esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced Phosphorus Removal from Wastewater Using RSPRC and a Novel Reactor
## 2 Materials and Methods
### Study on Decolorization and P Removal of RSPRC with Activated Clay  
The colored wastewater used in the experiment was derived from a river in the Jiulonghu Campus of Southeast University. A total of 250 mL wastewater was taken and stored in conical flasks. As shown in Table 3, 5 g adsorbents of different groups were respectively added. The colored wastewater was slowly stirred for 20 min and stood for 2 h. After that, the wastewater was filtered and the filtrate was taken to measure absorbance. The absorbance was measured by a spectrophotometer at a wavelength of 750 nm. The decolorization rate was calculated by the following formula:  
\[\text{Q}=\frac{(\text{A}_{0}-\text{A}_{\text{e}})}{\text{A}_{0}}\times 100\% \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material** & **Fly Ash** & **Steel Slag** & **Activated Clay** & **Binder** \\ \hline
**Composition of Group 1 (\%)** & 78.25 & 11.25 & 5 & 5 \\
**Composition of Group 2 (\%)** & 74.38 & 10.63 & 10 & 5 \\
**Composition of Group 3 (\%)** & 70 & 10 & 15 & 5 \\
**Composition of Group 4 (\%)** & 83.13 & 11.88 & — & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Percentage composition of substrate and binder in Residue and Soil Phosphorus Removal Composite (RSPRC) in four groups.  
Figure 2: RSPRC preparation process.  
where Q is the decolorization rate, A\({}_{0}\) is the initial absorbance of the colored wastewater and A\({}_{\rm e}\) is the absorbance of the colored wastewater after adsorption.  
A total of 10 g of different groups of adsorbents were placed into 250 mL Erlenmeyer flasks. Then, the simulated solution, with a phosphorus concentration of 2 mg/L, prepared with deionized water and KH\({}_{2}\)PO\({}_{4}\), was separately added to the Erlenmeyer flasks up to the mark. The pH of the simulated solution was adjusted to 7 by adding HCL and NaOH solution. Finally, they were oscillated in a constant temperature oscillator. When the adsorption duration was 60 min, 120 min and 180 min, the supernatant was taken, and the P concentrations were determined using the molybdenum blue spectrophotometric method stated in American Public Health Association (APHA) [20] in order to investigate the effect of the ratio of activated clay in the adsorbent on phosphorus removal.  
The phosphorus removal effect of RSPRC in the advanced treatment of wastewater was mainly reflected in the phosphorus removal rate and the amount of phosphorus adsorbed by RSPRC. The adsorption capacity (Qe, mg/g) or amount of phosphorus adsorbed by RSPRC and removal rate (R) of phosphorus were calculated from the following equations:  
\[\rm{Q_{e}=\frac{(C_{0}-C_{e})V}{m}} \tag{2}\]  
\[\rm{R\ (\%)=100\times\frac{C_{0}-C_{e}}{C_{0}}} \tag{3}\]  
where Co is the initial concentration of the P (mg/L), Ce is the equilibrium or residual P concentration (mg/L), V is the volume of the solution (L) and m is the mass of adsorbent (g).
# Capability Analysis of Earth Observation Data for Integrated Emergency Management
## 2 Conceptual Foundations  
### Climate Change Impacts on the Environment and Society  
Climate change is one of global society's most significant challenges, with natural hazards anticipated to become more frequent and intense due to the rise in weather and climate extremes [4]. Extreme weather events, e.g., wind and rainstorms, can result in high excess mortality and morbidity [5, 6] and lead to adverse impacts, such as cold homes [7]. Heavy snow and extremely low temperatures significantly impact critical systems and infrastructure [8, 9], e.g., leading to adverse highway conditions [10]. With transport representing a crucial lifeline, compromised roads can be highly detrimental to society as movements between locations become hindered or prevented [11]. Extreme weather events may also result in increased road traffic accidents, leading to injury and death [12], thereby imposing significant financial and economic costs on society [13].  
