esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantifying Uncertainties in OC-SMART Ocean Color Retrievals: A Bayesian Inversion Algorithm
## 3 Methodology for Quantifying Uncertainties
### A Priori Estimation  
The _a priori_ stems from our general knowledge of the uncertainties, and can be estimated as a covariance matrix  
\[\mathbf{S_{a}}={\sigma_{a}}^{2}. \tag{13}\]  
where \(\sigma_{a}\) represents the standard deviations of the _a priori_. To estimate \(\sigma_{a}\), we compare the MLNN predictions (Figure 3) with a training dataset comprised of synthetic data. From this comparison, we take the average percent error (APE) for each \(R_{rs}\) wavelength band. These APEs are multiplied by the corresponding retrieval values \(\mathbf{x}_{Ret}\) to form \(\sigma_{a}=\text{APE}\times\mathbf{x}_{Ret}\). This approach follows the standard procedure for choosing good _a priori_ values [45; 46]. Using this estimate of \(\sigma_{a}\) we can rewrite Equation (13) as:  
\[\mathbf{S_{a}}=\text{APE}^{2}\times\mathbf{x}_{Ret}{}^{2}. \tag{14}\]  
As a note, the _a priori_ estimate can be expressed in different ways as it is simply a representation of our prior knowledge. We have chosen to define the _a priori_ as stated above. If, however, a better (more complete) knowledge of the uncertainty becomes available the estimate of the _a priori_ can be adapted to accommodate this updated information.  
Figure 3: Verification of the remote sensing reflectance (\(R_{rs}\)) at 412, 443, 488, 531, 547, 667, 678, and 748 nm retrieved by the MLNN algorithm against a synthetic testing dataset (\(N=\) 10,000).
# Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction
## 6 Analysis of Recent Works
### OCR Deep-Learning Works
#### 6.1.1 CNN-LSTM-CTC Based-Works [47; 48; 49; 50; 51; 52; 53]
The researchers created a dataset of 18 different generated fonts, and the text to be generated was from over two million words from the Arabic Wikipedia dump. When tested on the same fonts, a 98.76% character-recognition rate (CRR) and a 90.22% word-recognition rate (WRR) were achieved. The authors performed a good study, experimenting with different types of test set for the proposed model by varying the fonts and dataset sizes and adding noise. It is noteworthy that when the model was tested on unseen data, it achieved a CRR of 85.15% and a WRR of 23.7%, and when tested on noisy images, it achieved a CRR of 77.29% and a WRR of 14.18%. [PERSON] et al. [53] proposed a model to solve the issue of Ottoman OCR, since Arabic OCR does not perform well in this case. They focused on the Naskh font and created a dataset from printed Ottoman documents. Their model consists of CNN layers, followed by LSTM layers and, finally, CTC layers. They compared their model on the test set of 21 pages against ABBY Finereader, Google OCR, Tesseract Arabic, and Tesseract Persian. The resultant character-recognition rate was 97.37% for joined text, 96.12% for normalized text, and 88.86% for raw text. [PERSON] [54] proposed an encoder-decoder convolutional recurrent neural network using connectionist temporal classification loss function. Five public datasets were used to train the network: Alexu, SRU-PHN, AHDB, ADAB, and IFN/ENIT, with a total of 63,403 words. The results revealed that the beam-search decoder with data augmentation achieved the best result, with a 97.38% word-recognition rate and a 99.06% characterization rate on the IFN/ENIT dataset. Although, without augmentation, it scored a lower word recognition rate of 88.93% on the same set, it achieved better results, of 90.65% CRR and 70% WRR, for the new dataset created for testing, containing 100 unseen Arabic words and images.
## 4 Performance Evaluation of the Proposed Framework
### Test Case of the Proposed CEAS Framework  
This subsection presents a test case of the proposed CEAS framework for sentiment analysis on Twitter data using MapReduce utility in Hadoop clusters.  
