esa-sceva/satcom-chunk-collection huggingface dataset:
# An Innovative Information Hiding Scheme Based on Block-Wise Pixel Reordering
## 2 Background of the Work
### Data Extraction of the Codeword Index Reordering  
When a receiver receives a stego codebook \(SCB=\{scw_{0},\ scw_{1},\ \cdots,scw_{m-1}\}\), the stego codebook needs to be sorted by the projected values of codewords using the received projecting line \(\overset{\rightarrow}{OD}\) before extracting secret data. The following steps showing how the secret data are extracted:  
Step 1: Project the codewords in \(SCB\) to the line \(\overset{\rightarrow}{OD}\) and sort the codebook by using the projected values of the codewords to obtain the recovered codebook \(RCB\).  
Step 2: Initialize recovered secret \(RS\).  
Step 3: Initialize the current codeword index \(ci\) to \(0\).  
Step 4: Match the current codeword \(scw_{i}\) with the codewords in the recovered codebook \(RCB\) and obtain the index \(si\) in the \(RCB\).  
Step 5: Convert \(\left(si\right)_{10}\) to a binary bit stream \(RS^{\prime}=\left(rs_{0}\ \middle\|\ rs_{1}\ \middle\|\ \cdots\ \middle\|\ rs_{k^{\prime}}\right)_{2}\).  
Step 6: Append \(RS^{\prime}\) to \(RS\).  
Step 7: Increase the current codeword index \(ci\) by \(1\).  
Step 8: Repeat Step 4 through Step 7 until all codewords in \(SCB\) are exhausted.  
Continuing to use the example above, Figure 4 demonstrates how the data are extracted from the stego codebook \(SCB\).
# Coupled-Error-Based Formation Control for Rapid Formation Completion by Omni-Directional Robots
## 5 Discussion  
As mentioned in the previous sections, the performance of trajectory tracking and the speed of cluster formation are determined by the coupling ratio \(q\), and these two elements have a trade-off relationship. Therefore, to enhance the performance of both factors, it is necessary to determine \(q\) according to the overall system's situation. Replacing the constant \(q\) used earlier, \(q\) is determined according to the completeness of the next formation as follows:  
\[q=k_{d}d\]  
Here, \(d\) represents the distance between the follower and its desired position needed to form the formation, and \(k_{d}\) is a positive constant. As evidenced in Figures 6 and 7, lower values of q enhance tracking performance, while higher values of q significantly influence the elements needed to complete the formation. Therefore, \(q\) is determined by d. The results, when \(k_{d}=50\), are illustrated in the following Figure 8. As can be seen in Figure 8a, at the beginning of the simulation, the leader moves towards the follower to maintain the formation at point A, after which the trajectory following performance improves, as seen at point B. Compared to Figure 7, it is evident that the robots complete a formation quickly with a large q applied, yet exhibit similar trajectory following characteristics as when q is small. In Figure 8c-e, it can be seen that the CE and the distance errors between each robot are bounded near zero even as q changes with distance. Additionally, as observed in Figure 8b, the distances converge rapidly.  
Figure 7: Performance changes due to variations in the constant coupling ratio, q: (**a**) q = 1; (**b**) q = 10; (**c**) q = 50; (**d**) comparison of distances between the cases of various q.
