esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Optimal Configurations for Different Models Across Different Performance Metrics
#### 4.2.5 Optimal Configurations for Intersection over Union (IoU)  
Table 7 showcases the configurations maximizing IoU, a spatial accuracy metric vital for tasks like segmentation, across the seven models.  
ResNet50 achieves the highest IoU of 0.926174 with AdamW (learning rate 0.0001), CosineAnnealingLR, and FocalLoss, extending its versatility to spatial tasks. This configuration's success likely stems from FocalLoss's focus on precise boundary predictions and CosineAnnealingLR's smooth optimization. ResNet101 (0.917219) and EfficientNet-B2 (0.911475) use SGD (learning rate 0.01), StepLR, and CrossEntropy, leveraging depth and scaling with momentum-driven stability.  
DenseNet121 (0.90301) and MobileNetV3 (0.90099) employ SGD (learning rate 0.01) and AdamW (learning rate 0.0001), respectively, with ReduceLROnPlateau and CrossEntropy, where adaptive scheduling enhances spatial consistency. ConvNeXt (0.898026) uses  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Optimizer** & **Learning Rate** & **Scheduler** & **Loss Function** & **F1** \\ \hline ResNet50 & AdamW & 0.0001 & CosineAnnealingLR & FocalLoss & 0.961672 \\ ResNet101 & SGD & 0.01 & StepLR & CrossEntropy & 0.956822 \\ EfficientNet-B2 & SGD & 0.01 & StepLR & CrossEntropy & 0.953688 \\ DenseNet121 & SGD & 0.01 & ReduceLROnPlateau & CrossEntropy & 0.940933 \\ MobileNetV3 & AdamW & 0.0001 & ReduceLROnPlateau & CrossEntropy & 0.947917 \\ ConvNeXt & Adam & 0.0001 & CosineAnnealingWarmRestarts & CrossEntropy & 0.946274 \\ ViT-B-16 & SGD & 0.001 & ReduceLROnPlateau & CrossEntropy & 0.935428 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Optimal hyperparameter configurations for maximizing F1-score across deep learning models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Optimizer** &
\begin{tabular}{c} **Learning** \\ **Rate** \\ \end{tabular} & **Scheduler** & **Loss Function** & **Recall** & **Training Time (s)** \\ \hline MobileNetV3 & Adam & 0.01 & StepLR & CrossEntropy & 1 & 345.73 \\ ConvNeXt & Adam & 0.01 & StepLR & CrossEntropy & 1 & 697.25 \\ ViT-B-16 & AdamW & 0.001 & CosineAnnealingWarmRestarts & LabelSmoothing & 1 & 1341.91 \\ EfficientNet-B2 & Adam & 0.0001 & StepLR & CrossEntropy & 0.979021 & 1155.55 \\ ResNet101 & Adam & 0.0001 & CosineAnnealingLR & LabelSmoothing & 0.972028 & 1085.43 \\ ResNet50 & SGD & 0.01 & StepLR & FocalLoss & 0.965035 & 1099.19 \\ DenseNet121 & SGD & 0.001 & ReduceLROnPlateau & CrossEntropy & 0.958042 & 646.64 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Optimal hyperparameter configurations for maximizing recall across deep learning models.  
Adam (learning rate 0.0001), CosineAnnealingWarmRestarts, and CrossEntropy, while ViT-B-16 (0.878689) relies on SGD (learning rate 0.001), ReduceLROnPlateau, and CrossEntropy, its lower IoU reflecting transformer challenges in spatial reasoning compared to convolutional models.
## 6 Application of the Optimization Techniques: Routing Protocols
### Multipath Routing Protocols
#### 6.4.1 SAR (Sequential Assignment Routing)  
SAR [25] is one of the first protocols for wireless sensor networks that provide the notion of QoS routing criteria. It is based on the association of a priority level to each packet. Additionally, the links and the routes are related to a metric that characterizes their potential provision of quality of service. This metric is based on the delay and the energy cost. Then, the algorithm creates trees rooted at the one-hop neighbors of the sink. To do so, several parameters such as the packet priority, the energy resources and the QoS metrics are taken into account. The protocol must periodically recalculate the routes to be prepared in case of failure of one of the active nodes.
