esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Dataset  
This project classifies the datasets utilized in the experiments into two categories: multi-scale organ information datasets and single-scale organ information datasets. A multi-scale organ information dataset is defined as a collection of samples containing multiple labels, requiring the model to label multiple objects within a single feature map. For instance, in the ACDC dataset, the network must accurately label the left ventricle, the right ventricle, and the myocardium in a single feature map. The objective of the multi-scale organ information dataset is to assess the model's capacity for generalization. This is because the network must learn the characteristics of multiple organs within the multi-scale organ information dataset and ensure that the labeled regions do not conflict with one another. In contrast, a single-scale organ information dataset comprises a single organ within a feature map, necessitating that the model discern the organ region from the background region. As the single-scale organ information dataset has a single label, it is possible to achieve higher test scores if the model can accurately delineate the organ's boundaries. Therefore, the single-scale organ information dataset places significant demands on the model's ability to capture details. In this project, the multi-scale organ information datasets employed for testing purposes are Synapse [7] and ACDC [46]. The single-scale organ information datasets utilized for testing are skin lesion segmentation datasets (ISIC2017 [47], ISIC2018 [48] with PH\({}^{2}\)[49]) and Kvasir-SEG [50].
## 3 Jitter Modeling
### Jitter Components
#### 3.2.1 Period Jitter  
The period jitter is the deviation of the cycle time from the ideal period [23], and may also refer to the jitter signal [24] that recurs over a certain period or frequency. Periodic jitter, sometimes referred to as sinusoidal jitter, is uncorrelated with the data signal and it is caused by adjacent circuits such as power supply noise, on-chip oscillators, and data buses. The most common time domain model for periodic jitter is sinusoidal jitter, which is often used for jitter tolerance testing, but rectangular jitter is also important for bit error rate.  
Here, a sinusoidal jitter is given by the following equation [25]:  
\[\begin{split}\triangle t_{\text{SIN}}[n]&=A\sin(2 \pi f_{0}(t-nT)+\Phi)\\ &=a\sin\left(\frac{2\pi f_{0}n}{f_{s}}\right)+b\cos\left(\frac{2 \pi f_{0}n}{f_{s}}\right)\end{split} \tag{6}\]  
where \(\triangle t_{\text{SIN}}[n]\) is sinusoidal jitter amount at sampling time \(nT\), \(f_{0}\) represents the frequency of sinusoidal jitter, and \(A\) is the amplitude of sinusoidal jitter.  
The mathematical model of the rectangular period jitter is given by the following equation:  
\[\triangle\,t_{\text{rect}}[n]=A_{\text{rect}}\times\text{sgn}[\sin(2\pi f_{j} nT)] \tag{7}\]  
where \(nT\) is the sampling time of the rectangular jitter, \(A_{\text{rect}}\) is the amplitude of the rectangular jitter, \(f_{j}\) is the frequency of the rectangular jitter, and \(\text{sgn}[\bullet]\) is the rectangular wave function  
\[\text{sgn}[T]=\begin{cases}1&T\geq 0\\ -1&T<0\end{cases} \tag{8}\]  
Figure 2: Digital synthesis.
# An Accelerated Slicing Algorithm for Frep Models
## 3 An Accelerated Slicing Algorithm
### Novel Acceleration Criterion  
Applying the spatial search solves the performance problem for the loop operation by reducing the number of iterations. It allows for the evaluation only of a part of an FRep model.  
The distance field calculation at every point of interest in the space leads to the computation of all the loop operations to build all the parts of the microstructure. It performs, even if these parts are very far from the point of interest and do not influence its distance field value (Figure 6a). Applying the spatial index allows for processing of only the unit cell closest to the point of interest. The Euclidean distance to the bounding box is used to find the nearest unit cell. Thus, the loop operation is replaced by a spatial search of the unit cell closest to the contours construction. During the calculation of the defining function of the FRep model, only the nearest unit cell (channel number 1 in Figure 6b) is taken into account for the computation, replacing the loop operation.  
