esa-sceva/satcom-chunk-collection huggingface dataset:
### Text Recognition  
Recognition of text or script is a procedure to draw out the typed text from images. For this purpose, OCR is utilized. Every linguist can have different OCRs based on text and image properties. OCR targets one linguistic need to recognize the guidelines of that linguistic to provide effective outcomes of the identified script. When converting documents into digital copies, individuals can easily change the written details as per need. Hence, the modifications can be easily made in the document. Therefore, there are many requirements for implementing text recognition solutions for Urdu to fulfill the basic needs of Urdu OCR. Finding its solution is the major obstacle in digitizing Urdu documentation. Several solutions can be found, but their limitations lie, which leaves a gap in finding the optimal solution. Urdu script is cursive and very different from Latin script, and it has multiple fonts and font styles. Due to a lack of research on Urdu, only a limited amount of data files can be found. The biggest challenge is to localize the words or sentences in a picture before recognizing the text. Pictures undergoing the process of text recognition can have different background colors or text types for each source. Therefore, localizing the text in the image before identifying the text is a necessary point. Furthermore, the central reason for low accuracy in past research is the lack of a data set. Each study with distinctive guidelines requires a data set that applies to those guidelines. Due to the complex nature of Urdu text styles, text detection and recognition is a complicated problem. The following are the main contribution of this research work:  
* A unified approach for position free Urdu text detection through Connected Component Analysis (CCA) and Long Short-Term Memory (LSTM).
* A hybrid Convolution Neural Network and Recurrent Neural Network (CNN-RNN) approach for text recognition.  
Figure 1: Different styles of an Urdu character used in cursive words.  
* Evaluation of the proposed methodology on self-synthesized and UPTI data sets.
## 6 Case Data and Simulation Results
### Optimal Scheduling Result Analysis
#### 6.2.1 The Summer Cooling Scenario Data  
Real-time electricity prices can effectively guide the user's behavior of electricity consumption [32]. The typical daily real-time electricity price changes in this region in summer are shown in Figure 2.  
Solar radiation passing through the windows changes the indoor temperature. The heat transferred to the interior of the building is determined by solar radiation power, window area and shading coefficient. Considering the angle of solar exposure and visor, the shading coefficient is set as 0.3. The variation curves of solar radiation power and outdoor temperature in summer are shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Upper Limit** & **Lower Limit** \\ \hline \(P_{\text{pv}}/\text{kW}\) & 125 & 0 \\ \(P_{\text{h}}/\text{kW}\) & 25 & 0 \\ \(P_{\text{ec}}/\text{kW}\) & 25 & 0 \\ \(P_{\text{b}}/\text{kW}\) & 30 & \(-\)30 \\ \(P_{\text{grid}}/\text{kW}\) & 150 & \(-\)150 \\ \(E_{\text{b}}/(\text{kWh})\) & 160 & 20 \\ \(E_{\text{h}}/(\text{kWh})\) & 20 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of microgrid operation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Magnitude** \\ \hline \(c_{\text{b}}/(\text{5}/(\text{kWh}))\) & 0.0038 \\ \(c_{\text{ec}}/(\text{5}/(\text{kWh}))\) & 0.0017 \\ \(c_{\text{h}}/(\text{5}/(\text{kWh}))\) & 0.0017 \\ \(c_{\text{pv}}/(\text{5}/(\text{kWh}))\) & 0.0140 \\ \(\eta_{\text{ec}}\) & 3.80 \\ \(\eta_{\text{h}}\) & 3.80 \\ \(\eta_{\text{bch}}\) & 0.95 \\ \(\eta_{\text{bdis}}\) & 0.95 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of microgrid equipment.  
Set the office building from 8:00 to 21:00 as working hours. The cold/heat load and hot water load demand generated during working hours. The PV generation, load demand and indoor heat source heating of the day-ahead prediction are shown in Figure 4.
