esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Advantages of JCAS Systems  
It is anticipated that JCAS systems offer several benefits through the integration of consistent communication and sensing functions. Achieving perfect spectral efficiency can be possible by fully sharing the accessible spectrum for both wireless communication and radar [1]. This spectrum sharing should not adversely impact the operational conditions of each aspect [4]. Additionally, the utilization of channel structures obtained from sensing can enhance the beamforming performance [1]. Joint systems can significantly decrease the expenses and dimensions of transceivers compared to two individual systems [1].  
Figure 1: A basic JCAS scenario [2].  
Furthermore, communication connections can provide well-organized collaboration among multiple nodes for sensing, while sensing can enhance the safety and performance by contributing to the environmental awareness of communications [1].  
However, practical considerations introduce essential restrictions to integrated sensing and communication. These include channel estimation faults, frequency offset, timing synchronization errors, and performance investigation challenges under diverse flexibility models [5]. These limitations underscore the importance of rigorous testing and optimization to ensure the seamless integration of communication and sensing functions in JCAS systems.
## 3 Proposed Routing Algorithm
### Operation of the Radio-Aware Routing Algorithm
#### 3.2.5 Link asymmetry of wireless network  
RA showed better performance than DD in lossy WSNs, but link asymmetry still existed in RA. The link asymmetry is a well known problem which may affect variation of upper layer in wireless networks [29]. Especially, link asymmetry has a greater impact on the routing layer than on MAC layer.  
In [29] the authors proposed solutions to cope with the asymmetric problem on wireless links at different layer: multi-round discovery, learning function, etc. A multi-round discovery technique needs multiple rounds of flooding to explore different paths with a large overhead. A learning function can be done with the help of RTS/CTS in the 802.11(DCF) MAC layer. This learning function allows a node to remember such an asymmetric link through verification of ACK to the response of CTS.  
However, DD just delegates a solution for addressing the link asymmetry to MAC layer. In contrast, RA adopted two available solutions among the proposed methods to overcome the asymmetric problem at different layer. A multi-round discovery was operated at network layer and the learning function was run at MAC layer. Updating the air time cost is a multi-round discovery technique at network layer in RA, whenever a node receives both the interest and data packet. This method reduces high overhead of DD due to the multiple round of flooding.  
In addition to the multi-round update of the air time cost, RA adopted a fast re-route scheme to guarantee the symmetry communication at MAC layer. If a node had lossy situation on a certain link, the fast re-route scheme was able to detect the link asymmetry. After the fast re-route scheme updated such asymmetric information on that link, the failed link was deleted from the neighbor table for an alternative route decision.  
As a result, RA is able to guarantee a symmetric link to transmit data across the selected route using the multi-round update of the air time cost and the fast re-route scheme as an active method. RA can also provide optimum selection of a route without additional overhead and complexity. When the WSNs became unstable, these additional features of RA showed more improvement than DD.
## 2 Materials and Methods
### Structure of the OFDM Signal  
The process of the OFDM signal generation in the transmitter is as follows. The input data stream is formed into complex Binary Phase Shift Keying (BPSK) constellation symbols. Each OFDM frequency domain symbol is composed of \(N_{s}\) samples, of which \(N_{b}\) samples are BPSK symbols, and the remaining \(N_{s}-N_{b}\) samples are zeros. Thus, each of the OFDM subcarriers carries an information bit as a binary phase of value \(\pi\) or \(-\pi\) rad. The ratio between \(N_{s}\) and \(N_{b}\) is equal to \(B/f_{s}\), where the transmission bandwidth \(B\) is equal to 5 kHz, and the sampling frequency \(f_{s}\) is equal to 200 kHz. The frequency-domain symbols are processed by Inverse Fast Fourier Transformation (IFFT) to obtain time-domain symbols, each of duration \(T_{OFDM}\). Each of them is extended by the cyclic prefix of duration \(T_{g}\) equal to \(\frac{1}{4}\) of the symbol duration \(T_{OFDM}\). Such prepared OFDM symbols preceded by a synchronisation preamble modulate the carrier wave of frequency \(f_{c}\) equal to 30 kHz, which is different from the resonant frequency of the transmitting and receiving transducers; however, for a broadband system, these frequencies do not need to be precisely equal.  
