esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data Collection
### GNSS Stations with 1 s Sampling Rate Data  
The station LASA from BDS Experimental Tracking Network (BETN) is located in Lhasa city, 645 km away from the epicenter. The recording of BDS and GPS data at LASA was applied to study the performance of BDS PPP to retrieve high-precision seismic displacements from the Nepal earthquake.  
In order to generate the high-rate GNSS satellite clock products, the data from 1 s GNSS stations from MGEX and BETN were collected and utilized (the blue and yellow dots in Figure 1). BETN network was built and maintained by Wuhan University as a part of continuous global observation network to solve the precise orbit and satellite clock products of BDS in real time. Most stations from BETN are equipped with UB240-CORS receivers manufactured by the Beijing Unicore Communication Company, which can collect both the L1/L2 pseudo-range and phase observation of GPS and B1/B2 of BDS. Two stations from BETN, NTSC in Xi'an and DHAB in United Arab Emirates, can record 1 s observations. Meanwhile, there are 17 stations from MGEX can gather 1 s sampling rate observations of BDS and GPS ([[ftp://cddis.gsfc.nasa.gov/pub/gps/data/campaign/mgex/highrate/](ftp://cddis.gsfc.nasa.gov/pub/gps/data/campaign/mgex/highrate/)]([ftp://cddis.gsfc.nasa.gov/pub/gps/data/campaign/mgex/highrate/](ftp://cddis.gsfc.nasa.gov/pub/gps/data/campaign/mgex/highrate/))). We chose 10 of these stations that had a good common view of constellation with station LASA, namely: CEBR (Spain), DLF1 (The Netherlands), KRGG (French Southern Territories), KZN2 (Russian Federation), MAL2 (Kenya), NNOR (Australia), REDU (Belgium), SIN1 (Singapore), THTG (French Polynesia), VILL (Spain).
# A UAV-Based Visual Inspection Method for Rail Surface Defects
## 2 Methodology
### Defect Segmentation Meltod Based on Gray Stretch Maximum Entropy
#### 2.3.1 A Brief Introduction for 2-D Discrete Wavelet Transform  
The discrete wavelet transform (DWT) can not only express some features of a signal easily and efficiently, but also provides a powerful insight into an image's spatial and frequency characteristics [(38; 46)]. Two-dimensional functions such as images can be expanded from one-dimensional wavelet transform [(46)]. In two dimensions, a 2-D scaling function and three 2-D wavelets are given by:  
\[\begin{array}{rcl}\varphi(x,y)&=&\varphi(x)\varphi(y)\\ \psi^{H}(x,y)&=&\psi(x)\varphi(y)\\ \psi^{V}(x,y)&=&\varphi(x)\psi(y)\\ \psi^{D}(x,y)&=&\psi(x)\psi(y)\end{array} \tag{7}\]  
where \(\varphi(x,y)\) is a two-dimensional scaling function, \(\psi^{H}\) corresponds to some variations along columns(such as horizontal edges), \(\psi^{V}\) corresponds to variations along rows (for example, vertical edges), and \(\psi^{D}\) responds to variations along diagonals. Each of them can be seen as products of two 1-D functions. If the 2-D scaling and wavelets functions are given, the 1-D DWT can be extended to two dimensions. Firstly, the scaled and translated basis functions are defined as:  
\[\varphi_{j,m,n}(x,y)=2^{\frac{j}{2}}\varphi(2^{j}x-m,\ 2^{j}y-n) \tag{8}\]  
\[\psi^{i}_{j,m,n}(x,y)=2^{\frac{j}{2}}\psi(2^{j}x-m,\ 2^{j}y-n),\ \ i=\{H,V,D\} \tag{9}\]  
where index \(i\) is a superscript that assumes the values \(H\), \(V\), and \(D\) in Equation (7). The discrete wavelet transform of \(M\times N\) image \(f(x,y)\) is given by:  
\[W_{\varphi}(j_{0},m,n)=\frac{1}{\sqrt{MN}}\sum_{x=0}^{M-1}\sum_{y=0}^{N-1}f(x, y)\varphi_{j_{0},m,n}(x,y) \tag{10}\]  
\[W^{i}_{\psi}(j,m,n)=\frac{1}{\sqrt{MN}}\sum_{x=0}^{M-1}\sum_{y=0}^{N-1}f(x,y) \psi^{i}_{j,m,n}(x,y),\ \ \ i=\{H,V,D\} \tag{11}\]  
where \(j_{0}\) is an arbitrary starting scale that is set to 0 by default, the \(W_{\varphi}(j_{0},m,n)\) coefficients are an approximation of \(f(x,y)\) at scale \(j_{0}\), and the \(W^{i}_{\psi}(j,m,n)\) coefficients add horizontal, vertical, and diagonal details for scales \(j\geq j_{0}\). The 2-D DWT is achieved by using digital filters and down-sampling, as shown in Figure 7. According to the 2-D DWT scaling and wavelet functions, we can take the 1-D FWT (fast wavelet transform) of the rows of \(f(x,y)\), followed by the 1-D FWT of the resulting columns. Therefore, an original 2-D image can be decomposed into four sub-image sets which contain different frequency characteristics by high-pass and low-pass filter: a scaling component \(W_{\varphi}\) involving low-pass  
Figure 6: The procedure of the GSME method.  
