esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Formation Mechanisms of Effective Reservoir in Ultra-Deep
### Sedimentation Lays the Foundation for Mineral Fabric and Primary Porosity of Reservoirs
#### 2.2.3 Inhibitory Effect of Low Geothermal Field on Diagenetic Evolution  
The diagenesis and stages of clastic rock reservoirs are closely linked to the paleo-geothermal conditions of the strata. As formation temperature increases, the solubility of authigenic minerals such as quartz and illite decreases in formation water, leading to mineral precipitation [41; 54; 55]. However, the prolonged lower paleo-heat flow values in China's western basins play a crucial role in delaying diagenesis and preserving reservoir pores. As shown in Figure 6, the Tarim Basin and the Junggar Basin exhibit relatively low paleogeothermal gradients, averaging \(\sim\)20 \({}^{\circ}\)C/km, classifying them as cold basins. At depths of 5000-8000 m, most reservoirs remain in the early sub-stage of the middle diagenetic stage A1 [47], preserving porosity levels of 15-20% (Table 1), primarily characterized by well-developed primary pores. In contrast, the Sichuan Basin and the Ordos Basin have higher geothermal gradients, 30-40 \({}^{\circ}\)C/km [11; 24]. As a result, their reservoirs have progressed into the late sub-stage of middle diagenetic stage A2 or even middle diagenetic stage B [41]. Consequently, these reservoirs generally exhibit poorer physical properties, with porosity mostly below 10% (Table 1).  
Figure 5: Relationship between overpressure development and porosity of Junggar Basin and Kuqa Depression. (**a**) Zheng-10 well of Junggar Basin; (**b**) Kela-2 well of Kuqa Depression.  
The stratigraphic burial processes and rates in different central and western China basins play a crucial role in reservoir pore evolution. In the Kuqa Depression and the southern Junggar Basin piedmont belt, intense compression from Tianshan orogenic uplift during the Himalayan period led to the formation of typical foreland basins. These basins exhibit a burial pattern characterized by slow, shallow burial in the early stage, followed by rapid deep burial in the late stage (Figure 7a,b). For example, in the Cretaceous Bashijiqike Formation, the main productive layer of the Kuqa Depression, subsidence was slow between the Jurassic and Paleogene (130-123 Ma), reaching only about 2000 m [53; 56]. However, since the Neogene (23 Ma-present), the strata have rapidly subsided, accumulating thick deposits. Due to the short duration of late-stage rapid deep burial, pore fluids between grains were not fully expelled, limiting compaction. As a result, grain contacts remain predominantly point-line (Figure 4f). In contrast, since the Permian, the Sichuan Basin and Ordos Basin have undergone gradual and continuous subsidence to depths of approximately 5000 m (Figure 7c,d). Consequently, compaction has been more thorough, leading to the complete disappearance of primary pores, with grains exhibiting intergranular fracture (Figure 4e).
## 1 Introduction
Generating a conformal 3D printing trajectory on a tessellated surface is challenging and may be an obstacle to innovative designs since the available software for 3D printing does not support generating continuous curved trajectories on triangle-defined surfaces. Therefore, the current paper presents an algorithm that can be used to project, in a given direction, a complex printing trajectory into any surface represented by faceted geometry, such as a standard tessellation language (STL) file. The algorithm uses mathematical point-projection fundamentals to transfer a set of ordered points known a priori and that usually lay on a two-dimensional plane (not mandatory) to achieve conformal printing on a non-planar surface. In this way, patterns or lattices based on parametric curves (sinusoidal, Hilbert, hexagonal, re-entrant, etc.) are successfully, conformally printed on aleatory non-planar surfaces. The trajectory's principal points can be generated using parametric equations, recursive functions, infill patterns or a combination of these. The algorithm optimizes the number of points projected as needed for proper curved printing, calculates the normal vector for each mapped point to be used as a tool orientation for multi-axis manufacturing systems, and generates the G-code for 3D printing. The main contribution of this work consists in the generation of the G-code for continuous printing trajectories for the fabrication of curved lattices, where the mandril or target surface does not need to be analytically represented. The algorithm was developed in Matlab(tm)(R2018a) and can be easily implemented using other programming languages. This present document is organized as follows: Section 2 presents a review of the relevant literature, and Section 3 presents a description of the algorithm in detail and the generation of target structures and printing trajectories. Furthermore, Section 4 presentsthe results of implementing the algorithm, and finally, concluding remarks and future insights are given in Section 5.
