esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Problem Description
### Weighting Factor Estimation Problem from Historical Data  
Noise and unnecessary information in the actual historical data interfere with the estimation of the weighting factors of the objective function. Therefore, we use the scheduling results (starting time of operations, production sequence of operations) and the parameters of the scheduling problem as input/output data. The possibility of using these input/output data for system identification to estimate the model is considered, but we refrain from using them because the relationship between the schedule and the weighting factors is difficult to ascertain. It is still difficult to estimate the weighting factors from the solution of the multi-objective scheduling problem because most of the scheduling problem is NP (non-deterministic polynomial-time) hard, and there is no one-to-one relation between the solution of the scheduling problem and the correct weighting factors of the objective function. Another challenge is how to find effective features from the input/output data. The problem can be used in many realistic situations due to the following reasons.  
1. When the plant is operated by human experts, they will set appropriate weighting factors in daily scheduling. The derived weighting factors can be used to understand the expert knowledge of human operators.
2. The solutions of the scheduling system can be used to set appropriate weighting factors of the objective function when an automated scheduling system is equipped in a real factory.  
The outline of the weighting factor estimation is shown in Figure 1. In this study, the near-optimal solutions of the scheduling problem are regarded as the actual data for estimating the weighting factors. We assume a parallel machine production scheduling problem. The processing time, job delivery time, and setup cost are given as input data. The output data includes job assignments, processing order, and objective function values. The weighting factor is estimated using these input/output data. Machine learning methods and inverse optimization are used to estimate the weighting factors. The model is evaluated by the mean squared error (MSE) between the estimated weighting factor and the true weighting factor. MSE is expressed by Equation (1), where \(y_{i}\) is the estimated weighting factor, \(\hat{y_{i}}\) is the true weighting factor, and \(N_{data}\) is the number of data.  
\[\text{MSE}=\frac{1}{N_{data}}(y_{i}-\hat{y_{i}})^{2} \tag{1}\]
## 2 Methodology
### LoRa-IoT Design Requirements  
Before designing the IoT terminal, it is important to define the design requirements of the terminal and the satellite receiver to ensure perfect communication between both of them. The design requirements of the IoT ground terminal are as follows:  
1. The IoT terminal shall be able to operate autonomously and under remote object control for different use-cases.
2. The IoT terminal shall be able to transmit LoRa-CSS packets.
3. The IoT terminal shall be able to transmit and receive LoRaWAN packets.
4. The IoT terminal shall be able to provide interfaces to sensors.
5. The IoT terminal shall be able to provide a transceiver power within the allowed range.
6. The IoT terminal shall be able to uplink data to the satellite with the specified frequency range.
7. The IoT terminal shall be able to withstand space environment temperatures.
8. The IoT terminal shall be able to carry a portable battery that power the terminal for a duration of 7 days.
9. The IoT terminal shall be able to carry a memory to store the collected data from sensors.
10. The IoT terminal shall be able to carry an antenna and a GPS.
11. The IoT terminal shall be able to transmit 50 bytes per LoRa message.  
Moreover, the design requirements of the IoT-LoRa payload (satellite receiver) are as follows:  
1. The LoRa Payload shall be able to fit within the 3U CubeSat platform.
2. The LoRa Payload shall be able to withstand the operating temperature of the space environment.
3. The LoRa Payload shall be work within the specific frequency range selected by IoT terminal.
4. The LoRa Payload shall be able to receive data from the ground terminal.
5. The LoRa Payload shall follow the CubeSat Space Protocol (CSP)
6. The LoRa Payload shall have high sensitivity Received Signal Strength Indicator (RSSI).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Simulation Parameters** & **SSO Value** & **Lower Inclined Value** \\ \hline Semi-major axis & 6928 km & 6928 km \\ Inclination & 98\({}^{\circ}\) & 0\({}^{\circ}\)–90\({}^{\circ}\) \\ Eccentricity & 0\({}^{\circ}\) & 0\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Orbital Elements of SSO and Lower Inclination Orbit Satellites.  
The LoRa Payload shall consume low power.
* 8 The LoRa Payload shall have a weight up-to 200 g.
## 3 Results
### Noise Emission  
The magnetic core under test was placed on a cork mat in the center of the room, where the background sound pressure level measured with the magnetic core de-energized (at the magnetic flux 0 T) was about 37 dB. Then, the equivalent continuous sound pressure levels were evaluated during core excitation from 0.4 T to the saturation flux density at 50 Hz, which is the characteristic operating frequency for distribution transformers. The same procedure was used to measure the noise of the reference, not the bonded core. The obtained results are shown in Figure 5.  
