esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods
### Decomposition of the HRV Signals
#### 4.2.2 Dwt  
The DWT is a widely used joint time-frequency analysis method [16]. Herein, the term wavelet refers to irregular, non-symmetric, and short-duration waveforms having finite energy, zero mean, and a real value of Fourier transform [19]. Wavelets have been used to analyze typical events that are embedded within a signal. Researchers have used DWT as an efficient tool for the processing of non-stationary signals. In DWT, the signals are decomposed into the detail and the approximation coefficients at each level by passing them through a high-pass and a low-pass filter, respectively. The detail coefficient is kept undivided, but the approximation coefficient is subjected to decomposition again in the next step, which results in the formation of a new set of detail and approximation coefficients. The decomposition process continues till the desired level of decomposition has arrived. Hence, a signal \(x(t)\) can be expressed as the sum of its approximation coefficient at a specific scale \(m_{0}\) (i.e., \(x_{m0}(t)\)) and the detail coefficients with scales ranging from \(-\infty\) to \(m_{0}\) as shown in Equation (2) [51]. The decomposition of the HRV signals using DWT was carried out in this study with the help of MATLAB software (2015a, MathWorks Inc., Natick, MA, USA).  
\[x(t)=x_{m_{0}}(t)+\sum_{m=-\infty}^{m_{0}}d_{m}(t) \tag{2}\]  
where, \(x_{m0}(t)\) = approximation coefficient at scale \(m_{0}\) and \(d_{m}(t)\) = detail coefficient at scale \(m\).
# A Non-WSSUS Channel Simulator for V2X Communication Systems
## 6 Simulation Examples
### Results over Non-Isotropic Scattering
The BER performance of both estimation techniques in isotropic conditions was always lower than for the non-isotropic scenarios. Such performance was always greater than 1% in the considered simulation conditions. In the case of the LS estimator, its performance attained a 10% BER with similar Eb/No levels. However, depending on the mean AOA \(\mu_{R}\), the Eb/No requirement could increase significantly. For instance, in Scenario II, a 10% BER performance was yielded with 4.43 and 8.14 dB, when the mean AOAs were \(3\pi/4\) and 0, respectively. With respect to the STA scheme with \(\alpha=20\), its performance was also affected by the mean AOA that defined the region where the IOs concentrated. This technique required higher Eb/No levels than observed for the LS estimator. In Scenario III, a 10% BER could be reached employing 7.03 and 19.03 dB, when the IOs were located around 0 and \(3\pi/4\), respectively, which was a significant increase in the Eb/Norequirement. In addition, the performance of the LS estimator could reach a 0.1% BER for high Eb/No regimes in some of the non-isotropic cases.
# Condition Rating Prediction Using an Interactive Deterioration Model Development Package
## 3 Dmdp  
The NBI requires regular inspections to be conducted biannually [47]. The resulting increase in the inspection data pool has enabled deterioration models to be developed by means of computational processes to access and analyze the inspection data from the numerous bridge assets. Maintenance agencies are required to monitor bridge conditions continuously and use such models to aid in the decision-making processes. Therefore, a comprehensive platform for conducting these tasks is necessary [12; 48].  
The DMDP is designed to conveniently interpret NBI records and develop deterioration models [49]. All bridge deterioration and inspection data used can be found at the Federal Highway Administration (FHWA) site [50]. One significant feature of the DMDP is that it allows the user to recall the deterioration model analysis results and update these when new inspection data are archived. In addition to an efficient protocol for loading inspection data, the DMDP provides multiple methods for the development of deterioration models, and thus the users can compare their performances in a straightforward manner.  
Figure 1 presents a flowchart of the DMDP, which is composed of three subtasks: the initiation of the DMDP, selection of explanatory variables, and development of deterioration models. Each subtask is matched to a MATLAB-based window developed using a graphical user interface platform. Once the user decides to initiate the analysis, a second window appears for the selection of explanatory variables. If a previously archived inspection dataset is accessible for a specific bridge element, the user can select the update option. Otherwise, the DMDP prompts the user to load the inspection data from the beginning and conducts an analysis to identify the significant variables. An explanatory variable list is obtained and used to divide the bridge data into multiple groups depending on the classification levels. Thereafter, a deterioration model is developed for each group. The window for the development of the deterioration models displays the deterioration curves for the selected group and the condition rating prediction results depending on the classification tree, if available.  
In the following section, NBI regulations, the weighted least absolute selection and shrinkage operator (LASSO), and an algorithm for the comparison of the condition rating prediction error are briefly described.
