esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods  
### Study Sites  
With the aim of analyzing different sites, the research was conducted in two urban and one rural locations in the Autonomous Community of Extremadura, in southwestern Spain (Figure 1). The urban sites are the two most populous cities of this region: Badajoz (around 150,000 inhabitants) and Caceres (around 95,000 inhabitants). The rural site is the Monfrague National Park. Extremadura (latitude between 37\({}^{\circ}\)57\({}^{\prime}\) and 40\({}^{\circ}\)29\({}^{\prime}\) N, longitude between 4\({}^{\circ}\)39\({}^{\prime}\) and 7\({}^{\circ}\)33\({}^{\prime}\) W) is one of the largest regions in Europe, with a surface area of approximately 41,600 km\({}^{2}\). Extremadura shows a great contrast, with wide agricultural and forest areas, and is considered to be one of the most important ecological enclaves in Europe. The main source of primary pollutants is the vehicle traffic because industry is not important in the region. Some small industrial activities can also contribute to air pollution.  
The climate of Extremadura is characterized by a variation in both temperature and precipitation, typical of a Mediterranean climate. However, this feature is modified by the interior location of the region and by oceanic influences that penetrate the peninsula because of its proximity to the Atlantic. Mean annual precipitation reaches less than 600 mm in the majority of the areas of the region, even less than 400 mm in the center of the Guadiana valley, but it can reach much more than 1000 mm in the northern (Gredos) and eastern (Guadalupe) mountainous areas. One of the most important characteristic of the precipitation is its intra-annual variability. There is a dry season, from June to September, and a wet season, from October to May (80% of the precipitation falls between these months). Extremadura is a semiarid region, where the water balance is negative.  
Figure 1: Location map of Badajoz, Caceres and the Monfragüe National Park, with reference to the Autonomous Community of Extremadura and Spain.
## 1 Introduction
[41] assessed the geotechnical features of rocks in coalfields under dry, semi-saturated, and saturated environments using ultrasonic S- and P-wave measurements. Their study showed a strong correlation, which was verified by the coefficient values for correlations between the P-wave velocity and other geotechnical parameters under dry conditions compared with semi-saturated and saturated conditions. [PERSON] et al. [42] reported on an analytical model to predict the UCS from V\({}_{\text{p}}\), density, and porosity for 13 saturated limestone specimens. They found that a multi-exponent model was appropriate for predicting the UCS and demonstrated that the UCS depends on V\({}_{\text{p}}\). However, a detailed study, which correlates natural, dry, and saturated P-wave velocities with the mechanical properties of rock, and investigates how the ultrasonic P-wave velocities and mechanical properties of rock are affected by the sample diameter, was missing in the field. In the present paper, we aimed to correlate the natural (V\({}^{\text{n}}\)p), dry (V\({}^{\text{d}}\)p), and saturated (V\({}^{\text{s}}\)p) P-wave velocities with mechanical features such as the UCS, the axial and diametrical point load indices (PLI-Point Load Index, PLI\({}^{\text{A}}\) and PLI\({}^{\text{D}}\)), indirect tensile strength (ITS), and Schmidt hammer value (SHV) of limestone. Furthermore, we examined how the P-wave velocities and mechanical properties of limestone are affected by the sample diameter.
# Hybrid-Supervised-Learning-Based Automatic Image Segmentation for Water Leakage in Subway Tunnels
## 2 Approach
### Framework  
As shown in Figure 3, the HSL approach adopts a two-stage structure, which is divided into a weakly supervised learning WL-CAM and a fully supervised learning WRDeepLabV3+. There are two steps in WL-CAM. Step 1, according to the characteristics of water leakage, is to propose the APS algorithm and the segment to obtain image-level labels. Step2 is to train WRes2 Net semantic segmentation network to obtain a CAM [31]. The generated CAM will obtain the label file (Mask Data) through the random-walk algorithm (RW). Image-level tags are combined with it. To further improve the completeness and accuracy of dataset labels, pixel-level labels are obtained. Feature extraction and semantic segmentation of water leakage are carried out on pixel-level labels using WRDeepLabV3+. In WRDeepLabV3+, the main network is WRes2 Net, ASSP is a spatial pyramid pooling module with atrous convolution, and transfer learning is performed through pixel-level labels and the original dataset. We use the channel attention mechanism (\(CA\)) to extract the high-level semantic information of water leakage, perform average pooling on the high-level semantic information to obtain a feature vector in the direction of the channel. Then use two nonlinear fully connected layers (FC) to obtain the correlation relationship between channels to limit the complexity of the model. Finally Sigmoid is used for channel feature vector normalization. The spatial attention mechanism (\(SA\)) is used to focus the water leakage target area to extract the low-level semantic information of the water leakage, the semantic segmentation accuracy is improved through the global convolutional network, and finally, the channel feature vector normalization processing is performed using Sigmoid.  
