esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions
\begin{tabular}{l l} \(C\) & Nominal radial clearance of bearing (m) \\ \(D_{33}\) & PZT coefficient of PZT stack (m/V) \\ \(d_{33}\) & PZT coefficient of PZT material (m/V) \\ \(E_{p}\) & Elastic modulus of the PZT material (N/m\({}^{2}\)) \\ \(F_{X}\), \(F_{Y}\) & Dynamic bearing reaction forces (N) \\ \(H\) & Normalized gas-film thickness, \(H=h/C\) \\ \(K_{p}\) & PZT stack stiffness (N/m) \\ \(k_{assy}\) & Structural assembly stiffness (N/m\({}^{3}\)) \\ \(k_{b}\) & Bump foil stiffness per unit area (N/m\({}^{3}\)) \\ \(k^{\prime}_{b}\) & Complex bump foil stiffness per unit area (N/m\({}^{3}\)) \\ \(k_{p}\) & PZT stack stiffness per unit area (N/m\({}^{3}\)) \\ \(k_{sp}\) & Holder spring stiffness per unit area (N/m\({}^{3}\)) \\ \(k(\theta)\) & Structural stiffness function (N/m\({}^{3}\)) \\ \(l_{b}\) & Axial bearing length (m) \\ \(l_{p}\) & PZT material length (m) \\ \(N_{p}\) & Number of PZT stacked materials \\ \(m_{r}\) & Journal mass (kg) \\ \(P\) & Normalized gas-film pressure, \(P=p/p_{a}\) \\ \(R\) & Journal radius (m) \\ \(r_{p}(\theta)\) & Piezoelectric preload function (m) \\ \(t_{p}\) & PZT material thickness (m) \\ \(u_{int}\) & Imbalance radius (m) \\ \(V_{3}\) & Driving voltage of PZT material (V) \\ \(w_{p}\) & PZT material width (m) \\ \(X\), \(Y\), \(Z\) & Stationary coordinate systems in a bearing (m) \\ \(\alpha\) & Constant related to function smoothness \\ \(\gamma\) & Bump foil loss factor, \(\gamma=0.1\) \\ \(p\) & Gas-film pressure (N/m\({}^{2}\)) \\ \(p_{A}\) & Local gas-film pressure (N/m\({}^{2}\)) \\ \(p_{a}\) & Ambient pressure (N/m\({}^{2}\)) \\ \(\delta_{PZT}\) & Piezoelectric preload (m) \\ \(\delta_{bump}\) & Deformation of bump foil (m) \\ \(\delta_{3}\) & PZT stack displacement (m) \\ \(\varepsilon_{i}\) & PZT material strain \\ \(\varepsilon_{X}\), \(\varepsilon_{Y}\) & Normalized journal eccentricities \\ \(\theta\) & Circumferential coordinate system (rad) \\ \(\theta_{p,n}\) & Preload locations, \(n=1\), \(2\), \(3\) (rad) \\ \(\theta_{s}\) & Angular length of the upper stinger (rad) \\ \end{tabular}
## 2 Module Design
### Design of the Thermal Control System
#### 2.2.2 Hardware Circuit Design of the Thermal Control System  
The main function of the system hardware circuit was to realize automated thermal control for the laser through exporting suitable drive power on the TECs. The diagram of the hardware circuit is shown in Figure 6. The circuit consisted of a STM32 microcontroller as control core, two PT1000 platinum resistor sensors to measure the upper surface temperature of heat sink, one PT1000 to measure the temperature of the MgO:PPLN crystal, an LCD12864 display to show the measuring temperature, setting temperature and operating state of the laser, a 24V DC power supply for the PWM fans, four H-bridges to drive the TECs, and a universal synchronous/asynchronous receiver/transmitter (USART) interface to communicate with a personal computer. The platinum resistor is a kind of temperature sensor with high stable performance and wide temperature measure ranging from \(-200\) to \(+650\)\({}^{\circ}\)C. The PT1000 temperature characteristics can be approximately expressed with the following equation:  
\[R_{t}=1000+3.9\times t. \tag{1}\]  
\(R_{t}\) is the resistance value (\(\Omega\)) at temperature of \(t\) (\({}^{\circ}\)C). An 0.1 mA precise constant current source flows into the PT1000 resistor. The voltage on two sides of the resistor is amplified by an instrumentation amplifier INA128 before entering the analog-to-digital converter (ADC) interface on STM32. Then, the STM32 chip calculates the temperature value according to the conversion formula.  
