esa-sceva/satcom-chunk-collection huggingface dataset:
# Maximum Correntropy Unscented Kalman Filter for Spacecraft Relative State Estimation
## 2 Preliminaries  
### Maximum Correntropy Criterion  
Given two random variables \(X,Y\in\mathbb{R}\) with joint distribution function \(\mathrm{F}_{XY}(x,y)\), the correntropy is a generalized similarity measure between \(X\) and \(Y\) defined by  
\[V(X,Y)=\mathrm{E}\left[\kappa(X,Y)\right]=\int\kappa(x,y)d\mathrm{F}_{XY}(x,y), \tag{1}\]  
where \(\mathrm{E}\) is the expectation operator, and \(\kappa(\cdot,\cdot)\) denotes a shift-invariant kernel following the Mercer condition. In this paper, the kernel function is chosen as the Gaussian kernel given by  
\[\kappa(x,y)=\mathrm{G}_{\sigma}(e)=\exp\left(-\frac{e^{2}}{2\sigma^{2}}\right), \tag{2}\]  
where \(e=x-y\), and \(\sigma>0\) is the kernel bandwidth.  
In many practical applications, we have only a limited amount of data and the joint distribution \(\mathrm{F}_{XY}\) is unknown. In these cases, the correntropy can be estimated by the sample mean estimator:  
\[\widehat{V}(X,Y)=\frac{1}{N}\sum_{i=1}^{N}\mathrm{G}_{\sigma}\left(e(i) \right), \tag{3}\]  
where \(e(i)=x(i)-y(i)\), with \(\left\{x(i),y(i)\right\}_{i=1}^{N}\) being \(N\) samples drawn from \(\mathrm{F}_{XY}\).  
Taking the Taylor series expansion of the Gaussian kernel yields  
\[V(X,Y)=\sum_{n=0}^{\infty}\frac{(-1)^{n}}{2^{n}\sigma^{2n}n!}\mathrm{E}\left[(X-Y) ^{2n}\right]. \tag{4}\]  
It can be seen that the correntropy is a weighted sum of all even order moments of the error variable \(X-Y\). In addition, the kernel bandwidth appears as a parameter weighting the second order and higher order moments. It is noted that the correntropy will be dominated by the second order moment when the kernel bandwidth is very large compared to the dynamic range of the data.  
Given a sequence of error data \(\left\{e(i)\right\}_{i=1}^{N}\), the cost function of MCC is given by  
\[J_{\mathrm{MCC}}=\frac{1}{N}\sum_{i=1}^{N}\mathrm{G}_{\sigma}\left(e(i)\right). \tag{5}\]  
Assume \(\mathbf{W}\) is a parameter vector of an adaptive system to learn, and let \(x(i)\) and \(y(i)\) denote the model output and the desired response, respectively. The MCC based learning can be formulated as the following optimization problem:  
\[\widehat{\mathbf{W}}=\operatorname*{arg\,max}_{\mathbf{W}\in\mathbf{\Omega}} \frac{1}{N}\sum_{i=1}^{N}\mathrm{G}_{\sigma}\left(e(i)\right), \tag{6}\]  
where \(\widehat{\mathbf{W}}\) denotes the optimal solution, and \(\Omega\) denotes a feasible set of the parameter.
