esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Selection of Variables for the Study
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Variables** & \begin{tabular}{c} **Total Sample** \\ **N = 94,489 (\%)** \\ \end{tabular} & \begin{tabular}{c} **Group A \textgreater{} 4 h** \\ **N = 29,621 (\%)** \\ \end{tabular} & \begin{tabular}{c} **RR (\textgreater{}4 h)** \\ **N = 64,868 (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Group B \textgreater{} \textless{} 4 h** \\ **N = 64,868 (\%)** \\ \end{tabular} &
\begin{tabular}{c} **RR (\textgreater{}4 h)** \\ **N = 64,868 (\%)** \\ \end{tabular} \\ \hline Level 0 & 11,473 (12.18\%) & 2013 (6.82\%) & **0.53 (0.51–0.55)** & 9460 (14.62\%) & **1.24 (1.22–1.25)** \\ \hline Level I & 113 (0.12\%) & 58 (0.2\%) & **1.64 (1.37–1.96)** & 55 (0.08\%) & **0.71 (0.59–0.86)** \\ \hline Level II & 5747 (6.1\%) & 3448 (11.69\%) & **2.03 (1.99–2.08)** & 2299 (3.55\%) & **0.57 (0.55–0.59)** \\ \hline Level III & 23,054 (24.47 \%) & 11,552 (39.16\%) & **1.98 (1.95–2.02)** & 11,502 (17.77\%) & **0.67 (0.66–0.68)** \\ \hline Level IV & 46,611 (49.46\%) & 10,848 (36.77\%) & **0.59 (0.58–0.61)** & 35,763 (55.25\%) & **1.26 (1.25–1.27)** \\ \hline Level V & 7233 (7.68\%) & 1584 (5.37\%) & **0.68 (0.65–0.71)** & 5649 (8.73\%) & **1.15 (1.14–1.17)** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Manchester triage standard levels values analysed with PMApp. * External referral (nursing home, primary care, or hospital outpatient clinics): RR 2.04; they usually arrive by ambulance and more frequently require admission;
* Dyspnea: RR 2.10;
- Abdominal pain: RR 1.84;
- Adult with poor general condition: RR 1.88. These variables were specifically chosen to identify complex patients who are most likely to benefit from early and targeted interventions. For example, advanced age and the need for mobility aids are indicators of frailty, while frequent visits and urgent triage levels highlight patients with higher care needs. This selection ensures that the fast-track pathway prioritises high-impact cases, optimising both clinical outcomes and resource utilisation. Variables with a high RR but low prevalence (\(\leq\)2%) such as seizures or gastrointestinal bleeding were discarded to maintain the parsimony of the model and avoid over-alerting. Outcome variables (admission and destination) were also excluded because they were not available at the time of triage. The combination of these predictors formed the FT formula, which was automatically applied at the end of triage:  
Age \(\geq\) 65 + frequent frequent requenter + Triage II-III + external referral + need for a stretcher/chair + (dyspnea and abdominal pain and poor general condition)  
When the criteria are met, the nurse immediately activates the FT pathway and assigns a bed in the Virtual Decision Room, as assessed in the simulation phase of the study.
## 3 Results
### Accuracy Assessment  
The relationship between CC at the ESU level and Landsat 8 NDVI green expressed by Equation (1) was assessed using the LOOCV method. The coefficient of determination was relatively large (\(R^{2}=0.703\)) indicating a good level of agreement between observed CC and predicted CC (Figure 8). The root mean squared error (RMSE) was used to compare the observed _vs._ predicted CC (RMSE = 15 g\(\cdot\)cm\({}^{-2}\)). RMSE was calculated using Equation (2):  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}(CCobs_{i}-CCpred_{i})^{2}}{n}}, \tag{2}\]  
where CCobs and CCpred are the observed and predicted CC, respectively.
# An Intelligent Warning Method for Diagnosing Underwater Structural Damage
## 4 Conclusions  
A vision-based approach for the diagnosis of damage on underwater structures was proposed by combining GLCM and SOM.  
The underwater structural micro-damage images database required for training, validation, and application were conducted with NDT (non-destructive testing) equipment. To secure a wide range of adaptability, the damage images were acquired from the database randomly.  
