esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Finite Element (FE) Modeling of Side Joints with URSP Connectors
### Modeling of Shear Connectors  
To consider the interfacial slip between the RC slab and steel beam, shear connectors are simulated by springs along the longitudinal direction. Meanwhile, the uplift action is simulated by springs along the vertical direction as per the numerical simulation performed by [PERSON], [PERSON] et al. [42]. As shown in Figure 5, to simulate each shear connector, the node on the shell element of the RC slab is connected  
Figure 4: Finite element model. (**a**) Geometrical configuration and material properties; (**b**) finite element model established with MSC-Marc.  
to the corresponding node on the shell element of the top flange of the steel beam by two springs including one vertical spring and one longitudinal spring. In this paper, the springs along the vertical and longitudinal direction are independent.  
For the shear connector, based on the theoretical analysis of the tensile stiffness of headed stud connectors in reference [43], a very large stiffness value is adopted for the definition of the spring in the vertical direction to simulate the uplift restraint, which is similar to the rigid link as reported in references [18; 44]. The shear force-slip relationship curve for URSP connectors illustrated in Figure 6 is adopted to simulate the nonlinear behavior along the longitudinal direction of the steel-concrete composite interface, which is a critical factor for the simulation precision of the whole finite element model. The mathematical expression can be described in Equations (1)-(4). The proposed curve is based on the previous experimental and analytical research conducted by the authors' research team [  
At the beginning of the curve, the initial stiffness \(k_{0}\) represents the bond stiffness between the concrete slab and the steel beam. After a small slip \(\delta_{0}\), the natural bonding surface fails, and the foamed plastic is pressed. Due to the small stiffness of foamed plastics, the stiffness in this stage is regarded as 0. When the slip \(\delta\)reaches \(t_{\text{s}}\)/3, the screw will be under shearing stress and the shear capacity \(V_{\text{u}}\) is also identical to that of the traditional headed stud. Lastly, when \(\delta\)is larger than \(\delta_{\text{u}}\), the shear force descends and fracture occurs on the connector. For the URSP connectors, the key factors are determined by referring to the experimental findings of [PERSON] [45], namely, \(k_{0}\) = 3.75 kN/mm, \(\delta_{0}\) = 0.15 mm, and \(t_{\text{s}}\) = 5 mm.  
For the traditional headed stud, the adopted constitutive relationship is proposed by [PERSON] et al. [32], as given in Equation (5) and Figure 7. Here, \(V\) is the shear force, \(V_{\text{u}}\) is the ultimate shear capacity, \(s\) is the interfacial slip, m and n are parameters obtained from experimental measurement. In most cases, \(m\) and \(n\) take the value of 0.558 and 1 mm\({}^{-1}\), respectively [46].  
\[V=V_{\text{u}}(1-\varepsilon^{-n_{\text{s}}})^{m} \tag{5}\]
# Classification of Precursor MicroRNAs from Different Species Based on K-mer Distance Features
## 1 Background
The tool izMiR implements all ML-based approaches and especially dissects the various feature sets that are used in ML-based miRNA prediction [20]. K-mers are short nucleotide sequences with the length k, and they have been used for ML-based ab initio detection of pre-miRNAs from the onset [25]. We were interested in finding out whether the pre-miRNA sequence (not considering the secondary structure) contains enough discriminating power to categorize miRNAs among species. It could be hypothesized that there may be sequence-based recognition via the protein machinery of the miRNA pathway. Additionally, we took into account the phylogenetic relationship among species to investigate whether there is a discernable difference allowing the separation of the miRNAs of various species. We set forth to answering these questions while removing the uncertainty of the quality of the negative data. For this, we employed the pre-miRNAs of one species/clade as positive data and the pre-miRNAs of another species/clade as negative data [26]. Hence, we only used positive data for two-class classification. For ML with the selected data, we used the random forest algorithm. We found that species that are phylogenetically distantly related could be distinguished by ML models established in this way. In another study [27], we had employed information-theoretic features to investigate their ability to categorize miRNAs among species/clades. However, we found that the usage of information-theoretic features did not outperform k-mers for this type of analysis. In this work, novel features based on k-mers, more specifically the distance among k-mers within a sequence, were used. The results were compared to k-mer performance and other previously published features. The average performance of the k-mer distance features was a bit higher than k-mers alone (\(\sim\)1%). On the other hand, they were somewhat less effective than selected features from all categories (\(\sim\)0.6% on average). We did not see any increase in performance when combining the k-mer distance-based feature set with the simple k-mer features. The novel k-mer distance features were, however, better at categorizing miRNAs at closer evolutionary distances. In conclusion, k-mer distance features can be useful for future studies aimed at categorizing miRNAs into their species of origin, especially for closer evolutionary distances. Categorizing miRNAs to their species of origin can aid in contaminant detection when predicting miRNAs from next-generation sequencing data. It further adds another line of evidence for miRNAs predicted from genomes and finally may present a different vantage point for the analysis of miRNA evolution. It is our aim for the future to create an automated system that can categorize miRNAs into their clade/species of origin.
