esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance of Adaptive Bit-Interleaved Polar Coded Modulation in FSOC System
## 3 Performance Simulation Results and Discussion of A-Bipcm under Different Turbulence Conditions
Furthermore, considering the desired BER = 3.8 \(\times\) 10\({}^{-3}\) of the system, the conventional polar coding and decoding scheme requires a received optical power greater than \(-\)17.69 dBm, and the A-BIPCM scheme requires a received optical power greater than \(-\)18.42 dBm. Thus, the A-BIPCM scheme achieves an improvement of approximately 0.73 dBm in terms of the required received optical power. Under weak turbulence conditions, the A-BIPCM scheme can provide better BER performance compared with traditional polar code encoding and decoding. In addition, as the received optical power increases, the BER shows a downward trend and increases by one order of magnitude. This shows that atmospheric turbulence has a significant impact on channel quality, and increasing  
Figure 6: BER performance curve of A-BIPCM scheme and traditional polar code encoding and decoding: (**a**) comparison of the BER performance curves of atmospheric coherence length \(r_{0}\) = 0.955, (**b**) comparison of the BER performance curves of atmospheric coherence length \(r_{0}\) = 1.884, (**c**) comparison of the BER performance curves of atmospheric coherence length \(r_{0}\) = 1.839, (**d**) comparison of the BER performance curves of atmospheric coherence length \(r_{0}\) = 3.282.  
transmit power can improve the information transmission quality of atmospheric optical channels.
## 5 Experimental Validation
### Comparative Experiments on Speed-Up and Speed-Down as Well as Forward and Reverse Operation  
To verify the synchronous performance of the new control method, a comparative experiment on speed-up and speed-down as well as forward and reverse operation is conducted. The multi-motor speed cooperative control system is started from an initial speed of 0, accelerated to 200 r/min, then to 500 r/min, and further to 700 r/min. Subsequently, it decelerates to 300 r/min and finally reverses to \(-300\) r/min. The results of the comparative experiment on speed-up and speed-down as well as forward and reverse operation are shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c} \hline
**Control Method** & **Parameter** & **Value** \\ \hline Traditional DCC method & \(K_{p,j}\) & 1.1 \\ \(K_{l,j}\) & 3 \\ \hline \multirow{6}{*}{New control method} & \(k_{1}\) & 5 \\  & \(k_{2}\) & 0.9 \\ \cline{1-1}  & \(k_{3}\) & 10 \\ \cline{1-1}  & \(p\) & 3 \\ \cline{1-1}  & \(q\) & 5 \\ \cline{1-1}  & \(r\) & 7 \\ \cline{1-1}  & \(s\) & 5 \\ \hline \end{tabular}
\end{table}
Table 2: Parameters of control methods.  
Figure 5: Comparative experimental results of speed-up and speed-down as well as forward and reverse operation. (**a**) Comparative curve of speed response. (**b**) Comparative curve of speed tracking error. (**c**) Comparative curve of speed synchronization error.  
From Figure 5a, the new control method exhibits no obvious overshoot phenomena during speed-up, speed-down, forward, and reverse operation conditions. The transition process is relatively smooth, with a settling time of around 2.5 s. In contrast, the traditional DCC method shows around 8% overshoot during speed-up, speed-down, forward, and reverse operation conditions, with a settling time of around 5 s. In steady-state, the new control method exhibits a chattering amplitude of around 3 r/min, significantly lower than the approximately 5 r/min steady-state process of the traditional DCC method. From Figure 5b, there is no significant overshoot phenomenon during forward and reverse operation conditions. From Figure 5c, the synchronization error of the new control method is around 3 r/min, which is lower than the approximately 8 r/min synchronization error of the traditional DCC method. Therefore, the new control method effectively reduces the system's chattering, while ensuring both the system's rapid performance and tracking capability.
# Narrowband Internet of Things (NB-IoT):
## 3 Narrowband-IoT Protocol Stack
### Control and User Plane Optimization
It further addressed the upper layers changes related to cellular IoT evolved packet system optimization through user and control planes to enhance the small data packets transmissions for end-to-end massive connectivity.
