esa-sceva/satcom-chunk-collection huggingface dataset:
# Iterative Learning Control for Vibration Suppression of a Robotic Arm
## 2 Control of Robotic Arm with Elastic Joints
### Integrating the ILC-EJ Controller with the VMD-HHT  
The vibration signal features are extracted via the VMD-HHT and fed into the ILC-EJ controller for vibration suppression. The proposed VH-ILC-EJ method, which integrates the VMD-HHT with an ILC-EJ controller, involves three steps:  
Step 1: Separate the error signal via VMD for every iteration. Use the algorithm to choose the appropriate mode number for decomposition.  
Step 2: Filter out useless information and allow the ILC-EJ controller to learn from the error signals caused by the vibration.  
Step 3: In each iteration, the error signal and the calculated root mean square (RMS) error of each IMF are decomposed. The error signal is separated if the RMS error continues to increase.  
Figure 4 shows the flowchart of the proposed VH-ILC-EJ controller, divided into two parts of the vibration signal analysis and vibration control strategies for the vibration suppression of a 6-DOF manipulator.
## 5 Case Studies
### Results and Discussion  
#### 5.2.1 Results for the First Case Study and Comparison  
In the first case study, 1009 images with a stable burning state, 235 images with an extinguishing state, 186 images with a final extinguished state, and 138 images with a non-flame state were randomly selected as the training set to construct the IFA-WSVM model. The rest of candidate images were regarded as the testing set to validate the model performance. Depending on Equation (31), the multi-feature vectors were formed by calculating the RCMIs of the pre-processed images and edge images. Then, the training set was input into the WSVM, and the best values of the WSVM parameters were sought by the IFA algorithm. Here, the optimal parameters were generated as follows: \(C=63.2396\), \(\tau=9.7631\). Next, the multi-feature vectors of the training set were input into the WSVM with the optimal parameters again, obtaining the IFA-WSVM model. In the end, the training set was input into the IFA-WSVM model to recognize the combustion state. In order to evaluate the performance of the proposed method, the comparison of the methods based on HMIs, ZMIs and RCMIs was performed. What's more, a comparison with the methods based on multi-features and region moment invariants was also carried out. The results of the first case study are shown in Table 4.  
From Table 4, it can be observed that the HMIs derived from non-orthogonal polynomials achieve the lowest recognition rate of 90.54%. RCMIs can achieve better accuracy of recognition than HMIs and ZMIs. In addition, the multi-feature vectors, formed by combining region moment invariants with contour moment invariants, can improve the recognition rate significantly, achieving the highest recognition of 99.07%. In terms of operating time, the work efficiency of RCMIs is apparently higher than that of ZMIs. Although a little lower than HMIs, the methods based on RCMIs can still satisfy the requirement of real-time. However, it also should be noted that the images used are in low definition, with a size of \(266\ \times\ 236\). This is one of the reasons for achieving a low time processing. As shown in Figure 5, the effectiveness of IFA-WSVM model was also confirmed by the fact that it achieved the highest rate of recognition during the optimization phase.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Region Moments**} & \multicolumn{3}{c}{**Region and Contour Moments**} \\ \cline{2-7}
**Parameter** & **Hu** & **Zernike** & **Radial-Chebyshev** & **Hu** & **Zernike** & **Radial-Chebyshev** \\ \hline Recognition amount & 1751 & 1808 & 1875 & 1796 & 1844 & 1916 \\ Recognition rate & 90.54\% & 93.49\% & 96.95\% & 92.86\% & 95.35\% & 99.07\% \\ Average operating time of a single image & 32.19 ms & 103.54 ms & 41.07 ms & 66.16 ms & 205.79 ms & 83.38 ms \\ \hline \hline \end{tabular}
\end{table}
Table 4: Recognition results of the testing set in the first case study.
