esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation and Discussion  
### Effect of Number of Fibers on the Coupled-MTF  
According to the sampling theorem, if a bandlimited signal is sampled at a sampling rate no less than twice the highest frequency of the signal, then the resulting discrete sampling value accurately determines the original signal. The Nyquist frequency \(f_{N}\) is the highest spatial frequency that can be theoretically resolved, which is half the spatial sampling frequency of the input signal. The fiber column sampling interval is \(2R\), and the sampling frequency is \(1/2R\), so then the Nyquist frequency \(f_{N}=\frac{1}{2R}\cdot\frac{1}{2R}=\frac{1}{4R}\). Assuming that the fiber radius \(R=3.2\)\(\upmu\)m, the fiber core layer radius \(r=2.67\)\(\upmu\)m, the initial position deviation is set to \(0.1R\), and the spatial frequency of the input cosine distribution target is the Nyquist frequency \(f_{N}\), then the image element coupling error along the x direction is \(\Delta i=0.5R\), and the image element coupling error along the y direction is \(\Delta j=0.7R\). The simulation results of the coupled-MTF with the number of fibers can be obtained using Equation (14).  
When the parameters of the hexagonally aligned optical fiber imaging bundle and area array CCD, as well as their coupling errors, are the same as the second case, and when the number of optical fiber imaging bundles increases sequentially from \(10\times 10\) to \(100\times 100\), \(1000\times 1000\), and \(2000\times 2000\), the coupled-MTF can be obtained. The simulation result of the oscillation convergence is shown in Figure 6.  
From Figure 6a-c, when the fiber parameters, initial position deviation, and image element coupling deviation are fixed values, the coupled-MTF oscillation converges faster at the Nyquist frequency as the number of fibers increases and converges to a stable value at \(1000\times 1000\) the number of fibers, as can be seen from Figure 6d. After this, the number of fibers increases again, but the oscillation period effectively remains the same. This has no effect on the final convergence value of the coupled-MTF.
# Internet of Drones: Routing Algorithms, Techniques and Challenges
## 5 Future Research Directions
### Energy Consumption  
In wireless sensor networks, energy efficiency has become an important issue and must be addressed with care [66]. Most of the network components like sensor nodes, cluster heads, and UAVs are operated on batteries and hence, battery life is a major energy conservation issue. This can be addressed with energy efficient batteries. In WSNs, clustering resolves the energy conservation issue as the transmission delay is minimized with the help of clustering because each cluster is responsible for the battery life of its members. The four major areas to be resolved are delay, security, energy, and distance; therefore, energy limitation is a real bottleneck in UAWSNs. Recent developments in battery improvement and the use of lithium-ion and hydrogen fuel batteries are best for long UAV paths [67]. Another technique is energy harvesting, used to extend the duration of UAV flight. The use of solar powered cells is also mitigating the limited battery effects. Another solution is wireless transfer of power, which is a smart solution to make the system energy efficient. In general, UAVs need more mobility, easy deployment, more programming, and scalability, so they can be applied to IoT systems with minimum battery requirements.
# Predicting Phubbing Through Machine Learning: A Study of Internet Usage and Health Risks
## 1 Introduction
In this respect, it is of great importance to consider the concept of phubbing as an important addiction and to consider the extent of the negative effects that it may cause in the coming years. This research aims to reveal the dimensions of the danger of \"phubbing\" by estimating the duration spent on the internet in 2030 and 2035, considering the correlation between internet use and smartphone use. In the Introduction of this study, the concept of \"phubbing\", related to excessive phone use, is discussed. The Section 2 of the study reviews similar works in the literature and provides an analysis of these studies. The Section 3, which is the Materials and Methods, describes the dataset and analysis techniques used. In the Section 4, the Findings and Discussion, the parameters used in the methods, the error and success metrics, and the results from the analysis,are presented. Finally, in the Section 6, the findings are interpreted, highlighting the unique, superior, and improvable aspects of the study, and its impact on the literature is discussed. This study provides a novel contribution by integrating phubbing analysis with machine learning-based time series forecasting. Unlike prior studies that predominantly used survey or traditional statistical methods, our work employs AI-based prediction models to estimate future internet usage patterns, directly addressing the digital behaviours that contribute to phubbing. The inclusion of diverse independent variables (e.g., urban-rural distribution, literacy rates, mobile traffic share) enriches the analysis, offering a comprehensive perspective on this social phenomenon.