## 3 Experiment and Results
This trend was also consistent with the change in the electromechanical coupling coefficient obtained from electrical characterization, further demonstrating that the PMUT exhibits good electromechanical coupling performance at higher temperatures. To investigate the repeatability of the devices after they were operated at high temperatures, a heating-cooling cycling experiment was conducted, and the change in the resonant  
Figure 12: Amplitude and mechanical resonant frequency variation with temperature for devices with a 1000 \(\upmu\)m back cavity (LDV test results). \begin{table}
\begin{tabular}{l l l l l} \hline
**Device** & **Initial Warp (\(\upmu\)m)** & **Warp at 50 \({}^{\circ}\)C (\(\upmu\)m)** & **Warp at 100 \({}^{\circ}\)C (\(\upmu\)m)** & **Warp at 150 \({}^{\circ}\)C (\(\upmu\)m)** \\ \hline
600 \(\upmu\)m No. 1 & 0.20 & 0.34 & 0.55 & 0.87 \\
600 \(\upmu\)m No. 2 & 0.21 & 0.36 & 0.57 & 0.93 \\
1000 \(\upmu\)m No. 1 & 0.19 & 0.58 & 1.12 & 1.83 \\
1000 \(\upmu\)m No. 2 & 0.3 & 0.71 & 1.24 & 2.15 \\ \hline \end{tabular}
\end{table} 
Table 5: Warpage variation of the devices with temperature (room temperature, 50 \({}^{\circ}\)C, 100 \({}^{\circ}\)C, and 150 \({}^{\circ}\)C). frequency of the devices was characterized. Both types of devices were slowly heated to 350 \({}^{\circ}\)C and then slowly cooled to room temperature, with measurements taken every 10 \({}^{\circ}\)C. The results are shown in Figure 13. The experiment revealed that both types of devices exhibited changes in the initial resonant frequency after the experiment returned to room temperature. When the resonant frequency of both devices was measured again after 24, 48, and 72 h, it was found that the frequency had not returned to its original value, indicating that PMUTs based on scandium-doped aluminum nitride experience performance changes after operating at high temperatures, which are difficult to recover in the short term. Figure 13: Resonant frequency variation during heating and cooling cycles for devices with different back cavity sizes: (**a**) 600 \(\upmu\)m and (**b**) 1000 \(\upmu\)m.  
A 3D profilometer was used to characterize the devices in the different states. However, because of the very small frequency deviation, only slight changes were observed, making it impossible to determine whether the changes were due to the effect of the high temperature or the measurement error. Therefore, these results are not detailed in this paper.
# Research on Public Service Request Text Classification Based on BERT-BiLSTM-CNN Feature Fusion
## 3 Model Design
### Bert-Bilstm-Cnn  
BiLSTM-CNN is the downstream module, and BERT is the upstream module in the BBLC model architecture. Figure 3 shows the approximate working process of the BBC model:  
1. First, process the data set. \(W_{1},W_{2}\ldots W_{n}\) is the input text in Figure 3. The input text is converted into word vectors \(T_{1}\), \(T_{2}\ldots T_{n}\) after the Transformer encoder, which can be trained as model parameters.
2. BERT output continues to feed into a two-way LSTM network. BiLSTM uses forward and backward LSTM units for bidirectional modeling of input sequences. The information included in the input sequence is captured by the hidden states of each time step, which are the BiLSTM outputs. CNN receives the spliced forward and backward outputs of the BiLSTM.
3. Multiple convolution kernels of the CNN convolution layer extract local features by sliding over the input data. The pooling layer receives local feature maps from the convolutional layer as input and performs max pooling operations for each feature map.
4. The fully connected layer receives the feature representation from the BiLSTM-CNN module and obtains the output \(y\) through the linear transformation of the weight matrix. Finally, the Softmax function receives the output of the full connection layer and processes it indexially and normalized to obtain a probability \(p_{i}\) for each category. The calculation formulas are (5) and (6). \[y=f(W*x+b)\] (5) \[p_{i}=\frac{e^{y_{i}}}{\sum_{j=1}^{n}e^{y_{j}}}\] (6)  
Formula (5): \(f\) represents the activation function; \(W\) represents the weight matrix, \(x\) is the input eigenvector, and \(b\) is the bias value. Formula (6): \(y_{i}\) represents the score of the \(i\) category, and \(n\) represents a total of \(n\) categories.
