esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Charging Method
### Multistage Constant Current (MCC) Method  
The multistage constant current (MCC) charging process comprises several constant current stages, wherein the current gradually decreases until the terminal voltage reaches a predetermined limit. The battery will undergo charging until it attains its maximum capacity. The MCC technique is demonstrable, as shown in Figure 15.  
The MCC charging method is designed to be compatible with a variety of battery types, including lead-acid, NiMH, and Li-ion batteries. When comparing the starting currents of both charging methods, it can be observed that the MCC charging process will require a slightly longer duration [72].  
Figure 14: CC-CV charging method for EV battery with battery current (green) and battery voltage (red).
# Data-Free Area Detection and Evaluation for Marine Satellite Data Products
## 3 Results
### Data Product Analysis and Evaluation
#### 3.2.1 Integrity of Ocean Color Information
However, the result of OLCI is extremely different, with an anomalous missing ratio of only 1.115% (i.e., the area is 6223.77 km\({}^{2}\)). It is less than 1% of the missing areas of the other two products. The main type of deficiency is turbid water, accounting for 40.762%, followed by clean water at 27.044%. The total percentage and total area of the anomalous missing data in the data-free area of GOCI and MODIS are much larger than the value of the anomalous missing data in the data-free areas of OLCI. Figure 11 shows the line graphs of the monthly abnormal missing area and abnormal missing rate. OLCI has the lowest abnormal missing ocean color information and the highest stable performance with respect to continuity in time patterns. The anomalous missing information of GOCI and MODIS ocean color information is higher than 30%, and the amount of missing data fluctuates greatly. Both have an abnormal missing rate exceeding 90% on March 13, and the information integrity is extremely low. The above findings indicate that OLCI is superior with respect to the completeness of ocean color information and the continuity of the missing time distribution, followed by GOCI, whereas the lowest performance is for MODIS. Turbid water, green tide algae and sea ice have reflectance magnitudes similar to that of clouds in the near-infrared band. This results in the cloud mask algorithm being much less capable of handling these types of marine coverage objects, particularly in the data-free area in this study. However, the proportion of clean water, as normal remote sensing information, is the largest for GOCI and MODIS, both with respect to all ocean coverage object types and anomalous missing information. The data-free area statuses of GOCI and MODIS have a high similarity, and this aspect is further analyzed in the following discussion.
# Speech Act Theory as an Evaluation Tool for Human-Agent Communication
## 3 Speech Act Theory
#### Speech Act Theory and Agent Communication  
When it was first introduced, speech act theory was meant to be a tool to interpret the verbal communication that takes place between human beings. SAT has been used to analyse different shapes of communication including questionnaires [20], written messages in forum posts [17], event logs [21], and email messages [22]. In the 1990s, artificial intelligence (AI) researchers adopted SAT as a design tool to work in the field of AI and inter-agent communication [23; 24; 25; 26]. SAT has been used as an integration approach to aid the design and interpretation of communication between humans and agents. In most research work that combines SAT and agent communication, SAT has been used as a reference to design the agent's communication language, understand the human-agent exchange of messages or for the analysis of mutual understanding.  
There are two main Agent Communication Languages (ACLs) that are based on SAT. The first one was proposed by the Foundation for Intelligent Physical Agents (FIPA-ACL), and the other was defined by [PERSON] [22] as a standard of ACL called Knowledge Query Manipulation Language (KQML). [PERSON], [PERSON] [27] used SAT as a foundation for giving semantics to messages received by an AgentSpeak (L) (which stands for Agents Speak Out in a Logical Computable Language) agent to fill the gap concerning neglected aspects in agent programming languages such as communication primitives.  
Speech act-based communication has been used to enable the agent to communicate arguments between agents, to share its internal state with other agents, and to influence other agents' states. In a more recent study [28], an approach (named an Agent Communication Model for Interacting Crowd Simulation: ACMICS) was presented to simulate the communication between agents in a crown simulation system. This approach used a message structure based on an ACL that depends on SAT.  