# An Evaluation Framework and Algorithms for Train Rescheduling
## 4 Framework Part I: Classification of Algorithm Capabilities and Characteristics
\begin{table}
\begin{tabular}{p{108.4 pt} p{108.4 pt} p{108.4 pt} p{108.4 pt} p{108.4 pt}} \hline \hline
**Algorithm Characteristic** & **Algorithm 1** ([PERSON] et al. [22])** & **Algorithm 2** ([PERSON] and [PERSON] [19]) & **Algorithm 3** ([PERSON] et al. [14]) & **Algorithm 4** ([PERSON] and [PERSON] [29]) \\ \hline Solution space exploration & Partial & Full & Partial & Full \\ \hline Solution approach & Branch and bound approach using a parallel depth-first search. & Branch and cut approach using a sequential tree search (defined by the applied commercial solver). & An iterated greedy approach using a reduced variable neighbourhood search, or tabu search. & Branch and cut approach (CPLEX solver to solve the formulated MILPs) \\ \hline Main ideas of the approach & A set of upper bounds associated with the set of best solutions is maintained and updated throughout the search. Pruning metrics corresponding to the multiple minimization objectives are used to prune off potentially undesirable solution branches. & The explicit MILP formulation is solved using a commercial solver, e.g., CPLC or Gurobi. & Each train’s new schedule is created by solving a shortest-path problem. A new timelable is obtained by rescheduling the trains one by one, according to their ranking, which is perturbed using a set of rules to obtain improved timelables. \\ \hline Control loop & Open & Open & Open \\ \hline Evaluation level [15] & Simulated experiments (real-world instances of Sweden). & Simulated experiments (real-world instances). & Simulated experiments (real-world and (To be) deployed in practice. & Simulated experiments (real-world instances of Italy). \\ \hline Evaluation context & Line & Network (of different lines in the south traffic district of Sweden). & Line & Line \\ \hline Applicable scenarios & Delayed train (i.e., initial primary delays of various sizes, various trains and various locations). & Delayed train (i.e., initial primary delays of various sizes, various trains and various locations). & Cannot be determined from the research paper. & Cannot be determined from the research paper. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._We apply the aforementioned part of the evaluation framework on four train rescheduling algorithms to compare their capabilities. The classification of the four algorithms, shown in Table 3, is based on the descriptions and demonstrations of the approaches in the mentioned references. Hence, detailed information is not available to cover all aspects to the same extent. Furthermore, other versions of those approaches may also exist and be in use in other settings.
## 2 Materials and Methods
### Image Quality Measurement Method  
The quality of data generated through GAN is not automatically measured and must be inspected with the naked eye, rendering the optical judgment difficult. Therefore, to use only high-quality data for learning, a quantitative criterion for evaluating the generated data is required. Many studies have proposed criteria to measure the GAN performance. The inception score proposed by [PERSON] [18] is the most widely used scoring algorithm for GANs. It measures the quality and diversity of the generated image by extracting the features of the real image and the image created using the pretrained Inception V3 neural network, where the higher the inception score, the better the quality of the model. The inception score can be calculated using Equation (2).  
\[\text{IS}(\text{G})=\exp(E_{x\sim pg}D_{KL}(p(y|x)||p(y))) \tag{2}\]  
where \(p(y|x)\) is the conditional class distribution, \(x\) is the generated image, and \(y\) is the label. \(p(y)\) is the marginal class distribution and can be calculated using Equation (3).  
\[p(y)=\int\ p(y|x)p_{g}(x) \tag{3}\]  
If the generated image is diverse, then \(p(y)\) approaches a uniform distribution. However, measuring the quality based on the inception score also encounters problems. If the model generates only one image per class, even if the diversity is low, \(p(y)\) may be close to a uniform distribution, thus resulting in incorrect performance.  
