esa-sceva/satcom-chunk-collection huggingface dataset:
# Sphere Fitting with Applications to Machine Tracking
## 2 Methods
### Algorithms
#### 2.3.4 Overview of Algorithm 3  
The input for Algorithm 3 is a set \(P\) of \(n\) points in \(\mathbb{R}^{d}\). The output of the algorithm is a coreset of size \(d+2\) for thinnest-sphere, as in Definition 3. The thinnest-sphere problem depends on the external sphere surface (convex hull) and the internal sphere surface (interior convex hull) that bound the set \(P\). Since the external sphere surface and the internal sphere surface are concentric spheres, it can be proved that the thinnest-sphere defined by exactly \(d+2\) points where at least one of the points placed on the internal sphere surface and one of the points placed on the external sphere surface. The thinnest-sphere can be computed in \(O(dn+1/\varepsilon^{3d})\) time using Theorem 6.8 in [29].  
```
Input: A set \(P=\{p_{1},p_{2},\ldots,p_{n}\}\subseteq\mathbb{R}^{d}\) of points. Output: A subset \(C\subseteq P\) which is a coreset of \(P\) for the thinnest-sphere, see Definition 3.
1 Compute \(S^{\prime}:=\operatorname{opt}_{\infty}(P)\) // Compute thinnest-sphere \(\operatorname{opt}_{\infty}(P)\) using [29]. See Equation (3).
2 Let \(x\in\mathbb{R}^{d}\) and \(r\geq 0\), such that \(S^{\prime}=S(x,r)\) // \(x\) and \(r\) are the center and radius of \(S^{\prime}\), respectively. Set \(q\in\operatorname*{arg\,min}_{p\in P}\|p-x\|\) // Select a point that is closest to the center of \(S^{\prime}\), i.e., on its internal surface. Set \(C:=\{q\}\) Set \(q\in\operatorname*{arg\,max}_{p\in P}\|p-x\|\) // Select a point that is farthest from the center of \(S^{\prime}\), i.e., on its external surface. Set \(C:=C\cup\{q\}\) // Select farthest \(d\) points from \(\operatorname{opt}_{\infty}(P)\). for\(d\) iterationsdo  Set \(q\in\operatorname*{arg\,max}_{p\in P\setminus C}\operatorname{dist}(p,S^{ \prime})\)  Set \(C:=C\cup\{q\}\) Return \(C\)
```  
**Algorithm 3**ThinnestSphereCoreset(\(P\))
# Research on a Real-Time Control System for Discrete Factories Based on Digital Twin Technology
## 2 Methods
### Design of the Interaction Architecture  
The model for the digital twin factory includes a physical factory, a virtual factory, and a management system, according to the aforementioned viewpoint. However, a gear factory mainly includes personnel, machine groups, processes, materials, environments, and related parameter data, as well as entities such as sensor transmission and reception, data integration, and interaction, with a wide range of multiple components and poor visualization. In order to realize real-time control and efficient management, it is necessary to install some surveillance cameras to monitor the physical factory on a computer screen.  
Figure 1: A model for digital twin gear factory.  
The virtual factory needs to include element simulation and process prediction, and the management system should include data exchange and real-time control. The digital twin factory is the equivalent of adding a computer network and a set of computer software to the initial factory. The hardware of the computer network belongs to the physical factory, while the software belongs to the digital factory. And the digital twin factory can also be regarded as a combination, assembled via adding a computer network containing the digital factory on the basis of the initial gear factory. The physical factory is the hardware of the initial entity factory and computer network. The digital factory is the software of the initial entity factory and computer network, and it exists in the form of computer software and consists of a monitoring system, a virtualizing system, and a controlling system. And it is simply the software of a real-time control system for the gear factory based on digital twin technology. The physical factory with the virtual factory, not with the digital factory, is a twin factory. At work, the digital factory appears as a human-machine interface with the three following parts: a monitoring window, a virtualizing window, and a controlling window. A real-time control architecture for the digital twin factory is designed, as shown in Figure 2.  