## 2 Numerical Modeling of Ropes and Large Floating Structures Based on the ANCF
### Mass Matrix and External Load Matrix
#### 2.3.2 Gravity and Buoyancy  
The work performed by external forces can be represented as the product of external forces acting on a point on the cable and the corresponding displacement along global coordinates. The equivalent external force vector \(\mathbf{F}_{U}\) at any point in the element can be derived based on the variation principle and through the integration of interpolation functions:  
\[\begin{array}{ll}\mathbf{F}_{U}&=\frac{\partial W_{p}}{\partial\mathbf{q}^{T}} =\frac{\partial}{\partial\mathbf{q}^{T}}\Big{(}\int_{0}^{l_{0}}u_{x}^{T}fdx \Big{)}=\frac{\partial}{\partial\mathbf{q}^{T}}\Big{(}u_{e}^{T}\int_{0}^{l_{0}} \mathbf{N}^{T}fdx\Big{)}\\ &=\int_{0}^{l_{0}}\mathbf{N}^{T}fdx\end{array} \tag{36}\]  
where \(W_{p}\) represents the work performed by the equivalent external force, and \(u_{x}\) and \(f\) represent the displacement per unit length and the external force received at the local coordinate \(x\), both of which are functions of the local coordinate \(x\). From Equation (36), the gravity and buoyancy acting on the ANCF element can be obtained as follows:  
\[\mathbf{F}_{W}=\int_{0}^{l_{0}}\mathbf{N}^{T}\rho\mathbf{g}dx=\bigg{(}p_{l}l_ {0}-\frac{1}{4}\pi\rho_{w}D^{2}l_{0}\bigg{)}\mathbf{N}_{a}\mathbf{g} \tag{37}\]  
\[\mathbf{N}_{a}=\int_{0}^{1}\mathbf{N}^{T}d\xi=\begin{bmatrix}\frac{1}{2}&0& \frac{1}{12}&0&\frac{1}{2}&0&-\frac{1}{12}&0\\ 0&\frac{1}{2}&0&\frac{1}{12}&0&\frac{1}{2}&0&-\frac{1}{12}\end{bmatrix}^{T}\]  
where \(\mathbf{g}=\begin{bmatrix}0&-9.807\end{bmatrix}^{T}\) is the gravitational acceleration vector, and \(D\) is the outer diameter of the cable.
# Ferroelectric Materials: A Novel Pathway for Efficient Solar Water Splitting
## 4 Ferroelectric Materials for Photoelectrochemical (PEC) Water-Splitting Devices
### (3) SrTiO\({}_{3}\)  
Among the various ternary metal oxides, SrTiO\({}_{3}\) (STO), which is a stable n-type semiconductor with a bandgap value of 3.25 eV [74], has been utilized as a promising photoelectrode for splitting water into hydrogen and oxygen, with many outstanding properties. However, a wide bandgap limits light absorption to the UV region.  
Since the first report by [PERSON] et al. [75] on the usefulness of STO for light-assisted photocatalytic water splitting, numerous studies have focused on bandgap engineering [76; 77; 78] and the tuning of the charge separation ability [79; 80; 81] to overcome the aforementioned drawbacks and achieve effective photoelectrochemical water splitting. It is expected that a heterojunction of STO with a small bandgap material should be able to narrow the overall bandgap and help suppress the limitation of visible light absorption.  
TiO\({}_{2}\) is a conventional n-type semiconductor, which is considered as the most promising photoanode material [82; 83; 84; 85], and Cu\({}_{2}\)O is one of the most investigated p-type semiconductors for PEC water-splitting. It is reported that loading Cu\({}_{2}\)O particles on TiO\({}_{2}\) nanotube arrays can significantly improve visible light absorption compared with pure TiO\({}_{2}\) nanotubes [86]. Meanwhile, there is a similar valence and conduction band position between STO and TiO\({}_{2}\), with a conduction band edge that is about 200 mV higher than TiO\({}_{2}\)[87]. Recently, Cu\({}_{2}\)O has been combined with STO with controllable thickness to form a Cu\({}_{2}\)O/STO heterojunction photoelectrode, which has been proven to be effective in driving the separation of charge carriers by an electric field generated at the Cu\({}_{2}\)O/STO interface [88]. Under illumination, the Cu\({}_{2}\)O/STO photoelectrode presented a p-type photocurrent used for hydrogen generation. As expected, the Cu\({}_{2}\)O/STO heterojunction with an optimal thickness of 343 nm exhibiteda photocurrent density of 2.52 mA/cm\({}^{2}\) at 0.8 V versus saturated calomel electrode (SCE), which is 25 times higher than that of pristine Cu\({}_{2}\)O (0.10 mA/cm\({}^{2}\) at 0.8 V versus SCE), as presented in Figure 7. This result is attributed to the synergy of broadening solar absorption and improved charge transportation in the Cu\({}_{2}\)O/STO heterojunction. In another report, the Cu\({}_{2}\)O/STO heterojunction photocathode was investigated using a less complicated method in which Cu\({}_{2}\)O nanoparticles (NPs) were loaded onto the surface of STO nanocubes (NCs) through a facile deposition--precipitation technique [89].
