esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Organic Compound Degradation and Biosurfactant Production
#### 2.8.2. Degradation of Aromatic Hydrocarbons
Genome mining by anti-SMASH 5.0 resulted in the prediction of seven gene clusters associated with secondary metabolite biosynthesis (Table S2). Many of the biosynthetic gene clusters (BGCs) are predicted to encode nonribosomal peptide synthetases (NRPSs), which produce lipopeptide (LP) and biosurfactants such as rhizomide B, viscosin, gacamide A, putisolvin, oramide B, and anikasin. The structures of these secondary metabolites were predicted using PubChem  
Figure 5: Degradation of hydrocarbons by the ZCR6 strain (mean \(\pm\) SD, n D 3). and are presented in Supplementary Figure S2. It is worth noting that LPs and rhamn lipids have been successfully used to enhance oil recovery in extreme environmental conditions [112]. In addition, bacteria use LPs for the chelation of heavy metals in a similar way to siderophores and incorporate them into their metabolism as micronutrients [113].
## 2 Materials and Methods
### Dual-Frequency IF Model  
In terms of BDS-3 data processing, the IGS ACs (Analysis Centers) commonly incorporate BII and B3I dual-frequency signals. To be consistent with the IGS ACs' convention, the dual-frequency IF combined clock with B1I/B3I signals, which assimilates code hardware delays on corresponding frequencies, is introduced with definition as:  
\[\left\{\begin{array}{l}c\hat{d}t_{r}=cdt_{r}+\frac{f_{2}^{2}}{f_{2}^{2}-f_{ 6}^{2}}d_{r}c_{2}+\frac{-f_{6}^{2}}{f_{2}^{2}-f_{6}^{2}}d_{r,C6}\\ c\hat{d}t^{s}=cdt^{s}+\frac{f_{2}^{2}}{f_{2}^{2}-f_{6}^{2}}d_{C2}^{s}+\frac{-f _{6}^{2}}{f_{2}^{2}-f_{6}^{2}}d_{C6}^{s}\end{array}\right. \tag{4}\]  
Inserting Equation (4) into Equation (1), the re-parameterized observation equations are obtained for B1I/B3I dual-frequency signals with IF combined model:  
\[\left\{\begin{array}{l}C26_{r}^{s}=\rho_{L26}+c\hat{d}t_{r}-c\hat{d}t^{s}+m _{r}^{s}zwd_{r}+\hat{e}_{r,C26}^{s}\\ L26_{r}^{s}=\rho_{L26}+c\hat{d}t_{r}-c\hat{d}t^{s}+m_{r}^{s}zwd_{r}+\lambda_  
IFCB can in principle be expressed as linear combinations of the hardware delays on corresponding frequencies, the resulting IFCB estimates will be close to zero once the raw observations are corrected with external observation-specific biases. With geometric distances fixed with known satellite and receiver coordinates, Equation (6) becomes the UC model for multi-frequency clock and IFCB estimation.
## 4 Numerical Simulations
In the third example, average MSE curves of SLMS-RL1 algorithm using reweighted selected factor \(\varepsilon=0.005\) with respect to impulsive-noise strength \(T\) are depicted in Figure 5. In addition, average MSE curves of the algorithm with respect to mixture parameter \(\phi\) are depicted in Figure 6. In the two figures, one can see that SLMS-RL1 algorithm using \(\varepsilon=0.005\) is stable for different GMM noises with impulsive-noise strength parameters \(T\in\{200,400,600,800\}\) as well as mixture parameters \(\in\{0.05,0.1,0.2,0.4,0.6,0.8,1.0\}\). The main reason of SLMS-RL1 algorithm is that sign function is utilized to mitigate the GMM impulsive noise. It is worth noting that SLMS-RL1 algorithm may be deteriorated by the enlarging the mixture parameter \(\phi\) of impulsive noise. In practical application scenarios, the mixture parameter \(\phi\) is very small (less than 0.1). Hence, the proposed reweighted factor for SLMS-RL1 is stable for GMM impulsive-noise. Figure 4: Monte Carlo simulations averaging for evaluating SLMS-RL1 algorithm over 1000 runs with respect to channel sparsity \(K\in\{2,4,8,12,16\}\) in the scenarios of mixture parameter \(\phi=0.1\), impulsive-noise strength \(T=400\), channel length \(N=80\), and \(SNR=10\) dB. Figure 5: Monte Carlo simulations averaging for evaluating SLMS-RL1 over 1000 runs with respect to impulsive-noise strength \(T\in\{200,400,600,800\}\) in the scenarios of mixture parameter \(\phi=0.1\), channel length \(N=80\), channel sparsity \(K=4\) and \(SNR=10\)dB.
