esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Low temperature affects the formation of internal structures in plant leaves; destroys the integrity and function of chloroplasts; and significantly reduces the maximum fluorescence (Fm), maximum photochemical efficiency (Fv/Fm), and actual photochemical and quantum efficiency (\(\Phi\)PSII), leading to a decrease in the rate of plant photosynthesis [17; 18]. The physiological and biochemical characteristics of different plants of different species and their chilling tolerance performance under low-temperature stress are different, and most of the previous studies used a single approach to evaluate chilling tolerance and lacked a systematic study to assess chilling tolerance; comprehensive evaluation and comparison play an important role in physiological response and resilience assessment [19]. A comprehensive evaluation method combining principal component analysis (PCA), membership function method, and cluster analysis has been applied to evaluate the chilling tolerance of _Arachis hypogaea_[20], _Cucumis melo_[21], _Solanum lycopersicum_[22], _Vitis vinifera_[23], and other plants. At present, there are few reports on the physiological characteristics and chlorophyll fluorescence characteristics of mustard under low-temperature stress, and comprehensive evaluations of the chilling tolerance of mustard cultivars are rare. In this study, 65 leafy mustard cultivars were treated at 5 \({}^{\circ}\)C and 25 \({}^{\circ}\)C, and the chlorophyll (Chl) content, soluble sugar (SS) content, proline (Pro) content, antioxidant enzyme activity, and malondialdehydedehyde (MDA) content of mustard leaves were determined under different treatments. Additionally, chlorophyll fluorescence technology was used to determine the photosynthetic performance of leaves. Furthermore, correlation analysis, principal component analysis (PCA), membership function method, and cluster analysis were used to comprehensively evaluate the chilling tolerance of mustard cultivars. The objective of this study was to clarify the tolerance of different leafy mustard cultivars to chilling injury, select cultivars with strong chilling tolerance, establish a mathematical model for evaluating the chilling tolerance of mustard and screen the main indicators for evaluating chilling tolerance. The results will provide a reference basis for the selection and breeding of chilling-tolerant cultivars of mustard, and lay a research foundation for the comprehensive evaluation of chilling tolerance of mustard cultivars.
## 1 Introduction  
The aluminum electrolysis process has continued to make improvements to energy conservation and emissions reductions, while increasing production and productivity at the same time [1, 2]. Once the cell is shut down and drained, every day of extended operation brings a substantial saving for the aluminum industry [3]. To achieve these goals, major efforts have been made to extend the service life of an industrial aluminum induction cell, which is the most important equipment in electrolytic production of aluminum [4]. The physical performance of the carbon cathode can be influenced enormously by the chemical contamination phenomenon [5]. In addition, cell life has long been associated with the properties of cathode blocks, often particularly with their degradation caused by the penetration of the melt and sodium towards higher temperatures [6]. Therefore, investigation of the sodium expansion of carbon cathodes is of vital importance for better controlling cathode quality and its performance in industrial aluminum reduction cells.  
A number of researchers have reported that sodium expansion is one of the main reasons for cathode deterioration during aluminum electrolysis. [PERSON] was the first to measure the sodium expansion of semigraphic cathode material with three current density values through the use of the Rapoport-Samoilenko-type apparatus. Moreover, he has established a model for the relationship between sodium expansion and time [7]. [PERSON] determined a ReaxFF parameter set to describe sodium interactions with graphitic carbon by applying molecular dynamics methods [8]. Recently, current density has received research attention due to its effect on the electrolytic process. Reinforced current is both the development trend for the current world aluminum electrolysis industry and the urgent demand of the aluminum industrial development of China. The stress and strain of graphite composite electrodes have recently been measured during electrochemical cycling [9]. It has been found that the current density affects the exchange rate of lithium ions and further affects the stress and strain of graphite electrodes. In addition, the current density can control the morphology and crystallinity of graphene nanoribbons by Joule heating [10]. In order to decrease cathode deterioration, an understanding of the microstructure change of carbon cathode during aluminum electrolysis on the atomic scales is desired.  
However, so far only limited information is available about the influence of current density on the microstructure of carbon cathodes during aluminum electrolysis. This work presents the in-depth microstructure investigations of carbon cathodes including the atomic arrangement and the formation of sharp zigzag and armchair edges.