## 4 Results and Discussion
Column (**a**) represents litho-fluid log normalization of water saturation (SWT), clay volume (VCL), and coal volume (COAL) logs. Figure 7: Comparison between (**a**) proposed method and (**b**) conventional method toward predicting P-Impedance from actual angle stacks seismic survey. \begin{table}
\begin{tabular}{c c c c c} \hline
**Well** & **Approach** & **P-Impedance** & **S-Impedance** & **Density** \\ \hline \multirow{2}{*}{Blind 1} & Proposed & **0.84** & **0.79** & **0.67** \\  & Conventional & 0.83 & 0.76 & 0.51 \\ \hline \multirow{2}{*}{Blind 2} & Proposed & **0.82** & **0.81** & **0.63** \\  & Conventional & 0.76 & 0.75 & 0.44 \\ \hline \end{tabular}
\end{table} 
Table 1: Elastic properties correlation coefficient comparison between proposed and conventional approach at blind well 1 and 2. Figure 8: Comparison between (**a**) proposed method and (**b**) conventional method toward predicting density from actual angle stacks seismic survey. However, the conventional method reveals the presence of scattered amplitude noises and prominent seismic imprints masking the reservoir, especially for density results. The proposed method, on the other hand, produces inversion results with a more stable, clearer definition and fewer noises. This is accomplished without the use of an initial model or wavelet extraction, unlike the standard approach. Despite better correlation, it is observed that the trained network model cannot accurately capture the relative variation closer to the actual elastic properties. This may indicate that the full correlation between the rock-physics driven synthetic data and real data used was not contextualized during the training. The unique characteristic signatures of synthetic and real seismic and well logs' data need to be investigated further. The introduction of noise modeling in catching the unique difference between synthetic and real data into the deep learning training process may be useful for prediction accuracy enhancement. In addition, the generation of rock-physics-driven synthetic data library may need to be refined. The current data simulation approach is only limited to two predominant facies. Utilization of more geologically realistic facies into the modeling can aid in establishing more realistic subsurface conditions and potentially enhance prediction accuracy. The approach also could be further improved by incorporating spatial neighboring input data for network training, since the current approach is only limited to trace-based input training. In doing so, the deep neural network may not only comprehend the relationship between seismic and rock physics but also be capable of capturing the structural and stratigraphical information of the subsurface. Aside from spatial-based input training, exploring more complex image-based deep learning algorithms techniques such as Generative Adversarial Networks (GAN), Graphomer, Etc., might also enhance the accuracy estimation.
# Sustainable Manufacturing Decisions through the Optimization of Printing Parameters in 3D Printing
## 4 Discussion  
### Printing Parameters in Different Single-Objective Optimizations  
The variation of the five printing parameters under different single-objective optimizations can be explained through the following points: First, to achieve ideal results in W, OW, OL, carbon dioxide emissions, and electricity cost, the fan rate must be set to maximum (100%); however, the T, material cost, and labor cost should be reduced to 50%. Second, when the printing speed is set to 20 mm/s, W, OW, OL, and T show the best performance. However, to achieve the minimum carbon dioxide emissions, labor cost, and electricity cost, the printing speed should be increased to 30 mm/s. [PERSON] et al. [19] and [PERSON] et al. [21] highlighted that the printing speed affects the printing time, as the printing time is the accumulation of the nozzle movement. Therefore, reducing the printing time can also reduce carbon dioxide emissions. For the minimum material cost, the printing speed must be changed to 25 mm/s. Third, when the nozzle and build plate temperatures were set to 190 \({}^{\circ}\)C and 30 \({}^{\circ}\)C, respectively, the smallest dimensional errors in W, OW, and OL were obtained, which is consistent with the nozzle setting temperature suggested by [PERSON] et al. [15]. However, to optimize T, it is necessary to increase the nozzle and build plate temperatures to 210 \({}^{\circ}\)C and 60 \({}^{\circ}\)C, respectively.  
Regarding the carbon dioxide emissions and electricity cost due to energy consumption, [PERSON] et al. [12] highlighted that the majority of the total energy consumption of an FFF-3D printer comes from heating and maintaining the nozzle and build plate temperatures. Since energy consumption is also positively related to electricity cost, setting the nozzle and build plate temperatures to 190 \({}^{\circ}\)C and 15 \({}^{\circ}\)C can help in obtaining minimal carbon dioxide emissions and electricity costs. Finally, a layer thickness of 0.1 mm can yield the best performance with regard to OL and T. Moreover, [PERSON] et al. [21] mentioned that the layer thickness affects the number of layer thicknesses and amount of material used. Furthermore, [PERSON] [23] stated that a higher layer thickness can shorten the printing time, thus reducing the printing time and material usage and inevitably reducing the energy consumption and printing costs. As for the carbon dioxide emissions and three printing costs, using a layer thickness of 0.3 mm would be the best choice.
