esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Field Survey and Testbed Site Selection  
Data from Korean local governments indicated that 433 domestic and industrial water intake facilities were in operation at the end of 2019. Among these, 183 intake facilities (42%) in 77 cities and counties used river water; of these, 56 (30.6%) and 118 (64.4%) were riverbed (sub-surface) water intake facilities, respectively (Figure 2).  
Water level and flow information are required for evaluating and predicting hydrological drought conditions at water intake points. Unfortunately, nearby water level observation data are only available for 109 intake points (Figure 3). Among the 109 water intake plants, facilities with an unspecified form of intake and those with a water intake tower were excluded. Water intake stations without water level and groundwater observation stations in the basin were also excluded. Finally, 13 sites were selected as preliminary candidates, among which two were finally selected based on their potential, with respect to the collection of various information, such as the river water level and groundwater data.  
Considering the pilot site selection criteria, continuous observation data on river water levels were required to determine the hydrological drought conditions at unmeasured river intake points. However, river water levels may not be measured during periods of drought. The main selection criteria for locations at which the groundwater level can be observed in the river and compared with the surrounding groundwater level were as follows:  
* Points without linkable water sources, such as dams, weirs, and reservoirs upstream, or where the main-stream water level is less affected by upstream structures.
* Points for which observation data for water and groundwater levels are available nearby.  
Figure 1: Drought condition criteria and monitoring procedure for the ungauged intake point.  
* Drought analysis is possible, and the correlation between observed data is assured.
* Areas vulnerable to drought.
* Points with few site-specific restrictions, such as the ability to install new flow measurement sensors.
* Points at which the water level can be observed in the river and groundwater adjacent to the water intake point.
* Ability to identify where the alluvial aquifer connects horizontally to the upstream water intake point.
* Points where monitoring equipment can be installed, such as locations where underground equipment is not present.
## 2 Method
### Optimization Algorithm
#### 2.4.1 PSO Algorithm  
In the PSO algorithm, each particle is a solution to a given optimization problem and is composed of two vectors: \"position\" and \"velocity\". A position vector \(x_{i}^{n}\) is used to store the positioning of the particles in the given dimensional space. The velocity vector \(v_{i}^{n}\) is updated using the following equation for each iteration k:  
\[v_{i}^{n}\ (k)=[v_{i}^{n}(k-1)+c_{1}r_{1}(\mathit{pbest}_{i}^{n}-x_{i}^{n}(k-1))+c_{ 2}r_{2}(\mathit{pbest}_{i}^{n}-x_{i}^{n}(k-1))] \tag{14}\]  
where \(x_{i}^{n}\) = position vector; \(v_{i}^{n}\)= randomly generated velocity vector of the particles at search initialization; \(\mathit{pbest}_{i}^{n}\) = vector representing the best solution achieved by the particle; \(\mathit{pbest}_{i}^{n}\) = vector representing the \"global best solution\" collectively achieved by the swarm; \(c_{1}\) and \(c_{2}\) = stochastic acceleration terms that pull each particle toward \(\mathit{pbest}_{i}^{n}\) and \(\mathit{pbest}_{i}\) positions respectively; \(r_{1}\) and \(r_{2}\) = random numbers in the uniform range [0, 1];  
The update of the position vector at the \(k^{th}\) iteration is given by the following equation:  
\[x_{i}^{n}(k)=x_{i}^{n}(k-1)+v_{i}^{n}(k) \tag{15}\]  
The PSO procedure can be outlined as follows:  
1. Generate an initial swarm with arbitrary values of the particle position and arbitrary initial velocities in the n-dimensional search space;
2. Evaluate the fitness of each particle of the swarm by searching a new velocity vector applied to each individual particle; the new velocity vector is influenced by its best position, the swarm's best position and its previous velocity;
3. Update the velocity of the particles as shown in Equation (14) and the position of the particles as shown in Equation (15) and evaluate the new fitness function;
4. Repeat steps 2 and 3 until the maximum number of iterations is reached or until the results no longer improve for a given number of iterations.  
