esa-sceva/satcom-chunk-collection huggingface dataset:
# Modified Dual-Site Langmuir Adsorption Equilibrium Models from A GCMC Molecular Simulation
## 4 Results and Discussion
### Modification of the DSL Model  
The form of the DSL adsorption equilibrium model [3; 35; 36; 37] was shown as follows:  
\[q=\frac{q_{m1}b_{1}P}{1+b_{1}P}+\frac{q_{m2}b_{2}P}{1+b_{2}P}, \tag{4}\]  
where \(P\) is the adsorption equilibrium pressure, \(q\) is the adsorption capacity at an equilibrium pressure, \(q_{m1}\) and \(q_{m2}\) represent the saturated adsorption capacities at adsorption sites 1 and 2, respectively, and \(b_{1}\) and \(b_{2}\) represent the Langmuir adsorption equilibrium constants of adsorption site 1 and 2, respectively, which can be written as follows:  
\[b_{i}=k_{i}\exp\biggl{(}\frac{E_{i}}{RT}\biggr{)}(i=1,2), \tag{5}\]  
where \(k_{i}\) is a pre-exponential factor, \(R\) is the gas constant, and \(T\) is the adsorption temperature.  
Equation (6) can be obtained by combining Equations (3) and (5):  
\[b_{i}=k_{i}\exp\biggl{(}\frac{a_{i}P+c_{i}}{RT}\biggr{)}(i=1,2). \tag{6}\]  
By substituting Equation (6) into Equation (4), a new modified DSL model (M-DSL) without the adsorption energy E as a parameter can be obtained. The form of the M-DSL model was described as follows:  
\[q=\frac{q_{m1}k_{1}\exp(\frac{a_{1}P+c_{1}}{RT})P}{1+k_{1}\exp(\frac{a_{1}P+c_ {1}}{RT})P}+\frac{q_{m2}k_{2}\exp(\frac{a_{2}P+c_{2}}{RT})P}{1+k_{2}\exp(\frac{ a_{2}P+c_{2}}{RT})P}. \tag{7}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(a\) (J-**moL-1**-**Pa-1**)** & \(C\) (**kJ-**moL-1**)** & **R** & **R2** & **SSE** \\ \hline
0.01687 & \(-\)23.61 & 0.9936 & 0.9873 & 0.1217 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Parameters of and errors in the linear regression for average adsorption energy.  
Figure 5: Linear regression curve for the average adsorption energies of CO\({}_{2}\) on MIL-101 (Cr) under different pressures.  
Because there are many parameters in Equation (7), it can be simplified by combining relevant parameters. The simplification was expressed in Equation (8), which was the simplified form of the M-DSL model:  
\[q=\frac{q_{m1}k_{m1}\exp(a_{m1}P)P}{1+k_{m1}\exp(a_{m1}P)P}+\frac{q_{m2}k_{m2} \exp(a_{m2}P)P}{1+k_{m2}\exp(a_{m2}P)P}, \tag{8}\]  
where \(k_{m1}\) and \(k_{m2}\) are two new parameters that are obtained by multiplying \(k_{i}\) by \(\epsilon^{c/\text{iRT}}\), and \(a_{m1}\) and \(a_{m2}\) are two new coefficients of the linear relationship between the adsorption energy and pressure.
# Memory-Non-Linearity Trade-Off in Distance-Based Delay Networks
## 4 Results
### Narma
We present a scatter plot of all network instantiations throughout the evolution in Figure 7. Here, we observe that, besides having a better task performance, optimized DDNs indeed achieved a higher task overlap than ESNs, confirming that what we observe in Figure 6 is true in general, not only for the best candidates. We summarize the NARMA-30 results for optimized and unoptimized networks in Table 1. In comparison with mixture reservoirs [2], which obtain an NRMSE of around 0.25 for NARMA-5, DDNs reach a much lower error for NARMA-30 with the same reservoir size. Figure 6: Second-degree IPC for optimized ESNs and DDNs (**left**) and DDNs (**middle**) and the NARMA-30 degree task capacity (**right**), shown as a heatmap over the lag and window size. A lighter hue indicates a higher capacity. The window size indicates the difference between the smallest and largest delay in the production of polynomials (the basis function) used to compute the capacity. We observe that the second-order task requirements are concentrated at a window size of 30.
