esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Aerodynamic Moment and Structural Stability Risk  
Aerodynamic moments are vital in understanding how wind loads can cause rotation or twisting in historical structures. Unlike modern buildings, many heritage temples were not designed with reinforced elements that can resist torsion, uplift, or shear forces. Consequently, it is important to analyze aerodynamic moments in three main directions. This type of analysis helps to identify structural vulnerabilities that may affect the long-term stability and preservation of these culturally significant buildings.  
The moment ratio graph in Figure 21 presents the percentage contribution of aerodynamic moments in three directions. The X direction, which represents horizontal rotation across the width of the building, had the highest moment ratio from the E wind (93.99%), followed by the SSE wind (56.34%) and the NE (54.50%). These results indicated that wind from these directions caused strong lateral rotational forces, which might place stress on side walls and column connections. In the Y direction, which corresponds to twisting along the length of the building, the highest moment ratios were from the S wind (33.48%) and the N wind (21.54%). These forces could introduce torque along long walls, leading to misalignment or fatigue in structural elements such as beams or load-bearing walls. The Z direction, which represents vertical torsion or uplift, was most influenced by the N wind (37.11%) and the S wind (34.19%), followed by the NE wind (25.96%). These vertical moments could cause deformation or displacement of the roof, particularly in buildings using traditional wooden frames or clay tiles.  
The numerical values of the aerodynamic moments supported the trends shown in the graphical results of Figure 18. The E wind produced the highest moment in the X direction (\(-\)224,114.48 Nm), indicating a major risk to the lateral stability of the structure. The N wind generated the strongest moment in the Y direction (66,661.54 Nm) and the highest moment in the Z direction (88,595.73 Nm), revealing a critical vulnerability to vertical lift and longitudinal twisting. In addition, the SSE wind presented a complex risk, with a high lateral moment (\(-\)101,788.18 Nm) in the X direction and a substantial uplift moment (\(-\)43,887.82 Nm) in the Z direction. Based on these findings, the moment analysis clearly showed that winds from the E and NE directions posed the greatest aerodynamic threats to the Temple structure. These wind directions generated high rotational forces across all three axes and should be a priority in structural evaluation and reinforcement strategies. Understanding the distribution of aerodynamic moments is essential for developing effective conservation strategies. There should be careful assessment of structural elements sensitive to rotational forces, such as roof joints, ridge beams, wall corners, and projecting eaves, with subsequent reinforcement to prevent long-term deterioration. Addressing these directional wind-induced risks will help to improve the structural stability of the Temple, while preserving its historical and architectural value.
# Back to the Metrics: Exploration of Distance Metrics in Anomaly Detection
## 2 Methodology
### Visualization Analysis of Different Metrics
#### 2.3.1 \(k\)-Nearest Neighbor Squared Distance Mean  
When \(k=1\), as shown in Figure 2(a), the algorithm ensures adequate sample coverage and representativeness. As \(k\) increases, as illustrated in Figure 3, the coverage and representativeness of outlier points significantly decrease while the robustness of the anomaly score calculation function to outlier points slightly improves.  
Larger \(k\) values can increase the robustness of the model and reduce the influence of outliers but at the expense of sample representativeness and coverage. When \(k>1\), we see a score distribution that conforms to Assumption 2. When the number of test samples is large enough and most of them are normal, it is a good choice to increase the \(k\) value appropriately to increase the robustness of the abnormal samples that may be introduced by Assumption 1. This fits the scenario of the AS task.  
However, as we can see from Figure 2(b), when \(k\) increases, there will be many \"gullies\" in the gradient plot of the abnormal fraction function. These \"gullies\" exist regardless of whether the outliers are normal or abnormal. Samples located in gullies will be assigned the same anomaly scores, losing their ability to distinguish anomalies. During patch core and BTF process AD tasks, the patch with the highest anomaly score plays a decisive role. In cases where a known sample is likely to be an anomaly (has a high probability of not being affected by outliers and reducing the anomaly score), the presence of these gullies is not conducive to the recall of anomalies.  
To sum up, MBBM uses the \(k\)-nearest neighbor squared distance mean to measure the \(k\) value that may be needed for processing AS tasks. It is best to maintain \(k=1\) when processing AD tasks.
