esa-sceva/satcom-chunk-collection huggingface dataset:
# Safety Assessment of Microwave Breast Imaging: Heating Analysis on Digital Breast Phantoms
## 1 Introduction
### Safety Assessment of Microwave Technology
This approach enables the assessment of the thermal effects of electromagnetic fields on biological tissues in the microwave frequency range, moving beyond the reliance on reference quantities commonly used in guidelines, such as SAR. The study emphasizes temperature as it is the physical metric most closely related to biological effects and is easily interpretable. Quantification of thermal effect aids in evaluating the risk/benefit profile of the diagnostic tool. The system considered here is the MammoWave device [11] (UBT Srl, Perugia, Italy), consisting of a transmitting horn antenna rotating around a holder where the patient's breast is located during the examination. The physical model adopted in this study for estimating the induced temperature increases could be applied to a range of breast MI devices, with minor necessary adjustments, in case they operate on the same principle.
# Buckling Analysis of CNTRC Curved Sandwich Nanobeams in Thermal Environment
## 5 Results and Discussion
### Comparison Study  
We start the numerical analysis by a comparative evaluation of our results with predictions from the open literature, while including possible thickness stretching effects. In Table 4, we summarize the results in terms of dimensionless critical buckling load for SS- and CC-CNTRC sandwich beams with and without thickness stretching effects and compare their accuracy against the numerical predictions by [PERSON] et al. [6], based on a differential quadrature method (DQM). The face sheets are made of poly methyl methacrylate (PMMA) as matrix, with \(E_{m}\) = 2.5 GPa and \(v_{m}\) = 0.3, and armchair (10, 10) SWCNTs as reinforcement phase, with \(E_{11}^{cnt}=5.6466\) TPA, \(E_{22}^{cnt}=7.08\) TPa, \(G_{12}^{cnt}=1.9445\) TPa and \(v_{cnt}=0.175\) (in 300 K). Titanium alloy (Ti-6 Al-4V) is used as core, with \(E_{m}=113.8\) GPa and \(v_{m}=0.342\). It is worth noticing the good correlation between our results (see Table 4) and the findings of [6] when the thickness stretching effect is neglected.
# Research on Fault Diagnosis Method for Rotating Machinery Based on Edge Computing
## 5 Experimental Validation
### Performance Evaluation Methods
#### 5.3.1 Average Accuracy  
As shown in Equations (18) and (19), the CNN-DS is run NNN times, and the accuracy for each run is defined as the percentage of correctly classified samples. The final accuracy is defined as the average accuracy over NNN simulations.  
\[\text{Accuracy}=\frac{\sum_{i=1}^{\text{N}}\text{Y}_{i}}{\text{N}} \tag{18}\]  
\[\text{Y}_{\text{i}}=\frac{\text{Y}_{\text{i}}}{\text{M}} \tag{19}\]  
where \(\text{y}_{\text{i}}\) is the number of correctly classified samples, M is the total number of samples, and N is the number of simulations. The M values for each label are summarized in the table. Without loss of generality, in this study, N is chosen as 30.
# Seamless Weather Data Integration in Trajectory-Based Operations Utilizing Geospatial Information
## 3 Performance Evaluation of the 4 DT-Wx Prototype System
### Performance Analysis of MET Data Processing on 4 DT-Wx Prototype System  
In this study, the performance of a distributed processing system using Apache Spark was analyzed from various perspectives. Particularly, we focused on the impact of the number of partitions and the buffer configuration on the system performance and observed performance changes according to various combinations of the number of files and partitions. The number of files was varied from 50 to 2000 and the number of partitions from 4 to 256 to measure the key performance indicators. Based on the data obtained through these extensive experiments, we directly and indirectly evaluated the parallelization performance by applying various methodologies such as speedup, optimal partitioning, efficiency, and suitability analysis.
# Efficient Argan Tree Deforestation Detection Using Sentinel-2 Time Series and Machine Learning
###### Abstract  
The organ tree (_Argania spinosa_) is a rare species native to southwestern Morocco, valued for its fruit, which produces organ oil, a highly prized natural product with nutritional, health, and cosmetic benefits. However, increasing deforestation poses a significant threat to its survival. This study monitors changes in an organ forest near Agadir, Morocco, from 2017 to 2023 using Sentinel-2 satellite imagery and advanced image processing algorithms. Various machine learning models were evaluated for organ tree detection, with LightGBM achieving the highest accuracy when trained on a dataset integrating spectral bands, temporal features, and vegetation indices information. The model achieved 100% accuracy on tabular test data and 85% on image-based test data. The generated deforestation maps estimated an approximate forest loss of 2.86% over six years. This study explores methods to enhance detection accuracy, provides valuable statistical data for deforestation mitigation, and highlights the critical role of remote sensing, advanced image processing, and artificial intelligence in environmental monitoring and conservation, particularly in organ forests.  
