esa-sceva/satcom-chunk-collection huggingface dataset:
# Town-Watching Workshop Using Disaster Information Tweeting and Mapping System +
## 3 Town-Watching Workshop
### Questionnaire Survey  
#### 3.3.1 Preliminary Survey  
A preliminary survey was conducted before the workshop. The questions are presented below. Figure 11 shows the results for Q.1-1 to Q.1-4. Table 2 shows the answer distribution of Q.1-3 by participant characteristics (gender, age groups, and groups of smartphone owners/non-owners).  
* Q.1-1: What type of cellular phone do you have? Answer choices: 1. Both smart and feature phones 2. A smartphone only 3. A feature phone (conventional cellular phone) only 4. None
* Q.1-2: What type of damage did you experience during the Hokkaido Eastern Iburi Earthquake? Please check all that apply (this question allows multiple answers). Answer choices: 1. Power was cut off 2. Water was cut off 3. Gas was cut off 4. Contact with his/her family was lost 5. Evacuation 6. Others 7. No damage
* Q.1-3: Do you think that posting on SNS, such as Twitter and Facebook, during disasters is helpful for mitigating damages? Answer choices: 1. Strongly disagree 2. Disagree 3. Neutral 4. Agree 5. Strongly agree
* Q.1-4: Select appropriate choice(s) about your experiences regarding the use of SNS during disasters. Please check all that apply (this question allows multiple answers). Answer choices: 1. I posted the pictures or texts about my suffering in a previous disaster. 2. I shared or retweeted other user's/users' post about my suffering in a previous disaster.  
* I shared or retweeted other user's/users' post since I never suffered a disaster. (4) I have never used SNS.
* Broadcasting cars should be provided to share local information.
- Safe evacuation sites, food and water for an emergency, evacuation support for vulnerable people, and local disaster prevention plan are required.
- Reliability of the infrastructure is important for hospitals supporting elderly people, and constant power supply is required in the houses where people with respiratory disturbance are living.
- Sufficient information about evacuation sites was not provided.
- Sharing information procedure about evacuation sites (e.g., whether the evacuation sites are open or not) is required.
- The utilization of SNS is required.
- We need to understand the routes and the location of the evacuation sites and routes to reach there in advance.
- The system to support elderly people living alone has not yet been provided. Many high school or college students do not join self-government associations; therefore, a system to support these people is needed in case of emergency.
- No preparation for lack of power.
- Drills lecture classes are necessary several times a year to know evacuation sites and understand the quantity of required foods and drinks during the emergency.
- Information sources and people's conduct during an emergency should be known in advance. In an emergency situation, information is difficult to obtain because of the power cut off.
- It was difficult to communicate relevant information.  
Figure 11: _Cont._
## 4 Experiments
### Performance Comparison  
We compared our method with previous works in the literature that utilized the IBM dataset, as summarized in Table 8. When compared with the most advanced techniques, our approach performed better. Notably, most prior studies focused on classical machine learning models. For instance, the authors of [12] used a Decision Tree model and achieved an accuracy of 82.44%. Logistic Regression (LR) was employed by several researchers, with [PERSON] [87] achieving 87% accuracy, the authors of [9] achieving 87.5%, and [PERSON] et al. [15] improving the accuracy to 88.43% using LR. Another effective model for predicting employee attrition was Support Vector Machine (SVM), which achieved 88.44% accuracy in [88], while the authors of [89] also reported 84% using another SVM variant. [PERSON] et al. [36] used L-SVM, achieving 87% accuracy, and further improved it to 92.5%  
Figure 14: The impact of each feature on employee turnover prediction in the Kaggle dataset.  
Figure 13: The ROC-AUC diagrams of the proposed model on Kaggle dataset.  
by combining features. Random Forest (RF) was another commonly used machine learning model, achieving accuracies of 80% (35), 85.11% (90), and 87.298% (17). Boosting algorithms also showed strong performance, with XGBoost achieving 86% (89) and CatBoost achieving 89.45% (26). Deep learning models have also been explored, with [PERSON] et al. (30) using a Deep Neural Network (DNN) to achieve an accuracy of 89.11%. [PERSON] et al. (37) introduced a hybrid Genetic Algorithm-Autoencoder-KNN model, achieving an accuracy of 90.95%.
