esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Enzymatically induced calcium carbonate precipitation (EICP) occurs when the activity of an enzyme alters the surrounding aqueous phase leading to precipitation of calcium carbonate. In this study, we focus on EICP via ureolysis by the enzyme urease of the bacterium _Sporosarcina pasteurii_, which is known for producing high amounts of urease. The enzyme urease catalyzes the hydrolysis reaction of urea (\((\mathrm{NH_{2}})_{2}\)CO) into ammonia (\(\mathrm{NH_{3}}\)) and carbon dioxide (CO\({}_{2}\)). The ureolysis reaction leads to an increase in pH as aqueous solutions of ammonia become alkaline. This results in higher concentrations of dissolved carbonate (CO\({}_{3}\)\({}^{2-}\)) as the dominant inorganic carbon species at high pH conditions. In the presence of calcium (Ca\({}^{2+}\)), this results in the precipitation of calcium carbonate (CaCO\({}_{3}\)). The overall EICP reaction is as follows:  
\[(\mathrm{NH_{2}})_{2}\mathrm{CO}+2\mathrm{H_{2}O}+\mathrm{Ca}^{2+}\longrightarrow 2 \mathrm{NH_{4}}^{+}+\mathrm{CaCO_{3}}\downarrow \tag{1}\]Similar to microbially induced calcium carbonate precipitation (MICP), EICP offers an engineering strategy to precipitate calcium carbonate in situ to change soil parameters such as mechanical strength, porosity, and permeability. As a technology, it can be used similarly to MICP to alter hydraulic flow conditions or for soil stabilization [1; 2; 3; 4; 5; 6]. EICP has already been applied for soil stabilization [7]. It could potentially also be used for building or monument restoration [8], and heavy metal coprecipitation [9; 10]. The capability of induced precipitation to seal highly permeable leakage pathways has been demonstrated for MICP in various studies [11; 12; 13; 14; 15; 16; 17].  
Successful application of EICP however depends on the interplay between the transport of urease as well as urea and calcium, determined by fluid dynamics, sorption of the urease to solid surfaces, and the reaction rates. Due to this interplay, the predictive planning of ureolysis-induced calcite precipitation and its impact is a major difficulty for practical engineering applications. One step towards overcoming this difficulty is the numerical model that we provide for EICP. For MICP, numerical models have been shown to be capable of capturing the complex interplay of hydraulics, precipitation reactions, and the change in hydraulic properties [12; 18; 19; 20].  
Numerical modeling is also an appropriate tool to assess the various emerging induced precipitation methods, providing the possibility to investigate each method's strong or weak points for a given setup to assist in the choice of the most suitable method for a given goal. After the choice of the specific method, models can further assist the application of induced precipitation methods, evaluating potential injection strategies or supporting the monitoring of an application by complementing sparse experimental measurements or by providing estimates for parameters and processes which are difficult to measure. This is of increasing importance as more and more induced precipitation methods are developed and applied.
## 6 Conclusions  
We have utilized the zero-forcing concept to maximize the weighted sum rate of an interference-free downlink MU-PhC-MIMO VLC system. First, we derive the precise expression for the system's weighted sum rate and propose the rate expression based on the ZF scheme. Furthermore, we employ mathematical tools to accomplish Gaussian approximations. Then, by employing variable substitutions and SCA, we recursively convexify and maximize the new objective. By comparing various possibilities, we arrive at the optimal solution for the complete optimization problem. After extensive simulation validation, our algorithms exhibit performance that allows saving 2.5 dB of transmit peak power compared to the classical ZF precoding scheme in low-transmit-power scenarios. Moreover, at higher power levels, the system's weighted sum rate significantly outperforms the repetition coding scheme. Additionally, the two proposed algorithms exhibit almost identical performance and showcase improved weighted sum rates with more active LED arrays and lower DC bias power.  
Methodology, Y.C. ([PERSON]), X.Z., J.W., Z.D. and Y.C. ([PERSON]); software, Y.C. ([PERSON]); writing--original draft preparation, Y.C. ([PERSON]); writing--review and editing, Y.C. ([PERSON]), X.Z. and J.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under Grant No. 61571135 and the Innovation Program of Shanghai Municipal Science and Technology Commission under Grant No. 21 XD1400300.  
Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [[[https://github.com/Cying98/Indoor_VLC_Channel_State_Information_with_Lambertian_Model](https://github.com/Cying98/Indoor_VLC_Channel_State_Information_with_Lambertian_Model)]([https://github.com/Cying98/Indoor_VLC_Channel_State_Information_with_Lambertian_Model](https://github.com/Cying98/Indoor_VLC_Channel_State_Information_with_Lambertian_Model)), accessed on 4 February 2024].  
The authors declare no conflicts of interest.  
Figure 7: Sum-rate performance of the proposed MU-PhC-MIMO VLC system under imperfect CSI.
# Reverse-Net: Few-Shot Learning with Reverse Teaching for Deformable Medical Image Registration
## 3 Materials and Methods
### Pixel Alignment Network
#### 3.3.2 Details of the Networks  
The U-net used in this paper is modified from the famous U-net with an encoder-decoder symmetric architecture. In the process of feature extraction, the main role of the encoder is to extract the image features layer by layer, and the decoder recovers the image information layer by layer. The output feature map of the second convolution in each stage of the encoder is transmitted to the decoder through a skip connection and is channel-spliced with the output feature map of the upsampling layer in the corresponding stage of the decoder after clipping to achieve the fusion of shallow and deep information and provide more semantic information for the decoding process. As shown in Figure 5, each downsampling block is composed of two 3 \(\times\) 3 convolutional layers and a 2 \(\times\) 2 max pooling layer. In the last layer, we used two 3 \(\times\) 3 convolutions with linear activations to obtain the final deformation field. After each convolution operation there is always batch normalization and leaky ReLU (unless otherwise stated). The channels of the U-net are set as 16, 32, 32, 64, 64, 64, 32, 32, and 16.
# An Efficient Ground Moving Target Imaging Method for Airborne Circular Stripmap SAR
## 4 Proposed Imaging Method
### Flowchart of the Proposed Imaging Method  
Based on the analyses presented in Sections 4.1 and 4.2, the flowchart of the proposed imaging method is presented in Figure 4. It starts with the target signal that has been extracted. First, a 2-D Fourier transform is applied to the target signal to transform the target signal into the 2-D frequency domain. Then, the strategy presented in Section 4.2 is utilized to solve the imaging optimization problem shown in (24). Note that, in using GA to solve the optimization problem, the fitness function is the contrast of the image that is focused with each chromosome, and the image is obtained via (26). Therefore, it is essential to use GA to find the chromosome that obtains the largest image contrast. Finally, with the optimal solution obtained by GA, the target focusing is performed efficiently via (27).  
Figure 4: Flowchart of the proposed imaging method.
## 5 Experimental Results
### Synthetic Data in Native Network Form
#### 5.2.4 Attributed Networks with Communities  
A model for generating attributed networks with communities is presented in [67]. This model is described as \"similar to the BTER model\" (described in Section 5.2.5), except that it uses the similarity of the vertex attributes to determine the inter-cluster edges, while BTER uses a scale-free distribution. The results in [67] suggest that the community structure of these graphs is most affected by the total number of edges in the graph. Fifty graphs were generated with 1000 nodes and 10 clusters (of varying sizes), varying the total number of edges to achieve a specified average degree. The high degree graphs have a strong community structure and should cluster with high accuracies. The low degree graphs should be more difficult to cluster.  
Table 5 illustrates the results as an average over 10 multiple graphs generated with the same parameters and average degree. Integrity does especially well on this type of graph, averaging 86% accuracy on the most difficult graphs and 100% accuracy on the easiest. Tenacity clustered every graph in one step, while integrity clustered 48 out of 50 graphs in one step. Additionally, VAT clustered these graphs in an average of two steps or less.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**10,000 Nodes**} & \multicolumn{4}{c}{**100,000 Nodes**} \\ \cline{2-9}  & & \multicolumn{4}{c}{**Percent Accuracy**} & \multicolumn{4}{c}{**Percent Accuracy**} \\ \# & **Edges** & \(\mu\) & **VAT** & **Integrity** & **Edges** & \(\mu\) & **VAT** & **Integrity** \\ \hline
1 & 325,380 & 0.01 & 100 & 100 & 275,542 & 0.001 & 100 & 97 \\
2 & 324,990 & 0.02 & 98 & 98 & 275,894 & 0.003 & 100 & 100 \\
3 & 324,000 & 0.03 & 98 & 98 & 275,270 & 0.005 & 100 & 92 \\
4 & 326,182 & 0.04 & 98 & 98 & 828,375 & 0.001 & 100 & 99 \\
5 & 323,552 & 0.05 & 96 & 98 & 825,222 & 0.005 & 98 & 99 \\
6 & 322,370 & 0.10 & - & 92 & 826,895 & 0.010 & - & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Clustering Results for LFR Nets of 10,000 and 100,000 Nodes.
