esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study of Primary Atomization Characteristics of Sonic Air-Assist Atomizers
## 2 Materials and Methods
The liquid flow rate varied from 100 kg/h to 350 kg/h, whereas the airflow rate varied from 1 kg/h to 35 kg/h with the corresponding liquid Weber number (Wei) of 17.2 to 844.9, respectively, which also corresponds to the air-to-liquid ratio (ALR) ranging from 0.00285 to 0.35. The main objective is to examine the effect of the sonic (converging) or supersonic (CD) air-assist atomizer on the annular sheet breakup and the resulting spray pattern. The major difference in both types of nozzles is that the converging type nozzle, after the nozzle is choked, develops the underexpanded sonic jet (Pexit > Pambient), which forms a Prandtl-Meyer expansion fan at inception. In contrast, the CD nozzle goes through overexpression (Pexit < Pambient), resulting in the initial formation of oblique shock waves. Thus, both configurations belong to a unique class that may result in entirely different breakup characteristics for the novel atomizer. A series of experiments with varying flow rates were performed to find out which configuration is more suitable for better primary atomization capability. All the ranges are mentioned in Table 1.
# UAV Formation Trajectory Planning Algorithms: A Review
## 4 Intelligent Algorithm
### Machine Learning Algorithm  
The machine learning algorithm mainly simulates or realizes human learning behavior, transforms the UAV formation trajectory planning problem into a decision-making problem, and formulates optimal or near-optimal search strategies through continuous learning and interaction in complex environments. With the rapid development of multi-agent  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Challenge** & **Optimization Criteria** & **Method** & **Dimension** \\ \hline R. [PERSON] and [PERSON] [97] & Time, Trajectory & Random operator & SSA & 3D \\ U. [PERSON] et al. [98] & Time & Parallel computing & ACO & 2D \\ Z. [PERSON] et al. [99] & Trajectory & Trajectory & Trajectory & IACO & 2D \\ L. [PERSON] et al. [100] & UAV clustering & Redefine, k degree smoothing & Voronoi + ACO & 2D \\ [PERSON] [101] & Communication, UAV clustering & DTN, Light lattice diagram & ACO & 2D \\ [PERSON]. [PERSON] et al. [102] & Time, Trajectory & MTS & ACO & 3D \\ [PERSON] et al. [103] & UAV, Trajectory & State transitions, Distributed & ISOA & 2D \\ [PERSON] et al. [104] & Environment & Parallel computing & ACO & 3D \\ [PERSON]. [PERSON] et al. [105] & Trajectory & Similarity measure & ACO & 2D \\ [PERSON]. [PERSON] et al. [106] & Time, UAV clustering & Location allocation, Node selection & ACO & 2D \\ Z. [PERSON] et al. [107] & Time, Environment & Mixed inspiration & MMACO + DE & 3D \\ [PERSON]. [PERSON] et al. [108] & UAV clustering, & Guidance mechanism, Adaptive, Bidirectional search & BSAPACO & 2D \\  & Multiple objectives & & CM & MMACO & 3D \\ [PERSON]. [PERSON] et al. [109] & Time, Trajectory & Chromosome update and rollback policies & ACO & 2D \\ [PERSON] and [PERSON] [110] & Time, Trajectory & Visibility matrix, Coverage matrix, Fitness, Reward & AACO & 3D \\ [PERSON] et al. [11] & Trajectory & SFS, SVG & ACO & 3D \\ [PERSON] and [PERSON] [112] & Environment, Cost & DBV & GWO & 2D \\ [PERSON] et al. [113] & Environment & Mapping & GWO & 3D \\ [PERSON] et al. [114] & Trajectory & Initialization, Attention factor, Position update & IGWO & 3D \\ [PERSON] et al. [115] & Trajectory & Multi-population, Chaotic search & MP-CCWO & 3D \\ [PERSON] et al. [116] & Trajectory & Update operators, Greedy algorithms, & HDGWO & 2D \\ [PERSON] et al. [117] & Trajectory & Separation compensation & AP-GWO & 3D \\ [PERSON] et al. [118] & Trajectory & Spiral update position, Adaptive adjustment & AP-GWO & 3D \\ [PERSON] et al. [119] & Time & Intersecting mutation operator, PH & MHS & 3D \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of the contents of SSA, ACO, GWO and HS in the review.  
