esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Temperature Dependence  
### Temperature Dependence of Linear Elastic Parameters  
The procedure proposed allows us to determine, for each temperature, the linear viscoelastic (velocity and damping) parameters. Indeed, by extrapolating the curves of the velocity and damping versus strain to zero strain, it is feasible to obtain a prediction of the material parameters in the unperturbed state. The results are reported in Figure 4.  
As expected, the material becomes softer (the velocity diminishes) for both the Berea and concrete. A significant reduction (a few percent) is observed. In particular, the velocity decrease is 5.5 percent for Berea and 3.6 percent for concrete. The behavior of damping is different for the two materials. In the case of Berea, the damping coefficient \(a_{\text{lin}}\) increases significantly (81 percent), while for concrete, no significant trend as a function of temperature is appreciable.
## 3 Experiment and Results
### Datasets
#### 3.1.1 Human-Eye Dataset  
We used data collection equipment to capture eccentric infrared pupil images in Jinzhou and Tacheng, two locations 3900 km apart. In Jinzhou, we used our acquisition device to collect 6413 eccentric infrared pupil images and measured their refractive errors using an automatic refractometer to serve as the image labels. These data were used as the full-range dataset, Dataset1, with 5149 image sets as the training set and 1264 image sets as the test set. Among the 6413 image sets collected, the number of images with high-diopter hyperopia (\(\geq\)+2 D) is very low, as shown in Table 1.  
Twenty types of real-human-eye eccentric infrared pupil images corresponding to the model eyes' refractive errors were selected, excluding images with incomplete pupils or severe eyelash occlusion. All images with refractive errors (\(\geq\)+2 D) were included in  
Figure 16: Schematic diagram of the Residual-6 Block structure.  
the test set, referred to as Dataset2, with 403 image sets assigned to the training set and 177 image sets to the test set.  
Typically, the precision unit of optometry measurements is 0.25 D, but due to the limited precision of the model eyes we created, we only made model-eye datasets with 0.5 D precision units. To ensure consistency between the model eyes and real eyes in the experiments, we divided the dataset into Dataset1, containing all real-eye data with 0.25 D precision unit, and Dataset2, which was selected to match the precision of the model eyes.  
In Tacheng, a total of 7087 images were collected, with 5702 images used as the training set and 1382 images as the test set, forming Dataset3. Additionally, the images were filtered with a precision of 0.5 D, and all images with refractive errors (\(\geq\)+2 D) were assigned to the test set, resulting in Dataset4, which consisted of 860 images for training and 237 images for testing.
# Nutritional Analysis of Plant-Based Meat: Current Advances and Future Potential
## 3 Nutritional Challenges in Plant-Based Meat Products
### Anti-Nutritional Factors
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Anti-Nutrients** & **Source** & **Interaction with Nutrients** \\ \hline \multirow{2}{*}{Phytic acid} & \multirow{2}{*}{Cereals, legumes, nuts, and seeds that are used to produce extruded plant-based meat products} & It can bind to amino acids and minerals such as iron, zinc, and calcium, making them less bioavailable for absorption by the body. \\ \hline \multirow{3}{*}{Lectins} & Legumes, grains, and vegetables belonging to the group of plants in the Solanaceae family & Some lectins may interfere with nutrient absorption and cause digestive issues. \\  & (ex. tomatoes, eggplant) & \\ \hline \multirow{2}{*}{Oxalates} & \multirow{2}{*}{Spinach, beet greens, and nuts} & Oxalates can bind to calcium and other minerals, potentially reducing their absorption. \\ \hline \multirow{2}{*}{Tannins} & Tea, coffee, and certain plant foods like legumes, nuts, and some fruits. & Tannins can interfere with iron absorption and may cause digestive discomfort. \\ \hline \multirow{2}{*}{Protease inhibitors} & Legumes and grains & Protease inhibitors can interfere with protein digestion. \\ \hline \multirow{2}{*}{Saponins} & \multirow{2}{*}{Legumes, whole grains, and some vegetables} & Saponins may interfere with nutrient absorption and cause digestive issues. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Plant-based sources and effects of anti-nutritional factors. of added and native minerals [48; 52; 53]. Hence, phytase can potentially enhance the effects of fortification and absorption of inherent amino acids and minerals in plant-based meat. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Food** & **Phytic Acid** \\ \hline Almonds & 0.4–9.4\% \\ \hline Beans & 0.6–2.4\% \\ \hline Brazil nuts & 0.3–6.3\% \\ \hline Hazelnuts & 0.2–0.9\% \\ \hline Lentils & 0.3–1.5\% \\ \hline Maize, corn & 0.7–2.2\% \\ \hline Peanuts & 0.2–4.5\% \\ \hline Peas & 0.2–1.2\% \\ \hline Rice & 0.1–1.1\% \\ \hline Rice bran & 2.6–8.7\% \\ \hline Sesame seeds & 1.4–5.4\% \\ \hline Soybeans & 1.0–2.2\% \\ \hline Tofu & 0.1–2.9\% \\ \hline Walnuts & 0.2–6.7\% \\ \hline Wheat & 0.4–1.4\% \\ \hline Wheat bran & 2.1–7.3\% \\ \hline Wheat germ & 1.1–3.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Examples of high-phytate plant materials, as a percentage of dry weight.
