esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Behavior of an Embedded Foundation under Horizontal Vibration in a Poroelastic Half-Space
## 3 Results and Discussion
### Effect of Poroelastic Material Parameters  
In order to investigate the effect of poroelastic material parameters under horizontal dynamic interaction, Figure 8 shows the dynamic impedance of embedded foundation versus \(a_{0}\) for \(L/R=1\). The only difference is the poroelastic material parameter. As it is indicated, the real part of dynamic impedance increases with the increasing poroelastic material parameters. As we proved, the increasing speed of the real part decreases when the relative value is larger than 100. The real part of dynamic impedance seems overlapped when a\({}_{0}\) is smaller than 1.5, and the increasing speed differs when a\({}_{0}\) is larger than 3. The imaginary part of dynamic impedance decreases with the increasing poroelastic material parameters when the poroelastic material parameters are smaller than 5 and then increases at a larger relative ratio. The imaginary part seems to increase to a maximum value at about \(a_{0}=1.5\) and then decreases for higher frequencies. As expected, both the real and imaginary part of all impedance shows the strong influence of the poroelastic material and the influence of excitation especially when \(10<M<100\) and \(a_{0}>1.5\). The parametric study indicates that both the real and imaginary part of the horizontal impedance component are independent when \(a_{0}<1.5\). However, it is affected by the poroelastic material properties significantly when \(a_{0}>1.5\).  
The dynamic response factor of embedded foundation affected by the poroelastic material properties versus \(a_{0}\) is depicted in Figure 9. It can be noticed that the dynamic response factor decreases with the increasing poroelastic material properties modulus. By increasing the poroelastic material properties, the dynamic response factor decreases obviously when the relative ratio is lower than 50. This decrease in value is very small when the relative ratio is larger than 100. The dynamic response factor decreases with the increase in frequency. In the case of frequency \(a_{0}<1.5\), the dynamic response factor for different poroelastic material properties are the same. The effect of poroelastic material properties pronounced for higher frequency. The dynamic response factor shows little dependence on M and when \(a_{0}>1.5\), the dynamic response factor decreases with the increasing M. Moreover, the dynamic response factor decreases with the increase in frequency.  
Figure 8: Horizontal dynamic impedance of embedded foundation under different poroelastic material properties.
# Hiding Stealth Optical CDMA Signals in Public BPSK Channels for Optical Wireless Communication
## 1 Introduction
The other investigation uses two joint routing and resource allocation algorithms for stealth and ordinary services in optical networks. The technique improves the success rate of optical steganography by increasing more wavelength consumption [22]. In this study, the stealth signal was encoded using a spectral-polarization-coded OCDMA (SPC-OCDMA) technique, which employs polarization beam splitters (PBSs) and arrayed waveguide gratings (AWGs) as the encoders and decoders. Chirped fiber Bragg gratings (CFBG) were used as the pulse stretcher and compressor to implement optical steganography. To restore the stealth signal, the public signal was removed using the balanced correlation detection/subtraction mechanism conventionally adopted in OCDMA decoders. A binary phase-shift keying (BPSK) demodulator was used to reject stealth OCDMA interference and system noise. Thus, the stealth signal and noise affect the public network only slightly. The remainder of this paper is organized as follows. In Section 2, the theories and principles of the proposed optical steganography scheme is described. In Section 3, the proposed optical steganography configuration setup and simulation results are introduced. In Section 4, system performance with stealth signal in the proposed system is interpreted, and brief conclusions are presented in Section 5.
# Clustering-Based Energy-Efficient Self-Healing Strategy for WSNs under Jamming Attacks
## 7 Main Findings  
From the attacker's point of view, the positions located near the contour of the network have certain benefits. In these positions, the attacker does not need to gain physical access to the property to perform the attack or deploy the jammer node. Therefore, scenarios four and seven extrapolate this condition and its impacts. Additionally, on average, the results show that the sixth scenario is the most detrimental to the WSN, with only 313,341 kb of information transmitted. On the other hand, the first scenario is the one with less residual energy at 56.66%, but the information transmitted is 629,810 kb. Considering the average results of the strategy on the analyzed scenarios, the power control domain surpasses the cooperation domain. The information transmitted for FP and PA is 571,963 kb versus the 137,976 kb and 215,354 kb by CP and PAC, respectively. Nevertheless, the residual energy of FP is very low, with a final value of 2.06%. Consequently, the self-healing strategy selection must consider the trade-off between WSN performance metrics.  