###### Abstract  
Compared with a scalar tracking receiver, the Beidou vector tracking receiver has the advantages of smaller tracking errors, fast loss-of-lock reacquisition, and high stability. However, in extremely challenging conditions, such as highly dynamic and weak signals, the loop will exhibit a high degree of nonlinearity, and observations with gross errors and large deviations will reduce the positioning accuracy and stability. In view of this situation, based on the concepts of cubature Kalman filtering and square root filtering, a square root cubature Kalman filtering (SRCKF) algorithm is given. Then, combining this algorithm with the concept of covariance matching based on an innovation sequence, an adaptive square root cubature Kalman filter (ASRCKF) algorithm is proposed. The algorithm was verified, and the tracking performance of the vector locking loop (VLL) realized by the algorithm was compared with the SRCKF VLL and the ASRCKF scalar locking loop (SLL). The simulation results show that, regardless of whether in a highly dynamic weak signal environment or in a general situation where the signal-to-noise ratio is higher than the tracking threshold, the tracking accuracy and stability of the ASRCKF VLL are higher than those of the SRCKF VLL and the ASRCKF SLL, the three-dimensional position error of the ASRCKF VLL does not exceed 36 m, and the three-dimensional velocity error does not exceed 3.5 m/s.  
Beidou; vector tracking; high dynamics; weak signal; ASRCKF; SRCKF; innovation +
Footnote †: journal: Sensors
## 2 Methods  
### Optical Experiment  
This study focused on GNRs with nominal diameters of \(10\pm 1\) nm and axial lengths of \(38\pm 3.8\) nm, obtained from Sigma-Aldrich(r)(St. Louis, MO, USA), The AR and effective radius of these GNRs was around 3.8 and 8.9 nm. Thus, the plasmonic resonance of the GNR solution was designed to be 780 nm. Transmission electron microscopy (TEM) images of particles in the solution were obtained to verify that these particle dimensions were accurate. In addition, the size distribution of the samples was measured by using dynamic light scattering (DLS) technique [39], noting that DLS provides effective hydraulic diameters (in suspension). The optical and thermal experiments were performed for five volume fractions of GNRs and with pure water as a control. Transmittance and reflectance measurements of the samples were obtained with a Perkin Elmer(r) Lambda 1050 UV/Vis Spectrophotometer (Waltham, MA, USA) with an integrating sphere. The absorbance of the samples was calculated using the following equation [40]:  
\[A=\log_{10}\frac{P}{P_{0}}=2-\log_{10}(T\times 100) \tag{1}\]  
where \(A\) and \(T\) are the absorbance and measured transmittance of the samples, \(P\) is the light intensity the sample received and \(P_{0}\) is the light intensity transmitted through the samples.  
The volume of each sample was \(1\times 1\times 3.5\) cm\({}^{3}\) (3.5 mL). Table 1 illustrates the concentration and volume fraction of these samples. The concentration shown in Table 1 indicates the number of GNRs per milliliter. Thus, Sample A contained pure water while Sample F had a non-diluted GNR solution as-purchased from Sigma-Aldrich(r) (St. Louis, MO, USA) in which, according to the specification sheet provided by the manufacturer, each milliliter solution contained at least 30 \(\upmu\)g of gold. Using this specification, the concentration and volume fraction of Sample F corresponded to \(5.2\times 10^{11}\) GNRs/mL and \(1.55\times 10^{-6}\) respectively. Samples B, C, D, and E were dilutions of Sample F to obtain the concentrations/volume fractions given in Table 1.
## 5 Experimental Scenarios and Results
### ADR-Det Stage 2 Evaluation Using Selected Datasets
#### 5.3.1 ADR-Det Stage 2 Detection Evaluation Using a Dataset of 500 ADR Images  
In this experiment, the ADR-Det algorithm stage 2 is put to the test in terms of detection accuracy. 500 photographs were selected for this evaluation scenario, all depicting vehicles with ADR plates and fed to the stage 2 of the ADR-Det algorithm. The experimentation results have shown that 498 out of 500 (99.60%) have been correctly detected. The average confidence rate was \(99.98\%\pm 2.24\%\). The minimum percentage of certainty was 50.32%, and the maximum percentage of certainty was 100.00% (typical in most cases). In the experimental photographs, the authors have also included vehicles with 2 ADR plates, vehicles with partially out-of-frame ADR plates, and diamond-shaped ADR plates. Figure 6 illustrates ADR algorithm detection capability.
