esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods of Atmospheric Density Inversion for Low Earth
### Product of Atmospheric Damping Coefficient and Effective Area  
As atmospheric molecules interact with each satellite panel, a drag effect is observed. The atmospheric drag force on each satellite panel is initially calculated, and the total drag force on all panels is superimposed. The atmospheric damping coefficient, \(C_{D}\), is calculated using the [PERSON] model [26], as detailed in the following formula.  
\[C_{D}*A=\left[\frac{P}{\sqrt{\pi}}+\gamma QZ+\frac{\gamma}{2}\frac{v_{out}}{v_{ at}}\left(\gamma\sqrt{\pi}Z+P\right)\right]*A \tag{2}\]  
The ratio \(S\) is equal to the thermodynamically best-distributed velocity of the atmospheric molecules. The symbol \(P\) is equal to \(\frac{1}{3\text{resp}(-\gamma^{2}*S^{2})}\), \(G\) represents a value of \(\frac{1}{2S^{2}}\), \(Q\) is equal to \(1+G\), and \(Z\) is equal to \(1+\text{erf}(\gamma*S)\). The symbol \(\gamma\) denotes the cosine of the angle normal to the incident atmospheric molecular flow and the satellite panel. The term \(v_{out}\) denotes the velocity of the atmospheric molecular flow after collision with the satellite panel, while \(v_{at}\) represents the atmospheric velocity in relative terms for the satellite [18].  
\[\frac{v_{out}}{v_{at}}=\sqrt{\frac{2}{3}\left[1+\alpha_{E}\left(\frac{T_{ wall}}{T_{in}}-1\right)\right]} \tag{3}\]  
The character \(T_{in}\) is defined as the molecular dynamics temperature of the incident atmosphere, represented by the equation \(\frac{\left(m_{a}*v_{at}^{2}\right)}{3R}\), where \(R\) can be defined as a constant equal to \(8314\,\text{J/mol}\). \(K\) is a thermodynamic constant, and \(m_{a}\) is equal to the average relative molecular mass of the atmosphere. The value of \(T_{wall}\) indicates that the temperature of the spacecraft panel is assumed to be \(300\) K. The energy exchange efficiency between the incident atmospheric molecular flow and the spacecraft panel, represented by \(\alpha_{E}\), is equal to 0.93 [19].  
\[S=\frac{v_{at}}{\sqrt{2 RT/m_{a}}} \tag{4}\]  
The atmospheric damping coefficient can be reduced to a functional equation that is solely dependent on the temperature \(T\), the mean relative molecular mass of the atmosphere denoted by the symbol \(m_{a}\), and so forth. \(T\) represents the local temperature of the atmosphere, which must be determined through empirical model calculations.
## 2 Materials and Methods
### EO Composition Analysis  
#### 2.3.1 Gas Chromatography (GC)  
Gas chromatographic analyses were performed on a Perkin-Elmer Clarus 400 gas chromatograph (Shelton, CT, USA) equipped with two flame ionization detectors (FID), a data-handling system, and a vaporizing injector port into which two fused-silica capillary columns with different polarities were installed: a DB-1 (polydimethylsiloxane, 30 m \(\times\) 0.25 mm i.d., 0.25 \(\upmu\)m film thickness) and a DB-17 HT (50% phenyl-methyl-polysiloxane, 30 m \(\times\) 0.25 mm i.d., 0.15 \(\upmu\)m film thickness), both from J & W Scientific Inc. (Rancho Cordova, CA, USA). The analytical conditions were an oven temperature from 45 to 175 \({}^{\circ}\)C at a rate of 3 \({}^{\circ}\)C/min followed by a temperature increase at 15 \({}^{\circ}\)C/min until 300 \({}^{\circ}\)C and hold during 10 min; injector and detector temperatures, 280 \({}^{\circ}\)C and 300 \({}^{\circ}\)C, respectively; carrier gas (hydrogen) at a linear velocity of 30 cm/s; and a split ratio of 1:50. The volume injected was 0.1 \(\upmu\)L of a _n_-pentane-EO solution (1:1). The percentage composition of the EOs was computed by the normalization method from the GC peak areas and calculated as mean values of two injections from each EO, without using correction factors, in accordance with ISO 7609 [42].
