esa-sceva/satcom-chunk-collection huggingface dataset:
# Cross-Domain Transfer Learning for Natural Scene Classification of Remote-Sensing Imagery
## 5 Conclusions  
This paper focuses on NaSC-TG2 dataset remote-sensing imaging for classification into ten different scenes facing multiple challenges. The main hazard is the non-availability of the scene texture and thus, the designing of handcrafted features is difficult to design. Therefore, deep learning is considered for automated and deep feature extraction and classification. Unfortunately, deep-learning structures require a number of weights to be optimized, which requires large datasets. Considering the non-availability of large datasets, transfer learning is considered. Unfortunately, the non-availability of an in-domain pre-trained model poses another challenge. Therefore, this study explored a cross-domain pre-trained model and in this regard, the ResNet-50 pre-trained on ImageNet was selected and fine-tuned on the NaSC-TG2 dataset. For this, we explored three different modes of transfer learning, i.e., fine-tuning the whole structure, fine-tuning only the last three layers, and from scratch. The experiments showed that the model from scratch was not performing well. On the other hand, the other two models performed almost the same (99.50% and 99.10% accuracies, respectively). It is because the initial layers extracted the basic gradient features, which are the same in these cross-domains, and thus, their final tuning is not affected significantly. Thus, we recommend cross-domain transfer learning for scene classification.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Precision (\%)** & **Recall (\%)** & **Accuracy (\%)** & **F1 Score (\%)** \\ \hline VGG-SA [28] & 98.57 & 98.57 & - & 98.57 \\ \hline MobileNetV3-small & & & & \\ pretrained [29] & - & - & 99.08 & - \\ \hline PyHENet [30] & - & - & 95.05 & - \\ \hline AlexNet [7] & - & - & 89.39 & - \\ \hline VGG-16 [7] & - & - & 89.59 & - \\ \hline GoogleNet [7] & - & - & 87.76 & - \\ \hline ResNet-34 [7] & - & - & 88.37 & - \\ \hline Inception v3 [7] & - & - & 86.75 & - \\ \hline ResNet-50 (Ours) & 99.50 & 99.50 & 99.50 & 99.50 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of published results with our state-of-the-art model.  
**Author Contributions:** Conceptualization, M.A. ([PERSON]), I.M., M.A. ([PERSON]) and A.S.; Methodology, I.M., M.A. ([PERSON]) and A.S.; Software, M.A. ([PERSON]); Validation, M.A. ([PERSON]); Formal analysis, M.A. ([PERSON]) and LM.; Investigation, I.M. and A.S.; Resources, I.M.; Data curation, M.A. ([PERSON]); Writing--original draft, M.A. ([PERSON]); Writing--review & editing, I.M., M.A. ([PERSON]) and A.S.; Supervision, M.A. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The APC was covered by UiT The Arctic University of Norway.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Simulation Results and Discussion
Figure 20 depicts the Power Spectrum (PS)-received signal before and after IIR NF processing. The results indicate that the proposed method is capable of detecting and mitigating CWI; they also show that by adjusting the depth of the NF, the CWI is removed according to the estimated JSR, which prevents the Signal of Interest (SoI) from being reduced. Most of the previous studies introduced in this paper emphasize the suppression of CWI and MCWI without focusing on adjusting the depth of the NF. These methods completely remove the interference, but they generate a significant amount of self-noise, which causes the distortion of some useful signals. In order to reduce the loss of some useful signals while removing interference, we proposed a low-complexity algorithm that uses a second-order IIR NF based on a direct-form structure to estimate the JSR by adjusting and controlling the depth of the NF, which has not been considered in previous studies--particularly [11]. Moreover, according to the comparison of the IIR NF with a lattice-form  
Figure 16: BER vs. JSR (using an S-ANF; full suppression means \(k_{1}=1\)): (**a**) \(E_{b}/N_{o}=15\) dB, and (**b**) \(E_{b}/N_{o}=20\) dB. Figure 17: BER vs. JSR (using MANF; full suppression means \(k_{1}=1\)): (**a**) \(E_{b}/N_{o}=15\) dB, and (**b**) \(E_{b}/N_{o}=20\) dB. Figure 20: Power Spectrum (PS)-received signal before and after IIR NF processing: (**a**) \(\text{SR}=-20\) dB, (**b**) \(\text{JSR}=20\) dB, (**c**) \(\text{JSR}=-15\) dB, (**d**) \(\text{JSR}=15\) dB, (**e**) \(\text{JSR}=-10\) dB, and (**f**) \(\text{JSR}=10\) dB.
