esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### The Limitations of This Study  
This study established a GPP loss probability assessment model under different heatwave, drought, and compound drought and heatwave scenarios. The model is based on the construction of a CDHI for rubber plantations on Hainan Island, using a two-dimensional Copula function. It analyzes the response of rubber plantations to various events from a statistical perspective. However, this study still has some limitations. Firstly, we only used five common Copula functions to construct the CDHI and conditional probability models. Although these Copula functions provide flexible methods for describing dependencies between variables, their assumptions and applicability are limited, especially in dealing with tail risks and complex multivariate dependency structures. Therefore, future research should consider more Copula functions to construct a CDHI and conditional probability models. Secondly, the GPP data used in this study come from the MODIS product from NASA, MODI7A2H. Although these data are widely used in ecological and climate research, the MODIS GPP algorithm has structural errors, such as the inability to accurately identify the contribution of leaf illumination and shading positions to total GPP [76; 77]. Therefore, future research should consider using other GPP data products to improve data accuracy.  
Additionally, the drought and heatwave indices used in this study are relative indices rather than absolute indices. The heatwave events indicated by the STI are only high temperatures relative to the multi-year average, not high temperatures specific to rubber tree growth. The drought indicated by the SPEI may also not cause water stress for rubber tree growth, meaning that meteorological heatwaves and droughts may not directly lead to ecological losses in rubber trees. This point can be seen from the inverse relationship between the probability of GPP loss and the intensity of CDHEs during the rainy season (June-October) in Figure 4. During the rainy season, excessive water resources exceed the ecological water demand for rubber trees, and the dry periods identified by the SPEI may instead promote photosynthetic production in rubber plantations due to more suitable moisture conditions. Similarly, even the high temperatures identified by the STI may be below the optimal temperature for rubber trees. Therefore, future research should focus on specific species and conduct quantitative studies on the impact of CDHEs based on ecological amplitude.
# The Effect of Preprocessing on Arabic Document Categorization
###### Abstract  
Preprocessing is one of the main components in a conventional document categorization (DC) framework. This paper aims to highlight the effect of preprocessing tasks on the efficiency of the Arabic DC system. In this study, three classification techniques are used, namely, naive Bayes (NB), k-nearest neighbor (KNN), and support vector machine (SVM). Experimental analysis on Arabic datasets reveals that preprocessing techniques have a significant impact on the classification accuracy, especially with complicated morphological structure of the Arabic language. Choosing appropriate combinations of preprocessing tasks provides significant improvement on the accuracy of document categorization depending on the feature size and classification techniques. Findings of this study show that the SVM technique has outperformed the KNN and NB techniques. The SVM technique achieved 96.74% micro-F1 value by using the combination of normalization and stemming as preprocessing tasks.  
document categorization; text preprocessing; stemming techniques; classification techniques; Arabic language processing Article
# An Object- and Shapelet-Based Method for Mapping Planted Forest Dynamics from Landsat Time Series
###### Abstract  
Large-scale afforestation in arid and semi-arid areas with fragile ecosystems for the purpose of restoring degradation and mitigating climate change has raised issues of decreased groundwater recharge and ambiguous climatic benefits. An accurate planted forest mapping method is necessary to explore the impacts of afforestation expansion on fragile ecosystems. However, distinguishing planted forests from natural forests using remote sensing technology is not a trivial task due to their strong spectral similarities, even when assisted by phenological variables. In this study, we developed an object- and shapelet-based (OASB) method for mapping the planted forests of the Ningxia Hui Autonomous Region (NHAR), China in 2020 and for tracing the planting years between 1991 and 2020. The novel method consists of two components: (1) a simple non-iterative clustering to yield homogenous objects for building an improved time series; (2) a shapelet-based classification to distinguish the planted forests from the natural forests and to estimate the planting year, by detecting the temporal characteristics representing the planting activities. The created map accurately depicted the planted forests of the NHAR in 2020, with an overall accuracy of 87.3% (Kappa = 0.82). The area of the planted forest was counted as 0.56 million ha, accounting for 67% of the total forest area. Additionally, the planting year calendar (RMSE = 2.46 years) illustrated that the establishment of the planted forests matched the implemented ecological restoration initiatives over the past decades. Overall, the OASB has great potential for mapping the planted forests in the NHAR or other arid and semi-arid regions, and the map products derived from this method are conducive to evaluating forestry eco-engineering projects and facilitating the sustainable development of forest ecosystems.  
