esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Online Label Distribution Feature Selection Based on Label Importance and Label Correlation
## 4 Proposed Method
### Improvement of NRS
The correlation between \(l_{i}\) and \(l_{j}\) is calculated using the mutual information as follows:_  
\[MI\big{(}l_{i},l_{j}\big{)}=\sum_{k=1}^{M}\sum_{q=1}^{M}P\big{(}l_{ik},l_{jq} \big{)}\log\frac{P\big{(}l_{ik}l_{jq}\big{)}}{P(l_{jq})} \tag{12}\]  
An undirected label graph \((\text{WUG})=(\text{V},\text{E},\text{W})\) can be constructed by applying Equation (12). Here, \(V=L=\{l_{1},l_{2},\ldots,l_{m}\}\) represents the set of nodes of the undirected graph, \(E=\big{\{}\big{(}l_{i},l_{j}\big{)}\mid l_{i},l_{j}\in L\big{\}}\) represents its set of edges, and \(w\big{(}l_{i},l_{j}\big{)}=MI\big{(}l_{i},l_{j}\big{)}\) represents the weight of each edge [48]. The importance of each node in this undirected graph is defined as follows:  
\[LW(l_{i})=(1-d)+d\sum_{l_{j}\in SN(l_{i})}\frac{LW\big{(}l_{j}\big{)}w\big{(} l_{i},l_{j}\big{)}}{SW(l_{j})} \tag{13}\]  
\[\text{SW}\big{(}l_{j}\big{)}=\sum_{l_{j}}w\big{(}l_{i},l_{j}\big{)} \tag{14}\]  
In the above equations, \(LW(l_{i})\) and \(LW(l_{j})\) represent the weight divisions of nodes \(l_{i}\) and \(l_{j}\), respectively. \(SN(l_{i})\) is the set of nodes with edges to label \(l_{i}\), and \(w\big{(}l_{i},l_{j}\big{)}=MI\big{(}l_{i},l_{j}\big{)}\) represents the correlation between nodes. Equation (10) is used to calculate \(SN(l_{i})\), which denotes the sum for the correlation for all edges starting from \(l_{j}\). In addition, \(d\) is the damping coefficient, where \(d=0.85\) is the recommended setting according to the method in Ref. [49]. For ease of calculation, an initial weight value can be set for all nodes; this is usually \(1/L\), where \(L\) is the total number of nodes, that is, the total number of labels. Using this algorithm, we can calculate the correlation between node \(l_{i}\) (i.e., label \(l_{i}\) ) and other nodes \(l_{j}\) related to it, as well as the structure of the graph (WUG). Through label correlation, we obtain the weight of each label in the label space, and this completes the exploration of label correlation.
# Thermal Analysis of Radiation Heat Transfer of Improved Fractal Solar Collectors
## 2 Design and Analysis  
The goal of thermal solar collectors is to absorb as much of the incoming light as possible and minimize the reflection. For this reason, absorber surfaces are usually provided with special coatings, usually black or dark in color. However, some light is inevitably reflected, and the amount and direction of the reflected light affect the collector's efficiency. Designers use a variety of techniques to reduce or redirect the reflected light. For example, selective surface coatings minimize heat loss while absorbing light. Some advanced designs use secondary reflectors or concentrators to recapture the reflected light. Concentrators direct incoming sunlight to a point or line, and one of the main types of concentrators, a parabolic dish-shaped reflector, can be used to focus the rays to a single point. The brightness and roughness of an object's surface directly affect how light is reflected. For example, a matte surface reflects the incoming light evenly in all directions, while shiny surfaces provide a more focused reflection. Therefore, directing the incoming and reflected rays is of critical importance in optimizing solar collectors. Thus, maximum energy conversion can be achieved, and the overall efficiency of the system can be increased.  
In this section, the design, working principle, and radiative thermal analysis of the solar collector developed in the concave dish-based fractal structure are reported. The design criteria are based on the experimental thermal behavior results of the 3-row fractal structure collector with a concave dish reported in [40], and their numerical analysis is carried out as 4-row and 5-row structures. In the radiative thermal performance analyses, radiative heat flux, radiation temperature data, and flow dynamics are obtained using the ANSYS-Fluent program (2023 R2).
