esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Deep Learning-Based Load Forecasting Model for Logical Range
## 3 Results
### Performance Analysis
#### 3.3.1 Ablation Experiment  
To rigorously evaluate the contribution of each component in the GCSG model for Logical Range load forecasting, we conducted systematic ablation studies using the following model variants:  
* GCSG: Full model (GAF+CNN+SENet+GRU).
* GCG: Ablated SENet (GAF+CNN+GRU).
* CSG: Ablated GAF (CNN+SENet+GRU).
* CG: Baseline (CNN+GRU).  
The experimental parameters maintained strict consistency across all model variants, with the detailed configuration provided in Table 3.  
Tables 4 and 5 present the load forecasting performance metrics (\(R^{2}\), MAE, and MSE) for each model in the Logical Range, evaluated across varying input window sizes \(w\) and prediction step lengths \(d\).  
Table 4 demonstrates the experimental results for the four models with fixed \(d=1\) and different values of \(w\). The four models (GCSG, GCG, CSG, and CG) exhibited stable performance across varying \(w\) values, with no significant fluctuations observed. Notably, when \(w=36\), the \(R^{2}\), MSE, and MAE values most closely approximated the average metrics across all tested values of \(w\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Category** & **Value/Range** \\ \hline Input window size (\(w\)) & \(16\leq w\leq 40\) (optimal \(w=36\)) \\ Prediction steps (\(d\)) & \(d\in\{1,3,\dots,29\}\) \\ Batch size & 100 \\ Training epochs & 2000 \\ Learning rate & 0.0001 \\ Optimizer & Adam \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental configuration for ablation study.  
Table 5 demonstrates that, as the prediction step \(d\) increases, the capability of the models for temporal dependency feature extraction in the Logical Range presents a decreasing trend. While all models exhibited declining \(R^{2}\) values, the GCSG model consistently maintained superior load forecasting accuracy compared to the other models. The model performance ranking was GCSG > GCG > CSG > CG, with MAE and MSE metrics confirming this trend. These results validate that the combined GAF-CNN-SENet-GRU architecture in GCSG achieves optimal prediction performance.  
The MAE and MSE values indicate that GCG outperforms CSG while approaching GCSG's accuracy, underscoring the GAF's greater impact on load forecasting accuracy compared to the SENet channel attention mechanism. GAF encoding effectively preserves temporal correlations while enhancing the relevance of inter-temporal values, enabling more comprehensive feature extraction. The GCSG model synergistically combines the GAF's temporal preservation effect with SENet's channel importance weighting.  
These experimental results conclusively demonstrate the GCSG model's superior accuracy across all prediction steps, proving that the simultaneous integration of SENet and GAF significantly enhances the resulting model's load forecasting performance.
## 3 Proposed Approach
### Real-Time Detection
#### 3.1.2 Note Labeling  
A note is essentially a labeled frequency. The system recognizes the frequency of the note played first, then assigns a label based on the detected frequency. Figure 4 demonstrates the frequencies of each note for the guitar when playing open strings. In order to detect the frequency of a note, we need to process the note signal first. We used a discrete Fourier transform (DFT) in our approach to translate the output audio signals generated by the guitarist from the time domain to the frequency domain; in particular, the DFT converts the audio signal from amplitude in time to frequencies and intensities using Equation (1). The frequency-intensity graph of a signal is called the magnitude spectrum. The DFT can transform an audio signal into a magnitude spectrum by translating each signal into a set of cosine functions at different frequencies. The DFT function was used whenever a note was detected from the guitarist, which allowed for the labeling of the note  
Figure 3: Mediapipe hand landmarks.  
Figure 2: Kinect false detection.  
being played, working in parallel with MediaPipe and the ML algorithms used to correct the finger motion mentioned in Section 3.3.  
\[X(k)=\sum_{n=0}^{N-1}x(n)\cdot e^{-i\pi kn/N}, \tag{1}\]  
where:  
\(X(k)\) is the transformed signal, which is a complex number (frequency domain) at index \(k\); \(x(n)\) is the discrete signal at sequence \(n\); \(N\) is the total number of samples in the input signal; \(e^{-i\pi kn/N}\) refers to [PERSON]'s formula, which encodes the amplitude of the frequency for \(k/N\) per time unit, where \(k\) is the frequency index range, \(N\) is the total number of samples, and \(i\) denotes the imaginary unit.  
