esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Partitioning Method  
In quantitative analysis, the data set division of training set and testing set influences model performance to some extent. Three partitioning methods, random selection (RS), KS (Kennard-Stone) [27; 28] and SPXY (sample partitioning based on joint X-Y distances) [29] were utilized in this paper to compare the performance of the regression model.  
The random selection method simply selects a certain number of samples as training sets and the rest as the test set without considering the actual spectral values and physical and chemical values. The key point of this method is to make the selected training samples have overall representativeness. However, if the data set is unevenly distributed and the sample size is small, the selected samples cannot represent the whole sample, which will lead to poor performance of the trained model.  
The KS method was proposed by [PERSON] and [PERSON], which calculates the Euclidean distance between the samples, and samples with large spectral differences are classified as the training set. The KS method ensures the uniformity of spatial distance distribution between selected samples. The samples selected by KS method have good uniformity in spatial distance distribution, which can guarantee the performance of the model. The Euclidean distance \(d_{xy}(p,q)\) between \(d_{x}(p,q)\) and \(d_{y}(p,q)\) is formulated as shown in Equation (1),  
\[d_{xy}(p,q)=\sqrt{\big{(}x_{p}-x_{q}\big{)}^{2}+\big{(}y_{p}-y_{q}\big{)}^{2}} \tag{1}\]  
The SPXY method was first proposed by [PERSON] et al. and is developed on the basis of the KS method, which considers both the spectral and physicochemical values when calculating the Euclidean distance between samples. \(d_{x}(p,q)\) and \(d_{y}(p,q)\) are divided by the maximum values of the data set \(max_{p,q\in[1,N]}\) in which they are located to ensure that the samples have the same weight in \(x\) and \(y\) space. Then, the standardized \(xy\) distance formula is as shown in Equation (2).  
\[d_{xy}(p,q)=\frac{\sqrt{\big{(}x_{p}-x_{q}\big{)}^{2}}}{max_{p,q\in[1,N]}d_{x} (p,q)}+\frac{\sqrt{\big{(}y_{p}-y_{q}\big{)}^{2}}}{max_{p,q\in[1,N]}d_{y}(p,q)} \tag{2}\]  
The advantage of the SPXY method lies in the ability to effectively cover the multidimensional vector space, thus improving the predictive ability of the trained model.  
In general, the number of training samples and testing samples should be appropriate to both guarantee enough training without either overtraining or undertraining and good prediction performance and the reliability of the model. From an empirical point of view, the overall 59 samples were divided into a training set and testing set according to the ratio of 7:3: namely, 41 samples are selected as the training set and the remaining 18 samples are used as the testing set. In this paper, the influence of three partitioning methods, RS, KS and SPXY, on the performance of the quantitative mode was studied.
# Improving Landslide Recognition on UAV Data through Transfer Learning
## 2 Study Site and Datasets
### The Source Domain Datasets  
Because of the limited landslide datasets, it is difficult to train a robust classification model. This paper uses the transfer learning method for small sample target detection. At present, there are few public datasets similar with landslide images. From the characteristics of the currently public datasets, there are three source domain datasets we used for this work: the Places365 dataset, the UC Merced land use dataset and the RSSCN7 dataset.  
The Places365 dataset is a scene image dataset published by MIT. The dataset contains a total of 10 million images, covering more than 400 different types of scene environments. The Places365 dataset includes a wide range of and huge data, which are suitable for model training of scene classification [41].  
The UC Merced dataset includes 21 types of land use images. The data were selected from the aerial remote sensing images of different cities from the United States Geological Survey. The dataset has 21 categories of images about agricultural fields, airports, baseball fields, sandy beaches, and buildings, each with 100 images and 2100 images. Each image has a resolution of 0.3 m and an image size of 256 \(\times\) 256 pixels.  
The RSSCN7 dataset is a remote sensing image dataset released by Wuhan University in 2015. It collected scene images of seven categories, including forests, grasslands, farmland, residential areas, river and lakes etc. Each category has 400 images, and the image size is 400 \(\times\) 400 pixels [33]. Figure 3 shows the samples of the source domain datasets.  
