esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation and Result of MANET-ESO in IoT Techniques
### Results and Analysis in Terms of Node Alive Node vs. Number of Rounds  
When using IoT approaches, the node alive time of MANET-ESO is compared with existing algorithms such as EECHS-ISSA DE [16] and Butterfly and Ant [17] method. Figure 2 illustrates the testing of the proposed methodology and the existing algorithm using 50 sensor nodes, 100 MANET nodes and one Gateway node in the ns-3 simulator. This shows the performance of the number alive node is better than the existing algorithm in a different number of rounds. The WSN constructed with MANET nodes using 10% of clusters was tested for 1800 rounds without dead nodes and produced better results. However, compared to other existing algorithms, the MANET-ESO protocol achieved high alive nodes up to 4500 rounds.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Field Area & \(1500\times 1500\) m\({}^{2}\) \\ Number of sensors nodes & 50 \\ Number of Mobile nodes & 100 \\ Percentage of cluster head & 10\% \\ Radio Propagation model & Free space \\ Mini. And Max Position of 5G BS & [0, 200] \\ Antenna Model & Omni directional Antenna \\ Initial Energy of the sensor node & 0.5 Jules \\ Packet Size & 500–1500 bits \\ MAC & 802.11 \\ Packet Scheduling Model & Random way \\ Mobility model & Random poisson Traffic Model \\ Traffic generation Model & 1.25 Jules \\ Initial Energy of the mobile adhoc node & Frequency and Time Division Model \\ Wireless Channel Allocation Models & 10, 15, 20, 25 and 30 m/s (Variable) \\ Node’s mobility & 2.4–5 GHz \\ IoT Frequency Band & MANET-ESO \\ Proposed Method & AODV \\ Adhoc Routing Protocol & Network Simulator (NS-3) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation Parameters.  
In the EECHS-ISSA DE method, all the nodes are connected via the clustering technique, and the cluster head selection process requires more energy to discover the route. Hence live nodes are limited after 1800 rounds.  
In the Butterfly and Ant method, the link to the BS station is discovered by two different algorithms. Firstly, it forms clusters and discovered routes. Both are utilizing less energy compared with EECHS-ISSA DE methods. However, the performance is not better than the proposed work. In the proposed work, the energy utilization of the network is reduced by the clustering algorithm. The cluster head is controlling all nodes in the cluster. So apart from cluster head remaining node energy utilization is less and increasing the alive in the network. Figure 2 shows the proposed algorithm performance, as the alive node is an average of 67.7% higher than EECHS-ISSA DE and an average of 10.7% higher than the Butterfly and Ant Method.
## 3 Methodology
### Feature Extraction  
The feature extraction process is a crucial step in our methodology, enabling the transformation of raw image data into a structured format suitable for machine learning models. We utilize a range of algorithms, namely Histogram of Oriented Gradients (HOG) [30], Local Binary Pattern (LBP) [31], Gabor Filters [32], and Canny Edge Detection [33]. This diversified strategy aims to capture a comprehensive set of features, taking advantage of each algorithm's unique characteristics. The Local Binary Pattern (LBP) encodes the local structure of an image by comparing the intensity of each pixel with its neighboring pixels, providing a robust representation of texture patterns. Gabor Filters analyze an image's spatial features by capturing the occurrences of pixel patterns with particular intensity values at specific orientations and scales, providing valuable information about the image's texture and structure. The Histogram of Oriented Gradients (HOG) divides an image into small regions, computes the gradient and orientation information for each pixel, and then creates a histogram of gradient orientations, capturing the underlying structure of objects. The Canny Edge Detection employs a multi-stage algorithm involving gradient computation, non-maximum suppression, and edge tracking with hysteresis to accurately identify and trace edges in an image.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**1522 Thermogram Images (56 Subject: 37 Sick IDs-19 Healthy IDs)**} \\ \hline \multicolumn{6}{c}{Desenvolvimento da Metodologia} & \multicolumn{3}{c}{12 Novos Casos de Testes} \\ \hline \multirow{2}{*}{Total} & Dentes & Saud à veis & \multirow{2}{*}{Total} & Dentes & Saud à veis \\  & (Sick) & (Healthy) & & (Sick) & (Healthy) \\ \hline
1282 & 640 & 642 & 240 & 120 & 120 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details about dataset classes.  
Figure 3: Process of gathering IDs and division into train–test sets in DMR-IR database.  
It is important to note that the parameters associated with each algorithm are not fixed but are subject to optimization. This flexible approach recognizes the complexity of the dataset, allowing for the fine-tuning of parameters to enhance the performance of the feature extraction process.  
Depending on the extractor, the feature vectors are subsequently extracted by dividing all values into N histogram bins, translating each image in the dataset to a feature vector of N length, and a feature length of 128 is constructed where each feature is a bin of values representing the frequency or count of a specific pattern in the image. Figure 4 offers sample extractions from each method and their captured data distribution, visually depicting their differences.  