## 3 Earth Surface Modelling Advances and Links with EO Datasets
### Land Surface Properties  
Land surface processes and parameters strongly depend on geomorphology, land use, water body distribution, vegetation cover and soil type, and therefore the climatological fields describing these characteristics are a key part of any land surface scheme.
# A Failure Risk-Aware Multi-Hop Routing Protocol in LPWANs Using Deep Q-Network
## 6 Simulation Results and Analysis
### Simulation Models
#### 6.1.3 Path Loss Measurement Model  
Results presented in this study were computed at carrier frequency \(f=868\) MHz and the path loss model defined in [33]:  
\[PL[\mathrm{dB}]=32.45+30\Big{(}e_{pl}-2\Big{)}+20\mathrm{lg}(f[ \mathrm{MHz}]), \tag{30}\] \[+e_{pl}\cdot 10\mathrm{lg}(d[\mathrm{km}])+\delta[\mathrm{dB}]\]  
where \(PL\) is the path loss at distance \(d\), \(e_{pl}\) is the path loss index, and \(\delta\sim N\big{(}0,\sigma^{2}\big{)}\) models random channel fluctuations resulting from shadowing. In this study, we use \(e_{pl}=5\) and \(\sigma=3\) dB.
## 3 Results and Discussion
### Experimental Results
We then take Cluster 1 as an instance where TP, TN, FP, and FN are 238, 455, 11, and 16. There are 16 points that should be classified in Cluster 1, but it is classified otherwise. It denotes only 6% of the points that have the incorrect class. For Cluster 5, the TP, TN, FP, and FN values are 70, 698, 26, and 66. There are 26 points that should be classified in Cluster 5, but it is classified otherwise. A total of 27% of the points have an incorrect class in Cluster 5. Since we obtained all the confusion matrices of the counterpart models, we could calculate precision, recall, and F1-score. These values are presented in Table 2. Through this table, we can compare the evaluation results between the models. Figure 8: The confusion matrix of the proposed model. Figure 7: The comparison between the actual values and the predicted values. In these evaluation metrics, the best result is achieved by a model with the highest values of precision, recall, and F1-score. Based on Table 2, The proposed model remains the best model. It achieves the highest values with precision, recall, and F1-scores of 0.70. The counterpart models mostly have values for precision, recall, and F1-score metrics below 0.70. The proposed model is followed by the LSTM model as the best model in the classification evaluation of clustering data between the actual values and the predicted values. The gap values between the LSTM and the proposed model's precision, recall, and F1-score metrics are 0.05, 0.06, and 0.06. It also shows that only the proposed model reaches these metric values above 0.70. Furthermore, these highest values denote the reliability of the proposed method compared to counterpart models.
# An Overview of Quantum Machine Learning Research in China
## 5 Challenges and Prospects
### Challenges
#### 5.1.1 Noise Interference  
To genuinely implement QML algorithms, real quantum computing devices are indispensable despite the existence of classical platforms for simulating quantum computing. A core technology of quantum computing devices is the preparation of qubits. At present, there are several kinds of physical qubits, mainly including semiconductor quantum dot qubits [95; 96], spin qubit [97], superconducting qubits [98], trapped-ion qubits [99], and photonic qubits [100], named after the preparation methods.  
For quantum computation, we are now in a so-called NISQ era (noisy intermediate-scale quantum) [101]. In quantum systems, noise refers to the interaction of qubits with uncontrolled degrees of freedom in the environment. There are different kinds of quantum noises, mainly including depolarizing noise, amplitude damping noise, phase damping noise, bit-flip noise, phase-flip noise, and others. Taking depolarizing noise as an example, its mathematical representation is \(\epsilon(\rho)=(1-p)\rho+p1/2^{n}\), where \(\epsilon\) denotes the noise channel, \(\rho\) is the density matrix, \(p\) is the probabilistic error rate that depends on both the device and the circuit, and \(n\) is the number of qubits [102]. This noise can cause qubits to decohere, transitioning from superposition/entanglement states to classical mixed states, thus resulting in quantum information loss.  