# Low Discrepancy Sparse Phased Array Antennas
## 2 Sampling Points on a Planar Aperture
### Low Discrepancy Sampling Approaches  
A low discrepancy sequence (LDS) is a set of points positioned on a surface that fills the aperture more uniformly than an equal set of uncorrelated random points [41]. Figure 2 shows the points on the x-y plane of a unit square for the case of uniform, random, and LDS Poisson disk distributions.
# Monitoring of Training Load and Body Composition in Elite Male Kayakers
## 2 Materials and Methods  
### Participants  
After receiving information about the study's objectives, experimental protocol, and procedures, eight elite male kayakers [5], which were training under the supervision of the same coaching staff, gave their written consent to participate in this study (Table 1). All keyakers were familiarized with the physiological and body composition procedures prior to the experiments. They were evaluated in three distinct moments: the beginning of the keyaking season (preparatory period, M1; first week of October), beginning of the transition period (M2; last week of February), and the end of the transition period, i.e., the beginning of competitive period of the season (M3; first week of April). M3 corresponded to the participation in international competitions. All participants were instructed to abstain from intensive training for 24 h prior to each experiment.  
The training program applied by the coach during the preparatory and transition period are described in Tables 2 and 3, respectively. Likewise, it is possible to observe the keyaking external training load, i.e., volume (km) and intensity (volume \(\times\) strokes per minute).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Mean \(\pm\) SD** & **95\%CI** \\ \hline
**Age** (y) & 23.1 \(\pm\) 5.6 & 19.3 to 27.0 \\
**Body Weight** (Kg) & 80.0 \(\pm\) 8.8 & 73.9 to 86.1 \\
**Height** (cm) & 177.0 \(\pm\) 6.9 & 172.2 to 181.8 \\
**Arm span** (cm) & 183.4 \(\pm\) 7.4 & 178.3 to 188.5 \\ \hline \hline \end{tabular} Data are presented as mean, standard deviation (SD), and 95% confidence interval (95%CI).  
\end{table}
Table 1: Elite male kayakers’ characteristics at the beginning of the keyaking season (N = 8).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Sessions**} & \multicolumn{1}{c}{**Monday**} & \multicolumn{1}{c}{**Tuesday**} & \multicolumn{1}{c}{**Wednesday**} & \multicolumn{1}{c}{**Thursday**} & \multicolumn{1}{c}{**Friday**} & \multicolumn{1}{c}{**Saturday**} & \multicolumn{1}{c}{**Sunday**} \\ \hline \multirow{6}{*}{**Moming Session**} & **Kayaking** & **Kayaking** & **Kayaking** & & & **Kayaking** & **Kayaking** & \\  & \(12\) km & \(2\times 30^{7}\) & \(14\) km & & & 14 km & 16 km & \\  & & Recovery: & & & & & & \\  & & 3 min & & Cycling & & & & \\  & SPM: 60 & SPM: 65 & SPM: 60 & & & & & \\  & Low intensity * & Low intensity * & Low intensity * & & & & \\  & & & & & & & \\ \hline \multicolumn{1}{c}{**Moming Session Volume and Intensity Description**} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & & & & \\ \multicolumn{1}{c}{(km)} & 12 & 15 & 14 & - & 14 & 16 & - \\
## 2 Materials and Methods
### Specim Preparation and Test Methods
#### 2.3.2 Exposed Aggregate Depth Test  
Exposed aggregate depth refers to the height of exposed coarse aggregates on the surface of EAC, serving as a critical indicator for evaluating the exposing effect. According to relevant literature [30], the optimal exposed aggregate depth should be controlled within the range of 2-3 mm. The disc method [17] was employed to measure the exposed aggregate depth. During testing, a transparent plexiglass disc with circular holes was placed on the surface of the EAC slab. A measurement probe was vertically inserted into the hole. If the reading on the probe exceeded the disc thickness, the measurement was considered valid. Conversely, if the reading was less than the disk thickness, it indicated that the measurement point was located at a protrusion of coarse aggregates, rendering the data invalid. Based on this method, ten random test positions were uniformly selected across the disk surface, with the mean of these ten measurements serving as a single valid measurement. The aforementioned steps were repeated five times to obtain five valid measurements, and the coefficient of variation (CV) of these five values was calculated. When \(CV\) exceeded 0.05, the disk was repositioned for additional sampling; otherwise, the exposed aggregate depth was obtained by subtracting the disk thickness from the valid mean measurement. The specific testing setup is illustrated in Figure 2.