## 2 Operation Principle for J-DBN-Based Equalization
### Cost Function of DBN-1 and DBN-2 Equalizers
#### 2.3.2 Cost Function of Blind DBN-2-Based Equalizer  
Unlike forwarding propagation steps, the weight values and model hyper-parameters are updated based on the backpropagation (BP) algorithm. According to the minimum mean square error (MMSE) algorithm, the cost function of the DBN-2 blind equalizer based on the DD-LMS algorithm can be defined as  
\[J_{n\_ddlms}=\frac{1}{2}{\sum_{n=1}^{S}}(T_{n}-O_{2}(n))^{2} \tag{8}\]  
Compared the obtained value \(O_{2}(n)\) with the corresponding expected value \(T_{n}\), the error \(e_{n}=T_{n}-O_{2}(n)\) is sent to the network with a reverse training algorithm. Thus, the connected weight vector \(w_{ij}^{k+}\) can be iteratively updated until the desired epoch or error value is reached, which can be represented as  
\[w_{ij}^{k+}=w_{ij}^{k}-\Delta w_{ij}^{k}=w_{ij}^{k}-\eta\frac{\partial f_{n\_ ddlms}}{\partial w_{ij}^{k}} \tag{9}\]  
where \(\eta\) is the learning rate and \(\Delta\) represents the gradient operation. In order to accurately update the weight value of every nonlinear node, we must calculate the gradient of the whole training sequence. Owing to the introduction of the blind error function, we will decrease the training size and improve the computation speed effectively.  
Thus, our proposed J-DBN equalizer comprises two parts, including the DBN-1 adaptive equalization and the DBN-2 blind equalization. Note that the former updating of the weight value is based on the traditional BP algorithm, while the latter deploys the blind equalization algorithm to optimize the network further.
## 2 Machine Topology and Simulation Results
### No-Load Characteristics Analysis  
In order to further reveal the advantages of the permanent magnet arrangement combined with the Halbach array and the magnetic flux focusing effect, a comparative analysis was conducted on the no-load operation of the basic model and proposed models one and two in this paper. Figure 5 shows the magnetic field distribution maps of three models under no-load conditions. Figure 5a is the magnetic field distribution map of the basic model, and Figure 5b,c are the magnetic field distribution maps of proposed models one and two, respectively. Compared with the basic model, proposed models one and two significantly increase the number of magnetic field lines in the C region of the stator. However, proposed model one has almost no magnetic flux flowing through zone A. The air gap magnetic density of proposed model one decreases almost to zero at a phase angle between 60 and 75 degrees in Figure 6. At the same time, the back electromotive force (EMF) is also affected accordingly. The back EMF of proposed model one exhibits greater fluctuations than that of proposed model two in Figure 7. Therefore, in proposed model two, a permanent magnet was added at B to fill the gap in magnetic flux in zone A. After adding the permanent magnet, it was found that not only did it increase the magnetic flux in zone A, but it also further increased excitation and increased the magnetic density in the stator. Although the maximum air gap magnetic flux density of proposed model two slightly decreased compared to the base model and proposed model one, the air gap magnetic flux density rebounded at a phase angle between 60 and 75 degrees in Figure 6. According to Table 2, regarding the air gap magnetic flux density, the root mean square (RMS) value of proposed model two is higher than that of the basic model, but slightly lower than that of proposed model one, and the RMS value of the back EMF is the highest among the three models.  
Figure 4: Vector diagram of proposed model 2.  
Figure 5: Distribution of magnetic flux lines under no-load conditions. **(a)** Basic model. **(b)** Proposed model 1. **(c)** Proposed model 2. A, B and C are applicable in all three models. A and C are two distinct regions in the stator and B is the topmost flux barrier in the rotor.  
Figure 6: Three models of air gap magnetic flux density under no-load conditions.  
Figure 7: Comparison of back EMF waveform over a period.
## 3 Design of IPFC Controller
### PID Control Algorithm  
The controller with proportional, integral, and differential links is called the PID controller, which is the most widely used control law in the field of industrial process control. The continuous PID control equation is shown in Equation (28) [29].  
\[u(t)=k_{p}\bigg{[}e(t)+\frac{1}{T_{I}}\int e(t)dt+T_{D}\frac{de(t)}{dt}\bigg{]} \tag{28}\]  
where, \(e(t)\) is the temperature deviation, \(k_{p}\) is the scale factor, \(T_{I}\) is the integration time, \(T_{D}\) is the differential time.  
Since the computer deals with digital quantities, Equation (28) is discretized, and the control law expression of digital PID is obtained as shown in Equation (29).  
\[u(k)=k_{p}e(k)+k_{i}\underset{j=0}{\overset{k}{\sum}}e(j)+k_{d}[e(k)-e(k-1)] \tag{29}\]  
where, \(e(k)\) is the input temperature deviation value when the sampling time is \(k;k_{p}\) is the scale factor; \(k_{i}=k_{p}\frac{T}{T_{I}}\), \(k_{d}=k_{p}\frac{T_{D}}{T}\), \(T\) is the sampling period.  
