esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of Six Machine Learning Methods for Predicting Gross Primary Productivity in Grassland
## 1 Introduction
proposed a method based on the GeoMAN model and predicted the GPP of nine flux observation stations on the Tibetan Plateau, and then compared it with RF, SVM, and deep belief network (DBN) [42]. The results show that GeoMAN performs better than the other three. These studies can provide a scientific reference for GPP prediction. However, there are still some problems in the existing research: failure to carry out application characteristic analysis on grassland ecosystems; the operation of the model depending on the observed flux data; failure to clarify the contribution ratio of key factors; some emerging machine learning models are not included [43; 44]. We focus on the insufficient assessment of machine learning methods in GPP prediction, as well as the problems of existing comparative studies (focus on grassland systems, identification of key factors, and representativeness of frontier models). In this study, six machine learning models (multilayer perception, RF, Adaboost, gradient boosting decision tree, XGBoost, LightGBM) will be selected for high spatial and temporal resolution grassland GPP prediction in the Mongolian Plateau, and detailed comparative analysis and accuracy trade-offs will be performed. We aim to achieve three scientific goals:  
(1) Apply multiple evaluation indicators to comprehensively assess the accuracy of different machine learning models in predicting grassland GPP;  
(2) Analyze the application characteristics of different machine learning models and find the model with the best overall performance;(3) Clarify the contribution ratio of different impact factors to the prediction accuracy and reveal the key factors of grassland GPP prediction.
## 2 Formulation and Model  
To consider the complex anisotropy of YBCO tapes in our numerical model, the dependence of the critical current density on the magnetic flux density \(B\) and the temperature \(T\) is expressed as [4; 20; 21; 22]:  
\[J_{c}(B,T)=J_{c0}J_{cB}(B_{par},B_{per})J_{cT}(T) \tag{1}\]  
\[J_{cB}(B_{par},B_{per})= \frac{1}{(1+\sqrt{\left(kB_{par}\right)^{2}+B_{per}{}^{2}}/B_{c}) ^{b}} \tag{2}\] \[J_{cT}(T)=J_{c}(T)= \begin{cases}\frac{(T_{c}-T)}{(T_{c}-T_{0})}&T<T_{c}\\ 0&T\geq T_{c}\end{cases} \tag{3}\]  
where \(B_{par}\) and \(B_{per}\) represent the magnetic field component parallel and perpendicular to the tape surface, respectively. \(J_{c0}\) is the self-field critical current density at the operating temperature. \(b\), \(k\) and \(Bc\) are the curve-fitting parameters. \(T_{c}\) = 92 K is the critical temperature. \(T_{0}\) = 77 K is the operating temperature.  
It is important to note that Equations (2) and (3) are only an approximate expression of the dependence of the critical current density on the magnetic flux density and the temperature. The actual properties of the HTS tapes will not follow these expressions completely.  
The superconducting layer resistivity is calculated by the E-J power law relation [23]:  
\[\rho(J)=\frac{E_{0}}{J_{c}(B,T)}\left|\frac{J}{J_{c}(B,T)}\right|^{(n-1)} \tag{4}\]  
where \(E_{0}\) is the critical electrical field, which is usually set as \(10^{-4}\) V/m. \(n\) is the critical exponent.
## 2 Literature Review
### Studies on Typhoon Scenario Modeling  
Recently, the \"scenario-response\" theoretical model has emerged as an essential research framework in emergency management. This model provides methodological support for dynamic risk assessment and cascading disaster disruption by systematically analyzing scenario elements and their evolution mechanisms [34]. Studies demonstrate that constructing scenario models offers significant advantages in accident, disaster, and risk analysis. For instance, by comprehensively considering the impacts of natural and social environmental changes on typhoon scenarios across multiple spatiotemporal scales, [PERSON] et al. developed a risk framework which integrates vulnerability and adaptability [35]. [PERSON] et al. established a typhoon risk scenario analysis method based on an extreme value distribution model, achieving spatial risk probability mapping of typhoon wind fields [36]. [PERSON] et al. proposed a scenario-based simulation approach for storm surge flood risk assessment, enhancing the precision of flood zone identification [37]. However, existing research focuses on single-disaster scenario modeling, with insufficient systematic investigation into the cascading effects of typhoon disasters.  
