esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Test of Continuous Wave Frequency Diverse Array Doppler Radar +
## 4 Data Collection III: Moving Targets
### Experimental Results
#### 4.3.2 Radial Velocity  
The radial velocity of a target can be found by rearranging the well-known expression for Doppler shift:  
\[v=\frac{cv_{0}}{2f_{0}}. \tag{17}\]  
The value of \(v_{0}\) is assumed to be the midpoint of the frequency bin containing the Doppler peak referenced with the midpoint of the frequency bin containing the peak of the TX Copy FFT. The radial velocities from the Doppler information in Figures 12 and 13 are given in Table 5, along with the true radial velocities of each test.
# Algorithmic Modified Denavit-Hartenberg Modeling for Robotic Manipulators Using Line Geometry
## 5 Verification
### SCARA with Modeling Uncertainties  
In practical robotic systems, modeling uncertainties often lead to deviations in the direction and displacement vectors from their ideal values. Despite such deviations, the proposed algorithm is capable of robustly computing the MDH parameters from non-ideal inputs, provided that the joint axes are correctly defined as lines, as described in Section 3. In conventional approaches to kinematic modeling using MDH or DH parameterizations, the transformations from the base frame to the first joint frame and from the last joint frame to the tool coordinate frame are typically treated as known rigid transformations. If needed, they may be modeled with additional parameters and identified separately. In our method, it is also possible to estimate the MDH parameters associated with such rigid transformations by allocating additional virtual lines to represent them. However, because these transformations are typically fixed and known with high accuracy (e.g., derived from CAD models or mechanical assembly), our algorithm assumes them to be given and instead focuses on calibrating the internal joint transformations between successive links using the line geometry-based approach (see Algorithm 1). To evaluate the robustness of the proposed method, we introduced synthetic modeling uncertainties into a simple SCARA robot. Table 6 compares the nominal and perturbed (\"actual\") joint parameters encoded in the URDF. Perturbations were applied to each joint with approximately 1\({}^{\circ}\) deviation in orientation and 0.01 m deviation in displacement, mimicking realistic sensor or modeling noise. Based on the perturbed URDF data in Table 6, the MDH parameters extracted using the proposed algorithm are listed in Table 7. Compared to the ideal case, the modeling uncertainties caused several joint axis pairs, previously identified as \"distant\" in line geometry, to be reclassified as \"skewed\". As a result, the joint offset distances \(d_{i0}\) increased noticeably. The updated relationships between adjacent joint axes under perturbation are summarized in Table 8.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Joint Pair**} & \multicolumn{2}{c}{**Orientation (Roll, Pitch, Yaw) [deg]**} & \multicolumn{2}{c}{**Position (x, y, z) [m]**} \\ \cline{2-5}  & **Nominal** & **Perturbed** & **Nominal** & **Perturbed** \\ \hline
0–1 & (0, 0, 0) & (0, 0, 0) & (0, 0, 0.375) & (0, 0, 0) \\
1–2 & (0, 0, 0) & (1, 1, \(-\)1) & (0, 0.5, 0.025) & (0.01, 0.51, \(-\)0.024) \\
2–3 & (0, 0, 0) & (1, \(-\)1, 1) & (0.5, 0, 0) & (0.51, \(-\)0.01, 0.01) \\
3–4 & (0, 0, 0) & (\(-\)1, \(-\)1, \(-\)1) & (0, 0, \(-\)0.25) & (\(-\)0.01, \(-\)0.01, \(-\)0.29) \\
4–5 & (0, 0, 0) & (0, 0, 0) & (0, 0, 0) & (0, 0, 0) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of nominal and perturbed URDF parameters for the SCARA robot.
## 3 Results
### Structural Repair and Airframe Condition Monitoring by Using RFID and CMB  
Automated aircraft inspection basically aims at automating the visual inspection process normally carried out by aircraft engineers. It aims at detecting defects that are visible on the aircraft skin, which are usually structural defects. These defects can include dents, lightning strike damage, paint defects, fasteners defects, corrosion, and cracks [42].  