Figure 2: Training dataset samples.
In this contribution, the ranging code accuracy and performance of two new BDS-3 civil signals B2a and B1C are preliminarily analyzed and compared with GPS L5C. The four-channel IF signal recorder is used by us to collect the raw IF signals of two BDS frequency points and conduct the zero-baseline experiment. The code ranging accuracy is analyzed by calculating the code signal path difference based on the waveform's extreme value difference, which can directly reflect the ranging performance, avoiding the complicated observation and data processing. This ranging accuracy evaluation method is first applied to the ranging accuracy analysis of BDS-3 BIC and B2a. A software-defined receiver (SDR) was used to process the raw intermediate frequency (IF) signal to calculate the code-level path differences. SDR, which is a general framework for highly flexible multiple system solutions, is also applicable to general GNSS equipment [27]. It realizes all signal processing processes through programmable software or microprocessors Its advantage is particularly important in the case of GNSS because of the series of signals used in PNT services. If traditional hardware receivers are used, new hardware components must be developed or purchased, while SDR can include the processing function of new signals only by updating the software [28]. Our research covers multiple signal frequency bands, so SDR is selected for signal processing. However, although SDR has greater flexibility, it has an obvious disadvantage that the signal processing speed is extremely slow. The amount of raw IF signal data used in this study is large. The remaining chapters of this paper are arranged as follows. In Section 2, the two civil signals of BDS-3 and other relevant details with the rationale of the zero-baseline experiment are described. In Section 3, the specific steps of our zero-baseline experiment will be described. In Section 4, we discuss the sampling results and the accuracy comparison among the three signal frequency points in detail. Finally, the conclusions and summary of the study, as well as the unsolved problems of this research, are discussed in Section 5.
## 2 Materials and Methods  
### The Fabrication of Filaments and Printed Parts  
ABS is a standard material used in 3D printers for fused deposition, and CB is a suitable filler material in conductive polymer composites. As such, ABS/CB composite pellets with 15 wt% CB, a composite with good conductive performance [34], was chosen. ABS/CB conductive composite pellets (Guangzhou Plastic Technology Co., Ltd., Guangzhou, China) were extruded into a 1.75 mm diameter filament to fit the 3D printer using a single screw extruder [35] (Supplementary Materials, Figure S1). Due to the swell phenomenon during extrusion, a discrepancy in diameter exists between the nozzle and the produced filament. A copper nozzle with an internal diameter of 1.70 mm was chosen to produce a filament with an average diameter of 1.75 mm and a standard deviation of 0.03 mm.  
An open source 3D printer, Prusa i3, was used to fabricate the samples (Figure S2). The dimensions of the cubic sample are 20 mm \(\times\) 20 mm \(\times\) 20 mm (length \(\times\) width \(\times\) height). Afterwards, a series of experiments was carried out using a three-level, three-factor factorial design to study the influence of three process parameters, i.e., layer thickness (\(A\)), raster width (\(B\)), and air gap (\(C\)), on the resistivity and its anisotropy. The typical parameter value was selected as the middle level, and the two other levels were chosen on the basis of the literature [28] as shown in Table 1. Each parameter was controlled independently through the slice software (Slic3r, Version 1.0), referring to the parameter combinations of the array in Table S1. Such experimental design could obtain all possible parameter combinations. The other fixed process parameters were as follows: nozzle diameter (0.4 mm), movement speed of the nozzle during printing (100 mm/s), extrusion temperature (230 \({}^{\circ}\)C), hot bed temperature (110 \({}^{\circ}\)C), and extrusion speed (generated automatically by the slice software depending on the nozzle movement speed and material diameter). Moreover, two parameters were set for all of the samples to ensure surface smoothness, thus facilitating resistance measurement. To improve the precision and to estimate the standard deviation, three samples for each parameter combination were produced.
