esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 The Evaluation of BOC-Like Receiving of MBOC  
### MBOC Overview and Receiving Strategies  
In the modernized GNSS, to help promote the availability of positioning service for civil use, interoperation should be obtained between different GNSS. Multiplexed Binary Offset Carrier (MBOC) [24; 25] is a candidate scheme for the L1 band civil signal, which is commonly accepted by GPS, Galileo and BDS as interoperation signal. MBOC defines that the signal spectrum should satisfy:  
\[G(f)=\gamma G_{\text{BOC}(m,n)}+(1-\gamma)G_{\text{BOC}(n,n)},\ m>n \tag{30}\]  
A MBOC(\(m\),\(n\),\(\gamma\)) signal comprises two BOC modulated signals: a wideband BOC(\(m\), \(n\)) and a narrowband BOC(\(n\), \(n\)), with \(m>n\), and \(\gamma\) is a power coefficient with \(0\leqslant\gamma\leqslant 1\). In practice, since the spectral characteristic Equation (30) is the only limitation for MBOC, various implementations can be used to generate the MBOC signal, including Composed Binary Offset Carrier (CBOC), Time-division Multiplexed Binary Offset Carrier (TMBOC), Quadrature Multiplexed BOC (QMBOC) [26], etc.  
Figure 5: Average range error envelope of ACE-BOC(15,10,[1,1,3,3]), under matched receiving, IMR and BLR. The double-sided receiving bandwidth 52 MHz. MDR is \(-5\) dB.  
As a composite signal, MBOC enables multiple receiving strategies, where different levels of accuracy can be obtained with the corresponding receiver complexity. Matched receiving of MBOC enables the optimum received performance. The unmatched receiving is more widely used, generally referred as BOC-like receiving, where only the low-order BOC(\(n\), \(n\)) component is processed in the receiver. Therefore, the local replica generation of BOC-like receiving is less complex than the matched way. Take MBOC(6,1,1/11) for example, matched receiving requires the bandwidth to be more than 14 \(\times\) 1.023 MHz, while narrowband BOC-like receiving requires a bandwidth of only 4 \(\times\) 1.023 MHz.  
It should be noted that discarding the high-order BOC(\(m\), \(n\)) component does not cause significant power loss. Because in MBOC(6,1,1/11), the low-order BOC(1, 1) accounts for 10/11 of the total transmitting power, ensuring the quality of primary positioning service for the vast unmatched receivers.  
Therefore, signal performance evaluation for the various receiving strategies of MBOC becomes essential in recent years for its widespread use. Reference [14] provides analysis of the unmatched equivalent Gabor bandwidth in Equation (25), which is available in the evaluation of code tracking accuracy, anti-multipath performance of a signal, along with its receiving strategy. Further, in this work, the performance evaluation criterion of non-white noise interference resistance is provided in Equation (24), which facilitates the signal analysis and comparison in multiple dimensions.
# Research on Testing Method of Oil Characteristic Based on Quartz Tuning Fork Sensor
## 3 Oil Ageing Processes and Sensors for Fluid Property Detection
### Metal Contamination  
Metal contamination comes from wear induced by bad lubrication and from corrosion. The main metals to be monitored are Fe, Cu, or Al, which are components of the main engine parts.  
Metal contamination is dangerous because it can accelerate oil degradation. Metal contamination is, above all, a sign of abnormal wear.  
Metal contamination can be measured in the laboratory with ICP spectrometric analysis (NFT 60 106). Metal contamination is expressed in mg/kg, equivalent to ppm mass [45].  
Iron (Fe) contamination measurements are presented in Figure 17. Engine oil (15W40) was stabilized at 100 \({}^{\circ}\)C and contaminated with <10 \(\upmu\)m Fe particles up to 105 ppm in mass. Oil homogeneity was ensured by magnetic stirring. The results demonstrate the capability of the fluid property sensor to detect and measure extremely high levels of Fe contamination with viscosity, density and dielectric measurements.  
Viscosity, density, and dielectric are strongly and linearly related to Fe contamination level. Fe contamination also induces a slight decrease in resistivity. These noticeable changes can be explained by the shape, high polarizability and charge characteristics of metal particles.  
