esa-sceva/satcom-chunk-collection huggingface dataset:
# Measurement of Wheel Radius in an Automated Guided Vehicle
## 2 Determination of the Vehicle Position and the Role of the Rolling Radius
### Wheel and Its Properties  
The wheel radius used to determine the position from odometry calculations in real movement conditions is not a constant value. Its size is influenced by many factors. The vehicle wheel shown in Figure 2a has a flexible belt. Thanks to this, the wheel has the ability to damp vibrations in selected operating conditions. The physical model of the wheel is shown in Figure 2b. The wheel has longitudinal and circumferential elasticity, which was modeled by a spring. Additionally, the model features damping elements designed to dissipate vibrations.  
In the case of a stationary wheel, the vertical force loading the wheel reduces the radius of the wheel by the value of deflection \(g\) (Figure 2b). In the case of a rolling wheel, circumferential deformations and slips occur in the wheel's contact with the ground. The rolling radius of the wheel changes. These changes are additionally dependent on the type of horizontal forces and moments acting on the wheels. There may be two cases here. In one of them, the wheel is driven; the other is braked. The occurring phenomena have been comprehensively described in the works [23; 24]. In real operating conditions of the wheel there is a slip and it is better than to use the concept of the rolling radius \(r_{t}\).  
In the range of small slips up to 2-3%, the relationship between pressure forces and slip is linear in both cases of operation. This is shown schematically in Figure 3a.  
Changing the slip value affects the value of the rolling radius. The course of changes in the rolling radius is shown schematically in Figure 3b [25].  
Figure 3: Changes in wheel radius (**a**) as a function of static load; (**b**) during motion, under the driving force/braking force.  
Figure 2: Vehicle wheel (**a**) view; (**b**) model, \(g\)—deflection, \(r_{S}\)—static radius.
# Implementation and Evaluation of HTTP/3 Connectivity Check Using Happy Eyeballs Algorithm
## 4 Implementation and Experimental Results  
### Implementation  
The logic of the proposed method was implemented in the client program ([[https://github.com/ebi-yade/quic-go](https://github.com/ebi-yade/quic-go)]([https://github.com/ebi-yade/quic-go](https://github.com/ebi-yade/quic-go)), accessed on 10 May 2022) by extending the existing HTTP/3 package in the Go language. Furthermore, because we could not find any programs that supported the client side of the existing Alt-Svc method, at least in the Go language, we implemented a parser ([[https://github.com/ebi-yade/altsvc-go](https://github.com/ebi-yade/altsvc-go)]([https://github.com/ebi-yade/altsvc-go](https://github.com/ebi-yade/altsvc-go)), accessed on 10 May 2022) that roughly met the specification. In this experiment, we evaluated the completion time of the initial handshake, and not the final HTTP/3 arrival time. Therefore, the program itself was embedded in the implemented client and its operation was confirmed, but the computation time and other factors resulting from the parsing did not affect the experimental results. The server program was built by applying a patch of the HTTP/3 implementation in Rust by Cloudflare ([[https://github.com/cloudflare/quiche](https://github.com/cloudflare/quiche)]([https://github.com/cloudflare/quiche](https://github.com/cloudflare/quiche)), accessed on 10 May 2022) to NGINX5 version 1.16.1.
