esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulations
### Numerical Modeling for Precise Sizing  
For precise sizing purposes, the V-shape system was modeled using finite solids and simulated in the Matlab-Simulink environment using the Simscape and Multibody toolbox. This numerical model is composed of twenty-four 5 cm long rigid elements linked to their neighbors by revolute joints whose stiffness is equal to \(10^{-3}\) N.m/rad, which was experimentally determined using the Fathom Slim tether cable of BlueRobotics(tm) [6]. The damping behavior is a result of the buoys' drag force. Figure 3 is a representative screenshot from the video that shows the dynamic behavior of the V-shape system returning to rest state. This validates the physical assumptions for the design of the proposed mechanical model. Moreover, it confirms that the V-shape system does not acquire a droplet shape, which is crucial for guaranteeing the monotony of the output signal provided by the flex sensor mounted at the ballast [27]. This embedded instrumentation provides a suitable signal related to the compliance state that is appropriate for use as feedback for the winch command.  
The V-shape system should not be excessive in size with respect to the robot's dimensions. A distance of 1.2 m between the buoys was chosen for a 1 m range of distance compliance between buoys and a minimal distance of 0.2 m at rest, which yields \(\ell=0.6\) m for each section \(CB_{1}\) and \(CB_{2}\). The value of \(V_{buoy}\) was set to approx. 36 cm\({}^{3}\).
## 4 Experimental Results and Analysis
### Dataset 1: Nearby Parking Lot
After obtaining the image that contains the changed part through Logarithm Background Subtraction, it is necessary to perform target detection on the image in order to obtain the change targets. In this paper, a CFAR target detection algorithm based on Gaussian distribution is used by calculating the statistical information of the changed image. The Gaussian distribution is utilized to model the environmental background, thereby computing the detection threshold. The target detection result is presented in Figure 12. CFAR detection is a method to determine pixels whose pixel value is greater than the threshold as the target and those whose pixel value is less than the threshold as the non-target. Therefore, after performing CFAR detection, a binary image is finally formed, as shown on the left side of the figure. In this image, the white areas represent the target points, while the black areas represent non-target points. In the right figure, the red dots corresponds to the white part of the figure on the left, which is'vehicle appeared' in the legend. Figure 11: Logarithm Background Subtraction result. (**a**) Input image. (**b**) Background. (**c**) Change part. Figure 10: Parking lot area for experiment. (**a**) Area for registration from Google Earth. (**b**) Optical image of parking lot area from Google Earth. (**c**) SAR image of parking lot area acquired on 15 September 2022 by Sentinel-1A satellite. The advantages of the CFAR algorithm are that it enables an adaptive determination of the detection threshold, thereby avoiding the problem of static threshold sensitivity to noise and interference. Since the algorithm only requires some local information to complete the detection task, its computational load is relatively small, and its real-time performance is good, making it suitable for target detection tasks in this paper. By multiplying the number of pixels with detected targets by the proportional coefficient, the vehicle count for each scene image is calculated, and the resulting vehicle count variation curve is obtained, as shown in Figure 13, of which Figure 13a shows the logarithm Background Subtraction method result and Figure 13b shows the result of pairwise comparison method.
# Calculation and Verification of the Real-Time Working Characteristics of a Viscous Coupling
## 3 The Temperature Calculation of the VC at Working Course
### The Temperature Calculation of VC Parts  
Except for the above-mentioned capacity of heat, the remaining energy is:  
\[Q_{f}=Q-Q_{12}-Q_{13}-Q_{14}-Q_{15} \tag{55}\]  
Supposing that the heat is overall absorbed by the silicone oil and the plates, their temperature is always the same. The ratio of the absorbed heat between them is \(R_{a}\), which can be determined when they raise the same temperature:  
\[R_{a}=\frac{C_{ps}m_{s}}{C_{pp}m_{p}} \tag{56}\]  
In the above, \(C_{ps},m_{s}\) are the specific heat coefficient of fixed pressure and the mass of the silicone oil; and \(C_{pp},m_{p}\) are the specific heat coefficient of fixed pressure and the mass of the plates.  