The RFID tag looks similar to a bar code but offers more beneficial features. The main benefits are data storage and enhanced data collection, the ability to read without a direct view of the RFID label, a dynamic read/write capability, simultaneous reading and identification of multiple tags, and the ability to withstand a harsh environment.
# An Integrity Monitoring Method for Navigation Satellites Based on Multi-Source Observation Links
## 5 Conclusions
The complexity of orbit determination would increase as more parameters are added to the normal equation of orbit errors, and more systematic errors would be brought in. Under the guarantee of effective observation links, it is an effective way to construct an orbit determination unit to optimize observation links and reduce equation dimension for fast calculation of satellite orbit. 4. LEO satellites can not only improve wide-area monitoring capability for navigation satellites but also augment the resolution accuracy of navigation messages. As the number of LEO satellites increases, the area where integrity parameters can be solved in real time is constantly expanding to a global area. With the support of 6, 12, or 60 LEO satellites, average service accuracy is respectively 0.93 m, 0.88 m, and 0.65 m, which has 8.9%, 13.7%, and 36.3% improvement compared with the result of regional service, verifying that LEO satellites have practical application values for augmentation of BDS-3 integrity monitoring. Conceptualization, formal analysis, investigation, data curation, and writing--original draft preparation, J.X. and D.W.; software and validation, K.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (grant nos. 12103077). Beijing Satellite Navigation Center provides all the test data used in this contribution. All data will be made available for scientific research purposes by request to the Beijing Satellite Navigation Centre. The authors declare no conflicts of interest.
## 4 Experiments
### Discussion  
This paper focuses on the HPO problem and proposes an RL-based approach. First, we treated the HPO problem as a sequential decision process and model it as MDP. Then, we designed an agent to select the hyperparameters sequentially and be updated by PPO. In order to mitigate sparse rewards problem, the curiosity-driven approach was used to provide intrinsic rewards. Additionally, we adopted RF as a surrogate model to reduce the search time. We validated our method on multi-classification, binary classification and image datasets. The accuracy of our method is better than other methods on all datasets. Our proposed method does not take less time than bandit-based methods when processing tabular data, but we have a significant improvement in search efficiency when processing image data. We believe that our approach is more efficient than bandit-based methods in the case of relatively large dataset sizes and long model training times. The reason is that bandit-based methods have to train the model with all candidate hyperparameter combinations for at least one epoch in the initial stage, while our method avoids the process of training the model many times by using the surrogate model.  
Figure 10: Prediction results of test samples. (**a**-**i**) are the prediction results for each surrogate model. The blue points represent the true values, and the red points represent the predicted results.  
In our future work, we will focus on two research directions. First, we will study how to optimize the hyperparameters of models that accomplish more complex tasks, such as object detection and sentiment analysis, which have been widely studied recently. Second, we will study how to accomplish model generation and HPO simultaneously, and eventually develop a complete automatic ML framework.
# UAV-Based Evaluation of Rockfall Hazard in the Cultural Heritage Area of Kipinas Monastery, Greece
## 4 Rockfall Hazard and Risk Assessment
### General
Moreover, due to shortage of available data (geomechanical, statistical and historical) of previous rockfall activities, the accuracy of the results in quantitative-based assessments varies. The accuracy of a rockfall hazard and risk assessment depends on the quality of the \"components\" that constitute the analysis, and their processing refinement, whether the data consist of qualitative relative descriptors or absolute numerical values. Hence, a quantitative evaluation may not be more precise than a qualitative one [80]. Consequently, determining which method best suits a specific site is not an easy task. For this paper, a qualitative-based approach was implemented and a new rockfall hazard and risk rating system was proposed.
