esa-sceva/satcom-chunk-collection huggingface dataset:
# Design Optimization of Underground Mining Vehicles Based on Regenerative Braking Energy Recovery
## 3 Analysis of Energy Consumption after Installing the Regenerative Braking Energy Recovery System  
To comprehensively assess the energy recovery efficiency of installing the regenerative braking system on underground mining vehicles, a continuous road condition was designed and simulated in this study. The aim was to replicate various scenarios encountered during actual underground operations, ensuring a more precise estimation of the energy recovery performance. The simulation began with the mining vehicle starting from the beginning point and traveling at a constant speed on a level track. This stage simulated the initial phase of the vehicle's journey from the working face to the transportation exit. The road segment designed for this phase featured a level slope, allowing the mining vehicle to maintain a stable speed in the initial state. Next, the road gradually transitioned to an uphill slope with a gradient of 5\({}^{\circ}\), incorporating curved sections to mimic the bends in actual underground mine tunnels. This segment of the road examined the braking system's performance during uphill and turning conditions, as well as the efficiency of the energy recovery system in these scenarios. After the uphill and turning section, the road gradually transformed into a downhill slope with the same 5\({}^{\circ}\) gradient, with the mining vehicle maintaining a constant speed during the descent. This stage aimed to evaluate the braking system's performance during downhill motion and assess the efficiency of the energy recovery system during continuous downhill movement. Each of these three road conditions involved a 300 m segment for the simulation experiment. Within this duration, an emergency braking test was included, where the braking force was suddenly increased to simulate the need for the underground mining vehicle to come to a quick stop in an emergency situation. This test assessed the braking system's performance in emergency  
Figure 6: Internal structure of the regenerative braking system.  
scenarios and evaluated the effectiveness of the energy recovery system during emergency braking. It should be noted that the underground mining vehicle investigated in this article always operates on the underground track road depicted in Figure 7.
# Noise-to-Norm Reconstruction for Industrial Anomaly Detection and Localization
## 3 Method
### Reconstruction Network  
As we know, U-Net [25] and its variants have been widely used in deep learning models for image segmentation and other fields [26; 27; 28; 29]. Our proposed network is based on the M-net [16], which originated from the field of image segmentation and has been proven to be effective in the domain of denoising. The overall architecture of the proposed reconstruction network is shown in Figure 2. Inspired by the SRMnet [30], we incorporated pixel shuffle operations into the encoder and decoder for upsampling and downsampling; this allows us to effectively manage resolution changes in the network and improve the reconstruction quality. The residual attention modules were merged after concatenating the features to enhance the feature representation and capture the relevant information. The encoder and decoder were connected through skip connections to facilitate the flow of information between different feature levels. The multiscale features were combined in the feature fusion module to generate the final reconstructed image. This design enables the network to effectively capture anomalies and produce high-ualitv reconstructions.
## 4 Discussion
Therefore, an area-based classification should be included in future evaluations of SRT dosimetry algorithms. With respect to our OCT dosimetry algorithm, this would probably lead to a higher sensitivity, as fewer false-negative events would be observed. Furthermore, an OCT device that evaluates the emanating pressure field might become more sensitive with either a larger or also smaller detection area, although vibrations obviously span more than the detection range of the ~250 \(\upmu\)m thick retina. Our results suggest that RPE cell damage could be successfully monitored by using an OCT dosimetry algorithm. Optimal performance of the algorithm was obtained for a pulse duration of 6 or 14 \(\upmu\)s (AUC 0.97). The OOP was determined for a \(\kappa\)-value of 13 at a laser pulse duration of 6 \(\upmu\)s (sensitivity: 0.89, specificity: 0.94). However, in the case of OCT dosimetry, sensitivity is of paramount importance because sensitivity is a measure of how many of the positive samples (RPE lesion created) have been predicted correctly by the dosimetry algorithm. Showing a high sensitivity in this case means that lesions within the RPE are detected reliably, such that further (unnecessary) laser pulses are not delivered. For successful SRT operation, overtreatment due to low-sensitivity dosimetry must be avoided. Therefore, the OOP could be biased in favor of sensitivity, which would lead to the use of lower \(\kappa\)-values (e.g., 12 to 10). When interpreting our results, consideration should be given to the fact that only the RPE, choroid and sclera were present during the SD-OCT M-scan acquisition. Thus, the algorithm had relatively little signal available for the fringe washout evaluation. Similar experiments should therefore be repeated on whole eyes, where the additional OCT signal from the neural retina would likely enhance the MBF detection. Furthermore, since the present results are based on a single pulse application, further studies with pulse sequences should be performed to verify the performance under repeated exposure.
