esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
Since energy is conserved in a system, a part of the transmitted power goes into the trace before it reaches the load. The energy is given by electric and magnetic fields and can be modeled by inductor and capacitance per unit length as it is illustrated in Figure 1a-c. Typical technologies applied in interconnects analyses are microstrip (Figure 1a) and striplines (Figure 1b) for top or bottom traces or internal layers in a multilayer printed circuit board (PCB). Signals are always pairs of current-current flows in one terminal and flows out the other one. On these interconnects, high data rates are used in order to get bigger bandwidth, meanwhile keeping as much as possible a compact size of a digital or analog communication system.  
The particularities of high versus low frequency signals focus on distortion at high frequencies such as ground bounce, ringing, variation of propagation time, crosstalk, balanced, or unbalanced interconnects that are easily framed in frequency domain or time domain. For a given type of technology, the equivalent circuit with per unit length parameters for a very short length comparing to the wavelength, \(\Delta x\ll\lambda\), is represented in Figure 1c.
## 3 Results
### Hydrological Regime Regulation Loss  
An estimate was made, in the period 2012-2020, of the impact that the increase in land consumption had on surface runoff (which represents the hydrological regime regulation service). Figure 9 shows the variations in \(\mathrm{hm}^{3}\) in this service caused by the consumption of land in Italy. At the national level, an increase of about 250 \(\mathrm{hm}^{3}\) of surface runoff was estimated. The biggest increases were recorded in the Veneto and Lombardy regions (50 and 44 \(\mathrm{hm}^{3}\), respectively), whereas the smallest increases (less than 10 \(\mathrm{hm}^{3}\)) were recorded in the Umbria, Basilicata, Liguria, Molise, and Aosta Valley regions.
## 3 Results
### Model Performance and Comparison  
#### 3.2.1 Sentinel-2 Bands Prediction Performance  
The results of the validation of the SOC content models using only the Sentinel-2 bands varied among the algorithms (Figure 8, Table A3 in Appendix B) but were relatively stable among the three iterations, with RMSEP equal to 3.57 (\(\pm\)0.79), 3.51 (\(\pm\)0.45), 2.33 (\(\pm\)0.1), for PLS, RF, and DNN, respectively. However, according to R\({}^{2}\), the third iteration always produce results of poor quality. Based on the RPD, only models calibrated using DNN performed moderately well in predicting SOC content (1.47 (\(\pm\)0.26)). But, based on the RPIQ, SOC predictions were very poor for the three algorithms (0.93 (\(\pm\)0.18), 0.90 (\(\pm\)0.11), and 1.31 (\(\pm\)0.08)).  
2.2 Prediction Performance of Sentinel-2 Bands Combined with Sentinel-2 and Sentinel-1 Indices and Sentinel-1 Soil Moisture  
Including the S1- and S2-indices and S1-soil moisture along with the Sentinel-2 bands in the models influenced calibration performance more than validation and the DNN algorithm performance more than the RF algorithm (Figure 9, Table A4 (Appendix B)). Using  
Figure 8: Scatterplots of measured vs. predicted soil organic carbon (SOC) content for the validation dataset using Sentinel-2 bands and the partial least squares (PLS), random forest (RF), or deep neural network (DNN) algorithms for the three iterations ((1), (2), and (3)). R\({}^{2}\): coefficient of determination, RMSEP: root mean square error of prediction, RPD: ratio of performance to deviation, RPIQ: ratio of performance to the interquartile distance.  
the DNN, the combination of S2-indices, S1-indices, and S2-indices + S1-indices + S1-soil moisture with Sentinel-2 bands increased the calibration performance the most (mean RPIQ = 2.59 (\(\pm\)1.30), 2.53 (\(\pm\)1.50), and 2.89 (\(\pm\)2.16), respectively), but this result was not stable among the three iterations. Model calibration performance was most stable when S1-soil moisture was included for DNN and RF (mean RPIQ = 2.17 (\(\pm\)0.2) and 1.48 (\(\pm\)0.08), respectively), but doing so had no strong effect on model validation performance. Thus, for all calibrated models, the RPIQ for model validation was always lower than the RPIQ threshold 1.7 considered for accurate predictions. Based on the mean RPD values, only the DNN model calibrated using S2 bands + S1-soil moisture showed a moderate validation performance of SOC prediction (mean RPD = 1.42 (\(\pm\)0.31)), but no improvement was observed compared to using only S2 bands (mean RPD = 1.47 (\(\pm\)0.32)). Mean RMSEP values ranged from 3.34 (\(\pm\)0.46) to 3.59 (\(\pm\)0.55) for RF and from 2.33 (\(\pm\)0.12) to 2.95 (\(\pm\)0.34) for DNN.
