esa-sceva/satcom-chunk-collection huggingface dataset:
# Lithium-Ion Battery State of Health Estimation Based on CNN-LSTM-Attention-FVIM Health Features
## 5 Verify the Feasibility of the Model and Analyze the Relevant Results
### Comparison and Verification of Different Models
LSTM stores long-term attenuation trends through a gating mechanism to capture capacity regeneration phenomena. Attention focuses on key stages, enhances the weight and noise suppression of degradation inflection points (such as capacity mutation cycles), and reduces the impact of non-critical cycles (such as the late stage of constant voltage charging), thereby reducing the errors caused by random charging fluctuations. The FVIM optimization algorithm improves the prediction performance of the model by optimizing key parameters such as the learning rate and regularization coefficient. The combination of these four models achieves accurate prediction of the dataset. The prediction error indicators of the B0006 and B0018 batteries are larger than those of the B0005 and B0007 batteries. This is due to the following reasons. First, based on existing deep learning-related research, due to the data characteristics contained in the B0006 battery dataset itself, the prediction accuracy is inferior to other datasets. Secondly, the deep learning model also has certain uncertainties. The prediction method of each model is different, resulting in prediction differences between similar models. This also causes the prediction error of the B0018 battery dataset to be inferior to that of the B0005 and B0007 datasets. At the same time, the high error of the B0018 battery may be related to data fluctuations or degradation characteristics, which affect the model fitting ability. But overall, the model proposed in this paper is still more competitive and stable than the  
Figure 7: Prediction graph and error graph of each battery when the training data is 60%.
## 2 Materials and Methods  
### Preparation of GO  
Graphite powder, sulfuric acid (H\({}_{2}\)SO\({}_{4}\)), phosphorus oxide (P\({}_{2}\)O\({}_{5}\)), potassium permanganate (KMnO\({}_{4}\)), and hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) were purchased from Wako Chemical Co., Ltd. (Osaka, Japan). Potassium persulfate (K\({}_{2}\)S\({}_{2}\)O\({}_{8}\)) and sodium nitrate (NaNO\({}_{3}\)) were obtained from Nakarai Tesque Co., Ltd. (Kyoto, Japan). GO was prepared by the Hummers method [21; 22]. First, 6 g of graphite powder, 50 mL of H\({}_{2}\)SO\({}_{4}\), and 10 g of K\({}_{2}\)S\({}_{2}\)O\({}_{8}\) were mixed, and the temperature of the mixture was elevated to 80 \({}^{\circ}\)C. P\({}_{4}\)O\({}_{10}\) was slowly added to the mixture, which was stirred for 3 h. The mixture was filtered and the filtrated powders were dried overnight. Then, 150 mL of H\({}_{2}\)SO\({}_{4}\) was added to the dried solid at 4 \({}^{\circ}\)C, and 6.5 g of NaNO\({}_{3}\) and 20 g of KMnO\({}_{4}\) were slowly added to the suspension. Subsequently, the ice bath was removed and the temperature of the suspension maintained at 35 \({}^{\circ}\)C for 0.5 or 30 h. After the oxidation process, 305 mL of distilled water (DW) was added, and the suspension was allowed to rest for 0.5 h. It was then diluted with DW to a total volume of 620 mL. 33 mL of H\({}_{2}\)O\({}_{2}\) was added to remove residual KMnO\({}_{4}\) and MnO\({}_{2}\), and the resulting suspension was stirred for 0.5 h. After dilution of the suspension with 500 mL of DW, the supernatant was removed. The pH of the GO suspension increased on DW dilution using a crossflow system with a hollow fiber (Spectrum, Repligen Co., Ltd., Waltham, MA, USA) and centrifugation (8000 rpm, 5 min) several times. We denoted GO samples subjected to the oxidation process for 0.5 and 30 h by KMnO\({}_{4}\) as GO (0.5 h) and GO (30 h), respectively.
