esa-sceva/satcom-chunk-collection huggingface dataset:
# From GPS Receiver to GNSS Reflectometry Payload Development for the Triton Satellite Mission
###### Abstract  
The global positioning system (GPS) receiver has been one of the most important navigation systems for more than two decades. Although the GPS system was originally designed for near-Earth navigation, currently it is widely used in highly dynamic environments (such as low Earth orbit (LEO)). A space-capable GPS receiver (GPSR) is capable of providing timing and navigation information for spacecraft to determine the orbit and synchronize the onboard timing; therefore, it is one of the essential components of modern spacecraft. However, a space-grade GPSR is technology-sensitive and under export control. In order to overcome export control, the National Space Organization (NSPO) in Taiwan completed the development of a self-reliant space-grade GPSR in 2014. The NSPO GPSR, built in-house, has passed its qualification tests and is ready to fly onboard the Triton satellite. In addition to providing navigation, the GPS/global navigation satellite system (GNSS) is facilitated to many remote sensing missions, such as GNSS radio occultation (GNSS-RO) and GNSS reflectometry (GNSS-R). Based on the design of the NSPO GPSR, the NSPO is actively engaged in the development of the Triton program (a GNSS reflectometry mission). In a GNSS-R mission, the reflected signals are processed to form delay Doppler maps (DDMs) so that various properties (including ocean surface roughness, vegetation, soil moisture, and so on) can be retrieved. This paper describes not only the development of the NSPO GPSR but also the design, development, and special features of the Triton's GNSS-R mission. Moreover, in order to verify the NSPO GNSS-R receiver, ground/flight tests are deemed essential. Then, data analyses of the airborne GNSS-R tests are presented in this paper.  
remote sensing; GPS receiver; GNSS reflectometry; DDM; Triton 2021 13 999 1
# Novel Resource Allocation Techniques for Downlink Non-Orthogonal Multiple Access Systems
## 4 Proposed Resource Allocation Techniques  
The NOMA performance depends mostly on power allocation and user pairing, which are joint optimization problems. To solve these problems, we analyze the power allocation and user pairing separately to reduce complexity. After that, the user pairing approach based on the HNG algorithm, power allocation solution using KKT (PKKT) and power allocation solution using DKL (PDKL) are determined. The hybrid NOMA-PKKT-HNG technique refers to applying PKKT solution after pairing users based on HNG to obtain the optimal sum rate in NOMA. On the other hand, NOMA-PDKL-HNG refers to using PDKL solution after pairing users based on HNG to get the optimal energy efficiency in NOMA.
# rStaple: A Robust Complementary Learning Method for Real-Time Object Tracking
## 4 Proposed rStaple Tracking Method
### Feature Fusion  
Researchers have demonstrated that feature extractors usually play the most important role in various object trackers [1]. Therefore, it is necessary to select appropriate features for target modelling. In this paper, fHOG and CN are combined to get a 42-D feature to meet the goal. The combined feature is used to train a translation filter which can learn more information about the tracking target and background.  
HOG is one of the most popular features in computer vision. Specifically, it is mainly used to describe the gradient change information in images, and is robust to translation, illumination variation, and posture changes. An input image is divided into many small \(2\times 2\) cells when calculating HOG, wherein several adjacent cells are combined into one block. A \([0,\pi)\) region is divided into nine unsigned gradient directions, then each block can be described as a 36-D feature. As an optimization, fHOG [6] divides a \([0,2\pi)\) region into 18 signed gradient directions to obtain a 108-D feature. For reducing the dimensions, the features of four cells in each direction are accumulated to obtain a 27-D feature, while nine unsigned gradient directions of four cells are accumulated to obtain a 4-D feature. Finally, this results in a 31-D feature. In fact, fHOG slightly outperforms HOG via our offline experimental verification. We evaluated the performance of using fHOG instead of HOG on 10 video sequences, under the same experimental conditions. Results show that fHOG operates 22.7% faster than the original HOG with similar accuracy. Therefore, fHOG is adopted in the proposed tracker for both translation estimation and scale estimation.  