# Operationalizing Dyadic Urban Traffic Interaction Studies: From Theory to Practice
## 5 Discussion
### Balancing Naturalism and Control in VR Environments  
While highly controlled environments allow for precise manipulation of variables and reproducibility, they risk diminishing ecological validity if participants perceive the scenarios as overly artificial. To mitigate this, several strategies may be used to simulate naturalistic behaviors effectively within VR environments. First, incorporating realistic environmental elements such as traffic flow, pedestrian density, and dynamic weather conditions can enhance the immersion of participants. Second, using adaptive algorithms to simulate the behavior of non-player agents (e.g., vehicles or pedestrians) ensures that their responses are contextually appropriate and resemble real-world interactions. These strategies help create a believable setting that encourages participants to engage naturally. Additionally, the design of interaction prompts can be subtle to maintain the flow of naturalistic behavior, as elaborated in Sections 4.2 and 4.4. For example, instead of explicit instructions or scripted cues, participants can encounter interactions organically through well-placed environmental triggers, such as the timing of traffic lights or the positioning of obstacles. This approach minimizes the intrusion of experimental control while preserving the natural dynamics of traffic interactions. The extent of naturalism required depends on the specific research question. Studies focusing on high-level behavioral patterns may tolerate slightly reduced naturalism in exchange for control, while those examining fine-grained decision-making processes may prioritize ecological validity.
## 3 Results
### Median Image Composite Creation  
Figure 5 shows the median composites for a Sentinel-2 tile in Guatem and Mato Grosso derived from cloud-free images acquired in 2019 with cloud cover <10%, and the corresponding sample change detection images acquired in July 2022 for Guatem and April 2022 for Mato Grosso.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Class** & **Band** & **Min** & **Max** & **Mean** & **Stdev** \\ \hline \multirow{4}{*}{1} & 2 & 74 & 668 & 207.49 & 31.83 \\  & 3 & 130 & 1130 & 398.40 & 57.63 \\  & 4 & 86 & 1352 & 290.00 & 49.20 \\  & 8 & 912 & 5144 & 2726.92 & 371.26 \\ \hline \multirow{4}{*}{3} & 2 & 211 & 2113 & 477.09 & 216.21 \\  & 3 & 391 & 2655 & 741.56 & 279.71 \\  & 4 & 323 & 3348 & 1034.24 & 453.30 \\  & 8 & 1160 & 4087 & 2246.23 & 632.23 \\ \hline \multirow{4}{*}{4} & 2 & 203 & 1872 & 415.48 & 97.21 \\  & 3 & 411 & 2334 & 724.29 & 145.90 \\  & 4 & 267 & 2837 & 621.16 & 241.67 \\  & 8 & 1723 & 5684 & 3644.23 & 646.96 \\ \hline \multirow{4}{*}{5} & 2 & 184 & 771 & 429.65 & 106.77 \\  & 3 & 340 & 1129 & 771.52 & 154.28 \\  & 4 & 214 & 1393 & 746.34 & 237.52 \\  & 8 & 1742 & 4003 & 2813.10 & 311.96 \\ \hline \multirow{4}{*}{11} & 2 & 146 & 626 & 300.66 & 55.23 \\  & 3 & 298 & 914 & 536.95 & 62.15 \\  & 4 & 176 & 1214 & 430.51 & 145.68 \\  & 8 & 1530 & 4324 & 2468.49 & 289.34 \\ \hline \multirow{4}{*}{12} & 2 & 189 & 906 & 466.51 & 99.30 \\  & 3 & 361 & 1336 & 774.15 & 133.53 \\ \cline{1-1}  & 4 & 225 & 1798 & 815.29 & 240.96 \\ \cline{1-1}  & 8 & 1320 & 4628 & 2897.72 & 362.87 \\ \hline \end{tabular}
\end{table}
Table 4: Spectral signatures of the training classes for the four selected Sentinel-2 image bands derived from the random forest model ‘mato_rf_toby_1’. Sentinel-2 bands: 2 = B, 3 = G, 4 = R, 8 = NIR.  