The harmonic product spectrum was used, according to Equation (2) (HPS) [29], which is a refined version of DFT that takes a number of power spectra generated by a signal and multiplies them. The product disappears for non-pure signals, resulting in a much more accurate detection of guitar frequencies.  
\[y(f)=\prod_{r=1}^{R}|X(fr)|, \tag{2}\]  
where:  
\(R\) is the number of magnitude spectra;  
\(x(f)\) is the magnitude spectrum of a signal.
# Optimizing Lattice Basis Reduction Algorithm on ARM V8 Processors
## 2 Technical Background
### Lll Algorithm  
The conventional LLL algorithm first uses Gram-Schmidt orthogonality with an mxn matrix B:\[B =\begin{bmatrix}\mathbf{b_{1}}&\cdots&\mathbf{b_{n}}\end{bmatrix}\] \[=\begin{bmatrix}\tilde{\mathbf{b}}_{1}&\cdots&\tilde{\mathbf{b}}_{n}\end{bmatrix} \begin{bmatrix}\mathbf{1}&\cdots&\mathbf{u_{1,n}}\\ \vdots&\ddots&\vdots\\ \mathbf{0}&\cdots&\mathbf{1}\end{bmatrix}\] \[=\begin{bmatrix}\frac{\tilde{\mathbf{b}}_{1}}{\left\lVert\tilde{\mathbf{ b}}_{n}\right\rVert_{2}}&\cdots&\frac{\tilde{\mathbf{b}}_{n}}{\left\lVert\tilde{\mathbf{ b}}_{n}\right\rVert_{2}}\end{bmatrix}\begin{bmatrix}\left\lVert\tilde{\mathbf{b}}_{1} \right\rVert_{2}&\cdots&\mathbf{0}\\ \vdots&\ddots&\vdots\\ \mathbf{0}&\cdots&\left\lVert\tilde{\mathbf{b}}_{n}\right\rVert_{2}\end{bmatrix} \begin{bmatrix}\mathbf{1}&\cdots&\mathbf{u_{1,n}}\\ \vdots&\ddots&\vdots\\ \mathbf{0}&\cdots&\mathbf{1}\end{bmatrix}\] \[=\mathbf{Q}\mathbf{D}\mathbf{U}, \tag{5}\]  
where \(\mathbf{Q}\) is an orthogonal matrix, \(\mathbf{D}\) is a diagonal matrix, and \(\mathbf{U}=\begin{bmatrix}\mathbf{u}_{i,j}\end{bmatrix}\) is an upper triangular matrix with a unit diagonal. If the decomposition satisfies the two convention conditions  
\[\mathbf{u}_{i,j}\leqslant\frac{1}{2},\ 1\leqslant i<j\leqslant n \tag{6}\]  
\[\mathbf{w}\left\lVert\tilde{\mathbf{b}}_{i-1}\right\rVert_{2}^{2}\geqslant\left\lVert \tilde{\mathbf{b}}_{i}\right\rVert_{2}^{2}+\mathbf{u}_{i-1,i}^{2}\left\lVert\tilde{ \mathbf{b}}_{i-1}\right\rVert_{2}^{2},\ 2\leqslant i\leqslant n \tag{7}\]  
where \(1/4<\mathbf{w}<1\), then the basis formed by the columns of \(\mathbf{B}\) is called a reduced basis.
## 3 Results and Discussion
### High-Speed Imaging
In Figure 3, the evolution of arc and droplet formation during the current pulse peak phase (for the same welding conditions corresponding to Figure 1) are shown for different exposure times when a 900 \(\pm\) 10 nm wavelength bandpass filter is used. In this figure, the camera exposure time is varied from 6.25 to 160 \(\upmu\)s. Compared to images acquired with the 516 \(\pm\) 10 nm filter, these images provide more details of the droplet formation sequence and weld pool motion. Using an exposure time of 20 \(\upmu\)s enables the simultaneous visualization of the arc, droplet formation, and molten pool motion. Indeed, at an exposure time of 20 \(\upmu\)s, one can distinguish an inner brighter cone in the arc, which can be compared to the 515 \(\pm\) 10 nm wavelength filter to reveal that this brighter inner cone is the iron vapor core of the arc. This is shown in Figure 4, where the snapshots using both filters are compared at equivalent pulse times. Therefore, the 900 \(\pm\) 10 nm wavelength filter enables visualization of both regions of the arc; i.e., the inner cone with a high fraction of iron vapor and the outer cone composed of mostly ionized shielding gas, which is Ar in the present investigation (see Figure 4b). For all the filters and camera settings tested in this work, it was found that when the 900 \(\pm\) 10 nm wavelength filter is used with camera exposure time of 80 and 160 \(\upmu\)s, it provides clear pictures of the events occurring during the background phase of current pulse profiles, allowing droplet transfer and weld pool motion to be monitored, as shown in Figure 5. Supplemental Video S2 shows a comparison of all the settings investigated. Figure 2: Pixel intensity at wire tip for a current of 55 A, observed with an aperture of f/22 using at narrow band pass filter of 515 \(\pm\) 10 nm wavelength and exposure time of 80 \(\upmu\)s: (**a**) frames of the high speed image, (**b**) pixel intensity plotted. Figure 3: Arc appearance for aperture of f/22 using at narrow band pass filter of 900 \(\pm\) 10 nm wavelength for different camera sensor exposure times.