planted forests mapping; shapelet-based classification; image segmentation; object-level time series; forestry eco-engineering projects Article  
An Object- and Shapelet-Based Method for Mapping Planted Forest Dynamics from Landsat Time Series  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], X. An Object- and Shapelet-Based Method for Mapping Planted Forest Dynamics from Landsat Time Series.  
Remote Sens. 2022146188[[https://doi.org/10.3390/rs14246188](https://doi.org/10.3390/rs14246188)]([https://doi.org/10.3390/rs14246188](https://doi.org/10.3390/rs14246188)) Academic Editor [PERSON]
# Failure Prediction for the Tearing of a Pin-Loaded Dual Phase Steel (DP980) Adjusting Guide
###### Abstract  
Owing to their outstanding strength, in recent years, there has been an increased use of advanced high-strength steel (AHSS) sheets in the automotive sector. Their low formability, however, poses a challenge to forming, and failure prediction requires accurate knowledge of its material behavior over a large strain range up to ultimate failure, in order to exploit their full capacity in forming, but also in crash events. For predicting the fracture of an adjusting guide loaded by a pin, first, the force-displacement data are extracted from tensile tests using DP980 specimens of diverse shapes, all of which represent a certain loading mode. Using digital image correlation (DIC), we determine the stress triaxialities corresponding to the diverse loading conditions and establish the triaxiality failure diagram (TFD), which serves as the basis for the generalized incremental stress state-dependent damage model (GISSMO). Then, the damage parameters (necking and failure strains) are determined for each loading mode by reverse engineering-based optimization. Finally, these damage parameters are applied to the adjusting guide, and the numerical results are compared with the experimental data. Comparisons of the external load-displacement curves and the local equivalent strain distributions show that using the damage model with the material parameters obtained in here allows for the accurate prediction of the guide's failure behavior, and the applicability of GISSMO to complex loading cases.  
damage; GISSMO; finite element analysis; DP980; advanced high-strength steels; digital image correlation; formability +
Footnote †: journal: Applied Soft. 2019, 9, 5460; doi:10.3390/appr9245460
# Data Downloaded via Parachute from a NASA Super-Pressure Balloon
## 2 Upgraded Hardware Design  
Each DRS capsule comprised a Raspberry Pi 3B attached to a custom 300 mm \(\times\) 125 mm printed circuit board (PCB; see Figure 1). The capsules were powered by switchable 24V DC from the main payload while they were attached, then by two internal 9V lithium batteries after release. These batteries were the only components of the DRS that could be considered potentially hazardous. However, they were compliant with safety test criteria T1-T8 defined in Section 38.3 of [5]. The DRS used a geared pincer mechanism to hold on to a loop of nylon cable tie underneath the main payload. After coordination with local air traffic control, we issued a sequence of three commands (to prevent errors and fault propagation) that used a servo inside the DRS to open the pincer, allowing the DRS to drop away.  
Each DRS was enclosed in a 3D-printed CPE hard plastic case and a closed-cell foam shell (see Figure 2). One case was printed in PLA, which also works fine but is harder to handle because it is brittle. The foam shell could absorb impacts and add a degree of waterproofing, which proved useful when one DRS landed in snow. It was manufactured by pouring liquid PU expanding foam mix into a pill-shaped mould a few millimetres less than 6\" in diameter, lined with a chicken roasting bag to create a waterproof outer surface on which contact details can be written [6]. The total mass including the parachute was 1.28 kg.  
