esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges
## 3 OCR Process
### Preprocessing
#### 3.1.2 Denoising  
Unwanted changes in the intensity of an image that cover up the underlying image structure are known as noise. Noise can appear in images or documents that contain text in different ways, i.e., scanning documents, compressing files, printing documents, and noise during text recognition in the form of errors. For scanning documents, the noise introduced is in the form of changes to document quality, exposure, lighting, and blurring of the text. Noise can also be introduced during file compression, as it is used to reduce the actual size of the files, so it can add noise in the form of quality loss. Noise can also be introduced while printing the document due to the lack of printing quality of that particular machine or due to variations in ink or toner density or blurring.  
Noise and image distortions significantly degrade OCR performance [55]. Noise removal is necessary for every image-processing task, and filters are used to remove unwanted variations in the image while preserving the essential details. Filters are used according to the filter behavior [56]; for example, a Gaussian filter is used to smooth an image by reducing high-frequency noise. A median filter works by replacing pixel values with the median value of the neighbouring pixels to remove impulse noise.  
The authors of [57] proposed a deep learning architecture based on a convolutional neural network (CNN) for detecting and recognizing text in distorted document images of different languages. The proposed approach combines two specialized modules for text detection and recognition for automatically learning discriminative features for character recognition; it achieves outstanding performance, surpassing the best competing models by at least 13% for text detection and 7.5% for text recognition. The developed global model demonstrates a high level of robustness and significantly outperforms all other schemes in comprehensive benchmarks.  
Denoising is also performed using morphological operations. Morphological operations process images according to their shapes; each pixel corresponds to its neighboring pixels. Salt-and-pepper noise is a common type of noise that appears due to random black-and-white pixels in an image, and morphological operations are used to remove such noise. Erosion and dilation are commonly used morphological operations for denoising [58]. Erosion eliminates the isolated noise pixels, and dilation fills up small, empty holes around the image caused by noise [59]. Opening removes small noisy pixels, whereas closing operations fill empty gaps in the image. The combination of opening and closing is generally used to denoise the image in the preprocessing step, as shown in Figure 5.
# Maximizing the Capacity of Edge Networks with Multicasting
## 2 Related Work
### Influence of Routing Scheme on the Capacity  
The system capacity provides a reliable index to reflect the network performance. The papers [3,9] first proposed the computability of network capacity. The authors designed an optimal scheduling transmission scheme and established the upper limit of wireless network capacity. The authors in [10] further studied the influence of routing and scheduling algorithms on the capacity of ad hoc networks [11]. The authors used \"shortest-path like\" selection strategies to incorporate the path-length requirement [12]. [PERSON] et al. [4] concluded that the gain in capacity is substantial even by minimally increasing the number of cell stations. A similar result is given in [13], where the transmission efficiency of device-to-device communication is improved with user-assisted cellular connections. In addition, the authors in [5] employed the scale-free characteristic to formulate network capacity. They rewired a small number of the links connecting to the central nodes to improve the transmission efficiency.  
The aforementioned works give general solutions for computing system capacity based on the unicast routing mode. Conversely, the multicast transmission scheme has shown superiority in saving energy and improving delays [14, 15, 16]. [PERSON] et al. [17, 18] studied the upper and lower bounds of multicast capacity in wireless networks. Their resultsindicate that the number of nodes in each multicast session layer plays a big role in system capacity. Furthermore, the authors of [19] showed that the achievable transmission rate per multicast session is bounded by the number of multicast flows in large-scale wireless networks. In addition, the authors of [20] studied the scaling law of capacity and delay for broadcast routing in highly mobile networks. Recent work [21] considered the capacity constraints for enabling workload sharing and load balancing between servers.
# Challenges, Threats, Security Issues and New Trends of Underwater Wireless Sensor Networks
## 4 Security Issues of UWSNs
### Security Mechanisms
#### 4.2.3 Trust Management  
As an important complement to security defense, a cryptography-based trust management mechanism has significant advantages in intrusion detection. Due to the particularities and constraints of UWSNs discussed in Section 2, the research on trust management mechanisms in UWSNs faces challenges [71].  
The existing trust management mechanisms can be classified into three categories: centralized scheme, distributed scheme, and hierarchical scheme.  
_Centralized Scheme_: in a centralized scheme, a root node or a base station supply trust management for each node in the network. The centralized schemes are inappropriate for UWSNs, because the energy consumption of trust values exchanging between sensor nodes and the base station is an expensive burden.  
