esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction System for Overhead Cranes Based on Digital Twin Technology
## 2 Literature Review
### Life Prediction for Overhead Cranes  
A bridge crane is typical mechanical equipment, and the service life of its components will be affected by various factors, including quality of raw materials, the installation process of the components, level of daily operation, maintenance of the equipment, etc. Therefore, in life prediction for bridge-crane components, it is necessary to clarify the principles of the roles of various influencing factors, thus establishing a more accurate life-prediction-calculation formula [16]. Through recording and analyzing the production and operation parameters of equipment, the parameters affecting the operating life of the equipment are integrated, and the remaining service life of the equipment is predicted using scientific analysis methods [17]. When there are sufficient equipment production and operation data, the remaining service life of the equipment can be predicted using intelligent algorithms [18]. [PERSON] et al. proposed a real-time fatigue-life-prediction method for bridge structures based on digital twins. The specific type of generic bridge crane was selected as the physical entity of the research object, and the information collection system was applied to obtain the current service-state information of the physical entity [19]. A fatigue-life-assessment method was proposed by [PERSON] et al. The finite element method and experimental stress analysis were employed to evaluate the service condition of a ship-unloading crane according to the relevant criteria and the nature of the risk [20].
# An Exploration of Circular Economy Practices and Performance Among Romanian Producers
## 3 Research Methodology
### Data Collection  
The final questionnaire was distributed via email using Qualtrics, an online research platform, to a database of 27,000 Romanian companies using the contact addresses provided by the Trade Registry of Romania during the months of October through December 2017. 377 respondents started the survey, 260 completed it with 98 respondents meeting the eligibility criteria of being engaged in manufacturing activities. The modest response rate may be attributed to the novelty of the topic in the Romanian economy or by the complexity of the questionnaire. Another possible explanation may be the quality of the database used which contains contact information provided by the official Trade Registry and which may not be updated. The profile of our respondents is summarized in Table 1. and it shows representation from seven types of producers' groups with a high interest in ecological issues, since they complete our questionnaire.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Business** & **Number** & **Percent** \\ \hline Groups of producers & & \\ Metallurgy, metallic and non-metallic constructions & 15 & 15.31\(\%\) \\ Woodworking, furniture manufacturing & 7 & 7.14\(\%\) \\ Electrical, electronic, printing & 18 & 18.37\(\%\) \\ Agriculture and food industry & 13 & 13.27\(\%\) \\ Textiles, leather & 11 & 11.22\(\%\) \\ Chemicals, pharmaceuticals, rubber, plastics, paper & 17 & 17.35\(\%\) \\ Motor vehicle, machinery and equipment & 17 & 17.35\(\%\) \\ Total & 98 & 100.00\(\%\) \\ \hline Ownership structure & & \\ Private-owned & 93 & 94.90\(\%\) \\ State-owned & 5 & 5.10\(\%\) \\ Total & 98 & 100.00\(\%\) \\ \hline Number of employees & & \\ Under 249 & 66 & 67.35\(\%\) \\ Over 249 & 32 & 32.65\(\%\) \\ Total & 98 & 100.00\(\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The profile of the Romanian respondents.  
The fact that our sample of 98 respondents includes representatives from most of the industries with impact on circular economy in Romania constitutes an argument to support the relevance of our study.  
At the same time, the 98 respondents forming our sample represent a true reflection of the structure of Romanian producers in regard to criteria such as \"ownership structure\" and \"number of employees\".