# Effect of Topography Truncation on Experimental Simulation of Flow over Complex Terrain
## 2 Experimental Method
### Experimental Set-Up  
The testing facility was the wind tunnel laboratory of Central South University, which is a closed-circuit atmospheric boundary layer wind tunnel with two test sections. The test was carried out in the low-speed section with a width of 12 m and a height of 3.5 m. The test wind speed was set to 10 m/s with a uniform profile. The incoming flow came from the west to the island, which is the most critical wind direction based on the local meteorological statistics. Furthermore, the altitude difference caused by topographic truncation is most obvious on the west side due to the high-west and low-east topography of the island. The Reynolds numbers based on model height \(h\) can be calculated as \(Re\) = U \(h\)/\(\
u\), where U is the streamwise velocity and \(\
u\) = 1.47 \(\times\) 10\({}^{-5}\) m\({}^{2}\)/s is the kinematic viscosity of air at 20 \({}^{\circ}\)C. The value of Reynolds numbers in this study was approximately 3.0 \(\times\) 10\({}^{5}\), which is in the range of 1.7 \(\times\) 10\({}^{5}\)-4.6 \(\times\) 10\({}^{5}\) studied by [PERSON] et al. [1]. According to their research, the flow behavior is generally less affected by the Reynolds number. For the other nondimensional numbers determining the flow similarity between the wind tunnel and the real island, the Richardson number and the Eckert number similarities were satisfied due to the consideration of the neutrally stratified boundary layer, and the Prandtl number similarity was satisfied by the air working fluid. Depending on the early study of flow over complex terrain [34], the Rossby similarity was relaxed because of the practical difficulties of modeling the Coriolis force in the wind tunnel. The Froude similarity was also relaxed, because no thermal stratification was reproduced in the wind tunnel.  
A turbulence flow instrument cobra probe with a high-frequency up-to 10 kHz and measurement accuracy within \(\pm\)0.5 m/s was used to measure the velocity at different locations on the terrain model. In this study, the data were sampled at 2 kHz. A minilaser range finder with measurement accuracy within \(\pm\)1 mm was used to measure the distance above the ground.
## 7 Conclusion, Limitations, and Future Research Work
### Conclusion  
During this research, multiple points of observation and concern have been concluded from data response and analysis, such as:  
* The industry professionals rate the impact of Customer Engagement and Employee Cultural Environment as very high.
* The ranking of the impact of critical success factors, such as Product Maintenance, Research, and Development, Usage of International Standards, and Political conditions, is very low. The average decomposed weight from the AHP process shows that construction industry rates impact investment in the Research and Development of in-house technologies to a low and medium extent. They mainly rely on products from computing firms.  
\begin{table}
\begin{tabular}{c c c} \hline
**Rank Category (C)** & **Predicted Rank Category (E)** & **ABS(1-(E/C))** \\ \hline
1 & 1 & 0.00 \\
2 & 1 & 0.50 \\
4 & 1 & 0.75 \\
5 & 5 & 0.00 \\
1 & 1 & 0.00 \\
2 & 2 & 0.00 \\
3 & 1 & 0.67 \\
2 & 5 & 1.50 \\
3 & 1 & 0.67 \\
1 & 1 & 0.00 \\
3 & 5 & 0.67 \\
1 & 1 & 0.00 \\
2 & 2 & 0.00 \\  & AIP\% & 36.11 \\ \hline \end{tabular}
\end{table}
Table 9: Validation results.  
* The political condition in the USA is more conducive. In a previous study, in the survey which was sent out to middle eastern countries, the impact of political conditions factor was rated as high and very high [42].
* The developed Hierarchical Fuzzy Model is based on the expert opinions of industry professionals. It is necessary to establish specific parameters or rubric, following which they answer the questionnaire. Personal bias leads to extensive outliers, thus making the model inefficient.
