esa-sceva/satcom-chunk-collection huggingface dataset:
# Achieving Low Latency Communications in Smart Industrial Networks with Programmable Data Planes
## 2 Concepts and Technologies
### Data Processing in Industrial Networks  
As mentioned in the previous subsection, features like communication protocols have a deep influence on performance. Thus, optimizing them is a crucial matter. There are plenty of methods and technologies available to improve communication parameters, such as network routing protocols, processing algorithms, or virtualization of resources. In this context, one of the most innovative concepts and with the most projection is in-network computing (INC), along with edge and cloud computing paradigms. INC extends the concept of edge computing by leveraging the data plane of networking devices to implement packet processing functions at line rate. This means that devices that traditionally have only provided forwarding of packets can now implement some intelligence to process the information transmitted in those packets and make decisions based on that information. Edge and cloud are different parts of a network that are distinguished by their locations, as the first one is closer to the end devices and the second one is usually in a remote station. These sites contain computing and storage servers, usually having more resources in the cloud than in the edge. Different locations make them suitable for different objectives. Edge servers offer superior results when it comes to response times from end devices, being adequate for services with strict timing constraints. Cloud servers, however, are usually gathered in data centers, centralizing network control in a single point. This simplifies network organization and reduces resource expenses, as having centralized equipment and servers is more efficient than duplicating every service in each local network. Figure 2 shows the communication in both cases, edge (red) and cloud (blue).  
Figure 1: MQTT protocol messages for a MQTT publication.  
INC offers numerous use cases in the industry. A clear example is the control of packet routing, which consists of processing packets or packet flows inside network elements (like switches) and taking decisions about how to route them without the intervention of a centralized controller. Filters can also be applied, which can be used for packet routing (as in the previous case) or even for detecting inconsistencies, such as duplicated packets or network attacks. Another interesting use case is data preprocessing; that is, the deeper inspection of packets in order to read higher level information before the packet reaches the final server. This can be really useful, as it can be combined with previous routing alternatives to process packets before they arrive at the destination, in this way providing a faster response.
## 2 3D Analytical Solutions to the Wave Equations of a RDGC  
### 3D Solutions for a Straight Rectangular Duct  
Figure 2 shows a uniform rectangular duct with a width of \(b\) and a height of \(h\). The 3D wave equation of the rectangular duct is given by [25]  
\[\frac{\partial^{2}p}{\partial t^{2}}=c^{2}\
abla^{2}p, \tag{1}\]  
where \(p\), \(t\) and \(c\) are the sound pressure, advancing time and sound velocity, respectively. The Laplacian operator \(\
abla^{2}\) is given as follows:  
\[\
abla^{2}=\frac{\partial^{2}}{\partial x^{2}}+\frac{\partial^{2}}{\partial y ^{2}}+\frac{\partial^{2}}{\partial z^{2}} \tag{2}\]  
where \(x\), \(y\) and \(z\) are the Cartesian coordinates shown in Figure 2.  
The general solution of Equation (1) [25] is  
\[p(x,y,z,t)=\left(C_{1}e^{-\mathrm{j}k_{z}z}+C_{2}e^{+\mathrm{j}k_{z}z}\right) \left(e^{-\mathrm{j}k_{z}x}+C_{3}e^{+\mathrm{j}k_{x}x}\right)\left(e^{-\mathrm{ j}k_{y}y}+C_{4}e^{+\mathrm{j}k_{y}y}\right)e^{\mathrm{j}\omega t} \tag{3}\]  
where j is the imaginary unit. \(C_{1}\), \(C_{2}\), \(C_{3}\) and \(C_{4}\) are the coefficients to be determined with boundary conditions. \(k_{x}\) and \(k_{y}\) are the wave numbers in the \(x\) and \(y\) direction, respectively. \(k_{z}\) is defined as  
\[k_{z}=\sqrt{k^{2}-{k_{x}}^{2}-{k_{y}}^{2}} \tag{4}\]  
where \(k=\omega/c\) and \(\omega\) is the angular frequency. The solution of the rigid-walled duct with a width of \(b\) and a height of \(h\) is given by  
\[p(x,y,z,t)=\sum\limits_{m=0}^{\infty}\sum\limits_{n=0}^{\infty}\cos\left( \frac{m\pi x}{b}\right)\cos\left(\frac{n\pi y}{h}\right)p(z,t) \tag{5}\]  
where \(p(z,t)=\left(C_{1,mn}e^{-\mathrm{j}k_{z,mn}z}+C_{2,mn}e^{+\mathrm{j}k_{z,mn}z }\right)e^{\mathrm{j}\omega t}\) and \(k_{z,mn}=\sqrt{k^{2}-\left(m\pi\pi/b\right)^{2}-\left(n\pi/h\right)^{2}}\). Here, \(\cos(m\pi x/b)\cos(n\pi y/h)\) is an eigenfunction representing the wave shape in the \(x\)-\(y\) plane at the (\(m\), \(n\)) mode. \(C_{1,mn}\) and \(C_{2,mn}\) are the amplitudes of the waves at the (\(m\), \(n\)) mode propagating in the positive and negative \(z\) directions.