_Distributed Scheme_: in a distributed scheme, each node needs to compute and maintain the trust values of entire network. But it is impossible for UWSNs, as discussed in Section 2, as underwater sensor nodes are extremely limited in hardware resources. Hence, the distributed schemes are also inappropriate for UWSNs.  
_Hierarchical Schemes_: this kind of trust management scheme is used by the PKI. There is a root CA which is directly trusted. The CA may certify certificates themselves, or may certify certificates that certify other certificates (trusted introducer) down some chain. The hierarchical scheme is represented as a tree structure. The leaf certificate's validity is verified by tracing backwards from its issuer to other issuers, until a directly trusted root (CA) certificate is found.  
As discussed above, it is obvious that neither pure centralized nor pure distributed schemes are suitable for UWSNs. In hierarchical schemes, the computation and transmission of trust value is implemented in a hierarchical way. The trust values are passed and merged from a lower layer to an upper layer. Therefore, the hierarchical schemes are more appropriate for cluster-based topology which was widely used in UWSNs. In order to broadcast control information and retrieve the readings from underwater sensor nodes, the sink node must be able to authenticate itself. Moreover, in most applications the sink node acts as an interface between the UWSN and the land network.
## 2 Materials and Methods  
### Materials and Instruments  
MWCNTs were purchased from Future Carbon GmbH (CNT-K, Future Carbon GmbH, Bayreuth, Germany). Prior to monolith synthesis, the particles were washed in 1 M HCl (VWR Chemicals GmbH, Darmstadt, Germany) overnight at 80 \({}^{\circ}\)C to reduce the catalytic residue and other contaminants, as suggested by the manufacturer. Surface oxidation of the nanotubes (oxCNTs) was realized in 3 M H\({}_{2}\)SO\({}_{4}\) (VWR Chemicals GmbH, Darmstadt, Germany) and HNO\({}_{3}\) (Merck GmbH, Darmstadt, Germany) (ratio 3:1 \(\
icefrac{{\
u}}{{v}}\), 30 min, 80 \({}^{\circ}\)C) based on the works of [PERSON] et al. [33] and [PERSON] et al. [34]. After oxidation, the nanotube slurry was filtered and thoroughly washed with deionized water (DI-water) until a neutral pH was reached. PVA (89,000-98,000, 99% hydrolyzed, Sigma Aldrich GmbH, Taufkirchen, Germany) was utilized to reinforce the monolith's structure [11; 17; 35]. SDBS was purchased from Sigma Aldrich GmbH. Maleic acid (absolute, Ph. Eur., AppliChem GmbH, Darmstadt, Germany) was used as model adsorbate and electrolyte.  
Particle characterization was performed with Fourier-transform infrared spectroscopy (FTIR, ALPHA II, Bruker Co., Billerica, MA, USA), transmission electron microscopy (TEM, 100-CX, JEOL GmbH, Freising, Germany), and light microscopy (Axio 7 observer, Carl Zeiss GmbH, Munich, Germany). CNTs were dispersed and gelled using a Branson Digital Sonifier ultrasonic probe (Wattage 400 W, Branson Ultraschall GmbH, Furth, Germany). Wet gels were dried in a drying furnace (Heraeus Oven, Thermo Fisher Scientific GmbH, Dreieich, Germany) or in a freeze-dryer (Alpha 1-2 LDplus, Martin Christ GmbH, Osterode am Harz, Germany). Monolith characterization was carried out using a Gemini VII surface analyzer (Micromeritics GmbH, Unterschleissheim, Germany), and a Z2.5 tensile strength testing machine (ZwickRoell GmbH, Furstenfeld, Germany). To measure electrical conductivity, the monoliths were clamped in a 3D printed rig pictured in Figure S1 and contacted by two-point gold pins. Electrochemical experiments were conducted using a Gamry I 1000 E potentiostat (Gamry Instruments, Warminster, PA, USA). For all electrochemical experiments, an asymmetrical two-electrode setup was used. Planar monoliths functioned as working electrodes while a porous steel foam (Alantum Europe GmbH, Munich, Germany) with a pore size of 450 \(\upmu\)m was utilized as a counter electrode (see Figure S2). The electrodes were placed parallel in a self-printed test rig (Filament: ABS, Rudolf Wiegand und Partner GmbH, Olching, Germany) (see Figure S2). For potential-controlled adsorption experiments, the amount of adsorbed and desorbed analyte was quantified as triplicates using an Agilent 1100 series high performance liquid chromatography (HPLC) system (Agilent Technologies Inc., Santa Clara, CA, USA).
