esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Accordingly, in order to realize cloud shadow-balanced compensation, this paper proposes a balanced shadow compensation model combining multi-level information, including pixel, super-pixel, and shadow-related region information. A consistent super-pixel in the shadow region is obtained as a supplementary compensation unit by means of super-pixel segmentation. Then, it applied in linear correlation correction compensation and integrated with region-level and pixel-level compensation information to realize the balanced compensation of a shadow area. The main contributions of this paper are illustrated as follows:  
1. A solution to the problem of uneven shading caused by cloud shadows in high-resolution images. Because a cloud has different areas of thickness, its shaded extent on ground objects is also inconsistent. This problem has not been discussed so far. However, if it is solved properly, high-resolution remote sensing images can be used more effectively. 2. Super-pixel information is introduced to compensate for shadow information. The super-pixel method segments the cloud shadow area into small regions with homo geneous information. Compared to traditional local region information extracted by windows around shadow pixels, a super-pixel can consider the complexity of an object in the shadow from the local shadow area to the global shadow region. 3. Multi-level information, including shadow pixel, super-pixel, shadow region, and non-shadow region, are composited together to solve the problems of unevenness in a cloud shadow. This can adaptively compensate for the shadow because coarse- to fine-level information is considered.
## 2 Methods
### Human-Machine Interface  
The University of Valladolid neuromuscular training system, UVa-NTS platform [8], was employed for these experiments. This training platform is a custom-designed human-machine interface [10] which makes real-time processing of EMG signals in order to interactively command a set of training tools or games. The platform follows the approach of designing portable devices meant to improve semi-autonomous therapy. It has been reported by [PERSON] et al. [11] as a benefit in home environments, often characterised by low supervision from clinical experts and low extrinsic motivation.  
The platform includes a set of graphical tools to achieve progressive adaptation to the environment. An overview of the implemented tools is shown in Figure 1 and is described in the following list:  
1. The _signal viewer_, where EMG signals are checked in the time domain;
2. The _state navigator_, allowing two-dimensional movements of a screen pointer depending on the detected EMG amplitude of two muscles and hence their contraction level. x-axis and y-axis displacements are related to the EMG amplitude of each muscle involved. In this way, targets can be located on screen by the user by contracting his or her muscles. These targets are plotted as circles as shown in Figure 1;
3. The _virtual arm_, a 3D-design arm allowing the simulation of movements for prosthetic training. This tool uses the targets defined in the _state navigator_. Thus, each movement of the virtual arm can be associated with each of the defined targets. This application is intended to provide an approximation to what would be the real myoelectric prosthesis control. Hence, the application can assess the prosthesis adequation to the patient;
4. _Myo-Pong_, which is a version of a table tennis video game [12]. In this game, two paddles are meant to maintain the control of a ball. Therefore, each paddle is controlled by the EMG amplitude from a different muscle suitably chosen depending on the type of disability. The paddle displacement downwards is proportional to the muscle contraction level.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Patient** & **Age** & **Gender** \\ \hline
1 & 34 & Female \\
2 & 38 & Male \\
3 & 41 & Male \\
4 & 66 & Male \\
5 & 37 & Male \\ \hline \hline \end{tabular}
\end{table}
Table 1: Set of patients with spinal cord injury (SCI).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Gender** & **Age** & **Mean Age** & **Number** \\ \hline Male & 24–46 & 31.3 & 10 \\ Female & 23–38 & 28.8 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Control group: Set of 16 subjects.
# Deceptive Jamming Detection for SAR Based on Cross-Track Interferometry
## 5 Experimental Analysis
### Extraction Results  
A slope-compensated mean filter [30; 31] is applied as shown in Figure 8a. Figure 8b shows a cross-section along range direction corresponding to the white line in Figure 8a. It can be seen that the interferometric phase of false targets is close to a constant with only little fluctuations while the surrounding real areas appear as varying phases, which again verifies the previous theoretical analysis.  
