esa-sceva/satcom-chunk-collection huggingface dataset:
# Advances in Graphene Field Effect Transistors (FETs) for Annine Neurotransmitter Sensing
## 2 Problem Outline
### GFET Characterisation
#### 2.5.3 Sensitivity and Limit of Detection  
The performance of GFETs as biological transducers is frequently referred to as sensitivity [148; 149]. Formally, analytical sensitivity reflects the sensor's ability to discriminate between minor changes in analyte concentration [150]. Interestingly, this feature is rarely tested in bioanalytical GFET research; instead, the most often reported performance parameter is the limit of detection (LOD) [151; 152; 153], which represents the lowest concentration at which an analyte can be reliably identified by the sensor. Both measures, sensitivity and LOD, are sometimes misconstured but represent independent standards, as shown in Figure 4.  
Sensitivity is an important performance metric for quantitation applications, especially when it comes to determining analyte concentration with high precision. Sensitivity can be determined using the calibration curve of the sensor, which is the evolution of a specified electrical metric (for example, Dirac point voltage, current) as a function of analyte concentration. Sensitivity is commonly defined as the slope S of the electrical metric's linear  
Figure 4: A typical calibration curve: Region 1 shows concentrations below the minimum detectable limit (LOD) of the analyte. Region 2 highlights the usable range of the sensor. Region 3 demonstrates that when concentrations are too high, no more binding sites are available, and the sensor is termed saturated.  
variation in response to a change in analyte concentration. Analyte-sensor interactions in GFETs are frequently represented by a change in Dirac point voltage; however, the exact relationship with analyte concentration is complex and depends on the precise architecture of the functionalised graphene interface and its coupling to analyte and media. However, sensitivity can be objectively tested. This is straightforward when the Dirac point is directly measured as the metric, such as in before-and-after studies of the transfer curves. Noise limits a GFET's sensitivity [154]; the greater the variance in the signal, and hence in the calibration curve, the more difficult it is to differentiate between close analyte concentrations. Maximising material mobility, sensor active area, and capacitive coupling helps to reduce noise and improve sensitivity [155].  
The LOD is another key performance metric that specifies the lowest concentration of analyte detected by the sensor. The LOD is calculated as the concentration at which the signal exceeds the baseline by an interval of confidence. The LOD is important for both analyte detection and quantitation since it sets the lower bound of the sensor's dynamic range, with the upper end limited by signal saturation. In the development of GFET biosensors, the most frequently reported metric of performance is the LOD, which is routinely benchmarked against other detection approaches.
# An Assessment Scheme for Road Network Capacity under Demand Uncertainty
## 4 Numerical Experiments
### Critical OD Pair Identification with Uncertain Demand  
In this section, based on the sensitivity analysis of link flow to nominal demand proposed in Section 3.2, we obtain the results of the sensitivity analysis of demand perturbations on key links link 10-16 and link 16-10. The result is shown in Figure 12. We obtain the five O-D pairs which affect the traffic of the critical road section the most and call them the critical OD pairs of the traffic network. O-D (9,16), O-D (10,7), O-D (11,16), O-D (10,16), and O-D (11,18) are the five O-Ds most affected by the disturbance of travel demand on link 10-16. O-D (16,9), O-D (7,9), O-D (16,11), O-D (16,10), and O-D (18,11) are the five O-Ds most affected by the disturbance of travel demand on link 16-10.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **80\% Reliability of Network** & **Network Capacity with** \\  & **Capacity with Uncertain Demand** & **Nominal Demand** \\ \hline Before improvement & 68,505 & 73,202 \\ After improvement & 84,376 & 84,020 \\ Improved ratio & 23\% & 15\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The comparison of network capacity.  
Figure 11: The improvement in network capacity under critical link.  