The digital factory includes three parts as follows: a monitoring system, a virtualizing system, and a controlling system, with three split windows as follows: a monitoring window, a virtualizing window, and a controlling window. The monitoring window is used to locate and monitor the physical factory. The analog signals or the digital signals can be used to achieve the real-time monitoring of the physical factory. The virtualizing window is used for virtualizing and simulating the physical factory, as well as predicting the results of various processes. The virtual factory is generated by the virtualizing system, and it is an important part of the virtualization system. The controlling window is used for the analysis and forecast. Each part is organically interconnected and interacts in real time.
## 4 Proposed Algorithm
### RRH Grouping Method  
When the RRH become deep Q-learning agents and perform learning, frequent state updates occur, resulting in critical communication overhead and maintenance cost. To reduce the overhead, similar RRHs are grouped. The similar RRHs means RRHs having similar traffic patterns consisted of amounts of servicing data and vehicles, and interference environments. Moreover, location of RRH is used to consider the similarity of RRHs because the data traffic of vehicular networks has spatial regularities. Then, each RRH group becomes an agent and learns to maximize system energy efficiency. In clustering similar RRHs into a group, we consider four factors; traffic volume, extent of data services, interference of VEs, and location of RRH. Traffic volume stands for the number of VEs going through each RRH per time unit. The extent of data services refers to the resourcesthe RRH serves VEs per hour. The interference of VEs means the average of the SINR values of VEs serviced by RRH per time unit. Considering the aforementioned factors, similarities of neighboring RRHs are calculated and stored in an array for each RRH. In addition, \(N_{s}\) RRHs are selected and assigned to the center of the cluster; those with maximum similarity are further incorporated into clusters for each RRH. The minimax method, which can minimize possible losses in game theory, is adopted as a method of selecting the center.
## 2 Mathematical Modeling  
In this study, incompressible, laminar, and transient flow of a resin/air mixture in a two-dimensional domain is considered. Moreover, the two phases are treated as immiscible. For the prediction of this kind of flow, conservation equations of mass and momentum for the mixture of resin/air and one transport equation for prediction of resin volume fraction are numerically solved. The conservation equations of mass and momentum for the resin/air mixture are given by [43]:  
\[\frac{\partial\rho}{\partial t}+\
abla\cdot\left(\rho\overset{\rightarrow}{V }\right)=0 \tag{1}\]  
\[\frac{\partial\left(\rho\overset{\rightarrow}{V}\right)}{\partial t}+\
abla \cdot\left(\rho\overset{\rightarrow}{V}\overset{\rightarrow}{V}\right)=- \
abla P+\
abla\cdot\overset{=}{\tau}+\overset{\rightarrow}{F} \tag{2}\]  
where \(\rho\) is the mixture density (kg/m\({}^{3}\)), \(\overset{\rightarrow}{V}\) is the velocity vector (m/s), \(t\) is the time (s), \(\mu\) is the dynamic viscosity of the fluid (Pa.s), \(\
abla P\) is the pressure gradient (Pa/m), \(\overset{\rightarrow}{F}\) is an external force vector per unit volume (N/m\({}^{3}\)) and \(\overset{=}{\tau}\) is the stress tensor of the fluid (N/m\({}^{2}\)), given by:  
\[\overset{=}{\tau}=\mu\left(\overset{\rightarrow}{V}\overset{\rightarrow}{V }+\
abla\overset{\rightarrow}{V}\right) \tag{3}\]  
The effect of the porous medium is included in the mathematical model by the insertion of a body force in the momentum equation based on [PERSON]'s law, as given by [44; 45; 43]:  
\[\overset{\rightarrow}{F}=-\frac{\mu}{K}\overset{\rightarrow}{V} \tag{4}\]  
To tackle the resin/air mixture, the volume of fluid (VOF) method is employed [42]. In this approach, an additional transport equation for resin volume fraction (\(f\)) is necessary to define the quantity of resin along with each cell of the domain. This transport equation is given by [42]:  
\[\frac{\partial f}{\partial t}+\
abla\cdot\left(\overset{\rightarrow}{V} \right)=0 \tag{5}\]  
With the definition of volume fraction, density and dynamic viscosity in each cell of the computational domain can be calculated by [46]:  
\[\rho=f\rho_{res}+(1-f)\rho_{air} \tag{6}\]  
\[\rho=f\mu_{res}+(1-f)\mu_{air} \tag{7}\]
## 3 Results and Discussion
### Derivation of the CO Distribution Function  
In previous studies, CO transport is often categorized into three phases, which are the mixing phase in the control zone, the diffusion phase of the peak CO in the tunnel, and the diffusion phase after the peak CO leaves the open end of the tunnel [13; 26]. In order to clarify the transportation process of CO and other pollutant gases during V-shaped tunnel construction, the ventilation distribution function of CO concentration is derived in this paper.  
