esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction and Analysis of the Surface Roughness in CNC End Milling Using Neural Networks
## 4 Conclusions  
In this study a BPNN was used to predict the surface roughness in CNC end milling. Furthermore, this study analyzed the influence of CNC parameters including cutting depth, spindle speed, feed rate, and milling pitch on surface roughness. The contributions of this study are summarized from the experimental results as follows.  
1. In the measurement experiment of surface roughness, the CNC parameters with a smaller cutting depth, a faster spindle speed, and a smaller feed rate will obtain a better surface roughness.
2. According to ANOVA, the contributions of the cutting depth, spindle speed, feed rate, and milling pitch in CNC were 51.86%, 77.48%, 92.3%, and 71%, respectively. This result shows that the feed rate has a greater influence on the surface roughness.
3. In the process of training neural networks, when the used BPNN with the number of iterations is set to 2,000,000, the learning rate is set to 0.8, the inertia factor is set to 0.5, and the number of hidden layer neurons is set to 20 it will have a lower RMSE.
4. In this study, linear regression and the used BPNN were compared. From the experimental results, it can be verified that the used BPNN has a lower RMSE and a higher \(R^{2}\). Furthermore, the prediction accuracy of linear regression and the used BPNN were 97.88% and 99.17%, respectively. According to the results, the used BPNN achieves excellent prediction of surface roughness.  
Conceptualization, C.-H.C. and C.-J.L.; Methodology, C.-H.C. and C.-J.L.; Software, S.-Y.J. and C.-H.C.; Data Curation, C.-H.C. and C.-J.L.; Writing--Original Draft Preparation, C.-H.C., S.-Y.J. and C.-J.L.; Writing--Review & Editing, C.-H.C., S.-Y.J. and C.-J.L.; Supervision, C.-H.C. and C.-J.L.; Funding Acquisition, C.-J.L. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **RMSE** & \(R^{2}\) \\ \hline Linear regression & 0.021215796 & 0.7794 \\ BPNN & 0.008338854 & 0.9995 \\ \hline \hline \end{tabular}
\end{table}
Table 13: BPNN parameter experiment results.  
**Funding:** This research was funded by the Ministry of Science and Technology of the Republic of China, grant number MOST 110-2218-E-005-016 and MOST 110-2221-E-167-031-MY2.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data sharing not applicable.  
**Acknowledgments:** The authors would like to thank the funding support of the Ministry of Science and Technology of the Republic of China.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Service-Constrained Positioning Strategy for an Autonomous Fleet of Airborne Base Stations
## 4 Positioning and Communication for a Fleet of UAVs
### Problem Formulation  
The proposed strategy is based on the maximization of a cost function to achieve a balanced rate distribution among users in different regions subject to several constraints imposed by the backhaul implementation. Specifically, the maximization of the cost function is carried out through the repositioning of the ABBSs and the allocation of bandwidth to the backhaul links. The proposed strategy consists in identifying the region with the lowest average rate per user (\(\overline{R}\)) and moving the appropriate UAVs in order to increase it. This procedure is repeated until all regions reach the same rate, thus achieving fairness among the service provided by different ABBSs.  
Regarding the constraints, the first one consists in limiting the total aggregated rate that each UAV can deliver as, in a real scenario, a BS is limited by the capacity of its backhaul link. The second one consists in limiting the total shared backhaul bandwidth from UAVs to the ground station. This leads to the following optimization problem:\[\underset{\{x_{i},y_{i},h_{i},B_{i}^{BH}\}_{i=1}^{P}}{\text{maximize}} \quad\min_{1\leq i\leq P}\overline{R}\big{|}_{A_{i}}\] (23) subject to \[\overline{R}\big{|}_{A_{i}}\cdot N_{i}\leq R_{i}^{BH},\quad 1\leq i\leq P \tag{24}\] \[\quad\sum_{i=1}^{P}B_{i}^{BH}\leq B_{BH}^{total}, \tag{25}\]  
where \(P\) is the number of deployed UAVs, \(R_{i}^{BH}\) is the rate supported by the backhaul link corresponding to the \(i\)th UAV, which is detailed later in Section 4.2.2, \(B_{i}^{BH}\) is the bandwidth of this link and \(B_{BH}^{total}\) is the total bandwidth that the backhaul ground station supports and that is distributed orthogonally among the UAVs. This implementation of the backhaul is also analyzed in [25] where, moreover, the tracking of each UAV is implemented using beamforming, which could be considered as a future work.  