The image composites were created by calculating the median of a stack of images (Sentinel-2 L2A products) of atmospherically corrected surface reflectance for each band, excluding missing values, cloud-masked pixels, and cloud-shadow-masked pixels. By comparing the composites on the left of Figure 5 to the sample images on the right, areas of forest loss are recognisable to a human observer given the change in colour of the RGB channels shown.
## 3 Results and Discussion
### Video-Inspection from the Inside of the Piping System  
After the pressure test, and due to the unexpected results, a video recording of the interior of the entire piping system was carried out using a robotic camera. The whole recording was provided to the authors of this work. In the several hours of video that were collected, a generalized deterioration due to corrosion is observed, which is of the same type as the one observed in the sample during the visual analysis.  
Figure 5 shows examples of the results of the inspections carried out. These records make it possible to verify that the corrosion was a very extended process, with a general  
Figure 4: Area (red ellipse) with deposits of reddish corrosion products; inner face of the tube. The arrow indicates a straight line identified as the level achieved by the stagnant water.  
Figure 3: Multiple pitting onsets on the inside of the tube.  
level of damage that can be classified as very advanced. Although it can be seen that the level of damage varies from one section to another, there does not seem to be any section of the piping system that is free of the corrosion process. In many of the pits observed (e.g., Figure 5b), a \"mushroom-shaped\" mark around the defect is evident. These marks may be due to the generation of gas during the corrosion process, which accumulates in pockets, bubbles, or tubercles around the defect and finally bursts (perhaps during the pressure test), leaving the aforementioned \"mushroom-shaped\" mark printed on the tube wall.  
Supporting the previous observation, as presented in Figure 6, in some sequences of the inspection videos, the presence of gas bubbles attached to the inner surface of the pipe can be observed. Here, it is important to note that the figure shows images of parts of the piping system where the water level inside meant that the camera had to be submerged. A more detailed observation of these bubbles reveals the existence of a corrosion pitting in the center of each one of them, as presented in Figure 6b. At the same time, for there to be a separation between gas and water, the existence of a film or membrane that separates both media must be considered.  
Figure 5: Video-inspection image of the interior of the piping system. (**a**) Detail of pitting; (**b**) detail of “mushroom-shaped” marks around two pits.
# Stochastic Network Calculus-Aided Delay Analysis of Wireless-Power Line Mixed Networks
## Appendix A  
Proof of the derivation of Equation (10): with the definition of the Mellin transform, we have  
\[\mathcal{M}_{s_{w}}(\theta)=E\bigg{[}(1+\gamma_{w})^{\frac{B(\theta -1)}{\ln 2}}\bigg{]}\] \[= \int_{0}^{\infty}\left(1+\frac{P_{w}x}{\sigma^{2}}\right)^{\frac {B(\theta-1)}{\ln 2}}\exp(-x)\mathbf{d}x\] \[= \int_{1}^{\infty}y^{\frac{B(\theta-1)}{\ln 2}}\exp\left(\frac{ \sigma^{2}}{P_{w}}(1-y)\right)\mathbf{d}\left(\frac{\sigma^{2}}{P_{w}}(y-1)\right)\] \[= \frac{\sigma^{2}}{P_{w}}\exp\left(\frac{\sigma^{2}}{P_{w}}\right) \int_{1}^{\infty}y^{\frac{B(\theta-1)}{\ln 2}}\exp\left(-\frac{\sigma^{2}y}{P_{w}} \right)\mathbf{d}y\] \[= \frac{\sigma^{2}}{P_{w}}\exp\left(\frac{\sigma^{2}}{P_{w}}\right) \int_{\frac{\sigma^{2}}{P_{w}}}^{\infty}\left(\frac{P_{w}}{\sigma^{2}}\right) ^{\frac{B(\theta-1)}{\ln 2}}z^{\frac{B(\theta-1)}{\ln 2}}\times\] \[\exp(-z)\mathbf{d}\left(\frac{P_{w}z}{\sigma^{2}}\right)\] \[= \exp\left(\frac{\sigma^{2}}{P_{w}}\right)\left(\frac{P_{w}}{ \sigma^{2}}\right)^{\frac{B(\theta-1)}{\ln 2}}\int_{\frac{\sigma^{2}}{P_{w}}}^{ \infty}z^{\frac{B(\theta-1)}{\ln 2}}\exp(-z)\mathbf{d}z\] \[= \exp\left(\frac{\sigma^{2}}{P_{w}}\right)\left(\frac{P_{w}}{ \sigma^{2}}\right)^{\frac{B(\theta-1)}{\ln 2}}\Gamma\bigg{(}\frac{B(\theta-1)}{\ln 2 }+1,\frac{\sigma^{2}}{P_{w}}\bigg{)},\]  
where in the last step, we take advantage of the definition of the incomplete Gamma function \(\Gamma(s,y)=\int_{y}^{\infty}x^{s-1}\exp(-x)\mathbf{d}x\).
