esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimation and Evaluation of Zenith Tropospheric Delay from Single and Multiple GNSS Observations
## 4 Results and Analysis
### ZTD from Single-GNSS
The biases and RMSs of ZTD differences are shown in Figure 5 between the GPS-only and the other single-system solutions for 55 stations, excluding the value between GPS and BDS at BSHM station. Horizontal coordinates are sorted from the low to high latitude. Between GPS and GLONASS, the RMS value is the smallest, approximately 2.5-7.9 mm. It is greater between GPS and Galileo, ranging from 2.7 to 10.9 mm, and the greatest value between GPS and BDS, approximately 4.3-12.2 mm, respectively. Between GPS and Galileo, the RMS values are relatively large at stations BREW and KERG. By examining the number of observable satellites for these two stations, we found that observable satellites for Galileo have averages of 2.6 and 3.0, respectively. However, the average number of observable satellites for GPS is 8 at both stations. The RMS of ZTD difference between GPS-only and GLONASS-only solutions is roughly equivalent with that between GPS-only and Galileo-only solutions. The possible reason is that the number of observable satellites is almost the same, with an average number not exceeding 1.5. BDS is significantly different from the other three systems; BDS-3 has not been fully established, and the limited number of observed values inevitably leads to significant errors in positioning and estimating parameters. This confirms that the number of observable satellites in one system greatly  
Figure 3: Linear correlation of GPS-derived ZTD to the other system-derived (BDS, Galileo, and GLONASS) ZTD at station DGAR (\(\mathbf{a-c}\)) and PTGG (\(\mathbf{d-f}\)). affects the accuracy of parameter estimation. The biases are mostly from \(-2\) to \(2\) mm, and the parts with large deviation values are almost all between GPS and BDS. In general, the RMSs show a decreasing trend from the low latitude to high latitude. Figure 4: Distribution of ZTD differences between the GPS-only and the other single-system (BDS, Galileo, and GLONASS) solutions at stations DGAR (\(\mathbf{a-c}\)) and PTGG (\(\mathbf{d-f}\)). Figure 5: Between the GPS-only and the other single-system (BDS, Galileo, and GLONASS) solutions, the top panel shows the RMSs of ZTD differences and the bottom panel shows biases of ZTD differences.
# A Digital Twin-Based Approach for Detecting Cyber-Physical Attacks in ICS Using Knowledge Discovery
## 5 Methodology
### Information Gathering  
#### 5.1.1 Register Identification and Data Types  
The initial step involves enumerating registers to identify (i) discrete input contacts and analog input registers (which may contain current measurements of physical variables) and (ii) discrete output coils and analog output holding registers (which may contain actuator commands). This task aims to compile a list of actively utilized registers and their corresponding data types (Boolean or numerical values). By scanning the Modbus address range, we map out the registers in use and identify those that respond to queries. It should be noted that certain registers may appear active even if their values never change; these are filtered out in subsequent stages. It should be noted that the underlying principle of our solution is based on querying the system for process-related data, regardless of the specific communication protocol used; for example, whether the system employs Modbus, DNP3, OPC UA, or another protocol, the critical aspect is the ability to access the PLCs and collect data from them. Figure 4 illustrates the values recorded over six hours for each register of the mixer PLC. This task ends by discovering the data type of the different registers (see Table 1).
###### Abstract  
This study aimed to evaluate the bone volume change at donor sites in patients who received mandibular body bone block grafts using intensity-based automatic image registration. A retrospective study was conducted with 32 patients who received mandibular bone block grafts between 2017 and 2019 at the Pusan National University Dental Hospital. Cone-beam computed tomography (CBCT) images were obtained before surgery (T0), 1 day after surgery (T1), and 4 months after surgery (T2). Scattered artefacts were removed by manual segmentation. The T0 image was used as the reference image for registration of T1 and T2 images using intensity-based registration. A total of 32 donor sites were analyzed three-dimensionally. The volume and pixel value of the bones were measured and analyzed. The mean regenerated bone volume rate on follow-up images (T2) was 34.87% \(\pm\) 17.11%. However, no statistically significant differences of regenerated bone volume were noted among the four areas of the donor site (upper anterior, upper posterior, lower anterior, and lower posterior). The mean pixel value rate of the follow-up images (T2) was 78.99% \(\pm\) 16.9% compared with that of T1, which was statistically significant (\(p<0.05\)). Intensity-based registration with histogram matching showed that newly generated bone is generally qualitatively and quantitatively poorer than the original bone, thus revealing the feasibility of pixel value to evaluate bone quality in CBCT images. Considering the bone mass recovered in this study, 4 months may not be sufficient for a second harvesting, and a longer period of follow-up is required.  