## 1 Introduction
### Contributions  
In multi-beam satellite communication systems, multi-dimensional coverage is adopted. In this paper, in response to the above problems, a multi-beam satellite cooperative transmission scheme based on cross-layer and cross-dimension resources optimization and irregular gradient adaptive packet segmentation is proposed. In the scheme, multi-layer multi-dimensional radio resources are abstracted as the multi-layer multi-dimensional distribution of fireflies. SINR and QoS are taken as the weight to influence the attraction between fireflies. Multi-layer and multi-dimensional resources optimization is carried out by using the multi-peak processing ability of the firefly algorithm, and the optimal resources are selected for allocation. In addition, the irregular gradient algorithm is introduced as the rule of packet segmentation, and the optimal resources are taken as the weight of gradient change to map different packets to radio resources, which realizes the mapping balance between packets and radio resources and ensures the efficiency and reliability of transmission. The main purpose of this paper is to solve the optimal allocation problem in the case of multi-layer and multi-dimensional radio resources multiplexing. The contributions are as follows:  
1. We propose a cross-layer and cross-dimension radio resources optimization model based on the weighted discrete firefly algorithm. CSI and QoS are taken as the weight to select optimal resources. The problem of high dynamic multi-dimensional radio resources optimization is solved in multi-beam satellite communication systems;
2. An adaptive packet segmentation scheme based on the irregular gradient algorithm is proposed. The optimal resources are weighted to realize the mapping balance between packets and radio resources. The problem of low spectrum utilization in satellite communication systems caused by the discontinuous optimal resources is solved. The cooperative transmission of multi-beam satellite is realized;
3. The normalized system throughput and user satisfaction index of the proposed cooperative transmission scheme in multi-beam satellite communication systems are analyzed and evaluated through simulation.  
The rest of this paper is organized as follows. Section 2 describes the related works about resource allocation. Section 3 describes the model of multi-beam satellite communication systems and introduces the user connection and mapping model of radio resources and packets. In Section 4, the multi-beam satellite cooperative transmission scheme based on cross-layer and cross-dimension resources optimization and irregular gradient adaptive packet segmentation is introduced, and the user satisfaction index is proposed to evaluate the proposed algorithm. In Section 5, the performance comparison of the proposed cooperative transmission scheme is analyzed, and the advantages of the scheme are illustrated by simulation. Section 6 summarizes this paper.
# Health Misinformation in Social Networks: A Survey of Information Technology Approaches
## 3 Fact-Checking
### Manual Fact-Checking
#### 3.1.2 Crowd-Source-Based Fact-Checking  
In crowd-sourced fact-checking, the detection of fake news is performed by a large population that rates the credibility of articles. This approach has been proposed by various organizations such as WikiTribune [42] and is much more economical than expert-based fact-checking. However, it carries the risk that the rating population might introduce their own biases in the process.  
The process consists of several steps. The first step is monitoring the news on TV, social media, newspapers, and websites and selecting the articles to be checked. When this selection is made by experts, the articles are filtered and balanced in order to be unbiased (e.g., they cover the whole political spectrum). This is a major differentiation from the case where the selection of articles is made up by the crowd since, in the latter case, it is difficult to certify that the selection of new claims is spread fairly across the news spectrum. The next step is researching the subject of the claims using multiple sources and assigning a rating to the news article. This step is difficult to be completed objectively by people who are not experts. There are concerns about the correctness of the rating, the availability of evidence, and the rater's motivation. Another drawback of this approach is that it is difficult for volunteers to fact-check a claim that needs to be assessed rapidly, as in the case of the pandemic.  
An example of a site that works with crowd-sourced fact-checking is Fiskkit [43], where users can select articles, upload them to the site, and rate them sentence-by-sentence. Users can also apply tags that evaluate the article's accuracy and see the ratings of other users.  
Regarding available datasets, the Multi-Genre Natural Language Inference Corpus (MNL) ([[https://cims.nyu.edu/~sbowman/multinli/](https://cims.nyu.edu/~sbowman/multinli/)]([https://cims.nyu.edu/~sbowman/multinli/](https://cims.nyu.edu/~sbowman/multinli/)), accessed on 10 December 2024) is a crowd-sourced collection of 433 K sentence pairs annotated for textual entailment. In other words, the data consist of pairs (p and h), where p is the premise and h is the hypothesis, and labels in {entailment, contradiction, and neutral}, which report whether the hypothesis entails, contradicts, or is neutral toward the premise.
