esa-sceva/satcom-chunk-collection huggingface dataset:
# Nonlinear Analysis and Reliability Analysis of Multilink Mechanism Considering Mixed Clearance
## 2 Creation of Kinematic Pair Clearance Model
### Establishment of a Kinematic Model for the Clearance in Prismatic Joints
#### 2.3.1 Establishment of Normal Impact Force Model  
When corners of two adjacent sliders contact with guide rail, the impact force generated between sliders and the guide rail acts on center of mass of embedded area, as shown in Figure 5a.  
Impact force is represented as:  
\[F_{n}=K\delta_{T} \tag{27}\]  
where the factor of stiffness \(K\) can be represented as [44]:  
\[K=\frac{a}{0.475(\sigma_{m}+\sigma_{n})} \tag{28}\]  
where a denotes half perimeter of the slide's rectangular contact surface, \(a=T+L\), as illustrated in Figure 2, \(\sigma_{m}=\frac{1-v_{m}^{2}}{E_{m}}\) and \(\sigma_{n}=\frac{1-v_{m}^{2}}{E_{m}}\), the symbols \(v_{m}\) and \(v_{n}\) indicate [PERSON]'s ratios of the material, and \(E_{m}\) and \(E_{n}\) represent elastic moduli.  
As shown in Figure 5b, when one or both of the opposing slider corners come into contact with the guide rail surface, assuming the contact occurs between a spherical surface and a plane, the contact force model can be represented as [44]:  
\[F_{n}=K\delta_{T}{}^{n}+D\dot{\delta}_{T} \tag{29}\]  
where the stiffness coefficient \(K\) can be formulated as:  
\[K=\frac{4}{3(\sigma_{m}+\sigma_{n})}\sqrt{R_{mn}} \tag{30}\]  
Figure 5: Contact modes between slider and guide rail: (**a**) impact occurs through surface contact; (**b**) impact occurs through contact between a plane and a spherical surface.  
Damping factor \(D\) is represented as:  
\[D=\frac{3K\big{(}1-c_{e}^{2}\big{)}\delta_{T}{}^{n}}{4\dot{\delta}^{(-)}} \tag{31}\]  
where \(R_{mn}\) represents the radius of the curvature of the slider's rotational angle.
## 3 Results
### Identification of Candidate Genes for Stable Loci  
According to the phenotype-SNP network of the GWAS results (Figure 5), the stable significant SNPs and pleiotropic SNPs were determined as stable loci. In addition, significant SNPs for HI and significant SNPs adjacent to the stable loci were also listed as stable loci. Twenty-two SNPs associated with the agronomic traits were filtered as promising QTLs. Potential candidate genes were identified in the intervals of significant marker-trait associations by investigating all genes within shared LD blocks or with physical proximity within 100 KB upstream or downstream of the SNPs (Figure S6). The genomic regions from 100 KB of both flanks of the significant SNPs were inspected for putative candidate genes for each agronomic trait. The candidate genes of different QTLs were selected by spatial and temporal expression and functional annotations. A total of 39 putative candidate genes were identified in these intervals (Table 1), and the gene expressions during bolting, initial flowering, full-bloom, podding, and maturation stage in the BrassicaEDB were displayed (Figure 6 and Table S4). All of the candidates were highly expressed in certain tissues of the bud, stem, carpel, inflorescence tip, silique pericarp, embryo, and so on. Figure 5: Phenotype-SNP network for 14 traits in rapeseed. Traits are solid triangles and SNPs are solid circles. Different traits are displayed in different colors. The links represent the significant correlations among them and the colors that vary from blue to red represent the partial correlation coefficients. SNPs associated to more than one trait or environment or adjacent to other significant SNPs are in pink. SNPs with a physical distance of fewer than 200 KB are connected by dotted lines. For HI, _BnaC05g11300D_ (_SCP50.1_, Serine carboxypeptidase-like 50), _BnaC05g11310D_ (_SCP50.2_), and _BnaC05g11350D_ (_CRP_, Cysteine-rich