Figure 21: A kind of HS trajectory diagram (colored areas: obstacles).  
algorithms, machine learning algorithms have gradually begun to be applied in UAV formation trajectory planning. The machine learning algorithms currently used for UAV formation trajectory planning include the neural network (NN) algorithm, reinforcement learning (RL) algorithm and deep reinforcement learning (DRL) algorithm.
## 4 Experiments and Discussion
### Visualization  
To systematically investigate the behavior of the framework, the Amazon Reviews (Book) dataset was visualized using t-SNE [48], and the resulting two-dimensional embeddings are presented in Figure 4. For the English->French task in Figure 4a, the high-dimensional feature representations produced by the multilingual pre-trained language model are subsequently mapped to a two-dimensional space via the t-SNE algorithm. The visualization clearly illustrates that the embedding representations of source and target language documents exhibit significant divergence in feature space, forming distinct clusters aligned with linguistic boundaries. This observation is consistent with the cross-lingual alignment hypothesis in representation learning. Additionally, while the multilingual pretrained language model demonstrates strong document embedding capabilities, the visualization reveals that this model still results in a considerable number of misclassifications.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline  & \multicolumn{4}{c}{**XGLUE**} & \multicolumn{2}{c}{**SLU**} \\ \cline{2-7}
**Model** & **German** & **French** & **Spanish** & **Russian** & **Spanish** & **Thai** \\ \hline mBERT & 80.17 & 76.52 & 79.98 & 76.81 & 72.40 & 70.22 \\ XLM & 81.29 & 76.17 & 80.23 & 76.91 & 60.24 & 89.93 \\ XLM-R & 82.93 & 76.77 & 81.75 & 77.96 & 88.57 & 81.49 \\ mT-5 & 83.14 & 77.82 & 82.13 & 78.42 & 92.29 & 84.90 \\ CLHG & 83.21 & 77.64 & 82.56 & 77.98 & 92.56 & 85.13 \\ Meta-X\({}_{NLG}\) & 81.28 & 77.22 & 81.58 & 75.40 & 83.24 & 81.77 \\ LS-mBERT & 83.54 & 78.19 & 83.20 & 77.72 & 79.93 & 60.72 \\ XCLHG & **84.82** & **79.17** & **85.16** & **79.14** & **93.52** & **90.73** \\ \hline \end{tabular}
\end{table}
Table 5: F1-score (%) for text classification on SLU and XGLUE datasets.  
To facilitate visualization, the feature points of the source and target languages in the model without the contrastive learning module (referred to as the Heterogeneous Graph (HG) model) are presented as two separate images in Figure 4b. The distributions of the source and target languages exhibit fundamental consistency, indicating effective knowledge transfer from the source language to the target language. In Figure 4c, the visualization of the model shows tightly clustered representations of positive and negative documents, respectively. A comparative analysis of subfigures (b) and (c) demonstrates that the XCLHG framework achieves enhanced cluster separation in target language document embeddings compared to the HG architecture. The t-SNE visualizations empirically validate the effectiveness of multi-view contrastive learning for cross-lingual knowledge transfer by improving latent space alignment.  
Figure 4: Visualizations of the document features; t-SNE is applied to reduce the dimensionality of features. (**a**) is the feature representation in the datasets. In (**b**,**c**), the subgraph on the left is source language features, and the one on the right is target language features.
## Abstract  
This article investigates and presents a case study on the Beijing Subway Line 12 excavation beneath the existing Qinghuayuan Tunnel. The composite pre-reinforcement technique was used in conjunction with the shallow tunneling method to control the distortion of the existing large-diameter tunnel. When building twin tunnels underneath, this strategy considerably decreased the impact on the existing large-diameter tunnel. To systematically study the mechanical response of the existing large-diameter tunnel, a variety of sensors was embedded in the prefabricated segments just above the new twin tunnels. During the undercrossing twin tunnels procedure, the earth pressure, tunnel crown settlement, opening width of the segment joint, and the circumferential strain of the large-diameter existing tunnel were all measured. The settlement development of the existing large-diameter tunnel was categorized under six stages: (1) sedimentation, (2) heave, (3) second sedimentation, (4) second heave, (5) third sedimentation, and (6) steady state. The joint opening of the existing large-diameter tunnel changed sharply during the new undercrossing twin tunnels. The earth pressure and concrete stress of the linings rapidly increased during the new undercrossing twin tunnels. The majority of the reinforcement and concrete stresses were compressive and far lower than the yield strength, indicating that the tunnel was in a safe working condition.  