# Improved Autocollimator for Roll Angle Measurement with an Enlarged Measuring Range
## 3 Experimental Results
### Applications of Improved Autocollimator  
This subsection provides two applications of the improved autocollimator. In the equipment manufacturing industry, the roll angle of a linear stage results from the shape error of the guide-rail, which will adversely affect its positioning accuracy. As an application, the improved autocollimator was used to measure the roll angle of the linear stage to facilitate the shape modification of the guide-rail. Figure 7a illustrates the experimental setup. The WP was fixed to a linear stage that could move along a guide-rail, and the improved autocollimator measured the roll angle of the linear stage. An electronic level (Qingdao Qianshao, Type WL9, Qingdao, China) possessing \(\pm\)(1 + 0.02 \(\times\) reading) accuracy was also placed on the linear stage to synchronize the roll angle measurement for comparison. The linear stage was moved by hand from near to far with an increment of approximately 50 mm and a travel of 400 mm. Figure 7b maps the measurement curves of both instruments. The deviations between the two curves ranged from \(-\)8.63 to 16.92 arcsec. The main reasons for the deviations may be that the WP and electronic level were in different positions on the linear stage, and the electronic level was marginally misdirected. Nonetheless, the  
Figure 5: Calibration experiment: (**a**) setup and (**b**) results.  
Figure 6: Results of resolution test.  
tendencies of the two measurement curves were greatly consistent, which exemplified that the improved autocollimator was suitable for this practical, concrete task.  
In the optical industry, the wedge angle of an optical wedge is a critical parameter that determines the direction of the refracted beam. As a second application, the improved autocollimator can be used to measure the wedge angle of an optical wedge specimen to perfect its processing. Specifically, the currently used WP can be replaced by the specimen. Then, the specimen is rolled by 360\({}^{\circ}\), and a circular moving path of light spot 2 around light spot 1, as well as the rotation radius \(L\), can be detected on the CMOS. Consequently, the wedge angle of the specimen is calculated to be \(L/[2f\times(n-1)]\).
# Sub-Band Backdoor Attack in Remote Sensing Imagery
## 7 Results of All-Bands BadNets Attack and Defense
### Results of All-Bands BadNets Attack  
The performances of the benign and backdoored DCNN models on the EuroSat dataset are shown in Table 2. It is observed that the backdoored DCNN model achieved similar accuracy as the benign model on clean samples for all trigger sizes, ranging from \(2\times 2\) to \(10\times 10\). The ASR is 100% for all different trigger sizes, demonstrating that the backdoored model can maintain a high ASR for the target label as well as good accuracy on the clean dataset.  
The performances of the benign and backdoored ResNet-56 [21] models on the EuroSat dataset are presented in Table 2. It is observed that the backdoored ResNet-56 model achieved similar accuracies to the benign model on clean samples for all trigger sizes, ranging from \(2\times 2\) to \(10\times 10\). The ASR is 100% for all different trigger sizes, demonstrating that the backdoored model consistently maintains a high ASR for the target label and good  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Trigger Size** & **DCNN** & \multicolumn{4}{c}{**ResNet-56**} \\ \hline  & \(Acc_{Clean}\) & \(Acc_{B}\) & \(ASR\) & \(Acc_{Clean}\) & \(Acc_{B}\) & \(ASR\) \\ \hline \(2\times 2\) & & 0.93 & 1.00 & & 0.96 & 1.00 \\ \(3\times 3\) & & 0.93 & 1.00 & & 0.97 & 1.00 \\ \(4\times 4\) & & 0.92 & 1.00 & & 0.96 & 1.00 \\ \(5\times 5\) & & 0.93 & 1.00 & & 0.96 & 1.00 \\ \(6\times 6\) & 0.93 & 0.93 & 1.00 & 0.96 & 0.97 & 1.00 \\ \(7\times 7\) & & 0.91 & 1.00 & & 0.96 & 1.00 \\ \(8\times 8\) & & 0.93 & 1.00 & & 0.96 & 1.00 \\ \(9\times 9\) & & 0.93 & 1.00 & & 0.97 & 1.00 \\ \(10\times 10\) & & 0.92 & 1.00 & & 0.97 & 1.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performances of all-bands BadNets attack with the EuroSat dataset. ‘\(Acc_{Clean}\)’: Acc of benign model, ‘\(Acc_{B}\)’: Acc of backdoored model, ‘\(ASR\)’: Attack Success Rate.  
Figure 4: Deep convolutional neural network (DCNN) architecture.  
accuracy on the clean dataset. We also employed EfficientNet [38] as a backbone in similar settings and observed comparable performances.  