The subcarrier spacing \(B_{OFDM}\) and symbol duration \(T_{OFDM}\) are matched to the transmission parameters of the UAC channel, in which the communication is performed. The coherence bandwidth \(B_{c}\) of the channel sets an upper limit on subcarrier spacing \(B_{OFDM}\). At the same time, \(B_{OFDM}\) should be much larger than the Doppler spread \(\
u_{M}\). The coherence time \(T_{c}\) sets an upper limit on the symbol duration \(T_{OFDM}\), which, on the other hand, should be longer than the delay spread \(\tau_{M}\) of the channel [8].
## 3 Three-Dimensional FDTD Parallel Algorithm on the CUDA Platform  
FDTD is computationally intensive, especially for large-scale simulations involving fine spatial grids or long-time iterations. NVIDIA's CUDA platform enables massive  
Figure 1: The FDTD Yee cell.  
parallelism by leveraging GPU hardware, offering significant speedups compared to CPU-based implementations.  
CUDA programming mainly involves two main parts: the host code and device code. The host code runs on the CPU and is responsible for data preparation, task scheduling, and results collection. The device code runs on the GPU and is responsible for executing the actual computing tasks. Developers need to use the CUDA compiler to compile the device code into GPU-executable binary files, and then call these functions in the host code to perform the computing tasks. The CPU and GPU each have independent memory address spaces, including host-side memory and device-side video memory. CUDA's memory operations are the same as those of general C++ programs. The GPU has a unique graphics memory system, and the operating graphics memory requires calling the memory management functions in CUDA's API, including opening, releasing, and initializing the graphics memory space, as well as transferring data between the host and device sides. The CUDA parallel computing function running on the GPU is called a kernel. A kernel is not a complete program, but a step in the CUDA program that can be executed in parallel. A complete CUDA program is composed of a series of device-side kernel functions and host-side serial steps, which are executed in the order of the corresponding statements in the program. Figure 2 is a hardware storage architecture diagram of a GPU.  
In parallel computing, a reasonable subdomain division scheme and an efficient subdomain communication strategy are the keys to achieving high-efficiency parallel computing. This section will provide the corresponding solutions for the two critical issues mentioned above.
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
###### Abstract  
In the realm of earth observation and space exploration, LiDAR technology offers humanity insights into the dynamics of our planet and beyond. This paper reviews spaceborne LiDAR instruments with attention to their evolution, capabilities, and achievements. We focus on the high-level LiDAR instrument design, their components, and their operational parameters in contribution to the study of Earth. Through examining selected space missions, this work illustrates the role of LiDAR technology in our understanding of environmental and atmospheric phenomena. Furthermore, the paper looks ahead, discussing the ongoing development of advanced LiDAR technologies.  
LiDAR; spaceborne LiDAR; remote sensing; earth observation; planetary exploration; atmospheric studies; LiDAR technology; topographic mapping; climate change monitoring; laser altimetry; space missions +
Footnote †: journal: Sensors
## 4 Results and Analysis
### Effect of Injection Conditions on Fracture Propagation
#### 4.3.2 Effect of Fracturing Fluid Viscosity  
To study the effect of the fracturing fluid viscosity on the fracture growth, the injection fluid viscosity in Case 4 was varied from 1 mPa\(\cdot\)s to 20 mPa\(\cdot\)s in Case 5, while the other parameters were kept unchanged.  