Opinion mining is a well-known service to orchestrate what users post on different social media platforms. Many users share their views on different aspects of life every day. Efficient techniques should be developed to collect a significant amount of social media data and extract meaningful information from them [26]. To investigate the CEAS framework, Twitter data is taken as it has the ability for users to tweet about events, situations, feelings, opinions in real time. A massive amount of Twitter data is accumulated from various Twitter sources by using Twitter API for sentiment analysis of Big Data [23; 27].  
This work aims to provide an automatic scaling system which predicts the sentiment of the tweets of the people posted on social media. Hadoop clusters are employed to process the data and MapReduce programming is used to analyze the sentiment statistics on the Twitter dataset due to its capability in performing distributed and parallel processing on large data sets [10; 14]. The data from different universities in the United Kingdom is collected by following a Twitter API link to perform sentiment analysis. It is worth noticing that the analysis of the universities' Twitter data in the proposed CEAS framework has benefits over those based on the previous data sets in the sense that the tweets are now analysed in real-time.
# Deep Learning-Based Spread-Spectrum FGSM for Underwater Communication
## 4 SS-FGSM Performance Analysis  
In this section, the performance of the proposed SS-FGSM is addressed in terms of ABER performance, energy efficiency, and receiver complexity.
# The Role of Extracellular Matrix (ECM) Adhesion Motifs in Functionalised Hydrogels
### Hydrogels as Sophisticated, Biomimetic Materials
#### 1.2.3. Hydrogels as \"Smart\" Biomaterials
##### 1.2.3.1. pH-Sensitive Hydrogels  
pH-responsive hydrogels are a subgroup of stimuli-sensitive matrices that can respond to changes in environmental pH [32]. These systems are particularly translatable to a range of biomedical applications, as many anatomical sites for tissue engineering can be susceptible to changes in pH, sometimes as a result of the presence of disease [32], inflamed tissues or wounds [35]. When hydrogels experience a change in pH, volumetric alterations occur, which promotes reversible and repeatable expansion of the gel [36]. Swelling is associated with charge carrying groups in the hydrogel [37]. An ion concentration is established, and osmotic pressure establishes on the surface of the hydrogel, causing ions to move in or out of the gel. Cationic hydrogels, e.g., chitosan, swell at low pH following the acquisition of protons to the charge carrying groups. The positively charged domains then cause repulsion, leading to swelling. Anionic hydrogels, e.g., carboxymethyl chitosan, swell at higher pH levels following the removal of protons from the pendant groups. The negatively charged domains cause repulsion, leading to swelling [37]. pH-sensitive hydrogels have been used as drug-delivery vehicles for releasing therapeutic agents, triggered by a change in pH [38].
# Deep Transfer Learning-Based Fault Diagnosis Using Wavelet Transform for Limited Data
## 3 Wavelet-Based Deep Transfer Learning
### Wavelet Transformation  
Various faults deteriorate the power spectrum characteristic of the signal in the temporal period rather than the stationary behavior [21]. Thus, it is necessary to jointly investigate the signal characteristics of the time and frequency domains.  
The CWT separates the signal into several frequency components, and each component is then evaluated by the appropriate scale [22; 23]. It efficiently analyzes the abrupt transient behavior of industrial signals with rapidly changing frequencies over the slowly varying behavior [24]. Similar to the Fourier transform, the CWT essentially measures the similarity between a signal \(s(t)\) and a basis function called a wavelet, \(\psi(t)\)[23]. By integrating the scaling parameter \(a\) and the translating parameter \(b\), a continuous wavelet function \(\psi_{a,b}(t)\) is obtained  
\[\psi_{a,b}(t)=\frac{1}{\sqrt{|a|}}\psi\bigg{(}\frac{t-b}{a}\bigg{)}\,. \tag{1}\]  
The CWT is basically defined as the inner product between the signal \(s(t)\) and the wavelet function \(\psi_{a,b}(t)\), namely,  
\[C(a,b,s(t),\psi(t))=\int_{-\infty}^{+\infty}s(t)\psi_{a,b}^{*}(t)\mathrm{dt}= \langle s(t),\psi_{a,b}(t)\rangle \tag{2}\]  
where \(\psi_{a,b}^{*}(t)\) is the complex conjugate of \(\psi_{a,b}(t)\), and \(\langle\cdot,\cdot\rangle\) is the inner product [23]. Each coefficient is multiplied by the correctly scaled and shifted wavelet to produce the constituent wavelets of the signal.  
