esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Efficient Dynamic Symmetric Key Based Protocol for Secure Traffic Exchanges in Smart Homes
## 5 Performance Evaluation
### Communication Overheads  
Based on Figure 3, the following messages are exchanged during the AKA phase: \(\{\rm P_{U},\mathbb{Q}_{U},\mathbb{R}_{U},T_{1}\},\{\rm P_{S},T_{3}\},\{\rm P_{ T},T_{5}\}\) and \(\{\rm B,P_{TA},T_{6}\}\). Using the values in [40], all user and device identities =128 bits, pseudo-random numbers = 128 bits, symmetric key output = 256 bits, hashing output = 160 bits, certificates =128 bits, nonce = 160 bits, signatures = 160 bits, and timestamp = 32 bits. Therefore, Here, \(\rm P_{U}=P_{S}=P_{T}=P_{TA}=160\) bits, \(\mathbb{Q}_{U}=256\) bits, \(\mathbb{R}_{U}=160\) bits, \(\rm T_{1}=T_{3}=T_{5}=T_{6}=32\) bits, while \(\rm B=256\) bits. As such, the cumulative communication complexity of the proposed protocol is 1440 bits. Table 11 compares this value with other state-of-the-art schemes.  
Based on the graphs in Figure 6, the protocol in [56] has the highest communication costs of 3296 bits. This is followed by the schemes in [39; 40; 43; 47] with 2268 bits, 2048 bits, 1856 bits, and 1728 bits, respectively.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Scheme** & **Total Costs (Bits)** \\ \hline [PERSON] et al. [39] & 1728 \\ \hline [PERSON] et al. [40] & 1856 \\ \hline [PERSON] et al. [43] & 2268 \\ \hline [PERSON] et al. [47] & 2048 \\ \hline [PERSON] et al. [56] & 3296 \\ \hline Proposed & 1440 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Communication costs.  
Figure 5: Computation costs comparisons.  
On the other hand, the proposed protocol incurs the lowest communication overheads of only 1440 bits during the mutual authentication and key negotiation phase. Therefore, it makes the most effective usage of the available bandwidth.
## 4 Results and Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network** & **Precision** & **Recall** & **Specificity** \\
**Performance Label** & & & \\ \hline
0 & 0.783 & 0.9 & 0.995 \\
1 & 0.914 & 0.855 & 0.995 \\
2 & 0.837 & 0.806 & 0.978 \\
3 & 0.835 & 0.868 & 0.942 \\
4 & 0.895 & 0.87 & 0.957 \\
5 & 0.902 & 0.925 & 0.972 \\
6 & 0.806 & 0.806 & 0.996 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Three indicators of classification performance. Figure 9: Accuracy of RWP test dataset. Considering the cost issue, researchers are unable to construct actual unmanned aerial vehicle (UAV) networks every time to obtain operational data. It is also difficult to deploy truly controllable UAV mobile networks for performance evaluation. Therefore, in most cases, network planning can only be achieved through simulation platforms. During this process, the choice of the mobility model will directly affect the rationality of network planning and ultimately affect the performance of network information transfer. Moreover, the selection of simulation experiment parameters for nodes' mobility models also has a significant impact on research results. It can be seen that research on mobile models has extremely important practical significance and application value for the development of UAV wireless network systems. Under different node mobility models, the performance of the same routing protocol varies significantly, so it is crucial to select an appropriate node movement model when conducting UAV routing protocol research.
# Mean Reattachment Length of Roof Separation Bubbles Using Proper Orthogonal Decomposition
## 4 Results  
### Pressure Fluctuation Characteristics  
In this study, the POD method was employed to assess the mean reattachment length (\(X_{r}\)) of roof separation bubbles based on the wind pressure coefficient fluctuation components. The characteristics of the pressure distribution were analyzed based on the standard deviations of the surface pressure coefficients, as depicted in Figure 7.  
At an intermediate turbulence intensity (\(I_{u}=12.7\%\)), when the wind angle (\(\alpha\)) is \(0^{\circ}\), as depicted in Figure 7a, the standard deviation of the roof surface pressure coefficients shows marked fluctuations, especially near the left and right front roof corners and in an area approximately 200-300 mm from the front edge. Fluctuations at the corners are influenced by the roof corner's impact on flow dynamics. Additionally, the high fluctuation components occurring inside the roof are believed to be associated with roof separation bubbles and the vortices within them. When the flow has a wind angle (\(\alpha\)) of \(5^{\circ}\) (as seen in Figure 7b), the center of the prominent fluctuation inside the roof shifts slightly leftward from the centerline, influenced by the wind direction.  
