esa-sceva/satcom-chunk-collection huggingface dataset:
# Carbon Dioxide Uptake by Brazilian Cement-Based Materials
## 1 Introduction
### International Climate Change Context  
The Intergovernmental Panel on Climate Change (IPCC), which is the United Nations body for assessing the science and establishing policy-relevant facts related to climate change, has recently developed an analysis of key findings on the impact of the increase in CO\({}_{2}\) emissions on climate change [1]. The IPCC's Sixth Assessment Report examines the sources of global emissions and provides an updated assessment of the progress on climate change mitigation. As is the case with most environmental science research, assessing causality is a significant challenge, which in this instance is to determine the relationship between the impact of national climate commitments and long-term emissions goals outcomes. The IPCC's Sixth Assessment Report (AR6) sets in its first part entitled \"Climate Change 2021: The Physical Science Basis\" [2] that \"the uptake of CO\({}_{2}\) in cement infrastructure (carbonation) offsets about one half of the carbon dioxide emissions from current cement production\". Furthermore, it is also mentioned that the carbon budget has been improved by adding a method for estimating the carbon dioxide emissions from cement production and the sink associated with mortar and concrete carbonation. Finally, in March 2023, the Intergovernmental Panel on Climate Change (IPCC) has considered the Synthesis Report, the closing chapter of the Sixth Assessment Report (AR6), at its 58 th Plenary Session which was held in Interlaken, Switzerland. This Synthesis Report integrates all the findings collected in the six reports released by the IPCC during the Sixth Assessment Cycle which began in 2015, i.e., three Special Reports plus three Reports made by the IPCC Working Groups [3].  
China has proposed a goal to achieve carbon neutrality by 2060 and [PERSON] and [PERSON] [4] recognized that the cement paste in concrete can take up as much carbon dioxide emissions as that produced during calcination. Moreover, accounting for carbon dioxide due to carbonation could represent a significant impact in decarbonizing China's cement industry. Considering 15% of the carbon dioxide uptake of all the carbon dioxide produced by calcination, carbonation will aid in reducing 8.8% of global emissions by 2060. Therefore, it is essential for China to add carbonation effects when constructing its carbon dioxide emission inventories for the cement industry. This approach is consistent with what is currently known regarding the carbon dioxide uptake by cement-based materials [5]. Summing up, achieving carbon neutrality in China's Cement Industry by 2060 is possible under advanced and aggressive abatement scenarios.  
[PERSON] and [PERSON] [6] proposed to develop a multifaceted approach to reach carbon peaking in the cement industry, i.e., to achieve the point at which carbon dioxide emissions from the cement industry attain the highest level and then subsequently drop. Furthermore, the idea that the global cement industry has a huge potential to bring about changes to minimize global warming was supported.
# A Very High-Resolution Urban Green Space from the Fusion of Microsatellite, SAR, and MSI Images
## 2 Methodology
### Accuracy Assessment, Data Conversion, and Area Calculation  
The classification results were subsequently assessed using the matrix contingency method. Finally, the classified images were exported to Google Drive, downloaded, and imported into the QGIS environment for the final visualization process.  
In the QGIS environment, the raster layer was first polygonized, and its geometry was subsequently fixed. Next, a new \"Class\" field was created to categorize each \"Digital Number (DN)\" value into distinct classes using a \"CASE statement\" within the field calculator feature. Afterward, the vector layer was dissolved based on the \"Class\" field. Using the resulting dissolved vector layer, the final area of each urban green space class was calculated. Only areas larger than 0.1 km\({}^{2}\) were considered for analysis, while the rest were excluded. To refine the data, Google's Open Building Data accessible through the GEE data catalog was utilized. Specifically, only data with a confidence level of 75% or higher were selected. Subsequently, the \"Difference\" function in the \"Geoprocessing Tools\" of QGIS was employed. Vector data were manipulated to create a highly accurate urban green space map for Jakarta, where the \"input layer\" and \"difference layer\" are urban green space data and open building data, respectively. Figure 3 provides a summary of the methodology that was developed and employed in this study.  
Figure 3: Methodology of the study.
