esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment and Analysis Results  
### Experimental Data Description  
With the full life cycle data of XJTU-SY rolling bearings as the test basis for analysis [35], the relevant parameters of the bearings are listed in Table 1. Two PCB-352C33 unidirectional acceleration sensors are fixed in the horizontal and vertical directions of the test bearing, and the DT9837 dynamic signal collector is applied to collect its vibration signal. The bearing test stand is shown in Figure 2. The bearing test platform can change the working conditions by adjusting the equipment parameters, including the rotational speed and radial force. An acceleration sensor is adopted to collect the vibration signal of the equipment. The sampling frequency is 25.6 kHz, the sampling interval is 1 min, and the sampling time is 1.28 s. Python was chosen as the software for writing and verifying the model in this article.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Bearing** & **Inside** & **Outside** & **Bearing** & **Ball** & **Number** & **Contact** \\
**Type** & **Diameter** & **Diameter** & **Mean** & **Diameter** & **of Rollers** & **Angle** \\ \hline LDK & & & & & & \\ UER204 & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tested bearing parameters.
## 4 Discussion
### Limitations  
It is crucial to acknowledge the system's limitation in terms of its ability to adapt to tissue deformations. The patient's scan remains insensitive to factors such as pressure, breathing, injection, and bladder filling. To address this problem, integrating supplementary imaging techniques such as US may be a viable solution. Although the developed needle tracking method proves effective if the needle is partially visible, further efforts are required to accurately track its position within the patient's body and estimate the injection depth.  
This study's main limitation is the use of phantoms. While on the one hand this allows the error to be estimated precisely, on the other it does not take into account the time between the CT scan and the biopsy, which for humans is the most important factor. If the time difference is greater, the deformation of the actual state compared to the scanned one can be significant. In conclusion, future work should be extended to cadavers and human subjects.
## 1 Introduction
RCS-based radar methods such as high-resolution range profiles and synthetic aperture radar (SAR) can be difficult when considering the similar RCS values of birds and drones [12, 13, 14, 15]. To avoid this, the benefits of leveraging the micro-Doppler signatures from targets were investigated using a continuous-wave (CW) radar system. Through processing algorithms, it was possible to discern the type of target (bird or drone) based on their micro-motions, such as spinning of propeller blades or flapping of bird wings. For automatic target recognition (ATR) purposes, many recent studies have utilized machine learning to classify images of micro-Doppler signatures between drones and birds. Unfortunately, the lack of real-world data makes improving classification efforts difficult. The remainder of this paper is structured as follows: Section 2 discusses the concepts and theory related to the micro-Doppler features of birds and drones. Section 3 describes \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sensor Type** & **Advantages** & **Disadvantages** \\ \hline \multirow{4}{*}{Acoustic} & No LOS & \\  & Low visibility & Ineffective in noisy conditions \\  & Low cost & Ineffective in windy conditions \\ \hline \multirow{2}{*}{Camera} & Easy operability & Very sensitive operability \\  & High accuracy & High cost \\  & High accuracy & Ineffective in darkness \\ \hline \multirow{4}{*}{Lidar} & \multirow{4}{*}{High accuracy} & Expensive \\  & & Ineffective in foggy and rainy \\ \cline{1-1}  & & conditions \\ \cline{1-1}  & & Very narrow FOV \\ \hline \multirow{2}{*}{Radar} & Low-cost & \multirow{2}{*}{Targets have small visibility} \\  & Weather resistant & \\ \cline{1-1}  & Robust & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sensing methods advantages and disadvantages. our simulation setup and results in order to provide an understanding of the experimental data. Section 4 discusses the design and operational characteristics of the constructed 10-GHz radar systems. Section 5 outlines the experimental setup and the results obtained. In Section 6, the classification setup and processing tactics are described. In Section 7, the conclusions are presented with possible directions for future research. In this paper, please note that \"UAV\" and \"drone\" are used interchangeably.
