esa-sceva/satcom-chunk-collection huggingface dataset:
# AsGCL: Attentive and Simple Graph Contrastive Learning for Recommendation
## 3 Preliminaries  
### Matrix Factorization (MF) and Graph Collaborative Filtering (GCF)  
We conceptualize users and items as nodes in a bipartite graph, where users are connected to the items they have selected. Consequently, the task of identifying the optimal recommended items transforms into a link prediction problem [17]. Graph collaborative filtering is an embedding-based model [23,24], with matrix factorization being the most commonly used approach in traditional recommendation systems, demonstrating significant practical value. As a result, it has become a baseline model for many subsequent studies.  
The Figure 1 illustrates the relationship between the matrix factorization process, the original graph, and the embedding matrices. Here, matrix R represents the user-item adjacency matrix, matrix \(H\) contains user embeddings, and \(W\) contains item embeddings.  
The user-item bipartite graph \(\mathcal{G}=(\mathcal{V},\mathbf{A})\), where \(\mathcal{V}=\mathcal{U}\cup\mathcal{I}\), has an adjacency matrix \(\mathbf{A}\in\mathbb{R}^{n\times n}\), which can be expressed as follows:  
\[\mathbf{A}=\begin{bmatrix}0^{|\mathcal{U}|\times|\mathcal{U}|}&\mathbf{R}\\ \mathbf{R}^{T}&0^{|\mathcal{I}|\times|\mathcal{I}|}\end{bmatrix}. \tag{1}\]  
For the embedding vectors, users with similar preferences will have similar embeddings. Embedding-based models aim to find optimal embeddings for users and items. Typically, we need to compute the optimal scoring function \(f\), where \(f\) is the scalar product of the two embeddings. The model is trained by minimizing the Frobenius norm of the matrix \(R-HW\).
# Route Planning for Autonomous Driving Based on Traffic Information via Multi-Objective Optimization
## 3 Proposed Method
### Travel Time  
Whether highway sections or daily commuting, driving time is a critical issue for travelers. Through the investigation of the actual road sections in the city, this paper dividesthe driving time into the following three parts: road driving time, intersection signal time and intersection traffic time.  
The road driving time is closely related to the traffic busy degree of the road section. After observing and investigating some actual road sections, it is found that when the number of vehicles in the road section is less than 60% of the capacity, vehicles can usually pass at the free flow speed (\(v_{f}\)). When the number of vehicles in the road section is less than 80% of the capacity, vehicles can usually pass at 60% of the free flow speed. When the number of vehicles in the road section is saturated, vehicles can usually pass at 35% of the free flow speed.  
The intersection signal time is closely related to the driving direction of the vehicle at the intersection. Through the observation of the signal lights at several actual traffic intersections, it can be seen that the vast majority of intersections do not need to wait for the traffic signal lights when turning right and the average signal time of straight sections is lower than that of turning left.  
The intersection traffic time is related to the capacity of the intersection, the number of vehicles at the intersection and the number of turnouts. The less the traffic volume at the intersection accounts for its capacity, the shorter the traffic time. The greater the number of intersections, the more complex the traffic behavior at the intersection and the longer the traffic time.  
Based on the above considerations, there should be several constraints as below:  
\[t_{ij}\leqslant\frac{d_{ij}}{0.35v_{f}} \tag{6}\]  
\[t_{node(x)}\leqslant t_{node(y)}\quad\forall degree(x)\leqslant degree_{y} \tag{7}\]  
\[t_{right}\leqslant t_{straight}\leqslant t_{left} \tag{8}\]  
where \(t_{ij}\) represents the travel time between nodes \((i,j)\), \(d_{ij}\) is the distance between nodes \((i,j)\), \(degree(x)\) represents the number of intersections at node \(x\), \(t_{node(x)}\) represents the time to pass through node \(x\), \(t_{right}\) represents the right turn time, \(t_{straight}\) represents the time to go straight through the intersection and \(t_{left}\) represents the left turn time. Constraints (6) ensure that the car avoids traffic jams. Constraints (7) and (8) limit the two situations of the intersection signal time.
