esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Framework
### Chebyshev Pooling
#### 3.2.2 Derivative and Gradient  
As usual, a CNN-based classifier was trained by using the back-propagation algorithm. For error propagation and weight adaptation in the fully connected layer and the convolutional layer, the standard back-propagation procedures could be employed before the pooling layer. In the network design, the pooling layer did not actually conduct any learning. On the contrary, it just reduced the sizes of the tensor after the Chebyshev pooling, leaving the feature \(t\). This \(t\) value obtained an error calculated from back-propagation, and the error was forwarded to the entire pooling block by processing Equation (3), then divided by \(k^{2}\). The result was further forwarded to its source, and all the units in the block affected its value. In addition to the error consideration, we also found the first derivative of Equation (3) about \(t\) as follows,  
\[\frac{d}{dt}P_{max}(t)=-\frac{2\sigma^{2}(t-\mu)}{\left(\sigma^{2}+\left(t- \mu\right)^{2}\right)^{2}},\]  
where \(\frac{d}{dt}P_{max}(t)\) becomes zero if \(t\rightarrow\infty\). This meant the gradient was vanishingly small and the training processes would stagnate somewhere. In order to alleviate this worst-case scenario, we proposed to employ the non-linear function _Softplus_ as the activation function to activate the gradient by applying it to \(t\). The reason for choosing _Softplus_ was that it could ensure the output within the activated range of \((0,+\infty)\), and it is monotonic and differentiable, having a positive first-order derivative in \(\mathbb{R}\). Meanwhile, there were several ways for the \(t\) selection, including taking the minimum or maximum value, and random selection. In our practice, the maximum and random selection could both cause the training convergence to be successful, but the minimum way could not, and the convergence of the maximum selection was faster than that of the random selection. Thus, we recommend using the maximum value as the sample \(t\). By stacking the aforementioned layers, we completed the feature extraction with the proposed pooling.
## 3 Methodology
### Loss Function
#### 3.3.1 Reconstruction Loss  
The reconstruction loss, \(L_{rec}\), was used to measure the difference between the sum of the upgoing wavefield, \(\widetilde{U}\), and the downgoing wavefield, \(\widetilde{D}\), predicted by the dual convolutional autoencoder and the original VSP record, \(X\), specifically defined as  
\[\text{L}_{\text{rec}}=\left|\left|X-\widetilde{U}-\widetilde{D}\right|\right|_ {h}, \tag{13}\]  
where \(||\cdot||_{h}\) represents the error measure based on the Huber function, which is specifically defined as:  
\[f_{h}(s)=\left\{\begin{array}{ll}\frac{1}{2}*s^{2},&if\ \ |s|\leq d\\ d*|s|-\frac{1}{2}*d^{2},&if\ \ |s|>d\end{array}\right. \tag{14}\]  
The Huber function uses squared loss for small errors and linear loss for larger errors, thus combining the advantages of both the mean squared error and absolute error. This effectively reduces the sensitivity to outliers. The introduction of the reconstruction loss ensures that when the model predicts the upgoing and downgoing wavefields, the overall wavefield can accurately reconstruct the original observed data. This loss term encourages the model to maintain the overall consistency and physical reasonability of the wavefield during the separation of the upward and downward waves.
## 1 Introduction
In addition, the location of the substation has a significant impact on the connection topology, and some studies have proved an optimal matching between the two [19; 20; 21; 22; 23; 24]. In these studies, the spatial coordinates of the substation were taken as variables, and the shortest cable length [19; 20; 21], minimum investment [22], maximum benefit [23], and minimum energy loss [24] were taken as optimization targets. Subsequently, the optimal substation locations and matching topology were obtained by using MST to generate connections and combine the optimization algorithms. A joint optimization of the wind turbine locations and the connection topology at a given annual wind speed distribution has also been carried out in some studies to improve the economics of the entire wind farm [25; 26; 27]. In the literature [10; 24; 28], when the energy loss of the topology was included in the cost model, the optimization results showed that the energy loss was effectively reduced by improving the topology structures. In the selection of optimization algorithms, the optimization of the power system involved complex connections between wind turbines and substations, and a large number of constraints on power parameters, being revealed as a non-convex, discontinuous, NP-complete complex optimization problem [29; 30]. To cope with such problems, evolutionary algorithms such as genetic algorithms [20; 22], PSO [25], and MOEA [31] were considered to be ideal optimization algorithms, since they could be applied to any number of design variables and tolerate random errors generated during the search process. In addition, classical optimization algorithms such as nonlinear programming [24; 32] and quadratic programme [33] have also been applied. From the review of the existing literature, it can be concluded that the cable cross-section of the power system is traditionally determined according to the principle of maximum current carrying capacity, and the matching optimization with the connection topology cannot be achieved. This situation may cause the overall topological resistance level to increase significantly, resulting in severe energy loss. In this pursuit, we develop a new coupling optimization method for the connection topology and the cable cross-section. The proposed method designed the power system of a typical discrete wind farm, and the results are compared with the traditional step-by-step method. Moreover, the effects of the energy loss on the power system economics and topology are also evaluated, and the effectiveness of the proposed method is discussed.