## 1 Introduction
This corrosion can significantly impact in-core materials, particularly fuel cladding, leading to fuel failures and the dispersal of fuel fragments and fission products into the coolant. Moreover, these chemical species can also affect the transport of radioactive materials from the core to downstream piping components, thereby enhancing out-of-core radiation fields and increasing exposure risks for reactor maintenance personnel [7]. Another key aspect of controlling SCWR water chemistry requires the ability to mitigate the effects of water radiolysis. It should be noted that the common practice of adding excess molecular hydrogen, hydrazine (N\({}_{2}\)H\({}_{4}\)), or ammonia (NH\({}_{3}\)) in concentrations sufficient to suppress net radiolysis (see, e.g., [16; 17; 18]) may not be effective in an SCWR [7]. The extreme conditions of high temperature and pressure, combined with the intense flux of ionizing radiations, render the experimental characterization of water radiolysis under expected SCWR/SCW-SMR operating conditions exceedingly challenging, if not unfeasible. As a result, computer simulations become crucial for investigating the radiation chemistry of the SCWR coolant. While experiments and simulations have examined the radiolytic yields (\(G\) values) of water decomposition products at temperatures up to 350 \({}^{\circ}\)C [13], most studies on radiolysis in SCW, ranging from 380 to 700 \({}^{\circ}\)C, have primarily relied on modeling (e.g., see [19] and the references therein). Recent experimental research investigating temperatures beyond the critical point of water (\(t_{\rm c}\) = 373.95 \({}^{\circ}\)C and \(P_{\rm c}\) = 22.06 MPa), with findings extending up to 500 \({}^{\circ}\)C, has been comprehensively reviewed by [PERSON] and [PERSON] [20]. In this work, we present Monte Carlo track chemistry simulations of the time-dependent \(G\) values for the primary species (e\({}^{-}_{\rm aq}\), H\({}^{\bullet}\), H\({}_{2}\), \({}^{\bullet}\)OH, and H\({}_{2}\)O\({}_{2}\)) resulting from the radiolysis of neutral, de-aerated water by monoenergetic 2 MeV neutrons. These simulations cover the temperature range from 300 to 600 \({}^{\circ}\)C at a pressure of 25 MPa. We selected 2 MeV neutrons because the in-reactor fission-neutron flux spectrum peaks around this energy [13; 21; 22]. Calculations spanned from 1 picosecond (ps) to 1 millisecond (ms). Concerning densities at 25 MPa for the studied temperatures, below the critical point of water, they are 0.743 g/cm\({}^{3}\) at 300 \({}^{\circ}\)C and 0.625 g/cm\({}^{3}\) near the reactor core inlet (350 \({}^{\circ}\)C). For SCW, densities are 0.167 g/cm\({}^{3}\) at 400 \({}^{\circ}\)C, 0.090 g/cm\({}^{3}\) at 500 \({}^{\circ}\)C, and 0.071 g/cm\({}^{3}\) at the reactor core outlet (600 \({}^{\circ}\)C) [23]. All available data on the physicochemical properties of SCW (such as dielectric constant, \(K_{\rm w}\), viscosity, etc.) up to 600 \({}^{\circ}\)C have been incorporated into the calculations.
# CFD-DEM Simulation of the Transport of Manganese Nodules in a Vertical Pipe
## 6 Conclusions  
The hydraulic lifting of manganese nodules was numerically investigated using a CFD-DEM coupling method. The effects of solid volume fraction, particle diameter, and flow velocity were discussed. The major conclusions drawn from the present study are as follows:  
1. With increasing initial solid volume fraction, the energy consumption is enhanced, which leads to the decreases in both seawater and manganese nodule velocities at the central part of the pipe. This effect attenuates near the wall. Moreover, a high initial solid volume fraction results in an increase in total pressure drop. 2. Particle diameter has a significant influence on the rising velocities of seawater and manganese nodules. At the central part of the pipe, the liquid velocity is relatively  
Figure 18: Relationship between \(I_{s}+I_{c}\) and local SVF. low for small particles, while high liquid velocity arises near the pipe wall for small particle flow compared to large particle flow. The total pressure drop decreases with the increase of particle size from 5.0 mm to 30.0 mm. 3. At different flow velocities, similar seawater velocity distributions are evidenced. The velocity of manganese nodules exhibits the same tendency, and the highest velocity is produced at the pipe axis and then decreases in a radial direction. Increasing initial mixture velocity is beneficial to the transport of manganese nodules, but it will consume more energy and result in the increase of total pressure drop. 4. For both small and large particles, the retention ratio declines with increasing initial mixture velocity and decreasing initial solid volume fraction. At an initial mixture velocity of 4.5 m/s, the particles of 30.0 mm in diameter are responsible for the lowest retention ratio, implying a significant effect of the initial mixture velocity on the transport performance. 5. A formula is proposed to describe the pressure drop due to the presence of solid particles and collisions. Combined with the pressure drop formula associated with clean water, the total pressure drop for the hydraulic transportation of solid particles can be well predicted. Conceptualization, C.K. and S.Z. ; methodology, S.T. and C.L. ; software, S.T. and K.D. ; validation, K.D., C.L. and S.Z. ; writing--original draft preparation, S.T. ; writing--review and editing, C.K. ; funding acquisition, C.K. All authors have read and agreed to the published version of the manuscript. This research was funded by the Science and Technology Plan Project of State Administration for Market Regulation of China (Grant No. 2021 MK060), the project of Science and Technology Commission of Shanghai Municipality (Grant No. 19 DZ1207303), the Postgraduate Research and Practice Innovation Program of Jiangsu Province (Grant No. KYCX20_3082), the National Key Research and Development Program of China (Grant No. 2021 YFC2801600), and the Science and Technology Innovation Project from China State Shipbuilding Corporation Limited. Not applicable.
