esa-sceva/satcom-chunk-collection huggingface dataset:
# Scanning Electron Microscopy Techniques in the Analysis of Gunshot Residues: A Literature Review
## 4 Discussion
### Collection Phase  
The collection phase often involves the use of adhesive stubs or swabss to gather trace evidence, such as GSRs, from surfaces or individuals involved in forensic investigations. This step is critical, as improper collection can lead to the contamination or loss of vital particles. Once collected, samples undergo a preparation phase that frequently involves mounting the samples on conductive tapes and coating them with a thin layer of conductive material, such as gold or carbon, to prevent charging under the electron beam. This preparation is essential to enhance the visibility and detection of particles under SEM [50; 51; 52; 53; 54]. Furthermore, advancements in automation for SEM-based methods, including SEM-EDX, have streamlined the process, enabling more efficient and accurate identification of trace elements like GSRs and glass fragments [27; 48; 49; 50; 51; 52; 53]. In the context of SEM-based GSR analysis, two distinct methods have been introduced to enhance the preparation process. These methods involve either direct analysis of collected particles or their extraction and concentration before SEM examination. Both approaches aim to optimize the detection of GSRs from various surfaces, including nasal hairs, which serve as a novel sampling site for forensic purposes [54]. Also, several best practices are recommended. First, conducting a reliability and validity assessment of the measurement model is crucial. This initial step ensures that the constructs being measured are both consistent and accurate, laying a solid foundation for subsequent analysis. Following this, a structural model assessment should be performed as part of a two-step analysis process. This step involves evaluating the relationships between variables, allowing for a comprehensive understanding of the underlying structure of the data. Additionally, utilizing multiple fit indices, such as the Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR), is a best practice for evaluating the model's goodness of fit. These indices provide a multifaceted view of how well the model represents the data, enhancing the robustness of the findings. Ensuring consistency between results obtained from different methods, such as the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and the Analytic Hierarchy Process (AHP), is also important for validating findings. This cross-method validation helps to confirm the reliability of the results and mitigate any method-specific biases [55].
###### Abstract  
Traditional modal testing has difficulty accurately identifying the ultralow-frequency modes of flexible structures. Ultralow-frequency excitation and vibration signal acquisition are two main obstacles. Aiming at ultralow-frequency modal identification of flexible structures, a modal testing method based on Digital Image Correlation method and Eigensystem Realization Algorithm is proposed. Considering impulse and shaker excitation are difficult to make generate ultralow-frequency vibration of structures, the initial displacement is applied to the structure for excitation. The ultralow-frequency accelerometer always has a large mass, which will change the dynamics performance of the flexible structure, so a structural vibration response was obtained through the Digital Image Correlation method. After collecting the free-decay vibration signal, the ultralow-frequency mode of the structure was identified by using the Eigensystem Realization Algorithm. Ground modal tests were conducted to verify the proposed method. Firstly, a solar wing structure was adopted, from which it was concluded that the signal acquisition using Digital Image Correlation method had high feasibility and accuracy. Secondly, an ultralow-frequency flexible cantilever beam structure which had the theoretical solution was employed to verify the proposed method and the theoretical fundamental frequency of the structure was 0.185 Hz. Results show that the Digital Image Correlation method can effectively measure the response signal of the ultralow-frequency flexible structure, and obtain the dynamics characteristics.  
flexible structure; ultralow-frequency modal identification; Digital Image Correlation method; Eigensystem Realization Algorithm +
Footnote †: journal:  
0000-0001-999-2010-185
# SPOT: Testing Stream Processing Programs with Symbolic Execution and Stream Synthesizing
## 5 Case Studies
### How JDU Path Coverage Can Be Achieved  
The second case study illustrates how SPOT behaviors in terms of coverage compared with PBT-based approaches, among which DiffStream and FlinkCheck are the most representative solutions for testing DSP programs. We take the HarassMap program as the first subject program that is introduced in [26]. It involves verifying the incidents about sexual harassment incidents from witnesses or victims and giving out the danger level to further create a map showing harassment hot spots. The incidents are streamed incident object by incident object, with each containing a zone ID (integer value) and a danger value (double value). As for the second subject program, we extend the SpeedRadar from a GitHub project [45], which detects overspeed incidents.  
