esa-sceva/satcom-chunk-collection huggingface dataset:
# Bearing Estimation for Indoor Localization Systems Using Planar Circular Photodiode Arrays
## 4 Measurements and Evaluation
### Localization Measurements
#### 4.4.2 Localization Width Tilted PCPA  
Another localization experiment was conducted utilizing five beacons, with a similar beacon arrangement as can be seen in Figure 14. The purpose of the measurements was to quantify the localization error caused by the tilt of the PCPA. Table 3 summarizes the statistical results of the experiments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Setup** & **4 Beacon (LS)** & **4 Beacons (FD)** & **6 Beacon (LS)** & **6 Beacon (FD)** \\ \hline normal & 8.37/5.64 & 16.37/9.80 & 6.63/3.47 & 14.36/3.92 \\ with reflections & 15.02/8.02 & 28.78/11.34 & 9.93/5.52 & 18.39/8.56 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean and standard deviation of the localization error (units in cm).  
Figure 14: Localization with various amount of reflections, utilizing 6 beacons: (**a**) LS estimator, (**b**) FD estimator. Dark big blue dots: beacon positions, light blue dots: localization with reflective surface next to the PCPA, yellow dots: localization without reflective surface.  
As the data in Table 3 indicates, the tilted and non-tilted measurements are almost identical. This indicates that the utilized hardware, more precisely the cylindrical shape of the photodiode is highly tolerant to the tilt of the PCPA.
# A Numerical Analysis on Lateral Resistance of Pile-Bucket Foundation for Offshore Wind Turbines
## 5 Analysis on _p-y_ Curve
### Modification of \(p\)-\(y\) Curves for P-BF
#### 5.3.2 Modified Eigenvalue \(y_{50}\) of Pile Displacement  
The eigenvalue of pile displacement is another important parameter of the \(p\)-\(y\) curve, which is referred to as the eigenvalue of the displacement of the pile. It is obtained from the \(p\)-\(y\) curve simulated by ABAQUS as shown in Table 9. There is a linear relationship between \(y_{50}\) and depth \(Z\). Then, a dimensionless coefficient k, which is related to depth \(Z\), is introduced to reconstruct \(y_{50}\), as shown in Formula (7).  
\[\left\{\begin{array}{l}y_{50}=kd_{s}\\ k=m+n(Z/h)\end{array}\right. \tag{7}\]  
Figure 16: Modified lateral ultimate soil reaction.  
The undetermined coefficients m and n in Formula (7) are solved by the least squares method. Formula (8) is the revised formula of \(y_{50}\).  
\[y_{50}=[0.0291(Z/h)+0.0035]d_{s} \tag{8}\]  
where  
\(h=\) the bucket height, m;  
Other parameters are the same as above.  
The revised eigenvalue is consistent with the fitting trend of the finite element simulation results, which are shown in Figure 17.
# Multi-User AF Relay Networks with Power Allocation and Transfer: A Joint Approach
## 5 Conclusions  
We studied a joint design of power allocation and transfer for multi-user AF-assisted networks with EH. We focused on maximizing the achievable sum rate using a deadline, subject to the battery and energy causality constraints at the \(S_{k}\), \(\forall k\), and \(R\) nodes, and proposed an iterative algorithm for obtaining the near-optimal solution using successive convex approximation and dual decomposition techniques. Through numerical simulation, we found that the multi-user AF-assisted network with energy transfer achieves a significant sum rate enhancement over the one without WPT.  
[][PERSON] simulated the proposed method and analyzed the results and wrote the paper; [PERSON] and [PERSON] proposed the problem and helped in designing the system model and the problem formulation; [PERSON] supervised this work.  
[]This research received no external funding.  
[]The authors declare no conflict of interest.
