esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Integration Options
### Integration at Platform Level  
A different approach could be the integration with a domain that implements the MEP and possibly some specific applications. This approach can be perceived as a Platform-as-a-Service (PaaS) offering, also in analogy with cloud computing world.  
The business rationale for this integration model could be the one of a primary provider, Domain A, willing to leverage on the applications of a secondary provider, Domain B, which could retain the rights for the integral exploitation of such applications, including the value added of the functionalities provided by the MEP itself, thus leading to the PaaS concept.  
The integration at PaaS level could present two different sub-scenarios: _(i)_ integration with the platform provider with infrastructure owned by the primary MEC provider, Domain A; and, _(ii)_ integration with the platform provider, Domain B, including its infrastructure. Both scenarios are shown in Figures 3 and 4, respectively.  
The first situation, when the primary MEC provider also provides the infrastructure, implies that the platform provider instantiates the MEP function in advance on top of the primary MEC provider infrastructure. This could be performed in the form of a Virtual Network Function (VNF), e.g., by leveraging on the integration model of MEC and NFV as defined in [12].  
In this case, it can be assumed that the virtualization infrastructure of Domain A will be fully controlled by the MEP of Domain B through the MD-Mp2 interface, as result of the indications from the MEPM of Domain A via the MD-Mm5 interface. The MEP from Domain B could interact with other MEHs from either Domain A or Domain B by means of the MD-Mp3 interface.  
Figure 3: Integration of MEC providers at platform level following a Platform-as-a-Service (PaaS) approach with infrastructure owned by Domain A provider.  
In the second situation, when the platform provider includes the supporting infrastructure, the platform provider could be a remote provider. In these circumstances, the MD-Mm5 interface will behave as before, however an integration with the VIM is required, which, as mentioned before, could be achieved through open interfaces in case the VIM is an open source solution such as OpenStack. Additionally, as in the IaaS case, the main MEC provider, Domain A, could leverage on the concept of VIM-on-demand for facilitating the integration and control of the resources granted by the platform provider to it.
## 2 Materials and Methods  
To develop this research, a partial differential equation was created that simulated the spread of the fungus PP in space and time during the 2018/2019 harvest over the state of Mato Grosso (Brazil). Mato Grosso is the largest soybean producer in the country. In the 2022/2023 harvest alone, it produced more than the entire South region (the second-largest grain-producing region in the country). In the 2023/2024 harvest, it was responsible for over a quarter of all production [10].  
The diffusion-advection equation is frequently used to simulate pollutant dissemination (or concentration). Examples include a concentration of animals infected by a virus, river contaminants, or a specific poison's dispersion in the air.  
The equation was used to model the dispersion of pollutants in an air-water system. Initially, the air transport of the pollutant and its deposit on the surface of the water body were modeled with a two-dimensional vertical domain. Next, the aquatic environment was included for polluting substances that penetrate water, with a two-dimensional horizontal domain [9].  
A system of partial differential equations of the susceptible, infected, and recovered (SIR) type was constructed, and the three equations were adjustments to the diffusion-advection equation. Thus, to simulate the behavior of foot-and-mouth disease in cattle, the first equation modeled the population susceptible to the disease; the second, the infected population; and the third, the recovered population [6].  
The evolution of pollutants from agro-industrial activity in the Lago Manso region in Mato Grosso was also modeled using the diffusion-advection equation from two different approaches. In the first, the domain was two-dimensional, as it characterized the surface of a body of water. With the algorithmic tool built from this research, it was possible to generate scenarios using wind variations and reducing the flow of agrochemical products [7]. In the second, the domain covered was three-dimensional, including the lake's depth. In addition to producing material and method for qualitative evaluation, with attention to this problem, the objective was to build a generic model that could be applied to any situation that fell into the same category as the second research [8].  
For all these reasons, this equation was chosen to model the spatiotemporal dynamics of the fungus that causes Asian soybean rust. Next, the classic model of the diffusion-advection equation is presented, and a brief explanation of the specific terms is provided.  
Some concepts concerning variational formulation, discretization of equations, and domain are also addressed and contextualized to address the problem of this research.
