esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Lightweight Morphological Detection for Chinese Mitten Crab Origin Tracing
## 3 Experimental Results and Analysis
### Crab Classification
#### 3.3.2 Comparison of Detection Algorithms  
The average iteration time and parameter size of the three models are shown in Table 4, the parameter size of our model is 40% and 17.4% of that of the AlexNet model and VGG16 model, the training time is 7.8% and 47.7% of the other two models, and the detection time is 9.5% and 50.2% of the other two models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **DT (ms/Image)** & **TT (min)** & **Parameter Size/M** & **mAP50/\%** \\ \hline AlexNet & 24.79 & 47.58 & 60 M & 99.81\% \\ VGG16 & 130.82 & 289.59 & 138 M & 99.90\% \\ Ours & 12.45 & 22.71 & 24 M & 99.57\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of iterations of AlexNet, VGG16, and our model.  
Figure 12: Comparative Analysis of Detection Performance between YOLOv10n and MC-RTLNet.  
As shown in Figure 13a, it can be seen that the accuracy of the AlexNet model increases the fastest, our model increases slightly slower, and the VGG16 model is the slowest, but the accuracy of the three models is still approximate, and the value of accuracy convergence is also very similar at 2000 iterations.  
We can see from Figure 14 that the loss of the AlexNet model decreases the fastest, our model decreases slightly slower, and the VGG16 model is the slowest, but the loss of the three models is still approximately the same, and the value of loss convergence is also very similar at 2000 iterations.  
In summary, when the appropriate learning rate is selected, our model has a faster convergence speed and higher accuracy. As shown in Figure 13, our model is slightly lower than AlexNet in terms of accuracy, recall, precision, and F1, but higher than VGG16. However, the proposed model is much smaller than the other two models in terms of single iteration time and model size. In the crab classification task, the actual training time of the proposed model is shorter, and the prediction time is also shorter. Therefore, considering the performance parameters and model size, the proposed model is more suitable for the task of crab classification.  
Figure 13: Comparison curves of four metrics across three models versus training iterations. (**a**) Accuracy. (**b**) Precision. (**c**) Recall. (**d**) F1-score.
# Research on Space-Based Visible Detection for Conical Space Targets
## 2 Radiation Sources of Space Target  
Solar radiation, Earth radiation, background star radiation, and the target's own radiation are the principal sources of radiation reflected by space targets. The sun, the Earth, and the background stars, for example, can be considered black bodies with temperatures of 5900 K, 300 K, and 3.5 K, respectively. The satellites in sun-synchronous orbit have an average temperature of 314 K in the sunlight zone and 198 K in the shadow zone [18]. Simulations may be used to obtain the radiation output of the sun, Earth, background stars, and satellites at various wavelengths, according to [PERSON]'s law (see Figure 1).  
Figure 1 shows that the radiation source reflected by the space target in the visible light wave band from 0.3 microns to 0.9 microns is mostly solar radiation. According to [PERSON]'s law, the sun is equal to a blackbody with temperatures of 5900 K, and the irradiance on the space target from the sun is estimated using [PERSON]'s law and expressed as \(E_{s}\).  
\[E_{s}=\int_{\lambda_{1}}^{\lambda_{2}}\frac{R_{s}^{2}}{R_{ESD}^{2}}\frac{c_{1} }{\lambda^{5}\exp(c_{2}/\lambda T_{s})-1}d\lambda, \tag{1}\]  
where the visible working wavelength range is from \(\lambda_{1}\) to \(\lambda_{2}\), \(c_{1}\) and \(c_{2}\) are both simplified constant values, \(T_{s}\) is the solar temperature with value of 5900 K, \(R_{s}\) is the solar radius, and \(R_{ESD}\) is the Earth-Sun Distance. The intensity of the solar radiation reflected by the space target can be expressed as follows:  
\[I_{s}=\int_{g}\frac{\rho E_{s}}{\pi}\cos\Theta^{\prime}_{s}\cos\Theta^{\prime} _{d}dA, \tag{2}\]where \(\rho\) is the space target surface reflectivity which can usually be set to 0.2, \(A\) is the effective incident cross-sectional area of the space target, \(\Theta_{s}^{\prime}\) is the angle between the incident direction of the solar radiation flux and the normal vector of the space target surface element, and \(\Theta_{d}^{\prime}\) is the angle between the normal vector of the space target surface element and the direction of the radiation flux reflected by the space target received by the detector. Obviously, \(\Theta_{s}^{\prime}\Theta_{d}^{\prime}\), and \(A\) directly determine the radiation intensity of the space target received by the detector. The radiation intensity model analysis for space targets generally adopts a two-dimensional model [14; 19]. In the visible light band, the three-dimensional radiation transfer model of conical space targets is rarely studied [17]. Next, the analysis will be done for the radiative transfer model of the conical space target.
