esa-sceva/satcom-chunk-collection huggingface dataset:
_Article_  
**Leakage Detection of a Spherical Water Storage Tank**  
**in a Chemical Industry Using Acoustic Emissions**  
**[PERSON], [PERSON](r), [PERSON], [PERSON] and [PERSON] * School of IT Convergence, University of Ulsan, Ulsan 44610, Korea; [EMAIL_ADDRESS] (M.S.); [EMAIL_ADDRESS] (M.I.); [EMAIL_ADDRESS] (J.K.); [EMAIL_ADDRESS] (D.-C.J.)**  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +82-52-259-2217  
Received: 22 November 2018; Accepted: 3 January 2019; Published: 8 January 2019  
**Featured Application: The proposed work is based on acoustic emission measurements and a machine learning technique to develop an intelligent fault diagnosis system. This work can be used to find cracks in industrial components such as spherical tank and pressure vessel.**  
**Abstract: Spherical storage tanks are used in various industries to store substances like gasoline, oxygen, waste water, and liquefied petroleum gas (LPG). Cracks in the storage tanks are unaccepted defects, as storage tanks can leak or spill the contained substance through these cracks. Leakage from contained hazardous substances storage tanks can contaminate the environment and may lead to fatal accidents. Therefore, the ability to detect cracks from spherical storage tanks is necessary to avoid damage to the environment and to ensure public safety. In this paper, we present a crack detection case study of a spherical tank. The detection was performed using time-domain statistical features and a machine learning algorithm. The proposed method consists of (1) extraction of statistical features from the acoustic emissions (AE) acquired from the spherical tank, and (2) classification of the nonlinear data using a support vector machine (SVM). We evaluate the proposed algorithm with AE data obtained from the spherical tank, demonstrating that the algorithm effectively discriminates between normal and crack conditions. These results show that the proposed algorithm is effective for detecting cracks in spherical storage tanks.**  
**Keywords: crack detection; leakage detection; spherical tank; statistical features; support vector machines**
# Traffic Noise Prediction Applying Multivariate Bi-Directional Recurrent Neural Network
## 3 Results and Discussion
### Development of RNN  
As mentioned above, the problem in this paper can be constructed with three different architectures: many-to-one, many-to-many and encoder-decoder. At each time step, along with the input traffic feature variables, a corresponding traffic noise output is expected. All these three architectures are trained with Blansko data, and corresponding model performances have been compared in this paper. After some basic tuning, such as number of layers and number of neurons, the trained models based on the three different architectures are obtained, as shown in Figures 13-15. Bidirectional recurrent neural network (BRNN) is applied, which is an extension of the conventional RNN [43]. BRNN can improve the model performance, because BRNN trains the model not only on the input sequence, but also on the reversed copy of the input sequence. Therefore, BRNN obtains additional context for predicting the current state by using the information from both the past and the future.  
Figure 12: Work flow in the traffic noise modelling with Blansko data.  
Figure 14: Model based on Architecture 2: many-to-many architecture.  
Figure 13: Model based on Architecture 1: many-to-one architecture.  
First model, based on Architecture 1, many-to-one, is stacked RNN network (Figure 13), with two bi-directional RNN layers and one fully connected dense layer, which is the output layer with a single neuron. The model output is a vector with the same length as the time steps of the sample sequence, n_steps.  
Second model, based on Architecture 2, many-to-many, has one bi-directional RNN layer and two fully connected dense layers including one output layer with a single neuron at each time step (Figure 14). Each of the two dense layers is wrapped by TimeDistributed API. TimeDistributed API applies the same operation to every temporal slice of the 3D tensor.  
The third model, based on Architecture 3, is an encoder-decoder network, where the encoder and decoder each consists of an RNN layer (Figure 15). Only the last output of the sequence from the encoder is sent to every temporal slice of the decoder, by RepeatVector API [44]. Two more fully connected dense layers were wrapped with TimeDistributed API, where one of them is output layer with single neuron, same as mentioned in Architecture 2.  
Among all these three trained models, dropout was also applied, in order to avoid model overfitting.
