esa-sceva/satcom-chunk-collection huggingface dataset:
# Application Study of Adaptive Tracking Algorithm in Active Noise Control System of Transformer
## 4 Discussion
### Prototype Configuration and Instrumentation  
The ANC system is consisted of two parts: electroacoustic devices and controllers. The electro acoustic device includes the primary noise pickup and residual noise monitoring by acoustic sensor, and the loudspeaker is used as the secondary sound source. The controller is used to achieve the hardware and software of the ANC algorithm. The characteristics of the noise sources should be used as a reference for the control algorithm, and the hardware should be placed on the industry standard substation control room.  
The hardware of controller includes the industrial personal computer (IPC), PCI data acquisition, and output board. The software and the key parameters of the algorithm are as follows: the main control program of industrial control computer realized by C ++ is visual, controllable, and monitoring. The adaptive tracking algorithm is used in the main control program, and facilitate to be maintained. The parameters of adaptive tracking algorithm are the same as in 4.1. The main reason for adopting the industrial computer as the hardware carrier of the ANC system controller is that it has good performance at anti vibration, dustproof and electromagnetic shielding, which can protect the internal  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **ANC off** & **Adaptive Tracking Method** & **LMS** & **FxLMS** \\ \hline X1 & 59.4 & 53.7 & 57.9 & 61.7 \\ X2 & 52.9 & 48.0 & 52.1 & 58.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The sound pressure level (dB(A)) of the measuring point.  
Figure 8: Simulation results.  
parts of the computer from affection of electromagnetic field in the substation. In addition, selecting the corresponding chassis according to the control cabinet model in the substation is good to arrange on shelves in actual operation. The main reason for using a PCI board is that it is plug-and-play and has efficient transmission rate and better expansibility. The other part is the electro acoustic device. The primary acoustic sensor is used to pick up the noise signal, and the secondary acoustic sensor is used to monitor the residual noise signal. The Secondary sound source array is composed of nine speakers and a power amplifier. Horizontal spacing of each speaker is 0.3 m, and vertical spacing is 0.3 m. The structure is shown in the Figure 9.
# Simulation of the Cooling Effect of Porous Asphalt
## 5 Finite Element Simulation of the Pavement Temperature Field
### Model Establishment  
A virtual experiment was used to simulate the temperature field distribution of porous asphalt pavement. Because the process of defining boundary conditions in ANSYS Workbench is complicated and the calculation speed is slow, the study only used ABAQUS to simulate the temperature change of the pavement within 24 hours. According to the simulation results, the maximum temperature variation in pavement with different air voids was analyzed, and the influence of air voids on the cooling performance of the porous pavement was evaluated.  
Pavement is a layered structure, so the study divided the models into six layers. The upper layer was AC-13/PAC-13, the middle layer was AC-20, the lower layer was AC-25, the base layer was a cement-stabilized macadam base layer, and the cushion layer was made of lime-stabilized soil. The order and thickness of each layer were as shown in Figure 6.  
In the finite element simulation process, every layer was treated as a complete and independent isotropic material. It was assumed that the pavement had a good compaction effect and that there was no thermal resistance between two adjacent layers with a tight contact interface. Therefore,  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Specimen** & **Upper Layer Air** & **Lower Layer** & **Surface Equilibrium** & **Convective Heat Transfer** \\
**Number** & **Void (Material)** & **Material** & **Temperature (\({}^{\circ}\)C)** & **Coefficient (W-m\({}^{2,\circ}\)C\({}^{-1}\))** \\ \hline AC & AC-13 & AC-20 & 28.5 & 19.058 \\ S1 & 16.68\% (PAC-13) & AC-20 & 28.2 & 20.642 \\ S2 & 18.11\% (PAC-13) & AC-20 & 27.8 & 24.639 \\ S3 & 19.66\% (PAC-13) & AC-20 & 27.4 & 26.551 \\ S4 & 20.79\% (PAC-13) & AC-20 & 27.5 & 27.639 \\ S5 & 23.05\% (PAC-13) & AC-20 & 26.6 & 34.148 \\ \hline \end{tabular}
\end{table}
Table 4: Surface equilibrium temperatures and convective heat transfer coefficients.  
Figure 6: Pavement model in ABAQUS.  
after meshing, it was only necessary to input the thermophysical property parameters of each layer into the model to complete the model establishment.  
