esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### The Optimization Process Architecture  
According to Table 1, in total, 14 parameters are involved in BPMSM structural design. To realize efficient performance improvements, the optimization process architecture is designed to focus on those dominant parameters. In the optimization process, the involved parameters and searching areas are determined by taking the motor design standard as the criterion, and the introduction of parametric modeling provides a basis for efficient and feasible comprehensive performance optimization. The optimization flowchart is presented in Figure 2:  
* First, BPMSM performances, as the optimization objects and the structural parameter candidates to be optimized, are specified.
* Second, a sensitivity index is proposed to judge the effects of the different structural parameters on the optimized performance of the BPMSM to avoid contradictions in the optimization process, and the searching range is determined according to the working conditions of the motor and the difficulty of assembly.
* Third, the orthogonal test method [26] is used out to efficiently extract representative sample data.
* Then, the linear relationship between individual BPMSM performance and the chosen structural parameter is derived by using the gradient descent method for parametric optimization modeling.
* Finally, a dimensionless multi-structural optimization model is developed based on the regression model and solved using a GA.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Description** & **Symbol** & **Value** \\ \hline Pole number of torque winding & \(P_{1}\) & 1 \\ Pole number of levitation winding & \(P_{2}\) & 2 \\ Outer radius of rotor & \(r_{1}\) & 14.5 mm \\ Inner radius of rotor & \(r_{2}\) & 5.5 mm \\ Turn number of torque coil & \(N_{1}\) & 160 \\ Turn number of levitation coil & \(N_{2}\) & 520 \\ Height of yoke & \(h_{1}\) & 5.25 mm \\ Length of air gap & \(\delta_{0}\) & 3.75 mm \\ Thickness of rotor & \(h\) & 7 mm \\ Diameter of armature & \(D\) & 36.5 mm \\ Inner radius of stator yoke & \(r_{3}\) & 14 mm \\ Outer radius of stator yoke & \(r_{4}\) & 30.75 mm \\ Height of stator tooth & \(h_{\text{t}}\) & 54.26 mm \\ Width of stator tooth & \(b_{\text{t}}\) & 7 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical structural parameters of BPMSM.
# Labeling Expert: A New Multi-Network Anomaly Detection Architecture Based on LNN-RLSTM
## 2 Literature Review
### Supervised Anomaly Detection  
Supervised learning-based anomaly detection methods work mainly through machine learning methods to study and analyze the existing training set of completed labeled network data to find out the laws contained in it and how to apply this law for anomaly detection. Supervised methods require a label for each of the data points in the training dataset. They use this information to learn the differences between normal and outlier instances by either discriminative or generative algorithms. Theoretically, supervised abnormality detection is superior in overall accuracy due to a clear understanding of normality and abnormality. Neural network algorithms have proven to be robust and highly accurate in applications, such as the prediction of medical time-series data [23].  
Methods, such as Linear regression model parameter estimation [20], design and implementation of a network anomaly detection system based on weighted plain Bayes [24] and research on deep neural network video anomaly target detection [25], use linear regression, Bayesian algorithm and neural network, respectively, and these methods can effectively ensure the accuracy of anomaly detection in different scenarios. However, the method is extremely dependent on the training set, and the accuracy of the model is easily affected if there are unlabeled anomalies. Moreover, for online network anomaly detection, it is simply impossible to perform enough labeling, so the supervised learning network anomaly detection method is difficult to adapt to online network anomaly detection.  
Several practical problems greatly hinder its use. Firstly, in the context of big data, the labels of much of the data are unknown, which poses a challenge for supervised anomaly detection algorithms. Data labels are often unavailable or too costly to obtain, e.g., the labeling of network configurations is not completely clear unless the network is run and inspected. Secondly, data labels may be unbalanced. Often, in practical anomaly detection problems, the number of normal samples greatly exceeds the number of anomalous samples, which leads to prominent biases in the classification model and may degrade the performance of anomaly detection [26].
# Suitability of Graph Database Technology for the Analysis of Spatio-Temporal Data
## 4 Design and Architecture
### Database Technologies  
This section covers the underlying technologies used to implement the databases being benchmarked. This is important to note in terms of how portable each technology is, how difficult the configuration is before each database can be used for a given project, or limitations on performance or hardware requirements due to software requirements. While each database technology has varying capabilities, in terms of being supported for a given operating system, they each provide support for being deployed in a containerized environment.  
**PostgreSQL** is a classical object-relational database queried via SQL queries. It is built to be extensible with a variety of existing extensions for additional features such as spatial indexing and thus caters for different kinds of data. PostgreSQL is quite a mature product often used in industrial applications and has a large amount of support from the open-source community. PostgreSQL version 11 and for the spatial extension PostGIS version 2.5 are used in this investigation.  
