esa-sceva/satcom-chunk-collection huggingface dataset:
# Modelling and Analysis of Hybrid Transformation for Lossless Big Medical Image Compression
## 4 Simulation and Analysis
### Comparative Analysis  
The decompressed images of different sizes were compared with the original image. Several image measures--mean square error (MSE), signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and bit error rate (BER)--were evaluated. The results are shown in Table 1. The performance of the proposed model was compared with those of other models--steganography, WT, segmentation, encryption and fusion, DCT, DWT, and classification [1; 2; 3; 4; 8; 9; 14; 15; 16; 17; 19; 21; 23; 24].  
This research proves that the evaluation parameters do not change significantly as the size of the information increases and gives the insight that, regardless of size, the error rate is low enough that the steganography or decompressed image will not be distorted. Regardless of changes to the input data, the embedding and compression algorithms do not deviate from expected results, proving robustness and accuracy to any dataset and the tricky challenges introduced. The compression ratios of all images also exceeded the 80% benchmark, demonstrating high compression ratios for lossless images. From the original 17.63 GB of the significant medical dataset, the total compression achieved was 8.62 GB, which is very efficient for medical repository storage. These proven results give a clear  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Measures** & **Original Image** & \
\begin{tabular}{c} **Decompressed** \\ **Image 1** \\ \end{tabular} &
\begin{tabular}{c} **Decompressed** \\ **Image 2** \\ \end{tabular} \\ \hline MSE & 41.89 & 41.88 & 41.88 \\ SNR & 25.79 & 25.79 & 25.79 \\ PSNR & 31.92 & 31.91 & 31.91 \\ SSIM & 0.99 & 0.99 & 0.99 \\ BER (number, ratio) & 1548, 7.38\% & 1548, 7.37\% & 1548, 7.37\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative analysis.  
Figure 8: MRI compression using core wavelet families.  
insight into how well the hybridization and fusion of these techniques have yielded results and give us a significant boost and inspiration to use this well-constructed technology.
## 2 Related Work  
Password guessing research can be divided into online guessing and offline guessing according to whether it interacts with the server, and wandering guessing and targeted guessing according to whether the user's personal information is used in the guessing process. The current mainstream wandering-guessing algorithms include Markov-based algorithms, PCFG-based algorithms, and neural-network-based algorithms.  
In 2005, [PERSON] et al. [12] used the Markov model to model a password dataset, which laid the foundation for follow-up research. In 2009, [PERSON] et al. [13] proposed Probabilistic Context-Free Grammar (PCFG). This research provided the foundation for subsequent research on password templates.  
Neural-network-based password generation research began earlier; however, it was slow to develop and did not receive much attention [14]. It was not until 2016 that the use of neural networks to research password security became a focus. In 2016, [PERSON] et al. [15] used recurrent neural network technology to conduct password generation and password strength evaluation research, and subsequent studies [16; 17; 18] were based on this research.  
In 2019, [PERSON] et al. [11] proposed a PassGAN neural network password-guessing method based on a generative adversarial neural network, and subsequent studies [19; 20] were based on this model. [PERSON] et al. [21] used multiple criteria to evaluate the performance of multiple password guessing attack models. Experimental data showed that, when the number of guesses was the same, the password-guessing method combined by the two methods performed better than a single method. Combination method password guessing research has also become a focus of current research.  
The above-mentioned password-generation model belongs to the category of wandering attacks, and the attacker does not conduct password guessing attacks against specific users. Another type of password-generation method focuses on specific user information. The attack scenario is often an online password guessing attack, with restrictions, such as the number of guesses. It is more stringent and belongs to the category of online targeted attacks as documented in [22; 23; 24] Password-generation model.  
Regarding previous password generation research, [9; 10; 11], in this paper, we highlight and analyze the ordered Markov enumerator (OMEN) model [9] and the PassGAN model [11], which are password-generation methods based on traditional Markov chains and a generative adversarial network (GAN) [25], respectively. The starting point of the OMEN model is that high-probability passwords are ranked in front of the password set to accelerate password cracking.  
