esa-sceva/satcom-chunk-collection huggingface dataset:
# Flow-Induced Strength Analysis of Large Francis Turbine Under Extended Load Range
## 2 Methodology and Mathematical Physics Equations
### Fluid-Induced Structural Dynamics Governing Equations  
With the calculated results by the fluid dynamics analysis, the fluid-induced force \(\left\{\,F^{f}\,\right\}\) acting on the structure surfaces \(S\) of the pumped-storage unit can be obtained by  
\[\left\{\,F^{f}\,\right\}\,=\,\iint_{S}p\,dS \tag{17}\]  
Taking the flow-induced force \(\left\{\,F^{f}\,\right\}\) applied on the pumped-storage unit into account, the fluid-induced structural dynamics equation can be expressed as  
\[[M^{s}]\{\,a\,\}\,+[D^{s}]\{\,v\,\}\,+[S^{s}]\{\,d\,\}\,\,=\,\,\{F^{s}\,\}\,+ \left\{\,F^{f}\,\right\} \tag{18}\]  
where \([M^{s}]\), \([D^{s}]\), and \([S^{s}]\) denote the mass, damping, and stiffness matrices of the structures for the pumped-storage unit, respectively. \(\{\,a\,\},\{\,v\,\}\), and \(\{\,d\,\}\) represent the acceleration, velocity, and displacement vectors of the unit structures, respectively. \(\{\,F^{s}\,\}\) is the external force acting on the unit structures but excludes the flow-induced force \(\left\{\,F^{f}\,\right\}\).  
The structural displacement \(\{d\}\) of the unit structures can be obtained by solving Equation (18), and the flow-induced stress \(\{\sigma\}\) of the structures for the pumped-storage unit can be obtained by the following equation:  
\[\{\sigma\}\,\,=\,\,[E^{s}]\Big{[}B^{d}\Big{]}\{d\} \tag{19}\]  
where \([E^{s}]\) and \([B^{d}]\) are the elasticity matrix and the strain-displacement matrix of the structures for the pumped-storage unit.  
With the maximum, middle, and minimum principal stresses (\(\sigma_{1}\), \(\sigma_{2}\) and \(\sigma_{3}\)), the equivalent stress \(\sigma_{eq.}\) can be calculated by Equation (20) to investigate the flow-induced stress behavior of the structures for the pumped-storage unit, including the head-cover bolts and bottom-ring bolts:  
\[\sigma_{eq.}=\sqrt{\frac{(\sigma_{1}-\sigma_{2})^{2}+(\sigma_{2}-\sigma_{3} )^{2}+(\sigma_{3}-\sigma_{1})^{2}}{2}} \tag{20}\]
Ex Vivo Fusion Confocal Microscopy of Liver Biopsies: Diagnostic Pattern Identification and Correlation with Conventional Microscopy  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON]  
1 Pathology Department, Hospital Clinic Barcelona, C/Villarroel 170, 08036 Barcelona, Spain; [EMAIL_ADDRESS] (S.L.-P.) [EMAIL_ADDRESS] (C.F.-A.)
2 Faculty of Medicine, University of Barcelona, C/Casanova 143, 08036 Barcelona, Spain; [EMAIL_ADDRESS] (O.B.)
3 Institut d'Investigacions Biomediques August Fri Sunyer (IDIBAPS), C/Rosello 149, 08036 Barcelona, Spain; [EMAIL_ADDRESS] (O.B.)