**JanusGraph**[19] is a highly scalable graph database ready to be clustered between multiple machines. JanusGraph is largely based on the Apache tech-stack making use of the property graph model from Apache TinkerPop. The native language through which JanusGraph is queried is Gremlin, but it can also be extended for Cypher queries. JanusGraph can store graph data via three backends: Apache Cassandra, Apache HBase, and Apache Berkeley. The configuration of JanusGraph used in this paper is with Apache Cassandra as the storage backend and ElasticSearch as the search engine for spatial and temporal query support. JanusGraph version 0.4 is used in this investigation.  
**TigerGraph** is an enterprise level graph analytics platform developed with insights from projects such as Apache TinkerPop and Neo4j and provides advanced features like native parallel graph processing and fast offline batch loading, see Refs. [20; 21]. Unlike JanusGraph, TigerGraph was developed from scratch in order to effectively create the next generation of graph database technology. Some of the use cases explicitly mentioned by TigerGraph ([[https://www.tigergraph.com/solutions/](https://www.tigergraph.com/solutions/)]([https://www.tigergraph.com/solutions/](https://www.tigergraph.com/solutions/))) are in geospatial and time series analysis. TigerGraph is queried using the GSQL querying language. GraphStudio is a web interface which is packaged along with TigerGraph which provides an interface to write, install and visualize queries, design and export one's graph schema, and monitor database performance. TigerGraph version 2.5 is used in this investigation.
# Research on Trajectory Planning and Tracking Methods for Coal Mine Mobile Robots
From the analysis of the current situation, it can be found that most of the current trajectory planning and tracking studies are for ground robots, and there are few trajectory planning and tracking studies based on coal mine robots considering the special underground environment. In this paper, we propose a trajectory planning algorithm based on MINCO trajectory and safety corridors for tracked coal mine robots and underground environments, combine it with the MPC trajectory tracking method to complete the overall program design of trajectory planning and tracking for tracked CMRRs, and finally verify the feasibility of the program through experiments.
# Cloud-Edge Collaborative Model Adaptation Based on Deep Q-Network and Transfer Feature Extraction
## 4 Experiment
### Experimental Environment  
The model proposed in the study is implemented in Python (version: 3.12.7 (packaged by Anaconda, Inc., released on 4 October 2024)). The fusion module is based on the Faster R-CNN target detection framework, and training and reasoning are completed under the PyTorch(PyCharm 2024.3 (Community Edition), Build #PC-243.21565.199, built on 13 November 2024) framework. Edge deployment reasoning is equipped with an NVIDIA GeForce RTX 4070 GPU and an SM7 AI acceleration chip. The backbone network of the edge target detection model uses ResNet-50. The edge model freezes the backbone and only trains the parameters of the category score output head (score.weight and score.bias) to achieve local migration optimization.
## 7 Conclusions  
This paper proposes a novel code design, named 3D-SWZCC code, which uses three encoding schemes: spectral, temporal, and spatial for the first, second, and third components, respectively, with the aim of implementing the proposed code in an adapted 3D-OCDMA-PON network. The proposed code overcomes most of the obstacles, mainly the MAI effects and limited capacity, as well as the PIIN effect, which becomes lower using the developed code compared to other 3D systems that use PD, PD/MD, DCS/MD, and PTZCC codes. Additionally, the proposed 3D-SWZCC code offers good simulation results and satisfies the optical communication requirements by presenting a good quality factor of more than 6 dB and a low BER value of less than \(10^{-9}\). The suggested SMF length for NG-OCDMA-PON systems can reach up to 122 Km for a data rate of 0.622 Gb/s without implementing any amplifier, which enables each user to have a high data rate with long SMF lengths like 10, 30, and 50 Km. This is achieved due to the ZCC features, which can perfectly restrict the MAI influence, making the proposed code a prospective candidate for satisfying the optical communication requirements in NG-OCDMA-PON networks.  
Conceptualization, A.C. and T.M.C.; methodology, A.S.K.; software, J.M.H.B.; validation, I.D; formal analysis, O.A.; investigation, A.C.; resources, T.M.C.; data duration, A.S.K.; writing--original draft preparation, I.D.; writing--review and editing. A.C.; visualization, O.A.; supervision, A.S.K.; project administration, A.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This research is supported by The General Directorate of Scientific Research and Technological Development 'DGRST'-Algeria.  
The authors declare no conflict of interest.
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 4 Discussion
### Limitations and Future Work  
Despite the significant improvements demonstrated by our methodology, several limitations should be acknowledged. First, while our approach dynamically adjusts weights based on evaluation metrics, it does not explicitly model inter-regional dependencies that might affect load patterns across adjacent areas. Incorporating spatial correlation analysis could potentially enhance the weighting mechanism, particularly for regions with strong economic or climatic connections.  