## 1 Introduction
However, this assumption is not always true for all the GNSS antenna types. As a rebuttal to this assumption, [PERSON] _et al_. [8] found that in some cases, the phase patterns between two antennas of the same type have a large difference amounting to several millimeters, which was further demonstrated by using a precise point positioning (PPP) analysis [16] and precise relative positioning experiments based on a very short baseline [17]. This indicates that individual absolute antenna calibrations, rather than using IGS type mean values, are recommended if tasks related to GNSS precise positioning are conducted. China has launched a large research project, namely, \"Crustal Movement Observation Network of China\" (CMONOC), which is a comprehensive observational network for real-time dynamic monitoring of changes in continental tectonic settings and to explore their effect on resources, the environment and disasters. The main observation technique in this project is the well-known GPS, combined with other space-based techniques, such as VLBI and SLR [18]. As the core observation technique within the CMONOC project, the GPS permanent networks consist of more than 200 tracking stations, which were equipped with different receivers and mixed antenna types, and some of them without precise calibration for GNSS antenna phase center. Aimed at high precise dynamic crustal movement monitoring, it is still necessary to carry out individual absolute antenna recalibrations. Therefore, one of goals of this project was to establish an absolute field antenna phase calibration platform using GPS to remove or greatly reduce the detrimental effect of phase center errors on mm-level precise geodetic and geophysical applications. The absolute field antenna phase calibration platform was established on the rooftop of a laboratory building on the campus of Wuhan University (China). Based on this calibration platform, a direct absolute calibration in field procedure has been attempted in this paper.
## 3 The Statistical Regression Adaptive Kalman Filtering Algorithm
### The Analysis of the Convergence of the Proposed Algorithm
#### 4.1.1 Case 1: Surface Lake Trial  
The AUV's positions can be renewed by the GPS for a period of time while refloating. When the GPS signal is blocked, the accuracy of the integrated navigation system will be extremely poor or even inaccurate. SRAKF performance is evaluated in the surface trial.  
In Case 1, Figures 7 and 8 show the trajectory comparison results between the SRAKF and other traditional algorithms. The position (latitude and longitude) errors are shown in Figures 9 and 10, respectively. Furthermore, the RMSE of position for SRAKF, UKF, and CKF are shown in Table 3.  
Figure 8: Longitude positions for the surface lake trial.  
Figure 9: Latitude errors for the surface lake trial.  
From Figures 7 and 8, it can be clearly seen that the performance of the proposed SRAKF is much better than UKF and CKF. In Table 3, we can see that by employing the proposed SRAKF, the RMSE\({}_{\text{La}}\) is reduced from 3.4998 \(\times\) 10\({}^{-4}\) deg to 2.4298 \(\times\) 10\({}^{-4}\) deg. The RMSE\({}_{\text{Lo}}\) for SRAKF is 8.9344 \(\times\) 10\({}^{-4}\) deg, which is the lowest of the three algorithms.
## 3 Numerical Simulation  
### Whole-Space Transient Electromagnetic Responses of Water-Bearing Collapse Column  
First, this paper establishes a sphere space modeled at 180 m and sets the 18 m thick outer boundary layer as an infinite meta-domain. A layered geoelectrical model is established, divided into three layers, representing roof, coal seam, and floor. The specific geometry and geoelectrical parameters are shown in Table 1. For the roadway model of the coal seam, we set the length of the tunnel being dug as 100 m and the roadway section dimensions as 4 m \(\times\) 4 m. The interior area of the laneway is set as air, the resistivity of which is 1 \(\times\) 10\({}^{5}\)\(\Omega\cdot\)m. The peripheral surrounding rock resistivity of the geoelectrical model is set to 100 \(\Omega\cdot\)m. A wire device serves as the excitation source, and the coil is taken as a circular coil with a radius of 1 m, the transmitting coil is 60 turns, the receiving coil is 40 turns, the coil is placed on the tunnel head, the detecting direction is in front of the tunnel head. The coil and the tunnel head have a 25 mm interval to be consistent with the actual situation. An abnormal geologic body is placed 20 m from the tunnel head, with a resistivity of 1 \(\Omega\cdot\)m; its parameters are listed in Table 2. To study the transient electromagnetic response law of the collapse column, this paper establishes a collapse column model with an upper surface radius of 10 m, a radius of 30 m on the lower surface, a height of 100 m, a resistivity of 1 \(\Omega\cdot\)m, and placed 20 m from the excavation head. Figure 5(a,b) shows the specific model structure.  