# Parallelized Swarm Intelligence Approach for Solving TSP and JSSP Problems
## 2 Materials and Methods
### Traveling Salesman Problem  
The traveling salesman problem (TSP) is a well-known NP-hard optimization problem. We consider the Euclidean planar traveling salesman problem, a particular case of TSP. For given \(n\) cities in the plane and Euclidean distances between these cities, we consider a tour as a closed path visiting each city exactly once. The objective is to find the shortest tour. A solution is represented as the list of indexes of the cities on the path-in the order of visiting them.
# AUV Adaptive Sampling Methods: A Review
## 3 Target of Interest
### Chemical Features  
Chemical feature detection and tracking is another application of adaptive sampling using AUVs. Three distinctive chemical features have been the typical subjects: hydrothermal vents, chemical plumes and oil plumes. Hydrothermal vent plumes often form chimney-shaped structures at their point of exit from the seafloor. The temperature of the seawater as it exits the chimney structures can reach up to 450 \({}^{\circ}\)C. They are formed as freezing seawater percolates down through fissures in the ocean floor in the abyssal zone (deep ocean layer below 3000 m), gets heated, then conveys and dissolves minerals and metals from the rocks nearby [54] and is driven up out of the seafloor, forming the chimney-like structure made up of the dissolved minerals and metals. The term chemical plumes in this paper refers to any unnatural plume consisting chemical components derived from anthropogenic activities, such as the produced water plumes during the process of oil drilling and extraction and the wastewater from plant sites. Hydrocarbon (oil) plumes range from natural seeps to accidental oil spills at various scales.  
Due to the unnatural or undesirable nature of chemical entities in the marine environment, the primary interest of AUV deployments generally tends to be looking for the feeding source or delineating the spatial extent of the chemical plume. It is useful to comprehend the characteristics and differences of each plume to effectively design an adaptive sampling strategy. Unlike biological features, chemical plumes are less likely to correlate with the physical properties of the surrounding water. Therefore, a particular chemical element itself is usually used as a proxy, resulting in the need for direct chemical sensors.
## 2 Materials
### Experimental Data  
Currently, 2D image datasets related to surface defect detection are relatively common, while 3D datasets, particularly for specialized applications such as surface defects in magnetic tiles, remain scarce. This lack of publicly available 3D datasets for magnetic tile surface defects presents a significant challenge for researchers and practitioners working to develop robust defect detection algorithms. As of now, no dedicated datasets for magnetic tile surface defects exist, especially ones that include diverse defect types such as cracks, dents, peeling, scratches, and microdefects.  
To address this gap, we designed and built a custom data acquisition system capable of capturing high-resolution 3D point cloud data from the surface of magnetic tiles. All point clouds were obtained with an XY scanning resolution of 5 \(\upmu\)m, resulting in high-density point clouds that captured sufficient defect details. For each point cloud file, we created two copies: one for the defect analysis and the other meticulously labeled for validation. Figure 3 shows a portion of the carefully labeled dataset, which primarily includes cracks, dents, peeling, scratches, and tiny defects. Additionally, we utilized some publicly available 3D point cloud models from the Stanford 3D Scanning Repository (the Bunny and Dragon models), which feature high levels of detail and complex surface structures, containing tens of thousands of vertices and faces. These models were employed to assess the robustness of the proposed method.  
Figure 3: Point cloud dataset of surface defects on magnetic tiles.
# Magnetic Nanofluid Droplet Impact on an AAO Surface with a Magnetic Field
## Materials and Methods  
### Preparation of Magnetic Nanofluids  
The magnetite (Fe\({}_{3}\)O\({}_{4}\)) nanoparticles were synthesized according to the co-precipitation method [41; 42]. In brief, a mixture of ferrous chloride tetrahydrate (FeCl\({}_{2}\cdot\)4H\({}_{2}\)O) and ferric chloride anhydrous (FeCl\({}_{3}\)) acid solution (100 mL, molarity: Fe\({}^{2+}\)/Fe\({}^{3+}\) = 1/2) was added to a sodium hydroxide (NaOH) alkaline solution (50 mL, 8 M) to obtain Fe\({}_{3}\)O\({}_{4}\) nanoparticles after the solution was cooled down to room temperature (25 \({}^{\circ}\)C). Then, these nanoparticles were washed by deionized water to remove the salt impurities several times. Next, the NaOH alkaline solution (12 M) was mixed with Fe\({}_{3}\)O\({}_{4}\) nanoparticles to obtain the 200 mL unstable water-based magnetite nanofluids. Because the Fe\({}_{3}\)O\({}_{4}\) nanoparticles may aggregate and precipitate under the action of a gravitational or magnetic force, 5 mL oleic acid (C\({}_{18}\)H\({}_{34}\)O\({}_{2}\)) was used to cover Fe\({}_{3}\)O\({}_{4}\) nanoparticles. The changes in fluid electrical conductivity over time have been used to estimate the stability of magnetic nanofluids [43]. From the obtained conductivity-time relationship, the electrical conductivity decreased by a maximum of 18.41% in one hour. To avoid the influence of fluid instability, all experimental measurement processes were set to be completed within 20 min. Finally, the volume fraction of particles of the stable water-based Fe\({}_{3}\)O\({}_{4}\) nanofluids used in this work was estimated to be 1.00%.