## 2 Study Area and Used Data  
The study area is the Futian District, located in the south-central part of Shenzhen City, with a total area of about 80 square kilometers. This district is a main commercial trade area in Shenzhen, with high-rise buildings and many floating populations (Figure 1). With the rapid development of Shenzhen's economy and society in the last 30 years, the pace of urban construction in Shenzhen has accelerated, leading to the deterioration of land subsidence. Shenzhen is a coastal city, featuring organic sand, clay, silt, and gravel in the underground soil. Its soil is also porous and prone to subsidence. At the same time, the overexploitation of groundwater, the soil compaction, the construction of subway tunnels, and the excavation of large foundation pits of high-rise buildings can disturb the soil. Human activities and natural forces may also cause disaster hazards in this area, so it is necessary to monitor such hazards [24].  
A dataset of the TerraSAR-X images acquired between 2008 and 2010, along the ascending orbit (39 scenes, see Table 1 for detail), were employed to analyze the deformation of Futian District. Observation parameters for all the images were as follows: path numbers, 167; incidence angle, 37.3\({}^{\circ}\); range pixel spacing, 0.9 m; azimuth pixel spacing, 2.0 m; polarization mode, VV. After setting the baseline and coregistration, we obtained 38 image pairs. The maximum spatial baseline is 228 m and the minimum is only 1 m; the time interval of interference pair was set to 11-440 days. Meanwhile, the 30 m resolution SRTM (NASA's Shuttle Radar Topography Mission) DEM data were used to remove the topographic phases [29].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**No.** & **Master** & **Slave** & **B (m)** & **T (day)** & **No.** & **Master** & **Slave** & **B (m)** & **T (day)** \\ \hline
1 & 27 Jul 2009 & 25 Oct 2008 & \(-155.116\) & \(-275\) & 20 & 27 Jul 2009 & 01 Oct 2009 & 114.078 & 66 \\  
(-41.524\) & \(-11\) & 38 & 27 Jul 2009 & 10 Oct 2010 & 50.539 & 440 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the formed image pairs. B and T represent the spatial baseline and time interval between the two acquisitions, respectively.  
Figure 1: (**a**) Optical image of the Futian District in Shenzhen, (**b**) geographic location of study area, and (**c**) Google Earth 3D model of the Futian District in Shenzhen.
## 3 Methods Used for Identification of Tonal Noise Sources
### Tonal Metrics
Rather than using the absolute value of the tone, the metric focuses on the position of the tone relative to the rotor speed and the tone level relative to the background sound pressure level. The distance of the tone level from the background can be defined using the tone-to-noise ratio criterion:  
\[L_{\text{TNR}}=20\cdot\log\left(\frac{p_{\text{ef},e_{\text{R}}}}{p_{\text{ef,noise}}}\right), \tag{7}\]  
where \(p_{\text{ef,e_{\text{R}}}}\) is effective pressure of the analysed excitation frequency ratio and \(p_{\text{ef,noise}}\) is the effective pressure of the surrounding frequency band (masking noise) corresponding to the frequency ratio width \(\varepsilon_{\text{R,band}}=0.2\), both without the application of weighting filters. Frequency bandwidth is quite an important parameter for the evaluation of masking noise. In cases where tonal components are sought to quantify the ability of the human ear to discriminate between different frequency tones, critical bands are used [61]. For a positive identification of a given tone from the ambient sound by a human, a minimum tone separation of 8 dB is assumed ([PERSON] and [PERSON] [61]). However, the metric used for this research focuses on the identification of noise sources; thus, a fixed frequency bandwidth is used because critical bands are significantly wide at higher frequencies. The size of the width is chosen to allow the identification of even tones with lower amplitude. As the bandwidth increases, the identification capability is partially reduced because the lower amplitude tones are masked by the surrounding stronger tones. Based on the tone-to-noise ratio values, the prominent tones \(L_{\text{PT}}\) are selected from the entire frequency spectrum as  
\[L_{\text{PT}}=\xi\cdot L_{\text{P}}, \tag{8}\]  
where \(L_{\text{P}}=20\cdot\log\left(\frac{p_{\text{ef}}}{p_{\text{ef}}}\right)\) is the sound pressure level from the measured effective pressure, \(p_{\text{ef}}\), \(p_{\text{ref}}=2\cdot 10^{-5}\) Pa is the reference sound pressure and \(\xi\) is the binary selection function. \[\xi= \begin{cases}1\text{ if }L_{\text{TNR}}\geq L_{\text{lim}}\\ 0\text{ if }L_{\text{TNR}}<L_{\text{lim}}\end{cases} , \tag{9}\]  
where \(L_{\text{lim}}\) is the limiting value of the tone-to-noise ratio to select prominent tones. In the case of this study, a value of 8 dB was chosen. After evaluating the prominent tones from the time interval, it is necessary to average the partial prominent tones from each time interval \(L_{\text{PT},i}\). During this averaging, it is necessary to ensure that the frequency ratios of the individual tones are corrected according to the actual rotor speed. The average prominent tone is determined as  
\[\widetilde{L}_{\text{PT}}=\sum_{i=1}^{N}L_{\text{PT},i} \tag{10}\]  
The algorithm for evaluating the tonal metrics is shown in Figure 1.  
