esa-sceva/satcom-chunk-collection huggingface dataset:
# Revealing the Spatial Pattern of Weather-Related Road Traffic Crashes in Slovenia
## 3 Results  
### Trends in Road Traffic Crashes in Slovenia  
In general, most Slovenian municipalities were characterized by a decreasing trend of road traffic crashes in all considered weather situations except wind. There were 147 municipalities with a negative trend in road traffic crash frequency (70%) and 62 with a positive trend (29.5%) (Figure 0(a)).  
Under rainy weather conditions, the ratio between negative and positive municipal road traffic crash trend coefficients was 135 to 75 (Figure 0(b)). The positive trend was most prominent in the Ljubljana and Celje Basins, with their surrounding municipalities, and in the most urbanized and the wettest parts of the Dinaric Alps region. The second cluster of municipalities with a positive trend in road traffic crashes in rainy conditions was detected in the eastern part of the Dinaric Alps region. However, during precipitation, the trend was mostly negative in high altitude municipalities with low population density. Similarly, under snowy weather conditions (Figure 0(c)), the observed trend was negative in 123 municipalities and positive in 77. The spatial footprints of positive or negative trends in road traffic crashes in snowy and rainy conditions were similar. However, more municipalities in the Alps region, which is the coldest and wettest region in Slovenia, had a positive road traffic crash trend in snowy weather conditions. Under strong air advection (wind), positive trends in road traffic crashes with serious injury or death were detected mainly in municipalities in the Mediterranean region, particularly in the Vipava valley (Figure 0(d)). Here, other plains in the Alps and Pannonian Basin regions were also exposed to turbulent wind conditions, which indirectly caused road traffic crashes between 2006 and 2018. Under dense fog, 115 Slovenian municipalities had a negative and 77 a positive trend in road traffic crash frequency. Basins in the Alps region as well as valleys and karst plateaus in the Dinaric Alps region showed increasing numbers of road traffic crashes in this kind of weather (Figure 0(e)).
# Analytical Model for Enhancing the Adoptability of Continuous Descent Approach at Airports
## 4 Model Development
### Adopting Queueing Theory to Our Model
#### 4.2.1 Assumptions and Parameters of the Model  
1. The space available for stacking aircraft arrivals in the TMA is considered as the maximum number of aircrafts (customers) that are permitted in the queue, and
2. the longitudinal separation distance between aircrafts conducting CDA are greater than the distance between aircrafts not conducting CDA  
Our model assumes that the number of aircraft arrivals over the period of time considered follows the Poisson probability distribution. This distribution has high variability and, thus, is likely to lead to the model results being conservative. Moreover, the fleet mix is assumed to be homogeneous, that is, dominated by two aircraft wake turbulence classes. The following parameters represent the fundamental components of our model: the space available to stack aircraft arrivals, the minimum allowable horizontal separation distance between a pair of consecutive same-weight-class aircraft arrivals, and the number of aircrafts that can be stacked for the approach. Figure 6 illustrates these components in our model. In this regard, while stacking space can be viewed as three dimensional, we model it as one dimensional, reflecting the longitudinal separation of stacked aircrafts as if the aircrafts within the space are all positioned in a single line.
