esa-sceva/satcom-chunk-collection huggingface dataset:
# Voting-Based Document Image Skew Detection
## 2 Skew Detection Algorithms
### Spatial Domain Lines
#### 2.3.2 Confidence  
The confidence for this algorithm, Equation (4), is computed as a product of one or multiple accuracy indicators, depending on which case we are in.  
For any set of parallel lines, we can compute the average absolute dot-product \(\widetilde{absDot}\), of the support vectors for all possible line pairs within the set. The closer this average is to one, the more parallel we can consider the lines, so the greater the algorithm's confidence should be.  
If we only have one set A, whose representative \(\widetilde{A}\) is pronounced as one of the document's axes, then only the first factor \((\widetilde{absDot}_{A})\) is returned as the method's confidence.  
For two sets of segments, A and B, whose representatives \(\widetilde{A}\) and \(\widetilde{B}\) are pronounced as the document's vertical and horizontal axes, then we can account on how close to perpendicular \(\widetilde{A}\) is to \(\widetilde{B}\) for computing confidence, by computing their dot product. The second factor in the confidence formula increases the measure when \(\widetilde{A}\) and \(\widetilde{B}\) tend to be perpendicular and decreases it when \(\widetilde{A}\) and \(\widetilde{B}\) tend to be parallel.  
Figure 6: Document orientation detection using the detected lines in the spatial domain (black background), the algorithm performed on various documents (white background).  
The third factor is responsible for a contribution equal to the ratio between the sum of lengths of all segments in A and B, which we write as \(\|A\|\) and \(\|B\|\), respectively, and the sum of lengths of all segments in all sets, including A and B. This is performed in order to encourage the contribution of the longer segments in the final confidence formula because longer segments are both more reliably detected and their support angle is also more accurate.  
\[Confidence=\left(\left(\widetilde{absDot}_{A}\widetilde{absDot}_{B}\right) \left(1-\frac{\left|arccos\left(1-\left|\overrightarrow{A}\cdot\overrightarrow{B} \right|\right)\right|}{\frac{5}{2}}\right)\left(\frac{\|A\|+\|B\|}{\sum_{i=1}^ {n\in Sets}\left|Set_{i}\right|}\right)\right)^{\tau_{3}} \tag{4}\]  
Again, \(\tau_{3}\) is a positive exponent used for fine-tuning the confidence to achieve balance and accuracy in the voting process.
## 3 The Proposed Model
### Image Pre-Processing  
At the initial stage, the AOBNN-BDNN algorithm applies WF-based noise removal and U-Net segmentation as a pre-processing step. The WF technique carries out 2D adaptive noise removal filtering using a window size of 3 \(\times\) 3 that eliminates the blur generated in Gaussian smoothing [17]. As a result, images are restored without noise. The local mean (\(\mu\)) and variance (\(\sigma^{2}\)) of every pixel can be defined as follows.  
\[\mu=\frac{1}{NM}\sum_{n_{1},n_{2}\in\theta}\alpha(n_{1},n_{2}) \tag{1}\]  
\[\sigma^{2}=\frac{1}{NM}\sum_{n_{1},n_{2}\in\theta}\alpha^{2}(n_{1},n_{2})-\mu^ {2} \tag{2}\]  
Next, the WF can be defined as follows:  
\[b(n_{1},n_{2})=\mu+\frac{\left(\sigma^{2}-\beta^{2}\right)}{\sigma^{2}}(\alpha (n_{1},n_{2})-\mu) \tag{3}\]  
where \(b(n_{1},n_{2})\) refers to a recovered image, \(\theta\) refers to the \(N\times M\) local neighborhood of every pixel in a scaled input image and \(\beta^{2}\) stands for the noise variance.  