# Simulation and Optimisation of Utility-Scale PV-Wind Systems with Pumped Hydro Storage
## 3 Computational Results and Discussion
### Optimisation of an LSS System (System with Load Consumption)
#### 3.2.1 Resources  
Zaragoza (Spain) (41.66\({}^{\circ}\) N, 0.88\({}^{\circ}\) W) was selected as the location of the system. The PV module slope was 35\({}^{\circ}\), which was optimal for maximising the annual irradiation over the tilted surface (2049 kWh/m\({}^{2}\)). Hourly irradiation (Figure 6) and temperature data were downloaded from PVGIS in 2020.  
Figure 5: Annual energy (GWh/year) supplied by the different components, unmet load, and sell energy during the 25 years of the simulation.  
Figure 6: Hourly irradiance (W/m\({}^{2}\)) in Zaragoza (slope of 35\({}^{\circ}\) and azimuth of 0\({}^{\circ}\)).  
The uncertainty of the hourly irradiation for each year was obtained using a standard deviation \(\sigma_{\text{G}}\) = 5% (this value was obtained by analysing the data from 1981-2021 downloaded from NASA [37]). Figure 7 shows the variation in the annual irradiation over the average.  
Wind speed was determined at a height of 53 m (hub height of the wind turbines is defined later) and downloaded from Renewables Ninja (Figure 8), which helped obtain an average of 6.96 m/s with a 2.9 form factor of the Weibull distribution that best fit the data. The uncertainty of the hourly wind speed for each year was obtained using a standard deviation \(\sigma_{\text{W}}\) = 2.7% (obtained after analysing the variation over 10 years). Figure 9 shows the variation in the annual average wind speed over the average, in percentage.
# Polar Codes and a M-ary Modulation-Based OFDM-PLC System
## 5 Performance Simulation and Results Analysis
### Giving the BER for Different Intensities of Impulse Noise  
The BER performance of polar codes under different impulse models and impulse interference conditions is studied in the above OFDM system using 16 APSK modulation. Firstly, the most commonly used Middleton-A class noise model is used, with a fixed Gaussian pulse power ratio of 0.1, which means that the power of the impulse noise is 10 times that of the background noise, and the impulse index \(A\) in the model, which measures the average number of impulses within one signal cycle, is changed. As shown in Figure 6, with a polar code length of 512, and a code rate of \(1/2\), when \(A\) is defined as 0.01, 0.1, and 0.5, it can be seen that as \(A\) increases, the impulse interference increases, the attenuation slope decreases, and the BER performance decreases. When the BER reaches \(10^{-3}\), the Signal-to-Noise Ratio (SNR) of \(A=0.01\) is 2 dB higher than that of \(A=0.1\). In order to better fit the background noise characteristics of the PLC channel, an improved noise model is given to characterize more realistic random impulses. The \(m\) parameters in the improved noise model are set to six values as shown in Figure 7, respectively, and the BER curves using AWGN as the background noise under the same system model are also  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameter** & **Setting** \\ \hline \multirow{8}{*}{OFDM} & constellation mapping method & BPSK/16 APSK/16 QAM/16 PSK \\  & cyclic prefix length (\(\upmu\)s) & 75 \\  & length of cyclic prefix (\(\upmu\)s) & 30 \\  & subcarrier spacing \(f\) & 1.5652 kHz \\  & sampling rate & 400 kHz \\  & number of subcarriers \(N\) & 36 \\  & FFT/IFFT size & 256 \\  & OFDM symbol spacing (\(\upmu\)s) & 715 \\ \hline \multirow{4}{*}{Polar code} & Code length & 512 \\  & Coding rate & 12,8,3 \\ \cline{1-1}  & Decoding algorithm & CA-SCL \\ \cline{1-1}  & Decoding list length & 8 \\ \cline{1-1}  & CRC length & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 1: System Parameter Settings.  
given as references. The figure shows that under 16 APSK modulation, as \(m\) increases, the number of impulse noises increases. When SNR > 10 dB, high-amplitude random impulse noises will reduce the error correction ability of the polar code. As the SNR continues to increase, the BER under the improved noise model tends to flatten, i.e., the error floor phenomenon (error caused by impulse noise only) appears as \(m\) increases and increases with \(m\).  