Intuitively, SPOT will perform better than PBT-based approaches in terms of path coverage, as SPOT integrates symbolic execution, which will go through as many program paths as possible even under resource constraints, and generate representative data corresponding to each path. In this study, we adopt the JDU path coverage as the metric to measure the coverage of DPS programs. For HarassMap and SpeedRadar, we will compare the minimum test data required to reach 100% JDU path coverage when the test data are generated by SPOT and existing PBT-based approaches individually.  
DiffStream and FlinkCheck are the only two PBT solutions directly for DSP programs to the best of our knowledge. Generators in both of these two solutions are generating random values as input data. Therefore, we generate test data for HarassMap and SpeedRadar  
Figure 8: Symbolic analysis results for filter program.  
using the Junit-Quickcheck [24], which works the same as the generation in DiffStream and FlinkCheck. To avoid divergence of generation data in PBT, we constrain the ranges of values specified in [26; 45]. We execute the process of data generation and coverage analysis seven times, and computed the trimmed mean value by removing the maximum and the minimum number of test data required. The results are shown in Table 2.  
We have observed that SPOT can achieve 100% JDU path coverage with fewer test data than PBT methods ( 2X to 65X less). This gap can be further widened if no data ranges are constrained when randomly generating data, as this means to sample data in a larger data space.  
In summary, we have shown that SPOT has a clear benefit in generating test data than the random strategy in PBT solutions.
# Experimental and Parametric Analysis of Pull-Out Resistance of Notched T-Perfobond Connectors
## 4 Numerical Simulation  
### General  
Figure 8 displays the simulated pull-out test for the notched T-perfobond shear connector, which was conducted utilizing the finite-element method. The numerical analysis aimed to elucidate the damage and pull-out resistance mechanisms inherent to these connectors. The simulation was executed using ABAQUS versatile analysis software [26], which served as a cost-effective and time-efficient alternative to physical pull-out tests. The analysis employed a dynamic explicit method, accounting for both material and geometric nonlinearities. Given the slow loading rate of the test, the loading was treated as quasi-static.
# A Review of Deep Learning Based Methods for Acoustic Scene Classification
## 5 Network Architectures
### Convolutional Recurrent Neural Networks  
The third category of ASC algorithms is based on convolutional recurrent neural networks (CRNN). [PERSON] et al. combined in two separate input branches CNN based front-ends for feature learning with bidirectional gated recurrent units (BiGRU) for temporal feature modeling [13]. In contrast to a sequential ordering of convolutional and recurrent layers, parallel processing pipelines using long short-term memory (LSTM) layers were used in [50; 63]. Two recurrent network types used in ASC systems require fewer parameters and less training data compared to LSTM layers: gated recurrent neural networks (GRNN) [11; 12; 64] and time-delay neural networks (TDNN) [20; 65].
# Pruning Deep Neural Network Models via Minimax Concave Penalty Regression
## 2 Methodological Models
### Preliminary Investigation of Convolutional Layer Parameters
#### 2.4.3 Reconstruction of Outputs  
Upon completion of channel selection, the coefficients, \(\beta\), associated with each channel are considered to be known. The parameters, \(W\), within the model can then be reconstructed by minimizing the mean square error. The optimization objective function is formulated as follows:  
\[\underset{\widetilde{W}}{\text{argmin}}(Y-\widetilde{X}\widetilde{W}^{T})^{2} \tag{16}\]  
where \(\widetilde{X}\ =\ [\beta_{1}X_{1},\beta_{2}X_{2},\ldots,\beta_{i}X_{i},\ldots, \beta_{c}X_{c}]\), with dimensions of \(N\times ck_{h}k_{\omega}\Algorithm 1 presents the pseudocode for parameter selection, where \(\alpha\) and \(\lambda\) represent the MCP parameters. The specified tolerance, \(\tau\), indicating the permissible number of channels within specific interval ranges post-model pruning, is set at 0.02 in our experiments. For instance, when configuring a pruning ratio of 0.1, the actual channel pruning ratio is considered to reach the threshold of 0.1 within a range of 0.098 to 0.12.  
Figure 9: Parameter selection flowchart.  
