esa-sceva/satcom-chunk-collection huggingface dataset:
# UWB Localization with Battery-Powered Wireless Backbone for Drone-Based Inventory Management
## 1 Introduction
* A large number of scientific papers focus on improving accuracies and assume small scale set-ups that do not scale cost-efficiently towards large warehouses [9; 10; 11]. * Many proposed UWB localization solutions require Ethernet or pre-existing WiFi access points to provide connectivity between anchor nodes [12; 13]. * Always-on UWB radios consume significantly more energy than traditional radios, thereby limiting the possibility of battery or energy-harvesting-powered anchor nodes. To remedy this, we propose a MAC protocol for an UWB localization system using battery-powered anchors with a long battery lifetime and which can be easy installed (e.g., plug and play) without requiring wired communication infrastructure. The main contributions of this paper are the following. * Design of a scalable localization system that requires minimal pre-existing infrastructure by combining two wireless technologies: sub-GHz for IoT-standardized long-range wireless communication backbone and UWB for localization. * Optimization of the energy consumption of the UWB radio by designing a multi-technology, duty cycling time-slotted UWB MAC protocol. * Theoretical evaluation of the design choices on overall system performance in terms of update rate, energy consumption, range and scalability
* Experimental validation of the system for two real-life scenarios: autonomous drone navigation and tracking of runners in sport halls. * Providing the source-code of the UWB hardware and MAC protocol software as open-source contributions [14]. The remainder of this paper is organized as follows. First, Section 2 describes related work regarding drone based inventory-taking and infrastructure-light, easy-installable UWB solutions. Section 3 describes the design of our UWB localization system. Next, Section 4 describes the implementation of the system. Section 5 theoretically and experimentally evaluates the overall performance. Finally, Section 6 mentions future work and Section 7 concludes the paper.
# Meta-Analysis of the Performance of Previous Concrete with Cement and Aggregate Replacements
## 2 Review Methodology  
The literature survey followed a systematic approach to search for relevant articles that evaluated the performance of PC with cement and aggregate replacements, as shown in Figure 1. Search strings were chosen to include the most significant number of the articles of interest. All search expressions had a string of \"pervious concrete\" and \"cement replacement\" and \"aggregate replacement\" and \"mechanical properties\" or \"porosity,\" or \"permeability,\" or \"abrasion OR resistance.\" Peer-reviewed journal articles accessible through the Web of Science and Scopus databases, including the search expressions mentioned above, were targeted for this study. Data were extracted and processed from each article to provide essential information for the analysis, including the material used to replace the cement and aggregates, replacement percentage, and the mechanical, hydraulic, and durability properties of the PC. It should be noted that the most and least evaluated criteria were the mechanical and durability performance, respectively, owing to the respective ease and difficulty in evaluating the associated properties. Additionally, none of the articles investigating aggregate replacement considered durability as a performance measure, suggesting the need for more research in this specific field.  
Figure 1: Flowchart of the methodology for selecting and screening the articles (n = number of articles).
## 4 Fire detection algorithms
### Absolute threshold based identification  
A preliminary classification is used to eliminate obvious non-fire pixels. Those pixels will not be considered in the following tests. To detect large fires, a simple absolute threshold test is performed for every non-obscured vegetation pixel throughout the whole image. This threshold should be set as sufficiently high as possible to identify the very unambiguous fire pixels, i.e. those have little chance of being a false alarm.  
The absolute threshold criterion remains the same with that proposed in the original algorithm [19], i.e., \(T_{3}\)\(>360\) K. This is necessary because this threshold can solve the known \"hole\" problem, which had been described in detail in the literature [19]. In despite of the higher threshold, an adequate sun glint removal is still necessary to reduce false alarm probabilities.
## 3 Audio Characterization and Recognition Tests
### Data Analysis
#### 3.1.2 Time Domain Waveform Shape  
To analyze the similarities and differences of the same type but with different sound sources and different audio types, one frame was randomly selected from the 10 frames of the audio file as a typical frame of the audio file. The first 100 ms of the time domain waveforms of the typical frames of the three types of audio are shown in Figures 3-5.  
