esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Motivation and Related Works
### Integrated HAP/Satellite Networks  
As mentioned at the end of Section 2.1, there are relatively few studies on the IHS architecture for energy-efficient transmissions in emergency communications. However, there are articles about the IHS networks for other related aspects in WSNs. In [4], the authors presented a review of the IHS networks in WSNs and demonstrated the technological advantages of the IHS networks for a wide range of monitoring, Earth observations and other sensing-based applications. However, the authors did not highlight the energy-efficiency-related problems. The energy constraint is crucial for battery-powered HAPs and remote sensor devices. In [25], the authors proposed schemes for an IHS-based network for delivering emergency calls and multicast and broadcast services (MBS) in case of large-scale disasters in mountainous areas. Appropriate standard options were chosen, and necessary adaptations were recommended by considering the special requirement of delivering emergency communication services from the HAP. [PERSON] _et al._[26] presented a heterogeneous network system comprised of a mission layer, an HAP layer and a satellite layer. The energy consumption was minimized using a deterministic annealing algorithm. [PERSON] _et al._[27] presented an innovative call admission control (CAC) scheme that uses transmission control protocol (TCP) statistics as one of its inputs and is able to manage different classes of users for an IHS communication system in emergency situations. [PERSON] _et al._[28] show how space technologies and new integrated telecommunication networks mitigate the impact of natural and man-made disasters. Potentially attractive telecommunication architectures to better manage a disaster scenario were discussed. Therefore, the existing studies have shown that IHS can offer a promising communication architecture for emergency scenarios.  
Although the deployments and demonstrations of emergency communications have been extensively investigated, many issues and research aspects regarding the possible integration of different architectures and technologies still have open questions that require deeper investigation. Quite a few works in the literature have addressed the issue of energy-efficient transmissions in the IHS architecture for emergency scenarios. As it is crucially important for the theoretical research and application of emergency communications, this paper will address the energy-efficient strategies and algorithms in an IHS architecture for an emergency scenario. The simulation will further prove that the proposed IHS architecture and transmission strategy are promising solutions for emergency communications.
## 3 Results and Discussion
### Constant Speed Phase--Noise  
The average values of the \(L_{Aeq}\) parameter oscillated around 70 dB (Figure A5, Table 3). Three trains achieved values lower than this figure--train D 65.9 dB, train B 67.7 dB, and train C 69 dB. The last two exceeded the 70 dB limit, train E by 1.7 dB and train A by 2.7 dB. As expected, the lowest \(L_{Aeq}\) values were obtained in each of the trains at points 3, 5, 6, 8, and 10, i.e., places away from the bogies. Their values range from 58.8 dB to 74 dB and are, on average, higher in the third wagon. In 70% of the measurement locations, train D achieved the lowest values. In the case of train A, the free flow of ventilation in the passenger compartment contributes to the increase in noise level. In 60% of the measurement sites, this train achieved the highest values. Interesting data comes from train E. The differences between neighboring measurement places are the smallest for this train and amount to a maximum of 3.3 dB (points 6 and 7), which indicates the steady level of soundproofing for this type of vehicle.
