esa-sceva/satcom-chunk-collection huggingface dataset:
# Activity Recognition Using Different Sensor Modalities and Deep Learning
## 3 Results  
In this study, a wireless sensor network was established with smart insoles and IMU sensors. From forty healthy people, sensor outputs, including foot contact states and accelerometer and gyroscope readings, were collected and recorded. They performed eight activities including walking on flat ground, walking uphill, walking downhill, ascending stairs, descending stairs, running, jumping, and sitting down/standing up. Figures 4-6 illustrate a part of the activity signals obtained from the IMU sensors placed on the thigh area of the left leg and FSR located on the sole of the left foot while walking on flat ground, respectively.  
The classification performances of the proposed models were evaluated using four metrics including precision, recall, \(F_{score}\), and accuracy [35]. These metrics are calculated using Equations (1)-(4), where \(TP\) is true positive, \(FP\) is false positive, \(TN\) is true negative, and \(FN\) is false negative.  
\[Precision=\frac{TP}{TP+FP} \tag{1}\]  
\[Recall(Sensitivity)=\frac{TP}{TP+FN} \tag{2}\]  
\[F_{score}=2*\frac{(Recall*Precision)}{Recall+Precision} \tag{3}\]  
\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{4}\]  
Figure 4: A Part of the Acceleration Data Collected from the Thigh Part of the Left Leg During Walking.  
The classification results show that the neural network has the best performance for each activity when the input set is a combination of all the datasets. The sit-down/stand-up activity has the highest rate for each neural network in terms of precision and recall. The average accuracies of the neural networks are 90.1%, 88.6%, and 93.4% for foot contact states, accelerometer and gyroscope, and the combination of all the datasets, respectively.  
It is seen from the average accuracy that the ConvLSTM NN with the foot contact state input set offers better performance around 1.5% than the accelerometer and gyroscope input set. Moreover, Table 4 shows the \(F_{score}\) values of the neural networks for each input set. The combination of all the datasets has better performance in terms of \(F_{score}\) with 0.958 than the other two categories.
## 3 Construction of Learning Framework
### Dynamic Adjustment Rating
\begin{array}{ll}DAR_{j}^{t-1}+\varphi_{i}^{t},&\quad if \ \DAR_{j}^{t-1}=0\ and\ \tau_{j}^{t-1}>th_{1}\\ DAR_{j}^{t-1},&\quad if\ \DAR_{j}^{t-1}=0\ and\ th_{2}\leq\tau_{j}^{t-1}\leq th_{1}\\ 0,&\quad if\ \DAR_{j}^{t-1}=0\ and\ \tau_{j}^{t-1}<th_{2}\end{array} \right. \tag{8}\]  
\[DAR_{i}^{t}=\left\{ \begin{array}{ll}\max\{DAR\},&\quad if\ \DAR_{j}^{t-1}=\max\{DAR\} \ and\ \tau_{j}^{t-1}>th_{1}\\ DAR_{j}^{t-1},&\quad if\ \DAR_{j}^{t-1}=\max\{DAR\} and\ th_{2}\leq\tau_{j}^{t-1}\leq th_{1}\\ DAR_{j}^{t-1}-\mu_{i}^{t},&\quad if\ \DAR_{j}^{t-1}=\max\{DAR\} and\ \tau_{j}^{t-1}<th_{2}\end{array} \right. \tag{9}\]  
In Equation (7), \(\varphi_{i}^{t}\) is the increment index and is defined as:  
\[\varphi_{i}^{t}=\left\{ \begin{array}{ll}\varphi_{j}^{t-1}+1,&\quad if\ \tau_{j}^{t-1}>th_{1}\\ 0\,&\quad if\ \tau_{j}^{t-1}\leq th_{1}\end{array} \right. \tag{10}\]  
That is, every time the transmission latency exceeds the higher threshold \(th_{1}\), \(\varphi_{i}^{t}\) will increase by one unit; otherwise, it will return to 0. Conversely, the decrement index \(\mu_{i}^{t}\) is given by:  
\[\mu_{i}^{t}=\left\{ \begin{array}{ll}\mu_{j}^{t-1}+1,&\quad if\ \tau_{j}^{t-1}<th_{2}\\ 0\,&\quad if\ \tau_{j}^{t-1}\geq th_{2}\end{array} \right. \tag{11}\]  