large-diameter tunnel; mechanical behaviors; undercrossing; composite pre-reinforcement; shallow tunneling method
# Advancements in Semi-Active Automotive Suspension Systems with Magnetroheological Dampers: A Review
## 6 Conclusions  
This paper presented a review of the advancements in semi-active suspension systems, with a particular focus on magnetorheological dampers (MRDs). The progression from traditional passive systems to active and semi-active configurations highlights the continuous pursuit of enhanced vehicle performance, comfort, and safety. Semi-active systems, especially those utilizing MRDs, offer a balanced solution that combines the simplicity of passive systems with the adaptability of active systems, without the high energy consumption typically associated with the latter. The contents of this paper can be concluded in the following aspects:  
1. The paper emphasized the significant attributes of MRDs, including their rapid response, reversibility, compact size, low energy consumption, continuous damping adjustment, and high reliability. These characteristics have enabled MRDs to be widely adopted in various industries. The design and optimization of MRDs involve complex interactions among fluid dynamics, magnetic fields, and structural mechanics, with notable innovations such as twin-coil and twin-tube configurations, self-inductive and self-powered dampers, and advanced controllers enhancing their performance.
2. The various control strategies employed in semi-active suspension systems were thoroughly discussed, which can be categorized into classical, modern, and intelligent methods. These strategies play a crucial role in optimizing suspension performance and improving vehicle ride comfort and stability. The current research focus on intelligent control is progressively pivoting towards hybrid control, which integrates modern control with intelligent control, which not only enhances the adaptability and performance of semi-active suspension systems but also potentially opens new avenues for the development of autonomous vehicles.
3. The successful implementation of the MagneRide system by Lord Corporation and Delphi Corporation in 1999 marked a significant milestone in the practical application of MRDs in automotive suspension systems. The widespread adoption of MagneRide in high-end and luxury vehicles demonstrated its effectiveness in enhancing vehicle dynamics and user experience, despite challenges related to cost and technical complexity.
4. Looking ahead, enhanced integration of artificial intelligence and machine learning, the development of hybrid control strategies, advancements in sensor technology, and a commitment to energy efficiency and sustainability will drive this progress. Moreover, seamless integration with autonomous and connected vehicles, along with increased collaboration and standardization among researchers, manufacturers, and industries, will facilitate the widespread adoption and consistent performance of advanced semi-active suspension systems across more vehicle models.
## 3 Results
### Patients' Response to Treatment and Adverse Events  
All the patients in the study received CPI as a second- or third-line treatment due to previous treatment failures. Among our patients, 10 out of 20 continued their immunotherapy for more than 6 months as they were categorized as responders by that time, and no serious adverse events were reported for them. On the other hand, 10 out of 20 patients were nonresponders. Six of them succumbed to disease progression after receiving 2-3 doses of CPI, while three patients switched to alternative treatment regimens due to disease progression, which was diagnosed during scheduled computed tomography (CT) scans (two switched at the 3-month timepoint and one at the 6-month timepoint). One patient passed away due to autoimmune pneumonitis 3 months after initiating CPI treatment without being marked as a responder or nonresponder (Table 1, Supplemental Table S2).  
Figure 1: Study design: Timepoints of the study.  
In terms of autoimmune complications within the first six months of immunotherapy, one patient, who was evaluated as a nonresponder, receiving pembrolizumab developed fatal pneumonitis. Three patients who were assumed as responders, two of them receiving nivolumab, experienced hypothyroidism, and the other one, who received pembrolizumabab, reported persistent diarrhea. It was noticed that the irAEs in these patients were developed within about 2-3 months after the initiation of ICI treatment, just before the third timepoint (Table 1).  
At timepoint 0, antinuclear antibody (ANA) screenings were available for all the patients. Among them, four were found to be ANA-positive, despite not having a history of autoimmune disease. Notably, half of the patients who eventually developed immune-related adverse events (irAEs) (two out of four) had a positive ANA test before the beginning of treatment. The patient who developed autoimmune pneumonitis had the highest ANA titer at baseline, which was 1/640.