Figure 6(a)-f shows the inductive electromagnetic field in the collapse column at different times after the current is switched off. When there is no current, the far-left part of the middle of the collapse column is the first to sense the disappearance of the field and produces an inductive electromagnetic field in the area. At this point, the inductive electromagnetic field peak is approximately 4.5 \(\times\) 10\({}^{-9}\) T, and in 10 \(\upmu\)s, the response of the electromagnetic field spreads to the inside of the collapse column and continues to spread to the center area. At 200 \(\upmu\)s, the response spreads to the center area of the collapse column, after which it stabilizes, with only the attenuation of the electromagnetic field strength.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Toward the Length (\(\upmu\)x/m)** & **Tending Length (y)/m** & **Thickness (z)/m** & **Apparent Resistivity (\(\Omega\cdot\)m)** \\ \hline Roof & 200 & 150 & 80 & 100 \\ \hline Coal seam & 200 & 150 & 4 & 50 \\ \hline Floor & 200 & 150 & 60 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geoelectric parameters of the model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Radius of the** & **Radius of the** & **Height/m** & **Apparent Resistivity (\(\Omega\cdot\)m)** \\ \hline Cylinder & 10 & 30 & 100 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Geoelectric parameters of the cylinder.  
Figure 6: Geoelectric model of a full-space sphere.
## 4 Feature Extraction and Control Methods
### Control Program  
In the active balancing system, the I/O card named PCI-6172 was used to output the control commands of the control unit and to connect with solenoid valves. The normal state of these channels in the I/O card is open and the control loop is disconnected, so that none of the solenoid valves work. When the control unit outputs a control command, the channel corresponding to the solenoid valve is closed, the control loop is turned on, and the corresponding solenoid valve starts to work. To ensure that the channels opened correctly, the corresponding channels of each solenoid valve were coded binary, that is, \(2^{0}\), \(2^{1}\), \(2^{2}\), and \(2^{3}\). If two solenoid valves needed to be opened at the same time, the output code of the control unit was the sum of the corresponding channel codes. Since only adjacent chambers had the possibility of opening at the same time and the contra-positioned chambers were forbidden to work simultaneously, there were eight codes to control the four solenoid valves: 1, 2, 4, 8, 3, 6, 9, and 12. The first four codes were used to open one solenoid valve, and the last four codes were used to open the two simultaneously. Based on the LabVIEW platform, the control program was developed, and a schematic diagram is shown in Figure 9. After debugging the program, two conclusions could be drawn: (1) when these codes were sent to the I/O card, the corresponding solenoid valve could open correctly; and (2) when the work time of the solenoid valves increased from 100 ms to 2000 ms by 100 ms steps, the control accuracy of each step was within 1 ms. Therefore, the control program could satisfy the need for control accuracy of the balancing system.  
Figure 8: Relationship between the initial phase and peak phase.  
Figure 9: Diagram of the control program.
## 3 Results and Discussion
### The Output Characteristics  
Figure 5a indicates the output characteristics of the HJLTFET and DMGE-HJLTFET at Vgs = 1 V, it can be seen clearly that HJLTFET showed worse saturation performance when gate voltage was less than 0.5 V, meanwhile, the maximum output saturation drain current of HJLTFET was only \(2.35\times 10^{-5}\) A/\(\mu\)m. While the DMGE-HJLTFET exhibited better saturation performance within the simulated voltage range, and the maximum output saturation drain current of DMGE-HJLTFET increased up to \(2.66\times 10^{-4}\) A/\(\mu\)m.  
As another important parameter to evaluate the analog performance of the devices, the output transconductance (gds) can be calculated by the first derivative of the drain current (Ids) with respect to VDS, Equation (2) is the formula of gds:  
\[\textsubscript{g}_{\text{ds}}=\frac{\text{d}\textsubscript{I}\textsubscript{ds}}{ \text{dV}\textsubscript{ds}}. \tag{2}\]  
The comparison of gds characteristics of the HJLTFET and DMGE-HJLTFET at Vgs = 1 V is shown in Figure 5b. It can be viewed that gds of the DMGE-HJLTFET was greater than that of HJLTFET within the simulated voltage range. The maximum output conductance of DMGE-HJLTFET was \(6.06\times 10^{-4}\) S/\(\mu\)m when Vds = 0.56 V, however, the maximum output conductance of the HJLTFET was \(9.46\times 10^{-5}\) S/\(\mu\)m when Vds = 0.38 V. Therefore, the maximum gds of the DMGE-HJLTFET was almost seven times more than HJLTFET.  
