esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Recently, display-to-camera (D2C) communication, including display field communication (DFC), has gained attention due to advancements in display technology and the widespread availability of cameras in handheld devices. In this study, we proposed an iterative pilot-based reference-frame estimation scheme to increase the data rate of a 2D-DFC system. To estimate the reference frame, pilot symbols are inserted between the data symbols of the transmitted image frames. Using pilot symbols, we can compensate for the distortion in the received frame and estimate the data pixels of the reference frames. After the first iteration, we use some of the data symbols as virtual pilot symbols for the next iteration. This process is repeated using both the original and virtual pilots; furthermore, by conducting several iterations, all the data pixels of the reference frame are estimated to reconstruct the reference frame. Simulation results show that the proposed scheme significantly boosts the achievable data rate of the 2D-DFC communication system by almost twofold, while maintaining the unobtrusiveness of the display.  
Keywords:display-to-camera communication; display field communication; iterative channel estimation; image reconstruction +
Footnote †: journal: Applied
# Data-Driven Policy Learning Methods from Biological Behavior: A Systematic Review
## 4 Explaining Behavior via Reward Function: Inverse Reinforcement Learning  
The goal of RL is for agents to attempt to learn optimal behaviors through experience. Reward functions are required for RL to determine policies during the interaction between the agent and the environment. IRL, in contrast, is the reverse process of RL; that is, the agent learns the corresponding reward function based on the observed behavior [98]. Although some studies have classified the IRL under the category of IL, in this review, IRL is listed as an independent section. This is because, unlike IL methods, such as BC and GAIL, which learn policies directly from demonstration data, IRL extracts reward functions from behavioral data in a transferable manner. If the reward function is recovered, the target strategy can be implemented efficiently using the reward function and RL. Compared with the human-designed reward function approach, IRL automates the reward design, and the learned reward function can handle complex and multimodal presentations. In addition, by learning the reward functions, the behavioral preferences of experts can be obtained, contributing to an understanding of biological behavior.  
IRL assumes that the behavior is consistent with a Markov decision process (MDP) and infers reward functions from behavioral data. MDP is a sequential decision process that simulates an agent [99]. The Markov property simplifies the state dependencies in real-world problems by ensuring that the action decision for the next state depends only on the current state-action pair and is independent of the previous state, where the transition from state to action \(\pi(a_{t}\mid s_{t})\) is determined by the policy of the agent and the transition from action to state \(P(s_{t+1}\mid s_{t},\,a_{t})\) is determined by the environment. The long-term reward based on MDP can be expressed as  
They demonstrate an extension of MDP. On this basis, IRL inversely estimates the reward function through a data-driven approach. IRL performs well in autonomous driving, path planning, and human-computer interaction (HCI) applications. It has also been used in the research of learning animal behavioral strategies.  
In this section, we review policy-learning methods using IRL. The listed IRL methods are divided into four main categories based on the optimization method: the maximum margin algorithm, entropy optimization, Bayesian optimization, and adversarial methods. The maximum margin IRL determines the ideal reward function for an expert trajectory by maximizing the margin between optimal and suboptimal policies. This IRL algorithm performs well in recovering expert behavioral trajectories. Entropy optimization and Bayesian optimization IRL are based on probabilistic methods. They can find a strategy that outperforms experts' suboptimal behavior when the demonstrated behavior is not optimal. In addition, IRL using adversarial methods builds on the entropy optimization IRL to make reward learning efficient and robust in dynamic environments.
## 3 Analysis of the Practical Engineering Data and Results
### Comparison of Calculation Results  
The model structure with 2-2-4-3 for the PCA-ANFIS model with four nodes is shown in Figure 2; the membership function plots of gbellmf of the four inputs were all the same, and the Levenberg-Marquardt algorithm of ANN was selected. Table 4 shows the RMSE of the ANFIS, ANN, PCA-ANN, and PCA-ANFIS models, and Table 5 shows the measured values and predicted values by the ANN, ANFIS, PCA-ANN, and PCA-ANFIS models. The RMSE of training and checking the results of the above models is shown in Table 5.
