esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology  
### A Brief Introduction to the Nishihara Creep Model [17]  
The Nishihara creep model, as a representative rheological model, is widely employed to simulate the time-dependent characteristics of various rocks due to its simple constitutive equations and physically interpretable parameters, which reflect the basic creep characteristics and can be simplified into other creep models, such as the Burgers and Bingham models. A schematic diagram of the model is shown in Figure 1. The Nishihara model consists of three parts connected in series. When the applied stress is below the yield stress [18] (\(\sigma<\sigma_{\mathrm{s}}\)), the elastic elements \(E_{0}\), \(E_{1}\) and the viscous element \(\eta_{1}\) are sequentially activated. The elastic element \(E_{0}\) captures the instantaneous stress response and the effects of loading on the rock's behavior. The viscoelastic component, formed by the parallel connection of the viscous element \(\eta_{1}\) and the elastic element \(E_{1}\), characterizes the primary creep stage and the steady-state creep stage. When the applied stress exceeds the yield stress (\(\sigma>\sigma_{\mathrm{s}}\)), the viscous element \(\eta_{2}\) and the plastic element \(\sigma_{\mathrm{p}}\) are activated, simulating the accelerated creep stage where the creep rate increases progressively with time.  
The theoretical expression of the Nishihara model can be expressed as  
\[\begin{split}\varepsilon(t)&=\frac{\sigma}{E_{0}}+ \frac{\sigma}{E_{1}}\Big{(}1-e^{-\frac{E_{1}}{\eta_{1}}t}\Big{)}(\sigma<\sigma _{\mathrm{s}})\\ \varepsilon(t)&=\frac{\sigma}{E_{0}}+\frac{\sigma }{E_{1}}\Big{(}1-e^{-\frac{E_{1}}{\eta_{1}}t}\Big{)}+\frac{\sigma-\sigma_{ \mathrm{p}}}{\eta_{2}}t\;(\sigma\geq\sigma_{\mathrm{s}})\end{split} \tag{1}\]  
where \(E_{0}\) represents the elastic modulus in instantaneous loading, GPa; \(E_{1}\) represents the visco-elastic modulus, GPa; \(E_{2}\) represents the visco-plastic modulus, GPa; \(\eta_{1}\) represents the visco-elastic viscosity coefficient, GPa-h; \(\eta_{2}\) represents the visco-plastic viscosity coefficient, GPa-h; and \(\sigma\) represent the current stress state, MPa.
## 2 Materials and Methods
### Field Spectral Measurements
#### 2.2.2 SIF and Vegetation Index Retrieval from Ground Data  
The three Fraunhofer line discrimination (3 FLD) algorithm [48] was used to retrieve far-red SIF for the O\({}_{2}\)-A band near 760 nm in this study. [PERSON] et al. [49] suggests that the SIF can be accurately extracted in the O\({}_{2}\)-A band near 760 nm rather than the O\({}_{2}\)-B band near 687 nm when the spectral resolution is 3.3 nm. Compared with the FLD [50] and the improved FLD (iFLD) algorithm [51], 3 FLD can get more accurate SIF values [52] and is more robust than the iFLD, which appears to be highly sensitive to noise [53; 54]. In this approach, three spectral channels are required for each absorption line, and the changes of fluorescence and reflectance in and out of the absorption line within a very narrow band are hypothesized to be linear. The radiance at a single outer channel is expressed as the weighted mean calculated from the two sides of the absorption valley (at the left and right wavelengths close to the absorption band). The weight factors (\(w\)) of the two outer channels are as follows [48]:  
\[w_{\text{left}}=\frac{\lambda_{\text{right}}-\lambda_{\text{in}}}{\lambda_{ \text{right}}-\lambda_{\text{left}}}\text{ and }w_{\text{right}}=\frac{\lambda_{\text{in}}-\lambda_{\text{left}}}{\lambda_{ \text{right}}-\lambda_{\text{left}}}, \tag{1}\]where \(\lambda\) is the wavelength, and the subscripts, left and right, represent the shorter and longer wavebands close to the Fraunhofer lines, respectively. The subscript, in, indicates the wavelength of the absorption band. The SIF can then be determined by [48]:  
\[SIF=\frac{L_{\text{in}}\times\left(I_{\text{left}}\times w_{\text{left}}+I_{ \text{right}}\times w_{\text{right}}\right)-I_{\text{in}}\times\left(L_{\text {left}}\times w_{\text{left}}+L_{\text{right}}\times w_{\text{right}}\right)}{ \left(I_{\text{left}}\times w_{\text{left}}+I_{\text{right}}\times w_{\text {right}}\right)-I_{\text{in}}}, \tag{2}\]  
where \(I\) is the solar irradiance, and \(L\) is the canopy radiance. The 760 nm was used as the absorption band and then the left and right wavelengths were looped through to determine the optimal shorter and longer channels. Finally, 757 and 772 nm were taken as the left and right wavelengths, respectively, which are close to those in [55].  
