esa-sceva/satcom-chunk-collection huggingface dataset:
# Track-Bridge Interaction of CWR on Chinese Large-Span Bridge of High-Speed Railway
###### Abstract  
The track-bridge interaction is a fundamental concern in the field of railway engineering, which plays an important role in the optimization design of railway bridges, especially for heavy-haul railway and high-speed railway bridges. This paper systematically introduces the research status of the CWR track-bridge interaction for large-span bridges of high-speed railway in China. The evolution process of the track-bridge interaction model from the simplest elastic bar and linear longitudinal resistance model to the complex beam-rail interaction model considering the loading history is described. In this paper, the modeling methods of the track-bridge interaction model for five types of long-span railway bridges, namely simply supported beam bridge, continuous beam bridge, cable-stayed bridge, and bridge, and suspension bridge, are systematically introduced, and the characteristics of longitudinal force distribution under the track-bridge interaction are analyzed. This paper discusses the practical application of the theory of the track-bridge interaction on extra-large-span bridges from the aspects of system dynamic performance evaluation and system safety evaluation. The practical application of track-bridge interaction theory under special conditions such as earthquake load, complex temperature load, shrinkage and creep load, and superposition of multiple loads is emphasized. It provides guidance for the further improvement of the track-bridge interaction model and the design of large-span high-speed railway bridges in the future.  
b +
Footnote †: journal: Applied Sciences  
railway bridges account for about 45.2% of the total length of high-speed railway lines, with two main spans over 1000 m. The preliminary scheme of the Ya'an-Linzhi section of the century project \"Sichuan-Tibet Railway\" includes one suspension bridge with a main span of 1000 m and one suspension bridge with a main span of 1020 m, three steel truss arch bridges with main spans of 400-500 m, and one continuous steel truss bridge with a main span of 240 m. Long-span railway bridges are characterized by a large-span, complex structure, temperature sensitivity, various loads, and a more complex track-bridge interaction, which puts forward new requirements for the design of CWR on bridges. For the large-span bridge of a high-speed railway under design or in service, it has become a common practice to evaluate the bridge by using the track-bridge interaction. Through the stress analysis of the track-bridge system, the actual service condition of the bridge can be better described, and the fatigue life assessment of the bridge can be more reliable. In this paper, the collation and review of the existing research on the track-bridge interaction of long-span railway bridges will provide strong support for the next in-depth study.
## 2 State of the Art
### Standardization of Information Containers  
The specification of the openCDE interface is based on information containers according to DIN EN ISO 19650 [8] (generally) and DIN SPEC 91391-2 [9] (specifically). These information containers achieve an efficient exchange of heterogeneous and interlinked construction data, e.g., for data drops or long-term archiving. In general, information containers are referred to as the interrelated addressable set of data or information in the storage hierarchy of a system [8]. In the context of data transfers, information containers are usually transferred as compressed, structured file archives. An information container can provide a set of metadata and a set of information content according to DIN SPEC 91391-2 [9]. The general openCDE-compliant interface does not specify a specific container format, but also allows for unstructured storage of documents in an information container. More specific and structured information containers can be, for example, the Dutch standardized COINS container [37], the multimodel or BIM-LV container [38], or the Information Container for linked Document Delivery (ICDD) [39]. Extensions of the openCDE interface for specific container types, for instance, using the ICDD container, were analyzed, conceptualized and implemented by [PERSON] et al. [39].  
With the employment of information containers and, in particular, the ICDD and the Semantic Web technologies utilized in it, decentralized applications and data storage are made feasible for the building life cycle. The stakeholders of projects in the AECO domain are used to operate in a decentralized manner in terms of their location and organization [40]. Therefore, more and more approaches focus on the development of decentralized CDEs [41], considering information containers to comprise heterogeneous information from distributed locations to be accessible for stakeholders [40]. This kind of decentralization is also facilitated by the use of Federated Linked Building Data as a novel approach for the BIM collaboration [42].
# A Visual Mining Approach to Improved Multiple-Instance Learning
## 3 Background and Related Concepts
### Instance Prototype Selection  
#### 3.2.1 Salient Instance Selection Strategy  
In MILSIS [16], the authors perform prototype selection in positive bags, obtaining Salient Instances via \"Rough Selection\" and \"Fine Selection\".  
