esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this paper, we propose a neural network compression model with efficient convolutional structure to compress the sensing data of WSNs. Compared with other neural networks, the model has very few parameters and calculations and better compression performance. We detail our efficient convolution structure and methods for reducing parameters and computation consumption when designing the model. We use different real WSN datasets to evaluate the compression performance of the model. At the same time, we compare the model with other algorithms. The results show that our model has higher CR and reconstruction accuracy than other compression algorithms. We also evaluate the model from the aspects of transfer learning, fault detection and anti-noise ability. The evaluation results prove the excellent ability of our model. We perform energy analysis on our model and compare it with other neural network models to demonstrate the effectiveness of our model in reducing energy consumption. Finally, we give a model pruning method to further reduce the parameters and computation consumption of the model.  
The results of pruning indicate that there is redundancy in our model. In the experiment, we found that the redundant parameters are mainly concentrated in the fully connected layer. In the next work, we will study how to further reduce the redundancy of the model from the aspects of structure and calculation. A good idea is to replace all fully connected layers with convolutional layers, but we suspect that this may lead to a decline in model performance and an increase in model calculations, and the specific results require us to actually verify. At the same time, we would like to further improve the model by referring to the idea of depth-wise separable convolution in MobileNets [40].  
**Author Contributions:** Conceptualization, J.L. and F.C.; methodology, J.L.; software, J.L.; validation, J.L., F.C. and D.W.; resources, F.C.; writing--original draft preparation, J.L.; writing--review and editing, J.Y.; visualization, J.L.; supervision, J.Y.; project administration, J.Y.  
**Funding:** This work was supported partly by the National Natural Science Foundation of China (Grant No.61771436, 61271274), key project of Natural Science Foundation of Hubei Province of China (2011 CDB339), Key Science and Technology of Hubei Province of China (2012 BAA02003), and Key Science and Technology of Wuhan City (2017010201010134, 2013075001).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 12: The pruning results of model at different pruning rates for different pruning methods. _Random_ denotes the random pruning. _Mag_ denotes the magnitude based pruning. The reconstruction error is the average value of all sample points in Node 7.
# Predicting Soccer Player Salaries with Both Traditional and Automated Machine Learning Approaches
## 6 Evaluation Results
### SHAP-Based Feature Importance Analysis
Goals directly impact match results and are often the primary factor fans, clubs, and sponsors associate with a player's effectiveness. Assists highlight a player's ability to create scoring opportunities for teammates, reflecting creativity and vision--qualities highly prized in professional soccer. These two statistics are widely recognized as the most concrete and quantifiable measures of offensive contribution, which strongly influence contract negotiations and salary levels. Their consistent importance across different models underscores the centrality of direct involvement in goal-scoring actions as a primary driver of player salary. Moreover, two advanced performance metrics, _xGChain_ and _xGBuildup_, emerge as key contributors in the SHAP analysis. _xGChain_ quantifies the total expected goals (xG) value of all attacking actions a player participates in during a possession sequence culminating in a shot. This metric effectively captures the player's cumulative influence throughout buildup and passing sequences, highlighting their integral role in offensive plays beyond merely taking shots. Conversely, _xGBuildup_ isolates the expected goals generated during the buildup phase by excluding key passes and shots. This emphasizes a player's contribution in advancing the ball and facilitating scoring opportunities prior to decisive attacking actions. Collectively, these metrics enrich the evaluation of a player's offensive impact by encompassing both direct and indirect contributions to goal-scoring chances, underscoring the multifaceted nature of attacking involvement. While we acknowledge the presence of highly correlated features such as _xG_ and _xGg_ or _aG_ and _aGg_, these engineered per game metrics provide important standardization for fair comparisons across players with differing playtime. Given the robustness of tree-based models used in our study to multicollinearity, we retain these features to capture complementary information.
# Privacy-Preserving Data Mining on Blockchain-Based WSNs
## 2 Related Work
### Blockchain Applications in WSNs  
The unique properties that blockchain networks have, and how they can benefit IoT and WSNs, has been studied extensively. Early research focused on using a blockchain network as an external component of systems that are mainly used as databases for key storage and WSN management [24; 25]. Blockchain networks have been used in combination with WSNs for data security, sensor node authentication [26], removing single points of failure in WSNs [27], and secure data accumulation [28].  
