esa-sceva/satcom-chunk-collection huggingface dataset:
# Classification of Alzheimer's Disease Patients Using Texture Analysis and Machine Learning
## 2 Materials and Methods
### ROI Selection  
volBrain is an MRI brain volumetry system available online. volBrain is a fully automated system that can deliver brain structure volumes without the need for human participation. volBrain is mainly composed of image processing tasks that strive to increase the performance of the input images and organize them in a predefined geometric and intensity space so that they may be segmented later [15].  
Figure 2 shows the functional block scheme of volBrain. It comprises the HIPS pipeline devised to cope with high-resolution or standard T1w images (Spoiled Gradient Echo (SPGR) and MPRAGE at 1.5T and 3T, respectively) without any preprocessing. T1 weighted sequences are used substantially in all MRI treatments and are best described as the most \"anatomical\" of images, that closely resemble the looks of tissues macroscopically. HIPS considers the use of the Winterburn segmentation protocol, which is freely available. MOPAL, which performs quick segmentation, offers accurate findings in less than twenty minutes and provides a mask of Winterburn segmentation protocol with reports stating the volumes of all the subfields and the total volume of the structure. HIPS exceeds other state-of-the-art algorithms regarding segmentation accuracy, earning a DICE of 0.661 for the Winterburn dataset. CA1, CA2/CA3, CA4/DG, SR/SL/SM, and Subiculum are the five subfields in the Winterburn dataset, as shown in Figure 3. volBrain is one of the first few freely available platforms that offer hippocampus segmentation based on the European Alzheimer's Disease Consortium (EADC) protocol, which will become the standard definition for AD in the next few years [15,16]. In terms of volume and shape estimation, volBrain is deemed more reliable than Freesurfer and FIRST [16].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Group** & **Description** & **Subject Age** \\ \hline \multirow{4}{*}{AD} & Number of Cases- 119 & - \\ \cline{2-3}  & Minimum & 55 \\ \cline{2-3}  & Maximum & 91 \\ \cline{2-3}  & Mean (Standard Deviation) & 75.10 (\(\pm\)8.60) \\ \hline \multirow{4}{*}{CN} & Number of Cases- 115 & - \\ \cline{2-3}  & Minimum & 59 \\ \cline{2-3}  & Maximum & 90 \\ \cline{2-3}  & Mean (Standard Deviation) & 74.80 (\(\pm\)7.95) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Demographical data of the Subjects in detail.
cal information, thus enabling the capture of more data features and improving the short-term prediction accuracy of urban-rail-transit interval OD passenger flows. 2. External factors related to interval OD passenger flow, such as date, time attributes, weather conditions, and air quality, are analyzed. The factors most strongly correlated with passenger flow are identified and incorporated into the model's predictions, enhancing the model's adaptability and generalization ability. 3. Using the urban rail transit system in the densely populated city of Chongqing as a case study, we validate the proposed model by conducting experiments on the city's interval OD passenger flow. The model's performance is tested under high-, medium-, and low-passenger-flow scenarios. The results demonstrate that the proposed model fits the actual variation trends of passenger flow more effectively, with the smallest prediction error under high-passenger-flow conditions. The findings are of significant relevance for future urban rail transit construction and daily operations.
## 7 Results Analysis
### Time  
Time is a crucial aspect of access control security, as it enables timely and dynamic updates to the system's access control policy, as well as prompt responses to access requests, therefore preventing the access control system from becoming a bottleneck. As a result, as depicted in Figure 7, the training time of the model was analysed along with the update time necessary for a policy decision point (PDP) to approve or deny an access request. The SVM algorithm required more training time than the other techniques, whereas the LR, KNN and DT algorithms required the same amount of time. Additionally, Figure 8 demonstrated that after applying data balancing techniques, the testing time results of various methods were analyzed, focusing on security access decision-making. The methods considered for analysis included random forest (RF), k-nearest neighbours (KNN), decision tree (DT), logistic regression (LR), and support vector machine (SVM). The analysis aimed to evaluate the performance of these methods in terms of their testing times after data balancing. The results indicated that RF, KNN, and DT exhibited testing times of zero seconds, while LR demonstrated a testing time of zero seconds as well. On the other hand, SVM exhibited a testing time exceeding 0.15 seconds. These findings provide valuable insights into each method's computational efficiency and resource utilisation in the context of security access decision-making. Furthermore, they contribute to understanding the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **RF** & **KNN** & **DT** & **LR** & **SVM** \\ \hline
**Accuracy** & 0.95 & 0.94 & 0.93 & 0.94 & 0.94 \\
**Precision** & 0.94 & 0.92 & 0.96 & 0.89 & 0.89 \\
**Recall** & 0.98 & 0.99 & 0.93 & 1.00 & 1.00 \\
**F1-score** & 0.94 & 0.92 & 0.93 & 0.92 & 0.92 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance results of different techniques without balancing the data set.  
