esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Safety Signs Using Computer Vision Based on Deep Learning
## 3 Deep Neural Network
### Attention Mechanisms  
The one-stage detection algorithm has been widely used in various fields because of its fast detection speed. [PERSON] [47] ran a variety of real-time object detection models on non-GPU computers, such as YOLO, Faster RCNN, R-FCN, RetinaNet, etc., and found  
Figure 1: The structure of YOLOV4-tiny deep neural network.  
that YOLO was faster and more accurate than most other algorithms. [PERSON] et al. [48] proposed a new detection algorithm, AP-SSD, based on the one-stage algorithm SSD, which reduced the computing cost. Nevertheless, it has a poor ability to extract target features containing small-scale pixel information, so it is expected that the one-stage detection algorithm could not perform well in detecting safety signs since safety signs are small-scale targets in coal mines. Adding attention mechanisms can greatly improve performance because the attention mechanism can highlight the characteristic information of the target and weaken the interference of background information. The central focus of the attention mechanism is to let the network focus on what needs more attention to reduce the arithmetic demand for network training. This paper adds the attention mechanism module after two feature outputs and upsampling, as shown in Figure 2. The implementation of the attention mechanism enhances the network's capability for information extraction, enabling the filtration and amplification of critical information within features while suppressing irrelevant data. Consequently, this allows the model to distinguish the target from the background more effectively with only minimal computational effort.  
Most attention mechanisms introduce extra variables and computations to improve the performance of the module. ECANet [49] is a new super strong channel attention mechanism based on SENet [50], which greatly reduces computation and optimizes speed and performance. ECANet's module structure is shown in Figure 3. After inputting the feature map \(\chi\) for the global averaging pooling across all channels without dimensionality reduction, ECANet achieves local interactions across channels using a one-dimensional  
Figure 2: YOLOV4-tiny structure with the attention mechanism.  
convolution of size (\(K\)). The size of the convolution kernel (\(K\)) represents the range of interactions across channels, which can be calculated by the adaptive function based on the size of the input channel (\(C\)), as shown in Equation (1).  
\[K=\frac{\log_{2}C+1}{2} \tag{1}\]
# Privacy-Preserving Byzantine-Resilient Swarm Learning for E-Healthcare
## 1 Introduction
In short, the main contributions of this paper include the following three aspects:  
* PBSL can not only protect the privacy of local sensitive data, but also ensure the confidentiality of the global collaborative model. Using the fully homomorphic encryption (FHE) scheme CKKS, PBSL encrypts the local gradient submitted by the medical center and aggregates to generate a global collaborative model without decryption. * PBSL can achieve swarm learning (SL) that resists Byzantine attacks. In order to resist Byzantine attacks, we propose a privacy-protecting Byzantine-robust aggregation method, which uses secure cosine similarity to punish malicious **M**Cs. Figure 1: (**a**) Traditional federated learning (FL) architecture and (**b**) swarm learning (SL) architecture. * PBSL prototype is implemented by integrating deep learning and blockchain-based smart contract. Moreover, PBSL is tested using real medical datasets to evaluate its effectiveness. Experimental results show that PBSL is effective. The remainder of this paper is organized as follows. We review related works in Section 2. We review some primitives in Section 3, and introduce our system model and design goals in Section 4. Then, we introduce our scheme in Section 5. Next, we present the security and performance analyses in Sections 6 and 7, respectively. Finally, we conduct a summary of our work in Section 8.
# IoT-Enabled Gas Sensors: Technologies, Applications, and Opportunities
## 5 Sensing Technologies
### Polymers  
Volatile organic compounds can be toxic above certain concentrations, causing harmful health effects, such as cancer, and are difficult to detect with electrochemical transducers. The polymer transducers used to detect gas are useful to sense these common organic compounds, present at the industry and household, in everyday use products [21; 39; 40; 64].  
Doping polymers by redox reactions, which is a reversible process, generates conductors or semiconductors which can be used to sense gases by the variation of these materials' conductivity. The doping level can be changed by the reaction between the polymer and the sensed gas, making the sensing process effective. The conductivity variation is affected by the exposition of these compounds to certain gases, proportionally to the target gases concentration on the ambient. Thus with this variation, it is possible to calculate the concentration of the gases, with a short response time and, depending on the sensing material and target gas, high accuracy. These materials can also sense inorganic gases, however the sensitivity and accuracy can be compromised, as the main target gases to these compounds are the VOCs [26; 28; 129; 130].  