Through Fourier transform of interferogram in the range direction, the resultant spectrum is given in Figure 9a. As can be seen, the false phase is close to zero in the frequency domain. Then, setting the threshold as \(\pm\)0.005 rad, the 0-1 valued mask is obtained as in Figure 9b. Figure 9b also shows the size of deception area detected through our method.  
Figure 6: Comparison between the SAR images before and after SAR deceptive jamming: (**a**) master image before jamming; (**b**) deceptive jamming template; and (**c**) master image after jamming.  
Figure 7: Comparison between the interferograms before and after SAR deceptive jamming: (**a**) interferogram before jamming and (**b**) interferogram after jamming.
## 5 Conclusions  
In this study, regarding the application and utilization of UT in a non-destructive examination of defects on piston rods, a measurement technique was established. Thus, ultrasonic defect signals were analyzed according to the types and depths of the defects on piston rods for automobiles in order to determine how to select the optimal inspection conditions.  
1. To evaluate ultrasonic beam behavior in defects on piston rods, the Rayleigh angle was determined using the optimal peak-to-peak amplitude based on the pulse-echo technique after machining the defects on the surfaces of the piston rods.
2. Regardless of the types of micro-defects on the surfaces, the peak-to-peak amplitude of the transmitted ultrasonic waves decreased as the size of the defect increased. The received ultrasonic signals decreased linearly as the distance increased between the transmitting and receiving transducers. Accordingly, the optimal inspection condition could be suggested to be in the range of 5-10 mm, where ultrasonic signals with a high resolution were received.
3. When the surface defect occurred in the direction of the propagation of the Rayleigh waves, the amplitude of the Rayleigh waves was high. In contrast, the lowest number  
Figure 14: Experiment profiles of peak-to-peak amplitude for defect types at a distance of 6.5 mm from the crack location.  
of ultrasonic waves was transmitted when the crack was perpendicular to the propagation of the Rayleigh waves. Although the defect detection resolution is proportional to the wavelength (\(\lambda\)) (usually \(\lambda/2\)) in ultrasonic inspection, a resolution of \(\lambda/3\) was found despite the loss of Rayleigh waves on the curved surface as a result of using piston rod samples in this study.
* Additional studies are required in the future to analyze the effects of different defect types and heat treatment characteristics using defects with more diverse depths in order to improve the reliability of piston rods in automobiles.  
[PERSON] and [PERSON] suggested and designed the experiments; [PERSON], [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON], [PERSON] and [PERSON] helped in the accomplishment of ideas and the administration of the experiments. The data were discussed and analyzed, and the manuscript was written and revised by all members. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) (No. 2021R11A3042195), and the experiments was helped by CNDE at Iowa State University, USA. Moreover, Human Resources Development Service of Korea (HRD Korea) (RJB19002) and LINC's research reports(2020-C-G025-010113) were partly utilized in Korea.  
Not applicable.  
Data Availability StatementData are contained within the article.  
The authors declare no conflict of interest.
# Analytical and Graphical Profiling of Thread-Milling Cutters for Forming Internal Threads
## 2 Literature Review
### Forming of Internal Threads by Milling
In this way, the use of modern CAD systems completely replaces the classical methods of grapho-analytical design, and there is no need to check the results analytically. Profiling is used in the manufacture of various types of metal-cutting tools. Through analytical or graphical profiling, the working parts of the tools are obtained, which in most cases represent surfaces with a complex shape. Such tools are, for example, thread-turning tools [1,2], taps [1,2], dies [1,2], thread-cutting heads [1,2], finger-milling cutters [1,2], end mills [1,2,6,7,8,9], thread-milling cutters [1,2,10], disk-shaped thread-milling cutters [1,2,11], ring-shaped tools for vortex thread milling [12], combined tools [13,14], profile turning tools [15,16,17], gear-shaped cutters [1,2,18], profile milling cutters [19], single-tooth thread-milling cutters [20] etc. The publication considers the profiling of the working part of thread-milling cutters by an analytical method and by a graphical method using a CAD system. The profiling of tools for forming metric threads is considered. These are the threads that are widely used in practice. If a tool needs to be designed for another type of thread, the proposed method for graphic profiling can be used. The method is applicable for all types of threads that have a straight profile. Analytical Determination of the Tool Surface Profile of Thread-Milling Cutters for Milling Internal Threads  
When milling threads with thread-milling cutters, the tools are oriented relative to the machined surface so that the axis of the rotary profile working part of the tool is parallel to the geometric axis of the machined thread. This positioning of the tool relative to the milled surface leads to deviations in the axial profile of the latter. The forming of the specified axial profile of the formed surface without deviations is achieved by implementing special profiling of the tool surface. To determine the profile of the tool surface on which the cutting edges of the teeth are located, with a known profile of the formed surface, an analytical method was used. Using a graphical method, the same profiling of the tool surface was performed through the SolidWorks 2022 CAD system, and its accuracy and reliability were validated.
