esa-sceva/satcom-chunk-collection huggingface dataset:
# Simplified Pushover Analysis for Rapid Assessment of Shear-Type Frames
## 5 Vulnerability Analysis through the Proposed Simplified Method  
The proposed analytical pushover analysis on the simplified (stick-)model of a building becomes very useful when studying the impacts of earthquakes and developing risk assessment maps, in order to prioritize regions for structural retrofitting policies. In this study, the aim is to divide the building stock considered into different typologies, based on some macro-features (structure type, number of stories, age of construction, etc.) and then correctly describe the vulnerability of each typology. This is performed through the so-called fragility curves, which may be based on either an observational approach accounting for the damages observed in previous earthquakes, or a mechanical approach based on numerical models. As for the latter approach, in the literature, the fragility curves may be obtained from either incremental dynamic analysis (IDA) or nonlinear static analysis. Despite the accuracy of IDA, researchers are often discouraged to use it, since the computational effort it requires renders the analysis of large-building inventories an onerous task. In this respect, static analyses may be preferred, especially if they make use of simplified mechanical models. Having a small number of basic variables, the computational effort becomes affordable. In that respect, the fully analytical approach proposed in this study is even more effective, since fragility curves can be obtained very straightforwardly by parametrizing the basic variables of interest and by running a large number of analyses on different models in a very short time. It is a very efficient method, especially when dealing with a large inventory of buildings at a territorial scale.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Story**} & \multicolumn{3}{c}{**Case 1 (Error \%)**} & \multicolumn{3}{c}{**Case 2 (Error \%)**} & \multicolumn{3}{c}{**Case 3 (Error \%)**} \\ \cline{2-10}  & **DLS** & **LLS** & **CLS** & **DLS** & **LLS** & **CLS** & **DLS** & **LLS** & **CLS** \\ \hline
1 & \(-16.7\) & \(-13.0\) & \(-5.3\) & \(-22.2\) & \(-16.7\) & \(-20.0\) & \(-20.0\) & \(-10.0\) & \(+12.5\) \\
2 & \(-4.5\) & \(-14.3\) & \(-7.0\) & \(-13.3\) & \(-15.2\) & \(-3.2\) & \(-22.7\) & \(+5.3\) & \(+9.6\) \\
3 & \(-6.1\) & \(-7.0\) & \(2.0\) & \(-7.7\) & \(-8.4\) & \(-4.8\) & \(-8.6\) & \(+7.1\) & \(+7.7\) \\
4 & & & & \(-6.3\) & \(-8.3\) & \(-2.2\) & \(-6.8\) & \(+3.4\) & \(+5.9\) \\
5 & & & & \(-7.7\) & \(-9.1\) & \(2.0\) & \(-8.6\) & \(+7.8\) & \(+5.3\) \\
6 & & & & & & & \(-6.3\) & \(+5.3\) & \(+2.5\) \\ \hline Average & \(-9.1\) & \(-11.4\) & \(-3.4\) & \(-11.4\) & \(-11.5\) & \(-5.6\) & \(-10.4\) & \(+2.7\) & \(+6.2\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Drift error between rigorous pushover and proposed method.  
Figure 7: Comparison of the capacity curves from the full pushover analysis (dashed lines) and the proposed analytical method (thick lines): (**a**) Case 1 (3 storeys); (**b**) Case 2 (5 storeys); (**c**) Case 3 (6 storeys).
# A Network Analysis Approach to Detecting Social Issues with Web-Based Data
## 2 Literature Review
### Social Issue Detection
To overcome the limitations mentioned above, the incremental approach was proposed. The incremental approach is a method to detect not only events, but also upcoming events, and has been used in various studies until recently. A study proposed an evolutionary model for event detection to capture the dynamics and evolving behavior of events [5]. The study defined an event as a collection of documents from social media that have common content. Another study proposed an event detection system called TwitterNews+ to identify both major and minor newworthy events from Twitter [11]. In the study, similar tweets were grouped and defined as an event. Additionally, based on document representation using word embedding, an adaptive, online clustering method was proposed for online news event detection using time slicing and event merging [10]. This study also defined a group of news documents with similar content as an event. Most of the studies that used the incremental approach to consider dynamic patterns of social issues grouped similar documents and defined them as an issue or event. When a few documents become a new event, it is not clear whether the event was created by people's concentration or a meaningless event. To explain the time when social issues or important events occurred, it is necessary to use the concept toward an issue or topic that is suddenly attracting people's attention. Furthermore, it can be difficult to rank issues by degree of urgency. Most studies either detect social issues or simply suggest that more frequent issues are more important. If a group of similar documents is defined as an issue, it can be recognized as a significant or urgent issue when enough documents are accumulated over time. This method requires continuous monitoring of numerous issues, making it difficult to detect urgent issues in a timely manner.
