esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Temporal Upscaling from Instantaneous to Daily SIF  
SIF can be expressed as a product of PAR, FPAR, and the quantum yield for fluorescence (LUE\({}_{\text{f}}\)), given that \(f_{\text{esc}}\) is almost constant for a given vegetation species [16]. As LUE\({}_{\text{f}}\) and FPAR have much smaller diurnal variations compared to the diurnal variation in PAR [19; 27], the SIF was regarded as approximately linear related to the PAR at a diurnal scale. Several studies have also demonstrated that there is a significant linear correlation between the SIF and PAR at the diurnal scale using in situ measurements [17; 20; 28; 29]. Therefore, the SIF value at any time of a day can be approximately calculated using the ratio of SIF to PAR for a given instantaneous time and with continuous diurnal PAR data, which is expressed as:  
\[\text{SIF}(\text{t})\approx\frac{\text{SIF}_{\text{inst}}}{\text{PAR}_{\text {inst}}}.\text{PAR}(\text{t}). \tag{6}\]  
By integrating \(\text{SIF}(\text{t})\) with time from sunrise to sunset over one day, the daily integrated SIF can be approximately calculated as follows:  
\[\text{SIF}_{\text{daily}}\approx\text{SIF}_{\text{inst}}.\frac{\text{PAR}_{ \text{daily}}}{\text{PAR}_{\text{inst}}} \tag{7}\]  
where PAR\({}_{\text{daily}}\) was calculated by integrating the half-hour PAR from sunrise to sunset for one day, which was similar to the calculation of SIF\({}_{\text{daily}}\) in Equation (2) and had a time-integrated unit of MJ/m\({}^{2}\)/d. PAR\({}_{\text{daily}}\)/PAR\({}_{\text{inst}}\) can be regarded as the temporal conversion factor from SIF\({}_{\text{inst}}\) (mW/m\({}^{2}\)/nm/sr) to SIF\({}_{\text{daily}}\) (J/m\({}^{2}\)/nm/sr/d).  
Recent studies (e.g., References [14; 27; 31]) used an approximate clear-sky PAR proxy (cos(SZA)) to replace PAR in Equation (7) for SIF upscaling from the instantaneous to daily scale. This cos-based method for SIF temporal upscaling assumes that the diurnal variation in SIF only depends on the SZA values and did not consider cloud and atmospheric scattering effects. In this study, we upscaled SIF\({}_{\text{inst}}\) at a given time of 9:30 to SIF\({}_{\text{daily}}\) using these two upscaling methods (i.e., the cos-based and PAR-based methods) with the tower-based measurements at the two sites and then investigated whether the PAR-based SIF\({}_{\text{daily}}\) can improve the SIF-GPP correlation instead of using SIF\({}_{\text{inst}}\) or cos-based SIF\({}_{\text{daily}}\).
# Depth-Optimized Quantum Circuit of Gauss-Jordan Elimination
## 2 Preliminaries
### Information Set Decoding (ISD)  
Information Set Decoding (ISD) is a well-known algorithm for tackling the Syndrome Decoding Problem. Essentially, ISD utilizes a brute-force strategy that systematically narrows the search space. The process is as follows: An information set \(S\) is chosen (randomly) from a matrix \(H\). If \(S\) is invertible, the Hamming-weight is assessed by multiplying the inverse of \(S\) (i.e., \(S^{-1}\)) by the syndrome \(c\). If the Hamming-weight of the resulting vector equals \(t\), the secret vector \(e\) can be recovered. If not, the process is repeated from the start. [PERSON] first proposed the basic ISD algorithm [11], and subsequently, various variants of the ISD algorithm have been introduced [12; 13; 14].  
In ISD, Gauss-Jordan elimination is employed to find the inverse of an information set \(S\); thus, it can compute \(S^{-1}\) (if \(S\) is invertible). This process involves transforming the matrix into an identity matrix through a series of row operations. What we aim to compute using Gaussian elimination is \(S^{-1}\cdot c^{T}\), which can be achieved without explicitly calculating \(S^{-1}\). By applying the row operations used in Gauss-Jordan elimination to \(c\), we compute a vector that corresponds to \(S^{-1}\cdot c^{T}\). Thus, instead of constructing \(S^{-1}\) explicitly, we apply the same operations to \(c\) to compute the desired result.
