esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion
### Clinical Implementation Considerations  
The proposed system is designed with real-world clinical applicability in mind, particularly in neonatal intensive care units of tertiary care or university-affiliated hospitals. Our solution leverages standard patient monitoring (Philips IntelliVue MP20/MP50) and affordable, off-the-shelf RGB cameras mounted above the infant's bed. Near-infrared (NIR) illumination ensures recording under low-light conditions, enabling 24/7 monitoring without disturbing the infant.  
From a computational standpoint, the system is efficient: the GRU-based model operates on precomputed features and can run in real time on standard GPU-equipped clinical workstations. A real-time implementation is currently under development, and preliminary performance benchmarks confirm its feasibility.  
Despite this, practical deployment in NICUs presents several challenges. One major issue is camera stability--cameras are sometimes repositioned by clinical staff during care routines, necessitating automated recalibration or realignment. Additionally, nighttime recordings must avoid overexposure or signal dropout, especially when bright lights or reflections interfere with the infrared spectrum. These technical factors affect both segmentation and motion feature quality.  
Beyond hardware constraints, successful implementation depends heavily on clinical acceptance and usability. A key barrier is the level of trust clinical staff place in automated systems. Throughout this project, we have worked closely with neonatologists and NICU nurses to ensure that the system aligns with practical workflows and clinical expectations. Ongoing efforts focus on developing an intuitive, interpretable user interface and incorporating clinician feedback through iterative usability testing.  
In summary, our findings suggest that automated behavioral state classification in neonates is not only technically feasible but also clinically promising. With further development, such systems may enhance individualized care, reduce caregiver workload, and offer novel insights into infant neurodevelopment within real-world clinical settings.
# Development of Automatic Crack Growth Simulation Program Based on Finite Element Analysis
###### Abstract
[16] developed FINAS/CRACK, automatic crack growth and fatigue evaluation program, and based on the standard low-cycle fatigue theory, calculate the amount of FCG at each point along the crack front to simulate natural crack growth. The results of crack growth using the program were verified by comparison with the test results performed by [PERSON] et al. [17]. [PERSON] et al. [18] performed stress corrosion crack (SCC) growth by automatic re-meshing for natural cracks of dissimilar metals using Abaqus and in-house code. This was verified by comparison with the crack growth test in a water environment. As such, various re-meshing techniques have been developed for crack growth modeling based on FEA. However, existing studies have focused on one of the FCG and SCC growth. In addition, these methods and programs have limited application to various geometries such as nozzles. This study developed the advanced iterative-finite element method (AI-FEM), a python-based automatic crack growth program that operates as a plug-in program in Abaqus. AI-FEM is based on linear elastic fracture mechanics, and the SIF is calculated along the crack front. For arbitrary crack shape, mechanical FCG and SCC growth behavior can be simulated as a non-idealized crack shape by utilizing the developed program. Sensitivity analyses were performed on various variables affecting the SIF value and crack tip element shape such as the number of elements in the circumferential direction inside the contour and radial direction of the contour. The appropriate criteria for crack tip modeling that can be applied as default settings of the program or can be referenced when using the program are presented. The developed program was verified by comparing it with the SCC growth calculated by the procedure presented in ASME code, Sec. XI. In addition, SCC growth verification was additionally performed in comparison with natural crack growth, and the validity of the specified cracking transition method was verified.
# Challenges of Malware Detection in the IoT and a Review of Artificial Immune System Approaches
## 2 Security Challenges and Malware Attacks in the IoT
### Malware Analysis and Detection
#### 2.2.1 Malware Analysis  
Malware analysis techniques are essential to developing effective malware detection methods. These techniques involve the analysis of the process and functionality of the malware to build a suitable defense method. Three main malware analysis techniques achieve the same goal of determining how the malware works and how the attack will affect the network (see Figure 3).  
* Static analysis, also called code analysis: In this technique, the infected file is inspected and analyzed without executing it. Low-level information is extracted such as the control flow graph (CFG), data flow graph, and system calls. Static analysis is fast at analyzing data and safe to use; also, it has a low level of false-positives, which means a higher detection rate. Moreover, the static analysis tracks all possible paths, which gives it a global view; however, it fails in detecting unknown malware using code obfuscation.