## 6 Conclusions
As reliance on UGS facilities intensifies globally, the imperative to consistently and comprehensively monitor ground deformation becomes even more pressing. InSAR, with its ability to deliver high-resolution observations across vast regions, provides an optimal solution. By integrating InSAR monitoring strategies for UGS, we can champion best practices and safety standards that hold universal relevance. This approach not only aids in the early detection of potential hazards but also promotes the efficient utilisation of storage facilities and highlights the broader implications of localized activities. In our quest for energy sustainability and safety, global-scale InSAR monitoring of UGS sites is not merely a recommendation, it is a necessity. This study serves not only as an exemplification of the versatility of the InSAR approach but also paves the way for future establishment and calibration of sophisticated geomechanical models of fractured layers. Such models are indispensable for simulating and foreseeing the profound deformations of reservoirs. The research results showed that the InSAR method enables general, regular, and specific monitoring of UGS activities and shows seasonal trends in vertical displacement above the reservoir. By providing a thorough site-specific perspective, our work offers invaluable insights that could significantly enhance the management of withdrawal and injection rates. Furthermore, this research can act as a pivotal reference when assessing effects near UGS facilities, thereby improving the reliability and safety of operations. G.F., Conceptualization, methodology, software, validation, formal analysis, investigation, writing--original draft preparation and figures preparation; T.B., methodology, software, investigation, writing-original draft preparation and figures preparation; M.D.S., writing--review and editing, conceptualization, supervision of the project and review of the final version; R.F., supervision of the project, review of the final version. All authors have read and agreed to the published version of the manuscript. This research received no external funding. **Data Availability Statement:** Publicly available datasets were analysed in this study. This data can be found here: [[https://land.copernicus.eu/pan-european/european-ground-motion-service](https://land.copernicus.eu/pan-european/european-ground-motion-service)]([https://land.copernicus.eu/pan-european/european-ground-motion-service](https://land.copernicus.eu/pan-european/european-ground-motion-service)). This study was developed in the context of the aim of the national doctoral program PON Research and Innovation 2014-2020 \"Education and research for recovery--REACT-EU\" program (Ministerial Decree 10 August 2021, n. 1061) Action IV.5, Ph.D. on green topics. The authors declare no conflict of interest.
## 2 Related Work  
### Security Computing Methods Based on Encryption Algorithms  
Fully homomorphic encryption (FHE) [16], as an advanced encryption technology, offers an ideal solution for secure data processing. Compared to basic homomorphic encryption, the most significant feature of FHE is its support for unlimited additions and multiplications on encrypted data [17]. This capability allows the execution of any arithmetic or logical operation while keeping the data encrypted, greatly expanding the possibilities for encrypted data processing. The fundamental principle of FHE is based on constructing encryption algorithms with complex mathematical structures that enable two basic operations on encrypted data: addition and multiplication. These operations are mathematically expressed as  
\[\mathrm{Enc}(a)\oplus\mathrm{Enc}(b) =\mathrm{Enc}(a+b) \tag{1}\] \[\mathrm{Enc}(a)\otimes\mathrm{Enc}(b) =\mathrm{Enc}(a\times b) \tag{2}\]  
where \(\mathrm{Enc}(x)\) denotes the encrypted form of data \(x\), and \(\oplus\) and \(\otimes\) represent the addition and multiplication operations on encrypted data, respectively. The key innovation of FHE lies in the fact that no matter how many times these operations are performed, there is no need to decrypt the data, ensuring absolute security during data processing.  
Despite the theoretical high security and powerful functionality of FHE, several challenges remain in its practical application [18]. The primary challenges include low computational efficiency and the complexity of algorithm implementation. Each operation involves complex mathematical computations, making FHE inefficient for processing large-scale data or real-time data processing [19; 20]. Additionally, the implementation and maintenance of the algorithm require a high level of expertise, limiting its widespread adoption in ordinary applications.
# Improved Performance on Wireless Sensors Network Using Multi-Channel Clustering Hierarchy
## 2 Multi-Channel Clustering Hierarchy (MCCH)
### Calculation of the Distance of Each Node and Cluster Head  
At this stage, the process of determining the distance between the nodes scattered on the WSN is initiated. Next, the point closest to CH will be searched. In calculating the distance of all sensors to the cluster head, the distance formula used is the Euclidean approach. In mathematics, the Euclidean distance is the distance between two objects or points. Euclidean distance can be used to measure the similarity (matching) between objects. The information collected is in the form of the Euclidean distance between nodes and neighbors. Euclidean distance is the length of a line connecting two points on a coordinate plane. If the coordinates used are two-dimensional planes, then the Euclidean distance between the two nodes (\(X_{1}\), \(X_{2}\)) and (y1, y2) can be calculated using the following equation:  
\[D(x_{2}\;,\;x_{1})=\sqrt{\sum_{j=1}^{d}\left|x_{2\;j}\;\;-\;\;\;x_{1\;j}\right| ^{2}}. \tag{2}\]  
Notes:  
\(d\) = Node point,  
\(X_{1}\) = Node on axis X (CH),  
\(X_{2}\) = Node on axis Y,  
\(X_{1j}\) = axis number X (CH),  
\(X_{2j}\) = axis number Y.  