The thermophysical properties of the materials used in the upper and middle layers were studied as mentioned above. The thermophysical property parameters of the other layers are shown in Table 5.
## 3 Empirical Fatigue Damage Estimation
### Modal Phase Reconstruction
Additionally, instead of decomposing the measurements so as to represent all the complex modes or frequency components (from 0 Hz to the Nyquist frequency, \(N=P/2\)), the number of modes, \(N\), is selected such that the frequency band from \(\omega_{1}\) to \(\omega_{N}\) preserves 99% of the total energy (defined as the sum of the variances of the strains at all theloggers). Figure 6a shows CF PSDs for all the input sensors (except sensor no. 4). In Figure 6b, the sum of PSDs of the twenty-three input CF strains (excluding the strain at the target sensor, i.e., logger no. 4) is represented by the blue line; the selected frequency band that preserves 99% of the total energy is indicated by the red dots. By decomposing the riser strain measurements into modes only over the 99%-energy frequency band, the number of frequency components, \(N\), is reduced to one-seventh of its original value. This greatly reduces the number of coefficients to be estimated in the MPR linear system solution and, hence, dramatically reduces computational time. Figure 6c shows that the energy distribution by frequency of the reconstructed CF strains at the target sensor (red dashed line) matches that based on the measured strains there (blue solid line) reasonably well over the 99%-energy frequency band. In Figure 6d, RMS values of CF curvature at the twenty-three input sensor locations based directly on measurements are indicated by the blue circles; at the location of the target sensor, the MPR-based interpolation (indicated by the blue line, which also shows estimated RMS values at other locations nearby) is very close to the RMS value (red cross) obtained from the measurements. The strains at the target sensor (no. 4) are interpolated quite effectively with a third-order polynomial fit using strains from four nearby sensors, i.e., sensor nos. 2, 3, 5, and 6 (again, other fits are also possible). As is the case with the POD procedure, riser response statistics, such as the RMS CF curvature, at any location within the spread of the sensors, may be estimated by such piecewise interpolation following application of the MPR procedure. The variability factor, representing the ratio of the estimated damage to that based directly on measurements, at the target sensor (i.e., sensor no. 4), is 1.25, which suggests that the fatigue damage  
Figure 6: The MPR procedure applied with twenty-three input sensors (sensor no. 4 is the target) for the NDP2350 (sheared current) data set: (**a**) PSDs of the strains measured at the twenty-three sensors; (**b**) summation of the strain PSDs and the frequency components that preserve 99% energy; (**c**) strain PSD at the target sensor, reconstructed (using modes that preserve 99% energy) vs. measured; and (**d**) RMS curvatures, reconstructed (using modes that preserve 99% energy) vs. measured. is overestimated by 25% when the MPR method is employed with the selected frequency components. Similar results for other choices of the target sensor are discussed in Section 4.
# Non-Maximum Suppression Performs Later in Multi-Object Tracking
## 4 Results
### Comparison with State-of-the-Art  
This paper compares the proposed method with the current state-of-the-art (SOTA) method. These methods include one-stage methods such as FairMOT [7], Corrtracker [8], MOTR [32], TrackFormer [33], CSTrack [6]; and detection-based two-stage methods such as TransMOT [13], and MPNTrack [34].
## 2 Related Work
### Optimal Resource Scheduling on General NOMA
#### 2.1.1 User Pairing and Scheduling  
User pairing plays a significant role in improving NOMA performance. [PERSON] et al. [10] demonstrated that pairing users with distinct channel state information (CSI) improves both throughput and fairness. Their work has inspired dynamic user-pairing algorithms that adapt to real-time channel conditions. [PERSON] et al. [13] further analysis on user-pairing strategies in NOMA, showing that optimizing pairing can significantly improve sum-rate performance. [PERSON] et al. [14] highlighted that pairing users with highly distinctive channel gains not only enhances system capacity but also maintains fairness in resource allocation. [PERSON] et al. [15] proposed a user pairing and power allocation for NOMA with possible use cases of vehicular communications. To effectively handle the non-convexity of the problem, the authors proposed to decouple the problem and to use deep Q learning. Despite the computational efficiency, the proposed system assumes a single base station which may limit the scalability. [PERSON] et al. [10] analytically demonstrated that pairing users with distinct channel gains significantly enhances system performance.