## 8 Future Directions and Innovations
Interestingly, novel tests such as the detection of fungal and bacterial infections, venereal diseases and alcohol and drug abuse are on the horizon to make the POC platform comprehensive in all possible disease fronts [104; 105]. The integration of IoT in healthcare enables practical, real-time data collection from wearable devices, sensors and POCT systems, supporting remote monitoring, chronic disease management and early diagnosis. It improves decision-making by linking patient-generated data directly to healthcare providers via cloud platforms and mobile networks. However, challenges include ensuring data privacy and security, managing interoperability between devices and systems, and maintaining data accuracy and reliability across diverse environments. As IoT merges with POCT, future challenges will involve harmonizing clinical validation standards, ensuring the cybersecurity of connected diagnostic devices and addressing infrastructure gaps in low-resource settings, all while maintaining low cost and user-friendliness for global scalability. Last but not least, investment in further research and optimization of POCT and digital health passports could be justified by the available data on economic advantages as a result of the reduction in the burden on centralized healthcare infrastructure and improving efficiency in patient management. Studies estimate that POCT can reduce the average length of hospital stays by 20-30%, cut diagnostic turnaround times by 50-75%, and lower overall treatment costs by USD 300-800 per patient in emergency settings due to faster therapeutic decisions [106; 107]. In parallel, digital health passports have enabled streamlined access to services during infectious disease outbreaks, such as COVID-19, reducing public health verification costs by up to 40% and enabling safe continuity of travel and commerce [108]. Additionally, countries piloting integrated digital credentials reported operational cost savings exceeding USD 100 million annually, particularly by avoiding paper-based systems and redundant verification [109]. These technologies support scalable, cost-effective public health strategies, especially when combined with mobile and AI-based diagnostics, making them economically sustainable across both high- and low-resource settings.
## 2 Theoretical Background: Deep Learning, Sparse Auto-Encoder (SAE) and Support Vector Machine (SVM)
Both first and last layers (encoder and decoder, respectively) have \(M\) processing units (neurons), \(M\) being the sampled signals' length. The central layer has \(K\) neurons, corresponding to the \(K\) internal components \(\mathbf{h}\) that are used to characterize each structural signal \(\mathbf{x}\) composed of \(M\) data points. It is noteworthy that \(K\ll M\), indicating the transformation of the input variables into new ones with a lower dimension. In order to achieve the objective of this work, the main SAE parameters--the number of encoding/decoding layers and the dimension of the vector \(\mathbf{h}\)--are analyzed for two different structures, described further in the text. It is important to note that the feature vector \(\mathbf{h}\) is used to assess relevant information about the structure's behavior. Then, such a vector is used as input to SVM for structural novelty detection. Figure 1: Three-layer AE structure. SVM is a statistical machine learning method that has been widely used to solve several pattern recognition problems. The fundamental idea of SVM is to construct an optimal separating hyperplane by maximizing the margin and minimizing the misclassification between two classes of data in a space [39]. The SVM performs the classifications by defining some training data points positioned in the margins (support vectors), where the distance between them is maximized through an optimization function that minimizes the Euclidean norm of the hyperplane's directional vector. When dealing with nonlinear problems, the input variables are mapped in a higher-dimensional feature space by using a kernel function. The SVM theory can be extended to multi-class classification problems through strategies based on a combination of binary classifiers, e.g., one-against-one and one-against-all [40]. For more detailed information about SVM, see [PERSON] (2013) [39] and [PERSON] (2006) [40].
###### Abstract  
_Background:_ Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a global threat impacting the lives of millions of people worldwide. Automated detection of lung infections from Computed Tomography scans represents an excellent alternative; however, segmenting infected regions from CT slices encounters many challenges. _Objective:_ Developing a diagnosis system based on deep learning techniques to detect and quantify COVID-19 infection and pneumonia screening using CT imaging. _Method:_ Contrast Limited Adaptive Histogram Equalization pre-processing method was used to remove the noise and intensity in homogeneity. Black slices were also removed to crop only the region of interest containing the lungs. A U-net architecture, based on CNN encoder and CNN decoder approaches, is then introduced for a fast and precise image segmentation to obtain the lung and infection segmentation models. For better estimation of skill on unseen data, a fourfold cross-validation as a resampling procedure has been used. A three-layered CNN architecture, with additional fully connected layers followed by a Softmax layer, was used for classification. Lung and infection volumes have been reconstructed to allow volume ratio computing and obtain infection rate. _Results:_ Starting with the 20 CT scan cases, data has been divided into 70% for the training dataset and 30% for the validation dataset. Experimental results demonstrated that the proposed system achieves a dice score of 0.98 and 0.91 for the lung and infection segmentation tasks, respectively, and an accuracy of 0.98 for the classification task. _Conclusions:_ The proposed workflow aimed at obtaining good performances for the different system's components, and at the same time, dealing with reduced datasets used for training.  