In this era, quantum devices are inevitably affected by noise, hence bringing negative impacts to QML models [34; 40; 42; 78]. To address the issue of noise interference, some  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Innovation** & **Datasets** & **Algorithms** & **Accuracy** & **References** \\ \hline \multirow{8}{*}{Algorithm/modelesign} & \multirow{2}{*}{Yelp} & CSANN & 83.11\% & \multirow{8}{*}{[38]} \\  & & QSANN & 90.09\% & \\  & & QSAM & **91.03\%** & \\  & & CSANN & 79.67\% & \\  & & IMDb & QSANN & 87.28\% & \\  & & QSAM & **88.35\%** & \\  & & QCNN & 74.30\% & \\  & & QSAM & **82.22\%** & \\  & & QSANN & 67.74\% & \\  & & QNN-MSFF & **96.77\%** & \\  & & NN & 86.36\% & \\  & & VSQL & **87.39\%** & \\  & & HQNN-Quanv & 67.00\% & \\  & & QNet & 81.00\% & \\ Strategy & & SEQNN & **87.56\%** & \\ selection & & Quantumphase & QCNN-Adam & 53.00\% & \\  & & QCNN-PSO & **84.00\%** & \\ Application & CIFAR-10- & CNN & 95.83\% & \\ scenario & small & HQconv & **99.45\%** & [54] \\ \hline \end{tabular}
\end{table}
Table 2: Typical research examples of the three innovation aspects and accuracy comparison of the relevant models.  
feasible approaches include developing noise suppression techniques, quantum error correction techniques, and models with noise-resistant quantum circuits [89; 103]. Taking noise-resistant quantum circuits as an example, the VCNN model designed in [34] exhibited excellent noise resistance since a new hybrid quantum-classical circuit framework was formulated; the VSQL model designed in [85] based on a variational shadow quantum circuit could introduce less noise by using fewer quantum gates.
## 5 Case Study
### Measurement of Atmospheric Parameters  
As shown in Figure 12, we built an atmosphere parameter sampling system, consisting of integrated sensor hardware and software.  
In fact, we use the temperature and humidity sensor to measure the actual atmospheric state and transmit the measured value onto a cloud server through the RS-485 field bus converter. The calibration process is conducted by this atmosphere sampling system shown in Figure 13. Generally, after a 2-3 s self-calibration process, the output of the measured sensor is the absolute value that meets the positioning requirements. Therefore, this study does not involve redundant calibration work.
# Linear Computation Coding: A Framework for Joint Quantization and Computing
## 5 Performance Analysis  
In order to analyze the expected distortion, we resort to a columnwise decoupling of the wiring optimization into target vectors and greedy wiring as in Sections 4.2.2 and 4.5, respectively. We assume that the codebook and the target vectors are IID Gaussian random vectors. We analyze the mean-square distortion for this random ensemble. The IID Gaussian codebook is solely chosen, as it simplifies the performance analysis. In practice, the IID Gaussian random matrix \(\mathbf{B}\) must be replaced by a codebook matrix with low computational cost, but similar performance. Simulation results in Section 6 will show that practical codebooks perform very similar to IID Gaussian ones.
# 5G-Compliant Authentication Protocol for RFID
###### Abstract  
The term \"Internet of Things\" was originally coined when radio frequency identification (RFID) technology was being developed to refer to applications where RFID tagged objects and sensors enabled computers to achieve effective situational awareness without human intervention. Currently, this term encompasses a myriad of medium/small devices connected to the Internet. On the other hand, 5G is a key enabling technology that will support next generation wireless communications. Moreover, 5G aims to realize the \"Internet of Everything\". Surprisingly, despite the expected relationship between these two technologies, RFID tags have not been properly integrated into 4G and it is not clear if this will change in 5G. RFID is considered as a parallel technology where, at best, it has connection to the core network using back-end servers as gateways between the two technologies. With the aim of overcoming this problem, this paper proposes a 5G compliant RFID protocol that allows RFID tags to act as fully fledged 5G subscribers while taking into account the main characteristics of RFID systems. This proposal leverages the separation between USIM and mobile equipment within the user equipment to implement a 5G compliant protocol where tags accomplish the authentication part, as 5G subscribers, while readers assume the mobile equipment role, carrying out the 5G communication and most of the resource consuming tasks.  
