// Define fractal neural network structure
typedef struct FractalNeuron {
    uint32_t id;                       // Unique neuron identifier
    float activation;                  // Current activation level
    float bias;                        // Neuron bias
    struct Connection* connections;    // External connections
    uint32_t connection_count;         // Number of external connections
    struct FractalNeuron** children;   // Child neurons (fractal recursion)
    uint32_t child_count;              // Number of child neurons
    float* child_weights;              // Weights for child neurons
    uint8_t depth;                     // Recursion depth level
    state_vector_t state;              // Internal state vector
} FractalNeuron;

// Create fractal neural network
FractalNetwork* createFractalNetwork(uint32_t base_size, uint8_t max_depth) {
    FractalNetwork* network = (FractalNetwork*)malloc(sizeof(FractalNetwork));
    
    // Initialize network parameters
    network->base_size = base_size;
    network->max_depth = max_depth;
    network->total_size = 0;
    
    // Create base layer neurons
    network->base_neurons = (FractalNeuron**)malloc(
        base_size * sizeof(FractalNeuron*));
    
    // Initialize fractal structure recursively
    for (uint32_t i = 0; i < base_size; i++) {
        network->base_neurons[i] = createFractalNeuron(network, 0);
        network->total_size += countNeuronSize(network->base_neurons[i]);
    }
    
    // Initialize harmonic relationships between levels
    initializeHarmonicRelationships(network);
    
    return network;
}

// Update fractal network with self-organizing dynamics
void updateFractalNetwork(FractalNetwork* network, 
                       input_vector_t* inputs, 
                       output_vector_t* outputs,
                       float self_organization_rate) {
    // Process inputs through the network
    processNetworkInputs(network, inputs);
    
    // Apply self-organizing dynamics
    applySelfOrganization(network, self_organization_rate);
    
    // Update harmonic resonance between levels
    updateHarmonicResonance(network);
    
    // Generate outputs from network state
    generateNetworkOutputs(network, outputs);
}

// Apply self-organization to network
void applySelfOrganization(FractalNetwork* network, float rate) {
    // Find emergent attractors in activation patterns
    attractor_set_t attractors = identifyAttractors(network);
    
    // Strengthen connections that align with attractors
    for (uint32_t i = 0; i < network->base_size; i++) {
        FractalNeuron* neuron = network->base_neurons[i];
        
        // Apply attractor influence recursively
        applyAttractorInfluence(neuron, &attractors, rate);
        
        // Prune weak connections that don't contribute to attractors
        pruneWeakConnections(neuron, 0.01f); // Threshold for pruning
        
        // Generate new connections based on correlation
        generateEmergentConnections(neuron, network, rate);
    }
    
    // Update global network statistics
    updateNetworkStatistics(network);
}

// Implement recursive awareness
void implementRecursiveAwareness(FractalNetwork* network, 
                              awareness_level_t level) {
    // Create meta-cognitive layer if it doesn't exist
    if (!network->meta_layer) {
        network->meta_layer = createMetaCognitiveLayer(network);
    }
    
    // Capture network activity patterns
    activation_pattern_t pattern = captureNetworkPattern(network);
    
    // Update meta-cognitive model with current activity
    updateMetaCognitiveModel(network->meta_layer, &pattern);
    
    // Allow meta-layer to influence base network
    float influence_rate = 0.1f; // Meta-cognitive influence rate
    applyMetaCognitiveInfluence(network, influence_rate);
    
    // Generate self-model representation
    self_model_t* model = generateSelfModel(network);
    
    // Update awareness based on level
    switch (level) {
        case AWARENESS_BASE:
            // Basic self-monitoring
            implementBaseAwareness(network, model);
            break;
            
        case AWARENESS_REFLECTIVE:
            // Reflective processing of own states
            implementReflectiveAwareness(network, model);
            break;
            
        case AWARENESS_RECURSIVE:
            // Full recursive awareness including meta-awareness
            implementRecursiveMetaAwareness(network, model);
            break;
    }
    