In summary, these results indicate that the remote sensing domain is highly susceptible to backdoor attacks in deep learning models, as evidenced by the maintenance of high ASRs for the target label and good clean accuracies across multiple architectures.
# Groove Formation in Glass Substrate by a UV Nanosecond Laser
## 3 Results and Discussion
### Analysis of Glass Surface Morphology Produced by Single Laser Scan
It is found that the groove with a very smooth surface is obtained when a relatively low scanning speed is used. Under this process condition, the groove with thermal cracks is generated as seen in Figure 6b,e,f,j. With the further increase of the scanning speed, the groove produced has a very rough morphology; however, this process condition does not nearly induce cracks around the groove. In the high power density laser-matter interaction, physical phenomena such as melting, vaporization, and plasma formation are involved, and material removal is mostly achieved through vaporization and melt ejection driven by high vapor pressure [24; 25]. According to Figure 7a,b, it is seen that molten materials removed from the irradiation regions splash out on the surface near grooves. This indicates that melting is the dominant material removal mechanism. At a relatively high scanning speed (see Figure 7c,d), many molten glass powders not ejected are observed inside the groove owing to less laser energy deposition on the glass surface, which in turn makes a rough surface morphology at the irradiation zone. Under a relatively high scanning speed, the shattering of the surface is also seen at the edge region of the groove. This is attributable to the shock wave induced by plasma breakdown during the process. The sharp and non-melt glass pieces found around the groove in Figure 7d are the outcome of shattering. Therefore, in this case, high temperature vaporization inducing the shock wave as well as the melting contributes to material removal. The generation of micron bubbles is seen in HAZ (see Figure 8). During the interaction between the laser beam and glass, thermal energy inside the glass could induce thermal dissociation and other chemical reactions of the components shown in Table 3. This may generate the oxygen gas to be the source of the bubbles inside the glass. On the basis of the results for single laser scan tests, some important findings could be described as follows. The interaction between the UV laser and material at a high laser power and low scan speed just induces volume melting underneath glass surface and large thermal crack without material  
Figure 6: SEM images of the glass surface damaged by a single laser scan. Laser power: (**a**–**d**) 2.2 W, (**e**–**h**) 2.8 W, and (**i**–**l**) 3.32 W. Scan speed: (**a**,**e**,**i**) 5 mm/s; (**b**,**f**,**j**) 15 mm/s; (**c**,**g**,**k**) 25 mm/s; and (**d**,**h**,**l**) 35 mm/s. The circle in the figure denotes a crack. removal. With the decreasing laser power and increasing scan speed, a groove with a smooth surface starts to form owing to the melting-dominant material removal process. A crack-free groove is only obtained at a relatively low laser power and high scanning speed. Under this process condition, both shattering induced by shock wave and melting contribute to the material removal. Process characteristics depending on the laser scanning condition are summarized in Table 4.
# Malware Detection Issues, Challenges, and Future Directions: A Survey
## 4 The Taxonomy of Malware Analysis and Detection Approaches
### Malware Analysis Approaches and Data Types
#### 4.1.1 Static Analysis  
The static analysis approach has been widely utilized by exploring the source code without running the executable files to extract a unique signature which is used to rep  
Figure 3: Malware Analysis and Detection Taxonomy.  
resent the file under investigation. Several types of static data can be collected via static analysis, including PE-header data [30; 31; 32; 33], derived data such as string-based entropy and compression ratio [34; 35; 36; 37]. Additionally, static analysis tools, such as IDA pro disassembler and Python-developed modules, are also used to collect static opcode and API calls [38; 39; 40; 41; 42]. Even though static analysis can track all the possible execution paths, it is influenced by packing and encryption techniques.