## 5 Field Experiment
### Discussion of Test Results  
In the process of the field test, the prototype could complete the operation process of seedbed preparation, seedling casting, rotary tillage soil covering, film covering outcrop, and film edge soil covering at one time when the target seedling spacing is set at 4.4 cm and the forward speed is 0.1, 0.15, and 0.2 m/s. When the machine moves forward at the speed of 0.1 m/s, the average planting spacing was 4.56 cm, the average variation coefficient of planting spacing was 0.08%, the average qualified rate of planting depth was 95.83%, the average qualified rate of planting posture was 94.17%, and the average qualified rate of film side outcrop was 93.33%. When the forward speed of the machine was 0.15 m/s, the average planting spacing was 4.87 cm, the average variation coefficient of planting spacing was 0.19%, the average qualified rate of planting depth was 92.5%, the average qualified rate of planting posture was 89.17%, and the average qualified rate of film side outcrop was 88.33%. When the forward speed of the machine was 0.2 m/s, the average qualified rate of planting spacing was 4.78 cm, the average qualified rate of planting depth was 90.83%, the average qualified rate of planting posture was 84.17%. While the machine moves forward at the speed of 0.1, 0.15, or 0.2 m/s, referring to JB/T 10291-2013 [29] \"Standard for dryland planting machinery\", the variation coefficient of planting spacing is less than or equal to 20%, the qualified rate of planting depth is greater than or equal to 75%, and the variation coefficient of planting spacing and qualified rate of planting depth both reach the standard requirements. The variation coefficient of planting spacing becomes larger with the increase in forward speed, and the qualified rate of planting depth decreases with the increase in forward speed, indicating that the speed of prototype operation has a great influence on the seedling spacing and planting depth. There are no relevant standard requirements about the qualified rate of planting posture and film side outcrop at present, so the qualified rate cannot be evaluated. It can be seen from the test results that the qualified rate of planting posture and film side outcrop are greatly affected by the speed of the machine and decrease with the increase in operation speed. It can also be concluded from the field test results that when the forward speed of the prototype is 0.1 m/s, the operating performance is better than that of 0.15 m/s and 0.2 m/s.
## 4 Conclusions  
The carbon content has little effect on the positions of the D band and G band. The parameters (P\({}_{\rm G}\), P\({}_{\rm D}\), I\({}_{\rm D}\)/I\({}_{\rm G}\), W\({}_{\rm D}\), and W\({}_{\rm G}\)) of the D and G bands were discussed. The parameters affected each other. Among them, I\({}_{\rm D}\)/I\({}_{\rm G}\) is used to quantitatively evaluate the degree of disorder of carbon materials.  
Figure 7: In-situ temperature dependence of the peak position of the G band for the samples pyrolyzed under an Ar atmosphere.  
During an in-situ high temperature-dependent pyrolysis process, the peak position of the D band seems related to the low molecular weight carbon organics. Since the low molecular weight carbon organics condensed and devolatilized, the intensity of the D band changed negligibly with an increase in temperature. Meanwhile, the position of the G band was shifted to blue, and its intensity became weaker with an increase in temperature, which was the result of aa decrease in devolatilized contents in the samples. The G peak position variation was affected by condensation and devolatilization of the low molecular weight carbon organics and the heavy molecular weight carbon organics inside the coal samples. This variation in the peak position of the G band reflects the coalification processes of condensation, crystallization, coking, and devolatilization during the entire pyrolysis process.  
Raman spectroscopy has clear potential for the diagnostic identification of coal and for coking optimization.  
Experiment and writing and original draft preparation, Y.X.; writing, review, and editing, J.Y. and L.L.; data curation, M.W. and J.W.  
This research was funded by the National Natural Science Foundation of China, grant number 21773152; the national 111 project (the Programme of Introducing Talents of Discipline to Universities), grant number D17002; the Shanghai Committee of Science and Technology Fund, China, grant number 12520709200; the Open Project Program of the State Key Laboratory of Advanced Special Steel, Shanghai University, China, grant number SKLASS2015-01; and the Special Fund Project of Shanghai Municipality for Science and Technology Development, grant number YDZX20173100001316.  
The authors declare no conflict of interest.