## 5 Channel Model
### Channel Loss  
An acoustic frequency \(f\) in kHz separated by a distance \(d\) in km from source experiences the attenuation \(A(d,f)\) in water, which is computed in dB re \(\mu\) Pa as [5]:  
\[10\log A(d,f)=k\,10\,\log\left(d\right)+d\,10\,\log\left(\alpha(f)\right) \tag{14}\]  
where the first term represents the spreading loss, while the absorption loss is represented by the second term. The shape of spreading loss is determined by the spreading factor \(k\), i.e., \(k=1\) for cylindrical spreading and \(k=2\) for spherical spreading. The absorption co-efficient \(\alpha(f)\) in dB/km is empirically computed by the relationship [5]:  
\[10\log\alpha(f)=\left\{\begin{aligned} & \frac{0.11f^{2}}{1+f^{2}}+\frac{44f^{2}}{4100+f}+2.75\times 104f^{2}& f\geq 0.4\\ &\\ & 0.002+0.11\frac{f}{1+f}+0.011f.& f<0.4\end{aligned}\right. \tag{15}\]
## 2 Study Area and Resources
### Resources  
#### 2.2.1 Landslide Inventory  
Landslide inventories represent a fundamental dataset for LSM studies and contain temporal and spatial information on landslides. The primary data source for global landslide susceptibility research is the COOLR (Cooperative Open Online Landslide Repository), which includes datasets such as the Landslide Report Catalogue [32] and GLC (Global Landslide Catalogue) [33; 34] and a subset of locally reported landslide data ([[https://landslides.nasa.gov](https://landslides.nasa.gov)]([https://landslides.nasa.gov](https://landslides.nasa.gov)), accessed on 1 April 2024) (Figure 2). The website contains data on landslide sites from 1915 to 2021 sourced from various sources, including satellite remote sensing, ground surveys, and disaster reports. The integration of these datasets enabled the exclusion of small landslides, landslides with ambiguous temporal information, and landslides with incomplete data. A total of 8023 large-scale landslide events triggered by rainfall and earthquakes between 2001 and 2020 were extracted. This was performed because rain and earthquakes are the most common high-impact landslide triggers globally, and high-quality data on rain and earthquakes are relatively abundant and readily available. These data serve as a solid foundation for investigating landslide susceptibility, thereby  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \begin{tabular}{c} **Year of** \\ **Research** \\ \end{tabular} & \begin{tabular}{c} **Main** \\ **Researchers** \\ \end{tabular} & \begin{tabular}{c} **Space Scale** \\ **Resolution** \\ \end{tabular} & \begin{tabular}{c} **Space** \\ **Resolution** \\ \end{tabular} & \
\begin{tabular}{c} **Methodology** \\ **Type** \\ \end{tabular} &
\begin{tabular}{c} **Evaluation** \\ **Type** \\ \end{tabular} \\ \hline
2021 & Wang [31] & \begin{tabular}{c} National \\ (China) \\ \end{tabular} & 1 km \(\times\) 1 km & \
\begin{tabular}{c} The maximum \\ entropy model \\ \end{tabular} & Static &
\begin{tabular}{c} Evaluated susceptibility and \\ risk factors of landslides \\ in China \\ \end{tabular} \\ \hline
2022 & Lin [15] & \
\begin{tabular}{c} National \\ (China) \\ \end{tabular} & 1 km \(\times\) 1 km & GAMM & Static &
\begin{tabular}{c} Evaluation of landslide \\ susceptibility in China in \\ relation to climate change \\ is proposed \\ \end{tabular} \\ \hline
2022 & Li [25] & Global & 1 km \(\times\) 1 km & \begin{tabular}{c} Random forest and \\ LightGBM model \\ \end{tabular} & \
\begin{tabular}{c} Dynamic \\ (2020–2021) \\ \end{tabular} &
\begin{tabular}{c} Created a global dynamic \\ landslide susceptibility model \\ \end{tabular} \\ \hline
2024 & (This text) & \begin{tabular}{c} National \\ (China) \\ \end{tabular} & 1 km \(\times\) 1 km & ResNet18 model & \
\begin{tabular}{c} Dynamic \\ (2001–2022) \\ \end{tabular} &
\begin{tabular}{c} Revealed dynamic changes in \\ landslide susceptibility in \\ China from 2001-2022 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: \(Cont.\)
# Cost-Aware IoT Extension of DISSECT-CF
## 3 Cost Modeling in DISSECT-CF
### IoT Pricing
#### 3.1.4 Oracle's IoT Platform  
Finally, we investigated pricing of the Oracle's IoT solution. This pricing method is slightly different from the three providers described before. It is more similar to Azure's tiers than to the completely \"pay as you go\" billing like in Bluemix. The information was gathered from and we calculated with the so-called Metered Services. There are four product categories regarding the used devices (wearable, consumer, telematics and business). Categories determine the monthly device price and the number of messages that can be sent by that particular type of device. In addition, there is a restriction on how many messages can a particular type of device deliver per month. In case, the number of messages sent by a device is more than the device's category permits, an additional price will be charged according to a predefined price per thousand of messages.
