esa-sceva/satcom-chunk-collection huggingface dataset:
# Digital Twin-Based Automated Guided Vehicle Scheduling: A Solution for Its Charging Problems
## 3 Principle of the Proposed DTDAS
### Scheduling Model
The waiting time for the AGV to perform this transportation task is determined by Equation (12). Equations (13) and (14) ensure that the time sequence of the transportation task meets the requirements. Equation (15) ensures that from the second transportation operation of each order, the start time of the transportation task must be greater than the processing completion time of the machine. Equation (16) ensures that each transportation task will only be executed once. Equation (17) ensures that the start time of this processing operation must be greater than the end time of the previous transportation operation. Among them, the first transportation operation is transported from the material storage area to the corresponding machine, so there is no corresponding processing operation. Minimizing the total energy consumption. Energy consumption is expressed by AGV battery SOC. The total energy consumption can be expressed by:  
\[\min E=\sum_{a=1}^{s}{(SOC_{0}^{a}+SOC_{rc}^{a}-SOC_{f}^{a})}, \tag{18}\]  
Subject to:  
\[SOC_{rt}^{a}\geq 20-d^{+},\;\forall a\in m, \tag{19}\]  
\[\text{Case 2}:C_{ij}^{a}=0\text{ \&\&\ (tSPO_{ij}+tPO_{ij}\geq tCA^{a}+tO_{ij}^{a})\]0  
\[\text{Case 2}:C_{ij}^{a}=0\text{ \&\&\ (tSPO_{ij}+tPO_{ij}\geq tCA^{a}+tO_{ij}^{a})\]1  
\[E_{ij}^{a}=\left\{ \begin{array}{ll}{C_{ij}^{a}(\mu_{iat}WO_{ij}^{a}+\mu_{emp} (tMO_{ij}^{a}+tBO_{ij}^{a})+\mu_{ij}tCO_{ij})},&{C_{ij}^{a}=1}\\ {C_{ij}^{a}(\mu_{iat}WO_{ij}^{a}+\mu_{emp}tO_{ij}^{a}+\mu_{ij}tCO_{ij})},&{C_{ ij}^{a}=0}\end{array} \right., \tag{22}\]  
\[SOC_{f}^{a}=SOC_{0}^{a}+\sum_{i=1}^{n}{\sum_{j=1}^{p_{i}}{C_{ij}^{a}SOC_{ij}^ {a}-\sum_{i=1}^{n}{\sum_{j=1}^{p_{i}}{E_{ij}^{a}}}}}, \tag{23}\]  
\[\mu_{ij}=\beta{mass_{ij}}+\kappa{v^{2}}+\chi, \tag{24}\]  
Equation (19) is the AGV real-time power soft constraint, which ensures that the AGV will not perform transportation tasks when the power is less than 20%. Where, \(d^{+}\) is a small positive number. Equation (20) ensures that the AGV battery will not be overcharged due to each charging process. Equation (21) calculates the total power charged to \(A_{a}\) when completing all transportation tasks. Equation (22) calculates the total electricity consumed by \(A_{a}\) in the transportation operation. Equation (23) calculates the final power of \(A_{a}\) when all transportation tasks are completed. The energy consumption rate of AGV during transport is determined in Equation (24) for different transport operation loads. Multi-objective evaluation. The objective function consists of two components: makespan and energy consumption, which can be expressed as:  
\[f=\delta_{1}MS+\delta_{2}(1-\delta_{1})E, \tag{25}\]  
where \(\delta_{1}\) is the weight coefficient of MS, and \(\delta_{2}\) is a coefficient to adjust the two objectives in the same order of magnitude.
