esa-sceva/satcom-chunk-collection huggingface dataset:
# Seg-Eigen-CAM: Eigen-Value-Based Visual Explanations for Semantic Segmentation Models
## 3 Methods
### Methodology Overview
#### 3.2.1 Gradient Weighting Strategy for Spatial Information
The computation process is defined as follows:  
\[O_{L=k}=W_{L=k}^{T}\cdot\left(\sum_{k}\left|\left|\frac{\partial Y^{c}}{ \partial A^{k}}\right|\right|\odot A_{k}\right) \tag{10}\]  
Here, \(\odot\) represents element-wise product. In addition, \(Y^{c}\) is replaced with \(\sum_{(i,j)\in\mathcal{M}}Y^{c}_{ij^{\prime}}\), indicating the generation of a localized explanation for the region of interest. When interpreting the behavior of deep learning models, gradients provide a natural way to quantify the influence of features on the model's output. According to [PERSON] et al. (2018), who reviewed and synthesized existing gradient-based methods, gradients reflect the sensitivity of predictions to changes in input, making them a local approximation of feature coefficients. This offers a reasonable starting point for analyzing the decision-making process of models. However, direct use of gradients can be affected by noise, especially when unrelated features contribute to noise. Researchers have pointed out that such noise primarily arises from imperfections in the method design rather than from the model relying on noise for predictions. This study emphasizes the necessity of evaluating the magnitudes of the gradients in absolute terms, as both negative and positive gradients can significantly impact the results. Considering only positive gradients can underestimate the contributions of certain critical features and potentially introduce noise through low-intensity responses. As shown in Figure 4, Grad-CAM emphasizes understanding which input features contribute most to the overall classification result, using the global average pooling of gradients to capture a global interpretation of features. In contrast, Seg-Xres-CAM adopts a local pooling approach, allowing it to capture the variations in the gradients within specific regions. Although this method preserves more spatial information, it may still introduce additional noise, particularly in edge regions, where larger gradient variations can lead to overestimating the influence of features near the boundaries, causing the activation of non-target areas to increase and affecting the accuracy of the visualizations. To address these limitations, we propose a method that focuses on gradient magnitude values, which not only reduces noise interference but also provides a more accurate quantification of feature importance. By multiplying absolute gradient values with activation maps, we obtain a weighted activation map with high spatial resolution that more faithfully representspixel-wise contributions. For further analysis of this weighted activation map, we employ SVD in subsequent steps to extract key information.
# Multi-formalism Models for Performance Engineering
## 3 Queueing Networks Techniques
### Timeout with Quorum based Join
#### 3.1.2 Fork/Join Paradigm  
The fork/join paradigm (see [27] for a formal discussion) is one of the most common extensions added to conventional queuing networks. For instance, one of the most interesting scenarios from the point of view of performance analysis consists of a load-balancer requiring to split a request into parallel processing units. In this context, a fork/join paradigm stems from the need of efficiently parallelizing _divide-and-conquer_ algorithms (see [28]), which are usually decomposed into a base case (that is immediately solved) and a recursive case, where the problem is decomposed into smaller subproblems assumed to be disjunct: in the end, the responses deriving from the solution of each subproblem are merged. As a result, the main idea is to fork the initial problem into more subproblems, execute them in parallel and finally synchronize them joining the solutions.  
Modern extensions of fork and join allow for _quorum-based joins_. Let us assume a request is split into \(M\) tasks by a fork node. A conventional join node would wait for all \(M\) tasks to complete the executions to reconstruct the request and let it continue through the model. Quorum-based joins are characterized by an extra parameter \(Q\leq M\). In this case, the fork/join part of the request is considered to be finished when the first \(Q\) out of \(M\) tasks reach the join node. The late \(M-Q\) tasks will be simply discarded when they will reach the join node.
# Numerical Design and Laboratory Testing of Encapsulated PCM Panels for PCM-Air Heat Exchangers
## 2 Methods
### Design Procedure
#### 2.1.3 2D Simulations  
Based on the five KPIs for selection, 13 surface geometries were generated in 2D (Table 4) as the selected 3D design is uniform along its width. The selected design has longitudinal ridges; these drive the air uniformly along its width and ensure a more uniform melting and solidification process. It also helps to avoid panel leaking due to unbalanced pressure and non-uniform stress due to panel expansion and contraction.  