Figure 2: Basic idea of HSL.  
Finally, through the combination of high-level semantic information and low-level semantic information, the water leakage area is generated.
## 3 Results and Discussion
### Alkaline Protease and Detergent Auxiliary Washing Effect Test
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Washing Performance**} & \multicolumn{2}{c}{**Reflectivity (\%)**} \\ \hline \multirow{8}{*}{Anti-redeposition agent} & Before washing & 10.78 \(\pm\) 1.04 \\  & Basal detergent & 14.93 \(\pm\) 1.36 \\  & Alkaline protease & 25.20 \(\pm\) 1.39 \\ \hline \multirow{8}{*}{Anti-redeposition agent} & PASP & 11.81 \(\pm\) 1.13 \\  & PEG & 11.43 \(\pm\) 1.05 \\  & CMC & 14.24 \(\pm\) 1.32 \\ \cline{1-1}  & PVA & 15.06 \(\pm\) 1.56 \\ \cline{1-1}  & HPMC & 24.40 \(\pm\) 2.04 \\ \cline{1-1}  & Sodium Hydroxyethyl Cellulose & 21.18 \(\pm\) 2.12 \\ \cline{1-1}  & Sodium polyacrylate & 19.14 \(\pm\) 1.67 \\ \cline{1-1}  & Disodium Maleate & 13.18 \(\pm\) 1.21 \\ \hline \multirow{8}{*}{Cyclodextrin} & \(\alpha\)-cyclodextrintrin & 26.04 \(\pm\) 2.41 \\ \cline{1-1}  & \(\beta\)-cyclodextrintrin & 23.38 \(\pm\) 2.05 \\ \cline{1-1}  & \(\gamma\)-cyclodextrintrin & 22.50 \(\pm\) 2.08 \\ \cline{1-1}  & Sulfobutyl-\(\beta\)-cyclodextrintrin & 31.44 \(\pm\) 0.47 \\ \cline{1-1}  & Methyl-\(\beta\)-cyclodextrintrin & 24.76 \(\pm\) 2.49 \\ \cline{1-1}  & 2-hydroxypropyl-\(\beta\)-cyclodextrintrin & 21.51 \(\pm\) 1.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Washing effect of anti-redeposition agent mixed with alkaline protease on blood dirty cloth. Adding cyclodextrin to the detergent can help remove blood stains. A variety of cyclodextrins [38] mixed with alkaline protease can be used to wash blood-dirty cloth. Results showed that the addition of \(\alpha\)-cyclodextrinrin and \(\beta\)-cyclodextrintrim to alkaline protease detergent was better than that of \(\gamma\)-cyclodextrintrin. The solubility of \(\alpha\)-cyclodextrintrin and \(\beta\)-cyclodextrintrin was not good (Table 3), and 2-hydroxpropyl-\(\beta\)-cyclodextrintrin showed better water solubility than \(\beta\)-cyclodextrintrin. Adding sulfobutyl-\(\beta\)-cyclodextrintrin to the alkaline protease detergent demonstrated a better washing effect, but it is currently mainly used in the medical field and is expensive.
# Preliminary Validation of Surface Reflections from Fengyun-3C Radio Occultation Data
## 2 Data and Quality Control
### Data Matching and Quality Control  
In order to obtain the collocated profiles, we matched the refractivity profiles of COSMIC and FY-3C with spatial and temporal criteria. At first, we set the matching window with the time interval limited to 1 h, and the latitude and longitude interval limited to 2.5 degrees. Then, we used this window as the matching criteria to extract FY-3C and COSMIC refractivity profiles. Finally, we used a spline interpolation method to interpolate the collocated refractivity profiles from 0 km to 25 km, with vertical steps of 0.2 km.  