## 3 Debonding Failure Modes  
The main causes of failure in FRP-strengthened concrete elements due to debonding are:  
-  Mode 1: Cutoff cross-section debonding;  
-  Mode 2: Debonding by flexural cracks;  
-  Mode 3: Debonding by diagonal shear cracks;  
-  Mode 4: Debonding by irregularities and roughness of surface.  
The first mode occurs at the FRP end, due to high tangential stresses at the interface over an anchorage area, and is related to a typical Mode II fracture failure located close to a superficial volume of the substrate (e.g., concrete cover, superficial portion of a brick) (Figure 2).  
In the case of strengthening with laminates (e.g., pultruded laminae), the interface may be also subject to normal stresses perpendicular to the interface (peeling stresses), due to the non-negligible stiffness of the reinforcement. These normal stresses may modify the failure mode, leading to a mixed Mode I and Mode II fracture failure mechanism.  
Figure 1: Composite with one-directional fabric (**left**), bi-directional fabric (**right**).  
Figure 2: Cutoff cross-section debonding.  
Mode 2 debonding is also due to a Mode II fracture failure, but generally located close to cross-sections of an element subject to a bending moment greater than the cracking moment. In such zones, discontinuities in the substrate generate stress concentrations at the interface that may cause full or partial debonding (Figure 3).  
Mode 3 debonding is caused by diagonal shear cracks, and may occur when shear is predominant over bending moment (e.g., in the case of thick beams) (Figure 4). The corresponding failure mode is characterized by a mixed Mode I and Mode II fracture failure mechanism, because the normal slip caused by the relative displacement between the edges of the crack generates significant normal stresses.  
Such a collapse does not generally occur in beams subjected to a distributed load, but rather takes place most commonly in laboratory tests of the four-point bending type, and less frequently in situations of transverse distributed load.  
Mode 4 occurs due to the unevenness and roughness of the concrete surface, and may initiate a full debonding of the material. This failure mode can be avoided by regularizing the surface of the strengthening application.
# Food Processing with UHP Waterjets
## 3 Waterjet Food Systems
### UHP Pumps and Plumbing  
Two main classes of pumps are used in UHP waterjet cutting. These are intensifier-type and direct drive pumps. Intensifier pumps are the most used in the food cutting industry due to the demand for responsiveness, reliability, and an extended lifetime. Figure 4 illustrates the principle of pressure intensification used in these pumps. A low pressure acting on a large area results in a higher pressure acting on a smaller area. Conventional variable output hydraulic pumps are used to generate about 20 MPa of pressure over a piston of an intensifier. A plunger with 20-to-33-times less area attached to that piston is used to transmit this hydraulic force to water, thus pressurizing it to 400 MPa or 600 MPa, respectively. An important component of the intensifier pump is a pressure attenuator which is a pressure vessel used to store energy to compensate for pressure drop during intensifier position reversal. A pump will consist of one or more intensifiers, most commonly two intensifiers. Figure 5 shows examples of intensifier pumps with a single intensifier with 621 MPa (90 Ksi) of operating pressure capability. Direct drive pumps are used when the system is of a small size such as a cake cutting system and when the pressure is relatively low, say around 200-300 MPa, to be more reliable.  
UHP plumping, shown in Figure 6, consists of tubing, coils, fittings, hoses, and swivel joints. Small-diameter (6 mm OD) tubing is commonly used due to its relative flexibility in the form of whips and coils. An alternative option, though rare in food systems, to achieve flexibility is to use swivel joints, and in this case, larger tubing (~9.5 mm OD) may be used. The tubing is typically autofrettaged to increase its lifetime. UHP hoses offer great flexibility and ease of replacement when the pressures used are relatively low (less than 345 MPa). Another approach to increase the lifetime of UHP components is not to close and open the valve, but rather a deflector is used to interrupt the jet instead of shutting it off. Also, fittings for pressures like 690 MPa have been developed, as stated in [PERSON] et al. [5], for an extended lifetime and are mostly used in UHP food sterilization systems.  