Next, the preprocessed images are passed onto the U-Net architecture to perform the segmentation process. This is a fundamental structure in the medical imaging automation society and has massive applications in the domain. The network structure consists of contractive and expansive paths [18]. The contracting path involves numerous patches of convolution with a filter size of 3 \(\times\) 33 \(\times\) 3 and unity strides in both directions, after the ReLU layer. Next, it draws data from the feature vector through up-convolution, and generates, through a consecutive operation, an output segmenting map, and from the contractive path, through cropping and copying. The building block of this architecture is the operation connecting both paths. This connection enables the network to accomplish a precise dataset from the contractive path, therefore producing the segmentation mask for the output.
## 3 Example Analysis
### Example Setup  
(1) The starting parameters of the OGGWO algorithm are as follows: population number S = 30, maximum iteration count Gm = 1000, maximum limit Ub = 100 for each variable, and minimum limit Lb = \(-\)100 for each variable. The microgrid is composed of a diesel generator with a power change range of 18-60 kW, a wind turbine with an installed capacity of 90 kW, a photovoltaic array with an installed capacity of 85 kW, and an energy storage system with a rated capacity of 900 kWh and daily load. The value of P\({}_{\text{LK}}\) is 230 KW. The initial values of \(P_{Gi,\text{min}}(t)\), \(P_{Gi,\text{max}}(t)\), \(P_{sj,\text{min}}(t)\), \(P_{sj,\text{max}}(t)\), \(P_{Grid,\text{min}}(t)\), and \(P_{Grid,\text{max}}(t)\) are 163 KW, 224 KW, 81 KW, 112 KW, 97 KW, and 156 KW, respectively.  
(2) The microgrid model in this paper consists of wind turbines, photovoltaic power generation, internal combustion engines, fuel cells, and batteries. Their relevant operating parameters include the maximum and minimum limits of output capacity for each device, ramp rate constraints, equipment maintenance factors, installation costs, etc., as shown in Table 1. These data are sourced from the internal company database of a wind farm in Jiangxi Province, China.  
(3) Due to the fact that wind and light are clean energy sources, the pollutants generated are almost zero. Therefore, only the treatment costs of pollutants generated by internal combustion engines, fuel cells, and large power grids are considered. The pollutant emission coefficients and treatment costs of various pollutants are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Equipment Type**} & **Power Upper Limit (KW)** & **Lower Limit of Power (KW)** & **Climbing Rate (KW/MIN)** & **Maintenance Rate (CNY/kW)** & **Capacity Factor (\%)** & **Depreciation Period (year)** & **Installation Cost (CNY/kW)** \\ \hline WT & 50 & 0 & 5 & 0.032 & 22.13 & 10 & 2.34 \\ PV & 30 & 0 & 10 & 0.006 & 29.34 & 20 & 6.61 \\ MT & 60 & 0 & 5 & 0.049 & 54.78 & 10 & 1.72 \\ FC & 40 & 0 & 5 & 0.029 & 33.62 & 10 & 4.23 \\ BAT & 30 & \(-\)30 & 5 & 0.002 & 32.67 & 10 & 0.09 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Relevant parameters of the power supply equipment.  
(4) This paper divides a day into three time periods, and the electricity prices for each period are calculated based on the usage time electricity pricing scheme: valley period: 0:00-7:00 and 23:00-24:00; during regular hours, 7:00-10:00, 15:00-18:00, and 21:00-23:00; and during the peak hours of 10:00-15:00 and 18:00-21:00. The corresponding electricity buying and selling cost data are shown in Table 3.  
(5) The output power of wind turbines and photovoltaic power generation introduced in this paper is taken from the typical daily power generation data in East China on December 29. The wind and solar energy output curves for Scenario 1 are shown in Figure 1(a) and the wind and solar energy output curves for Scenario 2 are shown in Figure 1(b).
