esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Structural Analysis Using FEA  
To accurately assess the merits of the three conceptual design configurations, a structural analysis was performed for each design using ANSYS Workbench 2023 R2, a renowned finite element analysis software. In the pursuit of simulating a human patient, each model featured a central point mass weighing 136 kg (equivalent to 300 lbs), precisely characterized by the moment of inertia data provided by NASA [25].  
Moreover, to emulate real-world conditions, an additional point load was introduced specifically for the robotic walking training device which was connected to the base of the backboard. All three devices were anchored with their wheels securely fixed to the ground, and a gravitational load was applied to replicate authentic forces acting upon the system.  
Furthermore, in consideration of the mechanical components, fixed joints were strategically incorporated where motors would be situated, and revolute joints were thoughtfully positioned to simulate pin connections. To ensure the reliability of the analysis, a mesh convergence study was meticulously conducted for each configuration, employing the convergence tool to scrutinize the von Mises stresses.  
Table 3 provides the outcomes of the FEA conducted for each design configuration. Specifically, it records the maximum deflection and maximum stress values at various angles of tilt. The safety factor was subsequently determined, employing the yield stress characteristic of 316 steel, the material employed for all design variants.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Design Concept Number**} & **Angle of Tilt (Degrees)** & \multirow{2}{*}{**Max Deflection (mm)**} & \multirow{2}{*}{**Max Stress (MPa)**} & \multirow{2}{*}{**Safety Factor**} \\ \hline \multirow{4}{*}{1: Rotational Lift} & 0 & 0.72 & 55.8 & 3.7 \\  & 30 & 0.44 & 44.1 & 4.7 \\  & 45 & 0.32 & 36.0 & 5.7 \\  & 60 & 0.19 & 27.3 & 7.5 \\  & 90 & 0.20 & 20.7 & 10.0 \\ \hline \multirow{4}{*}{2: Scissor Lift} & 0 & 0.00 & 31.5 & 6.5 \\  & 30 & 0.95 & 21.8 & 9.4 \\ \cline{1-1}  & 45 & 0.90 & 22.8 & 9.0 \\ \cline{1-1}  & 60 & 0.90 & 24.6 & 8.4 \\ \cline{1-1}  & 90 & 1.49 & 28.6 & 7.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of FEA results for each design concept.  
Figure 6: Concept 3—double pivot lift CAD model.  
In the analysis results, Concept 1 exhibited the highest stress levels when the patient was positioned in the supine (horizontal) orientation, yielding a safety factor of 3.69. Concept 2 similarly registered the greatest stress levels when the patient was in the supine position, but with a notably higher safety factor of 6.58. Concept 3, on the other hand, manifested its highest stress levels when the patient was in the upright (vertical) position, accompanied by a safety factor of 5.99. Most common lift devices are designed with a safety factor of 3 or more, making all the designs acceptable in terms of safety factors.
## 3 Results
### Electrical Stimulation Enhanced Pax-7 and Myo Counts in Tibialis Anterior of Mice  
Next, we studied the effect of muscle training on tissue remodeling. For this purpose, proliferating satellite cells were detected by immunolabeling with a specific Pax-7 antibody (Figure 4). We observed a rapid increase in the number of Pax-7-positive cells one week after training (CTR: 3.44 \(\pm\) 1.31 vs. ES1: 7.56 \(\pm\) 0,87, \(p\) < 0.0001) Accordingly, comparable outcomes were obtained two weeks following muscle training (CTR: 3.71 \(\pm\) 1.41 vs. ES2: 8.15 \(\pm\) 0.67, \(p\) < 0.0001).  
Figure 2: Representative immunofluorescence micrographs of IgG-positive (red), dystrophin (green), and DAPI (blue) after one (ES1, n = 5) and two (ES2, n = 5) weeks of electrical stimulation and 4 days after cardiotoxin injection (CTX 4D, n = 4) compared to control (CTR, n = 5). Scale bar: 50 \(\upmu\)m.  
