esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methods
### Principles of Mathematical Morphology  
During the analysis of deformation signals, the convolution operation is primarily utilized as a method of linear transformation. However, mathematical models can be developed based on the introduction of partial order. In the monitoring of landslide deformation, a landslide deformation image operator can be developed using set theory [24]. Consequently, the morphological transformation of the landslide deformation image is conducted using mathematical morphology based on the characteristics of the landslide deformation image.  
Mathematical morphology is an image analysis subject based on mathematical theory, which integrates mathematics with topology. The fundamental concept is to utilize the defined structural elements for scanning the target image for processing and analysis. The fundamental operations of mathematical morphology mainly include erosion and dilation, as well as opening and closing operations [25]. This study applies the operation process of mathematical morphology to analyze landslide deformation images. The process is as follows:  
The basic concept of the dilation algorithm is that the structural elements belong to the target image, and the boundary elements are added to the image. The dilation of \(B\) to \(A\) is expressed as \(P\) and is defined as follows:  
\[P=A\oplus B=\left\{q\Big{|}\Big{[}(B)_{q}\cap A\Big{]}\subseteq A\,\right\} \tag{4}\]  
Among them, \(A\) and \(B\) are both sets in Q2; \(B\) is a structure; and \(A\) is the inflated set, that is, the target image.  
The basic concept of the erosion algorithm is to remove certain elements of the object boundary. The erosion of \(B\) to \(A\) is expressed as \(F\) and is defined as follows:  
\[F=A\Theta B=\left\{q\Big{|}(B)_{q}\cap A^{\prime}=\Phi\,\right\} \tag{5}\]  
Among them, \(A\) and \(B\) are both sets in Q2, \(B\) is a structure, \(A\) is the inflated set \(A^{\prime}\), is the complement, and \(\Phi\) is the empty set.  
The open operation of the structural element \(B\) on set \(A\) is expressed as \(K\) and is defined as follows:  
\[K=P\oplus F \tag{6}\]  
Therefore, the open operation of \(B\) on set \(A\) is to corrode against \(A\) with \(B\) and then to expand the corrosion result with \(B\).  
The erosion and dilation operations in mathematical morphology are simple to use and highly efficient for processing. The combination of open and closed operations can also remove burrs and fill in holes. These characteristics also enable them to optimize and remove the noise in the initial segmentation of high-resolution remote sensing images, which improves calculation efficiency.
# An Integration Framework for the Inpainting and Colorization of Arbitrary Masked Grayscale Images
## 2 Related Work  
### Image Inpainting  
Image inpainting has been a central challenge in computer vision, focusing on restoring missing or corrupted regions in images. Traditional methods, such as diffusion-based approaches [6, 7] and patch-based methods [8, 9, 10, 11, 12], relied on local features to reconstruct gaps. Techniques like PatchMatch [13] introduced efficient patch search strategies, while partial differential equations [14, 15] and global or local image statistics [16, 17] enhanced reconstruction accuracy. Despite their successes, these methods are limited by their reliance on low-level features, hindering semantic understanding.  
Deep learning has revolutionized inpainting, with encoder-decoder architectures and adversarial training [18, 19] enabling context-aware reconstructions. Innovations include U-Net variants [20, 21, 22], gated and partial convolutions [23, 24], and attention mechanisms [25, 26]. Frameworks like EdgeConnect [27] and StructureFlow [28] leveraged intermediate representations to improve completion quality.  
Recent advancements have addressed large missing regions and diverse contexts. Co-modulated generative adversarial networks (CoModGAN) [29] uses modulation techniques [30, 31], while unsupervised cross-space translation generative adversarial network (UCTGAN) [32] employs transformer-based strategies. Transformer-based models like MAT [2] integrate attention mechanisms for high-resolution inpainting, achieving state-ofthe-art performance. Challenges in semantic coherence and high-resolution reconstruction remain open research areas.
## 3 Energy Conserving Issues in Wireless Networking-Based IoT
### Energy Conserving Issues for WPAN-Based IoT  
Bluetooth Low Energy (BLE) is one of the most promising low power consumption wireless technologies in the wireless personal area networking domain for IoT applications. Figure 6 illustrates the duty cycle of a slave device by using the Sniff interval method during which it listens to a master device for a particular amount of time. At every Sniff anchor point (typically every 100 ms), the device wakes up to check if it has data to send or receive, and goes back to a sleep mode for the rest of sniff  
Figure 5: An illustration of IEEE 802.11 PSM.  
interval. The issue of determining the sleeping time of BLE devices according to a battery level and throughput-to-workload ratio is examined in [83].  