autogenous bone grafts; bone regeneration; cone-beam computed tomography; intensity-based registration +
Footnote †: journal: _Sciences_
## 5 Proposition of YSC Demand Spectra for Design Purposes
### Method of Analysis and the Systems Considered  
The YSC demand spectrum for a fixed response ductility is computed for each simulated DRSCEM by the iteratively obtained yield strength of SDOF systems (Clough type nonlinear constitutive equation). The elastic natural period of SDOF systems to calculate YSC demand spectra ranges from 0.1 to 5.0 s with an interval of 0.1 s and a 5% viscous damping ratio (all the results presented in this paper are for this damping ratio) is used. The elastic natural period is obtained from the initial stiffness  
Figure 10: Comparison between the target response spectrum defined in the Chinese seismic design code of GB50011-2010 (red line) and recalculated spectrum by using a DRSCEM (black line).  
Figure 9: (**a**) Fourier amplitude of ten different observed ground motions and their mean and (**b**) YSC demand spectra obtained by simulated ground motions according to individual Fourier amplitude and their mean.  
of SDOF and its mass by Equation (20). After computing the YSC demand spectra for all DRSCEMs, statistical studies were completed based on these results.  
\[T_{n}=\frac{1}{2\pi}\sqrt{\frac{m}{k_{0}}} \tag{20}\]  
where \(k_{0}\) and \(m\) are the initial stiffness and the mass of the concerned SDOFs  
To perform statistical studies of YSC demand spectra, the mean and mean plus one standard deviation at each natural period were computed. Another two confidence levels at the 5 th and 95 th percentiles were also computed. Although this analysis process was conducted using response ductility factors of 1(elastic), 1.5, 2, 3, 4, and 6, we here show only the results from using a response ductility factor of 4 as an example. Figure 11a shows the mean, mean plus and minus one standard deviation, 5 th and 95 th percentiles of YSC demand spectra using a response ductility factor of 4. Additionally, the thin gray lines show the scattering of the YSC demand spectra when keeping the response ductility factor set to 4. Also, we compared the results obtained using phase spectra simulated by the modified fBm with those obtained by taking the phase spectra from observed ground motion. Figure 11b shows the fluctuation of the YSC demand spectra for a response ductility factor of 4, which are calculated by simulated DRSCEMs using modified fBm and taking the phase from observed ground motion together. From this figure, it seems that the fluctuation of the YSC demand spectra calculated using only 6 DRSCEMs obtained from the observed grand motion phases covered the fluctuation range of YSC demand spectra calculated using 1000 DRSCEMs based on the modified fBm. Therefore, a lot of DRSCEM is become necessary to define a design YSC demand spectrum.
# Efficient Feature Selection Using Weighted Superposition Attraction Optimization Algorithm
## 3 Results and Discussion
### Comparison with the State of the Art  
In this section, the WSA is compared with existing solutions from the literature. A neural network trained with PSO (PSO-NN) [59], random forest (RF) [60], Vote method [61], support vector machine (SVM) [62], decision tree (DT) [63] and genetic algorithm with fuzzy logic (AGAFL) [64] are compared with the current WSA-KNN. From Table 11, it is observed that despite the additional sophistication of neural networks over KNN, it is observed that the WSA-KNN's solution is on par with PSO-NN. Moreover, the current method is about 2% better than the AGAFL. Compared to traditional ML algorithms such as DT and SVM, the WSA-KNN is able to better its performance by as much as 18%. About 9% improvement in accuracy is seen even over ensemble ML algorithms such as RF.