* Dynamic analysis, also called behavioral analysis: In dynamic analysis, the infected file is inspected during execution, which is usually conducted on an invisible virtual machine, so the malware file does not change its behaviors. Dynamic analysis is time-consuming and vulnerable, and it can only detect a few paths based on triggered files. Furthermore, it is neither safe nor fast, and it suffers from a high level of false positives. However, dynamic analysis is known for its good performance in detecting new and unknown malware.
* Hybrid analysis: this technique was designed to overcome the challenges and limitations of the previous two techniques. First, it analyzes the signature descriptions of any malware code and then combines that with other dynamic parameters to improve the analysis of malware.  
The connection in IoT networks is currently enabled via cloud services. Static, dynamic, and hybrid malware analyses are mostly applied in the cloud to protect IoT devices.  
Figure 3: Malware analysis and detection.
# Analysis of the Use of Similarity Coefficients in Manufacturing Cell Formation Processes
## 4 The Research
### The Most Influential Similarity Coefficients  
Given machines \(i\) and \(j\), \(S_{ij}\) is the similarity between them, \(a\) is the number of parts that require both, \(b\) is the number of parts that require only \(i\), \(b\) is the number of parts that require only \(j\), and \(d\) is the number of parts that require neither. Table 2 presents the twenty coefficients considered and the results of the search.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Coefficient** & **Sites (in 2024)** & **Equation \(S_{ij}\)=** & **Range** \\ \hline [PERSON] & 510 & \(\frac{a}{a+b+c}\) & 0–1 \\ [PERSON] & 61 & \(\frac{(a+d)+(b+c)}{(a+d)+(b+c)}\) & \(-1\) to \(1\) \\ [PERSON] & 43 & \(\frac{(ad-bc)}{(ad+bc)}\) & \(-1\) to \(1\) \\ Simple matching & 74 & \(\frac{a+d}{a+b+c+d}\) & 0–1 \\ [PERSON] & 66 & \(\frac{2a+b+c}{2a}\) & 0–1 \\ [PERSON] and [PERSON] & 53 & \(\frac{a+d}{a+2(b+c)+d}\) & 0–1 \\ [PERSON] and [PERSON] & 117 & \(\frac{2(a+d)}{2(a+d)+b+c}\) & 0–1 \\ Russell and Rao & 34 & \(\frac{a}{a+b+c+d}\) & 0–1 \\ Baroni-Urbani and Buser & 46 & \(\frac{a+\sqrt{ad}}{a+b+c+\sqrt{ad}}\) & 0–1 \\ [PERSON] & 76 & \(\frac{a+b+c+\sqrt{ad}}{\sqrt{(a+b)(a+c)(b+d)(c+d)}}\) & \(-1\) to \(1\) \\ Ochiai & 46 & \(\frac{a}{\sqrt{(b+d)(c+a)}}\) & 0–1 \\ PSC & 40 & \(\frac{a^{2}}{(b+d)(c+a)}\) & 0–1 \\ Dot-product & 57 & \(\frac{a}{2a+b+c}\) & 0–1 \\ Kulczynsky & 36 & \(\frac{\left(\frac{a}{a+b}\right)^{2}+\left(\frac{d}{ac}\right)}{2}\) & 0–1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Incidences of similarity coefficients (adapted from [8; 38]).  
Figure 5: The research method.  
The six most prevalent coefficients, including Jaccard, Chandrasekharan and Rajagopalan (C&R), Sokal and Sneath, Phi, Simple Matching, and Sorensen, collectively represent over 65% of the occurrences. With the exception of Phi, all are of the Jaccardian type. Therefore, researchers and practitioners are advised to prioritize these six coefficients for future applications, as they are the most widely used in the literature.
# Structure, Biology, and Therapeutic Application of Toxin-Antitoxin Systems in Pathogenic Bacteria
## 2 Functions of Six Different Types of Bacterial TA Systems  
Most toxins from TA systems modulate cell growth by binding to diverse important components of the cellular machinery. Functionally validated toxins of six different types of TA systems are listed in Table 1.  
According to the mechanism by which the toxin is neutralized by the antitoxin, TA modules can be typically classified into six different types. Regulatory mechanisms of action of type I-VI TA systems are summarized in Figure 1.  