```
1  Set initial parameter left = 0, right = \(\lambda\)
2  Calculate the number of channels after pruning c_new = cxratio
3  Repeat:
4  Update the value of right right = right\(\times\)2
5  Solve using coordinate descent algorithm keep_num, keep_channels = solver( \(\alpha\),right)
6  Until keep_num \(<\ c_{\mathit{max}}\) # Until the number of channels retained is less than the number of channels left for pruning
7  Repeat:
8  Calculate the median as the value of \(\lambda\), \(\lambda\) = (left + right + right ) / 2
9  Use the coordinate descent algorithm to solve for keep_num, keep_channels = solver( \(\alpha\), \(\lambda\) )
10  if keep_num \(>c_{\mathit{max}}\cdot\left(\mathrm{l}+\tau\right)\)then # Determine if the number of reserved channels is greater than the maximum value of the set range.
11  left = \(\lambda\)
12  else if keep_num \(<\ c_{\mathit{max}}\)then # Determine if the number of reserved channels is greater than the minimum of the set range.
13  right = \(\lambda\)
14  End if
15 Until \(c_{\mathit{max}}\) \(<\) keep_num \(<c_{\mathit{max}}\cdot\left(\mathrm{l}+\tau\right)\) # Until the number of retained channels is within the set range
```  
**Algorithm 1** Pseudocode for parameter selection. (# indicates an annotation on this line of pseudocode)
## 3 Proposed Framework (Fed-DTB)
### Compliance Score  
The compliance score \(\tau_{C_{i}}(t)\) measures how vehicle \(v_{i}\) complies with traffic rules and regulations at time \(t\). Compliance indicates responsible behaviour and reliability.  
Using compliance ratios \(C_{ik}\) (e.g., adherence to speed limits, stop sign compliance) and weights \(W_{k}\):  
\[\tau_{C_{i}}(t)=\frac{\sum_{k=1}^{n_{r}}W_{k}\cdot C_{ik}}{\sum_{k=1}^{n_{r}}W_{ k}} \tag{16}\]  
Impact on Trust Score:Non-compliant vehicles may engage in risky behaviours, affecting the validity of the data they collect and potentially introducing bias or anomalies into the learning process. A lower compliance score reduces the trust score, discouraging the inclusion of such vehicles.  
The participation score for federated learning \(\tau_{P_{i}}(t)\) evaluates the contribution of the vehicle \(v_{i}\) to the federated learning process. It reflects both the quality and quantity of the vehicle's data and model updates.  
It is calculated as  
\[\tau_{P_{i}}(t)=\frac{\|\Delta LM_{i}(t)\|\cdot|D_{i}|}{\sum_{j\in V_{s}}\| \Delta LM_{j}(t)\|\cdot|D_{j}|} \tag{17}\]  
where  
* \(\Delta LM_{i}(t)\) is the local model update vector of vehicle \(v_{i}\).
* \(|D_{i}|\) is the size of the local dataset.
* \(V_{s}\) is the set of vehicles selected for federated learning at time \(t\).  
Impact on Trust Score:Vehicles that contribute more significantly to the model updates and have larger datasets are considered more valuable to the learning process. A higher participation score increases trust, incentivising vehicles to contribute effectively.  
The user feedback score \(\tau_{U_{i}}(t)\) assesses feedback provided by users regarding vehicle \(v_{i}\). Positive feedback indicates satisfaction and reliability.  
With normalised feedback indicators \(F_{ik}(t)\) (e.g., user ratings, satisfaction scores) and weights \(W_{k}\):  
\[\tau_{U_{i}}(t)=\frac{\sum_{k=1}^{n_{F}}W_{k}\cdot F_{ik}(t)}{\sum_{k=1}^{n_{F }}W_{k}} \tag{18}\]  
Impact on Trust Score:Vehicles receiving positive user feedback are likely to be reliable and perform well, increasing their trust scores. Conversely, negative feedback reduces the trust score, reflecting potential issues in reliability or performance.
# Replication-Based Dynamic Energy-Aware Resource Provisioning for Scientific Workflows
## 5 Experiment Setup
### Results and Discussion
#### 5.1.3 Execution Time  
The execution time for the proposed R-DEAR strategy for the Montage SW is shown in Figure 6. The proposed R-DEAR strategy decreased the execution time as compared with the existing R-PCP strategy. The reason for this was the provision of the replica manager for dealing with the failure of tasks and power management in the proposed R-DEAR strategy through its load-sharing approach.  
The execution time for the proposed R-DEAR strategy for the CyberShake SW is shown in Figure 7. The proposed R-DEAR strategy decreased the execution time as compared with the existing R-PCP strategy. The reason for this was the provision of the replica manager for dealing with the failure of tasks and power management in the proposed R-DEAR strategy through its load-sharing approach.  
