esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### The Deactivation Constant \(k_{d}\) and Half-Time \(t_{1/2}\)  
The transformation of Equation (6) allowed us to calculate the values of the deactivation constant at optimum temperature \(k_{\text{d}}\)(\(T_{\text{opt}}\)) when the parameter \(\theta\) and the reaction time of horseradish peroxidase assay \(t\) were known. Next, from Equation (4), knowing the optimum temperature \(T_{\text{opt}}\), the values of a pre-exponential factor of the kinetic constant for the deactivation process \(k_{\text{d0}}\) were determined. Then, based on calculated \(k_{\text{d0}}\) and \(E_{\text{d}}\) values (from Table 2), the values of deactivation constant \(k_{\text{d}}\) at temperatures in the range of 25 \({}^{\circ}\)C to 75 \({}^{\circ}\)C for horseradish peroxidase immobilized on the modified nanofibrous membrane were analyzed. For the calculations, the time of activity measurement was assumed to be 10 min. The half-time \(t_{1/2}\) was calculated as follows:  
\[t_{1/2}=\frac{ln2}{k_{\text{d}}} \tag{15}\]  
The values \(k_{\text{d}}\) and \(t_{1/2}\) are given in Table 3.  
The data presented in Table 3 were used to create a double-logarithmic plot for the half-time \(t_{1/2}\) as a function of the deactivation constant \(k_{\text{d}}\) (Figure 2).  
The obtained kinetic constants of the process deactivation \(k_{\text{d}}\) values were used in calculating the thermodynamic parameters for the deactivation of horseradish peroxidase on the nanofibrous membrane.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Horseradish Peroxidase**} & \multirow{2}{*}{_SSE_} & \multirow{2}{*}{_\(R^{2}\)_} & \multicolumn{3}{c}{_\(p\)_} & \multirow{2}{*}{_F_} & \multirow{2}{*}{_P_} \\ \cline{3-3} \cline{5-8}  & & & & \(E_{\text{d}}\) (\(k\)/mol) & & \(T_{\text{opt}}\) (\(K\)) & \(\theta\) (\(-\)) & \\ \hline Native & 0.0348 & 0.9538 & 0.0003 & \textless{}0.0001 & 0.0276 & 41.27 & 0.0021 \\ Nanofibrous membrane & 0.0177 & 0.9642 & 0.0174 & \textless{}0.0001 & 0.0776 & 53.92 & 0.0013 \\ \hline \end{tabular}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{_SSE_—the sum of squared errors; \(R^{2}\)—regression coefficients; \(F\)—Fisher test value; \(p\)-value—probability values for each parameter separately; \(p\)-value—probability value for all value: parameters \(E_{\text{d}}\), \(T_{\text{opt}}\), and \(\theta\). \\ \end{tabular}
\end{table}
Table 2: The statistical data of obtained parameters \(T_{\text{opt}}\), \(E_{\text{d}}\), and \(\theta\) values of horseradish peroxidase.
## Materials and Methods
### MOCT Laboratory Hardware
#### 2.1.5 Time-to-Digital Converter  
The Time-to-Digital Converter 2 (TDC-GPX2) was selected due to its high data throughput provided by LVDS stop inputs as well as LVDS serial outputs for each channel. Dissipating between 60 mW and 450 mW, the TDC-GPX 2 is capable of combining two channels or using the four individually, with a maximum of 70 megasamples per second for the former and 35 megasamples per second for the latter. The 9 mm \(\times\) 9 mm chip is located upon a development board, as seen in Figure 10, which is connected directly to the APD daughter board and a laptop via USB. The TDC-GPX2 is capable of 20 ps rms single shot resolution per channel, and the accuracy has been measured to be less than or equal to 40 ps. Currently, for the MOCT receiver, the TDC is on development board that is only able to capture 16 measurement segments consecutively followed by a resting period before collecting more measurements. This development board is the limiting factor; once the TDC-GPX2 chip is on its own PCB, optical pulses can be time stamped continuously.  