## 3 System Model
### Computational Model
#### 3.3.2 Edge Computing  
When the computing power of local devices is not enough to efficiently handle tasks or to handle tasks that require a fast response, edge computing can provide lower latency due to its proximity to the data source. The \(m_{i}\) is the size of the task from the requester \(UE_{i}\) and \(R_{i,b}\) represents the rate at which \(UE_{i}\) transmits tasks to the MEC server, transmission delay is  
\[t_{i,b}^{trans}=\frac{m_{i}}{R_{i,b}}. \tag{8}\]  
The time requirement of edge computing is  
\[t_{i,b}^{edge}=\frac{c_{i}^{edge}}{f_{i}^{edge}}. \tag{9}\]  
\(p_{i,b}\) denotes the transmission power that requester \(UE_{i}\) sends to the MEC server. The transmission energy loss is denoted as  
\[E_{i,b}^{trans}=p_{i,b}t_{i,b}^{trans}. \tag{10}\]  
Computation energy loss is denoted as  
\[E_{i,b}^{edge}=p_{i,b}t_{i,b}^{edge}. \tag{11}\]  
The total consumption, including the loss of time and energy, provides  
\[C_{i}^{edge}=\alpha\Big{(}t_{i,b}^{trans}+t_{i,b}^{edge}\Big{)}+\beta\Big{(}E_ {i,b}^{trans}+E_{i,b}^{edge}\Big{)}. \tag{12}\]
# Aromatic Amines in Organic Synthesis Part III; \(p\)-Aminocinnamic Acids and Their Methyl Esters
## 1 Introduction  
Chemical compounds containing an amino group (or its derivative) are widely used in many fields of science. They are intensively studied because the electron-donating properties of the amino group influence their biological activity and spectroscopic and electrochemical properties [1]. Particular attention is paid to organic compounds with amino groups in the preparation of dyes [2], spectroscopic probes [3; 4; 5], solar cells [6], polymerization photoinitiators [7; 8], and drugs [9].  
Our research group and others have investigated the influence of different amino groups on the spectroscopic and electron-donating properties of various classes of dyes, such as azomethine [10], stillazolium [11], hemicyanine [12; 13; 14], styrylquinolinium [15], oxazolome [16; 17], biphenyl [18], BODIFY [19; 20; 21; 22; 23], malcone [24; 25], phenothiazazine [26; 27], anthracene [28; 29], and pyrazolo [30; 31], for many years. The application of dyes in modern technologies based on multi-photon absorption, spectroscopic probes, and sensors requires compounds with an extended system of conjugated double bonds. Such compounds are synthesized using either \(p\)-aminocinnamic aldehydes or their esters instead of \(p\)-aminobenzzaldehydes (or their esters).  
The esters of \(p\)-aminocinnamic acid derivatives can be synthesized by applying several methods. The most common procedure is based on the general Knoevenagel reaction. In the first step, the corresponding cinnamic acid is prepared, which is then esterified [32; 33]. The esters can also be obtained from the appropriate benzaldehydehydes [34] or benzoyl bromide [35] and iodides [36] using the Wittig or Heck reactions, respectively.  
In this paper, we describe the synthesis of fifteen derivatives of \(p\)-aminocinnamic acids and show the optimal way to transform them into methyl esters. The esters of \(p\)-aminocinnamic acids were synthesized in a two-step reaction, starting with easily accessible benzaldehydes and applying the first of the above-mentioned methods. The aldehydes were synthesized according to the procedure given by [PERSON] et al. [37]. Only\(p\)-(dibutylamino)benzaldehyde was prepared by applying the methodology given by [PERSON] and co-workers [38]. The basic spectral properties of the synthesized compounds in two solvents of different polarities (methanol and ethyl acetate) arealso described.
# Dual-IoTID: A Session-Based Dual IoT Device Identification Model
## 4 Model Selection
### Algorithm Selection
#### 4.3.1 Initial Classification Model Selection  
As proposed in Section 3, there are significant differences in the payloads of sessions for different IoT devices. Therefore, we attempted to split the payloads of sessions and extract frequent items from the segmented content for the initial classification of IoT devices. We selected the confidence matrix outputted by the model, with a high initial classification accuracy as the input for the second-layer classification.  
Firstly, we proposed to slice the effective content of sessions into a fixed-length gram in N-gram mode and statistically analyze each gram to obtain a set of frequent items exceeding the threshold. A key parameter to determine in this process was the specific value of N. Table 3 illustrates the accuracy of classification predictions on the test set using different models under different values of N. In the experiment, we set the threshold for frequent items to 0.6, meaning that words appearing in a class of sessions with a frequency exceeding 60% were considered frequent items.  
