esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### AP Interdependency Analysis  
To verify the interdependence of RSS from various APs, we compared the static data collected at a single point by one device with the training data of the corresponding cell collected by the same device at multiple RPs. Intuitively, we chose the three strongest APs (APs 19, 57, and 74) for the two datasets to do the analysis.  
From the results shown in Table 3, the RSS properties of the three APs are quite distinct at a single point from within a room. Regarding the point data, the RSS values are more stable and have smaller variations compared with the cell data. The correlations between pairs of APs at RP1 are as small as  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Device ID** & \(M\) & \(C\) \\ \hline
1 & 0.9336 & \(-\)6.7754 \\
2 & 0.8825 & \(-\)11.4863 \\
3 & 0.8951 & \(-\)8.7945 \\
4 & 0.8600 & \(-\)11.9044 \\
5 & 0.7802 & \(-\)21.3151 \\
6 & 0.8709 & \(-\)13.8607 \\
8 & 0.9706 & \(-\)5.3788 \\
9 & 0.8701 & \(-\)10.6396 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Device calibration coefficients.  
0.086 which is similar to the results given in [57], while the correlation can also be as large as \(-0.27\) in complex, noisy and non-line-of-sight signals. In the cell data, the correlations between pairs of APs become so large that we can no longer assume the RSS samples from the visible APs are independent, which also explains why the proposed algorithm consider the correlations from RSS measurements between pairs of APs.
## 4 Experiments
### Dataset  
We conducted experiments using the VisDrone-DET2021 [34] and UAVDT [35] datasets, which rank among the most prominent benchmarks for small object detection in UAV imagery.  
The VisDrone-DET2021 dataset includes real-world images taken by drones across various urban and rural regions, covering a wide range of scenes, lighting conditions, and object categories. Due to variations in drone flight altitude and posture, there is a significant change in object scale, as shown in Figure 10a. Additionally, the presence of obstacles causes occlusions in the images, as depicted in Figure 10b.  
The dataset consists of 6471 images for training, 548 images for validation, and 1580 images for testing, and it covers 10 distinct object categories. An overview of the dataset is provided in Figure 11. The number of objects per category is shown in Figure 11a, with pedestrians and cars making up the majority. Figure 11b visualizes the sizes of the object  
Figure 10: (**a**) Images of different sizes (**b**) obstructed by objects.  
Figure 9: (**a**) Aspect ratio loss result. (**b**) Distance and angle loss result.  
bounding boxes, where all bounding box centers are fixed at a single point. This clearly indicates that the dataset is composed of a large proportion of small objects. The distribution of bounding box center coordinates is shown in Figure 11c, indicating that most of the centers are concentrated in the lower and central areas of the image. Figure 11d displays a scatter plot illustrating the widths and heights of the bounding boxes, with the darkest color located in the lower-left corner, further confirming that the dataset predominantly consists of small objects.  
The UAVDT dataset contains a total of 38,327 images, split into 23,258 images for training and 15,069 images for testing. Each image has a resolution of 1080 \(\times\) 540 pixels, providing a balanced level of detail while maintaining computational efficiency. The annotations in the dataset cover three vehicle types, i.e., car, truck, and bus, with each vehicle type annotated with bounding boxes, indicating the locations of the vehicles within the image. Among them, the total number of labels for cars is 755,688, for trucks is 25,086, and for buses is 18,021.
_Algorithms_**2015**, \(8\), 1143-1174; doi:10.3390/a8041143  
_algorithms_  
**ISSN 1999-4893**  
www.mdpi.com/journal/algorithms  
_Article_  
Generating Realistic Labelled, Weighted Random Graphs \({}^{\dagger}\)  
**[PERSON] \({}^{1,*}\), [PERSON] \({}^{2,*}\), [PERSON] \({}^{3}\), [PERSON] \({}^{3}\), [PERSON] \({}^{4}\) and [PERSON] \({}^{4}\)**  
\({}^{1}\) Organisation Europeene pour la Recherche Nucleaire (CERN), Route de Meyrin 385,  
1217 Meyrin, Switzerland  
\({}^{2}\) Pattern Recognition and Intelligent Systems (PRIS) Lab, Beijing University of Posts and  
Telecommunications (BUPT), 100876 Beijing, China  
\({}^{3}\) School of Electrical and Electronic Engineering and Computer Science, Queen's University Belfast,  
University Road, Belfast BT7 1 NN, UK; E-Mails: [EMAIL_ADDRESS] (W.L.);  
[EMAIL_ADDRESS] (P.M.)  
