esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology: Requirements Analysis, Selection of Buildings, and Technologies
### Choosing the Set of Buildings for Retrofitting Sensor and Control Networks
#### 3.2.1 Selection Criteria  
To ensure subsequent transferability of the developed approaches, they have to be tested in different buildings to cover the existing building stock and different usages. For the scope of this paper, two different groups of criteria for selecting buildings to be equipped can be distinguished.  
The first group of criteria is closely related to the scientific preparation for the design of experiments and the possibilities to compare and interpret the data. For this reason, it is advantageous to select a set of buildings with different physical properties and usages. By comparing data out of such different buildings, possible sensitivities can be calculated. An example of such evaluation based on building archetypes is, e.g., given in [20], where the authors point out which kind of building characteristics are beneficial for applying MPC. When clustering is possible, subsets of similar buildings allow for the evaluation of different kinds of automation measures taken in the same time spans.  
The second group of criteria relates to the practicability of equipping the buildings and running experiments. Physical access needs to be granted, and operators, as well as other stakeholders, need to be in line with the agreed-upon measures.
# Evaluation of the Acoustic Noise Inside the Main Steam Line of a BWR/5 Nuclear Reactor
## 4 Results
### Numerical Results (Scale 1:8)  
The internal pressure (Figure 12a) was evaluated initially with the computational fluid dynamic code. The peak values (maximum pressure fluctuations) were located at the intersection between the closed-side branch and the main steam line (MSL). When pressure fluctuations increase due to higher fluid velocity, vortex shedding may occur due to high localized turbulence. This can cause a coupling in the acoustic modes of the pipeline and the hydrodynamic modes producing excessive vibration. It propagates through the pipeline. The structural elements are loaded dynamically, and fractures are initiated [1; 2; 4; 10; 13].  
The velocity field is shown in Figure 12b. The velocity inside the branch was close to zero. Therefore, there was a significant pressure difference in the main line. This dynamic condition created a significant fluctuating condition, which generated the propagation of acoustic noise.  
Figure 11: Pressure average measurement over the interior surface of the MSL.  
Figure 10: Pressure average measurements of the interior of the MSL.  
Six cases were studied. The first one was considered as the baseline (100%). In the other five cases, the velocity increased to 110%, 120%, 130%, 150%, and 170%. In the next step, the strains were obtained with the FSI (one-way) model. The pressure field was imported directly from the domain of computational fluid dynamics (Figure 12(a)) to the finite element model (Figure 12(b)).  
Finally, the acoustic load and the resonant frequency of the first vibration's acoustic mode were obtained with the algorithm developed in MATLAB R2023a. For this purpose, the equations of the quarter wave resonator tube were considered. Figure 13 shows the pressures calculated around the mark line (Figures 13(a) and 12(a)) for the six cases considered (100, 110, 120, 130, 150, and 170%). The range of pressure was between 196 Pa to 553 Pa.  
Figure 13: Numerical imported pressure directly from the CFD.  
Figure 12: Pressure and velocity fields obtained with a CFD code.  
The acoustic power (Figure 15) was obtained with the MATLAB R2023 acode (Figure 6), which was the result of the finite element evaluation. In normal conditions (100%), it was 97 dB. In the following cases, it increased to 114 dB for the conditions of 170%.