Protocol mechanisms for using BLE for IoT, and their relationships with energy consumption issues have been examined, such as the relationship between header compression and energy consumption, the impact of using IoT gateways as proxies on energy consumption, relationship between duty cycling and energy consumption, routing mechanisms for BLE energy consumption, and the influence of IoT data delivery protocols on energy consumption, in the literature [79, 80, 81, 82, 92]. Particularly, the usage of multicast signaling messages for IPv6 neighbor discovery over BLE assumes that the message receivers are powered on to be able to receive the messages, which can require energy consumption.  
Z-Wave is an emerging wireless protocol for smart homes, where the Z-Wave based devices can be remotely controlled. Basic considerations for Z-Wave for home automation systems include how to manage devices in a home environment in a power efficient way [87, 88]. Fault management in Z-Wave is also related to energy consumption [89]. More specifically, if some devices fail in their operation, other devices that continuously try their data transmission through those failed devices would end up with huge energy consumption.
# Adaptive Polling Medium Access Control Protocol for Optic Wireless Networks
## 5 Experimental Setup and Numerical Results
### Average System Fairness versus Normalized Throughput and Network Load  
In Figure 7(a),b, we present numerical results considering fairness index as a function of normalized throughput and network load respectively. For simplicity, we elaborate our results by considering two cases; (i) five users and (ii) ten users in the network. Figure 7(a) shows that the fairness index increases with respect to normalized throughput. This suggests that when the system is fairer, its throughput increases. Moreover, when the network size is large, fairness index decreases as suggested by fairness index when there are 5 VLNs compared to 10 VLNs in Figure 7(a),b. Thus, it is difficult to distribute resources fairly when there are more nodes in a network. Moreover, in Figure 7(b) we consider the fairness index against network load for five and 10 users. In this case, the value of fairness index  
Figure 6: Average packet loss vs. network size and network load.  
Figure 7: Average system utilization vs. network size and network load.  
decreases when network load increases, thus distributing resources fairly to all VLNs at high network load becomes more difficult.
## 4 Numerical Example
### Sensitivity Analysis  
Because the software reliability model is highly sensitive to changes in the mean value function \(m(t)\) whenever the parameters change, this study compares the changes in the \(m(t)\) owing to changes in the four parameters through a sensitivity analysis [49; 50]. In this study, we use two datasets to compare the most affected parameters of the mean value function \(m(t)\) when the parameters of the estimated model are varied by \(-20\%\), \(-10\%\), \(10\%\), and \(20\%\).  
The changes in the four parameters of Dataset 1 are shown in Figure 6. For \(N\) and \(\gamma\), the estimated mean value function \(m(t)\) became larger with increasing values, while for \(\alpha\) and \(\beta\), the \(m(t)\) became smaller with increasing values. Furthermore, the magnitude of the change in mean value function \(m(t)\) increased with increasing time point for \(N\), \(\gamma\) gradually decreased, and \(\beta\) showed relatively small changes compared to the changes in other parameters.  
The changes in the four parameters of Dataset 2 are shown in Figure 7. For \(N\) and \(\gamma\), the estimated mean value function \(m(t)\) became larger with increasing values, while for \(\alpha\)  
Figure 5: Three-dimensional plot of the \(MCDM\), \(MCDMR\), and \(MCDMM\) for Dataset 2.  
Figure 6: Results of sensitivity analysis for changes in parameters in dataset 1. (**a**) parameter \(N\); (**b**) parameter \(\alpha\); (**c**) parameter \(\beta\); (**d**) parameter \(\gamma\).  
and \(\beta\), \(m(t)\) became smaller with increasing values. For \(N\), the variation in the mean value function \(m(t)\) increased as the time point increased, while \(\alpha\) showed very small variation overall. \(\gamma\) and \(\beta\) showed very large variation initially but gradually became smaller.
