esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Programs
### Loading Scheme  
The quasi-static cyclic test setup and loading scheme are illustrated in Figure 4. The vertical axial force exerted on the top of the column using the jack was 560 kN, as per the axial compression ratio of 6%. The footings were stably fixed in the ground through thread rebar. The two-column bents were subjected to quasi-static cyclic loading with the actuator located 3.35 m above the ground. The actuator's maximum load capacity and travel distance were 1000 kN and \(\pm\)250 mm, respectively. In order to clarify the destroy development of two-column piers, use L1-L4, R1-R4 to represent each surface of the left and right columns, as shown in Figure 4a.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Bent**} & \multicolumn{5}{c}{**Cubic Samples**} & \multicolumn{5}{c}{**Mean Strength**} \\ \cline{2-7}  & **1** & **2** & **3** & **4** & **5** & **6** & \(f^{\prime}_{cu}\) & \(f^{\prime}_{c}\) \\ \hline B2-CIP & 46.4 & 47.7 & 42.5 & 47.1 & 42.8 & 40.6 & & \\ B2-PGS & 42.6 & 45.2 & 44.4 & 43.7 & 41.9 & 41.1 & 43.9 & 34.7 \\ B2-PGT & 43.2 & 47.5 & 43.9 & 44.9 & 43.1 & 40.9 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cubic compressive strength of concrete.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Diameter** & \(f_{y}\)**/Mpa** & \(f_{us}\)**/Mpa** & \(\epsilon_{us}\)**\%** & \(E_{y}\)**/Gpa** \\ \hline  & & 505 & 611 & 16.5 & 197 \\ HRB400 & 16 mm & 519 & 630 & 15.3 & 204 \\  & & 492 & 603 & 16.1 & 205 \\ \hline  & & & 526 & 15.5 & 209 \\ HPB300 & 8 mm & / & 523 & 18.5 & 219 \\  & & & 535 & 15.0 & 211 \\ \hline \hline \end{tabular} Note: \(f_{y_{\text{s}}}\) is the yield strength, \(f_{us}\) is the ultimate strength, \(\epsilon_{us}\) is the ultimate strain, and \(E_{x}\) is the elastic modulus.  
\end{table}
Table 3: Mechanical properties of rebar.  
Figure 4: Test setup and loading scheme.  
The horizontal loading history according to preset incremental displacement is shown in Figure 4c. Each cyclic loading step was repeated three times with a given displacement increment. The displacement increment of 5 mm was applied until the ED rebar was fractured or the concrete was severely damaged.
## 4 Experimental Results
The pull-out tests on the Reference specimens, since they were affected by the mixed-mode failure, exhibit a kink in the loading branch due to the concrete cone that is more pronounced for the 30 mm anchors in comparison with \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Cored [MPa]** & **Teflon [MPa]** & **Reference [MPa]** \\ \hline
30 mm & 40.60 \(\pm\) 2.2\% & 39.89 \(\pm\) 2.7\% & 28.03 \(\pm\) 7.0\% \\
40 mm & 37.87 \(\pm\) 1.9\% & 38.28 \(\pm\) 6.9\% & 31.12 \(\pm\) 9.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Bond strength results for the three configurations obtained using the uniform bond model. Figure 7: Failure mechanisms for the tested configurations: Reference (**a**), Teflon (**b**), and Cored (**c**). the 40 mm anchors. The cone development additionally leads to a strong bond strength reduction in comparison with the Teflon and Cored cases. After the verification of the improvement on the bond properties' determination, the stability improvement is also verified. The test stability and test controllability are dependent on the system stiffness and on the machine PID control settings. The PID controller calculates the difference between the prescribed process variable value and the measured one. This is defined as an error and is continuously calculated. The aim of the controller is to minimise the error over time. More information about the PID and its numerical implementation in an explicit solver can be found in [21]. Figure 9 shows one of the pull-out tests performed in the current experimental campaign showing the recorded displacement from three different points along the threaded bar. Three curves are plotted to explain the improvement of stability and controllability brought on by the proposed setup. The dashed blue line represents the unloaded end displacement, the red dash-dotted line stands for the loaded end displacement, and the solid green line is the stroke displacement. As previously mentioned, all the tests were controlled with the unloaded end LVDT displacement. The loaded and unloaded end displacement curves are stable and controllable because they exhibit no snap-back. The remaining curve, the stroke displacement curve, is a classical example of a curve affected by snap-back, which makes the test unstable. In stroke displacement control, such a situation  
Figure 8: Test results of the experimental campaign for (**a**) Cored specimens, (**b**) Teflon specimens, and (**c**) Reference specimens. Also, a comparison of the 40 mm bonded length mean curves is provided (**d**). would lead to a failure at the snap-back point. However, it may still be possible to control this test if the proper PID setting is chosen. In this case, the test would be unstable but controllable. Such a situation leads to a dynamic vertical force drop due to the motion of the structure, except for at the loading point [22]. This phenomenon may lead to a load-displacement curve that is locally influenced by the machine's attempt to keep the test running and does not reflect the proper specimen behaviour.