# 1D Barcode Detection via Integrated Deep-Learning and Geometric Approach
## 6 Experiment and Evaluation
### Results and Comparison  
We compared the metrics, the accuracy \(J_{avg}\), and the detection rate \(D_{0.5}\) with previous works [2; 3; 4; 5; 6] using two datasets: the ArteLab Rotated Barcode Dataset and the WWU Muenster Barcode Database. We collected the results that they report in articles and compared them to the results from our approach. The comparison results for the two datasets are shown in Tables 1 and 2 (in [3], the accuracy \(J_{avg}\) is not reported). It can be seen that our method shows a noticeable improvement both in the accuracy \(J_{avg}\) and the detention rate \(D_{0.5}\). In particular, the accuracy \(J_{avg}\) reaches a score higher than 0.9, which has not been achieved in the previous.  
We also compared the speed, which is presented in Figure 7. The barcode location stage took 14.8 ms for the 1D barcode region location and rotation angle prediction. Note that we performed the same task as [PERSON] et al. [6] in terms of the barcode location stage but with a faster speed. The region extraction stage took 4.7 ms to propose an accurate 1D barcode region. The total process took 19.2 ms; this is also faster than the existing approaches mentioned above [2; 3; 4; 5; 6].  
Considering the 1D barcodes may be printed on soft packing surfaces, or packed in plastic film, which brings distortion, occlusion, and reflects light, we collected some images from the retail market using a phone camera to test whether our algorithm can overcome these harsh conditions. The results show that most 1D barcodes can still be captured precisely. The primary purpose of this test in a real-world scenario was to investigate the sensitivity of our approach; some 1D barcodes were not  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Approach** & **Accuracy \(J_{avg}\)** & **Detection Rate \(D_{0.5}\)** \\ \hline [PERSON] et al. [5], 2013 & 0.695 & 0.805 \\ [PERSON] and [PERSON] [2], 2015 & 0.763 & 0.893 \\ [PERSON] and [PERSON] [3], 2016 & - & 0.989 \\ [PERSON] et al. [6], 2017 & 0.816 & 0.926 \\ [PERSON] and [PERSON] [4], 2017 & 0.860 & 0.930 \\ [PERSON] & 0.912 & 0.997 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test results on the ArteLab Rotated Barcode Dataset.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Approach** & **Accuracy \(J_{avg}\)** & **Detection Rate \(D_{0.5}\)** \\ \hline [PERSON] et al. [5], 2013 & 0.682 & 0.829 \\ [PERSON] and [PERSON] [2], 2015 & 0.829 & 0.963 \\ [PERSON] and [PERSON] [3], 2016 & - & 0.982 \\ [PERSON] et al. [6], 2017 & 0.873 & 0.991 \\ [PERSON] and [PERSON] [4], 2017 & 0.882 & 0.966 \\ [PERSON] & 0.939 & 0.998 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test results on the WWU Muenster Barcode Database.  
Figure 7: The different execution times for the two datasets.  
decoded successfully when they were serious distorted and/or occluded. We show some examples in Figure 8.  
Figure 8: Some examples in different conditions: normal, occlusion, distortion, and light reflected.