# Route Planning for Autonomous Driving Based on Traffic Information via Multi-Objective Optimization
## 4 Experiments and Results
For example, in Figure 3e, the route (9, 10, 15, 19) and the route (9, 10, 17, 19) are very similar in terms of the travel distance and the number of intersections, but since intersection 17 has fewer branches (road complexity) than intersection 15, the route in Figure 3e is the optimal route. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Origin/Destination Pairs** & **(7, 24)** & **(1, 20)** & **(1, 19)** & **(2, 22)** & **(9, 19)** & **(16, 24)** \\ \hline (0.33, 0.33, 0.33) & 0.32 & 0.31 & 0.30 & 0.27 & 0.29 & 0.30 \\ (0.2, 0.4, 0.4) & 0.19 & 0.18 & 0.20 & 0.18 & 0.17 & 0.21 \\ (0.3, 0.2, 0.5) & 0.31 & 0.27 & 0.28 & 0.26 & 0.26 & 0.31 \\ (0.5, 0.3, 0.2) & 0.48 & 0.46 & 0.46 & 0.45 & 0.43 & 0.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The optimal comprehensive cost for six different OD pairs with four parameter settings. Figure 1: Transportation network used for experiments, (**a**) Map of Sioux Falls, (**b**) Complex network model of Sioux Falls. Through the above analysis of the results, it can be found that our framework can meet the requirements of designing better routes for travelers concerning travel distance, travel time, travel cost, personal preference and other aspects after the road traffic information has been collected. Compared with the paper [21, 22, 23, 24, 25, 26, 27, 28], our framework comprehensively considers multiple objectives, generates the optimal route that meets the driver's requirements and balances the relationship between total costs and personal preferences, which proves the feasibility and applicability of the general framework, completes the automatic driving framework and improves the safety and economy of automatic driving vehicles. Figure 3: _Cont._  
Figure 2: Basic statistics for traffic network of Sioux Falls, (**a**) Degree distribution, (**b**) Betweenness distribution, (**c**) Traffic flow distribution.
# Genetic Algorithm-Based Optimization of Clustering Algorithms for the Healthy Aging Dataset
## 5 Discussion
### Insight into the Clustering Based on Features Selected by the Best-Performing Algorithms  
From Table 2, it is seen that the GA with KMeans++ is the best-performing algorithm. The best performance metric was obtained for KMeans++ with the feature set [Age, Medication Keeps Patient from Sleeping, Gender]. Using training data, three clusters were formed with 323, 271, and 20 data points, respectively. The mean data points of the three clusters and the point nearest to the centroids, called the representative points for each cluster, are presented in Table 3.  
Table 3 provides some general observations for patients across all clusters, however, we would like to have a deeper insight into their behavior which can be obtained cluster-wise. The particular observations for the remaining features cluster-wise are presented in Table 4.  
