esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area and Data
### Data
#### 2.2.2 Geographical Data  
The elevation from the ALOS World 3D 30 m (AW3D30) provides height information above the sea level globally at 1 arcsecond (approximately 30 m) resolution. It was released in May 2016 and updated in April 2020 by the Japan Aerospace Exploration Agency. It was constructed using the Panchromatic Remote-sensing Instrument for Stereo Mapping boarded on the Advanced Land Observing Satellite [35]. Studies have shown that AW3D30 has a better skill core than Shuttle Radar Topography Mission Digital Elevation Model (DEM) or Advanced Spaceborne Thermal Emission and Reflection Radiometer DEM in the Philippines and Mexico that have rugged mountains at high altitudes [35; 36; 37]. In particular, AW3D30 showed the most stable results among the three models even on the high altitude and dense vegetation. The elevation data were used as input considering accessibility because areas at the high altitude are less accessible by people, reducing the chance of forest fire occurrences.  
The population density was derived from the Gridded Population of the World, Version 4, and Revision 11 data provided by the Society Economic Data and Applications Center [38]. The population density is provided as raster data at multiple spatial scales from 30 arcseconds to 60 arcminutes all over the world. As it is provided every five years from 2000, this study used 30 arcseconds (~1 km) data generated in 2015. The road density was calculated using the global road dataset provided by the Global Roads Inventory Project [39]. It contains five types of roads: highways, primary roads, secondary roads, tertiary roads, and local roads [40]. In this study, road density was calculated from the GRIP vector data (all road types) through the line density function in  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data** & **Source** & **Variables** & **Abbreviation** \\ \hline \multirow{3}{*}{Weather data} & \multirow{3}{*}{Korea Meteorological Administration} & Relative humidity & Rehu \\ \cline{3-4}  & & Temperature & Temp \\ \cline{3-4}  & & Wind speed & Wind speed \\ \hline \multirow{3}{*}{Geographical data} & JAXA ALOS & AW3D30 v3.1 & Elevation \\ \cline{2-4}  & NASA Gridded Population of the & & \\ \cline{1-1}  & World (GPW), v4 & & \\ \cline{1-1} \cline{2-4}  & GRIP global roads dataset & Road vector & Roaddens \\ \hline Fuel data & Forest Geospatial Information System & Forest density & DN \\ \hline Time-related data & & Day of the Year & DOY \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data description used for machine learning model building.  
the ArcGIS 10.4 program. The population and the road densities were also used as factors to indirectly consider accessibility.
## 3 Results and Discussion
### Physicochemical Properties of the Samples
#### 3.2.3 BET Analysis  
N\({}_{2}\) adsorption experiments were conducted to investigate the structural properties of these samples. As shown in Figure 5a, all of these samples exhibited typical type IV isotherms and type H3 hysteresis loops, implying the existence of mesopores with slit-shaped structures [2]. In addition, it is worth noting that all these samples displayed a sharp rise of nitrogen uptake in low relative pressure regions, signaling the presence of abundant micropore structures [42]. Figure 5b shows the corresponding pore size distributions of various samples, which confirms the co-existence between micropores and mesopores [41; 43]. The N\({}_{2}\) adsorption data are listed in Table 1, from which it can be found out that the adsorbent micropore surface area shrunk upon the introduction of the active species, but with no obvious variations detected in the external surface area. This result indicates that most of the added metal oxides entered the micropore channels to obstruct them. In addition, the Co\({}_{4}\)Fe\({}_{1}\)-ZSM-5 sample had the largest external surface area, which means the existence of iron oxide contributes to a satisfactory outcome of cobalt oxide distribution. This is consistent with the result of XRD measurement. Moreover, the relatively large specific surface area might provide an increased number of active sites for Hg\({}^{0}\) capture, which can partially explain the higher Hg\({}^{0}\) removal efficiency of Co\({}_{4}\)Fe\({}_{1}\)-ZSM-5 than the other samples.  
Figure 4: XRD patterns of a series of samples.
# A Routing Optimization Method for LEO Satellite Networks with Stochastic Link Failure
## 2 Mathematical Model  
### LEO Satellite Network Formation  
According to the area and function, the system can be divided into three parts: space, ground and user segment. In this paper, we assume that the space segment contains only satellites. The ground segment includes functional entities such as gateway stations and network management centers, and the user segment consists of various types of terminal equipment and application facilities.  
