esa-sceva/satcom-chunk-collection huggingface dataset:
# Compatible Ground Motion Models for South Korea Using Moderate Earthquakes
## 1 Introduction
Given this backdrop, this study aims to identify several suitable GMMs, if any, for use in South Korea among a subset of PEER GMMs, as PEER GMMs are derived from an extensive database through relatively robust techniques. In doing so, users are able to more confidently select appropriate GMMs for their earthquake and seismic risk studies in the Korean peninsula. This study starts by compiling earthquake event data and the necessary processing to derive useful ground shaking intensity measures from local ground motion recordings. These are compared to NGA ground motion model estimates per event with an evaluation on each model. Although not shown yet, a unique element of this study is that it considers mainland and offshore events; foreshocks, main shocks, and aftershocks; as well as a potentially triggered event.
## 2 Instruments and Methods  
### Methods for Measuring Atmospheric Opacity  
The optical depth of the atmosphere in zenith can be measured by several methods. Direct measurements of the opacity in a plane-parallel atmosphere are based on Bouguer's law:  
\[I(\
u)=I_{0}(\
u)e^{-\frac{\tau(\
u)}{\cos\theta}}\,, \tag{3}\]  
where \(I_{0}(\
u)\) is the intensity of cosmic source emission within the antenna beam, as it would be measured without atmosphere, \(I(\
u)\) is the measured intensity of this emission and \(\theta\) is the zenith angle. By measurements of \(I(\
u)\) at different zenith angles, the optical depth in zenith \(\tau(\
u)\) can be retrieved. This method requires a long time for a substantial change in zenith angle, stable atmospheric conditions during this time and negligible or well-known dependence of the antenna response on the zenith angle. The validity of the plane-parallel model has been analyzed in several works. According to [16; 27], it can be used at \(\theta\lesssim 85^{\circ}\), and according to [28] at \(\theta\lesssim 75^{\circ}\). Our calculations show that under typical atmospheric conditions, the error in the optical depth for the plane-parallel model is \(\lesssim\)5% at \(\theta\lesssim 80^{\circ}\) and grows rather rapidly at larger zenith angles.  
In practice, the value of the zenith opacity (optical depth) at millimeter wavelengths is usually derived from measurements of the atmosphere emission [16]. The sky brightness temperature in a plane-parallel atmosphere, neglecting the cosmic microwave background, equals  
\[T_{\rm s}(\theta,\
u)=T_{\rm a}(\
u)\left[1-e^{-\frac{\tau(\
u)}{\cos\theta}} \right]\,, \tag{4}\]  
where \(T_{\rm a}\) is the mean temperature of the atmosphere:  
\[T_{\rm a}(\
u)=\frac{\int_{0}^{\infty}\kappa(\
u,l)T(l)\exp[-\int_{0}^{l} \kappa(\
u,l^{\prime})dl^{\prime}]\,dl}{\int_{0}^{\infty}\kappa(\
u,l)\exp[- \int_{0}^{l}\kappa(\
u,l^{\prime})dl^{\prime}]\,dl}\,, \tag{5}\]  
where \(T(l)\) is the air temperature along the line of sight and \(\kappa(\
u,l)\) is the total air absorption coefficient, \(dl=dh/\cos\theta\) and \(h\) is the height. The value of the mean temperature of the atmosphere is discussed in [29]. At a relatively low opacity \(T_{\rm a}\sim(0.90\dots 0.95)T_{0}\), where \(T_{0}\) is the ambient temperature. At a higher opacity, the mean temperature increases, as expected, to \(\sim T_{0}\). Therefore, \(T_{\rm a}\) depends on the zenith angle.  
In principle, the zenith optical depth can be derived using Equation (4) from the absolute measurements of the sky brightness temperature. However, this method requires a precise absolute calibration of the brightness measurements, which can be challenging. A typical widely used approach, known as a \"sky dip\" (described below), is based on relative measurements of the sky brightness at several (minimum two) zenith angles.
## 3 Reliability Models of WSNs
### Path Model  
The path model is defined by composing basic blocks, _i.e._, node and link reliability blocks. A path includes at least two nodes (one hop), where one node starts (source) and another terminates (target) the path model. If there are more nodes involved (multiple-hop), they will be placed between the source and target blocks.  