## 4 Simulation Results
### Algorithm Analysis  
The optimal coefficients of the suggested model are obtained by various test functions. The first initial population is 60, \(\sigma=0.3\), \(\lambda=\text{N}/2\), repetition is 300, and the results are compared with NSGA, SPEA, and MOPSO models. The convergence of the optimal Pareto set (C-metric (C): C-metric) to cover the above objectives is considered:  
Suppose \(S_{1}\) and \(S_{2}\subseteq S\) sets of answers and C-metric is pairs \((S_{1},S_{2})\) and \([0,1]\) distances according to the following relation:  
\[C(S_{1},S_{2})=\frac{|\{a_{2}\in S_{2},\exists a_{1}\in S_{1}:a_{1}\prec a_{2 }\}|}{|S_{2}|} \tag{61}\]  
If \(C(S_{1},S_{2})=1\), answers in set \(S_{2}\) override the answers \(S_{1}\). If \(C(S_{1},S_{2})=0\), no \(S_{2}\) answers are covered via \(S_{1}\). Both \(C(S_{1},S_{2})\) and \(C(S_{2},S_{1})\) are compared, and obtained data are presented in Table 11. The NSGA, SPEA, and MOPSO are the mean values of 68.3%, 50.2%, and 48.3%, which are covered by our model. According to the data presented in Table 5, our model is worked better than other models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type of Operation** & **Error (\%)** & \(\text{ENS}_{P}\) & \(\text{ENS}_{Q}\) & \(\text{VNS}_{Q}\) & \(\text{VNS}_{Q}\) \\ \hline \multirow{4}{*}{Traditional distribution system} & 5 & 142.21 & 19.32 & 144.32 & 25.28 \\ \cline{2-6}  & 18 & 75.24 & 10.24 & 81.54 & 12.65 \\ \cline{2-6}  & 30 & 27.06 & 4.57 & 25.75 & 4.32 \\ \hline \multirow{4}{*}{Microgrid} & 5 & 101.45 & 13.67 & 112.15 & 15.51 \\ \cline{2-6}  & 18 & 64.79 & 6.13 & 71.28 & 10.78 \\ \cline{1-1} \cline{2-6}  & 30 & 26.37 & 3.25 & 26.32 & 4.32 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Error analysis in the designed microgrid.
# Rapid-DEM: Rapid Topographic Updates through Satellite Change Detection and UAS Data Fusion
## 2 Methods
### Rapid-DEM
#### 2.1.3 Prioritizing and Contextualization
Sort queue in descending order DESC(VPQ)  
\[\text{PQ}=\frac{\text{P}-min(\text{P})}{max(\text{P})-min(\text{P})} \tag{2}\]  
The priority queue is contextualized through a priority context map (PCM) that explains the type of land change. The PCM is created by assigning each land cover change transition and action. These actions are defined by two base types of land change: New features and removed features (Table A1). New features are represented by changes such as the construction of a new building, road, parking lot, while removed features represent change such as forest clearings or denolished buildings. Static land cover and land change likely due to noise (e.g., seasonal variation, misclassification) are not included. To assign a final context to each priority change object the mode land cover change context class is calculated for each object in the priority queue.
## 4 Data Description and Preparation
#### Air Density Correction for Improving Power Curve Accuracy  
The International Electrotechnical Commission (IEC) standard 61400-12-1 [46; 47; 48] proposes a technique for variable pitch regulated WTs to compensate for the air density effect on a power curve, where atmospheric pressure and ambient temperature are acknowledged as relevant parameters for air density correction. Therefore, the following equations (defined in the IEC standard) are used in this study for air density correction purposes:  
\[\rho\ =\ 1.225\left[\frac{288.15}{T}\right]\left[\frac{B}{1013.3}\right] \tag{3}\]  
and  
\[V_{C}\ =\ \ V_{M}\left[\ \frac{\rho}{1.225}\ \right]^{\frac{1}{3}} \tag{4}\]  
where \(V_{M}\) and \(V_{C}\) are the measured and corrected wind speed (m/sec), respectively. Ambient temperature (T) and atmospheric pressure (B) average 10 min SCADA data points are used in Equation (3) to calculate the corrected air density and then the corrected wind speed (\(V_{C}\)) using Equation (4). The corrected wind speed is then plotted against electrical power output to provide the corrected power curve. IEC air density correction is widely used in data-driven models to construct the power curve.  
Figure 2 shows the measured power curve of a turbine before filtering and air density correction. Adopting air density correction (as described in Section 3) and filtering, as outlined above, results are illustrated in Figure 3. The changes due to air density correction is not significant as data used in this study came from wind turbines from normal region where temperature is not significant [47]. It is worth noting that WT power curves are affected by both environmental and operational conditions and incorporating those could be useful for improving data-driven power curve accuracies [47; 48].  