The heat that the silicone oil absorbed is:  
\[Q_{s}=Q_{f}\bigg{(}\frac{1}{1+1/R_{a}}\bigg{)} \tag{57}\]  
The temperature increase of the silicone oil is:  
\[\Delta t_{s}=\frac{Q_{s}}{C_{ps}\rho_{s}V_{s}} \tag{58}\]  
The temperature of the silicone oil in time I is:  
\[t_{si}=t_{si-1}+\Delta t_{s} \tag{59}\]  
If we substitute \(t_{si}\) into the heat-transfer model, the transferred heat of all parts and the temperature can be calculated. Thus, the parameters such as the instantaneous density, the volume, the instantaneous viscosity of the silicone oil, and the filling rate of the VC are determined. If we substitute these instantaneous values of parameters into the torque calculation in Equation (1), the instantaneous torque of VC can be calculated. Therefore, the real-time torque-transmission characteristic of the viscous LSD can be calculated.  
The variation of the internal temperature of the VC during operation is shown in Figure 4. The torque characteristic of the VC is shown in Figure 5. All the characteristics were derived from a simulation of the above model. As shown in the figures, the temperature of the silicone oil rose rapidly at the beginning, and the torque declined rapidly as well. Then, the temperature of VC parts rose, the heat of the VC dissipated fast, and the temperature variation of the silicone oil became increasingly smaller. The torque transmitted by the VC tended to be stable.  
Figure 4: Internal temperature of the VC.
## 3 Results and Discussion
### Performance Evaluation of Defect Detection
In the hybrid dataset, the training effect of the YOLO11 model is shown in Figure 11. The calculations of L\({}_{\text{box}}\) and L\({}_{\text{cls}}\) are shown in Equations (7) and (8):  
\[L_{box}=1-IoU(b,b_{p}) \tag{7}\]  
\[L_{cls}=\frac{1}{N_{b}}\sum_{i=1}^{N_{b}}-\log(\hat{y}_{i},t_{i}) \tag{8}\]  
where \(b\) refers to ground truth box, \(b_{p}\) refers to predicted box, \(N_{b}\) is the number of samples in batch, and \(\hat{y}_{i},t_{i}\) is the probability of the real label category predicted by the \(i\)th sample. In the figure, L\({}_{\text{box}}\) represents the bounding box loss, that is, the loss between the predicted box and the marked box. As the training progresses, L\({}_{\text{box}}\) gradually decreases, with an obvious convergence trend, indicating that the training has been able to locate the defect. L\({}_{\text{cls}}\) represents the classification loss, which is used to measure the difference between the predicted category and the true category. There are fewer classification categories here, L\({}_{\text{cls}}\) converges quickly, and the category prediction ability training effect is better. L\({}_{\text{dfl}}\) represents the distribution focal loss [30], which is used to assist L\({}_{\text{box}}\) and improve recognition accuracy. It shows a convergence trend similar to the first two curves, indicating that the accuracy of model training has steadily improved. The final prediction results are shown in Table 3. After training with the hybrid dataset, the target detection effect is greatly improved. Of the same 12 images, 11 images can be successfully identified, with a recognition rate of 91.7%. Compared with the 41.7%  
Figure 11: Training loss curves of the YOLO11 model on the hybrid dataset, showing the convergence of bounding box regression loss (L\({}_{\text{box}}\)), classification loss (L\({}_{\text{cls}}\)), and distribution focal loss (L\({}_{\text{dfl}}\)) over training epochs. recognition rate of the model trained only with the simulation dataset, the recognition rate increased by 120%. At the same time, the images that failed to be successfully identified were checked, and the results showed that a solder ball missing defect was successfully marked, but the confidence was only 0.27, so it was ignored. In summary, in terms of real target detection, real datasets need to be added to further optimize detection capabilities and realize industrial applications.