* It is also observed that the majority of participants have rated the employee culture environment of their respective organizations as high and very high. However, the rating for employee compensation is low. This either represents that the employees of these organizations are not paid well, or the participants favor a better employee culture environment over employee compensation. This represents a personal bias in the responses.
# Automated Geological Features Detection in 3D Seismic Data Using Semi-Supervised Learning
## 2 Seismic Data and Geological Background  
### SEAM Synthetic Seismic Dataset  
The SEAM Phase I synthetic seismic dataset was designed to represent a deepwater Gulf of Mexico salt domain field. This dataset was modeled with complex and realistic geological features, including faults, overturned beds, overhanging salt, turbidite fans, and braided stream channels reservoir [21].  
The main advantage of using synthetic seismic data is that the location of geological features is known in the seismic section. Thus, it will be convenient to evaluate the proposed workflow and model on this synthetic dataset. The salt body is the main geological feature in the SEAM dataset. This salt body is reflected in the behavior of velocity values. The velocity in salt body is generated as a uniform random distribution of values centered at 4280 m/s [21]. Figure 1 shows the velocity values on SEAM seismic section.  
According to the velocity values, this salt body is made up of two parts: the overhanging salt and the mother salt. According to Figure 2, the convex surface at the bottom of the figure represents the mother salt, while the overhanging salt is shown in the middle of the figure.
# Process Parameter Optimisation in Laser Powder Bed Fusion of Duplex Stainless Steel 2205
###### Abstract  
Additive Manufacturing (AM) appears as a very interesting alternative to conventional production routes for alloys and metals, thanks to the fact that at the end of printing, the final product is obtained directly. The present article looks for the inclusion of duplex stainless steel 2205 (DSS-2205) in the commercial catalog of steels utilized in powder bed fusion (PBF) technologies, specifically applying the selective laser melting (SLM) technique. The main objective is to establish optimal printing parameters that reproduce the closest results to the base material properties. To achieve this, the response surface method was used in the methodology and experimental design, studying the parameters of laser power, scanning speed, and hatching distance. A reference material, machined from a hot-rolled plate, was utilized to compare the results obtained through tensile strength. Lastly, the optimal parameters have been obtained for this stainless steel. Additionally, a study of heat treatments has been developed, aiming to optimize the austenitization process, achieving an improvement in mechanical properties. A steel with mechanical properties practically identical to those of steel produced using conventional techniques has been obtained through SLM.  
metal additive manufacturing; laser powder bed fusion; selective laser melting; process optimization; duplex stainless steel 2205 +
Footnote †: journal: Applied sciences  
Acticle
## 4 The Proposed Method for Predicting the Reactive Power Adjustment
### Reactive Power Optimization Method Based on Big Data  
The big data reactive power optimization (BDO) method presented in this paper is targeted to solve the dynamic RPO problem in distribution network. It optimizes dispatching scheme of reactive power control devices of the day ahead, based on forecasting load, reducing active power losses and making voltage quality better. Compared with the traditional optimization method based on exact mathematical models, the big data RPO method relies on the historical RPO empirical data. By calculating the load similarity between the forecasting load random matrix and the historical load random matrix, dispatching scheme of the day ahead can be obtain from the best matching historical RPO dispatching scheme.
# Synthesis of Non-Linguistic Utterances for Sound Design Support Using a Genetic Algorithm
### Related Work on Design Methods, Models, and Systems for Producing NLUs  
Researchers [16; 22; 23] have drawn inspiration from human speech, music, and animal sounds when studying and generating NLUs. Compared to these sounds, NLUs are abstract, lack defined rules, and are not as easily recognizable. They are more open to interpretation and can originate from a wide variety of existing and imaginary sources.  
