esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Design of the POM-PSASIDEA Algorithm
### Progressive Optimization Mechanism
#### 4.1.3 Global Optimization Phase  
The global optimization phase (GOP) aims to minimize the response time as the optimization objective, consider the planning schemes in the allocation optimization phase and the time window optimization phase, and introduce conflict detection and conflict resolution methods to address the overlap of the planning schemes, so as to generate high-quality mission planning schemes to solve large-scale target scenarios. The high-quality task planning scheme can cope with large-scale target scenarios. Firstly, the sequential allocation scheme generated in the allocation optimization phase is integrated with the visible time window allocation scheme generated in the time window optimization phase as the initial population \(P_{0}\) for the evolutionary search of the POMPSAISIDEA algorithm, and the population individuals (contemporary mission scenarios) are traversed using a conflict detection method, i.e., by comparing the start times of the time windows of the neighboring missions in the same satellite resource to determine the time window overlap situation (by comparing whether or not intersection exists to judge the time window overlap). If there is a conflict between two neighboring tasks, the higher-priority task is retained, and the lower-priority task is stored in the candidate task queue \(candidate_{Task}\), which is treated as a candidate task for reassignment. Secondly, the conflict elimination method is used to cope with the time window overlap of the planning scheme, the allocated time window of the contemporary mission scheme and the set of satellite visible time windows are matched one by one, the satellite idle time window is screened out, the starting positions of the candidate visible time window and the satellite idle time window are judged one by one according to the priority of the candidate mission queue \(candidate_{Task}\), the candidate mission is inserted into the original satellite allocation sequence as a new mission, the successful candidate missions are deleted from the candidate mission queue until the candidate mission queue is empty,and the POM-PSAIDEA algorithm is used for the population individuals. The candidate task is inserted as a new task into the original satellite allocation sequence, and successful candidate tasks are deleted from the candidate task queue. The iteration continues until the candidate task queue is empty. Then, the POM-PSAIDEA algorithm is used to iteratively search for the optimality of the individual populations, and the output is used to generate the final MSIMPLTS-MLOO optimal allocation scheme. The algorithm framework is shown in Algorithm 4.
# Design and Analysis of a Low Latency Deterministic Network MAC for Wireless Sensor Networks
## 6 Performance Analysis
### Reliability  
Let the LLDN superframe contain \(M_{1}\) number of transmission slots and \(M_{2}\) number retransmission slots. According to IEEE 802.15.4e, if one transmission has failed in the \(k\)-th slot, the tagged node retransmits the data in the same superframe provided a retransmission slot is available. A failed node gets a retransmission slot, if either \((k-1)<M_{2}\) or the total failed nodes before the \(k\)-th slot is less than \(M_{2}\). Therefore, the probability that the node transmitting in the \(k\)-th slot may get a retransmission slot is:  
\[P_{RS}=\left\{\begin{array}{l}1,\ if\ \ Max\{k-1,number\ of\ failure\\ \ nodes\ before\ k-th\ slot\}<M_{2}\\ 0,\ otherwise.\end{array}\right. \tag{19}\]  
In the case of any transmission failure, the tagged node retransmits the data in the allotted retransmission slot after getting the transmission failure information in GACK with the available retransmission slot. However, in this slot, only the collided nodes will access the channel. Let the expected number of collided nodes be \(N_{c}\). Therefore, the retransmission successful probability of the node is given as follows.  
\[P_{RTS}=\frac{N_{c}\phi(1-\phi)^{N_{c}-1}}{1-(1-\phi)^{N_{c}}}(1-P_{b}(\varsigma ))^{L_{data}}. \tag{20}\]  
In the LLDN superframe, a transmitted packet by a node to the coordinator is consider as successful when the transmission or retransmission in the allocated slot is successful. Therefore, the successful transmission probability for a node in the LLDN superframe is given as follows.  
\[P_{success}=P_{TSSS}+(1-P_{TSSS})P_{RS}P_{RTS}. \tag{21}\]
# An IoT-Based System for Monitoring the Health of Guyed Towers in Overhead Power Lines
## 3 System Architecture
### Hardware Design of the System
#### 3.5.1 Mcu  
The master chip used in this monitoring system is an RX65N. It has the advantages of low power consumption, high performance, and low cost. The operation frequency is 120 MHz, 32 bit core, on-chip floating-point unit, up to 2 MB flash memory (supportive of the dual bank function), 640 KB SRAM, and various communications interfaces, including Ethernet MAC, quad SPI, CAN, 12 bit A/D converter, RTC, CMOS camera interface, Graphic-LCD controller, and a 2D drawing engine. These are standard computing power needs that allow a node to accomplish the remote monitoring function, including pre-processing and data handling to the multihop network.