## 5 Proposed Approach
### Prediction Module
#### 5.4.3 Regressor Based on Polynomial Integration  
The last model is an algorithm that we designed and created from scratch based on polynomial interpolation. The idea is to look for the polynomial of degree \(p\) that best approximates the time series in question. To do this, every time a prediction is performed, several polynomials with degree \(p\) ranging from 1 to \(n\) are generated, where \(n\) is the total number of polynomials that are thought to fit. In our case, we have chosen not to go beyond the ninth degree for each prediction; therefore, we chose among 9 polynomials. This choice was made because polynomials with a very high degree could be problematic to converge [72]. To choose the best polynomial, we carried out the following procedure:  
* We determined the prediction in the point preceding the one we are interested in making the prediction for;
* The error was calculated as the difference between the obtained element and its corresponding real value;
* We saved the error in a list.  
In this process, it was necessary to impose two constraints:  
* The first constraint requires that the forecast value (which is a number corresponding to a date) must always be greater than the date corresponding to the previous element, as it cannot be possible to obtain forecasts in the past;
* The second constraint imposes that the failure date must not tend towards an infinite value.  
This process is applied to all the polynomials and the one that at step \(i\)-1 provides the lowest error (and satisfies the two constraints defined above) is chosen to predict step \(i\)-th. This process tries to optimize the forecast by trying to follow the trend as much as possible.  
An example of how it works is included in Figure 4, where we have four points in the training set and are interested in the prediction of the fifth one (in purple). Three polynomials with a different degree are determined on the first three points. The predictionson the fourth point (the three blue points are the predictions, whereas the red point is the real value) are performed and the three errors are computed. The polynomial corresponding to the lowest error, \(c3\), is yellow, and it will be used to predict the fifth point.
## 4 Support Scheme Optimization
### Numerical Simulation  
To validate the rationality of the support scheme, we conducted a simulation analysis of the support effectiveness of the two schemes using the UDEC (Universal Distinct Element Code) numerical simulation software. UDEC has a distinct advantage in simulating rock fragmentation and large deformations. Table 4 shows the Brock physical and mechanical parameters.  
Based on the excavation and support plan for TBM excavation in soft sandstone at the Kekegai coal mine, a typical section at a depth of 200 m was selected. The UDEC 7.0 discrete element software was used to create a numerical model to simulate the disturbance of the surrounding rock and the stress on the support structures under different support schemes [22]. According to [PERSON]'s principle, if a force acting on a small surface of an elastomer is replaced by a statically equivalent system of forces acting on the same surface, this substitution produces a significant change in stress on only the local surface, and the effect is negligible further than the linear dimensions of the stress-changing surface, it was determined that the disturbance outside the area 3 to 5 fold the tunnel diameter from the excavation zone is negligible and can be disregarded. Therefore, the model dimensions were set at 80 m by 80 m, containing a total of 14,294 blocks. The circular TBM excavation tunnel was located at the center of the model with a diameter of 7.13 m, as shown in Figure 14.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Stratum** & **Rock Types** & **Density/(kg\(\cdot\)m\({}^{-3}\))** & **Bulk** & **Shear** & **Adhesive** & **Angle of Internal** \\  & & **Modulus/MPa** & **Modulus/MPa** & **Strength/MPa** & **Friction/(\({}^{\circ}\))** \\ \hline \multirow{3}{*}{Luohe Formation} & sandstone & 2510 & 1530 & 1080 & 0.5 & 32 \\  & mudstone & 2480 & 1300 & 920 & 0.6 & 30 \\  & fractured rock & 2510 & 920 & 650 & 0.3 & 28 \\  & stratum & 2530 & 2170 & 1490 & 1.5 & 28 \\  & sandstone & 2480 & 1750 & 1320 & 1.2 & 30 \\  & sandstone & 2710 & 7900 & 4800 & 2 & 26 \\  & mudstone & 2680 & 6500 & 3600 & 2 & 30 \\  & fractured rock & 2710 & 1000 & 900 & 1.7 & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Brock physical and mechanical parameters.  
Figure 14: Computational model and joint configuration.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Components** & **Bolt (Roots)** & **Cable (Roots)** & **Mesh (Sheets)** & **Steel Belt (Items)** \\ \hline Original scheme & 17 & 0 & 4 & 0 \\ Optimization scheme & 14 & 3 & 4 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of materials required by two support schemes.  
