esa-sceva/satcom-chunk-collection huggingface dataset:
# A Social Life Cycle Assessment Framework for Additive Manufacturing Products
## 3 SLCA Framework Development
### Framework Development
#### 3.2.4 Phase 4--Aggregation Method  
Following step 3, it is necessary to define a mathematical model to aggregate the scores of the indicators into a single final score that corresponds to the product life cycle score. In this framework, the scores are aggregated at each level of the assessment (i.e., subcategory, stakeholder category, life cycle stage, and finally the product life cycle). Figure 1 considers the \"Manufacturing\" life cycle stage to exemplify how the aggregation method works. The same procedure is applied to obtain the scores for other life cycle stages of an AM product. Finally, the scores of all life cycle stages are aggregated into an overall score that corresponds to the AM product life cycle score. This final aggregate score can be any value between 1 and 5, which demonstrates the social impact of the AM product towards its stakeholders.  
In order to aggregate the different scores, it is necessary to use a mathematical method. In [18] an aggregation method was developed to conduct a social assessment of the Indian steel sector. They assign equal weights to each indicator, facilitating indicator normalization. In order to normalize an indicator, after multiplying the indicator score by the indicator weight, it is necessary to divide the obtained value by the maximum score value that can be assigned to an indicator. The normalized scores are then aggregated within each life cycle stage by summing up the scores of their respective stakeholder categories. In this way, a score between 0.20 and 1 is assigned to each life cycle stage. The final score of the product's social impact is equal to the summation of all the life cycle stage scores, which is between 1 and 5. The structure of the aggregation method developed in the present study follows the method introduced by [18] and is illustrated in Figure 2. The method is comprised of the following steps:  
_Step 1: Indicator weights_  
Figure 1: Aggregation of scores for the Manufacturing stage.  
The first step is to determine the indicators' weights. As previously stated, equal weights are assigned to the indicators within each life cycle stage. The main reason for not assigning different weights to the indicators is to keep the approach as simple as possible due to the pilot nature of the study. As shown in Equation (1), the weight of each indicator is obtained by dividing 1 by the total number of indicators considered in each life cycle stage.  
\[\textit{indicator i Weight}=\frac{1}{\textit{Number of Indicators considered in the life cycle stage}} \tag{1}\]
# Evolutionary Approach to the Euclidean Steiner Tree Problem in n-Space
## 3 Results  
In this section, we present the results of the proposed algorithm for data from the \(\mathbb{R}^{3}\) space (3D real numbers). Two sets of problems were generated, one with 17 points and another with 100 points. Each set contains 50 problems, where the points were randomly generated from a uniform distribution over the cube \([0,1]\times[0,1]\times[0,1]\) in \(\mathbb{R}^{3}\). These sample problems and the obtained solutions can serve as a new benchmark for other researchers. They have been made available from the author upon request.  
First, the results for the basic version of the algorithm will be presented, followed by an examination of the impact of the appropriate preliminary sorting of the set \(P\) of points. The influence of the applied crossover operator will also be discussed.  
All runs of the genetic algorithm utilized the following parameter values:  
* Population size--80;
* Number of generations--70;
* Tournament size--3;
* Crossover probability--0.8;
* Mutation probability--0.1.  
The settings used are typical values applied in the case of genetic algorithms. They were validated through test runs of the algorithm and preliminarily compared with other configurations. It was verified that these settings perform best considering the computation time limitations. A full analysis of the impact of these settings is not presented here; they are kept consistent across all computations since it was observed that other factors, such as the type of crossover and the method of initializing the initial population, have a much more significant and interesting influence on the results.