# The Effect of Custom Insoles on Muscle Activity in Diabetic Individuals with Neuropathy
###### Abstract  
Foot ulcers are amongst the most serious complications of diabetes. Guidelines recommend that people with diabetes wear appropriate footwear or insoles to reduce repetitive stresses. Excessive plantar pressure has been recognized as the major risk factor for plantar ulcers in diabetic individuals; custom insoles are indicated as the gold standard treatment to unload the foot structure. The aim of this study was to investigate the effect of custom insoles on biomechanical and neuromuscular functions in diabetic neuropathic individuals. Ten diabetic subjects walked with and without custom insoles at their preferred speed; ten controls were assessed for comparison. Data were captured through seven video cameras, plantar pressure insoles, and surface electromyography. The electrical activity of Rectus Femoris, Tibialis Anterior, Medius Gluteus and Gastrocnemius Lateralis were acquired bilaterally. The plantar pressure and surface electromyographic variables were determined, while videos were used to detect the gait cycle. The following comparisons were made across the variables through the non-parametric SPM1D test (\(p<0.05\)): condition with vs. without insoles vs. controls. Custom insoles provided a reduction in plantar pressure through contact surface redistribution in association with a reduced electromyographic activity. Our results suggest optimizing the prevention approach by including personalized foot and ankle exercises.  
custom insole; plantar pressure; surface electromyography; diabetic neuropathy; gait analysis Article
## 4 Performance Analysis
### Overhead Estimation
#### 4.2.2 Switching Probability  
Switching between the two links results in increased signaling and also consumes more power. Therefore, SP is one more metric which is related to the overhead signaling of the proposed scheme. This (switching) probability can be evaluated using the complementary ones, i.e., \(P_{1,1}\) or \(P_{2,2}\), which are conditioned to the corresponding stationary distributions \(\pi_{1}\) and \(\pi_{2}\), which results in the following closed-form expression  
\[S_{p}=\pi_{1}(1-P_{11})+\pi_{2}(1-P_{22}). \tag{29}\]
# Photocatalytic Phenol Degradation by Silica-Modified Titanium Dioxide
## 4 Conclusions  
In this research, the photocatalytic removal of phenol from an aqueous solution by TiO\({}_{2}\)/SiO\({}_{2}\) composites was examined. The modification of titanium dioxide using silicon dioxide extracted from sand beach increased the photocatalytic activity, thereby decreasing the phenol concentration. The best ratio of TiO\({}_{2}\)/SiO\({}_{2}\) composite was 7:1, which also gave the highest crystallinity (92.52%), largest crystal size (16.28 nm for anatase and 26.06 nm for rutile), and smallest particle size (210.9 nm). The addition of silicon dioxide reduces the crystallinity, causing the volume of the anatase and rutile crystal lattice to decrease and the particle size to decrease. TiO\({}_{2}\)/SiO\({}_{2}\) composites also exhibited chemical bonding rather than a simple physical mixing process. However, there was no significant change in the shape and the band gap of TiO\({}_{2}\). By using the optimized condition, the reduction percentage of the phenol concentrations by using the TiO\({}_{2}\)/SiO\({}_{2}\) composite was 96.05% in 120 min and has 3.5 times more efficient compared to TiO\({}_{2}\) P25. These results indicate that the TiO\({}_{2}\)/SiO\({}_{2}\) composite can be used as a green approach for phenolic compounds removal in industrial wastewater. The following are available online at [[https://www.mdpi.com/article/10.3390/app11199033/s1](https://www.mdpi.com/article/10.3390/app11199033/s1)]([https://www.mdpi.com/article/10.3390/app11199033/s1](https://www.mdpi.com/article/10.3390/app11199033/s1)), Figure S1: Rietveld refinement of XRD pattern of TiO\({}_{2}\) P25, Figure S2: Rietveld refinement of XRD pattern of composite TiO\({}_{2}\)/SiO\({}_{2}\) 1:1, Figure S3: Rietveld refinement of XRD pattern of composite TiO\({}_{2}\)/SiO\({}_{2}\) 3:1, Figure S4: Rietveld refinement of XRD pattern of composite TiO\({}_{2}\)/SiO\({}_{2}\) 7:1. Conceptualization, D.R.E. and M.L.F. ; methodology, S.N.I. ; software, S.N.I. and M.D.P. ; validation, D.R.E., M.L.F. and I.R. ; formal analysis, S.N.I. ; investigation, S.N.I. and M.D.P. ; resources, S.N.I. ; data curation, S.N.I. ; writing--original draft preparation, S.N.I. ; writing--review and editing, D.R.E. and M.D.P. ; visualization, M.D.P. ; supervision, D.R.E. and M.L.F. ; project administration, D.R.E. ; funding acquisition, Y.A.E.-B., E.E.H. and Z.M.E.-B. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Photocatalysts** & **Kinetics Equation** & \(\mathbf{k_{1}\) **(min\({}^{-1}\))** & \(\mathbf{R^{2}}\) \\ \hline TiO\({}_{2}\) P25 & \(-\)In (Ct/Ci) = 0.0076 t + 0.1062 & 0.0076 & 0.9279 \\ TiO\({}_{2}\)/SiO\({}_{2}\) (1:1) & \(-\)In (Ct/Ci) = 0.0098 t + 0.0486 & 0.0098 & 0.9896 \\ TiO\({}_{2}\)/SiO\({}_{2}\) (3:1) & \(-\)In (Ct/Ci) = 0.0179 t + 0.0504 & 0.0179 & 0.9785 \\ TiO\({}_{2}\)/SiO\({}_{2}\) (7:1) & \(-\)In (Ct/Ci) = 0.0267 t + 0.2040 & 0.0267 & 0.9558 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The kinetic study of the photocatalysts.
