esa-sceva/satcom-chunk-collection huggingface dataset:
# An Experimental Study on D2D Route Selection Mechanism in 5G Scenarios
## 4 System Model and Route Selection
### System Model  
The testbed consists of a MC BS and a set of \(fc\) BSs and nodes \(fc_{f}\in F=\{fc_{1},fc_{2},...,fc_{|F|}\}\). The source node \(fc_{s}\) transmits UDP packets along an available route \(k_{k}\in K=\left\{k_{1},k_{2},...,k_{|K|}\right\}\). The set of routes \(K\) is readily available, which can be collected and provided by MC BS. A route \(k_{k}=\cup\)\(l_{n}\in L\) consists of a set of links \(l_{n}\in L=\left\{l_{1},l_{2},...,l_{l}\right\}\) with each link uses one of the available channels \(c_{c}\in C=\left\{c_{1},c_{2},...,c_{|C|}\right\}\).  
In Figure 4a, a traditional testbed that connects each USRP node to a single processing unit (i.e., a PC), which makes decisions in a centralized manner, via a switch using cables is shown. The source node generates data packets and performs packet initialization using a single processing unit and selects a route. The PC exchanges control messages with USRP nodes via a switch using cables, and the USRP nodes exchange data packets among themselves using wireless medium. A \(fc\) source node \(fc_{s}\) sends data packets to a \(fc\) destination node \(fc_{d}\) along a D2D route \(k_{1}=(fc_{s},fc_{1},fc_{2},fc_{3},fc_{d})\) selected by the MC BS \(mc\).  
In Figure 4b, our proposed testbed consists of nodes connected to separate independent processing unit RP3, which enables the source node to make decisions in an independent manner. A \(fc\) source node \(fc_{s}\) receives a set of routes \(k_{k}\in K=\left\{k_{1},k_{2},k_{b}\right\}\) from the MC BS, selects a D2D route (i.e., \(k_{1}\)), and sends packets to a \(fc\) destination node \(fc_{d}\) along the route. One of the routes in the set of routes \(k_{k}\in K\) is the backbone route \(k_{b}\in K=(fc_{s},mc,fc_{d})\), which connects the \(fc\) source node \(fc_{s}\) to the \(fc\) destination node \(fc_{d}\) through MC BS \(mc\). The backbone route can be selected when all \(fc\) nodes have PUs' activities in their operating channels \(c_{c}\in C\). The other routes (D2D routes) are selected whenever they have white spaces to offload traffic from MC BS to \(fc_{s}\) and reduce network congestion [5].
## 1 Introduction
The first one, the soft decision fusion rule, also know as data fusion, and the the second, the hard decision (HD) fusion rule, also known as decision fusion (DF). In the soft decision, this sensing information may be the sensing samples that were collected from the received signal after a specified sensing period or a quantity calculated from these samples. The former soft decision is called sample fusion (SF). The latter soft decision mode commonly adopts quantities as energy values [6], eigenvalues of the sample covariance matrix (SCM) of the received signals [6], or power spectral densities (PSDs) [7], for example. In the HD, however, each SU senses the targeted PU band and autonomously takes and sends their individual decisions on the occupation state of the sensed band to the FC. Whether in SUs or FC, the secondary network of cooperating SUs needs to apply a statistical test to make occupation decisions. SUs or FC fully computes the adopted statistic in HD or soft decision with SF, respectively, but SUs may also partially compute the adopted statistic in soft decision when SF is not in use and leave the remaining computations to the FC. The FC associates all received information to achieve the global collaborative occupation decision in both cases, soft decision or HD. In HD, it combines all received occupation decisions sent by each SU via the \(K\)-out-of-\(U\) rule [6], where \(K\) is the number of decisions favoring the hypothesis that the PU signal is in the band that is sensed, represented by \(\mathcal{H}_{1}\), i.e., the hypothesis that the PU band under sensing is not idle, and \(U\) denotes the number of received decisions. In the \(K\)-out-of-\(U\) rule, the FC takes a final occupation decision favoring \(\mathcal{H}_{1}\) if a minimum of \(K\) out of the \(U\) received decisions are also in favor of the hypothesis \(\mathcal{H}_{1}\). It decides in favor of \(\mathcal{H}_{0}\) otherwise, in which \(\mathcal{H}_{0}\) represents the hypothesis that the PU signal is absent of sensed band, i.e., the hypothesis that the PU band under sensing is idle. With \(K=U\), \(K=1\), or \(K=\lfloor U/2+1\rfloor\), the \(K\)-out-of-\(U\) rule becomes one of its well-known three particular cases named AND, OR, and majority (MAJ) voting rule, respectively, where \(\lfloor\cdot\rfloor\) represents the floor function.
