esa-sceva/satcom-chunk-collection huggingface dataset:
# Aluminum-Based Plasmonic Photodetector for Sensing Applications
## 3 Simulation of the Proposed Structure and Parameter Optimization
### Theoretical Framework for Parameter Optimization Problem
Figure 5: Angular dependences of light absorption in Si substrate for different values of the intermediate layer thickness for the aluminum-based structures. Other parameters: grating period \(a=0.63\) μm, etching depth \(D=50\) nm, and filling factor \(f=0.5\). Curves 1–7 correspond to thicknesses \(d\) of 7, 9, 11, 13, 15, 17, and 19 nm, respectively. The next parameter of the grating that needs to be optimized is the filling factor \(w_{2}/a\). To choose the optimal value of the filling factor, similar calculations of absorption in the Si substrate were carried out for the gratings with different values of \(w_{2}/a\). Figure 7a shows the dependencies of the max \(A(\theta)\) (Curve 1) and FWHM (Curve 2) on the filling factor, which have a non-monotonic character. However, a clear maximum was observed on the dependence of the goal function \(\max A(\theta)/\text{FWHM}\) on the filling factor (Figure 7b), which allowed for determining the optimal value of the filling factor for this structure: \(w_{2}/a=0.36\). Thus, for the considered aluminum grating with an etching depth of 50 nm and a grating period of 0.63 \(\upmu\)m, the optimal thickness of the intermediate layer is 14 nm, and the optimal filling factor is 0.36. Figure 6: (**a**) Dependence of the maximum light absorption in Si (max \(A(\theta)\)) (Curve 1), and the full width at half-maximum (FWHM) (Curve 2) on the thickness of the intermediate metallic layer \(d\). (**b**) Dependence of the goal function \(\max A(\theta)/FWHM\) on \(d\). Figure 7: (**a**) Dependences of the max \(A(\theta)\) (Curve 1) and the FWHM (Curve 2) on the grating filling factor. (**b**) Dependence of the goal function \(\max A(\theta)/\text{FWHM}\) on the grating filling factor. The calculations were carried out at the optimal depth of the intermediate Al layer (14 nm).
## 3 Results and Discussion
### Screening of Key Differential VFCs among the Four Types of WRTs
The compounds in the last class (V) showed significantly higher concentrations in the DHP samples than in the other samples, including methyl 2-fluoroate, \(p\)-methylacetophenone, methyl salicate, 1,1,6-trimethyl-1,2-dihydronaphthalene, methyl benzoate, 2-heptanone, mestilbene, 2,5-dimethylpyrazine, (\(E\),\(E\))-3,5-octadien-2-one, \(N\)-ethylpyrrole, 1-ethyl-1H-pyrrole-2-carboxaldehyde, 5-methyl furural, furural, 1-furfurfurylyrrole, benzaldehyde, 2-methylpyrazine, and 2-acetylfuran. The compounds were mainly composed of nitrogen, oxygen heterocyclic compounds and heterocyclic aldehydes, most of which presented a similar nutty and roasted odor. The above heterocyclic compounds have been reported to be correlated with applied heating temperature and time, and they have also been detected in other types of oolong teas [16], and some green [29] and black [30] teas. Only two compounds, naphthalene and dihydroactinidiolide, were included in class VI. Both of the above compounds showed significantly lower contents in the RG samples than in the other three types of samples; the former compound was more abundant in SX and JF, and the concentrations of the other compound were significantly higher in most of the JF and several of the DHP and SX samples. In general, it can be summarized that the compounds in classes I and V (DHP), classes I, II, III, and VI (JF), and classes III and IV (RG) were significantly abundant in WRTs, with the corresponding cultivars in parentheses. However, the distributions of the key differential VFCs with higher concentration levels among the SX samples were irregular. Instead, it was consistent that VFCs in classes I-IV were scarce in almost all the SX samples. The clustering analysis of key differential VFCs among the four types of WRTs showed that DHP and SX could be distinguished from JF and RG. This result was consistent with the PCA and PLS-DA trends. Moreover, the above key differential VFCs made different contributions to the tea aroma according to their own flavor characteristics. Some of the key differential VFCs, such as naphthalene (0.44 \(\upmu\)g/kg of odor threshold), linaloid (6 \(\upmu\)g/kg of odor threshold), and methyl salicate (40 \(\upmu\)g/kg of odor threshold), probably had a significant effect on the tea aroma due to their low odor thresholds and high contents [19; 22]. Figure 3: Heatmap based on the normalized peak area of the key differential volatile fragrant compounds (VFCs) among Wuyi rock teas (WRTs) with four different cultivars.