## 1 Introduction
As far as we know, none of the existing OFDM variants offer a connection between the guard sequence and the data symbols of different subcarriers within the OFDM block. Using a distinct PN sequence from a predefined list of possible sequences as a guard interval for each OFDM block, a novel method of identifying a subset of data symbols using the guard interval is provided. This sequence is constant for one OFDM block but changes based on the subset of data symbols in each block's data part. The contribution of our scheme lies in the fact that unlike TDS-OFDM, the PN sequence from block to block is not fixed. In the proposed scheme, the PN sequence is selected from a finite set of available sequences, and the selection is linked to a subset of data symbols, hence, providing the advantages of data symbol detection and additional channel estimation capabilities for various subcarriers. Particularly, our scheme demonstrates an improved bit error rate (BER) in the frequency-selective Rayleigh fading channel. The specific contributions of this paper include:  
* A new eOFDM block structure with a variable GI sequence for successive blocks of data, which links the selection of the GI sequence based on the data symbols of scattered data subcarriers;
* A versatile block structure that does not need scattered pilot tones in the OFDM block and still provides the option to use a subset of scattered data symbols as pilot subcarriers;
* The detection of the GI sequence improves the detection performance of a subset of data subcarriers, which results in the improved BER performance of the proposed system. * A unique and flexible block structure, which provides high spectral efficiency as all the subcarriers of the OFDM block are used as data subcarriers. Apart from these contributions, a flexible alternate mathematical expression is provided for polynomials of degree \(R\) with a flexible initial state vector. We highlight the novelty of the proposed scheme in Table 1 by comparing it with the salient features of the fundamental multicarrier OFDM schemes. For a fair comparison of the new scheme with CP-OFDM and TDS-OFDM, the symbol energy, block size, length of the guard sequence and multipath channel impulse response (CIR) for a block are kept the same for all schemes. The CIR varies for successive blocks, as explained in the subsequent section. The remaining paper is organized as follows. In Section 2, the system model for the proposed eOFDM scheme is presented. The new OFDM block structure is described and compared with the block structure of CP-OFDM and TDS OFDM. Section 3 is dedicated to guard interval analysis. This section describes how the PN sequences are generated and the necessary conditions to be fulfilled by the guard interval sequences. Section 4 presents the performance metrics for fair comparison of CP-OFDM, TDS-OFDM and eOFDM schemes. The numerical results are presented in Section 5, while Section 6 concludes the paper.
# Resource Allocation in Unmanned Aerial Vehicle (UAV)-Assisted Wireless-Powered Internet of Things
## 6 Conclusions  
In this paper, we have researched the resource allocation problems in a UAV-assisted IoT system based on a dynamic game. In the proposed model, the wireless nodes are divided into two categories for energy harvesting. Each node category has its own power level transferred from the UAV. The UAV can control its power for energy transfer based on the proposed dynamic game. Meanwhile, as the harvested energy of the wireless nodes is mainly controlled by the unit energy price, the optimal unit price of the transferred energy for the two categories of nodes can also obtained based on the proposed dynamic game model. In order to get the optimal solutions for the resource allocation problems, we use dynamic programming to obtain the Nash equilibriums of the proposed dynamic game. The simulation results show that the proposed game model can achieve optimal power control and price control for the UAV.  
[PERSON] conceived the main idea and the dynamic game theory model; all authors contributed to data analysis, simulations, and the writing of this paper.  
The authors would like to thank the editor and the anonymous reviewers for their valuable comments and suggestions that improved the quality of this paper. This work is supported by the National Key R&D Program of China, No.2018 YFB1003905, and the Fundamental Research Funds for the Central Universities, No.FRF-TP-18-008A3.  