In some challenging application tasks it is not desirable that trackers simply use fHOG, which lacks robustness to target deformation. Therefore, CN is exploited in the proposed tracking method. CN describes color characteristics of an object and is robust to rotation and deformation. Specifically, it refines the 3-channel RGB features in 11 basic color terms: black, blue, brown, grey, green, orange, pink, purple, red, white, and yellow. In this paper, the mapping given by [PERSON] et al. [7] is introduced to compute CN, which is learned from the Google image dataset.
## 4 Results and Analysis
### Inlet Air Velocities and Flow Rates  
The air flow rate is one of the most crucial parameters of an air-cooling BTMS. It reflects the cooling capacity and the energy consumption of the cooling system. A higher air flow rate delivers higher cooling capacity but unavoidably consumes more parasitic energy from the same battery pack which it cools. The frictional force to the air flow due to cell arrangement and internal structure could cause viscous losses and reduce the pressure and velocity. To have a fair comparison, the inlet air flow rate should be the same for each design to provide an identical initial cooling capacity. Since the inlet sizes of different designs are slightly different due to various vertical spacings, the inlet velocities for each design were, adjusted correspondingly to deliver the same flow rate (10.56 \(\times\) 10\({}^{-3}\) m\({}^{3}\)/s) as shown in Table 3.  
Design 1, 4, and 5 have the smallest inlet and outlet size, so their inlet velocity is the highest. On the other hand, Design 3 and 7 have the largest inlet and outlet size, so their inlet velocity is the lowest. Different flow rates require corresponding inlet velocities for different designs, as shown in Table 4. In this study, the five input air flow rates were 10.56 \(\times\) 10\({}^{-3}\) m\({}^{3}\)/s, 21.12 \(\times\) 10\({}^{-3}\) m\({}^{3}\)/s, 31.68 \(\times\) 10\({}^{-3}\) m\({}^{3}\)/s, 42.24 \(\times\) 10\({}^{-3}\) m\({}^{3}\)/s, and 52.8 \(\times\) 10\({}^{-3}\) m\({}^{3}\)/s.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Design**} & **Inlet/Outlet** & **Inlet/Outlet** & **Battery Pack** & **Inlet Velocity** & **Flow Rate** \\  & **Dimension (m)** & **Area (m\({}^{2}\))** & **Volume (m\({}^{3}\))** & **(m/s)** & **(m\({}^{3}\)/s)** \\ \hline
1 & 0.160 \(\times\) 0.066 & 0.010560 & 2.9568 \(\times\) 10\({}^{-3}\) & 1 & \\
2 & 0.166 \(\times\) 0.066 & 0.010956 & 3.0677 \(\times\) 10\({}^{-3}\) & 0.9639 & \\
3 & 0.172 \(\times\) 0.066 & 0.011352 & 3.1786 \(\times\) 10\({}^{-3}\) & 0.9302 & \\
4 & 0.160 \(\times\) 0.066 & 0.010560 & 2.9568 \(\times\) 10\({}^{-3}\) & 1 & \\
5 & 0.160 \(\times\) 0.066 & 0.010560 & 2.9568 \(\times\) 10\({}^{-3}\) & 1 & \\
6 & 0.166 \(\times\) 0.066 & 0.010956 & 3.0677 \(\times\) 10\({}^{-3}\) & 0.9639 & \\
7 & 0.172 \(\times\) 0.066 & 0.011352 & 3.1786 \(\times\) 10\({}^{-3}\) & 0.9302 & \\ \hline \end{tabular}
\end{table}
Table 3: Different inlet and outlet dimensions and inlet velocities to deliver the same flow rate (10.56 \(\times\) 10\({}^{-3}\) m\({}^{3}\)/s).
# Beyond Classical AI: Detecting Fake News with Hybrid Quantum Neural Networks
###### Abstract  
The advent of quantum computing has introduced new opportunities for enhancing classical machine learning architectures. In this study, we propose a novel hybrid model, the HQDNN (Hybrid Quantum-Deep Neural Network), designed for the automatic detection of fake news. The model integrates classical fully connected neural layers with a parameterized quantum circuit, enabling the processing of textual data within both classical and quantum computational domains. To assess its effectiveness, we conducted experiments on the widely used LIAR dataset utilizing Term Frequency-Inverse Document Frequency (TF-IDF) features, as well as transformer-based DistilBERT embeddings. The experimental results demonstrate that the HQDNN achieves a superior recall performance--92.58% with TF-IDF and 94.40% with DistilBERT--surpassing traditional machine learning models such as Logistic Regression, Linear SVM, and Multilayer Perceptron. Additionally, we compare the HQDNN with SetFit, a recent CPU-efficient few-shot transformer model, and show that while SetFit achieves higher precision, the HQDNN significantly outperforms it in recall. Furthermore, an ablation experiment confirms the critical contribution of the quantum component, revealing a substantial drop in performance when the quantum layer is removed. These findings highlight the potential of hybrid quantum-classical models as effective and compact alternatives for high-sensitivity classification tasks, particularly in domains such as fake news detection.  
