esa-sceva/satcom-chunk-collection huggingface dataset:
# On Edge Pruning of Communication Networks under an Age-of-Information Framework
## 3 Proposed Model
### Age of Information  
In this subsection, we briefly visit the standard definition of AoI used in this work, which is the metric used to measure the freshness of messages.  
Consider a source-destination network. Now, consider \(t_{k}\) to be the times at which the source node \(s\) receives a message. Let \(t^{\prime}_{k}\) be the times at which this message is received by the destination node \(d\). We then introduce the indexing function that takes \(t\) as an input and gives the index of the most recently received message at the destination node \(d\). So that, the indexing function \(N(t)\) is shown as follows:  
\[N(t)=max\{k|t^{\prime}_{k}\leq t\} \tag{1}\]  
To this end, we have everything set up to define the standard AoI of any message at time \(t\). The AoI that a system has which depends on the generation and receiving time of messages is given as follows [30]:  
\[\Delta(t)=t-t_{N(t)} \tag{2}\]  
In Figure 2, we see the basic representation of the AoI model that we use as the metric of message freshness. From now on in the paper, when we mention the AoI, we refer to the average AoI value that can be evaluated depending on the distribution of the generation times and delay of the network.  
In this work, we consider the case at which the time taken for a link (indexed \(i\)) to be activated, in order to transfer the message awaiting at one of its nodes, to follow the  
Figure 2: The progression of AoI in our model.  
exponential distribution where the average time taken for activation is \(\frac{1}{\lambda_{i}}\) units of times counted from the moment the message arrives at one of its nodes. For now, we study the case where the time taken for the message to be transferred through the link is always insignificant compared to the average wait time \(\frac{1}{\lambda_{i}}\). The activation times of the links are modeled as independent random variables.
# Neural Network-Assisted Interferogram Analysis Using Cylindrical and Flat Reference Beams
## 2 Materials and Methods
### Optical Setup
It was also shown that the use of a single-arm scheme for generating such interference fields allows us to provide a better quality of the object wavefront compared to the two-arm scheme.
# Adhesion Behavior between Multilayer Graphene and Semiconductor Substrates
## 1 Introduction
As a result, too many influencing factors were introduced to these processes, especially concerning the interlayer friction of the graphite. Moreover, they could not obtain the bonding strength between graphene and Si substrates directly. In our study, we concentrated on the bonding strength between graphene and the Si substrate, which is known to influence the performance of graphene-based MEMS/NEMS devices. Because of its layered structure, the properties of graphene may be significantly affected by the number of layers, as verified by many experiments and atomistic simulations. Due to electron-phonon coupling, it was found through friction force microscopy measurements that the friction force of graphene increased with the decreasing number of layers [22; 23; 24]. The friction reduction in graphene with an increasing number of layers was thought to be associated with changes in the viscoelastic properties of the graphene flakes, as measured by Atomic Force Microscope (AFM) [25]. Through AFM, it was found that the increase in the percentage of the contact area for a thickness of 0.5 nm versus 1.2 nm was much smaller than the decrease percentage of the average friction [26]. Our previous studies clarified that indentation-induced cross-linking between interlayers varied with the number of graphene layers, resulting in different friction coefficients [27; 28; 29]. Otherwise, the thermal conductivity can be influenced by the number of graphene layers, and reached as high as 5000 W/mK in pure monolayer graphene at room temperature due to C-C covalent bonds and phonon scattering [30; 31]. In this paper, the exfoliation process of one to ten layers of graphene from Si semiconductor substrates were simulated by the MD method, and the adhesion behavior between multilayer graphene and silicon substrates was studied. In order to simplify the model to exclude the interference of external factors (for example, factors, like friction, render the system much more complicated during sliding or wedge exfoliation, while errors may be introduced by fixed end during peeling or bubble exfoliation), we applied a series of external vertical upward velocities to the topmost layer to exfoliate graphene directly. The influences of the graphene layer number and exfoliation velocity on the bonding properties between graphene and silicon were clarified. In the system where graphene bonded to an Si semiconductor substrate, the bonding properties, critical velocities, adhesive forces, and adhesion energies were obtained. This research can promote further study of graphene-based MEMS/NEMS devices.