\({}^{4}\) Centre for Public Health, Queen's University Belfast, University Road, Belfast BT7 1 NN, UK;  
E-Mails: [EMAIL_ADDRESS] (R.H.); [EMAIL_ADDRESS] (F.K.)  
\({}^{\dagger}\) This paper is an extended version of our paper published in New Frontiers in Mining Complex Patterns--Second International Workshop (NFMCP 2013), held in conjunction with the European  
Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2013), Prague, Czech Republic, 27 September 2013.  
* Authors to whom correspondence should be addressed; E-Mails: [EMAIL_ADDRESS] (M.C.D.);  
[EMAIL_ADDRESS] (Z.M.); Tel.: +41-75-411-7224 (M.C.D.); +86-134-6632-3341 (Z.M.).  
Academic Editor: [PERSON]  
_Received: 1 June 2015 / Accepted: 20 November 2015 / Published: 8 December 2015_  
**Abstract:** Generative algorithms for random graphs have yielded insights into the structure and evolution of real-world networks. Most networks exhibit a well-known set of properties, such as heavy-tailed degree distributions, clustering and community formation. Usually, random graph models consider only structural information, but many real-world networks also have labelled vertices and weighted edges. In this paper, we present a generative model for random graphs with discrete vertex labels and numeric edge weights. The weights are represented as a set of Beta Mixture Models (BMMs) with an arbitrary number of mixtures, which are learned from real-world networks. We propose a Bayesian Variational Inference (VI) approach, which yields an accurate estimation while keeping computation times tractable. We compare our approach to state-of-the-art random labelledgraph generators and an earlier approach based on Gaussian Mixture Models (GMMs). Our results allow us to draw conclusions about the contribution of vertex labels and edge weights to graph structure.  
**Keywords:** network models; generative algorithms; random graphs; labelled graphs; weighted graphs; bayesian estimation; maximum likelihood estimation; beta distribution; mixture modeling; variational inference
## 2 Materials and Methods  
### Experimental Installation and Process  
The experimental measurement of DC-FluTENG was carried out on the system as shown in Figure 1a. Among the experimental apparatus, a microprocessor-controlled tubing pump of ISMATEC-Ecoline VC-360 with an Tygon silicone tubing manufactured by Saint-Gobain Performance Plastics (6.4 mm inner diameter, 1.6 mm thickness, and 90 cm length) was used to generate the flow of water droplets in the system. The fabrication of a DC-FluTENG cell is extremely simple, with a pin-type electrode (0.5 mm in diameter and 30 mm in length) located at a distance of 5 cm from the end of a pipe and connected to the ground, resulting in a single-electrode mode of operation. The average contact area between the droplet and the electrode is about 10 mm\({}^{2}\). A Digit Graphical Sampling Multimeter model DMM7510 of the Keithley Instruments, Inc. (Cleveland, OH, USA) (this measuring instrument has an enhanced accuracy DC-current function that can measure a 1 \(\mu\)A sleep mode current with 1 pA resolution and with 0.375 nA tolerance, an enhanced sensitivity with 100 mV range with 10 nV resolution, and a high input impedance of more than 10 G\(\Omega\)) was used to measure the potential and current from the electrode to the ground. To evaluate the output performance of the TENG, deionized water, tap water, and 0.3 M copper sulfate (CuSO4) solution were used as the test water; meanwhile, silver, gold, copper, and aluminum were considered as the material of electrodes. Simultaneously, various capacitors with capacitance in a range from 1 to 100 \(\mu\)F and resistors with resistance in a range from 10 k\(\Omega\) to 50 M\(\Omega\) were utilized as the external electrical loads to find out the optimal working conditions of the system. The droplet flow rates were controlled in a range from 113 to 676 mL/min.  
Based on the representative component layers of a conventional single-electrode-based TENG, the charge-generating layer and charge-collecting layer (electrode) are normally af-fixed together. However, in the case of DC-FluTENG, the electrode is completely separated from the charge-generating layer (pipe), which indicates an improved single-electrode configuration. It leads to the difference in output characteristics of the DC-FluTENG and conventional SE-TENGs. Conventional SE-TENGs normally come out with alternating current outputs, and then a rectifier module is needed to convert the AC output into DC output before powering electronic devices. Meanwhile, DC-FluTENG exhibits direct-current outputs that can power directly electronic devices without requiring a rectifier. This is an advance in comparison with conventional SE-TENGs, as shown in Figure 1b.  
