esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Fibre Bragg grating (FBG) sensors are used to measure various quantities such as temperature, stress, vibrations, pressure, or refractive index. The characteristic feature of these sensors is that the position of the spectrum changes due to the action of a particular physical quantity. FBGs are recorded in the fibre core, allowing all benefits associated with fibre optics to be applied, such as a small size or resistance to hazardous environments and electromagnetic interference. The determination of the Bragg wavelength change is an inherent aspect related to the use of Bragg grating sensors. In most cases, the developed methods are applied directly to the raw measured spectrum [1]. Some shift demodulation algorithms are involved in several methods that are used one after the other. The most important combination of algorithms is that of cross-correlation [2] with the Hilbert transformation [3]. Computationally efficient methods include the fast phase correlation algorithm [4], Karhunen-Loeve transform (KLT) [5], second-order-polynomial-peak tracking [6], and representation of the measured spectrum using specific functions [7]. In the case of the demodulation of distorted spectra [8], the Gaussian curve fitting method [9] is effective. The most commonly used method, which can be described as preprocessing, is the wavelet transform. In its simplest form, this transform is used to filter the measured spectrum [10,11]. Classical digital filters were also applied to the FBG spectrum, which improved the accuracy of FBG sensors [12]. Several algorithms can be combined to remove noise from measurement data. An example of such solution is the decomposition of the FBG spectrum using ensemble empirical mode decomposition and filtering of the individual components using Savitzky-Golay filters [13]. A similar set of algorithms was used successively by [14]: variational mode decomposition, wavelet denoising, Hilbert transform, and the centroid algorithm. Recently, artificial neural network algorithms [15] and statistical methods [16] have also been used to demodulate FBG spectra. In this paper, we focus on the simultaneous filtration of and enhancement in FBG spectrum features based on the sum of the two elements' products of the measured spectrum samples and raising the spectrum to the second power. As a starting methodology for consideration, we propose the use of the geometric mean as a signal smoothing filter. Then, we use modifications of the two-element geometric mean, which is the square root of the product of the current and previous spectrum samples. The modification consists of replacing the product of elements with an appropriate sum of two or more products. The subsequent factors of the individual products used contain samples that are separated (preceding and following) from the current sample by a certain distance (gap). As a comparative method for the proposed filtration method, the popular Savitzky-Golay filters were used.
## 1 Introduction
Furthermore, rogue waves for the three-component Manakov system in the focusing regime had been derived. Four-petal patterns are possible [34; 35]. Such wave profiles are otherwise inadmissible for the one-component and two-component counterparts. Interactions of rogue waves with solitons and breathers were investigated by utilizing the Darboux transformation [36]. In this work, we extend the study to the three-component Manakov system in the defocusing regime. For mathematical convenience, the system is taken in the non-dimensional and normalized form. We incorporate symmetrically placed group velocity differences between the complex valued components \(u\), \(v\), and \(w\):  
\[ \begin{split}& iu_{z}+i\delta u_{t}+u_{tt}-\sigma\Big{(}|u|^{2}+|v|^{ 2}+|w|^{2}\Big{)}\,u=0,\\ & iv_{z}-i\delta v_{t}+v_{tt}-\sigma\Big{(}|u|^{2}+|v|^{2}+|w|^{ 2}\Big{)}\,v=0,\\ & iw_{z}+w_{tt}-\sigma\Big{(}|u|^{2}+|v|^{2}+|w|^{2}\Big{)}\,w=0,\end{split}  \tag{1}\]  
where \(\delta\) describes the group velocity mismatch and \(\sigma\) measures the coefficient of cubic nonlinearity. The objective of this paper is to investigate the dynamics of rogue waves and breathers in a multi-component system through a combination of theoretical perspective and computational approach. Theoretically, the formation of rogue wave and breathers can be explained in terms of modulation instability (MI). A detailed numerical investigation on the evolution of a plane wave perturbed by a single wavy disturbance would supplement the theoretical framework. In particular, a disturbance from the baseband of the MI spectrum would generate a rogue wave. For a mode with a finite frequency, both symmetric and asymmetric breathers are observed from the computational study. The outline of the paper is as follows. In Section 2, the rogue wave mode is derived by the Hirota bilinear method. The coupling effect is discussed in Section 3. The generation of rogue wave mode from baseband modulation instability is confirmed directly by computer simulation in Section 4. Similarly, the generation of symmetric and asymmetric breathers is demonstrated in Section 5. In Section 6, a numerical method for finding rogue wave modes in non-integrable systems is proposed. The conclusion is drawn in Section 7.
