esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Generalized Service Point Distribution Problem
Moreover, each demand node \(i\in\mathcal{D}\) is associated with a demand \(a_{i}\geq 0\) and a subset of facilities \(\mathcal{F}_{i}\subseteq J\) of which each is able to cover the node's full demand. The goal of the MCLP is to select up to \(p\) locations for opening facilities in order to maximize the total demand covered. Given an instance to the MCLP, we construct a corresponding GSPDP instance, in which the set of locations \(V\) corresponds to the set of facilities \(\mathcal{J}\), and the set of users \(U\) corresponds to the set of demand nodes \(\mathcal{D}\). Moreover, each user \(u\in U\) only has a single use case with a single SPR and demand \(a_{i}\) with \(u=i\). Building costs \(z_{v}^{\rm frx}\) for a location \(v\in V\) are set to one, while the maintenance costs \(z_{v}^{\rm var}\) are zero. The budget of the GSPDP instance is set to \(p\), and the prize for a unit of covered demand \(q\) is set to one. The suitability value \(w_{r,p}\) is set to one for \(v\in V\) and \(r\in R\) if facility \(i\) can satisfy the demand of demand node \(j\), i.e., \(j\in\mathcal{F}_{i}\), and zero otherwise. Let \((x,y,h)\) be a feasible solution to this derived GSPDP instance. A corresponding feasible solution to the MCLP is obtained by opening facilities at all locations \(j\in\mathcal{J}\) for which \(x_{v}=1\). Due to the budget constraint (4), at most, \(p\) facilities are opened in the MCLP instance; thus, there is a bijective mapping of feasible GSPDP solutions to feasible MCLP solutions. Since each user in the GSPDP instance only has one use case, and each use case only consists of one SPR, the sets \(U\), \(C\), and \(R\) all contain the same elements. By our definitions, variables \(y_{u,c}\) indicating the covered SPRs, therefore, also indicate the covered demand nodes of the MCLP instance. More generally, we also have a bijective mapping of covered SPRs in the GSPDP instance to covered demand nodes in the MCLP instance. Last but not least, due to our definitions of the suitability values \(w_{r,p}\), the fixed and variable costs for opened stations, and the prize per unit of fulfilled demand, the objective values of corresponding GSPDP and MCLP solutions also correspond. Since all applied transformations require polynomial time, it follows that the GSPDP is NP-hard. In the next section, we present a cooperative algorithm for solving the GSPDP when no a priori information about the suitability values \(w_{r,\rho}\) of potential locations is known but can only be obtained by querying the users in a limited fashion.
## 3 Results  
Three types of shielding plates were manufactured using a tungsten plate, as shown in Figure 2. Although there was no difference in appearance, the difference in thickness could be distinguished through measurements.  
Figure 3 shows the EDS component analysis of the tungsten plate fabricated in this study. The plate contained tungsten with a purity of 99% or above.  
Figure 4 shows the results of the cross-section analysis of the same 0.1 mm-thick tungsten plate using an optical microscope. Figure 4a,b present plates manufactured by hot and cold rolling methods, respectively. No difference was observed in the cross-section during the two processes. However, a comparison between Figure 4(a-1,b-1) reveals a difference in the height between the layers indicated by the yellow dashes, which appeared in the form of a multilayer structure of a thin film during the hot-rolling process. Despite the use of similar multi-layer structures, the structure after hot rolling appeared to be irregular, while the structure after cold rolling appeared to be regular.  
Figure 3: Energy-dispersive X-ray spectroscopy component analysis of tungsten plate. (**a**) Hot-rolling process for the 0.3 mm plate, (**b**) hot-rolling process for the 0.1 mm plate, (**c**) cold-rolling process for the 0.1 mm plate.  
Figure 2: Tungsten plates. (**a**) A 0.3 \(\pm\) 0.002 mm (42.5 g/cm\({}^{3}\)) tungsten plate manufactured via hot rolling; (**b**) a 0.1 \(\pm\) 0.001 mm (21.2 g/cm\({}^{3}\)) tungsten plate manufactured via hot rolling; and (**c**) a 0.1 \(\pm\) 0.001 mm (19.1 g/cm\({}^{3}\)) plate manufactured via cold rolling.  
Changes to the surface of the tungsten plate due to the different rolling methods were observed, as shown in Figure 5, and it was found that the surface was slightly denser in the 0.1 mm (21.2 g/cm\({}^{3}\)) plate that was repeatedly forged to reduce the thickness. The process of increasing the density or adjusting the thickness can directly affect the shielding performance.  
