esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This study proposed a scheme that can produce linearly polarized \(\gamma\) photons in an unpolarized electron bunch interacting with a linearly polarized laser pulse. The normal and polarization properties of the emitted \(\gamma\) photons were investigated using 2D-QED-PIC simulation and a theoretical model. The simulation results indicated that the properties of the \(\gamma\) photons could be varied by changing either the laser intensity or electron bunch initial energy. The cutoff energy, brightness, and polarization degree of the \(\gamma\) photons were enhanced by properly choosing the laser intensity and electron bunch initial energy. Highly collimated (a divergence angle of \(5^{\circ}\)) \(\gamma\) rays were emitted with a brightness of \(1.8\times 10^{21}\) photons/(s mm\({}^{2}\) mrad\({}^{2}\) 0.1% BW) at 1 GeV. Further, the \(\gamma\) photons had high probabilities of being emitted into a polarization state parallel to the polarization of the background field and possessed a positive polarization degree, which attained its maximum \(\sim\)63% at a photon energy of approximately 1 GeV. Moreover, results showed that the low  
Figure 7: **(a)**\(\gamma\) photon energy spectrum and **(b)** polarization degree \(\Gamma_{1}=\frac{\rm{P}-\rm{P}}{\rm{H}+\rm{H}_{2}}\) from theoretical QED calculation for different laser intensities with a fixed electron bunch initial energy of 5 GeV. Other parameters are the same as in Figure 1.  
intensity laser and high-energy electron bunch could generate highly polarized \(\gamma\) photons with high energies, and the extremely intense laser could not achieve this effect. Our findings point to potential application in theoretical studies on upcoming QED experiments. The well-collimated, high-energy, ultrabright, and linearly polarized photon beam obtained using our scheme may be suitable for the generation of energetic polarized positrons and laboratory astrophysics research.  
Conceptualization, M.A.B.; methodology, M.A.B.; formal analysis, Y.H., B.Y. and M.A.B.; investigation, Y.H. and M.A.B.; data curation, Y.H., B.Y. and M.A.B.; writing--original draft, Y.H. and M.A.B.; visualization, Y.H. and B.Y.; supervision, M.A.B.; project administration, M.A.B.; funding acquisition, M.A.B. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Special Training Program of Science and Technology Department of Xinjang China (Grant No. 2024D03007) and the National Natural Science Foundation of China (NSFC) under Grant No. 12265024.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Ground Displacement in East Azerbaijan Province, Iran, Revealed by L-band and C-band InSAR Analyses
###### Abstract  
Iran, as a semi-arid and arid country, has a water challenge in the recent decades and underground water extraction has been increased because of improper developments in the agricultural sector. Thus, detection and measurement of ground subsidence in major plains is of great importance for hazard mitigation purposes. In this study, we carried out a time series small baseline subset (SBAS) interferometric synthetic aperture radar (InSAR) analysis of 15 L-band PALSAR-2 images acquired from ascending orbits of the ALOS-2 satellite between 2015 and 2020 to investigate long-term ground displacements in East Azerbaijan Province, Iran. We found that two major parts of the study area (Tabriz and Shabestar plains) are subsiding, where the mean and maximum vertical subsidence rates are \(-10\) and \(-98\) mm/year, respectively. The results revealed that the visible subsidence patterns in the study area are associated with either anthropogenic activities (e.g., underground water usage) or presence of compressible soils along the Tabriz-Shabestar and Tabriz-Azarshahr railways. This implies that infrastructure such as railways and roads is vulnerable if progressive ground subsidence takes over the whole area. The SBAS results deduced from L-band PALSAR-2 data were validated with field observations and compared with C-band Sentinel-1 results for the same period. The C-band Sentinel-1 results showed good agreement with the L-band PALSAR-2 dataset, in which the mean and maximum vertical subsidence rates are \(-13\) and \(-120\) mm/year, respectively. For better visualization of the results, the SBAS InSAR velocity map was down-sampled and principal component analysis (PCA) was performed on \(\sim\)3600 randomly selected time series of the study area, and the results are presented by two principal components (PC1 and PC2).  
