esa-sceva/satcom-chunk-collection huggingface dataset:
# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
## 7 Land-Based Systems Implementation-Evolution: Early Rail Defect Detection Capability
### Signal Pre-Processing
#### 7.3.4 Signal Stretching and Scaling  
Time varying EOCs influence the acquired signals, even in a defect-free rail: for this reason, compensation techniques not affecting the influence of defects on the processed signal are required. The group velocities of guided wave modes are influenced by temperature variations; in fact, the reflections appear to be shifted in time (or distance) if measurements are acquired at a different temperature. This effect can be compensated for by stretching the dispersion-compensated signals, in time or in distance domain [65]. Since dispersion compensation converts time domain signals in the distance domain, stretching in Ref. [65] has been performed in the last domain.  
The influence of temperature variations cannot be represented only through a phase shift, since the wave envelope can also be distorted. A piecewise linear stretch can be exploited to compensate for the envelope distortion; in doing so, the knowledge about the location of the welds appearing along the considered span of rails is used. If this method is adopted, the distorted envelope is stretched in a way that the reflection peaks corresponding to the welds are aligned with their known locations. In this phase, however, the phase information is discarded. The received signal might also be affected by variations in their amplitude during the monitoring period. These can originate from a change in the sensitivity of transmitters or in the attenuation of the rail with temperature, or from the rail itself gradually sinking into the ballast. To compensate for this effect, signal scaling based on the peaks originating from weld reflection can be performed. Guided waves are attenuated during propagation; for this reason, distant weld reflections appear smaller than those due to welds closer to the transducer array. To compensate for this effect, an energy-based normalization can be applied to each signal envelope by adopting the technique proposed by [PERSON] et al. in Ref. [PERSON] et al. (2018). The energy attenuation employed in normalization is computed by exploiting a moving average technique.  
The phase of the compensated signals is discarded, and only their envelope remains. Unluckily, the envelope is affected by a large _direct current_ (DC) component, affecting the performance of the defect detection algorithms (and, in particular, of the ICA; see Section 7.4). To reintroduce phase information, the wave envelopes of scaled signals are multiplied by a single-frequency sine wave. However, the employed method for defect detection is based only on the analysis of signal envelopes [PERSON] et al. (2018).
# Perceptual Similarities between Artificial Reverberation Algorithms and Real Reverberation
## 2 Background
### Hybrid Moorer-Schroeder Implementation
#### 2.4.2 The Binaural Optimised Structure of Reverberation Algorithms
The original D/R energy ratio of the impulse response generated by the reverberation algorithm is first found by Equation (15), then its reverberation portion is scaled by Equation (16) to match the D/R energy ratio of the desired measured impulse response. The direct-to-reverberant (D/R) energy ratio can then be used as an acoustic cue in a reflective environment in relation to a one-to-one distance, avoiding confusion with source characteristics [49]. In a typical listening room, the direct sound field energy decays proportionally to the (logarithmic) distance, while the energy of the reverberant sound field is independent of the distance. Therefore, D/R can in principle be used to estimate the distance of a sound source [50]. \[DRR\_orig=mean(rms(Direct))/mean(rms(Reverb)) \tag{15}\]  
where \(DRR\_orig\) is the original D/R energy ratio of the impulse response generated by the reverberation algorithm, \(mean\) represents the average of the left and right channels, \(rms\) represents the calculation of the root mean square, \(Direct\) represents the direct sound portion, and \(Reverb\) represents the reverberation portion:  
\[Reverb\_RA=(Reverb\times DRR\_orig)/DRR\_M, \tag{16}\]  
where \(Reverb\_RA\) is the scaled reverberation portion, \(Reverb\) is the original reverberation portion, and \(DRR\_M\) represents the D/R energy ratio of desired measured impulse response. Figure 12: The structure of the comb filter in the large-room HMS reverberation algorithm.