# Research on Stagger Coupling Mode of Pulse Duration and Tool Vibration in Electrochemical Machining
## 2 Analysis of the Simulation of a Dynamic PVECM Process  
### Traditional Coupling Mode  
Figure 1 depicts the traditional symmetry coupling mode of pulse duration and tool vibration. One period of the pulse goes from point B to point D. The amplitude is \(A\). The minimum gap \(\Delta\) between the tool and workpiece is at point C. The durations of stages B-C and C-D are the same, but their statuses clearly differ. In general, the machining process contains two stages: a gap-narrowing stage (B-C) and a gap-expanding stage (C-D). In the narrowing stage, the inter-electrode gap narrows quickly with the accumulation of electrolysis products and bubbles. In the expanding stage, the gap pressure decreases, and the accumulation of products slows down. Different machining conditions are expected if the pulse is completely in the gap-narrowing or gap-expanding stage. The dynamics of the PVECM process were simulated in order to analyse the characteristics of the electrolyte flow and of the electrolysis products in the periodic inter-electrode gap.  
Figure 1: Traditional symmetry coupling mode of pulse duration and tool vibration.
## 2 Materials and Methods
### Segmentation of the Myocardium in Tagged CMR Images
#### 2.2.5 Optimization  
A gradient scaler was used for training in parallel on two TITAN RTX 24 GB GPUs while a scheduler reduces the learning rate (initially set to 0.01) with logarithmic steps when validation loss hits a plateau with a patience value of ten epochs. The equally weighted sum of the Dice coefficient loss and pixel-wise cross-entropy loss was used as a loss function to optimize the network's weights with Adam stochastic optimization [19].  
Additionally, a shape distance loss was implemented to give more importance to the ground segmentation masks' boundaries. This loss is based on the pixel-wise difference between the sigmoid of the log odds prediction values and a boundary distance map of the ground-truth label.  
Let us denote \(g\) and \(p\) as the multi-channel binary ground-truth and prediction masks (each having \(C\) channels) and \(\phi\) as a signed normalized Euclidean distance map operator. Shape information for a binary mask \(m\) is computed as follows in Equation (2), which is in turn used to compute a boundary-aware loss \(\mathcal{L}_{S}(g,p)\) defined in Equation (3) and visualized in Figure 4. The parameter \(k\) in Equation (1) adjusts the softness of the boundary. In this work, it is set to 0.2 based on empirical tuning.  
\[H(x)=\frac{1}{1+e^{-x/k}} \tag{1}\]  
\[SI(m)=\left\{\begin{array}{ll}H(1-\phi(m))&\text{if }\phi(m)\geq 0\\ 0&\text{otherwise}\end{array}\right. \tag{2}\]  
\[\mathcal{L}_{S}(g,p)=\frac{1}{C}\sum_{c=1}^{C}\frac{1}{\sum_{Sci}}\int_{i\in \Omega}|p_{ci}-SI(g_{c})_{i}|\;di \tag{3}\]  
A grid search is performed to find the optimal weight contribution \(\gamma\) of this shape-aware loss on a ResNetVAE model trained from scratch on the manually annotated dataset of tagged images. The mathematical formulation of the loss function is presented in Equation (4).  
\[\mathcal{L}(g,p)=\underbrace{\left(1-\frac{1}{C}\sum_{c=1}^{C} \frac{2g_{c}p_{c}}{g_{c}+p_{c}}\right)}_{\mathcal{L}_{\text{D}}(g,p)}- \underbrace{\left(\frac{1}{C}\sum_{c=1}^{C}\int_{i\in\Omega}g_{ci}\log(p_{ci} )\;di\right)}_{\mathcal{L}_{CE}(g,p)}\\ +\gamma\underbrace{\left(\frac{1}{C}\sum_{c=1}^{C}\frac{1}{\sum_{ i}g_{ci}}\int_{i\in\Omega}|p_{ci}-SI(g_{c})_{i}|\;di\right)}_{\mathcal{L}_{S}(g,p)} \tag{4}\]
## 4 Experimental Validation of the Method
### Trajectories Chosen for Numerical and Experimental Comparison  
In order to evaluate and validate the proposed method, two benchmark examples representing two qualitatively different motions, as well as a pick-and-pass movement for a collaborative activity have been chosen.