[PERSON] et al. [23] used music theory and notation to design NLUs for happiness, sadness, fear, and disgust. Experiments were conducted to test emotion recognizability when using facial expressions, and NLUs together with facial expressions. [PERSON] et al. [5] suggested that the use of NLUs in some form or other as part of the communication system of a social robot would enhance its expressiveness. They developed a novel approach to the creation of NLUs, which was based on a \"sonic expression system\". They used a dynamic approach involving the modulation of parameters based on the context of the interaction between the robot and the user. The researchers proposed a concept called the quason, which they defined as \"the smallest sound unit that holds a set of indivisible psycho-acoustic features that makes it perfectly distinguishable from other sounds, and whose combinations generate a more complex individual sound unit\". The main parameters of the quason are amplitude, frequency, and duration. Individual quasons combine to form sonic expressions. Such expressions were created for communicative acts including agreement, hesitation, denial, questioning, hush, summon, encouragement, gre greeting, and laughter. Each sonic expression, made with the help of a sound designer, was made at three intensity levels, and was evaluated by 51 subjects in an experiment. The results of the experiment showed that, while most sounds were categorized correctly, some categories, such as agreement, encouragement, and greeting, were easily confused with each other. They also noted that deny, laughing, question, summon, and hush were more easily distinguishable. The researchers recommended integrating their Sonic Expression System into a full, multi-modal, communication system for robot agents. In addition, they determined that fundamental frequency, pauses, volume contour, rhythm, articulation, and speech rate affected the perceived emotion.  
[PERSON] et al. [6; 24] modeled NLUs in terms of dialogue parts using DAMSL (Dialogue Act Markup in Several Layers). A total of 53 sounds were created using PureData [25] and combining and modulating sine, saw, and square waveforms, in a similar way to [PERSON] et al. [5] while randomly varying the number of notes and frequencies of each note. A total of 31 subjects evaluated the sounds based on communicative dialogue actsincluding greeting, reject, question, thanking, accept, apology, non-understanding, and exclamation. Analysis including factor analysis showed that the pitch, timbre, and duration of the sounds had important effects on how participants interpreted them.
## 3 Security and Privacy-Preserving Solutions for IoE
### Edge AI and Secure Computing in IoE
#### 3.2.4 Edge AI Security Mechanisms for IoE  
In IoE environments, various AI-based security mechanisms are directly implemented at the edge layer to enhance security and privacy [15; 16; 108]. These methods enable real-time decision-making, significantly reducing cloud dependency and minimizing latency in data processing and threat detection [100; 104; 105]. Table 10 summarizes the core Edge AI security mechanisms that contribute to building trusted and resilient IoE ecosystems.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Mechanism** & **Function** & **Security Benefits** & **References** \\ \hline AI-Driven Intrusion Detection (Edge IDS/IPS) & Detects and mitigates real-time cyber threats at IoE edge nodes & Faster threat response, reduces cloud dependency & [25; 108] \\ \hline FL on Edge Devices & Local AI model training on edge devices without sending data to centralized servers & Prevonts data exposure, supports privacy compliance & [23; 65; 109] \\ \hline Adversarial AI Defense at the Edge & AI models that detect and counter adversarial attacks on IoT networks & Enhances robustness against AI-based cyber threats & [29; 30; 57; 64] \\ \hline Threat Intelligence and Anomaly Detection & AI continuously monitors edge devices for abnormal behavior & Prevonts zero-day attacks, malware propagation & [26; 31; 59] \\ \hline \hline \end{tabular}
\end{table}
Table 10: To enhance security, Edge AI employs the following mechanisms.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Criterion** & **Traditional AI** & **Federated Learning** \\ \hline Data Privacy & Centralized & Local Processing \\ Latency & High & Low \\ Security Risk & High & Low \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison between traditional AI and federated learning.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Model** & **Accuracy (\%)** & **Comm. Overhead (MB)** & **Convergence Rounds** & **Latency (ms)** \\ \hline FedAvg & 88.7 & 3.2 & 120 & 850 \\ FedProx & 90.1 & 3.4 & 110 & 910 \\ Quantum FL (QFL) & 91.8 & 2.1 & 100 & 760 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparative FL model performance and communication latency.  
The mechanisms listed in Table 10 demonstrate how AI is leveraged at the edge level to counter modern cybersecurity threats in IoE. Each method serves a unique function: AI-based IDS/IPS systems provide real-time threat detection and rapid response, while FL protects data privacy by performing model training locally. Adversarial AI defense mechanisms increase model robustness against manipulated inputs, and anomaly detection enables continuous monitoring to identify unknown or zero-day attacks. When these approaches are evaluated collectively, they provide a scalable foundation for creating secure and intelligent edge computing frameworks, reducing reliance on centralized infrastructures.