The normalized difference vegetation index (_NDVI_) and Vogelmann red edge index (_VOG_) were used in this study to compare SIF values over the growing stages of summer maize in 2015. The _NDVI_ is retrieved from the reflectance difference of red and near-infrared bands and is calculated as [56]:  
\[NDVI=\frac{R_{850}-R_{680}}{R_{850}+R_{680}}. \tag{3}\]  
The _VOG_ can be derived based on the simple reflectance ratio of the red edge portion (680-750 nm) [57]. The equation is:  
\[VOG=\frac{R_{742}}{R_{722}}, \tag{4}\]  
where \(R_{\text{xxx}}\) in Equations (3) and (4) denotes the reflectance at wavelength xxx in nm.
## 2 Results
### DFO Release Profile from the PLGA-PEG-PLGA NPs  
The DFO release pattern over the course of 30 days is depicted in Figure 3, showing that a total of 52.3% encapsulated DFO was released from the NPs over the first 5 days; while on day 30, it could still be detected and the cumulative release amount was 89.7%. Considering DFO half-life is very short, it is conceivable that DFO was almost completely incorporated in the NPs during preparation.  
The Effect of DFO-Loaded PLGA-PEG-PLGA NPs on the Viability of Human Bone Marrow Stromal Cells (hBMSCs)  
To determine whether the blank or DFO-loaded NPs had any cytotoxic effect on osteogenic-involved cells, Cell Counting Kit-8 (CCK-8) and a Live/Dead cell staining assay were performed. As shown in Figure 4, hBMSCs were viable in the blank or DFO-loaded NPs groups, displaying low levels of cell death, which indicated that both the NPs and the released DFO did not have a significant effect on the viability of hBMSCs for the experiment duration of 72 h.  
Figure 3: The in vitro DFO release profile of DFO-loaded NPs. Bars represent mean \(\pm\) SD (\(n=3\)).  
Figure 2: Field emission scanning electron microscope (FESEM) image (**A**) and dynamic light scattering (DLS) spectra (**B**) of DFO-loaded NPs.
# Multi-Defect Detection Network for High-Voltage Insulators Based on Adaptive Multi-Attention Fusion
## 4 Experiment Preparation  
### Experimental Environment and Dataset  
The experimental hardware setup comprised an Intel(R) Core(TM) i3-9100F CPU at 3.60 GHz, 16-GB RAM, an RTX 2080 Ti graphics card with 11 GB of video memory, and GPU acceleration through CUDA 11.0 and cuDNN 8.6.0. The software environment consisted of the Windows 10 Professional operating system, PyCharm 20.1.1, and the PyTorch DL 1.7.1 framework.  