## 3 Numerical Simulation  
In order to study the deformation of the model pile under vertical loads, a numerical model is established by COMSOL Multiphysics software. The software realizes the simulation of real physical phenomena by solving partial differential equations or partial differential equations based on the finite element method. COMSOL Multiphysics is widely used in scientific research and engineering calculations owing to its efficient computational performance and outstanding multi-field direct-coupling analysis capabilities [31; 32]. Compared with other FE software, the COMSOL Multiphysics software is superior in multi-field coupling calculation. This model test is prepared for single PCC pile and PCC energy pile experiments. Considering the advantages of the software, and the characteristics of the experiment, the COMSOL Multiphysics software was used to conduct an equal-sized numerical model of soil and pile (see Figure 6).  
In this simulation, soil and pile are both regarded as incompressible materials. The soil is an elastoplastic material, and the constitutive model uses the Drucker-Prager (D-P) model. The pile is a homogeneous isotropic linear elastomer, and subject to the William-Warnke (W-W) criterion. The pile body and the soil body were continuously coordinated and deformed. Creep was not considered. The physical and mechanical parameters used in the numerical simulation are shown in Table 1.  
The meshes of the model include two parts with soil meshes and pile meshes, which are all tetrahedral meshes, the meshes arranged by free subdivision. The maximum size of a mesh unit is 0.367 m, and the minimum is 0.0661 m; the maximum growth rate is 1.5. The curvature factor is 0.6 and the resolution of the narrow region is 0.5. When calculating, the number of iteration meshes is 4, and the maximum depth of the unit is 4 m. There were 6 areas, 33 boundaries, 65 sides, and 42 vertices in the numerical calculations, and the freedom degree of the solution was 199,401.  
In the stress field, the lower boundary of the sand was under fixed constraint, i.e., without displacement in three directions. The boundaries around the two layers of soil were all under horizontal constraints (roller bearing). The displacement only occurs in the direction of the Z axis, which is along the length of the pile. The top of the clay layer had a free boundary in which was contact with the surface of pile. The load was applied to the head of pile.
## 1 Introduction
### Services  
The definition of services is not as easy to find as that for goods or product because research on services stems from different scientific backgrounds, such as marketing or operations research. Thus, there is no explicit definition agreed upon by every discipline, but, instead, various agreed-upon definitions [24]. The most cited definition is in [25], as follows:  
\"Service is a process consisting of series of more or less intangible activities that normally, but not necessarily always, occur in interactions between the customer and service employees and/or physical resources or goods and/or systems of the service provider, which are provided as solutions to customer problems\" [25].  
Another characterization of services that is widely accepted conceptualizes it as containing four major features: Intangible, Heterogeneous, Inseparable, and Perishable (IHIP). These four features are known as the IHIP characteristics [26]. Intangible things, by definition, are not physical objects and thus are services based on the performance of a service provider [26]. Heterogeneity focuses on the difficulty of standardizing the services. In addition, depending on the customer's involvement and requirements, the outcome will change [26]. Inseparability is a service that must be produced and consumed simultaneously. Thus, the production of a service provider is directly linked to the consumption of a customer [26]. Perishable services cannot be stored or stockpiled. These criteria are broadly used and often criticized by scholars [27; 28; 29]. Nevertheless, they are quite useful as a commonly accepted definition.
# Instability of High Liquid Limit Soil Slope for the Expressway Induced by Rainfall
## 3 Numerical Simulations
### Rainfall Infiltration Theory of Unsaturated Soils
#### 3.1.3 Rainfall Infiltration Theory  
Many infiltration models based on [PERSON] equation have been established. These models are in good agreement with the results of field soil water experiments to some extent. As early as the beginning of the 20 th century, the G-A infiltration model [29] assumed that there was a thin layer of water in the upper layer of the dry soil at the initial stage of infiltration, and that there was always an infiltration front, that is, the wetting front. In addition, it is assumed that there are only two regions in the infiltrated soil: the wet regionwith saturated water content and the initial water content region below the infiltration front. The basic formula is as follows:  
\[i=k_{s}\frac{z_{f}+s_{f}+H}{z_{f}}=(\theta_{s}-\theta_{i})\frac{dz_{f}}{dt} \tag{4}\]  
where \(z_{f}\) is the wetting front depth; \(s_{f}\) is the soil matrix suction at the infiltration front; \(H\) is the surface water depth; \(s\) is the saturated water content; \(i\) is the initial soil water content.  