# Improved DeepFM Recommendation Algorithm Incorporating Deep Feature Extraction
## 2 Improved DeepFM Recommendation Algorithm Incorporating Deep Feature Extraction  
For the recommendation algorithm, the effective extraction of item features is an important factor affecting the recommendation effect. The user's interest changes with time. Recommending what the user liked in the past often makes the recommendation meaningless, so the recommendation algorithm should also consider timeliness. However, data sparsity is a common problem for recommendation datasets. In general, the word vector is sparse after One-hot coding, especially for textual item descriptions, and One-hot only exacerbates the sparsity. Therefore, a new model is designed in this paper, named fDeepFM.  
The algorithm model is shown in Figure 1. The model is divided into three parts, the _FM_ model, the _DNN_ model, and the GRU model. Firstly, item features are divided into word features and text description features. The word features are trained through the Embedding layer to alleviate the sparsity of data, the text description features are combined with Doc2 Vec [14] to process the item description information, and the two are spliced to form new vector representation of the item as input to the _FM_ model and _DNN_ model. Secondly, the GRU model can analyze the characteristics of the long-term sequence to predict the user behavior at the next moment, so the user's historical behavior records are trained according to different cycles through GRU [15] model to get the user's feature representation. Finally, the three parts of the model are trained in parallel, a fully connected layer is added to the output features, and the final results are output after the activation function is applied. The model can improve the effect of feature learning and learn the feature changes of the user's historical interest.
# Multi-Band Polarization Imaging in a Harsh Sea Fog Environment
## 5 Conclusions  
This paper focuses on the multi-band polarization imaging of a sea fog environment and the analysis of the polarization images obtained from the processing. Through outdoor experiments, the effects of multi-band on target polarization imaging and the effects of sea fog optical thickness on target imaging can be seen. With the increase of sea fog, the polarized light appeared to recede, and the quality of polarization image decreased. When the optical thickness was 0.75, the best effect was achieved in the 670 nm band in the sea fog environment, and the polarization contrast increased by 1.9%, where the polarization contrast values of 670 nm and 530 nm with the increase of optical thickness were about twice as high as the contrast values of intensity images. It provides strong technical and data support for multi-band polarimetric imaging in a sea fog environment. The outdoor experiments in this paper realize the imaging of static targets, followed by image computation and processing analysis; however, the shooting and real-time processing of moving targets in the sea fog environment is the general trend and because of its difficulty should be the focus of future research methods.  
Data curation, Q.F. and N.L.; formal analysis, H.G. and X.L.; investigation, D.G. and Y.Y.; methodology, Q.F.; project administration, J.D. and Q.F.; resources, S.Z.; software, J.Z.; supervision, J.D.; validation, X.L.; visualization, S.Z. and J.Z.; writing--original draft, Q.F.; writing--review and editing, N.L. All authors have read and agreed to the published version of the manuscript.  
National Natural Science Foundation of China (61890963; 61890960; 62127813).  
Not applicable.  
The study did not report any data.  
The authors declare no conflict of interest.
## 3 Performance Analysis
### Complexity  
In this paper, the computation complexity is defined as the total number of multiplications and additions which is required to generate the communication symbol and demodulate information. For VDES, \(N\) multiplications and \((N-1)\) additions are required to form a symbol. To demodulate \(N\) information bits, \(N\) multiplications and \(N\) additions are required. Thus, the total complexity of VDES is \(2N\) multiplication and \(2N-1\) addition. The complexity of TDOM is similar to that of VDES, except for the time-delay. Fortunately, the time-delay can be realized by means of digital signal processing, which is relatively simple to implement. At the receiver, solving the system of linear equations introduces additional complexity. It is well known that the complexity of solving a system of equations of dimension \(N\) is \(O(N^{3})\). Therefore, the complexity of TDOM is larger than that of VDES. In addition, OFDM-DCSK requires \(N\) IFFT and FFT to demodulate \(N\) information bits, and its complexity is also smaller than that of TDOM. However, the TDOM achieves a higher spectral efficiency and saves more maritime communication spectrum resources, which will enable maritime communication networks to achieve higher communication rates or accommodate more users. Meanwhile, when updating the traditional transceiver using TDOM, only the baseband modem module needs to be replaced, and other modules such as the RF modem module do not need any changes. Therefore, the system upgrading cost is low.