In this research, the tonal metrics were applied to a set of sound pressure measurements of the centrifugal compressor of a turbocharger. Examples of partial displays of the measured signal are presented in Figure 2.
# Convergence Analysis of the Phase-Scheduled-Command FXLMS Algorithm with Phase Error
## 2 Active Sound Profiling Algorithms  
### Command-FXLMS Algorithm  
A schematic of the command-FXLMS algorithm is presented in Figure 1, where it can be seen that the command signal \(c(n)\) is the predefined value target for the residual error signal \(e(n)\). The command signal is created from the reference signal, which ensures that the frequency components of the command signal are the same as those of the disturbance signal. The pseudo-error \(e^{\prime}(n)\), instead of the residual error signal \(e(n)\), is introduced for updating the adaptive filter. As the pseudo-error \(e^{\prime}(n)\) converges to zero with the updating of the algorithm, the residual error signal \(e(n)\) converges to the command signal \(c(n)\) which indicates the predefined value.  
As is shown in Figure 1, the error noise \(e(n)\) can be calculated by  
\[e(n)=d(n)+y^{\prime}(n)=d(n)+s(n)*y(n) \tag{1}\]  
where \(d(n)\) is the disturbance signal, \(y^{\prime}(n)\) is the counter noise signal, \(s(n)\) is the impulse response of the FIR filter \(S(z)\) of the secondary path and \(y(n)\) is the output of the adaptive filter \(W(z)\), for which it is given by  
\[y(n)=\mathbf{w}^{T}(n)\mathbf{x}(n) \tag{2}\]  
where the \(x(n)\) is the reference signal vector and the superscript \"\(T\)\" denotes the transpose of a matrix. The vector \(\mathbf{w}(n)\) is the adaptive weight vector of the least mean square (LMS) algorithm, and its iteration follows the method of steepest descent and can be expressed as  
\[\mathbf{w}(n+1)=\mathbf{w}(n)+\mu\mathbf{x}^{\prime}(n)e^{\prime}(n) \tag{3}\]  
where \(\mu\) is the step size, \(\mathbf{x}^{\prime}(n)\) is the filtered reference signal and can be expressed by  
\[\mathbf{x}^{\prime}(n)=[x^{\prime}(n)\quad x^{\prime}(n-1)\quad\cdots\quad x^ {\prime}(n-L+1)] \tag{4}\]  
where \(L\) is the length of vector, and the value of \(x^{\prime}(n-l)\) is obtained by  
\[x^{\prime}(n-l)=x(n-l)*\hat{s}(n) \tag{5}\]  
where \(l=0,1,\ldots L-1\), \(\hat{s}(n)\) is the impulse response of the modeled FIR filter \(\hat{S}(z)\) of the secondary path. The \(e^{\prime}(n)\) is the pseudo-error signal, which is expressed by  
\[e^{\prime}(n)=e(n)-c(n) \tag{6}\]  
It can be seen that when the pseudo-error signal converges to zero, the residual error signal \(e(n)\) converges to the command signal \(c(n)\) which indicates the predefined value.
# Intelligent TCP Congestion Control Policy Optimization
## 3 Methods  
### Deep Reinforcement Learning  
#### 3.1.1 Background  
Deep reinforcement learning is a combination of deep learning and reinforcement learning. Deep learning uses representation learning to refine data [14] and does not have to choose features, compressed dimensions, conversion formats, or other data processing techniques, offering better feature representation capabilities than conventional machine learning techniques and providing a distributed representation of data by mixing low-level  
Figure 2: DRL-based congestion control algorithm framework.  