L-band PALSAR-2; ground subsidence; synthetic aperture radar; East Azerbaijan Province 2020
## 5 Performance Evaluation
### Estimation Model Results Analysis
#### 5.2.2 Performance in Different Locations  
After knowing that LSTM works well in terms of overall performance, we analyze the performance of different methods for different locations. As satellites need to move around the globe, we expect the algorithm to maintain a low MAE and high prediction accuracy at any location. We tested the algorithm's performance separately for 147 cities around the world. These locations are found on different continents at different latitudes and longitudes and with different weather conditions.  
The test results are shown in Figure 5, where the vertical axis is a cumulative distribution function (CDF) plot composed of the test results from different locations around the world. These results indicate that the proposed method performs better than other algorithms in the vast majority of worldwide locations. Even in the worst-performing locations, the MAE of the proposed method is less than 0.07.
## 3 Results and Discussion
### Official Cartography: Errors and Inconsistences in Habitat Delimitation and Designation  
The analysis conducted in the present study revealed that the delimitation and designation of habitats do not respond to clear, standardized criteria, leading to multiple inconsidences that preclude comparative studies between different Natura 2000 areas with similar characteristics.  
One of the most common errors observed in this project was the excessive division of natural spaces into numerous habitats not related to the overall ecosystem functionality. In addition, incompatible habitats sometimes appear in the same space. Table 1 shows the main habitats designated as coastal lagoons, considered by official sources as only including bodies of water.  
In this study, we assumed that lake systems are complex habitats that include different environments and plant formations such as need beds (communities of _Phragmites australis_), rushes (formation dominated by _Juncus_ spp.) and aquatic vegetation (e.g., _Ruppieta_ maritiinae, _Potametea_, _Zostereeta_, and _Chareeta_). The coexistence of several formations in the same habitat is explained in the description of coastal lagoons [9], which include salt marshes as part of these complexes.  
Following on from this general framework for defining habitats in each lake in relation to the official cartography, we conducted an in-depth analysis of each sector in function of the composition of the water and sediments, as done for the delimitation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Code** & **Definition** \\ \hline
1150 & Coastal lagoons \\
7210 & Calcaneous fens with _Cladium mariscus_ and species of the _Cariçion davallianae_ \\
2130 & Fixed coastal dunes with herbaceous vegetation (grey dunes) \\
2150 & Atlantic decalcified fixed dunes (_Calluno-Ulliceta_) \\
3150 & Natural eutrophic lake with _Magnopotam_ or _Hydrocharition_–type vegetation \\
5230 & Arborescent matorral with _Laurus noblils_ \\
6220 & Pseudo-steppe with grasses and annuals of the _Thero-Brachyopdietea_ \\
91E0 & Alluvial forests with _Alnus glutinos_ and _Fraxinus excelsior_ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main, associated, and secondary habitats in the official cartography assigned to the studied lakes. The habitats that are not marked as relevant for the conservation in the official cartography were discarded. Source: Xunta de Galicia ©.
## 3 Denoising Diffusion Probabilistic Model (DDPM)
### Mathematical Foundations of DDPM
#### 3.2.2 Probabilistic Modeling  
DDPM formulates the denoising process within a probabilistic framework [48]. The likelihood of observing a clean image given the noisy counterpart is modeled using the conditional probability:  
\[p(\,X_{t}|X_{t-1})=N(f_{\theta_{t}}(X_{t-1}),2\eta_{t}I) \tag{2}\]  
This probabilistic formulation allows DDPM to learn the conditional distribution and infer the underlying clean image given its noisy observation.