# Evolutionary Optimization for Robust Epipolar-Geometry Estimation and Outlier Detection
## 5 Experimental Results and Discussion
### Experiments on Synthetic Data
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Description** \\ \hline _ltr_ & number of iterations before the termination of robust estimation \\ \hline \multirow{2}{*}{\(\mu_{d}\)} & precision of estimation (pixel\({}^{2}\)): the average of squared Sampson residuals over the detected inliers that shows how well the estimated fundamental matrix fits the detected inliers \\ \hline \multirow{2}{*}{\(\alpha\)} & accuracy of inlier classification (\%): percentage of correctly identified outliers and inliers among all the matches, which is calculated as the sum of true positives and true negatives divided by the total number of putative matches (applicable when the ground-truth is available) \\ \hline \multirow{2}{*}{\(TPR\)} & sensitivity or true-positive rate (\%): percentage of correctly identified inliers, which is calculated as the true positives divided by the sum of true positives and false negatives (applicable when ground-truth is available) \\ \hline \multirow{2}{*}{\(TNR\)} & specificity or true-negative rate (\%): percentage of correctly identified outliers, which is calculated as the true negatives divided by the sum of true negatives and false positives (applicable when ground-truth is available) \\ \hline \multirow{2}{*}{\(\mu_{d}\cdot CP\)} & accuracy of estimation (pixel\({}^{2}\)): an average of squared Sampson residuals on control points or true inliers that shows how well the estimated model fits the real inliers; the real inliers are noise free (applicable when the ground-truth is available) \\ \hline \multirow{2}{*}{\(D_{F}\)} & difference between the estimated fundamental matrix and the true one (expressed in pixels), which is measured using a method described by [46] (applicable when the ground-truth is available) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Criteria for performance assessment. the others are at higher altitudes (similar to aerial imagery); this also causes high variations of scale across the images. Therefore, the performance under various outlier ratios (Section 5.4) was tested on this dataset. The results obtained from the StaRSAC method were not represented in the graphs of Section 5.4 since they were not as good as the results of the other methods, and their representation would have caused mis-scaling of the graphs. The Multi-GS-RANSAC was not tested on this dataset since most stereo pairs contained thousands of correspondences, the processing of which with Multi-GS would have been too time consuming. Furthermore, a stereo pair from this dataset with a large number of matches (4510 matches) and a low level of noise was used to test the performance of inlier classification (Section 5.3). There is no specific structuring pattern in this stereo pair. Furthermore, there is no challenge or complexity regarding the relative poses of the cameras; i.e., the baseline is neither too short, nor too long, and the relative rotation between images includes yaw differences only.
## 5 Discussion
### Layering Strategy  
Through this research, it was found that different layering strategies applied for parts repaired using DED hybrid manufacturing can significantly influence the mechanical properties, particularly tensile strength and ductility, of the manufactured components. First, the thickness of each deposited layer plays a crucial role. Thinner layers tend to create finer microstructures, which can enhance mechanical properties like tensile strength due to reduced porosity and improved grain structure [32]. However, excessively thin layers might lead to increased heat accumulation and potential interlayer defects. Excessive heat accumulation involves localised melting and rapid solidification, which can increase the brittleness of the parts and in extreme cases can cause cracks and even delamination of the DED layers. Layer deposition strategies can impact the width and depth of the heat-affected zone in the substrate material, which ultimately impacts the grain growth and microstructure, affecting both strength and ductility. Proper deposition strategy can enhance the bond strength between layers, positively impacting tensile strength. Moreover, rapid cooling after deposition may lead to a higher number of dislocations and grain refinement, potentially enhancing strength but possibly compromising ductility, while slower
## 6 Discussion
### Results Obtained from ASMAG
#### 6.1.2 Maximum SLA and ELA  
Similar to the SCR and AAR but with less variability, values for the highest SLA within a season are systematically lower than ELA values. In years with a good match, the ranking of SLA values is also in agreement with ELA values albeit less often than for the SCR/AAR pairs. Considering the simplicity of the SLA calculation and the different way the ELA is derived from field measurements, this agreement is promising for applying the method to more frequently acquired satellite data. As for the SCR, most of the SLA outliers (e.g., in 1991, 1994, 2003, 2007 and 2012) can be explained with the absence of appropriate (cloud-free) images. If images from the end of the ablation season are not available, the automatically mapped maximum SLA cannot be used as a proxy for the ELA nor the glacier mass balance. However, all ASMAG derived snow cover maps can still be used to calibrate and/or validate results of mass balance models (Kal
## 3 System Tests
### Test in the Real Environment  
The second stage of the test was performed in Kielce University of Technology and at the European Rover Challenge international competitions in 2018 and 2019. Figure 14 presents a map of test track with control points (letter W), graphic markers (letter L) where the point number was coded using ARTags.  