Figure 4: Diagrammatic sketch of the thermoelectric cooler (TEC) layout.  
Figure 5: Picture of TEC layout.
## 4 Comparison and Discussion
### Comparison of Prediction Models  
The CNN-LSTM [40], the least squares support vector regression (LSSVR), LSTM [33], BP neural network, and the TPA-LSTM [26] model are compared to verify the validity of the model examined in this paper. In the comparison experiments discussed in this section, the input variables of the model are the six variables screened in Section 3.2 and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Control Group**} & \multicolumn{2}{c}{**Experimental Group**} \\ \cline{2-5}  & **MAE** & **MAPE/\%** & **MAE** & **MAPE/\%** \\ \hline Dataset 1 & 0.0379 \(\pm\) 0.0003 & 6.81 \(\pm\) 0.07 & 0.0401 \(\pm\) 0.0004 & 7.17 \(\pm\) 0.14 \\ Dataset 2 & 0.0165 \(\pm\) 0.0005 & 3.33 \(\pm\) 0.12 & 0.0178 \(\pm\) 0.0005 & 3.59 \(\pm\) 0.07 \\ Dataset 3 & 0.0327 \(\pm\) 0.0021 & 6.68 \(\pm\) 0.39 & 0.0384 \(\pm\) 0.0011 & 7.75 \(\pm\) 0.21 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The prediction comparison table of segmented training.  
Figure 14: The prediction comparison diagram of segmented training.  
From the results, it can be seen that the model examined in this paper achieves the best prediction performance on all three datasets. Dataset 1 and Dataset 3 are data from the regular operation of the unit. It can be seen from the figure that the model in this paper can better learn the characteristics of the bed pressure variation values. The test set of Dataset 2 contains fault data. Our model can also accurately predict the bed pressure variation in this case.  
The BP model and the LSSVR model cannot handle the time series information of the data in the input of the model. Therefore, both models perform worse than the other algorithms in all three datasets. To our surprise, the BP model performs close to the other compared algorithms in Dataset 2. After analysis, it is revealed that the main reason for this is the autocorrelation of the data. The sampling time is too short, resulting in autocorrelation between the data so that the temporal information of the data is not fully displayed. The LSTM model performs the selection of historical states through the forgetting gate. However, the inertia and delay properties unique to each feature are not fully considered during the LSTM model computation. Therefore, the model does not learn the target values correctly. The convolution operations in the TPA-LSTM and CNN-LSTM models are used to extract the period information in the MTS data. This type of period information is not obvious during the short-term operation of CFB units. Therefore, the CNN-LSTM model and TPA-LSTM cannot learn the periodic information. This problem leads to the degradation of the prediction performance of the models.
# RM-Line: A Ray-Model-Based Straight-Line Extraction Method for the Grid Map of Mobile Robot
## 1 Introduction
### Our Approach  
Our work targets grid maps for indoor mobile robots. The grid in the map has three possible values: OBSTACLE = 0, which means that there are obstacles in the grid; BLANK = 1, which means that there are no obstacles in this grid; UNKNOWN = 2, which means that the grid is not detected.  
When the existing HT methods are used to extract straight lines from grid maps, the computational load is large. When the existing perceptual grouping methods are used to extract straight lines from grid maps, there is a certain distance between the obtained straight line and the real obstacle contour due to less local information (the value of the pixel has only three discrete values).  
