esa-sceva/satcom-chunk-collection huggingface dataset:
# Population Bias on Tornado Reports in Europe
## 5 Discussion  
The Bayesian model used in this article only included the population effects, but previous studies using a similar approach have also included meteorological data [7]. These are large areas were the data collection is unreliable and thus can reduce the predictive capacity of the model. In their study of the probability of tornado occurrence across Canada, [PERSON] et al. [7] included the cloud-to-ground lightning climatology to account of for the spatial variability of tornadoes. For Europe, an indication regarding the predictive capacity of the model can be obtained by comparing the results from Figure 1 with lightning density over Europe between 2008-2012 developed by [PERSON] and [PERSON] [15] using data from the Arrival Time Differing NETwork. Over northern Norway, Sweden, and Finland, where NUTS3 have a large area and the collection of data is not as reliable as for other regions of Europe, the Bayesian model is introducing large adjustments. For these regions, the lightning density (Figure 4 from [15]) is lower (\(<\)0.4 flashes km\({}^{-2}\) yr\({}^{-1}\)) compared with almost any other regions in Europe. Thus, the adjustments in the number of tornadoes are less realistic for the regions from a meteorological point of view. For the Baltic states (i.e., Estonia, Latvia, Lithuania) the corrections from the model are realistic due to the relatively large lightning density (i.e., 0.4-2.5 flashes km\({}^{-2}\) yr\({}^{-1}\)) in this region. The predictive capacity of the model in these areas can be improved by improving data collection (e.g., ref. [16] used satellite data to obtain information on unreported tornadoes that occurred in forested regions) or by considering as covariate meteorological factors related to tornado occurrence [17].  
Future research will develop the model by considering meteorological covariates such as lightning density (e.g., from Arrival Time Difference long-range lightning detection network [18]) and tornadic environments (e.g., ERA5 reanalysis data [19]).  
Figure 2: As in Figure 1, but for the standard deviation of tornado occurrence from posterior distribution.
## 1 Introduction
Such effects can only be analyzed if the detail of the flow is obtained either by experiment or CFD simulation or both. The works of [PERSON] and [PERSON] [3] as well as [PERSON] et al. [4] have become the basis of many researchers doing ejector one-dimensional modeling. Many attempts have been done to improve the model's prediction of ejector behavior by considering the losses that occur inside it. Several loss coefficients are incorporated into the one-dimensional model and the values of these coefficients are obtained by matching the results of the model with experimental data. This approach was used by [PERSON] et al. [2] and [PERSON] et al. [5; 6]. [PERSON] and [PERSON] [7] have also developed a semi-empirical one-dimensional model based on the model of [PERSON] et al. [2] with a modification that considers the Fanno flow concept inside the mixing section to capture the effect of friction. An ejector mathematical model for critical mode operation has been developed by [PERSON] et al. [8] which was called the \"shock circle model\". The model took into account the velocity profile of the secondary flow at the entrance of the constant area chamber. [PERSON] [9; 10] has pointed out the importance of the pressure variation along the converging mixing section and attempted to develop a mathematical model considering this pressure variation. However, the pressure variation function used was an assumption and has not been validated extensively. The previous works presented above have indicated that the studies of pressure variation along the wall of the converging mixing section are still limited. Moreover, many of the one-dimensional models do not consider this pressure variation. Instead, they usually assume that the pressure variation at this location is negligible and equal to the pressure of the secondary flow inlet. Therefore, more researches about its effect on the accuracy of the one-dimensional model need to be conducted. The current research is an attempt to comprehend that effect further through CFD analysis, especially for ejectors operating in critical conditions. Additionally, the k-omega SST model and the transition SST model were tested to find the best-suited turbulence model for the ejector flow in this research. Hence, it will also contribute to the turbulence modeling test data for flow within an ejector, especially the use of transition SST model. The steps used in this research are as follows. Computational fluid dynamics simulations were used to obtain the pressure variation on the wall of the converging mixing section. Four different ejectors were simulated. An ejector was obtained from a published experimental work and used to validate the CFD simulations. The other three ejectors were a modification of the first one and used for the parametric study. The predicted secondary mass flow rates, \(\dot{m}_{s}\), obtained using the one-dimensional analytical model were compared before and after incorporating the above-mentioned pressure variation into the model.