The places with obstacles were marked in black (letter R). The robot's position on the track was marked on the map with a red dotted line. The side of the map grid shows a resolution of 1m. In Figure 15, the arrows show the corrections made during the driving. The black arrow indicates the correction made after detecting the markers number L4 and L5, while the red arrows indicate where the control system made corrections after detecting the L6 and L7 markers. As mentioned in [21], the recognition range of the system was limited to about 6 m, therefore corrections are calculated near the markers. During the main drive at the competition, the vehicle reached the checkpoints with the following accuracy. Checkpoints W1--2 cm, W2--5 cm, W3--20 cm. The W4 checkpoint was very difficult to reach due to the large wheels slip of the vehicle and the fact that the vision system did not have two markers in its field of view. The vehicle control system did not make position correction and decided that the vehicle had reached the checkpoint without disruption. The same situation happened with the WX point, which was hidden behind the hill.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Vision Sys. Turned off** & **Vision Sys. Turned on** \\ \hline Sum of \(x\) module deviations & 17.30 & 8.55 \\ Sum of \(y\) module deviations & 15.40 & 8.50 \\ Sum of the distance module deviations & 24.72 & 13.01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The quantitative results comparison of positioning accuracy without and with cameras.  
Figure 14: Software screenshot presenting the real position of the robot in test track.
# Model-Driven Approach to Cloud-Portability Issue
## 2 Related Works
### Cloud Standardization  
Cloud Computing is a relatively new IT industry trend and has not yet been fully standardized or even unified between providers. Currently, several standardization groups are trying to unify the use of CC environments from the perspective of users and providers.  
The first group is mainly composed of organizations dealing with the technological aspects of CC. The best-known representatives are standardization organizations NIST (National Institute of Standards and Technology) and ITU-T (International Telecommunication Union).  
ITU-T in Y.3500 Recommendation [13] defines CC as a service that allows access to a set of shared, scalable and user managed physical or virtual resources via networks. This paradigm mentions the key features, deployment models, user roles and cross-sectional aspects of CC services.  
In the NIST SP-500-291 [4] standard, CC is defined as a computing model that enables ubiquitous, practical, network-accessible and on-demand computing resources with minimal effort and without interaction with the provider of these resources. This model is composed of five basic features, three service models and four deployment models.  
The second group consists of organizations that deal with business relationships between individual participants. Those worth mentioning are European Commission and its working groups Cloud Select Industry Group (C-SIG) and European Commission Expert group on Cloud Computing Contracts [1,2]. Another organization is the European Telecommunications Standards Institute (ETSI) and its Cloud Standard Coordination group [3].
## 3 Research Model and Hypothesis Development
### Technological Factors
#### 3.1.1 Complexity  
Complexity, as defined in the literature, denotes the degree of difficulty in comprehending and utilizing an innovation [99]. Numerous studies have underscored the significance of technology complexity as a formidable obstacle for firms contemplating the adoption of Industry 4.0 technologies [59; 100]. Indeed, concerns have been raised regarding the detrimental effects of technology complexity on implementation success [23]. This complexity poses challenges for organizations in effectively deploying and integrating technologies  
Figure 2: Proposed research model.  
with one another [101], potentially undermining the synergistic benefits between different technological components. Furthermore, heightened complexity often coincides with increased mental workload and stress among employees [86]. Such conditions can lead to slower learning curves and diminished levels of satisfaction among employees, ultimately impeding their ability to fully harness the capabilities of the new technologies. Consequently, this may curtail the realization of the full potential offered by Industry 4.0. Expanding on this perspective, it is essential for organizations to recognize the nuanced ways in which technology complexity can impact their implementation efforts. By understanding these challenges, organizations can develop tailored strategies to mitigate complexity-related barriers and enhance the likelihood of successful implementation. To further investigate this relationship, this paper proposes the following hypothesis:  
**Hypothesis 1 (H1).**_Complexity negatively influences the implementation success of Industry 4.0 technologies._  
This hypothesis suggests that as the complexity of technologies increases, the likelihood of successful implementation decreases.