It is clearly visible that, in the case of the core bonded with sols, the noise starts to go beyond the background level for a much higher operating flux density than for the reference core, 1.4 T compared to 1.25 T, respectively. This means that the reference core starts to be noisy at a much lower operating load than the core bonded with sols. The observed effect can be explained by the counteraction of the applied sol-based bonding layers against the magnetostriction of amorphous steel [16]. Due to the applied bonding, the core vibrations caused by the magnetic forces between the laminations are being decreased, as a result of which the core noise also decreases. It is also worth noting that, at a saturation flux of about 1.5 T (saturation), the difference in noise levels between the reference core and the bonded core is 6 dB, which means that the reference core is twice as noisy as the bonded core.  
Additional confirmation of the noise reduction effectiveness due to the applied bonding system is clearly visible in the frequency spectrum of the sound pressure levels in the range from 100 Hz to 10 kHz, which is presented in Figure 6.  
Figure 5: Sound pressure levels at 50 Hz operating frequency as a function of flux density measured for the reference and the sol-bonded core.  
The spectrum shown clearly shows that, in most of the peaks in the frequency domain, the sound pressure amplitudes for the core bonded with sols are much lower than for the reference core, which is particularly visible for the frequencies of 400 Hz and 1 kHz, and harmonic frequencies of 1.1 kHz, 1.2 kHz, etc.
## 6 Limitations of UAVs
### Integration with Other Data Sources  
Another limitation is the integration of UAV datasets with other data sources. While multimodal datasets that combine UAV data with other sensor inputs (such as satellite imagery, GPS data, and environmental sensors) provide richer insights, they also introduce complexity in data alignment and fusion. The AU-AIR dataset, which includes visual data along with GPS coordinates and IMU data, exemplifies the potential and challenges of such integration. Ensuring the synchronized and accurate fusion of data from multiple sources remains a technical hurdle that needs addressing.
## 4 Conclusions and Future Research Directions  
Our study provides a focused analysis on specific examples of the wSAA framework, which is commonly used in prescriptive analytics for dealing with uncertain optimisation problems featuring non-linear objectives. We highlight a potential deficiency of the wSAA framework: when data samples are limited, the minimum relative regret--an indicator of the deviation between a data-driven policy and a full-information oracle--can approach one. This is illustrated through the analysis of two CSO problems: the PDP and the SMP. These analyses consider the wSAA framework's different capacities for extrapolation and interpolation. The primary implication of our finding is that the wSAA framework may not perform well when data is limited. This insight is of value to both researchers and practitioners who utilize the wSAA framework in their work, providing them with an important perspective on its limitations and potential areas for improvement.  
The findings of this study open up several avenues for future research. The first and most immediate would be to examine ways to mitigate the high relative regret identified in this study, especially when working with limited data samples. This could involve developing new frameworks or improving upon the existing wSAA model. Another direction could be to analyze whether the same deficiency apply to other prescriptive analytics approaches. This research could contribute to a broader understanding of how these models perform under the condition of limited data and inform the development of more robust strategies. Lastly, future research could apply our findings to more specific, practical scenarios, examining how the limitations of the wSAA framework impact decision-making in various industries. This could lead to industry-specific recommendations for enhancing the utility of prescriptive analytics approaches.  
Conceptualization, S.W.; methodology, S.W. and X.T.; software, X.T.; validation, S.W. and X.T.; formal analysis, S.W. and X.T.; investigation, S.W. and X.T.; writing--original draft preparation, S.W. and X.T.; writing--review and editing, S.W. and X.T.; visualization, X.T.; supervision, S.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
# An Alternative Procedure for the Description of Seismic Intensity Parameter-Based Damage Potential
###### Abstract  
This study presents an alternative statistical approach for describing the damage potential of R/C structures using various seismic intensity parameters. By employing a comprehensive set of 34 Intensity Measures (IMs) and three well-known Global Damage Indices (DIs), a correlation study was initially conducted to assess the predictive capacity of the selected IMs in estimating a structure's damage grade. Multiple regression analyses were performed to determine the most suitable IMs for damage prediction, utilizing only the highest correlated IMs with the selected DIs, employing both conventional regression models and a novel alternative approach by transforming the IMs for each predicted DI. The IMs were modified through exponentiation, using powers directly dependent on each IM's rank correlation coefficient with the respective DI. Employing the rank correlation coefficients to modify the IMs effectively amplifies the influence of those that present the highest agreement with the observed damage. The results demonstrate that energy-related and spectral-based IMs correlate highly with structural damage. The generated models exhibit high accuracy in predicting the observed damage grade, with the models based on the proposed approach showing improved performance in estimating the sustained damage grade while maintaining computational efficiency in terms of their computational time and results' accuracy.  