features to create more abstract high-level features. Reinforcement learning originated from the optimal control theory in cybernetics [15], which is mainly used to solve the problem of timing decision making, by ongoing environmental interaction and trial-and-error, and finally obtains the optimal strategy for a specific task and maximizes the cumulative expected return of the task. The mainstream methods of traditional reinforcement learning mainly include the Monte Carlo class method and the time difference classification method [16]. The former is an unbiased estimate with a larger variance, while the latter uses a finite step bootstrapping method with a smaller variance but introduces bias. Deep reinforcement learning combines the structure of deep learning with the idea of reinforcement learning for solving decision-making problems. With the help of the powerful representation ability of deep neural networks, any component of reinforcement learning can be fitted, including state value functions, action value functions, strategies, models, etc., and the weights in deep neural networks can be used as fitting parameters. DRL is mainly used to solve high-dimensional state action space tasks, integrating deep learning's powerful understanding ability in feature representation problems and reinforcement learning's decision-making ability to achieve end-to-end learning. The emergence of deep reinforcement learning has made reinforcement learning technology truly practical and it can solve complex problems in real-world scenarios. The most representative deep Q-network (DQN) is an extension of the Q-learning algorithm [17], which uses neural networks to approximate the action-value function, and the optimization goal is the minimization loss function:  
\[L_{i}(\theta_{i})=E_{\pi_{\theta_{i}}}[(y_{i}-Q(s,a;\theta_{i}))^{2}] \tag{1}\]  
where \(i\) represents the ith iteration, \(y_{i}=r+\gamma\underset{a^{\prime}}{\text{max}}Q(s^{\prime},a^{\prime};\theta _{i-1})\).  
[PERSON] et al. proposed the use of the deep double Q-network (DDQN) [18]. DDQN selects the action of the target Q value based on the current Q-network and uses the target Q-network to calculate the corresponding Q value of the action.  
\[y_{i}=r+\gamma Q(s^{\prime},\text{argmax}_{a}Q(s^{\prime},a;\theta_{i});\theta _{i}^{-}) \tag{2}\]
## 2 Methods
### Field Experiment Verification
In the field tests, the variations in wind velocity and pressure at points are recorded as 1.583 m/s and 19.021 Pa, respectively. For the RNG \(k\)-\(\varepsilon\), the corresponding variations are 1.451 m/s and 17.522 Pa. The fluctuation results predicted by the LES model are 1.497 m/s and 17.783 Pa, respectively. The fluctuations in the corresponding results calculated by the IDDES model are 1.472 m/s and 17.577 Pa, respectively. The LES model's predictions are found to be more in line with the field test results than those of the RNG \(k\)-\(\varepsilon\) and IDDES models, with discrepancies kept within a 7% margin. Upon comparing the peak wind speed moments, it is observed that the IDDES model's highest wind speed moments at 1.361 s are closest to the field test results at 1.471 s, with a difference of 7.5%. Similarly, the LES model's lowest wind speed moments at 1.087 s are nearest to the field test results at 1.012 s, with a variance of 7.4%. Comparing the corresponding moments of peak pressure, it can be seen that the moments (1.015 s, 1.252 s) of maximum and minimum pressure based on the LES model are the closest to the corresponding results (1.068 s, 1.21 s) of the field test (5.0%, 3.5%). To verify the reasonableness of the aerodynamic load calculation strategy for the sedan in the numerical method, the RNG \(k\)-\(\varepsilon\), LES, and IDDES models are built based on the numerical method in this study and the wind tunnel tests in the literature [21], and the aerodynamic drag and lateral forces of the sedan are presented in Figure 9. The differences between the aerodynamic load values obtained based on the RNG \(k\)-\(\varepsilon\), LES, and IDDES methods with the wind tunnel test results are marked in the figure. Under five yaw angle conditions (\(-6^{\circ}\), \(-3^{\circ}\), \(0^{\circ}\), \(3^{\circ}\), and \(6^{\circ}\)), the differences in the aerodynamic drag of the sedan obtained by the RNG \(k\)-\(\varepsilon\), LES, and IDDES-based methods with the experimental results are within 5.3%, 2.3%, and 4.0%, respectively. Regarding the lateral forces on the sedan, the differences between the RNG \(k\)-\(\varepsilon\), LES, and IDDES methods and the tests are within  
Figure 8: Comparison between the results at the 0.9 m height of the shoulder in the field test and the CFD numerical simulation: (**a**) wind speed; (**b**) pressure. Figure 7: Comparison between pressures and its averaged values measured by field tests and CFD methods. 6.2%, 3.7%, and 4.8%, respectively. Compared to the RNG \(k\)-\(\varepsilon\) and IDDES methods, the aerodynamic loads of the sedan predicted by the LES method are closest to the wind tunnel experiment results.
