esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The Principle of Generating Vector Optical Beams with Polarization Varying along a Longitudinal Direction in Free Space  
An arbitrary vector optical beam can be divided into two components with different polarization and complex amplitude [18]. Using VOFs with transverse distribution of polarization as examples, the VOF can be generally expressed as  
\[\mathbf{E}(r,\phi)=E_{1}(r,\phi)\exp[i\delta_{1}(r,\phi)]\mathbf{e}_{1}+E_{2}(r,\phi)\exp[i\delta_{2}(r,\phi)]\mathbf{e}_{2} \tag{1}\]  
where \(r\) and \(\phi\) are the radial and azimuthal coordinates, \(E_{1}\) and \(E_{2}\) are the amplitude distributions of these two beams, \(\delta_{1}\) and \(\delta_{2}\) are the phase distributions, and \(\mathbf{e}_{1}\) and \(\mathbf{e}_{2}\) represent orthogonal polarization base vectors. The complex spatial distribution of polarization in VOF, from another perspective, originates from the superposition of two orthogonally polarized beams with different complex amplitude distributions. The difference may occur in the amplitude, phase, or both. For example, assuming two superposed beams are carrying left- and right-hand circular polarizations and their amplitudes are the same, but the phase distributions are different \(\delta_{1}=-\delta_{2}=\phi\). In this configuration, the superposition of these two beams leads to the generation of localized linear-polarized VOF. If the phase distributions are the same but the amplitudes of the two beams are \(E_{1}=\cos\phi\) and \(E_{2}=\sin\phi\), localized linear-polarized VOF can also be generated when the polarization of the two beams are orthogonally linear-polarized. Therefore, to generate the vector optical beam  
Figure 1: Schematic diagram of the longitudinal variation of polarization in free space. with varying polarization along the longitudinal direction in free space, there are two strategies available: constructing the longitudinally varying phase difference and amplitude difference, respectively.
## 4 Creation of Models and Extraction of Geometrical Components
### Examples of Graph Representation  
Once geometric components have been described, it is possible to construct a graph for each piece of furniture.  
Let \(F^{*}\) be a piece of furniture (e.g., a table); therefore, as stated in Equation (1), the graph is described as:  
\[F^{*}=(V^{*},E^{*})\]  
where \(V^{*}\) has five vertices and \(E^{*}\) four edges (i.e., \(n_{v}^{*}=5\) and \(n_{e}^{*}=4\)). The five vertices correspond to: one vertex for the horizontal surface of the table and one for each of the four legs. The main vertex is the horizontal plane, and an edge will be added whenever two geometrical components are adjacent, so in this particular case, the edges correspond to the union between each leg and the horizontal plane.  
Figure 10a presents the graph corresponding to a dinning table. Similarly, Figure 10b shows the graph of a chair. It can be seen that the chair's graph has one more vertex than the table's graph. This extra vertex is a vertical component corresponding to the backrest of the chair.  
As mentioned earlier, a graph serves as a complete model for each piece of furniture because it contains all its geometrical components. However, as described in Section 3.3, the robot cannot view all the components of a given piece of furniture because of the perspective. Therefore, subgraphs are created in order to compare robot perception with models.  
To generate a small set of sub-graphs corresponding to partial views, several points of view have been grouped into four quadrants. These are: two subgraphs for the front left and right views and two more for the back view left and right (Figure 11). However, due to symmetry and occlusions, the set of subgraphs can be reduced. For example, in the case of a dining table, there is only one graph without sub-graphs because its four legs can be seen from many viewpoints.  
Consequently, graphs require also to specify which planes are on opposite sides (if there are any), because this information is important to specify which components are visible from every view.  
Figure 11: Visualizing the different points of view used to generate partial views.  
Figure 10: Example of complete graph models for: (**a**) a dinning table and (**b**) a chair; different shapes represent different types of geometric components.  
The visibility of a given plane is encoded at the vertex. For example, for a chest of drawers, it is not possible to see the front and the back at the same time.  
Figure 12 shows an example of a graph model and some subgraphs for a couch graph model. It can be seen from Figure 12a that the small rectangles to the side of the nodes indicate the opposite nodes. The sub-graphs (Figure 12b,c) represent two sub-graphs of the left and right frontal views, respectively. The reduction of the number of vertex of the graph is clear. Specifically, the backrest and the front nodes are shown, whereas the rear node is not presented because it is not visible for the robot from a frontal view of the furniture. Thus, a subgraph avoids comparing components that are not visible from a given viewpoint.