# Standing Wave Field Distribution in Graded-Index Antireflection Coatings
## 1 Introduction  
Laser systems for inertial-fusion research are required to possess the maximum possible power and energy. However, the output energy of laser system is usually limited by the damage of optical coatings and elements [1; 2; 3; 4; 5; 6]. Antireflection (AR) coating is widely used in laser system, which can effectively reduce the reflection of optical elements [7; 8; 9; 10; 11; 12; 13; 14; 15]. There are two kinds of AR coatings. One is the multi-layers AR coating [8] and another is Graded-index AR coating [7; 9; 16]. It was found that, comparing to multi-layers AR coating, graded-index AR has some obvious advantages: (i) Graded-index AR coating can reduce significantly the reflection over a wide range of wavelengths [17; 18; 1] and angles of incidence [9], while the multi-layers AR coating is usually workedat a specific wavelength; (ii) The laser damage threshold of Graded-index AR coating is higher than multi-layers AR coating [1]. Thus, the Graded-index AR coating attracts a lot of attention for high energy laser applications. When an incident laser beam enters into optical coating, the reflected part of this laser beam will interfere with the incident laser beam and form the standing wave. It is believed that standing wave distribution plays an important role for laser induced damage of AR coating [2] and a good design of standing wave distribution can obviously increase the damage threshold of it [19; 20; 21; 22]. Therefore, the study of Standing wave field distribution in AR coating is important for the high energy laser systems, which can be used for inertial-fusion research and other studies of intense laser physics [22; 23]. The standing wave field distribution of multi-layers AR coatings are known well. It was found that, for multi-layers AR coatings, the maximum intensity of Standing wave field is usually happened in the interfaces between layers. Because the absorption coefficients of interfaces are several orders of magnitude larger than other parts of film layer [24; 25], this standing wave field distribution is bad for the resistance of laser damage. The index in graded-index AR coating is changed continuously. Thus, graded-index AR coating can avoid this problem. There are three classic graded-index AR coatings that were found to be good for reducing the reflection over a wide range of wavelengths. One is the linear distribution of index [18], the second one is exponential distribution [26; 27] and the third one is polynomial distribution [26]. These graded-index AR coatings are potentially used in high laser system. So far, however, there is no study about the standing wave field distribution in these graded-index AR coatings. In this work, these graded-index AR coatings are designed at working wavelengths from 200 nm to 1200 nm, which are the working wavelengths for inertial-fusion research. The transmittance of the coatings are calculated by Fresnel coefficients matrix method. The standing wave field distributions in these three graded-index AR coatings are obtained by the numerical calculation of electromagnetic wave equation [24].
## 4 Implementation  
### The Pixel Energy Per Bit to the Spectral Noise Density Ratio Computation and Noise Modeling  
The pixel noise in the CCD/CMOS cameras is approximated [22] using Equation (1) as follows:  
\[n\,\sim N(0,\delta(s)^{2}) \tag{1}\]  
where \(s\) is the value of a pixel, \(\delta^{2}(s)=s\cdot a\cdot a+\beta\), \(a\) is the \"1\" and \"0\" amplitude, and \(\alpha\), \(\beta\) specify the fitting factors obtained in the experiments. Model-fitting coefficients were employed in our implementations and could be predicted experimentally [22]. Equation (2) [22] is used to calculate the pixel \(E_{b}/N_{0}\) on the receiver side, assuming that each symbol comprises one bit as follows:  
\[\textit{Pixel}\frac{E_{b}}{N_{0}}=\frac{\mathbb{E}\big{[}s^{2}\big{]}}{ \mathbb{E}[n^{2}]}\approx\frac{a^{2}\cdot\Delta}{a\cdot\alpha\cdot\Delta+\beta} \tag{2}\]  
where \(E_{b}\) is the energy for each bit, \(N_{0}\) is the noise density, \(s\) is the intensity pixel values, \(\Delta=T_{\text{exposure}}/T_{\text{bit}}\) refers to the camera exposure duration divided by the bit interval, and \(\alpha\), \(\beta\) are the fit parameters. The theoretical relationship between pixel \(E_{b}/N_{0}\) and image amplitude shown in Figure 4.
# Service-Aware Clustering: An Energy-Efficient Model for the Internet-of-Things
### Contributions and Outline
#### 1.2.1 Energy Awareness  
We consider a model where different energy levels are associated with different roles the sensor nodes can play in a clustering process and where the sensor nodes are allocated different roles in the network based on their residual energy: energy-depleted nodes are allocated lower energy-demanding roles as edge nodes playing the role of cluster member, while fully-energized nodes are allocated high energy-demanding roles as core nodes playing the role of cluster heads.
