esa-sceva/satcom-chunk-collection huggingface dataset:
# Scheduling the Exchange of Context Information for Time-Triggered Adaptive Systems
## 5 Results and Discussion  
### Evaluation Methodology and Performance Metrics  
This study addresses the limitations of static resource allocation in time-triggered systems, specifically their inflexibility in adapting to dynamic scenarios such as environmental changes, failures, and varying power demands. We introduce an advanced metascheduling method and a specialized algorithm for time-triggered multi-processor system-on-chip (MPSoC) environments, aimed at optimizing communication efficiency through precomputed adaptation schedules. This approach is expected to reduce overhead and enhance system performance.  
The evaluation of this research tests the effectiveness and efficiency of the proposed metascheduling algorithm in a time-triggered MPSoC context. Key areas of focus include  
* **Communication Efficiency:** The evaluation aims to validate our algorithm's ability to optimize context information exchange, assessing whether its selective communication strategy, aligned with decision points, reduces communication cost.
* **Comparative Analysis:** By comparing the new algorithm with existing methods, the evaluation highlights the advancements in our research, specifically in reducing communication costs and improving overall system performance.
## 2 Methodology
### Multi-Objective Constrained Optimization
#### 2.1.1 NSGA-II for Multi-Objective Optimization  
In the present work, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed to solve the multi-objective constrained optimization problem. NSGA-II [1] is widely used for its ability to maintain diversity in the population while efficiently converging to the Pareto front. The key components of NSGA-II are non-dominated sorting and a crowding distance. Non-dominated sorting categorizes the population into several fronts based on Pareto dominance, where each front consists of solutions that are not dominated by any other in the population. The crowding distance helps to maintain a diverse set of solutions by calculating the density of solutions around each individual, ensuring that the population does not converge prematurely to a narrow region of the search space.  
At the start of the optimization, a population of \(\mu\) individuals is initialized randomly. Each individual represents a potential solution in the design space. The objective functions and constraints are evaluated for each individual. If any individual violates the constraints, it is discarded, ensuring that feasible solutions are prioritized during selection.  
NSGA-II uses elitism by combining the parent and offspring populations from each generation, allowing the best solutions from both to survive. The population is sorted based on non-dominance, and the best individuals are selected. The algorithm also incorporates genetic operations, including recombination and mutation, to create new solutions in the next generation.  
During optimization, the offspring undergo recombination and mutation. Feasibility is ensured by discarding infeasible offspring and replacing them with new individuals if necessary. The selection of parents for the next generation is based on non-dominated sorting and the crowding distance.
# Optimizing Physics-Informed Neural Network in Dynamic System Simulation and Learning of Parameters
## 4 Error Metrics  
This section discusses the error metrics used for data-driven simulations, examining when and why specific metrics should be employed. These error metrics provide valuable insights into the accuracy and performance of data-driven models, such as physics-informed neural networks (PINNs). By evaluating the models based on these metrics, researchers can determine the quality of their predictions and compare the effectiveness of different approaches. In the context of learning the time-varying parameters for dynamical systems, it is important to consider the specific characteristics of the problem and the goals of the analysis when selecting the appropriate error metrics. For instance, if \(\theta\) symbolizes the actual data and \(\hat{\theta}\) represents the data predicted by the model, these error metrics are applied in our data-driven simulations:  
1. Mean absolute error (MAE): a significant advantage of using the mean absolute error (MAE) is its relevance to quantile regression. MAE represents an average of absolute differences between predicted and actual values. The formula for mean absolute error is given by: \[MAE=\frac{1}{N}\sum_{j=1}^{N}\left|\theta_{j}-\hat{\theta}_{j}\right|\]
2. Root mean squared error (RMSE): RMSE is in the same units as the predicted variable, providing a straightforward interpretation of the model's prediction error magnitude. The formula for root mean squared error is given by: \[RMSE=\sqrt{\frac{1}{N}\sum_{j=1}^{N}(\theta_{j}-\hat{\theta})^{2}}\]
3. Mean squared error (MSE): mean squared error (MSE) measures the amount of error in the models. It assesses the average squared difference between the observed and predicted values. \[MSE=\frac{1}{N}\sum_{j=1}^{N}(\theta_{j}-\hat{\theta})^{2}\]
# Mitigation of Gravity Segregation by Foam to Enhance Sweep Efficiency
## 4 Gravity Segregation in Layered Model
### Comparison to Model with Average Properties  
There is no rigorous mathematical proof for precise prediction of segregation length with foam injection in heterogeneous reservoirs. Following [19] work, we approximately calculate the segregation length in two-layer models by adjusting \(K_{\varphi}\) to represent the effective vertical permeability. The harmonic average permeability for the layered reservoirs is used:  
\[\overline{K_{\varphi,ave}}=\frac{h_{t}}{\sum_{i=1}^{n}\frac{\Delta h_{i}}{K_{ \varphi,i}}}, \tag{7}\]  
where \(n\) is the number of layers in the reservoir, \(h_{t}\) is the total thickness, and \(h_{i}\) and \(K_{i}\) are the thickness and vertical permeability of layer \(i\), respectively.  