Figure 9: The left picture shows the NOVA receiver board used in laboratory testing. The center picture is the NOVA receiver board inside of its housing for both thermal dissipation and positioning of the focusing lens. The right photo is looking at the active element through the focusing lens which increased the APD effective active area size from 200 \(\upmu\)m to \(\sim\)5 mm. The focusing lens was aligned to this magnification (\(\sim\)25\(\times\)) in order to have an effective active area larger than the 3.6 mm collimated amplifier output signal and maintain low laboratory alignment requirements.
# CTIMS: Automated Defect Detection Framework Using Computed Tomography
## 5 Defect Inspection
### Defect Localization  
The defect localization uses the image processing algorithm to localize the defect based on the residual image between the faulty image and the reference image (defect-free image). It is worth mentioning that the object might be noisy, tilted, or shifted. Thus, it  
Figure 16: The classification framework with data augmentation.  
Figure 17: Example of the training/validation loss and accuracy of the **ResNET18** classification model.  
Figure 15: Example of the general framework of Convolutional-Neural-Network (CNN)-based binary classification.  
is crucial to pre-process the images to ensure that the reference image has the maximum similarity to the faulty image. For 2D images, the defect localization is based on the analysis of the residual image and noise refinement to cancel the noise and neglect the non-relevant fault. The localization algorithm and flowcharts are presented in Figure 18 and Algorithm 1 respectively.  
```
1 Input:\(y_{0}\): 2D image before the use of the tool
2\(y_{1}\): 2D image after the use of the tool
3\(A_{min}\): minimal detectable defect area
4 Output:\(y_{2}\): 2D image with fault detection bounding boxes
5\(\Omega\): set faulty pixels
6
7\(\diamond\) Registration of the images \(y_{0}\) and \(y_{1}\)
8\(y_{0}^{\prime}\), \(y_{1}^{\prime}\) = registration(\(y_{0}\), \(y_{1}\))
9\(\diamond\) Background subtraction
10\(y_{0}^{\rm{s}}\) = subtract_background(\(y_{0}^{\prime}\))
11\(y_{1}^{\rm{s}}\) = subtract_background(\(y_{1}^{\prime}\))
12\(\diamond\) Compute the residual mask
13\(mask\) = create_mask(\(y_{0}^{\rm{s}}\), \(y_{1}^{\rm{s}}\))
14\(\diamond\) Fault detection
15\(\Omega\) = \(\emptyset\) Initialize the fault set for all area \(A\) in the mask do
16 if\(A\geq A_{min}\)then
17\(\Omega\) = \(\Omega\cup\{A\}\)
18
19 end if
20
21\(\diamond\) Show detected faults \(\Omega\)
22\(y_{2}\) = Bounding_box(\(y_{1}\), \(\Omega\))
```  
**Algorithm 1** The DSP-based defect localization algorithm  
Figure 18: The DSP-based defect localization flowchart.  
For a 3D volume, this same algorithm is extended to defect localization by applying it on every single slice of the volume. The output defect slices are concatenated, forming the final output 3D defect volume.  
The defect volume is further processed to categorize its defect by size based on binary erosion [44]. An example of the obtained results is shown in Figure 19.
## 2 Materials and Methods
### Field Tests  
Attractiveness of olean under natural conditions was evaluated in a field trial conducted from 11 th June to 17 th August 2021 in a private cork oak stand managed for cork production every 12-13 years (N 41.826718\({}^{\circ}\), E 2.580175\({}^{\circ}\), 531 m.a.s.l., Catalonia, NE Spain) and with clear symptoms of FOB infestation. Specifically, no cork harvesting had been undertaken prior to conducting the assay. The study plot (ca. 8 ha size) is almost dominated by _Q. suber_ of approximately 50-150 years old, with a low presence of _Q. alex_.  
Sticky purple three-sided prism traps (100 cm long, 53 cm wide, 0.4 cm thick, peak reflectance: 430 and 750 nm; La Digital Impser, Girona, Spain) [21] made of corrugated polypropylene cardboard were deployed following a previous methodology [18]. In brief, prism traps were hung in sun-exposed areas at 1.5-2 m above the ground level by attaching them to an iron rod (2 m height), spaced at least 40 m apart to each other, and with adistance >2 m from the oak trunk. The three outer faces of the prism were coated with Tangle-Trap(r) (The Tanglefoot Company, Grand Rapids, MI, USA).  