Fe contamination in engine oil can be modeled using the following equations at a constant temperature:  
\[\eta_{\text{th}}=\eta_{\text{i.}}\Big{(}1+2\times 10^{-6}.\text{FeCont} _{\text{(ppm.mass)}}\Big{)} \tag{40}\]  
\[\rho_{\text{th}}=\rho_{\text{i.}}\Big{(}1+0.4\times 10^{-6}.\text{FeCont} _{\text{(ppm.mass)}}\Big{)} \tag{41}\]  
\[\epsilon_{\text{th}}=\epsilon_{\text{i.}}\Big{(}1+0.8\times 10^{-6}.\text{FeCont} _{\text{(ppm.mass)}}\Big{)} \tag{42}\]  
\[\text{Rp}_{\text{th}}=\text{Rp}_{\text{i.}}\Big{(}1-0.1\times 10^{-7}.\text{FeCont} _{\text{(ppm.mass)}}\Big{)} \tag{43}\]These models are plotted in Figure 17 with the related accuracy specifications given as full blue lines.  
Similar results can be obtained with copper (Cu) or other metals.
## 4 Experiments and Result
### Problem Description  
The problem under consideration concerns a fragment of the production system consisting of three parallel mixed-model production lines, which are sub-assembly lines for semiproducts. Semiproducts are taken from dedicated buffers to the first production cell of each line (labeled M1, M2, and M3) (Figure 7). Each buffer (Q1, Q2, and Q3) is an overhead conveyor loop with capacity \(g_{1}=g_{2}=g_{3}=11\). Semi-finished products flow into the system according to the arrival rate \(\mu=\{A,D\}\), characterized by two statistical  
Figure 5: greenDigital twin—direct data exchange.  
Figure 6: Cyber–Physical Production System architecture.  
distributions \(A\) and \(D\), where \(A\) describes the distribution of durations between each arrival to the system, and \(D\) describes the distribution of \(n\)-versions of semi-finished products, from the following \(P\) set:  
\[P=\left\{p_{1},p_{2},\ldots,p_{n}\right\} \tag{1}\]  
where \(P\) is the set of possible versions that can be processed in the system, and \(p_{i}\) is the \(i\)-th version of semi-finished products.  
The DRL agent decides on the order in which semi-finished products are routed from the buffer to the production cell. If a product is introduced into the buffer that would exceed its capacity, the product from the buffer with the longest waiting time is redirected to the adjacent buffer through a bypass connecting buffers in the system. Thus, this problem under consideration is a special case of the dynamic mixed-model sequencing problem.  
Since the lead times in a production cell depend on the previous version of the product produced there, selecting a product from the buffers affects the work sequence and allows for load balancing and maximizing efficiency. Depending on the versions of the \(p_{s_{i}}/p_{s_{i-1}}\) pair, the product may require additional time for processing. Therefore, the product is called a work-intensive model or version. Processing times are known and defined in the \(n\times n\) time matrix T, whose columns and rows correspond to the vectors of individual product versions \(P\):  
\[\text{T}=\left(t_{i,j}\right)_{1\leq i\leq n,1\leq j\leq n}, \tag{2}\]  
where \(t_{i,j}\) is the processing time of version \(i\), if version \(j\) preceded it.  
Figure 7: Production system.  
The objective is to dynamically identify a production sequence, defined by the sequence of decisions of the agent regarding the selection of products from the buffer. The quality of a production sequence is evaluated based on the total working time obtained in relation to the minimum, for a given sequence of versions:  
\[max\sum_{i=1}^{k}\frac{min(t_{s_{i}(1,...n)}))}{t_{s_{i}s_{i-1}}}, \tag{3}\]  
where \(s_{i}\) is the version number processed in the \(i\)-th order in the sequence, and \(k\) is the sequence length for the period considered.  
In summary, the learning objective of the agent is to maximize the expected return, which is the total reward over the multiple episodes, and, in the discussed case, corresponds to the efficiency of the production cell M in a 4 h work period.