information and three wavelet components, \(W_{\Psi}^{H}\), \(W_{\Psi}^{D}\), and \(W_{\Psi}^{V}\), corresponding respectively to the horizontal, diagonal, and vertical details, as in Figure 5. The 1-level DWT method can also reduce noise and the background disturbances.
###### Abstract  
Applications based on Long-Short-Term Memory (LSTM) require large amounts of data for their training. Tesseract LSTM is a popular Optical Character Recognition (OCR) engine that has been trained and used in various languages. However, its training becomes obstructed when the target language is not resourceful. This research suggests a remedy for the problem of scant data in training Tesseract LSTM for a new language by exploiting a training dataset for a language with a similar script. The target of the experiment is Kurdish. It is a multi-dialect language and is considered less-resourced. We choose Sorani, one of the Kurdish dialects, that is mostly written in Persian-Arabic script. We train Tesseract using an Arabic dataset, and then we use a considerably small amount of texts in Persian-Arabic to train the engine to recognize Sorani texts. Our dataset is based on a series of court case documents in the Kurdistan Region of Iraq. We also fine-tune the engine using 10 Unikurd fonts. We use Lstmeval and Ocreval to evaluate the outputs. The result indicates the achievement of 95.45% accuracy. We also test the engine using texts outside the context of court cases. The accuracy of the system remains close to what was found earlier indicating that the script similarity could be used to overcome the lack of large-scale data.  
Keywords:optical character recognition; tesseract; printed-document OCR; Kurdish-OCR system; offline character recognition system +
Footnote †: journal: Applied Sci. 2021, 11, 9752. [[https://doi.org/10.3390/app11209752](https://doi.org/10.3390/app11209752)]([https://doi.org/10.3390/app11209752](https://doi.org/10.3390/app11209752))
## 2 Materials and Methods
### Equations and Analytical Methods
#### 2.4.2 Relationship among Thrust Force, Velocity and the Propulsion Power  
The mathematical model here was adopted from two previous studies on propellers used for the propulsion of an aircraft [31; 36]. Exemplary, the thrust force is measured as a function of the propulsion (engine) power.  
Airplane velocity/airspeed is a parameter for the propulsion system of an aircraft. Here, the velocity of the robot was calculated according to time traveled and the measured displacement from the following equation.  
\[\Delta x=u.\Delta t+\frac{1}{2}.a.\Delta t^{2} \tag{3}\]  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Measurements** & **Symbol** & **Data** & **Units** \\ \hline Propeller RPM & RPM & 8000–13,000 & RPM \\ Diameter of Propeller & D & 0.166 & m \\ Weight of Prototype & m & 0.808 & kg \\ Coefficient of Static Friction & \(\mu\) & 0.2–0.6 & / \\ Coefficient of Rolling Friction & CTT & 0.0012 & / \\ Displacement & \(\Delta x\) & 1.524 & m \\ Average Time & \(\Delta t\) & 2.73 & sec \\ Power of Motor & P\({}_{\text{engine}}\) & 1.44 & W \\ \hline \hline \end{tabular}
\end{table}
Table 1: Measurements and Data.  
where \(\Delta x\) is the displacement, \(u\) is the initial velocity, \(\Delta t\) is the time interval and \(a\) is the acceleration. Measured data and calculation results of the prototype according to the formula are shown in Tables 1 and 2.  
To find the relationship between robot velocity, propeller efficiency and motor power, the following formula was used [36]:  
\[\
u=\eta.\left(\frac{2.P}{\pi.\rho.D^{2}.(1-\eta)}\right)^{\frac{1}{3}} \tag{4}\]  
where \(P\) is the motor power, \(D\) is propeller diameter, \(\
u\) is the velocity of incoming flow, \(\eta\) is the propeller efficiency, and \(\rho\) is the density of the fluid in the calculation. The density of air is \(\rho_{air}=1.225\) kg/m\({}^{3}\).  