For cluster 1, it was observed that physical health varied from very good to good, while mental and dental health were very good for most of the cases. This cluster had a balanced count of male and female patients aged between 50 and 64 who had some trouble sleeping. For cluster 2, it is observed that there are more females aged 65-80 and having good to fair physical health, with very good mental health and good dental health. They also have some trouble while sleeping. Finally, cluster 3 is a balanced dataset with patients' age varying from 65 to 80, having average physical and dental health, good mental health, and some trouble sleeping.
# Unobtrusive Respiratory Flow Monitoring Using a Thermopile Array: A Feasibility Study
## 3 Results
### Dataset B  
Table 1 contains the average of the results obtained for each subject for the four different respiration patterns used, with both the periodogram and AR model. The last row shows the overall average results, in bold we indicate the best results for RMSE, RE, and SNR. Figure 6, instead, shows the time domain signals, the spectra evaluated through FFT on each window, and the RRs estimated in comparison with the reference for the five cases. It should be specified that in the case of guided breathing with a sudden change, an additional filter was applied. As can be expected from the results obtained in Dataset A, breathing at 30 BPM will have a lower amplitude compared to the 10-BPM one. This effect caused a delay in the detection of the frequency change. Therefore, a ramp filter has been applied to each window of the sudden change signals to amplify high frequencies in the breathing range. Correlation plots and Bland-Altman analysis for all the subjects and breathing scenarios are displayed in Figure 7. The mean bias was 0.23 BPM with the 95% limits of agreement being \(-5.05\)-\(5.51\) BPM. Moreover, the results also showed a strong correlation between the impedance pneumography estimated RR and the one estimated with the thermopile array, yielding an \(r=0.95\,(p<0.0001)\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Breathing Pattern**} & \multicolumn{3}{c}{**Periodogram**} & \multicolumn{3}{c}{**AR**} \\ \cline{2-7}  & **RMSE (BPM)** & **RE (\%)** & **SNR (dB)** & **RMSE (BPM)** & **RE (\%)** & **SNR (dB)** \\ \hline
10 BPM & 0.00 & 0.00 & 8.71 & 0.00 & 0.00 & 13.59 \\
30 BPM & 0.00 & 0.00 & 5.15 & 0.00 & 0.00 & 8.15 \\ Chirp & 1.41 & 1.19 & 4.09 & 1.41 & 1.21 & 5.87 \\ Sudden Change & 5.57 & 7.79 & 2.19 & 6.57 & 9.61 & 7.90 \\ Spontaneous & 0.98 & 1.59 & 4.27 & 0.99 & 1.72 & 5.81 \\ \hline
**Average** & **1.59** & **2.11** & 4.88 & 1.79 & 2.51 & **8.26** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average of five subjects of the results obtained on Dataset B.  
Figure 5: Respiration detection through shoulder motion: (**a,b**) the first figure shows a thermal image, and the second one shows the spectrum peak magnitude distribution of a subject seated and lying respectively at 50 cm of distance; (**c,d**) overview of the differences in amplitude, SNR, number of pixels, and RMSE when data were collected from a seated subject at 50 and 100 cm.  
Figure 6: Results obtained for Dataset B with different patterns; on the left the time domain signal, in the middle the Fourier spectrum per each window, and on the right the RR extracted with both the periodogram and AR and the impedance pneumography one. (**a**,**b**) Collected during constant breathing pattern at 10 and 30 BPM, respectively; (**c**) chirp pattern; (**d**) pattern with two sudden changes; (**e**) spontaneous breathing. The time axis in the FFT and in the extracted RR figures ends at 90 s even though the original signal has a duration of 120 s. This is due to the sliding window approach using a window size of 30 s.
## 3 Data Protection Risks of ML Systems and Relations with GDPR Provisions
### Reconstruction Attacks  
It should be pointed out that even if only the features extracted from the input data are moved to and stored on the servers of the computation unit, there is still a risk of so-called reconstruction attacks. In these attacks, the malicious user tries to reconstruct the input data using the knowledge he can extract from the model's feature vectors. Reconstruction attacks occur when the feature vectors of the model are known; this is the case when the feature vectors that have been used to train the model have not been deleted after building the machine learning model [42; 43]. Some examples of successful reconstruction attacks include fingerprint reconstruction, where the image of a fingerprint was reconstructed from features of the fingerprint, and screen unlock pattern reconstruction, where the screen unlock pattern was reconstructed from features of the pattern, such as direction and speed.  