Table 4 is the next step, namely the third algorithm, which is to calculate the distance between the cluster head and cluster members so that they are close together.
## 4 Design and Implementation of Proposed MPPT Algorithm  
The application of conventional Particle Swarm Optimization (PSO) algorithm for single power peak values of solar-PV modules is effective. However, nonuniform operating conditions (NUOC) generate multiple peaks, and conventional PSO is not capable of tracking these numerous peak weight components during tracking of maximum power. Hence, conventional PSO needs to be modified for implementation in the NUOC conditions. For this purpose, the concept of I& C has been utilized for updation of particle velocity and position of the PSO algorithm.
## 2 Modeling Methodology
The analysis cases considered in this study are summarized in Table 4. The input seismic waves were modified to match the design response spectrum from the north-south (NS) component recorded at the PHA2 station (bedrock motion) during the 2017 Pohang earthquake, accounting for the seismic characteristics of the Korean Peninsula. The NS component corresponds to the larger value of the two seismic waves (NS, EW) measured at the PHS station. Seismic accelerations of 0.1 g, 0.2 g, and 0.4 g were used as input motions and applied at the base of the numerical model. The acceleration-time histories of these input motions are illustrated in Figure 6. Additionally, to assess the influence of inertial and kinematic interactions in the soil-pile system, two different superstructure mass conditions and two relative soil density conditions were considered. As a result, a total of 12 three-dimensional numerical simulations were conducted. In addition, since velocity and displacement responses are mainly used when evaluating or designing seismic responses of superstructures such as bridges, following analysis results were investigated with a focus on dynamic velocity responses and displacement responses. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Case No.**} & **Input Earthquake** & \multirow{2}{*}{\(\mathbf{D_{r}}\)} & **Weight (Density) of** \\  & **Magnitude** & & **Superstructure** \\ \hline
1 & 0.1 g & 30\% & 2.4 ton/m\({}^{3}\) \\
2 & 0.1 g & 30\% & 12 ton/m\({}^{3}\) \\
3 & 0.1 g & 80\% & 2.4 ton/m\({}^{3}\) \\
4 & 0.1 g & 80\% & 12 ton/m\({}^{3}\) \\
5 & 0.2 g & 30\% & 2.4 ton/m\({}^{3}\) \\
6 & 0.2 g & 30\% & 12 ton/m\({}^{3}\) \\
7 & 0.2 g & 80\% & 2.4 ton/m\({}^{3}\) \\
8 & 0.2 g & 80\% & 12 ton/m\({}^{3}\) \\
9 & 0.4 g & 30\% & 2.4 ton/m\({}^{3}\) \\
10 & 0.4 g & 30\% & 12 ton/m\({}^{3}\) \\
11 & 0.4 g & 80\% & 2.4 ton/m\({}^{3}\) \\
12 & 0.4 g & 80\% & 12 ton/m\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Analysis cases (12 cases). Figure 5: Overview and output derivation point of analysis model. (**a**) Overview of entire analysis model. (**b**) Deviation point of velocity-time history response on pile cap.