peptide family) were located in the region of C05_6554451. They showed higher expression in inflorescence tip, embryo, and root. For TSW, three genes including _BnaA03g43740D_ (_TRANS11_, Translocase 11), _BnaA03g43820D_ (_AG1_, Floral homeotic protein AGAMOUS), and _BnaA03g43840D_ (_MYA1_, MYOSIN 1) were located in the candidate region of A03_22103527. For SL, _BnaA09g39690D_ (_PGF10_, Polygalaturonase clade F 10) and _BnaA09g33760D_ (_UBQ5_, Ubiquitin 5) were identified in the A09_28188769 interval, and _BnaC07g36830D_ (_PPR334_, Pentatricopeptide repeat 334)
# Deep Cross-Network Alignment with Anchor Node Pair Diverse Local Structure
## 3 Method
### Network Embedding with the Diverse Local Structure of Anchor Node
#### 3.2.1 Masked Network Encoder Based on Variational Graph Convolution
\(\mathbf{\tilde{A}}^{s}=\mathbf{D}^{(s)-\frac{1}{2}}(\mathbf{A}^{s}+\mathbf{I})\mathbf{D}^{(s)- \frac{1}{2}}\) is the symmetrically normalized adjacency matrix, and \(\mathbf{D}^{(s)}\in R^{n_{s}\times n_{s}}\) is a diagonal matrix containing each node's degree in \(G^{s}\). The same is for \(\mathbf{\tilde{A}}^{t}\). Through the above masked network encoder, nodes in two different networks, \(G^{s}\) and \(G^{t}\), are embedded into a low-dimension embedding space, and some labeled anchor nodes' influence is ignored by using the masked adjacency matrix. We denote the Gaussian distribution \(\mathcal{N}(\mu^{s}_{i},\sigma^{s}_{i})\) of each node in \(G^{s}\) as \(\mathbf{h}^{s}_{i}\), where \(\mu^{s}_{i}\) represents the structural information of \(v^{s}_{i}\) and \(\sigma^{s}_{i}\) implies the possible uncertainty of \(\mu^{s}_{i}\). Similar for \(\mathbf{h}^{s}_{i}\). For each network we reconstruct the input adjacency matrix, which represents the network's neighborhood structure, to maintain the network's global structure,  
\[\mathcal{L}_{rec}=\left\|\mathbf{\tilde{A}}^{s}-(\mathbf{A}^{s}+\mathbf{I})\right\|^{2}+ \left\|\mathbf{\tilde{A}}^{t}-(\mathbf{A}^{t}+\mathbf{I})\right\|^{2}, \tag{4}\]where \(\hat{A}^{s}=\mathbf{Z}^{s}(\mathbf{Z}^{s})^{T}\), \(\hat{\mathbf{A}}^{t}=\mathbf{Z}^{t}(\mathbf{Z}^{t})^{T}\) are the reconstructed adjacency matrix. \(\mathbf{Z}^{s}\) and \(\mathbf{Z}^{t}\) are the stochastic latent variables matrix, whose \(i\)-th row can be sampled from the Gaussian distribution \(\mathbf{h}^{s}_{i}\) and \(\mathbf{h}^{t}_{i}\):  
\[\mathbf{z}^{s}_{i}=\mu^{s}_{i}+\varepsilon\odot\sqrt{\sigma^{s}_{i}},\qquad\mathbf{z}^ {t}_{i}=\mu^{t}_{i}+\varepsilon\odot\sqrt{\sigma^{s}_{i}},\qquad\varepsilon \sim\mathcal{N}(0,\mathbf{I}). \tag{5}\]  
In addition, to ensure that the learned representations are indeed Gaussian distributions, we use the following regularization to constrain the latent representations:  
\[\mathcal{L}_{reg} =\sum_{i=1}^{n_{s}}KL(\mathcal{N}(\mu^{s}_{i},\sigma^{s}_{i})|| \mathcal{N}(0,\mathbf{I})) \tag{6}\] \[+\sum_{i=1}^{n_{t}}KL\big{(}\mathcal{N}(\mu^{t}_{i},\sigma^{t}_{i })||\mathcal{N}(0,\mathbf{I})\big{)}.\]  
\(KL[q(\cdot)\parallel p(\cdot)]\) is the Kullback-Leibler divergence between \(q(\cdot)\) and \(p(\cdot)\), it quantifies the difference between two probability distributions \(q(\cdot)\) and \(p(\cdot)\). Therefore, we obtain the following training objective function to learn node representations:  
\[\mathcal{L}_{cn}=\mathcal{L}_{rec}+\mathcal{L}_{reg}. \tag{7}\]
## 4 Experimental Results and Analysis  
An experimental design for the proposed SSO-DE algorithm in this study is conducted in Section 4.1 to find out the best parameters of this algorithm for the CCHP optimization problem. The parameter setting and experimental situation of other algorithms are explained in Section 4.2. Finally, in Section 4.3, experimental results are detailed, represented, and compared among the proposed SSO-DE and other algorithms.
