esa-sceva/satcom-chunk-collection huggingface dataset:
# Improvements of the xAAL Home Automation System
## 2 The xAAL Architecture
### Typical Work Flow  
Such a functional architecture allows managing the dynamic aspects of the infrastructure (modularity, scalability, adaptation, etc.). Thus, advanced functions such as HMI or automata of scenarios can adapt themselves automatically to the infrastructure and to its modifications (if equipment enters/leaves, if one fails or is replaced, etc.).  
The typical work flow of an HMI could be  
1. Query the xAAL bus to be aware of the present devices;
2. Query devices about their description (model, manufacturer, URL, etc.);
3. Optionally, download non-standard schemas from the URL of the description of those non-standard devices, or else use standard schemas;
4. Query the database of metadata about devices to get the configuration of the installation (a friendly name to display, location and other key values associated with each device);
5. Query the cache for the latest known states of devices;
6. Dynamically build display screens and control interfaces for these devices.
# Notes on Bus User Assignment Problem Using Section Network Representation Method
## 1 Introduction
The analyst uses the assignment model to anticipate the volume of lines as well as the critical design factor of time spent by passengers on the seeded bus network. Operators make judgments about transit planning issues in order to provide the desired level of service while balancing overall operation cost and user cost [8]. The assignment models in the literature may be divided into three approaches: simulation-based, schedule-based, and frequency-based (FB). In simulation models, the real-time route selection of transport customers is monitored. Vehicles and people are represented as independent entities in the models, allowing for far more realistic modeling capabilities. The schedule-based method considers each vehicle journey individually, and its modeling approach may express each bus's exit time via a diachronic graph. In contrast, the FB models take into account the cumulative frequencies on bus lines when computing the passenger share percentages [9]. Mathematically, the BNDP solution methods value FB models for their capacity to conduct assignments on real-world networks in a manageable amount of time. As previously indicated, several transit assignments are needed in the BNDP solution process. Therefore, the FB model aggregation would reduce the inherent complexity of the BNDP. Technically, no research has yet calibrated any current FB models using data from the actual world. In addition, the majority of BNDP studies have used transit assignment models  
Figure 1: Small bus system taken from [1]. based on naive (i.e., unrealistic) assumptions (this will be discussed in detail in the next section). The reliance on such inadequate models is due to the necessity, as referred to before, of conducting many assignments before arriving at convergence and the lack of a solid calibrated model upon which all BNDP solutions can be based. This paper addresses one of the most popular transit assignment FB models [10]. It attempts to clarify some aspects of the well-known traffic equilibrium over the section-based graph representation. The structure of the rest of this study is as follows: Section 2 offers a succinct summary of the current state of the art for FB models, Section 3 describes the fundamental ideas employed in the chosen model, and Section 4 provides the graphical depiction. In Section 5, the strategies for achieving equilibrium are described. Section 6 concludes with the conclusion and discussion.
## 3 Experimental Results  
This section presents a detailed overview of the experiments performed to evaluate the performance of the hybrid multi-laser module as a local oscillator signal generator. It also includes the results of the characterization of the single-gain lasers used for optical heterodyning, along with a comprehensive description of the experimental setup used in this study. These findings provide a basis for understanding the capabilities and limitations of the system under test.
# Surface-Wave Extraction Based on Morphological Diversity of Seismic Events
## 4 Examples
### Field Example  
The X-component field data of 2D3C seismic data shown in Figure 9 were acquired in Wangjiatun District, Daqing Oilfield, China with a sample interval of 4 ms, a geophone interval of 25 m, and a nearest offset of 400 m. It can be seen that several events of higher-mode surface waves overlap with the reflections. The surface waves are mainly at the range of low frequencies and low velocities, while reflections are in the whole f-v domain (Figure 10). Several branches of dispersive energy at frequencies of ~5 Hz and velocities of 800-1000 m/s, circled in Figure 10, are very close to each other, resulting in inaccurate phase velocities  
of the field data are reflections and other noises, except for a small number of surface waves (circled in Figure 11b). This is because the morphologies of the surface waves and reflections are slightly distorted by diffraction waves and other noises. These noises are caused by small-size geological subjects (pinch-outs or fractures) [33].  
Figure 10: An image of dispersive energy of the field data in the f-v domain.  