Figure 1: The DRS is based around this custom PCB with a Raspberry Pi in the middle. An Ethernet cable is plugged into the bottom right of the Pi and attaches to a zero-extraction force connector at the top right corner of the PCB. Two SD card readers are plugged directly into the Pi, and two are attached to USB extension cables to reduce heat production at the sockets. Servo-operated pincer mechanisms can be seen on the right, with the pincer on the front holding on to the main gondola and the release at the back holding the parachute.  
Figure 2: The DRS capsules hang underneath the payload (here, down is shown to the left). They are prevented from swinging or rotating by a 3D-printed ‘alignment crown’ fixed to their top, which fits inside a mount on the gondola that has an inverted shape.
## 2 Design of Components and Fabrication
### Test Specimens
#### 2.2.2 Dynamic Testing  
The dynamic impact testing setup, which included three-point bending specimens, is shown in Figure 4a,b. The system was designed to store potential energy by extending the springs, as depicted in Figure 4c. The same three-point bending frame, coupled with the dynamic impact setup, was used to test eight steel specimens, as summarized in Table 5. During testing, an AC motor applied tension to the springs supporting the impactor carriage, and, by varying the springs' extension distance, impacts were generated at different energy levels across the specimens.  
Maximum impact force and displacement values were obtained from the time-dependent graphs discussed in Section 3.1. Specifically, these values were derived by analyzing the peak values on the force-time and displacement-time curves, ensuring an accurate representation of the material's response under impact conditions.  
Figure 4: The proposed dynamic impact testing setup: (**a**) side view; (**b**) the setup with the specimen installed; (**c**) impact test actuation system.  
To ensure accurate data collection, various sensors and instruments were integrated into the testing setup. A load sensor was incorporated into the impact carriage to measure the force-time history during impact events. Additionally, a velocity sensor and accelerometer were attached to the setup to record the impactor's velocity and acceleration at the moment of impact. To record the response of the steel plates, displacement gauges were positioned on the tested specimen and a data acquisition system was employed.
## 9 Capacity Requirements of the Proposed Preventive Control Strategy  
The capacity requirements (MW) using the developed frequency support methods are analyzed based on the historical operating data. If the BESS method is utilized, the probability distribution of the required BESS power output is presented in Figure 15. The confidence level (CL) of the probability distribution is demonstrated in Table 10. For instance, 90% of confidence level requires 0.9031 MW capacity of BESS to ensure sufficient frequency stability under \(N\)-1 tripping events.  
If the DLC method is utilized, the probability distribution of the power requirement of DLC load shedding is presented in Figure 16. The confidence level (CL) of the probability distribution is demonstrated in Table 10. For 90% of confidence level, it requires 0.9031 MW capacity of DLC to ensure sufficient frequency stability under \(N\)-1 tripping events.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Confidence LEVEL** & **Required BESS** & **Required DLC** & **Required EDRP** \\
**(\%)** & **(MW)** & **(MW)** & **(MW)** \\ \hline
70–72 & 0.6281 & 0.6281 & 2.4135 \\
80–81 & 0.7531 & 0.7531 & 2.8135 \\
90–92 & 0.9031 & 0.9031 & 3.4135 \\
95–96 & 0.9781 & 0.9781 & 3.8135 \\
99–99.5 & 1.1031 & 1.1031 & 4.4135 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The comparison of capacity requirement.  
Figure 15: Probability distribution of the required BESS power output.  
If the EDRP method is utilized, the probability distribution of the power requirement of EDRP load reduction is presented in Figure 17. The confidence level of the probability distribution is demonstrated in Table 10. For 90% of confidence level, it requires 3.4135 MW capacity of EDRP to ensure sufficient frequency stability under _N_-1 tripping events.