Note that the previous optimization problem in Equation (23) is not convex [26]. The main consequence is that any practical algorithm, such as the one detailed in the following Section 4.2, will only be able to converge to a local optimum, that is, to a globally sub-optimum solution. Anyway, in many situations, such solution will provide good performance. In this paper, this performance is checked by means of simulations in Section 6.
# Thermal and Thermomechanical Analysis of Amorphous Metals: A Compact Review
## 2 Thermal Characterization of Amorphous Metals
### Fast Scanning Calorimetry of Amorphous Metals
#### 2.2.2 Expansion of Measurable Transitions by Utilizing FSC  
The utilization of this method extends to the study of rejuvenation processes in amorphous alloys. Annealing metallic glasses above their \(T_{g}\) followed by rapid cooling potentially increases their enthalpy, \(\Delta H\)[62; 63]. The magnitude of the material's rejuvenation has been found to depend on the ratio of effective cooling (\(\Phi_{c}\)) and post-annealing cooling rates (\(\Phi_{i}\)), whereas experimental evidence of rejuvenation saturation with long enough annealing time has also been revealed [64]. Rejuvenation, a structural change that is usually obtained thermally or mechanically, plays an important role in the mechanical properties of amorphous metals in their glassy state, and it is often described as a process where the glass undergoes heavy internal strain in order to introduce structural disorder, which improves the desired properties related to amorphous structures. FSC has significantly improved the cooling rate, which allows for the determination of the change in enthalpy under plastic deformation, ultimately providing information on the relaxation processes in ribbon-like melt-spun metallic glasses [65] and thin films [66]. BMGs also seem to show a sub-\(T_{g}\) shadow glass transition at very high heating rates, correlated to the heterogeneity of the glass, which increases with aging.
# Beyond Static Estimates: Dynamic Simulation of Fire-Evacuation Interaction in Historical Districts
## 3 Results
### Built Environmental Changes
#### 3.3.2 Impact on the Built Environment  
The extent of burned building area exhibits slow linear growth beyond 300 min from ignition, accelerating progressively afterwards. Case 2 demonstrates notable acceleration post-400 min, culminating in a final burned area proportion of 27.8%, substantially higher than the 12.1% observed in Case 16 (Figure 6a,b).  
Figure 6: Fire simulation environmental variables of the historical district: **(a)** Burnt buildings, burning buildings, and impassable areas in Case 2, **(b)** burnt buildings, burning buildings, and impassable areas in Case 16, **(c)** perimeter of the impassable area, **(d)** distance from the ignition point to the centroid of the impassable area, **(e)** road impassable length ratio in Case 2, **(f)** road impassable length ratio in Case 16, **(g)** number of dead-end roads, **(h)** number of T-junctions, **(i)** number of four-way intersections, **(j)** schematic diagram of the main roads in the historical district.  
Conversely, the actively burning area grows differently, experiencing gradual expansion up to 350 min, accelerating between 350-430 min. The result of Case 2 increases from 30.9% to 43.3% during this, surpassing Case 16 by approximately 38%. Beyond 430 min, Case 2 plateaus in growth rate, whereas Case 16 continues to escalate, ultimately achieving 56.5%--approximately 10% higher than Case 2 (Figure 6a,b).  
High-temperature zones typically encompass actively burning areas, demonstrating parallel developmental trends. Notable differences occur in Case 2 within the 400-600 min range, where actively burning area expansion slows while high-temperature zones continue to expand consistently (Figure 6a,b). Perimeter lengths of high-temperature areas similarly diverge beyond 360 min, with Case 2's perimeter expanding more rapidly, exhibiting a more irregular geometry compared to Case 16's elongated elliptical form (Figure 6c).