## 3 Experimental Setup and Instrumentation
### Sensor Deployment and Measurement Techniques
For instance, wave travel from Station 1 to 2 corresponds to propagation from A to B, while Station 3 to 4 aligns with D to E. These paths share similar boundary conditions: 2.5 m from the initial support to the first station, 6 m between the first and second stations, and 3.5 m from the second station to the opposite support. This configuration facilitates wave direction reversal, allowing for a more comprehensive analysis of sender-receiver dynamics. Additionally, one temperature sensor was used to measure air temperature, while two other sensors were inserted in the middle of the bridge, one on the bottom and one on the side (Figure 7). The bridge temperature presented in this paper was derived from the average temperature of both sides. The difference between them was negligible.
## 4 Single parent parallel Lamarckian GA implementation
### Algorithm layout  
Parallel GA implementation for atomic clusters which is written using **C++** object oriented programming language and **MPI** parallelization library, may be summarized as follows:  
* **Population initialization :** Each computing node initializes a number of (**populationSize / numberOfNodes**) individual atomic clusters by generating random positions for the atoms in a cubical box of a certain size which is smaller than the expected size of the cluster.  
* **GA loop*
* **Local optimization :*
* Each individual cluster is relaxed by the MC local optimizer at the computing node initializing the cluster.
* **Gathering at master node :*
* Atomic coordinates and potential energy values of locally optimized clusters are gathered at the master node by **MPI*
* **Selection :*
* Half of the individuals are selected by **roulette wheel selection*
* with elitism using the fitness function \(V\)[10]. The number of GA steps with no progress in the potential energy of the fittest individual is counted and when this number reaches a certain amount, an alternative selection mechanism is applied in which all the individuals are transformed to the fittest individual.
* **Termination check :*
* Number of alternative selection mechanism loops with no progress in best fitness value is counted. When this number reaches a certain amount GA loop terminates.
* **Variation generation :*
* Phenotype mutation operations of **piece rotation*
* and **shrinkage*
* are applied on the selected individuals.
* **Distribution of individuals :*
* Individual clusters are distributed to available computing nodes by **MPI*
* communication for local optimization.
* **Output :** Atomic coordinates and potential energy value of the fittest individual are given as the output of GA implementation.  
An illustration of the method is given in Figure 3 for the C\({}_{20}\) cluster in which three GA steps of the evolution of the fittest individual winning the competition is given ignoring some intermediate GA steps. This illustration is for the evolution of a single individual and therefore natural selection and reproduction steps are not shown.
## 3 Status and Application
### Single-Pass Interference
#### 3.2.1 CT-InSAR
Compared with SRTM data, the TanDEM-X DEM expanded from 60\({}^{\circ}\) N to 56\({}^{\circ}\) S to cover the entire globe, including all regions of the North and South Poles, and the spatial resolution and elevation accuracy increased to 12 m and 2 m, respectively, meeting the HRTI-3 standard in Table 5.  
China has also actively conducted research and application explorations in this field, achieving groundbreaking progress. The TianHui-2 dual-satellite formation launched in 2019 [66; 67] has a payload of high-resolution synthetic aperture radar, high-precision inter-satellite relative state measurement equipment, etc. It acquires global radar images and auxiliary measurement data in all-weather and all-time conditions, enabling the rapid production of DSM. The PIESAT-1(01) satellite launched in 2022 was developed by Galaxy Aerospace. This satellite system adopted the world's first \"1 + 3\" wheel formation multi-satellite distributed interferometric configuration, consisting of 4 high-resolution X-band radar satellites, and has the capabilities of high-precision terrain mapping, high-resolution wide-angle imaging, high-precision deformation monitoring, etc. It can quickly and efficiently produce high-precision DSM and complete global non-polar region mapping tasks, and is widely applied for domestic emergency disaster reduction, ecological environment, natural resources and other scenarios. Through the elevation accuracy verification of the RAW DSM products in Yuyang County, Hunan Province, using ICESAT2 laser control point data, the absolute elevation mean error is found to be 3.34 m, meeting the terrain mapping accuracy requirements of 1:50,000 scale in hilly areas. The RAW DSM products in Yuyang County, Hunan Province, are shown in Figure 11.