Therefore, in this study, quality was evaluated by applying the method proposed by [PERSON] [19] to objectively evaluate the generated image. Our proposed method is to train a deep-learning model with a training dataset consisting only of real data and test newly created data to select only images with an accuracy of 0.8 or higher, among correctly classified images. Through this process, not only the images generated through GAN but also the quality of data augmented by 25 policies of the CIFAR-10 dataset were evaluated. As shown in Figure 8, the Xception model was trained with an actual gastroscopy image, and the data expanded through the 25 policies of CIFAR-10 and DCGAN were composed of a test dataset and classified as normal or abnormal. Correctly classified images mean that they are similar to real images, and only high-quality images were selected by selecting only images with a classification prediction degree of at least 0.8. Through this method, the quality of image was judged based on a quantitative standard rather than subjective evaluation.
# Point Cloud Upsampling Algorithm: A Systematic Review
## 4 Deep Learning-Based Point Cloud Upsampling
### Supervised Upsampling
#### 4.1.3 Point Set Generation  
The point set generation component is the last step of the upsampling model, and reconstructs the expanded features into 3D features. Compared with the feature extraction and feature expansion components, this component is simpler in structure and usually consists of one or more MLP layers, such as PU-Net [5], MPU [16], or PUI-Net [35]. Although the component is simple in structure, there is still a requirement for improvement. Through an edge distance regression component, EC-Net [12] learns the perturbation of the position of the generated point cloud relative to the original point cloud to obtain distance features. The distance feature and the extended feature are connected and input into the point set generation component, and the point coordinates are obtained through two MLPs. This helps to supplement missing edge points on the surface of regular objects. PU-GAN [13] applies farthest point sampling after one layer of MLP, which can further improve the uniformity of point set distribution. PU-EVA [31] obtains the regression displacement error through the learned neighborhood features, adds it to the point coordinates obtained by the MLP layer, and calculates the final coordinates of the output point.
# Modeling and NVH Analysis of a Full Engine Dynamic Model with Valve Train System
###### Abstract  
The valve train system is an important source of vibration and noise in an engine. An in-depth study on the dynamic model of the valve train is helpful in understanding the dynamic characteristics of the valve train and improving the prediction accuracy of vibration and noise. In the traditional approaches of the dynamic analyses, the simulations of the valve train system and the engine are carried out separately. The disadvantages of these uncoupled approaches are that the impact of the cylinder head deformation to the valve train and the support and constraints of the valve train on the cylinder head are not taken into consideration. In this study, a full engine dynamic model coupled with a valve train system is established and a dynamic simulation and noise vibration harshness (NVH) analysis are carried out. In the coupled approach, the valve train system is simulated simultaneously with the engine, and the complexity of the model has been greatly increased. Compared with the uncoupled approach, more detailed dynamic results of the valve train can be presented, and the subsequent predictions of vibration and noise can also be more accurate. The acoustic results show that the difference from the experimental sound power level is reduced from 1.8 dB(A) to 0.9 dB(A) after applying the coupled approach.  
