esa-sceva/satcom-chunk-collection huggingface dataset:
# Empirical Perturbation Analysis of Two Adversarial Attacks: Black Box versus White Box
## 4 Adversarial Noise Visualization and Frequency Analysis
### Adversarial Noise Visualization  
The visualization of the noise that EA\({}^{\text{target},\mathcal{C}_{k}}\) and BIM add to \(\mathcal{A}_{q}^{p}\) to create the 0.999-strong adversarial images \(\mathcal{D}_{k}^{EA}(\mathcal{A}_{q}^{p})\) and \(\mathcal{D}_{k}^{BIM}(\mathcal{A}_{q}^{p})\) is performed in two steps. First, the difference \(\mathcal{D}_{k}^{\text{attk}}(\mathcal{A}_{q}^{p})-\mathcal{A}_{q}^{p}\) between each adversarial image and its ancestor is computed for each RGB channel. Second, a histogram of the adversarial noise is displayed. This leads to the measurement of the magnitude of each pixel modification. An example, typical of the general behavior regardless of the channel, is illustrated in Figure 2, showing the noise (the fact that the dominating colors of the noise representation displayed in Figure 2 are green, yellow, and purple stems from the 'viridis' setting in Python's matplotlib library, which could be changed at will, but still, the scale gives the amplitude of the noise per pixel in the range \([-\epsilon,\epsilon]=[-0.03,0.03]\) and hence justifies the position of the observed colors) and histogram of the perturbations added to the red channel of \(\mathcal{A}_{5}^{4}\) to tool \(C_{6}\) with the EA and BIM.  
Recall that both attacks perform pixel perturbations with a maximum perturbation magnitude of \(\epsilon=0.03\) (see Section 2.4). However, with BIM, the smaller magnitudes dominate the histogram and the adversarial noise is closer to a uniform distribution with the EA. Another difference is that, whereas with BIM, all pixels are modified, a considerable number of pixels (9.3% on average) are not modified at all with the EA. Overall, there is a  
Figure 2: Display of the noise and histogram of the perturbations added by the EA (**left** pair) and by BIM (**right** pair) to the red channel of \(\mathcal{A}_{5}^{4}\) to fool \(\mathcal{C}_{6}\).  
larger variety of noise magnitudes with the EA than with BIM, which can also be observed visually in the image display of the noise.
# Research on Rapid Recognition of Moving Small Targets by Robotic Arms Based on Attention Mechanisms
## 4 Experimental Results and Discussion
### Ablation Experiment  
To test the effectiveness of the improved model proposed in this study, we conducted comparative ablation experiments. These experiments were performed on the Fasternet module with the introduced attention mechanism (abbreviated as AFN), the small object detection layer (abbreviated as SODL), and the SIOU loss function. Where x represents not enabled, \(\surd\) represents enabled. As can be seen from Table 7, by replacing the original c2f module with the improved attention module proposed in this study, adding the small object detection layer, and improving the loss function, the mAP @ 0.5 was effectively increased. Furthermore, by using the SIOU loss function, the limitations of CIOU in real small object detection scenarios were addressed, which had a certain enhancing effect on the detection performance of the model.
# Failure Identification Method and LMI-Based Control System for Small Spacecraft
###### Abstract  
This study aimed to design a linear matrix inequality (LMI)-based robust control system and a failure identification method for small spacecraft. The key feature of the proposed method is its capability to withstand failure in the actuation system by means of the observer and controller gain definition. An effective approach for withstanding failure is required due to the small capabilities of the momentum exchange device (MED) and the system's sensitivity to external perturbations. Passive fault-tolerant approaches can be included in the design process of the control algorithm. In particular, the main objective of this study was the design of an \(H_{\infty}\) controller that started from an LMI formulation and considered the parametric uncertainties and matched failure of the actuation system. In addition, a fault detection method based on sliding mode observers was proposed to include an active disturbance correction algorithm and improve the system robustness and performance during spacecraft stabilization. The closed-loop system was evaluated for different initial conditions, including attitude positions that are far from the desired condition. The effectiveness of the proposed approach was demonstrated using extensive simulations that considered a pyramidal actuation configuration and hardware constraints.  
