esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Data Silos and Fragmentation:
### Pseudocode: Homomorphic Encryption and Integration of Federated Learning Model  
The proposed pseudocode outlines the integration of homomorphic encryption with a federated learning model which is explained in detail through Algorithm 2. By leveraging homomorphic encryption, the model updates can be securely exchanged and aggregated while preserving the privacy of the individual updates. The federated learning process, including model integration and local model refinement, is performed on the encrypted data, ensuring the confidentiality of the EMR information. This approach enables collaborative learning on distributed EMR datasets while maintaining data privacy [29].  
```
1:Initialization:
2: Each participating healthcare institution initializes its local model parameters.
3: The blockchain network is set up with the necessary smart contracts, consensus mechanisms, and transaction validation protocols.
4: The homomorphic encryption scheme parameters are generated.
5:Model Update Exchange:
6: The participating institutions securely exchange their locally trained model updates using the blockchain network.
7: Each institution encrypts its model update using the homomorphic encryption scheme.
8: The blockchain network records and validates the encrypted model updates, maintaining an immutable history of the updates.
9:Aggregation:
10: The blockchain network aggregates the encrypted model updates from the participating institutions.
11: Aggregation can be performed using techniques such as Federated Averaging or secure multi-party computation to ensure the confidentiality of the individual updates.
12: The aggregated encrypted model update is computed and shared back with the participating institutions.
13:Model Integration:
14: Each institution integrates the aggregated encrypted model update with its local model parameters.
15: Homomorphic decryption is applied to the aggregated encrypted model update using the corresponding decryption key.
16: The decrypted model update is integrated with the local model parameters, resulting in an updated local model.
17:Local Model Refinement:
18: Each institution continues training the refined model using its local EMR data.
19: Model refinement can be performed using standard machine learning techniques such as gradient descent or stochastic gradient descent.
20:Iterative Process:
21: Steps 2-6 are repeated iteratively for multiple rounds, allowing the model to learn from the distributed EMR data in a collaborative manner.
22: Convergence criteria, such as a maximum number of iterations or a defined improvement threshold, can be used to determine the termination of the federated learning process.
```  
**Algorithm 2** Homomorphic Encryption and Integration of Federated Learning Model
# Predicting the Quality of Tangerines Using the GCNN-LSTM-AT Network Based on Vis-NIR Spectroscopy
## 5 Conclusions  
In this study, we propose a novel GCNN-LSTM-AT network for the prediction of five quality parameters of tangerines using Vis-NIR spectroscopy. GCNN-LSTM-AT combines two-dimensional CNN and Bi-LSTM networks, aided by graph features and the attention mechanism, to effectively capture the spatial and wavelength sequential dependencies in spectra data. Experimental results demonstrate the superior performance of the proposed network compared to other traditional algorithms and two deep neural networks, Deep-Spectra2D and CNN-AT. The GCNN-LSTM-AT network achieves the lowest RMSECV, highest \(R^{2}\), and smallest MAE prediction of SSC, TA, A/S, and firmness. Although it is slightly inferior to DeepSpectra2D in the evaluation of VC, GCNN-LSTM-AT obtains more outstanding performance overall and shows better generalization ability than the other algorithms for diverse prediction targets. These results suggest that our method has strong potential for application on packing lines, allowing for the assessment of up to 10 fruits per second, quickly and accurately. However, in the future, more work ought to be conducted on online systems, which are specific to post-harvest applications, in which fruit of different qualities need to be graded and packed according to sorting categories. Future work should consider a dedicated design for an online application including a mechanical subsystem, communication subsystem, and spectral detection subsystem that prioritizes easy maintenance, easy modification for different products, and high working efficiency. For online systems, obtaining accurate spectral data from samples is difficult due to the complex working conditions and external parameters. It is, therefore, necessary to consider these parameters when establishing the whole system.  