Figure 6: BER performance of polar codes under different impulse indices A in Middleton-A class model.  
Figure 7: BER performance of polar codes under different impulse parameters m in the improved noise model.
# An Improved Lightweight Dense Pedestrian Detection Algorithm
## 5 Conclusions  
An improved YOLOv5 lightweight dense pedestrian detection algorithm is proposed in this article. The algorithm's running speed is improved to solve the problem of real-time detection of dense pedestrians under the premise of keeping a certain accuracy and robustness in pedestrian-dense scenes. For picture feature extraction and feature fusion, it employs a lightweight backbone network and neck. Furthermore, in dense scenes, the SIoU loss function is used to improve the prediction box overlap issue. The experimental results on the CrowdHuman dataset show that the GS-YOLOv5 model's number of parameters is reduced by 40% compared to the original YOLOv5, and the amount of calculation is reduced by 64%, significantly improving the detector's detection speed. The mAP value is raised by 0.3% after SIoU is added without increasing the computational cost. The limitation of this method is that when real-time pedestrian detection meets the requirements, the calculation accuracy is not significantly improved. Shortly, more advanced algorithms for dense pedestrian detection can be researched further to enhance the algorithm's overall performance and efficiency.  
Conceptualization, M.L., S.C. and C.S.; methodology, M.L. and S.C.; software, M.L. and C.S.; validation, S.C., C.S. and S.F.; formal analysis, M.L. and X.W.; investigation, J.H. and H.Y.; resources, C.S.; data curation, S.C.; writing--original draft preparation, M.L. and S.C.; writing--review and editing, M.L., S.C. and X.W.; visualization, S.C.; supervision, M.L. and X.W.; project administration, M.L.; funding acquisition, M.L. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **mAP0.5** & **mAP0.50.95** & **\(P\)** & **R** \\ \hline Model 1 & 0.601 & 0.316 & 0.779 & 0.38 \\ Model 2 & 0.597 & 0.319 & 0.834 & 0.33 \\ Model 3 & 0.598 & 0.318 & 0.85 & 0.319 \\ Model 4 & 0.599 & 0.321 & 0.854 & 0.317 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test results on the CrowdHuman dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **mAP0.5** & **mAP0.50.95** & **\(p\)** & **R** \\ \hline YOLOv5 + GhostNet + GSConv + SIoU & 0.599 & 0.321 & 0.854 & 0.317 \\ YOLOv5 + MobileNetV3 & 0.473 & 0.185 & 0.668 & 0.408 \\ YOLOv5 + ShuffleNetV2 & 0.473 & 0.186 & 0.654 & 0.414 \\ YOLOv5 + EfficientLite & 0.505 & 0.204 & 0.689 & 0.437 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison results of other algorithms on the CrowdHuman dataset.  
**Funding:** This research was funded by Jilin Provincial Department of Education [No: JKH20220593 KJ] and Jilin Provincial Natural Science Foundation of China [No: YDZJ202201 ZYTS432].  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Copper Nanoparticles Synthesis  
Metal nanoparticles can be produced using green, chemical and physical methods. In order to obtain nanoparticles, especially copper nanoparticles, three elements are needed. First, a precursor that provides copper ions. Second, in order to obtain copper atoms a reducing agent is required to supply electrons. Given the correct temperature and pH conditions, the third component, the surfactant, aggregates the copper atoms resulted from the reducing agent into copper nanoparticles [20].  
Depending on the source of electrons, we can categorize the synthesis methods into green, if the reducing agent is a biological organism (bacteria, molds, algae, plants), into chemical, if the reducing agent is a chemical compound and lastly physical, when the source is physical one, such as electricity [20].