# A 5 Gb/s Optoelectronic Receiver IC in 180 nm CMOS for Short-Distance Optical Interconnects
## 3 Chip Layout and Post Layout Simulation Results
This level of integration helps to minimize the overall chip area, and also contributes to a balanced power consumption profile, making the CORIC a well-rounded solution for compact, low-cost, and energy-efficient short-distance optical interconnects. Since many prior works exhibited limitations such as the requirements for multiple supply voltages, higher per-channel power consumption, or larger silicon area, despite being realized in more advanced technology nodes, the CORIC stands out by offering a practical and scalable solution that balances performance, integration, and power efficiency without relying on cutting-edge semiconductor processes.
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Deletion Strategy** & **Algorithm** & **Model Accuracy** & **Unlearning Efficiency** & **Residual Influence** & **Communication Overhead** & **Convergence Time** & **Unlearning Accuracy** & **Computational Cost** \\ \hline \multirow{3}{*}{Random Deletion} & FedEraser & 0.83 & 0.89 & 0.06 & 0.65 & 0.81 & 0.88 & 0.72 \\  & EG-FedUnlearn & 0.87 & 0.91 & 0.04 & 0.6 & 0.78 & 0.91 & 0.69 \\  & OFU-Conology & **0.91** & **0.92** & **0.03** & **0.58** & **0.75** & **0.94** & **0.67** \\  & VeFi & 0.79 & 0.85 & 0.07 & 0.68 & 0.80 & 0.86 & 0.73 \\ \hline \multirow{3}{*}{Class-based Deletion} & FedEraser & 0.85 & 0.90 & 0.05 & 0.62 & 0.80 & 0.92 & 0.67 \\  & EG-FedUnlearn & 0.89 & 0.92 & 0.03 & 0.57 & 0.78 & 0.94 & 0.64 \\  & OFU-Conology & **0.93** & **0.95** & **0.02** & **0.54** & **0.75** & **0.87** & **0.66** \\  & VeFi & 0.81 & 0.87 & 0.06 & 0.64 & 0.82 & 0.89 & 0.69 \\ \hline \multirow{3}{*}{Feature-based Deletion} & FedEraser & 0.83 & 0.89 & 0.05 & 0.63 & 0.78 & 0.91 & 0.69 \\  & EG-FedUnlearn & 0.87 & 0.92 & 0.03 & 0.59 & 0.75 & 0.94 & 0.67 \\  & OFU-Conology & **0.91** & **0.94** & **0.02** & **0.56** & **0.73** & **0.97** & **0.64** \\  & VeFi & 0.79 & 0.87 & 0.06 & 0.66 & 0.80 & 0.90 & 0.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of federated unlearning algorithms using three deletion strategies for the Purchase dataset. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Deletion Strategy** & **Algorithm** & **Model Accuracy** & **Unlearning Efficiency** & **Residual Influence** & **Communication Overhead** & **Convergence Time** & **Unlearning Accuracy** & **Computational Cost** \\ \hline \multirow{3}{*}{Random Deletion} & FedEraser & 0.85 & 0.9 & 0.05 & 0.6 & 0.8 & 0.88 & 0.7 \\  & EG-FedUnlearn & 0.89 & 0.91 & 0.04 & 0.55 & 0.78 & 0.9 & 0.68 \\  & OFU-Conology & **0.92** & **0.92** & **0.03** & **0.52** & **0.75** & **0.93** & **0.65** \\  & VeFi & 0.81 & 0.85 & 0.06 & 0.65 & 0.82 & 0.87 & 0.72 \\ \hline \multirow{3}{*}{Class-based Deletion} & FedEraser & 0.87 & 0.91 & 0.04 & 0.58 & 0.79 & 0.92 & 0.68 \\  & EG-FedUnlearn & 0.91 & 0.93 & 0.03 & 0.53 & 0.76 & 0.94 & 0.65 \\  & OFU-Conology & **0.93** & **0.94** & **0.02** & **0.50** & **0.74** & **0.96** & **0.6** \\  & VeFi & 0.83 & 0.87 & 0.05 & 0.62 & 0.80 & 0.9 & 0.7 \\ \hline \multirow{3}{*}{Feature-based Deletion} & FedEraser & 0.86 & 0.90 & 0.05 & 0.57 & 0.77 & 0.90 & 0.70 \\  & EG-FedUnlearn & 0.90 & 0.92 & 0.04 & 0.54 & 0.75 & 0.92 & 0.66 \\  & OFU-Onology & **0.93** & **0.93** & **0.03** & **0.49** & **0.72** & **0.95** & **0.62** \\  & VeFi & 0.82 & 0.86 & 0.06 & 0.64 & 0.79 & 0.88 & 0.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of federated unlearning algorithms using three deletion strategies for the CIFAR-10 dataset.