Figure 1: (**a**) Schematic showing the experimental setup of DC–FluTENG. (**b**) Structure diagram of DC–FluTENG in comparison with a conventional single–electrode–based TENG.
# Study on Corrosion Monitoring of Reinforced Concrete Based on Longitudinal Guided Ultrasonic Waves
## 1 Introduction
[23] monitored the RC corrosion process by the positive and negative bimodal amplitudes of GUWs on this basis and studied and analyzed three factors: the reinforcement type, the water-cement ratio and the concrete cover thickness. [PERSON] et al. [24] found that the embedded ultrasonic transducer method could detect concrete damage during the corrosion process and proposed a method that could determine the damage stage according to the relationship between the ultrasonic transducer data and the corrosion rate. [PERSON] et al. [25] proposed a histogram-based qualitative analysis method using contact-based ultrasonic excitation and non-contact laser-based sensing to monitor corrosion in concrete and the ultrasonic scanning of specimens with different corrosion degrees. The GUW method of identifying the corrosion state of reinforcement in concrete has achieved more results, but there are still some shortcomings: on the one hand, most of the existing finite element simulations of corroded reinforcement simplify corrosion as a regular notch, and this simplified method cannot accurately quantify the influence of corrosion damage on GUW propagation; on the other hand, many corrosion identification indexes of RC rely on the test results, and lack an explanation of the mechanism linking corrosion parameters and the rust expansion process. In this paper, GUWs are used to carry out numerical simulations and test studies on the corrosion process of reinforcement and RC. Firstly, five damage indexes (DIs) that can identify the corrosion process are proposed, and the indexes with good sensitivity are selected by a finite element simulation analysis. Secondly, the reinforcement and RC under corrosion tests are carried out to verify the effectiveness of the selected indexes. Finally, the correlation between the theoretical corrosion model of the RC and the measured DI is analyzed, which theoretically explains the rationality and effectiveness of the ultrasonic identification indexes.
# A W-Band Communication and Sensing Convergence System Enabled by Single OFDM Waveform
## 3 Proposed Method for Range Extension
Similarly, at \(\hat{d}=\hat{d}_{1}=(\hat{m}+N)\), \(A_{\hat{d}}(k)\) consists of last \(N_{\rm cp}\) samples of the \((q-g)\)th OFDM symbol and \(N-N_{\rm cp}\) samples of \((q-g+1)\)th OFDM symbol; therefore,  
\[{\bf H}_{\hat{d}_{1},(q-i)}=\frac{N-N_{\rm cp}}{NS_{(q-i)}}b{\bf S}_{(q-g)}e^ {-\frac{2\pi m}{N}N_{\rm cp}}+\frac{N_{\rm cp}}{NS_{(q-i)}}b{\bf S}_{(q-g+1)}+ \frac{{\bf Z}_{(q-g)}}{{\bf S}_{(q-i)}}. \tag{27}\]  
Using (26) in (24) provides the maximum of \(r_{\hat{d}}(0,\mu)\), which is same as \(r(d)\) in (20), whereas (27) indicates the additional peak with height reduced by a factor of \(N_{\rm cp}/N\) and affected by the ISI. Similar to \(D_{\rm conv}(d,p)\), where a processing of \(NM\) is assigned to a peak, \(D_{\rm pro}(d,p)\) also provides the same processing gain when \(i=g\) in (26); otherwise, \({\bf H}_{\hat{d},(q-i)}\) in (26) is interference. In (27), contrary to interference term, \(N_{\rm cp}\) samples are coherently added when used in (24) and the processing gain is \(10\log_{10}(N_{\rm cp}^{2}/(N-N_{\rm cp}))\). In a generalized scenario, there can be \(L\) echoes with delays not limited to CPI; the received signal \(y(k)\) is the summation of all echoes, each represented by the (19),  
\[ \begin{split} y(k)=&\sum_{l=0}^{L-1}\sum_{\mu=0}^{M -1}\sum_{n=0}^{N-1}b_{l}S(\mu N+n)e^{\left(\beta \pi\frac{n}{N}(k-m_{l}-\mu N_{\rm s}-N_{\rm cp})\right)}\\ &\cdot e^{\left(\beta\pi\frac{2\pi j_{l}/\varepsilon}{\varepsilon} k\lambda T\right)}\text{rect}\bigg{(}\frac{k-m_{l}-\mu N_{\rm s}}{N_{\rm s}} \bigg{)}+z(k),\end{split}  \tag{28}\]  
where \(m_{l}\) represents the delay associated with \(l\)th echo. Based on the delay \(m_{l}\), we split the \(y(k)\) into three portions such as \(y_{1}(k)\) for \(m_{l}\leq N_{\rm cp},y_{2}(k)\) for \(N_{\rm cp}<m_{l}\leq N_{\rm s}\), and \(y_{3}(k)\) for \(m_{l}>N_{\rm s}\), i.e.,  
\[y(k)=y_{1}(k)+y_{2}(k)+y_{3}(k)+z(k). \tag{29}\]  
Since \(y_{3}\) is formed by the summation of echoes that are outside the current OFDM symbol, therefore this part is only ISI. Unlike \(y_{3}(k)\), the ISI part of \(y_{2}(k)\) increases as \(m_{l}\) approaches to \(N_{\rm s}\). The detection of the echoes in \(y_{2}(k)\) and \(y_{3}(k)\) is possible if the processing gain \(G\) is sufficient to overcome the related ISI and noise.