In both cases, the malicious user actually exploited a data protection threat (caused by not protecting the characteristics of the extracted input data) and was able to mount a security attack on mobile device user authentication systems since, in both cases, the attacker was able to bypass the user's authentication process, imitating the actual user of the mobile devices. Although the goal of the attacks was to log into a mobile device as a specific user by fooling the mobile device user authentication systems, such attacks may also reveal other personal data of the user, such as the user age or the user location. Interestingly enough, one could say that the \"source\" for instantiating such privacy risks is noncompliance with the GDPR's storage limitation principle.
# Review and Evaluation of MAC Protocols for Satellite IoT Systems Using Nanoatellites
## 6 Discussion and Open Research Challenges  
In this section, we discuss the performance evaluation provided in Section 5 together with additional characteristics of the IoT scenario described in Section 3. Of the protocols reviewed, although several options offer a high throughput in terms of communications performance, such a good result is associated with a medium to high cost in terms of implementation complexity and energy consumption (see the first four protocols in Table 2). Our comparative analysis of protocols suggests that the Aloha-based are good candidates for the MAC layer in nanosatellites devoted to IoT connectivity. This is mainly due to their simplicity of implementation and their minimum hardware requirements. These protocols also report having a low sensitivity to delay. In terms of network topology, the Aloha-based protocols can benefit from the power imbalance among nodes because of the so-called capture effect. In such a case, the receiver can correctly receive a packet with a high signal strength despite the existence of interference with other transmissions with lower power levels. Among the protocols in this category, the E-Aloha is the current solution for commercial telemetry satellite systems, has been extensively tested, and is operative. The performance of E-Aloha is similar to that of S-Aloha, with no need of synchronization. However, the main problem with E-Aloha is it may lack scalability for massive applications, due to its poor performance even for moderate traffic loads, as shown in Table 2. SS-Aloha, on the other hand, has much better performance than other Aloha-based protocols. However, this mechanism does not benefit from the capture effect; on the contrary, its performance falls to values similar to S-Aloha in a situation of power imbalance. When examining the protocols based on interference cancellation (e.g., CRDSA and E-SSA in Table 2), the limitations in processing capacity in CubeSats, together with the adverse conditions to perform correct channel estimation in LEO orbits, make employing such protocols in the IoT study scenario infeasible. Other protocols in this category, such as MuSCA, CSA, and E-SSA, require the exchange of coordination or channel estimation information delivered in advance or through a separated channel, making it more demanding regarding channel resources. As for protocols that require the carrier sensing mechanism, i.e., CSMA/CA, they have been shown to be highly inefficient given a topology with a moderate to a high number of hidden nodes, which will be a reasonably common scenario given the random distributions of ground sensors and devices serving a variety of IoT applications' requirements. The performance of such protocols also
# Predictive Maintenance Based on Identity Resolution and Transformers in IIoT
## 2 Related Works
### Equipment Condition Prediction  
Many scholars focus on predicting the condition of industrial equipment. [PERSON] et al. [14] proposed a hybrid multi-stage methodology for predicting the remaining useful life (RUL) of control systems using the unscented Kalman filter (UKF) and dynamic Bayesian networks to handle uncertainty in nonlinear systems. The UKF models optimize the degradation process of a system and estimate its state through a cyclic iteration. [PERSON] et al. [15] developed a fault prognosis method combining stacked autoencoders and continuous deep belief networks (DBNs). They constructed a health index from vibration signals and used a continuous DBN to predict future performance, smoothing data with an exponentially weighted moving average. [PERSON] et al. [16] presented a real-time fault prediction approach for rolling element bearings, using a dynamic three-sigma interval and an LSTM model to estimate future trends and determine RUL. [PERSON] et al. [17] proposed a new RUL prediction method using deep reinforcement learning (DRL), which improves accuracy and stability by addressing temporal correlations that are often overlooked by deep learning (DL) methods. An autoencoder extracts features, and a DRL model based on the TD3 algorithm is trained, resulting in better performance on the XJTU-SY dataset. [PERSON] et al. [18] introduced MFSSCINet, which enhances RUL prediction accuracy by using a feature-sequence convolution network. It captures interactions across feature and sequence dimensions and integrates data from different sensors and time points. Validation on C-MAPSS and XJTU-SY datasets shows that MFSSCINet outperforms existing methods. [PERSON] et al. [19] proposed the deep subdomain adaptation time-quantile regression network (DSATQRN) model, which addresses this by aligning feature distributions and using temporal quantile regression for interval predictions, with uncertainty quantified by kernel density estimation. Experiments show that DSATQRN achieves high accuracy and robust performance.  
