esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed System
### Feature Extraction
#### 3.3.1 Binary File  
Byte_1_gram: The \(n\)-gram is a contiguous representation of \(n\) items of a given sequence, widely used in language modeling, DNA sequence detection, etc. In the field of computer security, malware samples can be efficiently represented as hexadecimal sequences using n-gram analysis to learn important details about different virus types. Based on this, this paper constructed 257-dimensional 1_gram frequency features from binary files.  
Byte_2_gram: As a text analysis tool, byte_2_gram is widely used in text mining and analysis in NLP. Numerous studies have shown that byte_2_gram features play an important role in the field of malware classification with their low system overhead and high classification accuracy. Therefore, this paper built a 2562-dimensional 2_gram frequency featured after 1_gram and selected the best 360-dimensional byte_2_gram combination from each binary file by cross-validation.
# Signal Quality Assessment for Transdermal Optical Wireless Communications under Pointing Errors +
## 1 Introduction
Specifically, the technical contribution of this paper can be summarized as follows:  
* A novel system model for the TOL is formulated, which accommodates the characteristics of this type of links. * A novel easily-computed channel model is extracted by employing experimental results concerning the pathloss and a stochastic process for the pointing errors between the TX and the RX. The utilization of this model in the analysis for the transdermal channel introduces a accurate but tractable alternative to the existing models, in terms of the signal propagation and the pointing errors. * Closed form expressions are derived for the instantaneous and average SNR, which take into account the transdermal channel particularities, the transceivers characteristics and the intensity of pointing errors. * Illustrative results reveal important technical insights that can be utilized in the design of OWC transdermal systems. The rest of this paper is organized as follows. Section 2 is devoted in presenting the system model and the characteristics of the transdermal optical channel. Section 3 focuses on providing the theoretical framework, which quantifies the TOL's quality in terms of average SNR. Respective numerical results and discussions are provided in Section 4. Finally, closing remarks are summarized in Section 5. _Notations_: Unless stated otherwise, in this paper, \(|\cdot|\) denotes absolute value, \(\exp(\cdot)\) represents the exponential function, and \(\log_{2}(\cdot)\) and \(\ln(\cdot)\) stand for the binary logarithm and the natural logarithm, respectively. In addition, \(P\left(\mathcal{A}\right)\) denotes the probability of the event \(\mathcal{A}\), and \(\text{erf}(\cdot)\) denotes the error function [18].
# Disentangling Long Trajectory Contributions in Two-Colour High Harmonic Generation
## 5 CTMC Simulations
### Displacement Gate  
Scanning the relative phase \(\varphi\) between the second harmonic and the fundamental allows for finding the \(\varphi_{\mathrm{max}}(n)\) for each harmonic order \(n\) in the spectrum, for which the recorded intensity generated at that harmonic order is maximised. Out of the complete cloud of trajectories started in the CTMC, every returning trajectory is assigned their harmonic order \(n\) with  
\[n=\frac{1}{\hbar\omega}\left(E_{\mathrm{kin}}(t_{r})+I_{\mathrm{p}}\right). \tag{13}\]  
Then, the summed up probability weights of all returned trajectories contributing to harmonic order \(n\) from the CTMC simulation with two-colour phase \(\varphi\) is shown in Figure 6A. These displacement gate plots are normalised to the maximum intensity within each harmonic order individually. The normalisation is to show the maximum intensity within each harmonic order more clearly in the plot.  
Figure 5: (**A**) phase mismatch for an example long and short trajectory, depending on the position of the gas jet after the laser beam focus; (**B**) phase matching efficiency for two example trajectories depending on the position of the gas jet \(z\).  
The horizontal line cut of each harmonic order can be fitted with a  
\[I(n,\varphi)=C(n)\cos\left[2(\varphi+\varphi_{\max}(n))\right]+B(n) \tag{14}\]  
function, where \(C(n)\) describes the contrast of displacement gate and \(B(n)\) is the constant background intensity. As an example, Figure 7 shows the line out for harmonic order 31 of the dataset displayed in Figure 6A, together with the fitted function.  
