esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Problem Statement and Data Collection  
### Problem Statement  
Due to complex traffic conditions at the diverging and the emerging area, the impact radiation area is large. Therefore, in order to avoid the mutual influence between interchanges at the early stage of construction, all countries have made relevant regulations on the minimum spacing of interchanges [47; 48].  
In the initial stage of freeway construction, when the freeway network is relatively sparse, and the spacing between interchanges is significant, it is feasible to design the interchanges as independent units. However, with the development of road transportation and the demand for freeways increases, the intersection of two freeways leads to more interchanges. For example, the construction of the Xi'an around-city freeway and the outer-ring freeway, accessing the surrounding national freeway network, has generated many nodes requiring interchange construction. At this time, if the new interchanges and existing interchanges are too close to each other, resulting in the mutual influence of traffic flow, it constitutes a compound interchange.  
However, the relevant specifications in China do not specify the design form of compound interchange auxiliary lanes. Research in this field has primarily focused on the width of the auxiliary lane, alignment indexes of the start and end points, and setting conditions. However, there is a lack of specific studies on the forms of continuous auxiliary lanes and their application scenarios. This leads to the lack of uniform regulations on the design form of auxiliary lanes and the lack of applicability evaluation.  
In certain highly urbanized cities in China, the road network is developed and dense, resulting in a significant density of interchanges. This has caused great challenges for the newly built interchanges. However, the small spacing interchange is an inevitable problem in current development. Therefore, it is urgent to study the forms of auxiliary lanes in order to propose solutions that can adapt to different traffic volumes.  
In this study, the typical compound interchange composed of the Baqiao and Tianwang interchange is taken as the research object (Figure 2). The interchange mainline has six lanes in both directions. The exit and entrance ramps each have two lanes, and two auxiliary lanes connect these two ramps. The mainline is designed to accommodate speeds of up to 120 km/h, and the exit and entrance ramps are designed for speeds of 60 km/h. The traditional auxiliary lane form is optimized by studying the impact of different auxiliary lane forms on traffic efficiency and safety.
# Lean Manufacturing Soft Sensors for Automotive Industries
###### Abstract  
Lean and flexible manufacturing is a matter of necessity for the automotive industries today. Rising consumer expectations, higher raw material and processing costs, and dynamic market conditions are driving the auto sector to become smarter and agile. This paper presents a machine learning-based soft sensor approach for identification and prediction of lean manufacturing (LM) levels of auto industries based on their performances over multifarious flexibilities such as volume flexibility, routing flexibility, product flexibility, labour flexibility, machine flexibility, and material handling. This study was based on a database of lean manufacturing and associated flexibilities collected from 46 auto component enterprises located in the Pune region of Maharashtra State, India. As many as 29 different machine learning models belonging to seven architectures were explored to develop lean manufacturing soft sensors. These soft sensors were trained to classify the auto firms into high, medium or low levels of lean manufacturing based on their manufacturing flexibilities. The seven machine learning architectures included Decision Trees, Discriminants, Naive Bayes, Support Vector Machine (SVM), K-nearest neighbour (KNN), Ensembles, and Neural Networks (NN). The performances of all models were compared on the basis of their respective training, validation, testing accuracies, and computation timespans. Primary results indicate that the neural network architectures provided the best lean manufacturing predictions, followed by Trees, SVM, Ensembles, KNN, Naive Bayes, and Discriminants. The trilayered neural network architecture attained the highest testing prediction accuracy of 80%. The fine, medium, and coarse trees attained the testing accuracy of 60%, as did the quadratic and cubic SVMs, the wide and narrow neural networks, and the ensemble RUSBoosted trees. Remaining models obtained inferior testing accuracies. The best performing model was further analysed by scatter plots of predicted LM classes versus flexibilities, validation and testing confusion matrices, receiver operating characteristics (ROC) curves, and the parallel coordinate plot for identifying manufacturing flexibility trends for the predicted LM levels. Thus, machine learning models can be used to create effective soft sensors that can predict the level of lean manufacturing of an enterprise based on the levels of its manufacturing flexibilities.  