## 4 Discussion
In previous studies, neither brachial artery compliance, brachial artery distensibility, nor brachial artery resistance has been reported after WBPA or WBPA-V-160 in humans. The non-invasive measures of brachial arteries, such as brachial artery distensibility, are effective in assessing cardiovascular risk [19]. Brachial artery distensibility is a measure of arterial stiffness [19]. Since arterial stiffness can be improved by nitric oxide [21], brachial artery distensibility was increased after 30 min of WBPA-V-160, which might imply that WBPA-V-160 might have the potential to improve arterial stiffness due to the increased nitric oxide. Some limitations were also present in this research. In this pilot study, the increased nitric oxide detected via NIOX after WBPA-V-160 cannot be isolated from inducible nitric oxide versus endothelial nitric oxide. We investigated the acute effects of a single 30 min trial of WBPA-V-160 at a constant frequency of 4 Hz on cardiovascular hemodynamics. Based on the preliminary findings of this pilot study, because we did not see any change in the BP and exhaled nitric oxide after the control non-WBPA without turning on the motor, we decided to investigate the acute effects of a single 30 min session of WBPA-V-160 without control. It is still unknown whether the long-term effects of a 60 min trial or a 3-month WBPA-V-160 program could have better effects on BP, or whether different effects can be found in WBPA-V-160 with various moving distances (2 mm, 6 mm or 2.4 cm) from headward to footward, or frequency. Because we did not video record the moving abdominal compartment in the pilot study, we cannot observe the relationship between the movement of the abdominal compartment and nitric oxide release. The moving distance of 2 mm in the WBPA-V-160 was enough to improve the blood pressure and cardiovascular hemodynamics in healthy subjects. It is unknown whether the therapeutic effects of WBPA-V-160 with a longer moving distance from headward to footward in hypertensive patients are better. Future studies will need to answer these questions.
# Traffic Light Detection and Recognition Method Based on YOLOv5s and AlexNet
## 3 Experiment
### Traffic Light AlexNet Recognition Network Model Construction  
A convolutional neural network (AlexNet)-based image recognition method is used for the problem of traffic light recognition. Before training the model, the collected individual traffic light images are first preprocessed, which is divided into two main parts: image resizing and data enhancement.  
Figure 8: Vertical and horizontal installation of traffic light.  
Figure 9: Results of image processing.  
The default input for the AlexNet network is 227 \(\times\) 227. As the size of the images in the dataset was not uniform, it was first necessary to standardize the resolution of the images and modify the resolution of all individual traffic light images to 227 \(\times\) 227.  
The number of samples in the dataset was increased by data augmentation. Common data enhancement methods include horizontal flipping, vertical flipping, random rotation, etc. However, considering the peculiarities of digital traffic light images, which may produce dissimilarity after geometric transformations such as flipping or rotation. Therefore, this experiment uses only pixel transformations to achieve data enhancement by adding salt-and-pepper noise and Gaussian noise. The processed image is shown in Figure 10.  
The original image is shown on the left in Figure 10, the image in the middle after adding salt-and-pepper noise, and the image on the right after adding Gaussian noise. After data enhancement, the final total number of samples was 4242. A total of 80% of the samples in each category were selected as the training set, and the remaining 20% constituted the test set in this paper.  
After the image preprocessing was completed, the data were fed into the network for training. The learning rate set for this image recognition experiment was 0.001, and the epoch was 100.
## 3 Results and Discussion
### NTP Activated the PI3K/AKT and STAT3 Pathways in HaCaT Cells  
Subsequently, the specific mechanism of NTP promoting cell proliferation was further explored. The expression of PI3K, AKT, mTOR, Cyclin D1, ERK, and STAT3 were detected by WB, and results showed that the expression levels of PI3K, AKT, mTOR, and ERK reached the peak in the 15 s NTP treatment group while the expression levels of Cyclin D1 and STAT3 peaked in the 10 s NTP treatment group compared with the control group. However, the expression levels of all proteins gradually decreased with the further extension of NTP treatment time (Figure 7A-D). Therefore, we suggested that low-dose NTP treatment promoted cell proliferation by activating the PI3K/AKT/mTOR signaling pathway, whereas high-dose NTP inhibited cell proliferation by inhibiting this pathway. In addition, the expression of STAT3 and Cyclin D1 also suggested that NTP might activate the JAK/STAT3 pathway and promote cell proliferation.  
