esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Framework Model for Optimizing Routing Performance in VANETs
## 1 Introduction
However, most of recent studies in literature succeeded somewhat in enhancing the routing performance for one or two features, but it also loses other features or part of its routing efficiency. This is because of the limitation capacity of MANETs routing protocols. Thus, the relationship should be detected between the routing performance and the influential factors of V2V system to correctly develop MANET protocols for VANET. Addressing the trade-off and balancing problem in VANET is critically required, it helps comprehending the trade-off status between QoS requirements and the routing efficiency. In this paper, we present a framework model designed specifically to tune the routing configuration parameters in high dynamic mobility environment. This model exploits the network resources to improve the stability and reliability of routing packets between vehicles, which it index the QoS metrics as a function to balances the QoS requirements and the nature of the dynamic mobility impact. This balance reduces the trade-off between the communication cost and time needed to deliver packets successfully. In addition, we present The routing protocol for vehicular networks in urban environment called Balanced Optimized Link State Routing-Particle swarm optimization (BOLSR-PSO). This protocol is the end result obtained when the framework tune Optimized Link State Routing-Particle swarm optimization (OLSR-PSO), which improved its efficiency using the network resources by maximizing PDR, Throughput, and minimizing both NRL and E2 ED. The remainder of this paper is organized as follows; Section 2 provides an overview of the work aimed at optimizing the routing performance in dynamic topology changes. The proposed protocol and framework components are described together with the problem statement in Section 3. Section 4 introduces the optimization-based multiple objective. Section 5 presents the experiment component, analyses, and compares the results of the proposed protocol. Finally, Section 6 concludes the paper.
# Wire-Grid and Sparse MoM Antennas: Past Evolution, Present Implementation, and Future Possibilities
## 5 Wire-Grid Sparse Antennas
### Wire Grid: Application and Example
#### 5.3.2 Modeling Using Wire Grids  
The structure of the parabolic antenna is modeled using a MoM wire grid with the thin-wire approximation [302]. The isometric view of the parabolic antenna structure is presented in Figure 7, and its geometrical parameters are as follows: the diameter \(D=400\) mm, the depth \(h=143.6\) mm, the focus \(F=75\) mm. As an excitation source, a dipole is located at a distance equal to the focus of the reflector from the center of the grid, and its length is chosen equal to \(l=\lambda/3.6\). The dipole radius is chosen equal to \(R_{1}=\lambda/3.6\) and the radius of grid wires \(R_{2}=\lambda/30\). The wire grid is built using 60 radial wires and 25 horizontal ones (Figure 7). According to the antenna datasheet [301], the simulation using the wire grid is performed at frequencies of 5.1 GHz and 5.9 GHz. The antenna gain is calculated in the E-plane and H-plane, and the results are compared to the measured ones and presented in Figure 8.
###### Abstract
The direct effect of the PSF on the image is due to the fact that the contribution of each ideal point source of radioactivity is spread over a wider volume, i.e., the content of each voxel is the sum of the contributions of neighboring point sources. This can result in an overestimation of the object size, but also in an underestimation of the real radioactivity concentration, because part of the signal from the source spills out in the background (spill out effect), hence it is seen outside the actual source, as well as, part of the background surrounding the source spreads into the source (spill in effect). The combined effect depends by the source/background activity concentration ratio and by the size of the source. Moreover, from a quantitative point of view, it becomes particularly important when the target is smaller in comparison to the FWHM of the imaging system [10]. In order to achieve an accurate quantification of the radioactivity concentration, PVE has to be considered and compensated, especially in small structures. To date, several techniques have been proposed to compensate for PVE, which are illustrated in details in many papers and reviews [11; 12; 13]. The method proposed in this work can be included in the post-reconstruction correction methods applied at regional level. The method is based on the exact analytical deconvolution of the PSF of the imaging system in the case of an activity distribution, which consist of a single source with homogeneous activity, surrounded by a homogeneous background. The method is experimentally tested by means of an IEC NEMA phantom acquisition, and the results are shown and discussed.
