esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Formulation and Model
### The Field Model  
The current density vector potential \(\mathbf{T}\) is defined in the superconducting layer to solve the current density distribution in HTS-coated conductors:  
\[\mathbf{J}=\
abla\times\mathbf{T} \tag{5}\]  
As can be seen in Figure 1, set the current density vector potential of the endpoint on one side of each HTS tape as zero, and the boundary condition for the 2-D T-formulation is:  
\[I_{su}=\int_{l}I_{zd}d{=}{=}\int_{l}-\frac{\partial T}{\partial y}dx=-(T_{1}-T _{2})=-T_{1} \tag{6}\]  
where \(I_{su}\) is the superconducting layer current of the HTS coil.  
In all domains of the generator, the magnetic vector potential \(\mathbf{A}\) is defined and the A-formulation is used:  
\[\mathbf{B}=\
abla\times\mathbf{A}\text{ all domains} \tag{7}\]  
\[\frac{1}{\mu}\
abla\times(\
abla\times\mathbf{A})=\begin{cases}\
abla\times\mathbf{T }&\text{superconducting layers}\\ \mathbf{J}_{cu}&\text{copper layers}\end{cases} \tag{8}\]  
\[-\frac{\partial\mathbf{A}}{\partial t}=\rho(I)\
abla\times\mathbf{T}\text{ superconducting layers} \tag{9}\]  
where \(J_{cu}\) is the copper layer current density of the HTS coil, calculated by coupling with the circuit part (introduced in Section 2.2). \(\mu\) is the permeability of the material. The relative permeability for all the materials in the model is one.  
Figure 1: Schematic diagram for the 2-D T-A formulation, the topology of the generator and the numbering sequence of each turn of the studied coil.  
The boundary condition for the A-formulation is set as:  
\[\mathbf{A}=\mathbf{0} \tag{10}\]
# Stabilization of Pickering Emulsions by Hairy Nanoparticles Bearing Polyanions
###### Abstract  
Pickering emulsions are increasingly applied in drug delivery, oil-water separation, composite materials preparation, and other fields. However, systematic studies on the stabilization of Pickering emulsions to satisfy the growing application demands in multiple fields with long-term conservation are rare. Compared to conventional solid nanoparticles, polyanion-modified hairy nanoparticles are more stable in practical environments and are investigated in this study. Poly (sodium \(p\)-styrenesulfonate) was grafted to a polystyrene (PS) core via a photoemission polymerization. A hairy nanoparticle bearing polyanions called poly (sodium \(p\)-styrenesulfonate) brush (PS@PSS) was synthesized. The size and uniformity of the Pickering emulsions stabilized by PS@PSS were investigated via a polarizing microscope. The stability of Pickering emulsions were optimized by adjusting critical factors like ultrasonic power and time, standing time, oil phases, salt concentration, and wateroil ratio. Results indicated that the Pickering emulsions could be stabilized by PS@PSS nanoparticles, which showed remarkable and adjustable partial wetting properties. It was found that the optimized conditions were ultrasonic power of 150 W, ultrasonic time of 3 min, salt concentration of 0.1 mM, oil phase of hexadecane, and wateroil ratio of 1:1. The formation and stability of Pickering emulsion are closely related to the hairy poly (sodium \(p\)-styrenesulfonate) brush layer on the nanoparticle surface.  
hairy nanoparticles; pickering emulsion; photoemulsion polymerization +
Footnote †: journal:
# Adaptive DecayRank: Real-Time Anomaly Detection in Dynamic Graphs with Bayesian PageRank Updates
## 4 Proposed Method  
In this section, we present our previous work, the DecayRank algorithm [38], along with our novel Adaptive-DecayRank algorithm. These approaches incorporate a dynamic node scoring function, an adaptive decay factor enhanced with Bayesian inference, and an anomaly detection metric. The components are illustrated in Figure 3 and are discussed in detail in Section 4.2.  