## 2 Materials and Methods
### Surge Detection  
The turbocharger was formerly characterized in a gas test stand according to the procedure explained by [PERSON] et al. [34], and the surge line was determined. However, this limit is not reliable once the turbocharger is coupled to the engine, because surge appearance is affected by the piping geometry around the compressor [19; 20; 21; 22; 23; 24] and engine pulsation [25; 26], as stated before. Besides, the surge margin may be modified under transient flow conditions [29]. Instead, the compressor outlet pressure (\(p_{2}\)) signal, acquired with a sampling of 0.2 crank-angle degrees, and its first derivative were used for surge detection. The surge characteristic frequency is generally below 30 Hz [35], while the engine firing frequency with four cylinders at 1500 rpm is 50 Hz. Hence, the signals of \(p_{2}\) were processed by means of a 5 th order low-pass Butterworth filter [36] with a cut-off frequency (\(f_{\rm C}\)) of 30 Hz, to identify surge with no interference from engine pulses. The moving average of \(p_{2}\) was also calculated by applying the same low-pass filter, but with an \(f_{\rm C}\) of 10 Hz to eliminate the oscillations due to the surge. The time derivative of the difference between the filtered pressure signal with a \(f_{\rm C}\) of 30 Hz (\(\tilde{p_{2}}\)) and the moving average pressure signal (\(\overline{p_{2}}\)) was chosen as the main surge indicator, and an amplitude threshold of 1 bar/s was considered. In this way, an oscillation in that time derivative was regarded as surge if its amplitude was higher than 1 bar/s.
## 3 Temperature-Based ET Models: Advantages, Drawbacks, and Evolution of Research
### Contexture-Based ET Models
With the aim of characterizing the energy exchanges within agricultural fields, encompassing four distinct surface components: bare soil, healthy green vegetation, non transpiring green vegetation, and mature senescent vegetation, ref. [100] presented the SEB-4S model (standing for Four-Sources Surface Energy Balance). The SEB-4S representation is attained through a unified physical interpretation of both the edges and vertices of two distinct polygons. The first one is created by plotting surface temperature against fractional green vegetation, while the second is generated by plotting T as a function of surface albedo. [PERSON] et al., 2015 proposed an Enhanced Two-source Evapotranspiration Model for Land (ETEML) that utilizes the concept of a trapezoid area of the vegetation fractional cover (VFC) and LST space, enabling spatially distributed estimation of ET [101]. ETEML employs a pixel-wise mixed surface temperature decomposition and the crop water stress index (CWSI) concept to separate soil evaporation and vegetation transpiration. These enhancements overcome the limitations typically associated with a simpler trapezoid interpolation model, expanding its applicability. The evaluation of the model in central Iowa (USA) demonstrated accuracy in the estimation of \(LE\) and surpassed simpler trapezoid models, reducing subjectivity and uncertainties. ETEML holds promise for evapotranspiration modeling in diverse landscapes. The Time-Domain Triangle Method (TDTM) was introduced in 2016 by [PERSON] et al. for estimating regional ET [102]. The TDTM enhances the parameterization of the Priestley-Taylor coefficient by focusing on the temporal dynamics of LST-VI pairs derived from MODIS and MSG-SEVIRI sensors. Validated in Sicily, the TDTM accurately predicts daily ET rates, making it a practical tool for estimating spatial and temporal changes in ET, vital for water resource management, agriculture, ecology, and climate change research. Existing models based on trapezoidal approaches have limitations in accurately determining the wet edge. To address this, a novel Two-source Model for estimating EF (TMEF) was developed by [103], incorporating a two-stage trapezoidal framework and an extended Priestly-Taylor formula. A comparison analysis was performed and the TMEF model outperforms TTME and OTEF (One-source Trapezoid model for EF), demonstrating improved accuracy in simulating and partitioning EF using MODIS products and field observations.