# Scan to BIM Mapping Process Description for Building Representation in 3D GIS
## 6 Prototype Development and Performance Analysis  
### Prototype Development  
Based on the previously defined SBMD, JSON-based Scan to BIM mapping Description Language (SBDL) was developed. SBDL has the following simple syntax so that the algorithm module parameters of the U4 use cases level can be defined as extensible.  
The following is the project example file using SBDL to execute the Scan to BIM process in 3D GIS with the SBMD tool developed by Python. In this example, the resolution for DEM generation is 0.2, and the contour z-value for classifying the building in DEM is defined as \"extract_pixel_min_z_value\" and \"ex-tract_pixel_max_z_value\". The \"simplify_factor\" parameter to simplify the footprint's polygons is 10.0, and the \"remove_area\" parameter is defined to remove small objects such as trees and vehicles.  
The SBMD project file described using SBDL is interpreted by the SBDL parser, and the objects and relationships based on UML defined in Figure 4 are automatically generated. When the SBMD project file is executed along with the input/output folder, \"[parameter]\" set is transmitted to the algorithm module specified in the \"type\" of each stage, the input data is calculated, and the output files are saved in the output data folder.  
Figure 5: Building roof height calculation method (dashed green = footprint, yellow = offset polygon, and red dot = z-value sampling point).  
The program call logic is implemented so that the algorithm logic module can be called with the name specified in \"type\". Figure 6 shows the SBMD execution sequence. For the SBMD test, the scan data (Tuborg Havnepark, Denmark) registered using the ALS tool was used. The scan data volume with a length of 566 m by 775 is 238.2 MB.  
In Figure 6, the SBMD in step 2 defined the following parameters for each step.  
Figure 6: Results of the SBMD execution.  
1. PCD to DEM step: pixel resolution for DEM generation = 0.2.
2. DEM to Geometry step: minimum height for extracting buildings from DEM = 60, maximum height for extracting buildings = 200, simplification factor = 10 [29], minimum area for removing small objects other than buildings from DEM = 20, dimension data output file including footprint polygon after geometry calculation = dimension.csv, and output property format = json.
3. Geometry to BIM step: link_pset = Define properties such as area and length to be added when creating the IFC file.  
As a result of running the SBMD Project, outputs such as SHP, GeoJSON, and IFC are created, and a projection file containing the GIS coordinate system information of the LAS input file for GIS mapping is generated, like below.
# My Smartphone tattles: Considering Popularity of Messages in Opportunistic Data Dissemination
## 9 Conclusions and Future Work  
In this paper, we have introduced the first implementation of an organic data dissemination model for opportunistic networks, called Keetchi. It targets data dissemination for destination-less scenarios and it is the first to leverage data popularity. We have shown that Keetchi outperforms Epidemic, the only other destination-free approach well known and evaluated, especially in terms of overhead. With a simple learning algorithm, we are able to learn over time the popularity of any kind of message and to adapt the dissemination of the messages to this changing popularity.  
Our immediate next steps include an implementation of Keetchi in real smartphones and testing it with real users to validate the results presented here. We will also continue working on refining the simulation models for opportunistic networks in general and to define simulation benchmarks for better reproducibility and comparison of results. Since cache management is an integral part of Keetchi, we will investigate the cache management work proposed in [28] to optimise the operations to improve the data availability in caches. Further, this paper assumes that all nodes are trustworthy. We plan to address security and authentication issues with OppNets as one of our immediate next steps.  