Figure 6: Analyze the performance metrics of various methods after data processing (balancing method).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **RF** & **KNN** & **DT** & **LR** & **SVM** \\ \hline
**Accuracy** & 0.95 & 0.91 & 0.94 & 0.53 & 0.64 \\
**Precision** & 0.96 & 0.91 & 0.94 & 0.54 & 0.64 \\
**Recall** & 0.95 & 0.89 & 0.93 & 0.63 & 0.67 \\
**F1-score** & 0.95 & 0.91 & 0.94 & 0.53 & 0.64 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance results of different techniques after balancing dataset.  
impact of data balancing on testing times and inform the selection of appropriate methods for security access decision-making tasks.
# Resizer Swin Transformer-Based Classification Using sMRI for Alzheimer's Disease
## 4 Evaluation
### Training Setup  
This experiment used the PyTorch deep-learning framework to construct the proposed network model. Two NVIDIA 3080 TI GPUs were implemented on a server for training the classification task. The model was first transferred to the sMRI dataset after being pre-trained on the ImageNet-1K dataset. Since the Resizer module may shrink the images to eliminate a tiny amount of incorrect information (dark parts of sMRI images), cropping the input images is unnecessary. We tested various batch sizes and learning rates to determine the settings that would produce the best experimental results. The experimental outcomes are displayed in Figure 5. Most curves are generally stable at epoch = 50, yet some still vary remarkably. It is the most stable when the batch size is 16 and the learning rate is 5 \(\times\) 10\({}^{-5}\). Therefore, the batch size was set to 16, and the learning rate was set at 5 \(\times\) 10\({}^{-5}\), considering parameters like processing speed and classification performance. With a patch size of 4 \(\times\) 4, and using cross-entropy loss as a loss function, all networks were trained using the Adam optimizer. The training for each stage was performed up to 50 epochs on 70% of randomly selected data, and the best model was selected based on separate randomly selected 10% of the data for validation. The results of this study are based on the remaining unseen test set of 20% with the epoch.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Image Dataset** & **AD** & **NC** & **Age** & **Sex (F/M)** \\ \hline ADNI & 388 & 800 & 75.76 \(\pm\) 6.75 & 574/614 \\ (N = 1188) & & [56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95; 96] & 574/614 \\ (N = 847) & 196 & 651 & [52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 72; 74; 76; 77; 78; 79; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 94; 95; 96] & 463/384 \\ \hline \hline ADNI: Alzheimer’s Disease Neuroimaging Initiative; AIBL: Australian Imaging Biomarker \& Lifestyle; AD: Alzheimer’s disease; NC: normal control; F: female; M: male. & \\ \end{tabular}
\end{table}
Table 1: Demographic information of ADNI and AIBL datasets.
# Addressing the Gaps of IoU Loss in 3D Object Detection with IIoU
## 6 Experiments
### Dataset
#### 6.2.1 KITTI  
KITTI [14] dataset consists of 3D point cloud data and RGB images. During the preprocessing, following the existing studies [70], the raw point clouds are clipped into (0, 70.4) m, (\(-\)40, 40) m, (\(-\)3, 1) m for X, Y, and Z axis ranges with voxel size (0.05, 0.05, 0.1) m, respectively. The training dataset was split into 3712 and 3768 samples for training and validation. Three significant classes in the KITTI dataset, such as car, pedestrian, and cyclist, were focused on. Performance is evaluated in terms of Average Precision (AP) for various IoU thresholds, such as 0.5 and 0.75. 0.5 and 0.75 represent 50% and 75% overlap of detection with ground truth.
