esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Influence of Bridge Saturation on Air-Gap Magnetic Field with Mixed Eccentricity  
In order to analyze the influence of bridge saturation on the air-gap magnetic field distribution with mixed eccentricity, the radial spatial component of air-gap flux density was calculated with the analytical model without considering saturation. Specifically, the calculation was performed by ignoring Equation (16) while solving Equations (17) and (18). Then, the radial un-symmetry \(USYnr\) and the radial total harmonic distortion rate \(THDnr\) were further calculated for the case without considering bridge saturation and were then compared with the results calculated in Section 3.2.1 in which the bridge saturation was considered, as shown in Figure 10.  
Figure 10: The influence of the bridge saturation on \(USYnr\) and \(THDnr\): (**a**) \(USYn\); (**b**) \(THDnr\).  
Figure 9: The influence of mixed eccentricity on the values of \(USYt\) and \(E_{OI}\): (**a**) \(USYt\); (**b**) \(E_{OI}\).  
It can be seen from Figure 10 that the values of \(USYnr\) are lower than those of \(USYr\) when the degree of mixed eccentricities are low. In other words, when the analytical model does not consider the bridge saturation, the calculation results show that the eccentricity has little effect on the magnetic field. So, it is difficult to identify the characteristics of the early stage of eccentricity fault. When the degree of mixed eccentricity is high, the increase in \(USYnr\) is greater than that of \(USYr\). That is, it seems that the impact on the magnetic field is greater than in reality when the model without considering the bridge saturation, which will cause misjudgment of the degree of eccentricity. In addition, we found that the bridge saturation has little effect on \(THDnr\).
# An Overview of Design Techniques for Two-Dimensional Leaky-Wave Antennas
## 3 Fabry-Perot Cavity Antennas  
This section deals with the design of FPCAs. While the theoretical working principles and design workflow for these radiating devices are presented in Section 3.1, the numerical techniques needed for describing such FPCAs are reported in Sections 3.2 and 3.3. The possible realizations of a feeder for such devices are then reported in Section 3.4 and the implementation of an efficient and fast full-wave simulation of FPCAs is shown in Section 3.5.
# Research on Coal and Rock Recognition in Coal Mining Based on Artificial Neural Network Models
## 2 Coal and Rock Image Acquisition and Image Feature Extraction  
### Collection of Coal Image and Rock Image Samples in Coal Mining Area  
The application of deep learning in image recognition requires a large number of image samples as support [13]. This paper designs an image acquisition system for a coal mining area, as shown in Figure 1.  
Illumination is an important factor affecting image quality and parameters. Even for the same piece of coal or rock, the characteristic parameters extracted under different illumination conditions are not the same [14]. The illumination conditions in coal mines are poor and uneven, so an adjustable light source is designed to fill the mining area, and the illuminance value of the surface of the mining area is measured in real time using an illuminance meter. In this paper, 7000 Lux is set as the standard illumination value when the sample image is collected, and the adjustable light source is used to ensure the unity of the experimental background conditions. After ensuring that the illumination value is unchanged, a mine explosion-proof camera is used to take pictures of the coal mining area to obtain the sample pictures.  
The experimental site is the 52603 fully mechanized mining face of the Tashan Coal Mine in Shanxi Province, with a total length of 135 m. The mine explosion-proof camera is used to video capture the mining area of the whole fully mechanized mining face. The captured video is intercepted every 1 s, and a total of 1860 pictures are obtained. Then, the sample maps that meet the experimental requirements are selected, and the number of samples is expanded by image cropping. A total of 270 sample maps that meet the experimental requirements are obtained. The acquired sample images are classified into coal seam images, rock stratum images, and coal-rock mixed-layer images. At the same time, these pictures are numbered. Some samples are shown in Figure 2. Finally, the sample images are randomly divided into training sets and test sets in a ratio of 2:1 for deep learning training.  
Figure 1: Coal mining area image acquisition system.
# Assessment of GNSS Galileo Contribution to the Modernization of CROPOS's Services
## 1 Introduction
Data processing at the system's side uses the mathematically optimal Kalman filter technique to process data (observations) from reference stations in the network, in that way modelling all relevant error sources [20]. Once the network errors are computed at the reference station, distance-dependent errors need to be interpolated at the location of the user receiver [10]. The main purpose of the VRS station is to reduce the baseline distance between the rover and the reference station to facilitate the efficient removal of the spatially correlated errors using differential processing and to incorporate error corrections obtained from the network [21]. One of the core issues of NRTK is how to interpolate the distance-dependent biases generated from the network to the user's (rover's) location. Over the years, several methods were proposed for the interpolation of the distance-dependent residual biases [17]. The network solutions increase the reliability and productivity of ambiguity resolution and the positioning accuracy of rovers working in the system [22]. As outlined by [PERSON] and [PERSON] [23], the overall procedure for NRTK positioning methodology consists of three steps: (1) the processing of the reference network GNSS data to derive the network corrections, (2) the interpolation of ionospheric and tropospheric corrections for the user location, and (3) user solution position determination with the application of the network-derived corrections. Current techniques (e.g., Network-RTK) are based on Local and Regional Networks of GNSS Reference Receivers, with inter-distances in the order of 70 km. Such networks are commonly managed by Land Administration or Geodetic Authorities [24]. That is the case with CROPOS (Croatian Positioning System)--a national permanent GNSS network in Croatia which was modernized and upgraded in 2019 to provide support for the European Galileo satellite system. The assessment of the Galileo satellite system's contribution to the performance of CROPOS's services is the core of this article.