# An Intelligent Method for C++ Test Case Synthesis Based on a Q-Learning Agent
## 2 Materials and Methods
### Action Selection Policy  
Once the agent has formed an internal representation of the expected utility of actions in various states via the Q-table, a strategy must be defined for selecting the next action. This strategy, or action selection policy, is a core component of the agent's model. During training, it regulates the trade-off between exploring new paths and exploiting the learned knowledge. During the TC synthesizing phase, the Q-values learned by the agent help avoid actions that do not contribute new traces or coverage, thus promoting the construction of effective TC.  
In our implementation, two alternative action selection policies are supported: the \(\varepsilon\)-Greedy policy and the Boltzmann (Softmax) policy. These are detailed below.  
* \(\varepsilon\)-Greedy policy. With probability \(\varepsilon\), the agent selects \(a_{t}\)--random action from the available action set \(\mathcal{A}(s_{t})\) for current state \(s_{t}\); with probability \((1-\varepsilon)\), it selects the action with the highest Q-value: \[a_{t}=\left\{\begin{array}{ll}\underset{a^{\prime}\in\mathcal{A}(s)}{\arg \max}Q(s_{t},a^{\prime})&(1-\varepsilon)\\ \mathrm{rand}\mathcal{A}(s_{t})&\varepsilon\end{array}\right.\] (4)
* Boltzmann policy (Softmax) [31]. In this strategy, actions are selected probabilistically based on their Q-values scaled exponentially, according to the softmax distribution: \[P(a_{t}|s_{t})=\frac{e^{\frac{Q(s_{t}+a_{\mathit{target}})}{\tau}}}{\sum_{a^{ \prime}\in\mathcal{A}(s_{t})}e^{\frac{Q(s_{t}+a^{\prime})}{\tau}}}\] (5)  
\(\tau\) is the temperature parameter that controls the influence of stochasticity. A high \(\tau\) encourages exploration by making all actions nearly equally likely, while as \(\tau\to 0\) the behavior approaches that of a greedy policy. In addition to basic training, an important aspect is the reuse of acquired experience. The next subsection describes the limited knowledge transfer between TCs.
# Employing Ray-Tracing and Least-Squares Support Vector Machines for Localisation +
## 2 Experimental Setup and Methodology
### Localisation Algorithms Used for Comparison  
The two localization algorithms used to compare the performance of the proposed direct method for localization in LOS and NLOS environments, are TDOA and a hybrid TOA/AOA scheme. TDOA uses 3 Base stations, and one should be the same BS that is used by the other schemes which only require one BS. Detailed information on these algorithms, is provided in [2].