## 5 Accuracy and Uncertainty of COSMIC-2 Refractivity, Temperature, and Water Vapor Retrievals
### Accuracy and Uncertainty of COSMIC-2 Temperature in the Lower Stratosphere  
The upper troposphere and lower stratosphere (UT/LS) is a critical region for understanding the radiative balance and climate processes. Studies have demonstrated that COSMIC temperature profiles in the lower stratosphere are very accurate (+/\(-\) 0.1K comparing with RS92 [46]) and stable (with a structural uncertainty of +/\(-\) 0.3 K in the trend [31]).  
To further quantify the temperature uncertainty of COSMIC-2 in the lower stratosphere, we compare COSMIC-2-RS41 and COSMIC-2-RS92 pairs in Figure 10a,b, respectively. Figure 10 depicts that the mean bias between COSMIC-2 and RS92 temperature from 8 km to 32 km is equal to \(-\)0.09 K with a std of 1.64 K, where the mean COSMIC-2 and RS41 temperature difference in the same height range is equal to \(-\)0.02 K with a std of 1.77 K. The RS92 and COSMIC-2 have a close to 0.1 K negative temperature difference between 18 km and 25 km altitude. Note that RS92 also has a close to 0.1 K warm bias compared to R41 [36]. This warm bias may be owing to incomplete radiative warming correction for RS92.  
80 degrees, Figure 11b). Figure 11 shows that the mean difference between COSMIC-2 and RS41 is close to zero for both day and night. This result gives us confidence in the quality of temperature measurement for both RS41 and COSMIC-2 in the low stratosphere.  
Moreover, these results are very compatible with those from COSMIC. Comparing COSMIC temperature profiles from 200 hPa to 10 hPa to those from Vaisala-RS92 in 2007 with more than 10,000 COSMIC and Vaisala-RS92 pairs, ref. [46] confirmed that their mean bias in this height range was equal to \(-\)0.01 K with a standard deviation of 2.09 K.
# Post-Quantum Biometric Authentication Based on Homomorphic Encryption and Classic McEliece
## 1 Introduction
### Biometric Authentication Architectures  
In a device-centric authentication architecture (Figure 1a), all the phases (enrollment and verification) and, thus, all the operations (acquisition, feature extraction, storage, comparison and decision) of the recognition scheme are carried out locally in the same client device. The client device authenticates the user and, then, an external server authenticates the client device on behalf of the user. Advantages of the device-centric authentication architecture are that the security perimeter is very reduced (it is constrained to the device) and user's biometric data, which are sensitive data, as established by the data-protection regulations from several countries, do not go out of that perimeter. The drawback is that many applications require external evidence about the authenticity of the user and this architecture requires additional secure hardware.  
In a non-device-centric authentication architecture, acquisition and feature extraction are performed on the client device, and storage, comparison and decision are performed on external servers. In addition to the user and the device, several authors model a generic non-device-centric biometric system with three entities, which act as database, computation and authentication servers [3]. At the enrollment and verification phases, the device captures and extracts the biometric features from its user and sends them to the external servers. At the enrollment phase, the database server stores the reference (template). At the verification phase, the client device provides the biometric queries and the database server provides the stored reference. The computation server compares the stored reference and the biometric queries. Then, the authentication server applies a threshold value to the comparison result to authenticate the user. In this case, there is an external authentication of the user's physical entity. However, since communication, storage and authentication are performed on the cloud, biometric features should be protected. Homomorphic encryption not only allows for features to be protected at communication and storage, but also whenever they are compared [4; 5; 6; 7; 8; 9; 10]. Only the comparison result is obtained in the unprotected domain by the authentication server (as illustrated in Figure 1b).