# Imaging Microstructure on Optically Rough Surfaces Using Spatially Resolved Acoustic Spectroscopy
## 4 Discussion and Conclusions
### Challenges Due Roughness  
Of the materials presented, L-PBF presents the biggest challenge from the small-scale roughness perspective (Figure 12c). However, it is important to note that the optimisation of the L-PBF process to reduce surface roughness is an ongoing research domain, and within the last few years clear progress has been made. Therefore, it is realistic to foresee a future where the ever-increasing capability of detector systems and smoother as-built surfaces of L-PBF mean that measurements on unprepared parts become routine.  
In addition, the surface roughness of L-PBF components are lower than any other powder based-technique [45]. The results presented in this paper deal only with L-PBF and not other varieties of powder-bed manufacture, such as selective laser sintering (SLS), but it is reasonable to assume the roughness seen in SLS components would prove a greater challenge to SRAS measurements.  
The dropouts are primarily caused by loss of detection light and so multiple scans can be combined and stacked to reduce this effect as discussed earlier. Another approach is to broaden the detection laser spot into a line or a number of spots spaced wider than the correlation length of the roughness so that some light will always return to the sample for each scan location.
# Rapid Synthesis of Silver Nanowires in the Polyol Process with Conventional and Microwave Heating
## 2 Materials and Methods  
### Chemicals  
Silver nanowires were synthesised using polyols with different molecular weights and lengths of aliphatic chains: 1,2-ethanediol p.a. (Chempur); 1,3-propanediol 99% (Alfa Aesar); 1,5-pentanediol, 97% (Alfa Aesar); 1,4-butanediol, +99% (Acros Organics); and 1,3-butanediol, 99% (Acros Organics). AgNO3, p.a., (POCH), polyvinylpyrrolidone (PVP) K30 MW 40 kDa pure (Carl Roth GmbH) and CuCl2 2H2O p.a. (POCH) were used as a precursor, capping agent and mediating agent, respectively. All chemicals were used without additional purification. Figure S1 (Supplementary Materials) presents a graphical relationship between dynamic viscosity and temperature for each applied polyol. The data required for drawing the graphs were prepared using an Arrhenius-type equation from the commercially available software ChemCAD(tm):  
\[\eta=\exp\left(A+\frac{B}{T}+C\ln(T)\right) \tag{1}\]  
whereby \(A,B\) and \(C\) are individual constants dependent on the polyol used and \(T\)-temperature. It can be noticed that the viscosity of polyols depends on the molecular weight, length of the aliphatic chain and its structure. Table S1 (Supplementary Materials) shows the dielectric constant and \(tg(\delta)\) values for the applied multi-hydroxyl alcohols [37; 38; 39; 40].
## 2 Materials and Methods
### The Voxel Grid and the Approach to Capture Data
The overarching goal is to create a comprehensive and accurate representation of an environment, leveraging the strengths of each agent while overcoming individual limitations. The centralised communication method, utilising a server or broker, offers several benefits, including improved coordination among agents, simplified data package management, and a clear focus on specific goals for the agents. However, these advantages come with trade-offs in three primary areas:

* Agents must be predefined and registered with the central coordinator before joining the colony or domain. * Errors occurring within the system may propagate throughout the entire colony. * As the number of agents increases, the centralised system must manage vast amounts of data to maintain coordination among the expanding agent population. Contemporary efforts by [28] propose a hash-graph-based framework for common knowledge. Although there is a recognised need for a common knowledge base in the research space, no consensus has been reached on the ideal solution. Referring back to [28], an early consideration was the use of blockchain for a common knowledge base. One identified weakness of this idea revolved around the \"majority attack\", in which a single entity controls 50 percent of the network and, consequently, dictates the entire blockchain. This issue undermines a crucial aspect of transitioning to decentralised communication. While the proposed approach in this paper addresses this problem, the transition-based approach overlooks the potential to examine all the data in the environment. By only recording data deemed useful or necessary to share, it misses opportunities to identify trends in areas where no change has occurred. In contrast, the voxel grid approach presented in this paper offers a more generalised solution, enabling agents to observe trends even in areas with no observable changes. This is achieved by storing historical information inside each voxel that builds over time. The Algorithm 4 shows the communication approach used in this paper. ```  Cluster objects in voxel grid at time T Remove outliers from object data Alingment for For each time step Tdo  Cluster image voxel gets for different objects Define centroid for objects Measure the Euclidean distance from the agent position to the centroid Measure the Euclidean distance from the agent perspective to any visible centroids Communicate with other local agents if that agent can view the position of the current agent.