A triangle algorithm was proposed to generative criterion optimization, which has extracted comprehensive aggregation for the GLCM constructed. The generative criterion was confirmed based on the difference maximization principle: \(d_{5}\)-\(\theta\)-\(g_{7}\), which was the texture average of distance, delta = 4, four directions (0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), 135\({}^{\circ}\)), and image gray level, \(g\) = 128.  
The SOM standard performance parameters were obtained by applying the DFS method, including the angular second moment, entropy, variance of grayscale, correlation inverse matrix, variance, significant clustering and sums of mean. The numbers of layers, numbers of nodes in hidden layers, and learning steps were set to optimal values by training the SOM network model and analyzing the SOM topology diagram. The measurement project example proved the robustness and adaptability of the method. The application result showed high performance under complicated conditions, including adverse environmental conditions, limited sample space, and complex defect types.  
The results show that the proposed method exhibited good performance and can indeed diagnose structure damage in realistic underwater situations. In the future, intelligent warning techniques will replace human-conducted on-site inspections for the detection of underwater infrastructure. The method will also be combined with an unmanned aerial vehicle (UAV) to monitor the damage of civil structures.  
[PERSON] took part in the entire researching process. [PERSON] contributed to the experiment design. [PERSON] revised the manuscript.  
This research was funded by the Fundamental Research Fund for the Central University (Project No.2572018 AB31), Heilongjiang Provincial Postdoctoral Scientific Research Development Fund (Project No.LBH-Q15011).  
Figure 16: Sample prediction classifications.
# Static and Dynamic Response of Micropiles Used for Reinforcing Slopes
## 2 Model Test
### Test Materials and Preparation Process  
#### 2.2.1 Model Box  
The two models were constructed independently in two identical rigid model boxes (the inner size of the model box was 2.6 m long, 1.4 m wide and 1.67 m high). The model box was supported by a steel frame to improve the stiffness. To observe the damage within the model, a transparent tempered glass was installed on two lateral sides. Before preparation of the model slopes, a layer of gravel (maximum particle size 2.3 cm) was glued on the inside bottom of the box to reduce the relative displacement between the box bottom and the soil. In addition, the front and rear ends of the box wall in the direction of seismic wave excitation are lined with 10 cm polystyrene foam panels to act as shock absorption layers to reduce boundary effects. The completed model box is shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Similarity Law** & **Similarity Constants** & **Remarks** \\ \hline Geometry (L) & \(C_{L}\) & 8 & Control Variable \\ Elasticity Modulus (E) & \(C_{E}\) & 3 & Control Variable \\ Density (\(\rho\)) & \(C_{\rho}\) & 0.75 & Control Variable \\ Load (F) & \(C_{F}=C_{E}C_{L}^{2}\) & 192 & \\ Stress (\(\sigma\)) & \(C_{\sigma}=C_{E}\) & 3 & \\ Strain (\(\epsilon\)) & \(C_{\epsilon}=1\) & 1 & \\ Flexural Stiffness (EI) & \(C_{EI}=C_{E}C_{L}^{4}\) & 12,288 & \\ Poisson Ratio (\(\mu\)) & \(C_{\mu}=1\) & 1 & \\ Time (T) & \(C_{T}=C_{L}C_{E}^{-0.5}C_{\rho}^{0.5}\) & 4 & \\ Frequency (f) & \(C_{f}=C_{L}^{-1}C_{E}^{0.5}C_{\rho}^{-0.5}\) & 0.25 & \\ Velocity (v) & \(C_{\rho}=C_{E}^{0.5}C_{\rho}^{-0.5}\) & 2 & \\ Damping Coefficient (c) & \(C_{c}=C_{E}^{0.5}C_{L}^{2}C_{\rho}^{0.5}\) & 96 & \\ Mass (M) & \(C_{M}=C_{\rho}C_{L}^{3}\) & 384 & \\ Stiffness (K) & \(C_{K}=C_{E}C_{L}\) & 24 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The similarity ratios of the test model.  
Figure 2: Plan and section view of the landslide model (unit: mm).