## 1 Introduction
The first comparison of sensitivity and response of the spectral and refractive detection approaches was proposed by [PERSON] [13], who made use of a Sagnac interferometer for detection of the probe phase changes; however, much simpler systems using Kerr lens-induced beam displacement or beam propagation effects have been investigated as early as the 1990s [14; 15] and subsequently used extensively in other papers by the same and other groups [16; 17; 18; 19], and more recently by [PERSON] et al. in [12]. Here, the radially dependent phase changes are detected by measuring the Raman-induced Kerr lens effect on the probe beam propagation using a detector placed behind a pinhole in the beam far field. Detection of the nonlinear Kerr lens by measurements of the beam propagation modification using a small-size detector in the far field is the basis of the well-known Z-scan technique [20], originally proposed for third-order nonlinear optical characterization of transparent samples. In this technique, the sample position (Z) is scanned along the propagation direction of a focused laser beam, and the changes in the beam profile due to the nonlinear Kerr lens produced by the beam itself are detected by measuring the transmitted intensity through a pinhole in the far field (so-called closed-aperture detection). A direct relationship between \"true\" Z-scan measurements, i.e., obtained by scanning the sample position, and femtosecond impulsive stimulated Raman experiments was proposed in [21]. However, in that paper, the occurrence of cumulative thermal effects [22] due to the use of a high-repetition rate laser oscillator was not properly addressed, thus preventing correct interpretation of the observed signals. In fact, in [21], an 80 MHz repetition rate oscillator was used, corresponding to 12.5 ns laser pulse spacing, and during the measurements, the sample was illuminated for 1 ms, i.e., with a train of closely spaced pulses; however, as shown in [22; 23], in these conditions, the observed Z-scan is essentially due to thermo-optical effects, which actually produce a large negative \"nonlinear\" refractive index that overwhelms the much smaller RIKE contribution. As a matter of fact, one major difficulty in experiments aimed at detecting ultrafast effects such as femtosecond SRS by Z-scan measurements in setups based on low-energy femtosecond oscillators is the occurrence of thermo-optical effects produced by the inherently high repetition rate of self-Kerr lens modelocked oscillators. The unambiguous detection of the RIKE component in Z-scan measurements is the goal of the current work. This objective was pursued by means of a Fourier-transform ISRS setup based on a femtosecond laser oscillator equipped with a cavity dumper, allowing control of the repetition rate to virtually eliminate cumulative thermal effects. By performing Z-scan measurements with the modulated Z-scan technique (MZ-scan) in different pump-probe conditions and delays on a dibromomethane (CH\({}_{2}\)Br\({}_{2}\)) sample, we were able to observe clear variations in the Z-scan traces, showing the unmistakable signature of coherent Raman oscillations.
# Real-Time Estimation for Cutting Tool Wear Based on Modal Analysis of Monitored Signals
## 4 Method Validation and Application  
### Validity of Tool Wear Estimation  
Tool wear estimation is mainly based on the modal features of vibration signals in this work as the cutting vibration is highly sensitive to tool wear and can be measured with fairly good accuracy. The first step is to filter out the noise and remove non-cutting signals using EEMD. Then the main features were identified and captured from the vibration signals using statistical methods.  
Regardless of the emergency, tool wear should be a degradation process, similar to the wear process of most of mechanical part, and is an irreversible process. The actual degradation process of tool should be a monotonous process, and difficult to be obtained in the actual production, so the tool degradation process can be observed only through indirect methods. According to the method in the 'Feature Extraction of Test Data' section, vibration signals of test set are processed. Figure 8, shows the frequency spectrum of vibration signal IMF1 at different times of a milling experiment.  
The feature value, Gn, can be calculated by the similar formula in Section 3.2 and the results are shown in Table 3.  
The number of milling can be converted to the time according to the milling experiment scheme. So the estimated tool wear and the actual tool wear are calculated and compared in Table 4, simultaneously, to get the accuracy of the results, absolute error is also calculated and shown in table below.  
Figure 8: Frequency spectrum of vibration signal IMF1 at different times of milling experiment. (**a**) Frequency spectrum of vibration signal IMF1 at 85 th milling experiment; (**b**) Frequency spectrum of vibration signal IMF1 at 185 th milling experiment; (**c**) Frequency spectrum of vibration signal IMF1 at 285 th milling experiment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Cut** & **85** & **185** & **285** \\ \hline Gn & 1.1210 & 2.5329 & 2.9481 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Gn value of in the milling.  
These calculated results in table above suggest that the method can be used to estimate tool wear. In addition, Equation (11) can be optimized to improve accuracy, which will be researched in future work.