Figure 5: The contour extraction of one layer of the FRep model (contours—green lines, quadtree cells—black lines): (**a**) using a common adaptive criterion (**b**) using the novel compound adaptive criterion.  
The computational complexity of the search of the nearest elements to the point of interest is close to O (log \(n\)) using a spatial index, in contrast to performing the full loop operation with complexity O (\(n\)). The spatial index based on the K-d tree or R-tree is constructed for every layer of the sliced 3D model and is used to calculate the distance field value for 2D contour construction. It should be noted that the performance can be further improved by applying interval or affine arithmetic to the found unit cells.  
Using this criterion makes the defining function of the FRep model discontinuous. It does not affect the contouring process, but the obtained model cannot be used as a primitive in further modelling process. This means that this criterion has to be applied at the end of the modelling process.  
The accelerated slicing algorithm was implemented using C++17 and incorporated into the FRepCAM system. The template-based Boost library was used for R-tree construction with the quadric creation algorithm [44]. ALGLIB 3.16.0 was used to construct the K-d tree with the median thresholding strategy for space division [45].  
Two examples of the application of the accelerated slicing algorithm will be given in the following sections. The tests for these case studies were performed using the following hardware: a PC with Intel Core i5-8250U CPU @ 1.80 GHz and 8 GB RAM.
# Energy-Aware 3D Path Planning by Autonomous Ground Vehicle in Wireless Sensor Networks
## 6 Numerical Results
### 50-Node Scenario  
This subsection evaluates the proposed algorithms numerically for a scenario with a single AGV and 50 nodes. Figure 2 illustrates 50 nodes located in a 500 m \(\times\) 500 m \(\times\) 500 m space.  
In Figure 2, we generate the locations of 50 nodes by considering uniform random distribution for them. The locations of the 50 nodes are given as {(441, 21, 473), (501, 58, 326), (284, 290, 66), (293, 310, 107), (56, 148, 389), (453, 21, 46), (237, 382, 179), (424, 10, 17), (158, 462, 37), (76, 29, 451), (142, 73, 154), (291, 355, 347), (3, 214, 112), (147, 178, 126), (157, 208, 284), (448, 262, 361), (107, 140, 229), (449, 483, 171), (427, 111, 222), (488, 280, 22), (258, 241, 185), (324, 161, 226), (140, 21, 385), (295, 107, 401), (413, 354, 410), (438, 61, 13), (240, 6, 28), (257, 271, 220), (318, 389, 243), (208, 298, 389), (168, 155, 442), (149, 464, 95), (186, 104, 279), (271, 446, 27), (347, 262, 304), (388, 233, 381), (398, 415, 212), (30, 369, 62), (151, 119, 370), (206, 66, 490), (117, 351, 125), (224, 360, 459), (459, 202, 138), (14, 99, 243), (339, 187, 19), (59, 446, 315), (429, 153, 167), (213, 157, 282), (209, 331, 94), (101, 334, 421)).  
With different subsets of these parameters, we evaluate performance of the Nearest Neighbour (NN)-, Grey Wolf Optimizer (GWO)-, and Genetic Algorithm (GA)-based approaches.
# Adaptive Frame Sampling and Feature Alignment for Multi-Frame Infrared Small Target Detection
## 3 Our Method
### Loss Function  
In the feature alignment module, in order to ensure the quality of alignment and guide the learning direction of the alignment module, we introduce a triplet loss without involving an attention mask as in [44], i.e.,  
\[L_{t}=\sum_{i=1}^{N-1}|f_{i}^{\prime}-f_{N}|-|f_{i}-f_{N}| \tag{10}\]  
where \(f_{i},f_{i}^{\prime}\) are the feature map before and after alignment, and \(f_{N}\) is the current feature map.  
