esa-sceva/satcom-chunk-collection huggingface dataset:
# Hypersentized Metamaterials Based on a Corona-Shaped Resonator for Efficient Detection of Glucose
###### Abstract
In order to increase the sensitivity, researchers usually consider different approaches to design the sensing resonators. Based on the hypothesis that the symmetrical circular shape of a corona architecture might be helpful in generating a homogenous electromagnetic wave distribution across the resonator components, we expect that any trivial interaction of the external field with the dielectric behavior of the investigated sample would result in an interesting observation in the transmission response of the sensor. Therefore, in this study, we designed and fabricated a resonator based on a corona shape. The resonator dimensions were tuned by using a genetic algorithm embedded in the Computer Simulation Technology (CST) Microwave Studio program in order to optimize the whole design of the sensor. This work gained motivation from the fact that a highly sensitive glucose biosensor can be developed using a proper resonator design with a high Q factor [34]. It has been proved that when glucose concentration is changed, the effective permittivity (\(\epsilon_{\text{eff}}\)) of the biosensor is ultimately modified, which in turn leads to the shift in resonance frequency or amplitude [35; 36; 37; 38; 39; 40; 41]. In the current work, the dielectric constant parameters were measured by an 85070E open-ended dielectric probe kit. The obtained parameters were simulated by the CST Microwave Studio program. The simulation and experimental studies showed good agreement. The findings of this study confirmed that glucose content in water or in blood can be efficiently sensed by the proposed metamaterial-inspired corona-shaped sensor.
## 5 Experimental Results and Discussion
### Characterization of Individual SEBs
This example illustrates the major difficulty facing \"gate reflectometry\" where changes in the admittance of the device are measured through the gate port: it yielded very small SNR, which stemmed from extreme impedance mismatch. The reduction of \(C_{pud}\) would have greatly improved the sensitivity, yet in the experimental setup it is very difficult to reduce it significantly below a fraction of a picofarad using bonding pads to wire the devices. The use of a single bond pad for connection of all the sources for an entire array with \(N>>1\) devices significantly alleviates this problem, as will be shown below. Figure 7: **(a)** MN used for probing single SEB. The values for \(R_{DC}\) = 0.02 \(\Omega\) and \(R_{SE}\) = 0.35 \(\Omega\) at 199 MHz are adjusted for low temperature. The series resistance of parasitic parallel capacitor (57.5 \(\Omega\)) is tuned to obtain the best fit. The value of parasitic parallel capacitor (260 ff) used in simulations is equal to its room temperature value, while \(R_{\rm C}\) (57.5 \(\Omega\)) is an adjustable parameter. **(b)** Comparison between the simulated and measured response of the network shown in Figure 7a; upper panel—magnitude and lower panel—phase of reflection. Dashed line—simulations; solid line—experiment. Figure 8: Experimentally obtained reflectometry data (blue lines) along with simulations (black lines) for magnitude (**a**) and phase (**b**) of the RF signal reflected from SEB. Base temperature of the fridge is 25 mK. Both curves are obtained by averaging of 3049 scans; effective bandwidth is 2.7 kHz. Calculated oscillations of admittance magnitude (**c**) and phase (**d**) using Formula (2) from Section 2. Parameters used in simulations: \(C_{g}=2.88\) aF, \(C_{J}=65\) aF, \(R_{J}=45\) k\(\Omega\), \(T=50\) mK. The calculated phase response is offset by a constant ( \(-40\) degrees) for easy comparison.
## 2 Materials and Methods
### Real-Time Implementation and Synchronization  
It is important to note that the objective function for MPC is expressed in distance, while in the supervisory controller, it is in time. Careful choice of horizon length, prediction update rate (\(t_{c}\)), and EF update rate (\(t_{s}\)) is crucial for real-time implementation. A long horizon window is not practical due to the substantial computational load. Additionally, longer-term predictions are more uncertain due to uncertain future traffic. It was found that the nonlinear MPC with DP as solver can complete one optimization over 600 m in well under 10 s. Hence, the prediction horizon window is set to 600 m, and DP is recalculated every 10 s. For \(t_{s}\), an update rate that is too rapid will react to every minor change and may cause oscillations in the torque demand. On the other hand, updating too slow can cause  
Figure 4: Speed limit based on US06.  