## 5 Graph Simulations and Results
### Results
#### 5.2.3 Shortest-Path Mode over Non-Trivial Link Metrics  
The use of adaptive jitter in the shortest-path mode of route discovery is also beneficial, although not due to the same reasons (RREQ path quality improvement, mainly) as in the shortest-delay mode. The fact that routers are able to forward RREQs indefinitely, any time that they receive an RREQ with a better route than the last forwarded RREQ, entails that RREQ flooding ideally provides the optimal route between source and destination, if it terminates successfully (without packet losses, collisions or inaccuracies in link quality estimation). However, the shortest-path mode with static jitter (\"standard\" jitter, window jitter) presents a relevant drawback: as every packet may forward each RREQ several times, and the source may send several RREP to the same destination, the probability of packet collisions and route discovery failure also increases; more significantly for dense networks. Figure 13a,b depicts the evolution of RREQ retransmissions and RREP transmissions per route discovery, when the network density increases. It can be observed that the use of adaptive jitter, by increasing the quality of the firstly-discovered paths, entails a reduction in the number of control packets per route discovery (RREQ retransmissions and route replies) up to \(30\%\), with respect to the static configurations.  
Figure 14 depicts average RREQ delays for the different types of jitter when using shortest-path (sh-p) and shortest-delay (sh-d) modes. For any given type of jitter, the delay for the shortest-path mode is always longer or equal to the delay for the shortest-delay mode: in the later, the flooding terminates when the destination receives the first RREQ; in the former, the flooding terminates when the destination receives the RREQ through the best path, which can correspond to the first or to a posterior reception. More interestingly, two observations can be drawn from Figure 14. In the first term, RREQ delay caused by adaptive jitter decreases with the network density (a result consistent with what was depicted in Figure 12), while, in contrast, \"standard\" jitter and window jitter are present in the shortest-path mode, a roughly constant delay with respect to network density. In the second term, the gap between RREQ delays in shortest-path and shortest-delay modes, _i.e._, the additional delay caused by reception in the destination of better RREQ packets later than the first, is different for each type of jitter. The adaptive type of jitter has the smallest gap between modes, which is consistent with the previous observation about the quality of first-received RREQs at the destination. The significant difference between modes  
Figure 12: Route discovery delay, shortest-delay RREQ flooding. (**a**) Fixed grid; (**b**) Constant router density.  
when using window jitter is another indication, in turn, of the poor performance achieved by this type of jitter in networks with diverse link qualities, as the non-trivial link metrics scenarios considered in this section.
# Isotropic IoT-Based Magnetic Flux Density Meter Implementation for ELF Field Measurements +
## 7 MF-EH Experimental Results  
The experimental measurements showed that at a relatively close distance from the tested magnetic source (hair dryer), the (DC) harvesting voltage can reach up to 4.2 volts, as shown in Figure 11b. The storage capacitor (\(C_{s}\)) of the system can be fully charged in 3 min with a 33 \(\upmu\)T magnetic field caused by the operation of an electric hair dryer, as depicted in Figures 12 and 11a, respectively. The storage capacitor's (\(C_{s}\)) charging curves demonstrate that the maximum charging voltage (\(V_{max}\)) is proportional to the ambient magnetic field strength, as shown in Figure 12. The dynamic behaviour of the rectifier diodes depends to a large extent on the levels of the received signal, consequently affecting the equivalent resistance of the rectifier circuit (\(R_{thleevin}\)), with the change in the time constant charging (\(t_{charge}\)) as a final result. The experimental measurements were also extended to the utilization of the alternating magnetic field near medium voltage substations of the electrical public distribution network. Firstly, magnetic field strength measurements were performed around the perimeter of the metal cabin where the transmission lines of the distribution network are driven, with the Narda EHP-50C PMM field meter, detecting magnetic field values of up to 60 \(\upmu\)T (at peak time within an urban environment). The experimental setup developed an (open circuit) voltage that reached 8 Volts at points where the magnetic field density was maximum, as shown in Figure 13a,b, respectively. It should be noted that the harvesting voltage is a function of the spatial placement of the tuning circuit of the device, as it is not possible to harvest energy isotopically using the original design of the prototype (axes \(X\), \(Y\), \(Z\)).  
Figure 11: **(a)** The irradiated magnetic flux density value can reach up to 33 \(\upmu\)T, near a common-use hair dryer, as shown by the Narda PMM EHP-50C analyzer. **(b)** Experimental measurements showed that the (DC) harvesting voltage can reach up to 4.2 volts from this magnetic field value using the proposed MFEH system.  