After obtaining the \(\overline{K_{\varphi}}\), we directly linearly interpolate the foam parameters based on Table 1. Then we can approximate the segregation length in these layered reservoirs by running simulations. The results are shown in Table 3. With a larger thickness ratio, i.e., the average permeability is close to that of the top layer, the model possessing average properties demonstrates the capability to predict the distance of complete segregation with precision in scenarios where the layer with higher permeability is positioned at the top. However, if the thickness of the two layers is not significantly different, the approximation overestimates or underestimates \(L_{g}\) depending on which permeability is on the top. When heterogeneity is more severe, the average properties cannot be used to predict the steady-state segregation length accurately.  
Figure 13: Sweep efficiency with different injection strategies in layered reservoirs. The light green and light red regions represent the sweep efficiency of homogeneous porous media.
## 3 Method
### Feature Selection Using Wolf Search Algorithm  
WSA was performed using Weka in this study. The attribute evaluator adopted for the experiment was a correlation-based feature selection (CFS) algorithm. CFS finds a subset of features highly correlated with the target class and lowly correlated with the other features [81; 83]. Wolf search algorithm was performed to find the best feature set based on this CFS evaluator. Both the number of iterations and the population size of WSA were set to 100. The start points for search were set to 1 and 10.
# DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation
## 6 Results and Analysis
### RQ3: Ablation Studies
In addition, the DACSR-C model removed the extraction net and directly concatenated the representations of sequences and items from two sequence encoders. It obtained worse performance than the DACSR model, showing the importance of the extraction net. On the MI-1m dataset, the \(C_{KL}\)@20 of the DACSR model is 0.7262, which is slightly better than the 0.7433 of the DACSR-C model. But the recommendation accuracy of the DACSR model is higher than the DACSR-C model (e.g., 0.1338 vs. 0.1257 in terms of MRR@20). On the Tmall dataset, our DACSR model also obtains better performances in terms of accuracy, and close performance of calibration. The extraction net takes the concatenation of sequence/item representations as inputs, and provides more suitable representations for the two objectives.
# Educational 5G Edge Computing: Framework and Experimental Study
## 4 Measurements and Experiments
### Measurement Study of Campus Service  
Figure 2 refers to a hypothetical campus as a case study. It shows that in a campus, different buildings and areas are given specific and distinguished functionalities with different requirements regarding communications and computing. For example, in a VR classroom, the real-time computing requirement will be intensive. In a gym, only delay-tolerant trajectory services are required. This characteristic should be supported and captured by the educational network, which further supports application-level service and resource management.  
General research shows that users on campus have strong temporal and spatial regularity, but this has not been quantitatively analyzed in research on smart educational applications. The research team conducted long-term data research on a well-known university in western China (with a population of no less than 30,000 people on a single campus). The data dimensions included, but were not limited to, the spatio-temporal activities of personnel, the curriculum arrangement of the university, the distribution of operator infrastructure in the university, operator data statistics, the spatio-temporal characteristics of campus users' traffic, the characteristics of campus users' uplink and downlink data, etc. [26] The heatmaps of the number of connected users are depicted in Figure 4, which reflects the temporal and spatial differences. In addition, the quantitative change of throughput and user connections in 24 h are shown in Figures 5-7. Some interesting characteristics of network servers in campus can be observed:* _Spatial correlation:_ the distribution of service is correlated with geographic location, where most of the connections are located in functional areas, such as the classroom, lab, and library.
