esa-sceva/satcom-chunk-collection huggingface dataset:
# A Large-Deformation Gradient Damage Model for Single Crystals Based on Microdamage Theory
## 6 Numerical Results and Discussion
### Analysis of a 1D Bar  
The regularization of a localized deformation with the proposed approach is demonstrated in a numerical model problem, where a bar with a length of 2.5 mm under tension is studied (Figure 3). An opening plane, normal to the direction of the bar is defined and at the center element, an initial defect is introduced where the damage threshold value is slightly reduced. Considering a rate independent damage evolution with linear isotropic softening and only active for opening mode, a microforce balance and the generalized balance law for the alternative case take the following form:  
\[\tilde{\sigma}=S_{a}+Hd+\mathcal{I}(d-^{X}d)+B(d-^{X}d) \tag{59}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\mu\) & \(\kappa\) & \(\beta\) & \(B\) & \(Y_{0}\) & \(H\) & \(\mathcal{I}\) \\ \hline \(7.0\times 10^{10}\) & \(1.5\times 10^{11}\) & \(3.0\times 10^{-7}\) & \(1.0\times 10^{0}\) & \(2.0\times 10^{8}\) & \(-1.0\times 10^{-9}\) & \(6.0\times 10^{9}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model Parameters.  
where \(\bar{\sigma}\) is equivalent tensile stress, \(H\) is the softening modulus and \(S_{a}\) is the initial material resistance. Using Equation (52) one can rewrite Equation (59) as follows:  
\[\bar{\sigma}=S_{a}+H({}^{x}\!d-\frac{\beta}{B}\Delta^{x}\!d)+(\mathcal{I}+B)(- \frac{\beta}{B}\Delta^{x}\!d) \tag{60}\]  
Further simplifying Equation (60)  
\[\bar{\sigma}=S_{a}+H{}^{x}\!d-(\mathcal{I}+B+H)l^{2}\Delta^{x}\!d \tag{61}\]  
where  
\[l^{2}=\frac{\beta}{B} \tag{62}\]  
solving Equation (61) for \({}^{x}\!d\) gives:  
\[x\!d=\frac{\bar{\sigma}-S_{a}}{H}+\frac{(\mathcal{I}+B+H)}{H}l^{2}\Delta^{x}\!d \tag{63}\]  
Note that for a 1D problem, Equation (63) is a second order homogeneous differential equation with a general form:  
\[y^{\prime\prime}-\omega y=C \tag{64}\]  
where  
\[\omega=l\sqrt{\frac{H}{(\mathcal{I}+B+H)}} \tag{65}\]  
which provides two types of solution depending on the sign of \(\omega\)  
\[A\sinh(x\sqrt{\omega})+B\cosh(x\sqrt{\omega})+C\qquad\qquad if\qquad\omega>0 \tag{66}\]  
\[A\sin(x\sqrt{\omega})+B\cos(x\sqrt{\omega})+C\qquad\qquad if\qquad\omega<0 \tag{67}\]  
Note that for a softening problem (providing \(\mathcal{I}>|H|\)) the solution is sinusoidal an the wave length reads  
\[\frac{2\pi}{\omega}=2l\pi\sqrt{\frac{(\mathcal{I}+B+H)}{H}} \tag{68}\]  
The numerical result presented in Figure 3 is well in line with the analytical solution provided in Equation (68).