Figure 17: CO concentration curves of each measurement point under different \(L_{d}\) values.  
By analyzing Figures 18 and 19, In(C/C\({}_{0}\)) satisfies parabolic, linear, linear, and linear functions for the four factors \(L_{d}\), \(\theta\), \(t\), and \(u_{0}\), respectively. Therefore, the distribution function of CO concentration in the control area of the V-shaped tunnel is obtained from the experimental results:  
\[\ln\left(\frac{C}{C_{0}}\right)=f(L_{d},\theta,u_{0},t)=a\frac{L_{d}}{D}+b \left(\frac{L_{d}}{D}\right)^{2}+c\theta+du_{0}t+eu_{0},x<L_{0}, \tag{4}\]  
where \(a\), \(b\), \(c\), \(d\), and \(e\) are constant coefficients, and the results of the fitting can be seen in Table 5; \(C\) and \(C_{0}\) are the CO concentration and the initial CO concentration within the model, respectively, in ppm; \(L_{d}\) is the digging length of the model, m; \(\theta\) is the slope of the uphill section of the model, %; \(u_{0}\) is the average air velocity of the wind duct outlet, m/s; \(t\) is the ventilation time, s; \(D\) is the equivalent diameter of the tunnel model, m.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Calculation Results** \\ \hline a & \(-0.03062\) \\ b & \(0.00147\) \\ c & \(-0.072\) \\ d & \(-5.289A/V_{0}\) \\ e & \(0.0447/u_{\min}\) \\  & R\({}^{2}=0.84\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of fitting the CO distribution function in the control zone.
# Toward Intelligent Roads: Uniting Sensing and Communication in Mobile Networks
## 3 Cooperative Data Fusion in JSC Network for Vehicular Scenarios
### Monostatic Multisensor Data Fusion and Target Recognition
#### 3.3.3 Target Recognition  
While target recognition has been extensively researched for radar systems, a significant gap exists in the literature concerning vehicular scenarios, particularly in JSC systems that leverage communication-type waveforms. Target recognition is especially critical in scenarios involving dim targets, such as pedestrians. For instance, when a pedestrian is near a vehicle, the stronger reflections from the vehicle may overshadow those of the pedestrian, making it difficult for the system to maintain track of the pedestrian, even when its presence is known. In such cases, target recognition can fine-tune the tracking algorithm by extrapolating information about the obscured pedestrian from the available data. This is possible because each target produces a distinct reflection pattern based on its geometric shape and RCS, forming what can be described as its \"reflection signature\". In this context, a CNN can be utilized to directly classify the target category based on the combined soft maps that contain this information [34; 35]. Initially, image cropping for each target is performed, with each target framed within a square window of size \(W_{\text{size}}\) pixels, centered at the predicted target position at time \(t\), which is obtained via tracking using information from the previous time step \(t-1\). To generate a training dataset for the CNN, the actual positions and categories of the targets should be known. To improve the classifier's performance and robustness against imperfect target state predictions, which may cause misalignment between the targets and reference frames, the true target position is perturbed during training. Gaussian noise, representing random displacement with a standard deviation \(\sigma_{\text{w}}\) in both the \(x\) and \(y\) directions, is added to simulate this misalignment. By introducing variability into the training dataset, this approach enhances target classification accuracy during testing and reduces generalization errors, improving the system's ability to perform well on unseen samples. Upon completion of the training phase, the CNN can perform real-time target classification in a variety of scenarios. This capability is highly beneficial for recognizing objects and making decisions based on the target category. For instance, the tracking measurement and prediction models can be adapted according to the specific target type. Numerical results demonstrating the advantages of target recognition for position estimation will be provided in the following section.