For infrared small targets, the area produced by the two-dimensional Gaussian expansion of the true label is still insufficient. In order to further balance the positive and negative samples, a mask loss is introduced to supervise the learning of the model, The mask matrix \(M\) is defined as follows:  
\[M(m,n)=\begin{cases}1,&g_{t}(m,n)>\alpha\\ 0,&\text{else}\end{cases} \tag{11}\]  
where \(\alpha\) is the probability threshold, and finally our loss function can be expressed by the following formula:  
\[L_{h}=\|p_{t}-g_{t}\|_{2}\odot(M+\beta(1-M)) \tag{12}\]  
where \(\odot\) means element-wise multiplication, and \(\beta\) in the formula is used to control the proportion of background areas in supervision. Since the number of negative samples of small infrared targets is much larger than the positive samples, the value of \(\beta\) will be set smaller to balance the positive and negative samples. The value of \(\alpha\) should be set to a number slightly larger than 0 but not too large, to ensure that \(M\) accurately represents the real target area. In this paper, after some experiments, \(\alpha\) was set to 0.2 and \(\beta\) was set to 0.3. The final total loss function can be written as  
\[L=\lambda L_{t}+L_{h} \tag{13}\]  
Since the value of \(L_{t}\) is much larger than \(L_{h}\), in order to ensure the dominance of \(L_{h}\), we set \(\lambda\) to 0.01 to highlight the dominance of \(L_{h}\).
## 3 Results and Discussion
### Complex Ecospatial Networks Evaluation
#### 3.2.2 Ecospatial Network Integrity  
The results of the ecos spatial network integrity evaluation in the study area are shown in Table 2. These results indicate that the ecos spatial network has a low degree of closure and fewer loops; in other words, there are fewer paths for species migration and diffusion, and the overall circulation and circulation of the network are general. The \(\beta\) index is 1.76, which is greater than 1, indicating that the ecos spatial network has a more complex connection level compared with the tree-like structure and demonstrates strong resistance and recovery; the \(\gamma\) index is 0.59, which indicates that the node connectivity is at a medium level and not every node is connected to each other. Overall, the ecos spatial network integrity of the study area is average, and it is necessary to optimize and improve it.
## 6 Results
### Performance of Converter in Stand-Alone
#### 6.1.1 Boost Converter Closed Loop Performance  
Boost converters in the DCMG are able to operate in two modes, output voltage regulation using and MPPT. Firstly, the both versions of boost converter in regulation mode behavior are verified with the same load sequence. The simulation test starts at 100% load condition, then, 22.5% load steps are removed until reach a final load at 10%. At 0.5 s, 22.5% load steps are connected back until meet 100% load condition again. First version, 380 V output voltage boost converter behavior appears in Figure 14.  
Figure 14: Boost converter 2.5 kW at 380 V with state feedback controller. (**a**) input voltage, (**b**) inductor current and (**c**) output voltage.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(LGD2\) & \(LGD1\) & \(LBT2\) & \(LBT1\) & \(LLED\) & \(EC\) \\ \hline \(B5\) & \(B4\) & \(B3\) & \(B2\) & \(B1\) & \(B0\) \\ MSB & & & & LSB \\ \hline \hline \end{tabular}
\end{table}
Table 12: \(LC\) vector.  
No significant dynamics is observed in capacitor input voltage at starting or during load steps, see Figure 14a. Inductor current ripple is not greater than 10% nominal value and converter performs in CCM all the simulation time, see Figure 14b. Overshot is noticed in DC bus voltage at the starting because not higher level of control is included, in steady state, transitions are smooth during load changes, see Figure 14c. Boost converter at 190 V output voltage simulation results appears in Figure 15.  
Again, no important transients are observed in capacitor input voltage at starting neither during load transitions, see Figure 15a. Inductor current ripple keeps in criteria value and the converter performs in CCM, see Figure 15b. Starting oscillations take more time than those in 380 V version, they appear at starting and load transitions but overshoot is not significant, see Figure 15c. A simulation to evaluateas a 380 V boost converter in MPPT mode is perform using \(P\&O\) algorithm, Figure 16.  