The retrieved \(\varphi_{\max}\) for the two different simulation cases are shown on top of the experimental displacement gate plot in Figure 8. Neglecting the Coulomb potential (cyan) does not have any significant effect on the HHG spectra for most of the harmonic spectrum. Comparable \(\varphi_{\max}(n)\) are found both in the CTMC with Coulomb and the CTMC without Coulomb cases, and only around the cutoff energy do the two curves diverge. In addition, the \(\varphi_{\max}(n)\) curves extracted from the different CTMC simulations show remarkable agreement with the experimental spectrum for harmonics \(n\geq 25\). A constant shift between the experimental and CTMC values can be attributed to the unknown absolute phase difference in the experiment [18].  
Figure 6: CTMC with Coulomb simulation, the two displacement gate plots show the influence of long trajectories. (**A**) only short trajectories with extracted \(\varphi_{\max}(n)\) shown in white; (**B**) both long and short trajectories are considered.  
Figure 7: Intensity oscillation of harmonic order 31 while scanning the two-colour phase \(\varphi\). Blue dots are extracted from Figure 6A, the red line is the fit of Equation (14).
# Real-Time Monitoring of Wind Turbine Bearing Using Simple Neural Network on Raspberry Pi
## 2 Methodology
### Acceleration Sensor and Data Logger  
As shown in Figure 3a, the primary purpose of the acceleration sensor located on the bearing is to monitor and assess the condition and functionality of the bearings. These sensors offer an efficient means for detecting and preventing possible malfunctions by monitoring the acceleration of the vibration in the bearing while it is in operation [31; 32; 33].  
As shown in Figure 3b, the data logger obtains an analog signal from the acceleration sensor at the bearing, and uses the A/D converter to convert it to a digital signal. Then, the conversion signal is stored [34; 35].
## 4 Results
### Within-Season Yield Prediction  
To further explore the performances of different models over time, the Informer model was compared with other machine learning models, including RF, XGBoost, and AtLSTM. We excluded the LASSO model in the comparison since the LASSO model had the poorest performance among all models (Figures 5 and S1). For overall comparison among models, we used each model's average \(R^{2}\), \(RMSE\) (t/ha), \(MAP\) (%), and \(d\) from 2009 to 2016. The within-season prediction was achieved by each model using data from different periods, with results shown in Figure 8. With the progress of the growing season, the yield prediction performance could be improved by more training data. The yield prediction performances  
Figure 7: Spatial patterns of the relative errors of (**a1–a4**) LASSO, (**b1–b4**) RF, (**c1–c4**) XGBoost, (**d1–d4**) AtLSTM, and (**e1–e4**) Informer from (1) 2013 to (4) 2016.  
of all the models were stable since interval 8 (mid-September), which indicated that the models could achieve the optimal prediction within 2 months before rice maturity. For rice yield prediction in all testing years, the proposed Informer model performed better than the other models since interval 9 (late-September) with the highest \(R^{2}\) of 0.78, lowest _RMSE_ of 0.44 t/ha, lowest _MAPE_ of 16.56%, and highest \(d\) of 0.72, while the AtLSTM model had better performance than the other models during interval 5 (late-July) to 8 (mid- September) with the lowest _RMSE_ and _MAPE_. The results demonstrate that the Informer model had the best performance in predicting rice yield, especially with more information at the end of the growing season. AtLSTM had the second-best within-season prediction performance among all models, illustrating its excellent ability to predict yield using long-term series data and the attention mechanism.  
The spatial patterns of the average absolute relative error in all testing years using the Informer model and information from different periods are shown in Figure 9. The average absolute relative errors of the Informer model were mainly prominent in the early growth periods, indicating that adequate information was critical for the yield prediction. As the growing season progressed, the performance of the Informer model was significantly improved in the Punjab and Haryana States. In contrast, fewer improvements were obtained in the southern Uttar Pradesh State. The spatial patterns of relative errors had been stable since mid-September, consistent with the results shown in Figure 8.  
Figure 8: Average \(R^{2}\), _RMSE_ (t/ha), _MAPE_ (%), and \(d\) of different models with time in all testing years (2009–2016).
## Case Study: Data Analysis and Results
### Bayesian Statistics and Hierarchical Model Fitting
#### 4.3.4 MCMC Results  
The solution of the hierarchical model describes all parameters at the same time. Nevertheless, the results can be visualized step by step.  