# Extrinsic Bayesian Optimization on Manifolds
###### Abstract  
We propose an extrinsic Bayesian optimization (eBO) framework for general optimization problems on manifolds. Bayesian optimization algorithms build a surrogate of the objective function by employing Gaussian processes and utilizing the uncertainty in that surrogate by deriving an acquisition function. This acquisition function represents the probability of improvement based on the kernel of the Gaussian process, which guides the search in the optimization process. The critical challenge for designing Bayesian optimization algorithms on manifolds lies in the difficulty of constructing valid covariance kernels for Gaussian processes on general manifolds. Our approach is to employ extrinsic Gaussian processes by first embedding the manifold onto some higher dimensional Euclidean space via equivariant embeddings and then constructing a valid covariance kernel on the image manifold after the embedding. This leads to efficient and scalable algorithms for optimization over complex manifolds. Simulation study and real data analyses are carried out to demonstrate the utilities of our eBO framework by applying the eBO to various optimization problems over manifolds such as the sphere, the Grassmannian, and the manifold of positive definite matrices.  
Bayesian optimization; optimizations on manifolds; embedding; extrinsic gaussian process Article
# Anti-Fouling Properties of Phosphonium Ionic Liquid Coatings in the Marine Environment
## 1 Introduction
By choosing an antimicrobial anion, one can adjust an ionic liquid's antimicrobial properties [32]. In a 2020 investigation, [PERSON] et al. looked into the antibacterial properties of SLIPSs injected with various counterions of phosphonium ionic liquids using different anions such as bis(trifluoromethylsulfonyl)imide and docusate [9]. Figure 1: Process of marine biological fouling—Stage 1: Biofilm formation (by adsorbing organic molecules to the submerged surface, a conditioning biofilm is created); Stage 2: Primary colonization (a planktonic bacteria settles on the surface and grows exponentially in the nutrient-rich media); Stage 3: Secondary Colonization (colonization of microorganism including algal spores, barnacle crypts and diatoms); Stage 4: Macro fouling formation (settlement of larger marine species) [1]. Figure has been reproduced with permission from reference [12]. We perceive several advantages in using phosphonium-based ionic liquids for producing anti-fouling coatings. There are four possible alkyl chain attachments onto the phosphonium ion, [PR\({}_{4}\)]\({}^{+}\), which can be used to significantly enhance hydrophobicity or toxicity depending on the appropriate choice of alkyl substituent. Additionally, phosphonium ionic liquids are thermally more stable than imidazolium and other common ionic liquids, more stable to bases and nucleophiles (no acidic protons), and less expensive [22; 33]. Explored chemistries can provide phosphonium ionic liquids with improved heat stability, lower viscosity, improved gas transport, and greater stability under basic and reducing conditions [29; 34]. In this work, we describe the synthesis and explore the use of hydrophobic phosphonium-based poly(ionic liquid) ion gels to prevent biofouling in marine environments. We observe the effect of hydrophobicity, ionic liquid content, and the inclusion of copper as a toxic component. We determine correlations between hydrophobicity and antimicrobial properties of the phosphonium ion gels with anti-fouling performance in marine environments.
# Transforming Petrochemical Processes: Cutting-Edge Advances in Kaolin Catalyst Fabrication
## 7 Kaolin in Petrochemical Catalysis: From Fundamentals to Innovations
### Kaolin-Driven Catalysis in Petrochemical Processes
#### 7.4.2 Hydroracking
The modified crystal morphology and pore architecture of the Y zeolite enhanced mass transfer and acidity accessibility, leading to higher aromatic contents in middle distillates and lower Bureau of Mines Correlation Index (BMCI) values of tail oil. This study provided deeper insights into the structural characteristics that influence hydrocracking efficiency. Recently, in 2024, [PERSON] et al. (2014) synthesized bifunctional Ni/Mo-impregnated ZSM-5 catalysts using Badau Belitung kaolin. These catalysts demonstrated exceptional performance in converting heavy petroleum distillates into medium and light fractions, achieving conversions of 92.47% and 92.06% by mass for two optimized formulas, respectively. This performance significantly outperformed commercial catalysts, which yielded conversions of 70.40% and 87.19% by mass under the same conditions. Overall, these studies underscore the potential of kaolin-derived catalysts in enhancing hydrocracking efficiency and product yield, with applications ranging from renewable oil hydrotreatment to heavy petroleum distillate conversion.