# Multiple Pedestrian Tracking in Dense Crowds Combined with Head Tracking
## 3 Main Architecture
### Match  
Bipartite Graph. Head bounding boxes and body bounding boxes obtained in Sections 3.1.1 and 3.1.3 can be viewed as a bipartite graph. It is a special graph dividing vertices into two disjoint and independent sets. The vertices in these two sets are connected by edges, but not self-connected in one set. In our method, head bounding boxes and body bounding boxes respectively constitute the two sets of the bipartite graph, and the edges between two vertices were evaluated by the IoC calculation between the head bounding boxes and the the full-body bounding boxes.  
IoC and Cost Matrix. The IoC reflects the extent to one bounding box covered one other bounding box, and is calculated in the ratio of the intersecting area between head and full-body bounding boxes to the area of the entire-body bounding box. As shown in (7), where \(H_{i}\) and \(B_{j}\) represent the \(i\)th head bounding box and the \(j\)th body bounding box. The IoC's value is normalized to \([0,1]\). IoU is calculated in slightly different ways than IoC. The IoU is the ratio of the intersecting area to the area of both two bounding boxes. Figure 5 shows the definition and difference between IoC and IoU.  
Figure 4: The architecture of the body detector in our proposal. In this figure, on the far left side at Section (a) is the basic structure of Faster-RCNN. After the fully connected layer, each proposal predict multiple predicted bounding boxes (\(box_{A}\) and \(box_{B}\)). After using EMD Loss to solve the losses between each predicted result and the ground truth bounding boxes, we used our patched NMS to suppress redundant bounding boxes. The refinement module was used to further refine the final results.  
We have  
\[IoC(H_{i},B_{j})=\frac{|H_{i}\bigcup B_{j}|}{|B_{j}|} \tag{7}\]  
\[\textbf{CostMatrix}=\left(\begin{array}{cccc}IoC(H_{1},B_{1})&\ldots&IoC(H_{1}, B_{n})\\ \vdots&\ddots&\vdots\\ IoC(H_{m},B_{1})&\ldots&IoC(H_{m},B_{n})\end{array}\right). \tag{8}\]  
Hungarian algorithm. An IoC operation was performed between each head bounding box and each body bounding box in the current frame. The cost matrix is shown in (8), where \(m\) is the number of rows and \(n\) is the number of columns, i.e., \(m\) head boxes and \(n\) body boxes detected in the frame. Then the cost matrix was processed by the Hungarian algorithm. As an allocation algorithm, the Hungarian algorithm completed the matching of the targets' (pedestrians) head bounding boxes and body bounding boxes as (9). We have  
\[indices_{H},indices_{B}=Hung(\textbf{CostMatrix}). \tag{9}\]
## 5 Evaluation
### Evaluation of the Final Architecture Design
#### 5.3.3 Fidelity with Real World Phenomenon  
Energy consumption optimisation of buildings is the ultimate goal of our work. Particularly, we enabled predictions in edge-nodes, which could be located in buildings. Realising of energy consumption optimisation is left for future work.  
Implementation of energy consumption optimisation in the real world depends on the feasibility of the architecture and applied technologies (see Section 5.3.1). The main technologies may be additionally analysed in terms of industry adoption. K8S has become the de-facto technology for running workloads in computing clusters. Rancher is vendor independent (a solution objective; Figure 1). Particularly, Rancher is open source and does not lock users into vendor-specific solutions [18]. Both Rancher and k3s are utilised increasingly in the industry [18].
# GraphDBSCAN: Optimized DBSCAN for Noise-Resistant Community Detection in Graph Clustering
## 4 Evaluation
### Real Datasets  
Real-world datasets provide diverse network topologies for evaluation. Table 1 summarizes their original characteristics before sampling.  
Each dataset was sampled to 1000 nodes, preserving edges within the subgraph to maintain structural integrity. The optimal DBSCAN parameters (bestEps, minPts) were dynamically determined using the K-nearest neighbor (KNN) algorithm, ensuring adaptive noise removal and improved clustering accuracy.