_Rough Selection_ obtains two _optimal_ positive instances from all positive bags, which are basically those with the highest and lowest values of salience. First, all instances in negative bags are grouped in a set \(B^{-}\). Then, the salience \(Sal(B_{ij}^{+})\) for each instance in a given bag \(B_{i}^{+}\) is computed as follows:  
\[Sal(B_{ij})=\sum_{B_{ik}\in B_{i}\backslash\left\{B_{ij}\right\}}d(B_{ij},B_{ ik}), \tag{1}\]  
where \(d(.,.)\) is the Euclidean distance function. A high salience value indicates that the instance is different from the other instances in the bag.  
After computing saliences inside \(B_{i}^{+}\), instances are sorted from the maximum (\(j=1\)) to the minimum (\(j=m\)) salience values. This is used to estimate the probability that \(B_{i1}^{+}\) (maximum) and \(B_{im}^{+}\) (minimum) instances are positives given the set \(B^{-}\) (see Equation (2)), and then select an _optimal_ positive instance, which will represent \(B_{i}\):  
\[Pr(l(B_{ij})=+1|B^{-})=1-\exp(-D(B_{ij},B^{-})/\sigma^{2}), \tag{2}\]  
where \(l(.)\) is the label function, \(\sigma\) is a scaling factor larger than 0; \(D(B_{ij},B^{-})\) is the minimum distance between \(B_{ij}\) and all instances in \(B^{-}\):  
\[D(B_{ij},B^{-})=\min_{B_{tr}\in B^{-}}d(B_{ij},B_{rt}), \tag{3}\]  
Since \(Pr(l(B_{ij})=+1|B^{-})\) is proportional to \(D(B_{ij},B^{-})\)[16], from Equation (3), it is possible to estimate how likely an instance is to be labeled as positive or negative by its distance to the set of negative instances. Finally, the probabilities of each bag are compared in order to find the _optimal_ positive instance, i.e., the one with maximum distance to \(B^{-}\).  
In _Fine Selection_, an _optimal_ negative instance is selected from \(B^{-}\) by its maximum distance to the optimal positive ones. Then, starting with the optimal positive instances obtained in the rough selection: \(B_{i1}^{+}\) and \(B_{im}^{+}\), it finds a _true_ positive instance in each bag. These _true_ positive instances will be part of the Salient Instances. Algorithm 1 summarizes the whole procedure. Note that \(B=B^{+}\cup B^{-};n^{+}\) and \(n^{-}\) are, respectively, the number of instances inside positive and negative bags; and _SalNum_ is the number of salient instances.
# Design and Experiment of a Moving Magnet Actuator Based Jetting Dispenser
###### Abstract  
Developing actuators to drive high-frequency jetting dispensers in the dispensing technology for electronic assembly applications has become a concern in recent years. This study proposed a new jetting dispenser without a displacement amplification mechanism directly actuated by a moving magnet actuator (MMA) to jet small fluid droplets. In this article, the main geometric dimensions of the dispensing cluster including the needle, chamber, and seat-nozzle follow those of previous studies and existing dispensers to reduce design time. The necessary root-mean-square force applied to the dispenser needle, which is the key value for the MMA design, is calculated in detail. An ANSYS Maxwell finite element analysis (FEA) is used to simulate the MMA thrust force and modify geometric dimensions. The droplet size produced by the proposed dispenser is empirically investigated under various backpressure, operating frequency, needle displacement, and fluid temperature conditions. The experimental results indicated that the jet dispenser driven by the moving magnet actuator stably operated at the maximal operating frequency of 80 Hz. Some errors, such as losing droplets and generating small satellite dots around the main dots, did not occur during the operating process. In addition, the size of the proposal dispenser without the displacement-magnifying mechanism was also significantly reduced. To conclude, the jetting dispenser driven by the proposal moving magnet actuator can operate well at a medium frequency and shows great potential for dispensing technologies.  
linear motor; moving magnet actuator; jetting dispenser; droplet; electronics packaging Article
## 6 Open Challenges, Future Prospects and Research Directions  
FSO communication systems have developed into an innovative technology for high-speed wireless communication. Despite their potential, various challenges need to be overcome to fully harness their capabilities. This section explores the key open challenges, discusses future prospects, and outlines potential research directions in FSO communication. By identifying critical areas, such as AT mitigation, aperture averaging effects, and advanced modulation techniques, we aim to offer an in-depth overview of the current landscape, fostering a deeper understanding of where focused efforts and innovative solutions are needed. This discussion is crucial for guiding ongoing and future research, ensuring continued advancement and addressing the critical needs of the field.
