esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
Due to the varying technological levels of the testing installation (limited number of monitored parameters, depending on the sensors placed on the installation), evaluating trends can be challenging. However, by calculating additional parameters, functional trends become evident, and the early detection of these trends can differentiate between possible failure modes. Considering the above points, combined with the rapid technological development and application of ML techniques, the proposed algorithm represents a significant advancement in naval maintenance. It contributes to the safe operation of naval equipment by immediately reporting parameter deviations and improving the activities of personnel involved in ship operation, thereby enhancing autonomous maritime transport. Furthermore, new research directions are thus outlined, aiming at the development of an application that enables (1) automation of the diagnosis process for naval equipment by integrating the application into the ship's maintenance management system; (2) application of the study to other onboard installations by replicating the steps described in the SIPOC diagram; (3) mapping fault matrices for other naval installations and automating the diagnosis process; (4) updating the application with new technological capabilities (additional sensors for measuring operational parameters) to unequivocally differentiate between the various and complex possible failure modes; (5) creating support materials (operating manuals and training videos) to develop new competencies for operating personnel. This work aims to serve as a foundation for developing an application that integrates the study conducted by creating a pilot model to diagnose faults in naval installations and evaluate operational parameters. The proposed model is intended to estimate functional deviations and assist maintenance decision-making.
# Evaluating Swarm Robotics for Mining Environments: Insights into Model Performance and Application
## 3 Swarm Robotics Design and Simulation
### Swarm Model Design
#### 3.3.4 Honeybee Model  
The honeybee model represents the most advanced approach, specifically designed for high-precision mining tasks such as selective ore extraction in an underground environment. It introduces a hybrid control system that combines centralized and decentralized controls, optimizing exploration, precision of ore extraction, and efficiency of material transport,  
Figure 6: Finite-state machine for the ant model.  
Figure 7: Finite-state machine for the firefly model.  
particularly in settings where accurate localization and targeted extraction are critical. This model builds upon the capabilities of earlier models by incorporating enhanced localization and communication systems, inspired by the foraging behavior and structured division of labor found in honeybee colonies [15; 38]. In the context of ore mining and transportation, the honeybee model's hybrid control system ensures precise ore extraction and an efficient flow of materials from the mining site to the processing site. Scout robots are tasked with identifying resource locations and communicating detailed information back to the central \"hive\" using digital \"waggle dances\". Observation robots process this information to accurately pinpoint resource locations [39], while a master robot oversees the recruitment and deployment of harvesting robots, optimizing the entire process. This approach results in significant advancements in role specialization, communication, and localization capabilities. The operational flow of the honeybee model, as directed by the FSM, is depicted in Figure 8.
# Towards an Efficient Method for Large-Scale Wi-SUN-Enabled AMI Network Planning
## 6 Discussions and Future Work
In fact, the results in Table 8 demonstrate that with its original path loss model, AIDA presents a better processing time than AIDA using the Erceg-SUI model that does not demand a detailed analysis of the terrain characteristics. Even with the results representing satisfactory values, it is important to highlight that the method can be improved to reduce the processing time since processing bottlenecks were identified and deserve improvements in future method updates. For example, for a region with 2375 smart meters used in a special experiment for execution profile analysis, from a total processing time of 244.0 s, the time consumed for the main processes was as follows (Table 9): multihop connection analysis, 11.2 s; clustering with a bottom-up approach, 4.4 s; clustering with top-down approach, 1.6 s. However, loading, selecting, grouping, filtering, and saving datasets summed 169.5 s, especially due to the high volume of disk I/O operations involved. The remaining 57.3 s of the required processing time were consumed by smaller processes, such as calls to external functions (necessary to obtain topographic profile data) and other processes inherent to the execution platform used. In summary, the results obtained with the AIDA method proposed by this study indicate that the experiments carried out (considering case studies 1 and 2) were able to obtain results with coverage and connection values within established values for all evaluated regions. Even when comparing the AIDA method with its original loss model and the AIDA method with a path loss model that does not use a detailed terrain analysis, the results obtained (especially concerning the number of CPs selected) were better. Additional experiments can be performed after the bottleneck mitigation and future modifications expected for the path loss model used by the method. Future work will consider the improvement of the link propagation model, including the existing vegetation model, as well as the type of environment (e.g., urban, dense \begin{table}
\begin{tabular}{l r} \hline \hline
**Process** & **Execution Time (s)** \\ \hline Multihop connection analysis & 11.2 s \\ \hline Clustering with bottom–up approach & 4.4 s \\ \hline Clustering with top–down approach & 1.6 s \\ \hline Dataset load, select, filter, save processes & 169.5 s \\ \hline Miscellaneous & 57.3 s \\ \hline Total: & 244.0 s \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Processing time (execution profile analysis) for a special experiment using 2375 smart meters. urban, rural area). Additional experiments may explore the use of AIDA in expanding and optimizing already installed smart grid communication networks. For rural or sparse regions with less DA equipment or no backhaul infrastructure coverage, there may be a need to assess the network expansion to ensure integration between the AMI and Backhaul networks. However, as these are particular situations, they are not part of the scope of the current study and are reserved for future works.