FCPA stands out among self-healing strategies with 967, 961 kb of information transmitted and 63% of residual energy on average. Whenever FCPA was used, the first node to exhaust its battery was a cluster head, demonstrating the high energy efficiency of this strategy. The integration of multiple techniques that increase the efficient use of resources is essential in IoT networks with devices with limited resources. On the other hand, UAVs can assist self-healing strategies by data collection from small clusters.  
To summarize the results acquired from both metrics, we tabulate the data in Table 3 and show results in Figures 16 and 17.  
For all the scenarios, the FCPA strategy exhausts the batteries of the CH nodes. Therefore, it reaches the maximum information transmitted, while the residual energy is always over 50% for all scenarios.  
Figure 15: Seventh scenario: where, exceptionally, there exists a node that cannot communicate with any neighbor node.  
Figure 16 shows the results from the jammer in the positions moving horizontally, while Figure 17 shows the results from the jammer in the positions moving diagonally. The algorithms FP, PA, CP, and PAC have a poor tradeoff since they cannot distribute the load evenly, so the best performance was always achieved by FCPA. In Figure 17, the tradeoff is worse than in Figure 16. Consequently, the jammer moving into a formed cluster notably impacts the integrity of the cluster and its communications.
# Scale-Adaptive Simulation of Unsteady Cavitation Around a Naca66 Hydrofoil
###### Abstract  
The present paper focuses on the numerical simulation of unsteady cavitation around a NACA66 hydrofoil to improve the understanding of the cavitation effects on hydraulic machinery. For this aim, the Zwart-Gerber-Belamri cavitation model was updated and uploaded as a library file for OpenFOAM's solvers using C++ language. Furthermore, the hybrid Reynold average Navier-Stokes (RANS)-large eddy simulation (LES) model \(k-\omega\) SST scale adaptive simulation (SAS) was implemented as a turbulence model for the present study of scale adaptive simulation. For validation, numerical results were compared with experimental results obtained by [PERSON] at the Naval Academy Research Institute in France. In order to highlight the benefits of computational consumption and reproduction of the phenomenon the \(k-\omega\) SST SAS model was compared against implicit large eddy simulation (ILES). Results show that the cavitation evolution including the maximum vapor length, the detachment and the oscillation frequency were reproduced satisfactorily using \(k-\omega\) SST SAS. Moreover, \(k-\omega\) SST SAS results predicted a lower total vapor volume on time than ILES, which is related to observed pulses of pressure coefficient, \(C_{\rm p}\), and those match fairly well with the experimental results. To summarize, the \(k-\omega\) SST SAS model predicts with good accuracy unsteady cavitation behavior around hydrofoils and shows improved versatility over the ILES approach.  
Keywords:\(k-\omega\) SST SAS; LES; OpenFOAM; unsteady cavitation +
Footnote †: journal: Applied Physics Letters
## 5 Flying Base Station Capacity Gain Benefit  
We study the capacity benefit of utilizing a flying base station. The flying base station, while amplifying the coverage and communication performances, operates in addition to the stationary base station and cannot replace it. The stationary base station is required even if the user's direct communication link is from/to the flying base station, as the flying base station relays to the stationary base station for backend/internet access. The user can receive the connectivity from the stationary base station or the flying base station. We study if/when the flying base station is beneficial to use over the stationary base station by providing greater channel performance.
# A Novel Application Based on a Heuristic Approach for Planning Itineraries of One-Day Tourist
## 2 The One-Day Tourist Itinerary Planning Problem
Different trip solutions can be provided by the application based on the user choice regarding the stop time at each PoI and preferred way of transport: (1) fastest, (2) by foot, (3) by metro/bus. The tourist is also asked to indicate the starting and destination points of the roundtrip, that are different from the origin/final point (i.e., the port), as well as the time deadline for the roundtrip. In addition, the tourist is asked to decide which PoIs to be visited in the first and second part of the day, indicating the priority and the desired time to dedicate to the visit. On this basis, the application try to generate the customized outward and return tours of the day by applying the heuristic procedure presented in Section 3. If the deadline time both for the outward and return trips are respected, the heuristic procedure investigates the addition of secondary importance PoIs and generates the final roundtrip itineraries. On the contrary, if the deadline time of the outward and/or the return tours is violated, the heuristic procedure can exchange PoIs between the first and the second tour. In case some feasible solutions are determined, i.e., the deadlines of the outward and return trips are satisfied, the procedure delegates to the user the choice of a solution from a list created by the application. Afterwards, the heuristic procedure determines the final customized outward and return tours of the day including possible addition of secondary PoIs. Finally, if the deadline time of the outward and/or the return tours is violated and no feasible solution is achievable, a PoI deletion procedure must be implemented in order to respect the deadline travel time. Let us summarize the necessary input and output information and data of the proposed itinerary planning application:Initial inputs from the user:  
* starting and ending PoI;
* deadline time both for the outward and return trips;
* stop times;
* priority list of PoI;
* list of PoIs both for the outward and return trips;
* preferred mode of travel;  
Moreover, other inputs are required to the user from the application while running in order to refine the roundtrip customization as described in detail in Section 3. Real time inputs from the user:  
* preferred itinerary from a list of feasible solutions determined by the proposed automatic procedure. Outputs by the application:
* outward and return paths;
* outward and return travel times.