Conceptualization, A.U., A.F. and K.K.; methodology, A.U., A.F. and A.T.-G.; software, A.U., J.D. and Z.V.; validation, A.U., J.D. and V.K.; formal analysis, A.U., J.D., A.F. and V.K.; investigation, A.U., J.D. and V.K.; resources, A.F. and A.T.-G.; data curation, A.U. and J.D.; writing-original draft preparation, A.U., J.D., A.F., V.K. and Z.V.; writing-review and editing, A.U., J.D., A.F., V.K., K.K., A.T.-G. and Z.V.; visualization, A.U., J.D. and A.F.; supervision, A.F.; project administration, A.F.; funding acquisition, A.F. and A.T.-G.  
This research received no external funding.  
The authors declare no conflict of interest.
## 7 Applications in SDN-based VANETs
### Applications-based Safety Services  
The functionalities of the SDN in the VANETs upgrade the roads' wellbeing by utilizing V2I, V2V, and V2H correspondences. The SDN-based VANETs can verify or limit specific frequencies or channels for later usage. Emergency traffic or other special exercises could be used to save the frequencies [55]. The principle bit of leeway of SDN-based VANETs over regular emergency channels is that the description of repetition should be possible progressively. The SDN-based controller could hold the channels for the emergency administrations dependent on traffic situations, programs, and application needs. Essentially, these channels could be used to oblige different applications or administrations. Emergency communication, helpful for concentrated communication, can profit from the advantages of this. Different protection plans can likewise be executed to improve two security administrations, namely, path change cautioning and forward impact [40,106]. The taxonomy of the safety services are listed in Figure 25.  
Figure 25: Taxonomy of safety services in SDN-based VANETs.
# PSDFH: A Phase-Space-Based Depth from Hologram Extraction Method
## 4 Experimental Results
### Hyperparameter Tuning  
When each object in the scene spans the entire 3D space, the value \(N_{z}\) must be large enough in order to correctly refine the depth of each object. However, as shown in Figure 12, there is a trade-off between choosing the right interval size and the used focus measure. The larger the interval size, the greater the risk of having a large estimation error due to the lack of sensitivity of the focus measures. Therefore, when increasing the size of the interval, some points may converge to the correct value that will be included in the chosen interval, and others may diverge because the used focus measure is not robust enough to detect the optimal focus plane given a very large number of images, which may lead to a bad final result.  
Figure 11: Result of the application of the iterative method on 2D holograms.  
A visual example of the different steps involved in our proposed methodology is given in Figure 7, where the initially extracted ROI perfectly encapsulate the scene points, and their refinement falls either close to the objects or inside them. The curvature is estimated in the last step of the algorithm using the DFF method on a new fixed-size ROI of size 31 around the refined ROI. It can be observed that the final estimate is relatively close to the ground truth value; however, some regions are poorly estimated by the GLVN operator, which results in a dispersion of the point cloud clearly visible in the figure.  
In summary, depth estimation without ROI is a tedious task since the possible depth values for each pixel span a large interval. The performance can be improved using the ROI which reduces the possible depth interval for each pixel and therefore reduces uncertainty during the final estimation. However, if the processed scene has complex geometry with points that are close to each other and span over the entire X-axis, then the extracted ROI may have a large depth extent; therefore the gain when using the ROI is reduced.  
The ROI refinement and the use of fixed-size ROI can manage the large depth extent and produce better interval bounding. The use of refined ROI may not be the best choice when processing scenes with objects spanning a large depth range. In that case, the best choice for \(N_{z}\) may be large, and the produced depth interval can be approximately \([0,z]\).  
The \(N_{z}\) values in the DFF method can be selected adaptively by starting from the ends of the representation lines with a small \(N_{z}\) value, and gradually increasing it as the process moves towards the center of the line, with the maximum value determined by prior knowledge about the object's curvature.