# A Low-Complexity Lossless Compression Method Based on a Code Table for Infrared Images
## 3 Redundancy Analysis of Line-Scan Panoramic Infrared Images
Additionally, the infrared panoramic images processed in this study consist of 3072 pixels per column, with an image width of approximately 60,000 columns. When employing inter-column or inter-row differencing, preserving the first column or row is necessary for the restoration of subsequent rows or columns. As the first column of the image is generated at the beginning of the detector's operation, only 3072 original pixels need to be immediately stored for inter-column differencing as opposed to 60,000 for inter-row differencing at different times. To maximize the compression speed, we only utilize inter-column differencing to eliminate image redundancy. After the image redundancy is removed, the subsequent entropy coding requires the probability distribution information of the pixels. For a 14-bit infrared image, the dynamic range of pixel values is \([0,2^{14}-1]\), but the differential image dynamic range is doubled to \([-2^{14}+1,2^{14}-1]\). The experimental results also show that differential images often contain many 0 pixels, approximating a Laplace distribution. Therefore, through extensive experimentation, a general probability distribution model can be found to predict other differential images. A general distribution probability model can generate a general Huffman code table applicable to generic images. The higher the prediction accuracy of the probability distribution model, the better the compression. \begin{table}
\begin{tabular}{c c c c} \hline
**Image** & **Original Image** & **Entropy1 1** & **Entropy2 2** \\ \hline A & 10.3854 & 4.9182 & 6.3071 \\ B & 9.91121 & 4.3792 & 5.7613 \\ \hline \end{tabular} \({}^{1}\) entropy1 represents inter-column differential image entropy (bit/symbol). \({}^{2}\) entropy2 represents inter-row differential image entropy (bit/symbol).  
\end{table} 
Table 1: Image information entropy. Figure 1: Frequency statistics and correlation analysis of infrared images. (**a**) Original image A. (**b**) Original image A pixel frequency. (**c**) Difference image A. (**d**) Difference image A pixel frequency. (**e**) Correlation analysis of A. (**f**) Original image B. (**g**) Original image B pixel frequency. (**h**) Difference image B. (**i**) Difference image B pixel frequency. (**j**) Correlation analysis of B.
## 1 Introduction
Nevertheless, the protocols mentioned above have limited consideration in supporting the QoS features and have not prioritized different types of sensor data packets, thus limiting their use to only applications with homogeneous data. Therefore, this paper proposes an Energy-Aware QoS MAC protocol for Multi-hop and Prioritized Data in WSNs (EQPD-MAC), that provides both QoS and multi-hop routing features. Additionally, the receiver employs a mechanism that reduces latency for the highest priority data packets and enhances the network's energy efficiency. The performance of the EQPD-MAC is compared with MPQ-MAC [30], PMME-MAC [32], and RMP-MAC [33]. The following is a list of the contributions made by this work:  
* The EQPD-MAC takes into account data packets with multiple priorities. * AODV routing is incorporated to enable multi-hop routing and integrated with the MAC to ensure an energy-efficient protocol. * A novel approach that provides best energy efficiency while still maintaining good end-to-end latency. * In order to assess the performance of EQPD-MAC, real-world measurements are used. * EQPD-MAC provides improved energy consumption, average end-to-end latency, throughput, and packet delivery ratios. The remainder of the paper is organized as follows: The discussion and summary of related works on WSN from Application and MAC layer standpoints are provided in Section 2. In Section 3, the description of the proposed QoS MAC protocol with multi-hop routing feature, EQPD-MAC, is given. Details of the design and implementation at the Application, Network, and MAC layer levels are also provided. Section 4 presents the extensive performance evaluation, discussion and analysis of the EQPD-MAC and other comparative protocols. Finally, Section 5 concludes the work.
