esa-sceva/satcom-chunk-collection huggingface dataset:
# A Space-Interconnection Algorithm for Satellite Constellation Based on Spatial Grid Model
## 3 Experiments and Results  
### Experiment Design  
According to the proposed methods, the following three sets of experiments were designed: (1) aerospace grid indexing BigTable, (2) satellite visibility analysis, and (3) satellite routing planning. Considering the large number of satellites in the low-orbit satellite constellation, the experiments used  
Figure 8: Dynamic routing planning method: (**a**) routing method; (**b**) topology of time-slice based on grids.  
the simulated Walker constellation, with a configuration of 12000/200/2, that is, 200 orbitals, each with 60 satellites. The \(satellite_{ij}\) is the \(j_{th}\) satellite in the \(i\)th orbit of the constellation. The simulation ran from 04:00 26 February 2020 UTC to 04:00 27 February 2020 UTC (Coordinated Universal Time). The STK software was used to generate the simulation constellation, and the J2000 coordinate system was selected in STK.
## 2 Measurement Principle and Setup  
A schematic of the proposed fluidic birefringent sensor (FBS) is depicted in Figure 1. Its design includes inserting a KTP thin plate in a cylindrical glass cell. When an incident \(s\)-wave is parallel to the \(n_{z}\)-axis of KTP plate, the phase difference between the two orthogonal \(p\)- and \(s\)-waves of the probe light is represented by  
\[\phi=\frac{2\pi}{\lambda}\cdot t\cdot\left(\sqrt{n_{z}^{2}-n_{i}^{2}\cdot\sin^{ 2}\!\theta_{i}}-\sqrt{n_{y}^{2}-n_{i}^{2}\cdot\left(\frac{n_{y}^{2}}{n_{x}^{2} }\right)\sin^{2}\!\theta_{i}}\right)\,,\]  
where \(\gamma\) is the wavelength of the probe light, \(t\) is the thickness of the KTP, \(n_{i}\) is the refractive index of the liquid medium and \(\theta_{i}\) is the incident angle. The KTP is a biaxial birefringent crystal. There are three different refractive indices represented by \(n_{x}\), \(n_{y}\) and \(n_{z}\). The inlet and outlet are connected by a plastic tube. The fluidic liquid is transported by a tubing pump. The phase variation \(\Delta\phi\) of the probe beam is dependent on the incident angle and relative refractive index between the KTP plate and the liquid. The liquid's RIs are also dependent on the concentrations of mixed contents. Therefore, the liquid concentrations are measured according to the phase variations.  
Figure 1: Device structure and photo of a fluidic birefringent sensor (FBS).  
Figure 2 represents the schematic diagram of a measurement system for fluidic liquid concentrations. A 632.8 nm He-Ne laser (LS) is used as a probe light in the system. A linear polarizer (PL) and a half-waveplate (HWP) are used to control the probe light with equal power for both orthogonal polarizations. The polarized probe light through an objective lens (L1) is launched into a waveguide-type Zn-indiffused phase modulator (ZIPM) [24]. The sinusoidal voltage from a function generator (FG) is applied on the ZIPM for the homodyne modulations. The output beam is focused through a lens (L2), and a pinhole (PH) is used to filter out the scattering light. The probe beam passes through the FBS, an analyzer (AL), a cylindrical lens (CL) and a photodetector (PD). The tested liquids are injected in the FBS by the tubing pump (TP). The received voltage signals through the PD are connected to a multi-data acquisition module (PXI); the procedure employs a LabVIEW (Version 2010, National Instrument, Austin, TX, USA) platform [24] performed on a personal computer (PC). The measured phase data is saved and shown on the display. In common-path homodyne interferometry, the received interferometric intensity applied with a sinusoidal phase modulation. The modulated interferometric signal is analyzed by a fast Fourier transform (FFT) scheme. The spectrum, with different harmonic signals, can be used for calculating phase information during measurements.
# STPA-RL: Integrating Reinforcement Learning into STPA for Loss Scenario Exploration
## 6 Threats to Validity  
Threats to the internal validity of the STPA-RL approach, such as biases in system modeling, are carefully addressed by ensuring a thorough analysis of the system's control structure and validating the RL models against real-world scenarios and operational data. During the initial implementation of the RL environment, we found that it is influenced by the contents analyzed by STPA and the control algorithms constructed, which can lead to an inaccurate environment. However, performing STPA analysis during the conceptual design phase enables the discovery of potential vulnerabilities through RL, facilitating the proactive identification of such issues and enhancing the generalizability of results. This process involves iterative testing and refinement to closely align the model with actual system behavior.  
