esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Architecture of the Platform and Related Software and Hardware Aspects
### Core Facilities for Nodal Interactions and Ad Hoc Networking
#### 3.1.5 Overall Operation and Event Dynamics  
The description given in the previous subsections can be summarized by representing the operation of each node through an appropriate Finite State Machine (FSM). The FSM for the mobile node is depicted in Figure 6a, while the modified versions for the fixed source and destination nodes appear in Figure 6b,c, respectively. In all cases, transitions between states are indicated by arrows denoting the direction of change, and each transition is annotated with a description of the triggering condition. Moreover, transitions related to the localization process, the beacon-based signaling, and the routing protocol are marked in blue, red, and green, respectively, in conformance with the colors used in Figure 2. In the FSM for the fixed source node, transitions relating to the message generation process are marked in brown.
## 3 Results and Discussion
### Rheology
These parameters are determined from the plot double log \(\eta\)*-\(\omega\). The parameter \(K\) is known as the consistency coefficient, is the y-intercept of the log \(\eta\)*-log \(\omega\) fitted plot. The parameter \(n\) is the non-Newtonian or flow behavior index which is the slope of the log \(\eta\)*-log \(\omega\) fitted curve. For \(n<1\) the material is pseudo-plastic, when \(n=1\) the material is Newtonian, and \(n>1\) the material is dilatant [(31; 61; 62; 63)]. However, a drawback to this model is that the power-law model does not accurately represent experimental data in the low and high shear ranges, so it should not be extended to these regions [(62; 63)]. The goodness of fit for the models \(R^{2}\) was used. All the PHBV samples had \(R^{2}\geq 0.92\). The \(K\) and \(n\) parameters, and complex viscosity at the frequency of \(\omega=6.28\) rad\(\cdot\)s\({}^{-1}\) (1 Hz) for PHBV-15 to -84 are summarized in Table 8. The Power-law model was well fitted to the data, with \(K\) ranging from 3.27 to 28.07 Pa\(\cdot\)s. The \(n\) ranged from 0.645 to 0.888 showing pseudo-plastic behavior. Reported values for PHBV (5% HV) at 160 \({}^{\circ}\)C at were \(K=1.35\times 10^{6}\) Pa\(\cdot\)s and \(n=0.0823\)[(58; 61)]. The \(\eta\)* of the PHBV samples at 1 Hz ranged from 1.7 Pa\(\cdot\)s  
Figure 3: Complex viscosity (\(\eta\)*) versus shear rate (\(\omega\)) plots for PHBV samples (180 \({}^{\circ}\)C) and power-law fitted models (dotted lines). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & \(K\) **(Pa-s)** & \(n\) & \(\eta\)* (Pa-s)** & \(R^{2}\) \\ \hline PHBV-15 & 3.27 & 0.645 & 1.7 & 0.982 \\ PHBV-32 & 9.12 & 0.824 & 6.6 & 0.940 \\ PHBV-39 & 15.48 & 0.827 & 11.26 & 0.931 \\ PHBV-46 & 28.07 & 0.717 & 16.7 & 0.921 \\ PHBV-61 & 17.91 & 0.888 & 14.57 & 0.951 \\ PHBV-71 & 10.43 & 0.853 & 7.95 & 0.945 \\ PHBV-78 & 4.71 & 0.778 & 3.13 & 0.945 \\ PHBV-84 & 6.19 & 0.843 & 4.64 & 0.938 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Power–Law parameters (\(K\) and \(n\)) and complex viscosity (\(\eta\)*) at \(\omega=6.28\) rad\(\cdot\)s\({}^{-1}\) (1 Hz) determined at 180 \({}^{\circ}\)C for the purified PHBV samples. to 14.57 Pa\(\cdot\)s. The PHBV samples with higher HV content showed higher \(\eta^{*}\) at 1 Hz. It is expected an increase in the HV content leads to a reduction in \(\eta^{*}\)[64; 31; 65]. This difference can be attributed to the simultaneous effect of higher crystallinity which increases the \(\eta^{*}\) and lower \(PDI\) which decreases the \(\eta^{*}\) in polymers [60].
# Information-Aware Secure Routing in Wireless Sensor Networks
## 3 Model and Methods
### Attack Probability
#### 3.5.1 Attack Probability of the Sensor Node  
To find the optimal relay node for a given node \(i\), we have to calculate the attack probabilities of its adjacent nodes. Figures 1 and 2 show the calculation method of the attack probability. As shown in Figures 1 and 2, for an adjacent node \(j\) of \(i\), we use the former communication behaviors between them to calculate the attack probability. The attack probability \(Attack_{\langle i,j\rangle}\) of \(j\) to \(i\) consists of two parts: (1) Direct attack probability \(Attack_{d\langle i,j\rangle}\) and (2) indirect attack probability \(Attack_{id\langle i,j\rangle}\).  