# The Mobility Analysis of a 3-CCR Parallel Manipulator with Three Screw-Type Terminal Constraints
## 4 Mobility Analysis of 3-CCR PM
In Figure 3, the blue dots indicate that the 3-CCR PM at this pose has three rotational DOFs corresponding to the 8 th screw system, and the magenta dots indicate that the 3-CCR PM at this pose has three helical DOFs corresponding to the 11 th screw system. The region in the workspace of the 3-CCR PM that only contains 3H DOFs is shown in Figure 3b. To better display the distribution of points in the workspace, the points closer to the origin are set to have higher transparency. This result indicates that the DOFs property of the 3-CCR PM is variable in the workspace, and there are alternating forms of 3H and 3R DOFs in the workspace. In order to intuitively demonstrate the independent helical DOFs of the 3-CCR PM, four poses corresponding to different \(d_{11}\) that have 3H DOFs are shown in Table 2. The first position parameters are given as \(X_{o}\) = 30.25 mm, \(Y_{o}\) = 55 mm, and \(Z_{o}\) = 86.5 mm. The active limbs are solved as \(d_{11}\) = 40.91 mm, \(d_{21}\) = 67.98 mm, and \(d_{31}\) = 97.60 mm. The second position parameters are given as \(X_{o}\) = 48.25 mm, \(Y_{o}\) = 46 mm, and \(Z_{o}\) = 41.5 mm. The active limbs are solved as \(d_{11}\) = 60.60 mm, \(d_{21}\) = 57.87 mm, and \(d_{31}\) = 53.73 mm. The third position parameters are given as \(X_{o}\) = 66.24 mm, \(Y_{o}\) = 34.75 mm, and \(Z_{o}\) = 66.25 mm. The active limbs are solved as \(d_{11}\) = 77.07 mm, \(d_{21}\) = 46.41 mm, and \(d_{31}\) = 78.90 mm. The fourth position parameters are given as \(X_{o}\) = 25.75 mm, \(Y_{o}\) = 65.25 mm, and \(Z_{o}\) = 55 mm. The active limbs are solved as \(d_{11}\) = 38.07 mm, \(d_{21}\) = 79.42 mm, and \(d_{31}\) = 65.77 mm. After solving for the types of DOFs corresponding to feasible poses in the workspace of the 3-CCR mechanism, the author selected four poses with 3R DOFs. The \(X_{o}\), \(Y_{o}\), and \(Z_{o}\) values of these poses, along with the calculated \(\alpha\), \(\beta\), and \(\gamma\) values, are substituted into Equations (22)-(23) to obtain the joint vector values for those poses. Then, the joint vector values were sequentially substituted into Equations (4), (5), and (16)-(18) to calculate the values of \(\mathbf{\xi}^{r}_{pj}\) and \(h_{j}\) shown in Table 2.  
The principal screws and the pitch of the constraint screw system in these poses are shown in the third and fourth columns of Table 2. The principal screws of the motion screw system in each pose can be calculated based on screw theory. The pitches of the principal screws in the motion screw system are denoted as \(h_{1}^{m}\), \(h_{2}^{m}\), \(h_{3}^{m}\), where the superscript \(m\) indicates that these are the principal screws of the motion screw system. The calculations yield \(h_{1}^{m}=-h_{1}\), \(h_{2}^{m}=-h_{2}\), \(h_{3}^{m}=-h_{3}\). Next, MATLAB (R2022b) was used to plot the principal screws of the motion screw systems, as shown by the blue lines in Figure 4, in which the black labeled numbers represent the pitch of the principal screws of the motion screw system.