Keywords:seismic Intensity Measures; Damage Indices; damage analysis; correlation study; multiple regression analysis; structural assessment; damage prediction
## 3 Results and Discussion
### Element of Materials on the Side Wall of the Electrode  
Figures 12 and 13 show the results of the analysis of the alloy elements mapped on the surface of the copper electrode and the average volume contents of copper, carbon, and iron in the region marked in Figure 5 for the conventional and step cylindrical electrodes, respectively. The weight percentage (%wt) of alloy elements was averaged by EDX, analyzing at least three positions in the different regions of the electrode surface, as shown in the SEM images. These positions included an area where the surface is bright and clear, an opaque surface, and the surface over the boundary joint area. Figure 12a shows the elemental mapping in the regions close to the end of electrode (EDX1 in Figure 5a). Carbon (C) of approximately 52.99%wt and ferrous (Fe) of approximately 35.01%wt accumulated more in the region that further away from the end of electrode (EDX2 in Figure 5a) as shown in Figure 12b which results element of carbon by approximately 44.57%wt and ferrous by approximately 27.81%wt. This is evidence of the severity of repeated sparks occurring in the process of joining the wall debris around the wall within the drill hole, leading to higher carbon and iron accumulation on the surface of the electrode. However, the content of copper (Cu) of approximately 26.29%wt on the step cylindrical electrode (EDX3 in Figure 5b), as shown in Figure 13a, was higher than the copper content of approximately 12.00%wt on the conventional electrode at the same region (Figure 12a). This can be explained by the reduction in secondary sparking occurring on the side surface of the electrode due to the removal of debris and bubbles during machining, which easily escape from the sparking area. In addition, the increased escape area with the increased gap distance in the machining process when using the step cylindrical electrode led to the elimination of secondary sparking, resulting in low damage and alloy accumulation on the surface of the electrode (Fe \(\approx\) 0.09%wt, C \(\approx\) 40.30%wt, and Cu \(\approx\) 57.61%wt), as shown in Figure 13b. These results confirmed that the machining performance of the electrical discharge machining process for deep hole drilling can be improved by using a step cylindrical electrode.  
Figure 11: Profiles of the machined surface on the side wall of the hole drilled with the step cylindrical electrode (SCE). (**a**) SCE \(\O\) 12 mm; (**b**) SCE \(\O\) 9 mm; (**c**) SCE \(\O\) 6 mm.
# Multitarget Search Algorithm Using Swarm Robots in an Unknown 3D Mountain Environment
## 4 Multitarget Ground Search Algorithm for Swarm Robots in a 3D Mountain Environment  
### 3D Virtual Force Model Roaming Search  
When no target signal is obtained, each robot conducts roaming search to quickly detect more areas. Here, a virtual force model is adopted [28]. When the distance between robots is less than \(\min(d_{com},d_{tar})\), the robots repel each other, making the robots spread quickly to quickly evaluate the search area. To simplify the calculation, a robot is repulsed only by the nearest two robots.  