# Band Gap Analysis for Materials with Cookie-Shaped Auxetic Microstructures, Using Finite Elements
## 3 Materials and Methods
### Setting Up the Numerical Experiment  
Structures that guide waves are common in the study of metamaterials. A method to determine the dispersion curves of a periodic waveguide is to use a Periodic Condition to prescribe that the displacements on two different sets of boundaries with the same geometry are related, as in a structure with periodicity. The so-called Floquet boundary conditions, which are available in some software packages, such as COMSOL used in this work, are technically realized by means of multi-point constraints. The source of the wave and its destination are applied to the edges of the unit cell, one to the left and right edges and one to the top and bottom edges.  
The eigensolver analysis is set as a parametric sweep, using the parameter k. The values of this parameter range between 0 and 3. Group 0-1 defines a wave number that scans the \(\Gamma\)-X edge, 1-2 the X-M edge, and 2-3 the M-\(\Gamma\) edge of the IBZ triangle. We solve for the lowest frequencies and then we plot the wave propagation frequencies at each value of k. Every finite element mesh for the RVE uses an average of 9500 triangular finite elements of plane elasticity type. The number of degrees of freedom is approximately 25,000.  
The properties of the materials that were used in the numerical experiment are given in Table 2.  
It should be mentioned that Material1 is used for the outer-part of the \"cookie-shaped\" structure (Figure 1) and Material2 for the inner-part.  
For the simulations that followed, the initial characteristics of the geometry are the following:  
* Dimensions of the outer ellipse: 200 \(\times\) 100 (mm)
* Dimensions of the inner ellipse: 170 \(\times\) 80 (mm)
* Dimensions of the cross: 150 \(\times\) 150 (mm)
* Thickness for each of the cross rods: 6 (mm)
* Number of eigen frequencies: 8
* Periodic condition parameter value range: k 0-3 with step 0.08  
The variations that followed involved different values for the size of the inner ellipse, the thickness of the cross surrounding the geometry, as well as the presence or absence of a circular inclusion within the auxetic microstructure. The latter inclusion, in cases of contact activation, will modify the microstructure and eventually influence the behavior.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Material Name** & **Density** & **Poisson’s Ratio** & **Young’s Modulus** \\ \hline Material1 & 8000 kg/m\({}^{3}\) & 0.34 & \(2\times 10^{11}\) Pa \\ \hline Material2 & 1000 kg/m\({}^{3}\) & 0.45 & \(2\times 10^{9}\) Pa \\ \hline \end{tabular}
\end{table}
Table 2: Properties of materials.
## 4 Case-Study
### [PERSON]'s Steel Beam Splice Connection
#### 4.1.2 Material Modelling  
Accurate PhNLFEA relies on detailed non-linear material models that include both plasticity and damage. Both standard-defined limit thresholds and information on plausible steel properties are needed to establish these models. Pertaining to the former, the case study steel grade was 50 as per the ASTM A572/A572M standard [61], with a minimum yield strength of 345 MPa, a minimum ultimate strength of 450 MPa, and an elongation no less than 0.18 for a 200 mm gauge and 0.21 for a 50 mm gauge.  
