esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Edge Extraction and Peak Detection Counting Method  
#### 2.4.1 Edge Extraction  
For the segmented plant area, the improved Diag-Canny edge detection algorithm was used to extract the boundary lines of the plant area, as shown in Figure 7.  
In step 1, for each pixel in the image, directional matrices \(H_{45^{\circ}}\) and \(H_{135^{\circ}}\) were added for expanding and refining gradient direction based on directional matrices \(H_{x}\) and \(H_{y}\) of the classical Canny algorithm. The specific calculation process is shown in Equations (10) and (11).  
\[M[i,j]=\sqrt{H_{x}[i,j]^{2}+H_{y}[i,j]^{2}+H_{45^{\circ}}[i,j]^{2}+H_{135^{ \circ}}[i,j]^{2}} \tag{10}\]  
\[D[i,j]=\arctan\frac{H_{y}}{H_{x}} \tag{11}\]  
where \(i\) and \(j\) represented pixel coordinates, \(H_{x}[i,j]\), \(H_{y}[i,j]\), \(H_{45^{\circ}}[i,j]\), and \(H_{135^{\circ}}[i,j]\) represented different directional matrices, \(M[i,j]\) was gradient amplitude, and \(D[i,j]\) was gradient direction.  
Figure 6: The process of extracting plant areas.  
In step 2, all points on the gradient matrix were traversed, and their gradient directions were approximated as eight directions (horizontal and vertical directions, and expand in the 45 degree and 135 degree directions). Gradient intensity of pixels \(\mathrm{A_{i+1}}\) and \(\mathrm{A_{i-1}}\) in the positive and negative directions of gradients were compared. If the gradient intensity of \(\mathrm{A_{i}}\) was maximum, it was retained, and if otherwise it was suppressed. The specific calculation process is shown in Equation (12).  
\[\mathrm{M^{\prime}[i,j]}=\left\{\begin{matrix}\mathrm{M[i,j]},\mathrm{if}\ \mathrm{M[i,j]}>\mathrm{M[i-1,j]}\ \mathrm{and}\ \mathrm{M[i,j]}>\mathrm{M[i+1,j]}\\ 0,\mathrm{otherwise}\end{matrix}\right. \tag{12}\]  
where \(\mathrm{M^{\prime}[i,j]}\) represented the result of pixel processing, and \(\mathrm{M[i-1,j]}\) and \(\mathrm{M[i+1,j]}\) represented the gradient intensity of pixels in the positive and negative directions of their gradients.  
In step 3, an edge detection method based on dual thresholds was used to refine the edge curve. The maximum and minimum gradient thresholds, Max and Min, were set. If the gradient intensity of a pixel was greater than Max, the pixel was confirmed as an edge point. If the gradient intensity of a pixel was between Max and Min, and was connected to the edge point, it was determined as the edge point. Otherwise, they would be judged as non-edge points. After the above processing, the final edge of the plant area was obtained.
# Damping Estimation of an Eight-Story Steel Building Equipped with Oil Dampers
## 2 Building Description and Its Structural Monitoring System  
### Overview of the Building  
The investigated structure, as shown in Figure 1, is the administration building of Tohoku Institute of Technology, located in Sendai, Japan. It was constructed in 2003 and is steel framed with precast concrete slabs. The building has a rectangular plan of dimensions 48 by 9.6 m, while the total height is 34.2 m, with eight stories above the ground. The story height is 3.8 m, except for the first floor, which has a height of 8 m since the first two stories merge for a large public place.  
The building was designed according to the Japanese Building Standard Law without considering the incorporation of damping devices. For enhanced seismic protection, a total of 56 oil dampers were additionally installed in the building. Each floor has 8 oil dampers equally distributed in the  
Figure 1: The administration building: (**a**) front view; (**b**) allocation of oil dampers and accelerometers.  
two orthogonal directions, as shown in Figure 1b. Two types of such oil dampers are fixed on floors and are connected with the adjacent upper floors through V-type steel braces. These dampers have different orifice specifications and stroke limits for the 1 st floor (Type I) and 3 rd to 8 th floor (Type II), as described in Figure 2 using notation 1F and 3-8F.  