## 3 Results and Discussion
### Structural Characterization of JSP  
#### 3.2.1 ATR-FTIR Spectroscopy  
The FTIR footprints of JSP and JDP fractions were performed and are shown in Figure 1. The absorption bands at 3331 and 2927 cm\({}^{-1}\) were attributed, respectively, to OH- stretching and CH-asymmetric vibrations of polysaccharides [41; 42]. The absorption signal around 1609 cm\({}^{-1}\) was ascribed to (-COO-) carboxylate groups, whereas those at 1417 cm\({}^{-1}\) suggested the presence of (C=O) ester carbonyl groups of acid residues, confirming the weak electrolytic character of JSP (5.63%) [41]. FTIR spectra showed characteristic bands (1236, 1073, 1023, 934 and 875 cm\({}^{-1}\)) of agar-like polysaccharide (agarans structure) [1; 37; 38].  
The absorption band observed at 1236 cm\({}^{-1}\) could be attributed to asymmetric O=S=O stretching vibration of sulfate ester [30; 38; 41]. The peaks near to 1073 and 1148 cm\({}^{-1}\) were assigned to C-O-C and C-O glycosidic band vibrations, which suggested the presence of carbohydrates. The strong absorption peak near 1023 cm\({}^{-1}\) corresponded to the stretching vibration of carbohydrates (pyranose ring of galactose units). The weak signal at around 935 cm\({}^{-1}\) could be assigned to the vibration C\(-\)O\(-\)C bridge in 3,6-\(\alpha\)-\(\iota\)-AnGal\(p\) residues [43; 44; 45].  
The absorption region between 950 and 750 cm\({}^{-1}\) was expanded to better determine the sulfate group position in agaran-type polysaccharides [11]. The characteristic region (agar-specific bands) between 855 and 830 cm\({}^{-1}\) could be attributed, respectively, to 3-O-sulfate and/or 2-O-sulfate groups present in \(\alpha\)-\(\iota\)-Gal\(p\) units [11], while the peaks at around 875 and 812 cm\({}^{-1}\) were ascribed to (6-O-SO\({}_{3}\)\({}^{-}\)) stretching of sulfate groups on \(\beta\)-d-Gal\(p\) units and to the 3,6-AnGal\(p\) (2-O-SO\({}_{3}\)\({}^{-}\)) residues, respectively [1; 46]. Regarding the literature, alkaline treatment increased the intensity of the signal at 935 cm\({}^{-1}\) (950-930 cm\({}^{-1}\)) by conversion of (1,4)-linked \(\alpha\)-\(\iota\)-Gal\(p\) (6-O-SO\({}_{3}\)\({}^{-}\)) to 3,6-AnGal\(p\)[11; 16].  
JDP, which was obtained from JSP after desulfation (see Section 2.4), showed a drop in intensities for the characteristic signals (region 800-1240 cm\({}^{-1}\)) corresponding to sulfate groups. This desulfation step, with a yield of 46.35% (_w/w_), was also confirmed by colorimetric assays (2.08%).  