Equation (1) shows the method of calculating the direct attack probability of \(j\) according to its former communication behaviors with \(i\).  
\[Attack_{d\langle i,j\rangle}=\frac{|P_{ij}-P_{j}|}{P_{ij}}, \tag{1}\]  
where \(P_{ij}\) is the number of packets sent from \(i\) to \(j\), and \(P_{j}\) is the number of packets successfully forwarded by node \(j\) in the past. The direct attack probability is defined as the packet loss ratio of node \(j\), i.e., the ratio between the number of lost packets and the number of packets sent to \(j\) from \(i\).  
The indirect attack probability, as shown in Equation (2), calculates the attack probability of \(j\) according to the former communication behaviors with the nodes that are adjacent to both \(i\) and \(j\)[11].  
\[Attack_{id\langle i,j\rangle}=\frac{\sum_{m=1}^{r}\alpha_{m}Attack_{d\langle m,j\rangle}}{\sum_{m=1}^{r}\alpha_{m}},(\alpha_{m}=1-Attack_{d\langle i,m \rangle},m\
eq i), \tag{2}\]  
where \(m\) is a node that is adjacent to both \(i\) and \(j\), \(\alpha_{m}\) is the trust value of node \(m\) from \(i\), and \(r\) is the number of nodes that are adjacent to both \(i\) and \(j\). The indirect attack probability of node \(j\) is determined by the attack probability of \(j\) to \(m\). It is a weighted average of the direct attack probabilities of \(j\) to all nodes that are adjacent to both \(i\) and \(j\). In this way, even if there are some nodes that deliberately reduce the attack probability of a malicious node by propagating an error value, the attack probability of a malicious node can be calculated by other nodes. Thus, the error has little influence on the overall attack probability, and collusion attacks can be resisted.  
Summarizing Equations (1) and (2), we have the attack probability of node \(j\), as follows:  
\[Attack_{\langle i,j\rangle}=\frac{\sum_{m=1}^{r}\alpha_{m}Attack_{d\langle m,j \rangle}}{\sum_{m=1}^{r}\alpha_{m}},(\alpha_{m}=\left\{\begin{array}{ll}1,&m= i,\\ 1-Attack_{d\langle i,m\rangle},&m\
eq i.\end{array}\right.). \tag{3}\]
## 2 Materials and Methods
### Scheme Selection  
In order to perform the regression analysis and develop an RS-derived Chl-\(a\) (Chl\({}_{\text{BS}}\)) model for a given scenario (i.e., a given satellite and data category combination), the primary (most significant) feature (i.e., the retrieval index) as well as atmospheric correction processor for the Chlags model were first identified. Random Forest (RF) scoring was primarily used in the selection of the optimal scheme for each satellite and data category. While in most cases the top-ranked RF importance score determined the best scheme (20 out of 28 scenarios), we occasionally selected the second-ranked scheme when it demonstrated significantly higher correlation coefficients (for both Pearson's \(r\) and Spearman's \(\rho\)).
# Multi-Channel Expression Recognition Network Based on Channel Weighting
## 3 Experimental Results and Analysis
### Experimental Procedure  
The experiments in this paper were carried out on NVIDIA GTX 2080 Ti CPU. Based on the pytorch deep learning framework, the optimizer adopted SGD, the momentum was set to 0.9, the initial learning rate was set to 0.1, the batch size was set to 16, and the number of training rounds was set to 300 epochs.  
In this paper, the data was first preprocessed: the Oulu_CASIA and RAF-DB data sets were detected according to key points, the face part was selected, and the background information was removed; the resolution of the dataset FER2013 itself was 48 \(\times\) 48, and no other processing was performed. The images in the input model were cropped to a size of 44 \(\times\) 44, followed by simple data augmentation. Then these were input into the multi-channel feature extraction network for feature extraction, and channel weighting and the attention module were used to help improve the recognition accuracy. Finally, the data was used for expression recognition and classification.