# The Impact of Spatial Resolutions on Nature-Based Solution Suitability Mapping for Europe
## 3 Materials and Methods  
For the aim of gaining clarity on the impact of spatial resolution on the suitability mapping of NBS, we used a GIS-based approach (Figure 2) for the test study area and NBS. The results of this test study shall inform the wider application of the suitability mapping for the European-focused NBS Toolkit.  
The GIS-based approach of this study included, firstly, the selection of open-source input layers at the European scale (Section 3.1), and secondly, the multi-criteria suitability mapping utilising the selected raster and vector input layers (Section 3.2). The suitability mapping was performed by applying the Boolean multi-criteria analysis [40] to three different LULC input layers (CORINE Land Cover, LUISA base map, and OpenStreetMap), which differ in their spatial resolution. The suitability mapping resulted in three outputs (one for each LULC input layer). To gain a better understanding of the impact of the three different spatial resolution layers on the output suitability maps, the following analysis and validation methods were used in the final step (Section 3.3): patch and landscape statistics, cohesion analysis, and satellite validation.
###### Abstract  
The current research aims to examine the long-term evolution of the western cliffs of Lefkada Island following the occurrence of the last two strong earthquakes, on 14 August 2003 and 17 November 2015, respectively. Medium resolution satellite data (Landsat) and very high-resolution data (Ikonos, Pleiades, and airphotos) were processed in Google Earth Engine and Erdas imagine software, respectively. The study area covers a 20 km-long region of the western cliffs of Lefkada Island, extending from Egremmi beach to the South to Komilio beach to the North. Relief, vegetation, and inclination changes were detected in the ArcGis environment. The results were associated with in situ data provided through the installation of a sediment trap. The analysis of the results proved that seismicity is the main factor that formed the western coastline of Lefkada Island, affecting the integrity of the cliffs. Specifically, large earthquakes cause immediate vegetation and topographic (inclination changes, mass movements) modifications in the western cliffs of the island. Meanwhile, small earthquakes (magnitudes < 4.1) contribute to the diff's evolution during the inter-seismic era. The intensity of these aforementioned changes was closely related to the seismic activity that occurred in the vicinity of the study area. In addition, it was found that precipitation and wind do not exert a similar influence on the cliff's evolution.  
type: analysis; seismicity; remote sensing; geomorphological analysis; Lefkada coast
## 3 Results
### ANK and TPR Domains Work Together as a Molecular chaperone
These structural shifts were confirmed by the HPLC-SEC experiment (Figure 9b,d). Upon exposure to 60 \({}^{\circ}\)C for 30 min, both ANK-D and TPR-D were predominantly present as HMW complexes, indicating that the monomeric and oligomeric forms of each truncated protein interacted to form HMW complexes. Figure 7: Comparison of the chaperone activity and hydrophobicity of AtTPR10, ANK-D, and TPR-D. (**a**) Schematic representation of AtTPR10 and its truncated variants. (**b**) Purification of homogeneous recombinant proteins analyzed by SDS-PAGE gel. Each protein was separated on a 12% denaturing gel by SDS-PAGE and visualized by Coomassieie Brilliant Blue staining. Numbers indicate the molecular weight standards in kDa. (**c**) Relative chaperone activity of AtTPR10 and its truncated forms. The data presented are the averages of at least three independent measurements. (**d**) Comparison of the hydrophobicity of AtTPR10, ANK-D, and TPR-D, following incubation with bis-ANS at 23 \({}^{\circ}\)C. Fluorescence of bis-ANS was measured at an excitation wavelength of 390 nm and emission spectra of 430–630 nm. Only bis-ANS treatment was used as control. Overall, our results demonstrate that AtTPR10 acts as a molecular chaperone to protect plants from heat stress, and the ANK and TPR domains of AtTPR10 work together to enhance its chaperone function. Given that _Arabidopsis_ contains a number of AtTPR isotopes (Figure 1), it is possible that most of these proteins perform the same basic function as AtTPR10, in addition to other specific roles in the regulation of diverse plant processes. Figure 8: Comparison of the chaperone activity and hydrophobicity of AtTPR10, ANK-D, and TPR-D at different temperatures. (**a**) Relative chaperone activity of AtTPR10, ANK-D, and TPR-D at 23 \({}^{\circ}\)C, 50 \({}^{\circ}\)C, and 60 \({}^{\circ}\)C. (**b**) and (**c**) Hydrophobicity analysis of ANK-D (**b**) and TPR-D (**c**), along with that of AtTPR10, upon incubation with bis-ANS at 23 \({}^{\circ}\)C, 50 \({}^{\circ}\)C, and 60 \({}^{\circ}\)C for 30 min. The fluorescence of bis-ANS was measured using a fluorometer, with excitation at 390 nm and emission at 430–630 nm. The representative results are means of at least three independent experiments. Figure 9: Heat shock-dependent structural changes in AtTPR10, ANK-D, and TPR-D. (**a**)–(**d**) Analysis of heat shock-dependent structural changes by 10% native gel electrophoresis (**a**) and (**c**) and SEC using HPLC (**b**) and (**d**) upon incubation at different temperatures (23 \({}^{\circ}\)C, 50 \({}^{\circ}\)C, and 60 \({}^{\circ}\)C) for 30 min. SM represents the size marker (**a**) and (**c**).