A sequence from 0% to 100% PV array rating power shows: in Figure 16a boost converter output voltage to set primary bus, mainly voltage value keeps around 380 V but, voltage ripple performing in this mode is larger than in regulation mode deu to. In Figure 16b waveform of power taa  
Figure 16: Sixth set of simulation results, (**a**) primary DC bus and (**b**) power harvested from PV array.  
Figure 15: Boost converter 2.5 kW at 190 V with state feedback controller. (**a**) input voltage, (**b**) inductor current and (**c**) output voltage.
## 3 Method
### Preliminary
The color \(\mathbf{\hat{C}}(\mathbf{r})\) of the pixel in the image that aligns with the ray \(\mathbf{r}(t)\) is determined via discrete integration as follows:  
\[\mathbf{\hat{C}}(\mathbf{r})=\sum_{l=1}^{N}T_{i}(1-\exp(-\sigma_{i}\delta_{i }))\mathbf{c}_{i} \tag{5}\]  
\[T_{i}=\exp\left(-\sum_{j=1}^{i-1}\sigma_{j}\delta_{j}\right) \tag{6}\]  
\[\delta_{i}=t_{i+1}-t_{i} \tag{7}\]  
where \(\mathbf{c}_{i}\) and \(\sigma_{i}\) are the color and density of point \(\mathbf{r}(t_{i})\), \(\delta_{i}\) is the distance between adjacent samples, and \(T_{i}\) is the cumulative transmittance along the ray, which denotes the probability that the ray does not hit any particles from \(t_{j}\) to \(t\). **Optimization**. The NeRF network is trained under the guidance of a color-loss function, which aims to reduce MSE loss between the rendered color of ray \(\mathbf{r}\) and its corresponding ground-truth color:  
\[\mathcal{L}_{\text{MSE}}=\sum_{\mathbf{r}\in\mathbf{R}}\left\|\mathbf{\hat{C }}(\mathbf{r})-\mathbf{C}(\mathbf{r})\right\|_{2}^{2} \tag{8}\]  
where \(\mathbf{C}(\mathbf{r})\) represents the ground-truth value and \(\mathbf{\hat{C}}(\mathbf{r})\) represents the color of the generated ray \(\mathbf{r}\). Figure 2: NeRF network architecture. The position encoding of the input position is passed through 8 fully connected layers with 256 channels each. Additional layers output volumetric density and feature vectors. This feature vector is concatenated with the position encoding of the input viewing direction, and finally the RGB color is output.
# Principal Subspace of Dynamic Functional Connectivity for Diagnosis of Autism Spectrum Disorder
## 4 Results and Discussion
### Classification of ASD Using PWC FC Images
#### 4.4.1 Evaluation of PWC-Based ASD Classification Using 10-Fold Cross Validation  
In this section, we evaluate the performance of SFC, WC and PWC images for ASD classification. Essentially, this experiment is about observing the impact of PWCs on  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **SFC** & **WC** & **PWC** \\ \hline AccU & \(72.3\pm 0.9\) & \(86.6\pm 1.6\) & \(95.2\pm 1.7\) \\ SenS & \(72.4\pm 1.3\) & \(84.8\pm 1.9\) & \(96.7\pm 2.4\) \\ SpeC & \(73.2\pm 1.1\) & \(88.4\pm 1.8\) & \(94.3\pm 4.5\) \\ PreC & \(72.8\pm 1.1\) & \(85.4\pm 1.3\) & \(94.8\pm 3.7\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Percentage of average AccU, SenS, SpeC and PreC (\(\pm\)standard deviation) of the 3L-CNN using SFC, WC and PWC images based on 10-fold CV framework.  
