esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulations and Experiments
### Experimental Result  
Figure 19 shows the experimental waveform of the circular multi-pointer filter. Figure 19a shows the grid voltage, the circular structure multi-pointer filter output, and the analog filter output waveform. It can be seen that setting the cutoff frequency of the circular structure multi-pointer filter to 0.6 Hz to safely remove the noise from the actual voltage results in good characteristics. Figure 19b shows the steady-state waveform of Figure 19a, which is distorted because the grid voltage contains a low-order harmonic component, and also shows that the output of the circular-structured multi-pointer filter almost matches the grid voltage. In Figure 19c, the waveform for the phase stream matches the grid voltage to compensate for the phase difference between the grid voltage (Vg_f) and the actual grid voltage (Vg) sampled in the steady-state waveform shown in Figure 19b [20].  
Figure 20 shows the experimental results when the grid voltage changes from no load to a 1.0 kW load in the state of 200 V as in the simulation to analyze the excellence of the characteristics of the proposed system. Figure 20a shows a case-control by the conventional method, in which a harmonic component appears in the output voltage owing to the harmonic of the grid voltage. As shown in Figure 20b, although there is a harmonic in the grid voltage, in the proposed method, the output voltage becomes sinusoidal with an effective value of 220 V  
Figure 19: Filter characteristic analysis. (**a**) The waveform with grid voltage, circular structure, multipoint filter output, and an analog filter output. (**b**) The steady-state waveform with grid voltage, circular structure multipoint filter output, and an analog filter output. (**c**) The phase difference waveform with grid voltage, circular structure multipoint filter output, and analog filter output.  
Figure 20: Results of the DVR voltage compensation experiment. (**a**) The waveform with conventional method, in which a harmonic component appears in the output voltage owing to the harmonic of the grid voltage. (**b**) The waveform with proposed method in which the output voltage becomes a sine wave.  
Figure 21 shows the experimental result when the load of the DVR changes from 1.0 kW to 2.0 kW. The test results show that the load fluctuation responds immediately and maintains a constant voltage without a sag at the output voltage.  
Figure 22 shows the waveform used to analyze the voltage compensation characteristics of the DVR. As shown in the figure, the command voltage waveform of the inverter generates a waveform that removes the voltage and harmonic components to control the magnitude of the fundamental wave voltage from the grid voltage (Vg), which contains the harmonics. A single-phase full-bridge inverter forms a PWM waveform to generate a command voltage. Therefore, when the sum of the grid voltage and PMW voltage passes through the LC filter, a sinusoidal output voltage can be formed.
# Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks
## 6 Lessons Learned, Open Issues, and Research Challenges
### Open Issues and Research Challenges
#### 6.2.4 Trade-Off between Exploration and Exploitation  
Exploration is an attempt to discover a new state in the search space that may provide a better reward compared with the existing experience of an RL agent. Exploitation refers to performing the best action according to existing experience. Exploration aids in determining the global optimal solution. However, during exploration, the action performed might be good or bad because excessive exploration may produce unnecessary detours, retransmissions in FANETs, and delay the convergence of the algorithm. Therefore, in FANET routing decision making using RL, a strategy is required to balance the trade-off between exploration and exploitation to attain the global optima.  
Some RL algorithms consider \(\in\)-greedy [24] and UCB [51] strategies to control the exploration rate. However, in the \(\in\)-greedy strategy, the exploration rate depends on the parameter \(\in\), which is frequently approximately 10%, resulting in a very low exploration rate. The UCB strategy can control the exploration rate by jointly considering the sum of the average cumulative reward and number of times a specific action is selected within a specific time. In [22], the authors reported that the exploration rate should be controlled according to the network condition and degree of mobility changes in FANETs, instead of exploration based on time. This is logical because when the relative neighbor state is stable, UAVs can exploit according to the existing Q-value. Otherwise, when the relative neighbor state is not stable, UAVs can perform exploration according to the predicted link duration with the neighbor links to achieve a more stable routing path.  