[PERSON] and [PERSON] [29] provide a general agent automated negotiation protocol based on speech act theory in MAS. [PERSON] and [PERSON] [17] designed a speech act model using a dynamic Bayesian Network (DBN) that provided a communication bridge to help virtual agents reason about different dialogue contexts that include norms, social relations, emotion, personality, intention, or goals among agents in a dialogue scene. [PERSON], [PERSON] [30] make use of the accurate and expressive communication mechanism of SAT in agent-agent communication in a multi-agent system. The agents send messages (such as requesting, ordering, informing, or promising) to socially capable acquaintances in order to affect their mental states. The interacting agents used an ACL that was designed using SAT. [PERSON] and [PERSON] [31] used SAT as a model to understand the human-agent exchange of speech acts in a plan-based situation. [PERSON] and [PERSON] [32] used SAT to analyse the achievement of mutual understanding between participants in a conversation, i.e., to provide grounding.
# Advancements in Machine Learning-Based Intrusion Detection in IoT: Research Trends and Challenges
## 6 ML Performance Review in IDS
### Binary Classification
#### 6.2.2 Challenges in Comparison  
Many studies prioritize accuracy, neglecting other key metrics like recall, precision, or F1 score, masking the impact of class imbalance in the datasets. The choice of dataset significantly affects model performance due to variations in traffic volume and class distribution. Hyperparameter tuning and dataset-specific optimizations further limit the validity of direct comparisons.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Paper** & **Accuracy [\%]** & **Precision [\%]** & **Recall [\%]** & **F1 Score [\%]** & **AVT [ms]** & **Highest Accuracy ML Technique** \\ \hline
[45] & 99.9987 & 100 & - & 99.9993 & 0.0011 & XGBoost \\
[78] & 99.99 & - & - & - & - & JRip classifier \\
[82] & 99.96 & 99.96 & 99.96 & 99.96 & - & RF \\
[76] & 99.92 & - & - & - & - & Stacking using XGBoost \\
[67] & 99.9 & 99.9 & 99.9 & 99.9 & 0.0011 & RF \\
[40] & 99.7 & 99.7 & 99.7 & 99.7 & - & RF \\
[28] & 99 & 100 & 99 & 99.16 & - & EWMA + CUSUM + KNN \\
[63] & 99 & 99 & 100 & 99 & - & RF \\ \hline \hline \end{tabular}
\end{table}
Table 7: Binary classification performance for BoT-IoT (sorted by accuracy); AVT: Average Validation Time.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Paper** & **Accuracy** & **Precision** & **Recall [\%]** & **F1 Score [\%]** & **AVT [ms]** & **ADT [ms]** & **Highest Accuracy ML Technique** \\ \hline
[61] & 100 & 100 & 100 & 100 & - & 130 & DT \\
[55] & 100 & 100 & 100 & 100 & 0.3173 & - & RF \\
[35] & 98.17 & 97.63 & 98.08 & 97.90 & - & 14.22 & EWEA \\
[53] & 99.1 & 98.4 & 99.1 & 98.7 & - & - & XGBoost \\
[58] & 99 & 100 & 100 & 100 & - & - & SVM \\
[75] & 97.31 & 95.8 & 96.88 & 96.39 & - & - & Stacking (DT, MLP RProp, Logistic Regression) \\
[38] & 95 & 95 & 95 & 94 & 0.0074 & - & GADAD-ET \\ \hline \hline \end{tabular}
\end{table}
Table 8: Binary classification performance for TON_IoT (sorted by accuracy); AVT: Average Validation Time, ADT: Average Detection Time.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Paper** & **Accuracy [\%]** & **Precision [\%]** & **Recall [\%]** & **F1 Score [\%]** & **AVT [ms]** & **Highest Accuracy ML Technique** \\ \hline
[72] & 99.74 & 100 & 99 & 100 & 37.02 & CNN, VGG19 \\
[64] & 98.78 & 99.03 & 99.35 & 98.48 & - & CNN + LSTM \\
[68] & 98.5 & 98.5 & 98.4 & 98.44 & 0.1234 & TabNet algorithm \\
[49] & 97.39 & - & - & - & DT + RF + SVM \\
[65] & 94.5 & 93.3 & 95.3 & 94.3 & - & K-means + Gaussian mixture+one-class SVM \\ \hline \hline \end{tabular}
\end{table}
Table 9: Binary classification performance for CIC-DDoS2019 (sorted by accuracy); AVT: Average Validation Time.
