esa-sceva/satcom-chunk-collection huggingface dataset:
# Smartphone-Based Task Scheduling in UAV Networks for Disaster Relief
## 3 System Model  
The system model is shown in Figure 1. There are three types of objects in the system: UAVs, SPs, and disaster relief centers (DRCs).  
Let us suppose that \(N\) UAVs are evenly distributed throughout the disaster area, and the set of UAVs is denoted by \(U=\{1,2,\cdots,n,\cdots,N\}\). Each UAV has a task queue waiting to be executed. The tasks in the task queue of UAV \(n\) are denoted by \(T=\{t_{1},t_{2},\cdots,t_{1}\}\). Each UAV covers a designated area at a constant altitude, where \(h\) is the minimum safe cruising altitude that aims to minimize the energy consumption for maintaining altitude while ensuring safety [16; 17]. We assume there is no interaction between UAVs, and the coverage areas of each UAV do not overlap. In Figure 1, each UAV is responsible for collecting tasks within its coverage area and offloading some tasks to the SPs within the coverage range of the UAV. The coverage range of the UAV is set as \(r_{0}\).  
After the disaster, several DRCs need to be deployed in the disaster area. These DRCs can provide different types of assistance to nearby residents, such as accommodation and disaster relief supplies, depending on the severity of the disaster. There are a certain number of SPs within each DRC which are carried by the people located within the DRC.  
As the most common portable smart devices in daily life, smartphones have computing, storage and communication capabilities. Most of the time, most of the computing resources of a smartphone are idle. Therefore, smartphones are ideal equipment to assist UAVs in completing computation-intensive tasks in disaster rescue. For UAV \(n\), let us suppose that there exist \(D\) DRCs in its coverage area, each with a certain number of SPs. The SPs set in the coverage range of UAV \(n\) is given as \(M=\{1,2,\cdots,m\}\).  
To prompt SPs to assist the UAVs in performing the offloading, the system can provide incentives for the owner of the SP that executes the offloading of tasks. Based on the operating experience of the peer to peer (P2P) file system [18], about 30% of users are willing to offer assistance for the common interests of the community. These users are enough to support the computing operation of the system. Moreover, a certain number of users are willing to share resources without obtaining benefits.  
For relatively selfish users, the system can motivate users to perform tasks through incentives, such as distributing shopping vouchers, monetary rewards or phone bill coupons.
## 4 Discussion
### Strengths of the Approach  
One of the key advantages of the developed system is its automation. The use of optimizers to find the optimal parameters (size and shift) of the error signal eliminates the need for manual tuning and allows for efficient exploration of the parameter space. Parallelizing the optimization process with joblib significantly reduces computation time, making the system applicable to problems requiring fast data processing.  
The use of wavelet decomposition in the intervals_errors algorithm, although not central to the classification of error types, demonstrates the ability to integrate different signal processing techniques to improve the analysis and detection of errors at different frequency levels. The find_peaks algorithm provides an effective tool for detecting characteristic features in signals that may be associated with errors or anomalies.  
The modularity of the developed algorithms, including auxiliary functions such as pad_array and resize_vector, increases the flexibility and reusability of the system. The mse_classification function, although replaced by the more efficient classification_parallel, demonstrates the basic MSE-based classification approach that underlies the entire system. The clean_signal function shows the possibility of preprocessing the signal to remove known types of noise, which can improve the quality of classification.
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 3 Security Protections in IoT Environments
### Defense at the Device Layer
#### 3.3.2 Robust Operating Systems and Attack Surface Reduction  
In IoT environments, adopting an operating system with strong security and minimizing the attack surface is a key strategy for protecting devices from threats. It is wise to elect an operating system designed specifically for IoT security, and examples for OSs optimized for IoT devices include FreeRTOS, RIOT OS, and Zephyr [49]. These operating systems are usually designed to be lightweight and include only the necessary functional modules to reduce the potential attack surface. More importantly, unnecessary system services and applications, especially those that may listen to network ports or provide remote access capabilities, are disabled or removed from the operating systems. In these scenarios, system processes and applications are running with elevated privileges to mitigate the risks of being maliciously exploited.