## 1 Introduction
[23] proposed using wavelet transform for analyzing recorded AE signals. The influence of material properties on AE signal propagation was characterized comprehensively through AE descriptors, waveforms, and wavelet analysis. The combination of the Weibull damage function and AE technology enables quantitative studies on the failure of fiber-reinforced concrete beams with varying water-cement ratios under pure torsion conditions [24]. The integration of fractal theory in AE technology provides theoretical guidance for analyzing the evolution of correlation dimensions of AE signal characteristic parameters in prestressed reinforced concrete beams [25]. The choice of AE devices for monitoring structural damage varies according to the type of building and monitoring requirements. In high-rise buildings and bridges, AE sensor networks and vibration sensors are commonly utilized to detect acoustic signals resulting from structural cracks or material fatigue. For dam and levee structures, AE sensors are employed to detect acoustic signals within soil and concrete components, thereby monitoring potential issues such as leaks and fractures. Additionally, strain sensors are integrated to monitor structural deformation and assess damage [26]. In underground structures like tunnels, AE sensors are deployed to monitor acoustic signals associated with cracks, leaks, or geological activities. Simultaneously, seismic monitoring equipment is employed to gauge the seismic-induced vibrations within subterranean structures. Withincultural heritage edifices, infrared thermography is commonly employed to track surface temperature fluctuations, aiding in identifying latent concerns such as humidity, leaks, or structural degradation [27; 28]. Moreover, AE sensors are employed to capture internal acoustic signals within cultural heritage sites, offering insight into issues like sonic reverberations caused by structural degradation [29]. However, AE technology in the analysis of concrete damage evolution still has several limitations: (1) There is a lack of quantitative analysis and research on AE technology in the field of reinforced concrete research. (2) Concrete damage models need further optimization, and there is limited research on the influencing factors of damage evolution. (3) Further refinement is needed in the processing and parameter extraction of AE signals. The study conducted static loading tests on reinforced concrete beams under three different working conditions. The study examined the mapping relationship between acoustic emission (AE) characteristic parameters and the damage of reinforced concrete beams. A comparative analysis was conducted to examine the variation patterns of AE parameters during the damage evolution process of the test beams in relation to the theoretical load values, strain test values, and deflection test values. The study confirmed the applicability of AE non-destructive testing technology and analyzed the impact of loading methods and prestressing on the damage of reinforced concrete beams.
## 4 Representation and Characterization: Scenarios and States
### Considerations on Scenarios and States  
As a final note on scenario classification, it should be mentioned that for scenarios S6-S0, we considered the fixed CSFs not only to be equal, but we also treated them as a monolithic block. This means that they cannot mix with the other CSFs in the sequence; otherwise, the number of states would significantly increase. The analysis of scenarios S6-S0 demonstrate not only the obvious fact that the more fixed CSFs we have, the fewer possibilities there are to bring the system back to equilibrium, but it also shows the different ways in which it can be carried out. In other words, we have determined the various sub-scenarios. To discuss the equilibrium of a system, unfortunately, the current representation or classification into scenario families is not effective, just as it is inadequate for trend analysis and forecasting. For these reasons, in the following section, we will adopt a more advanced representation suitable for achieving the stated aim.
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 3 Modelling
### Screening Models
#### 3.1.4 CapSim  
CapSim was developed entirely in the Python programming language [390], utilizing additional libraries such as NumPy, SciPy, and Matplotlib for visualization purposes. It is a multi-layered 1D model designed to simulate processes occurring in heterogeneous soil materials during in situ capping. CapSim provides users with the flexibility to modify soil properties and layer thickness. Moreover, it encompasses common processes in porous environments including advection, diffusion, dispersion, sorption, and reaction. Additionally, CapSim enables more intricate simulations such as bioturbation, deposition, or water exchange [360].
## 3 Application and Experiments
### Experiments Setup  
First, we used the method in Section 2.2 to assemble and label the flight data sets and BIT records from test flight No.18100223, No.19012332 and No.19012433. We randomly sampled 200 instances from the labeled flight data of each test flight, and labeled them manually to verify the accuracy of the labels.  
Secondly, in order to indicate the performance of the diagnosis models in all compound fault modes (serious imbalanced), we used the StratifiedShuffleSplit() to set 80% of the labeled data as the training set and 20% as the test set, by stratified sampling. Moreover, the NULL values in data were replaced by the value of the next sample point.  
Then, we compared the classification performance of the proposed ensemble learning models, XGBoost (num_leaves = 15, max_depth = 5, subsample = 0.9, random_state = 42) and LightGBM (num_leaves = 15, max_depth = 5, subsample = 0.9, random_state = 42), with the original GBDT model (min_samples_leaf = 20, max_features ='sqrt', subsample = 0.9, random_state = 42). The classification performance of the proposed five-layer modified CNN model (tanh as the activation function of convolution layers, Softmax as the activation function of fully connected layers, Adam as the optimizer, categorical_crossentropy as the loss function, learning_rate = 0.001, iterations = 15) which selected 450 features by Analysis of Variance (ANOVA), was compared with the original three-layer FCNN model (ReLU as the activation function between layers, Sigmoid as the activation function of output layer, Adam as the optimizer, categorical_crossentropy as the loss function, learning_rate = 0.001, iterations = 15). The structure diagrams of modified CNN and FCNN models are shown in Figure 4a,b, respectively.  