###### Abstract  
The deployment of new radio access technologies always provides a good opportunity and timing to optimize the existing mobile front- and backhaul (commonly called \"anyhaul\"). The legacy systems (Long-Term Evolution (LTE), High-Speed Packet Access (HSPA), third-generation mobile (3G), second-generation mobile (2G)) already extensively utilize the transmission and transport capacities of the mobile anyhaul. With the current launch of 5G (fifth-generation mobile) and recent LTE-A (Advanced Long-Term Evolution), additional new transmission capacities are required again. Depending on the traffic and network topology, additional cell sites are built, and even more locations are connected with fiber optics. The existing microwave and millimeter-wave links are rotated toward those aggregation points that already have optical-fiber access. Due to the increased cell-site density, the average distance of the radio access links can be reduced by network and topology optimization. The reduced hop lengths combined with adaptive modulation and automatic power control bring an opportunity for capacity increase in shortened radio links. Links newly deployed for 5G find a wide spectrum in the millimetric V, E, W, and D frequency bands. This paper discusses the availability and hop-length targets of the anyhaul links that should be carefully kept by proper planning and monitoring.  
5G; mobile anyhaul; hop length; rainfall rate; system gain; availability; outage +
Footnote †: journal: Applied Physics Letters  
_Applied_
## 2 Materials and Methods
### Volatile Compounds Analysis  
Volatile compound analysis employed solid-phase microextraction-gas chromatography/mass spectrometry (SPME-GC/MS). A total of 1 mL of the sample was combined with 3 mL of deionized water spiked with the internal standard, 2-octanol, in a 10-milliliter headspace vial and immediately capped. SPME extraction utilized an autosampler (AOC6000, Shimadzu, Kyoto, Japan) featuring a heated agitator set at 60 \({}^{\circ}\)C and an 80 \(\upmu\)m DVB/C-WR/PDMS Smart SPME fiber (Shimadzu, Kyoto, Japan). Fiber preconditioning lasted 10 min, sample incubation lasted 30 min, and sample extraction lasted 40 min. Volatile compound separation occurred on a Rxi 5-MS column (Restek, Bellefonte, PA, USA). The SPME fiber was desorbed over 10 min in the injector port maintained at  
Figure 1: Scheme of vinegar production and analysis.  
250 \({}^{\circ}\)C with a splitless helium column flow of 1 mL per minute and a temperature program as follows: hold at 40 \({}^{\circ}\)C for 5 min, ramp to 220 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min, ramp to 300 \({}^{\circ}\)C at 15 \({}^{\circ}\)C/min, and hold at 300 \({}^{\circ}\)C for 5 min (GC2030, Shimadzu, Kyoto, Japan). MS parameters (TQ8040 NX, Shimadzu, Kyoto, Japan) included an ion source temperature of 280 \({}^{\circ}\)C, interface temperature of 300 \({}^{\circ}\)C, electron impact ionization, and a mass scan range from 40 to 350 \(m\)/z. Kovat's retention index for each compound was calculated against a mix of standard alkanes using the same temperature program. Compounds were identified utilizing both the Kovat's index and the NIST17 database. The obtained peak areas were normalized against the internal standard and expressed as mg/L.
## 3 Examples
### The Process of Damage Evolution  
To study the influence of the pitting morphology on the damage evolution, a pre-corroded steel wire with a mass loss rate of 0.25% under a stress range of 418 MPa was selected to present the damage evolution process.  