# Modeling and Multi-Objective Optimization Design of High-Speed on/off Valve System
## 2 New High-Speed on/off Valve Structure and System Modeling  
### New High-Speed on/off Valve Structure  
The operation of the solenoid valve can be affected by eddy current losses, which have an impact on its performance. In the case of the HSV, these losses primarily occur during the attraction and reset stages of the permanent magnet high-speed solenoid valve, thereby increasing the action time of the HSV. The eddy current losses of the HSV exhibit a positive correlation with the electrical conductivity of the material and a negative correlation with the magnetic saturation intensity [26].  
Figure 1 illustrates the structure of the new HSV. At the bottom of the solenoid coil, there is a radially magnetized permanent magnet ring structure. By producing a permanent magnetic field in the reverse direction of the excitation coil at the contact iron, the permanent magnet reduces the magnetic induction intensity throughout the magnetic circuit and prevents magnetic saturation. Furthermore, the use of a low magnetoresistive material with slightly lower saturation magnetic induction and high resistivity for the core, as well as the selection of a low magnetic resistance material for the contact iron, helps minimize eddy current losses. The introduction of the permanent magnet not only reduces the eddy current losses of the contact iron but also enhances the responsiveness of the electromagnetic coil.  
The HSV possesses the capability of achieving a three-position, two-way function. In its resting state, the valve remains in a normally closed position, where the high-pressure inlet is designated as the P port and maintains closure under hydra  
Figure 1: Schematic diagram of the structure of the new HSV.  
P port is closed, a connection is established between the T port and the A port. Upon the input of a control signal to the electromagnetic coil, the armature is attracted and undergoes downward movement, thereby driving the spool to close the T port and establishing a connection between the P port and the A port. Following the termination of the control signal, the pilot valve ascends due to the influence of high-pressure fluid, resulting in the closure of the P port.
## 3 Discussion of the Noise Results
### Effect of the Chordwise Spacing  
Figure 16 presents the \(\Delta\)SPL for structured porous trailing edges with different chordwise spacings, while Figures 17 and 18 plot the corresponding typical noise spectra and \(\Delta\)OASPL, respectively. It can be seen that the noise reduction performance is slightly different for a zero angle of attack of \(\alpha=0^{\circ}\) and a non-zero angle of attack of \(\alpha=10^{\circ}\). At \(\alpha=0^{\circ}\), small-to-medium size chordwise spacing of \(c_{h}=1.0\) mm and \(c_{h}=2.0\) mm alternately provide the best noise reduction. On the other hand, the smallest chordwise spacing of \(c_{h}=1.0\) generally outperforms \(c_{h}=2.0\) mm and \(c_{h}=4.0\) mm at \(\alpha=10^{\circ}\).  
Figure 16: \(\Delta\)SPL as a function of free-stream velocity and frequency for structured porous trailing edges with different chordwise spacings: (**a**) \(c_{h}=4.0\) mm, \(\alpha=0^{\circ}\); (**b**) \(c_{h}=2.0\) mm, \(\alpha=0^{\circ}\); (**c**) \(c_{h}=1.0\) mm, \(\alpha=0^{\circ}\); (**d**) \(c_{h}=4.0\) mm, \(\alpha=10^{\circ}\); (**e**) \(c_{h}=2.0\) mm, \(\alpha=10^{\circ}\); and (**f**) \(c_{h}=1.0\) mm, \(\alpha=10^{\circ}\).  