Due to the limited defect dataset of overhead transmission lines or power fields and considering the ability to comprehensively evaluate the condition of insulators, we conducted this study using the insulator dataset provided by a company affiliated with the State Grid Corporation of China that was captured by drones. During image acquisition, the mutual positioning of the investigated object and the camera was set to 0\({}^{\circ}\), \(\pm\)90\({}^{\circ}\), and \(\pm\)45\({}^{\circ}\) to ensure that multi-angle information was fully considered. To enhance the model's generalization ability and to mitigate overfitting, various data augmentation techniques, such as angle rotation, saturation adjustment, image flipping, and panning, were applied to increase the sample size. The augmented dataset comprised 1593 images. These images were labeled using the Labellmg tool, and the labeling process is illustrated in Figure 12. The labeling categories were divided into three groups: the insulator, breakage defect (posun), and flashover defect (wushan). Sample images representing these defects are shown in Figure 13. The broken defects could be chipping, breaking, etc., and the degree of breakage includes single-piece and multi-piece umbrella skirt breakage. According to the different variations of the lighting conditions, the form of dirty flash defects includes single-piece and multi-piece umbrella skirt flashing, and there is diversity in the area region of the flashing. The acquired images have a consistent spatial resolution of 3216 \(\times\) 2136 pixels. In order to speed up the model training efficiency, the acquired images were resized to a resolution of 640 \(\times\) 640 pixels before training. The dataset was split into a training set and a validation set in a 9:1 ratio, with 1416 images in the training set and 177 images in the validation set. The sample distribution is presented in Table 2.  
Figure 11: Comparison of the convergence of the two loss functions.
## 6 Discussion  
Even if CNES is a member of the IGS real-time working group, effort has been put recently into the computation of the so-called post-processed code and phase biases using the rapid precise orbit and clock products. It can be seen throughout this study that using these products brings a real benefit from a user's point of view in terms of positioning accuracy. This has been the case in a static scenario with a geodetic station equipped with a good GNSS receiver as well as in a dynamic scenario with a car moving in narrow streets of the city center of Toulouse, France. In addition to this, the use of the POST products shortens the convergence intrinsic to PPP with ambiguity resolution. The improvements brought by these products cannot be obtained for real-time applications. An in-between solution has thus been proposed with the \"Snapshot\" concept. This positioning technique has been made available thanks to the third and fourth frequencies of Galileo and Beidou. The positioning performances cannot be as good as the PPP-AR mode, but they are still better than the float mode in static conditions and without convergence time. The Snapshot solution could be  
Figure 20: Online positioning service result examples.  
improved using meteorological information, such as an external ionosphere provided by an SBAS system.  
The fact that independent evaluations of the products computed at CNES can be found in the scientific literature for academic partners or have been made by private companies strengthen the ability for users to improve their positioning performances adopting CNES products or using the provided online positioning service.
## 4 Recurrence-Analysis-Based Detector
### Analytical Expression of the Probability of Detection  
Taking into account the presence of the communication signal \(s(n)\) and keeping the same approach as under \(H_{0}\), we demonstrate that  
\[\overline{\textbf{u}}_{\Delta/H_{1}}\mapsto\mathcal{N}\left(\mu_{\Delta/H_{1 }},\sigma_{\Delta/H_{1}}^{2}\right) \tag{41}\]  
