esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Low-Frequency Pressure Fluctuation in Open-Jet Wind Tunnel
Researchers have conducted extensive theoretical research and wind tunnel testing to determine an optimal solution [7]. Low-frequency pressure fluctuations can pose a number of hazards to open-jet wind tunnels. The pressure pulsations cause a poor quality of the flow field in the jet region, thereby affecting the stability of both velocity and pressure within the wind tunnel. Consequently, this leads to a reduction in measurement accuracy [16; 22]. The infrasonic waves generated by low-frequency pressure fluctuations in the test environment may pose health risks to the participants. The vibration of the wind tunnel structure may cause vibration fatigue of the test equipment and compromise test safety. Strong low-frequency pressure fluctuations may even cause vibrations in the wind tunnel structure, thus endangering the safety of the wind tunnel building [4; 5; 26]. In addition, the vibrations in the flow field and structure lead to energy dissipation. Therefore, attenuating or suppressing these low-frequency pressure fluctuations is of positive significance for improving measurement accuracy and enhancing energy utilization efficiency and safety in open-jet wind tunnels, as well as creating a better working environment for testers. It should be remarked that the primary focus of this paper is to investigate the mechanisms and control methods associated with wind tunnel design and enhancement, without delving into the intricate flow physics of low-frequency pressure pulsation. The paper is primarily concerned with the low-frequency pressure fluctuations observed in subsonic open-jet wind tunnels, not taking into account transonic and supersonic wind tunnels where shock waves introduce a different scenario. In addition, this review is based on an extensive analysis of existing literature conducted by the authors. While striving for objectivity throughout the analytical process, it is inevitable that subjective opinions become intertwined. The paper is organized as follows: Section 2 provides a comprehensive overview of the mechanism underlying low-frequency pressure fluctuations, including the sources of excitation and resonance mechanisms. Section 3 examines various methods for low-frequency pressure fluctuation suppression. Lastly, Section 4 presents concluding remarks.
## 5 Conclusions  
To improve the macroscopic morphology and reduce the defects of Mg alloy additive components, this paper first carried out the oscillating laser-arc hybrid additive manufacturing (O-LHAM) of AZ31 Mg alloy. It focused on the effect of an oscillating laser on process characteristics such as macroscopic morphology, porosity defects, microstructure, and mechanical properties. The main conclusions obtained are as follows.  
(1) The thin-walled components of Mg alloy with good macroscopic morphology and excellent performance could be obtained using the O-LHAM process. Increasing the oscillating frequency helped to suppress the lack of fusion, height difference, and humps because the spreading of the melt pool and the regulation of the reflux of liquid metal were promoted.  
(2) As the oscillation frequency increased from 0 Hz to 300 Hz, the porosity decreased from 24.45% to 0. The reason for this was that the high-speed movement of the beam could stabilize the laser keyhole, and second, the oscillating effect of the oscillating behavior could melt the solidified pores.  
(3) Increasing the oscillating frequency was beneficial for reducing the average grain size and promoting the precipitation of the Al\({}_{8}\)Mn\({}_{5}\) and Mg\({}_{17}\)Al\({}_{12}\) phases. Compared to the case without oscillation, the average grain size range at 300 Hz decreased from 22-32 \(\upmu\)m to 18-20 \(\upmu\)m, while the precipitation percentage increased from 1.42-1.61% to 2.55-3.32%. This was because the stirring effect of the oscillating laser was enhanced with increasing frequency.  
Figure 15: Schematic diagram of microstructure evolution induced using oscillating laser. (**a–c**) without oscillation; (**d–f**) laser oscillation.  
(4) For the component without pores, the ultimate tensile strength was 205 MPa, slightly less than the base, but the elongation was 20.7%, twice that of the base. The tensile fractures presented ductile fracture characteristics, consisting mainly of dimples and some steep ductile tearing ridges. Laser oscillation had almost no effect on the hardness values of thin-wall components but could significantly improve their ductility.  