## 3 Diagnosis Using Simulation Data  
The goal of the diagnosis is to isolate the fault information from the raw signal mixed with other noisy sources. The steps are taken in reverse order of the way the virtual raw signal is made and are summarized in Figure 5. It consists of three steps: First is the discrete signal separation to remove signals from the other components. Second is the demodulation band selection to find out the band containing resonance frequencies, using which the band pass filtering is carried out to obtain only the signal in the band while removing those elsewhere. Last is the envelope analysis to restore the original fault signal out of the modulated signal around the resonance frequencies. Usually, the first  
Figure 4: Simulation of bearing fault signal: (**a**) discrete signal, (**b**) white noise, (**c**) raw signal, in the time domain; (**d**) discrete signal, (**e**) white noise, (**f**) raw signal, in the frequency domain.  
two steps are denoted as the signal pre-processing. Note that the raw signal includes the mixture of four signals: bearing fault signal, resonance signal, and discrete signal with frequencies \(f_{f}\), \(f_{r}\) and \(f_{d}\) and the background noise.  
Regarding the algorithms in each step, autoregressive (AR) models are applied [14; 23] for the discrete signal separation at first. Spectral kurtosis (SK) and Short Time Fourier Transform (STFT) are employed in the second step to select frequency band where the bearing resonance is dominant. In the third step, Hilbert transform is applied for the envelope analysis to obtain the analytic signal. Then, its magnitude becomes the envelope signal. By taking the FFT of the envelope signal, the fault information is retrieved from the amplitudes at the fault frequencies. More details are explained in the following sub-sections. MATLAB code with 63-lines is provided in the Appendix B to implement the process, which consists of four parts: (1) problem definition, (2) discrete signal separation, (3) demodulation band selection, and (4) envelope analysis.
# A Fast Identification Method of Gunshot Types Based on Knowledge Distillation
## 2 Principles Relating to Sniper Gunshot Recognition  
A gunshot signal is complex in composition. It is composed of a muzzle blast [9], a shock wave [10], a secondary mechanical sound, and more. In the gun barrel the gunpowder burns in a narrow space to generate an expanding airflow, which propels the bullet out of the gun chamber. After the bullet escapes the muzzle, the air flow ejected out of the muzzle forms a muzzle blast, which is a very brief sound wave (measured in milliseconds) but of a high intensity (usually 120-160 dB) [11]. The muzzle blast supplies an important basis for interpreting the type and location of the gunshot. But the muzzle wave decays quickly with distance, and is hard to capture at a low signal-to-noise ratio. Therefore, it is difficult to identify the exact type of the gunshot relying on the muzzle wave only [12].  
The warhead flying in the air rubs against the surrounding air and when the bullet travels faster than the speed of sound, this process generates a shock wave, which propagates outward from the bullet's path. Since the wave front of the shock wave is characterized by a rapidly rising positive pressure and an immediately dropping negative pressure, the sound wave propagating in the air is a longitudinal wave whose vibration direction is parallel to the propagation direction [13]. Therefore, the wave front of the shock wave features a rapid rise to the maximum amplitude, to be followed by a drop to the minimum amplitude, but it will eventually get back to the initial state of the waveform, as shown in Figure 1.  