A parametric analysis varying ridge height, ridge pitch and ridge radius evaluated pressure drop and heat transfer. These dimensions are represented in Table 4. The flow rate in terms of Reynolds number used for all 2D simulations were based on the inlet hydraulic diameter of the channel and ranged from 7200 to 21,600.  
The optimum design selected from 2D simulations was used for refinements. With surface dimensions unchanged, nine new designs evaluated the gap between panels, panel thickness and number of panels. The refined shapes (24 to 32) were based on the number of panels (4-9) and the thickness of the panel [\(0.069<T_{t}/\mathrm{T}<0.151\)] in order to increase the volume of PCM per stack. Cost of manufacturing and pressure drop were the KPI's analysed in this simulation.
# Efficient Filtering for Edge Extraction under Perspective Effect
## 4 Implementation
Meanwhile, in order to solve the problem of arbitrary scale assignment for the kernel, we use the technique of integration map to realize the real-time calculation. In other words, by combining the advantages of image rotating and integration map, the proposed oSoB filter can not only achieve nearly the same real-time performance of the box filter, but can also maintain the ability to obtain the filter response of any orientation and scale. Given the parameter maps, we can compute the response map based on the proposed algorithm. First, we rotate the image with different orientation steps (e.g., in this project, we use 18 orientations); then, for each rotated image, the integration map is computed, giving us 18 integration maps. When we compute the response for location (_x,y_), we look up both the scale parameter \(s\) and the orientation parameter \(\gamma\) from the parameter maps. With these two parameters and the aspect ration known before, the kernel shape and orientation are determined. To avoid rotating the kernel, we compute the respond in a standard DOB manner, but at the integration map of the rotated image. According to the parameters of the filter at location (_x,y_), we can find out the integration map corresponding to the given orientation and calculate the response using the DoB filter with given scale parameter. The response \(M\) at \((x,y)\) is obtained in a standard DoB manner with given kernel shape at the chosen integration map,  
\[M(x,y)=I^{I}(V_{F})-I^{I}(V_{E})+I^{I}(V_{D})-2I^{I}(V_{C})+2I^{I}(V_{B})-I^{I }(V_{A}). \tag{30}\]  
A DoB contains two rectangles, where \(R\)(_A,B,C,D_) is the left side of the rectangle (negative part) and \(R\)(_B,E,F,C_) is the right side (positive part). The area of these two rectangles can be computed efficiently. The process is summarized in Algorithm 1. ```
1:Image \(I\) with height H and width W
2:Scale \(s(x,y)\), orientation \(\gamma(x,y)\) for kernels at (_x,y_) and aspect ratio \(\lambda\) for all the kernels
3:The response map \(M\)
```  
**Algorithm 1** Workflow of oSoBo. ```
1:\(I^{P}\gets Pad(I,[max(H,W),max(H,W)])\)\(\triangleright\) Padding boundaries
2:for each orientation \(\theta\)do
3:\(I^{R}_{\theta}\gets Rotate(I^{P},\theta)\)\(\triangleright\) Generating rotated images
4:\(I^{I}_{\theta}\gets Integrate(I^{R}_{\theta})\)\(\triangleright\) Generating integration maps
5:endfor
6:for each valid position \((x,y)\)do
7:\(I^{I}_{\theta\leftarrow Select(I^{1},\gamma(x,y))\)\(\triangleright\) Selecting integration map by orientation
8:\(\{V_{i}\}\leftarrow Position((x,y),s(x,y),\lambda)\)\(\triangleright\) Positioning vertexes by scale and aspect ratio
9:\(M(x,y)\gets DoB(I^{I}_{\theta^{\prime}},\{V_{i}\})\)\(\triangleright\) Calculating difference of box (DoB)
10:endfor
11:return\(M\)