## 2 Materials and Methods
### Experiment Setup and Evaluation Metrics  
The experiments are performed on an NVidia RTX 380 GPU computer with 64 GB of RAM and an Intel Core i9 processor. In addition, the Tensorflow-gpu library has been employed in a Python 3.8 environment.  
Figure 4: ParaNet+ topology. FF-VGG19 stands for the Feature-Fusion VGG19 network, which is an essential component of ParaNet+. GAP stands for global average pooling. MIBI stands for the radioactive tracer \({}^{99m}\)Tc-Sestamibi.  
We considered the 10-fold cross-validation methodology for evaluating the agreement between the experts and the model, using images labelled by the human experts. The performance is assessed based on the total number of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN). These scores are used to calculate the accuracy, sensitivity, specificity, and positive and negative predictive values (PPV and NPV, respectively). Moreover, the F1 and AUC scores are reported. The F1 score is a commonly used metric in machine learning to evaluate the performance of a classification model. It measures the model's accuracy, considering both precision and recall. Precision is the ratio of true positive predictions to the total number of positive predictions. At the same time, recall is the ratio of true positive predictions to the total number of actual positive instances. The F1 score is the harmonic mean of precision and recall.
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 4 Discussion
### Design Parameters
Particularly, for localization systems intended for general public use as an add-on service, the privacy rules and regulations of the general data protection regulation (GDPR) [132] should be taken into account, as to avoid jeopardizing the users' privacy and security. However, this important issue has received limited attention so far in the related literature. For instance, in [98], the device owners had no means of noticing that they were being tracked; in [133], the system had been reported to compromise user privacy; while in [96], privacy issues may be raised since users were adding their location in a database. Additionally, for the commercial localization systems, the corresponding rules and regulations should be followed, such as the federal aviation regulations (FARs) and government laws. _Localization and tracking accuracy_: Accuracy is the degree to which the estimated position from the localization system conforms to the actual position. Accuracy also depends on the particular application. Some applications require cm level accuracy, while for others, a few meters accuracy is satisfactory. For example, GPS accuracy is acceptable to be within a 4.9 m radius under LoS [19], while other technologies, such as Wi-Fi and 5G, can achieve sub-meter localization accuracy (i.e., [97; 134]). It is affected by the environment, such as weather conditions, obstacles, and noise. For commercial localization systems, high accuracy is required; therefore, advanced signal processing and noise elimination techniques are employed, which can be highly challenging tasks. _Fault tolerance_: Fault tolerance defines the network's capability for continued uninterrupted operation, despite the failure of one or more nodes. Although it is of vital importance, it is rarely discussed in the existing literature. In particular, only in [55] are the collaborative efforts of the sensor nodes in a WSN promoted for providing a fault-tolerant localization system. _Latency_: Latency defines the amount of time required for a packet of data to be captured, processed, and transmitted through multiple devices, until the time it is received and decoded at its final destination. It is proportional to the nodes' density, and it is important for real-time localization systems, which may require milliseconds of response time. _Scalability_: Scalability is the ability of a system to continue to function when changes occur, such as additional nodes or demands introduced into the system. In particular, a system can increase or decrease its performance, according to the changes in demands, for instance, by increasing the nodes' density in the network, which increases the coverage area [8]. _Real world environment deployment_: Both indoors and outdoors environments include various dynamics, such as construction materials, people movements, and weather conditions. A usual assumption in designing localization systems is the existence of at least one LoS path between the nodes, as well as noise-free conditions, which may not always be realistic.
# Spatiotemporal Variations in Vegetation Canopy Interception in China Based on a Revised Gash Model
## 2 Materials and Methods
### Data Analysis  
#### 2.4.1 Model Validation  
The validation of the revised Gash model was assessed by comparing the canopy interception both with those acquired from the PML_V2 dataset by randomly selecting 400 sites and with the results of in-situ measurements from 53 sites in 35 peer-reviewed journal articles in China during the 2000-2018 period (File S1). We set the interception from the PML_V2 dataset or in-situ measurements as the benchmark and employed normalized root mean square error (_NRMSE_), the mean relative error (_MRE_), and determination coefficient (R\({}^{2}\)) to assess Gash model's performance. _MRE_ and _NRMSE_ were calculated using the following equations:  
\[MRE=\frac{Y-\overline{X}}{\overline{X}}\times 100\% \tag{5}\]  
\[NRMSE=\frac{\sqrt{\frac{1}{n}\overline{\sum_{i=1}^{n}\left(Y_{i}-X_{i}\right) ^{2}}}}{\overline{X}}\times 100\% \tag{6}\]  
where \(Y_{i}\) is the canopy interception simulated by the revised Gash model, \(X_{i}\) is the interception obtained from the PML_V2 dataset or in-situ measurement, \(n\) is the number of simulation datasets.