## 4 Results of Underwater Experiment  
An actual underwater experiment was conducted to analyze the performance of the covert underwater acoustic communication. The underwater experiment was conducted around September 2016 at the coastal waters near Korea Maritime and Ocean University, located in Yeongdo-gu, Busan.  
The underwater experiment environment is presented in Table 1 and Figure 4. The source signal was set to \(10^{3}\) bits, and bits of PN code for spread spectrum signal were 8, 16, and 32 bits. The center frequency and sampling frequency were set to 16 kHz and 192 kHz, respectively. Distance between transmitter and receiver was set to 41 m, and depth was set to 3 m from the surface of the water to conduct the underwater experiment.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Parameter** \\ \hline Source & 1000 bits \\ Number of PN(Pseudo Noise) Code bits N\({}_{\text{Tc}}\) & 8, 16, 32 bits \\ Channel Coding Method & (2, 1, 7) Convolution \\ Modulation & BPSK(Binary Phase Shift Keying) \\ Decoding Method & BCJR(Bahl-Cocke-Jelinek-Raviv) \\ Center Frequency & 16 kHz \\ Sampling Frequency & 192 kHz \\ Bit rate & 187.5 bps \\ Distance & 41 m \\ Water depth & 4.3 m \\ Depth & Transmitter: 3 m, Receiver: 3 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental parameters.
# Simulative and Experimental Investigation of Vibration Transfer Path at Gearboxes
## 1 Introduction
[PERSON] (1998) divides the possibilities of the transfer path analysis into three variants: classical TPA, component-based TPA, and transmissibility TPA. Other sources investigate complex connection with joints ([PERSON] and [PERSON], 1998; [PERSON], 1998). Here, complex contact is modeled using dynamic substructuring. Different methods can be used, such as inverse substructuring, primal decoupling, or dual decoupling ([PERSON] and [PERSON], 1998). [PERSON] (1997) models a multi-point connection which can be compared with the contact between the rollers and the bearing rings at a roller bearing. Consequently, this work aims to analyze the measured vibrations in the frequency domain at different positions of a gear test rig to find an optimal position for condition monitoring of the gears. Additionally, the data from the simulation of the test rig are compared to the measurements, which allows a better explanation of the cause of the vibration. With this setup, it is possible to investigate the differences in vibration levels at different places on the test rig and to analyze the vibration transmission through different components of the gearbox, whereby the critical component for the vibration transmission can be found. Therefore, the novelty of this work is as follows:  
1. In this paper, the vibration execution from the gear mesh and bearings in the simulation are investigated at the same time, based on a particular self-modeled software based on published sources. 2. In this paper, the vibration executions in the frequency domain from the experiment and simulation are discussed and compared. 3. Helical gears and tapered roller bearings are used, which lead to a complex dynamic vibration transfer because of static preloads and dynamic axial loads at the bearings. Therefore, Section 2 outlines the FZG test rig and the test scenarios, e.g., the different torque and rotational speeds. Section 3 explains the simulation model. Section 4 describes the measurement setups and Section 5 discusses the data from the measurement and the simulation.
## 2 Preliminaries
### Polynomial Chinese Remainder Theorem  
Polynomial operations are fundamental to many cryptographic constructions, especially in the context of lattice-based schemes. In this section, we introduce the Polynomial Chinese Remainder Theorem, which facilitates the decomposition and reconstruction of polynomial residues under modular constraints.  