Metaheuristic algorithms, such as GWO and SAO, are very efficient at exploring the search space to determine the global optimal solution [68]. The hybrid mechanism with metaheuristics and RL can provide a better solution for FANET routing decision making, as RL agents can decide when to explore or exploit. In [18], the authors used SAO to control the exploration rate defined by the temperature parameters according to the mobility in the FANETs.
## 2 Materials and Methods
### Follow-Up Examination  
Patients were asked preoperatively to complete demographic information and the following questionnaires: the Knee Injury and Osteoarthritis Outcome Score pain subscale (KOOS-pain), symptoms subscale (KOOS-symptons), function in daily living subscale (KOOS-LDL), function in sport and recreation subscale (KOOS-sport), and knee-related Quality of Life subscale (KOOS-quality); the International Knee Documentation Committee subjective knee evaluation form (IKDC); the Lysholm knee scoring scale (Lysholm); and the Western Ontario and McMaster Universities Arthritis Index (WOMAC). With a minimum follow-up of 55 days, all patients came back to our department and postoperative KOOS-pain, KOOS-symptons, KOOS-ADL, KOOS-sport, KOOS-quality, IKDC, Lysholm, and WOMAC questionnaires were administered. Each KOOS subscale and the IKDC and Lysholm scales calculate and grade an overall score from 0 to 100 based on the answered questions. Higher scores indicate better function and fewer symptoms. Conversely, higher scores (from 0 to 96) on the WOMAC indicate functional limitations, worse pain, and stiffness. Subsequently, all patients underwent ultrasound examination performed by 2 orthopaedic surgeons experienced in musculoskeletal sonography (Esaote My Lab X5) using a 4-15 MHz linear transducer. The examiners were blinded to any previous imaging data. The protocol of the sonographic examination was as follows: a supine position, the knee in 90\({}^{\circ}\) of flexion, a linear transducer placed over the lateral knee in a coronal oblique plane, the location of the lateral collateral ligament (LCL) extending from the femur to the fibula in its long axis, and identification of the ITB strip in its short axis as a rounded structure below the LCL. The visible scar on the skin after lateral extra-articular tenodesis and characteristic bone landmarks, such as the lateral condyle of the femur, Gerdy's tubercle, and the fibular head, assisted in ITB identification. During examination, the following sonographic parameters were documented: measurements of width and height, colour Doppler assessment of the ITB strip, ITB strip echogenicity, the presence of effusion around the ITB strip, and the location of the ITB strip according to designated zones. Throughout the follow-up, intraoperative and postoperative complications and reinjury were documented.
## Materials and Methods
### Microencapsulation Method  
Complex coacervation technology was applied according to the following major steps: 50 g of 3% \(w/w\) gum arabic (GA) aqueous solution was prepared by dissolving gum arabic at room temperature under continuous stirring by the means of a magnetic stirrer (Arec,Velp Scientific, Usmate, Italy) at 800 rpm. Fifty grams of 3% gelatin (GE) aqueous solution was prepared under continuous stirring and concomitant heating up to 50 \(\pm\) 2 \({}^{\circ}\)C using a magnetic stirrer at 800 rpm. Gelatin type A with a gel strength of approx. 175 Bloom was used; 2.7 g of yarrow essential oil was emulsified into the formerly prepared 3% \(w/w\) gelatin solution by using an IKA DI 18 B type ultaturrax (IKA\({}^{\circledR}\)-Werke GmbH & Co. KG, Staufen, Germany) equipped with an IKA S 18 N-19 G type dispersion tool (IKA\({}^{\circledR}\)-Werke GmbH & Co. KG, Staufen, Germany) for 10 min at a rotation speed of \(\approx\) 5000 rpm. This emulsion was added to the formerly prepared aqueous gum arabic solution under continuous stirring. At addition, the temperature of both components was in the interval of 50 \(\pm\) 2 \({}^{\circ}\)C. The pH adjustment to pH = 4.0 \(\pm\) 0.05 of the mixture was made by using 0.5 N HCl. After gradual cooling to 10 \({}^{\circ}\)C in an ice bath, the crosslinking agent glycerol (1 g) was added to harden the formed shell. Separation and purification of microcapsules were performed by decantation and washing with purified water twice (volume 2 \(\times\) 50 mL). After decantation, 50 mL of water was added to the microcapsule slurry and the mixture was stirred by the means of a magnetic stirrer for 15 min. The washing procedure was repeated two times (Figure 1).  