Threats to external validity, including the generalization problem due to the empirical study's size, are acknowledged. Although STPA-RL was applied on the small scale of the PSD system, it provided sufficient research findings. Yet it is crucial to consider the target system's characteristics and environment. Conducting further validation and experiments on larger-scale systems is indispensable for ensuring external validity in practical applications. This method effectively reduces biases in system modeling and strengthens the validity of the findings through iterative testing and refinement of the model to closely mirror actual system behavior.
# Research on High-Efficiency Routing Protocols for HWSNs Based on Deep Reinforcement Learning
## 5 Algorithm Simulation and Result Analysis
### Simulation Experiment of Total Energy Consumption in HWSNs  
As shown in Figure 9, six algorithms were executed with the same initial parameters for a maximum of 1800 rounds to observe how the total energy consumption of heterogeneous wireless sensors changes with the number of rounds. By the time 900 rounds were completed, the energy consumption of the DEEC, NCOGA, EEHCHR, and 2L-HMGEAR algorithms had all exceeded 40,000 mJ. The second-best performing SEP algorithm also reached an energy consumption of 40,000 mJ after approximately 1000 rounds. It can be observed that as the number of operational rounds increases, the total energy consumption  
Figure 8: Comparison of average remaining energy of nodes in HWSNs.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & **Average** & **Average** & **Average** & **Average** & **Average** \\  & **Remaining Energy** & **Remaining Energy** & **Remaining Energy** & **Remaining Energy** & **Remaining Energy** \\  & **at 500 Rounds** & **at 600 Rounds** & **at 700 Rounds** & **at 800 Rounds** & **at 900 Rounds** \\ \hline EEHCHR & 94.47 & 64.754 & 41.895 & 28.111 & 18.696 \\
2L-HMGEAR & 74.1528 & 41.648 & 21.203 & 11.391 & 5.909 \\ NCOGA & 52.647 & 30.94 & 16.57 & 6.72 & 1.631 \\ DEEC & 58.44 & 31.002 & 16.912 & 10.675 & 6.478 \\ SEP & 131.190 & 93.780 & 63.691 & 40.212 & 24.038 \\ HDQN & 128.635 & 94.514 & 66.263 & 45.641 & 31.338 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation data of average remaining energy of nodes in HWSNs (unit: mJ).  
of the networks using the DEEC, NOOGA, 2L-HMGEAR, EEHCHR, and SEP algorithms rises sharply. This can be attributed to the fact that the proposed HDQN algorithm, designed to optimize energy efficiency in HWSNs, comprehensively considers the energy variability among different heterogeneous nodes. Additionally, it utilizes improved deep reinforcement learning to optimize the selection of information transmission paths in real time.  
The specific data from the simulation experiment comparing the total energy consumption of HWSNs are presented in Table 4. A lower total energy consumption indicates better energy efficiency optimization, which can enhance network reliability and reduce the costs associated with maintaining and replacing nodes. From Table 4, it can be observed that after 600 rounds, the total energy consumption of the network using the proposed HDQN algorithm is 32,548.6 mJ, which is the lowest among the six compared algorithms. Specifically, the total energy consumption of nodes at rounds 600, 700, 800, and 900 using the HDQN algorithm is lower than that of the second-best SEP algorithm by 73.4 mJ, 257.3 mJ, 543.6 mJ, and 729.4 mJ, respectively. The reduction in total energy consumption is 0.23%, 0.73%, 1.45%, and 1.88% correspondingly. This demonstrates the excellent performance of the HDQN algorithm in terms of total energy consumption in HWSNs.