To improve the profiles quality for comparison, we rejected refractivity profiles with relative bias higher than 10% from the altitudes of 0 km to 25 km.
# Preamble-Based Adaptive Channel Estimation for IEEE 802.11p +
## 4 Adaptive Channel Estimation Scheme Based on Preamble
### LS Estimation  
The channel estimate of the long training synbols is first obtained through LS estimation using (1).
# Mapping Atmospheric Exposure of the Intertidal Zone with Sentinel-1 CSAR in Northern Norway
## 2 Materials and Methods  
### Data  
#### 2.1.1 Study Area and Field Data Sites  
Our study area was the Tromso Municipality in Northern Norway, extending from 69\({}^{\circ}\)N to 70\({}^{\circ}\)N and from 18\({}^{\circ}\)E to 20\({}^{\circ}\)E (Figures 1 and 2). Field sites were all on the islands of Kvaley and Tromsoy and were easily accessible to the authors under the Covid-19 pandemic travel restrictions. With a tidal range of more than 2 m, important ITZ areas, including vast mudflats, beaches, and rocks, are present in this region. It is, therefore, well suited as study area. Field data were collected on seven occasions at five different locations, four on Kvaloy and one on Tromsoy: Lille Grindeya (twice), Hillesey (twice), Finnvika, Gretfjorden, and Langnes (Tromso Airport on Tromsoya), respectively. Gretfjorden is a popular sandy beach, whereas all others are mainly mudflats. A drone flight was performed over the Hillesey site. The locations are indicated in Figure 1. At Grindeya, Hillesey, and Langnes, GPS tracks of the water line at several tidal reference levels were collected during a half tidal cycle covering the full mean spring tidal range. At Finnvika and Gretfjorden, only single GPS tracks of a low-tide waterline were collected. Field data collection is described in more detail in Section 2.2.
## 3 Microalgal Mechanisms for Wastewater Bioremediation
### Nitrogen Sources Assimilation  
Ammonium nitrogen (NH\({}_{4}\)-N), nitrate nitrogen (NO\({}_{3}\)-N), and nitrite nitrogen (NO\({}_{2}\)-N) enter microalgal cells via active transport [142]. Ammonium nitrogen (NH\({}_{4}\)-N) is a key component in microalgae growth [145]. For this reason, before being assimilated, nitrate and nitrite are converted to ammonium by enzymes nitrate reductase and nitrite reductase [142] as demonstrated by the equations below (3)-(5):  
\[\begin{split} NO_{3}^{-}-(\mathit{Nitrate~{}reductase})\to NO _{2}^{-}-(\mathit{Nitrite~{}reductase})\to NH_{4}^{+}\end{split} \tag{3}\]  
\[\begin{split} NH_{4}^{+}+Glutamate-(Glutamine~{}\mathit{synthae })\to L-Glutamine\end{split} \tag{4}\]  
\[\begin{split} L-Glutamine\to Amino~{}acids\to Proteins\end{split} \tag{5}\]  
Here, Equation (3): ammonium-based nitrate and nitrite reduction; Equation (4): reaction of glutamine synthesis; Equation (5): proteins formation.
## 2 Related Work
As shown in Figure 1 below, Keypoints Detection is the main technique for the human pose estimation task, which aims to accurately find the location of each key node of the human body contained in an image or a video frame. The number of keypoints is determined by the dataset used, and the number of keypoints and their locations vary slightly from dataset to dataset. Recently, graph convolutional neural networks (GCNNs) have become increasingly popular due to their ability to model local structures and node dependencies on graphs. The first GCNN was proposed by [PERSON] et al. in 2013, which defined graph convolution in the spectral space based on graph theory [34]. However, the initial spectral method had the drawback of high time and space complexity [35]. Subsequent methods such as ChebNet and GCN parametrized the convolution kernel in the spectral method [36], which greatly reduced the space-time complexity. Inspired by these methods, spatial methods were applied, and attention mechanisms and serialization models were used to model the weights between nodes in the node domain. With the improvement in convolutional operators, researchers began to consider various graph characteristics such as how to model higher-order information on the graph, and fine-grained design for graphs with features on edges and heterogeneous graphs. In addition, the scalability of models to large-scale graphs and the speed of training have also received wide attention. In 2019, [PERSON] et al. proposed a High-Resolution Network (HRNet), a network model that distinguishes itself from previous low-resolution networks by connecting convolutional group networks of different resolutions together in a parallel way. This network uses parallel connections to maintain the input resolution from beginning to end and allows for multi-scale information fusion. By fusing feature information from different resolutions, HRNet effectively extracts feature information from the target and allows for better estimation of a single pose. In human pose estimation, the general process of HRNet involves target detection, which is used to detect the approximate location of the human body [37]. This location is then used to detect all keypoints of the human body, and a regressor is used to predict the individual key locations. After the estimation of all keypoints, the key points are returned to the original map in the form of a heatmap, achieved by adding a convolutional layer to the last layer of the high-resolution network model. This converts the key point locations into a full-resolution heatmap [38]. Figure 1: Human skeleton diagram composed of key points. Overall, HRNet represents an advancement in GCNNs for human pose estimation, allowing for effective multi-scale information fusion and feature extraction. Future research may focus on further improving the scalability and efficiency of GCNNs for large-scale graph [39].