The oil damper, designed by [PERSON] et al. [30], consists of a central cylinder and a pair of pistons. The gaps between them are sealed by using viscoelastic polymers. The pistons are fixed on a U-type abutment on floors, and the central cylinder is attached to a V-type brace moving between the two pistons. Figure 2 shows the detailed layout and dimensions of the installed oil dampers. The relative movements between the cylinder and the pistons create the damper resisting force, which consists of two parts: the pressure resistance coming from the flowing infilled oil through a small orifice and the viscoelastic resisting force caused by the shearing deformations of the sealing materials.  
Forced vibration tests were conducted after completing the building frame in June 2003, to investigate the dynamic characteristics of the structure with and without oil dampers. The identification results from vibration tests showed an apparent increase in natural frequencies, the fundamental frequencies increased from 1.05 to 1.12 Hz along the transversal direction and 1.02 to 1.10 Hz along the longitudinal direction. The increased natural frequencies stem from the additional stiffness provided by oil dampers, which motives this study to investigate such stiffness effect during earthquakes.
## 3 Results and Discussions
### Diffusion Coefficient  
#### 3.5.1 Dependence of the Diffusion Coefficient on the Number of Particles  
The dependence of the diffusion coefficient \(D\) on the number of particles \(N\) for the dense beryllium and Yukawa system at 8000 K is shown in Figure 8, where we also compare the results computed using the NVT and NVE ensembles. It can be seen that in the NVT ensemble the diffusion coefficient is systematically lower than that from the NVE ensemble. This is due to the effect of the thermostat limiting the particle velocity fluctuations. This clearly indicates that the transport properties must be computed using the NVE ensemble  
Figure 7: Velocity autocorrelation function (VACF) for dense beryllium from the QMD and Yukawa–MD simulations at various temperatures.  
to avoid the effects of a thermostat. These results also confirm that the correct calculation of the diffusion coefficient requires the scheme NVT \(\rightarrow\) NVE, at which the system is first brought to equilibrium by the thermostat and then freely evolves without artificial temperature control.  
From the dependence of the diffusion coefficient on the number of particles presented in Figure 8, we see that the effect of a finite number of ions in the main simulation cell becomes minor at \(N>800\).
## 4 Overview of the Privacy-Protection Regulations and Frameworks
### The NIST and Five Safes Privacy Framework
While the NIST provides comprehensive and detailed guidance applicable to various organisational contexts [49], the Five Safes framework is specific and mainly used in research settings. It focuses on ensuring responsible data usage for research, balancing data utility and privacy [50].
## 3 Processing Methods  
This section will first introduce two methods for determining the LEO satellite clocks using a Kalman filter, i.e., the epoch-processing and the arc-processing. Next, the impacts of the latency and continuity of the GNSS real-time products on the LEO satellite clocks determination will be explained analytically.
## 2 System Model
### Problem Formulation  
In this paper, we consider the network EE as the objective function, and study the joint optimization problem of power control and user association in the scenario of massive MIMO enabled HetNets. The network EE is defined as shown in Equation (4).  
\[\eta\left(\mathbf{x},\mathbf{p}\right)=\frac{\sum\limits_{j}\sum\limits_{k}x_ {jk}R_{jk}}{P_{tot}}=\frac{R_{tot}}{P_{tot}} \tag{4}\]  
Here, \(x_{jk}\) denotes whether the user \(k\) is associated with the BS \(j\). If the user \(k\) is associated with the BS \(j\), \(x_{jk}=1\), otherwise \(x_{jk}=0\). \(\mathbf{x}=\left\{x_{jk}\right\}\), \(\mathbf{p}=\left\{p_{j}\right\}\). We consider the proportional fairness and define \(R_{jk}=\ln\left(r_{jk}\right)\) as the proportional fair rate [36]. Therefore, the EE defined in this paper can be understood as the network proportional fair throughput in the case of energy consumption per unit spectrum. Then, the energy efficient joint power control and user association optimization problem can be formulated as shown by Problem P0.  
\[P0: \max\limits_{\left\{\mathbf{x},\mathbf{p}\right\}}\eta\left( \mathbf{x},\mathbf{p}\right) \tag{5}\] \[s.t.C1: 0\leq p_{j}\leq p_{j,\max},\forall j\] (6) \[C2: \sum\limits_{j}x_{jk}=1,\forall k\] (7) \[C3: x_{jk}\in\left\{0,1\right\},\forall j,k \tag{8}\]  
Here, C1 represents the maximum transmit power constraint of each BS. C2 shows the principle of user association in the network, that is, each user can be associated with only a single base station. C3, which constrains the variable of each user's association, is a binary variable.