## 2 Materials and Methods
### Statistical Analysis  
For the analysis of the present study, the Statistical Package for the Social Sciences (SPSS) version 23 (IBM, New York, NY, USA) was utilized. The level of significance for all the analyses was set at \(p\leq\) 0.05. The first step followed was to analyze the normality of the sample size in the analyzed variables. In this process, significant differences were obtained in the Kolmogorov-Smirnov and the Shapiro-Wilk tests. Thus, the non-parametric tests were selected for the following analysis.  
First, to confirm how self-efficacy may influence physical activity levels, the investigators conducted a Kruskal-Wallis one-way analysis to compare the differences in self-efficacy among patients performing high, moderate, and low physical activity levels, according to the IPAQ classification explained before. Moreover, we performed pairwise comparisons through the Mann-Whitney U test when significant differences were obtained.  
Second, to examine the relationships among the fear of movement, the impact of fibromyalgia, fatigue dimensions, and self-efficacy dimensions, the investigators conducted the [PERSON]'s rank correlation coefficient test.  
Finally, given that the relationship between self-efficacy and physical activity is already well-known, we explored the relationship between the fear of movement and self-efficacy, as well as the potential mediating role of the impact of fibromyalgia and fatigue. To do that, the investigators conducted multiple variable mediation analyses, using the macro package of PROCESS version 4, designed by [PERSON] [37] for IBM SPSS Statistics. To calculate the unstandardized regression coefficients, model 6 with an interval confidence of 95 and 10,000 bootstrap samples was utilized. Moreover, the total effect was calculated to quantify the influence of the fear of movement on self-efficacy for coping with physical function without the influence of the mediators. For establishing the mediation role of the variables, the following criteria stated by [PERSON] and [PERSON] [38] were considered: (a) the relationship between the independent and the dependent variables must be statistically significant; (b) the independent variable and each of the mediator variables must be significantly related; (c) the dependent variable and each of the mediator variables must be significantly related; and (d) the relation between the independent variable and the dependent variable is reduced when each of the mediator variables is introduced in the analysis.
## 2 Experimental Method
### Wind Tunnel Tests
The calibrated thermosensitive sensor (RTDs, pt1000 \(\Omega\)) was used to measure the temperature on the water side with a measurement accuracy of \(\pm\)0.05 \({}^{\circ}\)C.  
The heat transfer and flow characteristics were evaluated using Colburn factor \(j\) (\(j\) factor) and friction factor \(f\) (\(f\) factor) according to the structural parameters of the different flow channels. In plate-fin heat exchangers, the \(j\) factor and \(f\) factor can be expressed as [25]:  
\[j=\frac{Nu}{Pr^{1/3}Re} \tag{1}\]  
The friction factor in the equal and curved cross-section flow channel is described as:  
\[f=\frac{d_{h}}{2L_{f}}\frac{\Delta P}{\rho u_{in}^{2}} \tag{2}\]  
Figure 3: Schematic diagram of test table [18]. In Equation (2), \(u_{m}\) is the mean velocity in the equal and curved cross-section flow channels. \(d_{h}\) is the hydraulic diameter of the flow channels. \(L_{f}\) and \(\rho\) represent the length of the flow channels and the density of the air. In the above equations, the Nusselt number (\(Nu\)) and the Prandtl number (\(Pr\)) can be expressed as follows:  
\[Nu=\frac{hd_{h}}{\lambda} \tag{3}\]  
\[Pr=\frac{\mu c_{p}}{\lambda} \tag{4}\]  
where \(h\) is the convective heat transfer coefficient, \(\mu\) is dynamic viscosity and \(\lambda\) is heat conductivity. The uncertainties of experimental results can be calculated by using the error evaluation method [26; 27]. The expression of uncertainty is  