# Talent Supply and Demand Matching Based on Prompt Learning and the Pre-Trained Language Model
## 1 Introduction
These computational methods have shown promising results in specific contexts. However, significant challenges remain in refining these models to effectively address the complexity and dynamic nature of talent supply and demand matching in real-world scenarios. For instance, many demands and talent abilities are described in natural language [23; 24; 25], and manual processing methods (e.g., rule-based parsing [26], keyword extraction [27], and text categorization [28]) are insufficient for feature extraction from such unstructured data. Furthermore, existing methods commonly use talent evaluation indices (TEI) [29] to assess talent skills. However, TEI often relies on human expertise, which hinders the model's ability to uncover the true underlying patterns in raw data. Additionally, missing data in TEI can lead to biased learning and inaccurate model representations. To address the aforementioned challenges, we propose a novel approach, TSDM, for matching talent supply and demand. TSDM employs prompt learning [30] to extract talent ability and their corresponding demand information from the unstructured documents. Subsequently, a BERT-based pre-trained language model [31] is utilized to encode the talent ability and demand data, generating feature embeddings. The talent-ability-specific and demand-specific encoder are designed to capture the contextual relationships within these embeddings, along with a decoding network to learn the similarity between talent ability and demand. In a series of comparative experiments, TSDM demonstrated superior performance, highlighting the effectiveness of prompt learning and large language models in predicting talent supply and demand matching. In summary, TSDM has three main contributions as follows:  
1. Proposing a method that leverages prompt learning combined with large language models to extract highly discriminative descriptions of talent ability and demand information from the unstructured data. 2. Utilizing a pre-trained large language model (BERT) to generate feature embedding, that effectively capture the contextual relationships within the text, reflecting the nuanced dependencies and long-range interactions inherent in the data. 3. Leveraging talent-ability-specific and demand-specific encoding networks, which consist of 1D-CNN and Bi-LSTM, to capture both local and global representations of talent ability and demand, thus providing a comprehensive expression of these features.
## 2 Materials and Methods  
### Experimental Design  
To ensure reaction between peridotite and a migrating melt, the experiments were designed to contain an alkaline melt (lamproite) at one end of the capsule, peridotite in the middle, and a vitreous carbon melt trap at the other end of the capsule (Figure 1). The primary function of the melt trap is to facilitate movement of melt through the peridotite, which works due to the pore space within the trap _initially_ offering a lower pressure than in the rest of the capsule. Experiments were conducted with the melt trap at the top of the capsule so that melt movement had to occur against gravity. The ratio of vitreous carbon trap to silicate charge was kept low (0.035-0.046) to allow complete filling of the pore space with melt and subsequent equilibration of the melt: this allows equilibration of the melt within 24 h [18], circumventing issues with the attainment of equilibrium [19; 20; 21], which only occurs when the pore space in the trap is too large [18]. All experiments in this study were run for 24 h or more. A similar constellation of three layers was used for basalt/peridotite experiments, but with a larger melt trap that was not intended to collect melts for analysis [22; 23]. Graphite-lined Pt capsules were used to limit the loss of iron during the experiments: the Pt capsules had an inner diameter of 3.6 mm and the graphite liners had an inner diameter of 1.9-2.0 mm. Water was added to the charge, then the capsule was welded shut whilst cooled below 0 \({}^{\circ}\)C in an iced brass block. After welding, capsules were checked for leaks by heating at 110 \({}^{\circ}\)C for \(>\)30 min and re-weighing. The amount of water was \(\approx\)10 wt% for the lamproite and \(\approx\)3 wt% for the whole charge; this amount of water ensures that the lamproite is molten under the P-T conditions of the experiments [24]. One experiment (G141, 3 GPa, 1100 \({}^{\circ}\)C) was run with the peridotite and lamproite intimately mixed before the experiment to compare results with those of the layered configuration. This should produce similar mineral assemblages to the reaction zone, but in which minerals are easier to identify [25; 26]. Experiments were conducted at 2 and 3 GPa in standard piston-cylinder apparatus at the University of Gottingen as part of the Diploma thesis of [PERSON] [27]. Boron nitride was used for sample sleeves and NaCl as pressure medium apart from the 1250 \({}^{\circ}\)C experiment at 3 GPa, for which BaCO\({}_{3}\) was used as the melting point of NaCl was exceeded. Temperature was controlled to within \(\pm\)1 \({}^{\circ}\)C by an 818P Eurotherm controller and Pt/Pt\({}_{90}\)Rh\({}_{10}\) thermocouples and run durations varied between 24 and 132 h. After experiments, capsules were cut lengthwise with a WS22 wire saw and the resulting halves and thin sample discs were mounted in epoxy and polished with diamond paste. Details of experiments are listed in Table 1.
