esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Bridge Degradation Identification and Model Update Results
### Analysis of Deformation Detection Results  
The study employed the deformation detection model, as outlined in the previous sections, to analyze the spatial changes in both the overall bridge structure and its individual components. Utilizing the \"registration _icp_\" point cloud registration algorithm from Open3D, the research examined the point cloud data of Hedong Bridge for the years 2017 and 2018. This approach enabled the determination of the rigid body displacement for the overall bridge structure, which was subsequently used to assess the spatial shape changes in Hedong Bridge.  
The detection results revealed that the overall structure of Hedong Bridge has experienced small translational and rotational movements along/around the x-, y-, and z-axes of the bridge. Specifically, the overall bridge structure experienced a translation  
Figure 16: Particle swarm optimization results and crack detection result graph.  
Figure 17: Three-dimensional graph of parameter correlation.  
of 0.0784 m along/around the x-axis, \(-0.1577\) m along/around the y-axis, and 0.0164 m along/around the z-axis. Notably, the fourth span exhibited more significant changes, with translations of 0.5673 m along/around the x-axis, \(-0.2243\) m along/around the y-axis, and 0.3800 m along/around the z-axis. The corresponding rotations were measured as 0.1253\({}^{\circ}\), \(-0.0569^{\circ}\), and 0\({}^{\circ}\) along/around the x-, y-, and z-axes, respectively. These findings indicate that while the overall bridge structure underwent minimal movements, the fourth span experienced more pronounced spatial alterations, highlighting the importance of detailed component-level analysis in structural health monitoring.
## 4 Numerical Examples
### L-Shaped Al/SiC Plate
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(h\) & **Point** & **Method** & \(g=0\) & \(g=2\) & \(g=4\) & \(g=6\) & \(g=8\) & \(g=10\) \\ \hline \multirow{4}{*}{0.2} & \multirow{2}{*}{P\({}_{1}\)} & Ansys & \(3.089\times 10^{-3}\) & \(8.246\times 10^{-3}\) & \(9.745\times 10^{-3}\) & \(1.037\times 10^{-2}\) & \(1.083\times 10^{-2}\) & \(1.121\times 10^{-2}\) \\  & & Present & \(3.088\times 10^{-3}\) & \(8.215\times 10^{-3}\) & \(9.714\times 10^{-3}\) & \(1.034\times 10^{-2}\) & \(1.078\times 10^{-2}\) & \(1.117\times 10^{-2}\) \\  & & \multirow{2}{*}{P\({}_{2}\)} & Ansys & \(4.480\times 10^{-3}\) & \(1.188\times 10^{-2}\) & \(1.400\times 10^{-2}\) & \(1.488\times 10^{-2}\) & \(1.551\times 10^{-2}\) & \(1.606\times 10^{-2}\) \\  & & Present & \(4.485\times 10^{-3}\) & \(1.183\times 10^{-2}\) & \(1.394\times 10^{-2}\) & \(1.481\times 10^{-2}\) & \(1.544\times 10^{-2}\) & \(1.599\times 10^{-2}\) \\  & & \multirow{2}{*}{P\({}_{1}\)} & Ansys & \(2.376\times 10^{-2}\) & \(6.357\times 10^{-2}\) & \(7.431\times 10^{-2}\) & \(7.852\times 10^{-2}\) & \(8.163\times 10^{-2}\) & \(8.442\times 10^{-2}\) \\  & & Present & \(2.464\times 10^{-2}\) & \(6.330\times 10^{-2}\) & \(7.396\times 10^{-2}\) & \(7.813\times 10^{-2}\) & \(8.122\times 10^{-2}\) & \(8.400\times 10^{-2}\) \\  & & \multirow{2}{*}{P\({}_{2}\)} & Ansys & \(3.448\times 10^{-2}\) & \(9.166\times 10^{-2}\) & \(1.071\times 10^{-1}\) & \(1.131\times 10^{-1}\) & \(1.176\times 10^{-1}\) & \(1.216\times 10^{-1}\) \\  & & \multirow{2}{*}{Present} & \(3.324\times 10^{-2}\) & \(9.136\times 10^{-2}\) & \(1.067\times 10^{-1}\) & \(1.127\times 10^{-1}\) & \(1.171\times 10^{-1}\) & \(1.212\times 10^{-1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Deflections at P\({}_{1}\) and P\({}_{2}\) of the Al/SiC L-shaped corner support plate for various gradient indexes and thicknesses. Figure 12: Three-dimensional geometry and 2D NURBS midplane of an L-shaped corner support plate. (**a**) Three-dimensional geometry; (**b**) 2D NURBS midplane. Figure 13: Converged 3D response of Al/SiC L-shaped corner support plate under a uniform load.