## 3 Measurement Results and Discussions
Figure 8: The measured frequency response of the reference and proposed quintuple band-notched UWB antenna (**a**) Measured reflection coefficient of the reference antenna; (**b**) Measured vs. simulated reflection coefficient of the proposed quintuple band-notched antenna. \begin{table}
\begin{tabular}{c c c c c c} \hline  & **WiMAX** & **INSAT** & **LWLAN** & **UWLAN** & **ITU 8 GHz** \\ \hline Simulated (f\({}_{1}\)) & 3.60 & 4.66 & 5.43 & 6.14 & 8.27 \\ Measured (f\({}_{2}\)) & 3.57 & 4.70 & 5.51 & 6.15 & 8.28 \\ Shift in frequency (\(\Delta\)f = f\({}_{2}\) – f\({}_{1}\)) & 0.03 & 0.04 & 0.08 & 0.01 & 0.01 \\ \hline \end{tabular}
\end{table} 
Table 3: Discrepancies between the simulated and measured results when simulated with \(W_{s}\) = 0.348 mm. Figure 11: Measured radiation patterns of the reference vs. quintuple band-notched antenna (**a**) XZ plane (3.9 GHz); (**b**) XZ plane (6.1 GHz); (**c**) XZ plane (7.8 GHz); (**d**) YZ plane (3.9 GHz); (**e**) YZ plane (6.1 GH _z_); (**f**) YZ plane (7.8 GHz). Figure 10: Radiation pattern measurement setup for the reference and proposed antenna at different orientations in the anechoic chamber. Figure 9: Simulated vs. measured frequency response of the proposed quintuple band-notched UWB antenna when \(W_{s}\) = 0.348 mm in the simulation setup.
# Graphene Passively Q-Switched Nd:YAG Laser by 885 nm Laser Diode Resonant Pumping
## 2 Experimental Setup  
Figure 1 shows the experimental scheme of the passively Q-switched Nd:YAG laser based on inclusion of the graphene. A fiber-coupled LD was employed as the pump, the core diameter and \(N\)A of which were 200 \(\upmu\)m and 0.22, respectively. The central wavelength and maximum output power of the LD were 885 nm and 175 W, respectively. The dimensions of the Nd: YAG crystal with an Nd\({}^{3+}\) doping fraction of 0.5% were 40 mm (length) \(\times\) 6 mm (width) \(\times\) 2 mm (high). The crystal, both cross-sections of which were anti-reflective at 0.8-0.9 \(\upmu\)m and 1.0-1.1 \(\upmu\)m, was wrapped in the heat sink made of copper and controlled at 15 \({}^{\circ}\)C by the thermoelectric cooler. The pump beam converged into the center of the crystal with a radius of 160 \(\upmu\)m through the coupling system.  
The cavity had a L-shaped structure with a physical length of 160 mm. Compared with the straight cavity, the L-shaped cavity could reduce the space and effectively avoid the pump feeding back to the LD. In the device, the distance between the lenses, M1 and M2 was 65 mm, and the distance between M1 and M3 was 95 mm. M1\(\sim\)M4 were all flat mirrors. M1 had high transmittance at 0.8-0.9 \(\upmu\)m and high reflectance at 1.0-1.1 \(\upmu\)m at an angle of 45\({}^{\circ}\); M2 had high transmittance at 0.8-0.9 \(\upmu\)m and high reflectance at 1.0-1.1 \(\upmu\)m; M3 had high transmittance at 0.8-0.9 \(\upmu\)m and a transmittance of 30% at 1.0-1.1 \(\upmu\)m; M4 had high reflectance at 1.0-1.1 \(\upmu\)m at an angle of 45\({}^{\circ}\). The graphene was laid between M1 and M3. The substrate of SA was a mirror made of a CaF\({}_{2}\) crystal, and a graphene material that was dissolved in ethanol was coated on one surface of the CaF\({}_{2}\) mirror using a rotary  
Figure 1: Experimental scheme of the Nd:YAG laser.  
coater (KW-4A, Chinese Academy of Sciences, Beijing, China). The mirror diameter of the SA was 20 mm, which was a multilayer graphene. The transmittance of the SA at 1.064 \(\upmu\)m was measured and found to have been approximately 87.2%.  