Figure 9: The X-component field data of 2D3C seismic data acquired in the Wangjiatun District, Daqing Oilfield, China.  
Figure 11: (**a**) The extracted surface waves by the proposed method and (**b**) the rest of the field data.  
To display the effectiveness of surface-wave extraction further, the details of waveforms are compared in Figure 12, where the original field data (Figure 9) and the results of surface-wave extraction (Figure 11a) in section A and section B are magnified. For section A, the original data (Figure 12a) are dominated by reflections, while the events of surface waves can be clearly identified in the results of surface-wave extraction (Figure 12b). For section B, the events of surface waves (Figure 12d) are clearer and more continuous than those before surface-wave extraction (Figure 12c). The image of dispersive energy of the extracted surface waves using the proposed method is shown in Figure 13a. After surface-wave extraction, the dispersive energy of different modes is separated, and the ambiguity of the phase velocities in Figure 10 is eliminated. As shown in Figure 13b, we can easily pick dispersion curves from Figure 13a. For comparison, the dispersive energy of surface waves separated by the f-k filtering method is displayed in Figure 14, where it is
# Building a Realistic Virtual Simulator for Unmanned Aerial Vehicle Teleoperation
## 2 Building a Realistic UAV Simulator
### Modeling
#### 2.2.5 Non-Conservative Effects in the Quadrotor  
A more realistic model requires to include non-conservative effects such as friction. So, a model that considers frictions in the dynamics of rotation and translation can have the following form:  
\[\left\{\begin{array}{l}M_{T}(\vec{\xi})\vec{\xi}+\vec{g}_{T}(\vec{\xi})+f_{ f}(\vec{\xi})=\vec{f}\\ M_{R}(\vec{\eta})\dot{\vec{\eta}}+C_{R}(\vec{\eta},\dot{\vec{\eta}})\dot{\vec{ \eta}}+\tau_{f}(\dot{\vec{\eta}})=\tau\end{array}\right. \tag{25}\]  
where \(\vec{f}_{f}\) in (26) is the friction vector due to the translation velocity \(\dot{\vec{\xi}}\) of the quadrotor, and \(\tau_{f}\) in (27) is the friction vector due to the rotation velocity \(\dot{\vec{\eta}}\) when the quadrotor interacts with the air.  
\[\vec{f}_{f}=\left[f_{x},f_{y},f_{z}\right]^{T} \tag{26}\]  
\[\tau_{f}=\left[\tau_{\phi},\tau_{\phi},\tau_{\phi}\right]^{T} \tag{27}\]  
In this framework, a viscous friction is considered in the translational dynamics, so the components of \(\vec{f}_{f}\) in (26), are \(f_{\dot{x}}=b_{\dot{z}}\dot{x}\), \(f_{\dot{y}}=b_{\dot{y}}\dot{y}\) and \(f_{\dot{z}}=b_{\dot{z}}\dot{z}\), with \(b_{\dot{x}}\), \(b_{\dot{y}}\) and \(b_{\dot{z}}\) as viscous friction coefficients. Similarly, this type of friction is also considered for the dynamics of rotation and the components of \(\tau_{f}\) in (27) are \(\tau_{\phi}=b_{\phi}\dot{\phi}\), \(\tau_{\phi}=b_{\dot{\theta}}\dot{\theta}\) and \(\tau_{\phi}=b_{\dot{\psi}}\dot{\psi}\), with \(b_{\phi}\), \(b_{\theta}\) and \(b_{\dot{\psi}}\) as viscous friction coefficients.  
For completeness, the developed forms of \(\vec{f}_{f}\) and \(\tau_{f}\) are presented in Appendix A.3.
# An Overview of GEO Satellite Communication Simulation Systems
## 3 Resource Management and Scheduling
### Energy Efficiency  
Additionally, energy efficiency optimization has gained increasing attention. The authors in [26] investigated the multigroup multicast beamforming design for high-throughput GEO satellite communications as an energy efficiency maximization problem, which considered the power-consumption outage and total power constraints. In [27], an energy cost minimization problem was formulated with joint optimizing route selection and radio resource allocation constrained by QoS requirements in satellite networks where both the inter-satellite data transmission and the satellite-ground stations direct transmission were deployed.