Representative values for this alloy's commercial properties can be found in steel mill test data reports [62] and several research publications [63], including the aforementioned [PERSON]'s works in [30; 58].  
Moreover, steel properties models are available, such as FHWA-HRT-17-04\({}^{\prime}\)s [59], but require sufficient experimental data to calibrate them.  
Consequently, a trilinear hardening model, as presented in Figure 2, was suggested. Its Young's modulus first stage was elastic with E\({}_{0}\) = 199.9 GPa, the second stage showed plastic hardening behaviour with a plastic modulus of E = E\({}_{0}\)/339, and the last stage showed softening (damage) of the instantaneous modulus with E = E\({}_{0}\)/1000. A standard isotropic yield model was coupled with the non-linear hardening model. The former values were attained, establishing the ASTM A572/A572M standard values as a lower bound and accounting for the published experimental data. This model was named the _\"Material model, Trilinear with fictitious hardening\"_ and will be employed for the nonlinear analyses.
## 3 Results
The results presented in Table 4 are  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{\(\overline{e}(al2,b,0)\): \(R_{\alpha}/h=4\)} \\ \hline  & \(N=1\) & \(N=2\) & \(N=3\) & \(N=4\) & \(N=5\) & \(N=6\) & \(N=7\) & \(N=8\) & \(N=9\) & \(N=10\) & \(N=11\) & \(N=12\) \\ \hline \(M=1\) & \(-91.308\) & \(0.273\) & \(-11.682\) & \(-7.335\) & \(-7.671\) & \(-7.630\) & \(-7.631\) & \(-7.631\) & \(-7.631\) & \(-7.631\) & \(-7.631\) & \(-7.631\) & \(-7.631\) \\ \(M=2\) & \(-31.308\) & \(6.919\) & \(6.776\) & \(7.078\) & \(7.707\) & \(7.707\) & \(7.707\) & \(7.707\) & \(7.707\) & \(7.707\) & \(7.707\) \\ \(M=5\) & \(-4.599\) & \(10.601\) & \(10.759\) & \(10.776\) & \(10.776\) & \(10.776\) & \(10.776\) & \(10.776\) & \(10.776\) & \(10.776\) & \(10.776\) & \(10.776\) \\ \(M=10\) & \(3.603\) & \(11.326\) & \(11.377\) & \(11.379\) & \(11.379\) & \(11.379\) & \(11.379\) & \(11.379\) & \(11.379\) & \(11.379\) & \(11.379\) & \(11.379\) & \(11.379\) & \(11.379\) \\ \(M=20\) & \(7.600\) & \(11.500\) & \(11.514\) & \(11.514\) & \(11.514\) & \(11.514\) & \(11.514\) & \(11.514\) & \(11.514\) & \(11.514\) & \(11.514\) & \(11.514\) & \(11.514\) \\ \(M=40\) & \(9.584\) & \(11.544\) & \(11.548\) & \(11.548\) & \(11.548\) & \(11.548\) & \(11.548\) & \(11.548\) & \(11.548\) & \(11.548\) & \(11.548\) & \(11.548\) \\ \(M=80\) & \(10.243\) & \(11.553\) & \(11.554\) & \(11.554\) & \(1  
Material (FGM) layers. The problem is solved for different kinds of geometry: plates, cylinders, spherical and cylindrical shells can be analyzed via a unique formulation. The second order differential equations are reduced to the first order differential equations redoubling the number of variables. The system of first order equations is solved through the thickness using the exponential matrix method. This method allows for obtaining as a direct result the displacement amplitudes and the relative derivatives with respect to \(z\). The coefficients of the differential equations are not constant as they depend on the thickness coordinate. This dependence is related to the material (as the mechanical properties of FGM layers change across the thickness) and to the geometry (as the curvature is considered by means of parametric coefficients). The structures are divided into \(M\) mathematical layers to approximate these values in the middle of each layer. The third order of expansion for the exponential matrix combined with the opportune value of \(M\) always give stable results. Several parameters play a significant role in the definition of these values, but these effects cannot be a priori determined. The complexity of the FGM law is one of the most important parameters. The influence of geometry and thickness ratio is also fundamental. Using an expansion \(N=3\) combined with \(M=300\) mathematical layers, the convergence to the exact results for each possible case is always guaranteed. [PERSON] implemented the analytical code; [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper. The authors declare no conflict of interest.