# Preddiagnosis of Heart Failure (HF) Using Deep Learning and the Korotkoff Sound
## 3 Materials and Methods
### Deep Learning (DL) Network
#### 3.5.5 Data Augmentation  
Deep neural networks are data-greedy learning algorithms, which means that the network's training effect is proportional to the size of the data. Data augmentation technology is an important technical support for neural network algorithms, as it can increase the number of samples and prevent the algorithm from overfitting [64]. In our experiments, we applied the following augmentation strategies to the original feature image.  
**Flipping**: The patient's and healthy person's feature images are flipped 180 degrees horizontally.  
**Add Gaussian noise:** The feature maps of patients and healthy people are augmented with Gaussian noise. The variance of Gaussian noise is set at 0.02 and the mean value is set at 0.2.  
**Contrast enhancement:** During the patient's feature map processing, we generated enhanced images by linear mapping of color values greater than 0.3 \(\times\) 255. In the feature image processing of healthy people, we performed three types of contrast enhancement: the first is to set the color values of less than 0.3 \(\times\) 255 to 0, and the enhanced image is generated by linear mapping; the second is to generate the image by nonlinear mapping of the original image, with the nonlinear coefficient gamma = 0.6. The third step is to non-linearly map the 0.3 \(\times\) 255-255 color value of the original image into the 0.3 \(\times\) 255-255 color image, gamma = 0.9.  
After data augmentation, the KS image feature set of HF patients increased threefold, from 249 to 996; and the image feature set of healthy people increased fivefold, from 116 to 696.
## 3 Silicon Photonics
### Gratings  
We used two types of grating structures in our design. The first are vertical modulated distributed Bragg reflector (DBR) gratings integrated into waveguides and into Echelle gratings (Figure 9a,b, respectively). The second are laterally modulated distributed feedback (DFB) slow-light grating structures, depicted in Figure 9c,d.  
As can be seen in Figure 9c, the pattern transfer of the laterally modulated structures was distorted from the original layout, which was caused by lithography limitations. To account for this, we modified the layout using optical proximity correction (OPC), as shown in Figure 9d top (green pattern). The resulting lateral grating demonstrates sufficiently sharpened corners as well as an increased lateral degree of modulation.
## 2 Gaussian Integers
These technologies depend on the high-speed accuracy of transmitting huge volumes of data in noisy and interfered areas. To overcome such issues, there is a need for the formulation of enhanced error-correction methods. This work proposes a new method in modern systems of communication by modeling the alternant codes built over the Gaussian integers. These codes are constructed based on parity-check matrices produced from finite commutative local rings created out of Gaussian integers modulo \(n\). Thus, by integrating these alternant codes into data transfer processes, we advance the error detection and correction features of data transmission, thereby increasing its integrity and effectiveness. This study confirms that through the society of alternant codes, one can have strong support in responding to the challenges of modern communication systems; therefore, the given work can be regarded as significant progress in the sphere of coding and data transmission.
# Non-Classic Atmospheric Optical Turbulence: Review
## 6 Influence in Applications
### Imaging Systems  
The analysis of imaging systems using random illumination and/or operating through random environments relies on the modulation transfer function (MTF) describing a filter acting on the continuum of spatial frequencies characterizing the system [95]. The MTF can be directly derived from the CSD function, discussed above. In the clear-air (particle-free) atmospheric turbulence, the MTF is defined by the following convolution:  
\[MTF(\Omega)=MTF_{0}(\Omega)\star MTF_{T}(\Omega), \tag{51}\]  
where the first term prescribes the properties of the system in the absence of turbulence, and the second term characterizes spatial filtering due to turbulence, its form depending on long or short exposure options. Parameter \(\Omega\) represents the spatial frequency scaled by wavelength and the collecting aperture diameter. Imaging through various regimes of non-classic turbulence was discussed in References [60, 105-108]. The influence of both non-Kolmogorov power spectra and anisotropy on the MTF profiles, and thus, on the quality of the formed images, is illustrated.