Figure 4: Pressure intensifier.  
Figure 5: Example of UHP Pumps, Flow International, Kent WAUSA (**a**) Single intensifier pump with quick change intensifier. (**b**) Dual intensifier or redundant intensifier pump.  
Figure 6: Examples of plumbing components used in waterjet systems. (**a**) Common fitting used in UHP plumbing, Autoclave Engineers, Erie PA (**b**) Trussed fitting for elevated pressures (~120 Ksi), as stated by [PERSON] et al. [5].
## 3 Methodology
### Feature Extraction
#### 3.2.3 Energy Computation  
The computed energies were derived using a sliding window approach applied to the mean envelope in each brain hemisphere for each sub-band separately. The envelope signals \(\hat{\rm e}_{band,hemisphere}[n]\) were divided into overlapping windows. Let \(w\) represent the window index and \(N\) the window length (set to 2000 samples, approximately equivalent to 7.8 s, selected experimentally). Research suggests that 8 s provides valuable information for certain types of EEG analyses [32].  
Figure 5: Example of a peak envelope: The black dotted lines represent the envelopes of each of the 10 individual channels, while the red line indicates the mean of 10 envelopes from the left hemisphere. Each different color’s signal represents a separate EEG channel.  
Different overlap ratios were tested, ranging from a stride of \(1750\) samples to a stride of \(250\) samples by a step of \(250\) samples, to determine the ratio that yielded the best performance. The windowed signal envelope \(\hat{\mathrm{e}}_{w}^{band,hemisphere}[n]\) is defined by Equation (5):  
\[\hat{\mathrm{e}}_{w}^{band,hemisphere}[n]=\hat{\mathrm{e}}_{band,hemisphere}[n+w.(N-\tau)] \tag{5}\]  
where \(\tau\) represents the number of overlapping samples. The energy \(En_{w}^{band,hemisphere}\) of the envelope within each window is computed as the sum of the squared envelope values over the window, as shown in Equation (6):  
\[En_{w}^{band,hemisphere}=\sum_{n=0}^{N-1}|\hat{\mathrm{e}}_{w}^{band,hemisphere }[n]|^{2} \tag{6}\]  
Overlapping windows help maintain the continuity of the EEG signals and ensure that important features between successive windows are not missed. This overlap improves the feature capture and enhances the model's performance. A slight overlap enables the model to detect patterns that might otherwise be missed with non-overlapping strides. Research has demonstrated that overlapping sliding windows positively impact the classification accuracy [34; 35; 36; 37]. Without an overlap, each window is independent, potentially missing transitional features [38].
## 5 Discussion  
This paper proposes the MSFFCNN and introduces the muti-scale and feature fusion mechanism into the traditional CNN for EMA fault diagnosis.  
Compared with the traditional CNN, the method in this paper utilizes a muti-scale structure to effectively and adaptively extract multi-scale, high-level features at different time scales. Moreover, the proposed attention mechanism can enhance the multi-scale features related to faults, thereby achieving a high diagnostic accuracy rate in the case of strong noise and variable loads. In order to evaluate the superiority of the proposed method in real-world industrial environment, this paper establishes a fault-diagnosis platform of the EMA system and conducts fault-injection experiments for several typical faults. The experimental results demonstrate that the proposed method has better performance than several state-of-the-art methods in scenarios with strong noise and variable loads.  
**Author Contributions:** Conceptualization, S.L.; Methodology, Y.S.; Software, Y.S. and S.L.; Validation, S.L.; Formal analysis, Y.L. ([PERSON]); Investigation, Y.L. ([PERSON]); Resources, D.L.; Data curation, Y.L. ([PERSON]) and Y.W.; Writing--original draft, Y.S.; Writing--review & editing, J.D. and Y.L. ([PERSON]); Visualization, Y.L. ([PERSON]) and Y.W.; Supervision, J.D. and D.L.; Project administration, J.D.; Funding acquisition, J.D. and D.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Natural Science Foundation of China, grant number 52277065, and the National Key R&D Program of China, grant number 2020 YFA0710500.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 14: Performance comparison under variable load.
