esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Verification
### Description of the Target Object in a Community (TOC): Historical Structure in Kantza, Greece  
The TOC in this case study, the Church of Agios Nikolaos (Figure 8a) is a cultural heritage building and historical structure, and its exterior condition is of concern for city maintenance. This 400-year-old building is 5 m \(\times\) 12 m and is currently in use in Kantza-Pallini, Greece. It was originally built in 1592 AD, and was restored in 1872. As a regional landmark structure, together with a colossal lion sculpture made of Penteli mountain marble located at the NW of the temple, they offer various crowdsourcing opportunities that could involve both citizens and online resources. As shown in Figure 8b, black and green-colored graffiti markings were present on all four of its walls, where its light-colored surfaces made the graffiti even more obtrusive and ugly.  
Exploring appropriate and stable crowdsourcing resources was important in this method. In this work, we focused on the technical implementation procedures and assumed proper crowdsourcing resources were already retained. Here, we assumed that a large volume of visual data was collected as a part of a series of tasks in a scavenger-hunt-like game application that users were asked to complete [49], and there were a sufficient  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Predicted Class**} \\  & & **Positive** & **Negative** \\ \hline \multirow{2}{*}{Actual Class} & Positive & 482 & 83 \\  & Negative & 61 & 574 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Confusion matrix for calculating the accuracy of the trained R-CNN.  
Figure 8: (**a**) The Church of Agios Nikolaos in Kantza, Greece, the historical structure used in this case study; and (**b**) samples of existing graffiti markings present.  
Figure 7: Example results with test images on which multiple complicated graffiti markings existed within the scene image.  
quantity and variability to satisfy the minimum image overlap criterion in order to perform the SfM procedure described in Section 2[43]. The game application was developed in the scope of the STORM project [50]. Thus, the community data collection step here was done by our team members, not random individuals. Furthermore, to demonstrate the potential for enabling a lifecycle assessment perspective, we collected data twice within an eight-month interval, including 268 images in May 2018 and 337 images in January 2019.
## 1 Introduction
These molds are not only costly but also time-consuming to prepare, limiting the stent design to the diameter, wall thickness, and length of the fabricated tube. Consequently, stent designs based on molding and extrusion lack flexibility. In recent years, 3D printing has also been applied to the production of biodegradable stents. [PERSON] et al. [43] designed a rotary printer that uses polycaprolactone (PCL) to develop a biodegradable stent. They analyzed three different printing pathways to determine the optimal printing pathway for producing stents, adjusting the printer parameters (such as nozzle temperature, flow rate, and printing speed) to find the correlation between the parameters and the stent's geometric dimensions. [PERSON] et al. [44] used self-produced rotary 3D printing to create numerous PLA stents with different structural shapes and investigated the correlation between adjusting the parameters and the stent structure and dimensions. [PERSON] et al. [45] analyzed the mechanical properties of a 3D-printing PLA filament on the market and found that when the strain was 3%, the filament reached the upper limit of the fully elastic deformation zone. However, previous studies utilizing rotary 3D printing to fabricate biodegradable stents have not addressed the specific requirements for stents intended for use in human vascular applications. For instance, the fabricated stents had a strut width of 400 \(\upmu\)m or more [43; 44], which is significantly larger than the strut width required for crimping into a standard 6 Fr (2 mm) stent delivery system. Additionally, there has been limited investigation and measurement of the functional requirements for 3D-printed biodegradable stents after implantation, such as the radial force and compliance needed for vascular expansion. Furthermore, previous literature has rarely employed finite element analysis to optimize the design of 3D-printed biodegradable stents, particularly for self-expanding biodegradable stents, where research is almost non-existent. An ideal AVF stent must be flexible after expansion, preventing the risk of damage due to forces external to the arm, and biodegradable, avoiding the risk of AVF blockage or complications in the stent after assisting the AVF maturation. Moreover, these stents must be able to be crimped into a stent delivery system with a small diameter, namely 2 mm, and released to expand the venous fistula, with sufficient radial force, to a diameter larger than 4.2 mm. In this study, by combining the biodegradability of PLA with the elasticity of thermoplastic polyurethane (TPU) to form the stent material, a new type of biodegradable self-expanding stent was designed that can be applied to assist AVF maturation. A prototype was produced using a rotary 3D printer, and stent performance measurement tests were conducted on the prototype to analyze and verify whether the designed stent could meet the requirements.