tree detection; change detection; deforestation; remote sensing; Sentinel-2; Google Earth Engine; machine learning; classification; phenology 2020
# Multipoint Thermal Sensing System for Power Semiconductor Devices Utilizing Fiber Bragg Gratings
## 3 Sensors' Calibration and Installation  
This section describes the practical installation procedures for the FBG sensors utilized in this study. The temperature-wavelength (\(T-\lambda\)) relationship was measured both before and after integrating the sensors into the SEMIKRON inverter. This is necessary to obtain the sensitivity of the sensor, and to determine whether the proposed installation meets the requirement for onsite calibration. In addition, the performance of the sensors was examined in different installation configurations. The details of the FBG sensors and the process of installation are discussed in the following subsection.
# Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication
## 4 Analysis of Experimental Results
### Suggestions for Future Experiments  
By processing and analyzing the actual data in the water tank experiment, we have thought about the problems that may be encountered in future experiments. In order to deal with these problems, we will provide suggestions and references for all scholars to pay attention to the process of experimental verification in the future.  
* Influence of experimental equipment  
It is necessary to consider the negative impact of the PA on the transmitted signal in the experiment. When the PA amplifies the signal, it usually introduces the corresponding nonlinear distortion [36]. The additional high-order components obtained after the PA will cause certain distortion to the amplitude and phase of the signal. This influence will produce phase modulation components and cause clutter interference. Digital pre-distortion technology [37] can be used to compensate for the nonlinear distortion caused by the PA by distorting the signal before passing through the PA in the digital domain to reduce the impact on the performance of the neural network. In addition, the frequency response of the preamplifier, filter, and transducer will also affect the signal waveform. It is necessary to adopt more strict standards to screen the instruments and equipment that may be used in the test process.  
* Variation of UWA channel and coping strategies  
The environment of the water tank is relatively stable and the channel conditions will not change much. However, the marine environment is more complex and changeable, and the channel will change due to marine dynamic factors such as sea surface floating. It is necessary to \"saturation train\" the network model. For the specific application scenario of underwater fixed communication nodes, it can be considered to wake up the underwater nodes regularly in non-working hours, send training signals to the receiver to obtain more diverse UWA channel information, and periodically strengthen the neural network model to improve the performance of the system.
## 1 Introduction
### State of the Art
#### 1.1.1 Concept of the National Institute of Environmental Studies of Japan  
The National Institute of Environmental Studies of Japan (NIES) proposes a floating wind farm that is not moored [2]. The concept has multiple wind turbines arranged in a line on semi-submersible hulls, as shown in Figure 1. The energy is converted to hydrogen by electrolysis of seawater and later transported to shore by a supply ship. The whole platform is mobile, which allows it to avoid storms and increase energy yield. Based on simulation studies, an annual average capacity factor of 42.6% is expected to be achieved [3].
# Optimized Scheduling of IoT Devices in Healthcare Facilities: Balancing Cost and Quality of Care
## 1 Introduction
This enables scalable, interpretable scheduling with better resource use, improved patient-device matching, and transparent trade-off evaluation. This paper makes several key contributions to the field of healthcare resource optimization through the design, formulation, and implementation of a bi-objective scheduling model for IoT device allocation. First, it introduces a novel optimization framework that explicitly balances operational costs with quality of care--two often conflicting objectives rarely addressed together in prior work. By modeling real-world constraints such as patient priority, device compatibility, setup times, and availability windows, the proposed formulation reflects the complexity of clinical environments more accurately than existing approaches. Second, the paper develops and tailors a heuristic algorithm based on NSGA-II, capable of generating a diverse set of high-quality solutions under realistic time constraints, which is essential given the NP-hard nature of the problem. Third, the method is designed to support real-time, adaptive re-optimization, allowing healthcare facilities to respond dynamically to changes such as new admissions or evolving patient conditions. From a practical standpoint, the system has broad applicability in modernizing healthcare facility operations, offering a scalable and interpretable decision support tool that enhances efficiency and patient outcomes. It can be deployed across various healthcare settings--from small outpatient facilities to large multi-ward hospitals--where it automates complex planning tasks, reduces human error, and improves responsiveness to clinical needs. By integrating intelligent scheduling into real-world medical center workflows, this work extends current scientific understanding of IoT-enabled healthcare optimization and bridges the gap between theoretical modeling and operational decision-making in digital healthcare environments. The topic of this paper strongly aligns with the scope and objectives of ''_Applied Sciences''_ and its Special Issue ''_The Internet of Things (IoT) and Its Application in Monitoring''_. By addressing a real-world challenge at the nexus of IoT deployment, healthcare optimization, and intelligent scheduling, our proposed framework directly contributes to the advancement of practical, data-driven solutions for patient monitoring. This work not only highlights the increasing importance of IoT in enhancing clinical operations but also introduces methodological innovations that support the journal's mission of connecting applied research with tangible technological impact.