The most notable aspect in these samples is that each extractor converted the images into unique sets of feature distributions as well, where the distinctive patterns of classification became varied. This variety was intentional to determine whether the features would align visually when different extractors undergo optimization through metaheuristic algorithms.
## 4 Nanodelivery Platform for Vaccine
### Bio-Nanocarriers
#### 4.1.3 Virus (Virus-like Particle)/Bacterium-Based Nanocarriers  
Immunogenic viruses or recombinant viruses as cancer vaccine vectors can stimulate tumor antigens to generate an immune response. Adenoviruses (Ads) are one of the most well-studied and promising viral vectors [118]. [PERSON] et al. demonstrated that the gorilla Ad vaccine targeting tumor neoepitopes combined with \(\alpha\)PD-1 in a mouse model could increase neoantigen-specific CD8\({}^{+}\) T cells, improving immunogenicity and antitumor efficacy while potentially alleviating resistance to the PD-1 blockade [119].  
Virus-like particles (VLPs) are hollow particles containing one or multiple viral structural proteins. VLPs have virus-like structures and properties, which can be effectively absorbed by APCs to induce immune responses; relatively, they are safer due to the absence of infectious genetic material, and they have the ability for autonomous replication [120]. Virus-like particles (VLP) extracted from phage P22 can be used as carriers of neoantigen peptides to design personalized therapeutic vaccines. Such vaccines can induce robust specific cellular and humoral immune responses and immune memory in vivo. Interestingly, this delivery platform can overcome the immunosuppressive microenvironment of the tumors [95]. VLPs have the advantages of clear composition, excellent biocompatibility, fixed spatial structure, and clinical translational potential.  
Bacteria as a means of vaccine delivery has also attracted the attention of many researchers. \(\Delta\)ppGpp S. typhimurium has been found to highly target tumors, induce immune cell infiltration, and promote tumor suppression [121]. [PERSON] et al. then used Salmonella typhimurium, which has high tumor-infiltrating abilities, to piggyback on a neoantigen peptide that has high tumor-targeting abilities. A single injection revealed a large increase in CD4\({}^{+}\)/8\({}^{+}\) T cells in mice, inhibition of tumor growth, and prolongation of survival [122].  
At this stage, tumor immunotherapy with exosomes, proteins, or viruses as carriers shows significant biological advantages: good biocompatibility, low cytotoxicity, and alleviation of immune tolerance, and it is worthwhile to further explore the development of biological nanocarriers as a vaccine platform for tumor immunotherapy.
# Which Features Are More Correlated to Illuminant Estimation: A Composite Substitute
###### Abstract  
Computational color constancy (CCC) is to endow computers or cameras with the capability to remove the color bias effect caused by different scene illuminations. The first procedure of CCC is illuminant estimation, i.e., to calculate the illuminant color for a given image scene. Recently, some methods directly mapping image features to illuminant estimation provide an effective and robust solution for this issue. Nevertheless, due to diverse image features, it is uncertain to select which features to model illuminant color. In this research, a series of artificial features weaved into a mapping-based illuminant estimation framework is extensively investigated. This framework employs a multi-model structure and integrates the functions of kernel-based fuzzy c-means (KFCM) clustering, non-negative least square regression (NLSR), and fuzzy weighting. By comparing the resulting performance of different features, the features more correlated to illuminant estimation are found in the candidate feature set. Furthermore, the composite features are designed to achieve the outstanding performances of illuminant estimation. Extensive experiments are performed on typical benchmark datasets and the effectiveness of the proposed method has been validated. The proposed method makes illuminant estimation an explicit transformation of suitable image features with regressed and fuzzy weights, which has significant potential for both competing performances and fast implementation against state-of-the-art methods.  