###### Abstract  
Wayfinding is important for everyone on a university campus to understand where they are and get to where they want to go to attend a meeting or a class. This study explores the dynamics of mobile navigation apps and the spatial ability skills of individuals on a wayfinding performance and perceived workload on a university campus wayfinding, including indoor-outdoor navigation, by focusing on three research objectives. (1) Compare the effectiveness of Google Maps (outdoor navigation app) and MazeMap (indoor-outdoor navigation app) on wayfinding performance and perceived workload in university campus wayfinding. (2) Investigate the impact of participants' spatial ability skills on their wayfinding performance and perceived workload regardless of the used navigation app. (3) Highlight the challenges in indoor-outdoor university campus wayfinding using mobile navigation apps. To achieve this, a controlled experiment was conducted with 22 participants divided into a control (using Google Maps) and an experiment group (using MazeMap). Participants were required to complete a time-bound wayfinding task of navigating to meeting rooms in different buildings within the Glashaugen campus of the Norwegian University of Science and Technology in Trondheim, Norway. Participants were assessed on spatial ability tests, mental workload, and wayfinding performance using a questionnaire, observation notes and a short follow-up interview about the challenges they faced in the task. The findings reveal a negative correlation between overall spatial ability score (spatial reasoning, spatial orientation, and sense of direction) and perceived workload (NASA TLX score and Subjective Workload Rating) and a negative correlation between sense of direction score and total hesitation during wayfinding task. However, no significant difference was found between the Google Maps and the MazeMap group for wayfinding performance and perceived workload. The qualitative analysis resulted in five key challenge categories in university campus wayfinding, providing implications for designing navigation systems that better facilitate indoor-outdoor campus navigation.  
Keywords: indoor-outdoor navigation; wayfinding; campus navigation; mobile navigation app; spatial ability skills +
Footnote †: journal: Medical Informatics
# A Trusted Routing Scheme Using Blockchain and Reinforcement Learning for Wireless Sensor Networks
## 3 Approach  
In this section, we introduce a novel trusted routing scheme based on the blockchain and reinforcement learning. First, we put forward the threat model of our scheme and briefly describe the attack and cheating methods of malicious nodes in the routing environment. Then, we propose a blockchain-based network architecture to strengthen the credibility of routing information. We also design a specific routing scheduling algorithm based on reinforcement learning for the designed blockchain-based network architecture, labeled as RLBC (reinforcement learning and blockchain based) routing algorithm, in which the reinforcement learning is used to help the routing node select the next optimal routing node. Finally, we analyze the security of the proposed trusted routing scheme.
## 3 UBSS Algorithm Based on TF Soft Mask  
To tackle the above challenge, we propose a new blind separation strategy based on TF soft mask. The process consists of two main stages. The first step is to determine the number of sources through the KDE algorithm, followed by peak detection. After that, the contribution degree of the source signal in the observed signal is calculated by the contribution degree function to construct the TF soft mask matrix, which is used to separate the TF aliasing signals. All variables involved in the section are described in Table 2.
## 4 Results and Discussion
### Divergence of Attributes  
While the previous findings show consensus among the papers, we also collected attributes for which there were differences in findings among papers. In this section, we describe attributes that were viewed as beneficial attributes in some papers, while in other papers they were identified as a challenge.
## 3 Results
### Antimicrobial Activity of Fructan Isolated from Dandelion  
To the best of our knowledge, the reports about the antimicrobial potential of fructans, especially inulin, are insufficient, and their mechanisms against pathogens have still not been elucidated in depth. It is well known that the soluble oligosaccharides mimic the sugar chains situated in the glycolipids and glycoproteins of the gut epithelial cells, and in this way they prevent pathogen adhesion [40]. This study aimed to perform a preliminary antimicrobial study of dandelion inulin because little is known about the direct antimicrobial effect of fructans. The results obtained from the antibacterial activity of isolated dandelion and commercial chicory inulin are summarized in Table 5.  
