esa-sceva/satcom-chunk-collection huggingface dataset:
# Wind Tunnel Test on Windblown Sand Two-Phase Flow Characteristics in Arid Desert Regions
## 2 Experimental Apparatus, Methods and Design
### Field Measurement  
Due to the unique climatic conditions and terrain complexity in desert regions, the characteristics of the near-ground impurity-free wind may be different from those of similar landform types as specified in existing wind codes and standards. To ensure the accuracy of the study and guide subsequent wind tunnel tests, the impurity-free wind characteristics in a typical desert region are obtained by using the field measurement method in the early stage of the test (A tiny sand concentration in the air stream or sand particles are not raised by wind, namely impurity-free wind flow field). As shown in Figure 4, the observation site is located in the photovoltaic power station on the southeastern edge of the Tengger Desert in Zhongwei, Ningxia Hui Autonomous Region, China. The measuring system consists of a wind monitoring tower, cup anemometers, directional vanes and data acquisition instrument. The cup anemometers and the directional vanes are installed at 1.4 m, 2.8 m, 4.2 m, 5.6 m, 7.0 m, 8.4 m and 10 m of the tower. More details about the experimental process and apparatuses had been provided by [PERSON] et al. [33]. Sand flux  
Figure 3: The wind profiler.  
data are missing in the field measurement, so we analyze the characteristics of windblown sand flow through wind tunnel tests.
_applied sciences_  
_Article_  
**Novel Approach to Introduce Alkyl Chains into PEDOT:PSS and Its Effect on the Performance as a Flexible Electrode**  
**[PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 2, [PERSON] 3 and [PERSON] 1,*  
* Department of Polymer Engineering, College of Engineering, The University of Suwon, Hwaseong-si 18323, Korea; [EMAIL_ADDRESS] (I-S.H.); [EMAIL_ADDRESS] (C.-W.P.); [EMAIL_ADDRESS] (H.-I.K);
* Center of Advanced Materials Analysis, The University of Suwon, Hwaseong-si 18323, Korea; [EMAIL_ADDRESS]
* EverChemTech Co., Ltd., 38, Cheongwonsandan 7-gil, Mado-myeon, Hwaseong-si 18323, Korea; [EMAIL_ADDRESS]
* Correspondence: [EMAIL_ADDRESS]; Tel: +81-312-202-156  
**Abstract: We here report a synthetic route to introduce alkyl chains into poly (3,4-ethylenedioxythiophene):poly (4-styrenesulfonate) (PEDOT:PSS) by the reaction with epoxylankanes. The reaction was analyzed by FT-IR, TGA, and XPS studies, and the conductivities of derivatives were discussed as a function of the length of alkyl chains. PEDOT:PSS-C6, which is the product from a reaction with epoxylhexane, was well dispersed in methanol and transparent films from this dispersion were successfully prepared. PEDOT:PSS-C6 film showed an increase in hydrophobicity, resulting in enhanced water resistance compared to pristine PEDOT:PSS film, and a morphological study of the film exhibited clear phase separation similar to PEDOT:PSS doped by DMSO. We also observed an improvement in the conductivity and flexibility of PEDOT:PSS-C6 film compared to those of pristine PEDOT:PSS film. This study proposes a promising method to introduce alkyl chains into PEDOT:PSS and to develop a flexible electrode applicable to an environment where contact with water is unavoidable.**  
**Keywords: PEDOT:PSS; epoxylankanes; phase separation; water resistance; flexibility**
# Comprehensive Review of RF MEMS Switches in Satellite Communications
## 3 Strategies for Optimizing RF MEMS Switch Performance
### High Reliability
#### 3.1.1 Reducing the Impact of Shocks
Figure 4c illustrates the electrode or dielectric patterning design, where the field lines associated with the patterned array begin to rapidly separate as the upper electrode approaches the dielectric during the actuation process. This separation causes the array elements to act as isolated capacitors, rapidly reducing the effective area. Such a decrease in capacitance leads to a reduction in electrostatic potential energy, thereby lowering impact velocity. This technique also helps in reducing dielectric charging, as the contact area decreases, thereby extending the switch's lifespan.