# Knowledge Distillation-Based GPS Spoofing Detection for Small UAV
## 5 Methodology  
The conventional methods mainly focus on forged signal processing, which needs many computational sources to identify the hackers. Also, signal processing needs additional devices to receive signals, which reduces the loading capacity of UAVs and are hard to deploy on a large scale. Different from conventional methods, we identify the patterns of the movements of an UAV and predict the trajectory to classify the forged signals. LSTM has good performance in time-sequential data prediction [35]. Also, GPS localization has a strong relationship with time sequence. In this section, we first propose an LSTM-based detection model that can identify GPS attacks. And then, we leverage the knowledge distillation method to obtain a lightweight classifier that is able to be installed on computation-limited platforms like UAVs.
# Logistics Transportation Vehicle Supply Forecasting Based on Improved Informer Modeling
## 3 Problem Description and Model Design  
This section describes the spatiotemporal evolution patterns of supply data and the principles of the Informer model temporal prediction algorithm. In this section, a spatiotemporal convolutional network is designed to extract the spatiotemporal features of the data and propose a supply prediction method for logistics transportation based on the improved Informer model.
# A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges
### Challenges  
Some challenges are faced while designing an OCR system for the Arabic language. Arabic script uses diacritics and ligatures to indicate short vowels and certain consonant combinations, and OCR systems need to recognize and process these diacritics and ligatures correctly. The Arabic language has several types of two- and three-letter consonant combinations, i.e., shadda, sukoon, and tashkeel. An OCR system needs to recognize around 70 to 80 symbols in total for the Arabic language, including basic letters, diacritic marks, and other symbols used in the Arabic script. The Arabic script has 28 basic letters and several complex characters formed by joining multiple basic letters, and OCR systems  
Figure 1: Types of OCR systems in Arabic and their modes of processing.  
need to recognize and separate these complex characters. Arabic handwriting can vary greatly, making it more difficult for OCR systems to recognize the characters correctly. Arabic OCR datasets are usually smaller than those of other languages, leading to difficulty with training and fine-tuning the model. The images for OCR can be in multiple forms, i.e., computer-rendered images, scanned images, photographed images, and handwritten scans. These image types have challenges regarding the recognition rate for the OCR process.  
[PERSON] et al. [7] also discussed some challenges of the Arabic OCR system. Dots come in Arabic in different places, sometimes above or below the baseline. These dots have much importance in the Arabic language; if you miss any dot somehow or during skew detection/correction, it will change the meaning of the letter or word, reducing the accuracy of the OCR model. Many of the scripts have a non-cursive style, meaning the letters present in a word have some gaps, making them easy to recognize, reducing the challenge, and making the task easy. However, the Arabic language has a cursive style, and the connectivity of letters makes text recognition more complicated. As Arabic letters are compounded to form a word, every font style shows a different level of ligature in words.
# Intelligent End-Edge Computation Offloading Based on Lyapunov-Guided Deep Reinforcement Learning
## 4 End-Edge Computation Offloading Based on Lyapunov-Guided DRL
### Complexity Analysis  
The complexity analysis of the proposed LyA3C algorithm is based on the structure of the neural network used for the critic network and actor network. The critic network contains the input layer \(K_{i}\), the hidden layer \(K_{h}\) and the output layer \(K_{o}\). The complexity of the critic network is calculated as \(\mathcal{O}(K_{i}K_{h}+K_{i}K_{o})\). Similarly, the actor network also contains the input layer \(K_{i}\), the hidden layer \(K_{h}\), and the output layer \(K_{o}\). The complexity of the actor network is calculated as \(\mathcal{O}(K_{i}K_{h}+K_{i})\). For each agent, the overall complexity is the sum of the complexity of the critic network and the actor network: \(\mathcal{O}(2K_{i}K_{h}+K_{i}K_{o}+K_{i})=\mathcal{O}(K_{i}(2K_{h}+K_{o}+1))\).