Figure 12: Charging curves of the storage capacitor (100 \(\upmu\)F) as a function of the intensity of the changing magnetic field (50 Hz) environment. The maximum voltage (\(V_{max}\)) of the storage capacitor depends on the intensity of the magnetic field, having also an effect on the charging time constant.
# Evaluation of UV-Curable Solid Rocket Propellants' Properties for Advanced 3D Printing Technologies
###### Abstract  
Challenges in the traditional cast-and-care manufacturing of composite solid propellants, such as the use of mandrels and the toxicity of curing agents, are being addressed through new propellant formulations and additive manufacturing techniques. Within this framework, this study aimed to investigate the properties of UV-curable composite solid rocket propellants, focusing on their compatibility with advanced 3D printing technologies. Polybutadiene-based propellants incorporating a specific photoinitiator were examined. Key rheological properties, including the pseudoplasticity and pot-life, were assessed to evaluate the material's behavior during the printing process. Furthermore, photopolymerization tests were performed using a customized delta illuminator to evaluate the conversion efficiency under UVA and UVC light sources. Concurrently, a modular Cartesian 3D printer was developed and preliminary tests were performed. Rheological tests also revealed a flow index n of 0.32 at 60 \({}^{\circ}\)C and 0.46 at 80 \({}^{\circ}\)C, indicating significant pseudoplastic behavior. The pot-life tests showed that the viscosity of the propellant reached the upper limit of 10\({}^{6}\) cP more quickly at higher temperatures, indicating a shorter time range of printability. UVA irradiation resulted in a polymerization conversion rate of about 90%, while UVC exposure did not significantly enhance the conversion rate beyond this value. Finally, the 3D printing tests confirmed the feasibility of producing solid propellant, though challenges related to material segregation and the extrusion consistency were observed. Material separation resulted in a significant impact on the printability, causing underextru-sion and nozzle clogging, particularly with smaller nozzle diameters and higher extrusion pressures. Overall, this research represents a significant step forward in the development of UV-curable propellants for additive manufacturing, building on previous advancements by the research group. It demonstrates tangible progress in addressing key challenges such as the printability, material performance, and curing efficiency, while also highlighting areas requiring further refinement. These findings underscore the continuous evolution of this technology toward higher readiness levels, paving the way for its broader application in composite solid propellant manufacturing.  
solidrocket motor; composite solid rocket propellant; additive manufacturing; rheology; polybutadiene; photopolymerization; polymer chemistry +
Footnote †: journal: Applied Sciences  
Article
## 3 Chimera Algorithm
The columns of \(M\) correspond to the sub-carriers, whereas the rows correspond to the channels. In each row, each 1 identifies a sub-carrier assigned to the corresponding node. Each column can have at most one element equal to 1 and the others are equal to 0. The columns with all zeros correspond to not assigned sub-carriers. By using matrix \(M\), the capacity \(\tilde{C}_{n}\) assigned to each node can be expressed as:  
\[\tilde{C}_{n}=\sum\
olimits_{k=1}^{K}m_{nk}C_{nk}=\sum\
olimits_{k=1}^{K}m_{ nk}log_{2}(1+SNR_{nk}) \tag{3}\]  
where \(m_{nk}\) is the element of \(M\) corresponding to the frequency response \(n\) and the sub-carrier \(k\). The aim of the procedure is to assign to each charging station a capacity proportional to the number of plugs it feeds. This rule can be expressed by the following statement:  
\[\frac{1}{V_{1}}\sum\
olimits_{k=1}^{K}m_{1k}C_{1k}=\ldots=\frac{1}{V_{N}}\sum \
olimits_{k=1}^{K}m_{Nk}C_{Nk} \tag{4}\]  
The allocation problem consists in defining the matrix \(M\). As the unknowns are binary, the previous series of equations has not, in general, an exact solution, therefore the following objective function is defined, which has to be minimized:  