An improved model [30] which applied the G-A model to rainfall scenarios believed that the allowable soil infiltration \(f_{p}\) was related to the rainfall intensity \(I\) and the permeability coefficient \(k_{s}\), which can be divided into three cases to discuss, shown in Figure 5.  
1. \(I<k_{s}\): Surface runoff does not occur and all rainfall infiltrates.
2. \(k_{s}<I\leq f_{p}\): For all rainfall infiltration, the soil saturation near the surface gradually increases over time, and after a certain time, the soil shifts to the Situation (3).
3. \(I\geq f_{p}\): Since the rainfall intensity is greater than the infiltration capacity of the soil, part of the rainfall does not infiltrate, resulting in surface runoff. At this time, the soil on the slope is basically in a saturated state. The infiltration rate gradually decreases after the rainfall reaches the infiltration capacity.  
The influence of slope angle on infiltration and runoff under rainfall was studied by the G-A model [31]. It is found that slope angle had a certain influence on the rainfall infiltration at the beginning of rainfall, and with the increase of rainfall time, the difference became smaller which can be ignored.  
Based on the G-A model, the stability of slope under the rainfall condition was studied using the numerical simulation in this paper.
## 3 Results  
### Spatiotemporal Distribution of NPP from 2000 to 2019  
As shown in Figure 2, there were obvious spatial differences in the NPP in Ordos City from 2000 to 2019, basically showing a distribution of low values in the northwest and high values in the southeast. The years 2000, 2001, 2005 and 2011 presented more obvious low values of NPP in the northwest of Hanggin Banner, the west of Otog Banner and the west of Otog Front Banner in Ordos City. The NPP values of Ejin Horo Banner, Dongsheng District, Jungar Banner, Kangbashi District and Ukin Banner were generally high. The NPP values in the central part of Ordos City from 2000 to 2011 were mainly concentrated in the range of 1000 to 1500 kg C/m\({}^{2}\), and from 2011 to 2019, the NPP values in the central part of Ordos City were mainly concentrated in the range greater than 1500 kg C/m\({}^{2}\).  
As shown in Figures 3 and 4, the total NPP in Ordos City exhibited a more obvious upward trend. The lowest total NPP value was 362,241,992 kg C/m\({}^{2}\) in 2001, and the highest total NPP value was 754,386,572 kg C/m\({}^{2}\) in 2016, followed by 753,298,833 kg C/m\({}^{2}\) in 2018. Comparing the NPP for 2000 and 2019, as shown in Figure 5, it is clear that the areas showing a large increase in NPP are mainly located in the eastern side of Ordos City, namely, Jungar Banner, Dongsheng District, Kangbaishi District and Ejin Horo Banner, whereas the areas showing a decrease in NPP are mainly concentrated in the northwestern part of Hanggin Banner, the northwestern part of Otog Banner and the central part of Dalad Banner.  
Figure 2: Spatial distribution of NPP over the years.  
Figure 4: Inter-annual change in total NPP by district or banner. (**a**) Dalad Banner; (**b**) Dongsheng District; (**c**) Jungar Banner; (**d**) Ejin Horo Banner; (**e**) Otog Banner; (**f**) Uxin Banner; (**g**) Hanggin Banner; (**h**) Otog Front Banner; (**i**) Kangbashi District.  
Figure 3: Inter-annual change in total NPP.
# Online Attentive Kernel-Based Off-Policy Temporal Difference Learning
## 2 Background and Preliminaries
### Reinforcement Learning  
Reinforcement learning problems are typically modeled as Markov Decision Processes (MDPs), represented by the quintuple \(\langle S,A,P,R,\gamma\rangle\), where \(S\) is the set of states, \(A\) is the set of actions, \(P\) is the state transition probability function, \(R\) is the reward function, and \(\gamma\in[0,1)\) is the discount factor. The core characteristic of an MDP is its Markov property, where the next state \(s_{t+1}\) depends only on the current state \(s_{t}\) and action \(a_{t}\), independent of previous states.  