## 6 Limitations and Challenges  
This section discusses the limitations of the proposed BOTA-LSTM model and its potential challenges:  
1. The proposed model uses historical data for training, which makes it effective for predicting known patterns. However, the predictive capability of the model may be limited when it is faced with new or anomalous situations that significantly deviate from the training data. This limitation also exists in current machine-learning models.
2. The proposed model is a data-driven model. Therefore, it relies heavily on the quality of the monitoring data from sensors and the geological data from experimental tests.
3. In this study, the shield machine tunnels through the soft top and hard bottom strata. A standardization method for stratum parameters is used, which helps the machine learning model handle and integrate data from different strata. However, more advanced methods, such as physics-informed neural networks, may be necessary to more accurately reflect the specific characteristics of soft top and hard bottom strata.
# A CNN-LSTM-Attention Model for Near-Crash Event Identification on Mountainous Roads
## 4 Near-Crash Event Identification Model
### LSTM Layer  
Following the CNN layer, the extracted features are processed by the LSTM layer, which plays a pivotal role in capturing temporal dependencies. The LSTM network, a variant of recurrent neural network, incorporates gating mechanism [31] to regulate the flow of information. It consists of three control gates: (1) the input gate, which controls the information entering the memory cell; (2) the forget gate, which manages the portions of historical data to discard, and (3) the output gate, which determines the inclusion of current memory in the final output. This design effectively counters the challenges of gradient vanishing and exploding that are prevalent in regular recurrent neural networks [32].  
As shown in Figure 6, the used LSTM network typically features three control gates (forget gate, input gate, and output gate) along with a conveyor belt for carrying long-term memory. The operations of these gates are governed by Equations (7)-(12), ensuring control over the information's entry, retention, and output within the network. Through these gating mechanisms, the LSTM network controls the extraction, discarding, and updating of historical information in a refined manner, thus maintains the network's sensitivity to both short-term and long-term dependencies without succumbing to gradient issues [33].  
\[f_{t}=\sigma(W_{f}\cdot[x_{t},h_{t-1}]+b_{f}) \tag{7}\]  
\[i_{t}=\sigma(W_{i}\cdot[x_{t},h_{t-1}]+b_{i}) \tag{8}\]  
\[\widetilde{C}_{t}=\tanh(W_{\text{C}}\cdot[x_{t},h_{t-1}]+b_{\text{C}}) \tag{9}\]  
\[C_{t}=f_{t}\odot C_{t-1}+i_{t}\odot\widetilde{C}_{t} \tag{10}\]\[o_{t}=\sigma(W_{o}\cdot[x_{t},h_{t-1}]+b_{o}) \tag{11}\]  
\[h_{t}=o_{t}\odot\tanh(C_{t}) \tag{12}\]  
where the sigmoid function (\(\sigma\)) and hyperbolic tangent (\(tanh\)) are activation functions; (\(W_{f}\), \(b_{f}\)), (\(W_{i}\), \(b_{i}\)), (\(W_{C}\), \(b_{C}\)), and (\(W_{o}\), \(b_{o}\)) are respectively the weight matrices and bias terms for the forget gate, input gate, cell state, and output gate; \(x_{t}\) represents the input vector, \(h_{t-1}\) the hidden layer state sensitive to short-term conditions, \(C_{t}\) the cell state that stores long-term conditions, \(f_{t}\) the output of the forget gate, \(i_{t}\) the output of the input gate, \(\overset{\sim}{C}_{t}\) the candidate value of the input gate's output, \(o_{t}\) the output of the output gate, and \(h_{t}\) the output of the LSTM model.
# Performance Optimization of Object Tracking Algorithms in OpenCV on GPUs
## 4 Performance Optimization of the OpenCV Object Tracking Algorithm  
Based on the analysis in the previous section, we propose two optimization strategies. First, we improve the utilization of the GPU hardware by improving the parallelism of the OpenCL kernels. This strategy can be applied to the object detection algorithm since it shows low kernel occupancy, which is a major culprit of under-utilizing the parallelism  
Figure 7: Experimental results of the Farneback optical flow algorithm by resolution. (**a**) Frame processing time. (**b**) Frame processing breakdown.  
of GPU hardware. Second, we propose to alleviate the frequent kernel call overheads of an OpenCL program. The frequent kernel calls not only incur the direct cost of kernel call overhead, but also waste unnecessary time to transfer data between consecutive kernels. This strategy is suitable for the optical flow algorithm. The following two subsections describe the two proposed optimization strategies, respectively.  