# Graphene-Based Flexible Electrode for Electrocardiogram Signal Monitoring
## 2 Research on Graphene-Based Flexible ECG Electrodes
### Modified-Graphene-Based ECG Electrodes  
Appropriate modifications can give materials superior properties, and graphene is no exception as a material for ECG electrodes. In this section, we will introduce some typical graphene modification methods, including physical doping and chemical modification, that give graphene electrodes better performance.
# A Bayesian Control Chart for Monitoring Process Variance
## 4 The Construction of the EWMA Variance Chart with the Bayesian Approach
### Evaluation of the EWMA Variance Control Chart  
To realize the performance of the proposed Bayesian EWMA variance chart, the out-of-control ARL will be calculated. First of all, the coefficients of the control limits, \(k_{1}\) and \(k_{2}\), are determined from the procedure described in the previous subsection. When the process is out-of-variance-control and the parameters of the prior distribution of \(p\) are changed to \(\alpha_{1}\) and \(\beta_{1}\), the \(ARL_{1}\)s of the proposed Bayesian EWMA variance chart are evaluated by the Monte Carlo simulation approach. The procedure of evaluation is presented as follows:  
1. Given \(\alpha_{0}\), \(\beta_{0}\), \(n\), \(\lambda\), \(k_{1}\), and \(k_{2}\), then \(UCL_{EWMA}\) and \(LCL_{EWMA}\) can be calculated by Equations (31) and (32), respectively;
2. Let \(EWMA_{0}\) be equal to the mean of \(EWMA\), i.e., \(n\alpha_{0}/(\alpha_{0}+\beta_{0})\);
3. Simulate random numbers \(M_{t}\) from \(Beta\)-\(Binomial(n,\alpha_{1},\beta_{1})\) and compute \(EWMA_{t}\) by Equation (25) until \(EWMA_{t}>UCL_{EWMA}\) or \(EWMA_{t}<LCL_{EWMA}\), then record the run length;
4. Repeat step 3  
The results in Table 4 show that the \(ARL_{1}\) decreases quickly as \(E_{1}(p)\) shifts away from \(E_{0}(p)\) in each set of sample size, documenting that the proposed Bayesian EWMA variance chart possesses excellent detecting power for any shift of the process variance.
## 3 Materials and Methods
### Model Validation
Finally, three result attributes were identified in the last layer using a sigmoid activation function. Finally, the model was compiled with the instruction to perform a binary cross-entropy algorithm with the Adam optimizer. Model validation, which enables the classification of the video gamer level, reveals a high degree of accuracy in both the training and validation phases. The results demonstrated a precision of 99.6% in the training phase and 99.83% in the validation phase. These findings suggest that the Feedforward Neural Network architecture used in this study is highly effective in accurately classifying the level of a video gamer. The high accuracy achieved by the model in this study can be attributed to the ability of Feedforward Neural Networks to learn complex patterns and relationships within the data. During the training phase, the network adjusts the weights of its connections to minimize the difference between the predicted and actual outputs. This learning process continues until the network can accurately classify new data. The deep learning model's predictions are represented by ranges from 0 to 2 and divided into three distinct categories based on the player's level of expertise. A prediction of 0 indicates that the player has a high level of experience in the game, whereas a prediction of 1 suggests an intermediate level of experience. Finally, a prediction of 2 indicated that the player had a low level of experience in the game. Dynamic difficulty adjustment (DDA) was developed with this deep learning model to adjust game difficulty based on player performance. When the model classifies the player as having a high level of experience (0), the game difficulty increases by 3. When a player is classified as having an intermediate experience level (1), the game difficulty increases by 1. Conversely, when the player is classified as having a low level of experience (2), the game difficulty is decreased by one, allowing for the dynamic adjustment of game complexity based on the player's detected level. This approach to DDA has significant implications for game design as it creates personalized and challenging gaming experiences that can adapt to the player's skill level in real-time. Furthermore, this approach can enhance player engagement and satisfaction by providing a more immersive and enjoyable gaming experience. To validate the player engagement through DDA and adjust the game difficulty in real-time based on the player's performance, we conducted a user experience evaluation. The following section presents a study with users to evaluate the effectiveness of DDA in enhancing player engagement in the video game, Astro. The evaluation aimed to investigate how the DDA algorithm adapts to a player's skill level and affects the game experience. The findings of this study provide insights into the potential of DDA to improve player engagement and inform the development of future games. Finally, Figure 8 presents a flowchart to illustrate our research framework visually. This diagram offers readers a quick overview of the research process.