Recent advances in blockchain protocols have enabled the design of light clients, which are nodes participating in the network without needing to maintain the entire chain. This enables clients to run on sensor nodes with low computing power and storage requirements. Coupled with smart contract platforms, new applications, such as malicious node detection [29] and anomaly detection [30], have surfaced.  
Although we use blockchain for access control, which is a typical way to ensure trust between actors in a decentralized setup, the originality of our work lies in the use of smart blockchain contracts to ensure the authenticity of the onion messages, which is a novel approach to blockchain when combined with onion routing, as in this context.
## 3 Performance Characterization and Reliability Assessment
### Temperature and Humidity Test  
Figure 14 presents the results of comprehensive temperature and humidity testing conducted according to rigorous military standards (MIL-STD-810E [22]) to verify the  
Figure 13: Thermal shock testing results showing (**a**) the temperature cycling profile between \(-50\)\({}^{\circ}\)C and \(+145\)\({}^{\circ}\)C for 10 complete cycles and (**b**) insertion loss measurements before and after thermal shock testing for all three configurations, confirming no bonding interface failure occurred during the test.  
environmental reliability of all three resonator width configurations. The figure displays a detailed bar chart showing insertion loss measurements (in dB) for all five samples of each configuration (L1, L2, and L3), with paired measurements taken before and after exposure to extreme environmental conditions. Following protocols from MIL-STD-810E Method 501.4 (high temperature), MIL-STD-810E Method 502.4 (low temperature), and MIL-STD-810E Method 507.4 (humidity), the devices were subjected to extended periods of environmental stress designed to simulate real-world deployment conditions. The consistent performance values between pre-test and post-test measurements across all configurations demonstrate exceptional environmental stability, with no significant degradation in RF performance after testing. This data confirms that the Au-Au thermocompression bonding technique provides excellent resistance to temperature and humidity variations across all resonator width configurations. Most importantly, the L3 configuration (100% width matching) maintains its superior insertion loss performance even after environmental testing, demonstrating that the optimized resonator design delivers both enhanced electrical performance and environmental reliability. These results further validate the suitability of the L3 design for deployment in demanding applications where environmental stability is a critical requirement alongside optimal RF performance.
# Threat Analysis and Risk Assessment (TARA) Analysis of an Autonomous Emergency Braking (AEB) System
## 6 Risk Analysis
### High-Risk-Level Threats and Mitigation Strategies
#### 6.2.2 Tampering with Radar_to_ESC_CAN_3  
**Security Risk**. Due to this, the information exchanged between the radar and the ESC system using the CAN communication channel can develop a very serious security breach, as attackers with physical access to the OBD connector would be able to manipulate the radar data. An \"adjacent\" attack against the AEB system would eventually destroy the proper operation or result in its failure and increase the chance of accidents due to false data. Although privacy is not directly affected, the operational and safety impacts are severe, potentially making the vehicle unsafe to drive. In general, the risk is considerable given the medium feasibility of the attack and its potentially severe consequences. **Security Measures**. For risk mitigation in regard to tampering with the radar-to-ESC CAN, cryptographic message integrity checks are required, like HMAC, to ensure the authenticity and integrity of radar data. The regular auditing and monitoring of the communication system would help in response and detection against any type of anomalies or unauthorized activities. These measures would also be important in ensuring the integrity of radar data, the reliable performance of the AEB system, and improving general safety through the prevention of accidents occurring as a result of tampered data.
## 2 Methodology
### EnGS-DGR Model  
In this subsection, EnGS-DGR model is proposed to capture the spatial-temporal correlation characteristics of traffic flow, which is mainly composed of two parts: a dynamic graph reconfiguration algorithm with a specific forecasting interval and a deep-learning integration strategy combining the graph convolutional network (GCN) with the sequence-to-sequence (Seq2 Seq) learning modular. The framework of the proposed EnGS-DRG model is shown in Figure 4.  
Figure 3: The process of traffic flow forecasting.  
Figure 2: The definition representation of traffic flow forecasting problem.  