The simplified model of the LEO satellite network is shown in Figure 1, which includes five satellites. Usually, the user segment selects the upper satellite (SA I) and communicates with it through microwave, and then transmits data to the satellite (SA V) above the ground station via the relay satellite through ISLL, and finally the SA V communicates with the ground segment via microwave. Since the system adopts ISLL, the quality and range of transmission are greatly improved compared to microwave. On the one hand, link failure may occur during the communication due to the transmission errors and system noise. On the other hand, the complexity of the topology will increase significantly with the number of satellites due to the adoption of ISL.
# An Observation Scheduling System for Radio Telescope Array
## 4 Main Function Testing  
### Trajectory Prediction Module  
In this section, we assess the accuracy of our trajectory prediction model on a set of well-known targets using Equation (1). The six targets selected for this evaluation are PSR B0329+54, B0531+21, J1846-0258, FRB190523, SGR1935, and FRB20220912A. The predicted trajectories for these targets are detailed in Table 3. Columns 1 to 3 show the target name as well as the RA and DEC positions of the target. Columns 4 to 6 present the UT time with the observation date set for 1 April 2024. Specifically, columns 4 and 5 indicate the rise and set times of the targets, respectively. The rise time refers to the time when the target rises to the observable elevation (\(15^{\circ}\)), and the set time is the time when it drops to the unobservable elevation (\(15^{\circ}\)). Column 6 lists the final visible time range of the targets within a 24-h period for the telescope array. These visibility time ranges were determined using a threshold elevation (\(\theta_{\text{el}}\)) of 15 degrees. The distribution of \(\theta_{\text{el}}\) across UT time is visualized on the system interface, as depicted in Figure 7.  
To assess the precision and practicality of the trajectory predictions generated by this module, it was utilized in target-observation tasks involving the radio telescope array from August 2022 to March 2024. The module was employed to generate a 24-h \(\theta_{\mathrm{el}}\) curve for each target. Subsequently, the telescope was directed to track the corresponding target at the designated times in conjunction with the telescope array control software [23]. The findings revealed that the actual observational outcomes were highly consistent with the computed predictions, which underscores the high computational accuracy and reliability of this method.
## 3 Results and Discussion
### Results of Problem 1  
The Pareto front related to the solutions of Problem 1 is represented in Figure 5a. Considering the whole amount of 75 optimized individuals (three runs with 25 individuals each), only 11 individuals satisfy the three conditions stated in Section 3.1, and their power vs. position profiles are reported in Figure 5b, highlighting with thicker lines the profiles relevant to particular individuals described in the following analysis. All the profiles, apart from the one denoted with the \"A\", exhibits the typical behavior of the series-series compensation where, when the mutual inductance, M, decreases, there is an initial increase in the transferred power and then a steep descent. This behavior requires the adjustment of the supply voltage of the transmitting coil, while the distance increases in order to avoid excessive solicitation of the coils and of the power converters. The characteristics of solutions A, B, C, and D of Figure 5 are summarized in Table 3, together with those of other individuals obtained from the optimization algorithm applied to the other two problems. Table 4 lists the reactances forming the CNs of the individuals characterized in Table 3.  
Considering the original requirement of transferring a power of 600 W, the individuals that originate the power profiles denoted with \"A\" and \"B\" are the most promising. The first one allows to transfer a power of approximately 0.97 kW that is quite constant without the need of adjusting the supply voltage from the aligned position to the maximum position, \(\mathrm{xp_{max}}\), of 100 mm. The second individual enlarges the range where it is possible to transfer the power, \(\mathrm{P_{L}(0)}\), up to \(\mathrm{xp_{max}}=173\) mm and limits the maximum transferred power to 1.76 times \(\mathrm{P_{L}(0)}\). This latter characteristic is highlighted in Figure 6, where \(\mathrm{P_{L,p\text{-u}}}\) is plotted  
When the transferred power must be maximized, individuals that give the profiles denoted with C or D should be selected. Individual C transfers a P\({}_{\text{L}}\)(0) = 3.15 kW and maintains this power capability up to \(\text{xp}_{\text{max}}\) = 123 mm with \(\text{P}_{\text{L,p-u-,max}}\), equal to 1.64. The individual D maintains the power capability of the aligned position up to \(\text{xp}_{\text{max}}\) = 151 mm, but at the expense of a lower \(\text{P}_{\text{L}}\)(0), equal to 2.25 kW, and a higher \(\text{P}_{\text{L,p-u-,max}}\), which reaches 2.27.  
The power transfer efficiency corresponding to individuals A, B, C, and D is plotted in Figure 7, and its value in the aligned position is reported in Table 3. As a general result, it can be noted that an increase in the transferred power causes a decrease in the efficiency and that the effect is stronger when the coils are misaligned.