Lean manufacturing; manufacturing flexibilities; soft sensor; automotive; neural networks +
Footnote †: journal: _Journal of Applied Soft  
is the key to achieving these goals. The following subsection introduces manufacturing flexibilities in lean manufacturing.
# Multi-Scale Flame Situation Detection Based on Pixel-Level Segmentation of Visual Images
## 1 Introduction
The main contributions of this paper are as follows. * In the context branch, squeeze-enhanced axial attention (SEA attention) squeezes the fire feature map into compact column/row information and computes self-attention to acquire the dependencies between flame pixels. In the spatial branch, the fusion block fuses low-level spatial information and high-level semantic information to obtain a more comprehensive flame feature. * The light segmentation head maps the flame features to the pixel-level flame segmentation results using a lightweight convolution operation. Based on the flame segmentation mask, flame parameters (flame height, width, area, rate of change of flame height, and so on) are calculated to analyze the flame situation. * The experimental results on both private and public datasets show that Flame-SeaFormer outperforms existing fire segmentation models. It extracts reliable and valid flame state information and mines flame development dynamics to support intelligent firefighting. The rest of the paper is organized as follows. In Section 2, the related work on fire segmentation techniques is reviewed. Section 3 provides a detailed description Flame-SeaFormer. In Section 4, experiments are described to demonstrate the effectiveness of Flame-SeaFormer. Finally, conclusions are drawn in Section 5.
## 3 Scheme of the Proposed Method
### Overview of the Method  
The components are small-sized, multiplex-shaped, and complex-structured, with blurred surfaces and edges. Fast, flexible assembly is a big challenge since it is very difficult to achieve the correct position and posture accurately via efficient 2D vision processing. Therefore, a 3D-vision-based method is proposed for spatial positioning and posture estimation of MCB components with a novel point cloud registration method. The procedure of the method is as follows (illustrated by Figure 7):  
1. The computer-aided design (CAD) models of the components are sampled to obtain surface point clouds that contain richer feature information, which improves the precision of point cloud registration.
2. The scene point cloud is filtered, clustered, and segmented to obtain candidate-matching regions.  
Figure 6: Stereo vision system.  
Figure 7: Flowchart of the process.  
3. Point cloud features are extracted by Intrinsic Shape Signatures (ISSs) [23] from the templates and the candidate-matching regions are described by a Fast Point Feature Histogram (FPFH). Then, Sample Consensus Initial Alignment (SAC-IA) [24; 25] is applied to achieve a rough matching.
4. Fine registration is used to improve the precision of the results obtained by rough matching. To perform a fast search, fine registration employs Iterative Closest Point (ICP) [23; 26] with a K-dimensional Tree (KD-tree) [27] to obtain a set of point pairs of minimum Euclidian distances between the source and target point clouds. Meanwhile, Random Sample Consensus (RANSAC) [28] is employed to remove incorrectly matched point pairs to further raise the performance of the ICP algorithm.
5. Finally, the result is visualized by moving a design model to the registered position and posture.
## 3 Results and Discussion
### Advanced Oxidation Process UV/H\({}_{2}\)O\({}_{2}\)
#### 3.1.3 Selection of Pretreatment UV/H2O2 Operation Parameters  
Figure 7, based on the results of Section 3.1, shows the interaction effects of irradiation time and H2O2 dose on the organic matter removal for optimization. In terms of energy and oxidant reagent consumption, the combination that allows a shorter irradiation time and lower consumption of reagent without compromising the pretreatment removal efficiency was considered.  
Hence, 75 and 120 min of UV irradiation and H2O2 dosage between 49 and 65 mM were the optimal conditions to achieve satisfactory removal efficiencies for the subsequent treatment stage. Table 2 describes the more favorable treatment configurations, which were selected to be 90 min with 65 mM, since same removal efficiencies are achieved with higher irradiation time and/or concentration of H2O2. The second option, 90 min and 57 mM, was excluded because TOC and malathion removal efficiencies are lower in comparison with the third option (\(p\) = 0.008). Furthermore, such efficiencies are practically equal to the first option, which use shorter irradiation time and reagent volume (Table 2). The third option was selected over the first one, despite the longer irradiation time, because removal efficiency was 50% higher for both malathion and TOC. In this regard, the lowest concentrations of TOC and malathion to be treated in the biological systems were considered in order to reduce the organic load to the CWs which have better performance at low organic loads [5; 39].  