In Figure 3, the Adaptive-DecayRank framework begins by transforming the dynamic graph input into snapshots based on timestamps, which serve as the foundation for node score computations. The algorithm then iteratively upd  
Figure 3: Adaptive-DecayRank Framework: The framework consists of the _Graph Input and Snapshot Generation Phase_, where dynamic graphs are converted into snapshots; the _Dynamic Node Scoring Phase_, which computes node importance; the _Adaptive Bayesian Updating Phase_, where decay factors are adjusted based on graph changes; and the _Normalization and Anomaly Detection Phase_, which identifies nodes with significant deviations in real-time.  
Figure 2: Node-level and structural anomalies across two snapshots (t and t + 1), with nodes and edges labeled to indicate changes and anomalies. These changes could represent cyber-attacks, such as DDoS, or bursts of fraudulent transactions in financial systems.  
dynamically adjusting the decay factor through Bayesian inference. This adaptive approach ensures sensitivity to observed changes in the graph structure, enabling the quick detection of evolving patterns. Finally, normalized anomaly detection metrics are applied to identify nodes exhibiting significant deviations over time. The framework emphasizes the integration of real-time updates, adaptive mechanisms, and robust anomaly detection tailored to dynamic graph environments.
## 5 Principles Related to Traveling Wave Detection Methods
### Hilbert Transform  
The Hilbert transform is a signal processing method that reflects the local variation characteristics of a signal. It is based on the theory of Hilbert spaces and associates the signal with an analytical function on the imaginary axis [35; 36; 37]. For a given real signal \(X(t)\), its Hilbert transform \(Y(t)\) can be represented as follows:  
\[Y(t)=\frac{1}{\pi}P\int\frac{X(\tau)}{t-\tau}\mathrm{d}\tau \tag{5}\]  
In the equation, the Cauchy principal value of the real signal \(X(t)\) is denoted by \(P\); \(\tau\) and \(t\) are time variables. The essence of the Hilbert transform is to create an analytic signal by convolving with an analytical function. The real part of this analytical function is the Cauchy principal value, typically used for extracting the phase information of the signal. Convolution of the original signal with this analytical function yields the analytic signal \(Z(t)\), which contains both the magnitude and phase information of the original signal. This process is akin to introducing a filter in the frequency domain, aiding in a more comprehensive understanding of the signal's characteristics. The constructed analytic signal \(Z(t)\) is given by  
\[Z(t)=X(t)+\mathrm{i}Y(t)=a(t)\mathrm{e}^{\mathrm{i}\varphi(t)} \tag{6}\]  
In the equation, \(a(t)\) represents the instantaneous amplitude of the given real signal \(X(t)\), \(\varphi(t)\) represents the instantaneous phase of the given real signal \(X(t)\), and \(\omega(t)\) represents the instantaneous frequency of the given real signal \(X(t)\). The expressions for \(a(t)\), \(\varphi(t)\), and \(\omega(t)\) are calculated accordingly:  
\[a(t)=\sqrt{X^{2}(t)+Y^{2}(t)} \tag{7}\]  
\[\varphi(t)=\arctan\frac{Y(t)}{X(t)} \tag{8}\]  
\[\omega(t)=\frac{\mathrm{d}\varphi(t)}{\mathrm{d}t} \tag{9}\]
# Real-Time Path Planning of Driverless Mining Trains with Time-Dependent Physical Constraints
## 5 Solution  
The traditional approach to solve the above MIP problem is to use an offline scenario, which optimises the train schedule for a predefined time window. However, this approach is not suitable for the online scenario, where real-time updates are needed to keep up with the dynamic nature of the entire system. In the online scenario, the solving time often turns out to be unacceptable. For example, it may take several days to generate a train schedule for the next 24 h. It is not feasible to wait several days to generate a train schedule. Therefore, a more efficient solution framework is needed to handle the real-time requirements of the online scenario. We here propose a solution framework for the online scenario, which aims to address the limitations of the traditional approach by finding a balance between computational efficiency and optimisation accuracy.  
In particular, we adopt the rolling-window approach. In this approach, a \"rolling window\" of time is defined, and the train scheduling problem is modeled as a MIP over this time horizon. The window is then moved forward in time, and the MIP is solved again for the new time period, taking into account the updated information. This solution framework can continuously monitor the train system, incorporate new information, and generate updated schedules in real time. This helps ensure that the trains are operating optimally, even in the face of unexpected events or changes.  