## 3 Results and Discussion
### OBs Morphological Analysis and Thermal Stability
Concerning the circularity values, as expected, they suggest that the OB shape is rather similar to a sphere (i.e., circularity is 1 for a perfect circle) at 25 \({}^{\circ}\)C with a minimum value at 100 \({}^{\circ}\)C as shown by the mean values in Table 3. Conversely, the mean diameters had a minimum value at 150 \({}^{\circ}\)C. Figure 8 reports the OB parameters of D10, D50 and D90 for each temperature corresponding to the percentages 10%, 50% and 90%, respectively. According to the microscope observations, the vast majority of OB sizes (90%) have diameters shorter than 1.5 \(\upmu\)m and larger than 0.8 \(\upmu\)m, are spherical in shape, and the OB structures are stable after the thermal steps. Moreover, such small sizes lead us to expect an efficient stability of an OB-based emulsion, as it is well known [8,23]. Regarding the results of the Shapiro-Wilk tests, we determined for the set temperatures that the \(p\) values < 0.05. These normality tests allowed us to define that our data significantly deviate from a normal distribution. According to these results, we chose a non-parametric statistic test for valuating if there were differences from the diameters groups at each temperature. The results of the Kruskal-Wallis tests have shown \(p\) values < 0.05; hence, there were differences among the diameters recorded at set temperatures. Afterwards, the Dunn tests identified the temperatures of 150 and 200 \({}^{\circ}\)C as the groups which have generated the differences. Therefore, an alteration in the OBs' diameters was evaluated between 150 and 200 \({}^{\circ}\)C; this chance in diameter value is influenced by different water contents of the structures and depends on the different evaporation processes used to obtain higher temperature.
## 3 Analytical Methods and Applications  
This section addresses the first guiding question of our inquiry and presents the six different types of geospatial analysis discovered, as well as the IoT projects and initiatives using these analysis techniques. The IoT projects identified cover a variety of application areas such as tourism, utility networks, disaster monitoring, health and disease detection, transportation, logistics, wildlife monitoring, agriculture, crime prediction, sports and gaming, and the physical environment.
# Wheat Powdery Mildew Detection with YOLOv8 Object Detection Model
## 3 Results and Discussion
Selecting 0.6 as the confidence score, where the F1 score has fallen sharply, remains a logical option. The precision values of the model decrease with confidence scores less than 0.6. According to these results, the model with the highest detection success was saved and tested in the test dataset. The test dataset consists of 45 images. Some sample detections performed on the test dataset are shown in Figure 8. The model demonstrates high detection accuracy in different scenarios; for example, different overlapping leaves, multiple objects, and leaves containing powdery mildew at different densities. Figure 9 illustrates the use of Eigen-CAM visualizations to understand the model's predictive detection of wheat powdery mildew. The data are structured into four rows, with each row corresponding to a unique wheat leaf. The leftmost column presents the original images. The middle column shows the model's detection of the disease. The right-most column displays Eigen-CAM visualizations, which use color gradients to indicate the most important regions for the model's decision-making process, with red areas representing the most critical regions. When comparing the model predictions and Eigen-CAM visualizations, it becomes evident that the model accurately directs its attention to the appropriate regions. Figure 6: YOLOv8m model results in training. Figure 7: YOLOv8m evaluation metrics against the confidence score. When the selected model was used on the test dataset, the precision, recall, F1, mAP50, and mAP50-95 values were 0.796, 0.749, 0.770, 0.765, and 0.383, respectively. It has been observed that False Positive and False Negative errors occur for various reasons, such as changes in natural light, blurring of the background due to the camera's autofocus feature (Figure 10). To address these issues, a more diverse training dataset that considers varying lighting conditions can be collected from additional images to improve detection efficacy. Adaptive histogram equalization can be applied as preprocessing to enhance image contrast, reducing the impact of lighting variations. Turning off the camera's autofocus feature during image collection will prevent background blurring, allowing for more accurate detection of diseased areas that might otherwise be missed. Future research could also focus on predicting the severity of the disease rather than detecting only diseased regions. These steps will help reduce false positive and false negative errors, improving the overall accuracy and reliability of the model in the real-time detection of powdery mildew. Figure 8: Powdery mildew detection results in some test images. Figure 10: Powdery mildew misclassification examples. Figure 9: Eigen-CAM visualization of powdery mildew detection.