The electrical conductivity of polymers alone is extremely low, making the use of these compounds alone unpractical for sensing gases; the conductive polymers were reported to be used in biosensors, generating electric signals from biological data. Non-conducting polymers can be used with other sensing techniques to improve sensibility, accuracy and response time. Research has demonstrated the use of these compounds with capacitive, mass sensitive, calorimetric and wave-dependent sensors [28; 130; 131].  
Gas sensors based on polymers have been used for over 35 years, with good accuracy and response time dependent on the chosen materials, reaching 11 min and recovery time of a few minutes; these sensors also present low stability, in some cases after one month of operation [126]. They run at room temperature, with low energy demand, while they can be easily affected by environmental changes, present inadequate selectivity, and are consumable, with a lifetime of approximately 6 months. The sensors can be easily reproduced in large scale, into portable devices, with low fabrication costs, although more researches on these compounds should be conducted to achieve better reliability levels. These transducers can be used with other sensing techniques, to achieve greater sensing properties [26; 28; 126; 129].
# Efficient Distributed Method for NLOS Cooperative Localization in WSNs
## 5 Numerical Simulations  
In this section, we present the simulation results to demonstrate the strengths and weaknesses of the ADMM_P algorithm. We considered the worst case, where NLOS connections are unidentifiable and the probability of these NLOS connections is up to 95%. We preliminarily considered a network that had 10 anchor nodes and 40 source nodes randomly distributed in a \([-50\) m, \(50\) m\(]\times[-50\) m, \(50\) m\(]\) square. Noisy range measurements were available when distances \(r_{i,j}\leq 30\) m. The coordinate dimension is \(d=2\). Range measurements were generated according to Equations (1) and (2),where NLOS error \(\varepsilon_{i,j}\) is exponentially distributed with mean parameter \(\alpha_{\mathrm{NLOS}}\). For a noisy environment, we assumed that the measurement noise was independent and identically distributed, i.e., \(\sigma_{i,j}=\sigma_{n}\).  
We compared the accuracy of ADMM_P with four state-of-art algorithms, which are named:  
1. SDP: SDP method of Reference [36] designed for NLOS environments;
2. SDPH: SDP model based on a heuristic solution [35];
3. EDM: EDM model based on three-block ADMM [37];
4. ADMM_SF: the parallel algorithm of Reference [20], which was only designed for LOS environments.  
It is worth noting that ADMM_P is an iterative algorithm working in the parallel distributed framework, and that there are few authoritative publications about distributed algorithms for NLOS localization. Therefore, we only analyzed the convergence property for our algorithm. In the convergence analysis, we provide the estimation results of SDPH of Reference [35] as references.  
All simulations were carried out in MATLAB. Local convex Problems (22) were handled via the MATLAB Optimization Toolbox with \(f\)_mincon_ solver. Algorithm performance was evaluated via Cumulative Distribution Function (CDF), Root Mean Square Error (RMSE), and objective Function Value (FVAL):  
\[\begin{split}\text{RMSE}&=\sqrt{\frac{\sum\limits_ {i\in\mathcal{S}}\|(\mathbf{z}_{i})_{1}-\mathbf{p}_{i}\|^{2}}{N}}\\ \text{FVAL}&=\sum\limits_{i\in\mathcal{S}}\left[F_{i }(\mathbf{z}_{i})+\frac{1}{2}c_{i}\|\mathbf{z}_{i}-\mathbf{A}_{i}\mathbf{x}+\tilde{\mathbf{\lambda}} _{i}\|^{2}\right]\end{split} \tag{42}\]  
where \(\mathbf{p}_{i}\) is the true position, and \((\mathbf{z}_{i})_{1}\) is the estimated location of node \(i\).
# Fall Detection Based on Recurrent Neural Networks and Accelerometer Data from Smartphones
## 2 Materials and Methods
### Methodology  
We used the same data to train and test the solution as in the reference [3] article to ensure that our results were comparable to the competing solution. Accordingly, two approaches were applied to divide the data. The first was the \(k\)-Fold Cross-Validation method with \(k=5\), and the second was the Leave-One-Subject-Out validation. After prior shuffling, we split the data into 5 and 30 folds, respectively. In the first method, 5 groups of equal size are created (independently of the subjects' identifiers). Leave-One-Subject-Out involves the establishment of samples about one subject as a test set and the remaining subjects as a training set, which provides a global approach to the problem. This way, we were able to reproduce the results from the reference article.