To date, it has been frequently reported that low-dose NTP treatment promotes wound healing by stimulating HaCaT cell proliferation [38,56]. However, the specific mechanism of NTP promoting cell proliferation was not very clear. The PI3K/AKT/mTOR signaling pathway was the key pathway for cell proliferation, and its expression was significantly increased in proliferating HaCaT cells [35,40,50]. In addition, studies had shown that ROS played an important role in the PI3K/AKT/mTOR signaling pathway [52]. In this study, the intracellular ROS level increased in a time-dependent manner, while the expression levels of PI3K, AKT, mTOR, and ERK reached the peak at 15 s, which indicated that ROS produced by low-dose NTP activated this signaling pathway while ROS produced by high-dose inhibited it. Studies have shown that Cyclin D1 was a key protein regulating the G1 phase of the cell cycle [57]. The results here were consistent with the cell cycle results mentioned above, so ROS produced by low-dose NTP stimulated the G1-to-S cycle transition of HaCaT cells and thus promoted cell proliferation by promoting the expression of Cyclin D1. Furthermore, the increasing expression of STAT3 was also confirmed to be closely related to the proliferation of HaCaT cells [58]. Therefore, ROS produced by  
Figure 7: NTP activated the PI3K/AKT and STAT3 pathways in HaCaT cells. (**A**) The expression of PI3K, AKT, mTOR, Cyclin D1, ERK, and STAT3 were determined by WB. (**B**) The quantification by measuring the protein expression with Photoshop software (version 20.0.4). Data represent the mean \(\pm\) SD of three independent experiments. ** \(p<\) 0.001, **** \(p<\) 0.0001 with ANOVA compared with the control.  
low-dose NTP might also activate the expression of JAK/STAT3 pathway proteins and promote the proliferation of HaCaT cells.
# Delamination Detection in Bimetallic Composite Using Laser Ultrasonic Bulk Waves
## 2 Theory and Model  
### Generation of Laser Ultrasonic Based on the Thermo-Elastic Mechanism  
The ablation mechanism will cause damage to the surface of the material, so the thermo-elastic mechanism is used to excite the ultrasound. When the pulse laser irradiates the surface of aluminum, the material in the irradiated area absorbs heat and expands instantly, and ultrasonic waves are excited. The thermal conduction equation [25; 26] can be described as  
\[\rho C_{p}\frac{\partial T}{\partial t}-\
abla\cdot(K\
abla T)=Q \tag{1}\]  
where \(T\) is the temperature field in aluminum, \(K\) is the conductive thermal coefficient, \(Q\) is the power density of aluminum surface generated by pulse laser, \(C_{p}\) is the constant volume specific heat, and \(\rho\) is the material density. The power density \(Q\) can be described as  
\[Q=I_{0}(1-R)f(r)g(t) \tag{2}\]  
where \(I_{0}\) is the total power density of pulsed laser, \(R\) is the reflection coefficient of laser, \(f(r)\) is the spatial distribution function of laser energy, and \(g(t)\) is the temporal profile of the laser pulse. \(f(r)\) and \(g(t)\) are given as following:  
\[f(r)=\exp-\left(\frac{r^{2}}{r_{0}^{2}}\right) \tag{3}\]  
\[g(t)=\exp\left(-\left(\frac{t-t_{0}/2}{\tau}\right)^{2}\right) \tag{4}\]where \(r_{0}\) is the spot radius of pulse laser on aluminum surface, \(\tau\) is the rise time of the laser pulse, and \(t_{0}\) is the duration of the laser pulse. The thermos-elastic displacement equation is given as following:  
\[(\lambda+2\mu)\
abla(\
abla\cdot\mathbf{u})-\mu\
abla\times\
abla\times\mathbf{ u}-\alpha(3\lambda+2\mu)\
abla T=\rho\frac{\partial^{2}\mathbf{u}}{\partial t^{2}} \tag{5}\]  
where \(\mathbf{u}\) is the transient displacement vector, \(\lambda\) and \(\mu\) are the same constants, and \(\alpha\) is the expansion coefficient.
