esa-sceva/satcom-chunk-collection huggingface dataset:
# Settlement Prediction and Differential Settlement Criterion for Heightening and Thickening Leveee
## 3 Case Study
### Differential Settlement Control Criterion
#### 3.3.2 Relationship between Horizontal Stress of Pavement and Differential Settlement of HTL  
Figure 14 gives the relationship between the horizontal stress and differential settlement under different load conditions. As mentioned above, the reason for pavement cracking is that the horizontal tensile stress exceeds the ultimate tensile stress. Hence, the horizontal tensile stress of the pavement is a very important factor to control pavement cracking. Based on Figure 14, when asphalt concrete pavement (the ultimate tensile stress is 0.12 MPa) is used, the corresponding differential settlement criterion is approximately 4.3 cm.  
Figure 13: Horizontal stress of pavement.  
In fact, the differential settlement of the HTL is inevitable. Objectively, differential settlement that does not cause pavement cracking and reduces stability should be acceptable. A differential settlement control criterion can define how much differential settlement needs to be controlled, which is of great significance to guide engineering construction. Specifically, the settlement prediction value should be compared with the settlement control criterion. If the settlement prediction value is higher than the settlement control criterion, this section needs to be controlled.  
The control criterion of differential settlement is common in expressway widening projects [41; 42]. [PERSON] et al. [41] proposed that the postconstruction settlement of the embankment of an expressway splicing section should not exceed 10 cm. Here, 10 cm is the control criterion. [PERSON] [42] proposed a settlement ratio of 2.3% as a control criterion for mountainous highways.  
The differential settlement control criterion established in this study is not constant, which is the main difference from previous studies [41; 42]. The reasons are as follow: 1. The main cause of pavement cracking is differential settlement rather than uniform settlement, so it is not appropriate to use final settlement as the control criterion; 2. Pavement cracking is related to the tensile strength of the pavement structure. The ultimate tensile strength of different pavements is different. Therefore, differential settlement criterion should not be constant. Thus, the differential settlement criterion should be a value relative to the ultimate tensile strength of the pavement.
# Medium Access Control Protocols for Cognitive Radio Ad Hoc Networks: A Survey
## 3 Survey of State-Of-The-Art MAC Protocols
### Random Access Protocols
#### 3.1.4 Single-Transmit, Dual-Receive Radios  
Similarly to the WFC-MAC protocol, the authors proposed a distributed contention-based MAC protocol for multi-hop ad hoc networks, which uses the \"dual-receive\" capability of radios [69]. The main design goal of this protocol is to maximize network throughput by allowing several simultaneous transmissions to perform at the highest possible rate. A common channel has been assigned for control purposes. Nodes not transmitting always have one of their two receivers listening to the CCC, while receiving data over the other chain can alleviate the hidden terminal problem in the multi-channel scenario.  
To decrease the possibility of collision, every CR node \(i\) keeps up a list of channels that are available (AC\({}_{i}\)) and a list of nodes that are busy (BN\({}_{i}\)). AC\({}_{i}\) comprises channels that are not occupied and are free to use. BN\({}_{i}\) comprises the identification of nodes currently transmitting or getting information bundles in the area of node \(i\). Imagine a scenario where CR \(i\) has an information bundle to transmit to CR \(j\). First, it checks AC\({}_{i}\) and BN\({}_{i}\); if \(j\in\) BN\({}_{i}\) and AC\({}_{i}\
eq\ \emptyset\), node \(i\) fights over the CCC using a CSMA/CA-type protocol. After getting an RTS bundle from node \(i\), the expected recipient, for instance node \(j\), utilizes AC\({}_{i}\) alongside its own particular AC\({}_{i}\) to decide the fitting channel and rate to be used for the information delivery.
## 2 Mathematical Description of the Proposed Hybrid System Components
### Diesel System  
The diesel generator is used as a back-up, working just in case there is a need, is connected directly with the load, and starts when the battery is fully discharged and the load is more than 30% of its rated capacity. The model of the diesel generator regarding its output power is presented by the following Equation [25]:  
\[P_{dg}=\frac{F_{dg}(t)\ -\ A_{g}\times{P_{dg,out}}}{B_{g}} \tag{7}\]  
where \(F_{dg}\) is fuel consumption, \(P_{dg,out}\) is the output power of diesel generator, \(A_{g}\) and \(B_{g}\) are the constants of the linear consumption of the fuel.