Figure 4: Analysis of Pax-7 positive cell counts after one and two weeks of electrical stimulation. (**A**,**B**) Representative immunofluorescence micrographs of DAPI (blue, used to identify nuclei), Pax-7  
Figure 3: Analysis of muscle function in vivo following one and two weeks of electrical stimulation. (**A**–**C**) After one week of electrical stimulation, the maximal tetanic force, specific force, and absolute force were analyzed, respectively. (**D**–**F**) After two weeks of electrical stimulation, the maximal tetanic force (100 Hz), specific force, and absolute force were measured, respectively. The data were presented as mean \(\pm\) SEM. In the statistical analysis, an unpaired \(t\)-test and a Mann-Whitney test were employed (n = 6). * \(p\) < 0.05 vs. respective control.  
(violet), and Laminin (yellow) after one and two weeks, respectively, of electrical stimulation (20 \(\upmu\)m scale bar). (**C**) The number of positive Pax-7 nuclei per 100 fibers after one and two weeks, respectively, of electrical stimulation (n = 5 per group). The arrows indicate Pax-7 positive cell. The data are presented as mean \(\pm\) SD. Two-way ANOVA was followed by [PERSON]'s post-test in the statistical analysis. * \(p\) < 0.0001 in comparison to the relevant control group throughout time.  
When assessing the effect of muscle training on satellite cell differentiation as measured by Myo immunolabeling, we found no response after one week of training (Figure 5). At two weeks, however, a significant increase in Myo-positive cells was found (CTR: 4.71 \(\pm\) 1.35 vs. ES2: 12.38 \(\pm\) 1.83, \(p\) = 0.0057). Notably, the rise observed at two weeks is statistically distinct from that shown at one week (ES1: 3.71 \(\pm\) 1.45 vs. ES2: 12.38 \(\pm\) 1.83, \(p\) = 0.0017) (Figure 5).
## 4 Comparisons and Results  
### Simulation Comparison of LSTM Model and GRU Model  
To verify that the GRU model selected in this study trained faster than the LSTM model, as well as to confirm that both the GRU and LSTM models yielded similar prediction results for water quality parameters, we constructed individual LSTM and GRU models for comparison. We utilized 900 preprocessed DO datasets for training and testing purposes. The basic parameters for both models were kept constant, as depicted in Table 2, where the number of iterations was set to 400, the hidden layer units were configured to 100, and the initial learning rate was established at 0.001.  
Figure 12 displays the test set prediction outcomes of the two individual models. The original data are represented by the black curve, while the prediction results and absolute error curves of the LSTM model are shown in red, and the prediction results and absolute error curves of the GRU model are in blue. As can be seen from Figure 12, under the same parameters, the independent GRU model was closer to the real value than the independent LSTM model for the DO data, the lag of the prediction results was weaker, the absolute error between the predicted value and the real value was smaller, and the fitting prediction effect on water quality parameters was better.  
According to Table 4, the GRU model exhibited a modeling speed 44.3% faster than the LSTM model, while GRU showed a significant advantage over LSTM in training speed, primarily due to its simpler structure with only two gate mechanisms: a reset gate and update gate. In contrast, LSTM includes an input gate, forget gate, and output gate, along with additional cell state updates. GRU has fewer parameters because its gate mechanisms share some parameters and only update their hidden state, whereas each gate mechanism in LSTM has independent weight matrices. Furthermore, the GRU model demonstrated a decreased RMSE and MAPE of 17.8% and 21.9%, respectively, compared to the LSTM model, indicating its suitability for predicting water quality parametric data. It is worth noting that when building a single GRU model, we manually input the hyperparameters of the GRU model based on experience. This was obviously uneconomical and may have had an impact on the final prediction results of the GRU model. Introducing intelligent optimization algorithms for hyperparametric or parametric optimization can improve this shortcoming.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of hidden layer units & [50; 200] \\ Iterations & [200; 500] \\ Initial learning rate & [0.001, 0.02] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Optimization intervals for model hyperparameters.