## 2 D2D Communications in LTE-A Standards
### Resource Allocation for D2D Communications  
Resource allocation (RA) is an important technical element that determines the interference level of DUEs and CUEs. The easiest way to avoid interference between D2D and cellular transmission modes is to allocate an unlicensed spectrum to DUEs. However, a licensed spectrum is allocated to D2D communications in LTE-A ProSe because assigning an unlicensed spectrum to D2D mode is impractical due to various technical problems. FFR is a typical method used to allocate the same radio resources to DUEs and CUEs. As described in the previous section, FFR is divided into strict FFR and soft FFR. Strict FFR can certainly avoid interference between DUE and CUE, but radio resource reuse is low. On the other hand, the entire radio resources can be dynamically reused to DUEs and CUEs, and,  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **UE1** & **UE2** & **Serving Cell** & **Communication Path** & **Interference Sources** \\ \hline
**1A** & Out & Out & n/a & direct & n/a \\ \hline
**1B** & In & Out & n/a & direct & Cellular User Equipments (CUEs) in the same cell \\ \hline
**1C** & In & In & Same cell & direct &
\begin{tabular}{c} Cellular User Equipments (CUEs) in the same cell \\ and D2D User Equipments (DUEs) in the same cell \\ and neighboring cells \\ \end{tabular} \\ \hline
**1D** & In & In & Different cell & direct &
\begin{tabular}{c} Cellular User Equipments (CUEs) in the same cell \\ and D2D User Equipments (DUEs) in the same cell \\ and neighboring cells \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 1: Device-to-Device (D2D) scenarios and interference sources.  
Figure 2: Proximity discovery scenarios.  
accordingly, the overall cell throughput increases in soft FFR. The drawback of soft FFR is that DUEs or CUEs can be suffered from inter-cell interference. Therefore, it is necessary to study an efficient resource allocation scheme for D2D communications that can efficiently mitigate interference without throughput loss.
## 1 Introduction
However, despite certain clues which might be present on orthopantomograms or periapical X-ray images, which are mainly used in daily clinical practice, radiographs are two-dimensional and it is often difficult to detect the presence of anatomical anomalies such as the \"C\"-shaped canal, radix entomolaris or paramolaris, or to determine the shape of multiple root canals and their possible merging into one canal. Therefore, cone-beam computed tomography (CBCT), which provides a three-dimensional analysis, is now widely used for diagnosis and in epidemiology as it is considered a suitable method to study the aforementioned anatomical variations in root canals, especially in epidemiologic studies [16; 17; 18; 19]. It is a non-invasive method that allows the observation and analysis of teeth in all three planes, coronal, sagittal and transversal [20]. CBCT analysis is indicated and can offer insight into root canal morphology in cases with unusual findings on intraoral radiographs and should only be used when examining the anatomy of endodontic space or diagnosis is not possible using lower-dose conventional dental radiographs. Furthermore, CBCT scans can be helpful for treatment planning in teeth with a challenging root canal morphology such as a \"C\"-shaped root canal, as 3D images can help identify number, shape, location, and size of the canals and detect potentially dangerous zones which present a risk for stripping perforation. In the Croatian population, no radiological analysis of the lower second molars, including the frequency of the \"C\"-shaped canal, the occurrence of additional roots and the morphology of root canals in mesial roots, has been conducted. [PERSON] et al. [21] investigated \"C\"-shaped canals in lower second molars by only analyzing the cross-sections of the extracted teeth. Therefore, the aim of this study was to investigate the morphological variations in the endodontic space of lower second molars using CBCT scans of a Croatian subpopulation in the city of Zagreb.
## 3 Results
### Multilayered Circuit Construction Using SWT  
To further characterize the applicability of SWT as a versatile synthetic regulator, we constructed a multilayered circuit with SWT as the top regulator. In this design, T500 SWT regulates the expression of TetR in the first layer, which in turn regulates GFP reporter expression in the second layer (Figure 7a). Since transcription of GFP reporter from T7 promoter can be strongly inhibited by TetR binding to its operator site (TetO), any leaky expression of TetR from the first layer can strongly inhibit the GFP reporter expression (Figure 7b and Figure S7) [52; 53]. When TetR was under the control of STAR Target AD1 or THS in the first layer, GFP reporter expression was repressed by leaky TetR even in the absence of cognate trigger RNA. GFP expression levels could be partially restored when anhydrotetracycline (aTc) was treated, indicating that TetR was, in fact, inhibiting GFP expression in the second layer. However, when the T500 SWT was connected to TetR in the first layer, the GFP repression by leaky TetR was not observed despite the fact that a strong constitutive promoter J23119 was driving the expression of T500 SWT-TetR (Figure 7c) [54]. In the presence of cognate trigger RNA, T500 SWT-TetR could strongly repress GFP reporter expression (Figure 7d), indicating proper expression of TetR in its ON state. Even in the presence of cognate trigger RNA, GFP expression levels were recovered to some extent when higher aTc levels were used (Figure S8). Together, SWT design can be utilized in multilayered complex circuits thanks to its extremely low leakage and large dynamic range.  