\[\frac{\delta_{R}}{R}=\sqrt{\left(\frac{\partial R}{\partial v_{1}}\right)^{2 }\left(\frac{\delta v_{1}}{v_{1}}\right)^{2}+\left(\frac{\partial R}{\partial v _{2}}\right)^{2}\left(\frac{\delta v_{2}}{v_{2}}\right)^{2}+\ldots+\left( \frac{\partial R}{\partial v_{n}}\right)^{2}\left(\frac{\delta v_{n}}{v_{n}} \right)^{2}} \tag{5}\]  
In Equation (5), \(R\) is a function of the dependent variables \(v_{i}\) (\(i\) = 1, 2,..., \(n\)), and \(\delta v_{i}\) (\(i\) = 1, 2,..., \(n\)) represents the uncertainties of variable \(v_{i}\). Therefore, \(\delta R/R\) represents the uncertainty of the variable. As for uncertainties of the \(j\) and \(f\) factors, they can be described as [27]  
\[\frac{\partial j}{j}=\sqrt{\left(\frac{\partial h}{h}\right)^{2}+\left(\frac{ \partial\rho}{\rho}\right)^{2}+\left(\frac{\partial u}{u}\right)^{2}+\left( \frac{2}{3}\frac{\partial Pr}{Pr}\right)^{2}} \tag{6}\]  
\[\frac{\partial f}{f}=\sqrt{\left(\frac{\partial\Delta P}{\Delta P}\right)^{2} +\left(\frac{\partial\rho}{\rho}\right)^{2}+\left(2\frac{\partial u}{u} \right)^{2}} \tag{7}\]  
So, the uncertainties of the \(j\) and the \(f\) factors are 2.12% and 3.19%, respectively.
# Building Information Modelling Facility Management (BIM-FM)
## 7 Theoretical Requirements for BIM-FM
### Theoretical Functional Requirements
#### 7.2.2 Intelligent Maintenance Schedules  
An acknowledged issue with current FM practices is the tendency to undertake reactive maintenance rather than proactive maintenance [47], which can be an inefficient and often expensive use of resources. To enable proactive maintenance BIM-FM needs to perform two functions: monitor future maintenance requirements, and record previous maintenance activities.  
Several works have employed BIM-FM to actively track the urgency level of future maintenance requirements, often by colour-coding objects according to the urgency/severity [40; 44]. Setting up this approach generally requires good prior knowledge of maintenance programmes, but ongoing programmes may then be updated according to the newly inputted data. Most existing case studies are in the implementation stage of this function, so do not discuss its long-term success.  
There is evidence to support the argument that storing previous maintenance records within BIM enables proactive maintenance. For example, [PERSON] et al. [18] found that storing previous work orders in a BIM format enabled them to identify the cause of previous issues by providing a visual correlation between incidents originally considered unrelated. However, as previously discussed, [PERSON] et al. [79] found that when a model element was majorly updated or deleted and replaced to maintain visual accuracy, previous information associated with that model was removed. Whilst the data were still accessible within the FM database, their availability would rely on the personal experience of the user, so would not be viable in the long term.
# A Survey on Satellite Communication System Security
## 4 Attacks in Satellite Communication Systems
### Confidentiality Threats
Utilizing common, moderately priced equipment, they captured and decrypted a live call session from the Thuraya network in under an hour using standard PC hardware. This was achieved by exploiting weaknesses in the A5-GMR-1 cipher and solving a system of linear equations to recover the session key. Also, ref. [35] presents an approach for eavesdropping on optical communications between LEO satellites and High-Altitude Platform Stations (HAPSs), focusing on both downlink and uplink transmissions. It investigates the secrecy performance of these communications, particularly under scenarios where an eavesdropping spacecraft is positioned close to the satellite within its optical beam's convergence area. This study, validated through Monte Carlo simulations, reveals that downlink communications are generally more secure, with increased beam leakage to the eavesdropper significantly impairing the secrecy performance.
## 3 Results and Discussion
### Clustered Heat Maps  
The cluster map shown in Figure 3 indicates the similarities among the samples based on seed characteristics, pressing conditions, and cake characteristics.  