fake news detection; quantum machine learning; natural language processing; hybrid quantum classical model; text classification Article
# 3D Skeletal Joints-Based Hand Gesture Spotting and Classification
## 4 Experiments and Results
### Results and Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**NVIDIA Hand Gesture**} & \multicolumn{2}{c}{**Chaleearn LAP ConGD**} \\ \cline{2-5}  & **Frame-Wise** & **Edit Distance** & **Frame-Wise** & **Edit Distance** \\  & **Accuracy** & **Score** & **Accuracy** & **Score** \\ \hline
2S-RNN [4] & 80.3 & 74.8 & 87.5 & 86.3 \\ Proposed in [3] & 84.6 & 79.6 & 90.8 & 88.4 \\ R-3 DCNN [5] & 90.1 & 88.4 & 90.4 & 90.1 \\
**Our proposed** & **91.2** & **89.6** & **93.1** & **93.8** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Gestures spotting performance comparison with different methods on NVIDIA Hand Gesture dataset. Bold values are highest indices. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Accuracy on 20 BN-Jester**} & \multicolumn{2}{c}{**Accuracy on NVIDIA**} \\  & **Dataset** & **Hand Dataset** \\ \hline iDT-HOG [2] & - & 59.1 \\ C3D [8] & 91.6 & 69.3 \\ R-3 DCNN [5] & 95.0 & 79.3 \\ MFFs [9] & **96.2** & **84.7** \\
3D\_ResNet (RGB + Optical flow) & 92.8 & 75.5 \\ Ours M3D & 93.3 & 77.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Gesture classification performance comparison of different methods on the 20 BN_Jester dataset and NVIDIA Hand Gesture dataset. Bold values are highest indices. From the results illustrated in Table 4, the mean Jaccard Index on the test set of the Chalearn LAP ConGD dataset shows that the proposed method achieves satisfactory performance on the dataset. By using 3D key joint features and multiples, the recognition performance is significantly enhanced.
# An Innovative Internal Calibration Strategy and Implementation for LT-1 Bistatic Spaceborne SAR
## 3 Calibration Equipment  
As seen from the hardware composition of the radar system in Figure 1, the calibration equipment of the internal calibration system includes the internal calibrator, antenna calibration network, and interconnecting coaxial cables. Among them, the internal calibrator is the core component for achieving internal calibration of the SAR system, and which is located inside the satellite cabin. It performs the switching of various internal calibration loops and the selection between non-delay and delay paths. Together with the antenna calibration network located outside the satellite cabin, it fulfills the internal calibration function of the SAR system. It samples signals from the frequency modulation signal source, microwave combination, synchronization transceiver, and T/R modules transmitting output via power dividers or directional couplers; after amplification and digital attenuation processing, the calibration sample signals are converted into calibration signals that can be received by the receiving channels. By analyzing and processing the calibration signals, the measured system parameters can be obtained.  
The antenna calibration network is the calibration circuitry within the Active Phased Array Antenna (APAA), consisting of the coupler in T/R modules, the power division networks, and the RF cables. It is a crucial component connecting the distributed T/R modules and the internal calibrator. Considering that the APAA is located outside the cabin and exposed to harsh temperature environments, and the antenna array has a large size with significant temperature gradients across different parts of the APAA, precise calibration of the antenna calibration network is essential.
