esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Object Clustering  
The object clustering method segments nonground points into independent objects. Due to the sparse characteristics of LiDAR points, as the scan range increases, the points change from dense into sparse. The fixed distance threshold cannot meet the clustering requirements of either near or far distance, and over- or undersegmentation appears. To address this problem, we propose a clustering method fusing Euclidean clustering with adaptive distance thresholds according to semantic segmentation category information, as shown in Figure 7.  
Inspired by [38], the clustering distance thresholds were set to different values according to the density of the point clouds in different regions in order to address the problem of remote oversegmentation under fixed clustering distance thresholds. The category of clusters is determined according to the category of each point in the cluster. If all the points in the cluster are in the same category, the category is the clustering category. Otherwise, the clustering category is uncertain. As shown in Figure 7b, points in each red rectangle correspond to the same clustering result, labeled as the clustering category. As the truck length is large, it is oversegmented into three clusters.  
Point clouds with semantic categories can be used as a clustering criterion to address the problem of oversegmentation. However, the result of semantic segmentation may include false segmentation, as shown in Figure 7b, and there may be multiple categories in the points of segmented clustering results. The clustering categories should be filtered.  
To overcome this problem, we present a statistical category filtering method to filter out semantic segmentation noise in each cluster. For each cluster, we statisticized the number of points in each category. The category with the maximal number of points was the category of all points in the cluster. Then, all points of the cluster were set to this category. As shown in Figure 7c, for truck category points with the maximal number, the uncertain category was filtered to the truck category.  
For each cluster, the merge threshold was calculated according to the clustering category and the distance of the cluster center to the origin of coordinates. For different clustering categories, such as 'truck','vehicle', and 'pedestrian', different distance thresholds were set to merge the clustering results. As shown in Figure 7d, three clustering results were merged into a cluster to address the problem of oversegmentation. On the basis of the category filter method, the clustering category was determined to be 'truck'. The merging threshold was defined as in Equation (3).  
\[d_{m}=w_{c}*w_{r}*r \tag{3}\]  
where \(d_{m}\) is the clustering merger threshold, \(w_{c}\) is the parameter of the clustering category, \(w_{r}\) is the parameter of the distance of the cluster center to the origin of coordinates, an \(r\) is the distance of the cluster center to origin of coordinates.
## 3 Improved Grey Wolf Particle Swarm Hybrid Optimization MSVR Localization Model Construction
### The IPSOGWO Model
#### 3.3.2 Improved Location Updating Strategy  
In the grey wolf optimization algorithm's position update process, the method focuses on information exchange between individual wolves and the population. It neglects each wolf's relationship with its historical position. Ref. [46] introduced an enhanced GWO algorithm that incorporates concepts from Particle Swarm Optimization. The position update strategy for the wolves in this hybrid algorithm is as follows:  
\[\mathbf{X}_{i}(i+1)=c_{1}r_{3}(w_{1}\mathbf{X}_{1}(t)+w_{2}\mathbf{X}_{2}(t)+w_{3}\mathbf{X}_{ 3}(t))+c_{2}r_{4}(\mathbf{X}_{ibest}-\mathbf{X}_{i}(i)) \tag{25}\]  
In Equation (25), \(c_{1}\) is the social learning factor and \(c_{2}\) is the cognitive learning factor. These values respectively indicate how the individual's best experience and the swarm's best value influence the algorithm's search direction. Both \(r_{3}\) and \(r_{4}\) are random variables within [0, 1]. \(\mathbf{X}_{ibest}\) denotes the historically best position that a particular wolf has encountered. The terms \(w_{1}\), \(w_{2}\), and \(w_{3}\) adjust the weights of \(\alpha\), \(\beta\), and \(\delta\) to help the algorithm avoid local optima. They are determined by the following equation:  
\[\left\{\begin{array}{l}w_{1}=\frac{|\mathbf{X}_{1}|}{|\mathbf{X}_{1}+\mathbf{X}_{2}+\bm {X}_{3}|}\\ w_{2}=\frac{|\mathbf{X}_{2}|}{|\mathbf{X}_{1}+\mathbf{X}_{2}+\mathbf{X}_{3}|}\\ w_{3}=\frac{|\mathbf{X}_{3}|}{|\mathbf{X}_{1}+\mathbf{X}_{2}+\mathbf{X}_{3}|}\end{array}\right. \tag{26}\]  
Incorporating concepts from Particle Swarm Optimization has undoubtedly improved the algorithm's optimization capabilities. However, the authors in [46] assigned constant values to \(c_{1}\) and \(c_{2}\). They neglected that the weights of the learning factors should adjust dynamically with each iteration. To address this, our study introduces a grey wolf position update strategy with adaptive learning factors. Instead of being fixed, the modified learning factors \(c_{1}\) and \(c_{2}\) adjust complementarily: as \(c_{1}\) decreases, \(c_{2}\) increases. This method ensures a balance between global search capability and enhanced local search ability. The equation is presented as follows:  
\[\left\{\begin{array}{l}c_{1}=c_{1}(\max)-\frac{(c_{1}(\max)-c_{1}(\min))\cdot i }{c_{2}=c_{2}(\min)+\frac{(c_{2}(\max)-c_{2}(\min))\cdot i}{I}}\\ \end{array}\right. \tag{27}\]  
Substituting this into Equation (25) yields the final position updated equation.
