esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Machine Learning Classifiers
#### 3.6.1 Support Vector Machines  
Support Vector Machines is a supervised machine learning algorithm to solve classification issues based on the theory of statistics [52]. For a given training dataset of \(n\) points,  
Figure 5: Phases of correlation-based feature selection method.  
\((\mathbf{x}_{1},y_{1})(\mathbf{x}_{2},y_{2}),\ \ldots(\mathbf{x}_{n},y_{n})\), where \(y_{i}\) is 1 or \(-1\), representing the class to which the point, \(\mathbf{x}_{i}\) belongs to. A hyperplane is defined as a set of data points \(\mathbf{x}\) that satisfy the equation  
\[\mathbf{w}^{\text{T}}\mathbf{x}-b=0 \tag{2}\]  
where \(\mathbf{w}\) is a normal vector, perpendicular to the hyperplane's surface, and b is the value of the offset from the origin. The \(\frac{b}{\|\mathbf{w}\|}\) parameter defines the hyperplane's offset from the origin along the normal vector \(\mathbf{w}\).  
Kernel or Kernel trick is the process of utilizing a linear classifier to resolve nonlinear problems. The commonly used SVM kernels are Polynomial, Radial Basis Function (RBF), and Pearson VII Universal Kernel (PUK).  
The Polynomial kernel for the degree d is defined as  
\[K(x,y)=\left(x^{T}y+c\right)^{d} \tag{3}\]  
where \(x\) and \(y\) are the feature vectors of input space, and \(c\) is a constant. The second most widely used kernel is RBF, defined as  
\[K\Big{(}\mathbf{x},\ \mathbf{x}^{{}^{\prime}}\Big{)}=\exp\left(-\frac{\|\ \mathbf{x}-\mathbf{x}^{{}^{\prime}}\ \|^{2}}{2\sigma^{2}}\right) \tag{4}\]  
where \(\mathbf{x}\) and \(\mathbf{x}^{{}^{\prime}}\) are the feature vectors of input space, \(\sigma\) is a constant. The Pearson VII function of PUK is defined as  
\[f(x)=\frac{H}{\left[1+\left(\frac{2(x-x_{0})\sqrt{2\frac{b}{b}-1}}{\sigma} \right)^{2}\right]^{\omega}} \tag{5}\]  
where the \(H\) is the peak height at \(x_{0}\), \(x\) is an independent variable, and the parameters \(\omega\), and \(\sigma\) control the width and tailing factor of the peak [53].
## 2 Data and Methodology
The irrigation rate is relatively high in arid regions (>60%) and relatively low in humid regions (<20%) (Figure 1e). Since the quantitative range of values for LST and precipitation differ, we normalized each of their pixels for July and August between 2000 and 2020. These normalized LST and precipitation products are known as the Temperature Condition Index (TCI; [32]) and the Precipitation Condition Index (PCI; [33]). In particular, they are widely used indices in drought research (Equations (1) and (2)). \begin{table}
\begin{tabular}{c c c c} \hline
**Type** & **Product** & **Spatial Resolution** & **Units** \\ \hline Land surface temperature (LST) & MODIS/Terra Monthly LST (MOD11C3) & 0.05\({}^{\circ}\) & Kelvin \\ Gross primary production (GPP) & MODIS/Terra 8-day GPP (MOD17A2) & 1 km & kg C/m\({}^{2}\) \\ Precipitation & Climate Hazards Group InfraRed & & \\ Precipitation with Station data (CHIRPS) & 0.05\({}^{\circ}\) & mm \\ Crop yield data for maize and soybeans & US Department of Agriculture National & & \\ Agricultural Statistics Service Quick Stats & & \\ \hline \end{tabular}
\end{table} 
Table 1: Data used in this study. The spatial resolution is described based on the original product. \[\text{TCI}=(\text{LST}_{\text{max}}-\text{LST}_{\text{i}})/\ (\text{LST}_{\text{max}}-\text{ LST}_{\text{min}}) \tag{1}\] \[\text{PCI}=(\text{PR}_{\text{i}}-\text{PR}_{\text{min}})/\ (\text{PR}_{\text{max}}-\text{PR}_{\text{min}}) \tag{2}\]  
where LSTmax and LSTmin, and PRmax, and PRmin are the maximum and minimum values of LST and precipitation of given study periods. LSTi and PRi are the LST and precipitation values of the pixel of a particular ith year. Moreover, the county-level and state-level TCI, PCI, and GPP data were produced by mean aggregation. The correlations were analyzed between TCI and PCI, TCI and GPP, and PCI and GPP for pixel-level, county-level, and state-level for July and August. We further assessed the correlation between crop production and the climate variables (i.e., TCI and PCI) based on the respective state levels for the major maize and soybean production. As mentioned above, maize and soybean have positive trends because of the developments in crop genetics and agriculture. Therefore, the linear trend of maize and soybean yield was removed.