## 4 Methodology and Prototype Implementation
### Evaluation Process and Validation Criteria  
The prototype connects the widely researched topic of DSM with the newly introduced SMGW infrastructure, which is becoming an integral part of the energy regulation in Germany. In order to answer the posed research questions, it is necessary to successfully implement and test a DSM approach based on the exploitation of flexibility provided by a decentralized EMS. As stated before, it is neither our goal to propose novel DSM approaches or control algorithms, nor to evaluate their performance. Instead, we provide an analysis of the functionality of these novel communication devices in the context of EMS-enabled DSM. The criteria presented in Table 2 determine whether we consider the prototype to work as intended or not.  
Figure 3: Controllable local system (CLS) management systems encapsulate SMGW-specific requirements and provide interfaces for DSMgrs that offer a direct communication channel to control boxes.  
The above criteria are specific to the combination of SMGW and HKS. Theoretically, for each SMGW three HKSs can be implemented and evaluated, given the SMGW supports the respective HKS. The prototype is validated step by step based on the criteria, as outlined in the following:  
1. Configuration 1.1. Send request for adding new HAN communication profile from GWA to SMGW. 1.2. Send request for adding new WAN communication profile from GWA to SMGW. 1.3. Send request for adding new proxy profile from GWA to SMGW.
2. Communication 2.1. Physical setup for connectivity of all components. 2.2. Align communication infrastructure and configuration profiles (e.g., IP addresses). 2.3. Start systems of EMS and aEMP. 2.4. Set the grid state to green. 2.5. Establish communication utilizing the respective HKS. 2.6. Validate network traffic.
3. Steering by aEMP and control by EMS 3.1. Connect electric vehicle to charging station. 3.2. Request charging process via user interface. 3.3. Validate if charging power is as expected regarding grid state and time-variable prices. 3.4. Change grid state to yellow. 3.5. Validate if charging power is as expected regarding grid state and time-variable prices. 3.6. Change grid state to red. 3.7. Validate if charging power is as expected regarding grid state and time-variable prices. 3.8. Stop charging process via user interface.
4. Evaluate success It is worth noting that the prototype implementation and the physical setup can be used with different SMGWs with only minor adjustments such as updated IP addresses. An evaluation run is labeled a success when all steps are performed without errors.
## 4 Case Studies
### Corrective Voltage Control  
#### 4.1.1 Case Study 1  
A total of around 9000 valid EMS snapshots representing actual system operating conditions in East China were collected at 5 min intervals for training and testing RL agents to provide voltage control actions, among which around 6000 snapshots were used for RL agent training and the remainder for testing.  
The first case study was designed to regulate voltage profiles of a provincial transmission power grid (220 kV+) only. The secure voltage level is defined as [0.97, 1.07] p.u. The control performance of the RL agent is shown in Figure 3. As can be observed from the reward accumulation, during the training phase, the RL agent was able to learn voltage control strategies effectively. The trained RL agent performed well during the testing phase with a maximum of two iterations to fix all voltage violations, indicating all voltage magnitudes of buses above 200 kV return to the secure range. This case study verifies the feasibility of the proposed control framework in regulating voltage in an actual power system considering various operating conditions.
# Numerical Modeling Technique of Damage Behavior of
## 4 Numerical Verification
In the infill panel, some inclined cracks that start from the top-left corner and bottom-right corner are well captured by the analysis. As the load increases, the cracks and deformation gradually increase, and expand to the bottom-left corner and top-right corner. Finally, a diagonal compressive failure is formed, and a large number of cracks and larger deformation of masonry blocks are observed, which shows the serious damage that has occurred in the infill panel. However, the crack distribution at the top of columns predicted by the analysis is not clearly observed in the experiment, which may be due to the out-of-plane failure, which dramatically reduces the damage to columns. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Bare Frame**} & \multicolumn{3}{c}{**Infilled Frame**} \\ \cline{2-7}  & **Peak Load** & \begin{tabular}{c} **Peak Dis-** \\ **placement** \\ \end{tabular} & \begin{tabular}{c} **Initial** \\ **Stiffness** \\ \end{tabular} & **Peak Load** & \
\begin{tabular}{c} **Peak Dis-** \\ **placement** \\ \end{tabular} &
\begin{tabular}{c} **Initial** \\ **Stiffness** \\ \end{tabular} \\ \hline Experimental result & 223.72 & 87.10 & 16.80 & 332.88 & 15.99 & 77.18 \\ Numerical result & 227.56 & 106.70 & 22.07 & 340.05 & 16.10 & 90.78 \\ \hline Relative error (\%) & 1.72 & 22.50 & 31.37 & 2.15 & 0.69 & 17.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of experimental and numerical results. (unit: kN and mm). Figure 11: Numerical and experimental damage patterns for the infilled frame. Lateral displacement: (**a**,**d**) 2.4 cm, (**b**,**e**) 4.0 cm, (**c**,**f**) 4.8 cm.