## 4 Optimization Design
### Implementation and Comparison of Optimization  
In engineering, a commonly used optimization method is to mix finite element software with optimization tool software or other optimization functions. The principle of this method is that the optimization part transfers the design variables generated by a single exploration to the finite element software, which calculates the stress-strain results and returns them to the optimization software for judgment. The optimization software then proceeds to the next step of optimization until the optimal solution is found. Although this method solves the problems of limited algorithm types and low adaptability in traditional finite element software's built-in optimization tools, it still does not solve the problems of slow operation and low efficiency, because each optimization requires one operation of finite element, which is the most time-consuming part of the optimization process. Therefore, the maximum number of optimization times becomes the bottleneck of traditional optimization methods. If neural networks are used to replace the role of finite element software in the optimization process, this deficiency can be compensated for. After the neural network prediction model is trained, it is equivalent to finding the mapping relationship between the design variables and the objective function and constraint conditions. Therefore, during the optimization process, finite element calculations are no longer needed.  
The result of the trained BP neural network is saved to a file with the suffix \"mat\", and the optimize calculation is conducted by using the global optimization tool Pattern Search in the MATLAB toolbox. The specific format for calling this optimization algorithm is as follows:  
\[[\text{x,fval,exit,output}]=\text{patternsearch}(\text{fun,x0,A,b,Aeq,beq, lb,ub,nonlcon,options})\]  
Here, fun is the function name for the objective function with respect to design variables, matrix A and vector b, respectively, represent the terms at the left and right endsof the linear inequality constraint, the matrix Aeq and vector beq, respectively, represent the terms at the left and right ends of a linear equation constraint, and lb and ub represent the upper and lower limits of design variables.  
The flowchart for implementing the optimization idea is shown in Figure 17a. As a comparison, the traditional optimization method is shown in Figure 17b. It can be seen that in the traditional optimization method, the calculation results of each finite element software are not fully utilized, but only the values required for the objective function and constraints are provided. In this paper, the results of ANSYS calculation are retained and used for training the neural network, which can be fully and comprehensively utilized.
## 4 Case Study and Results Discussion  
This study aimed to establish a predictive maintenance policy by analyzing operational data in a real-world scenario involving data from power transformers supplied by a Chilean energy distribution company. Therefore, data were collected from four databases, including transformer details, external tests, operational data, and intervention data, which were processed and merged into a single dataset for this case study.  
As context, the company relied on its manual solution, requiring external input at every step. Their method initially generated a list of potential solutions with their respective Weibull parameters, covariate weights, and flags to indicate the feasibility of the solution. A user then selected the most appropriate solution from this list. The process was repeated if no feasible options were available.  
The proposed framework aimed to streamline that process by integrating the covariate selection and weight and band estimation processes to obtain a feasible solution without a user having to initially decide. Two analyses were conducted to test this approach's effectiveness. The first study evaluated the decision policy quality derived from a preliminary solution and the optimal log-likelihood solution obtained in previous studies. Additionally, a sensitivity analysis was conducted to check the impacts of the resultant maintenance policy. The second study focused on methods for selecting the optimal covariates, examining how they affected the decision policy, and comparing them with the previous results.
## 4 Conclusions  
A numerical investigation of the heat transfer characteristics and deposition mechanism of SCR spray/wall interaction under low load diesel exhaust conditions has been performed. The developed numerical model accounts for the relevant physical processes, which include mass transfer, momentum transfer, heat transfer, water evaporation, spray/wall interaction, and wall film formation. The heat transfer characteristics are verified by the experimental data in ref. [1]. The numerical model can give accurate evaluations of the temporal and spatial distribution of wall temperatures and heat fluxes. The main conclusions of this study are as follows:  
* The derived model contributes to quantitatively predicting the spatial distribution of the wall film and the deposits in the SCR system.
* The spray cooling effect on the wall surface is localized, and the temperature drop decreases significantly from the core region to the peripheral region along the radial direction because the mass flux in the core region of the spray impingement is larger than that in the peripheral region.
* The cooling effect of spray is instantaneous since the initial wall temperature is less than or equal to the critical transition temperature of wall film formation.  