SARS-CoV-2; CT scans; deep learning; lung segmentation; infection segmentation; classification; 3D reconstruction +
Footnote †: journal: _Sciences_  
_Article_  
A Deep Learning-Based Diagnosis System for COVID-19 Detection and Pneumonia Screening Using CT Imaging
# Burnup Credit Evaluation for BWR Spent Fuel from Full Core Calculations
## 5 Results and Discussion  
### BWR Full-Core Analysis  
#### 5.1.1 Defining the Cycle Length  
For the core map developed in [29] and used in this study, the cycle length for the average discharge burnup of 50 GWd/tU is 12.6 GWd/tU per cycle. However, the full core model described in [29] did not consider the effects of the fuel reloading process. A series of calculations were performed for different shuffling procedures to define an optimal cycle length and possible adjustments of the core configuration to achieve the target burnup of 50 GWd/tU while not exceeding limits set in [38] (Table 5). On the one hand, the analysis showed that the limit on maximum assembly average burnup of 60 GWd/tU was exceeded for the SP_C procedure (Figure 6), indicated by the appearance of several fuel assemblies with higher burnup (last bar on the right end of the right figure). The average discharged burnup over the core in this case (49.7 GWd/tU) is close to the set target of 50 GWd/tU. On the other hand, the SP_A procedure produces the narrowest discharged burnup distribution, indicating a very efficient fuel use for most fuel assemblies. The average discharged burnup is 50.8 GWd/tU, thus higher than in SP_C for the same cycle length, which makes it the preferred one in terms of fuel utilisation. However, as seen in Figure 6, there are still fuel assemblies detached from the main distribution (56 and 58 GWd/tU) which could be further optimised to improve the average discharge burnup. In this study, we refer to these assemblies as assembly-burnup outliers.  
Further analysis showed that the assembly located at the position (3, 10) caused the outlier effects described above. To resolve this problem, the full core map was slightly modified/optimised compared to the reference, as shown in Figure 6. The FA from the (3, 10) position was moved to (3, 14), while (3, 14) was placed to (3, 12) and (3, 12) back to (3, 10). For the improved full core layout, the optimal cycle length was estimated as 11.75 GWd/tU. The impact of the modification on discharged FA burnup distribution will be discussed more extensively in the next section.
# Modified Smith Input-Shaper Crane-Controller for Position Control and Sway Reduction
## 1 Introduction
Integral performance criteria [36] were used to assess the proposed control structure's impact on the crane's dynamics and compare it with the PD controller with and without the input shaper technique.
# Real-Time Mitigation of the Mobility Effect for IEEE 802.15.4g SUN MR-OFDM
## 4 Adaptive Estimation and Compensation for the CFO Scheme
### Proposed Scheme  
As mentioned in Section 3, estimating the CFO technique by using the CP has certain advantages, such as spending less time in estimation and using less computing power. The range of the CFO can be estimated as [\(-0.5\), \(+0.5\)]. In this section, we take advantage of the CP technique for the CFO estimator in each OFDM symbol. CP is an important part of the OFDM symbol, which is the tail of the symbol copied to the top of the symbol for reducing ISI. We assume that the symbol synchronization is perfect, and the CFO of the \(\varepsilon\) value causes a phase rotation of \(2\Pi n\varepsilon/N\) at the receiver side. If the channel equalizer is perfect, the channel effect of the OFDM symbol can be considered negligible. In this case, the phase difference between the CP part and the corresponding tail of the OFDM symbol is \(2\Pi N\varepsilon/N=2\Pi\varepsilon\) as follow:  
\[y_{l}[n]=x_{l}[n]e^{\frac{2\Pi n\varepsilon}{N}}\text{ with }n=-N_{CP},-N_{CP}+1,....,-1 \tag{12}\]  
\[y_{l}[n+N]=x_{l}[n]e^{\frac{2\Pi(n+N)\varepsilon}{N}}\text{ with }n=-N_{CP},-N_{CP}+1,....,-1 \tag{13}\]  
Then, the CFO value can be detected by comparing the phase of the CP part with the phase of the corresponding tail of the OFDM symbol, which is defined as follows:  
\[\widehat{c}=\frac{1}{2\Pi}\text{arg}\Big{\{}y_{l}^{*}[n]y_{l}[n+N]\Big{\}} \text{ with }n=-N_{CP},-N_{CP}+1,....,-1 \tag{14}\]  
To reduce the effects of noise, the estimated value of the CFO is taken as the average of the total \(N_{CP}\) samples as follows:  
\[\widehat{c}=\frac{1}{2\Pi N_{CP}}\text{arg}\Bigg{\{}\sum_{n=-N_{CP}}^{-1}y_{l} ^{*}[n]y_{l}[n+N]\Bigg{\}}\text{ with }n=-N_{CP},-N_{CP}+1,....,-1 \tag{15}\]  
If \(N_{CP}\) is the number of CP, and \(N\) is the DFT size, then the argument operation \(\text{arg}\{\}\) is \(\tan^{-1}\{\}\) with the CFO estimate in the range [\(-0.5\), \(+0.5\)].  
