esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation and Discussion  
Based on the above theoretical analysis, we theoretically analyze the effectiveness of the observer enhanced cascade SMC-PD algorithm. In this section, some simulation results are given to prove the performance of the proposed control method. Next, we conducted simulations of HAUV motion control and selected the two most typical working conditions (3D spiral water exit and vertical climb water exit) to verify the robustness of the proposed control strategy in dealing with coaxial HAUV cross-media motion with environmental disturbance and parameter perturbation. The controller parameters used in our simulation are given in Table 1.  
The wind, wave, and current interference parameters used in the simulation are given in Appendix A. At the same time, the lumped uncertainty is described in advance as the form of a random function:  
\[\left\{\begin{array}{l}d_{x}=1.3 sin(0.1\pi t)+0.3 cos(0.13\pi t)+0.1k_{s}\\ d_{y}=1.3 cos(0.12\pi t)+0.3 sin(0.12\pi t)+0.1k_{s}\\ d_{z}=1.3 sin(0.13\pi t)+0.3 cos(0.1\pi t)+0.1k_{s}\\ d_{\phi}=0.5 sin(0.21\pi t)+0.1 cos(0.41\pi t)\\ d_{\theta}=0.5 sin(0.31\pi t)+0.1 cos(0.31\pi t)\\ d_{\psi}=0.5 cos(0.41\pi t)+0.1 cos(0.21\pi t)\end{array}\right. \tag{66}\]  
The initial status is set as \(\left[x_{0},y_{0},z_{0},\dot{x}_{0},\dot{y}_{0},\dot{z}_{0}\right]^{\rm T}= \left[0.2,1.8,-21,0,0\right]^{\rm T}\), considering the constraint rule condition of quaternion \(q_{0}^{2}+q_{v}^{\rm T}q_{v}=1\); take the initial state of the quaternion as \(Q_{0}=\left[0.8,0.4,0.4,0.2\right]^{\rm T}\). Because of the transformation relationship, the initial value of the defined quaternion \(Q_{0}\) is equivalent to the initial value of the defined Euler angle. The initial value of the angular velocity is set as \(\omega_{0}=\left[0.1,0.1,0.1\right]^{\rm T}\).  
In order to conduct a comparative study, the basic controller in [16] was selected as the control group to verify the superiority of the designed DO-enhanced SMC-PD controller. The basic controller adopts the PID-PD control algorithm, that is, the position loop and the attitude loop adopt the gain-adjustment PID algorithm and PD algorithm, respectively. The following two classical cases (spiral cross-domain maneuver and vertical cross-domain maneuver) were simulated comprehensively to explain the superiority of our proposed controller and compare which cross-domain maneuver trajectory is better.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Parameter Symbol** & **Value** & **Parameter Symbol** & **Value** \\ \hline \multirow{3}{*}{controller parameter} & \(k_{10}\) & 1.5 & \(k_{3}\) & 5 \\  & \(k_{2}\) & 1.5 & \(\delta_{p}\) & 0.1 \\  & \(k_{\
eq 0}\) & 210 & \(k_{d}\) & 6 \\ \hline disturbance observer & \(\kappa_{1}\) & \(5000\cdot\lambda^{3\times 3}\) & \(\kappa_{2}\) & \(50\cdot\lambda^{3\times 3}\) \\ \hline adaptive law & \(\gamma_{1}\) & 1.5 & \(\gamma_{2}\) & 0.002 \\ \hline learning rate & \(b\) & 45 & \(\xi\) & 0.1 \\ \hline \end{tabular}
\end{table}
Table 1: Control system parameters.
## 5 Current Treatments to Avoid or Reduce PPO Activity in Fresh-Cut Produce
### Calcium Washing Treatments  
Calcium solutions are usually used (especially in fruits) in order to reinforce cell structures (and therefore firmness) by cross-linking with cell wall and middle lamella pectins. As explained in previous sections, PPO is highly influenced by cell integrity. Therefore, these treatments are usually related to a decrease in PPO activity and the subsequent browning. Thus, PPO activity is expected to be reduced when using calcium treatments due to the fact that cell structures are stronger, avoiding the enzyme-substrate contact. The main calcium treatment used is calcium lactate due to the fact that it does not add flavor to the produce (such ascalcium chloride, its antecessor, does) [8; 102]. In the case of fresh-cut produces, it is used as a complementary treatment, especially for heat-treatments. Therefore, many studies involved the use of calcium treatments [86; 103]. For instance, in the case of fresh-cut iceberg lettuce with increasing calcium lactate concentration, a decrease in PPO activity was found. However, minimal color evolution was found for a concentration of 1.5%, and more concentrated solutions showed some evolution [15]. Consequently, the optimization of calcium treatments (regarding addition) is required. Depending on the salt that calcium comes from, its effect might vary especially due to the hydrogen ion concentration during the washing treatment [68]. The use of calcium citrate and ascorbate (assuming 0.4% Ca at 60 \({}^{\circ}\)C for 1 min in both cases) was studied for fresh-cut eggplant. Both of them were effective at reducing PPO activity, but the latter offered a stronger texture decay resistance [28]. The use of calcium ascorbate has been proved in fresh-cut pears, finding browning reduction probably due to o-quinones reduction by ascorbate, competing with browning substrates for the active site of pear PPO [69]. This way, there was a competition between the enzyme and the additive which resulted in a decrease in PPO activity.