Pattern Search (PS) is a direct search optimization method that is used to minimize a function by comparing its value at each iteration with its value at its previous iteration in a finite set of trial points. The method is outlined as follows:  
1. Select the initial solution;
2. Explore the direction search by evaluating each objective function value, one at a time;Evaluate the objective function. If the objective function is minimized, then update it; otherwise, do not update it;
4. Evaluate the next values of the objective function following the previous steps 2 to 3;
5. After exploring the search of all possible values of the objective function and the minimization of the overall objective function, the PS method follows a pattern move; and
6. If the overall objective function has not been minimized, repeat the process.
## 3 Method for Improving the Pointing Error  
The acquisition protocol involves the shipborne ATP irradiating the satellite to initiate a link. The ATP is fixed on the ship. As the ship moves, the ATP is affected by sea waves, thus producing three periodic yaw, pitch, and roll disturbances, which cause the ATP's line-of-sight axis to wobble a few degrees. The divergence angle of the uplink beacon laser cannot be increased indefinitely; it is generally several milliradians and much smaller than the swaying amplitude. To achieve the acquisition of a satellite, the acquisition performance can only be improved by reducing the pointing error. The absolute pointing error is defined as the angular separation between the actual direction and the intended ATP line-of-sight [26]. In invisible satellite target acquisition, the pointing accuracy of the telescope is one of the important indices for the establishment of optical links. In our experiment, several factors contribute to the pointing error, including the attitude of the ship platform pre-compensation accuracy, installation error calibration accuracy, and systematic errors calibration accuracy.
## 3 Experimental Test Results and Analysis
### Compressive Strength (CS)
For instance, relative to the control paste, there is a little decrease of 10% for the addition of 10% OWA. After this level, the samples containing 15% and 20% OWA show a more noticeable drop in IRCS, with declines of 23% and 33%, respectively. These fluctuations highlight OWA's impact on the IRCS measurements. More precisely, as IRCS describes the strength gain at an early age, it can be deduced that OWA delays the hydration process and, thus, leads to a drop in IRCS values [20]. Similarly, the UCS characteristics followed a clear trend similar to the pattern for compressive strength. For instance, the control paste showed the highest UCS of 51 MPa among all mixtures. This value sharply drops for the inclusion of 5% OWA, reaching a magnitude of 34 MPa, which is equivalent to 33% relative to the reference paste. However, this reduction is followed by a significant growth in the compressive strength in the long-term period in the samples containing 10% of OWA. For example, the UCS value is 47 MPa, which corresponds to a slight loss of 8%. The UCS values drop again with the excessive addition of this ash. Particularly, the samples incorporating 15% and 20% of OWA show UCS values of 32 and 20 MPa, indicating average rates of 41% and 62% decline. This negative behavior in mechanical performance pertains to the excessive amount of OWA, which provides the matrix with a significant number of voids, weakening the long-term strength development [28]. All in all, it can be considered that 10% is the optimal level of cement substitution in terms of improving gains of compressive strength at an early age (IRCS) and in the long-term period (UCS) where the pozzolanic activity is effective for strength development. Therefore, this level is well-suited for several paving applications, including pedestrian sidewalks and light traffic zones.