# Rapid-DEM: Rapid Topographic Updates through Satellite Change Detection and UAS Data Fusion
## 2 Methods
### Case Study  
Rapid-DEM was implemented and tested at the Walnut Creek Watershed (119 km\({}^{2}\)) in Wake County, North Carolina, USA (Figure 2). The Walnut Creek watershed is part of the Neuse River Basin and includes parts of the City of Raleigh and the Town of Cary, North Carolina. The watershed is predominately developed, but still partially forested, with open water [49] and includes Walnut Creek, which is a tributary to the Neuse River. Based on the National Land Cover Dataset (NLCD) high and medium intensity development are the fastest growing land classes between 2016 and 2019 in the Walnut Creek watershed [49; 50]. Medium intensity land cover increased 40.9% from 14.4 km\({}^{2}\) to 20.2 km\({}^{2}\) mostly converting low intensity development (6.6 km\({}^{2}\)). Similarly, high-intensity land cover increased 57.5% from 4 km\({}^{2}\) to 6.4 km\({}^{2}\) mostly converting medium intensity development (1.9 km\({}^{2}\)). The data suggests the development density and total amount of impervious surface in Walnut Creek are increasing. We chose the Walnut Creek watershed as the study area because it enabled us to perform UAS flights on locations identified by the priority queue.
## 5 Numerical Simulation  
ANASYS was used to simulate the settlement of the pile body and the displacement field of the undisturbed soil around the pile under the loads. The size of the model pile and the range of the soil around the pile were consistent with those in the model test. The parameters of pile and soil in the numerical simulation are shown in Table 3.  
Figure 15: The relationship between displacement and the load at different burial depths under the pile bottom: (**a**) vertical displacement in the undisturbed soil; (**b**) horizontal displacement in the undisturbed soil; (**c**) vertical displacement in the sand; (**d**) horizontal displacement in the sand.  
Figure 16 shows the relationship between load and settlement of pile top in a numerical simulation and model test. The values in the two curves are very similar. The development trends of the two curves are also relatively consistent. In order to compare the displacement field of the soil around the pile, the vertical displacement cloud picture when the load was 0.557 kN was used for comparative analysis, as shown in Figure 17. From the vertical displacement cloud picture, it can been seen that the displacement field distribution of soil around the pile obtained by numerical simulation and model test is relatively close.  
Figure 16: Relationship between load and settlement of pile top in numerical simulation and model test.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Materials** & **Pile** & **Soil** \\ \hline Density (\(\times 10^{3}\) kg/m\({}^{3}\)) & 7.85 & 1.8 \\ Elastic Modulus (MPa) & 20e4 & 25 \\ Poisson’s ratio & 0.2 & 0.35 \\ Cohesion (MPa) & — & 0.04355 \\ Internal friction angle (\({}^{\circ}\)) & — & 10.7 \\ Expansion angle (\({}^{\circ}\)) & — & 10.7 \\ Pile-soil friction coefficient & 0.3 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: The parameters of pile and soil in the numerical simulation.  
Figure 17: The vertical displacement cloud picture (0.557 kN): (a) numerical simulation; (b) model test.  