# Grid Anchor Lane Detection Based on Attribute Correlation
## 4 Experimental Results
### Experimental Summary  
The experimental results demonstrate that the proposed model achieves impressive lane detection performance across both the CULane and Tusimple datasets. The model excels in various scenarios, including normal (93.05%), curve (73.27%), and arrow (89.01%) scenarios, outperforming existing methods such as LaneATT and UFLDv2. The grid anchor-based lane representation, which allows for flexible and precise lane localization, is a key factor in the model's success, especially in curved scenarios. The introduction of convolutional reordering upsampling and the integration of a multi-layer perceptron (MLP) further enhanced the model's feature extraction capabilities, improving overall accuracy. Additionally, the loss function incorporating both intra-anchor and inter-anchor loss components contributed to more stable convergence and better detection results. However, the model's performance in extreme environments (e.g., fog and rain) remains relatively average, likely due to the dataset imbalance and the absence of post-processing modules. Future improvements could focus on incorporating more diverse data and enhancing post-processing steps to address these challenges.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**FPN** & **Upsampling** & **MLP** & **F1** \\ \hline ✓ & & & 75.09 \\ ✓ & ✓ & & 75.68 \\ ✓ & & ✓ & 75.27 \\ ✓ & ✓ & ✓ & 75.83 \\ \hline \hline \end{tabular} The symbol \(\mathbf{\check{\gamma}}\) indicates that the corresponding method is included in the calculation of the \(F1\) score.  
\end{table}
Table 7: Improved ablation experiments for FPN.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\mathbf{L_{si}}\) & \(\mathbf{L_{so}^{ed}}\) & \(\mathbf{L_{so}^{ok}}\) & **F1** \\ \hline ✓ & & & 75.80 \\ ✓ & ✓ & & 76.07 \\ ✓ & & ✓ & 76.31 \\ ✓ & ✓ & ✓ & 76.41 \\ \hline \hline \end{tabular} The symbol \(\mathbf{\check{\gamma}}\) indicates that the corresponding loss is included in the calculation of the \(F1\) score.  
\end{table}
Table 8: Ablation experiments for loss functions.
# Millimeter-Wave Multi-Channel Backscatter Communication and Ranging with an FMCW Radar
## 5 Signal Processing Framework
### Equalization  
To further minimize the error vector magnitude (EVM), we need to make the constellation points as concentrated as possible. This can be done via blind equalization.  
To concentrate the constellation points, the interference signal from another tag needs to be removed. The \(TX_{2}\) part needs to be removed from \(RX_{1}{}^{\prime}\), and the \(TX_{1}\) part needs to be removed from \(RX_{2}{}^{\prime}\). Since \(\Delta\omega_{1}\), \(\theta_{1}\), \(\Delta\omega_{2}\) and \(\theta_{2}\) are estimated from the previous step, the interference signal in \(RX2{}^{\prime}\) can be removed as  
\[\begin{array}{c}RX_{2}{}^{\prime\prime}=RX_{2}{}^{\prime}-\varepsilon_{1} \times RX_{1}{}^{\prime}\times e^{-j[\theta_{2}-\theta_{1}+(\Delta\omega_{2}- \Delta\omega_{1})t]}\\ =a_{2}TX_{1}e^{-j[\theta_{2}-\theta_{1}+(\Delta\omega_{2}-\Delta\omega_{1})t] }+b_{2}TX_{2}-\varepsilon_{1}\times a_{1}TX_{1}\times e^{-j[\theta_{2}-\theta _{1}+(\Delta\omega_{2}-\Delta\omega_{1})t]}\\ -\varepsilon_{1}\times b_{1}TX_{2}\\ \end{array} \tag{21}\]  
\[=(a_{2}-\varepsilon_{1}\times a_{1})TX_{1}e^{-j[\theta_{2}-\theta_{1}+( \Delta\omega_{2}-\Delta\omega_{1})t]}+(b_{2}-\varepsilon_{1}\times b_{1})TX_ {2},\]  
where \(\varepsilon_{1}\) is an adjustable coefficient that needs to be optimized to minimize the EVM. We do a search of \(\varepsilon_{1}\) from its possible value to get the smallest EVM. Equation (19) indicates\(a_{1}\gg b_{1}\), \(a_{2}\ll b_{2}\). When the first component in (21) is removed, we have the smallest EVM. In this case,  
\[a_{2}-\varepsilon_{1}\times a_{1}=0 \tag{22}\]  
Then, (21) becomes  
\[RX_{2}{{}^{\prime\prime}}=(b_{2}-\varepsilon_{1}\times b_{1})TX_{2} \tag{23}\]  
Follow the same procedure to remove the interference signal in \(RX_{1}{{}^{\prime}}\) and get  
\[RX_{1}{{}^{\prime\prime}}=RX_{1}{{}^{\prime}}-\varepsilon_{2}\times RX_{2}{{} ^{\prime}}\times e^{[\theta_{2}-\theta_{1}+(\Delta\omega_{2}-\Delta\omega_{1} )t]}=(a_{1}-\varepsilon_{2}a_{2})TX_{1} \tag{24}\]  
when  
\[b_{1}-\varepsilon_{2}\times b_{2}=0 \tag{25}\]  
The simulated constellation diagrams after equalization with different \(\Delta\omega_{2}-\Delta\omega_{1}\) are shown in Figure 7. Compared with Figure 6, the constellation points are more concentrated.