## Materials and Methods
### Recommendation Approaches
#### 2.4.3 Hybrid Approach: Content-Based and Quality-Based Recommendation (CB + Q)  
A hybrid recommender system was developed with the aim of examining the usefulness of learning object quality scores for generating OER recommendations and for enhancing existing content-based OER recommender systems. This system combines the traditional content-based recommendation technique, described in Section 2.4.1, and the quality-based non-personalized recommendation technique, described in Section 2.4.2. The hybrid recommender system calculates the overall score on a 0-1 scale for each candidate OER as follows:  
\[S_{CB+Q}\Big{(}\rm O_{x},\rm O_{y}\Big{)}\ =\ 0.8\times S_{CB}\Big{(}\rm O_{x}, \rm O_{y}\Big{)}+0.2\times S_{Q}\Big{(}\rm O_{y}\Big{)} \tag{4}\]  
where \(\rm O_{x}\) is the OER the user is currently viewing and \(\rm O_{y}\) is the candidate OER, \(S_{CB}\Big{(}\rm O_{x},\rm O_{y}\Big{)}\) is a score calculated according to Equation (1), and \(S_{Q}\Big{(}\rm O_{y}\Big{)}\) is the quality score of \(\rm O_{y}\) on a 0-1 scale.  
The recommendation process followed by the hybrid recommender system is the same as the one followed by the traditional content-based recommender system, except for the way in which scores are calculated. Therefore, the hybrid recommender system, first, calculates a score for each candidate OER using Equation (4), then, sorts all candidate OER according to the calculated scores, and finally generates the recommendation as a sorted list of the N OER with the highest calculated scores.  
This recommendation approach allowed to analyze the specific added value of incorporating learning object quality scores into the OER recommendation process by comparing its performance against the one obtained by the traditional content-based approach. This comparison also made it possible to determine if OER quality scores are useful for enhancing existing recommender systems for OER repositories.
## 1 Introduction  
The fabrication of removable partial dentures (RPDs) is a fundamental aspect of prosthetic dentistry, playing a crucial role in restoring oral function, aesthetics, and patient comfort [1]. The lost-wax casting technique has traditionally been the standard method for fabricating RPD frameworks, due to its reliability and adaptability [2]. However, concerns have been raised about its labor-intensive nature and susceptibility to errors, potentially resulting in a suboptimal fit [3]. Inaccuracies in fit can lead to various clinical complications, such as discomfort, increased plaque accumulation, tissue irritation, and accelerated bone resorption, emphasizing the necessity for advanced fabrication methods [4].  
The advent of digital (CAD/CAM) technologies over the past two decades, both subtractive (milling) and additive (3D printing), offers the potential to revolutionize RPD fabrication by improving precision, reducing production time, and enhancing clinical outcomes [5].  
Fit accuracy significantly impacts RPD success, influencing its retention and stability, as well as the long-term health of the supporting tissues. Consequently, there is growing interest in investigating whether digital fabrication techniques yield superior fit accuracy compared to conventional methods [6]. For decades, conventional methods, such as lost-wax casting, have been the cornerstone of removable partial denture fabrication. However, these techniques are labor intensive and prone to manual errors that affect fit accuracy [5; 6]. On the other hand, digital fabrication methods, including CAD/CAM and 3D printing, have introduced new possibilities for improving precision and reducing production time [5; 6]. Recent studies have demonstrated that these digital methods can offer superior accuracy and consistency in terms of denture fabrication [5; 6].  