## 4 Comprehensive Link Budget Analysis of the BDS-3 ISL
### Analysis of Inter-Satellite Link Signal Quality  
During the inter-satellite measurement and communication process, it is necessary to analyze the signal transmission quality to determine whether it meets various system performance requirements. Signal quality is typically measured using the signal-to-noise ratio (SNR), but the value of SNR depends on the noise bandwidth of the receiver, which can complicate its application. To facilitate performance comparisons between differentreceivers, the carrier-to-noise ratio can be used as an evaluation metric, and its equation can be expressed as follows:  
\[C/N_{0}=\frac{P_{r\_BDS}}{N_{0}} \tag{14}\]  
where \(C/N_{0}\) is the carrier-to-noise ratio of the received signal in dBHz, and \(N_{0}\) is the noise power spectral density in dBW/Hz. The equation of \(N_{0}\) can be expressed as follows:  
\[N_{0}=N/B_{f}=kT_{s} \tag{15}\]  
where \(N\) is the noise power of the receiver in dBW, \(B_{f}\) is the received bandwidth of the signal in Hz, and \(k\) is Boltzmann's constant, with a value of \(1.38\times 10^{-23}\) J/K. The noise temperature of the receiving system primarily consists of three components: the noise temperature from external sources, the noise temperature generated by cable losses, and the noise temperature produced by the series components in the system. According to Friis' formula, the total noise temperature of the receiving system can be calculated as follows:  
\[T_{s}=T_{a}+(L_{N\_BDS}-1)T_{0}+(F-1)L_{N\_BDS}T_{0} \tag{16}\]  
where \(T_{a}\) is the noise temperature from external sources in K, \(F\) is the noise figure in dB, and \(T_{0}\) is the ambient temperature in K. Considering various losses, Equation (14) can be expressed in dB form as follows:  
\[[C/N_{0}]=[\text{EIRP}]+[G/T]-[L_{BDS\_ISL}]-[L_{P\_BDS}]-[L_{D\_BDS}]-[k]-[L_{ M}] \tag{17}\]  
For inter-satellite communication systems employing BPSK or QPSK digital modulation, a more commonly used evaluation metric is the normalized signal-to-noise ratio, defined as the ratio of energy per bit to noise power spectral density (\(E_{b}/N_{0}\)). The relationship between \(E_{b}/N_{0}\) and \(C/N_{0}\) can be expressed as follows:  
\[E_{b}/N_{0}=\frac{C/N_{0}}{R_{b\_ISL}} \tag{18}\]  
where \(R_{b\_ISL}\) is the information transmission rate of the ISL signal in bps.
## 3 Precoding Matrix Design Based on Type I 5G-NR for SU-MIMO
### Type I Multi-Panel  
The precoding matrix design for multi-panel antenna arrays is just an extension of Type I single-panel. In this case, the wideband PMI index is defined as \(\mathbf{i}_{1}=[i_{11},i_{12},i_{13},i_{141},i_{142},i_{143}]\), and the frequency-selective index is \(\mathbf{i}_{2}^{n}=\left[i_{20^{\prime}}^{n},i_{21^{\prime}}^{n},i_{22}^{n}\right]\). The main problem with using several antenna panels is that the distance between two consecutive antennas of different panels does not have to be equal to the spacing between two consecutive antennas in the same panel. Therefore, the assumption of uniform arrays is no longer fulfilled. To solve this problem, the beamforming design is based on the assumption that all antenna panels are identical. Each panel is composed of \(N_{1}\) antenna elements in the horizontal direction and \(N_{2}\) in the vertical direction. Then, the idea is to design the precoding matrix \(\mathbf{W}_{SP_{n}}\mathbb{C}^{2N_{1}N_{2}N_{s}}\) for a single-panel. Finally, this matrix is repeated in the remaining panels but adding a phase offset \(e^{j\alpha_{p}}\), where \(p\in\left\{1,...,N_{g}-1\right\}\), to compensate the non-uniform distance between panels.  