## 4 Results and Discussion
### Performance Loss Rate Calculation Using LR and STL  
PLR has been evaluated using linear regression and STL decomposition. Figure 8 shows the monthly PR\({}_{\text{ann}}\). time-series, their fitting lines, and trend components. As shown, the data do not have the same length, ranging from two to nine years. The grey lines are PR\({}_{\text{ann}}\), also shown in blue lines for PV systems with data less than a five-year period. The dashed orange lines are the applied linear regression fits of PR\({}_{\text{ann}}\), and the green lines are the STL-extracted \"trend\" component of the PR\({}_{\text{ann}}\). time-series. For almost all systems, the difference between the regression fits with the trends is very small, which means that the STL method is useful for an accurate calculation of changes in performance.  
The quantities of PLR using STL are presented in Figure 9, grouped by module technologies and climate zones. The values of PLR are presented as relative values (chart on the left-hand side) and absolute values (chart on the right-hand side). Reading the mean of relative values, p-Si shows the lowest PLR of \(-0.6\%\)/year, followed by CIGS at \(-0.8\%\)/year, mono-Si at \(-0.84\%\)/year, HIT at \(-1.01\%\)/year, and CdTe at \(-1.18\%\)/year. The strongest degradation was experienced by a-Si modules at \(-1.58\%\)/year. CdTe systems have the largest range, from \(-0.38\%\)/year to \(-2.20\%\)/year. In contrast, the range of PLR values of p-Si systems was the smallest, from \(-0.19\%\)/year to \(-0.97\%\)/year. The results are in line with a previous similar study [6].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Climate** & **Country** & **PR** & **PR\({}_{\text{stc}}\).** & **PR\({}_{\text{ann}}\).** \\ \hline BWh & Australia & \(0.81\pm 0.03\) & \(0.92\pm 0.02\) & \(0.81\pm 0.02\) \\ Af & Indonesia & \(0.81\pm 0.03\) & \(0.90\pm 0.03\) & \(0.81\pm 0.03\) \\ Cfb & Italy & \(0.84\pm 0.04\) & \(0.89\pm 0.03\) & \(0.84\pm 0.03\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance ratio of p-Si systems and their standard deviation by climate.  
Figure 7: Performance ratio by technology.  
Figure 8: Annual-averaged monthly temperature-corrected performance ratios, PRann, of the PV systems (grey or blue lines), their linear fit (dashed orange line), and trend components (green line) over the monitoring period. Numbers at the top right of the charts indicate the lengths of the data in years.  
Figure 9: Performance loss rate of the PV systems using STL. (**a**) Grouped by module technologies, (**b**) grouped by climate. Left: relative values, right: absolute values.
## 4 Experiments
### Image Manipulation Detection  
As recent works only consider pixel-level tampering detection and not image-level detection, there are no standard datasets for benchmarking.  
Since the CASIA dataset and the RTD (Realistic Tampering Dataset) [31] contain both forgery images and its corresponding original image, we select 500 images from the CASIA dataset, 300 images from the NIST dataset and 220 images from the RTD with 50% forged and 50% original as a test dataset. From Table 3, it cn be seen that our model achieves good performance, especially on the nist dataset: our model achieves an F1 score of 84.8% and an AUC of 92.2%, which shows that our method is effective in capturing the manipulation artifacts.