## 5 Conclusions and Future Works  
In this study, a series of experiments were conducted to inspect subsurface defects in polypropylene/glass fiber (PP-GF) composites using the LIT. Various defect conditions were simulated for defects that may occur during product production and operation in the aerospace and automobile industries. Two representative multiple lock-in frequency modulated algorithms, viz., HA and FFT, were discussed. Phase images from raw data calculated by HA and FFT algorithms were considered. In the qualitative aspect, all algorithms showed appropriate and satisfactory results. However, shallow defects with a defect depth of 1 mm or less were hardly noticeable. Defect length and width hardly affect defect detection using LIT. On the other hand, the defect depth had a significant influence on the LIT detectability, and it has been proven that several tests are needed to select the optimal modulation frequency. That is, detectability tended to differ depending on the amount of energy of the total heat source provided. Defects with a depth of more than 1.4 mm showed the best phase contrast and SNR at a modulation frequency of 0.05 Hz, whereas defects below a defect depth of 1.4 mm showed poor results at all modulation frequencies. Additionally, the HA has superior detectability than FFT in terms of phase contrast and SNR. Finally, the defects showing sufficient phase contrast of 0.2 radian or more showed a correlation indicating an SNR of 30 dB or more.  
Based on these results, in order to apply LIT to actual industrial sites, it is necessary to consider the dimension of defects and appropriate thermal excitation conditions. In this way, LIT can be utilized as a part of defect characterization in composites. However, in this study, the limited sample condition as a thin plate was applied, and investigation into real-field defect conditions is needed. In addition, improving advanced signal processing and image processing for deep defects will provide innovative solutions that can be used for defect characterization to enhance the reliability of composites.  
Conceptualization, W.K., Y.C. and R.S.; data curation, R.S.; formal analysis, R.S. and S.L.; funding acquisition, W.K.; investigation, W.K., Y.C., R.S. and S.L.; methodology, Y.C., R.S. and S.L.; project administration, W.K.; resources, W.K.; software, Y.C., R.S. and S.L.; supervision, W.K.; validation, W.K. and R.S.; visualization, R.S.; writing--original draft, Y.C. and R.S.; writing--review and editing, S.L. and W.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF-2022R1A2C1011413) funded by the Korean Government, Ministry of Education, Science and Technology (MEST).  
Figure 17: SNR value according to different defect depth in sample group 4: (**a**) HA, (**b**) FFT.