## 5 Sensitivity Analysis
### Electricity Price  
According to the electricity price regulations in Taiwan Electric Power Company, the maximum range of each electricity price adjustment is 3%. Therefore, the sensitivity analysis of this study is based on the comparison and analysis of the electricity price for four cases, without adjustment for 20 years, 3% adjustment every three years, 3% adjustment every two years, and 3% adjustment every year. According to the analysis results of Table 6, the electricity price adjusted more frequently leads to higher accumulative electricity bills in 20 years including both increased NPC and LCOE. However, the Project benefit, ROI, and IRR will increase, and the discounted payback will be shortened under the condition of PV/BESS. As an example by taking the scenario of adjusting 3% every three years, LOCE will be as high as $0.12044/kWh without PV/BESS installation, but LOCE will be reduced to $0.10539/kWh and IRR will be increased to 5.87% with PV/BESS installation. In the context of high electricity prices, the results show the benefits of installing PV/BESS can be more prominent, even in effect for Taiwan's electricity prices at low levels in the world.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Types of Electricity Price Rates** & **10 Years of Electricity** & **Increased by 3\%** & **Increased by 3\%** & **Increase by 3\%** \\
**for Different Years** & **Price Rate Unchanged** & **Every Three Years** & **Every Two Years** & **Every Year** \\ \hline NPC-Without PV/BESS (\$) & \(-\)\$54,813,205 & \(-\)\$59,431,490 & \(-\)\$62,110,835 & \(-\)\$70,229,116 \\ \hline NPC-With PV/BESS (\$) & \(-\)\$51,222,008 & \(-\)\$54,285,866 & \(-\)\$56,063,396 & \(-\)\$61,449,217 \\ \hline Project benefit (\$) & \$3591,197 & \(\$5,145,624\) & \(\$6,047,439\) & \(\$8,779,899\) \\ \hline ROI (\%) & 1.77\% & 2.46\% & 2.86\% & 4.07\% \\ \hline IRR (\%) & 3.08\% & 4.00\% & 4.50\% & 5.87\% \\ \hline Discounted payback (year) & 15.41 & 14.44 & 13.95 & 13.95 \\ \hline LOCE-Without PV/BESS (\$/kWh) & 0.09401 & 0.10193 & 0.10652 & 0.12044 \\ \hline LOCE-With PV/BESS (\$/kWh) & 0.08785 & 0.09310 & 0.09615 & 0.10539 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Analyzed results of PV/BESS combinations corresponding to different contract capacities.  
Figure 14: Effect of varying GHI on the LCOE and discounted payback.
## 2 Experimental System and Principle
### Experimental System  
The experimental system was mainly composed of the SHPB bars (Luoyang Liwei Technology CO., Ltd., Luoyang, Henan, China), control system (Luoyang Liwei Technology CO., Ltd., Luoyang, Henan, China), data acquisition system (Jiangsu Donghua Testing Technology Ltd., Jingjiang, Jiangsu, China), and high-speed photography system (IX-Cameras Inc., Rochford, Essex, UK), as shown in Figure 1. The bars were made from steel with a 206 GPa Young's modulus and 5160 m/s elastic wave velocity. The length and diameter of the striker bar, the incident bar, and the transmission bar were 400 mm \(\times\) 50 mm, 2000 mm \(\times\) 50 mm, and 1800 mm \(\times\) 50 mm, respectively. At the other end of the transmission bar, a damper was arranged to absorb the transmitted wave. The dynamic resistance strain gauges (DC-97A), which were connected to an oscilloscope, were mounted at midpoints of the incident and transmission bars. The deformation signals were stored by the stress pulse recorder. For capturing the images of fracture process, the high-speed camera (i-speed 716, IX-Cameras Inc., Rochford, Essex, UK) was arranged in the direction perpendicular to the circular section of rock specimen. Two sets of lights (LEDs) were used as illuminations for the camera, providing continuous light output of 200 W power. Under this configuration, images were captured at a frame rate of up to 100,000 fps (frames per second) with a resolution of 672 \(\times\) 216 pixels [28]. In order to avoid damage to the equipment by the ejection of rock fragments, the specimen was placed in a recycling box with a shooting window, and a polymethyl methacrylate (PMMA) shield was placed between the camera and specimen for protection.
