esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Examples
### Specific Examples
#### 5.2.3 Example 3  
The parameters in example 3 keep the baseline values of example 1a, with the only modification being a periodic harvesting parameter defined as  
\[h(t)=\begin{cases}0\,\text{day}^{-1}&\text{if $t$}\mod 250\leq 240\,\text{ days},\\ 0.1\,\text{day}^{-1}&\text{if $t$}\mod 250>240\,\text{days}.\end{cases} \tag{77}\]  
We refer to Figure 4 for illustration: The harvesting leads to a simultaneous reduction in \(S\) and \(I\), which is accompanied by a reduction in \(U\) and \(W\). The decrease in \(W\) towards a  
Figure 3: Simulation results for Example 2. Change in parameters: (**a**) \(\beta=0.01\) (vector \({}^{-1}\) day\({}^{-1}\)), (**b**) \(g=0.01\) (day\({}^{-1}\)), (**c**) \(h=0.01\) (day\({}^{-1}\)), (**d**) \(\mu=0.01\) (day\({}^{-1}\)).  
minimum allows \(S\) to obtain a maximum, that is accompanied by a maximum in \(U\). After reaching their maxima, \(S\) and \(U\) decrease towards an asymptotic state, whereas \(I\) and \(W\) continue to increase towards an asymptotic state.  
Increasing the harvesting parameter changes the phenomenon quantitatively, but not qualitatively. Remarkable, higher harvesting parameters \(h(t)\) might lead to negative \(U\) values. Similarly, changing the mortality of the vector species might also result in negative values of \(U\). As negative values of \(U\) are not physically plausible, a model modification is necessary to exclude negative values by design. Since the negative values can be traced back to the term in the large parenthesis, there are two possible fixes:  
\[U\bigg{(}1-\frac{U+W}{K(S+I)}\bigg{)},\qquad\max(0,\bigg{(}1-\frac{U+W}{K(S+I) }\bigg{)}). \tag{78}\]  
Multiplying the potentially negative factor by \(U\) ensures that the values of \(U\) remain within the non-negative invariance region, with \(U=0\) as an asymptotic boundary that cannot be crossed. Taking the maximum of the potential negative value and \(0\) enforces a non-negative value.
## 2 Materials and Methods
### Methodology
The new satellite data with high frequency, such as HJ-1/CCD and Sentinel2/MSI, were firstly used to construct monthly time series surface reflectance and they were subsequently used in the LCMM method [19] for time series analysis to make the high-quality land cover datasets at HRB from 2011-2015. In this study, the land cover dataset at HRB from 2011-2015 was made, and publicly released at the Western Data Center of China ([[http://westdc.westgis.ac.cn/](http://westdc.westgis.ac.cn/)]([http://westdc.westgis.ac.cn/](http://westdc.westgis.ac.cn/)), accessed on 19 April 2021) [19] and this dataset was well tested by many applications under the support of the HiWATER project [31], so it was directly used in this study. This takes advantage of the newly available satellite data. 2. Instead of making the land cover year by year for 10 years, the machine learning method was chosen as the classifier to lower the labor and time costs. While employing machine learning, the training sample is always the key to its performance and it usually requires a lot of labor for manual sampling; subsequently, an automatic sampling strategy was established in this study to retrieve enough accurate training samples from the high-quality land cover dataset at step 1 by comprehensively using the land cover maps from all five years. The details of the strategy are presented in Section 2.4.2. 3. Due to the inconsistency of seasonal surface reflectance composites in Figure 2, the samples from step 2 could only satisfy the requirement for 2011-2015 and could not be directly transferred for application at earlier years; therefore, a strategy for transferring the training samples to earlier years was established. The details of the strategy are presented in Section 2.4.3. 4. Due to its advantages (detailed in Section 2.4.4. ), the random forest model was employed to train the selected yearly samples and the land cover map for every year was subsequently made. A long time series land cover dataset including 1986, 1990, 1995, 2000, 2005, 2010, 2011, 2013, 2014, and 2015 was made. 5. Finally, comprehensive analysis and validation were carried out for evaluation.