The proposed scheme that uses CP to estimate and compensate for the CFO is shown in Figure 3. After the signal goes through the equalizer block, the signal is divided into two parts. First, the signal goes through the CFO estimation block using the CP technique. Second, the signal is given over to the CFO compensation block with input from the CFO estimation block. Figure 4 shown the constellation of the MR-OFDM with the CFO effect and after CFO compensation over the Gaussian channel. We can see that the system performance is improved after using the proposed scheme.  
Figure 3: Block diagram of the architecture of the proposed scheme.  
Figure 5 evaluates the CFO estimation effectiveness of the CP method using four options based on the mean-squared error algorism. From Equation (15), the estimated value of the CFO will be better if the DFT windows sizes are larger. Figure 5 shows that the estimated performance of Option 1 is the best because of the DFT size of 128, and the estimated performance of Option 4 is the lowest because of the DFT size of 16 at the same noise level.
# Design and Modelling of a Novel Integrated Photonic Device for Nano-Scale Magnetic Memory Reading
## 2 Materials and Methods  
In this section, materials and geometries of the parts that contribute to the overall device are explained. In addition, the device concept, optical simulation and mathematical modelling methods are explained in the subsections.  
The material which stores the magnetic information (memory bit) is a multi-layered ferromagnetic metal thin-film structure, whose stack order is given in the previous section. These multi-layers display PMA, where the magnetization vector is perpendicular to the film plane [22]. PMA is highlighted due to its relatively large magneto-optical efficiency [23]. The multi-layers are placed on top of the waveguides as the top cladding. The rest of the photonic device is fabricated on InP membranes since the devices are based on the IMOS platform [21]. The waveguides have a cross-section of 300 \(\times\) 400 nm (height and width) and the multi-layered top claddings have the dimensions of 400 \(\times\) 50 \(\times\) 12 nm (width, length and height).
# STA-GCN: Spatial-Temporal Self-Attention Graph Convolutional Networks for Traffic-Flow Prediction
## 3 Dataset and Methodology
### Spatial-Temporal Self-Attention Graph Convolution Networks (STA-GCN)  
In this section, the detailed description of the STA-GCN (spatial-temporal self-attention graph convolution network) model structure is provided. As shown in Figure 2, the model consists of an input layer, an output layer, and a spatial-temporal layer. The input layer is a fully connected neural network that maps the input spatial-temporal information \(\chi\in\mathbb{R}^{S\times N\times 4}\), which is encoded with time, from 4 channels to a higher-dimensional channel. The spatial-temporal layer is composed of a temporal layer and a spatial layer, and we describe the model structures of these two layers in detail below.
# Fault Detection and VSC-HVDC Network Dynamics Analysis for the Faults in Its Host AC Networks
## 4 Performance Analysis
### Effect of Sampling Frequency  
Higher sampling frequencies are selected during the analog-to-digital conversion process so that the high-range frequency components generated during the fault conditions can be extracted and applied for better analysis of the condition. But choosing a higher sampling frequency may lead to a higher burden on the relay memory, and the overall decision process may become delayed. To see the impact of various sampling frequencies on the response of the HHT and proposed methods, 2, 5, and 8 kHz sampling frequencies were considered. A detailed comparison is presented in Table 5. From the results, it can be observed that the DTE values estimated using the HHT and SST were almost the same, but the response time of the proposed method for various sampling frequencies was lower compared with the HHT. Thus, the proposed method has high immunity to the sampling frequency. It was also observed that the response time of the proposed method was almost the same for different sampling frequency conditions.  