# Enhancing Clay Soil's Geotechnical Properties Utilizing Sintered Gypsum and Glass Powder
###### Abstract  
The growing number of end-of-life (EoL) photovoltaic (PV) panels as waste materials is forcing many countries to face the challenge of addressing this issue. The presented research explores the utilization of a by-product of this waste material, namely glass powder, with gypsum in geotechnical engineering to improve clay-soil properties. The approach is to integrate these materials to address the sustainable management of EoL PV panels, an underutilized resource in geotechnical applications. Furthermore, the study extensively examines the physical properties of clay soil, gypsum, and glass powder. Composite samples are created by adjusting the proportions of gypsum (0%, 5%, 10%, and 15%) and glass powder (0%, 4%, 8%, and 12%) relative to the soil's dry mass. Compaction processes are performed at dry densities of 1500 and 1700 kg/m\({}^{3}\), with 7, 28, and 56 days of curing duration. Various tests, including ultrasonic pulse velocity (UPV), unconfined compressive strength (UCS), assessments of wet and dry cycle durability, scanning electron microscope (SEM) analyses, and X-ray diffraction (XRD) analyses, are conducted. The results reveal that gypsum consistently improves the soil's strength and stiffness features, while initially adding glass powder reduces these properties before showing improvement at a 12% content. Correlations have been proposed to determine the unconfined compressive strength (\(q_{u}\)), initial shear modulus (\(G_{0}\)), and modulus of elasticity (\(E\)) to be acquired utilizing just a single test. Moreover, a correlation has been developed to predict the unconfined compressive strength and elastic modulus of any specimen through non-destructive testing. Additionally, microstructural analyses unveil intricate interactions, showcasing the progress of pozzolanic reactions, identifying silicon-rich compounds from glass powder, and elucidating how additives transform soil structure.  
glass powder; sustainable management; waste utilization; clay stabilization; microstructures +
Footnote †: journal: Applied sciences
# First Results from Sentinel-1A InSAR over Australia: Application to the Perth Basin
## 4 InSAR Datasets and Methods
### Sentinel-1A and TerraSAR-X Datasets  
Over Perth, Sentinel-1A acquisitions were made in an ascending orbit at 24-day intervals (i.e., every other cycle) between 27 August 2015 and 17 May 2016 (12 images in total). Four descending scenes are also available between November 2015 and March 2016, but due to the long and irregular acquisition intervals between these images, we do not include these data in this study.  
Sentinel-1A data are provided via the online Sentinel-1 Scientific Data Hub in Interferometric Wide-swath (IW) focused Single Look Complex (SLC) format. Each SLC is split into three sub-swaths that are subsequently subdivided into \(\sim\)10 bursts. In total, the IW SLCs are \(\sim\)150 km in the along-track direction and \(\sim\)250 km across-track, with a spatial resolution (pixel size) of \(\sim\)14 m. We use the central sub swath, which covers the Perth metropolitan area and is \(\sim\)85 km across-track (Figure 2).  
We use 22 TerraSAR-X images obtained from the German Aerospace Center (DLR) under science project LAN1499, that cover the same time period as the Sentinel-1A dataset to within four days (29 August 2015-19 May 2016). Images are separated by 11-day intervals, with the exception of a 33-day gap in March 2016. Unlike the Sentinel-1A dataset, all imagery was acquired in a descending orbit in strip map mode. The images have a higher spatial resolution than Sentinel-1A (\(\sim\)3 m pixels), but cover a smaller area that is \(\sim\)50 km and \(\sim\)35 km in the along- and across-track directions, respectively (Figure 2).
# Multi-Subject Image Retrieval by Fusing Object and Scene-Level Feature Embeddings
## 4 Experiments
### Single Subject Image Retrieval  
In Table 1, the performance of image retrieval using previous methods such as SIFT-based BoVW [14], ResNet [51], R-MAC [10], R-GeM [9], and our method using DETR-based BoVW and random-based BoVW are compared. Image retrieval in the COCO dataset can be regarded as OBIR, which searches for images containing the same object as the query image. As shown in Figure 10, the object-level feature in DETR-based BoVW performed better than other image retrieval methods in OBIR. Because SIFT [18] extracts feature vectors without distinguishing between objects and backgrounds, the image embedding of SIFT-based BoVW exhibited low performance in OBIR. The global features of ResNet or scene-level features of random patch-based BoVW could not represent object information effectively when the query image and database had complex backgrounds, or when the objects were small or occluded; therefore, the detection of the scene-level features performed poorly. Contrarily, in the AID, and Paris6K and Oxford5K datasets, the global- and scene-level features that represent scene information performed well, as shown in Figure 10. R-MAC performs well on the COCO and Oxford datasets. In R-MAC, the feature map of CNNsis truncated to different sizes and applies max pooling to multiple regions to improve the image representation ability. R-GeM shows the best results on both AID, Paris6k, and Oxford5k. Owing to GeM pooling and whitening being performed as a post-processing step, it is enabled as an image representation that is suitable for instance retrieval.  
