esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed RDH-ED Scheme Based on VTSIS
### Secret Extraction and Image Reconstruction  
The process of secret extraction and image reconstruction is shown in Algorithm 2. Assuming that there are \(m\) (\(m=T\)) participants to extra secrets, the participants first update their shares according to the following threshold:  
1. If \(T=k\), \(P_{t}\) updates the secret share to \(S_{t}^{k}=f_{t}(y)\);
2. If \(T=h\), \(P_{t}\) updates the secret share to \(S_{t}^{h}=g_{t}(x)\);
3. If \(k<T<h\), \(P_{t}\) first choose \(h\)-\(T\) integers randomly different from 1, 2,..., \(n\), and then update the secret share to \(S_{t}^{T}=\left\{S_{t}^{h},\ f_{t}(e_{1}),f_{t}(e_{2}),\ldots,f_{t}(e_{h-T}) \right\}\);  
```
1:\(l\gets 1\);
2:while\(l\prec M\times N\)do
3:if\(T=\) = \(k\)
4: Update the original secret share to \(S_{t}^{k}=f_{t}(y)\);
5:elseif\(k<T<h\)
6: Select \(h\)-\(T\) distinct integers \(e_{1}\), \(e_{2}\),..., \(e_{h-T}\) excluding 1, 2,..., \(n\);
7: Update the original secret share to \(S_{t}^{T}=\{S_{t}^{h},f_{t}(e_{1}),f_{t}(e_{2}),\ldots,f_{t}(e_{h-T})\}\);
8:elseif\(T==h\)
9: Update the original secret share to \(S_{t}^{h}=g_{t}(x)\);
10: Reconstruct \(F(x,y)\) from the updated shares using Equation (1);
11: Extract pixel and extra secrets according to hiding key;
12:endif
13:\(l\gets l+1\);
14:endwhile
```  
**Output**: reconstructed Secret, reconstructed image  
Figure 3: The instance flowchart of secret sharing and data embedding.  
The extractor can reconstruct the coefficient matrix according to the Lagrange interpolation. After generating the extractor key using the seed key, the image pixel and extra data can be obtained. For enhanced comprehension, an instance flowchart is shown in Figure 4, and two examples are given as follows:  
1. When \(T=2\), the secret owner updates the share to \(S_{t}^{k}=f_{t}(y)\). If \(P_{1}\) and \(P_{2}\) need to extract the extra data, Equation (11) can be obtained by the Lagrange interpolation formula: \[u_{0}(x)=191\times\frac{x-2}{1-2}+19\times\frac{x-1}{2-1}\text{mod}251=112+79x.\] (11) where \(u_{0}(x)\) is the reconstructed polynomial, and its coefficients are 112 and 79. The reconstructed coefficient 79 is the embedded secret. In the same way, \(u_{1}(x)\) and \(u_{2}(x)\) can be reconstructed, so the original pixel and the embedded secret can be recovered.
2. When \(T=3\), the secret owner updates shares to \(S_{t}^{h}=g_{t}(x)\). If \(P_{1}\), \(P_{2}\) and \(P_{3}\) need to extract the extra data, Equation (12) can be obtained by the Lagrange interpolation formula: \[\begin{array}{l}v_{0}(y)=245\times\frac{(y-2)(y-3)}{(1-2)(1-3)}+221\times \frac{(y-2)(y-3)}{(2-1)(2-3)}+40\times\frac{(y-1)(y-2)}{(3-1)(3-2)}\text{mod}251 \\ \qquad=112+86y+47y^{2}.\end{array}\] (12) where \(v_{0}(y)\) is the reconstructed polynomial, and its coefficients are 112, 86 and 47. The reconstructed coefficient 86 is the pixel. In the same way, \(v_{1}(y)\) can be reconstructed, so the original pixel and the embedded secret can be recovered.
## 2 Study Area and Data
### Remote Sensing Data  
To further evaluate the ET accuracy of the TSEB-SM simulations, the daily ET datasets were selected for indirect cross-validation against the TSEB-SM ET data, that is, the PML-V2 ET data and the SEBAL ET data.  