Table 1 presents the shielding performance of the tungsten shielding plate. As tungsten was used, all the manufactured plates generally showed high shielding performances. The  
Figure 4: Sectional analysis of tungsten plate: (**a**) hot-rolling method; (**b**) cold-rolling method. (**a**-**1**) shows an irregular laminated structure, and (**b**-**1**) shows a regular laminated structure.  
Figure 5: Surface analysis of tungsten shielding plate. (**a**) A 0.1 mm tungsten plate using hot rolling; (**b**) a 0.3 mm tungsten plate using hot rolling; (**c**) a 0.1 mm tungsten plate using cold rolling.  
shielding performance of the hot-rolled plates was superior to that of the cold-rolled plate. The 0.3 mm plate exhibited the best shielding performance.  
In addition, the shielding performance of tungsten and lead plates of the same thickness was compared. The results are shown in Figure 6. The trend in the shielding performance of both plates is similar, indicating that a tungsten shielding plate can be used instead of lead. Furthermore, in the high-energy range, the 0.3 mm tungsten plate exhibited a better shielding performance than the lead plate.
# Survey of Smart Parking Systems
## 3 Types of SPS Reported in the Literature
### Centralized Assisted Parking Search  
Here, a central processor or server is entirely in charge of processing the information. It collects the information from sensors that are located in the parking places and, after processing these data, can send this information directly to the users or display information that can serve the drivers [43].  
This concept is not very different from, for example, PGIS. However, these types of SPS have quite marked conceptual differences. CAPS bases its operation on a single central processing unit or server,while a PGIS can offer a decentralized structure applied to VANETs or other networks. In addition, CAPS indicates the number of parking slots available and does not offer the possibility of indicating to drivers the route or a guide to the selected parking slot. These two main differences mark CAPS as an SPS that aims to be used more in small car parks with a controlled area [44], and they greatly limit the scalability of the system in case the infrastructure changes or grows. This type of controlled parking where this kind of SPS is generally applied may contain physical visual indicators, like LED screens, so that drivers can identify the parking slots available and go directly to them.  
Complete information processing and decision-making tasks depend on a single server. Parking position sensors are only responsible for transmitting the availability of parking slots to servers. The server's real-time two-way communication with vehicles and parking sensors requires a heavier network infrastructure, both wired and wireless. In a first-come-first-served (FCFS) form, the server queues requests and satisfies them, delegating to a vehicle the parking slot that is closest to its destination [43].  
Another important difference in a CAPS and distributed PGIS has to do with their installation, operation, and maintenance costs. A fixed centralized infrastructure requires a large investment [45], as well as a design that forecasts system maintenance [46]. In addition to other difficulties related to the scalability of the system, as mentioned above, there are further ones such as the number of parking slots monitored and the burden of resizing the detection network. Vehicle nodes that can be established in distributed detection systems, such as VANETs [47], are fully cooperative, whereas, in the CAPS approach, cooperation is partial or non-existent. This can lead to selfish and/or malicious behavior by drivers that can significantly undermine the system's performance. Therefore, detecting and penalizing inappropriate behavior is a challenge.
## 3 Results and Discussion
### Data Analysis  
The PCA biplot in Figure 3, with two principal components (PC1 and PC2), accounts for 73.7 per cent of the total variance observed. The distribution of the samples in the PCA plot suggests a distinct differentiation of nutrients among the samples. Principal component 1 (PC1) shows a positive correlation with protein, Cu, Na, and Fe and a negative correlation with carbohydrates, fat, and K. In contrast, principal component 2 (PC2) is positively correlated with ash, fibre, and Mg and negatively correlated with P, Zn, Ca, and Mn.  
_Ulva_ sp. is strongly associated with ash, Cu, and Na content, while _S. maticum_ from Buarcos is associated with higher fibre content and, to a lesser extent, with ash content, suggesting its potential use in dietary fibre supplements. _G. multipartita_ is linked to higher Zn, Mn, and P content, highlighting its role as a source of trace elements. _S. maticum_ from San Marcos is strongly correlated with carbohydrate, fat, and K content, indicating that its value is suitable for applications requiring energy-dense biomaterials. This analysis could be useful for bioresource applications, such as identifying species with specific nutritional profiles for food or industrial uses. Regional variations (UB--_Ulva_ sp., GMB--_Gracilaria_  
Figure 3: Projection of the algae analysed and the analytical parameters measured in the space defined by the first two principal components. UB—_Ulva_ sp., GMB—_Gracilaria_ multipartite_, SMB—_Sargassum_ _muctium_ from Buarcos beach, SMS—_Sargassum_ _maticum_ from San Marcos beach.  
multipartia_, SMB--_Sargassum muticum_, all from Buarcos beach; SMSM--_Sargassum muticum_ from San Marcos beach) may reflect environmental influences, which should be further investigated in future work [75].  