## 4 Results and Discussion
### Proposed Actions
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Action** & **Headline** & **Description** \\ \hline \multirow{8}{*}{7} & & When IFC is used to transfer product data (e.g., the catalogue of a manufacturer), it should include the unique identification of the manufacturer, the manufacturing site (where relevant) and the product. Regulations can specify mandatory identifications (for example, the _product type_ for certain products in Europe). The product can be identified at the level “model”, “batch” or “item”. This identification should be based on the future standard EN 18219, which is currently being developed in CEN/CLC 24/WG 2 “Unique identifiers”. Current identification systems such as the GTIN [57] can be used. \\ \hline \multirow{8}{*}{8} & & The incorporation of product EPD data requires the compatibility of the LCA assumptions and boundaries with the actual built asset under consideration. To automate the transfer and minimize human intervention, the product information must be defined with compatible parameters in the digital EPD and in the IFC model. To ensure machine interpretability, it is essential to uniquely identify LCA information, such as the RSL, the scenarios, and the processes excluded in the EPD (where relevant at building level). These parameters should be defined, when possible, in the PCRs. Automation should incorporate the automatic selection of the scenario (in the EPD) most suitable for the actual building, applying the relevant parameters, where relevant, and identifying any divergence. \\ \hline \multirow{8}{*}{9} & & The relations between the information available in the model and the scenarios in the EPD are very important to determine whether the data can be directly transferred. For instance, an insulation material can be incorporated into the EPD in various scenarios, such as installation in the roof or in external walls. BIM should be able to extract the necessary data for the project based on the information in the model. This action will require extensive additional research to ensure a standardized mapping. \\ \hline \multirow{8}{*}{10} & & Metadata can be incorporated into digital EPDs to improve its direct incorporation in IFC. IFC can provide requirements and criteria for such metadata, to ensure that dEPDs can be transferred to BIM. It will require the implementation of this metadata in dEPD standards, such as ISO 22057. \\ \cline{1-1}  & & A governance system for the the properties (and the generation of the related unique identifiers) should be implemented to ensure a common understanding in different domains. Governance must be common to other formats and, therefore, this issue cannot be resolved solely by revising the IFC schema. \\ \hline \hline \end{tabular}
\end{table} 
Table 11: _Cont._in the construction industry. Therefore, it is essential to ensure that EPD data is included in IFC in order to improve the sustainability assessment of built assets based on BIM tools.
# Comparative Analysis of Image Classification Models for Norwegian Sign Language Recognition
## 2 Literature Review
### Convolutional Neural Network  
Convolutional Neural Network (CNN), inspired by the organization of the visual cortex in the human brain, is a popular, supervised machine learning model for image classification tasks due to its ability to generalize well and handle large-scale datasets [15]. CNN models generally consist of convolutional, pooling, and fully connected layers and are trained using backpropagation to recognize visual patterns from the pixels of images [16]. The process is as follows in [6]. First, the convolutional layer extracts the input data by a convolution operation, reduces feature dimensionality through a pooling layer, then passes the most significant features to the fully connected layer to categorize the images.  
When creating CNN models, the creator has many options, and the hyper-parameters one chooses will significantly impact the model's accuracy [15]. A large dataset is crucial for training an effective deep learning model such as a CNN [17]. Dropout is also often an added technique used in CNN models to prevent overfitting. Randomly dropping out neurons during training with a specified probability makes the model more robust and generalized, reducing reliance on any single neuron [18].  
In [19], they used CNN for their real-time ASL recognition model, skin color detection, and convex hull algorithm for segmentation of the hand location. Then resized that hand region to 28 \(\times\) 28 pixels and converted it to grayscale as part of their feature extraction. They achieved 100% accuracy on their test data and 98.05% on their real-time system. Their model was trained on a static fingerspelling dataset created by [20], which consists of 900 images of 25 hand gestures. Reference [21] created a SLRNet-8 CNN model ASL recognition, which was trained and tested on four different datasets containing a combined size of 154,643 in 38 classes (alphabet, 0-9, delete, nothing, space). They gravscaled the images, performed normalization, and resized them to 64 \(\times\) 64 pixels. Their model achieved an average accuracy of 99.92% on the mixed dataset. Reference [22] created a CNN model using a public dataset from MNIST to create a better-performing model. They performed augmentation to create a total of 34,627 28 \(\times\) 28 static images of the alphabet (excluding J, Z). The paper was written to compete with state-of-the-art methodologies and achieved an accuracy of 99.67%, which beat other models such as SVM, DNN, and RNN.