# Automotive OEM Demand Forecasting: A Comparative Study of Forecasting Algorithms and Strategies
## 4 Experiments and Results
The highest proportion of under-estimates was obtained in ML streaming models based on the Hoeffding inequality, reaching a median of underestimates above 70%. In Experiments 5-10, we consistently observed global models created considering the magnitude of the median of past demand outperformed those created based on demand-type when considering the proportion of forecasts with an error higher to 90%. On the other side, global models based on demand-type scored better on MASE. However, these differences did not prove statistically significant in most cases when comparing top-ranking models of both groups. Having explored different ML models, we then trained statistical models for each product considering demand data available for the last three years (Experiment 11) and contrasted results obtained with the top three models from Experiment 8 (see Table 6). When preparing demand data for the statistical models, we applied differencing to remove stochastic trends. We observed that the ML models outperformed the statistical ones in almost every aspect. R\({}^{2}\)adj was consistently low for statistical models, and though their MASE was better compared to the baseline models, ML models performed better. When assessing the ratio of forecasts with less than 30% error, ML models displayed a better performance. We observed the same when analyzing the under-estimates ratio. Even though the random walk had a low under-estimates ratio, the rest of the metrics indicate the random walk model provides poor forecasts. We consider the best overall performers are the SVR, RFR, and GBRT models, which achieved near-human performance in almost every aspect considered in this research. Even though differences regarding R\({}^{2}\)adj, MASE, and the ratio of forecasts with less than 30% error are not statistically significant between them in most cases, they display statistically significant differences when analyzing under-estimates.
## 3 Proposed Graphic Equalizer Designs
### Third-Octave GEQ using Symmetric Band Filters
The widths of the filters were a compromise between their ability to produce flat regions in their magnitude response and, on the other hand, large gain changes between neighboring bands. The prototype gain was used to calculate the initial interaction matrix, and since the prototype gain was predefined, the initial interaction matrix could be calculated beforehand and stored [6]. The value of the prototype gain was not as crucial here as, for example, in [22], where no iterative step was used. Due to the iterative step, where a new interaction matrix was determined based on the first set of optimized filter gains, the initial WLS solution could be seen as a frequency-dependent, improved prototype gain vector. The first iterative step had the biggest effect on the optimized gains [3], and thus, it was useful in improving the accuracy of the GEQ. However, during the optimization, further iterative steps, while improving the accuracy in certain cases, actually decreased the accuracy overall. This might be because there were only 61 design points in the entire audible frequency range, in which case, the WLS solution optimized these points while simultaneously neglecting the frequency areas between them. Figure 4: All the band filters for the (**a**) third-octave and (**b**) Bark cases. The squares indicate the frequency points used to optimize the bandwidths. Note that not all filters are zero at the Nyquist frequency due to the symmetric band filter design. For the third-octave design, the magnitude response of the first 22 band filters always stayed at approximately 0 dB at the Nyquist limit, whereas the remaining band filters benefited from adjusting the gain \(G_{\text{Nq},m}\) as a function of the peak gain \(G_{m}\), as seen in Figure 5a. The polynomial approximation utilized third-order polynomials for bands \(m=23,24,\ldots,31\):  
\[\mathcal{g}_{\text{Nq},m}=q_{0,m}+q_{1,m}g_{m}+q_{2,m}{g_{m}}^{2}+q_{3,m}{g_{ m}}^{3}. \tag{16}\]  
The polynomial coefficients \(\mathbf{q_{m}}\), obtained for each band filter using curve fitting in MATLAB, are shown in Table 2. This way, the peak dB gain \(g_{m}\) was the only parameter that had to be provided when designing the band filters for the SGE3 design. Note that the coefficients \(q_{0}\) and \(q_{2}\) were always zero, since the values given by the curve fitting were so small that they could be rounded to zero. The polynomial approximation produced accurate results compared to the targets shown in Figure 5a, with the maximum error being \(\pm 0.5\) dB for the 31 st band and less than \(\pm 0.2\) dB for the other bands.
## 3 Comparative Parameters and Dimensional Analysis
### Parameters of Flow Path Characteristics  
In this study, both the initial pressure \(P_{0}\) and ambient pressure \(P_{B}\) are maintained at constant values. Based on the previous equations, it is clear that the inlet and outlet area \(A\) of the cavity is the primary geometric factor influencing the CIDZ area. According to the simplified flow path structure presented in Figure 1, the research focuses on the cavity flow path and the combination of the cavity and tube flow paths, as illustrated in Figure 8. In addition to \(A\), the key geometric parameters include the shape of the cavity and the length of the tube. The aforementioned influencing factors have been non-dimensionalized, as shown in Table 5. In the combined cavity and tube flow path, the diameter of the tube matches that of the cavity inlet and outlet. Under the specified conditions for \(A\), it is sufficient to increase the non-dimensional length of the tube to accurately characterize it.