# Improving Bundle Routing in a Space DTN by Approximating the Transmission Time of the Reliable LTP
## 4 Numerical Evaluation of the Optimal Performance
### Performance Impact of the Packet Loss Ratio  
The impact on the flow routing performance of the packet loss ratio can be observed in Figure 4 for the four methods identified to calculate the bundle service time. The per flow bundle sending rate was fixed to a low value of \(\lambda=0.02\), given that by increasing the packet loss ratio, the effective bundle service rate decreased. Cases for propagation delays of 0.01 s and 0.1 s are shown. The results indicate that increasing values for the packet loss ratio contributed to extending the gap between the performance of the service time methods. No significant difference between methods 2 and 3 was observed. The reason for this is that the disruption times (100 s) mainly drove the performance of the scenario rather than the service times of one second with method 2 and at most two seconds with method 3 (when \(p=0.5\)). Method 4 did produce noticeably lower response times for the worst transmission conditions.
## 3 Results
### Comparison of Reddyproc and MLP Interpolation Effect
#### 3.3.2 Monthly Scale NEE Variation Characteristics  
The Reddyproc and MLP models estimated the monthly change in the NEE at the Longbao Station, which is shown in Figure 7. The filling effect of missing data had almost the same effect on the whole-year data. The net absorption reached its maximum in July, which was \(-1.26\) umol S\({}^{-1}\) M\({}^{-2}\). The emission rate reached its maximum in April, which was \(0.57\) umol S\({}^{-1}\) M\({}^{-2}\). With the development of the vegetation structure, the carbon absorption increased, and it reached its maximum in the middle of the growing season. The results showed that the estimated value of Reddyproc was about 1.5 times lower than that of MLP in January, February, March, and October. However, from April to September, the monthly average of the former was higher than that of the latter by about 1.5-2 times. These results indicated that the machine learning algorithm tended to predict the carbon source more in the cold season (nongrowing season), and carbon sequestration was more likely to be predicted in the warm season (growing season).  
Figure 6: Comparison of regression accuracy between measured and simulated NEE values. (**a**) Reddyproc and (**b**) MLP.  
Figure 7: Comparison of monthly variation characteristics of NEE estimated by Reddyproc and MLP models.
## 3 Remote Sensing Image Scene Understanding
### Datasets Used for Scene Understanding  
With the development of the remote sensing sensor production level, there are many kinds of remote sensing sensors, ranging from the multi-spectral (MS) sensor Landsat-8, to the high-resolution sensor WorldView-III. In addition, we usually need to preprocess the raw data before RSISU. One of the main difficulties in the quantification of remote sensing is the adjustment of the measured values of remote sensing images according to the atmospheric conditions to eliminate the atmospheric impact. The core of this problem is solving the radiative transfer problem in the Earth's atmosphere and estimating the optical parameters of this atmosphere. Among them, the atmospheric correction model based on the radiation transfer model is a method with high accuracy, and the classical models are the 6S model, LOW Resolution TRANsmission (LOWTRAN) model, and MODerate resolution TRANsmission (MODTRAN) model. In addition to the atmospheric correction model based on the radiation transfer model, there are atmospheric correction models based on image characteristics, the typical dark target method, plane field model, internal average method, and so on. At present, many remote sensing scene data sets come from Google Earth, where Google Earth images are an integration of aerial and satellite images. Some of the satellite images are captured through QuickBird and WorldView satellites. Their spatial resolution is less than 1 m, and all sensors contain panchromatic bands, whereas most sensors contain near-infrared bands. The imaging mode is push-broom, rather than swing-broom. Although very high-resolution (VHR) remote sensing images can clearly identify the types of objects and have a high amount of data, the huge amount of data requires high computational efficiency in our classification process. Meanwhile, scene classification in the field of remote sensing is more inclined to semantic segmentation [18]. Thus, for richer semantic information, we need to train the scene-level remote sensing dataset to obtain the semantic label of the scene. The most commonly used scene data sets are shown in Table 1:In addition, as a non-optical remote sensing method, the synthetic aperture radar (SAR) also plays a pivotal role in remote sensing images. Due to its independence on atmosphere and sunlight conditions, SAR images are also obtained to categorize the scene [25] like remote sensing images that rely on spectral information. However, the SAR image is also limited by the existence of speckle noises and the absence of effective feature representation.  