In Figure 3, the background noise, A1-A4 have no similar waveforms, and the waveforms are irregular. In Figure 4, mechanical vibration, B1-B4 have 10 similar waveforms and the time interval is about 10 ms, the waveforms are sparse and gentle, and the shapes are like sine waveforms. In Figure 5, the waveforms of C1-C4 are dense and steep, C2 and C4 have acoustic pulse groups with pulse intervals of about 20 ms and 10 ms, respectively, C1 and C3 have no acoustic pulse groups and correlation, and the waveforms vary irregularly.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Audio Type** &
\begin{tabular}{c} **Audio File** \\ **Number** \\ \end{tabular} & \(min(S_{ij})\) & \(max(S_{ij})\) & \(min(F_{ij})\) & \(max(F_{ij})\) \\ \hline \multirow{3}{*}{Background} & A1 & \(-0.063\) & \(0.108\) & \(0.102\) & \(0.692\) \\  & A2 & \(-0.065\) & \(0.036\) & \(0.347\) & \(0.784\) \\ \multirow{3}{*}{noise} & A3 & \(-0.396\) & \(0.313\) & \(0.521\) & \(0.838\) \\  & A4 & \(-0.097\) & \(0.094\) & \(0.635\) & \(0.857\) \\ \multirow{3}{*}{Mechanical} & B1 & \(-0.84\) & \(0.835\) & \(0.94\) & \(0.978\) \\  & B2 & \(-0.134\) & \(0.913\) & \(0.863\) & \(0.997\) \\ vibration & B3 & \(-0.212\) & \(0.345\) & \(0.732\) & \(0.895\) \\ \multirow{3}{*}{PD} & B4 & \(-0.06\) & \(0.956\) & \(0.806\) & \(0.994\) \\  & C1 & \(-0.113\) & \(0.173\) & \(0.731\) & \(0.847\) \\ \cline{1-1}  & C2 & \(-0.103\) & \(0.155\) & \(0.462\) & \(0.884\) \\ \cline{1-1}  & C3 & \(-0.023\) & \(0.024\) & \(0.75\) & \(0.781\) \\ \cline{1-1}  & C4 & \(-0.023\) & \(0.028\) & \(0.764\) & \(0.801\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Similarity calculation of 10 frames of data generated a symmetric matrix of \(10\times 10\) similarities, from which the minimum value and the maximum value other than \(1\) were selected.  
Figure 4: Time domain waveform of mechanical vibration.  
Figure 3: Time domain waveform of background noise.  
The analysis shows that the time domain waveform of background noise is usually irregular and does not have autocorrelation. The mechanical vibration generated by the operating switchgear has an regular waveform pattern, stable amplitude, and autocorrelation. Although acoustic pulse clusters appear in the PD about every 10 ms or 20 ms, the occurrence and duration of the acoustic pulse clusters and the amplitude changes are not the same, resulting in a low autocorrelation of the time-domain waveforms.
# Studies on Influence of Chromium Layer on Inner Surface of Steel Tube on Heat Transfer
## 3 Numerical Simulation and Results Analysis
### Temperature Distribution along the Tube Thickness for a Series of Seven Heat Impulses  
The temperature \(T_{i}(t,r,z)\) along the wall thickness for different chromium layer thicknesses for a sequence of seven heat impulses in zone S30 and after one, four and seven heat impulses in zone S30 are shown in Figure 15.  
Figure 15: Temperature distribution \(T_{i}(t,r,z)\): left side—along the wall thickness for the tube with different chromium layer thicknesses for a sequence of seven heat impulses (color-coded for the distance from the inner surface of the wall); right side—for the 1, 4 and 7 heat impulses.  
Temperature distributions \(T_{i}(t,r,z)\) shown in Figure 15 (right side) were visualized at the time of the heat impulse, i.e., when the so-called highest temperature occurs. Already after the first impulse, the temperature of the wall at a depth of 0.5 mm below the inner surface is twice as high for a tube with a 200 \(\upmu\)m thick chrome coating compared to a tube without this coating--Figure 15 (right side). After the fourth impulse, the temperature difference stabilizes and amounts to approximately 60 \({}^{\circ}\)C and, after the seventh impulse, a little more, i.e., 65 \({}^{\circ}\)C.  
Distribution of the highest peak temperatures (upper figure) and the lowest temperatures of the peak base (lower figure) along the tube length with different chromium layer thicknesses after seven heat impulses are shown in Figure 16. After seven impulses, the distribution of the lowest peak base temperatures along the tube length is an ascending curve, and the highest temperature value is in the S27-S30 zones--Figure 16.  