From Equations (7) and (10), it can be seen that the DAR will increase at accelerating levels each timeslot if transmission delay is consistently higher than the higher threshold \(th_{1}\). On the contrary, the more successive timeslot single-hop latency falls below the lower threshold \(th_{2}\), the larger the decrement of DAR will be. Thus, the DAR transfers multiple levels at each time step, which helps the transmit power of SU nodes reach a reasonable range rapidly and enhances the efficiency of power adaptation. Every DAR maps to a value of transmit power \(p_{i}\) (\(p_{\min}\leq p_{i}\leq p_{\max}\)), and the mapping relation is given by:  
\[p_{i}\Big{(}DAR_{i}^{t}\Big{)}=\left(1-\frac{DAR_{i}^{t}}{|DAR_{i}|}\right)p_{ \min}+\frac{DAR_{i}^{t}}{|DAR_{i}|}p_{\max} \tag{12}\]  
where \(|DAR_{i}|\) denotes the size of \(\Big{\{}DAR_{i}^{t}\Big{\}}\). Consequently, with DAR, the transmit power is allocated efficiently to reach a reasonable tradeoff between energy efficiency and routing latency. Moreover, the DAR is able to transform the large action space problem to an issue with a huge state space, which reduces computation complexity and makes it compatible with DRL framework.
## 2 Materials and Methods
### Characterization
#### 2.3.5 Mechanical Test  
In accordance with the UNE-EN ISO 527 standard [52], the mechanical properties (Young's modulus, yield stress, stress at break point, and elongation at break) of materials were measured through a tensile test using a Testometric M350-20 CT (Rochdale, UK). This test involved gradually applying a uniaxial force until the sample fractured. The testing speed was set at 100 mm/min, adhering to the specific guidelines of the standard. The specimens used for testing were stamped and measured 2.5 \(\times\) 10 cm and 120 \(\pm\) 10 microns, were rectangular in shape, and, in accordance with the standard, a minimum of 10 repetitions were conducted for each sample to ensure consistency and comparability of the obtained results.  
Elastico-plastic properties in the range from the yield strength until the tensile strength were analyzed by the Hollomon equation, expressed as \(\sigma_{\mathrm{T}}\) = K\(\cdot\)\(\epsilon_{\mathrm{T}}\)\({}^{n}\). In this equation, \(\sigma_{\mathrm{T}}\) denotes true stress, \(\epsilon_{\mathrm{T}}\) represents true strain, K is the strength coefficient, and n is the strain-hardening exponent. The Hollomon equation enables the understanding of how materials harden as they deform, providing essential insights for optimizing manufacturing processes and ensuring the structural integrity of materials.
## 2 Numeric Modeling of NPP Equipment  
### Structural Configurations  
In this study, an electrical cabinet provided by Innose Tech company in Korea was used as a representative of NPP equipment [11; 21]. This prototype was used to carry out some experimental tests for assessing the seismic performance of NPP facilities. The details of the configuration are shown in Figure 1a. The specifications of the components, including plates, subframes, mainframes, were assigned to SS400 steel. The material characteristics had 200 GPa of Young's modulus, the Poisson's ratio \((\mathrm{v})\) and density \((\mathrm{\rho})\) were 0.3 and 7850 kg/m\({}^{3}\), respectively. The external dimensions of the cabinet were 800\(\times\)800\(\times\)2100 (mm\(\times\)mm\(\times\)mm), and its weight was 287 kg. The thickness of the plates was 2.3 mm, and the detailed cross-sections of the electric cabinet's components include C-shape and rectangular, as expressed in Figure 1b.  