# Power Efficient Random Access for Massive NB-IoT Connectivity
## 2 Literature Survey Random Access in NB-IoT  
Low cost, low power, reliable and robust applications are becoming popular for services like traffic surveillance, military sensing, industrial automation, manufacturing, environment monitoring, medical services and physical security [11; 12]. NB-IoT has been specifically introduced to provide low-cost, low-power and wide-area cellular connectivity [1]. It is designed to operate at 180 kHz system bandwidth, that corresponds to one physical resource block (PRB) in the legacy LTE transmission [13]. Its design is based on existing LTE functionalities. However, the 180 kHz bandwidth presses for several simplifications and modifications over existing procedures [1]. To this regard, a novel NPRACH is designed for NB-IoT. In this section, we first delineate the novelty of NPRACH while highlighting the repetition mechanism. Subsequently, we present a concise review of existing literature on random access in NB-IoT.
## 3 Existing Pathfinding Techniques
### Metaheuristic Approach Algorithms
#### 3.3.2 Ant Colony Optimization (ACO)  
Ant Colony Optimization was first introduced in 1992 by [PERSON] [80]. As the name suggests, this Algorithm is based on the way of communication between ants in a colony [81]. These colony ants make use of chemical signals called pheromones, helping them to communicate trails from their nest to a food source [82].  
Figure 14 represents the general flowchart for ACO. In the first stage, the trail of pheromone is initialized. The second stage involves iteration in which, through probabilistic state transition rule, a complete solution is constructed by every ant to the problem [83]. The pheromone state determines the state transition rule. Once a solution is generated by the ants, another rule of global pheromone is applied. It involves two phases:  
1. Evaporation phase wherein a pheromone fraction is evaporated.  
Figure 12: **Top left: crossover point, top right: exchanging genes among parents, bottom: new offspring.**  
Figure 13: Mutation: before and after.  
2. Reinforcement phase wherein an amount of pheromone is deposited by every ant which is proportional to the fitness of solution. The process is repeated until halting criteria is achieved.  
Even if we place obstacles across their path, the pheromone will help them in discovering a minimum cost path which takes less time. Similarly, based on this concept of natural behavior, this popular Algorithm can be applied for pathfinding applications in video games [84].
# An Efficient Delay Tolerant Networks Routing Protocol for Information-Centric Networking
###### Abstract  
Delay tolerant networks (DTN) is a good candidate for delivering information-centric networking (ICN) messages in fragmented networks due to disaster. In order to efficiently deliver ICN messages in DTN, the characteristics of multiple requester nodes for the same content and multiple provider nodes for the same request should be used efficiently. In this paper, we propose an efficient DTN routing protocol for ICN. In the proposed protocol, requester information for request packet, which is called an Interest in ICN, is shared by exchanging status table with requested Data ID, requester ID, and satisfaction flag, where satisfaction flag is defined to show the delivery status of Data, so that unnecessary forwarding of Data is avoided. Data is forwarded to a more probable node by comparing average delivery predictability to a set of requesters. Performance of the proposed protocol was evaluated using simulation from the aspect of Data delivery probability and Data overhead, for varying buffer sizes, number of relay nodes, and time-to-live (TTL) of Data. The results show that the proposed protocol has better Data delivery probability, compared to content distribution and retrieval framework in disaster networks for public protection (CIDOR) and opportunistic forwarding (OF) protocols, although there is a tradeoff from the aspect of Data overhead for varying buffer sizes and number of relay nodes.  
delay tolerant network; information-centric networking; delivery probability; overhead; ONE simulator +
Footnote †: journal: Electronics
# Feasibility Study of Passive Bistatic Radar Based on Phased Array Radar Signals
## 2 Overview of Experimental PBR System
### Hardware of the PBR System  
The receiving antenna we designed consists of a reference antenna and a surveillance antenna. Figure 2 shows the photographs of the reference antenna. A Yagi antenna with 30 elements is chosen as the reference antenna due to its good directivity and simplicity, which is used to receive the direct  
Figure 1: Geometry of the passive bistatic radar (PBR) system.  
wave signal to estimate the detailed parameters of the transmitted signal. The Yagi antenna has a gain of 13.5 dBi and a half-power beam width (HPBW) of 14\({}^{\circ}\). The surveillance antenna is used to receive the scattered wave signal. Since the scattered wave is extremely weak relative to the direct wave, it requires a high gain and the ability to cover a wide range of airspace. Therefore, we use a phased array antenna, which has a total of 64 array elements with 8 rows and 8 columns in triangle arrangement; each array element has a gain of 9 dBi and is circularly polarized.  