Figure 6: Pshysicochemical parameters behavior in UV/H2O2-advanced oxidation process at three different H2O2 dosage during malathion removal. The values represent the mean values of five replicates. (**a**) pH and (**b**) oxidation–reduction potential.
## 2 Remarks on the Topology of Microgrid
### Interconnection and Mode of Operation of Microgrids  
The topology of a microgrid is essential for determining its efficiency, stability, and ability to integrate with other energy systems. Interconnection and operation mode influence the overall performance of a microgrid. Microgrids can operate in two modes: (i) grid-connected and (ii) isolated. In grid-connected mode, the microgrid is synchronized with the primary electrical grid, allowing bidirectional energy exchange [24; 25]. This setup enhances system stability and demand management, as the primary grid can serve as a backup during fluctuations in local generation or demand. However, this mode requires a robust infrastructure and precise coordination to maintain frequency and voltage at the point of common coupling. The microgrid operates independently in isolated mode, crucial in remote areas or during emergencies. This mode presents additional challenges, such as balancing generation and consumption in real time. Integrating ESS, like batteries, is vital to managing the intermittency of renewable sources and ensuring a continuous energy supply [26].  
On the other hand, most microgrids operate in AC due to compatibility with traditional electrical systems and the majority of electrical equipment that runs on AC. Once phase and frequency synchronization is maintained, AC microgrids can integrate various generation sources, such as diesel generators, WT, and PV systems [27]. DC microgrids are gaining popularity, especially in applications with critical energy efficiency, such as PV systems and battery storage. DC operation eliminates losses associated with AC-DC conversions, leading to higher energy efficiency. However, integrating with existing grid infrastructure and ensuring system protection pose significant challenges [28]. Hybrid mode combines the advantages of AC and DC systems within a single microgrid. This approach allows greater flexibility in managing diverse generation and storage sources. Hybrid microgrids can leverage the efficiency of DC systems for specific applications, such as electric vehicle charging, while maintaining compatibility with existing AC infrastructure [29]. Table 1 compares microgrids' operation modes and power supply types. It highlights key features, challenges, and typical applications associated with each mode, understanding their advantages and limitations.  
Figure 3: Standard classifications of microgrids in terms of operation.
## 2 Materials and Methods
### Research Methods
#### 2.3.3 Classification of Vegetation Coverage Levels  
Vegetation coverage levels were classified into four categories using NDVI, ranging from high vegetation coverage to bare soil. The categories are defined as follows: high vegetation coverage (NDVI \(>0.6\)), moderate vegetation coverage (\(0.3<\) NDVI \(\leq 0.6\)), low vegetation coverage (\(0.1<\) NDVI \(\leq 0.3\)), and bare soil (NDVI \(\leq 0.1\)).
# Integrated Photonics on Glass: A Review of the Ion-Exchange Technology Achivements
## 2 Ion-Exchanged Waveguides
### Waveguide's Performances
#### 2.3.3 Exotic Substrates  
Some exotic glasses like fluoride glasses [43] or germanate glasses [44; 45] have also been used for the realization of ion-exchanged waveguides but the difficulty in making sufficiently good wafers available at a reasonable cost, strongly limited the research in these directions.
## 4 Implementation and Discussion
### Performance Analysis
#### 4.2.11 Comparison with a Few Other Consensus Algorithms
That is, at least 75% of the participated entities must agree to take control of a chain [72]. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Fact** & **Algorithm** & **PoF** & **PoW**[35] & **PoS**[38] & **PBFT**[37] & **Ripple**[39] & **Tendermint [40] \\ \hline Node management & Permissioned & Open & Both & Permissioned & Open & Permissioned \\ Transmission rate & Low & Low & Medium & High & High & Medium \\ Energy consumption & Low & High & Medium & Low & Low & Low \\ Scalability & High & Low & Low & Medium & Medium & Low \\ Finality process & Immediate & Probabilistic & Probabilistic & Immediate & Probabilistic & Probabilistic \\ Adversary tolerated & 0.75\% & \(<\) = 25\% & \(<\) 33\% & & \(<\) 20\% faulty & \(<\) 33.3\% Voting \\ Power & diversity & Computing & \(<\) 51\% stake & Voting & nodes in UNL & power \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of proof of familiarity (PoF) with other consensus algorithms.
