esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussions
### Uncertainties of Model  
In this study, based on ground survey data from the Xilingol grassland for the years 2005-2012 and MODIS productivity data for the same time period, we developed statistical models based on the relationship between the PSNnet data and aboveground biomass data and then selected the optimal model to estimate the grassland aboveground biomass of the Xilingol grassland. However, our estimate still retains some uncertainties for the following reasons. First, there exists the scale effect between remotely sensed data and ground survey data. The spatial resolution of MODIS productivity data is 1 km, differing from the size of quadrat. In the course of the sampling, we have taken the average value of multiple samples to reduce the estimation error caused by the scale effect. Second, the remotely sensed data used in this study came from the MOD17A2 eight-day PSNnet product, which is a global land vegetation net productivity product calculated by model and has been validated widely, across most of the world. In the process of performing these calculations, nevertheless, since some of the maintenance respiration costs and all of the growth respiration costs have not been accounted for in the daily timestep, the daily output from this algorithm is termed PSNnet (net photosynthesis), to differentiate it from the true daily NPP. This reason has magnified the contrast between net photosynthesis and actual biomass, and becomes the important uncertainty source for biomass estimation. In addition, although the MODIS-PSNnet dataset has been processed with a series of corrections, there is still a certain level of deviation, which causes uncertainty in the estimation. Understanding and identifying the sources of uncertainties and then devoting efforts to improving them are critical in improving grassland aboveground biomass estimation performance; therefore, more research is needed in the future for reducing the uncertainties from different sources in the aboveground biomass estimation procedure.
## 2 Computational Model
### Elastic Deformation  
The most commonly used method for calculating elastic deformation in the MBD system is through modal superposition, where the total body deformation is estimated as a linear combination of its mode shapes. In other words, the physical deformation is depicted by a small number of eigenvectors:  
\[\mathbf{u}=\mathbf{\Omega}\mathbf{q}_{\mathrm{d}}, \tag{4}\]  
where \(\mathbf{u}\) represents the vector of linear deformations, and the modal matrix \(\mathbf{\Omega}\) stores the vector of the modal coordinates of the mode shapes \(\mathbf{q}_{\mathrm{d}}\). Notably, the vector of modal coordinates is a subset of generalized coordinates. This means that the number of mode shapes used for the deformation calculus increases the number of degrees of freedom in the MBD model.  
To correctly estimate the deformation, one must have a proper database of the mode shapes. To respect the boundary conditions, the component mode synthesis (in this case, the Craig-Bampton method [27]) is employed, as it enables the exclusion of the specific degree of freedom from modal superposition. The Craig-Bampton method belongs to a group of reduction methods that reduce the number of degrees of freedom of the finite element model. [PERSON] and [PERSON] [19] presented improved reduction methods for calculating the elastic deformation of a lubricated body in the MBD system and compared them with the original Craig-Bampton method [27]. These methods are applied in the finite element software so that the user must create their script or use the default script of the application of the reduction method. The result of the finite element software is a reduced body, called the modal neutral file, which represents an input to the MBD model.  
The expensive projection of the load in physical form \(\mathbf{F}\) to the modal coordinates is used only once when creating the modal neutral file. This way is more advantageous than repeatedly calculating the projection during the simulation, because the MBD solver requires only the modal form of the load \(\mathbf{f}\):  
\[\mathbf{f}=\mathbf{\Omega}^{\mathrm{T}}\mathbf{F}. \tag{5}\]  
Equation (5) represents the most general way of estimating the modal force vector. Since this paper focuses on monolithic modelling of EHL in the MBD system (solution within one numerical integrator), the modal force is calculated within the user-written subroutine. The subroutine is called by the MBD solver whenever it must estimate the contact forces. Nevertheless, the use of Equation (5) in the subroutine bears the burden of high computational cost, as presented by [PERSON] et al. [28].  
The magnitude of the modal force depends on the lubrication conditions of the contact pair. Moreover, the contact forces between the lubricated surfaces are carried by the lubricant or by the surface asperities, according to the distance of the contact surfaces. Based on the size of the ratio between these two contributors, the lubrication regime is identified as a boundary lubrication regime, a partial (mixed) lubrication regime or a hydrodynamic lubrication regime.