Figure 1: Schematic regulatory mechanisms of cellular functions of type I–IV’ TA systems. (**A**) Type I TA system. Antisense RNA acts as an antisense that forms base pairs with toxin mRNA and thereby inhibits toxin synthesis. The toxin damages cell membrane, which inhibits essential cellular functions such as cell division and ATP synthesis. (**B**) Type II TA system. The toxin and antitoxin are proteins. Under normal growth conditions, the antitoxin inhibits the activity of the toxin by forming the TA complex. Commonly, the antitoxin alone or in complex with the toxin can bind to the TA promoter to repress transcription. Under unfavorable conditions, cellular proteases, such as Clp and Lon, are activated and degrade the antitoxins, which liberates the toxin to inhibit translation or replication. (**C**) Type III TA system. The antitoxin RNA binds to the toxin protein, forming the RNA pseudoknot-toxin complex. In the complex, the toxin is inactivated. (**D**) Type IV TA system. The protein antitoxin stabilizes filamentous cytoskeleton proteins (FsZ and MreB), whereas the protein toxin destabilizes them and inhibits cell division. (**E**) Type V TA system. The protein antitoxin acts as a ribonuclease specific for the toxin mRNA. The peptide toxin is involved in membrane lysis. (**F**) Type VI TA system. The protein antitoxin behaves as a protease adaptor that delivers the protein toxin to a cellular protease, promoting its degradation. In the absence of the antitoxin, the toxin binds to the sliding clamp and inhibits DNA replication. Promoter and Shine-Dalgarno sequence are abbreviated as P and SD, respectively. Toxins and antitoxins are orange and green, respectively.
## 5 Experiments and Result
### Experimental Configurations  
We employed the raw waveforms as input, and the minibatch was configured by setting the length of the input utterance to 59,049 samples (\(\approx\)3.69 s) in the training phase. In the testing phase, to prevent a mismatch with the training utterance input length, we applied a test time augmentation by sampling 10 temporal segments at regular intervals to make the length of an input utterance the same as in the training [9].  
The baseline and the proposed systems were trained using 200 and 1920 batch sizes, respectively, which reported the best performance empirically in each experiment. In baseline experiments, the AMSGrad optimizer [38] was used with a learning rate (LR) of 0.001, decaying exponentially with every iteration. Furthermore, we used a LARS [39] optimizer with an LR of 3 and cosine annealing LR policy [40] with warm up [41] for the first three epochs in the proposed system. These are the optimal combination of learning hyperparameters found by the evaluation results of internal experimentation for each experiment. We applied a weight decay with \(\lambda\) = 0.0001.
# Landslide Susceptibility Prediction Based on a CNN-LSTM-SAM-Attention Hybrid Model
## 2 Research Methodology (CNN-LSTM-SAM-Attention Model)
### Process Framework
#### 2.4.1 CNN Module  
As illustrated in Figure 2, the CNN module architecture is designed to extract hierarchical features from raw sequences.  
Function: Hierarchical feature extraction from raw sequences.  
Selection justification: Convolutional layers efficiently capture local spatial patterns in sensor data. The multi-scale design (progressively increasing receptive fields) models both short- and mid-range dependencies critical for early landslide indicators.