We compared the efficacy of unbaited prism traps (\(n\) = 20 traps, referred to as control) against prism traps baited with clean at the release rates of ca. 0.75 mg/day (\(n\) = 20 traps) and 3.75 mg/day (\(n\) = 20 traps). For this purpose, the sticky purple traps were arranged in twenty randomized complete blocks (\(n\) = 3 traps per block), with a minimum distance of 40 m among blocks. Olean-releasing lures consisted of a 400 \(\mu\)L polyethylene tube with a snap-on cap (Beckman Coulter, Deltalab, Spain), each loaded with 140 \(\mu\)L of the synthetic compound. Thus, the 3.75 mg/day release rate was achieved by combining five dispensers. Lures were hung up inside the upper part of the prism trap by means of a metal wire. To ease the emission of the odor plume, six holes (2 cm diameter) were vertically drilled in the middle of each side of the prism trap. Prior to setting up the assay, the release rate of the dispenser was estimated based on the daily weight loss for two weeks under laboratory conditions. For this purpose, dispensers (\(n\) = 4 replicates) were hung up in a glass wind-tunnel (180 \(\times\) 50 \(\times\) 50 cm) at 25 \(\pm\) 2 \({}^{\circ}\)C, 50% \(\pm\) 10% RH, and exposed to a constant airflow of 19 cm/s [18]. Traps were checked every week for two months, and the FOB were counted and sexed. No reapplication of the sticky coating was undertaken after each week of collection. Females were dissected under a stereomicroscope (Leica S8 APO, Leica Microsystems, Wetzlar, Germany) to determine their reproductive status, and for comparison, virgin females (\(n\) = 10) emerged from net-covered _Q. super_ trunk sections were also dissected.
## 3 Results and Discussion
### Effect of CH\({}_{4}\) and NH\({}_{3}\) on the Product Selectivity of the DBD-SCR Hybrid System
The enhancement in hydrocarbon activation by plasma processes is attributed to those active particles, and the entire activation reaction is given by:  
\[\text{CH}_{4}+2\text{NO}+\text{O}_{2}\rightarrow\text{N}_{2}+2\text{H}_{2} \text{O}+\text{CO}_{2}.\]  
For this reason, CH\({}_{4}\) enhanced the removal efficiency of NO for DBD-SCR with excess O\({}_{2}\) and DBD-NH\({}_{3}\)-CH\({}_{4}\)-SCR achieves the best selectivity for NO abatement. Table 1 shows the concentration of feed gas and product at outlet for DBD-NH\({}_{3}\)-SCR and DBD-NH\({}_{3}\)-CH\({}_{4}\)-systems. According to this table, there were fewer N\({}_{2}\)O, NO\({}_{2}\), and NO\({}_{3}\) in the outlet production of DBD-NH\({}_{3}\)-CH\({}_{4}\)-SCR. Furthermore, because the synthesis effects of DBD-SCR and assistant reductant agent, the N\({}_{2}\) selectivity of DBD-NH\({}_{3}\)-CH\({}_{4}\)-SCR reached 69.9%, while the N\({}_{2}\) selectivity of DBD-NH\({}_{3}\)-SCR was only 31.2%.
# Contour Mission Flight Planning of UAV for Photogrammetric in Hillside Areas
## 2 Methodology  
This study describes the methods used for flight planning in mountainous areas. Two mission flight planning methods were used: the grid-based mission commonly used in UAV surveys and the contour-based mission proposed in this study. The following sections describe each process, outlining their respective procedures and considerations.
# Establishment of a Standard Method for Boundary Slip Measurement on Smooth Surfaces Based on AFM
## 1 Introduction  
With the development of measurement technology, studies on micro/nanoscale fluid characteristics has become possible. Advanced equipment such as atomic force microscope (AFM) can be used for studying the effects of fluid flow on micro/nanoscale factors. Among them, the measurement of boundary slip is important in the design and application of micro/nanofluidic systems whose surface area ratio to volume are very large [1; 2; 3; 4; 5; 6]. Without precise boundary conditions, it would be impossible to accurately calculate the flow characteristics of the fluid.  
There are many methods for the measurement of boundary slip, including surface force apparatus (SFA) [7; 8; 9], particle image velocimetry (PIV) [10; 11] and colloidal probe of atomic force microscope (CP-AFM) [12; 13; 14; 15; 16; 17; 18; 19], among which the CP-AFM method is believed to have the highest degree of accuracy due to its sub-nanometer resolution.  