# Path Planning Strategy for Implementing a Machine Control System in Grader Operations
## 1 Introduction
The purpose of this study was to develop a construction equipment work path generation algorithm, a key component of machine control (MC) technology, to perform effectively earthworks in road construction projects. The construction equipment under study is a motor grader (hereafter referred to as the grader), which is primarily used in road construction for leveling, grading, drainage, resurfacing, and maintenance. Graders are particularly optimized for evenly cutting or leveling soil and play a crucial role in forming or maintaining road foundations. Modern graders are powered by GPS and sensor data, enabling real-time control of the path and depth that they need to work, thereby greatly increasing their efficiency. However, there remains a lack of automated work path generation for graders. The contributions of this research can be summarized as follows:  
Automated work path generation reflecting earthwork BIM, design information, and construction equipment specifications  
Provision of basic technology for integrating the grader with the MC system  
In this study, the type of work was limited to grading, excluding the control of the blade from the scope of the study. To develop the work-path generation algorithm, the heuristics of the grader operations were identified by interviewing the grader operators. Additionally, a survey was conducted with site managers to understand the work methods and patterns of graders onsite, which were then incorporated into the algorithm. What sets our work apart from previous studies is the consideration of workers' heuristics, work methods, and grader operation patterns. We generated work paths not only by applying optimization  
Figure 1: Smart earthwork process. techniques based on the given information but also by incorporating practical know-how to work efficiently and meet the required quality standards in real-world operations. A case study was conducted to validate the developed algorithm by arbitrarily selecting an actual road-construction site.
# Detection of Mineralization Zones Using Aeromagnetic Data
## 4 Results and Discussion
From the magnetic maps, subsurface structural lineaments were traced using [41] concepts. The RTP, FVD, HGR, AS, regional, and residual maps, and CFT grid analysis are all used to trace the lineaments. The N-S, NE-SW, NW-SE, and E-W are the principal tectonic trends impacting the study region, with the major trends traced from the geology map being in the NEE-SWW, NE-SW, NW-SE, and E-W directions (Figure 11). Thrust faults that run NE-SW and NEE-SWW and dip NW separate the island arc rock groups represented by metasedimentary and metavolcanic layered units, intrusive gabbro to diorite plutons, and ophiolitic rocks [42; 43]. The N-S trend coincides with the New Counter Lode trend, which [44] termed the East African trend and regarded as an ongoing direction of dike intrusion and faulting in Egypt. The applied filters and the application of CET techniques helped to detect three different alteration zones distributed in the area, with the first alteration zone located NE of the Barramiya mine area and extending in the N-SW direction. The second alteration zone is located at the southeast of the Barramiya mine area and extends in the E-W direction and coincides with the E-W structural trend. Our results for these two alteration zones are consistent with findings by [28]. They integrated fieldwork and satellite datasets with geochemical and mineralogical data to investigate new gold discoveries in two alteration zones in the northeast (zone 1) and southeast (zone 2) in the ultramafic-mafic successions of the Barramiya gold mine. Talc, magnesite, ankerite, calcite, and quartz are found in varying concentrations in regions 1 and 2, according to mineralogical studies. Other studies using an ore microscope indicated that these zones contain sulphide minerals that hold gold [28]. The third alteration zone (zone 3) lies in the central part of the mine area and extend in the NW-SE direction. [42] described the major cleavage direction NEE-SWW and two joint systems of granites striking NNE, SWW, and dipping toward the east. In the Barramiya mine site, NNE is represented by left-lateral strike-slip faults, which were evidenced by horizontal slickensides. In comparison to sophisticated magnetic data applications, gravity datasets are widely employed for advanced applications such as monitoring mass variations and groundwater resources [45; 46; 47; 48].