Figure 22 shows the fracture shape in Case 5, in which the injection rate is 30 L/min and the injection fluid viscosity is 20 mPa\(\cdot\)s when \(t=200\) s and \(t=270\) s. In this case, the hydraulic fracture in Well 3 interacts with the natural fracture when \(t=200\) s. From Figure 22b, the propagation of the hydraulic fracture induced by Well 3 along the natural fractures is evident. The time required for the hydraulic fracture induced by Well 3 to intersect the natural fracture in Case 5 is 200 s, which is longer than that (90 s) in Case 1 and shorter than that (265 s) in Case 4. Compared with Figure 19b, from Figure 22b, it is evident that with the increase in the injection fluid viscosity, the opened area of the natural fracture is greater than that in Case 4.  
Figure 23 shows the temporal variation in the bottom-hole pressure. Notably, once the viscosity of the fracturing fluid reaches 20 mPa\(\cdot\)s, the convergence of the hydraulic and natural fractures becomes more rapid compared with that in Case 4. Consequently, the time taken for the bottom-hole pressure of Well 3 to sharply decrease is shorter. Figure 24 shows the temporal variation in the hydraulic fracture area under different fracturing fluid viscosities. When the viscosity of the injection fluid is 20 mPa\(\cdot\)s, the area propagation rate is greater than that when the viscosity is 1 mPa\(\cdot\)s. By comparing the temporal variation in the pressure at the bottom hole and hydraulic fracture area under different fracturing fluid viscosities, it can be found that when the viscosity of the fracturing fluid increases, the hydraulic fracture intersects with the natural fracture more quickly, leading to a more rapid fracture expansion. Consequently, a higher fracturing fluid viscosity results in an accelerated propagation of the hydraulic fractures.  
Figure 21: Hydraulic fracture area with time for Cases 1 and 4.
_applied_  
_sciences_  
_Article_  
A Combination of Laboratory Testing, RCE, and Corrosion Loop for Inhibitor Selection  
**[PERSON] 1,2,*, [PERSON] 2,3 and [PERSON] 1(c)  
1 Energy and Environmental Innovation Group, Faculty of Engineering, Malvinas Institute,  
National University of La Plata, La Plata B1900, Argentina  
2 National Scientific and Technical Research Council (CONICET), Buenos Aires C1425 FQB, Argentina  
3 Faculty of Engineering, National University of Patagnia, San Juan Bosco,  
Comodoro Rivadavia U9000, Argentina  
*Correspondence: [EMAIL_ADDRESS]  
**Featured Application: Specific applications for the oil and gas industry. Testing of corrosion inhibitors to select the one with the best performance simulating field conditions.**  
**Abstract: Corrosion inhibitors are evaluated in the oil industry with electrochemical tests of resistance to linear polarization with rotating cylinders following ASTM G170 and NACE 3T199 standards. With these tests, we can determine the corrosion rate (CR) and efficiency of corrosion inhibitors. In this work, a corrosion test protocol used by hydrocarbon-producing companies for the testing of corrosion inhibitors was used. This protocol consists of a 1045 carbon steel working electrode in a NACE solution composed of 9.62% NaCl, 0.45% CaCl\({}_{2}\), 0.19% MgCl\({}_{2}\), and 89.74% H\({}_{2}\)O, at a temperature of 65 \({}^{\circ}\)C and saturated with CO\({}_{2}\). Each inhibitor tested was subjected to a series of 6000-4000-2000-4000-6000 rpm tests using rotating cylinder electrodes (RCEs). These electrochemical studies were carried out with the rotating cylinder to evaluate the ability of the inhibitor to prevent the corrosion of carbon steel in the presence of a centrifugal force. In our opinion, this test does not provide corrosion engineers with enough information to be used as a predictive tool, since what is obtained is the CR in a very short testing time. This document proposes the use of two more appropriate test methodologies, the rotating cylinder electrode (RCE) and the flow loop (FL), to evaluate the performance of the corrosion inhibitor. For