The first result is the estimated RADARSAT-2 drift. The estimated drift is shown in Figure 11. The error bars indicate the range of values which defines the 95% probability intervals. From this it can be seen that the drift between the first and the second overpass is statistically not significant, but the drift between the last two overpasses, for instance, is. The drift was estimated with a standard uncertainty (according to [7]) of always less than 0.1 dB.  
This estimated RADARSAT-2 drift can now be applied to the measured data. The original data in Figure 8 appears now much more uniform, see Figure 12. No apparent systematic drift remains.  
Figure 10: Diagram of the Bayesian model. The ellipsis symbol...indicates a family of probability distributions (per group \(g\) or overpass \(d\)), whereas \(\sim\) means that a parameter is drawn from the respective distribution.  
Figure 11: Estimated daily drifts \(r_{d}\) of the RADARSAT-2 system. The error bars indicate 95% highest-probability density intervals.  
Figure 12: Measured impulse response powers with RADARSAT-2 drift compensation (see Figure 11) applied. The dispersion within one target group is now greatly reduced with respect to Figure 8.  
After RADARSAT-2 drift compensation, the estimated transponder drift can be taken out from the upper plot in Figure 12. The plot in Figure 13 results. Already visually it becomes clear that the transponder drift is small in comparison to the remaining dispersion of the measurement data. This can also explain why at times the drift compensation results in updated values which lie further away from the population mean.  
Now the most important result of the MCMC simulation is the derivation of the transponder ERCS \(\varsigma_{t}\) and its standard uncertainty. The transponder ERCS was estimated to be \(60.80\,\mathrm{dBm}^{2}\) with a standard uncertainty according to [7] of \(0.206\,\mathrm{dBm}^{2}\). The 95% highest probability density interval is given as \([60.38,61.17]\)\(\mathrm{dBm}^{2}\). Note that the standard uncertainty is clearly dominated by the assumed ERCS knowledge uncertainty of the 1.5 corners of \(0.2\,\mathrm{dBm}^{2}\).
## 4 AI Applications and Their Impact
### Garment Defect Detection  
#### 4.1.1 Fabric Pilling Detection  
[PERSON] et al. [49] addressed the challenge of evaluating fabric pilling, a defect often assessed manually, leading to subjective and inconsistent results. They proposed a double-branch convolutional neural network (D-DCFNet) designed to objectively evaluate the degree of pilling in woolen knitted fabrics, semi-worsted knitted fabrics, and nonwovens using a pilling box method. Their model utilized a fusion of high-level features and cross-level representations, outperforming traditional inspection methods. However, the computational complexity of the model may restrict its adoption in resource-limited settings. Additionally, the dataset was limited to specific fabric types, potentially reducing the model's generalizability to other textiles.  
[PERSON] et al. [50] introduced a deep principal component analysis-based neural network (DPCANN) for fabric pilling classification. This study focused on automating pilling identification, eliminating inefficiencies associated with traditional inspections. The methodology involved feature extraction using deep PCA followed by classification using either neural networks or support vector machines (SVMs). While computationally efficient, the reliance on smaller datasets which did not include details of the types of fabrics assessed could limit the model's robustness in real-world applications, particularly for fabrics outside the study's scope.
## 2 Modeling of PV Module  
The PV module is used to convert light into electricity. To model the PV module, the required input variables are weather data, such as irradiance and temperature and output variables, may be voltage, current, power, or all the three. Though, to trace the I-V or P-V characteristics, there is a need for these three variables. The output changes immediately if any change in input. So, it is important to design an accurate model for the PV module [31].  
In solar panels, Maximum Power Point Tracking (MPPT) is used to achieve the maximum possible power extraction during variations of irradiance, shading, and temperature. The equivalent circuit of PV cell shown in Figure 1.  
Figure 1: The equivalent circuit of photovoltaic (PV) cell.  