4 Barcelona Clinic Liver Cancer (BCLC) Group, IDIBAPS, C/Rosello 149, 08036 Barcelona, Spain
5 Centro de Investigacion Biomedica en Red de Enfermedades Hepticas y Digestivas (CIBERehd), Av. Monforte de Lemos, 3-5, 28029 Madrid, Spain
6 Hepatobilipancreatetic Surgery and Liver and Pancreatic Transplantation Unit, Department of Surgery, Hospital Clinic Barcelona, C/Villarroel 170, 08036 Barcelona, Spain
7 Liver ICU, Liver Unit, Hospital Clinic Barcelona, C/Villarroel 170, 08036 Barcelona, Spain
Ex vivo Fusion Confocal Microscopy (eFuCM) VivaScope 2500M-G4 (MAVIG GmbH, Munich, Germany) has emerged in response, enabling real-time, high-resolution digital Hematoxylin-Eosin (H&E)-like imaging of tissue specimens with cellular and subcellular detail [4]. It does not waste tissue, keeping all sample intact for further FFPE, H&E, histo- and immunohistochemical stains and/or molecular analysis, since it does not alter the tissue composition. In recent years, the non-invasive nature of eFuCM has allowed for faster intraoperative decision-making and simplifying intraoperative samples workflow as it reduces the need for traditional frozen section evaluation [4; 5; 6; 7; 8].  
eFuCM is mainly used in Mohs intraoperative surgery for the assessment of the surgical margins of skin tumors and has shown good results [6; 7; 9]. It has also been used in the diagnosis of prostate, kidney and breast cancer with promising results [10; 11; 12; 13; 14; 15; 16; 17].  
Regarding the liver, few studies have been conducted to date. The initial studies involved a limited number of cases of liver pathology focused on neoplastic pathology, although they did not define it precisely [18]. Other studies have examined the utility of eFuCM in the transplantation setting [19], with only one study attempting to explore its usefulness in non-tumoral parenchyma by comparing different types of samples, both surgical and biopsy specimens [20]. None of these studies have considered the experience of the professional reading the eFuCM images.  
The aim of our study was to evaluate the performance of Ex vivo Fusion Confocal Microscopy on liver biopsies for the identification of basic lesions and patterns and the evaluation of the tissue integrity as well as to determine the influence of eFuCM image reading experience among pathologists.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 3 Limitations and Challenges
### Smart Grid Challenges
\\ \cline{2-3}  & \multirow{8}{*}{Electromagnetic Radiation} & If not well studied and regulated, it proves to be a challenge since smart-grid systems rely on wireless communications, which emit electromagnetic radiation that can potentially cause a health risk to humans and animals alike, especially if the exposure area is high and in the presence of individuals with certain medical conditions. \\ \cline{2-3}  & \multirow{8}{*}{Electrical Hazards} & This remains challenging since they can target workers/operators and the public. They are linked to fire, electric shock, electrocution, and arc flash, which can release hazardous materials and radiation. \\ \cline{2-3}  & \multirow{8}{*}{Privacy Challenges} & More particularly, data privacy, which is frequently compromised in smart grid applications, especially when transferred in real time. This often proves to be a safety and security challenge since the disclosure of information can expose operators and public personnel. \\ \cline{2-3}  & \multirow{8}{*}{Cyber–Physical Challenges} & The cyber part is frequently linked to a cyberattack that interferes with the smart grid’s performance. As for the physical part, it is more prone to physical damage such as vandalism, theft, or terrorism. Securing them proves to be challenging since it requires a combined cyber–physical security effort to evade such malicious events. \\ \cline{2-3}  & \multirow{8}{*}{Cyber–Physical Challenges} & The presence of complex networks of interconnected systems and device types to ensure effective and safe communications proves to be challenging as it is a critical task to maintain the smart grid’s safety and reliability. If not well established, this may reduce its efficiency and effectiveness. \\ \cline{2-3}  & \multirow{8}{*}{Cyber–Physical Challenges} & The cyber part is frequently linked to a cyberattack that interferes with the smart grid’s performance. As for the physical part, it is more prone to physical damage such as vandalism, theft, or terrorism. Securing them proves to be challenging since it requires a combined cyber–physical security effort to evade such malicious events. \\ \cline{2-3}  & \multirow{8}{*}{Cyber–Physical Challenges} & The presence of complex networks of interconnected systems and device types to ensure effective and safe communications proves to be challenging as it is a critical task to maintain the smart grid’s safety and reliability.