## 3 Methods
### Vanishing Gradient and Context-Sensitivity  
LSTM uses the gating unit to overcome the drawbacks of the traditional RNN not being able to memorize long-term information on the time axis. Regrettably, the LSTM still inevitably has the problem of vanishing gradient in the depth direction. As explained in [16], verified with an automatic speech recognition task on the AMI dataset, the LSTM recognition rate drops sharply when the number of network layers increased from three to eight layers. Only the information before the current time has an impact on the output of the LSTM current time point, which is far from enough for the time sequence task. GridLSTM adds a Depth-LSTM block in the depth dimension and encapsulates it with a Time-LSTM block in the time dimension in a grid, trying to solve the gradient problem in both time and depth dimensions. In the GridLSTM time dimension, the current time hidden layer of Time-LSTM is affected by the hidden layer of the previous moment and current time input. Therefore, only the information before the current time can be obtained, but the context-dependent characteristics of the timed task are ignored. However, the output of the time sequence task is affected by the context information, which makes the output more accurate with the content of the moments before and after. The bidirectional RNN uses two separate recurrent hidden layers to scan the input sequence in opposite direction, overcoming the shortcomings of GridLSTM not being able to take advantage of the context information. The RNN in each direction of the bidirectional RNN has the same disadvantages as the normal unidirectional RNN and LSTM. As the number of layers is deepened to a certain extent, the depth dimension will have a phenomenon of vanishing gradient or gradient explosion.  
Figure 3: Bidirectional RNN expands in time.
###### Abstract  
Digital multimedia elements such as text, image, audio, and video can be easily manipulated because of the rapid rise of multimedia technology, making data protection a prime concern. Hence, copyright protection, content authentication, and integrity verification are today's new challenging issues. To address these issues, digital image watermarking techniques have been proposed by several researchers. Image watermarking can be conducted through several transformations, such as discrete wavelet transform (DWT), singular value decomposition (SVD), orthogonal matrix Q and upper triangular matrix R (QR) decomposition, and non-subsampled contourlet transform (NST). However, a single transformation cannot simultaneously satisfy all the design requirements of image watermarking, which makes a platform to design a hybrid invisible image watermarking technique in this work. The proposed work combines four-level (4L) DWT and two-level (2L) SVD. The Arnold map initially encrypts the watermark image, and 2L SVD is applied to it to extract the s components of the watermark image. A 4L DWT is applied to the host image to extract the LL sub-band, and then 2L SVD is applied to extract s components that are embedded into the host image to generate the watermarked image. The dynamic-sized watermark maintains a balanced visual impact and non-blind watermarking preserves the quality and integrity of the host image. We have evaluated the performance after applying several intentional and unintentional attacks and found high imperceptibility and improved robustness with enhanced security to the system than existing state-of-the-art methods.  
DWT; SVD; imperceptibility; robustness; amold map +
Footnote †: journal:  
Article  
Image Watermarking Using Discrete Wavelet Transform and Singular Value Decomposition for Enhanced Imperceptibility and Robustness
## 1 Introduction
[27] assimilated satellite infrared brightness temperature (TB) from Advanced Baseline Imager (ABI) with EnKF method, Doppler radar reflectivity, and radial velocity using OSSEs for a cool-season extratropical cyclone. Results indicated that assimilation improved the analysis of water vapor and cloud hydrometeor. [PERSON] et al. [28] assimilated radar radial velocity and reflectivity together with cloud water path and total precipitation water retrieved from satellite, based on En3 DVar method. It shows that simultaneous assimilation outperformed radar data assimilation only, especially in an imperfect storm environment. Using EnKF method, [PERSON] et al. [29] assimilated both radar data and satellite infrared radiance for a tornadic supercell thunderstorm, revealing that satellite data increased the forecast and warning leading time, and radar data provided the information on three-dimension wind field. Combined assimilation of both data yielded the best forecast. Overall, assimilating both satellite and radar observations makes contributions to the improvement of model forecasts, especially for convective weather. However, many studies in the past focus on the assimilation with traditional methods, such as EnKF. The simultaneous assimilation study of satellite and radar data based on POD-4 DEnVar methods is not yet available. The purpose of this study is to realize the assimilation of satellite radiation, radar radial velocity and reflectivity based on POD-4 DEnVar method. Firstly, the assimilation system is designed and established. Then, the performance of the assimilation system is evaluated based on statistical results from two series of continuous rainfall process occurred in 2016 and 2017. The specific contribution to forecast improvement of assimilation is analyzed through a rainstorm case. Finally, the advantages of assimilating satellite and radar data with POD-4 DEnVar method are discussed.