## 1 Introduction
Conventional skyline query algorithms can only process given values, such as price or distance from the hotel to the beach [12; 13]. For more complex problems, users must first perform all necessary calculations before inputting the values to the system. We aimed to remove this time-consuming step by designing a new method. In addition, the computation time of skyline query algorithms increases exponentially with the amount of input data [13; 21; 22]. An irrigated region is usually composed of many areas, so a method for simplifying the computation involved was necessary. Finally, skyline queries judge each objective fairly and independently but tend to produce too many solutions [23; 24; 25], particularly when the number of objectives is high. While water allocation problems are complex, if the output is inconvenient for decision-makers, the system will not fulfill its role of decision support. We, therefore, modified our approach to output more reasonable solutions. This paper contributes two novel algorithms to overcome the difficulties described above: a genetic algorithm (GA) to realize the decision support system and objective ranking to sort the solutions. We anticipate that the target system will produce \(n\) feasible solutions for each generation. The values of the decision objectives must be obtained for this generation, and a skyline query is performed to derive the fitness scores. The system must then generate the next generation of suitable solutions based on these fitness scores. As the number of generations increases, the system will identify the final skyline solutions. The logic of a GA is to (i) generate individuals, (ii) calculate fitness scores, and (iii) select outstanding individuals to survive. Thus, it is well suited to the problem, as skyline solutions can be obtained before all solutions are obtained. Furthermore, the GA distributes the skyline query algorithm to various generations for execution, so for each round, the number of solutions that the skyline query needs to process is not high. The second algorithm asks decision-makers to rank each decision objective in order of priority. Then, once the system has obtained all of the skyline solutions, it can calculate the degree to which each solution is likely to be favored. The solutions are, thus, ranked, increasing the ease of use for decision-makers. The effectiveness of the proposed method is verified using data collected from the largest irrigation system in Taiwan, the Cho Main Canal. The remainder of this paper is arranged as follows. Section 2 discusses related works. Section 3 introduces the dataset and the naive objective values we used in this work. Section 4 describes the details of our algorithms. Section 5 presents our simulation experiments and their results. Finally, we draw our conclusions and discuss future works in Section 6.
## 2 Related Work  
### Virtual Reality Serious Games for Natural Disasters  
Floods are one of the most recurring and damaging natural disasters, universally, with adverse aftermaths and economic losses affecting 1.6 billion people worldwide [15]. In the last 20 years, the Center for Research on the Epidemiology of Disasters (CRED) emergency events' database recorded 7348 natural disasters, claiming approximately 1.2 million lives. The majority of these natural disasters were floods and storms, with an average of 44% and 28%, respectively. Numerous hazardous flooding events in Kyushu, Nepal, Indian, and most recent ones in Uttarakhand have repeatedly reminded humans of this imminent threat. Training people for disaster preparedness or emergency evacuation during floods can reduce the risk of injury and provide a better likelihood of survival.  
In contrast to traditional disaster preparedness methods such as drills or seminars, extended reality (XR) technologies such as VR have gained prominence for training people at low cost through serious games (SGs) [16; 17]. SGs are defined as \"digital games that are used for purposes other than entertainment [18]\" VR SGs allow its users to experience digitally simulated scenarios that are impossible to be experienced in the real world due to cost, time, and safety.  
These VR SGs engage users by providing life-like scenarios, where they can interact with in-game objects, solve challenges, and become familiar with disaster preparedness methods [19] thus developing life-saving skills. Various VR SGs studies have been performed on the use of VR as a procedural training tool and provide empirical evidence on how VR training skills can be transferred to real-world [20]. Researchers [21] have investigated the impact of game-based learning for flood risk management and researched how training-based SGs can support lifelong learning in people.  
Several research studies have focused on understanding the underlying concepts and principles of disaster preparedness in VREs. A VR framework for disaster awareness and emergency response training was proposed by [PERSON] et al. [19] which presented a realistic VR gaming environment to increase public awareness, train and evaluate respondents in emergencies using simulated real-time flooding scenarios. Research has shown that participants experiencing a VR simulation of flooding showed increased motivation to evacuate, seek information, and preference to buy flood insurance compared to the other disaster preparedness methods tested [22].  
VR simulates a realistic environment in which a person can navigate and interact freely in the virtual environment. When VR is used to create training simulations such as flood evacuation, complex scenarios are formed inside the VRE. In order to guide the VR users inside these complex representations, effective design strategies incorporating navigational aids such as wayfinding cues are required [23].