Figure 8a illustrates a path with sensor nodes \(A\), \(B\) and \(C\) where the sensor node \(A\) is the source, the sensor node \(C\) is the target, and the sensor node \(B\) routes the packet between the source and target. The corresponding RBD model is illustrated in Figure 8b.  
As RBD is a success oriented model, all system components, that is, _Node \(A\), Link (\(A^{-}B\)), Node \(B\), Link (\(B^{-}C\)),_ and _Node \(C\)_ should be arranged in series, since if at least one component fails the whole system fails. Additionally, this RBD model has only one minimal path (\(MP=\{Node\,A,Link\,\,(A-B),Node\,B,Link\,\,(B-C),Node\,C\}\)).  
A sensor node can also send or forward a packet through multiple paths as shown in Figure 9a. In this case, node \(A\) sends packets in broadcast, which are sent to nodes \(B\) and \(C\). Nodes \(B\) and \(C\) send the packet to the same target (node \(D\)).  
Figure 8: Example of a (**a**) WSN path and (**b**) its RBD model.  
The RBD model of the network illustrated in Figure 9a has two or more paths in parallel where two or more nodes forward the same packet from the same transmitter. For building the RBD model, lets first name the basic blocks: _Node A, Node B, Node C, Node D, Link (A\(-\)B), Link (A\(-\)C), Link (C\(-\)D)_ and _Link (B\(-\)D)_. The RBD is depicted by two minimal paths (a minimal path is a path that is not composed of any other path): \(MP_{1}=\{Node\ A,Link\ (A-B),Node\ B,Link\ (B-D),Node\ D\}\) and \(MP_{2}=\{Node\ A,Link\ (A-C),Node\ C,Link\ (C-D),Node\ D\}\). This RBD is shown in Figure 9b. It is worth observing that the path model depends on the routing algorithm used (see Section 3.5), because it determines which nodes will participate (and how to participate) in the path.
# Getting over High-Dimensionality: How Multidimensional Projection Methods Can Assist Data Science
## 4 Multidimensional Projection Approaches and Domains
### Part-Linear Multidimensional Projection  
Part-Linear Multidimensional Projection (PLMP) [72] is a global and partially linear multidimensional projection method that can deal with huge amounts of data due to its low complexity level. It consists of two steps: (i) a nonlinear step that projects a subset of representative samples; (ii) a linear step that projects the remaining instances with a linear transformation, justifying the \"part-linear\" name.  
PLMP selects \(s\) control points (see Section 4.8), a subset of representative instances of \(\bar{X}\) dataset that are projected with the Force Scheme, generating the \(\bar{Y}\) low-dimensional set. In the next step, a linear transformation \(\Phi:\mathbb{R}^{m}\rightarrow\mathbb{R}^{p}\) minimizes the differences between the projected distances and the original dissimilarities, such as  
\[\Phi=\underset{\Phi\in\mathcal{L}_{m,p}}{\arg\min}\Bigg{\{}\frac{1}{\sum_{ij} \delta(x_{i},x_{j})^{2}}\sum_{ij}(\delta(x_{i},x_{j})-\|\hat{\Phi}(x_{i})-\hat{ \Phi}(x_{j})\|)^{2}\Bigg{\}}, \tag{27}\]  
\(x_{i},x_{j}\in X\) and \(\mathcal{L}_{m,p}\) being a space of linear transformations that maps \(\mathbb{R}^{m}\) to \(\mathbb{R}^{p}\).  
Sadly, Equation (27) is computationally impractical in large datasets. To mitigate this issue, PLMP determines an approximation \(\hat{\Phi}\), which takes information from a subset \(\bar{X}\) of the projected control points. Consequently, the final solution is a linear mapping that approximates the transformation of the control points [72].  
To execute the approximation, PLMP creates a new system \(\hat{\Phi}_{m\times p}\bar{X}_{s\times m}=\bar{Y}_{s\times p}\), where \(\bar{X}\) and \(\bar{Y}\) are, respectively, the control points and their projections. Each line of \(\hat{\Phi}_{m\times p}\) generates a sub-system, and the least square is applied to solve it. The projection of the remaining instances is achieved by repeating the process to \(\Phi\) (Equation (27)), that is \(X\Phi=Y\), a simple matrix multiplication with \(O(n)\) computational complexity.  
