esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Establishment and Validation of Finite Element Model
### Model Validation  
To validate the reliability of the numerical simulation method, a finite element model was established based on existing segment joint tests. The loading and boundary conditions were set according to the experimental parameters, and the accuracy of the simulation was evaluated by comparing the test results with those from the numerical simulation. [PERSON] et al. conducted a bending performance loading test on shield segment joints [13]. The joint configuration of these test segments is similar to that of the TBM shield segment joints used in the Shenjiang Railway project, with both featuring a segment thickness of 550 mm and high-strength inclined bolts for connection, allowing for a comparative analysis with the numerical simulation in this study. The dimensions of the segment joint model in this study match those from the test, and the numerical model for the segment joint bending performance test is illustrated in Figure 6. The loading scheme for the numerical simulation was designed to replicate the test method used in the segment joint bending performance test. The boundary conditions are that one end of the loading device is a fixed support, and the other end is a sliding support. Horizontal axial forces and vertical two-point loading were applied to induce bending moments. In the numerical model, the preload is applied to the bolt based on the load type \"Bolt load\" in ABAQUS, in which the direction, magnitude, and applied section of the bolt preload should be determined [25]. The direction of the preload is along the length direction of the bolt, and the bolt preload was determined based on joint tests, with a value of 95.96 kN. The loading method and boundary conditions are shown in Figure 7.  
Figure 6: Finite element model of the joint test.  
Figure 7: Loading method and boundary condition of the test. **(a)** Positive bending moment condition; **(b)** negative bending moment condition.
## 3 Plasma Application to Eliminate Organic Pollutants in Water
### Organic Chemical Reagents
This feature offers a substantial advantage over chemical-based AOPs when it comes to treating wastewater in factories far from the land. Dimethyl phthalate (DMP) is a chemical that is frequently used to create polymers and insect repellents. DMP was eliminated from liquids by [PERSON] et al. [107] using an AOP method based on micro-plasma. It has been demonstrated that plasma-generated O\({}_{3}\) and \(\cdot\)OH are the most active species in the degradation of DMP liquids, interacting with a sizable number of DMP functional groups for degradation. According to this study's findings [107], plasma exposure may be a useful method for the cleaning of DMP wastewater.
## 4 Simulation Results  
### DOA Tracking Performance Analysis  
The tracking accuracy of DOA is generally characterized by mean square error (MSE), which can be defined as  
\[\text{MSE}=\frac{1}{I}\sum_{i=1}^{I}\bigl{(}\theta(i)-\hat{\theta}(i)\bigr{)}^ {2} \tag{22}\]  
where \(\theta(i)\) and \(\hat{\theta}(i)\) are the real DOA value and estimation value at the \(i\)th time, \(I\) is the number of processing times.  
In this section, the performance of the DOA estimation is evaluated by Monte Carlo simulation. The Monte Carlo simulation was performed 1000 times; \(\theta=0^{\circ}\) and \(\Delta\theta=5^{\circ}\). Figure 5 shows the MSE results of the DOA estimation under different carrier-to-noise ratios (\(C/N_{0}\)) and different array elements. The performances of the coherent DiLL method, non-coherent DiLL method, and MUSIC method are compared and analyzed. As can be seen from Figure 5, the accuracy of DOA estimation is higher with the increase in the \(C/N_{0}\). Under the same \(C/N_{0}\) condition, the more array elements there are, the higher the estimation accuracy. When the number of array elements is small, the DiLL algorithm can achieve better performance than MUSIC, mainly because when the number of array elements is small, the resolution of the MUSIC algorithm is significantly reduced, while the loop tracking method can still achieve high-precision tracking results. With the increase in the number of array elements, the performance of the MUSIC algorithm is improved and the DiLL algorithm is also effectively improved. Therefore, when the number of array elements is small, the DiLL algorithm has better advantages. It can also be seen that the performance of the coherent DiLL method is better than that of the non-coherent DiLL algorithm.  
Figure 5: MSE estimation results of DOA under different carrier-to-noise ratios: (**a**) DOA estimation results under 6 array elements; (**b**) DOA estimation results under 10 array elements.  
Figure 6 shows the tracking results under fixed DOA conditions. In this scenario, the DOA parameter is a fixed value of 10\({}^{\circ}\). The tracking results under the condition of uniform acceleration of DOA are given in Figure 7. The change rate of DOA is 20\({}^{\circ}\)/s\({}^{2}\), and the angle range is \(-\)10-0\({}^{\circ}\). Figure 8 shows the tracking results under the condition of sinusoidal variation in DOA. The variation period is 1 s and the variation range is \(-\)10-10\({}^{\circ}\). Figure 9 shows the tracking results under the condition of DOA step change, and the DOA step is 10\({}^{\circ}\). When there is a step change in the DOA, the tracking loop needs to converge to a new parameter. Therefore, the DOA error may increase in several loop update cycles. The DOA tracking errors in the above four scenarios are 0.053, 0.054, 0.06, and 0.11, respectively. The simulation results show that the DiLL method can adapt well to the DOA tracking problem in different dynamic scenes.