## 4 JTAG Security Solutions
### Security Level Based Approach  
[PERSON] et al. (2006) (2006) proposed new protected JTAG architecture solution for microcontrollers and processors. The main motive of this paper is to protect the program residing inside controller. Thus, to prove this, they proposed a separate hardware protecting mechanism that guarantees reliable authorization. This solution gives not only facility of three protection levels to user but four different access modes as per their requirement. AM0 access mode discussed in this paper might be useful if it becomes implemented since this mode restricts all kinds of data through JTAG interface, giving highest security to sensitive data. Moreover, [PERSON] et al. (2013) also proposed and simulated multi-level JTAG architecture. They offered VHDL implementation of JTAG architecture with four levels of privilege access modes: unprotected level (p1), low level protection level (p2), medium level protection level (p3), and maximum protection level (p4). AES encryption or decryption method is adopted for private key generation and authorization. These privilege level accesses are based on different stages involved in product life cycle development. Unprotected level (p1) can used by developers in initial development stage since they need full access to internal registers and flash memory. Low protection level (p2) can be used for just circuitry functions and uploading firmware. Medium protection level (p3) gives limited debugging facility such as board level interconnection testing for certified product distributor or service center. Maximum protection level (p4) is a locked state in which JTAG port is completely disabled hence giving maximum security to owner or user of product. Figure 4 shows block diagram of Secure JTAG Architecture.  
Conventional JTAG architecture gets added by an Authentication and Authorization Module (AAM) and access provider. AAM register is implemented for communication protocol and user level access settings whereas Access Provider is a memory module that holds different access levels to actual JTAG architecture. The authors modeled this using VHDL and simulated using MODEL SIM 10.0d. In this paper, VHDL programming details are limited but the concept of level-based security seems promising for IoT devices.  
Figure 3: IC with anti-tamper JTAG (2018).
# Theranostic Nanoplatforms of Thiolated Reduced Graphene Oxide Nanosheets and Gold Nanoparticles
## 3 Results and Discussions
In particular, in the ATR-FTIR spectra of citrate-capped AuNS, bwGO@AuNS, and rGOSH@AuNS, the peaks for the -COO- asymmetric stretching modes were visible, respectively, at 1560, 1569 and 1556 cm-1, largely redshifted in comparison to the original peak position in the citrate at 1582 cm-1 [96]. On the other hand, the peak for carboxylate symmetric stretching at 1388 cm-1 in the citrate reference sample was found at 1398 cm-1 for both AuNS and bwGO@AuNS, while it was slightly shifted to 1391 cm-1 in the rGOSH@AuNS spectrum. Noteworthy, a weak absorption at 676 cm-1, attributed to the O-Au bond stretching mode, was visible in the AuNS spectrum [97]. Also, the characteristic features of C=O carboxylic and carbonylic vibrational modes of GO were discernable in the bwGO@AuNS and rGOSH@AuNS spectra at 1702 and 1701 cm-1, respectively. Moreover, characteristic C-O and C-H vibrational modes were quite visible in the GO@AuNS spectra below 1200 cm-1 as well as the weak absorption attributed to S-H stretching mode (at 2664 cm-1) in the rGOSH spectrum [98]. Figure 7: Infrared spectra of rGOSH in the comparison with GO: **(a)** Experimental ATR-FTIR spectra of citrate-capped AuNS and hybrid GO@AuNS samples in the comparison with trisodium citrate; **(b)** experimental and calculated IR spectra of AuNR and GO@AuNR hybrids in the comparison with cetyl trimethylammonium bromide (CTAB) surfactant. The experimental IR spectra of CTAB-capped AuNR and of the hybrids with GO showed the characteristic absorption peaks of CTAB, with no significant shifts compared to CTAB alone, indicating the likely presence of unbound CTAB on the gold surface (Figure 7b). Specifically, the spectrum of CTAB showed the strong absorption bands attributed to the symmetric and asymmetric C-H stretching modes at 2916 and 2849 cm\({}^{-1}\), respectively. Furthermore, the asymmetric and symmetric C-H scissoring of CH\({}_{2}\) of the hydrocarbon tail of CTAB were visible at 3017 and 1488 cm\({}^{-1}\)[99]. The peaks at 1463 and 1473 cm\({}^{-1}\) were characteristic of CH\({}_{3}\)-N\({}^{+}\) scissoring modes [100], while the C-N stretching mode exhibited a peak at 960 cm\({}^{-1}\), as well as the vibrational mode at 719 cm\({}^{-1}\) corresponding to the bending of more than four adjacent CH\({}_{2}\) groups [101]. As to the calculated IR spectra, Figure 7b shows that the presence of AuNR on the surface of GO or rGOSH induced both hyperchromic and bathochromic shifts for the stretching modes of O-H (at 3490 cm\({}^{-1}\)) or S-H (at 2730 cm\({}^{-1}\)), respectively. To note, the computational approach unravels enhanced vibrational modes for the GO@AuNP hybrids that are commonly observed in surface enhanced Raman scattering (SERS) [102], such as graphitic band (at 1570 cm\({}^{-1}\)), representative of the sp\({}^{2}\) hybridized network of carbon in GO and C=C bonds at 1510 cm\({}^{-1}\) in rGOSH, respectively.