Conceptualization, S.H.; methodology, Y.W.; software, Y.W.; validation, Y.W.; formal analysis, Y.W.; investigation, X.Z.; resources, Q.H.; data curation, Y.W. and Y.Z.; writing--original draft preparation, Y.W.; writing--review and editing, Y.W., J.E. and S.H.; visualization, Y.W.; supervision, S.H.; project administration, S.H.; funding acquisition, S.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by \"Pioneer\" and \"Leading Goose\" R&D Program of Zhejiang Province (grant number 2023C03135), Ningbo Science and Technology Project under Grant 2021Z076 and National Natural Science Foundation of China (11621101).  
Not applicable.  
The authors are grateful to [PERSON] of Zhejiang University for valuable discussions.  
The authors declare no conflict of interest.  
Figure 9: Predictions of the proposed GCNN-LSTM-AT algorithm on the test set for five quality parameters of tangerines. (**a**) Soluble solid content (SSC) results. (**b**) Total acidity (TA) results. (**c**) Acid–sugar ratio (A/S) results. (**d**) Firmness results. (**e**) Vitamin C (VC) results.
## 5 Experimental Evaluation
### Algorithm Performance Results
#### 5.2.3 Graceful Degradation and Fallback Mechanisms  
When timing correction algorithms fail to achieve target performance thresholds, SMART DShot employs a multi-layered fallback strategy: (1) Graceful degradation to traditional CRC validation: The system seamlessly reverts to conventional DShot CRC checking while maintaining \(>\) 50% control authority as specified in our security requirements (Section 3.4), (2) Emergency algorithm switching: Real-time performance monitoring triggers automatic selection of the best-performing individual algorithm based on current error characteristics, (3) Adaptive update rate reduction: Frame update rates automatically decrease from 8 kHz to 1 kHz when correction probability drops below 20%, improving success likelihood while maintaining safe control margins, (4) Proactive pilot alerting: The APB interface (Figure 6) generates immediate notifications to the flight controller enabling manual intervention when automated correction consistently fails.  
The 31% success rate represents the worst-case scenario under simultaneous coordinated attacks designed to maximize algorithm failure. This is analogous to testing a bulletproof vest against armor-piercing rounds--the 31% survival rate under extreme conditions actually demonstrates remarkable robustness.
## 3 Early Warning Model
### Fast Fourier Transform  
Fast Fourier transform (FFT) is an efficient algorithm for a discrete Fourier transform (DFT). Given the finite sequence length x(n) with length N, the DFT is described as:  
\[\mathrm{X(k)}=\sum\limits_{x=0}^{N-1}x(n)W_{N}^{nk} \tag{12}\]  
FFT uses the symmetry, periodicity, and reducibility characters of \(W_{N}^{nk}\), i.e., Equations (13)-(15), to decompose a large scale DFT into a combination of several small scale DFTs.  
\[\left(W_{N}^{nk}\right)^{*}=W_{N}^{-nk}=W_{N}^{(N-n)k}=W_{N}^{n(N-k)}, \tag{13}\]  
\[W_{N}^{nk}=W_{N}^{(N+n)k}=W_{N}^{n(N-k)} \tag{14}\]  
\[W_{N}^{nk}=W_{mN}^{mnk}=W_{N/m}^{nk/m} \tag{15}\]  
The following is a time-based 2-FFT algorithm. Given N = 2M, then x (n) can be divided into two groups. When n is even, let n = 2r. When n is odd, let n = 2r. Let x (2r) = x1(r), X1 (k)= DFT [x1 (r)], x (2r+1) = x2 (r), X2 (k) = DFT [x2 (r)], where r = 0, 1,..., N - 1. Then formula (12) can be rewritten as:  
\[\mathrm{X(k)}=\mathrm{X_{1}(k)}+W_{N}^{k}\mathrm{X_{2}(k)}, \tag{16}\]  
\[\mathrm{X(k+N/2)}=\mathrm{X_{1}(k)}+W_{N}^{k}\mathrm{X_{2}(k)}. \tag{17}\]  
We can calculate that an N-point FFT operation needs (N/2) log2N complex multiplication and Nlog2N complex addition, which greatly improves the operation efficiency of DFT.