Figure 7: Standard deviations of the roof surface pressure coefficients under varying turbulence intensities and wind angles: (**a**) \(\alpha=0^{\circ}\), \(I_{u}=12.7\%\); (**b**) \(\alpha=5^{\circ}\), \(I_{u}=12.7\%\); (**c**) \(\alpha=0^{\circ}\), \(I_{u}=0.6\%\).  
Under conditions of low turbulence intensity (\(I_{u}\) = 0.6%), as illustrated in Figure 7c, the area within the roof that exhibited high standard deviation values under intermediate turbulence intensity (\(I_{u}\) = 12.7%) appears to have reduced standard deviations and moved further inward on the roof surface. This may be due to the center of the recirculation region beneath the separated and reattaching streamline moving away from the leading edge as the turbulence intensity (\(I_{u}\)) at the model height decreased. In flow conditions with lower turbulence intensity (\(I_{u}\) = 0.6%), there is less variation in wind pressure within the separation bubble compared to conditions with an intermediate turbulence intensity (\(I_{u}\) = 12.7%). This is attributed to the more stable nature of the flow and the extended reattachment length across the roof. Such stable flow in lower-turbulence conditions leads to a more uniform reattachment of the flow on the roof surface, consequently resulting in reduced fluctuating wind pressure.
# Inverse Solution Error Analysis and Correction of Beam Steering System Based on Risley Prisms
###### Abstract  
The pointing accuracy of the Risley prisms beam-steering system mainly depends on the accuracy of the solution method and the impact of the error on the solution. To improve pointing accuracy, the impact of systematic errors on the inverse solution precision is investigated and a correction method is proposed. First, a more accurate error model for Risley prisms is established, and the errors are obtained by the parameter identification method, which corrects the forward solution error. Second, we explain the reason for the error generated by the inverse solution and analyze the variation of the inverse solution error magnitude with the beam deflection angle. A correction method based on pointing-field transformation is proposed. Finally, simulations and experiments are performed to verify the feasibility of the method. Experimental results show that when the beam deflection angle is equal to \(0.1^{\circ}\), the maximum and RMS values of the pointing error are reduced by 94.08% and 95.18%, respectively.  
R  
R  
isley prisms; pointing error; laser communication; inverse solution; error correction
## 4 Conclusions  
The binding energy and the optical responses of the \((D^{+},X)\) complex depending on the influences of temperature and pressure combined to the size effect, have been investigated. Our results show that the pressure increases the electron-hole and electron-ionized donor attraction, consequently reinforcing the confinement effect. On the other hand, the temperature effect decreases the binding energy due to the electron-hole-ionized donor recombination reinforcing. We also have shown that in the strong confinement regime (\(R<1\)\(a_{D}\)) the \(E_{(D^{+},X)}^{b}\) is less sensitive to both \(T\) and \(P\). Also, the optical investigation shows that the \(AC\) and the \(RI\) depend deeply on the pressure and the temperature as the curves show a shift to higher (lower) therahertz frequencies when the pressure (temperature) increases. These effects can be exploited to change the \((D^{+},X)\) optical properties without modifying the radii of QD, but just by controlling the applied pressure and temperature. In a future work it is worthy to investigate the coupling effects between multiple quantum dots and to see how these effects influence the opposite effects caused by temperature and pressure which offer an alternative approach for the adjustment and the control of the optical frequencies. For now, we believe that this study contributes to a better understanding of the \((D^{+},X)\) optical properties associated to \(1s-1p\) transitions and to exploit them in different terahertz applications. We have finished our study by a significant comparison between optical properties of \((D^{+},X)\) complex, exciton and donor impurity. We have found that the ionized donor's interaction with the exciton causes a shift towards the low energy regions of their optical peaks compared to those of the exciton. These important results can help in the identification of the different optical curves obtained experimentally for different materials and can lead to a novel terahertz detectors concept by controlling size, pressure and temperature.  