# Multi-Object Deep-Field Digital Holographic Imaging Based on Inverse Cross-Correlation
## 2 Principle
### Digital Holographic Reconstruction
In addition to improving the quality of imaging, this method also introduces a fresh perspective for tackling the digital holographic imaging challenges that involve multiplanar objects.
# Wireless Home Energy Management System with Smart Rule-Based Controller
###### Abstract  
Despite the increasing utilization of renewable energy resources, such as solar and wind energy, most residential buildings still rely on conventional energy supply by public utility services. Such utility services often use time-of-use energy pricing, which compels residential consumers to reduce their energy usage. This paper presents a wireless home energy management (HEM) system that enables the automatic control of home appliances to reduce energy consumption to assist such energy users. The system consists of multiple smart sockets that measure the energy that is consumed by the connected appliances and are capable of implementing on/off commands. The system includes other support components for supplying data to a central controller, which utilizes a rule-based HEM algorithm. The control rules were designed, such that the lifestyle of the user would be preserved while the energy consumption and daily energy cost were reduced. The experimental results showed that the central controller could effectively receive data and control multiple devices. The system was also found to afford significant reductions of 23.5 kWh and $2.898 in the total daily energy consumption and bill of the considered household setup, respectively. The proposed HEM system promises to be particularly useful for households with a high daily energy consumption.  
home energy management; Zigbee; smart socket; monitoring; appliances scheduling 1
## 2 Theory  
### FFT-Based Gaussian Filtering  
Gaussian filtering is used to remove high-frequency noise from images and create a smooth effect. Through this, the effect can be achieved of reducing the sharpness of the image and reducing noise. However, it has the disadvantage of blurring clear edgeswithin the image. By combining this with the FFT algorithm, frequency components can be analyzed more clearly, noise can be removed, and quantitative data can be acquired in local feature areas. Gaussian filtering is defined in the spatial domain and mainly utilizes 2D Gaussian filtering [18].  
\[H(x,y)=e^{-\frac{x^{2}+y^{2}}{2\sigma^{2}}} \tag{1}\]  
where x and y are coordinates in the spatial domain and \(\sigma\) means standard deviation of the Gaussian filtering. Afterwards, the image and the Gaussian filtering are combined.  
\[g(x,y)=f(x,y)\cdot H(x,y) \tag{2}\]  
where \(f(x,y)\) means the input image and \(H(x,y)\) means the Gaussian filtering. Afterwards, the FFT is applied to the synthesized image.  
\[G(u,v)=FFT\{g(x,y)\} \tag{3}\]  
where \(G(u,v)\) means the image in frequency domain. Afterwards, the image is acquired by applying the Gaussian filtering in the frequency domain.  
\[\hat{F}(u,v)=G(u,v)\cdot H(u,v) \tag{4}\]  
where \(\hat{F}(u,v)\) means the image in frequency domain with the final Gaussian filtering applied. Afterwards, the inverse FFT (IFFT) is applied to convert the frequency domain image with the Gaussian filtering applied to the spatial domain.  
\[\hat{f}(x,y)=IFFT\{\hat{F}(u,v)\} \tag{5}\]  
where \(\hat{f}(x,y)\) means the final Gaussian filtered image.  
Figure 1 shows the process of removing noise and performing thermal equalization using Gaussian filtering. The weighted average is calculated by applying a kernel to each pixel of the image, and the larger the kernel size, the greater the influence on the surrounding pixels. This removes noise by replacing values around noisy pixels with smooth values. At the same time, the FFT algorithm is applied to convert to the frequency domain and then process the non-uniform heat source.  
Figure 1: The principle of Gaussian filtering-based FFT algorithm.