###### Abstract  
Background Due to the large number of undiagnosed obstructive sleep apnoea (OSA) patients, our aim was to investigate the applicability of artificial intelligence (AI) in preliminary screening, based on simple anthropometric, demographic and questionnaire parameters. Methods Based on the results of the polysomnography performed, the 100 patients in the study were grouped as follows: non-OSA, mild OSA and moderately severe-severe OSA. Anthropometric measurements were performed, and the Berlin and Epworth questionnaires were completed. Results OSA prediction based on body mass index (BMI), gender and age was accurate in 81% of cases. With the completion of the questionnaires, accuracy rose to 83%. The Epworth questionnaire alone yielded a correct OSA prediction in 75%, while the Berlin questionnaire was correct in 62% of all cases. The best results for categorization by severity were obtained by combining BMI, gender and age parameters, together with responses to the questionnaires (71%). Supplemented with neck circumference, this result improves slightly (73%). Conclusion Based on the results, it can be concluded that OSA can be effectively and easily categorized using AI by combining anthropometric and demographic parameters, as well as questionnaire data.  
obstructive sleep apnoea; artificial intelligence; questionnaire; anthropometric parameters; demographic parameters; prediction
## 3 System Model and Working Mechanism
### Traffic Transmission Model between LEO Satellites and HAP Drones
We define a traffic-drone matching matrix \(\mathbf{Y}_{I\times\mathcal{Q}}[m]\) to describe the transmission status of different traffic types at each HAP drone in the \(m\)-th time slot. If the \(q\)-th traffic type of the \(i\)-th HAP drone is sent in the \(m\)-th time slot, there is \(y_{i,q}[m]=1\); otherwise, \(y_{i,q}[m]=0\). Thus, in the \(m\)-th time slot, the maximum traffic volume from the \(q\)-th traffic type of the \(i\)-th HAP drone to the \(j\)-th LEO satellite can be expressed as follows:  
\[U_{i,q,j}[m]=x_{i,j}[m]y_{i,q}[m]R_{i,j}[m]T_{i,j}[m]. \tag{6}\]  
The transmitted traffic value of the \(q\)-th traffic type of the \(i\)-th HAP drone in the \(m\)-th time slot can be represented as follows:  
\[\widetilde{U}_{i,q}[m]=\min\left(S_{i,q}[m],\sum_{j=1}^{l}U_{i,q,j}[m]\right), \tag{7}\]  
where \(S_{i,q}[m]\) is the traffic volume of the \(q\)-th traffic type stored at the \(i\)-th HAP drone in the \(m\)-th time slot, which can be obtained as follows:  
\[S_{i,q}[m]=S_{i,q}[m-1]-\widetilde{U}_{i,q}[m-1]+G_{i,q}[m-1], \tag{8}\]  
where \(G_{i,q}[m-1]\) denotes the traffic volume of the \(q\)-th traffic type newly generated at the \(i\)-th HAP drone in the \((t-1)\)-th time slot. It is a random variable that follows the traffic generation model defined in Section 3.1. Therefore, the total transmitted traffic value of the space-air-ground integrated network can be given as follows:  
\[U_{\text{total}}=\sum_{m=1}^{M}\sum_{i=1}^{l}\sum_{q=1}^{Q}\mu_{q}\cdot \widetilde{U}_{i,q}[m]. \tag{9}\]
# Analysis of Electromagnetic Interference Effects of 5G Signals on Radio Altimeters
## 4 Injection Experiment of 5G Signal Interference into an RA Receiver
### Analysis of the Influence of 5G Signal Interference on RA Measurement Performance  
In order to assess the influence of 5G signal interference on RA measurement performance, interference injection experiments were conducted using three common frequencies (3800 MHz, 4000 MHz, and 4200 MHz) within the adjacent frequency bands of the RA. A 10 MHz bandwidth 5G signal was injected gradually, increasing the power output by 1 dB steps from the signal generator. The output altitude from the RA was observed under the influence of the 5G signal interference. Due to fluctuations in the altitude output of the RA during interference and poor numerical stability, the test employed averaging within each group to determine the height and calculate the height error ratio. The test results are presented in Figure 11.  
Figure 10: IF output under the interference of 5G signals with varying powers.  
Figure 9: Diagram of the experimental platform.  