Firstly, the multi-view subspace clustering theorem is adopted to evaluate and quantify the correlation degrees of each traffic node with the specific forecasting interval, then a subgraph is reconfigured by taking into consideration the traffic nodes with correlation degrees higher than a defined threshold. Taken the time series of reconfigured subgraph as the input, the overall spatial characteristics of traffic network are aggregated by GCN. Fusing the output of GCN with the local information of the selected nodes as the input, we adopt the Seq2 Seq modular to extract the temporal characteristics. In the end, by mixing the obtained spatial-temporal correlation characteristics through cross correlation coefficients, we obtain the traffic flow forecasting result. The special details of the EnGS-DRG model are described as follows:
# Cognitively Driven Arabic Text Readability Assessment Using Eye-Tracking
## 5 Results and Discussion
### Readability Prediction at the Word Level
#### 5.1.2 Model Comparison  
Finally, to answer our last question, we compared model performance using WEKA by setting linear regression as our base model. The results are illustrated in Tables 5 and 6, with and without using eye-tracking data, respectively.  
As can be seen from Table 5, when we used only linguistic features, MLP, SMOreg, and M5P showed statistically significant improvement over linear regression in their correlation coefficients between the predicted values and the actual values. In contrast, REPTree had a significantly worse correlation coefficient than linear regression. MLP and SMOreg showed a significant decrease for different types of error, and SMOreg had better performance (lower errors) than did MLP.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Performance Measure** & **LR** & **MLP** & **SMOreg** & **MSP** & **REPTree** \\ \hline CC & 0.14 & 0.76 v & 0.93 v & \(-\)0.02 & 0.00 \\ MAE & 12.43 & 8.00 * & 4.90 * & 12.68 v & 12.58 \\ RMSE & 15.43 & 9.94 * & 6.25 * & 15.60 & 15.63 \\ RAE & 98.76 & 64.20 * & 39.28 * & 100.92 v & 100.00 \\ RRSE & 98.76 & 63.86 * & 40.51 * & 100.05 & 100.00 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Model comparison when using eye-tracking data. The asterisk (*) indicates statistically significantly worse and (v) indicates statistically significantly better.  
Figure 4: Correlation ranking attribute evaluation (with eye data).  
For decision trees, M5P showed significant improvement in the mean absolute error and the relative absolute error only, and REPTree did not show any significant difference compared to linear regression. Thus, the best-performing model using linguistic features only was SMOreg, followed by MLP.  
We can see from Table 6 that when we used both linguistics and eye-tracking features, MLP and SMOreg showed a statistically significant improvement over linear regression in their correlation coefficients. In contrast, neither decision tree showed a significant difference compared to linear regression. For different types of error, similar to Table 5, MLP and SMOreg showed a significant decrease, and SMOreg had better performance (lower errors) than did MLP. For decision trees, M5P showed a significant increase in the mean absolute error and the relative absolute error, and REPTree did not show any significant difference compared to linear regression. Thus, similar to the previous results, the best-performing model in our dataset and the features used was SMOreg, followed by MLP.
## 2 Public Schools Case Study  
Generally, consultants, engineers, and designers spend the majority of their efforts in the pre-retrofit implementation phase, where different suggestions, calculations, and assessments are carried out. Based on these calculations and assessments, the customer or the building owner is promised a certain projected impact of the energy retrofit project, in terms of saving energy consumption and, hence, improving the overall performance of the targeted building or facility. However, very little is being done after the retrofit project is implemented, which is the most important part of the project for the customer or the user. This post assessment phase or continuous commissioning of the retrofit project is crucial to ensure that the implemented improvement measures have yielded the positive impacts that were projected at the design phase. In this work, a case study of three public schools is considered for post-retrofit process evaluation and assessment. The three schools were heavily retrofitted by September 2018 with various improvement measures targeting buildings envelope and energy systems. Below, overall information on the three schools will be presented. The three schools' identity is kept anonymous and, thus, will be called 'School A', 'School B', and 'School C'.