## 1 Introduction
For more than 40 years, _Bacillus cereus_ has been recognised as an agent of food poisoning and has been associated with foodborne emetic and diarrhal syndromes [18]. _Bacillus licheniformis_ has been associated with a variety of clinical syndromes and food poisoning events in humans, bovine toxaemia, abortions, and food spoilage events [19]. _Bacillus subtilis_ is not usually considered a human pathogen but may occasionally contaminate food and cause food poisoning [20]. Total aerobic bacteria were higher in spoiled hams than in normal hams in the study of [PERSON] et al. [21]. _Pseudomonas_ sp. and _Moraxella oslensis_ are both aerobic bacteria often associated with spoiled meat [22; 23]. The corrosion of the hams is related to the proteolytic and lipolytic reactions in the ham, especially in which the odour generated is closely associated with the metabolic action of the microorganisms [10]. Several studies have investigated the compositions and dynamics of microbial communities, as well as their impact on the production of volatile compounds in dry-cured ham [10; 15; 24; 25]. However, there is still a lack of comprehensive knowledge on the microbial diversity and flavour development of Xuanwei ham, especially the relationship between abnormal microbial growth and off-odour formation. Furthermore, no study comprehensively highlights the chemical, sensory, and volatile profiles of Xuanwei ham on the same experimental batch and comparatively on different muscles. Recently, headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) has been widely used in the analysis of volatile compounds in meat products, offering a precise method for both qualitative and quantitative analysis of volatile compounds [26]. Due to the complex composition of meat products, analytes need to be extracted before introduction into the chromatographic system. Solid-phase microextraction (SPME) is a potential food analysis sample preparation method due to its simplicity, speed, solvent-free nature, analyte separation, and preconcentration [27]. HS-SPME-GC-MS has been widely used in the investigation of meat products, including the analysis of volatile profiles in fermented sausages and dry-cured hams [28; 29]. Therefore, the aim of this study was to investigate the composition and diversity of microorganism communities and volatile compounds in the BF and SM muscles of Xuanwei ham with different quality grades (normal ham and spoiled ham). Meanwhile, comparisons of the physicochemical properties and quality characteristics of hams were also conducted. Furthermore, the potential correlations between core microorganisms and major volatile compounds were explored, and the core bacteria responsible for the off-odour of the Xuanwei ham were determined. This study may be helpful for regulating the spoilage in the production of Xuanwei hams and improving the flavour and quality consistency.
# Parameter Analysis on Seismic Response of Long Lined Tunnel by 2.5D Substructure Method
## 6 Conclusions  
In this paper, a 2.5D substructure method is proposed for the analysis of half-space tunnel wave scattering. On the surface of the structure, the high-precision 2.5D zigzag boundary is used. In the dynamic analysis of soil-structure interaction, the finite domain can only be composed of tunnels, which saves the calculation cost. At the same time, the 2.5D finite element method can effectively avoid the longitudinal truncation effect. Actual seismic records are used in numerical examples. The influence of the incident angle when P-SV waves are incident is mainly discussed. The influence of the position of the soil-rock interface on the seismic response of the lining structure is introduced.  
The seismic response of underground tunnels is unique; for example, tunnels are less affected by their own inertia force, so are mainly controlled by the free field deformation of surrounding media. Their seismic response analysis needs to consider the dynamic interaction between the tunnel and surrounding media. When the incidence angle of seismic wave is considered, the seismic response of the vertical incidence is not necessarily the maximum value, and the seismic response may be amplified in the case of oblique incidence. For different site conditions, the same underground tunnel will show different seismic response characteristics. The seismic response of the underground structure near the soil-rock interface of a long linear tunnel may be greater than that of the underground structure in a uniform site.  
In addition, in the next work, this method can be used to consider the dynamic interaction between soil and structure of multiple structures. It provides an effective calculation method for studying the seismic response interaction between 3D structures.  
Figure 16: The maximum bending moment (**a**) \(M_{c}\), (**b**) \(M_{v}\) and (**c**) \(M_{h}\) of tunnel lining at different soil-rock interfaces when SV wave is incident.  