# Intelligent Space Object Detection Driven by Data from Space Objects
## 2 Dataset Construction and Labeling
### Dataset Labeling
The label of \"body\", which is more complex in shape but with no consistency in pattern, maintains an accuracy of 0.95 or higher. In comparison, the labels like \"antenna\", \"optical-load\", and \"antenna-rod\" perform slightly worse when the accuracy is maintained at above 0.9, despite those missing and incorrect labels. After an analysis of this issue, this is found to result mainly from a significant variation between these three label categories within the dataset. To compound this, they are characterized by a confined area and low quantity, which hampers the capability to train a robust labeling model for these categories given a small sample size. It is also implied that semi-automatic labeling is unlikely to completely replace manual labeling. Overall, this interactive, task-specific method of semi-automatic labeling demonstrates a significant advantage over manual labeling in time cost and the complexity of labeling, as evidenced by the final results of labeling shown in Figure 7.
# A Survey on Change Detection and Time Series Analysis with Applications
## 2 Decomposition Methods into Frequency Domain
### Least-Squares Spectral Analysis (LSSA)
It is shown that LSS follows the beta distribution from which the critical value at \((1-\alpha)\) confidence level is calculated as \(\zeta=1-\alpha^{2/(n-q-2)}\), where \(\alpha\) is the significance level, usually 0.01 or 0.05, \(n\) is the time series size, and \(q\) is the number of constituents of known forms [13]. Therefore, if \(s(\omega_{k})>\zeta\), then the spectral peak in the spectrum corresponding to \(\omega_{k}\) is statistically significant at a certain confidence level, usually 99% or 95%. It is worth mentioning that the least-squares minimization has many applications in wireless networking and telecommunications. For instance, [PERSON] et al. in [17] showed that if \(\mathbf{\Phi}\) is a weighing design whose entries are \(\pm 1\), and the covariance matrix associated with \(\mathbf{f}\) is \(\mathbf{C}_{\mathbf{f}}=\sigma^{2}\mathbf{I}\), then \(\mathbf{\Phi}\) is optimal if \(\mathbf{\Phi}^{\mathrm{T}}\mathbf{\Phi}=n\mathbf{I}\), and so Equation (6) gives \(\mathbf{C}_{\mathbf{\xi}}=(\sigma^{2}/n)\mathbf{I}\). That means \(\mathbf{\Phi}\) is optimal, i.e., the variances of the elements of \(\mathbf{\xi}\) are minimum, if \(\mathbf{\Phi}\) is a \(\pm 1\) matrix whose columns are orthogonal and are taken from a Hadamard matrix (a Hadamard matrix is a square matrix \(\mathbf{H}\) of order \(n\) with \(\pm 1\) entries such that \(\mathbf{HH}^{\mathrm{T}}=n\mathbf{I}\)) [18]. [PERSON] [3] showed that the Fourier analysis is a special case of LSSA when the time series is strictly stationary, equally spaced, equally weighted, and with no gaps. The Fourier transform of a function \(f\) for cyclic frequency \(\omega\) is obtained by considering the entire domain of \(f\) similar to LSSA that obtains the spectrum of a time series by fitting sinusoidal functions of cyclic frequency \(\omega\) to the entire time series; this is not a satisfactory approach for analyzing non-stationary time series comprising constituents with a variability of amplitude and frequency over time.
## 4 Discussion
### Limitations and Considerations for Future Work  
Despite the contributions of our study, several limitations should be acknowledged. Our sample size (N = 14) is relatively small, potentially limiting the generalizability of our findings. [PERSON] and [PERSON] (2024) ([PERSON] and [PERSON], 2024) determined that at least 19 participants would be necessary to discriminate, with adequate statistical power, the participant's fall history using BBS scores. This suggested that our study may be underpowered, which could increase the risk of Type II errors and reduce the reliability of our results. Notwithstanding, our participant recruitment from the community with diversity in ethnicity, gender, age, and health background ensured this study's generalizability.  
Moreover, IMUs could be subject to inherent measurement errors. These include drift, where small errors accumulate over time leading to gradually increasing inaccuracies, as well as noise, which can introduce random fluctuations in the data. To mitigate this, we only used the gyroscopes' data for one movement metric (the RMS angular velocity) and applied a low-pass filter to eliminate the noise in the acceleration data. Also, as such errors can affect the accuracy of long-term measurements ([PERSON] and [PERSON], 2024), we chose a short-duration data collection where most tasks lasted for less than two minutes; thus, we expected these errors to be marginal.  