## 2 Background and Related Concepts
### Privacy Challenges  
Privacy is the behaviour or attitude of a company toward protecting its information resources and the personally identifiable information of its customers [27]. Organisations are increasingly leaking personal information, either deliberately or due to compromised information systems. Users of BYOD devices express concerns about their ability to manage personal data and trust organizations to protect their users. According to [PERSON] and [PERSON] [28], there has been an ongoing debate on whether individuals should sign contracts with organizations to manage and safeguard their information or if it should be the responsibility of the enterprise to protect individual privacy. According to various publications, organisations devote regular financial resources to implementing safeguards and information privacy programmes to protect information from leaks and other threats. However, they fail to effectively utilise these protections and programmes [29]. This strategy can easily result in privacy breaches, which have historically been a significant concern. The primary objective of these hacking attacks is to steal, delete, or alter sensitive information. Organisations must recognise the significance and necessity of protecting personal data because it presents various privacy issues, risks, and other data breaches. New, difficult-to-detect vulnerabilities are generated for hackers as technology progresses and becomes more sophisticated [30]. BYOD has and will continue to raise concerns about data and user privacy.
# Domain-Invariant Few-Shot Contrastive Learning for Hyperspectral Image Classification
## 3 Proposed Method
### MSAAN Model  
This study proposes a feature extraction network for HSI few-shot learning, aimed at improving feature discriminability by integrating multi-scale spatial information and selectively extracting crucial information from channels and spaces. As illustrated in Figure 2, the input initially passes through a spectral feature extraction module that produces a feature map of size \(128\times 9\times 9\). This is followed by a multi-scale spatial feature extraction module, consisting of three stacked Multi-Scale Adaptive Attention (MSAA) units and channel attention mechanisms, and finally, high-discriminability features are selected through max pooling. The network architecture mainly consists of three components: the spectral feature extraction module, the Multi-Scale Adaptive Attention (MSAA)module, and the channel attention mechanism, which together work to extract rich and discriminative feature maps.