## 5 Long-Short-Range Communication
### Long-Range Communication
#### 5.1.1 Sigfox  
The most widely used and simplest low-power network standard is Sigfox with limited functionality. Sigfox is a protocol developed for the OSI model using the initial four layers. Its base station is similar to that in cellular networks; the antenna is mounted on a pillar based on a star topology network constructed in such a way that at the time of uplink, its battery efficiency is improved. It is also possible for a node to include a downlink, but it requires a request to receive the acknowledgement and is limited to four requests a day.  
Figure 4: Comparison of scheduling approaches. STF denotes the small-task-first approach, and FAFS denotes the first-arrived, first served approach.  
Its range can be further extended up to 50 km [76] but is limited in terms of high latency for healthcare applications. It operates in various unlicensed bands in different countries in Europe, working at 868 MHz, and 915 MHz in the US. No global band exists for the use of Sigfox, whereas some other low-power network technologies function at sub-GHz frequencies. It requires high bandwidth because it functions in the unlicensed band, which increases interference that may harm the healthcare system. In order to minimize this disadvantage, Sigfox follows three successive frames with a unique pseudo-random carrier using dissimilar propagation paths, which affect and improve the likelihood of an intact message being received, thereby reducing the chances of interference. Sigfox can handle approx. fifty thousand nodes using a single gateway to increase its network capacity. Compared to narrowband IoT, which covers around fifty thousand nodes, Sigfox supports 40 devices per household with an intersite length of 1732 m [77].
## 5 Experimental Results
### Memory Analysis
#### 5.2.1 Aho-Corasick Algorithm  
In order to carry out the pattern-matching process on the AC automaton, the system employs three tables in the memory: a state transition table, a failure transition table, and an output table. The state transition table determines existing transitions from the current state to another state for a given character in the AC automaton. If there is no transition to another state, the failure transition table restarts the pattern detection process. The output table serves to understand whether there is a detected pattern in the current state. Each transition in the automaton has an entry in the state transition table, while each state has a failure transition and output information in the memory. Table 7 shows the memory space required for the AC tables specially created for datasets containing different numbers of patterns.