From the experimental results, it can be observed that an increase in the value of N led to a gradual decline in the accuracy of each model. Consequently, when setting the length of frequent items at N = 2, the initial classification model exhibited the highest classification performance.  
In terms of classification performance, XGB, RF, and DT were the best-performing algorithms, with initial classification accuracies exceeding 90%. NB consumed the least amount of time compared to the other algorithms but had the lowest accuracy. SVM also  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mathbf{N}\) & \(\mathbf{2}\) & \(\mathbf{3}\) & \(\mathbf{4}\) & \(\mathbf{5}\) & \(\mathbf{6}\) \\ \hline RF & 0.9058 & 0.8655 & 0.8435 & 0.7784 & 0.7731 \\ DT & 0.9056 & 0.8654 & 0.8434 & 0.7784 & 0.7729 \\ XGB & 0.9055 & 0.8651 & 0.8431 & 0.7781 & 0.7728 \\ NB & 0.6537 & 0.5931 & 0.5671 & 0.5304 & 0.5259 \\ SVM & 0.9018 & 0.8578 & 0.8327 & 0.7669 & 0.7628 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of ML algorithms with different N.  
achieved good results in the initial classification model, but it also required much more time than the other methods. As a result, in the remaining work, we will try to use the confidence matrix outputted by XGB, RF, and DT as inputs for the second-layer model.
# Seasonal Changes in Body Composition, Jump, Sprint, and
###### Abstract  
Assessing physical fitness throughout the entire season can provide valuable insights for designing effective training programs to enhance handball performance. Therefore, the aim of this study was to investigate seasonal changes in body composition, jump, sprint, and agility performance among elite female handball players. This study involved fourteen elite female handball players (age 21.98 \(\pm\) 1.22) who participated in the Serbian Handball Super League and the European Handball Federation (EHF) European Cup during the 2022/23 season. Within the framework of seasonal changes, five measurements were made. Players were tested for height, weight, BMI, body fat and muscle mass percentage, jump performance (CMJ, CMJA, SJ, CMJ left), linear sprint (5 m, 10 m, 20 m), and agility performance (zig-zag and slalom). Repeated measures ANOVA revealed no changes in body composition parameters during the season. Among the five jump performance tests, a significant change was observed only in the CMJ left test, where better values were achieved in the fourth and fifth measurements compared to the initial measurement. In sprints over 5 and 10 m, significant changes were noted between specific measurement points, while no differences were observed in the 30-m sprint. For agility, differences were recorded in four out of five tests, with the best performances in the Zig-Zag tests observed in the third measurement, while in other tests, the best results were most often recorded in the second and fifth measurements. These results indicate that body composition remained stable, while performance improvements were specific to certain physical capacities and time points during the season. These findings can help coaches design targeted strength and agility training aligned with key performance periods, emphasizing short sprints and agility with/without the ball, rather than focusing on body composition changes. However, this study is limited by its small sample size and single-season scope, which may affect the generalizability of the results.  
Seasonal variation; body fat; muscle mass; physical fitness; women's handball players +
Footnote †: journal: Applied sciences  
Article
# APT Detection via Hypergraph Attention Network with Community-Based Behavioral Mining
## 7 Hypergraph Construction  
In the set of behavioral communities, provenance graphs struggle to model high-order interactions between entity groups. We use hypergraphs, where hyperedges connect multiple nodes, to naturally represent these multi-variate relationships. Community detection identifies related entity groups, which are modeled as hyperedges to analyze group-level associations efficiently.  
Specifically, define the community set as \(C=\{C_{1},C_{2},\ldots,C_{m}\}\), where each community \(C_{j}\) is a subset of the entity set \(E\) (i.e., \(C_{j}\subseteq E\)). For each community \(C_{j}\), a corresponding hyperedge \(e_{H_{j}}\) is constructed in the hypergraph, with its node set given by \(V_{H_{j}}=\{v_{i}\mid e_{i}\in C_{j}\}\). This mapping upgrades the representation from \"entity-entity binary relationships\"to \"entity-group multi-variate relationships\", encapsulating the collective semantics of the community within a single hyperedge.  