## 2 Method Applied to BIPV Project Evaluations
### Cost  
The selected BIPV projects were financially evaluated by means of levelized cost of electricity (LCOE), and return on investment (ROI) according to Equations (1) and (2):  
\[LCOE=\frac{\left(CAPEX+\sum_{t=0}^{Lifetime}\frac{\text{\small{OPEX}}\star(1- \mu)-CAPEX\star d\star\mu}{(1+\
u)^{t}}\right)}{E_{BIPV}} \tag{1}\]  
\[\text{ROI (\%)}=\text{CAPEX}/(\text{In}_{\text{self-consumption}}+\text{In}_{ \text{feed-in}}-\text{OPEX}) \tag{2}\]  
where CAPEX is the capital expenditure (in EUR), OPEX is the operational expenditure or maintenance (2.5-5% of the CAPEX, in EUR), t is time, d is the depreciation rate (%), \(\mu\) is the tax rate and \(r\) is the discount rate (%). \(\text{E}_{\text{BIPV}}\) is the amount of electricity (kWh) produced by BIPV throughout its lifespan (usually taken as 25 years). \(\text{In}_{\text{self-consumption}}\) indicates how much import and cost is avoided and \(\text{In}_{\text{feed-in}}\) indicates the economic value of the BIPV energy sold to the grid, both resulting as income (c), as yearly energy production values derivate (as described in the following section).  
In this study, the country regulations and electricity end-user prices, as well as a market analysis and the potential stakeholders of the project, were investigated. The cost of the panels and inverters were acquired from manufacturers. Hourly resolution annual energy consumption and PV production, self-consumption, and electricity fed into the grid were investigated further to establish a more precise levelized cost of electricity (LCOE) and incomes of the PV system, compare it with the incentives given, and obtain the return on investment (ROI).
## 2 Data and Methods
### LSTM Building, Training, and Evaluation  
Before the data were used to train the LSTM network, they were split into a training set and a test set. The data were split by assigning the first 80% to the training set and the last 20% to the testing set. Next, the DateTime columns were removed from the training and test sets, leaving a single column of energy production data in each set. Those columns were then normalized to between zero and one using feature scaling. Finally, the training and testing sets were split into input and target arrays. Each row of the input arrays contained a vector of length 192 and represented the input for a single training example. Each row of the target arrays contained a vector of length 6 and represented the 6 target values for a single training example. Each of the 6 target values were the next 6 energy production values directly following the corresponding input vector of 192 values. The time window of inputs and outputs then shifted by 6 values so that the first value in a given input vector is the same as the 7 th value in the previous input vector. This way, the outputs and targets do not overlap, but rather are continuous in time.  
To illustrate the building of the input and target vectors consider the following theoretical PV data column: [A1, A2, A3,..., An]. The first input vector would be [A1, A2, A3,..., A192] and the corresponding first target vector would be [A193, A194, A195, A196, A197, A198]. The second input vector would be [A7, A8,..., A198] and the second target vector would be [A199, A200, A201, A202, A203, A204].  
The Keras Python library was used to build, train and test the LSTM network. The LSTM model was built upon [PERSON]'s sequential class. A single hidden layer of 50 neurons and an output layer of 6 neurons were added. The \"Adam\" optimizer was chosen for training. Once built, the LSTM was trained and tested using the training and test sets, respectively. The model underwent 100 epochs of training with a batch size of 32, dropout rate of 0.2, and the \"Adam\" optimizer function. In addition to the test results, a 10-fold cross-validation process was used to evaluate the model.  
This entire building, training and testing process was conducted twice, once with the time-interpolated data sets and once with the linearly interpolated data sets.