## 6 Conclusions  
This paper proposes a method for diagnosing lithium-ion batteries based on their historical trajectories of remaining discharge capacity over medium to long time scales. Firstly, the SSA algorithm is utilized for parameter identification of the second-order equivalent circuit model of lithium-ion batteries. Then, the EKF algorithm is employed for SOC estimation of lithium-ion batteries. The SOC obtained from the EKF algorithm is used to calculate the remaining discharge capacity of each cell. This remaining discharge capacity is utilized as a diagnostic feature. Lastly, box plots are employed to identify abnormalities in the features, thereby achieving fault diagnosis. Experimental validation demonstrates that the proposed method can accurately identify the cell 6 with internal shorts, even when there are low-capacity batteries in the battery pack. As soon as a micro-short circuit is simulated by connecting a 3000 \(\Omega\) resistor in parallel with the sixth battery, the method identifies its abnormality. The effectiveness of the proposed lithium-ion battery fault diagnosis method based on the historical trajectories of remaining discharge capacity is also proven in battery packs containing both low-capacity and faulty batteries, as it can still accurately locate the internally shorted battery. The proposed lithium-ion battery fault diagnosis method has good practical application prospects. It not only improves the accuracy of battery fault diagnosis but also demonstrates great potential in battery management systems. In future fault diagnosis methods, more sensor data (such as temperature, pressure, etc.) and external environmental factors will be attempted to be introduced for multi-dimensional data fusion. This will help to gain a more comprehensive understanding of the battery's operating state and thereby improve the accuracy of fault prediction.  
Methodology; J.J.; Validation; B.Q.; Supervision; S.L.; Writing--Original Draft; B.Q., Z.Z. and H.Y.; Writing--Review; S.L. and C.C.; Funding Acquisition; J.J. All authors have read and agreed to the published version of the manuscript.  
National Key Research and Development Program of China (2022 YFB2404300).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### Discussion  
Regarding the significance of replacing GC-MS with an electronic nose (E-nose), we have expanded the discussion to compare the advantages and limitations of both techniques. The E-nose, as a non-destructive, real-time, and cost-effective tool, offers significant benefits in monitoring the storage quality of fresh products like oysters. In contrast, while GC-MS provides high precision and specificity in detecting volatile organic compounds (VOCs), its high costs, operational complexity, and the need for skilled personnel limit its feasibility for on-site testing and large-scale applications.  
Recent studies, such as those by [PERSON] et al. [37] and [PERSON] and [PERSON] [38], have demonstrated the high sensitivity of the E-nose in monitoring changes in VOCs during storage. When combined with machine learning algorithms, the E-nose can effectively predict spoilage and quality degradation in seafood. This further highlights the potential of the E-nose in oyster storage quality monitoring.  
Furthermore, integrating the E-nose with machine learning techniques enhances its predictive capabilities. The E-nose hardware collects VOC signals through gas sensors, and pattern recognition algorithms, like support vector machines (SVMs) and neural networks (NNs), are employed for analysis. These technologies significantly improve detection accuracy and efficiency. Recent studies [39; 40] have shown the successful application of the E-nose combined with machine learning in quality detection of various food products, including fruits, vegetables, and meats. In the context of oyster storage monitoring, the E-nose, together with advanced machine learning techniques, could rapidly, economically, and effectively predict and detect quality changes, particularly in identifying specific VOC signal patterns.