# Graph Dilated Network with Rejection Mechanism
## 4 Limitation of Existing GNNs  
Although GNN models significantly outperform many state-of-the-art methods on some benchmarks, there remain some problems that limit the performance of GNNs.  
In point of fact, the message propagation scheme in GNNs can be taken as a random walk [21]. The influence score of node \(u\) on node \(v\), which measures the effect of the input feature of node \(u\) to the representation of node \(v\), can be defined as  
\[I(v,u)=\sum_{i}\sum_{j}\left|\frac{\partial h_{v,i}^{(k)}}{\partial h_{u,j}^{ (0)}}\right| \tag{4}\]  
where \(h_{v}^{(k)}\) is the representation vector of node \(v\) at the \(k\)-th layer, \(h_{v,i}^{(k)}\) refers to the \(i\)-th dimension of \(h_{v}^{(k)}\), and \(h_{u}^{(0)}=X_{u}\). Then, the expected distribution of the normalized influence score follows a slightly modified \(k\)-step random walk distribution \(P(u|v,k)\) starting at the root node \(v\)[21]. Thus, the message propagates from node \(u\) to node \(v\) in a random walk pattern.  
As we know, the node sequence \((v_{t}:t=0,1,\dots)\) generated by a random walk is a Markov chain. When \(k\rightarrow\infty\), the probability distribution \(P(u|v,k\rightarrow\infty)\) converges to a stationary distribution (i.e., \(P(o|x)\equiv P(o|y)\) for any node \(x,y,o\in V\)) [31]. It means that, for the message propagation scheme, the representation of each node is influenced almost equally by any other nodes (i.e., \(I(x,o)\approx I(y,o)\)) [21] when GNNs go deeper (i.e., a large value of \(k\)). In other words, the node representations may be over-smoothed and lose their focus by the information from distant nodes [20], which is called the over-smoothing problem.  
Therefore, one of the limitations of existing GNNs is that most GNN models cannot go deeper. A deeper version of these models even performs worse than a shallow version, and the best performance of these models is usually achieved within two or three layers [17]. Although a sufficient size of neighborhoods is especially important, which allows the models to explore a more complex graph structure and aggregate useful neighbors' information [20; 21], existing GNNs can only capture limited structure information in a small size of the neighborhoods.  
In summary, on the one hand, when we stack too many layers in a GNN model, the node representation will be over smoothed, which makes the node representation indistinguishable. On the other hand, there always exists a need for a sufficient size of neighborhoods to learn a more effective representation [20; 21]. Therefore, in this paper, we propose a new neural network architecture to tackle the over-smoothing problem and capture a larger size of neighborhoods at the same time.
## 6 Conclusions  
HAP can provide a remote sensing platform that is more responsive and more persistent (_i.e._, longer observation time) than satellite and airplane. In this paper, we present an azimuth-variant signal processing in HAP-borne passive BiSAR with spaceborne/airborne transmitter. The system models, azimuth-variant characteristics and two-dimensional spectrum model are investigated. Conceptual design examples and simulation results are also provided. We show that HAP-borne BiSAR has azimuth-variant resolution and Doppler characteristics, which brings a challenge for developing high precision data processing algorithms. To address the azimuth-variant signal processing problem, we present an imaging algorithm with equivalent velocity and NCS. This imaging algorithm is verified by numerical simulation results. This paper assumes that the HAP is moving at a slow speed. In fact, HAP can also move at a high speed. High-speed HAP can serve as a good platform for wide-swath SAR remote sensing, but there is a technical challenge in suppressing the range ambiguities. This topic will be further investigated in our future work.
## 2 Problem Description
### Symbol Definition  
The relevant symbols are described below:  
\(T=\{t_{i}|1\leq i\leq n_{t}\}\) represent the set of data transmission tasks where \(|T|=n_{t}\), \(S=\left\{s_{j}|1\leq j\leq n_{s}\right\}\) represents the collection of satellites where \(|S|=n_{s}\). \(G=\left\{g_{k}|1\leq k\leq n_{g}\right\}\) represents the set of ground stations where \(|G|=n_{g}\). \(A_{s_{j}}=\left\{a_{s_{j}m}\mid 1\leq m\leq m_{s_{j}}\right\}\) represents a collection of antennas, a set of antennas. The antennas at the ground station are \(g_{k}\) with \(\left|A_{g_{k}}\right|=d_{g_{k}}\). Each antenna \(a_{g_{k}}\) is associated with a frequency band \(af_{gkd}\). For each task \(i,\ j,\ k,\ m\in R\), the task symbols' definitions are given in Table 1.  