As a result, chicory and UAE dandelion inulin demonstrated moderate antimicrobial activity against _Listeria monocytogenes_. Moreover, dandelion inulin mainly demonstrated low-to-moderate antimicrobial activity against _Salmonella throphy_ 745 and _Bacillus subtilis_ 6633 at a concentration of 1 mg/mL, while chicory inulin was inactive at this concentration. Dandelion inulin demonstrated an antibacterial potential comparable to that of Biseptol (400 mg/mL) used as a positive control. However, none of the tested inulin samples were active against _St. aureus_ 745, nor against Gram-negative bacteria _E. coli_ 3398. To the best of our knowledge, this is the first study to evaluate the antimicrobial potential of dandelion inulin.
# An Energy-Efficient Coverage Algorithm for Macrocell--Small Cell Network Systems
## 3 Problem Description
### Problem Formulation and Goal
In mathematical terms, the optimization problem in question can be formulated in a rigorous manner as follows:  
\[\underset{z,h}{\text{minimize }}P_{\text{total}}=P_{\text{s,basic}}|\aleph^{(z)}|+ \sum\limits_{k=1}^{K}P_{\text{trans},k}^{(z)}+P_{\text{m\_basic}}+P_{\text{m\_ trans}}^{(h)} \tag{11}\]  
subject to  
\(z\in\Omega_{\text{s}}\);  
\(h\in\Omega_{\text{m}}\);  
\(y_{\text{m}}^{\ell}+\sum\limits_{k=1}^{K}x_{k}y_{k}^{\ell}\geq 1\), \(\ell\in\text{Y}\);  
\(\sum\limits_{\delta\in\mathbb{N}}I_{\delta}^{(i)}=1\), \(i=1,2,\ldots,M\). In light of this, the ideal solution would consist of smartly adjusting the power levels of all the SBSs, including the full deactivation of a previously active SBS or the activation of a previously inactive SBS within the system, according to the dynamics of the given network traffic, so as to actively manage the overall power consumption of the system. In other words, the goal of the study was to determine a feasible solution, given the available configurations and under various traffic scenarios, for providing users with complete signal coverage for wireless devices, while also yielding the minimum possible level of total power consumed. Given these conditions, it should be clear that the considered optimization problem would necessarily require the use of integer-nonlinear programming. That said, problems of this type have a high degree of complexity, such that an exhaustive search is typically needed if the optimal solution is to be found. To put that another way, this sort of problem can be seen as numerically tractable. It should also be noted, moreover, that an optimal solution will not be achievable at all if the level of traffic is so excessive that it surpasses what the system can handle. With these points in mind, and with the aim of giving system operators a practical way of handling their operations in real-life contexts, this study sought to work out an effective approach for addressing the stated problem. Consequently, the following section proposes a simple-but-efficient strategy for managing power aimed at achieving the stated goal in an efficient and effective manner.
## 2 Methodology
### Regression Modelling
#### 2.2.2 Negative Binomial Regression  
We employed two negative binomial regression models to analyse the relationship between the count of train running time delay increases (1)/decreases (2) and a set of explanatory variables. The regression coefficients were estimated using the glm.nb function in R (2023.06.2). The equation for the model is as follows:  
\[\log(E[Y|X])=\beta_{0}+\beta_{1}X_{1}+\beta_{2}X_{2}+\beta_{3}X_{3}+\beta_{4}X _{4}+\beta_{5}X_{5}+\log(\varepsilon_{i}) \tag{3}\]  
where:  
* \(E[Y|X]\) is the expected count of running time delay increase (1 min) for the first model and of running time delay decrease (1 min) for the second model given the predictor variables;
* \(X_{1},X_{2},\dots,X_{5}\) are the predictor variables in the model (trackwork, track type, train subtype, train enter status, and night, respectively);\(\beta_{0}\) is the intercept term, and \(\beta_{1},\beta_{2},\ldots,\beta_{5}\) are the coefficients for each predictor variable.
* \(\log(\varepsilon_{i})\) is the natural logarithm of the exposure variable for observation.  