Figure 2: Raw power curve data.
## 4 Experimental Setup
### Datasets Used for Evaluation  
In the experiments, two datasets were employed, namely, IoT-23 and SIMARGL2021.  
IoT-23 [28] is a dataset containing network traffic sourced from the IoT (Internet of Things) devices, containing three captures for benign IoT device traffic and twenty malware captures. It was circulated in January 2020 for the first time; the captures come from 2018 and 2019. The particular IoT network traffic was captured in the Stratosphere Laboratory, AIC group, FEL, CTU University, in the Czech Republic. The main objective was to provide a comprehensive dataset of real and labelled IoT malware infections and IoT benign traffic for researchers to develop machine learning algorithms. Both the dataset and the research associated with it were sponsored by Avast Software, Prague.  
SIMARGL2021 [29] is a dataset assembled from a real-world, academic network, from which real-life traffic was gathered after having carried out a variety of attacks. The format  
Figure 3: The model cascade used for anomaly detection and threat identification (there are two steps of classification in the proposed approach: anomaly detection and threat identification).  
selected for the network data schema is Netflow v9. It encompasses 44 specific features; each frame is labelled.
# Sleep Quality in Older Women: Effects of a Vibration Training Program
###### Abstract  
Background: Sleep is an important phenomenon to restore the body, both physically and emotionally, providing a state of balance in the person. It has been proven that adequate sleep at night is one of the main needs of older people in order to maintain an active and healthy life; among other factors, regular physical exercise can improve the quality of sleep. The aim of this research is to evaluate the effects of a physical exercise program supplemented with vibration training on sleep quality and the use of sleep drugs in women over 65 years of age. Methods: Fifty-two independent, physically active adult women were randomised into two groups: a physical exercise program (n = 26, control group) and the same physical exercise program supplemented with vibration training (n = 26, experimental group). The control group performed two weekly sessions of exercise, and the experimental group added another two sessions of vibration training to these two sessions for 12 weeks. Sleep quality was assessed using the Pittsburgh index. Statistical significance was established as \(p<0.05\). Results: After the intervention, there were significant changes to the quality of sleep (\(p=0.001\)) and hours of sleep (\(p=0.002\)) in the experimental group. The consumption of drugs decreased in this group, although not significantly; however, it did have a moderate effect size (\(p=0.058\); d = 0.36). The control group, on the other hand, reported significantly worsened sleep quality (\(p=0.001\)) and increased drug use (\(p=0.008\)). Conclusion: Three months of vibration training, as a complement to a conventional physical exercise program, improves sleep quality and reduces the consumption of sleeping pills in women over 65 years of age.  
whole-body vibration; physical exercise; quality of sleep; health; quality of life; ageing; drugs +
Footnote †: journal: Applied Soft. 2020, 10, 8391; doi:10.3390/app10238391
# Review of the AI-Based Analysis of Abdominal Organs from Routine CT Scans
## 1 Introduction  
Medical image segmentation is essential in modern healthcare, providing critical support for diagnosis, treatment planning, surgical guidance, and patient monitoring. Over recent decades, the field has progressed from manual analysis to sophisticated automated systems, fueled by advancements in computing power and artificial intelligence (AI). While manual and semi-automatic methods are still in use, they are time-intensive, dependent on operator expertise, and often costly, highlighting the urgent need for fully automated, reliable segmentation solutions. This review explores the evolution of medical image segmentation for abdominal and liver trauma analysis, emphasizing key technological advancements that have transformed clinical workflows. Precise segmentation of liver and abdominal organs is particularly valuable in pre-operative planning and surgical guidance, where accurate lesion and trauma segmentation can allow for more conservative resections, preserving healthy tissue and improving patient outcomes. AI-driven techniques, especially deep learning, have led to substantial improvements in segmentation accuracy and speed, with convolutional neural networks (CNNs), U-Net architectures, and novel adaptations like MSLUNet showing promising results in trauma analysis. Regarding the segmentation masks used in CT scan analysis, distinguishing between tumors in soft tissue organs and located tissues from trauma can be challenging, particularly if the segmentation is solelybased on the image data without clinical context. Additionally, automatic segmentation for abdominal and liver trauma remains challenging due to:  
* Artifacts in CT images and variations in intensity information [1];
* Irregular shapes and diverse sizes of anatomical structures [2];
* Low contrast between liver parenchyma and lesions [3];
* Weak boundaries between the liver and adjacent abdominal organ [4].  