## Methods
### ML Approach
#### 3.2.2 Feature Permutation  
Although it is difficult to investigate in detail the structure of the ML model in a black-box model such as an artificial neural network, the importance of input variables can be calculated by various methods. In particular, some features (input variables) cannot contribute to improving the accuracy of ML models and only make them more complex. For investigating the trained ML model, a feature permutation test, the most commonly used method, was conducted for each input variable [43]. Feature permutation, initially proposed for Random Forest models, can be widely used for ML models [44]. For conducting the feature permutation test, we randomly permuted the order of one variable and assessed the decrease in the performance of the ML model; we repeatedly conducted this process for all input variables; finally, we calculated the mean decrease in the accuracy for each variable [45]. Because the arrangement of each variable differs from its arrangement when training the model, the performance is generally reduced compared with the accuracy when applying the original order of each variable. A feature with a larger mean decrease in accuracy is a more important feature in the ML model because the data quality of the feature has a greater influence on its performance. If the performance does not decrease significantly when a specific feature is permutated, it can be assumed that the feature is unimportant to the ML model or that the information in the feature is included in the other features [46]. In this study, when each variable was randomly permutated and applied to the model, the increase in the root mean square error (RMSE) was calculated as the decrease in its accuracy. We repeated the permutation test 10 times to calculate the mean decrease in accuracy with each input variable and ranked the input variables with respect to their mean decrease in accuracy.
# UV-Induced Aging in Thermochromic Pigment-Integrated Food-Grade Polymers: A Performance Assessment
## 2 Materials and Methods
### Visual Inspection of the Surface Area  
Visual observations were performed at 0, 500, and 1000 h of UVA exposure to assess changes in specimen characteristics under indoor-simulated (RS 559-934) and outdoor-simulated (QUV/Solar Eye) UV conditions. Each sample was photographed using a Canon EOS 90D DSLR (Canon Europa, Amstelveen, The Netherlands) paired with a fixed-focus lens. Imaging was conducted under standardized LED lighting with a high color rendering index (CRI > 90) in a light-controlled chamber. The samples were positioned on a matte white backdrop at a uniform height and distance to maintain visual consistency. This qualitative approach enabled documentation of pigment fading, surface oxidation, and color retention, complementing the quantitative colorimetric analysis described in Section 2.7. Consistent imaging geometry and lighting minimized reflection artifacts, allowing direct visual comparison of degradation trends across timepoints and exposure types.  
Figure 1: Spectral power distribution comparison between natural sunlight, Q-Sun with a daylight filter, and QUV with UVA-340 lamps. The UVA-340 spectrum replicates solar UV radiation from 295–365 nm, validating the use of the QUV/Solar Eye device for outdoor photodegradation simulation (adapted from Q-Lab Corporation [62]).
# Authentication-Based Secure Data Dissemination Protocol and Framework for 5G-Enabled VANET
## 4 System Model for A-MAC  
In the system model, we consider a network of distributed vehicles, following Distributed Coordination Function (DCF) mechanism. We consider a twin-layer network scenario comprising vehicles and TAs. It is assumed that TAs are fully authenticated and are a part of DCF. Each TA is assigned a network region and is responsible for vehicle registration and generating various system parameters in the network. It is assumed that all vehicles in a network are equipped with an OBU which renders data transmission and reception. Moreover, each vehicle is equipped with a device which is used to store encrypted data. Table 2 shows the main notations and their corresponding meanings.  
In the below sections, we define and explain A-MAC protocol to reach inconsequential certification of V2V communication. According to this protocol, only authenticated vehicles are permitted to disseminate messages among each other. The protocol is further subdivided into three sub-protocols namely initiation level, assessment level, and validation level.
# Evaluation of Radio Communication Links of 4G Systems
## 2 Background  
A radio communication link transports information from a transmitter to a receiver. The installation of such a link requires a calculation to improve its performance. This chapter will present the process of calculating the radio communication links of 4G systems. Therefore, the link budget makes it possible to calculate the maximum permissible propagation losses denoted MAPL (maximum allowable power losses) for a mobile located at the edge of the cell, which can reach it while maintaining the level of sensitivity of the base station. It predicts cell coverage radius, propagation pattern, transmitter, and receiver parameters. The transmit power is the maximum power of the base station (eNB), having a typical value for the macro cell (43-46) dBm at the antenna connector. Antenna gain is the power radiated by the antenna in a given direction relative to the energy released by a perfect unidirectional antenna that radiates evenly in all directions. It is expressed in dB but to indicate that the reference antenna used is isotropic, it is customary to speak of dBi. A concept related to that of gain is the effective area of an antenna, which connects to the size and shape of the antenna. The relationship between the gain and the effective area of the antenna is given by [14]:  
\[\mathrm{G}=4\pi\mathrm{A}/\lambda^{2}, \tag{1}\]  
With:  
A: Equivalent area of the antenna.  