## 3 Finite Element Analysis of Welding Residual Stress  
### Welding Finite Element Model  
The orthotropic steel bridge deck and U-ribs are connected by welding. During the welding process, uneven temperature fields, local restrained expansion and contraction, and local plastic deformation may occur. The enclosed colder material will suppress the shrinkage of the heating material, resulting in residual tensile stress near the weld seam. Higher residual tensile stress will accelerate the growth of fatigue cracks [24; 25; 26]. Following are directions for preparing the model. Select a half U-shaped rib with a transverse direction of 300 mm and a longitudinal direction of 200 mm for simulation of welding residual stress and divide the weld area into a 1 mm refined grid. The surrounding area adopts a 4 mm grid, and a 2 mm transition element is used between the two. Apply a symmetric constraint in the z-direction at the symmetrical centerline of the deck and U-rib, which constrains the translation in the z-direction and the rotation in the x and y directions. On the other side of the bottom surface of the deck, constrain the translation in the y direction, as shown in Figure 5b.  
The welding simulation process involves complex material nonlinear problems, and the calculation requires defining the physical and thermodynamic parameters of the material at different temperatures. Due to the incomplete thermophysical parameters of steel  
Figure 4: Transverse stress history curves: (**a**) weld toe; (**b**) weld root.  
Figure 5: Welding details: (**a**) weld dimensions (unit: mm); (**b**) weld finite element model. The black dashed box represents the enlarged area, the red dashed box represents the boundary area, and the blue box represents the display of boundary conditions in software ABAQUS.  
materials at present, the temperature-dependent curve of Q345 steel material parameters was obtained from references [27; 28], and the radiation Stephen Boltzmann constant was taken as 5.67 \(\times\) 10\({}^{-8}\) W/(m\({}^{2.\circ}\)C\({}^{4}\)) according to references [29]; the convection heat transfer coefficient is 13 W/(m\({}^{2}\cdot\)K); absolute zero is \(-\)273.15 \({}^{\circ}\)C. The finite element analysis of welding residual stress is carried out using the sequential thermal coupling method, and the welding heat input process is simulated using the \"life and death element\" method to simulate the weld filling method. According to the boundary criterion of the welding pool, the isothermal surface is formed by taking the steel melting point of 1500 \({}^{\circ}\)C as the boundary, and the temperature equal to the melting point temperature forms the contour of the welding pool. The gray area is the area above the melting point, as shown in Figure 6. The shape of the welding pool is relatively close to the actual weld dimensions (Figure 5a), indicating that the selection of heat source model parameters is reasonable.
## 2 Materials and Methods
### Analysis Methods Applied
#### 2.4.1 Statistical Analysis  
The first analysis of the experimental results was performed statistically by analyzing the scatter plots to visually check the distribution of FSR sensing data (ASUM) collected for the actual weight each experimenter is holding (load). Also, the \(R^{2}\) values of a univariate multiple linear regression was calculated to determine the correlation between the sensing data values (LSUM, RSUM, ASUM) and the differential weights. However, the correlation between the variables was found to be somewhat low because of the wide variation characteristics of sensitive sensing data values. Therefore, a machine-learning-based analysis method has been developed to increase the accuracy in classifying how much weight is carried by construction workers wearing smart safety shoes.
# Solution-Processed Organic Light-Emitting Diodes Using a Lamination Method
## 3 Results
### Laminated Two-Substrates OLED Devices  
Through experiments on single-substrate OLED devices, it was confirmed that mixing PDY-132 and PBD in a 1:1 ratio resulted in optimal luminescence performance. Based on this, an OLED device was fabricated by laminating two substrates, and its characteristics are shown in Figure 7 and the maximum values are summarized in Table 3.  