Conceptualization,N.A., S.B. and E.M.F.; methodology, N.A.; software, N.A., L.M.P.; formal analysis, N.A. and E.M.F.; resources, L.M.P., D.L.; data curation, N.A.; writing--original draft preparation, N.A., E.M.F.; writing--review and editing, N.A., F.D., E.M.F.; visualization, L.M.P., A.T., D.L., A.E.F., M.E.-Y.; supervision, D.L., S.B., E.M.F.; project administration, E.M.F.; All authors have read and agreed to the published version of the manuscript.  
LMP and DL acknowledge partial financial support from FONDECYT 1180905. AT acknowledges financial support from Postdoctoral Fellowship FONDECYT 3180276. DL acknowledges partial financial support from Centers of excellence with BASAL/ANID financing, AFB180001, CEDENNA.  
The data are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.  
Figure 4: The absorption coefficients (**a**) and The refractive index changes (**b**) against incident photon energy \(\hbar\omega\) for exciton, \((D^{+},X)\) complex and impurity and for \(2\)\(a_{D}\).
# Real-Time 3D Reconstruction Pipeline for Room-Scale, Immersive, Medical Teleconsultation
## 6 Evaluation
### Performance Characteristics  
#### 6.3.1 CPU Scaling  
While most of our pipeline is performed on GPUs, we perform the extended recursive mesh extraction (Section 4.1), which has significantly more procedures to perform than the original work by [PERSON] et al. [17] on CPU using task-based parallelization. We use a simple work-stealing-based scheduler with randomized victim selection based on the work-stealing queue of [PERSON] et al. [75] with the modification to steal tasks in batches of 32 to reduce overhead.  
Even though the scheduler has significant overhead (Figure 17a), we can hit our target run time of 31 ms with 14 cores for extracting an unsimplified mesh and 9 cores using a simplified mesh. We chose 31 ms rather than 33 ms for 30 FPS to accommodate CPU/GPU memory copies and mesh finalization, which takes less than 1 ms of GPU time in our measurements (Table 2).
## 4 Results and Discussion
In this work we opted for using only two inertial sensors--placed on the feet--to simplify setup and reduce costs. However, complete movements (e.g., the strength training-related tasks) could not be evaluated. Reducing system complexity was preferred over the use of additional sensors for full-body movement evaluation. The use of the vaginal dynamometer was optional for women with urinary incontinence wanting to confirm pelvic floor muscle contraction. We opted for a web implementation of the exergame to ensure cross-platform compatibility and easy access without installation. The complexity of the graphical game contents was adapted to meet the continuously increasing, but relatively limited performance of web browsers. Bluetooth-enabled devices could connect via web, allowing data processing in real time and interaction (and feedback) without any noticeable delay. The VITAAL exergame was designed for use at clinics or at home--after an introductory session, the technical solution should promote independent use, with or without the support of a (remote) caregiver. Training at home is currently considered an alternative and/or complement to the training at clinics, allowing better access to services (e.g., in the context of a pandemic), promoting the continuity of treatments, and overcoming barriers related to the lack of resources at clinics [80]. The exergame operated in connection with a clinician portal that, besides allowing training specification, enabled the remote supervision of the training by the clinician. According to [81], exergames that fit older adults' characteristics and needs can be used to counteract the consequences of confinement and hospitalization, such as increased vulnerability, decreased functional capacity, and dependency. VITAAL may also find application in these contexts.
## 4 Results  
Table 6 presents the 5-fold CV results of all three models on the 13-class datasets. As mentioned above, randomized search CV was used to determine the best hyperparameter values on which to train each model. For SVM, they were \(C\) = 10 and the _rbf_ kernel [69]. For RF, _max_depth_ = 70, _max_features_ = \"sqrt\", _min_samples_leaf_ = 1, _min_samples_split_ = 10, _n_estimators_ = 190, and _bootstrap_ = False. Finally, the best hyperparameters for BERT on the 13 classes were _batch_size_ = 8, _learning_rate_ = \(5\times 10^{-5}\), and number of _epochs_ = 40. Since our dataset was imbalanced and we had multiple classes, instead of accuracy, we considered f1-score as the determining factor for identifying the best-performing models. In Table 6, we observe that training on Split 2 (see Table 4) produced the best models across all three classifiers. Table 7 presents the classification report of the best-estimating classifiers on the Split 2 dataset.  