## 3 Related Work
Comparing this FedARX approach to conventional machine learning models, it achieves accurate and reliable heart rate prediction. A meta-heuristic method called artificial bee colony (ABC) optimization was suggested by the authors in [29] as a way to efficiently manage and optimize the calculation of offloading for IoT-based applications. Their method effectively controls the computing workload for IoT applications with limited resources. The authors in [30] suggested a fast-convergent technique that accomplishes intelligent selection of each device at every round of training the model in order to maximize the convergence speed of federated learning. To increase the convergence rate, their approach employs precise and efficient approximation for the transmission of a nearly optimum distribution of device selection. Other approaches, such as a hybrid technique combining a linear discriminant analysis with modified ant lion optimization for classification [31], a gradient boosting decision tree with fuzzy logic algorithm [32], a hybrid of modified scalp swarm optimization and adaptive neuro-fuzzy inference system [33], and a multi-objective function using meta-heuristics [34], were also presented in the literature as strategies for predicting heart disease. The use of a hybrid classifier and a modified neural network with a deep learning focus was presented in [35; 36; 37; 38] as a method for monitoring and predicting cardiac problems. Methods such as the Boltzmann-based model for higher order [39], modified hybrid method using classifiers and optimizers [40], two-stage-based localization of the classifiers [41], and hybrid classifier based on naive Bayes and random forest [42] were also anticipated to improve classification accuracy with less error.
## Main Design of DERM
### Geographic Routing for EO-DRA
#### 4.2.2 Void Handling for EO-DRA  
Greedy forwarding for EO-DRA may also encounter the communications void problem that a node (termed _void node_) fails to locate a next-hop forwarder closer to the destination region than it. The long-known perimeter routing [43] has been proved to be an efficient way to bypass the void area. However, it cannot be directly applied in DERM. We make the following modifications to adapt it for DERM: (1) the physical destination node is undetermined in advance unlike in traditional geo-routing. Thus, we define the _virtual destination_ of a node as the point closest to the node on the sink's travel  
Figure 3: An example of greedy forwarding for EO-DRA.  
path within the delay constraint. When a packet enters the void handling mode, it will be forwarded along the faces intersected by the line connecting the void node and its virtual destination using the right-hand rule; (2) we set the traversal direction (clockwise or counterclockwise) to be the same as the direction in which the void node, the current sink location, and the virtual destination can be visited orderly. In this way, the destination node will be closer to the sink, and the waiting time for the final delivery will be reduced.  
As shown in Figure 4, a packet gets stuck at void node \(u\). The shortest distance from \(u\) to the travel path \(L\) is \(d_{uL}\). \(u\) cannot find a one-hop neighbor in the greedy forwarding region where the nodes' distances to \(L\) are less than \(d_{uL}\). Then, the stuck packet enters the void handling mode. Its virtual destination is \(D\) and the sink location is \(A\). Points \(u\), \(A\), and \(D\) are sequentially visited in the clockwise direction. To find a path towards \(L\) clockwise, the packet traverses the faces crossed by the line \(\overline{uD}\) using the counterclockwise direction of the right-hand rule. It will return to the greedy forwarding mode at node \(N_{3}\).  
It can be observed that the greedy destination region in EO-DRA is larger compared to the traditional geo-routing, which means that a packet suffers the communications void with a lower probability and can return to the greedy mode more easily.  
We prove the efficacy of the modified perimeter routing for EO-DRA.  
Theorem 4.1: _The modified perimeter routing for EO-DRA guarantees that a route from the void node to the destination region can always be found if it does exist._  
Proof: Assume that there exists a route from a void node \(u\) to the destination node \(N_{D}\), \(Path(u,N_{D})\). We add a virtual link connecting \(N_{D}\) and the virtual destination \(D\) named \(l(N_{D},D)\), and thus a route from \(u\) to \(D\) named \(Path(u,D)\) is formed. We have  
\[Path(u,D)=Path(u,N_{D})\cup l\left(N_{D},D\right). \tag{11}\]  
Figure 4: An example of void handling for EO-DRA.  
Based on the principle of the right-hand rule, travelling along the faces intersected by \(\overline{uD}\) can find \(Path(u,D)\). \(Path(u,N_{D})\) is a part of \(Path(u,D)\), and thus \(N_{D}\) will be reached when travelling towards \(D\). Figure 4 also illustrates this proof.
# Timed-SAS: Modeling and Analyzing the Time Behaviors of Self-Adaptive Software under Uncertainty
## 4 Implementation of the Timed-SAS Approach
### Definition on Time Behaviors
#### 4.1.3 Time Evaluation Metrics  
The time evaluation metrics, i.e., _Self-adaptive Adjusting Time_ and _Self-adaptive Steady Time_, are defined to evaluate the performance and response-time of the self-adaptation logic.  
**Definition 3**.: (Self-adaptive Adjusting Time \(T_{\text{Adjust}}\)). _The time evaluation metric of Self-adaptive Adjusting Time refers to the minimum time required for the SAS systems to recover from beyond the limits to within the limits. It is represented as \(T_{\text{Adjust}}\), as shown in Figure 7._  
**Definition 4**.: (Self-adaptive Steady Time \(T_{\text{Steady}}\)). _The time evaluation metric of Self-adaptive Steady Time refers to the minimum time required for the SAS systems to recover to and maintain  
Figure 6: The uncertainty time behavior of Delay.  