robust controller; observers; failure rejection; spacecraft dynamics; attitude control; attitude stabilization
## 2 Materials and Methods
### Sample Collection, Preparation and Evaluation of Lipids and Inflammatory Markers  
Three blood samples were collected from the peripheral vein of the athletes (in fasting conditions): (1) the day before the race (baseline), (2) within 20 min after the end of the half-marathon (post) and (3) 24 h after the run (24 h, recovery period). Blood samples were immediately centrifuged at 2500 \(g\) for 10 min and stored at \(-80\)\({}^{\circ}\)C until assayed.  
Ceramides, SM and DAG were evaluated from 20 uL of plasma (upon being deproteinized using 200 uL of cold methanol) in 13 healthy Caucasian trained runners.  
An aliquot of 20 uL of a mix of internal standards SM (d18:1/17:0), ceramide (d18:1/17:0), (Avanti Polar Lipids, Alabaster, AL, USA); DAG (17:0/17:0) (Larodan Solna SE) was added to the sample before deproteinization. The extract was injected in high performance liquid chromatography (Agilent UHPLC 1290, Santa Clara, CA, USA), coupled with a quadrupole time-of-flight mass spectrometry QTOF (QTOF-MS, Agilent 6540, Santa Clara, CA, USA), equipped with electrospray ionization source (ESI). For liquid chromatography analysis, we used ZORBAX Eclipse Plus C18 2.1 \(\times\) 100 mm 1.8-micron column at 50 \({}^{\circ}\)C (Agilent, Santa Clara, CA, USA). The mobile phase A was made by water with 0.1% formic acid and the mobile phase B was made by isopropanol/acetonitrile (1:1, _v:v_) with 0.1% formic acid. Injection volume was 1 uL Lipids were measured in positive electrospray ionization and identified using an internal spectral library. The data were normalized by internal standard representative lipids present in the sample and the analysis of the peaks was performed with Agilent MassHunter software program [11]. DAG species founded in plasma were 32:0, 32:1, 34:1, 34:2, 36:3 and 36:4, while ceramide species evaluated in plasma sample were 18:1/16:0, 18:1/18:0, 18:1/25:0, 18:1/26:0, 18:1/22:0, 18:1/24:1, 18:1/24:0, 18:0/24:0. The metabocard for ceramide species was reported as supplementary material (supplemental data). Fractalkine CX3 CL1, VEGF, IL6 and TNF\(\alpha\) were analyzed in 25 uL of plasma by a specific assay (MILLIPLEX MAP Millipore corporation, Billerica, MA, USA) using an integrated multi-analyte detection platform (high-throughput technology MagPix system, Luminex xMAP technology) with combined analyst software (MILLIPLEX(r)) for the biomarkers quantification developing new curve fitting algorithms and optimizing mathematical methods to minimize fitting errors.