Severe weather can also cause power outages, as high wind speeds can make power lines unstable and increase the chances of trees falling onto overhead lines [14]. Floods can cause damage to power and utility services if flood water enters buildings, in addition to posing difficulties in repairs to electrical points. While the UK energy regulator, Ofgem, states that power outages must be restored within 12 hours of average weather or 24 hours of abnormal weather events [15], this is often exceeded and secondary impacts, such as rail service disruptions, can also persist.  
North East England has experienced several severe winter storms in recent years, including Storm Desmond (December 2015), leading to record-breaking rainfall and severe flooding in parts of northern England, especially in Cumbria and parts of Scotland [16]: \"the Beast from the East\" (February to March 2018), associated with Storm Emma, bringing 50 cm of snow and blizzard conditions to the UK [17]; Storm Arwen (November 2021), bringing strong winds, heavy rain, snow and ice, causing widespread power outages, transportation disruptions, and damage to infrastructure across northern England and Scotland [18]. Storm Arwen in particular demonstrated that the existing UK energy network was not sufficiently resilient to severe weather, as falling trees caused extensive damage to power lines, leading to extended electricity disruption for over one million customers [19]. Research suggests that extreme weather events will likely become more frequent and intense [20]; therefore, supporting multi-agencies in managing emergencies through relevant data on critical infrastructure is pivotal to improving reliability and resilience.
# Dynamic Response Analysis of Soil around Curve Section Tunnel under Train Vibration Load
## 5 Analysis of Computational Results
### Analysis of Vertical Deformation Response
#### 5.3.1 Vertical Deformation under Different Curve Radii  
The vertical deformation variations after a single load at different curve radii while maintaining a constant speed of 80 km/h are shown in Figures 21 and 22. When the train vibration load acts on the ballast bed, the vertical deformation of the soil mass decreases with an increase in the tunnel curve radius. After a single load is applied to the tunnel, a rebound trend is observed in the vertical deformation, and displacement rebound occurs after reaching the maximum vertical displacement for different curve radii. In terms of the magnitude of rebound after reaching the maximum vertical deformation at each node, the smaller the tunnel curve radius, the slower the rebound velocity and the flatter the trend.  
The peak vertical deformation and attenuation rate decrease with an increase in the depth distance from the tunnel base. Smaller curve radii result in larger vertical displacement fluctuations in the soil mass at the tunnel bottom. Additionally, larger curve radii lead to smaller peak vertical displacements in the soil at the tunnel bottom. As the curve radius increases, the peak vertical deformation at the tunnel base gradually  
Figure 21: Vertical deformation changes after a single loading of different curve radii.  
Figure 22: Peak comparison of single-loaded vertical deformation of different curve radii.  
decreases, and the maximum settlement difference under different curve radii is 0.045 mm. This is because in tunnels with smaller curve radii, the lateral load caused by the subway train increases, resulting in larger soil settlement. The farther away from the tunnel, the smaller the disturbance caused by subway vibrations. Therefore, the magnitude of vertical deformation peak decreases with distance from the tunnel, eventually stabilizing. The influence of different curve radii on the soil layers beneath the tunnel is mainly concentrated within 0-15 m. When the distance from the tunnel base exceeds 15 m, the variation trend and peak of vertical deformation become consistent under various working conditions. The curve radius of the tunnel affects the vertical deformation of the soil at the tunnel base.  
The vertical deformation trends and patterns in different sections of the tunnel, including straight sections and curved sections, are consistent. However, the vertical displacements in the straight sections are smaller than those in the curved sections. This is because centrifugal forces are generated when the train runs through curved sections with smaller radii. In order to balance the centrifugal force, the outer rail superelevation will be set at design time. The lateral load induced by the subway train increases, resulting in larger settlements compared to straight sections.
## 3 Results and Discussion
### Area Reduction of Extreme Pressure Coefficient  
#### 3.2.1 Extreme Pressure Coefficient for Different Area Sizes under Different Wind Angles  
This paper studies the area reduction effects of wind pressure for four faces; the subordinate area of each facade is 1 m\({}^{2}\), 5 m\({}^{2}\),10 m\({}^{2}\), 25 m\({}^{2}\), 33 m\({}^{2}\), and 72 m\({}^{2}\) (full scale). The different subordinate areas contain several different measuring points. Figure 11 shows the comparison of the area-averaged extreme pressure coefficient under four typical wind  
Figure 10: Contour of extreme pressure coefficient at 90 \({}^{\circ}\) wind direction angle: (a) N, (b) E, (c) S, (d) W.  