# Wide-Area Protection System for Radial Smart Distribution Networks
## 4 Simulation Study

Table 4: Accuracy of the ANNs in Scenarios 1 to 3. Figure 5: Zoomed 2D scatter plot illustrating superimposed Voltages and Currents. and the FZRC matrices. The FZRC matrices for the test network in the connected mode and in the islanded mode of operation are as follows:  
\[FZRC_{connected}=\left[ \begin{array}{cccccccccccc}+1&+1&+1&+1&+1&+1&+1&+1&+1&+1 \\ -1&+1&+1&+1&+1&+1&+1&+1&+1&+1\\ -1&-1&+1&+1&+1&+1&+1&+1&+1&+1\\ -1&-1&-1&+1&+1&+1&+1&+1&+1&+1\\ -1&-1&-1&-1&-1&+1&+1&+1&+1&+1\\ -1&-1&-1&-1&-1&-1&+1&+1&+1&+1\\ -1&-1&-1&-1&-1&-1&-1&+1&+1&+1\\ -1&-1&-1&-1&-1&-1&-1&-1&+1&+1&+1\\ -1&-1&-1&-1&-1&-1&-1&-1&+1&+1&+1\\ -1&-1&-1&-1&-1&-1&-1&-1&-1&+1&+1\\ -1&-1&-1&-1&-1&-1&-1&-1&-1&-1&+1\\ \end{array} \right]\]  
\[FZRC_{islanded}=\left[ \begin{array}{cccccccccccc}0&0&0&0&0&0&0&0&0&0&0\\ -1&0&0&0&0&0&0&0&0&0&0\\ -1&-1&0&0&0&0&0&0&0&0&0\\ -1&-1&-1&0&0&0&0&0&0&0&0\\ -1&-1&-1&-1&0&0&0&0&0&0&0\\ -1&-1&-1&-1&-1&+1&+1&+1&+1&+1\\ -1&-1&-1&-1&-1&-1&-1&+1&+1&+1\\ -1&-1&-1&-1&-1&-1&-1&+1&+1&+1\\ -1&-1&-1&-1&-1&-1&-1&-1&+1&+1\\ -1&-1&-1&-1&-1&-1&-1&-1&-1&+1\\ -1&-1&-1&-1&-1&-1&-1&-1&-1&-1&+1\\ -1&-1&-1&-1&-1&-1&-1&-1&-1&-1&+1\\ \end{array} \right]\]  
As can be seen, the proposed approach requires only the fault flag and fault direction from the network relays to detect faults, without needing any additional information such as network voltages and currents. Moreover, the method is independent of synchronization and time calculations, as each UFDM makes fault decisions individually, based solely on the RMS and phase angle values of the voltages and currents at the connection point.
## Materials and Methods
### Fourier-Transform Infrared (FT-IR) Analysis  
FT-IR spectra of ZnSO\({}_{4}\), SP, and ZnSO\({}_{4}\)/SP NCs were obtained using a FrontierTM FT-IR spectrometer (PerkinElmer Inc., Buckinghamshire, UK) with the attenuated total reflectance (ATR) mode. Transmittance (%) of each group was scanned in a range from 400 to 4000 cm\({}^{-1}\).
## 1 Introduction
Still, they do not include the known graph attributes in the reasoning process, which reduces these methods' prediction veracity rates and restricts their availability in practice [21]. A large number of studies have shown a hierarchical, scale-free nature of biological networks [5; 22]. This attribute makes most nodes in the network sparsely connected, where a few positively associated genes account for most of the interaction, which are hub nodes. The hub node is a node with high network centrality. The connection rules between nodes reflect the relative position information between nodes to some extent. Recently, the LDCNET algorithm has successfully merged MI and network topology centrality for reconstructing GRNs [23]. In this article, we develop a novel method, namely DCNTC, which incorporate the distance correlation and network topology centrality into GRNs inferring algorithms and test the performance. Our approach adopts a novel estimation of measurement, and the sparse (scale-free) structure of the gene regulatory network is used to calculate the network topology centrality. Compared with the traditional methods, we use the latest distance correlation statistics to measure the nonlinear relationship, and combine the network topology centrality to infer the gene regulation network. At the same time, we select the optimal threshold according to the distribution characteristics of the values calculated by the distance correlation of each gene pair. Real data from the SOS DNA repair network and DREAM-simulated data suggest that the DCNTC algorithm can improve the GRNs' inference accuracy.
## 5 Limitations  
This work shows comparative results, in terms of discomfort perceived, effort needed, and ergonomics risk, regarding the execution of simple and common tasks both on a ladder and on the ground. Some limitations need to be acknowledged:  
26 participants in the test cannot be considered sufficient to carry out an in-depth study of the issues, even if the sample is statistically representative. Furthermore, the sample was composed of only young people, and a group of only 4 women, out of a sample of 26, is considered a limiting factor.  
No EMG acquisition was performed to provide objective information measures of a person's behavior and effort. The test setup, the choice of activities to simulate, and the procedure were found to be adequate to be able to acquire both subjective and subjective data. However, the setup was the same for the entire sample without taking into account the different anthropometry among the participants and the difference in terms of force and exertion that each subject had to need to accomplish a task.  