## 2 Power Internet and High Voltage Transmission Line Condition Monitoring Intelligent Sensor Design
### Smart Grid High-Voltage Transmission Line Condition Monitoring Intelligent Sensing Design
#### 2.3.2 High-Voltage Transmission Line Condition Monitoring Sensing Network  
Node Design  
The main functional modules of the sensor network node consist of a real-time multi-tasking management module, a Socket communication module, a configuration management module, an IEEE1451 protocol processing unit, a TEDS electronic data form, and a TII intelligent interface module. The main parameters measured by the sensor network, including phase volume, have a signal over-zero detection method and discrete Fourier algorithm. The over-zero detection method is simple to implement but susceptible to interference, and the discrete Fourier algorithm suffers from frequency leakage when the frequency of the collected signal changes.
# Quantitative Model for the Value of the 3D Product Model Use in Production Processes
## 3 Results
### Smoother Transition to Field Use  
In alignment with the prior operator training example, there are 350 stations per production line (\(N_{m}\)). Prior to implementing the 3D product model, the automotive manufacture discussed in this example has a reliability of each station on a given line (\(R_{m,n}\)) producing a quality product of 99.97% per station. This results in an overall reliability of the production line (\(R_{m}\)) producing quality products 90% of the time, meaning 10% of the products need to be re-worked. The average price to re-work a product (\(\overline{\xi}_{m}\)) is USD 60 (two operator labor hours) on this production line, which with the production throughput and profitability rates noted earlier in the paper, result in a rework cost of over USD 2 million per year on one line (\(Q_{m}\)). With the implementation of the 3D product model in the production planning and design process, the product is designed such that it improves the ease of manufacturability and removes possibilities for incorrect assembly (poka-yoke). This increases the probability of each station on the line producing quality products at their station by two hundredths of a percent to 99.99%. The result is rework costs reducing to less than USD 0.74 million per year on that production line, saving the company over USD 1.4 million each year in rework on that assembly line (\(P_{Q_{m}}\)) via a 65% decrease in re-work costs.
## 2 Problem Description
### Goal  
This study aims to maximize overall system throughput to the greatest extent possible while also ensuring that both the required QoS for the RT traffic and fairness among all the users are maintained. A novel algorithm is proposed to achieve this goal efficiently and effectively, with this aim including improved energy efficiency for the overall system. The algorithm will be described in Section 3.
# A Novel DV-HOP and APIT Localization Algorithm with BAT-SA Algorithm
## 1 Introduction  
WSN has many uses because it is one of the most efficient and dependable techniques for gathering and processing data. In order to study and monitor environmental changes in locations where people are unable to travel, aircraft are used to deploy the wireless sensor nodes. The WSN nodes collect environmental data and are typically randomly dispersed around the monitoring region. It is found that in these circumstances, the nodes' deployment position is significant. Due to limitations on cost, power, and stability, it would be very difficult to manually install a GPS receiving device for every sensor node [1].  
The effectiveness of the WSN as a whole is significantly impacted by the absence of location data in such environments. A WSN is thus strongly dependent on localization technologies. The nodes convey the data they have collected to the destination node or monitor node across a number of hops. Since the WSN is self-organizing, each node may recognize or learn details about its surrounding nodes independently. It is found that the nodes of the wireless network have greater power than conventional nodes in a communication link. Data derived from nodes are transmitted to the base station using a multi-hop method [2; 3; 4; 5]. Unknown nodes and beacon nodes are the two main categories of nodes in a WSN. In general, the unknown nodes are unaware of their location, as opposed to beacon nodes, which are aware of their whereabouts thanks to the GPS device that is deployed with them. The two types of localization algorithms are as follows:  
* Distance-free;* Distance-based. Received signal strength indicator (RSSI), the angle of arrivals, time difference of arrivals, and the time of arrival are a few of the distance-based algorithms. Algorithms for localization without regard to distance or range include MAP, approximate triangle point test, amorphous algorithms, and DV-HOP. The perfect distance between the deployed node is identified by the distance-based localization algorithm in order to locate the closest nodes in the network. Therefore, location awareness is essential in highly sensitive applications like military and underwater communication, among others [6]. The availability of the geographic locations of the sensor nodes is thus discovered to be complicated by localization. Triangulation, multilateration, or trilateration are the geometric methodologies used for position determination depending on the characteristic of the communication signals. Although distance-based localization approaches have a high degree of precision, they are not economical [7] and shown in Figure 1.