Figure 9: Contour of extreme pressure coefficient at 0 \({}^{\circ}\) wind direction angle: (a) N, (b) E, (c) S, (d) W.  
direction angles (\(0^{\circ}\), \(90^{\circ}\), \(180^{\circ}\), \(270^{\circ}\)) for the different facades with different area sizes. It can be seen from Figure 11 that the area-averaged maximum wind pressure coefficient decreases as the area size increases, and the wind pressure on this area decreases. The area-averaged minimum wind pressure coefficient increases as the area size increases, and the suction on this area decreases.
## 1 Introduction  
Clustering is a technique used for analyzing statistical data [1]. The process involves partitioning the data into clusters, so that data within the same cluster have the highest similarity, while data in different clusters have less similarity. Various clustering approaches, such as hierarchical, distance-based, density-based, and graph-based, have been proposed so far. Figure 1 depicts a diagram illustrating different clustering approaches.  
Figure 1: Diagram illustrating different clustering techniques.  
The clustering technique has various applications in medical science [2], architecture [3], drug discovery [4], image processing [5], computer networks [6], and communication [7], among others. It is also an important tool for solving controller placement problems (CPPs) [8] in software-defined networks (SDNs) [9]. In CPPs, the goal is to determine the optimal number and location of controllers in a network, with the key objectives being the delay between controllers and switches, the delay between controllers, and load balancing [10].  
A learning automaton (LA) is one type of machine learning algorithm with a finite number of actions and different probabilities assigned to each action [11]. In each round, the LA takes an action and evaluates the reinforcement signal from its surrounding environment. Then, the LA updates its action probability vector according to the reinforcement signal until a satisfactory solution is achieved. The LA has been used in various computer network applications to enhance existing solutions. The LA has also been applied to improve the k-means clustering method in [12]. In the proposed clustering algorithm, LAC, each LA is assigned to a data point, and the LAs determine the clusters to which their associated data points belong. LAC has been shown to outperform other clustering algorithms such as k-clusters, k-means, k-medians, k-means++, and k-medoids in UCI datasets. However, LAC, like k-means, is sensitive to the selection of initial points.  
To address this issue, we propose an optimization method of LAC using k-Harmonic means (KHM) in this paper. Our approach, LAC-KHM, yields better results than LAC on various datasets. In addition to considering the distance between data points and their corresponding cluster centers, we also take into account the distance between individual cluster centers and load balancing for solving CPPs.  
The rest of this paper is organized as follows: In Section 2, we present related works. Section 3 provides a thorough explanation of the preliminaries. In Sections 4 and 5, we present the proposed algorithms, LACKHM and CLAC-KHM, respectively. Section 6 evaluates the performance of the algorithms, starting with LACKHM in the presence of seven datasets from the California Irvine (UCI) clustering benchmarks and then discussing CLAC-KHM performance on four topologies. Finally, Section 7 concludes the paper.
## 1 Introduction
In sEMG studies, basic movements (e.g., forward lumge, Bulgarian squat, and lateral step-ups) are often used to make them easily reproducible [33; 34], and these exercises do not cause excessive fatigue, allowing soccer teams to use them at any time during the season. These are simple exercises used to strengthen the hamstring and quadriceps muscles, and their study can provide information on the activation of the different muscles of these muscle groups and see the differences in activation in the phases of exercise [30]. These exercises can be decisive for soccer players, taking into account the muscle group they are targeting, since recording the electrical activity of each of the muscular bellies would help reduce the time in the process of recovery from an injury and also help in its prevention [30]. Exercises with higher H:Q activation ratios may be preferred during early rehabilitation after injury [35]. Previous studies have suggested that the H:Q force ratio should be at least 0.6 to prevent injuries to both the hamstrings and the knee [36]; however, this has been studied using isokinetic machines rather than sEMG. With the use of sEMG, some studies have been carried out with soccer players, most of them males, reporting H:Q ratios from 0.21 to 0.81 [31; 32; 37] and few studies have compared the ratio of males to females and with a non-soccer specific sample [37; 38]. Therefore, the main objective of this study was to determine if there are differences in muscle activation patterns between female and male soccer players for better understanding and application. The following hypotheses were formulated: (1) the H:Q ratio will be different between male and female soccer players; (2) the vastus medialis (VM):vastus lateralis (VL) ratio will be different between male and female soccer players; and (3) there would be a different pattern of intra-hamstrings and intra-quadriceps muscle group coactivation between male and female soccer players.