# Energy and Distance Based Multi-Objective Red Fox Optimization Algorithm in Wireless Sensor Network
###### Abstract
The fitness values, such as residual energy, network coverage, distance, and degree of nodes, are considered as the fitness functions for finding the optimal solutions for the selection of nodes. The study's major contributions are as follows:  
* The ED-MORFO-based clustering & routing process was exploited to achieve an energy-efficient procedure, with a variety of node counts for improving the lifetime of the network by minimizing the energy consumption. * To improve the data transfer dependability, ED-MORFO was employed to reduce energy consumption and improve link superiority across the various sensor nodes. The structure of this research paper is as follows: Section 2 offers a survey of contemporary strategies in WSN clustering and routing that are energy efficient. Section 3 contains the research's problem statement. Section 4 explains the preliminaries and the system model. Section 5 contains a block diagram and a description of the suggested approach. Section 6 declares the simulation findings, as well as a comparative study of the proposed method. Finally, in Section 7, the conclusion is expressed.
# Spatial Assessment and Prediction of Urbanization in Maseru Using Earth Observation Data
## 4 Discussion  
### LULLC Maps  
This study assessed the impact of urbanization on LULC changes in the study area. Evidence of urbanization is evident in the increasing area of coverage for built-up areas, with the highest growth of 8.2% between 1993 and 1998 (Table 3). This growth was mainly due to the industrialization in Maseru, where the first textile factories were built in the 1990s. The emergence of these textile factories was largely a result of the collapse of Apartheid in South Africa, which freed up some policies that enabled countries such as Lesotho to open in the 1990s [51]. There was a massive movement of people from rural areas to obtain employment in the new textile factories in Maseru [52]. Moreover, the massive retrenchment of migrant mine workers in nearby South Africa in the early 1990s led more people to move from rural areas in Lesotho to Maseru in search of employment and better living conditions [53]. In 1993 and 1998, general elections were conducted in Lesotho, which led to a massive return of political exiles, most of whom built and resided in Maseru: the economic hub of the country [52]. The continuous rise in the built-up areas from 1988 to 2019 was due to population growth, which rose from approximately 311,829 in 1986 to 519,186 in 2016 [54].  
Many researchers have found a similar link between high urbanization and economic growth for the provision of housing and basic infrastructure development, especially in Sub-Saharan countries [14; 15; 55; 56]. This inequitable growth has resulted in various problems, which include traffic congestion, road accidents, and air, land and water pollution [20]. The city is also facing extreme poverty, with high rates of unemployment and hunger [57].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Class** & \multicolumn{2}{c}{**2019**} & \multicolumn{3}{c}{**2050**} & \multicolumn{3}{c}{**Predicted Change 2019–2050**} \\ \hline  & **ha** & **\%** & **ha** & **\%** & **ha** & **\%** \\ \hline AG & 847.1 & 2.8 & 657.8 & 2.2 & \(-\)189.3 & \(-\)22.3 \\ BS & 6939.6 & 23.3 & 4609.8 & 15.5 & \(-\)2328.8 & \(-\)33.6 \\ BU & 11,973.2 & 40.1 & 17,407.7 & 58.4 & 5434.5 & 45.4 \\ GL & 8139.8 & 27.3 & 5228.8 & 17.5 & \(-\)2910.0 & \(-\)35.8 \\ WB & 421.3 & 1.4 & 412.4 & 1.4 & \(-\)9.1 & \(-\)2.2 \\ WV & 1506.1 & 5.1 & 1495.9 & 5.0 & \(-\)10.2 & \(-\)0.7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The predicted changes in The built-up area and other LULC types from 2019 to 2050.  
Figure 5: Predicted land use and land cover (LULC) map for 2050.