Conceptualization, M.G. ([PERSON]); methodology, L.C. and L.J.; writing--original draft preparation, L.C.; validation, M.G. ([PERSON]); formal analysis, L.C.; investigation, L.J. and S.Z.; supervision, M.G. ([PERSON]); funding acquisition, M.G. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the National Natural Science Foundation of China (Grant No: 52275335 and Grant No: 52205465).  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
The authors declare no conflict of interest.
# A Hybrid Adversarial Attack for Different Application Scenarios +
## 2 Related Work
### Different Measures of Textual Similarity  
An important problem is that the generated adversarial examples must not only fool the target model, but also keep the perturbation undetected. A good adversarial example should convey the same semantics as the original text, so we need some metrics to quantify the similarity. There are three metrics that used on vectors and documents [20; 21].  
**Euclidean Distance.** In text, Euclidean Distance measures the linear distance between two vectors in Euclidean space.  
**Cosine Similarity.** Cosine similarity represents the semantic similarity of two words by calculating the cosine of the angle between two vectors. Cosine distance is more concerned with the direction difference between two vectors. The smaller the angle between two vectors (the larger the cosine value), the greater the similarity.  
**Word Movers Distance (WMD).** WMD [22] mainly reflects the distance between documents, so it is not suitable for finding similar words. Its semantic representation can be based on the embedding vector obtained by word2 vec. Of course, it can also be based on other word embedding methods. This algorithm constructs the document distance into a combination of the semantic distances of the words in the two documents. For example, the Euclidean distance is obtained from the word vectors corresponding to any two words in the two documents and then weight and sum. The WMD distance between two documents \(A\) and \(B\) is:  
\[WMD(A,B)=\sum_{i,j=1}^{n}T_{ij}\cdot D(\overrightarrow{i},\overrightarrow{j}) \tag{1}\]  
where \(D(\overrightarrow{i},\overrightarrow{j})\) is the Euclidean distance of the word vectors corresponding to the two words \(i\) and \(j\). The Bag Of Words model is used to get the word frequency of the word in the document (as the weight of a word in the document), and the problem then becomes how to \"carry\" all the word units of document \(A\) to the corresponding word units of document \(B\) with the minimum cost, and finally get the weight matrix \(T\). The WMD algorithm is a special case of the EMD [23] algorithm, and some improved algorithms based on WMD, such as WCD and RWMD.
# Coronary Centerline Extraction from CCTA Using 3D-UNet
## 4 Experimental Results and Discussion
### Experimental Setup  
The algorithm was implemented in python using _Tensoflow_ with _Keras_. The full source code is publicly available at [35]. The provided source code contains the dataset converter and the segmentation algorithm in the form of Python files with executable cells. An example network layout is available in Figure 11. For loading the dataset, we used the _SimpleITK_ python library, and for thinning the obtained centerlines, the _skimage_ package was utilized.  
The eight-input volumes with ground truth were split into a training set of seven and a validation set with the remaining volume, leaving one volume out; therefore, the holdout validation was employed. While the number is small, using augmented patches helps reduce the problem of a really small dataset.  
The models were trained and tested on an NVIDIA GeForce GTX 1060 with 6 GB VRAM. Training required around 8 h on the formerly mentioned video card. The predicting processing time for a patch size input varies between 400 ms and 600 ms, depending on the patch size and model size. For a full volume, this time is multiplied by the number of patches inside a volume. At around 80 patches and 500 ms per patch, the full output is computed in around 40 s. Stitching and postprocessing for saving are negligible in time. The training time makes hyperparameter tuning a strenuous operation, and better configurations can be found by integrating the algorithm into automated design space exploration frameworks. The parameter search space must be more rigidly defined, and the proposed parameters here are a good start.  