Figure 4: (**a**) Original diagnosis model based on Fully Convolutional Network (FCNN); (**b**) Proposed diagnosis model based on modified CNN. All the experiments were performed on an Intel Core i7-10700F 2.90 GHz machine with 16 GB RAM. The codes of methods were implemented in R 4.0.3 and Python 3.7, with Keras 2.3.1, Scikit-learn 0.22.2.post1, Tensorflow 2.1.0, Lightgbm 2.3.1 and Xgboost 1.0.2. The hyper-parameters related to the model were optimized by Grid Search and manual adjustment. All models were evaluated in the test data set.  
Finally, the model training speeds of all the classifiers were compared to consider the feasibility of real-time model updating in future.
## 3 Results and Discussion
### Water Entry of 3D Torpedo  
This section systematically investigates the water entry process of a 3D torpedo at different entry velocities and entry angles. The corresponding schematic and detailed parameters are shown in Figure 8. The length, the width, and the height of the pool filled with water are 8, 8, and 4 m, respectively. The length and the diameter of the torpedo are 2.79 and 0.32 m, respectively. The center of mass of the torpedo is located at a distance of 1.18 m from its head. The density of the torpedo is assumed as 1000 kg/m\({}^{3}\) and the rotational inertias of the torpedo are set as \(I_{x}=I_{z}=88.01\) kg\(\cdot\)m\({}^{2}\) and \(I_{y}=2.31\) kg\(\cdot\)m\({}^{2}\). The torpedo is initially placed above the water with its head placed tangentially to the surface of the water. In addition, its center of mass is fixed at (\(x_{0},y_{0},z_{0}\)). At the beginning of the water entry, the entry velocity of the torpedo is set along the same direction as the axis of the torpedo. Therefore, the entry angle \(\beta\) formed between the entry velocity and the water surface is the same as the attitude angle \(\theta\) of the torpedo formed between the axis of the torpedo and the water surface.  
Figure 8: Schematic of (**a**) the torpedo and (**b**) the calculation domain of the water entry of the torpedo.  
Figure 7: Evolutions of the velocities, within the middle plane along the z direction, as achieved from this work and the reported experimental work [44].
## 1 Introduction
In a limited-view situation, imaging is possible with the aforementioned algorithms, but the imaging performance is very poor compared to when full-view data are given. Therefore, research has been constantly conducted to solve this problem. Some research presented mathematical analyses of non-repetitive imaging algorithms performed when limited-view data are given (see [37; 38; 39; 40; 41; 42; 43; 44; 45; 46] for instance). However, compared to the studies on the non-iterative imaging techniques in the full-view situation, there have been few studies in the limited-view situation. Figure 1: Schematic diagram of full- and limited-view inverse scattering problems. (**a**) Full-view situation; (**b**) Limited-view situation. Therefore, in this study, in order to improve the imaging performance in the limited-view problem, the artificial neural network model was developed that outputs the corresponding full-view data when the limited-view data are input for the case of two and three scatterers. For this purpose, the artificial neural network learned artificial neural network in various ways using the data generated for each case. Consequently, a learning model in which the mean square error between the output data from the artificial neural network and the actual data are not large can be developed. For the purpose of verifying the effectiveness of the output data drawn from the artificial neural network, it was confirmed that the ground truth data and the imaging result were very similar as a result of imaging the output data several times using the subspace migration algorithm. In this way, it was confirmed that artificial neural networks can be used importantly to solve the inverse scattering problem. This paper is organized as follows. In Section 2, we briefly introduce the basic concept of the two-dimensional direct scattering problems in the presence of small scatterers located in a homogeneous space and subspace migration imaging technique from the generated multi-static response (MSR) matrix whose elements are measured in the far-field pattern. In Section 3, we design an adopted artificial neural network model for generating complete elements of the MSR matrix and corresponding training results. The models developed for the case of two scatterers and three scatterers are described respectively. In Section 4, we exhibit various numerical simulation results to show the pros and cons of the designed artificial neural network. Finally, a short conclusion including an outline of future work is provided in Section 5.