Furthermore, our study focused on balance during specific BBS tasks, which may not fully capture all aspects of balance control in daily life. For example, task no. 12 (placing an alternate foot on a stool) was easier than actually climbing up or down the stairs. We did not account for factors such as environmental conditions (e.g., uneven surfaces, poor lighting), cognitive load (e.g., dual-task conditions), and fatigue, which can significantly influence balance performance ([PERSON] and [PERSON], 2024). For instance, cognitive load can divert attention from balance maintenance, while fatigue may impair muscle response times critical for maintaining stability.  
Additionally, IMU data from the head and torso may be influenced by non-balance-related movements, requiring careful interpretation. These extraneous movements could potentially mask or mimic balance-related signals, complicating the analysis and potentially leading to misinterpretation of the data. Addressing these limitations in future research will be crucial for advancing the use of IMUs in balance assessments and fall prevention strategies for the elderly population.  
Finally, the cross-sectional nature of our study limits our ability to assess changes in balance over time or the long-term predictive value of our IMU-derived metrics. A longitudinal study design would provide more robust evidence for the prognostic capabilities of these measures in fall risk assessment ([PERSON] et al., 2018).
# Real-Time Monitoring of Passenger's Psychological Stress
###### Abstract  
This article addresses the question of passengers' experience through different transport modes. It presents the main results of a pilot study, for which stress levels experienced by a traveller were assessed and predicted over two long journeys. Accelerometer measures and several physiological signals (electrodermal activity, blood volume pulse and skin temperature) were recorded using a smart wristband while travelling from Grenoble to Bilbao. Based on user's feedback, three events of high stress and one period of moderate activity with low stress were identified offline. Over these periods, feature extraction and machine learning were performed from the collected sensor data to build a personalized regressive model, with user's stress levels as output. A smartphone application has been developed on its basis, in order to record and visualize a timely estimated stress level using traveler's physiological signals. This setting was put on test during another travel from Grenoble to Brussels, where the same user's stress levels were predicted in real time by the smartphone application. The number of correctly classified stress-less time windows ranged from 92.6% to 100%, depending on participant's level of activity. By design, this study represents a first step for real-life, ambulatory monitoring of passenger's stress while travelling.  
wellbeing; stress; physiology; wearable sensors; human-machine interface; affective computing 1
# Contactless Palm Vein Recognition Based on Attention-Gated Residual U-Net and ECA-ResNet
## 4 Experiments
### Experimental Setup  
Initially, the palm images are normalized and aligned to a vertical orientation, which is required for calculating the RDF, and resized to 112 \(\times\) 112 in the ROI extraction step. This orientation change introduces a minor impact as when presented with horizontally oriented palm images, a few adaptations may be necessary, such as adjusting the ROI extraction to account for changes along the x axis. However, it is critical to note that once the ROI images were identified and extracted, orientation changes do not significantly impact the performance of the U-Net segmentation model as the model can overcome the problem of rotation and scale transformation. When training ECA-ResNet-50, data augmentation methods such as random cropping and random rotation (0.6) are performed. To optimize the network, an Adam optimizer with the weight decays of 2 \(\times\) 10\({}^{-4}\) is used. Regarding the learning rate, an adaptive learning rate strategy is used to update the learning rate from 1 \(\times\) 10\({}^{-4}\) at the initial epoch to 1 \(\times\) 10\({}^{-5}\) at the max epoch. The network is trained for a total of 60 epochs for the CASIA dataset. The training for the segmentation model took approximately 3 h, and the training for recognition only required approximately 2 h. In terms of models' complexity and size, the attention-gated residual U-Net segmentation model comprises 2.4 M parameters with a model size of 9.2 MB. In contrast, the ECA-ResNet recognition model contains 26.9 M parameters with a model size of 98 MB. In our experiments, the average computational time per single palm image through the entire pipeline was approximately 40 milliseconds. This rapid inference time ensures a better user experience, especially in applications where instant identification is required. All experiments are simultaneously run in parallel on four NVIDIA TitanX GPUs (12 GB of RAM per GPU) with the computation from several GPUs.