However, some conflicting conclusions can be found in the previously reported studies on the measurement of boundary slip with AFM. For example, some studies show that the shape of the atomic force microscopy cantilever [20] affects slip measurements. Some studies point out that the cantilever beam with low stiffness [1; 21; 22] can measure the length of boundary slip, while the slip cannot be measured by the cantilever beam with high stiffness. Actually, boundary slip is a characteristic between fluid and solid, and does not change when the measurement tool changes. It has been confirmed that the stiffness and shape of the cantilever beam do not affect the measurements of boundary slip. The effect mechanism of surface roughness [23; 24; 25; 26] to the boundary slip is also controversial. Relativelyrough surfaces are often measured as having larger length of boundary slip, which is different with the results of theoretical analysis.  
Such conflicts occur because the boundary slip measurement methods based on AFM lack the definite scope of measurement methods and a standard process. Nanoscale boundary slip length is prone to errors when exposed to many disturbance factors in the process of measuring. As a result, ignoring these factors brings non-ignorable errors to the results.  
In this study, disturbances and deviations were analyzed comprehensively to establish a method for eliminating their effects. A standardized measurement method is presented, and its application scope to provide important reference for measurements on boundary slip in the future is discussed.
### Background of Marine Traffic Management  
Marine traffic management is an intricate system that plays a pivotal role in ensuring the smooth operation of the global maritime transportation network [14]. It involves a multitude of processes, ranging from vessel traffic monitoring, port and harbor operations, and shipping route optimization, to regulatory compliance and safety measures [15; 16].  
The past few decades have witnessed a significant surge in maritime traffic due to the globalization of trade and industries. This increased traffic has, in turn, resulted in marine environments becoming highly congested [17], especially in the vicinity of ports and along popular shipping routes. The heavy traffic, coupled with the inherently unpredictable nature of marine environments--characterized by changing weather conditions, currents, and tides--has made marine traffic management an increasingly challenging task.  
Traditional methods of marine traffic management rely heavily on human skills and judgment. Vessel Traffic Services (VTSs) [18] are akin to Air Traffic Control for ships, utilizing radar, Automatic Identification System (AIS), and radio communication to provide navigational advice and manage marine traffic in designated areas [19]. However, these methods often struggle with the increasing scale and complexity of modern maritime systems. They are susceptible to human error, have limitations in handling large volumes of data, and can be less efficient in predicting potential situations that may lead to maritime accidents or delays.  
The advent of modern technology and data-driven approaches, such as Machine Learning, presents a compelling alternative to these traditional methods. By leveraging a vast amount of data generated in maritime environments, Machine Learning techniques can be employed to develop more sophisticated, accurate, and automated marine traffic management systems [20]. This potential shift from human-centric methods towards a more technologically advanced approach could revolutionize marine traffic management, driving improved safety and efficiency in the industry.
## 4 Results
eigenvalues situated farthest from the real axis and closer to the imaginary axis supports the claim that these two approaches were the worst-performing ones. Thus, PIDF-EPSCA is the optimal method for enhancing SMIB system stability. The parameters' (\(K_{\text{P}}\), \(K_{\text{D}}\), \(N\), and \(K_{\text{I}}\)) objective functions, iterations, and settling times for Scenario A-4 are shown in Table 6. The suggested hybrid EPSCA strategy offered better angle deviation damping and eigenvalue location than existing optimisation techniques. The suggested EPSCA algorithm shifted eigenvalues closer to the left of an arbitrary axis line to enhance system stability while limiting abrupt changes in angle deviation amplitude. Unlike other optimisation techniques, the PIDF-EPSCA system took 14 iterations to converge the solution (Table 6). To converge the maximal objective function, the PIDF-SCA system needed 42 iterations, whereas the other optimisation techniques needed only one iteration (Table 6). The MFO and EP systems took 49 and 8 iterations, respectively, to converge on the results. The PIDF-EP system had a higher rate of convergence with lower objective function values. The proposed technique had a superior objective function and a higher computing efficiency than the three other techniques and the PIDF-EP system. Therefore, the suggested optimisation technique, which uses MO as the objective function, can be used to optimise the system's performance while simultaneously improving the damping DR and the DF. This ability demonstrates how the suggested EPSCA technique may adjust the TCSC-PIDF controller parameters by using a maximum objective function as the proposed MO function and stabilise the signal in an SMIB power system. Figure 25: Speed deviation response of the TCSC-PIDF controller using PIDF-EPSCA, PIDF-SCA, PIDF-MFO, and PIDF-EP techniques for Scenario A-4.