# Attention-Based Background/Foreground Monocular Depth Prediction Model Using Image Segmentation
## 4 Experiments
### Implementation Details
#### 4.2.2 Training Settings  
We used the PyTorch framework to build and train our proposed model on an Nvidia RTX3090 GPU with a 24 GB memory. For the foreground depth prediction model, we setthe batch size and learning rate as 1 and \(10^{-4}\), respectively. For the background depth prediction model, we set the batch size and learning rate as 5 and \(10^{-4}\), respectively. The two models were trained for 50 epochs with the Adam optimizer. We chose a ResNet 101 network that was pre-trained on ImageNet [69] as the feature extractor. The weight of the ResNet 101 was fixed during training.
## 3 Data Collection and Processing
### Spearman Correlation Analysis  
Spearman correlation analysis [24] discriminates the strength of the correlation by the Spearman rank correlation coefficient; hence, it is also called the Spearman rank correlation coefficient. The principle is to analyze the nonlinear data of 25 critical parameters affecting blast furnace production by solving the ranking position of the original data, ranking the critical parameters (\(x_{1},x_{2},x_{3},\ldots,x_{25}\)) by the magnitude of the values and establishing the order table, respectively. The position of the order table is called rank order, and the Spearman rank correlation coefficient is calculated as shown in Equation (5):  
\[P=1-\frac{6\sum_{i=1}^{n}(R_{i}-Q_{i})^{2}}{n(n^{2}-1)}\,i=1,2,\ldots,n \tag{5}\]  
where: \(P\) is the Spearman rank correlation coefficient; \(n\) is the number of variables; \(R_{i}\) and \(Q_{i}\) are the rank order of the two parameter data sets.  
A heat map of the Spearman correlation analysis results is shown in Figure 5. The range of the Spearman correlation coefficient is \([-1,1]\), and the larger the absolute value, the higher the correlation between the two. In general, the absolute value of [0.8, 1] indicates that the correlation between the two is extremely strong; the absolute value of [0.4, 0.8] indicates that the correlation between the two is strong; the absolute value of [0.2, 0.4] indicates that the correlation between the two is weak; the absolute value of [0, 0.2] indicates that the correlation between the two is extremely weak or irrelevant. In this study, key parameters with correlation coefficients greater than 0.3 were selected as data-driven preliminary input variables (\(x_{2},x_{3},x_{4},x_{6},x_{7},x_{8},x_{15},x_{17}\)).  
Figure 5: Heat map of [PERSON]’s characteristic correlation analysis.
# Information-Aware Secure Routing in Wireless Sensor Networks
## 4 Algorithm
In regard to the above, the explanations are as follows. First, it is enough for each sensor node in the network topology to know the adjacent nodes connected to itself. Second, while it is calculating the cost of the adjacent nodes, the sending node requests the adjacent node to send its associated information to itself (the information can be transmitted by means of the message packet); thus, the information of the adjacent node is enough. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Algorithm** & **Weight Factor** & **Weight** & **Weight Calculation** \\  & & **Calculation** & **(Path)** \\  & & **(Node)** & \\ \hline Traditional Dijkstra & \(Attack_{\langle i,j\rangle}\), \(e_{rj}\), \(e_{\langle j,sink\rangle}\) & \(Attack_{\langle i,j\rangle}\)+\(E_{j}\) & Sum of all node weights in a path \\ Improved Dijkstra & \(Attack_{\langle i,j\rangle}\), \(e_{rj}\), \(e_{\langle j,sink\rangle}\) & Equation (9) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the improved Dijkstra algorithm and the traditional Dijkstra algorithm. The associated information includes the trust value of other adjacent nodes, the location and residual energy of the adjacent node, and the cost of the path from the adjacent node to the sink. The secure route in the proposed model is reversely constructed by the Dijkstra algorithm. Therefore, the secure path from the next-hop node to the sink node has been acquired, and the cost value of the path has been calculated in the previous procedure. In the process of secure route selection, it is unnecessary to distribute the information to the whole network, since the local information is enough. In summary, the proposed model can obtain the global optimal path from the source node to the sink node using only the local information obtained.