## 3 Results and Analysis of Influencing Factors
### The Influence of Frequency on Signal Attenuation  
Using the model in Figure 3, the drill string is set to be 240 m in length, with an inner diameter of 46 mm and an outer diameter of 76 mm. The casing is 240 m in length, with an inner diameter of 124 mm and an outer diameter of 154 mm. The electrical conductivity of both the drill string and casing is 1.12 \(\times\) 10\({}^{7}\) S/m. The drilling fluid's resistivity is set at 1 \(\Omega\cdot\)m with a relative permittivity of 80, while the formation resistivity is set at 10 \(\Omega\cdot\)m. The helical loop antenna is installed at a position 10 m above the bottom of the drill string. A magnetic current excitation of 1 V is applied, and the frequency range of the short transmission signal studied is set from 10 Hz to 10,000 Hz. The relationship between transmission frequency and received magnetic field amplitude is shown in Figure 4a. According to: \(E=N\mu HS2\pi f\), Let \(k=2\pi N\mu S=1\), the relationship between the transmitting frequency and the signal is as shown in Figure 4.  
Frequency has a significant impact on the propagation of electromagnetic waves, especially higher frequencies leading to more severe attenuation. For instance, over a propagation distance of 100 m, as the transmitting frequency increases from 10 Hz to 10,000 Hz, the signal attenuation rapidly climbs from 33.1 dB to 233 dB. This indicates that using extremely low frequencies for transmission is more appropriate when covering longer distances. However, at shorter transmission distances, such as 70 m or 40 m, high-frequency transmission demonstrates its advantages.  
Figure 4: The influence of frequency on signal attenuation.  
When electromagnetic wave signals propagate through a cased well, reducing the transmitting frequency is an effective way to increase the signal's propagation distance. Although high-frequency electromagnetic signals have limited transmission distances, they exhibit advantages unmatched by low-frequency signals for short-range transmission. In practical applications, it is necessary to consider both transmission distance and rate, finding a balance between frequency and transmission power. In this study, after analysis, the frequency of 1000 Hz has been chosen as the standard frequency, which meets the transmission distance requirements of the researched content. All subsequent model experiments will use this frequency unless otherwise specified.
## 2 Related Works  
### Static HIN Embedding  
Static information networks include static homogeneous information networks and static HINs. In traditional homogeneous information embedding methods [7], the nodes are represented as low-dimensional dense vectors. Representative models include DeepWalk [11], which is a combination of random walk, skip-gram, and LINE [12], which comprehensively considers the similarity of first-order and second-order neighbors. The emergence of Graph Neural Networks (GNN [13]) has accelerated the development of static homogeneous network embedding, typical representatives such as GAT [14], which uses an attention mechanism to perform weighted summation of adjacent node features; GraphSAGE [15] uses an inductive learning approach for training to adapt to large-scale network embeddings; SDNE [16] uses deep auto-encoders to extract network structures and nonlinear features. There are also many methods to improve node representation by using the content of nodes or text, images and labels, etc. Some review articles comprehensively summarize the work in this area [17; 18].  
The homogeneous information network often only extracts part of the information of the real network or does not distinguish the differences between objects and relationships in the real network, which can be regarded as a simplification of the real information network [19]. To model the real-world network more naturally, more and more researchers have focused on the analysis of HINs [8; 9; 20], the most classic of which is the Metapath2 vec [8], which is based on meta-path-guided random walks. Metapath2 vec utilizes domain knowledge, the meta-paths are pre-defined to guide random walk sampling to deal with the heterogeneity of the graph, and the representations of nodes are learned through Skip-Gram. HIN2 Vec [9] realizes the performance of the prediction task while learning the representation vectors of the nodes and meta-paths. SHNE [20] jointly optimizes Skip-Gram and deep semantic coding to capture structural and non-structural semantic relations. GNNs have also made great progress in static HIN embedding [21]. The core idea of neural graph networks is to aggregate the feature information of neighbors through a neural network to learn node-independent information and corresponding structural information. Typical representatives include the relational graph convolutional network (RGCN) [22] and the heterogeneous graph attention network (HAN) [23].
# Popularity-Aware Closeness Based Caching in NDN Edge Networks
## 2 Background and Motivations  
### NDN in a Nutshell  
In NDN, caching operations are embedded in the forwarding process of the Interest and Data packets, which is shown is Figure 1.  