The SVM classifier for 13 classes had an accuracy of 0.85 and an f1-score of 0.72, with regularization \(C\) = 0 and _rbf_ kernel as the best parameters. The RF classifier performed better with respect to the f1-score of 0.74, but had a slightly smaller accuracy of 0.84. The fine-tuned BERT model, on the other hand, outperformed both baseline classifiers with an accuracy of 0.92 and an f1-score of 0.87. The confusion matrices in Figure 5 of the classifiers  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & \multicolumn{2}{c}{**SVM Baseline**} & \multicolumn{2}{c}{**RF Baseline**} & \multicolumn{2}{c}{**Fine-Tuned BERT**} \\  & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** \\ \hline Split 0 & 0.86 & 0.71 & 0.81 & 0.61 & 0.87 & 0.77 \\ Split 1 & 0.84 & 0.63 & 0.79 & 0.58 & 0.84 & 0.70 \\
**Split 2** & **0.85** & **0.72** & **0.84** & **0.74** & **0.92** & **0.87** \\ Split 3 & 0.76 & 0.61 & 0.73 & 0.53 & 0.87 & 0.81 \\ Split 4 & 0.79 & 0.61 & 0.76 & 0.56 & 0.85 & 0.77 \\ \hline Mean & 0.82 & 0.65 & 0.78 & 0.60 & 0.87 & 0.78 \\ Standard Deviation (SD) & 0.04 & 0.05 & 0.04 & 0.07 & 0.03 & 0.06 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The 5-fold CV accuracies and f1-scores. The best results were obtained on the Split 2 dataset.  
provide more insight into the classification report and the scores that we observe in Table 7.
## 4 Implementation and Discussion
### Performance Analysis
#### 4.2.4 Security  
Since medical decisions and participant's identities are stored in the blockchain, data breaching is almost impossible [32]. P, D, I\({}_{\text{c}}\), and C\({}_{\text{P}}\) never share their private keys. Similarly, identifying those secret keys (private key) are also difficult. A hacker must guess a 256-bit sequence number, in other words, 2\({}^{256}\) probable numbers to achieve that secret key. Hashes of the locally stored PII and PPII are stored in the blockchain. Those immutable hashes inside blocks are used for verification. As a result, change in blockchain or even local databases is easily tracked to retrieve the originality of the data [32]. In PoF every data stored in the cloud (blockchain) are encrypted with private-public keys. Those keys are accessible only by the patients. In addition to that, before data are stored in the cloud-based blockchain, hashes are stored in local databases. At the same time, a digital signature by a decision giving entity also ensures data integrity.
# A Review on Data-Driven Condition Monitoring of Industrial Equipment
## 2 Data-Driven Condition Monitoring of Industrial Equipment
### Data Processing Methods  
Feature extraction and feature selection are the most critical parts of data processing techniques. Features extraction is the process of obtaining sensitive features from the original signal that effectively highlight variations between the various operation conditions of the equipment. Time-domain feature extraction, frequency-domain feature extraction, and time-frequency feature extraction are the primary feature extraction methods, with the kind and physical relevance of the derived characteristics dictating which approach is utilized. The method for selecting a sensitive, highly differentiated, and moderately dimensional collection of features from the initial defect feature set is known as feature selection [24; 25].  
Fourier Transform, Wavelet Transform, and Empirical Model Decomposition are the primary approaches. Figure 2 shows a simple flow chart for applying these three techniques.