**Author Contributions:** Conceptualization, Q.Z. and X.D.; methodology, Q.Z. and M.Z.; software, Q.Z.; validation, Q.Z. and J.H.; data curation, J.H.; writing--original draft preparation, Q.Z. and J.H.; writing--review and editing, M.Z.; visualization, J.H.; supervision, X.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work described in this paper is supported by the National Natural Science Foundation of China (52278476) and National Key Research and Development Program (2022 YFC3003603, 2022 YFC3004304), which are gratefully acknowledged.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The relevant data are all included in the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Study on the Anti-Interference Performance of Substrate-Free PEDOT:PSS ECG Electrodes
## 2 Materials and Methods
### Electrode Adhesion Mechanism  
When PEDOT:PSS dry electrodes are attached to the skin surface, they make partial contact with skin primarily through van der Waals forces, as illustrated in Figure 2a. Upon hydration and gelation, as depicted in Figure 2b, the PEDOT:PSS electrode turns into soft hydrogel and the modulus increase to a low level, enabling conformal contact with the skin. Following the redrying of the PEDOT:PSS electrode on the skin surface, shown in Figure 2c, it regains its original Young's modulus and thickness. Because the gelation and drying processes of the PEDOT:PSS electrode occur predominantly in the thickness dimension, the redried electrode could maintain conformal contact with the skin. In addition, during the rehydration and subsequent drying of the PEDOT:PSS electrode, water molecules facilitate the formation of intermolecular hydrogen bonds between the electrode and the skin surface. Since hydrogen bonds are stronger molecular forces compared to van der Waals forces, the adhesion strength between the PEDOT:PSS electrode and the skin significantly enhances, enabling self-adhesion. Moreover, this process increases the contact area with the skin, thereby further boosting the adhesion strength between the PEDOT:PSS electrode and the skin [7].  
Figure 1: (**a**) Ag/AgCl electrode; (**b**) PEDOT:PSS electrode.  
Figure 2: (**a**) PEDOT:PSS dry electrode was attached to the skin surface, dominated by van der Waals force; (**b**) PEDOT:PSS was wetted and gelled on skin; (**c**) Conformal contact of PEDOT:PSS with skin, dominated by intermolecular hydrogen bonds.
###### Abstract
Although preferable for ease of input collection, image-based solutions have some drawbacks. Firstly, to achieve high accuracy, large amounts of uniform training data are required. Secondly, the data acquisition process needs to be closely connected to the manufacturer. The key factors that need to be considered during this process are not always straightforward to derive. In the aspect of initial problem definition, signals are a better solution, as the measurement process focuses on detecting any changes in them. At the same time, the computation required to process large amounts of data is an immediate issue that needs addressing. Using sensor-based data as an input for neural networks can pose some problems. To do it efficiently, a new handling method was required, which in this case includes a novel approach to dealing with discrepancies and variations in sensor data. While changes will occur when the tool state deteriorates, not all sensors will produce the same amount of feedback. Because of this, pinpointing the actual moment when the tool needs to be replaced can be challenging. Additionally, the size of data obtained from different sensors differs significantly. The data files will be much larger, especially for sensors requiring higher precision. One of the main problems to consider is optimally using the obtained data while retaining the advantage of precise measurements. One way to approach this issue is by transferring the signals into an image. For example, the authors of [18] do this with sound signals using short-time Fourier transform. The raw data were denoised and later converted into images, while a pre-trained CNN model performed deep feature extraction. The final step involves using a support vector machine for classification. A different approach uses the scalogram as a target format [19] for induction monitor state diagnosis. In this case, Constant-Q transform with non-stationaryGabor transform is used. Vibration and acoustic single features are fused with a multi-input CNN solution. In the authors' opinion, the continuous wavelet transform (CWT) was too time-consuming given the initial input structure. Although the overall computation was faster, it also decreased the obtained. This paper primarily aims to critically compare various machine learning approaches for tool condition monitoring in milling processes, utilizing time series data collected from the machining operations. The specific algorithms include support vector machines (SVMs), boosting ensemble decision trees, and long short-term memory (LSTM) networks. The research seeks to understand the comparative effectiveness of those methods in terms of their ability to accurately classify the tool state based on various performance metrics. This study hypothesizes that each algorithm will exhibit distinct advantages and limitations depending on the complexity and nature of the data involved. This comparative analysis is intended to identify which method provides the most reliable and accurate results under varying operational conditions.