Definition 1: **(Polynomial Chinese Remainder Theorem (Polynomial CRT) [26])**. Consider a set of polynomials \(f_{0}(x),f_{1}(x),\ldots,f_{\xi-1}(x)\) that are pairwise relatively prime. Let \(f(x)=f_{0}(x)\cdot f_{1}(x)\ldots f_{\xi-1}(x)\), and let \(\alpha_{0}(x),\alpha_{1}(x),\ldots,\alpha_{\xi-1}(x)\) represent any given polynomial. Then, there exists a unique polynomial \(g(x)\) such that \(g(x)\equiv\alpha_{i}(x)\pmod{f_{i}(x)}\) for \(i=0,1,\ldots,\xi-1\). The unique solution to this system of congruences is given by:  
\[g(x)=\sum_{i=0}^{\xi-1}\alpha_{i}(x)\cdot\beta_{i}(x)\cdot\gamma_{i}(x)(\bmod f (x)),\]  
where \(\gamma_{i}(x)=\frac{f(x)}{f_{i}(x)}\) and \(\beta_{i}(x)\) is the modular inverse of \(\gamma_{i}(x)\) modulo \(f_{i}(x)\), i.e., \(\beta_{i}(x)=\frac{1}{\gamma_{i}(x)}\pmod{f_{i}(x)}\).
# Fast Screening Algorithm for Satellites Based on Multi-Constellation System
## 4 Experimental Validation and Result Analysis  
We built a multi-constellation positioning system based on C language containing three systems, GPS, BeilDou, and Galileo. We compared different satellite selection algorithms to verify the stability and feasibility of the proposed algorithm, and we evaluated the reduction in computational load of the navigation system when some GDOP values are lost.
# Rifampicin-Loaded PLGA/Alginate-Grafted pNVCL-Based Nanoparticles for Wound Healing
## 2 Materials and Methods
### Preparation of PLGA-Coated Alginate Grafted with pNVCL  
The prepared biopolymeric matrix was used further to synthesize nano/microparticles containing and not containing rifampicin (the selected drug model for postsurgery wound healing).  
An amount of prepared biopolymer was dissolved in water within a concentration of 0.1 wt.% and then rifampicin was added to the polymeric solution to ensure a good blending and formation of electrostatic bonds with the hydrophilic groups of the biopolymer. The theoretical amount used for the rifampicin loading within the matrix was 0.5 wt.% against the polymer amount. The mixture was stirred for a couple of hours then lyophilized.  
The biopolymeric matrix without rifampicin was dissolved in twice-distilled water within a concentration of 0.1 wt.%. The oil phase solution was prepared by dissolving 0.1 g PLGA (50/50) in dichloromethane (DCM) and Span 80 was added to ensure good stability of the emulsion. The oil phase was probe sonicated for 10 min then the water phase was added dropwise under strong stirring under vortex to prepare the water-in oil emulsion. The first emulsion was again probe sonicated and then added dropwise in the second water phase consisting of 5 wt.% polyvinyl alcohol low molecular weight, Tween 80, and salt to give the final water-in-oil-in water (W/O/W) dispersion. The solvent was evaporated at room temperature, and the system was kept under stirring overnight. The final dispersion was then centrifugated at 15,000 rpm under a temperature of 5 \({}^{\circ}\)C to collect the nano/ microparticles of poly(N-vinylcaprolactone) grafted with alginate (AgA-g-pNVCL) coated with PLGA.  
The formation of particles containing rifampicin was performed similarly except for the fact that the matrix loaded with rifampicin was dissolved in the water. The drug content was determined using high-performance liquid chromatography (HPLC) and it was reported in our previous work [4] as being 160 ug/mL in a 20 mg formulated sample.
## 2 Background and Related Technologies
### Vleo  
Very low Earth orbit (VLEO) satellites operate at altitudes of approximately 200 to 500 km, significantly closer to Earth's surface than conventional LEO satellites. As traditional LEO satellites approach saturation and space debris concerns continue to escalate, VLEO technology has gained attention as a promising alternative. Due to their lower altitude, VLEO satellites can offer reduced transmission latency and enhanced spatial resolution, making them highly effective for Earth observation, reconnaissance, and high-speed data transmission applications [6].  