# GNSS-Assisted Visual Dynamic Localization Method in Unknown Environments
## 2 GNSS-Assisted Visual Dynamic Localization Method Framework
### Initialization
#### 2.1.3 Model Scores  
\[\mathbf{R}_{C_{2}}^{C_{1}}=\mathbf{U}\mathbf{R}_{D}\mathbf{V}^{T},\text{ } \mathbf{n}=\mathbf{V}\mathbf{n}_{D},\text{ }t_{C_{2}}^{C_{1}}=d\mathbf{U}t_{D} \tag{4}\]  
The epipolar geometry constraint and homography matrix were introduced as (4) shows above. In a real scenario, it is necessary to judge which model to choose for a positional solution according to the distribution of feature points in a shooting scene.  
Here, we performed model scoring by judging the number of interior points. Equation (5) shows the defined ratio, and score \(H\) and score \(F\) represent the \(H\) matrix and \(F\) matrix scores, respectively:  
\[ratio=\frac{scoreH}{scoreH+scoreF} \tag{5}\]  
Here, the _ratio_ threshold was set to 0.6 based on the results of several experiments, and when the _ratio_ was greater than 0.6, the homography matrix was selected for solving.
## 3 Results and Discussion
### Adsorption Experiments
#### 3.2.5 Influence of Temperature  
Figure 10 shows the variation of the percentage of removal of IV2R dye ions in the MWCNTs. It can be concluded that the maximum percentage of removal of dye was obtained at 30 \({}^{\circ}\)C with 91.15% and reduced with the rise in temperature from 30 to 55 \({}^{\circ}\)C for the IV2R dye in the MWCNTs. With a rise in temperature from 25 to 30 \({}^{\circ}\)C, the diffusion rate of the adsorbate molecules within the pores, because of the decreasing solution viscosity, also modified the equilibrium capacity of the MWCNTs for a particular adsorbate. A further increase in temperature (above 30 or 35 \({}^{\circ}\)C) led to a decrease in the percentage removal. This is mainly due to the decreased surface activity [58; 59]. This decrease in the efficiency of the adsorption may be ascribed to several causes, such as deactivation of the adsorbent's surface, a growing tendency for the dyes to escape from the solid stage to the bulk stage, or the destruction of specific active sites on the surface of the adsorbent owing to bond ruptures [16].  
Figure 10: Influence of temperature on the removal of IV2R.  
Figure 9: Influence of the initial IV2R dye concentration.
## 3 Automatic Recognition Algorithm of VLPT  
Clustering analysis, as an unsupervised learning method, involves grouping or categorizing samples according to their measured similarity. This paper aims to use unsupervised clustering learning methods to automatically recognize VLPTs with varying energies, as shown by the different energy vertical lines in Figure 1b. The algorithm flowchart for VLPT recognition is shown in Figure 2.  
Figure 1: Two spectrograms with a duration of 8 s. (**a**) The 103 rd spectrogram of orbit number 109601; (**b**) the 15 th spectrogram of orbit number 109621.  
Figure 2: Flow chart of automatic recognition algorithm of VLPT. Among them, the yellow box represents the object handled in this article, the red box indicates the preprocessing process for the spectrogram, and the green box represents the workflow for unsupervised clustering.  
Grayscale: The color image is converted into a grayscale image.
2. Edge feature enhancement: Edge enhancement algorithms are applied to enhance the edge features of vertical lines in the grayscale image.
3. Binarization: The enhanced grayscale image is converted into a binary image, where each pixel has only two values, typically black and white.
4. Data dimensionality reduction: Each pixel column is treated as a recognition sample.
5. Unsupervised clustering: The K-means++ clustering algorithm is used to cluster pixel columns into different clusters or categories based on their similarity.