## 2 Materials and Methods
### Image Processing Module  
For the third step of the camera-aided algorithm presented in the Introduction, which supposes the projection of the surrounding satellites' coordinates onto the camera reference frame using the vehicle heading computed from the reference trajectory, the image processing module is needed. It transforms each image acquired by the fisheye camera into a probability map. The first step of the algorithm which implements the image processing module is the segmentation of each image.
## 3 Proposed Channel Estimation Schemes
### Scheme II: Channel Estimation Using Orthogonal Pilots  
In this scheme, the pilot signals and data signals are multiplexed on the subcarriers within a subframe in the frequency domain before the \(Q\)-point IDFT, as shown in part (II) marked in Figure 1a. We defined \(\mathbf{P}=[P(0),P(1),\cdots,P(N_{p}-1)]^{T}\) as a \(N_{p}\)-length pilot symbol. The pilot and data symbols are mapped to the subcarriers as follows:  
\[G((u-1)\eta+1)=P(u), \tag{17}\]  
\[G((u-1)\eta+\
u+1)=F((u-1)\eta+\
u), \tag{18}\]  
where \(\{G(i),i=0,1,...,Q_{0}-1\}\) denotes the multiplexed frequency-domain symbol with the length of \(Q_{0}=Q+N_{p}\), \(\eta=\left\lfloor Q_{0}/N_{p}\right\rfloor\), \(\left\lfloor\cdot\right\rfloor\) represents the floor operation. \(u=0,1,...,N_{p}\) and \(\upsilon=1,2,...,\eta\). As a result, the CSI can be directly estimated via LS without interpolation as follows:  
\[\mathbf{\hat{h}}=\mathbf{\hat{P}}./\mathbf{P}, \tag{19}\]  
where \(./\) represents the element division. Then, the complete CIR can be obtained by interpolation algorithms such as linear interpolation and DFT interpolation [37]. As the pilot symbols are inserted into the subcarriers as the data symbols after the data block generation, the pilot and data symbols share the same time-frequency resources in an orthogonal manner. Therefore, the two-dimensional packing of the data symbols has no negative impact on the pilot symbols. Thus, the channel estimation can be implemented independently within each subband, which is different from Scheme I.
## 4 Experiment and Analysis
### Ablation Study
#### 4.5.2 Algorithm Ablation Experiment  
Combined with the CDML proposed, this subsection compares the effects of the proposed enhancement algorithms on the model performance. The experiments are conducted in the datasets CMU-MOSI and CMU-MOSEI, respectively. Tables 6 and 7 show the results of the algorithmic ablation experiments for CMU-MOSI and CMU-MOSEI. Comparing Tables 6 and 7, it can be concluded that when either enhancement algorithm is missing, all performance evaluation indexes of the model are reduced to some extent. That is, the proposed enhancement algorithm can improve the performance of sentiment prediction within the model and validate the effectiveness of the enhancement algorithm.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model Design** & **Acc-7 (\(\uparrow\))** & **Acc-2 (\(\uparrow\))** & **F1 (\(\uparrow\))** & **Corr (\(\uparrow\))** & **MAE (\(\downarrow\))** \\ \hline CDML (full model) & **39.1** & **83.7** & **84.3** & **0.725** & **0.864** \\ (-) multitask learning & 38.7 & 83.0 & 83.7 & 0.717 & 0.871 \\ (-) adversarial training & 38.4 & 82.9 & 83.6 & 0.714 & 0.875 \\ (-) attention mechanism & 37.9 & 82.4 & 82.8 & 0.704 & 0.887 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of enhancement algorithm ablation experiments on CMU-MOSI. In each row, the corresponding component is progressively removed from the CDML.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline Acoustic & Visual & Textual & Acc-7 & Acc-2 & F1 & Corr & MAE \\ Modality & Modality & Modality & (\(\uparrow\)) & (\(\uparrow\)) & (\(\uparrow\)) & (\(\uparrow\)) & (\(\downarrow\)) \\ \hline ✓ & & & 47.6 & 79.9 & 80.2 & 0.598 & 0.704 \\  & ✓ & & 48.5 & 80.4 & 80.9 & 0.625 & 0.693 \\  & & ✓ & 48.9 & 80.4 & 80.6 & 0.614 & 0.698 \\ ✓ & ✓ & & 50.7 & 81.2 & 81.6 & 0.648 & 0.659 \\ ✓ & & ✓ & 50.3 & 81.3 & 81.8 & 0.632 & 0.662 \\  & ✓ & ✓ & 51.2 & 81.7 & 82.0 & 0.702 & 0.624 \\ ✓ & ✓ & ✓ & **52.9** & **84.3** & **84.3** & **0.761** & **0.553** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Modality combination comparison experiment on CMU-MOSEI. ✓ means information containing this modal.  