the adaptation to become unstable and cause SOC to deviate far from the reference value. Based on this reasoning and studies found in the literature [28], \(t_{s}=30\) s is chosen. Figure 5 shows a timing diagram of the DP-ECMS energy management system. The horizon length is converted to time, \(t_{h}\). This time is not fixed and depends on the horizon length (600 m) and the speed of the vehicle. The horizon length was chosen to be long enough so that \(t_{c}\) will always be much shorter than \(t_{h}\) regardless of the ego vehicle speed. This ensures that the vehicle will never travel further than the horizon length before updating the prediction.
# Fabrication and Characterization of Modified Graphene Oxide/PAN Hybrid Nanofiber Membrane
## 2 Materials and Methods
### Synthesis of Graphene Oxide (GO) and Modified GO by PolydiallyIdimethylammonium (PDDA) (GP)
The pore diameters of nanofiber membranes were analyzed with a capillary porometer (Porolux 1000, IB-FT GmbH, Berlin, German) under wet and dry conditions using a Porewick standard solution, and the effective diameter of the nanofibers was fixed at 1.9 cm. The porosity of the samples (5 cm \(\times\) 5 cm) was examined by measuring the dry and wet weights of the nanofiber membranes after soaking in n-butanol for 1 h. The hydrophilicity of nanofiber membranes was confirmed with a contact angle analyzer (Phoenix 300, SEO Inc, Seoul, Korea) using a water droplet. The mechanical testing was performed using a universal tensometer (MYUNGJI TECH, TENSO, Seoul, Korea) with a crosshead speed of 500 mm/min and a sample size of 100 mm \(\times\) 30 mm. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample Name** & \begin{tabular}{c} **GP** \\ **(wt.\%)** \\ \end{tabular} & \
\begin{tabular}{c} **PAN** \\ **(wt.\%)** \\ \end{tabular} &
\begin{tabular}{c} **DMF** \\ **(wt.\%)** \\ \end{tabular} \\ \hline PAN & 0 & 10 & 90.0 \\ \hline GP01 PAN & 0.1 & 10 & 89.9 \\ \hline GP02 PAN & 0.2 & 10 & 89.8 \\ \hline GP03 PAN & 0.3 & 10 & 89.7 \\ \hline GP04 PAN & 0.4 & 10 & 89.6 \\ \hline GP10 PAN & 1.0 & 10 & 89.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Component ratio of polyacrylonitrile (PAN) and graphene oxide modified by polydiallyldimethylammonium chloride/polyacrylonitrile (GP/PAN) electrospinning solution. (DMF = N,N-dimethyl formamide).
# A Review of Recent Progress of Carbon Capture, Utilization, and Storage (CCUS) in China
## 5 Progress of CO2 Storage in China
### Development Status of CO2 Geological Storage in China
#### 5.1.1 CO2 Storage in Deep Saline Aquifers in China  
In China, the widespread distribution of trap structures in saline aquifers matches well with high-concentration CO2 emission sources, which can reduce transport and comprehensive costs. Meanwhile, deep saline aquifers in China have a huge storage potential, with a theoretical storage capacity of up to 2417 billion tonnes [50]. Therefore, saline aquifers are currently the most widely used CO2 storage method in China. China's current technology and equipment required for storing CO2 in saline aquifers can completely meet the demands of practical applications.  