###### Abstract  
To prepare lightweight, wideband, strong absorption and low-cost microwave absorbing materials, carbon black/polylactic acid (CB/PLA) composite filaments were prepared using a high-energy mixer and twin-screw extruder. Coaxial ring test pieces were printed by fused filament fabrication (FFF) technology with polylactic acid as the matrix and carbon black as the absorbent. The crystal texture and micromorphology of the composites were observed by XRD and SEM. The electromagnetic parameters were measured at 2-18 GHz using a vector network analyzer. The influence of CB content on the absorbing performance of the composites was studied, and the loss mechanism was explained. The results show that when the CB content (refers to PLA) is 25%, the composite has suitable impedance matching, conduction loss, and polarization loss, resulting in the best microwave absorption performance. The minimum reflection loss reached \(-\)45.47 dB at 13.68 GHz, and the corresponding effective absorption bandwidth was 5.36 GHz (10.72-16.08 GHz) with a matching thickness of 2.1 mm. At the same time, the density of the composite was only 1.19 g/cm\({}^{3}\). This work sheds light on the development of lightweight and high-efficiency microwave absorption materials (MAMs) in a simple and low-cost manner.  
carbon black; FFF filament; electromagnetic parameters; loss mechanism; microwave absorption performance
# Antennas for Licensed Shared Access in 5G Communications with LTE Mid- and High-Band Coverage
## 3 Dual-Band Antenna for LSA Bands and LTE Mid and High Bands
### Antenna Configuration  
The dual-band antenna is designed to operate in the 1427-2690 and 3300-3800 MHz bands to support LSA, TDD/FDD LTE, UMTS, DCS, PCS, GSM1800, GSM1900, WLAN (2.4 GHz), and WiMAX (2.5 and 3.3 GHz), as shown in Figure 6. There is a small vertical plate in the antenna, which is shown as unfolded along the +y-axis in Figure 6 for better presentation. The substrate size, positioning of the antenna, feed arrangement, ground clearance, and back view are similar to Ant#1, as shown in Figure 1. The overall dimensions of the antenna are 39 mm \(\times\) 15 mm \(\times\) 4 mm or 0.18\(\lambda_{1}\)\(\times\) 0.08\(\lambda_{1}\)\(\times\) 0.02\(\lambda_{1}\), where \(\lambda_{1}\) is the free-space wavelength at 1.39 GHz (the lowest frequency of the antenna's first operating band, i.e., the mid band). Although the antenna has a small vertical plate, the area occupied by this antenna is 3% less than that occupied by Ant#1.  
This antenna is composed of three elements: an L-shaped element MNO in the middle, a U-shaped large element GQUH loaded with the vertical plate on the left, and an L-shaped element VKL on the right. Here, the vertical plate is aligned with the edge GQ. The dimensions are optimized using a parameter analysis conducted in CST for good impedance matching. The optimized design parameters of the dual-band antenna (Ant#2) are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \(w_{1}=2\) & \(w_{2}=1.5\) & \(w_{3}=1.6\) & \(w_{4}=0.5\) & \(w_{5}=3\) \\ \hline \(w_{6}=3.4\) & \(w_{7}=2\) & \(w_{8}=1\) & \(w_{9}=1\) & \(w_{10}=3\) \\ \hline \(w_{11}=4\) & \(w_{12}=1.5\) & \(l_{1}=7.4\) & \(l_{2}=4.25\) & \(l_{3}=22\) \\ \hline \(l_{4}=11\) & \(l_{5}=7.75\) & \(l_{6}=4\) & \(h_{1}=4\) & \(r=2\) \\ \hline \(L_{1}=39\) & \(W_{1}=15\) & & & \\ \hline \end{tabular}
\end{table}
Table 1: Design parameters of the dual-band antenna (Ant#2). All dimensions are in millimeters.  
Figure 6: Configuration of the dual-band antenna (Ant#2) for the mid (1427–2690 MHZ) and high (3300–3800 MHz) bands. For better presentation, the vertical plate is unfolded along the +y-axis.