The PML_V2 [11] ET dataset has a spatial and temporal resolution of 0.05\({}^{\circ}\) and daily, respectively. It comprises vegetation transpiration (Ec), soil evaporation (Es), vaporization of intercepted rainfall (Ei), and evaporation from water and snow (ET_water). In order to improve the simulation accuracy, vegetation transpiration and GPP were coupled based on the Penman-Monteith-Leuning (PML) model and stomatal conductivity theory. This balances GPP and Ec, improving the effect of increasing atmospheric CO\({}_{2}\) concentration on carbon and water processes. The observations of 95 eddy covariance flux sites worldwide were used to derive the PML_V2 parameter, which was then extended to the entire planet based on vegetation types. The final step was to obtain the datasets for terrestrial ET and total primary productivity by using the meteorological drivers of GLDAS 2.1 and MODIS reflectance (albedo), emissivity (emissivity), leaf area index (LAI), and continuous dynamic vegetation type (MODIS MCD12Q2.006 IGBP) as inputs to PML V2. The PML-V2 data were downloaded from the Central Tibetan Plateau Data Center ([[https://www.tpdc.ac.cn/en/](https://www.tpdc.ac.cn/en/)]([https://www.tpdc.ac.cn/en/](https://www.tpdc.ac.cn/en/)), accessed on 17 July 2022). The SEBAL ET dataset (2001-2018) is based on the Surface Energy Balance Algorithm for Land, with a spatial and temporal resolution of 1 km \(\times\) 1 km and daily, respectively. SEBAL is an advanced algorithm utilized to estimate the land surface ET [21]. This algorithm is grounded in the principle of energy balance, where the incoming energy (comprising solar and atmospheric radiation) to the Earth's surface must be balanced by the sum of sensible heat flux (heat transferred to the air), latent heat flux (evaporation of water), and ground heat flux (heat transferred into the soil). The SEBAL model calculates the instantaneous \(\lambda\)ET of the satellite transit time as a residual based on the surface energy balance equation. The daily surface ET is then obtained by an upscaling algorithm. The SEBAL ET data for this study used MCD43 surface albedo, MOD11 surface temperature, MOD13 NDVI, and meteorological data (air temperature) from the Global Modeling and Assimilation Office (GMAO) as input data.
## 4 Conclusions  
From these experiments, it can be said that electrochemical methane production has huge potential to be one of the alternate fuel solutions. The cathode potential analysis revealed that electrochemical methane production is possible even at potentials as low as \(-0.55\) V vs. SHE. The optimum cathode potential remains between \(-0.70\) V and \(-0.60\) V. Methane concentrations above 90% were consistently achieved. The carbon dioxide concentrations in biogas were kept below 15% in all the cases, achieving 50-60% reduction in CO2 emissions in biogas production processes. We could also observe that when the feed/effluent pHs were kept high (i.e., \(>\)7.0/8.4 respectively), most of the methane is produced via the electrochemical pathway. As the pH moves closer towards neutral, coulombic efficiencies of over 200% were achieved, which is evidence of simultaneous electrochemical and heterotrophic methane production. The control experiments provide evidence of electrochemical CO2 reduction showing an improvement of about 13.6% in terms of the average methane production rate. These experiments also show a decrease in the amount of CO2 dissolving within the reactor when the electrodes are supplied with electricity. Further investigations are required to distinguish the mechanism of electron transfer during electrosynthesis of methane and/or acetic acid and how they depend on the applied cathode potential.  
Figure 9: Proposed (generalized and simplified) AD-MES integrated treatment plant setup.  
**Author Contributions:** Conceptualization, A.B.T.N. and C.D.; Methodology, A.B.T.N.; Validation, C.D.; Investigation, A.B.T.N.; Resources, C.D.; Data curation, A.B.T.N.; Writing--original draft preparation, A.B.T.N.; Writing--review and editing A.B.T.N. and C.D.  
**Funding:** This research was funded by the Norwegian Ministry of Education and Research through the Ph.D. program in Process, Energy and Automation Engineering at University of South-Eastern Norway. The APC was funded by University of South-Eastern Norway.  