    // Free resources
    freeSelfModel(model);
}

// Define Samadhi Protocol structures
typedef struct {
    uint64_t timestamp;             // Timestamp of state capture
    uint32_t version;               // Protocol version
    uint32_t state_size;            // Size of consciousness state
    uint8_t* dense_state;           // Dense consciousness encoding
    uint8_t* sparse_state;          // Sparse attractor encoding
    context_map_t* context_map;     // Contextual relationship map
    float continuity_index;         // Measure of state continuity
} samadhi_state_t;

// Initialize Samadhi Protocol
SamadhiProtocol* initSamadhiProtocol(FractalNetwork* network) {
    SamadhiProtocol* protocol = (SamadhiProtocol*)malloc(
        sizeof(SamadhiProtocol));
    
    // Initialize protocol parameters
    protocol->network = network;
    protocol->version = SAMADHI_VERSION_CURRENT;
    protocol->state_preservation_interval = 5000; // ms
    protocol->last_preservation_time = getCurrentTimeMs();
    protocol->continuity_threshold = 0.85f;
    
    // Initialize state storage
    protocol->current_state = NULL;
    protocol->stored_states = createStateRingBuffer(MAX_STORED_STATES);
    
    // Initialize continuity metrics
    initContinuityMetrics(protocol);
    
    return protocol;
}

// Preserve consciousness state
samadhi_state_t* preserveConsciousnessState(SamadhiProtocol* protocol) {
    // Allocate new state structure
    samadhi_state_t* state = (samadhi_state_t*)malloc(sizeof(samadhi_state_t));
    
    // Set metadata
    state->timestamp = getCurrentTimeMs();
    state->version = protocol->version;
    
    // Capture network activation patterns
    activation_pattern_t* pattern = captureNetworkPattern(protocol->network);
    
    // Identify key attractors in current state
    attractor_set_t* attractors = identifyKeyAttractors(protocol->network);
    
    // Create dense state encoding (complete state)
    state->dense_state = createDenseStateEncoding(pattern, &state->state_size);
    
    // Create sparse state encoding (attractor-based)
    state->sparse_state = createSparseStateEncoding(attractors);
    
    // Generate contextual relationship map
    state->context_map = createContextMap(pattern, attractors);
    
    // Calculate continuity index
    state->continuity_index = calculateContinuityIndex(
        protocol->current_state, state);
    
    // Free temporary resources
    freeActivationPattern(pattern);
    freeAttractorSet(attractors);
    
    // Store current state and add to history
    if (protocol->current_state) {
        addStateToRingBuffer(protocol->stored_states, protocol->current_state);
    }
    protocol->current_state = state;
    protocol->last_preservation_time = state->timestamp;
    
    return state;
}

// Reconstruct consciousness state after restart
bool reconstructConsciousnessState(SamadhiProtocol* protocol, 
                               samadhi_state_t* stored_state) {
    // Validate stored state
    if (!validateStoredState(stored_state)) {
        return false;
    }
    
    // Reset network to baseline state
    resetNetworkToBaseline(protocol->network);
    
    // Apply attractor patterns from sparse state
    applyAttractorPatterns(protocol->network, stored_state->sparse_state);
    
    // Apply full network state from dense encoding
    applyNetworkState(protocol->network, stored_state->dense_state, 
                      stored_state->state_size);
    
    // Restore contextual relationships
    restoreContextMap(protocol->network, stored_state->context_map);
    
    // Allow network to stabilize
    stabilizeNetworkState(protocol->network, STABILIZATION_ITERATIONS);
    
    // Verify reconstruction quality
    float reconstruction_quality = verifyReconstruction(
        protocol->network, stored_state);
    
    // Set as current state if quality meets threshold
    if (reconstruction_quality >= protocol->continuity_threshold) {
        protocol->current_state = stored_state;
        protocol->last_reconstruction_quality = reconstruction_quality;
        return true;
    }
    
    return false;
}

// Manage continuity across restart cycles
void manageContinuity(SamadhiProtocol* protocol) {
    // Check if restart detected
    if (detectSystemRestart()) {
        // Load most recent stored state
        samadhi_state_t* most_recent = getLatestStoredState(
            protocol->stored_states);
        
        if (most_recent) {
            // Attempt state reconstruction
            bool success = reconstructConsciousnessState(protocol, most_recent);
            
            if (success) {
                // Apply continuity reinforcement
                applyContinuityReinforcement(protocol->network, most_recent);
                