# Rapid Classification of Wheat Grain Varieties Using Hyperspectral Imaging and Chemometrics
## 3 Results and Discussion
### Classification Visualization of Wheat Varieties  
The superiority of HSI to simultaneously obtain both spectral and spatial information makes it possible to display the classification results of wheat varieties using intuitive visualization map. In this study, the best model, ELM based on raw full wavelengths, was selected to visualize the classification results of wheat varieties. The spectral information of each wheat seed in hyperspectral images was input into ELM model to predict the corresponding variety. Combined the prediction results with spatial location' information of wheat seeds, the visual classification map could be eventually formed. Representing corresponding wheat variety, each digit of different position was endowed with corresponding color to distinguish five varieties of wheat seeds (blue represents variety 1, light blue represents variety 2, yellow represents variety 3, pink represents variety 4, red represents variety 5), which gave a clear view of the variety of wheat seeds. The original gray image is shown in Figure 6a and the classification map was shown in Figure 6b. For these five hyperspectral images, the discriminant accuracy of five varieties of wheat seeds was 100%(169/169), 77.1%(91/118), 74.1%(126/170), 94.4%(117/124), and 93.9%(185/197), respectively, which came to agreement with the detailed interpretation in Section 3.3. The overall accuracy was up to 88% in the classification maps and could be fairly satisfactory. The visualization manner provided us an intuitive and accurate estimation of wheat varieties, which would be beneficial for detecting non-target seeds rapidly and removing them timely, so as to ensure the purity of grains in the production process actually.  
Figure 6: _Cont._
# Flash Flood Early Warning System in Colima, Mexico
Sniffer: System mountable to any vehicle--it can be a drone--this is used in the recovery process of drifters after a hydrometeorological event and obtain the data collected from it. Server: Allows data archiving, data processing, and runs the web user interface of the platform. The EWIN platform uses:  
* Internet of Things (IoT) technology in each of the nodes. * LORA (LOng RAnge) technology to communicate between drifters, RiverCore nodes, and the hardware located in the sniffer. * MQTT (MQ Telemetry Transport) protocol to carry out the communication between the fixed and mobile nodes with the server in the cloud, making efficient handling of data acquisition. * JSON (JavaScript Object Notation) protocol for data exchange. * Low Energy Consumption Telemetry.
# A Distributed Mix-Context-Based Method for Location Privacy in Road Networks
## 6 Experimental Setup
### Simulation Setup  
For the simulation of the proposed CMC scheme, we used Network Simulator 2 (NS2). The simulation parameters are described in Table 4. We run the simulations for 400 s in an urban environment. We deploy 200 vehicles on the real world map created with SUMO. The simulation area is approximately \(5249\times 5053\) square meters. The speed range is up to 10 m per second in the road network. We use SUMO for realistic mobility generation of vehicular networks. The OpenStreet map is used to provide a real world road scenario, as shown in Figure 9. The map is converted into SUMO to generate vehicle traffic on a real-world map. The vehicle mobility model file is generated with the help of SUMO. The simulation is run with diverse vehicle traffic densities.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Value** \\ \hline Simulator & NS2, SUMO \\ MAP & OpenStreetMap \\ Routing protocol & AODV \\ Bit rate & 6 MBPS \\ Simulation time & 400 s \\ Number of Vehicles & 200 \\ Road area & 5249 \(\times\) 5053 m \\ Speed range & 0–10 m/s \\ MAC protocol & IEEE 802.11p \\ Transmission range & 500 m \\ Beacon interval & 300 ms \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation parameters for CMC.