# A Review of Vertical Shaft Technology and Application in Soft Soil for Urban Underground Space
## 4 Technological Advancements in Mechanized Systems
### Blind Shaft Boring  
Blind shaft drilling refers to the mechanical excavation of large-diameter vertical shafts [40] into the earth using a full-face drill head, with the excavated material transported to the surface through the shaft. In urban infrastructure, it serves as access and ventilation shafts for metro tunnels and parking facilities. This method facilitates the transportation of excavated material through the shaft to the surface. A blind SBM is employed when a pilot hole is not feasible. To date, the South Deep Shaft in South Africa is the deepest lift blind vertical shaft, reaching a depth of approximately 2991 m below collar [41].  
Blind shaft drilling presents several advantages over conventional shaft sinking techniques. The process is highly automated, requiring only a crew of three to four individuals.  
Figure 5: Overview of (**a**) shaft boring system after [31] and comparison of the main structures: (**b**) SBM and (**c**) TBM [37].  
During development and operation, personnel do not enter the shaft; all work is conducted on the surface. However, some drawbacks of blind shaft boring include limited access for ground support installation, constrained remedial support access, and the necessity for precise descriptions of rock mass conditions. Over the past decade, significant advancements in blind shaft boring technologies have emerged (Figure 6).
## 3 Methodology
### Data Processing
#### 3.2.5 Dataset Construction
1:For\(ais_{i}\) in \(init\_ais\)do
2: Find the MMSI trajectory that \(ais_{i}\) belongs to in \(MMSI_{Traj}\)
3:\(P_{Om}\leftarrow\{cg_{i},sg_{i},lon_{i},lat_{i},ns_{i},t_{i}\}\) from \(ais_{i}\)
4: Add \(P_{Om}\) to \(Traj_{j}^{min}\)
5:End For
6:For\(Traj\) in \(MMSI\_Traj\)do
7:\(Tmp\_Traj\) \(\leftarrow\) Create an empty set
8:For\(P_{Oi}\) in \(Traj\)do
9:Add \(P_{Oi}\) to \(Tmp\_Traj\)
10:If current \(P_{Oi}\) is stop point do
11:If the length of \(Tmp\_Traj\geq l_{min}\)do
12:Add \(Tmp\_Traj\) to \(ALL\_Traj\)
13:Clear the set \(Tmp\_Traj\)
14:End For
15:End For
16:For\(Traj_{i}\) in \(All\_Traj\)do
17:For\(P_{Oi}\) in \(Traj_{i}\)do
18:If\(P_{Oi}\) is removal points do
19:remove \(P_{Oi}\) from \(Traj_{j}\)
20:End For
21:\(Traj_{i}\gets Repair\Big{(}Traj_{i},\ T_{interp}\Big{)}\)
22:\(Traj_{i}\gets Resample\Big{(}Traj_{i},\ T_{sample}\Big{)}\)
23:End For // Step 2 Differential processing and create dataset. 24:For\(Traj_{i}\) in \(ALL\_Traj\)do
27:\(Tmp\_Diff\leftarrow\) Create an empty set
28:For\(P_{Oi}\) in \(Traj_{i}\)do
29:\(\Delta lon_{i},\ \Delta lat_{i}\)\(\leftarrow\) Calculate the latitude and longitude differences between \(P_{Oi}\) and \(P_{Oi-1}\)
30:\(cg_{i},\ sg_{i}\)\(\leftarrow\) the heading angle and speed in \(P_{Oi}\)
31:Add \([cg_{i},sg_{i},\Delta lon_{i},\Delta lat_{i}]\) to \(Tmp\_Diff\)
32:End For
33:For\(m=1\) to M do
34:\(X\gets Tmp\_Diff[\text{m:m}+\text{H}]\)
35:\(Y\gets Tmp\_Diff[\text{m}+\text{H:m}+\text{H}+\text{P}]\)
36:\(Z\gets Traj_{i}[\text{m}]\)
37:Add \([X,Y,Z]\) to TD
38:End For
39:End For
```  
**Algorithm 1**Data processing algorithms
# Estimation of Live Load Distribution Factor for a PSC I Grider Bridge in an Ambient Vibration Test
## 1 Introduction
In a study, the LLDF of a prestressed concrete (PSC) I-girder bridge was measured using a vision-based system in the static loading test, and the finite element model was updated using the measured response [35]. In another study, the finite element model of a reinforced concrete I-girder bridge was updated using the results of the vehicle loading test. LLDF was calculated and the load-carrying capacity was evaluated using boundary conditions as variables [36]. The vehicle loading test was conducted on a hollow slab bridge, and the model was updated using the test results. The influence of various parameters, such as span length, skew, and bridge deck thickness, on the distribution factor for the cross section was examined [37]. The vehicle loading test was conducted on bridges in use and the measured LLDF was compared with the criterion of ASSHTO Load and Resistance Factor Design (LRFD) in studies [38; 39; 40; 41; 42]. In general, a vehicle loading test is conducted to measure the LLDF of a bridge. It is impossible to control vehicles on bridges in use since it interferes with traffic flow. Therefore, this study proposed a method of measuring the