As shown in Figure 2, it runs iteratively. The cycle length is predefined (e.g., every 5 min), which is decided as per the business requirements (e.g., problem size, algorithm running time, etc.). For each cycle, it contains 4 steps as follows.
## 5 Verification on Robustness of the Used Methods against Noise
### Field Test Experiment Verification  
The planetary gearbox test rig shown in Figure 24 was designed to perform verification experiments for the multiwavelet analysis method on fault diagnosis of planetary gearbox by noisy vibration signals. The test rig includes a 20 HP drive motor, a level gearbox, two planetary gearboxes, two speedup gearboxes and a 40 HP load motor. The load was applied through the drive motor. Sensors were installed at the output shaft of the second stage planetary gearbox. There are three planet gears in the first stage planetary gearbox and four planet gears in the second stage planetary gearbox. The first stage sun gear is connected to the level gear by shaft \(\#1\). The first stage planet gears are mounted on the first stage carrier, which is connected to the second stage sun gear by shaft \(\#2\). The second stage carrier is located on shaft \(\#3\). Ring gears of the first stage and the second stage planetary gearboxes are mounted on the housing of their stages, respectively.  
Figure 23: The multiwavelet information entropy values of all the simulated signals.  
Figure 24: The planetary gearbox test rig.  
In this test rig, accelerometer sensors are installed on the casing of the second stage planetary gearbox. The sampling frequency is 10 KHz. A planetary gearbox dataset containing two subsets is obtained from the experimental system under the two different operating conditions. The two conditions include faulty and normal conditions on planetary gearbox. Planet gear in experimental planetary gearbox with tooth breakage fault is shown in Figure 25. Each data subset corresponds to one of the two conditions and it consists of 100 samples. Each sample is a vibration signal containing 5000 sampling points. Figure 26 gives typical raw data samples for each condition. Figure 26a,b, respectively, show the two conditions of planetary gearbox: normal condition and tooth breakage fault in planet gear. From the raw vibration signals, it is not easy to identify the different faults and it is extremely difficult to separate the normal condition and fault conditions using only the raw vibration signals. Thus, in order to identify the two different conditions of planetary gearbox accurately, it is necessary to apply an efficient method.  
The proposed method is adopted for this task. The normalized multiwavelet transform information entropy of the condition vibration signals is computed according to Equation (19), which is shown in  
Figure 26: Typical raw data samples of experimental planetary gearbox in normal condition (**a**) and fault condition (**b**).  
Figure 25: The photograph of tooth breakage fault in planet gear.  
Figure 27. According to Figure 27, we can find that the multiwavelet entropy values of vibration data on normal condition are 0.62-0.71. Meanwhile, the multiwavelet entropy values of vibration data on fault condition are 0.39-0.52. The result shows that the proposed method can accurately identify the fault condition of planetary gearbox based on noisy condition vibration data.
# Multiparameter Fusion Decision Routing Algorithm for Energy-Constrained Wireless Sensor Networks
## 2 System Model and Routing Evaluation Parameter
### Routing Evaluation Parameter  
In general, the design of the routing algorithm needs to consider node traffic load, residual energy, relative location, etc. In this paper, we introduce some routing evaluation parameters, including energy equilibrium degree, single-hop transmission ratio, effective forward rate, cache queue index, and residual energy level factor. Based on the five parameters, we establish a routing model.  
Among the five parameters, the energy equilibrium degree reflects the degree of residual energy balance in the forward neighbor area, so that the node energy attenuation speed is kept as consistent as possible to prolong the network lifetime. Because of the data transmission capacity of the current node, the single-hop transmission distance is linked to its residual energy. Hence, a node with high residual energy can choose the forward neighbor with longer single-hop transmission distance. Conversely, those closer forward neighbors will be selected as much as possible. The effective forward rate reflects the location of the relationship between the nodes, which enable the packets to arrive at the sink as quickly as possible. The cache queue index and the residual energy level factor reflect the traffic load and residual energy of the forward neighbors, respectively, so that the packets in the transmission can avoid the congestion area and nodes with massive energy loss.