# Introduction to Reconfiguration
## 2 Specifying and Analyzing Reconfiguration Graphs
### Defining Problems
The fact that reachability may be trivial under TAR for unbounded solution sizes leads to the question of the size needed for there to be a path between a source and a target solution. The term _reconfiguration index_[28] has been used to denote the minimum value of solution size required for a yes-instance; the _threshold_[19] expresses the same concept in terms of the difference between the largest and smallest solutions in the reconfiguration sequence. As such values are not easy to determine, many of the results have focused on approximations [29]; here, we use the terms _inapproximable, approximation algorithm_, and _polynomial-time approximation scheme_ (a family of approximation algorithms) without providing details. Results include inapproximability results for Clique Reconfiguration [4], Satisfiability Reconfiguration [4], and Set Cover Reconfiguration [4], approximation algorithms for Power Supply Reconfiguration [4] and Vertex Cover Reconfiguration [28], a polynomial-time approximation scheme for Subset Sum Reconfiguration [30], and a characterization of properties of graphs that result in large thresholds for Independent Set Reconfiguration [19].
## 2 Background of DSRC
### Standardization and Collaboration  
Standardization organizations play a crucial role in the development and establishment of standards for DSRC systems. Among these organizations, the Institute of Electrical and Electronics Engineers (IEEE) stands out with its significant contributions. The IEEE is actively involved in shaping the field of wireless communication in vehicular environments through its IEEE 802.11p standard. This standard specifically addresses the wireless communication protocols and requirements for DSRC, ensuring reliable and efficient communication between vehicles and infrastructure. Another prominent organization in the realm of automotive and mobility-related technologies is SAE International (Society of Automotive Engineers). SAE International has published standards related to DSRC, including SAE J2735, which defines message sets used for vehicle-to-vehicle and vehicle-to-infrastructure communication. Their contributions play a crucial role in ensuring interoperability and standardized communication within the DSRC ecosystem.  
In Europe, the European Telecommunications Standards Institute (ETSI) takes the lead in developing standards for information and communication technologies. ETSI has published standards specific to DSRC, such as EN 302 571, which outlines the requirements for DSRC implementation within the European Union. Their efforts aim to harmonize and regulate DSRC deployments in Europe, promoting consistent and interoperable communication systems. The International Organization for Standardization (ISO) is a global standardization body that also contributes to the development of intelligent transportation systems. ISO has developed standards like ISO 15628, which provides guidelines for utilizing DSRC in road vehicle communications. Their work ensures a cohesive and standardized approach to DSRC implementation across various regions.  
In the United States, the Intelligent Transportation Society of America (ITS America) plays a vital role in promoting the deployment and advancement of intelligent transportation systems. They actively support the implementation of DSRC and advocate for its benefits in enhancing road safety and mobility. Additionally, the CAR-2-CAR Communication Consortium operates as an esteemed international organization with a primary focus on the development and standardization of cooperative intelligent transportation systems (C-ITSs) that are based on the DSRC technology. Its establishment stemmed from the purpose of advancing and promoting the implementation of vehicle-to-vehicle (V2V)and vehicle-to-infrastructure (V2I) communication systems, which aim to enhance road safety, optimize traffic efficiency, and foster environmental sustainability.
## 2 Related Work and Our Contribution in Context
### Re-Pair and Repetition-Aware CSAs  
Re-Pair [34] is a grammar-based compression method that factors out repetitions in a sequence. It is based on the following heuristic: (1) Find the most repeated pair \(ab\) in the sequence; (2) Replace all its occurrences by a new symbol \(s\); (3) Add a rule \(s\to ab\) to a dictionary \(R\); (4) Iterate until every pair is unique.  
The result of the compression of a text \(T\), over an alphabet \(\Sigma\) of size \(\sigma\), is the dictionary \(R\) and the remaining sequence \(C\), containing new symbols (\(s\)) and symbols in \(\Sigma\). Every sub-sequence of \(C\) can be decompressed locally by the following procedure: Check if \(C[i]<\sigma\); if so the symbol is original, else look in \(R\) for rule \(C[i]\to ab\), and recursively continue expanding with the same steps.  
The dictionary \(R\) corresponds to a context-free grammar, and the sequence \(C\) to the initial symbols of the derivation tree that represents \(T\). The final structure can be regarded as a sequence of binary trees with roots \(C[i]\).  