## 4 Simulation Analysis of Roughness on Shot Peening Residual Stress and Surface Deformation
### Effect Analysis of Roughness on Residual Stress and Deformation of Shot Peening
#### 4.1.4 Analysis of Average Deformation of Different Roughness Surface  
Average the displacement sampling data of the surface stress and strain area of the target with the same roughness after impact; the average displacement of the deformation area after impact is shown in Figure 7. The positive displacement in Figure 7 is caused by pellet impact, consistent with the impact direction in the modeling process.  
Figure 7 shows that when the pellet vertically impacts the rough target surface at 50 m/s, the average material displacement and material accumulation on the target material surface both increase with the increase of surface roughness. The displacement of the small rough surface is relatively small after impact, and the displacement is almost the same under different roughness conditions. The material accumulation is also symmetrically distributed on both sides of the pit. The width of the pit is also approximately the same, with a width of 0.21 mm. The displacement of 10 \(\upmu\)m and 16 \(\upmu\)m roughness surface has little difference, which are 11.6 \(\upmu\)m and 11.5 \(\upmu\)m, but slightly higher than the displacement of 0 \(\upmu\)m after shot peening, which is 10.1 \(\upmu\)m. Due to the relatively si  
Figure 7: Schematic diagram of surface material deformation.  
of a small rough surface, the material displacement is less affected by the shape limitation and the different impact points of the pellet. Therefore, when the target material surface roughness is small, the material surface displacement has little difference.  
The deformation of the large rough surface caused by the impact is more evident than that on the small rough surface with the displacement of 12.7 \(\upmu\)m and 13.1 \(\upmu\)m. Due to the impact position and the more complex surface morphology of the target, the width of the pit caused by impact on the large roughness surface is relatively small than the impact on small surface roughness; the width of the pit is 0.15 mm. The material accumulation presents high accumulate volume, narrow deformation range, and dissymmetric accumulation.
## 5 Conclusions  
In summary, a microwave absorber with an ultra-broadband operating bandwidth is proposed here. The absorber is made of carbon-loaded ABS polymer material and fabricated by a low-cost 3D printing technology based on FDM. The dielectric characteristic of the printing materials is first measured and modeled in the band of interest. By exciting the fundamental DR mode, higher order DR mode and grating mode of the dielectric resonator, an ultra-broadband operating bandwidth can be obtained. The dimensions of the absorber are optimized using the optimization algorithm and the prototype is fabricated and measured. The simulated and measured results show that, over the whole band from 3.9 to 12 GHz, the absorber exhibits an absorptivity higher than 90%, and the relative bandwidth of the absorber can reach over 100%. The operating band of the absorber can cover the whole C-band and X-band. The working principle of the absorber is discussed in detail with the help of numerical simulation, and the absorptivity under incident angles is also simulated and discussed. As a lightweight microwave absorber with an ultrabroad operation band, the proposed absorber can be used to suppress the radar cross-section of the antenna array, which is useful in military applications [71]. Compared with the metamaterial-based absorber, consisting of the metal wire, the absorber proposed here has a wider operating band and can decrease the complexity of the system. In addition, the absorber can be used in the ultra-wideband (UWB) communications systems  
Figure 6: Schematic diagram of oblique-incidence waves for (**a**) the TE mode and (**c**) TM mode. Absorption spectra for oblique-incidence waves with different angles of incidence for (**b**) the TE mode and (**d**) TM mode.  
to enhance the system's performance [72; 73]. Besides, as the DR mode of the resonator is excited in the absorber, the relative permittivity of the material is significant for the performance of the absorber. As shown above, the relative permittivity of the dielectric used here is about 10. If we choose the lossy material with higher relative permittivity the miniaturization of the absorber can be achieved. In the future, other nanoparticles with extremely high dielectric constants may be considered to enhance the dielectric constant of ABS composite material.  
[PERSON] and [PERSON] conceived the idea. [PERSON] did the numerical simulations. [PERSON] fabricated the samples and conducted the measurements. [PERSON] conducted the theoretical analysis. [PERSON] wrote the manuscript, and all of the authors reviewed the manuscript.  
This research received no external funding.  
The authors would like to thank the reviewers for their valuable comments.  
The authors declare no conflict of interest.