computer color constancy (CCC); illuminant estimation; kernel-based fuzzy c-means clustering (KFCM); non-negative least square regression (NLSR); image enhancement +
Footnote †: journal: Applied Sciences  
1 Faculty of Light Industry, Qlui University of Technology (Shandong Academy of Sciences), Jinan 250353, China: [EMAIL_ADDRESS]  
22 Juanary 2022
# Wireless Remote Control for Underwater Vehicles
## 4 Scenario Description and Simulation Setup  
The simulated scenario, depicted in Figure 4, included an underwater hybrid vehicle (HROV) able to operate both as an ROV and an AUV, a control station used to pilot the vehicle, and three relays used to extend the communication range between the vehicle and the control station. Both the control station and HROV were equipped with MF, HF acoustic, and optical modems, while the acoustic relays were equipped only with MF acoustic modems.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **SRM Mode** & **MRM mode** & **LRM Mode** \\ \hline Joystick position control & ✓ & ✗ & ✗ \\ Way-point position control & ✓ & ✓ & ✓ \\ Video High Quality & ✓ & ✗ & ✗ \\ Video Very Low Quality & ✓ & ✓ & ✗ \\ Gripper remote control & ✓ & ✗ & ✗ \\ Transmit HD image & ✓ & ✗ & ✗ \\ Take and store HD image & ✓ & ✓ & ✓ \\ Light intensity control & ✓ & ✓ & ✓ \\ Status Monitoring & ✓ & ✓ & ✓ \\ LBL positioning & ✓ & ✓ & ✓ \\ Optical modem & ✓ & ✗ & ✗ \\ HF acoustic modem & ✗ & ✓ & ✗ \\ MF LBL acoustic modem & ✓ & ✓ & ✓ \\ Max range & 10 s of meters & 100 s of meters & 10 km \\ ROV -> control station & 2 Mbps, low latency, no jitter & 15 kbps & 76 bps \\ Control station -> ROV & 4.5 kbps, low latency & 160 bps & 50 bps \\ \hline \hline \end{tabular}
\end{table}
Table 4: System modes comparison.  
All simulations were performed with the DESERT Underwater [117] simulation framework. As presented in Section 2.4, we selected the BlueComm 200 optical modem, Subnero WNC, and the EvoLogics S2C HS communication devices; therefore, the simulations were configured according to the modem specifications, as will be presented later in this section and is summarized in Table 5.
## 6 Results
### Execution History and Performance Results
#### 6.3.2 Execution History for the 25-Location Problem  
Figure 5a illustrates the fitness evolution over generations, showing that GAAM-TS achieves the highest fitness values and converges more rapidly than GA-AM and Standard GA, confirming its superior optimization capability. Figure 5b presents the diversity evolution, demonstrating that GAAM-TS maintains a higher diversity level throughout the generations. This prevents premature convergence and allows for a broader exploration of the solution space, contributing to improved optimization. Similarly, Figure 5c tracks  
Figure 5: Execution history analysis for the 25-instance problem. (**a**) Fitness evolution shows GAAM-TS achieving superior optimization. (**b**) Diversity evolution demonstrates GAAM-TS maintaining higher diversity and avoiding premature convergence. (**c**) Distance evolution highlights GAAM-TS achieving the most significant travel distance reduction. (**d**) Time evolution confirms GAAM-TS minimizing delivery time more effectively than the other variants.  
the evolution of total travel distance. GAAM-TS achieves the most substantial reduction in travel distance, outperforming the other variants in optimizing the sales route. This confirms its effectiveness in minimizing unnecessary travel. Lastly, Figure 5d shows the evolution of delivery time across generations. In line with the distance optimization trend, GAAM-TS significantly reduces the total travel time, ensuring faster and more efficient delivery routes compared to GA-AM and Standard GA.  
The evolution plots confirm that GAAM-TS not only achieves the best final performance but also maintains steady progress across generations, which aligns with the improvements observed in route quality.
# Methodology for Simulating 5G and GNSS High-Accuracy Positioning
## 3 Characterization of the Observables
### cmWave
Since this section focuses on cmWave scenarios, the UMi, UMa and RMa scenarios of the WINNER II models are considered for the sake of simplicity. These models are based on tabulated large-scale parameters, such as the delay spread, for each propagation scenario, and the probability of LoS is defined as function of the distance between mobile and BS. Considering these models, our approach is to characterize the ranging error or estimation bias, i.e., \(b(\hat{\tau})=\text{E}\left[\hat{\tau}\right]-\tau\), independently of the distance between mobile and BS. This implies to compute the cumulative density function (CDF) of the ranging error for multipath channel realizations with LoS and for realizations with NLoS, considering the threshold-based estimator for a certain signal bandwidth and propagation scenario. Then, the LoS conditions are computed according to the distance between mobile and BSs \(d_{\text{BS}}\), as in [29], and the corresponding CDF for LoS or NLoS conditions is randomly evaluated to generate ranging errors. These errors are finally used with the distance to the BS to compute the cmWave ToA observables, i.e., \(\hat{d}=d_{\text{BS}}+c\cdot b(\tau)\), where \(c\) is the speed of light. This approach reduces the computational burden of the generation of observables, while they are representative of the standard channel models. As an example, let us consider an OFDM cmWave signal, i.e., at a carrier frequency of 2 GHz, with a signal bandwidth of 90 MHz, which is formed by 6000 contiguous pilot subcarriers and a subcarrier spacing of 15 kHz. The range of the time-delay estimation is set to \(\tau\in\left[-20,100\right]/c\) and the likelihood threshold is defined as \(\Lambda_{\text{thr}}\simeq\max\left(\Lambda\left(\tau\right)\right)/4\). The resulting CDF of the threshold-based ranging errors is shown in Figure 6 for LoS and NLoS conditions over UMi, UMa and RMa scenarios. As it can be seen, the NLoS ranging bias is above 10 m for the 95% of the cases, while the LoS ranging error is below one meter.