[PERSON] and [PERSON] [35] used Re-Pair to compress the differentially encoded suffix array, \(A^{\prime}[i]=A[i]-A[i-1]\). They showed that Re-Pair achieves \(|R|+|C|=O(r\log\frac{n}{r})\) on \(A^{\prime}\), \(r\) being the number of _runs in_\(\Psi\). A run in \(\Psi\) is a maximal contiguous area where \(\Psi(i+1)=\Psi(i)+1\). It was shown that the number of runs in \(\Psi\) is \(r\leq nH_{k}+\sigma^{k}\) for any \(k\)[36]. More importantly, repetitions in \(T\) induce long runs in \(\Psi\), and hence a smaller \(r\)[25]. An exact bound has been elusive, but [PERSON] _et al_. [25] gave an average-case upper bound for \(r\): If \(T\) is formed by a random base sequence of length \(n^{\prime}\ll n\) and then other sequences that have \(m\) random _mutations_ (which include indels, replacements, block moves, _etc._) with respect to the base sequence, then \(r\) is at most \(n^{\prime}\) + \(O(m\log_{\sigma}n)\) on average.  
The RLCSA [25] is a CSA where those runs in \(\Psi\) are factored out, to achieve \(O(r)\) cells of space. More precisely, the size of the RLCSA is \(r(2\log(n/r)+\log\sigma)(1+o(1))\) bits. It supports accesses to \(A\) in time \(O(s\log n)\), with \(O((n/s)\log n)\) extra bits for a sampling of \(A\).
# An MMIC LNA for Millimeter-Wave Radar and 5G Applications with GaN-on-SiC Technology
## 2 Analysis and Design of MMIC LNA
### Co-Design Strategy for Broadband Matching and Low NF
Careful selection of high-impedance stages can control the value of \(\mathsf{Q_{IS}}\) and suppress performance losses. The OMN and ISMNs are designed to widen the bandwidth and maintain inter-stage impedance matching. By considering these factors, designers can optimize the performance of the MMIC LNA, ensuring optimal small-signal gain, NF, and input/output return loss characteristics.
## 2 Design of the MR Transmission Device  
Figure 1 shows a schematic diagram of the high-temperature MRF and SMA spring friction compound transmission device. The MR transmission device consists of an input shaft, an output shaft, a coil, an input disc, an output housing, an SMA spring, a driving piston, a friction ring, and other components. The coil is connected to the electric slip ring through the wire hole on the output housing. The input disc is circumferentially fixed to the input shaft by a slide key, and the friction ring is fixedly connected to the input disc by a screw. The surface of both the input disc and the output housing is a semi-circular arc, and the arc gap between the two parts is filled with MRF. The shape of the MRF working gap is shown in Figure 1b.  
The total torque of the compound transmission consists of two parts: MR torque and SMA compensation torque. The operating principle of the transmission is as follows.  
First, in the initial state, the input shaft starts to rotate under the action of the input torque from the power source. At this time, no current is passed into the coil, and the torque transmitted by the zero-field viscosity of the magnetorheological fluid alone is not sufficient to drive the output housing to rotate.  
Second, after the excitation coil is energized, the magnetic flux generated by the coil passes perpendicularly through the gap of the magnetorheological fluid, and the magnetic particles in the MRF are arranged into a chain-like structure in the direction of the magnetic flux. The shear yield stress of the magnetic chain transmits torque to drive the output housing to rotate synchronously. The current continues to increase until the MRF is magnetically saturated, the magnetic flux cannot be further increased, and the shear torque reaches its maximum value.  
Finally, when the system needs to stop the power input, the coil is disconnected, and the shear yield stress of the MRF decreases rapidly. Then, the input disc and the output housing are disconnected, and the output shaft stops rotating.
## 5 Conclusions  
Presented in this paper is an integral system for pre-processing of point data based on a cross-sectional RE approach. The system, provisionally entitled SyPreF, is of modular structure with a total of five modules containing the developed software tools for: 3D filtering, extraction of point data in cross-sections, filtering and smoothing of point data within cross-sections, reduction of point data and generation of output formats for application in dedicated software for surface reconstruction.  