Figure 17: Typical noise spectra between the structured porous trailing edges and the baseline trailing edge with different chordwise spacings. (**a**) \(V_{\infty}=30\) m/s, \(\alpha=0^{\circ}\); (**b**) \(V_{\infty}=60\) m/s, \(\alpha=10^{\circ}\). Different colored circles in the zoomed inset figures illustrate the peak SPL of the different configurations.
## 3 Simulation Method and Numerical Assessment  
The nonlinear Schrodinger equation (NLSE) of Equation (1) represents the propagation of the optical signal in the silica fibers, such as the SSMF and DCF, where chromatic dispersion and nonlinear Kerr effect dominate [17].  
\[\frac{\partial A_{j}}{\partial z}=-\frac{\alpha}{2}A_{j}-\frac{i}{2}\beta_{2j }\frac{\partial^{2}A_{j}}{\partial T^{2}}+\frac{1}{6}\beta_{3j}\frac{\partial ^{3}A_{j}}{\partial T^{3}}+i\gamma_{j}\big{|}A_{j}\big{|}^{2}A_{j}+2i\gamma_{ j}\big{|}A_{k}\big{|}^{2}A_{j}, \tag{1}\]  
In Equation (1), \(j\), \(k=1\), 2,..., 24 (\(j\
eq k\)), \(A_{j}\) represents the complex amplitude of the optical signal of the \(j\)-th channel, \(z\) is the propagation distance, \(\beta_{2j}\) is the group velocity dispersion (GVD), \(\beta_{3j}\) is the dispersion slope, \(\gamma_{j}\) is the nonlinear coefficient, and \(T=t-z/v_{j}\) is the retarded frame. The last two terms of (1) express the effect of nonlinearity including self-phase modulation (SPM) and cross-phase modulation (XPM). The XPM has little impact on the system performance because of the high dispersion coefficient of the SSMF and, hence, its high walk-off [18]. Thus, the XPM effect on dense WDM signals was omitted in our simulation. The most common approach for solving (1) is the split-step Fourier (SSF) method [17].  
The SSF method divides the transmission medium dominated by NLSE into small steps and processes linear calculations in the frequency domain and nonlinear calculations in the time domain separately and continuously through Fourier transform and inverse Fourier transform for each step [17]. In this study, the DM link was modeled by applyingthe SSF method at intervals of 0.1 km. The multiplexed signal with RZ pulses having different wavelengths as described above is propagated through the DM link modeled in this way. The demultiplexed signal was recovered using the direct detection method for each channel. All coding was carried out using MATLAB.  
\[EOP\ [dB]=10\ log_{10}\frac{EO_{rec}}{EO_{bb}}, \tag{2}\]  
We used the eye opening penalty (EOP) and timing jitter of the received optical signal as the assessment factor. The EOP is expressed by Equation (2), where \(EO_{rec}\) and \(EO_{bb}\) are the eye opening of receiving optical signals and the back-to-back optical signals (i.e., the reference signals), respectively. The eye opening is defined as twice the averaged power of all of the received signals divided by the smallest power level of the \"one\" optical signal minus the largest power level of the \"zero\" optical signal.  
The compensation performance criterion is a 1-dB EOP, which is equivalent to the pulse broadening (the ratio of root mean square (RMS) width of the received pulse to RMS width of the initial pulse) of 1.25 and corresponds to a bit error rate (BER) of \(10^{-12}\)[19]. We also used 2.5 ps, which is one-tenth of the duration of 40 Gb/s, as the performance criterion for timing jitter.
# Identification of Industrial Heat Source Production Areas Based on SDGSAT-1 Thermal Infrared Imager
## 2 Materials and Methods
### Data Sources
#### 2.2.2 Landsat-8 OLI Product  
Landsat 8 is the eighth satellite in the U.S. Landsat program, launched successfully on 11 February 2013 from Vandenberg, California. It carries the Operational Land Imager (OLI) with a 30 m resolution and a TIS with 100 m [36]. With a radiometric resolution of 12 bits and a temporal resolution of 16 days, each Landsat-8 scene covers an extensive area of 185 \(\times\) 185 km\({}^{2}\)[37]. Landsat-8 data can be applied across a range of scientific research fields, such as agriculture, urban planning, and environmental monitoring, to provide crucial Earth observation data. Currently, only Collection-2 data are available for distribution. The Collection-1 product stopped production on 31 December 2022, and the Collection-2 product started production on 1 January 2022 [38]. Now, only Landsat Collection-2 products will be produced. While Level-2 data can be utilized directly for quantitative studies, Level-1 data require radiometric calibration for other research purposes, as they are only geometrically rectified.  