# A Novel Piezoelectric Ceramic Actuator with Scissoring Composite Vibration for Medical Applications
## 3 Finite Element Modeling  
### Eigen Frequency Analysis  
In order to verify the design effectiveness and advantages of the proposed actuator, finite element modeling (FEM) is carried out by using COMSOL Multiphysics software. To conveniently analyze the proposed design, two models with different lengths of beam tip are established for Eigen frequency analysis, harmonic analysis and transient analysis under the same input conditions. In FEM, the length, width and thickness of the back mass was 14 mm, 4.5 mm and 4.5 mm, respectively. The diameter of the front mass and circular holes of the netted structure was 8 mm and 1 mm, respectively. The length of the cylinder, horn and beam was 26 mm, 12 mm and 20 mm, respectively. The actuator is made of stainless steel (SUS304) material.  
Figure 2: The equivalent model of beam driven by longitudinal vibrations.  
Furthermore, two models with arbitrary beam tip length of 7 and 9 mm are established for Eigen frequency analysis. The beam parameters and material constants are summarized in Table 1. In the simulation models, the piezoceramic stack is comprised of ceramics slices of lead zirconate titanate, simplifying as an integrated structure. The left end of the back mass is clamped as a fixed boundary condition, whereas other boundaries remained free.  
Figure 3 shows that the scissoring modes are successfully excited at the resonance frequency of 3518.8 and 2223.7 Hz with beam tip of 7 and 9 mm, respectively. Obviously, the scissoring bending vibration only concentrates on the beam tip and does not cause any obvious deformation in other parts of the actuator, which coincides with the proposed design. These results indicate that the longitudinal driving vibration can be efficiently transferred and converted into bending vibration at the beam tip, demonstrating the effectiveness of the proposed design. Moreover, even with the arbitrary lengths of beam tips, both models successfully excited the scissoring mode, which indicates that the desired scissoring vibration mode can be easily obtained by the proposed design. These results also indicate that the proposed actuator can be miniaturized or increased for some practical minimal invasive surgeries based on their exact demands because the scissoring mode is independent of beam length. One should note that the output frequency of actuator can be tuned by adjusting the structure parameters, which demonstrates the potential of proposed actuator in medical applications, requiring various output frequencies. Furthermore, the scissoring-type vibration with double opposite vibrations obviously has the potential to provide more efficient cutting and stirring function compared with the normal phaco needle [5; 6], scalpel [17], and micro-stirrer for crushing blood clot [18] with single vibration. Overall, the FEM results confirmed the effectiveness of proposed design.
## 4 Experiments
### Image Classification on the ImageNet1K Dataset  
**Experiment Settings** There are 1.28 M training images and 50 K validation images from 1000 classes in Imagenet1K [52] dataset, we train our model for 300 epochs with the same training settings as other vision transformers [9; 21; 23] are employed. We take an AdamW [55] optimizer with a cosine decay learning rate scheduler and 20 epochs of linear warm-up. The input image size is set as \(224\times 224\) by using the same data augmentation and regularization strategies in Swin Transformer [23], including RandAugment [56], Mixup [57], Cutmix [58], random erasing [59] and stochastic depth [60]. The proposed model is trained on 8 RTX 3090 GPUs with a batch size of 1024, an initial learning rate of 0.001, and a weight decay of 0.05. For classification, we use the top-1 accuracy as the metric.  
**Results** The results are shown in Table 3. It is obvious that the proposed LGViT outperforms other methods with similar FIOPs and higher accuracy. Specifically, LGViT improves over the newly proposed Swin transformer [23] 0.8% and is 1.3% better than the Deit-Small [6]. The ViL-Small [61] achieves close performance with our model but we still outperform it with 0.1G FLOPs less and 0.1% accuracy higher.  
\begin{table}
\begin{tabular}{l c} \hline
**Method** & **Complexity** \\ \hline ViT [5] & \(\Omega(N^{2})\) \\ DeiT [6] & \(\Omega(N^{2})\) \\ PvT [50] & \(\Omega(N^{2})\) \\ CvT [7] & \(\Omega(N^{2})\) \\ \hline Twins [51] & \(\Omega(N)\) \\ Swin-T [23] & \(\Omega(N)\) \\ LGViT (ours) & \(\Omega(N)\) \\ \hline \end{tabular}
\end{table}
Table 2: Complexity comparison of different vision transformers. \(N=h\times w\), \(h\) and \(w\) are the height and width of the input image.  
