esa-sceva/satcom-chunk-collection huggingface dataset:
# Fourier Neural Operator Network for Fast Photoacoustic Wave Simulations
## 2 Materials and Methods
### Conventional Solvers for the Wave Equation  
Numerical approaches, such as the finite difference and finite element methods, are commonly used to solve PDEs by discretizing the space into a grid [36]. However, these methods are often slow for time domain modeling broadband or high-frequency waves due to the need for a fine grid with small time steps [16]. Computational efficiency can be improved using pseudo-spectral and k-space methods. The pseudo-spectral method fits a Fourier series to the data and reduces the number of grid points per wavelength required for an accurate solution [37]. The k-space method incorporates a priori information regarding the governing wave equation into the solution [38]. This allows for larger time steps and improves numerical stability in the case of acoustically heterogeneous media. The k-Wave toolbox, a widely used MATLAB tool for photoacoustic simulations, uses the pseudo-spectral k-space approach for solving time domain photoacoustic wave simulations [39].  
Conventional numerical approaches are typically used to solve a single instance of PDEs and require the PDEs' explicit form. Because these approaches solve the PDE via approximation on a mesh, there is a trade-off between accuracy and computation time. In comparison, the FNO network is a data-driven and black-box approach that learns a solution for a family of PDEs from the training data and does not require the PDEs' explicit form. It is also resolution- and mesh-invariant, meaning that the trained network can be used for solving PDEs at varying levels of resolution and discretization. However, the FNO network is dependent on the quality of the data and is slow to train. Although modifications to the functional form of the PDE can be easily accounted for in conventional approaches to solve a single instance, the FNO network would need to be retrained with a new training dataset.
# Performance Analysis of Dual-Hop AF Relaying with Non-Linear/Linear Energy Harvesting
## 7 Conclusions  
In this paper, a WP DH AF relaying system has been considered and a comprehensive performance analysis has been undertaken considering linear and non-linear EH models. In the studied system, S and R have been assumed to be battery-less, and that they harvest their power from a dedicated PB. For a comprehensive analysis, the results have been obtained considering the non-linear EH model together with the linear EH model which is mostly assumed in the literature. The BEP and throughput of the system for both linear and non-linear EH models have been analytically derived and compared with Monte Carlo simulation results, which verify our theoretical derivations. Moreover, results have been provided for different system parameters. It has been shown that the linear EH model misrepresents the system performance at high amounts of harvested energy and only provides reasonable results at low amounts of harvested energy. Hence, this causes an inaccurate understanding of the process of EH system design. However, the studied non-linear EH model provides a realistic result for the system performance, both at low and high harvested energy levels, which provides a comprehensive insight into the EH system architecture. We considered the case of a single antenna in all nodes in this paper. However, it is possible to have more antennas at the S, R, and D nodes and to analyze the improved system performance based on transmit antenna selection and maximum ratio combining. The analysis of these systems will be part of future work.  
[PERSON] made substantial contributions to the conception, theoretical derivations, and design of the work; [PERSON]-A. and U.A. were major contributors to the interpretation of results and writing of the manuscript. In addition, all authors have agreed to be both personally accountable for the author's own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by The Scientific and Technological Research Council of Turkey (TUBITAK) under Project: 120E307. The work of [PERSON] was supported by the Research Fund of the Istanbul Technical University under Project MDK-2019-42438.  
Not applicable.  
**Data Availability Statement:** The data leading to the results presented in this study are available on request from the corresponding author.  
Figure 11: BER performance of the WP DH AF relaying system versus \(P_{T}\) dB where \(P_{th}=35\) dB, \(d_{sd}=1\).
## 3 Results and Discussion
### Method Validation
#### 3.3.2 Recovery, ME, and Intra- and Interday Precision  
The recovery and ME ranged for Hu7691 from 96.7 to 98.5% and 96.1 to 105.1%, separately. The precision values were both less than 10%, as summarised in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Analyte** & **Precursor Ion, \(m/z\)** & **Product Ion, \(m/z\)** & **Cone Voltage, V** & **Collision** \\  & & & & **Energy, eV** \\ \hline Hu7691 & 415.11 & 196.09 & 5 & 22 \\ Loratadine & 383.08 & 267.02 & 5 & 32 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mass spectrometry detection conditions for Hu7691 and IS.  