The experimental results of the attention-based mechanism, adversarial training, and multitask learning augmentation algorithm show significant improvement compared with CMJRT. Regarding the evaluation metrics Acc-7, Acc-2, and F1, the amalgamation of all augmentation algorithms (CDML) improved by 1.2%, 1.3%, and 1.5%, respectively, over CMJRT on the CMU-MOSI, and by 1.3%, 1.4%, and 1.7%, respectively, on the CMU-MOSI. The experiments illustrate that the attention mechanism can focus on filtering features that are more important to the effective task. The unimodal subtask in multitask learning shares feature information with the main sentiment prediction task, which makes the model pay more attention to samples with greater variability and helps provide more comprehensive information for the model sentiment prediction task.
## 3 Proposed System Model
### Performance Measurement of Machine Learning Classification Algorithms
#### 3.4.6 ROC Curve  
The ROC curve is used to assess the performance of classification models, particularly in imbalanced datasets [78]. It plots the True Positive Rate (TPR) against the False Positive Rate (FPR) across various thresholds. The TPR represents the proportion of correctly identified positive cases, while the FPR shows the proportion of negative cases incorrectly identified as positive. The curve helps visualize the model's ability to distinguish between classes and adjust its decision boundary effectively [79].
## 3 Test Results and Analysis  
Characterization of Seepage in a Coal Rock Body Within the Caving Zone of Multiple Protected Mining in the Coal Seam Group  
Three cycle loading and unloading seepage tests were conducted on coal rock samples with different mixing ratios, following the test scheme in Figure 5. The test results are shown in Figure 6.  
Figure 6 shows that the permeability of broken coal rock samples with different mixing ratios decreases as the effective stress increases. Even under high-stress conditions (16 MPa effective stress), the permeability remains above 40 md, indicating that the porosity between the broken coal rock particles in the protective layer working face goat remains relatively high. This suggests that the permeability of the broken coal rock body in the goat stays relatively high, even with compaction. Additionally, samples with a higher proportion of rock bodies exhibit greater permeability under the same stress and show higher stress sensitivity in terms of permeability during the loading and unloading cycles.  
Additionally, the permeability change trend in the three loading and unloading processes of the broken coal rock samples is consistent. The permeability during the first loading seepage process is much higher than in the last two loading processes. In each cycle, the permeability during the loading stage is always higher than during the unloading stage. As the number of cyclic loading and unloading increases, the decrease in permeability after each unloading cycle gradually diminishes. The obtained regularity is the same as that reported in [26].  
Figure 6: Plot of permeability versus effective stress for cyclic loading and unloading of broken coal rock samples with different mixing ratios: (**a**) specimen P1, (**b**) specimen P2, and (**c**) specimen P3.
## 6 System Evaluation
### Search Efficiency
#### 6.4.2 Search Time  
In this experiment, we investigate the performance of our proposed scheme in terms of matching time. Our scheme requires the searching cost of index tree \(O((k.p)log_{R}(m))\) to compute the search request. As shown in Figure 6, \(Bob\) is able to search a database of 10,000 within a period of 162.3 ms using SEPIM. SEPIM induces a higher search cost of just 14% over schemes which search images using plaintext [21; 22; 24; 25]. The additional time cost of our work can be considered as a reasonable cost for achieving a secure matching. Figure 6 presents ways in which lessening dimension might  
Figure 4: Index tree building cost for 10,000 images of variable dimension reductions. PCA: principal component analysis; BRIEF: binary robust independent elementary features; SYBA: SYrhetic BAsis.  