Assuming that the robot nearest to robot \(R_{i}\) is \(R_{i_{1}}\) as shown in Figure 2, the positions of the two robots are \(\mathbf{R_{i}(t)}=\left[xu_{i|t},yu_{i|t},zu_{i|t}\right]\) and \(\mathbf{R_{i_{1}}(t)}=\left[xu_{i_{1}|t},yu_{i_{1}|t},zu_{i_{1}|t}\right]\). In addition, \(dr_{i_{2}|t}\leq\min(d_{com},d_{tar})\). The repulsive force of \(R_{i_{1}}\) on \(R_{i}\) is calculated using Equation (7), and the repulsive force direction is that the former points to the latter:  
\[\mathbf{f_{i,t_{1}}(t)}=\frac{c\cdot l_{m}^{2}}{dv_{i_{1}i_{1}|t}^{3}}\Big{[}\left( xu_{i|t}-xu_{i_{1}|t}\right)\Big{,}\left(yu_{i|t}-yu_{i_{1}|t}\right)\Big{,} \left(zu_{i|t}-zu_{i_{1}|t}\right)\Big{]} \tag{7}\]  
where \(\mathbf{f_{i,t_{1}}(t)}\) is the repulsion of \(R_{i_{1}}\) on \(R_{i}\) at time \(t\). \(l_{m}\) strengthens the obstacle avoidance distance, and \(c\) optimizes the robot movement path.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{tabular}{c} **Serial** \\ **Number** \\ \end{tabular} & \begin{tabular}{c} **Robot** \\ \end{tabular} & \begin{tabular}{c} **Target Type** \\ \end{tabular} & \begin{tabular}{c} **Intensity of the** \\ **Response** \\ \end{tabular} & \begin{tabular}{c} **Nearest** \\ **Communication Robot** \\ \end{tabular} & \
\begin{tabular}{c} **Distance from** \\ **Communication Robot** \\ \end{tabular} &
\begin{tabular}{c} **Priority** \\ **Sorting** \\ \end{tabular} \\ \hline
1 & \(R_{2}\) & II & - & \(R_{14}\) & 213.2349341 & 11 \\
2 & \(R_{3}\) & II & - & \(R_{19}\) & 209.3224293 & 9 \\
3 & \(R_{5}\) & I & 2.099988287 & - & - & 2 \\
4 & \(R_{9}\) & II & - & \(R_{14}\) & 33.53008801 & 5 \\
5 & \(R_{11}\) & II & - &If the two robots closest to \(R_{i}\) satisfy \(dv_{i,l\prime}\leq min(d_{com},2d_{tar})\), then the virtual force on \(R_{i}\) is as shown in Figure 2. In the figure, \(xu_{i_{1}|t}>xu_{i_{2}|t}>xu_{i_{2}|t},yu_{i_{1}|t}>yu_{i_{1}|t}>yu_{i_{2}|t}, zu_{i_{1}|t}>zu_{i_{2}|t}>zu_{i_{1}|t}\), and the virtual forces \(\mathbf{f_{i,i_{1}}}(\mathbf{t})\) and \(\mathbf{f_{i,i_{2}}}(\mathbf{t})\) satisfy Equation (7). The virtual forces applied to the robot satisfy the vector sum \(\mathbf{f_{i}}(\mathbf{t})=\mathbf{f_{i,i_{1}}}(\mathbf{t})+\mathbf{f_{i,i_{2}}}(\mathbf{t})\). The speed of the robot in the roaming search state is the direction indicated by the virtual force; that is, the next expected speed of the roaming search is as Equation (8):  
\[\mathbf{Ve_{i}}(\mathbf{t+1})=Vm\frac{\mathbf{f_{i}}(\mathbf{t})}{\|\mathbf{f_{i}}(\mathbf{t})\|} \tag{8}\]
## 3 Results
### Influence of the Zenith Angle on the Destruction of Mud Plugs and Well Cleaning  
Analyzing Figure 5a it can be seen that, with an increase in the zenith angle \(\alpha\) from 35\({}^{\circ}\) to 55\({}^{\circ}\), when cleaning with a Newtonian liquid, the mud removal decreases by 40-56%, depending on the flow rate of mud No. 1 \(Q=1.25\div 1.67\times 10^{-3}\) m\({}^{3}\)/s and drill speed \(n=0.83\div 2.50\) s\({}^{-1}\). At a flow rate of mud No. 1 \(Q=1.25\times 10^{-3}\) m\({}^{3}\)/s, a sufficient upward flow rate is not provided and cleaning deteriorates by 51-60% in the presence of the three half-waves pipe, up to the complete absence of 100% mud removal during rotation of the straight pipe and the pipe with the first half-wave.  
The subsequent increase in the zenith angle \(\alpha\) from 55\({}^{\circ}\) to 70\({}^{\circ}\), ceteris paribus, has a positive effect on the quality of well cleaning, and cuttings removal is improved by 30-48%.  
When comparing the results of experiments, with an increase in the zenith angle \(\alpha\) from 35\({}^{\circ}\) to 70\({}^{\circ}\), the mud transportation deteriorates by 17-42%. In Pipe \(L_{1}\), deterioration is by 34-60%, but is still insufficient for pipe \(L_{0}\).  