Figure 11 demonstrates the trend of the height error ratio with the increase in the 5G interference signal power. It can be seen that the height error ratio shows an exponentially increasing trend as the 5G interference signal power increases, which is evident at all three measurement frequency points (3800 MHz, 4000 MHz, and 4200 MHz). When the 5G signal power is low, the height error ratio essentially remains stable. However, when the signal power increases further, especially when the height error ratio exceeds 1%, the performance of the RA starts to degrade significantly, and the height error increases. As the 5G interference signal increases, the height error ratio continues to increase, and when it exceeds 6%, the RA is unable to output a valid height value and enters the no-computed-data (NCD) state.  
Specifically, under the injection of 5G signals at the three center frequencies of 3800 MHz, 4000 MHz, and 4200 MHz, the height error ratio curves start to rise significantly near \(-9\) dBm, \(-15\) dBm, and \(-18\) dBm, respectively, and quickly reach and exceed the threshold value of 6%. This indicates that when the 5G signal power reaches a certain threshold, the high performance of RA measurements is seriously affected, preventing reliable height information. In addition, the closer the interfering signal frequency is to the operating frequency band of the RA, the more pronounced the impact on the RA becomes. In particular, when the interfering signal frequency is 4200 MHz, its interference effect is the most significant. This indicates that as the 5G interfering signal frequency approaches the RA operating frequency band, the nonlinear effect becomes stronger, leading to a sharp increase in the height error ratio.
## 4 Analysis of Factors Affecting the Reflection Coefficient
### Effect of Porosity on Reflection Coefficients  
The relation between the reflection coefficient and the porosity of sea ice is shown in Figures 7 and 8. The porosities were chosen as 0.1, 0.2, 0.3 and 0.4. It is shown that there is a trend that reflection coefficients decrease with the increase in porosity, except at some special large angles. It is because sea ice contains more pores when the porosity increases. Then, waves are more easily trapped in many pores and absorbed during the process of propagating in the ice. Therefore, porosity should be included in sea ice modeling.  
Figure 5: Reflection coefficients at different frequencies.  
Figure 8: Contour plot of reflection coefficients at different porosities.  
Figure 6: (**a**) Reflection coefficients at different thicknesses of the first layer. (**b**) Reflection coefficients at different thicknesses of the second layer. (**c**) Reflection coefficients at the same thicknesses of the single-layered and double-layered models.  
Figure 7: Reflection coefficients at different porosities.  
acoustic research. Furthermore, it is necessary to take the layering and porosity characteristics into account in sea ice modeling.
## 3 Results
### Molecular Docking Studies
#### 3.3.2 Molecular Docking for the Enzyme Acetylcholinesteraseterase  
Molecular docking was performed for acetylcholineesterase with the compounds identified by GC-MS of ethanolic extract of _Z. pentandra_ and the standard compound Eserine (physostigmine). The results showed [1,3] diazepan-2,4-dione, 1,2-benzenedicarboxylic acid, 2-hydroxy-n-(2-phenylethyl)benzamide, phthalic acid, lactose, dioctyl phthalate, and \(\gamma\)-sitosterol are the best-docked compounds with strong binding affinity scores. The binding energy of the standard Eserin was calculated at \(-8.4\) kcal/mol, which is equal to phthalic acid and [1,3] diazepan-2,4-dione; \(\gamma\)-sitosterol, 1,2-benzenedicarboxylic acid, 2-hydroxy-n-(2-phenylethyl)benzamide, and [1,3] diazepan-2,4-dione have \(-11.5\) kcal/mol, \(-9.5\) kcal/mol, \(-9.4\) kcal/mol, and \(-9.4\) kcal/mol binding affinity scores, respectively, and showed better results than the standard compound (Eserine). Table 7 and Figure 2 show the binding energy and interactions between enzyme and ligands.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline \multirow{2}{*}{**Sr No.**} & \multirow{2}{*}{**Compounds**} & **H-Bond Interacting** & **Binding Affinity** \\  & & **Amino Acids** & **(kcal/mol)** \\ \hline
1 & \(\gamma\)-sitosterol & N/A & \(-11.5\) \\ \hline
2 & 2-hydroxy-n-(2 phenylethyl)benzamide & GLY B:121, TYR B:124 & \(-9.4\) \\ \hline
3 & Eserin (standard) & TRP B:286 & \(-8.4\) \\ \hline \hline \end{tabular} N/A: not observed.  
\end{table}
Table 7: The binding affinity of GC–MS compounds and bond interaction with acetylcholinesteraseasease.  
Figure 1: Interaction between \(\alpha\)-glucosidase and ligands. (**A**) 2-hydroxy-n-(2-phenylethyl) benzamide, (**B**) lactose, and (**C**) Acarbose.