The incremental PID control algorithm is:  
\[\Delta u(k)=k_{p}\Delta e(k)+k_{i}e(k)+k_{d}[\Delta e(k)-\Delta e(k-1)] \tag{30}\]
## 3 Result and Discussion
### Characterization of the Positively Charged Au NPs in MIHs  
Dry MIHs were ground into fine powders, ultrasonically dispersed in tetrahydrofuran (THF) at room temperature, and transferred onto a carbon-coated copper grid by dipping. The elemental distribution of MIHs was then determined by EDX mapping technique in high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) mode. In a selected area (outlined in red in Figure 3a), the corresponding element mapping of gold (Figure 3c,d) from positively charged Au NPs and the mapping of nitrogen (Figure 3e), oxygen (Figure 3f), and carbon (Figure 3g) from polyacrylamide hydrogels clearly indicate that there were a lot of gold nanoparticles distributed in the MIHs, and the MIHs could be served as an SERS active substrate for the detection of New Red.  
Figure 2: TEM image (**a**), Zeta potential distribution (**b**), size (**c**), and energy dispersive X-ray (EDX) spectrum (**d**) of the positively charged Au NPs.
## 2 Preliminaries  
### Graph-Theoretic Terms  
Given a simple graph \(G\), we use \(V(G)\) and \(E(G)\) to denote the vertex set and the edge set, respectively. An edge with end vertices \(u\) and \(v\) is denoted by \((u,v)\). For each vertex \(v\in V(G)\), if \((u,v)\in E(G)\), we say that \(u\) is a neighbor of \(v\) or \(u\) is adjacent to \(v\). The set of all the neighbors of \(u\) is called the neighbor set of \(u\) in \(G\), denoted by \(N_{G}(u)\). The total number of vertices of graph \(G\) is denoted by \(N\).  
Pathway \(P\) is a sequence consisting of \((x_{1},x_{2},\ldots,x_{n})\) with a length of \(n\), and there is \((x_{m},x_{m}+1)\in E(G)\) for any \(0\leq m\leq n\). For any two different paths, \(P_{1}\) and \(P_{2}\), the starting and ending points are the same, named \(x\) and \(y\), respectively. If \(V(P_{1})\cap V(P_{2})=\{x,y\}\), then \(P_{1}\) and \(P_{2}\) will be vertex disjoint paths. Furthermore, if all vertices in \(P\) are fault-free, then \(P\) will be a fault-free path. If \(P_{1}\) is a path between \(u\) and \(x\), and \(P_{2}\) is a path between \(x\) and \(v\) in \(G\), then let \((P_{1}\),\(P_{2})\) be a path between \(u\) and \(v\) in \(G\). The reverse of \(P\) is \((x_{n},x_{n-1},\ldots,x_{1})\), denoted by \(P^{-1}\).  
Let \(F\subset V(G)\) and \(F\) be a set of faulty vertices. For any vertex \(x\) in \(G\), if \(x\in F\), then \(x\) is faulty; otherwise, \(x\) is fault-free. The connectivity of graph \(G\) is defined as \(\kappa(G)\). This is the minimum number of vertices needed to make the graph disconnected or trivial. If we specify that every vertex has at least one fault-free neighbor, we call the connectivity in this case the limited connectivity, which is defined as \(\kappa^{1}(G)\).
# High Throughput and Acceptance Ratio Multipath Routing Algorithm in Cognitive Wireless Mesh Network
## 3 Multipath Routing and Spectrum Allocation Algorithm
### Primary Path Construction Algorithm  
The primary path construction algorithm (PPC) contains the following steps: First, if the link (\(u\),\(v\)) belongs to the backup path of some wireless request, then the link (\(u\),\(v\)) is hidden. The purpose is not to include the backup link in the process of constructing the primary path. If the link (\(u\),\(v\)) does not belong to the backup path of some wireless request, then the link weight of all the available channels is calculated according to Equation (8). Second, the primary path is constructed using the Dijkstra algorithm. If a primary path is constructed, then the \(\alpha(s_{i},d_{i})\) is returned, otherwise, the null path is returned.