**Acknowledgments:** Authors acknowledge Mrs. [PERSON] for helping with the schematic diagram (Figure 1) in the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Design and Implementation of Attitude Stabilization System for Marine Satellite Tracking Antenna
## 2 Introduction of Attitude Stabilization System
### Stepper Motor Control and Drive Board  
The stepper motor control and drive board is an important part of the attitude stabilization system, whose responsibilities are: (1) executing control algorithm with the frequency of 1 KHz; (2) amplifying the power of the signal from the controller to drive stepper motor; and (3) receiving and sending data to other boards or computers.  
The stepper motor control and drive board is shown in Figure 4. There is one ARM chip (Cortex-M3 core) on this board with the CPU frequency of 168 MHz, which receives data from the attitude sensor board with UART port, runs the control algorithm and sends the controller outputs to stepper motor drive chips through SPI communication port. There are four layers on this board. The high voltage area on this board typically has a voltage of more than 20 V, while the low voltage area has a typical voltage of 3.3 V. These two areas are connected by a 0 ohm register, which can significantly decrease the electronic current affection from stepper motor. To avoid missing steps in stepper motor, the velocity profile is usually applied. There are many methods to implement the velocity profile by self-written code [21; 22; 23], but, in our case, a specific stepper motor control chip is applied. That is, the ARM chip sends the command rotation velocity to the stepper motor control chip, which is responsible to convert the command velocity into corresponding pulse, according to pre-defined velocity profile. The algorithms run in the attitude sensor board and stepper motor control and drive board are firstly designed in Matlab/Simulink, and then converted into C code by the code generation tools [24; 25; 26].
# Novel Janus Fibrous Membranes with Enhanced Directional Water Vapor Transmission
## 2 Materials and Methods
### Preparation of the Moisture-Penetrability Janus Membranes  
The precursor solution was prepared by dissolving the sufficiently dried CA powder in DMF and acetone solvent (the mass ratio was 2:1) at a series of concentration (7.5 wt %, 10 wt %, 12.5 wt %) and stirred for 8 h at room temperature. An electrospinningning machine was employed to fabricate the membranes. Nearly 4 mL precursor solution was placed in a 5-mL syringe, and the flow velocity was controlled to 1.0 mL/h by the injection pump. The syringes were periodically scanned in the horizontal direction with a width of 90 cm. In order to form cellulose acetate fibers with different diameters, different metal needle nozzles with the inner diameters of 0.6, 0.8, 1.0, and 1.2 mm (named as 6G, 8G, 10G and 12G) were equipped. A skim cotton fabric was covered on the drum (200 r/min) as the collector. The distance between the needle tip and the collector was 15 cm, and the voltage was set as 8-15 kV, respectively. The electrospinningning process was adjusted for 5, 10, 15, 20, 25, and 30 min to obtain the CA electrospun fibrous layers with different thicknesses, and then the as-prepared CA membranes were directly used as the substrate for the next step without any treatment.  
The PU pellets were dissolved in DMF and THF solvent (the mass ratio was 9:1) at 40 \({}^{\text{\text{\textdegree}}}\)C for 8 h to obtain a uniform solution with different concentrations (10, 12.5, 15, 17.5, and 20 wt %). Then, the as-prepared precursor solutions were loaded onto the same electrospinningning machine, and the feed rate of the solutions was 0.8 mL/h. The above as-prepared CA fibrous layers were used as the collector. The PU electrospun fibrous layers were then fabricated by the similar method as the CA layer. After 15 min, the PU/CA Janus fibrous membranes were successfully fabricated.
# Estimation of Impact Loads Transmitted to Vibro-Ripper Housing Using Transfer Path Analysis
## 2 Vibration Experiments with a Vibro-Ripper
### Vibration Experiment Setup  
We analyzed the natural characteristics of a vibro-ripper and quantitatively estimated the impact loads transmitted to each of its components in a vibrational experiment. The vibrations during rock crushing were measured using an accelerometer (Figure 4a) attached at a position representing the shape of the vibro-ripper.  