# Quantitative Reconstruction of Absorption Coefficients for Photoacoustic Tomography
###### Abstract  
Photoacoustic (PA) tomography (PAT) is a cutting-edge imaging modality for visualizing the internal structure and light-absorption distribution in tissue. However, reconstruction of the absorption distribution has been limited by nonuniform light fluence. This paper introduces a novel method for quantitative reconstruction of the distribution of optical absorption coefficients in tissue. In this method, we implement an iterative algorithm for recovering absorption coefficients from optical absorbed energy maps based on a 3D Monte Carlo simulation of light transport and integrated with fluence compensation to obtain the initialization parameters. In the iteration algorithm, we calculate the deviation between the detected and the computed absorbed energy distribution at each iteration. By minimizing the deviation in the absorbed energy, the recovered values converge to the true absorption distribution. The results of numerical simulation and phantom experiment theoretically and experimentally demonstrate that the proposed method performs an accurately quantitative estimate of the distribution of optical absorption coefficients. This work expects to provide accurate quantitative information for absorbers within tissues or organs, and thereby broaden the clinical applications of PAT.  
photoacoustic tomography; quantitative reconstruction; absorption distribution; fluence compensation; iteration algorithm +
Footnote †: journal: Applied Sciences
# Design of 1-Bit Digital Reconfigurable Reflective Metasurface for Beam-Scanning
## 2 Materials and Methods
### The Theory of Fuzzy Quantification  
Depending on the fuzzy quantification theory, the elements on the RRM are divided into two groups, coding \"0\" and coding \"1\". In order to get the two states of the 1-bit digital element, we need to quantify the phase compensation. The quantization standard is that if we want to get the K-bit quantization, the number of the phase states is \(2^{K}\) for \(360^{\circ}\). That is to say, \(K\) is 1 for a 1-bit digital element and the phase compensation of the unit only needs to be over \(180^{\circ}\).  
When \(\theta_{r}=0^{\circ}\), \(30^{\circ}\) and \(60^{\circ}\), the actual and the digital phase compensation of the 1-bit digital RRM computed by MATLAB 2017a are illustrated in Figures 2-4, respectively. The black blocks are coding \"0\" and the white blocks are coding \"1\". Based on the above results, the models of the RRM are determined and simulated by using HFSS 15.0.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(L\) & 25 mm & \(Lx\) & 22.5 mm \\ _Gap_ & 0.5 mm & \(Ly\) & 22.5 mm \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of the 1-bit digital RRM element.  
Figure 2: Reflection phase distribution on the RRM when \(\theta_{r}=0^{\circ}\) (**a**) Accurate phase distribution; (**b**) Digital phase distribution.
# Effective Techniques for Protecting the Privacy of Web Users
## 3 Background
### Data Collection Practice by Websites  
Almost every website can generate millions of data points, which are typically collected at two major interfaces, the front-end, and the back-end [12]. The front-end interface can be viewed in the visitors' browser as the website itself. It contains several scripts, primarily written in JavaScript so that it can collect and transfer the data to one or more servers. While the back-end interface is the server that serves the visited website and manages the data it receives from the front-end interface. It is virtually invisible to site visitors, and most are unaware of it. However, it might also be a potential source of data collection.  
In order to clarify different front-end data collection methods, the following are some popular JavaScript program codes that are currently used by most websites.  
* navigator.languages, which could disclose the nationality and the native language of the visitor.
* navigator.userAgent, which reveals whether the visitor is on a desktop or mobile device.
* screen, which records information about the visitor's screen settings such as height, width, pixel depth, resolution, default orientation, etc.
* document.location, which gives valuable details about the visited webpage including the host, protocol, port, the entire URL of the webpage, the origin, and many other details.
* document.cookie, which is one of the most discussed methods for tracking website visitors provides a list of all cookies that the website use to track its users.  
Finally, all front-end JavaScript programs will be performed automatically by the websites and the data will be collected for every movement of the visitors including every keyboard press, every mouse click and movement, every scrolling activity, every visited website, and much more [13; 14]. All this data will be compressed and stored into a JavaScript array, which will be sent to one or more server locations (might include tracking servers) where it can be analyzed and stored.  
This work intends to limit the number of tracking JavaScript programs automatically in the front-end interface in order to achieve a minimum level of sensitive data leakage.
## 2 Materials and Methods
### Mechanical Properties
#### 2.3.2 Surface Roughness and Wear behavior  
Composite discs (\(\O\) = 8 mm; 2 mm-thickness) were fabricated using a customized mold and cured (Labolight, DUO, GC, Tokyo, Japan) for 1 min per side to deliver 2330 mW/cm\({}^{2}\) radiant emittance. The surface roughness parameters were noted from the surface exposed to the simulated wear, as described above, using a surface roughness tester (Surftest SJ-310; Mitutoyo America, Aurora, IL, USA). At a sustained speed of 0.5 mm/s, a force of 4 mN, a 0.25-mm cutoff value, and 1.5-mm tracing length, each sample was traversed by the stylus tip (5 \(\upmu\)m) [28]. An average of five readings from each sample was taken. Then, the following parameters were measured:  
* _Average surface roughness (Ra)_: Ra represents the average surface roughness from the roughness profile's mean line.