The authors declare no conflicts of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 4 Results and Discussion
### Potential Health Hazards Assessment
#### 4.3.5 Excess Lifetime Cancer Risk (ELCR)  
The ELCR is an essential radiological risk assessment parameter because it predicts a person's probability of acquiring cancer due to low-dose radiation exposures during his lifetime. Using Equation (8), the ELCR is attributable to the effective gamma dose excess incurred yearly indoors due to the use of granite as superficial materials is calculated [66].  
\[\text{ELCR}=\text{YEGD}\times\text{ALE}\times\text{RF} \tag{8}\]  
where ALE and RF are the life average expectancy (66 years) [19] and risk fatal for stochastic impact (0.05 Sv\({}^{-1}\) for the overall population), respectively [67].  
The values of ELCR originating from the studied granitic samples, when used as superficial material (Table 4) are plotted against their global average values originating outdoors and indoors (Figure 11). It is clear that the values of the indoor ELCR originating from ElSibai-Abu ElTiyur granites are scattered near and around the line representing the ELCR global average values outdoors 0.29 \(\times\) 10\({}^{-3}\) (arising from gamma dose rate outdoor as background calculated according to UNSCEAR) [68], as well as having a mean value of 0.3 \(\times\) 10\({}^{-3}\) (approximately on the line). This mean value of ELCR (0.3 \(\times\) 10\({}^{-3}\)) is smaller by a factor of 4 than its corresponding indoor global average of ELCR (1.16 \(\times\) 10\({}^{-3}\) arising from gamma dose rate indoor due to only walls, floor, and ceiling [PERSON] et al. [69]). The indoor global average value of ELCR (1.16 \(\times\) 10\({}^{-3}\)) [PERSON] et al. [69], is calculated for a typical room with concrete walls, floor, and ceiling having the following activity concentrations 50, 50, and 500 Bq/kg for \({}^{226}\)Ra, \({}^{232}\)Th, and \({}^{40}\)K, respectively. According to the ELCR increment (being 0.3 \(\times\) 10\({}^{-3}\)) for the rock samples under investigation, in addition to what was reported by [PERSON] et al. [68]: \"equivalent values of ELCR equal to 1, 10, 100, and 1000 mSv/y will cause a mortal cancer of 0.004, 0.04, 0.4, and 4%, respectively\". Accordingly, cancer risk possibility, in a lifespan, due to the usage of ElSibai-Abu ElTiyur granites as superficial material, is still insignificant to indoor ELCR.  
Figure 10: YEGD values variation in the investigated samples.
## 4 Results and Discussion  
### Discussion on Performance of Keyword-Based Vulgarity Extraction and Classification Baselines  
In this section, we present the results and analysis of three different methods for vulgar word extraction using keyword matching described in Section 3.3 and Figure 7. These methods involve various approaches to filtering non-vulgar words and determining the relevance of extracted words based on _TF-IDF_ scores and probability of occurrences. The goal of this proposed method was to evaluate the effectiveness of these methods in identifying and extracting vulgar words from a given text.
# An Efficient MAC Protocol for Blockchain-Enabled Patient Monitoring in a Vehicular Network
## 3 Proposed Superframe Structures
### Proposed Algorithm
#### 3.2.3 Case 3  
In case the number of requesting slots by patients' vehicles is more than the maximum allowed limit of the proposed protocol, then the TDMA session cannot accommodate all data-requesting patients' vehicles. In this case, the data-requesting vehicles are optimally assigned data slots by applying the knapsack optimization algorithm. A complete flow chart of the proposed scheme is shown in Figure 4.  
3.2.4. 0/1 Knapsack Algorithm  
The 0/1 Knapsack algorithm allows utilizing the limited capacity with the most valuable items of different weights within its carrying capacity. Our proposed algorithm can have a maximum of 255 slots to restrict the control overhead. In case slot requirements are more than 255, then the knapsack algorithm selects the slots of vehicle nodes that have sensitive data requirements so that data can be transmitted on priority. All nodes have pre-configured sensitivity values: the higher the sensitivity level, the higher will be the value.  
Our designed problem maps with the knapsack problem, as mentioned in Table 2.  
The knapsack algorithm optimally scrutinizes items by filling a knapsack table by following the below-mentioned two steps:  
Figure 4: Proposed algorithm for adaptive data requirements in vehicular networks.  
1. Creating and filling knapsack table;
2. Selection of patients' vehicles that have sensitive data.  
The knapsack algorithm starts with creating the knapsack table that has \(N+1\) rows and \(S_{max}+1\) columns, where \(N\) is the total number of vehicles and \(S_{max}\) is the maximum number of slots, i.e., 255. After the creation of the table, the knapsack algorithm starts filling the table. A complete algorithm for filling the \(0/1\) knapsack table in \(TAPD_{VN}\) is shown in Figure 5. After creating and filling the knapsack table, the most sensitive data-requesting vehicles that have accumulative slots equal to or less than \(S_{max}\) is selected. Successful data-requesting vehicular node selection is shown in Figure 6.  