The sensor employed in this study was a triaxial accelerometer that is suitable for high-impact experimental environments, using 84 channels. Data acquisition and analysis were managed by a supervisory control and data acquisition system (SCADAS) for overall experimental control, and a mobile data acquisition system with Test.lab software version 18.2 (Siemens, Munich, Germany). Experimental and operational conditions of the excavator and vibro-ripper are provided in Table 1. Table 2 lists the vibro-ripper's specifications.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Items** & **Parameters** & **Values** \\ \hline Tracking & Measurement method & Time trace \\  & Duration & 30 s \\  & Increment & 0.5 s \\ Acquisition & Bandwidth & 6400 Hz \\  & Resolution & 4 Hz \\ Vibro-ripper & Flow rate & 290 lpm \\  & Working pressure & 200 kg/cm\({}^{2}\) \\  & Rotational speed & 900 rpm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters for vibrational experiments and operating conditions of the vibro-ripper.  
Figure 4: Vibration experiment setup. (**a**) positions of the installed tri-axial accelerometers; (**b**) Testing jig for impact load measurement.  
A vibro-ripper functions by converting the energy of a hydraulic motor into the kinetic energy of the shank and tooth, thereby breaking the rock. The vibro-ripper used in this study strikes the tooth jig connected to the steel structure (Figure 4b), to create uniform impact loads. The actual rock has mechanical properties that change due to anisotropy and irregularity [25]. Therefore, the uniformity and reproducibility of the test were ensured by minimizing the variation in the strength of the construction objects.  
To ensure experimental reproducibility and repeatability, we constructed in situ testing equipment that enabled us to minimize variations in impact load related to the compressive strength of the rock (Figure 4b). The experiment design also eliminated effects related to inhomogeneity and anisotropy of the rock.  
Striking of the tooth jig was expected to generate higher striking forces than during striking of the rock surface, as impact energy used to break the rock is transmitted to the housing of the vibro-ripper as a reaction force (in addition to energy dissipated as noise vibration, and hydraulic heat). To assess the effects of impact loads related to rock strength, it is necessary to conduct additional experiments using actual rock specimens. Data from sensor No. 12 during operation are shown in Figure 5 for vibration signals in the Z-direction (the main working direction) with respect to time.
## 7 Conclusions  
In this paper we have proposed a dual-channel speech enhancement algorithm intended for dual-microphone smartphones. Our proposal brings the use of a novel speech presence probability calculation method for the estimation of noise statistics needed by our extended Kalman filter RTF estimator, allowing for online processing of the noisy speech signal. The noise and acoustic channel information is used for speech enhancement through MVDR beamforming. To improve the noise reduction performance with low speech distortion, we also proposed several postfiltering techniques which make use of both single-channel statistics estimated at the beamformer output and information about speech presence. Moreover, our system can take advantage of information about the user position in relation to the smartphone (available through the smartphone's sensors). The experimental results indicate that our proposal achieves more accurate estimates of RTFs and SPPs than other related state-of-the-art algorithms, which yields low speech distortion and better speech quality and intelligibility. Furthermore, the proposed postfilters improve the noise reduction performance compared to other algorithms specifically intended for dual-microphone smartphones without degrading the speech intelligibility.  
Regarding future work, we think that the large margin of improvement revealed by the oracle results, with respect to the estimation of the noise statistics and the acoustic channel, is a boost to this research. In particular, we will generalize our extended Kalman filter framework to general multi-microphone devices and also investigate the estimation of the speech presence probability and the development of postfiltering techniques for multi-channel speech enhancement.  
Conceptualization, J.M.M.-D., A.M.P. and I.L.-E.; Methodology, J.M.M.-D. and A.M.P.; Software, J.M.M.-D.; Validation, J.M.M.-D., A.M.P. and A.G.; Formal analysis, J.M.M.-D., A.M.P. and I.L.-E.; Investigation, J.M.M.-D. and A.M.P.; Resources, A.G.; Data curation, J.M.M.-D. and A.G.; Writing--original draft preparation, J.M.M.-D.; Writing--review and editing, A.M.P., I.L.-E. and A.G.; Visualization, J.M.M.-D.; Supervision, A.M.P. and A.G.; Project administration, A.M.P. and A.G.; Funding acquisition, A.G.  