## 4 Simulation and Discussion  
In this section, simulations based on the MATLAB platform are used to examine the performance of the proposed sensing strategy for SS. In the simulation, the BPSK modulated signal is considered as the primary signal with the bandwidth 2000 Hz, the background noise is the additive white Gaussian noise (AWGN) [35] and the sampling frequency at the receiver of the local detector is the same as the bandwidth of the primary signal. ED is regarded as the local detector and the Rayleigh fading channel is considered. In the CR network, 15 SUs are considered. We assume that the time for each SU to send its sensing results to FC is much shorter than the sensing duration. Two different types of detectors are designed in the paper to simulate the situation of heterogeneous detectors, where the signal-to-noise ratio (SNR) of one kind of detector is \(SNR1\) and the SNR of the other detectors is \(SNR2\). The number of the first types of detectors is denoted as \(N_{1}\).  
In addition, the Rayleigh fading is generated based on the built-in function of MATLAB in Monte Carlo simulations, where the sampling frequency and the Doppler shift are fixed. As a result, an ensemble of fading random variables is made with the fixed parameters to get a sense of expected performance in the Monte Carlo experiment of this paper.  
It states that the main contribution of this paper consists in the closed-form expression of the optimal sensing threshold at the FC for the N-out-of-K rule-based CSS scheme under the HCRN situations by Poisson distribution. In consequence, the comparisons of the optimal sensing threshold at the FC between the proposed scheme and the classical scheme are mainly considered in the simulations under various scenarios. For the proposed scheme, the optimal sensing threshold is obtained by the closed-form solution provided in this paper while the optimal sensing threshold of the classical scheme is obtained by iteration and comparison. The optimal sensing threshold of the classical scheme is the theoretical value and the optimal sensing threshold of the classical scheme is the evaluated value.
###### Abstract  
Eco-friendly synthesis of silver nanoparticles (SN) by using a naturally occurring plant, such as _Nitrosalsola (Salsola) vermiculata_ (SV), could be a novel way for appropriate wound healing. AgNO\({}_{3}\) was reduced by SV to produce safe SN (SN-SV) extract and hasten the wound healing process. The obtained SN-SV were characterized by size, charge, wavelength, and surface morphology. The optimized formulation was dispersed in O/W cosmetic cream. Then, it was characterized in terms of pH, viscosity, homogeneity, and permeability. The ex vivo and in vivo studies have been conducted in a rat animal model to assess the potential of SN-SV cream on skin tissue regeneration. A skin punch biopsy was obtained to investigate the histopathological (HP) changes in the skin lesions of all rats by the H&E staining and PCNA immunostaining methods. The skin wounds in all subgroups were examined on days 5, 11, and 15 to analyze the effectiveness of SN-SV cream for treating surgical skin wounds. The prepared SN-SV had a particle size of 37.32 \(\pm\) 1.686 nm, a charge of \(-1.4\pm 0.7\) mV, non-aggregated SN-SV, and a \(\lambda_{\max}\) of 396.46 nm. The formed SN-SV cream showed a pH near the skin's pH, with suitable viscosity and homogeneity and an apparent permeability of \(0.009\pm 0.001\). The HP changes in the SN-SV subgroups revealed a substantial reduction in wound size and improvement in wound granulation tissue formation and epidermal re-epithelialization (proliferation) compared to the healing in the SN subgroups. The current work revealed that SN-SV could be a novel skin-wound-healing agent with a practical application as a wound-healing platform.  
eco-friendly; _nitrosalsola vermiculata_; anti-inflammatory; wound granulation tissue; epidermal re-epithelialization
## 2 Data  
### In Situ Data  
The \(i\)Quam collects in situ SSTs from multiple types of platforms, including conventional and scientific ships, drifting buoys, tropical and coastal moored buoys, and Argo floats available from various data sources, such as ICOADS, FNMOC, CMEMS, AOML, GDACs, and IMOS. Detailed descriptions of types of platforms and sources of in situ information can be found in [2; 9]. The full set of in situ data from September 1981 -present, with \(i\)Quam QLs appended, is available at [1]. The \(i\)Quam QC employs five binary checks, including the Duplicate Removal (DR), the Plausibility/Geolocation check (GC), the platform Track Check (TC), the SST Spike Check (SC), and the platform ID Check (IC) [2; 9]. Two other checks, the RC and the Buddy Check (BC), employ the Bayesian method [10]. The highest \(i\)Quam QL = 5 is set when all seven checks pass. If the \(T_{IS}\) fails at least one of these checks, then QL = 3 or 4 are assigned. Note that the \(i\)Quam does not exclude low-quality \(T_{IS}\)'s from records but rather assigns those lower QLs, so users have access to these data and may explore them. This study focuses on QC'ing \(T_{IS}\)'s from drifters and tropical moored (DTM) buoys, which sample \(T_{IS}\) with frequencies sufficient for resolving the diurnal signal. Figure 1 shows that the number of DTM measurements from September 1981-December 2021 had increased by more than two orders of magnitude and, today, represents the major source of in situ data for satellite Cal/Val.