Keywords: valve train; dynamics; coupled approach; NVH +
Footnote †: journal: Applied Physics Letters
# Research on Two-Dimensional Digital Map Modeling Method Based on UAV Aerial Images
###### Abstract  
Accurate acquisition of two-dimensional digital maps of disaster sites is crucial for rapid and effective emergency response. The construction of two-dimensional digital maps using unmanned aerial vehicle (UAV) aerial images is not affected by factors such as signal interference, terrain, or complex building structures, which are common issues with methods like single-soldier image transmission or satellite imagery. Therefore, this paper investigates a method for modeling two-dimensional digital maps based on UAV aerial images. The proposed Canny edge-enhanced Speeded-Up Robust Features (C-SURF) algorithm in this method is designed to enhance the number of feature extractions and the accuracy of image registration. Compared to the SIFT and SURF algorithms, the number of feature points increased by approximately 44%, and the registration accuracy improved by about 16%, laying a solid foundation for feature-based image stitching. Additionally, a novel image stitching method based on the novel energy function is introduced, effectively addressing issues such as color discrepancies, ghosting, and misalignment in the fused image sequences. Experimental results demonstrate that the signal-to-noise ratio (SNR) of the fused images based on the novel energy function can reach an average of 36 dB.  
digital maps; feature extraction and registration; image stitching; UAV image 2020 IEEEexample:BSTcontrol
## 8 Conclusions  
Stochastic diffusion search has been placed in the context of swarm intelligence techniques in contrast to more traditional search and optimisation methods.  
A variable population size has been shown to be a key factor when designing SDS systems, with coupled SDS shown to be the most flexible and best performing variant of SDS.  
All SDS varieties appear to suffer from the no free lunch issue; configured to perform well for a certain problem (for example, hyperplane estimation in twelve dimensions) automatically sets them up to perform badly on another problem (for example, hyperplane estimation in three dimensions). For this reason, it is suggested that further work is carried out to provide SDS with some mechanism of auto-tuning itself; dynamically modifying population size along with some method of varying the number of datum checked during the test phase.  
There may be some fundamental relationship between the number of hypothesis agents (in CSDS) needed for best performance and the total number of hypotheses in the system. Given the data-driven techniques adopted, this possible number of hypotheses is finite. Similarly, it seems plausible that there is an optimum number of data agents required for any given problem; some fraction of the total number of data elements, for example. While some attempts have been made here to approximate these relationships, further work is necessary to establish their fundamental properties or simply to provide better heuristics to guide their approximation.  
SDS has been shown to be an excellent swarm intelligence search and optimisation technique. This is due to SDS's partial evaluation of the objective function, in comparison with many existing techniques' approach of full evaluation. The communication phase of SDS allows good hypotheses to spread throughout the agent population quickly and efficiently, while the discarding of bad hypotheses is generally reliable.  
Given that the relate phase can be applied to CSDS in the usual manner, further work in, for example, the realm of image and video analysis could include an investigation into the performance of SDS in context-sensitive modes in order to entertain multiple hypothesis solutions in a dynamic search space;something RANSAC is unable to achieve. For example, applying this kind of technique to object recognition during real-time video analysis appears to offer a rich vein of potential.
# Fault Detection Algorithms in Medium-Voltage Direct-Current (MVDC) Grids
## 3 Study Case and Results
There are some limitations associated with the simulations:
* Simplified modeling: Some components of the protection system, such as the protective relays and other control devices, are modeled with ideal characteristics. More detailed modeling of these components could affect the results, particularly in terms of the time delays and reliability. On the other hand, although in this analysis the operation of the circuit breakers is not considered [31], DC hybrid circuit breakers operate in 2 ms after detecting the fault. So, the correct operation of the fault detection algorithm is considered if it detects the fault in less than 5 ms.
* Real-world implementation constraints: The simulations do not account for practical implementation constraints such as sensor accuracy, hardware limitations, or environmental factors (e.g., temperature or electromagnetic interference), which could impact the performance of the protection algorithms in real-world settings. * Computational resources: The complexity of running large-scale simulations involving numerous fault scenarios with varying parameters requires significant computational resources. While the simulations covered a wide range of fault types, they are still limited by the available computing power, particularly for highly detailed transient analysis over extended time periods.