# Biodegradation of a Mixed Manure-Lignocellulosic System--A Possibility Study
## 2 Materials and Methods
### Preparation of Standard Solutions and Calibration Curves for Glucose and Xylose  
Standard stock solutions of glucose, xylose and internal standard (ISTD) phenyl-\(\beta\)-D-glucopyranoside were prepared by weighing 10 mg of each into separate 10 mL glass volumetric flasks and dissolving them in methanol (\(\gamma\) = 1000 mg L\({}^{-1}\)). Afterwards, the flasks were placed in an ultrasonic bath for at least 15 min. Calibration solutions were prepared by combining different aliquots (20 to100 \(\upmu\)L) of glucose and xylose with 50 \(\upmu\)L of ISTD stock solution into separate conical glass flasks. These solutions were evaporated to absolute dryness using a rotary evaporator. Then, 100 \(\upmu\)L of pyridine and 200 \(\upmu\)L of BSTFA with 1% TMCS were added, and derivatization (silylation) was carried out by heating the samples for 90 min at 80 \({}^{\circ}\)C on a sand bath. Before the analysis, the solutions were diluted with toluene to the same final volume of 1 mL, and then, 1 \(\upmu\)L of each solution was injected into the GC-FID system in triplicate. Linear curves (concentration range from 20 to 100 mg L\({}^{-1}\)) were constructed using linear regression of the peak-area ratio of individual monosaccharide to ISTD versus the concentration.
# Adaptive Seasonality Anomaly Detection Algorithm under Intricate KPI Profiles
## 4 Results
### Seasonal Adaption Evaluation  
First, we assess the _ASAD_ algorithm's seasonal adaptation function. With respect to seasonal adaptation functions, we compare the _ASAD_ method to similar anomaly detection algorithms as shown in Table 7. The seasonal length detection function of the KPI anomaly detection algorithm is gradually growing toward intelligence, from manual and automatic configuration of seasonal parameters to the process of self-adaptation. Both Argus and EGADS, according to tests, require human configuration of the seasonal length as a parameter in KPI anomaly detection and are unable to achieve seasonal adaptation. Period does not require the seasonal length parameter to be configured, but it is unable to adapt to the seasonal feature, because Period assumed that KPI time series data have a seasonal length of 1 day. As a result, only the _ASAD_ algorithm has seasonal adaptive capability to detect anomalies under an intricate KPI profile.  
Second, we evaluate the accuracy of the _ASAD_ algorithm's seasonal length detection. There are no other anomaly detection algorithms to compare, so we use auto-correlation as the comparison method. We divide the KPI time series data into multiple segments with a length of 1 week to half a month to test the performance.  
Finally, we employ the _ASAD_ method and auto-correction to determine the seasonal length of each test instance. According to Table 8, _ASAD_ algorithm has the 75% accuracy rate for detecting seasonal length. The auto-correction algorithm's accuracy rate for detecting seasonal length is only 43%. The accuracy rate of the _ASAD_ algorithm is higher than that of auto-correction, and the _ASAD_ algorithm's error rate is lower than that of auto-correction. To summarize, _ASAD_ algorithm with a higher detection accuracy rate is better than auto-correction method for automatically detecting the seasonal length.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**ID** & **Index Name** & **Sampling Interval (s)** & **Total Duration (Day)** \\ \hline A & Transactions per Second & 300 & 180 \\ B & CPU utilization & 300 & 60 \\ C & Number of full table scans Second & 300 & 60 \\ D & Memory utilization & 300 & 60 \\ E & Number of TCP connections & 300 & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 6: KPI indicator statistics of experimental dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Argus** & **EGADS** & **Period** & _ASAD_ \\ \hline Manually & & & & \\ configure & & & & \\ Auto-configure & & & & \\ Seasonality & & & & \\ adaptation & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Seasonal adaptive function comparison.