Following the format of the reference file, we have created a tool to generate a volumetric mask for each input image of the training set, which serves as the ground truth in the supervised learning algorithm. The tool can be parametrized to specify the width in voxels of the centerline. Instead of having a single voxel for one reference point, a cube of a given size is generated, with the center voxel overlapping the centerline. This was found useful in combating the huge class imbalance when working with a single-voxel-wide centerline. The tool outputs the mask in the same format as the input images, with the exception that it can compress the raw data, which is useful when the mask is very sparse. The network will learn centerlines that are thicker than one voxel. A new step of morphological thinning (as described in [36]) must be applied to convert the output of the network into thin centerlines.  
The overlap and the accuracy are presented with respect to the wide centerlines, not the thinned ones.
## 3 Results
### Architecture Selection  
Both models were trained using the same data and hyperparameters to ensure a fair comparison, despite introducing some bias in the architecture selection. Augmented images were excluded from the set, as the primary goal was to identify the best architecture rather than achieve precise results at this stage. A pixel-weighted BCE loss function was employed, combined with the Adam optimizer and the He-Normal kernel initialization [42] for robust performance without additional adjustments [52]. The models used a constant learning rate of 0.001, ReLU activation, and a batch size of 2 was used due to memory constraints. All models were trained over 30 epochs, which preliminary tests showed to be adequate for validation IoU convergence without significant overfitting.  
Table 1 presents the average performance metrics from three training runs for both ResNet and U-Net models, with dropout rates varying from 0.0 to 0.7. U-Net encountered convergence issues at dropout rates above 0.3, so results for these rates were excluded. These results highlight ResNet's potential to outperform U-Net, which can be attributed to its 50% higher parameter count, despite both models requiring equivalent GPU memory and ResNet being faster. This greater parameterization enables ResNet to model more complex transformations inherent in the dataset, albeit with increased susceptibility to overfitting. In terms of inference speed, U-Net achieves an unoptimized rate of 19 FPS on 640 \(\times\) 368 resolution images when tested on an Intel 11600 KF CPU, whereas ResNet achieves 21 FPS, further underscoring its efficiency despite its larger size. Based on these findings, ResNet is selected as the preferred architecture for subsequent experiments.  
The ResNet architecture was retrained from scratch with augmented samples, testing dropout rates from 0.0 to 0.4. Using augmented samples increases the importance of regularization methods like dropout, as augmented images do not contain purely new information, introducing biases into the training. The averaged results of three training runs for this training are shown in Table 2. The highest IoU was achieved at a dropout rate of 0.3, making it the selected model for further fine tuning.
## 2 Materials and Methods
### Description of Winding Rotor  
The winding rotor is the key component of a winder for filament, its main structure is shown in Figure 1. To illustrate its structure more clearly, the reduction ratio of the winding rotor in the radial direction is smaller than that in the length direction.  
To facilitate doffing the filament packages from the winding rotor, the winding rotor is designed as a cantilever beam structure. The supporting arm is fixed on a frame. Three sets of flexible supports with rubber O-rings are installed between the supporting arm and the shaft. The flexible support includes rubber O-rings, bushing and bearings, as shown in Figure 1b. The shaft is driven by a motor, and the interference fit between the shaft and the sleeve is used to maintain synchronous rotation. The paper tubes are rigidly connected with the sleeve through the tightening device to realize synchronous rotation, and the filament bundle is wound on the paper tubes. Usually, there are 12 packages on the winding rotor, and each package weighs about 10-15 kg in full volume.  
To ensure constant linear speed winding, the winding rotor starts to rotate and its speed increases quickly from 0 to \(\omega_{\rm s}=7730\) rpm, and the filament bundle begins to be wound on the paper tubes. As the filament package diameter increases, the rotor speed nonlinearly reduces from the highest \(\omega_{\rm s}\) to the lowest \(\omega_{\rm w}\), then the rotational speed gradually decreases to zero, and a single cycle of the winding task is usually accomplished in 1-3 h according to different types of filaments, as shown in Figure 2.  
