esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Underwater Routing Protocols
### Mc  
The protocol in [33] achieves the maximum coverage (MC) of the network in terms of data gathering from the sensor nodes using two mobile sinks. The mobility of the sinks in a circular fashion ensures the consumption of the energy of nodes in a balanced manner and also reduces packet loss. However, rather than moving to the targeted locations, where nodes have data to send, the sinks follow the predefined circular paths. This leads to dropping out of some packets and increases the delay, especially in sparse conditions.  
The difference between MC and SBR-DLP is that the former uses two mobile sink nodes while the latter uses only one. This allows greater coverage of the network in the former than the later. In addition, the sink nodes in MC follow circular trajectories to cover the network, while the movement of the sink (destination) node is random in SBR-DLP. This eliminates the need for sensing a specific underwater zone.
## 2 Virtualization in Wireless Sensor Networks
### Operating Systems
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Platform** & **Contiki** & **TinyOS** & **MANTIS** & **OpenWSN** & **LiteOS** \\ \hline
**Real-Time** & No & No & No & No & No \\ \hline
**Hardware** & ESB, TelosB, Tmote & MICA (z)(2), TelosB, & MICA(2)(z), Telos, & & & \\
**Platforms** & Sky & Iris, Shimmer & MANTIS nymph & & & \\ \hline
**Virtualization** & Serial Execution & Yes & Semaphores & Yes & Synchronization primitives \\ \hline
**Static or Dynamic** & Dynamic & Static & Dynamic & Dynamic & Dynamic \\ \hline
**Network Support** & uIP, uIP6, Rime & Active message & Comm &
\begin{tabular}{c} 6 LoWPAN, RPL, \\ CoAP \\ \end{tabular} & Message-based \\ \hline
**Simulation** & Cooja. MSP-Sim, & \
\begin{tabular}{c} TOSSIM, Viptos, \\ NetSim \\ \end{tabular} & XMOS &
\begin{tabular}{c} OpenVisualizer, \\ OpenSim \\ \end{tabular} & AVRORA \\ \hline
**OTA** & Yes & Yes & No & Yes & Yes \\ \hline
**Latest build** & 2.2.1 & 2.0 & 1.0 Beta & 1.8.02 & 1.0 \\ \hline
**Multi-threads** & Yes & Tiny-threads & Yes & Yes & Yes \\ \hline
**Release date** & 2004 & 2000 & 2005 & 2011 & 2008 \\ \hline
**Concurrent** & Yes & Yes & Yes & Yes & No \\ \hline
**References** & [52; 53; 54] & [31; 55; 56] & [36; 57; 58] & [32; 59] & [37] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A comparison of some of the operating systems of interest.
# The Influencing Factors of CO2 Utilization and Storage Efficiency in Gas Reservoir
###### Abstract  
Carbon Capture, Utilization and Storage (CCUS) technology is one of the most practical means to meet zero greenhouse gas emission goal of the Paris Agreement and to ensure profitability, which could achieve permanent sequestration of CO2. Due to the cost constraints of CCUS implementation, improving recovery and maximizing storage efficiency have become a critical part of ensuring economic efficiency. This research aims to analyze the effects of key factors on enhancing gas recovery and storage efficiency, combined with the validation of CO2 displacement and storage mechanisms. Therefore, long core experiments and different dimensional simulations were established based on R gas reservoir (one of the actual gas reservoirs in Northeast China), which were designed for sensitivity analyses of different influencing parameters and quantitative analyses of different storage mechanisms during CCUS process. When the conditions (temperature and pressure) were closer to the CO2 critical point, when the following parameters (the CO2 purity, the injection rate and the dip angle) became larger, when the reservoir rhythm was reversed and when the irreducible water was is in existence, the final displacement and storage effects became better because of weaker diffusion, stronger gravity segregation and slower CO2 breakthrough. The contributions of different storage mechanisms were quantified: 83.78% CO2 existed as supercritical fluid; 12.67% CO2 was dissolved in brine; and 3.85% CO2 reacted with minerals. Some supercritical and dissolved CO2 would slowly transform to solid precipitation over time. This work could provide theoretical supports for CCUS technology research and references for CCUS field application. At the same time, countries should further improve CCUS subsidy policies and make concerted efforts to promote the globalization and commercialization of CO2 transport.  
