esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Numerical Illustration  
Here, we have considered six different types of machines, i.e., milling machine, lathe, grinder, jig saw, drill press, band saw, for industrial production planning problem [48] and formulated it with deterministic parameters. Their capacities are to be dedicated to the production of three products. The current capacity of each machine type is available in hours per week. Table 1 gives information about the total available time of each machine and the time required by each product. Using the above information as given in Table 1, the multi-objective industrial programming problem with a deterministic parameter can be formulated as follows: \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Machine Type**} & \multirow{2}{*}{**Available Time**} & \multicolumn{4}{c}{**Machine time**} \\ \cline{3-5}  & & **Product (I)** & **Product (2)** & **Product (3)** \\ \hline \hline \multirow{2}{*}{Milling machine (\(m\))} & 1400 & 12 & 17 & 0 \\ \hline \hline \multirow{2}{*}{Lathe (\(l\))} & 1000 & 3 & 9 & 8 \\ \hline \hline \multirow{2}{*}{Grinder (\(g\))} & 1750 & 10 & 13 & 15 \\ \hline \hline \multirow{2}{*}{Jig saw (\(s\))} & 1325 & 6 & 0 & 16 \\ \hline \hline \multirow{2}{*}{Drill press (\(d\))} & 900 & 0 & 12 & 7 \\ \hline \hline \multirow{2}{*}{Band saw (\(b\))} & 1075 & 9.5 & 9.5 & 4 \\ \hline \hline \multicolumn{2}{c}{Profit (\(P\))} & 50 & 100 & 17.5 \\ \hline \multicolumn{2}{c}{Product liability (\(L\))} & 0.72 & 0.85 & 0.78 \\ \hline \hline \multicolumn{2}{c}{Quality (\(Q\))} & 92 & 75 & 50 \\ \hline \multicolumn{2}{c}{Workers’ satisfaction (\(W\))} & 25 & 100 & 75 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Portfolio of available capacities with deterministic parameter. **[Ist Level]**  
\[\underset{\mathrm{x_{1}}\mathbb{N}_{2}}{\text{Max}}\left( \begin{array}{l}Z_{1} =\ 50x_{1}+100x_{2}+17.5x_{3}&\text{Profit}\\ Z_{2}\ =\ 0.72x_{1}+0.85x_{2}+0.78x_{3}&\text{Product liability}\end{array} \right)\]
**[IInd Level]**  
\[\underset{\mathrm{x_{3}}}{\text{Max}}\left( \begin{array}{l}Z_{1}\ =\ 92x_{1}+75x_{2}+50x_{3}&\text{Quality}\\ Z_{2}\ =\ 25x_{1}+100x_{2}+75x_{3}&\text{Worker's satisfaction}\end{array} \right)\]  
Subject to  
\[ \begin{array}{ccc}12x_{1}+17x_{2}\leq 1400&\text{(Milling machine)}\\ 3x_{1}+9x_{2}+8x_{3}\leq 1000&\text{(Lathe)}\\ 10x_{1}+13x_{3}+15x_{3}\leq 1750&\text{(Grinder)}\\ 6x_{1}+16x_{3}\leq 1325&\text{(Jigsaw)}\\ 12x_{2}+17x_{3}\leq 900&\text{(Drill press)}\\ 9.5x_{1}+9.5x_{2}+4x_{3}\leq 1075&\text{(Band saw)}\\ x_{1},x_{2},x_{3}\geq 0\end{array} \]  
The above problem formulated with deterministic parameter while there also may be a situation in which this parameter may have multiple choices. Keeping this in mind, we have considered these deterministic parameters as a multi-choice parameter which are given in the Table 2:
# A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges
### Applications  
OCR systems are used now in many fields to make the workflow fast and accurate, so for this kind of digitization, OCR is used. In [8], the authors present a survey of the application of OCR and perform experiments for some applications. The OCR applications discussed are as follows:  
* Invoice Imaging: Used in many businesses to track business records.
* Legal Industry: To digitize documents and enter the data directly into the databases, OCR is used.
* Banking: OCR is also widely used in banking services. For example, to process check payments, cheques are scanned and transferred in seconds.
* Healthcare: In healthcare, many forms, reports, and insurance applications are processed into databases and for other purposes; OCR helps to transfer all kinds of patient data.