The finite element model (FEM) of the electric cabinet was created by using SAP2000 software (Figure 1c), in which the linear beam and shell elements were used to simulate the frame and plate members, respectively. Torsion and warping [40] of plate members of the structure were ignored in this study. The link elements were used to model their connections. Particularly, the connections between the plates and frames were simulated as rigid link elements. The hinges between doors and frames were also considered to be fixed at five degrees of freedom; only the rotation around the hinge axis was released. Meanwhile, the locks between panels and frames were fixed at three translational degrees of freedom. For the boundary condition of the structure, a total of 8 bolts connect the base-frames with the floor. These connections were assumed to be fixed at all degrees of freedom.  
In this study, the electrical cabinet was assumed to respond elastically with a 5% inherent (viscous) damping. A few experimental studies have shown that the viscous damping ratio of typical NSCs could be well below the conventional value of 5% [41; 42]. Previous numeric studies had illustrated that the value of NSCs damping ratio could significantly affect the acceleration demands on NSCs, especially when the frequency of NSCs was close to those of the supporting structures [1; 43]. Furthermore, a few previous studies had shown that the inelastic behavior of NSCs could significantly reduce their seismic force and displacement demands, especially for tuned NSCs [44; 45; 46]. Given the importance of the  
Figure 1: Test structure: (**a**) configuration; (**b**) main cross-sections (mm); and (**c**) finite element model.  
damping ratio and the nonlinear behavior of NSCs identified by the above-mentioned studies, future research works on electrical cabinets should consider the effect of these parameters.
## 7 Heuristic Security Analysis
### Anonymity and Untraceability  
In the authentication phase, the vehicle's real identity \(ID_{i}\) is concealed, utilizing the pseudonym \(RID_{i}\). This approach ensures that adversaries cannot determine the actual identity of the vehicle, thereby maintaining anonymity. Moreover, the information uploaded to the blockchain by RSUs, \((TID_{i},n_{j},h(RID_{i}||TID_{i}||k))\), only serves to verify the vehicle's RID without revealing the RSU details. The dynamic temporary identity \(TID\) updates via blockchain, and refreshed \(TID\) values using RSU-specific nonces \(n_{k}\) break long-term pseudonym linkage. With \(TID_{i}\) being refreshed during each handover authentication, it becomes impossible for adversaries to track the vehicle's movement across RSUs, ensuring untraceability.
## 3 Materials and Methods
### Framework Architecture and Cloud Enablement  
The overall architecture, depicted conceptually in Figure 1, establishes a systematic flow from real-time data acquisition to optimized parameter recommendations. This figure illustrates the conceptual framework proposed in this study, which leverages a cloud computing infrastructure as a foundational element, recognizing the trend toward cloud manufacturing and the need for advanced data analytics in modern systems [22; 23]. For specific applications of machine learning in manufacturing process modeling and optimization. The cloud platform is crucial for the following:Data Ingestion and Storage: Handling high-speed, high-volume data streams from numerous sensors and manufacturing systems (ERP, MES) across potentially many production lines [24, 25].
* Scalable Computation: Providing on-demand computational resources required for demanding tasks such as process discovery algorithms (e.g., Inductive Miner), real-time LCA calculations, and complex multi-objective optimization solvers (NSGA-II) [23, 26]. Moreover, the efficient utilization and optimization of these cloud resources themselves are critical aspects, often addressed through exploratory data analysis and machine learning techniques [27].
* Integration and Centralization: Acting as a central hub to integrate diverse data sources and make processed information and optimization results accessible to relevant stakeholders and downstream control systems.
* Real-time Analytics: Facilitating the timely processing and analysis needed to derive actionable insights from live data streams, moving beyond traditional batch processing.  
The methodology follows a structured sequence of steps, illustrated in Figure 2, ensuring a systematic approach from data collection to actionable insights.  
Figure 1: Architecture of the Cloud-Based Integrated Optimization Framework. This conceptual model, developed for this study, shows the data flow from sensors/MES through Process Mining (PM) and Life Cycle Assessment (LCA) modules to the Multi-Objective Optimization Model (MOOM-OEP) engine.