While some studies indicate that digital methods may offer a more accurate fit and potentially improve clinical outcomes and patient satisfaction, others suggest that there is no significant difference between the two approaches [7]. This inconsistency highlights the critical need for a comprehensive synthesis of the available data, to gain a deeper understanding of the impact of fabrication techniques on RPD fit accuracy.  
This systematic review and meta-analysis aim to assess the fit accuracy of RPDs fabricated using digital versus conventional techniques. By integrating data from various studies, this review aims to provide greater insight into the advantages and limitations of digital fabrication methods compared to traditional approaches. Additionally, the current review seeks to assess the broader implications of these findings for future research in prosthodontics.
# Solving Non-Permutation Flow Shop Scheduling Problem with Time Couplings
## 2 Problem Formulation
### Mathematical Model
A left-shifted schedule for solution \(\pi\), with \(C_{\max}=27\), is shown as a Gantt chart in Figure 1. Note that \(\pi\) is not optimal, as any change in \(\pi_{3}\) will result in a smaller makespan._  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \(a\) & \(p_{a,1}\) & \(p_{a,2}\) & \(p_{a,3}\) & \(p_{a,4}\) & \(p_{a,5}\) & \(\hat{r}_{a}\) & \(\hat{d}_{a}\) \\ \hline
2 & 1 & 2 & 1 & 2 & 1 & 1 & 2 \\
3 & 2 &Finally, we will define the goal function to be optimized. Let \(C_{\max}(\pi)\) be the completion time of the operation that completes last in the left-shifted schedule corresponding to \(\pi\). From constraints (1)-(3), it follows that \(C_{\max}(\pi)=C_{m,n}\). The goal is to find a solution \(\pi^{*}\), such that  
\[C_{\max}(\pi^{*})=\min_{\pi\in\Pi}C_{\max}(\pi), \tag{4}\]  
where \(\Pi\) is the set of all feasible solutions. The solution \(\pi^{*}\) is called the optimal solution. In Graham notation, we will denote this problem as \(F|\hat{r}_{i},\hat{d}_{i}|C_{\max}\). We will also consider several special cases of the \(F|\hat{r}_{i},\hat{d}_{i}|C_{\max}\) problem. When \(\hat{d}_{i}=0\) (for all \(i\)), we obtain the classic no-idle constraint, i.e., the \(F|\text{no-idle}|C_{\max}\) problem. When \(\hat{d}_{i}=\infty\), we have only the minimum idle time constraint, denoted as \(F|\hat{r}_{i}|C_{\max}\). When \(\hat{r}_{i}=0\), we have only the maximum idle time, denoted by \(F|\hat{d}_{i}|C_{\max}\). Finally, when both \(\hat{r}_{i}=0\) and \(\hat{d}_{i}=\infty\), then we obtain the classic FSSP, i.e., \(F|C_{\max}\). It should be noted that the results from this paper can be applied to any of those special cases, especially for \(F|\hat{d}_{i}|C_{\max}\) and \(F|f_{i}|C_{\max}\), for which more specific approaches are not yet formulated.
# Enhancing Smart Agriculture by Implementing Digital Twins: A Comprehensive Review
## 4 Reviewing DT Applications in Agriculture and Farming Domain  
Regarding the methodology, we opted for our research. Initially, an extended search for literature and bibliography referring to DT in agriculture was conducted in Google Scholar, Scopus, and Web of Science. We used the query item \"digital twin\" incorporating the logical operation of conjunction with the concepts of \"agriculture\" including concept association as \"crop\", \"farm\", \"aquaculture\", \"animal\" and \"smart farming\" to step on cases of DT in subfields of agriculture. Queries as such provided 144 relevant papers from Google Scholar, 153 papers from Web of Science, and 135 papers from Scopus.  
Secondly, we narrowed down all references to 105 papers by omitting duplicates, research papers with almost identical titles, with insufficient or less relevant content, and following our research individual issues related to the main criteria and title as initially stated in the introduction, i.e., crop modelling, precision agriculture, and predictive maintenance in smart farming via the DT concept.  
Diving deeper into the selection criteria, we used keywords such as plants, smart agriculture, agriculture 4.0, IoT, aquaculture, food, horticulture, urban farming, DT models, 3D simulation, AR, VR, and Blockchain ledgers. Totally 89 papers, including those for initially referenced material, were chosen as being deemed relevant to the context of our research.  
Deriving from the central idea of our research based on our study's concepts, eminently disclosed in the preceding paragraphs of the current Section 2, we finally selected a total number of 26 papers fulfilling the criteria to utilize our review as graphically viewed in Figure 8.  