## 5 Case Study: An AHP/TOPSIS-Based Optimal Gas Station Site Selection
### Ranking the Alternatives Using TOPSIS
\begin{table}
\begin{tabular}{l l|l l l l} \hline
**ID** & **Candidates** & **Traffic** & **Popularity** & **Vehicle Owners** & **Competitors** \\ \hline
1 & 40.8839, \(-\)73.8561 & 9.24677 \(\times\)\(10^{-6}\) & 1.14721 \(\times\)\(10^{-3}\) & 1.02115 \(\times\)\(10^{-2}\) & 2.21658 \(\times\)\(10^{-1}\) \\
2 & 40.8095, \(-\)73.8807 & 1.23605 \(\times\)\(10^{-5}\) & 5.09158 \(\times\)\(10^{-4}\) & 2.06362 \(\times\)\(10^{-2}\) & 6.74612 \(\times\)\(10^{-2}\) \\
3 & 40.8347, \(-\)73.9177 & 7.23926 \(\times\)\(10^{-4}\) & 4.06682 \(\times\)\(10^{-3}\) & 1.56490 \(\times\)\(10^{-2}\) & 3.08394 \(\times\)\(10^{-1}\) \\
4 & 40.7931, \(-\)73.9675 & 5.12669 \(\times\)\(10^{-2}\) & 7.54714 \(\times\)\(10^{-3}\) & 7.21901 \(\times\)\(10^{-3}\) & 6.74612 \(\times\)\(10^{-2}\) \\
5 & 40.7586, \(-\)73.9731 & 2.34279 \(\times\)\(10^{-1}\) & 9.87122 \(\times\)\(10^{-2}\) & 9.25446 \(\times\)\(10^{-3}\) & 6.74612 \(\times\)\(10^{-2}\) \\
6 & 40.7699, \(-\)73.9271 & 8.32164 \(\times\)\(10^{-3}\) & 8.22387 \(\times\)\(10^{-3}\) & 1.71489 \(\times\)\(10^{-2}\) & 1.25285 \(\times\)\(10^{-1}\) \\
7 & 40.7496, \(-\)73.8692 & 1.31949 \(\times\)\(10^{-3}\) & 1.28901 \(\times\)\(10^{-3}\) & 1.88036 \(\times\)\(10^{-2}\) & 1.54197 \(\times\)\(10^{-1}\) \\
8 & 40.7231, \(-\)73.914 & 1.60848 \(\times\)\(10^{-3}\) & 2.12686 \(\times\)\(10^{-4}\) & 1.60403 \(\times\)\(10^{-2}\) & 9.63731 \(\times\)\(10^{-2}\) \\
9 & 40.6686, \(-\)73.9321 & 3.51319 \(\times\)\(10^{-4}\) & 2.19131 \(\times\)\(10^{-3}\) & 1.12921 \(\times\)\(10^{-2}\) & 3.66218 \(\times\)\(10^{-1}\) \\
10 & 40.6599, \(-\)73.8701 & 8.17112 \(\times\)\(10^{-6}\) & 7.86295 \(\times\)\(10^{-4}\) & 1.23497 \(\times\)\(10^{-2}\) & 1.92746 \(\times\)\(10^{-1}\) \\ \hline  & \(V_{j}^{+}\) & 2.34279 \(\times\)\(10^{-1}\) & 9.87122 \(\times\)\(10^{-2}\) & 2.06362 \(\times\)\(10^{-2}\) & 6.74612 \(\times\)\(10^{-2}\) \\  & \(V_{j}^{-}\) & 8.17112 \(\times\)\(10^{-6}\) & 2.12686 \(\times\)\(10^{-4}\) & 7.21901 \(\times\)\(10^{-3}\) & 3.66218 \(\times\)\(10^{-1}\) \\ \hline \end{tabular}
\end{table} 
Table 5: Weighted normalized decision matrix. By comparing the \(P_{i}\) values, the ranking of alternatives is determined: the higher the value, the better the rank. Table 6 shows the \(S_{i}^{-}\), \(S_{i}^{+}\), and \(P_{i}\) values along with the candidates/alternatives ranking. From the Table 6, it is deduced that candidate having ID 5 is ranked first; therefore, a new gas station can be opened at this optimal site, followed by candidate IDs (CID) 4 and 2. The performance scores suggest that CID 9 must be avoided because it ranks worst among all of the candidate sites.