# Multi-Objective Optimisation in Multi-QoS Routing Strategy for Software-Defined Satellite Network
## 5 Proposed Methodology
### Linguistic Description and Rule Base  
In this section, we define the linguistic description and rule:  
Traffic Class A: The flow of typical delay-sensitive applications, such as some real-time communication and audio applications.  
Traffic Class B: The flow generated by the typical video applications and requiring throughput assurance.  
Traffic Class C: The flow generated by the packet-loss sensitive applications and needing to keep a small packet-loss rate.  
Traffic Class D: The flow generated by the applications that has no special QoS requirements.  
When a flow is sent to a LEO satellite, multi-QoS requirements are discussed: delay, throughput and packet loss rate. The criteria can fully denote current network state at the moment. However, the criteria cannot be calculated instantaneously and other measures are needed to express these three QoS requirements.  
In this paper, we use the queuing length of traffic class A (\(L_{A}\)), the queuing length of traffic class B (\(L_{B}\)), the queuing length of traffic class C (\(L_{C}\)) and the queuing length of traffic class D (\(L_{D}\)) as the input of proposed routing algorithm to the indirectly describe of the current network state. The queuing length of traffic class can infer the congestion degree of ISLs. For each queuing length of traffic class, there are eight kinds of evaluation: \"AL\", \"VL\", \"ML\", \"M\", \"MH\", \"H\", \"VH\" and \"AH\". Their corresponding values is given by Table 1.
# Compression Behavior of Rubber-Bentonite Mixture under Different Salinities
## 1 Introduction
### Test Soil Sample, Rubber, and Solution  
In this investigation, we focused on employing specific materials that would yield significant and insightful experimental outcomes. To achieve this, we utilized AQUAGEL GOLD SEAL bentonite (US HALLIBURTON Energy Limited Company, Tianjin, China) that falls under the BAROID brand). The selected bentonite demonstrated an impressive montmorillonite content of 94%, a water content of 8.4%, and a free swelling rate of 620%. Additionally, this study determined that the plastic limit was 29.7%, and the liquid limit reached 393.75%, resulting in a plasticity index of 364.05, highlighting the unique characteristics of bentonite.  
For the rubber component, we sourced the particles from Lingshou County Jingjia Mineral Products Processing Plant. Produced in Shijiazhuang City, Hebei Province, China.These rubber particles possessed a density of 1.1 and 99.9% of the particles fell within the size range of 1-2.5 mm, ensuring consistency in the experimental setup.  
Previous studies have consistently demonstrated a positive relationship between the cation valence present in the pore fluid and the permeability and compressibility of rubber-bentonite mixed soil [29,30]. To ensure that our experimental observations were more pronounced and easily interpreted, we chose to use NaCl solution as the salt solution in our experiments. By doing so, we could effectively investigate and analyze the effects of salt concentration on the behavior of the rubber-bentonite mixture, providing valuable insights into the permeability and compressibility characteristics of the material system.
# An Architecture for a Tri-Programming Model-Based Parallel Hybrid Testing Tool
## 2 Background
### An Overview of Common Run-Time Errors
#### 2.3.2 Livelocks  
Livelocks are similar to deadlocks; however, in a livelock, the state of the affected tasks continues to change but never makes any real progress. In some cases, all the tasks stall and remain unfinished [25; 26]. Nevertheless, in a livelock, a thread will never be caught in a permanent blockage. As livelocks are plagued by busy-wait cycles, they are not only power-consuming but may perform poorly. Furthermore, both deadlocks and livelocks can cause parallel systems to become unresponsive and may require manual intervention to resolve.  
An example of a livelock would be when two computing processes, A and B, use an MPI to transfer messages; A relays the message to B and then awaits a response, but B relays its own different message to A, rather than sending an instantaneous response for the first message. Therefore, both A and B end up stuck in an endless period of waiting for a response from the other, creating a livelock.
# A Graph-Based Representation Method for Fashion Color
## 3 Graph Representation of Fashion Color
### Color Graph Construction
#### 3.2.2 Geographical Information  
The geographical information is another important but missing category of information for color data extraction in the conventional solutions. The geographical information is of great importance to the human perception of overall color style. Here, geographical data are collected based on the visual relationships of the different colors and are built as the edge set \(E\). To quantitatively represent the relationships and keep the logic concise, we abstracted the definition of the relationships into the following three categories:  
* Discrete: where two colors are nonadjacent. As shown in Figure 4b,c, the red area and yellow area in Figure 4b and red area and purple area in Figure 4c are discrete based on our definition.