## 3 Results
### Analysis of Correlation between Ignition Properties and Dielectric Strength  
_Experiment Results_  
To determine which variables affected the dielectric strength experiment results, a correlation analysis was performed between ignition properties and measurement results for the dielectric strength of air during combustion of the 6 mm materials. Table 6 presents the results of the analysis showing the relationships between ignition properties and the vertical dielectric strength experimental results. Most factors had a negative correlation with dielectric strength. Other than CO yield, the variables showed correlations with significance probabilities of 0.01 or less. The dielectric strength of the flame showed strong negative correlations with the average heat release rate, average heat release rate per unit area, and effective heat of combustion. These results are statistically significant with a significance probability of 0.01 or more. However, if effective heat of combustion is high, the heat release rate is usually high as well. Therefore, it is not certain that all variables affect the electric field strength. High effective heat of combustion when the material is combusted indicates a high heat release rate; therefore, the problem of multicollinearity must be considered [15].  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Division** & **Dielectric Strength** \\ \hline \multirow{3}{*}{HRR} & Pearson correlation coefficient & \(-\)0.704 ** \\ \cline{2-3}  & Significance probability & 0.001 \\ \cline{2-3}  & N & 20 \\ \hline \multirow{3}{*}{HRRPUA} & Pearson correlation coefficient & \(-\)0.702 ** \\ \cline{2-3}  & Significance probability & 0.001 \\ \cline{2-3}  & N & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Relationship between dielectric strength test results in vertical direction and fire properties.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multirow{2}{*}{**Thickness**} & \multicolumn{6}{c}{**Breakdown Voltage(kV)**} \\ \cline{3-8}  & & **1 st** & **2 nd** & **3 rd** & **4 th** & **5 th** & **Average** \\ \hline \multirow{3}{*}{PE} & 2 mm & 11.6 & 11.4 & 11.3 & 11.3 & 11.8 & 11.48 \\ \cline{2-8}  & 6 mm & 10.07 & 11.2 & 10.9 & 10.8 & 10.9 & 10.9 \\ \cline{2-8}  & 480 mm\({}^{2}\) & 11.6 & 11.7 & 11.4 & 11.7 & 11.2 & 11.52 \\ \hline \multirow{3}{*}{PP} & 2 mm & 11.6 & 11.6 & 11.6 & 12.4 & 12.5 & 11.94 \\ \cline{2-8}  & 6 mm & 12.5 & 12.7 & 12.8 & 12.6 & 12 & 12.52 \\ \cline{2-8}  & 480 mm\({}^{2}\) & 12.1 & 11.4 & 11.5 & 11.7 & 12.2 & 11.78 \\ \hline \multirow{3}{*}{Cypress} & 6 mm & 14.4 & 14.9 & 14.6 & 14.7 & 15 & 14.72 \\ \cline{2-8}  & 480 mm\({}^{2}\) & 14 & 14.7 & 14.3 & 14 & 14.6 & 14.32 \\ \hline \multirow{3}{*}{Douglas fir} & 6 mm & 12.9 & 12.7 & 12.9 & 12.7 & 13.3 & 12.9 \\ \cline{2-8}  & 480 mm\({}^{2}\) & 13.5 & 12.9 & 13.3 & 12.8 & 12.7 & 13.04 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Dielectric breakdown voltage and average value of dielectric strength test.
# Low-Power, High Data-Rate Digital Capsule Endoscopy Using Human Body Communication
## 3 Techniques to Improve Performance in the CEHBC System
### LineSync Algorithm  
The LineSync section is placed at the front of each line of data, as shown in Figure 3. When receiving the individual line of data in one transmission frame, the LineSync section is used to create the data synchronization that corresponds to \(\pm\)1 bit variation based on the ideal sampling signal.  
The LineSync generator produces the 5 bit PN sequence signals formed with the LSp (z) = \(2^{5}+x^{3}+1\) polynomial. The serial signal of 32 bits is changed to serial data of 64 bits through the Manchester encoder, which can change \"1\" to \"10\" and \"0\" to \"01\". The LineSync generator then produces the final 128 bits that are repeated twice. Here, the initial value of the PN generator polynomial is \"10,000\".  
By using the LineSync generator utilizing these two repeating structures, the CEHBC system has the advantage of reducing the size of the correlators in the receiver to approximately half and could acquire a large correlation value. The LineSync generator has another advantage: it can cope with the synchronization of the varied data that can occur during the interval of a long transmission frame. The location of the capsule endoscope often varies within the human digestive tract. Therefore, the polarity of the received signal could be changed depending on the situation. Thus, the LineSync generator should perform real time corrections that are conducted using the sign of the correlation values calculated by the LineSync of the receiving block.  
Figure 8: Flow chart of the electrode selection algorithm.