# Data Hiding and Authentication Scheme for Medical Images Using Double POB
## 4 Analysis of Simulation Result
### Encryption Performance
#### 4.1.1 Key Space Analysis  
For the encryption method in this paper, the four initial values of the hyperchaotic Lorentz system are \(K=\{x_{0},y_{0},z_{0},w_{0}\}\), which are double precision numbers, so the size of the key spaces are \((10^{16})^{4}\approx 2^{213}\). It can be seen that the key space of the hyperchaotic Lorentz system is very large, which makes it very difficult to crack the key. In addition, when using the POB for hiding and authentication, converts a 10-bit POB (10, \(r\)) string to an equivalent POB value, it can range from 0 to \(\binom{n}{r}-1\). For example, when n is 10 and r is 4, there are 210 possible POB values. Thus, the probability of successfully analyzing a Share image for an image of size \(M\times N\) is \(\left(\frac{1}{210}\right)^{M\times N}\).
## 3 Background
### The Relationship between Arguments and Labelings  
There is a one-to-one correspondence [17] between labelings and arguments. In an abstract argumentation framework \(\Gamma=\langle A,R\rangle\), each extension \(E\) implicitly defines a three-valued labeling of arguments: if an argument \(a\) belongs to \(E\), then \(a\) is labeled as \(in\); if \(a\) is labeled as \(out\), it indicates that there exists an argument in \(E\) attacking \(a\); if neither condition applies, \(a\) is labeled as \(undec\). Here, the \(in\) label signifies explicit support for the argument, the \(out\) label denotes explicit opposition to the argument, and the \(undec\) label indicates that the status of the argument remains undecided.  
**Definition 7**: _(Labeling Model): Given an argumentation framework \(\Gamma=\langle A,R\rangle\) and argument \(a\in A\), let \(\mathit{Lab}:A\rightarrow\{in,out,undec\}\) be a mapping from arguments of the argumentation framework \(\Gamma\) to labels._  
**Definition 8**: _(Legal in Label): Given an argumentation framework \(\Gamma=\langle A,R\rangle\) and arguments \(a,b,c\in A\), let \(\mathit{Lab}:A\rightarrow\{in,out,undec\}\) represent a mapping from arguments of the argumentation framework \(\Gamma\) to labels. It is known that \(\mathit{Lab}(a)=in\), for any \(b\in\{a\}^{+}\), so it holds that \(\mathit{Lab}(b)=out\), and, for any \(c\in\{a\}^{-}\), it holds that \(\mathit{Lab}(c)\
eq in\)._  
**Definition 9**: _(Legal out Label): Given an argumentation framework \(\Gamma=\langle A,R\rangle\) and arguments \(a,b\in A\), let \(\mathit{Lab}:A\rightarrow\{in,out,undec\}\) represent a mapping from arguments of the argumentation framework \(\Gamma\) to labels. If \(\mathit{Lab}(a)=out\), then there exists \(b\in\{a\}^{-}\) such that \(\mathit{Lab}(b)=in\)._  
**Definition 10**: _(Legal undec Label): Given an argument framework \(\Gamma=\langle A,R\rangle\) and arguments \(a,b\in A\), let \(\mathit{Lab}:A\rightarrow\{in,out,undec\}\) denote the mapping from arguments of the argumentation framework \(\Gamma\) to labels. If \(\mathit{Lab}(a)=undec\), then there exists \(b\in\{a\}^{-}\) such that \(\mathit{Lab}(b)=undec\)._  
In the mapping of arguments to labels within an abstract argumentation framework, a set of labels is considered a complete labeling when all three types of label are legal. This complete labeling serves as the foundation for defining other semantic labeling. Consequently, when the set of \(undec\) labels in a set of complete labeling is empty, this complete labeling is also a stable labeling. In the subsequent discussion of stable extension algorithms, it is assumed that the \(undec\) label set is empty; hence, the \(undec\) label will not be included in the labeling propagation process of the stable extension algorithms.