Saline aquifers located in the Ordos Basin, Bohai Bay Basin, Qaidam Basin, Tarim Basin, Junggar Basin and Songliao Basin are considered to be the most ideal areas for storing CO2 due to their excellent storage capacities and geological conditions [50]. The Ordos Basin was selected as the site for the first demonstration project of full-process CO2 capture and deep saline aquifer storage in China. The CO2 injection experiment was implemented in May 2011, and the injection was stopped in 2015. The target of 30 \(\times\) 10\({}^{4}\) tonnes of CO2 injection was completed, and then the monitoring period has begun. The CO2 source of this demonstration project came from the tail gas of the conversion unit of the coal-to-hydrogen plant, and the CO2 with a purity of 88.8% was purified to over 99.99% through gas-liquid separation, oil removal, desulfurization, purification, and distillation processes. Then the CO2 was transported to the storage area by cryogenic tank truck, firstly into the buffer tank, and later injected into the ground after pressurization and heating. Monitoring data such as buffer tanks, injection wells, monitoring wells, pressure, and temperature could be transmitted to monitoring staff in real-time [172]. Monitoring data for the past nine years show that the groundwater quality, pressure, temperature, land subsidence, surface CO2 concentration, and other indicators in the storage area have not changed significantly, and no CO2 leakage has been monitored using tracer technology [173]. Another demonstration project of full-process CO2 capture and deep saline aquifer storage, operated by CHN ENERGY, began construction in 2019 and is currently in progress. It will be utilized for CO2 storage in saline aquifers after completion with an estimated annual CO2 storage capacity of 15 \(\times\) 10\({}^{4}\) tonnes [174]. China's CO2 storage projects in deep saline aquifers are listed in Table 9.
## 5 Experimental Results
### Security Analysis
#### 5.6.4 Integrity  
The protocol maintains the integrity of the derived shared key through consistent modular exponentiation. However, to safeguard the integrity of transmitted data, it is recommended to incorporate an MAC derived from the shared key (\(SK\)). This additional layer of protection ensures that any tampering with encrypted messages can be detected and mitigated, enhancing overall communication security.
## 2 Theoretical Background
### Network Performance Statistics  
The cross-layered analysis framework is based on the IEEE 118-bus system, which uses TCP/IP protocols to imitate the Modbus RTU. This model, which resembles the Poisson traffic model [27], sends packets in groups of four every four seconds. Each bus represents the M/M/c queue [28], i.e., cc 1, in which packet arrival is Poisson and queue service time is exponential. The traffic intensity or utilization is represented by the following equation:  
\[P_{util}=\frac{\lambda}{\mu} \tag{6}\]  
The packets' arrival rate is denoted by \(\lambda\), while the packets' service time is represented by \(\mu\). The time difference (\(\Delta t\)) between packet arrivals is known as the inter-arrival time (\(IAT\)). With parameter\(\lambda\), it has an exponential distribution. For \(t\geq\)0, the probability density function is defined as follows:  
\[f(t)=\lambda e^{\lambda t}. \tag{7}\]  
The average \(IAT\) is defined as  
\[IAT=\frac{1}{\lambda} \tag{8}\]  
The service time follows an exponential distribution with parameter \(\mu\). The probability density function is as follows:  
\[g(s)=\mu e^{-\mu s},\forall\geq 0 \tag{9}\]  
where \(\frac{1}{\mu}\) is the average service time of the system. Utilizing [PERSON]'s theorem, the total waiting time is defined as transmission delays (\(TD\)), and represented as the following:  
\[W=TD=\frac{1}{\mu-\lambda} \tag{10}\]  
The normal distribution of network packet arrivals (i.e., non-attacked packets) into each system was decided by the probability of witnessing a number of packet arrivals in a period from [0, T]. This equation is used to model the traffic volume of the bus:  
\[P(n\ arrivals\ in\ interval\ T)=\frac{(\lambda T)^{n}e^{-\lambda T}}{n!} \tag{11}\]  
whereas \(T\) is the \(IAT\), and the \(n\) represents the number of packets. The packet count (PC) is modeled as the following:  
\[PC=\lambda T \tag{12}\]
## 2 Materials and Methods
### Silver Nanoparticle Characterization  
#### 2.4.1 UV-Vis Spectrophotometry  
In a quartz cuvette, AgNP were diluted in ultrapure water at a 1:43 ratio. The wavelength of the absorption spectrum was set between 380 and 600 nm in a UV-Vis spectrophotometer (Shimadzu UV-160, Tokyo, Japan).