###### Abstract  
Several factors have been identified to have an influence over the accuracy of a digital impression: ambient lighting conditions, the presence of liquid on the intraoral structures, and the scanning patterns. The purpose of this study was to identify which protocol produced the most accurate digital impression regarding a one-preparation scenario in which data were acquired from a single uninterrupted scan, a rescanning of the area of interest in order to obtain more data, or the deletion of the area of interest followed by a rescan. An in vitro scenario using a typodont involving a single full-crown restoration was conceived alongside the selection of an in vivo case of a patient presenting an unrestored right mandibular first molar. The STL (standard tessellation language) reference models for each group were obtained using a high-resolution scanner (Freedom, HD; DOS). With the use of a Medit I700 (Medit, Seoul, South Korea) intraoral scanner, three different scanning protocols were conducted on the typodont and on the clinical case. The measured data (IOS scans) were sorted and further analyzed with the help of Geomagic Control X (Version:16.0.2.16496, 3D Systems, Wilsonville, OR, USA). The Kolmogorov-Smirnov test for normality was conducted on the whole set of data. The nonparametric data set underwent Kruskal-Wallis test analysis while the parametric data set underwent a one-way ANOVA test (the level of significance was set to \(\alpha=0.05\)). For the in vitro case, the \"SINGLESCAN\" group displayed the best trueness; the \"DELETE&RESCAN\" group displayed the best precision. Regarding the in vivo case, the \"SINGLESCAN\" group also displayed the best trueness; the \"RESCAN\" group displayed the best precision. Statistical differences were found in the trueness and precision of the IOS scans captured with the three different scanning protocols, but the differences were so small that they could be considered clinically negligible.  
digital impression; accuracy; mesh holes; scanning protocol Article
## 4 Experimental Results and Analysis
### Parameter Analysis
#### 4.4.1 Inter-Manifold and Intra-Manifold near Neighbors a and b  
To investigate the influence of inter-manifold near neighbor a and intra-manifold near neighbor b on the final dimensionality reduction effect on the spectral dimensional map, 15 samples are selected as training samples using the IP and PU datasets, and the other samples are used as test samples. The variations in parameters a and b are chosen as {1, 2, \(\ldots\), 8} and {1, 2, \(\ldots\), 8}, respectively. Figure 6 shows the classification results after dimensionality reduction obtained by setting different parameters a and b using the two datasets.  
As can be seen in Figure 6, the classification accuracy of the algorithm increases with the increase in parameters a and b and then tends to be basically stable for two datasets. As the number of inter-manifold near neighbors increases, the effective information extracted gradually increases. The spectral lines of the same class of samples reveal more about the original high-dimensional structure of this class of samples, and the classification accuracy is improved. Similarly, with the increase in the number of intra-manifold near neighbors, the differences in the structure of spectral features of different categories of samples are better represented by the constructed graphs. However, it can be observed in Figure 6 that  
Figure 6: Classification results of different parameters a and b using two data sets: (**a**) IP dataset; (**b**) PU dataset.  
when b is too large, the information will reach saturation, meaning that the classification accuracy of the HSI will not continue to increase. For the IP dataset, a = 4 and b = 5 are chosen in the experiment. a = 6 and b = 7 are chosen in the experiment for the PU dataset.
## 3 Physiological Signal Monitoring and WBSNs Overall System
### Security on Medical Information  
Data security is a major problem for healthcare and medical emergency systems due to the handling of significantly delicate information. Sharing and transmitting private patient records via Internet can lead to interception of information by unauthorized parties. Security and integrity of the medical information is an important issue to be considered in the development of WBSNs. We briefly describe some important security considerations from the reviewed literature.  
The Health Insurance Portability and Accountability Act (HIPAA) Security Rule was created for regulation of different kinds of medical services, and specifically focuses on the safeguarding of Electronic Protected Health Information (EPHI) [27]. The EPHI created, received, maintained, or transmitted by an entity must be protected against reasonably anticipated threats, hazards, and impermissible uses and/or disclosures. The security and privacy of patient-related data are two indispensable components for the system security of WBSN [28].  
