esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Medical Screening and Certification Prior to Space Travel
AI-based system designed to guide non-physicians on the proper acquisition of medical diagnostic testing using The Edge TPU and deep reinforcement learning  
AI-enhanced 3D-imaging technology (e.g., micro-CT scanners) [101]  
Utilization of the Edge TPU and other similar low-power AI ASICs to provide the necessary processing power for high-performance parallel-processing space research [102]  
**Intervention**  
AI-guided minor surgical procedures [such as incision and drainage (I&D)] using next-generation High Performance Spaceflight Computing (HPSC) [103]  
AI-assisted, remotely controlled robotic PCI and robotic laparoscopic surgery (e.g., telechelocystectomy and teleappendectomy); made possible by a reduction in communication latency beyond a lag of 200 ms [103; 104; 105; 106; 107]  
Using AI predictions of drug metabolism and effectiveness based on an individual’s multicomic data prior to medication or supplement distribution (e.g., melatonin, immune supplements, probiotics) [108; 109]  
**Disease Prevention**  
AI-integrated space suits (e.g., exoskeletons) to maximize EVA time and operating pressure, and minimize space radiation exposure [110; 111]  
3D printing of personalized devices (e.g., ear plugs to prevent noise source generated from man-made sources), space shields, space suits for use in emergency scenarios [112; 113; 114]  
AI-based chatbots or social media could potentially be used to prevent anxiety and depression during long-duration space travel. The Edge TPU could potentially be used in advancing an internet or social media for the moon, known as LunaNet [115]. AI-improved augmented reality/virtual reality to reduce/prevent Spaceflight-Associated Neuro-Ocular Syndrome (SANS), space motion sickness (SMS), and post-flight motion sickness (PFMS) [116; 117; 118]  
AI identification of an individual’s radiosensitivity (genotype-phenotype) using multimotics to prevent the negative effects of ionizing radiation [119; 120; 121]  
Emerging technology could allow for the transfer of large volumes of data at faster rates in order to facilitate medical research in space [122]  
AI can be used to create novel metrics (e.g., travelers’ satisfactions, post spaceflight measurements for travelers) and ethics (e.g., health insurance)
## 2 Problem Formulation  
The searching region is usually divided into grid cells. The target existence probability (TEP) represents the probability of target existence in a cell. Environmental uncertainty (EU) indicates the degree to which UAVs do not know about the environment. These two variables are regarded as prior information. Every cell is modeled as the key or non-key region, and then the search probability graph of the whole searching region is formed. UAVs accomplish the search task via environment perception and information interaction. This section includes three parts: the environment model, UAV kinematic model and the analysis of UAV closed search.
## 2 Materials and Methods  
### Sample Preparation  
The powder chosen is tungsten carbide powder, whose average particle size is 200 nm and purity is 99.99%. The analyzed elements were Cobalt (Co). Because the element content in metal samples is fixed, only parts of the samples were selected for the analysis; and their content are 4%,8%, 12%, 16%, 20%, 24%, 28%, 32%, as Table 1 shows. The second kind of sample we analyzed was pressed samples, as Figure 1 shows.  
* _Configure the required gradient of the standard solution, and mix 3 mL of standard solution with 2 g of powder sample in the reagent bottles._
* _Place the reagent bottles containing the mixed solution in the ultrasonic cleaning machine, and apply the ultrasonic oscillation for 10 min._
* _After the ultrasonic oscillation, place the reagent bottles on the heating device to completely evaporate the solution._
* _Transfer the dried powder into mortar and grind evenly._
* _Press the powder sample into pellets with a diameter of 40 mm under a pressure of 40 Mpa, 50 Mpa, 60 Mpa, 70 Mpa, 80 Mpa, 90 Mpa, 10 Mpa, and 11 Mpa, as Figure_ 2 _shows. Figure_ 2 _shows the surface morphology of WC pellets prepared with different Co content and compaction pressures. At lower pressure (40 MPa), the pellet surface exhibits notable roughness and micro-voids, especially for samples with low Co content. As the pressure increases to 70 MPa and 110 MPa, the morphology becomes smoother and more uniform, indicating improved densification and reduced porosity. These results highlight the combined effect of Co content and compaction pressure on pellet quality, which is critical for achieving consistent LIBS measurements.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Sample Ratio** & **WC (g)** & **Co (g)** & **Ratio** \\ \hline
1 & 96 & 4 & 4\% \\
2 & 92 & 8 & 8\% \\
3 & 88 & 12 & 12\% \\
4 & 84 & 16 & 16\% \\
5 & 80 & 20 & 20\% \\
6 & 76 & 24 & 24\% \\
7 & 72 & 28 & 28\% \\
8 & 68 & 32 & 32\% \\ \hline \end{tabular}
\end{table}
Table 1: Sample Preparation.  