Figure 7: Classification accuracy (%) of 3L-CNN using PWC images reconstructed using first principal component through the first ten principal components. When using WC without application of SVD as input to the CNN, the classification accuracy of the 3L-CNN is 86.6%, as listed in Table 4.  
classification performance. Table 4 shows the results of ASD classification of SFC, WC and PWC images using our proposed 3L-CNN model in terms of accuracy, sensitivity, specificity and precision using a 10-fold CV framework. From Table 4, classification using images from PWC outperforms images from SFC and WC, with the best average accuracy, sensitivity and specificity of 95.2%, 96.7% and 94.3%, respectively. ASD classification using PWC images was 8.6% and 22.9% higher than the WC and SFC images, respectively, based on 10-fold CV. The model performance based on the images from WC achieved 86.6% accuracy, which is a good result compared to the SFC images' 72.3% accuracy. However, it is worth noting that the use of SFC images achieves marginal improvement compared to previously published outcomes [18].  
In addition, Figure 8 shows the ROC curve for the 3L-CNN model using SFC, WC and PWC images, indicating the value of the AUC. The ROC curve evaluates the correlation between sensitivity and specificity at different cut-points, which allows better assessment of the accuracy of the classification algorithm. Figure 8 shows lower AUC values for the images of SFC (0.72) and WC (0.86), while the highest value is for the images of PWC (0.95). These results indicate that ASD has the highest discriminatory power using PWC images. It is possible that the SVD helped to show the distribution of time-frequency components more consistently. Figure A1 in the Appendix B shows the differences in the distribution of the time-frequency components for one of the biomarkers in one of the subjects with ASD and a subject with NC before and after SVD.  
proposed classification framework based on PWC images and 3L-CNN is a robust and effective ASD classification model for completely unseen data.
## 2 Mathematical Modeling and Analysis
### Thickness Variation of the Plate  
In this study, three different parabolic thickness variations of plates is considered for analysis and is reported in Figure 2. The radial direction is considered for thickness variation by keeping the total mass of the plate plus patch constant. In the radial direction the plate thickness is given by h\({}_{\rm x}\) = h [1 - T\({}_{\rm x}\) [f(x)]\({}^{n}\)], where 'h' is the maximum thickness of the plate where,  
\[f(x)=\{^{0,x=b}_{1,x=a}\text{ and }f(x)=\frac{x-b}{a-b}\text{ where }\text{b }<\text{x }<\text{a} \tag{12}\]  
The taper parameter or taper ratio (T\({}_{\rm x}\)) is given by the equation:  
\[T_{x}=\left(1-\frac{h_{\rm min}}{h}\right) \tag{13}\]  
The Case I plate of (Figure 2 with parabolically decreasing thickness variation is given by the equation:  
\[h_{x}=h\bigg{\{}1-T_{x}\bigg{(}\frac{x-b}{a-b}\bigg{)}^{n}\bigg{\}} \tag{14}\]  
The Case II plate (parabolically decreasing-increasing) and Case III plate (parabolically increasing-decreasing) thickness variation of (Figure 2) are given by the equations:  
\[h_{x}=h\bigg{\{}1-T_{x}\bigg{(}1-abs\bigg{(}1-2\frac{(x-b)}{(a-b)}\bigg{)} \bigg{)}^{n}\bigg{\}} \tag{15}\]\[h_{x}=h\bigg{\{}1-T_{x}abs\bigg{(}1-2\frac{(x-b)}{(a-b)}\bigg{)}^{n}\bigg{\}} \tag{16}\]  
where, n = 2 for parabolic thickness variation. The total volume of the plate plus patches as well as the unloaded plate is kept constant and is given by the equation:  
\[\text{Volume}=\pi(a^{2}-b^{2})h=\int\limits_{b}^{a}{(a^{2}-b^{2})h_{x}dx} \tag{17}\]  
In this paper, a comparison for the effect of frequency parameters, effect of sound power levels, average radiation efficiency and peak sound power level is obtained for parabolic tapered plates. The out of plane (m, n)th modes in \(Z\) direction for the plate with different attachment of rectangular and concentric stiffener patches at different positions with different parabolically tapered varying thickness is considered. The plate is made tapered with different taper ratios of 0.25, 0.50 and 0.75. The mass of the plate plus rectangular or concentric patches are kept constant for this analysis. The inner clamped and outer free boundary condition is taken. Three arrangements of plates with different combinations of rectangular or concentric stiffener patches are considered as shown in Figure 3. The selection of different combinations of rectangular or concentric stiffener patches are such that the mass of the unloaded plate is equal to mass of the rectangular or concentric stiffener patches plus plate and in all three cases mass of the plate with stiffener patches remains constant. The specifications and the material properties of an annular circular plate with attached rectangular and concentric stiffener patches are reported in Table 1. Rayleigh integral has been used for sound power calculation and ANSYS has been used as a tool for computation.