```  
**Algorithm 2** Workflow of oSoBo.
# Webcam Eye Tracking for Monitoring Visual Attention in Hypothetical Online Shopping Tasks
## 2 Literature Review
While some studies suggest that endogenous factors have a stronger influence, others point to the decisive importance of exogenous factors [7; 22; 23; 24]. Regardless of this discussion, however, it is clear that endogenous factors of potential customers can hardly be controlled by e-commerce retailers, but they can indeed control exogenous factors in relation to their own online shop. Knowing in which way these exogenous factors are associated with visual attention of customers visiting an online shop could help to optimise visual aspects of the shop or the presentation of its products. Position effects of objects on visual attention has already been studied both in virtual and in real environments using traditional eye tracking (e.g., [7; 22; 25; 26]). In horizontal orientation, higher visual attention is paid to the centrally located option, e.g., the centrally located item within a product category in a virtual supermarket shelf or within a website scene [25]. [PERSON] [27] describes a scene and screen centre bias when visual stimuli are presented on a computer screen. However, [PERSON] et al. [26] report that pictures placed in the upper area of a hotel booking website capture more visual attention than the same pictures when positioned farther away from the top of the website. Analysing viewport (the part of a webpage that is visible at any given time to a user) data, [PERSON] and [PERSON] [28] demonstrate that participants pay considerably more attention to upper parts of a website than to lower parts when reading online news. [PERSON] et al. [13] describe positions effects on visual attention within recommendation interfaces of e-commerce websites, with the upper positions of a vertical layout and the middle positions of a horizontal layout receiving more visual attention. [PERSON] et al. [29], who investigated the effect of incorporating human images into B2C websites on visual attention using traditional eye tracking, conclude that the effect of human images on gaze behaviour depends on the type of product. On websites selling entertainment products such as clothing, human images attract more visual attention, but not on websites selling utilitarian products [29]. [PERSON] and [PERSON] [30] report similar findings: models are the product presentation feature which most strongly influences visual attention on websites selling clothing.
## 4 Discussion
### Piglet Dataset  
The baseline piglet LDCT images exhibit poor quality (SSIM: 0.6152, PSNR: 25.12) with significant noise. The PViT model achieves a marked improvement (SSIM: 0.9631, PSNR: 43.34) with low uncertainty, demonstrating robust denoising capability. Gradient attention modules (GAMs) further enhance performance, with the diagonal-vertical-horizontal (DVH) configuration achieving the highest SSIM (0.9730) and PSNR (43.62) with low uncertainty. The gradient-Laplacian attention module (GLAM) paired with a combined loss function (MSE + SSIM + PL) delivers the best results (SSIM: 0.9756, PSNR: 43.84) with consistency, outperforming benchmark models like RED-CNN, TED-Net, and DSC-GAN.
## 4 Rice Monitoring Results and Discussion
### H/\(\bar{\kappa}\) Decomposition
It is observed that \(\alpha_{1}\) for FCP is close to 45\({}^{\circ}\) at stage 4 and fluctuates within the range of 40-65\({}^{\circ}\) at stage 5, whereas \(\alpha_{1}\) for DCP is close to 45\({}^{\circ}\) at stage 3 and fluctuates within the range of 60-85\({}^{\circ}\) at stages 4 and 5. Thus, these results confirm the necessity of statistical interpretation in terms of \(\bar{\alpha}\) values and/or \(H/\bar{\alpha}\) space for the discrimination of physical scattering mechanisms. Figure 8: Scattering probabilities for the FCP and DCP modes. (**a**) FCP mode; (**b**) DCP mode. Figure 7: \(H/\bar{\alpha}\) decomposition results of the FCP, DCP, and DLP modes. (**a**) Entropy; (**b**) Mean alpha. Figure 10 displays the \(H/\tilde{\alpha}\) 2D plane plots obtained from the entropy and \(\tilde{\alpha}\) values for FCP (Figure 10a), DCP (Figure 10b), and DLP (Figure 10c) measurements. In the FCP mode, the distributions mainly lie in zones 6, 5, and 4, indicating a vegetation-type scattering event. Roughly, four different clusters can be identified: germination stage, tillering stage, stem elongation stage, and booting plus ripening stage, and thus the booting and ripening stages cannot be separated effectively from FCP data. The results for the DCP mode in Figure 10b also reveal a broadened \(H/\tilde{\alpha}\) pattern, but the two observations (DoY 159 and 174) in the tillering stage are moved away from each other more than the FCP case. Visually, the DCP mode affords better separation between booting and ripening stages compared to the FCP mode, but this cannot be regarded as better capability because the DCP mode has less information content than the FCP mode. Although the \(H/\tilde{\alpha}\) plane plots for the FCP and DCP modes exhibit some different features, each mode yields satisfactory discrimination of the phenological intervals. In contrast, the \(H/\tilde{\alpha}\) plane plots for the DLP mode cannot achieve adequate discrimination capability for our rice targets, since almost all intervals are not resolved successfully, as seen from Figure 10c. Figure 10: The \(H/\tilde{\alpha}\) 2D plane. (**a**) FCP mode; (**b**) DCP mode; (**c**) DLP mode. Figure 9: Alpha angle corresponding to each eigenvector. (**a**) FCP mode; (**b**) DCP mode. It is also worth noting that entropy and \(\bar{\alpha}\) values for crop backscattering usually increase with increasing incidence angle. For lower incidence angles, surface reflection from soil is dominant whereas, for high incidence angles (e.g., greater than 20\({}^{\circ}\)), backscattering from vegetation stems and leaves make dipole-like scattering dominant, as demonstrated in [30]. Therefore, because of our high incidence angle (70\({}^{\circ}\)) feature, relatively higher \(H\) and \(\bar{\alpha}\) values from our experiment than the usual spaceborne SAR incidence angle (common operational mode: \(<\)70\({}^{\circ}\)) can be expected.