## 5 Experimental Setup and Results
### Simulation Results
The clustering algorithm may often select two CHs for two distinct clusters, even if one of the clusters has just one node or if the two selected CHs are sometimes quite close to one another. The energy efficiency is decreased when certain kinds of CHs are chosen. Some protocols completely disregard other criteria and select CH based only on the residual energy and neighbor's degree of distance. This technique reduces the network lifetime as well. The distance from the sink, the average distance between cluster nodes and residual energy were some of the characteristics used in this proposed algorithm to choose the CHs. Using the reliability parameter, which is also introduced in this algorithm, the problem of a cluster with a single node is also resolved. Let the number of clusters be \(N_{c}\) and the total number of nodes be \(N\). For accessing \(N_{c}\) number of clusters, \(\Theta(N_{c})\) times are required and within each cluster to find the average distance between every pair of nodes present in the cluster, it needs \(\Theta(N^{2})\) time. The time needed to calculate the residual energy and distance from the sink are constant \(\rho_{1}\)(say). Hence, the time complexity of the algorithm is \(\Theta(N_{c}N^{2}+\rho_{1})\approx\Theta(N_{c}N^{2})\). In the situation where the number of clusters and nodes are equal, the maximum running time (worst-case time complexity) will be \(\Theta(N^{3})\). Now, to validate the results, we conduct a statistical hypothesis. We have stated the hypothesis as follows:  
Figure 9: Number of rounds vs. number of nodes alive using the PSO and OCAB algorithms. **Hypothesis H0. **_The average number of simulation rounds ends with a 95% confidence interval, i.e., the simulation round is between 2570 to 2630._  
**Hypothesis H1. **_The number of simulation rounds will not end up with 95% confidence interval._  
Let the level of significance be \(\alpha=0.05\),and \(T\) is a random variable having \(t\) distribution. Here, 95% a confidence interval is [2570, 2630]. The simulation has been performed 50 times. The average simulation result has been considered for the final simulation result which is 2600. Using \(t\) statistics, we calculated \(T-Score\) as 0.4691 and \(p=\text{Prob}(T>0.4691)\) as 0.6460. As \(p>\alpha=0.05\), then we do not have enough evidence to reject the null hypothesis H0.Therefore, average number of simulation rounds ends with a 95% confidence interval.
## 3 Federated Learning Solutions in the Medical Domain  
This study proposes a federated learning-based AI solution for applying AI in the medical domain. The proposed solution focuses on overcoming the limitations faced by AI models in the medical field by addressing the issues of data decentralization and privacy and creating an environment in which data from various institutions can be integrated. The focus was on solving three key challenges.  
First, it addresses the problem of data imbalance that frequently occurs in the medical field. By introducing federated learning, a global model was built that reflected the diversity of data owned by each institution. This allows the model to be validated using data from different institutions while maintaining its performance.  
Second, it addresses the issues of data security at each institution. Stringent regulations and privacy requirements often restrict the direct sharing of data. The proposed solution enables the utilization of such data while ensuring privacy, thereby facilitating collaboration between institutions by allowing them to fully leverage their data without compromising security.  
Third, it provides a sustainable AI solution. In medical environments, new data are continuously generated, and existing models must be retrained to accommodate these changes. The proposed federated learning solution offers a sustainable learning lifecycle in which local models are updated based on newly generated data at each institution and are then reflected in the global model. Throughout this lifecycle, the global model progressively improves its performance and remains up to date with the latest medical data.  
