esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Related Work
Results from the experiments show that Sub-Curve HMM outperforms existing methods, achieving over 94% accuracy in detecting six malware families, compared to 83% for baseline HMM and 73% for Information Gain. The study highlights improvements in detecting potential data exfiltration incidents but does not address computational resource requirements. Table 1 summarizes the malware detection systems reviewed in this section. Several studies [2; 4; 6; 11; 29] on malware detection using API calls have utilized various machine learning and deep learning techniques, but few have explored the potential of Generative Adversarial Networks (GANs). GANs can generate realistic synthetic API call sequences, enhancing training data for imbalanced datasets and improving the detection \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Authors**} & \multirow{2}{*}{**Algorithm Used**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{
\begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular} } & \multicolumn{4}{c}{**Computational Efficiency**} \\ \cline{4-7}  & & & & **Training** & **Testing** & **Memory** \\  & & & **Time** & **Time** & **Usage** \\ \hline [PERSON] et al. [2] & Hybrid logistic Regression and RNN & API-Call-Sequences & 84.0 & 0.40 & \(\times\) & \(\times\) \\ [PERSON] et al. [4] & RF/CatBoost/XGBoost/BILSTM/BiGRU & API-Call-Sequences/APHDS & 99.3 & 6.673 & 0.011 & \(\times\) \\ [PERSON] et al. [6] & RF/SVM/XGBoost/HGBoost/LSTM & VirusName/ VirusSample & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ [PERSON] et al. [11] & DCNCNNs/LSTM & API-Call-Sequences & 92.44 & \(\times\) & \(\times\) & \(\times\) \\ [PERSON] et al. [14] & LSTM & Mal-API-2019 & 98.50 & 2569.2 & \(\times\) & \(\times\) \\ [PERSON] et al. [29] & CNN/BiGRU & APHDS/MallbackV-V1 & 99.07 & 35.721 & 0.177 & \(\times\) \\ [PERSON] et al. [31] & MSA/LCS & APHDS & 99.80 & \(\times\) & \(\times\) & \(\times\) \\ [PERSON] et al. [36] & HMM & VirusName & 99.70 & \(\times\) & \(\times\) & \(\times\) \\ Our Proposed & APHDS/APH-Call- & See & See & See & See \\ Model & GRU-GAN & Sequences/MelebahvD-V1 & Section 4 & Section 4 & Section 4 & Section 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of detection techniques used in malware detection using API Calls. of sophisticated malware. Despite these advantages, GANs are underutilized due to challenges like high data requirements and training complexity. This paper aimed to overcome these challenges by developing a GAN-based model optimized for efficiency and reduced computational demands. The proposed GAN model demonstrated improved evaluation metrics, including higher accuracy and AUC, lower false-positive rates (FPRs), and reduced false-negative rates (FNRs), owing to its enhanced data generation and advanced learning capabilities.
# MA-Xnet: Mobile-Attention X-Network for Crack Detection
## 2 Proposed Methodology  
### Proposed MA-Xnet  
MA-Xnet is proposed for crack segmentation, and it aims to reduce parameters and utilize feature information effectively. The MA-Xnet network structure is shown in Figure 1. The encoder part uses MobileNetV2 [24] for feature extraction, and Table 1 shows the overall architecture of MobileNetV2, where \(t\) is the expansion factor; \(n\) is the number of repeated blocks; \(s\) indicates the stride; and \(c\) is the final number of channels after the last convolution operation. MobileNetV2 uses depth-separable convolution to replace the standard convolution of U-Net to reduce the parameters of the model, and the bottleneck residual structure can reduce the information loss caused by image compression, especially in the case of low-image resolution, with better robustness.  
Figure 1: Proposed MA-Xnet architecture.  