According to the result of O-D pair identification, we can take traffic demand management measures to control the change of key O-D pairs reasonably and improve the coupling between the demand distribution and the road network structure, for example, increasing the proportion of bus trips between critical O-D pairs to replace cars, thus reducing the total demand between the critical O-D pairs. This will improve the overall capacity of the road network. This study assumes a 30% reduction in critical O-D demand, an increase in demand matrix multiplier from 0.204 to 0.225, and an 8.72% increase in road network capacity as shown in Figure 13.
## 4 Conclusions  
Three native plant species were collected in different locations around the industrial area of Puchuncavi-Ventanas, and the concentrations of 11 trace elements and their corresponding transfer factors were determined. Preliminary results showed a high concentration of trace elements in _O. picensis_, _S. velutina_, and _A. subfusiformis_ species, which was inversely correlated to the distance of the primary emission sources, especially for those elements associated with industrial complex activities, such as Cu, As, Cr, Zn, V, and Ni.  
The high concentrations of trace elements such as Cu, As, Cr, and V, close to the toxic limits in the native plant species of the industrial area, suggest the need for continuous monitoring of the region given that the area is under massive pressure from industrial activities. Nevertheless, no hyperaccumulator plants were found in any location. However, the results indicated that _O. picensis_, _S. velutina_, and _A. subfusiformis_ species exhibited accumulation or potential accumulation for Cu, Pb, As, Ni, and Cr in the more industrial affected areas. _O. picensis_ was identified as the plant with the highest capacity for TE accumulation.  
Low bioavailability for V was evidenced by the low transfer factors obtained in most of the native plant species, despite the high soil concentration, suggesting the exclusion of this trace element.  
In summary, this study provides preliminary baseline results on the element compositions of important native plants and soil in the Puchuncavi-Ventanas areas, which can serve as the basis for future resource management decisions concerning pollution assessment, plant toxicity and phytoremediation plans.  
**Author Contributions:** Conceptualization and methodology, S.S.-G., X.F.-R., E.P.-G., and F.C.-B.; validation, S.S.-G.; investigation, S.S.-G. and X.F.-R.; writing--original draft preparation, S.S.-G. and X.F.-R.; writing--review and editing, E.P.-G., F.C.-B., and C.M.-R.; supervision, E.P.-G. and F.C.-B.; project administration, E.P.-G. and F.C.-B.; funding acquisition, E.P.-G. and F.C.-B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was funded by the Spanish Agency of International Cooperation for Development (AECID project A1/037813/11), Junta de Extremadura and FEDER (project IB-16114) and CETAM-UTFSM.  
**Data Availability Statement:** Data is contained within the article.  
**Acknowledgments:** Useful comments from [PERSON] are gratefully acknowledged.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Discrete-Time Position Control for Autonomous Underwater Vehicle under Noisy Conditions
## 3 Discrete-Time Controller Design in Trajectory Tracking  
In this section, the design of the proposed discrete-time controller for AUV positional control in trajectory tracking noisy conditions is presented.  
The control objective is to design a discrete-time control law, where the AUV can follow the reference trajectory as the time increases. The schematic diagram of the proposed controller is shown in Figure 2. The symbols used throughout this paper are presented in Table 1.  
Figure 2: Schematic diagram of the proposed discrete-time position control for trajectory tracking.
## 3 Synopsis of KDN Paradigm
### Detailed KDN Architecture
#### 3.2.3 Data/Infrastructure Layer  
The data layer is made up of transferring components that can analyze, save, or transfer data in compliance with the traffic rules transmitted by the control layer. Furthermore, the infrastructure layer is needed to transmit the fresh facts sought by the measurement and control layers, which are used to generate insight in KDN [198]. Such knowledge-based awareness is used by the controller to make choices about network control, by the management layer to make selections about network surveillance and setting up, and by the application layer to continuously alter policies.  
In comparison to switching devices in SDN, data-transfer components in KDN demand more capacities, such as throughput and computing power, owing to the greater communication load used for transmitting data to surveillance and control layers. Real switching devices, virtualized switching devices, routing devices, wireless connection points, base stations, etc. are examples of forwarding equipment [199]. While network traffic flow rules have been created by the controller, these devices are set up and observed by the measurement layer. There are several switch-transferring models that employ protocols like OpFlex [200], ForCES [201], OpenFlow [202], Protocol-Oblivious Forwarding (POF) [203], Path-Computation Element Path-Computation Client (PCEPCC) [204], OpenState [205], and others.  