# A Novel Approach to Multi-Provider Network Slice Selector for 5G and Future Communication Systems
## 5 Proposed Solution
### NSSF DAF: Network Slice Selection Function Decision-Aid Framework
Information about traffic conditions on slices, as well as extra options for configuring the structure, are available on a panel in the Graphical User Interface (GUI) or via Command Line Interface (CLI) when accessing the slice selection service. It is possible to combine more than one selection strategy or method, for example using genetic algorithms, fuzzy logic or multi-criteria decision methods. It is also possible to define which QoS parameters should be considered in the selection process.
## 3 Methodology
### Experimental Goals  
With the use case defined, our experiments aimed to evaluate how well each model could perform that task under varying conditions. The experimental focus of this work was on the comparative analysis of four open-source and one closed-source LLM on the presented use case. GPT-4 served as a performance benchmark due to its strong results in general and legal benchmarks such as Lawbench [16] and LaiW [18], making it an ideal baseline for assessing whether open-source models could achieve similar accuracy and reliability in compliance-oriented applications. Comparing open-source models with GPT-4 was crucial for several reasons:  
* Accessibility and cost. Open-source models are accessible and free to use, making them a viable option for organizations with limited resources. Demonstrating that they can perform comparably to GPT-4 can encourage wider adoption and innovation of open-source LLMs in the legal domain.
* Transparency and control. Open-source models allow organizations to understand and control the underlying mechanisms, ensuring better compliance with data privacy regulations such as the GDPR.
* Privacy and security. Open-source models can be deployed locally, mitigating the risks associated with transmitting data to third-party servers, which is a major concern with closed-source models.  
This study also examined the rate of improvement in open-source models when using prominent techniques for enhancing input context, namely, few-shot prompting [5] and Retrieval-Augmented Generation (RAG) [6]. Few-shot prompting and RAG were explored as techniques for improving model performance by providing additional contextual information. This approach is particularly valuable for GDPR-related tasks, where accuracy and context adherence are essential. Achieving this would be a significant step towards establishing LLMs as compliant legal assistants under regulations such as the GDPR.  
Figure 4: Use case for LLM application in document drafting for GDPR compliance.  
Alternative evaluation frameworks, such as LegalBench [17] and LawBench [16], were considered during the design of this study. However, these frameworks primarily evaluate tasks such as classification, extraction, and general legal reasoning, which are not directly aligned with the structured output and domain-specific requirements of GDPR documentation. The chosen framework emphasized criteria and tasks uniquely tied to GDPR compliance, ensuring alignment with the objectives of the study. By tailoring our evaluation to the specific needs of compliance tasks, we aimed to provide a more targeted assessment of LLM capabilities in this critical regulatory domain.
# A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting
## 3 The Architecture of Deep Learning Algorithms
### Convolutional Neural Network (CNN)  
CNN is considered to be robust for various applications, since it can learn high-level features from sequences without any human intervention [97]. The CNN algorithm is widely utilized in deep learning. The most notable advantage of CNN is its remarkable ability to capture non-linear information. CNN comprises three layers: the convolutional, pooling, and fully connected layers [98; 99]. The most significant component of CNN is the convolution layer, which consists of many convolution kernels used to construct new feature maps. Figure 4 depicts the architecture of CNN, which consists of convolutional, pooling, and fully linked layers [49]. The following tasks are assigned to each layer: The convolution layer is involved in local feature extraction, which is effective when the kernel weights are equally distributed across all input maps. The convolutional layers are utilized to extract spatial patterns from the underlying training sample and its related input variables. The mathematical formula for the convolutional layer is expressed via Equation (10).  
\[y^{k}\,i_{k}=f((w^{k}*h)i_{j}+b_{k}) \tag{10}\]  
where \(f\) represents an activation function, \({}^{*}\) represents a convolutional process operator, and \(w^{k}\) represents the weight of the kernel.  
A pooling layer is commonly utilized to minimize the in-plane dimensionality of input maps, hence minimizing the number of learnable parameters and preventing overfitting. Typically, pooling layers can be divided into two types: maximal pooling and average pooling. Both types are used in the procedures outlined, as shown in Equation (11):  
\[f\left(x\right)=\max\left(0,x\right) \tag{11}\]  
The fully connected layer is utilized extensively for high-level inference, since it is responsible for transferring the features filtered by the convolutional and pooling layers to the output layer. Next, linear activation and non-linear activation functions are employed for both layers. Finally, a full connection layer is added to the CNN structure to forecast the output using the extracted features [100]. CNN is commonly used in the development of data pattern-based filters and the extraction of hidden features. More importantly, the key  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Activation Function** & **Equation** & **Plot** \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 4: The architecture of CNN [49].  
characteristics of Convolutional Neural Networks include local connections and weight sharing [101, 102].