## 1 Introduction and Related Works
The next condition of successful implementation of an assisted assembly into the production process is synchronization with the recognition system mainly based on vision devices with some SMART features like object detection, assembly parts identification and localization with their actual orientation in a workspace. Based on the obtained knowledge from the research on diagnostics of errors at the component surface by vision recognition systems using machine learning algorithms [22] we have started to use a convolutional neural network (CNN) for the recognition of standardized industrial parts (hexagon screw/nut and circular hole assembly elements) trained by real image input [23]. CNN with deep learning can work reliably for parts recognition, but the problem is manual preparation of input data for the learning process, because a very large quantity of input samples need to be prepared, usually several hundred for one part. The solution is to replace real image samples with 3D virtual models [24], but an important task is an automated sample generation from 3D models, which can be simplified using a web interface [25]. Besides automated input data preparation for CNN training, automated image analysis is also important. Some principles of this process were described in [26]. An interesting case study on the recognition of mark images using deep CNN was published in [27]. A methodology for synthesizing novel 3D image classifiers by generative enhancement of existing non-generative classifiers was proposed and verified in [28]. A viewpoint estimation in images using CNNs trained with rendered 3D model views was published in [29] and an accurate and fast CNN-based 6 DoF object pose estimation using synthetic training in [30]. The sections of this article are structured in the following manner: following the introduction and related works in this Section a methodology of CNN training using 3D virtual models is introduced in Section 2. Section 3 describes the experimental assisted assembly work cell and the assembled product, in Section 4 the principles of the 3D virtual model preparation and 2D sample generation for CNN training are presented. Section 5 contains results and discussion, including implementation of parts recognition into the collaborative work cell. Finally, Section 6 presents a summary of the article along with some ideas for future work. The main novelty and the innovation contribution of the article is a complex methodology for CNN training by virtual 3D models and design of a communication framework for assisted assembly devices like collaborative robot and mixed reality device.
# Non-Terrestrial Networks with UAVs: A Projection on Flying Ad-Hoc Networks
## 2 Structure of NTN and its Key Components
Furthermore, the UAV networks can support services proposed by the international telecommunication union (ITU), which are enhanced mobile broadband (eMBB), massive machine type communications (mMTC), and ultra-reliable and low latency communications (URLLC) [10; 62; 63]. For these use cases, the actual value of UAVs is in the payloads they can carry and their ability to effectively establish networks of flying nodes, i.e., FANETs.
## 3 Results  
Figure 8 shows the average values of all NASA-TLX scores. As shown in Figure 8, the workload of the MR method is lower in all aspects compared to the traditional method. The MR method significantly reduced participants' mental effort. Mental demand decreased from 45.50 with the traditional method to 18.67 with the MR method, and the mental demand for these two methods was statistically significantly different (\(p<0.001\)). Physical demand decreased from 27.00 with the traditional method to 17.50 with the MR method, and the physical demand for the two methods was statistically significantly different (\(p<0.001\)). Temporal demand decreased from 38.50 with the traditional method to 21.27 with the MR method, and the temporal demand for these two methods was statistically significantly different (\(p<0.001\)). Performance demand decreased from 37.50 with the traditional method to 20.83 with the MR method, and the performance demand for these two methods was statistically significantly different (\(p<0.001\)). Effort decreased from 48.33 with the traditional method to 26.57 with the MR method, and the effort for these two methods was statistically significantly different (\(p<0.001\)). The frustration level decreased from 27.83 with the traditional method to 21.10 with the MR method, but the level of frustration for these two methods is statistically insignificantly different (\(p>0.05\)).  
Figure 9 shows the average scores from the SUS questionnaire. For the method proposed in this paper, users gave a total score of 80.92. On the Bangor adjective rating scale, this score corresponds to a \"B\" rating, categorized as EXCELLENT. In contrast, the traditional method's score is only 63.25, which is far below our method and even below the SUS average score of 70. The proposed method achieved an average learnability score of 75.83 and an average usability score of 82.19, which is higher than the traditional method's scores of 53.75 and 65.63, respectively. The total score, the learnability score, and the usability score for the two methods are statistically insignificantly different (\(p<0.001\)). Table 3 shows the inferential statistics for all measures.  
Figure 8: Statistical graphs of NASA-TLX for the traditional method and our method during the task [15]. The whiskers indicate the mean +/ – standard deviation, and * denotes that data are statistically significantly different, \(p<0.001\).  
Finally, the proposed method can complete tasks within 2 min and significantly reduce collision compared to the traditional method, ensuring the safety of operation.
# Low-Latency Bit-Accurate Architecture for Configurable Precision Floating-Point Division
## 3 Predict-Correct Algorithm for Division  
Inspired by fast division method [18], this paper proposes a Predict-Correct algorithm which will increase iteration speed by bringing about \(n\) more quotient bits than fast division without consuming many areas.  
Our proposed division is similar to fast division in that both use multiplication for divisor multiple formation and look-up tables to obtain an initial approximation to the reciprocal of divisor. Their differences lie in the number and type of subsequent operations used in each cycle and the technique used for quotient-digit selection.  
Figure 1: Basic layout of very high-radix class algorithm.