The experiments show that the passwords generated by OMEN are arranged in the order of the combined probability of the character sequence. In addition, the passwords generated by the PassGAN model possess the distribution features of the original password set. Based on the existing research, we attempt to find a better password-generation method, e.g., to analyze the complementary advantages that can be better utilized.
# A Systematic Literature Review of Autonomous and Connected Vehicles in Traffic Management
## 1 Introduction
### Prior Research  
To the best of our knowledge, there do not seem to be many surveys and systematic literature reviews (SLRs) examining the various approaches autonomous vehicles use to control traffic at junctions. Even though different AV- and CV-related topics [11; 12; 13; 14; 15; 16; 17] have been the subject of several research studies, our study is distinct from those in terms of techniques, content, and research interests. Articles [15; 16; 17] summarized the methods used in traffic management systems to assess various traffic flow patterns and/or different types of crossings. Recently, a study document from 2022 primarily concerned with driverless vehicles' environmental effects was published [18]. Our research thoroughly evaluated the scientific literature, extending the focus beyond air pollution to include water, land, noise, and light pollution.  
Only a small fraction of traffic management strategies has been examined in the above research, emphasizing one particular traffic scenario (junction) and a set of constrained driving goals. There is still room for more research and understanding in suggesting or advising acceptable procedures. To guide future research directions, it is essential to deliver a detailed summary of the most recent journal articles, specifically on traffic management strategies used by AVs and CVs at all three major traffic conflicts (intersections, roundabouts, and interchanges). The study examines and contrasts how well the ways are reviewed on the outcomes of the assessment. First, the goals of the methods were defined and classified, such as increasing efficiency and safety. The study, then, evaluated how effectively various strategies accomplished a specific objective.
## 5 Experimental Results
### Experimental Results of Multi-Objective Optimization Scheduling
#### 5.2.1 Fixed Edible Oil Weight
Similarly, for [PERSON]'s algorithm, only Task 1 had an available oil pipeline path and was therefore assigned as the fifth task. At this point, there were temporarily no available pipeline resources. Consequently, the A* algorithm proceeded to execute oil transportation tasks for Tasks 8, 5, 2, 4, and 3 concurrently, while [PERSON]'s algorithm executed oil transportation tasks for Tasks 8, 5, 2, 3, and 1 simultaneously. The sixth scheduling iteration entered a standby state, with Tasks 1, 6, and 7 remaining for the A* algorithm, and Tasks 4, 6, and 7 remaining for [PERSON]'s algorithm. Both algorithms awaited the completion of prior tasks and the release of pipeline resources. Upon resource availability, the path planning algorithms recalculated accessible routes and executed new oil transportation tasks accordingly. A subsequent task analysis indicated that none of the remaining tasks met the criteria to form a Pareto front. Therefore, the scheduling order from the sixth to the eighth iterations proceeded as follows: for the A*  
Figure 10: Scheduling process with fixed edible oil weight: (**a**) 1 st iteration; (**b**) 2 nd iteration; (**c**) 3 rd iteration; (**d**) 4 th iteration; (**e**) 5 th iteration; (**f**) 6 th to 8 th iterations. algorithm, Tasks 6, 1, and 7; for [PERSON]'s algorithm, Tasks 6, 4, and 7, as illustrated in Figure 10f. As shown in Figure 11, a comparative summary of the execution sequences and time data between the multi-objective optimization scheduling method and the FIFO method for edible oil transportation tasks is presented. As shown in Figure 11a, the A* algorithm scheduled Task 6 immediately after Task 5, Task 1 after Task 4, and Task 7 after Task 3. The estimated total completion time was 124.65 min. The [PERSON] algorithm results are shown in Figure 11b, where Task 6 followed Task 5, Task 3 began after Task 4, and Task 7 was executed immediately after Task 1. The estimated total task completion time was 132.86 min. By contrast, Figure 11c illustrates the task execution sequence and completion time using the FIFO scheduling method, with the total completion time extended to 220.67 min. This comparison demonstrates that the multi-objective optimization approach offers higher efficiency and better performance in task parallelism and resource allocation.