## 5 Experiment Evaluation
### Experiment Results  
#### 5.2.1 Energy-Consumption  
The proposed BFAN is validated on the energy consumption (Network and CPU) with D2D [36] with respect to numerous costs in communication as shown in Figure 4.The web-based applications are tested to calculate power consumption. Figure 5 shows the traffic of the real workload, which is further normalized for different communication. Figure 2 shows the distributed and integrated FN. The BFAN's numerical results for the average time and energy report per round are simulated for FN2 FN, t2 FN, and t2t. The medium of communication can be wireless or wired. As per [36], single-hop D2D for IEEE802.11b is used for t2t communication. The results demonstrate in Figure 6 that the D2D platform is less efficient than the proposed BFAN platform. The D2D platform is affected by the path loss, fading, retransmission of packets and TCP time-out. The primary, interprimary and secondary average power consumption are shown in the Figure 7.
# Design of Multifunctional Mesosphere-Ionosphere Sounding System and Preliminary Results
## 2 System Description
### Sounding Waveform
\[R_{d}=L.\frac{cT_{p}}{2}=30.72\ \mathrm{km} \tag{4}\]  
By considering that the height range of the mesosphere or the lower ionosphere visualized by MAI system in the mesospheric sounding model is 70-100 km, and the concerned height range of the ionosphere is even higher, this interference of the fade zone will not cause any seriously adverse effects. In addition, except for the Adelaide MF Rada and Saura radar with a large cross antenna array, whose average power are 240 W and 600 W [16; 17; 32], the average transmitting power of most MF radars is only about 100 W. However, in the case of MAI system, even though the peak transmitted power \(P_{peak}\) is only 6 kW, the pulse coded waveform can generate a larger duty ratio \({}_{D}\), and the average power \(P_{aver}\) can reach 300 W, as Equation (5) shows [31]. \[D=\frac{LT_{p}}{T_{r}}=5\%,\ P_{aver}=P_{peak}D=300\ \mathrm{W} \tag{5}\]  
Also because of the monopulse mode and small duty ratio, for the traditional MF radars, despite the coherent accumulation, it is still quite difficult to obtain enough processing gains. Therefore, a large peak power is often required to ensure the signal-to-noise ratio (SNR) of the received echoes. Comparatively, complementary code can provide an additional correlation compression gain benefits from its good correlation characteristic. For \({}_{L}\)-bit complementary code sequences \(A=\{a_{n}\}\),B=\(\{b_{n}\}\), the correlation function \(R_{A}(\tau)\), \(R_{B}(\tau)\) can be determined as Equation (6)  
\[\left\{ \begin{array}{c}R_{A}(\tau)=\sum\limits_{k=1}^{L}a_{k}a_{k+\tau},R_{ B}(\tau)=\sum\limits_{k=1}^{L}b_{k}b_{k+\tau}\\ R_{A}(\tau)+R_{B}(\tau)=\left\{\begin{array}{c}2L,\tau=0\\ 0,else\end{array}\right.\end{array} \right. \tag{6}\]  
Therefore, a pair of 16-bit sequences will bring compression gain of 12 dB. After a certain number of coherent accumulations, the processing gain would reach more than 20 dB, which makes it possible to obtain a higher SNR at a certain receiving threshold level for both the mesospheric and ionospheric sounding.