# Efficient Tensor Sensing for RF Tomographic Imaging on GPUs
## 4 The Implementation and Optimization of Efficient GPU Tensor Sensing
### Optimizations of the GPU Tensor Sensing
```
0: Data on CPU memory: randomly-initialized \(\mathbf{U}^{0}\), measurement vector \(\mathbf{y}\in\mathbf{R}^{M}\), matrix \(\mathbf{A}\in\mathbb{R}^{M\times N_{1}N_{2}N_{3}}\) converted from \(M\) sensing tensors \(\mathcal{A}_{m}\)
0:\(\mathbf{X}\in\mathbb{R}^{N_{1}N_{3}\times N_{2}}\)
1: apply for memory on GPU device: \(dy,dA,dW,dyL\) (\(dA(\mathcal{A})\) means that the content in \(dA\) is \(\mathcal{A}\), the same below)
2: data transfer: \(\mathcal{A},\mathbf{y},\mathbf{U}^{0}\overset{cudaMemcpyHostToDevice}{ \longrightarrow}dA(\mathcal{A}),dy(\mathbf{U}^{0}),dyL(\mathbf{y})\)
3:for\(i=0\to IterNum\)do
4:\(dW(\text{uninitialized}),dA(\mathcal{A}),dy(\mathbf{U}^{i})\overset{cublas<t>gemmStridedBatchd}{ \longrightarrow}dW(\mathcal{A}\operatorname{diag}(\mathbf{U}^{i})),dA( \mathcal{A}),dy(\mathbf{U}^{i})\)
5:\(dy(\mathbf{U}^{i}),dyL(\mathbf{y})\overset{cudaMemcpyDeviceToDevice}{ \longrightarrow}dy(\mathbf{y}),dyL(\mathbf{y})\)
6:\(dW(\mathcal{A}\operatorname{diag}(\mathbf{U}^{i})),dy(\mathbf{y})\overset{ cusolver<t>qr}{\longrightarrow}dy(\mathbf{V}),dW(\text{uninitialized})\)
7:\(dW(\text{uninitialized},dA(\mathcal{A}),dy(\mathbf{V}^{i}))e^{cublas<t>gemmStridedBatchd}dW(\mathcal{A} \operatorname{diag}(\mathbf{V}^{i})),dA(\mathcal{A}),dy(\mathbf{V}^{i})\)
8:\(dy(\mathbf{V}^{i}),dyL(\mathbf{y})\overset{cudaMemcpyDeviceToDevice}{ \longrightarrow}dy(\mathbf{y}),dyL(\mathbf{y})\)
9:\(dW(\mathcal{A}\operatorname{diag}(\mathbf{V}^{i})),dy(\mathbf{y})\overset{ cusolver<t>qr}{\longrightarrow}dy(\mathbf{U}^{i}),dW(\text{uninitialized})\)
10:endfor
11:return\(\mathbf{X}=\mathbf{U}\mathbf{V}\)
```  
**Algorithm 3** Computation flow of the tensor sensing on the GPU.
# Machine Learning and Deep Learning Based Model for the Detection of Rootkits Using Memory Analysis
###### Abstract  
Rootkits are malicious programs designed to conceal their activities on compromised systems, making them challenging to detect using conventional methods. As the threat landscape continually evolves, rootkits pose a serious threat by stealthily concealing malicious activities, making their early detection crucial to prevent data breaches and system compromise. A promising strategy for monitoring system activities involves analyzing volatile memory. This study proposes a rootkit detection model that combines memory analysis with Machine Learning (ML) and Deep Learning (DL) techniques. The model aims to identify suspicious patterns and behaviors associated with rootkits by analyzing the contents of a system's volatile memory. To train the model, a diverse dataset of known rootkit samples is employed, and ML and deep learning algorithms are utilized. Through extensive experimentation and evaluation using SVM, RF, DT, k-NN, and LSTM algorithms, it is determined that SVM achieves the highest accuracy rate of 96.2%, whereas Execution Time (ET) shows that k-NN depicts the best performance, and LSTM (a DL model) shows the worst performance among the tested algorithms. This research contributes to the development of advanced defense mechanisms and enhances system security against the constantly evolving threat of rootkit attacks.  
memory analysis; rootkits; deep learning; machine learning; execution time