Figure 2: Typical UPLC-MS/MS chromatograms of (a) blank plasma; (b) blank plasma spiked with Hu7691 at LLOQ; and (c) an actual sample acquired from a dog 2 h after gavaging Hu7691.
# Compressive Sampling with Multiple Bit Spread Spectrum-Based Data Hiding
## 3 Data Hiding Model
### Noisy Environment  
Note that the compressed and watermarked audio in this paper is the coded audio. A human cannot directly listen to the coded audio without decoding it first. This means that the signal processing attacks against the coded audio are not the same as the attacks against the real audio signal. The signal processing attacks against the real audio signal were standardized in the Stirmark benchmark [24]. However, the Stirmark benchmark is not appropriate for the robustness evaluation of this proposed method except for the additive noise attack. The additive noise attack is the signal processing attack that we can generally use to evaluate the watermarking compression robustness. In the real situation, this additive noise attack in the receiver happens due to the existing thermal condition of the hardware. In this subsection, we describe mathematically how our proposed method is robust to additive noise attack. If the compressed and watermarked signal \(\mathbf{y}_{i}\) is under an additive noise environment, then (23) becomes:  
\[K_{ij} =\left|\left(\mathbf{y}_{i}+\mathbf{n}_{i}\right)\mathbf{p}_{j}^{T }\right| \tag{42}\] \[=\left|\left(\mathbf{p}_{i}\mathbf{S}_{\mathbf{r}}+\mathbf{n}_{i }\right)\mathbf{p}_{j}^{T}\right|.\] \[=\left|\mathbf{p}_{i}\mathbf{S}_{\mathbf{r}}\mathbf{p}_{j}^{T}+ \mathbf{n}_{i}\mathbf{p}_{j}^{T}\right|\]  
Assume \(\mathbf{p}_{i_{l}}=\mathbf{p}_{j}\), then (42) becomes:  
\[K_{ij}=\left|\mathbf{p}_{j}\mathbf{S}_{\mathbf{r}}\mathbf{p}_{j}^{T}+\mathbf{n }_{i}\mathbf{p}_{j}^{T}\right|. \tag{43}\]Because \(\mathbf{n}_{i}\) is independent of \(\mathbf{p}_{j}^{T}\), thus \(\mathbf{p}_{j}\mathbf{S}_{\mathbf{r}}\mathbf{p}_{j}^{T}\gg\mathbf{n}_{i}\mathbf{ p}_{j}^{T}\), then (43) becomes:  
\[K_{ij}\approx\left|\mathbf{p}_{j}\mathbf{S}_{\mathbf{r}}\mathbf{p}_{j}^{T} \right|=K_{a}. \tag{44}\]  
Thus, we confirm that the data inserted with the proposed method can be detected even in the additive noise environment. The performance evaluation of the proposed method, when attacked by additive noise, depends on the power ratio between the host audio and the additive noise represented by the Signal-to-Noise power Ratio (SNR) with the following formula:  
\[\text{SNR}=10\log_{10}\left(\frac{\sum\limits_{i=1}^{r}y_{i}^{2}}{\sum\limits _{i=1}^{r}n_{i}^{2}}\right) \tag{45}\]  
where \(i\) is the row index at \(y\) and \(n,y_{i}\) is the signal after being compressed using CS at row \(i\), \(n_{i}\) is the noise at row \(i\), and \(r\) are the number of rows from \(y\).
## 4 Results
### Ionospheric Data Analysis
#### 4.3.2 Swarm Satellite Data Analysis  
The analysis of satellite magnetic field data from the Swarm constellation has reported two electromagnetic anomalies, which could be correlated with the Fano EQ. Specifically, applying the MASS algorithm to the Swarm A satellite, considering the 90 days before the EQ and 10 days after, an anomaly was detected 4 days after the EQ (Figure 14a), potentially associated with the aftershock period. Another anomaly was also detected 75 days before the EQ, very close to the edge of the analyzed track (Figure 14b).  
Figure 14: Anomalies found 4 days after (**a**) and 75 days before the Fano EQ (**b**) by means of an automatic search for magnetic anomalies 90 days before and 10 days after the EQ; MASS algorithm (kt = 2.5) applied to Swarm A satellite. The anomalies are evidenced by coloured rectangles. The vertical red line on the geographical map represents the satellite track.  