###### Abstract
Additionally, a mathematical model is developed and integrated into a new cluster-head rotation algorithm, optimising and balancing energy consumption. These contributions ensure stable, energy-efficient network performance and sustainable operations. 2. A novel mathematical model is developed to dynamically identify the optimal region for relay selection, allowing any node within this region to be chosen as a relay based on resource distribution and energy costs. An algorithm is developed that incorporates the dynamic selection of relay nodes to effectively balance inter-cluster energy consumption. The proposed method significantly improves energy efficiency, by preventing premature node failure, and ensures long-term network performance. 3. A novel algorithm is developed to implement dynamic relay-role rotation among nodes within the identified optimal region. The proposed role rotation prevents excessive energy depletion in any single node and optimises inter-cluster communication. By distributing the communication load more evenly, the algorithm significantly extends the network's lifespan and stability. The remainder of this paper is structured as follows: Section 2 provides a critical review of the related literature. Section 3 describes the network and energy models, utilised for the analysis and evaluation of the proposed methods. Section 4 details the proposed schemes, including corresponding mathematical models, flow charts, and pseudo codes. Section 5 outlines the results and discussion. Finally, conclusions and future research directions are outlined in Section 6.
# A Method for Underwater Biological Detection Based on Improved YOLOXs
## 3 Methods
### Loss Function Improvement  
The localization loss function, also known as the regression loss function, aims to measure the distance deviation between the final predicted box and the ground truth box. This distance difference is calculated through a specific function, transforming it into a loss value. Subsequently, the weight parameters are adjusted through backpropagation of errors to gradually bring the predicted box closer to the ground truth box. The original YOLOX algorithm uses IoU Loss as the localization loss function. IoU is a simple metric for measuring the distance between the predicted box and the target box by calculating the intersection over union of the predicted box and the ground truth box. When the predicted box and the ground truth box do not intersect, the IoU value is 0, which does not accurately reflect the distance between them. Additionally, IoU cannot precisely indicate the degree of overlap between the predicted box and the ground truth box.  
To address the aforementioned issue, this paper replaces IoU Loss with Focal_EIoU Loss [32], as shown in Equation (4). Focal_EIoU Loss effectively tackles the problem of a sample imbalance and enhances the accuracy and robustness of object detection by combining the class recognition capability of Focal Loss and the positional accuracy of EIoU Loss. Specifically, Focal_EIoU Loss focuses on difficult and misclassified samples by reducing the weight of easily classifiable samples. Here, \(\gamma\) is an adjustment factor controlling the curvature of the curve, so that the loss for correctly classified samples is reduced, while the loss for misclassified samples is increased. EIoU Loss consists of three parts: IoU loss, center distance loss, and width-height loss, which consider the overlap area of the bounding box regression, the distance between centers, and the differences in width and height of the edges. This formulation helps mitigate the issues that are present in IoU Loss to some extent.  
\[\begin{split}& Focal\_EIoU\ Loss=IoU^{\gamma}EIoULoss\\ & EIoULoss=Loss_{IoU}+Loss_{dis}+Loss_{asp}\\ &=1-IoU+\frac{p^{2}(b,b^{gt})}{c^{2}}+\frac{p^{2}(w,w^{gt})}{C _{b}^{2}}+\frac{p^{2}(b,b^{gt})}{C_{b}^{2}}\end{split} \tag{4}\]
## 3 Results and Discussions
### Packet Delivery Ratio Comparison  
The EMBLR routing scheme exploits the HETX metric that decreases the number of intermediate devices in the optimal path selection, which minimizes the number of dropped packets during data transmission. In addition, the HETX metric decreases the number of trials for data retransmission and reduces link failure probability resulting from the dynamic nature of networks, thereby keeping a higher packet delivery ratio compared to the other multi-path routing schemes. Besides, the EMBLR routing scheme considers the rate of data transmission and queue length size of the intermediate devices during data transmission, which minimized the congestion level at the device. Therefore, it forwards the data packets towards the device with a smaller queue length size and a higher rate of data transmission, which reduces the chance of packets dropped in the network. For high-speed device scenarios, it can be seen from Figure 7 that the EMBLR routing approach attains better PDR performance compared with the other routing approaches. Consecutively, the improvement percentages with an average of all device speed are approximately 33.67% and 23.84% as compared with the MP-OLSRv2 and MEQSA-OLSRv2 routing approaches, respectively.