## 2 Materials and Methods
### Proposed MP/DD Method
In the first stage, multiple peaks of the test variable are selected as candidates of the test variables. In the second stage, the final estimation parameter is singled out from the previously multiple peaks of the test variable. \[\sigma=\frac{C}{N_{0}}-10\log_{10}\left(B_{pd}\right)+10\log_{10}(F_{s}\times T_{ int})-L_{sq}-L_{d}, \tag{6}\]  
where \(B_{pd}\) is the noise band, \(F_{s}\) is the sample frequency, \(T_{int}\) is the coherent integration time, and \(L_{sq}\) and \(L_{d}\) are square and digital process loss. Statistically, the first dwell detection probability \(P_{D}^{1}\) and the first dwell false detection probability \(P_{F}^{1}\) can be expressed in Equation (7). Moreover, the second dwell detection probability \(P_{D}^{2}\) and the second dwell false detection probability \(P_{F}^{2}\) are represented in Equation (8). The difference between the first and the second dwell is that \(\sigma^{\prime}\) is 3 dB higher than \(\sigma\). \[P_{D}^{1}(M,N,\sigma)=\int_{V_{th}(M,N,\sigma^{\prime})}^{\infty}f_{H_{1}}(x, \sigma)dxP_{F}^{1}(M,N,\sigma)=\int_{0}^{V_{th}(M,N,\sigma)}f_{H_{0}}(x,\sigma )dx \tag{7}\]  
\[P_{D}^{2}(1,M,\sigma^{\prime})=\int_{V_{th}(1,M,\sigma^{\prime})}^{\infty}f_{ H_{1}}(x,\sigma^{\prime})dxP_{F}^{2}(1,M,\sigma^{\prime})=\int_{0}^{V_{th}(1,M, \sigma^{\prime})}f_{H_{0}}(x,\sigma^{\prime})dx \tag{8}\]  
Since explicit formulation descriptions of detection probability and false detection probability are difficult to integrate with Equations (7) and (8), numeral calculation and Monte-Carlo simulation of the presented acquisition algorithm are carried out. Threshold is not required in the second dwell if the satellite is determined to exist, so the false alarm probability of MP/DD is the complementary set of detection probability, as Equation (9) shows. Based on the above analysis, the AMCO of MP/DD consisting of three parts is derived in Equation (10). \[P_{F}=1-P_{D} \tag{9}\]  
\[\overline{C}_{MP/DD}=\frac{1}{P_{D}}(T_{int}NC_{cell}+2T_{int}MC_{cell}+C_{S}) \tag{10}\]
# Performance Evaluation Method for Intelligent Computing Components for Space Applications
## 4 Benchmark Execution and Evaluation
### High-Performance Computing Benchmark Experiment Results
#### 4.3.2 Test Results on CPUs  
OpenMP (open multi-processing) is an open parallel computing framework that can be used for multi-core CPU systems with shared memory architecture. It achieves the parallelization of tasks by using compiler directives and provides a set of API interfaces for writing parallel programs in a multi-threaded environment. The OpenMP framework has advantages such as simplicity, cross-platform compatibility, automated parallelization, and performance scalability. It can provide convenience and efficiency in CPU development for space applications, accelerate the computation process, and improve application performance.  
Optimized implementations of OpenMP were also performed, and the average results of 20 experiments are shown in Table 4. The table shows the total program execution time of 12 computing benchmark modules on the experimental platforms. The CPUs of the three  
Figure 5: A comparison of the total execution time of the computational benchmark module based on the CUDA framework on the three experimental platforms, also including the optimization implementation, where (**a**) is the cifar_10 benchmark, (**b**) is the cifar_10_multiple benchmark, (**c**) is the convolution_2D benchmark, and (**d**) is the correlation_2D benchmark.  
experimental platforms are 8-core Nvidia Carmel, 4-core Intel i7-8550U, and Loongson 3A4000. This experiment used four multi-core CPUs for computation on all three platforms.  