The results show a clear difference between the samples, whether due to taxonomic differences or environmental conditions. The _S. muticum_ sample from San Marcos appears to have been exposed to less nutrient-rich waters, resulting in lower protein and mineral content compared to the Buarcos sample.
# Electric Field Distribution Induced by TMS: Differences Due to Anatomical Variation
## 1 Introduction
### EF Modeling  
The induced EF is computed by applying numerical approaches, such as the finite element method (FEM) or the boundary element method (BEM) [24; 25] on either tetrahedral meshes or voxel grids [26], depending on the specifics of each study. As far as the head model anatomical complexity is concerned, the EF can be computed in anything in the range from homogenous spheres [27; 28] to high-resolution, anatomically realistic head models [29]. Personalized models with a few tissues [30], typically consisting of white matter, grey matter, cerebrospinal fluid (CSF), skull, scalp, and occasionally eyes, have become a popular choice as they balance anatomical accuracy to computational cost.
## 5 Conclusions  
In view of the problem of low prediction accuracy of the remaining life of small samples of mechanical products caused by multiple time scales and multiple fault states, the failure time data and performance degradation data are fused, and the failure rate and standard deviation are introduced as the remaining life prediction criteria, intuitively reflecting the possible failure of a component or system at a certain moment. Aiming at the current demand for self-adaptation of residual life prediction data for small samples, a statistical matching method is used to establish a preprocessor model of life feature self-adaptive matching. On this basis, the LSTM neural network is used to establish a small-sample adaptive residual life prediction model.  
Using the bearing life dataset of Xi'an Jiaotong University as the research object, a small amount of sample data in the dataset is randomly selected for remaining life prediction, substituted into the constructed small-sample adaptive remaining life prediction model for prediction, and compared with the standard remaining life of bearings. The prediction results show that the remaining life of the RV reducer is about 6121.1-5921.1 h, and compared with the standard remaining life of the RV reducer, the results show that the overall prediction error is small, which verifies the accuracy of the proposed model and feasibility of the proposed model.  
The proposed model can match the life characteristics of mechanical products based on a small number of life characteristics samples and give accurate remaining life prediction curves, which overcomes the shortcomings of traditional life prediction methods with large feature requirements and provides a feasible method for the remaining life prediction of mechanical products with small samples, and this method can also provide reference significance for other equipment with similar life prediction, which can effectively reduce the economic loss of enterprises and help improve the efficiency of enterprises.  
Data curation, J.S.; Formal analysis, Y.L. and G.Y.; Funding acquisition, Z.Z.; Investigation, Z.L.; Methodology, Y.L. and G.Y.; Project administration, Z.Z.; Software, J.S. and G.Y.; Validation, Z.L.; Visualization, Y.Z.; Writing--review & editing, Y.L., Z.Z. and Z.L.; Writing--original draft, J.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The Open Project Foundation of Anhui Provincial Engineering Laboratory on Information Fusion and Control of Intelligent Robot, China, grant number No.IFCIR2020001; Industrial Equipment Quality Big Data Ministry of Industry and Information Technology key laboratory, grant number No.2021-IEQBD-05; University-level scientific research project of Anhui Polytechnic University, grant number No.KZ420222068; and Research start-up Fund project of Anhui Polytechnic University, grant number No.S022022067.  
Not applicable.  
Supporting data is not available.  
The authors declare no conflict of interest.
# Use of Surface Acoustic Waves for Crack Detection on Railway Track Components--Laboratory Tests
## 1 Introduction
### Current Technical Solutions for Condition Monitoring of Railway Tracks  
Preventure maintenance strategies dictate regular maintenance activities to grind or mill the top surface layer of rails, to remove these cracks and ensure safe railway operation.  
In order to schedule rail maintenance interventions effectively, it is vital to detect these surface defects as early as possible. Railway infrastructure providers generally have two complementing measurement options for measuring and monitoring the track condition: (a) measurements with instrumented trains that regularly scan the whole track network, and (b) stationary measurements currently limited to sensors placed at hotspots within the track network or on critical safety components such as switches and crossings. The latter is referred to as wayside condition monitoring. Currently available technical solutions for wayside monitoring of the track are limited to strain and load measurements toidentify unbalanced and overloaded freight wagons, and vibration sensor instrumentation to identify defective wheels (e.g., flat spots). Driven by the need for condition-based maintenance (CBM) of rolling stock and track, extensions of these concepts are currently gaining increased interest [2].