# Research Progress of Visual Inspection Technology of Steel Products--A Review
## 4 Detection Methods
### Methods Based on Filtering
#### 4.2.1 Spatial Domain  
Spatial filtering is an enhancement method based on neighborhood processing that directly conducts operations in the two-dimensional space where the image is located. The most common operation of spatial filtering is template arithmetic, and the basic idea is to use the value of a pixel as a function of its own gray value and the gray value of its neighboring pixels. In spatial filtering, the gradient filters are mainly used to detect edges, lines, and isolated points. Sobel, Robert, Canny, Laplacian, and Deriche filters are popular tools for measuring edge density. [PERSON] et al. provided a method that used the Prewitt filter to extract edge information and realize defects of sheet products [18]. [PERSON] et al. [61] used the Sobel gradient edge detection operator combined with the Fisher discriminant to detect defects on steel surfaces. Spatial filtering methods are also discussed in the literature [25; 34; 35; 45; 46; 62; 63; 64] for the defect detection of various steel products. For the optical properties of highly reflective surfaces of cold-rolled strip, [PERSON] et al. [36] adopted a kind of homomorphic filtering algorithm based on a partial differential equation (PDE). Recently, the application of filter banks has been expressed in [PERSON] [46], [PERSON] [65], [PERSON] [47], and particularly in [PERSON] [47], which used mean filtering combined with a local annular contrast (LAC) detection method, which led to better performance.
# Phase Imbalance Analysis of GF-3 Along-Track InSAR Data for Ocean Current Measurement
## 3 Data Processing and Interpretation
### Analysis of Channel Phase Imbalance
#### 3.1.2 Phase Imbalance Caused by Squint Imaging  
According to Equation (19), the phase imbalance caused by the effect of squint imaging between Channel A and Channel B is formulated as:  
\[\Delta\phi_{squint}=\frac{\pi(d+\Delta y)}{V_{s}}f_{dc}. \tag{24}\]  
The Doppler centroids estimated by the image of the two sets of GF-3 ATI data are shown in Figure 9, which is no longer zero and varies with the slant range. It can be found that the Doppler centroids of the two sets of GF-3 ATI data change by about 23 Hz and 20 Hz from near range to far range. Their change trend is basically consistent with that of the phase imbalances. Correspondingly, due to the existence of squint, the phase imbalances between channels changed by about 4.1\({}^{\circ}\) and 3.9\({}^{\circ}\) in range. Therefore, it is enough to show that the squint angle error exists in the imaging process of GF-3 ATI mode, and it is the main reason for the space-varying of the phase imbalance in range.  
However, whether the squint imaging is caused by attitude control error or antenna electronic miss-pointing needs further discussion. The Doppler centroid frequency calculated from the attitude information is not consistent with the Doppler centroid frequency estimated by the GF-3 image. The Doppler centroids of the two sets of GF-3 ATI image data for phase imbalance error analysis are shown in Figure 10.  
Figure 10: The Doppler centroid frequency of the two sets of GF-3 ATI image data of the same channel. (**a**) The estimated Doppler centroid frequency from real data; (**b**) the calculated Doppler centroid frequency according to the attitude parameters.  
Figure 9: The relative change of the Doppler centroid in range. (**a**) The first set of GF-3 ATI data; (**b**) the second set of GF-3 ATI data.  
The estimated Doppler centroid frequency difference between the two sets of data is about 60 Hz, and the Doppler change trend between different scenes is also different. Furthermore, the calculated Doppler centroid frequency difference between the two sets of data is much smaller than that of the estimated Doppler centroid frequency. Therefore, when the GF-3 attitude measurement results can truly reflect the actual attitude, we believe that the main reason for GF-3 squint imaging is the antenna electronic miss-pointing caused by the antenna thermal deformation. According to the Doppler centroid frequency of these two sets of data, the squint angle error can be calculated to be about 0.03\({}^{\circ}\) and 0.04\({}^{\circ}\), respectively. A Doppler centroid frequency error of 100 Hz will cause a radial velocity measurement error of 2.8 m/s. Therefore, to eliminate the dependence on the land scene, the calculated value of the Doppler centroid frequency from attitude measurements must be accurate enough for ocean current measurement. At the same time, it is necessary to ensure that the antenna electronic pointing is accurate enough for ocean current measurements.