Network executives may adjust network layouts dynamically without altering the core network infrastructure by using simulated switches, which are software components that connect virtual computers and real network hardware. Moreover, modern virtual switches such as VMware NSX, Open vSwitch, etc. can separate data flow between several emulated machines or collections of emulated machines, allowing for the creation of network sectors and enhancing network security [206].  
Circuit switching is a necessary component of fiber optic networks, and KDN switches that are a part of a fiber optic network are dependent on light circuit switching. Dedicated path switching, in contrast to connectionless switching, occurs at the infrastructure plane of the OSI concept, employing light routes to link multiple optical switches via fiber optics. On the other hand, infrastructure plane transmission in packet switching networks typically uses wired or wireless media.
# Image-Based Gimbal Control in a Drone for Centering Photovoltaic Modules in a Thermal Image +
###### Abstract  
Recently, there have been many types of research applying drones with a thermal camera to detect deteriorations in photovoltaic (PV) modules. A thermal camera can measure temperatures on the surface of PV modules and find the deteriorated area. However, a thermal camera generally has a lower resolution than a visible camera because of the limitations of cost. Due to different resolutions between the visible and thermal cameras, there are often invalid frames from a thermal camera. In this paper, we describe a gimbal controller with a real-time image processing algorithm to control the angle of the camera to position the region of interest (ROI) in the center of target PV modules to solve this problem. We derived the horizontal angle and vertical position of ROI in visible images using image processing algorithms such as the Hough transform. These values are converted into a PID control signal for controlling the gimbal. This process makes the thermal camera capture the effective area of target PV modules. Finally, experimental results showed that the photovoltaic module's control area was properly located at the center of the thermal image.  
drone; gimbal; thermal image; photovoltaic module; image processing Article Image-Based Gimbal Control in a Drone for Centering Photovoltaic Modules in a Thermal Image +
Footnote †: journal: Applied Physics Letters  
[PERSON]  
1 Department of IT Convergence Engineering, Gachon University, Seongnam 13120, Korea; [EMAIL_ADDRESS]  
Department of Software, Gachon University, Seongnam 13120, Korea; [EMAIL_ADDRESS]  
Department of Fire Service Administration, Chodang University, Mu-An 58530, Korea  
Correspondence: [EMAIL_ADDRESS]; Tel.: +82-61-450-1229  
A preliminary version of this article was presented at the 3 rd International Conference on Advanced Engineering and ICT-Convergence, Jeju Island, Korea, 11-13 July 2019.
## 2 Material and Methods  
### Overview of KOMPSAT-3 and KOMPSAT-3 Satellite Datasets  
The Korea Aerospace Research Institute (KARI, Daejeon, Republic of Korea) developed and launched the high-resolution KOMPSAT-3 and KOMPSAT-3A Earth observation satellites in 2012 and 2015, respectively. KOMPSAT-3 is equipped with 2.8 m multispectral channels and a 0.7 m Panchromatic (PAN) channel from the optical camera (AEISS; Advanced Earth Imaging Sensor System) capable of providing precise optical images of the Earth's surface, which are used for purposes such as environmental monitoring, disaster response, and resource management. KOMPSAT-3A, an upgraded version of KOMPSAT-3,features the existing AEISS sensor along with a newly developed infrared (IR) sensor, enabling Earth observation at night or in cloudy weather conditions. Table 1 presents the spectral channels and specifications of KOMPSAT-3 and 3A. In this study, we used datasets that the KARI refined and constructed from KOMPSAT-3 and KOMPSAT-3A for training and preparing cloud detection data, which are publicly available on AI Hub ([[https://www.aihub.or.kr/](https://www.aihub.or.kr/)]([https://www.aihub.or.kr/](https://www.aihub.or.kr/)) (accessed on 7 April 2025)).