Figure 1: Schematic diagram of the preparation process for pressed tungsten carbide samples containing Cobalt gradients. Powder mixing procedure using a small-scale stirrer to ensure uniform blending of the powders. The process includes solution mixing, ultrasonic dispersion, drying, grinding, and pellet pressing under varying pressures for LIBS analysis.
# A Comprehensive Survey on Real-Time Applications of WSN
## 3 Applications of WSN
### Environment Monitoring
#### 3.1.2 Reliability  
To prevent an unforeseen crash, the network should perform very simple and expected operations. The end user may not have enough knowledge and the interested areas are isolated most of the time. During the ruthless situation of weather (like very cold weather and a lot of rain in), the packet could be lost so it becomes harder to achieve reliability.
# A ROTI-Aided Equatorial Plasma Bubbles Detection Method
## 1 Introduction  
Equatorial plasma bubbles (EPBs) are very frequent ionospheric events associated with the depletion in plasma density, which are widely believed to be generated by the Rayleigh-Taylor (R-T) instability in the F-layer [1, 2]. The EPBs can cause the scintillations and huge ionospheric gradients of radio signals, leading to severe effects on navigation and communication systems [3]. As a result, the study on climatology and the forecasting of EPBs has remained an open question among the scientific community during the last two decades [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]. Many measurements, such as in situ satellite data [4, 5], ionosonde [6], and airglow imaging [9, 16] are applied to investigate the EPBs characteristics. In recent years, the Global Navigation Satellite System (GNSS) ionospheric Total Electron Content (TEC) has been an important tool for the study of EPBs due to its high spatial-temporal resolution [10, 11, 12, 13, 14, 15].  
To study the EPBs using GNSS TEC data, the first step is to extract the EPBs signals in the TEC series. One common method is using a polynomial fitting with a specific window length to fit the TEC series; then the detrended TEC series that might include EPBs signals are obtained by subtracting the fitted TEC series from the observed TEC series [11, 12, 13, 14, 15]. However, the detrended TEC series achieved by this method not only contain depletion but also enhancement, which is similar to the feature of the Medium-Scale Travelling Ionospheric Disturbances (MSTIDs). This exerts an adverse effect on distinguishing EPBs and MSTIDs. [PERSON] et al. [17] presented an Ionospheric Bubble Seeker (IBS) method to detect the EPBs. The IBS method applies the TEC variation and its variance to acquire the initial and end times of the EPBs; then, a linear fitting strategy is used to calculate the background TEC values during the period of EPBs. This method can obtain a good profile of the EPBs compared to the common method. However, the assumption that the profile ofthe background TEC is a straight line during the period of EPBs is inaccurate, especially for a long duration.  
High-precision EPBs signals can help to understand the fine characteristics of EPBs and evaluate their effect on GNSS positioning accurately. To further improve the accuracy of the extracted EPBs signals, in this study, we attempted to employ the so-called Rate of TEC Index (ROTI) to seek the occurrence times of the EPBs and then use the low-order, polynomial fitting to calculate the background TEC values during the period of EPBs. In the following sections, we present the ROTI-aided EPBs detection method in detail and test it using GNSS data in Hong Kong.
# Cross-Domain Echocardiography Segmentation with Multi-Space Joint Adaptation
## 2 Methods
### Training Procedure and the Overall Loss Function  
We use source domain images (X\({}^{5}\)) and labels (Y\({}^{5}\)) and target domain images (X\({}^{T}\)) as training data. The images from both the source domain and the target domain are input into the segmentation module ASDB to gain the predicted masks and boundaries. The ASDB is optimized with the \(\mathcal{L}_{seg}\) between the prediction \(P_{X^{5}}\) on the source domain images and manual annotations Y\({}^{5}\) (Equation (1)). Multi-space discriminators distinguish the entropy maps and features from two domains, respectively. Two discriminators are optimized independently.  