Figure 1: FTIR spectra of JSP (original sulfated fraction, in blue) and JDP (desulfated fraction, in red).
# A Multi-Parameter Comprehensive Optimized Algorithm for MPTCP Networks
###### Abstract  
The transmission performance of a multi-path transmission control protocol (MPTCP) is affected by many parameters, such as path management, congestion control, buffer size, and subflow bandwidth. Most of the previous studies have focused almost exclusively on the improvement of a single parameter, without a holistic view. In this paper, a multi-parameter comprehensive optimized algorithm (MPCOA) is proposed to comprehensively optimize the above parameters. The MPCOA algorithm can find a smaller buffer size and select an appropriate congestion control and path management algorithm on the premise of ensuring better throughput. Experiments in three scenarios show that MPCOA can save buffer space and subflow resources, and achieve a high throughput. Meanwhile, a set of quantitative improvement results given by MPCOA is convenient for us to evaluate the quality of the MPTCP network and provide reference for our ongoing future work.  
MPTCP; MPCOA; buffer size; path management; congestion control Article
# New Protocol and Architecture for a Wastewater Treatment System Intended for Irrigation
## 1 Introduction
These algorithms may also consider reducing the amount of forwarded data in order to reduce energy consumption and the amount of generated traffic. Fault tolerance is another aspect that is usually considered by the algorithms employed in wireless sensor networks (WSNs) and Internet of Things (IoT) solutions. Moreover, some solutions consider soil moisture and other factors such as weather predictions to determine whether the irrigation system must start or stop [11]. In this paper, a communication protocol and a data analysis algorithm for a wastewater treatment system intended for irrigation are presented. Our system includes a smart group-based wireless sensor network that is able to detect, locate, monitor, and track high salinity levels and pollution stains, such as oil spills. Moreover, a new kind of water treatment plant is proposed to depollute the water before the irrigation. The system will use artificial intelligence techniques and data fusion methodologies in order to quantify the amount of pollution in the source and to detect and predict its movement and form. These techniques will allow the system to be conscious of what is happening in the water environment and send alarms through the warning system. This information will be used to define one treatment or other before using the water for smart irrigation. The rest of the paper is organized as follows. The description of our proposal is given in Section 2. The results are presented in Section 3. Section 4 discusses the related work. Finally, the conclusion and future work are presented in Section 5.
# Space-Ground Remote Sensor Network for Monitoring Suspended Sediments in the Yellow River Basin
## 2 Data and Methods
### Data
#### 2.2.2 Satellite Imagery  
The Digital Elevation Model (DEM) data used for analyzing the water and sediment conditions in the Qingshui River basin were obtained from the Geospatial Data Cloud site (GSCloud, [[https://www.gscloud.cn/](https://www.gscloud.cn/)]([https://www.gscloud.cn/](https://www.gscloud.cn/)), accessed on 10 May 2024), with a spatial resolution of 30 m. The normalized difference vegetation index (NDVI) data were derived from the median NDVI values of Landsat-8 imagery acquired between April and August 2023, with a spatial resolution of 30 m. The Landsat-8 imagery was downloaded from the U.S. Geological Survey (USGS, [[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)), accessed on 10 May 2024). For analyzing the suspended sediment concentration (SSC) in the Xiaolangdi reservoir and the adjacent Yellow River estuary, a total of 12 satellite images were used, including CB04A,  
Figure 2: Schematic diagram of the integrated remote sensing monitoring network for water and sediment in the Yellow River basin.  
HJ2A/B, and CM1 imagery. SSC inversion was performed on 11 of the 12 downloaded images (excluding the CM1 imagery) obtained from the China Centre for Resources Satellite Data and Application (CRESDA, [[https://data.cresda.cn/](https://data.cresda.cn/)]([https://data.cresda.cn/](https://data.cresda.cn/)), accessed on 25 June 2024), with all images resampled to a spatial resolution of 16 m.  