# Study on Magnetic Resonance Characteristics of Coal Sample under Progressive Loads
## 3 Materials and Methods  
### Test Equipment  
As an extremely complicated medium with double pore structures, coal is composed of coal matrix and fissures. The former contains pores while the latter cuts the coal matrix [29; 30; 31]. Due to the coexistence of pores and fissures, coal is featured with inhomogeneity and anisotropy. Meanwhile, the strength of coal mass, mechanical property, and deformation characteristics all change correspondingly. Under the effect of progressive load, the evolution process of pores in coal is highly complex. To test the evolution mechanism of pore and fissures in the coal sample under progressive loads in a non-destructive way, low-field NMR is the best testing method [32].  
The test equipment is mainly the MR 12-type low-field NMR analysis system (Suzhou Niumag Analytical Instrument Co., Ltd, Suzhou, Jiangsu, China). Its technical parameters are as follows: magnet strength 0.3 \(\pm\) 0.05 T, magnet temperature 25-35 \({}^{\circ}\)C, Pulse Freq Range 1-30 MHz, the diameter of probe coil 60 mm. As is shown in Figure 4, MR12-type low-field NMR analysis system is composed of five parts, namely, 1 control module, 2 Imaging module, 3 display module, 4 NMR analyzer with large probe, and 5 probe coil. When testing, the coal sample to be tested should be put in the center of probe coil first, and then the probe coil is placed in the NMR analyzer. By means of control module, the porosity and pore size distribution can be measured, as is shown in Figure 4a. Then the imaging module is used to image the pore and fissure distribution inside the coal sample, which is shown in Figure 4b.  
Figure 4: MR12-type low-field NMR analysis system: (**a**) the porosity and pore size distribution, (**b**) the pore and fissure imaging.
# Code Similarity and Location-Awareness Automatic Program Repair
## 1 Introduction
A heuristic algorithm preferentially finds repair ingredients in the smallest search space. If no suitable repair ingredients are found, the search space is gradually expanded. This strategy limits the size of the search space to a certain extent so that the search algorithm can quickly find suitable repair ingredients. Compared with the approaches that have been proposed, SLARepair can effectively alleviate the problems caused by the large search space. For the patch generation process, SLARepair uses code similarity as heuristic information to guide the search process. Recent research shows that the more similar a program is to a buggy program, the more likely it is to contain correct repair. To this end, we design a new fitness function for the genetic programming algorithm to select high-quality offspring populations. It combines the code similarity and the number of failed test cases for subvariant programs to calculate the fitness value. The population selected by SLARepair is more likely to contain the correct repair than other approaches. In addition, we utilized a test case prioritization method combined with test filtering to further reduce the time spent on patch verification. The main contributions of this paper are as follows:  
* An approach for automatic program repair based on code similarity and location-awareness is proposed. It improves repair efficiency by further subdividing the search space. * A new fitness function is used to calculate the similarity between candidate patches and the original individuals to guide the search process. * A new test case prioritization approach combined with test case filtering reduces patch verification time. * Experimental results on Defects4J show the effectiveness of SLARepair, which outperforms the compared approaches.
# A Hybrid DNN Multilayered LSTM Model for Energy Consumption Prediction
## 2 Related Studies
### Traditional Models  
Traditional algorithms, including artificial neural networks [9], support vector machines [10], extreme learning machines [11], energy-aware clustering schemes [12], and random forest algorithms, did not perform optimally in predictions. Nevertheless, they showed promising performance in feature mining. Therefore, improving the models' predictive performance requires adding more selection and feature extraction algorithms, which remains challenging [13; 14]. Artificial learning techniques are further classified into deep learning and machine learning techniques. Several studies have been conducted on energy consumption prediction for machine learning algorithms.  