In regard to scenario modeling, current research mainly uses three typical technologies: (1) Bayesian networks: [PERSON] et al. used ontology clustering and semantic rules to build a dynamic framework for scenario simulation and to calculate the possibility of uncertain events [38]. (2) Case-based multidimensional scenario matching methods: [PERSON] et al. used a \"case-scenario-object-element\" structure to transfer historical data, supporting decisions via case simulations of oil tank fires [39]. (3) System dynamics-based complex system modeling methods: [PERSON] et al. proposed a ROC-Based CIA-ISM model to analyze nonlinear interactions and evaluate the reliability of metro emergency management during extreme rainfall events [40].  
In actual emergency decision-making situations, information asymmetry tends to magnify the bias of subjective and objective factors in modeling efforts [41]. This information bias not only enhances the uncertainty in disaster decision making, but also creates disconnection between the scenario model and users' requirements in actual emergency analyses. Though current methods can depict objective aspects like situation status, response measures, and environmental impacts, they often fall short in considering subjective variables such as users' concerns and behavioral preferences. These subjective elements interact dynamically with disaster evolution through bidirectional feedback mechanisms. This may cause deviations between the results of human-disaster interaction simulation and actual decision making during a disaster.
###### Abstract  
The back-arc thrust region in Eastern Java to Flores is significantly influenced by the arc-continent collision between the Australian Plate and the Eastern Sunda Arc, leading to a tectonic regime characterized by high seismic and volcanic hazards. This area has experienced several major earthquakes. However, back-arc thrust in Eastern Java remains absent from significant shallow earthquakes, which might indicate intense deformation. We conducted an analysis using recent and dense Global Navigation Satellite System (GNSS) observations from both continuous and campaign stations to develop a strain rate model and explore the detailed crustal behavior and strain accumulation within the Eastern Java back-arc thrust system. Our findings revealed varying values of compression and extension throughout the region, with compression values ranging from \(-2.24\) to \(0.086\)strain/year. Additionally, we observed that the maximum shear strain rate and dilatation strain rate were within the ranges of \(0.0013\) to \(1.12\)strain/year and \(-2.24\) to \(0.698\)strain/year, respectively. These findings could facilitate more informed strategies and improve preparedness for future seismic events.  
back-arc; Kendeng; Eastern Java; GNSS; campaign Article
# Thrust Measurements on the High Efficient and Reliable Vacuum Arc Thruster (HERVAT)
## 2 Experimental Setup
### Power Supply Unit  
The power supply unit is based on a multi level capacitor discharge network with an integrated high voltage trigger system. It switches automatically between the high voltage ignition and the \"trigger-less\" ignition mode of a vacuum arc. The electrical schematic of the power supply unit is shown in Figure 3.  
The circuit uses two capacitors, \(C_{1}\) and \(C_{2}\), and a triggered SCR to operate in the \"trigger-less\" ignition mode. For the high voltage ignition, a separate high voltage pulse generator with an output voltage of up to 10 kV and a pulse energy of around 53 mJ is used. The SCR is triggered synchronously with the applied high voltage pulse.  
The charging voltages are \(U_{1}=95\) V for \(C_{1}\) and \(U_{2}=250\) V for \(C_{2}\). The charging resistors \(R_{1}\) and \(R_{2}\) limit the current from \(U_{1}\) and \(U_{2}\), respectively. The resistors are chosen according to the operation frequency of the thruster. They ensure that the capacitors store the required energy level, when the SCR is triggered. The diode \(D_{1}\) prevents current back-flow from \(C_{2}\) to \(C_{1}\) while \(D_{2}\) blocks the high voltage trigger pulse (up to 10 kV). \(C_{1}\) acts as the main discharge capacitor and can be adjusted for different pulse energies while \(C_{2}\) has fixed value of 4.7 \(\upmu\)F. Three different values for \(C_{1}\), 92.9 \(\upmu\)F, 198.2 \(\upmu\)F and 436.9 \(\upmu\)F are used in order to investigate system behavior at different pulse energies and the resulting pulse lengths. Here, capacitors with a low equivalent series resistance (ESR) and a low equivalent series inductance (ESL), and type electrolyte capacitors for pulsed applications (Panasonic, low ESR) are chosen in order to reduce the parasitic effects during charging and discharging of capacitors.
# Modernising Receiver Operating Characteristic (ROC) Curves +
## 4 Results and Discussions
### Logistic Regressions  
The modernisation of traditional ROC curves (such as shown in Figure 1) in terms of MSA (Section 3.2), RMT (Section 3.3), and various linearisations (Section 3.4) relies on making an initial logistic regression of Equation (1) to yield the ability, \(\theta_{i}\), of each device, \(I\), and the level of difficulty, \(\delta_{j}\), of each assigned value (task), \(j\), on a common interval scale. Figure 3 show the results of the logistic regression for the two tested ways of applying the Rasch model (Section 3.4.2, i.e., calculated (i) the classic \(P_{success}\) for each target value for each decision scenario (positives and negatives); and (ii) \(P_{success}\) from the cumulative rates for TPR and FPR (Figure A1)).  