# Automatic Age and Gender Recognition Using Ensemble Learning
## 3 Dataset and Methods
### Ensemble Learning
#### 3.3.1 Majority Voting  
In classification problems, the voting method is often used to combine multiple base-level classifiers. Majority voting and weighted voting are two widely used voting methods [27]. In majority voting, the class label of the unknown sample is predicted according to the class with the highest votes among the members forming the ensemble model. This technique is also known as plurality voting and does not require any parameter optimization after training the base-level classifiers. This method is expressed as follows:  
\[class(x)=\arg max\Big{(}\sum_{k}f(y_{k}(x),c_{i})\Big{)} \tag{1}\]  
where \(y_{k}(x)\) is the prediction of the \(k\)'th model and \(f(y,c)\) is the indicator function with the following definition:  
\[f(y,c)=\begin{cases}1,\ y=c\\ 0,\ y\
eq c\end{cases} \tag{2}\]  
The weighted majority voting is a trainable version of majority voting. In this method, different weights are assigned to the predictions of the base-level classifiers in the ensemble according to the performance or reliability of each classifier. After multiplying the predictions of each classifier by the weight assigned to it, these values are summed to calculate the weighted votes for each class. The final prediction is made according to this value, and the class with the highest weighted vote is determined as the class of the input data. In weighted voting, the selection of the weights of the base-level classifiers is a critical issue. Various approaches based on artificial intelligence such as genetic algorithms, particle swarm optimization, and fuzzy sets have been proposed on this issue [28].
## 4 IEC61850 Multicast Communication for Wide-Area Applications
### WAN Communication in Wide-Area Applications
It allows remote substations to use cellular communication to connect to the public IP network and therefore to communicate over the existing network infrastructure. However, the substation routers must be cellular routers that support cellular communication. Accordingly, wide-area applications can be economically implemented by using  
Figure 6: IP-Multicast communication from Substation 1 to Substation 2 and 3 via public IP network. cellular routers that tunnel IP-multicast traffic (R-SV/R-GOOSE) over IP traffic within the existing public IP network as shown in Figure 6. GRE [34] is a well-known protocol that can be used for tunneling. GRE is a tunneling protocol between two networks, which encapsulates IP-multicast packets and delivers them to the destination network. In wide-area applications, WAN communication over a private or public network requires careful attention in terms of network determinism and cybersecurity in order to satisfy the application's real-time requirement and thus ensures that it functions correctly.
## 3 Results
### Significance Test of Spatial Factors  
The factor scales were unified from \(1\) to \(-1\) through numerical standardization to solve the inconsistency of the factor value distribution. Then, the correlation between the spatial factors and landslides based on the slope units was examined. The correlation coefficient values were expressed as positive or negative. As seen in Figure 5, the correlation coefficients of Siaolin Village (yellow bar) were between \(-0.47\) and \(0.43\), and those of Putunpunas River (dark-blue bar) were between \(-0.42\) and \(0.36\). Hypothesis significance  
Figure 4: Fourteen spatial factors used in this study. These observations in 2017 are for Siaolin Village (**left**) and Putunpunas River (**right**).  
testing was performed, and the probability of obtaining the test resulted in the \(p\)-value, as shown in Table 1. Then, the significant spatial factors were screened on the basis of a 99% reliability as the test threshold. There were five significant spatial factors in the Siaolin Village area (rock mass strength, aspect, terrain roughness, slope, and dip slopes) and six significant spatial factors in the Putunpunas River area (rock mass strength, aspect, vegetation index, water distance, terrain roughness, and dip slopes).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Spatial Factor**} & \multicolumn{2}{c}{**Siaolin Village**} & \multicolumn{2}{c}{**Putunpunas River**} \\ \cline{2-5}  & **Correlation Coefficient** & \(p\)**-Value** & **Correlation Coefficient** & \(p\)**-Value** \\ \hline Rock mass strength & \(-0.47\) & \(1.00\) & \(-0.30\) & \(1.00\) \\ Aspect & \(-0.25\) & \(1.00\) & \(-0.22\) & \(1.00\) \\ Vegetation index & \(-0.06\) & \(0.49\) & \(-0.42\) & \(1.00\) \\ Water distance & \(-0.04\) & \(0.37\) & \(-0.21\) & \(1.00\) \\ Annual rainfall & \(0.13\) & \(0.85\) & \(-0.13\) & \(0.98\) \\ Terrain roughness & \(0.31\) & \(1.00\) & \(0.17\) & \(1.00\) \\ Slope & \(0.27\) & \(1.00\) & \(0.05\) & \(0.66\) \\ Folds & \(0.07\) & \(0.57\) & \(-0.11\) & \(0.95\) \\ Dip slopes & \(0.24\) & \(0.99\) & \(0.36\) & \(1.00\) \\ Elevation & \(0.16\) & \(0.93\) & \(0.03\) & \(0.37\) \\ Profile curvature & \(0.11\) & \(0.81\) & \(0.07\) & \(0.78\) \\ Annual displacement velocity gradient & \(0.08\) & \(0.62\) & \(0.02\) & \(0.29\) \\ of InSAR & \(0.07\) & \(0.59\) & \(0.13\) & \(0.98\) \\ Road distance & \(0.02\) & \(0.28\) & \(0.01\) & \(0.23\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Correlation coefficients and \(p\)-values, quantified according to the relationship between landslides and the 14 spatial factors in the slope units.  
