esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimenting with Agent-Based Model Simulation Tools
## 3 ABM Tools Overview
### ABM Tools Description
_Pandora_[68] is an ABM framework for large-scale distributed simulation providing two identical programming interfaces exposing the same functionalities in two different programming languages. pyPandora allows non-expert developers to develop models using Python quickly. C++ Pandora offers a more efficient interface in C++ to implement complex models, including the automatic generation of parallel and distributed code. Pandora includes Cassandra, a GUI tool with functionalities to design and analyze a single model execution or to set up a model exploration process. This tool can run large-scale ABMs, and deal with thousands of agents with complex behavior. _Repast_ (REcursive Porous Agent Simulation Toolkit) [69] is a family of agent-based modeling and simulation platforms available in several programming languages. Repast Simphony [70] is a Java-based modeling system that provides automated methods to perform all the common tasks required in a simulation and supports several crucial additional functionalities. The Simphony platform is based on a modular architecture adopting a plugin system that enables adding a wide range of external tools. Any other Repast version implements the core features of Repast Simphony. Repast4 Py [71] is a Python-based framework that includes functionalities to develop distributed ABMs. _RepastHPC_ (Repast for High-Performance Computing) [72] is another member of the Repast suite; specifically, it is a C++-based modeling system designed for running on large computing clusters and supercomputers. This toolkit enables the execution of massive simulations containing hundreds of thousands of agents of very complex behavior whose execution requires high computational power. Although some built-in functions are available for developing a model, RepastHPC still requires users to have good programming experience since they have to manage different aspects of the parallel execution.
# Image-Based Scratch Detection by Fuzzy Clustering and Morphological Features
## 1 Introduction
In this paper, the light-field surface detection system is shown in Figure 1. The hardware part is composed of two strip light sources, a lens with little distortion, an industrial camera, and the piezoelectric ceramic plates on a conveyor belt. The industrial camera and lens are mounted directly above the conveyor belt. In order to enhance the quality of images of surface scratch defects, the length of the strip light source is twice as long as that of the ceramic chip, and the strip light source irradiates the ceramic plate at a low angle. A new detection method utilizing the fuzzy c-means (FCM) algorithm and morphological characteristics is presented to extract piezoelectric ceramic plate surface scratches. The method aims to enable automated recognition and a simple offline learning process. The research is focused on detecting surface scratches on a piezoelectric ceramic plate. In the preparation process of a piezoelectric ceramic plate, when there are improper processing methods, scratches will form on its surface, which degrade the piezoelectric, dielectric, and elastic properties. When working under high voltage, it is easy to crack, which affects the service life and utilization value of the product. In the method proposed in this paper, FCM was used to cluster the foreground and background of the ceramic image offline, and the foreground membership degree of the gray value was calculated. Online, the membership degree and a normalization operation were  
Figure 1: Low-angle strip light illumination light-field surface imaging system schematic diagram. used to enhance the ceramic image, then the scratch was segmented according to the morphological analysis method. This provides a new method for surface scratch detection on piezoelectric ceramics. The structure of this paper is as follows. In Section 2, the FCM algorithm and its application in image enhancement are briefly reviewed. Section 3 is devoted to the analysis and extraction of morphological features for binary enhanced images. In Section 4, the outline of the scratch detection working steps and experimental results are presented. Finally, this article ends with a conclusion in Section 5.
## 2 Data and Methods
### Study Area  
The Dongting Lake includes four prefecture-level cities and one district in Hunan Province and Hubei Province. The Dongting Lake is located at 27\({}^{\circ}\)98\({}^{\prime}\)-30\({}^{\circ}\)23\({}^{\prime}\) N, 110\({}^{\circ}\)20\({}^{\prime}\)-114\({}^{\circ}\)14\({}^{\prime}\) E (north latitude). The area of the whole district is 60,500 km\({}^{2}\), with a total population of 22 million residents. The main topography of the Dongting Lake includes vast alluvial plains, lakes and water networks, and the surrounding mountains and hills have a complex topography that also includes a large area of wetlands. Dongting Lake is an important base for commercial food, aquaculture and farming in China. The annual mean temperature range of the Dongting Lake is 16.4-17.0 \({}^{\circ}\)C, the frost-free period is 260-280 d, and the annual mean precipitation range is 1200-1550 mm. The geographic location and spatiotemporal distribution of land use types of the Dongting Lake in 2018 are shown in Figure 1.  