As in the before-mentioned techniques, the users can manually define the position of the projected control points. Nonetheless, PLMP requires a number of control points superior to the data attribute number, that is, \(s>m\), to build a good quality layout, and this characteristic can limit its use with very high-dimensional datasets such as text represented with a bag of words.  
The results presented by [PERSON] et al. [72] highlighted that PLMP is faster than projections such as LSP. Moreover, besides the initial step, PLMP formulation does not need data dissimilarities information, reducing computational complexity.
###### Abstract  
Currently, the construction and operation of buildings are responsible for 36% of global final energy usage and nearly 40% of energy-related carbon dioxide (CO\({}_{2}\)) emissions. From the perspective of sustainable development, and taking into account economy and thermal comfort, it is crucial to consider the influence of multi-objective realization on design parameters. In this paper, high-rise residential buildings in the cities of Xi'an and Yulin, which have differences in solar radiation, in the western solar enrichment area of China are taken as the research objects. The four objectives of building energy consumption, thermal comfort, life-cycle cost, and life-cycle carbon emissions are weighed using the SPEA-2 algorithm by adjusting eleven design variables, thereby obtaining the Pareto non-dominated solutions. The TOPSIS method is applied to obtain the suitable parameter combinations under different scenarios. The results show that the differences in climate and solar radiation influence the solution distribution, the range of objective function values, and the values of the design variables in Pareto non-dominated solutions. The obtained optimal scheme for the Xi'an area has an energy-saving rate of 61.7%, a TDHP improvement rate of 20.3%, an LCC of 254.8 CNY/m\({}^{2}\), and an LCCO\({}_{2}\) of 72.3 kgCO\({}_{2}\)/m\({}^{2}\). The corresponding values in the Yulin area are 69.7%, 19.4%, 230.2 CNY/m\({}^{2}\), and 0 kgCO\({}_{2}\)/m\({}^{2}\). This reflects the potential of solar energy utilization to reduce buildings' energy consumption and carbon emissions. The methodology and findings can provide references for high-rise residential building design in Northwestern China.  
high-rise residential building; multi-objective optimization; energy consumption; life-cycle cost; carbon emissions; solar energy utilization +
Footnote †: journal: Applied Sciences
# Predicting the Failure of Dental Implants Using Supervised Learning Techniques
## 5 Conclusions  
Supervised learning techniques have been widely applied in the medical field to search for and analyze relevant information in the medical literature to uncover key medical information that can serve clinical workers in their decision making. Therefore, this study on predicting dental implant failure was conducted using supervised learning techniques to quantify and analyze collected data. The key findings are listed below:  
* Comparing the prediction performance of the three tested supervised learning techniques, the results revealed that DT is the optimal model for forecasting dental implant failure.
* Fixture width, implant system, betel nut chewing, ridge augmentation, fixture length, and alcohol consumption are the top five most influential independent variables on dental implant failure prediction.
* This study not only demonstrates the influencing factors for dental implant failure, but also extends to clinical practice for the benefit of the public. However, there are still areas in which future study can advance our understanding. The retrospective data collection adopted in this study led to a considerable lack of information, which could have affected the analysis results; prospective research methods should be adopted in future work to collect more complete information, enabling the correctness of the analysis results to be closer to actual clinical results.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] contributed reagents/materials/analysis tools; all authors wrote the paper.  
The authors declare no conflicts of interest.
## 7 System Architecture and Implementation
### CSP Conflict Solver Module  
The Conflict Solver is used to resolve arising conflicts during the solving of the CSP either on the resource or on the component side and is implemented as a separate class. Within these classes, several methods are available. The find_violated_soft_constraints method is used to identify constraints that cause a conflict in the constraint network. For this purpose, all combinations of soft constraints are started as a solution process in which the selected soft constraint combination is not considered in the solution search. Individual csp_solver objects are created in each case for the solution search. Since these solving processes of the combinations run independently, the computation time is reduced by running the solving processes in parallel (multi-threading). If a solution is found when a combination is suppressed, the combination triggers a conflict. These combinations are then stored in a list. Once the conflicts have been identified, the user is asked whether they should be resolved on the resource side or on the component side. In the case of component-side resolution, all input variables that are directly related to the conflicting constraints are searched for first. For this purpose, a path search with the Dijkstra algorithm is performed within the CSP graph. The Dijkstra algorithm is available within the implemented library _NetworkX_[53]. If an optional domain is assigned to the identified input variables, it is taken over. If this is not the case, the domain is directly expanded to a quasi-continuous range of integer numbers.  