# Preliminary Field Results of a Dedicated 5G Positioning Network for Enhanced Hybrid Positioning +
## 2 Dedicated 5G Positioning Network  
### HOP-5G High-Level Architecture  
The HOP-5G testbed is based on a dedicated 5G positioning network. The key objective of this first-of-a-kind demonstrator is to assess the enhanced hybrid 5G and GNSS-positioning capabilities using dedicated ground and aerial BSs. Furthermore, the HOP-5G testbed operates in real-time and in post-processing, which allows for a wide range of experiments. The main building blocks of this testbed are:  
* Ground support equipment (GSE): The testbed operations are performed within this building block. The GSE is used to control, command, collect data, monitor, and perform post-processing functionalities within the HOP-5G testbed.
* 5G BSs: The 5G BSs (or 5G gNBs as in the 3 GPP nomenclature) can be deployed in ground or in the UAV payloads. The BSs are based on software-defined radios (SDRs) synchronized with GNSS-disciplined oscillators. For each BS, dedicated positioning reference signals (PRS) are transmitted in specific downlink slots.
* User Equipment (UE): The UE can be deployed in ground or in the UAV payload, and it is also based on SDR. In downlink operations, the UE exploits a specific upgrade of OpenAirInterface (OAI) to obtain 5G real-time ranging measurements. In addition, a COTS GNSS receiver is used to collect the raw observables and position estimates. Then, a positioning engine performs the UE position estimation also in real-time by combining either or both GNSS and 5G time of arrival (ToA) in a weighted least squares (WLS) or extended Kalman filter (EKF).  
The baseline HOP-5G deployment is based on two ground-fixed BSs, two UAV BSs, and two UEs. Further details of the HOP-5G testbed architecture can be found in [6].
## 3 Experimental Results  
### Resonance Curves  
For each temperature (and each sample), the NRUS protocol allowed the resonance curves (output amplitudes vs. frequency) to be plotted for different conditioning drive amplitudes \(A_{\text{cmd}}\). Also, resonance curves at baseline drive amplitude (immediately following  
Figure 2: Spectra for concrete at \(T=298\) K. (**a**) Superposition of experimental data and fit (using Equation (2)) for a given drive amplitude. (**b**) Conditioning spectra for selected drive amplitudes. (**c**) Baseline spectra for the same drive amplitudes as in (**b**). Colors indicate the drive amplitudes, solid lines refer to loading, and dashed lines refer to unloading. For the description of subplots (**b**,**c**), refer to Section 3.  
the conditioning phase) were derived. A few selected curves (for concrete at \(T=293\) K) are shown in Figures 2b and 2c, respectively.  
As expected, subplot (b) shows a decrease in the resonance frequency and an increase in the peak width and asymmetry with increasing drive amplitude (solid lines). When the measurement protocol consists of an upward-trending and downward-trending sequence of drive amplitudes \(A_{\text{drive}}=A_{\text{end}}\) in Equation (1), a difference between the results for equivalent amplitudes is present (compare solid with dashed lines), which indicates hysteresis.The behavior of the baseline strain (panel (c)) is also consistent with other measurements: slow dynamics is responsible for the softening and damping increase observed at a constant (low) excitation amplitude (\(A_{\text{drive}}=A_{\text{baseline}}\)) after conditioning at different drives.
# Automated Aviation Wind Nowcasting: Exploring Feature-Based Machine Learning Methods
## 3 Materials and Methods
### Methods  
In this section, the research methodology employed is presented, emphasizing the major steps followed to analyze the more important features in wind nowcasting. The prediction process was based on feeding the model with the last 2 min of data for predicting the targeted 2 min, 10 min, and 20 min values, corresponding to the next step, 5 steps, and 10 steps of the dataset, respectively. Specifically, the 2 min and 10 min durations are defined by the World Meteorological Organization (WMO) for local and international wind reports, respectively, while the 20 min duration is drawn from the timeframes used in previous wind nowcasting studies [2; 26; 27]. A concise illustration of the stages followed in this work is provided in Figure 4.  
Figure 2: Violin plots representing the full range of wind direction recorded by the official wind sensors at Madeira International Airport over 2018 and 2019.