## 4 Q3: What Are the Methodologies Used to Overcome the Challenges in UIoT?
### Methods to Overcome the Technical Challenges of UIoT
#### 4.1.5 Localization Methods  
In [83; 84; 85; 86], the existing techniques for solving the localization problem in UIoT are discussed, and some methods are indicated herewith. In [83], [PERSON] et al. anticipated that localization is a crucial element in the protocol design given the proposed geographic routing protocols for underwater sensor networks. Suggestively, they resulted in accuracy and coverage of localization as essential factors for performance based on the surveyed centralized and distributed localization algorithms. In addition, [PERSON], [PERSON] et al. proposed the integrated navigation of the Inertial Navigation System (INS) in AUV with limited doppler velocity log (DVL) to update the depth of the system based on the pressure sensor integrated with AUV [84].
## 4 Prediction Model of WCFZ Height Based on DEWGO-BPNN
### Training and Prediction Results  
Through the hybrid DEGWO algorithm, the optimal weight and threshold are found and exported into the BPNN model. The improved prediction model first iteratively trains 104 samples to determine the best training model. The relationship between the prediction height of WCFZ simulated by the training model and the actual value is shown in Figure 5a. The deviation between the predicted value and the actual value of the training samples is relatively smaller, and the height of WCFZ can be predicted more accurately. In terms of the evaluation indexes reflecting the performance of the training model, the MAE, RMSE, MAPE, and R\({}^{2}\) are 2.3952, 3.4674, 5.3148%, and 0.99077, respectively. The prediction accuracy is 94.6852%. Moreover, 25 testing samples further verify the reliability of the model as shown in Figure 5b. The predicted value of the testing samples fluctuates around the actual value, and the deviation is relatively smaller.  
In addition, the fitness value can also determine whether the prediction results of the model reach the expected accuracy. RMSE is treated as a fitness value in this study, and the lower the RMSE value of the model, the better the prediction performance of the model. In Figure 6a, when the iteration number of the DEGWO algorithm is 10, the fitness value decreases to less than 6.0 and then tends to be stable. In the improved BPNN model, MSE can be used as a quantitative reflection of training performance. When the maximum number of training rounds is reached, the optimal training performance value is 1.2392 \(\times\) 10\({}^{-3}\) as shown in Figure 6b.  
Figure 5: Comparison between the actual and prediction values for training and testing data.  
Figure 6: DEGWO-BPNN model error iteration curve. (**a**) is fitness value of DEGWO algorithm iteration; (**b**) is training performance of improved BPNN model.
## 2 Materials and Methods
### Extraction Configurations  
One hundred forty pipelines performed brain extraction with one of the ten BET configurations described in Table 1. These ten BET configurations differ in their specific parameters or \"options\" set in the BET command. BET option \"B\" incorporates intensity inhomogeneity correction, also called bias-field correction. BET option \"R\" robustly locates the brain's center of gravity more accurately by performing multiple iterations of BET to refine the center-of-gravity estimation. Options \"B\" and \"R\" are mutually exclusive and optional. BET option \"f\" adjusts the fractional intensity threshold, which adjusts the size of the brain outline estimation, with values closer to 0 yielding larger outlines and values closer to 1 yielding smaller outlines. BET option \"g\" adjusts the vertical gradient in the fractional intensity threshold, modifying brain outline estimations along the vertical axis such that positive values increase the outline size at the brain's bottom and decrease it at the top, while negative values do the opposite. Unless different options are specified, BET runs with options f = 0.5 and g = 0 by default. The ten BET configurations we tested utilize one or none of the mutually exclusive primary options, \"B\" and \"R\", along with a combination of \"f\" and/or \"g\" option values. Fourteen pipelines performed brain extraction with the default configuration of opti BET from [PERSON] et al. (2018). The difference between optiBET and BET is that optiBET is a shell script designed to perform brain extractions even in brains with significant pathology. The first step of the optiBET script is performing a preliminary brain outline estimate using BET with options \"B\" and \"f = 0.1\" (BET | B,f0.1 in Table 1). This specific configuration of BET was found by [PERSON] et al. (2018) to generate the most accurate BET extractions in MRIs with multiple sclerosis. BET | B,f0.1 was further shown to improve brain extractions over default BET in T1-MPRAGE MRIs with TBI by [PERSON] et al. (2018). Following the initial BET extraction, optiBET uses both linear and non-linear registration to map the extraction onto the MNI template space. Third, it inverts the projection of a standard MNI brain-only mask from the template space back to the subject's original space. Finally, this brain mask is applied to the input image to mask out non-brain tissue, generating the final brain. The configuration of optiBET we tested was the default, which uses an MNI152 T1
# An Efficient BGV-type Encryption Scheme for IoT Systems
## 2 Background Knowledge  
### Plaintext Encoding  
Based on the underlying algebra, we know that a cyclotomic polynomial can be factorized into \(l\) irreducible polynomials as \(\Phi_{m}(x)=\prod_{i=1}^{l}F_{i}(x)\bmod p\). Hence, the quotient ring \(R_{p}=\mathbb{Z}_{p}[x]/\Phi_{m}(x)\)\(\cong\mathbb{Z}_{p}[x]/F_{1}(x)\)\(\otimes\)\(\cdots\)\(\otimes\)\(\mathbb{Z}_{p}[x]/F_{l}(x)\). Let \(d\) be the order of \((m,p)\), such that \(p^{d}=1\bmod m\). We have \(l=\phi(m)/d\). Here, \(\phi()\) is Euler function, \(d\) is the degree of each \(F_{i}(x)\), and \(l\) is the number of \(F_{i}(x)\), which represents the number of plaintext slots. Subsequently, a message can be encoded with the coefficients and modulus of a polynomial ring as the plaintext.  
