esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Modified Smith Predictor Control Scheme
### The Disturbance Rejection Controller \(H(s)\)
Consequently, if \(\Lambda_{k}(\omega)<1\), \(SP-H\) provides better disturbance rejection than \(SP\). Conversely, if \(\Lambda_{k}(\omega)>1\), \(SP\) offers better disturbance rejection than \(SP-H\). After selecting \(\lambda_{11}=0.01\), \(\lambda_{22}=0.3\), and \(n_{11}=n_{22}=3\), the transfer function \(F(s)\) is as follows:  
\[\mathbf{F}(s)=\left[ \begin{matrix}\frac{1}{(0.01\cdot s+1)^{3}}&0\\ 0&\frac{1}{(0.3\cdot s+1)^{3}}\end{matrix} \right] \tag{40}\]  
So:  
\[\mathbf{H}(s)=\left[\begin{matrix}\frac{s^{3}+0.9973.s^{2 optimized: (1) increasing \(\lambda\) values can widen the effective disturbance rejection frequency range but may risk stability or increase sensitivity to noise, or (2) decreasing \(\lambda\) values reduces disturbance rejection effectiveness, potentially narrowing the frequency range, but can improve the stability of the system. Figure 3 plots \(\Lambda_{1}(\omega)\) and \(\Lambda_{2}(\omega)\). The green line represents the threshold line at \(\Lambda_{k}(\omega)=1\). This line serves as a reference: when \(\lambda_{k}(\omega)<1\), \(SP-H\) provides better disturbance rejection than \(SP\); and when \(\lambda_{k}(\omega)>1\), \(SP\) performs better. The red curve corresponds to \(\Lambda_{1}(\omega)\), which evaluates the robustness function for the pitch angle \(\psi\). The black curve corresponds to \(\Lambda_{2}(\omega)\), which evaluates the robustness function for the yaw angle \(\phi\). The figure demonstrates that \(SP-H\) rejects disturbance effects more effectively than \(SP\) in the frequency range of \([0,1.1]\) rad/s for the pitch angle \(\psi\), and in the range of \([0,2.4]\) rad/s for the yaw angle \(\phi\).
# PD Detection and Analysis Triggered by Metal Protrusion in GIS Through Various Methods
###### Abstract  
Defects in GIS can be effectively detected by detecting the partial discharge (PD). The common methods of detecting partial discharge are pulse current, ultrasonic and UHF (ultra-high frequency). However, the results of different methods may be different due to the different physical quantities detected. It is important to research the differences between the PD detection methods for the PD detection and analysis. In this study, we designed metal protrusion defects in GIS, including protrusion on the conductor and enclosure. Then, we detected the PD of defects using pulse current, UHF and ultrasonic methods at the same time. The PRPD patterns, maximum discharge amplitude of different defects and PD inception voltage (PDIV) detected by the three methods were analyzed. The PRPD patterns and discharge amplitude of the different methods were very similar to each other, but the PDIVs were different. It can be concluded that the process from the PD inception to breakdown can be divided into four sections based on the PRPD and the maximum discharge amplitude. The similarity between the three methods is because their signals are all related to the pulse current during the PD process, and differences in their PDIVs are caused by the differences in sensitivity. The sensitivity of the pulse current is the lowest among the three methods due to its poor anti-jamming capability. The sensitivity of UHF is higher, and that of ultrasonic is the highest.  
partial discharge; protrusion; GIS; pulse current; UHF; ultrasonic; PRPD; sensitivity 2025  
1
## 3 CCUS3O Model
### Objective Values
#### 3.2.3 Connectivity  
Efficient information sharing is of prime importance for UAV swarms, which are highly mobile ad hoc networks relying on distributed decision making. In CCUS3O, it is considered that every UAV \(u\) asynchronously shares its local information with every UAV in its communication neighbourhood depicted by \(\mathcal{N}^{c}(u)\). As a consequence, every UAV in the same connected component of the communication graph has similar local information about the area that has been covered. Therefore, this connectivity objective aims at minimising the average number of connected components in \(G^{c}\). For this purpose, a discretisation of time \(T=\left\{t_{1},t_{2},\cdots\right\}\subset\mathbb{R}\) is considered, and for a solution \(S\), \(T_{S}\subset T\) contains every time lower than or equal to the current time of \(S\). The objective value for the connectivity \(O^{(comn)}(S)\) is then obtained as:  
\[O^{(comn)}(S)=\frac{1}{\left|T_{S}\right|}\sum_{t\in T_{S}}c_{t}\]  
where \(c_{t}\) is the number of connected components in \(G^{c}\) at time \(t\).  