Major emphasis was placed on the module for point data reduction, which was designed according to a novel approach with integrated deviation analysis and fuzzy logic reasoning. Building on the weak spots and deficiencies of current approaches to reduction of point data by sampling methods--_i.e._, the lack of information on the level of deviation in reduced point clouds and necessity to employ parameters which are abstract to user--a novel approach was developed for analysis of the level of deviation of the reduced point cloud in comparison with the initial point cloud.  
The approach was tested with three different methods of sampling--chord, straightness and spatial--showing a significant improvement of the maximum deviation/level of reduction ratio, _i.e._, improvement of average deviation and level of reduction. Beside the improvement of the deviation/mean level of reduction ratio, the novel approach allowed a more user-friendly and intuitive application as the reduction process is controlled by simply entering the deviation tolerance, allowing the user to gain better insight into the quality of the reduced point cloud.  
Verification of the proposed pre-processing system was conducted through its application in three case-studies selected on the basis of geometry complexity, dimensions and applied system for 3D digitization. Achieved results--high reduction levels and low deviations of reconstructed modelsconfirm the system's effectiveness on point data from parts of versatile geometries, obtained by contact and laser triangulation digitization systems. Additionally, productivity and speed of surface reconstruction based on pre-processed point clouds are significantly increased, maintaining stability of process on an average hardware configuration.  
Future research will be oriented towards further development through implementation of additional modules/tools (e.g., for point data segmentation and registration) as well as on further advancement of point data reduction through improvement of the calculation speed and development of an expert system for proposing an optimal method for the given point data.  
The research was supported by the Ministry of Education and Science of the Republic of Serbia and the Ministry of Higher Education, Science and Technology of Republic of Slovenia.
# HCL Control Strategy for an Adaptive Roadway Lighting Distribution
## 3 Results
### The Benefits of the Human-Centric Intelligent Roadway Lighting System  
After a long-term observation using the imaging luminance meters, we have established a roadway lighting adaptation model. Figure 22 compares the lighting conditions for when the road is wet and dry. The level of luminance value is shown in Table 7. The average luminance of the wet road is around 8.5-12%, which is lower than that of the dry road, and the luminance uniformity drops below 0.5. Therefore, according to the data obtained, we can use this to set up control logic of the output of the street light type. When the system considers the road condition to be wet and slippery, it will switch to the uniform luminance light distribution, if the road surface is dry it will switch to a uniform illuminance light distribution.  
According to the records of road luminance uniformity by the ILM from September, we collected the highest and lowest value per day from 19:00-04:00. The standard deviations are shown in Table 8:  
Figure 22: The luminance level in Sect. 5 of Gaotie South Road: (**a**) dry surface; (**b**) wet surface.  
Taking Section 5 of Gaotie South Road as an example, from left to right, Figure 23 shows Type A distribution on dry roads, Type A distribution on wet roads and Type B distribution on wet roads. From what we can tell, the Type A distribution shows good uniformity on dry roads. When applied to wet roads, the change in road surface reflectance produces a strong contrast (causing the zebra effect). However, if the light is switched to the Type B distribution it can provide drivers with a relatively safer and more comfortable driving experience where they are able to see the road surface more clearly. Therefore, the intelligent roadway lighting system proposed in this paper used an imaging luminance meter to detect the roadway lighting status and automatically switch and adjust to the optimal output of light distribution to achieve the best road lighting conditions. The main focus of our research is decreasing the zebra effect; due to the road luminance factor q0 (related to the overall reflectance of the pavement) it is different for each roadway surface. Before designing the secondary optical lens for roadway lighting, it is important to define which kind of road surface is our target. In this case, we chose wet road surface (W1) as our design target, and one of our two lenses named Type B (uniform luminance) achieved the target of uniform luminance for improving the zebra effect on wet roads.