The segmentation network and multi-space discriminators achieve the alignment of the source and target domain through adversarial learning. The segmentation network generates fused features and entropy maps for both of the domain datasets. Multi-space discriminators need to identify the source domain membership as true and the target domain membership as false. For the whole framework, the adversarial loss of \(\text{D}^{f}\) and \(\text{D}^{e}\) are:  
\[\mathcal{L}_{adv}^{f}=\frac{1}{N}\sum_{x^{e}\in X^{T}}\mathcal{L}_{D}\Big{(}p _{x^{e}}^{f},1\Big{)}, \tag{5}\]  
\[\mathcal{L}_{adv}^{e}=\frac{1}{N}\sum_{x^{e}\in X^{T}}\mathcal{L}_{D}(E_{x^{ e}},1). \tag{6}\]  
The minimum of the adversarial loss renders the segmentation prediction on the target domain and the source domain indiscernible, and more similar features and entropy maps force discriminators to improve their discriminating ability. The ASDB and discriminators are optimized in turn for the domain adaptation at the model level and finally complete the segmentation task in the target domain.  
The overall loss function of the MACS is:  
\[\mathcal{L}_{total}=\mathcal{L}_{seg}+\lambda\Big{(}\mathcal{L}_{adv}^{f}+ \mathcal{L}_{adv}^{e}\Big{)}, \tag{7}\]  
where \(\lambda\) is the variadic parameter for the function balance.
## 3 Application of Hyperspectral RS into Plant RS and Military Actions
### Military Application of Hyperspectral RS
Thermal infrared camouflage of military targets mainly uses low-emissivity barriers to reduce the target's radiation energy. If the coupling temperature between the target and the background is close to that of the target, the thermal infrared detector may not be able to detect or identify it. However, if hyperspectral detection is used and linear assumptions are used to construct equations in the thermal infrared band, the true temperature and emissivity of the target surface can be calculated. Hyperspectra break through the limitations of hypothetical temperature measurement and make the measuring solution closer to the real temperature of the object surface. This enables more effective identification of camouflaged targets and backgrounds.
# 3D DNA Nanostructures: The Nanoscale Architect
## 2 3D DNA Nanostructure Design
### Fundamental Techniques for Design of 3D DNA Nanostructures
#### 2.2.4 DNA Bricks  
While the DNA origami methodology has been a fundamental mainstay in most strategies, the DNA bricks technique pioneered by the [PERSON] group has been one that has been otherwise successful without it. DNA bricks construct structures without the use of any scaffold strand, instead opting to only use short ssDNA referred to as _bricks_. Each brick is a pair of same length antiparallel helices joined by a phosphate linkage. Eachhelix of the brick is split into 2 equal length domains, such that the brick has a total of 4 domains, and each domain is a consistent length regarded as the _motif._ Motifs are either 8 or 13 nt, and the choice of motif affects the scale of designed structures due to the number of unique sequences possible for a specific length of DNA, as well as yields and melting temperature. In a simple one-pot synthesis, the numerous strands self-assemble with each strand representing a small volume of the structure like bricks filling in a shape [34]. This technique has been shown to scale up to megadalton scales with high shape complexity, which was also accompanied by the release of its own software tool Nanobricks [16].
# Characterization of the Migration of Soil Particles in Lateritic Soils under the Effect of Rainfall
## 4 Particle Migration within the Soil
### Spatial Structure Evolution of Soil Particles  
The loss of fine particles from soil slopes consists of two aspects: one is the sediment mixture carried away by rainfall erosion of the slope surface, and the other is the migration of fine particles within the soil mass and their eventual loss at the toe of the slope.  