## 3 Results and Discussion  
### Distribution of Anticancer Drugs in River Waters and STP Effluents  
Table 5 summarized the detected concentrations of the target anticancer drugs. All anticancer drugs were present in both the river waters and the STP effluents [30]. The median concentrations of BLT, TAM, CP, CAP, DFUR, and TGF were 32 ng/L, N.D., 2 ng/L, 2 ng/L, N.D., and N.D., respectively, in the main stream samples, 30 ng/L, N.D., 3 ng/L, 1 ng/L, N.D., and N.D., respectively, in the tributaries, and 245 ng/L, N.D., 10 ng/L, 6 ng/L, N.D., and 23 ng/L, respectively, in the STP effluents (excluding the ozonation STP). The concentrations of BLT, CP, CAP, and TGF in the STP effluents were thus several times higher than those in the river waters. In the previous studies, CP and TAM were detected in the range of N.D. to several tens of ng/L in river water [12; 45; 62; 63] and from N.D. to 100 ng/L in the STP effluent [12; 19; 47; 63; 64; 65]. On the other hand, in the case of BLT, which is frequently used to treat prostate cancer [66], the maximum concentrations were about 2-10-fold higher than the concentrations of the other drugs: 254 ng/L in main stream samples, 151 ng/L in tributaries, and 1032 ng/L in STP effluents (excluding ozonation). Detection frequencies in the main stream and tributary samples were quite high (83-100%) for BLT and CAP, but low--in the range of 6-44%--for DFUR, TAM, and TGF. The corresponding values for CP were moderate (56-63%). In the cases of TAM and DFUR, their detected concentrations appreciably decreased in the STP effluents. This indicates easiness of degradation of these anticancer drugs by the usual treatment at STPs. Although concentration differences were detected among the seasons, the orders were about the same throughout the year. In addition, the quantities of water to be treated at these STPs and the river flow rates in the target basin were fairly stable throughout the year [36]. Consequently, the data shown in this article suggest that the target six anticancer drugs were being used all year round.  
The concentrations of the anticancer drugs detected in the STP effluents (BLT, CP, CAP, and TGF) tended to be several times higher than those in the river waters. Clarification of the levels and mass balances of all discharged anticancer drugs in the urban river environment, together with the optimization of clean-up treatments at STPs, will be helpful in maintaining the health of residents in the sampling area. In the effluent samples from the STP that used ozonation, the mean concentrations of all anticancer drugs ranged as low as from N.D. to several ng/L, which were roughly one-tenth to one-hundredth of the concentrations detected in the effluents from STPs with chlorination after biological treatment (Table 5). This kind of observation was in accord with those of the previous studies [67; 68; 69], indicating the effectiveness of ozonation treatment for removing a wide range of pharmaceutical compounds, including anticancer drugs from water samples.
# Comparative Study of Natural Fibres to Improve Insulation in Wooden Beehives Using Sensor Networks
## 3 Results
### External Heating Source
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & **Material** & **Average** & **Standard** & **Coefficient of** & **\(p\)-Value** & **Pairwise** \\  & & & **Deviation** & **Variation (\%)** & & **Comparison** \\ \hline \multirow{6}{*}{**Area/thickness**} & White agglomerated cork & 234.547 & 27.675 & 11.799 & & ab \\ \cline{2-6}  & Black agglomerated cork & 188.473 & 16.23 & 8.615 & & a \\ \cline{2-6}  & Rock mineral wool & 255.197 & 4.709 & 1.845 & & ab \\ \cline{2-6}  & Wood fibres & 218.227 & 32.12 & 14.719 & & b \\ \cline{2-6}  & Control & 418.137 & 38.174 & 9.129 & & c \\ \hline \multirow{6}{*}{**Indoor temperature after 1 h at 60 \({}^{\circ}\)C**} & White agglomerated cork & 26.14 & 0.470 & 1.799 & & a \\ \cline{2-6}  & Black agglomerated cork & 25.57 & 0.278 & 1.090 & & a \\ \cline{1-1} \cline{2-6}  & Rock mineral wool & 26.093 & 0.090 & 0.347 & & a \\ \cline{1-1} \cline{2-6}  & Wood fibres & 25.563 & 0.524 & 2.053 & & a \\ \cline{1-1} \cline{2-6}  & Control & 26.967 & 0.402 & 1.493 & & b \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Descriptive statistics and ANOVA results for the variables: area/thickness and interior temperature after 1 h at 60 \({}^{\circ}\)C (* \(p\)-value < 0.05). Last column summarises the pairwise comparison after Fisher LSD. “a”, “b”, and “c” represent the groups within the pairwise comparison. Figure 6: (**a**) Graph with averages of the area/thickness ratio for each type of insulation material. (**b**) Graph with averages of the maximum indoor temperature after 1 h at 60 \({}^{\circ}\)C outdoor temperature for each type of insulation material: S (control), F (wood fibres), L (rock mineral wool), CN (black agglomerated cork), and CA (white agglomerated cork).