## 1 Theoretical Background
To fill this gap, this paper investigates the benefits of using an increasing amount of distance sensor coupling to improve the accuracy of distance estimation. Additionally, this paper investigates alternative ways of connecting sensors to the Arduino for excluding the multiplexor from the system. This paper validates this idea through distance measurement laboratory experiments in which a number of distance sensors are connected to microcontrollers. Furthermore, to solve the system's dependency on an attached computer, this paper uses low-cost data acquisition equipment based on a low-cost Linux computer (Raspberry Pi). Raspberry Pi is programmed to receive and manage the microcontroller outputs without any additional connected computer and gives wireless access to the data acquisition process. In addition, this paper provides the standard deviation and distribution functions of the sensors used in the experiments. Currently, there are many sensor fusion works in the literature [38; 39; 40; 41; 45]. What separates this paper from those works is how this paper uses the combination of identical sensors to increase the accuracy of low-cost sensors. Moreover, this paper presents the impact of a number of sensor combinations on the overall data accuracy for static distance sensors. For the first time in the literature, this paper presents a device consisting of 25 analog sensors and 50 digital sensors connected to two Arduino microcontrollers for estimating the distance of an object. Platforms for holding ultrasonic and ToF sensors have been printed using a 3D printer to make sure that all the various ranging sensors are the same distance from the object under study. This system uses a programmed Raspberry Pi that can save the outputs of the Arduninos in a text file. This Raspberry Pi serves as a low-cost data acquisition system that can easily connect to the Internet to control the distance device and access the saved estimations wirelessly. This study investigates the beneficial effects of combining the estimations of several similar sensors to improve the overall accuracy. This paper is organized as follows: First, Section 2 presents the methodology for making a distance measurement device including 25 of each type of sensors (L530 LX, VL53L1X, and HC-SR04). Section 3 describes the characteristics of the performed laboratory experiments and validates the benefits of combining sensors. Section 4 discusses the results of the performed experiments. Finally, Section 5 offers a conclusion.
## 2 Endophytes-Current State of the Art
### Endophytes as Inhabitants of Agricultural Plants
#### 2.2.1 Biotic and Abiotic Stresses Related to Crop Productivity
It was shown that two bacterial endophytes, _Pseudomonas fluorescens_ YsS6 and _Pseudomonas migulae_ R86, isolated from tomatoes, can provide protection and growth stimulation to plants at very high (185 mM) salt stress. These endophytic bacteria produce ACC deaminase, IAA and siderophores. Under salinity stress, plants inoculated with these endophytes showed significantly higher chlorophyll content, yield and lower sodium (Na) content compared to plants inoculated with endophytic strains not synthesizing ACC deaminase [65]. Meanwhile, among fungi, endophytic _Epi thloe_ sp. play an important role in improving salt tolerance of wild barley (_Hordeum brevissubultatum_) by modifying polyamine metabolism [66]. Environmental stress also includes trace element pollution (metals and metalloids) of soils, the presence of which results in disruption of numerous biochemical and physiological processes, including photosynthesis, cellular respiration, N metabolism and nutrient uptake [67]. Nevertheless, endophytic bacteria can mitigate the effects of toxic concentrations of trace elements. As most of the techniques used for soil remediation are very costly and detrimental to soil structure, methods using plants and associated microorganisms to lower levels of trace elements are being used [68; 69]. [PERSON] et al. [70] reported that two bacterial strains _Bradyrhizobium japonicum_ E109 and _Azospirillum brasilense_ Az39 effectively colonized As-polluted soil and accumulated it in cellular biomass, stimulating plant growth [70].
## 2 Materials and Methods
### Datasets and Preprocessing of Images  
The MRI scans were retrieved from the picture archiving and communication system. To convert the images for use by the training database, they were converted from the Digital Imaging and Communications in Medicine (DICOM) format to the Joint Photographic Experts Group (JPEG) format using a dedicated DICOM software (XTREK view, J-MAC SYSTEM Inc., Sapporo, Japan). The window width and level of the DICOM images were used to preset values in the DICOM tag. The DICOM images were converted to JPEG images with a size of \(512\times 512\) pixels. The size of the image was unified for inputting as the same size in the software because the middle sagittal images in this study were obtained by several different MRI scanners. JPEG files were loaded into the in-house MATLAB software program (The MathWorks, Inc., Natick, MA, USA). The software was used to draw regions of interest (ROIs) with sizes of \(64\times 64\) pixels and \(32\times 32\) pixels at the center of specific structures. The ROIs were drawn according to the anatomies of the root of the nose (Nose), inferior border of the pons (Pons), the AC, and the PC (Figure 1). The ROI data were outputted as a text file, which included the object name, coordinates, and size of each ROI. The dataset was divided into six subsets to complete the six-fold cross-validation. Two hundred images were included in each subset, with a total of five subsets (1000 images) used for training and the other five subsets used for validation (Figure 2). Data augmentation [21; 22] was performed involving 1000 images for the improvement of the training. The training images were applied as a data augmentation dataset to the image rotation, which involved angles from \(-30\) to \(30\) degrees in 3-degree steps (Figure 3).  