# Congestion Control in CoAP Observe Group Communication
## 4 Evaluation Setup
### Performance Metrics  
In Obs, appropriate performance metrics were chosen to evaluate the congestion control mechanism, concerning features of message transmission and congestion mechanisms on the CoAP. These metrics were designed to allow the measurement of the efficiency of the mechanisms as well as the performance in the network. They involved a simulation setup, traffic scenarios, and network typologies which were set under varying congestion levels for the test.  
In the continuous traffic scenario and burst traffic scenario, the setting time, response time and packet loss were chosen as the important performance metrics since it was assumed that the CoAP transactions varied as a result of the changing topologies. Consequently, this would provide the whole picture of changes in the congestion level and the network size and help us to analyze the behaviors of the mechanism in each scenario.
# Quality Assurance of Chest X-ray Images with a Combination of Deep Learning Methods
###### Abstract  
Background: Chest X-ray (CXR) imaging is the most common examination; however, no automatic quality assurance (QA) system using deep learning (DL) has been established for CXR. This study aimed to construct a DL-based QA system and assess its usefulness. Method: Datasets were created using over 23,000 images from Chest-14 and clinical images. The QA system consisted of three classification models and one regression model. The classification method was used for the correction of image orientation, left-right reversal, and estimating the patient's position, such as standing, sitting, and lying. The regression method was used for the correction of the image angle. ResNet-50, VGG-16, and the original convolutional neural network (CNN) were compared under five cross-fold evaluations. The overall accuracy of the QA system was tested using clinical images. The mean correction time of the QA system was measured. Result: ResNet-50 demonstrated higher performance in the classification. The original CNN was preferred in the regression. The orientation, angle, and left-right reversal of all images were fully corrected in all images. Moreover, patients' positions were estimated with 96% accuracy. The mean correction time was approximately 0.4 s. Conclusion: The DL-based QA system quickly and accurately corrected CXR images.  
quality assurance; X-ray; deep learning; artificial intelligence Article
## 3 Materials and Methods
### Proposed Method
#### 3.3.3 Dataset Balancing
The above-mentioned data augmentation procedure has to be carried out \(N\) times with \(N\) equal to the number of images to be added to class \(\alpha\) for dataset balancing. Figure 6 shows some examples of augmented images which were produced from generic images from the _filtered dataset_. Figure 6: Examples of augmented images created by the proposed method. Downsampling Processing Technique  
Typical downsampling approaches involve the removal of excessive images. A novel approach for data balancing without sample scraping is indicated in this paper. Let class \(\beta\) be a generic class of the _filtered dataset_ for which downsampling is necessary. The conceived technique provides that class \(\beta\) is split into several sub-classes with an equal number of images. The number of sub-classes generated by class \(\beta\) is computed as the ratio between the cardinality of \(\beta\) and the median of the _filtered dataset_. Images belonging to class \(\beta\) are then distributed in each obtained sub-class by choosing to perform an additional shuffling procedure at random intervals. During the step of splitting the generic class \(\beta\) into multiple sub-classes, a json file keeping track of the associations between the original class \(\beta\) and all the generated sub-classes is created. These associations are then used for the evaluation of the classification performance. A classification error does not occur if an image belonging to class \(\beta\) is misclassified and assigned to a sub-class of \(\beta\). By adopting the proposed approach, the representation and interpretation of the confusion matrix also change. The confusion matrix, usually representing a diagonal pattern, is characterized by a diagonal-block distribution, since the majority of classification errors represent instances of class \(\beta\) predicted as one of its sub-classes. When computing the performance scores, all the misclassified instances falling in the sub-block of class \(\beta\) must not be interpreted as errors. The output of the balancing process is named the _balanced dataset_ and represents the input for the next steps of the designed methods. Figure 7 shows the distribution of images per category and highlights how intra-class imbalances are reduced by using the proposed procedure.