Figure 4: (**a**) The transfer characteristics of the HJLTFET and DMGE-HJLTFET; (**b**) transconductance of the HJLTFET and DMGE-HJLTFET.  
Figure 5: (**a**) Output characteristics of the HJLTFET and DMGE-HJLTFET at gate-to-source voltage (Vgs) = 1 V; (**b**) output transconductance of the HJLTFET and DMGE-HJLTFET at Vgs = 1 V.
# Performance Analysis of Internet of Vehicles Mesh Networks Based on Actual Switch Models
## 3 Network Architecture and System Model  
The IoV mesh network constitutes a vital component of ITS. Within this network, each vehicle is equipped with communication and computing devices. Vehicles possess the capability to directly communicate with each other while in motion at high velocities, even in situations where communication infrastructure such as base stations and wireless access points are unavailable nearby. It is essential to recognize that, within an IoV mesh network, vehicles not only transmit and receive data, but also serve as routers. Consequently, when the sender node is distant from the receiver node, the sender node transmits data through neighboring nodes in a multi-hop manner. The IoV mesh network architecture aims to facilitate communication among proximate vehicles, as well as between vehicles and adjacent facilities, which can be divided into two parts: inter-vehicle communication (V2V) and communication between vehicles and infrastructure (V2I). A typical IoV mesh network architecture is shown in Figure 1.  
We consider a simplified motion scenario where the newly added vehicles are not involved in the task and, hence, the total number of network nodes remains unchanged for the simulation duration. We set the starting position of the originally existing vehicle that will not leave the simulation area based on its maximum movement speed.  
Each vehicle and RSU autonomously generates diverse tasks. For example, when a vehicle encounters factors such as occlusion or bad weather, and is unable to make a correct judgment on the current traffic light or the traffic light changes in the coming period, the RSU can send traffic light information to the neighboring vehicles, and the vehicle can clarify the traffic light status and forward it to other vehicles after obtaining this information. At the same time, it can also determine the time for the vehicle to arrive at the intersection according to its own location and the map, so as to realize traffic light speed guidance. Vehicle tasks include augmented reality, real-time video analytics, and human behavior recognition for drivers, pedestrians, etc.  
Figure 1: The architecture of IoV mesh network.  
When a vehicle or RSU requires task collection and transmission, it sends requests to neighboring vehicles or RSUs so that different tasks have different destinations. This framework enables vehicles and RSUs to engage in communication, information exchange, collaborative task assignment, and data processing. Such collaboration allows for real-time environmental sensing and timely adaptation of collaboration strategies across diverse application scenarios. If the network is faulty (e.g., node failure or link outage), there may be an impact on network performance. For example, when a task has been transmitted to a node, but that node suddenly fails and is unable to process and forward the task, at this point, the packet loss rate of the network will increase, the task arrival rate will decrease, etc.
# Neural Network-Assisted Interferogram Analysis Using Cylindrical and Flat Reference Beams
## 3 Results
### Neural Network Training  
The neural network based on Xception [58; 59; 60] architecture was trained using a dataset corresponding to the superposition of two aberrations (6000 images: 4000, 1000, and 1000 images were used for training, testing, and validation, respectively), examples of which are presented in Table 2.  
Upon completion of the learning process lasting 80 epochs for the mean absolute error (MAE) function, results were obtained on linear-type and cylindrical-type interferograms (Figure 7) with the parameter \(\alpha=2\pi\). As a result of this learning process (Figure 8b), we get a model capable of making a prediction in the form of an array of eight elements, where each element corresponds to a level (or power) of an aberration type \(c_{nm}\). Predictions are made using only an image of an interferogram as input (Figure 8a).  