Figure 8: Schematic presentation of the paper selection procedure for reviewing.  
Being familiarized with the relevant literature, we achieved insights into the extent of DT penetration in agriculture, addressing methodologies, designs, data analysis techniques, technologies, and use cases utilized in application domains to gain a deeper understanding of the approaches employed by others regarding the DTs in agriculture. The main idea lines of our study concepts, though, as previously stated, targeting DTs, plants, 3D modelling, simulation, IoT, and ICT gave reason to review the relevant literature. The main concepts of our study are graphically displayed in Figure 9.
## 6 Discussion
### Limitations  
The results in the qualitative damage analyses of CT tests in Figure 6 and OHT tests in Figure 10 show that damage localises in one row of elements. This is a typical shortcoming of local CDM-based material models such as the one used here and presented in Section 2.2. Damage is expected to be more distributed irrespective of the element height. The scaling of the damage saturation strains according to [PERSON]'s crack band scaling in Equation (4) provides a reasonable solution while maintaining computational efficiency. Non-local damage analyses [34] or gradient-based phased field methods [37; 38] offer more mesh-independent solutions resulting in more realistic qualitative representations of damage. Note that the calibration of such non-local methods becomes more challenging and the computational cost is increased.  
If delamination becomes a dominant failure mode, higher fidelity models are needed to capture progressive damage within the interfaces of the laminates. The individual plies with different fibre/grain orientation can be stacked up with embedded cohesive interfaces between them [12]. Similar to the remark about the incorporation of non-local CDM, the computational cost of such higher fidelity models increases significantly, hence limiting their coupling with data-driven calibration methods. With regards to the investigated mechanical tests here, both CT and OHT analyses revealed that delamination can be considered as negligible [6].  
Since the presented study investigates laminates subjected to tensile loads, it remains to be seen whether the simulation of compressive damage in wood veneer laminates can be equally adopted from FRPs. The densification capabilities of wood and its accompanied plastic behaviour in compression may necessitate a more comprehensive calibration process for a compressive damage-plasticity model as opposed to veneers subjected to tensile loads.
## 3 Channel Model
### RF Channel  
The RF channel coefficient \(I_{j}\), \(j\in\{SR_{i},R_{i}D\}\) can be modeled by Nakagami-m distribution, which is closer to the actual fading of the AWACS-to-UAV and UAVs-to-GS channel. The PDF is presented as [19]  
\[f_{I_{j}}(I_{j})=\frac{I_{j}^{m-1}}{\Gamma(m)}\left(\frac{m}{\overline{\gamma _{j}^{RF}}}\right)^{m}\exp\left(-\frac{mI_{j}}{\overline{\gamma_{j}^{RF}}} \right),I_{j}\geq 0,j\in\{SR_{i},R_{i}D\}, \tag{29}\]  
where \(\overline{\gamma_{j}^{RF}}=P_{r,j}\mathrm{G}_{j}/\sigma_{rf}^{2}\) is the average SNR, and \(m\) is the Nakagami-m parameter known as the RF fading factor.  
The path loss \(G_{j}\) in Equations (5) and (6) can be expressed as  
\[G_{j}(dB)=G_{t}+G_{r}-20\log_{10}\left(\frac{4\pi L_{j}}{f_{RF}}\right)-\left( \kappa_{oxy}+\kappa_{w}\right)L_{j},j\in\{SR_{i},R_{i}D\}, \tag{30}\]  
where \(G_{t}\) and \(G_{r}\) are the gains of the transmitting and receiving antennas, \(f_{RF}\) is the RF, \(\kappa_{oxy}\) and \(\kappa_{w}\) are the attenuation coefficients due to the oxygen and weather condition, respectively. \(\kappa_{w}=KM_{CLWC}\), where \(K\) is specific attenuation coefficient. The CDF of the RF link is given by  
\[F_{\gamma_{j}^{RF}}(\gamma_{th_{RF}})=\frac{1}{\Gamma(m)}\Gamma\left(m,\frac{m \gamma_{th_{RF}}}{\overline{\gamma_{j}^{RF}}}\right)=\frac{1}{\Gamma(m)} \Gamma\left(m,\frac{m\sigma_{rf}^{2}\gamma_{th_{RF}}}{P_{r,j}G_{j}}\right),j=SR_{ i},R_{i}D, \tag{31}\]  
where \(\Gamma(\alpha,x)\) is the incomplete gamma function.