Figure 7: Characterization of Multilayered Circuits. (**a**) Design of circuits where TetR is under the control of SWT. (**b**) Simplified schematic of SWT-TetR and pT7 (TetO)-GFP multilayered circuit. This multilayered circuit is analogous to a NOT logic gate where TetR expression from the first layer suppresses the expression of GFP reporter output in the next layer (see Figure S7 for details). In case there is a leaky TetR expression, GFP output would be suppressed even in the absence of trigger RNA, which can be partly reversed by aTc treatment. (**c,d**) T500 SWT, STAR target WT, or THS A was attached upstream of TetR to regulate its expression. GFP fluorescence outputs were measured in the absence of trigger RNA (**c**) and in the presence of trigger RNA (**d**). TetR negative did not contain RBS upstream of the TetR gene, and GFP negative did not contain RBS upstream of the GFP gene. Relative errors for GFP fluorescence are from the s.d. of three biological replicates.
# Optimized Design of Floor Plan and Components of Prefabricated Building with Energy-Cost Effect
## 4 Results and Discussion
### Correlation Analysis Results
#### 4.2.2 Correlation Analysis of Component Selection Method  
In the phase of correlation analysis, only the relationship between the percentage of the thickness of the insulation in each orientation to the total insulation thickness and the total energy consumption is required, given that the types of material and construction have already been determined (Table 8).  
A negative correlation could be observed between the insulation thickness in the east/south directions and total energy consumption--i.e., increasing the proportion of the insulation in the east and south directions will decrease the total energy consumption; conversely, increasing the proportion of the insulation in the west and north directions will increase the total energy consumption. From a correlation point of view, in order to reduce the total building energy consumption, the insulation of east- and south-oriented buildings should be appropriately increased and the insulation of west- and north-oriented buildings should be reduced under a certain cost limit.
# Efficient Metaheuristics for the Mixed Team Orienteering Problem with Time Windows
## 3 Problem Definition
* \(q^{k}(e^{d})\): a binary variable set equal to \(1\) if, in the walk \(W_{k}\), the node \(t(e^{d})\) is visited just before the traversal of the edge \(e\) in the direction \(d\in\{+,-\}\). In all other cases, \(q^{k}(e^{d})=0\). * \(r^{k}(e^{d_{1}}_{1},e^{d_{2}}_{2})\): a binary variable which is defined only for edges \(e_{1}\), \(e_{2}\) with \(h(e^{d_{1}}_{1})=t(e^{d_{2}}_{2})\). Its value is \(1\) only if i) the edge \(e_{1}\) is traversed in the direction \(d_{1}\) along the walk \(W_{k}\), just before the traversal of the edge \(e_{2}\) in the direction \(d_{2}\) and ii) the node \(h(e_{1}^{d_{1}})\) is not visited at that moment, that is, it is only passed by while traversing \(e_{1}\) and \(e_{2}\). In all other cases \(r^{k}(e_{1}^{d_{1}},e_{2}^{d_{2}})=0\). From the definition of these variables, it is clear that for any two edges \(e_{1}\), \(e_{2}\) and directions \(d_{1}\) and \(d_{2}\), the variable \(r^{k}(e_{1}^{d_{1}},e_{2}^{d_{2}})\) cannot be equal to 1 when at least one of the variables, \(p^{k}(e_{1}^{d_{1}})\) or \(q(e_{2}^{d_{2}})\), is equal to 1. * \(\text{start}_{u}\): a real variable denoting the starting time of the single visit at the node \(u\). * \(\text{start}_{e}\): a real variable denoting the starting time of the single traversal of the edge \(e\). * \(M\): a large number (larger enough from the parameters of the problem). * \([K]\): the set \(\{0,1,\ldots,K-1\}\)  
For avoiding many special cases in the formulation of MTOPTW, we assume that all the endpoints of the walks are different nodes and also none of these nodes are POIs. In case that some of the nodes are the same or POIs, copies of the original nodes are created which are considered as different nodes with no profit. Then, these nodes are used as endpoints of the walks. For instance, if the \(K\) walks all start and end at the same node which happens to be a POI, \(2K\) copies of this node are created and each of these copies has zero profit and visit time and time window as long as the interval between the starting and ending time of the corresponding walk. Also, all the copies of the node and the node itself are connected with each other through zero-length shortest path edges belonging to \(E^{\prime}\). Regarding the remaining nodes of the graph, these copies \"inherit\" the edge connectivity along with the associated edge costs of the original node.