Figure 12: Cumulative wall film mass curve at different air flow temperatures during an injection cycle.  
Figure 13: Wall surface temperature curve across the centerline of each cooling zone in different initial wall temperatures: (**a**) upstream, (**b**) midstream, (**c**) downstream.  
* The approximate relationship between the critical transition temperature of wall film formation and the critical heat flux temperature is clarified.
* The Stochastic Kuhnke Model was adopted as a spray/wall interaction model to improve the drawback of a sudden change in the Kuhnke Model in wall film deposition prediction.
* When the wall temperature is less than the critical transition temperature, the wall film mass is sensitive to the wall temperature and increases as the wall temperature decreases.  
Conceptualization, C.B. and K.L.; methodology, C.B. and T.Z.; formal analysis, C.B. and J.L.; validation, C.B. and J.L.; writing--original draft preparation, C.B.; writing--review and editing, C.B. and K.L.; visualization, C.B.; supervision, K.L.; funding acquisition, K.L. and T.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 51876175.  
Not applicable.  
The authors declare no conflict of interest.
# Surface Plasmon Resonance-Based Fiber Optic Sensors Utilizing Molecular Imprinting
## 5 Molecular Imprinting
### Elements of Molecular Imprinting
#### 5.3.1 Template  
The main objective of molecular imprinting is to synthesize polymers having high stability and specificity which are comparable to biological receptors that may be replaced by MIPs in real applications. An ideal template should satisfy three essential conditions: the template should not have functional groups which restrict its polymerization; it should show high chemical stability during the polymerization process and should contain functional groups which can create complexes with functional monomers. Several types of templates such as ions [101; 102], organic molecules (pesticides, pharmaceuticals, amino acids, sugars, endocrine disrupting chemicals, etc.) [103; 104; 105], bio-macromolecules, cells and viruses [90; 106] have been reported in MIPs, but the imprinting of proteins and a few bio-macromolecules is still a great challenge for researchers [107].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Characterization Technique** & **Application** \\ \hline SEM, TEM, AFM & Morphological study \\ FTIR, ATR-FTIR, UV-Vis, NMR & Bonding characteristics, monomer:template screening \\ XPS, XRD & Structural evaluation \\ VSM & Magnetic nature analysis \\ TGA & Thermal analysis \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characterization methods and the information sought for the MIP formation confirmation.
## 3 Experimental Results and Discussion
### Diversity Analysis  
In order to comprehensively evaluate the performance of the GSLEO-AEFA optimization algorithm, it is imperative to thoroughly investigate the impact of Gaussian mutation and specular reflection on its diversity. To accomplish this, we must calculate the population diversity of both GSLEO-AEFA and AEFA using the following equation [20].  
\[\textit{Diversity}=\frac{1}{N}\sum_{i=1}^{N}\sqrt{\sum_{j=1}^{D}}\big{(}x_{i,j }-\overline{x}_{j}\big{)}^{2},\overline{x_{j}}=\frac{\sum_{i=1}^{N}x_{i,j}}{N} \tag{36}\]  
Here, \(D\) denotes dimension, \(N\) represents the number of particles, \(x_{j}\) is \(j\)th dimension for the particle at the center of the population and \(x_{i,j}\) represents \(j\)th dimension of a given particle \(i\). To validate the success of GSLEO-AEFA on diversity over the algorithm which is originated from, diversity plots for two unimodal; F1 and F2 and two multimodal; F10 and F12 functions are plotted in Figure 5.  
According to the diversity plots for all of the functions analyzed GSLEO-AEFA produces higher diversity than AEFA at each time when it is analyzed. This is an indication of well applied and well suited principles that GSLEO-AEFA contains. Gaussian mutation with local escaping operator successfully enhance the diversification ability of the original algorithm and this is well observed in the plots.  
On the other hand, AEFA produces lower diversity and as a result its convergence speed is high. This was expected because of the hybrid nature of the GSLEO-AEFA, since it contains multiple methods to be executed during the optimization.