By varying the values of the scales \(a\) and the positions \(b\), we obtain the CWT coefficients as a function of two variables from the time-series signals with a size of \(N\times N\), as shown in Figure 1. The abrupt transitions are separable from smoother signal features since these result in large absolute values of the wavelet coefficients.  
Finding the optimal size of the image depends upon the capacity of the original signal and the computational complexity. While the computational complexity is generally proportional to \(N\), decreasing the value of \(N\) incurs a significant loss of the features. In this paper, we set \(N=64\) and scale it to \(224\times 224\) as the input image for the pre-trained GoogLeNet [20].
## 3 Results
### Real Time (RT) Analysis  
In the RT analysis, there were significant condition-by-time interactions for distance (F(10, 90) = 340.681, \(p<0.001\)), speed (F(10, 90) = 3.342, \(p=0.022\)), and HR (F(10, 90) = 3.052, \(p=0.002\)), but not VO2 (F(10, 90) = 2.667, \(p=0.054\)) (see Figure 2B, Figure 3B, Figure 4B,D and Figure 5B). The following is a description of the results for each variable in this analysis.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Analysis**} & \multirow{2}{*}{**Chronometer**} & \multirow{2}{*}{**HR (bpm)**} & \multicolumn{2}{c}{**VO2**} & \multicolumn{2}{c}{**RPE (6 to**} & \multirow{2}{*}{**Speed (m/min)**} & \multirow{2}{*}{**Distance (km)**} \\  & & & **(mL/kg/min)** & & & & \\ \hline \multirow{4}{*}{Overall} & Faster & 157 \(\pm\) 8 & 43.1 \(\pm\) 5.0 & 14 \(\pm\) 2 & 191.7 \(\pm\) 16.7 & 10.3 \(\pm\) 0.9 a  \\  & Normal & 159 \(\pm\) 9 & 42.5 \(\pm\) 5.0 & 14 \(\pm\) 1 & 191.5 \(\pm\) 18.3 & 11.5 \(\pm\) 1.1 a  \\  & Slower & 156 \(\pm\) 7 & 42.4 \(\pm\) 4.5 & 14 \(\pm\) 2 & 188.3 \(\pm\) 20.0 & 12.4 \(\pm\) 1.3 a  \\  & Faster & 169 \(\pm\) 7 & 46.8 \(\pm\) 6.7 & 17 \(\pm\) 2 & 206.7 \(\pm\) 18.3 b  & 2.06 \(\pm\) 0.18 b  \\ L10 & Normal & 169 \(\pm\) 7 & 44.9 \(\pm\) 5.7 & 17 \(\pm\) 2 & 201.7 \(\pm\) 23.3 & 2.02 \(\pm\) 0.23  \\  & Slower & 166 \(\pm\) 7 & 46.1 \(\pm\)5.8 & 17 \(\pm\) 2 & 198.3 \(\pm\) 20.0 b  & 1.96 \(\pm\) 0.20 b  \\ \hline \hline \end{tabular} Notes: Values are mean \(\pm\) SD for each variable (except for distance, which is the total in each condition for overall analysis and the total of the last 10 min in each condition for L10 analysis). a significant difference between all three conditions. \(p<0.001\) and d \(>0.75\). b significant difference between Faster and Slower Chronometers. \(p=0.003\) and d \(>0.40\).  
\end{table}
Table 1: Overall and Last 10 min (L10) analyses.  
Figure 2: Distance (km) in the (**A**) observed Time (OT) and (**B**) real time (RT) analysis. Mean (\(\pm\)SD) data for distance for each condition with respect to (**A**) OT (every 10-min period of the observed time and (**B**) RT (every 10-min period of the real time). Notes: * Significant differences between the three conditions, (\(p<0.001\)) for all comparisons. + Significant differences between NC and FC (\(p=0.026\)) in T1. ** Significant differences between NC and SC in T1 (\(p=0.046\)) and T2 (\(p=0.032\)).  