Based on the aforementioned parameters, the examined samples were divided into three groups. The first group includes samples 1, 4, 5, 6, and 11. These samples were found to have a total hull and impurity content over 15%. The greatest similarity within the first  
Figure 3: Clustered heat map based on the seeds characteristics and composition (h—hull content, i—impurity content, Ms—moisture content, Os—oil content), cake composition (Mc—moisture content, Oc—oil content), and pressing characteristics (Ep—pressing efficiency, t—pressing time, Qp—material flow rate, Q—quantity of produced oil) of the tested samples.  
cluster was observed between samples 4 and 11, which have the highest total hull and impurity content (22.5% and 20%, respectively), with a Euclidean distance value of only 0.27. The least similarity was found between samples 1 and 5. The total hull and impurity content in these samples differs the most (15% and 25%, respectively), and the Euclidean distance value was 1.21. The second group includes samples 2, 3, 8, and 10, which are very similar in the examined characteristics. Among the samples in this group, the greatest similarity (0.55 Euclidean distance) was observed between the samples with the lowest hull and impurity content, 0% (sample 10) and 5% (sample 8), while the greatest difference was found between samples 2 and 3 (1.55 Euclidean distance). The third group consists of samples 7 and 9, which have a low impurity content (2.5% and 0%, respectively) compared to their hull content (15% and 10%, respectively). These findings highlight a synergistic interaction between hull and impurity content, emphasizing their combined impact on pressing yield and capacity, as well as cake characteristics.  
Similarity between parameters was also examined. The oil content in the seed and cake, pressing capacity, and oil produced per unit of time formed one cluster, while the hull content, impurities, moisture content in the seed and cake, as well as efficiency (yield) of extracted oil, clustered together in a second cluster. Pressing time did not belong to any cluster.  
A statistical validation of the cluster analysis was also performed. High cophenetic correlation coefficients of 0.869 (in the cluster analysis of samples) and 0.871 (in the cluster analysis of parameters), as well as low values of delta 0.5 and delta 1.0 of 0.123 and 0.152, respectively (for parameters), and 0.107 and 0.141, respectively (for samples), indicate a good fit of the data.
## 3 Data Analysis and Results
### The Zakurthos Aftershock Sequence  
Herein, we study the seismic sequence that began on 25 October 2018 with a shallow \(M_{w}\) = 6.6 earthquake off the coast Zakurthos (Ionian Sea, Greece) (Figure 1). Based on detailed examination of the 1668 aftershock sequence, which were located by the station network of the Hellenic Unified Seismological Network (HUSN), we conclude that the duration corresponds to the 1-year time period i.e., from 25 October 2018 up to 19 October 2019. According to the catalogue used, aftershocks occurred with a magnitude greater than \(M_{w}\)2.1.  
Plotting the CDF, \(P(>T)\) = \(N(>T)/N_{0}\) of aftershocks interevent times on a double-log scale, a typical \(Q\)-exponential pattern presents for \(T\) < \(T_{c}\), with \(q\) = 1.68 (Figure 7a). The transition from NESP to BF statistics is estimated to be at about \(T_{c}\approx\) 5607 s (Figure 7b). The \(T_{c}\) value (red dashed line in Figure 7c) suggests that the Tsallis entropy controls the early stages of the aftershocks' evolution. Certain traits of the early aftershock sequence related to the NESP become less significant as time passes by, and the statistical physics of BG is recovered.  
Figure 6: (**a**) The interevent times CDF of the 2019 \(M_{w}\)5.1 Parnitha Earthquake, for the aftershocks with \(M\) > \(M_{c}\). The scarlet stroke is the \(Q\)-exponential fitting with \(q\) = 1.71. The change of colors indicates the crossover between the NESP (blue circles) and BG statistics (green triangles). (**b**) The \(ln_{Q}\)P(>\(T\)) as a function of \(T\), where the red line is the fitting with \(q\) = 1.71 and correlation coefficient 0.9912 up to \(T_{c}\). \(T_{c}\) value close to 175,460 s is suggested by the deviation from linearity. (**c**) Interevent time \(T\) evolution with time \(t\) since the main shock. The scarlet line illustrates the \(T_{c}\) value.