Meanwhile, to effectively utilize more contextual semantic feature information about cracks, a backbone network, with X-net as the backbone, is constructed to allow different feature information extracted by the two parts of the encoder to interact in the decoder part through cross-skip connections, which reduces the dependence of the deconvolution layer on the features extracted by the convolution layer in the U-Net skip connection fusion approach and further improves the utilization of crack-feature information in upsampling learning. Then, the position attention module (PA) and channel attention module (CA) are used in the penultimate layer of the two decoder sections of the model to capture global contextual information in two different dimensions, spatial dimension and channel dimension, respectively, to focus on enhancing the semantic features of the cracks to integrate the semantic information of deep and shallow layers and, thus, enhance the feature representation. Finally, the two parts of the semantic features through the PA module and the CA module are summed and the resulting segmentation map, with a better pixel-level feature representation, is output.
# An In-Line Coaxial-to-Waveguide Transition for Q-Band Single-Feed-Per-Beam Antenna Systems
## 3 Results  
The transition was designed through a FEM electromagnetic solver, Ansys Electronics Desktop 2016.2. The waveguide and the coaxial outer conductor were modelled in aluminium, the coaxial inner conductor was modelled in copper. By considering from the air gap in the coaxial to the smallest ridge in the WR-22, the transition was 13.24 mm long. It is worth noting that the main part of the transition in terms of space occupation (9.52 mm) consisted of the Chebyshev impedance transformer, the rest of the matching network consisted only of the pin and the first ridge where this was shorted and it was 1.6 mm long inside the waveguide, 3.32 mm considering from the air gap in the coaxial cable to the same ridge (the backshort cavity lays inside this length), which was 0.55 \(\lambda_{0}\) at the higher frequency considered (50 GHz).
## 1 Introduction
### Fixation Unit Anchor System
#### 1.2.2 Design Standards and Failure Modes for Concrete-Embedded Anchor Bolts  
In this study, the design load was calculated by referring to the domestic \"Concrete Structure Standard (2012) Appendix II Concrete Anchors\" design standards, and the level of safety was examined through a comparison with the results of static tests (pull-out and shear tests) [6]. The basic concepts of the anchor bolts subjected to tensile and shear loads are shown in Equations (1) and (2) below. Regarding the nominal tensile strength and shear strength, the smallest values among the possible anchor failure modes were used, as presented in Table 1. Anchor bolts were installed.  
\[\emptyset N_{n}\geq N_{ua} \tag{1}\]  
where \(N_{ua}\) is the required tensile strength and \(N_{n}\) is the nominal tensile strength.  
\[\emptyset V_{n}\geq V_{ua} \tag{2}\]  
where \(V_{ua}\) is the required shear strength and \(V_{n}\) is the nominal shear strength.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Division** & **Tensile Strength** & **Shear Strength** \\ \hline Steel Failure & \(N_{sa}\) & \(V_{sa}\) \\ Pullout & \(N_{pn}\) & \\ Side-Face Blowout & \(N_{sb}\) & \\ Concrete Breakout & \(N_{cb}\) & \(V_{cb}\) \\ Concrete Splitting & & \\ Concrete Pryout & & \(V_{cp}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Failure mode of anchor bolt subjected to strength.  
Figure 1: Typical post-installed anchors.
# Deep Learning Enhances Radiologists' Detection of Potential Spinal Alignancies in CT Scans
###### Abstract  
Incidental spinal bone lesions, potential indicators of malignancies, are frequently underreported in abdominal and thoracic CT imaging due to scan focus and diagnostic bias towards patient complaints. Here, we evaluate a deep-learning algorithm (DLA) designed to support radiologists' reporting of incidental lesions during routine clinical practice. The present study is structured into two phases: unaided and AI-assisted. A total of 32 scans from multiple radiology centers were selected randomly and independently annotated by two experts. The U-Net-like architecture-based DLA used for the AI-assisted phase showed a sensitivity of 75.0% in identifying potentially malignant spinal bone lesions. Six radiologists of varying experience levels participated in this observational study. During routine reporting, the DLA helped improve the radiologists' sensitivity by 20.8 percentage points. Notably, DLA-generated false-positive predictions did not significantly bias radiologists in their final diagnosis. These observations clearly indicate that using a suitable DLA improves the detection of otherwise missed potentially malignant spinal cases. Our results further emphasize the potential of artificial intelligence as a second reader in the clinical setting.  