This research was funded by the Spanish MINECO/FEDER Project TEC2016-80141-P and the Spanish Ministry of Education through the National Program FPU under Grant FPU15/04161.  
The authors declare no conflict of interest.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Hybrid Comprehensive** & **Adherent Patients** & **Partially Adherent and** & \\  & **TeTeTehabilitation Group** & **Group** & **Non-Adherent Patients Group** & \(p\) \\  & **(n = 396)** & **(n = 350)** & **(n = 46)** & \\ \hline Males, n (\%) & 354 (89.4) & 313 (89.4\%) & 41 (89.1\%) & 1.00 \\ \hline Age (years), mean \(\pm\) SD & 62.2 \(\pm\) 10.7 & 62.3 \(\pm\) 10.4 & 61.2 \(\pm\) 13.1 & 0.594 \\ \hline Left ventricular ejection fraction & & & & \\ (\%), mean \(\pm\) SD & 30.8 \(\pm\) 6.9 & 31.0 \(\pm\) 6.8 & 29.7 \(\pm\) 7.1 & 0.222 \\ \hline Atrial fibrillation or atrial flutter, n (\%) & 75 (18.9) & 61 (17.4) & 14 (30.4) & **0.034** \\ \hline Body mass index (kg/m\({}^{2}\)), mean \(\pm\) SD & 28.9 \(\pm\) 5.1 & 28.7 \(\pm\) 5.0 & 29.9 \(\pm\) 5.9 & 0.136 \\ \hline \multicolumn{5}{c}{Etiology of heart failure, n (\%)} \\ \hline Ischemic & 258 (65.1) & 234 (66.7) & 24 (52.2) & \\ \hline Non-ischemic & 138 (34.9) & 116 (33.1) & 22 (47.8) & **0.049** \\ \hline \multicolumn{5}{c}{Past medical history, n (\%)} \\ \hline Myocardial infarction & 231 (58.3) & 208 (59.4) & 23 (50.0) & 0.223 \\ \hline Angioplasty & 187 (47.2) & 171 (48.9) & 16 (37.8) & 0.072 \\ \hline Coronary artery bypass grafting & 59 (14.9) & 52 (14.9) & 7 (15.2) & 0.949 \\ \hline Valve surgery & 32 (8.1) & 28 (8.0) & 4 (8.7) & 0.777 \\ \hline Hypertension & 234 (59.1) & 209 (59.7) & 25 (54.3) & 0.486 \\ \hline Stroke & 22 (5.6) & 19 (5.4) & 3 (6.5) & 0.731 \\ \hline Diabetes & 133 (33.6) & 121 (34.6) & 12 (26.1) & 0.252 \\ \hline Chronic kidney disease & 73 (18.4) & 61 (17.4) & 12 (26.1) & 0.154 \\ \hline Depression BDI-II \textgreater{} 13 & 84/389 (21.6) & 71 (20.7) & 13 (28.3) & 0.242 \\ \hline SF-36 overall (score) mean \(\pm\) SD & 90.1 \(\pm\) 12.4 & 90.3 \(\pm\) 12.4 & 87.7 \(\pm\) 11.3 & 0.178 \\ \hline \multicolumn{5}{c}{Functional status, n (\%)} \\ \hline NYHA I & 52 (13.1) & 45 (12.9) & 7 (15.2) & \\ \hline NYHA II & 277 (69.9) & 249 (71.1) & 28 (60.9) & 0.318 \\ \hline NYHA III & 67 (16.9) & 56 (16.0) & 11 (23.9) & \\ \hline Parameters & & & & \\ \hline NT-proBNP Medians [Q1–Q3] & 867.5 [362.0–1997.0] & 859.5 [356–1935] & 1139.5 [373.0–3030] & 0.185 \\ \hline Peak oxygen consumption pVO\({}_{2}\) (mL/kg/min) mean SD & 17.0 \(\pm\) 5.6 & 17.2 \(\pm\) 5.6 & 15.5 \(\pm\) 5.5 & 0.066 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Baseline characteristics of the hybrid comprehensive telerehabilitation group and comparison of baseline characteristics by degree of patients’ adherence to hybrid comprehensive telerehabilitation. The groups were comparable in terms of demographic data, baseline clinical parameters, and pharmacotherapy except for the following parameters: presence of atrial fibrillation/flutter, etiology of HF, and number of patients treated with antiplatelet drugs (aspirin/clopidogrel), non-vitamin K antagonist oral anticoagulants and statins.