## 3 Results and Discussion
### Variable Wind Speeds and Variable Wind Directions  
The ideal wind condition of variable wind speeds and variable wind directions was studied, as shown in Figure 9. As can be seen, three different wind speeds i.e., 8 m/s, 12 m/s, and 17 m/s were considered and each wind speed on each wind direction were assigned a particular value indicating its probability of occurrence. Figure 10 shows the optimization results of total wind farm power output and power losses. As the number of wind turbines increased, the total wind farm power output increased linearly and the total power output losses showed a general linearly increasing pattern. Figure 11 shows the optimal wind farm layout with 40 wind turbines optimized. Under this particular wind condition, the wind turbines were distributed all across the wind farm area while they were more aligned to each other compared to the optimal wind farm layouts of the above two cases. The minimum distance apart for any two wind turbines was 7.28 turbine diameters and the average distance of all wind turbines was 36.1 turbine diameters.  
Figure 8: Optimal wind farm layout with 40 wind turbines under constant wind speed and variable wind directions.  
Figure 7: Results of wind farm layout optimization with different number of wind turbines under constant wind speed and variable wind directions: (**a**) wind farm power production and (**b**) wind farm power losses.  
Figure 9: Probability of variable wind speeds and variable wind directions.
## 2 Methodology
### Patent Indicators
#### 2.2.2 RPP and RPA as a Combined Indicator  
Relative patent position (RPP) is combined with revealed patent advantage (RPA) as a new indicator in the study. RPP means the ratio of a specific technique for a company to the leader one (which owns the most patent counts). It represents the \"innovation leadership\" of the company. It further indicates the \"innovation ability\" of the company. The RPP is defined in Equation (3):  
\[\text{RPP}=\frac{\text{The issued patent counts of one company in the specific technology field}}{\text{The most issued patent counts of the company in the specific technology field}} \tag{3}\]  
When the value is 1, it means that the company is the leader in the specific technology field of interest and shows superior innovation ability to others. The difference of the RPP value to 1 indicates the relative difference in innovation ability between a specific company and the leader.  
The revealed patent advantage (RPA) indicator was created by [PERSON] [21] in 1995. [PERSON] used RPA to fairly compare the technological advantages among companies of different sizes, since each company has different commercial strategies to develop its own technology. When one company has a higher RPA value, it means that it has better \"innovation advantage\" than others in the specific technological field. It further represents the company's specific technology is more important. In this study, the RPA is defined in Equation (4):  
\[\text{RPA}_{\text{ij}}=100\text{tanh}\ \ln\left(\frac{\text{P}_{\text{ij}}/\sum_{ \text{i}}\text{P}_{\text{ij}}}{\sum_{\text{i}}\text{P}_{\text{ij}}/\sum_{ \text{i}}\text{\sum}_{\text{j}}\text{P}_{\text{ij}}}\right) \tag{4}\]  
where i represents the one specific technological field in three considered electrolyte technologies, j represents the one specific company. Pij is the issued patent counts of the company j in the technological field i. Hence, \(\sum_{i}\text{P}_{\text{ij}}\) is the company j issued patent counts in three technological fields, and \(\sum_{\text{j}}\text{P}_{\text{ij}}\) is all company granted patent counts in technique field i. \(\sum_{i}\sum_{j}\text{P}_{\text{ij}}\) is the sum of all granted patents by all company in all technological fields. The introduction of the logarithm leads to an asymmetric range of values and a neutral point 0, the tangents hyperbolics to a limitation to the boundaries of \(\pm 100\).  
The RPP value suggests the innovation ability and RPA represents an innovation advantage. RPP and RPA can be combined into a new indicator to compare innovation activity of a patent assignee, whereby the relationship between innovation ability and advantage is expected to provide additional insights to the patenting strategies of the companies of concern.