Figure 10: The results of image retrieval for single-subject datasets.
# Research on Path Planning of Mobile Robot Based on Improved Theta* Algorithm
## 3 Simulation
### Experiment of Comparing W-Theta\({}^{\star}\) Algorithm with Other Algorithms
The W-Theta* algorithm reduces the turning angles by 87.06, 77.67, and 34.57% on average compared to the A* algorithm, Dijkstra algorithm, and Theta* algorithm. From the results of this comparison experiment, we can conclude that the W-Theta* algorithm proposed in this paper is substantially optimized in terms of time complexity and space complexity compared with the A* algorithm, Dijkstra algorithm, and Theta* algorithm, and can achieve fast planning of high-quality paths in various complex environments. The W-Theta* algorithm plans paths with a smaller total turn angle and better overall path smoothing. In general, W-Theta* has significantly improved the efficiency of the algorithm while guaranteeing the resultant path, and the path smoothing process makes it more consistent with the motion of the robot in real situations.
## Appendix A.2.1 Architecture
### 4G Long-Term Evolution (LTE)
#### Architecture  
As shown in Figure 11, the organization of the devices that are used to access the network in LTE and the way in which we describe them are the same as in UMTS networks: user equipment (UEs) equipped with USIMs.  
Unlike GSM and UMTS, the evolved UMTS terrestrial radio access network (E-UTRAN) that manages the radio communications between the devices and the core network consists of just one component: the evolved base station or eNodeB (eNB). An eNB is essentially a base station that controls the mobile devices in one or (usually) more cell.  
The core network, called the evolved packet core (EPC), includes all the elements needed for conveying the user traffic over the network and all the services for session management, security, and administration. Unlike the previous technological solutions, EPC contains only the PS module, as both voice and data services are managed without the need for establishing a circuit connection.
## 5 Furrowing Depth Stability Control--Automatic Control System
### System Downforce Adjustment  
Sowing downforce affects the quality of seeding. When the downforce of the seeding machine is insufficient, it is easy to cause insufficient depth of furrow opening, resulting in loose soil in the seed furrow and increasing the occurrence of air pockets. When the downforce is too large, it is easy to cause soil compaction in the seed furrow, resulting in low seed germination [83]. Stable downforce can improve the stability of furrowing depth and increase crop yield [84]. Therefore, experts and scholars in relevant fields began to utilize downforce control to improve the stability of open furrow depth. [PERSON] et al. installed force sensors on the furrow depth-adjusting member and used PID closed-loopcontrol based on CAN communication. The field test results showed that the downforce via electronically controlled hydraulic control system improved the stability of sowing depth by 1.05-2.23% and the pass rate by 3.12-34.38% [85]. [PERSON] et al. designed an active force sensor-based downforce regulator using a linear motor instead of a compression spring. The experimental results showed that the active control system had 8.11% less variance in downforce stability than the traditional passive system and could regulate the downforce according to the agronomic requirements of the crop [86]. Precision Planting has developed a SeederForce downforce control system. The system utilizes air springs or hydraulic cylinders instead of mechanical springs for downforce control of furrow depth. A SmartDepth furrow depth control system was also developed, which utilizes a motor-driven adjustment handle to adjust the height of the depth limit wheel. It also utilizes the 20/20 SeedSense monitoring system to monitor the downforce and sowing depth in real time, realizing the online adjustment of downforce and sowing depth [87].