Figure 13: Ionosonde anomaly for the 9 November 2022 M5.7 Fano EQ (red square), compared to the relationship between \(\Delta\)T-R and M previously found by the analysis of the most powerful Central Italian EQs since 1984 (red line and black squares).
# A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery
###### Abstract  
Rotating machinery is widely applied in various types of industrial applications. As a promising field for reliability of modern industrial systems, early fault diagnosis (EFD) techniques have attracted increasing attention from both academia and industry. EFD is critical to provide appropriate information for taking necessary maintenance actions and thereby prevent severe failures and reduce financial losses. A massive amounts of research work has been conducted in last two decades to develop EFD techniques. This paper reviews and summarizes the research works on EFD of gears, rotors, and bearings. The main purpose of this paper is to serve as a guidemap for researchers in the field of early fault diagnosis. After a brief introduction of early fault diagnosis techniques, the applications of EFD of rotating machine are reviewed in two aspects: fault frequency-based methods and artificial intelligence-based methods. Finally, a summary and some new research prospects are discussed.  
early fault diagnosis; rotating machinery; signal processing; feature extraction +
Footnote †: journal: Energy
## 1 Introduction
[PERSON] et al. [24] considered the problem of fault diagnosis without a large number of fault samples and proposed a model based on autoencoder and capsule network, which was tested on a bearing data set and achieved nearly 100% diagnostic accuracy. [PERSON] et al. [25] studied the bearing fault detection problem under small samples by using the Siamese neural network. Compared with traditional methods, they achieved the highest accuracy in small sample fault diagnosis. [PERSON] et al. [26] combined the symmetrized dot pattern image and convolutional neural network method to classify rotor faults. [PERSON] et al. [27] solved the problem of data enhancement by combining FEM and generative adversarial networks (GAN) and verified it on rotor fault classification data. [PERSON] et al. [28] and [PERSON] et al. [29] reviewed the application of deep learning in rotating machinery and pointed out the shortcomings and development direction of current research. In addition, [PERSON] et al. [30,31] introduced an Internet of Things system that provides new ideas for solving industrial big data and fault diagnosis. At the same time, [PERSON] et al. [32,33] studied the fault diagnosis method based on thermal imaging technology and deep learning. Different from the above fault diagnosis using time series vibration data, this method uses thermal imaging technology to collect fault data, which is an economical nondestructive testing technique. The fault detection method based on deep learning mainly extracts the high-dimensional features of fault signals through autonomous learning and then carries out fault classification or quantitative detection. The deep learning method can overcome the background noise of the actual industrial site, and the training process and recognition process of the model do not require too much human participation, so it has low requirements for the theoretical foundation of the actual operators. Based on the above research background, we found that the existing fault diagnosis methods based on deep learning mainly focus on bearings and gearboxes, and there is a lack of research on rotors, especially asymmetric cracked rotors. In order to realize the crack fault detection of asymmetric shaft systems, we design and independently build an experimental platform for crack fault detection of asymmetric shaft system in this paper. The intelligent detection method of crack fault detection based on experimental signals is studied. The rest of the paper is organized as follows: The proposed methodology will be introduced in Section 2. In Section 3, the experimental platform of asymmetric shaft crack fault detection is introduced. Then, Section 4 analyzes the performance of the adopted method on the data set collected by the experimental platform in detail. Finally, Section 5 gives a summary.
## 5 Influence Analysis of the Main Structural Parameters on Dynamic Characteristics
### Inclination Angle Changes of Stayed Cables  
The unique shape of this type of cable-stayed bridge in this paper has a wide field of vision, but also makes the mechanical properties of the cable-stayed bridge complicated and unique. Because the torsional stiffness decreases, the difficulty of construction increases accordingly. The effect of inclination angle changes on cables at the maximum cantilever state is not ignored. Therefore, it is necessary to analyze its effects on the dynamic characteristics of cable-stayed bridges.  
The influence of inclination angles of the stayed cables is analyzed in three cases. Namely, compared with the original mode, the average inclination angle of cables changes from an increase of 4.5% to a decrease of 4.5%. The analysis results based on the original mode are shown in Figure 8.  
Figure 7: Influences of the key components’ stiffness on vibration frequencies of typical vibration modes: (**a**) The influences of main girder rigidity; (**b**) the influences of bridge tower; (**c**) the influences of stayed cable stiffness.  