###### Abstract  
This paper develops a novel method of bridge damage detection using statistical analysis of data from an acceleration-based bridge weigh-in-motion (BWIM) system. Bridge dynamic analysis using a vehicle-bridge interaction model is carried out to obtain bridge accelerations, and the BWIM concept is applied to infer the vehicle axle weights. A large volume of traffic data tends to remain consistent (e.g., most frequent gross vehicle weight (GVW) of 3-axle trucks); therefore, the statistical properties of inferred vehicle weights are used to develop a bridge damage detection technique. Global change of bridge stiffness due to a change in the elastic modulus of concrete is used as a proxy of bridge damage. This approach has the advantage of overcoming the variability in acceleration signals due to the wide variety of source excitations/vehicles--data from a large number of different vehicles can be easily combined in the form of inferred vehicle weight. One year of experimental data from a short-span reinforced concrete bridge in Slovenia is used to assess the effectiveness of the new approach. Although the acceleration-based BWIM system is inaccurate for finding vehicle axle-weights, it is found to be effective in detecting damage using statistical analysis. It is shown through simulation as well as by experimental analysis that a significant change in the statistical properties of the inferred BWIM data results from changes in the bridge condition.  
bridge health monitoring; BWIM; structure dynamics; damage detection; vehicle-bridge interaction; SHM Article Using Statistical Analysis of an Acceleration-Based Bridge Weigh-In-Motion System for Damage Detection
# An Optimal Power Control Strategy for Grid-Following Inverters in a Synchronous Frame
## 3 Discrete LQR-ORT Controller Design
### Optimal Reference Tracking Matrix  
The reference signal \(r[k]\) introduces an additional equation to the optimization problem similar to (13). This equation is solved offline to obtain a control law that minimizes (12) [18]:  
\[\
u[k+1]=\left(\overline{A}_{T}-\overline{B}_{1T}K_{d}\right)^{T}\
u[k]+C^{T} Q_{p}r[k] \tag{15}\]  
where \(\
u\) is an auxiliary matrix similar to \(S_{p}\). Assuming a step reference signal \(r[k]\), and solving (15) with infinite horizon such that \(\
u[k]=\ \
u[k\ +\ 1]\):  
\[\
u\ =\left[I-\left(\overline{A}_{T}-\overline{B}_{1T}K_{d}\right)^{T}\right]^ {-1}C^{T}Q_{p}. \tag{16}\]  
For the LQR-ORT, the control law is given by [18]  
\[E_{dq}[k]\ =\left(\overline{B}_{1T}^{T}S_{p}\overline{B}_{1T}+R_{p}\right)^{-1} \overline{B}_{1T}^{T}\left(-S\overline{A}_{T}X[k]+\mathrm{vr}[k]\right), \tag{17}\]  
or, in a compact form:  
\[E_{dq}=\ -K_{d}X[k]+K_{\
u}\mathrm{vr}[k]. \tag{18}\]  
where \(K_{\
u}=\left(\overline{B}_{1T}^{T}S_{p}\overline{B}_{1T}+R_{p}\right)^{-1} \overline{B}_{1T}^{T}\). The product \(K_{\
u}\
u\) is the optimal reference tracking matrix (ORT). As shown in Figure 3, this matrix transforms a vector in the units of \(r[k]\) to a vector in the units of \(E_{dq}[k]\) such that (12) is minimized.
## 5 Experimental Results and Analysis  
### Algorithm Performance Analysis  
Using a telemetry communication system in a specific test range as an example, the key parameter configurations are shown in Table 1.  
The ratio of bit energy to power spectral density was tested in the range of 1 dB to 10 dB. The three-symbol, five-symbol, and seven-symbol traditional MSD algorithms [23] (referred to as the Tr-MSD in this paper); the improved MSD algorithm proposed in reference [16], namely, the decision feedback MSD algorithm (referred to as the DFB-MSD in this paper); and the improved MSD algorithm proposed in this paper (referred to as the Lc-MSD in this paper) were evaluated through simulation experiments. The simulation and hardware measurement results are shown in Figure 9.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value/Type** \\ \hline symbol rate (\(f_{\tau}\))/Mb/s & 40 \\ sampling rate (\(f_{\text{s}}\))/Mb/s & 160 \\ intermediate frequency carrier (\(f_{\text{c}}\))/Mb/s & 80 \\ modulation index (\(h\)) & 0.629 \\ pre-filter & square-root raised cosine filter \\ channel noise & gaussian white noise \\ FPGA model & xc7k325 ffgg676-2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation key parameter configuration.  