###### Abstract  
The relationship between the average annual collapse probability and collapse safety margin of structures is identified to evaluate structural collapse performance quantitatively. A method is then proposed to determine the acceptable collapse margin ratio (CMR) with a certain annual collapse probability. Two methods, namely adopting steel braces and enlarging column cross sections, are used to retrofit a four-story, low-ductility reinforced concrete (RC) frame structure. On the basis of the acceptable CMR, the seismic collapse resistance of the structure is assessed before and after strengthening. Furthermore, a four-story RC frame structure, which is designed in conformity to the minimum design criteria of the building code, is constructed. The incremental dynamic analysis method is used in consideration of collapse uncertainties. Results show that when the acceptable annual collapse probability is equal to \(1.24\times 10^{-4}\), which is calculated using the collapse probability at maximum considered earthquake (5%, as proposed in CECS 392), the collapse safety margin of the four structures does not satisfy the seismic collapse resistance requirements with large collapse uncertainty. The structures that are retrofitted and designed in conformity to the code can satisfy the collapse safety margin requirements when the acceptable annual collapse probability is increased to \(2\times 10^{-4}\). The comparison of the two retrofitting schemes used to improve the seismic collapse resistance of the structure indicates that the steel brace-retrofitting method is better than increasing the column section. This work is an important reference for the reinforcement of the seismic resistance of structures and for corresponding research on collapse resistance.  
collapse safety margin; annual collapse probability; acceptable value; collapse resistance performance; seismic resistance reinforcement +
Footnote †: journal: Applied Sciences
## 5 Conclusions  
In this paper, we conducted a detailed evaluation of the propagation characteristics of low-altitude A2G communication using UAVs. We clarified the effect of differences in the environment and the directivity characteristics of the antenna on the propagation characteristics. We conducted radio wave propagation measurements in a simple propagation environment (S environment) and a complex propagation environment (C environment). The following results were obtained, which will contribute to the establishment of optimal design guidelines for A2G communications.  
The Rician K-factor was high in the S environment, reflecting the high stability of communication. In the C environment, the effects of multiple reflections and scattering were large, decreasing the Rician K-factor. This clearly showed the effect of the complexity of the environment on the propagation characteristics. In addition, we were able to specifically understand the impact of distance from the building and the UAV altitude on communication performance, highlighting the importance of this research.  
By comparing horizontal and vertical installation of the antennas, we were able to accurately capture the changes in propagation characteristics due to the environmental conditions of radio wave propagation. Especially in the C environment, the communication performance was very stable with a horizontal installation, so it is important to pay attention to the antenna directivity. This finding will serve as a guideline for selecting the optimal polarization conditions for each environment in future communication system designs.  
The results of this research can not only improve the performance of A2G communications using UAVs but also provide an important foundation for a variety of practical applications, such as ensuring communication during disasters and building effective networks in urban areas. However, this research was limited to evaluations in the 2.5 GHz, and an analysis of other frequency bands, more complex urban environments, and the dynamic characteristics of moving UAVs remain future challenges. It is expected that overcoming these challenges will further improve the versatility of UAV-based communication systems.  
Conceptualization, K.A. and K.H.; methodology, K.A. and K.H.; software, K.A.; validation, K.A. and K.H.; writing--original draft preparation, K.A.; writing--review and editing, K.H. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. The authors declare no conflicts of interest.
# A Pilot-Based Integration Method of Ranging and LS Channel Estimation for OFDM Systems
## 3 Ranging  
The traditional OFDM system will discard these pilots after the receiver utilizes them for channel estimation and equalization and then decode and demodulate the data. However, since these pilots are replaced by ranging codes in this paper, the role of pilots has been expanded in this paper. In addition to the channel estimation and equalization, these pilots can be utilized for ranging. Thus, the receiver will acquire and track these pilots to range the transmission distance of the signal. In this way, the problem of the low spectral efficiency of traditional LS channel estimation is solved.
## 3 Satellite-to-Ground QKD Simulator
### Atmospheric Link Modeling
#### 3.2.6 Fiber Coupling Efficiency
The estimated coupling efficiency can then be determined by  
\[\eta_{FC}=\eta_{sys}\eta_{0}SR. \tag{15}\]  
At this point, it is important to note that multi-mode fiber (MMF) coupling could be employed instead of SMF coupling to enhance the coupling efficiency. However, in this study, we focused on SMF coupling as it is more widely used in long-distance telecom photon distribution due to its lower degree of loss. A comparison between MMF and SMF coupling could be valuable, but the model is fundamentally different for a MMF and is beyond the scope of the current work. Therefore, we have not included MMF coupling in our analysis.