## 5 Results and Analysis
### Comparison of Loose and Tight Solutions
#### 5.3.1 Segment 1  
To better perform a comparison among the analyzed configurations with LC and TC architectures, the RMS errors of position, velocity and attitude are presented in Figure 16. The main remarks deduced from this figure are:  
* in the baseline GPS/INS integration, TC architecture provides significantly better horizontal solution and similar altitude result than LC, as expected and as shown in previous work;
* including GLONASS observations provides meaningful performance improvements for both LC and TC architectures in terms of position, velocity and azimuth estimation;
* in case of GPS/GLONASS without other aiding, the TC architecture provides only slightly better solution relative to the LC case;
* the loosely coupled GPS/GLONASS and tight coupled GPS-only configurations (no other aiding) provide very similar performance;
* including GNSS-derided azimuth aiding and velocity/height constraints produces significant improvements for both the LC and TC cases in terms of position, velocity and azimuth;
* the results obtained with using loose and tight integration for the GG/INS YVH configurations are very similar.  
The practical consequences of the above considerations are two-fold. First, in this type of environment, the loosely coupled GPS/GLONASS configuration could replace the tightly coupled GPS-only configuration without performance degradation. Such an approach is simpler to implement thus saving development costs. Furthermore, the GNSS-only solution (_i.e._, no inertial) is available in this case as well. Second, adding vehicle constraints is equally beneficial to the LC and TC cases, further blurring the benefits of the TC approach.  
Figure 16: Comparison between LC and TC architectures in terms of position, velocity and attitude RMS errors (Segment 1).
## 2 Materials and Methods
### Activation Maps  
The intermediate outputs of network layers during the forward pass for a specific input are called activation maps. They are computed by applying filters on inputs of the corresponding layer. Activations usually start with relatively dense maps at the first layers and get more localized for deeper layers. Correspondingly, activation maps of lower layers tend to show more basic patterns, while those derived from deeper layers are smaller and may localize semantically meaningful regions of the input objects. Activation maps illustrate the network behavior for the selected input, however one layer may contain more than a thousand filters and the same number of activation maps. Thus, understanding network decisions by studying all activation maps of a single input is not a feasible endeavor. Activation maps are also not easily interpreted. Different methods [13; 14] have been proposed to find a meaningful interpretation of activation maps.  
In CNNs, outputs of a layer become inputs for the next layers. Finding the strongest activation map (and corresponding filter) for a selected layer and desired class does not mean that the subsequent layers will only use the most powerful result to decide. A network can amplify some other important intermediate results by applying weights to the next layers. Furthermore, some activations with higher values may be reduced by subsequent filters of the next layers. To find out which filters in a selected layer are more important for a specific class in the final layer, weights are be applied to them. Gradients are one of the existing solutions to computing these weights for the desired activation maps. Because functions of neural networks are differentiable, it is possible to compute the desired class' gradient with respect to the selected layer. The result is a measure for the sensitivity of the desired class to each activation map, with higher values corresponding to more relevance. For segmentation networks it is possible to find more important activation maps (with respect to a specific class) by exploiting localization information from the final layer.