CO2 utilization; CO2 storage; long-core experiment; numerical simulation; gas recovery 2023
# Farm Monitoring System with Drones and Optical Camera Communication
## 5 Experimental Results
### Results  
#### 5.2.1 Model Confirmation  
First, we confirmed the mathematical model based on the coordination transformation. We measured the ground coverage of the drone-mounted camera in the real environment. Then, we compared the measured results with the theoretical model formulated in (5). Table 3 summarizes the difference of the measured values from the theoretical values. From this result, we confirmed the feasibility of the mathematical model.
# Panchromatic and Multispectral Image Fusion Combining GHS, NSST, and PCA
## 2 Methods
### High-Frequency Fusion Rules  
After NSST decomposition, each source image can be obtained as a series of high-frequency sub-band images. The high-frequency coefficients at different scales of NSST provide rich edge and texture information for the source images. The absolute values of the coefficients are larger when the edge and texture features are more pronounced [18]. Therefore, a considerable absolute value is usually used as the high-frequency coefficient selection rule. However, this rule ignores the correlation between neighboring pixels and may introduce noise into the fused image. The local spatial frequency (LSF) can reflect the pixel neighborhood activity index: the larger the _LSF_ value, the more active the pixel points in the local region. Therefore, _LSF_ is used to fuse high-frequency coefficients. The equation of _LSF_ is as follows:  
\[LSF(x,y)=\sqrt{LRF^{2}(x,y)+LCF^{2}(x,y)}. \tag{6}\]  
where _LRF_ and _LCF_ denote the image's local row frequency and column frequency, respectively, with the following equations:  
\[LRF(x,y)=\sqrt{\frac{1}{(2M+1)(2N+1)}\sum_{m=-M}^{M}\sum_{n=-N}^{N}\Big{[}H_{ j,l}(x+m,y+m)-H_{j,l}(x+m,y+m-1)\Big{]}^{2}}, \tag{7}\]  
\[LCF(x,y)=\sqrt{\frac{1}{(2M+1)(2N+1)}\sum_{m=-M}^{M}\sum_{n=-N}^{N}\Big{[}H_{ j,l}(x+m,y+m)-H_{j,l}(x+m-1,y+m)\Big{]}^{2}}, \tag{8}\]  
where \(M\) and \(N\) represent the neighborhood size. Then, the SF-based HF coefficient selection rule can be written as:  
\[H_{j,l}^{F}(x,y)=\left\{\begin{array}{cc}H_{j,l}^{PC}(x,y)&LSF_{j,l}^{PC}(x, y)\geq&LSF_{j,l}^{PAN}(x,y)\\ H_{j,l}^{PAN}(x,y)&LSF_{j,l}^{PC}(x,y)<LSF_{j,l}^{PAN}(x,y)\end{array}\right.. \tag{9}\]
# Portable L-Band Radiometer (PoLRa): Design and Characterization
## 5 Conclusions  
We have introduced a small, low-mass, and cost-effective L-band radiometer design, and provided characterization results to demonstrate its performance. L-band, with the lowest-frequency passive-protected band, from 1400-1427 MHz, provides penetration into natural media, such as soil and vegetation.  
By mounting a portable low-mass radiometer on a multi-copter drone, pixel size of ~6 m or less is achievable. The PoLRa is also convenient as a ground-based radiometer for satellite validation networks, or any brightness temperature time-series measurement, and can be mounted on a simple automatic weather station type of infrastructure. This paper introduced the hardware design, calibration, characterization, and uncertainty analysis of the radiometer. The drone-based demonstration and results are reserved for a following publication.  
We presented the block diagram of the direct-detect total power radiometer and the measured front-end filter response of the system. The radiometer is estimated to have a system noise temperature of \(T_{\text{sys}}~{}=~{}627\) K, based on the cascaded noise figure of the front-end and first LNA. The unique air-gapped patch antenna array design was shown, and the simulated and measured return loss and gain pattern presented. The antenna has a half-power full beamwidth of 37\({}^{\circ}\), and is nearly symmetric with azimuth angle, resulting in a circular nadir-viewing pixel.  
Section 3 presented the characterization of the active cold source (ACS) reference, the noise equivalent delta (\(\Delta\)) temperature (NEAT), and the total radiometric uncertainty. The ACS was characterized along with the cable and antenna loss factor to a noise temperature root mean square error (RMSE) of 0.66 K. The experimentally determined NEAT for an integration time of \(\tau\cong 1\) s is 0.14 K, which is in close agreement with the theoretical value of 0.12 K, determined from the system noise temperature, integration time, and bandwidth. An integration time of 1 s actually takes about 4.4 s total, due to calibration views and views of the two polarizations. An integration time more realistic for future drone-based operation is about 100 ms, corresponding to a total measurement time of 480 ms and NEAT of 0.4 K.  