# A Performance-Consumption Balanced Scheme of Multi-Hop Quantum Networks for Teleportation
## 2 Preliminary
### Fidelity  
Because of the channel noise, the quantum state we finally get may be different from the theoretical value. Fidelity is used to measure this deviation, defined in [26]:  
\[F=\sqrt{\langle\psi|\rho|\psi\rangle}, \tag{5}\]  
where \(|\psi\rangle\) is the wanted quantum state, \(\rho\) is the density matrix of the quantum state received finally. The density matrix is  
\[\rho=\sum_{i}p_{i}|\psi_{i}\rangle\langle\psi_{i}|, \tag{6}\]  
where \(p_{i}\) is the probability of state \(|\psi_{i}\rangle\). Fidelity takes values from 0 to 1.
## 3 Results and Discussion
### Soil Disturbance Characteristics
#### 3.2.2 Longitudinal Disturbance  
The simulation showed the effects of rake angle and belt motion on longitudinal disturbance (Figure 9). The area of longitudinal disturbance (_Axz_) decreased as the rake angle increased (Figure 10). Meanwhile, the failure boundary became curvier gradually (Figure 9). The _Axz_ values for the regular tools and the belt tools were maximal at the rake angles of 30\({}^{\circ}\), then decreased at different rates as the rake angle increased. The _Axz_ values for the belt tools were 3.54%, 4.46%, and 6.98% lower than those for the regular tools at the rake angles of 45\({}^{\circ}\), 60\({}^{\circ}\) and 75\({}^{\circ}\), respectively (Figure 10). Additionally, the failure boundaries were straighter for the belt tools than those for the regular tools (Figure 9). These results indicate that the belt motion reduces the area of longitudinal disturbance and changes its shape especially when the rake angle is larger.  
The decrease in the area of longitudinal disturbance (_Axz_) caused by the belt motion was related to the decreases in rupture angle (\(\beta\)) and forward rupture distance (\(L\)) (Figures 11 and 12). The \(\beta\) and \(L\) values for the belt tools were about 1-5\({}^{\circ}\) and about 7% lower than those for the regular tools, respectively. Therefore, the changes in these two parameters as well as the increase in upheaval height (\(H\)) contributed to the changes in the area of longitudinal disturbance (_Axz_).  
Figure 10: Area of longitudinal disturbance (_Axz_).  
Figure 9: Longitudinal disturbance and particle velocity distribution: (**a–d**) Regular tools; (**e–h**) Belt tools (rake angle \(\alpha=30^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\), 75\({}^{\circ}\)).  
In general, small rake angles lead to relatively straighter failure boundaries, larger rupture angles, and smaller rupture distances, whereas large rake angles have the opposite trends [41]. These are the differences between \"cutting\" and \"pushing\" [35]. The belt motion tends to reduce the \"pushing\", though its effect is also different from the \"cutting\". This means the direction in which the tool applies forces to the soil and the direction in which these forces cause the soil to move has changed [1]. The changes in longitudinal disturbance are closely related to the change in tool force. The draught component of the tool force decreases for the reduced \"pushing\". The tool force decreases since less soil interacts with the tool, which is reflected in the reductions in the area of longitudinal disturbance and rupture distance [39]. Therefore, the comprehensive effect of belt motion on longitudinal disturbance is one of the reasons for the reduction in tool force.
# Toward Explainable AutoEncoder-Based Diagnosis of Dynamical Systems
## 1 Introduction  
### Autoencoders in Diagnosis  
Data-driven diagnosis using deep learning is starting to have a big impact in diagnostics. Applications have been developed for applications such as rotating machinery [1; 2], as well as for other areas, such as medicine [3]. The common theme in most of these applications is the use of image data (e.g., X-rays in medicine) or time-series data. Hence, few applications that apply to complex systems, such as large process-control systems, have been developed.  
One of the most common deep learning architectures for diagnosis is the autoencoder [2]. The autoencoder (AE) has been used for fault detection and for fault isolation. AE performance is comparable to using traditional signal-processing methods, but it has been supplanting these methods since data pre-processing is not needed in AE-based approaches, whereas it is a manual and time-consuming requirement for traditional signal-processing methods. A major drawback to AE-based approaches is the need for large volumes of (labeled) data, and the lack of explainability for the results.  