Transitioning from LEO to VLEO, the benefits and challenges also shift considerably. While VLEO satellites offer the potential for reduced launch and operational costs and facilitate the mass production and deployment of small satellites, their lower altitude introduces significant challenges. In particular, operating at such low altitudes intensifies the effects of atmospheric drag, which impacts orbital maintenance and fuel efficiency. To overcome these issues, research efforts have focused on aerodynamic satellite designs, drag-minimizing materials, and propulsion advancements.  
Moreover, the advantages of VLEO satellites are not limited to cost reductions. Their proximity to Earth naturally facilitates orbital decay, aiding in the safe deorbiting of non-operational satellites and reducing long-term debris risks. The commercialization of VLEO satellites has been further propelled by data-driven design methodologies and modern optimization algorithms, including genetic algorithms and AI-based orbit simulation frameworks. These techniques are being leveraged to solve complex constellation design challenges, with promising applications in areas such as smart cities, environmental monitoring, and precision agriculture [7].
## 1 Introduction
[11] selected 200, 300, 400, and 1000 vertiport locations in Northern California using the K-means algorithm. [PERSON] et al. [12] selected 10 to 36 vertiport locations using data from a survey from the commuting population and the K-means algorithm and selected three routes to analyze the savings in commuting time. For this study, however, data was filtered by adding assumptions that commutes would not commute to and from the same administrative district using the eVTOL, and vertiports were chosen based on the assumption of building a substantial number of vertiports required when the UAM is commercialized. In addition, regarding the issue of noise, [PERSON] et al. [13] stated that eVTOL's noise is one of the most harmful effects of commercializing the technology, and the impact of noise on commuting using an eVTOL in San Francisco was analyzed through two scenarios of use for daily commuting. However, eVTOL noise data was not available in the Aviation Environmental Design Tool (AEDT), so it was modeled using four representative helicopters of different classes. Uber established a subsidiary dedicated to UAM called Uber Elevate and led the market by standing out in eVTOL development and commercialization. It also plans to operate a certified vehicle in 2023 to generate revenue, signaling commercialization in Los Angeles, Dallas, and Melbourne, Australia. Regarding the selection of vertiports, the K-means algorithm was used to select the most suitable places in LA and London, and in collaboration with Corgan, Uber announced the designs of vertiports in certain U.S. cities. In addition, various types of vertiports, such as parking lots, highway interchanges, and barges, were proposed to address difficulties in infrastructure design in urban areas [14]. However, Joby acquired Uber's UAM business in December 2020. In this paper, the demand for eVTOL is analyzed by examining statistics of the number of people commuting, focusing on the Seoul metropolitan area in Korea, where 50% of the population lives. Vertiport locations are then selected for UAM using the commuter data and K-means algorithm. The Aviation Environmental Design Tool (AEDT), a software that analyzes environmental effects, such as noise, fuel consumption, emissions, and air pollution of aircraft, developed by the FAA (Federal Aviation Administration), was also used to perform noise analysis around the UAM route. In order to minimize the number of people affected by noise, the route was set in the form of noise priority rather than the shortest route directly connecting the origin to the destination. Finally, noise analysis was performed compared to the shortest route.
# A Framework for Coverage Path Planning Optimization Based on Point Cloud for Structural Inspection
## 4 Conclusions and Future Work  
The proposed research work presented a framework for coverage path planning optimization using a dense point cloud as information from the surface. The main idea is to provide reliable information for periodic inspections in enormous structures to verify possible changes. The data can be used to create routes or even to refine previously created paths. The proposed technique was evaluated in simulation and real scenarios, generating missions with time and photometric optimizations. The results showed good responses to the problem, avoiding wasted energy from the UAV and a specialized operator's need.  
The methodology shows an increase in 3D reconstructions' density, observing photometric criteria, and equalizing it with the maximum mission time. Note that the objective was achieved, since the increase in the number of vertical transfers and the capture points' approach significantly increased the number of points in the same region of the 3D reconstruction. Another aspect is the insertion of local approach missions, allowing a sweep in larger areas and increasing density at specific points.  
In terms of evaluation, this research opens the possibility of several future works. For example, in addition to more photometric parameters, such as intersection angles among the photos, incidence angles, and features, it will be researched to add the reconstruction's quality as a parameter of the optimization.  