For the 30 HN2 MFA steel, for which a phase transition takes place, the temperature of 750 \({}^{\circ}\)C will appear the fastest for a tube with a protective coating of 200 \(\upmu\)m in thickness--Figure 16. This means that the tube with 200 \(\upmu\)m Cr will be the most exposed to wear.
## 4 Panoramic Live Broadcast
### Current Challenges for Panoramic Live Broadcast  
Panoramic video provides an immersive volume of video that is not available in traditional 2D. A panoramic video is spherical in nature, which brings about many challenges regarding its acquisition, storage, coding, transmission, and display.  
There is a variety of distortions from capture to display. To solve the distortion problem in 360-degree video streams, efficient splicing, projection, and encoding methods with better exploration results and a lower bandwidth should be explored. With the popularity of virtual reality technology and the formulation of the next-generation video coding standard, 360-degree video has been receiving increasing attention from academia and various fields in industry. Innovating video projection methods and efficient compression coding to meet bandwidth and quality requirements represent the current direction of mainstream research and exploration [14].  
Sphere-to-plane projection is a common procedure for panoramic video before encoding. There are, so far, many projection formats. Considering that different plane projection formats may be adapted to different applications, we often need to convert from one projection format to another, for which the interpolation algorithm is thus critical [40].  
It is necessary to focus on designing quality evaluation methods and indicators for panoramic video. This is a complex challenge, for traditional video QoE (quality of experience) models are not suitable for 360-degree content. Although most studies have carried out various subjective and panoramic video objective evaluations, most evaluation methods still follow the traditional video evaluation standards. Due to the lack of unified and standardized factors affecting 360-degree video, the standard evaluation method has not been finalized. This represents a complex and challenging problem [15].
## 4 Experimental Setup and Results
### Experiment Results
#### 4.3.3 Human-Machine Cooperation Level  
Cooperation ratio (number of times checking AI advice/total rounds) and cooperation but inconsistent ratio abbreviated as CNAR (number of times checking AI advice but not following/number of times checking AI advice) were used to measure human-machine cooperation. ANOVA results are shown in Tables 8-11.  
Participants in the negative emotion group showed higher levels of human-machine cooperation compared to those in the positive emotion group.
# Automatically Extracted Antarctic Coastline Using Remotely-Sensed Data: An Update
## 3 Method
### The Automated Edge Detection Method
For each image, the high threshold is defined as the minimum of the highest-gradient cluster, and the low threshold is the maximum of the lowest-gradient cluster (Figure 2e). Based on this selection, the potential edge pixels are those having the gradient falling between the low and high thresholds (the blue area in each histogram). These pixels are examined to confirm their identity as being part of an edge or not using the same concept introduced in the [PERSON] algorithm, as described above. To check the revised Canny algorithm, we conducted two experiments to construct the edge. The first used the pair of high and low thresholds for each image, and the second used the maximum threshold only, and defined the edge pixels as those that have a gradient higher than that of the threshold. Results from the two experiments, after applying the complete method (including the post-processing step described in the next section), are presented in a1a2, b1b2, and c1c2 of Figure 2,  
Figure 1: Comparison between edge points from using a fixed pair threshold—for maximum and minimum (**b**), and an adaptive pair of thresholds (in the improved algorithm) (**c**). The corresponding Landsat-7 image shown in (**a**) was acquired on 25 January 2017. The improved edge detector yields much less noise and better continuity in the extracted coastline. Figure 2: Three pairs of Landsat-8 false color images (locations identified in (**d**)) are shown in (**a1,a2**), (**b1,b2**), and (**c1,c2**). Histograms of the pixel gradients are shown in (**e**–**g**), respectively. The two thresholds are marked by the blue-dashed lined in each histogram, while the blue gap between the two lines represents the gradient range for the potential edge points. Coastline extraction results using two thresholds and one threshold (the maximum threshold) are overlaid on the Landsat-8 images in the left and middle panels, respectively. The color composition we use is from bands 1, 2, and 3.  
respectively. It is clear that using a pair of adaptive thresholds (high and low) warrants the continuity of the edge (i.e., the coastline), which cannot be achieved using a single threshold when the edge is constructed from a sequence of spatial images.