## Appendix A Sensitivity Analysis of Airport Preselection Method  
The preselection of candidate airports as described above influences the climate mitigation potentials. For the selected individual flight example from Heathrow Airport (LHR) in London to Abu Dhabi International Airport (AUH) with an Airbus A380 at a cruise altitude of 29,000 ft, we find that climate-optimised and climate-optimal ISO airport are not identical if all possible refuelling airports are considered. However, differences are moderate in terms of location and associated ATR as displayed in Figure 11. We find that the climate-optimal refuelling location is at Rene Mouawad Airport Qlayaat (KYE) in Lebanon, whereas the selected airport according to the described methodology is at Al Jouf-Sakaka-Almatar Airport (AJF) in Saudi Arabia. The deviation is below 2% (1.67% in ATR20 and 1.89% in ATR100).  
Looking at the 25 most relevant routes in terms of ASK, we aim to perform a sensitivity analysis on the airports location to support our approach of airport preselection (Section 2.2). In this case study, we cover approximately 35,000 flights of the total sample and a share of 5% with regards to all ASK of this study. Thereby, we can confirm that the results in ATR100 and ATR20 change gradually so that no significant errors are caused through the preselection process. While ATR results in the climate-optimal case are on average10% above the solution resulting from the applied approach (+9.62% in ATR20, +9.66% in ATR100), deviations in fuel consumption are below 0.5% (+0.37%).  
However in this context, it has to be noted that an investigation of intermediate airport locations and the impact on stakeholders in terms of additional take-offs and landings at the selected intermediate stop airports needs further analysis, as these are influenced by the preselection method.
## 2 Generator Dynamic State Estimation Model  
### Mathematical Model of Dynamic State Estimation  
State space model can be used to describe the dynamic system as  
\[\left\{\begin{array}{l}X_{k+1}=\Phi X_{k}+\Gamma U_{k}+W_{k}\\ Z_{k}=CX_{k}+V_{k}\end{array}\right. \tag{1}\]where \(k\) indicates the time instant, \(W_{k}\) represents system noise, \(V_{k}\) represents measurement noise, \(\Phi\) denotes the state transition matrix, \(\Gamma\) is the input matrix, and \(C\) is the observation matrix.  
The statistical characteristics of noise \(W_{k}\) and \(V_{k}\) are given as follows  
\[\left\{\begin{array}{l}E[W_{k}]=E[V_{k}]=0\\ E[W_{k}W_{k}^{T}]=Q\delta_{kj}\\ E[V_{k}V_{k}^{T}]=R\delta_{kj}\end{array}\right. \tag{2}\]  
where \(Q\) is the variance matrix of system process noise, \(R\) is the variance matrix of system measurement noise, \(\delta_{kj}=\left\{\begin{array}{l}1,k=j\\ 0,k\
eq j\end{array}\right..\)
# Research on Leaf Area Density Detection in Orchard Canopy Using LiDAR Technology
## 1 Introduction
Considering the shapes of the leaf point clouds in a canopy, a leaf area density detection algorithm based on leaf fitting is proposed to process the fused point cloud data. Experiments detecting the canopy leaf area density using LiDAR were conducted and a mathematical calculation model for detecting the canopy leaf area density was established. The feasibility and accuracy of the detection algorithm were verified through laboratory-simulated canopy tests and detection tests conducted in an orchard.
## 3 Results and Discussion  
### Effect of Liquid Smoke Treatment on Tensile Strength of Single Fiber  
The study of BSF treatment with liquid smoke showed that the micromechanical properties of BSF were increased, and there was a tendency of fiber strength to be different without and with treatment. The tensile strength of TP fiber was 148.54 MPa, while P1J, P2J, and P3J increased this to 219.19 MPa, 264.21 MPa, and 239.46 MPa. The percentage change in the tensile strength of the fiber compared to TP fiber for P1J, P2J, and P3J was 32.23%, 43.78%, and 37.97%. A significant increase occurred in P2J, which was 43.78%. This change is influenced by changes in fiber composition, whereas in previous studies conducted by [8; 17], the changes in fiber composition could increase fiber strength.  
Figure 2: A tensile test specimen ASTM 3379-02.  