## 4 Experimental Process and Result Analysis
### Comparative Experiments
#### 4.4.1 Comparison of the Presented Method with Other Common Methods  
Common methods in the GNSS positioning error estimation research mainly include the extended Kalman filter (EKF), linear regression (LR), support vector machine (SVM), decision tree (DT), and RF. These five common methods and the presented method are compared in the first comparative experiment.  
By incorporating the MAGU module into the MLP model, the presented method minimizes the influence of unimportant features on the model, enabling the model to achieve better performance. It can be seen from Table 3 that the presented method obtains smaller RMSE values for both datasets and has obvious advantages over other common methods. This shows that the estimation accuracy of the presented method is higher.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **RMSE Value** & **MAE Value** & **Standard Deviation** \\ \hline RF & 0.047 & 0.022 & 0.016 \\ Ours & 0.029 & 0.014 & 0.010 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative analysis experimental results of the proposed method and random forest algorithm (unit: m).  
Figure 12: Residual histogram with the presented method and the RF algorithm.
## 3 Results and Discussion
### Volatile Flavor Profile during BBP-Meju Fermentation with Different Salt Concentrations  
Volatile flavor compounds play a crucial role in determining the flavor and texture of fermented foods and are essential for their quality and consumer acceptance [48]. To  
Figure 4: The volatile compounds of BBP-meju with different salt concentrations based on E-nose data: (**a**) PCA scores and (**b**) radar chart. further elucidate the differences in flavor compounds during the fermentation of BBP-meju with different salt concentrations, HS-SPME-GC \(\times\) GC-MS was employed for detection and analysis in this study. The results showed that a total of 66 volatile flavor compounds were identified, which could be classified into 7 major groups, including 11 aldehydes, 12 esters, 4 ketones, 22 alcohols, 5 phenols, 6 acids, and 6 other compounds (Table 2). The relative abundance of the seven groups of volatile flavor compounds in different meju samples, as well as the changes in their contents, are statistically presented in Figure 5. The total amount of volatile flavor compounds increased significantly during BBP-meju fermentation, with increases of 143.19% \(\pm\) 4.14% (L), 174.94% \(\pm\) 0.23% (M), and 124.90% \(\pm\) 1.11% (H), respectively (Figure 5B). Alcohols, esters, aldehydes, and acids consistently remained the main volatile flavor compounds in BBP-meju, and alcohols were always absolutely dominant (Figure 5A). At the three different salt concentrations, the medium-salt (8%) meju had the highest content of volatile flavor compounds, while the high-salt (12%) meju had the lowest content (\(p\) < 0.05) (Figure 5B). This result differed from the reported by [PERSON] et al. [23]. Specifically, the content of alcohols, acids, esters, ketones, and phenols in low- and medium-salt meju was higher than that in high-salt meju (Figure 5B). This result agreed with the study of [PERSON] et al. [4]. Figure 5: Content distribution of volatile flavor compounds during fermentation of BBP-meju with different salt concentrations: (**A**) relative abundance of different types of volatile flavor compounds; (**B**) content of different types of volatile flavor compounds; (**C**) variation in the content of different types of volatile flavor compounds across samples. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Compounds** & **CAS N.** & **RI** & **MW** & **L7** & **M7** & **H7** & **L35** & **M35** & **H35** \\ \hline Aldehydes & 71.36-3.