# A Security-Oriented Data-Sharing Scheme Based on Blockchain
## 4 Experiment and Analysis  
### Experimental Environment  
To assess the performance and security of our proposed scheme, we conducted experiments that evaluated storage efficiency, encryption/decryption performance, and the time overhead associated with NFT functionalities. We then analyzed the security implications of the data-sharing mechanism. Finally, we compared our scheme with similar approaches, underscoring its usability and security benefits.  
This paper introduces a blockchain-based data security sharing framework utilizing Hyperledger Fabric, with IPFS serving as the underlying storage solution. Hyperledger Fabric ensures authenticated access for all participants and features customized permission controls to safeguard sensitive user data. The software configuration of the experimental setup is detailed in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Contract Functions** & **Contract Logic** & **Contract Methods** & **Contract Description** \\ \hline Identity Registration & Register Legal Identity & userRegistration() & \begin{tabular}{c} Generate public and private keys to register \\ legal identities \\ \end{tabular} \\ \hline NFT Minting & \begin{tabular}{c} Initialize NFT assets \\ Add NFT \\ \end{tabular} & \begin{tabular}{c} initLedger() \\ mine() \\ \end{tabular} & \begin{tabular}{c} Initialize NFT assets on the blockchain \\ Add a new NFT after initializing the NFT asset \\ \end{tabular} \\ \hline NFT Transfer & \begin{tabular}{c} Asset Authorization \\ Asset Transfer \\ \end{tabular} & \begin{tabular}{c} approve() \\ transFrom() \\ \end{tabular} & \
\begin{tabular}{c} Authorizes the data demander \(u_{j}\) \\ Transfer the NFT asset to the privileged data \\ demander \(u_{j}\) \\ \end{tabular} \\ \hline NFT Query & Find NFT Information & getAddress() &
\begin{tabular}{c} Legitimate users look for NFT information and \\ get file address information \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 1: Architecture of the smart contracts.  
Figure 4: Shared data flow history tracking mechanism based on NFT.
## 5 Performance Evaluation  
In this section, we evaluate the RCU-style synchronization schemes by answering the questions below.  
* Do RCU-style synchronization techniques show performance scalability in a manycore-based operating system?
* How well does each synchronization scheme perform when simultaneously accessing the linked list and hash table, which are representative data structures?
* How does each synchronization mechanism perform according to the read/write ratio of the workload?
# Multilayer Model in Soil Moisture Content Retrieval Using GNSS Interferometric Reflectometry
###### Abstract  
The global navigation satellite system-interferometric reflectometry (GNSS-IR) was developed more than a decade ago to monitor soil moisture content (SMC); a system that is essentially finished has emerged. The standard GNSS-IR model typically considers soil to be a single layer of medium and measures the average SMC between 1 and 10 cm below the soil surface. The majority of the SMC is not distributed uniformly along the longitudinal axis. This study is based on a simulation platform and suggests a SMC-stratified measurement model that can be used to recover the SMC at different depths in the sink and reverse osmosis to address the issue that conventional techniques cannot accurately measure soil moisture at different depths. The soil moisture of each layer was assessed by utilizing the GNSS signals reflected by various soil layers, and this study employed total transmission when the vertical linearly polarized component of the electromagnetic wave was conveyed by the GNSS signal reflected by the soil. This work employed the Hilbert transform to obtain the interference signal envelope, which increases the visibility of the interference signal's \"notch\" and reduces the burr impact of the interference signal brought on by ambient noise. The accuracy of the SMC measurement at the bottom declines due to the soil's attenuation of the GNSS signal power, but the correlation between the predetermined value and SMC retrieved by the GNSS-IR multilayer SMC measurement model similarly approached 0.92.  