## 4 Result
As the ratio increases, the _Accuracy_ generally decreases, with the lowest _Accuracy_ recorded at the 32:1024 ratio, which is 85.0%, suggesting a diminishing return or even a detrimental effect with excessive attention complexity. The highest _Sensitivity_ is seen at the 2:64 and 4:128 ratios, both achieving 98.2%. This suggests that these configurations are particularly effective at identifying TPs, crucial for not missing actual falls. _Sensitivity_ drops significantly at the 32:1024 ratio, reaching a low of 76.4%, indicating that overly complex attention mechanisms might diffuse focus rather than enhance it. The highest _Precision_ is found at the 2:64 ratio, with a value of 92.9%, demonstrating that this configuration has the most accurate positive predictions. _Precision_ varies across different ratios, with the lowest _Precision_ at the 1:32 ratio, which is 88.7%, indicating that insufficient attention heads or channels might lead to more FPs. The highest _F1-Score_ is observed at the 2:64 ratio, with a value of 95.5%, indicating that this configuration offers the best balance between _Precision_ and _Sensitivity_. The _F1-Score_ decreases at higher ratios, with the lowest value at the 32:1024 ratio, which is 83.5%, reinforcing the notion that the 2:64 ratio provides the optimal trade-off between accurately detecting falls and minimizing false alarms. Overall, the 2:64 ratio appears to be the most effective configuration across all metrics, providing the highest _Accuracy_, _Sensitivity_, _Precision_, and _F1-Score_. As the ratio of attention heads to key channels increases beyond this optimum, the performance of the model generally declines, suggesting that an excessively complex attention mechanism can lead to overfitting or an inability to generalize effectively. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Architecture**} & \multicolumn{4}{c}{**Evaluation Metrics [\%]**} \\ \cline{2-5}  & _Sensitivity_ & _Precision_ & _Accuracy_ & _F1-Score_ \\ \hline Raw+LSTM & 100 & 50 & 50 & 66.7 \\ Raw+LSTM+Attention & 100 & 50 & 50 & 66.7 \\ HOG+LSTM & 94.5 & 88.9 & 91.4 & 91.6 \\ HOG+LSTM+Attention & 98.2 & 92.9 & 95.3 & 95.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Validation results of fall detection of four types of proposed fall detection systems.
# Heterogeneous Network Switching Strategy Based on Communication Blind Area Dwell Time
## 4 Simulation Results  
This experiment uses a room layout of 6 m \(\times\) 6 m \(\times\) 3 m, where the coordinates of LED light sources with a covering radius of 1 m are (1, 3), (3, 1), (3, 5), and (5, 3). The coordinates of LED light sources with a covering radius of \(\sqrt{2}\) m are (1, 1), (1, 5), (3, 3), (5, 1), and (5, 5). The user-receiving device moves randomly in the speed range of 0.3 to 0.7 m/s. The time that the user receiving device moves at a value higher than the normal communication BER is the communication blind spot dwell time, and the time required for network handover is the handover delay time, as shown in Table 1.  
In the whole visible optical communication networking system, in order to highlight the communication stability of the CBDHVHO solution, the BER and the probability of handover failure are first used as performance indicators. The BER is a measure of the accuracy of data transmission within a specified period of time, and the size of the handover failure probability affects the user's voice call and Internet experience. The average number of handovers reflects the signaling cost of the visible optical communication system, and the lower number of handovers avoids the ping-pong effect it generates. Finally, the average network throughput is used as a communication quality indicator, and high data throughput can support functions such as high-definition video calls and livestreaming even when users are mobile. Also, the I-VHO scheme [19] and the D-VHO scheme are used as comparison experiments to highlight the better performance of the CBDHVHO scheme through comparison experiments.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Room size & 6 m \(\times\) 6 m \(\times\) 3 m \\ Velocity & 0.3–0.7 m/s \\ Movement time duration & 1–30 s \\ Throughput for VLC & 400 Mbps \\ Throughput for Wi-Fi & 100 Mbps \\ Number of VLC APs & 9 \\ Radius of APs & 1 m, \(\sqrt{2}\) m \\ Number of iterations & 1000 \\ Number of RF APs & 1 \\ Handover delay time & 0.1–1 s \\ Time frame for response time & 1 s \\ Maximum waiting time & 2 s \\ BER Scope & \(10^{-7}\)–0.025 \\ Communication blind area dwell time & 0–7 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.