# Resource Mapping Allocation Scheme in 6G Satellite Twin Network
## 5 Serverless-Based Decentralized Simulation Development Model
### The Three-Dimensional Hierarchical Simulation Framework  
The 6G satellite twin network includes simulation network initialization, simulation task segmentation, simulation task assignment mapping, function combination, resource balance coordination, and simulation task process. In this paper, a three-dimensional hierarchical simulation framework is proposed, as shown in Figure 4. In this framework, CPU, memory, and bandwidth are collectively referred to as resources. The resources are gridded according to the distribution of the docker containers of the servers and modeled as \(s_{pq}=\left\{\mathrm{caps}_{pq},\mathrm{m}\mathrm{m}\mathrm{e}_{pq}, \mathrm{b}\mathrm{a}\mathrm{a}\mathrm{p}_{q}\right\}\). All tasks of the docker containers are performed by the function \(F_{pq}^{C}=\left\{f_{0}^{C},f_{1}^{C},\cdots,f_{J}^{C}\right\}\). The resources support the functions to be executed. The docker containers interact according to the contact of the function.  
In the 6G satellite twin network, we decouple resources, transport, control, and task. The resources of each docker container are logically layered according to function into the control management layer, service resource layer, and network ontology layer.
# Using Explainable AI (XAI) for the Prediction of Falls in the Older Population
## 4 Discussion
### Limitations
#### 4.6.5 Alternative Algorithms and Explainability Considerations  
Maximising the utility of machine learning models requires the selection of the correct model that best suits the context of the problem. These considerations include the type of problem, the characteristics of the features, versatility, and the need for explainability. Real life application then further considers the technical feasibility of it such as its computational intensity. For a binary classification problem as per our research question, supervised learning alternatives could have been decision trees and the supervised vector machine (SVM). Deep learning models such as neural networks could have also been alternatives [47]. Amongst these considerations, explainability is crucial, particularly in analysing population datasets of health and healthcare relevance, where it is essential to allow both clinicians and patients to interpret the feature relevance of the model's prediction. This can help to personalise and streamline the preventative measures and medical management for a maximum amount of individual patient benefits. Decision trees allow for direct visualisation of how the algorithm makes a prediction using the features that are provided. Explainability is achieved easily as both the patient and clinician can interpret the tree results visually, without requiring extensive knowledge in artificial intelligence [22]; however, decision trees have high variance and are more prone to overfitting, which reduces the consistency and quality of their predictions in real datasets. This is important to be avoided especially when the consequences of erroneous predictions are significant, such as in the healthcare context. Utilising the strength of ensemble learning, random forests are able to provide more accurate and stable predictions through the aggregation of results from randomly built decision trees. This is at a slight expense of their interpretability, where the expression of feature relevance may be less straightforward for the general population.  
On the other side of the explainability spectrum, deep learning models such as neural networks may offer better prediction performance but with minimal transparency over the derivation of the predictions. This may make clinical correlations of the results more difficult and less attractive for their practical application in medicine [48]. Moreover, the small dataset that is present in each of our classification problems may also restrict the performance of neural networks, which require a large training dataset for the delivery of stable and accurate results. However, in recent years there has been increasing interest in the optimisation of the performance of neural networks for small datasets, which can increase their applicability to the medical field [49; 50].
###### Abstract  
This study presents a fast Fourier transform (FFT) kernel for multistandard applications, which employ multiple-input, multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM). The proposed design uses a mixed-radix, mixed-multipath delay-feedback (MRM\({}^{2}\)DF) structure, which enables 4/5/6-stream 64/128-point FFT. This approach allows the effective usage of guard intervals (GI) in conjunction with a novel resource-sharing scheme to improve area efficiency. An area-reduced constant multiplication unit and sorting buffer with minimal memory size further reduced an area overhead. A test chip was designed using UMC 90-nm technology, and was evaluated through post-layout simulation. The proposed design outperformed previous works in terms of the throughput per area.  
fast Fourier transform; FFT; kernel; MIMO; OFDM; multistandard 1
# Influence of Interference Fit and Temperature on High-Speed Permanent Magnet Motor
## 3 Static Strength Check of Interference Fit
### Measures to Reduce Maximum Stress  
#### 3.4.1 Chamfers Position of Interference Fit Structure  
According to the structure of interference fit in the rotor system, two methods of cutting 45\({}^{\circ}\) chamfers, meaning that the top of both sides of the magnet and the top of the two end covers are added, respectively, are adopted. Comparing the two different structures under the condition of keeping the speed, temperature, and interference consistent above, to change the position of the chamfers, rebuild the model, and simulate, the result is the chart of stress distribution along the radial section, as shown in Figure 9. Setting the chamfers to 0.5 mm as an example, chamfers are added on the magnet and the end covers, respectively, to compare the parameter changes in them. The maximum principal stress on the sheath is 337.55 MPa and 308.42 MPa, respectively, which is significantly lower than the structure without chamfers, and the maximum stress is also within the range of allowable stress.  