## 4 Conclusions  
TK221 holds substantial promise for effectively recovering rare earth elements (REEs) from matrix solutions. The characterization results obtained using SEM-EDX, ATR-FTIR, and XPS analyses confirm the presence of crucial functional groups (such as C=O, P=O, C-O) associated with DGA and CMPO, facilitating the adsorption of metal ions. Furthermore, we achieved adsorption equilibrium within a mere 360 min; notably, for Nd, Ce, Y, and Er, the data aligned well with the pseudo-second-order model, highlighting chemisorption as the primary adsorption mechanism with a maximum R\({}^{2}\) value of 1.00. In the investigation of Nd, Ce, Y, Er, and Fe, the Langmuir adsorption isotherm model provided an excellent fit, revealing a maximum adsorption capacity (q\({}_{\text{max}}\)) of 45.2 mg/g for Nd. These results emphasize TK221's potential as a highly effective adsorbent for REE recovery from matrix solutions. However, taking proactive measures to address factors that influence the adsorption process remains imperative. Furthermore, the applications of this resin extend beyond REE recovery, encompassing fields like environmental remediation and resource recovery.  
Conceptualization, J.L.R. and C.B.T.; methodology, J.L.R. and C.B.T.; software, J.B.Z.; validation, J.L.R., C.B.T., I.P. and T.A.; formal analysis, J.L.R., I.P. and C.B.T.; investigation J.L.R., T.M. and J.B.Z.; resources, T.L., M.I., R.D.A. and S.H.; data curation, J.L.R., C.B.T. and J.B.Z.; writing--original draft preparation, J.L.R.; writing--review and editing, J.L.R., C.B.T., T.A. and I.P.; visualization, J.L.R., I.P. and C.B.T.; supervision, V.J.T.R., C.B.T., R.D.A., I.P. and N.H.; project administration, V.J.T.R.; funding acquisition, V.J.T.R. and L.C.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Department of Science and Technology through the Engineering Research and Development for Technology (ERDT) for the MS scholarship granted to [PERSON] and the APC was funded by Curtin University.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors wish to acknowledge the support provided by Triskem International for their generous provision of the TK221 resin used in this research.  
The authors declare no conflict of interest.
# 5G-MEC Testbeds for V2X Applications
## 2 Background
### Overview of the 5G-MEC V2X Testbed Elements  
To develop and deploy a 5G-MEC testbed for V2X applications, the above-mentioned elements need to be considered. The 5G-MEC system delivers the support needed for V2X applications. Figure 5 shows a real-time scenario that involves V2X communication and backend activities within 5G and MEC that satisfies the performance requirements. In addition, we investigated and identified the generalized elements involved in building these testbeds. This section provides an overview of the testbed and is divided intotwo parts. The first discusses the hardware components and the second discusses the software components.  
_Hardware elements_  
The hardware components are fundamental aspects of the testbed. In terms of vehicular communication applications, the testbed consists of multiple vehicles equipped with On-Board Units (OBUs) with RSU connectivity. The vehicles can communicate with each other and the infrastructure via standard technologies. The RSUs are connected with the rest of the network using Ethernet links or other industry standards. The end-user application for the VUE is referred to as the client app and runs from within the OBU. The OBU establishes a communication link with the RAN infrastructure via eNB, AP, or gNB. Each RAN infrastructure is equipped with an MEH connected to the AP, eNB, and gNB. The MEH may or may not be present at the RAN infrastructure. The RAN infrastructure can be provided by the MNOs or can be built using a Software-Defined Radio (SDR) such as a USRP B210. The MEH is a compute node that provides resources for running MEC applications and delivering services related to the MEC. The MEH is a high-performance computing unit. The transport network is the overlay network that provides connectivity between the RAN, MEC systems, and the data center/cloud. The data center/cloud can be private or public. In the case of a private cloud, high-performance servers are needed, with or without a GPU, depending upon the orchestration responsibilities.  