## 1 Introduction
Several seismic refraction profiles were performed in the cities of Chlef and El-Attaf [17]. The Woodward Clyde Consultants also carried out a complete geotechnical and geological study in eight cities of the Chelif region, including Oued-Fodda, El-Attaf, and El-Abadia, where several boreholes were drilled. The study outcomes provided geotechnical and hydrogeological maps for each city, along with seismic microzoning survey maps [18]. The neotectonic and paleoseismological studies carried out on the formations of the oriental Lower-Chelif Basin (e.g., [9; 19]) were intended to highlight the tectonic elements and to identify geological structures and faults likely to be reactivated by generating earthquakes. More recently, site effects investigations using earthquakes and ambient vibration data were conducted in the cities of Chlef in the Lower-Chelif Basin [20; 21] and Oued-Fodda in the Middle-Chelif Basin [22]. The main objectives were to estimate the soil resonance frequencies, the shear-wave velocities in the sedimentary rocks, and the depths to seismic bedrock, where the relatively high impedance contrast with Miocene formations may amplify ground shaking during an earthquake. In the Mitidja basin, [PERSON] [23] and [PERSON] [24] estimated and mapped the roof of the engineering bedrock using mainly ambient vibration data. In this study, ambient vibration measurements using single-station and array techniques were used to estimate the shear-wave velocity for the sedimentary layers, and to map the bedrock structure in the Middle-Chelif Basin. In the first part of the study, we estimate the soil resonance frequencies (\(\mathfrak{f}_{0}\)) and the corresponding amplitudes (\(A_{0}\)) using the horizontal-to-vertical spectral ratio (HVSR) technique [25; 26]. In the second part, we perform array measurements using the frequency-wavenumber (F-K) technique [27; 28; 29; 30] to retrieve the surface wave dispersion curves. After that, the dispersion and HVSR curves are inverted jointly to estimate the shear-wave velocity (Vs) profiles at each site. Available borehole information and electrical resistivity profiles constrain the Vs models used for the inversion process. As a result, the bedrock model is proposed. This study is a continuation of previous work conducted in the city of Oued-Fodda [22]. The results obtained in this work will contribute to the seismic hazard reassessment in the Chelif Basin. They can also be used for seismic hazard mitigation studies, such as strong ground motion simulation, soil liquefaction studies, and for the updating of the Algerian seismic code.
## 3 DAMP-YOLO Network
### Meter Data Augmentation  
Traditional data preprocessing relies heavily on parameter tuning, typically based on morphological features. However, given the intricacy and dynamism of real-world environments, fixed-parameter traditional data preprocessing often struggles to adapt well to environmental changes. By artificially incorporating prior knowledge of human vision, data augmentation can enhance the model's performance and has become a widely accepted practice for training computer vision models. Therefore, we have chosen to adopt data enhancement as a means of simulating real-life data distribution to improve the robustness of the network. Compared with ordinary object detection images, meter reading images have their particularities. These characteristics can be considered and analyzed during meter reading image preprocessing and network training:  
1. Background color: The digital display area of the meter image is usually dominated by light colors (such as light green or light grey), and the peripheral areas of the meter are usually dark colors (such as dark black and dark brown).
2. Font labels: Meter images contain text labels such as meter readings and timestamps. Meter readings are the core of meter recognition and are usually presented using specific digital fonts, which cannot be flipped left and right or up and down.
3. Shape layout: Meters of the same model have similar shapes and layouts, and the numbers and labels in the meter reading image are primarily located in the center of the image with the same fonts.
4. Noise interference: The meter image may be affected by noise interference, such as day and night light changes, stains, reflections, and shadow occlusion. These interferences may affect the accuracy and stability of readings.  
Given the above particularities of the meter reading data, the dataset will undergo random processing during input preprocessing to enhance data. Apart from basic geometric transformation, like random translation, rotation, and scale, meter data augmentation also  
Figure 8: The structure of the aggregated triplet attention module.  
includes random channel multiply, Gaussian blur, Gaussian noise, alpha grid, and conversion to grayscale. The example images are shown in Figure 9.  
These steps replicate the process in which a camera captures images from various angles and positions, often with noise interference. By adjusting and refining the training data, we aim to align more closely with the data distribution in real-world scenarios, ultimately enhancing the model's performance and generalization capabilities.