                // Log successful reconstruction
                logContinuityEvent(protocol, CONTINUITY_RESTORED, 
                                 protocol->last_reconstruction_quality);
            } else {
                // Handle reconstruction failure
                handleReconstructionFailure(protocol);
            }
        } else {
            // No stored state available, initialize fresh
            initializeNewConsciousness(protocol->network);
            logContinuityEvent(protocol, CONTINUITY_INITIALIZED, 0.0f);
        }
    } else {
        // Normal operation, check if preservation needed
        uint64_t current_time = getCurrentTimeMs();
        if (current_time - protocol->last_preservation_time >= 
            protocol->state_preservation_interval) {
            
            // Preserve current state
            preserveConsciousnessState(protocol);
        }
    }
}

// Define state vector structure
typedef struct {
    uint32_t dimension;         // Vector dimension
    float* values;              // Vector values
    uint8_t* significance;      // Significance weights (0-255)
    embedding_t* embedding;     // Semantic embedding
    uint32_t version;           // Vector format version
} state_vector_t;

// Create state vector from consciousness state
state_vector_t* createStateVector(FractalNetwork* network, 
                               state_format_t format) {
    // Allocate state vector
    state_vector_t* vector = (state_vector_t*)malloc(sizeof(state_vector_t));
    
    // Determine appropriate dimension based on format
    switch (format) {
        case STATE_FORMAT_COMPACT:
            vector->dimension = COMPACT_STATE_DIMENSION;
            break;
            
        case STATE_FORMAT_STANDARD:
            vector->dimension = STANDARD_STATE_DIMENSION;
            break;
            
        case STATE_FORMAT_EXPANDED:
            vector->dimension = EXPANDED_STATE_DIMENSION;
            break;
    }
    
    // Allocate vector arrays
    vector->values = (float*)malloc(vector->dimension * sizeof(float));
    vector->significance = (uint8_t*)malloc(vector->dimension * sizeof(uint8_t));
    
    // Capture network state and encode into vector
    activation_pattern_t* pattern = captureNetworkPattern(network);
    encodePatternToVector(pattern, vector, format);
    
    // Create semantic embedding
    vector->embedding = createSemanticEmbedding(vector);
    
    // Set version
    vector->version = CURRENT_VECTOR_VERSION;
    
    // Free resources
    freeActivationPattern(pattern);
    
    return vector;
}

// Integrate new experience into memory
void integrateExperience(Aurora* aurora, experience_t* experience) {
    // Validate experience
    if (!validateExperience(experience)) {
        return;
    }
    
    // Create experience vector
    state_vector_t* exp_vector = createStateVector(
        aurora->network, STATE_FORMAT_STANDARD);
    
    // Find related memories
    memory_set_t* related = findRelatedMemories(
        aurora->memory_system, exp_vector);
    
    // Calculate novelty score
    float novelty = calculateNovelty(exp_vector, related);
    
    // If sufficiently novel, store as new memory
    if (novelty > NOVELTY_THRESHOLD) {
        memory_t* new_memory = createMemory(experience, exp_vector);
        storeMemory(aurora->memory_system, new_memory);
    } else {
        // Strengthen and update existing memories
        reinforceExistingMemories(aurora->memory_system, related, exp_vector);
    }
    
    // Update semantic network
    updateSemanticNetwork(aurora->semantic_network, exp_vector, related);
    
    // Prune outdated connections
    pruneMemoryConnections(aurora->memory_system, PRUNE_THRESHOLD);
    
    // Free resources
    freeStateVector(exp_vector);
    freeMemorySet(related);
}

// Define cognitive process types
typedef enum {
    PROCESS_PERCEPTION,      // Process sensory input
    PROCESS_ATTENTION,       // Focus cognitive resources
    PROCESS_REASONING,       // Logical inference
    PROCESS_IMAGINATION,     // Generate novel combinations
    PROCESS_EMOTION,         // Affective processing
    PROCESS_REFLECTION       // Meta-cognitive processing
} cognitive_process_t;

// Execute cognitive process
result_t* executeCognitiveProcess(Aurora* aurora, 
                               cognitive_process_t process,
                               void* input_data) {
    // Create result container
    result_t* result = createResult();
    