* _Temporal correlation:_ the network activity is correlated with the school timetable, where the network connection and servers are concentrated at duty time on weekdays and afternoons on the weekend.
* _Combined correlation:_ The distribution of network services is affected by the combined correlation of both spatial and temporal elements. For instance, the peak of network traffic is found in the classroom or laboratory at duty time on weekdays and in the lakeside park on the weekend afternoon.  
In summary, there is indeed a significant spatio-temporal correlation in network services for mobile users in campus, which provides heuristic information for a series of network optimizations, such as network configuration, network slicing, resource scheduling, and channel allocation, in a flexible and efficient manner.
# Extensive Experiments on Opportunistic Routing in Wireless Sensor Networks
## 3 Proposal: ODYSSE
### Packet Erasure Codes in ODYSSE  
This section describes how ODYSSE is able to combat wireless transmissions losses.  
Because ODYSSE operates at the routing layer, our focus is on the packet level rather than the bit level. Thus, we consider, as in RFC 5170 [36], a packet erasure channel, that is a communication path where packets are either dropped or received. When a packet is received, it is assumed that it is not corrupted. Consequently, ODYSSE considers Erasure codes (EC) [38], which are based on data redundancy. Indeed, instead of sending \(k\) original packets, the encoder adds \(m\) redundant packets to recover the lost packets. When the number of the received packets is sufficiently high, the decoder decodes them and retrieves the original packets. Different coding methods exist. In our work, we rely on the LDPC (Low-density parity-check) codes [37]. The latter is a block code defined by a  
Figure 2: Illustrative example for data routing in ODYSSE. The node U is a source node sending a packet to node V. The node U has as neighbors nodes N\({}_{1}\) and N\({}_{2}\). Node N\({}_{2}\) has as neighbors nodes U and N\({}_{3}\), while N\({}_{3}\) has as neighbors nodes N\({}_{2}\) and V. Node V has as neighbors node \(N_{3}\). Messages are denoted: “B”: Beacon, “R”: Reply, “D”: “Data”, “SLEEP”: sleep period, “ACTIVE”: active period.  
sparse parity-check matrix (majority of entries are zero), and is known to provide excellent decoding performances [45].
## 2 Results
### Identification of Allium Species Non-Volatile Metabolites via UPLC/PDA/qTOF-MS
#### 2.3.2 Identification of Flavonoids  
Medicinal plants rich in polyphenols can retard the oxidative degradation of lipids and improve the quality and nutritional value of food [14]. Garlic and particularly onion are considered one of the richest sources of phenolic compounds i.e., flavonoids [15]. Photodiode array inspection of peaks assisted in capturing an overview of their main flavonoid subclass. Flavonol glycosides constituted the most abundant class mostly enriched in _A. cepa_ red cv. extracts as revealed from their UV max near 270 nm and a second maximum (345-360 nm). MS/MS analysis led to the identification of the aglycone (Ag) moiety, where the sugar type in \(O\)-glycos could be determined from the respective loss of 162, 146, and 132 amu corresponding to hexose, deoxyhexose, and pentose [16]. MS spectral interpretation led to the identification of four quercetin (\(m/z\) 301) conjugates including quercetin-diglucoside (\(m/z\) 625.1405, C\({}_{27}\)H\({}_{29}\)O\({}_{17}\), [M - H]\({}^{-}\) peaks L16/18, (Figure S5A), quercetin glucoside (\(m/z\) 463.0883, C\({}_{21}\)H\({}_{19}\)O\({}_{12}\), [M - H]\({}^{-}\) peak L21, Figure S5B), and quercetin rhamn (\(m/z\) 447.0933, [M - H]\({}^{-}\), C\({}_{21}\)H\({}_{9}\)O\({}_{11}\), peak L23). Similarly, kaempfer aglycone (\(m/z\) 285) was detected in kaempfer-\(O\)-glucose (astragalain) (\(m/z\) 447.0933, C\({}_{21}\)H\({}_{19}\)O\({}_{11}\), [M - H]\({}^{-}\) L22, Supplement Figure S5C) and isorhamnetin, a methylated derivative of quercetin, was identified as an aglycone in peak L38 with [M - H]\({}^{-}\) of 315.051. Other characteristic fragments of quercetin and kaempfer are those at \(m/z\) 151 and 179 corresponding respectively to the A\({}^{-}\) ring fragment released after RDA fission (Figure S5D) and confirming the agylcone structure in the respective flavonoid peaks. In contrast, isorhamnetin, exhibited the loss of methyl from (\(-\)15 Da), (Figure S5E). Both flavonoid peaks L22 and L23 are for the first time reported in _Allium_. It should be noted that no peaks for anthocyanins in either negative or positive modes were detected in _A. cepa_ red cv. extract which could be attributed to the level of detection or the analysis protocol.