# Sensorless HSPMSM Control of an Improved SMC and EKF Based on Immune PSO
## 3 Design of the Speed Controller Based on Improved SMC
### Improvement of Speed SMC  
The exponential convergence law is improved to further reduce the convergence time to the SMS and to increase the convergence speed, which causes jitter in the system due to overly fast convergence [37]:  
\[\dot{s}=\left[-\xi\text{arctan}|x_{1}||sat(s)/\left[\eta\exp(-\delta|s|) \right]\right]-qs \tag{20}\]where \(\eta\), \(\delta\), \(\epsilon\), and \(q\) are constants greater than \(0\).  
The exponential term \(e^{-\beta|s|}\) is thus introduced into the system. When \(|s|\) is large, that is, when the system state is far from the SMS, the exponential term approaches \(0\), the isokinetic term \(esgn(s)\) is amplified, and the system accelerates to approach the SMS under \(esgn(s)\) and \(qs\). When \(|s|\) approaches \(0\), the exponential term approaches \(1\) and gradually fails. \(\eta\) can adjust the distance from the sliding mode to the system state whose denominator is near \(0\). Furthermore, the boundedness of the range of the inverse tangent function ensures that the speed is not overly large and avoids system chattering. In addition, \(sat(s)\) is used instead of \(sgn(s)\) to further weaken the chattering phenomenon of the system.  
\[sat(s)=\left\{\begin{array}{ll}-1&s<-\triangle\\ ks&|s|\leq\triangle&k=1/\triangle\\ 1&s>\triangle\end{array}\right. \tag{21}\]  
The improved asymptotic LSMC, GSMC, and ISMC are obtained from the above as follows:  
\[u^{\prime}=\left\{cx_{2}+[\text{arctan}|x_{1}|sat(s)]/[\eta\exp(-\delta|s|)] +qs\right\}/D \tag{22}\]  
\[u_{1}^{\prime}=\left\{c_{1}x_{2}+\lambda h(0)\exp(-\lambda t)+[\text{arctan}| x_{1}|sat(s_{1})]/[\eta\exp(-\delta|s_{1}|)]+qs_{1}\right\}/D \tag{23}\]  
\[u_{2}^{\prime}=\left\{c_{2}x_{2}+c_{3}x_{1}+[\text{arctan}|x_{1}|sat(s_{2})] /[\eta\exp(-\delta|s_{2}|)]+qs_{2}]\right\}/D \tag{24}\]
## 4 Classification of MAC Protocols Based on Channel
### Cooperative Multi-Channel MAC Protocols
#### 4.7.2 Recv-Mac  
Reliable and efficient cooperative (RECV) MAC is a novel protocol proposed to provide efficient communication with high throughput. The protocol provides better packet dropping rates (PDRs) and lowers delay for safety messages. CSMA/CA is used with random access as in IEEE 802.11p. Modified control messages are used to provide cooperative communication. Negative acknowledgement (NACK) and keen to help (KTH) messages are introduced for exchange of safety messages. Cooperative wave service advertisement (CWSA) and willing to involve (WTI) are introduced for non-safety messages. Packets transmit NACK; if the node hears broadcasts but does not receive a notification in short inter-frame space (SIFS), there is failed transmission. These packets are sent through cooperative transmission to improve the transmission reliability. After NACK, the nodes with improved transmission rate, SINR, and proper channel condition transmit KTH. Upon receiving KTH, an optimal helper node (having optimal SINR) can be chosen among neighboring nodes. Basic service set (BSS) is used for non-safety message transmission, with a period broadcasting WSA in the CCH interval. The receiver sends WTI, and when a neighbor node has better SINR and channel conditions, it sends CWSA to the sender. The node with optimal SINR is chosen as the optimal WAVE helper node; after hearing the selector helper message (SHM), other nodes suspend cooperation with WTI. The optimal helper joins BSS, and thus, the sender transmits through the optimal helper to the receiver.  
RECV MAC provides an optimal helper selection mechanism for reliable data transmission. However, the performance of the protocol in unsaturated conditions and in high vehicle density is not analyzed. The power consumption factor is not taken into consideration.