It is worth mentioning that through being confronted with higher spatial resolution, scene classification datasets have been greatly developed. However, training large-scale remote sensing datasets from scratch are still subject to many limitations. The database, which only has three bands--R, G, and B--is also different from the real remote sensing spectral band. Like Aerial Image Dataset++ (AID++) and Remote Sensing Image (RSI), we can make more use of the tagging of crowd-sourced geographic data, such as OpenStreetMap (OSM).
## 5 Satellite Stabilization Problem
### Example 1. The Joint Estimation and Control Problem
#### 5.1.3 Case C  
The measuring system model is described by the following relationship:  
\[z(t)=(r(t),q(t))^{\mathrm{T}}\]  
The behavior of the trajectories set for different sizes of mini-batch is shown in Figure 3:  
Table 3 shows the results of solving the problem depending on the mini-batch size.  
\begin{table}
\begin{tabular}{c c} \hline \hline \(d\) & \(J[K_{d}]\) \\ \hline
1 & 2722.1031 \\
10 & 455.5253 \\
20 & 311.6676 \\
27 & 236.1635 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Solution results for different mini-batch sizes.  
Figure 3: The behavior of the satellite coordinates \(p,q,r\) (red) and estimations (blue) for different mini-batch sizes: (**a**) \(d=1\); (**b**) \(d=10\); (**c**) \(d=20\); (**d**) \(d=27\).  
From Figure 4 and Table 4, a similar character of convergence for different models of the measuring system is observed.
## 3 Experiment Results and Observations
### Simulation and HILS Experiment Results  
#### 3.2.1 Longitudinal Driving Simulation Conditions  
In the longitudinal driving simulation, the vehicle was driven at a fixed speed of 70 km/h, and the longitudinal driving conditions were separated into two: a flat (0%) and an inclined (30%) road driving. First, the road conditions of the vehicle driving longitudinally were set up at fixed intervals to check the power distribution system's control performance. To simulate the vehicle driving on a longitudinal incline, the experiments were performed at full throttle (100%) on a dry road with a 30% incline, which was the ground's maximum incline.
## 2 System Overview  
This paper presents an autonomous localization system for rescuers, which enhances and integrates APDR and visual localization techniques. The overall framework of the system is illustrated in Figure 1 and consists of four primary modules, which are described in detail in the following sections.  
The first is a sensor module composed of a magnetometer, accelerometer, gyroscope, and binocular camera. These sensors are mounted on the front chest area of the soldier's vest to collect multi-source raw data during movement, including three-axis magnetic field strength, three-axis acceleration, three-axis angular velocity, and binocular image data.  
The second is the advanced APDR module, designed to optimize the sensor layout and establish adaptive step length estimation and dead reckoning models. This module effectively acquires positional data by adaptively detecting step segmentation, optimizing sensor placement, adaptively estimating step lengths, and employing robust dead reckoning methods, significantly reducing the instability in localization accuracy that typically arises from step length estimation errors in conventional PDR systems.  