This paper proposes an algorithm for extracting straight lines from grid maps, called RM-Line. The following terms are used in the algorithm:  
1. Connected domain. The connected blank grids in the grid map are clustered, and the area formed by the grids in the cluster is called the connected domain;
2. Edge point. The obstacle grids located at the edge of the connected domains;
3. Active points. The obstacle grids that belong to a line.  
RM-Line first uses the connected domain of the grid map to obtain edge points, and then uses the screening model to obtain active points. Lastly, a ray model is used to derive line segments.
# Measurement of Quasiparticle Diffusion in a Superconducting Transmon Qubit
## 2 Device and Quasiparticle Injection  
The single-junction transmon qubit that we used in the experiment was centered in a 3D cavity that was made of aluminum (Al 6061-T6 alloy) and had a size of \(35.56\times 5.08\times 17.78\) mm. The bare frequency of the cavity \(\omega_{bare}/2\pi\) was 9.0524 GHz. The Josephson junction was located in the middle of two thin-film electrodes that had the size of \(250\times 500\) um, as illustrated in Figure 1b. The qubit was cooled down to 30 mK in an Oxford DR200 cryogen-free dilution refrigerator with the transition frequencies of \(\omega_{ge}/2\pi\) = 7.0905 GHz and \(\omega_{ef}/2\pi\) = 6.748 GHz and the ratio of Josephson coupling energy to charging energy of \(E_{\uparrow}/E_{C}=58\). The average measured energy relaxation time \(T_{1}\) was 7.29 us. The data were obtained by repeating the measurement 1000 times with a visibility of 50%. More details about the sample can be found in Appendix A.  
In this work, we modified the technique of quasiparticle injection reported in [21] by changing the injection duration time to 3-5 us from a hundred microseconds. This allowed us to study the diffusion of the quasiparticles in the qubit. The injection pulse was input at the frequency of \(\omega_{bare}\) with the power [21,22] corresponding to the one on the right side of the red line in Figure 1c. For convenience, we mark the power of 0 dB with the red line. The gap between the injection pulse and the measurement pulse in Figure 1d is the diffusion time of the quasiparticles.  
Figure 1: (**a**) Quasiparticle-induced transitions with quasiparticles’ energies near the superconducting gap \(\Delta\) and above \(\Delta+E_{ge}\). The blue dotted line indicates that the quasiparticles absorb energies, while the red one indicates that they emit energies. (**b**) Diagram of the Josephson junction centered between the two electrodes and placed in the 3D cavity. (**c**) Sketch of the response at \(\omega_{bare}\) versus the high input power. To inject quasiparticles, the injected microwave power needs to be above 0 dB, which is marked by the red line. (**d**) Pulse sequence of measurement for injecting quasiparticles.
## 2 Materials and Methods
### Antioxidant Activity
#### 2.6.2 ABTS Assay  
The ABTS radical scavenging activity was determined by the decolorization assay described by [PERSON] [32] with some modifications. Briefly, the working solution of ABTS was prepared by mixing 7.4 mM ABTS stock solution and 2.6 mM potassium persulfate solution in equal quantities and allowing them to react overnight at room temperature in the dark. Then, the working solution was diluted with distilled water to achieve an absorbance of 1.10 \(\pm\) 0.02 at 734 nm. After that, 2.85 mL of working solution were added to 150 \(\mu\)L of fresh hydrolysate or a solution of 1 mg/mL of lyophilized sample. The mixture was incubated in the dark for 120 min and absorbance was measured at 734 nm. Trolox was used as a positive control. The percentage inhibition of ABTS+ to ABTS was calculated using the following equation:  
\[\textit{ABTS\ decoloration}\ \left(\%\right)\ =\ \left(\frac{Abs\ control-Abs\ sample}{Abs\ control} \right)\times 100\]  
where _Abs sample_ is the absorbance of the tested sample with the ABTS solution, while _Abs control_ is the absorbance of ABTS solution without sample. Antioxidant capacity relative to that of Trolox was calculated using the equation generated with a Trolox standard curve as follows:  
\[\textit{Trolox\ equivalent}\ \left(\frac{mg}{mg}\right)\ =\ \left(\frac{\left(ABTS\ decoloration\ \%-b\right)}{a}\right)\div\textit{sample\ concentration}.\]  
where \(a\) and \(b\) are the two coefficients of the Trolox standard curve. To estimate the umol Trolox/g of product, the molecular weight of Trolox was used (25,029 g/mol). Results were reported as the mean of three independent reads (_n_ = 3).