## 8 Experiment & Results
### Quantitative Studies  
First of all, it can be noticed that the results obtained using the attention and residual modules outperform those obtained through the simple CNN version, but only resulting in a slight degree of accuracy improvement. Assume here that the visual features of the piano roll frames are relatively simple compared to the visual features of the images, spanning fewer feature layers. Therefore, the presence of different connections in the residual module may hinder the learning of the model rather than improve its performance. On the other hand, the different attention modules greatly improve the prediction accuracy of the model. As expected, the results demonstrate that the accuracy of all datasets has been significantly improved even using the simple dot product attention module, although the use of the multi-headed attention mechanism also further improves the performance of the experimentally designed model to a high accuracy, but does not surpass the 3D-SCN and TP-LSTM-NADE models, as shown in Table 4.  
The three-dimensional sequential convolution model BoYaTCN and its simplified version BoYa+TCN in this paper outperform all previous architectures, and a stronger modulated signal (relying on the product rather than a simple sum) on the fully connected layer of the attention matrix yields higher accuracy. Figure 12 depicts the testing procedure for the cutting-edge models BoYaTCN and the other three models, including BoYa+TCN, 3D-SCN, and TP-LSTM-NADE. The above confirms the merits of the present model approach and demonstrates the benefits of building spatial embedding modules and attention mechanisms between elements of a music sequence for efficient encoding and prediction of music data in a low-dimensional space.
# Improving IMES Localization Accuracy by Integrating Dead Reckoning Information
## 7 Experiment of the Interference of the Human Body  
The experimental results of the previous section suggest that the human body interferes with the IMES radio propagation. To clarify its influence, the variation of the CNR according to the positional relation between the transmitter and receiver antennas and the human body is investigated in this section.
## 2 Dynamic Modeling
### Analysis of the Interference Mathematical Model
#### 2.6.2 Ocean Current Interference  
The influence of ocean currents is similar to the influence of sea wind, and its magnitude is proportional to the square of the relative velocity. The force \(F_{c}\) and moment \(M_{c}\) generated by ocean currents can be expressed by the following expression:  
\[\left\{\begin{array}{l}F_{c}=-C_{dc}|v_{rc}|v_{rc}\\ M_{c}=r_{b}\times f_{c}\end{array}\right. \tag{18}\]  
In the simulation, the ocean current is generally considered constant and uniform, and it is assumed that the ocean current has a constant inertial reference velocity, \(v_{c}\in R^{3}\); \(v_{rc}=v-v_{c}\) is the relative velocity of the HAUV in the ocean current, and \(C_{dc}\) represents the water damping coefficient.
# Deep Learning-Based Segmentation of Various Brain Lesions for Radiosurgery
## Materials and Methods
### Deep Learning Models
#### 2.4.3 PSPNet  
Pyramid scene parsing network, or PSPNet, is a state-of-the-art model in scene parsing tasks [39]. We included it because it is also suitable for our segmentation task. The PSPNet utilizes the high-level representations extracted by a pretrained network, and a novel design of the pyramid pooling module serves as a backend to predict the segmentations. The pyramid pooling modules pool the extracted feature maps to obtain features of different scales. The pretrained model is typically a ResNet trained on an ImageNet dataset [39]. However, on an MRI dataset, the features are not transferable due to the large consistency and the absence of common pretrained models to process MRI images. In our implementation, we randomly initialized the ResNet backend and also removed the deep supervision loss.
## 4 Conclusions  
In this work, the SiC ceramic and Si/SiC ceramic were manufactured via direct LPBF and CVI. A comprehensive investigation of their microstructure characterization and strengthening mechanisms was conducted. The principal findings are as follows:  
1. The densities of LPBF-SiC specimens were 65.7%; the compressive strength was 6 \(\pm\) 1 MPa. After CVI, the density increased to 76.4% and the compressive strength increased to 39 \(\pm\) 13 MPa.
2. A 70.5% density and 19 \(\pm\) 8 MPa compressive strength were obtained for LPBF-Si/SiC specimens after adding Si. After CVI, the density of the LPBF-CVI Si/SiC specimen was increased to 78.3% and the compressive strength was increased to 90 \(\pm\) 8 MPa.
3. During the LPBF-forming process, silicon carbide undergoes slight decomposition, and the subsequent CVI densification treatment enables the decomposed silicon to re-react and regenerate silicon carbide.
4. The nano-hardness values of the LPBF-CVI and LPBF-CVI SiC ceramic specimens differ in different regions. The nano-hardness values of the LPBF-formed SiC and Si/SiC regions are 24.2 \(\pm\) 1.0 GPa and 25.9 \(\pm\) 4.4 GPa, respectively, whereas the nano-hardness values of the CVI-formed SiC region can be as high as 55.3 \(\pm\) 9.3 GPa.  