# MEMS and IoT in HAR: Effective Monitoring for the Health of Older People
###### Abstract  
The aging population has created a significant challenge affecting the world; social and healthcare systems need to ensure elderly individuals receive the necessary care services to improve their quality of life and maintain their independence. In response to this need, developing integrated digital solutions, such as IoT based wearable devices combined with artificial intelligence applications, offers a technological platform for creating Ambient Intelligence (AI) and Assisted Living (AAL) environments. These advancements can help reduce hospital admissions and lower healthcare costs. In this context, this article presents an IoT application based on MEMS (micro electro-mechanical systems) sensors integrated into a state-of-the-art microcontroller (STM55 WB) for recognizing the movements of older individuals during daily activities. human activity recognition (HAR) is a field within computational engineering that focuses on automatically classifying human actions through data captured by sensors. This study has multiple objectives: to recognize movements such as grasping, leg flexion, circular arm movements, and walking in order to assess the motor skills of older individuals. The implemented system allows these movements to be detected in real time, and transmitted to a monitoring system server, where healthcare staff can analyze the data. The analysis methods employed include machine learning algorithms to identify movement patterns, statistical analysis to assess the frequency and quality of movements, and data visualization to track changes over time. These approaches enable the accurate assessment of older people's motor skills, and facilitate the prompt identification of abnormal situations or emergencies. Additionally, a user-friendly technological solution is designed to be acceptable to the elderly, minimizing discomfort and stress associated with using technology. Finally, the goal is to ensure that the system is energy-efficient and cost-effective, promoting sustainable adoption. The results obtained are promising; the model achieved a high level of accuracy in recognizing specific movements, thus contributing to a precise assessment of the motor skills of the elderly. Notably, movement recognition was accomplished using an artificial intelligence model called Random Forest.  
ambient assisted living; MEMS; Internet of Things; edge machine learning; IMU; HAR; healthcare
# The Effect of Particle Type and Size on CoCr Surface Properties by Fine-Particle Shot Peaning
## 3 Results  
### Surface Observation  
The surface morphology was evaluated by optical microscope. The results showed that there were significant differences in the surface morphologies between the CoCr alloys with and without the fine-particle shot peening process, as shown in Figure 1. The fine-particle shot peening can change surface microstructure by creating many small dimples on the surface of the materials, which represent the plastic deformation of the surface materials.  
There were two media types, namely ceremic beads and silica beads, and it was found that ceramic beads generated multiple small dimples that were clearer and deeper than those of the silica beads (Figure 1). Not only did the surface morphology of the CoCr alloy change after the fine-particle shot peening process, but there was a significant difference in effect between the two particle types. The hardness of the ceramic particle was higher than the silica particle. These results showed that particles with greater hardness can create clearer, deeper, and smaller dimples on surface materials, and therefore, particle type affects the change in surface morphology.  
Figure 1: Surface morphology evaluated by optical microscope: (**a**) without peening process, (**b**) with fine-particle shot peening (FPSP) with silica, and (**c**) FPSP with ceramic.  
In order to compare different particle sizes, this study used three distinct sizes for both the silica and ceramic particles. The size of the media is shown in Table 2. The results showed that the larger particle size creates bigger and clearer dimples than the smaller particle size (Figure 2). However, the observation by optical microscope only proved that fine-particle shot peening can change the surface morphology of CoCr alloy but cannot explain the change of material properties.
## 3 FM-ACSK System's Description
### FM-ACSK Modulation  
FM-ACSK communication system is made of two modulation layers--ACSK and FM modulations. As shown in Figure 3, FM is added on top of the ACSK modulation. In the ACSK layer, three chaos generators are used--one master chaos generator in the transmitter's ACSK modulator and two slave chaos generators in the receiver's ACSK demodulator. In the transmitter's ACSK modulator part, the master chaos generator's output \(R_{out}\) gets passed through a switch, where \(R_{out}\) gets inverted if the transmitted data bit is \"1\", or gets passed without a change if data bit \"0\" is transmitted. In the receiver's ACSK demodulator part, input signal \(R_{in}\) is passed into two slave chaos generators--into one without signal inversion and into another one with inverted \(R_{in}\) signal. This helps to determine if the transmitted chaotic signal was inverted or not in the transmitter by comparing two slave chaos generator outputs. At any given moment, one of the generators will provide the smallest estimated synchronization error compared to another generator. In order to decide the value of the transmitted data bit, RMS values of both slave chaos generators' synchronization errors are compared.  