In Figure 9, \"Lc SIM Ns\" denotes the Lc-MSD algorithm under different observation intervals in the simulation; \"DFB SIM Ns\" denotes the DFB-MSD algorithm under different observation intervals in the simulation; \"Tr SIM Ns\" denotes the Tr-MSD algorithm under different observation intervals in the simulation; \"Lc FPGA Ns\" denotes the Lc-MSD algorithm measured on an FPGA platform under different observation intervals; and \"FM REAL\" represents the orthogonal coherent demodulation algorithm.  
From Figure 9, it can be observed that, compared to the coherent demodulation algorithm, all three MSD algorithms show significant improvements in demodulation performance. Additionally, larger observation intervals yield more pronounced performance gains. At \(N=3\), \(N=5\), and \(N=7\), the performances of the DFB-MSD and Lc-MSD algorithms are comparable, with virtually no performance loss relative to the Tr-MSD algorithm.  
For example, at an observation interval of \(N=7\), the three MSD algorithms achieve a performance gain of approximately 3.5 dB over coherent demodulation at a bit error rate of \(10^{-4}\). The performance difference between the Lc-MSD and DFB-MSD algorithms and the Tr-MSD algorithm is no more than 0.05 dB, which can be negligible. Additionally, at \(N=3\) and \(N=5\), the hardware-measured performance of the Lc-MSD algorithm shows a loss of less than 1 dB compared to its simulation performance, which is within the acceptable range.
# Influence of the Microwave Heating Time on the Self-Healing Properties of Asphal Mixtures
## 2 Materials and Methods
### Test Specimens Preparation  
A total of 40 Marshall specimens were manufactured using the following procedure:  
* Before the mixing process, the aggregates were heated during 24 h at 150 \({}^{\circ}\)C, while the bitumen and the fibres were heated at 150 \({}^{\circ}\)C during 2 h.
* Raw materials were added to the mixer in a specific order: bitumen and fibres, coarse aggregate, fine aggregate and at last, filler.
* Raw materials were mixed in a metallic bowl during approximately 3.5 min at a speed of 100 rpm and keeping a constant temperature of 150 \({}^{\circ}\)C.
* After mixing, asphalt mixtures were poured into a previously heated Marshall mould (10 cm diameter, 6 cm height) and compacted using a Marshall hammer applying 75 blows on each face.
* Marshall specimens were left to cool at ambient temperature during 24 h and then extracted from the mould.  
After manufacturing, bulk density and air void content of all Marshall specimens were determined. Finally, each Marshall specimen was sawed into 4 semi-circular samples obtaining 160 semi-circular samples in total (with dimensions 10 cm diameter, 5 cm height and 2.5 cm thickness), 80 of which were used in the heating tests, while the other 80 were used in the crack-healing cycles. In both cases, 4 samples for each mixture type and heating time were tested.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Sieve Size (mm)** &
\begin{tabular}{c} **Cumulative Mass** \\ **(\% Retained)** \\ \end{tabular} & **Mass (g)** & **Density (g/cm\({}^{3}\))** \\ \cline{2-5}  & 12.5 & 16 & 176 & \\  & 10 & 29 & 143 & 2.779 \\  & 5 & 53 & 264 & \\ \cline{2-5} Aggregates & 2.5 & 69 & 176 & \\  & 0.63 & 86 & 187 & \\  & 0.315 & 90 & 44 & 2.721 \\  & 0.16 & 93 & 33 & \\  & 0.08 & 95 & 19 & \\ \cline{2-5}  & \textless{}0.08 & 100 & 58 & 2.814 \\ \hline \multirow{3}{*}{Bitumen} & **Type** & **Amount (\%mass/Aggr.)** & **Mass (g)** & **Density (g/cm\({}^{3}\))** \\ \cline{2-5}  & CA24 & 5.3 & 58.3 & 1.039 \\ \hline \multirow{3}{*}{Steel Wool Fibres} & **Amount (\% vol./Bitumen)** & **Length Range/** **Avg. Diameter (mm)** & **Mass (g)** & **Density (g/cm\({}^{3}\))** \\ \cline{2-5}  & 2 & & 8 & \\ \cline{1-1}  & 4 & 2–8/0.157 & 16 & \\ \cline{1-1}  & 6 & & 24 & 7.180 \\ \cline{1-1}  & 8 & & 32 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of the asphalt mixtures used in the study.