Figure 18 is the vertical displacement of different data points, where the data point positions are as shown in Figure 12. It can be seen that the difference between the vertical displacement of each point in the numerical simulation and the model test is small. The errors of vertical displacement obtained by numerical simulation and experimental tests are mostly within 10%.
# Travel-Time Inversion Method of Converted Shear Waves Using RayInvr Algorithm
## 5 Conclusions  
We skillfully performed the travel-time inversion of converted S-waves based on RayInvr algorithms by transforming the conversion interface of the sedimentary layers to the seafloor. The iterative inversion is terminated by reaching the conditions of the travel-time residuals (RMS) and the number of tracing phases (NTP). The P-wave velocity model and the inverted S-wave velocity mode are presented to calculate the crustal Vp/Vs ratios. Our results show the crust has a Vs. of 3.0-4.4 km/s, and the crustal Vp/Vs ratios are 1.71-1.80, which are reliable due to the small difference, with a \(\Delta\)Vs of 0.1 km/s and \(\Delta\)Vp/Vs of 0.03, between the inversion and forward model. In addition, the high-resolution inversion model has revealed many details of the crustal structures, including magma conduits.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app11083571/s1](https://www.mdpi.com/article/10.3390/app11083571/s1)]([https://www.mdpi.com/article/10.3390/app11083571/s1](https://www.mdpi.com/article/10.3390/app11083571/s1)), Figure S1: Results of the forward P-wave velocity, Figure S2: The F-test results for the uncertainty of the Moho depth and velocity in the layer of high-velocity lower crust, Figure S3: The velocity grid nodes for the inversion, Table S1: The polarization angle for each OBS.  
G.W.: Conceptualization, Methodology, Software, Writing--Original Draft. K.W.: Methodology, Resources, Project administration, Writing--Review and Editing. S.X.: Methodology, Resources, Project administration, Writing--Review and Editing. H.X.: Resources, Project administration, Supervision. C.F.: Methodology, Writing--Review and Editing. J.C.: Writing--Review & Editing. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) (GML2019 ZD0204). National Natural Science Foundation of China (NSFC, U1701641, 41906055), Science and Technology Planning Project of Guangdong Province--Guangdong Collaborative Innovation Center for Earthquake Prevention and Disaster Mitigation Technology (2018B020207011).  
Not applicable.  
Obs Data were collected onboard of R/V Shiyan 2 implementing the open research cruise NORC2014-08 supported by NSFC Shiptime Sharing Project. We are grateful to the captain, crew and scientists for their help during the cruise. We also thank [PERSON] for helpful discussions. The figures are plotted by GMT [29].  
The authors declare no conflict of interest.  
References
# Lateral Capacitance-Voltage Method of NanoMOSFET for Detecting the Hot Carrier Injection
## 3 Simulation Methodology
The density of oxide traps in the charged area was chosen to be \(10^{12}\) cm\({}^{-2}\), which can take place at hot carrier injection stress in MOSFETs [28]. The changes in threshold voltage \(\Delta\)V\({}_{\rm th}\) and capacitances of lateral depletion regions \(\Delta\)C\({}_{\rm ds}\) and \(\Delta\)C\({}_{\rm ss}\) were simulated for different sizes of the local oxide charge.