## 4 Research Methodology  
An inclusive literature review shows that previous studies have highly accepted the survey methodology. The quantitative result of any investigation leaned entirely on the knowledge and skills of the respondents in online games. For the present analysis, empirical research was employed, and data were collected using a similar survey approach. The survey was completed by respondents who are involved in online games, mostly from three cities in the Central Zone of India (i.e., Agra, Gwalior, and Indore). The initial questionnaire was divided into two sections. The first section had two questions, used to gather the demographic information, such as gender and age. All questions are made to maintain symmetry in data. The second section of the questionnaire contained a major component with 23 items to explore the 8 factors discussed in the theoretical model. Each question was evaluated on a 7-point Likert scale with \"strongly disagree (1), strongly agree (7)\" endpoints. The content validity of the questionnaire is mentioned in Table 1. The data were collected as non-probability purposive sampling method, in which 450 questionnaires were distributed in central India (i.e., Agra, Gwalior, and Indore), between the age group of 16 to 35 years of age; out of those, 304 data samples were used for further analysis. The collected data was analyzed through various tools and techniques, such as SPSS, used for descriptive statistics of male and female. Along with the KMO tested, a rotation matrix was also tested by principal component method, and AMOS 18 Tools were used for CFA and SEM to test the framed hypothesis. In addition to statistical analysis, to find more robust results, machine learning was used, in which various methods were executed.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**S.No** & **Construct** & **Questionnaire Source** \\ \hline
1 & Use Context & [55] \\
2 & Social Interaction & [56] \\
3 & Perceived ease of use & [45] \\
4 & Perceived usefulness & [57] \\
5 & Perceived enjoyment & [57] \\
6 & Attitude & [48] \\
7 & Flow & [48] \\
8 & Behavior intention & [46] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Content validity of questionnaire.  
Figure 2: The building blocks for the proposed system.
## 1 Introduction
In [19], a terrain identification-enabled exoskeleton system which integrated only two attitude and heading reference system (AHRS) sensors and three GRF sensors was designed, tested by three healthy subjects and successfully identified ramp and level ground, with a mean accuracy of 97.77%. The number and categories of the applied sensors and the classification accuracy of the above studies, along with some other relevant studies [17; 33; 34; 35], are summarized in Figure 1.  
We can infer from Figure 1 that fruitful results have been made by relevant studies in order to obtain a robust identification accuracy and low complexity at the same time. Nevertheless, a multi-sensory terrain identification system utilizing neuromuscular signals such as EMG, which has proved to have a better performance in interpreting and predicting the intention of the user compared to mechanical signals [28; 36; 37; 38], with both a simple structure and high detection accuracy, has not been reported yet. To fill the gap, in this article, a smart neuromuscular-mechanical fusion terrain classification technique based on GRF detection, EMG and machine learning is presented. Here, only four sensors, including two EMG sensors attached to two muscles (tibialis anterior and soleus) and two GRF sensors placed at the hallux and heel, were used for classifying uphill, level ground and downhill. The collected signals are preprocessed and analyzed to draw the typical profiles of the EMG and GRF, based on which novel features reflecting the changing patterns of the EMG and GRF were selected for the support vector machine (SVM) machine learning algorithm to classify the terrains. To verify the proposed technique, standard experimental procedures compatible with relevant studies on different terrains were performed. Comparisons were made using the EMG and GRF information, respectively, and the combination of them to evaluate the effectiveness of the sensors' fusion. A paired \(t\)-test and Kruskal-Wallis test were applied to evaluate the correlation of the selected features and terrain changes. Ten-fold cross-validation accuracy together with sensitivity and specificity were used to evaluate the goodness of the SVM machine learning model. An overview process of this work is conceptually described in Figure 2.  
The paper is organized as follows. In Section 2, we explain the methodology of the experiment setup, data pre-processing, feature extraction, establishment of SVM model and evaluation of the selected features and the classifier. In Section 3, the experimental results are demonstrated and discussed, including the EMG and GRF profiles, statistical features of the different terrains and the performance of the SVM model.
## 3 Results and Discussion
### Stability of CA-Cu and CO\({}_{2}\) Capture Ability
Comparing the proposed method to using bacteria for bioccmentation, cultivation of the latter could be challenging if in situ temperatures and pH were unsuitable for bacterial growth and survival. To understand the CO2 capture of the synthesised CA-Cu and free CA, the water chemistry was investigated, and the pH (Figure 5b), electrical conductivity (Figure 5c) and calcium ion content (Figure 5d) were determined. As shown in Figure 5b, CA-Cu and free CA systems showed an increased electrical conductivity compared to the water-only system. This difference in electrical conductivity is due to the CO2-H2O system, whereupon CO2 dissolution in the water, there is the formation of HCO3- leading to increased conductivity. In the control samples with water only, the electrical conductivity and pH remained relatively the same, as in the water-only system, the dissolution of CO2 in water was limited. Consistently with what was previously reported, the results of the present study showed that immobilised CA is more stable over time and contributes to CO2 capture and precipitation [40; 52]. Bioprecipitation is confirmed by the reduction in calcium ion content as the CA captures CO2 from the atmosphere and forms biominerals. Conversely, the water-only system did not show any changes in calcium concentration (Figure 5d) as no precipitate was observed. Enzyme immobilisation has been used in industrial biotechnology to reduce costs [20; 53]. Thus, this study postulated that the proposed method would reduce the costs of bioccmentation, as the immobilisation process is inexpensive and could allow large amounts of recoverable and reusable enzyme to be prepared for large-scale bioccmentation. Although, arguably, the hybrid carrier synthesis would incur some extra costs compared to the use of free enzymes, in the longer term, the savings coming from the reuse of the hybrid carriers (which are recoverable and can maintain their activity at various temperatures and pH conditions for a long time) would outweigh the synthesis costs.