global navigation satellite system-interferometric reflectometry; soil moisture content; multilayer; Hilbert transform; sink model; reverse osmosis model +
Footnote †: journal: Sensors  
ultilayer Model in Soil Moisture Content Retrieval Using GNSS Interferometric Reflectometry
# Ride Comfort Prediction on Urban Road Using Discrete
## 4 Results
### Correlations between DRI and MTVV
#### 4.2.3 The Combined Effect of the Left and Right Wheel Paths  
Similar to the IRI, the DRI can only reflect the pavement roughness of one single road profile, while actually the vibration in the vehicle is provoked by both the left and right wheel path. Many research employed the average IRI of the two profiles to consider the combined effect, which is reliable in long-road-section measurements but does not perform well in the measurement of short sections. Figure 7 shows the relations between the DRIleft and the DRIright in the cases of four different lengths of road sections. Note that the DRIleft and the DRIright are the mean value of the DRI of the left and right profiles, respectively.  
Since the mean value of the DRI converges to the IRI when the whole length of the road section becomes larger, the DRI in Figure 8a, whose length of road is 100 m, is almost equal to the IRI. Therefore, the significant correlation between the DRIleft and the DRIright demonstrates the reliability of using the average IRI to characterize the pavement roughness of the whole road because the two profiles have close pavement roughness in long sections. On the contrary, for short-distance sections, there is a significant difference in the DRI calculated on the left and right wheel paths. As seen in Figure 8b-d, the correlation is becoming weaker as the length becomes shorter. While driving on short sections, any change in one side will affect the ride comfort on both sides. Generally, the left wheel path makes more contribution to the vibration of the driver's seat (assuming that the driver's seat is on the left), whereas the road profile on the right wheel path has more influence on the vibration of the passenger's seat. Therefore, it is necessary to discuss the combined effect when studying the correlations between pavement roughness and ride comfort.
## 5 Discussion
### Analysis of Extreme Offset, Dynamic Bandwidth Strategy, and ITS Set Aggregation  
Extreme offset analysis. Due to airborne laser lidar having poor penetration and cannot scan complete individual trees (especially in locations with severe canopy occlusion) [8], at this time, the calculated individual tree center point has a huge gap with the actual individual tree center point, which seriously interferes with the learning effect of the deep network, resulting in the poor ability to enhance the discrimination among trees.  
Figure 12: Comparative test results. (**a**) Overall precision, recall, and F-score on different methods; (**b**) F-score on different plots and methods.  
Considering the treetop points are distinct and stable, an extreme offset loss function for ITS is designed based on HAIS, which makes the points offset to their respective treetops to enhance the discrimination among different trees. Then, the density of the treetop increases significantly after points are offset to their treetops.  
Dynamic bandwidth strategy analysis. Due to the deep network randomness, the offset effect of some points is poor. Directly using the mean shift algorithm for clustering may suffer from serious under-segmentation and over-segmentation problems. Considering the treetop density increases significantly after extreme offset, the average proximity distance is adaptively generated as the bandwidth of the mean shift algorithm so that the dense points near the treetop are clustered into one class. At the same time, the isolated points with poor offset are clustered separately to eliminate the under-segmentation error as much as possible.  
ITS set aggregation analysis. Due to the erroneous offset point, this inevitably causes an under-segmentation issue of the mean shift algorithm. The HAIS set aggregation approach merely aggregates sets that are close to one another, which may result in the improper aggregation of neighboring sets of different trees. In order to effectively prevent neighboring sets of various trees from being aggregated, the ITS set aggregation approach takes into account the gradient features of the individual trees. This method reduces over-segmentation mistakes and boosts segmentation accuracy.