## 4 The Physical Layer Security Performance of the Relay Transmission Model
### Influence of Relay Location for \(P(C_{S}>0)\)  
#### 4.2.1 Simulation Results with Various Relay Location  
We assume \(P_{r}\) equals \(P_{s}\), and \(x(x=d_{SR}/d_{SD})\) is the normalized distance from \(S\) to \(R\). Let \(x\) change from 0 to 1. The simulation result of \(P(C_{S}>0)\) is shown in Figure 8. There exists an optimal relay location where \(P(C_{S}>0)\) would reach a peak value in both cooperative and selfish behavior of a relay.  
In the simulation \(\gamma_{SD}=5\) is the SNR of S to D channel, the optimal location is in the middle of source and destination (\(x=d_{SR}/d_{SD}=0.5\)). \(P(C_{S}>0)\) will decreases quickly when R moves towards D with a malicious behavior relay.
## 2 Materials and Methods
### HPLC-MS/MS Method Validation
#### 2.5.1 Linearity, Lower Limit of Quantification and Selectivity  
All calibration curves included one blank plasma sample, one blank plasma fortified only with IS, and eight-point calibration standards at 0.3, 1.5, 2.5, 5, 10, 25, 50 and 150 ng/mL for Fingolimod or seven-point at 1.5, 2.5, 5, 10, 25, 50 and 150 ng/mL for Fingolimod-P. Responses, i.e., peak area ratio of the analyte to IS, were plotted against the corresponding drug concentration, fitting the data with a weighted (1/x\({}^{2}\)) linear regression curve. The determination coefficient (r\({}^{2}\)) and the mean accuracy of calibrators were used to assess the quality of calibration curve. The lowest calibration standard point (0.3 and 1.5 ng/mL for Fingolimod and Fingolimod-P, respectively) was defined as the lower limit of quantification (LLOQ) of the method if its signal was at least five times the signal of the blank sample.  
Selectivity of the method was verified analyzing plasma matrices from six different donors fortified or not with Fingolimod and Fingolimod-P at the LLOQ and their IS at the concentration used.
# Underlay Loosely Coupled Model for Public Safety Networks Based on Device-to-Device Communication
## 3 Underlay D2D Network Based on ProSe Service
### D2D Synchronization Procedure  
The synchronization procedure is a precondition for direct communication and direct proximity discovery services. This procedure defines physical resources in time and frequency that carry D2D control and traffic data. The Primary Sidelink Synchronization Signal (PSSS) is deployed in the synchronization of the initial timing and frame boundary estimation. PSSS signals are designed to simplify the detection of synchronization sources. It can be broadcasted by a cellular antenna for in-coverage UEs or even by an independent UE for partial and out-of-coverage scenarios. The synchronization procedure happens naturally as long as UEs are in coverage of the same eNB or scattered over synchronized neighboring eNBs. For UE located in the coverage of an eNB that supports sidelink operations, a resource pool with a preconfigured list of transmission and reception parameters will be provided to configure the D2D direct communication.
## 4 Optimal Frame Generation Time Threshold Calculation
### Algorithm Design
#### 4.2.1 Clipped Surrogate Objective  
The clipped surrogate objective function \(\kappa^{CLIP}(\theta)\) defined in this paper is  
\[\kappa^{CLIP}(\theta)=\mathbb{E}_{t}\big{[}\min\big{(}r_{t}(\theta)\hat{A}_{t },clip(r_{t}(\theta),1-\epsilon,1+\epsilon)\hat{A}_{t}\big{)}\big{]} \tag{18}\]  
where \(r_{t}(\theta)=\frac{\pi_{\theta}(a_{t}|s_{t})}{\pi_{\theta_{\rm{old}}}(a_{t}|s _{t})}\) represents the probability ratio of the current policy and the old policy. \(\hat{A}_{t}\) represents the advantage estimation of the time interval \(t\), and the advantage function represents the advantage of taking action \(a_{t}\) under the state \(s_{t}\). \(\epsilon\) is a clipping coefficient used to limit the range of policy updates, which is used to limit the range of policy updates. \(clip(r_{t}(\theta),1-\epsilon,1+\epsilon)\) limits \(r_{t}(\theta)\) to the range of \([1-\epsilon,1+\epsilon]\). Thisshearing mechanism ensures that the policy update does not cause the \(r_{t}(\theta)\) to be too large or too small, thereby avoiding excessively deviating from the old policy. At the same time, the clipping constraint of the policy update range is  
\[1-\epsilon\leq r_{t}(\theta)\leq 1+\epsilon \tag{19}\]  
The difference between the new policy and the old policy is limited to a small range, avoiding the instability caused by excessive policy updates.