The thrust of a propeller depends on the volume of air accelerated per time unit and on the density of the medium. Based on the momentum considerations, it can be expressed as [36]:  
\[T=\frac{\pi}{4}.D^{2}\left(\
u+\frac{\Delta\
u}{2}\right)\rho.\Delta\
u \tag{5}\]  
where \(T\) is the thrust, \(D\) is the propeller diameter, \(\
u\) is the velocity of incoming flow (Equation (4)), \(\Delta\
u\) is the additional velocity by the propeller and \(\rho\) is the density of the fluid.  
From Equations (4) and (5), the relationship between thrust force and engine power can be established.  
In this study, only one propeller (RAYCorp(r) 6,045 6 \(\times\) 4.5 Bullnose Propellers, RAYcorp Inc., Tinley Park, IL, USA) was applied in experiments to confirm the concept. To illustrate the feasibility of the mathematical model, the results in Tables 1 and 2 were used to confirm the consistency of the measured data and calculation results shown in Table 4. The results in Table 4 were calculated using Equations (4) and (5) when assuming that a series of propeller efficiency from 0% to 100% can be realized, which are also shown in Figure 4.
# Service Interface Translation. An Interoperability Approach
## 3 Service Interface Translation
### Runtime Consumer Interface Deployment  
The last step to achieve an overall interoperability solution is the injection of the generated code into the consumer device. This step involves several challenges. In contrast to previous steps, the deployment of the code in an external device implies considering, among others, the hardware limitations and the security of the device. The most significant aspects are outlined in Table 1.  
The autonomous updating of software is not a new concept. From smart devices to industrial equipment, new versions of the software have to be updated periodically. This situation has led to the investigation of autonomous and decentralized approaches to address the deployment of the new code. For example, [PERSON] et al. [29] proposed a new approach based on blockchain to autonomously and securely update the firmware of IoT systems.  
Nevertheless, software deployment solutions are designed for a specific device or system and rely on proprietary technologies. Devices are designed with the capabilities and tools to receive software updates. In contrast, in industrial scenarios, due to the heterogeneity in the devices and the variety of vendors, devices do not share a common technology that can be used for code deployment. Finding a generic solution for all types of devices can be seen as an impossible task. To facilitate deployment, two steps are suggested.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{284.5 pt}} \hline \hline
**Aspect** & **Definition** \\ \hline Hardware & Hardware characteristics and constraints must be considered at the time of injecting and executing the autonomously generated code. The generator system requires access to the Device Registry and the information about the device type, computational power, memory and operating system. \\ \hline Security & Devices can include a variety of security measures to prevent threats and malware. These security measures must be considered, and the privileges or access policies have to be updated in order to inject and execute the code without introducing new vulnerabilities. \\ \hline Generality & The heterogeneity of the devices needs to be considered. The deployment mechanism must be adaptable to the different device types and architectures. Devices may be from different vendors; consequently, they do not share a previous dedicated middleware installation to inject the code. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Significant aspects of interface deployment.
## 4 The Proposed New SMO Variant
### The SMO Basic Algorithm
#### 4.1.7 Global Leader Decision (GLD) Phase  
This step in the SMO algorithm is based on the status of the GLL phase counter. If the latter is greater than or equal to the Global Leader Limit, the population is divided. This division leads to the creation of new local leaders for each part of the population and a global leader for the entire population. These actions, including fragmentation, creation of new local leaders, and global leader creation, are monitored by the status of the GLL phase counter until the maximum number of divisions, equal to the Maximum Group Limit, is reached. At this stage, all groups created are combined into a single global group. It is important to note that the GLL phase counter is reset to zero after each division and aggregation. Algorithm 4 provides a detailed explanation of the GLD phase.
## 5 Data Correction Module  
The analysis of the datasets performed in the EDA phase has shown that the anomaly detection system must integrate a data correction module. In fact, this module should be capable of detecting faulty transmissions before those data reach the machine learning module. The development made in this work has led to the specification, and the later implementation, of a solution that should be capable of: (i) detecting and removing problematic observations; and (ii) introducing missing values when a period of data loss is identified. In the following, we describe these features of the data correction module.