Currently, the precoding matrix can be obtained according to two different codebook modes supporting the spatial multiplexing up to 4 parallel layers. The codebook mode 1 supports both \(N_{g}=2\) and \(N_{g}=4\). The co-phasing between each panel is obtained using only wideband coefficients. On the contrary, in the case of codebook mode 2, the co-phasing is configured by applying a combination of wideband and subband parameters. This mode is only supported for two antenna panels. In general, the precoding vector for the \(n\)th subband and \(d\)th layer can be written as  
\[\left[\mathbf{W}_{n}\right]_{d}=\begin{bmatrix}\mathbf{W}_{SP_{n}}^{[d]}\\...\\ e^{j\alpha_{p}}\mathbf{w}_{SP_{n}}^{[d]}\\...\\ e^{j\alpha_{Ng-1}}\mathbf{w}_{SP_{n}}^{[d]}\end{bmatrix}, \tag{12}\]  
where  
\[\mathbf{w}_{SP_{n}}^{[d]}=\begin{array}{cc}\left[\mathbf{b}^{[d]}&\mathbf{0 }\right]\\ \mathbf{0}&\mathbf{b}^{[d]}\end{bmatrix}\begin{bmatrix}1\\ e^{j\phi_{n}^{[d]}}\end{bmatrix},\;\;\phi_{n}^{[d]}\in\left\{0,\frac{\pi}{2}, \pi,\frac{3\pi}{2}\right\}. \tag{13}\]  
The phase offset \(e^{j\alpha_{p}}\) is computed according to the wideband sub-index \(\mathbf{i}_{14}\) in codebook mode 1, such that \(\alpha_{p}=\frac{\pi}{4}+\frac{\pi i_{14}}{2}\), where \(i_{14p}\in\left\{0,1,2,3\right\}\). The coefficients for the higher layer are also adjusted based on the subindex \(i_{13}\) to obtain an orthogonal precoding matrix.
# Novel Scoring for Energy-Efficient Routing in Multi-Sensored Networks
## 3 The Proposed Mechanism
### Proposed Mechanism
#### 3.2.3 Selfish Node Management  
It is possible that a node with the highest \(\lambda\) does not respond to an RREQ during \(n\) number of attempts. The source node then marks such a node as a selfish node and recalculates the value of \(\lambda\) by considering the remaining nodes. Moreover, the source node piggybacks the address of the selfish node with its RREQ to let the other nodes know about it. The nodes stop entertaining the selfish nodes once they get their information. However, selfish nodes can still be requested for the route. If the selfish node entertains an RREQ,  
Figure 8: Selection of relay nodes.  
Figure 7: Initial self-configuration of nodes.  
then the source node announces it as a normal node again. In case a node does not respond to an \(s\) number of RREQs, it is then broadcasted as a blacklisted node. The network nodes omit the blacklisted nodes from their connected nodes' lists for a specified period of time \(t\). The features and capabilities of nodes in terms of their cooperation levels are indicated with blue, yellow, and red in Figure 9.
## 5 Applications of Integrating Federated Learning and Blockchain in Vehicular Networks
### Federated Learning and Blockchain for Security in Vehicular Networks  
The fundamentals of information security CIA must be adhered to by the FL developers and adopters. Many end devices are included in the exposure and training of model characteristics through a decentralized approach, making FL vulnerable to several open risks and attacks. Current research regarding vulnerabilities and frameworks for mitigating risks in the FL technology is limited.  
By using BC technology, several researchers and developers made efforts to improve the security of the VANET in recent times. Reference [131] proposed a novel secure spectrum sharing technique for VANET cellular networks based on blockchain; the proposed technique is for VANET and network operators where a new Stackelberg framework is presented for the optimal spectrum approaches. Reference [132] presented BC-as-a-service (BaaS) for IoT devices cohesive with MEC. VANET is used as a base station in the proposed  
\begin{table}
\begin{tabular}{c l l l} \hline \hline
**Characteristics** & **Traditional FL** & **FCN Private Data** & **FVN Combined** \\ \hline Flexibility & Easy to deploy & Easy to launch & Harder to deploy \\ \hline Processing & Data parallel & Data parallel & Model parallel + data parallel \\ \hline Comp. unit & Mobile device & Vehicle & FVC \\ \hline Computation & Limited & Medium & High \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation of FL paradigms in the VANET environment.  
framework, and BC is used for computation-intensive task offloading. For IoT networks based on MEC, [133] proposed a framework for secure data collection. In the proposed framework for authentication, end devices transfer private data to MEC servers. A BC-based decentralized framework is proposed by [134] for the ground to air data sharing in IoT networks. A Cournot framework is designed to achieve maximum advantages from the ground to air sensors. For efficient and secure key distribution and recovery in VANET, [135] proposed an essential distribution technique based on the decentralized group by exploiting mutual healing and private BC protocol. For the security of VANET, several frameworks are proposed by different researchers based on the BC technique; some of the proposed frameworks are based on the BC network implementation under the FL framework for the applications of MCS. Reference [136] proposed a novel technique of privacy-preserving and secure FL for VANET. The author also presented a decentralized FL framework based on the BC techniques focused on user privacy to protect data contribution verification and data training between VANET. Table 3 provides solutions to FL limitations.