# Maximizing Efficiency in Energy Trading Operations through IoT-Integrated Digital Twins
## 3 Proposed Architecture
### Evaluation
#### 3.4.3 Latency Analysis  
Latency analysis pertains to the assessment and enhancement of the time delay, commonly known as latency, encountered by tasks or jobs within a scheduling system. The process entails the examination of the duration required for tasks to initiate or conclude their execution from the point at which they are submitted or received within the system. The latency metric was utilized for the same objective as that of RTT. The formula for latency calculation is shown below, which comprises task start time, task submission time, task execution time, and task completion time.  
\[\text{Latency}=T_{\text{start}}-T_{\text{submit}}+T_{\text{execution}}+T_{ \text{completion}} \tag{12}\]
## 3 Context Modeling
### Context Model
#### 3.2.4 Activity Class  
An _activity_ may be either _instant_, e.g., the air-conditioning was turned on, or _durative_, e.g., the TV is showing a movie. Some models presented in the literature distinguish between action and event, implying an active (caused by users) or passive nature. Since the proposed model represents both user and device in the same class, all activities can be assumed to be triggered by a device or user, thus a thing object. Furthermore, conceptually, it is more efficient to model the triggering of an activity by capturing all of the related context.  
An activity may be part of or occur after another activity, properties that are modeled with appropriate self-referential relations. As already mentioned in the time class, an activity engages one or more thing(s). The activity class is not directly related to a location, but rather indirectly through the engaged things and their locations. Thus, an activity occurring in different locations (e.g., a phone call) can be accurately modeled.  
This class illustrated in Figure 5, aims to represent a wide range of cases, from typical human activities, to events triggered by human decisions or device actuation. Furthermore, it models not only observations of occurring activities, but also tentative activities. In order to model this distinction, an activity is related to the time class in different ways. It may occur in a specific timeframe, or is connected to time through the _is scheduled_ relation. An activity has similar profiling classes with location: general profile and authorization profile to model privileges of things in respect to the activity, e.g., users permitted to turn on/off a device.  
Finally, the measurement of a sensor device is considered an activity and can be accurately captured by the activity class. It includes a thing, a timestamp and a value. Similarly, a service is considered an activity (a tentative action to support a need). Overall, the activity class is wide enough to include all types of systems or user behaviors.
# ARIBC: Online Reporting Based on Identity-Based Cryptography
## 4 Implementation
### ARIBC Communication and Computation Overhead  
The communication and computation overheads imposed by the ARIBC depend on the security level that has been chosen at the initial setup phase. Stronger security implies larger cryptographic parameters, and these in-turn imply more communication and computational overhead. The following estimates are derived based on the work in [5, 45, 47].
