esa-sceva/satcom-chunk-collection huggingface dataset:
# Temperature Estimation Adaptive to Variables over Distance Using Infrared-LiDAR
## 4 Experimental Results
### Experiment: Temperature Estimation for a Person  
In body temperature measurement, since the exact value of the surface temperature of a person cannot be known, body temperature is estimated similarly at each distance, showing that the proposed method is also suitable for body temperature measurement. It also shows the possibility of estimating body temperature through fusion of infrared cameras and LiDAR in the future. Figure 16 is an image of a person obtained from an infrared camera by distance. Digital count is acquired from the person face area and used for temperature estimation. Furthermore, Figure 17 shows the LiDAR map at 6 m. Distance to a person can be obtained obtained through LiDAR (\(x,y,z\)) by distance. Unlike a blackbody, for person measurements, since the exact temperature value cannot be known, the estimated temperature for each distance is similar, confirming the validity of the proposed method. In addition, when estimating the temperature for an object other than a blackbody, the emissivity is an important factor and should be reflected in the radiation. Therefore, in the experiment, the human emissivity is set to 0.98, and the radiation is obtained through \(L(G_{obj})*\frac{\textit{ emissivity of blackbody}}{\textit{ emissivity of object}}\) and T is estimated through this. As a result of estimating the temperature by defining a new mapping function by obtaining the distance to a person through LiDAR and obtaining a digital count through it, it can be seen that similar temperature for each distance is estimated as shown in Table 8.  
Figure 15: Temperature estimation result according to distance by temperature.  
Figure 16: Measurement image of a person at about 6 m, 7 m, 7.5 m, 8 m.  
Figure 17: Measurement LiDAR map for a person at about 6 m.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **6 m** & **7 m** & **7.5 m** & **8 m** \\ \hline
**T (\({}^{\circ}\)C)** & 33.62 \({}^{\circ}\)C & 33.57 \({}^{\circ}\)C & 33.55 \({}^{\circ}\)C & 33.50 \({}^{\circ}\)C \\ \end{tabular}
\end{table}
Table 8: Body temperature measurement result by distance.  
Through the proposed method and the fusion of LiDAR and IR cameras, it will be possible to quickly and accurately screen people with fever from the general public by checking the body temperature of many people. Through this, it can be usefully used in airports or buildings with frequent access by people.
# Predicting Dynamic User-Item Interaction with Meta-Path Guided Recursive RNN
## 4 Methodology
### Overall Objective Function  
In the final prediction step, we aim to predict the embedding \(\widetilde{J_{t}}\) of the item that a user will interact with. Most existing models that give the highest interaction probability among all user-item pairs usually need to accomplish the neural-network forward process for each item, which is very time-consuming. Instead of searching the highest interaction probability from all the user-item pairs, we directly outputs the item embedding vector. Thus, our model only needs to accomplish the forward process once and predicts the item embedding in the shift embedding module, and then the item whose embedding vector is closest to the predicted one in the embedding space will be selected. Thus our model is much more efficient than existing models. The item embedding prediction function is given as follows.  
\[\widehat{j_{t+\Delta}}=W_{1}\widehat{u_{t+\Delta}}+W_{2}\overline{u}+W_{3}i_{ t+\Delta-1}+W_{4}\overline{i}+b \tag{7}\]  
where \(W_{1},...,W_{4}\) are trainable parameters and \(b\) is a bias vector, \(\overline{u}\) and \(\overline{i}\) represent static embeddings of users and items, respectively. \(\widehat{u_{t+\Delta}}\) is the output of shift embedding module. Note that \(i_{t+\Delta-1}\) is the dynamic item embedding before \(t+\Delta\).  