## 3 Problem Formulation  
Given two geometric point clouds with coordinates, denoted as \(V_{src}=\{\mathbf{x}_{1},\ldots,\mathbf{x}_{n}\}\in\mathbb{R}^{3}\) and \(V_{tgt}=\{\mathbf{y}_{1},\ldots,\mathbf{y}_{n}\}\in\mathbb{R}^{3}\), and their corresponding edge relationships \(E_{src}=\{\mathbf{e}_{x1},\ldots,\mathbf{e}_{xx}\}\in\mathbb{R}^{2}\) and \(E_{tgt}=\{\mathbf{e}_{y1},\ldots,\mathbf{e}_{yn}\}\in\mathbb{R}^{2}\), our goal is to align the source graph structures with the target graph structure within the same coordinate system through rigid rotation (\(\mathbf{R}\)) and translation (\(\mathbf{T}\)). Ideally, the points in the graph structures correspond to each other and can be represented by the following equation:  
\[\mathbf{y}_{i}=\mathbf{R}\mathbf{x}_{i}+\mathbf{T}+\mathbf{N}_{i} \tag{1}\]  
where \(\mathbf{N}_{i}\) is the noise vector. The objective is to compute the rotation and translation matrices (\(\mathbf{R}\) and \(\mathbf{T}\)) to minimize the least-squares error:  
\[\text{error}(\mathbf{R},\mathbf{T})=\frac{1}{N}\sum_{i=1}^{N}\|\mathbf{R} \mathbf{x}_{i}+\mathbf{T}-\mathbf{y}_{i}\|^{2} \tag{2}\]  
However, during the registration process, the points in the two subgraphs do not correspond perfectly. Therefore, it is essential to employ specific methods to identify the corresponding points. In this paper, we utilize the existing point and edge information topredict the correspondence matrix \(\mathbf{C}_{x}\in\mathbb{R}^{|V_{src}|\times|V_{tgt}|}\) through an end-to-end neural network. Once the correspondences are established, the rigid transformation parameters can be obtained using Singular Value Decomposition (SVD).  
Given two partially overlapping geometric graph structures \(G_{src}=\left(V_{src},E_{src}\right)\) and \(G_{tgt}=\left(V_{tgt},E_{tgt}\right)\), where \(V=\{\mathbf{x}_{i}\in\mathbb{R}^{3}\}\) represents the set of vertices and \(E=\{\mathbf{e}_{ij}\in\mathbb{R}^{2}\}\) denotes the set of edges, the registration objective is to determine the rigid transformation \((\mathbf{R},\mathbf{T})\) that minimizes the error as follows:  
\[\text{error}(\mathbf{R},\mathbf{T})=\frac{1}{N}\sum_{i=1}^{N}\|\mathbf{R} \mathbf{x}_{i}+\mathbf{T}-\mathbf{y}_{\pi(i)}\|^{2} \tag{3}\]  
where \(\pi(i):V_{src}\to V_{tgt}\) denotes the point correspondence. After predicting the correspondence matrix \(\mathbf{C}_{x}\) using an end-to-end neural network, the optimal transformation parameters are obtained via SVD.
###### Abstract  
We report here the synthesis, characterization and behavior of a series of Pt(II) cyclometalated rollover complexes with two substituted bipyridines, 6-ethyl-2,2\({}^{\prime}\)-bipyridine (bpy\({}^{6\text{\scriptsize{HE}}}\)) and 6-methoxy-2,2\({}^{\prime}\)-bipyridine (bpy\({}^{60\text{\scriptsize{Me}}}\)), in comparison with previously studied 2,2\({}^{\prime}\)-bipyridine complexes. The two ligands have similar steric hindrance but different electronic properties. As a result, the reactivity of the two series of complexes follows very different routes. In particular, the new complexes behave differently towards protonation reactions, differences given by substituents and ancillary ligands, added to the presence of several nucleophilic centers. Reaction of complex [Pt(bpy\({}^{6\text{\scriptsize{Me}}}\)-H)(PPh\({}_{3}\))Me]) with [H\({}_{3}\)O:18-crown-6][BF\({}_{4}\)] results in a retro-rollover reaction whose final product is the cationic adduct [Pt(bpy\({}^{6\text{\scriptsize{Me}}}\))(PPh\({}_{3}\))Me]\({}^{+}\). Surprisingly, only the isomer with the _cis_-PPh\({}_{3}\)-OMe geometry is formed; in spite of an expected instability due to steric hindrance, Density-Functional theory (DFT) calculations showed that this isomer is the most stable. This result shows that the cone angle is far from being a real \"solid cone\" and should lead to a different interpretation of well-known concepts concerning steric bulk of ligands, such as cone angle. Proton affinity values of ligands, neutral complexes and their protonated counterparts were analyzed by means of DFT calculations, allowing a comparison of their properties.  
cyclometalated compounds; rollover cyclometalation; bipyridines 24 August 2020; 10, 6665; doi:10.3390/appj.10196665; doi:10.3390/appj.10196665