While these methods effectively predict the state of equipment, they often overlook data dependencies. To address this, the authors apply a self-attention mechanism to extract features from time series data, enhancing the ability of model to capture long-range dependencies and complex patterns. This leads to more accurate and robust predictions, especially for non-linear or time-varying data. Integrating these features with the original data enriches the representation for advanced predictive modeling and analysis.
# Fault Location Method of Multi-Terminal Transmission Line Based on Fault Branch Judgment Matrix
## 5 Conclusions  
To address the problem of fault location in multi-branch grid-connected lines, this paper proposes a CEEMDAN-TEO-based fault ranging method for multi-terminal transmission lines. This method combines CEEMDAN and TEO to accurately extract fault mutations under strong noise interference and accurately capture fault-traveling wave-heads, providing a foundation for subsequent accurate fault location. The traditional double-ended weakness-ranging algorithm is also improved by proposing a double-ended ranging method based on line mode components, which eliminates the influence of line wave speed uncertainty on fault-ranging results. This method does not need to consider line wave fold reflection or fault occurrence time.  
For fault branch determination in multi-terminal transmission lines, this paper constructs a fault branch determination matrix based on the difference between the fault transmission distance and the line branch length. It combines the traveling wave head capture method and the improved double-end fault ranging form to achieve accurate fault location. It compares it to the current multi-terminal transmission line location method to achieve precise fault branch determination. The proposed method can achieve accurate fault branch determination and high-ranging accuracy, meeting the requirements of actual working conditions.  
Conceptualization, Q.Z. and Y.Y.; methodology, M.W. and X.W.; software, M.W.; validation, M.W., E.Q.; formal analysis, X.W.; investigation, Y.Y; data curation, X.W. and E.Q.; writing--original draft preparation, Q.Z.; writing--review and editing, Y.Y. and Q.Z.; visualization, E.Q. All authors have read and agreed to the published version of the manuscript.  
This research was supported by [Jilin Province Science and Technology Development Program Project], grant number [20220201075 GX].  
Not applicable.  
The data used to support the findings of this study are included in the article.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fault Point** & **Fault Type** & **Beginning** & \begin{tabular}{c} **Actual** \\ **Failure** \\ **Distance/km** \\ \end{tabular} & \
\begin{tabular}{c} **Calculation** \\ **of Faults** \\ **Distance/km** \\ \end{tabular} &
\begin{tabular}{c} **Positioning** \\ **Error/m** \\ \end{tabular} \\ \hline \multirow{3}{*}{N2-T2} & Bg & N2 & 40 & 40.376 & 376 \\  & ABg & N2 & 40 & 40.341 & 341 \\  & BC & N2 & 40 & 39.795 & 205 \\  & ABC & N2 & 40 & 40.267 & 267 \\ \hline \multirow{3}{*}{T2 node} & Bg & N2 & 100 & 100.401 & 401 \\  & ABg & N2 & 100 & 99.654 & 346 \\  & BC & N2 & 100 & 100.317 & 317 \\  & ABC & N2 & 100 & 100.397 & 397 \\ \hline \multirow{3}{*}{T1-T2} & Bg & N2 & 110 & 110.295 & 295 \\  & ABg & N2 & 110 & 110.364 & 364 \\ \cline{1-1}  & BC & N2 & 110 & 109.629 & 371 \\ \cline{1-1}  & ABC & N2 & 110 & 109.713 & 287 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The results of reference [20] under different experimental conditions.