esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Graphene Waveguide Arrays  
Now we consider the realization of Jackiw-Rebbi modes in graphene sheet arrays. Figure 1 depicts the geometry of graphene pair arrays under consideration. The SPPs propagate along the \(z\) axis. The structure is composed of two different arrays. For each array, there are two graphene sheets in each unit cell with alternating chemical potential. The middle of the structure is a defect as the sequences of chemical potential at left and right arrays are different.  
The propagation constants of SPPs are different with different chemical potentials. In such an arrangement, the center defect will support Jackiw-Rebbi modes. The arrays are embedded in the dielectrics with permittivity denoted as \(\mathrm{\epsilon_{d}}\). Here they are initially assumed to be freestanding in air with \(\mathrm{\epsilon_{d}=1}\). The intra- and inter-layer spacings are uniform and denoted as \(\mathrm{d}\). The surface conductivities are represented by \(\sigma_{1}\) and \(\sigma_{2}\), which are related to temperature T, chemical potential \(\mu\), momentum relaxation time \(\tau\), and photon frequency \(\omega\). The surface conductivity of graphene can be determined by the Kubo formula [6]  
\[\sigma(\omega,\mathrm{T},\mu,\tau)=\mathrm{i}\frac{\mathrm{e}^{2}\mathrm{k_{B }T}}{\pi\hbar^{2}(\omega+i\tau^{-1})}\big{[}\frac{\mu}{\mathrm{k_{B}T}}+2\ln \left(\mathrm{e}^{-\frac{\mu}{\mathrm{k_{B}T}}}+1\right)\big{]}+\mathrm{i} \frac{\mathrm{e}^{2}}{4\pi\hbar}\ln\big{[}\frac{2|\mu|-\hbar(\omega+i\tau^{- 1})}{2|\mu|+\hbar(\omega+i\tau^{-1})}\big{]}, \tag{4}\]  
where \(\mathrm{e}\) is the electron charge, \(\hbar\) is the reduced [PERSON]'s constant, and \(\mathrm{k_{B}}\) is Boltzmann constant. In this work, the room temperature (T = 300 K) is assumed. The spatial spacing, momentum relaxation time, and incident wavelength are fixed at d = 70 nm, \(\tau\) = 1 ps, and \(\lambda\) = 10 \(\upmu\)m. The chemical potential of graphene sheet is kept at \(\mu_{1}\) = 0.15 eV and chemical potential of the other sheets \(\mu_{2}\) is changed. The parameters of graphene are typically used for simulation [24], which coincides with the current experiment. Under these parameters, the graphene sheets are weakly coupled.  
We only focus on the TM polarized SPPs propagating along z axis. The propagation constants and mode profiles of supermodes in waveguide arrays can be figured out by transfer matrix method [1; 55],  
Figure 1: The geometric scheme of graphene waveguide arrays. The red and blue colors represent graphene sheets with different chemical potentials. The center of the structure is a defect. \(\mathrm{n}\) denotes the waveguide number.  
which is based on the boundary condition of the electromagnetic field. Moreover, we also calculate the wave propagation in the structure performed based on the finite element method. The graphene is modeled by using the surface current boundary condition. The domain has been discretized by using homogeneous mesh with the maximal element size being less than 1/12 of the graphene SPP wavelength.
## 4 Results
### Accuracy Assessment at Two Sites  
#### 4.3.1 Adaptation of the Proposed Thresholds at the Ji'An Site  
To quantitatively measure the suitability of the proposed template at the Ji'An site, a visual inspection was conducted at the site using the GF-3 images from June 10. The results show that the metrics obtained using our proposed thresholds for the Ji'An site also perform well (Table 2). The overall accuracies of our algorithm (94.5-96%) were similar in the three periods. The kappa coefficient ranged from 0.74 to 0.81, and the commission and omission errors of the water ranged from 16% to 26%, while those of the land ranged from 1% to 3%. Moreover, the six indicators for 13 June are better than for the other two dates. This may be due to our selection of high-resolution images closest to that date.  
Therefore, pseudo-color composite images of Sentinel-1 were used (Table 2). The overall accuracies were above 95.0%. The kappa coefficient ranged from 0.77 to 0.84; the commission and omission errors of the water ranged from 12% to 19%, while those of the land ranged from 1% to 2%. This indicates that our classification accuracy for land is higher than that for water bodies. The data for 13 June continues to yield the best results.
## 4 Grid Study and Validation  
For computational grid independency, three computational grids with interval sizes of 0.2, 0.1 and 0.05 were selected. According to the results, as the interval size decreased from 0.1 to 0.05, the SOT did not change significantly. Therefore, a structured computational grid (interval size of 0.1 mm) waschosen. Figure 2 shows the effect of time step size variation on the output response (SOT). As can be seen, reducing the time step up to 0.01 s, SOT decreases. Further reduction of the time step does not have a significant effect on SOT.  