# A Survey on Botnets: Incentives, Evolution, Detection and Current Trends
## 4 Incentives
### Benevolent Botnets  
While exceedingly rare, not all botnets are malicious. A scant few, such as the Hajime botnet, is an example of a neutral if not beneficial botnet [48]. Built on a similar method of infection as Mirai, Hajime distinguishes itself from its cousin in a number of ways, such as:  
* A decentralised P2P distributed hash table, rather than Mirai's C&C approach.
* A far greater number of ways to infect new hosts.
* The usage of a custom made protocol for disseminating files.  
Another interesting difference, is that is has never been used in a documented hostile attack on a service or platform. The only instances of potentially questionable actions performed by Hajime have been acts of broadening its sphere of influence to new IoT devices. In a remarkable act of selflessness, the botnet actively _patches_ discovered security holes on infected devices, rendering many attack vectors used by other botnets mute. Other botnets are created by researchers to intentionally overtake and disable malicious botnets, propagating the harmless version instead [49].
## 3 Numerical Calculation Results and Analysis
### Comparison of Numerical Simulation Results with Wind Tunnel Test Results
When five and six antennas are used in the arrangement, the variation curves of the tower's overturning force coefficient with the wind direction are approximately sawtooth-shaped, but with five antennas, the coefficient is more sensitive to the wind direction, with a larger range of variation. Overall, when a single layer of antennas is arranged on the tower, the overturning force coefficient tends to increase with the antenna arrangement number, but the extent of the increase gradually diminishes as the antenna arrangement number increases. The pattern of change in the overturning moment coefficient of the telecommunication tower with the antenna arrangement number is basically consistent with that of the overturning force coefficient, but with an increase in the antenna arrangement number, the magnitude of increase in the overturning moment coefficient is about twice that of the overturning force coefficient. Figure 14: Velocity contour and streamline diagram at the mid-height cross-section of the antennas when 4 antennas are arranged. (**a**) Form 1. (**b**) Form 2. (**c**) Form 3. Figure 15: Impact of single-layer antenna arrangement on the wind load of telecommunication towers. (**a**) Drag coefficient. (**b**) Windward base overturning moment coefficient. (**c**) Lateral force coefficient. (**d**) Crosswind base overturning moment coefficient. (**e**) Overturning force coefficient. (**f**) Overturning moment coefficient.
# Bayesian Network Demand-Forecasting Model Based on Modified Particle Swarm Optimization
## 2 Bayesian Network Model Construction
### Parameter Estimation  
This section mainly discusses how to estimate the model parameters \(\eta\), \(\gamma\), \(\beta\), \(\mu\), and \(\phi\) in the case of given historical demand data \(y\) = (\(y_{1},y_{2},\,\ldots,\,y_{T}\)) of a particular product.  
The maximum posterior probability estimation is used to estimate the above parameters, and the problem can be expressed as Equation (15).  
\[\left\{\hat{\eta},\,\hat{\gamma},\,\hat{\beta},\,\mu,\phi\right\}=\underset{ \eta,\gamma,\beta,\mu,\phi}{argmax}P(\eta_{1:T},\,\gamma_{1:T},\beta,\mu,\phi) \tag{15}\]  
Here, let us think of \(P\) as a function of the variables \(\eta\), \(\gamma\), \(\beta\), \(\mu\), and \(\phi\).  
\[\left\{v_{id}(t+1)=wv_{id}(t)+c_{1}r_{1}(\frac{\mathit{best}_{id}+\mathit{ best}_{d}}{2}-x_{id}(t))+c_{2}r_{2}(\frac{\mathit{best}_{id}-\mathit{best}_{d}}{2}-x_{id}(t))\right. \tag{21}\]  
\[\left\{v_{id}(t+1)=wv_{id}(t)+c_{1}r_{1}(p_{ad}-x_{id}(t))+c_{2}r_{2}(\mathit{ gbest}_{d}-x_{id}(t))\right. \tag{22}\]  
Equation (21) introduces the speed update strategy in [40], and the position update strategy adopts \"\(x=wx+(1-w)v\)\" so as to improve the global development ability of particles in the early stage of the search. In Equation (22), the position update strategy of \"\(x=x+v\)\" is used to improve the local exploration ability of particles. Combined with the idea of [41], the average dimension information pad of particles is introduced into the velocity update formula to improve the local search efficiency of particles. The calculation formula of \(p_{  
The implementation steps of the MPSO algorithm are as follows:
## 7 Conclusions  
In this paper, a Bayesian method is proposed to estimate the residual life of Weibull-distributed components of on-orbit satellites by fusing multi-source information. The contributions of this paper were concluded as follows:  
1. By fusing prior information, including historical lifetime data, degradation data, similar data and expert information, a more precise estimation of residual life is produced. Both the Bayesian estimate and credible interval are considered in this paper.