Figure 7: Self-adaptive Adjusting Time and Self-adaptive Steady Time.  
at the final value of \(\pm 5\%\) after the limits being violated. It is represented as \(T_{\text{Standy}}\), as shown in Figure 7._  
The two metrics are different, as \(T_{\text{Adjust}}\) represents the response time to recover to safe states, while \(T_{\text{Steady}}\) depicts the response time to recover to steady states.  
In the SUIS example of Section 2.1, the monitoring variable is the CPU utilization. At time \(t_{1}\), the variable \(Util\) is detected exceeding the upper limit, and the subsequent self-adaptation strategies are triggered. Subsequently, the variable \(Util\) falls back to normal range at time \(t_{2}\), and returns to steady state at time \(t_{3}\). And the time of (\(t_{2}\)-\(t_{1}\)) is called Self-adaptive Adjusting Time, while the time of (\(t_{3}\)-\(t_{1}\)) is called Self-adaptive Steady Time. The two metrics can be used to evaluate the performance and response-time of the self-adaptation strategies.
# A Vision Servo System for Automated Harvest of Sweet Pepper in Korean Greenhouse Environment
## 3 Results and Discussion
### The Result of Harvest Experiment
The thickness and length of the peduncle were calculated on the basis of the end-effect's contact point of cutting, and the vertical angle of the peduncle to the system was set up at 0\({}^{\circ}\). The thickness of the peduncle was classified by its diameter of 0-10 mm, 10-15 mm, and more than 15 mm, thus examining their rate of successful harvest. As for the diameter, the portion that has contact with the  
Figure 21: Field experiment results of sweet pepper’s position extraction algorithm (vertical axis). Figure 22: Field experiment results of distance compute algorithm. end-effector's cutting point was measured. The most frequent diameter of the sweet pepper's peduncle appeared in the scope of 10-15 mm. The number of experiments was 30 times, and 16 of them were successfully harvested. The scope of 10-15 mm was most successful with 60.0% of 9 successes in 15 trials. The success rate of the scope of 0-10 mm was 58.3% of 7 successes in 12 trials. In the scope of 15 mm and above, it was impossible to harvest. The failure of harvest in the scope seems to be because the diameter of peduncle was bigger than the entrance of the cutting portion. The rate of successful harvest according to the length of the peduncle is presented in Table 3. The rate was examined by dividing the length of the peduncle into 0-3 mm, 3-6 mm, 6-9 mm, and more than 9 mm. Most of the harvest of sweet peppers where the length of the peduncle was less than 6 mm was unsuccessful. It was seemingly because the guide installed in front of the end-effector was too thick. The success rate of harvesting according to diameter and length of peduncle was analyzed and the overall success rate was 53.3%. The approximation rate of the end-effector to the peduncles of sweet pepper was 86.7%. When the same harvesting procedure was repeated four times using the sweet pepper which failed to harvest, the maximum harvesting success rate of about 60 sweet peppers was 70%.
## 3 Research Model and Hypotheses
### Organizational Factors  
The adoption of cloud computing requires the integration of resources and a supportive environment [40]. Top management involvement in the cloud computing adoption decision and implementation process is essential as it guarantees that sufficient resources will be allocated to support implementation [5] and the value of adopting will be communicated throughout the organization [6]. Thus, we propose:  
**Hypothesis 7 (H7).**_Top management support is positively related to the adoption of cloud computing for HPC._  
Adequate resources refer to the resources needed for the adoption of cloud computing. Previous studies show that organizations with the necessary resources are more likely to adopt cloud computing [41]. Cloud computing adoption for HPC is a large project that requires top management commitment, ample time, money, competent human resources, and technological competencies [9; 42]. Thus:  
**Hypothesis 8 (H8).**_Adequate resources are positively related to the adoption of cloud computing for HPC._  
Exstant literature suggests that cloud computing provides organizations with direct and indirect benefits. For instance, cloud computing enables organizations to gain access to hardware, software, or other ICT infrastructure not available in their own data centers [8]. Such advantages are termed direct benefits. In addition to these direct benefits, organizations are also motivated by the indirect benefits, such as improving the organizational image, competitive advantage, or relationships with customers or business partners [43]. We posit:  
**Hypothesis 9 (H9).**_Indirect benefits are positively related to the adoption of cloud computing for HPC._  
Cloud computing adoption allows organizations to reduce upfront capital expenditure and operational costs, thereby enabling them to gain a cost reduction advantage [41; 44]. Studies show that cost reduction is positively associated with organizations' perceptions of the ease of use and convenience of adopting cloud computing, thus increasing adoption intention [33]. HPC infrastructure has extremely high operating costs compared to more general IT [17]. Thus, we posit:  
**Hypothesis 10 (H10).**_Cost reduction is positively related to the adoption of cloud computing for HPC._
## 3 Materials and Methods
### Feature Selection
#### 3.3.3 Sequential Forward Selection (SFS)  
The SFS algorithm incrementally adds features to a model, aiming to enhance the performance index while minimizing the count of chosen features [25]. The performance index, often based on metrics such as classification accuracy or precision, guides the feature selection process, ensuring the best features are chosen to optimize the model. SFS initiates the process with an empty feature set and progressively incorporates one feature at each step, selecting the feature that maximizes the objective function. This process continues iteratively, with each new feature being evaluated for its contribution to the performance index until the predetermined number of features has been selected. However, SFS is considered a naive approach because it does not account for dependencies between features, potentially leading to suboptimal feature subsets.