At the reception of an Interest packet, the node looks in the CS and, in case of a name matching, it immediately sends the Data back. Vice versa, the node checks if an equal  
Figure 1: Forwarding process in NDN.  
request is pending in the Pending Interest Table (PIT) and, in case of a positive outcome, the incoming interface of the Interest is added to the PIT entry and the packet is discarded, thus reducing the traffic in the network. If the PIT matching fails, the Interest is forwarded according to named-based forwarding rules towards the original source.  
When the Data packet arrives, the node checks for a name matching in the PIT and, in case of a positive outcome, the packet is cached and then forwarded towards the consumer(s). Vice versa, if the PIT matching fails, the Data is considered unsolicited and it is discarded.  
The vanilla NDN caching strategy, CEE, typically leads to poor performance due to the lack of cache diversity [2]. A conventional scheme that limits the cache redundancy without introducing much complexity is Fixed Probability-based caching, where the Data packets are cached according to a fixed probability, usually set to 0.5 [9].
## 4 Proposed Method  
The intelligent fault diagnosis real-time procedure starts with obtaining the _abc_ stator currents from SC-IM with various states of combined ITSC and BRB faults. The SC-IM operates at 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100% of loading conditions with one BRB, two BRB and different ITSC faults: 2%, 5%, 7%, and 10% of ITSC faults. The _abc_ currents are converted to _qd_ signals. DWT pre-processed the _qd_ signals. The low-frequency component is used as input data in the ANFIS model. The target of faults is shown in Table 1. The data set is used to train and test the ANFIS technique. A full schematic illustration of the work is given in Figure 3. A Fuzzy Logic Controller (FLC) model is built on Matlab Simulink to diagnose the SC-IM faults. The ANFIS based on the DWT method is compared to ANFIS without data pre-processing and ANFIS based on an auto-regressive model. ANFIS based on DWT realized higher performance. Several experimental tests are performed to diagnose combined ITSC and BRB faults in SC-IM under different loading conditions.
## Materials and Methods
### Determining Rates of Shore Erosion and Shoreline Location Modeling  
In order to determine the net changes of shoreline and annual rates of shore erosion, the shoreline location was specified using ortho-mosaic compiled from UAV-based surveys during 2018 to 2019. Multi-temporal ortho-mosaics were manually co-registered to align each other with one-to-one-pixel accuracy. Ground control points (GCPs) were set as unchanged objects in the coastal zone such as large stones, notable trees, road crossings, or artificially-coloured ground markers.  
The shoreline positions on different dates were evaluated using modified Hausdorff distance as a measure of distance between two trajectories. This technique is widely used in geographical research for assessing distances between two complex linear objects [51,52]. Modified Hausdorff distances were calculated in ArcGIS Model Builder using an algorithm based on successive calculation of the shortest distances between shoreline positions. Additionally, a transition matrix was constructed of calculated distances between shoreline positions on different dates. As a result, the attribute table of modified Hausdorff distances also includes information specifying the type of coasts for each distance.  
Shoreline position modeling within the framework of Roadmap for Lake Water Level Raising [34] was performed using DSMs compiled from UAV-based surveys. Since the shoreline positions show intra-annual movements, we modeled them in increments of 10 cm. Modeling was performed using a Water Rise Calculation Tool in Global Mapper software. The modeling results enabled identification of flooding zones and assessment of the size of built-up areas at risk of submerging and waterlogging.