Each UAV keeps in memory the representation of the solution (based on its distributed knowledge), i.e., the path made by each UAV so far. When two UAVs communicate, they share that information and keep for each path the most recent one, as shown in Figure 3.
# A Hierarchical Decision Fusion Diagnosis Method for Rolling Bearings
###### Abstract  
In order to achieve accurate fault diagnosis of rolling bearings, a hierarchical decision fusion diagnosis method for rolling bearings is proposed. The hierarchical back propagation neural networks (BPNNs) architecture includes a fault detection layer, fault isolation layer and fault degree identification layer, which reduce the calculation cost and enhance the maintainability of the fault diagnosis algorithm. By wavelet packet decomposition and signal reconstruction of the raw vibration signal of a rolling bearing, the time-domain features of the reconstructed signals are extracted as the input of each BPNN and the accuracy of fault detection, fault isolation and degree estimation are improved. By using the majority voting method, the diagnosis results of multiple BPNNs are fused, which avoids the missed diagnosis and misdiagnosis caused by the insensitivity of a vibration characteristic to a specific fault. Finally, the proposed method is verified experimentally. The results show that the proposed method can accurately detect the fault of rolling bearings, recognize the fault location and estimate the fault severity under different operating conditions.  
rolling bearings; fault diagnosis; decision fusion; neural network +
Footnote †: journal: Applied sciences
# Construction Progress and Aviation Flight Test of BDSBAS
## 3 BDSBAS Service Performance Evaluation
### Positioning Accuracy  
The positioning accuracy of SBAS is usually measured from both horizontal and vertical directions, and it is defined as the 95% quantile of the positioning error within a period of time when the protection level is less than the alarm limit, which can be expressed as follows:  
\[\left\{\begin{array}{c}\mathrm{P}(|\mathrm{HPE}|<\mathrm{HPA}(|95\%|))=95 \%\\ \mathrm{P}(|\mathrm{VPE}|<\mathrm{VPA}(|95\%|))=95\%\end{array}\right. \tag{24}\]  
where HPE and VPE denotes the horizontal positioning error and the vertical positioning error, respectively, and HPA and VPA denotes the horizontal positioning accuracy and the vertical positioning accuracy, respectively.
## 3 Results and Discussion
### Back-Extraction Stage  
The back-extraction stage was always limitative in BA\(\upmu\)E, since substantial manipulation, many times up to six steps (i.e., removing the device from the sampling flask, placing the device into a convenient organic solvent inside a vial, sonication treatment, removing the device from the vial, evaporating the organic microextract until dryness, solvent switch) were required. However, the implementation of an innovative analytical cycle allowed negligible manipulation during the back-extraction stage, which is now performed in \"only single LD step\", making BA\(\upmu\)E a much more simple approach. In this technique, after the microextraction stage, the devices were placed into glass vial inserts, having 100 \(\upmu\)L of mobile phase to desorb the target analytes, in which, after sealing and ultrasonic treatment, became ready for instrumental analysis. Therefore, desorption times of 10, 15, 30, and 60 min were assayed, in which the results (data not shown) proved that 10 min of ultrasonic treatment was enough to desorb both UV filters from the PS-DVB sorbent phase. Besides the results obtained, the back-extraction stage showed to be very user-friendly, once the several  
Figure 1: Average extraction efficiency obtained by BA\(\upmu\)E-\(\upmu\)LD/HPLC–DAD using different activated carbons and polymers sorbent phases for the microextraction of PBS and BZ4 in ultra-pure water, using standard experimental conditions. The error bars represent the standard deviation of 3 replicates.  
steps of cumbersome manipulation were avoided. Since the analytical device has small dimensions (\(7.5\times 1.0\) mm), the desorption stage can be performed inside common glass vial inserts, having enough space for the needle of the conventional HPLC injection systems, with the possibility of automation for routine work. Moreover, due to the very easy preparation and low cost of the analytical devices, they are used only once, being discarded at the end of the sample analysis.