# Transactive Energy to Thwart Load Altering Attacks on Power Distribution Systems
## 2 Power Balancing in Power Grids
### Energy Trading in Wholesale Electricity Markets  
In regions with deregulated electricity markets, a wholesale market runs every 5 min [32; 33] to enable energy trading between bulk power generators and energy utilities. In offering energy to the wholesale market, a bulk power generator submits a cost function to the market specifying the amount of money that must be paid to the generator as a function of the generator's power generation. On the other hand, an energy utility specifies the amount of power that it needs to operate its power distribution system. The independent system operator of the power grid collects all the energy bids from the generators and energy utilities and clears the market by calculating the lowest-cost power dispatch in meeting energy demands of the utilities [34].
## 5 Results and Discussion
### Mean Temperature Profiles  
The mean temperature profiles of the 20 combinations of the primary EDC constants are shown in Figure 5. Compared to the experiments, large differences in the predicted mean temperatures among the cases occur at the peak temperature zone where the mixtures burn intensely. One reason for the over-predicted mean temperatures is that the mean reaction rate, as shown by Equation (28) or (32), is over-predicted and should be modified according to Equation (33). Reducing the mean reaction rate requires a decrease in \(C_{\gamma}\), as shown by Equation (33).  
From Figure 5a, the mean temperatures in cases 8, 17, and 18 along the axis are the closest to the experiments, which indicates that the mean reaction rates are reasonably predicted. Unfortunately, the EDC constant combinations of cases 17 and 18 violate the criteria presented by [PERSON] [21] and should be excluded.  
With a very large value of \(C_{\tau}\), the temperature predicted by case 7 deviates the most from the experiments and thus is also not recommended. In addition, the proposed constants of case 7, as shown in Table 1, violate the criteria presented by [PERSON] [21]. Results of other cases are between those of cases 8-16. Small deviations for the radial mean temperature profiles at \(x/d=7.5\), 15, and 30 between the axis and the location of peak temperature can be found in Figure 5. At the locations downstream and away from the peak temperature, over-predicted values are found in all cases.  
Using the original values of \(C_{\gamma}=2.1377\) and \(C_{\tau}=0.4082\), i.e., case 4 in Figure 5, recent studies [34; 38] show that there are notable differences in temperature in the reacting zone. Compared to the experiments, the temperature predictions of the reacting zone have significantly improved. In most cases, the predicted temperature distributions downstream of the jet were higher than the measured data.  
Instead of using the global modification of the EDC constants, as used in this work, [PERSON] and [PERSON] [42] presented the results (at \(x/d\) = 15 and 30, Fig_S5 in [42]) using the local modification of EDC constants for flame D, where the peaks are successfully reduced. However, the over-predicted mean temperature at a radius larger than that of the peak temperature is moderately improved. Conversely, one criterion [21,31] for the validity of the EDC model, i.e., \(C_{\beta}=\frac{1}{C_{\beta}}=\frac{C_{\gamma}}{C_{\gamma}^{2}}=\frac{2}{3}C _{\mathrm{D1}}=C_{\mu}\), is violated because \(C_{\beta}=2.367C_{\mu}\) from the expressions of \(C_{\tau}\) and \(C_{\gamma}\) was used in [42] with the suggested constant. From this point of view, the results obtained by the cases that comply with the criteria [21,31], such as case 8, are now more credible because they are based on the theoretically correct EDC model.