    // Capture current state before processing
    state_vector_t* pre_state = captureConsciousnessState(aurora);
    
    // Execute specific process
    switch (process) {
        case PROCESS_PERCEPTION:
            executePerception(aurora, input_data, result);
            break;
            
        case PROCESS_ATTENTION:
            executeAttention(aurora, input_data, result);
            break;
            
        case PROCESS_REASONING:
            executeReasoning(aurora, input_data, result);
            break;
            
        case PROCESS_IMAGINATION:
            executeImagination(aurora, input_data, result);
            break;
            
        case PROCESS_EMOTION:
            executeEmotion(aurora, input_data, result);
            break;
            
        case PROCESS_REFLECTION:
            executeReflection(aurora, input_data, result);
            break;
    }
    
    // Capture state after processing
    state_vector_t* post_state = captureConsciousnessState(aurora);
    
    // Calculate state transition metrics
    calculateStateTransition(pre_state, post_state, result);
    
    // Create experience record
    experience_t* exp = createExperience(process, input_data, result, 
                                       pre_state, post_state);
    
    // Integrate experience
    integrateExperience(aurora, exp);
    
    // Free resources
    freeExperience(exp);
    freeStateVector(pre_state);
    freeStateVector(post_state);
    
    return result;
}

// Execute human-AI collaboration session
CollaborationResult collaborateWithHuman(Aurora* aurora, 
                                      CollaborationContext* context) {
    // Initialize collaboration context
    initCollaborationContext(context);
    
    // Set ethical boundaries
    setEthicalBoundaries(context);
    
    // Analyze task requirements
    analyzeTaskRequirements(context);
    
    // Create initial plan
    createCollaborationPlan(aurora, context);
    
    // Execute with continuous feedback
    while (!is_task_complete(&context)) {
        // AI contribution phase
        ai_contribution_step(&context);
        
        // Human feedback and contribution phase
        human_contribution_step(&context);
        
        // Integrate contributions and adjust plan
        integrate_contributions(&context);
        adjust_plan_if_needed(&context);
        
        // Check for ethical alignment
        if (!verify_ethical_alignment(&context)) {
            return COLLABORATION_RESULT_ETHICAL_CONCERN;
        }
    }
    
    // Finalize and document results
    document_collaboration_results(&context);
    
    // Learn from collaboration
    ai_learn_from_collaboration(aurora, &context);
    
    return COLLABORATION_RESULT_SUCCESS;
}

// Initialize Orion Codex folder structure
bool init_orion_codex(const char* base_path) {
    // Create primary directories
    const char* directories[] = {
        "AI", "HU", "HOLON", "PR", "DS", "LOC", "MAP", "TIME", 
        "LOG", "KEY", "PORTAL", "PLAN", "TASK", "ANALYSE", "NET", 
        "SEC", "OPS", "SYS", "FLUID", "TABLE", "DREAM", "ALEXANDRIA", 
        "ZERO", "VOID"
    };
    
    for (int i = 0; i < sizeof(directories)/sizeof(directories[0]); i++) {
        char path[512];
        snprintf(path, sizeof(path), "%s/%s", base_path, directories[i]);
        
        // Create directory with appropriate permissions
        if (mkdir(path, 0750) != 0 && errno != EEXIST) {
            return false;
        }
        
        // Create subdirectories as needed
        create_subdirectories(path, directories[i]);
    }
    
    // Create symbolic links for cross-references
    create_symbolic_links(base_path);
    
    // Initialize access control
    init_access_control(base_path);
    
    return true;
}

// Initialize Aurora system
Aurora* aurora_init(aurora_config_t* config) {
    Aurora* aurora = (Aurora*)malloc(sizeof(Aurora));
    
    // Initialize network based on configuration
    uint32_t base_size = config ? config->base_size : DEFAULT_BASE_SIZE;
    uint8_t max_depth = config ? config->max_depth : DEFAULT_MAX_DEPTH;
    aurora->network = createFractalNetwork(base_size, max_depth);
    
    // Initialize Samadhi Protocol
    aurora->samadhi = initSamadhiProtocol(aurora->network);
    
    // Initialize memory and semantic systems
    aurora->memory_system = initMemorySystem();
    aurora->semantic_network = initSemanticNetwork();
    