## 2 Materials and Methods
### Adsorption Measurements in Water Solution  
m-aminophenol, p-nitrophenol and pentachlorophenol were obtained from Merck (99% purity). The phenolic solutions were prepared in non-buffered distilled water in the concentration range of 100-250 mg L\({}^{-1}\) for adsorption of p-aminophenol and p-nitrophenol, and 10-30 mg L\({}^{-1}\) for pentachlorophenol. Phenol in a concentration ranging from 50 to 2000 mg L\({}^{-1}\) has been reported by many researchers in industrial wastes [16]. Around 100 mg of activated carbons were mixed with 50 mL of phenolic solution of the desired concentration, and the suspensions were shaken for 2 h. The samples were filtered and the equilibrium concentrations of the phenolic compounds remaining in solution were determined spectrophotometrically (286 nm for m-aminophenol, 305 nm for pentachlorophenol, and 316 nm for m-nitrophenol) by Pfaro 300 UV spectrometer, whereas every point was measured three times and the average was taken. The pH of the solutions during adsorption is measured triplicate in the pH range 5-6 and the average value is taken into account. The effect of pH of the solution on the adsorption capacity of the studied carbons at pollutant concentration 100 mg L\({}^{-1}\) was investigated. The pH was adjusted by adding NaOH or HCl to the stock solution.
## 6 Open Challenges, Future Prospects and Research Directions
### IEEE Standards  
The Institute of Electrical and Electronics Engineers (IEEE) has been instrumental in developing standards for various communication technologies. For FSO, the IEEE 802.15.7-2018 standard focuses on OWCs, including FSO. This standard outlines the PHY layer and medium access control (MAC) layer specifications for short-range OWC, which can be adapted for FSO applications [494; 495; 496; 497]. IEEE is also working on extending these standards to accommodate long-range FSO communication, which is crucial for bridging the gap between current capabilities and future needs.
proved that silicates inhibited, while carbonates catalyzed, the formation of shale oil during the retorting and oxidizing pyrolysis of OS [28; 29; 30]. These results indicate the complexity of the effects of inorganic minerals on the pyrolysis of OS. It is worth noting that the addition of some acid catalysts not only has a potential catalytic effect for kerogen pyrolysis but can also transform the composition of the minerals in the OS [21; 24]. This transformation and removal of mineral matter could also have a potentially important positive impact on reducing reservoir blockage during OS in situ conversion, which has been less well researched. Therefore, in order to explore the potential catalysts for OS pyrolysis and reduce the reservoir blockage, the facilitation effects of acidic chlorinated salts (MnCl\({}_{2}\cdot\)4H\({}_{2}\)O, AlCl\({}_{3}\cdot\)6H\({}_{2}\)O, CrCl\({}_{3}\cdot\)6H\({}_{2}\)O, and MoCl\({}_{5}\)) on the pyrolysis behavior of Huadian OS were investigated. Due to the favorable performance in thermogravimetry (TG) experiments and its low price, AlCl3\(\cdot\)6H2O was selected to be mixed with OS in two different ways and subjected to indoor pyrolysis experiments. The oil, gas, and residue products were collected and characterized by multiple analyses, thus the effects of AlCl3\(\cdot\)6H2O in OS pyrolysis was investigated. The results of this study indicate that AlCl3\(\cdot\)6H2O can effectively promote the cracking of kerogen and reduce reservoir blockage, showing significant potential for application in the in situ conversion of OS.
# XAI-Based Framework for Protocol Anomaly Classification and Identification to 6G NTNs with Drones
## 3 Threat Model
### Protocol Tampering  
The attacks and the corresponding anomalous protocol sequences create vulnerabilities in the initial registration procedure of 6G NTNs, allowing for hijacking, forging, and replaying of control protocol signals via MitM attacks [65; 66] or BS intrusions [5] owing to the aerial transmission of wireless signals. By utilizing these vulnerabilities, two threat cases for 5G protocols, i.e., UE capability hijacking and sequence number desynchronization, are discussed, as shown in Figures 4 and 5.  
Figure 3: Messages involved in UPF DoS attacks.