## 3 Results and Discussion
### Volatile Compounds
#### 3.4.10. Furanoids and Lactones
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & & & & & \multicolumn{8}{c}{**Differences in Concentration**} \\ \cline{6-13}  & & & & & & \multicolumn{4}{c}{**One-Way ANOVA**} & \multicolumn{4}{c}{**Two-Way ANOVA**} \\ \cline{6-13}
**Co.** & **Volatile Compounds** & **ID** & **LRI\({}_{\text{e}}\)** & **LRI\({}_{\text{l}}\)** & \multicolumn{4}{c}{**2021**} & \multicolumn{4}{c}{**2022**} & \\ \cline{6-13}  & & & & & **SCE** & **SPA** & **SCE** & **SPA** & **Y** & **H** & **Y**\(\times\)**H** \\ \hline FL01 & 2-Pentylfuran & B, C & 1229 & 1231 & 0.86 & 0.71 & n.d. & n.d. & - & - & - \\ FL02 & 2-Butyltetrahydrodofuran & B & 1267 & - & **35.47 \({}^{\text{a}}\)** & **25.44 \({}^{\text{b}}\)** & **21.66 \({}^{\text{a}}\)** & **14.86 \({}^{\text{b}}\)** & **SCE** & **21** & n.s. \\ FL03 & _cis_-2(1-Pentenyl)furan & B & 1399 & - & n.d. & n.d. & 0.07 & 0.07 & - & - & - \\ FL04 & Furfural & A, B, C & 1472 & 1460 & **2.24 \({}^{\text{b}}\)** & **3.31 \({}^{\text{a}}\)** & 7.69 & 9.36 & **SPA** & **22** & n.s. \\ FL05 & _cis_-2(2-Pentenyl)furan & B & 1505 & - & n.d. & n.d. & 0.14 & 0.13 & - & - & - \\ FL06 & 3-Acetyl-2,dimethylfuran & B & 1577 & - & n.d. & n.d. & 0.28 & 0.
# 3D U-Net for Skull Stripping in Brain MRI
## 2 Materials and Methods  
### Datasets  
The performance of 3D-UNet and the other methods (BSE and ROBEX) have been evaluated on a brain MRI dataset: \"Neurofeedback Skull-stripped (NFBS) repository\" that is publicly available [45]. There are 125 scans of 77 females and 48 males in the 21-45 age range (average: 31 years) with a variety of clinical and subclinical psychiatric symptoms. Each subject data comprises of skull-stripped T\({}_{1}\)-weighted images along with the manually corrected gold standard. The size of the individual scan is 256 \(\times\) 256 \(\times\) 192 and each voxel size is 1 \(\times\) 1 \(\times\) 1 mm\({}^{3}\). The first two dimensions of each scan represent the individual size of a 2D slice and the third dimension indicates the total number of slices present in the scan. Initial skull-stripped brain mask from T\({}_{1}\)W MRIs have been obtained using the semi-automated skull stripping software BEAST [34]. The results were checked by visual inspection and manual correction was made to the worst results using a Free-view Visualization Tool [46]. These corrected brain masks were incorporated into the BEAST library and the process was iterated until satisfactory brain masks were available for the whole image (slices). Experts and the research community have differing opinions on the standard of what to include or exclude in the brain [9].  
The brain ground truth standard used for this dataset follows from the paper of [PERSON] et al. [34]. Brain tissues consist of the cerebrum, cerebellum, brainstem, internal vessels, and arteries along with the cerebrospinal fluid (CSF) in ventricles, internal cisterns, and deep sulci. Skin, skull, eyes, dura mater, external blood vessels, and nerves are included in non-brain tissues.
## 3 Test Results and Analysis
### Test Phenomenon and Failure Modes
#### 3.1.3 Strengthened Beam A1  
The reinforced beam A1 was in the elastic stage during the initial loading. When the load was increased to 29.72 kN (18% \(P_{\rm m}\)), the first vertical crack appeared in the concrete layer in the tensile area of the pure curved section. As the load increased, the number of concrete cracks gradually increased, and the crack distribution was fine and dense. When loaded to 120.00 kN (71% \(P_{\rm m}\)), concrete cracks in the tensile area appeared at the bottom. When loaded to 139.09 kN (82% \(P_{\rm m}\)), the longitudinal tendons reached the yield load. When the loading continued, the specimen deflection increased rapidly to 160.00 kN (95% \(P_{\rm m}\)) and the concrete in the tensile area in the protection layer of the longitudinal horizontal cracked. When loaded to 168.72 kN, the peak load was reached and the reinforced thin layer of epoxy mortar broke near the left loading point. The load dropped sharply to 129.67 kN (77% \(P_{\rm m}\)). As the load increased slowly, the deflection increased rapidly. The load continued to increase to 138.10 kN (82% \(P_{\rm m}\)) when the concrete in the pressure area was crushed. The load dropped sharply to 92.41 kN (55% \(P_{\rm m}\)), and the test was stopped. The distribution of the cracks is shown in Figure 10c.