## 4 Results of Numerical Simulation  
### Mechanical Properties  
Figure 8 represents the shear stress-shear displacement curves of the specimens under different shear rates or different, normal stresses. As can be observed from Figure 8, the curves exhibit that the shear process could be determined and identified as the elastic, strengthening, plastic, and residual stress stages, respectively (I-IV). Figure 8b shows a comparison of the stress-displacement curves under three normal stresses, for the elastic stage (I) the change of normal stress has had a certain effect on the slope of the curve. Moreover, the cohesive elements unit did not reach the yield limit at the elastic stage, and no cracks occurred on the specimen. Under the shear load (Figure 8b), it gradually entered the strengthening stage (II). At this stage, the cohesive elements began to enter the linear damage stage, some of the cohesive elements reached the stress yield limit and disappeared immediately after a fracture. At this time, the initiation of cracks could be observed. It is worth noting that crack initiation led to stress change. Since the rock bridge was not coalesced by cracks at this time, the rock bridge was the main provider of the shear strength; therefore, the stress-displacement curve fluctuated at this stage, and the overall shear strength showed an upward trend. When the shear load was further increased, the specimen reached the peak shear strength, and it is noteworthy that the normal stress and shear rate were positively correlated with the peak shear stress. Immediately afterward, we entered into the plastic stage (III). At this stage, the crack expanded and coalescence cracks were generated, and the shear strength of the specimen decreased significantly. When the crack developed further, forming a continuous main crack, the curve entered the residual stress stage (IV). The residual shear strength was provided by the friction and mechanical occlusal forces of the upper and lower parts of the specimen at this stage. The larger the shearing rate was, the bigger the residual shear strength was.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Numerical Number** & **Shear Rate (mm/s)** & **Normal Stress (MPa)** \\ \hline AI & 0.01 & 1 \\ AII & 0.02 & 1 \\ AIII & 0.04 & 1 \\ BI & 0.02 & 1 \\ BII & 0.02 & 2 \\ BIII & 0.02 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Shear rate and normal stress of six cases in the numerical model.  
Figure 7: A conceptual model for rock-like materials containing two holes.
# Driver Behavior Classification System Analysis Using Machine Learning Methods
## 5 Experimental Results and Comparison
### Traffic Status Dataset
#### 5.2.4 Neural Networks  
The starting network parameters were the same as those used for the \"lane detection\" dataset. The number of epochs and batch sizes were varied over the same range. Similar results to those observed with the \"lane detection\" dataset could be observed, with the overall model accuracy not exceeding 0.29. In addition, multiple epochs, a varied number of layers and multiple other parameters including the activation functions were run independently as well as together in loops, providing an accuracy at best of 0.29. The vast and yet highly varied data sets, with different sample numbers, result in poor overall results again.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline
**Class 0 (normal)** & 0.61 & 0.52 & 0.56 & 7840 \\
**Class 1 (aggressive)** & 0.88 & 0.56 & 0.68 & 6394 \\
**Class 2 (drowsy)** & 0.55 & 0.79 & 0.65 & 7652 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Multi-class evaluation metrics with random forest classifier for traffic status.  
Figure 8: Random forests simulation results: (**a**) precision–recall curve; (**b**) ROC curve.
## 1 Introduction
\[-\ln(1-X)=kt^{m}\quad;\quad\text{for the Avrami model} \tag{3}\]  
Although the unreacted shrinking core and the Avrami models can satisfactorily fit the experimental observations, they lack the ability to describe the physicochemical particularities of the process. In particular, from our previous results on Li and Co extraction from LiCoO\({}_{2}\) particles via acidic leaching [24], two important observations were pointed out:  
1. Li and Co extraction does not happen according to the stoichiometry of 1:1 expected from the dissolution of LiCoO\({}_{2}\) particles. More specifically, the molar amount of Li extracted seemed to be twice as much as that of Co.
2. Leaching of LiCoO\({}_{2}\) particles does not happen to a full extent in reasonable times. The leaching process slows down to an apparent plateau in around 65-70% of Li extraction. As mentioned before, to the best of our knowledge, the SCM, USCM, and the Avrami model do not deal properly with the aforementioned limitations (Li:Co extraction in a proportion 2:1 and limiting Li extraction to around 65%). Therefore, a mathematical model is proposed for the leaching kinetics of LiCoO\({}_{2}\) particles. The model is described from the stoichiometric reactions reported in the bibliography and takes into account the formation of an insoluble crust of Co\({}_{3}\)O\({}_{4}\), which resembles the USCM. The formation of the Co\({}_{3}\)O\({}_{4}\) crust was detected through X-ray photoelectron spectroscopy in [24]. The model is validated with two sets of experiments at different conditions of pH and solid to liquid ratios.