Simultaneously, microcapsules without essential oil, referred to as placebo (PL), were produced using the aforementioned method. The schematic process of obtaining microcapsules is presented in the flow chart (Figure 1).
# Constraint Preserving Mixers for the Quantum Approximate Optimization Algorithm
## 3 Construction of Constraint Preserving Mixers
### Optimality of Mixers  
On current NISQ devices, the noise level of two-qubit gate (CX) times and error rates are one order of magnitude higher than for single qubit gates (\(U_{3}\)). In addition, most devices lack all-to-all connectivity. The CX gates between these require SWAP operations, which consist of additional CX gates. _An optimal mixer will therefore contain as few CX gates as possible_. Since Pauli-strings are implemented according to Equation (33), we define the cost to implement \(e^{-itH_{M}}\) as  
\[\text{Cost}(H_{M})=\sum_{\begin{subarray}{c}i_{1},\cdots,i_{n}=1\\ |c_{i_{1},\cdots,i_{n}}|>0\\ \text{len}(\sigma_{i_{1}}\otimes\cdots\otimes\sigma_{i_{n}})>1\end{subarray}}^{ \mathbf{4}}2(\text{len}(\sigma_{i_{1}}\otimes\cdots\otimes\sigma_{i_{n}})-1), \tag{36}\]  
where \(\text{len}(P)\) is the _length of a Pauli-string_\(P\) defined as the number of literals that are not the identity. For instance, \(P=IXIIY=I_{1}X_{2}I_{3}I_{4}Y_{5}=X_{2}Y_{5}\) has \(\text{len}(P)=2\). The \(\text{Cost}(H_{M})\) specifies the number of CX gates that are required to implement the mixer. A lower cost  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Algorithm 1** & **Algorithm 2** & **Algorithm 3** \\ \hline runtime & \(\mathcal{O}(2^{5n})\) & \(\mathcal{O}(n\gamma)\) & \(\mathcal{O}(n\gamma)\) \\ memory & \(\mathcal{O}(2^{2n})\) & \(\mathcal{O}(n\gamma)\) & \(\mathcal{O}(n\gamma)\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the complexity of the two algorithms for \(n\) qubits. Here, \(\gamma\) is the number of nonzero entries of \(T\).  
means fewer and/or shorter Pauli-strings. There are four interconnected factors that influence the cost to implement the mixer for a given \(B\).
# Train-Induced Vibration Analysis and Isolation Trench Measures in Metro Depot Structures
## 1 Introduction
Subsequently, an air-cushion-filled trench was also implemented in Dusseldorf, Germany [26] to isolate vibration from high-speed railways. Additionally, the Brussels Public Transport Company [27] employed composite barriers made of polystyrene cores and concrete side panels near a test track. Apart from isolation trenches, other methods such as wave impeding blocks (WIBs) and pile rows have also been studied. [PERSON] [28] and [PERSON] et al. [29] analyzed the effectiveness of WIBs for vibration isolation. [PERSON] et al. [30], [PERSON] et al. [31], and [PERSON] et al. [32] investigated the vibration isolation performance of pile rows. Currently, research by scholars in various countries on vibration isolation in the propagation pathways of rail transit primarily focuses on the sections of subways or high-speed rail. However, studies on vibration isolation measures for the propagation pathways in metro depots are relatively scarce. In conventional railway sections, the above-ground or underground tunnel lines are generally located far from building structures. High-frequency vibrations become very weak after attenuation through the soil, resulting in building floor vibrations that are predominantly low-frequency. In contrast, metro depots typically have track lines that are located close to the over-track buildings. The high-frequency vibrations induced by trains do not completely attenuate and are transmitted through structural columns to the floors of the over-track buildings. The dominant frequency range of floor vibrations in this case is higher compared to conventional lines. Therefore, the vibration characteristics of the floors differ in these two scenarios. This paper focuses on the metro depot as the research target and investigates the impact of open trenches and infilled trenches on the vibration isolation effects for the over-track building of the metro depot.