## 2 Reverse Bias Characteristic of a PV Module Due to Voltage Mismatch Conditions
### Non-Uniformity of Irradiance  
An experiment, in which a reverse current flowed to the PV module by a non-uniformity of irradiance, was conducted to confirm the open-circuit voltage and current characteristics. The amount of irradiance was reduced by 100 W/m\({}^{2}\) from 1000 W/m\({}^{2}\) to 600 W/m\({}^{2}\), thereby simulating a non-uniformity of irradiance. The open-circuit voltage, through which the reverse current flowed, was repeatedly measured, while gradually increasing the voltage of the power supply from 0 V at each irradiance. The open-circuit voltage and current characteristics, as the reverse current begins to flow for each irradiance, are shown in Figure 2. At 1000 W/m\({}^{2}\), the reverse current began to flow from 37.14 V, which is the open-circuit voltage of the test PV module. At 900 W/m\({}^{2}\), which reduced the amount of irradiance by 100 W/m\({}^{2}\), the reverse current flowed from 36.42 V, which is 0.72 V lower than 1000 W/m\({}^{2}\). At 800 W/m\({}^{2}\), 700 W/m\({}^{2}\), and 600 W/m\({}^{2}\), the reverse currents flowed from 35.86 V, 35.36 V, and 34.91 V, respectively. From the experimental results, even if irradiance decreased to 400 W/m\({}^{2}\), compared to 1000 W/m\({}^{2}\), the open-circuit voltage difference of the PV module was 2.23 V. Figure 3 is the I-V curve of the PV module, measured when the irradiance was 1000 W/m\({}^{2}\) and 600 W/m\({}^{2}\). As shown in the figure, the PV module has the characteristics of the current significantly decreasing and the voltage slightly decreasing, when the irradiance is decreased. Due to these characteristics, even if the non-uniformity of irradiance occurs, the open-circuit voltage of the PV module does not decrease significantly, so the voltage mismatch between the PV modules is not significant. Therefore, even if the non-uniformity of irradiance on the PV modules occurs due to the soling of front glass or a cloud, it can be estimated that little or very little reverse current may flow to PV modules.  
Figure 1: (**a**) Schematic diagram and (**b**) experimental equipment for measuring the reverse voltage and current of a PV (Photovoltaic) module in voltage mismatch situations.
## 3 Numerical Simulation (FE)  
In order to validate the result of above analytical solution, finite element (FE) simulation results and experimental data are used to compare. In this section, the three-dimensional FE model was used to simulate the temperature evolution during the SLM process of In718. Heat conduction, Gaussian surface heat source and latent heat of phase change were considered. A commercial software, ANSYS, is used to implement the numerical simulation.
## 2 Materials and Methods
### Grid Partitioning  
To enhance the detection of small objects, the proposed NSSRD model employs grid partitioning. Large objects are easier to detect due to sufficient image detail and resolution [17]. In contrast, small objects, defined by a limited number of pixels, pose a challenge because detectors struggle to learn meaningful features and accurately identify them. To address this issue, the image is divided into smaller grids for detection. The partitioned images are resized to match the input size of the detector as follows:  
\[\mathrm{I}_{(i,j)}^{\mathrm{grd}}=\mathrm{I}\left[\frac{\mathrm{W}\times( \mathrm{i}-1)}{\mathrm{D}}:\frac{\mathrm{W}\times(\mathrm{i})}{\mathrm{D}}, \frac{\mathrm{H}\times(\mathrm{j}-1)}{\mathrm{D}}:\frac{\mathrm{H}(\mathrm{j} )}{\mathrm{D}}\right]. \tag{1}\]  
Here, W and H represent the number of pixels along the x-axis and y-axis of the image, respectively, and when W and H values are equal, the sub-images maintain a square shape; D represents the number of divisions along both the horizontal and vertical axes, resulting in D square sub-images.  
This process ensures that the partitioned images retain a square shape when input into the detector, preventing any loss of object information due to aspect ratio distortion. Such distortion can alter object features and degrade detection performance, making its prevention critical. When the original image (Figure 2a) is partitioned according to Equation (1), it results in square sub-images as shown in Figure 2b. This grid partitioning approach enhances the clarity of small objects, improving both detection accuracy and reliability.
# Caroenoid Production from Microalgae: Biosynthesis, Salinity Responses and Novel Biotechnologies
## 3 Salt-Stress Treatment for Caroenoid Production from Microalgae  
Microalgae have flexible metabolic systems, which means that external environmental changes will affect biochemical components of microalgal cells. Stress factors, such as high light intensity, salinity, nitrogen starvation and high/low temperatures, will provoke metabolic changes in microalgae and lead to different cell compositions.  
Though carotenoid production could be boosted by several environmental abiotic stress factors, salt-stress treatment is much more attractive because it is cheap, easy to operate and has great commercialization value for seawater culture of microalgae. Here, we elucidate the contributions of salinity in microalgal culture and production of carotenoids and summarize the current strategies of salt-stress treatment in carotenoid production.