## 3 Results
### Models Statistical Significance Analysis  
In addition to the metrics mentioned above, the nonparametric Wilcoxon signed-rank test was used to evaluate the statistical significance of the differences between pairs of landslide susceptibility models. Table 9 shows the results of the Wilcoxon test applied to the six susceptibility models resulting from this research.  
The results obtained using the Wilcoxon test analysis reveal notable differences in the efficacy or characteristics of the variants of the XGBoost models analyzed. Specifically, there are significant differences between the 6 factors (sampling 1) and 6 factors (sampling 2) models, as well as between 6 factors (sampling 1), DEM (sampling 1 and 2) and NO DEM (sampling 2), with \(p\)-values close to 0.01 and lower. On the other hand, the results indicate that there are no significant differences between the models 6 factors (sampling 2) with DEM (sampling 1); between DEM (sampling 1) with DEM (sampling 2) and NO DEM (sampling 1 and 2) and between DEM (sampling 2) and NO DEM (sampling 2). All of them show values close to and above 0.75.
###### Abstract  
Ensuring optimal performance and reliability in rotor-bearing systems is crucial for industrial applications. Imbalances and shaft bowing in these systems can lead to decreased efficiency and increased vibrations. The early detection and mitigation of a rotor's faults are essential, and model-based fault identification has gained much attention in the manufacturing industry over the years. Over the past two decades, however, the development of fault diagnosis rules with data-driven and artificial intelligence (AI) methods has become a trend, and in the foreseeable future the combination of AI with big data will become mainstream. Nevertheless, the critical role of rotating machinery in manufacturing introduces a challenge, as often insufficient fault data are available. This limitation renders the establishment of diagnostic rules using data-driven methods and AI technologies impractical. In light of these challenges, this study proposes a novel hybrid approach that combines a physical model with machine learning (ML) techniques for the diagnosis of multi-faults (imbalances and shaft bowing are demonstrated) in a Jeffcott rotor. To overcome the lack of real-world labeled fault datasets, a physics-based Jeffcott rotor model is first derived and then used to generate abundant fault datasets for ML. Subsequently, simulated data are employed for the training of an artificial neural network (ANN), enabling the network to learn from and analyze the vast array of generated data. The results prove that a well-trained feed-forward neural network (FNN) can accurately isolate and diagnose imbalance and shaft bowing faults using the simulated and real data from the Jeffcott rotor experiment. These physics-based and ML approaches prove effective particularly for multi-faults, offering new possibilities for advanced rotor system monitoring and maintenance strategies in industrial applications.  
hybrid approach; multi-fault diagnosis; machine learning; ANN; imbalance and bow
## 3 Results
### Mapping Flight Test  
#### 3.2.1 Georeferencing Performance  
Using the methodology described in Section 2.5, the aerial images from mapping experiment with AIRS positioning were processed in PhotoScan to evaluate the accuracy of AIRS positioning. The reprojection error was 0.96 pixels, and the root mean square errors (RMSEs) in 5 GCPs were 1.4 cm, 0.9 cm, and 1.5 cm in X, Y, and Z coordinates, respectively; and the RMSEs of 5 check points were 1.1 cm, 1.0 cm, and 1.9 cm in X, Y, and Z coordinates, respectively. For the photogrammetric block type used here, the expected RMSEs of the perspective center positions are less than 5 cm and of the rotations well below 0.03\({}^{\circ}\)[32]. They are thus suitable reference values for the assessment of RTK and PPK quality. Thus, the AIRS orientation data was evaluated with the Photoscan orientations as a reference. RTK position had an RMSE accuracy of 0.255 m in horizontal and 0.14 m in vertical when compared to the Photoscan positions. The PPK position had an RMSE accuracy of 0.13 m in horizontal and 0.08 m in vertical when compared to the Photoscan positions. PPK data obtained 92.2% of the flight track with an ambiguity-fixed solution and the RTK solution obtained 21.2% of the track with an ambiguity-fixed solution. Roll and pitch were found to have an accuracy of 0.4\({}^{\circ}\) and heading having an accuracy of 7.3\({}^{\circ}\). The error in INS heading was slowly drifting during the flight, with the beginning of the flight having up to 8\({}^{\circ}\) bias in a clockwise direction and in the end up to 8\({}^{\circ}\) in the opposite direction.