## 2 Experimental Database and Methods
### Chi-Squared Test  
Various techniques have been employed to eliminate overfitting in prediction methods. Overfitting occurs when a data-driven model sticks on training data and fails to predict when new data are introduced. Even though algorithmic metrics may show increased accuracy, this may be a false result. The chi-squared test is utilized to ensure that all input features are statistically significant [26] and a difference that may be observed between observed and expected values is due to a hidden relationship between them or at random. It is given by:  
\[\chi^{2}=\sum_{i=1}^{R}\sum_{j=1}^{K}\frac{\left(O_{ij}-E_{ij}\right)^{2}}{E_{ ij}} \tag{3}\]  
where, \(E_{ij}\) the expected value, \(O_{ij}\) the observed value, and \(R\)x\(K\) the total outcome number. The test has given all \(p\)-values below the 0.05 threshold, above which it would be implied that there exists a relationship between the observed and the expected value.
# Networked Unmanned Aerial Vehicles for Surveillance and Monitoring: A Survey
## 2 General Target Surveillance
### Coordination of UAVs  
The deployment and navigation of UAVs involve a number of hot research topics such as motion control [29; 30], path planning [31; 32], trajectory tracking [33], etc. We now introduce some representative work on navigation of the surveillance UAV. The problem of navigating surveillance UAVs to periodically monitor a set of moving targets was studied in [31; 34]. Specifically, to periodically monitor a group of moving ground targets on the 3D terrain, the authors of [31] proposed a reactive sliding mode control algorithm to navigate a team of communicating surveillance UAVs with ground-facing video cameras. Particularly, the authors of [31] adopted a Voronoi partitioning technique to decrease the movement range of the UAVs and to reduce the revisit times of the targets. Reference [34] studied the navigation of a group of solar-powered UAVs for periodical monitoring of a set of scattered mobile ground targets in urban environments. In the considered scenario, the number of targets is larger than that of the UAVs, so that the UAVs need to carry out periodical surveillance. In addition, the authors considered the existence of tall buildings in urban environments, which may block the Line-of-Sight (LoS) between a UAV and a target. The tall buildings may also create some shadow region, so that the solar-powered UAV may not be able to harvest energy from the sun and the surveillance may become invalid. In [34], such a periodical surveillance problem is formulated as an optimization problem to minimize the target revisit time while considering the impact of the tall buildings. To solve this problem, the authors proposed an autonomous navigation algorithm based on rapidly exploring random tree (RRT) to guide the movements of the UAVs in real-time. To further narrow UAVs' area in which they can move and to reduce the target revisit time, a partitioning scheme was also adopted to group targets. The limitation of [31; 34] is that the considered targets are all assumed to move on some given trajectory.  
When these trajectories are unavailable, the accuracy of the target position predictions may significantly decrease and the UAVs may lose some targets. To solve this problem, some search operations can be adopted during the surveillance mission. In the problem of monitoring a moving disaster area using UAVs equipped with ground-facing cameras, the authors of [35] proposed a sliding-mode control algorithm that navigates the UAVs to monitor the faster-moving segment of the disaster area's frontier. The authors in [35] proved that the proposed method tracks the fastest spreading parts of the frontier of the moving disaster area and converges to the global maximum in the optimization problem considered. The proposed method requires the initial positions of the UAVs to be near the frontier of the moving disaster area.
## 5 Simulation Results and Analysis  
In this section, our work's performance is evaluated from four aspects. First, the effects of different MinPts and Eps values on DOIDS were compared. Second, a set of appropriate parameters is selected to compare the average hop count, delivery rate, and energy tax of the network under a normal, malicious attack without IDS, and malicious attack with IDS. Then, we simulate and analyze the defense effects of DOIDS under different types of attacks. Finally, the detection rate and false detection rate of IDS and SARA against route attacks are compared.
# Assessment of Cardiovascular Disease in Autosomal Dominant Polycystic Kidney Disease
## 5 Heart Rate Variability (HRV)
One study conducted on 65 patients evaluated the HRV in mild hypertensive patients with ADPKD (21 patients) versus patients with hypertension and organ damage (20 patients) and versus healthy controls (24 patients). HRV was more significantly reduced in ADPKD patients than in healthy controls, and the LF and LF/HF ratio were higher than in healthy controls. In hypertensive patients with organ damage, the same level of HRV abnormality was observed as in the ADPKD patients [41]. Since HRV variation is also detectable in young ADPKD patients without hypertension and/or CKD, it plays an important role as a predictive risk factor and therefore can be detected early in the disease course. The evaluation of HRV can contribute to risk stratification by evaluating the imbalance of the sympathetic/parasympathetic system linked to cardiac complications such as coronary artery disease, arrhythmia events, and sudden cardiac death. In conclusion, although no specific guidelines or standardized protocols for HRV measurement are available in ADPKD patients, it is an important, easy, and economic tool which contributes to the better assessment of cardiovascular risk in this cohort of patients.