## 3 Results
### Statistical Analysis
The 90% confidence intervals (CI) provide a measure of the uncertainty associated with the reliability estimations. The variation in the width of the intervals indicates differences in the stability of estimations for each component and time point. The sorting station trough exhibits the widest confidence intervals, indicating greater uncertainty. The decrease in reliability highlighted in Table 11 underscores the need for preventive maintenance strategies. The confidence intervals can be used to establish maintenance schedules that account for the uncertainty of estimations to minimize downtime and optimize costs. It is recommended to evaluate alternative materials for components with low reliability, such as the sorting station trough, and to conduct more detailed analyses of the causes of wear and implement appropriate protection measures. This analysis demonstrates the importance of evaluating the reliability of critical components for optimizing quarry operations. The use of statistical methods allows for a more robust estimation of reliability and associated uncertainty. For the comparison of reliability curves, the log-rank test was employed. This is a non-parametric test used to compare the survival (or reliability) curves of two or more groups. The results of the log-rank test indicate whether there are statistically significant differences between the reliability curves of the compared components. The data were implemented in Python 2.0 software, and the simulation results obtained were as follows: knife vs. tooth: \(p\) = 0.001; knife vs. trough: \(p\) = 0.0005; and tooth vs. trough: \(p\) = 0.03. The obtained reliability values have significant practical implications for the operations of the Patars quarry. By implementing proactive maintenance planning, based on the reliability values, unplanned downtime can be significantly reduced. Effective maintenance planning allows for the optimization of costs by avoiding excessive or insufficient maintenance. Preventive maintenance contributes to enhancing operational safety by reducing the risk of catastrophic failures. Reducing downtime and optimizing maintenance lead to an increase in productivity and operational efficiency. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Component** & **Time Point (Hours)** & **Mean Reliability** & **Lower Bound (90\% IC)** & **Upper Bound (90\% IC)** \\ \hline \multirow{3}{*}{Front loader knife} & 720 & 0.70 & 0.65 & 0.75 \\  & 1512 & 0.40 & 0.35 & 0.45 \\  & 2160 & 0.20 & 0.15 & 0.25 \\ \hline \multirow{3}{*}{Excavator tooth} & 500 & 0.85 & 0.80 & 0.90 \\  & 1000 & 0.70 & 0.65 & 0.75 \\  & 1752 & 0.50 & 0.45 & 0.55 \\ \hline \multirow{3}{*}{Sorting station trough} & 2160 & 0.60 & 0.50 & 0.70 \\  & 4368 & 0.30 & 0.20 & 0.40 \\ \cline{1-1}  & 6480 & 0.15 & 0.05 & 0.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Component reliability and intervals of confidence (IC).
## 2 Materials and Methods
### Physicochemical Measurements during Osmotic Dehydration  
Water activity of oyster mushrooms and \({}^{\circ}\)Brix of the hypertonic osmotic solution were measured by an \(\mathrm{a_{w}}\)-meter (AquaLab Dew Point Water Activity Meter 4 TE, METERGroup, Inc., Pullman, WA, USA) and by a hand-held refractometer (Atago, Master refractometer, Yorii, Japan), respectively. The color of the samples was determined with a tristimulus chromatometer (model CR-400, Minolta, Tokyo, Japan) calibrated with a white standard plate (L*: 97.83, a*: \(-0.45\), b*: +1.88). The CIELAB color scales were used, with coordinates (L*, a*, b*) being directly read from the chromatometer. The total color change (\(\Delta\)E) was calculated according to Equation (3):  
\[\Delta\mathrm{E}=\sqrt{(L_{t}^{*}-L_{0}^{*})-(a_{t}^{*}-a_{0}^{*})-(b_{t}^{*}- b_{0}^{*})} \tag{3}\]  
where \(\Delta\)E is the total color change, L*, a*, and b* are the luminosity, redness, and yellowish of the samples, respectively. Subscripts \"t\" and \"0\" are referring to time t and zero, respectively [3; 10; 45]. All measurements were performed in triplicate.  