## 3 Seismic Fragility Assessment Procedure
### FEMA P695 Procedure
An example of the seismic fragility curve in the form of a cumulative lognormal distribution function is shown in Figure 7, to illustrate those two collapse prevention criteria. In Figure 7, the CMR is defined as \(\hat{\mathbf{S}}_{\text{CT}}/\mathbf{S}_{\text{MT}}\), where \(\mathbf{S}_{\text{MT}}\) is the spectral acceleration corresponding to the fundamental mode period of the analytical model at the MCE level spectrum, and \(\hat{\mathbf{S}}_{\text{CT}}\) is the spectral acceleration with 50% collapse probability in the fragility curve. The collapse probability is evaluated at \(\mathbf{S}_{\text{MT}}\). The spectral acceleration at the first mode period \(S_{a}(T_{1})\) was used to represent the intensity measure (IM) of ground motion suites for IDA and the fragility assessment. Higher-mode contribution and period elongation are important factors in the case of high-rise buildings responding to earthquakes inelastically. An intensity measure, such as an average spectral acceleration for multiple periods including an elongated one, may be a better IM that can reduce dispersion and bias in the prediction of collapse [32]. Nevertheless, \(S_{a}(T_{1})\) was adopted for the IM in order to achieve consistency in the seismic hazard between design and fragility evaluation as well as to take into account variability in spectral shape by scaling ground motion records to the distribution of conditional spectra anchored to \(S_{a}(T_{1})\). The standard deviation \(\beta_{\text{TOT}}\) of the lognormal distribution to account for uncertainty in the fragility curve was determined in accordance with FEMA P695 [4]. \(\beta_{\text{TOT}}\) is calculated by Equation (4). \[\beta_{\text{TOT}}=\sqrt{\beta_{\text{RTR}}^{2}+\beta_{\text{DR}}^{2}+\beta_{ \text{TD}}^{2}+\beta_{\text{MDR}}^{2}} \tag{4}\]  
where \(\beta_{\text{RTR}}\) is the record-to-record collapse uncertainty and was calculated based on the dynamic analysis results in this study. \(\beta_{\text{DR}}\) is the design requirements-related collapse uncertainty, \(\beta_{\text{TD}}\) is the test data-related collapse uncertainty, and \(\beta_{\text{MDR}}\) is the modeling-related collapse uncertainty. In FEMA P695, values of 0.1 to 0.5 are assigned to \(\beta_{\text{DR}}\), \(\beta_{\text{TD}}\), and \(\beta_{\text{MDR}}\) according to \"Superior\" to \"Poor\" grade of  
Figure 7: Example of seismic fragility curve. quality ratings for each uncertainty source. In this study, 0.2 was used uniformly for the three standard deviation assuming \"Good\" grade.
## Appendix A  
Variations among decision-makers in terms of work experience, expertise, and depth of understanding regarding strategic issues lead to divergent views on the significance of this decision-making issue. To ensure the rationality and accuracy of the evaluation results, this study employs EEM to determine the weights of indicators. EEM, an objective method of assigning weights, leverages the concept of entropy value from information theory to gauge the level of informational disorder across various indicators. It measures the informational content of each indicator, thus determining its impact on the decision-making process. A higher entropy value indicates increased informational disorder, suggesting that the information has less utility and, thus, lesser importance of the indicator; conversely, lower entropy values denote greater importance of the indicator. The specific computational steps are as follows.  