Ideally, we use an AE as both a residual generator [4] and decision-logic tool to isolate faults. The question is how to train the AE to generate good diagnostic results, i.e., to best distinguish among the different failure conditions (or modes).  
An AE has two steps: (1) _the encoding step_ transforms the input \(\xi\subset\mathcal{X}\) into a representation \(\zeta=f(\zeta)\) that resides in a subspace \(\mathcal{W}\) of \(\mathcal{X}\); (2) _the decoding step_ performs the inverse transform, performing the mapping \(\xi^{\prime}=f(\zeta)\). The standard metric (or loss function) used in an AE is the \(L_{2}\), or mean-squared error (MSE), metric defined over the input \(\xi\) and reconstructed input \(\xi\), i.e., \((\xi-\xi)^{2}\). For diagnostics purposes, the goal is that the subspace \(\mathcal{W}\) will enable us to classify different fault modes, i.e., it disentangles \(\xi\)[5; 6]. The key to successfully using an AE for diagnosis is to identify the subspace \(\mathcal{W}\) that best distinguishes the different nominal and failure modes of the system under analysis. Unfortunately, this \(L_{2}\) loss function does not have clear semantics for diagnostics isolation in comparison to a metric based on an encoded subspace whose principal angle [7] best distinguishes thenominal and failure modes of the system. An example of such a metric is the gap metric [8]. In comparing a nominal input \(\xi\) and new input \(\hat{\xi}\), the gap metric is \([0,1]\)-bounded, and has well-defined interpretations for \(0\) (behaviors \(\xi\) and \(\hat{\xi}\) are identical) and \(1\) (behaviors \(\xi\) and \(\hat{\xi}\) are maximally different) [8]. In contrast, two behaviors can be similar even though their \(L_{2}\) metric can be arbitrarily large ([9], p. 349).
# Variational Autoencoders for Network Lifetime Enhancement in Wireless Sensors
## 3 Methodology
### Discussion  
The performance of the proposed work was verified in a common network setup that is shown in Table 2 with the standard ODV routing with LEACH cluster head selection model. The performance of the proposed work can also be improved in terms of network lifetime, delay or energy consumption with an alternative routing or improved cluster head selection approach. However, the performance of the proposed work can be affected with several factors like hardware limitations, signal interference and power source availability on real-time deployment. Therefore, a careful consideration of computational components, memory units, and power resources are required for a successful ML- based implementation. Also, the complexity of the ML algorithm, number of connected sensors and the amount of data to be processed require a powerful CPU, GPU and memory units. For implementing the proposed work, a multi-core CPU of 3.0 GHz or higher is required for handling the coordination between the connected components. Similarly, a GPU of 8-12 GB and a RAM of 16-32 GB are required for processing the huge data generated by the sensors in parallel to the execution of the VAE algorithm without any buffering. In such cases, power consumption by the compression algorithm would be a constraint, and that can be rectified by implementing an efficient batch processing technique along with a careful thermal management process.
## 4 Discussion
### Uncertainty and Potential Refinements  
The PB-CB method used in this study has dramatically improved the results of previous studies. However, certain factors and uncertainties still continue to influence the accuracy of the mapping results. Firstly, GLC_FS30 is used as auxiliary data for land mask processing and has some errors that may lead to propagation errors in the final crop classification map. In addition, the MOD13Q1 data has a spatial resolution of 250 m, which will cause the problem of mixed pixels and lead to errors in crop recognition.  
Although the work of this study is carried out in North China, the methods used in this study can also be applied in other regions. Several improved ways may be more applicable to other crop identification. First of all, time series was used for crop identification, and these data still face the challenge of intra-class variation. Multi-source data fusion can be used to classify crops [57]. Crops are affected not only by phenology but also by longitude, dimension, sowing time, and climate change [58; 59]. Therefore, the phenological, longitude, and latitude information can be adopted in the recognition algorithm of crop area [60]. In addition, medium spatial resolution data have limited acquisition and coverage capabilities, and rough spatial resolution, such as MODIS, still has some problems in mixed-pixel recognition [61; 62; 63; 64]. In future research, higher spatial resolution data can be used for the crop recognition.