\[J=\sum\
olimits_{n=1}^{N}\Bigl{(}\frac{1}{V_{s}}\sum\
olimits_{k=1}^{K}m_{sk }C_{sk}-\frac{1}{V_{n}}\sum\
olimits_{k=1}^{K}m_{nk}C_{nk}\Bigr{)}^{2} \tag{5}\]  
where \(s\) identifies the channel with the lowest total capacity:  
\[s=\operatorname*{argmin}_{n\in[1,\ldots,N]}\Bigl{(}\frac{1}{V_{n}}\sum \
olimits_{k=1}^{K}C_{nk}\Bigr{)} \tag{6}\]  
The quadratic function (5) allows to obtain the most homogeneous distribution of capacities, but it does not guarantee the total capacity is maximized. For this reason, a further term is added to the objective function, which weighs the capacity assigned to the channel with the lowest total capacity, obtaining:  
\[J=\sum\
olimits_{n=1}^{N}\Bigl{(}\frac{1}{V_{s}}\sum\
olimits_{k=1}^{K}m_{sk }C_{sk}-\frac{1}{V_{n}}\sum\
olimits_{k=1}^{K}m_{nk}C_{nk}\Bigr{)}^{2}- \alpha\sum\
olimits_{k=1}^{K}m_{sk}C_{sk} \tag{7}\]  
where \(\alpha\) is the weight coefficient; a small value favors the homogeneity among the channels, while a large value tends to increase the capacities assigned to all the channels. Hence, the best \(\alpha\) is the one that maximizes the lowest allocated capacity. In order to avoid both the trivial solution of null matrix \(M\), and the same sub-carrier be assigned to different channels, the following constraint has to be stated:  
\[\sum\
olimits_{n=1}^{N}m_{nk}=1\quad\forall k=1,\ldots,K \tag{8}\]  
In case one sub-carrier is not suitable for the assignment, the corresponding column of the matrix \(M\) has to be removed. Summarizing, the allocation problem can be defined as the following quadratic, binary minimization problem:  
\[ \begin{array}{c}\min J\\ \text{s.t.}\\ \sum_{n=1}^{N}m_{nk}=1\quad\forall k=1,\ldots,K\\ m_{nk}\in[0;1]\end{array}  \tag{9}\]
# Rotating Machinery Fault Detection Using Support Vector Machine via Feature Ranking
## 5 Explore Different Machine Learning Models with ClassificationLearner
### Improved Quadratic SVM Model with 58 Predictors  
To improve the existing Quadratic SVM Model with all 58 predictors, a new Quadratic SVM Model was created but using only 11 important predictors found during the Decision Tree Study. Table 7 shows the model performance in terms of accuracy and training time.  
The overall accuracy of the improved Quadratic SVM decreases slightly by 0.1%, from 99.8% to 99.7%, with accuracy differences highlighted in the confusion matrix of Figure 14 which uses green boxes for higher accuracy and red boxes for lower accuracy.  
Figure 13: Comparison of Linear, Cubic, and Quadratic SVMs, highlighting the slight accuracy advantage of the Quadratic SVM. (**a**) Linear SVM, (**b**) Cubic SVM, with red boxes indicating lower accuracy, and (**c**) Quadratic SVM, with green boxes indicating higher accuracy.  
Despite the minor decrease in prediction quality, the training time for the SVM model with \(11\) predictors is \(57\%\) shorter compared to the model with all \(58\) predictors.
## 3 Methodology
### Data Preprocessing
#### 3.2.2 Removing Outliers Using LOF
The DoS GoldenEye class saw a reduction from 4000 to 3554, and the FTP-Patator class decreased in number from 2000 to 1964. Classes such as SSH-Patator and DoS Slowloris also experienced reductions, from 900 to 868 and from 1700 to 1540, respectively. Proportionally, smaller classes like Infiltration and Web Attack--Sql Injection saw more significant reductions, from 36 to 15 and from 21 to 9, respectively. The Heartbleed class remained relatively unchanged, with a slight reduction from 11 to 10 samples. Overall, the \begin{table}
\begin{tabular}{c c c} \hline
**Class Type** & **Number of Samples Before LOF** & **Number of Samples After LOF** \\ \hline Normal & 99,000 & 93,053 \\ \hline Attack & 76,368 & 73,546 \\ \hline \end{tabular}
\end{table} 
Table 4: Sample distribution of CICIDS2017 dataset for binary classification using LOF. LOF method helped create a more balanced dataset by removing outliers, improving the reliability and performance of the classification models for multi-class tasks.