## 3 Algorithm Improvement and Scheme Evaluation  
### Improvement Strategy for the NSGA-III Algorithm  
The basic framework of the NSGA-III algorithm is similar to that of the NSGA-II algorithm, but its mechanisms have undergone significant changes [30]. NSGA-II maintains population diversity through crowding distance, focusing on preserving the physical distance between solutions. However, as the number of objectives increases, mere distance calculation is insufficient to fully capture the diversity among solutions. NSGA-III addresses this limitation by introducing reference points. During the algorithm's initialization phase, a set of uniformly distributed reference points is generated to guide the population's development direction in subsequent evolutionary processes. By calculating the proximity of individuals to these reference points--typically using normalized Euclidean distance or angular distance--representative individuals are selected to advance to the next generation. This approach enables the algorithm to explore complex Pareto frontiers composed of multiple objectives. However, in practical multi-objective optimization problems, fixed reference points cannot reflect the dynamic changes of the true Pareto frontier, leading to ineffective reference points that interfere with the evolution of population individuals. To address this issue and make the algorithm more adaptable to constrained multi-objective optimization problems, adjustments are necessary. This paper improves the algorithm in three aspects: Reference point improvement Strategy, Dynamic Solution Retention Mechanism, and optimization selection strategies.
# MIMO Signal Detection Based on IM-LSTMNet Model
## 3 Experiments and Results of Signal Detection
### The Result of the Signal Detection
#### 3.2.4 Influence of Channel Matrix  
In the experiments in the above section, offline training and online deployment use the same channel matrix. However, in practical applications, there may be a mismatch between these two processes--offline training and online deployment use different channel matrices. As a consequence, it is significant that the trained IM-LSTMNet is robust in this condition.  
To prove the robustness of the proposed IM-LSTMNet network for the signal detection of the FrFT-based MIMO-OFDM system, the experiments in this section are carried out when the FrFT order is 1.6, the subcarrier number is 64, and the CP number is 16. In offline training and online signal detection, a different channel matrix H is selected to discuss the signal detection performance of the FrFT-based MIMO-OFDM system. The experimental results are shown in Figure 18.  
As shown in Figure 18, when the offline training and the online detection adopt different values of H, the neural network still has a certain signal detection ability for the FrFT-based MIMO-OFDM system. The IM-LSTMNet network proposed by us has lower BER and more effective performance than the other two neural network detection algorithms when using different values of H. It can be concluded that the IM-LSTMNet network proposed in this paper is more robust than the FrFT-based MIMO-OFDM system.
## 3 Results and Analysis  
### Production and Characterization of Pentalene in V. nutriegens  
Pentalenene production was achieved using a metabolically engineered strain of _V. nutriegens_. The plasmids 3933-fpps-pents and pAC-6409-oriT-mva (Figure 1A) were co-transformed into _V. nutriegens_ ATCC14048 via conjugative transformation.  
Following shake-flask fermentation, the n-decane overlay was collected and analyzed by GC-MS. The chromatogram of the pentalenene standard exhibited a distinct peak at a retention time (RT) of 6.84 min (Figure 1C). A corresponding peak at 6.84 min was also observed in the fermentation sample (Figure 1B). Mass spectral analysis revealed that the molecular ion peak (204.2 \(m/z\)) and fragment ions in the sample (Figure 1B) matched those of the pentalenene standard (Figure 1C). Based on the retention time and mass spectral similarity, the compound was identified as pentalenene. These results confirmed  
Figure 1: **(A) Illustration of the metabolic engineering vector assembly. (B) Represents the total ion chromatograms (TIC) and mass spectrum, respectively, of pentalenene detected in the samples obtained after inducing fermentation following the introduction of the pentalenene biosynthetic pathway into _V. nutriegens_. (C) Represents the total ion chromatogram (TIC) and mass spectrum of pentalenene standard, respectively (from left to right).**  
that engineered _V. natriegens_ is capable of producing pentalenene through heterologous expression of the biosynthetic pathway.