## 2 Optimization Algorithm of TMD
### Optimization Goal  
The following dynamic system is consisted of a single degree of freedom primary structure and a TMD, excited by an external excitation \(F(t)\), shown in Figure 1.  
When the external excitation is assumed to take the form \(F(t)=F_{0}\mathrm{e}^{i\omega t}\), where \(F_{0}\) is the forcing amplitude and \(\omega\) is its frequency. The motion equations of the dynamic system under an external excitation can be written as [1]:  
\[\left(\begin{array}{cc}m_{p}&0\\ 0&m_{s}\end{array}\right)\left(\begin{array}{cc}\ddot{u}_{p}\\ \ddot{u}_{s}\end{array}\right)+\left(\begin{array}{cc}c_{p}+c_{s}&-c_{s}\\ -c_{s}&c_{s}\end{array}\right)\left(\begin{array}{cc}\dot{u}_{p}\\ \dot{u}_{s}\end{array}\right)+\left(\begin{array}{cc}k_{p}+k_{s}&-k_{s}\\ -k_{s}&k_{s}\end{array}\right)\left(\begin{array}{c}u_{p}\\ u_{s}\end{array}\right)=\left(\begin{array}{c}F_{0}\mathrm{e}^{i\omega t}\\ 0\end{array}\right) \tag{5}\]  
where \(m_{p}\) and \(m_{s}\) are the mass of the primary structure and the TMD, respectively. \(c_{p}\) and \(c_{s}\) are the viscous damping coefficient of the primary structure and the TMD, respectively. \(k_{p}\) and \(k_{s}\) denote the stiffness coefficient of the primary structure and the TMD, respectively. \(u_{p}\) and \(u_{s}\) denote the absolute displacement of the primary structure and the TMD, respectively. The raised dot indicates the derivative with respect to time.  
Figure 1: The dynamic system coupled with a tuned mass dampers (TMD) under an external excitation.  
Besides, the excitation frequency ratio \(\beta=\frac{\omega}{\omega_{p}}\), the TMD frequency ratio \(\gamma=\frac{\omega_{s}}{\omega_{p}}\) and the TMD mass ratio \(\mu=\frac{m_{s}}{m_{p}}\) are utilized.  
Under human-induced vibrations, the maximum acceleration of the pedestrian bridge is one of the most important indexes to evaluate the serviceability problem. In order to depress the maximum acceleration dynamic amplification factor of the pedestrian bridge under external excitation and strengthen the robustness of the TMD, in this section, the novel optimization method of TMD is based on the AFSA, considering the structural damping ratio. The optimization goal is to minimize the maximum acceleration dynamic amplification factor of the pedestrian bridge under external harmonic excitations, based on the AFSA.  
The acceleration dynamic amplification factor of the pedestrian bridge under a harmonic excitation can be written as:  
\[N=\frac{\tilde{X}}{\frac{\tilde{Y}}{\gamma^{2}}+\frac{\tilde{Z}}{\gamma}}=\frac{ \beta^{2}\sqrt{\left(1-\frac{\tilde{Z}}{\gamma^{2}}\right)^{2}+4\left(\frac{i \tilde{Z}}{\gamma}\right)^{2}}}{\sqrt{\left(\frac{\tilde{Z}}{\gamma^{2}}- \frac{\tilde{Z}}{\gamma^{2}}-\beta^{2}(1+\mu)-4\frac{\tilde{\zeta}\tilde{z} \tilde{z}}{\gamma}+1\right)^{2}+2\frac{\tilde{z}\tilde{z}\tilde{z}}{\gamma}+2 \frac{\tilde{z}\tilde{z}\tilde{z}\tilde{z}}{\gamma}(1+\mu)-2\frac{\tilde{z} \tilde{z}\tilde{z}}{\gamma}-2\zeta_{p}\beta\right)^{2}}} \tag{6}\]  
where \(\zeta_{p}\) and \(\zeta_{s}\) are the damping ratio of primary structure and TMD, respectively.