[PERSON] et al. [15] developed a dynamical-empirical model to predict energy consumption using physical models. The developed models were found to be unreliable in energy prediction based on the high computational space and memory used and needed. Relative to physical learning models, statistical learning models are not as expensive computationally as they apply autoregressive models, such as ARIMA [16], GARCH [17], and linear regression techniques. Learning models were developed using linear datasets. [PERSON] et al. [8] used the GARCH model to determine predictive uncertainty, but its ability was limited to capturing nonlinear and nonstationary elements of energy consumption data. Studies involving linear regression have also been conducted. [PERSON] et al. [18] developed a multiple and linear regression model for energy consumption prediction, while [PERSON] et al. [19] and [PERSON] [20] developed multiregression energy consumption prediction models. However, statistical energy consumption models are not capable of accurately capturing uncertainty trends directly; instead, they employ other methods, as shown in [PERSON] et al.'s [13] study, to minimize prediction errors.  
[PERSON] et al. [21] used support vector regression (SVR) to predict energy consumption using temperature and consumption datasets. [PERSON] and [PERSON] [22] applied a hybrid SVR model to forecast seasonal energy consumption. In their work, the SVR algorithm was combined with the fruit fly algorithm to improve the model's performance. [PERSON] et al. developed an SVR model to predict energy consumption and change the nonlinearity of the features found in the dataset into linear to deal with the nonlinearity problem of the model and the dataset used [23]. [PERSON] et al. used a random forest regression algorithm for energy consumption prediction. The researchers extracted the required features based on frequency using fast Fourier transforms and then applied the random forest model in their simulation to make predictions [24].
## 2 Framework
### Mathematical Model
To add the cooling load constraint to the mathematical model, the following constraint can be added: \[\sum_{\text{j}=1}^{1}\text{Number of AC}^{\prime}\text{s}*\text{AC capacity}*\text{X}_{\text{ij}}\geq\text{Cooling Load}\] (7) where j represents the different AC types available. * Heating Load Heating load is defined as the amount of heat that would need to be added to a building. Heating load calculations should be performed in the peak winter when temperatures are at their lowest levels. The estimate of the heating load is the sum of the heat loss transmission (through fenestration, opaque walls, roof, and floor) and the outside air heat losses. Formulas on how to calculate the cooling load can be found in [14]. * Visual Comfort Analysis Since lighting replacement is one of the energy-saving measures, the illumination level should be taken into consideration when retrofitting a building. Visual comfort can be achieved by providing the minimum level of illuminance (density of light in lux or foot-candle (FC)) required by the building [33]. The Illuminating Engineering Society (IES), formerly the IES of North America (IESNA), provides the recommended levels of illumination for different spaces (presented in Table 1) [34]. The illumination is determined by dividing the lumen (light output) of lamps over the area of space in square meters or feet [33]. When replacing old lamps with energy-efficient ones, the illumination level and the required number of lamps should be taken into consideration. \begin{table}
\begin{tabular}{c c} \hline \hline
**General Building Area** & **IES Standards Illumination Level (Lux)** \\ \hline Corridors & 100 \\ Lift and stairs & 150 \\ Kitchen & 150 \\ Living rooms general & 50 \\ Bedroom general & 50 \\ Bathroom & 100 \\ Halls and landing & 150 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Required illumination level. The following formula is used to calculate the illuminance [34]:  
\[\text{Lux(fc)}=\text{Lumens}/\text{Area(m${}^{2}$)} \tag{8}\]  
To calculate the required number of lighting fixtures needed for a specific area, the following equation will be used [34]:  
\[\text{Number of fixtures}=\frac{\text{illuminance }\times\text{Area}}{\text{lamps per fixture }\times\text{lumens}} \tag{9}\]  
To add the illumination constraint to the mathematical model, the following constraint can be added:  
\[\sum_{\text{j}=1}^{\text{J}}\text{Lumen}(\text{per lamp type j})\ast\text{X}_{ \text{ij}}/\text{space area}\geq\text{Required LUX level} \tag{10}\]  
where j represents the different lamp types available [33].