## 3 BeiDou PPP Strategy
### Static PPP
The results illustrate that the accuracy of GPS static PPP do not improve obviously with the introduction of BeiDou data. Probably, the orbit error of BeiDou satellites may still have biases with respect to that of GPS satellites.
# Load Forecasting Based on LVMD-DBFCM Load Curve Clustering and the CNN-IVIA-BLSTM Model
## 3 Methodology
### The Proposed CNN-IVIA-BLSTM Forecasting Model
#### 3.3.3 Hybrid Forecasting Model  
The CNN-IVIA-BLSTM model is a combined prediction model consisting of a CNN and BLSTM. BLSTM is a commonly used model architecture of deep learning models for sequence modelling tasks, and it has achieved good performance in many tasks. The CNN is first used to extract the feature vectors consisting of load influencing factors, which can be considered a \"feature extractor\", then the extracted feature vectors are used for load prediction with the BLSTM model optimized by the IVIA. The hybrid prediction model  
Figure 11: BLSTM network structure diagram.  
Figure 10: LSTM network structure diagram.  
structure is shown in Figure 12, which takes full advantage of both the CNN and BLSTM network to ensure the accuracy of the power load.  
The forecasting steps are as follows:  
1. Selected information as model input.
2. LVMD decomposes and denoises the original sequence, and DBPCM performs clustering.
3. The IVIA population size \(N\), the maximum number of iterations \(M\), and the initial search range of the parameters (the number of neurons in the hidden layer \(H\), training number \(E\), learning rate \(\eta\), and regularization factor \(L2\)) are set. The root mean square error (\(y_{RMSE}\)) is used as the objective function in the optimization algorithm, and finally, the model of the influenza virus immunization algorithm coupled with the bidirectional long and short-term memory network is developed.
4. The 1D CNN reads the load sequence with a sliding time window of 10 and a step size of 1 for feature extraction.
5. \(m\) prediction models are obtained by inputting the CNN-IVIA-BLSTM prediction models for each component separately.
6. Finally, the predicted values of the m prediction models are combined to obtain the predicted values of the load.
## 2 Materials and Methods
### Methods
#### 2.4.1 MLR Model
\\ \cline{2-2}  
Evaluation of MLR Model  
The linear fit between the GPP inverted by the MLR model based on comprehensive parameter optimization and the GPP measured at three ecological stations shows that the inverted GPP can capture 86% of the changes in GPP in farmland areas, 82% in grassland areas, and 89% in desert vegetation areas. Overall, over 80% of the changes in each underlying surface can be effectively explained. Therefore, the GPP model inverted based on the improved SIF and MLR models can effectively represent the spatial comprehensive variation pattern of GPP in arid areas (Figure 3). In addition, for the three underlying surfaces, the range of RMSE after fitting the measured GPP and the inverted GPP is between 0.0029 and 0.0916, the range of SD is between 0.0052 and 0.2712, and the range of MB is between \(-0.0002\) and 0.0003. This further demonstrates the good applicability of the MLR model in GPP inversion in arid regions. Figure 3: Applicability evaluation of the GPP inverted based on the improved SIF and MLR models on different underlying surfaces. In the graph, each point represents the average over an 8-day period. The shaded area in the figure represents the 95% confidence interval.