* Captcha: Captcha is used to secure systems. A few letters, numbers, or both are used in a captcha, and the image is distorted. Humans can easily read this captcha, but not an average computer program.
* Automatic Number Recognition: It is used for surveillance systems to track vehicles' records by getting their number plates. OCR is also used to recognize the characters and numbers from the number plates.
* Handwriting Recognition: in this application of OCR, the text is extracted from handwritten documents and photographs. For this purpose, the model learns and identifies fonts and languages for better results.
* Scanned Receipts: some challenges comes while scanning receipts for extracting information from them, i.e., variations in receipt layout, noise, and distortion [9].
# Periodic Surface Defect Detection in Steel Plates Based on Deep Learning
## 3 Periodic Defect Detection Algorithm Based on CNN + LSTM
### Periodic Defect Recognition Based on LSTM  
#### 3.2.1 The Principle of Periodic Defect Recognition Based on LSTM  
Figure 7a shows the overall flow of CNN + LSTM. The features were extracted from the samples through the CNN to obtain their corresponding feature vectors X. Then, the feature vectors were inputted into the LSTM in a time sequence, and the outputs O are the recognition results.  
Figure 7b shows the specific flow of the LSTM [16] algorithm at time t in Figure 7a. In Figure 7b, C\({}_{t-1}\) is the information of the previous time, including all the information inputted into the network at all previous times. Combined with the information Xt inputted at the current time, the information in the previous time is filtered first, and then the inputted information at the current time is filtered. By filtering, information at times without a defect, such as X2, Xt in Figure 7a, was forgotten, while information at times with a defect, such as X0, Xt-1 in Figure 7a, was remembered. Thereby, LSTM remembers the defect features better and is prevented from forgetting the defect features for too-long periods.  
Figure 6: Structure of the VGG16 feature extraction network.
## 3 Results and Discussion
### Characterization of the Adsorbent Biomaterials
Figure 2: IR spectrum of YSR before and after removal of Cr (VI) and Ni (II). bridges with overtone patterns indicating the presence of carboxylic acids (-COOH) [48], by O-H stretching, as shown in the change of intensity of the adsorption peak at 2927.94 cm\({}^{-1}\) attributed to the vibrations of C-H methyl, methylene, and methoxy groups present in the biomass that facilitate the adsorption process and justify the high retention percentages of Cr (VI) ions at high temperatures and Ni (II) ions at ambient conditions [49]. Similar to the bio adsorbents under study, other biomaterials, after the adsorption process, were characterized by FTIR and showed the presence of similar functional groups, for example, Sakura residues showed the presence of groups such as -CH, C=O, which favor Cr (VI) adsorption [50]; sugar cane bagasse showed some functional groups responsible for Ni (II) adsorption, such as, OH group attributed by a broad absorption band shown at 3350 cm\({}^{-1}\)[10]. Activated coffee and coffee husk also showed the presence of the OH group due to the bands located at 1440, 1364, 698, and 626 cm\({}^{-1}\), which denotes the interaction with Ni (II) ions [51]. Figure 3: SEM micrographs and EDS of the PSR (**a**) natural and after adsorption of (**b**) Cr (VI) and (**c**) Ni (II). SEM images of the biomaterials after adsorption of Cr (VI) and Ni (II) clearly show their smoother and more diffused structure due to the surface coverage with the metals. Previous studies report similar results when removing metals with biomaterials from _Citrus limetta_[19], _Citrus limettoides_[20], coconut sawdust, and cane bagasse [58]. After adsorption, appearance of Cr (VI) was observed in the high-intensity peaks 0.6, 5.6, and 6 keV; additionally observed were the increase of C, the decrease of O, K, and the disappearance of Na at 1 keV and K at 3.4 keV, which could be due to the formation of bonds between the ion and the active centers of the bio adsorbent [59]. Additionally, the formation of white precipitates on the material and the smoothing of the cylindrical surface are observed, which determines that ion exchange between the metals under study and the mechanism of ions in biomaterials is given by ion exchange between the metals under investigation active centers of the material [13]. Regarding the adsorption of Ni (II), we observed its appearance in the 0.8, 7.5, and 8.2 keV peaks for both biomaterials, as well as the disap  
Figure 4: SEM micrographs and EDS of the YSR (**a**) natural and after adsorption of (**b**) Cr (VI) and (**c**) Ni (II). pearance of chlorine, sodium, and potassium, which would be the active centers to which the ion adhered, with the conversation of the presence of the cover, which indicates that the adsorption of Ni (II) is highly competitive for the active centers resulting in an ion exchange process [13].