Figure 5: Index tree building cost of various image datasets using PCA-256 dimension reduction.  
extend the enhancement of the search efficiency of SEPIM due to gains in cost among secure dot product computation, together with a reduced input/output (I/O) cost through search the index tree. In particular, PCA-256 can compress the file size from 35 MB to fewer than 3 MB of 10,000 images' vectors allocated to the index tree (see Figure 7), and these can also be cached simply within memory to speed up the search process.  
Figure 6: Search cost required for over 10,000 images with various dimension reductions.  
Figure 7: Vectors’ size reduction over 10,000 images with various dimension reductions.  
Clearly, PCA transform results in some data loss in relation to specific image features, possibly impacting the search precision in a limited way and somewhat offsetting the self-evident relationship between improved system effectiveness and lowering the descriptor vector using PCA transform. In the next part, we will analyse the search precision of SEPIM, and evaluate how to establish correct PCA strength might help to balance the effectiveness of a SEPIM-influenced system with regard to its efficiency and precision.
## 4 Challenges and Future Research Directions
### Ultra-High Capacity Backhaul Link  
The technology of 6G can provide a thousand times higher wireless connectivity than 5G. To provide a high data rate, high area capacity, and high connection density for the wireless communication of smart grid networks, the backhaul connectivity should cope with a high capacity to support the huge amounts of traffic [9]. Ultra-high capacity backhaul refers to the high-speed and high-capacity links that connect the core network or central hub of a telecommunications system to the access points or base stations in the network. Backhaul is a critical part of the network infrastructure as it is responsible for carrying a high volume of data traffic between the core and edge of the network.  
Thus, various emerging technologies such as free space optical communication (FSO), high-speed fiber optics, and OAM-based backhaul communication can be potential solutions through which to provide high traffic capacity for huge volumes of traffic for smart grid networks [9; 131; 132]. A number of studies are required for reliable, efficient ultra-high capacity, and ultra-high speed FSO/OAM communication for 6G networks.
## 3 Results and Discussion
### What Are the Current Physiological Signal Analysis Methods?
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Category** & **Technologies and Sensors Used** & **Applications** & **References** \\ \hline Web-based and mobile applications, Section 3.1.3 & Android apps, web platforms, Bluetooth, MQTT, AI, facial recognition, cloud-based dashboards, telemedicine & Remote health tracking, medication reminders, telemonitoring, real-time alerts, doctor-patient communication & [57; 89; 93; 100; 101; 102; 103; 104; 105; 106; 107; 108; 109; 110] \\ \hline Integrated healthcare systems, Section 3.1.4 & ARM-embedded platforms, AI, IoMT, fuzzy inference systems, BLE, IMUs, cloud computing, holographic visualization & Multi-parameter monitoring, real-time health alerts, gait assessment, telemedicine & [85; 111; 112; 113] \\ \hline \end{tabular}
\end{table} 
Table 5: _Cont._a two-channel convolutional neural network for improved recognition efficiency was developed for human activity recognition and classification. Principal component analysis (PCA) was used for the extraction of characteristics and to enable a reduction in data dimensions [124]. The corresponding description of each listed work can be found in Table 11, which is organized by year of publication. Discussion of Key Studies in This Subsection  
Recent studies on remote health monitoring for the elderly highlight significant advancements in non-invasive, real-time solutions using technologies such as RFID, wearable sensors [124], radar [114], and edge computing. These systems leverage AI and machine learning for accurate activity recognition, vital sign monitoring, and fall detection, with some methods achieving over 90% accuracy [118; 119; 121]. Real-time predictions using LSTM and radar techniques contribute to improved elderly care and early intervention [114; 116; 118]. Notably, approaches such as RFID-based respiration monitoring [43], LSTM-based health risk assessment [116], and radar networks for vital sign detection [114] have shown promising results. However, challenges remain, including limited operational ranges, signal interference, and usability concerns for elderly users, particularly those with physical or cognitive limitations [115]. Additionally, several methods require further validation in diverse real-world environments. The challenge of achieving consistent performance across different settings and user conditions persists [123], along with issues related to power and resource constraints that affect the systems' scalability and practical application [118; 119; 120].