Figure 3 shows that BSFs with 2 h of treatment have a higher tensile strength per single fiber, where the shape of the cross-section does not break as brittle or change the shape of the fiber before breaking, as shown in Figure 4. Following previous research conducted by [16; 17], fiber treatment with liquid smoke can form strong C-C elemental bonds caused by the H\({}_{2}\)O degradation process in BSFs, hence carbon atoms (C) are dense, and in conditions of excessive H\({}_{2}\)O degradation, the fiber strength will become brittle and the liquid smoke can increase the tensile strength of the fiber. Based on these data, the tensile strength of the fiber with treatment for 2 h with grade three liquid smoke is the most significant, and in line with previous research, which stated that the treatment of liquid smoke on the fiber increased the tensile strength of a single fiber [8; 17].  
Figure 4: The surface of BSFs with SEM Photo of TP, P1J, P2J and P3J.  
Figure 3: Single fiber strength test (TP, P1J, P2J, and P3J).
## 4 Implementation
### Objectness Clarification Process
#### 4.4.4 Model Performance  
To evaluate the performance enhancement achieved by our two methods for objectness enhanced images, we calculated again the model's mAP and the precision, recall, and F1 score each object class. Table 4 presents the results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Background Color** & \multirow{2}{*}{**Class**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F1**} & \multirow{2}{*}{**mAP**} \\
**and Dilation** & & & & & \\ \hline \multirow{3}{*}{white} & Bus & 0.62 & 0.81 & 0.70 & \\  & Car & 0.51 & 0.65 & 0.57 & 50.65 \\  & Truck & 0.42 & 0.71 & 0.53 & \\ \hline \multirow{3}{*}{black} & Bus & 0.59 & 0.64 & 0.62 & \\  & Car & 0.52 & 0.50 & 0.51 & 40.14 \\  & Truck & 0.50 & 0.48 & 0.49 & \\ \hline \multirow{3}{*}{green} & Bus & 0.64 & 0.83 & 0.72 & \\  & Car & 0.59 & 0.91 & 0.72 & 66.20 \\  & Truck & 0.65 & 0.55 & 0.60 & \\ \hline \multirow{3}{*}{white with dilation} & Bus & 0.56 & 0.86 & 0.68 & \\  & Car & 0.54 & 0.87 & 0.67 & 69.45 \\  & Truck & 0.43 & 0.89 & 0.58 & \\ \hline \multirow{3}{*}{black with dilation} & Bus & 0.57 & 0.85 & 0.68 & \\  & Car & 0.54 & 0.87 & 0.67 & 70.98 \\ \cline{1-1}  & Truck & 0.54 & 0.88 & 0.67 & \\ \hline \multirow{3}{*}{green with dilation} & Bus & 0.55 & 0.88 & 0.68 & \\ \cline{1-1}  & Car & 0.56 & 0.90 & 0.69 & 75.34 \\ \cline{1-1}  & Truck & 0.59 & 0.90 & 0.71 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Inference Performance with different background colors and dilation application (DSSIM \(>\) 0.3).  
Figure 12: Inference time with difference operations: Three colors were applied to the non-ROI areas, and the dilate operation was either applied or not applied.  
The mAP was found to be around 40.14% in the black color background case. This is the same with that presented in Table 3 earlier. However, the mAP increased to be 50.65% and 66.20% when the background had filled with white and green colors, respectively. The F1 scores also increased in all the object classes when the background was colored with white or green.  
When the ROI areas in the video were expanded with dilate operations, in all the cases of white, black, and green, the mAP considerably increased to be 69.45%, 70.98%, and 75.34%, respectively. Note that the mAP of YOLOv4 presented with the original dataset is around 68.2%, as described above.
## 4 Results  
### Time of TLS Data Conversion  
Table 1 presents the average time of TLS data conversion for particular cartographic projections and test site in minutes and seconds. For planes detection, the Hough 2D method were used [44].  
It can be seen from Table 1 that the shortest time for data conversion into the raster form is obtained in the case of the spherical images and the longest time in the case of the orthoimage (Test site III). The time of processing TLS data to the orthoimages depends on the number of detected planes and it is an influence on the complexity of the process of computations [44].