## 5 Results and Discussion  
_Presentation of the database and discussion of execution times and complexity_  
We used two images to illustrate the treatments:  
* Lena image, which is widely used in image processing;  
Figure 4: Illustration of the adaptive model effects on the Lena image.  
Tiger image, because it contains the most significant data in image processing. The tiger is present within the focal depth, while the rest of the scene, which is less important, should be homogenised.  
The execution of the programme mainly depends on the smoothing factor and the number of pixels to be smoothed. The higher the smoothing factor, the more a pixel must be modified. Processing continues until every pixel in the image is stabilised. The complexity is in \(\mathcal{O}(kN)\), where \(N\) is the number of pixels in an image and \(k\) is a constant representing the disorder in the original image. By increasing the value of \(\lambda\) in detailed areas, the proposed model reduces the processing time. Figure 5 shows the results of the adaptive model on noise.  
Figure 5: Illustration the effects of the adaptive model. From top to bottom, the original images, the result of applying the Sobel filter, then the result of applying the adaptive algorithm, and finally the result of applying the Sobel filter. The application of the proposed adaptive method reduces the number of noise and contour points for the Lena image from 21,571 pixels to 19,552 pixels (a reduction of 9.35%) and for the Tiger image from 15,600 to 14,374 (a reduction of 7.86%).  
We used the adaptive model on 100 images of the Berkeley database set [19]. This database contains colour photographs of various scenes. The results of these studies are presented in the following sections.
# Energy Efficiency in Short and Wide-Area IoT Technologies--A Survey
## 3 Energy Efficiency and Consumption Metrics and Models
### Energy and Power Consumption Models  
In this section, we provide a description of models proposed for estimating the energy/power consumption of (some of) the IoT technologies described in Section 2. In particular, we first focus on LPSANs, reporting the models proposed for Zigbee, Bluetooth and BLE; we then move on LPWANs, and provide a review of consumption models for LoRaWAN, Sigfox, LTE, and NB-IoT.  
As analyzed in [72], a generic IoT device switches back and forth over five main operation modes having different power consumption level:  
* _From Sleep to Wake-up_, that includes the time it takes for an IoT device to switch from sleep mode, where it is neither transmitting or receiving, to Wake-up mode;
* _Wake-up_, during which the device is connected to the network and is able to perform data exchange with the IoT network infrastructure and/or other devices;
* _Sensing_, during which the device scans its surroundings for possible data exchange;
* _Processing_, during which the sensors embedded in the IoT device collect and possibly (pre)process the gathered data, in order to later initiate a transmission toward the network infrastructure and/or other devices nearby;
* _Transmission_, during which the device transmits gathered and processed data.  
A preliminary analysis of the energy consumption of the above modes is presented in [73]. It is shown that Sensing and Processing modes have the lowest and highest energy consumption, respectively, while the consumption during the Transmission mode depends on the specific applications and services delivered by the device.