# On the Role of Matrix-Weights Elements in Consensus Algorithms for Multi-Agent Systems
## 4 Simulations
### Diagonal Matrix-Weights  
The consensus algorithm of the graph with all matrix weights equal to some scalar multiples of the identity matrix is simulated and the state trajectory is plotted in Figure 5. The matrix weights are chosen as  
\[W_{12}=3I_{3\times 3},\quad W_{32}=1.9I_{3\times 3},\quad W_{13}=5I_{3\times 3}. \tag{16}\]  
The graph structure per state is the same for all states with similar convergence properties when the matrix-weights are scalars multiplying the identity matrices. When the matrix weights are instead chosen to be general PD diagonal matrices as  
\[W_{12}=\text{diag}\{1.3,4.0,6.1\},\,W_{32}=\text{diag}\{0.8,2.1,3.1\},\,W_{13} =\text{diag}\{8.1,1.4,3.1\},\]  
the consensus algorithm is simulated and the state trajectory is shown in Figure 6. The convergence properties are different from state to state. The choice of \(w_{kk}^{ij}\) will affect the rate of convergence of the consensus algorithm of the \(k\)th state according to Equation (15). In all cases, a global consensus (GC) is achieved based on Theorem 3.  
When the matrix weights are diagonal PSD matrices chosen as  
\[W_{12}=\text{diag}\{1.3,4.0,0\},\,W_{32}=\text{diag}\{0,0,0\},\,W_{13}=\text{ diag}\{8.1,0,3.1\}.\]  
the consensus algorithm is simulated and the state trajectory is shown in Figure 7. Since the 1-state graph is connected due to \(w_{11}^{12}\
eq 0\) and \(w_{11}^{13}\
eq 0\), there is a 1-global consensus (IGC). For 2-states, there is a 2-cluster consensus (ZCC) because \(w_{22}^{13}=w_{22}^{32}=0\). There are no links between \(\{x_{1}^{[2]},x_{2}^{[2]}\}\in\mathcal{C}_{1}^{2}\) and \(\{x_{1}^{[2]}\}\in\mathcal{C}_{2}^{2}\). The same analogy applies for the 3-cluster consensus (3 CC) in 3-states: \(\{x_{1}^{[3]},x_{3}^{[3]}\}\in\mathcal{C}_{1}^{3}\) and \(\{x_{2}^{[3]}\}\in\mathcal{C}_{2}^{3}\) because \(w_{33}^{12}=w_{33}^{32}=0\).  
Figure 5: State trajectory for diagonal matrix weighted graph: scalar multiple of the identity matrix (GC with similar convergence properties across the states).
# Enhanced Bit Repair IP Fast Reroute Mechanism for Rapid Network Recovery
## 6 Discussion of the Obtained Results
### Speed Comparison of EB-REP and Existing FRR Mechanisms  
Comparing the speed of EB-REP to alternative FRR solutions, it is necessary to describe the FRR procedure in more detail.
# Viability of ABO Blood Typing with ATR-FTIR Spectroscopy
## 3 Results and Discussion
### Principal Component Analysis (PCA)  
The graphical representation of the samples in the Principal Component (PC) space (Figure 2) does not seem to reveal a clear aggregation, neither with respect to the ABO-type nor to the Rhesus factor. Nonetheless, A-type (red) and B-type (green) tend to segregate, which can be explained by taking into account the different chemical natures of their respective antigens. AB-type appears mixed since it shares both antigens, and O samples, which do not present a specific antigen, seem to be randomly distributed.  
These results suggest that minimizing the intra-class variance while maximizing the inter-class variance could improve the classification success rate. This kind of classification is obtained when performing Linear Discriminant Analysis studies.