_Software elements_  
The software elements represent the main virtualized components of the data plane. The application server/controller administrates and monitors the end-user application of the VUE. An application server can be present in the MEH or a separate cluster. The 4G and 5G CNs represent the core network functionalities. The MEC system is deployed using different MEC building blocks: the MEP, MEPM, VIM, and MEO. An MEP needs to be located within each MEH, whereas the rest of the MEC elements have no constraints on their location. In addition, the testbed can include an SDN controller to perform network management and control the traffic flow. As presented by the ETSI, an MEC in an NFV environment offers an NFVO for orchestrating the VNFs and VNFMs. The NFVO is located alongside the MEO. Based on our studies, the orchestration of MEC systems and VNF needs high-performance servers, and their allocation would be most appropriate and suitable within the data center/cloud infrastructure.  
Figure 5: Overview of the 5G-MEC V2X testbed elements.
## 3 Results  
As shown in Table 1, height resulted in strong correlation with performance (\(r=0.68\), \(p=0.014\)) in male rowers. Body mass (\(r=0.83\), \(p<0.001\)) and body muscle (\(r=0.81\), \(p<0.001\)) had a very strong correlation but percentage of body fat and Body Mass Index (BMI) were not correlated with performance test. Jump tests heights (\(h_{\text{SJ}}\), \(h_{\text{CMJ}}\) and \(h_{\text{RJ}}\)), RI, EI and MP showed low correlation with performance test. Besides, the power output prediction equation based on body mass for SJ showed strong correlation with performance (\(r=0.58\), \(p=0.048\)). Slightly higher correlations were found for CMJ (\(r=0.70\), \(p=0.012\)). Mean velocity (\(r=0.84\), \(p<0.001\)) and mean propulsive velocity (\(r=0.87\), \(p<0.001\)) in BP test showed very strong correlation with performance. Likewise, mean power (\(r=0.85\), \(p<0.001\)) resulted in very strong correlation and maximum power (\(r=0.73\), \(p=0.007\)) showed very strong correlation with performance. Female rowers showed strong to nearly perfect correlation between the same anthropometric variables than males with performance: height (\(r\) = 0.67, \(p\) = 0.101), body mass (\(r\) = 0.66, \(p\) = 0.107) and body muscle (\(r\) = 0.94, \(p\) = 0.002). Similarly to male rowers, the power output prediction equation based on body mass for SJ (W\({}_{\text{SJ}}\)) showed strong correlation (\(r\) = 0.57, \(p\) = 0.179), and stronger correlation values were found for CMJ (W\({}_{\text{CMJ}}\)) with performance (\(r\) = 0.59, \(p\) = 0.159). Finally, female rowers showed strong correlation in mean velocity (\(r\) = 0.63, \(p\) = 0.126), mean propulsive velocity (\(r\) = 0.67, \(p\) = 0.102) and mean power (\(r\) = 0.64, \(p\) = 0.124) in BP test with performance. Furthermore, very strong correlation in maximum power (\(r\) = 0.79, \(p\) = 0.036) were found. The results of the stepwise multiple linear regression analysis in male rowers indicated that body mass is the only predictor variable for anthropometric characteristics explaining 69% (R\({}^{2}\) = 0.69, \(p\) < 0.001) of W\({}_{2000m}\). Similarly, the only predictor variable for BP test were the mean propulsive velocity that explained 76% of rowing performance (R\({}^{2}\) = 0.76, \(p\) < 0.001) (Table 2). The rest of the anthropometric and power variables in jump and BP tests did not contribute significatively and were excluded from the prediction equation. The best predictor of rowing performance among anthropometric characteristics for female rowers was body muscle, accounting for 89% of variance (R\({}^{2}\) = 0.89, \(p\) = 0.002) and maximum power for BP measures, explaining 62% of rowing performance (R\({}^{2}\) = 0.62, \(p\) = 0.036). As with the male linear regression models, the inclusion of the remaining variables resulted in models predicting less than 50% of the variance so they were excluded from the equations.