# Hybrid Wind-Redox Flow Battery System for Decarbonizing Off-Grid Mining Operations
## 6 Results and Discussion
### Model Results
#### 6.2.1 General Trend Results
\begin{table}
\begin{tabular}{c|c c c c|c c c} \hline \multicolumn{2}{c}{**Number of Wind**} & \multicolumn{3}{c}{**160 MWh Redox Battery**} & \multicolumn{3}{c}{**120 MWh Redox Battery**} \\ \hline
**Turbines** & 0 gen & **1 gen** & **2 gen** & **3 gen** & 0 gen & **1 gen** & **2 gen** & **3 gen** \\ \hline
**12** & \(-63.98\%\) & \(-58.66\%\) & \(-51.98\%\) & \(-41.84\%\) & \(-62.72\%\) & \(-57.43\%\) & \(-50.82\%\) & \(-40.88\%\) \\ \hline
**11** & \(-62.83\%\) & \(-57.69\%\) & \(-51.27\%\) & \(-41.28\%\) & \(-61.56\%\) & \(-56.44\%\) & \(-50.08\%\) & \(-40.37\%\) \\ \hline
**10** & \(-61.45\%\) & \(-56.59\%\) & \(-50.35\%\) & \(-40.79\%\) & \(-60.20\%\) & \(-55.34\%\) & \(-49.12\%\) & \(-39.76\%\) \\ \hline
**9** & \(-59.82\%\) & \(-55.34\%\) & \(-49.33\%\) & \(-40.13\%\) & \(-58.63\%\) & \(-54.10\%\) & \(-48.15\%\) & \(-39.08\%\) \\ \hline
**8** & \(-57.85\%\) & \(-53.71\%\) & \(-48.14\%\) & \(-39.32\%\) & \(-56.71\%\) & \(-52.58\%\) & \(-46.95\%\) & \(-38.23\%\) \\ \hline
**7** & \(-55.19\%\) & \(-51.75\%\) & \(-46.75\%\) & \(-38.29\%\) & \(-54.20\%\) & \(-50.68\%\) & \(-45.61\%\) & \(-37.17\%\) \\ \hline
**6** & \(-51.24\%\) & \(-49.12\%\) & \(-44.79\%\) & \(-37.13\%\) & \(-50.51\%\) & \(-47.99\%\) & \(-43.78\%\) & \(-36.02\%\) \\ \hline
**5** & \(-45.15\%\) & \(-44.70\%\) & \(-42.17\%\) & \(-35.43\%\) & \(-44.82\%\) & \(-44.12\%\) & \(-41.11\%\) & \(-34.44\%\) \\ \hline
**4** & \(-37.87\%\) & \(-37.96\%\) & \(-37.46\%\) & \(-32.95\%\) & \(-37.79\%\) & \(-37.70\%\) & \(-36.86\%\) & \(-32.18\%\) \\ \hline
**3** & \(-30.06\%\) & \(-29.83\%\) & \(-29.52\%\) & \(-27.95\%\) & \(-29.96\%\) & \(-29.86\%\) & \(-29.46\%\) & \(-27.49\%\) \\ \hline
**2** & \(-20.62\%\) & \(-20.62\%\) & \(-20.34\%\) & \(-19.21\%\) & \(-20.65\%\) & \(-20.59\%\) & \(-20.29\%\) & \(-19.20\%\) \\ \hline
**1** & \(-10.14\%\) & \(-10.20\%\) & \(-10.07\%\) & \(-9.23\%\) & \(-10.19\%\) & \(-10.17\%\) & \(-10.17\%\) & \(-9.21\%\) \\ \hline
**0** & \(0.73\%\) & \(0.74\%\) & \(0.61\%\) & \(0.96\%\) & \(0.71\%\) & \(0.66\%\) & \(0.68\%\) & \(1.09\%\) \\ \hline \end{tabular} Legend: Dark green = very good economy; Light green = good economy; Yellow = fairly good economy; Red = bad economy; gen = generator.  
\end{table} 
Table 9: Amount of diesel saved, in the cases of 120 MWh and 160 MWh redox battery. 160 MWh batteries (see Table 9). In contrast, with four wind turbines added to the grid and one generator in continuous operation, the configuration with the largest storage capacity (160 MWh battery) remains the best option, with an estimated diesel savings rate of 37.96%. Scenario of Two Generators in Operation and 120 MWh Battery: Impact of Adding Wind Turbines to the 25 kV-G Network  
This scenario evaluates the performance of a hybrid system with two continuously operating diesel generators and a 120 MWh redox flow battery. The number of additional E-115 wind turbines varies from 0 to 12 to assess their impact on system costs and operation. The results are shown in Figure 14:
# Application of Electrical Network Frequency of Digital Recordings for Location-Stamp Verification
## 2 Enf Extraction and Database Formation Based on Location Specific Recordings
### Extraction of Enf Sequences
#### 2.2.1 Enf Sequences of Power Recordings  
**Step-1:** Each 5 min long power signal is processed through a 2 nd order Butterworth band-pass filter designed with a frequency band of [40; 70] Hz. The filtered signal is then segmented into a number of time frames for ENF estimation. In this work, each ENF time frame is set as 5 s long empirically.  