## 4 Experiments and Results
### Dataset Preparation  
The simulation for each IEEE 802.11 protocol and LTE standard has been run independently to generate signals and save the radio spectrogram images for \(T(s_{nm})\) and \(R(s_{nm})\), under various channel scenarios and SNR values. The radio spectrogram images correspond to as many preambles for each IEEE 802.11 protocol and LTE subframes.  
Each spectrogram represents the _Short-Time Fourier Transform_ (STFT) [57] of the raw time series \(s_{nm}(t)\) corresponding to the signal of a preamble. Spectrograms were selected as signal representations because of their ability to capture the behavior of multiple received signals. Each spectrogram image represents the STFT for transmitted or received signal as a function of the (discretized) time \(\tau\) and frequency \(\omega\). Each spectrogram image consists of \(64\times 3782\) pixels where 3782 time intervals are captured, and for each interval, 64 frequencies are computed. To reduce the high dimensionality in the spectrograms, they were binarized according to Equation (2).
# Interplanetary Student Nanospacecraft: Development of the LEO Demonstrator ESTCube-2
## 2 ESTCube-2 Mission
### Satellite Design
#### 2.3.6 COTS Subsystems  
Despite the mission requirements to create a deep-space platform in-house, two commercial systems have been acquired. They will assist in the payload experiments.  
The GOMSpace NanoProp CGP3 _cold-gas propulsion (CGP)_ system is used for the IPB tether deployment and maintenance. While it is possible to deploy the tether without additional propulsion in LEO, it will be required for E-sail deployment in deep space; therefore, for testing purposes, it will be used in LEO as well.  
Figure 13: Subsystem software architecture.  
Compressed butane gas is used as the propellant. At operational conditions, the system would generate 1 mN thrust, and two nozzles at total capacity can run continuously for ten orbits (15 hours).  
The system will be used only to desaturate the RWs, because of its suboptimal design (see Section 3.3).  
A _high-speed communications system (HSCOM)_ is required to download the spectral images made by the EOP. An S-band high-speed communications system HiSPiCO from IQ Spacecom is capable of up to 1 Mbps downlink speed. As our partner, who planned to use their high-speed communications system on board ESTCube-2, failed to deliver the system, our team was forced to find an alternative [18]. The HiSPiCO module size has allowed us to integrate the system easily because its form factor is not based on the CubeSat standard, and its dimensions fit into the envelope of the planned communications system. The system's compact dimensions have allowed it to be located near the S-band patch antenna.  
Even though most of the system is a COTS product, the S-band antenna was developed and tested in-house (see Figure 14). The antenna's size was limited by the dimensions of the side of the satellite and the PB electron gun opening (see Figure 14's RBF lid). The antenna is a circularly polarized patch-type antenna whose resonant frequency is 2.4 GHz. It has an \(\sim\)8 dBi gain, and its half-power beam width is \(\sim\)70\({}^{\circ}\).  
The link budget of the HSCOM is visible in Table A7 in Appendix C.
# Air Pollution: Sensitive Detection of PM2.5 and PM10 Concentration Using Hyperspectral Imaging
## 4 Results
### Estimated Results of PM2.5 and PM10  
The comparison of the estimated PM2.5 and PM10 concentrations in the visible light band with the data measured by the measuring station is shown in Figure 5 (see Supplementary Materials Figures S4 and S5 for the estimated results of PM2.5 and PM10 in the FIR and NIR spectra respectively). The comparison demonstrates that the visible light band had many concentration values that were evenly scattered around the concentration value of the measuring station, but some deviations remained between the estimated data and the data of the measuring station, which may be attributed to the influence of the environment or sunlight. The above results indicate that the visible light and near-infrared bands are affected by PM2.5 and PM10, while the far-infrared band is unaffected. To understand the calculation error of visible light and near infrared bands, the concentration of suspended particles was measured, and the calculated concentrations were analyzed using the root mean square error. The result is shown in Figure 6, which shows that the error in the visible light band was lower than that in the near infrared band. There are various active methods to reflect a beam from air-suspended solids, such as laser, sonar, and radar; however, in these methods, the acquisition price is high, and it is difficult to interpret the returning output signal independently [43]. In contrast, the passive method discussed in this study is easy to interpret, has no interference problems with the environment, and is cheaper to build.  