## 2 Preliminary Experiments
### LOHC Material and Catalyst  
There are several candidate LOHC materials, which share certain attractive characteristics: high hydrogen storage capacity, the ability to undergo repeated hydrogenation and dehydrogenation under specific operating conditions, thermal stability, and low flammability (or non-flammability) [15]. In addition, the U.S. Department of Energy has set a guideline for hydrogen storage capacity at a minimum of 5.5 wt% [16]. The advantages and disadvantages of these LOHC materials have been discussed previously [9; 17; 18; 19; 20].  
Dibenzyl-toluene (DBT or H0-DBT) was selected as the LOHC-material for the present study. The physical properties of H18-DBT and H0-DBT required for designing the dehydrogenation system have been previously reported [21; 22; 23]. DBT is most extensively employed for large-scale LOHC technology projects [24] because it is commercially available (Marlotherm SH by Sasol Inc. in Sandton, Republic of South Africa), is non-toxic, and has been widely used as an excellent heat transfer fluid. The LOHC+ material for the present study was prepared in-house by hydrogenating H0-DBT using a 10 L batch  
Figure 1: Schematic of the triple loop dehydrogenation system.  
reactor under operating conditions of 180 \({}^{\circ}\)C and 70 bar [17]. Table 1 presents the physical properties of DBT at representative temperatures. As dehydrogenation progresses and hydrogen is released, the total volume of liquid LOHC within the dehydrogenation system gradually decreases because the density of the LOHC\(-\) is relatively higher than that of the LOHC+. At 300 \({}^{\circ}\)C, full dehydrogenation results in an approximately 11.5% decrease in volume.  
The 0.5 wt% Pt pellet catalyst was purchased from Sigma Aldrich (Saint Louis, MO, USA). Detailed information on the catalyst properties can be found in [25]. The catalyst pellets had a diameter of approximately 3 mm and an apparent density of approximately 540 kg/m\({}^{3}\) when loosely packed within the cage. Thus, the required cage volume for 1.5 kg of catalyst was approximately 2.8 L.
## 3 Results and Discussion  
### Axial Cumulative Strain  
The axial cumulative strain is one of the important indexes to characterize the deformation ability of the lightweight soil. In this study, the axial cumulative strain is defined as the residual strain when the overlying load is removed. As shown in Figure 4 for sandy lightweight soil, the axial cumulative strain increases with the increasing cyclic loading times, as well as the axial total strain of the specimen. There exists a good linear relationship between the axial cumulative strain and the axial total strain (see Figure 5), and the ratio of axial cumulative strain to axial total strain remains constant. The same also applies to the tested silty clay lightweight soil specimens. However, the influence of confining pressure, cement content, and soil type on the ratio is very small through inspecting all of the tested specimens in this study (not shown here).  
Figure 3: Test procedure for the consolidated undrained (CU) test in the laboratory under cyclic loading.  
Under lower confining pressures, the cementing structure (with relatively high cement content) of the mixed lightweight soil plays the main role in bearing the load capacity, and the damage of the specimen is very small. In this condition, the EPS particles are still constrained by the cementation force, indicating that the characteristics of larger elastic deformation cannot be brought into full activity. Although the elastic deformation of the lightweight soil would mainly come from the elastic deformation of the EPS particles, the elastic strain is always at a lower level when the stress state is relatively small. Therefore, the difference of the elastic strains under different conditions is not obvious.
## 6 Results and Discussions
### Results of Introducing Confidence Levels
#### 6.1.2 Confidence Level \(\alpha\)  
As shown in Table 3 and Figure 3, with the increase of confidence level \(\alpha\), the system operational cost shows a similar tendency to that of \(\beta\).  