# Digital Twin and Internet of Things--Current Standards Landscape
## 2 Classification Schema  
Both IoT and DT center on resources. In the context of IoT, resources are internet-connected devices, whereby communication between consumer and device can happen either directly or via some kind of software system. In the context of DT, resources are defined in a broader sense, e.g., as assets, devices, and physical or virtual entities. Both concepts share the vision that communication between resources should happen mostly without human interaction, i.e., machine-to-machine (M2M).  
Figure 1 depicts basic functions that are both required for realizing IoT and DT. As M2M communication requires a formal representation of the resource to interact with, the first step that needs to happen in such a scenario is always the creation of a resource description. A resource description is exactly that formal representation required and describes a resource with all its capabilities and interfaces that should be exposed to others. The creator of a resource description has the role of the provider. Once a resource description is created, it can be discovered by a consumer. A consumer can be a software system (i.e., M2M) or a human. Different modes of resource discovery can be supported, e.g., peer-to-peer (P2P) or directory-style (see Section 2.2). Once discovered, a consumer can parse/read the resource description and extract the information needed to access the described resource.  
Although these are basic functions used in many other domains, these are the very functions enabling the IoT and DT to revolutionize many aspects of today's IT landscape.  
In the following, we present a classification schema for IoT and DT standards centered on the identified three main functions: resource description, resource discovery, and resource access.
## 3 Design Optimization
### \(h\)  
Figure 7 shows the MD, PL, \(\text{Re}(N_{\text{eff}})\) and \(\text{Re}(\Delta N_{\text{eff}})\) of two modes in the DBOSW modulator versus the middle silica layer thickness \(h\). As shown Figure 7a, \(\text{Re}(N_{\text{eff}})\) of both modes reduce consistently with the increasing of \(h\). Furthermore, when \(h\) = 100 nm, the \(\text{Re}(N_{\text{eff}})\) of the TM mode and complex mode show a very close minimum, which can be confirmed in Figure 7b. Though \(\text{Re}(\Delta N_{\text{eff}})\) can already be restrained at a low level (<0.115) when \(h\) is 50 or 200 nm, the thickness of silica layer apparently has a more significant impact on the polarization dependence. As shown in Figure 7c, MD and PL of two modes decrease simultaneously with the increasing of \(h\). These trends are attributed to less confinement of the light field in the middle silica layer as well as the weakening of the interaction with graphene. This is depicted in the illustration in Figure 7c. As \(h\) decreases, the electric field intensity of the TM mode and the \(y\)-axis electric field component of complex mode increases in the middle silica layer. The increment of mode power in the metal layer invites higher optical loss. To be noted, the MD difference between the TM mode and complex  
Figure 6: (**a–c**) are \(\text{Re}(N_{\text{eff}})\), \(\text{Re}(\Delta N_{\text{eff}})\), and modulation depth (MD) as well as propagation loss (PL) of the TM mode and complex mode in the DBOSW modulator as a function of \(h_{\text{b}}\), respectively.  
mode remains at a low level when \(h\) varies from 50 to 200 nm. However, the PL of the TM mode becomes lower than that of complex mode when \(h\) is higher than 95 nm. To constrain the performance difference between the TM mode and complex mode, \(h\) is chosen to be 100 nm.
# UAV Dynamic Non-Terrestrial Transmission Channel Analysis Based on SSCM-RT Model
## 4 Spatial Consistency of Dynamic Channels
### Spatial Consistency and Motion Models  
The measurement of channel properties can be made to fit realistically and exhibit smooth evolution by the introduction of spatial consistency. Figure 9 illustrates the time-varying path loss comparison using SSCM for a linear motion model. As can be observed, spatially correlated shadow fading changes smoothly and corresponds to motion trajectories, but independent shadow fading changes dramatically over a range of around 25 dB. It indicates the significance of channel spatial consistency.  