For training the parameters of the model, we minimize the \(L_{2}\) difference between the predicted item embedding \(\widetilde{j_{t}}\) and the ground truth ground item embedding \(j_{t}\) at every interaction. We aim to minimize the following loss.  
\[minimize\sum_{(u,j,t,f)\in S}\|\widetilde{j_{t}}-j_{t}\|_{2}+\lambda_{U}\|u_ {t}-u_{t-1}\|_{2}+\lambda_{I}\|i_{t}-i_{t-1}\|_{2} \tag{8}\]  
The first loss term is the error of the predicted embedding vector. To prevent the embeddings of users and items change sharply, the last two terms are embedding smoothness regularization, and \(\lambda_{U}\) and \(\lambda_{I}\) are scaling parameters.
# Effect of Wheel Path in Raster Grinding on Surface Accuracy of an Off-Axis Parabolic Mirror
## 4 Discussion
### Analysis of the Factors Affecting the Scallop Height of the Grinding Surface  
#### 4.2.1 Effect of Grinding Wheel Radius on Scallop Height  
The scallop height \(h\) is a function of the wheel radius \(r\), and the partial derivative of the wheel radius \(r\) occurs on both sides of Equation (6):  
Figure 7: Schematic of the scallop height: (**a**) step along the meridian direction; (**b**) step along the arc vector direction.  
\[\frac{\partial h}{\partial r}=-\frac{2r+2\sin^{2}(l/2R)(R-r)}{\sqrt{r^{2}-(R-r)^{2} \sin^{2}(l/2R)}}+\cos(l/2R) \tag{8}\]  
According to Equation (8):  
\[\frac{\partial h}{\partial r}<0 \tag{9}\]  
The scallop height \(h\) is negatively correlated with the radius \(r\) of the grinding wheel. Simulation calculation is shown in Figure 8. It can be seen that the larger the grinding wheel radius \(r\), the smaller the scallop height.  
In the grinding process, to obtain a lower scallop height, a larger grinding wheel radius should be selected as far as possible for processing. To avoid local interference, Equation (10)'s condition should be satisfied. The increase in the radius of the grinding wheel gradually reduces the effect of reducing the scallop height and increases the manufacturing cost, the shape accuracy of the grinding wheel is more difficult to guarantee, and the wheeling requirements are higher.  
\[r\leq R_{\text{min}} \tag{10}\]
###### Abstract  
Joint communication and sensing (JCS) is becoming an important trend in 6G, owing to its efficient utilization of spectrums and hardware resources. Utilizing echoes of the same signal can achieve the object location sensing function, in addition to the V2X communication function. There is application potential for JCS systems in the fields of ADAS and unmanned autos. Currently, the NR-V2X sidelink has been standardized by 3 GPP to support low-latency high-reliability direct communication. In order to combine the benefits of both direct communication and JCS, it is promising to extend existing NR-V2X sidelink communication toward sidelink JCS. However, conflicting performance requirements arise between radar sensing accuracy and communication reliability with the limited sidelink spectrum. In order to overcome the challenges in the distributed resource allocation of sidelink JCS with a full-duplex, this paper has proposed a novel consecutive-collision mitigation semi-persistent scheduling (CCM-SPS) scheme, including the collision detection and Q-learning training stages to suppress collision probabilities. Theoretical performance analyses on Cramer-Rao Lower Bounds (CRLBs) have been made for the sensing of sidelink JCS. Key performance metrics such as CRLB, PRR and UD have been evaluated. Simulation results show the superior performance of CCM-SPS compared to similar solutions, with promising application prospects.  
joint communication and sensing (JCS); NR-V2X sidelink; resource allocation; radar sensing; Q-learning +
Footnote †: journal: Journal of Communications and Systems  
11 November 2024  
Revised: 17 December 2024  
Accepted: 6 January 2025  
Published: 7 January 2025  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] Reinforcement Learning-Based Resource Allocation Scheme of NR-V2X Sidelink for Joint Communication and Sensing_Sensors** **2025**, 25.302. [[https://doi.org/10.3390/s250200302](https://doi.org/10.3390/s250200302)]([https://doi.org/10.3390/s250200302](https://doi.org/10.3390/s250200302))  
Copyright: 6 2025 by the authors.  
License MDPI, Basel, Switzerland.  
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/))).