\[\overline{\textbf{u}}_{d/H_{1}}\mapsto\mathcal{N}\left(\mu_{d/H_{1}},\sigma_ {d/H_{1}}^{2}\right) \tag{42}\]  
where  
\[\mu_{\Delta/H_{1}}=2\sqrt{\sigma_{s}^{2}+\sigma_{b}^{2}}\;\frac{\Gamma\left( \frac{m+1}{2}\right)}{\Gamma\left(\frac{m}{2}\right)} \tag{43}\]  
\[\sigma_{\Delta/H_{1}}^{2}=\frac{2(\sigma_{s}^{2}+\sigma_{b}^{2})}{K_{\Delta}} \left[m-2\left(\frac{\Gamma\left(\frac{m+1}{2}\right)}{\Gamma\left(\frac{m}{2} \right)}\right)^{2}\right] \tag{44}\]  
\[\mu_{d/H_{1}}=2\sigma_{b}\frac{\Gamma\left(\frac{m+1}{2}\right)}{\Gamma\left( \frac{m}{2}\right)} \tag{45}\]  
\[\sigma_{d/H_{1}}^{2}=\frac{2\sigma_{b}^{2}}{K_{d}}\left[m-2\left(\frac{\Gamma \left(\frac{m+1}{2}\right)}{\Gamma\left(\frac{m}{2}\right)}\right)^{2}\right] \tag{46}\]We deduce the PDF of \(T\) under the hypothesis \(H_{1}\) as follows:  
\[f_{T/H_{1}}(t)=\frac{1}{\sigma_{1}\sqrt{2\pi}}\left[e^{-\left(\frac{t-p_{1}}{ \sigma_{1}\sqrt{2}}\right)^{2}}+e^{-\left(\frac{t+p_{1}}{\sigma_{1}\sqrt{2}} \right)^{2}}\right] \tag{47}\]  
with \(t\geq 0\). \(\mu_{1}\) and \(\sigma_{1}^{2}\) are given by  
\[\mu_{1}=\mu_{\Delta/H_{1}}-\mu_{d/H_{1}}, \tag{48}\]  
\[\sigma_{1}^{2}=\sigma_{\Delta/H_{1}}^{2}+\sigma_{d/H_{1}}^{2}-2 Cov\left( \overline{\mathbf{u}}_{\Delta/H_{1}},\overline{\mathbf{u}}_{d/H_{1}}\right) \tag{49}\]  
Consequently, the Probability of Detection \(P_{d}\) becomes  
\[\begin{split} P_{d}&=\int_{\lambda}^{+\infty}f_{T/H _{1}}(t)dt\\ &=1-\frac{1}{2}\bigg{[}erfc\bigg{(}\frac{-\lambda-\mu_{1}}{\sigma _{1}\sqrt{2}}\bigg{)}-erfc\bigg{(}\frac{\lambda-\mu_{1}}{\sigma_{1}\sqrt{2}} \bigg{)}\bigg{]}\end{split} \tag{50}\]
## 2 Optimization Design Process and Modeling Method
### Pump Model
#### 2.2.1 Pump Geometry and Losses Analysis  
The structure of automotive electronic pumps used in this study is shown in Figure 2a. The coolant liquid is sucked into the impeller, obtains pressure head after rotating with the impeller, and enters into the volute passage under centrifugal force. In this process, electrical energy is converted into the pressure energy of the fluid with a lot of losses, such as mechanical loss, leakage loss, disc friction losses, and hydraulic loss. Mechanical loss refers to the energy loss due to the mechanical friction in the pump, including the disk friction loss, bearing loss, and shaft seal loss. Leakage loss refers to the clearance leakage in the pump including the leakage loss at the front ring, rear ring, and balance holes. Hydraulic loss refers to the energy loss through the flow passage, including the inlet section, outlet section, pump cavity, impeller, and volute. The flow inside the pump passage is full 3D unsteady turbulence flow, including flow separation, backflow, circulation, instability flow, jet-wake flow, vortex, and even cavitation. As the most important part of energy transfer, the geometry of the impeller has a significant effect on hydraulic losses, pumping ability, and inherent reliability. Impeller-related losses dominate the pump efficiency. Therefore, the optimization of this study is mainly aimed at the impeller.  
The pump performance characteristic curves at different rotational speeds are shown in Figure 2b. The pump would work at point \(A\) in actual operation at design rotational speed \(n_{\text{d}}\) because of system resistance. Similarly, the pump would work at point \(A_{1}\) in actual operation at rotational speed \(n_{1}\) based on similarity law if Reynolds number changes within a certain range. The related losses of the pump under different rotational speeds also follows the similar law. Thus, it is equivalent to evaluate the pump performance only at the design rotational speed. The hydraulic parameters of the selected model pump are as follows: design rotational speed \(n_{\text{d}}\) is 5400 r/min, design head \(H_{\text{d}}\) is 7.5 m, design flow rate \(Q_{\text{d}}\) is 1.4 m\({}^{3}\)/h. Table 1 provides a list of the main geometric parameters of the pump. Blade angle is relative to tangential direction. Considering the inlet pressure of the pump in the real system, this study does not involve cavitation problems.  