Figure 4: The regression analysis results of visible light band spectrum data and particulate matter: (**a**) PM2.5, (**b**) PM10.0.
# Aircraft Structural Stress Prediction Based on Multilayer Perceptron Neural Network
## 3 Dataset Creation and Model Variable Definition
### Model Variables  
In structural analysis, node coordinates serve as fundamental information for describing the geometry of a structure. The stress distribution within the structure is influenced by both its geometry and the external loads applied. Consequently, the positional information of the nodes directly impacts the stress distribution. In complex aeronautical structures, abrupt changes in geometry can lead to stress concentrations. In cruise conditions, the stress levels in the wing structure are predominantly influenced by aerodynamic loads, with the angle of attack and inflow velocity being critical parameters affecting the distribution of these loads. To enhance the accuracy of stress magnitude and distribution predictions, node coordinates can be integrated with the angle of attack and inflow velocity. The expressions for the eigenvectors are provided below, and the ranges of the parameters in these eigenvectors are detailed in Table 3.  
\[\alpha_{i}=[x_{i},y_{i},a_{i},v_{i}] \tag{1}\]  
In the equation, \(x_{i}\) and \(y_{i}\) represent the node position information, while \(a_{i}\) and \(v_{i}\) denote the angle of attack and flow velocity, respectively. Thus, the stress prediction model can be represented as a function:  
\[\overline{\textit{von\ miss}}=f(\alpha_{i},\theta)=W\alpha_{i}+b \tag{2}\]  
In the equation, \(\overline{\textit{von\ miss}}\) represents the predicted von Mises stress, \(f\) is the model function, \(\theta\) denotes the model parameters, \(W\) indicates the weight matrix learned by the model, and \(b\) is the bias matrix.
## 5 Interpretation and Discussion
### Mineral Matter in the Coal  
Common minerals in the coal samples are calcium-iron minerals such as calcite, ankerite, and siderite and pyrite, and also silica in the form of quartz and silicate of clay minerals. The latter minerals are frequently derived from groundwater passing through cracks and fissures in the hardening or hardened coal seams. When saturated, the ions and elements in the groundwater precipitate out, resulting in the formation of mineral, which often takes the shape of the cracks or fissures in which they are precipitated/deposited. The minerals could have formed by water passing through the xylem phloem or cortical cells (supportive and \"feeding\" cellular tissue) in the tree trunks and also led to the precipitation of minerals in those cellular cavities, thereby locking the mineral into those cell structures.  
The observed mineral matter ranges from 21 to 26 vol.% in the top samples (Figure 9). The results show that sample MM-1 (Middle middle seam) has the lowest silica mineral percentage of about 2.8 vol.%. Silicate minerals (which can be quartz or clay) are the most abundant and represent an average of approximately 21.7 vol.% of the total mineral matter associated with coal in the upper-middle seam. Mineral genesis is complex; they may have a detrital origin or may be a secondary product from aqueous solutions [31]. Additionally, the top samples have a high amount of epigenetic carbonate minerals, which are minerals deposited after peat formation at any time during coalification. Carbonate minerals in the samples appear to have shattered the vitrinites with the lowest effect in sample BM-2 (Bottom middle seam) and highest with 8.6 vol.% in sample TM-1 (Top middle seam). It is supposed that the secondary carbonate minerals precipitate from magma-derived fluids percolating through the coal following the emplacement of the intrusion. The textures and distribution of carbonate minerals suggest that the temperatures and pressures of the fluids may be just as significant in the developing fractures near dykes, particularly those that have multiple phases of geometries [27]. The pyrite content is also high in sample TM-1, with a concentration of about 5.4 vol.%, which is a contrast to the other samples where pyrite content is less than 1 vol.% or completely absent/not observed under the microscope. Sulfur is commonly present in the organic fraction of the coal, whereas inorganic or mineral  
Figure 8: Photomicrograph of coal showing: (**A**) semi-fusinite (yellow arrow) with pyrite (red arrow); (**B**) vitrinite macroal with pyrite minerals; (**C**) fusinite (yellow arrow); and (**D**) semi-fusinite macroal.  
sulfur is in the form of pyrite. Pyrite may be present as a primary as authigenic minerals in coal and black shale or as secondary pyrite as a result of the reduction of sulfur in marine waters (pyrite framboids are mostly identified in marine anoxic sediments).