## 4 Experiments
### Highway Crack Detection Experiments
#### 4.1.3 Ablation Experiments  
This subsection focuses on the impact of each proposed improvement to the original network and the effect of each added module on the effectiveness of the experiments on the UAVRoadCrack dataset.  
The ablation experiment uses the DeepCrack network model as the baseline network to explore the effects of GE, MHDC, and MHSA on the baseline model. The \"+\" in Table 3 represents an improvement point of the model. The ODS, OIS, and AP of the baseline model on the UAV remote sensing highway crack dataset are 0.601, 0.613, and 0.608, respectively. When the MHDC module is added to the baseline model, the model further increases the receptive field in the underlying feature extraction process while obtaining multi-scale feature information, resulting in improvements in all indexes compared to the baseline. In the extreme model with the MHDC module, the BR in the original convolutional layer is replaced with GE, allowing the model to maintain a small training error when the batch size is small, and the ODS, OIS, and AP are 0.008, 0.009, and 0.009 higher, respectively, than those with the MHDC module only. When the CrackNet network with three improvements to the baseline model was used, the evaluation indexes of the network model on the UAV remote sensing highway crack dataset were improved by 10% compared with the baseline model, further demonstrating the reasonableness of the three improvements to the baseline model. The CrackNet model performs better on the UAVRoadCrack dataset than without the pretrained weights when using the training weight files on the public dataset CrackL315.
# Design and Evaluation of Large-Scale IoT-Enabled Healthcare Architecture
## 3 Proposed IoT-Enabled Healthcare Architecture
### Communicator  
Communication between health devices, regardless of type or number, is an important issue in the proposed healthcare system. There are three main communication challenges: coverage, device type (active or passive), and the numerous health devices. The first communication challenge is solved by using a powerful communication architecture that is described in [29]. The main idea of this communication architecture is to use three coverage tools. These tools are the Internet, high-altitude platforms (HAPs), and satellites. Each coverage tool is used in the case of the unavailability of the other two coverage tools. For example, if the device is not connected to the Internet and does not fall within the scope of coverage of an HAP, it can use the satellite as a coverage method. If there is more than one coverage method, the most-used method (Internet) is chosen.  
Regarding the second communication challenge, different network technologies are used, such as wireless sensor networks (WSNs), RFID, mobile ad hoc (MANET), and cellular. Hence, RFID technology can be used for passive health devices to include them in the proposed healthcare system. The third communication challenge is solved by dividing the entire health environment into a number of clusters. Determination of each cluster size depends on the number of things (patients, users, devices, and others), the complexity of computation processes, which may be achieved within a period, in each cluster region, and the specifications of the cluster's manager (see Figure 2). For example, assume that there is a healthcare unit in a densely populated area. Most of these residents in this area are elderly, which means that the rate of patients' visits to this healthcare unit will be high, and the nature of the diseases of these residents may be classified as high risk. That is why this healthcare unit must be equipped with all of the equipment necessary to serve these types of patients. Hence, it is expected that the number of things and the size of exchanged data in this healthcare unit is extremely large and, of course, their rate of increase will be very high. Therefore, when integrating this healthcare unit into the proposed architecture, it is necessary to specify an appropriate number of homegrown managers (in case of considering this healthcare unit as one cluster) or dividing it into many simple clusters.