In Figure 2, \(t_{\rm s}\) is the end time during speeding up, \(t_{\rm w}\) is the end time during winding and \(t_{\rm d}\) is the end time during deceleration.  
Due to its cantilever beam structure with a large aspect ratio and multi-coupling components with flexible support, the first-order critical speed point of the winding rotor is low. Within the maximum working speed of 7730 rpm, there are two critical speed points of the winding rotor. So, frequently passing through the critical speed points is the normal working state of the winding rotor.  
To pass steadily through the critical speed points, the transmission with flexible support should be adopted according to the literature researches [14].  
Due to the presence of rubber O-rings, changes in the speed of the winding rotor may change the frequency-dependent characteristics of the supporting stiffness and damping parameters of the flexible support. Therefore, the winding rotor has characteristics of time-varying rotational speed and frequency-dependent supporting parameters, as well as structural coupling, which makes the dynamic behavior of the winding rotor extremely complicated.  
Figure 1: Layout of main structure of the winding rotor. (**a**) Winding rotor, where I II III denote the flexible supports with different rubber O-rings, respectively; (**b**) flexible support structure.  
Figure 2: Diagram of the single cycle of winding task.
# Real-Time Prediction of Operating Parameter of TBM during Tunneling
## 4 Prediction Results  
### Initial Conditions and Prediction Procedure  
To simulate tunneling, 30% of the entire dataset is assumed as the initial \"in-sample\". The tuning spaces of the hyperparameter set concerning ARIMAX were specified by trial and error and by referring to the literature (Table 4) [23].  
As mentioned in Section 2.2, prediction values for current thrust were sequentially stacked step by step until the WF's final step is achieved, and then all of the predicted values were evaluated at once using a validation index, mean absolute percentage error  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyperparameter** & **Range** & **Sampling Method** \\ \hline \(p\) and \(r\) & 1–5 & \\ \(d\) & 0–2 & Grid search \\ \(q\) & 0–2 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hyperparameter tuning space related to ARIMAX.  
Figure 3: \(n\)-th weight parameters corresponding to \(n\)-th segmental lining.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Input Category** & **Contents** \\ \hline Geological parameter & UCS, RQD, RMR, Pw, Sw, De, Ab \\ Weight parameter & W \\ Operating parameter & Thrust (previous) \\ \hline W, weight parameter; UCS, uniaxial compressive strength; RQD, rock quality designation; RMR, rock mass rating; Pw, P-wave velocity; Sw, S-wave velocity; De, deformation modulus; Ab, absorption ratio.  
(MAPE) (Equation (4)), which is a kind of indicator expressing the TBM penetration error as a percentage in reference to the actual value:  
\[\text{MAPE}\left(\%\right)=\frac{1}{\text{n}}\times\sum_{\text{i=1}}^{\text{n} }\left|\frac{\text{actual value}_{\text{i}}-\text{predicted value}_{\text{i}}}{\text{actual value}_{\text{i}}}\right|\times 100 \tag{4}\]
## 2 Materials and Methods
### Methodology
#### 2.4.6 Graph Model  
At this stage, the goal is to optimize the robotic arm's path between the workstations, based on the spatial configuration previously determined by the GA. To achieve this, a fully connected directed graph is constructed, where each node represents a workstation located at coordinates (_x,y_), and each edge corresponds to a possible path between two stations.  
The graph is formally defined as  
\[G=(V,\ E) \tag{10}\]  
where  
\(V=\{v_{1},\ v_{2},\ \ldots,\ v_{n}\}\) is the set of nodes representing the workstations in the optimal layout produced by the GA.  
\(E=\{(v_{i},\ v_{j})\ |\ i\
eq j\}\) is the set of edges representing all possible transitions between station pairs.  
Each edge (\(v_{i}\),\(v_{j}\)) is associated with a heuristic cost \(\eta_{ij}\), which can be computed based on the Euclidean distance between stations or estimated travel time considering the robot arm's constraints, such as maximum speed and acceleration.  