```
Input: \(G=(V\),\(E\)),\(\gamma_{i}=(s_{i}d_{i}\),\(b_{i})\) Output: \(\alpha(s_{i},d_{i})\) \(\left|\right.\) while (\(\forall(u,v)\in E\)){ (2) if((\(u\),\(v\)) is Backup link for the accepted request){ (3) Hide (\(u\),\(v\)); (4)else{ (5) while(\(\forall k\in K(u,v)\)){ (6) Calculate \(f_{1}^{k}(u,v)\) according to Equation (8); (7) \(\left|\right./\emph{end while (8) \(\left|\right./\emph{end if (9) \(\left|\right./\emph{end while (10) \(\alpha(s_{i},d_{i})\leftarrow\mathrm{Dijkstra}(s_{i},d_{i})\); (11) if(\(\alpha(s_{i},d_{i})\) is found){ (12) return \(\alpha(s_{i},d_{i})\); (13) }else{ (14) return null; (15) }//end if
```
**Algorithm 2** PPC
# Underwater Excavation Records Using Underwater Acoustic Survey: A Case Study in South Korea
## 2 Background Information  
Underwater visibility is limited even in clear water, which makes it difficult to obtain a photograph in which relics and remains are easily identifiable, as can be done with aerial images for land excavations. Therefore, the most effective way to illustrate the overall shape of surveyed objects is on-site drawings. However, the available diving time for on-site investigations and measurements is limited, and relics on the seafloor may be swept away by tidal currents. Moreover, underwater on-site measurements and photogrammetry cannot be performed below a certain level of underwater visibility, and the accurate recording of remains is challenging if relics are dispersed over a large seafloor area. For example, the Myeongnyang site is characterized by a turbid underwater environment and strong tides, significantly affecting photographing and surveying the site. Consequently, the current survey report comprises a map indicating previously surveyed sections only and does not provide enough information to infer the state of the remains, such as the topographic profile of the seafloor or the presence of foreign objects.  
Similar to land archeology, where burial depths and structures of relics can be investigated by geophysical surveys that eliminate the need for excavations for research and exploration, underwater archeology can utilize MGS devices for investigations. However,the geophysical surveying of underwater environments requires different devices to those used for land surveys. For example, [PERSON] et al. (2019) utilized ground-penetrating radar in a land archeological investigation using electromagnetic waves to probe below the surface. However, electromagnetic waves are not suitable for underwater investigations because they are strongly dampened in water. In contrast, sound waves travel faster and farther in water than they do in air. This characteristic has led to the development of acoustic MGS devices, such as the SiSS and the echo sounder, which can detect objects on the seafloor, and sub-bottom profilers, which are used to map the strata beneath the seafloor. As geophysical survey devices use sound waves that are not affected by underwater turbidity, they yield relatively high-quality images under conditions that limit visual or optical photography ([PERSON] and [PERSON], 2019).
# RBF-Based Monocular Vision Navigation for Small Vehicles in Narrow Space below Maize Canopy
## 2 Materials and Methods  
### System Overview  
The requirements for the design of the machine platform depend on its environment and purpose, as shown in Figure 1. In the late growth stage of corn, the machine must enter the field for pest and disease control. Following the distance between two rows of corn planted at the maximum acceptable range of 60-80 cm, this article designs an autonomous moving and spraying robot. The autonomous moving and spraying robot consists of three parts: the image acquisition and transmission system, the control platform, and the moving and spraying execution system. The purpose of image acquisition is to collect the front area data to be traversed by the vehicle. After applying the codec for video networking via a wireless bridge (Breeze NET DS802.11, Version 4.1, Alvarion Technologies Ltd., Rosh Ha'ayin, Israel, 2003), the image data are transferred to the control platform. The control platform mainly consists of the Industrial Personal Computer (IPC), signal transmitting and receiving system, and display components.  
Path planning is based on the camera's capacity for detection through the corn stalks, the calibration of the camera to calculate the relative coordinates of the corn on the ground, and its capacity to calculate the most appropriate path for the vehicle to pass. This paper first presents the calibration of the camera in relation to the ground reference plane and the correspondence of the calibration between the images and ground coordinates. Next, the paper presents image acquisition, identification of stalk images, and calculation of the stalk on the ground plane. Finally, the study solves the trajectory path algorithm using the radial basis function (RBF) by the coordinates of stalks on the ground.
## 4 Statistical Optimization and Model Validation
### Errors and Uncertainties
#### 4.4.1 Measurement Error  
The experimental data may contain errors from several sources. Reference [32] indicate the accuracies of the applied measurement devices. We account furthermore for the fact, that the measurement implementation contains errors. Therein, we consider the error that occurs, if the location of the thermocouples deviates by 0.5 mm from the indicatedcoordinates and if the measurement is delayed. Due to the large temperature gradients in the reactive bed, the influence of already a small shift of the thermocouple results in a notable difference in temperature. Especially in the first seconds, the temperature rises sharply and thus, a small delay causes large errors. Lastly, we take systematic errors into account. The different simulation runs of Reference [31] do not always show full conversion and the results in Reference [20] to the same setup even show conversions larger than 100%. We assign therefore uncertainties as the full conversion being within the confidence interval.