# COVID-Related Misinformation Migration to BitChute and Odysee
## 3 Methodology
### Video Retrieval in BitChute and Odysee
#### 3.2.3 Manual Similarity  
In the case where the retrieved videos had no similar titles to the titles of the YouTube cases, the textual and visual similarity was not applicable. In this case, we applied manual annotation to the videos. This required two annotators and \(\approx\)50 h per annotator to inspect the videos. The annotators were asked to manually annotate the BitChute and Odysee videos based on the metadata provided for the YouTube query and references to debunking articles and other sources on the Internet. For example, for a video entitled 'Coronavirus and 5G by dr [PERSON]' the annotator found a debunking article on CBC([[https://www.cbc.ca/news/science/fact-check-viral-video-coronavirus-1.5506595](https://www.cbc.ca/news/science/fact-check-viral-video-coronavirus-1.5506595)]([https://www.cbc.ca/news/science/fact-check-viral-video-coronavirus-1.5506595](https://www.cbc.ca/news/science/fact-check-viral-video-coronavirus-1.5506595))), where screenshots of the original video were provided and the annotator could label BitChute and Odysee videos that depict these screenshots as duplicates. In total, 523 and 329 YT cases were manually annotated for BitChute and Odysee, respectively. Regarding BitChute, 15,813 videos were manually annotated, resulting in 1293 duplicates corresponding to 182 YouTube cases. Similarly, for Odysee, the manual inspection resulted in 968 duplicates from 222 YouTube cases.  
In summary, we created a dataset of 5906 videos posted on the BitChute and Odysee platforms after their removal from YouTube, which addresses RQ1. These videos were derived from 1048 and 591 COVID-related misinformation cases posted on YouTube, which amounts to \(\approx\)15% of the Oxford dataset, that is 1114 unique YouTube videos. We observe that a relatively small percentage of the videos migrated to BitChute and Odysee, but we also need to consider the following:  
* We used a semi-automated process to recover these videos, so there may be videos that migrated, but our process could not recover;
* We solely relied on the video titles in the first step to search and compare videos; the videos might have been posted on the target platforms with a different title, in which cases it would not be possible to recover them.  
Table 3 presents the number of BitChute and Odysee videos retrieved in each process and the number of YouTube videos they correspond. We collected the metadata of 3994 (due to using a custom-built scraper for collecting metadata, there are videos where the metadata are missing) Bitchute videos and 1810 Odysee videos. The unique value derives from the fact that, as presented above, the Oxford dataset contains duplicate cases of COVID-related misinformation, which means that the collected videos are the same for several cases.
###### Abstract  
Many applications in image analysis require the accurate classification of complex patterns including both color and texture, e.g., in content image retrieval, biometrics, and the inspection of fabrics, wood, steel, ceramics, and fruits, among others. A new method for pattern classification using both color and texture information is proposed in this paper. The proposed method includes the following steps: division of each image into global and local samples, texture and color feature extraction from samples using a Haralick statistics and binary quaternion-moment-preserving method, a classification stage using support vector machine, and a final stage of post-processing employing a bagging ensemble. One of the main contributions of this method is the image partition, allowing image representation into global and local features. This partition captures most of the information present in the image for colored texture classification allowing improved results. The proposed method was tested on four databases extensively used in color-texture classification: the Brodatz, VisTex, Outex, and KTH-TIPS2b databases, yielding correct classification rates of 97.63%, 97.13%, 90.78%, and 92.90%, respectively. The use of the post-processing stage improved those results to 99.88%, 100%, 98.97%, and 95.75%, respectively. We compared our results to the best previously published results on the same databases finding significant improvements in all cases.  