## 1 Introduction
### PHA Synthesis
The synthesis through related carbon sources (fatty acids) is more accessible than from unrelated carbon sources (carbohydrates), and the addition of fatty acids to the substrate with a structure similar to the monomer of the PHA can improve the synthesis of MCL-PHA [27]. The process of \(\beta\)-oxidation synthesis involves the enzymatic activity of (R)-specific-enoyl-CoA hydratase, acyl-CoA oxidase, and 3-ketoacyl-CoA reductase, which are then polymerized by PHA synthase to produce PHA [28]. One instance involves the operational mechanism of this specific pathway in _Aeronomas_ spp., in which acyl-CoA derived from fatty acids undergoes degradation, resulting in the formation of enoyl-CoA intermediates. These intermediates can then be transformed into (R)-3-hydroxyoxyl-CoA through the activity of (R)-specific-enoyl-CoA hydrase. Subsequently, this (R)-3-hydroxyoxyl-CoA can participate in the elongation of the polyester chain by means of PHA synthase [29]. In the pathway of de novo fatty acid synthesis, an intermediate within the fatty acid synthesis pathway undergoes conversion by the transacylase PhaGPp into (R)-3-hydroxydecanoylI-CoA, which exists in its carrier protein form. This conversion process facilitates the utilization of (R)-3-hydroxydecanoylI-CoA as a substrate for the production of PHA [30]. The use of inexpensive substrates, such as glycerol residues, simple sugars, and short-chain volatile fatty acids derived from fermented residues, can be facilitated by this particular approach [31]. A fundamental enzyme in the synthesis pathways is PHA synthase or PhAc en-zymes, which is responsible for the polymerization of hydroxyacyl monomers to obtain PHA polymers. Several classes of PhAc with varying substrate specificities have been isolated from various microorganisms. PhAc of _Ralstonia eutrpha_ (PhACRe) is specific for SCL-PHA. The PHA synthases of _Pseudomonas_ sp. are specific for MCL-PHA monomers. An additional example is the PHA synthase of _Aeronomas cariae_, which has a high affinity for units of 3 HB and 3 HHx and can therefore synthesize the copolymer P(3 HB-co-3 HHx) [32; 33]. PHA is stored as an energy reserve when nutrients such as nitrogen, oxygen, and phosphorus are scarce or when the pH changes. The use of these energy stores for bacterial development occurs when the nutrition supply is restricted, as seen in Figure 1[34]. This effect is demonstrated in mixed microbial cultures, with the limited nitrogen and phosphorus action resulting in the efficient production of PHA [35], just as the limitation of phosphorus and carbon was advantageous for the production of mcl-PHA [33]. The limitation of sodium nitrate and supplementation with sodium bicarbonate and sodium acetate optimized PHB production in _Stigeccolonium_ sp. B23 microalgae [36].
# Surrogate-Based Robust Design for a Non-Smooth Radiation Source Detection Problem
## 3 Detection of Nuclear Radiation Sources Using Surrogate Model  
The problem of estimating the location and intensity of a radiation source when several detectors with associated measurements are available represents a classic inverse problem. The source \(\boldsymbol{\theta}_{0}\) is unknown and has to be inferred from realizations \(v_{i}\) of the random variables \(\mathrm{Y}_{i},\ i=1,\ldots N\) whose statistical model is described in (13).  
The Gaussian likelihood function \(\pi:\mathcal{X}\rightarrow[0,\infty)\) is given by  
\[\pi(\boldsymbol{V}|\boldsymbol{\theta})=\frac{1}{\sqrt{(2\pi)^{N}\prod_{i=1} ^{N}\sigma_{i}}}\exp\bigg{(}-\sum_{i=1}^{N}\frac{1}{\sigma_{i}^{2}}[v_{i}- \tilde{F}_{i}(\boldsymbol{\theta})]^{2}\bigg{)}, \tag{14}\]  
where \(\boldsymbol{V}=[v_{1},\ldots,v_{N}]\) is the vector of all the available observations.  
A standard technique to estimate the location and intensity of a radiation source, based on measured data, is to apply maximum likelihood estimators. Due to the monotonicity of the logarithm function, maximizing (14) is equivalent to minimizing the negative logarithm of the likelihood  
\[\min_{\boldsymbol{\theta}\in\mathcal{X}}J(\boldsymbol{\theta}),J(\boldsymbol{ \theta})=\frac{1}{2}\sum_{i=1}^{N}\frac{1}{\sigma_{i}^{2}}[v_{i}-\tilde{F}_{i }(\boldsymbol{\theta})]^{2}. \tag{15}\]  
We omit the constant in front of the exponential term in Equation (14) since it does not affect the solution. The objective of this investigation is to solve the optimization problem (15) with minimal uncertainty. The solution to this problem (15) is a maximum likelihood estimator which is a random variable. In this context, uncertainty represents the covariance of this estimator which is shown to be the inverse of the Fisher information matrix (23). To achieve this, we apply the robust design strategy described in next section.