# Comprehensive Review of UAV Detection, Security, and Communication Advancements to Prevent Threats
## 4 Communication Methods and Architecture  
Today, one may see the variety of several drones being opted for commercial and domestic use. This is because they are cost effective and are controlled remotely from anywhere. In military operations, mostly the micro or miniature-type UAVs are used, but there are some major limitations in terms of size and weight. In Figure 3, tier II and III UAVs have several requirements such as being able to deploy sensors, and having a global positioning system (GPS), communication module, and efficient batteries. This is illustrated further in Figure 4. Although there are huge advancements being noticed in the field of drones or unmanned aerial vehicles (UAVs), at the same time, there are some limitations associated with the software and hardware support.  
Figure 4: Components of an unmanned aerial vehicle.  
Figure 3: Classification of UAVs in terms of altitude.  
The UAVs which are proposed for military operations have some advanced sensors which are not accessible to ordinary people. These sensors enable drones to carry additional payloads. After studying different UAVs, one may subdivide UAVs into their five major components as follows:  
* Drone airframe;
* Onboard controller;
* Payload capability;
* Communication system;
* Efficient batteries.  
Discussing the airframe of a UAV, one must consider aspects such as aerodynamics, a lightweight structure, and stability. These can be some of the constraints to designing the UAV airframe. Moreover, the onboard controller is the main thing that maneuvers the drone. Therefore, it must be equipped with all essential sensors such as the accelerometer, gyro sensor, pressure sensor, GPS, and camera. While designing the drone, one should consider the factor of payload variation. In this way, the drone may carry some nominal amount of weight from one place to another [25]. Another important component is the communication system where the drone requires some communication equipment such as a sitcom, modem, or radio channel-based equipment. This will ensure the communication and control between a UAV and the ground control room (GCR). Lastly, the UAV must have a reliable power source that can help it to fly for a specific time to fulfil the task. Mostly, lithium batteries are considered as the main power source for these UAVs [26].
## 4 Indoor AR Positioning Techniques
### Conversion of Local Coordinate Values to a Global Coordinate System  
Although high-precision spatial tracking (on the order of centimeters) is necessary for indoor navigation with camera-based systems, the standard approach initializes the coordinate space based on the camera's initial position and orientation at launch, with the \(z\)-axis aligned with the camera's orientation. This local coordinate system differs from the global reference frame used to register objects within indoor space. To leverage these existing objects in navigation tasks, it becomes crucial to convert the camera's position from the local frame to the global reference system. This subsection outlines the procedure for performing such a conversion.  
Figure 3 illustrates converting the local coordinate values to a global coordinate system for indoor AR positioning by using a smartphone (Figure 1, A5). The position (global coordinates) and direction (global direction, \(0^{\circ}\) points north) of the marker board \(MB_{i}\) in the real world (e.g., the floor map signboard) are defined as \((la_{i},lo_{i})\) and \(\theta_{i}\). The position (local coordinates) of the smartphone camera \(Cam_{j}\) is defined as \(\left(la_{j},lo_{j}\right)\), and the distance and direction from \(Cam_{j}\) to \(MB_{i}\) are defined as \(d_{i}\) and \(\Psi_{i}\), respectively. The relationship between these variables is defined by Equations (1) to (4). \(Local2 Global\) is a function that converts the local 2D coordinate values in meters to global 2D coordinate values (longitude and latitude) and \(Rotate_{m}\) is a function that rotates the 2D coordinate values around the origin by \(m\).  