Figure 10: (**a**) Current waveform during zero-crossing fault. (**b**) Current waveform during peak crossing fault. (**c**) DTE during zero-crossing fault. (**d**) DTE during peak crossing fault.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sampling Frequency (kHz)**} & \multicolumn{2}{c}{**HHT**} & \multicolumn{2}{c}{**SST**} \\ \cline{2-5}  & **DTE (p.u.)** & **Time (ms)** & **DTE (p.u.)** & **Time (ms)** \\ \hline
2 & 38.05 & 1.52 & 42.82 & 1.28 \\ \hline
5 & 41.75 & 1.45 & 46.50 & 1.25 \\ \hline
8 & 44.25 & 1.31 & 48.95 & 1.20 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Effect of sampling frequency.
## 3 Results
### Research on Tomato Leaf Disease Classification Based on LBFNet Model
#### 3.1.2 The Impact of Different Learning Rate Parameters on the Model  
The learning rate is an important hyperparameter of convolutional neural networks. A learning rate that is too large can cause the loss function to miss the global optimal point, while a learning rate that is too small can increase the convergence complexity of the network. Therefore, to explore the optimal learning rate for LBFNet in tomato disease recognition, we conducted experiments with learning rates set to 0.0001, 0.001, 0.01, and 0.1.
# Post-Quantum Key Exchange and Subscriber Identity Encryption in 5G Using ML-KEM (Kyber) 1
## 6 Experimentation and Performance Analysis
### Hardware and Software Setup  
Table 5 shows the hardware and software specification that we use in our experimental setup. We implement our system using a computer with an AMD Ryzen 7 5700U running at 1.8 GHz with 16 GB RAM to simulate both user equipment and the core network. Additionally, we also perform our experimentation with resource constraints, such as a Mini-PC equipped with an Intel 12 th N95 processor running at 3.4 GHz with 8 GB RAM, as well as a Raspberry Pi featuring a BCM2711, Quad-core Cortex-A72 (ARM v8) 64-bit SoC clocked at 1.8 GHz with 4 GB RAM, to represent the UE, and implement all three ciphers on both devices for measuring the computational time of public-key generation and shared-key generation. All of the machines are running Ubuntu 22.04 LTS as their operation system (OS).  
We perform our experimentation as the implementation design described in Section 5. We follow steps 1, 2, and 5 of Section 5.1 for the pre-quantum ECIES and use open-source python library _cryptography version 42.0.5_[46] for the implementation. For the post-quantum algorithm, we follow steps 1, 2, and 4 of Section 5.2. We use the open-source Open Quantum Safe (oqs) library _libqs version 0.10.0_[47] to realize the steps. We write Python scripts to run the experiments on a computer, a MiniPC, and a Raspberry Pi hardware as mentioned earlier, while the compiler version installed on each machine is Python 3.10.6. For our experimental data analyses, we collect the time and power measurement data. We measure only the computation time required by each algorithm in key pair generation, encapsulation/encryption, and decapsulation/decryption. The computational time gives us an indication of the energy required for each computation in the user equipment.  
For the power measurements, we use a power analyzer hardware (ZHURUI PR10-E US15A power recorder [48]; Shenzhen Zhurui Technology CO., LTD.; Shenzhen, China) to measure power consumption during the execution of our cryptographic processes. Specifically, for the pre-quantum ECIES, we measure power consumption during steps 1, 2, and 5 of Section 5.1. Meanwhile, for Kyber-512, we follow the procedures outlined in steps 1, 2, and 4 of Section 5.2. After collecting this data, we compute the average power consumption and multiply it by the time duration of the process to obtain the total energy consumed.