Figure 10 shows the stress and damage distribution of the steel wire specimen at the 195 th step, when it is about to fracture. Figure 10a shows that the maximum stress appears at the bottom of the pit, and the high stress zone presents a zonal distribution perpendicular to the tension direction. The minimum stress appears at the edge of the pit, and the low-stress zone is distributed along the axis. Figure 10b shows that fatigue failure mainly occurs in the high-stress zone and presents a zonal distribution perpendicular to the tension direction.  
Figure 11 shows the damage evolution process and stress distribution on a cross section of the model (z = 7 mm). The stress concentration occurs in the middle of the pit, where the damage begins to accumulate gradually. As the number of cyclic loads continues to increase, damage accumulates and penetrates into the inner zone of the steel wire far from the pit. In addition, the stress distribution in the steel wire also changes with the cyclic loading process. Due to the stress concentration, the elastic modulus of the material near the pit will gradually decline with cyclic loading, so that the deformation energy of the material can be changed locally and its stiffness will decrease compared with other areas. However, under these circumstances, the damaged material still has the ability to transfer stress and is still continuous with the surrounding material, which makes the adjacent material bear a higher stress.  
Figure 12 presents the time-varying curves of the stress intensity factor and the size of defect. As shown in Figure 12, with the increase of the loading step, the defect size gradually increases, the damage gradually accumulates, and the rate of damage evolution gradually increases. When the step equals 195, the defect size is 1.75383 mm, the stress intensity factor is 64.967 MPa\(\cdot\sqrt{m}\), which is close to the fracture toughness, and the steel wire could begin to fracture.  
Figure 10: Stress and damage contours of corroded steel wire (step = 195): (**a**) Stress distribution; (**b**) Damage distribution.
## 6 Magnetic Properties  
The magnetic characteristics of electrical steel are influenced by the heat treatment employed, as larger average crystal sizes result in reduced hysteresis loss. Hysteresis loss is measured using a standard Epstein tester and typically ranges from approximately 2 to 10 watts per kilogram (1 to 5 watts per pound) at 60 Hz and a 1.5 tesla magnetic field density for common grades of electrical steel.  
Electrical steel can be delivered in a semi-processed state, allowing for final shaping through punching. After shaping, a final heat treatment can be applied to achieve the desired grain size, usually around 150 micrometres. Fully processed electrical steel is delivered with an insulating coating, full heat treatment, and well-defined magnetic properties. It is utilized in applications where punching does not significantly degrade the electrical steel's properties. However, excessive bending, incorrect heat treatment, or rough handling can negatively impact the magnetic properties of electrical steel and may also increase noise due to magnetostriction [54]. The internationally standardized Epstein frame method is employed to test the magnetic properties of electrical steel.  
Numerous properties contribute to the final product of electrical steel. Below is a description of the most crucial properties involved.
# Remote Sensing Image Segmentation of Marculture Cage Using Ensemble Learning Strategy
###### Abstract  
In harbour areas, the irrational layout and high density of marculture cages can lead to a dramatic deterioration of the culture's ecology. Therefore, it is important to analyze and regulate the distribution of cages using intelligent analysis based on deep learning. We propose a remote sensing image segmentation method based on the Swin Transformer and ensemble learning strategy. Firstly, we collect multiple remote sensing images of cages and annotate them, while using data expansion techniques to construct a remote sensing image dataset of marculture cages. Secondly, the Swin Transformer is used as the backbone network to extract the remote sensing image features of cages. A strategy of alternating the local attention module and the global attention module is used for model training, which has the benefit of reducing the attention computation while exchanging global information. Then, the ensemble learning strategy is used to improve the accuracy of remote sensing cage segmentation. We carry out quantitative and qualitative analyses of remote sensing image segmentation of cages at the ports of Li'an, Xincun and Potou in Hainan Province, China. The results show that our proposed segmentation scheme has significant performance improvement compared to other models. In particular, the mIoU reaches 82.34% and pixel accuracy reaches 99.71%.  
a pacs: 12.62.-h _applied_