## 1 Introduction
However, it is difficult to find studies investigating the strength characteristics of hardfill materials in different aggregate PSDs and mixing ratios and the resulting stability of hardfill dams by applying the same hardfill materials through indoor tests, field test construction, and numerical analyses at a time. In this study, the maximum (coarse) and the minimum (fine) PSDs of aggregates with a maximum particle size of 80 mm, considered to be a suitable aggregate PSD range for the construction of hardfill dams [11], were selected for analysis. The analysis was based on the results of PSD investigations of the riverbed aggregates for each major river system in South Korea presently without hardfill dam construction. After fabricating large hardfill specimens by mixing the aggregate, cement, and water, the strength characteristics of the hardfill were investigated through laboratory uniaxial compressive strength tests. In addition, test constructions were conducted on-site by applying the same mixing ratios and aggregates as those for hardfill samples in the laboratory tests. Then, the hardfill's constructability was not only investigated using the on-site compaction equipment, but also the strength range was obtained from uniaxial compressive strength tests on the cores collected from the test fills. Subsequently, by applying the properties of the hardfill obtained from the laboratory test results, a finite element analysis was performed for hardfill dams with three different cross-sectional slopes (i.e., 1:0.6, 1:0.8, and 1:1) and a height of 50 m, subjected to earthquake. The seismic stability of the hardfill dams was evaluated according to the changes in the strengths and cross-sections (slopes) of the hardfill dams.
# Light Graph Convolutional Recommendation Algorithm Based on Hybrid Spreading
## 2 Related Work  
### Spreading-Based Recommendation Algorithm  
Widely known spreading-based recommendation algorithms include probabilistic spreading algorithms (ProbS) [4], heat spreading algorithms (HeatS) [5], hybrid spreading algorithms (HybridS) [6], etc. These algorithms model user-item interaction data as a bipartite network and, by assigning initial resource value to items and simulating the spreading process, generate recommendations through two rounds of the spreading process: from items to users and from users to items. This approach effectively leverages user-item interaction data. Many scholars have extended these algorithms in various directions. For instance, [PERSON] et al. considered that users have different preferences for different items, introduced item popularity in the second round of the probabilistic spreading process of the probabilistic spreading algorithm to allocate resources, significantly improving recommendation accuracy and diversity compared to the original probabilistic spreading algorithm [7]. [PERSON] et al. proposed a novel hybrid spreading algorithm that assigns equal weight to probabilistic spreading and heat spreading algorithms, achieving higher accuracy and diversity than traditional hybrid spreading-based recommendation algorithms [8]. [PERSON] et al. proposed a bidirectional spreading algorithm based on probabilistic spreading, which penalizes the weights of popular items during the bidirectional spreading process, effectively improving both accuracy and diversity [9]. [PERSON] explored the theoretical upper bound of accuracy for spreading-based recommendation algorithms using quantitative methods, highlighting that further improvements in accuracy are still needed [10]. [PERSON] utilized user labels and item labels weight factors to weight the initial user-item interaction matrix, performed bipartite graph spreading on the optimized matrix to derive user similarity based on label weighting, and employed a combination of similarity based on ratings and label-weighted similarity for collaborative filtering recommendations. This method significantly improved accuracy and better reflects user preferences compared to traditional collaborative filtering recommendation algorithms [11].  
Although these works have enhanced the accuracy and diversity of recommendations, several issues remain: (1) the focus on modifying resource update configuration during the spreading process overlooks the fact that user interests and preferences influence resource spreading from users to items. This oversight leads to underutilization of potential relationship between the user and the item; and (2) these algorithms rely solely on user-item interaction data and neglect auxiliary information such as user features and item features. This limits their application potential and applicable scenarios.