The absorbed pump power and average output power of the Nd:YAG laser in the CW and Q-switching operation were measured using an Ophir power meter (power range: 20 mW-20 W). The temporal characteristics of the Q-switched Nd:YAG laser were measured using the Si-biased photodetector from Thorlabs, DET025 AL/M, and recorded using the oscilloscope from Tektronix MSO 3034.
## 3 Key Technologies of UAV-Assisted SAGINs
### Resource Management
#### 3.4.4 SDN/NFV Technology  
The collaboration of SDN and NFV enables efficient network management, including managing core functionalities such as traffic scheduling and computational task offloading, offering innovative design concepts for the complex structure of SAGINs. Currently, the integration of SDN and NFV with SAGINs and edge computing, especially in AI-driven network management, VNF deployment, and IoT device support, has garnered significant interest in research. Applying SDN to SAGINs first requires addressing the structural complexity introduced by heterogeneous layered networks. Figure 5 illustrates the design of an NFV/SDN-supported SAGIN framework. As the control center of the whole network, SDN controllers must monitor global network resources in real time to achieve deterministic task scheduling. However, in traditional deployment schemes, SDN controllers are typically placed in satellite networks, which struggle to overcome hierarchical network limitations. During cross-layer transmissions, intermittent link disruptions and signal fluctuations can severely impact the delivery of critical instructions. To address this, [PERSON] et al. [122] proposed a hierarchical multi-controller deployment scheme, with the primary controller located in the TN and secondary controllers placed in satellite nodes. This architecture not only enhances resource awareness but also effectively alleviates the load on a single controller. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Key Technologies**} & \multicolumn{1}{c}{**Ref.**} & \multicolumn{1}{c}{**Method**} & \multicolumn{1}{c}{**Contribution**} \\ \hline \multirow{6}{*}{\begin{tabular}{c} Task \\ scheduling \\ \end{tabular} } & \multirow{3}{*}{\
\begin{tabular}{c} Computation \\ offloading \\ \end{tabular} } & \multirow{3}{*}{SCA} & The research solves non-convex optimization problems using the successive convex approximation (SCA) method in multi-hop satellite communication and computing offloading, minimizing energy consumption and optimizing UAV control and user scheduling. \\ \cline{3-5}  & & \multirow{3}{*}{
\begin{tabular}{c} DDPG \\ PA \\ \end{tabular} } & \multirow{3}{*}{SCA} & The study integrates a framework that optimizes task offloading and control in SAGINs using deep reinforcement learning and perception-aided (PA) methods, enhancing decision quality and network stability. \\ \cline{3-5}  & & & \\ \cline{3-5}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Significant research on resource management. Figure 5: An NFV/SDN framework design integrating a UAV-assisted SAGIN.
## 3 Results and Discussion
### Pyrolysis Compound Specific Isotopic Analysis (Py-CSIA)
Thus, the remaining compounds would be \({}^{13}\)C depleted relative to the original biomass, as reflected in Figure 4.  