The numerical method used in this study was validated by experiments carried out by [PERSON] et al. (2018). The authors studied a PCM-based heat sink equipped with vertical fins to enhance the heat sink thermal conductivity. The electrical power applied to the bottom wall was 45 W, while the other walls were well insulated. The ambient temperature was 27 \({}^{\circ}\)C, hence the heat sink initial temperature was 27 \({}^{\circ}\)C. Applying heat flux on the bottom wall, its temperature begins to rise. The temperature variations of the bottom wall are shown in Figure 3. As can be seen, the base temperature variations can be divided into three zones. In the first and third zones, the rate of the variations (slope) is greater than in the middle zone. In the middle zone, the PCM undergoes phase change. The thermal energy in this zone is stored as the latent heat, but in the other two zones, the heat stored is sensible. The same trend can be seen for the simulated results. As shown, the numerical method can well predict the temperature variations of the PCM-based heat sink at three zones.  
Figure 2: Independency of the time step size \((\Delta t)\).
## 2 Materials and Methods
### Signal Preprocessing
#### 2.3.3 Noise Reduction  
Signals or one-dimensional data usually contain unwanted or unknown components that can be the result of the capturing, transmitting, or storing device. In this investigation, the focus was on removing the noise from the collected data and not identifying its root. Noise removal techniques serve so as to remove irregular fluctuations by running filters (mathematical operations) throughout the entire signal and replace the \"noisy\" values with \"smooth\" ones. These methods enable the ML algorithms to better learn the trends and fluctuations instead of random value variations. In this analysis, the median filter [59] was used as a noise removal method. This kind of filter is a nonlinear one within which each output sample is calculated as the median value of input samples under the selected window. This corresponds to a result after the sorting of the input values. Furthermore, the median filtering of signals includes a horizontal window having an odd number of taps. After examining various values, eleven taps were utilized. In addition, no isolated extreme values (such as a possible large-valued sample as a result of impulse noise) appeared in the filtering phase, since outliers were removed manually beforehand. The median filter's effect on the signals is demonstrated in Figure 3.  
Figure 3: Collected raw signals and processed signals after median filter application.
## 4 Building the Walker Dataset
### Data Acquisition  
For the IMU-based fall detection algorithm, the data acquisition process involves capturing motion and orientation data using the sensor integrated into the device. In this project, the fall detection problem is treated as a classification task, where four classes are considered: idle, motion, step, and fall.  
* Idle: The idle class represents periods of no movement or minimal activity when the walker is stationary, and the user is not actively engaged in any motion.
* Motion: The motion class captures random motion, which means that the walker is being moved, but not necessarily by someone actively walking with it.
* Step: The step class corresponds to the specific movement when someone takes a step with the walker. This class focuses on capturing the gait pattern associated with normal walking using the walker.
* Fall: The fall class represents situations where the walker falls to the floor, indicating a potential fall event of the walker user.  
Data for this study were collected using the walker in five groups. Each group contains about 100 to 150 samples of fall, motion, and step data.  
To record idle data, multiple 5-minute duration files were recorded with the walker placed in various positions and kept still throughout the recording. The walker was placed in different orientations to capture variations in sensor readings, ensuring a diverse dataset.  
For motion data, random movements were recorded by actively moving the walker in different directions and at varying speeds. This allowed for capturing a wide range of motion patterns, simulating everyday movements that may occur between adjacent walking steps.  
To simulate the walking of an elderly person, walker data were recorded in an indoor open space. Pauses were introduced between each step to mimic the typical walking pattern of an elderly person and facilitate the separation of steps.  
For falls, deliberate actions were performed in which the walker was intentionally thrown to the floor multiple times and then picked up again. Additionally, external forces, such as pushing the walker, were applied to simulate the motion associated with a fall event. These intentional actions aimed to capture the distinct patterns and characteristics of falls in the acquired sensor data.
# Fuzzy-Based Control System for Solar-Powered Bulk Service Queueing Model with Vacation
###### Abstract  
This study highlights the effects of fuzzy-based control on key performance metrics, including queue stability, waiting time, and system utilization. The results can be used to enhance operational efficiency and service continuity compared to traditional queueing, with obvious advantages.  