Figure 5: Execution cost for CyberShake scientific workflow.  
Figure 6: Execution time for Montage scientific workflow.  
Scientific workflows have some special characteristics, such as tasks that are integrated, disintegrated, and executed sometimes in parallel and sometimes in sequence. Workflows such as Montage and CyberShake differ from each other based on these special characteristics. Per the structure of CyberShake, it has a higher number of parallel task executions with minimum inclusion of other workflow characteristics, which explains why there is a strong correlation between R-DEAR and R-PCP through small, medium, and large workflows.  
The results conclusively show that on average, the proposed R-DEAR strategy reduces energy consumption, execution cost and execution time by 9%, 15% and 18%, respectively, as compared with the existing state-of-the-art strategy. The reason is that the proposed R-DEAR strategy assigns resources by finding the best resource for each task. The R-DEAR strategy manages the power by finding and powering off idle resources. The R-DEAR strategy also ensures the successful execution of tasks by providing a replication-based fault-tolerant mechanism.
# Designing a Collaborative Virtual Conference Application: Challenges, Requirements and Guidelines
## 1 Introduction  
This research is primarily focused on a virtual conference application that has been overlooked for the past few years. With the recent COVID-19 pandemic still taking its course around the world, more people are resorting to online communication applications to adapt to the new normal [1]. This includes performing different online tasks such as meeting, learning, discussing, and problem solving through a video conference. However, the current pandemic has shown that 96 percent of users are frustrated when collaborating on any online communication format [2]. A previous study found that users prefer interactive sharing spaces to just video in one of its user studies [3]. Current virtual conference applications still use text, audio, and video to perform complex collaborative activities [4; 5; 6; 7]. This paper focuses on the virtual conference application by identifying the issues and limitations encountered while performing collaborative activities. The following is how the paper is organized: Section 2 presents several theories and concepts on Human-Computer Interaction (HCI), collaboration, video conference, communications and the increase in the use of virtual conferences application during the current pandemic, and user experience. Section 3 describes the usability study that was conducted to evaluate user experience with the current virtual conference application. The results of the usability study are presented in Section 4. Section 5 discusses the findings of the usability study. Section 6 contains design recommendations, proposed system architecture, and an introduction to the userinterface design for the virtual conference application. Finally, the work of this paper concludes with a discussion on its limitations and recommendations for future work.
# Angular Trajectory Design for MR-SPS Using Bezier Shaping Approach
## 2 Model Description
### Sun and Earth Orientation Analysis
In fact, as long as \(\gamma\) and \(\theta_{j}\) are confirmed, the attitude of the MR-SPS is determined, and according to the geometric relationship, \(\gamma\) and \(\theta_{j}\) can be calculated by  
\[\gamma=\arctan(-\frac{\hat{\mathbf{x}}_{o}\cdot\hat{\mathbf{y}}_{a}}{\hat{\mathbf{y}}_{0} \cdot\hat{\mathbf{y}}_{a}}) \tag{1}\]  
\[\theta_{j}=\arctan(\frac{\hat{\mathbf{x}}_{a}\cdot\hat{\mathbf{z}}_{ij}}{\hat{\mathbf{z}} _{a}\cdot\hat{\mathbf{z}}_{sj}}) \tag{2}\]  
For the purpose of precise solar orientation of the arrays and earth orientation of the antenna, the \(y_{a}\)-axis needs to be vertical to \(\hat{\mathbf{r}}_{s}\) and \(\hat{\mathbf{r}}_{a}\) simultaneously and the \(z_{sj}\)-axis needs to be parallel to \(\hat{\mathbf{r}}_{s}\) which means \(\hat{\mathbf{z}}_{sj}=\hat{r}_{s}\). In view of the symmetrical structure of the SPS, \(\hat{\mathbf{y}}_{a}\) and \(\hat{\mathbf{x}}_{a}\) can be obtained as follows:  
\[\hat{\mathbf{y}}_{a}=\pm\frac{\hat{r}_{s}\times\hat{\mathbf{r}}_{a}}{|\hat{r}_{s} \times\hat{\mathbf{r}}_{a}|} \tag{3}\]  
\[\hat{\mathbf{x}}_{a}=\hat{\mathbf{y}}_{a}\times\hat{\mathbf{z}}_{a} \tag{4}\]  
Substitute Equations (3) and (4) into Equations (1) and (2) and in this case, the ideal angular trajectory is obtained and the energy receiving efficiency reaches to 100%.