Sensors_**2010**, _10_, 3444-3479; doi:10.3390/s100403444  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Information Warfare-Worthy Jamming Attack Detection Mechanism for Wireless Sensor Networks Using a Fuzzy Inference System**  
**[PERSON] 1,*, [PERSON] 1 and [PERSON] 2**  
1 School of Information Technology, Indian Institute of Technology, Kharagpur-721302, WB, India; E-Mail: [EMAIL_ADDRESS]  
2 Department of Mathematics, Indian Institute of Technology, Kharagpur-721302, WB, India; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +91-3222-282338; Fax: +91-3222-255303.  
_Received: 8 February 2010; in revised form: 18 February 2010 / Accepted: 24 March 2010 / Published: 8 April 2010_  
**Abstract:** The proposed mechanism for jamming attack detection for wireless sensor networks is novel in three respects: firstly, it upgrades the jammer to include versatile military jammers; secondly, it graduates from the existing node-centric detection system to the network-centric system making it robust and economical at the nodes, and thirdly, it tackles the problem through fuzzy inference system, as the decision regarding intensity of jamming is seldom crisp. The system with its high robustness, ability to grade nodes with jamming indices, and its true-detection rate as high as 99.8%, is worthy of consideration for information warfare defense purposes.  
**Keywords:** wireless sensor networks; jamming detection; fuzzy inference system; information warfare
## 3 Results and Discussions
### Growing Environment Measurement by the WSN
#### 3.1.1 Growing Environment Data Acquired by the WSN
When you use the getObservation request, you should set \"eventTime\", that sets both the start time and end time, and \"observedProperty\" which decides what sensor to choose, then one can obtain the monitored data described by the O & M language based on XML. Using these methods, the standardized data are sent to the server located at the Mie University, then XML tags are parsed and the extracted data are stored in the database constructed in this study (Figure 11). Verifying the existence and accuracy of the weather data obtained from the weather station is necessary. Therefore, we used the \"getObservation\" method of cloudSense to make sure whether this system can acquire information about the growing environment from the WSN and the connection between the data link server and cloudSense, then we could confirm that the data obtained from the WSN are stored in the cloudSense. Concerning the values from the soil moisture sensor, there are some deficit points. In our web application, each missing value is replaced with an average value of the value former day and the value of the following day. In addition, we also compare the data from the SE-WS700 weather station and the data from the reference weather station. The reference weather station is added to the WSN and deployed next to the SE-WS700. As a result, almost all the observed data were correct, except for the precipitation and air temperature data [20; 21]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sensor Name** & **Physical Quantity** & **UOM** \\ \hline air\_temperature & Temperature & Cel \\ air\_pressure & Air pressure & hPa \\ wind\_speed & Wind speed & m/s \\
10 min\_maximum\_wind\_speed & Wind speed & m/s \\
10 min\_minimum\_wind\_speed & Wind speed & m/s \\
10 min\_average\_wind\_speed & Wind speed & m/s \\ wind\_direction & Aind direction & deg \\
1 min\_precipitation & Precipitation & mm \\ solar\_irradiance & Global radiation & W/m\({}^{2}\) \\
10 min\_maximum\_solar\_irradiance & Global radiation & W/m\({}^{2}\) \\
10 min\_minimum\_solar\_irradiance & Global radiation & W/m\({}^{2}\) \\
10 min\_average\_solar\_irradiance & Global radiation & W/m\({}^{2}\) \\ relative\_humidity & humidity & \% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Sensor name, physical quantity and UOM.