# A Comprehensive Experimental Emulation For OTFS Waveform RF-Impairments
### Organization  
The remainder of this paper is organized as follows. Section 2 presents the modulation and demodulation of both OTFS and OFDM waveforms. Section 3 discusses the channel effects, The RF impairments' impact, and shows the real implementation results. Finally, Section 4 concludes the work. (Notation: Matrices and column vectors are represented by bold, capital, and lowercase letters, respectively. \(E(.)\), \((.)^{T}\), \((.)^{H}\), \(|\left|.\right|\rfloor\) and \(\left|.\right|\) represent expectation, transposition, Hermitian transposition, floor, and absolute value operations, respectively.)
# SACT: A New Model of Covert Communication Based on SDN
## 4 System Model
### Watermark Extraction Module
#### 4.3.2 Viterbi Decoding  
SACT system uses convolutional code with constraint length of 9, so there are eight shift registers in the encoder and the status of each register is \((0,1)\). An input sequence is entered at a time, the state of the register affects the others, and the cumulative state is the encoder state. Therefore, there are 28 states in the encoder.  
According to the (2, 1, 9) convolutional coding scheme selected by the system, the Viterbi decoder makes 256 cumulative comparison and selection for the received data at a time. Each of the 256 states is followed by two branches (the state of the register is 0 or 1).  
Then, they are transferred to two different registers along the two branches to create two paths. The corresponding output of the two paths is compared with the received convolution output, and the one with the smallest Hamming distance is selected to save. This path is called survival path.  
Add this survival path to the Hamming distance of the survival path preserved at the previous time, the path selection elimination process is performed for all 256 states. At the end of decoding, the shortest Hamming distance from the 256 surviving paths generated by the above process is selected, and the final decoding output is obtained by backtracking. The algorithm is implemented as follows:  
* Suppose that the binary convolution sequence received at time \(t\) is \(s\), the leading state of the first state is \(p(i,j)\), and the codeword of the corresponding time is \(c(i,j),j=1,2\).
* Select the branch path, sum it and the measurement of the previous time path: \[BM(i,j)=M((c(i,j,1)+1,s(t,1)+1)+M((c(i,j,2),s(t,2)+1)\] (14) \[PM(i)=\max(PM\_l(p(i,j))+BM(i,j))\] (15) Eliminate cumulative overflow: \[PM(i)=PM(i)-BM(1,\max\_j(1))\] (16)
* Update current surviving path. Read the state data of the last time: \[save(i,2:50,:)=save\_l(p(i,\max\_j(i)),1:49,:)\] (17)Save candidate codewords: \[save(i,1,:)=c(i,\max\_j(i),:)\] (18)
* Output the decoding result at the current time. By calculating the maximum path measure, the maximum likelihood path is obtained: \[\max(PM(i))\] (19) Output the final path and cycle the codeword of next time: \[out(save\_l(\max\_i,50,:))\] (20)
# An Automated Framework for Streamlined CFD-Based Design and Optimization of Fixed-Wing UAV Wings
## 4 Evaluation Metrics  
The reliability of CFD results must consider the quantification of numerical errors, especially in an automated framework where human intervention is minimized. Thus, it is essential to ensure that the solutions generated are accurate, especially when considering critical aerodynamic quantities like lift and drag. The error in CFD modeling can be categorized into round-off errors, discretization errors, and iterative errors [50; 51; 52].  