# Multi-Path Data Distribution Mechanism Based on RPL for Energy Consumption and Time Delay
## 2 Related Work  
### RPL: Routing Protocol for Low Power and Lossy Networks  
Recently, researchers on routing protocol have mainly concentrated on two aspects: hierarchical models and flat models. The hierarchical model is to construct the network into multiple clusters, with every node in the cluster in communication with each other. In this case, the implementation of load-balanced routing depends on the structure of these clusters. This model requires the nodes in the clusters to communicate with each other directly, thus the cluster communication distance is one-hop and is not suitable for a large-scale network. In the flat model, the data is transmitted through multi-hop, and it is suitable for large-scale network deployment, but it cannot communicate with an IP network directly and needs gateways to convert protocols.  
The IETF ROLL working group designed the wireless sensor network routing protocol, RPL. It is a kind of distance vector routing protocol which is suitable for low power and lossy networks. It constructs DODAG (Destination-Oriented Directed Acyclic Graph) through one or several routing metrics to perform data communication. In the process of building and maintaining a DODAG, four RPL messages are needed:  
* DIO (DODAG information object): This message is used to build DODAG, which is broadcasted by the root node initially.
* DIS (destination advertisement solicitation): When a node wants to join DODAG, but it does not receive DIO, the node can actively send a DIS message to apply to join DODAG.
* DAO (destination advertisement object): Used to produce reverse routing information. Apart from root nodes, every other node can send DAO.
* DAO-ACK (DAO acknowledgement): Used to confirm DAO.  
According to RPL protocol, a network can contain several DODAGs. During the process of constructing a DODAG, the root node broadcasts the DIO message at first including the instance ID, DODAG ID, increasing version number, the rank value and the objective function. After listening to the DIO message, the neighbor of the root node decides whether to join the DODAG. If they join the DODAG, they will calculate their own rank values independently, and send information about the final version number and DODAG identification to neighbors. The same procedure will continue until the leaf nodes.  
RPL is designed for LLNs and performs routing in a distributed way. However, load balance is missing in the original RPL. For fault tolerance, each node needs to save a list containing multiple parent node information. Nevertheless, only the preferred parent, which is selected by routing metrics or constraints, is responsible for the communication task, while other parent nodes act as backups. In the real network, some nodes have more neighbors than others and thus spend more energy. If these nodes are chosen to be the preferred parents, a network hole will appear because of their energy exhaustion and the network will disconnect.