Additionally, the hydrological station measurement data, as well as the sediment transport, discharge, and sediment concentration data for the Yellow River, are sourced from the Yellow River Daily Hydrological Bulletin, Yellow River Sediment Bulletin, Yellow River Water Resources Bulletin, and the Yellow River Hydrological Reports. These data are available for download from the official website of the Yellow River Conservancy Commission of the Ministry of Water Resources ([[http://www.yrcc.gov.cn/](http://www.yrcc.gov.cn/)]([http://www.yrcc.gov.cn/](http://www.yrcc.gov.cn/)), accessed on 25 June 2024).
## 3 Design of Concise Robust Path-Following Control with RRR
### Guidance System for Underactuated Ships  
As shown in Figure 4, the purpose of the traditional guidance principle is to provide desired waypoints-based planned routes for marine ships [5; 6; 13]. The most used method at present is LOS, which can simulate the pilot's vision field to determine the specific track tracking points. However, in marine practice, path planning will set a track error, thus forming a track-belt, if the ship is sailing within the track-belt, course-keeping can ensure the safety of navigation with less rudder movements, otherwise it is necessary to correct the track error, as shown in Figure 4. The advantage of this design is that both navigation safety and economy have been considered. For indirect straight-line path-following, LOS guidance principle is often incorporated with course-keeping autopilot to solve the way-point ship path following control problem. Consequently, an indirect path following system  
Figure 3: Signal flow indication for guidance, optimal control and navigation.  
Figure 2: Ship’s course and roll response to rudder motion of port 5\({}^{\circ}\).  
based on LOS with course-keeping in track-belt is brought up. Given the track error \(d\) and the range of track belt \(E_{r}\), the set course \(\psi_{d}\) is defined as follows  
\[\begin{array}{l}d\leq E_{r},\ \psi_{d}=\psi_{WP_{i-1}WP_{i}}+\alpha_{d}\\ d>E_{r},\ \psi_{d}=\psi_{\text{los}}+\alpha_{d}\end{array} \tag{7}\]  
where \(\alpha_{d}\) is the drift angle, \(WP_{1},WP_{2},\cdots WP_{i-1},WP_{i}\) are the waypoints in marine practice, \(\psi_{WP_{i-1}WP_{i}}\) is the direction angle from \(WP_{i-1}\) to \(WP_{i}\), and \(\psi_{\text{LOS}}\) is defined by LOS guidance as the set course \(\psi_{\text{SP}}\) from point S to point P shown in Figure 4. They can be obtained as the following:  
\[\begin{array}{l}\psi_{W_{i-1}W_{i}}=\arctan\left(\frac{y_{WP_{i-1}}-y_{WP_{i -1}}}{x_{WP_{i}}-x_{WP_{i-1}}}\right)\\ \psi_{\text{LOS}}=\psi_{\text{SP}}=\arctan\left(\frac{y_{WP_{i-1}}}{x_{WP_{i -1}}-x_{S}}\right)\end{array} \tag{8}\]  
As shown in Figure 4, \((x_{S},y_{S})\) is the ship's position, \((x_{P},y_{P})\) is the cross position coordinates of the planned route and the vision field arc in LOS, which depends on the radius of the visual field \(R_{\text{LOS}}\). To obtain faster track error convergence, a variable \(R_{\text{LOS}}\) is adopted to obtain faster track error convergence, as shown in Equation (9). Furthermore, when the ship enters the range of next waypoint within the turning radius \(r_{w}\), it is switched to the next route, and \(r_{w}\) is set as \(2L\), with \(L\) the ship's length.  
\[R_{\text{LOS}}=L\left(2+\frac{d}{E_{r}}\right) \tag{9}\]  
The validity and superiority of the LOS-CT are proven Section 5. LOS-CT guidance performs as well as LOS near the waypoints. It is worth pointing out that LOS-CT reduces the rudder movements greatly by keeping the ship in track-belt rather than following the route.