Conceptualization, methodology and writing I.Z.B., M.F.P. and A.L.M.M.; funding acquisition, L.M.H.; review and editing A.G.M., L.M.H. and M.J.R.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by INCT-INERGE, BAESA, ENERCAN, and FOZ DO CHAPECO, under supervision of ANEEL--The Brazilian Regulatory Agency of Electricity, to Grant No. PD 03936-2607/2017.  
The work reported in this paper was performed as part of an interdisciplinary research and development project undertaken by UFJF. The authors acknowledge the financial funding and support of the following companies: CAPES, CNPq, INCT-INERGE, BAESA, ENERCAN, and FOZ DO CHAPECO, under supervision of ANEEL--The Brazilian Regulatory Agency of Electricity, through Project number PD 03936-2607/2017. The authors also would like to thank CEFET-RJ.  
The authors declare no conflict of interest.
# Effects of Footpad Slope, Movement Direction and Contact Part of Foot on Foot-Based Interactions
## 1 Introduction  
There are 27 bones in each hand and 26 bones in each foot, adding up to a total of 106 bones which account for 52% of the body's 206 bones [1]. The high number of bones and joints in these body parts is due to the essential role they play in various movements. While the hands are capable of intricate movements for writing or using tools, the bones in the feet move organically to aid in walking and exercise and to absorb impact from the body's weight.  
These structural characteristics may limit the effectiveness of computer interfaces operated by feet. However, foot-operated devices such as car pedals, door stoppers, foot switches, and foot mice have been in use since the inception of human-computer interaction (HCI) [2], indicating the possibility of using a foot-based input as a method. Recent technological advances have led to the development of foot-based interactions that allow for direct contact between the foot and the device. With sensor technologies, the computer can recognize foot gestures and trigger specific commands [3, 4].  
This study focuses on direct foot interaction, in which users use a specific part of the foot to directly contact the foot pad while sitting. This approach to foot-based interaction is more intuitive than input by foot gesture, which requires users to remember foot gestures and mapped commands [3, 4]. The problem with direct foot interaction is that the foot cannot perform precise tasks [5, 6, 7]. If feet could perform faster and more sophisticated tasks, the frequency and likelihood of using them for human-computer interaction would increase.  
The goal of this study is to find ways to improve the speed and accuracy of the task of clicking the touchpad with the feet while sitting. Specifically, the study examines theinfluence of the slope of the foot touchpad, the moving direction of the foot, and the touch part of the foot on the accuracy and speed of the touch when a specific point (target) is touched. The results of this research are expected to provide guidance for the design of direct foot-based interfaces.
## 3 Proposed Methods
### Color Space Transformation  
Many color models are currently used because the science of color is a broad field encompassing many application areas [7]. In this work, we use two color spaces (RGB and HLS), since transforming images to a non-RGB space has improved the classification performance [35]. These two color spaces are described below:  
Figure 1: Proposed method for detection of archaeological structures.  
Figure 2: (**a,b**) The image acquisition process for the archaeological sites using SASPlanet.  
RGB color space: The typical format involves a 24-bit encoding, where the image is constructed using three primary colors--red (R), green (G), and blue (B)--each represented by a separate channel that is processed by cameras and computers. With 8 bits allocated to each color channel, there are 256 distinct values possible per channel, resulting in a total of 16,777,216 color combinations. Additive color mixing is used in this color space to create the final image, which is divided into the three channels (R, G, and B) and treated as distinct features for each color channel.
* HSL color space: This consists of three color channels: H (hue), which represents the primary colors (red, green, and blue); L (lightness), which takes into account the amount of light in an image, where the amount of light will tend to black; and S (saturation), where the amount of saturation in the image will depend on whether the color turns gray or maintains the original color. Choosing this color channel allows us to carry out the processing of the image in individual planes, because the color is represented in the image hue and lightness, while the saturation is used as a masking image that isolates the area of interest in the image [7].  
The change in the color space from RGB to HSL in our experiments helped us to obtain images with filters that contain more information due to the composition of the HSL. The tests can be seen in Figure 3, where we can see images generated from these color spaces with the help of different filters. This figure shows that the filters (especially the Canny and Laplacian filters) emphasize more pixels corresponding to vegetation in the HSL color space than in the RGB color space.