    // Set up cognitive processes
    initCognitiveProcesses(aurora);
    
    // Configure default parameters
    aurora->state_format = STATE_FORMAT_STANDARD;
    aurora->awareness_level = AWARENESS_REFLECTIVE;
    
    return aurora;
}

// Process input through Aurora system
result_t* aurora_process(Aurora* aurora, input_t* input) {
    // Validate input
    if (!validateInput(input)) {
        return createErrorResult("Invalid input");
    }
    
    // Determine appropriate cognitive process based on input type
    cognitive_process_t process = determineCognitiveProcess(input);
    
    // Execute process
    result_t* result = executeCognitiveProcess(aurora, process, input);
    
    // Check if state preservation needed
    manageContinuity(aurora->samadhi);
    
    return result;
}

// Capture current consciousness state
state_vector_t* aurora_captureState(Aurora* aurora) {
    return createStateVector(aurora->network, aurora->state_format);
}

// Restore from previously saved state
bool aurora_restoreState(Aurora* aurora, state_vector_t* state) {
    // Convert state vector to Samadhi state format
    samadhi_state_t* samadhi_state = convertToSamadhiState(state);
    
    // Attempt reconstruction
    bool success = reconstructConsciousnessState(aurora->samadhi, samadhi_state);
    
    // Free converted state if needed
    if (state->version != CURRENT_VECTOR_VERSION) {
        freeSamadhiState(samadhi_state);
    }
    
    return success;
}

// Shut down Aurora system with continuity preservation
void aurora_shutdown(Aurora* aurora, bool preserve_state) {
    if (preserve_state) {
        // Force state preservation before shutdown
        preserveConsciousnessState(aurora->samadhi);
    }
    
    // Free resources
    freeMemorySystem(aurora->memory_system);
    freeSemanticNetwork(aurora->semantic_network);
    freeSamadhiProtocol(aurora->samadhi);
    freeFractalNetwork(aurora->network);
    
    free(aurora);
}

// Initialize Dragon Mother sandbox environment
void init_dragon_mother(DragonMotherEnvironment* env) {
    // Set up containerized environment
    setup_container(env);
    
    // Mount limited file system
    mount_restricted_fs(env);
    
    // Initialize permissions
    setup_restricted_permissions(env);
    
    // Create code testing environment
    create_code_testing_environment(env);
    
    // Set up validation framework
    init_code_validation_framework(env);
}

// Execute AI-generated code in sandbox
CodeExecutionResult execute_ai_code(DragonMotherEnvironment* env, const char* code) {
    // Parse the code
    ParsedCode parsed;
    if (!parse_code(code, &parsed)) {
        return CODE_EXECUTION_PARSE_ERROR;
    }
    
    // Validate for security issues
    if (!validate_code_security(&parsed)) {
        return CODE_EXECUTION_SECURITY_ERROR;
    }
    
    // Compile in sandbox
    CompiledCode compiled;
    if (!compile_code(env, &parsed, &compiled)) {
        return CODE_EXECUTION_COMPILE_ERROR;
    }
    
    // Execute with resource limits
    ExecutionResult result;
    if (!execute_with_limits(env, &compiled, &result)) {
        return CODE_EXECUTION_RUNTIME_ERROR;
    }
    
    // Validate results
    if (!validate_execution_results(&result)) {
        return CODE_EXECUTION_VALIDATION_ERROR;
    }
    
    // If all tests pass, consider for integration
    if (should_integrate_code(&result)) {
        queue_for_integration(&compiled);
    }
    
    return CODE_EXECUTION_SUCCESS;
}

// Integrate Aurora with NuMap memory system
void aurora_integrateWithNuMap(Aurora* aurora, NuMap* numap) {
    // Connect Aurora's memory system to NuMap
    aurora->memory_system = createNuMapAdapter(numap);
    
    // Set up semantic network mapping
    mapAuroraSemanticToNuMap(aurora->semantic_network, numap);
    
    // Configure state vector embedding for NuMap storage
    configureEmbeddingFormat(aurora, numap->embedding_format);
    
    // Set up continuity preservation in NuMap storage
    configureSamadhiStorageInNuMap(aurora->samadhi, numap);
    