ACSK demodulator analyzes a relatively long interval of the chaotic signal to estimate the received data bit, as seen in Figure 4. Therefore, it can be said that the received signal's despreading in the ACSK demodulator is carried out by means of chaotic synchronization, which cardinally differs from the approaches used in classical SS systems. In classical SS systems, despreading is performed by means of correlation, which requires signifiifi  
\begin{table}
\begin{tabular}{c c c c} \hline \(p^{\prime}_{1}\) & \(p^{\prime}_{2}\) & \(p^{\prime}_{3}\) & \(p^{\prime}_{4}\) \\ \hline
0.5 & 0.6 & 0.7 & 0.8 \\ \hline \end{tabular}
\end{table}
Table 2: Initial system’s state conditions of slave chaos generator.  
cantly higher computation resources compared to chaotic synchronization. In [26], more information about ACSK can be found.  
On top of ACSK modulation, FM is applied. The main purpose of FM is to spread transmitted chaotic signal even further, over the wider frequency range (see Figures 5 and 6), to diversify the signal and increase immunity to intentional or unintentional interference. Additionally, employment of non-coherent FM also increases immunity to CFO by lowering requirements for the stability of the carrier frequency. Additionally, the transmitted FM signal has a constant envelope, which allows using energy-efficient power amplifier (PA) with small backoff.  
Figure 4: Spreading of the data bits using a continuous chaotic waveform. Transmitted data bits are drawn in the upper graph, switched chaotic waveform-lower graph. Each data bit is spread with approximately 9 oscillations of the chaotic waveform.  
Figure 5: Baseband spectrum of the ACSK waveform (spread data bits).  
Figure 3: Block diagram of SDR-based FM-ACSK transceiver.
# Leveraging Digital Twin Technology for Enhanced Cybersecurity in Cyber-Physical Production Systems
## 2 Background and Related Works
### Advanced Persistent Threats in CPPS  
The vulnerabilities detailed in the previous section can be sequentially exploited, leading to the formation of advanced persistent threats (APTs) [28].  
Figure 1 illustrates the vulnerability chain within CPPS, where _V-x_ denotes various vulnerabilities and _A-x_ signifies stages of an advanced attack. An attacker could exploit the default password setting (_V-1_) of a design engineer's account, leading to account compromise (_A-1_). This breach could enable the attacker to target a related designer workstation in a follow-up attack (_A-2_). The likelihood of success for this follow-up attack (_A-2_) increases if the designer workstation suffers from poor authentication management (_V-2_). Furthermore, the attacker might launch an additional attack (_A-3_) to gain entry into a database server via the compromised workstation. This entry could be facilitated by weak access control (_V-3_), paving the way for another attack (_A-4_) aimed at altering geometry  
computer-aided manufacturing (CAM) programs on the control server, especially if the database lacks encryption (_V_-4). The absence of effective communication between CAM engineers and security personnel (_V_-5) might allow such alterations to CAM program codes to remain unnoticed. Communication barriers between IT and OT staff, exacerbated by their physical separation, could result in the modified CAM file being used without correction. This oversight could lead to numerical control (NC) machines operating incorrectly, potentially causing significant disruptions in the manufacturing process due to these communication failures.  
The chained vulnerabilities discussed can be pinpointed either through the insights of security experts or by tracing the interrelations among _CWE_, common attack pattern enumeration and classification (_CAPEC_) [29], and MITRE adversarial tactics, techniques, and common knowledge (_ATT&CK_) [30] attributes associated with specific vulnerabilities. The sequence of vulnerabilities illustrated in Figure 1 is effectively represented using these standardized enumerations, as depicted in Figure 2. Particularly, _CAPEC-560_ is capable of advancing beyond both _CAPEC-49_ and _CAPEC-70_, showcasing a complex attack graph that evolves along the vulnerability chain. Recognizing such sequences is crucial for conducting a comprehensive vulnerability assessment at the system level.