## Methods
### Simulation Quality Assessment  
When using simulations for algorithm training and validation, it must be considered that characteristics such as the spatial distribution of the trees can have severe impacts on a successful registration and the transferability of the method. Therefore, a high similarity between the actual and simulated forest stands has to be guaranteed. Assuming full comparability between the synthetic and the original datasets, similar point statistics should be achieved. Consequently, we evaluated the number of trees, the number of detections per reference tree, and the mean nearest-neighbor distance \(\overline{NND}\) (Equation (3)) for all training datasets overall and on the plot level.  
\[\begin{split}\overline{NND_{A}}&=\sum_{d\in NND_{a }}\frac{d}{|A|}\\ \underset{a\in A}{NND_{a}}&=\min_{b\in A\setminus\{a \}}\|b-a\|_{2}\end{split} \tag{3}\]  
To assess the suitability of the simulated datasets, various statistical analyzes of the previously mentioned variables have been performed. To decide if the values between the original and simulated datasets generally differ, the two sample Wilcoxon rank sum test (comparison of medians) and the F-test (comparison of variances) have been performed. To evaluate the correlation between the original and the simulated values on plot level, the Pearson's correlation coefficient has been calculated. To investigate the overall agreement on plot level, the RMSE has been calculated and a paired Wilcoxon rank sum test has been applied for each plot. We have chosen the nonparametric Wilcoxon test to not rely on normally distributed data.  
Figure 3: (**a**) Height grids of the original airborne laser scanning (ALS) point cloud and (**b**) a corresponding synthetic point cloud with 1 m resolution. The given stand is characterized by predominant oaks, some tall spruces and young beeches. The median height of the detected trees is about 24 m.
## 1 Introduction
### Background  
Unlike Power Transmission Systems (PTSs), PDSs accommodate various loads, including both single-phase and three-phase loads. Single-phase loads are predominantly residential and consist mainly of household appliances such as televisions and refrigerators. Additionally, small-scale agricultural and industrial equipment also operates on single-phase voltage. In contrast, three-phase loads primarily comprise electric motor loads, which are extensively utilized in industrial facilities such as factories, large shopping malls, and agricultural applications. When electric motors are controlled via inverters, issues such as inrush current are mitigated. However, in the absence of such control, as seen in Direct-On-Line (DOL) motors and certain agricultural machinery such as crushers, the substantial inrush current can adversely affect the STPC.  
In order to supply a three-phase voltage, KEPCO currently installs three conductors on Concrete Poles (CPs) to supply a three-phase voltage distribution. Additionally, for low-load applications, three transformers are installed to ensure power supply. However, this conventional power distribution method incurs substantial construction costs, even for low-power loads. Since electricity consumption is low, the corresponding electricity bills are also minimal, making cost recovery virtually impossible. Furthermore, the ongoing maintenance of extensive power infrastructure results in a continuous accumulation of operational costs.  
In addition, in mountainous regions, both high-voltage and low-voltage lines must be installed to deliver three-phase voltage to end-use loads, raising concerns about forest fires due to potential faults such as short circuits. To address these issues, this paper analyzes the topology of a power semiconductor-based STPC for converting single-phase to three-phase voltage, and evaluates its applicability for effective for integration with connected loads. Since the loads in the PDS are directly connected to the Power Transmission System (PTS), a comprehensive PDS analysis is essential when integrating Power Distribution Equipment (PDE). The variables of the PDS were simulated through computer program and subsequently validated via experimental demonstration.  
The main objective of this paper was to develop and validate a SiC-MOSFET-based STPC suitable for LVDSs. Specifically, the research aims to achieve the following:  
* Design a compact and modular STPC topology capable of generating a stable three-phase voltage from a single-phase input.
* Evaluate the performance of the STPC under various load conditions (nonlinear, unbalanced, and motor loads) through both simulation and field testing.
* Assess the applicability of the STPC in real PDS, especially in rural areas where a conventional three-phase supply is economically constrained.