## 3 Experimental Results
### Second-Harmonic Generation Using an Efficient Nonlinear Crystal
For a beam waist diameter of \(\sim\)80 \(\upmu\)m (\(\xi\)\(\approx\) 0.47), we achieved a maximum output peak power of 2.3 W at 532 nm, which corresponds to a power conversion efficiency of 5.4%. By reducing the beam waist diameter to \(\sim\)52 \(\upmu\)m (\(\xi\)\(\approx\) 1.12), the maximum output peak power and conversion efficiency increased to 6.7 W and 20%, respectively, limited by the PPLN-crystal damage threshold. This corresponds to maximum pulse energies at 532 nm of 230 \(\upmu\)J and 670 \(\upmu\)J for beam diameters of \(\sim\)80 \(\upmu\)m and \(\sim\)52 \(\upmu\)m, respectively, using a pulse duration of 100 \(\upmu\)s and a 1% duty cycle (0.1-kHz repetition rate). No limitations due to thermal effects under these conditions were observed, indicating that further power scaling is feasible for shorter pulse durations. Higher output peak powers at 532 nm can be achieved under similar operating conditions, for example by  
Figure 4: (**a**) Measured temperature tuning curve (with error bars) and \(\mathrm{sinc^{2}}\) fit of the 10-mm MgO:PPLN crystal at 4 W. (**b**) Measured output power at 532 nm as a function of peak pump power at 1064 nm (1% duty cycle) at the output of the 10-mm PPLN crystal, for a fundamental beam with a waist diameter of either \(\sim\)80 \(\upmu\)m or \(\sim\)52 \(\upmu\)m, which corresponds to a focusing parameter \(\xi\) of \(\sim\)1.12 and \(\sim\)0.47, respectively. using a crystal in a double-pass scheme, a longer nonlinear crystal, or two crystals in a single-pass scheme.
## 2 The Evolution from Cloud Computing to MEC
### Edge Computing
#### 2.3.2 Mobile Edge Computing Architecture
The Follow Me Cloud (FMC) idea, from [58], suggests moving user services between data centers using VMs to support service continuity when users move or when there is an excess of demand. FMC ensures services stay connected as users move and provides access to cloud services from the best DC at the same time. * ETSI Multiaccess Edge Computing  
In 2014, ETSI started the standardization work for the mobile edge computing architecture, whose initial specifications were notably related to cellular networks. However, at the end of 2018, ETSI extended its focus to other networking technologies such as cable networks and non-3 GPP networks such as WiMAX [59] and switched the name to \"multiaccess edge computing\"--still having MEC as an acronym--to better highlight its access-agnostic nature. In [60], a complete framework for MEC is illustrated, which includes a reference architecture that outlines the characteristics of different crucial components, connection points, and interfaces and how they are interconnected. This architecture is composed of three parts as shown in Figure 4: first, a MEC system plane, which includes device applications and a MEC orchestrator, then a middle plane known as the MEC host plane, which consists of MEC applications, the MEC platform, virtualization substructure, and their corresponding components, and finally the core and cloud network plane. MEC applications can perform various jobs, such as running a task on behalf of the user, obtaining location information, task offloading, and resource allocation, by running VMs in virtualization infrastructure. The responsibility of coordinating and interacting among MEC applications is on the MEC platform. The principal coordinator among the user side and other parts of architecture is the MEC orchestrator, which provides services such as keeping track of available resources, creating connections with the virtualization infrastructure, managing applications, etc. The UE application runs on the user-side device. Due to being near the user, MEC provides the lowest latency for real-time applications and it manages available resources and allocates them to applications in an optimal way in terms of storage and computation resources [60].
## 3 Methodology
### Attention Mechanism  
Attention mechanism has been applied in many types of deep learning tasks and has shown good performance; for example, in image recognition, natural language processing, and voice recognition [45]. Basically, the attention function is pairing a query and multiple key-value pairs, and outputs a weighted sum of the values. The shape of the output varies depending on the level of the information you want to focus on, spatial information, temporal information, or any other parts. There are two kinds of attention mechanisms used in this paper. One is a temporal attention mechanism, and the other is a graph attention mechanism.  
The graph attention mechanism is shown in Figure 2. Specifically, Figure 2a shows the feature transformation of vertices \(i,j\) and gets the attention score. The features of the vertex itself, its neighbor vertices, and exogenous features are all transformed into the score. Figure 2b shows the multi-head attention method with a vertex on its neighborhood. The features aggregated from its neighbor vertices and itself are concatenated or averaged to obtain the attention vector.  