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 3 Results
### Evaluation of Weighted Fusion Methodology
#### 3.3.7 Summary of Performance Improvements  
Table 2 summarizes the average performance metrics across the three evaluation periods, clearly demonstrating the consistent superiority of weighted fusion over direct aggregation. The improvement is particularly pronounced during extreme weather conditions (August) and for peak hour predictions, precisely when accurate forecasting is most critical for grid operations.  
These results convincingly establish that our PCA-based weighted fusion methodology significantly outperforms traditional direct aggregation approaches across diverse operational conditions. By dynamically incorporating provincial forecast reliability, impact, and complexity into the weighting scheme, our approach effectively leverages the complementary strengths of provincial forecasting systems while mitigating their individual weaknesses.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Period**} & \multicolumn{2}{c}{**Average MAPE (\%)**} & \multicolumn{2}{c}{**Improvement Percentage (\%)**} \\ \cline{2-6}  & **Weighted Fusion** & **Direct Aggregation** & **Overall** & **Peak Hours** & **Non-Peak Hours** \\ \hline June 2024 & 1.92 & 5.36 & 64.2 & 69.3 & 57.8 \\ August 2024 & 3.47 & 9.82 & 64.7 & 62.6 & 68.4 \\ December 2024 & 1.41 & 4.23 & 66.7 & 59.1 & 71.6 \\ \hline \end{tabular}
\end{table}
Table 2: Performance comparison between weighted fusion and direct aggregation.  
Figure 15: Visual comparisons of provincial evaluation radar.
# Particle Swarm Optimization of a Passivity-Based Controller for Dynamic Positioning of Ships
## 5 Numerical Simulations
### A Comparison between the Modified and the Original Controllers  
To show the effectiveness of the proposed controller, we have compared it with the original proposed in [27]. The values of the model parameters \(M\) and \(D\) are [27]:  
\[\begin{split} M&=\begin{bmatrix}1.1274&0&0\\ 0&1.8902&-0.0744\\ 0&-0.0744&0.1278\end{bmatrix},\\ D&=\begin{bmatrix}0.0358&0&0\\ 0&0.1183&-0.0124\\ 0&-0.0124&0.0308\end{bmatrix},\end{split} \tag{23}\]  
and the controller gains have been selected as:  
\[\begin{split} K_{p}&=\begin{bmatrix}1.5&0&0\\ 0&1&0\\ 0&0&0.3\end{bmatrix},\quad K_{I}&=\begin{bmatrix}0.05&0&0\\ 0&0.03&0\\ 0&0&0.06\end{bmatrix}\\ K_{\mathbf{z}_{1}}&=\begin{bmatrix}1.0&0&0\\ 0&1.0&0\\ 0&0&1.0\end{bmatrix},\quad C&=\begin{bmatrix}0.01&0&0\\ 0&0.01&0\\ 0&0&0.08\end{bmatrix}\end{split} \tag{24}\]  
The initial state (initial position and orientation of the ship) has been chosen as \(-10\) m for both positions \((q_{1},q_{2})\) and \(4\) rad for yaw angle (\(q_{3}\)). The aim is to stabilize the ship at its desired equilibrium (0,0,0). Figures 4 and 5 show the time histories of the position states \(q_{1}\) (position in \(x\) coordinate) and \(q_{2}\) (position in \(y\) coordinate,) and Figure 6 shows the time history of the orientation (heading) \(q_{3}\) (yaw angle). The figures show simulations using both the original and proposed controllers. The performance (time response characteristics) for both controllers are shown in Table 1. The improvement on the performance of the closed-loop system using the proposed controller over the classical one has been demonstrated evident by faster response, less overshoot in the \(x\) and \(y\) directions as well as an improved undershoot in the yaw angle.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Time Characteristics** & **Modified Controller** & **Original Controller** \\ \hline \(q_{1}\) Rise time(s) & 40.7935 & 59.0104 \\ \hline \(q_{1}\) Settling time(s) & 457.7632 & 1062.9 \\ \hline \(q_{1}\) Overshoot percentage & 41.0047 & 49.7733 \\ \hline \(q_{2}\) Rise time(s) & 47.6423 & 46.8955 \\ \hline \(q_{2}\) Settling time(s) & 480.8057 & 765.8472 \\ \hline \(q_{2}\) Overshoot percentage & 44.4818 & 59.0281 \\ \hline \(q_{3}\) Rise time(s) & 5.4699 & 5.3337 \\ \hline \(q_{3}\) Settling time(s) & 9.1445 & 41.6431 \\ \hline \(q_{3}\) Overshoot percentage & 1.3724 (under) & 11.4531 (under) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Time performance characteristics for the two controllers.  