During rainfall, fine particles on soil slopes are washed by rainwater and flow along the slope surface, forming slope erosion. On the slope surface, due to the random distribution of larger gravels on the slope surface, as well as the formation of gullies, and the appearance of small surface failures all show strong randomness, so the sand transport rate of the sediment mixture on the slope surface with the rainfall ephemeral time shows strong randomness, as shown in Figure 13. This also shows the changes in the sand transport rate of the runoff on the slope surface with the rainfall time.  
For the loss of fine particles within the slope, limited by the minimum pore size of the soil pores, the fine particles usually block at the minimum pore size, leading to an increase in the localized fine particle content of the slope. However, with further rainfall infiltration, the hydrodynamic forces gradually increase, and the pore water pressure further increases, which will break through this blockage and lead to the re-migration of fine particles, thus leading to the loss of fine particles. This process of hydrodynamic changes can cause alternating phenomena of blockage and loss of fine particles. In addition, the randomness and inhomogeneity of soil pore distribution will also have a significant effect on the sand transport rate of the seepage slurry within the slope. Therefore, under the same rainfall conditions, the sand transport rate of the seepage slurry inside the slope body shows a strong randomness. As shown in Figure 14, the variation in sand transport rate of internal seepage with time is demonstrated. Even though the sand transport rate of internal seepage shows strong randomness, the overall sand transport rate gradually decreases and stabilizes. When the rainfall intensity is low (rainfall intensity: 50 mm/h), the change in the sand transport rate of internal seepage is relatively stable and small, while the change in the sand transport rate increases with the increase in rainfall intensity.  
Figure 13: The variation of sand transport rate of slope runoff with rainfall time.
# An Estimation of Speech Privacy Class Based on ISO Parameter
## 2 Background
### Ambient Noise and Speech Level in the Room
on the sound reduction index of the partition \(R\) exposed to sound and its surface area \(S_{12}\). Consequently, a speech signal with a defined level value, \(L_{2,spnch}\), is transmitted into the receiving room. The level of the speech signal at the point of origin, \(L_{1,spnch}\), is determined by the potential range of sound power that the vocal tract can generate. Standards that prescribe conditions for partitions in a building are usually based on data regarding the expected sound level produced by normal human conversation. In residential buildings, this is somewhat limited by house rules. According to regulations in Serbia, the expected equivalent sound level in residential premises should not exceed 70 dB(A) [49]. Previous research provided detailed insights into the sound level in residential premises, illustrating the potential impact of noise during daily activities [46]. As part of these studies, information on measured sound levels during conversations in apartments revealed that the equivalent speech level in rooms typically ranges from 60 dB(A) to 73 dB(A). In commercial buildings, the noise level in the premises is inherently controlled by work requirements. Research results that illustrate the speech sound level in meeting rooms, typically found in office buildings, are presented in [41]. Based on measurements in 79 halls, it was concluded that the equivalent speech level in them is 60 \(\pm\) 2 dB(A). This level is significantly lower than the speech level occasionally occurring in apartments. Sound insulation requirements in buildings are defined by construction norms and adjusted to ensure that human speech generated at an expected level at one side of the walls is not understood, but it is noticed on the opposite side. In that process, some expected value of ambient noise in the premises of the buildings was implicitly adopted as a constant. However, the engineering approach to this issue faces the fact that the level of ambient noise in the premises has a rather variable value for multiple reasons. Numerous examples of compromised privacy in buildings exist, even when the sound insulation requirements from the current regulations are fulfilled. This indicates that engineering practice requires an improved approach for determining the state of privacy and thus the quality of buildings. This approach needs to take into account both quantities that determine the signal-to-noise ratio at the listener's position--the speech level and ambient noise level.
# An Automatic Transformer from Sequential to Parallel Java Code
## 3 Proposed Approach  
The proposed approach uses static analysis to gather data from the source code of the analyzed software. Parsing activities are based on the JavaParser library. This is an automatic parser that takes one or more.java files as input, generates an abstract syntax tree (AST) for each one, and provides means to perform operations on ASTs, such as reading, inserting, deleting, and updating [37]. Algorithm 1 shows the pseudo-code at a high level of abstraction of our analysis. The procedure takes the source code of the analyzed application as input, and, after parsing, it returns the instances of CompilationUnit for the application. A CompilationUnit is a class modeling a single Java file represented as an AST. Afterward, the method's declarations are extracted from each CompilationUnit instance to obtain a list containing all the methods implemented in the source code. A method body generally contains many instructions; in our analysis, we focus on method calls since they are the instructions that will be evaluated to be run in parallel among all the instructions in the method's body. Each method call is analyzed according to its context, data dependence, and the number of instructions performed. All three analyses are covered in Sections 4 and 5. Once a method call passes all the checks, it is transformed to a parallel version and the method is updated with the proper synchronization statement, according to the data-dependent statement found. Finally, all the compilation units containing at least one transformed method will be written in new Java files.  