# A Reliability Engineering Approach for Managing Risks in CubeSats
## Acronyms and Abbreviations  
ADCS & Attitude Determination and Control Subsystem CDR & Critical Design Review CN & Criticality Number D Detectability index DET & Direct Energy Transfer EPS Electrical Power System ESA European Space Agency FAM Fuzzy Associative Map FDIR Fault Detection, Isolation and Recovery FMEA Failure Modes and Effects Analysis FMECA Failure Modes, Effects and Criticality Analysis FTA Fault Tree Analysis IMU Inertial Measurement Unit InGaAs Andium-Gallium-Arsenide NASA National Aeronautics and Space Administration O Occurrence index OBDH On-Board Data Handling PDR Preliminary Design Review PPT Peak Power Tracking RAMS Reliability, Availability, Maintainability, Safety RPN Risk Priority Number S Severity index SEU Single Event Unit SPEC Stellar Population Evolution with CubeSats TQM Total Quality Management TRL Technology Readiness Level TT&C Telemetry, Tracking and Command UHF Ultra-High Frequency
## 2 Modeling Uncertainty in Logistics and Transportation  
In the L&T arena, decision makers face various challenges due to the uncertainty that exists in these systems [17]. Uncertainty can stem from several factors such as travel times, customers' demands, electric battery durability, vehicle reliability, etc. Thus, for instance, the travel time of vehicles cannot be defined solely as a function of time, because external factors such as heavy traffic, weather conditions, traffic accidents, and road conditions can cause delays. These delays create uncertainty in travel times, and this uncertainty must be taken into consideration when making decisions to ensure that the decisions reflect a reasonable majority of real-world scenarios [18].  
Uncertainty in L&T activities has to be differentiated into stochastic random or fuzzy variables. On the one hand, a stochastic random variable follows a probability distribution, and the parameters of this distribution are estimated based on historical data using fitting techniques like input analyzers [19]. For example, the delay in travel time between node \(i\) and node \(j\) could be modeled as a lognormal or a Weibull probability distribution, among others. Since many optimization challenges that arise in real-life L&T systems are not just NP-hard and large-sized but also subject to uncertainty conditions, simulation-optimization approaches are required to make informed decisions. Hence, simulation methods are often integrated with optimization algorithms in simulation-optimization approaches like simheuristics [8] to recommend the most promising solution to a given stochastic optimization problem.  
On the other hand, fuzzy uncertainty refers to a type of uncertainty where precise values cannot be given, and instead terms like'short','medium', and 'long' are used [20]. Fuzzy sets are used to model fuzzy uncertainty, and these sets allow crisp values to have partial memberships with more than one set (Figure 2). The degree of membership, \(\mu(x)\), takes a value between 0 and 1, where 1 assigns full membership and 0 refers to non-membership. For example, the travel time between two nodes in a network could be affected by a delay of ten minutes. This delay could be interpreted as'short' or'medium'. Thus, it has a membership with more than one set. Fuzzy logic employs 'if-then' inference rules to consider different variables and draw conclusions. Qualitative variables can also be represented through linguistic variables using fuzzy rules. For instance, if both the road condition and the weather are bad, it can be inferred that travel and delivery times will be long.  
Several researchers have considered fuzzy attributes in their study of L&T problems. For example, [PERSON] et al. [21] defined a fuzzy-based assessment model of sustainable transportation, and [PERSON] et al. [22] considered a spherical fuzzy environment in the assessment of city logistics concepts.  
Figure 2: Fuzzy sets for a crisp variable \(x\).
# Feature Augmentation Based on Pixel-Wise Attention for Rail Defect Detection
## 3 Materials and Methods
### Feature Augmentation
To be more specific, the rows of the similarity metric correspond to the pixels of the sample feature, while the columns of the metric correspond to pixels of all support features in sequence. The whole metric can be viewed as \(N\) blocks \([\omega^{1},\omega^{2},\cdots,\omega^{N}]\), each representing relations between the sample feature and one support feature. Global softmax is performed to highlight the most relevant defect patterns while suppress less similar features. The support value maps can be weighed by \(\omega\) through another matrix inner-product, and then concatenated with the query value map to form the final output feature map:  
\[F=concat[V_{q},\omega\cdot V_{s}]\in\mathbb{R}^{HW\times C}. \tag{6}\]  
This feature is then reshaped to \(H\times W\times C\) to fit the input sample dimension and used for subsequent object classification and bounding box regression. Our pixel-wise attention within feature augmentation framework can be treated as a combination of self attention and cross attention, since we retrieve hidden representations from both the sample feature and the support features. Previous meta-learning trials either perform class-wise feature vectors to fuse attention feature maps [15] or perform concatenation over \(N\) class-wise results to obtain the final soft attention [20]. However, our approach considers a global pixel-wise attention on all \(N\) classes, making our model extremely sensitive to pixel-level detailed features of the most relevant support defect, while reducing potential ambiguities from less similar classes or noises. It also makes our model applicable to pretrain-finetune paradigm. In addition, we adopt random selection to generate support class prototype features instead of average calculation to reduce training resource consumption without losing detection performance.