Information security can be seen from different perspectives such as privacy, integrity, and authentication. Privacy of patient's data is an important issue of systems for healthcare. In terms of integrity, information needs to be intact and complete to ensure an accurate diagnosis. By authentication, the system must provide protocols where only authorized medical staff has access to information. In the emergency scenario, it is necessary to provide and define data security components due to the handling and transmission of patient information via public wireless networks, e.g., GPRS, 3G, 4G.
## 5 Implementation
### Lab Setup and Tools  
We set up our experiment by using four HP Proliant DL380 G7 servers. All servers had two 3.47 GHz Intel Xeon X5690 processors with six cores each, 196 GB of DDR3 memory and 1 Gbit network adaptors (In a testbed provided by Eidsiva broadband, Oslo, Norway). The servers ran Ubuntu 18.04.2 LTS with kernel 4.15.0-47-generic. We used Vagrant 2.2.4 and qemu-kvm 1:2.11 with virtual network adapters by libvirt. For every Compute Node, we disabled Hyper-Threading, Turbo Boost, and power saving.  
We simplified the instantiation of the VMs by only using one VM template for every VM. This VM template was set up with Ubuntu Linux 18.04.2 LTS, 2 GB RAM and one virtual CPU. The pre-installation of the template included the P4 framework ([[https://github.com/jafingerhut/p4-guide](https://github.com/jafingerhut/p4-guide)]([https://github.com/jafingerhut/p4-guide](https://github.com/jafingerhut/p4-guide))) where we used the P4 version P4_16 with the behavioural model 2 (BMv2) from Barefoot [36]. For running the P4 code, we used the inner virtualisation software from Barefoot named the \"simple_switch\".  
For all communication between the VMs, we simulated RESTconf by the use of simple web services running over secure netcat (socat) [37].  
According to the scenario and the service plane topology, we created 21 VMs for data plane forwarding, including two endpoints (Figure 6). In addition, for the federated top-level controller,  
Figure 8: Hierarchy of network control.  
we combined the network controller, the service provisioner and the AuC in one common VM (22 VMs in total).  
For end-to-end traffic testing, we used Iperf 3.0.11 for measuring the performance and provisioning time. By sending a fixed stream of packets per seconds, we measured the network outage time by counting the packet loss. For packet injection tests, we used the Python tool scapy [38] and, for packet monitoring, we used Tcpdump on the virtual Compute Node interfaces (veth).
# A Versatile Resilience Packet Ring Protocol Model for Homogeneous Networks
## 5 Conclusions  
Our objective, in this work, is to propose a VRPR-based network model, which provides reliable communication between network devices within the LAN. We developed the model and simulated it in OMENeT++. The model is a secure and almost error-less communication model with 0% packet loss within the LAN. We have tested the difference between wired and wireless media by measuring their transmission time. We have found that wired media is faster than wireless, especially fiber. VRPR is implemented in every Network Element (NE) of the LAN. VRPR provides a nominal convergence time, which results in no blockage of traffic. We have found some delay if the main wired link is broken and traffic flows over the wireless link, and this is due to the limitation of the wireless link delay. However, the main objective of seamless communication has been achieved.
## 5 Results and Analysis  
It is useful to recall that the numerical prediction of heat transfer and entropy generation in a 2D square cavity is the main concern of this work. This has been performed for different Pr from Pr = 2.10\({}^{-2}\) to Pr = 70 and at a fixed Rayleigh number ( Ra = 10\({}^{4}\) ) with irreversibility distribution \(\varphi\) = 10\({}^{-7}\). Figure 7a, b illustrates the evolution of \(S_{T}\), \(S_{l,d}\), \(S_{l,h}\) and \(Nu\) (on the hot wall) according to the Prandtl number. A careful review of this figure shows that the effect of Pr on \(S_{l,h}\) and \(Nu\) is obvious for low values of Pr. For small Pr (\(<\)1 ), the thermal boundary layer becomes very thin, indicating that the heat transfer irreversibility ( \(S_{l,h}\) ) is dominative in the vicinity of boundaries, although the inner part of the simulation domain is dominated by the fluid friction irreversibility. We find that \(S_{l,h}\) decreases quite sharply until Pr = 5, from which it reaches its constant value, which stays up to 70. As for \(S_{l,d}\), it remains insignificant until Pr = 30, from which it starts to increase substantially. Furthermore, the effect of Pr on \(S_{l,h}\) is more significant for the range of 0.02 \(<\) Pr \(<\) 30. Beyond Pr = 30, the effect of Pr on \(S_{l,d}\) becomes very important. This is well reflected in Figure 8, where we plot the influence of the Bejan number _versus_ Pr. On the other hand, the Nu varies inversely to \(S_{l,h}\), especially in the case of low Pr. This shows that an enhanced heat transfer coefficient contributes to decreasing losses in the system.  