This solution is designed to operate through FedOps (v.1.1), enabling lifecycle management of federated learning, including building, aggregation, testing, evaluation, version control, and deployment. The global model was deployed to clients at each institution, where it was retrained on local data, and the results were integrated back into the global model. By repeating this process, the model incrementally enhances its performance by incorporating new data from each institution. The proposed solution can be applied to various AI solutions within the medical domain.
## 3 EM Techniques for CH Diagnosis
### X-Ray Fluorescence (XRF) in CH Applications
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{2}{c}{**Monitoring and Surveillance**} \\ \hline Dosimetry: & Personal dose badges (film, thermoluminescent, or optically stimulated luminescent dosimeters) worn by staff to monitor cumulative exposure. \\ \hline Area Monitoring: & Use of fixed and portable survey meters to assess ambient radiation levels. \\ \hline Record Keeping: & Maintaining detailed logs of exposures, equipment maintenance, and safety audits. \\ \hline Emergency Procedures \\ \(\bullet\) & Protocols for dealing with accidental exposures, spills, or equipment malfunctions. \\ \(\bullet\) & Immediate reporting and medical evaluation for accidental overexposures. \\ \hline \hline \multicolumn{2}{c}{**Radiation Dose Units**} \\ \hline Absorbed Dose: & Gray (Gy)—the amount of energy deposited per unit mass. \\ \hline Equivalent Dose: & Sievert (Sv)—absorbed dose adjusted for type of radiation; accounts for biological effect. \\ \hline Effective Dose: & Sievert (Sv)—weighted sum considering the sensitivity of tissues and organs. \\ \hline \multicolumn{2}{c}{**Dose Limits**} \\ \hline Occupational Exposure: & Typically limited to 20 mSv/year averaged over 5 years, with no more than 50 mSv in any single year. \\ \hline Public Exposure: & Limited to 1 mSv/year. \\ \hline \multicolumn{2}{c}{**Dose Optimization Strategies**} \\ \hline Selection of Imaging & Adjust kilovoltage (kV), milliamperage (mA), and exposure time to achieve diagnostic quality with minimal dose. \\ \hline Use of Dose-Reduction & Implementing automatic exposure control, iterative reconstruction algorithms, and \\ Technologies: & digital radiography. \\ \hline Patient Positioning: & Proper alignment to avoid repeat exposures. \\ \hline Limiting Field Size: & Collimation to restrict radiation to the region of interest. \\ \hline \multicolumn{2}{c}{**Practical Recommendations for Implementation**} \\ \hline \(\bullet\) & Conduct routine safety audits and risk assessments. \\ \(\bullet\) & Establish clear protocols aligned with national and international guidelines (e.g., the International Commission on Radiological Protection (ICRP), the National Council on Radiation Protection and Measurements (NCRP), and the World Health Organization (WHO)). \\ \(\bullet\) & Promote a safety culture where staff feel responsible and empowered to follow safety procedures. \\ \(\bullet\) & Invest in modern equipment with dose-saving features. \\ \(\bullet\) & Educate employed staff about radiation risks and benefits to facilitate informed consent. \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Radiation Safety Protocols and Dosage Considerations for CH Conservation [129; 130; 131; 132].
# Remote Sensing Image Dehazing via Dual-View Knowledge Transfer
## 3 Results and Discussion
### Datasets  
Extensive experiments were conducted on four public remote-sensing-image-dehazing datasets: SateHaze1k [40], RS-Haze [28], RSID [19], RRSD300 [41].  
* SateHaze1k: The SateHaze1k dataset [40] consists of 1200 image pairs, divided into three sub-datasets with different haze densities. The SateHaze1k-thin sub-dataset contains images with thin haze, the SateHaze1k-moderate sub-dataset contains images with moderate haze, and the SateHaze1k-thick sub-dataset contains images with thick haze. The images in each sub-dataset are split among three categories: 320 for training, 35 for validation, and 45 for testing.
* RS-Haze: The RS-Haze dataset [28] is a comprehensive and challenging dataset designed for image dehazing tasks, featuring 51,300 image pairs. Among these, 51,030 pairs are designated for training purposes, while the remaining 270 pairs are reserved for testing. It encompasses various scenarios and levels of haze density, such as urban landscapes, forests, beaches, and mountain regions.
* RSID: The RSID dataset [19] comprises 1000 image pairs, with each pair consisting of a hazy image and a corresponding haze-free image. In line with prior research [41], 900 of these pairs are randomly chosen for the training set, while the rest are utilized for testing.