## 1 Introduction
### Tree Cover Maps for Natural Resource Monitoring  
Land cover products are increasingly valuable inputs to a range of scientific studies and resource management activities [11]. Researchers have focussed on land cover classification [12; 13; 14; 15; 16] and monitoring change in vegetation cover [7; 9; 17; 18; 19; 20]. These studies have been used to assess the extent of remaining natural forest, the location of threats to biodiversity as well as the effectiveness of existing protected-area networks. The challenge is to produce consistent maps across large areas while maintaining local relevance and utility [9].  
The ability to produce global maps of forest loss using Landsat is relatively new [9; 10; 18]. The methods are similar to studies previously limited to continental scales [15; 19; 21; 22]. However, these large-area studies do not perform equally across all landscapes. For example, tree cover can be particularly difficult to quantify in grassy and semi-arid woodlands that feature naturally open canopies with large gaps between tree crowns. Tree cover in these areas is often underestimated by global maps, whereas mapping at regional scales allows classification schemes to be more finely tuned [16; 17; 23; 24; 25; 26; 27]. Producing tree cover maps over large areas of fragmented forest and open woodland remains a challenge.  
Many studies are focused on forested areas rather than examining all tree cover, resulting in maps that are sensitive to the definition of forest used. For example, estimates of Canada's net forest loss doubled when low and high tree cover strata were included, largely due to extensive burning in open boreal woodlands [9]. Previous regional estimates of tree cover and change in Australia have also varied due to the definition of forest, with woodlands and open forest under-represented [28; 29].  
The work presented here was designed to overcome the issues described above, and map the heterogeneous patterns of fragmented forest and open woodland through using high spatial resolution satellite data. The method benefits from combining automated data processing suitable for continental scale mapping with manual editing learnt from regional scale mapping. The result is a tree cover map that can be adapted to suit many definitions of forest, and can be used for natural resource management at small scales, such as state-wide reports, and large scales, such as local planning. We have defined tree cover as trees and shrubs taller than two metres that are visible at the resolution of the imagery used in the analysis (5 m).
## 4 WPAN MAC: Designing and Analysis
### Approach and Assumptions  
As we mentioned earlier most of the studies on WPAN MAC are focused on MAC scheduling, superframe optimization, etc., rather then performance analysis of WPAN MAC from a protocol architecture point of view. In this paper our main focus is to analyze and optimize the performance WPAN MAC with different ACK policies and proposed improvements to WPAN MAC under error channel conditions. Here, the optimization problem is defined as how to find the maximum throughput and channel utilization for a given channel bit error rate (BER) during the CTA/CAP period with various ACK policies under varying payload size. In this paper, we assume a Gaussian wireless channel model. Although the Gaussian channel model cannot capture the multi-path fading characteristics of a wireless channel, it is widely used because of its simplicity. The capture effect is also not considered. The Back off algorithm (During CAP period) performed in a time-slotted fashion. A node attempts to attain the access the channel only at the beginning of a slot. Furthermore, all nodes are well synchronized in time slots and propagation delay is negligible compared to the length of an idle slot. In the WPAN MAC, the channel time request command should be successfully transmitted between a node (in the remainder of the paper we use the terms 'DEV' and 'node' interchangeably) and the PNC to realize the communication during CTA. Usually, data transmission is performed during the CTAs and command transmission during the CAP. Here, for the simplicity we assume that a data frame is successfully transmitted if both data and ACK frames (except for No-ACK) are successfully received by receiver and transmitter, respectively. The analytical model presented in this paper does not depend on the technology adopted at the physical layer. However, the physical layer technology determines some network parameter values, e,g., SIFS, DIFS. Whenever necessary, we choose the values of these physical layer technology dependent parameters by referring to [16]. According to [16] we consider two different transmission rates for data signal and control signal, respectively.