For ease of interpretation, in line with the coefficients obtained from the negative binomial regression, we computed the incidence rate ratio (IRR) by taking the exponent of the estimated coefficients, which is expressed as \(\text{IRR}=e^{\beta_{i}}\). This allows us to directly interpret the proportional change in the count of running time delay increases or decreases associated with a one-unit change in the predictor variable, with all other variables held constant.
# The Evaluation of Industry 5.0 Concepts: Social Network Analysis Approach
## 2 Literature Review
### Industry 5.0: Sustainability Approach  
Considering sustainability issues, three types of factor appear--social, economic, and ecological [41]. Social factors are related to the society and its needs, while economic factors reflect the economic steadiness of a particular firm or industry, and ecological factors represent the state of the environment [27; 41; 42]. These factors are also referred to throughout the Sustainable Development Goals (SDGs) established by the United Nations [6].  
Industry 4.0 addressed sustainability, but only partially on a micro-organizational level [43]. Industry 5.0 approaches sustainability in a broader manner, taking into consideration not only issues on a micro-organizational level, but on a macro-organizational level, and on a global level as well [44].  
It is expected that firms will develop corporate social responsibility, which supports informing and educating employees about current social and environmental problems, and encourages them to find new ways of doing business that have a weak or no negative influence on the environment, through implementing the use of new materials, recycling the products or their parts, etc. [1; 6; 45]. It is also expected that production planning will be performed according to planet Earth's possibilities and constraints, because an increase in the production volume leads to an increase in gas emissions [1; 46].  
Additionally, when purchasing goods, modern customers seek to support businesses that not only offer high-quality products but also align with their values--this trend has led to an increasing emphasis on green business and the circular economy [35; 47].  
In this research, the parameters used for measuring sustainability were: (1) the possession of a certified environmental management system; (2) the possession of a certified energy management system; (3) the implementation of measures for improving efficiency in material consumption; (4) the implementation of measures for improving efficiency in energy use; (5) the implementation of measures for improving efficiency in water use; (6) the use of technologies for recycling and re-use of water; (7) the use of technologies to recuperate kinetic and process energy; (8) the use of technology that resulted in a significantly more efficient use of materials when first implemented; (9) product revamping or modernization; and (10) offering take-back services.
# Analysis of Hybrid Ship Machinery System with Proton Exchange Membrane Fuel Cells and Battery Pack
## 3 Numerical Background of Fuel Cell Model
The membrane material's properties, including density, specific heat capacity, and thermal and ionic conductivity, as well as water diffusion and electro-osmotic drag values are obtained from the literature [25]. Regarding the technical details, the fuel cell model is from the software AVL CRUISE M. In the software, only the dimensions and number of single cells are specified for the stack, and the effective power is calculated according to the available parameter sets (available in the software manual). For the experimental setup, which was not used in this work, it would be required to model the system for 300 kW output power, capable of producing a voltage of 600 V at a current of 500 A. At the moment, high-performance fuel cell stacks are able to produce current densities of 2.0 A/cm\({}^{2}\) with single-cell voltages of 0.6 V. In order to achieve a total current of 500 A, each individual cell should have an active area of at least 250 cm\({}^{2}\). Additionally, the stack should be composed of a minimum of 1000 single cells. To our knowledge, commercial stacks only go up to 250 kW. The dimensions of high-end commercial stacks (e.g., Toyota Mirai, with power output of around 115 kW) have a specific power density of 3.0 kW/dm\({}^{3}\), which would mean that the volume of the fuel cell stack would be at least 100 dm\({}^{3}\). There are additional approaches for numerical simulation of PEMFC-s, and in CRUISE M, there is simplified model that uses Tafel slope, and the characteristic of the PEMFC stack or single cell is defined as a polarization curve, where it is possible to vary the angle of the curve with coefficients, which makes this simulation zero-dimensional. The most complex numerical approach is with computational fluid dynamics (CFD), with which the PEMFC is considered as a stand-alone system with high complexity, but this requires intensive computation resources and time, so it is useful to define precise parameters in a simplified numerical model, as it can calculate basically any parameter that is required to define the output parameters of heat, current, and voltage production together with consumption of gasses and amount of generated water. One example of CFD simulation for a single-cell PEMFC is given in [26].