\begin{table}
\begin{tabular}{l c c c} \hline
**Method** & **\#Params** & **FLOPs** & **Top-1 Acc** \\ \hline ResNet-50 [3] & 25 M & 4.1 G & 76.2 \\ Reg-4G [62] & 21 M & 4.0 G & 80.0 \\ DeiT-S [6] & 22 M & 4.6 G & 79.8 \\ PVT-S [33] & 25 M & 3.8 G & 79.8 \\ T2T-14 [63] & 22 M & 5.2 G & 81.5 \\ ViL-S [61] & 25 M & 4.9 G & 82.0 \\ TNT-S [64] & 24 M & 5.2 G & 81.3 \\ CViT-15 [65] & 27 M & 5.6 G & 81.0 \\ CPVT-S [66] & 23 M & 4.6 G & 81.5 \\ Nes-T [67] & 17 M & 5.8 G & 81.5 \\ CAT-S [68] & 37 M & 5.9 G & 81.8 \\ CvT-13 [7] & 20 M & 4.5 G & 81.6 \\ Swin-T [23] & 29 M & 4.3 G & 81.3 \\ \hline LGViT (ours) & 30 M & 4.8G & **82.1** \\ \hline \end{tabular}
\end{table}
Table 3: Performance comparison of image classification task on the ImageNet-1K dataset for different models. All models are trained and evaluated on \(224\times 224\) resolution.
## 3 Methodology
### Statistical Methods
#### 3.3.4 Holt-Winters Exponential Smoothing  
Exponential smoothing (or ETS) is a forecasting method for uni-variate data where the prediction is a weighted linear sum of recent past observations, in which the weight assigned to past observations decreases exponentially. Therefore, the ETS model gives more importance to the most recent observations, i.e., the more recent are the observations, the higher is the associated weight. As our data contains trend and seasonality, we need to consider Holt-Winters Exponential Smoothing, which provides support for both of them as in the following equation.  
\[\widehat{Y_{t+m}}=\left(S_{t}+mb_{t}\right)I_{t-L+m} \tag{4}\]  
where:\(y\) is the observation in time \(t\)
* \(S\) is the smoothed observation
* \(b\) is the trend factor
* \(I\) is the seasonal index
* \(L\) is the number of periods is a season
* \(\widehat{Y_{t+m}}\) is the forecast at \(m\) periods ahead  
The reason we chose to test this forecasting method is that it performs well in those situations where there is a changing trend in the data and the last observations are the most important ones to consider.
# Improvement of [PERSON]'s Stripe Noise Suppression Method for Side-Scan Sonar Images
## 3 Experiments
### Experimental Design
#### 3.2.3 Validation with Real Noisy Images  
Considering that the objective of this study was to suppress stripe noise in side-scan sonar images, simulated stripe noise does not fully reflect the characteristics of real stripe noise. Therefore, the next step involves denoising side-scan sonar images with real stripe noise using the method proposed in this paper. The effectiveness of removing stripe noise under different conditions is analyzed and compared with the Fourier-based denoising method.  
Figure 6 depicts three side-scan sonar images with real stripe noise, with each exhibiting different textural characteristics.  
Figure 5: Denoising results of six sets of models. (**a**) Group 1; (**b**) Group 2; (**c**) Group 3; (**d**) Group 4; (**e**) Group 5; (**f**) Group 6.  
The method proposed in this paper and the Fourier transform were used to denoise the above three images, and the denoising results are shown in Table 4.  
An analysis of Table 4 shows that the method proposed in this paper effectively removes stripe noise of a certain width and performs well in restoring texture. However, regarding the denoising of image (a), the method shows some disadvantages when dealing with narrow or less prominent stripe noise. In contrast, Fourier transform-based denoising performs better in removing such noise, and it also provides a smoothing effect. Therefore, after using the method proposed in this paper to remove stripe noise of a certain width, applying the Fourier transform for smoothing may lead to better results.