The total uncertainty of the radiometer is the combination of systematic and statistical uncertainty contributions. The systematic uncertainty is determined from propagation of the calibration reference uncertainties, whereas the statistical uncertainty is equivalent to the NEAT, and is a function of the integration time. The total uncertainty is found to range between 0.6 K and 1.4 K over a range of expected natural brightness temperatures from 50 K to 350 K. This value is less than the radiometric uncertainty of the ESA SMOS satellite, and comparable to that of NASA's SMAP instrument.
# Intelligent Traffic Model for Unmanned Ground Vehicles Based on DSDV-AODV Protocol
## 2 Method
### Proposed UGVs  
Unmanned Ground Vehicles (UGVs) are becoming increasingly popular in the field of Vehicular Ad Hoc Networks (VANETs). UGVs are able to navigate autonomously through their environment, making them ideal for providing efficient, low-cost solutions to urban traffic problems. UGVs can be used to monitor and control traffic flow, detect objects in the environment, and provide real-time navigation guidance to drivers. Additionally, UGVs can be used to aid in the implementation of smart traffic lights. Smart traffic lights can be used to reduce traffic congestion, prioritize emergency vehicles, and improve overall safety. By using UGVs to monitor the traffic flow, smart traffic lights can adjust the signals accordingly. This can help reduce the amount of time a driver spends at a red light, minimize travel time, and improve overall efficiency. UGVs are also being used to detect and identify obstacles in the environment, such as pedestrians, cyclists, and other vehicles. This can help improve the safety of drivers by providing early warnings of potential hazards. UGVs can also be used to monitor traffic conditions and provide real-time navigation guidance. This can help reduce the amount of time drivers spend stuck in traffic, as well as reduce the number of emissions.
# Entity-Alignment Interaction Model Based on Chinese RoBERTa
## 2 Related Works  
Entity alignment represents a crucial undertaking within the domain of knowledge graphs. Its objective is to establish correspondences and alignments between entities with analogous semantics across disparate knowledge graphs, thereby facilitating the integration and consolidation of knowledge across diverse data sources. In the related work of entity alignment, researchers mainly improve the alignment effect by utilizing graph-structure information and additional information associated with entities.
## 3 Results and Discussion
### Model Development
#### 3.1.2 Machine Learning Models
This is because of the over-fitting phenomenon during the training of ANNs when the number of hidden nodes is relatively too high. Therefore, we can define that the MLFN-2 is the most suitable model for the prediction using the data provided by [PERSON] _et al._[25]. Table 4 and Figure 4d show that the SVM has the lowest RMSE (15.79), shortest training time (2 s), and highest prediction accuracy (99.2%). However, it is significant that the best predicted result presented in Table 4 and Figure 4d has a higher RMSE than those in Tables 1-3. A possible explanation of this phenomenon is that experimental details in different experiments may generate different deviations when acquiring the compressed liquid density of R227 ea because the three groups of data come from three different research groups in different years [1; 24; 25]. Therefore, the combination of three groups of experimental data may generate additional noise, leading to deviations in training processes and, hence, the tested results have higher RMSEs. However, it should be noted that although the results of the best model here have higher RMSE than those in Tables 1-3 these testing results are still acceptable and it is also far more precise than the RMSEs generated by the theoretical equation of state.
## 3 Results and Discussions  
### Bird Strike Force at Different Rotational Speeds  
The bird strike simulation was performed in LS-DYNA to obtain the impact forces transmitted to the rotor system. The bird state at different times during the impact process is shown in Figure 10, and it shows that the bird body SPH model has been cut into particles after the strike.  
The bird strike process was simulated at four rotational speeds, and the contact forces in the x and y directions (in coordinate system depicted in Figure 7) are extracted and shown in Figure 11.  
Figure 11: The bird impact forces at four rotational speeds: (**a**) in the X direction, and (**b**) in the Y direction.  
Figure 9: The vibration response of normal rotor system at 1500 r/min: (**a**) test rig (**b**) numerical calculation.  
Figure 11 shows that the peaks of the magnitude of the bird impact forces in both the x and y directions increase with the rotational speed of the rotor. Moreover, the force in the x direction is significantly larger than that in the y direction, which is caused by the geometry of the fan blade.  