Dongting Lake is the second largest freshwater lake in China. The lake area is known as the \"land of fish and rice\". It is an important commercial grain base, aquatic product and breeding base in China. The Dongting Lake ecosystem can play a role in regulating runoff, storing floods and preventing droughs, conserving water sources, purifying water quality, purifying air, regulating climate, protecting organisms and enriching species. According to the statistics of biodiversity in Dongting Lake wetland ecosystem, there are 170 families,  
Figure 1: The geographic location and spatiotemporal distribution of land use types in the Dongting Lake in 2018.  
637 genera and 1428 species of plants, 11 orders, 22 families and 119 species of fish, 16 orders, 43 families and 216 species of birds, eight orders, 13 families and 22 species of mammals and 27 species of amphibians and reptiles [60]. It is also an important habitat for the rare species of finless porpoise in the Yangtze River, and is listed as one of the 200 important ecological areas in the world by the World Wildlife Fund. Therefore, the protection and sustainable utilization of the Dongting Lake ecosystem is of great significance for ensuring the steady development of the regional economy and society and improving the ecosystem environment.
## 2 Principles of TU and CEBU  
CEBU is a novel upsetting method that was developed from the deformation characteristics of traditional upsetting (TU). To highlight the difference between CEBU and TU in upsetting, Figure 1 schematically illustrates the two upsetting methods. The TU is schematically illustrated in Figure 1a. During the upsetting process, due to the friction between the anvil and billet, two large, rigid deformation zones are formed at the billet ends. Meanwhile, outside the rigid deformation zones, the metal flows nonuniformly from the billeet inside to its surface. Therefore, due to the non-uniformity of metal flow, buckling will occur and longitudinal cracks may be formed simultaneously on the billeet surface.  
According to the deformation characteristics of TU, the metal flow around the billeet end is a crucial factor that leads to bulging formation. In other words, improving the ability of metal to flow around the billeet end is a fundamental way to restrain bulging. CEBU is proposed on the basis of this idea. The principle of CEBU is schematically shown in Figure 1b. By prefabricating a cone shape at the billeet end, the contact area between the billeet end and the anvil is reduced. Compared with TU, the metal flow in the cone zone is greatly improved, which reduces the influence of the rigid deformation zone at the billeet end. In addition, metal flow in the cone part can be considered independent of other parts, resulting in homogeneous deformation in the center section of the billeet. As a result, CEBU is able to effectively prevent bulging and longitudinal cracking during upsetting, especially for LHDR billets.  
According to Figure 1b, the cone angle \(\alpha\) and the height \(h_{\mathrm{c}}\) are two crucial parameters in CEBU. In practice, for a cylindrical billet, the billeet ends should first be tapered. However, a large cone height will result in an excessive overall height of the billeet, which is not favorable for upsetting. It is therefore recommended that the height of the cone end be generally less than the diameter of the billet. It should be acknowledged that CEBU adds an end forming process compared to TU. This is a disadvantage when using CEBU in practice.
# A Practical and Efficient Node Blind SignCryption Scheme for the IoT Device Network
## 4 Functionality Comparisons and Security Analysis
### Fast Sign-Cryption Operation  
The proposed scheme only needs three hash operations, one \(\oplus\) operations, five multiplication operations, and one symmetric encryption in the offline-signing phase. In this situation, our proposed scheme is more efficient than [2]. In addition, the sensor node \(DS_{i}\) can blind the sensed data to the base station efficiently and with data confidence. The base station \(BS\) cannot be aware of the sensed data content. If the base station is compromised by a malicious attacker, \(DS_{i}\) can also protect this data to prevent its exposure outside the IoT network. At the same time, it also guarantees the protection of user's personal information.