## 3 Establishment and Simulation of the Error Model
### Ray Tracing and Error Representation
#### 3.2.2 Tilt Motion Error around X Axis  
Similar to the calculation process of the tilt motion error around the Y axis, the tilt motion error around the X axis can be obtained by the following equation:  
\[\varepsilon_{x}=\theta_{SCx}-\theta_{SCx(t_{1}=0)}-\varepsilon_{px}cos\theta+ \varepsilon_{px(t=0)}-\varepsilon_{ybc}sin\theta-\varepsilon_{xbc}cos\theta+ \varepsilon_{xbc} \tag{11}\]  
The perpendicularity error \(\varepsilon_{px}\) and the installation errors \(\varepsilon_{xbc}\) and \(\varepsilon_{ybc}\) have an effect on the measurement of the tilt motion error around the X axis and are related to the rotation angle of the target rotary axis. Among them, \(\varepsilon_{px}cos\theta\), \(\varepsilon_{ybc}sin\theta\), and \(\varepsilon_{xbc}cos\theta\) are trigonometric function terms, and \(\varepsilon_{px(t=0)}\) and \(\varepsilon_{xbc}\) are constant terms. These errors can be compensated by the triangular fitting method.
## 4 Overview of DSRC, LTE C-V2X, and 5G NR V2X
### DSRC vs. C-V2x
The study highlights the challenges arising from the nature of hybrid V2X and argues that the operation of the two technologies in the same channel for concurrent transmissions, without a mutual synchronization solution, would result in harmful co-channel interference. Both co-channel and adjacent-channel interferences remain open research problems in hybrid V2X systems. [PERSON] et al. [45] proposed an architecture and a suite of protocols to enable DSRC and C-V2X hybrid vehicular networks. The protocol suite comprises an enhanced network protocol stack, an adaptive RAT selection mechanism, a vertical handover (VHO) algorithm, and dynamic communication management (DCM) algorithms to address various challenges in the hybrid network. Simulation results using Matlab demonstrate the effectiveness of this architecture and protocol suite in terms of packet delivery ratio, latency, and throughput. [PERSON] et al. [44] introduced the traffic differentiated clustering routing (TDCR) mechanism within a software-defined network (SDN)-enabled hybrid vehicular network. This mechanism comprises a centralized one-hop clustering approach and an optimization method for data delivery. Specifically, the optimization aims to strike a balance between cellular bandwidth cost and end-to-end delay, allowing cluster heads (CHs) to deliver their aggregated data either through multi-hop vehicle-to-vehicle (V2V) transmissions or via cellular networks.
## 5 Results  
### Coupled Flow-Valve Dynamics  
#### 5.1.1 Sealing Integrity  
The integrated kinematic fluid dynamic analysis delineated flow characteristic during a complete operational cycle (T = 0.5714 s). Transient velocity distribution contours (Figure 5) exhibited near-zero magnitudes in discharge valve clearance regions (<0.01 m/s) throughout the suction phase, confirming complete sealing integrity with no detectable fluid bypass. Symmetrical flow suppression occurred in suction valve regions during discharge operations, further corroborating the clearance model's ability to emulate physical valve closure mechanisms.
## 4 Experimental Results and Analysis
### Simulation Results
#### 4.2.3 Performance Comparison of Transmission Probability of CAM Messages  
Figures 9 and 10 highlight the transmission probability of CAM messages with respect to intra-platoon spacing and the total number of platoon members. From the plot, it can be inferred that the probability of CAM message transmission tumbles with the broadening of intra-platoon space for the benchmark algorithms. Concerning the MADDPG and DDPG algorithms, the growth of vehicle population from 30 members to 50 in the environment highly influences the descent of transmission probability. At times, as the vehicle population densities, the probability of decentralized MADDPG diminishes to 65 percent, which is inferior to Centralized DDPG, primarily due to deficits in interference management. The densification of the number of vehicles deteriorates the performance of transmission probability.  
Figure 10: Transmission probability of CAM messages vs. number of platoon members for \(P=7\), intra-platoon spacing \(=25\) m.  
Figure 9: Transmission probability of CAM messages vs. intra-platoon spacing for \(P=7,V=5\).  
However, the proposed CMADDPG algorithms exhibit a reliable and robust nature towards variation in the spacing of platoons and the size of platoons. The transmission probability of CAM messages of the CMADDPG algorithm is retained at 99 percent even at 25 m intra-platoon spacing. Conversely, the transmission probability decrease is witnessed in the other benchmark algorithms for similar settings, as demonstrated in all the plots. It is evident that the proposed Cascaded Multi-Agent Deep Deterministic Policy Gradient significantly outperforms the other benchmark algorithms and demonstrates a resistant response against any fluctuations in the metric.