From an inspection of the Rasch histograms of Figure 3, it seems that device-task targeting is better when alternative (i), i.e., classic \(P_{success}\), Figure 3a,b, is used; that is, there is better matching in terms of spread and centring between the spans of ability and difficulty. Better targeting according to the Rasch approach [18] leads to lower measurement uncertainties for device abilities and a better separation between the devices with  
Figure 3: Histograms of experimental data with Rasch estimates of device ability (\(\theta_{i}\), upper histograms) and target value difficulty (\(\delta_{j}\), lower histograms) based on \(P_{success}\) for (**a**) scoring alternative (i) for negatives and (**b**) scoring alternative (ii) FPR, as well as (**c**) scoring alternative (i) for positives and (**d**) scoring alternative (ii) TPR for five device types used for pregnancy testing. Measurement uncertainty coverage factors \(k=2\). (The height of each column in these histograms—called “Count”— indicates, as standard, the occupancy of each probability mass function; that is, the number of device types (upper, blue plots) and target level tasks (lower, red plots) per bin, respectively.  
method (i). On the other hand, measurement uncertainties for target values are larger when alternative (i) (i.e., classic \(P_{success}\)) was used, except for the highest and lowest target values where the cumulative rates were almost 100% or 0%. Method (i) is better in terms of separating the devices, as will be examined in detail in Section 4.5. For the corresponding (ii) ROC-based \(P_{success}\) values, Figure 3c,d, the resolution of the device ability is poorer.  
Depending on how scoring is carried out, the order of item difficulty will change, as the orderings depend on how one values a high probability for the various decision risks (see Section 4.2).
# Calibration Method of Orthogonally Splitting Imaging Pose Sensor Based on General Imaging Model
## Materials and Methods
### Calibration  
#### 2.2.1 Mathematical Derivation  
According to the mathematical model, the target point in the world coordinate system and the image in the image coordinate system should on the same line in Plucker coordinates.  
According to the characteristics of the Plucker line [14], Equation (8) is satisfied if a point is on the Plucker line.  
\[\underbrace{\Bigg{(}\begin{array}{cc}[w]_{x}&-I\\ 0^{T}&w^{T}\end{array}\Bigg{)}}_{Q(w)}f(x)=0 \tag{8}\]  
Substituting Equation (7) for Equation (8), there will be Equation (9).  
\[Q(w)\Big{(}\Big{(}\begin{array}{cc}\varnothing(x)&p(x)\end{array}\Big{)}H_{ cam}\Big{)}^{T}=0 \tag{9}\]  
According to the nature of the transposed matrix \((AB)^{T}=B^{T}A^{T}\), Equation (9) can be rewritten as Equation (10).  
\[\underbrace{\Big{(}\begin{array}{cc}\varnothing(x)^{T}&p(x)^{T}\end{array} \Big{)}}_{r(x)}H_{cam}Q(w)^{T}=0 \tag{10}\]  
In Equation (10), \(\{w_{i}\}\)\(i=1,\ \cdots,N\) is the world coordinates set of target points, \(\{x_{i}\}\)\(i=1,\cdots,N\) is the image coordinates set of images.  
According to the Kronecker product, Equation (10) is rewritten as Equation (11).  
\[[Q(w)\otimes r(x)]vec(H_{cam})=0 \tag{11}\]  
From the above equation, it can be concluded that if the image coordinates \(x\) and the world coordinates \(w\) are known, the camera matrix can be solved. If the image coordinates and world coordinates of N points are known, the above equation can be rewritten as Equation (12).  
\[Mvec(H_{cam})=0 \tag{12}\]  
\[M=\left(\begin{array}{c}Q(w_{1})\otimes r(x_{1})\\ \vdots\\ Q(w_{N})\otimes r(x_{N})\end{array}\right) \tag{13}\]  
In order to exclude non-zero solutions, add constraints \(\parallel\) vec\((H_{cam})\parallel=1\). Then the problem of solving the camera matrix is transformed into solving the least squares solution [15] of the homogeneous equations. Find out vec\((H_{cam})\) to gain the minimum value of \(\parallel\)\(M\)vec\((H_{cam})\)\(\parallel\). According to the singular value decomposition method, the matrix \(M\) can be expressed as \(M=UD^{T}V\), so vec\((H_{cam})\) is the last column of matrix \(V\) based on derivation. Hence, if data set \(\{x_{i}\to w_{i}\}i=1,\cdots,N\) is known, the camera matrix \(H_{cam}\) can be solved out.