* Adjacent: where two colors share the same boundary but one of them is not surrounded by the other. As shown in Figure 4a-c. The red and blue in the objects are all adjacent colors.
* Surround: where one color is in the other color and the pixels of the inner color are all surrounded by the outer one. As shown in Figure 4d, the red color is surrounded by the blue color.  
We used a directed graph to capture the geographical information of the nodes, since it can show the surrounded status of the colors--the adjacent colors have a bi-directional edge, and the surrounded colors have a single direction of the edge. As shown in Figure 5a, the red and blue colors are adjacent and blue and yellow are also adjacent, so these two pairs both have a bi-directional edge to each other, while the red and yellow are discrete, so they do not have an edge between them.  
Figure 5b depicts a case in which a red color is surrounded by a blue color, so they have a single direction of edge that points from the one that is surrounded to the one that surrounds it. In this way, the number of colors, the ratio of each color, and the relationship between the colors are captured and represented as a graph. Although these examples are for regular images with simple patterns, the method can be used for fashion apparel with complex color patterns and irregular shapes due to the ignorance of shape and the scalability of the number of colors in our solution.
# Colored Petri Net Modelling and Evaluation of Drone Inspection Methods for Distribution Networks
## 4 Results
### Determining a Suitable UAV Configuration  
Let us now consider the effect of the range of each UAV on the frequency of object monitoring. For the study, we will choose the monitoring scheme \(P\)0-_P_2, \(P\)2-_P_8 and \(P\)3-_P_6, as it allowed us to achieve the highest monitoring frequency in Section 4.4 when using three drones.  
The shortest permissible range of the drone is determined by the longest segment length, taking into account the 5% navigation error, and equals 150 km.  
The longest range is limited by the maximum take-off weight of the aircraft including batteries and payload, the transmitter range and the design scheme of the UAV.  
Medium and large UAVs with a weight over 25 kg have a very long range. Thus, the range of military UAVs can reach 1200 km and more. However, the use of such UAVs is impossible without a specially equipped runway. Therefore, we will consider small UAVs here.  
The record range of a small UAV was established by the company IN-FLIGHT Data Inc. in 2018 in Canada [38]. The range of the UAV of the flying-wing type was 414 km. Thus, we will limit the theoretical permissible range of the UAV to 400 km.  
The simulation result for a UAV with a range of 150 to 400 km in increments of 25 km is shown in Figure 8. Increasing the range of the UAV to 225 km allows for increasing the frequency of monitoring the entire object from 1.8 to 2.0 times per day. The next threshold is a UAV range of 350 km. Then, the whole object monitoring frequency would be 2.3. However, such a significant increase in range would be costly in practice.  
It can be concluded that the range of a UAV has a significant impact on the monitoring frequency. Since the range of a UAV is highly dependent on other parameters that can be easily changed, such as battery capacity, payload weight, fairings, trajectory and speed, it is not always necessary to change the UAV model to increase the range. Sometimes it is possible to increase the frequency of flight by making the UAV lighter or by installing a larger battery or winglets.
# Effect of Magnetic Nanofluids on the Performance of a Fin-Tube Heat Exchanger
## 3 Methodology
### Effect of Three Permanent Magnets on Heat Transfer  
To solve the problem of the temperature difference between tubes and stagnation flows, two additional permanent magnets were added. When compared to Case B, several vortexes were made over the entire chamber, including the edges (Figure 8). This means that the heat transfer between the Figure 1a region and tubes was more active, and kinetic energy loss was reduced. This effect can be seen in Case 2 of Figure 9. Unlike Figure 7, which shows cooling at \(1.8\times 10^{7}\) A/m, it is confirmed that cooling was already in progress  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Case** & \(\mathbf{T_{s}}\) (\(\mathbf{K}\)) & \(\mathbf{T_{m}}\) (\(\mathbf{K}\)) \\ \hline Case 1 (\(6.0\times 10^{6}\) A/m) & 352.18 & 324.68 \\ Case 2 (\(1.2\times 10^{7}\) A/m) & 343.35 & 320.14 \\ Case 3 (\(1.8\times 10^{7}\) A/m) & 334.11 & 315.42 \\ Case 4 (\(2.4\times 10^{7}\) A/m) & 326.34 & 311.47 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Temperature values in the case of one permanent magnet (Case B).  