High-purity and crystalline silicon carbide can be fabricated using direct LPBF combined with CVI. The addition of silicon will increase the relative density, change the microstructure morphology, and improve the compressive strength, but there will be a small amount of silicon residue. This research is anticipated to serve as a foundation for the advancement of SiC ceramics with refined microstructures and enhanced strength through direct LPBF.  
Methodology, N.K.; Formal analysis, J.P.; Investigation, J.Z. and L.L.; Writing--original draft, Y.W. and K.G.P.; Writing--review and editing, P.W., Y.Z., K.G.P., X.W. and M.E.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Key Research and Development Program of China (Grant No. 2024 YFB4608600), National Science and Technology Major Project (Grant No. J2022-VI-0011-0042), the Joint Fund of Henan Province Science and Technology R&D Program (Grant No. 225200810002 and 235200810030), the program for overseas high-level talents introduction of Henan Province (HNGD2025040) and the Startup Research Fund of Henan Academy of Sciences (No. 242021011).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Data-Driven System Identification
#### 2.4.3 Complex Curve Fitting by Nonlinear Least Squares Optimisation  
A second step of the identification follows once the FRF data have been obtained from the previous step. The problem of approximation of the complex data by a rational continuous- or discrete-time transfer function model  
\[P(s,\theta)=\frac{b(s)}{a(s)}=\frac{\sum_{i=0}^{n_{b}}b_{i}s^{i}(s)}{s^{n_{a}} +\sum_{i=0}^{n_{a}-1}a_{i}s^{i}},P(z,\theta)=\frac{b(z)}{a(z)}=\frac{\sum_{i=0} ^{n_{b}}b_{i}z^{i}(s)}{z^{n_{a}}+\sum_{i=0}^{n_{a}-1}a_{i}z^{i}}, \tag{29}\]with a vector of unknown coefficients  
\[\theta=\{a_{i};i=0,..,n_{a}-1,b_{i};i=0,..,n_{b}\}. \tag{30}\]  
can be formulated as an optimisation in the least-squares sense. The goal is to minimise the cost function  
\[\chi^{2}(\theta)=\tfrac{1}{2}\sum_{i=1}^{m}\left[\tfrac{r_{i}(\theta)}{w_{i}} \right]^{2}=\tfrac{1}{2}r(\theta)^{T}Wr(\theta),\theta^{*}=\underset{\forall \theta}{argmin}\{\chi^{2}(\theta)\}, \tag{31}\]  
\[r(\theta)=\left[\begin{array}{cc}r_{1}(\theta)&r_{2}(\theta)&\ldots&r_{m}( \theta)\end{array}\right]^{T};\,r_{l}(\theta)=|P(i\omega_{l})-\hat{P}(i\omega _{l},\theta)|,l=1..m\]  
The nonlinear least squares problem can be approximated by a series of simple linear subproblems  
\[\chi^{2}_{LLS}(\theta)=\tfrac{1}{2}r_{LLS}(\theta)^{T}W_{L}r_{ LLS}(\theta),\] \[r_{LLSl}(\theta)=|P(i\omega_{l})\hat{A}(i\omega_{l})-\hat{B}(i \omega_{l},\theta)|,l=1..n_{f},\,\,\hat{P}(i\omega)=\tfrac{\hat{B}(i\omega)} {\hat{A}(i\omega)}.\]  
An iterative procedure is formed by finding a linear least-squares estimate in each step as  
\[\theta^{*}_{k+1}=\underset{\forall\theta}{argmin}\{\frac{1}{2}r_{LLS}^{T}( \theta)W_{k}r_{LLS}(\theta)\}, \tag{32}\]  
with the weights \(W_{k}\) adjusted to the uncertainty of the individual samples of the non-parametric model to form a maximum-likelihood estimate.  
The result of this step is used as an initial guess for the subsequent nonlinear optimisation. In our application, the Levenberg-Marquardt method [30] is employed to solve the nonlinear least squares problem. Orthogonal parameterisation of the transfer function polynomials is used to improve numerical conditioning of the problem. The reader is referred to publication [2] for a detailed treatment of the identification topic.