# Artificial Intelligence Applications in Smart Healthcare: A Survey
## 4 Applications
### Virtual Health Assistant  
The virtual health assistant is an online platform designed to streamline disease identification for users by providing access to a centralized clinical database in an interactive format [56]. Leveraging NLP technology, it efficiently handles large volumes of user-generated natural language data, including text and audio [57]. For pattern recognition and classification problems, the SVM classifier performs best, and XGBoost is used for feature extraction to develop new feature combinations for training the SVM model [58].  
The assistant engages intuitively with users through interfaces, responding to queries, sharing health information, and offering self-care recommendations [59]. It is not merely an information provider but a comprehensive health management tool. For example, users can input symptoms via voice or text, and the virtual assistant will use NLP technology to parse these inputs and retrieve relevant information from the clinical database. It then employs the optimized SVM model to identify possible diseases and provide suggested diagnostic results and care measures [60]. Additionally, the virtual health assistant shows great potential in managing chronic diseases. For instance, for diabetic patients, it can track blood glucose levels, diet, and physical activity, offering personalized advice to optimize health management. By integrating with wearable devices like smartwatches and glucose monitors, the virtual health assistant can receive real-time health data from users, providing dynamic feedback and recommendations. Real-time interaction and continuous monitoring help to improve patient adherence to treatment and overall health outcomes [61]. The virtual health assistant also plays a crucial role in mental health support. For users with anxiety or depression symptoms, the virtual assistant can guide cognitive behavioral therapy (CBT) techniques to help manage their emotional state. By analyzing users' emotions and behavior patterns, the virtual assistant can offer tailored advice to help users develop healthier psychological habits [62].
# Enhancing Machine Learning Models Through PCA, SMOTE-ENN, and Stochastic Weighted Averaging
## 2 Materials and Methods
### Methodology  
Figure 1 outlines a generalizable methodology that can be applied to any classification problem, including survival prediction. While the methodology was demonstrated on the Titanic dataset, the individual steps--data preprocessing, feature engineering, resampling, dimensionality reduction, model selection, and optimization--can be adapted to other datasets with similar characteristics.  
The proposed 'Structure of Survival Prediction Research System' study consists of six major steps: (1) Feature Engineering and PCA, (2) Modeling Pipeline, (3) Resampling, (4) Optimization of hyperparameters, (5) Ensemble and SWA, and (6) Performance Evaluation and Comparison. These steps are summarized in Figure 1.  
The first part of this study involves conducting data statistics for independent variables and preprocessing. The validity test of each variable was performed using exploratory data analysis (EDA). The data were divided into survival groups by removing redundant information or outliers, and additional independent variables were generated based on the distribution of features, such as age, fare, and embarkation location. This resulted in the creation of 17 variables.  
Next, dimensionality reduction techniques using PCA were employed to select the optimal model. Third, imbalanced data were addressed using the best-performing of the six resampling methods. Fourth, a comprehensive comparison of each model using grid search, random search, and Bayesian optimization is essential for evaluating their performance and selecting the best hyperparameters. Fifth, the models were compared by combining Stacking and Voting ensemble techniques and Stochastic Weighted Averaging (SWA) to perform the evaluation, and finally, the performance evaluation was verified.  
Feature engineering involves the extraction of key features relevant to the problem at hand. In the case of the Titanic dataset, domain knowledge was used to engineer features such as passenger titles, family size, and deck information. However, this step is adaptable across datasets. For other datasets, domain-specific features can be engineered based on available information. While specific features like 'Title' were used in the Titanic case, in other datasets, features that capture important domain-specific relationships would be  
Figure 1: Structure of a survival prediction research system.  
engineered. Thus, the methodology is flexible enough to be applied to other datasets, with the feature engineering step being tailored to the characteristics of the new dataset. This flexibility ensures generalizability across different applications.