## 3 Results and Discussion  
### Preliminary Chemical Characterisation of R. rugosa Root Polysaccharides  
Crude rugosa rose root polysaccharide (CPL-Rx) was obtained using the previously optimised extraction methodology [12]. Its chemical composition was characterised using spectrophotometric, electrophoretic (CE, SDS-PAGE), and chromatographic (LC) techniques and mass spectrometry (MS).  
CPL-Rx constitutes a water-soluble fraction of root polysaccharides obtained after hot-water extraction of plant material. The preliminary analyses of the CPL-Rx composition revealed that it contains 28.94 \(\pm\) 0.01% of sugars, followed by a lower amount of phenolics (4.55 \(\pm\) 0.12%), uronic acids (2.61 \(\pm\) 0.41%), and proteins (Table 1). Besides this water-soluble fraction obtained with the yield of 0.80 \(\pm\) 0.02%, the rose root was found to contain water-insoluble carbohydrates. This was revealed during the determination of the total glucan content, where the procedure started with dry plant material and involved its extraction combined with acidic and enzymatic hydrolysis. The determined amount of glucans (16.65 \(\pm\) 0.39% of root dry weight; Table 1) exceeded the CPL-Rx yield and the content of soluble sugars (Table 1). This carbohydrate fraction was composed mostly of \(\beta\)-glucans constituting 12.95 \(\pm\) 0.30% of root d.w. (Table 1). This result is particularly interesting since \(\beta\)-glucans are regarded as one of the most valuable bio-active carbohydrates showing, for example, immunomodulatory, antiviral, antitumor, hypoglycaemic and hypolipidemic activity [23; 24]. However, recent research has shed new light also on \(\alpha\)-glucans, revealing their bio-medical (e.g., prebiotic and immunostimulatory), nutraceutical, and technological potential [25]. The content of \(\alpha\)-glucans in the rose roots was found to be a few times lower than that of \(\beta\)-glucans (3.70 \(\pm\) 0.09% d.w.). However, their amount was still relatively high. The results are in agreement with previous reports showing remarkable amounts of glucan-type polysaccharides in rose organs [12; 26].  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Result** \\ \hline Yield (g/100 g d.w.) & 0.80 \(\pm\) 0.02 \\ Water soluble sugars (g/100 g d.w.) & 0.23 \(\pm\) 0.00 \\ Total glucans (g/100 g d.w.) & 16.65 \(\pm\) 0.39 \\ \(\alpha\)-Glucans (g/100 g d.w.) & 3.70 \(\pm\) 0.09 \\ \(\beta\)-Glucans (g/100 g d.w.) & 12.95 \(\pm\) 0.30 \\ Sugar content (\% of CPL-Rx) & 28.94 \(\pm\) 0.01 \\ Uronic acids (\% of CPL-Rx) & 2.61 \(\pm\) 0.41 \\ Phenolic content (\% of CPL-Rx) & 4.55 \(\pm\) 0.12 \\ Protein content (\% of CPL-Rx) & 0.88 \(\pm\) 0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Yield and chemical composition of crude polysaccharide obtained from _R. rugosa_ root, results given in g/100 g of dry weight (d.w.) or in % of crude polysaccharide. Values are presented as mean \(\pm\) standard deviation (\(n\geq 3\)).