## 3 Both End Explanations for Reinforcement Learning (BEERL) Method
### Generating Explanation
#### 3.5.1 Attributive Explanation  
Our presented approach is independent of the XAI method used to generate feature importance, i.e., any method (e.g., SHAP [4], LIME [5], etc.) can be applied. When a deep network is employed for the RL agent, the neural network explainer can also be applied to fit the required characteristics, e.g., the complexity of the operation, consistency, the accuracy of the explanation, etc. Without reward decomposition, the XAI methods generate a feature importance for the entire Q-function (i.e., the entire DRL network), as depicted inFigure 4a. In the proposed solution, the explainer analyzes the Q-function by calculating the contribution of every input feature to every reward component:  
\[U_{ic}=E(Q_{c}(s,a),s,a) \tag{3}\]  
where \(E(h,x,y)\) is the explainer that takes \(h\) as the model to be explained (Q-function of reward component \(c\), \(Q_{c}(s,a)\)) together with the input data \(x\) and the predicted output \(y\) (state \(s\) and action \(a\) in DRL, respectively). The generated attribution values, \(U_{ic}\), explain the contribution in a normalized domain, which are independent of reward prioritization, and we refer to them as the Q-function explanations. Furthermore, we apply the reward prioritization to these explanations to generate the RL-level explanations (\(V_{ic}\)).  
\[V_{ic}=Z_{c}(U_{ic}) \tag{4}\]  
In this manner, the effect of reward prioritization is shown and each reward component can be individually analyzed. Any scope of explanations can be produced using this method, where the global explanation is an aggregation of local explanations using the absolute mean formula, as shown in Equation (5) (\(P_{ic}\) refers to either level of explanation, i.e., \(U_{ic}\) or \(V_{ic}\)).  
\[P_{ic}=\frac{\sum_{l=1}^{T}|P_{ict}|}{N_{T}} \tag{5}\]  
The above generated explanations show the importance of input feature towards different reward components, but currently, there is no mechanism to compare the importance of input features with reward components. With these detailed explanations (\(U_{ic}\) and \(V_{ic}\)), the normalized input feature importance (\(L_{i}\)) is calculated by summing its importance to all reward components and dividing it by the number of reward components (\(N_{C}\)), as shown in Equation (6). Similarly, Equation (7) shows the calculation of normalized reward component importance (\(L_{c}\)) where all input feature importance are summed and then divided by the number of input features (\(N_{I}\)). In this manner, both input feature (\(L_{i}\)) and the reward component's (\(L_{c}\)) importances are measured on the same basis and can be properly compared.  
\[L_{i}=\frac{\sum_{c=1}^{C}P_{ic}}{N_{C}} \tag{6}\]  
\[L_{c}=\frac{\sum_{i=1}^{I}P_{ic}}{N_{I}} \tag{7}\]
# Lightweight Deep Learning for Road Environment Recognition
## 4 Experimental Results
### Metrics  
To validate the accuracy of object detection and lane detection, we used the following metrics to evaluate each model. Object detection mainly uses precision, recall, and average precision as shown in Equations (4)-(7). Lane detection mainly uses mean intersection over union (mIoU) and mean pixel accuracy (mPA). T/F denotes true/false, which indicates whether the prediction is correct, and P/N denotes positive/negative, which indicates a positive or negative prediction result.  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}, \tag{4}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}. \tag{5}\]  
However, in some data sets with unbalanced distribution, if the number of negative samples is very small it will lead to precision close to perfection and recall close to zero, obviously this situation alone using any one indicator is not able to fully evaluate the system, so average precision is introduced. The AP is calculated using the difference-average accuracy measure, which is the area under the precision-recall curve, as shown in Equation (6).  
\[\text{AP}=\left(\frac{1}{n}\sum_{(r\ \in\ \frac{1}{n},\ \frac{2}{n}\ \dots\ \frac{n-1}{n},\ 1)}P_{\text{interp}}(r) \right)^{2}, \tag{6}\]  
where \(n\) denotes the number of detection points and \(P_{\text{interp}}(r)\) represents the value of the accuracy at a recall of \(r\).  