# A Component-Based Approach to Early Warning Systems: A Theoretical Model
## 4 Discussion
[PERSON] and [PERSON] (2018) have also analysed the causes and consequences of natural hazards, confirming that the assessment of natural hazards is crucial as it identifies potential causes and their manifestations, enabling systematic screening of hazard combinations. This understanding helps mitigate risks and ensures the safe operation of critical infrastructures against various natural phenomena [137]. However, natural hazards often exhibit a cascading effect, where the manifestation of one event can trigger another. [PERSON] and [PERSON] (2014) [138] noted that geophysical and hydrological hazards are receptors that can be triggered by most of the other types of hazards, while geophysical and atmospheric causes are the most common triggers. The results of such studies contribute to a broader understanding of complex interactions, which could be integrated into EWSs and rapid response tools. However, their research focused on classifying 21 types of natural hazards [138]. Since the entire monitoring and early warning system relies on data collection through sensors, a flowchart illustrating the application of sensors in EWSs is presented. The diagram clearly depicts the critical areas in data acquisition and the subsequent data flow leading to warnings or alerts sent to affected businesses, entities, or residents. The purpose of this diagram is to visually highlight the essential elements of the system and to highlight the importance of addressing this topic. The development and implementation of EWSs face numerous challenges, primarily due to the complexity of these systems and their components. While significant emphasis is placed on comprehensive and technologically advanced systems, there is also a growing role for low-cost alternatives. These simplified systems are particularly useful in remote communities and less developed countries. The use of basic EWSs with SMS notifications has proven to be effective in reducing damages [139]. Such low-cost micro-electromechanical systems have been developed in Taiwan for on-site earthquake warnings [140], in the Himalayan region for landslides [141], and in Pakistan for flood early warning systems [142]. What these solutions have in common is the use of simple technologies, which are significantly cheaper, even if they are not as precise. However, they can effectively detect and assess environmental changes. The implementation of such systems is particularly meaningful in remote communities and developing countries, where resources for more advanced systems are limited. Further research is needed to analyse the advantages and disadvantages of these systems, providing a deeper insight into their benefits and potential applications.
## 1 Introduction
Unfortunately, the neighbors cannot be guaranteed to successfully receive the data packet again at the next time due to the error-prone nature of wireless links. Furthermore, if the routing mode is changed, the protocol requires a number of control messages and wastes much node energy, because it has to get the positions of all neighbors to detour the holes. These unnecessary and redundant control messages may reduce the entire network performance. Therefore, in order to both increase forwarding opportunities and also prevent packet duplication due to the hidden problem, we propose a semi-distributed beaconless geographic routing protocol, which gives a priority-based delay function for the forwarding node selection into each region in the sub-areas of the greedy forwarding area. In the proposed protocol, nodes in the high prioritized region that are mutually-communicable with each other find a next-hop forwarder immediately in a fully-distributed manner after receiving a sender's broadcast data. On the other hand, nodes in the low prioritized region have to wait until the contention of the high prioritized region is done. Namely, our protocol has two phases for the contention process. After the primary contention process, the low prioritized region begins the secondary contention process for finding an appropriate next-hop forwarder. Such a temporal difference easily allows a sender to know whether there is a partial hole in the high prioritized region without any control messages. Consequently, this information could give the sender an opportunity to select an appropriate next-hop forwarder in the rest of the area without the packet duplication problem. In other words, instead of the primary contention, only the secondary contention process works with an acknowledgement-based forwarding rule in order to prevent packet duplication due to the hidden problem. Experimental results show that the proposed protocol has better performance than previous restricted region-based fully-distributed beaconless routing protocols in terms of the data throughput, the total number of packets, the packet delivery ratio and the end-to-end delay. The rest of this paper is organized as follows. Section 2 shows related work for existing beaconless geographic routing protocols. We describe the proposed protocol in detail in Section 3. Experimental results are given for evaluating the performance of the proposed protocol in the Section 4. Section 5 concludes this paper. Figure 1: An example of next-hop forwarder selection failure in the restricted forwarding area-based beaconless routing protocol.