In the model, normal displacement constraints were applied to the left and right sides as well as the bottom surface. Additionally, maximum horizontal stresses were imposed on the computational elements, with a magnitude of 14.12 MPa (x-direction), a minimum horizontal stress of 9.95 MPa (out-of-plane direction), and a vertical stress of 7.68 MPa (y-direction).
# Soil Moisture Retrieval from Multi-GNSS Reflectometry on FY-3E GNOS-II by Land Cover Classification
###### Abstract  
The reflected GNSS signals at the L-band is significantly advantageous in soil moisture monitoring as they are sensitive to the dielectric properties determined by the volumetric water content of topsoil, and they can penetrate vegetation, except in very dense forests. The Global Navigation satellite system Occulation Sounder (GNOS-II) with a reflectometry technique onboard the Fengyun-3E (FY-3E) satellite, launched on 5 July 2021, is the first mission that can receive reflected Global Navigation Satellite System (GNSS) signals from GPS, BeiDou and Galileo systems. This paper presents the soil moisture retrieval results from the FY-3E GNOS-II mission using 16 months of data. In this study, the reflectivity observations from different GNSS systems were firstly intercalibrated with some differences analyzed. Observations were also corrected by considering vegetation attenuation for 16 different land cover classifications. Finally, an empirical model was constructed for volumetric soil moisture (VSM) estimation, where the reflectivity of GNOS-II was linearly related to the SMAP reference soil moisture for each 36 km ease grid. The overall root-mean-square error of the retrieved soil moisture is 0.049 compared with the SMAP product, and 0.054 compared with the in situ data. The results of the three GNSS systems show similar levels of accuracy. This paper, for the first time, demonstrates the feasibility of global soil moisture retrieval using multiple GNSS signals.  
GNOS-II; FY-3E; GNSS-R; volumetric soil moisture
## 1 Introduction
These materials face intrinsic limitations in high-voltage environments: perovskites degrade under humidity and UV exposure, hydrogels exhibit temperature-dependent performance collapse above 40 \({}^{\circ}\)C, and most systems necessitate hermetic sealing to prevent oxygen degradation, constraints that render them impractical for outdoor power infrastructure. While electroluminescent materials have seen significant advances in low-voltage applications, their implementation in high-voltage power equipment remains remarkably limited. This demands concurrent solutions to unique challenges, including ultra-high field endurance and dielectric integrity preservation, while maintaining EL functionality for fault diagnosis [30; 31; 32]. The few existing high-voltage applications primarily focus on GIS components, which explored EL-based field mapping for basin-type GIS insulators, but this work was confined to medium and low voltage electrical equipment in indoor environments and did not address transmission line monitoring challenges [33; 34; 35; 36; 37]. In response to these challenges, this study proposes an on-line monitoring sensing strategy for identifying zero-value insulators based on electroluminescence. The core innovation involves integrating copper-doped zinc sulfide (ZnS:Cu) electroluminescent powder into an epoxy coating, which is applied to the critical junction between the insulator's steel cap and its ceramic or polymer shed. This region, which experiences concentrated mechanical and electrical stresses, serves as a sensitive indicator of insulator health. The primary objectives of this work are threefold: (1) developing and fabricating a durable EL composite coating capable of withstanding the harsh conditions of high-voltage transmission lines; (2) establishing a quantitative relationship between luminescent intensity and voltage magnitude, enabling fault severity assessment; and (3) validating the proposed method's diagnostic accuracy, response speed, and practicality through comparative experiments.
# Comparative Study of Different Approaches for Islanding Detection of Distributed Generation Systems
## 2 Literature Review
### Aggressive Signal Modeling and Support Vector Machine Based Anti-Islanding Method [65]:  
System parameters like voltage and current are extracted from the point of common coupling. This is then used as input to the machines and an additional advanced machine learning technique is used to determine different scenarios of islanding and non-islanding conditions. Thus, with few parameters, predictions can be made about the islanding sate of the system. With the accuracy of 98.74%, this method was able to detect the islanding condition within 50 ms of the event occurrence.
## 3 Proposed System
### Proposed System Development Environment  
We used a PC with the following specifications and software for our development environment.  