Focusing on PS compounds, their isotopic composition pointed to a modification of the molecular chains as remaining fragments derived from the initial biomass (differences up to 6-8%), leaving thus more enriched residues. Most of these identified pyrolysis products may indicate that this organic matter is modified, leading us to believe in a more continuous process of humification during the composing process, and/or their selectively preservation [69; 70]. On the other hand, the great isotopic shift of EST from T2 to T3--although not statistically significant--seems to constitute an indicator of the thriving in the composing pile by microorganisms. Consequently, these data point out to a complete degradation of these compounds by the microbiota, showing fast turnover times. \({}^{13}\)C values of lignin moieties were up to 6%-enriched relative to T1, only significantly regarding LG and LS units, which agrees with previous findings [35; 69]. However, this isotopic enrichment cannot be fully explained, as lignin moieties are not expected to be degraded by any sort of microbial or bacterial assimilation, aside from specific enzymatic pathways. One possible explanation to this \({}^{13}\)C enrichment relies on the modification on the side chains of lignin phenolic units during the composing process: the change on the position of the carbon [35], because of breaking down the C3 side alkyl chain linkages (as supported by the relevant indexes calculated in Table 2). Likewise, this could apply to the enrichment of high molecular weight \(n\)-alkanes and isoprenoids, defined by their highly recalcitrant structures. The stable isotope composition of carbon for ALK ranged from \(-15.43\)% (C-23) to \(-29.28\)% (C-29). These values conform to the established knowledge of \(n\)-alkanes from C\({}_{4}\) plants [63]. Short odd-chain alkanes had a higher level of \({}^{13}\)C enrichment than larger chains: an enrichment up to 4.3% of odd-chain alkanes suggests that, during the biosynthesis of these aliphatic carbon structures, there is a differential carbon isotopic fractionation along the composing process [71]. This may indicate that \(n\)-alkanes constitute a group of biomarkers based on the disposition of their chains, rather than the compounds itself. For ISO, these compounds showed an intermediate enrichment. The majority of these identified pyrolysis products could indicate a more humified carbon pool, confirming the technical suitability of analytical pyrolysis to monitor the composing process.
###### Abstract  
Purpose: The aim of this study was to assess the efficacy of an artificial intelligence (AI) algorithm that uses radiomics data to assess recurrence and predict survival in hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE). Methods: A total of 57 patients with treatment-naive HCC or recurrent HCC who were eligible for TACE were prospectively enrolled in this study as test data. A total of 100 patients with treatment-naive HCC or recurrent HCC who were eligible for TACE were retrospectively acquired for training data. Radiomic features were extracted from contrast-enhanced, liver computed tomography (CT) scans obtained before and after TACE. An AI algorithm was trained using the retrospective data and validated using the prospective test data to assess treatment outcomes. Results: This study evaluated 107 radiomic features and 5 clinical characteristics as potential predictors of progression-free survival and overall survival. The C-index was 0.582 as the graph of the cumulative hazard function, predicted by the variable configuration by using 112 radiomics features. The time-dependent AUROC was 0.6 \(\pm\) 0.06 (mean \(\pm\) SD). Among the selected radiomics features and clinical characteristics, baseline_glszm_SizeZoneNonUniformity, baseline_glszm_ZoneVariance and tumor size had excellent performance as predictors of HCC response to TACE with AUROC of 0.853, 0.814 and 0.827, respectively. Conclusions: A radiomics-based AI model is capable of evaluating treatment outcomes for HCC treated with TACE.  
haptocellular carcinoma; chemoembolization; radiomics; artificial intelligence; contrast-enhanced CT; treatment response; overall survival
## 3 Numerical Examples
### Kelvin-Helmholtz Instability  
This problem is simulated by using a two-dimensional single-mode perturbed initial condition to assess the precision and detail of the numerical scheme [51]. The perturbation  
Figure 16: Two-dimensional Riemann problem (configuration 11): Density contours with 35 equally spaced isolines ranging from 0.5 to 1.2 were obtained using the LF, ROE, and URLBFS schemes. The color gradient represents the corresponding density values, with blue indicating the minimum density value. The simulation was conducted at t = 0.4 with a resolution of 400 \(\times\) 400. Panels (**a–c**) show results using the WENO5 reconstruction method with LF, ROE, and URLBFS schemes, respectively. Panels (**d–f**) display corresponding results with the WENO7 reconstruction method.  
takes the form of a sinusoidal wave with a wavelength along the x-direction, perturbing the vertical velocity component with an amplitude of \(\delta=0.01\).  
\[(\rho,u,v,p)=\left\{\begin{array}{ll}(2,-0.5,0.01\sin(2\pi x),2.5),&0.25\leq y \leq 0.75,\\ (1,0.5,0.01\sin(2\pi x),2.5),&\text{otherwise.}\end{array}\right. \tag{55}\]  
The computational domain spans from [0, 1] in both the x and y directions, and a regular grid of 400 \(\times\) 400 is applied. Apply periodic boundary conditions on all boundaries and the ultimate time is \(t=0.5\) with a fixed-time step size of \(DT=1\times 10^{-4}\).  