Figure 3: Speed (m/min) in the (**A**) observed Time (OT) and (**B**) real time (RT) analysis. Mean (\(\pm\)SD) data for speed for each condition with respect to (**A**) OT (every 10-min period of the observed time) and (**B**) RT (every 10-min period of the real time). Notes: * Significant differences between FC and both NC and SC.
## 4 Model Establishment  
### Model Information  
The primary distinction between mountainous and conventional structures lies in the uneven embedment of foundations. Below are the names of the various parts of a mountainous step-terrace frame structure, as illustrated in Figure 4. The structure is divided into two parts at the upper ground interface. The portion above the upper ground surface is referred to as the upper slope portion, while the portion below is termed the lower slope portion or step-terrace portion. The floors adjacent to the upper ground are termed upper ground floors, and the columns contained within are referred to as upper ground columns. Conversely, the floors adjacent to the lower ground are termed lower ground floors, with their respective columns known as lower ground columns. The floors within the step-terrace portion are labeled sequentially from top to bottom, starting from first floor, second floor, and so forth. Similarly, floors within the upper slope portion are labeled from bottom to top, starting from Upper 1 st floor, Upper 2 nd floor, etc. Now, let CmKn represent a step-terrace frame structure with m dropped layers and n dropped spans. For instance, a mountainous step-terrace frame structure with two dropped layers and three spans is denoted as C2K3. If this structure includes additional rocking walls and buckling-restrained braces (BRBs), it is termed a mountainous step-terrace frame structure with energy dissipation rocking walls and BRBs, abbreviated as C2K3 YBR, where \"R\" signifies the presence of buckling-restrained braces (BRBs). The ends of energy dissipation components and rocking walls are articulated at both ends and at the base, respectively.  
Using OPENSEES to establish a finite element analysis model, reinforced concrete frames were simulated using force-based beam-column elements, integrating stiffness matrices through fiber section integration. Concrete was modeled using the concrete02 linear tensile softening model, while steel was simulated using the Steel02 uniaxial isotropic strain-hardening model for reinforcement. The expected performance goal of the rocking wall is to remain elastic under major earthquakes. Therefore, elastic beam elements were used in the modeling. Connections between the rocking wall and the main structure were established using connectors to transmit horizontal shear forces. Truss elements were employed to simulate the connectors, limiting rotation outside the plane of the swinging wall. Acting as truss elements, they solely transmit horizontal shear forces [23]. Buckling-restrained braces (BRBs) were positioned at the base of the rocking wall to efficiently dissipate energy due to the concentration of deformation at the bottom of the rocking wall, facilitating easy replacement post-earthquake.
## 5 Conclusions  
In this paper, two control algorithms and control strategies were applied to the isolation system consisting of MR damper and isolation rubber bearing. The numerical simulation and shaking table test of model structure were carried out. The following conclusions can be achieved:  
1. Semi-active isolation system is shown to be adaptable to the external seismic input intensity, which may change the energy consumption mode with respect to external earthquake input magnitudes. It can effectively limit the deformation of the isolation layer as well as to reduce the acceleration response of the superstructure. Therefore, it demonstrates better damping effect than passive LRB isolation system.
2. The LQR control algorithm has been proven to be effective in terms of controlling the real time structural vibrations. Regarding the two control strategies, the switching control strategy is simpler than the continuous control strategy. However, in the switching control strategy, the deformation of the isolation layer is limited, and the acceleration of the upper structure is enlarged.
3. The MR damper responds more quickly to external excitations. When the structure is subjected to nine-degree earthquake, the damping force of the damper can reach the maximum capacity instantaneously to guarantee the energy dissipation performance. This can ensure the overall stability of the structure during super large earthquakes.  