# Coarsely Quantized Decoding and Construction of Polar Codes Using the Information Bottleneck Method
## 6 Numerical Results
### Code Construction
Hence, we are encouraged to consider the code and the decoder design separately and choose the least computationally intensive code construction method, i.e., the Gaussian approximation. Figure 13: Frozen charts for half-rate polar code with \(N=1024\) and design \(E_{b}/N_{0}=3\) dB, constructed using (**a**) [PERSON]’s method with \(|\mathcal{T}|=512\), (**b**) [PERSON]’s method with \(|\mathcal{T}|=16\), (**c**) information bottleneck method with \(|\mathcal{T}|=16\), (**d**) information bottleneck method with \(|\mathcal{T}|=32\), and (**e**) Gaussian approximation.
# Feature Fusion Text Classification Model Combining CNN and BiGRU with Multi-Attention Mechanism
## 4 Experimental Results and Analysis
### Experimental Results and Analysis
#### 4.5.4 Effect of Cumulative Contribution Rate of Eigenvalues on Classification Accuracy  
To study the influence of the cumulative contribution rate of principal component eigenvalues on the classification performance of MATT-CNN+BiGRU model, this paper analyzed the classification accuracy of different cumulative contribution rates for the MATT-CNN+BiGRU model under an MRD dataset. The relationship between classification accuracy and eigenvalue cumulative contribution rate is shown in Figure 12.  
Figure 11: Analysis of the influence of PCA on training time.  
Figure 12: Cumulative contribution rate.  
Figure 12 shows the cumulative contribution rate of eigenvalues (CCRE), from 100% to 95%; the text features extracted by the MATT-CNN model were processed by PCA, and the redundancy in the text features was eliminated gradually. Accuracy increased when CCRE = 95%, the redundant information in the text feature was eliminated more completely, and the classification accuracy was also the highest; when CCRE gradually was reduced from 95%, the partially used text also was eliminated, resulting in a decrease in classification accuracy. It follows that the dimension of the principal component is critical to the accuracy of the classification.
## 3 Broadcast Ephemeris Performance
### Broadcast Ephemeris Clock Offset Performance
The QZSS broadcast ephemeris clock offset difference from DOY 283 to 289 is presented in Figure 6. It can be clearly seen that the clock offset difference time series is rather stable, and the value is between \(-\)2 ns and 2 ns, the mean is 0.75 ns, 0.71 ns, 0.88 ns, and 1.64 ns for J01, J02, J03, and J07 satellites, respectively. Figure 5: Clock offset accuracy of GPS/BDS-2/BDS-3/Galileo/GLONASS/QZSS satellites from broadcast ephemeris. Figure 6: Clock offset difference time series of QZSS satellites from broadcast ephemeris.
## 2 Related Studies
### Unsupervised NMT  
Since NMT requires a large parallel corpus, an unsupervised NMT that relies solely on a monolingual corpus can be an initial solution for language pairs, including low-resource languages [8; 9]. There are two approaches to training an NMT system in an unsupervised manner [10].  
Denoising is the same technique as is used in denoising autoencoders [11], in which a system is trained to reconstruct the original version of a corrupted input sentence. To build an unsupervised NMT system using the denoising technique, [PERSON] et al. [10] proposed an NMT system that consisted of one encoder shared by two languages and two decoders for each language. The training process was as follows: The NMT system takes an input sentence in a given language, encodes the sentence using the shared encoder, and then reconstructs the original sentence using the decoder of that language. This training process is essentially a trivial copying task. In order to make the system truly learn the compositionality of its input sentence, a random noise is inserted into the input sentence. As the system tries to recover the original input by denoising the random noise from an input, it can eventually learn about the internal structure of the languages. After completing the learning process, the system can translate an input sentence to a target sentence by replacing the decoder with that of the target language.  