## 3 Layout and More PEX and EM Simulation Results  
Figure 8 presents the layout of the broadband LNA, whose core size (without pads) is 0.53 \(\times\) 0.46 mm\({}^{2}\). PEX simulations were conducted using Calibre xACT v2022.2_38.20 and Cadence Spectre RF v20.1, while the EM simulations were conducted using Cadence EMX v6.3.1 (3D planar solver, Ref. [18]). The first stage operates with a supply voltage of 0.9 V, while the second stage operates at 1 V, with DC currents of 9.3 mA and 7.4 mA, respectively.  
Figure 6: \(S_{21}\), \(S_{11}\) and \(N\)F comparison on our 2-stage LNA: resistive feedback vs. no resistive feedback (from pre-PEX simulations).  
Figure 7: Comparison of simulated noise circles at 24 GHz at the LNA input in 0.4 dB increments. (**a**) Without resistive feedback (pre-PEX). (**b**) With resistive feedback (pre-PEX). (**c**) With resistive feedback (PEX simulation using \(RCC\) extraction option; more on the extraction option in the next section).
## 4 Experiment and Discussion  
### Experimental Settings  
Speech and other audio data are involved in the experiment. One of the most important features of self-fitting is that any sound could be used in fitting [18; 19], unlike the traditional method that uses speech, pure tone or noise. Therefore, some environmental data are used in the fitting evaluation, which quantitatively demonstrates improvement in audio quality. Four types of environmental sounds are used, which include 5 natural climate sounds, 33 daily life sounds, 19 musical instrument sounds and 5 animal sounds. A total of 4-5 similar audio segments are used in every sound, and every sound lasts 5-10 s.  
The speech signals are all from the professional listening and fitting software OTOsuite and the TIMIT speech database. The OTOsuite software includes English samples and Chinese samples. English samples include the AB ([PERSON]) word list, BKB (Bamford-Kowal-Bench) sentences [32] and IHR (International Health Regulations) sentences [33], and the Chinese samples [34] include monosyllable word lists, two-syllable word lists and sentences. Because all subjects are Chinese, the Chinese two-syllable word lists are adopted in the experiment.  
Seven hearing-impaired patients are tested, 2 females and 5 males, with an average age of 52.4 years (standard deviation 16). The Danish international audiometer AD226 is used to test the audiograms of seven subjects, which are shown in Figure 5. Among these subjects, five subjects have moderately severe hearing loss and two have severe hearing loss.  
The comparison methods include the proposed fitting method (P), the GP-based algorithm (G) [19] and the IEC-based algorithm (I) [18]. A baseline setting is based on the prescription formula NAL-NL2 [22], which is the initial setting based on the patient's audiogram through the professional fitting software [35]. It is the initial setting of the hearing aids without additional personalization.
# A Novel Deep Learning Network Model for Extracting Lake Water Bodies from Remote Sensing Images
## 2 Materials and Methods
### Model Performance Evaluation Metrics
#### 2.4.1 Pixel Accuracy (_Pa_)  
_PA_ is the number of pixels predicted to be correctly classified divided by the total number of pixels in the observed labeled image, which can be interpreted as the percent  
Figure 6: R50A3-LWBENet holistic network instructive.  
age of correctly classified pixels in the image, and is calculated as shown in Equation (3) as follows.  
\[PA=\frac{\sum_{i=1}^{c}n_{ii}}{\sum_{i=1}^{c}\sum_{j=1}^{c}n_{ij}} \tag{3}\]  
where \(n_{ii}\) is the number of pixels in which the observed category \(i\) is predicted to be category \(i\); \(n_{ij}\) is the number of pixels in which the observed category \(i\) is predicted to be category \(j\); and \(c\) is the number of categories.
## 3 Proposed Method
### Flowchart of the Proposed Method  
The discussed approach stabilizes the frequency, dampens oscillations, and ensures that the VSGs can be effectively controlled, leading to a more resilient power system. A flowchart of the proposed optimization method is given in Figure 1, and it comprises eight steps, marked (1)-(8). The flowchart shows the systematic procedure for obtaining optimal values of virtual inertia constants and damping coefficients using a genetic algorithm.  
Firstly, using input parameters (\(H\), \(D\)) and network parameters (internal machine voltage \(E\), admittance \(Y\), rotor angle \(\delta\)) (step 1), we define the state-space model (step 2) to compute damping ratios, the controllability Gramian, and the rate of change of frequency (RoCoF), with penalties applied when the system is unstable, which happens if any eigenvalue has a positive real part. Next (step 3), two objective functions \(J_{1}\) and \(J_{2}\) are defined: \(J_{1}\) to maximize controllability through controllability Gramian-based metrics (EV\({}_{\min}\), EV\({}_{\min}\), EV\({}_{\max}\), TR, and DET), while \(J_{2}\) minimizes RoCoF, with additional penalties for insufficient damping. Subsequently, parameters for the GA are specified (step 4), including the number of variables, lower bounds (\(lb\)), upper bounds (\(ub\)), and algorithm settings. In the following (step 5), the GA initializes a population and evolves it using non-dominated sorting and crowding distance to generate a Pareto front. This  
Figure 1: Flowchart of the proposed method for obtaining optimal values of inertia constants and damping coefficients.  
loop continues until the stopping criterion is met (step 6), and if the Pareto set is satisfactory (step 7), the optimal solutions, including the final Pareto front and corresponding \(H\) and \(D\) values, are output (step 8).