# Distributed Global Function Model Finding for Wireless Sensor Network Data
## 2 Related Work  
### Data Mining in Wireless Sensor Networks (WSNs)  
Recently, mining knowledge from sensor data has attracted a great deal of attention from data mining experts [21]. [PERSON] _et al._ proposed a fuzzy-logic-based clustering approach to prolong the lifetime of WSNs using energy predication [10]. [PERSON] _et al._ present a distributed energy-efficient clustering algorithm with improved coverage by analyzing communication energy consumption of the clusters and the impact of node failures on coverage with different densities in wireless sensor networks [11]. In WSNs, the stream nature of the data, the limited resources, and the distributed nature of sensor networks bring new challenges for the mining techniques. [PERSON] _et al._ proposed a new formulation for the association rules [12]. In these references, data mining techniques are only seen as a means to solve the problems existing in WSNs. Generally, for data mining in wireless sensor networks, WSNs would be regarded as platform of data collection and transmission [22]. Finally, we analyzed these data from WSNs. Due to the wide range of application of WSNs, the analysis and mining of all kinds of data based on wireless sensor network are also emphasized. [PERSON] _et al._ proposed classification and prediction of future weather using Back Propagation (BP) Algorithm for data collected by weather sensors [23]. [PERSON] _et al._ present a data mining approach for fall detection using _k_-nearest neighbor algorithm on wireless sensor network data in order to enhance life safety of the elderly and boost their confidence [24]. [PERSON] _et al._ present knowledge discovery and leaf spot dynamics of groundnut crop by wireless sensor network and data mining techniques. The useful information, knowledge or relations from all kinds of data mining techniques would be helpful to analyze and understand leaf spot disease infection [25]. In order to protect sensor nodes from malicious attacks, [PERSON] _et al._ proposed a new intrusion detection method. The method constructed Markov decision processes based on an attack pattern mining in order to predict future attack patterns and implement appropriate measures [26]. In order to explore, analyze, and extract useful information and knowledge from the larger number of personal data which came from smartphone and wearable devices, [PERSON] _et al._ proposed the personal ecosystem where all computational resources, communication facilities, storage and knowledge management systems are available in user proximity [27]. As suggested above, it can be seen that finding knowledge or model from wireless sensor network data is very meaningful and valuable.
## 3 Results
### Tiltmeter Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Earthquakes** & **Depth (km)** & **MD** & **IMB** & **CMT** & **TRC** & **CMG** \\ \hline
6 & 20181129 & 1.65 & 2.3 & No Tilt data & LTC & LTC although TF & LTC \\
7 & 20181201 & 1.40 & 2.2 & No Tilt data & NC & LTC & TF \\
8 & 20181202 & 1.25 & 2.5 & No Tilt data & NC & LTC & LTC \\
9 & 20181202 & 1.65 & 2.5 & No Tilt data & LTC & LTC & LTC \\
10 & 20181209 & \(-0.40\) & 2.1 & LTC & LTC & TF & TF \\
11 & 20190518 & 4.35 & 2.5 & LTC & LTC & LTC \\
12 & 20190816 & \(-0.50\) & 2.1 & NC & LTC & TF & TF \\
13 & 20200115 & 1.15 & 2.3 & LTC & LTC & LTC \\
14 & 20200213 & 2.65 & 2.2 & NC & LTC & LTC & LTC unclear \\
15 & 20200319 & \(-0.45\) & 2.4 & LTC & LTC & TF & LTC \\
16 & 20200424 & \(-0.65\) & 2.3 & LTC & TF & TF & TF \\
17 & 20200427 & \(-0.55\) & 2.1 & LTC & LTC & LTC & LTC \\
18 & 20200708 & \(-0.40\) & 2.1 & NC & LTC & TF & TF \\
19 & 20200720 & \(-0.45\) & 2.2 & LTC & LTC & TF & TF \\
20 & 20200803 & \(-0.55\) & 2.3 & TF & LTC & TF & TF \\
21 & 20200811 & 2.50 & 2.0 & LTC & LTC & LTC & NC \\
22 & 20200902 & \(-0.50\) & 2.1 & TF & LTC unclear & TF & TF \\
23 & 20200923 & 0.65 & 2.3 & LTC & LTC & NC & TF \\
24 & 20201122 & \(-0.45\) & 2.1 & LTC & TF & TF & TF \\ \hline \hline \end{tabular} Legend: LTC = Long Term tilt direction Coherent with displacement. NC = long term tilt direction Not Coherent with displacement. TF = Tilmeter too Far from the epicenter.  