\(\lambda\): Length of the radiated wave.  
The gain of a typical base station antenna is between (15 to 18) dBi, and the gain of the UE (user equipment) antenna is in the range of (\(-\)5 to 10) dBi.  
The link budget of the transmission system for all gains and losses is the total received signal. It is the received power \(\mathrm{Pr}\) expressed in dBm by the following equation [15]:  
\[\mathrm{Pr}=\mathrm{Pt}+\mathrm{Gr}+\mathrm{Gt}-\mathrm{L}, \tag{2}\]  
where:  
Pt: transmitter power in dBm.  
Gr: receiver antenna gain in dB.  
Gt: transmitter antenna gain in dB.  
L: loss in the considered direction.  
A receiver is characterized by its sensitivity (S) which is the minimum power level of the received signal that allows the reception of the signal (minimum reception power level) [16]. It is given by the following formula:  
\[\text{S}=10\text{ log(K. T. BRx)}+\text{SINR}_{\text{required}}+\text{NF}, \tag{3}\]  
With:  
K: Constant of Boltzmann (1.388062 \(\times\) 10\({}^{-23}\)).  
T: Ambient temperature (Kelvin).  
BRx: Receive bandwidth.  
\(\text{SINR}_{\text{required}}\): The value of the SINR needed at the receiver is an indicator of system performance; the lower it is, the more efficient the system, depending on the number of resource blocks, information rate required, etc.  
NF: RF Noise Figure (dB): depends on the duplexing mode and the duplex deviation.  
4G operates in several bandwidths and the bandwidth selection relates directly to base station capacity. During practical planning, for a first-time deployment, the dimensioning is done by a bandwidth of 5 MHz and 10 MHz.
# TEC and ROTI Measurements from a New GPS Receiver at BOWEN University, Nigeria
## 5 Conclusions  
The TEC and ROTI data from the newly installed CASES GPS receiver at Bowen in the equatorial region of Nigeria for January-November 2021 are studied. These observations from Bowen's CASES GPS receiver are compared to the GPS receiver at BJCO, an equatorial station for the period of January-December 2010. The choice of comparing 2021 and 2010 is that they are both periods of low solar activity with the solar flux index of \(\sim\)82 sfu and \(\sim\)80 sfu, respectively. Both measurements revealed a similar monthly average TEC diurnal pattern with slight variations in TEC values that might be associated with the slight difference in the solar flux values coupled with differences in the antenna type. In addition, the ROTI values derived from the 30 s ROT values show a strong significant agreement when observing total suppression of ionospheric irregularities. These uniformities in the measurements support our motivation for the use of the Bowen CASES GPS receiver for TEC studies in the African equatorial region.  
Conceptualization: O.S.B., W.A.S. and J.B.F.; Methodology: J.B.F., R.O.K. and J.O.F.; Visualization, Validation, Software: O.S.B., J.B.F., A.O., A.B.R. and Y.P. Formal Analysis: O.S.B. and J.B.F.; Investigation: J.B.F., R.O.K. and J.O.F. Resources: W.A.S. and O.S.B.; Data Curation: J.B.F. and R.O.K. Writing--Original Draft Preparation: J.B.F. and R.O.K. Writing--Review And Editing: O.S.B. and A.B.R.; Project Administration, Supervision: O.S.B.; Funding Acquisition: W.A.S. and O.S.B. All authors have read and agreed to the published version of the manuscript.  
We would like to appreciate the Engineering VT funding sources: the National Science Foundation, NSF, and the Bradley Department of Electrical and Computer Engineering.  
Not applicable.  
The data used for this study is collected from the newly installed GPS receiver. We would like to acknowledge [PERSON] for his support with the newly installed GPS  
Figure 7: Monthly ROTI variation over the BJCO receiver during a low-solar-activity period (2010).  
instrumentation. The dataset used for the analysis of BJCO was retrieved from [[https://www.sonel.org](https://www.sonel.org)]([https://www.sonel.org](https://www.sonel.org)) website (accessed on 22 January 2023).  
The authors declare no conflict of interest.