## 4 K-Anonymity-Based SHFL
### Secure Hierarchical Distributed Architecture
#### 4.1.1 Clustering Index of SHFL  
The proposed SHFL framework formulates centralized clustering using a global model trained on the server. GPS-equipped smart devices voluntarily participate in hierarchical FL and exchange key parameters to initialize the clustering process. The primary task of the global model is to select the anchor device that can directly transmit the information of the nodes to the cluster members for training after cluster aggregation. As mentioned in the network model of SHFL, the global model receives the computation and energy calculations from the network nodes. The global model computes the similarity index by using the following equation:  
\[Similarity-Index=\frac{R_{e}\times A_{Pc}}{d}, \tag{18}\]  
where \(R_{e}\) is the residual energy resource, which indicates the primary availability index of the device, \(A_{Pc}\) represents the available processing capacity, and \(d\) is the distance of the node from the global network model. The global model utilizes the threshold of the similarity index to determine the rule of the network device as an anchor node or member node. _APP_ and _PPV_ also play critical roles in selecting the anchor nodes. All anchor nodes act as local model nodes \(M_{j}\), and member nodes act as intra-local model nodes \(L_{i}\). This initial cluster formulation provides the basic setting for K-anonymity-based regrouping to establish anonymous clusters.
## 5 Discussion
Prediction models based on weather information alert civil protection and fire services on areas of increased readiness. Fire risk models and maps (such as the one proposed in this study) are supplementary to weather data, indicating high risk areas usually at higher spatial resolution and where proactive measures can, or should, be taken. Those measures can include the optimal allocation of observatories and/or fire service areas by using GIS tools such as visibility, network, or other suitable analysis. We utilized GIS technology for fire risk model development, not only because of GIS's analytical and presentation capabilities, but also due to information dissemination and its integration ability along with other organizational workflows. Developing the proposed model, a key issue addressed is data availability. As the reader can see all data utilized are from reliable [38; 40] and freely referenced or downloaded sources [41; 42]. As a result, local authorities, which are responsible in specifying precautional policies and measures (especially in high fire risk periods), such as increased supervision, temporal road closures to traffic, prohibition of certain activities, and more optimal resource allocation, have the means to identify areas of higher risk. The spatial resolution of the presented model is limited to the resolution (25 by 25 m) of the DEM used to calculate it [38]. Higher-resolution DEMs are calculated by mapping agencies during orthophoto production workflows. They can also be acquired by drones [54]. A question of further research and investigation is if higher resolution DEMs help in a better understanding and mapping fire risk or just add higher frequency data, which sometimes are noise rather than actual information. A constrain of our model is the lack of meteorological factors. Furthermore, it is important to note that in order to determine the fire risk of an area, meteorological factors such as temperature, wind, and humidity play a very important role [26]. These factors change dynamically, and therefore it is hard to establish a baseline risk map to compare results from different years. Human activity layers of information (road network and settlements) were derived from OpenStreetMap in testing our model. More important than the accuracy of road network is completeness and the level of update. State agencies and local authorities are advised to use the most updated information they have access to. Other data sources of human activities, such as electricity grids and landfills, which are of great importance, can be incorporated in our model, creating multi-buffer rings, similar to how road network and settlement areas were treated.