## 2 Materials and Methods
### Processing Methods
#### 2.4.2 Google Earth Engine  
The processing to obtain the NDWI images was carried out in Google Earth Engine (hereafter GEE), because it is capable of processing a large quantity of data in a short time [73]. The number of raw images necessary would have also required significant amounts of space on any physical drive, a problem that can be avoided by using the cloud-based GEE platform. A script was written to generate the NDWI images for every year and areas using Equation (2) seen at Section 2.4.1. Each image was acquired at the TOA reflectance, and a cloud masking function was applied to the data as well. During the process, a filter for a low cloud pixel percentage was applied; however, this percentage was tweaked in cases when there were no available images that satisfied those criteria. Nonetheless, there were still some problematic years where images without dense cloud cover were available, e.g., 1985 for the Haditha Dam, and December 2018 for the Mosul Dam. After the image selection and the generation of the NDWI, the annual composite was created by taking the median of the NDWI values at each pixel through the collection of NDWI images in each year interval [71] (p. 158). The same procedure was applied for the generation of the 2018 monthly composites. One NDWI composite was also generated for the year before the construction of each dam. The Hamrin Dam was completed before the first Landsat 5 satellite acquisition (1984); therefore, the NDWI image was generated using a Landsat 2 image. Given that the SWIR band was not present on the Multispectral Scanner System (MSS) sensor of Landsat 2, the NIR band (Band 7) was used instead, and the image underwent a transformation process from digital numbers (DN) to reflectance values (the process for the Landsat 2 images was compiled on a separate script). The low cloud filter and the cloud masking function were applied to minimize potential impactsfrom the atmosphere [74] (see the discussion Section 4), while the median usually helps in removing noise and minimizing the effects of clouds and shadows [53]. In R, the process used in GEE was carried out using the _RGEE_ package [75]. After obtaining all the processed images needed, the subsequent analyses were carried out in QGIS.
## 2 Thermal Methods
The efficiency of this process depends on factors such as the composition of the food, its electrical conductivity, and temperature. In liquid foods, conductivity increases with temperature but decreases with increasing viscosity. [PERSON] et al. (2018) [45] studied microbial inactivation by ohmic heating [45], and [PERSON] et al. (2020) [46] showed that the treatment of whey beverages by this method is more effective in reducing _L. monocytogenes_ and has less impact on sensory and nutritional qualities [46] compared to conventional methods [22]. Ohmic heating (OH) is an interesting method of food processing because it produces heat when an electric current is passed directly through the food. The electrical conductivity of the material, the size of the product to be treated, the heat capacity and heating rate of the system, the electrode material, and the combination of applied current and voltage are all important factors that influence the rate and efficiency of ohmic heating. All of these parameters work together to create a well-performing ohmic heating system [47]. Direct steam injection, a UHT technology, allows for rapid and precise heating of liquids by directly injecting steam into the product. This is considered an effective method for preserving the nutritional qualities of milk, although it has disadvantages such as high cost and operational complexity. The water used for steaming must comply with strict hygiene standards to prevent contamination of the finished product [48]. RF technology has seen a significant increase in industrial use for food processing. Initially used for drying, disinfection, and defrosting, RF has been applied in recent years in pasteurization and sterilization processes, offering promising results for a variety of food products [49]. A better understanding of the influence of fat content and food structure on RF thermal inactivation will contribute to the development of more effective treatments [50]. To date, the use of RF has been studied for the treatment of various dairy products, such as milk [33, 35, 36, 40, 41, 51, 52], yogurt [36, 39, 53], goat milk [54], skimmed or whole milk powder [43, 44, 55, 56, 57, 58], and infant formula [20, 59, 60, 61].
## 4 Discussion
### Limitations of the Models  
In terms of remote sensing spatiotemporal data fusion, the ESTARFM and the new model still have limitations:  
(1) The spatiotemporal data fusion model is data-driven. Therefore, it is difficult for it to accurately generate details and textures that only exist on the target date.  
(2) In the process of model calculation, the size of the sliding window and the selection rules of similar pixels are very important. The optimal settings for these parameters still cannot be automatically generated by the program.