This study proposes a Fuzzy-Based Control System (FBCS) for a Bulk Service Queueing Model with Vacation, designed to optimize service performance by dynamically adjusting system parameters. The queueing model is categorized into three service levels: (A) High Bulk Service, where a large number of arrivals are processed simultaneously; (B) Medium Single Service, where individual packets are handled at a moderate rate; and (C) Low Vacation, where the server takes minimal breaks to maintain efficiency. The Mamdani Inference System (MIS) is implemented to regulate key parameters, such as service rate, bulk size, and vacation duration, based on input variables including queue length, arrival rate, and server utilization. The Mamdani-based fuzzy control mechanism utilizes rule-based reasoning to ensure adaptive decision-making, effectively balancing system performance under varying conditions. By integrating bulk service with a controlled vacation policy, the model achieves an optimal trade-off between processing efficiency and resource utilization. This study examines the effects of fuzzy-based control on key performance metrics, including queue stability, waiting time, and system utilization. The results indicate that the proposed approach enhances operational efficiency and service continuity compared to traditional queueing models.  
fuzzy-based control system; bulk service queueing model; vacation policy; high bulk service; medium single service; low vacation; fuzzy inference system; fuzzy Mamdani Inconsec MNP, Basel, Switzerland.
## 2 Materials and Methods (Testing and Simulation Methodologies)  
The _Escuela Tecnica Superior de Ingenieria Aeronautica y del Espacio_ (ETSIAE) at the _Universidad Politecnica de Madrid_ (UPM) is the university's aerospace engineering faculty. It was created by merging two older engineering schools within the same university: the _Escuela Tecnica Superior de Ingenieros Aeronautics_ (ETSIA) and the _Escuela Universitaria de Ingenieria Tecnica Aeronautica_ (EUITA). This followed the university's implementation of the new degree programs regulated by Royal Decree 1393/2007, within the framework of the European Higher Education Area. The available data, from academic year 2016-2017, indicate a total of 3468 students. This makes the ETSIAE one of the UPM's largest faculties, with a surface area of 36,500 m\({}^{2}\), 240 professors and researchers, and 140 service and administrative staff.  
Following the renovations undertaken in recent years, ETSIAE has four buildings dedicated to teaching, known as buildings A, B, C, and E (see Figure 1). Due to the high number of students at the school and the degree programs offered and depending on the time of year, it is normal for many students to spend all or part of their school day using only artificial lighting, with the visual fatigue that this entails. For this reason, it is essential for classroom lighting to meet regulatory requirements [43] in order to be suitable for the tasks performed at this faculty.  
This study comprises several successive steps, starting with measurement of the present illumination levels (i.e., illuminance, _E_) of a significant number of ETSIAE classrooms. Four different types of classrooms were then selected as representative of the entire set, and the current situation was reproduced using DIALux(r) lighting simulation software. Once the current situation in the selected classrooms was properly simulated, three proposals for improvement were analyzed, using as reference the simulation of the current situation.  
Figure 1: Aerial view of _Escuela Tecnica Superior de Ingenieria Aeronautica y del Espacio_ (ETSIAE): there are four buildings dedicated to teaching (**A**,**B**,**C**,**E**), and two buildings housing the library, laboratories, offices, cafeteria, and common areas (**D**,**F**). Source: _Universidad Politecnica de Madrid_.
## 3 Materials and Methods
### Determination of Free Tocopherols and Phytosterols by High-Performance Liquid Chromatography (HPLC)
#### 3.5.2 HPLC Analysis of Free Topoberols and Phytosterols  
Quantification of free topoberols and phytosterols was done using a 1250 Series HPLC system (Waters, Milford, CT, USA) equipped with a UV detector (2487, Waters, Milford, CT, USA) and a C18 reversed-phase column (250 \(\times\) 4.6 mm; 5 \(\upmu\)m). The injection volume and column temperature were 20 \(\upmu\)L and 30 \({}^{\circ}\)C, respectively. Tocopherols were detected at 300 nm wavelength. The mobile phase was a mixture of methanol and high-purity water (98:2, \(v/v\)). The flow rate of the mobile phase was set at 1 mL-min\({}^{-1}\). Phytosterols were detected at the 210 nm wavelength. The isocratic mobile phase (acetonitrile: high-purity water = 98:2, \(v/v\)) was set at a flow rate of 1.5 mL-min\({}^{-1}\). Each component was quantified using an external standard method with pure standards of tocopherols and phytosterols. Waters Breze software (Waters, Milford, CT, USA) was used to calculate the peak areas.