From the PV equivalent circuit shown in Figure 1, the following parameters are calculated. The output current from the equivalent circuit is described as Equation (1)  
\[I=I_{ph}-I_{sc}\bigg{(}\exp\frac{q(V+R_{s}I)}{NKT}-1\bigg{)}-\bigg{(}\frac{V+R_{s }I}{R_{slt}}\bigg{)} \tag{1}\]  
where \(I_{ph}\)-photocurrent, \(I_{sc}\)-short circuit current, \(V\)-cell voltage, \(R_{s}\)-series resistance, and \(R_{slt}\)-shunt resistance, respectively. \(I_{ph}\) is expressed as Equation (2).  
\[I_{ph}=\frac{[I_{sc}+K_{i}(T-298)]I_{r}}{1000} \tag{2}\]  
where \(K_{i}\)-temperature coefficient, \(T\)-cell temperature, and \(I_{r}\)-solar insolation in Watts/m\({}^{2}\). The saturation diode current is specified in Equation (3). Where \(E_{go}\)-Band gap energy, _Tr_-Nominal temperature, \(V_{t}\)-Diode thermal voltage.  
Reverse saturation diode current is  
\[I_{o}=I_{rs}\bigg{[}\frac{T}{T_{r}}\bigg{]}^{3}\exp\bigg{[}\frac{E_{go}}{nV_{t }}\bigg{(}\frac{T}{T_{r}}-1\bigg{)}\bigg{]}. \tag{3}\]  
Solar cells are arranged in series and parallel combination to obtain a PV module. Where \(N_{s}\)-Number of cells in series and \(N_{p}\)-Number of cells in parallel. In MSX 60 W panel, the number of cells connected in series is 36 and parallel are 1. The Equation (4) represents the output current of the PV module.  
Output current of the PV module is  
\[I=N_{p}I_{ph}-N_{p}I_{o}\left[\exp\Bigg{(}\frac{q\bigg{(}\frac{V}{N_{s}}+\frac{ lnR_{s}}{N_{p}}\bigg{)}}{KnT}\Bigg{)}-1\right]-\Bigg{(}\frac{\frac{N_{p}V}{N_{s}}+ IR_{s}}{R_{sh}}\Bigg{)}. \tag{4}\]  
These equations are modeled in MATLAB and the P-V, I-V curves are obtained in different irradiance and temperatures are shown in Figures 2 and 3. Table 1 shows the parameter specifications of the MSX-60 Watt panel.
# Method for Generating Panoramic Textures for 3D Face Reconstruction Based on the 3D Morphable Model
## 5 Conclusions  
This study proposed a method for generating a panoramic texture for a 3D face mesh that was reconstructed from a single 2D image using 3 DMM. Furthermore, we proposed a method for generating an inexpensive face texture dataset. The unwrapped texture was converted into a panoramic view using an image-to-image translation method and a panoramic texture translator. Additionally, we evaluated the performance of existing face reconstruction methods and concluded that the addition of the texture translator module improved the overall quality of the textures. After optimization, the average PSNR value in the texture test sets improved by 0.3262, 0.4960 and 0.4891, whereas the SSIM of the texture improved by 0.0367, 0.0473 and 0.0909 for DECA, PRNet and 3 DDFA v2, respectively. This showed that the proposed GAN network could create texture information for invisible regions, thereby improving the quality of the original 3D face textures and making them more comprehensive.  
When the input face image has hair on the face in addition to various accessories and occlusions, the approach cannot achieve satisfactory results. This is because the GAN network cannot fully learn delicate information and the generated results contain a significant amount of noise. We will incorporate skin segmentation algorithms in future research to explore ways to solve this problem.  
Conceptualization, S.H., M.W. and K.C.; methodology, software, validation, writing--original draft preparation, S.H.; writing--review and editing, M.W.; supervision, project administration, funding acquisition, K.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (2022R1A2C2006864).  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [[https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html](https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html)]([https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html](https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html)) (2020) (accessed on 28 July 2022).  
Not applicable.  
The authors declare no conflict of interest.