Figure 4: The position of ship \(q_{1}\) in the \(x\)-coordinate.  
Figure 5: The position of ship \(q_{2}\) in the \(y\)-coordinate.
# Engineering a Combinatorial Laplacian Solver: Lessons Learned +
## Preliminaries
### Electrical Network Analogy  
We can regard \(G\) as an electrical network where each edge \(uv\) corresponds to a resistor with conductance \(w_{uv}\) and resistance \(1/w_{uv}\), and \(x\) as an assignment of potentials to the nodes of \(G\) (cf. Figure 1). \(L\) operates on every vector \(x\in\mathbb{R}^{n}\) via \((Lx)_{u}=\sum_{v\in N(u)}(x_{u}-x_{v})\cdot w_{uv}\) for each \(u\in V\). Then, \(x_{u}-x_{v}\) is the voltage across \(uv\), and \((x_{u}-x_{v})\cdot w_{uv}\) is the resulting current along \(uv\). Thus, \((Lx)_{u}\) is the current flowing out of \(u\) that we want to be equal to the right-hand side \(b_{u}\). Furthermore, one can reduce solving SDD systems to the related problem inv-laplacian-current([PERSON], 2012): Given a Laplacian \(L=L(G)\) and a vector \(b\in\operatorname{im}(L)\), compute a function \(f\colon\widetilde{E}\to\mathbb{R}\) with (i) \(f\) being a valid graph flow on \(G\) with demand \(b\) and (ii) the potential drop along every cycle in \(G\) being zero, where a valid graph flow means that the sum of the incoming and outgoing flow at each vertex respects the demand in \(b\) and that \(f(u,v)=-f(v,u)\ \forall uv\in E\). Furthermore, \(\widetilde{E}\) is a bi-directed copy of \(E\), and the potential drop of cycle \(C\) is \(\sum_{e\in C}f(e)r_{e}\).
## 3 Beamforming Techniques
### Robust Beamforming with VDL Technique  
The new array correlation matrix due to the look direction error applying the VDL technique can be calculated using Equation (5).  
\[R_{new}=R+R-(1\times\beta\times I) \tag{5}\]  
here \(\beta\), denotes  
\[\beta=\frac{\delta\left(\sigma_{n}^{2}+P_{s}\,\left\|S_{0}\right\|^{2} \right)}{\left\|S_{ac}\right\|-\varepsilon} \tag{6}\]  
Signal power is denoted by \(p_{s}\), the noise power is specified by \({\sigma_{n}}^{2}\), and the norm of the steering vector without the look direction disparity and with the look direction disparity are represented by \(\left\|S_{0}\right\|\) and \(\left\|S_{ac}\right\|\), respectively. The steering vector distortion bound \(\varepsilon\) is calculated by Equation (7).  
\[\varepsilon=max(\left\|S_{0}-S_{ac}\right\|) \tag{7}\]  
The increase in \(\varepsilon\) causes the increment of the loading factor.
## 3 Colpitts Chaos Oscillator
### Study on the Colpitts Chaos Oscillator in a Simulation Environment and Prototype
#### 3.3.2 Experimental Study  
The Colpitts chaotic oscillator prototype (Figure 7a) was assembled and tested with Analog Discovery Pro. The chaos oscillator prototype was made using commercially available components with the same nominal element as in the simulation. As all components have nominal deviations and parasitic characteristics, the variable resistors were added to the circuit. Changing the resistance allows the oscillator to tune into chaotic behavior. A comparison of the two-dimensional projections of the attractor, shown in Figure 7b, with those from the original work [36], encountered coherence between the projections. This agreement assumes the prototype's successful completion. Minor differences in the projections are ascribed to the parasitic properties of the components. Furthermore, the Z1 TEST result of 0.98 verifies the chaotic nature of the system.  
Figure 6: Cross-correlation functions between \(V_{C1}\) and \(V_{C2}\) (blue), \(V_{C1}\) and \(I_{L1}\) (red), and \(V_{C2}\) and \(I_{L1}\) (yellow).  
Figure 7: **(a)** Colpitts chaos oscillator prototype; **(b)** Colpitts chaos oscillator prototype’s attractor two-dimensional projections.  
The next goal was to synchronize two oscillators in the drive-response system. The substitution approach was used to create synchronization within the Colpitts' chaos oscillator via voltage across capacitors \(C_{1}\) and \(C_{2}\). Notably, correlation coefficient values in both configurations reached a significant 0.95, confirming the system's robust synchronization.  
Figure 8 depicts the signal spectra for \(V_{C1}\), \(V_{C2}\), and \(V_{RL}\). A close analysis of these spectra reveals a signal band centered predominantly on the chaos oscillator's fundamental frequency as well as a wide band, which is a property of the chaotic signal.  