A Texture Analyzer (TA-XT2i of Stable Micro Systems, Godalming, UK) was used for texture analysis of all samples, and a TPA (Texture Profile Analysis) test was carried out. The test was performed on a non-lubricated flat platform, using a 6 mm cylindrical compression probe and a 25 Kg load cell, under the following instrument parameters: pre-test speed--5 mm/s; test speed--2 mm/s; post-test speed--5 mm/s, at 50% deformation. Texture characteristics, such as hardness, were calculated [45; 46]. All measurements were performed in triplicate.  
Salt intake was measured using silver nitrate solution by the Mohr Method with some modifications [37]. More specifically, 2 g of finely chopped mushrooms were taken and put into a beaker with 250 mL distilled water. After 10 min, the solution was filtered, and 50 mL of final solution was put into an Erlenmeyer flask. A total of 1 mL of K\({}_{2}\)CrO\({}_{4}\) (indicator) was added. The burette was filled with 0.1 N of AgNO\({}_{3}\), and the samples were titrated until the first perceptible pale red color. The color should remain constant for 30 s. Titration volume was recorded. Calculations were based on the following reaction:  
\[\mathrm{AgNO_{3}}\;+\;\mathrm{NaCl}\;\rightarrow\;\mathrm{AgCl}\;+\;\mathrm{ NaNO_{3}}\]
## 2 Materials and Methods
#### 2.1.2 COPRAS (The Complex Proportional Assessment) Technique  
COPRAS was developed by [PERSON] and [PERSON] in 1996 and is used to evaluate the criteria of the options by taking into account the benefit and cost objectives [60]. Its biggest advantage is that it compares the options and expresses proportionally how good or bad they are compared to each other. The technique has 6 steps and can be implemented by using the Equations (8)-(13) [60; 61; 62].  
**Step 1. Creating the decision matrix**: The matrix X in Equation (1), which consists of m rows and n columns, is created.  
**Step 2. Normalizing the decision matrix:** The normalized matrix is derived from decision matrix (1) using Equation (8)  
\[X_{ij}^{*}=\frac{x_{ij}}{\sum_{i=1}^{m}X_{ij}} \tag{8}\]  
**Step 3. Weighting the normalized matrix:** The normalized matrix is weighted with Equation (9). In this context, the normalized matrix is multiplied by the weights obtained by the PSI method.  
\[r_{ij}=x_{ij}^{*}w_{j} \tag{9}\]  
**Step 4. The sum of the criteria values:** The sum of the criteria values is calculated by using Equation (10) for the benefit (maximum) oriented criteria and Equation (11) for the cost (minimum) oriented criteria.  
\[S_{i}^{+}=\sum_{j=1}^{k}r_{ij} \tag{10}\]  
\[S_{i}^{-}=\sum_{j=k+1}^{k}r_{ij} \tag{11}\]  
**Step 5. Relative weights of the options:** The relative weights of the options are calculated using Equation (12).  
\[Q_{i}=S_{i}^{+}+\frac{\sum_{i=1}^{m}S_{i}^{-}}{S_{i}^{-}\sum_{i=1}^{m}\left(\frac{1 }{S_{i}^{-}}\right)} \tag{12}\]  
**Step 6. Calculating the performance index of the options:** The performance indices of each alternative are determined and ranked using Equation (13).  
\[P_{i}=\left(\frac{Q_{i}}{Q_{max}}\right)*100 \tag{13}\]  
The \(P_{i}\) values of the options are ranked from biggest to smallest.