## 3 Experimental Data and Results
### Utility of Environmental Sensors in Mobile Telephony in the Direct Communication Process
The thresholds set in the application have different iterative repetitive loops for each color gradient, thus obtaining a response from the extraction performed. Through the received light intensity, we can see that we also have a sufficient amount of data to be decrypted. There is also the possibility that the data may become false positive in terms of external lights, headlights, braking lights and street lighting. In this case, the use of optical filters is imperative when combined with hardware filters (optimized high-pass filter or low-pass filter). The existence of a high degree of saturation or parasitic light can distort or congest the communication channel, but in this case, the application of an optical filter to eliminate the noise of the signal becomes necessary. This approach was also applied in the case of external scenarios between vehicles (V2V), having an 80 nm bandpass optical filter with a central wave of 645 nm. It was suitable for the given situation because it was also tested in conditions of exposure to light sources, including the red one redirected from the external environment (traffic lights, brake lights, street advertisements). This filter can eliminate up to 85% of ambient optical noise. At the next stage, the inclusion of an optical decoder based on the PDA100A PIN photodiode is considered, but this version would mean the standard modification and the transition to an implementation that includes the outsourcing of the portable device and the inclusion of new modules. Thus, the optical decoder generates an electric current that depends directly on the power of the incident light, then the internal circuit transforms the electric current into an operable voltage so that information can be decoded. In this case, the analyzed information was shaped around the established intensities, only by using DroidStudio, and data-frame-type synchronization headers were created to be able to extract the data through the modulation used. Therefore, the data presented provide a qualitative and quantitative perspective on the long-term implementation of this solution. Direct communication between portable devices and infrastructure is limited because most pre-installed components are in an adapted version and do not have the characteristics of commercial ones dedicated to industrial implementations. Currently, the results are satisfactory in the conditions in which there was no direct access to a traffic light system, this being the next step in which an autonomous intersection will be implemented that can communicate directly with traffic participants and pedestrians.
# Optical Technologies Supporting 5G/6G Mobile Networks
## 2 An Overview of Published Articles
and titled \"A Distance-Weighted Dynamic Bandwidth Allocation Algorithm for Improved Performance in Long-Reach Passive Optical Networks for Next Generation Networks\" [33], presents the Distance-Weighted Bandwidth Allocation (DWDBA) algorithm, which significantly increases the efficiency of the use of optical resources in Long-Reach PONs (LRPONs). During planning, the algorithm establishes appropriate weight vectors for the Optical Network Units (ONUs) based on its distance from the Optical Line Termination (OLT)headend. It was assumed that the LRPON network operates on a maximum of four optical channels, necessitating the tuning of the laser as an optical carrier source in accordance with the assumed WDM grid. Simulation results showed that the proposed DWDBA algorithm outperforms the traditional Interleaved Polling with Adaptive Cycle Time (IPACT) algorithm, assuming that the network range does not exceed 100 km. In order to assess the effectiveness of the DWDBA algorithm, two metrics were used, i.e., delay resulting from waiting in the queue and available bandwidth. Simulations were carried out using OPNET modeler software for four distance scenarios, where the differential distance between ONUs did not exceed 50 km. The future of 5G/6G mobile systems is not only radio communication. The IEEE 802.11 bb standard is now available, which is based on optical wireless communication. In this context, there are basically no significant limitations due to the photonic ranges used, but Visual Light Communication (VLC) systems are gaining the most popularity. The optical communication system from the 802.11 family has been referred to as mentioned before Li-Fi. Since Li-Fi systems operate in the near-infrared range, they can successfully cooperate with optical systems based on single-mode and multimode glass fibers. The fifth paper, authored by [PERSON] et al. and titled \"Investigation of OFDM-Based HS-PON Using Front-End LiFi System for 5G Networks\" [34], presents an example of a hybrid solution in which Li-Fi communication is effectively combined with the transmission of optical signals in the High-Speed PON (HS-PON) link. It was assumed that the Orthogonal Frequency Division Multiplexing (OFDM) technique, combined with 16-order band modulation and 4-channel multiplexing in the WDM format (for each direction separately), was used in the optical HS-PON link, and a simple 2-order modulation was used in the Li-Fi link. The simulation was performed with the OptiSystem v.21 software. The simulation results were presented in graphs for each of the links, both fiber optic and wireless, as well as for the total. Simulations were performed for various lightpath lengths, ranging from 10 to 100 km. A similar approach was used for the wireless Li-Fi link length up to 20 m. Based on the simulation and analysis results, it can be concluded that the proposed hybrid HS-PON/Li-Fi system can be used in future 5G/6G mobile networks to transport data more effectively with increased resistance to electromagnetic interference.