Differently from the confidence level \(\beta\), the confidence level \(\alpha\) reflects the possibility of accomplishing the objective function in an uncertain environment. Moreover, because the objective function contains uncertain variables, the confidence level \(\alpha\) also can be used to represent the activity of the uncertain variables in the model. The smaller value of \(\alpha\) represents the lower requirement of achieving the constraints of the function and the greater flexibility of the uncertain variables, which can lead to a lower operational cost. On the contrary, the potential risk is increased because of the uncertain factors, namely the control of the renewable generators. Therefore, according to the varied-curve surface diagram of the system operational costs shown in Figure 3, the power system's economical operation and stable operation can be compromised by selecting appropriate confidence levels \(\alpha\) and \(\beta\).
# Monocular Vision Guidance for Unmanned Surface Vehicle Recovery
## 2 Materials and Methods
### Visual Localization of the USV
#### 2.4.1 Coordinate System Conversion  
The PNP localization method involves the conversion of coordinates among four coordinate systems: the image coordinate system, pixel coordinate system, camera coordinate system, and world coordinate system. The image coordinate system \(uO^{\prime}v\) is established with the image plane center as the coordinate origin. The pixel coordinate system \(xOy\) is established with the top-left corner of the image plane's first pixel as the coordinate origin. The camera coordinate system \(X_{c}Y_{c}O_{c}Z_{c}\) is established with the camera's optical center as the origin. The world coordinate system \(X_{w}Y_{w}O_{w}Z_{w}\) is established to determine the target  
Figure 5: Principle diagram of cross-ratio invariance.  
Figure 6: The camera’s shooting scene.  
position. Visual localization can be achieved through the transformation relationships between the four coordinate systems.  
The transformation relationship between the world coordinate system and the camera coordinate system is described by Equation (9), where \(R\) and \(T\) represent the rotation matrix and translation matrix, respectively.  
\[\left[\begin{array}{c}X_{c}\\ Y_{c}\\ Z_{c}\\ 1\end{array}\right]=\left[\begin{array}{cc}R&T\\ 0&1\end{array}\right]\left[\begin{array}{c}X_{w}\\ Y_{w}\\ Z_{w}\\ 1\end{array}\right] \tag{9}\]  
The transformation relationship between the camera coordinate system and the pixel coordinate system is described by Equation (10), where \(f\) represents the camera focal length.  
\[\left[\begin{array}{c}x\\ y\\ 1\end{array}\right]=\left[\begin{array}{cccc}f&0&0&0\\ 0&f&0&0\\ 0&0&1&0\end{array}\right]\left[\begin{array}{c}X_{c}\\ Y_{c}\\ Z_{c}\\ 1\end{array}\right] \tag{10}\]  
The transformation relationship between the pixel coordinate system and the image coordinate system is described by Equation (11), where (\(dx\), \(dy\)) represents the size of each pixel in the CCD/CMOS image sensor.  
\[\left[\begin{array}{c}u\\ v\\ 1\end{array}\right]=\left[\begin{array}{ccc}\frac{1}{dx}&0&0\\ 0&\frac{1}{dy}&0\\ 0&0&1\end{array}\right]\left[\begin{array}{c}x\\ y\\ 1\end{array}\right] \tag{11}\]  
By combining Equations (9)-(11), the transformation relationship between the image coordinate system and the world coordinate system can be obtained, as shown in Equation (12). In Equation (12), \(M_{1}\) represents the camera's intrinsic matrix, which contains parameters obtained through camera calibration. \(M_{2}\) represents the projection transformation matrix, also known as the extrinsic matrix, which mainly consists of the rotation matrix \(R\) and the translation matrix \(T\).  
\[\left[\begin{array}{c}u\\ v\\ 1\end{array}\right]=\left[\begin{array}{ccc}\frac{1}{dx}&0&0\\ 0&\frac{1}{dy}&0\\ 0&0&1\end{array}\right]\left[\begin{array}{ccc}f&0&0&0\\ 0&f&0&0\\ 0&0&1&0\end{array}\right]\left[\begin{array}{c}R&T\\ 0&1\end{array}\right]\left[\begin{array}{c}X_{w}\\ Y_{w}\\ Z_{w}\\ 1\end{array}\right] \tag{12}\]