Figure 2: Automobile electronic pump model: **(a)** Structure diagram; **(b)** Performance characteristic curves.
# A Versatile 100 Hz Laser System with Few-Cycle and TeraWatt Pulses for Applications
## 4 Few-Cycle Branch  
The few-cycle branch of the system is the unit that shortens the several ps, mJ level pulses below three optical cycles (Figure 1). Part of the stretched pulses from the front end is compressed in BK7 bulk (Figure 3), cut in a'snooker' shape [17], so that the total length in the dispersive media can be tripled compared to a single pass. The following 24 reflections on 4 large aperture chirped mirrors (CM1512 by Ultrafast Innovations, Munchen, Germany) compressed the pulses to 28.1 fs (Figure 4). For spectral broadening, these pulses with 450 \(\upmu\)J energy were steered through a non-stretched glass hollow core fiber (HCF) with a length and core diameter of 1 m and 250 \(\upmu\)m, respectively [18]. The optimal broadening was found under 1.5 bar of static Ar gas, along with shaping the spectrum and fine-tuning the higher order spectral phase of the pulses with the Dazzler. The transmission of the HCF was better than 65%. After collimation, the pulses were compressed by three pairs of double-angle chirped mirrors (PC70 by Ultrafast Innovations, Munchen, Germany), providing a full-width-half-maximum (FWHM) pulse duration of 7.1 fs (Figure 4) and a pulse energy of 250 \(\upmu\)J. The pulse duration was measured in all cases by a dispersion-scan device (D-cycle R XR, by Sphere Ultrafast Photonics, Boiro, Spain) [19]. The transform-limited pulse duration of the compressed FE pulses was 27.9 fs, which indicates an optimal choice of the amount of bulk and type of chirped mirrors. The transform-limited pulse duration after the HCF was 6.6 fs. The noticeable difference between the measured and achievable shortest pulse duration determined by the spectrum can be attributed to the \"sub-7 fs\" measurement range of the d-cycle device, as well as to the uncompensated higher order dispersion. It is worth mentioning that the peak power was still increased upon pulse compression from 16 GW to 35 GW. The beam profile was nearly ideal Gaussian (see insert of Figure 3) with aberration-free caustic having a size of 3.36 mm \(\times\) 2.95 mm (FWHM). The pointing stability of the compressed beam was \(\pm\)34 \(\upmu\)rad.  
Figure 3: Layout of the few-cycle branch. Insert: the beam profile of the compressed few-cycle pulses.
# Automotive Cybersecurity Application Based on CARDIAN
## 2 Background on Cybersecurity in Automotives: Methods and Related Works
### Network Segmentation  
The network segmentation mechanism [7] is the most direct protection system. Using this technique, it is possible to establish a priori which components of the system must participate in the communication of a given subnetwork; any attack is thus limited to a particular area. The connection between the subnetworks is managed through particular ECUs that act as gateways. This protection model is currently used on commercial vehicles as it is very simple to implement. This system is not sufficient to guarantee the protection of the vehicle when the compromised node actually turns out to be the gateway. Furthermore, this type of network configuration makes the maintenance of the network itself more expensive and difficult.
# An Efficient Approach to Manage Natural Noises in Recommender Systems
## 3 The Proposed Approach of Managing Natural Noises  
In recommender systems, the prediction results usually depend on the historical ratings provided by users. It is generally assumed that the ratings can accurately reveal users' opinions on items. However, in fact, the ratings are often influenced by some accidental factors, such as the mood, environment, and evaluations provided by friends. This means that the ratings affected by accidental factors cannot represent the real intention of users and probably have some negative effects on recommendations. These ratings are regarded as natural noise. To deal with natural noises, we first need to detect whether they exist in a rating and then correct them.