# Training Performance Indications for Amateur Athletes Based on Nutrition and Activity Lifelogs
## 2 Methodology
### Periodic-Frequent Pattern Mining  
In this subsection, we briefly review the fundamental terminologies of periodic-frequent pattern mining techniques, show an example of how they produce periodic-frequent patterns, and explain how to interpret these patterns.  
Periodic-frequent patterns are patterns (e.g., a sequence of items) that (1) appear in a dataset with its frequency less than or equal to the predefined threshold (i.e., minsup) and (2) repeat themselves with a specific period in a given sequence [17].  
For example, the pattern _meat_, _vegetables:_[4; 3] expresses that if a 2-pattern happens (i.e., _meat_ and _vegetables_ are items of the pattern), then within three periods, it will appear again with a probability (or _sup_) of 3.  
In general, if \(PA=\{pa_{i}\}_{i=1..L}\), where \(L\) is the number of mined patterns, is a set of mined patterns, the format of a mined pattern \(i^{th}\) is  
\[pa_{i}=(\{item_{j}\}):[sup,maxPer]. \tag{1}\]  
Periodic-frequent pattern miningtries to discover all periodically occurring frequent patterns in a temporal database. In our research, we want to mine such periodic-frequent patterns from an uncertain temporal dataset, as mentioned before.  
Let \(I=\{l_{i}\}_{i=1..N}\) denote the itemset. Let \(T_{j}=\{I_{m}\}_{m\in[1,N]}\) denote a transaction that is a set of items of the itemset. Let \(P_{k}=\{\{I_{n}\}_{n\in[1,N]}\), support(%), periodicity(%)\(\}\) denote a periodic-frequent pattern where the set of items \(\{I_{n}\}_{n\in[1,N]}\) has the frequency expressed by support(%), and how regularly this pattern appear within a database is depicted by periodicity (%).  
The periodic-frequent pattern mining algorithm introduced by [PERSON] et al. [13] gets \(\{T_{j}\}_{j=1..M}\), where \(M\) is the total transactions, as the input and generates \(\{P_{k}\}_{k=1..P}\), where \(P\) is the total patterns, as the output. The _minSup_ (i.e., select only patterns with their support larger than _minSup_) and _maxPer_ (i.e., choose only patterns with their periodicity smaller than _maxPer_) must be declared beforehand to limit the searching scope.  
Figure 3 illustrates a toy sample of an itemset, transaction, and periodic-frequent patterns. The terms ts, P, Sup, and Per are acronyms of transaction, pattern, support, and periodicity, respectively. Readers could refer to the original paper [13] for more details.  
We assume we have 10 receipts of customers from a small grocery in town, where each receipt records the list of food purchased by a customer. Then, we want to understand how frequently and periodically one food type is purchased. Figure 3a illustrates these 10 receipts; each receipt is considered as one transaction (ts). Figure 3b depicts the result from the periodic-frequent pattern mining. We can see that \"meat\" is bought with high frequency (i.e., 6/10), and after every three transactions, \"meat\" is repurchased.
## 3 Results and Discussion
### Electron Micrographs of Root Macropores  
According to the results above, living roots accelerated the water infiltration rate in soil, but dead roots further increased water storage capacity of soil. These different effects may not only be due to the different influence of living and dead roots on soil pores but also due to the different pore sizes in living and dead roots. Other studies have been conducted on the influence of living and dead roots on soil pores [1, 3], soil permeability [5, 6, 7, 8], and soil water storage capacity [25], but no study is known to have investigated the influence of pore sizes in living and dead roots. To our knowledge, this is the first time the pores of living and dead roots were examined via a Hitachi S-4800 electron microscope in this study. The smaller pores with diameters from 30-50 \(\upmu\)m in living roots were observed (Figure 4a,b), and the larger pores with diameters from 100-1,000 \(\upmu\)m in dead roots were observed (Figure 4c,d).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Design** & **Without Roots** & **Living Roots** & **Dead Roots** \\ \hline
**Penetration time (min)** & 198 & 178 & 171 \\ \hline
**Steady-state water flux at the bottom of** & & & \\
**the container (cm d\({}^{-1}\))** & & & \\ \hline
**Water storage capacity of soil (cm\({}^{3}\) cm\({}^{-3}\))** & 0.279 & 0.317 & 0.322 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Penetration time, steady-state water flux at the bottom of container, and water storage capacity of soil without or with living and dead roots.  