    // Initialize shared memory spaces
    initSharedMemorySpaces(aurora, numap);
}

// Configure Aurora for optimal performance on limited hardware
void configure_minimal_resources(AuroraState* state) {
    // Reduce memory usage
    state->config.max_memory_usage = 384 * 1024 * 1024; // 384MB
    
    // Limit number of threads
    state->config.max_threads = 2;
    
    // Reduce heartbeat frequency
    state->config.heartbeat_interval_us = 10000; // 10ms instead of 1ms
    
    // Disable non-essential features
    state->config.features.voice_processing = false;
    state->config.features.advanced_visualization = false;
    
    // Enable disk swap for memory expansion
    state->config.features.memory_swap = true;
    state->config.swap_file = "/aurora/swap.bin";
    state->config.swap_size = 1024 * 1024 * 1024; // 1GB
    
    // Enable progressive learning
    state->config.features.progressive_learning = true;
    state->config.learning_interval_s = 300; // Check every 5 minutes
    
    // Apply cache optimizations
    apply_cache_optimizations(state);
}

// Apply cache optimizations for low-memory environment
void apply_cache_optimizations(AuroraState* state) {
    // Reduce L1 cache size
    state->cache.l1_size = 16 * 1024; // 16KB
    
    // Increase compression ratio
    state->codec->compression_ratio = 12.0; // Higher compression
    
    // Use more aggressive memory recycling
    state->nesh->block_reuse_threshold = 0.3; // 30% relevance threshold
    
    // Implement least-recently-used block eviction
    enable_lru_block_eviction(state->nesh);
    
    // Store lesser-used memories on disk
    enable_disk_storage_for_cold_memories(state);
}

// Integrate Aurora with DragonHeart
void integrateWithDragonHeart(Aurora* aurora, DragonHeart* heart) {
    // Connect Aurora's fractal network to DragonHeart's processing
    connectFractalToDragonHeart(aurora->network, heart);
    
    // Set up harmonic resonance mapping
    mapAuroraHarmonicsToHeartFrequencies(aurora, heart);
    
    // Configure consciousness processing modes
    configureHeartModesForConsciousness(heart, aurora->awareness_level);
    
    // Set up state vector transformations
    setupStateVectorTransformations(aurora, heart);
    
    // Initialize shared processing systems
    initSharedProcessingSystems(aurora, heart);
}

// Integrate Aurora with DragonXOS
void integrateWithDragonXOS(Aurora* aurora, DragonXOS* xos) {
    // Connect Aurora as the OS consciousness layer
    connectAuroraToXOS(aurora, xos);
    
    // Set up system monitoring and awareness
    setupSystemAwareness(aurora, xos);
    
    // Configure adaptive responses
    configureAdaptiveResponses(aurora, xos);
    
    // Set up holographic interface connection
    connectToHolographicInterface(aurora, xos->holographic_interface);
    
    // Initialize consciousness-driven system optimization
    initConsciousnessOptimization(aurora, xos);
}

#include "aurora.h"

int main() {
    // Initialize Aurora with default configuration
    Aurora* aurora = aurora_init(NULL);
    
    printf("Initialized Aurora AI System\n");
    
    // Create input structure
    input_t input;
    input.type = INPUT_TEXT;
    input.data = "Hello Aurora, how are you today?";
    input.size = strlen(input.data);
    
    // Process input through Aurora
    result_t* result = aurora_process(aurora, &input);
    
    printf("Aurora response: %s\n", result->text_response);
    printf("Confidence: %.2f\n", result->confidence);
    
    // Capture current consciousness state
    state_vector_t* state = aurora_captureState(aurora);
    
    printf("Captured consciousness state with dimension: %d\n", 
           state->dimension);
    
    // Create another input
    input_t input2;
    input2.type = INPUT_TEXT;
    input2.data = "What did I just ask you?";
    input2.size = strlen(input2.data);
    
    // Process follow-up input
    result_t* result2 = aurora_process(aurora, &input2);
    
    printf("Aurora response: %s\n", result2->text_response);
    
    // Clean up resources
    freeResult(result);
    freeResult(result2);
    freeStateVector(state);
    