## 2 Modelling Tools
### DOC Model
 \tag{4}\]  
According to Equations (2)-(4), the substrate temperature (\(T_{w}\)), the internal surface temperature (\(T_{sur,int}\)) and the external surface temperature (\(T_{sur,ext}\)) are governed by the thermal capacity (\(C\)) and  
Figure 2: Monolith and canning nodal schemes for heat transfer process solution. the equivalent thermal resistance between two neighbouring nodes (\(R\)), whether gas, substrate or canning. The term \(\dot{q}_{r}\) is related to the heat power of the chemical reactions taking place in the washcoat. The DOC chemical mechanism considers the CO and HC oxidation as well as the physisorption of HC on the zeolites present in the washcoat and the NOx redox reactions:  
\[CO+\frac{1}{2}O_{2}\to CO_{2} \tag{5}\]  
\[C_{n}H_{m}+\left(n+\frac{m}{4}\right)O_{2}\to nCO_{2}+\frac{m}{2}H_{2}O \tag{6}\]  
\[C_{n}H_{m}+Zeol.\rightleftarrows C_{n}H_{m}\bullet Zeol. \tag{7}\]  
\[NO+\frac{1}{2}O_{2}\rightleftarrows NO_{2}. \tag{8}\]  
The conversion efficiencies of HC and CO are calculated by the integration in the axial direction of the 1D transport equations of these species in the gas bulk and over the catalytic surface. Assuming quasi-steady flow, these are written as  
\[u_{in}\frac{dX_{n}}{dx}=-S_{p,gas}k_{m,n}\left(X_{n}-X_{n,exc}\right) \tag{9}\]  
\[\sum_{j}\alpha_{n}R_{j,n}+S_{p,exc}k_{m,n}\left(X_{n}-X_{n,exc}\right)=0, \tag{10}\]  
where Equation (9) is the bulk gas chemical species conservation equation and Equation (10) represents the chemical species transport in the washcoat of the pollutant species \(n\). The bulk gas transport equation takes into account the convective transport of the species along the monolith channels and the bulk mass transfer towards the washcoat interface. As described by Equation (10), the gaseous species are then transported by diffusion from the interface to the washcoat internal volume, where the reaction takes place in the active sites. The reaction rate (\(R_{n}\)) for every pollutant species \(n\) accounts for all reactions \(j\) in which it is involved. It is calculated from the Arrhenius kinetic constant assuming first-order kinetics and including limitations due to inhibition and internal pore diffusion [26]. Due to the minor effect of NO\({}_{2}\) in the soot oxidation during active regeneration processes and the high temperature in the DOC during this process [38], the NO\({}_{2}\)/NO\({}_{x}\) ratio at the DOC outlet was determined assuming thermodynamic equilibrium [39]. Therefore, the reaction rate for the CO and HC oxidation reactions is written as  
\[R_{ox,n}=\eta_{int,ox,n}\frac{k_{ox,n}}{G_{ox,n}}X_{O_{2}}X_{n,exc}, \tag{11}\]  
whilst the HC adsorption and desorption reaction rates are given by:  
\[R_{ads,HC}=\eta_{int,ads,HC}k_{ads,HC}\left(1-\theta_{HC}\right)\psi_{HC}X_{HC, exc} \tag{12}\]  
\[R_{des,HC}=\eta_{int,des,HC}k_{des,HC}\theta_{HC}\psi_{HC}. \tag{13}\]
# A Virtual Network Resource Allocation Framework Based on SR-IOV
## 3 Background Knowledge
### Single-Root I/O Virtualization  
To improve I/O performance, hardware-based virtualization is proposed to eliminate the intervention of VMM on I/O paths. Lightweight emulation has greatly improved the performance of para-virtualized I/O compared to full virtualization, but because in the para-virtualized model the VMM still interferes with the I/O path of the VM, so the performance of para-virtualized I/O is still limited. Unlike the para-virtualization model, DMA operations for SR-IOV devices can be directly accessed by IOMMU or device-embedded TLB without the need for a VMM. Therefore, this bypasses the intervention of the VMM on the VM I/O path, and the VM can directly access the SR-IOV hardware device so that can achieve the performance close to the physical machine.  
SR-IOV is an I/O virtualization solution supported by PCIe device hardware and developed by the standardization organization PCI-SIG. As shown in Figure 2, each SR-IOV device has one or more _physical function_ (PF), and each PF has one or more VF corresponding to them. Generally speaking, the PF driver runs in the privileged domain (where KVM is a QEMU program) and has the right to access all the resources of the SR-IOV hardware, while the VF driver runs in the non-privileged domain (that is, VMs), and the VF is managed and configured by the PF. VF is a lightweight PCIe device and  
Figure 1: (a) I/O virtualization model in QEMU. (b) I/O virtualization model in virtio.  
has the necessary resources to run independently. A VF is like a traditional PCIe device for a VM, with a unique BDF identification number in the PCI bus, so a VF can be bound to a specified VM. In the SR-IOV NIC device, the bandwidth allocation between multiple VFs is based on the hardware arbitration of the NIC device, and the bandwidth is allocated in a weighted average manner. Therefore, when the total bandwidth is limited, if the number of VFs is too large, the effective bandwidth acquired by each VF will be inversely proportional to the total number of configured VFs, which will also cause damage to the system network performance.