* Round-off errors arise from the finite precision of numerical calculations performed by computers, particularly in large-scale simulations with many iterations, where the accumulation of small errors can impact the solution.
* Discretization errors, on the other hand, result from the approximation of continuous governing equations using numerical schemes and finite spatial or temporal resolution. They encompass truncation errors, which arise from the omission of higher-order terms in the numerical approximation, and additional artifacts introduced by grid quality, boundary condition definition, and numerical schemes. Discretization errors are often treated as an epistemic uncertainty [53], reflecting incomplete knowledge of the true solution due to mesh and scheme limitations.
* Iterative errors stem from the incomplete convergence of the solver to a stable solution, either due to insufficient iterations or suboptimal solver settings, and are similarly treated as an epistemic uncertainty [54].  
While these errors cannot be entirely eliminated, estimating their impact is essential for assessing the numerical fidelity of CFD results. Such error estimation safeguards against misinterpreting numerical artifacts as physical phenomena and helps ensure that the automated workflow produces robust and reliable outputs.  
To complement the error estimation process, this study also introduces a rapid reliability assessment methodology for evaluating the overall quality of CFD results. This assessment uses established best practices and guidelines for CFD simulations in the subsonic regime [41; 48]. By systematically evaluating critical parameters--such as boundary layer resolution, the number of discretized cells within the boundary layer, and the convergence behavior of key forces--the methodology identifies unreliable simulations and highlights potential sources of inaccuracy. These quality checks act as a double-layer safeguard, ensuring that the solutions meet predefined standards of fidelity before progressing further in the design process.  
Together, error estimation and rapid reliability assessment form a comprehensive framework for validating CFD outputs. The framework not only quantifies numerical uncertainties but also ensures solution quality through practical and experience-based reliability checks. This dual approach provides designers with robust and reliable outputs, enabling informed decision-making during the design and optimization process.
## Article
### YOLOv5s-D: A Railway Catenary Dropper State Identification and Small Defect Detection Model
The shooting time of high-speed railway catenary dropper images is mainly concentrated at night. Therefore, the background of the catenary dropper images is mainly black. In this kind of background, small defects in the catenary are difficult to identify and detect. Generally, they may cover only a dozen pixels, as shown in Figure 1. The overall characteristics of the image are relatively weak. Thus, the feature information extracted during the detection process will contain little information and lots of noise, which will seriously affect the test results. YOLOv5 is one of the better performing versions in the YOLO series of algorithms. By improving the architecture of YOLOv5 [25], changing the feature fusion method of the network model [26; 27], and adding attention mechanisms to the network model [28], the performance of small-target detection in the network is improved. In response to these issues, this paper proposes an improved model based on YOLOv5s by introducing the selective kernel (SK) attention module [29], the bidirectional feature pyramid network (BiFPN) [30], and the decoupled head [31]. The model enhances the attention to small targets in catenary dropper images, enabling the recognition of the state of the catenary dropper and the detection of small defects against dark backgrounds. The experimental results using a self-made contact network dropper dataset show that the detection accuracy of the proposed approach is better than that of the original model, improving the detection of dropper defects in high-speed rail networks. The contributions of the proposed approach are as follows:  
1. The SK attention module was optimized, resulting in reduced module complexity and improved performance, and further increasing the focus on small-target features. 2. The feature fusion network structure of the YOLOv5 model was optimized by utilizing BiFPN for multi-scale feature fusion, enabling a better balance of information across different scales. 3. The detection head of YOLOv5 was improved by replacing the original YOLO head with an enhanced decoupled head, leading to an improved detection accuracy and the faster convergence of the model. The rest of this paper is organized as follows. Section 2 introduces the YOLOv5 model and the improved model in this article, and explains the structure of the improved SK attention module and decoupling head structure, respectively. In Section 3, firstly, the preparation for the experiments is explained, including the contact network suspension dataset and experimental parameter settings, and then the results of the ablation experiments and comparative experiments are analyzed. Section 4 concludes this study. Figure 1: Small defects of the dropper in images. In the red box is the small defect of the dropper.