## 4 Experimental Results
### Experimental Data  
The experimental dataset consists of real-world samples collected from an actual industrial production line. The data is carefully screened, including 400 defective samples and 4000 normal samples. The defects encompass four classical types: black spots, scratches, tears, and indentations, with 100 samples for each type. Utilizing an introduced data augmentation algorithm, each defective sample is expanded to 1000 images. Additionally, 400 normal images without defects are added as background inputs during model training to reduce the false positive rate. The dataset is randomly split into training, testing, and validation sets in an 8:1:1 ratio.
# An On-Demand Fault-Tolerant Routing Strategy for Secure Key Distribution Network
## 4 Proposed Solution
### Overview  
As shown in Figure 4, this routing strategy provides full life cycle management for communication keys, which is divided into six parts: path discovery, key transmission, fault detection, evidence collection, information aggregation, and decision making.  
Path Discovery:  
An improved on-demand path discovery mechanism based on signatures and flooding not only reduces the overhead but also prevents path discovery failures caused by tampering or selective loss of routing messages to a certain extent.  
Key Transmission:  
After the path is discovered, the node sends the communication key to the next hop node in the corresponding routing table entry of the path; at this time, the communication key status is uncertain and cannot be used by the upper layer.  
Fault Detection:  
The destination node will acknowledge the communication key. An abnormal acknowledgment rate will trigger the fault detection. The detection will start from the intermediate node of the path to detect the upstream and downstream status and recursively detect the faulty subpath until the faulty link is confirmed. This will allow the location of the faulty node to be narrowed down to a single link.  
Evidence Collection:  
Characterize the acknowledgment rate, bitmap autocorrelation coefficient, and local key status of the current path as evidence of the cause of the link fault, and define and derive the mass function corresponding to each piece of evidence; weight the evidence and perform fusion calculations to obtain the combined mass function.  
Information Aggregation and Decision Making:  
Select the cause of fault that can maximize the pignistic transformation of the combined mass function, conduct a corresponding new round of path discovery, and convert the communication key status generated during evaluation.
## 4 Simulation Verification and Physical Experiment
### Experimental Results and Analysis  
The inverter system hardware experiment platform is shown in Figure 11. The experimental platform mainly comprises a DC voltage source, a T-type three-level inverter, and a drive module, while the control circuit implemented uses the digital signal processor (TMS320F28335). Specifically, the right image in Figure 11 provides a magnified view of the drive circuit and inverter module in their powered-off state. During the experiment, OC faults in the switches are induced by injecting fault control signals via the PSIM software platform. Table 4 presents the relevant parameters of the experimental platform.  
Figure 12 shows the three-phase current waveforms measured under normal operating conditions, with the platform powered on and no fault simulation signals applied. The three-phase output currents demonstrate sinusoidal waveform characteristics, maintaining a stable frequency of 50 Hz with consistent 120\({}^{\circ}\) phase differences between any two phases. These results are consistent with the simulation outcomes under normal operating conditions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline DC side voltage U\({}_{\text{dc}}\) & 110 V \\ Rated voltage U & 36 V \\ Rated output power \(P\) & 270 W \\ Rated current \(I\) & 7.5 A \\ Filter inductance \(L\) & \(4*10^{-3}\) H \\ Load resistance \(R\) & \(24\,\Omega\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental platform parameters.  
Figure 11: Inverter system hardware experiment platform.