# Atmospheric Density Inversion Based on Swarm-C Satellite Accelerometer
## 2 Materials and Methods
### Method and Model
#### 2.2.4 Accelerometer Atmospheric Density Inversion  
There are many difficulties in the accurate modeling of atmospheric reverse drag. Since the surface shapes of the Swarm-C satellite and GRACE-A satellite are non-spherical, it is necessary to consider the satellite's attitude changes during flight and the interaction between gas particles on the satellite surface [25; 26].  
In atmospheric density calculation, the determination of ballistic coefficient \(B\) is very important, and its calculation method is shown in Formula (3) [36], where \(C_{D}\) is the drag coefficient of the satellite, \(A\) is the effective area of the satellite, and \(m\) is the satellite's mass.  
\[B=\frac{C_{D}A}{m} \tag{3}\]  
Figure 8: Comparison of conical shading model modeling and column shading modeling.  
The atmospheric drag coefficient \(C_{D}\) and the effective area \(A\) of the satellite constantly change with the satellite's movement. \(C_{D}\) is related to the satellite flight's attitude and to the physical properties of the materials on the satellite surface and the atmospheric composition state around the satellite orbit; therefore, it is difficult to determine. The atmospheric drag coefficient is calculated by the Sentman model [30; 36]. The calculation process will not be repeated.  
For the \(k\)-th panel of the satellite, the product formula of the atmospheric drag coefficient and the effective area of the panel are shown in Equation (4), where \(\gamma_{k}\) represents the angle between the normal vector of the k-panel and the incident atmospheric molecules, \(A_{k}\) represents the area of the panel, \(C_{D_{k}}\) is the drag coefficient of the panel, \(\overset{\rightarrow}{v}_{r}\) represents the velocity of k-panel at location r, and \(\overset{\rightarrow}{v}_{out}\).represents the gas particle's velocity after collision.  
\[C_{D_{k}}\cdot A_{k}=(\frac{P}{\sqrt{\pi}}+\gamma_{k}QZ+\frac{\gamma}{2} \frac{\overset{\rightarrow}{v}_{out}}{\overset{\rightarrow}{v}_{r}}( \gamma_{k}\sqrt{\pi}Z+P))\cdot A_{k} \tag{4}\]  
In Equation (4), the incident atmospheric molecules are recorded as \(\gamma_{k}\), \(P=\frac{1}{(S\cdot\exp(-\gamma_{k}2^{S}))}\), \(Q=1+\frac{1}{2S^{2}}\), \(Z=1+\left(\frac{2}{\sqrt{\pi}}\right)\int_{0}^{\gamma_{k}S}e^{-t^{2}}dt\), where \(S\) represents the scale of molecular velocity.  
The product of the effective area of the drag coefficient of all satellite panels is added to obtain the result of the overall drag coefficient and the product of the effective area of the satellite. The final atmospheric density \(\rho\) around the satellite orbit can be calculated using Equation (5), where \(a\) represents the drag acceleration.  
\[\rho=\frac{a}{\left(\left.\frac{1}{2}B\right|\overset{\rightarrow}{v}_{r} \right|^{2}\right)} \tag{5}\]
## 4 Performance Analysis
### Baseline Methodology
It preserves the current best position through the local memory of individual particles, while using the particle swarm optimization (PSO) algorithm as a local optimizer to obtain the final results. * LIPS (2013) [43]: The Locally Informed Particle Swarm (LIPS) algorithm forms niches by calculating Euclidean distance neighborhoods, using local information to enhance the local search capabilities. It employs multiple local best particles to guide each particle's search, thus identifying multiple optimal solutions via the fine-tuning ability of particle swarm optimization (PSO). * LoISDE (2014) [44]: This method first employs a local information matrix to enhance the mutation phase of DE. Then, it integrates this refined mutation phase with an information-sharing mechanism to elevate the algorithm's search efficiency. Subsequently, it implements a niche-aware population synchronization strategy to minimize niche loss. Together, these sequential strategies empower LoINDE to outperform existing DE-based niching algorithms. * LMCEDA and LMSEDA (2017) [45]: A novel local search scheme based on Gaussian distribution was proposed to refine the obtained solutions, thereby constructing the Local Search Clustering-Enhanced Distribution Estimation Algorithm (LMCEDA) and the Local Search Speciation Distribution Estimation Algorithm (LMSEDA). * FDLS-ADE (2023) [13]: This method proposes a novel fitness- and distance-based individual local search technique, FDLS, which can avoid ineffective local searching in local optimal solutions and similar regions. After applying FDLS to DE with an adaptive parameter scheme, the effectiveness of the algorithm has been significantly improved. * SA-DQN-DE (2024) [9]: This method assigns mutation operations to each individual based on the effectiveness of different mutation operators in historical evolution. Then, deep reinforcement learning is introduced to select multiple local search operators to improve the accuracy of potential optimal solutions.