These complexities necessitate robust, adaptive artificial intelligence (AI) models that can handle noisy, diverse data while achieving high segmentation accuracy. Emerging approaches, such as multiscale feature extraction and attention mechanisms, have shown potential in addressing these issues, and reliable segmentation even in complex anatomical regions. This review provides a comprehensive overview of state-of-the-art methods and discusses future directions to improve the performance and clinical applicability of AI-driven segmentation tools in abdominal trauma, specifically liver analysis.  
The rest of this article is structured as follows: Section 2 is a summary of the historical evolution of medical image analysts. Section 3 presents abdominal medical image datasets and then briefly explains evaluation metrics for medical image analysis. Section 4 presents a comparison between different network approaches, and finally, the conclusion is presented.
## 7 Conclusions  
In order to assess the condition of the structure and determine the degree of earthquake resistance of the bell tower of the parish Church in C\(\ddot{\text{u}}\)ntic, an OMA was performed under ambient conditions.  
Accelerations of the bell tower walls were measured in two sets at a total of 16 measurement points, where the vibrations of the bell tower were caused by the wind, micro-vibrations from the movement of people and the ringing of the bells. The analysis of the measured signals was performed using the EFDD and SSI-UPC techniques. The first two identified modes can be classified as bending (f\({}_{1}\) = 4.395 Hz and f\({}_{2}\) = 4.639 Hz), and the third identified mode can be classified as torsion (f\({}_{3}\) = 10.303 Hz). Despite the very low ambient vibrations during the AVT, the OMA proved to be effective in identifying the modal parameters.  
The comparison between the measured and the originally calculated modal parameters (preliminary NM) served to verify the assumptions used in the formulation of NM. In particular, a good correlation between experimental and numerical modal shapes was obtained for relatively high values of the elastic modulus of the bell tower masonry. However, the correlation between experimental and numerical results in terms of modal frequencies is rather poor. This, in addition to the visible damage, is an indication that it is more reasonable to choose a much lower value for the model E-modulus of the masonry in the most damaged areas of the bell tower. After updating NM (with significantly smaller values of elastic modulus varying by tower height), a good correlation between the experimental and numerical modal behaviour was obtained. Therefore, the numerical model with the assumed material and geometry properties is reliable for evaluating the seismic resistance of the bell tower.  
It has been shown that the differences between the provisionally established and the finally adopted structural parameters are significant. In this sense, the contribution of AVT is very important because, based on the results of the OMA, the actual structural parameters of the building can be determined. The application of the proposed method can provide a reliable assessment of the seismic safety of historic buildings, as well as their resistance to other forces.  
Conceptualization, M.S. and I.B.; methodology, M.S., I.B., N.G. and A.B.; software, M.S. and I.B.; validation, M.S., I.B., N.G. and A.B.; formal analysis, M.S., I.B., N.G. and A.B.; investigation, M.S., I.B., N.G. and A.B.; resources, N.G.; data curation, M.S., I.B., N.G. and A.B.; writing--original draft preparation, M.S. and I.B.; writing--review and editing, N.G. and A.B.; visualization, M.S. and I.B.; supervision, A.B.; funding acquisition, N.G. All authors have read and agreed to the published version of the manuscript.  
This research is partially supported by project KK.01.1.1.02.0027, a project co-financed by the Croatian Government and the European Union through the European Regional Development Fund--the Competitiveness and Cohesion Operational Programme.
## 4 Results and Discussion
### Experimental Procedure  
The performance of the proposed approach was evaluated over the FIFO FA (baseline approach) and Adaptive FA Conv. approach [11] in terms of the system throughput performance while considering the minimum delay. FIFO FA (baseline approach) does not employ an adaptive aggregation scheme; the Adaptive FA Conv. approach [11] is an adaptive aggregation algorithm that considers channel errors but ignores the expense of delay, aiming to achieve maximum throughput only. The acronyms 'FA' and 'Conv.' in this paper refer to 'frame aggregation' and 'conventional', respectively. In Section 4.2, we will evaluate the cost of delay experienced in different aggregation algorithms and the performance of the proposed approach achieved in terms of the minimum delay. The proposed adaptive approach will be evaluated under the effects of different traffic models such as Pareto, Weibull, and fBM in Section 4.3. Then, the performance of the proposed approach under the effect of channel conditions while considering SNR = 5, 8, and 20 dB is evaluated in Section 4.4. Finally, the performance of the proposed approach under a varying number of STAs (two, three, or four) is evaluated in Section 4.5. All experiments were conducted with a traffic mix of 75% VoIP and 25% video with a constant frame size of 100 bytes or 1000 bytes and a data rate of 10 kbps or 100 Mbps, respectively.