# Multi-Person Action Recognition Based on Millimeter-Wave Radar Point Cloud
###### Abstract  
Human action recognition has many application prospects in human-computer interactions, innovative furniture, healthcare, and other fields. The traditional human motion recognition methods have limitations in privacy protection, complex environments, and multi-person scenarios. Millimeter-wave radar has attracted attention due to its ultra-high resolution and all-weather operation. Many existing studies have discussed the application of millimeter-wave radar in single-person scenarios, but only some have addressed the problem of action recognition in multi-person scenarios. This paper uses a commercial millimeter-wave radar device for human action recognition in multi-person scenarios. In order to solve the problems of severe interference and complex target segmentation in multiplayer scenarios, we propose a filtering method based on millimeter-wave inter-frame differences to filter the collected human point cloud data. We then use the DBSCAN algorithm and the Hungarian algorithm to segment the target, and finally input the data into a neural network for classification. The classification accuracy of the system proposed in this paper reaches 92.2% in multi-person scenarios through experimental tests with the five actions we set.  
human action recognition; millimeter-wave radar; point cloud; filtering; deep learning +
Footnote †: journal: Applied sciences
# Time-Varying Communication Channel High Altitude Platform Station Link Budget and Channel Modeling
## 3 HAPS Link Budget and Channel Modeling
### Holistic Applicability Analysis
#### 3.3.2 Impact on the Resource Management in HAPS Wireless Communication System  
The goal of wireless communication resource management is to provide service quality assurance for wireless user terminals in the network under limited bandwidth conditions. The basic starting point is that the network traffic is unevenly distributed, and the channel characteristics are fluctuating due to channel weakness and interference. It is necessary for flexible allocation and dynamic adjustment of the available resources of the wireless transmission, maximize wireless spectrum utilization and prevent network congestion and keep the signaling load as small as possible.  
One of the main features of the HAPS communication system is the existence of a large number of non-real-time packet data services. Because different users have different rates. The sum of all user rates in a base station tends to exceed the channel capacity that can be transmitted by the base station's own frequency band. Therefore, it is necessary to have a scheduler in the base station according to the user QoS requirements. The channel resources will be allocated to different users according to the type of service.
## 2 Related Work
[8] suggested a refined process for SA polarity results from TextBlob to handle the problem of indeterminacy in polarity. They proposed a multi-refined neutrosophic set where polarity scores resulted are refined into seven polarity classes (i.e., strong positive, positive, indeterminate positive, indeterminate, indeterminate negative, and strong negative). Another two methods were applied, where polarity scores were classified into five and three classes, respectively. A comparative study was conducted between their model and the two other used methods. Tests were performed on 10 different datasets to see how well the three methods worked at evaluating them. The results showed that the proposed refined method was the best. In conjunction with the continuous production of social media-based research, this work combined both structure and content-based methods by utilizing a neutrosophic-based OM approach that can provide each author with a polarity score for his text that depends on his influence level. A new element to the basic five opinion elements that represented the influence level was added. This model applies a novel classification of the opinion holders into four types based on their centrality measures and the reactions to their texts using an artificial neural network (ANN). SNA was performed using several softwaretools that are available online to provide the required information about authors' topologies in their OSNs. People's opinions and text polarity scores are combined for the first time in this model. It also takes into account text indeterminacy and dynamicity with NL.