# Optimization Algorithms for Scalable Stream Batch Clustering with k Estimation
## 4 Scalable Distribution-Based Clustering Algorithms
### Computational Complexity Analysis  
Our three optimization methods based on the model distribution have two cases: the worst-case scenario, where the full clustering (G-means, PG-means, or Pk-means) must be used and reused to update the model from scratch; and the best-case scenario, which occurs when full clustering is not needed. Both cases are presented in Table 2. The size of the micro-batch is given by \(n_{mb}\), \(d\) is the dimensionality of the data, \(mc\) is the number of micro-clusters, \(m\) is the number of worker nodes of a distributed system, \(n_{w}\) is the size of the landmark window, \(k_{m}\) is the maximum number of clusters found during the process, \(i\) is the maximum iterations of \(k\)-means, \(g\) is the maximum generations of F-EAC based algorithms, \(|P|\) is the population size, and \(l\) is the number of EM iterations.  
The main difference in the computational cost of SPGMS and SPKMS resides in the data dimensionality: the former requires a matrix inversion for EM, which implies an \(O(d^{3})\) cost; the latter uses a \(k\)-means, which is linear considering the data dimensionality \(d\) for most common distance measures. Such a characteristic makes SPGMS unfeasible for data sets with high dimensionality, scenarios for which SPKMS is preferable. Both algorithms need a sort step bounded by \(O(n\log n)\). However, as \(\log n<<k*d\) holds for most scenarios, other parts of the algorithms asymptotically dominate this step.
## 4 Experimental Results
### Training and Validation  
Training and validation loss curves (Figure 1) show the convergence behavior of XCeption, ResNet-50, and VGG16 on the DFDC dataset. XCeption achieves steady convergence with minimal overfitting (final validation loss: 0.23), while ResNet-50 shows fluctuating loss patterns (final validation loss: 0.41). VGG16 converges more gradually (final validation loss: 0.28), indicating a balanced learning approach.
## 1 Introduction  
According to the European Commission [1], a high proportion of students in Norwegian upper secondary schools attend a school with a high level of infrastructure and connectedness. Since 2009, most upper secondary students have been assigned their own digital device from the county. Norwegian upper secondary schools are close to 1:1 access to technology, with students gaining a sense of personal ownership and responsibility over their own devices, an important aspect in developing digital competence. Schools expect students and teachers to use ICT (Information and communication technology) in teaching and learning at school [2]. However, with the increased access to technology, it is important that students develop Internet safety awareness as concerns privacy, copyright and safety issues.  
When research is conducted on hierarchical or nested data from schools, for example students belonging to one classroom in one school, the researcher has to examine whether there is an effect of students being clustered. It is likely that students within the same classrooms could have some of the same characteristics, because they are taught and learn in the same environment. In order to take into account variation between clusters, _i.e._, classrooms or schools, techniques for multilevel analysis have been developed [3, 4]. Multilevel analysis is a kind of regression analysis technique dealing with data where individuals are nested in clusters.  
The aim of this paper is to identify Internet safety awareness among upper secondary school students in a school context, and to examine the relationship between upper secondary students' level of Internet safety awareness, social background and academic aspirations.
## 3 Calculation Mesh and Boundary Condition Setting
### Example Verification  
In order to verify the accuracy of the numerical simulation method in this paper, the flow field of the missile body and the submerged inlet in the literature [40] was simulated and compared with the relevant experimental data. The calculation model is shown in Figure 8.  
In the case of no sideslip, the flow field is symmetrical, so the half model is used for mesh generation to reduce the computational cost. Unstructured polyhedral mesh is adopted, and the far field is in the shape of a cuboid, with the length, width, and height of 40 m, 10 m, and 10 m, respectively. The local mesh of the missile body surface and inlet is shown in Figure 9. The total number of meshes in the computational domain is 0.5 million, the number of boundary layers is 10, the height of the first layer is 0.01 mm, and the growth rate is 1.2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Time Step (s)** & **Average \(\sigma\) in 0.2–0.5 s** & **Error** \\ \hline
0.001 & 0.966 & 0.2\% \\
0.0001 & 0.968 & / \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average total pressure recovery coefficient in 0.2–0.5 s.  