# Denoising Diffusion Models on Model-Based Latent Space
## 4 Experiments and Evaluation
### Image Generation
#### 4.2.2 Continuous-Style Diffusion Model  
We begin this evaluation by analyzing the case in which the forward and backward processes are kept unchanged compared with the continuous case. Hence, we optimize for the objective in Equation (4) while leveraging the codeword-to-index assignment introduced in Section 3.2.1. As anticipated, the generative process is able to infer and reproduce the high-level structure of real images and model the textures and details. Where this approach fails is in the last steps of the inverse process, since the ambiguity introduced by the approximation to the continuum seems to limit the overall definition of the finest details in the image, resulting in an extremely noisy generated image.  
As an impromptu solution, we opted to train a trimming model to improve the quality of the generated image \(\tilde{x}=\mathcal{D}(\tilde{z})\) after it has been decoded to RGB space. The refiner is based on the same U-Net architecture with a smaller configuration with only 1.7 M parameters, and it is trained in a fully supervised fashion by synthetically generating noisy images \(x_{n}\) by slightly corrupting their encoded representation, \(x_{n}=\mathcal{D}(\mathcal{E}(x)+\gamma)\), where \(\gamma\) is scaled-down Gaussian noise. The objective function for the refiner is a combination of \(L1\) and SSIM losses, which easily achieves a PSNR of over 30 on both datasets. The clear downside of this solution is the necessity of training a custom refiner for each dataset and the increased cost of generating new images. In Figure 5 the effect of the refiner model can be appreciated, while in Figure 6 a batch of refined generated samples are reported for qualitative evaluation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**FID Score (\(\downarrow\))**} \\ \cline{2-3}  & **LSUN-Cat** & **LSUN-Church** \\ \hline Continuous & 14.01 & 12.46 \\ Continuous + Refiner & **11.92** & **10.05** \\ Categorical Rev. (\(\tau=1.0\)) & 13.97 & 12.06 \\ Categorical Rev. (\(\tau=2.2\)) & 13.75 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation of unconditional image generation on LSUN datasets. Best results in **bold**.  
Figure 5: (a) Generated Image. (b) After Refinement.
# 3D Reconstruction Based on Iterative Optimization of Moving Least-Squares Function
## 3 Methods
### Moving Least-Squares Surface Generation  
After the GRU module described in Section 3.2, shape features \(f\) with contextual information can be obtained. Where \(f\) is calculated as \(f=MLP\Big{\{}\left.f_{h}^{i}\bigoplus\widehat{f}_{h}^{j}\right\}\), and \(\oplus\) represents the feature concatenation of each channel, \(f_{h}^{j}\) and \(\widehat{f}_{h}^{j}\) are calculated as described in Section 3.2. \(h\) represents the current octree depth, and \(j\) represents the octree node at depth \(h\).  
We generate MLS point cloud \(Q\in R^{3}\) through a shallow MLP, where \(q_{i}\in Q\) represents the MLS point within the octant and \(o_{i}\) represents the smallest octant center point where \(q_{i}\) is located. Through the above steps, a collection of MLS point clouds can be obtained, denoted as \(\{q_{i},n_{c}\}\).  