# Data-Driven Computational Methods in Fuel Combustion: A Review of Applications
## 3 Analysis of the State of the Art
### Methods and Algorithms
#### 3.1.4 Support Vector Machines (SVMs)  
In Ref. [7], SVM, in addition to FNN and GPR, were compared in terms of predicting the rate of chemical reactions of alkenes with the OH radical. While FNN scored the best, SVM also achieved good accuracy. In Ref. [20], a comparison with random forests showed their limited effectiveness in specific conditions. In the study [22], SVM was used to classify the oxidation state of carbon based on terahertz data, and an accuracy of 87.5% was achieved, which confirmed the usefulness of this method in detecting spontaneous ignition hazards. Ref. [42] compared five ML models for predicting combustion phases in hydrogen-powered Wankel engines; the GPR model achieved the best generalization, and SVM and EnTR obtained high accuracy for key combustion phase stages. In the case of SVM applications for the classification of combustion states, it is important to choose the kernel function, which determines how the data is transformed into a feature space and the model's ability to separate nonlinear relationships. In the analyzed works, RBF (radial basis function) functions are mainly used, which work well in conditions of a large number of features and non-linear relationships between process parameters (e.g., temperature, pressure, component concentrations). In the context of combustion data, which is often characterized by a high correlation of variables and the presence of measurement noise, SVM offers a good balance between prediction accuracy and robustness to learning. However, these models require careful selection of parameters--such as the regularization factor C and the gamma parameter for the RBF function--which in practice is often carried out using a search grid or optimization algorithms. For example, in the classification of cyclic combustion variability in ZI engines, SVM with a properly selected nucleus allows for precise detection of limit states, which is directly important for ignition and emission control. The results highlight the importance of algorithm selection for the precision of alternative fuel engine control. In Ref. [43], an adaptive PSO-SVM model was used to predict the behavior of the S-CO\({}_{2}\) CFB boiler in the scaling process, effectively replacing costly CFD simulations. In Ref. [44], an optimized model for the prediction of cycle variability was developed for Wankel engines powered by gasoline and n-butanol with the addition of hydrogen, combining SVM with a genetic algorithm. On the other hand, Ref. [45] proposes the use of a neural network (BP-ANN) improved by a genetic algorithm (GA) to optimize combustion parameters in a coal boiler, enabling precise tuning of the fuel and air ratio to improve efficiency and reduce emissions.
# LESS: Link Estimation with Sparse Sampling in Intertidal WSNs
## 7 Performance Evaluation
### Evaluation in Real Systems
#### 7.2.6 Time Complexity Analysis  
Both LESS and CTP require nodes to calculate link qualities with neighbors by sending beacons. Taking a network with \(n\) nodes as an example, every node needs to exchange beacons with a maximum of \(n\) nodes to maintain a routing table, and the time complexity of CTP is \(\mathcal{O}(n^{2})\). If the sampling rate of LESS is \(s\) (\(s\in(0,1)\)), \(n*s\) nodes should exchange beacons with a maximum of \(n\) nodes for link quality reconstruction, and the time complexity of LESS is also \(\mathcal{O}(n^{2})\).
## 2 Review of Model Construction and Parameter Determination
### Organismic Constraints
#### 2.1.1 Structural Constraints  
As a rule of thumb, forward-dynamics simulation models have remained as simple as possible while maintaining sufficient complexity to answer the research question [4]. For instance, [PERSON] contrasted simple and complex models of walking to show that a simple model (an inverted double pendulum) was able to identify basic features of muscle function during gait, but that a more complex model (3-dimensional, 10-segment, 23-degree-of-freedom musculoskeletal model) was required to discern the functional roles of specific muscles in the movement [31]. Similarly, for jumping, [PERSON] was able to use a planar two-segment, single muscle model to estimate optimum touchdown conditions for the plant leg in running jumps that were in close agreement with competitive performances [14]. To realistically predict muscular control during jump performances required a three-dimensional model consisting of ten segments and 54 muscle actuators [32] or 17 segments and 46 muscle actuators [33].  
It is often claimed that the simpler the model, the easier it is to quantify the contribution of its features to the observed effect [14; 34]. However, in reality, the complexity of forward-dynamics simulation models are more often limited by computational power and the ability to construct scientifically robust constraints. Forward-dynamics simulation models investigating maximal effort sporting movements have, therefore, typically represented relatively simple planar movements such as bilateral vertical jumps where relatively little error is introduced by the assumptions of planar movement [9; 10] or bilateral symmetry [12; 35; 36]. Three-dimensional forward-dynamics models have been restricted to angle-driven models [37] or muscle-driven models with muscle parameters either scaled from values in the literature [32; 33] or determined via optimization [38; 39]. This has limited their application to neuromuscular coordination, internal loading, and injury risk [38; 39; 40; 41; 42], rather than the optimization of maximal effort sporting performance.