## 1 Introduction and State of the Research
[15], employing pristine DFT calculations to examine in depth the most likely sites of water molecules and acids, along with other structural details. They also studied the process of dealumination in acid-hydrated zeolites by combining forcefield MC and an ab initio optimization strategy using hybrid DFT [16]. Adding to this, [PERSON] et al. [17] examined the adsorption of arsenic acid (AsO(OH)\({}_{3}\)) and arsenous acid (As(OH)\({}_{3}\)) on the Al(m\({}_{\text{I}}\))-modified natural clinoptilolite using DFT under both hydrated and dehydrated conditions. In addition, [PERSON] et al. [18] examined the adsorption of phosphate (H\({}_{2}\)PO\({}_{4}\)\({}^{-}\) and HPO\({}_{4}\)\({}^{2-}\)) and phosphoric acid using QM/MM in ONIOM and a periodic model with DFT. Finally, [PERSON] et al. [19] utilized simulated annealing and dispersion-corrected DFT to predict adsorption, separation, ion exchange, and catalytic properties in different natural zeolites including clinoptilolite. [PERSON] et al. [20] evaluated the potential of clinoptilolites for CO\({}_{2}\) capture by investigating the adsorption capacity using a predictive molecular simulation (MS) model, which was validated through their experimental data. Most state-of-the-art studies in the field relied on force-field simulations combined with Monte Carlo (MC) methods, while a limited number employed electronic structure codes using either the plane wave approach or Gaussian orbitals. In contrast, the current study integrates the latter two approaches, utilizing the open-source electronic structure software package CP2K, version 7.1. The objective of this study is to make exploring such complicated systems computationally feasible, establishing a comprehensive guideline to complement the existing literature on the distribution of Al and cations. Understanding Al and cation distributions will aid in creating suitable clinoptilolite bulk models from which surface models will be generated. Examining various clinoptilolite models can aid in predicting how a material might be 'tailored' for the adsorption of specific species, or in other words, assist in the development of clinoptilolite-based adsorbents for PPCP removal and/or drug delivery applications.
## 1 Introduction
Influencing stability, reactivity, and solubility in water are all parameters that need to be considered before a sample's preparation for analysis. Despite the variability in the PPCPs' chemical structures, for most laboratories specialized in the analysis of these compounds, the use of multiresidue methods is very attractive because not only attains a reduction of cost and time, but also offers global patterns of contamination with only one analysis. Moreover, these methods easily facilitate an eco-friendly analysis with decreases in waste. In these multiresidue methods, the sample preparation becomes the heart of analysis that influences any other procedural steps from sample collection and storage to the specific instruments selected for final quantification (high performance liquid chromatography-mass spectrometry (HPLC-MS), gas chromatography-mass spectrometry (GC-MS), etc.). The choice of a liquid or gas chromatography (LC or GC), to analyze the final extract, is guided by the analytes' polarity. Generally, compounds with polar characteristics are more suitable for LC, and those with non-polar properties are more amenable to GC; most PPCPs are polar or moderately polar [24]. Surprisingly, some contemporary review articles either cover broader aspects of environmental analysis [25; 26; 27], or focus on a particular type of extraction process (e.g., microextraction, use of nanomaterials, magnetic, ultrasonic, etc.) [28; 29; 30] but do not cover the entire sample preparation. Therefore, the goal of this review was to critically analyze the status of sample preparation to determine PPCPs in an all-water matrix. Each step of sample preparation, including all analytical variants (conventional and innovative methods) used to detect these contaminants were considered. Furthermore, each sample preparation method was critically analyzed, highlighting advantages and disadvantages. This review performs an examination of all studies published from January 2018 to May 2020. The search was conducted on the database Scopus (Elsevier), with two different inputs: \"extraction pharmaceutical environmental\"/\"extraction personal care products environmental\" and \"extraction pharmaceutical water\"/\"extraction personal care products water\". More than one thousand one hundred works have been viewed. The selection criteria to choose the studies were based on (i) the presence at least of 10 PPCPs to include attractive multiresidue methods; and (ii) water compartmentation, in all variants (wastewater, rivers, irrigation channels, lakes, drinking water, seas, urban storms, swimming pools and thermal water), was chosen. In addition, some reviews outside the interval of time were also chosen.