Data curation, W.F.; algorithm development, C.Z.; formal analysis, M.L.; funding acquisition, W.F.; investigation, W.F.; project administration, C.Z.; software, M.L. and C.D.; writing--original draft, W.F.; writing--review and editing, C.Z. and C.D.  
The research work was funded by the Ministry of Science and Technology Of China (grant number: 2017 YFC0703603), the Nation Natural Science Foundation of China (grant number: 51,678,322) and the Shandong Provincial Natural Science Foundation, China (grant number: ZR2019 MEE020).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**El Centro Earthquake**} & \multicolumn{1}{c}{**Taft Earthquake**} \\ \hline
**Earthquake Magnitude** & **Max Acc. 0.36 g** & **Max Acc. 0.72 g** & **Max Acc. 0.72 g** \\ \hline Acceleration response of the shaking table & \(-\)0.351 & 0.727 & \(-\)0.368 \\ Peak acceleration of 1 st layer & \(-\)0.281 & \(-\)0.596 & \(-\)0.503 \\ Peak acceleration of 5 th layer & \(-\)0.440 & \(-\)0.662 & \(-\)0.662 \\ The 5 th layer acceleration amplification & 1.254 & 0.91 & 1.79 \\ factor under LRB isolation & 0.89 & 0.64 & 0.69 \\ The 5 th layer acceleration amplification & 8.38 & 24.15 & 14.36 \\ factor under MR isolation & 3.80 & 15.18 & 7.04 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The peak response isolating by the lead rubber bearing (LRB).
# Statistical Analysis of the Weight and Center-of-Gravity Position of an Empty Container Ship
## 4 Analysis of the Test Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} **Measurement** \\ \(\boldsymbol{M_{1}}\) \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Regression (34)** \\ \end{tabular} } & \multicolumn{6}{c}{**Weight \(\boldsymbol{M_{1}}\) Calculated from the Formulas in the Publication:**} \\ \cline{3-11}  & & & \multicolumn{2}{c}{**[PERSON] [14].**} & \multicolumn{2}{c}{**[PERSON] [29].**} & \multicolumn{2}{c}{**[PERSON] [15].**} & \multicolumn{2}{c}{**[PERSON] [15].**} \\ \cline{3-11}  & & **v** & **e** & **v** & **e** & **v** & **e** & **v** & **e** & **v** & **e** \\ \hline A & 4153.8 & 4462.7 & \(-\)7.4\% & 3888.3 & 6.4\% & 4671.6 & \(-\)12.5\% & 4364.7 & \(-\)5.1\% & 2349.7 & 43.4\% \\ \hline B & 9331.0 & 8778.7 & 5.9\% & 8029.7 & 13.9\% & 9514.4 & \(-\)2.0\% & 8219.6 & 11.9\% & 5383.8 & 42.3\% \\ \hline \hline \end{tabular}  
\end{table} 
Table 8: Weight of empty ship without equipment (weight group 1—\(M_{1}\))—Comparison of calculations with measurements for different methods. The comparison of the calculations shows that the developed regression formulas in Table 4 are, for the most part, more accurate than those presented in the literature. One reason for this is the fact that those methods were developed in the 1960s, i.e., for ships much older than those used in the herein presented analyses.