Second, the current implementation uses a predefined set of evaluation indicators; exploring adaptive indicator selection based on specific grid characteristics could yield further improvements. Different grid systems may benefit from customized evaluation frameworks that emphasize indicators most relevant to their particular operational challenges.  
Third, our evaluation is based on data from the Eastern China power grid, and the applicability to other geographic areas with different characteristics requires further investigation. Testing the methodology across more diverse grid systems would provide valuable insights into its generalizability and potential areas for refinement.  
These limitations present several promising avenues for future research:  
1. Integration of extreme weather event prediction models with load forecasting to enhance grid resilience: Combining advanced meteorological forecasting with our weighted fusion methodology could significantly improve prediction accuracy during critical weather events when grid stability is most challenged.  
2. Application of explainable AI techniques to increase transparency in the fusion process: Enhancing the interpretability of weight determination would build operator trust and provide deeper insights into the factors driving regional forecast reliability variations.  
3. Development of adaptive weighting schemes that can continuously learn and evolve based on performance feedback: Implementing reinforcement learning approaches could enable the system to autonomously refine weights as regional forecasting capabilities change over time.  
4. Extension of the methodology to incorporate emerging data sources such as electric vehicle charging patterns and behind-the-meter generation: As distributed energy resources continue to grow, integrating these new data streams will become increasingly important for comprehensive load forecasting.  
5. Integration with emerging edge computing architectures to enable more distributed fusion approaches: Leveraging edge intelligence for local processing could reduce communication latency and enhance system resilience, particularly relevant for large-scale implementation across extensive grid networks.  
These future directions demonstrate the potential long-term impact of our methodology beyond its immediate application, providing a roadmap for continued advancement in grid-level forecast integration in increasingly complex and distributed power systems.
# Dynamic Compensation of a Fluxgate Magnetometer Based on a Hybrid Optimizing Algorithm
## 3 Simulation and Signal Test
### Simulation Results and Discussion
#### 3.2.1 Verification of the Step Magnetic Field Compensation Effect  
The dynamic compensator obtained from Equation (10) is utilized to compensate for the rising edge signal. The output signal after the dynamic compensator is shown in Figure 14. The time domain dynamic performance index of the magnetometer before and after the dynamic compensator is shown in Table 4.  
Figure 12: GA, SA, and GA-SA algorithm simulation comparison: (a) fitness convergence curve; (b) compensated response curve.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Overshoot/\%** & **Response Time/ms** & **Fitness** \\ \hline Before dynamic compensation & — & 10.70 & \(4.98\times 10^{-3}\) \\ \hline After GA compensation & 0.35 & 4.20 & \(2.93\times 10^{-4}\) \\ \hline After SA compensation & 0.92 & 2.10 & \(1.10\times 10^{-4}\) \\ \hline After GA-SA compensation & 0.17 & 1.95 & \(8.21\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: GA, SA, and GA-SA algorithm performance comparison.  
Figure 13: Magnetometer input and output of the rising edge of the square wave.  
As shown in Figure 14 and Table 4, the response time is shortened from 10.91 ms to 0.63 ms (calculated by 2% steady-state error), and the response time is shortened to 5.5% of that before dynamic compensation. Although there is a small overshoot in the step, the compensated waveform is basically consistent with the input signal. In addition, the error of the compensator output is much less than the magnetometer output, which is from 22932.55 nT to 231.74 nT. It shows that the output signal can quickly follow the changes in the input magnetic field under the step response signal excitation, which is a typical system identification and compensation of the excitation signal. Since the step signal contains all the frequency components, the fast response result to the step input signal can prove the validity of the dynamic compensation method.
# MCCM: An Approach for Connectivity and Coverage Maximization
###### Abstract  
The internet of Things (IoT) has attracted significant attention in many applications in both academic and industrial areas. In IoT, each object can have the capabilities of sensing, identifying, networking and processing to communicate with ubiquitous objects and services. Often this paradigm (IoT) using Wireless Sensor Networks must cover large area of interest (AoI) with huge number of devices. As these devices might be battery powered and randomly deployed, their long-term availability and connectivity for area coverage is very important, in particular in harsh environments. Moreover, a poor distribution of devices may lead to coverage holes and degradation to the quality of service. In this paper, we propose an approach for self-organization and coverage maximization. We present a distributed algorithm for \"Maintaining Connectivity and Coverage Maximization\" called \(\mathit{MCCM}\). The algorithm operates on different movable devices in homogeneous and heterogeneous distribution. It does not require high computational complexity. The main goal is to keep the movement of devices as minimal as possible to save energy. Another goal is to reduce the overlapping areas covered by different devices to increase the coverage while maintaining connectivity. Simulation results show that the proposed algorithm can achieve higher coverage and lower nodes' movement over existing algorithms in the state of the art.  
coverage maximization; connectivity; wireless sensor networks (WSNs); heterogeneous devices; Internet of Things (IoT) +
Footnote †: journal:  
_Article_
# Modeling and Simulation of Sky Survey
## 3 Proposed Methods
### Assumptions and Simplifications  
The sky survey scheduling optimization is characterized by many variables and many constraints, and the optimization model contains a huge solution space, which is a non-deterministic polynomial hard problem [37]. In order to simplify the model by mainly studying the important constraints and ignoring the secondary constraints, this paper makes assumptions about some of the constraints in the problem to facilitate our study of the problem.  