# NFT Technology for Enhanced Global Digital Registers: A Novel Approach to Tokenization
## 8 Experimental Evaluation
### Smart Contract Implementation
#### 8.2.3 Rationale Behind Design Choices  
* ERC-721 Standard Adoption: We chose to build upon the ERC-721 standard due to its widespread adoption, robust security features, and compatibility with existing NFT ecosystems. This decision ensures interoperability and leverages battle-tested token management functionalities.  
* Identifier-to-Token Mapping: The use of a mapping between string identifiers and token IDs allows for efficient lookups and verification, which is crucial for a global registry system that may need to handle millions of entries.
* Metadata Storage: Storing metadata directly on-chain ensures data integrity and immediate availability. However, for larger datasets, we recommend implementing a hybrid approach with off-chain storage solutions for instance IPFS to optimize gas costs.
* Access Control: Functions that modify the state (for example, registerItem and updateMetadata) include ownership checks to ensure data integrity and prevent unauthorized modifications.
* Batch Operations: The inclusion of batch registration functionality addresses scalability concerns and provides cost-effective solutions for large-scale data entry.
* Gas Optimization: Throughout the contract, we've prioritized gas-efficient operations, such as using mappings for lookups and minimizing storage operations where possible.
* Upgradability Considerations: While not implemented in the current version, the contract structure allows for future upgradability patterns, ensuring the system can evolve with changing requirements and technological advancements.  
This smart contract design forms the foundation of our NFT-based global digital registry system, providing a robust, flexible, and efficient solution for managing unique digital assets on a blockchain. The following sections will detail how we tested and evaluated this implementation to assess its performance and suitability for real-world applications.
# A Review of Parallel Heterogeneous Computing Algorithms in Power Systems
## 1 Introduction
FPGA architecture allows application of the Multiple-Instruction, Multiple-Data (MIMD) technique where different instructions can be executed on multiple data points simultaneously. On the other hand, GPU architecture provides the possibility to use the Single-Instruction, Multiple-Data (SIMD) technique for common tasks where the same operation is performed on multiple data points at the same time. Due to the optimized GPU architecture for SIMD operations and developed toolkits and frameworks to program GPU instructions in non-graphic programming languages, such as C, C++, and FORTRAN, researchers have discovered how to use GPU in different power system applications where common instructions are recurrent. The literature review revealed more than 200 works using PHC in power system applications over the last three decades. The review mainly focuses on research works where PC and HC are implemented in the following areas to limit the scope of the survey: power flow analysis, transient stability, contingency analysis, smart grids, Optimal Power Flow (OPF), electromagnetic transient simulation, renewable energy integration, dynamic state estimation, power quality, dynamic models, electrical vehicles, probabilistic power flow, Security Constrained-Optimal Power Flow (SCOPF), Transient Stability-Constrained Optimal Power Flow (TSCOPF), power system visualization, short circuit analysis, power system planning, power system reliability, electricity market, small-signal analysis, security-constrained economic dispatch, and hydrothermal scheduling. The review classifies the works into two groups: works where GPU is used and works that use CPU clusters, fog or cloud computing, or FPGA in power system studies and applications. This classification helps to organize the review since GPU applications represent more than half of all the articles. However, GPU applications are the most recent works of PHC techniques in power systems since 80% of the articles published during the last five years correspond to GPU implementations. This document is organized as follows. Section 2 presents an overview of the PHC techniques applied to power system studies and analysis. Section 3 organizes all works by technology and power system applications. Section 4 presents the conclusions, and Section 5 outlines the future work of PHC in power system applications.
# Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels
## 3 Methods
### Experimental Setup
#### 3.4.1 Environment and System  
We performed simulations for its experimental investigation by utilizing a Jupyter Notebook (6.3.0) environment running Python (3.8.8) on Windows 11 [29, 30]. A personal computer used to run the simulation was equipped with an Intel Core i7 8565U processor and 16 GB of memory. In addition, we also investigated the models' performances in the constrained computational environment, i.e., Raspberry Pi 2 Model B. The Raspberry Pi runs Raspbian OS with Python (3.9.2) on a Broadcom BCM2863 SoC (900-MHz quad-core ARM Cortex-A7 CPU 1 GB RAM). We selected this particular device as it has less computational capabilities than the planned future system implementing a CubeSat (Raspberry Pi CM3+) onboard. Raspberry Pi CM3+'s in-orbit performance has been proven in a recent operational satellite project (KITSUNE). It is an acceptable system representation to demonstrate the feasibility of model deployment. Finally, the simulation libraries were utilized as follows: pre-processing, ML modelling, evaluation, and plotting (sklearn, PyOD, pickle, NumPy, pandas, SciPy, matplotlib, scikitplot, and seaborn) [18, 23, 30, 31, 32, 33, 34, 35, 36].
# Non-Thermal Methods for Ensuring the Microbiological Quality and Safety of Seafood
## 1 Introduction  
Raw fish and seafood products belong to the most traded foodstuff worldwide (FAO, 2020) and are much appreciated because of their high nutritional value. In general, it is becoming clear that there is a parallel trend in world fish production and the human demand for seafood products. Over 150 million tons of fish were utilized in 2018 for human consumption, and in 2030, this is expected to increase [1], showing the fish industry production dynamic. However, raw fish may lose their quality rapidly due to various physicochemical changes, enzyme activity, and microbial growth [2, 3]. Together, particular care must be given to avoid seafood contamination during post-harvesting from chemicals and various bacterial pathogens [3].  
Thermal methods are generally performed in the seafood industry to prolong the product's shelf life and inactivate the spoilage and pathogenic microorganisms. However, inhibition of spoilage microorganisms is not always the main objective, but it is an unavoidable positive effect [4]. Traditional thermal processing techniques, such as pasteurization and sterilization applied by the seafood industry, may be very efficient in inactivating or inhibiting bacterial pathogens but usually result in undesirable nutritional, chemical/biochemical, and sensorial changes in foods. These changes reduce the consumer's acceptance of now seeking minimally processed products with improved safety, added value, and increased shelf life [5, 6].  
As an outcome, to ensure safety and maintain the quality of seafood, several non-thermal techniques have been adopted and are continuously developing over the lastyears. High hydrostatic pressure processing (HHP), ultrasounds (US), non-thermal atmospheric plasma (NTAP), pulsed electric fields (PEF), and electrolyzed water (EW) are some methods showing the potential to be applied by the seafood industry. Research on these non-thermal methods has shown significant bactericidal activity with minimal organoleptic changes [7; 8; 9; 10; 11; 12]. Among them, HHP is the best-established method in the food industry, with many HHP processed products being commercially available in Europe, Japan, and the United States [13]. Among them, EW is another processing method with good results and notable research progress, providing safe and minimally affected raw fish and seafood products [14], while it is approved for use in the Japanese seafood industry [15]. The purpose of this review is to highlight and summarize past and recent studies on non-thermal technologies (HHP, US, NTAP, PEF, and EW), focusing on their efficacy to inactivate bacteria and to produce healthier seafood products with retained physicochemical properties.