5G; 5G-AKA; security; USIM; primary authentication; RFID; EPC Article
## 2 Materials and Experimental Set-Up  
### Samples  
GaN nanowires were grown on a 2.5 nm-thin n-doped AlN buffer layer deposited on n-type doped Si(111) substrate by Plasma-Assisted Molecule Beam Epitaxy (PA-MBE) [18]. They were grown under nitrogen-rich conditions (V/III = 1.36) at temperature higher than 760 \({}^{\circ}\)C. The GaN NWs grown following this procedure are vertically oriented and present a hexagonal cross-section shape delimited by \(\{10-10\}\) planes [19]. Depending on the growth conditions, the NW diameter ranges between 50 and 70 nm and their density evolves between \(10^{8}\) and \(10^{10}\) NW/cm\({}^{2}\). Finally, their length, which depends on the growth time, is approximately of 1.00 \(\pm\) 0.12 \(\upmu\)m.  
The elastic properties of the GaN NW/HSQ composite layer, playing the role of active layer in piezoelectric devices, depend on the morphological properties of the NWs, which are embedded into a polymer matrix presenting lower elastic constants. In order to investigate the influence of NW characteristics, we have considered two types of NW morphologies. The first type of samples corresponds to GaN NWs grown at 800 \({}^{\circ}\)C (Figure 1). In these conditions, the NW density reaches \(10^{10}\) NW/cm\({}^{2}\) and their diameter is around 50 \(\pm\) 10 nm. The second type of samples has been grown at lower temperature (760 \({}^{\circ}\)C). Due to this growth temperature, a parasitic and thick 2D-layer is formed at the bottom of the NWs, as shown on Figure 2. These conditions lead to the formation of wider (diameter of 70 \(\pm\) 20 nm) and less dense (5 \(\times\) 10\({}^{8}\) NW/cm\({}^{2}\)) GaN NWs. This type of NWs will be referred to as \"_pyramidal_\" NWs.  
To evaluate the effective elastic properties of GaN NWs-based composite structures, the as-grown GaN NWs of the two morphologies have been embedded into HSQ matrix. This matrix, spin-coated homogeneously onto the nanowire array, has been annealed at 400 \({}^{\circ}\)C to cure the polymer. Then reactive ion etching was used to remove polymer excess from the nanowire summits for metallic contact deposition. Finally, after a de-oxidation of the nanowire top part, CrPt contacts were deposited through a shadow mask with 2 mm openings and a bottom TiAu contact was made on the conducting silicon substrate. In addition, three samples corresponding to the second NW morphologies (i.e., with a rough parasitic layer) were prepared following the same procedure but with different annealing temperature (300 \({}^{\circ}\)C, 400 \({}^{\circ}\)C and 500 \({}^{\circ}\)C).  
Figure 1: Scanning electronic microscope picture of GaN NWs (Gallium nitride nanowires) on n-doped Si (111) substrate before the encapsulation into a polymer matrix. All NWs are vertical.The density is of the order of 10\({}^{10}\) NWs/cm\({}^{2}\). The filling factor is 33–40%.
## 3 Analysis of the Transition between Shock Reflections
### Position (c)  
From the results thus far, it is expected that moving the deposition position downstream and not using excessive energy will be effective for the shock reflection transition, especially from Mach reflection to regular reflection. The focus of the laser energy was moved further downstream and on the centerline (point (c) in Figure 8). Figures 15-17 show the results from when energy levels of 107.5 mJ, 215.0 mJ, and 430.0 mJ are applied. In this case, the shock wave disturbance due to the blast wave occurs in only a short interval of T = 10-160 \(\upmu\)s.  
Figure 10: Interaction between blast wave and oblique shock, deposition upstream of the wedges. Energy: 215.0 mJ.  
Figure 11: Interaction between blast wave and oblique shock, deposition upstream of the wedges. Energy: 430.0 mJ.  
Figure 12: Interaction between blast wave and oblique shock, deposition at the middle of the upper wedge. Energy: 107.5 mJ.  
Figure 13: Interaction between blast wave and oblique shock, deposition at the middle of the upper wedge. Energy: 215.0 mJ.  
Figure 14: Interaction between blast wave and oblique shock, deposition at the middle of the upper wedge. Energy: 430.0 mJ.  
Figure 15: Interaction between blast wave and oblique shock, deposition downstream of the wedges. Energy: 107.5 mJ.  
Figure 16: Interaction between blast wave and oblique shock, deposition downstream of the wedges. Energy: 215.0 mJ.  