deep learning; computed tomography; malignancies; AI detection; second reader; spine; vertebral lesions +
Footnote †: journal: Medical Imaging  
11
# Performance Impact of Optimization Methods on MySQL Document-Based and Relational Databases
## 1 Introduction
With this idea, this paper is focused on the issue of increasing database performance and proposes several methods to optimize the database structure and queries. The most important optimizations considered are those on the database structure and indexes. To show the efficiency of the proposed optimization methods, a testing architecture was developed that involves the implementation of two versions of a Java application, using IntelliJ IDEA [13] (IntelliJ IDEA, version 2020.2.4, build #IC-202.8194.7, built on 24 November 2020, JetBrains, Prague, Czech Republic, known as Intellij IDEA), with one using relational MySQL, and one using document-based MySQL. Furthermore, optimization methods were applied to both relational MySQL and document-based MySQL, and performance tests were run on CRUD operations before and after these optimizations to observe the impact of these optimization methods on each database. This paper is organized as follows: The first section contains a short introduction emphasizing the motivation of the paper, followed by Section 2 that reviews related work. The structures of the databases, methods, and testing architecture used in this work are described in Section 3. The database optimization methods are described in Section 4; furthermore, the experimental results and their analysis regarding the impact of the optimization methods on the two databases in an application that uses large amounts of data are presented in Section 5. Some discussion regarding the performance tests over different complexities of queries and data volumes is carried out, and, finally, some conclusions regarding the analysis are presented.
## 2 Literature Review
### Imaging Geometries
#### 2.2.2 Scanned Imaging  
The second way to form an image is by scanning. [PERSON] filed a patent for using a scanning method to transmit an image using facsimile [38]. Later, scanning was used in the early development of television. A scanning optical microscope was developed by [PERSON] and [PERSON] [39]. In this system, which they called a flying-spot microscope, the object is illuminated with a scanned, focused spot of light. A signal is picked up with a single, bucket detector, and an image is built up by scanning in synchronism with the illuminating spot. Recently, such a system has also been called a single-pixel camera. The light emanating from the sample is then merely detected, rather than imaged. Therefore, unlike a conventional imaging system, the image is basically formed by the illumination system. Further, as the light from the sample is only detected, the direct line of sight from the object to the detector is unnecessary and can be obstructed by a scattering medium.  
The principle of the flying-spot microscope was discussed earlier and attributed to [PERSON], by [PERSON] and [PERSON] in 1949 [40], p.383. Interestingly, the flying-spot microscope was widely investigated in those days, culminating in a meeting of the New York Academy of Sciences in 1962. For some reason, interest in the topic declined soon after.  
Scanning systems can also be used in various imaging modes where the illuminating light is used to produce some physical response that can be used to generate an image [41]. Examples include many types of spectroscopy, photoacoustic and photothermal generation, and the generation of charge carriers in semiconductors.  
Using the principle of reciprocity, it can be shown that scanning and conventional systems usually give identical images, even in the presence of multiple scattering [42]. However, this property breaks down if there is a change in wavelength in the interaction with the sample, as in fluorescence with a Stokes shift or in two-photon microscopy. In the case of fluorescence, if a Stokes shift is present, then the scanning system gives a better resolution, as the wavelength of the illuminating light is shorter than the fluorescent light.  
In the case of two-photon fluorescence (2 PF) or SHG, the emitted radiation has a shorter wavelength than the incident light [43].  
Instead of using a single, focused spot of light, a patterned illumination can be used. In this case, a set of patterns must be used, and the resulting signals must be demodulated to produce an image.
## 4 Results and Discussion
### Fuel Consumption  
As expected, all algorithms showed significant reductions in fuel consumption compared to a baseline lead vehicle. The greatest fuel savings occurred at lower speeds and in scenarios with frequent speed changes. Using SP as the benchmark, fuel savings ranged from a minimum of 6.9% in rural conditions to a maximum of 22% in urban driving conditions. These results reflect the theoretical best performance that an ACC control could achieve if the entire lead vehicle's speed profile for the whole drive cycle was known in advance. Thus, it is no surprise that the MPC controller achieves on average about 70% of the fuel benefit. In urban driving scenarios, MPC achieved fuel savings of up to 17.3%.  