1. With \(n\) evaluation subjects and \(m\) evaluation indicators, a judgment matrix \(\mathrm{X}\) is constructed: \[\mathrm{X}=\left(x_{ij}\right)_{n\times m}(i=1,2,\ldots,n;j=1,2,\ldots,m)\] (1)
2. Standardization Transformation  
Given the varying dimensions and units of different indicators, standardization is implemented to render these indicators dimensionless to facilitate better comparability:  
\[x_{ij}{}^{\prime}=\frac{\left(x_{ij}-\overline{x_{j}}\right)}{s_{j}} \tag{2}\]  
where \(\overline{x_{j}}\) represents the mean value of the \(j\)-th indicator and \(s_{j}\) represents the standard deviation of the indicator values. To eliminate negative values, a coordinate shift is per formed to identify the minimum value \(p_{j}\) for each indicator. Let \(Z_{ij}=-p_{j}+0.01+x_{ij}\), and substitute \(x_{ij}\) with \(Z_{ij}\) for evaluation.
3. Homogenization of Various Indicators The weight \(f_{ij}\) of the indicator value for the \(i\)-th solution under the \(j\)-th indicator is computed as follows: \[f_{ij}=\frac{Z_{ij}}{\sum_{i=1}^{n}Z_{ij}},i=1,2,\ldots,n;j=1,2,\ldots,m\] (4) (4) Determination of the entropy value \(H_{i}\) for the evaluation indicator \[H_{j}=(-e)*\sum_{i=1}^{n}f_{ij}*Inf_{ij}\] (5) where \(e=\frac{1}{\ln n}\). If \(f_{ij}=0\), it is defined that \(\lim\limits_{f_{ij}\to 0}f_{ij}Inf_{ij}=0\). Then, \(G_{i}=1-H_{i}\). The greater the value of \(G_{i}\), the more important the indicator \(j\) is in the comprehensive evaluation.
5. Weight of the evaluation indicator, \(\omega_{j}\) \[\omega_{j}=\frac{G_{j}}{\sum_{j=1}^{m}G_{j}}=\frac{1-H_{j}}{\sum_{j=1}^{m} \left(1-H_{j}\right)}\] (6) It satisfies \(\sum_{j=1}^{m}\omega_{j}=1\), with\(\leq\omega_{j}\leq 1\). The aforementioned formula reveals that lower entropy values are associated with higher weights, suggesting that the informational content of a particular evaluation indicator is more effective, and thus, the indicator is deemed more important. Conversely, an indicator with a higher entropy value is considered less important.
6. Computation of the synthetic indicator, \(v_{i}\) \[v_{i}=\sum_{j=1}^{m}\omega_{j}*f_{ij}\] (7) where \(v_{i}\) represents the comprehensive evaluation value of the i-th solution.
## 4 Reliability Assessment Considering ME
### Sources of ME  
The ME inevitably arises from the observed data in practice. In terms of the lateral displacement data monitored by an inclinometer, the ME could be caused by inclinometer accuracy and human error. The former consists of bias-shift error, sensitivity drift, rotation error, depth positioning error and others [51; 55; 56]. For example, the bias-shift error (also known as zero-shift error) is due to the initial checksum at the probe (c.f. A\({}_{0}\) and A\({}_{180}\) in Figure 3b) not being zero. Ideally, the sum of the readings of A\({}_{0}\) and A\({}_{180}\) should be zero with identical numbers but opposite signs. However, there is often a slight bias at the initial readings due to the imperfection of the probe. Details for other types of ME can be found elsewhere [51; 55]. It was argued in [51] that the systematic error due to inclinometer accuracy, which accumulates arithmetically with the number of measurement intervals, is more important and significant than the random error (human error). As such, in this paper, only the ME due to inclinometer accuracy is considered. Taking into account its randomness by nature, the ME is represented by a random variable. The impact of ME on the deformation reliability of retaining structures is formulated in the next Section.  
Figure 3: Inclinometers used to measure the lateral displacement of retaining walls. (**a**,**b**) Elevation and top view of a typical inclinometer probe [54]. (**c**) A tube (PVC plastic, ABS plastic or aluminium) is installed serving as the inclinometer casing for the purpose of ground measurement. (**d**) Measurement of lateral displacement [51].