Figure 5: Histogram of correlation coefficient between landslides and the 14 spatial factors in the slope units.
## 4 Experimental Setup and Results  
This part offers an in-depth description of the system setup and results from the experimental phase of the research. The setup was created to test the performance of AI-based temperature control algorithms in real time using sensor data. A prefabricated test chamber was installed with some HVAC equipment, such as an air conditioner, electric heater, and circulation fan, controlled by an NVIDIA Jetson Orin Nano. The system was configured to mimic standard indoor environmental setpoints, with sensors measuring temperature, humidity, and energy consumption in real time. Data captured from the experiment were utilized to train AI models, such as an LSTM model for forecasting temperature and a DQN model for control optimization. Actual performance results from the AI-based control system, which were tested on the basis of real-time data measurement and prediction from models, are discussed in this part, illustrating the capabilities of algorithms in controlling energy consumption to achieve the desired temperature setpoint with reduced wear on mechanical components.
# Underwater-Acoustic-OFDM Channel Estimation Based on Deep Learning and Data Augmentation
###### Abstract
In this paper, we propose a DL architecture to implement channel-sample generation and channel estimation for UWA-OFDM communication. The main contributions of this study are summarized as follows:  
1. we propose a DL-based UWA-OFDM-channel-estimation scheme for the defects in the traditional UWA-OFDM communication system. We consider the shortage of underwater-acoustic channel samples and we introduce the CWGAN-GP model to generate enhanced channel samples, whose generator contains some channel feature parameters to realize different types of channel model generation. 2. We propose a novel deep-learning channel-estimation architecture. First, in order to attenuate the effect of feature information loss, we extracted channel features by constructing a convolutional network structure in the way of U-Net, and we then designed a Channel-Attention-Denoising-(CAD) module, to further optimize the reconstructed channel information. 3. We provide a validation of the channel quality in different UWA environments. The numerical results show that the proposed model is better than the traditional OFDM communication system, DNN-based, and ChannelNet-based models, in terms of Mean Square Error (MSE) and Bit-Error Rate (BER). Significant performance improvements are particularly evident in harsh UWA environments. The rest of this paper is organized as follows: In Section 2, we briefly introduce the UWA-OFDM communication system. In Section 3, we present the proposed deep-learning-based-channel-estimation scheme. In Section 4, we experiment and evaluate the estimated performance. The paper concludes with Section 5.
# Recognizing Urban Functional Zones by GF-7 Satellite Stereo Imagery and POI Data
## 3 Methodology
### Multimodal Data Feature Extraction and Fusion
#### 3.3.3 Feature Fusion and UFZs Identification  
The image features extracted by ST-CNN and the semantic features extracted by LDA are concatenated. The image feature dimension (128 dimensions) is about 25 times that of the POI semantic feature dimension (5 dimensions), and direct concatenating leads to fusion features that overly emphasize image features, it is necessary to reduce the dimensionality of image features. PCA is a dimension-reduction technique that linearly transforms the original space into a new space of smaller dimensions while simultaneously describing the variability of the data as much as possible [59]. PCA projects along the eigenvectors of the covariance matrix corresponding to the largest eigenvalues, where the eigenvectors point in the direction with the highest amount of data variation. In general, the first few principal components whose cumulative variance contribution exceeds 95% are considered dimensionality-reduced data and often contain nearly all information from the original data. In this study, PCA is employed to reduce the dimensionality of image features. The specific image dimension after dimensionality reduction is determined through experimentation. After reducing the dimensionality of the image features, they are concatenated with the semantic features. Following the feature fusion, three classifiers, namely, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF), are utilized for identifying UFZs [60; 61; 62].  
Figure 8: Perplexity values for the different number of topics.