From the experimental results in Table 4, it can be observed that the performance of the Nvidia Carmel CPU is very close to that of the Intel i7-8550U CPU, and overall, the performance of the Intel i7-8550U CPU is slightly stronger.  
To present the above results more intuitively, the experimental results of the OpenMP framework were plotted into a bar chart using MATLAB (2018.a) software, as shown in Figure 6. From the graph, it can be seen that the execution time of the Loongson platform is relatively longer, at about five times longer than that of the other two platforms, and there is still a certain gap even after optimization. The main reason for this difference may be that the CPU of the Loongson platform is designed for desktop and mobile computing, focusing on comprehensive performance and general computing capabilities. On the other hand, Jetson AGX Xavier is designed specifically for embedded devices and edge computing, emphasizing low power consumption and high efficiency.
## 4 System Development and Experimental Verification
### System Development
#### 4.1.1 Hardware System Development  
The hardware system is the foundation of intelligent obstacle avoidance design, primarily consisting of front-end sensors and onboard hardware. The front-end sensor integrates high-performance LiDAR and high-definition cameras (as shown in Figure 10), including eight onboard cameras and eight LiDARs. The LiDAR can perform area scanning within an 80\({}^{\circ}\)\(\times\) 25\({}^{\circ}\) range, with a high scanning point density of up to 240,000 points per second. It can accurately obtain the distance and orientation information of obstacles and can monitor the space ahead in real time. The high-resolution (5 M) and high frame rate (30 fps) of the cameras provide users with rich image information, assisting in identifying the shape and nature of obstacles. The onboard hardware, including servers, power supply boxes, displays, amplifiers and speakers, routers, etc., is primarily responsible for processing the data collected by the sensors and implementing corresponding obstacle avoidance strategies. The server, as the core of the entire system, runs the obstacle avoidance algorithm and coordinates the work of various modules. The power supply box provides stable electrical support to all components, ensuring the stable operation of the system under harsh conditions. The display is used to show the operating status and alarm information of the obstacle avoidance system in real time, helping operators understand the working conditions of the system. The amplifier and speaker are used to emit sound alerts, reminding operators of potential risks. The router is responsible for ensuring communication between the various components, ensuring efficient and stable data transmission.
## 5 Experimental Results
### Security Analysis
#### 5.6.3 Authentication  
Although the protocol does not inherently provide an explicit authentication mechanism, it assumes either a secure initial key exchange or the involvement of a trusted third party for public key verification. Without such measures, the protocol remains vulnerable to man-in-the-middle (MITM) attacks. To mitigate this risk, integrating a digital signature or certificate-based authentication mechanism is recommended. This would ensure the authenticity of public keys ( \(PK_{A}\) and \(PK_{B}\) ), preventing impersonation by malicious actors and safeguarding the key exchange process.
# Spatial Performance Indicators for Traffic Flow Prediction
## 6 Conclusions  
This study presents spatial KPIs as a novel approach for evaluating traffic flow prediction methods, complementing the conventional average prediction accuracy metrics. Each of the proposed spatial KPIs offers unique capabilities for evaluating the traffic network prediction performance from a spatial perspective. Our study demonstrates that Global Moran's I, Getis-Ord General G, and the univariate analysis of Adapted PageRank Algorithm Modified (APAM) are sensitive to the synthetic clustering cases. However, the evaluation of the four traffic prediction methods showed less pronounced differences. We found Getis-Ord General G to be the most sensitive, as it can differentiate ASTGCN and STSGCN, two methods with similar average RMSE, and utilize the predefined adjacency matrix. This information helps to determine a traffic prediction method that has better spatial characteristics. The practical implications of this study are substantial and provide researchers and stakeholders with more nuanced measures for evaluating and selecting traffic prediction models, ultimately leading to more robust traffic management systems. There are several directions for improving the evaluation of traffic prediction methods in future studies. First, the integration of temporal KPIs, such as the propagation of errors over time, further enhances the evaluation of traffic prediction methods. Second, broader case studies and datasets that reflect real-world situations, such as validation in urban traffic network settings, the consideration of external factors (e.g., weather conditions and events), and larger traffic networks, will show whether spatial KPIs can be applicable as scalable general tools for traffic management systems. Finally, the investigation of other crucial traffic state predictions such as travel time and vehicle speed will broaden the application of spatial KPIs.  