# Document Image Shadow Removal Based on Illumination Correction Method
## 3 The Dark Illumination Correction Net
### The Loss Function  
To detect the shadow mask and correct the dark illumination, the loss function of DICNet includes two components: shadow detection loss \(L_{shn}\) and illumination correction loss \(L_{ill}\). The shadow detection loss is designed to measure the discrepancy between the true shadow mask \(\rm\bar{\textbf{M}}\) and the predicted shadow mask \(\rm\textbf{M}\). As shown in Equation (7), it includes two components: \(L_{sha1}\) and \(L_{sha2}\).  
\[L_{sha}=\lambda_{sha}L_{sha1}+(1-\lambda_{sha})L_{sha2} \tag{7}\]  
Figure 4: Simplified spatial attention structural diagram.  
where \(\lambda_{sha}\) is a trade-off parameter and used to balance the influence of the terms, \(L_{sha1}\) is the L1 loss, and \(L_{sha2}\) is the mean square error; they are described in Equation (8) and Equation (9), respectively.  
\[L_{sha1}=\left\|\mathbf{\tilde{M}}-\mathbf{M}\right\|_{1} \tag{8}\]  
\[L_{sha2}=\frac{1}{N}\sum_{i=1}^{N}\left(\tilde{M_{i}}-M_{i}\right)^{2} \tag{9}\]  
where \(N\) is the total number of pixels, and \(\tilde{M_{i}}\) and \(M_{i}\) are the \(i\)th pixel values in \(\mathbf{\tilde{M}}\) and \(\mathbf{M}\), respectively.  
The illumination correction loss is used to ensure accurate shadow removal, as shown in Equation (10), and also includes two components: \(L_{ill1}\) and \(L_{ill}\).  
\[L_{ill}=\lambda_{ill}L_{ill1}+(1-\lambda_{ill})L_{ill2} \tag{10}\]  
where \(\lambda_{ill}\) is the trade-off parameter. As shown in Equations (11) and (12), \(L_{ill1}\) and \(L_{ill2}\) are the L1 loss and weighted mean square error, which are used to measure the discrepancy between the real shadow-free image \(\mathbf{\tilde{O}}\) (i.e., the ground truth) and the predicted shadow-free image \(\mathbf{O}\).  
\[L_{ill1}=\left\|\mathbf{\tilde{O}}-\mathbf{O}\right\|_{1} \tag{11}\]  
\[L_{ill2}=\frac{1}{N}\sum_{i=1}^{N}M_{i}\big{(}\tilde{O}_{i}-O_{i}\big{)}^{2} \tag{12}\]  
where \(\tilde{O}_{i}\) and \(O_{i}\) are the \(i\)th pixel values in \(\mathbf{\tilde{O}}\) and \(\mathbf{O}\), respectively.  
The total loss of DICNet \(L_{total}\) is shown as follows:  
\[L_{total}=\lambda L_{sha}+(1-\lambda)L_{ill} \tag{13}\]  
where \(\lambda\) is the trade-off parameter, and it is used to balance the effect of \(L_{sha}\) and \(L_{ill}\). Although both L1 loss and mean square error are commonly used, this combination of losses can simultaneously measure the errors in shadow detection and illumination correction.
# A Review of Test Stimulus Compression Methods for Ultra-Large-Scale Integrated Circuits
## 2 Overview of Test Stimulus Compression Techniques  
### Encoding-Based  
Encoding-based test stimulus compression techniques initially divide the original test set into distinct symbol blocks, which are then represented by code words. These code words constitute new test vectors, achieving the objective of compressing the original test data [23]. During the decoding process, the decoder interprets the original symbol blocks based on the code words to reconstruct the test vectors [24]. The length of the symbol blocks in the original test set and the compressed code words can be either fixed or variable. Consequently, encoding-based test stimulus compression methods can be categorized into four types, which are fixed-length to fixed-length, fixed-length to variable-length, variable-length, and variable-length to fixed-length [20].