# Combinatorial Optimization Problems and Metaheuristics: Review, Challenges, Design, and Development
## 2 Materials and Methods
### Combinatorial Optimization Problem Classification System
#### 2.3.3 Subproblems (Single-Problem Versus Multi-Problem)  
Some problems, due to their complexity, need to be split into subproblems in order to be adequately resolved (\"divide to conquest\" strategy). Therefore, the problems, considering the number of subproblems, can be classified into two classes: (1) single optimization problems (single-problems), which are composed of only one problem, and (2) multiple optimization problems (multi-problems) [48; 49; 50], which are a non-trivial arrangement of two or more problems. A point that needs to be emphasized is that a problem can be composed of two subproblems (multi-problems), where the first subproblem has one objective (single objective) and the second subproblem has two objectives (multi-objective).  
Multi-objective problems require extra efforts to select the optimal solutions (Pareto optimal) and also demand additional work because it is necessary to balance the results of the subproblems to attend to the global perspective of the higher-level problem. The higher-level problem may have global objectives that must also be achieved, and they cannot be entirely perceived by the parts separately.  
Figure 1: Combinatorial optimization problem—building blocks.  
A typical example is the artificial problem named Traveling Thief Problem (TTP) proposed by [PERSON] et al. (2013) [48]. It is an optimization problem composed of two well-known problems: TSP (Travel Salesman Problem) and KP (Knapsack Problem), organized in a higher-level problem. By way of example, guaranteeing the best loading ships and logistics may not ensure the best profitability. In other words, \"the fittest\" profitability may not be guaranteed by \"the fittest\" ship loading solution (equivalent to KP part) and \"the fittest\" ship scheduling (equivalent to TSP part).
# Traffic Noise Reduction Strategy in a Large City and an Analysis of Its Effect
## 2 Materials and Methods
### Sound Absorption Material  
As the noise over-limit values obtained in Section 2.1 were quite large, the concept of a high-performance absorptive barrier is proposed in the present study. Absorptive noise barriers are usually developed with the installation of sound absorption materials on the structures of normal noise barriers [35]. According to the \"Standard requirement of acoustic design and measurement for noise barrier\" (HJ/T90-2004) [37], the noise reduction coefficient of a sound absorption material for a noise barrier should be greater than 0.5. In the present study, four materials, namely, rock wool, slag wool, polyurethane foam, and superfine glass wool (5 mm thickness), were selected for comparison based on their good sound absorption performance with respect to what was provided in the \"Optimization study of road noise barriers\" [38]. As shown in Table 1, the noise reduction coefficients (NRCs) of rock wool, slag wool, and superfine glass wool are almost the same, while polyurethane foam has the smallest noise reduction coefficient. As the price of rock wool is much cheaper than that of the other wool materials, rock wool was selected as the sound absorption material for the noise barrier modeled in the present study, with an NRC of 0.75.
# Performance Evaluation of Classification Algorithms to Detect Bee Swarming Events Using Sound
## 4 k-NN, SVM, and U-Net CNN Algorithms Evaluation
### U-Net Classifier Results  
For the U-Net CNN classifier, the model's accuracy was examined ten days before the swarming event. The results are presented in Table 12, using the default network parameters mentioned in Appendix A.  