2. The Monte Carlo simulation validated the accuracy of the method. The results proved that the Bayesian approach provided more satisfactory and robust estimates than MLE, especially when the sample size of the lifetime data was small. The precision can be improved by fusing multi-source reliability information.
3. The momentum wheel of an on-orbit satellite was taken as an example. The applicability and flexibility of the proposed method were illustrated in the case study. The results were in agreement with those of the simulation study.  
This method can be applied to many problems of survival time prediction, and will be of interest to readers in many applications. To extend our research, we think that various kinds of expert information, including point estimation and the CI of the lifetime, merits further research.  
Conceptualization, Q.Z. and X.J.; Methodology, Q.Z. and Z.C.; Validation, X.J.; Formal Analysis, Q.Z. and B.G.; Writing--Original Draft Preparation, Q.Z.; Writing--Review & Editing, Z.C., X.J. and B.G.  
This research was funded by the National Natural Science Foundation of China (No. 71801219 and No. 61573370), the Natural Science Foundation of Hunan Province (No. 2019J50730) and the Pre-research Foundation of National University of Defense Technology under agreement ZK17-02-08.  
The authors declare no conflict of interest.
## 4 Experimental Results
### Comparison of Average Precision with Traditional Methods  
[PERSON] et al. [20] provided significant and valuable research results on steel surface defects. They presented the performance evaluations of steel surface defect detection with traditional ML algorithms on the NEU dataset. In this section, we provide a comparison between the proposed method and the traditional methods, which are HOG and LBP with two classifiers, neighbor classifier (NNC) and a support vector machine (SVM), based on the experimental results in [20]. Table 3 illustrates the outperformance of the proposed method with the other traditional models on all defects. Figure 6 displays the overall mAP compared to the other traditional approaches. The graph shows that the proposed approach is the best among all the traditional approaches by a 30% margin.
## 4 Experimental Validation
### Results and Analysis
Figure 14 shows the error bar plot according to different means and errors. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Inception V3 + ECA** & **Inception V3** & **CNN** & **FCN** & **TCN** \\ \hline Precision & 0.9725 & 0.9668 & 0.9299 & 0.9064 & 0.9447 \\ Recall & 0.9728 & 0.9668 & 0.9304 & 0.9062 & 0.9443 \\ F1 Score & 0.9726 & 0.9669 & 0.9307 & 0.9069 & 0.9447 \\ Model Parameters & 58,284 & 58,279 & 97,670 & 94,023 & 117,319 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Diagnostic results of different networks. Figure 13: t-SNE visualization results. (**a**) Innovation and residual sequences. (**b**) Filtered data. (**c**) Innovation sequences. From the experimental results, it can be observed that, by extracting innovation and residual sequences of the UKF, the fault features are distinctly represented, and all the neural networks achieve relatively good results. Among them, the Inception V3 network, with its ability to extract features at multiple scales, demonstrated a superior performance in handling multiple fault types, even in situations with relatively high levels of noise interference. Other neural networks, due to reasons such as slow convergence speed and an inability to accurately extract fault features, exhibit a relatively poorer performance in such situations. Additionally, Inception V3 maintains network sparsity while ensuring a good performance, as it has significantly fewer parameters compared to other networks. This achieves a balance between network performance and computational resources. Furthermore, the inclusion of the ECA attention mechanism strengthens the influence of key features, leading to further improvements in performance with a minimal increase in the number of parameters. The above experiments demonstrate the effectiveness and advantages of the proposed approach based on the UKF and Inception V3 model for fault diagnosis in the missile air data system.