To approximate the signed distance function, we employ the classic moving least-squares (MLS) function, treating it as an implicit signed distance function for 3D shape surfaces. (We adopt a sampling strategy like DeepMLS, which makes the sampling points dense enough and the distance between the sampling points small enough to make MLS approach SDF). This method effectively applies the moving least-squares technique to three-dimensional shape analysis, enabling the estimation and calculation of implicit signed distance functions on surfaces. This provides crucial support for further geometric calculations and surface reconstruction. By constructing and applying MLS functions, we can more accurately describe and represent the features and topological structures of three-dimensional shapes. In the paper, the function \(F(\cdot)\) is defined to represent the moving least-square function, which is used to estimate the signed distance value of the point cloud:  
\[F(x)=\frac{\sum_{q_{i}\in Q_{mls}}\omega(x-q_{i})\cdot\langle x-q_{i},n_{c} \rangle}{\sum_{q_{i}\in Q_{mls}}\omega(x-q_{i})}, \tag{8}\]  
\[\omega(x)=e^{-\frac{x^{2}}{\rho^{2}}}, \tag{9}\]  
where \(a,b\) denotes the inner product of \(a\) and \(b\), \(Q_{mls}\) represents all MLS points of the local patch contained in the sphere with \(x\) as the center and \(r\) as the radius, where \(r\) is 1.8 times the width of the octant where \(x\) is located. \(\sigma\) is defined as:  
\[\sigma=\sqrt{\frac{d_{Qmls}}{|Q_{mls}|}}, \tag{10}\]  
where \(d_{Q_{mls}}\) indicates the length of the bounding box of the local patch where \(Q_{mls}\) is located. And \(|Q_{mls}|\) is defined as 200.
# Bangla Character Detection Using Enhanced YOLOv11 Models: A Deep Learning Approach
## 4 Analysis and Findings from Experiments
### Result Analysis
#### 4.4.2 False Detection Using the Original YOLOv11  
The original YOLOv11 model demonstrated several false detections when recognizing Bangla characters, as illustrated in Figure 8. Despite reporting high confidence levels (up to 0.99 for each class), some predictions did not match the actual characters present in the image. These errors highlight the model's difficulty in distinguishing visually similar letters, often due to overlapping structural features among certain Bangla characters.  
These misclassifications can be attributed to several factors, including class imbalance, suboptimal feature extraction, and limited ability to differentiate fine-grained visual details. Expanding the dataset, particularly with samples of visually ambiguous characters, is critical for improving model performance. In addition, applying advanced data augmentation methods and fine-tuning the training parameters can further reduce false detections and enhance overall detection accuracy.
## 3 Component Specifications
### Energy Specification
It is the weight of the fuel, rather than that of the fuel cell, that increases. This is one of the main advantages of fuel cells over batteries. The energy density of a fuel depends on the applied hydrogen storage technology. If hydrogen is stored at 300 bar of pressure, in electrical terms, its energy density will be nearly 33.3 kWh/kg [21]. If the efficiency of the fuel cell used in the HPS is 40%, then the energy density of the hydrogen will be reduced to 13.2 kWh/kg. By dividing the total energy required (8.4 kWh) by this value, the weight of hydrogen required was estimated to be approximately 0.63 kg.
## Wind Turbine Blade Design  
**[PERSON] * and [PERSON]**  
Faculty of Engineering, Division of Materials, Mechanics and Structures, University of Nottingham, University Park, Nottingham NG7 2 RD, UK  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +44-(0)-115-95-13979.  
_Received: 23 April 2012; in revised form: 21 June 2012 / Accepted: 30 August 2012 / Published: 6 September 2012_  
**Abstract:** A detailed review of the current state-of-art for wind turbine blade design is presented, including theoretical maximum efficiency, propulsion, practical efficiency, HAWT blade design, and blade loads. The review provides a complete picture of wind turbine blade design and shows the dominance of modern turbines almost exclusive use of horizontal axis rotors. The aerodynamic design principles for a modern wind turbine blade are detailed, including blade plan shape/quantity, aerofoil selection and optimal attack angles. A detailed review of design loads on wind turbine blades is offered, describing aerodynamic, gravitational, centrifugal, gyroscopic and operational conditions.  
**Keywords:** wind turbine; blade design; Betz limit; blade loads; aerodynamic