Figure 6: Velocity contours in the case of one permanent magnet (Case B).  
Figure 7: Temperature contours in the case of one permanent magnet (Case B).  
at \(1.2\times 10^{7}\) A/m. As the strength of magnetization increased, even cooling proceeded between the three tubes. \(T_{s}\)(K), which represents the surface temperature of the tube, also decreased by about 3.5 \({}^{\circ}\)C based on \(2.4\times 10^{7}\) A/m when compared with Tables 4 and 5.  
As a result of arranging three permanent magnets, the heat transfer performance was improved by solving the temperature non-uniformity. Although the cooling performance was improved, the efficiency was decreased because of the addition of external permanent magnets.
# Crustal Structure of Hainan Island and Surrounding Seabed Based on High-Resolution Airborne Gravity
## 4 Results  
### Bouguer Gravity Anomaly Analysis  
From the continental area to the sea, the Bouguer gravity anomaly in the study region changes from negative to positive. The Bouguer gravity anomaly reflects large-scale tectonic information and sedimentary basin features. In the Yinggehai Basin, the value  
\begin{table}
\begin{tabular}{c c} \hline
**Order** & **Average Source Depth (km)** \\ \hline D1–D3 & 3 \\ D4 & 5 \\ D5 & 11 \\ D6 & 19 \\ D7 & 27 \\ \hline \end{tabular}
\end{table}
Table 1: Depth of each decomposed gravity anomaly source layer.  
ranges from \(-50\) to \(0\) mGal with the NW-trending negative anomaly. The Yinggehai Basin is separated from the South China Block by the Red River Fault. The Red River Fault is apparently a gravity gradient belt, and the gradient can reach \(2.4\) mGal/km, indicating that it reflects the boundary of different blocks. The Central Hainan Island Low Anomaly Zone is characterized by a central negative anomaly surrounded by positive anomalies, which clearly separates Central Hainan Island from other units. The large negative Bouguer gravity anomaly of Central Hainan Island reflects the large-scale intrusion of granite in Hainan Island and the subsequent stronger uplift of this terrain. Local anomalies show the superimposed northeast and northwest directions. There is a striking high-value anomaly zone in the north of Hainan Island, with high anomalies and low anomalies arranged regularly, reflecting the extensive distribution of Leiqiong basaltic rocks on the surface. The overall Bouguer gravity anomaly of BBWB is negative, and the basin boundary is reflected well from the gravity anomaly. The northern part of the gravity anomaly background is lower than the southern part, which is consistent with the feature that where the northern part is a sag, the southern part is an overthrust. The Qiongdongnan Basin is situated on the southeast side of Hainan Island. The gravity anomaly value is significantly higher than the other area, increasing gradually from land to sea. It is separated from Hainan Island by a clear anomaly gradient band, indicating the Qiongdongnan Fault. Northeast Hainan Island, formerly known as the Hainan Uplift [50], exhibits alternating positive and negative gravity anomalies. This area is the southern extension of the South China Block.
## 3 Concepts, Models and Technologies
### Machine Learning (ML) and Deep Learning (DL)  
Machine learning enables computers to learn from data through optimization techniques or control mechanisms by leveraging calculations and weight adjustments. ML, along with its subcategory DL, has captured significant attention in recent years, particularly in the fields of data communication networks, audio signal processing, voice and video over IP, discussed on [4; 16; 27]. Given that networks play a fundamental role in distributed learning systems--including collaborative learning at the edge, whether via Fog Computing or IEC--it is understandable that research in this area has seen substantial growth [28].  
With the advent of cloud computing and the adoption of application architectures based on containers, companies and research centers have observed increasing complexity in network infrastructures and data centers, along with their configurations and the rapid expansion of applications operating within this new architecture. This scenario demands network intelligence with a more holistic perspective to address critical issues effectively.  
The complexity is already evident in metropolitan wireless networks, large public or private spaces, and Smart Cities, particularly with the explosion of 5G and 5G-IoT networks [7]. This complex environment presents a fertile field for research, especially in the context of distributed application approaches.