Figure 1: The software for training outlined ROIs over the noteworthy anatomical characteristics. The red circle is the center of the target anatomy, the yellow bounding boxes are the ROIs measuring \(64\times 64\) pixels, and the green bounding boxes are the ROIs measuring \(32\times 32\) pixels.  
Figure 2: A total of 1200 images were divided into six subsets to complete the six-fold cross-validation.
# Advanced Optimization Techniques for Federated Learning on Non-IID Data
## 2 Background
### Pre-Training  
Model pre-training in deep learning is a powerful technique that enhances performance and accelerates the convergence of neural networks. This process occurs prior to the actual training phase and helps in preparing the models by allowing them to gain a basic understanding of the data's features before they are applied to specific training data for further specialization. Pre-training is especially useful in applications such as federated learning, where available data may be limited or insufficient.  
According to research [39], pre-training can lead to significant improvements in neural network performance, enabling the models to discover features more effectively and adapt better to new, unexplored data. It has been found that pre-training can provide more efficient initialization of the network's weights, encouraging the acquisition of useful representations that facilitate the learning of complex data structures. In addition to performance improvements, pre-training reduces the risk of overfitting by increasing the robustness of the models, as they are exposed to a broader range of data.  
This technique has been shown to improve the resilience and accuracy of models in various challenges, such as adversarial perturbations. Pre-trained models on large and diverse datasets, such as ImageNet, have demonstrated significant improvements in both resistance to adversarial attacks and prediction accuracy compared to models trained from scratch [40].  
In the context of federated learning, model pre-training becomes even more valuable as it can significantly enhance the performance of FL models by providing a strong knowledge base that facilitates faster convergence and improved final model performance [5]. Specifically, it addresses some of the challenges related to the instability of federated training in scenarios with non-IID data across client devices. This helps the models require fewer training iterations to achieve comparable or superior performance, making FL more feasible and efficient even in environments with limited computational resources. Additionally, this approach reduces the need for extensive data communication between devices and the central server, lowering communication costs and enhancing data privacy protection.  
Model pre-training is proven to be a powerful technique for boosting deep learning in both conventional applications and federated learning. Its ability to improve model generalization, reduce overfitting, and accelerate training makes pre-training an essential process in the development of neural networks.
# A Mobile Solution for Enhancing Tourist Safety in Warm and Humid Destinations
## 5 Discussion
### The Impact of Weather and Safety Information on Tourist Safety and Risk Mitigation
Notably, the system achieved a perfect score of 100% in accuracy, indicating its ability to deliver precise and reliable readings without any errors or deviations. This level of accuracy is crucial for environmental monitoring, where even minor inaccuracies could lead to erroneous conclusions and misinformed decision-making. Equally impressive are the system's response times, ranging from 0.000 to 0.002 s, which sets a new standard for swift data processing. Such near-instantaneous response times are of paramount importance in environmental monitoring, as timely data acquisition is vital for understanding rapidly changing environmental conditions and detecting potential hazards promptly. The system's precision, also achieving a perfect score of 100%, showcases its consistency in producing identical results for repeated measurements. This level of precision fosters confidence in the system's reliability, particularly in critical applications where consistent and dependable data is essential for drawing accurate conclusions. It is evident from the comprehensive dataset that the system performs exceptionally well across diverse environmental factors. The ability to monitor temperature, humidity, weather conditions, vital signs, and health risks (heat stroke and dehydration) with such high accuracy and precision render this system an invaluable asset for various sectors, including public health, agriculture, and disaster management.