## 3 Methodology
### Fuzzy Fault Tree Analysis
#### 3.3.3 Aggregation  
For the failure probability of each BE, different experts have made a separate subjective linguistic evaluation, which needs to be integrated to generate the final quantitative data. This study uses the fuzzy aggregation analysis algorithm proposed by [PERSON] and [PERSON] for aggregation [63]. The fuzzy aggregation analysis algorithm is implemented by the following steps.  
Step 1: Calculation of the degree of similarity (S)  
\[S_{uv}(\widetilde{E}_{u},\widetilde{E}_{v})=1-\frac{1}{3}\underset{i=1}{\overset {3}{\sum}}|a_{ui}-a_{vi}| \tag{8}\]  
where \(S_{uv}\) (\(\widetilde{E}_{u}\), \(\widetilde{E}_{v}\)) is the degree of agreement for different opinions between expert \(E_{u}\) and \(E_{v}\), \(\widetilde{E}_{u}\) = (\(a_{u1}\), \(a_{u2}\), \(a_{u3}\)) and \(\widetilde{E}_{v}\) = (\(a_{v1}\), \(a_{v2}\), \(a_{v3}\)) are represented as two TFNs (\(u\
eq v\)).  
Step 2: Calculation for the average agreement (\(AA\)) degree for each expert viewpoint  
\[AA(E_{u})=\frac{1}{U-1}\underset{u\
eq v,v=1}{\overset{U}{\sum}}S_{uv}( \widetilde{E}_{u},\widetilde{E}_{v}) \tag{9}\]  
where \(AA(E_{u})\) is the \(AA\) degree of expert \(E_{u}\), \(U\) is the total number of experts.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Scale**} & \multicolumn{4}{c}{**TFN**} \\ \cline{2-4}  & \(\mathbf{a_{1}}\) & \(\mathbf{a_{2}}\) & \(\mathbf{a_{3}}\) \\ \hline Very low (VL) & 0.00 & 0.04 & 0.08 \\ Low (L) & 0.07 & 0.13 & 0.19 \\ Medium low (ML) & 0.17 & 0.27 & 0.37 \\ Medium (M) & 0.35 & 0.50 &.065 \\ Medium high (MH) & 0.63 & 0.73 & 0.83 \\ High (H) & 0.81 & 0.87 & 0.93 \\ Very high (VH) & 0.92 & 0.96 & 1.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Linguistic measurement scale.  
Step 3: Calculation for the relative agreement (\(RA\)) degree for each expert viewpoint  
\[RA(E_{u})=\frac{AA(E_{u})}{\sum\limits_{u=1}^{U}AA(E_{u})} \tag{10}\]  
where \(RA(E_{u})\) is the \(RA\) degree of expert \(E_{u}\).  
Step 4: Estimation of the consensus coefficient (\(CC\)) for each expert  
\[CC(E_{u})=\beta\times WEI(E_{u})+(1-\beta)\times RA(E_{u}) \tag{11}\]  
where \(CC(E_{u})\) is the CC of expert \(E_{u}\), \(WEI(E_{u})\) is the weighting coefficient of expert \(E_{u}\), \(\beta\) (\(0\leq\beta\leq 1\)) represents the importance of \(WEI(E_{u})\) over \(RA(E_{u})\). In this study, \(\beta=0.5\) was considered [64].  
Step 5: Calculation for the aggregated results of the experts' viewpoints  
\[\widetilde{R}_{AG}=CC(E_{1})\times\widetilde{E}_{1}+CC(E_{2})\times\widetilde {E}_{2}+\ldots+CC(E_{U})\times\widetilde{E}_{U} \tag{12}\]  
where \(\widetilde{R}_{AG}\) is the aggregated result value, and it is a fuzzy number.