Based on the radial and axial stress distribution of the original structure, the stress distribution data of magnet chamfers and the end cover chamfer structure are added. As shown in Figure 10, a comparative diagram of stress distribution is drawn.  
The simulation parameters of the three structures are compared and analyzed, as shown in Table 2. Under the normal working condition, there is little difference in the total deformation and elastic strain among the three structures. However, there is a great difference in the maximum principal stress between the original structure and the two chamfered structures. The maximum principal stress of the magnet chamfers structure and the end cover chamfers structure is on the sheath, and the maximum stress of the end cover chamfers structure is smaller than the former.  
Figure 8: The distribution of axial and radial stress curves. (**a**) Radial direction; (**b**) axial direction.  
Figure 9: Two kinds of structural stress distribution. (**a**) The magnet chamfered structure; (**b**) the end covers chamfered structure.  
Considering the processing factors, it is difficult to process the magnet because of its brittleness and easy-to-produce cracks at the top. Therefore, to choose the chamfered structure of the end cover, on the one hand, the material of the end cover is structural steel, which is convenient for processing; on the other hand, the maximum principal stress of the chamfered structure of the end cover is smaller.
## 2 Materials and Methods
### Metagenomic Sequencing, Assembly, and Annotation  
The community genomic DNA (from triple DNA extractions from each replicate of the bioreactor) of the samples was sheared into 500- and 800-bp length fragments and multiplex-sequenced on the Illumina HiSeq 2500 platform (Illumina, Inc., San Diego, CA, USA), which generated 2 \(\times\) 250-bp pair-end sequencing reads. Sequencing was carried out at Novogene Bioinformatics Technology (Beijing, China). The adapter sequences were removed from the generated reads, which were trimmed using Trimmomatic v0.30 with a quality cutoff of 30, a sliding window of 6 bp, and a minimum length cutoff of 45 bp [30]. The functional composition of the microbial communities was obtained from the results of the high-quality reads compared with the sequences in the Kyoto Encyclopedia of Genes and Genomes (KEGG, a database resource for understanding high-level functions and utilities of the biological system) [31] and Carbohydrate-Active EnZymes (CAZy, a database describes the families of structurally-related catalytic and carbohydrate-binding modules of enzymes) databases [32] using thresholds of e < e\({}^{-10}\) and an aligned length > 150 nt. High-quality reads from the PSD and AnMBR were pooled and assembled into one metagenome using IDBA-UD 1.1.1 [33] with the following parameters: minimum k value of 60 and maximum k value of 120; increment of the k-mer of each iteration of 10 and minimum multiplicity for filtering k-mers when building the graph of 5; seed k-mer size for alignment of 5; and minimum length of contigs of 1000. The other parameters were set as default. The contigs and unmapped reads from the IDBA-UD assembly were re-assembled using theparameters as described. The contig coverage was determined by mapping the initial reads to contigs larger than 1 kbp with Bowtie2 software [34]. Subsequently, Samtools [35] was used to convert, sort, and merge the Sam files. Then, coverage (describes the number of reads that align to the contigs) was determined with the Genomecov software of the Bedtools package [36]. As low coverage and short contigs are known to be error-prone, contigs with a length \(<1\) kb and average coverage \(<3\) were discarded from the subsequent binning step.