* Only the smallest sky area is accurately observed for each observation, and there is no misalignment of observations, which means that the satellite CMG can be accurately positioned on the smallest sky area block of the observation;
* It assumes satellites meet data storage and total energy limits (CMG energy limits are not included);
* Assuming that the steady image time is already within the CMG rotation time, no additional steady image time is calculated;
* It is assumed that there are no sudden events that affect camera imaging during the simulation time, such as high-energy rays from solar activity.
# Mining Pool Selection under Block WithHolding Attack +
## 2 Related Work
### Mining Pool Selection under BWH Attack  
The authors in [25] investigated the process of mining pool selection under BWH attack. In this study, they also used the evolutionary game theory to model the mining pool selection process of miners. However, they focused on this problem from the perspective of mining pool managers and formulated the rewards of mining pools instead of miners, which is quite different from this paper. In addition, the authors did not consider the revenues from attacked pools in the formulation, which may result in less accurate and convincing insights into the problem and make the incentive of launching the BWH arguable. In addition to the minimum required hash power and the size of blocks to be mined, the authors also considered the population of attackers as an additional strategy parameter. They investigated the properties of the population states and described how pool managers can change the mining strategies to drive the population of miners to stable states. The authors in [38] considered a scenario where miners can select to join an attacking pool to perform honest mining or the BWH attack to another pool, and modeled the selections between miners and attacking pools by indicator variables. A novel anti-attack mining revenue optimization algorithm was proposed to determine the pool selection so as to improve the group revenue of the attacking pools. In [39], the BWH game among multiple mining pools was modeled as a stochastic game and the reinforcement learning techniques were applied to analyzed the game. During the game analysis, the pool selection of miners was also considered, where each miner randomly chooses the pool to join based on the attractiveness of the pools. In [40], the authors investigated the power splitting problem of a miner under the BWH attack from the game-theoretic perspective, where a miner can choose to devote its mining power to one pool or split its mining power among multiple pools to maximize its payoff. The power splitting game can be regarded as one variant of the mining pool selection problem.
## 3 Performance Evaluation  
### The Effect of the Prediction Mechanism  
We evaluate our scheme through simulations implemented in the LTE-Sim [17] simulator. LTE-Sim can provide a thorough performance verification of LTE networks. We also utilize Video Trace Library [18] with LTE-Sim to present real-time streaming video for network performance evaluations. The simulation parameters are summarized as Table 1.  
The accuracy of handover prediction affects the pre-scheduling time (\(t_{r}\)) for BL and EL transmission rate. In Figure 4, as user equipments (UEs) velocity is 30 km/h and the actual TTT of handover is 79.924 s, we can have an error rate smaller than 0.8% while the prediction is made after 59 s. On the other hand, as UE velocity is 120 km/h, the actual TTT of handover is 25.981 s and the error rate can be contained smaller than 0.5% as the prediction is made after 15 s. Faster UE results in shorter pre-scheduling time for transmissions accordingly. On the contrary, more pre-scheduling time can be used for transmissions. Therefore, we can adaptively trigger the pre-scheduling procedure and adjust the transmission rates between BL and ELs with limited resource.  
\begin{table}
\begin{tabular}{c c} \hline \hline Parameter & Values \\ \hline Simulation Time & 100 s \\ \hline Simulation Rounds & 100 \\ \hline Cell Radius & 1 km \\ \hline Number of User Equipments (Ues) (per cell) & 30, 40, 50 \\ \hline Velocity of UEs (km/h) & 30, 120 \\ \hline UE Application Flows & Video Application \\ \hline Modulation and Coding Scheme & QPSK, 16 QAM, 64 QAM \\ \hline Number of Cells & 7 \\ \hline Number of eNodeB & 7 \\ \hline Bandwidth & 10 MHz \\ \hline Number of PRBs & 50 (12 Subcarriers/PRB) \\ \hline \multirow{3}{*}{Transmission Time Interval} & 0.5 ms/slot \\  & 1 ms/subframe \\ \cline{1-1}  & 10 ms radio frame/TTI \\ \hline Mobility Model of UEs & Random Direction \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of simulation.  
Figure 4: The prediction of time-to-trigger (TTT) of handover. (**a**) User equipments (UEs) velocity = 30 km/h and (**b**) UE velocity = 120 km/h.