Briefly, our performance optimization framework can be summarized as follows:  
* When the VGPR usage is high and the LDS memory has free space, our optimization strategy is to remap variables from VGPRs to LDS memory (Section 4.1.1).
* When the number of generated active wavefronts is lower than the total capacity available in the GPU, our optimization strategy is to increase the number of wavefronts to fully utilize the SIMD (Section 4.1.2).
* When many kernel invocations are observed from the profiling results, our optimization strategy is to merge consecutive kernels that have an identical global/local work size into one merged kernel (Section 4.2).
# Smart Warehouse Management System: Architecture, Real-Time Implementation and Prototype Design
## 6 Design and Development of the Prototype
### Receiver Module  
This is a module designed to receive information from the WMS. The data being entered and updated in the communication network of the WMS are displayed here [46]. This can be a platform for user accessibility and interaction. Various platforms can be used for this purpose, such as a web or mobile application, a customized user interface, or a specifically designed software [47]. For the case study, the frontend of the web application was designed using the Angular framework along with a database developed using a SQL server and.NET backend. The web application can be used on all the devices that require access to the data. The scanned data reach the subscriber via the broker, then are displayed on the frontend using JavaScript and WebSockets. The frontend provides different functionalities such as user authentication, purchase and sales records, and inventory stock. A customized frontend software was designed for every department that catered to their needs. Different departments share tables in the database to exchange information so that the data are accessible to every element in the system at all times. A centralized database was also maintained in addition to the databases of each department.
## 2 Results and Discussion  
### Characterization of Silica Particles  
Surface functionalization of silica particles with mPEG ligands was confirmed by spectroscopic and gravimetric techniques. Surface area and pore volume of bare and mPEG-grafted silica particles were determined by Brunauer-Emmett-Teller (BET) analysis.
# No-Reference Quality Assessment of Transmitted Stereoscopic Videos Based on Human Visual System
## 6 Conclusions  
This study aims of incorporating binocular rivalry measure in QA which will lead us to design a robust error-resilient 3D video communication free from perceptual ambiguity and binocular rivalry with the focus on developing error concealment strategies that are applicable in real-time and various categories of 3D displays which do not deteriorate the user's visual perceptual comfort. To avoid the detrimental effect of binocular ambiguity and visual discomfort, the transmission system could be designed to take into account these challenging psychovisual issues; for example, based on feedback regarding the measure of 3D video quality at the receiver end, parameters of the transmission system could be changed \"on the fly\" to mitigate distortions and generate 3D views with minimal distortions [44]. To mitigate the loss of quality, the transmission system can allocate extra resources to that view or enhance the level of protection to reduce error for that 3D video channel. This type of distortion measure or impairment meter is useful for assuring error-resilient video communication and boosts the possibility of efficiently fusing 3D video content and improving overall user QoE.  
Conceptualization, M.M.H. and J.F.A.; methodology, M.M.H.; software, M.M.H.; validation, M.M.H., M.A.I. and S.R.; formal analysis, M.M.H.; investigation, M.M.H. and S.R.; resources, M.A.I.; data curation, M.M.H. and M.A.I.; writing--original draft preparation, M.M.H.; writing--review and editing, S.R., M.R.F. and J.F.A.; visualization, S.R.; supervision, M.R.F. and J.F.A.; project administration, J.F.A. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by the University of Dhaka.  
Not applicable.  
The authors would like to express their gratitude to the University of Dhaka for funding the project and Professor [PERSON] for continuously providing research guidance.  
The authors declare no conflict of interest.
## 2 Materials and Methods  
### Experimental Design and Data Collection  
#### 2.1.1 Experiment Materials  
The corn samples tested are from Kaiyuan City, Liaoning Province, China, and belong to the 'M81' variety, which is commonly grown in northeastern China. The samples are freshly harvested corn kernels from the current season and have a moisture content ranging from 23.01% to 21.18%. The experimental site is Shenyang City, Liaoning Province, China, which is located at 123\({}^{\circ}\)25\({}^{\prime}\)31\({}^{\prime\prime}\) E, 41\({}^{\circ}\)48\({}^{\prime}\)12\({}^{\prime\prime}\) N.