Keywords:colored texture pattern classification; global-local texture classification; color-texture features; color-texture feature extraction; bagging post-processing; BQMP and Haralick global-local feature integration +
Footnote †: journal: Applied
## 3 Proposed Procedure  
As can be seen in the mathematical equations of the original DSSN model, the basic nonlinear term of this model is the quadratic term which is repeated in all parts of the model. DSSN neuron is implemented on an FPGA digital board, but the quadratic terms of the equations cause the final system to be no more efficient. In this situation, the best method is to convert the nonlinear terms to new functions which have two basic conditions: first is the high degree of similarity between the original and proposed method and second is the low-cost digital implementation in terms of FPGA resources and higher frequency (speed-up) compared with the original DSSN model. In this approach, there are different acceptable ways to approximate and modify the original models, such as piecewise linear functions, absolute functions, and hyperbolic terms. When the original models are approximated by linear functions, the error level in the proposed model can be increased, but using the hyperbolic functions reduces the error calculation, and a high-degree of similarity will also be achieved. Thus, in this paper, we used the hyperbolic-based modifications. Another advantage of this method is that by using these hyperbolic terms, all nonlinear terms and functions in the differential equations are converted to digital SHIFTs and ADDs without any multiplications. Consequently, we have a new model with all aspects of the original model that it is efficient in terms of speed and costs compared with the original main DSSN model. In the proposed model, the equations are reformulated as follows:  
\[\frac{dV}{dt}=\frac{\phi}{\tau}(F(V)-n+I_{0}+I_{Stimulus}) \tag{5}\]  
\[\frac{dn}{dt}=\frac{1}{\tau}(G(V)-n) \tag{6}\]  
where  
\[F(V)=\left\{\begin{array}{l}a_{n}(Func(V))-c_{n}\quad;\quad V<0\\ -a_{p}(Func(V)-4 Vb_{n})+c_{p}\quad;\quad V>0\end{array}\right. \tag{7}\]  
\[G(V)=\left\{\begin{array}{l}k_{n}(Func(V))+q_{n}\quad;\quad V<r\\ k_{p}(Func(V))+q_{p}\quad;\quad V>r\end{array}\right. \tag{8}\]  
where  
\[Func(V)=0.6 sinh(0.65V)+0.6 cosh(1.5V)-0.5 \tag{9}\]  
In other words, based on Equations (3) and (4), when these equations are simplified, we have a new nonlinear function which is repeated in all equations. This new function can be formulated as follows:  
\[NL\text{-}Func(V)=V^{2}+0.5V+0.0625 \tag{10}\]  
Consequently, this nonlinear function can be replaced by \(Func(V)\) in all parts of the original mathematical equations. By this modification, the proposed model can be implemented in low-cost and high-speed states (This procedure is elaborated in detail in the hardware section). In this approach, as can be depicted in Figure 2, the original nonlinear function (\(NL\text{-}Func(V)\)) and the proposed hyperbolic term (\(Func(V)\)) have a high degree of similarity, and the error level between these two equations is in the low state, which is shown in the next sections. It is noticeable that based on Figures 1 and 2, the permissible range of parameter value changes is given between \(-0.5\) and \(+0.5\). Indeed, the modifications of the nonlinear terms of the neuron model have been conducted in this variation range.
## 3 Results
### Practicability of the rGMFM-66  
The rGMFM66 significantly reduced the time required to assess participants' gross motor function (_p_ < 0.001), decreasing from 38.5 min to 17.2 min. With an effect size of 1.9 (Cohen's d for paired samples), this demonstrates a large effect (Table 2 and Figure 1A).  
1. Boxplot illustrating the application times for the GMFM-66 and rGMFM-66. The rGMFM-66 required significantly less time for administration compared to the GMFM-66 (_p_ < 0.001).
2. Scatter plot showing the correlation between GMFM-66 and rGMFM-66 scores. Each point represents one participant. The dashed line represents the line of equality, indicating strong agreement between both measurements.
## 3 Results
### Heavy Metals Chemical Analysis (AAS)  
Generally, all study sites for river and borehole sources had concentrations below 0.2 mg L\({}^{-1}\) for the analyzed heavy metals (Table 4). The chemical results (Table 4) indicated that similar values below 0 mg L\({}^{-1}\) were obtained mostly for Cd and Pb using the PLS method (Table 3).  