# Research on Shore-Based River Flow Velocity Inversion Model Using GNSS-R Raw Data
## 2 River Flow Velocity Inversion Model
### GNSS-R Reiver Velocity Inversion Process  
Under the shore-based experimental conditions, it could be assumed that the river flow velocity of the target area is a fixed vector over a short time. Therefore, velocity inversion results are calculated every two minutes in this paper. The whole inversion process includes three steps:  
First of all, by preprocessing to remove corrupted raw IF data, the Lissajous figure is used to judge the multi-frequency relationship and phase difference between two waveform signals (I branch, Q branch) in the field of signal processing. It plays an important role in regulating the signal-tracking loop [31]. In addition, the Lissajous figure is also used as a criterion for judging whether the carrier phase replica \(\varphi_{Local}\) could take the place of the direct signal carrier phase \(\varphi_{direct}(t)\). Figure 1 is the Lissajous figure of the GPS PRN 9 for 2 min on 22 April 2021, 10:40 am\(-\)10:42 a.m. local time (LT).  
Then, in the data processing stage, the residual phase \(\varphi_{raw}\) is obtained by processing the raw IF data. The details of processing raw IF data and how to obtain \(\varphi_{out}\) will be introduced in Section 2.2. After further processing of fast Fourier transform (FFT) data, the flow rate \(V_{flow}\) is inverted. The method of obtaining the Doppler frequency from the interferometric phase and using the river inversion model for inversion is introduced in Section 2.3. During the experiment, the average river flow velocity \(V_{real}\) was recorded every 5 min in the experimental area using a contact current meter. The current meter model is H-ADCP CM300, and the system frequency is 300 kHz, which calculates the flow velocity by analyzing the frequency shift of the Doppler.  
Eventually, the inversion river flow velocity \(V_{flow}\) is compared with the real velocity \(V_{real}\) to verify the precision and conduct further error analysis. Figure 2 is the flowchart of the GNSS-R river flow velocity inversion method.  
Figure 1: The Lissajous figure of the GPS PRN 9 direct signal in 2 min.  
Figure 2: Flow chart of stream velocity inversion method.
# Ferroelectric Materials: A Novel Pathway for Efficient Solar Water Splitting
## 4 Ferroelectric Materials for Photoelectrochemical (PEC) Water-Splitting Devices
### (4) PbTiO\({}_{3}\)
#### (5) YFeO\({}_{3}\)  
YFeO\({}_{3}\) is one of the ferrite perovskites featuring a bandgap energy of approximately 2.3-2.4 eV. Although several studies have been performed on the utilization of YFeO\({}_{3}\) as a photocatalyst for water splitting [98; 99; 100], research on YFeO\({}_{3}\) as a photocathode material in PEC devices remains limited. YFeO\({}_{3}\) thin films are prepared by two different methodologies: nanoparticle thin film electrode via an ionic liquid protocol [68], and compact thin film electrode via a sol-gel method. Both types of electrodes have been evaluated for their PEC activity. They show exhibited cathodic photocurrent responses with an onset potential of 1.05 V versus RHE with complex dynamic features that should be addressed to application in PEC water splitting [102].
# Monte-Carlo-Based Estimation of the X-ray Energy Spectrum for CT Artifact Reduction
## 2 Materials and Methods
### Monte Carlo Simulation
#### 2.1.2 Calculation of the Emitted X-ray Spectrum  
For beam hardening and scattering artifact reduction methods, it is required that the X-ray energy spectrum be independent from the flat panel energy response. Therefore, after validation of the modeled FleXCT system including the X-ray tube and flat panel (see Section 3, \"Results and Discussion\"), the X-ray tube of the FleXCT scanner was simulated and the emitted X-ray energy spectrum was calculated in front of the target just after the beryllium window, using a point detector (tally type F5). The model used for calculating the tube spectrum for 2 different cases is shown in Figure 2: (1) for tube voltage of 230 kV and without any external filter (see Figure 2a), and (2) for tube voltage of 230 kV and with a 1 mm copper filter (see Figure 2b).  
Figure 1: Schematic view of the simulation model of the FleXCT scanner, showing the geometry of the reflection type X-ray tube and different material layers present in the flat panel X-ray detector.  
Figure 2: Schematic view of the simulated FleXCT scanner’s X-ray tube for calculating X-ray spectrum independent of the detector response for 2 different cases: (**a**) without additional filter, (**b**) with a 1 mm copper filter.