# Action Recognition Network Based on Local Spatiotemporal Features and Global Temporal Excitation
## 4 Experiments
### Comparisons with the State of the Art
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Backbone** & **Frames** & **FLOPs (G)** & **Top-1 (\%)** & **Top-5 (\%)** \\ \hline TSN [16] & Resnet-50 & 8 & 33 & 27.8 & 57.6 \\ TSN [16] & Resnet-50 & 16 & 66 & 30.0 & 60.5 \\ TRN [32] & BNInception & 8 & 16 & 48.8 & 77.6 \\ CPNet [33] & Resnet-18 & \(16\times 6\) & - & 54.1 & 82.1 \\ CPNet [33] & Resnet-34 & \(16\times 6\) & - & 57.7 & 84.0 \\ TSM [17] & Resnet-50 & \(8\times 6\) & 198 & 59.1 & 85.6 \\ TSM [17] & Resnet-50 & \(16\times 6\) & 390 & 63.4 & 88.5 \\ GST [23] & Resnet-50 & 8 & 29.5 & 61.6 & 87.2 \\ GST [23] & Resnet-50 & 16 & 59 & 62.6 & 87.9 \\ TEI [19] & Resnet-50 & 8 & 33 & 61.3 & - \\ TEI [19] & Resnet-50 & 16 & 66 & 62.1 & - \\ LSCT (Ours) & Resnet-50 & 8 & 34 & 61.4 & 86.9 \\ LSCT (Ours) & Resnet-50 & 16 & 68 & 62.3 & 87.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results compared with state of the arts on Something-Something V2. work. These findings prove the ability of the LSCT network for improving action recognition performance. Something-Something v2. Table 2 compares the results on the Something-Something V2 dataset and demonstrates a considerable improvement over the baseline methods TSN and TSM. When sampling eight frames as input, the Top-1 accuracy of our LSCT network is 2.3% higher than that of TSM. Since TSM uses two-clip and three-crop strategy, the eight sampled frames are increased to 48, which results in 198G FLOPs. Moreover, our LSCT network also achieves competitive results compared with advanced action recognition methods such as GST and TEI. Our LSCT network also outperforms other popular methods, which demonstrates its effectiveness in action recognition tasks.
# Simulation of Lid-Driven Cavity Flow with Internal Circular Obstacles
## 3 Results and Discussion
### Location of Vortex Centers and Their Comparison  
This section describes the locations of the vortex centers formed inside the cavity, such as the left and right bottom vortices and the primary vortices, and a comparison of the current work with the existing work of GHIA is shown in Table 1. In the table, \(Re\) varies from 100 to 5000 and the symbols \(x\), \(y\), and \(\psi\) indicate the values in terms of the horizontal and vertical locations and stream function, respectively. In order to analyze the flow variation inside a cavity, the stream function (symbol \(\psi\)) was used to compare the quantity of moving fluid with the existing data ([PERSON] et al. [9]). The stream function has a constant value along each line for a two-dimensional incompressible potential flow. In order to compare the results obtained from stream function, the table lists only the maximum value of stream function (e.g., negative in the case of a counter-clockwise rotation). As shown in the table, compared with the previous work, the locations of the vortex centers obtained in this study have some scatter depending on the location, but the deviation of location (\(=\bigtriangleup d/p\times 100\), where \(\bigtriangleup d\) and \(p\) are the deviation and present value, respectively) is quantitatively a bit higher (around 20%) than those by [PERSON] and [PERSON] [17] who obtained the deviations from 4-10% in terms of energy and entropy for steady lid-driven flow with \(Re=1000\) and 5000. Therefore, it should be careful to consider the locations of the vortex center in the table.  
Figure 10: Vertical and horizontal velocity profiles for various \(Re\).
## 4 eLSNN: The Optimized Embedded LSNN
### Current-Driven Input  
In this work, we consider also an alternative to input events, using continuous (e.g., real) values in the vector \(x\)[19]. While in this case it is not possible to perform the optimisations described in Algorithm 2 for solving \(v^{(l)}:=W^{l}x\), we provide an optimised version of the LSNN using this type of input to evaluate the trade-offs lead by the two encoding methods and reported in Section 5. We note that in this case the contribution of internal neuronal activity \(v^{(H)}\) (hidden/recursive layer) is still spike-based, however in the input-layer it is not possible to remove multiply operations as it is possible using input spike events.  
In the literature, networks using continuous input typically observe a sample for a period called the exposure time \(t_{exp}\). Within the network exposure time, the same values of \(x\) are always presented. This leads to the formation of three time domains (although it is common for the time domains of input and output to coincide) represented in Figure 7:  
* Temporal domain of the input, where the input varies over time.
* Temporal domain of the network, for each tick in the input-time-domain the network performs \(t_{exp}\) iterations always observing the same value of \(x\)
* Temporal domain of output, where output varies over time.  