## 4 Results
### Contour Profile Inspection
#### 4.1.1 Form Deviation Analysis on Curve Profile  
Figure 27 outlines the trends in form deviation, showing the correlation with \(M_{\Delta}(k)\) values obtained from \(I_{d,C}(x,y)^{SG}\). The materials involved in the analysis include aluminum, nylon, delirin, and steel.  
Figure 26: Third signal slope.  
Figure 24: Third signal curve.  
Figure 25: Third signal line.  
It is crucial to highlight that \(M_{\Delta,C}(k)\) serves as an indicator for quantifying the form deviations in the curve profiles of these materials. A reduced \(M_{\Delta,C}(k)\) value indicates a closer adherence to the ideal curve, implying minimal form deviations. Conversely, a higher value suggests more pronounced deviations, potentially exceeding the tolerance limits.  
Aluminum, with a mean of \(M_{\Delta,C}(k)=0.1726\), exhibits the least form deviations among the materials under consideration. The results show that the curve profile of aluminum closely matches the ideal curve, reflecting a high degree of accuracy and precision in its manufacturing.  
Nylon, with a mean of \(M_{\Delta,C}(k)=0.7359\), shows moderate form deviations compared to aluminum. While nylon may have some imperfections in its curve profile, the deviation is still within an acceptable tolerance, and the material can be considered suitable for certain applications.  
With a mean of \(M_{\Delta,C}(k)=0.6067\), delrin falls within a similar range as nylon regarding form deviations. The results imply that delrin also exhibits a moderate level of accuracy in its curve profile, making it a viable option for specific applications where such deviations are acceptable.  
On the other hand, steel stands out for tolerance, with a relatively higher mean \(M_{\Delta,C}(k)\) value of 1.16, indicating more significant form deviations than the other materials. The results may be attributed to the inherent characteristics of steel or the specific manufacturing processes involved, which result in a curve profile that deviates noticeably from the ideal.  
In summary, aluminum demonstrates the least deviations, nylon and delrin exhibit moderate levels, and steel shows a higher degree of deviation.
# A Sampling-Based Unfixed Orientation Search Method for Dual Manipulator Cooperative Manufacturing
## 2 Previous Work and Paper Organization  
In our previous work, a novel method called the Sampling-based Operation Space Map Search (SbOSMS) method (picture c in Figure 1), which combines the map search method (picture a in Figure 1) with the time-sampling-based method (picture b in Figure 1), was proposed. Equivalently, a local planning map was established during each time interval, and an optimal local path was determined following the local planning map. All chronologically planned path segments are glued together to generate a collision-free path, which is highly effective at the cost of forgoing the global optimum.  
Figure 1: Schematic diagram of four different path-planning strategies for industrial manipulators. Picture (**a**) shows the planning strategy of map-based planning algorithms such as A*; Picture (**b**) shows the planning strategy of sampling-based planning algorithms such as RRT, PRM, and APF, etc.; Picture (**c**) is SOGSMS strategy which combines a map-based planning strategy and a sampling-based planning strategy to reduce both the search space and search time, as well as overcome the defect of random planning results for RRT; Picture (**d**) shows the proposal of this paper, which changes the node map in picture c into local-global sphere search space around EEF position point during every sampling period.  
When focusing on how to build a suitable search map during each sampling period, we find that the nodes constituting the search map are several specific points selected by a particular rule on the surface of the entire search space (usually a sphere space) in that sampling period (Figure 2). Therefore, calculating the optimal path points in the whole search space (sphere space) surface rather than merely the constructed search map will be the central focus of this paper's study, as shown in picture d in Figure 1.  
In this paper, a brief path-planning method review of the dual manipulator cooperative system is shown in Section 1. Section 2 provides a concise snapshot of our initial efforts on this issue and the limitations we encountered. Section 3 proposes a mathematical model for the non-linear optimization of dual manipulator path planning in detail. To convert all equation terms in the set of non-linear optimization equations into equations with the same independent variables in Section 4, we use spatial geometry to linearize the minimum distance prediction equation within a specific sampling period. Subsequently, in Section 5, spatial vector geometry is used to quickly solve the set of equations with the same independent variables that have been transformed in Section 4. Section 6 presents the simulation results and relevant analyses. Finally, in Section 7, some conclusions are offered.