6. Automatic marking: The identified lines are automatically marked, facilitating further observation and analysis.
## 3 Methods
### Experiment Setting
#### 3.2.2 MG Excavation  
Prior to starting the MG excavation, a 3D model of each 4 \(\times\) 4 m footing was created using specialized design software (e.g. Autodesk Civil 3D etc.) and uploaded to the excavator's onboard system. This model contained precise depth (1.2 m) and slope (1:0.2) data that guided the operator in real time. The machine guidance setup included dual GNSS receivers mounted on the boom and the counterweight, along with inertial measurement units on the excavator body. These sensors collectively provided positioning and orientation data to the system, which was calibrated by aligning the bucket's reference point with known coordinates on the ground. Once calibration was complete, the 3D model was overlaid with live excavator data, allowing the operator to see exactly how far to dig and at what angle to maintain the required slopes. During the excavation process, a screen inside the cabin displayed a color-coded interface indicating the bucket's vertical distance from the target depth. Real-time updates alerted the operator if the bucket approached or exceeded the design grade, helping to reduce over-excavation.
###### Abstract
Traditional encoder-decoder architectures demonstrate limitations in their ability to efficiently extract temporal features, which subsequently impair their predictive accuracy. (2) Low Gradients and Blurry Features in Radar Echo Images: Radar echo imagery frequently exhibits low gradients and indistinct features. The application of RNNs for sequence-to-sequence prediction may encounter challenges such as long-term dependencies and the accumulation of errors ([PERSON] et al., 2018), which can lead to prediction inaccuracies, especially in regions characterized by medium- to high-intensity echoes. Such complications may result in the failure to accurately forecast significant areas. This paper presents an innovative sequence-to-sequence radar echo extrapolation technique that combines a translator encoder-decoder with a spatiotemporal dual-discriminator conditional generative adversarial network (STD-TranslatorNet). This methodology effectively tackles the challenges associated with radar echo feature extraction and reduces prediction loss in medium- to high-intensity echo regions during long sequence forecasting. Within the STD-TranslatorNet framework, the generator enhances the encoder-decoder architecture by integrating a TAU ([PERSON] et al., 2018) at its core, facilitating mutual influence for a more thorough reconstruction and extraction of image features. The discriminator is structured with a spatiotemporal dual framework that imposes constraints on the generator across both temporal and spatial dimensions. The temporal discriminator ensures the consistency of the generated sequence over time, while the spatial discriminator preserves the quality of spatial features in each individual frame. This configuration enables the generator to more effectively learn complex spatiotemporal relationships, leading to the production of more realistic and coherent image sequences. The efficacy and adaptability of the STD-TranslatorNet model are further validated through comparative analyses with other established spatiotemporal prediction models utilizing the SRAD and SEVIR datasets. Moreover, to assess the reliability of TAU as the translator, various representative modules are employed as alternatives in validation experiments to compare and evaluate the actual predictive performance of TAU.
# Evaluating MPTCP Congestion Control Algorithms: Implications for Streaming in Open Internet
### Congestion Control Algorithms in TCP Networks  
CC algorithms are responsible for managing the flow of data packets in a network, ensuring that the network does not become overwhelmed with traffic, which can lead to packet loss, increased latency, and reduced throughput [26]. The choice of an adequate CC  
Figure 1: Data transfer in MPTCP architecture. Solid lines represent the transmission of data and the dashed ones—the acknowledgments. The MPTCP buffer is shared between the protocol stack and the user application, whereas individual buffers are allocated for each SPTCP controller.  
algorithm is even more important in the context of MPTCP given the complexities arising from the concurrent management of multiple network paths.  
The reference design of MPTCP does not follow the single-responsibility programming pattern. The SPTCP CC algorithms used at the path level are tailored to the application needs or link specifics; thus, besides the default one (currently Cubic [27], both for Windows and Linux), there are several other variants [17; 28]. The application may engage a given version on request. The idea explored in this work is allowing the applied algorithms to be responsible for one task only. In the experiments, we modified the kernel and allowed for the selection of an arbitrary CC algorithm for each link autonomously and independently of the MPTCP controller.