# A Secure Blockchain-Based Authentication and Key Agreement Scheme for 3 GPP 5G Networks
## 6 Performance Evaluation
### Resistivity to DoS and DDoS Attacks
#### 6.4.2 Average Successful Authentications under DoS Attacks  
The 5 GSBA can alleviate the effect of DoS attacks by verifying the timestamp and the HMAC code in the authentication request. To prove that the 5 GSBA can provide the best DoS attack resistivity among all related schemes, a simulation model was built in MATLAB 2020b with the following assumptions. There are 1000 mobile devices sending authentication requests to a server at every one millisecond, which can hold--at most--1000 sessions simultaneously without any performance degradation. Then, for each authentication request, there could be a percentage chance that it is a fabricated DoS request. In case the request is legitimate, the server will complete the protocol operation normally following the protocol specifications. However, if the request is a DoS attack, either of the following results could happen. If the server can identify it as a DoS request, it will terminate the session immediately to accept another new request. If the server cannot identify it, the session will hold until it reaches the protocol expiry time at 3 s, which is a common default  
Figure 10: Average delay under many attacks.  
value for RADIUS server timeout. This simulation is run for 60 s. The number of successful authentications of different schemes is recorded in Figure 11.  
Figure 11 shows that when there is no DoS attack in the network, the 5G-AKA performs the best among all schemes with the highest number of successful authentications. However, when the percentage of DoS attacks increases, the performance of the 5G-AKA [4] drops drastically, while the 5 GSBA maintains the highest number of successful authentications. This is because the 5G-AKA has no way to identify if the authentication requests are fabricated, so the server wastes some sessions for holding until their expiry. On the other hand, the SE-AKA [20] can also provide DoS attack prevention, similar to our 5 GSBA. However, the 5 GSBA runs faster, so it has a higher number of successful authentications.
# Multi-Agent Reinforcement Learning Framework in SDN-IoT for Transient Load Detection and Prevention
###### Abstract
Deep Reinforcement Learning (DRL), which combines Deep Learning (DL) and Reinforcement Learning, has seen breakthroughs in complex game development, robotics, and network automation, among others, by using hidden layers to capture features and intricate details relevant to the RL agents [2; 22]. The integration of DL in RL has also reduced the curse of dimensionality and improved the ability to solve high-dimensional problems [22]. The security of DL models and data privacy protection has been studied in [23; 24] and remains a concern to service providers, especially with the massive connectivity of objects. These security threats can lead to inaccurate models for training the RL agents and adversely affect performance especially for time-bound applications. Compromises to data through network attacks [23; 24] lead to the availability of sensitive information to hackers and the occurrence of this phenomenon will compromise the confidentiality of the system. In this research, we propose a multi-agent RL framework in SDN for three use cases, as shown in Figure 2: traffic burst detection, elephant flow, and DDoS. We extended Deep Deterministic Policy Gradient (DDPG) in multi-agent environment, Multiagent Deep Deterministic Policy Gradient (MADDPG) algorithm, and used Markov Decision Process  
Figure 1: Reinforcement learning. (MDP) to mathematically model the state-action-reward representation. The rest of the article is organized as follows. Section 2 lays the foundation for single-agent RL (SARL) and multi-agent RL (MARL) in the SDN architecture. Section 3 reviews similar literature involving MARL. Sections 4-6 describe the system into details. Section 7 discusses the experimental results, and we conclude in Section 8.
## 5 Experimental Results: Comparison and Analysis
### Comparison of Bending Moments
for the K-SC specimen, which used a cross-shaped connector plate, stiffness and the internal force were maintained, or showed a gradual increase at 0.04 rad, at the end of the cycle. In the case of loading in the horizontal direction (short edge of the connector plate), this behavior difference resulted from the ability of the vertical plate cross-section to sufficiently resist bending.
# The Role of Fungi in Food Production and Processing
## 2 Understanding the Research Prospects
### Methods for Fungal Identification and Characterization in Food Samples
#### 2.2.4 Immunological Methods  
Immunological methods, such as enzyme-linked immunosorbent assays (ELISA) and lateral flow immunoassays, utilize specific antibodies to detect fungal antigens or toxins in food samples. These methods offer rapid and convenient detection of fungal contamination, particularly mycotoxins, with minimal sample preparation [84]. However, immunological methods may lack specificity and cross-reactivity with structurally similar compounds, leading to false-positive results.