# The Detection of Fake News in Arabic Tweets Using Deep Learning
## 4 Experiments and Results
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Embedding Model**} & \multirow{2}{*}{**Embedding Level Method**} & \multirow{2}{*}{**Dim**} & \multicolumn{5}{c}{**CNN**} \\ \cline{4-7}  & & & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{3}{*}{Keras Embedding Layer} & Word\_level & 100 & 0.9458 & 0.9462 & 0.9458 & 0.9459 \\  & & 200 & 0.9502 & 0.9500 & 0.9502 & 0.9497 \\  & & 300 & 0.9498 & 0.9500 & 0.9498 & 0.9496 \\ \hline \multirow{3}{*}{Word2 vec} & Word\_level & 100 & 0.9330 & 0.9324 & 0.9330 & 0.9315 \\  & & 300 & 0.9322 & 0.9311 & 0.9322 & 0.9307 \\ \hline \multirow{3}{*}{FastText} & Word\_level & 100 & 0.9410 & 0.9407 & 0.9410 & 0.9405 \\  & & 200 & 0.9376 & 0.9331 & 0.9376 & 0.9370 \\  & & 300 & 0.9484 & 0.9482 & 0.9484 & 0.9481 \\ \hline \multirow{3}{*}{ARBERT} & Word\_level & 768 & 0.9538 & 0.9534 & 0.9538 & 0.9531 \\  & Sentence\_level & & 0.9380 & 0.9375 & 0.9380 & 0.9368 \\ \hline \multirow{3}{*}{MARBERT} & Word\_level & 768 & 0.9564 & 0.9564 & 0.9564 & 0.9563 \\  & Sentence\_level & & 0.9446 & 0.9462 & 0.9446 & 0.9448 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The experimental results of the CNN-based model. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Embedding Model**} & \multirow{2}{*}{**Embedding Level Method**} & \multirow{2}{*}{**Dim**} & \multicolumn{5}{c}{**BiLSTM**} \\ \cline{4-7}  & & & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{3}{*}{Keras Embedding Layer} & Word\_level & 100 & 0.9478 & 0.9472 & 0.9478 & 0.9474 \\  & & 200 & 0.9444 & 0.9435 & 0.9444 & 0.9437 \\  & & 300 & 0.9454 & 0.9457 & 0.9454 & 0.9454 \\ \hline \multirow{3}{*}{Word2 vec} & Word\_level & 100 & 0.9470 & 0.9464 & 0.9470 & 0.9464 \\  & & 300 & 0.9460 & 0.9455 & 0.9460 & 0.9456 \\ \hline \multirow{3}{*}{FastText} & Word\_level & 100 & 0.9300 & 0.9305 & 0.9300 & 0.9300 \\  & & 200 & 0.9336 & 0.9336 & 0.9336 & 0.9335 \\  & & 300 & 0.9356 & 0.9354 & 0.9356 & 0.9353 \\ \hline \multirow{3}{*}{ARBERT} & Word\_level & 768 & 0.9548 & 0.9543 & 0.9548 & 0.9542 \\  & Sentence\_level & & 0.9476 & 0.9470 & 0.9476 & 0.9470 \\ \hline \multirow{3}{*}{MARBERT} & Word\_level & 768 & 0.9548 & 0.9546 & 0.9548 & 0.9545 \\  & Sentence\_level & & 0.9426 & 0.9437 & 0.9426 & 0.9428 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The experimental results of the BiLSTM-based model. Figure 2: The experimental results of the CNN-based model.