For the resource-side resolution, the resource-related variables are identified within the conflicting constraints. Since the constraints follow a fixed scheme, these are found on the right-hand side of the equation. Subsequently, the opening of these variables also follows a quasi-continuous range of integer numbers. The resulting configuration problem thus also includes the specification of the required manufacturing resources of the CSP modified by the component-sided or resource-sided solution. Again a solution process isstarted via a csp_solver object. The resulting solutions then represent possible alternative solutions that enable the production of the examined component. If several alternative solutions are to occur, these can be evaluated finally over the resulting costs. For this purpose, the costs are added to each solution by adding the costs of the constraints that were previously suppressed in the process.  
All classes are merged into a separate main script, which coordinates the processes according to Figure 10 of the manufacturing stage model. This also involves the integration of the Graphical User Interface in Flask. Overall, this system architecture provides a useful implementation for solving CSP problems as it is modular and easy to use through the use of classes and methods and can be adaptively applied to other problems as well.
## 3 Joint Optimization of Cashing and Recommendation
### Problem Formulation  
The problem of joint optimization of caching and recommendation can be mathematically formulated as follows:  
\[\begin{array}{cc}\underset{X,R}{\text{max}}&H=\sum_{u=1,2,\ldots,m}\sum_{i= 1,2,\ldots,n}x_{i}\hat{P}_{i}^{u}\\ s.t.&\sum x_{i}w_{i}\leq c\\ D(k)_{u}=1-\frac{\sum_{i\in K}P_{i}}{\sum_{i\in K_{best}}P_{i}}\leq d_{u}\\ R_{i}^{u}\leq(1+\gamma)F_{i}^{u}\\ \hat{P}_{i}^{u}\leq(1+\alpha)P_{i}^{u}\\ 0\leq d_{u},\alpha\leq 1,-1\leq\gamma\leq 0\end{array} \tag{5}\]  
Here, \(i\) denotes the content, and \(u\) denotes the user. The first constraint indicates that the capacity of all cached content will not exceed the cacheable capacity \(c\) of the server. The second constraint indicates that the distortion value of the recommendation for user \(u\), namely warping degree \(D(k)_{u}\), will not exceed its tolerance limit \(d_{u}\). The third and fourth constraints respectively set the upper limits on the recommendation score \(R_{i}\) and modification frequency \(\hat{P}_{i}\) with respect to the original (request) frequency. Condition \(0\leq d_{u}\leq 1\) states that the tolerance limit of distortion \(d_{u}\) is at least 0 (no distortion) and at most 1 (100% distortion), \(-1\leq\gamma\leq 0\) ensures that the recommended score is non-negative and at most the original frequency, and \(0\leq\alpha\leq 1\) ensures that the corrected frequency after recommendation is at least the original frequency.
## 4 Results and Discussion  
The final ensemble predictions from EEMD-RNN, EEMD-LSTM, EEMD-GRU, EEMD-SVM, EEMD-ELM, and EMD-LSTM are shown in Figure 9. The evaluation metrics, including MAE, MSE, MAPE, NRMSE, R\({}^{2}\), and KGE, are shown in Figure 10. As shown, satisfactory predictions were achieved, with R\({}^{2}\) values greater than 0.98, which demonstrate the effectiveness of the \"decomposition-ensemble\" learning model.  
Figure 9: Time series plots of observed and predicted landslide displacement. The training set is shown with the white background, while the testing set is shown with the blue background.
# A Deep Evolution Policy-Based Approach for RIS-Enhanced Communication System
## 6 Conclusions  
In this paper, we investigate an RIS-aided MU-MISO downlink communication system. We propose a DEP-based algorithm for the joint optimization of both active and passive beamforming. Unlike optimizing one single DNN through gradient descent-based algorithms, the proposed DEP-based approach simultaneously generates multiple agents composed of DNNs in one population. After the processes of selection, crossover, and mutation, the active and passive beamforming can be optimized through the agent with the highest score in the last generation. This approach eliminates the need for gradient back-propagation and optimization for individual agents, allowing for the concurrent calculation of fitness scores for multiple agents. Furthermore, it facilitates the processes of selection, crossover, and mutation to produce offspring with enhanced characteristics. In conclusion, we propose a new way to solve beamforming optimization with faster convergence speed and higher system performance. Ultimately, the simulation results demonstrate the effectiveness and superiority of the DEP-based method in comparison to the DRL algorithm that relies on gradient descent.  