## 1 Introduction
Plant resistance responses to pathogen attack commonly involve the accumulation of specific compounds with either signaling or antimicrobial properties. The latter can include both structural modifications to impair pathogen ingress and direct toxic effects on the pathogen (e.g. phytoalexins). Key components in plant disease resistance include salicylic acid [19] and phenolic compounds, including flavonoids [20]. As phenolic compounds generally have the property of emitting fluorescence after UV excitation [21], they provide an elegant way to reveal stress symptoms [22, 23]. Ethylene involved in systemic plant resistance, is a plant growth regulator and its molecular simplicity is in contrast with its complex role in the physiology of plants [24]. An ethylene sensor based on laser photo-acoustic spectroscopy (LPAS) was designed [25]. LPAS technique offers an elegant and accurate solution to perform high sensitivity trace gas detection (typically under 1 ppb) for small molecules and posses a time resolution of only a few minutes. Disease warning systems have been developed as tools for Integrated Pest Management (IPM) to help growers decide when to apply a fungicide spray. The systems use data about the weather, crops, and/or pathogens and provide outbreak risk information. Warning systems can save fungicide sprays and reduce production costs. Weather data is the main component of the disease warning system, but depends on accuracy, costs and logistics. Weather stations usually have air temperature, radiation, relative humidity, rainfall, and wind speed sensors. An additional sensor important for disease detection is the leaf surface wetness sensor (LSW) which works both on the soil-air interface and as an electronic leaf sensor. The capacity to detect wetness at the soil-air interface and to estimate wetness duration would be used in future disease development models [26, 27]. Airborne remote sensing technology has been employed for detecting crop disease and assessing its impact on productivity [28, 29]. Satellite remote sensing has been used to detect some pest problems. QuickBird imagery was evaluated for detecting citrus orchards affected by sooty mould [30]. Researchers have used Landsat [31] and SPOT [32, 33] satellite imagery with coarse spatial resolutions to detect and assess insect damage to forests. This review analyzes the main non-destructive technologies for fruit and vegetable disease detection, both in field and for postharvest management. Field diseases have to be predicted just in time to avoid losses while infected fruit have to be removed. It is impossible to analyze all the fruits and vegetables, so the most important detection techniques (mango, avocado, potato, tomato, cucumber, apple, orange, lemon and grapes) are mentioned together with risk-disease management and remote sensing.
## 3 Avispa
### Security Goal Specification  
Using the back-end tools of AVISPA, secrecy and different forms of authentication goals could be validated. AVISPA supports four types of goal predicates: witness, wrequest (for weak authentication), request (for strong authentication) and secret. In the following, we explain how the predicates are used to specify security goals: secrecy and authentication.  
Secrecy is verified with the help of goal predicate secret(E, id, S), which confirms that the secret information E should be known only by the agents of set S. The label id (of type protocol_id) is used to identify the goal. In the HLPSL goal section, the statement secrecy_of id should be given to refer to it. An intruder attempting to break a secret mounts different attacks. On a successful attack,  
Figure 5: Architecture of the AVISPA tool.  
the intruder learns a value that is considered as secret and that he is not allowed to know, and hence, the secrecy property is violated.  
Authentication is verified with the help of different goal predicates: witness(A, B, id, E), request(B, A, id, E) (for strong authentication) and wrequest(B, A, id, E) (for weak authentication). The witness predicate is used for (weak) authentication of A by B on E. This is a confirmation that A is a witness for the information E. The request predicate is used for the strong authentication property (similarly, wrequest is for weak authentication) of A by B on E, which declares that B requests a check of the value E. The authentication property is expressed in the HLPSL goal section, which is written as authentication_on id (similarly, weak_authentication_on id for weak authentication). In this expression, id is a label (of type protocol_id) to tag the goal. If any of the back-end tools finds a trace in which the request event is preceded by a witness event originated by an agent other than A, an attack will be reported. Moreover, an attack trace will be reported if no valid witness is found for a request.
# WIG-Net: Wavelet-Based Defocus Deblurring with IFA and GCN
## 3 Methods  
The network architecture presented in this work is illustrated in Figure 2. Our approach was built upon U-net [2] by integrating wavelet transform into the encoding stage, replacing the conventional pooling layer. The sparse nature of wavelet coefficients simplifies the deblurring process while providing a larger receptive field to the network by decreasing the size of the feature maps. In the decoding stage, wavelet inverse transform, instead of the upsampling layer, was used to generate high-resolution feature maps from low-resolution ones, effectively mitigating the information loss due to pooling layers. The Iterative Filter Adaptive module (IFA) [4] and Graph Convolutional Network between the encoding and decoding stages were also introduced. The network employs a filter size of 3\({}^{\circ}\)3 and uses Leaky Rectified Linear Unit (Leaky ReLU) [17] as the activation function. The Mean Squared Error (MSE) loss function was also adopted, which is widely used in image deblurring tasks and is the most suitable for our approach.