# OBU for Accurate Navigation through Sensor Fusion in the Framework of the EMERGE Project
## 2 EMERGE Onboard System Architecture
### EMERGE Navigation System Implementation  
The HW, SW or service components that drive up the onboard navigation system are selected according to the functional requirements of the EMERGE project. For each use case, requirements are mapped to performance measures that sensors must satisfy in the first layer. Specifically, two main elements are considered: the GNSS receiver and the inertial sensor. A GNSS augmentation service is used to improve the satellite positioning performance through corrections received via the OBU connection to the Internet.
## 2 PV Model Modeling and Problem Formulation
### Solar Cells  
PV cells, often known as solar cells, are electronic devices designed to convert solar energy into electrical energy. Semiconductor elements, such as silicon, are utilized in the construction of these devices. These materials possess the ability to absorb photons emitted by sunlight, thus liberating electrons and facilitating the flow of electric current. Solar panels are commonly interconnected in both series and parallel configurations to enhance their voltage and electrical output. Solar panels possess a diverse array of applications, encompassing the generation of electricity for both residential and commercial purposes, the provision of power in distant settings, and the facilitation of portable electronic devices such as calculators and mobile phones.  
The efficiency and affordability of solar cells are consistently advancing, rendering them an increasingly attractive option for harnessing clean and sustainable energy. Every aspect of the I-V spectrum of a cell or component is represented by equivalent circuit models using a series of sequential functions that are determined based on predefined operational constraints.
## 4 Experiments
### Baselines  
We compared our model with several baselines. The compared models were as follows:  
* NovelTagging [29] treats the extraction problem as a sequence labeling problem by merging entities and relational roles;
* CopyR [30] applies a sequence-to-sentence architecture;
* MultiHead [31] uses a multi-headed selection technique to identify the entity and the relation;
* GraphRel [32] uses graph convolutional neural networks to extract entities and their relations;
* OrderCopyR [33] uses reinforcement learning in seq-to-seq models to extract triples;
* ETL-span [24] proposed a decomposition-based tagging scheme;
* RSAN [34] proposed a relation-specific attention mechanism network to extract entities and relations extraction;
* CasRel [17] applies a cascade binary tagging framework to extract triples;
* TPLinker [25] firstly finds all token pairs and then uses map to tag token links to recognize relations between token pairs;
* TDEER [18] proposes a translating decoding network to relational triples;  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{4}{c}{**\#Sentence**} & \multicolumn{6}{c}{**Details of Test Data**} \\ \cline{2-11}  & **Train** & **Valid** & **Test** & **Normal** & **SEO** & **EPO** & **SOO** & \(N=\textbf{1}\) & \(N>\textbf{1}\) & **\#Triples** & **\#Relations** \\ \hline NYT* & 56,195 & 4999 & 5000 & 3266 & 1297 & 978 & 45 & 3244 & 1756 & 8110 & 24 \\ WebNLG* & 5019 & 500 & 703 & 245 & 457 & 26 & 84 & 266 & 437 & 1591 & 171 \\ NYT & 56,196 & 5000 & 5000 & 3071 & 1273 & 1168 & 117 & 3089 & 1911 & 8616 & 24 \\ WebNLG & 5019 & 500 & 703 & 239 & 448 & 6 & 85 & 256 & 447 & 1607 & 216 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of four datasets used in our experiments, where \(N\) is the number of relational triples in a sentence.We divided the overlapping patterns into four categories: _Normal_, _Single Entity Overlap (SEO)_, _Entity Pair Overlap (EPO)_, and _Subject Object Overlap (SOO)_.  
* PRGC [19] proposes a framework based on a potential relation and global correspondence;
* R-BPtrNet [35] proposes a unified network to extract explicit and implicit relational triples. For a fair comparison, all baseline results are taken directly from the original literature.