Landsat-8 OLI data, with the same spatial resolution as the SDSGAT-1 TIS data and stable data quality, were used to extract the optical features of IHSPAs. Therefore, Landsat-8 Collection-2 Level-2 data on 16 June 2022 were chosen from the United States Geological Survey (USGS) website ([[https://www.usgs.gov](https://www.usgs.gov)]([https://www.usgs.gov](https://www.usgs.gov)), accessed on 26 August 2022) in this study.
# Optimization of Mechanical and Setting Properties in Acrylic Bone Cements Added with Graphene Oxide
## 3 Results and Discussion
### Setting Properties
#### 3.1.1 Maximum Temperature  
The analysis of variance is shown in Table S2 (support information). The \(T_{max}\) delivers that all the effects of the interaction were significant at levels below 0.021 (sonication time * BPO/solid ratio; sonication time * GO/liquid ratio; BPO/solid ratio * GO/liquid ratio and sonication time * BPO/solid ratio * GO/liquid ratio). A determination coefficient of 94.64% was obtained, which is a good measure of the controlled effects' contribution (studied factors) to the total variation.  
Figure 4 exhibits the interaction plot for \(T_{max}\). Increasing the sonication time and the BPO/solid ratio promoted the higher values of \(T_{max}\), while the higher GO percentages decrease the \(T_{max}\) reached during polymerization. \(T_{max}\) values of all the formulations studied were found between 41 and 58 \({}^{\circ}\)C, below the maximum value allowed by the ISO 5833-02 standard, 90 \({}^{\circ}\)C. In ABC, a reduction in the \(T_{max}\) reached during polymerization is desirable since it reduces the thermal necrosis of the periprosthetic tissue [6; 12; 54], which is related to the loosening of the prosthesis [55]. Several researchers have highlighted the influence of the BPO content on the maximum temperature [5; 6; 55], finding that a higher BPO content can increase the concentration of free radicals available during polymerization, generating an increase in the amount of energy released during the reaction [11; 56; 57].  
The sonication time is related to the degree of dispersion [50] and the thickness of the GO particle [44]. Longer exfoliation times decrease the size of GO [45; 47] and possibly the heat dissipation capacity during the polymerization reaction due to the increase of the defects in the GO structure [49; 50; 58]. The decrease of \(T_{max}\) with the increase in GO content has been attributed to this component's inhibitory effect during the polymerization reaction because it acts as a sink for free radicals [34; 37; 38; 59]. According to the results reported by [PERSON] et al. [60], this inhibitory effect is concentration-dependent because, in their study, they showed by differential scanning calorimetry that the addition of 0.1 wt.% GO had no significant impact on the heat generation during the polymerization reaction of MMA.  
Figure 4: Interaction plot for the variable \(T_{max}\).  
[PERSON]'s multiple comparison tests shown in Table 2 indicate that group G formulations are the most suitable from a \(T_{max}\) point of view since they had the lowest \(T_{max}\) (<48 \({}^{\circ}\)C). This suggests that formulations 1-2.5-0.5; 1-2.0-0.5; 5-2.0-0.3; 1-2.5-0.3; 5-2.0-0.5 are statistically equal to a significance level of 5%. All group G samples are characterized by presenting the highest GO contents studied, which indicates that this variable's effect was the most significant on \(T_{max}\). Compared to the other two studies, the lower \(p\)-value shown for the GO/liquid ratio confirms the previous statement (Table S2).