###### Abstract  
Diagnosing bipolar disorder (BD) and schizophrenia (SCH) presents significant challenges due to overlapping symptoms, reliance on subjective assessments, and the late-stage manifestation of many symptoms. Current methods using structural magnetic resonance imaging (sMRI) as input data often fail to provide the objectivity and sensitivity needed for early and accurate diagnosis. sMRI is well known to be capable of detecting anatomical changes, such as reduced gray matter volume in SCH or cortical thickness alterations in BD. However, advanced techniques are required to capture subtle neuroanatomical patterns critical for distinguishing these disorders in sMRI. Deep learning (DL) has emerged as a transformative tool in neuroimaging analysis, offering the ability to automatically extract intricate features from large datasets. Building on its success in other domains, including autism spectrum disorder and Alzheimer's disease, DL models have demonstrated the potential to detect subtle structural changes in BD and SCH. Recent advancements suggest that DL can outperform traditional statistical methods, offering higher classification accuracy and enabling the differentiation of complex psychiatric disorders. In this context, this study introduces a novel deep learning framework for distinguishing BD and SCH using sMRI data. The model is specifically designed to address subtle neuroanatomical differences, offering three key contributions: (1) a tailored DL model that leverages explainability to extract features that boost psychiatric MRI analysis performance, (2) a comprehensive evaluation of the model's performance in classifying BD and SCH using both spatial and morphological analysis together with classification metrics, and (3) detailed insights, which are derived from both quantitative (performance metrics) and qualitative analyses (visual observations), into key brain regions most relevant for differentiating these disorders. The results have achieved an accuracy of 78.84%, an area under the curve (AUC) of 83.35%, and a Matthews correlation coefficient (MCC) of 59.10% using the proposed framework. These metrics significantly outperform traditional machine learning models. Furthermore, the proposed method demonstrated superior precision and recall for both BD and SCH, with notable improvements in identifying subtle neuroanatomical patterns. Depending on the acquired result, it can be said that the proposed method enhances the application of DL in psychiatry, paving the way for more objective, non-invasive diagnostic tools with the potential to improve early detection and personalized treatment.  
keywords: bipolar; deep learning; psychological disorders; schizophrenia; ResNet50; structural MRI; Grad-CAM; Bayesian-optimized network
## 2 Weather Nowcasting
Several industrial players have also shown an interest in the problem of weather forecasting and have developed various solutions in this direction: IBM Deep Thunder [15] aims to provide high-resolution weather prediction for a variety of applications, Panasonic Global 4D Weather [16] is a weather forecasting platform that uses Panasonic patented atmospheric TADMAR sensor collected data, ClimaCell company [17] offers weather forecasting solutions tailored for various weather-sensitive industries while TempoQuest [18] is a company putting on the market a proprietary forecasting software for commercial users and government agencies.
# Error Probability of a Coherent \(M\)-ary PSK FSO System Influenced by Phase Noise
## 3 Error Rate Analysis
### Asymptotic Analysis  
For finding a limit when \(\tilde{\gamma}\to+\infty\), we need to re-examine the derivation of (17). Namely, to obtain this equation, it is necessary to perform an integration step in (14), or more precisely, in (11), under the assumption that the average SNR tends to infinity. Strictly mathematically speaking, the integral (11) itself is an improper integral of the first kind that is in Riemann sense defined as the limit of proper integral when the upper bound tends to infinity. This in turn presents a problem, as we need to find a double limit  
\[f_{\psi}(\psi)=\lim_{h,\gamma\to+\infty}\int\limits_{0}^{h}f(\psi|\gamma)f_{ \gamma}(\gamma,\bar{\gamma})\,\mathrm{d}\gamma. \tag{33}\]  
In the previous equation, instead of \(f_{\gamma}(\gamma)\), we use notation \(f_{\gamma}(\gamma,\bar{\gamma})\) in order to emphasize that PDF depends on average SNR. On the other hand, when average SNR tends to infinity, according to (5), the probability of any other value being realized becomes infinitesimally small, as  
\[\lim_{\gamma\to+\infty}G_{1,3}^{3,0}\left(\alpha\beta\kappa\cdot\frac{g+\Omega ^{\prime}}{g\beta+\Omega^{\prime}}\cdot\frac{\gamma}{\bar{\gamma}}\left| \xi^{2}_{\alpha,\kappa}\right.\right)=0,\quad 0\leq\gamma<+\infty, \tag{34}\]  