## 5 Discussion  
This summary of different orbit configurations and release maneuver options is not exhaustive, but presents some key concepts to consider if a MET-deployed constellation were to be developed. Answering the five discussed orbit design questions can help in determining the nature of maneuvers needed to establish a constellation. These questions essentially all revolve around the similar theme of how similar or dissimilar the orbits for each member satellite are allowed to be, where missions allowing more dissimilar orbits can be deployed quicker and/or easier, while missions requiring more similar orbits can take longer and/or be more challenging to achieve. The deciding factors on whether these orbits can be similar or dissimilar are sourced from major requirements of the overall mission architecture itself, which is specific to the teams developing them and the proposed constellation's intended application(s). The selection of orbit design options can also inform the generation of requirements needed for associated spacecraft design.  
The methods of deploying payloads by tether described here are not exhaustive, but give some initial insight to the thought processes that would likely be followed for designing such a system. The example given in this article is just one method (and the most chaotic or unoptimized of the five mission design options in Figure 16) but is still one viable means of dispersing satellites around an orbital plane for a constellation. A more optimized case would require some additional assistance in terms of attitude control and propulsion capabilities on the deployer, which would be easily achievable for moderately sized deployer spacecraft (such as ESPA class equivalent and larger).
## 3 Methods
### Road-Vehicle Interaction Model
#### 3.1.1 Features and Limitations of the Bicycle Model
The cornering forces on each axle can be expressed as  
Figure 2.0  
Figure 2.1  
The expressions for the side friction factors on each axle are  
Figure 2.2  
Figure 2.3  
where \(\mu_{yf}\) is the side friction factor on the front axle and \(\mu_{yr}\) is the side friction factor on the rear axle. Substituting Equations (4) and (8) into Equation (10), one obtains  
Figure 2.4  
Substituting Equations (5) and (9) into Equation (11), one obtains  
Figure 2.5  
Equations (12) and (13) depict the quasi-static friction demands for the front and rear axles for cornering during braking, respectively. These equations demonstrate the weight transfer from the rear axle to the front axle, resulting in an increased vertical force on the front axle. Consequently, a lower \(\mu_{yf}\) is sufficient to meet the cornering requirements. Conversely, the vertical force on the rear axle decreases, necessitating a higher \(\mu_{yr}\) to satisfy the cornering demands. It is assumed here that \(F_{bf}\) and \(F_{br}\) are known. Then, the longitudinal friction factors on each axle can be determined by utilizing their basic definitions as follows:  
Figure 2.6  
Figure 2.7  
where \(\mu_{xf}\) is the longitudinal friction factor on the front axle and \(\mu_{xr}\) is the longitudinal friction factor on the rear axle. The side friction supply factors on each axle can be found based on the friction ellipse as follows:  
Figure 2.8  
Figure 2.9  
where \(\mu_{yf,supply}\) is the side friction supply on front axle; \(\mu_{yr,supply}\) is the side friction supply on rear axle; and \(\mu_{xmax,supply}\) is the maximum longitudinal friction supply. Note that when longitudinal friction factors (\(\mu_{xf}\), \(\mu_{xr}\)) exceed the maximum longitudinal friction supply \(\mu_{xmax,supply}\), it is assumed that the side friction supply factors (\(\mu_{yf,supply}\), \(\mu_{yr,supply}\)) are zero. The lateral friction margins are formulated as  
\[\Sigma F_{x}=F_{bf}+F_{br}=ma_{x}+mgi \tag{1}\]0  
\[\Sigma F_{x}=F_{bf}+F_{br}=ma_{x}+mgi \tag{1}\]1where \(\mu_{yf,margin}\) is the lateral friction margin on the front axle and \(\mu_{yr,margin}\) is the lateral friction margin on the rear axle. Equations (18) and (19) can be used to predict the skidding potential of the front and rear axles of the vehicle. 1. Limitations of the bicycle model  
Automotive theory indicates that, during the braking process, the actual braking force distribution among different axles of a vehicle is dynamically varying. The bicycle model does not consider the braking characteristics of trucks and lacks parameters related to the truck's braking system, so it fails to accurately depict the dynamic changes in brake force distribution, vertical tire load, and longitudinal and side friction during braking. Consequently, it is unable to calculate the dynamic variations of \(F_{bf}\), \(F_{br}\), and \(\mu_{yr,margin}\). Furthermore, it does not provide accurate predictions of the skidding potential of the front and rear axles of trucks.