In the ladder-knee contact, which has been discovered as the most impacting factor on perceived discomfort, no physical parameters, such as contact pressure, contact area, and contact time, have been investigated. This can be an improvement to do in future experiments.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Sideways Object Picking** & \begin{tabular}{c} **RULA** \\ **(Right)** \\ \end{tabular} & \begin{tabular}{c} **RULA** \\ **(Left)** \\ \end{tabular} & \begin{tabular}{c} **Perceived** \\ **Discomfort** \\ \end{tabular} & \begin{tabular}{c} **Effort** \\ **Needed** \\ \end{tabular} & \begin{tabular}{c} **Left** \\ **Knee** \\ \end{tabular} & \
\begin{tabular}{c} **Right** \\ **(Right)** \\ \end{tabular} &
\begin{tabular}{c} **CAMan** \\ **(Left)** \\ \end{tabular} \\ \hline Mean (Ladder) & 5.04 & 5.04 & 3.33 & 1.26 & 2.30 & 2.00 & 8.60 & 8.74 \\ St. Dev. (Ladder) & 0.92 & 1.00 & 2.06 & 0.45 & 2.92 & 2.77 & 1.01 & 0.96 \\ Mean (Ground) & 4.96 & 4.96 & 2.44 & 1.19 & 0.19 & 0.19 & 8.72 & 8.90 \\ St. Dev. (Ground) & 0.82 & 0.82 & 1.34 & 0.48 & 0.96 & 0.96 & 0.90 & 0.42 \\ Mean Difference & 0.08 & 0.08 & 0.89 & 0.07 & 2.11 & 1.81 & 0.12 & 0.16 \\ Stochastic Significance & NO & NO & YES & NO & YES & YES & NO & NO \\ \hline \hline \end{tabular}
\end{table}
Table 8: Frontal object picking Ground-Ladder difference.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Frontal Object Picking** & **Perceived Discomfort** &
\begin{tabular}{c} **Effort** \\ **Needed Perceived** \\ \end{tabular} \\ \hline Right arm & 0.568 ** & 0.589 ** \\ Left arm & 0.567 ** & 0.509 ** \\ Right forearm & 0.508 ** & 0.394 * \\ Left forearm & 0.405 * & - \\ Neck & 0.574 ** & 0.523 ** \\ Right knee & 0.523 ** & 0.460 * \\ Left knee & 0.574 ** & 0.460 * \\ Upper back & 0.533 ** & 0.473 * \\ Lower back & 0.516 ** & 0.402 * \\ Perceived Discomfort & 1 & 0.676 ** \\ CaMan global & \(-\)0.676 ** & - \\ \hline \end{tabular}
\end{table}
Table 9: Frontal object picking Spearman coefficients.
## 4 Methodology
### Temporal Convolution Network (TCN)  
A TCN has input and output of the same length with dilated and convoluted layers. Only observed inputs \(x_{1},x_{2},\cdots,x_{N}\) can be used to predict the output \(y_{i}\). In a TCN, fixed steps between adjacent filters are used rather than the whole history in dilation. To overcome the vanishing gradient problem and speed up computation time, the method also uses residual connections [37]. Given a time series \(q=(q_{0},\cdots,q_{t})\) and filter \(f:\{0,\cdots,k-1\}\), on element \(j\) of the sequence, a layer F is defined as:  
\[F(j)=(q\times_{d}f)(j)=\sum_{i=0}^{k-1}f(i)\cdot q_{s=d\cdot i} \tag{9}\]  
where \(d=5\) is the dilation factor, \(k=10\) is the filter size, and \(s-d\cdot i\). This study takes the values of \(d,k\), and \(s\) as 5, 10, and 5, respectively. TCNs have been shown to have longer memory retention, and are simpler and clearer than recurrent networks [38]. TCNs have been deployed to predict river discharge in different basins worldwide [39; 40; 41].