The third is an enhanced VIO algorithm module that utilizes step length information to enhance the visual tracking process, aiming to improve the reliability and accuracy of position estimation. This module incorporates adaptive step length data into the visual positioning and tracking algorithm, specifically addressing the challenge of rapid divergence in traditional visual navigation systems, particularly complex environments. By increasing the stability and adaptability of the visual positioning module, it ensures reliable positioning even in challenging environments.  
The fourth is the fusion module, which uses an EKF to integrate enhanced APDR with VIO. This integration achieves higher accuracy and more stable localization, significantly enhancing system performance in complex and occluded environments.
## 3 Results and Discussion  
To process the collected data and better understand the effects of the factors and their interactions on the process responses, statistical models were created using MATLAB (R2018b, MathWorks). In particular, the measured data was utilized to generate quadratic regression models by fitting the second-order polynomial as shown in Equation (2).  
\[y=b_{0}+\sum_{i}b_{i}x_{i}+\sum_{i}b_{ii}x_{ii}^{2}+\sum_{i}b_{ij}x_{i}x_{j} \tag{2}\]  
Figure 4: Stylus profilometer MarSurf GD 26.  
Figure 5: (a) Energy measuring device Voltcraft Energy Check 3000 and (b) analytical balance Sartorius Extend ED224S.  
where \"\(y\)\" is the output variable (percentage error for dimensional accuracy, surface roughness, energy consumption, and productivity), \"\(b_{0}\),\" \"\(b_{i}\),\" \"\(b_{\bar{u}}\),\" and \"\(b_{\bar{y}}\)\" are the regression coefficients or predictors, and \"\(x_{i}\)\" is the value of the \(i\)th factor (infill percentage, layer thickness, printing speed, printing temperature, and surface inclination angle).  
Predicted responses were plotted to show effects of process parameters and their interactions on process responses. In Figure 6, Figures 8, 11, 13, and 15, the predictions, the green lines on the plots, were based on quadratic regression models, and show the effects of each process parameter on the response value, when keeping the other parameters constant. The dotted red lines show the 95% confidence bounds. These interactive prediction plots allow optimization of the process parameters. Figure 7, Figures 9, 12, 14, and 16 show the effect of interaction (quadratic model) of process parameters on each process responses individually. Analysis of variance (ANOVA) was conducted to identify the most significant terms affecting the response values. These were located through the \(p\)-value in the ANOVA. A term is regarded as significant if the \(p\)-value is below 0.05 and considered as highly significant if the \(p\)-value is below 0.01.
# Light-Weighted Password-Based Multi-Group Authenticated Key Agreement for Wireless Sensor Networks
## 4 Security Analysis
### Mutual Authentication  
In the proposed group key agreement scheme, only legal sensor node \(SN_{i}\) who has the correct \(ID_{i}\) and \(pw_{i}\) can compute \(C_{i}=h(DID_{i}||Q_{i}||K_{1}||X_{i}||C_{i}||T_{i})\), where \(Q_{i}=h(ID_{i}||pw_{i})\). \(AS\) then authenticates sensor node by checking \(C_{i}=h(DID_{i}||Q_{i}||K_{1}||X_{i}||C_{i}||T_{i})\). Also, sensor node authenticates \(AS\) by checking \(CS_{i}=h(K_{1}||Q_{i}||Y_{i+1}||Y_{i+1}||GID_{im}||T_{S})\). Additionally, only legal \(SN_{i}\) in \(G_{i,m}\) can compute \(sk_{i,m}=T_{r_{1}\cdot r_{2}\cdot r_{5}}(x)\cdot T_{r_{2}\cdot r_{3}\cdot r_{ 5}}(x)\cdot\ldots\cdot T_{r_{n}\tau_{1}\cdot r_{5}}(x)\text{mod}p\). Then, \(SN_{i}\) authenticates \(SN_{j}\) by checking \(Auth_{i,m}=h(DID_{i}||sk_{i,m}||GID_{im}||T_{S})\) for \(j\
eq i\). Therefore, the participants of the proposed protocol authenticate each other.