# Application of Soil Moisture Active Passive (SMAP) Satellite Data in Seismic Response Assessment
## 3 Results
### Integration of \(\Delta SM_{SMAP}\) with Seismic Records
#### 3.3.7. Gorkha, Nepal  
For the 2015 M 7.8 Nepal event, the USGS liquefaction model predicted the highest probability of liquefaction in regions that were not inspected by reconnaissance teams [87]. However, SMAP identified seismic-induced soil moisture increases (i.e., grids with \(MDI>0.08\) and \(0.04\) cm\({}^{3}\)/cm\({}^{3}\) in Figure 17) in those areas. Similar to Muisne, Ecuador, SMAP data might have been used a priori by field reconnaissance teams to support the USGS model results and to focus regions for inspection.  
Figure 16: \(\Delta\)_SMAP_ (9 km \(\times\) 9 km) in grids with (**a**) available soil moisture data, (**b**) \(MDI>0.04\), (**c**) \(MDI>0.02\), and (**d**) \(MDI>0\) cm\({}^{3}\)/cm\({}^{3}\) integrated with seismic records for Muisne, Ecuador 2016 earthquake.  
Figure 17: \(\Delta\)\(SM_{\rm SMAP}\) (9 km \(\times\) 9 km) in grids with (**a**) available soil moisture data, (**b**) \(MDI>0.08\), (**c**) \(MDI>0.04\), (**d**) \(MDI>0.02\), (**e**) \(MDI>0\) cm\({}^{3}\)/cm\({}^{3}\) integrated with seismic records for Gorkha, Nepal 2015 earthquake.
## 2 Materials and Methods
### Corrosion Resistance and Macroscopic Observations  
The pitting corrosion resistance test was carried out using the potentiodynamic method in accordance with the recommendations of PN-EN ISO 10993-15 [39]. In the first stage, the opening potential value E\({}_{\text{cop}}\) was determined under current-free conditions. In the second stage, polarization curves were recorded.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **1—Sandblasting** & **2—Mechanical** & **3—Heat** & **4—Steam** \\  & & **Polishing** & **Treatment** & **Sterilization** \\ \hline PW 1,2 & x & x & & \\ \hline PW 1,2,4 & x & x & & x \\ \hline PW 1,2,3 & x & x & x & \\ \hline PW 1,2,3,4 & x & x & x & x \\ \hline DMLS 1,2 & x & x & & \\ \hline DMLS 1,2,4 & x & x & & x \\ \hline DMLS 1,2,3 & x & x & x & x \\ \hline \hline \end{tabular} All baseline samples were after sandblasting and mechanical polishing. In addition, some of the samples were subjected to heat treatment and sterilization, to check whether other material modifications affect the properties of the specimens from the bar after plastic working and 3D-printed samples. PW—plastic working; DMLS—printed samples.  
\end{table}
Table 1: Division of samples for testing.  
The measurement setup for conducting this study consisted of the following components: the \"VoltaLab PGP201\" potentiostat (Radiometer, Villeurbanne Cedex, France), computer with VoltaMaster software 4, an electrochemical cell comprising a Ag/AgCl 3M KCl reference electrode, an auxiliary electrode of platinum, a test electrode of the test sample, 250 mL of PBS solution and the test temperature (T = 37 \({}^{\circ}\)C \(\pm\) 1 \({}^{\circ}\)C).  
In each case, based on the obtained polarization curves obtained, the corrosion potential was determined using the Stern linear polarization method-(\(E_{corr}\) [V]), polarization resistance (\(R_{p}\) [k\(\Omega\cdot\)cm\({}^{2}\)]) and by simple Tafel extrapolation of the current density (\(i_{cor}\) [mA/cm\({}^{2}\)]).  
In addition, before and after the corrosion resistance test, surface observations were made on a Leica DVM6 digital microscope at 100\(\times\) magnification.
## 2 Materials and Methods
### ICEEMDAN Decomposition Method
Add a group of Gaussian white noises to the first-order residual to construct a new signal \(r_{1}(t)+\beta_{1}E_{2}\big{(}\eta^{i}(t)\big{)}\) to be decomposed and obtain the second-order residual \(r_{2}\) and second _IMF_ component using EMD as follows: \[r_{2}(t)=\Big{\langle}M\Big{(}r_{1}(t)+\beta_{1}E_{2}\Big{(}\eta^{i}(t)\Big{)} \Big{)}\Big{\rangle}.\] (4) \[IMF_{2}=r_{1}(t)-r_{2}(t).\] (5) 3. Similarly, in the \(k\)-th order mode, calculate: \[r_{k}(t)=\Big{\langle}M\Big{(}r_{k-1}(t)+\beta_{k-1}E_{k}\Big{(}\eta^{i}(t) \Big{)}\Big{)}\Big{\rangle}.\] (6)\[IMF_{k}(t)=r_{k-1}(t)-r_{k}(t). \tag{4}\]  
Step (3) is repeated until the residual component meets the termination condition or the modal component is less than the local extreme value of the first three orders, and the algorithm outputs all obtained residual and _IMF_ components. Through the ICEEMDAN process, the characteristic signals at different time scales in the original pipeline signals are decomposed into several _IMF_ components, including the IMF components of the signals indicating the pipeline defects; each modal component is arranged according to frequency. This effectively removes the noise remaining in the intrinsic modal component and greatly reduces the generation of false modal components.