LLDF of a bridge under ambient vibration conditions without vehicle control. This method measures LLDF by extracting the displacement of the static component from the vertical displacement response caused by vehicles traveling on the bridge. Since the measured vertical displacement response includes both static and dynamic components, the displacement response of the static component is extracted using empirical mode decomposition (EMD). In this study, a static loading test and dynamic loading test were conducted to verify the validity of the method capable of measuring the LLDF of a PSC I girder bridge under ambient vibration conditions using EMD. The results were compared with those of the ambient vibration test.
# Evaluating Convolutional Neural Networks and Vision Transformers for Baby Cry Sound Analysis
## 2 Methodology
### Classification
#### 2.4.1 Convolutional Neural Network with Mel Spectrograms (CNN)  
We employed a CNN architecture specifically designed for cry sound analysis using _Mel spectrograms_[40]. The model consists of several convolutional layers, activation functions, and pooling layers, followed by fully connected layers and a softmax output layer. The convolutional layers in the model serve as feature extractors, capturing relevant patterns and acoustic features from the Mel spectrograms. The activation functions introduce non-linearity, enabling the model to learn complex relationships within the cry sounds. The pooling layers help reduce the spatial dimensions and retain the most salient features. The output of the convolutional layers is flattened and passed through fully connected layers to further refine the representations. The final softmax activation function produces the predicted probabilities for each cry class.  
1. _Convolutional layers:_ Three convolutional layers are used with increasing filter sizes (32, 64, 128). This helps in learning hierarchical features from Mel spectrograms. The Rectified Linear Unit (ReLU) activation function is applied after each convolutional layer to introduce non-linearity and enhance feature representation.
2. _Pooling layers:_ Max-pooling layers with a pool size of (2, 2) follow each convolutional layer. This reduces spatial dimensions and extracts essential features, contributing to computational efficiency.
3. _Flatten layer:_ Positioned after the convolutional layers, the flatten layer transforms the spatially structured output into a flat vector. This facilitates the integration of the model with subsequent dense layers for classification.
4. _Dense layers:_ Two dense layers with 256 and 128 neurons are incorporated for high-level feature learning and classification. Dropout layers with a rate of 0.5 are introduced after dense layers to prevent overfitting. Dropout randomly deactivates connections during training, promoting a more robust model.
5. _Output layer:_ The output layer has five neurons that work together with a softmax activation function. This is because the classification task needs five classes corresponding to a different baby cry pattern.  
The described architecture is designed to process and sort audio signals efficiently by focusing on obtaining hierarchical features from spectrograms and stopping overfitting using dropout layers. The overall design aligns with established practices in audio signal analysis and classification.
## 3 Results
### Analysis of Perturbations in Fluxes Propagated by FSEOF  
By introducing reaction flux perturbations with the FSEOF algorithm, we apply flux increases for each of the 76 reactions. To define the magnitude of perturbations, the fluxes of each reaction were determined after maximizing the production of tryptophan (4.52 mmol/L/h) with minimal biomass production (Equation (3)). Under these conditions, the enzyme DHQS rends a flux of 4.54 mmol/L/h. The flux was multiplied by a perturbation factor of 1.5, giving a flux of 6.81 mmol/L/h to ensure that fluxes overpass the maximal capacity for tryptophan production; in this way, when the FSEOF method is applied, the L-tryptophan pathway can only consume as much as 4.54 mmol/L/h of DHQS flux, and the rest of the flux is propagated in the rest of the metabolic network. As such, we had ten fluxes for each of the 2713 reactions in the GEM and 76 perturbation simulations to have around 2 million fluxes for classification and analysis. However, this classification was challenging because biochemical or biological information only supports some of these reactions.  