# Review of Power Electronics Technologies in the Integration of Renewable Energy Systems
## 1 Introduction
### Demands for Wind Power Generation  
The wind turbine uses wind energy to generate mechanical energy, which a generator uses to create electrical energy in wind power systems. After this, a power converter controls this electrical energy to meet local loads or utility grid requirements [59; 81; 82]. Three main points can be employed to describe the particular demands made on wind energy facilities, which are illustrated in Figure 6.  
**Wind Generator Side:** The power electronics converter maintains the generator's electromagnetic torque by managing the generator's rotor or stator current. Improving the energy harvesting and maintaining energy balance are the fundamental objectives of current control on the generator side, particularly when the mechanical and electrical power operate with distinct inertia [81].  
**Wind Power Conversion System Side:** The power conversion system is the primary component of the wind power system, and any failure here can have a significant effect on how well the system operates as a whole, resulting in high maintenance costs, particularly for greater wind power capacity. As a result, reliability assurance is becoming increasingly crucial in wind power systems [60; 72].  
**Utility Grid Side:** The network must fulfill particular demands, such as grid synchronization, reaction under unusual grid conditions, and grid support, in order to assure safe operation even with significant amounts of wind energy integration [5; 8; 80]. The primary requirements are fault ride-through operation, frequency management, reactive power injection, and power quality. The practical concerns also include power forecasts, communication, ramp rate constraints, and other essential needs for offshore wind power plants [46].  
A transformer typically increases the voltage level to facilitate proper power transmission when connected to the grid. However, because of the restricted physical area in  
Figure 6: Demands for wind power generation.  
the nacelle and tower of wind power systems, power density and heat dissipation issues must be resolved. In addition, it is essential to incorporate energy storage or balancing capabilities into the power conversion step to avoid extra expenses arising from temporary power imbalances between the utility grid and the wind turbine [47, 81].
## 3 The Improved 3D Reconstruction Method of Satellite Images
### Altitude Estimation  
Altitude estimation is the final step of the proposed method, which requires triangulation in combination with the dense disparity map generated in Section 3.3 and the corresponding RPC file of the satellite image. We bring the matching point pairs into the epipolar curve equation and iterate over it to obtain the final altitude value.
## 3 Simulation Testing  
Since the space in an underground coal mine is limited, the intrinsic safety of underground coal mines has significant impacts on the wireless transmission power and antenna gain; the working frequency of mine wireless signals should be 700 MHz. Compared with other working frequencies, the wireless signal at 700 MHz has the advantages of low wireless transmission energy loss and long distance, can reduce the number of base stations, and has the advantage of low networking costs [29].  
The rectangular roadway model can be described using 3D Cartesian coordinates, where x is the horizontal direction of the roadway, y is the direction along the tunnel, and z is the vertical direction. The roadway is 5 m in width, 4 m in height, 20\({}^{\circ}\) in tilt angle, and 2000 m in length. The wavelength of a wireless signal with a frequency of 700 Hz is 0.43 m. In the horizontal and vertical direction of the roadway do not meet the condition that the geometric dimensions of the object must be larger than the wavelength, but the longitudinal size of the roadway reaches 2000 m, thus the 700 MHz wireless signal in this direction satisfies the conditions. The signal transmitting end (Tx) and receiving end (Rx) are located at half the width and half the height of the roadway, respectively. A roadway cross section with a height of one half is selected to measure the field strength distribution of wireless signals in the roadway.  
A comparison of Figures 5 and 8 reveals that when the transmitting end and receiving end are located in the center of the roadway, the attenuation of the wireless signal is relatively low, and the fluctuation is relatively stable; when the transmitting end is close to the roadway wall, the signal attenuates more dramatically and fluctuates more significantly.