Back-translation is another approach to building an unsupervised NMT system [10; 12]. First, an NMT system translates an input sentence into a target sentence that is decoded with a greedy approach. While the input sentence is genuine, the target sentence might be synthetic and artificial. In a training process with back-translation, the unsupervised NMT system learns to translate a synthetic translation into the original input sentence. The system can be updated by this training process and can generate more natural pseudo-parallel sentence pairs. This process is repeated until the NMT system reaches acceptable accuracy.  
In this study, we adopt a back-translation approach to build an unsupervised NMT system because MASS utilizing a shared encoder and decoder for both languages can easily apply the back-translation approach. Both approaches assume that cross-lingual word embeddings can be built between two languages before training an unsupervised NMT system. Most embedding mapping methods independently learn the embedding for each language using monolingual corpora separately, and then map them onto a shared space by linear transformation and a bilingual dictionary.  
In this study, we train cross-lingual word embeddings using a shared Byte Pair Encoding (BPE) [13] between the two languages. Generally speaking, monolingual corpora containing as many shared lexical entries as possible between the two languages are collected and utilized to maximize the benefit from the shared BPE of an unsupervised NMT.
# Communication Architecture for Grid Integration of Cyber Physical Wind Energy Systems
## 4 Simulation Results  
The WF communication architecture is modeled using the OPNET Modeler [30], as shown in Figure 3. The proposed communication architecture for the Southwest WF consists of 22 subnetworks (20 wind turbines, one met-mast, and one offshore substation) [31]. Upstream data from WF subsystems are transmitted to the local control center at the offshore substation. We simulated different scenarios for WF monitoring, protection, and control, as shown in Table 8. The details of simulation cases are as follows:  
* Standalone wind turbine: includes data transmission of the condition monitoring system during different operation modes.
* Wind farm: consists of 20 wind turbines configured in a cascade architecture.
* Met-mast: includes data transmission of sensors and measurement devices installed at the met-mast.
* Substation: consists of protection and control devices (IEDs) at wind turbines, feeders, and offshore substation.
## 4 Experiment
### Judgement Criteria  
To assess the performance of the prediction model, this study employs the following four metrics to measure the model's predictive accuracy: mean absolute percentage error (MAPE), which takes into account the error between predicted and actual values as well as the proportionality of the error to the actual values; root-mean-square error (RMSE), a measure of the discrepancy between observation values and actual values; mean absolute error (MAE), an indicator reflecting the real error in predicted values; and the coefficient of determination (\(R^{2}\)), a gauge of the predictive power of a statistical model. The respective formulas are as follows:  
\[\left\{\begin{array}{l}e_{MAPE}=\frac{1}{n}\frac{\sum\limits_{i }^{n}{\lvert\hat{y}_{i}-y_{i}\rvert}}{y_{i}}\\ e_{RMSE}=\sqrt{\frac{1}{n}\frac{\sum\limits_{i}^{n}{(\hat{y}_{i}-y_{i})^{2}}} }\\ e_{MAE}=\frac{1}{n}\frac{\sum\limits_{i}^{n}{\lvert\hat{y}_{i}-y_{i}\rvert}} \\ R^{2}=\frac{\sum\limits_{i}^{n}{(\hat{y}_{i}-\overline{y})^{2}}}{\sum\limits_{ 1}^{n}{(y_{i}-\overline{y})^{2}}}\end{array}\right. \tag{9}\]  
In these equations, \(y_{i}\) and \(\hat{y}_{i}\) denote the actual and predicted values of the price spread data at time I, respectively; n stands for the sample size of the test dataset; and \(\overline{y}\) represents the average value of the dataset. Typically, the smaller the values of MAPE, RMSE, and MSE, the less the deviation between the predicted and actual values. The value of \(R^{2}\), which falls within the range [0, 1], is utilized to measure the accuracy of predictions by a statistical model. Ordinarily, a higher R-squared implies superior modeling performance.