From Figure 5b it can be seen that when using a pseudo-plastic liquid as a mud, with an increase in the zenith angle \(\alpha\) from 35\({}^{\circ}\) to 55\({}^{\circ}\), with a flow rate of mud No. 2 \(Q=0.83\div 1.25\times 10^{-3}\) m\({}^{3}\)/s, the mud removal decreases by 29-46%, when rotating at a frequency of \(n=0.83\div 2.50\) s\({}^{-1}\) pipe with the first half-wave, and by 32-44% when rotating the direct pipe \(L_{0}\) and DS with three half-waves.  
A further increase in the zenith angle \(\alpha\) from 55\({}^{\circ}\) to 70\({}^{\circ}\), ceteris paribus, provides an increase in the removal capacity of the cuttings by 25-44%, 24-48% and 25-43%, with pipe \(L_{0}\), pipe \(L_{1}\) and pipe \(L_{3}\), respectively.  
When comparing the results of experiments with an increase in the zenith angle from 35\({}^{\circ}\) to 70\({}^{\circ}\), by passing the angle \(\alpha=55^{\circ}\), the mud transportation deteriorates by only 13-30%, with a flow rate of pseudo-plastic liquid \(Q=0.83\div 1.25\times 10^{-3}\) m\({}^{3}\)/s and rotation of the straight pipe \(L_{0}\), pipe \(L_{1}\) and pipe \(L_{3}\).  
Experimental data on the change in the quality of well cleaning, depending on the zenith angle \(\alpha\), confirm the presence of a critical angle \(\alpha=55^{\circ}\) in the zone with a range of 45-60\({}^{\circ}\).
## 3 Experimental Results and Analyses
### Sample Deformation Characteristics
#### 3.3.2 Relationship between the Deformation and Loading Stage  
The relationship between the loading stage and the strain, as shown in Figure 9, demonstrated that the axial strain varied approximately linearly with the fatigue loading stage for a given loading frequency. In addition, the rate of change of the axial cumulative strain in each stage of the loading process remained nearly constant (this was relevant to the sample loading conditions), whereas the rate of change of the radial cumulative  
Figure 8: (**a**–**d**) Axial deformation characteristics of the argillaceous samples tested under the different increasing-amplitude fatigue levels. The loading frequencies were 0.5 Hz, 0.2 Hz, 0.1 Hz, and 0.05 Hz, respectively.  
strain gradually became larger. The change in the volumetric strain was divided into three stages: (1) a steady increase, (2) a steady decrease, and (3) a sharp decrease toward negative values. This trend also indicated that, as the stress increased, cracks were generated and expanded within the sample, causing the volume of the sample to change from initially compressed to expanded. The sample went through a shear expansion process before it was damaged. The location of the expansion point appeared to vary owing to the different loading stages. For the different loading frequencies, the cumulative strain occurring at the point of final damage also varied. Overall, this showed that the higher the frequency, the higher the accumulated strain at the final deformation. This result corresponded well with the influence of the frequency on the fatigue life, indicating a consistency between the before and after results.
###### Abstract  
Color preservation of esthetic dental restorative materials in the oral environment represents, besides longevity, a concern, and there is still limited knowledge related to the effect of aging on the optical behavior of resin matrix ceramics. The study analyzed the finishing and thermocycling of resin matrix ceramic material surfaces, in order to assess their consequences on optical properties. Five resin matrix CAD/CAM ceramics, namely a polymer-infiltrated ceramic and four types of nanoparticle-filled resins, were selected for the study, and finished by polishing and glazing. Thermocycling was chosen as the in vitro aging method. Surface microroughness, optical and hardness evaluations were achieved before and after artificial aging. Statistical analyses were performed with IBM SPSS Statistics software at a significance value of \(p<0.05\). Micro-roughness values increased after thermocycling, but were kept under the clinically accepted values. The optical characteristics of resin matrix ceramics were not significantly modified by thermocycling. Values of the glazed samples became closer to those of the polished ones, after hydrothermal aging, even if the differences were insignificant. Thermocycling significantly decreased the microhardness, mainly for glazed samples. This could be the consequence of glaze removal during thermocycling, which means that glazes provide a surface protection for a limited time.  
resin matrix ceramics; thermocycling; optical properties; surface analysis Article