Illustrate: 1 th Order Means 1 th Vibration Mode  
It can be seen from Figure 8 that an increase or decrease in the inclination angle can change the first-order torsional stiffness of the structure, but the change is small. However, first-order vertical bending and lateral bending torsional vibration frequency decrease obviously, and the lateral bending torsional vibration mode has the greatest influence. The anchorage accuracy of stayed cables should be strictly controlled during the construction process, the independent inspection system should be established, the cables must be reliably fixed, and auxiliary facilities need to be checked carefully. In a modal analysis, different modes have different participation masses, participation coefficients, and direction factors in the X, Y, and Z directions. For example, in the participation coefficient, the first mode tran-x is \(-\)0.04 and tran-y is 18.03, while the second mode tran-x is 45.07 and tran-y is 0.03, which leads to different change rates of different modes under the same parameter, and the curve is not smooth enough, but the curve change trend is the same. Obviously, the change rate of the original cable angle from the first mode to the second mode is greater, which indicates that the dynamic characteristics of the original cable angle are more sensitive than those of the angle increased by 4.5% in the first and second modes.
# Nanomaterials in Photocatalysis: An In-Depth Analysis of Their Role in Enhancing Indoor Air Quality
## 14 Cost and Economic Feasibility
### Material Costs: Sourcing and Synthesis of Nanomaterials  
The first and most immediate factor influencing the cost of nanomaterial-based photocatalysts is the synthesis and procurement of the materials themselves. Titanium dioxide (TiO\({}_{2}\)), the most commonly used photocatalyst, is widely available and relatively inexpensive [283; 284] compared to other nanomaterials such as carbon-based materials (e.g., graphene), and more exotic catalysts like CeO\({}_{2}\)[285]. However, the specific synthesis methods required to enhance the photocatalytic properties--such as doping TiO\({}_{2}\) with metals (e.g., silver or copper) or non-metals (e.g., nitrogen)--can increase costs significantly. These methods often require precise control of reaction conditions, advanced equipment, and the use of rare or expensive precursors. For example, doping with noble metals such as platinum or palladium introduces high material costs, which could be prohibitive for large-scale applications [286].  
Furthermore, the synthesis of carbon-based nanomaterials, which are increasingly being explored for their superior adsorption properties, results in higher costs due to the complexity of their production processes [287]. These materials are often produced via chemical vapor deposition or other high-temperature methods, which are energy-intensive and require significant technical expertise. In addition to raw material costs, the scale-up of production processes from laboratory to industrial scale often requires significant investment in infrastructure, which could further elevate costs.
###### Abstract  
Considering the significance of an energy efficient, delay tolerant and reliable communication protocol for underwater acoustic wireless sensor network (UWSN), this paper proposes a novel systematic random linear network coding (SRLNC) based transmission system examined over a robust statistical UWSN channel model. The derived statistical channel model deals with both the small-scale fading primarily caused by scattering and small wavelength changes and large-scale fading introduced due to node dislocation in the underwater acoustic medium. The proposed SRLNC transmission-based routing approach has been applied over the proposed underwater acoustic (statistical) channel model, and respective performance assessment has been conducted in terms of throughput, energy efficiency, delay and computational complexity by varying network condition parameters. The contributions such as low coefficient vector and Galois filed, low redundant message requirements, computationally efficient pre-coding scheme, iterative buffer flush and enhanced FEC based decoding make the SRLNC based routing protocol sufficiently robust to enable reliable, energy-efficient and delay resilient communication over UWSN. The proposed SRLNC based UWSN routing protocol and its efficacy over dynamic channel conditions affirm that it can be a potential solution for QoS-oriented mission critical underwater communication purposes.  
delay tolerant; energy efficient; quality of service; random linear network coding; statistical channel model; underwater acoustic wireless sensor network Article
## 3 Protection Coordination Constraints
### Pilot Protection  
In the case of pilot protection, it is assumed that protective relays are installed in sets of \(\mathcal{P}_{\xi}\) relays, where each relay is again associated with a branch terminal, as illustrated in Figure 4, and typically the set cardinality is 2. For protection, the same quantities are used as that of [PERSON] et al. [5; 7], where line-ground faults are detected by operating on the measured positive-sequence impedance between the relay and the fault:  
Figure 8: Oneline diagram for an ideal protection scheme.  