* _Maximum peak height (Rq)_: denoted as the highest peak produced by the chewing simulation. A high Rq value indicates a high amount of wear.  
* _Maximum valley depth (Rv):_ stated as the deepest valley produced by the chewing simulation. A high Rv value indicates a high amount of wear.
* _Maximum high of the profile (Rt):_ Rt estimates the distance between the maximum peak height and the maximum valley depth.  
By estimating these roughness parameters before and after the simulated wear, the variation of Ra, Rq, Rv, and Rt for each sample and each group (\(\Delta\)Ra, \(\Delta\)Rq, \(\Delta\)Rv, \(\Delta\)Rt) were calculated. The delta values(\(\Delta\)) were obtained by subtracting the final values from the initial ones. The results were expressed in \(\mu\)m.
## 3 Demand-Side Management Strategy and Resource Forecasting
### Wind and Solar Forecasting
#### 3.2.2 Random Forest Algorithm  
Algorithm 1 outlines the pseudo-code procedure of the random forest algorithm.  
```
Prerequisite: Training data set \(M\), \(n\) forest trees and \(P\) traits (Temperature, wind speed, solar Irradiance.etc.)
1. Growing \(n\) forest trees.
2.For\(i\in 1...n\)
3. Select a random sample \(m(i)\) from training set \(M\).
4. Grow a tree without pruning.
5. Split-attribute: Randomly select attribute \(n\) for splitting.
6. Best split: Evaluate the gain of split-attribute for \(m(i)\) samples.
7.EndFor
8. Provided a new data set y:
9. Predict the new data by averaging the predictions of each individual \(n\) forest trees by averaging
10.End: Terminate the algorithm
```  
**Algorithm 1**Random forest pseudo-code  
For a training data set \(M\), we extract a random sample \(S_{i}\) with replacement, where \(i\) indexes the \(i\)-th bootstrap sample. Based on the decision-tree algorithm, we grow \(n\) trees for each bootstrap sample without pruning. For each tree, a restrained randomly selected data set attribute \(p\) is selected. \(p\) equals the number of split-attributes randomly selected out of all possible split attributes \(P\): \(p\in P\). The bounded selected attribute \(n\) is a subset of \(N\) and is always very small compared to \(P\); \(p<<P\). This superior property of the RF algorithm gives its fast speed trait and over-fitting elimination attribute. The outcome of the prediction is calculated by finding the averaging of the individual predictions of each tree.
## 2 Materials and Methods
### PEGylation of NPs  
A double PEGylation of aminomethyl NPs **(1)** was carried out. Briefly, aminomethyl NPs **(1)** were conditioned in N,N-Dimethylformamide (DMF). Separately, an Fmoc-4, 7, 10-trioxa-1, 13-tridecanediamine succinamic acid (Fmoc-PEG-COOH) spacer (MW = 542 g/mol) (15 eq) was dissolved in DMF, then Oxyma (15 eq) and N,N\({}^{\prime}\)-Disopropylzendboimide (DIC) (15 eq) was added and mixed for 10 min at room temperature. Then, this mixture solution was added to NPs and mixed for 2 h at 60 \({}^{\circ}\)C. Subsequently, the NPs were washed by centrifugation (13,400 rpm, 3-10 min) with DMF, methanol, and water to obtain Fmoc-PEGylated NPs **(2)** (100% yield, 0.054 mmol g\({}^{-1}\) of amino groups). Next, Fmoc deprotection was achieved by treating nanoparticles with 20% piperidine/DMF (3 \(\times\) 20 min). PEGylated NPs **(3)** were obtained by centrifugation and subsequently washed with DMF, MeOH, and deionized water. Next, a second PEGylation and deprotection step was carried out to obtain the double-PEGylated NPs **(4)** (100% yield, 0.053 mmol g\({}^{-1}\) of amino groups).
## 6 Discussion
### Implications for Methane Mitigation  
The ability to detect and quantify methane emissions in near real-time has significant implications for methane mitigation efforts. By identifying super-emitters promptly, corrective actions can be taken to reduce emissions, thereby contributing to global climate change mitigation. The case studies of the Sanyuan Nan Yao Coal Mine in Shanxi and the EOG Shale Gas Well in Eddy County illustrate how satellite observations can inform operational decisions and regulatory actions. For instance, the observed reduction in emissions from the EOG Shale Gas Well following operator intervention highlights the potential for satellite data to drive timely and effective emission reductions. This capability is crucial for achieving the methane reduction targets set forth in international climate agreements.