# A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images
## 3 Proposed Methodology
### Preprocessing  
The MRI images produced from different MRI machines could have a bias field or intensity inhomogeneity, which is an artifact and should be corrected as it affects the segmentation results [52]. In the preprocessing step, we applied the level set method for bias field correction [53]. To obtain the enhanced image, a median filter was applied. The median filter is considered better as compared to linear filtering for eradicating noise [54].
## 2 Materials and Methods
### The Vibration Attenuation Concepts  
Material damping of the rotors made of steel or other metal materials is very low, and so is damping produced in rolling element bearings. Therefore, to reduce amplitude of the lateral vibration of rotors, especially if they operate in the vicinity of their critical speed, the damping must be increased. This is offered by inserting rolling element bearings into squeeze film dampers.  
The first possibility of reducing the vibration amplitude of rotors passing through the area of the critical speed consists in increasing their acceleration or deceleration. This manipulation requires the rotors to be driven by motors of large power. In addition, the inertia effects cause additional loading of the individual components of rotating machines, which results at reducing their service life.  
The second possibility is offered by controlling the damping effect in the rotor supports. Higher damping shifts the resonance peak to higher angular velocities, as evident from the frequency responses depicted in Figure 1. To minimize the vibration amplitude of accelerating or decelerating rotors, damping in the support elements should be maximum for low velocities and as low as possible for higher angular speeds. Changing the damping at the moment when the angular velocity corresponds to the point of intersection of the frequency responses related to the low and high damping leads to the minimum vibration amplitude during running the rotor through the area of the critical speed.  
The experience and analyses performed in the past show that sudden change in the damping effect induces a transient component of the rotor lateral vibration of large amplitude. This implies that the change in damping cannot be sudden, but it must be distributed in some time interval. The parameters that influence the magnitude of the rotor vibration in the resonance area are: (i) the moment of time when the change in the damping effect begins, (ii) the duration of the decrease/increase in the damping magnitude, (iii) the phase shift of the vibration at the time when the rotor passes the critical speed, and (iv) the rate of change in the rotor angular velocity.  
The rate of acceleration or deceleration often depends either on the design of the rotating machine or on the requirements put on realization of the technological process. The value of the phase shift has a random character. Nevertheless, if the change in the speed starts at the point where the lateral displacement and velocity are the same as in the optimum case, the influence of this factor can be eliminated. The optimum values of the moment of starting and the period of duration of changing the damping effect can be determined by solving the optimization problem.
# Enhancing Malware Detection via RGB Assembly Visualization and Hybrid Deep Learning Models
## 4 Methodology
### Step 1: Data Preprocessing
2. Transition Matrix Computation and Image Generation  
The conversion process involves calculating transition probability matrices at three distinct levels--byte, opcode, and quality--by analyzing sequential character patterns within the assembly code. Each matrix captures the statistical relationships between consecutive elements, with byte-level transitions reflecting raw data patterns, opcode-level transitions revealing instruction sequences, and quality-level transitions encoding structural features of the code. These matrices are then normalized to ensure consistent scaling across different samples, enabling fair comparison and analysis. The normalized matrices are subsequently mapped to the RGB color channels of an image, where the byte, opcode, and quality transitions are represented in the red, green, and blue channels, respectively. This transformation effectively converts the statistical properties of the assembly code into a structured visual representation, preserving both local and global patterns in a format suitable for deep learning-based analysis. The resulting image encapsulates the semantic and syntactic relationships within the original code, facilitating the detection of malware-specific features through computer vision techniques. This approach leverages the discriminative power of color-encoded structural information while maintaining computational efficiency for large-scale datasets. 2. Image Storage and Parallel Processing  
The system stores the generated malware images in 8-bit RGB PNG format, which provides an optimal balance between image quality and file size efficiency. This standardized format ensures compatibility with common computer vision and deep learning frameworks while preserving the discriminative features encoded in the three-channel visualization. To handle large-scale datasets efficiently, the framework implements parallel processing capabilities that distribute the computational workload across multiple CPU cores. This parallel  
Figure 5: Asm2 Image Transformation. execution architecture significantly improves processing throughput, enabling the timely analysis of extensive malware collections. The system maintains organized storage by preserving the original directory structure and file naming conventions while incorporating progress tracking and error-handling mechanisms to ensure reliable operation during batch processing. The combination of optimized image compression and parallel computation makes this approach practical for real-world malware analysis scenarios where processing thousands of samples is required. Structured Output Organization  
The resulting RGB images are stored in a user-defined directory while preserving the original class-based folder hierarchy. This facilitates organized storage, efficient retrieval, and streamlined use in downstream tasks such as classification or clustering.