# Dynamic Energy Management Strategy of a Solar-and-Energy Storage-Integrated Smart Charging Station
## 5 Simulation Inputs and Results  
### Simulation Inputs  
#### 5.1.1 PV Power Generation  
Utilizing Python, a model for the electric vehicle charging station (EVCS) was constructed to validate simulation results under PV generation conditions for 30 days each in summer and winter. The simulation was conducted using historical data from a Taiwan central region solar farm for the periods of 1 July 2022 to 30 July 2022 and 1 January 2023 to 30 January 2023. The total electricity generation for July amounted to 60,792.89 kWh, approximately 1.5 times the electricity generation of 40,756.35 kWh observed in January.
# Optimization Design of Lattice Structures in Internal Cooling Channel of Turbine Blade
## 4 Results and Analysis
### Validation of Mechanical and Heat Transfer Performances
#### 4.2.1 Validation of Bearing Load Performance  
Although function of bearing load performance was used as a constraint, its value can be obtained by corresponding experiment to verify the accuracy of the mathematical optimization model. Equivalent elastic modulus (\(E\)) was used to evaluate bearing load capacity of XLS cores. A relevant experiment was developed to obtain \(E\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Coefficient** & \(\mathbf{a}\) & \(\mathbf{b}\) & \(\mathbf{c}\) \\ \hline Op-I & \(-\)49.434 & \(-\)13.253 & 176.796 \\ Op-II & \(-\)54.888 & \(-\)13.542 & 176.984 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Coefficients of the set of Pareto optimized solutions in fitting function.  
Figure 10: Pareto fronts and fitting curves of XLS of (**a**) Op-I and (**b**) Op-II.  
Firstly, both the initial model and the optimal model were built. Because XLS has the property of periodicity, only a single XLS core was made, as shown in Figure 11, and two side walls of LS core were removed. Secondly, according to the GB/T 1041-2008 test standard, the unidirectional compression test of the structure was carried out by a ctm2000 universal material testing machine. The pre-cyclic loading method was adopted in the experiment, and the loading rate was 0.5 mm/min. The force displacement curve of the XLS core was obtained (Figure 11). It can be found that the black curve was the force displacement curve of the initial model of the XLS core, and the maximum force was 8543.267 N, which was converted into the elastic modulus of 680.196 MPa. The theoretical calculation value of equivalent elastic modulus was 830.265 MPa. The error between the maximum valve of the XLS core and the theoretical calculation value was 10.3%. Therefore, \(E\) can accurately evaluate the bearing load capacity of the XLS core. Moreover, it can be observed from Figure 11 that the maximum bearing capacities of the optimization result of Op-I and Op-II were 11,089.41 N and 12,404.22 N, respectively. Compared with the initial model, the maximum bearing load capacity of the optimized structure was significantly improved. From the results of Figure 10, the maximum bearing load capacity for the optimal structure of Op-I was increased by 29.8% compared to that of the initial model. The maximum capacity of bearing load for the optimal structure of Op-II was increased by 45.2% compared to that of the initial model. Therefore, the mathematical optimization model was accurate for bearing load performance.  
Figure 11: Validation XLS model and validation results: (**a**) XLS core; (**b**) force displacement curves; and (**c**) comparison for the maximum bearing load.