## 5 The Verification Experiment of VAPP
The working pressure of Ports B and T fluctuate periodically around the loading pressure, and the system performance is good. It can also be concluded that the pressure pulsation amplitude of Port T is slightly bigger than that of Port B. The pulsation amplitude of flow increases synchronously when loading of Ports B and T is equal. Figure 16d presents the test pressure of Port B when the port is loaded alone and when Port T and Port B are loaded at the same time. The two sets of curves are measured respectively at pressure levels of 10 MPa and 20 MPa. It can be deduced from Figure 16d that pulsation amplitude of pressure when Port B is loaded alone is bigger than the pulsation amplitude of pressure when Ports B and T are loaded at the same time. The pulsation amplitude of pressure when Port B is loaded alone is 1.5 times that of when both Port T and Port B are loaded at the same time and at the pressure level of 10 MPa. The pressure pulsation amplitude when Port B is loaded alone is 2.3 times that of when Ports T and B are loaded at the same time and pressure levels of 20 MPa. Figure 16: The comparison of experiment and simulation. (**a**) Slot B output pressure under Ten and twenty megapascals load alone in Slots B; (**b**) Slot B output pressure under Ten and twenty megapascals load in Slots B and T at the same time; (**c**) Slot T output pressure under Ten and twenty megapascals load in Slots B and T at the same time; (**d**) Slot B output pressure under two different operating conditions and two pressure levels. When the pump speed is set at 1500 r/min and swash plate angle is 18\({}^{\circ}\), the flow test result is as illustrated in Figure 17. The no-load working condition of discharge Port B and Port T is presented in Figure 17a, whereas the working condition is indicated in Figure 17b, when discharging Port B and Port T are loaded at 10 MPa. As shown in the figure, the output flow of Ports B and T is 29 L/min and 20.5 L/min, respectively, when Port B and Port T are no-load. The output flow of Ports B and T is 24.5 L/min and 16 L/min, respectively, when Port B and Port T are loaded with pressure of 10 MPa. The flow pulsation of each discharge oil port is small but the flow pulsation of Port T is bigger than Port B.
## 4 Discussion
### Feasibility Module
In addition, discrepancies between the ideal and calculated plans are expected due to the dose calculation method that accounts for tissue heterogeneities in primary and scatter interactions (e.g., convolution/superposition, Monte Carlo, or grid-based Boltzmann transport equation solvers) or different geometry of beam/arc arrangements (e.g., coplanar vs. non-coplanar configurations, number of beams or arcs). These factors can lead to minor or significant deviations from the ideal distribution calculated from fDVH, making unfeasible plans feasible.
# OTFS-Based Handover Triggering in UAV Networks
## 7 Numerical Analysis  
In this section, an extensive numerical analysis is conducted to prove the efficiency of the proposed UAV-to-ground handover-triggering scheme based on the DD-estimated channel over that based on the conventional RSS or the FT channel gains using OFDM modulation. A simulation area of 10 Km\({}^{2}\) is constructed, where several UAVs, i.e., working as ABSs, hovering at an altitude of 2000 m, are randomly distributed inside it to cover an HST. We selected the UAV-HST scenario given in Figure 1c, because it is extensively investigated in [8], enabling us to use their practical time-varying DD channel model and its associated parameters. The operating frequency of communication between UAV and HST is set to 5.06 GHz and \(P_{Tx}\) of 10 Watts. Also, the total frame duration is set to 50 OTFS symbols: other simulation parameters are listed in Table 2, unless otherwise stated.  
As for performance metrics, we are interested in measuring the following handover metrics:  
* **Handover Overhead [s]**, which is defined as follows [30]: \[O_{HO}=\text{Number of Handovers}\times\text{Single Handover duration}\] (54) Actually, there are many components of single-handover duration, including, channel estimation overhead, signaling overhead, processing delay, data packet loss and re-transmission delay, and synchronization time. In this paper, for fair comparisons, we were more concerned about physical-layer handover issues than the network-layer handover issues. Thus, we only considered channel estimation overhead, signaling overhead, and synchronization time. Also, we considered that only one packet can be used to do all this. Thus, the length of this packet will be \(P_{q}T/M\) s in the case of RSS, a single OFDM symbol duration of \(T\) s in the case of OFDM, and a single OTFS symbol duration of \(NT\) s in the case of OTFS. Apparently, OTFS has the largest handover duration while the RSS scheme has the lowest one.
* **Average Throughput [bps]**: For the average throughput calculation, we followed the formula given in [30]. In this formula, the average throughput is simply the data rate multiplied by the percentage of the handover overhead as follows: \[\Psi=M\Delta f\psi_{\hat{\mathcal{H}}}\bigg{(}\frac{\text{OTFS symbols duration}}{\text{OTFS symbols duration}+\text{Handover Overhead}}\bigg{)},\] (55) where \(M\Delta f\psi_{\hat{\mathcal{H}}}\) is the achievable average data rate resulting from multiplying the total bandwidth \(M\Delta f\) with the average spectral efficiency \(\psi_{\hat{\mathcal{H}}}\) given in (38), and the ratio \(\Big{(}\frac{\text{OTFS symbols duration}}{\text{OTFS symbols duration}+\text{Handover Overhead}}\Big{)}<1\) is the loss in the achievable data rate resulting from the accumulated handover overhead due to the transmission of the OTFS symbols.