## 4 Beam Steerability and Beam Forming
### Impact of Fabrication Techniques  
Antennas need to be robust mechanically and should be efficient with wide bandwidth as well as having desirable radiation characteristics [65; 66; 67; 68; 69; 70; 71; 72] which might ensure high simulation accuracy [31]. Radiation pattern and gain of the antenna are required to be tested for distortion or degradation. In the light of the literature studies, here is the summarized comparison on the bases of advantages and disadvantages in terms of bandwidth, efficiency, and gain. A brief summery has been studied in the literature about how different fabrication techniques affect the performance of an antenna. A summary of different antenna performances along with fabrication techniques is presented in Table 2.
# Integrating Digital Twins of Engineering Labs into Multi-User Virtual Reality Environments
## 3 Workflow for the Development of a New Crawling Engine Experience
### Development of the Technological Solution
#### 3.2.3 Multi-User Environment  
The last stage consisted of the development of a multi-user environment in Unity using Photon, a platform specialized in real-time data synchronization for virtual environments. The laboratory engine was scanned using Polycam, generating a detailed 3D model that was integrated into the virtual environment.  
Interactive text boxes were designed within the environment to display engine signals in real time, using code developed to extract information directly from the Blynk server. This environment allows users to visualize relevant data and experiment immersively with the operating conditions of the engine.  
With this workflow, it was possible to implement a safer and more interactive educational environment, aligned with the technologies used in today's industry, as shown in the following images, in which the environment with real-time sensing can be appreciated. In Figure 6, the interface in Unity is shown, displaying its components and the result can be seen in Figure 7.  
Figure 6: Unity interface image.  
Figure 7: Image of the environment.  
The implemented code shown in Table 2 facilitates real-time data acquisition, processing, and visualization within the virtual environment. It utilizes API calls to retrieve sensor data stored on the Blynk server, which is then processed in Unity to update the corresponding real-time values in the VR environment. By employing UnityWebRequest, the system periodically fetches sensor readings, formats the data, and dynamically updates UI elements such as temperature, pressure, and RPM indicators. This ensures that users in the multi-user VR space can observe live changes in engine performance, fostering an interactive and immersive learning experience.
# Deep Learning in Financial Modeling: Predicting European Put Option Prices with Neural Networks
## 5 Discussion
### Limitations and Future Perspectives  
Despite its promising results, the proposed model has several limitations that must be addressed in future work. One significant limitation is its dependency on synthetic data. Since the training dataset was generated using the Black-Scholes analytical solution, the model's ability to generalize to real-world data remains uncertain. Real-world financial systems are influenced by exogenous factors such as economic announcements, transaction costs, and market crises, which are not represented in synthetic datasets. The exceptional accuracy observed in this study may decrease under volatile or unsimulated conditions.  
Another limitation is the model's sensitivity to extreme parameters. Slight losses in precision were observed for deep out-of-the-money options or very short maturities. This issue likely arises from the limited parameter distribution in the synthetic dataset, which did not fully encompass these edge cases. Addressing this limitation will require augmenting the dataset with additional scenarios to improve the model's robustness.  
To overcome these challenges, future research should focus on several key areas. First, the model's performance should be validated on historical market data to assess its robustness in noisy and dynamic environments. This step is critical for ensuring the model's applicability in real-world financial systems. Second, extending the network architecture to include physics-informed neural networks (PINNs) could enhance its ability to capture the underlying dynamics of options in non-standardized contexts. Similarly, exploring advanced architectures such as recurrent neural networks (RNNs) or Transformers may enable the model to incorporate temporal dependencies and better handle financial time series data. Finally, introducing realistic perturbations into the synthetic dataset, such as volatility jumps or rapid changes in interest rates, would further enhance the model's generalization capacity in unstable environments.  
In summary, while the proposed DNN provides a flexible and efficient framework for option pricing, its reliance on synthetic data and sensitivity to extreme scenarios highlight the need for further exploration. These findings emphasize the importance of integrating deep learning methods into financial modeling while acknowledging their current limitations. The next section will summarize the contributions of this work and outline its broader implications.