## 2 Resources and Methodology
### Methodology for Obtaining the Robot Digital Twin
#### 2.2.2 Nonlinear Trajectory Tracking: Lyapunov-Based Controller  
For the nonlinear trajectory tracking, the strategy developed by [PERSON] et al. in the Elektra project [36] is applied for the control of an autonomous vehicle. The design process follows a structured sequence of steps, summarized as follows.  
1. Consider the kinematics model of a unicycle mobile (Equation (1)) \[\begin{split}\dot{\theta}&=w,\\ \dot{x}&=v\cdot cos(\theta),\\ \dot{y}&=v\cdot sin(\theta),\end{split}\] (1) being \(v\) the linear velocity, \(w\) the angular velocity, \(x,\ y\)_and_\(\theta\) the robot pose in Cartesian space, and the upper dot means the time derivative. 2. Define the pose error \(e\) (Cartesian space): \(x_{e},\ y_{e}\)_and_\(\theta_{e}\), respect to the desired values. 3. Obtain the time derivative of the pose error \(\dot{e}\). 4. Propose the Lyapunov Function \(V(e)\), in this case: \[V(e)=\ \frac{1}{2}\,x_{e}^{2}+\ \frac{1}{2}\,y_{e}^{2}+\ \frac{1}{2}\,\theta_{e}^{2}\] (2) 3. Evaluate the time derivative of the Lyapunov Function \(\dot{V}(e)\), where the control vector (\(v\), \(w\)) and the desired velocities (\(v_{d}\), \(w_{d}\)) according to the trajectory reference are involved, \[\dot{V}(e)=K_{2}x_{e}(v_{d}cos\theta_{e}-v)+\theta_{e}(w_{d}-w)+K_{2}y_{e}v_{d }sin\theta_{e}\] (3) 4. Propose a control law that guarantees stability of the control system, that means \(\dot{V}(e)<0\). In this case, the control law is: \[\begin{bmatrix}v\\ w\end{bmatrix}=\begin{bmatrix}k_{1}\ x_{e}+\ v_{d}\ cos\theta\\ \\ w_{d}+\ k_{2}\ v_{d}\ y_{e}+\ k_{3}\ \theta_{e}\end{bmatrix}\] (4) being \(k_{1},\ k_{2}\)_and_\(k_{3}\) positive control gains. 5. Adjust the gains involved in the control law according to the desired trajectory tracking (see Appendix A.2).  
At this stage of the design process, the dynamics related to the velocity servosystem (as described in [43]) is incorporated to validate the complete Simulink model, illustrated in Figure 5. The Kalman Filter provides the estimated state \(\hat{x}\) and the filtered output \(\hat{y}\) to close the servosystem loop. All the control gains involved in the global control solution depicted in Figure 5 are detailed in Appendix A.2.  
Figure 4: Simulink project to be downloaded on the DT-RBP working as a DT of the identified robotic unit.  
Finally, following the same approach used for the implementation of DT, all control components shown in Figure 5--excluding the Robot Model--are deployed to the ECU-RBP using the Simulink Coder tool [36], allowing the acquisition of experimental results.
## 1 Introduction
However, with the traditional full network coding technique as illustrated by Figure 1, nodes in a network have to wait for the data packets from all other nodes to arrive before starting decoding, which inevitably increases the network delay. To tackle this issue, [PERSON] _et al._[14] proposed the partial network coding (PNC) solution that can effectively reduce the network delay. In this work, we propose GPNC, a novel geographic information routing protocol that adopts partial network coding for efficient forwarding of data packets through reducing retransmissions and network delays and improving data delivery rates. The GPNC protocol employs a novel forwarding strategy to route data packets to any destination node by choosing candidate forwarding nodes according to each node's normalized packet advance and its residual energy in order to appropriately balance energy efficiency and data delivery rate. Simulation results show that GPNC can significantly improve network throughput and packet delivery ratio, while reducing energy consumption and network latency when compared with other routing protocols. The rest of this paper is organized as follows: Section 2 reviews previous work on geographic information routing and network coding. Section 3 describes the network model and the partial networking coding model. Section 4 presents the GPNC routing algorithm, followed by its performance evaluation in Section 5. Finally, Section 6 concludes the paper with a summary of major contributions and future work.