# Configuration Optimization of Mobile Photovoltaic-Diesel-Storage Microgrid System Based on CPS-MOEA
## 2 Mathematical Model of the Mobile Photovoltaic-Diesel-Storage Microgrid System
### Photovoltaic Power Generation Unit Model  
Photovoltaic power generation does not utilize fuel, emit any substances including greenhouse gases and other waste gases, pollute the air, or produce noise, making it environmentally friendly. Simultaneously, photovoltaic panels utilize solar radiation for power generation, allowing for the maximum utilization of energy, and solar energy is inexhaustible. However, photovoltaic power generation also has certain shortcomings. Its efficiency is highly dependent on weather and seasons and exhibits intermittency and instability to some extent. Consequently, photovoltaic power generation is typically suitable for use as an auxiliary power supply method.  
To calculate the power output \(P_{PV}\) of a photovoltaic panel, several factors must be considered, including weather-related factors like light intensity, environmental temperature, and the panel's conversion efficiency, as well as installation-specific factors such as the panel's position and its inclination angle. The formula to calculate \(P_{PV}\) typically incorporates these variables to accurately estimate the power generation capacity under varying conditions.  
Generally, the power output of a photovoltaic panel can be expressed as follows:  
\[P_{PV}=A\cdot G\cdot\eta\cdot cos(\theta) \tag{1}\]  
where \(A\) is the area of the photovoltaic panel (in square meters), \(G\) is the solar irradiance on the panel (in watts per square meter) and \(\eta\) is the conversion efficiency of the panel (as a decimal). \(\theta\) is the angle between the solar radiation and the panel.
# Optimization of the Weighted Multi-Facility Location Problem Using MS Excel
## 3 Performance Evaluation
### Results
#### 3.3.3 Histograms and Solutions for Benchmark C  
The results for benchmark C show that, for randomly distributed points in the plane, the results of the evolutionary algorithm are better than benchmark A. In this case, it can be again stated the positive effect of powerful hardware, on which the optimizations were calculated with the evolutionary algorithm. The hardware configuration has no effect on the performance of the metaheuristic algorithm, only on the optimization time, see Table 11.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Benchmark Instance** & **A** & **B** & **C** \\ \hline Reference value & 310,048.77 & 360,186.79 & 3,399,273.00 \\ Minimal & 310,048.77 & 360,186.79 & 3,400,034.97 \\ Average & 313,718.26 & 360,207.91 & 3,422,185.23 \\ Dif. ref-min (\%) & \(2\times 10^{-7}\) & \(2\times 10^{-7}\) & \(6.22\times 10^{-3}\) \\ Dif. ref-avg (\%) & 1.18 & \(5.87\cdot 10^{-3}\) & \(0.66\) \\ Standard dev. & 6288.90 & 41.25 & 15,981.47 \\ Coefficient of variation & 0.0200 & 0.0001 & 0.0047 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Calculations MHW3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Benchmark Instance** & **A** & **B** & **C** \\ \hline Reference value & 310,048.77 & 360,186.79 & 3,399,273.00 \\ Minimal & 310,048.77 & 360,186.79 & 3,399,823.32 \\ Average & 312,993.06 & 360,484.42 & 3,419,586.89 \\ Dif. ref-min (\%) & \(2\times 10^{-7}\) & \(0\) & \(0.02\) \\ Dif. ref-avg (\%) & 0.95 & 0.08 & 0.60 \\ Standard dev. & 5639.52 & 2671.73 & 13,667.40 \\ Coefficient of variation & 0.0180 & 0.0074 & 0.0040 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Calculations MHW2.  
Figure 4: Histograms—benchmark instance A.  
Figure 5: _Cont._  
Figure 5: Histograms—Benchmark instance B.  
Figure 6: Cont.  
Based on the results of 100 repetitions of calculations on three benchmarks, it can be stated that the evolutionary method depends on the hardware on which the optimizations are performed. The difference between average values and the reference values ranges from 3.7% to 19.2%. The smallest values are for benchmark C. The coefficient of variation describing the variability of the results ranged from 0.016 to 0.101. The smallest values were again reached for benchmark C.  
The difference of the minimal values and reference values is more important for practical use. For the evolutionary method it ranges from 0.04% to 2.97%, depending on the hardware and the topology of benchmarks.