# Photoacoustic Imaging with a Finite-Size Circular Integrating Detector
## 5 Conclusions  
This study proposes a PSF estimation method based on coordinate transformation and polynomial standard factor extraction. The algorithm simplifies the complexity of extracting spatially varying PSFs by transforming the image into polar coordinates. It estimates the blurring kernel from the blurred pressure distribution, effectively eliminating the influence of the blurring kernel. Using deconvolution, the influence of the blurring kernel is removed from the photoacoustic image. Test results show that the PSF extracted using this method can effectively be applied to the deblurring of photoacoustic images affected by circular integral detectors, significantly reducing \"spin blurring\" and restoring image edges and details. This algorithm supports the selection of two columns of pressure data to save computation time when solving blurring kernel problems. Its high reconstruction accuracy, low computational complexity, and strong robustness make it suitable for photoacoustic imaging systems constrained by the size of ring detectors and noise. Furthermore, the method has demonstrated promising results in breast photoacoustic imaging, where it helps to recover fine details and improve structural fidelity in complex tissue structures. Future work will test it on real photoacoustic data and improve its robustness to noise through optimized regularization techniques for deconvolution.  
Conceptualization, [PERSON] and [PERSON]; methodology, [PERSON] and [PERSON]; software, [PERSON] and [PERSON]; validation, [PERSON]; formal analysis, [PERSON]; investigation, [PERSON] and [PERSON]; resources, [PERSON]; data  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Processing Method** & **PSNR (dB)** & **SSIM** \\ \hline Blurred image & 16.81 & 0.4941 \\ Wiener deconvolution image & 22.61 & 0.5164 \\ Processed image (proposed) & 36.77 & 0.9912 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantitative comparison of objective metrics for breast photoacoustic images under different processing methods.  
Figure 14: Comparison of image processing methods on breast photoacoustic images. (**a**) Blurred Image; (**b**) Wiener deconvolution; (**c**) proposed method.  
curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON], [PERSON] and M.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (42174162).  
Not applicable.  
The data underlying the results presented in this study are not publicly available at this time but may be obtained from the authors upon reasonable request. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
## 3 Concept of an Automaton for Data Validation
### States  
The first parameter we consider are the states \(Q\). To create an automaton from the example process above, we determine the power set of the process steps: \(\{\emptyset\), \(\{S\}\), \(\{M\}\), \(\{T\}\), \(\{SM\}\), \(\{ST\}\), \(\{MT\}\), \(\{SMT\}\)}.  
We use the elements of this set as states of our automaton. We ignore the empty set (see Figure 3). Sets containing only one element form the start states in each case. We characterize states that follow the process run as potentially consistent (\(\{\{S\},\{SM\},\{SMT\}\}\)). Potentially consistent means that it is ensured that the existing messages have arrived in the correct order, but it remains to be checked whether there are inconsistencies within the  
Figure 2: Example process to visualize states, transitions, and algorithms.  
messages (category 3 and 4). Furthermore, we add an inconsistent, a _cat4_ state, in which a data set waits until it has been checked for category 4 inconsistencies, and a consistent state to our automaton. This gives us a total of ten states for our example process.  
As mentioned, we define a state as a tuple \(Q=(q,E)\). With \(q\), we denote the unique name of a state. \(E\) is a list consisting of all products, waiting in the corresponding state. An element of \(E\) is a triple \(e=(id,r(id),t)\). The parameters of this triple are a unique product identifier (\(id\)) to be able to track each product and group related data, a rank (\(rk(id)\)), to detect overtakings, and a process-specific timer (\(t\)), to be able to terminate in the case of a missing message. Depending on the current manufacturing conditions, these timers can be extended or shortened.
# An Experimental Tethered UAV-Based Communication System with Continuous Power Supply
## 2 Background
### The Role of UAVs in Wireless Communications
#### 2.2.4 Load Balancing  
During mass user concentration events, such as concerts, BSs may experience overloading, leading to denial of service or a decrease in service quality [25]. Although infrastructures usually have a larger-than-usual capacity for users, these situations can temporarily saturate them. Therefore, using UAVs can help alleviate network congestion by acting as temporary BSs and signal repeaters, providing additional connectivity and extending network coverage. This helps to distribute the data load and improves network capacity.