Based on the AP, mAP (Mean Average Precision) can be calculated as shown in Equation (7).  
\[\text{mAP}=\frac{1}{n}\sum\text{AP}. \tag{7}\]  
In the evaluation metric of lane detection, IoU is used to represent the ratio of the intersection of the predicted result and the true value for a category to the merged set, and pixel accuracy (PA) represents the ratio of the number of pixels correctly predicted for a category to the total number of pixels, as shown in Equations (8) and (9).  
\[\text{mIoU}=\frac{\text{TP}}{\text{TP}\ +\ \text{FP}\ +\ \text{FN}}=\frac{1}{n+1} \sum_{i=0}^{n}\frac{p_{ii}}{\sum_{i=0}^{n}p_{ii}+\sum_{i=0}^{n}p_{ji}-p_{ii}}, \tag{8}\]  
\[\text{mPA}=\frac{\text{TP}+\text{TN}}{\text{TP}\ +\ \text{FP}\ +\ \text{FN}+\text{TN}}=\frac{1}{n+1} \sum_{i=0}^{n}\frac{p_{ii}}{\sum_{i=0}^{n}p_{ij}}, \tag{9}\]  
where \(n\) represents the number of classes. \(i\) represents the true value, \(j\) represents the predicted value. Then, \(p_{ii}\) denotes TP, \(p_{jj}\) denotes TN, \(p_{ij}\) denotes FP, and \(p_{ji}\) denotes FN.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Items** & **Description** \\ \hline \multirow{4}{*}{H/W} & CPU & Intel(R) Core(TM) i7-10700 \\  & RAM & 32 GB \\  & SSD & Samsung SSD 512 GB \\  & Graphics Card & NVIDIA GeForce GTX 1650 \\ \hline \multirow{4}{*}{S/W} & Operating System & Windows 10 Pro, 64 bit \\  & Programming Language & Python 3.6 \\ \cline{1-1}  & Learning Framework & TensorFlow 1.13.2 \\ \cline{1-1}  & API & Keras 2.1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The experimental environment.
## 2 Data and Methods
### Datasets
#### 2.2.2 Precipitation Products  
The ground rain gauge observations were obtained from the CMA automatic weather station in Naqu (white triangle in Figure 1b). The data are available from 2006 to present at hourly temporal steps with a data availability of greater than 90%. Meanwhile, it has undergone rigorous quality control as described by [PERSON] et al. [55]. In this study, the CMA precipitation observations were served as a benchmark for evaluating the SM2 RAIN estimates.  
The level 3 IMERG product was used mainly to investigate the representativeness of the CMA rain gauge station. This product integrates infrared, passive microwave, and  
Figure 3: (**a**) Comparison of the time series of average daily in situ soil moisture over stations with good time series completeness (stations numbered 1–35, i.e., those above the red horizontal line in Figure 2) (AVE1) and all stations (AVE2) during JJAS 2015–2018, and (**b**) its corresponding scatterplot.  
Figure 2: Time series of half-hourly surface soil moisture (0–5 cm) observed across the 56 stations of CTP-SMTMN from June–September (JJAS) 2015–2018. Note that blanks denote missing data. Stations above the red horizontal line are those with good time series completeness.  
radar observations from the Global Precipitation Measurement (GPM) satellite constellation and provides Early, Late, and Final releases with different latencies. The data are accessible from the Goddard Earth Sciences Data and Information Services Center (GES DISC; [[https://disc.gsfc.nasa.gov/](https://disc.gsfc.nasa.gov/)]([https://disc.gsfc.nasa.gov/](https://disc.gsfc.nasa.gov/)), accessed on 26 December 2022). Studies have suggested that the Final product, which is corrected using monthly gauge data, outperforms the Early and Late products by capturing precipitation events more effectively and accurately, reflecting the spatiotemporal pattern of precipitation, as well as daily variability [56; 57; 58; 59; 60]. In this study, the 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\), half-hourly IMERG Final V06 product was used.