## 2 Stereo Reconstruction Algorithm for Unstructured Scenes
### Triangulation and Texture Mapping  
After calculating the spatial information for the feature points by calculating the depth value, these feature points are scattered in the three-dimensional space. If we want to complete the reconstruction of the three-dimensional scene, we also need to build these scattered three-dimensional points into a three-dimensional mesh skeleton model. Considering that the \(x\) and \(y\) projections of some of the point cloud data in the \(xyz\) coordinate system overlapped, we combined the Delaunay triangulation algorithm with the idea of a partitioning algorithm to perform region segmentation, triangulate each subset of points according to the Delaunay triangulation algorithm, and, finally, merge each subset of points to triangulate the boundary of the subset of points.  
Texture mapping is the process of mapping the 2D pixel coordinates of an image to spatial 3D coordinates. The three-dimensional mesh skeleton model formed with triangulation cannot yet make the objects in the scene feel realistic or achieve a realistic display of the scene. Texture mapping extracts the texture from the 2D image and maps it onto the stereo mesh skeleton model to recover the real texture of the surface and provide a realistic 3D model of the scene.
# A Reliability Engineering Approach for Managing Risks in CubeSats
## 4 Results
### Step 4: Overall Risk Analyses
In this case study, some areas are vulnerable due to the low TRL (Technology Readiness Level) of some of the components (mainly payload), for the lack of redundancies and for the low-cost sub-units. Almost all the interfaces, considered with no redundancies in previous analyses, showed--as expected--higher scores for RPN than when introducing redundant and different components. The obtained highlighted the expected features and put into consideration other aspects. The study showed RPN below 80 for each item, except for structure, whose top value reaches 100. However, the structural subsystem is the area that may be better tested, allowing mitigation actions to reduce the related risks. The maximum value of the RPN values for each subsystem are summarized in Table 17.  
More details about the distribution of the RPN scores are summarized in Figure 7, which confirms that the majority of the items belongs to the 25-49 area. The method showed interesting results from a design point of view. Areas remarked by higher RPN include the ones that were already thought to be the weaker part of the design phase (i.e., considerations about the design of the payload, that is the item with the lowest TRL, with a configuration never used and not built before). Several other items have emerged as more critical than what supposed. STR-3.4-PLD | THM (damage to the mechanical interface between payload and thermal subsystem due to the structure vibration) reaches RPN = 80, and STR-3.3-PLD-5 (when the vibration of the structure implies damage to the payload, in particular, damaging the filters), whose RPN reaches 100, resulted more critical than expected. On the contrary, the large number of items in the EPS RPN 1-24 slot provides an example of a subsystem initially conceived as more critical than as emerged from a systematic analysis: (e.g.,) EPS-3.2-ADC-1 (lost in space for the ADC damaged by the EPS _non continuous power_ failure mode) has RPN = 15. Generally, highest RPN values relate to non-redundant items, whose failure jeopardize the mission's success, i.e., single point of failures. Consequently, the outcomes of the analysis led to revising the SPEC design, in order to introduce redundancies, where possible, or to adopt fault tolerant strategies. Nevertheless, the results are based on modeling assumptions and numerical coefficients which pragmatically compensate the lack of precise statistical data, in order to generate systematic assessments, mainly for the purpose of layout-rather than component-selection. \begin{table}
\begin{tabular}{c c} \hline \hline
**Score** & **MAX Risk Priority Number** \\ \hline
1–24 & none \\ \hline
25–49 & none \\ \hline
50–74 & TT\&C = 50 \\  & PLD = 60 \\ \hline
75–99 & THM = 75 \\  & EPS = 75 \\  & OBH = 75 \\  & ADC = 80 \\  & Interfaces = 80 \\ \hline
100–125 & STR = 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Maximum RPN of each subsystem.
## 3 Results and Discussions
### Results of Combined Loading
#### 3.3.2 Lateral Loading Effect on Uplift Capacity of Helical Pile  
To study the effect of lateral load on the uplift capacity of a helical pile, numerical analyses were carried out on fully embedded helical piles. Four helical piles having a different number of plates and spacing ratios were analyzed. Figure 17 shows the  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Pile Type**} & **Lateral** & **Uplift** & **Applied Uplift Load (kN)** & \multicolumn{3}{c}{**Numerically Calculated**} & \multicolumn{2}{c}{**Percentage Decrease**} \\  & **Capacity** & **Capacity** & & **Lateral Load (kN)** & & **(\%)** & \\  & **Q\({}_{\text{l}}\) (kN)** & **Q\({}_{\text{u}}\) (kN)** & **Q = 0.25 Q\({}_{\text{u}}\)** & **Q = 0.  
comparison on load-displacement curves of helical pile uplift capacities when subjected to pure uplifts and with lateral loads. It is clear that the uplift capacity of a helical pile is enhanced when it is subjected to lateral load. The loading is done in two phases; first the ultimate lateral load calculated previously is applied on the pile head and then the combined uplift load is applied, which is the load required to displace the pile by 4 mm (10% of D). The increment in uplift capacity is also observed by [PERSON] and [PERSON] [46] for straight shaftted piles embedded in cohesionless soil when subjected to combined uplift and lateral loads.  