The curve at 3772 r/min includes several peaks, which is due to the splitting of the bird body into multiple pieces by the fan blades during the impact process. The magnitude of each peak is highly dependent on the size of the colliding bird body. The number of peaks varies at different speeds, which is due to the fact that the number of blades cutting the bird body varies with the fan rotational speed. The faster the fan rotates, the more blocks the bird body is divided into and, as a result, the more peaks appear. Additionally, is it notable that the direction of the force changes in some curves, especially in the y direction at the speed of 1984 r/min. This is as the strike process between the bird body and the blade can be divided into two parts. As the fan is rotating, the bird hits the blade, which results in the blade slapping the bird body. As can be seen in Figure 10, after the bird was cut by the fan, the blades may hit the bird body, thus causing a change in the direction of the force in Figure 8.  
After obtaining the curve of variation of \(Bx\) and \(By\) with time, the data were substituted into the dynamic equation by linear interpolation method to calculate the rotor dynamic responses.
# UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey
## 4 Anti-UAV System
It emphasizes the importance of creating scalable anti-UAV systems with a platform at the core. Such a design enables easier integration and maintenance of sensors in the future. By adopting a scalable approach, anti-UAV systems can adapt to evolving threats and technological advancements, ensuring flexibility and efficiency in the long run. Fourth, data analytics and machine learning techniques can be employed to enhance the accuracy and efficiency of UAV detection and tracking. Indeed, machine learning techniques have already found extensive application in the field of UAV detection. The continuous advancements in machine learning algorithms and the availability of more comprehensive datasets are key factors in enhancing UAV detection capabilities. By leveraging more efficient learning methods and utilizing diverse and representative datasets, the accuracy and effectiveness of UAV detection systems can be significantly improved. This ongoing development in machine learning holds promise for further advancements in UAV detection technology. Fifth, although accuracy is an important evaluation indicator, lightweight anti-UAV systems that sacrifice part of the accuracy seem to be more in demand in some non-important scenarios. In mobile scenarios or situations where budget constraints exist, lightweight anti-UAV systems that can operate on portable devices offer a more suitable solution. These systems, designed to be lightweight and portable, can be easily deployed and utilized in various environments. They provide flexibility and cost-effectiveness, making them a practical choice for scenarios where mobility and budget considerations are important factors. By leveraging lightweight anti-UAV systems, organizations can enhance their capabilities for UAV detection and mitigation while maintaining operational efficiency. Furthermore, compatibility with existing sensors, such as ubiquitous video surveillance equipment, could significantly reduce the cost of anti-UAV systems. This suggestion of leveraging existing video surveillance equipment, inspired by Dedrone's products, is indeed valuable. By ensuring compatibility and utilization of the already deployed video surveillance infrastructure, the deployment costs of anti-UAV systems can be significantly reduced. This approach aligns with the concept of using edge computing to assist in UAV detection, as previously discussed. By calibrating the physical locations of these monitoring devices, it becomes possible to detect and track illegally intruding UAVs effectively. This application scenario showcases the potential for cost-effective and efficient UAV detection by leveraging existing resources and edge computing capabilities.
## 3 Evaluation Metrics
### Intersection over Union (IoU)  
IoU measures the overlap between predicted and ground-truth collision regions, normalized by their union. In binary classification settings, it reduces to comparing predicted collision frames with actual ones. IoU quantifies the accuracy of the model in predicting the location and presence of collision-critical events in space or time. It is particularly useful when spatial localization is required. This quantifies the spatial localization accuracy of collision-critical regions in frames.  
\[\text{IoU}=\frac{\text{TP}}{\text{TP}+\text{FP}+\text{FN}} \tag{5}\]
# Design and Fabrication of a MEMS Bandpass Filter with Different Center Frequency of 8.5-12 GHz
## 3 Fabrication and Measurement of the Filter
(7) The fabrication of the top electrode: We sputtered 2 \(\upmu\)m Al film as a top electrode. The photoresist AZ4620 was spin-coated on the Al film at a speed of 4000 rad/min. A photolithography plate was used as a mask for exposure and development. After that, O\({}_{2}\) plasma was applied to the base film for 5 min. Then, a 30% phosphoric acid water bath was used to heat it to 50 \({}^{\circ}\)C and corrode it for 12 to 15 min. Finally, the water was flushed with acetone to remove the photoresist in it. (8) Sacrifice layer release: Finally, the sacrificial layer was released in the atmosphere of oxygen plasma to complete the whole fabrication process. The sample was added to the test system, and the transmission status (S11, S21), test frequency, and appropriate IF bandwidth were set. For more accurate measurements, the instrument was fully port calibrated by using a calibration tool (the machine model was T40A-GSG500 from MJC Probe Incorporation (MPI) company, and it was calibrated with an AC5 calibration sheet). According to the filter requirements, the maximum resonance point of the filter was selected. Accordingly, the return loss and the insertion loss at the frequency point could be seen on the screen. Comparing the test results with the simulation results, as shown in Figure 6b, we found that the test results were basically consistent with the simulation results, and they exhibited better stopband rejection than the simulation results in the frequency band above the center frequency. The specific results are compared in Table 3 and Figure 9.  