# W-GUN: Whale Optimization for Energy and Delay-Centric Green Underwater Networks
## 1 Introduction
The aforementioned underwater relay node optimization techniques majorly rely on either geolocation-centric node selection or the quality of service-centric node selection, without considering the dynamic self-mobility of the medium of communication in underwater environments, such as in whale optimization [30]. In this context, this paper proposes an adapted whale optimization algorithm-based energy and a delay-centric green UWSNs framework (W-GUN). It focuses on exploiting dynamic underwater network characteristics by effectively utilizing underwater whale-centric optimization in relay node selection. The significant insights and offerings of this paper can be listed as follows. * Firstly, an underwater relay node optimization model is mathematically derived, focusing on underwater whale dynamics for incorporating realistic underwater characteristics;
* Secondly, the optimization model is used to develop an adapted whale and grey wolf optimization algorithm for selecting minimal energy consumption and stable relay node-centric underwater communication paths;* Thirdly, a complete workflow of the green underwater networking framework W-GUN, is presented with an optimization flowchart;
* Finally, a comparative performance evaluation of the prosed framework W-GUN, has been carried out considering the state-of-the-art techniques in the literature regarding underwater networks. The rest of the paper is organized as follows: in Section 2, related works on green computing-centric underwater networks are critically reviewed, considering strengths and weaknesses. Section 3 presents the detail of the proposed green underwater networking-centric framework W-GUN. In Section 4, the experimental setting and result analysis are discussed. Our conclusions and possibilities for future work are been presented in Section 5.
## 4 Proposed Scheme
### Security Proof  
**Theorem 1**.: _Suppose that the CPDHP problem according to the lush function is safe and extended mapping of chaotic is legal. Thus, under Definition 2, our work is the security key negotiation method._  
Proof.: Let \(A\) be an attacker, utilize \(q_{s}\) to indicate the value of times the value queried the **Send**, utilize \(q_{r}\) to indicate the value of times the value queried the **Reveal**, and \(q_{e}\) to indicate the value of times the attacker queried the **Execute**. Construct a Challenger \(\mathcal{C}\) to imitate the true protocol work via **Send** Oracle query. Describe Game \(Game_{i}:\) i = 0, 1, 2...; by continuously altering the Oracle replies of adjacent games, it can be shown that the diversity in the probability of an attacker gaining the game is negligible.  
In the last game, the probability of evaluating the adversary successfully was only \(\frac{1}{2}\). Therefore, it is evaluated that the probability of win of the attacker method can be ignored. Assume that the event of Repeat represents that the scenario working example has chosen the \(x_{i}\) that has been chosen. The active event probability is:  
\[Pr[Repeat]\leq\frac{(q_{s})^{2}}{2^{k+1}} \tag{4}\]  
**Guess:** Once all queries are completed, attacker \(A\) guesses \(b^{-}\) of \(b\). Once \(b^{-}\) = b, then the attacker has broken the security of the system successfully. This event is indicated by \(Succ_{A}\), and \(Att_{A}\) is described to indicate the attack advantage of any attacker \(A\) against the scheme, where \(Att_{A}\frac{\text{def}}{\left[2 Pr[Succ_{A}]-1\right]}\).  
**Game0:** The challenger \(C\) replies to the attacker's query based on the execution operation of the true method. Thus, the probability of winning against the adversary is equal to the probability of win of the adversary attacking the true method. Hence, the following can be concluded:  
\[Pr[E_{0}]=Pr[Succ_{A}] \tag{5}\]  
**Game1:** In **Game1**, the Oracle will follow **Game0**. Once Repeat or Forge events occur, the challenger \(C\) ends the game. Hence, the following can be concluded:  
\[Pr[E_{1}]-Pr[E_{0}]\leq Pr[Forge]+Pr[Repeat] \tag{6}\]  
**Game2:** In this game, for the \(\text{Send}(\sum_{e}^{i}M_{2})\) query, the challenger \(C\) initially tests when the working example is Corrupt and successfully passes it to \(\sum_{e}^{i}\), and the challenger  
Figure 5: The simulation results under OFMC and CL-A4 Se back-ends. (**a**) OFMC back-end; (**b**) CL-A4 Se back-end.  
randomly selects the signature to a random number \(SM_{vi}\). Since the signature is a uniformly distributed random number; hence, the following can be concluded:  
\[Pr[E_{2}]=\frac{1}{2} \tag{7}\]  
By merging Equations (4)-(7), the following can be concluded:  
\[\begin{split}& Att_{A}=|2\cdot Pr[Succ_{A}]-1|\\ &\leq\frac{(q_{s})^{2}}{2^{k+1}}+2 Att_{dlp}+2\cdot q_{s}Att_{ CPDHP}\end{split} \tag{8}\]  
Ultimately, it is evaluated and proved that the attacker's probability of breaking the security of our work is negligible.