Figure 5: Total entropy production _vs._ dimensionless time \(\theta\) (= \(\alpha t\) / \(L^{2}\) ).  
Figure 6: Bejan number (Be) _vs._ dimensionless time \(\theta\).  
In what follows, we focus on the transient entropy generation with phase change. It is well known that during melting, the material undergoes an isothermal phase with a release of latent heat. Here, we ask the question about the effect of this phenomenon on the evolution of the generation of entropy through time. For this purpose, two models with different Pr have been investigated. Following this simulation, a comparison of the losses between the transfer of sensible and latent heat is carried out.
## 5 Results and Discussion
### Algorithm Performance Evaluation
#### 5.1.2 Convergence Performance Analysis
Moreover, the algorithm maintains a competitive time complexity, demonstrating efficiency without increasing computational cost.
## 3 Methodology
### Scalable Scenario  
The iFogSim2 toolkit was also used to run the simulation in the scalable scenario by changing the number of devices in the following sequence 20, 40, 60, 80, 100, 120, 140, 160, 180, and 200 smart IoT devices for the CCTV, CO\({}_{2}\), and light sensors. The scalable scenario was designed with a changing number of smart IoT devices, which started from 20 and was scaled up in increments of 20 to a final 200 smart IoT devices. The parameter values such as MIPS, RAM, Uplink, and Downlink bandwidth were set to the same specifications as the fixed scenario, using real-world values, and remained constant during the simulation experiment. In the scalable scenario, only the number of smart IoT devices could be increased or reduced as required, while other network configuration settings remained constant. The main aim of the scalable scenario was to provide insight into how the system performed as the number of smart IoT devices was scaled up, identifying the system's adaptability and performance metric efficiency as the number of devices increased.  
In the fixed scenario, the environment was fixed with the number of smart IoT devices reflecting a real-world smart building infrastructure. In contrast, the scalable scenario scaled the number of smart IoT devices upwards at increments of 20 devices to a final 200 devices, in order to represent a high volume of IoT devices within the infrastructure. The methodology for the fixed scenario provides a stable and controlled environment with a fixed impact on performance metrics; meanwhile, the scalable scenario provides a less stable environment with a dynamic increase in smart IoT devices and the consequent impact on performance metrics.
## 6 Performance Evaluation of the Proposed Protocol
### Energy Consumption  
In this part of the article, we have calculated the energy consumption for each of the presented protocols and compared them with the proposed protocol.  
To complete the protocol process, communication entities use several operations, such as point multiplication, bilinear peering, octal functions, etc., for which communication devices must consume energy to perform. This energy consumption is measured in milli-joules (mJ), which can be calculated through the relationship EC = T\({}_{\text{CT}}\) * C [62; 63]. In this regard, EC expresses energy consumption, T\({}_{\text{CT}}\) expresses total computational time, and C expresses maximum processing power (10.88 W) during wireless communications. Using Table 4, which provides the computing cost of each protocol and the maximum processing power (10.88 W), the energy consumption of each of the key-agreement protocols has been calculated. Table 6 compares the proposed protocol with other related protocols based on energy consumption criteria.  
As the results in Table 6 show, the proposed protocol has been able to provide more optimal energy consumption than other protocols. This low energy consumption provides the conditions that the presented protocol has suitable compatibility with nodes with low processing power and energy.