# Optimal Path Routing Protocol for Warning Messages Dissemination for Highway VANET
## 5 Performance Evaluation  
The effectiveness of our suggested OPRP procedure is assessed in this section concerning DABFS [8] and ID-LAR [7]. We employed the Mobility Model Generator for Vehicular Networks (MOVE) [35], Simulation of Urban Mobility (SUMO) [36], and ns-2.35 to assess performance in a realistic vehicular environment. Route information, intersections, and node positions were produced by SUMO and MOVE and used by ns-2.35. Unless otherwise stated, all simulations were based on Scenarios 1 to 5, depicted in Figure 4 in Section 4. The performance evaluation parameters for the protocols mentioned above are listed in Table 3. Nodes with omni-directional antennas are dispersed at random. The nodes were moving bi-directional at varying rates belonging to a set, \(\chi\), with lower and upper bounds of 0 m/s and 42 m/s, respectively. The acceleration or deceleration that nodes achieved was in the range of 1 to 6 m/s\({}^{2}\). In addition, Table 4 shows the number of nodes on the highway was categorized as sparse, medium, or dense. This classification adjusts the node density by actual traffic [37].  
Figure 5: Average packet loss rate during the dissemination of WM. (**a**) Scenario 1: \(H\) (\(S\), \(D\)) = 1, \(S\) is rear and \(D\) is front node. (**b**) Scenario 2: \(H\) (\(S\), \(D\)) = 1, \(S\) is front and \(D\) rear. (**c**) Scenario 3: \(H\) (\(S\), \(D\)) = 0, \(S\) and \(D\) are moving toward each other. (**d**) Scenario 4: \(H\) (\(S\), \(D\)) = 0, \(S\) and \(D\) are moving away from each other.(**e**) Scenario 5: \(H\)(\(S\), \(D\)) = 0, \(S\) and \(D\) are moving toward each other.  
Figure 6: _Cont._  
Figure 6: End-to-end delay during the dissemination of WM. (**a**) Scenario 1: \(H\) (\(S\), \(D\)) = 1, \(S\) is rear and \(D\) front node. (**b**) Scenario 2: \(H\) (\(S\), \(D\)) = 1, \(S\) is font and \(D\) is rear node. (**c**) Scenario 3: \(H\) (\(S\), \(D\)) = 0, \(S\) and \(D\) are moving toward each other. (**d**) Scenario 4: \(H\) (\(S\), \(D\)) = 0, \(S\) and \(D\) are moving away from each other. (**e**) Scenario 5: \(H\) (\(S\), \(D\)) = 0, \(S\) and \(D\) are moving toward each other in opposite directions.  
Figure 7: _Cont._
## 3 Results and Discussion
### Corrosion Resistance and Macroscopic Observations  
The polarization curves for the bar after plastic working and 3D-printed samples are presented in Figure 11.  
The parameters determined from the polarization curves obtained are presented in Table 3.  
Based on the graphs obtained and the parameters determined, it was concluded that the Ti6 Al4V alloy bar after plastic treatment and the DMLS specimens showed full resistance to pitting corrosion over the entire range tested. Surface treatment, including grinding and mechanical polishing, contributed to improving the corrosion resistance already in the initial state of the samples. In addition, heat treatment also significantly increased corrosion resistance compared to samples not subjected to these processes (\(p<0.05\)). Surface analysis confirmed the absence of corrosion changes and pitting for the samples from both groups.  
Figure 11: Logarithmic diagrams for plastic working (PW) and DMLS samples.  
Figure 10: Examples of contact angle drops.  
Black images in microscopy can be the result of optical phenomena associated with a mechanically polished sample surface. Mechanical polishing gives the surface a high degree of smoothness and significantly increases its ability to reflect light in a directional manner.