## 3 Results
### ASA and Anti-Fas Ab Impact on the Phenotype of Hct116 and Ht29-Derived Colonospheres  
Cells of CRC lines cultured in the form of colonospheres and treated for 10 days with ASA and/or anti-Fas Ab were subjected to cytometric analysis of commonly used stem cell surface markers. We have previously presented that HT29 and HCT116 cell lines cultured in spherical form are highly heterogeneous and enriched in cells bearing some CSC-like features [20; 23]. Multicellular spheroid model of cancer cells' expansion seems to partially simulate naturally-occurring heterogeneity in regard to cellular morphology, heterogeneous exposure to environmental factors and specific gene expression [27; 28]. Since the CSCs are the most important clinical challenge we decided to focus our attempts on this critical cancer cells' population. In the current study we could observe that the influence of treatment on colonosphere cells depended on the cancer cell line used (Figure 3).  
Our results revealed that the percentage of CD133+, CD44+ and CD29+ cells in both CRC cell lines presented the same pattern of changes. We could observe that ASA reduced the number of CSCs bearing particular markers and ASA combined with anti-Fas Ab-intensified this phenomenon (Figure 3A-C). The most significant differences were observed for MFI values of CD29 protein. CD133+ cells number significantly decreased after the combined treatment (Figure 3A), and this turned out to dep  
Figure 3: The cytometric analysis of HCT116 and HT29 cells expanded 10 days in spherical forms with agonistic anti-Fas antibody (200 ng/mL) and/or aspirin (ASA) (2.2 mM or 1.8 mM for HCT116 or HT29, respectively). Y-axis presents mean fluorescence intensity (MFI) related to unstained control. Statistically significant differences were assessed by Kruskal-Wallis test followed by Dunn’s test as a post hoc procedure or U Mann-Whitney test. Bars and whiskers represent median \(\pm\) interquartile range (”_p_ < 0.05 vs control unstimulated cells, \(\bullet\)_p_ < 0.05 ASA vs ASA/ anti-Fas antibody, \(n\) = 12 for each option). Triplicate independent experiments were performed. (**A–D**). The analysis of CD133+, CD44+, CD29+ and Fas+ colorectal cancer stem cells (CSCs), respectively.  
(data not shown). Subsequently, we checked cytometrically how the introduced treatment influenced CD133+ Fas+ (CD95+) cell number among both cancer cell lines (Figure 3D). HCT116 and HT29 cells reacted in similar way with significant elevation of MFI values after simultaneous treatment with ASA and anti-Fas Ab.
# A Feedback Control Method to Maintain the Amplitude of the RF Signal Applied to Ion Traps
## 3 FPGA-Based Feedback Control
### FPGA-Based Feedback Controller
#### 3.3.2 Aluminum Enclosure  
The magnetic field around the feedback controller can affect the cables connected to the DAC, ADC, FPGA, and printed circuit board (PCB). The devices are mounted inside the aluminum enclosure to shield them from outside electromagnetic fields. We use two linear  
Figure 4: A diagram of the finite-state machine of the FPGA-based feedback controller.  
Figure 3: A block diagram of the closed-loop feedback control system for reducing the fluctuation of the amplitude of the RF signal, using the field-programmable gate array (FPGA)-based feedback controller.  
power suppliers to supply power with little noise to the DAC and PCB, including two regulators, a voltage adder, and a voltage subtractor. The resolution of the FPGA-based feedback controller is increased by the voltage subtractor and voltage adder. In the circuits, regulators (STMicroelectronics LM317, Geneva, Switzerland) are used. The op amps (Texas Instruments LM324, Dallas, TX, USA) are used in the circuits, such as the voltage follower, voltage adder, and voltage subtractor. The aluminum enclosure and the layout of the PCB are shown in Figures S1 and S2, respectively.