## 3 Results and Discussion
### Mechanism Performance
#### 3.1.3 Qualitative Analysis by Functional Groups of Mineral Powder  
Given the small amount of ZnO detected by XRF, the study applied AAS to obtain Zn contents in a pure asphalt binder and sample C, according to the standard GB/T 15337-2008. Based on the atomic vapor for a given characteristic line, AAS analysis achieved qualitative and quantitative analysis by assessing the degree of attenuation [38]. The results show that the concentration of Zn in pure a asphalt binder and sample C were 2.75 mg/kg and 48.36 mg/kg, respectively; this approximately corresponds to 0.005% for sample C and agreed well with the XRF result. Therefore, the AAS test also proves that the mineral powder adsorbed part of asphalt binder from the solution.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Composition**} & \multicolumn{3}{c}{**Content (\%)**} \\ \cline{2-4}  & **Sample A** & **Sample B** & **Sample C** \\ \hline Na\({}_{2}\)O & 0.325 & 0.357 & 0.392 \\ MgO & 7.835 & 8.488 & 8.413 \\ Al\({}_{2}\)O\({}_{3}\) & 15.749 & 16.755 & 16.218 \\ SiO\({}_{2}\) & 32.612 & 33.651 & 32.987 \\ SO\({}_{3}\) & 2.373 & 2.315 & 2.674 \\ K\({}_{2}\)O & 0.445 & 0.423 & 0.442 \\ CaO & 39.237 & 36.788 & 37.600 \\ TiO\({}_{2}\) & 0.758 & 0.615 & 0.638 \\ MnO & 0.133 & 0.141 & 0.155 \\ Fe\({}_{2}\)O\({}_{3}\) & 0.241 & 0.218 & 0.194 \\ SrO & 0.102 & 0.085 & 0.094 \\ Y\({}_{2}\)O\({}_{3}\) & 0.012 & 0.010 & 0.012 \\ ZrO\({}_{2}\) & 0.049 & 0.041 & 0.044 \\ BaO & 0.103 & 0.114 & 0.133 \\ ZnO & 0 & 0 & 0.005 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Composition and content for the three investigated samples.  
Figure 2: XRD spectra for three investigated samples.  
Afterwards, the study attempted to calculate the adsorption ratio by comparing the peak intensity in FTIR spectra for a pure asphalt binder and the three samples (Figure 3). It can be found that, in the wavenumber range 2750-3000 cm\({}^{-1}\), both sample C and 70# asphalt binder clearly displayed two peaks (2919-2845 cm\({}^{-1}\)), while no absorption peak appeared in sample A or sample B (Table 4) [39]. Based on the existing literature concerning gasoline [40], mineral powder [41] and asphalt binder [42], the two peaks registered in the range 2750-3000 cm\({}^{-1}\) prove the mineral powder's adsorption capacity.
# Automated Enemy Avoidance of Unmanned Aerial Vehicles Based on Reinforcement Learning
## 4 Problem Solving
### Formulate the Problem as the MDP
#### 4.1.2 Action Space  
At each decision making step, the agent needs to decide an action for execution, so as to change the altitude of the decision making UAV. Six actions are defined in this paper: flying toward the goal, accelerating, decelerating, increasing the heading angle, decreasing the heading angle, and attacking the enemy. Denote these actions with \(A=\{a_{0},a_{1},...,a_{5}\}\). Each action corresponds to a way to change the desired attitude. The details are listed as follows.  
* \(a_{0}\): Fly toward the goal straightly, while keeping the desired velocity as the same in the previous step.
* \(a_{1}\): Increase the desired velocity with \(\Delta V\) and fly with the same desired heading angle as the previous step.
* \(a_{2}\): Decrease the desired velocity with \(\Delta V\) and fly with the same desired heading angle as the previous step.
* \(a_{3}\): Increase the desired heading angle with \(\Delta\phi\) and fly with the same desired velocity as the previous step.
* \(a_{4}\): Decrease the desired heading angle with \(\Delta\phi\) and fly with the same desired velocity as the previous step.
* \(a_{5}\): Attack the enemy UAV, while the desired attitude changes as that of \(a_{0}\).  
The desired height is set with a fixed value \(h\) in all cases, for the flying height is not considered in this problem.