Focusing on early detection, the accuracy results of the U-Net classifier present an 89% mean accuracy. For late detection, a 92% mean accuracy is achieved for both swarming  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline
0.0 (Non-Swarming) & 74\% & 70\% & 72\% & - \\
1.0 (Swarming) & 70\% & 91\% & 90\% & - \\ Accuracy & - & - & - & 85\% \\ Macro avg & 81\% & 80\% & 81\% & - \\ Weighted avg & 85\% & 86\% & 85\% & - \\ \hline \hline \end{tabular}
\end{table}
Table 11: k-NN Classifier results applied ten days before the swarming event.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Days before the** & **Swarming** & **Non-Swarming** & \\
**Swarming Event** & **Probability** & **Probability** & **Accuracy** \\
**(Recall)** & **(Recall)** & **(Recall)** & \\ \hline
1–3 & 94\% & 96\% & 95\% \\
4–6 & 91\% & 93\% & 92\% \\
7–8 & 89\% & 90\% & 90\% \\
9–10 & 88\% & 90\% & 89\% \\ \hline \hline \end{tabular}
\end{table}
Table 12: Results of the U-Net CNN classifier.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline
0.0 (Non-Swarming) & 99\% & 98\% & 99\% & - \\
1.0 (Swarming) & 98\% & 98\% & 98\% & - \\ Accuracy & - & - & - & 98\% \\ Macro avg & 98\% & 98\% & 98\% & - \\ Weighted avg & 98\% & 98\% & 98\% & - \\ \hline \hline \end{tabular}
\end{table}
Table 9: k-NN Classifier results applied 5 days before the swarming event.  
and non-swarming events. These are fairly-good accuracy results for both early and late detection cases.  
Since the U-Net CNN utilizes the whole 10 kHz spectrum for its training and detection process, the results are an indication that it will outperform both k-NN and SVM if a more targeted data input is used; that is if the training applies in the frequency band of up to 1-2 kHz, followed by appropriate fine-tuning of its network parameters, such as the number of training steps.
# An Intelligent Collaborative Charging System for Open-Pit Mines
## 4 Technical Implementation Scheme for Intelligent Collaborative Charging in Open-Pit Mines
### The Explosive Vehicle and the Loading Robot Work Intelligently in Coordination  
(1) The explosive vehicle completes the optimal driving route planning for transporting explosives, and the loading robot completes the optimal hole-finding path planning. After the explosive vehicle stops at each parking point, the loading robot conducts hole search, positioning, and loading with the explosive vehicle as the center to ensure that the loading work of all blast holes within the area can be completed. That is, the explosive vehicle and the loading robot, under the optimal driving route and the optimal hole-finding path, aim for the shortest path, the least time, and the most accurate loading, and efficiently complete the loading task of the blast holes in the entire blasting area.  
(2) Influencing factors of optimal driving route planning for explosive vehicles. The size range of the explosion zone, the position of the blast hole opening, and the maximum and minimum loading radius of the loading robot (the size range of the mixed loading explosive delivery pipe between the explosive vehicle and the loading robot) influence the optimal driving route plan.  
Influencing factors of the optimal hole-finding path planning for loading robots. The size range of the explosion zone, the position of the blast hole opening, the driving route of the explosive vehicle, the parking position and parking posture, and the maximum and minimum charging radius of the charging robot influence the optimal hole-finding path plan.  
(3) Collaborative operation between the explosive vehicle and the loading robot. (1) After the explosive vehicle travels and stops along the pre-determined optimal planned route, the instructions are conveyed to the loading robot, which then begins to search for holes and position and load the explosives. (2) After the loading robot automatically locates and precisely positions the holes along the planned hole-finding path, it feeds back the position information of the blast hole opening to the on-board terminal of the explosive vehicle. (3) The explosive vehicle completes the matching of the blast holes based on the position information of the found blast holes, obtains the amount of explosives designed for loading, and conveys the command to the loading robot to start loading and quantitatively dispensing the explosives. (4) After the loading robot completes the loading of the blast hole, it automatically feeds back the completion information of the loading operation to the explosive vehicle. (5) After the loading robot has completed the loading of all the blast holes around the parking position of the explosive vehicle, it follows the explosive vehicle to the next parking position in the explosive area and starts the next cycle of hole searching and loading. (6) The operation is repeated until all the blast holes in the explosion area have been loaded, and the explosive vehicle carrying the charge loading robot then drives out of the explosion area.