The relative motion relationship can be described using the memory-based, time-based Gauss-Markov model in actual communication scenarios. The Gauss-Markov model is often used in cellular networks and ad hoc networks. The use of the Gauss-Markov model for analysis has practical significance. In the Gauss-Markov model, node movement rate \(v\) and direction \(\theta\) are updated according to a certain time slot interval. It can be expressed as  
\[\left\{\begin{aligned} v_{t+1}&=\alpha v_{t}+(1- \alpha)\overset{-}{v}+\sqrt{(1-\alpha^{2})}v_{n}\\ \theta_{t+1}&=\alpha\theta_{t}+(1-\alpha)\overset{ -}{\theta}+\sqrt{(1-\alpha^{2})}\theta_{n}\end{aligned}\right., \tag{9}\]  
where \(t\) represents a certain moment and \(t+1\) represents the next moment. Both \(v\) and \(\theta\) obey the Gaussian distribution \(v_{n}\) and \(\theta_{n}\), and the motion of the whole process depends  
Figure 8: Comparison of path loss in the SSCM-RT channel model with varying \(\frac{d_{1}}{d_{2}}\) value.  
on the motion of the previous time, as well as the mean and variance of the Gaussian distribution. The position of the moving object can be expressed as  
\[\begin{cases}x_{t+1}=x_{t}+v_{t}\cdot t\cdot cos\theta_{t}\\ y_{t+1}=y_{t}+v_{t}\cdot t\cdot sin\theta_{t}\end{cases} \tag{10}\]  
Assume that the movement rate of the UAV ranges from 5 to 20 m/s. \(v_{t}\sim N(10,0.2)\), \(\theta_{t}\sim N(\pi,0.2)\). The number of simulations is set to 200. The trajectory of the two-dimensional Gauss-Markov model is shown in Figure 10.  
The process of the random direction model can be described as follows: Based on the beginning position, the speed, direction, and distance of travel are randomly chosen. After arriving at the destination, the movement's pace, direction, and distance are chosen again. The node can pause and modify the direction of motion while moving in this paper's improved random direction model. The improved random direction model is more accurate in simulating actual motion. Assume that the movement rate ranges from 5 to  
Figure 10: Gauss–Markov model.  
Figure 9: Time varying path loss of linear motion model by using SSCM.  
20 m/s, each pause time is 0.1 s, and the number of simulations is set to 100. The trajectory of the two-dimensional random direction model is shown in Figure 11.
## 2 Materials and Methods
### Hardware Implementation  
Due to electro-mechanical functionality, the hardware implementation of this LF amplifier is characterized by modularity and structural symmetry. We paid particular attention to ensuring convenient access to electronic components and to the appropriate assembly of semiconductor elements that are responsible for dissipating excess heat. The device was intentionally built using industrial nonlinear, active components--i.e., components that are not used in commercial, specialized solutions for LF power signal processing. The complete amplifier module (including mounting platform and heat sinks) measures \(115\times 210\times 300\) mm and weighs just under 2.5 kg (Figure 4). The power consumption of the device is less than 30 W for 10 W of output power at the load resistance.  
The PCB of the \(1/2D\) input signal system with a two-channel operational amplifier NE5532 AP measures 130 \(\times\) 102 mm (cf. Figure 5a). It is not integrated with the device's motherboard. It is fixed over a part of the power follower using metal mounting pins. The board has a classic \(\pm 15\) V power supply based on voltage stabilizers LM317 and LM337. To ensure a high level of stability of the OP1 supply voltage, LM-type voltage stabilizers were used, which were equipped with local heat sinks to remove excess heat from their surfaces. Electronic elements have been arranged to ensure (a) the possibly shortest signal paths for the \(1/2D\) signal to the operational amplifier input and output; and (b) routing a processed signal to a subsequent amplifier voltage follower stage, i.e., bases of bipolar transistors T1 and T2 located on the motherboard. At the same time, convenient access to the quiescent current adjustment potentiometer of the MOSFET transformer pair (T3 and T4) was ensured.  
The device's motherboard (Figure 5b) measures 130 \(\times\) 211 mm. The motherboard covers the voltage follower system, power follower system, and supply voltage filtering elements of these stages. It is fixed using metal assembly pins to a chassis between radiators that dissipate heat from active elements. The motherboard path mosaic has been designed so the \(1/2D\) signal ground of the preamplifier stage is connected at one point with the ground of the motherboard power supply system and the grounds of the power suppliesfor the operational amplifier, voltage follower, and power follower. A common ground point is connected to the mechanical structure chassis.  
Figure 4: Sample views of the LF power amplifier module.  