## 2 Results and Discussion
### Methods
#### 2.1.3 Memory usage and scaling
The time for building up all clusters for \(i\)-th similarity threshold is:  
_time(i) =O(In(N)*(Nst-i)*AvgFrag*exp^{\left/\left(\ln(N)*(Nst+1)/Nst\right)}\)/Nst)._  
Taking \(Ng\)\(>>\)1, one can evaluate overall processing time to construct clusters tree as:  
\(\sum\limits_{i=0}^{i<Nst}time(i)=O(N*\ln(N)*AvgFrag*Nst/2)\). Square dependence of calculation time on dataset size in divisive hierarchical clustering algorithms is given in [6]. Modern algorithms have time dependency better than \(O(N*\ln(N))\)[32]. There are three improvements, which help to perform calculations faster in proposed algorithm:  
Division of a cluster into many clusters but not into two as in traditional divisive cluster algorithms. Storing and using non-zero components only for centroid and scalar products calculations  
Fast selection of most diverse compounds to make probe dataset. Memory requirements and overall processing should be compared with other algorithms applied to large databases (\(>\) 1M size) described in the literature [7,13]. Some databases were successfully clustered with a Xeon Intel 3 MHz processor with 2G RAM [13] running Linux, but the memory used and algorithm complexity were not reported. From the time required for clustering different size datasets, one may conclude the algorithm complexity to be \(O(N)\), and minimal memory requirement to be 124*\(N\) bytes (from 988-bit fingerprint length). This value is some smaller than that used in our work 140*\(N\) bytes (see above), required to store non-zero indexes in glossary. The complexity of algorithm [13] is better than that proposed in this work, but the calculation time normalized by processor frequency has the same order or, is even larger for small datasets. It should be pointed out that estimated complexity and calculation time in [13] are reported for single similarity threshold-algorithm [13] without cluster tree building. Minimal memory, estimated for [7] is 256*\(N\) bytes (2048-bit Daylight fingerprint). This value is greater than that used in our work (140*\(N\)). Estimated algorithm complexity is \(O(N*\ln(N))\)[7], which is better than ours, but the calculation time was much larger - 16 days on a SGI Origin 300 single-processor computer for a 1.1M dataset. High speed of scalar product calculation and generation of multiply clusters at each step might explain why the algorithm with the worst complexity has a lower calculation time. In reality, 2048 multiplication and additions are required to calculate scalar product for a Daylight fingerprint [7]. In the proposed algorithm only \(AvgFrag\)(some tens) additions and multiplications are required.
## 3 Results
### SVM Model
#### 3.3.3 Error Distribution  
The error distribution for SVM models with SA optimization was analyzed using boxplot charts, starting with the cooling period presented in Figure 21.  
The boxplot with the error distribution for the cooling period based on SVM models with SA shows that the range of errors for every hour is much lower than that in Figure 15 (based on SVM without SA). Most of the errors are located above 0, except at 8:00 and 17:00, meaning that SVM models tend to underpredict cooling energy demand, as they had in Figure 15.  
The boxplot with the error distribution for the forecasted values regarding heating energy demand, based on SVM models with SA, is presented in Figure 22.  
Figure 22 shows that the error range is, once again, much smaller than in Figure 16, due to the use of SA, and the trend of SVM models to underpredict heating energy demand is kept.  
Figure 19: Load profile comparison between SVM+SA model C5 and reference for the cooling period.  
Figure 20: Load profile comparison between SVM+SA model H4 and reference for the heating period.
## 4 Discussion
### Threshold Response of Grassland Productivity to Atmospheric Drought  
Our study revealed a distinctive response mechanism of grassland ecosystems in Xinjiang's arid region to atmospheric drought, identifying a critical VPD threshold of 0.61 kPa. This provides a key theoretical basis for regional ecohydrological research. Unlike [PERSON] et al. (2023) [34], who proposed VPD percentile thresholds (e.g., the 40.07 th percentile triggering mild GPP loss) for Chinese grasslands, our study--based on the extreme arid climate and desert vegetation water regulation strategies in Xinjiang--used ridge regression to determine a specific kPa threshold (0.61 kPa). This discrepancy likely arises from the unique TEM-moisture coupling characteristics of Xinjiang--where annual PRE is less than 150 mm and vegetation rapidly responds via stomatal regulation (Figure 5a)--resulting in a lower water stress tipping point than in more humid regions. This reflects the high sensitivity of arid ecosystems to atmospheric drought.  