Figure 8: Reference [40] Submerged inlet.  
Figure 7: Calculation results of total pressure recovery coefficient at outlet of the inlet in two time steps of 0.2 to 0.5 s.  
The calculation conditions are set according to the experimental conditions in the literature, \(M_{0}=0.73\), and the altitude is 5 km. The pressure far-field boundary condition is adopted in the far field of the computational domain, and the outlet of the inlet is set as the pressure outlet. The inlet and missile body walls are set as non-sliding walls.  
The experimental results show that \(\sigma\) of the missile body and the submerged inlet is 0.9220 when \(M_{0}=0.73\) and \(M_{e}=0.4\). In the calculation results, \(\sigma\) at the outlet of the inlet is 0.9244, and the error between the numerical simulation results and the experimental results is only 0.26%. Figure 10 shows the total pressure distribution at the outlet of the inlet, which is also in good agreement with the experimental results. Therefore, the accuracy of the numerical simulation method in this paper is verified.
# BIMO: Bootstrap Inter-Intra Modality at Once Unsupervised Learning for Multivariate Time Series
## 3 Results and Discussion
### Implementation  
**Sample Generation:** Time-series augmentation can be divided into magnitude-based and time-based methods. In this study, we used the time-series augmentation set \(t\) and \(t^{\prime}\), which comprises magnitude-based magnitude warping and scaling methods, and time-based time-slicing and time-warping methods [35; 36].  
The time-series subsampling strategy is based on the literature [23]. We randomly extracted a part of the samples by selecting the length and starting point. We selected different lengths and starting points for each epoch and trained them with various lengths of subsamples to learn a sufficient inter-temporal modality representation.  
**Encoder Selection:** Time-series data should comprise temporal orders, which are required to consider temporal information, accommodate unequal lengths, and be efficient in terms of both time and memory. Note that deep convolutional neural networks (CNNs) do not consider temporal information and are difficult to apply to data of various lengths. Long short-term memory (LSTM) is inefficient in terms of time and memory. Thus, we used exponentially dilated causal convolutions to handle these issues [23; 29; 30].  
To verify the conformity of our encoder selection, we measured the classification performance on the UCR datasets using dilated causal convolutions, ResNet, and a two-layer LSTM encoder. Each model outperformed the other two on 65%, 35%, and 5% of the first 20 UCR datasets, respectively. This result confirmed that the encoder with dilated causal convolution was the most suitable for the proposed BIMO method. The accuracy results are detailed in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **DConv. (BIMO)** & **ResNet** & **LSTM** \\ \hline Adiac & **0.760** & 0.482 & 0.342 \\ ArrowHead & **0.814** & 0.763 & 0.388 \\ Beef & **0.800** & 0.625 & 0.313 \\ BeetleFly & **0.850** & 0.688 & 0.750 \\ BirdChicken & **0.900** & 0.750 & 0.563 \\ Car & **0.917** & 0.688 & 0.417 \\ CBF & **0.998** & 0.992 & 0.401 \\ ChlorineConcentration & 0.635 & **0.731** & 0.534 \\ CinCECG Torso & **0.757** & 0.629 & 0.283 \\ Coffee & **1.000** & **1.000** & 0.625 \\ Computers & 0.681 & **0.729** & 0.571 \\ CricketX & **0.750** & 0.651 & 0.107 \\ CricketY & **0.716** & 0.628 & 0.216 \\ CricketZ & **0.758** & 0.378 & 0.102 \\ DiatomSizeReduction & **0.977** & 0.911 & 0.336 \\ DistalPhalancyOutlineAgeGroup & 0.743 & **0.820** & 0.523 \\ DistalPhalancOutlineCorrect & 0.786 & **0.809** & 0.581 \\ DistalPhalancxTW & 0.684 & **0.688** & 0.422 \\ Earthquakes & 0.765 & 0.727 & **0.767** \\ ECG200 & 0.900 & **0.906** & 0.698 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Accuracy scores depending on encoder type with first 15 UCR datasets. Encoder type includes dilated convolution (DConv.), LSTM, and ResNet. Bold text represents the best accuracy.