In order to sample the signal from reference antenna and surveillance antenna simultaneously, a high performance Analog-to-Digital Converter (ADC) chip EV10 AQ190 of E2V company was applied in the sampling module. The highest sampling frequency of the chip is 5 GHz, and the 3 dB bandwidth is 3 GHz. A high performance signal processing platform based on VPX is utilized to deal with large amounts of real-time data. Figure 3 shows the photographs of the high performance sampling module and signal processing platform based on VPX.
# Similarity-Based Decision Support for Improving Agricultural Practices and Plant Growth
## 3 Results
### Sensitivity Analysis of Input Variables
#### 3.1.2 Disadvantages  
The KNN method struggles with large datasets, as it requires computing the distance from the selected point to all other data points. As the dataset grows, the time needed to compute recommendations increases accordingly.  
While ANN-LSH improves speed and scalability, it may sometimes yield approximate rather than exact neighbours. This approximation can overlook subtle distinctions between farmers that would otherwise be captured by KNN.  
Both algorithms rely on the availability and accuracy of the input data. If inconsistencies exist in the dataset, they can negatively affect the quality of the resulting recommendations [47].  
Although KNN and ANN-LSH can effectively identify similar profiles, they may struggle to capture complex interactions, such as the combined effects of soil type and climate conditions on crop yield.  
KNN provides a practical, easy-to-implement tool for recommending practices based on similarities between farmers. However, it performs best with clean, structured data and when applied to datasets of manageable size. When combined with other analytical methods or integrated into a broader system, KNN can significantly enhance decision-making, making it a valuable asset.  
While the current implementation relies on synthetic and static input data, ANN-LSH offers the computational efficiency required for future real-time or large-scale deployment. These comparative advantages are summarised in Table 7.
## 3 Experimental Results
### Models Comparison  
The comparison results between the proposed model and other models are shown in Table 1. In this paper, some state-of-the-art models are used to test the superiority of the proposed model (DSTP-FC), which are introduced as follows. LSTM [29]: The problem of RNN gradient disappearance can be avoided and the long-term dependence in series learning can be captured. CNN-LSTM [21] used CNN to extract the actual characteristics and spatial correlation from the input data, and used LSTM to predict the future PM\({}_{2.5}\) concentration. LSTM-FC [20] used the spatial combination based on fully connected neural network to integrate the prediction results of neighboring sites, and the final PM\({}_{2.5}\) prediction result of the central site is given. DA-RNN [22]: Attention mechanism is introduced not only in the input stage of decoder but also in the encoderstage. The encoder stage attention mechanism realizes the function of feature selection and timing dependence.  
The results in Table 1 show that the comprehensive result of LSTM-FC is better than that of LSTM, which shows that the method of using a fully connected neural network to integrate the prediction results of neighboring sites is effective. The comprehensive results of DA-RNN model are better than LSTM, LSTM-FC and CNN-LSTM models. It shows that the introduction of an attention mechanism in encoder and decoder can better grasp the timing dependence. At the same time, the comprehensive results of the proposed DSTP-FC model in this paper are better than that of DA-RNN, which shows the effectiveness of the proposed model.  
To show the performance of the proposed method, the prediction results of PM\({}_{2.5}\) concentration at the central site from 1 November 2014 to 30 November 2014 based on the proposed model are shown in Figure 4, where the deviations between the prediction of the model and ground truth increase with the increase of prediction time. The reason to select this time frame to visualize model prediction is that the change of the PM\({}_{2.5}\) concentration in the winter of China is very big. There are several sudden change points of PM\({}_{2.5}\) concentration in the figure, and it can be seen that PM\({}_{2.5}\) concentration changes obviously. For example, the PM\({}_{2.5}\) concentration decreases by nearly 187 \(\mathrm{\SIUnitSymbolMicro g}/\mathrm{m}^{3}\) in one hour, but the proposed DSTP-FC model can give out accurate prediction results. To further show the performances of the PM\({}_{2.5}\) concentration prediction using different methods, one scatter plot is shown in Figure 5. Here, only the eighteenth hour prediction of the PM\({}_{2.5}\) concentration for a total of 100 h is given out, to make the paper more readable. It is easy to see that the performance of the proposed model is better than other methods.