# Securely Computing the Manhattan Distance under the Malicious Model and Its Applications
## 4 The MPC Protocol of Computing Manhattan Distance under the Semi-Honest Model  
In Reference [29], the method of calculating the Manhattan distance between two points is converted into the Hamming distance between vectors. The specific MPC protocol is as follows (Algorithm 1):  
```
Input: [PERSON] owns point \(P(x_{1},y_{1})\) and [PERSON] owns point \(Q(x_{2},y_{2})\). Output:\(|x_{2}-x_{1}|+|y_{2}-y_{1}|\). Preparation: [PERSON] and [PERSON] construct the vectors \(A=(P_{11},\ \cdots,\ P_{1n},\ P_{21},\ \cdots,\ P_{2n})\) and \(B=(Q_{11},\ \cdots Q_{1n},\ Q_{21},\ \cdots,\ Q_{2n})\) corresponding to point \(P\) and point \(Q\) respectively using the coding rules.
1. [PERSON] gets the public-private key \(pk(A)\) and \(sk(A)\) of GM encryption algorithm and sends \(pk(A)\) to [PERSON]. [PERSON] encrypts \(A\) to obtain \(E(A)=(E(P_{11}),\ \cdots,\ E(P_{1n}),E(P_{21}),\cdots,\ E(P_{2n}))\), and sends \(E(A)\) to [PERSON].
2. [PERSON] encrypts vector \(B\), calculates \(R=(E(P_{11})E(Q_{11}),\ \cdots,\ E(P_{1n})E(Q_{1n}),\ E(P_{21})E(Q_{21}),\ \cdots,E(P_{2n})E(Q_{2n}))\), disturbs the order of elements in \(R\) to get \(R\), and sends it to [PERSON].
3. [PERSON] decrypts \(R\) using the private key \(sk(A)\), obtains \(D(R)=(d_{11},\cdots,d_{2n})\), calculates \(y=d_{11}+\cdots+d_{2n}\), and sends \(y\) to [PERSON]. The protocol ends.
```  
**Algorithm 1** Securely computing the Manhattan distance under the semi-honest model  
This protocol is secure for [PERSON] and [PERSON] under the semi-honest model. However, if either [PERSON] or [PERSON] is malicious, the protocol will no longer be secure. Solutions need to be designed for possible malicious behavior.
# Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction
### Motivation and Why This Is an Important and Interesting Work  
Pragmatically and concisely, the motivation of this work is to fill this noticeable gap in the literature, and the obvious need for a work that critically analyzes both recent OCR and text-correction works and applies deep-learning techniques. Specifically, this analysis helps to identify recent trends and challenges and provides strong recommendations for the future for researchers and practitioners, along with potential future applications.  
The use of OCR is of great importance as it allows users to extract text from images, which supports the digitization process by saving resources, such as time and money. Arabic OCR is still not sufficiently mature in comparison to other languages. This study critically analyzes and presents current trends, challenges, and future trends in deep-learning OCR and text correction in Arabic.  
Researchers, practitioners of OCR and deep learning, and interested readers can gain enormous benefits from this analysis. Researchers can learn what the current challenges are and the areas of focus that will be required in future work. Practitioners can determine the current trends in models for different uses and which potential applications need to be developed. In addition, practitioners from multiple sectors, such as government, healthcare, and the legal sector, can benefit from this analysis by discovering the latest models and their potential uses. Interested readers in Arabic may find the introduction and formal background sufficient to introduce themselves to this field. Moreover, they can be introduced to Arabic OCR holistically in a way that further increases their interest. Furthermore, researchers from other fields, such as calligraphy, paleontology, and paleography, can cooperate with deep-learning researchers and determine the current status of the field and its future directions.  
This work can be used by multiple audiences to discover both the general progress of the field and specific developments within it, according to their needs. The findings of this review provide clear remarks and guidance on the current challenges in models, datasets, and future trends and extensions.
# Review of Anomaly Detection Algorithms for Data Streams
## 7 Application of Data Stream Anomaly Detection Algorithms
### Significance, Value, and Potential Impact of Using Data Stream Anomaly Detection Algorithms
Therefore, when applying data stream anomaly detection algorithms based on offline learning, semi-online learning, and online learning, careful consideration of specific business scenarios, data characteristics, and application requirements is necessary. Ensuring the selection of appropriate algorithms; conducting thorough experimentation and evaluation; and balancing factors such as performance, accuracy, interpretability, and resource consumption are essential to achieve optimal anomaly detection effectiveness and practical value.