# Multifunctional Hyperelastic Structured Surface for Tunable and Switchable Transparency
## 1 Introduction  
Metasurfaces or planar metamaterials are engineered surfaces that use the tailored polarization response of resonant meta-atoms to control incident electromagnetic waves over electrically small thicknesses [1]. Metasurfaces have numerous applications, including in antennas [2], superlenses [3], sensors [4], holograms [5, 6], imaging devices [7, 8, 9], polarizers [10], high impedance surfaces [11], and structural color generation [12]. Passive metasurfaces, made of fixed resonant meta-atoms, are restricted to a single pre-determined fixed frequency response. Modifying a passive metasurface in such a way to enable external reconfiguration and tuning of its resonance frequency/bandwidth is a much sought functionality in many high-end applications, from reflect/transmit arrays to smart conformal telecommunication systems. Tunable metasurfaces can be achieved through several approaches such as geometric reconfiguration of the resonators mediated by mechanical deformations [13] or electronic components, like varactor diodes [14], PIN diodes [15], and micro-electro-mechanical systems (MEMS) switches [16]. In addition, one can change the electromagnetic properties of the substrate by using materials that respond strongly to an external electric or magnetic bias, such as liquid crystals [17], graphene [18], silicon [19], orferrite [20]. Reconfigurability can further be increased by intelligent mechanical designs, such as origami-based structures [21]. Often, however, the required bias network detrimentally increases the mass and complexity of the design, making it impractical especially for applications requiring large surfaces [22; 23]. In such cases, mechanical deformation is the best option as it allows reconfiguring the entire surface at once by acting only on its boundaries. Such design, however, requires careful modeling of the mechanical deformation, especially of the boundary effects and non-linear deformations that can occur when the deformations are large.  
In this paper, we propose the use of hyperelastic substrates to create a multifunctional hyperelastic structured surface (HSS) with large electromagnetic reconfigurability. We employ copper resonators and a hyperelastic substrate made of Polydimethylsiloxane (PDMS), operating between 10 and 13 GHz. To find the resonator geometry that results in an optimal level of mechanical and electromagnetic reconfigurability without reaching the maximum allowed value of local stress, we follow an optimization procedure seeded by common resonator geometries [13; 24; 25; 26] in order to find the optimal design in terms of resonance frequency shift (RFS). We fabricate the optimized HSS through a cost-effective method and measure its scattering parameter S\({}_{21}\) under several mechanical deformations.
# Asymmetric Distance in K-Means Clustering Enhances Quality of Cells Raman Imaging
## 5 Discussion  
Within the k-means clustering framework, the proposed asymmetric distance metric captures directional relationships in the data, a property particularly important for Raman signals characterized by gradual chemical or structural gradients. Although the core of this study is the direct comparison of clustering metrics within the KCA framework, in this section we briefly discuss how our approach relates to dimensionality reduction and clustering techniques commonly employed in Raman hyperspectral analysis.  
Principal Component Analysis (PCA) is frequently used to reduce the dimensionality of Raman datasets prior to clustering [27; 28; 29]. However, PCA projects the data onto orthogonal components that maximize variance, which can result in the loss of fine spectral features critical for accurate biochemical discrimination. In contrast, the present approach operates directly in the original spectral space without dimensionality reduction, thereby preserving subtle but potentially important variations.  
t-Distributed Stochastic Neighbor Embedding (t-SNE) [30; 31] and Uniform Manifold Approximation and Projection (UMAP) [32; 33; 34] are powerful non-linear dimensionality reduction techniques that have been used to visualize high-dimensional spectral data. However, these methods are primarily designed for visualization rather than clustering. Moreover, t-SNE and UMAP embeddings can distort global structures, potentially obscuring underlying chemical relationships.  
Hierarchical Clustering Analysis (HCA) has also been applied in Raman hyperspectral studies [35; 36]. Compared to KCA, HCA does not require the number of clusters to be specified a priori, but it becomes computationally intensive for large datasets. Like KCA, HCA is highly sensitive to the choice of distance metric. However, KCA only requires local distance computations between data points and centroids, and can naturally incorporate a directional, asymmetric attraction toward centroids. In contrast, HCA relies heavily on a precomputed global distance matrix, and classical linkage criteria assume symmetric distances, such as Euclidean distance. As a result, applying an asymmetric distance in HCA could introduce ambiguities during the merging of clusters, making KCA a more appropriate framework for direct use of directional information in the data.  
In summary, compared to commonly used dimensionality reduction and clustering techniques in Raman imaging, the main advantages of the proposed asymmetric clustering approach are: (i) it preserves full spectral resolution without the need for dimensionality reduction; (ii) it introduces directionality into the distance calculation, improving sensitivity to gradual biochemical changes. Future work could explore further applications of the asymmetric distance, such as its integration after PCA-based dimensionality reduction or its combination with hierarchical methods adapted to asymmetric distance.