# Cooperative Opportunistic Pressure Based Routing for Underwater Wireless Sensor Networks
## 4 Simulations and Results
### Simple Sensor Nodes  
The simulation parameters [4] are; three dimensional network of size 1 km \(\times\) 1 km \(\times\) 1 km is used. Different number of nodes varying from 120 to 300, are deployed randomly in the network. These nodes are not static, and move with the speed of 0.3 m/s. Transmission range is 250 m for these nodes. Nodes move independently, and after every 60 s, they forward their sensed data towards the destination.  
Figure 16 shows packet delivery ratio (PDR). Hydrocast has lower throughput than the other proposed scheme due to fast mobility of nodes which leads to path loss. However, the PDR improved in dense networks as shown in Figure 16. Improved-Hydrocast have shorter routing paths to overcome path loss due to the mobility of sensor nodes which minimize void regions with strategic deployment of nodes. Co-Hydrocast and Co-Improved Hydrocast has higher PDR due to cooperation technique, a data packet that has high BER is not discarded at the destination but it is combined with the copy of  
Figure 14: Number of transmissions for packet delivery.  
Figure 15: Average propagation delay between source and destination.  
same signal received from the relay node using MRC technique. Cooperation technique improves the throughput, and reduces the packet drop ratio in the network at the cost of propagation delay and energy consumption.  
Average energy consumption of successfully transmitted data packets is plotted in Figure 17. Hydrocast consumes less energy for a data packet delivery when the network is denser due to less number of void nodes, short recovery paths and no cooperation. Co-Hydrocast and Co-Improved Hydrocast schemes have high energy consumption due to opportunistic cooperation technique which has influential factor of redundant transmissions between the source and the destination which leads to high energy consumption in dense networks as shown in Figure 17.  
Figure 18 shows the average propagation delay of the proposed schemes. Co-Hydrocast has high delay than Co-Hydrocast due to opportunistic cooperation while both suffer from lengthy routing paths. Hydrocast has more propagational delay due to lengthy recovery paths from void regions. As shown in Figure 18 that the delay is minimum in our proposed schemes due to short paths in the sparse networks.  
Figure 16: Successfully transmitted data packets to sink.  
Figure 17: Average energy consumption between source and destination.  
Figure 19 shows the successfully transmitted data packets from a source to a destination. In dense networks, PDR of proposed techniques decreased due to the selection of a relay node which has at least one neighbor as forwarder to avoid a void node selection. However, cooperation technique improved PDR due to the deployment of sensor nodes at strategic locations.
## 2 Theoretical Basis and Related Work  
This section briefly describes the framework of CS [1], including sparse representation of signals, low dimensional measurement and reconstruction. Considering the importance of reconstruction in CS, numbers of greedy reconstruction algorithms have been proposed to solve this challenging problem. Therefore, we also review some representative greedy reconstruction algorithms.
## 4 Discussion
### Limitations  
A simple stratified remote sensing reflectance model considering Chla(z) vertical distribution was tested to illustrate the relationships between \(r_{rs}(0^{-})\) and \(IOP^{\prime}(\lambda,z)\) with vertical variations. This model related \(r_{rs}(0^{-})\) to absorption and backscattering coefficients, and used model parameters, varying in wavelength and sun zenith angle, to express the relationship between \(IOP^{\prime}(\lambda,z)\) of the top two layers (z = 0-0.1 m and 0.1-0.2 m) and \(r_{rs}(0^{-})\) in stratified waters. One limitation is that this model is suitable for eutrophic lakes with maximum Chla value on the water surface. The Ecolight simulation was processed based on field measurement in Lake Chaohu, a shallow eutrophic lake with frequently occurring algal blooms. Under self-regulation and appropriate environmental conditions (e.g., temperature, light, and wind), algae typically accumulate on the water surface and exhibit vertical inhomogeneous distribution with the maximum Chla value on the water surface [7]. Moreover, the performance of this model depends on the accuracy of weighting average functions; hence, one huge challenge in its further application is developing appropriate and accurate weighting functions for waters with different vertical distribution types. Another limitation is that SPIM and CDOM were assumed to be vertically uniform based on the field data of Lake Chaohu. However, SPIM and CDOM do not covary with phytoplankton and may not be vertically uniform in natural waters. Thus, attention should be directed on the different vertical structures of phytoplankton, sediments, and CDOM in Case 2 waters [10]; and it is common that they are distribute irregularly in the natural waters.