## 2 Related Work and Motivation
Major objectives behind this research are: (i) to study the effects of incremental relay-based cooperation with a different number of cooperative relays/nodes; (ii) to improve the EE of the conventional cooperative scheme by using the incremental relay-based cooperative scheme; and (iii) to implement incremental cooperation schemes in WBAN protocols to improve the overall network PER and EE.
# Analysis and Verification Results of Manual Inspection of Pavement Condition Index
## 4 Discussion  
Repeatability and accuracy are both essential indicators for evaluating the quality of pavement condition assessments. Accuracy reflects the closeness of the measured PCI value to the actual condition of the pavement, while repeatability reflects the consistency of measurements conducted by the same or different inspectors. In this study, variability among inspectors before training was notable, but both repeatability and accuracy improved significantly after structured training and validation processes [17].  
By adopting the Coefficient of Variation (CV) and Mean Relative Error (MRE) as statistical indicators, this study establishes clear, quantitative criteria to assess the performance of manual inspections. These indicators provide an objective framework to standardize the evaluation of PCI results--filling the gap in previous practices where inspector consistency and accuracy were not systematically measured [12, 18, 19].  
Although there has been increasing focus on automated or intelligent pavement inspection systems in recent years, manual inspections remain widely used in many regions, especially where resources for automation are limited. Therefore, improving the reliability of manual inspection methods continues to hold practical significance. The validation framework proposed in this study serves not only to enhance manual practices but also to support the future calibration and benchmarking of automated systems. It is recommended that validated manual PCI procedures be integrated into Quality Control (QC) and Quality Assurance (QA) frameworks to improve the overall consistency in pavement management. These efforts will contribute to better resource allocation, more consistent maintenance planning, and ultimately a safer and more efficient road environment for the public [20].
# Application of a Hybrid Model in Landslide Susceptibility Evaluation of the Western Tibet Plateau
## 3 Results
### Landslide Susceptibility Mapping by the IVM Model
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Factors** & **Class** & **Class Pixel** & **Class Pixel** & **Landside** & **Landside Pixel** & \(\mathbf{W_{i}^{+}}\) & \(\mathbf{W_{i}^{-}}\) & \(\mathbf{W^{*}}\) & \(\mathbf{W^{-}}\) & **IVM** \\ \hline Slope & 0–10 & 121,295 & 12.29 & 3699 & 6.97 & \(-\)0.592 & 0.062 & \(-\)0.529 & \(-\)0.654 & \(-\)0.819 \\  & 10–20 & 255,220 & 25.85 & 14,338 & 27.00 & 0.046 & \(-\)0.017 & 0.030 & 0.063 & 0.063 \\  & 20–30 & 329,166 & 33.34 & 21,463 & 40.42 & 0.205 & \(-\)0.118 & 0.086 & 0.323 & 0.278 \\  & 30–40 & 216,075 & 21.89 & 11,389 & 21.45 & \(-\)0.021 & 0.006 & \(-\)0.015 & \(-\)0.027 & \(-\)0.029 \\  & 40–50 & 58,428 & 5.92 & 2084 & 3.92 & \(-\)0.430 & 0.022 & \(-\)0.408 & \(-\)0.452 & \(-\)0.593 \\  & 50–60 & 6637 & 0.67 & 128 & 0.24 & \(-\)1.061 & 0.005 & \(-\)1.057 & \(-\)1.066 & \(-\)1.480 \\  & 5–60 & 487 & 0.05 & 0 & 0.00 & 0.000 & 0.001 & 0.000 & 0.000 & 0.000 \\ \hline Aspect & Flat & 2630 & 0.27 & 131 & 0.25 & \(-\)0.081 & 0.000 & \(-\)0.081 & \(-\)0.081 & \(-\)0.111 \\  & North & 143,044 & 14.49 & 8196 & 15.43 & 0.067 & \(-\)0.012 & 0.055 & 0.079 & 0.091 \\  & Northeast & 110,889 & 11.23 & 4947 & 9.32 & \(-\)0.197 & 0.023 & \(-\)0.174 & \(-\)0.219 & \(-\)0.270 \\  & East & 95,795 & 9.70 & 2725 & 5.13 & \(-\)0.663 & 0.052 & \(-\)0.611 & \(-\)0.716 & \(-\)0.919 \\  & Southeast & 126,716 & 12.83 & 1872 & 3.53 & \(-\)1.333 & 0.108 & \(-\)1.225 & \(-\)1.440 & \(-\)1.864 \\  & South & 155,703 & 15.77 & 4716 & 8.88 & \(-\)0.599 & 0.083 & \(-\)0.515 & \(-\)0.682 & \(-\)0.828 \\  & Southwest & 120,834 & 12.24 & 8712 & 16.41 & 0.313 & \(-\)0.051 & 0.261 & 0.364 & 0.423 \\  & West & 103,712 & 10.50 & 10,998 & 20.71 & 0.736 & \(-\)0.128 & 0.608 & 0.863 & 0.979 \\  & Northwest & 127,984 & 12.96 & 10,804 & 20.35 & 0.484 & \(-\)0.093 & 0.390 & 0.577 & 0.650 \\ \hline Elevation & 3869–4200 & 30,654 & 3.15 & 304 & 0.58 & \(-\)1.738 & 0.028 & \(-\)1.710 & \(-\)1.765 & \(-\)2.442 \\  & 4200–4500 & 143,803 & 14.77 & 6284 & 11.98 & \(-\)0.220 & 0.034 & \(-\)0.186 & \(-\)0.254 & \(-\)0.302 \\  & 4500–4800 & 256,027 & 26.30 & 15,152 & 28.89 & 0.100 & \(-\)0.038 & 0.062 & 0.137 & 0.136 \\  & 4800–5100 & 266,070 & 27.40 & 19,377 & 36.94 & 0.319 & \(-\)0.148 & 0.170 & 0.467 & 0.431 \\  & 5100–5400 & 157,095 & 16.13 & 9563 & 18.23 & 0.130 & \(-\)0.027 & 0.103 & 0.156 & 0.176 \\  & 5400–5700 & 80,207 & 8.24 & 465 & 0.89 & \(-\)2.279 & 0.082 & \(-\)2.197 & \(-\)2.360 & \(-\)3.216 \\  & 5700–6000 & 37,286 & 3.83 & 1262 & 2.41 & \(-\)0.486 & 0.016 & \(-\)0.470 & \(-\)0.501 & \(-\)0.671 \\  & 6000–6357 & 1819 & 0.19 & 47 & 0.09 & \(-\)0.764 & 0.001 & \(-\)0.763 & \(-\)0.765 & \(-\)1.060 \\ \hline Lithology & Hard rock & 101,483 & 10.28 & 21,463 & 40.42 & 1.671 & \(-\)0.439 & 1.22 & 2.110 & 2.110 \\  & Hard rock & 176,351 & 17.86 & 1726 & 3.25 & \(-\)1.749 & 0.174 & \(-\)1.575 & \(-\)1.923 & \(-\)2.458 \\  & Soffer rock & 402,465 & 40.76 & 17,828 & 33.57 & \(-\)0.204 & 0.122 & \(-\)0.083 & \(-\)0.326 & \(-\)0
# Conductive Textiles for Signal Sensing and Technical Applications
## 2 Materials Used to Impart Conductivity on Textiles
### Conductive Metals
#### 2.2.1 Silver Nanoparticles (AgNPs)  
AgNPs are known to be antibacterial, and their therapeutic properties have been proven against a broad range of microorganisms that can cause diseases. Ag can be synthesized through various methods ranging from simple chemical reduction processes to photochemical, biochemical, sonochemical, and Tollens' reagent methods [51]. The deposition of nanowire films around threads can be achieved through dip-coating [52]. Chemically metallized Ag threads are used to produce textiles with antistic properties or to shield against electromagnetic waves. Ag-coated fibers are used to improve the behavior of smart textiles [50].