# Motor On-Line Fault Diagnosis Method Research Based on 1D-CNN and Multi-Sensor Information
## 2 Motor Multi-Source Information Fusion Processing
### Motor Fault Diagnosis Process
#### 2.2.2 Data Preprocessing and Fusion Processing  
The data preprocessing included denoising of collected data, normalizing, slicing and labeling, etc. For data filtering and denoising, we first apply suitable low-pass and high-pass filters to filter out perturbations caused by vibrations in ambient conditions. Second, in this paper, we adopt a wavelet threshold denoising algorithm based on the sym8 wavelet function and soft threshold to denoise the collected data [13], which improves the algorithm's robustness and reduces the error caused by external noise.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Homogeneous Sensor Information** & **Signal** & **Units** \\ \hline Multi-source heterogeneous & A phase winding temperature _Ta_ & \({}^{\circ}\)C \\   information 1 & A phase winding temperature _Tb_ & \({}^{\circ}\)C \\   Multi-source heterogeneous & A phase winding temperature _Tc_ & \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Heterogeneous Sensors Information** & **Signal** & **Units** \\ \hline Multi-source heterogeneous & Fusion stator voltage _Ue_ & V \\   information 1 & Fusion stator current _le_ & A \\ Multi-source heterogeneous & Fusion bearing vibration _Ve_ & mm/s \\   information 3 & Fusion temperature _Te_ & \({}^{\circ}\)C \\   information 4 & Fusion temperature _Te_ & \({}^{\circ}\)C \\   Multi-source heterogeneous & Rear bearing vibration _Va_ & mm/s \\   information 5 & Stator temperature _Tf_ & \({}^{\circ}\)C \\   multi-source heterogeneous & & \\   information 6 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Composition of multi-source heterogeneous information of motor.  
The data fusion processing is based on the correlation adaptive weighting method, which fuses the multi-source homogeneous information of the motor into multi-source heterogeneous information.
# Resource Capacity Requirement for Multi-Terminal Cooperation in Container Ports
## 2 Resource Profiles for Representing Workloads  
According to [PERSON] et al. (2019) and [PERSON] et al. (2020), capacity requirement planning in container ports can make use of the planned container arrivals from various modes (i.e., vessels, rail, and trucks) of container transportation to calculate the workload for specific resources. To estimate the handling capability of the resources over time-shifts, capacity planners require information on the capacity requirement for different resource types to meet the workload. In particular, capacity planning should take into account the interferminal container flows and deployment of shared resources arising from the cooperation efforts among neighboring terminals.  
When an operational task (for examples, unloading or loading container onto a vessel, retrieving a container from yard, or putting a container into storage etc.) is distributed to resources, the corresponding workload is formed at each resource. The workload distribution representing the capacity requirement on the resources at different time points are recorded in individual resource profiles that can be used to manage the operational tasks over time-shifts. Sections 2.1 and 2.2 introduce resource profiles and cumulative workload distributions for a resource, Section 2.3 provides the resource profile simulation to examine capacity requirement on resources when events leading to cargo handling are triggered over time-shift in multiterminal operations. This study uses the terminology TEUs instead of containers to indicate workload for container arrivals and/or handling.
## 4 Case Study of Data Collaboration
### Space-Time Travel Data Mining
#### 4.2.2 GPS Data Process  
Firstly, according to Haversine Formula as shown in Formula (3), the distance between the two points is calculated from the latitude and longitude as the length of the target section.  
\[\text{hav}\bigg{(}\frac{d}{R}\bigg{)} =\text{hav}(\varphi_{2}-\varphi_{1}) \tag{3}\] \[+\cos(\varphi_{1})\cos(\varphi_{2})\,\text{hav}(\Delta\lambda)\]  
where,  
\[\text{hav}(\theta)=\sin^{2}(\theta/2)=(1-\cos(\theta))/2 \tag{4}\]  
Including, \(R\) is the radius of the earth, and the average value is 6371 km; \(\mathbf{\varphi_{1}}\), \(\mathbf{\varphi_{1}}\) are the latitude of two points; \(\Delta\lambda\) represents the difference in longitude between two points.  
Then, the time difference of each vehicle passing through the two groups of longitude and latitude points was obtained by looking for vehicle ID, and we take the maximum time difference as the baseline and record the number of vehicles.  
Finally, since GPS data are sampled at a time interval of 1 s, in order to obtain the average speed on the whole road, we process the vehicles with the same ID after data screening and calculate their average speed as the vehicle speed on the road based on the longitude and latitude of the intersection at both ends of the road. Table 3 shows partial calculation results.