* RRSD300: The RRSD300 dataset [41] comprises 300 real-world hazy images collected from DIOR dataset [42] and Microsoft Bing. This dataset is used to verify the generalization of various models.
# Exploring the Potential of Sensing for Breast Cancer Detection
## 6 Challenges for Existing Techniques and Prospects
However, microfluidic biosensors have a smaller dimension and high surface-area-to-volume ratios, which ultimately leads to a lower flow rate and a high-pressure difference over a small distance. The Debye shielding effect, which can be observed in any FET-based biosensor, is considered to reduce the LOD. The electrostatic field of the surface charges associated with biomolecules will vanish after the Debye screening length [182]. The number of accounts near the surface of the biomolecule and the same number of opposite directions also exist around the biomolecules. These opposite charges can materialize the electric field due to surface charges. In a buffer solution, the electrostatic field exponentially decreases and becomes zero after a particular distance, and this critical distance is known as Debye screening length. In FEB, the LOD is rigorously dependent on the Debye length limitation [182]. The sensitivity of an FET-based biosensor is significantly reliant on the strength of the ions. Biosensors face several challenges in determining the status of a biomarker in a sample. For example, a lower concentration of biomarkers is one of the most critical factors. In that case, a lower biomarker concentration produces weak electrochemical interactions and associated weak signals, which most biosensors have difficulty detecting. In that case, several secondary antibodies or antibody-like substances are to be considered for amplifying the electrochemical signal [52]. On the other hand, the interference between different meanings generates different signals/noises. To resolve these complexities, several ideas have been considered in sensing techniques. Examples include the following: (a) quantum nanomaterials have been considered as an amplifier of the weak primary signals; (b) secondary antibodies can be introduced to amplify the raw signals; (c) secondary/indirect and larger indirect signals can be detected, and these secondary/indirect signals can be decomposed to observe the properties of primary/direct signals [52]. The sensitivity of a biosensor could be improved under the consideration of detection strategies for secondary/indirect signals. Background interference can be minimized if the sample undergoes a pretreatment. For MIP, it can be considered that biomolecules have difficulty coping with harsh environments. Finally, different biomarkers can simultaneously exist in a single sample, but most biosensors can detect one at any time. Table 2 represents the linear range and the limit of detection of different biosensors for different biomarkers.
## 4 Simulation and Experimental Verification
### Simulation Verification
As seen from the enlarged local figure, the speed of the motor under the four conditions does not change when it reaches a steady state, indicating that it can meet the SynRM full-load requirements under the control of the EI-BELBIC in the magnetic saturation state. Figure 19b shows the comparison of torque output of the SynRM in four states. Due to the influence of magnetic  
Figure 19: Performance comparison diagram of the SynRM under different working conditions: (**a**) speed; (**b**) torque. Figure 18: Comparison diagram of current waveforms at different \(i_{d}\): (**a**) Current of d-axis and q-axis; (**b**) Three-phase stator current. saturation, the maximum output torque of the motor remains at about 60 Nm, less than 80 Nm under ideal conditions, but it can still meet the load requirements under the control of the EI-BELBIC. As shown in Figure 20, under the condition of consistent d-axis current settings, the current limiting value of the q-axis is kept at about 46 A, which drives the motor to run with load. Given the different loading conditions of the SynRM in different states, the final stable values of \(q\)-axis current are different after the motors are started with load and kept stable. Figure 21 shows the variation in the magnetic saturation parameter of the SynRM under different loads in the magnetic saturation state. Due to the same current of the \(d\)-axis, the inductance values of both are reduced by 25%. However, the inductance stabilized at different values due to different loads. Moreover, the inductance value of the SynRM under full load conditions was even smaller, only 80% of that under ideal conditions.
## 1 Introduction
In addition to the challenges of low power, large number of channels, and radiation resistance, the SALT is the first readout ASIC in HEP experiments capable of digitising and processing data in each channel with the LHC bunch crossing frequency of 40 MHz. Moreover, it can cope with 100% hit occupancy in a single channel thanks to a dedicated shaping of the analogue signal with a very short pulse tail. This paper is focused on the SALT design and contains a basic set of test results, to verify the functionality of the chip. Section 2 describes the overall SALT architecture and the design of its main blocks. In Section 3, the test results of the chip are discussed.