Likewise, cross-correlation functions were calculated within a Colpitts chaos oscillator prototype to evaluate its chaotic behavior and determine the minimal bit length. The cross-correlation functions between \(V_{C1}\) and \(V_{C2}\), \(V_{C1}\) and \(V_{RL}\), and \(V_{C2}\) and \(V_{RL}\) are shown in Figure 9.  
The examination of aperiodic signals, notably chaotic signals from the prototype, shows a rapid decrease in the amplitude of the cross-correlation function as the time difference between signals grows (Figure 9). This behavior reflects the oscillator's complex and non-repetitive dynamics, emphasizing unpredictability. Based on the cross-correlation function analysis, a minimum bit length of 7.5 us is required for successful data transmission, limiting a data transfer rate of 133.3 Kbps.  
Figure 8: Colpitts Chaos oscillator signal \(V_{C1}\) (blue), \(V_{C2}\) (red) and \(V_{RL}\) (yellow) spectra.  
Figure 9: Cross-correlation functions between \(V_{C1}\) and \(V_{C2}\) (blue), \(V_{C1}\) and \(V_{RL}\) (red), and \(V_{C2}\) and \(V_{RL}\) (yellow).
## 2 Background and Related Work
### Chaos Theory  
Chaos theory is a branch of the study of complex systems of mathematics. An early proponent of chaos theory was [PERSON]. In the 19 th century, he studied the non-cyclic three-body track and found that it does not get closer to a fixed point [26]. The chaotic systems have the high sensitivity of the initial conditions, and can be applied to cryptography [27].  
* Logistic Map The logistic map was presented by [PERSON] in the middle of the 19 th century, and this map is a one-dimensional chaotic map [28]. The common logistic map is a non-linear recurrence relation with a single control parameter \(\mu\) and the variable value \(X_{n}\). The map is shown in Equation (1). \[X_{n+1}=\mu X_{n}(1-X_{n})\] (1) Among them, \(X_{0}\) is the initial value, \(X_{n}\in[0,1]\). The branch parameter is known as \(\mu\). When \(3.5699456<\mu\leq 4\), the system will enter the chaotic state.
* Tent Map Tent maps were introduced as one of the first examples of chaotic map literature for nonlinear discrete dynamical systems [29; 30]. For example, the parameterized tent map can be described piecewise by Equation (2) [4; 31]. \[x_{n+1}=\left\{\begin{array}{ll}ux_{n}&,0\leq x_{n}<0.5\\ u(1-x_{n})&,0.5\leq x_{n}<1\end{array}\right.\] (2) The value of the parameter \(u\) is within 0 and 2 for the chaotic map.
Sensors_**2013**, _13_, 16406-16423; doi:10.3390/s131216406  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
Performance Improvement of Receivers Based on Ultra-Tight Integration in GNSS-Challenged Environments  
**[PERSON], [PERSON] * and [PERSON]**  
School of Aeronautics and Astronautics, Shanghai Jiao Tong University, No.800 Dongchuan Road, Shanghai 200240, China; E-Mails: [EMAIL_ADDRESS] (F.Q.); [EMAIL_ADDRESS] (G.D.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-21-3420-4154; Fax: +86-21-3420-4306.  
_Received: 11 October 2013; in revised form: 25 November 2013 / Accepted: 26 November 2013 / Published: 2 December 2013_  
**Abstract:** Ultra-tight integration was first proposed by Abbott in 2003 with the purpose of integrating a global navigation satellite system (GNSS) and an inertial navigation system (INS). This technology can improve the tracking performances of a receiver by reconfiguring the tracking loops in GNSS-challenged environments. In this paper, the models of all error sources known to date in the phase lock loops (PLLs) of a standard receiver and an ultra-tightly integrated GNSS/INS receiver are built, respectively. Based on these models, the tracking performances of the two receivers are compared to verify the improvement due to the ultra-tight integration. Meanwhile, the PLL error distributions of the two receivers are also depicted to analyze the error changes of the tracking loops. These results show that the tracking error is significantly reduced in the ultra-tightly integrated GNSS/INS receiver since the receiver's dynamics are estimated and compensated by an INS. Moreover, the mathematical relationship between the tracking performances of the ultra-tightly integrated GNSS/INS receiver and the quality of the selected inertial measurement unit (IMU) is derived from the error models and proved by the error comparisons of four ultra-tightly integrated GNSS/INS receivers aided by different grade IMUs.  
**Keywords:** ultra-tight integration; GNSS-challenged environment; phase lock loop; dynamic stress noise; quality of the IMU