The form of the wave looks like the letter \"N\" [14]. Not all muzzle blasts look similar, and not all firearms produce shock waves. Most common pistol projectiles are subsonic and therefore generate no shock waves when flying in the air. As a feature specific to the bullet itself, the shock wave is an important basis for determining the caliber of the gun [15]. Therefore, it is an efficient way to identify the type and caliber of firearms by considering both the muzzle blast and the shock wave, two audio components of the gunshot.
# HTSA-LSTM: Leveraging Driving Habits for Enhanced Long-Term Urban Traffic Trajectory Prediction
## 3 Experiments and Evaluation
### Results and Comparison
The results for prediction horizons of 1 to 6 s demonstrate that HTSA-LSTM yields significantly lower RMSE and NLL values than other baseline models, indicating higher accuracy in predicting future vehicle trajectories. The analysis of the RMSE and NLL values for the STA-LSTM and HTSA-LSTM models reveals significant differences in their performance across prediction horizons. As with the overall trend observed in Table 3, both models show increased RMSE and NLL values as the prediction horizon extends, indicating a decline in accuracy. However, HTSA-LSTM consistently outperforms STA-LSTM across all time frames, demonstrating its superior ability to incorporate vehicle interaction information into trajectory predictions. Specifically, HTSA-LSTM achieves markedly lower RMSE and NLL values compared to STA-LSTM, particularly in the shorter prediction horizons, highlighting its effectiveness in accurately forecasting future vehicle trajectories. This emphasizes the importance of utilizing interaction data from neighboring vehicles for improved prediction accuracy. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Evaluation** & **Prediction** & \multirow{2}{*}{**CV**} & \multirow{2}{*}{**S-LSTM**} & \multirow{2}{*}{**M-LSTM**} & \multirow{2}{*}{**A-LSTM**} & \multirow{2}{*}{**CS-LSTM**} & \multirow{2}{*}{**STA-LSTM**} & **HTSA-LSTM** \\ \cline{5-8} \cline{6-9} \multicolumn{1}{c}{**Metric**} & & & & & & & & \\ \hline \multirow{6}{*}{RMSE} & 1 & 0.953 & 0.683 & 0.584 & 0.648 & 0.623 & 0.615 & 0.499 \\  & 2 & 1.826 & 1.538 & 1.263 & 1.325 & 1.268 & 1.256 & 0.898 \\  & 3 & 3.352 & 2.466 & 2.128 & 2.163 & 2.065 & 2.047 & 1.314 \\  & 4 & 5.885 & 3.847 & 3.245 & 3.258 & 3.137 & 3.044 & 2.225 \\  & 5 & 8.943 & 5.236 & 4.661 & 4.562 & 4.366 & 4.281 & 3.461 \\  & 6 & 11.127 & 6.542 & 6.157 & 6.037 & 5.879 & 5.761 & 4.897 \\ \hline \multirow{6}{*}{NLL} & 1 & 3.718 & 2.023 & 1.173 & 1.012 & 0.576 & 0.630 & 0.603 \\  & 2 & 5.372 & 3.634 & 2.851 & 2.487 & 2.142 & 1.088 & 1.115 \\ \cline{1-1}  & 3 & 7.389 & 4.628 & 3.806 & 3.362 & 3.028 & 1.718 & 2.056 \\ \cline{1-1}  & 4 & 9.164 & 5.352 & 4.479 & 4.158 & 3.692 & 2.735 & 2.769 \\ \cline{1-1}  & 5 & 10.895 & 6.295 & 5.095 & 5.036 & 4.351 & 4.077 & 3.264 \\ \cline{1-1}  & 6 & 12.026 & 7.036 & 6.432 & 6.347 & 6.028 & 5.852 & 4.318 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Shows the evaluation metrics of the models over a 6 s prediction horizon.
## 3 Materials and Methods
### Data Collection and Preprocessing  
The main data included in this research are the NPP-VIIRS nighttime light (NTL) data, gross primary productivity (_GPP_) data, road maps data, land surface temperature (_LST_) data, and land cover data (Table 1).  