## 1 Introduction
When a receiver cannot find a segment with a \((t+1)\) over broadcast, it will be able to request and receive the segment with \((t+1)\) over broadband. Based on the real test in the paper [13], another study investigates the reliability gain of broadcast and broadband cooperation [14]. It describes the system models of broadcast and broadband networks as well as the delivery failure performance of cooperative over broadcast and broadband networks. The proposed system primarily utilizes a broadcast transmission for streaming, while broadband backup transmissions are requested whenever the broadcast signal is not decodable. As above, the papers that focus on IP-based broadcast delay have proposed a method or a QoE model to improve QoE. The experimental environment of the papers is an emulator or local network, and the actual experimental paper focuses on the success of implementation and data transmission. These papers experiment in LTE networks or combine terrestrial broadcast and broadband. The author thinks there is no related research about the delay and resource efficiency of the terrestrial broadcast network when transmitting DASH segment files especially in the terrestrial broadcasting networks, despite there being a problem due to the _fixed resources_ and the _variability of the DASH segment file size_ of UHD media. This paper proposes a transmission structure using the token bucket and leaky bucket methods already widely used in the IP system for analysis of the delay and resource efficiency. Therefore, the structure can be used without restrictions in an IP-based system and can be easily implemented. The proposed transmission structure can accommodate the burst characteristics of the DASH segment file and the characteristics of a broadcasting system that must be transmitted at a constant speed. In addition, this paper proposes a transmission structure capable of transmitting a best-effort service for the efficient use of broadcast transmission resources during media transmission. This paper models and analyzes the proposed transmission structure based on modeling the efficiency of the broadcasting resources when the real DASH media are transmitted. Moreover, the transmission delay simulation results are presented. The analyzed transmission delay is expected to contribute to synchronization in heterogeneous networks and can prepare for the delay. The proposed model is expected to contribute to future system development because it can check the maximum delay of users accessing broadcasting.
# Improved Design of LNG Marine Loading Arm Docking Method Based on TRIZ Theory
## 4 Innovative Design of Problem-Solving Method Based on TRIZ
### The Method of Numerical Analysis Is Adopted for Innovative Design
#### 4.6.2 Topological Optimization  
After conducting an analysis of the external flow field simulation results of the marine loading arm, it was determined that several components, including the upper rope wheel, column, lower rope wheel, counterweight, and counterweight support, are subjected to considerable loads. As the column houses a complex pipeline system, any alterations to its structure could result in repercussions for the unloading transmission system. Therefore, the optimization analysis was restricted to the upper and lower rope wheels, along with the counterweight support.  
Based on the established finite element model of the rope wheel and counterweight support shown in Figure 17, the topology optimization of their structure was carried out by defining the optimization and non-optimization areas according to the simulation results. The objective of the optimization, as indicated by [31], was to minimize the compliance, which corresponds to maximizing stiffness. Additionally, the target volume was set as a constraint to minimize the structural deformation energy under a given  
Figure 16: Velocity distribution in different sections of the LNG marine loading arm flow field.  
Figure 15: Velocity cross-section of the LNG marine loading arm flow field.  
load. The volume retention rates for the rope wheel and counterweight support topology optimization settings were set to 35% and 30%, respectively. The optimization process used an iterative approach, with an iteration accuracy of \(5\times 10^{4}\) and 59 and 37 iterations for the rope wheel and counterweight support, respectively. The optimization criterion method was employed for the topology optimization process.  
Based on the topological results presented in Figures 18 and 19, and in conjunction with the manufacturing process, the new structure can be achieved through selective material reduction or additive processing.