## 3 Results
### Analysis of Relationship Using the SSR Markers  
The number of alleles amplified by the selected markers varied from one to five, with each showing a PIC value \(>\) 0.5 (Table 5). However, the SSR markers identified in this study exhibited PIC values lower than those of the previously established markers [26; 27; 28; 29; 30; 31] (Table 5), although the sizes of the alleles amplified by the previous and new SSR markers were similar (Table 5).  
The tree generated for the previously established six SSR markers [26; 27; 28; 29; 30; 31] had three large agglomerative clusters, with clusters A-I, A-II, and A-III comprising 45, 31, and 24 varieties, respectively. Although the EM ecotype in cluster A-I and the MM ecotype in clusters A-II and A-III were the largest ecotypes, their distribution was under 50% in each cluster (Figure 5a). Another tree that was constructed based on the six newly developed SSR markers also had three clusters: B-I, B-II, and B-III, which included 31, 34, and 35 varieties, respectively. In cluster B-I, 18 (58.1%) varieties belonged to the EM ecotype, while 24 (70.6%) varieties in cluster B-II and 25 (71.4%) varieties in cluster B-III belonged to the MM and LM ecotypes, respectively (Figure 5b).  
Figure 4: Predicted schematic diagrams of the transcripts selected for analyzing expression profiling in three different soybean ecotypes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Marker** & **No. of Alleles** & **Amplified Size of Allele** & **PIC 1** \\ \hline Satt079 & 4 & 133–154 & 0.74 \\ \hline Satt150 & 3 & 189–210 & 0.66 \\ \hline Satt429 & 4 & 241–274 & 0.74 \\ \hline Satt481 & 5 & 124–169 & 0.77 \\ \hline Sat\_038 & 4 & 99–131 & 0.68 \\ \hline Satt292 & 3 & 215–248 & 0.63 \\ \hline Sub-average & 3.8 & & 0.70 \\ \hline Glyma.03g160900.1 & 1 & 212 & 0.59 \\ \hline Glyma.03g201600.1 & 3 & 114–144 & 0.62 \\ \hline Glyma.07g057900.3 & 4 & 285–315 & 0.63 \\ \hline Glyma.19g158400.1 & 4 & 135–168 & 0.71 \\ \hline Glyma.19G164800.1 & 3 & 178–192 & 0.52 \\ \hline Glyma.19g198600.1 & 4 & 232–253 & 0.59 \\ \hline Sub-average & 3.2 & & 0.61 \\ \hline Average & 3.5 & & 0.66 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Polymorphism of SSR markers developed in previous and present studies.  
Figure 5: Agglomerative clustering trees constructed using the (**a**) previously established and (**b**) newly developed SSR markers. In each cluster, the green circle represents the EM ecotype, the yellow circle represents the MM ecotype, and the red circle represents the LM ecotype.
###### Abstract  
Machine learning and deep learning algorithms have proved to be a powerful tool for developing data-driven signal processing algorithms for challenging engineering problems. This paper studies the modern machine learning algorithm for modeling nonlinear devices like power amplifiers (PAs) for underwater acoustic (UWA) orthogonal frequency divisional multiplexing (OFDM) communication. The OFDM system has a high peak to average power ratio (PAPR) in the time domain because the subcarriers are added coherently via inverse fast Fourier transform (IFFT). This causes a higher bit error rate (BER) and degrades the performance of the PAs; hence, it reduces the power efficiency. For long-range underwater acoustic applications such as the long-term monitoring of the sea, the PA works in full consumption mode. Thus, it becomes a challenging task to minimize power consumption and unnecessary distortion. To mitigate this problem, a receiver-based nonlinearity distortion mitigation method is proposed, assuming that the transmitting side has enough computation power. We propose a novel approach to identify the nonlinear power model using a modern deep learning algorithm named frequentative decision feedback (FFB); PAPR performance is verified by the clipping method. The simulation results prove the better performance of the PA model with a BER with the shortest learning time.  
underwater acoustic OFDM communication; machine learning; neural networks; peak to average power ratio; power amplifier; clipping +
Footnote †: journal: Applied Soft. 2020, 10, 4986; doi:10.3390/app10144986