# Compp-DDPG: Computation Offloading with Privacy Preservation in a Vehicular Edge Network
###### Abstract  
Vehicular edge computing (VEC) is emerging as a prospective technology in the era of 5G and beyond to support delay-sensitive and computation-intensive vehicular applications. However, designing an efficient approach for joint computation offloading and resource allocation is challenging due to the limited resources of VEC servers, the highly dynamic vehicular networks (VNs), different priorities of vehicular applications, and the threat of privacy disclosure. In this work, we propose a cooperative optimization for privacy-preserving and priority-aware offloading and resource allocation in VEC network (VECN) based on deep reinforcement learning (DRL). Firstly, we employed a privacy-preserving framework where the certificate authority (CA) is integrated into the VEC architecture. Furthermore, we formulated the dynamic optimization problem as a Markov decision process (MDP) by constructing a weighted cost function that integrates the priority of stochastic arrival tasks, privacy-preserving of offloading, and dynamic interaction between the edge servers and intelligent connected vehicles (ICVs). To solve this problem, a cooperative optimization for privacy and priority based on deep deterministic policy gradient (COPP-DDPG) is proposed by learning the optimal actions to minimize the weighted cost function. The simulation results show that COPP-DDPG has good convergence and outperforms the other four comparison algorithms in many aspects.  
2022
# Implementation and Security Test of Zero-Knowledge Protocols on SSI Blockchain
## 6 Security Tests and Evaluation
### Man-in-the-Middle Attack  
Although this type of attack has not been implemented in the application, it is interesting to study how a Man-in-the-Middle attack would affect the ZKP algorithms studied.  
A diagram showcasing the flow of this attack is presented in Figure 8. If a third person, let us call her [PERSON], were to gain access to the communication channel between the Prover who is trying to authenticate their identity and the Verifier who is providing the challenges, she would be able to impersonate the Prover and authenticate as such.  
To perform this, [PERSON] would simply have to intercept the messages exchanged in the communication, posing as the Verifier in front of [PERSON] and repeating the challenges proposed, while she uses [PERSON]'s answers to authenticate herself in front of the Verifier.  
This vulnerability exists for all three ZKP algorithms--Feige-Fiat-Shamir, Guillou-Quisquater, and Schnorr. ZKP tests are not designed to protect against MITM attacks, only to protect against information leakage. To prevent them, the following measures can be established:  
* Strong synchronization: Decrease the waiting time allowed for the responses of the Prover so that there is no time for a retransmission of the messages.
* Communication security: Like any protocol vulnerable to this attack, the first line of defense would consist of not allowing a third party to intercept the communication.
## 6 Implementation and Evaluation
### Performance Evaluation
#### 6.3.1 Experiment Setup
However, to still get an idea about the impact of RDC on our solution, we repeated some of the tests using Xmac. Xmac uses a simple RDC, but has less stringent requirements in terms of memory consumption than ContikiMAC. In all the experiments presented in this subsection the multicasts were sent using none-confirmable CoAP messages as required by the group communication draft [14] and the unicast were sent using confirmable CoAP messages to achieve reliability.
## 2 Materials and Methods
### Methods
#### 2.3.3 Validation of Multiple GPP Products at Footprint Scale  
Firstly, we directly compared the field GPP with MODIS GPP products and the downscaled GPP. Considering the co-registration errors between downscaled high resolution t images and field flux towers, the average GPP from a 3 \(\times\) 3 pixels window around the flux towers was used when validating the downscaled GPP. Then, the weighted MODIS GPP or downscaled GPP in the footprint was compared with the measured data. Specifically, the weight of each pixel in the footprint was obtained according to the contribution rate to the total source area [3]:  
\[R=\frac{f(x,y,z_{m})_{i}}{\sum_{i=1}^{N}f(x,y,z_{m})_{i}}\times 100\% \tag{5}\]  
where \(R\) is the weight, \(N\) is the total number of the pixels of GPP products in the footprint, and \(f(x,y,z_{m})_{i}\) represents the footprint of \(i\)-th pixel.  
The weighted average of the MODIS GPP or downscaled GPP was calculated and compared with measured GPP, and the determination coefficient (R\({}^{2}\)) and root mean square error (RMSE) were used to quantify the accuracy of GPP products.