## 9 Limitations and Future Perspectives
Polymer chemistry specialists have developed methods to design multi-branched polymers that can be used for targeted drug delivery, imaging, and even precision medicine. The incorporation of targeting motifs allows for more effective retention through ligand-receptor interactions. Stimuli-responsive polymers offer a more direct approach to achieve on-demand release in response to the abnormal microenvironment conditions of cancerous tissues. Star polymers offer opportunities for intelligent self-assembly design through synthetic manipulation of the composition of the branched polymeric structure, facilitating accumulation and absorption at the site of disease for increased biodisponibility and therapeutic effect. It is true that the direct comparison of formulations based on linear block copolymers with those based on star compositions is a crucial aspect that has not been sufficiently addressed in polymer research. Star compositions have potential advantages over linear block copolymers in terms of physical properties, stability, biodegradability, and functionalization. However, the complexity of controlling all properties, such as overall and individual molecular masses of polymeric arms, hydrophilic fraction, etc., makes direct comparison between these two types of polymers challenging. Additionally, it may be difficult to generalize results from one study to another, as formulations can vary significantly depending on the synthesis method, reaction conditions, and monomers used. Despite this, comparative analysis of the properties of linear block copolymers and star compositions is important as it can help determine which structure is best suited for a specific application. The results of such analyses can also provide further justification for the use of branched structures in the design of effective therapies. In summary, although direct comparison between linear block copolymers and star compositions is challenging due to the complexity of controlling all properties, it is important to continue research in this area to determine which structure is best suited for a given application. The results of such analyses can provide further justification for the use of branched structures in the design of effective therapies.
# Integration of Machine Learning-Based Attack Detectors into Defensive Exercises of a 5G Cyber Range
## 9 Exercises
### DoH Flooding Attack Detection Scenario
#### 9.2.3 Application and Integration of Machine Learning for DoH Flooding Attack Detection
In conclusion, similar to the cryptomining exercise, this exercise provides a scenario that perfectly mimics a real-life situation, tasking the trainee to evaluate different ML models to compare their performance and allowing the end user to judge which one is best suited to their particular needs. In this way, this exercise aims to reveal the potential of this technology and its possible pitfalls when applied incorrectly.
## 4 Experiments and Results of the Algorithm  
Selecting nine functions from the test function for testing, as shown in Table 1, IHO was compared with five optimization algorithms: HO, ZOA [24], CSO [25], BWO [26], and BES [27]. Population size N = 30. The maximum number of iterations is T = 500. The performance of the algorithm is compared with the optimal value (Best), mean (Mean), standard deviation (Std) and convergence curve. Figure 4 presents the running results of the six algorithms in nine test functions. F1, F2, and F3 are unimodal functions used to test the convergence rate of the algorithm and local search ability. Table 2 shows that IHO significantly outperformed the other five optimization algorithms on the unimodal function. For functions F1, F2, and F3, the optimal value and mean value of IHO are also optimal. This shows the excellent local search ability of IHO. Multimodal test functions are used to test the performance of the algorithm for solving multimodal complex optimization problems. Specifically, the functions F10, F12, and F13 \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Function Type** & **Function** & **Function Name** & **Minimum Value** \\ \hline \multirow{2}{*}{Unimodal testing} & F1 & Sphere & 0 \\  & F2 & Schwefel’s 2.22 & 0 \\  & F3 & Powell Sum & 0 \\ \hline \multirow{2}{*}{Multimodal testing} & F10 & Ackley 1 & 0 \\  & F12 & Penalized & 0 \\ \multirow{2}{*}{function} & F13 & Penalized 2 & 0 \\ \hline \multirow{2}{*}{Composite Test} & F17 & Branin Function & 0.3 \\  & F21 & Shekel5 & \(-\)10 \\ \multirow{2}{*}{Function} & F22 & Shekel7 & \(-\)10 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Test function. are multimodal test functions that test the ability of global search and jump out of the local optimal solution. According to the table, IHO has, compared with other algorithms, the best average on F10, F12, and F13 and searches for the smallest optimal value in all three test functions. Especially in F12 and F13, the standard deviation and mean value of IHO are orders of magnitude different from the remaining algorithms. Comparing the optimization results of the algorithm on the multimodal problem shows that IHO has good global search ability and jumps out of the local optimality. The composite benchmark function is composed of multiple simple functions. Each simple function is a univariate function but is combined to form a composite function of high dimensions. Such functions can test how much algorithms can tolerate unfeasible solutions and their ability to solve large-scale optimization problems. IHO also showed a significant advantage in these three composite functions, with the mean, optimal value, and standard deviation being the lowest among all contrast algorithms. Figure 4: _Cont._  
Figure 4: Running results of the six algorithms.