## 3 Applications
### Sensitivity Analysis  
Firstly, the sensitivity of Equation (16) to various acoustic parameters were analyzed. Table 2 shows the range and true value for the inversion parameters. The results of the one-dimensional sensitivity analysis of Equation (16) are shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Range** & **True Value** \\ \hline \(c_{\text{s}}\) (m/s) & 1450 to 1700 & 1600.00 \\ \(d\) (m) & 0 to 50 & 20.0 \\ \(\rho_{\text{s}}\) (g/cm\({}^{3}\)) & 1.1 to 2.0 & 1.50 \\ \(c_{\text{b}}\) (m/s) & 1700 to 2200 & 1800.00 \\ \(\rho_{\text{b}}\) (g/cm\({}^{3}\)) & 1.5 to 3.0 & 2.0 \\ \(n\) & [1,3,5] to [15,17,19] & [9,11,13] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Range and truevalue of parameters.  
Figure 5: One-dimensional cost function value of each inversion parameter. (**a**) the sound speed of the sediment (**b**) the thickness of the sedimentary layer (**c**) the density of the sediment (**d**) the sound speed of the basement (**e**) the density of the basement (**f**) the order of the interference fringe.  
As shown in Figure 5, the value of the cost function changed greatly with \(c_{\rm s}\), \(d\), and \(n\) within the range of inversion parameters, and thus, these parameters of \(c_{\rm s}\), \(d\), and \(n\) were sensitive to the cost function. The value of the cost function varied unobviously with \(\rho_{\rm s}\), \(c_{\rm b}\), and \(\rho_{\rm b}\), and thus these parameters were not sensitive to the cost function. The reason is that these three parameters of \(\rho_{\rm s}\), \(c_{\rm b}\), and \(\rho_{\rm b}\) are only related to \(Q\) in Equation (5). Compared with \(c_{\rm s}\), they have less influence on the shifting trend of the fringe position. When the thickness \(d\) is less than 10 m, the cost function value cannot be calculated because there is no corresponding fringe in the angle-frequency domain. Figure 6 shows the normalized value of the two-dimensional cost function between the parameters.
# A Laser-Based Method for the Detection of Honey Adulteration
## 3 Results
### Dimensionality Reduction and Classification of LIBS Spectra for Adulteration Detection
In Figure 5a, the important features (blue color lines) identified using the ERT algorithm are presented. As can be seen, there are several spectral peaks that are recognized as important by the algorithm, although they appear rather weak in the experimental LIBS spectra. Figure 5b,c present the spectral regions where the most important features occur together with the corresponding spectral regions of the LIBS spectrum. Therefore, in Figure 5b, the spectral features of Ca (II) at 393.3 and 396.8 nm are shown, exhibiting the highest importances. It is interesting to note that the algorithm seems to identify spectral features that are not clearly observed in the LIBS spectra, such as those of Mg (II) at 279.6 and 280.3 nm, and that of Ca (I) at 422.7 nm. In Figure 5c, the most important features corresponding to the K (I) and Na (I) spectral lines are shown. These findings confirm the importance of the spectral lines of these inorganic elements for the detection of adulteration in honey. It is interesting that the spectral lines of Mg (II) and Ca (I) have been also suggested by [PERSON] et al. [36] as important for the selection of the most important features for botanical origin classification. Similarly, many of the spectral features presented in the present work were also identified by [PERSON] et al. [37] using the backward interval partial least squares method. In the same spirit, [PERSON] et al. [38], trying to select the most efficient variables for the quantification of honey adulteration with high-fructose corn syrup, used different algorithms and also concluded with the same spectral features determined in the present work.
# Super-Resolution Multicomponent Joint-Interferometric Fabry-Perot-Based Technique
## 4 Results and Discussion
### Evaluation and Analysis
Based on the above analysis, the maximum OPD \(L\) of the Michelson interferometer and the interval \(d\) of the FPI jointly determine the spectral resolution, and the reflectivity \(R\) of the FPI affects the calculation accuracy.