Figure 8: An example of a prediction made by a trained neural network: is an input image (an image of an interferogram) (**a**) and an array of predicted coefficients as an output (**b**). For this image, the true coefficients are 0.3 for \(Z_{20}\) and 0.2 for \(Z_{44}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Phase Distribution** & **Reconstructed Phase Distribution** & **Interprogram** \\ \hline  & \(0.3Z_{20}(x,y)+0.2Z_{44}(x,y)\) & \\ \hline  & \(0.4Z_{22}(x,y)+0.1Z_{33}(x,y)\) & \\ \hline  & \(0.3Z_{42}(x,y)+0.2Z_{20}(x,y)\) & \\ \hline \hline \end{tabular}
\end{table}
Table 5: The model phase distribution, the reconstructed phase, and corresponding distribution interferogram for angle \(\alpha=6\pi\).
## 3 Results and Analysis
### Statistical Analysis of Results
#### 3.3.2 C80 and C50  
Table 6 shows the correlations between the measurement and the simulations with ODEON and CATT-Acoustics of energy parameters C80 and C50. In the case of C80, a high  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Measurement/** & **Measurement/** & **ODEON/** \\  & **ODEON** & **CATT-Acoustic** & **CATT-Acoustic** \\ \hline T30 & 0.534 & 0.576 & 0.870 \\ EDT & 0.544 & 0.258 & 0.519 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Correlations between measurement and simulations for T30 and EDT.  
Figure 10: (**a**) Dispersion of T30 in measurement at point 1. (**b**) Dispersion of T30 in measurement at point 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Cronbach’s Alpha** & **Intraclass Correlation Index** \\  & & **(Single Measures)** \\ \hline T30 & 0.841 & 0.638 \\ EDT & 0.645 & 0.377 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Reliability statistics and interclass correlation for T30 and EDT.  
correlation is observed between the measurements and the simulation with ODEON (0.728) and between both simulations (0.777). However, the correlation between measurement and simulation with CATT-Acoustic is moderate (0.558). For the C50 parameter, a moderate correlation is observed both between the measurements and the simulations (ODEON 0.643 and CATT-Acoustic 0.524), and between both simulations (0.683).  
Figure 11 shows the variation by frequencies of C80 and C50 as a function of the location of the source for the measurement point 1. In this case, both measurements and simulations present a lower dispersion compared to the results obtained for T30 and EDT.  
Table 7 shows the reliability statistic and the interclass correlation for C80 and C50. The internal consistency in both cases is good (\(0.8<\alpha\leq 0.9\)). On the other hand, C80 presents an interclass correlation coefficient with a substantial degree of agreement (\(0.6<ICC\leq 0.7\)), while the degree of agreement for C50 is moderate (\(0.4<ICC\leq 0.6\)).
# Clustering-Based Modified Ant Colony Optimizer for Internet of Vehicles (CACOIOV)
###### Abstract  
The Internet of Vehicles (IoV) has recently become an emerging promising field of research due to the increasing number of vehicles each day. IoV is vehicle communications, which is also a part of the Internet of Things (IoT). Continuous topological changes of vehicular communications are a significant issue in IoV that can affect the change in network scalability, and the shortest routing path. Therefore, organizing efficient and reliable intercommunication routes between vehicular nodes, based on conditions of traffic density is an increasingly challenging issue. For such issues, clustering is one of the solutions, among other routing protocols, such as geocast, topology, and position-based routing. This paper focuses mainly on the scalability and the stability of the topology of IoV. In this study, a novel intelligent system-based algorithm is proposed (CACOIOV), which stabilizes topology by using a metaheuristic clustering algorithm based on the enhancement of Ant Colony Optimization (ACO) in two distinct stages for packet route optimization. Another algorithm, called mobility Dynamic Aware Transmission Range on Local traffic Density (DA-TRLD), is employed together with CACOIOV for the adaptation of transmission range regarding of density in local traffic. The results presented through NS-2 simulations show that the new protocol is superior to both Ad hoc On-demand Distance Vector (AODV) routing and (ACO) protocols based on evaluating routing performance in terms of throughput, packet delivery, and drop ratio, cluster numbers, and average end-to-end delay.  
ant colony optimization; clustering; 5G wireless networks; routing; internet of vehicles; vehicular ad hoc networks; particle swarm optimization +
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