the FL, the selected flow rate was 1.2 m/s, the same rate that fluids have in oil company pipelines installed in Neuquen, Argentina. Firstly, according to the company's protocol, inhibitors are required to have an efficiency greater than or equal to 90% in RCE tests; therefore, inhibitors that meet these requirements were tested in the FL test. Unlike the RCE test, the FL test represents the experimental conditions of the laboratory that are closest to reality, for the evaluation of the performance of the inhibitors used in the pipelines of the oil and gas industry. FL tests have several problems involving corrosion, erosion, abrasion, biphasic fluids, the time it takes for the inhibitor to become effective, and the duration of its effectiveness.**  
**Keywords: rotating cylinder electrodes; corrosion loop; resistance to linear polarization**
# Welding Bead Inspection Using Image and Multi-Sensor Fusion
## 2 Related Works
Additionally, other methods have been proposed, such as a novel image segmentation approach optimized using the sine cosine algorithm (SCA), an approach analyzing welding surfaces using the faster R-CNN algorithm to determine defects, a method that segments welding areas using the entropy algorithm and evaluates defect presence through convolutional neural networks, and an approach that employs deep neural networks (DNNs) to evaluate defects after training on images captured by cameras with CCD (charge-coupled device) image sensors [26; 27; 28; 29]. However, the accuracy of these methods may not reach levels suitable for commercial use, and further experimentation is required to account for various variables.
# A High-Resolution Spectral Analysis Method Based on Fast Iterative Least Squares Constraints
## 2 Theory
### Objective Function  
Compared to L2 regularization, L1 regularization can also help prevent overfitting to a certain extent. The L1 norm promotes a sparse model by reducing some unimportant regression coefficients to zero. This reduction in computation can lead to lower average errors, thereby decreasing model complexity and potentially improving fitting performance. Therefore, to enhance the convergence speed of the spectral analysis method, we can replace the L2 norm in objective functional 3 with the L1 norm. The modified equation can be expressed as follows:  
\[\min_{x}\frac{1}{2}\|\mathbf{F}_{w}\mathbf{m}_{w}-\mathbf{W}_{d}\mathbf{d}\|^{ 2}+\lambda\|\mathbf{m}_{w}\|_{1} \tag{5}\]  
However, since the regularization term in Equation (5) is the L1 norm, it is non-differentiable, which prevents the use of gradient descent for optimization. Therefore, this paper employs the Iterative Shrinkage-Thresholding Algorithm (ISTA) to address this issue, utilizing a proximal gradient descent approach.
## 2 Materials and Methods
### Calculations of the Main Parameters  
ET (mm) was calculated as:  
\[ET=LE/\lambda \tag{1}\]  
where LE (w m\({}^{-2}\)) is the output latent heat flux, \(\lambda\) (MJ kg\({}^{-1}\)) is the latent heat of vaporization of water, which can be calculated from air temperature (T, \({}^{\circ}\)C):  
\[\lambda=(2.501-0.00237\times T)\times 10^{6} \tag{2}\]  
The NEE was linked to the eddy covariance CO\({}_{2}\) flux (F\({}_{\rm c}\), \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)) and the rate of canopy CO\({}_{2}\) storage change below the sensor:  
\[NEE=F_{\rm c}+F_{\rm st} \tag{3}\]  
where F\({}_{\rm st}\) is the storage flux, which reflects the accumulation and depletion of CO\({}_{2}\) in the canopy volume, and was estimated based on the assumption that half hour changes in CO\({}_{2}\) concentration at the 2 m and 32 m levels are representative of the whole layer below the eddy covariance systems [44], as follows:  
\[F_{\rm st}=\rho_{\rm a}h\Delta c/\Delta t \tag{4}\]  
where \(\rho_{\rm a}\) is the molar density of air (mol m\({}^{-3}\)); h is the height of the eddy covariance system (m), \(\Delta\)c is the change in CO\({}_{2}\) concentration (\(\mu\)mol mol\({}^{-1}\)) during the time interval, \(\Delta\)t (s).  