## 6 Discussion
### Additional Considerations
Deep learning frameworks like TensorFlow and PyTorch facilitate the development of custom models tailored to specific applications. For processing 3D point cloud data from LiDAR or stereo cameras, models such as PointNet, PointNet++ and VoxelNet are frequently utilized. Semantic segmentation models like U-Net, SegNet and DeepLab aid in image segmentation and environmental context understanding, while instance segmentation models like Mask R-CNN provide both object location and segmentation masks. Reinforcement learning models contribute to decision-making and navigation by learning optimal paths and actions through trial and error. Additionally, OCR models such as Tesseract and custom CNN-based OCR models are used to interpret text, such as labels and barcodes, within the forklift's environment. YOLO and Faster R-CNN are particularly prevalent for their robust real-time object detection performance, which is crucial for the dynamic environments in which autonomous forklifts operate. Mask R-CNN is also commonly used due to its combined object detection and segmentation capabilities. Current autonomous forklift technology, despite significant advancements, faces several hurdles on the road to widespread adoption. These challenges include navigating complex and dynamic environments like busy warehouses with human workers and moving equipment. Sensor limitations, particularly in poor lighting and with reflective surfaces, impair navigation and object detection accuracy. Integrating these forklifts into existing infrastructure can be expensive and requires substantial initial investment, posing a barrier for smaller businesses. Additionally, they lack flexibility, often needing significant reprogramming to adapt to new tasks or layout changes. Safety and regulatory concerns are paramount. Proving the safety of human-robot interactions requires complex and time-consuming efforts. Reliability and maintenance remain ongoing challenges, as technical issues can disrupt operations. Interoperability with human workers presents another hurdle, necessitating sophisticated systems for seamless coordination. Data security and privacy are also critical concerns due to the vast amount of data generated and used. Scaling up from pilot projects to full-scale deployments requires careful planning to handle increased volume and complexity. Addressing these limitations, shown in Table 15, necessitates ongoing research, development and collaboration among technology providers, warehouse operators and regulatory bodies. Advancements in sensor technology, improved decision-making capabilities for complex environments and increased adaptability to varied tasks are crucial. Additionally, reducing costs, improving safety protocols and establishing robust data security measures are essential for the wider adoption of autonomous forklifts.
# Analytical Approach in the Development of RF MEMS Switches
## 2 Analytical Approach to the Development of RF MEMS Switches of Electrostatic Type
### Technological Route of Manufacturing of the Developed Design of a RF MEMS Switch  
The technological process of manufacturing an integrated RF MEMS switch is shown in Figure 18. The process is characterized by a low deposition temperature of layers (not more than 350 \({}^{\circ}\)C) in order to prevent damage to the CMOS IC.  
Figure 16: The electrical circuit of the integrated RF MEMS switch with LC isolation control signal.  
Figure 17: Passing RF signal through the switch: (**a**) in down state; (**b**) in up state.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline \(L\) & 0.0375 nH \\ \(R\) & 0.0341 \(\Omega\)-m \\ \(C_{1},C_{2}\) & 1.1 pF \\ \(C_{3},C_{4},C_{5},C_{6}\) & 22.12 pF \\ \(C_{up-state}\) & 41.4 pF \\ \(C_{down-state}\) & 0.011 pF \\ \hline \end{tabular}
\end{table}
Table 5: Calculated parameters of the electrical circuit.  
At the beginning of the CMOS IC, it is manufactured using a standard CMOS process with formation of through holes. Then, the lower structural elements of the switch are formed, such as coplanar waveguide, fixed down electrode, anchor areas. The sacrificial layer is the photosensitive polyamide at various stages of formation of structural elements of the switch. Then, the upper structural elements such as the membrane, elastic suspension elements, the pair of side electrodes, the fixed upper electrode are formed. After forming the walls and cover for the capsule sealing, the sacrificial layers of polyamide are removed through holes in the capsule and fixed upper electrode membrane using dry etching. Finally, the holes in the capsule are sealed. The layer of sealing prevents switch failures that can be caused by contamination or high humidity. Consequently, the integrated RF MEMS switch shown in Figure 6 is manufactured using CMOS IC technology.  
Figure 18: Technological route of integrated RF MEMS switch manufacturing.