## 4 Experimental Results and Analysis
### Time Complexity Analysis
#### 4.5.2 Computation Time Comparison  
In this section, we compare the computation time of the proposed method with other methods (PSO, BF, ABF, NMS, and RCGA). In Table 5, the time computation of our proposed method for 1 K (1024) pixels is overwhelming compared to other methods. For the comparison methods, PSO takes the least time, which is 0.1057 S. However, our proposed method only took 0.008 s, which is a huge gap. It fully proves that the time cost of our proposed method is tiny.
# An Efficient Framework for Securing the Smart City Communication Networks
## 2 Related Work
### Information and Communication Technology (ICT) in a Smart City  
As presented in Figure 1, the smart city system has been divided into four layers, each with its own set of needs in order to be more efficient, reliable, and intelligent. Furthermore, efficiency, reliability, scalability, and intelligence have become increasingly vital in order to be fast, better, secure, and resilient controls and communication. The deployment of ICT in smart grid has involved different applications such as advanced metering infrastructure, wide area measurement system, substation automation system, and common information models [7].  
In a nutshell, overall, the primary purpose of a smart city is to focus on urban residents, not just to meet current requirements but also to ensure that future generations are kept in mind with regards to cultural, social, economic, and environmental aspects.
## 2 State of the Art
### Four-Dimensional Modelling
#### 2.2.3 Machine Learning and Hybrid Methods  
Traditional algebraic approaches, such as in classical photogrammetry, employ equations, e.g., to detect, describe and match geometric features in images [97]. In contrast, machine learning is based on a statistical model developed via training data (or using self- and cross-attention layers in transformers), e.g., to detect features [118]. Current evolutions in computer vision are linked to the renaissance of machine learning since the 2010s [119], driven by the development of convolutional neural networks (CNNs, e.g., [120]). Machine learning approaches are currently mainly researched and employed for image and 3D point cloud analytics in cultural heritage (recent overview: [121]), but also increasingly serve 3D model creation tasks. There, machine-learning-based technologies are currently primarily used for particular tasks to preselect imagery [122; 123], in semantic segmentation to classify parts of images [124; 125; 126] and to recognize and identify specific objects [127; 126; 128; 129]. Traditional machine-learning-based technologies do require large-scale training data [121; 126; 127; 128] and therefore are mainly capable of recognizing visually distinctive and well-documented landmark buildings [4]. These approaches usually fail to deal with less distinctive architecture, such as houses of a similar style, or fail when few images are available. Even using more advanced machine learning approaches or by combining different algorithms such as DELF and SuperGlue [130] only allows the realization of prototypic scenarios [6; 131; 132]. Another approach bypasses the modelling stage to generate visualizations directly from imagery [127; 133; 134], e.g., by transforming or assembling image content (recent image generators like DALL-E [135]). Recent approaches include neural radiance fields (NeRFs) [136; 137], which predict shifting spatial perspectives even from single images [138], and can predict 3D geometries [139]. Generative adversarial networks (GANs) are a combination of proposal and evaluation components of machine learning. They are frequently employed as approximative techniques in 3D modelling, e.g., for single photo digitization [140], completion of incomplete 3D digitized models [141; 142] and photo-based reconstructions [143]. Regarding transparency, most current machine learning approaches work within black box settings [121; 144]. Although some newer machine-learning-based approaches are promising for processing small samples, they fail to process non-digital (historical) imagery with robustness and by design work on predictions; in addition, machine learning is limited by being grounded in historical original sources.
# Guided Mode Resonance in a Low-Index Waveguide Layer
## 4 Conclusions  
In conclusion, we demonstrate the guided mode resonance in a low-index waveguide layer with a periodic metal grating. Any structure which can provide coupling and sufficient reflection can excite the guided mode resonance in almost any dielectric material. The ability to control the number and position of resonance peaks by defining the geometry has been demonstrated. Considering the guiding mode theory behind it, this simple structure may allow characterization of the optical property of various low refractive index films which is impossible by a traditional method such as prism coupling. Compared with the high-index waveguide, the low-index waveguide could be used in various functional polymer materials, which would find many applications such as fluorescence enhancement, photodetectors and sensors.  
Conceptualization, T.G., J.E. and Y.S.; validation, J.E., N.W. and Y.J.; formal analysis, T.G.; investigation, T.G.; resources, Y.S.; data curation, T.G.; writing--original draft preparation, T.G.; writing--review and editing, Y.S., J.E. and J.H.; supervision, Y.S. and J.H.; All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the National Natural Science Foundation of China (No. 91833303, 11621101, 61550110246, 61875174, 62007029), the National Key Research and Development Program of China (No. 2017 YFA0205700).  
Not applicable.  
We thank [PERSON] for the helpful discussion and [PERSON] for providing LN substrate.  
The authors declare no conflict of interest.