for \(\forall(i,j,k)\in\mathcal{P},\ \forall\phi\in\Phi_{ijk}:\)  
\[\hat{Z}^{lg+}_{ijkf}=\frac{V^{\phi}_{if}}{I^{\phi}_{ijkf}+k_{ijkf}I^{0}_{ijkf}}. \tag{49}\]  
Similarly, line-line faults are also detected by operating on the measured positive-sequence impedance:  
for \(\forall(i,j,k)\in\mathcal{P},\ \forall\phi\in\Phi_{ijk}:\)  
\[\hat{Z}^{ll+}_{ijkf}=\frac{V^{\psi}_{if}-V^{\phi}_{if}}{I^{\phi}_{ijkf}-I^{ \psi}_{ijkf}}. \tag{50}\]  
The quantity \(k_{ijkf}\) is related to the ratio of measured positive- and negative-sequence line impedance as:  
for \(\forall(i,j,k)\in\mathcal{P}:\)  
\[k_{ijkf}=1-\frac{\hat{Z}^{0}_{ijkf}}{\hat{Z}^{+}_{ijkf}}. \tag{51}\]  
A trip will occur when all relays in the set detect a trip. A directional comparison blocking scheme (DCB), illustrated in Figures 9 and 10, is recommended [12]. However, for the purposes of analysis, pilot relaying is assumed to be ideal and error-free. This is expressed in terms of the restoration branch status as:  
\[\delta_{ijkr}=\begin{cases}0,&(\hat{Z}^{lg+}_{ijkf}\in\mathcal{Z}^{lg,op}_{ ijk})\wedge(\Re(I^{-}_{ijkf}/V^{a}_{if})\geq 0)\wedge(\hat{Z}^{lg+}_{ijkf}\in \mathcal{Z}^{lg,op}_{ijk})\wedge(\Re(I^{-}_{ijkf}/V^{a}_{if})\geq 0)\\ 0,&(\hat{Z}^{ll+}_{ijkf}\in\mathcal{Z}^{ll,op}_{ijk})\wedge(\Re(I^{-}_{ijkf}/V^ {a}_{if})\geq 0)\wedge(\hat{Z}^{ll+}_{ijkf}\in\mathcal{Z}^{lg,op}_{ijk}) \wedge(\Re(I^{-}_{ijkf}/V^{a}_{if})\geq 0)\\ 1,&\text{otherwise},\end{cases} \tag{52}\]  
which serves as a constraint to set \(\delta_{ijk}\), given the minimum-load-shed formulation in (45a)-(48).  
Figure 10: Schematic for one relay in a directional comparison blocking (DCB) scheme.  
Figure 9: Oneline diagram for a DCB scheme.
## 4 Results
### Comparison of Models  
#### 4.6.1 Global Comparison and Analysis  
Table 14 indicates the performance comparison of all-time series classifications on our dataset. The 2-D encoding methods, such as the recurrence plot and Gramian angular, performed better than the 1-D input methods. The performance of recurrence plot encoding is highest in all aspects, including accuracy, loss, and average accuracy, compared to the other models trained on our dataset. The results of the encoding are undoubtedly better than the 1-D methods. The RP and Gramian angular methods exhibit an accuracy higher than 70%, whereas the LSTM solely achieves an accuracy of 60% and an average accuracy of 58.25%. The kNN-DTW with a warping distance value of 15 performs similarly to LSTM, but it has a significant disadvantage, owing to the time complexity and fine-tuning challenge. Here, the MTF encoding method performs poorly compared to kNN-DTW, owing to its characteristics for highly dynamic data only. In comparison, our dataset has only 50 states that significantly reduce the number of features in the encoding of the CNN steps. We utilized two approaches in the recurrence plot to obtain the best classification. The first activation function uses SoftMax, whereas the other operates the L2-SVM method. The RP (SoftMax) and RP (SVM) achieved the highest accuracy and the best classification, with an average accuracy of 76%. The standard deviation of the RP (L2-SVM Adam) is the lowest, that is, closest to 0 compared to other \(\sigma\) values in the table, which means that the classification of all the classes is uniform and better in RP (L2-SVM Adam) than the rest of the models. Although RP (L2-SVM) has a better class accuracy, it is susceptible to outliers. In addition, it is not easy to fine-tune as it penalizes quadratically, so the optimizer's convergence rate becomes high, which leads to model overfitting.