# DSA-SOLO: Double Split Attention SOLO for Side-Scan Sonar Target Segmentation
## 2 Literature Review  
### Instance Segmentation for Sonar Images  
Instance segmentation is a crucial, intricate, and challenging task in machine vision research. It localizes different classes of object instances that appear in diverse images in order to forecast object class labels and instance masks. The existing methods can be divided into two types: two-stage based methods and one-stage based methods. Based on the two sub-tasks, the process of detection and segmentation are the main keys of two-stage methods, while the detection-based method firstly generates a priori-bounding box of the target region, based on which a semantic segmentation is performed. For example, the Mask R-CNN [22] is a classical detection-based method, which uses a fully convolutional network (FCN) to generate a mask for each priori-bounding box. Segmentation-based methods firstly make a label for each pixel, and then cluster the pixels into different object instances. Representative algorithms include SGN [23] and SSAP [24]. In recent years, some one-stage methods are proposed following the one-stage target detection methods, such as YOLACT [25], Polar Mask [26], and SOLO [27]. Compared with other instance segmentation algorithms, SOLO adopts a fully convolutional, box-free, and grouping-free approach to directly output the instance mask and the corresponding class, which balances speed and accuracy better.  
All the above methods are proposed based on RGB images. Aiming at the differences between RGB images and sonar images, many researchers try to adopt these methods for imaging sonar systems. [PERSON] et al. [28] proposed a novel model named active Mask-Box Scoring R-CNN, which uses a model header to balance the boxIoU and NMS score. In addition, in order to improve the segmentation performance, a triplets-measure-based active learning (TBAL) method and a balanced-sampling method are used. [PERSON] et al. [29] replaced the Resnet50/101 with a 32-layer feature extraction network in Mask RCNN, which not only reduces the training parameters but also guarantees detection performance. [PERSON] et al. [30] used speckle to automatically subdivide the image into regions of interest (ROI) and background regions. However, most of the recent sonar target segmentation methods based on instance segmentation adopt two-stage methods, yet those methods are indirect and usually have a low efficiency.
###### Abstract  
The desalination of aquifers and seawaters is a viable choice to meet primarily domestic and industrial global water requirements. It removes salts from seawater to obtain freshwater with sufficient quality for different purposes, as well as a highly salt-concentrated waste stream known as brine. This residue is usually returned to the ocean, provoking, among other impacts, changes in temperature, salinity and oxygen and overall local aquatic ecosystem stress, as well as social rejection. Desalination in inland aquifers is more complicated because brine disposal is complicated or impossible. The current study presents a new zero-brine discharge technology able to achieve ecological liquid purification through distillation for the separation of the dissolved solids as crystallized salts (Adiabatic Sonic Evaporation and Crystallization, ASE&C). This new technology was used with seawater and three types of brine to test how it would work when coupled with reverse osmosis desalination plants. Analysis of the byproducts after treatment of the seawater and the different brines are presented here. A basic economic approach to calculating potential revenues is also presented. The results of the analyses revealed a complete depuration of water as distilled water, and crystallized solids with highly concentrated commercial salts (with different composition depending on their origin). The estimated economic value of annual revenue (taking into account only seven element recoveries and treatment of a volume of 1000 m\({}^{3}\)/d) for three types of brines ranged between 1 and 11 million euros, compared to between 3.6 and 9.3 million euros when ASE&C is employed with seawater. The treatment of greater volumes for seawater desalination would increase these numbers significantly. ASE&C supposes a solution coupled (or not) to desalination plants to reduce the ecological impacts associated with brine discharges to zero, obtaining two significant commercial byproducts: (seawater: freshwater and commercial elements Br, Ca, Cs, Cl, NaOH, Mg, N, K, Rb, Na, Sr, Li, U, B, Sr, Ga, etc.; aquifers: a larger list than for saltwater, depending on the nature of the water body). It can solve environmental issues associated with brine discharge, with null CO\({}_{2}\) emissions (renewable energy) and profitable (i.e., with no costly pretreatment) technology.  
desalination; seawater treatment; aquifer desalination; ASE&C; brine mining optimization; circular economy
# Revisiting the Analysis of Radiative Mid-Range Wireless Link for Pacemakers
## 3 Results
### Wireless Link Analysis Based on Related Formulas  
The main purpose of this section is to examine the power transmission to the implanted antennas for the pacemaker application. A variety of formulas discussed in the previous section are applied to the case of the near-field and the far-field wireless link. Based on the discussion of the radiation characteristics for different positions, biomedical applications of the formulas are provided in this section. The formulas have been applied to the on-axis and rotational scenarios which are commonly used in biomedical applications.