As a concrete example, assume \(p=17\) and \(m=8\). We have \((p=17)^{d=1}=1\bmod(m=8)\) and thus \(l=(\phi(m)=4)/(d=1)=4\). As a result, \(\Phi_{8}(x)=x^{4}+1=(x-2)(x-2^{3})(x-2^{5})(x-2^{7})\bmod 17\). For a message \(5491=17^{3}+2\times 17^{2}+3\times 17+4\), we can express it as \(x^{3}+2x^{2}+3x+4\bmod(x^{4}+1,17)\). Because  
\[x^{3}+2x^{2}+3x+4 \cong 9\bmod(x-2,17)\] \[\cong 5\bmod(x-2^{3},17)\] \[\cong 15\bmod(x-2^{5},17)\] \[\cong 4\bmod(x-2^{7},17)\]  
\(x^{3}+2x^{2}+3x+4\) can be further represented by a vector (9,5,15,4). Next, we have \((x^{3}+2x^{2}+3x+4)\)\(\cdot\)\((x^{3}+2x^{2}+3x+4)\)\(\bmod\)\(x^{4}+1=3x^{3}+7x^{2}+3x+6\). Meanwhile, (9,5,15,4)\(\cdot\)(9,5,15,4)\(\bmod\)\(17=(81,25,225,16)\)\(\bmod\)\(17=(13,8,4,16)\). We may verify that \(3x^{3}+7x^{2}+3x+6\) can be represented by (13,8,4,16). Similarly, \((x^{3}+2x^{2}+3x+4)+(x^{3}+2x^{2}+3x+4)\bmod\)\(x^{4}+1=(2x^{3}+4x^{2}+6x+8)\cong(1,10,13,8)=(9,5,15,4)\)\(
## 2 Computational Approach  
Our first-principles calculations are based on the local density approximation (LDA) to the density functional theory and have utilized the Vienna ab initio Simulation Package (VASP) [19]. Here, the self-consistent Kohn-Sham equations [20] are solved in the LDA calculations. To approximate the exchange-correlation energy term, we used the Ceperly-Alder functional. This technique has been used in previous investigations of Si and Ge clathrates [16; 21; 22; 23]. Starting with structural optimization, we determine the lattice constant at the equilibrium geometry through a conjugate gradient (CG) algorithm. The energy cutoff parameter here is selected to be a default value (245.7 eV) according to pseudopotentials obtained from the projector augmented wave (PAW) method. Next, several pairs of acquired data describing the LDA energy vs. volume are fitted to the third-order Birch-Murnaghan equation of state (EOS) [24], giving rise to an energy-volume relation. Such a fitting procedure yields a determination on the equilibrium energy \(E_{0}\), equilibrium volume \(V_{0}\), bulk modulus \(K\) and \(K^{\prime}\)s pressure derivative \(K^{\prime}=\text{d}K/\text{d}P\) at absolute zero temperature. It is also noted that the total energy convergence is adjusted to be \(10^{-7}\) eV.  