## 3 Results and Discussion
### Enhanced CO\({}_{2}\) Conversion by Modified Cathodes  
After 14 d operation of the MES, the final biomass of _R.__shareoides_ and succinic acid production were analyzed (Figure 6). Although the planktonic biomass (blue squares) decreased, the attached biomass (red circles) in the MES reactors equipped with two modified GFE cathodes increased substantially. The calculations of total mole concentrations, which sum up the planktonic and attached biomass, were as follows: 1.08, 2.25, and 2.64 mol for the bare, chitosan-modified, and chitosan/carbodiimide-modified GFE cathode, respectively. In addition, the largest amount of succinic acid production (bar graph) was observed at the MES reactor equipped with the chitosan/carbodiimide-modified GFE cathode (33.4 umol). The faradaic efficiency of the chitosan/carbodiimide-modified GFE cathode-equipped MES reaction was significantly higher (98.0%) than that of the bare (34.9%) and chitosan-modified GFE cathode (77.8%), respectively. [PERSON] et al. reported that a modified cathode with chit/CDI in MES by using _S.__ovata_ as microbial catalyst increased acetate production 7.6-fold higher than the sample without cathode modification with Faradaic efficiency 86 \(\pm\) 12% [12]. On the other hand, our study is shown to  
Figure 5: Scanning electron microscope images of graphite felt electrodes (GFEs) as cathodes before (**a–c**) and after (**d–f**) microbial photosynthesis reaction by _R.__shareoides_ for 14 d. (**a,d**) bare GFE, (**b,e**) chitosan-modified GFE, (**c,f**) chitosan/carbodiimide compound-modified GFE. Scale bar, 100 μm (inset, 5 μm).  
have better performance attributed to chit/CDI modified cathode, triggering succinic acid production as much as 33.4 umol with faradaic efficiency by 98%.  
Some electroactive bacteria can obtain electrons from an electrode directly through physical contact between the cathode and the microbial electron transport system [27]. This physical contact was developed in the form of a biofilm on the cathode [1]. For example, _Geobacter sulfureducens_, a well-known electroactive microorganism, was genetically engineered to enable autotrophic growth by introducing genes for an ATP-dependent citrate lyase (it is named _G. sulfureducens_ strain ACL) [27]. The designed strain grew thicker confluent biofilms (ca. 35 um) on graphite cathodes, resulting in higher current consumption (\(\geq\)10-fold) than that of the wild type. Recently, [PERSON] et al. reported that _R. shareoides_ produces H\({}_{2}\) with its biomass growth through direct electron transfer from cathode to bacteria in the MES reactor [7]. Taken together, these may reflect that cathode modification using chitosan/carbodiimide composite may facilitate electron utilization by improving direct contact between an electrode and _R. shareoides._
# Conditional Temporal Aggregation for Time Series Forecasting Using Feature-Based Meta-Learning
## 4 Empirical Evaluation
### Base Forecasts  
We produce base forecasts using the exponential smoothing family of models, as proposed by [PERSON] et al. (2018) and implemented in the _forecast_ package for R ([PERSON] et al., 2018).  
Exponential smoothing is a univariate forecasting method that extrapolates series by smoothing the historical data and assuming that the most recent observations available are the most relevant for predicting the future ones. Depending on the model, exponential smoothing may account just for the level of the series or handle trend and seasonality. Therefore, the ETS (error, trend, seasonality) framework, which involves a total of 30 exponential smoothing models and selects the best one based on information criteria, is an ideal method for automatically producing forecasts at multiple temporal aggregation levels, each having different time series features.  
Since exponential smoothing forecasts are intuitive, fast to compute, and accurate compared to other methods, the method has become popular in the industry and forecasting literature, being the standard method of choice in numerous forecasting applications ([PERSON] et al., 2017). Moreover, exponential smoothing has been one of the most accurate benchmarks considered in the M4 and M5 competitions, outperforming many of the participating teams.
# Potassium Recovery from Potassium Solution and Seawater Using Different Adsorbents
## 3 Results and Discussion
### K Recovery from Seawater
Based on the K sorption on each adsorbent, the K recovery from the seawater was generally less than that from the KCl solutions (Figure 6). This could be attributed to the interference of other ions such as chlorine and Na, which account for more than 80% in seawater. As the K concentration in seawater increases, so do the other ions in seawater, and they will compete with K for adsorption. Considering such competition between mixtures of ions, K from waters and seawaters containing K and other ions can be more efficiently recovered using zeolite-based adsorbents than manganese nodule.