Nighttime NEE is equal to R\({}_{\rm eco}\), and daytime R\({}_{\rm eco}\) was estimated from a regression model using the relationship between nighttime NEE and soil temperature (T\({}_{\rm s}\)) at a depth of 10 cm. This method is based on the Vant Hoff equation as described by [PERSON] and [PERSON] (1994) [45]:  
\[R_{\rm eco}=R_{\rm ref}exp(bT_{\rm s}) \tag{5}\]  
where R\({}_{\rm ref}\) (the reference respiration at T\({}_{\rm s}\) of 0 \({}^{\circ}\)C) and b are the regression parameters.  
GPP was calculated as the difference between \(\mathrm{R_{eco}}\) and NEE, which represent the CO\({}_{2}\) assimilation and release of the ecosystem, respectively.  
\[GPP=R_{eco}-NEE \tag{6}\]  
Based on gas exchange approaches, water use efficiency can be calculated at ecosystem, canopy, and leaf scales. In this study, the daily ecosystem WUE (g C kg\({}^{-1}\) H\({}_{2}\)O) was calculated through dividing GPP (g C m\({}^{-2}\) d\({}^{-1}\)) by ET (kg H\({}_{2}\)O m\({}^{-2}\) d\({}^{-1}\)) [8]:  
\[WUE=GPP/ET \tag{7}\]  
To distinguish these parameters between the two ecosystems, the GPP, NEE, ET, and WUE were denoted as GPP\({}_{\mathrm{W}}\), NEE\({}_{\mathrm{W}}\), ET\({}_{\mathrm{W}}\), and WUE\({}_{\mathrm{w}}\) in WWE, while they were denoted as GPP\({}_{\mathrm{a}}\), NEE\({}_{\mathrm{a}}\), ET\({}_{\mathrm{a}}\), and WUE\({}_{\mathrm{a}}\) in AFE.
## 3 Experimental Setup  
### G-THGEMs  
Table 1 summarises the four THGEMs, with their respective combinations of substrate material, electrode coating and hole shape and size. Both the G-THGEMs and FR4 THGEM have a 179 mm diameter, with a 163 mm diameter active area.  
Two substrate materials were tested for the G-THGEMs: SCHOTT Borofloat 33 [24; 25; 26; 27] and fused silica [27; 28; 29]. Borofloat 33 is relatively low cost, enabling potential large scale  
Figure 5: A comparison between the abrasively machined holes in (**a**) Borofloat 33 G-THGEM and the drilled holes in the (**b**) FR4 THGEM. Both G-THGEM/THGEM holes are 500 \(\upmu\)m in diameter.  
Figure 6: A Borofloat 33 substrate G-THGEM, coated on the top and bottom face with an ITO resistive coating, with micro-patterned hole formation created via a masked abrasive machining process.  
production of relatively inexpensive G-THGEMs, whereas fused silica is radiopure, potentially allowing use in experiments which are very sensitive to background radiation (for example, dark matter TPCs).  
The abrasive machining process allows for more complex THGEM hole topography than conventional manufacturing methods. To highlight this capability, a G-THGEM with hexagonal holes was also tested. Hexagonal holes can be packed more densely (shown in Figure 3), offering higher open areas and possibly increased electron transparency. The open area of the hexagonal G-THGEM is 62%, compared to 35% of the circular holes.  