## 3 Experiment and Application
### Engineering Application: Multi-UAVs Cooperative Path Planning
#### Path Cost Function
Moreover, \(h_{j}\) is the flying altitude of the current UAV, \(d_{T}\) refers to the distance from the UAV to the symmetrical axis of the peak, \(d_{T_{min}}\) denotes the minimum distance allowed on the terrain, and \(\theta\) refers to the slope of the terrain. The terrain threat is depicted in Figure 6.  
Figure 6: Terrain threat map. Cooperative cost function implies time cooperation. All UAVs are required to reach the target point simultaneously as far as possible. If the course of a certain path cannot satisfy the time cooperative constraints, then the path must be corrected. Assuming that the flying speed of the UAV is in the range of \([v_{min},v_{max}]\) and the course of the UAV \(i\) is \(L_{i}\), its flight time period is \(T_{i}\in\left[T_{min}^{i},T_{max}^{i}\right]\). Similarly, assuming that the flying time of UAV \(j\) is in the range of \(T_{j}\in\left[T_{min}^{j},T_{max}^{j}\right]\), if the flight time of the two UAVs intersects, then temporal cooperation is feasible, that is  
\[T_{inter}=T_{i}\cap T_{j}\
eq\varnothing, \tag{22}\]  
in accordance with the temporal cooperation evaluation formula between paths in Reference [74], the temporal cooperation cost function is obtained on the basis of the planning model in the present work:  
\[J_{coopt}=\left\{ \begin{array}{cc}&1\quad T_{inter=\varnothing}\\ &2\quad 0<T_{inter}<rand\times T_{min}\\ &\frac{3T_{inter}}{T_{min}}\quad T_{inter}>\frac{1}{3}\;T_{min}\end{array} \right. \tag{23}\]  
where \(T_{min}\) refers to a time period with a small range of a certain path in the flight time period, and \(T_{inter}\) represents the intersection of the flight time for two paths.
## 7 Numerical Results  
In the present section, several applications are presented to show the validity of both the present theoretical approach and the numerical methods for the evaluation of the free vibrations of various laminated composite shells. For this purpose, a preliminary convergence analysis is performed for two different mechanical configurations. Then, six shell structures are investigated. The results are compared with the natural frequencies available in the literature and the solutions obtained by means of a Finite Element model (FEM). The GDQ analysis is performed through a MATLAB code [106], whereas the commercial software Strand 7 is employed to obtain the FEM solution. It should be noticed that a specific tool has been developed to export the geometry of the considered doubly-curved shells into the commercial finite element code, as specified in the work [98]. For the sake of completeness, it should be specified that a different FEM model is used according to the investigated structure. For thick plates and shells, a three-dimensional model made of 20-node hexahedrons (\"Hexa20\") is employed. On the other hand, a two-dimensional model made of 9-node quadrilateral elements (\"Quad9\") is used for thin structures. The notations 3D-FEM and 2D-FEM are introduced to denote these solutions, respectively.
# Patch-Wise-Based Diffusion Model with Uncertainty Guidance for Low-Light Image Enhancement
## 1 Introduction
While [PERSON]'s approach incorporates global structure-aware regularization, its reliance on clustering-based methods for extracting matrix blocks from low-light images can be problematic, particularly in cases where images are extremely dark, resulting in failed task execution. This paper proposes a novel patch-wise-based diffusion model for high-quality low-light image enhancement. The model leverages patch selection to capture the global structure and introduces uncertainty in the patch area to achieve stable optimization reverse in the diffusion training process. To achieve a stable optimization path, we employ a random approach to select non-overlapping patches of varying sizes from the entire image. Within each patch, we compute the matrix rand between the generated image and the ground truth as part of the loss function, thereby enforcing a stable optimization path in the diffusion model during training. Furthermore, we utilize uncertainty weights within each patch to guide the generation of noise, focusing on more challenging regions and strengthening the diffusion model training process. Experimental results on various datasets demonstrate the effectiveness of our method in the LLIE task. We summarize our main contributions as follows:  
* Development of the first random patch-wise strategy to achieve stable parameter optimization reverses in the diffusion model training process. * Novel uncertain guidance in the patch-wise level to enhance the constraint of the challenge regions. * Experiments on popular benchmarks show that our method achieves competitive results, and our method can generalize well to the real environment.