* **Ping-Pong Ratio**, which is defined as the number of handovers, where the HST switches back and forth to the same UAV, divided by the total number of handovers. This ratio can give an indication of the number of unnecessary false handovers, which highly degrades the overall system performance.
# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 6 Echocardiography
### Detection  
[PERSON] et al. [43] proposed an architecture for the detection of ES and ED phases from echocardiography videos. The architecture included a CNN model, an RNN, and a regression module. For the CNN module, residual network (ResNet) performance was better than DenseNet. For the RNN module, long short-term memory (LSTM) and gated recurrent units yielded comparable performance. Only high-quality data were considered in this study. The measurement of the aortic valve area helps in the diagnosis of certain conditions, such as aortic stenosis. [PERSON] et al. [44] proposed a detection system for automatic segmentation of aorta valves using an SSD and faster RCNN. The dataset consisted of 30 videos, out of which 23 were used for training, 2 were used for testing, and 5 for validation. MobileNet and Inception were used as feature extractors for this model. A faster RCNN gave the best accuracy of 94%, while an SSD with mobileNet achieved the highest mean frame rate of just 34.21%. The former's accuracy was promising, but minimal data were used for testing and validation purposes. [PERSON] et al. [45] proposed a Bayesian framework for detecting the keyframe in echocardiography videos. They achieved an \(R^{2}\) score of 66%.  
For the detection task, the limitations are the same as segmentation, such as the quality and size of the dataset.
# Texture and Materials Image Classification Based on Wavelet Pooling Layer in CNN
## 3 Materials and Methods
### Network Training and Parameter Setting  
The algorithms are implemented and developed using the Python language and Keras API with Tensorflow as Backend. Keras is one of the deep learning frameworks with tools to create classification models. Moreover, it is an open-source project, and its manner of programming is sequential through blocks [35]. The hardware specifications of the training device are an Intel(r) Core(tm) i7 processor with an NVIDIA GeForce RTX(tm) 2080 graphics card, 12 GB of RAM, and Ubuntu 18.04 64-bit operating system.  
The base architecture is the VGG network, and it is one of the first deep models with good results in a large-scale visual recognition challenge (ILSVRC-2014) with 92.7% top-5 accuracy [36]. This architecture is designed to facilitate the creation of a _classification model_--three convolutional blocks with their pooling layer and one classification stage. The process is as follows: using the base VGG architecture, combined with the preprocessed  
Figure 1: Example of the shortcoming of Max and Average Pooling against the contribution of wavelet pooling, preserving the essential features.  
CIFAR-10, DTD, and FMD datasets, through supervised learning. Before training our CNN, the loss function and the optimizer need to be specified. These parameters determine how the network weights should be updated during the training process. In order to compile the network with Keras, we use the compile() function. Training a CNN means finding the best set of weights to map the inputs (_images_) to the outputs (_labels_) in the training dataset and, at the same time, in the validation dataset. Training is processed over epochs. An epoch is an iteration through all samples of the training dataset. Moreover, it is common for an epoch to be split into minibatches. Each minibatch consists of one or more samples. After each batch iteration, the weights of the network will be updated. In order to train the network with Keras, we use the fit() function. The training parameters for the proposed models are listed in Table 1.  
We perform a complete analysis with each of the proposed pooling methods: MaxP, AveP, DWTP, DWTaP, and DWTdP. In addition, we combine them with the regularization methods DropOut [37; 38], Data Augmentation [17; 39], and Batch Normalization [40; 41]. In this manner, a learning model is obtained, and we can predict the objects, textures, and materials in the dataset (test) images with better accuracy.