Land cover data were used to calculate assessment indexes, which include complexity, fragility, and fragmentation obtained from the Resource and Environment Science and Data Center of the Chinese Academy of Sciences ([[https://www.resdc.cn](https://www.resdc.cn)]([https://www.resdc.cn](https://www.resdc.cn)), accessed on 31 December 2019). Afterwards, the indexes were validated using nationwide field surveys [35; 36]. The vector data from 1990, 2000, 2010, and 2018 were interpreted by Landsat TM and ETM remote sensing data with spatial resolution of 30 m and the interpretation precision reached 93%. According to the LUCC classification system of China and regional characteristics, land use types were divided into six categories: cultivated land, forest land, urban land, grassland, water, and saltern [37].  
NPP-VIIRS NTL data from 2018 were downloaded from the National Oceanic and Atmospheric Administration ([[https://www.ngdc.noaa.gov/eog/viirs/download_ut_mos.html](https://www.ngdc.noaa.gov/eog/viirs/download_ut_mos.html)]([https://www.ngdc.noaa.gov/eog/viirs/download_ut_mos.html](https://www.ngdc.noaa.gov/eog/viirs/download_ut_mos.html)), accessed on 31 December 2018). Data were rigorously processed to remove the effects of clouds [38].  
_GPP_ data and _LST_ data from 2018 were obtained from the United States Geological Survey ([[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)), accessed on 31 December 2018). Furthermore, MOD17A2 and MYD11A2 products from the Moderate resolution imaging spectroradiometer (MODIS) of National Aeronautics and Space Administration (NASA) were used, respectively.  
Road maps and county administrative boundaries were derived from the database of the National Basic Geographic Information Center ([[http://www.ngcc.cn/ngcc/](http://www.ngcc.cn/ngcc/)]([http://www.ngcc.cn/ngcc/](http://www.ngcc.cn/ngcc/)), accessed on 31 December 2018). Additionally, socioeconomic data on a county level, including gross domestic production and population density, were collected from the China Statistical  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Name** & **Data Type** & **Time Period** & **Data Source** \\ \hline Land cover data & Grid & 1990, 2000, 2010, 2018 & Resource and Environment Science and Data Center National Oceanic and Atmospheric Administration United States Geological Survey (USGS) United States Geological Survey (  
Yearbook (1990-2018) published by the Municipal Statistics Bureau of each city. Finally, all spatial data were georeferenced in Albers Conical Equal Area projection depending on the different source data.
## 3 Dynamic Routing Algorithm for Inter-Satellite Network Based on Q-Learning
### Dynamic Routing Algorithm Based on Q-Learning
The reward function R for the agent to perform an action is related to the operation and maintenance strategy of the satellite network. It can be a single performance parameter, such as delay, throughput, or comprehensive parameters [19]. The routing algorithm proposed in this chapter is based on the classic reinforcement learning technique Q-learning for finding the optimal state-action policy for MDP. Under certain conditions, Q-learning has been shown to be optimal [20]. On the other hand, in the Q-routing algorithm, the decision strategy \(\epsilon-\)decreasing is a reasonable exploration and utilization strategy, which also meets the requirement of optimality, so the Q-routing algorithm proposed in this chapter can converge and approach the optimal solution. Regarding the convergence efficiency, it has been proved in [21] that Q-learning will sufficiently converge to the \(\xi\) neighborhood of the optimal value after \((N\log(1/\xi)/\xi^{2})(\log N+loglog(1/\xi))\) iterations, where N represents the number of states in the MDP, which also ensures that the algorithm proposed in this chapter can converge in a finite number of steps. In this chapter, the routing problem is reduced to let the agent learn a path from the source node to the destination node. In the routing problem, according to the established  
Figure 6: Reward function curve based on link bandwidth remaining.