# A Multilayer Data Processing and Aggregating Fog-Based Framework for Latency-Sensitive IoT Services
## 6 Conclusions  
This work proposes a multilayer data processing and aggregation fog-based framework. The proposed framework meets the requirements of IoT context: wide distribution, massive uploading, latency-sensitive, location awareness, and real-time interaction. For evaluation, extensive experiments were performed to evaluate the response time and the delay time of the proposed framework and its aggregation rate. Results show that comparing with the traditional cloud architecture, CFC framework reduces the average response time (i.e., whether the newest data or the historical data are being queried). Results show that the CFC framework has a better behavior than the conventional cloud architecture since fog nodes are placed closed to the clients allowing the response time to be noticeably minimized, while the cloud is placed in a distant region.  
The proposed framework also minimizes the amount of data transferred to the cloud. Data from multiple clients will be aggregated and processed at the nearest fog node before being transferred to the cloud. Data aggregation will be performed on IoT clients that are located on the same geographic region before being transferred to the cloud, which is potentially located in a distant geographic region. Many tests, adaptations, and experiments have been left for future work (i.e., performing additional experiments with real data, implementing additional services for smart city applications). Future work concerns deeper analysis of the proposed framework, such as adding a load-balancing algorithm between fog nodes.  
Conceptualization, E.-Y.D.; methodology, M.-C.W.; formal analysis, M.-C.W.; writing--original draft preparation, M.-C.W.; writing--review and editing, E.-Y.D.; supervision, S.-M.Y.; and project administration, S.-M.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external fund.  
Not applicable.  
Data supporting this study are available within the article.  
We thank National Chiao Tung University (NCTU) and Palestine Technical University--Kadoori (PTUK) for their support.  
The authors declare no conflict of interest.
## 3 System Description
### System Constraints  
The constraints are defined to guarantee that vehicle motion complies with the mechanical and safety constraints. Specifically, the vehicle's speed is not allowed to reverse at any time, and it should also not exceed the maximum speed \(v_{max}\). The vehicles' acceleration is bounded between minimum acceleration \(a_{min}\) and maximum acceleration \(a_{max}\). Thus, the mechanical constraints are formulated as:  
\[0\leq v_{i}(k)\leq v_{max},\;i\in\{1,2,\ldots,N\}\;\text{and}\;k\in\{0,\ldots,N-1\} \tag{27}\]  
\[a_{min}\leq a_{j}(k)\leq a_{max},\;j\in\{1,2,\ldots,m\}\;\text{and}\;k\in\{0, \ldots,N-1\} \tag{28}\]  
To prevent the risk of rear-end collisions between vehicles, the distance between the adjacent vehicles should be greater than or equal to the minimum distance \(d\). Thus, the safety constraint is represented as:  
\[x_{i-1}(k)-x_{i}(k+1)\geq d,\;i\in\{1,2,\ldots,N\}\;\text{and}\;k\in\{0,\ldots, N-1\} \tag{29}\]  
where \(x_{i-1}\) and \(x_{i}\) are the preceding and following vehicles, respectively. The value of \(d\) is related to the vehicles' length and it is set as \(5\) m because the length of most vehicles falls between \(4\) and \(4.5\) m.  
In mixed traffic flow, another safety constraint involves ensuring the satisfaction of the safety threshold metric of the vehicle under speed control, expressed by the time-to-collision (TTC) indicator. TTC serves as a safety performance metric, representing the time required for a rear-end collision risk to occur when the leading and following vehicles maintain their current speeds. Two integrated safety metrics, time-exposed time-to-collision (TET)and time-integrated time-to-collision (TIT), are utilized to evaluate the safety of traffic flow  
\[\left\{\begin{array}{l}TTC(t)=\frac{x_{v-1}(t)-x_{n}(t)-l}{v_{n}(t)-v_{n-1}(t) },v_{n}(t)>v_{n-1}(t)\\ TET=\sum\limits_{n=1}^{N}\sum\limits_{l=1}^{T_{f}}\Delta t,\ 0<TTC(t)<TTC_{threshold}\\ TIT=\sum\limits_{n=1}^{N}\sum\limits_{l=1}^{T_{f}}[TTC_{threshold}-TTC(t)] \Delta t,\ 0<TTC(t)<TTC_{threshold}\end{array}\right. \tag{30}\]  
where \(l\) represents the length of the vehicle and \(l\) = 4.9 m; \(t\) is the time; \(T_{f}\) is the total simulation time; \(n\) is the vehicle code; and \(N\) is the total number of vehicles. According to the previous literature [55], \(TTC_{threshold}\) is set to 2 s.