Figure 17 illustrates the density distributions calculated using the LF, ROE, and URLBFS schemes. It is evident that the high-order WENO7 reconstruction method offers a better resolution compared to WENO5, enabling a more detailed representation of the flow. Among the three schemes, the URLBFS method captures small-scale vortices with greater accuracy, showcasing its superior ability to resolve intricate flow features, such as fine vortices, which are crucial for accurately modeling complex fluid dynamics.
## 2 Materials and Methods
### Measurement of VOC  
A five-time repetition of sample for each treatment was analyzed for VOC. Headspace solid-phase microextraction (HS-SPME) was employed to extract the VOC following the method described by [PERSON] et al. [26]. Samples weighing 5 g each were placed in 20 mL SPME vials and sealed with septa and crimp caps. The sealed vials were equilibrated for 25 min in a constant-temperature water bath at 60 \({}^{\circ}\)C to establish an equilibrium state for the VOCs in the headspace. An SPME fiber (50/30 \(\upmu\)m DVB/Carboxen/PDMS, Supelco, Bellefonte, PA, USA) was exposed to the headspace and allowed to adsorb VOCs for 30 min. The VOCs adsorbed onto the SPME fibers were analyzed using an Agilent 8890 GC/5977B MSD (Agilent Technologies, Palo Alto, CA, USA). Agilent GC/MS Enhanced MassHunter software (version 10.0.368) was used for device operation, and Aligent MassHunter software was used to collect and analyze data. Helium was used as the carrier gas, and a DB-5 MS column (30 m \(\times\) 0.25 mm id, 0.25 \(\upmu\)m) was used as the analytical column, at a flow rate of 1.3 mL/min. The injection port temperature was set at 250 \({}^{\circ}\)C, and the detector temperature at 280 \({}^{\circ}\)C. The oven temperature was maintained at 40 \({}^{\circ}\)C for 10 min in gradient mode, followed by an increase to 250 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min and a 5-minute hold. The aroma compounds adsorbed onto the SPME fibers were desorbed by exposure to the injector for 15 min. The aroma compounds present in the samples were identified based on the linear retention index, using alkane standards (C10-C26) and mass spectral library data [27]. The concentration of the aroma compounds was expressed in area units (a.u.) \(\times\) 106. Additionally, the flavor characteristics of each aroma compound were determined using FlavorDB ([[https://cosylab.iiitd.edu.cn/flavordb/](https://cosylab.iiitd.edu.cn/flavordb/)]([https://cosylab.iiitd.edu.cn/flavordb/](https://cosylab.iiitd.edu.cn/flavordb/)) (accessed on 5 October 2023) and FooDB Flavornet, as described by [PERSON] et al. [28].
# Dual Histogram Equalization Algorithm Based on Adaptive Image Correction
## 4 Analysis of Algorithm Results
### AICHE Algorithm Effect  
To demonstrate the effectiveness of the AICHE algorithm, Figures 4-13 simulate the environment of insufficient light, fog, and smoke, and compare the image enhancement effects of seven histogram equalization algorithms with those of the AICHE algorithm. These include the classical algorithms HE, BBHE, and CLAHE and several more advanced algorithms, BHEPL, RSIHE, ESIHE, and MEBEBEHE.  
Figure 2: Image enhancement effect of AICHE algorithm: (**a**) original image; (**b**) effect of HE algorithm; (**c**) effect of histogram segmentation; and (**d**) effect of adaptive local grayscale correction.