As shown in Table 2, the two MPC controllers exhibit similar behavior, with the fuel consumption-based one performing marginally better, achieving improvements of approximately 0.2% to 0.5%.  
To thoroughly assess the controllers' performances, the percentage of fuel consumption reduction is considered. As shown in Table 3, the percentage savings for each proposed controller along the five driving cycles exhibit two trends:  
* The performance difference between the two MPC controllers is minimal, with a maximum variation of 0.5%, indicating highly comparable results;  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **UDDS** & **ARDC** & **AUDC** & **RD1** & **RD2** \\ \hline Duration (s) & 1369 & 1082 & 993 & 381 & 435 \\ \hline Mean velocity (m/s) & 8.7520 & 15.9487 & 4.8992 & 5.5080 & 4.9888 \\ \hline Max velocity (m/s) & 25.347 & 30.972 & 16.028 & 14.427 & 18.073 \\ \hline RMS acceleration (m/s\({}^{2}\)) & 0.6091 & 0.6289 & 0.7785 & 0.7370 & 0.7013 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mean velocity and RMS of acceleration for each driving cycle.  
* Both MPC controllers and DP show higher beneficial impacts in urban areas and driving cycles characterized by lower average speeds and higher-acceleration RMS, reflecting the efficiency of the control strategies in more dynamic driving conditions.  
The difference in fuel savings between DP and MPC can be attributed to the nature of each method. As an offline optimization technique, DP is capable of exploring a broader solution space to find the global optimum. In contrast, MPC, being an online optimization method, focuses on local optima within the prediction horizon. However, MPC's ability to continuously update its predictions based on real-time data allows it to adapt to changing conditions effectively.
# Interval Extended Kalman Filter--Application to Underwater Localization and Control
## 5 Simulation Results
### Extended Kalman Filter  
Figure 5 shows that in the right scenario, the localization with the Extended Kalman Filter is more precise than interval analysis. Indeed, the estimated position is visually close to the real position. However, in the second scenario (see Figure 8), the Kalman filter diverges because there is another local minimum for the covariance. Furthermore, this local minimum is closer to the estimated position than the real position. So this filter is more precise, but not robust.
# HERMES CubeSat Payload Thermal Balance Test and Comparison with Finite Volume Thermal Model
## 7 Conclusions  
The thermal balance test and test setup procedure for the HERMES CubeSat Payload DM have been presented and the conclusions are here summarized:  
1. the gathered data highlight the time needed to reach steady-state, the temperature jumps caused by the simulated electronic heat power dissipation and showed how the internal components of the payload DM are not much affected by the temperature changes at the interface with the rest of the spacecraft.
2. the test showed the actual thermal behaviour of the payload pushing the payload team to modify the thermal design. Indeed, new thermal interfaces have been introduced and strategically placed to deviate heat flux from flowing towards the detectors which must be kept below \(-5\,
## 4 Questionnaire Results and Analysis
### Relationships between Subjective Evaluation and Physiological Parameters  
Table 7 shows Pearson correlation coefficients between the physiological parameters and the subjective evaluation indexes. It can be seen from the table that the \(p\)(sig.) values between TSV and various parameters were all less than 0.05, and there was a significant positive correlation between physiological parameters and TSV. The correlation between \(T_{sk}\) and TSV was greater (\(r\) = 0.534 **). It can be seen that skin temperature was the main factor affecting the evaluation of human subjective thermal sensation. There was also a significant positive correlation between HSV and physiological parameters (\(p\) < 0.01). The correlation between \(SW_{p}\) and HSV was greater (\(r\) = 0.509 **), followed by the mean skin temperature. This suggests that as sweat increases the moisture of the human skin, the more the human body sweats, the higher the participant's HSV.  
There was a significant negative correlation between TCV and various physiological parameters (\(p\) < 0.05). Among them, \(T_{sk}\) had the highest correlation with TCV (\(r\) = \(-0.510\)**). Hence, skin temperature had a major impact on the subjective thermal comfort evaluation.