# Passive Vibration Control of a Semi-Submersible Floating Offshore Wind Turbine
## 3 Optimization for TMD Parameters
### Genetic Algorithm for Higher Stiffness TMD  
Genetic algorithm (GA) is a stochastic global optimization and search method inspired by natural biological evolutions [25]. It has been widely applied in extensive practical engineering problems. For a specific optimization problem, a population of individuals is encoded with a simple chromosome-like data structure firstly. Then, in the iteration of generation development, new sets of individuals that contain high fitness genes and newborn random information evolve gradually. The operators such as selection, recombination and mutation can ensure the diversity and superiority of the newborn population. Similar to natural adaptation, this evolution process leads to the individuals  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Mass (kg)**} & \multicolumn{3}{c}{**Optimization Value**} & \multirow{2}{*}{\(f_{n}\) (Hz)} & \multirow{2}{*}{\(\xi\)} & \multirow{2}{*}{\(\varpi_{\text{TTD}}\) (m)} & \multirow{2}{*}{\(\text{Reduction}\)} \\  & & & & & & & Ratio \\ \cline{1-1}  & **Spring Coefficient** & & & & & & Ratio \\  & **(N/m)** & & **(N/(m/s))** & & & & \\ \hline
6000 & 200 & 3400 & 0.02906 & 155.2\% & 0.09318 & 5.86\% \\
8000 & 100 & 3900 & 0.01779 & 218.0\% & 0.09176 & 7.29\% \\
10,000 & 300 & 5200 & 0.02757 & 150.1\% & 0.09045 & 8.61\% \\
12,000 & 200 & 6200 & 0.02055 & 200.1\% & 0.08923 & 9.85\% \\
14,000 & 100 & 7100 & 0.01345 & 300.0\% & 0.08808 & 11.01\% \\
16,000 & 500 & 7900 & 0.02813 & 139.7\% & 0.08669 & 12.41\% \\
18,000 & 0 & 8600 & 0 & - & 0.08586 & 13.25\% \\
20,000 & 800 & 10,100 & 0.03183 & 126.3\% & 0.08489 & 14.23\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Optimal results of spring and damping coefficients by method of exhaustion.  
Figure 6: 3D Surface of \(\sigma_{\text{TTD}}\) for different TMD mass (**a**) 10,000 kg; (**b**) 20,000 kg.  
that are optimal to environment (fitness criteria) finally. The feasibility of the genetic algorithm in TMD parameters optimization has been proven by [PERSON] (1975) and [PERSON] [PERSON] et al. (2008). In this study, the population size of each generation is 50, the ratio of crossover is 0.8, and the convergence criterion is that the average relative change of the best fitness is less than \(1\times 10^{-6}\). Eventually, the optimized results in the higher range of stiffness are listed in Table 5.  
According to the displacement of TTD shown in Figures 4 and 5, the first fore-aft bending mode frequency of the 5 MW wind turbine with a 10,000 kg TMD is around 0.43 Hz. The frequencies of the optimized TMD devices in Table 5 show a well agreement with the wind turbine's fore-aft natural frequency. Furthermore, the gradually decreased frequency is induced by the effects of incremental TMD mass. The reduction ratios for all the eight masses are relatively close to each other, which prove the more stable behavior of higher stiffness TMD.
## 5 Case Study  
In order to validate the proper achievement of the design goal presented in Section 4, we performed a case study to answer the following research question: Does the analytics feedback collected by the tool provide insights about the collaboration constructs? To get insights on this matter, we decided to compare two different teamwork activities, with high contrast between collaborative and non-collaborative work. The first activity, namely Collaborative Activity, aimed to explore whether the MMLA visualizations on subjects interacting collaboratively effectively support the observation by the teacher of behavioral indicators and traces of collaboration. The second activity, namely Competitive Activity, aimed to identify indicators and traces of non-collaborative behavior in an activity designed to produce conflict and more chaotic interactions among the subjects.  
We collected data automatically (through the MMLA platform) and manually (taking field notes) in both activities. Field notes allowed us to describe the flow of interaction of the subjects and observations about the six collaboration constructs of the framework [17]. The automatic data collection performed by the MMLA platform followed the requirements presented in Table 2: measurement of the number of spoken interventions, speaking time (per intervention), type of posture (open, closed, hands on the heaps, hands on the head, and hugging the opposite arm). The visualizations and the field notes were handed to two members of the research team that hold the degree of Master in Teaching for Higher Education, namely the reviewers. In the two activities, subjects received a task to be performed in five minutes, without further instructions about how to interact to achieve it. For both activities were considered the same four subjects. We recorded audio and video of each of them during the whole activity.  
The four subjects are students from different careers and universities: Psychology, Auditing Accountant, Industrial Management Execution Engineer and Business Administration. All participated voluntarily and gave their informed consent. The group is composed of 3 women and 1 man between 25 and 28 years old and they do not know each other.