There are several mechanical advantages afforded by G-THGEMs which makes them suitable for large-scale LArTPC experiments. Firstly, both Borofloat 33 and fused silica are stiffer than FR4 and can be produced in large flat surfaces, with thickness variation tolerances typically superior than what is possible for FR4. Large-scale experiments, for example DUNE, utilise PCB substrate LEMs with reported thickness variations approaching 5%, with a rejection rate of less than 1 LEM per charge readout plane (CRP) (18 LEMs) [11]. Borofloat 33 sheets are available with thickness variations of \(\leq\)15 \(\upmu\)m for 1 mm thick substrates [30], resulting in a thickness variation of less than 1.5%. This is an important consideration in achieving high THGEM gain uniformity. Differential surface flatness and THGEM bowing both contribute to THGEM field distortions. SCHOTT Borofloat 33 and fused silica are reported with flexural moduli of 64 GPa [24,25] and 72 GPa [28,29], respectively. The flexural strength of epoxy laminate/FR4 used to produce THGEMs does not typically exceed 23 GPa (crosswise) [31,32].  
Glass is less porous than FR4, making it less susceptible to contamination. This presents obvious advantages to detectors with high purity requirements. A common type of G10/FR4 is Textolite G 10 FR4, manufactured by General Electric, and this has been reported to have a total mass loss (TML) and collected volatile condensable material (CVCM) outgassing rate of 0.44% and 0.01%, respectively [33]. Silica-based glasses/ceramics such as borosilicate (Borofloat), fused silica or aluminium silicate typically have relatively lower outgassing rates [33].
## Data Collection
Therefore, the mean values of the strength and deformation in the positive and negative directions were adopted for the following analysis. The strength and deformation capacities of 1163 RC columns were determined according to the above approach, and used as the outputs of the ANN model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & \multicolumn{2}{c}{**Feature**} & **Category** & **Feature** \\ \hline \multirow{4}{*}{Cross Section} & \(B\) & Width of column section & \(\rho_{t}\) & Longitudinal reinforcement ratio \\  & \(D\) & Depth of column section & \(f_{dt}\) & Yield strength of longitudinal bar \\  & \(A_{c}\) & Gross area of column section & \(f_{dt}\) & Ultimate strength of longitudinal bar \\  & \(l_{c}\) & Moment of inertia of column section & \(d_{l}\) & Dameter of longitudinal bar \\  & \(d_{e}\) & Effective depth of column section & \(A_{dt}\) & Area of a single longitudinal bar \\  & \(d^{\prime}\) & Effective cover thickness & \(N_{l}\) & Number of longitudinal bars \\ \hline \multirow{2}{*}{Span} & \(L\) & Effective height of specimen & \(f_{dt}\) & Yield strength of transverse steel \\  & \(\lambda\) & Span-to-depth ratio & \(f_{dt}\) & Ultimate strength of transverse steel \\ \hline \multirow{2}{*}{Vertical loading} & \(P\) & Applied axial load & Trans. bars & \(d_{dt}\) & Dameter of transverse reinforcement bar \\  & \(n\) & Axial compression ratio & \(A_{dt}\) & Area of one transverse reinforcement bar \\ \hline \multirow{2}{*}{Concrete} & \(\frac{P}{E_{c}}\) & 28-day concrete compressive strength & \(s\) & Spacing of transverse reinforcement \\  & \(E_{c}\) & Elastic modulus of concrete & \(\rho_{ac}\) & Transverse reinforcement volumetric ratio \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Features of reinforced concrete (RC) columns. Figure 1: Schematic diagram and lumped plasticity model of a typical RC column. Consequently, the model parameters of the LP model (e.g., yielding moment \(M_{y}\), initial stiffness \(K_{1}\), and the coefficient of the post yielding stiffness \(r\)) can be determined by the four parameters: The strength (\(F_{\max}\)), the yielding (\(\delta_{y}\)), capping (\(\delta_{c}\)) and ultimate drift (\(\delta_{u}\)), as shown in Tables 2 and 3. As can be seen, \(M_{y}=F_{y}L+P\delta_{y}L\) and \(M_{u}=F_{\max}L+P\delta_{c}L\) are the yielding moment and ultimate moment, respectively. Consequently, the four variables (\(F_{\max}\), \(\delta_{u}\), \(\delta_{c}\), \(\delta_{y}\)) are used as the outputs of the ANN model.