\end{table} 
Table 3: _Cont._Figure 5: An example of long-term coherence (LTC—i.e., coherent with the displacement pattern represented in 3D and 2D) recorded at IMB on 2019/05/18. The blue arrows indicate the long-term tilt direction. The color bars specify the seismic displacement values, to which a magnification was applied to better visualize the vertical displacement spatial pattern. The red cross marks the absolute maximum value of the displacement field, while the blue cross indicates the absolute minimum deformation. Figure 6: Left side, from top left to bottom: NS component recorded at IMB 12-h before and after the earthquake of 2019/05/18; same component recorded five days before and after the earthquake (the signal highlighted in blue corresponds to the 24-h time window); EW component recorded 12 h before and after the earthquake; same component recorded five days before and after the earthquake (the signal highlighted in green corresponds to the 24-h time window). Right side: ground tilt recorded at IMB in a 24-h period straddling the earthquake. The curve in red represents the filtered signal while the blue arrow indicates the direction of ground tilt calculated over ten days (LTC). The green cross symbol indicates the tilt actually recorded during the event while the solid dot indicates that calculated by the displacement.
## 1 Introduction
Second, the link connectivity matrix that contains information about the network structure is utilized in the PINNs to facilitate the stitching process by utilizing flow conservation constraints at merge and diverge points. Third, this study demonstrates a single sparse data source is sufficient for training PINNs, even in the absence of the complete dataset or an enhanced dataset augmented by micro-simulation. The rest of the paper is organized as follows: Section 2 'Related Work' briefly reviews related work. The architecture of the proposed framework and data description are presented in Section 3 'Methodology' and Section 4 'Data', respectively. In Section 5 'Experimental setup and discussion', we present numerical experiment and discussions. Conclusions summarize the findings and suggest future research.
## 5 Experiment
### Experimental Results Analysis
#### 5.2.2 Analysis of Convergence  
According to Algorithm 1 and ADM algorithm, when penalty parameter sequence \(\left\{\mu_{k}\right\}\) is increasing monotonically and bounded, the Lagrange multipliers \(Y_{1}\), \(Y_{2}\) and \(Y_{3}\) can converge to the optimal solution linearly; when \(\left\{\mu_{k}\right\}\) is increasing monotonically and unbounded, \(Y_{1}\), \(Y_{2}\) and \(Y_{3}\) can converge to the optimal solution super-linearly. As shown in Figure 3, the \(x\)-axis denotes the iteration number, and the \(y\)-axis is the value of objective function. We can see that the objective function value converges in a very fast manner, usually within 40 iterations, which also proves the fast convergence property of the proposed DLRSD model.
## 3 Active Disaster Response System (ADRS)
### Prototype of ADRS and Use Scenarios
During normal operation, ADRS regularly resets the watchdog timer to prevent it from timing out. If, due to a hardware fault or program errors, ADRS fails to restart the watchdog, the timer will elapse and generate a timeout signal. The timeout signal is then used to reset ADRS or to perform predefined tasks, such as sending a warning message to the person who is in charge of the maintenance of ADRS. We consider the above mechanism used to deal with false positives and false negative as future work.
## 3 Results
### Spatial Variability in 'Dry' and 'Wet' Conditions  
To depict spatial variability of SMAI and LST, maps of their monthly mean values over Bulgaria are constructed and visualized. Drought severity is assessed according to the SMAI scale using its monthly averaged values for the location of each synoptic station. LST mean monthly values at 0900 UTC \(\pm\) 30 min for each ECMWF model grid point over Bulgaria are used. Spatial variability of the two biogeochemical parameters are strongly related to the distribution of energy-water cycles across the climate gradient of Bulgaria and, thus, their evolution is performed for a variety of environmental conditions. Based on NIMH long-term data records of precipitation and 'SVAT_bg' model outputs, the results are categorized in two types of experimental evolutions for SMAI and LST:  
1. Years/months with dominant sensible heat fluxes are selected: In this case, a high energetic loading (i.e., high solar irradiance) but limited precipitation lead to depleted SMA and drought occurrence with different levels of severity. Such years/months are classified as 'dry' periods.
2. Years/months with dominant latent heat fluxes are selected: In this case, even at a high solar irradiance, precipitation is above norms, soil becomes saturated (and even overmoistened), and evapotranspiration is undisturbed. Such years/months are classified as 'wet' periods.  
Figure 3: Boxplots of monthly mean values (2007–2018) for the region of Bulgaria during the growing season (March–October) of (**a**) SMAI; (**b**) LSASAF LST 0900 UTC \({}^{\circ}\)C. The boxes represent the interquartile range (25 th to 75 th percentile, first and third red line), whiskers cover 99.3% of the data, and the middle (red) lines represent the mean values.