## 4 Unloaded Static Transmission Error of Internal Spur Gears with Tooth Wear  
In the above theoretical derivation and verification for the analytical model of internal spur gear pair with tooth wear, the new mesh relationship is obtained and it further verifies the mesh stiffness with tooth wear. In this section, the effect of tooth wear on the unloaded static transmission error (USTE) will be studied using the new mesh relationship.  
USTE is usually caused by tooth profile error, assembly error and so on, which is defined as the deviation between the theoretical position and the actual position of the  
Figure 19: Comparisons of the mesh stiffness between the theoretical calculation and the FEM simulation.  
Figure 18: Finite element model of internal gear pair.  
driven gear under unloaded condition. This paper only considers the USTE caused by tooth wear and the expression is as follows:  
\[e_{\text{wear}}=\frac{R_{b1}}{R_{b2}}\varphi_{1}-\varphi_{2} \tag{62}\]  
Because tooth wear changes the mesh relationship, USTE also changes. According to the obtained mesh relationship between \(\varphi_{1}\) and \(\varphi_{2}\) of the internal spur gear, we can directly derive the USTE with wear. Furthermore, USTE is introduced in both cases.  
According to the multi-tooth mesh relationship with non-uniform wear, as shown in Figure 15, we can calculate and obtain the USTE by Equation (62). The USTE of non-uniform wear is given in Figure 20, which illustrates that the USTE of the internal spur gear varies periodically as the gear pair meshing. Meanwhile, the amplitude increases obviously with the increase in wear depth.
# The State of Ethereum Smart Contracts Security: Vulnerabilities, Countermeasures, and Tool Support
## 4 Smart-Contracts Vulnerabilities and Countermeasures
### Vulnerabilities and Countermeasures
#### 4.2.5 Freezing Ether  
This vulnerability is also known as greedy contract [43], and locked money [38]. A smart contract is designed to be able to receive and send Ether. A freezing-Ether situation is a contract that could only receive Ether but has no means to send Ether out. A contract can be greedy and the Ether sent to its address is frozen if the contract does not define any withdraw functions.  
_Countermeasures_. It is a good practice to avoid this situation at the smart-contract designing phase. Those contracts that could receive Ethers should have functions to withdraw Ethers [43].
## 3 Results and Discussions
### Analysis of Monitoring Results of High-Rise Buildings
The natural frequency of different buildings may be slightly different. Therefore, it is difficult to measure the natural frequency of multi-story buildings. The natural vibration period of the building is calculated with an empirical formula [33]:  
\[T=K\times 0.04N \tag{17}\]  
where \(N\) is the number of floors, \(K\) is the foundation coefficient, and the rock foundation is 0.7. Analysis of the floor and height of the newly built 20-story residential building with brick-concrete structure shows that its natural vibration coefficient period is 0.56 s, and its natural vibration frequency is ~1.79 Hz. In this paper, the blasting vibration frequency is monitored with the automatic monitoring system. As illustrated in Figure 12b, the blasting vibration frequency is gradually attenuated with the height of the floor, and the blasting  
Figure 12: (**a**) The curve of the blasting vibration speed with time on a building. (**b**) Variation of the primary frequency of vertical vibration of blasting with floor. vibration frequency is attenuated at different speeds with different blast center distances. However, the low-level zone (\(-2\) to \(3\) layers) is a fast attenuation zone, whose attenuation speed is fast. The middle and upper layers (\(4\) to \(16\) layers) are slow attenuation zones. The blasting vibration frequency of top layers (\(17\) to \(20\) layers) has been attenuated to the lowest value, which has remained stable at -\(10\) Hz. During the T6 tests, the lowest value of the top layer's blasting vibration frequency is \(\sim\)8 Hz. By calculation, it is found that the dynamic coefficient \(\beta\) is \(\sim\)1.3, indicating that the blasting vibration frequency of the top layer is close to the building's natural frequency, which causes the building's blasting vibration speed to increase, and the top-level amplification effect. Through the prediction of the intelligent system, it is found that the predicted value of Peak Velocity\(-\)Z can reflect the high-level amplification effect to a certain extent, and the error value is controlled within \(10\%\). The average relative error between blasting vibration velocity and measured value calculated by Satovsky equation is \(26.47\%\), and the prediction error is large. It can be seen that the artificial neural network prediction model established by the automatic blasting monitoring system has a more reliable blasting vibration velocity prediction effect. In engineering applications, the blasting vibration velocity can be predicted more accurately in the future, so as to remind construction units to adjust parameters in time and avoid serious environmental damage.