The QPA uses the FSEOF to analyze how perturbations propagate in the metabolic network. Taking the tryptophan production pathway as context, the overall capacity of QPAML to identify potential reactions to be overexpressed is greater than that of the FSEOF only.  
QPAML can recognize 26 of the 27 reactions reported in the literature to overproduce tryptophan, while the FSEOF can only predict 20 (See Figure 3, Supplementary Table S4 and S5) of experimentally validated reaction on tryptophan production [6; 7; 8; 9; 11; 12; 13; 14; 15; 16].  
None of the tools classify the NAD transhydrogenase reaction, which is overexpressed to compensate for the overexpression of NAD(P) transhydrogenase [9]. However, QPAML predicts this reaction after simulating NAD transhydrogenase overexpression.
# Future Directions for Electric Propulsion Research
## 5 Strategies for Addressing Technical Challenges for Electric Propulsion Development
### Lifetime Extension and Qualification  
Another objective that addresses many of the technical challenges of EP systems is to increase thruster longevity and improve qualification techniques. As mentioned in Section 4.2.1 in the context of Hall thrusters, current qualification standards are impractical for long-lived EP systems. Qualification challenges cannot entirely be mitigated by better ground test facilities, but instead, self-consistent numerical modeling may fill this role. Higher fidelity models currently complement ground tests in that they are used to verify performance and lifetime measurements. However, using these models to supplement ground tests could allow long-term thruster performance and failure to be predicted with high-fidelity codes based on short-duration experiments. For example, models of gridded ion thruster failure modes have been compared against experimental data and used practically in conjunction with them to inform future test conditions [145].  
In terms of increasing thruster lifetime, the exploration of electrodeless technologies are a natural research direction for circumventing life-limiting failure mechanisms associated with plasma-wetted systems. However, even in these latter devices, new designs are being explored to extend operating  
Figure 17: Phenomenological thrust values as a function of pressure compared to on-orbit data from the Russian Express missions, repeated from [143].  
lifetimes. For example, the application of magnetic shielding design principles to Hall thrusters has increased the longevity of these devices and, with continuing research into other erosion mechanisms in these thrusters, may prove a key asset in the future of high-power electric propulsion. The 12.5 kW HERMeS thruster, for example, is designed for 10,000 h of operation, and preliminary ground testing has indicated that it can indeed meet this goal [53]. A thruster like that--with sufficient longevity and power--is in a position to serve as the workhorse of high-power electric propulsion for the near future. Additionally, continuing research into heaterless cathodes is simultaneously promising to diminish lifetime risks due to traditional cathode erosion [18].
# Toward a Detailed Evaluation of Wireless Industrial Data Distribution Approaches +
## 3 Methods for Industrial Data Distribution
### Building Blocks
#### 3.1.1 WirelessHART (IEC 62591)  
WirelessHART is a wireless sensor networking technology based on the Highway Addressable Remote Transducer Protocol (HART). Developed as a multi-vendor, interoperable wireless standard, WirelessHART was defined for the requirements of industrial field device networks. The protocol utilizes a time-synchronized, self-organizing and self-healing mesh architecture. An important characteristic of the WirelessHART MAC layer which differentiates it from the typical 802.15.4 CSMA/CA MAC layer is that it combines slow frequency-hopping and a TDMA scheme that utilizes a centralized a priori slot allocation mechanism [30]. Two different mechanisms are provided for message routing: graph routing and source routing. In this work, we use the source routing option, which uses ad hoc created routes for the messages (instead of the pre-determined, redundant routes of graph routing) without providing any path diversity.
## 1 Introduction
In addition, in the case of path failure, since other nodes in the cluster could be delegated the role of existing nodes without any process, it takes significantly less time than in the case of existing schemes in terms of path recovery. The remainder of this paper is organized as follows. In Section 2, we describe the existing node-/radio-disjoint multipath routing protocols. In Section 3, we explain the proposed cluster-disjoint multipath routing protocol for real-time and reliable packet transmission. The performance evaluation results are provided in Section 4. Finally, the proposed scheme and simulation results are summarized in Section 5.