In RL, at each time step \(t\), the agent selects an action \(a_{t}\in A\) based on the current state \(s_{t}\in S\). The environment responds by transitioning to a new state \(s_{t+1}\in S\) and providing an immediate reward \(r_{t+1}\). The agent's objective is to learn a policy \(\pi:S\times A\rightarrow[0,1]\) through this interaction with the environment so that it can select the optimal actions in different states to maximize the long-term cumulative reward \(G_{t}\), which is defined as follows:  
\[G_{t}=\sum_{k=0}^{\infty}\gamma^{k}r_{t+k+1}.\]  
The value function \(V^{\pi}(s)\) is defined as the expected return the agent obtains in state \(s\) and acts according to policy \(\pi\):  
\[V^{\pi}(s)=[G(\pi)|s_{0}=s], \tag{1}\]  
the action-value function \(Q^{\pi}(s,a)\) is defined as the expected return the agent begins in state \(s\), where it takes an action \(a\), and then acts according to policy \(\pi\):  
\[Q^{\pi}(s,a)=[G(\pi)|s_{0}=s,a_{0}=a], \tag{2}\]  
additionally, we use \(V(s)\) and \(Q(s,a)\), instead of \(V^{\pi}(s)\) and \(Q^{\pi}(s,a)\), for convenience in the following section of our paper.
# Malware Detection on Byte Streams of Hangul Word Processor Files
## 6 Conclusions  
In this paper, to detect malicious actions of HWP files, we designed a convolutional neural network (CNN) that works on byte streams. We extracted byte streams from HWP files and manually identified whether malicious actions were embedded in the streams. By experimental results on our manually annotated 534 HWP files, we demonstrated that the proposed CNN achieves the best performance (e.g., accuracy) amongst comparable machine learning models. We believe that our study will help to prevent the web users from the malicious HWP files by warning them before opening the files. As new contaminated files keep appearing, we need to keep up the study of developing  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Runtime (Seconds)** \\ \hline GNB & 3.3028 \\ RF & 0.0976 \\ LR & 0.0026 \\ CNN & 1.6988 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Runtime comparison, where the elapsed time is specified in seconds.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Structure** & **F1** \\ \hline Embed(\(E=512\)) + Conv(\(K_{1}=32,K_{2}=64\)) + Pool(\(P=100\)) + FC(\(F_{1}=32,F_{2}=32\)) & 92.61/92.63 \\ Embed(\(E=128\)) + Conv(\(K_{1}=32,K_{2}=64\)) + Pool(\(P=100\)) + FC(\(F_{1}=32,F_{2}=32\)) & 93.00/93.07 \\ Embed(\(E=256\)) + Conv(\(K_{1}=32\)) + Pool(\(P=100\)) + FC(\(F_{1}=32,F_{2}=32\)) & 92.52/92.50 \\ Embed(\(E=256\)) + Conv(\(K_{1}=32,K_{2}=64\)) + Pool(\(P=100\)) + FC(\(F_{1}=128\)) & 92.63/92.63 \\ Embed(\(E=256\)) + Conv(\(K_{1}=64,K_{2}=64\)) + Pool(\(P=100\)) + FC(\(F_{1}=32,F_{2}=32\)) & 92.97/92.99 \\ Embed(\(E=256\)) + Conv(\(K_{1}=32,K_{2}=8,K_{3}=8\)) + Pool(\(P=100\)) + FC(\(F_{1}=32,F_{2}=32\)) & 92.47/92.43 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Samples of different CNN structures, where the two values \(b/m\) of each cell correspond to benign and malicious cases, respectively.  
improved detectors. We will continue collecting more malicious HWP files and also investigate better model structures.  
Conceptualization, Y.-S.J.; Investigation, A.R.K.; Methodology, Y.-S.J.; Validation, A.R.K.; Writing--original draft, Y.-S.J.; Writing--review & editing, J.W. and A.R.K.  
This research received no external funding.  
This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.2019-0-00026, ICT infrastructure protection against intelligent malware threats). This work was supported by the Soonchunhyang University Research Fund. We thank [PERSON] for his comment on the malware exploiting Hangle word processor.  
The authors declare no conflict of interest.
###### Abstract  
The rising climate change concerns over the excessive exploitation of non-renewable sources have necessitated the need for alternative renewable and eco-friendly resources for the production of innovative materials, achieving the targets of bioconomy. Lignocellulosic biomass (LB) constituted by polymeric sugars and lignin is an abundantly available plant-based renewable material in the form of agro-industrial food waste and crop residues that can be exploited for the production of an array of value-added chemicals and bioproducts. Cellulose is the most abundant natural and biodegradable organic polymer on earth derived from LB, with wide scale applications in the lignocellulosic biorefineries and food industries. The negative effects of food waste from agro-industrial activities could be reduced through the recovery of cellulose from these wastes and converting it into valuable forms. However, the extraction of cellulose from LB is a difficult task owing to the recalcitrant nature of biomass that restricts the easy accessibility of cellulose for value addition. Therefore, a suitable cellulose extraction method through the fractionation of LB is necessary for a better cellulose yield. Furthermore, owing to the enormous potential of nanocellulose (NC), researchers are keenly interested in developing ecologically acceptable cellulose extraction methods. Cellulose nanofibrils and nanocrystals confer excellent mechanical properties, non-toxic characteristics and biodegradability, due to which they possess wide-scale applications in diverse industrial sectors. The current review emphasizes the potential role of cellulose extraction and NC production from agro-food waste. The different pretreatment methods for their extraction from LB are outlined. The applications of nanocellulose in different areas are also discussed. The review also highlights the recent trends, challenges and future directions in the development of cellulose and NC-based commercial products.  