## 4 Simulation and Experimental Results
### Simulation Analysis
All three methods can make the system stable, namely, PD (proportional differential), SMC (sliding mode control), and RASMC (robust adaptive sliding mode control). For joint 1, the convergence time of the three methods was 3 s, 1.1 s, and 0.8 s, respectively. For joint 2, the convergence time of the three methods is 0.9 s, 2.8 s, and 6.4 s, respectively. The maximum steady-state error was reduced from 0.06 rad to 0.015 rad. It was shown that the proposed RASMC method was superior to the other two methods in convergence speed, which is consistent with the theoretical analysis. Figure 8: The estimated performance of the dead zone characteristics of joint 1 (**left**) and joint 2 (**right**). Figure 7: Input signal tracking performance of joint 1 (**left**) and joint 2 (**right**) without dead zone compensation. According to Figure 10, with the estimation and compensation of the dead zone, the gain of a robust term in RASMC was greatly reduced compared to that in SMC, and the chattering was significantly weakened. In this way, the control input was smoother, and the control performance was improved significantly. The necessity for dead zone identification and feedforward compensation was further highlighted in the experimental tests described in the following.
# The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development
## Materials and Methods
### Field Site and In Situ Crop Measurements
#### 2.1.3 Destructive Sampling and Uncertainty Analysis  
Destructive analyses were carried out to sample the winter wheat vegetation at five of the revelation trial plots, covering the range of N application from 0 to 200 kg (Figure 1). These destructive measurements, conducted on three dates (25 May, 13 June and 4 July 2018), were used to correct biases and quantify the uncertainty of LAI and LCC non-destructive measurements, which were also acquired on the same day as the destructive sampling. The measurements entailed randomly placing a 0.25 m\({}^{2}\) quadrat within the destructively-sampled plots and removing all above-ground vegetation. The leaves were separated from the remainder of the vegetation and the LAI was then estimated by passing the collected leaves through a Li-3100C leaf area meter (Li-Cor, Nebraska, USA). The non-destructive SunScan LAI estimates could then be directly compared to the destructive measurements to firstly correct for biases. This bias correction was performed through reduced major axis linear regression, which accounts for the variance in both the destructive and non-destructive measurements [36]. Specifically, the resultant linear fit equation was used to correct the bias in the non-destructive measurements. The uncertainty of the bias corrected non-destructive measurements was then calculated as the normalised root-mean-square-error (NRMSE):  
\[NRMSE=\frac{\left[\sqrt{\frac{\sum_{i=1}^{n}\left(E_{i}-O_{i}\right)^{2}}{n}} \right]}{\left[\max(O)-\min(O)\right]}, \tag{1}\]  
where \(E_{i}\) and \(O_{i}\) represent the bias corrected non-destructive and destructive values, respectively, and \(n\) is the number of non-destructive and destructive comparisons.  
Past research has demonstrated a significant relationship between LCC and leaf N content [14]. We, therefore, quantified the uncertainty of the in situ LCC measurements by estimating the leaf N content of the destructive samples. This analysis involved drying and milling the samples for each vegetation material type (i.e. stem, leaves and ears). The samples were then weighed and analysed for percentage C and N content using a Flash 2000 elemental analyser. The SPAD LCC estimates were then compared to the average leaf N percentage for each of the five destructive sample plots.
# Hyperspectral Image Inpainting Based on Robust Spectral Dictionary Learning
###### Abstract  
To address the problems of defective pixels and strips in hyperspectral images affecting subsequent processing and applications, we modeled the hyperspectral image (HSI) inpainting problem as a sparse signal reconstruction problem with incomplete observations using the theory of sparse representation, and proposed an HSI inpainting algorithm based on spectral dictionary learning. First, we studied the HSI observation model under the assumption of additive noise. We subsequently proposed a new algorithm for constructing a spectral dictionary directly from hyperspectral data by introducing an online learning optimization method and performing dictionary learning using a robust function. Afterwards, the image was sparsely encoded by applying the variable decomposition and augmented Lagrangian sparse regression method. Finally, the inpainted HSI was obtained by sparse reconstruction. The experimental results showed that compared with the existing algorithms, the algorithm proposed herein could effectively inpaint the defective HSI under different noise conditions with a shorter calculation time than those of existing methods and other dictionary learning inpainting algorithms.  
Keywords:image processing; hyperspectral image (HSI); image inpainting; spectral dictionary learning; robust function +
Footnote †: journal: Applied Physics Letters