# Characterization of Monovacancy Defects in Vanadium Diselenide Monolayer: A DFT Study
## 1 Introduction
First-principles calculations have demonstrated that the defect formation energy in a novel 2D PdSe\({}_{2}\) is significantly lower than these in other 2D TMDs, which sheds light on the reason for the presence of a large number of Se defects in the PdSe\({}_{2}\) monolayer obtained via the mechanical exfoliation [59]. DFT-based simulations have been utilized to show the fundamental role of defects in the fabrication of WSe\({}_{2}\)-based quantum emitters [60] and to explain the role of defects in the thermal conductivity of 2D WSe\({}_{2}\)[61]. Despite the fact that defects in various 2D TMDs have been widely studied, there is still a niche in a systematic study of defects in a recently emerged VSe\({}_{2}\) monolayer [62]. A deeper study for connecting the presence and type of defects in the VSe\({}_{2}\) monolayer to its electronic characteristics at a mechanistic level is needed. To fulfil this scientific question, the most common native defects, chalogene and transition metal single vacancies, in the VSe\({}_{2}\) monolayer are studied using DFT-based calculations.
## 2 Method
### Adaptive Adjustment of Control Parameters  
In the conventional DE algorithm, F and CR are fixed values. Different problems usually need to set different parameters to obtain optimal performance of the algorithm. To avoid manually adjusting these parameters, it was an ideal strategy to adopt an adaptive method to update F and CR based on successful experience continuously. Considering that the improved DE algorithm shared the same architecture with JADE [31], in this part, a detailed description of parameters adjustment will be instructed.  
Firstly, \(F_{i}\in(0,1]\) is defined as,  
\[F_{i}=randc_{i}(\mu_{F},\ 0.1), \tag{10}\]  
where \(randc_{i}(\mu_{F},\ 0.1)\) denotes the Cauchy distribution with location parameter \(\mu_{F}\) and scale parameter \(0.1\). If \(F_{i}\leq 0\), it was regenerated by the execution of Equation (11) until an effective value was obtained; if \(F_{i}>1\), it was truncated to \(1.0\). The initial value of \(\mu_{F}\) was \(0.5\). In the subsequent evolution process, it was updated according to the scale factor \(F_{i}\) fed back from the successful individuals. At the end of each iteration, these successful \(F_{i}\) are stored in the set \(S_{F}\), and \(\mu_{F}\) is updated as follows,  
\[\mu_{F}=(1-c)\mu_{F}+c\cdot mean_{L}(S_{F}), \tag{11}\]  
where the learning rate \(c\) is a positive constant in the range \((0,1)\); \(mean_{L}(\cdot)\) is the Lehmer mean and calculated as follows,  
\[mean_{L}(S_{F})=\frac{\sum_{F\in S_{F}}F^{2}}{\sum_{F\in S_{F}}F}. \tag{12}\]  
In addition, each individual in JADE has an independent crossover rate \(CR_{i}\) in the range \((0,1)\). Unlike \(F_{i}\), the generation of \(CR_{i}\) is controlled by the normal distribution,  
\[CR_{i}=randn_{i}(\mu_{CR},\ 0.1), \tag{13}\]  
where \(randn_{i}(\mu_{CR},\ 0.1)\) returns a random value with a normal distribution \((\mu_{CR},\ 0.1)\). If \(CR_{i}<0\) or \(CR_{i}>1\), it is truncated to \(0\) or \(1\). \(\mu_{CR}\) is updated as follows [31],  
\[\mu_{CR}=(1-c)\mu_{CR}+c\cdot mean_{A}(S_{CR}), \tag{14}\]  
where \(mean_{A}(\cdot)\) is the arithmetic mean; \(S_{CR}\) is the set of successful \(CR\) values in the current generation.