## 2 Materials and Methods
### Data Reduction
#### 2.4.2 Downsampling  
Based on a preliminary analysis, the original COP data sets, recorded at 1200 Hz for 30 s by [PERSON] et al. [21], resulted in lower estimates of ApEn because the embedding time window was too small. Therefore, for this study, MATLAB's built-in function was used to decimate the data from 1200 Hz to 60 Hz. The function low pass filters the data before downsampling to prevent aliasing. Figures 3-6 show the time series before (Figures 3 and 5) and after (Figures 4 and 6) downsampling from a representative participant tested with EOFT and ECTanDF, respectively. We believe it was important to examine and show the results of downsampling for both the EOFT and ECanDF conditions because they were clearly different based on the subsequent entropy analyses. The figures show that downsampling did not affect the observed major trends in either of the stability conditions.  
\begin{table}
\begin{tabular}{c c} \hline
**Balance Condition** & **Description** \\ \hline EOFT & Eyes Open, Feet Together \\ ECFT & Eyes Closed, Feet Together \\ EOTanDF & Eyes Open, Feet Tandem, Dominant Foot Forward \\ ECTanDF & Eyes Closed, Feet Tandem, Dominant Foot Forward \\ EOTanDB & Eyes Open, Feet Tandem, Dominant Foot Back \\ ECTanDB & Eyes Closed, Feet Tandem, Dominant Foot Back \\ \hline \end{tabular}
\end{table}
Table 1: Quiet Standing Balance Test Conditions.  
Figure 2: Data processing functional block diagram.  
Figure 4: Representative time series for downsampled center-of-pressure (COP) data of participant 1 eyes open feet together (EOFT) Trial 4, where _f_s = 60 Hz, in the anterior–posterior (AP) and medial–lateral (ML) directions, respectively.  
Figure 3: Representative time series for raw center-of-pressure (COP) data of participant 1 eyes open feet together (EOFT) Trial 4, where _f_s = 1200 Hz, in the anterior–posterior (AP) and medial–lateral (ML) directions, respectively.
## 4 Discussion
### Recommendations for Agricultural Producers and Environmental Policymakers  
#### 4.4.1 Recommendations for Agricultural Producers  
(a) Based on Soil Salinity: Soil salinity can be divided into five levels according to its salinity (see Figure 12). For soil in different salinity levels, the planting areas should be reasonably allocated and the planting structure optimized according to soil type and crop salt tolerance: (1) Non-saline Areas: Suitable for growing staple crops such as wheat, corn, and rice. (2) Slightly saline Areas: Suitable for growing salt-tolerant crops such as soybeans and cotton. This area can support crops with relatively high salt tolerance but still requires proper water management and salt control. (3) Moderately Saline Areas: Suitable for planting high-quality forage crops such as alfalfa. Planting forage provides food for livestock while maximizing the utilization of saline-alkali land. (4) Strongly Saline Areas: Mainly suitable for planting halophyte economic crops, such as soda grass. These crops have strong adaptability to high-salinity environments and possess certain economic and ecological value. (5) Extremely Saline Areas: These areas have the most severe salinization and are generally not suitable for agricultural planting. However, wild plants such as need and other halophytes can be considered for ecological restoration and wetland recovery.  
(b). Technological Integration and Agricultural Management. To efficiently utilize saline-alkali land, in addition to the reasonable allocation of planting areas, it is necessary to strengthen technological integration and improve agricultural management: (1) Soil Improvement and Fertility Enhancement: Using organic fertilizers, green manure, and microbial fertilizers to improve soil structure, enhance soil fertility, and reduce salt accumulation. Methods such as adjusting soil pH and irrigation management can also be employed to effectively reduce the impact of salinity on crops. (2) Integration of Agricultural Machinery and Remote Sensing Technology: Combining advanced agricultural machinery, drones, and remote sensing technologies for precision farming. Remote sensing monitoring technologies can be used to dynamically track soil salinity and moisture conditions, enabling real-time monitoring of changes in water and salt content in saline-alkali land, and guiding precise fertilization and irrigation.