## 3 Underwater Acoustic Sensor Network Models and Query Models
### Underwater Acoustic Sensor Network Backgrounds  
As explained in the introduction, our main focus is to provide practical and efficient data management framework for underwater sensor networks. An underwater sensor network usually consists of sensor nodes, clusters and base stations. Each terminal node transmits the sensor data toward the cluster header. The cluster header collects sensor data from a number of terminal nodes and tosses the packet up the tree. Using certain routing mechanisms, these intermediate nodes transfer the packet to each other and eventually deliver the packet to a base station placed at the surface, where the acoustic data signals are transformed into corresponding RF signals and transmitted to the user applications [23, 24]. Figure 1 shows the underwater acoustic sensor network model.  
One major disparity between the common RF based sensor networks and underwater acoustic sensor networks is time synchronization. As explained in the introduction, low signal propagation speed and limited human accessibility to underwater sensor nodes makesdifficult. In our network model, therefore, sensor nodes are assumed to execute queries individually without any agreement on time, and the following network database and query models are modified on such basis.
# Stalling in Queuing Systems with Heterogeneous Channels
## 2 QS with Heterogeneous Channels and Stalling Buffer  
### State Graph of QS  
Let us consider a queueing system consisting of \(m+n\) heterogeneous channels, where \(m\) is the number of fast channels, \(n\) is the number of slow channels, \(K\) is the length of the stalling buffer, and \(M\) is the length of the waiting line and the buffer (see Figure 1). Assumethat the interarrival time to the system is distributed exponentially with parameter \(\lambda\) and the service time is distributed also under this law with parameters \(\upmu 1\) and \(\upmu 2\), respectively, for fast and low channels, i.e., \(\upmu 1>\upmu 2\); the queries are served in the FCFS discipline. If the query--after being released into the system--finds a free fast channel, then it is served immediately; otherwise, it goes to a stalling buffer of \(K\) length, where it waits until the efficient channels become free. One query is served only by one channel without a break. If all the places in the stalling buffer are occupied, then the arrived query transfers to the slow channel. If the slow channel is occupied as well, then the application waits in the queue at a waiting buffer of length \(M\). If all the places in the waiting and stalling buffers are occupied, then the query is rejected and it is deemed lost.  
The main parameter of the QS is the coefficient of utilization \(\rho=\frac{\lambda}{m\cdot p_{1}+n\cdot p_{2}}\). Note, if the system is full (\(\rho>1\)), then all the channels, i.e., slow and fast ones, should work because the service is most efficient if the system works at maximal capacity. If some channels work with idle time, then the system will process a smaller number of queries. If more queries come in than the system can serve, then unserved queries will be deemed lost, if the waiting line is finite.
# Development and Application of a Novel Tsunami Monitoring System Based on Submerged Mooring
## 2 Overall System Design
The system mainly implements the following functions: (1) communication function: real-time reception of BeiDou satellite communication data sent by surface communication buoys and verification of the messages; (2) storage and query functions: these realize the storage of various parameters such as the status, position, and tsunami monitoring pressure values of the parsed buoys and provide efficient and convenient database access services; (3) display function: this displays the status, location, and data information of the tsunami monitoring system, as well as the status and pressure monitoring values of the tsunami units.