* OS: Window 10
* CPU: AMD Ryzen 7 1700X Eight-Core Processor 3.40 GHz
* RAM: 16.0 GB
* GPU: NVDIA GeForce GTX 1060 6 GB
* Framework: Unity3D 2019.4.18f, Visual Studio 2019
* Language: C#
* VR Device: HTC Vive Pro Eye, Oculus Rift S  
The proposed system was developed with Unity3D (2019.4.18f), and C# scripts were programmed using the Microsoft Visual Studio 2019 integrated development environment. For VR interaction, we used the XR Interaction Toolkit plugin supported by Unity3D and the server was built with Photon PUN2. The VR devices used for development were HTC Vive Pro Eye and Oculus Rift S.  
The proposed system was developed as shown in Figure 1. First, a simulation was planned. We needed to design educational fields and scenarios that our system would be applicable to. Furthermore, modelling files as well as desktop and VR devices were required for use in our system.  
Figure 1: Proposed system development environment.  
Next, we divided the simulation production into a virtual environment and a network. First, we used Unity3D to build a virtual environment. This virtual environment was programmed in Visual Studio and developed as a heterogeneous type that can be used without restrictions on VR devices to ensure high versatility. The tested VR device models were Oculus Rift S, Quest2, and HTC Vive Pro Eye, all of which were confirmed to work. We implemented 3D models that were necessary for environmental configuration, as well as a virtual avatar that acts as the user's agent. Finally, we implemented Text-To-Speech (TTS), a speech synthesis function for smooth interaction between users and objects.  
We then implemented a network. The library used was Photon PUN2, a cloud-based networking platform that can be used in Unity3D. Photon PUN2 was integrated with Unity3D to provide an API that is easy to develop, and users can freely log in and link with the simulation VR environment [49]. We implemented a client-server-based network, where user actions are sent to a cloud server and forwarded to the rest of the clients. By enabling voice conversation through Photon Voice, we provided users with an effective means of communication.  
The proposed system was completed by building a VR-based immersive simulation virtual environment and implementing a cloud-based network. Through the proposed system, users can wear VR devices and use objects in a virtual world similar to the real world, as well as interact and talk with others using virtual avatars. Since all user actions are transmitted to the server and other users in real time, users can meet and interact in the same virtual environment regardless of physical distance.
## 2 Mathematical Formulations
### The Setting of the Boundary Conditions in the Computational Domain  
All the numerical studies are carried out in a two dimensional computational domain of 0.01 m \(\times\) 0.01 m as shown in Figure 2.  
The details of the boundary conditions applied in the case study are as follows:  
(1) Velocity-inlet boundary  
\[u_{l}=u_{0},\;v_{l}=0,T_{l}=T_{0},u_{s}=0,v_{s}=0,T_{s}=0,\alpha_{s}=0\;. \tag{22}\]  
(2) Outflow boundary  
The flux of all the variables at the outflow boundary is set as zero, which means the state in the fluid zone are not affected by the conditions at the outflow boundary  
\[\frac{\partial u_{l}}{\partial x}=0,\;\frac{\partial v_{l}}{\partial x}=0,\; \frac{\partial T_{l}}{\partial x}=0,\;\frac{\partial u_{s}}{\partial x}=0,\; \frac{\partial v_{s}}{\partial x}=0,\;\frac{\partial T_{s}}{\partial x}=0\;. \tag{23}\]  
(3) Wall boundary with constant temperature  
\[u_{l}=0,\;v_{l}=0,T_{l}=T_{0},u_{s}=0,v_{s}=0,T_{s}=T_{0},\frac{\partial \alpha_{s}}{\partial n}=0\;. \tag{24}\]  
Figure 2: The setting of the computational domain.  
(4) Adiabatic wall boundary  
The heat flux passing through this face is set as zero:  
\[u_{l}=0,\;v_{l}=0,\frac{\partial T_{l}}{\partial n}=0,u_{s}=0,v_{s}=0,\frac{ \partial T_{s}}{\partial n}=0,\frac{\partial\alpha_{s}}{\partial n}=0\;. \tag{25}\]  
Different combinations of the above four boundary conditions are adopted in different cases, which are noted in the subsequent sections.