The vibrational properties are calculated using the harmonic approximation in VASP. Our approach to conduct the predicted phonon dispersion curves, along with the subsequently derived effective force constant for the guest \"rattlers\" in the Si hexakaidecahedron cage, is summarized as follows. The first step is to obtain the dynamical matrix \(\mathbf{D(q)}\) after moving each guest atom initially located at the Si\({}_{28}\) cage center by a small finite displacement (\(U_{0}\) = 0.02 A), in the presence of a \(2\times 2\times 2\)\(k\)-point grid [25] as well as for wave vectors in the vicinity of the gamma (\(\Gamma\)) point [\(q\) = (0,0,0)]. Second, diagonalization of the dynamical matrix \(\mathbf{D(q)}\) allows us to determine the vibrational eigenvalues\(\omega^{2}(\mathbf{q})\) (squared frequencies) and eigenvectors. On the basis of derived acoustic phonon spectrum arising from the knowledge of \(\mathbf{D}(\mathbf{q})\), we predict the lattice thermal conductivity through simple kinetic theory, \(\kappa_{\rm L}=(1/3)C_{v}v_{\rm s}\lambda\)[26], where the mean free path \(\lambda\) is evaluated as the separation distance between the neighboring guests encapsulated in adjacent hexakaidacaehedron cage. Furthermore, the inverse cubic of phonon velocity of sound \(v_{\rm s}\) is obtained through the average of the inverse third power of the long-wavelength phase velocities of the three acoustic modes (see subscript i): \(1/(v_{\rm s})^{3}=1/3\Sigma\int(1/({\rm d}\omega_{1}(\mathbf{q})/{\rm d}\mathbf{q})^{3} )(1/4\pi){\rm d}\Omega\). \(C_{\rm p}\) is the specific heat per unit volume. Other property such as atomic displacement parameters can be gained from the localized, flat vibrational modes of guest rattling.
## 4 Real-Time Prediction
### KF-FFT-SVM Model Building
#### 4.2.3 SVM Model Training  
Before the SVM model was trained, we marked the displacement acceleration data of BD1, BD2, and BD3 manually. The data between 3 November and 4 November were marked with the label \"Positive\", which signified that there was an early warning, and the others were marked with the label \"Negative\", which signified that there was no early warning. Then, we use the marked BD1, BD2, and BD3 data to construct the displacement acceleration sequence \(A^{\prime}_{n}\),  
\[A^{\prime}_{n}=[A_{n},A_{n+1},\ldots,A_{n+m-1},\textit{label}] \tag{26}\]  
Figure 14: FFT of the displacement acceleration data in the secondary landslide.  
where \(m=2,3,4,5,6,7,8\), which is given in part 4.2.2; \(n=1,2,\ldots,336-m\); and _label_ is the marked value of \(A_{n}\).  
Formula (27) is an example of data set when \(m=2\). Here, \(A^{\prime}_{n(BD1)}\), \(A^{\prime}_{n(BD2)}\), and \(A^{\prime}_{n(BD3)}\) were chosen as training data. We also constructed \(A^{\prime}_{n(BD4)}\) as testing data with the same method.  
\[\left\{\begin{array}{c}A^{\prime}_{n(BD1)}=[A_{n},A_{n+1},\ldots,A_{n+m-1}, \textit{label}]_{(BD1)}\\ \textit{training data}:A^{\prime}_{n(BD2)}=[A_{n},A_{n+1},\ldots,A_{n+m-1}, \textit{label}]_{(BD2)}\\ A^{\prime}_{n(BD3)}=[A_{n},A_{n+1},\ldots,A_{n+m-1},\textit{label}]_{(BD3)}\\ \textit{testing data}:A^{\prime}_{n(BD4)}=[A_{n},A_{n+1},\ldots,A_{n+m-1}, \textit{label}]_{(BD4)}\\ \end{array}\right. \tag{27}\]  
Figure 15: Prediction results at different step length sequences.  
In SVM training, the radial basis function (RBF) function and SMO algorithm are used to search for the best \(C\) and \(\gamma\). The early warning prediction results of the testing data A\({}_{n(BD4)}^{{}^{\prime}}\) with different step sequences are shown in Figure 15. Figure 15 also shows the three step length training data scattered in 3D space, and it is clear that they cannot be well-separated in 3D space. Therefore, we separated them in a higher dimension. From Figure 15, we know that when \(m=6\), the optimal result is achieved. In this situation, the highest accuracy = 0.915, \(C\) = 4, and \(\gamma=1\). The result is in great agreement with the FFT analysis, which shows the best precursor landslide character is near 0.2 Hz, which is equal to six step lengths in the time domain.