## 2 Spatial Distribution  
In order to be able to characterize the distributions of CMLs in a given region, we suggest partitioning the region into sub-regions, each with a spatially uniform CML density. Such sub-regions are expected to correspond to the common environmental terms: urban, suburban, and rural. If such a partition to homogeneous regions was not performed and the CMLs were clustered together, their spatial distribution would need to be addressed more meticulously in order to evaluate reconstruction potential, as it would not have been simply uniform.  
When partitioning a region, it is critical to maintain a nominal area size which is appropriate for capturing relevant rain phenomena. Typical rain clouds over Israel tend to stretch over an area of up to 10 \(\times\) 10 (km\({}^{2}\)) [14]. It is recommended to maintain a minimum of such region size. Accordingly, in this paper we examined regions that are indeed 10 \(\times\) 10 (km\({}^{2}\)).
## 2 MD Simulation Model
model was then minimized using the conjugate gradient method (cutoff at 10 kcal/mol A). Subsequently, an equilibration period using the Verlet algorithm composed of the normal volume temperature (NVT) dynamics at 300 K for 1 ns and the normal pressure temperature (NPT) dynamics at 300 K and 0.1 MPa for 1 ns was allowed, with the time step of 1 fs. All interatomic behaviors of constituent atoms were simulated using a polymer consistent force field (PCFF) [15].  
One key aspect of interest in the present study was the dependence of the physical properties on the crosslink density of the BC. An illustration of the crosslinking procedure and the corresponding BC microstructure, for the crosslinking ratio of 80%, are shown in Figure 1b. First, reactive points were assigned to each component, and the distance between them was measured. Then, covalent bonds between the reactive points were newly created starting from the shortest distance, until the total crosslinking degree in the unit cell reached approximately 80%. Regarding the crosslinking reaction, when the crosslinking ratio reaches 80%, almost all molecules inside the unit cell participate in polymerization as shown in Figure 1c. To observe the changes in the internal microstructure and interaction characteristics according to the crosslinking ratio, BC models under four different conditions (0%, 20%, 50%, and 80%) were considered. Each selected BC model was further equilibrated by the NVT dynamics at 300 K for 1 ns, and by the NPT dynamics at 300 K and 0.1 MPa for 2 ns. The details of the equilibrated BC microstructures are listed in Table 1. In all cases, the unit cell density after the NPT ensemble was in the 1.07-1.08 g/cc range, which was confirmed to converge under conditions similar to the typical values observed experimentally [16; 17; 18]. Note that these modeling procedures and crosslinking mechanisms for polymer microstructures used in this study can also be found in our previous works [19; 20; 21].  
Figure 1: The all-atom BC model used in this study. (**a**) Molecular structures of the MMA liquid and the PMMA powder, and (**b**) a configuration of the BC unit cell with the crosslinking ratio of 80%. (**c**) Snapshots of the PMMA-based BC as the crosslinking reaction proceeds. The constituent molecules participated in the reaction are highlighted in reddish brown.
# A Simple Water Retention Model Based on Grain Size Distribution
## 2 Key Phenomena for Water Retention Curve Models
### Contact Angle between Water and Soil Surface  
The contact angle of a mineral soil grain is an important factor affecting the predicted sections of GSD-WRC models, as it determines the drainage suction of a pore from the capillarity law. However, because most soil minerals are hydrophilic with relatively low contact angle values (for example, as low as 17\({}^{\circ}\) for quartz [35; 36]), many GSD-WRC models assume a contact angle of zero and achieve good predictions for many soils [11; 12; 13; 14; 15; 16; 17]. In contrast, the materials of interest in this study include coal tailings that have significant coal content, and coal is known to be hydrophobic [37; 38; 39; 40; 41; 42], which means that a contact angle of 0\({}^{\circ}\) cannot be used nor justified.  