## 4 Discussion
A comparison with results from other optical missions, such as Sentinel-2, could subsequently be performed to evaluate the advantages and limitations of using PlanetScope, as well as explore potential scenarios for data integration. Fifth, the research should address the integration of machine learning and deep learning techniques applied to optical satellite data for agricultural crop mapping. Machine learning approaches such as Random Forest, Support Vector Machines, and k-Nearest Neighbors have become widely adopted due to their robustness and ability to handle high-dimensional data, particularly when paired with spectral, textural, and temporal features extracted from multispectral imagery. More recently, deep learning models--especially convolutional neural networks (CNNs)--have revolutionized crop classification by enabling end-to-end learning from raw satellite imagery and often outperform traditional methods in accuracy. Despite their high performance, challenges remain in generalizing across regions, requiring large, annotated datasets and significant computational resources. Sixth, future developments should also address the specific challenge of parcel-boundary extraction in fragmented agricultural regions, such as those characterized by small field sizes, irregular shapes, or poorly defined boundaries. In such contexts, traditional segmentation techniques may face limitations due to low contrast and spectral ambiguity along field edges. Addressing this would require fine-tuning segmentation parameters, enhancing the masking strategy, and possibly integrating contextual or morphological features to better capture subtle spatial cues. Indeed, validating and refining the proposed workflow in fragmented landscapes would extend its operational applicability and contribute to a more inclusive approach to LPIS modernization across diverse agroecological settings. Overall, while this research is still in its early stages, the integration of very-high-resolution (VHR) optical satellite data within the framework of CAP monitoring presents a promising field of investigation. The preliminary findings suggest that combining multitemporal sets of vegetation indexes with existing orthophoto sets could mitigate the limitations of both data types. It is evident that temporal sequences would furnish a more comprehensive seasonal perspective than a singular acquisition snapshot, thereby providing a more exhaustive dataset comprising land conditions. On the other hand, orthophotos would continue to offer a level of detail that remains challenging for spaceborne sensors. If effectively leveraged, additional satellite data could enhance the extraction of land-parcel boundaries, ultimately increasing both the efficiency of LPIS maintenance and the accuracy of its updates.
# Full Field Inversion in Photoacoustic Tomography with Variable Sound Speed
## 4 Numerical Simulations
### Quantitative Error Analysis  
For all results shown in Figure 5, we stopped the steepest decent method after five iterations, since the reconstructions did not significantly improve by using more iterations. In fact, even after a single iteration, the \(\ell^{2}\)-reconstruction error and the \(\ell^{2}\)-residual error are quite close to their minimal values. This phenomenon is investigated in a more quantitative manner in Figure 7. The images on the left-hand side show the evaluation of the relative \(L^{2}\)-reconstruction error \(\|\mathbf{f}_{k}-\mathbf{f}_{*}\|/\|\mathbf{f}_{*}\|\) in dependence of the iteration index \(k\). The images on the right show the relative residual errors \(\|\mathcal{W}\mathbf{f}_{k}-\mathbf{h}_{*}\|/\|\mathbf{h}_{*}\|\). The rapid convergence of both error metrics indicates that the finite time inversion problem is well conditioned, which is also suggested by our theoretical analysis presented in Section 3. The relative data errors, residuals and reconstruction errors for all reconstructions shown above are summarized in Table 1.  
\begin{table}
\begin{tabular}{l c c c c} \hline  & \(\|\mathbf{g}-\mathbf{g}_{*}\|\) & \(\|\mathbf{x}^{\lambda}\mathbf{g}-\mathbf{h}_{*}\|\) & \(\|\mathbf{W}\mathbf{f}_{\text{rec}}-\mathbf{h}_{*}\|\) & \(\|\mathbf{f}_{\text{rec}}-\mathbf{f}_{*}\|\) \\ \cline{2-5}  & **Noise** & **Error (after Step 1)** & **Residual (for Step 2)** & **Error (after Step 2)** \\ \hline Correct SS (simulated data) & 0 & 0.208 & 0.052 & 0.170 \\ Correct SS (noisy data) & 0.853 & 1.760 & 0.874 & 0.227 \\ Wrong SS (simulated data) & 0 & 0.208 & 0.510 & 0.560 \\ Fourier method [31] & 0 & - & - & 0.564 \\ \hline \end{tabular}
\end{table}
Table 1: Relative error metrics for the FFD data and reconstructions. The first column shows the relative \(\ell^{2}\)-norm of the noise added to the data. The second column shows the relative \(\ell^{2}\)-reconstruction error after step one (not applicable to Fourier reconstruction). The third column shows the relative residual error which are minimized in step 2 of the iterative algorithms. The last column shows the relative \(\ell^{2}\)-error of final reconstruction.  