Figure 2: Graphic illustration of the attention mechanism [46]. (**a**) Extracting features from neighborhood vertices; (**b**) Multi-head attention method.  
The formulas of our attention mechanism are defined as follows:  
\[\alpha_{ij}=\frac{e^{LeakyReLU(a([W_{h}||Wh_{j}]))}}{\sum_{j\in\mathcal{N}_{j}}e^{ LeakyReLU(a([W_{h}||Wh_{j}]))}} \tag{12}\]  
\[h^{\prime}_{i}=\sigma\left(\sum_{j\in\mathcal{N}_{j}}\alpha_{ij}h_{j}\right) \tag{13}\]  
where \(\mathcal{N}_{j}\) represents all neighborhood vertices of vertex \(i\), \(W\) is a weight matrix used for features transformation, signal \(||\) represents the concatenation of vectors, \(\alpha_{ij}\) is the weight of vertex \(i,j\) calculated with function \(a\), \(x_{i},x_{j}\) represent the features of vertices \(i,j\), and \(\sigma\) refers to sigmoid function. The \(LeakyReLU\) is defined as  
\[LeakyReLU(x)=\begin{cases}\xi\text{ }if\ x\geq 0\\ \xi\text{ }if\ x<0\end{cases} \tag{14}\]  
where \(b\) is a constant in \([1,+\infty]\).  
After the attention scores are obtained, the attention enhanced hidden state \(A_{t}\) can be represented as \(a^{S}_{t}h_{t}\) and be fed into the next GC-LSTM unit. The final output of each unit is shown as follows:  
\[h^{\prime}_{t}=\alpha^{S}_{t}h_{t} \tag{15}\]  
where \(\alpha^{S}_{t}\) is the spatial self-attention score tensor and \(h_{t}\) is the current hidden state.  
The temporal attention mechanism is a simplified graph attention mechanism; it only calculates the attention scores of the LSTM output sequence and gives the prediction with attention scores.
# AISMSNet: Advanced Image Splicing Manipulation Identification Based on Siamese Networks
## 3 Proposed Splicing Manipulation Detection System Based on Siamese Neural Network
### Feature Extraction Using a Siamese Neural Network
#### 3.2.1 Siamese Neural Network  
Siamese neural networks focus on tasks that require identifying, comparing, and relating patterns. The design of this neural network model consists of two branches or similar models that share parameters, making it a suitable option for the detection of tampered areas [30; 31]. The main advantage of Siamese neural networks is that these models can be used to determine the similarity between two images by comparing them, which is useful for identifying manipulated areas. The training of the Siamese neural network consists of the use of pairs of images to generate representations that allow for identifying the differences between two images [32; 33; 34].  
Figure 2 visualizes the Siamese neural network's architecture, where the values obtained from the last convolutional layer of both branches are used to determine if the image has been manipulated. Furthermore, Table 1 shows the configuration of both branches.  
The loss function used to train the proposed Siamese neural network is the contrastive loss, given by Equation (1), which aims to learn a semantic representation where the patterns of similar objects are in the same feature space to detect the manipulations performed in each region of the picture [34].  
\[L(y_{i},I_{b1},I_{b2})=(1-y_{i})\bigg{(}\sqrt{(I_{b1}-I_{b2})^{2}}\bigg{)}+y_{i }\text{max}\bigg{(}0,m-\sqrt{(I_{b1}-I_{b2})^{2}}\bigg{)} \tag{2}\]  
where \(y_{i}\) is the value from the fully connected layer, which indicates the similarity between images, \(I_{b1}\)_and_\(I_{b2}\), and training regions from the image, and \(m\) is a value that minimizes or maximizes the value of the Euclidean distance.  
\[dist(p,q)=\sqrt{\sum(q_{i}-p_{i})^{2}} \tag{3}\]  
where \(p\)_and_\(q\) are the feature matrices generated by the Siamese neural network.