while, for \(\gamma\to+\infty\), it is indeterminate. Therefore, probability density function \(f(\gamma,\bar{\gamma})\) has the property of  
\[\lim_{\gamma\to+\infty}f(\gamma,\bar{\gamma})=\begin{cases}0,\quad\gamma<+ \infty\\ \Phi,\quad\gamma\to+\infty\end{cases}, \tag{35}\]  
where \(\Phi\to+\infty\), in order to satisfy a probability normalization condition. A specified value for \(\Phi\) stems from the indeterminate nature of a corresponding limit in (34), which permits this value to be arbitrary, including an infinite value. The property is equivalent to the following statement:  
\[\lim_{\bar{\gamma}\to+\infty}f_{\gamma}(\gamma,\bar{\gamma})=\lim_{\bar{\gamma }\to+\infty}\delta(\gamma-\bar{\gamma}), \tag{36}\]  
where \(\delta(\cdot)\) represents Dirac's delta function. Then, we can write (33) as  
\[f_{\psi}(\psi)=\lim_{\bar{\gamma}\to+\infty}\int\limits_{0}^{+\infty}f(\psi| \gamma)\delta(\gamma-\bar{\gamma})\,\mathrm{d}\gamma=\lim_{\bar{\gamma}\to+ \infty}f(\psi|\bar{\gamma}) \tag{37}\]  
Therefore, when \(\bar{\gamma}\to+\infty\), Equation (11) tends to  
\[f_{\psi}(\psi)=\frac{1}{2\pi}+\sum_{n=1}^{+\infty}\cos(n\psi)\lim_{\gamma\to+ \infty}a_{n}(\gamma) \tag{38}\]  
According to definition in (10), and ([34], (07.20.06.0007.01)), we have  
\[b_{n}=\lim_{\gamma\to+\infty}a_{n}(\gamma)=\frac{1}{\pi}, \tag{39}\]  
which proves the proposition when SNR tends to infinity. Then, \(f_{\psi}(\psi)=\delta(\psi)\). In that case, when SNR tends to infinity, based on (22), SEP can be estimated as  
\[p_{s}^{\text{floor}}=1-\frac{1}{M}-\sum_{n=1}^{\infty}\frac{2c_{n}}{n}\sin \left(\frac{n\pi}{M}\right). \tag{40}\]
# Research on Pore Pressure Detection While Drilling Based on Mechanical Specific Energy
## 2 Theory  
[PERSON] defined MSE as the energy required to break and remove a unit volume of rock by the bit, which is related to the strength of the rock (Equation (21)). It was confirmed by experiments that the minimum MSE is roughly equal to the compressive strength of the material drilled [17]. The effective stress is also related to the compressive strength of the rock [39]. Moreover, the dependence of MSE on pore pressure was experimentally verified in rock samples [40; 41]. Therefore, the MSE obtained while drilling can reflect the effective stress of the rock to some extent. The higher the effective stress, the greater the strength of the rock, and the more MSE is required. At the same depth, overpressure intervals with lower effective stress require less MSE than normal pore pressure intervals, and then the pore pressure can be reflected indirectly.  
\[MSE=\frac{WOB}{A_{\text{b}}}+\frac{120\pi\times N\times T}{A_{\text{b}}\times ROP} \tag{21}\]
# Open Radio Access Networks For Smart IoT Systems: State of Art and Future Directions
## 2 Background
### Traditional Radio Access Network (RAN)  
The RAN, popularly known as the access network, is the radio component of the cellular network. It is an essential element of a wireless telecommunication system that employs radio links to connect each device to other network sections. The RAN connects user equipment (UEs) over a fiber or wireless backhaul connection, such as a phone, computer, or any remotely operated machine. From the first generation (1G) of cellular networking to the fifth generation (5G), RANs have undergone several developments. The core network and radio access network underwent a significant change due to the introduction of long-term evolution (LTE) RAN by the Third Generation Partnership Project (3 GPP) in the 2000s with the advent of fourth-generation (4G) technology. With 4G, system connectivity was replaced by circuit-based networks for the first time and was based on the internet protocol (IP). The first cellular networks were developed in the early years of the 2000s. Text messages, voice calls, streaming video, and audio have all become part of RAN's capabilities. Many more types of UEs use these networks, including autonomous cars, drones, and IoT devices.  
Despite these remarkable advancements, traditional RANs are monolithic nowadays (i.e., inflexible, protocol-based, proprietary-driven), and inefficient in handling a number of vast dynamic requirements brought by 5G and future 6G. This calls for open, flexible, intelligent, software-driven connectivity solutions capable of handling these requirements and resolving the traditional RAN's deficiencies in a more cost-effective and energy-efficient manner. To address the shortcomings posed by RAN architecture, a number of research and standardization initiatives proposed O-RAN as the new paradigm shift for the future of RAN. Notwithstanding, O-RAN implementations use software-based, virtualized, and disaggregated components that are interoperable across multiple vendors and connected by open, standardized interfaces [37; 38].