## 3 Results
### Main Steps in FEA of Human-Seat Interactions
#### 3.2.1 Model Data Acquisition  
Generally, the methods used for model acquisition in the literature can be categorized into three groups: (1) non-destructive imaging techniques such as magnetic resonance imaging (MRI) scans, computed tomography (CT) scans, and 3D laser scans; (2) anthropometric measurements; and (3) various software tools. MRI scans are used when the inclusion of specific organs or the accuracy of the results is important. CT scans are another data source used for accurate models. Three-dimensional scans are used when the important geometry is the outer shape of the subject. Another source of data is anthropometric resources, which are the measurements of different percentiles within a population and are obtained from different databases that are available online. A popular site is the Civilian American and European Surface Anthropometry Resource (CAESAR) database [11], which contains the anthropometric variability of men and women, aged 18-65 years, covering various weights, ethnic groups, genders, geographic regions, and socio-economic status. Finally, the last category of geometry sources includes software such as: (1) POSER Pro [12], (2) MADYMO [13], (3) ANYBODY [14], and (4) CASIMIR [15]. POSER [12] is a 3D computer  
Figure 2: Flow chart showing the steps required to perform FEA simulation of human body–seat interaction [1].  
graphics program and provides optimized 3D modeling of human figures. MADYMO [13] is a standard software used for analyzing and optimizing vehicle safety designs and consists of a unique combination of multibody, finite elements, and computational fluid dynamics technologies. This software focuses on vehicle occupants with a variety of human body models. ANYBODY [14] is a software package that simulates the human body's interaction with its environment using various musculoskeletal models and provides force and motion simulation data. CASIMIR [15] is a computer-aided engineering solution for the simulation of static and dynamic seating comfort.  
A few papers [16; 17; 18; 19; 20; 21] have used multiple sources to create a human model for simulation. Bones are either obtained from MRI or CT scans [22; 23; 24; 25; 26; 18; 20; 22] whereas skin is obtained from CT scans or 3D laser scans [27; 28; 17; 21; 22]. Since MRI scans are usually performed in a lying-down position, an additional step of modifying the model is required to convert the posture to a seated position, which could alter the accuracy of the models, especially the soft tissue as it would differ in form and size. Other papers [22; 19; 28] have used scans from a seated position. A 50 th percentile male model is the most used model in different papers [22; 23; 24; 25; 26; 18; 27; 28; 21; 29; 15; 16; 17; 18; 21; 23; 26; 19; 2] as it is considered to be the average human being. A few papers [19; 34; 38] have used a 95 th percentile male model, and one paper [39] used an 80 th percentile male model.
###### Abstract  
There is a growing interest in high-altitude platform stations (HAPSs) as potential telecommunication infrastructures in the stratosphere, providing direct communication services to ground-based smartphones. Enhanced coverage and capacity can be realized in HAPSs by adopting multicell configurations. To improve the communication quality, previous studies have investigated methods based on search algorithms, such as genetic algorithms (GAs), which dynamically optimize antenna parameters. However, these methods face hurdles in swiftly adapting to sudden distribution shifts from natural disasters or major events due to their high computational requirements. Moreover, they do not utilize the previous optimization results, which require calculations each time. This study introduces a novel optimization approach based on a neural network (NN) model that is trained on GA solutions. The simple model is easy to implement and allows for instantaneous adaptation to unexpected distribution changes. However, the NN faces the difficulty of capturing the dependencies among neighboring cells. To address the problem, a classifier chain (CC), which chains multiple classifiers to learn output relationships, is integrated into the NN. However, the performance of the CC depends on the output sequence. Therefore, we employ an ensemble approach to integrate the CCs with different sequences and select the best solution. The results of simulations based on distributions in Japan indicate that the proposed method achieves a total throughput whose cumulative distribution function (CDF) is close to that obtained by the GA solutions. In addition, the results show that the proposed method is more time-efficient than GA in terms of the total time required to optimize each user distribution.  
H  
HAPS; cell configuration; self-organizing network; dynamic area optimization; machine learning; supervised learning; neural network +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 3 Act
### APC and ARC
#### 3.4.2 ARC  
The ARC controls the output packet rate by re-encoding the transit packets in the relaying queue. It consists of RLC, IRLC, ADPCM, and IADPCM units. Figure 8 shows its block schematic. Transit packets are in a compressed state after RLC is performed, and therefore, the ARC first decodes them with the help of IRLC. Then, the decompressed transit packets are reconstructed by IADPCM and compressed again with a different quantizer step size, which is affected by the congestion state.