# Cryptanalysis of Permutation-Diffusion-Based Lightweight Chaotic Image Encryption Scheme Using CPA
## 3 Cryptanalysis of the Original Scheme
### Computational Complexity Analysis  
In the proposed cryptanalysis structure, the original diffusion phase can be cracked with only one full zero image, and the original permutation phase can be broken by \(\left\lceil\log_{L}(M\times N)\right\rceil\) chosen-plaintext images. Thus, the total plaintext images \(1+\left\lceil\log_{L}(M\times N)\right\rceil\) are required to collapse the original cryptosystem, which is acceptable for the requirements of the computational complexity and cryptanalysis feasibility. In addition, The running times of attacking permutation and diffusion phases for different sizes of images, including \(256\times 256\), \(512\times 512\), and \(1024\times 1024\), are also shown in Table 1. From Table 1, one can easily find that given an image size \(256\times 256\) (\(512\times 512\)), the average running times for attacking permutation and diffusion are \(0.4249\) and \(2.4225\) (\(1.6362\) and \(8.2446\)), respectively; the average running times for breaking the total cryptosystem is \(2.8473\) (\(9.8808\) for \(512\times 512\)). Therefore, the proposed attack strategy is satisfactory in terms of executive efficiency. In addition, as expected, the running time is increased with the increase of the size of an image; in other words, the proposed attack strategy can be applicable for breaking the cryptosystem with an image of any size.
# Time Optimization of Unmanned Aerial Vehicles Using an Augmented Path
## 3 Proposed Augmented Path-Time Architecture
### Euclidean Distance  
For the aerial transport to follow the path of ascent and descent accurately, the Euclidean distance is used. This function is used to calculate the distance between two different points. The two points (initial and final) denote the centre of the path detected. The two points are given coordinates assigned to them by the GPS module and then the Euclidean distance is calculated by a tracking algorithm that tracks the movement of the AV and calculates the coordinates and guides the vehicle to the path accurately and keeps checking if it is on track while simultaneously using the GPS module.  
All these calculations are done simultaneously with the data being analyzed and used in real-time. These algorithms and calculations are carried out using different modules and chipsets on the flight controller of the UAVs. Figure 1 shows the final path followed by the UAVs as calculated by the algorithm.  
Now, in this path, we considered only the x and y-axis as in contradiction to the 3d world to keep it simple, but the z-axis is taken as being equal to x and y for the ascent as it has to keep a constant angle with the path (45 degrees).  
The Euclidean distance interprets all the data in coordinates identifying every object with their coordinates and with its help, the drone will not only have decreased processing time but also by reading the coordinates of an incoming danger or a threat, the drone will also obtain help to identify the threat so it can work out the protocol to avoid it, thus helping in the task at hand and minimizing the chances of failure.  
For the descent, the Brachistochrone curve is mainly for the x and y-axis so the z-axis could be kept constant.
# Practical Performance Analysis of Interference in DSS System
## 5 Field Test and Performance Analysis
### Neighbor Cell Interference Test
#### 5.2.4 NR Uplink Interference of LTE Terminal to DSS Cell  
The test results are shown in Figure 17.  
As can be seen from Figure 17, the impact of the LTE terminal on the raised background noise of the first circle DSS NR cell is basically the same as that of the NR terminal, and the difference in raised background noise is about 2.66 dB. In the first circle of DSS cell, the uplink background noise of the NR cell is raised by about 7%. The terminal has no effect on the background noise increase of the NR cell in the second and third rounds of DSS. It can be seen that the NR uplink interference from LTE terminals to DSS cells can be neglected.  
Through the analysis of the above test results, we can find that in the DSS networking scenario, LTE in DSS cells will not be affected by NR in neighbor DSS cells, and there is also no significant interference from LTE and NR terminals to NR and LTE in DSS cells, respectively. However, the NR of the DSS cell will be strongly interfered by the LTE of the neighbor DSS cell, which will seriously affect the user experience. Therefore, it is necessary to take interference mitigation solutions to improve network performance.