# A Simple Technique for Studying Chaos Using Jerk Equation with Discrete Time Sine Map
## 5 Numerical Simulation & Results  
Extracting nonlinear measures from time-series is a multifaceted problem, and care must be taken to elucidate its outcomes. Various theoretical measures like Lyapunov  
Figure 1: Chaotic invariants for approximated discrete–time Sine Map: (**a**) Bifurcation Diagram, (**b**) Lyapunov Exponent, (**c**) Cobweb Plot, (**d**) Pseudo Space Trajectory, (**e**) Time Series.  
exponent and fractal dimension are used to quantify the chaotic time-series as they are invariants of the attractor. These quantifiers turn a time-series into a single number, which is handy for highlighting important information about the system complexity and degree of freedom (DOF). Chaotic invariants are usually found to validate the presence of chaos, and ensure that the results obtained show sensitive dependence on the initial condition and are not just random white noise. To validate the route to chaos in the proposed system, numerical simulations were carried out in MATLAB. Runge-Kutta method was used to solve the system of Equations (7), (8) and (10). A step-size of \(\triangle t=0.08\) was used. The values of fixed control parameters \(\alpha,\gamma\), and \(\sigma\) are set to 1, -6, and -6 respectively, throughout the simulations.  
Figure 2 shows the resulting time series for \(x_{3}\) with its values on the y-axis and no. of samples on the x-axis for different values of \(\beta\). The light green line is for '\(\beta=9\)' which shows a period 1 behavior (limit cycle), the blue line is for '\(\beta=6\)' which corresponds to period 2 behavior; the black line is for '\(\beta=5.82\)' corresponding to period 4, and the red line for '\(\beta=5.535\)' shows more interesting aperiodic time series also known as chaotic time series.  
The presented system shows the infamous sensitive dependence on the initial conditions of the system. Figure 3 shows the phase portrait between \(x_{2}\) & \(x_{3}\) for two different sets of initial conditions for \((x_{1},x_{2},\) and \(x_{3})\).  
Figure 2: Time Series for for \(x_{3}\): Presence of multiple attractors in the proposed discrete-time map based chaotic jerk system.
# A Simplified Analysis to Predict the Fire Hazard of Primary Lithium Battery
## 4 Conclusions  
The combustion properties of 1, 2, 4, 9, 36, and 100 primary lithium batteries were experimentally examined in this paper. Based on the t\({}^{2}\) fire principle and total heat release results from the experiments, a simplified analysis was further developed to predict the fire hazard, and especially the heat release rate of primary lithium batteries. An exponential relationship between thermal energy released and the number of batteries was added into the simulation of a fire of multiple lithium batteries. The most difficult problem for simulation was that the ignition time of each battery in the bundle is random. Even in the same external environment and under the same heating power, the batteries did not always ignite at the same time. The ignition time difference parameter was introduced into the simulation to achieve similar results to the heat release rate of the multiple battery combustion. By comparing the experimental and simulation results, the heat release rate curves based on the oxygen consumption measurements of 1, 2, 4, 9, 36, and 100 primary lithium batteries agreed well with the simulation. The overall trend and curve shape were similar to the experimental results, and the values of maximum HRR and THR were close to each other.  
The experiments and simulations with different numbers of batteries in the current study provide some insights into the fire hazards of primary lithium batteries and indicate the possibility of predicting their heat release rate with a more accurate and simplified model. This prediction analysis is applicable to primary lithium batteries that are smaller than or equal to the dimensions used in this study. The results cannot be directly extrapolated to larger or even industrial scale batteries unless further validation is conducted.  
Figure 6: Comparison results of fires of 36 (**a**) and 100 (**b**) lithium-ion batteries.  
**Funding:** This research was supported by the National Key Research and Development Program of China (2018 YFC0808600), the opening fund of Key Laboratory of Firefighting and Rescue Technology, Ministry of Public Security (KF201801), Programs of Senior Talent Foundation of Jiangsu University (17 JDG036), and the Shanghai Sailing Program (18 YF1409600).  
**Conflicts of Interest:** The authors declare no conflict of interest.