Table 4 provides a comparison of the results between other filters and the filter designed in this study. Compared with the current bandpass filters, the filter based on MEMS switches has the advantages of a low insertion loss, a wide range of switching frequency bands, and a high stopband rejection \begin{table}
\begin{tabular}{c c c c c c} \hline  & \begin{tabular}{c} **Center** \\ **Frequency** \\ **(GHz)** \\ \end{tabular} & \begin{tabular}{c} **Insertion** \\ **Loss(dB)** \\ \end{tabular} & \begin{tabular}{c} **Return Loss** \\ **(dB)** \\ \end{tabular} & \
\begin{tabular}{c} **Stopband** \\ **(dB)** \\ \end{tabular} &
\begin{tabular}{c} **Drive** \\ **Voltage** \\ **(V)** \\ \end{tabular} \\ \hline Simulation & 10.5 & \(-1.13\) & \(-21.28\) & \(-25.03\) & 6.48 \\ Measurement & 10.5 & \(-1.12\) & \(-21.3\) & \(-28.3\) & 5.80 \\ \hline \end{tabular}
\end{table} 
Table 3: Filter simulation results and test results.
## 2 Materials and Methods  
### Preparation of Activated Carbon from Plastic Waste  
The plastic waste used for this study came from the rejected fraction of a municipal solid waste facility. The composition of the plastics was determined as follows (wt. %): 55.0% polypropylene, 8.6% high-impact polystyrene, 10.1% expanded polystyrene, and 27.7% polyethylene film [44]. The material was crushed to a size smaller than 1 mm and subjected to pyrolysis in a horizontal tubular furnace (Nabertherm) under an N\({}_{2}\) flow of 50 L h\({}^{-1}\). The temperature increased at 10 \({}^{\circ}\)C/min from room temperature to a final pyrolysis temperature of 500 \({}^{\circ}\)C, with a holding time of 90 min. The resulting material was denoted as \"char\".  
The pyrolyzed char was chemically activated using Na\({}_{2}\)CO\({}_{3}\) (PanReac AppliChem, Barcelona, Spain). The char was ground and mixed with the activating agent at a 1:1 mass ratio. The mixture was heated in a tubular furnace under N\({}_{2}\) flow (12 L h\({}^{-1}\)) under the following program: heating from room temperature to 300 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min, held at 300 \({}^{\circ}\)C for 60 min, and then heated to 800 \({}^{\circ}\)C (10 \({}^{\circ}\)C/min) and held for 60 min. The amount of Na\({}_{2}\)CO\({}_{3}\) and the activation temperature were based on previous studies [10; 45]. After activation, the activated char was washed with 1 M HCl (PanReac AppliChem, Barcelona, Spain) solution (20 mL per 1 g of char), filtered, and rinsed with water until a neutral pH was achieved. The final activated material was dried at 120 \({}^{\circ}\)C overnight and labeled \"AC\". For comparison purposes, the non-activated \"char\" was also washed following the same procedure.  
For the citric acid (PanReac AppliChem, Barcelona, Spain) functionalization, the prepared \"AC\" was immersed in a citric acid solution ranging from 0.5 to 1.5 M, with a liquid-to-solid ratio of 25:4 (mL of solution per gram of activated carbon). The mixture was stirred continuously at room temperature for 30 min to ensure uniform contact between the citric acid and the activated carbon's surface. After impregnation, the mixture was filtered and washed several times with distilled water to remove the residual citric acid. Finally, the washed activated carbon was dehydrated at 50 \({}^{\circ}\)C for 24 h and re-dried at 100 \({}^{\circ}\)C to remove moisture before storage. The resulting materials were labeled \"AC-CA-X\", where X refersto the citric acid concentration during the impregnation step (0.5-1.5 M). For comparison purposes, the non-activated char was also modified with citric acid, leading to the material denoted as \"char-CA-X\". Figure 1 schematizes the activation of the char to prepare AC and the functionalization with citric acid, AC-CA-X