# A Method for Autonomous Generation of High-Precision Time Scales for Navigation Constellations
## 5 Simulation Results and Analysis
### Experimental Analysis of Time-Frequency Steering Algorithm
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Atomic Time Type** & **ADEV@3 \(\times\) 10\({}^{2}\)** & **ADEV@1 \(\times\) 10\({}^{3}\)** & **ADEV@1 \(\times\) 10\({}^{5}\)** & **ADEV@1 \(\times\) 10\({}^{6}\)** \\ \hline G1 & \(8.71\times 10^{-14}\) & \(4.29\times 10^{-14}\) & \(1.54\times 10^{-14}\) & \(1.49\times 10^{-13}\) \\ G3 & \(8.28\times 10^{-14}\) & \(4.10\times 10^{-14}\) & \(1.04\times 10^{-13}\) & \(6.21\times 10^{-13}\) \\ G4 & \(8.29\times 10^{-14}\) & \(4.05\times 10^{-14}\) & \(2.13\times 10^{-14}\) & \(1.62\times 10^{-13}\) \\ G6 & \(8.46\times 10^{-14}\) & \(4.16\times 10^{-14}\) & \(6.71\times 10^{-14}\) & \(5.35\times 10^{-13}\) \\ G2 & \(5.49\times 10^{-13}\) & \(1.53\times 10^{-13}\) & \(1.64\times 10^{-14}\) & \(8.97\times 10^{-14}\) \\ G5 & \(9.45\times 10^{-14}\) & \(2.54\times 10^{-13}\) & \(1.13\times 10^{-14}\) & \(8.02\times 10^{-15}\) \\ G20 & \(8.89\times 10^{-13}\) & \(2.68\times 10^{-13}\) & \(9.78\times 10^{-15}\) & \(6.30\times 10^{-15}\) \\ G31 & \(5.88\times 10^{-13}\) & \(1.61\times 10^{-13}\) & \(1.09\times 10^{-14}\) & \(1.97\times 10^{-14}\) \\ TA (Steered) & \(5.73\times 10^{-14}\) & \(2.79\times 10^{-14}\) & \(8.38\times 10^{-15}\) & \(8.87\times 10^{-16}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Table of Frequency Stability Statistics. Figure 16: The frequency stability of TA(Steered) and 24 atomic clocks. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Atomic Time Type** & **1 DAY** & **5 DAY** & **10 DAY** \\ \hline G1 & \(8.35\times 10^{-9}\) & \(5.03\times 10^{-9}\) & \(5.70\times 10^{-8}\) \\ G3 & \(3.89\times 10^{-9}\) & \(6.82\times 10^{-8}\) & \(2.38\times 10^{-7}\) \\ G4 & \(1.08\times 10^{-9}\) & \(7.75\times 10^{-9}\) & \(5.26\times 10^{-8}\) \\ G6 & \(6.53\times 10^{-9}\) & \(6.09\times 10^{-8}\) & \(2.53\times 10^{-7}\) \\ G2 & \(1.77\times 10^{-9}\) & \(2.77\times 10^{-9}\) & \(3.03\times 10^{-8}\) \\ G5 & \(1.79\times 10^{-9}\) & \(3.05\times 10^{-9}\) & \(6.06\times 10^{-9}\) \\ G20 & \(2.46\times 10^{-9}\) & \(7.51\times 10^{-9}\) & \(2.58\times 10^{-8}\) \\ G31 & \(1.90\times 10^{-9}\) & \(9.05\times 10^{-9}\) & \(3.84\times 10^{-8}\) \\ TA & \(3.05\times 10^{-9}\) & \(1.57\times 10^{-9}\) & \(4.57\times 10^{-9}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Clock prediction accuracy. From Figure 17 and Table 3, it can be seen that the time scale TA(Streed) forecast has the smallest forecast residual for ten days, which can reach the level of 4.57 \(\times\) 10\({}^{-9}\) s, which is two orders of magnitude higher than that of the rubidium clock G6 with the largest forecast residual. The clock error prediction error of a single atomic clock gradually increases with time, and can reach the order of 400 ns in ten days. Therefore, it can be seen that the time scale generation algorithm proposed in this paper can effectively improve the frequency stability of the output signal and improve its predictability.