Figure 5: The \(0/1\) knapsack algorithm.
# Energy and Distance Based Multi-Objective Red Fox Optimization Algorithm in Wireless Sensor Network
## 6 Results and Discussion
### Performance of PDR  
Figure 4 shows the results of PDR for both planned and existing technologies. When the number of nodes increases, the size of the routing path results in an increase in delay. The performance comparison for the PDR is shown in Table 3. Table 3 clearly illustrates that the suggested ED-MORFO achieves a higher PDR of 99.4%, whereas the PDR of the existing MCH-EOR [24] attains 98.7%, and RDSAOA-EECP [25] achieves a PDR of 97.4%.
# A New Zenith Tropospheric Delay Grid Product for Real-Time PPP Applications over China
## 3 Real-Time ZTD Grid Product Generation
### Inverse Scale Height Modeling  
Before modeling the inverse scale height (\(1/H\)), we did a sensitivity test first to get a thumb rule regarding the relationship between \(1/H\) and ZTD errors. ERA-Interim products for one day (1 January 2014) are used to estimate \(1/H\) at each grid point in China. The average value of \(1/H\) at all grid points is 1.35 \(\times\) 10\({}^{-4}\) m\({}^{-1}\), with minimum and maximum values of 1.21 \(\times\) 10\({}^{-4}\) m\({}^{-1}\) and 1.49 \(\times\) 10\({}^{-4}\) m\({}^{-1}\), respectively. The average value of \(1/H\) instead of the actual \(1/H\) was then used in (8) to estimate ZTD at each grid point and the differences between the estimated ZTD and the actual ZTD were calculated. According to the statistics, we found a change of \(1/H\) with 0.1 \(\times\) 10\({}^{-4}\) m\({}^{-1}\) corresponds to about 2.8 cm in ZTD.  
Figure 2 presents the distribution of mean \(1/H\) at different latitudes over China. The amplitude is larger than 0.1 \(\times\) 10\({}^{-4}\) m\({}^{-1}\), which corresponds to about 2.8 cm ZTD according to the sensitivity test. In addition, according to our statistics, the differences of \(1/H\) at different longitudes in the same latitude can reach 0.2 \(\times\) 10\({}^{-4}\) m\({}^{-1}\) (corresponds to 5.6 cm of ZTD). Obviously, \(1/H\) has considerable spatial variations, which needs to be taken into account when modeling \(1/H\).  
The temporal variations of \(1/H\) were studied on the bases of 4-year (2011-2014) time series of \(1/H\). As an example, the time series of \(1/H\) at nearest grid points from four GPS stations (HLHG, BJFS, LHAS and GDZJ) are shown in Figure 3 where we can find obvious periodical signals. The Power Spectral Density (PSD) of \(1/H\) time series at all grid points over China are presented in Figure 4, where we can find the time series mainly contain annual and semi-annual periodical signals. Therefore, we used the model as expressed in (10) to fit \(1/H\) at each grid point:  
\[\begin{array}{ll}1/H=&a_{0}+a_{1}\cos(2\pi\frac{d\omega}{365.25})+a_{2}\sin (2\pi\frac{d\omega}{365.25})+\\ &a_{3}\cos(4\pi\frac{d\omega}{365.25})+a_{4}\sin(4\pi\frac{d\omega}{365.25}) \end{array} \tag{10}\]  
where \(a_{0-4}\) are coefficients.  
Figure 3: The 4-year (2011–2014) time series of parameter \(1/H\) at the nearest grid points of four GPS stations.  
Figure 2: Distribution of inverse scale height with latitudes.  
Based on the above analysis, ZTD time series covering the period from 2011 to 2014 at each grid point (0.75\({}^{\circ}\)\(\times\) 0.75\({}^{\circ}\) resolution) derived from ERA-Interim were used to estimate \(1/H\) by (8). The inverse scale height time series at each grid point were then fitted by (10) to generate a coefficient (\(a_{0}\), \(a_{1}\), \(a_{2}\), \(a_{3}\), \(a_{4}\)) table. The Mean Absolute Error (MAE) and RMS of fitting residuals are 0.03 \(\times\) 10\({}^{-4}\) m\({}^{-1}\) and 0.039 \(\times\) 10\({}^{-4}\) m\({}^{-1}\), corresponds to 0.84 cm and 1.09 cm of ZTD, respectively.