# Mueller Matrix Decomposition and Image for Non-Destructive Testing of UAVs Skin
## 3 Experiments  
To validate the feasibility and the accurateness of the method, two different instruments have been used. The first one is an imaging Mueller polarimeter operated with a CCD. Briefly, in the instrument, the polarization of the incident light is modulated by a polarization state generator (PSG) comprising a linear polarizer followed by one retarder. The polarization state analyzer (PSA) is made of the same elements as the PSG but in a reverse order. The second one is a scientific-grade inverted biological microscope (Nex-cope NIB900 produced by Nanjing Jiangnan Yongxin Optical Co., Ltd., Nanjing, China). Structural microscopic imaging is performed by using the biological microscope. The parameters of the instrument are objective lens 20 \(\times\), camera zoom 0.5\(\times\). The resolution of the camera (DMK 27 AUJ003 produced by Imaging Source Co., Ltd., New Taipei City, Taiwan) is 3856 \(\times\) 2764, and the pixel size is 1.76 \(\upmu\)m.  
Experiments are carried out in an optical darkroom. In this setup, the illumination beam (from a He-Ne laser, wavelength 633 nm) is focused at the center of the sample surface, which is in turn imaged on the CCD. Figure 1 is a UAV skin sample. The UAV skin is a multi-layer transparent composite material. The inner and outer two layers of glass fiber composite material are used as the skin material, and the honeycomb core material is used as the multi-layer structure. On the macro scale, the fabric appears as a collection of interwoven yarn systems. On the meso scale, it appears as a single yarn. The width of each yarn varies from 15 to 25 \(\upmu\)m, and every 10 yarns make up a thick thread. The width of the thick thread varies from 0.3 mm to 0.5 mm. The warp and weft yarns are perpendicular to each other and cross-wound, which forms lots of regular knots on the surface.
## 3 Results and Discussion
### Optimizing the Solar Cell with the Busbar Number  
As graphene has conductive properties and is located between the semiconductor and the busbars in the cell, we consider that it can benefit the collection of carriers. For this purpose, a minimodule with one HIT cell (15.675 cm side) encapsulated with EVA rubber and glass is considered in the modeling. The busbars are 30 \(\upmu\)m height and 540 \(\upmu\)m width. The considered values for graphene's series resistance are those shown in Figure 9. Metallization modeling is conducted with SunSolve software [(64)] on a 244.315 cm\({}^{2}\) solar cell as well as the busbar area.  
Figure 10 in the Appendix A shows the solar cell modeling with ITO and with one graphene layer instead of ITO, using the cell's values shown in Table 3. Here, the solar cell is modelled, varying the number of busbars from 2 to 10. Figure 10 shows that the efficiency increases with the number of busbars, with a limit of five busbars. As the number of busbars increases, \(l_{sc}\) and \(V_{oc}\) show a slight decay due to larger shade on the cell. However, the addition of busbars (at least up to ten) raises both the fill factor and the efficiency of the minimodule.
# Recent Progress in Fast Distributed Brillouin Optical Fiber Sensing
## 3 The Speed Limitations of the Distributed Measurement for the Classical BOTDA Scheme  
For the classical BOTDA scheme, the temperature or strain measurement time is usually several seconds to several minutes long, which limits its applications to a static or a slowly-varying measurement. Based on References [32; 33; 34], the expression of the maximum sampling rate can be modified to become the equation below.  
\[f_{s\_BOTDA}=\frac{1}{\left(T_{round\_trip}\cdot N_{code}\cdot N_{ave}+T_{ switch}\right)\cdot N_{f}} \tag{6}\]  
where \(T_{round\_trip}=2 nL/c\) is the round-trip time of the pump pulse, which is an inherent limitation related to the length of the sensing fiber and \(L\). \(N_{code}\) is the code length of the pump pulse number. For instance, this value should be set to 2 for the differential double-pulse (DPP) BOTDA scheme [32] while it depends on the encoding bits and rules for the coding approach [33]. \(N_{ave}\) is the times of the averaging. \(T_{switch}\) is the frequency-switching time of the microwave generator (MWG). \(N_{f}\) is the effective frequency-sweeping number for the probe wave, which is used to measure the whole BGS and restricts the dynamic range. For the classical BOTDA, generally, \(N_{code}=1\), \(N_{ave}\) is several tens for a polarization maintaining fiber (PMF) and thousands for a standard SMF, the \(T_{switch}\) is on the order of -ms scale that is limited by the property of the MWG, and \(N_{f}\) is usually ~100 for a frequency interval of several MHz to obtain a wide dynamic range of several hundreds of MHz.  
To increase the sampling rate of the BOTDA scheme, the recent hotspots researched are focused on reducing the \(T_{switch}\) by using the high-performance electrical devices, decreasing the \(N_{f}\) by using novel sensing curves instead of BGS, and BPSS and de-multiplexing \(N_{f}\) in a frequency-domain and a time-domain.