## 6 Discussion  
As a result of the measurements, it can be stated that the parameters of the HTS tapes, regarding the thickness of their component layers, differ from those provided by the manufacturer. At the same time, there is a large inhomogeneity in the thicknesses of these layers for all samples, which was shown in the microscopic images. Thicker silver layers were found for the tape with a declared thickness of 4 \(\upmu\)m (this tape had a lower resistance after leaving the superconducting state). Despite the lack of macroscopic changes for the tested HTS tape samples, it was possible to identify microscopic changes that could indicate the occurrence of diffusion processes responsible for changes in the I\({}_{\rm C}\) values of the HTS tapes. In the photos of the samples subjected to surge currents (higher than I\({}_{\rm C}\)), expanded layers are visible, which were initially identified as the superconductor layers. However, the analysis of their chemical composition showed the presence of elements from both the Ag and YBCO layers (especially for the areas closer to the Ag-YBCO boundary).  
The top layer of the HTS tapes, including the silver and superconductor layers and the buffer layers, was subjected to detailed analysis. The evaluation of the chemical composition of the individual layers allows for the indication of their position in the sample. In the samples with a reduced critical current value, there were lighter areas for which an increased presence of oxygen (approximately 20%) was observed in the chemical composition--the presence of oxygen is characteristic of the superconductor layer. The presence of lighter areas may correspond to the presence of silver oxides. On the other hand, the analysis of the chemical composition of the superconductor layers of these samples indicates the occurrence of damage to the YBCO layer (changes in thickness and the presence of silver), which may be caused by diffusion processes, and which may consequently lead to a decrease in the critical current value.  
The tested samples showed defects in the form of deformations and cavities in the buffer layers, and cracks at the silver-YBCO interface and in the superconductor as a result of the action of surge currents on the tape.
## 1 Introduction
However, due to the different ground stresses in the vertical and horizontal directions, the external loads on the horizontal frozen wall are nonuniform, which fundamentally differs from the axisymmetric loads on the vertical frozen wall. Even the optimized design formula for the vertical frozen wall is theoretically not applicable to the horizontal frozen wall [23]. In order to gain a detailed understanding of the deformation and plastic zone contour characteristics of the horizontal frozen wall, and to design the horizontal frozen wall more reasonably, theoretical research on the elastic-plastic design of the horizontal frozen wall urgently needs to be addressed. Traditionally, while studying the mechanical properties of frozen walls, the considered mechanical model was often assumed to be an infinitely long elastoplastic thick-walled cylinder under confining pressure loading conditions [6; 31]. However, because the interaction between the frozen walls and surrounding unfrozen soil, as well as the radial unloading of the frozen wall, were often neglected, this led to an inaccurate analysis of the frozen walls, which consequently led to structural failures. However, in recent years, several scholars have used an unloading model to optimize their study of the mechanical properties of the frozen wall [8; 9; 10; 32; 33], which has led to suitable results. Nevertheless, analyses based on the elastoplastic theory for frozen walls using the excavation unloading model and considering the interaction between the frozen wall and unfrozen soil under nonuniform load conditions has not been conducted [34]. Therefore, in this study, considering the unloading effect of excavation, an elastoplastic mechanical model for the interaction between the horizontal frozen wall and surrounding unfrozen soil under nonuniform load conditions is established. In particular, the stress, deformation, and plastic zone contours in the elastic zone of the frozen wall are theoretically analyzed; in this case, the Mohr-Coulomb yield criterion is used as the plastic yielding criterion for the frozen wall. Based on the Mohr-Coulomb yield criterion and considering the shear stress in the plastic zone, the traditional method for calculating the plastic zone of the frozen wall is modified, and thus, a modified analytical solution of the contour equation for the plastic zone of the frozen wall is obtained. Finally, the distribution law of stress and displacement is analyzed for construction wherein the horizontal freezing method is used; based on this analysis, factors influencing the evolution of the elastoplastic zone are studied.