The ultimate uplift capacity is interpreted as per previous studies and IBC [7] for the analyzed helical piles under the lateral load and results after the combined influence of uplift and lateral loading is presented in the Table 5. It is clear that even when the number of helical plates increases, its effect on increasing the uplift capacity is not effective. However, a maximum increase of almost 6% is observed.  
Figure 18 shows the displacement contours of a cross section at midway of helical piles under combined loading. Compared with Figure 13, the developed failure contour near the pile head, the soil seemed to mobilize with pile movement due to the applied lateral load. This formed the failure surface consistent with the existing cylindrical shear failure surface observed while piles were subjected to uplift load.  
Figure 17: Effect of lateral loading on uplift capacity of helical pile.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Pile Type**} & **Uplift Capacity** & **Applied Lateral** & **Numerically Calculated** & **Percentage** \\  & **Q\({}_{\mathbf{u}}\) (kN)** & **Load Q\({}_{\mathbf{l}}\) (kN)** & **Uplift Load (kN)** & **Increase (\%)** \\ \hline SHP-1 & 0.5083 & 0.0928 & 0.5254 & 3.36 \\ SHP-2 & 0.7911 & 0.2459 & 0.8355 & 5.61 \\ SHP-3 & 0.8858 & 0.2864 & 0.9358 & 5.65 \\ SHP-4 & 0.9282 & 0.3107 & 0.9765 & 5.21 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ultimate uplift capacity with and without lateral load.
# Application of Image Recognition Methods to Determine Land Use Classes
## 3 Materials and Methods
### Satellite Data Post-Processing  
Following the classification stage, a confidence-based filtering process was applied. Pixels with classification probabilities below a defined threshold were reclassified as \"Unclassified\" in order to minimize misclassification errors. Additionally, spatial smoothing techniques were applied to reduce noise and enhance the consistency of classification results.  
The final classification output was validated using independent ground truth datasets and accuracy assessment metrics. This post-processing approach ensured that the final classification maps were both accurate and reliable for practical applications.  
The subsequent post-processing operational steps are presented in Figure 7.  
Figure 7: Detailed representation of the satellite data post-processing process. Dotted line—message flow.
# Motor Imagery and Sport Performance: A Systematic Review on the PETTLEP Model
## 3 Results
### Motor Imagery and Strength Increase  
Strength-based disciplines, such as weightlifting or powerlifting, can benefit from MI to reduce anxiety and improve the execution of movements. However, increasing performance in these disciplines could also be achieved by increasing strength output. Furthermore, during the rehabilitation period following an injury, it is crucial to prevent excessive strength loss, and MI has been revealed to be particularly helpful in this regard. The so-called psyching-up strategies (which include MI) were found to be effective in increasing strength output in weightlifters [99]. However, some studies did not find any improvement in strength-based tasks after MI training [100; 101]. The ineffectiveness of this kind of training could be related to differences in the experimental design or imagery instructions [24]. Indeed, most of the studies that investigated this issue did not use a PETTLEP approach in all of its components but rather focused on the physical and task components. [PERSON] [102] found an increase in strength after imagined maximal voluntary isometric contraction (MViC) on a task which involved isometric bench pressing. However, this increase was restricted to the first training session.  
Another study [103] compared the effects of MViC on isometric bench press, leg press, triceps extension, and calf raise. In this study, different ratios of imagined-to-actual muscle contraction where compared. The 100% physical training group was the one with the higher strength increase. However, replacing part of the physical training with MI did not result in significant strength loss [103]. However, [PERSON] et al. [104] suggested that, in order to have greater results from MI practice, more repetitions and time devoted to MI are required.  
Larger strength gains are observed when imagining MViC compared to submaximal dynamic contractions [105]. Furthermore, muscles with larger cortical representation (more distal muscles) seem to benefit the most from MI [24,38], but this hypothesis requires further investigations.