# Unmanned Aerial System-Based Data Ferrying over a Sensor Node Station Network in Maize
###### Abstract
Each sensor node station of the network is equipped with three soil water content sensors and one infrared thermometer. The UAS-based data ferrying design is based on criteria and constraints based on the experimental site (research farm) and the nature of the experiment to be conducted. The UAS-based data ferrying system was designed to include wireless technology, a UAS, a radio power source, and memory storage. The major constraints assessing the system were design cost and adoptability. The design objective was to assemble and test a UAS-enabled data ferrying system that communicates wirelessly with a soil water/crop canopy temperature measurement WSN system with the purpose to monitor soil water content, plant canopy temperature, and air temperature at strategically selected locations across a field in real-time. Six designs based on design objective and constraints were considered and assessed; the best design that met the desired requirements (based on how the system meets the design objective for a field and constraints like that used in this study) was selected and validated in maize over different heights and lateral distances.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} MOPSO-h & Hybrid multi-objective particle swarm optimization algorithm \\ NLP & Non-linear programming \\ GA & Genetic algorithms \\ PSO & Particle swarm optimization \\ ACO & Ant colony optimization \\ PD & Proportional derivative \\ SNR & Signal-to-noise ratio \\ NSGA-II & Non-dominated sorting genetic algorithm-II \\ NSGA-III & Non-dominated sorting genetic algorithm-III \\ MOEA/D & Multi-objective evolutionary algorithm based on decomposition \\ HMS & Hybrid mutation strategy \\ HV & Hypervolume \\ MMD & Mean miss distance \\ ETP & Extra time percentage \\ TLS & Terminal landing speed \\ TLA & Terminal landing angle \\ PSA & Peak surge altitude \\ TC & Time cost \\ \(a_{m},\beta_{m}\) & The azimuth angles and elevation angles of the anti\(-\)ship missile in \(Ox_{m}y_{m}z_{m}\) \\ \(l_{lux},l_{by},l_{bz}\) & Projection of the line-of-sight vector \(I\) on the \(x,y\), and \(z\) axes in \(Ox_{y}y_{h}z_{h}\) \\ \(\alpha,\beta\) & In \(Ox_{m}y_{m}z_{m}\), the azimuth angles and elevation angles of the anti-ship missile \\ \(\hat{\alpha},\tilde{\beta}\) & In \(Ox_{m}y_{m}z_{m}\), the azimuth angles' and elevation angles' speed of the anti-ship missile \\ \(\tilde{\alpha},\tilde{\beta}\) & In \(Ox_{m}y_{m}z_{m}\), the azimuth angles' and elevation angles' accelerations of the \\  & anti-ship missile \\ \(V\) & Current cruise speed of the anti-ship missile \\ \(\hat{\theta}\) & In \(Ox_{y}y_{h}z_{h}\), the azimuth angles of the target relative to the anti-ship missile \\ \(\epsilon_{\theta}\) & In \(Ox_{y}y_{h}z_{h}\), the random error in the azimuth angles of the target relative to the \\  & anti-ship missile \\ \(t_{emd}\) & Total task duration \\ \(F\) & Planning frequency \\ \(R\) & Earth radius \\ \(N\) & Particle counts \\ \(\Delta I\) & Iteration count interval \\ \(H,H_{1},H_{2},H_{3}\) & The trajectory change in altitude of the anti-ship missile \\ \(c_{1},c_{2},r_{1},r_{2}\) & Random numbers within the interval [0, 1] \\ \(n_{\max}\) & Maximum normal overload of the anti-ship missile \\ \(R_{1},R_{2},\ldots,R_{6}\) & Distance thresholds for each stage \\ \(a_{1},a_{2},a_{3},a_{HV}\) & Adjustment coefficient for particle speed and position update \\ \end{tabular}
# Experimental Evaluation of a MIMO Radar Performance for ADAS Application
## 3 Results  
### Calibration of the Radar  
The first measurements carried out were intended to ensure the accuracy of the radar in determining the position of the targets. We were interested in doing that, not in an anechoic chamber (where, moreover, the maximum distance is quite limited), but rather in the same scenario chosen for the other measurements. To this end, we draw a grid on the parking floor with the help of a laser rangefinder, with points located every 5 m in one direction and every 3 m in the perpendicular direction. The global precision in the position of the marked points was of \(\pm 0.1\) m. Several measurements were taken with a turning retroreflector sited over those grid points. The set of results can be seen in Figure 2.  
In that figure, the points on the ground are marked as red circles. Squared blue points are the set of echoes recovered during the measurements. Although some measurements were very clean and accurate, with all the echoes recovered on the reference red point, others were not so clear. In the case of the closer point to the radar, in coordinates (0, 3.8), some false targets were generated; actually, all the isolated light blue squares are present in the figure near the \(x=0\ line\) (about eight false targets). The other, less accurate set of points corresponds to the reflector in the coordinates (\(-0.2\), 13.5). In this particular case,the problem could be a metallic pillar situated very close to that position, a little close in the direction towards the radar. We think there were double reflections from that pillar, provoking an error in the determination of the reflector position. With the exception of these two circumstances, the mean radar error is 0.2 m, and the maximum error is, in one case, 0.6 m.