Figure 6: Reconstruction \(\mathbf{f}_{\text{rec}}\) (**left**) and the difference \(|\mathbf{f}_{\text{rec}}-\mathbf{f}_{*}|\) (**right**) using the Fourier algorithm. Note that the Fourier algorithm assumes constant sound speed \(\mathbf{c}_{0}\) in the image reconstruction, similar to the results shown in the bottom row of Figure 5.
## 3 Resources and Demand Assessment of the Case Study
### Load Profile  
The estimated load for Kibran Gabriel island is depicted in Figure 5 below. In this paper, considering the environmental conditions of the island and the community living there, loads are categorized as summer (June to October) load, winter (November to May) load, and weekend load. According to the estimation, a total of 16.00 kWp power, of which 6.00 kWp were deferrable power, 86.24 kWh/day of winter load, 77.84 kWh/day of summer load, and 62.22 kWh/day of weekend loads were considered for the analysis. However, the scaled annual average load taken for the analysis was 76.94 kWh/day. In addition, depending on the demand and its time of use, the load is categorized into primary and deferrable load. The primary loads are loads that require a certain amount of operating reserves to respond immediately to the sudden increase in the electric load and/or due to a decrease in power output [32], whereas deferrable loads permit some flexibility when power is insufficient. That is, deferrable load is the second priority after the primary load; however, its importance comes ahead of charging batteries. In this study, lighting loads, fans, sound amplifiers, refrigerators, sewing machines, flourmills, and water pumps were the major loads estimated for the analysis. However, the flourmill and sewing machines were excluded from the weekend loads considering the community religious practices in the island. Similarly, since the summer season is rainy and relatively cold, water pumps and fans were excluded from the summer load. In addition, water pumps are considered as deferrable load during the winter season. The estimated load frequency distribution is shown in Figure 6. According to the scaled frequency distribution, 75% of the distribution is 2 kW-5 kW, and the remaining 25% is 6 kW-22 kW.  
Figure 4: Average wind speed data.  
Figure 5: Scaled load profile for each month: (**a**) daily load profile, (**b**) monthly average.
# Damage Identification in Warren Truss Bridges by Two Different Time-Frequency Algorithms
###### Abstract
Concerning this issue, the studies in [31; 32] demonstrate how damage affecting different members can cause different effects on the global response of the structure. The main objective of the paper is to examine the feasibility to detect the presence of a defect in a steel truss bridge, from the processing of the acceleration recorded on a bogie frame of the train. While in the real system the acceleration would be measured on the bogie frame, in this paper the acceleration is simulated considering the train travelling on the bridge. Simulations of train-track-bridge interaction are carried out by means of the software ADTreeS [33]. Some steps forward with respect to the literature are considered:  
* Use of a finer schematization of the bridge, so that it is possible to consider the individual damaged element, and not an equivalent representation as it is necessarily adopted for a simply supported beam model. * The schematization considering all the main structural members also enables location of the damage and better reproduction of its influence on train dynamics, following the real path from the defect to the point of acceleration detection on the train. * The track and its irregularity are included in the model, considering also track irregularity variation while comparing two different train runs. The paper is organized as follows: Section 2 describes the bridge model and briefly recalls the vehicle models used for the simulation of the bridge/track/train interaction. The damage consideration and the way track irregularity is taken into account are described, outlining the simulation plan. Section 3 describes the algorithms exploited for the signal processing. Section 4 discusses the results obtained and compares the performances of the Continuous Wavelet Transform against the Huang-Hilbert Transform, when the level of track irregularity is changed between damaged and heathy scenarios. Final conclusions are drawn in Section 5.