## 3 Study Cases
### Case 1: Four-Bar Linkage Mechanism  
The conceptual design model for the four-bar linkage mechanism is presented in Figure 3, where \(r_{1}\), \(r_{2}\), \(r_{3}\), and \(r_{4}\) are the link lengths, \(\theta_{1}\) is the ground link angle, [\(x_{0}\),\(y_{0}\)] is the Cartesian position of the mechanism with respect to the coordinate system \(x-y\), and \(\theta_{2}^{i}\)\(\forall\)\(i=\{1,2,\ldots,n_{f}\}\) is the \(i\)-th crank angle.  
The dimensional synthesis problem for this mechanism consists of finding the link lengths that approximate the coordinate \([x_{p}^{i},y_{p}^{i}]\) of the coupler link point \(P\) to the coordinates \([\bar{x}_{p}^{i},\bar{y}_{p}^{i}]\) of the rehabilitation trajectory. Figure 3 shows the design variable vector, and it is defined in (14):  
\[x=\left[r_{1},r_{2},r_{3},r_{4},\theta_{1},x_{0},y_{0},r_{c_{x}},r_{c_{y}}, \theta_{2}^{1},\theta_{2}^{2},\ldots,\theta_{2}^{n_{f}}\right] \tag{14}\]  
The dimensional synthesis optimization problem is presented in (15)-(19), where the constant values \(w_{1}=1\) and \(w_{2}=0.01\) weight the design goals:  
\[\underset{x}{\text{Min}}\;w_{1}\sum_{i=1}^{n_{f}}[(x_{p}^{i}-\bar{x}_{p}^{i})^ {2}+(y_{p}^{i}-\bar{y}_{p}^{i})^{2}]+w_{2}\sum_{i=1}^{n_{f}}(\theta_{2}^{i}- \bar{\theta}_{2}^{i})^{2} \tag{15}\]  
subject to:  
\[g_{1}(x)\leq 0:\;\;r_{2}+r_{1}-r_{3}-r_{4}\leq 0 \tag{16}\] \[g_{2}(x)\leq 0:-r_{4}-r_{1}+r_{2}+r_{3}\leq 0\] (17) \[g_{3}(x)\leq 0:-r_{3}-r_{1}+r_{2}+r_{4}\leq 0\] (18) \[x_{min}\leq x\leq x_{max} \tag{19}\]  
The first design goal minimizes the error between the desired precision points \([\bar{x}_{p}^{i},\bar{y}_{p}^{i}]\) and the path \([x_{p}^{i},y_{p}^{i}]\) of the mechanism's coupler link point. The second design goal minimizes the angular displacement error between the desired angles \(\bar{\theta}_{2}^{i}\) and the crank angles \(\theta_{2}^{i}\). In this problem, the desired angles \(\bar{\theta}_{2}^{i}\) are established by (20):  
\[\bar{\theta}_{2}^{i}=\theta_{2}^{1}+2\pi\frac{i-1}{n_{f}-1}\;\forall\;i=\{1,2,\ldots,n_{f}\} \tag{20}\]  
The \(i\)-th desired precision point \([\bar{x}_{p}^{i},\bar{y}_{p}^{i}]\) is established based on the ankle trajectory for rehabilitation routine. In this work, two trajectories are considered for the synthesis process representing a change in the human anatomy. The Pearson correlation between both trajectories is 95%. The trajectory points are shown in Figure 4, where Figure 4a,b are considered as trajectory 1 and trajectory 2, respectively, for the synthesis process. The Cartesian coordinates of the trajectory points are shown in Table A1.  
Figure 3: four–bar linkage mechanism.  
The synthesis problem has three inequality constraints (16)-(18). These constraints define a crank-rocker mechanism according to the Grashof criterion. The design variable vector \(x\in R^{23}\) for the four-bar linkage mechanism is bounded by \(x_{min}\) and \(x_{max}\) limits. These limits are established according to [29].