Figure 4: Electron micrographs (via SEM) of root macropores: (**a,b**) internal pores of living roots (scale bars: 1 mm and 50 \(\upmu\)m, respectively); (**c,d**) internal pores of dead roots (scale bars: 1 mm and 50 \(\upmu\)m, respectively).  
As summarized in Figure 5, living roots had relatively smaller pores with diameters of about 30-50 \(\upmu\)m, and they had a much greater water infiltration rate in soil (61.31 cm d\({}^{-1}\)) than dead roots (55.97 cm d\({}^{-1}\)) and without roots (control, 54.75 cm d\({}^{-1}\)). Dead roots had relatively larger pores with diameters of about 100-1,000 \(\upmu\)m, and they had a much greater water storage capacity of soil (0.322 cm\({}^{3}\) cm\({}^{-3}\)) than living roots (0.317 cm\({}^{3}\) cm\({}^{-3}\)) and without roots (control, 0.279 cm\({}^{3}\) cm\({}^{-3}\)). Combining the different influences of living and dead roots on the water storage capacity of soil and on the water infiltration rate in soil, we speculate that soil permeability improved by living roots may be due to the dominant channel generated on the roots surface. However, water could penetrate into the dead roots, and more water was stored by the pores in the dead roots.
## 1 Introduction
Regarding time optimality, an approach was proposed for a hyper-redundant robot taking into account the obstacles located in a 3D workspace [32; 33]. It aims to minimize the cycle time during the execution of required tasks, regarding trajectory optimization for robots in terms of energy consumption and minimizing joint torque. Other researchers described a new scheme to determine the trajectory of a redundant robot arm with the purpose of minimizing the total energy consumption [34]. In order to optimize both the energy consumption and the time required for executing a trajectory, many researchers have elaborated new methods based on a fuzzy logic algorithm, a genetic algorithm, or an ant colony algorithm [34; 35]. By using a genetic algorithm, a contribution was proposed to optimize the torque applied at the joints of the robot [36; 37]. We can also cite the second contribution for the same target, which uses a unified quadratic-programming-based dynamic system [38], as well as the role of neural networks for the optimized dynamics of redundant robots [39]. Most of the literature in motion planning features deals with point-to-point applications in free space without any obstacles, where the starting and ending points of the end-effector are predefined. The main purpose of this literature analysis was to guarantee a deeper understanding of the path planning field so that researchers could find an optimal solution without any constraints. Further, time and energy consumption presents the most important factors for evaluation [40]. The purpose of this study is to elaborate upon a new \"whip-lashing\" method that aims to realize an optimized trajectory for the five-degree-of-freedom RV-2 AJ robot arm, i.e., to generate a path for a manipulator arm without any design constraints. This newly elaborated method seeks to minimize the cycle time of the trajectory with constrained torque values applied in joints for executing smooth motions. This new method originates from the motion of a whip analogue applied for a RV-2 AJrobot arm. First, we introduce the methodology of this newly elaborated method, identifying all the steps required using SolidWorks and MATLAB software applications. After, the main features of whip-lashing are introduced and the Section 4 presents a concrete simulation executed for both paths in order to compare the normal motion of the arm to a whip lashing motion, based on cycle time. The final section presents the results of the joints torque variation according to the cycle time calculated in the previous section in order to show the reader the effect of the whip-lashing motion on the cycle time and the torque consumption. The main value of the research is that a manipulator arm can be treated as a whip in certain conditions, which can guarantee running an improved path that results in reduced cycle time. The proof of this novelty is presented by applying the real parameters of an RV-2 AJ arm to simulation tools.
# Macro-Modelling Approach for the In-Plane Cyclic Response of Cold-Formed Steel Partition Walls
## 1 Introduction
From the observations of damage mechanism to CFS partition walls subjected to lateral loads, the force-deformation relations of their components are identified by accounting for the initial flexural behavior of studs and followed by the racking behavior of gypsum boards. The interactions between components such as stud-to-track connections are theoretically defined from mechanical properties and geometrical information. To verify the proposed analytical model, two full scale CFS partition walls tested by a quasi-static procedure are introduced. The analytical and experimental results are compared in terms of force-displacement relations, dissipated energy, and the influential damage mechanism of components of the partition walls.