# A New Click-Through Rates Prediction Model Based on Deep&Cross Network
## 4 Experimental Results
### Experimental Settings  
All the experiments have been carried out on a Linux server of Ubuntu 18.04 LTS (64 byte), with a CPU of Intel(R) Xeon(R) @2.10 GHz, a GPU of NVIDIA TITAN RTX of 24G, and 64G RAM. In these experiments, Area Under the Curve (AUC) and logloss are used as the main evaluation metrics of the model, which are the most commonly used indicators to measure the performance of a CTR model. AUC is the area under ROC curve. The larger AUC value is, the better the prediction performance of the model is. Logloss is the cross entropy loss. The smaller the logloss value is, the smaller the error and the better the performance of the CTR prediction model is.
# A Global Continuous Multi-Scale Terrain Contour Simplification Method Based on the Level Set
###### Abstract  
Multi-scale spatial representation has been widely used in geographic information and online mapping systems. Terrain contour, which provides a reference for understanding and monitoring the Earth's surface, is an important data category. For the multi-scale representation of contour lines, simplification is a fundamental step in providing different levels of detail for linear features. However, achieving a global continuous multi-scale simplification of contours remains a challenge. Therefore, based on the concept of level set, a novel contour simplification method labeled the continuous changing surface model (CCSM) was proposed in this paper. The CCSM was built by using a non-uniform rational B-spline constrained with characteristics and was then intersected with a set of horizontal planes with progressive height values. The generated intersection lines are considered continuous multi-scale simplified contours. Experiments were conducted on a 1:50,000 real contour dataset to verify the effectiveness of CCSM. Results showed that the changes in the shape of the simplified contours generated by CCSM are more natural and progressive than those generated by two other significant simplification methods. CCSM can also effectively balance local and global structures and has potential applications in obtaining a continuous multi-scale representation of terrain contours.  
c 2021 April 2021  
1
## 1 Introduction
First, SAR image is a special kind of microwave image and the regions with the same gray level do not necessarily have similar optical properties. Therefore, the existing methods for optical images based on the deep learning framework may not be suitable for PolSAR image processing. Second, the existing PolSAR image classification methods based on deep learning only consider the polarization features of the image, while ignoring the spatial features. To solve the above problems, this paper proposes a dual-branch deep convolution neural network (Dual-CNN) method for PolSAR image classification. The proposed method is composed of two CNNs: one is used to extract the polarization features of the real matrix of the six channels (6 Ch-CNN) and the other is used to extract the spatial features of the Pauli RGB image (PauliRGB-CNN). These two kinds of features are fed into a fully connected layer to achieve mutual harmony, and then the Softmax classifier is followed immediately to complete the classification work. The remainder of this paper is structured as follows. Basics of the CNN are introduced in Section 2. In Section 3, we present the architecture of the proposed method. Experiment results and discussions are given in Section 4. In Section 5, the conclusion is made.
# Optical Realization of Wave-Based Analog Computing with Metamaterials
## 2 Computational Metamaterials
### Metasurface Approach
It means that the amplitude and phase of impinging optical wave will be modulated by \(e^{ik\Delta}\propto\big{(}iy\big{)}^{n}\) when propagating through the transfer function subblock (suppose the thickness of transfer function subblock is \(\Delta\)), where \(k=(2\pi/\lambda_{0})\sqrt{\varepsilon_{\Delta}(y)\mu_{\Delta}(y)}\), with free space wavelength \(\lambda_{0}\), \(\varepsilon_{\Delta}\) and \(\mu_{\Delta}\) are relative permittivity and permeability of transfer function subblock. For the cases of integration and convolution operations, the transfer functions become to \(H(y)\propto\big{(}iy\big{)}^{-1}\) and \(H(y)\propto\sin c(y)\), respectively. In addition, considering the limitation of lateral dimension, the transfer function \(H(y)\) has to be normalized to ensure the energy conservation (without gain materials) and keep the reflection (transmission) coefficient blow unity. Now, the question is, how to find a proper spatially-variant meta-structure that fulfills the required electromagnetic response. Here, we divide recent achievements of the MS approach into two categories according to the construction type.