# Progress on the Studies of the Key Enzymes of Ginsnoside Biosynthesis
## 1 Introduction  
_Panax_ species--including _Panax ginseng_, _Panax quinquefolius_ and _Panax notoginseng_--have been widely used as traditional herbal medicine in Asia for thousands of years [1; 2]. As the main bioactive constituents of _Panax_ species, ginsengosides exhibit broad pharmacological activities such as anti-cancer, anti-oxidative, antiaging, anti-inflammatory, anti-apoptotic and neuroprotective activities [3; 4; 5]. The ginseng code structure is comprised of a triterpene aglycone, which can undergo a variety of glycosylations. Ginsenosides are categorized into three groups based on their aglycones: oleanane-type pentacyclic triterpeneoid saponin Ro, protopanaxadiol (PPD)-type saponins and protopanaxatriol (PPT)-type saponins. The PPD-type and PPT-type saponins are dammarane-type tetracyclic triterpenoids, which are the major types of ginsengides and the main bioactive constituents. To date, more than 180 ginsenosides have been isolated from _Panax_ species.  
The biosynthetic pathways of ginsenosides have been elucidated as shown in Figure 1[6; 7]. The whole pathway can be divided into three stages. (1) Isopentenyl diphosphate (IPP) and its isomer dimethylallyl diphosphate (DMAPP) are formed through mexolonate (MVA) pathway; (2) IPP and DMAPP are converted into 2,3-oxidosqualene; (3) Ginsenosides are produced through three reaction steps of 2,3-oxidosqualene consisting of cyclization, hydroxylation and glycosylation [8]. The biosynthetic pathways of ginsenosides comprise more than 20 steps of continuous enzymatic reactions, in which a series of key enzymes such as 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR), famresyl pyrophosphate synthase (FPS), squalene synthase (SS), squalene epoxidase(SQE), dammarenediol-II synthase (DS), \(\beta\)-amyrin synthase (AS), cytochrome P450 (CYP450) and UDP-glycostransferase (UGT) are involved.  
The contents of ginseng in plants, cell cultures and hairy roots are too low to satisfy the increasing medical demand, and large-scale production of ginseng by chemical synthesis is not practical at present due to its complexity and high cost [9; 10]. These problems seriously limit further pharmacological research and new drug development. In this review, we summarize recent progress on the studies of the key enzymes involved in ginseng biosynthesis to lay a foundation for improving the yield of ginseng in plants, cell cultures and hairy roots through regulation of the biosynthetic pathway and to provide building blocks for ginseng production by synthetic biology.
# Emulating Software-Defined Disaggregated Optical Networks in a Containerized Framework
## 2 Proposed Architecture for SDN-DON Emulation
### Advanced Automatic Deployment of Emulated SDN DON  
The main target of this work is to enhance the dynamicity and the scalability of the automatic deployment of an emulated SDN DON environment. To achieve this challenging objective, we leverage the previous work focusing on two major and significative changes: (i) automatic container management by Kubernetes and (ii) a novel scalable agent definition.
###### Abstract  
Aging may affect quality of life (QOL). This study aimed to evaluate the impact of semi-immersive VR-based exercise on the QOL of young-old and middle-old adults. This study was a randomized controlled trial involving older adults aged 65-85 years. Methods: Two age groups were each randomly assigned to experimental (EG) and control (CG) groups. The EG underwent a 75-90 min semi-immersive VR-based exercise intervention twice a week for 12 weeks, whereas the CG continued with their usual daily activities. Each participant's psychological QOL was assessed using the World Health Organization's QOL Instrument-Older Adults Module (WHOQOL-OLD). Results: Compared with their CG counterparts, the EG older adults exhibited significantly higher QOL scores in four WHOQOL-OLD dimensions (i.e., sensory abilities, autonomy, social participation/isolation, and death and dying) and had a superior overall QOL. Furthermore, we observed significant decreases in the autonomy and overall QOL dimensions in CG older adults. On comparing the young-old and middle-old adults, a significant decrease in the past, present, and future activity QOL dimension was exclusively found in CG young-old adults. Conclusions: Semi-immersive VR-based exercise is a promising digital tool for supporting the psychological QOL of older adults across different age groups. This suggests that older persons, particularly young-old adults, should be encouraged to maintain physical activity habits in their daily lives in order to improve their QOL.  