In order to compare the performance with the laminated OLED used in the previous study [10], a reference device with the same structure was also measured. The reference device was turned on at 7 V, and the maximum current density was 546 mA/cm\({}^{2}\), and the maximum brightness was 2840 cd/m\({}^{2}\). The new laminated OLEDs in which the adhesive layer was formed by mixing PEG or D-sorbitol into PEI showed almost similar performance to the existing single-substrate OLEDs. The device mixed with PEG was measured to have a turn-on voltage of 4 V, a maximum current density of 1518 mA/cm\({}^{2}\), and a maximum brightness of 9688 cd/m\({}^{2}\), but a rapid performance drop was observed at high voltage. This is because the adhesive layer was melted and destroyed by the heat generated during OLED operation due to the low melting point of PEG, which is approximately 60 \({}^{\circ}\)C. On the other hand, the device mixed with D-sorbitol operated stably even at high voltage, and recorded a  
\begin{table}
\begin{tabular}{c c c} \hline
**ET/HB Material** & **Jmax (mA/cm\({}^{2}\))** & **Lmax (cd/m\({}^{2}\))** \\ \hline None & 446 & 3432 \\ TPBi & 1405 & 7103 \\ PBD & 1607 & 8987 \\ PDY-132:TPBi & 1088 & 6505 \\ PDY-132:PBD & 1725 & 9871 \\ \hline \end{tabular}
\end{table}
Table 2: Maximum current density and luminance values of single-substrate OLEDs.  
\begin{table}
\begin{tabular}{c c c} \hline
**Adhesive Material** & **Jmax (mA/cm\({}^{2}\))** & **Lmax (cd/m\({}^{2}\))** \\ \hline Reference & 546 & 2840 \\ PEI:PEG & 1518 & 9688 \\ PEI:D-sorbitol & 1506 & 10,168 \\ \hline \end{tabular}
\end{table}
Table 3: Maximum current density and luminance values of laminating OLEDs.  
turn-on voltage of 4 V, a maximum current density of 1506 mA/cm\({}^{2}\). The laminated OLED device with D-sorbitol demonstrated a maximum luminance of 10,168 cd/m\({}^{2}\), surpassing the performance of the PEG-based device (9688 cd/m\({}^{2}\)). Furthermore, the higher melting point of D-sorbitol (95 \({}^{\circ}\)C) than PEG ensured structural integrity under high voltages, allowing stable operation without significant performance degradation. These findings represent a notable improvement over previous studies, which reported higher turn-on voltages and lower luminance for comparable devices [9, 10].
# Physical-Layer Security Analysis over M-Distributed Fading Channels
###### Abstract  
In this paper, the physical layer security over the M-distributed fading channel is investigated. Initially, an exact expression of secrecy outage probability (SOP) is derived, which has an integral term. To get a closed-form expression, a lower bound of SOP is obtained. After that, the exact expression for the probability of strictly positive secrecy capacity (SPSC) is derived, which is in closed-form. Finally, an exact expression of ergodic secrecy capacity (ESC) is derived, which has two integral terms. To reduce its computational complexity, a closed-from expression for the lower bound of ESC is obtained. As special cases of M-distributed fading channels, the secure performance of the K, exponential, and Gamma-Gamma fading channels are also derived, respectively. Numerical results show that all theoretical results match well with Monte-Carlo simulation results. Specifically, when the average signal-to-noise ratio of main channel is larger than 40 dB, the relative errors for the lower bound of SOP, the probability of SPSC, and the lower bound of ESC are less than 1.936%, 6.753%, and 1.845%, respectively. This indicates that the derived theoretical expressions can be directly used to evaluate system performance without time-consuming simulations. Moreover, the derived results regarding parameters that influence the secrecy performance will enable system designers to quickly determine the optimal available parameter choices when facing different security risks.  
physical layer security; M-distributed fading channels; SOP; SPSC; ESC +
Footnote †: journal:  
_Article_  
Physical-Layer Security Analysis over M-Distributed Fading Channels