# Resource Allocation Schemes for 5G Network: A Systematic Review
## 5 Discussion
### Q2. What Is the Importance of Resource Allocation in 5G?
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Ref** & **Algorithm/Scheme/Strategy** & **Problem Addressed** & **Improvements/Achievements** & **Limitations/Weakness** \\ \hline
[89] & Cooperative Online Learning & Extreme interference between the multi-tier users. & \(\bullet\) & Maximizes spectral efficiency data rate by ensuring QoS. & \(\bullet\) & Limited to tier and downlink. \\ \hline
[90] & Game-theoretic approach & Cross-tier interference. & \(\bullet\) & Improves spectral efficiency. & \(\bullet\) & Limited to two tiers only. \\  & & & \(\bullet\) & Energy efficiency. & \(\bullet\) & Does not support uplink. \\ \hline
[91] & Genetic Algorithm Particle Swarm Optimization-Power Allocation (GAPSO-PA) & The allocation of power in heterogeneous ultra-dense networks. & \(\bullet\) & Reduces the system outage probability. & \(\bullet\) & Solves non-linear optimization. \\ \hline
[92] & Estimation of Goodput based Resource Allocation (EGP-BASED-RA) & Enhance Goodput (GP): (a specific metric of performance). & \(\bullet\) & The performance of the UFMC system was boosted. & \(\bullet\) & Limited to a particular packet format. \\ \hline
[93] & The social-aware resource allocation scheme & D2D multicast grouping; & \(\bullet\) & Fairness. & \(\bullet\) & Working on limited \\  & & Ineffective D2D links. & \(\bullet\) & Throughput. & & \(\bullet\) \\  & & & \(\bullet\) & Has substantial benefits & & \\  & & & over other algorithms. & & & \\ \hline
[94] & PGU-ADP algorithm & Dynamic virtual RA problem. & \(\bullet\) & Drastically minimizes the outage probability. & \(\bullet\) & Considers specific slice rate. \\  & & Expansion of the total user rate. & \(\bullet\) & Enhances user data rate & \(\bullet\) & Slice state. \\ \hline
[95] & Efficient Resource Allocation Algorithm & Enhance system capacity and maximum computational complexity. & \(\bullet\) & Improves system capacity. & \(\bullet\) & Power allocation is done on the sub-carriers of the fixed group. \\  & & & & & & sub-carriers of the fixed group. \\ \hline
[96] & GBD Based Resource Allocation Algorithm & Enhances allocating algorithm’s efficiency. & \(\bullet\) & Minimizes complexity performance. & \(\bullet\) & Limited \\  & & & & & & parameters. \\ \hline \end{tabular}
\end{table} 
Table 4: Characteristics of selected resource allocation techniques in 5G.
## 5 Complexity Analysis  
In this section, we will analyze the computational complexity of the proposed JCSCR and the 4 th-PD-VV algorithm in the QPSK modulation format. The real multipliers and real adders required for the two whole schemes when the length of the participating frequency-offset-estimation symbol block and the phase-offset-estimation symbol block were N1 and N2, respectively, are shown in Table 1. The complexity of the 4 th-PD-VV scheme using fourth-power operations to remove the modulation-phase operation increased considerably with increases in block length. Additionally, the M power operation requires more real multipliers when the number of powers increases [30]. In contrast, the JCSCR algorithm  
Figure 11: Experimentally measured BER as a function of transmitting power for different values of N1/N2.: (**a**) different N1 values, (**b**) different N1 values.  
Figure 12: Experimentally measured BER as a function of transmitting power for the proposed algorithm and the 4 th-PD-VV algorithm: (**a**) \(6.5\times 10^{-14}\) m\({}^{-2/3}\), (**b**) \(5\times 10^{-15}\) m\({}^{-2/3}\), and (**c**) \(6\times 10^{-16}\) m\({}^{-2/3}\).  
signal-preprocessing stage avoids the resource-consuming fourth-power operation. For complex signals of length N1, the signal-preprocessing stage requires only 11N1 real adders to perform the modulation-phase-removal operation. The JCSCR only requires preprocessing to remove the modulated phase of the signal and subsequent joint compensation operation, which helps to further reduce the complexity of the overall scheme. In field programmable gate array (FPGA),multiplication is implemented through multiple shift operations and multiple addition operations, and the complexity of multiplier implementation is much greater than that of adders. Therefore, the complexity of a scheme is mainly discerned through the number of multipliers. Based on Table 1, the number of multipliers required in the operation of the proposed JCSCR was less than \(\frac{1}{3}\) of that for the 4 th-PD-VV algorithm, which is more favorable for implementing the algorithm in FPGAs.