agre; older adults; virtual reality-based fitness; quality of life; sports intervention Article
# Deep Class-Guided Hashing for Multi-Label Cross-Modal Retrieval
## 3 Methodology
### Overview of DCGH Framework  
As illustrated in Figure 3, the proposed DCGH framework, in general, consists of two modules: the feature learning module and the hash learning module.  
Feature learning module. Inspired by [4; 27], we introduce feature extraction based on a Transformer encoder into cross-modal retrieval to obtain representative semantic features from both the image and text modalities. Specifically, the image Transformer  
\begin{table}
\begin{tabular}{l l} \hline
**Notation** & **Definition** \\ \hline \(\mathcal{D}\) & Training dataset \\ \(N\) & Number of samples \\ \(\mathcal{G}\) & Feature extractor \\ \(\mathbf{F}\) & Features of samples \\ \(\mathbf{B}\) & Hash code \\ \(K\) & The length of hash codes \\ \(C\) & The number of categories \\ \(\mathbf{S}\) & Similarity matrix \\ \(\mathcal{H}\) & Hash functions \\ \(\mathbf{x}_{i}\) & The i-th image sample \\ \(\mathbf{y}_{i}\) & The i-th text sample \\ \(\mathbf{I}_{i}\) & Label corresponding to the i-th sample \\ \(\mathbf{p}\) & Learnable proxies \\ \(\mathcal{I}\) & Indicator function \\ \(\mathcal{V}\) & Variance function \\ \hline \end{tabular}
\end{table}
Table 1: A summary of frequently-used notations and symbols.  
Figure 3: An overview of our proposed DCGH framework, including two parts: (1) Feature Learning: Two feature extractors with different Transformer Encoders are designed to extract the representative semantic features from image modalities and text modalities, respectively. (2) Hash Learning: Ingeniously combining proxy loss and pairwise loss while exploring relationships between points and between points and classes, preventing semantic bias, variance constraints are introduced to ensure the consistency of the relationship between data and relevant proxy points.  
encoder has the same structure as the VIT encoder [40], which is composed of stacked 12-encoder blocks. Each encoder block has the same structure, including Layer Normalization (LN), Multi-Head Self-Attention (MSA), and MLP blocks. The number of MSA is 12. For the image semantic feature \(\mathbf{F}_{x}=\mathcal{G}_{x}(\mathbf{X},\mathbf{\theta}_{x})\), \(\mathcal{G}_{x}\) represents the image semantic encoder and \(\mathbf{\theta}_{x}\) represents the parameters of the image semantic encoder. The text Transformer encoder consists of 12 encoder blocks, each with 8 MSA. For the text semantic feature \(\mathbf{F}_{y}=\mathcal{G}_{y}(\mathbf{Y},\theta_{y})\), \(\mathcal{G}_{y}\) represents the text semantic encoder, and \(\mathbf{\theta}_{y}\) is the parameter of the text semantic encoder.
## 6 Results and Discussion
### Overview of Performance Measurement  
The model's performance is evaluated based on precision, sensitivity, accuracy, and F-measure. These parameters are essential for assessing the model's effectiveness and predictive capability. The calculated values of these parameters are derived from the predicted outcomes, including true positives, true negatives, false positives, and false negatives.  
Figure 23: Final process diagram illustrating the results of the research.