## 4 Experiments
### Results  
#### 4.3.1 Results of Personnel and Feature Object Detection  
The subset is partitioned into training and validation sets, with a ratio of 8:2. The detection performance of various YOLOv7 models are evaluated based on this dataset, as shown in Table 5. The experimental results presented in Table 5 reveal an important trade-off in regard to model performance. The baseline YOLOv7 model achieves the fastest processing speed, with a latency of just 12.5 milliseconds, owing to its smaller 640 px \(\times\) 640 px input resolution. However, this advantage comes at the cost of a reduced detection capability, particularly for small objects like cigarettes and mobile phones that appear at low resolution in the input frames. The YOLOv7-e6 variant demonstrates superior performance, with a recall rate of 0.745, and achieves the highest [EMAIL_ADDRESS]:0.95 score of 0.514. This model exhibits particularly impressive efficiency, delivering comparable [EMAIL_ADDRESS] accuracy, while achieving an 18.6% reduction in inference latency relative to the computationally heavier YOLOv7-d6 architecture. The YOLOv7-e6e model, while attaining peak precision of 0.838 among all the tested models, suffers from a substantially lower recall rate of 0.703. This performance imbalance raises significant concerns regarding potential missed detections of critical feature objects, which could severely compromise the system's ability to accurately identify and classify anomalous behaviors in practical deployment scenarios.  
After a comprehensive evaluation of the detection accuracy, small-object sensitivity, and deployment feasibility, we selected YOLOv7-e6 as the core detection model. Its superior recall rate and robust feature extraction capabilities provide a stable and reliable foundation for target localization, which is critical for subsequent behavior recognition tasks. This architecture demonstrates an optimal balance between detection performance in regard to challenging small targets and practical implementation requirements.  
Therefore, the YOLOv7-e6 model is selected for object detection throughout this research, providing a robust and reliable foundation for object localization. This, in turn, supports subsequent behavior recognition tasks.
## 2 Materials and Methods  
### Study Area  
The study area is located in the UYR, in the southeastern part of the Qinghai-Tibet Plateau, with geographic coordinates ranging from 35\({}^{\circ}\)25\({}^{\prime}\) N to 36\({}^{\circ}\)25\({}^{\prime}\) N and from 100\({}^{\circ}\)32\({}^{\prime}\) E to 103\({}^{\circ}\)46\({}^{\prime}\) E, covering an area of approximately 15,000 km\({}^{2}\). The region is characterized by plateaus and mountains with significant variations in altitude (Figure 1a,b). The climate is predominantly plateau monsoon and sub-cold monsoon, with cold and dry winters, cool and humid summers, and precipitation concentrated in the summer, though it is unevenly distributed throughout the year. The UYR is rich in water resources, flowing through a section of approximately 381.9 km and forming several reservoirs, such as Longyangxia and Lijiaxia, which together constitute a group of \"dragon-type\" reservoirs (Figure 1c) (Table 1). These reservoirs provide crucial water sources for the provinces of Qinghai, Gansu, and Ningxia and regulate the distribution of water resources, playing a key role in regional socio-economic development and ecological protection. While the construction of these reservoirs has led to the full utilization of hydraulic resources, it has also impacted the local climate and ecological environment, particularly in terms of climate change and land use. Studying the climatic and ecological impacts of reservoir construction in this region will contribute to a deeper understanding of its integrated effects on both the environment and water resources.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Name of Reservoir**} & **Year of Construction** & **Normal** & \multirow{2}{*}{**Mean Depth**} & **Catchment** & \multirow{2}{*}{**Storage**} & **Long-Term** \\  & **Construction** & **Storage Level** & & **(DEP)** & & **Area** \\  & **(YEAR)** & **(FSL)** & & & **CATCH** & & **(DIS)** \\ \hline \hline  & & **m** & **m** & **km\({}^{2}\)** & **Billions of m\({}^{3}\)** & **Billions of m\({}^{3}\)** \\ \hline LX (Laxiwa) & 2010 & 2452 & 215 & 4.5 & 10.79 & 150 \\ NN (Nina) & 2000 & 2335.5 & 15.3 & 4.45 & 0.262 & 210 \\ LJ (Lijiaxia) & 2001 & 2180 & 128.3 & 6.78 & 16.5 & 210 \\ ZG (Zhigang) & 2006 & 2050 & 14 & 7.57 & 0.154 & 216 \\ \hline \hline  & & & & & & \\ \end{tabular}
\end{table}
Table 1: Basic properties of each reservoir.