Conceptualization, M.F.F. and S.G.; methodology, M.F.F.; software, M.F.F.; validation, M.F.F.; formal analysis, M.F.F. and S.G.; investigation, M.F.F.; resources, M.F.F.; data curation, M.F.F.; writing--original draft preparation, M.F.F.; writing--review and editing, M.F.F. and S.G.; visualization, M.F.F.; supervision, S.G.; project administration, S.G.; funding acquisition, S.G. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The original data presented in the study (PeMSD8 dataset) are publicly available on GitHub at [[https://github.com/wanhuaiyu/ASTGCN](https://github.com/wanhuaiyu/ASTGCN)]([https://github.com/wanhuaiyu/ASTGCN](https://github.com/wanhuaiyu/ASTGCN)) accessed on 7 September 2023.  
The authors declare no conflicts of interest.
## 3 Materials and Methods
### Data Preprocessing  
Data preprocessing encompassed several critical stages to safeguard the quality and integrity of the EEG recordings. The original dataset included epoched EEG signals that had already been segmented according to different learning conditions. In this study, the raw EEG signals were filtered using an infinite impulse response (IIR) Butterworth bandpass filter (4 th order) with a cutoff frequency of 1-49 Hz. The cutoff frequency range of 1-49 Hz was chosen to effectively eliminate low-frequency noise, such as drift and movement artifacts, as well as high-frequency noise, including power line interference at 50/60 Hz. Following that, to improve the spatial resolution, a common average reference (CAR) was implemented. To facilitate analysis, the data were subsequently divided into 2 s epochs. To reduce the likelihood of encountering artifacts, an automated artifact rejection system was integrated. This system eliminated segments containing data that exceeded \(\pm\)100 microvolts, with particular attention given to eye-blink artifacts. Following an extensive preprocessing stage, a grand total of 2284 artifact-free segments were preserved for further examination. This refined and immaculate dataset was utilized to extract features and classify classroom EEG recordings according to learning styles.
### Study Aim  
As previously demonstrated, variability, whether spatial or temporal, is insufficiently taken into account in biodiversity studies, even though it plays a major role in the implementation of biological responses to climate change. Our objective is to quantify the spatio-temporal variability effect in mountain environments on the activity time and persistence index of an endemic lizard species and to demonstrate the importance of its consideration to lead more appropriate conservation policies. We have made the following assumptions. Activity times and persistence indices would differ depending on exposure and elevation of the study sites. These two metrics could also vary between and within sites due to the habitat and micro-climate generated by the micro-habitat. Finally, climate variability would lead to inter-year variability. This environmental diversity could provide refuge locations where the micro-habitat would buffer rising temperatures. To achieve these, we measured the operative temperature using biomimetic loggers deployed over several sites and years, then we computed activity time and persistence indices and conducted statistical tests.  
Given the context, the novelties of our study are the spatio-temporal study in the mountains thanks to biomimetic logger deployment--an original remote sensing tool for recording data specific to biological organisms--with the biodiversity conservation objective and the definition of a new persistence index.  
First, we present the study area and the biomimetic loggers deployment sites. Second, we introduce the methods to calculate the activity time and the persistence index. Third, we discuss spatial variability on various levels (intra-site, inter-slope, inter-altitude, and inter-site) and inter-year temporal variability. On the basis of our study case, we demonstrate the interest of multi-site and multi-year studies. Finally, we provide guidelines for the use and deployment of biomimetic loggers.