## 3 Methodology
### Fuzzy TOPSIS-Based Decision-Making Model for Construction Fall Protection Systems
#### 3.2.1 Calculation of the Normalized Decision Matrix  
In the fuzzy TOPSIS method, in order to effectively evaluate the safety performance of fall protection alternatives, the fuzzy decision matrix is firstly established. The fuzzy decision matrix with \(m\) rows and \(n\) columns of the fuzzy MCDM problem can be expressed as Equation (6), where \(m\) is the number of safety planning alternatives and \(n\) is the number of criteria.  
\[\widetilde{D}=\begin{array}{c}A_{1}\\ A_{1}\\ \vdots\\ A_{1}\end{array}\left[\begin{array}{cccc}C_{1}&C_{2}&\cdots&C_{n}\\ \widetilde{x}_{11}&\widetilde{x}_{12}&\cdots&\widetilde{x}_{1n}\\ \widetilde{x}_{21}&\widetilde{x}_{22}&\cdots&\widetilde{x}_{2n}\\ \vdots&\vdots&\ddots&\vdots\\ \widetilde{x}_{m1}&\widetilde{x}_{m2}&\cdots&\widetilde{x}_{mn}\end{array}\right] \tag{6}\]  
where \(\widetilde{D}\) represents the fuzzy decision matrix with alternatives (\(A_{1},A_{2},\ldots,A_{m}\)) and criteria (\(C_{1},C_{2},\ldots,C_{n}\)), and \(\widetilde{x}_{ij}\) is the fuzzy number representing the performance of alternative \(i\) with respect to criterion \(j\).  
Then, assuming that \(\widetilde{P}=(p_{1},p_{2},p_{3})\) and \(\widetilde{Q}=(q_{1},q_{2},q_{3})\) are two fuzzy numbers, the mathematical relations can be expressed as Equations (7) and (8) based on the calculation rules of the fuzzy set theory:  
\[\widetilde{P}\oplus\widetilde{Q}=(p_{1},p_{2},p_{3})\oplus(q_{1},q_{2},q_{3}) =(p_{1}+q_{1},p_{2}+q_{2},p_{3}+q_{3}) \tag{7}\]  
\[\widetilde{P}\bigotimes\widetilde{Q}=(p_{1},p_{2},p_{3})\bigotimes(q_{1},q_{2},q_{3})=(p_{1}q_{1},p_{2}q_{2},p_{3}q_{3}) \tag{8}\]  
After the initial fuzzy decision matrix is generated, it can be normalized to matrix \(\widetilde{R}\) as Equation (9):  
\[\widetilde{R}=\left[r_{ij}\right]_{m\times n^{\prime}},\ i=1,\ 2,\ \ldots,\ j=1,\ 2, \ \ldots,\ n \tag{9}\]In this study, the criteria can be summarized into two category types: the \"benefit criteria\" and the \"cost criteria\". To this end, the criteria can be normalized through Equation (10) or Equation (11) based on the corresponding criterion type.  
\[\widetilde{r}_{ij}=\left(\frac{a_{ij}}{c_{j}^{*}},\frac{b_{ij}}{c_{j}^{*}},\frac {c_{ij}}{c_{j}^{*}}\right),j\in B \tag{10}\]  
where \(c_{j}^{*}=\underset{i}{\max}c_{ij},j\in B\), and \(B\) represents the set of benefit criteria.  
\[\widetilde{r}_{ij}=\left(\frac{a_{j}^{-}}{c_{ij}},\frac{a_{j}^{-}}{b_{ij}}, \frac{a_{j}^{-}}{a_{ij}}\right),j\in C \tag{11}\]  
where \(a_{j}^{-}=\underset{i}{\min}a_{ij},j\in C\), and \(C\) represents the set of cost criteria.