# A Survey on Moving Target Defense: Intelligently Affordable, Optimized and Self-Adaptive
## 1 Introduction
### Comparison with Existing MTD Surveys
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Refs.** & **Year** & \begin{tabular}{c} **MTD** \\ **Mechanism** \\ \end{tabular} & \begin{tabular}{c} **Intelligent** \\ **MTD** \\ **Introduction** \\ \end{tabular} & \
\begin{tabular}{c} **Intelligent** \\ **MTD Traits** \\ \end{tabular} &
\begin{tabular}{c} **Intelligent MTD** \\ **Development** \\ **Trend** \\ \end{tabular} & **Insights** \\ \hline [PERSON] et al. [3, 4] & 2013, 2018 & Five layers & - & - & - & From passive defense into proactive defense Function-and-movement model \\ [PERSON] et al. [5] & 2016 & Three layers & - & - & - & movement model \\ [PERSON] et al. [6] & 2018 & Four layers & \(\surd\) & Coarsely & Coarsely & From proactive defense into reactive defense The perspective of architectural structure A simple and yet general notion of defense \\ [PERSON] et al. [7] & 2019 & Three layers & Coarsely & Coarsely & - & - \\ [PERSON] et al. [8] & 2019 & Five attack surface shifting & Coarsely & Coarsely & - & - \\  & & ways & & & & defense \\ [PERSON] et al. [9] & 2020 & SDR & \(\surd\) & Partially & Coarsely & Toward proactive, adaptive defense Optimized defense behaviors \\ [PERSON] et al. [10] & 2020 & GT-related & GT-related MLs & - & - & - \\  & & & & & Intelligently affordable, optimized and self-adaptive defense \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of related works. metrics, evaluation methods, and application areas. Remarkably, this survey discussed the pros and cons of each aspect of MTD investigated in detail. Meanwhile, [PERSON] et al. [10] focused on the game-theoretic decision-making approaches of MTD and systematically introduced the application scenarios of combining MTD with four different game-theoretic models (static game, signal game, Markov game, differential game, and evolutionary game) with some new perspectives and interpretations of game-theoretic MTD research, and proposed a direction for future development. The surveys above do not provide a more detailed description of the development of intelligent MTD, which leads to insufficient analysis and guidance facing the intelligent network world. This is what we are trying to introduce: the development trend of intelligent MTD, as well as the shortcomings and prospects of intelligent MTD at the current stage of development. We are committed to discussing more technical details and helping researchers to obtain more detailed knowledge of the future development of MTD as much as possible.
# Broadband Spectral Domain Interferometry for Optical Characterization of Nematic Liquid Crystals
## 4 Thermotropic Characterization of \(MLC2132\)
### Results  
For the experiments, we characterized two LC cells with 25 \(\upmu\)m thickness, respectively filled with \(E7\) and \(MLC2132\). E7 measurements are first performed: the transition temperature is found at 331 K in excellent agreement with the previous studies [31]. In Figure 4 are reported the experimental group index evolution and the theoretical predictions [31] for both axis. The acquired data agree very well with the theoretical thermotropic prediction, over the whole nematic order, demonstrating that \(\Delta n(T)\) and \(\Delta n^{\delta}(T)\) have the same evolution with the temperature, over the considered spectral range.  
Figure 4: \(E7\) and \(MLC2132\) refractive indices dependence with the temperature. Dots features experimental data. \(E7\) theoretical curves from [32] are shown with solid lines. Dashed lines: \(MLC2132\) data fit for \(n_{e\rho}\). The nematic-to-isotropic phase transition temperatures for both mixtures are also indicated.  
Therefore, the same experimental process is applied to \(MLC2132\). The clearing point is found higher, around 386 K, confirming the furnisher data. Experimental results for both axes appear in Figure 4. As explained in [32], these data can be fitted in a two-steps process: \(D\) and \(E\) are obtained by fitting the average refractive index, while \(\Delta n_{0}\) and \(\beta\) are obtained with the birefringence excursion as a function of the temperature. The coefficients of Equation (9) for the \(MLC2132\) are then determined, as summarized in Table 3. The birefringence excursion on the extraordinary \(\Delta n_{e}(T)\) is also found larger by a factor \(\sim\)1.2 compared to \(E7\).