# Detecting Change at Archaeological Sites in North Africa Using Open-Source Satellite Imagery
## 3 Results
### Aswan and Kom Ombo
#### 3.1.3 True Negatives  
For most of the sites, no changes were detected within the buffered areas, and this could be confirmed by visual examination of imagery and through field visits to a sample of these sites in January 2020. While many of these sites consisted of features relatively far from current human activity, for example, prehistoric sites further up Wadi Kubbaniya, others were in the vicinity of locations where changes were detected. Therefore, monitoring should continue, with sites with recent changes prioritised for field validation in future seasons. The change detection workflow gives an early warning of human activities and natural processes.
## 3 Results
### Production of Ectoine Using High Density Cells in a Fermentor  
To evaluate the productivity and capability of engineered BL-SsEct in commercial application, ectoine bioconversion was also performed with higher density cells (OD\({}_{600}\) = 20) in a 5-L fermentor (Figure 9). Under the initial biosynthesis condition of 200 mM sodium aspartate, 200 mM KCl and 100 mM sodium phosphate buffer (pH 6.5) at 25 \({}^{\circ}\)C, the concentration of extracellular ectoine exhibited a linear increase and obtained the yield of 3.30 g/L in 2 h. In order to maintain the supply of aspartate in the reaction mixture, the feeding solution (2 M sodium aspartate, 2 M glucose and 100 mM KCl) was added into the bioreactor with a flow speed of 20 mL/h at the end of first 2-h bioprocess. As expected, this whole-cell catalysis system operated successfully in the fermentation, and a significant quantity of ectoine was synthesized and excreted at a rate of 0.94 g/L-h. After a reaction time of 24 h, the ectoine concentration reached 22.5 g/L in the medium, thus increased at over six times compared with that in the flasks reactions.  
Figure 8: Optimization of ectoine production. The initial reaction mixture containing 100 mM sodium phosphate buffer (pH 7.0), 200 mM sodium aspartate, 100 mM KCl, 100 mM glucose, and recombinant _E. coli_ cells of 10 OD/mL was incubated at 25 \({}^{\circ}\)C for 24 h. **(A)** Effects of sodium aspartate concentration on the production of ectoine. (**B**) Effects of KCl concentration on the production of ectoine (the concentration of sodium aspartate was 200 mM). **(C)** Effects of temperature on the production of ectoine (the concentration of sodium aspartate was 200 mM and KCl was 100 mM). **(D)** Effects of pH on the production of ectoine (the concentration of sodium aspartate was 200 mM and KCl was 100 mM). Error bars represent standard deviations from triplicate biological replicates.
## 4 FL-Based Controller: Formulation, Constraints and Algorithm
### Constraints and Formulation of Charging and Discharging Control
#### 4.2.1 SoC Membership Function  
In order to consider the energy requirements that should be met by battery owners and that can be applied in different ways, it is important to limit the energy that is allowed for use in B2G and G2B operation, ensuring that the battery will not be completely depleted or overcharged. Thus, the SoC  
Figure 5: Flow chart of the implemented controller and GA.  
must be limited by two different thresholds to insure safety, longevity, and performance [42]. In the present study, the battery cannot discharge to less than 20% or charge to more than 100% of its capacity. Moreover, the input SoC is fuzzified into four ranges, as illustrated in Figure 6; 'Very low SoC' (VLSoC), referring to SoC between 20% and 35%, 'Low SoC' (LSoC), referring to SoC between 36% and 60%, 'Medium SoC' (MSoC) for SoC between 61% and 80%, and 'High SoC' (HSoC) for SoC between 81% and 100%.