Concentrations of Ag (0 mg L\({}^{-1}\)) using the PCA method were also similar in both water sources to the AAS values (\(<\)0.020 mg L\({}^{-1}\)). Both the PLS and PCA methods were able to detect Cu (0 mg L\({}^{-1}\)) similar to the chemical results (0.020 mg L\({}^{-1}\)). Even though the concentrations were slightly high using the AAS as contrasted to the FT-IR, they were mostly within acceptable threshold levels for national recommended standards in drinking water sources (i.e., Ag \(\leq\) 0.05 mg L\({}^{-1}\); Cd \(\leq\) 0.003 mg L\({}^{-1}\); Cu \(\leq\) 2 mg L\({}^{-1}\); Zn- \(\leq\) 5 mg L\({}^{-1}\)); WHO, [4] and SANS, [5]. The only expectation was for Pb, which was slightly higher (0.015 mg L\({}^{-1}\)) than the threshold levels (Pb \(\leq\) 0.01 mg L\({}^{-1}\)). Both the PLS and PCA methods detected higher Zn values (Table 3) than the actual concentrations (\(<\)0.060 mg L\({}^{-1}\)) obtained from an AAS analysis (Table 4).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Source**} & \multirow{2}{*}{**Site**} & \multirow{2}{*}{**Element**} & **October–December 2019** & **January–February 2020** \\ \cline{3-4}  & & & **Value (mg L\({}^{-1}\))** & **Value (mg L\({}^{-1}\))** \\ \hline \multirow{8}{*}{River} & \multirow{8}{*}{QM-2} & Ag & \textless{}0.020 & \textless{}0.020 ** \\  & & Cd & \textless{}0.003 * & \textless{}0.003 * \\  & & Cu & 0.018 & 0.008 \\  & & Pb & \textless{}0.015 & \textless{}0.015 * \\  & & Zn & \textless{}0.020 & \textless{}0.020 \\  & & Ag & \textless{}0.020 & \textless{}0.020 \\  & & Cd & \textless{}0.003 * & \textless{}0.020 \\  & & Cu & 0.015 *** & 0.013 \\  & & Pb & \textless{}0.015 *** & \textless{}0.015 * \\  & & Zn & \textless{}0.020 & \textless{}0.020 \\  & & Ag & \textless{}0.020 & \textless{}0.020 \\  & & Cd & \textless{}0.003 * & \textless{}0.003 * \\  & & Cu & 0.020 & 0.011 \\  & & Pb & \textless{}0.015 & \textless{}0.015 *** \\  & & Zn & 0.023 & \textless{}0.020 \\  & & Ag & \textless{}0.020 & \textless{}0.020 \\  & & Cd & \textless{}0.003 * & \textless{}0.003 * \\  & & Zn & 0.020 & 0.020 \\  & & Ag & \textless{}0.020 & \textless{}0.020 \\  & & Cd & \textless{}0.003 * & \textless{}0.003 * \\ \multirow{8}{*}{Borehole} & \multirow{8}{*}{QM-7} & Ag & \textless{}0.015 * & \textless{}0.005 * \\  & & Cd & \textless{}0.015 *** & \textless{}0.015 * \\  & & Zn & 0.129 & 0.217 \\  & & Ag & \textless{}0.020 *** & \textless{}0.020 \\  & & Cd & \textless{}0.003 *** & \textless{}0.003 * \\  & & Cu & 0.015 *** & \textless{}0.005 \\  & & Pb & \textless{}0.015 *** & \textless{}0.015 \\  & & Zn & 0.060 & \textless{}0.020 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Chemical analysis of heavy metals in water samples.
# Dual Attention Fusion Enhancement Network for Lightweight Remote-Sensing Image Super-Resolution
## 4 Results
### Ablation Study
#### 4.8.3 Effects of the FFEM
Moreover, the results of the combined version of CCA and SCGC achieve a higher Diffusion Index (DI), indicating that more input pixels are utilized, which leads to increases in PSNR and SSIM values. Therefore, by employing \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Variant**} & \multirow{2}{*}{**Params**} & \multirow{2}{*}{**Multi-Adds**} & **RS-T1** & **RS-T2** & **BSD100** & **Urban100** \\  & & & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** \\ \hline W/ 1x1 Conv & 443K & 22.4G & 29.81/0.7695 & 27.59/0.7722 & 27.54/0.7367 & 26.00/0.7828 \\ W/ BFM & 431K & 21.8G & 29.79/0.7689 & 27.61/0.7735 & 27.56/0.7373 & 26.01/0.7831 \\ FFEM w/o SCGC & 303K & 15.8G & 29.73/0.7671 & 27.53/0.7705 & 27.49/0.7350 & 25.80/0.7765 \\ FFEM w/o CCA & 406K & 20.4G & 29.79/0.7688 & 27.60/0.732 & 27.54/0.7365 & 25.95/0.7814 \\ FFEM & 431K & 21.8G & 29.82/0.7701 & 27.62/0.7737 & 27.57/0.7376 & 26.01/0.7832 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Ablation experiments on the design of the FFEM on RS-T1, RS-T2, BSD100, and Urban100 datasets for \(\times\)4 SR.  