There are a variety of existing WRC models that do consider the contact angle of soils [43; 44; 45], typically for the purpose of fitting wetting and drying paths to WRC data (i.e., capturing hysteretic behavior) where the advancing contact angle and receding contact angle are relevant to the wetting and drying path WRCs, respectively. In addition, it has been shown that many of the WRC models that assume a contact angle of zero during the model development can be easily altered to consider the contact angle of a soil [37]. Thus, consideration of the contact angle of a material is relatively simple to implement, at least for materials that have the same contact angle for every particle. However, coal tailings contain a mixture of coal and mineral particles, and the coal particles can be more prevalent for particular size ranges, as indicated by the correlation between specific gravity (G\({}_{\text{s}}\)) and particle size for one studied coal tailings [46]. No models have been found that can consider contact angle variations with particle size; however, for GSD-WRC models in particular, it would be simple to implement such the correction in [37], as a suction is associated with each particle size range.
# Characterizing Growing Season Length of Subtropical Coniferous Forests with a Phenological Model
## Materials and Methods
### Response of Summer Day-to-Day Phenological Process  
Previous studies have reported the long-term precipitation dynamics of these sites during the study period (2009-2017) [40; 42], and showed short-term seasonal annual drought and water stress from June to August [38]. This caused significant day-to-day fluctuations in summer ecosystem productivity (GPP decline and recovery) and changes in summer phenological processes across sites.  
To study the impact of weather events on summer phenology, here we defined the length of response to stabilization (LORS, d) as the number days between the start of disturbance (SOD) and peak of recovery (POR) (Figure 5). The length of fading (LOF, d) was defined as the number of days between SOD and ROD, and the length of recovery (LOR, d) as the number of days between ROD and POR. The fading rate (g C m\({}^{-2}\) d\({}^{-1}\) d\({}^{-1}\)) was defined as the slope of GPP fading between SOD and ROD, and the recovery rate (g C m\({}^{-2}\) d\({}^{-1}\) d\({}^{-1}\)) as the slope of GPP recovery between ROD and POR.  
Figure 4: Schematic diagram of: (**a**) pattern of modeled cumulative daily GPP, solid black rectangle represents the standard length of peak (LOP) threshold (60 d), blue double-headed arrow indicates the normal LOP (>60 d), and the red double-headed arrow indicates the water-limitation induced short LOP (<60 d); and characteristic GPP growth rate in (**b**) normal year (2013) (black arrow point at the local extrema), with one maximum and minimum peak value (0.09 and \(-\)0.06 g C m\({}^{-2}\) d\({}^{-1}\) d\({}^{-1}\), respectively), and (**c**) short-term, summer drought year (2010), with multiple maximum and minimum peak values.  
Figure 5: Schematic diagram of summer phenological process after short-term, summer drought. SOD = start of disturbance (d), when modeled GPP starts to decline after the spring peak; POR = peak of recovery (d), when modeled GPP reaches peak recovery; ROD = recovery day of disturbance (d).
# A Lightweight Chaotic Map-Based Key Agreement Scheme for the Advanced Metering Infrastructure
## 4 Proposed Scheme
### Initialization  
The data concentrator is installed by the power company in the substation/distribution station, which is considered reliable. In order to achieve authentication and key agreement with the smart meter in the area under its jurisdiction, the data concentrator \(DC_{j}\) performs the following initialization process:  
\(DC_{j}\) selects a Chebyshev polynomial function \(T_{n}(x)\) with seeds \(x\in(-\infty,+\infty)\), a large prime number \(p\), selects collision-resistant hash functions \(H(\cdot)(SHA\)-160) and \(H_{0}(\cdot)(SHA\)-320), and sets a time threshold \(\Delta T\) to confirm the timeliness of the message. \(DC_{j}\) selects a random number \(s\in Z_{p}^{*}\) and calculates \(S=T_{s}(x)\), where \(s\) is the private key and \(S\) is the public key.  
\(DC_{j}\) publishes the system parameters \(\{T_{s}(x),x,\ H(\cdot),H_{0}(\cdot)\}\).