# Autonomous Loading System for Load-Haul-Dump (LHD) Machines Used in Underground Mining
## 2 Background and Related Work  
### Problem Description  
Autonomous loading of fragmented rock involves a sequence of steps: As the LHD's operator approaches the draw point, a swift assessment of the condition of the rock pile is made. The bucket of the LHD is tilted downwards until the tip is pressed against the ground. Next, the machine is commanded to charge at the rock pile. After the LHD makes contact, it buries the bucket in the rock pile, and bucket tilt and lift movements are issued as it advances through the fragmented rock. When sufficient penetration is achieved, the bucket is fully retracted, and the vehicle is withdrawn with a reverse motion. Then, the operator shakes the bucket briefly and assesses whether or not enough ore has been loaded. If necessary, a new loading maneuver is carried out.  
The step in which the machine is being controlled to dig into the rock pile will herein be referred to as the excavation process. During excavation, bucket-rock interaction forces affect the bucket motion through the material, and ultimately define the amount of ore loaded. Granular material inter-particle forces, as well as bucket-rock interactions, have been studied previously, but their complexity has led only to stochastic modeling [19]. In consequence, an analytical approach to bucket motion control is not possible, and accurately replicating these interactions to develop a system in simulation is highly unlikely. Hence, only a real environment offers suitable conditions for development of, and experimentation with, an excavation procedure. Furthermore, fragmented rock, unlike piled sand or gravel, is much more difficult to load. This is aggravated due to the environmental conditions in underground mining. Depending on the mining method, the column of blasted rock might exert large compression forces on the open face of the draw point (see Figure 3). Humidity can also increase cohesion forces, making the overall process more difficult [20]. Moreover, large rocks can be present in the draw point. For this reason, the operator needs to detect the presence of large rocks while approaching the draw point, and, depending on their size, could be forced to change the goal of the maneuver from filling the bucket to just loading one large rock. If the rock is too big to be loaded, the task is aborted. Furthermore, other conditions, such as narrow tunnels, uneven or unprepared terrain, and/or significant amounts of water and mud near the draw point, can make the loading process very challenging. An autonomous system should be prepared to handle all these situations in addition to performing certain processing and steps that a human operator deals with while driving the LHD, such as detecting an accurate enough position of the rock pile before charging at it, making sure not to hit the tunnel while pulling back from the rock, and estimating if the bucket is full enough.
# Measurement of Contrast and Spatial Resolution for the Photothermal Imaging Method
## 4 Results of Numerical Analysis
### Image Quality Change with \(R_{b}/L_{d}\)  
Figure 13 shows the ERF according to changes in \(R_{b}/L_{d}\). Unlike the case of \(L_{f}/L_{d}\), there was a minor change in the ERF intensity as \(R_{b}/L_{d}\) increased in the case of \(R_{b}/L_{d}\).  
Because the ERF change was insignificant, the LSF also showed no major change. As a result, the spatial resolution change derived from the LSF, as shown in Figure 14, showed a difference of only 10% from the average. The MTF indicating the contrast in Figure 15 also showed the same tendency. In addition, the positions where the contrast decreased by half showed a difference of less than 10% from the average. The reason for this is that the diameter of the thermal wave in the radial direction cannot have influences on the detection of subsurface structure.  
Figure 14: LSF according to changes in \(R_{b}/L_{d}\).  
Figure 13: ERF according to changes in \(R_{b}/L_{d}\).  
Figure 15: MTF according to changes in \(R_{b}/L_{d}\).  
Numerical analysis was conducted for each of the 20 cases in which both \(L_{f}/L_{d}\) and \(R_{b}/L_{d}\) changed, as well as the cases in which each parameter was fixed, and the results are presented in Table 5. Likewise, the image quality change according to changes in \(R_{b}/L_{d}\) was insignificant. In contrast, for the image quality change according to changes in \(L_{f}/L_{d}\), the spatial resolution increased as \(L_{f}/L_{d}\) increased.  
This tendency suggests that the pump-beam radius has an insignificant effect on image quality and that an increase in \(L_{f}/L_{d}\) within an experimentally measurable range is essential to achieve good image quality.