The graph is implemented using an adjacency matrix, where each cell stores the heuristic value and the corresponding pheromone level for that edge. This graph model serves as the foundation for applying the ACO algorithm, which aims to find the most efficient route visiting each station exactly once while minimizing the robot's total cycle time. Figure 5 shows an example of a graph with four stations, where the heuristic cost and pheromone level associated with each edge are illustrated.
## 3 Results and Discussion
### Retinal Dihedral Analysis
#### 3.6.1 Dihedral Energy Analysis  
The trends in dihedral energy values were analyzed and it was evident that for the dihedral C12-C13 = C14-C15 (Figure 5b), there were greater energy values in the presence of graphene. This is most clearly visible in the case of C11 = C12-C13 = C14 dihedral (Figure 5d) and C14-C15 = NZ-CE dihedral (Figure 4d) introduced in Section 3.5. In the bacteriorhodopsin _Three State Model_, the protein reaches an energy barrier during photoexcitation to achieve a 90\({}^{\circ}\) twist around the C13 = C14 double bond and advance from ground state (S\({}_{0}\)) to S\({}_{1}\)[32; 33]. It is envisaged that with increasing rest state energies, observed in the bR-graphene dihedrals, it is easier to overcome this barrier as well as reach energy values that are required to undergo sufficient twisting for a complete photocycle.  
Figure 4: Analysis of different energies at the interface of bR and graphene: (**a**) Total energy within bR in the absence and presence of graphene; (**b**) Electrostatic energy of bR in the presence and absence of graphene; (**c**) Van der Waals energy at the interface of bR and graphene; (**d**) Dihedral energy of the key double bond between retinal and LYS216 of bR (C14-C15 = NZ-CE) in the presence and absence of graphene.
# Validation of Seismic Performance of Stainless Press-to-Connect Piping System under Cyclic Loadings
## 4 Experimental Test of Large-Scale Piping System  
To evaluate the seismic performance of a large-scale piping system with a press-to-connect elbow system, an experimental test must be conducted. The common failure mode related to low-cycle fatigue or ground motion is concentrated at the elbow joints or T-joint systems in the piping system [8]. Therefore, in this study, a large-scale piping system with two press-to-connect elbow systems and one press-to-connect T-joint system with riser pipes based on NFPA 13 [10] were constructed in this study. In addition, according to guideline NFPA 13 [10], two flexible couplings, that is, sockets in the riser piping system, to absorb the energy of the vibration from the ground motion or other mechanical vibrations were installed within 610 mm from the upper bound (ceiling) and 305 mm from the lower bound (bottom floor), as shown in Figure 7.  
The piping equipment was anchored by bolts at all ends. To measure the load-displacement relationship of the system, three-axis load cells (LC1, LC2, and LC3) were installed at the ends of the piping system, as illustrated in Figure 8. Furthermore, three-axis rosette strain gauges were applied to the crown at the elbow joint, and to analyze the effect of the performance of the press-to-connect system on the piping, a strain gauge was attached to the middle of the socket. As shown in Figure 8, the actuator was connected to the upper slab of the steel frame, in order to apply the loading protocol. To simulate the allowable side sway during and after an earthquake, the hinge system condition was applied to the steel frame as a geometric boundary condition.  
Figure 7: Schematic design of large-scale press-to-connect piping system.  
Moreover, the cyclic loading protocol based on displacement control was repeated 10 times or more to satisfy the maximum drift ratio of the building system corresponding to design codes KBC 2016 [15] and IBC 2015 [16], as listed in Tables 1 and 2, where h and hxx denote the story heights for design codes KBC 2016 and IBC 2015, respectively.  
In addition, during the experiment the press-to-connect piping system was filled with water to detect the damage state with respect to the leakage and pressurized with an internal pressure of 2 MPa using an air-water booster. Finally, the 20 mm/min rate loading protocol was executed in the test using the displacement control.