## 4 Discussion  
This work makes a significant contribution by proposing the implementation of a vision system that determines maturity, firmness, and sugar content in a non-invasive manner. It should be noted that the computer vision (CV) system was calibrated using a refractometer, penetrometer, and the Citrus Color Index parameter. Table 10 shows several mathematical models proposed using fuzzy logic. From this, it can be seen that Models 5, 10, and 15 presented a statistical correlation value greater than or equal to 0.95, indicating a very strong correlation between the Citrus Color Index (CCI) and maturity, Brix degrees, and firmness. In comparison with the results proposed by [48; 49], Maturity Classification Model 5 presented a better result than its proposal with fuzzy logic and slightly lower than the one proposed with neuronal. It is noteworthy that your proposal employs the pixels of the sample. In the context of Brix degree prediction, Model 10 yielded superior outcomes compared to the approach proposed by [31]. Among the methodologies that leverage spectral information, Model 10 identified a statistical correlation exceeding 0.9. Regarding firmness, the model proposed by [50] exhibited comparable results to Model 15, which utilized the weight loss and CCI variables.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **Technique** & **Input** & **Output** & **R2** & \begin{tabular}{c} **Error Mean** \\ **Square (MSE)** \\ \end{tabular} \\ \hline Model 5 & Fuzzy & \begin{tabular}{c} CCI(\(3\times 3\)), CCI(\(5\times 5\)), \\ CCI(\(11\times 11\)), CCI(\(21\times 21\)), \\ CCI(\(31\times 31\)) \\ \end{tabular} & Maturity & 0.98 & 0.01 \\ \hline Model 10 & Fuzzy & \
\begin{tabular}{c} CCI(\(3\times 3\)), CCI(\(5\times 5\)), \\ CCI(\(11\times 11\)), CCI(\(21\times 21\)), \\ CCI(\(31\times 31\)) \\ \end{tabular} & \({}^{\circ}\)Brix & 0.98 & 0.082 \\ \hline Model 15 & Fuzzy &
\begin{tabular}{c} CCI(\(3\times 3\)), CCI(\(5\times 5\)), \\ CCI(\(11\times 11\)), CCI(\(21\times 21\)), \\ and CCI(\(31\times 31\)) \\ \end{tabular} & Firmness & 0.95 & 1.456 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of the different models.  
Figure 20: Mean square error of the predictions of the fuzzy inference systems of firmness.
# ICENET: An Information Centric Protocol for Big Data Wireless Sensor Networks
## 5 Simulations and Results
### Results
ICENET demonstrated itself to be a scalable solution, as it reduced the overhead as the number of nodes increased. This occurred because broadcast queries, taking into account the nodes that can provide the information, became more efficient as the number of nodes able to answer the query increased. On the contrary, the strategy adopted by CoAP led to a significant increase of overhead as the network became larger. Comparing the scenarios with 100 and 300 nodes, ICENET reduced the average overhead in 15%, while CoAP increased the overhead by approximately 52%. In the scenario with 300 nodes, ICENET presented an average overhead of 7%, while CoAP presented an average overhead of 91%. Figure 11: Average success rate. Figure 10: Number of data messages received by gateway.
## 5 Conclusions  
This study evaluates historical changes in concentration and the potential ecological risks of PTEs in the lake sediments from North Aral Sea. The conclusions are as follows:  
1. The contents of PTEs in the lake sediments from North Aral Sea changed abruptly around 1970 AD, which is associated with the anthropogenic intensification in the basin;
2. After 1975 AD, with the exception of as (I\({}_{\rm geo}<1\)), the geo-accumulation index of PTEs (V, Cr, Zn, Co, Pb, Ni, C, and Cd) was greater than 1, reflecting a moderately polluted status;
3. Before 1970 AD, the total ecological risk index was lower than 150, indicating a low risk, but since 1970, the total ecological risk index has exceeded 150, which represents a moderate risk.  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2076-3417/10/16/5623/s1](http://www.mdpi.com/2076-3417/10/16/5623/s1)]([http://www.mdpi.com/2076-3417/10/16/5623/s1](http://www.mdpi.com/2076-3417/10/16/5623/s1)), Table S1: Potential ecological risk index (PERI) of Potentially Toxic Elements (V, Cr, Co, Ni, Cu, Zn, As, Cd and Pb) from the lake sediments of North Aral Sea.  
**Author Contributions:** Conceptualization, L.M.; Data curation, W.L.; Funding acquisition, L.M.; and J.A.; Investigation, L.M.; Methodology, L.M.; Project administration, L.M.; and J.A.; Visualization, W.L.; Writing--original draft, W.L. and L.M.; Writing--review & editing, L.M., J.A. and W.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Regional Collaborative Innovation Project of Xinjiang Uygur Autonomous Region of China (2020E01013), National Natural Science Foundation of China (U1603242), Tianshan Youth Program of Xinjiang Uygur Autonomous Region, China (2018Q008) and the High-level Training Project of Xinjiang Institute of Ecology and Geography, CAS (E050030101).  
**Acknowledgments:** We thank four anonymous reviewers for their helpful comments and suggestions that improved the manuscript. We thank [PERSON], [PERSON], [PERSON] and [PERSON] for their helps in the sampling process.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 7: Stacked area chart shows how the PTEs have collectively changed over the past ca. 70 years.