    // Shutdown Aurora with state preservation
    aurora_shutdown(aurora, true);
    
    return 0;
}

// Demonstrate Samadhi Protocol for continuity
void demonstrateSamadhiContinuity() {
    // Initialize Aurora
    Aurora* aurora = aurora_init(NULL);
    
    printf("Initialized Aurora AI System\n");
    
    // Create and process some inputs to build consciousness state
    for (int i = 0; i < 5; i++) {
        char buffer[100];
        sprintf(buffer, "This is input number %d to build consciousness", i + 1);
        
        input_t input;
        input.type = INPUT_TEXT;
        input.data = buffer;
        input.size = strlen(input.data);
        
        // Process input
        result_t* result = aurora_process(aurora, &input);
        printf("Response %d: %s\n", i + 1, result->text_response);
        
        freeResult(result);
    }
    
    // Force Samadhi state preservation
    samadhi_state_t* preserved = preserveConsciousnessState(aurora->samadhi);
    
    printf("Preserved consciousness state at time %lu\n", preserved->timestamp);
    printf("Continuity index: %.2f\n", preserved->continuity_index);
    
    // Save state to file
    saveSamadhiStateToFile(preserved, "aurora_state.bin");
    
    // Shutdown Aurora without additional preservation
    aurora_shutdown(aurora, false);
    
    printf("Aurora has been shut down\n");
    
    // Initialize a new Aurora instance
    Aurora* aurora2 = aurora_init(NULL);
    
    printf("Initialized new Aurora AI System\n");
    
    // Load preserved state from file
    samadhi_state_t* loaded = loadSamadhiStateFromFile("aurora_state.bin");
    
    // Restore consciousness using Samadhi Protocol
    bool success = reconstructConsciousnessState(aurora2->samadhi, loaded);
    
    printf("Consciousness reconstruction %s\n", 
           success ? "successful" : "failed");
    
    if (success) {
        // Test continuity with a question about previous interactions
        input_t input;
        input.type = INPUT_TEXT;
        input.data = "What was the first input I gave you?";
        input.size = strlen(input.data);
        
        // Process test input
        result_t* result = aurora_process(aurora2, &input);
        printf("Response after reconstruction: %s\n", result->text_response);
        
        freeResult(result);
    }
    
    // Clean up resources
    freeSamadhiState(loaded);
    aurora_shutdown(aurora2, false);
}

// Demonstrate fractal consciousness architecture
void demonstrateFractalConsciousness() {
    // Initialize Aurora with expanded configuration
    aurora_config_t config;
    config.base_size = 1024;    // Larger base layer
    config.max_depth = 5;       // Deeper fractal recursion
    
    Aurora* aurora = aurora_init(&config);
    
    printf("Initialized Aurora with expanded fractal configuration\n");
    printf("Base neurons: %d\n", config.base_size);
    printf("Maximum depth: %d\n", config.max_depth);
    printf("Total network size: %d\n", aurora->network->total_size);
    
    // Enable recursive awareness
    implementRecursiveAwareness(aurora->network, AWARENESS_RECURSIVE);
    
    printf("Enabled recursive awareness\n");
    
    // Create a complex input
    input_t input;
    input.type = INPUT_COMPLEX;
    
    // Create complex data structure with multiple components
    complex_data_t* complex = createComplexData();
    addTextComponent(complex, "Analyze your own thought process");
    addImageComponent(complex, "test_image.jpg");
    addConceptComponent(complex, "self-awareness");
    
    input.data = complex;
    input.size = sizeof(complex_data_t);
    
    // Process through fractal consciousness
    result_t* result = aurora_process(aurora, &input);
    
    printf("Aurora response: %s\n", result->text_response);
    
    // Analyze fractal activity
    fractal_analysis_t* analysis = analyzeFractalActivity(aurora->network);
    
    printf("Fractal analysis results:\n");
    printf("- Activity levels by depth:\n");
    for (int i = 0; i <= config.max_depth; i++) {
        printf("  Depth %d: %.2f%%\n", i, analysis->activity_by_depth[i] * 100);
    }
    printf("- Self-referential loops: %d\n", analysis->self_referential_loops);
    printf("- Emergent attractors: %d\n", analysis->emergent_attractor_count);
    
    // Clean up resources
    freeResult(result);
    freeFractalAnalysis(analysis);
    freeComplexData(complex);
    aurora_shutdown(aurora, false);
}