## 3 The Data Filtering Based Recursive Least Squares Algorithm
From Equations (6) and (7), once the estimate \(\hat{\mathbf{\vartheta}}(t)\) is obtained, the estimate \(\hat{w}(t)\) can be computed by  
\[\hat{w}(t)=y(t)-\hat{\mathbf{\phi}}^{\mathrm{ T}}(t)\hat{\mathbf{ \vartheta}}(t-1).\]  
Replace the unmeasurable noise terms \(w(t-i)\) in \(\mathbf{\psi}(t)\) with estimates \(\hat{w}(t-i)\) and define the estimate of \(\mathbf{\psi}(t)\) as  
\[\hat{\mathbf{\psi}}(t):=[-\hat{w}(t-1),-\hat{w}(t-2),\cdots,-\hat{w}(t-n_{c})]\in \mathbb{R}^{n_{c}}.\]  
Use the estimate \(\hat{\mathbf{c}}(t):=[\hat{c}_{1}(t),\hat{c}_{2}(t),\cdots,\hat{c}_{n_{c}}(t)]^{ \mathrm{ T}}\in\mathbb{R}^{n_{c}}\) to form the estimate of \(C(q)\) as follows:  
\[\hat{C}(t,q):=1+\hat{c}_{1}(t)q^{-1}+\hat{c}_{2}(t)q^{-2}+\cdots+\hat{c}_{n_{c }}(t)q^{-n_{c}}.\]  
The estimates of the filtered input \(u_{j\mathrm{f}}(t)\) and the filtered output \(y_{\mathrm{f}}(t)\) can be computed through  
\[\hat{u}_{j\mathrm{f}}(t) = \hat{C}(t,q)u_{j}(t)=u_{j}(t)+[u_{j}(t-1),u_{j}(t-2),\cdots,u_{j} (t-n_{c})]\hat{\mathbf{c}}(t),\] \[y_{\mathrm{f}}(t) := C(q)y(t)=y(t)+c_{1}y(t-1)+c_{2}y(t-2)+\cdots+c_{n_{c}  
\[= \boldsymbol{\phi}_{\mathrm{f}}^{\mathrm{\tau}}(t)\boldsymbol{\phi}+v(t). \tag{13}\]0  
Replacing \(\mathbf{\phi}_{\mathrm{f}}(t)\) and \(y_{\mathrm{f}}(t)\) in Equations (16)-(18) with their estimates \(\hat{\mathbf{\phi}}_{\mathrm{f}}(t)\) and \(\hat{g}_{\mathrm{f}}(t)\), and replacing \(\mathbf{\psi}(t)\) and \(w(t)\) in Equations (19)-(21) with their estimates \(\hat{\mathbf{\psi}}(t)\) and \(\hat{w}(t)\), we can summarize the data filtering based recursive generalized least squares (F-RGLS) algorithm for the multi-input OEAR systems:  
\[y_{\mathrm{f}}(t) := C(q)y(t)=y(t)+c_{1}y(t-1)+c_{2}y(t-2)+\cdots+c_{n_{c}  
\[= \boldsymbol{\phi}_{\mathrm{f}}^{\mathrm{\tau}}(t)\boldsymbol{\phi}+v(t). \tag{13}\]1 (22) \[y_{\mathrm{f}}(t) := C(q)y(t)=y(t)+c_{1}y(t-1)+c_{2}y(t-2)+\cdots+c_{n_{c}  
\[= \boldsymbol{\phi}_{\mathrm{f}}^{\mathrm{\tau}}(t)\boldsymbol{\phi}+v(t). \tag{13}\]2 \[y_{\mathrm{f}}(t) := C(q)y(t)=y(t)+c_{1}y(t-1)+c_{2}y(t-2)+\cdots+c_{n_{c}  
\[= \boldsymbol{\phi}_{\mathrm{f}}^{\mathrm{\tau}}(t)\boldsymbol{\phi}+v(t). \tag{13}\]3 (39) \[y_{\mathrm{f}}(t) := C(q)y(t)=y(t)+c_{1}y(t-1)+c_{2}y(t-2)+\cdots+c_{n_{c}  
\[= \boldsymbol{\phi}_{\mathrm{f}}^{\mathrm{\tau}}(t)\boldsymbol{\phi}+v(t). \tag{13}\]4  
The F-RGLS estimation algorithm involves two steps: the parameter identification of the system model--see Equations (22)-(29)--and the parameter identification of the noise model--see Equations (30)-(37). The F-RGLS algorithm can generate the parameter estimation of the multi-input OEAR system; however, the algorithm uses only the measured data \(\{u_{j}(i),y(i):i=0,1,2,\cdots,t\}\) up to time \(t\), not including the data \(\{u(i),y(i):i=t+1,t+2,\cdots,L\}\). Next, we will make full use of all the measured data to improve the parameter estimation accuracy by adopting the iterative identification approach.