To further quantify the strength of the relationships represented by hyperedges, we introduce the concept of hyperedge weight \(w_{H_{j}}\). This weight is calculated using a specific formula to characterize the tightness or functional correlation among entities within the community, providing a quantitative basis for subsequent complex network analyses based on hypergraphs.  
\[w_{H_{j}}=\frac{1}{|C_{j}|}\sum_{\epsilon_{i}\in C_{j}}s(e_{i})\]  
where \(|C_{j}|\) represents the number of entities in community \(C_{j}\), and \(s(e_{i})\) represents the metric value between entities. In this paper, the weight composed of features is used as the metric value.  
Through the above definitions of nodes and hyperedges, the hypergraph constructed in this paper can be expressed as \(H=(V,E_{h},w)\), where \(V\) is the set of nodes, \(E_{h}=\{e_{h1},\ldots,e_{hm}\}\) is the set of hyperedges, and \(w=\{w_{H1},\ldots,w_{Hm}\}\) is the set of hyperedge weights. By constructing a hypergraph, the complex relationships in the provenance graph are represented by more expressive hyperedges, providing further high-level behavioral representations for subsequent attack detection.
# Signal Enhancement of Helicopter Rotor Aerodynamic Noise Based on Cyclic Wiener Filtering
## 2 Theory
### Cyclic Wiener Filter  
The cyclic Wiener filter is optimal in the sense of the least mean square, and it is proposed based on the spectral coherence theory of cyclostationary signals. The characteristic cyclic frequencies of cyclostationary signals are only required in advance. Then, the original signal traverses all characteristic cyclic frequencies to perform a set of frequency shifts. Finally, the adaptive filtering of the signal is realized through a filter bank.  
If the target signal is cyclostationary, the adaptive filter for the stationary signal will no longer be applicable. Some researchers applied the basic ideas of Wiener filters, such as the Wiener-Hough equation, the steepest descent method, and the LMS (least mean square) algorithm, to cyclostationary signals and gradually formed the theory of cyclic Wiener filters that can be used for adaptive filtering of cyclostationary signals [27; 28; 29; 30].  
The so-called cyclic Wiener filter first shifts the frequency of the input signal, \(x(t)\), with cyclostationarity:  
\[x_{\eta}(t)=x(t)\cdot e^{2\pi\eta t} \tag{5}\]  
where \(\eta\) contains all the cyclic frequencies of \(x(t)\). The shifted signal, \(x_{\eta}(t)\), is selected as the new input signal. Then, \(x_{\eta}(t)\) is passed through the filter bank, \(h_{\eta}(t)\), to obtain the respective output signal, \(y_{\eta}(t)\), and \(y_{\eta}(t)\) is summed to acquire the total output signal, \(y(t)\). The error function of \(e(t)\) is obtained by performing the difference operation with the expected output signals, \(d(t)\) and \(y(t)\). The filter coefficients of each group are adaptively adjusted following \(e^{2}(t)\) to achieve optimal filtering. Figure 1a,b demonstrates the structural block diagrams of the adaptive filter and cyclic Wiener filter, respectively.  
As observed in Figure 1, the cyclic Wiener filter is equivalent to the superposition of multiple adaptive filters.  
However, the cyclic Wiener filter in practical applications is always implemented in a discrete form. Therefore, the relationship between the input and output of the cyclic Wiener filter can be obtained:  
\[y(n)=\sum_{\eta}\sum_{m=0}^{M-1}w_{\eta,m^{*}}\Big{[}x(n-m)e^{2\pi\cdot\eta( n-m)}\Big{]} \tag{6}\]  
Figure 1: (**a**) Diagrams of the adaptive filter; (**b**) diagrams of the cyclic Wiener filter.  
where \(\sum\) indicates the summation operator, and \(w_{\eta,0}\), \(w_{\eta,1}\),..., and \(w_{\eta,L-1}\) represent the weight coefficient of the filter, \(h_{\eta}(t)\), corresponding to each cyclic frequency, \(\eta=\alpha_{0}\), \(\alpha_{1}\),..., and \(\alpha_{L-1}\), respectively.