# Nonlinear Maximum Power Point Tracking Control Method for Wind Turbines Considering Dynamics
## 4 Deficiencies of the Conventional MPPT Method from Other Perspectives
### Assumption of the Optimal TSR All the Time  
As mentioned above, there is no absolutely steady-state for VSWTs because of the stochastic nature of wind. TSR will fluctuate around the optimal TSR rather than maintain the absolute optimal TSR all the time. The conventional OT method operates under a fixed collective blade-pitch angle corresponding to the optimal TSR. However, the optimal collective blade-pitch angle varies with the instantaneous TSR. \(C_{\text{p}}\), as a function of TSR and the collective blade-pitch angle, is calculated using the AeroDyn standalone code. The contour map of \(C_{\text{p}}(\lambda,\beta)\) of a 5 MW wind turbine is shown in Figure 4.
## 3 Dynamic Disturbance Model of Crowd Panic Behaviors
### Dynamic Disturbance Model of Crowd Panic Behaviors  
The [PERSON] crowd flow model, as shown in Equation (1), is widely used in the study of crowd macro flows. However, the crowd behavior represented by the [PERSON] model is homogeneous; that is, it assumes that pedestrians make the same responses under the same stimuli. In reality, as affected by individual subjective consciousness, pedestrian movements exhibit evident acceleration inconsistencies. For example, the rear crowd can directly observe the movement changes of the front crowd, and then change their movement characteristics. However, the front crowd cannot see the rear crowd, so the front crowd is hardly disturbed by the rear crowd, reflecting the anisotropy of crowd disturbance propagation. Therefore, in addition to the hyperbolic conservation equation shown in Equation (1), another equation is needed to reflect the anisotropy of crowd disturbance propagation.  
Pedestrian acceleration, velocity, and density, as the three basic motion variables, have complex nonlinear couplings, but meet the basic momentum conservation. Therefore, many scholars in fluid mechanics have proposed second equation models based on momentum conservation. [PERSON] and [PERSON] used a convection derivative to replace the spatial derivative of \"pressure\" in a second-order model, and developed the Aw-Rascle prediction (AR) model, thereby improving the non-physical characteristics pointed out by [PERSON] [32]. The AR model is a typical anisotropic traffic flow model.  
In this study, based on the AR model and the proposed crowd disturbance pressure as shown in Equation (5), a propagation dynamic model for the internal disturbance of a crowd is established, as shown in Equations (6)-(8).  
\[\frac{\partial\rho}{\partial t}+\frac{\partial(\rho v_{h})}{\partial x}+\frac{ \partial(\rho v_{l})}{\partial y}=0 \tag{6}\]  
\[\frac{\partial\big{(}v_{h}+\mathrm{P}_{gh}\big{)}}{\partial t}+v_{h}\frac{ \partial\big{(}v_{h}+\mathrm{P}_{gh}\big{)}}{\partial x}=\frac{1}{\tau}(V_{eh }-v_{h}) \tag{7}\]  
\[\frac{\partial\big{(}v_{l}+\mathrm{P}_{gl}\big{)}}{\partial t}+v_{h}\frac{ \partial\big{(}v_{l}+\mathrm{P}_{gl}\big{)}}{\partial x}=\frac{1}{\tau}(V_{el }-v_{l}) \tag{8}\]  
where \(V_{eh},V_{el}\) represent the horizontal and lateral equilibrium velocities, respectively; these are represented by the steady-state relationship between velocity and density (fundamental diagrams). \(\tau\) is a relaxation factor. \(P_{gh},P_{gl}\) are the horizontal and lateral crowd disturbance pressures, as shown in Equations (9) and (10).  
\[P_{gh}=\rho^{7}\cdot\xi_{p}\cdot\frac{1}{\sqrt{2\pi t}}e^{-\frac{(x-a)^{2}}{2t}} \tag{9}\]  
\[P_{gl}=\rho^{7}\cdot\xi_{p}\cdot\frac{1}{\sqrt{2\pi t}}e^{-\frac{(y-b)^{2}}{2t}} \tag{10}\]