## 4 Determination of the Controller Gain  
If one looks at Figure 4 again, the control loop model can be described with the knowledge that has now been gained. The model is linearized, as shown in Figure 5, so that the stability and oscillation tendency of the control circuit can be determined.  
The controller used is a \(P\) controller with gain \(K_{P}\). The reason for this is that the system itself exhibits integral behaviour. If the pole of the controller at the origin is added to the pole of the system at the origin, there is likely no longer be a suitable controller configuration in which the control loop is stable.  
Next, the pre-control generates a steering angle \(\delta V\), which is added to the manipulated variable \(u_{\phi(t)}\). The steering angle setpoint is denoted as \(\delta w(t)\). The dynamics delay the \(G_{S}(s)\) of the steering, resulting in the real steering angle \(\delta t\). With the linearized variant of the single-track model, it is transformed into a change in orientation \(d\phi/dt\) by simple amplification. The
# Bio-Inspired Sleep Control for Improving the Energy Efficiency of a MEC System
## 3 System Model
### Overview of Sleep Control Mechanism  
A MECS sleep control method has to decide both when to sleep and when to wake up sleeping MECSs. When a MECS \(i\) decides to sleep, a sleep control method has to determine the way to process the tasks in \(Q_{i}\). When a MECS \(i\) decides to wake up sleeping neighbors, it has to determine the set of neighbors to be woken up and the way to distribute the tasks in \(Q_{i}\) to the woken-up neighbors.  
In Figure 1, we illustrate an overview of our sleep control mechanism. Figure 1 shows a sleep decision procedure. Each MECS \(i\) periodically exchanges its utilization \((\mu_{i}(t))\) and the Delta activation level \((d_{i}(t))\) with its active neighboring MECSs \(j\) and \(k\). By incorporating the relative utilization and the relative Delta activation level in the Delta-Notch inter-cell signaling model, a MECS \(i\) determines its sleep probability. The sleep probability increases as the relative utilization of a MECS becomes smaller. Therefore, it is likely that a relatively underutilized MECS decides to sleep. When a MECS \(i\) decides to sleep (i.e., \(\beta_{i}=0\)), the tasks in \(Q_{i}\) are transferred to all its active MECSs \(j\) and \(k\) in inverse proportion to their utilizations to avoid the situation where an active MECS becomes overloaded by accommodating the tasks transferred from the MECSs that decide to sleep.  
In Figure 1, we depict a wake-up procedure. The task service delay increases with the length of the task queue in a MECS. Thus, when an active MECS \(i\) detects that \(Q_{i}\) is larger than a predefined threshold, it wakes up all its sleeping neighbors \(j\) and \(k\) and distributes a fraction of the tasks in its task queue to them to decrease its service delay.
# The Effect of High-Pressure Treatment and Skimming on Caprine Milk Proteins
###### Abstract  
Background: Proteins are susceptible to HP-treatment and there is a need to determine the applicability of HP-treatment in dairy production. The aim of this study was to determine the effect of HP-treatment at 200-500 MPa (t\({}_{\text{const.}}\) = 10 min; T\({}_{\text{const.}}\) = 20 \({}^{\circ}\)C) and skimming of HP-treated milk on the content of nitrogen compounds and protein composition of caprine milk. Methods: The content of nitrogen (total, non-casein, non-protein) was determined using the Kjeldahl method. Casein fractions and whey proteins were separated using SDS-PAGE electrophoresis. Color parameters were measured in the CIELAB color space. Results: HP-treatment decreased (\(p\) < 0.05) the content of non-casein nitrogen and soluble whey proteins. Skimming decreased the content of nitrogen compounds, and the noted decrease was more pronounced in HP-treated milk. Pressure and skimming had no influence on the proportions of \(\alpha\)-, \(\beta\)-, \(\kappa\)-casein, \(\beta\)-lactoglobulin and \(\alpha\)-lactalbumin. Total color difference (\(\Delta\)E) increased with a rise in pressure, particularly in skim milk. Conclusion: HP-treatment led to a loss of protein solubility at pH 4.6 in caprine milk. In HP-treated milk, skimming did not induce changes in protein composition, despite a decrease in the content of nitrogen compounds after the separation of the cream layer. Higher values of \(\Delta\)E in skim milk than in whole milk point to changes in colloidal phase components.  
caprine milk; high-pressure treatment; nitrogen compounds; protein profile +
Footnote †: journal: Applied Sciences