As shown in the figures, no transition occurred under these deposition conditions. When 107.5 m of energy was deposited, the transition was not observed even though the interaction between the  
Figure 17: Interaction between blast wave and oblique shock, deposition downstream of the wedges. Energy: 430.0 mJ.  
blast wave and the oblique shock wave continued up to the expansion zone. In contrast, when the energy was increased further, the interaction was terminated in the contraction zone. It seems that the difference in the transition characteristics resulted from the location of the deposition. In other words, when the focus was placed on the centerline, the structures of the shock waves from both wedges were changed at the same time, which is similar to the phenomena observed in the case of 430.0 mJ of energy at other positions. This concurrent distortion prevents the generation of an asymmetrical shock structure.  
The residence time of the blast wave in the computational domain was also reduced as energy was deposited downstream. This results in Mach reflection after the disturbance passes the domain. This means that when introducing symmetrical disturbance, the transition from Mach reflection to regular reflection is not achieved regardless of the deposited energy. From these results, it is assumed that there is an optimal position that can cause a transition within a minimum time in the contraction section. Furthermore, even with a structurally symmetrical double wedge, it is possible to induce the transition by temporarily creating a virtual asymmetric wedge by selecting the energy deposition position unevenly.
# Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics
## 3 Summary of ROS-Based RL Applications
### Notable Works Using On-Policy
#### 3.1.8 Navigation I  
In [66], the authors provide a framework for the execution of a completely decentralized graph neural network (GNN) for real-world applications (for more info about the GNN, please refer to [88]). This includes finding a way to adapt GNN policies from centralized simulations to real-world applications that involve decentralized execution, communication that occurs at different times, and noisy communication channels. This work tackles the significant challenge in multirobot coordination.  
The authors in [66] utilized the PPO algorithm for training the GNN models using a reward function that encourages desired behaviors, such as reaching goals and avoiding collisions. The framework uses an ROS middleware for communication and control, enabling asynchronous episode execution and quick agent discovery. This is a significant step toward utilizing GNNs in a complex real-world application. For more information on the hardware and software used, please see Table 4.  
The advantage of using GNNs is that multirobot systems exhibit a graph-like structure, where robots that appear as nodes and interactions (communication and proximity) can be modeled as edges. The above-mentioned work made a notable contribution by creating a practical and comprehensive framework for implementing GNN-based policies in real-world scenarios for multirobot use cases. This work provides valuable insights into performance trade-offs between execution modes and network setups, paving the way for future research to integrate GNNs in robotics. It highlights the importance of considering real-world constraints and challenges during the development and deployment of GNNs for robotic applications.
# A Framework for Fall Detection Based on OpenPose Skeleton and LSTM/GRU Models
## 2 System Architecture and Method
### Data Pre-Processing  
Pre-processing of data is a very important part, because the data initially acquired may not have the same format or unit. Therefore, it is necessary to adjust the format, remove outliers, fill in missing values, or scale features. The following subsections explain the processes for data format adjustment, data normalization, and missing values recovery.
# Control Strategy Development of Driveline Vibration Reduction for Power-Split Hybrid Vehicles
## 3 Vibration Reduction Control Methods
### Proportional-Integral-Derivative Control (PID)  
PID control was applied in this research as another control method in addition to PC. When the vibration was sensed, the vibration was neutralized by corresponding torque feedback provided by the motors, which was also able to meet the power demand. The vehicle speed of the previous time step was recorded, and the corresponding transmission rotation speed was calculated as the reference. The calculation is shown in Equation (14). Among them, \(N_{t}\) is the base speed of the transmission, \(V_{vehicle}\) is the vehicle speed, and r is the effective radius of the tire.  
\[N_{t}=V_{vehicle}/r*(30/\pi) \tag{14}\]  
The previous time step actual rotation speed of the transmission was compared with reference speed, and the difference was sent into the PID controller to obtain the required vibration reduction torque feedback. The feedback started when a large change in speed occurred. The calculation is shown in Equations (15) and (16). Among them, \(N_{t\_exact}\) is the actual rotational speed of the transmission, \(\Delta N_{t}\) is the difference value of the rotational speed, \(K_{p}\), \(K_{I}\), and \(K_{D}\) are the gain values of the PID control, and \(u\) is the control effort needed for vibration reduction.  
\[\Delta N_{t}=N_{t}-N_{t\_exact} \tag{15}\]  
\[u=K_{p}\Delta N_{t}+K_{I}\int_{0}^{t}\Delta N_{t}dt+K_{D}\frac{\Delta N_{t}}{dt} \tag{16}\]  
Finally, the control effort needed for vibration reduction was sent to the M/G instruction controller to adjust the torque for vibration reduction.