**Step-2:** Root MUSIC algorithm is applied to extract the ENF sequence of each 5 s long signal segment. Thus, the power signatures embedded in a power recording are estimated.
# Dynamic Path Planning for Vehicles Based on Causal State-Masking Deep Reinforcement Learning
## 4 Causal State-Masking TD3 Algorithm
### CSM-TD3 Architecture  
Based on the above design of the critic and actor networks, the pseudo-code for the overall training and optimization steps of the CSM-TD3 algorithm is as described in Algorithm 1.  
Finally, the complete framework of the proposed CSM-TD3 algorithm interacts with the vehicle path-planning environment, as illustrated in Figure 4. This framework comprises three core components: the vehicle dynamic path-planning environment, the actor-critic network, and the experience replay buffer. The vehicle dynamic path-planning environment consists of the vehicle dynamics model and the environmental model, designed to interact with the DRL algorithm to generate the data required for training. This dynamic model is capable of simulating the vehicle's motion states and responses under various environmental conditions, providing diverse training scenarios so that the agent can learn effective path-planning strategies in complex and dynamic environments. The actor-critic network component includes both the online and target actor neural networks, which are responsible for generating appropriate control actions. The online actor network outputs the action policy based on the current state, while the target actor network is utilized to stabilize the training process and mitigate fluctuations during policy updates. Concurrently, the DRL critic component comprises two online critic neural networks and two target critic networks. These are designed to evaluate the value of actions and provide reliable feedback for updating the actor network. The design of dual critic networks effectively reduces the overestimation bias in value estimation, enhancing the stability and performance of the algorithm. The experience-replay buffer stores historical experience data generated by the agent during interactions with the environment. These data include information such as states, actions, rewards, and future states. By leveraging the experience-replay buffer, the algorithm can break the temporal correlations between consecutive samples, improve data efficiency, and stabilize the training process.
# Effect of Surfactant on Water Content of Phosphogypsum
## 1 Introduction  
Key processes during the wet process of producing phosphoric acid are the leaching behavior and crystallization process of phosphogypsum. CaSO\({}_{4}\cdot\)2H\({}_{2}\)O is the main content of phosphogypsum; its crystal structure and particle size can directly affect the filtration rate and the loss rateof phosphate. Many materials can improve the crystal structure during the production process of CaSO\({}_{4}\cdot\)2H\({}_{2}\)O, e.g., activated carbon, activated silicone, aluminum sulfate, and some inorganic compounds [1; 2; 3; 4] as well as some polymers and surfactants, phosphoric acid, and carboxylate.  
[PERSON] [5; 6; 7; 8; 9; 10; 11] simulated the production process of phosphate acid with sulfate and calciumrophosphateand showed that the existence of hex-adecyl trim-ethyl ammonium bromide, 1,2-benzodiazepepines, Al\({}^{3+}\) and Mg\({}^{2+}\) can improve the crystallization of phosphogypsum, while citric acid has the opposite effect. [PERSON] [12] found that increased crystallization temperature can enlarge particle size and improve the crystal structure in optimal crystallization time. [PERSON] [13] simulated the production process with calcium chloride and sulfate, finding that the crystal structure of phosphogypsum can change from acicular crystal to prismatic crystal, bowknot shape crystal, and spherical crystal, thus enlarging the particle size. Most research is focused on improving crystal structure of CaSO\({}_{4}\cdot\)2H\({}_{2}\)O with pure chemicals, while ignoring the effect of impurities such as Mg\({}^{2+}\), Al\({}^{3+}\), Fe\({}^{3+}\), and SiO\({}_{2}\) during the process [14; 15; 16]. During the wet process of producing phosphoric acid, the effect of the impurities in the phosphate rock cannot be ignored. However, few studies focusedanalyze the effect of surfactants on the growth behavior of phosphogypsum crystal and water content of phosphogypsum.  
This study investigated the effects of single surfactant SDS, SDBS, SR and PDMS on the water content of phosphogypsum. At the same time, the effect of SDBS+SDS, SDBS+SR and SDBS+PDMS on the water content of phosphogypsum was also investigated. The regulation mechanism of single surfactants and two-element compound surfactants on water content of phosphogypsum was analyzed from the following aspects: the interfacial tension of solution, the contact angle of system solid-liquid interface, the morphology of CaSO4 xH2O, and the fractal growth behavior of phosphogypsum.