Figure 9: Visualization results for the ablation experiments on the CSLB design. the dual-branch structure, the coverage of utilized pixels is expanded, further validating the effectiveness of the proposed FFEM.
# Non-Uniform Distribution of Contamination on Composite Insulators in HVDC Transmission Lines
## 3 Contamination Test in Wind Tunnel
### Experimental Methods  
The wind tunnel contamination test procedure is as follows: Before the test, wash the insulators samples thoroughly with Na\({}_{3}\)PO\({}_{4}\) solution to remove all traces of grease and dirt on insulators surface, and then put the insulators in a dry and clean place and let them dry naturally to prepare for the test.  
During the test, firstly control the parameters such as wind speed, particle (SiO\({}_{2}\)) concentration in the wind tunnel to be stable and meet the test setup requirements, then suspend samples into the wind tunnel, where the concentration of particles in the wind tunnel is 15 mg/m\({}^{3}\), the diameter of the particles is 250, 400, 800 and 2000 meshes respectively, the wind velocity is 1 m/s, 3 m/s, 5 m/s and 7 m/s respectively, relatively humidity is 60%, 70%, 80% and 90% respectively. Finally electrify the insulator with voltage of DC + 35 kV. Contamination particles were accumulated in 8 h.
## 3 Results and Discussion
### Single Galvanometer Scanner Model  
The laser scanning system design uses the AGV14 HPO galvanometer scanner manufactured by Aerotech Inc. The dimension of the X and Y mirrors are 28.2 \(\times\) 17 mm and 33.4 \(\times\) 24 mm, respectively. The face-sheet thickness of the mirror is 0.8 mm, with a rotation range of the mirror of \(\pm\)20 degrees. The parameters are set to increase the simulation model's reliability and accuracy. For the 2D scanner, the distance between mirror one (M1) and mirror two (M2) is 16.5 mm, and 27 mm for the distance between mirror two and the f-theta lens, as derived from the scanning system technical material. The detailed parameters related to the simulation model are listed in Table 1. The design parameters of a 1D scanning system using two galvanometers changes the mirror's surface 5 and 11 properties with the mentioned thickness and dimension, and places the center of gravity of the mirror on the rotation axis to increase the accuracy for simulation. These parameters for the 1D scanner will be optimized by ZEMAX software with merit functions. After optimization, the distance M1-M2 is 36.8 mm, and M2-the f-theta lens is 40 mm. Then, beam sizes X and Y are measured, and values, such as beam ellipticity, are calculated for efficient evaluation. The three-dimensional layout of the structure of the 1D scanner uses two galvanometers in combination with the designed f-theta lens with a focal length of f = 125 mm, as depicted in Figure 7.  
Figure 6: (a) The modulation transfer function (M.T.F.) and (b) the field curvature and distortion of the designed f-theta lens group. Figure 7: The three-dimensional layout of the structure of the 1D scanner uses two galvanometers in combination with the designed f-theta lens. Figure 8a shows simulation results comparing the roundness of the 1D scanning system using two galvanometers in the red circle and the 2D scanning system in the blue square. The cyan line shows the roundness of the ablations in ISO 11145:2018. The criterion for the roundness of the laser beam has been calculated as the ratio of the minimum diameter to the maximum beam diameter. For ISO standards with beam roundness requirements over 87%, the recommended effective extension length of the 1D scanning system is 3.16 mm on the maximum scan length of 75 mm of the f-theta lens, corresponding to 4.21% of the effective scan length. The maximum working distance with roundness greater than 87% is 48.1 mm, which corresponds to 64.13% of the effective scanning length of the f-theta lens. The telecentricity compensation is determined by the RAED operator in Zemax, shown in Figure 8b. This shows increased telecentricity in f-theta lenses through the use of a second mirror to change the pivot point.