```
procedureTransformsequentialToParallel(Sc) \(compilationUnits\leftarrow\mathit{parseAllPaths}(Sc)\) forcu, compilationUnitsdo \(methods\leftarrow\mathit{visitMethods}(cu)\) endfor form, methodsdo \(methodCalls\gets m.getMethodCalls()\) formCall, methodCallsdo ifContextAnalysis(mCall)then \(ddStatement\leftarrow\textsc{DataDependenceAnalysis}(m,mCall)\) ifCFGAAnalysis(m, mCall, ddStatement)then \(mCall\gets transformToParallel(mCall,ddStatement)\) endif endif endfor endfor \(printParallelCu()\) endprocedure
```  
**Algorithm 1** The algorithm of the proposed approach.
# QoE Optimization in a Live Cellular Network through RLC Parameter Tuning
## 2 Radio Link Control  
This section provides an overview of the operation of the RLC layer-2 sublayer following the 3 GPP [20] specifications. In the protocol stack, the RLC layer is located above the medium access control (MAC) layer and below the packet data convergence protocol (PDCP) layer. The operation of the RLC layer is controlled by the radio resource control (RRC) layer and is executed by RLC entities located at both the transmitter and receiver. The main functions of this layer are the segmentation and reassembly of service data units (SDUs) into protocol data units (PDUs) and the detection and correction of errors caused by interference, noise, and fading in the transmission medium--the radio interface.  
In order to adapt the RLC layer performance to the requirements of the different services, this layer can be configured in one of the following modes: transparent mode, UM, and AM. Transparent and UM modes are considered unidirectional communication modes, since they do not retransmit lost packets. To perform the communication, both a transmitting and a receiving entity at each communication endpoint are required. While the UM mode peforms the segmentation and reassembly of the RLC SDUs before delivering them to adjacent layers, the transparent mode does not. Transparent mode is used to exchange control messages with the RRC layer, and UM is normally used in real-time services, in which limiting delay takes precedence over achieving good reliability values. Real-time services such as voice transfer make use of this RLC mode. AM mode supports the retransmission of lost packets. In this sense, this mode is considered as bidirectional. Thus, the RLC AM entity at each endpoint has a transmitter and a receiver side. AM also performs the segmentation and reassembly of the packets. AM is used for services that require high reliability in the transmission at the expense of higher delay values. Some examples of services using RLC AM are browsing the web or downloading files.  
This paper is focused on the study of the UM and AM modes. Next, the main characteristics of these two modes are presented.
# Automatic Modulation Classification Using Hybrid Data Augmentation and Lightweight Neural Network
## 4 Simulation Results and Analysis
### Simulation Results  
#### 4.2.1 Comparative Experiments  
This paper compares our AMC method with three comparison methods, i.e., support vector machine (SVM), CVResNet, and lightweight CVResNet. SVM uses support vector machine to classify the original samples, CVResNet uses a non-lightweight network structure to implement classification, and lightweight ResNet uses a lightweight network structure to classify signal samples.
## 5 Factory Test of the System  
### Test of Auto-Switching Function  
The developed protection and control system has been satisfactorily tested in the factory using Real-Time Digital Simulator (RTDS). The 10 kV network model used for the test is shown in Figure 9 below. Factory test results showed that the delay of 4G LTE communication network is less than 80 ms, and the time synchronization error is less than 10 us. The system's response time is within 200 ms. The system was installed and commissioned on site and was put into service at the end of 2018. The system can significantly improve the reliability of power supply in the distribution network after it is in service.  
Figure 8: Final status when fault occurs at f2.  
Figure 7: Final status when fault occurs at f1.