\[\Psi_{ij} =\left(\Phi_{j}-\pi\right)-\theta_{i} \tag{1}\] \[\left(X_{L},Y_{L}\right) =Local2 Global\left(d_{j},0\right)\] (2) \[\left(X_{L}^{\prime},Y_{L}^{\prime}\right) =Rotate_{(\theta_{i}+\Psi_{ij})}(X_{L},Y_{L})\] (3) \[\left(la_{j},lo_{j}\right) =\left(la_{i},lo_{i}\right)+\left(X_{L}^{\prime},Y_{L}^{\prime}\right) \tag{4}\]
## 2 The Study Area
### Unmanned Aerial Flights--UAV  
UAV systems permit different payloads to be transported, among which the most common are RGB photographic cameras. In this way, images with a geometric range of altitudes between 20 m and 120 m (the legal limits in Spain) can be obtained from a UAV. For the use of convergent photogrammetric methods these are simple aircraft equipped with an economical RGB camera, with which three-dimensional models can be obtained from dense point clouds [48; 49; 50].  
Through the use of a UAV over the Corral del Veleta rock glacier, the aim was to obtain a detailed cartography on a 1:200 scale. To do so, an advanced DJI Phantom 4 multicopter aircraft was used equipped with a 20 Mpix RGB camera with a sensor size of 2.54 cm \(\times\) 2.54 cm, a fixed focus of 8.8 mm, and also a GNSS system of decimetric precision. The UAV photographic camera was calibrated before the flight, which was programmed for a constant altitude of 70 m measured from the point of take-off. The longitudinal and transversal overlaps were of 80%, in which the overlap of the images is calculated automatically for the altitude of the flight established according to the parameters of the camera. Another two parameters were used: the flight \"in grid\", in which flights are the result of intersecting two flight programmes perpendicularly in the same designated region; and the gradient of the camera to the vertical, which in these flights was \(10^{\circ}\). Ground control and check points were obtained with GNSS (RTK). As a result, 3D models were generated from over 10 million points, the uncertainty of which was \(\pm\)4 cm.
## 3 Comparison of Application Cases and Methods
### Comparison of Different K Values for Troubleshooting  
The choice of the \(K\) value plays a crucial role in effectively extracting information related to the state of gearbox transmission. It also significantly impacts the time taken for modal decomposition. The assertion that \(K=5\) is superior in fault diagnosis is examined in this context. To validate this claim, the \(K\) value is varied, and 750 sets of gearbox transmission vibration data are employed, all collected under a state of 100% rated pressure. The proposed method is then applied to extract characteristics indicative of wear faults in the gearbox transmission and to identify the specific types of faults present. The diagnostic outcomes, derived from this comprehensive analysis, are systematically presented in Table 6. This tabulation provides a detailed perspective on the efficacy of different \(K\) values in capturing and elucidating fault characteristics, offering valuable insights into the optimal \(K\) value for enhanced diagnostic accuracy and efficiency.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**K-Value** & **Modal Decomposition Time/s** & **Test Set Correctness/\%** \\ \hline
4 & 2544 & 91.3 \\ \hline
5 & 3486 & 100 \\ \hline
6 & 6504 & 96.7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Fault diagnosis results of different K values.  
Figure 6: Fault diagnosis results under different pressure conditions.  
As illustrated in Table 6, a modal decomposition number of \(K=4\) proves to be inadequate, resulting in an insufficient extraction of transmission vibration information. This shortfall precipitates a nearly 10% decline in the diagnosis accuracy rate. Conversely, with \(K=5\), a balance is achieved; the modal decomposition is optimal, ensuring a moderate decomposition time and attaining a 100% correct diagnosis rate. When \(K\) is increased to 6, the decomposition time almost doubles compared to \(K=5\). This increase in modal decomposition time correlates with an over-decomposition scenario. The excess decomposition yields frequency components void of effective transmission-state information, leading to a nearly 10% reduction in the correct diagnosis rate. These findings corroborate that \(K=5\) is the optimal number for modal decomposition, striking a balance between efficiency and accuracy in extracting pertinent transmission vibration information, thereby ensuring diagnostic precision.