Since PSO is typically used for solving continuous problems, and this scheduling problem is discrete, we use a binary encoding method to represent each satellite's selection state. Among them, we use binary encoding to represent the selection status of each satellite. Specifically, the selected satellites are denoted by 1, and the unselec  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Explanation** \\ \hline \(p_{t_{i}}\) & The priority of satellite \(t_{i}\) \\ \(g_{t_{i}}\) & The priority of ground stations \(t_{i}\) \\ \(s\) & Satellite \\ \(g\) & Ground stations \\ \(t_{i}\) & The number of tasks \\ \(a_{s_{i}m}\) & The number of antennas \\ \(af_{gkd}\) & The ground station antennas \\ \(af_{g_{g}m}\) & The satellite antennas \\ \hline \hline \end{tabular}
\end{table}
Table 1: Symbol of task.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Explanation** \\ \hline \(w\) & The visibility time windows \\ \(sw_{h}^{z}\) & The visibility start-time windows \\ \(ew_{h}^{z}\) & The visibility end-time windows \\ \(ST\) & The scheduled start time \\ \(ET\) & The scheduled end time \\ \(d_{t_{i}}\) & The minimal transmission duration \\ \(s_{t_{i}}\) & The task start-time window \\ \(e_{t_{i}}\) & The task end-time window \\ \(stw_{i}\) & The transmit start-time window \\ \(etw_{i}\) & The transmit end-time window \\ \hline \hline \end{tabular}
\end{table}
Table 2: Symbol of visibility time window.  
by 0. Based on this condition, the decision variables for the data transmission tasks are established. This decision variable ensures the data transmission between the satellite and the ground station matches.  
\(\xi_{ikj}=1\) indicates that satellite \(S\) transmits data to ground station \(G\) during the time window. \(\xi_{ikj}=0\) indicates that satellite \(S\) does not transmit data to ground station \(G\) during the \(u\) time window. \(\chi_{ikj}=1\) indicates that ground station \(G\) receives data from satellite \(S\), while \(\chi_{ikj}=0\) indicates that ground station \(k\) does not receive data from the satellite.
## 3 Results and Discussion
### Differential Scanning Calorimetry  
Figure 5 shows the DSC curve of the raw material PCDL and all types of PCUs. The left one was the DSC curve of PCDL with number 2 indicating the second heating process. The three melting peaks were quite obvious, which were the short-range order peak, long-range order peak, and micro crystal melting peak, respectively. The left one exhibited that the glass transition temperature of PCDL was approximately 40 \({}^{\circ}\)C, while the right one described the second heating process of the DSC curves of the polyurethane films based on different contents of the hard segment. There was an apparent distinction of the glass transition temperatures between the PCDL and the prepared polymer while the melting temperature of the polyurethanes stayed almost constant with little change at about 55 \({}^{\circ}\)C, attributed to the crystallization of the microphase separation. The relative percent crystallinity (\(R_{c}\)) of the PDCL in the PCUs was evaluated according to the enthalpy of fusion with the following equation:  
\[R_{c}=\Delta H/(w_{ss}\times\Delta H^{0})\times 100\% \tag{2}\]  
where \(w_{ss}\) is the theoretical mass fraction of the soft segment, \(\Delta H\) is the enthalpy of melting of the PCUs obtained experimentally, while \(\Delta H^{0}\) is the enthalpy of 100% crystalline PCDL (136 J\(\cdot\)g\({}^{-1}\)) [38].  
Figure 5 shows the DSC curves of all types of PCUs; with the increment of the hard segment, the crystallization process was slightly enhanced, indicating that the polymers containing a relatively high content of hard segment were conductive to the crystallization process. Since we know that the hard segments have sufficient mobility when the temperature increases in the hard domains, to a certain extent, this phenomenon could be largely ascribed to the long HDI-BDO sequences. The results were also in agreement with the analysis of AFM and the mechanical properties.  
Figure 4: Surface contact angles of the different contents of hard segments of the polyurethane.