# Activity Recognition with Combination of Deeply Learned Visual Attention and Pose Estimation
## 3 Proposed Algorithm
### Pose Estimation  
#### 3.2.1 3D Pose Estimation and Data Alignment  
Recent development in deep learning has enabled hierarchical systems to learn powerful filters from data ([PERSON] et al., 2017). Furthermore, filters that are learned from large datasets can effectively be used for problems with insufficient data, using what is called transfer learning. The VNCT approach propose in ([PERSON] et al., 2017) is one of the systems that use transfer learning for effective 3D pose estimation directly from RGB images. VNCT is based on a CNN pose regression that allows the real-time estimation of 2D and 3D skeletons using RGB image. For each estimated human joint, the network is trained to estimate a 2D confidence heatmap along with locations maps (for each of the three dimensions). One of the main advantages of estimating a 3D pose is the ability to estimate the positions of corresponding 3D points in different viewpoints. In which case, 3D pose alignment can be estimated with a closed-form solution. To further explain, let \(x_{1}\) and \(x_{2}\) be the estimates of the same subject's 3D pose from two different viewpoints. Assuming the mean of the estimated pose  
Figure 4: Soft attention mechanism. The CNN takes video frame as its input and produces a feature cube. The model computes the current input \(x_{t}\) as an average of the feature slices weighted according to the location softmax \(l_{t}\).  
is centered, the alignment of the estimated pose is performed by estimating the rotation \(R\) through the following optimization:  
\[\underset{R}{\operatorname{argmin}}\|x_{1}-Rx_{2}\|_{2}^{2} \tag{13}\]  
The Equation (13) has a closed-form solution given as  
\[\tilde{R}=VU^{T} \tag{14}\]  
where \(U\sum V^{T}=x_{1}x_{2}^{T}\), with \(U\) and \(V\)being unitary matrices and \(\sum\) a diagonal matrix corresponding to the singular value decomposition (SVD) of \(x_{1}x_{2}^{T}\). The matrix \(\tilde{R}\) denotes the estimated rotation matrix. Given two sequences on \(n\) poses estimated from two different viewpoints \(X_{1}=x_{1}^{1},\cdots,x_{1}^{n}\) and \(X_{2}=x_{2}^{1},\cdots,x_{2}^{n}\), we estimate the alignment between the first corresponding poses \(x_{1}^{1}\) and \(x_{2}^{1}\) using Equation (14). Afterwards, the estimated rotation matrix \(R\) is used to align the rest of the subsequent poses of the sequence.
## 3 The Design of _HeaLow_ and Implementation  
In this section, we explain the overall design of _HeaLow_ first. After that, we explain the implementation details and the techniques used in _HeaLow_. In addition, we give a detailed explanation about the offloading decision process in the availability checker.  
It is to be noted that the design and implementation lie in the practical side. Thus, unlike existing studies relevant to offloading or cooperative computing, we focused on devising techniques dealing with practical issues which are difficult to be predicted, not on conducting time scheduling. As _HeaLow_ attempts to offload data when the recipient confirms the request, it does not have to deal with complex scheduling issues. Scheduling algorithms may reduce network burdens, but our system cannot afford to try to solve a complex problem with the presence of a tight deadline. For these reasons, we tried to devise techniques to cope with unpredictable situations while conducting heavy-computation and low-latency processing in our system.
# An Ontology for Spatio-Temporal Media Management and an Interactive Application
###### Abstract  
In addition to traditional viewing media, metadata that record the physical space from multiple perspectives will become extremely important in realizing interactive applications such as Virtual Reality (VR) and Augmented Reality (AR). This paper proposes the Software Defined Media (SDM) Ontology designed to describe spatio-temporal media and the systems that handle them comprehensively. Spatio-temporal media refers to video, audio, and various sensor values recorded together with time and location information. The SDM Ontology can flexibly and precisely represent spatio-temporal media, equipment, and functions that record, process, edit, and play them, as well as related semantic information. In addition, we recorded classical and jazz concerts using many video cameras and audio microphones, and then processed and edited the video and audio data with related metadata. Then, we created a dataset using the SDM Ontology and published it as linked open data (LOD). Furthermore, we developed \"Web360\", an application that enables users to interactively view and experience 360\" video and spatial acoustic sounds by referring to this dataset. We conducted a subjective evaluation by using a user questionnaire. Web3602 is a data-driven web application that obtains video and audio data and related metadata by querying the dataset.  
content management; ontology; resource description framework; digital media; audio visual; proof-of-concept; virtual reality +
Footnote †: journal: Future Internet