## 4 Test-Bed Implementation Setup
### Software Configuration  
Each node is accompanied by an operating system provided by the manufacturer, which aims to provide the majority of the hardware functions, and a keen focus is on the performance. A lot of operating systems are available for raspberry pi in the market, but Raspbian OS is chosen due to its affiliation with the hardware manufacturer, and the performance it provides is beyond any other OS except the operating systems without x-server (which is GUI for Linux). The latest Raspbian OS (sometimes called Raspberry Pi OS) is available at the experiment's time, and Debian version 10 (buster) is installed. Buster comes with Linux kernel version 5.10, but it was later upgraded to kernel 5.15 before any experiment was conducted. Although batman-adv is already part of Linux as a kernel module, any modification is not possible directly into the kernel module. Therefore, the latest version of batman-adv (2022.0-13-ge009 ae23-dirty) was downloaded from GitHub, recompiled from scratch, and then loaded into kernel memory through the available _modprobe_ program in Linux for kernel module management.  
All the wireless interfaces of the raspberry pi OS are managed by the _wpa_supplicant_ program which is responsible for connecting and managing infrastructure-based networks that support Wi-Fi Protected Access (WPA). Since all the nodes do not need any infrastructure access point in ad hoc mode to enable communication, _wpa_supplicant_ utility is terminated on all nodes, and _invconfig_ (also a free utility in Linux to manage wireless interfaces) is used to create an ad hoc network with the essid \"batman-network\". After setting all the parameters through the _invconfig_ and _ifconfig_ utility, the batman-adv module was loaded through the _butctl_ command. _Batctl_ is the controlling application for batman, which includes all the commands for controlling batman routing protocol in user space.  
Since, the operating system boots and loads all the kernel modules and utilities by pushing them into RAM, which takes some time. Therefore, a delay of 30 s was included before the script began. Eliminating the delay between the execution of the script and the point where _crontab_ is executed will result in misconfiguration. During the experiments,  
Figure 7: Top view of a node comprising raspberry pi (below the touch-screen), touch-screen, and power bank.  
several parameters of the initialization script were changed over the runtime, including the wireless interface transmit power and batman routing algorithm. Regarding the steps required for initialization of the batman network, Table 2 highlights the tasks that need to be executed in that specific order at boot.
# Gradient-Guided and Multi-Scale Feature Network for Image Super-Resolution
## 2 Related Works
### CNN-Based SR Models  
CNNs have achieved great success in SR tasks. [PERSON] et al. [21] proposed a deep Laplacian pyramid network (LapSRN), which gradually predicts the sub-band residuals of HR images in a coarse-to-fine manner, and efficiently restores the reconstructed images by the predicted sub-band residuals. [PERSON] et al. [22] designed a deep plug-and-play super-resolution model (DPSR), which utilizes an iterative optimization scheme based on variable splitting to estimate the blurred kernels to cope with the challenges of different degradation models. [PERSON] et al. [23] exploited a detail-fidelity attention network (DeFiAN), which employs the proposed Hessian filtering to extract the high-frequency features, and improves the extracted Hessian features through a dilated encoder-decoder mechanism as well as a distribution alignment unit, respectively, to achieve high-quality reconstruction. [PERSON] et al. [24] developed a degradation-aware SR model (DASR), which introduces a degradation-aware module to discriminate different degradation models by learning different feature representations, thus achieving good super-resolution performance.  
As the network depth increases, the number of model parameters increases dramatically. To reduce computational complexity while improving super-resolution performance, [PERSON] et al. [25] developed an information distillation network (IDN), which effectively utilizes the local long and short-path features to enhance the quality of reconstructed images by combining an enhancement unit with a compression unit. Further, to improve the performance of IDN, [PERSON] et al. [26] proposed an information multi-distillation network (IMDN) by adopting the residual block, which utilizes the proposed adaptive cropping method to achieve super-resolution restoration at arbitrary scales. In addition, to decrease the computational cost, [PERSON] et al. [27] propose a linearly-assembled pixel-adaptive regression network (LAPAR), which transforms the conventional learning of LR to HR mappings into a multiple predefined regression task on linear filter coefficients. [PERSON] et al. [28] proposed a sparse mask SR model (SMSR) to boost the inference efficiency by exploring the sparsity in SR tasks. SMSR determines significant and redundant regions by spatial mask learning and channel mask learning, respectively, and maintains superior performance while skipping redundant computations.