# Electromagnetic Induction Imaging with Atomic Magnetometers: Progress and Perspectives
## 3 Application Areas and Limitations of Inductive Coils
### Biomedical Imaging  
Several medical conditions are associated with a variation of the conductivity of the tissues affected by the specific disorder. Such a variation may be the cause or the consequence of such illness. In any case, it represents a marker of many conditions. These include: head injuries, atrial fibrillation and cancer. EMI is the natural tool to detect and characterise these conductivity anomalies. The potential of EMI for biodemedical imaging was recognised long time ago [9]. Advantages of EMI for specific conditions are reviewed here.
# Human Factors Analysis of Air Traffic Safety Based on HFACS-BN Model
###### Abstract  
Air traffic control (ATC) performance is important to ensure flight safety and the sustainability of aviation growth. To better evaluate the performance of ATC, this paper introduces the HFACS-BN model (HFACS: Human factors analysis and classification system; BN: Bayesian network), which can be combined with the subjective information of relevant experts and the objective data of accident reports to obtain more accurate evaluation results. The human factors of ATC in this paper are derived from screening and analysis of 142 civil and general aviation accidents/incidents related to ATC human factors worldwide from 1980 to 2019, among which the most important 25 HFs are selected to construct the evaluation model. The authors designed and implemented a questionnaire survey based on the HFACS framework and collected valid data from 26 frontline air traffic controllers (ATCO) and experts related to ATC in 2019. Combining the responses with objective data, the noisy MAX model is used to calculate the conditional probability table. The results showed that, among the four levels of human factors, unsafe acts had the greatest influence on ATC Performance (79.4%), while preconditions for safe acts contributed the least (40.3%). The sensitivity analysis indicates the order of major human factors influencing the performance of ATC. Finally, this study contributes to the literature in terms of methodological development and expert empirical analysis, providing data support for human error management intervention of ATC in aviation safety.  
Keywords:air traffic control; human factors; aviation safety; HFACS; Bayesian networks; noisy MAX model +
Footnote †: journal: Applied Sciences
# Diffusion-Denoising Process with Gated U-Net for High-Quality Document Binarization
## 4 Experiments and Results  
### Dataset and Implementation Details  
We built a new training dataset by combining several existing document binarization datasets to evaluate and compare the performance of our proposed model against other methods. The training dataset comprised images from H-DISCO [45; 46; 47], DIBCO [48; 49; 50], the Bickley diary dataset [51], the Persian heritage image binarization (PHIDB) dataset [52], and the Synchromedia Multispectral (S-MS) dataset [53]. Given that most of the document images were of substantial size, we patchified each image into 256 \(\times\) 256 patches to enhance training efficiency. To diversify the dataset, we applied random rotation augmentation to the segmented patches, resulting in a dataset totaling approximately 160,000 images. For training purposes, 90% of the constructed dataset was allocated for training and the remaining 10% was reserved for validation. For evaluation, we used H-DIBCO 2016 [23], DIBCO 2017 [24], H-DIBCO 2018 [25], and DIBCO 2019-B [26]. These datasets comprise 10-20 document images, each subjected to various levels of general noise, with dimensions typically exceeding 1000 \(\times\) 1000 pixels. Additionally, we incorporated DIBCO 2019-B, encompassing 10 images featuring novel noise characteristics that the model had not previously encountered. Notably, the test dataset was not included in the training and validation set.  
To train the proposed model, the time step of the diffusion-denoising process was set to 1000, and the AdamW optimizer [54], with an initial learning rate of \(lr=1\times 10^{-6}\), was used. Furthermore, the autoencoder was pre-trained with an 8192-sized codebook at a learning rate of \(lr=4.5\times 10^{-6}\) over 20 epochs for the training set and was frozen during denoising network training. During the diffusion-denoising process, the latent space was utilized through the 8192-sized codebook of the pre-trained autoencoder. We used _LeakyReLU_ as the network's activation function. The denoising network underwent training for about 1M steps with a batch size of 2. We used three NVIDIA RTX 3090 GPUs (24 GB) for training. The network consisted of 590M parameters, and the model inference was carried out with 12 GB of VRAM.