# Joint User Pairing, Channel Assignment and Power Allocation in NOMA based CR Systems
###### Abstract  
The fifth generation (5G) wireless communication systems promise to provide massive connectivity over the limited available spectrum. Various new transmission paradigms such as non-orthogonal multiple access (NOMA) and cognitive radio (CR) have emerged as potential 5G enabling technologies. These techniques offer high spectral efficiency by allowing multiple users to communicate on the same frequency channel, simultaneously. A combination of both techniques may further enhance the performance of the system. This work aims to maximize the achievable rate of a multi-user multi-channel NOMA based CR system. We propose an efficient user pairing, channel assignment and power optimization technique for the secondary users while the performance of primary users is guaranteed through interference temperature limits. The results show that, at small values of the power budget or high interference threshold, optimizing channel allocation and user pairing proves to be more beneficial than optimal power allocation to the user pairs. The proposed joint optimization technique provides promising results for all values of the power budget, interference threshold and rate requirement of the communicating users.  
Keywords:non-orthogonal multiple access; underlay communication; cooperative communication; cognitive radio +
Footnote †: journal: Applied Sciences
# Solving the Moment Amplification Factor of a Lateral Jet by the Unsteady Motion Experimental Method
## 4 K\({}_{\text{M}}\) Solution  
By taking the derivatives of the curves in Figures 14 and 16 with respect to time, the variation of the model's pitching moment with the angle of attack can be obtained when the jet is on with wind tunnel airflow, as shown in Figure 18.  
The range of attack angles considered in solving \(K_{\text{M}}\) in this paper is \(5^{\circ}<|\,\alpha\,|<25^{\circ}\). However, the moment is also obtained for the data outside this range, and the results are provided in the following curves for reference.  
Figure 18 shows the moment curves of the revolution body for three conditions (jet on at 2.0 MPa, jet on at 4.0 MPa, and jet off) at \(M=0.6\). In Figure 18, the main analysis range is \(5^{\circ}<|\,\alpha\,|<20^{\circ}\) to reduce interference and to ensure a more realistic test analysis. As the angle of attack increases, the curves approach each other, indicating that the influence of the jet becomes less significant, especially when the angle of attack is greater than \(30^{\circ}\), and the aerodynamic moments for the three conditions tend to be equal. These results show that the influence of the jet is weakened with increasing angle of attack, and the moment generated by incoming flow is dominant. This is also in accordance with physical reasoning, which objectively shows that the data of this test are reliable.  
Figure 16: Angle of attack curve of revolution body at \(M=0.6\) with a jet at 4.0 MPa and without a jet.  
Figure 17: Interaction between jet region, jet flow, and wind tunnel flow at different pressures.  
Let \(K_{M}=(M_{jet\ on/wind}-M_{jet\ off/wind})/M_{jet\ on/no\ wind}\), where \(K_{M}\) is the pitch moment amplification factor of the lateral jet, \(M_{jet\ on/wind}\) is the moment on the revolution body with wind and jet on, \(M_{jet\ off/wind}\) is the moment on the revolution body with wind and jet off, and \(M_{jet\ on/no\ wind}\) is the moment on the revolution body no wind and jet on. The curves corresponding to this paper are Figure 18 for \(M_{jet\ on/wind}\) and \(M_{jet\ off/wind}\), and Figure 11 for \(M_{jet\ on/no\ wind}\). Thus, curves of \(K_{M}\) versus angle of attack with the jet on at 2.0 and 4.0 MPa can be obtained.  
As seen in Figure 19, when the pressure is 2.0 MPa, \(K_{M}\) at \(5^{\circ}<|\alpha|<20^{\circ}\) is generally less than 1, indicating that the pitching moment provided by the lateral jet has no pronounced effect. When the pressure is 4.0 MPa, \(K_{M}\) at \(5^{\circ}<|\alpha|<16^{\circ}\) is greater than 1 and even reaches about 2.25 at \(5^{\circ}\), indicating that the lateral jet plays a role in promoting the pitching moment. When \(16^{\circ}<|\alpha|<20^{\circ}\), \(K_{M}\) is less than 1, showing that the pitching moment provided by the lateral jet has no significant effect.