## 8 Technical Challenges and Potential Solutions
### Column-to-Beam Intra-Modular Joints Failure Issues  
The joint's stiffness controls the lateral performance by properly transferring the load to the columns [113]. Researchers investigated the endplate, the fastened fin plate, the welded connections, and the stiffened endplate [6; 179]. Due to the low moment-resisting capacity of the fin-plate and endplate connections, they are unsuitable for high-rise PFISBs [180]. Moreover, they necessitate extra-strengthening [6; 181]. Additionally, such joints result in a significant slab deformation and pry failure in the event of an accident [122]. While welded connections have the maximum capacity, they are adopted in high-rise PFISBs [66; 67]. However, they cause a brittle failure and necessitate diagonal stiffeners to prevent weld fractures. After destructive seismic events, the building structure remains out-of-plumb. Therefore, recentering is a significant concern for the separability. According to some studies, hybrid industrial systems with SCLVD dampers on welded internal joints exhibit a self-centering efficacy in high-rise PFISBs [115]. The self-centering efficiency of the joints with haunch braces on the multi-story frame was also observed [65]. However, research on the internal joints is insufficient, resulting in direct and indirect losses during severe seismic occurrences. The authors suggest using the smart connections developed by FREEDAM-PLUS [177] capable of self-centering the hysteretic dampers, lead extrusion dampers, buckling restrained bracing, friction dampers, dampers, and dampers based on shape memory alloys. Moreover, the authors suggest that plug-and-play joints [178] are an efficient system that can also be adapted. Using truss beams can improve the seismic performance of the system and joint. However, more research is required to manufacture damage-free and demountable column-beam intra-modular joints after being subjected to lateral forces.
## 3 Results
### Model Evaluation  
The confusion matrix, which comprehensively reflects the performance of the classifiers, can derive many evaluation indicators. Here, the calculated evaluation indicators, including accuracy, precision, recall, and F1-score, were used to evaluate classification performance for the binary classifier. The SVM model was estimated by 4-fold cross-validation, and the indicators were computed by averaging the folds. Accuracy represents the ratio of correct samples to the total samples without considering the positive and negative. Recall refers to the ratio of the correctly classified positive samples to the total true positive samples, and precision refers to the ratio of correctly classified positive samples to all classified positive samples. The F1-score indicator is proposed based on precision and recall to evaluate the indicators as a whole. The F1-score can be used to comprehensively consider the pros and cons of the classification models.  
Table 4 shows the results of model evaluation indicators for machine learning classifiers. Figure 5 shows the histograms of the evaluation indicators (accuracy and F1-score) of the training set and test set from machine learning classification models. According to the summaries of the model evaluation indicators, GA-SVM shows both higher accuracy and F1-score than other machine learning methods. Accuracy can reflect the classification correctness of the global results of the model. The F1-score can reflect the weighted average between precision and recall, and the results show that the GA-SVM classifier can be  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ANNs** & **Training Accuracy** & **Test Accuracy** \\ \hline GA-BP & 92.14\% & 86.32\% \\ ELM & 95.00\% & 89.47\% \\ PNN & 100.00\% & 71.58\% \\ LSTM & 100.00\% & 96.84\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification accuracy of training sets and test sets from ANN models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Optimizations** & **Training Accuracy** & **Test Accuracy** & **Best Gamma** & **Best c** \\ \hline Grid Search & 100.00\% & 97.89\% & 0.0039 & 48.50 \\ Least Square & 94.29\% & 96.84\% & 250.00 & 20.00 \\ Cuckoo Search & 100.00\% & 97.89\% & 0.56 & 25.51 \\ Genetic Algorithm & 100.00\% & 98.95\% & 0.33 & 66.43 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification accuracy of training sets and test sets from SVM models.  
considered as an optimal model for drum filter-circulating pump linkage technology in a variable-flow RAS because the model indicators satisfied the criteria.