## 5 Conclusions
To enhance the accuracy of simulations, future research could consider incorporating higher-resolution data, optimizing the selection of input variables, systematically tuning model parameters, and employing uncertainty quantification techniques to improve the reliability of the results. Furthermore, integrating socioeconomic data or human activity factors and exploring their impacts on ecological processes could provide valuable new directions for advancing the model. Regarding the integration of five ET products using the multiple linear regression model to simulate ET, while the model outperformed simple averaging methods, it still exhibits significant shortcomings. On the one hand, this approach may result in biased estimates in certain regions, thereby neglecting the uniqueness of regional characteristics. On the other hand, the model is highly sensitive to the quality of input data, and the limited and uneven distribution of validation data points can significantly affect the accuracy of the results. To further improve ET simulation accuracy, future studies could explore more advanced fusion techniques, such as deep learning models, which can effectively capture the nonlinear relationships among different ET products. Additionally, incorporating spatiotemporal weighted regression models or regional zoning models could dynamically adjust the weights for different spatiotemporal regions, allowing for a more nuanced representation of regional characteristics and ultimately improving the reliability and scientific validity of the results. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs17030527/s1](https://www.mdpi.com/article/10.3390/rs17030527/s1)]([https://www.mdpi.com/article/10.3390/rs17030527/s1](https://www.mdpi.com/article/10.3390/rs17030527/s1)), Figure S1: Spatial pattern of regGPP and its interannual trend; Figure S2: Spatial distribution of the annual mean and interannual variation trends of temperature and precipitation; Table S1: Construction of random forest model; Table S2: Multiple linear regression model. Methodology, software, visualization, X.W. ; conceptualization, T.Z. ; methodology and revision, J.Z. (Jingyu Zeng) and Y.Z. ; discussions, J.Z. (Jingzhou Zhang), E.T., Y.Y., Q.Z. and Y.Q. All authors have read and agreed to the published version of the manuscript. This study was supported by the second Tibetan Plateau Scientific Expedition and Research Program (2019 QZKK0405-02) and the National Natural Science Foundation of China (42277206). Data Availability Statement: Data will be made available on request. We thank all data providers for environmental datasets and climate datasets. Special thanks to the editor and several anonymous reviewers for their help and support. The authors declare no conflicts of interest.
## 5 Results and Discussion
### Comparative Performance with External Datasets  
To assess the generalizability of the VRFormer model and the value of the VRDeepSafety dataset, we conducted a comparative analysis by training the VRFormer model on two other publicly available datasets--nuScenes and V2X-Sim--and then evaluating all trained models using the VRDeepSafety testing set. This experiment allows us to compare  
Figure 16: System robustness to pose error and latency.  
the model's performance when trained with data from other sources. The results from this analysis are detailed in Table 8.  
As Table 8 shows, VRFormer trained on the VRDeepSafety dataset achieved significantly better results than when trained on nuScenes or V2X-Sim. Specifically, the VRDeepSafety-trained model achieved a 90% APA, compared to 73% and 70% for nuScenes and V2X-Sim, respectively; this trend was also seen in the mIOU and mAP scores. These results validate our hypothesis that VRFormer, when trained with VRDeepSafety data, yields superior accident prediction performance. The nuScenes-trained model's lower performance is likely due to its lack of V2X data and limited scenario diversity. Similarly, while V2X-Sim incorporates some V2X data, its idealized communication model resulted in poorer performance. In summary, training with VRDeepSafety data enabled VRFormer to better predict accidents on VRDeepSafety testing data due to its realistic sensor simulations, multi-modal data, and inclusion of real-world V2X challenges. These results support the argument that VRDeepSafety provides significant advantages for training robust, generalizable models for autonomous driving in V2X scenarios.
## 6 Discussion  
### Analysis of SBAS-InSAR Results  
Figures 10 and 11 respectively show the cumulative vertical displacement and ground-level annual average vertical displacement speed situation in the Ehuobulake Coal Mine area from 1 January 2019 to 6 September 2022 obtained via the application of SBAS-InSAR technology. Displacement quantity is represented by different colors, the negative value indicates the land subsidence, the positive value indicates the land uplift, and a deeper color denotes greater vertical displacement. As can be seen from Figure 10, two obvious deformation regions (1\(\#\) and 2\(\#\)) appear in the study area during the study period. The maximum vertical displacement in the study area is \(-225\) mm, which occurs in the 2\(\#\) deformation region. As can be seen from Figure 11, the maximum vertical displacement velocity in the mining area is \(-67\) mm/a, which occurs in the 2\(\#\) deformation region.