Figure 7: Flowchart modified to handle current-driven input. In this application scenario the input and output have two different time domains from the network. \(v^{in}\) and \(z\) vectors are used to pass information from the input to the network and from the network to the output.  
In order to efficiently handle this LSNN variant, the time domains are decoupled by vectors \(v^{in}\) and \(\hat{z}\). The first vector decouples the input from the network; it is computed by a row-column multiplication between the matrix \(W^{I}\) and the vector \(x\) each time the input changes. Then, the vector \(v^{in}\) will be used to increment \(v\) at each tick of the network. The number of operations to compute \(v^{(I)}\) then becomes \((\
icefrac{{1}}{{t_{exp}}}(I-1)N+N)t_{inf}\) sums and \(t_{inf}/t_{exp}IN\) multiplications.  
The second vector decouples the network from the output. The output will no longer process the spikes coming from the network but will use the average activity of the network within the exposure time as information. At each tick, the network must accumulate the emitted spikes inside the vector \(\hat{z}\). In the time domain of the output the content of the vector will be averaged (\(\hat{z}\)) and used for the calculation of \(y^{(H)}:=W^{O}\hat{z}\).  
The number of operations for calculating \(y^{(H)}\) then becomes \((\
icefrac{{1}}{{t_{exp}}}(N-1)O+\hat{z})t_{inf}\) sums and \(t_{inf}/t_{exp}NO\) multiplications.  
In the following, we will refer to this LSNN version as current-driven eLSNN, since the continuous and constant input over the exposure time can be interpreted as a constant current stimulus. The version working with input spikes (e.g., binary values) will be referred to as event-driven eLSNN instead.
# Object/Scene Recognition Based on a Directional Pixel Voting Descriptor
## 2 Related Works
### State of the Art for Scene Recognition  
For scene recognition, deep learning-based approaches remain at the forefront of the current state of the art, offering significant advancements in accuracy and robustness. Convolutional Neural Networks (CNNs) have emerged as the backbone of several recognition systems, capable of automatically learning visual features from images. One notable trend in scene recognition is the integration of contextual information and spatial relationships between objects within a scene [30; 31].  
Finally, the fusion of multi-modal data sources, such as images, textual descriptions, and semantic annotations, have demonstrated high performance for scene recognition. Techniques such as multi-modal fusion networks and cross-modal retrieval provide complementary information from different modalities in order to improve the scene understanding [32].
# Development and Application of a Novel Non-Iterative Balancing Method for Hydronic Systems
## 6 Conclusions
### Stepwise Method  
It is a step-by-step procedure consisting of the following main actions:  
1. Preliminary Setup. All the dampers are fully opened, and the pump is adjusted so that the total flow rate results to be slightly (normally about 10%) greater than the design value (same as RM procedure).  
2. As the flow rate at the terminal closest to the pump will typically be the highest, adjust the balancing valve aiming to a flow rate value approximately 10% below the design flow requirements, then move to the following terminal and repeat the same action. As the adjustment proceeds to the end of the system, the remaining terminal flow values will increase.  
3. Repeat the adjustment procedure throughout the system until all terminals are within \(\pm 10\%\) of the design flow requirements and at least one path valve is wide open.  
4. If necessary, adjust the pump velocity to set all terminals at \(\pm 10\%\) of the design flow, check once again all the terminals, register final values and eventually mark the position of all the balancing valves.  
As in the case of the Ratio Method, the Stepwise method involves iterations (with the related drawbacks); in addition to that it's not clear from the beginning which path is the most disadvantaged, so it may occur the operator closes a valve belonging to the MD path, disregarding in that way the minimum-energy/pressure requirement.
## 3 Method and Model Preparation
### Experiment Preparation and Machine Learning Model
#### 3.3.2. LSTM (Long Short-Term Memory)  
The full name of LSTM is Long Short-Term Memory, which is a kind of RNN (Recurrent Neural Network). LSTM is very suitable for modelling time series data due to its design characteristics. Recently, a new neural network was proposed, called LSTM [8]. The three core arithmetic structures contained in LSTM determine that it can achieve long- and short-term memory based on RNN. The algorithm structure is shown in Figure 8.