It is noteworthy that although current VPD levels below the threshold may optimize water use efficiency (WUE) by improving stomatal conductance, according to the stomatal hydraulic feedback hypothesis proposed by [PERSON] et al. (2005), plants rapidly close their stomata to reduce xylem tension and thus prevent embolism formation [35]. However, complex interactions among multiple factors may mask deeper underlying risks. Our SEM revealed a standardized path coefficient of \(-0.58\) (\(p<0.05\)) for the effect of VPD on GPP. This finding contrasts with [PERSON] et al. (2024), who identified soil moisture as the dominant driver in 44.6% of the Northern Hemisphere study area [5]. Similarly, research conducted in Southwest China in 2023 highlighted that those grasslands, due to their shallow root systems, are less capable of accessing deep soil moisture and are therefore more sensitive to atmospheric drought compared to deep-rooted forests [36]. Moreover, the global model projections by [PERSON] et al. (2019) suggest that the continued rise in VPD may further offset the CO\({}_{2}\) fertilization effect [8]. The observed exceedance of the VPD threshold in parts of our study region (Figure 8b,c) may serve as an early regional warning signal of this emerging trend.
# Design of a Modified Compact Coupler with Unwanted Harmonics Suppression for L-Band Applications
## 3 Design of The Proposed BLC  
The proposed BLC is designed at 1 GHz. In this design, the dimensions of the transmission lines and open stubs are modified to reach the desired performances. The structure of the proposed BLC is depicted in Figure 12. Size reduction of the proposed microstrip coupler is 86%, compared to the conventional BLC.  
Figure 11: Final vertical resonator: (**a**) layout and (**b**) frequency response. All dimensions are in the millimeter unit.  
Figure 10: Improved initial vertical resonator: (**a**) layout and (**b**) frequency response. All dimensions are in the millimeter unit.  
The structures of the proposed and the conventional BLCs are compared in Figure 13. As seen, the proposed BLC only occupies 20.7 mm \(\times\) 20.7 mm (0.093\(\lambda\)\(\times\) 0.093\(\lambda\)), where the conventional one occupies 56.3 mm \(\times\) 57.2 mm (0.25\(\lambda\)\(\times\) 0.25\(\lambda\)). The proposed coupler only occupies less than 14% of the conventional coupler and shows more than 86% size reduction.
# On the Secrecy Sum-Rate of Internet of Things Networks: Scheduling and Power Control +
## 2 System Model
### System Parameters  
As illustrated in Figure 1, we consider a time-division duplexing (TDD) uplink multiuser network which consists of \(N\) transmitters (users or IoT devices), a single desired receiver (base station: BS), and \(K\) independent eavesdroppers. We assume that each transmitter is equipped with a single antenna, and base station and each eavesdropper have \(M\) antennas, respectively. We consider a block-fading channel model, where the channel is constant within a single block and independently varying in the next block. During one symbol time, \(S\) transmitters are scheduled for data transmission. Thus, it can be modeled by a single-input and multiple-output (SIMO) multiple access channel (MAC).  
The term \(\alpha_{n}\mathbf{h}_{n}\in\mathbb{C}^{M\times 1}\) denotes the channel vector from the \(n\)-th transmitter to the base station, where \(\alpha_{n}\) and \(\mathbf{h}_{n}\) for \(n\in\{1,\cdots,N\}\) represent the large-scale and small-scale fading components, respectively. Similarly, the term \(\beta_{nk}\mathbf{g}_{mk}\in\mathbb{C}^{M\times 1}\) denotes the channel vector from the \(n\)-th transmitter to the \(k\)-th eavesdropper, where \(\beta_{nk}\) and \(\mathbf{g}_{nk}\) for \(k\in\{1,\cdots,K\}\) represent the large-scale and small-scale fading components, respectively. Each element of \(\mathbf{h}_{n}\) and \(\mathbf{g}_{nk}\) is assumed to be an independent and identically distributed (i.i.d) complex Gaussian random variable with zero mean and unit variance. The term \(\mathcal{N}_{S}\) denotes a selected user index set with \(|\mathcal{N}_{S}|=S\).