# Sensitivity Analysis of an Implanted Antenna within Surrounding Biological Environment
## 4 Numerical Results and Discussion
### Data Prediction  
Another tremendous advantage of using PCE metamodel is that it could predict data points by using only limited numbers of sample points. This could reduce largely the calculation time, especially when the calculation model is complicated or a great number of calculations are needed. In Section 4.1, the LOO error value shows that the estimation model could be established with at least 30 samples (LOO error less than 1%0). Here, an estimation model is built basing on 60 samples in order to be more accurate. In Table 4, some random data points (except for the 60 that build the estimation model) are estimated by using the PCE model and compared with the values that calculated by CST Studio. The absolute errors and relative errors are calculated and presented in Table 4. The test sample vector is (bone radius \(B\), reflector distance \(R\), fat thickness \(F\), muscle thickness \(M\), implantation depth \(I\)).  
As presented in Table 4, the five randomly chosen points are well estimated with an average relative error around 1.3%. Therefore, this surrogate model established by 60 samples could be used to estimate the real simulation results with an error tolerance less than 2%.
## 3 Artificial Intelligence Techniques for 6G Networks
### Terahertz Band Communication  
The use of very high-frequency electromagnetic waves for wireless communication in 6G networks is investigated by terahertz band communication. In comparison to conventional radio frequencies, this technology offers much better data rates and capacity. AI can help to improve signal processing, beamforming, and channel estimation for effective terahertz communication. AI-enhanced algorithms can be used to solve the problems associated with propagation and absorption at these frequencies [12].
## 5 Results and Discussion
The PMVO algorithm exhibits the narrowest distribution in both configurations, reflecting its stable convergence behavior and lower outcome variability. This visual result is coherent with the low standard deviation values previously reported in Tables 3 and 4. In contrast, the CBGA and PVSA methods show wider violins, especially in the self-powered mode, suggesting more dispersed solutions and less predictable performance. Figure 7b compares the distributions for CO2 emissions. Once again, PMVO yields the most compact distribution, indicating consistently optimized behavior for environmental criteria. The violin shapes confirm the observations made from numerical indicators: PVSA provides competitive results but exhibits slightly higher variability, while CBGA shows more scattered emissions values. These violin-scatter plots offer a visual confirmation of the robustness and reliability of the PMVO approach. The plots highlight that not only does PMVO achieve the best average and best-case performance, but it also maintains tighter distributions across optimization runs, which is an important feature in practical planning contexts where reproducibility and consistency are desirable. It is worth reiterating that although the PMVO algorithm involves a longer computational time, the nature of the BESS scheduling problem (executed offline within a broad planning window) renders the solution time a non-critical factor. Therefore, selecting a strategy that guarantees higher quality and consistency in the solutions, such as PMVO, is more valuable than prioritizing minimal computation time.
## 2 Critical Line Set and Its Search Method
### Search Method of CLS Based on Improved Shortest Path Algorithm
Find the minimum value of the path length among all the paths recorded by _Pathsback_, denoted as \(Z_{\text{min}}\). Check again whether the paths satisfy \(Z(P_{i})\leq\epsilon Z_{\text{min}}\) and eliminate the paths that do not satisfy the condition; at this time, the remaining paths all satisfy \(N\leq 10\) and \(Z(P_{i})\leq\epsilon Z_{\text{min}}\). These paths are the first \(K\) shortest paths, and all the branches on these paths constitute the CLS of the removed branch. Based on the improved shortest path method for searching for the CLS, the number of the first \(K\) shortest paths is limited by the minimum value of the length of all shortest paths. Therefore, when the shortest paths are determined, the number of the first \(K\) paths that can satisfy the restriction is very small. Due to the large number of circuits in the transmission grid, many loops include a small number of branches and nodes. When the number of nodes exceeds a certain value, it can be assumed that this loop is invalid due to the excessive length of the corresponding electrical distance. Therefore the method in this paper is not affected by the number of nodes in the grid. In this section, we fully use the information of the adjacent nodes of the first K-1 paths obtained to discover the Kth shortest path and finally obtain the CLS based on the first K shortest paths that have been searched to satisfy the conditions. The steps of the improved algorithm are shown in Figure 3. Therefore, when the WAMS monitors the power grid topology changes in real-time, the CLS of the removed branch can be quickly found based on the improved shortest path algorithm, and the acquisition range of wide-area information used to identify LE can be further determined. Figure 3: The steps of the improved algorithm.