Figure 5: PCB layout design: (**a**) board with a preliminary amplifier system; and (**b**) LF amplifier motherboard.
# Real-Time Interaction for 3D Pixel Human in Virtual Environment
## 7 Conclusions  
In this paper, we proposed a novel pixel-style 3D virtual avatar for virtual meetings that requires only a monocular RGB camera and can be generated in real-time. Our proposed pixel avatar contains both a high-resolution face and low-resolution point cloud body representing user features, such as clothing. It meets both the needs of real-time facial expression communication through head video stream and realistic interaction in a virtual environment using 3D arms. According to [45], human visual systems take 50 ms to process a frame. Our method generated pixel avatars of 160 \(\times\) 120 resolution and below at 20 FPS, which satisfy humans' processing and reaction speed for virtual interactions. The generation speed of the pixel avatar with 320 \(\times\) 240 resolution is also higher than the minimum frame rate to avoid performance degradation in a virtual environment of 10 Hz proposed by [46; 47]. According to experimental results, our method combines the advantages of 2D video conferences and artificial 3D avatars, compensates for the shortcomings of these methods, and obtains higher user evaluations for virtual meetings and interactions.  
**Author Contributions:** Conceptualization, H.D. and Q.Z.; methodology, H.D.; software, H.D. and H.J.; validation, C.-H.K.; paper, H.D. and Q.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was partly supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. IITP2022-0-002880101002) and National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF2022R1A4A101886911).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Intelligent Reflective Surfaces for Wireless Networks:
## 3 IRS-Assisted Wireless Networks
### Optimization and Design of IRSs
#### 3.3.5. Single-User MIMO Scenarios  
The authors in [84] propose different schemes to enhance the composite channel (including the cascaded channel and the direct path) power resulting in an improvement for the achievable rate of a single-user MIMO system with LOS. It is also shown that the incorporation of the IRSs in LOS environments increases the rank of the channel matrix, and improves the achievable rate by enabling spatial multiplexity.  
The authors in [85] focus their attention on achieving the capacity gain of a point-to-point MIMO system with LOS by optimizing the IRS phase matrix to improve the rank of the channel matrix.  
Table 5 summarizes the system models, the metrics assumed to evaluate the performance, and the main results of [84; 85].
# Numerical Study of the Filtration Performance for Electrospun Nanofiber Membranes
## 2 Numerical Simulation Method
### Theoretical Basis of Numerical Simulation
#### 2.2.2 Pressure Drop Analysis Model
Meanwhile, periodic boundary conditions are used to simulate the behavior of the fluid in the flow direction and tangential direction. To ensure the representativeness of the selected computational domain, the Brinkman screening length is introduced. It is determined by the square root of the permeability and used as a qualitative criterion. According to the research results of [PERSON] and [PERSON] [22], to prevent the closure of material channels and eliminate local inhomogeneities, the width of the computational domain should be at least 14 times the Brinkman characteristic length (i.e., the fiber diameter). In the thickness direction (\(Z\)-axis), empty voxels (air-filled regions) are added at the inlet and outlet of the numerical model. The number of empty voxels increases with the fiber diameter (e.g., 500 voxels for 280 nm fibers, 800 for 1300 nm fibers) to ensure stable flow field development before the fluid enters the fibrous region. At this time, the width of the computational domain is more than 20 times the Brinkman screening length to avoid the influence of material channel closure and flow field disturbances on the results. In addition, appropriately increasing the number of empty voxels helps to save memory when dealing with the computational domain and ensures that the calculation time is within a reasonable range. \"Empty voxels\" are air-filled regions added at the inlet and outlet along the thickness (\(Z\)-axis). The number varies with fiber diameter (e.g., 500 voxels for 280 nm fibers, 800 for 1300 nm) to ensure: (1) flow field development before entering fibrous regions; (2) accurate pressure drop measurement without boundary interference. When simulating the filtration resistance of the electrospun nanofiber filter media, the following physical parameters were selected: the face velocity is set to 6.89 cm/s, the temperature is set to 20 \({}^{\circ}\)C, and the density and viscosity of air are set to 1.204 kg/m\({}^{3}\) and 1.834 \(\times\) 10\({}^{-5}\) kg/(m\(\cdot\)s), respectively.