Figure 12: Sum rate versus total time steps under different subspace proportions, i.e., \(\{2/P,0.5,1\}\), \(P_{t}=5\) dBm, \(M=4,N=32,K=4\), mutation rate = 0.001, population size = 500. Shaded regions represent 95% confidence interval over 10 random seeds for each result.  
**Author Contributions:** Conceptualization, Z.S., Y.L. and B.W.; methodology, K.Z.; software, K.Z. and X.L.; writing--original draft preparation, K.Z. and X.L.; writing--review and editing, L.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All data generated or analyzed during this study are included in this published article.  
**Conflicts of Interest:** All authors were employed by the 54 th Research Institute of China Electronics Technology Group Corporation. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# A Mobility-Assisted Localization Algorithm for Three-Dimensional Large-Scale UWSNs
## 4 Mobility-Assisted Localization Scheme
### Phase II  
Since sensor nodes may be randomly distributed in the edge region, a small number of nodes cannot satisfy their localization conditions by receiving localization messages from anchor nodes. However, according to the above, the ordinary sensor node that has been located in Phase I can be used as a new reference node. Therefore, ordinary sensor nodes in Phase II can be located by the rational application of these new reference nodes.  
To alleviate the error propagation effect, the new reference node can calculate its confidence value, \(\eta_{a_{i}}\), and compare it with the confidence threshold, \(\lambda\)[29]. When \(\eta_{a_{i}}\geq\lambda\), the located sensor node can be used as a new reference node to participate in Phase II of the localization scheme.  
\[\eta_{a_{i}}=\left\{\begin{array}{c}1,\text{ if node is the initial anchor}\\ \\ 1-\dfrac{\sum_{j}\left|\left(x_{a_{i}}-x_{b_{j}}\right)^{2}+\left(y_{a_{i}}-y_{ b_{j}}\right)^{2}+\left(z_{a_{i}}-z_{b_{j}}\right)^{2}-r_{a_{i}b_{j}}^{2} \right|}{\sum_{i}\left(x_{a_{i}}-x_{b_{j}}\right)^{2}+\left(y_{a_{i}}-y_{b_{j }}\right)^{2}+\left(z_{a_{i}}-z_{b_{j}}\right)^{2}},\text{ others}\end{array}\right. \tag{26}\]  
Ordinary sensor nodes in Phase II send their localization request to obtain the localization message of the nearby reference node. When the sensor node obtains enough localization messages, the received information is sorted according to the confidence value of the reference node and selects the top-ranking localization message to estimate its position. If there are multiple solutions, one localization message is incremented until a unique estimation value appears. Moreover, if the confidence values of the two reference nodes are the same, the localization message that arrives first is selected.  
The detailed pseudo algorithm of MALS-TSF for 3D large-scale UWSNs is listed as Algorithm 1 shows. Moreover, Table 3 gives the definitions of related symbols used in Algorithm 1.
## 4 Results and Validation
### Selected Results
#### 4.2.3. Tharthar, Lake
By using the polynomial fit, the resulting RMS slightly improves to \(16.71\,\mathrm{km}^{2}\) which is 0.67% of the maximum area. It can be expected that there will be a further improvement in the RMS by using a higher degree polynomial fitting. But overall, the resulting RMS are very good as they are smaller than 1.0% of the maximum surface area.
## 5 Simulation Results  
In this section, we use the opportunistic network environment (ONE) simulator to evaluate the performance of our proposed LQFC protocol. We compare it with the other nine schemes, namely the LQFC-G, the MTAP [6], the CORV [7], the First Contact [34], the Direct Delivery [35], the Epidemic [33], the Spray and Wait [36], the Prophet [37], and the MaxProp [38]. In the LQFC-G, the vehicle is selected as the RN. Moreover, the MTAP and the CORV are respectively proposed in [6; 7]. Specifically, the MTAP uses the flooding technique for message delivery, and the CORV predicts the transfer probability based on the UAV course information for relay selection. Furthermore, other schemes are respectively proposed in [33; 34; 35; 36; 37; 38]. The detailed simulation parameters are summarized in Table 1.