agro-industrial food waste; lignocellulosic biomass; cellulose; nanocellulose; pretreatment; circular bioconomy +
Footnote †: journal: Physics Letters B
# Soft Error-Tolerant and Highly Stable Low-Power SRAM for Satellite Applications
## 3 Simulation Results and Analysis
### Electric Quality Metric Comparison  
Critical charge, \(Q_{C}\), effectively indicates SRAM cell performance in space. However, the performance of SRAM cells is influenced by multiple factors beyond \(Q_{C}\). Parameters such as RAT, WAT, RSNM, WWTV, power consumption, and area are also crucial for SRAM cell operation. Each of these parameters has trade-offs; improving one may deteriorate another. Therefore, instead of comparing cell performance based on each parameter individually, a comprehensive performance metric that considers all factors affecting SRAM cell operation is required. The Electric Quality Metric (_EQM_) represents the overall performance of an SRAM cell and is given by the following formula [3]:  
\[EQM=\frac{Q_{C}\times RSNM\times WWTV}{H_{PWR}\times WAT\times RAT\times Area} \tag{3}\]  
To compare the _EQM_ values of the proposed RHSP16T cell and all comparison cells, the relative _EQM_ (\(EQM_{Cell}\) / \(EQM_{RHS16T}\)) values for all the cells are shown on a logarithmic scale in Figure 8. As shown in Figure 8, the proposed RHSP16T cell exhibited the highest _EQM_ value, indicating its superior performance compared with the other cells.  
Figure 6: Two equivalent circuits for generating transient pulses (**a**) when a particle strikes an NMOS transistor of a ‘0’-storing sensitive node and (**b**) when a particle strikes a PMOS transistor of a ‘1’-storing sensitive node.  
Figure 7: Soft-error recovery waveforms when sensitive nodes are impacted by SEUs: (**a**) node Q and (**b**) node S0.
## 2 Materials and Methods  
### Construction of the CW-MFC System  
The unplasticized polyvinyl chloride tube was used to construct the up-flow _L. hexandra_ CW-MFC system with a diameter of 30 cm and an effective height of 60 cm (Figure 1). The bottom layer of the device was 8-16 mm gravel (15 cm), the middle layer was 5-8 mm ceramsite (15 cm), and the anode and cathode layers were 3-5 mm activated carbon (20 cm and 10 cm, respectively). The outside of the anode and cathode were wrapped with 100 mesh stainless steel mesh, and connected to the resistance box (1000 \(\Omega\), Fuyang Precision Instrument Factory, Hangzhou, China) by a copper wire to form a closed loop. The voltage information was collected by a data acquisition card (NI USB-6009, Suzhou Fuyutong Electronics Co., Ltd., Nanjing, China). The system uses synthetic Cr (VI) wastewater with _L. hexandra_ as wetland plants, and a peristaltic pump connects the inlet reservoir with the reactor (BT100-2), Baoding Lange Constant Flow Pump Co., Ltd., Baoding, China). The aeration device was arranged on the surface of the system, and the aeration rate (0-8 L\(\cdot\)min\({}^{-1}\)) was changed by the gas flowmeter. Five devices were operated under the conditions of aeration rates of 0 L\(\cdot\)min\({}^{-1}\), 2 L\(\cdot\)min\({}^{-1}\), 4 L\(\cdot\)min\({}^{-1}\), 6 L\(\cdot\)min\({}^{-1}\), and 8 L\(\cdot\)min\({}^{-1}\)respectively, until the water quality and voltage data were stable after 3 months. The substrate and plant samples of each device were collected for the following research. Each device is placed in the artificial climate chamber of the school, and the day and night temperature was 20-35 \({}^{\circ}\)C. The whole study lasted about 7 months.