# Object Semantic Grid Mapping with 2D LiDAR and RGB-D Camera for Domestic Robot Navigation
## 2 System Overview  
The proposed system is shown in Figure 1. First, a 2D laser-based SLAM is used to build an occupied grid map and obtain a robot trajectory. Then, an object detection algorithm is employed to detect the semantics of the objects located in the color image of the RGB-D sensor. Based on the timestamp of the image and the robot trajectory, the camera pose is interpolated to obtain a more accurate result. Based on object detection, depth images, and interpolated poses, a point cloud with object semantic information is generated. To more accurately approximate the occupied spaces of objects, the dominant directions of the room are extracted to generate OOMBRs. To make the robot select goal points conveniently and socially, object goal spaces are created. Finally, the object semantic grid map is generated.  
We divide the system into two parts: the front-end and the back-end. The front-end is used for data processing, including laser-based SLAM, camera pose interpolation, and object detection. The back-end is used to generate an object semantic grid map, including object point cloud generation, room dominant direction detection, and object goal space generation. The following first introduces the front-end of the system.  
Figure 1: System overview. LiDAR: light detection and ranging and SLAM: simultaneous localization and mapping.
## 2 Preliminaries
### Classification Metrics  
We computed the performance of the classification models using the python scikit-learn library [39] and the following metrics: precision, accuracy score, recall, balanced F score, MCC, ROC, and PRC. Precision represents the classifier's ability of not label a sample incorrectly and is given by the equation:  
\[Precision=\frac{TP}{TP+FP} \tag{3}\]  
where, \(TP\) represents the true positives and \(FP\) represents the false positives. The accuracy score calculates the total performance of the model using the equation:  
\[A(y,\hat{y})=\frac{1}{n}\sum_{i=0}^{n-1}1(\hat{y_{i}}=y_{i}) \tag{4}\]  
where, \(\hat{y_{i}}\) represents the value that the model classified the sample, \(y_{i}\) represents the real value of the sample, \(n\) is the total number of samples, and \(I(x)\) is the indicator function [27].  
The recall corresponds to the hit rate in the positive class and is given by  
\[Recall=\frac{TP}{TP+FN} \tag{5}\]  
where, \(FN\) represents the false negatives. The balanced \(F\)-score or \(F\) measure is a weighted average between precision and recall:  
\[F1=2*\frac{Precision*Recall}{Precision+Recall} \tag{6}\]  
The \(MCC\) is used to assess the quality of ratings and is highly recommended for imbalanced data [40], given by the following equation:  
\[MCC=\frac{TP*TN-FP*FN}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}} \tag{7}\]  
where, \(TN\) represents the true negative. Besides, the _FMI_ is used to measure the similarity between two clusters, the measure varies between 0 and 1, where a high value indicates a good similarity [41]. _FMI_ is defined as the geometric mean between precision and recall, given by the equation:  
\[FMI=\sqrt{\frac{TP}{TP+FP}*\frac{TP}{TP+FN}} \tag{8}\]  
The ROC calculates the probability estimates that a sample belongs to a specific class [42]. For multi-class problems, ROC uses two approaches: one-vs-one and one-vs-rest. Finally, the PRC is a widely used metric for imbalanced datasets that provides a clear visualization of the performance of a classifier [43].
## 3 Control Layer Architecture
### Directive Dissemination  
Although it is a controller that generates a directive, it is stored, carried, and forwarded by any node in the network that receives it. Figure 4 shows this behaviour. When a node receives a directive (\(dir_{n_{1}}\)) (_step \(s_{4}\)_), if the directive is newer than the one the node might be carrying (_steps \(s_{5}\)_-\(s_{6}\)), the node discards the old one (_step \(s_{7}\)_), executes the action encapsulated in the new directive (_step \(s_{8}\)_), and stores, carries, and forwards the new directive (_step \(s_{9}\)_).  
Discarding the older directive is a measure to deal with possible inconsistent directives as several controllers are allowed in the network. The newer a directive is, the closer the node is to the controller and, therefore, the more appropriate the directive is. If a node receives several directives, if they are consistent, it means that the controllers are also nearby and are mainly sensing the same context. Conversely, if the received directives are inconsistent, it indicates that the controllers are sensing different parts of the network. In this case, the node likely belongs to the network \"segment\" controlled by the controller generating the newer directive.  
In the following section, we will describe how to apply the control layer for the specific use case of congestion control.