# Hybrid Machine Learning-Statistical Method for Anomaly Detection in Flight Data
## 3 Local Outlier Factor
### Implementation
#### 3.2.7 Post Processing  
In this step, further plots were plotted to obtain more insight into the flights. These plots included LOF scores and flight parameters at each time stamp during the flight. Furthermore, flight parameters responsible for anomalous behavior were identified and plotted. In order to find parameters responsible for the anomalous behavior of any anomalous flight, the mean of all flight parameters for all normal flights was calculated. Then for each flight parameter of the anomalous flight, the value of each flight parameter was subtracted from the mean value of that parameter of all normal flights. This was performed to obtain the flight parameter that was furthest away from the mean value of the same flight parameter of normal flights. In this way, top \(n\) flight parameters that may be responsible for the anomalous behavior can be found, which are further away from the mean values of those \(n\) parameters of normal flights. These plots are discussed in detail in the following section.
# UV System Architecture: Challenges and Evolution
## 1 Introduction
* Drive (mobility): physical environment, class (land, air, water, or hybrid), energy consumption, power needed, and operating requirements. * Solution deployment: solution requirements (local or cloud resources) and amount of UV. The goal of this paper is to show a general architecture for building systems/applications with UVs. This objective is directly related to the wide use of UVs. It is expected that in each case, a specific architecture can be determined for each application, but this objective is motivated by the idea of presenting an architecture that can be specialized and serve as a guide for many UV applications, which involve much more than the vehicles themselves. It is important to note that at a mass level, a lot of emphasis is placed on vehicles (drones, rovers, etc. ), while the complete applications are most important, which involve much more than the vehicles themselves. Complete applications necessarily involve communication systems, trajectory management, data storage and processing, etc. All these tasks exceed the possibilities of being implemented in the vehicles themselves and are essential parts of any application with UVs. This paper is written as a report of the findings and challenges encountered along the way, and it is organized into sections according to the versions and evolution of the architecture. In each section, the architecture is presented with a proof of concept and a discussion. The paper starts with an introduction and related work, and then a section explains the first version of the UV system architecture. In this section, the basis of the first version is presented. Later, Section 3 uses a heterogeneous approach to the evolution of the architecture, but only for the UV side. The last section presents the complete UV system architecture by adding the base station specifications. The paper finishes with conclusions and future work. A sequence of UV system architecture versions is presented in Sections 2-4. Thus, starting in Section 2, each architecture version is presented to solve a number of problems, implementing a set of functionalities. Challenges not solved or not taken into account by each architecture version are presented at the end of the corresponding section and/or at the beginning of the next section, and the following sections include some alternatives to solve challenges and/or functionalities that the previous architecture version/s did not solve/approach.
## 5 Multifunctional Material Integrated Devices Enhance Sports Data Collection and AI Analysis  
With the advancement of materials science, integrated devices that combine multiple basic functional materials into multifunctional materials have provided a brand-new solution for sports data collection due to their excellent mechanical adaptability, high sensitivity and customizable characteristics. These devices can efficiently convert multi-dimensional information, such as mechanics and physiology during movement into digital signals, and achieve in-depth data analysis and intelligent feedback through artificial intelligence (AI) technology, thus, demonstrating great potential in fields such as athlete training and health management.  
With the help of modern data collection technology, we can not only collect the physiological and sports data of athletes, but also extract key performance parameters from intelligent sports equipment.  
In 2025, [PERSON] et al. [79] reported a smart insole for real-time visualization and analysis of foot pressure monitoring and gait, as shown in Figure 13. This insole is equipped with 22 pressure sensors, which can achieve spatially resolved pressure mapping. Meanwhile, it integrates a Support Vector Machine model to further accurately identify eight motion states, including static (such as sitting and standing postures) and dynamic (such as walking, running, and squatting) activities. In addition, it can also detect diseases related to foot pressure, achieve fatigue prediction, exercise optimization, and customized fitness training.  
In 2024, [PERSON] et al. [80] reported a self-powered and intelligent badminton racket to achieve multi-functional, real-time and convenient sports monitoring t, as shown in Figure 14. The customized electrode was constructed by using the silver paste coating method, and a frictional induction array was formed on the racket string, achieving the  
Figure 13: (**A**) Use the overall structure of the smart insole system. (**B**) Schematic diagram of pressure sensing is realized. The nonlinear components in the mechanical response and electrical response of the sensor cancel each other out, generating a linear sensing response. (**C**) The workflow of the smart insole system includes: pressure data collection, visualization of plantar pressure distribution on mobile terminals, and data classification based on the SVM learning model. (**D**) Photos of the smart insole system and the core sensing layer in their initial, folded, twisted and stretched states [79].  
monitoring of the hitting position. Meanwhile, a flexible piezoelectric film is embedded in the hand glue, which can recognize the grip posture. These induction arrays can directly convert mechanical signals into electrical signals. By processing the collected multi-channel data through machine learning, this study achieved a 95% accuracy rate for hitting the ball position. SIBR provides a powerful reference for badminton training and opens a new path and method for badminton monitoring.