# Sea Drift Trajectory Prediction Based on Quantum Convolutional Long Short-Term Memory Model
## 3 Methods
### The Input of Model  
In this study, we conducted comparative experiments between the CNN, CNN-LSTM, and QCNN models. The input of the QCNN and QCNN-LSTM models needs to quantize the data, which means rotating the quantum state on the Bloch sphere [37] around the \(X\), \(Y\), and \(Z\) axes by angles, so it can bring about a change in the probability amplitude. However, only phase changes and the use of these three operations together can make the quantum state move freely on the whole Bloch sphere [38] (Figure 2).  
As mentioned in the introduction, the CNN model cannot be directly used for time series prediction, so we improved several models for this purpose. For drift trajectory data, sometimes the data is periodic, while other times it is discrete and non-periodic. To enhance the models' versatility, they were designed to accept two types of inputs: discrete data input (without providing short-term memory to the model) and periodic data input (with input length corresponding to the autocorrelation period).  
For the first type of input, all input data points are considered discrete, so accuracy can be achieved by simply fitting the regression to each data point, and the model's historical input does not affect it; each row of data is independent of historical data. Figure 3 shows the QCNN model for discrete input data with quantum circuits encoding the data into dense-angle representation. Each quantum circuit accepts feature data and rotates the input quantum state along the \(X\) and \(Y\) axes. Here, \(X_{1}\) represents the first variable of the data, \(X_{2}\) represents the second variable, and so on.  
The second type of input that considers historical input data is the simulated \"time window\" input, which accepts input features of length n, where n is the observed autocorrelation period. Short-term memory is simulated for the model by providing input features of length n. The QCNN model with short-term memory and the QCNN-LSTM model needs historical data as input. We want the data set of each circuit to be stacked as a sequence as the input of the QCNN model. However, the input of each quantum circuit is always one quantum bit. In order to solve this problem, the input circuit will be connected  
Figure 3: A densely angular coded quantum circuit representing some data points of discrete data input QCNN-LSTM.  
Figure 2: Visualization of the Hadamard gate on the Bloch sphere, acting on the input state.  
in series with the time circuit as the historical data of the time circuit. The structure of the input quantum circuit is shown in Figure 4.  
By testing the data set, it was found that the autocorrelation of the data set is set to 3 days, which is the best effect, and the sliding window with a length equal to the autocorrelation period is used to split the data set into small data subsets for input. If the autocorrelation period of the data is 3, then we only need to concatenate t with \(t-2\) and \(t-1\) like Figure 4.
# Network Performance Optimization for Low-Voltage Power Line Communications
## 4 Network Performance Optimization Based on Dynamic Game Theory
### Network Performance Model
#### 4.1.1 Assumptions  
1. The load impedance matches the output impedance in the network.
2. A single contention domain with \(n\) stations (\(1\leq n\leq 30\)) exists.
3. Data packets are of a constant length L.
4. Each station always contains data packets in the transmission buffer, and packets are never discarded until a successful transmission.
5. The stations do not use request-to-send (RTS) and clear-to-send (CTS) handshake mechanisms.
6. The communication distance of the stations is constant at a certain time scale.
7. Propagation delays are much shorter than the slot time, and are, therefore, neglected.
# Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods
## 4 Learning to Optimize An RIS
### Supervised Learning  
In this paradigm, the optimal phase shift \(\mathbf{e}\) under a specific channel realization and network setting is obtained by traditional optimization approaches (as detailed in Section 3). This channel realization and the corresponding optimized phase shift are treated as a training sample. If we have many training samples corresponding to different channel realizations, a DNN can be trained to approximate the behavior of a traditional optimization method. The advantage of this approach is that the learning results inherit the solution quality from optimization methods [18; 38]. However, it has an additional burden of generating training samples; however, low-complexity methods, such as GD, the manifold method, and CR-PG, help to reduce this burden compared to the SDR and MM methods. Furthermore, supervised learning can be extended to directly solve problem (4) by treating  
Figure 8: Performance comparisons of six optimization methods: (**a**) Average computation time versus the number of antennas at the BS when \(M=10\). (**b**) Average computation time versus the number of reflecting elements when \(N=10\).  
the channel realization as input and all resources (both \(\mathbf{x}\) and \(\mathbf{e}\)) as the desired output of the DNN.