# Comparing the Performance of Robust Controllers for Vibration Suppression in Long Rotor Systems
## 6 Performance Evaluation of the Minimum-Effort Controller  
According to [12], the minimum-effort controller \(\mathbf{K}\) can be expressed by the following:  
\[\mathbf{K}(s)=\mathbf{k}(s)\ \rangle.\langle\mathbf{h} \tag{48}\]  
In (48), the symbol \(>.<\) represents the outer products of \(\mathbf{k}(s)\) and \(\mathbf{h}\). The \(\  
In (49), \(K=-1627\) and \(T=17.11\). \(K\) and \(T\) are found by optimisation techniques, according to the flow charts in Figures 5 and 6. By considering the weights in (43) and (44), the maximum and minimum singular values of the \((W_{1}S,W_{2}KS)\) for the minimum-effort controller are drawn in the Bode plot of Figure 11. It is shown that at frequencies over 5 rad/s, the maximum and minimum singular values drop below the 0 db line.  
The singular values of the sensitivity matrix are shown in the Bode plot of Figure 12, from which disturbance rejection, when using the minimum-effort controller, can be explained. Figure 12 indicates that the maximum singular value is bounded by the 0 db line, while the minimum singular value, at lower frequencies, drops to -33 db.  
This minimum singular value explains why such excellent step responses were achieved in [12]. Therefore, any attempt to bring the maximum singular value below the 0 db line does not contribute to the performance of the controller.  
Another excellent property is that both the maximum and minimum singular values converge at the line 0 db for the frequencies of interest, and this explains why the attenuation filter in the minimum-effort controller suppresses the oscillations significantly, with the lowest possible control effort.  
The performance of the minimum-effort controller can also be confirmed via the singular values of the control effort matrix \(KS\) without any weight. In Figure 13, the maximum singular values of \(KS\), for three types of controllers, are drawn for the purposes of comparison. It is seen that the maximum singular value of the minimum-effort controller indicated by the solid line is always below the curves for the PI controller of [12], and the \(H_{\infty}\) optimal PI controller. Moreover, by using the minimum-effort controller, the control effort matrix \(KS\) becomes singular, enabling the minimum singular value to be zero, scalar, or \(-\infty\) db and hence cannot be drawn for comparison purposes. Therefore, the lower bound of the control effort matrix \(KS\) for minimum-effort controller is always lower than any other type of controller that can be implemented.  
Figure 11: The Bode plot of maximum and minimum singular values of the \((W_{1}S,W_{2}KS)\) mixed-sensitivity matrix for the minimum-effort controller.  
Figure 12: The Bode plot of maximum and minimum singular values of the \(S\) sensitivity matrix for the minimum-effort controller.  
Figure 13: Maximum singular values of the control effort matrix \(KS\) for three types of controllers: dashed line, \(H_{\infty}\) optimal PI controller; dotted line, PI controller; and solid line, minimum-effort controller.