# Development of Attached Cavitation at Very Low Reynolds Numbers from Partial to Super-Cavitation
## 4 Conclusions  
In the present article, the development of attached cavities under cavitating conditions in a highly degassed silicone oil has been explored, at Reynolds numbers in the range \(Re\in[800;2000]\). The regime map in terms of Reynolds and cavitation numbers have been established. Four different regimes have been observed: type-1 small cavities that are almost steady, type-2 periodically shrinking cavities of intermediate sizes, type-3 vortex bursting with no clear period and type-4 supercavities. For \(Re\leqslant 1000\), the background flow is steady and laminar and the cavitating flow gives rise to steady attached cavities whose length slowly increases with a decrease of the cavitation number. The first footprint of unsteadiness in the cavitating flow consists of the type-2 periodic regime. It appears for a sufficiently great Reynolds number (\(Re\geqslant 1200\)), first in a very narrow range of cavitation numbers and then in a wider range as \(Re\geqslant 1400\). The frequency is almost proportional to the velocity of the upstream flow and gives a constant Strouhal number of very small value. More modal analysis with POD (Proper Orthogonal Decomposition) or DMD (Dynamic Mode Decomposition) is in progress to characterize better this regime. These experiments will require higher temporal resolution. The analysis of the length of the cavity, of the transition to super-cavitation and of the evolution of the pressure drop are consistent with a smooth transition in the cavitating flow for \(1200\geqslant Re\geqslant 1400\), which corresponds to the range where the single-phase (non-cavitating) flow is transitional. It would be interesting to extend further the range of Reynolds numbers, using an oil of lower viscosity, in order to give a lower bound in Reynolds number for the periodical cloud shedding regime that is known to take place in this geometry in fully developed turbulent flows.  
Conceptualization, F.R., F.B., A.D. and S.K.; methodology, F.R.; software, F.R.; validation, F.R., F.B., A.D. and S.K.; formal analysis, F.R.; investigation, F.R. and A.D; resources, K.C. and C.S.; data curation, F.R. and A.D.; writing-original draft preparation, F.R.; writing-review and editing, F.R.; visualization, F.R.; supervision, F.B. and S.K.; project administration, F.R.; funding acquisition, F.B. and S.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors acknowledge [PERSON] and [PERSON] for technical support, and [PERSON] for fruitful discussion.  
The authors declare no conflict of interest.
# State of the Art and Trends Review of _Smart Metering_ in Electricity Grids
## 2 Trends of the _Smart Metering_ Systems
### Architecture of a Smart Metering System
#### 2.1.4 Control Centre  
The _CC_ or _Data Management System_ (_DMS_) is in charge of receiving and storing the metering data for processing purposes. The _CC_ can be seen as a modular system formed by the _Meter Data Management System_ (_MDMS_), which manages the metering data, and additional secondary modules in charge of end-users applications, weather forecasting systems, geographical information systems, control applications, and load management, among others. The _MDMS_ includes the tools that enable communication among different modules, as well as being in charge of validating, processing, and editing the metering data for a suitable information interchange among the different parts of the _Smart Metering_ system [12]. _CCs_ have evolved with the progressive increase of _Smart Metering_ systems' capabilities from mere data compilers and storage devices, typical of _AMR_ systems, to more sophisticated systems able to take decisions and manage the entire system in real time. Generated data from measurements is a very valuable resource for utilities, since they are able to make a wide range of forecasts (available energy, probability of power failures, customers' consumption predictions) by using predictive analysis, which enables utilities to take proactive action rather than simply reacting to events after they happen [13]. Making the most of information from _SM_s and _S_Gs increasingly requires dealing with what is called _Big Data_. [PERSON] _et al._ present a roadmap of _Big Data_ analytics in _Demand Energy Management_ that includes, among other strategies, load patterns categorization, predictive analytics, distributed data mining, and cloud computing, to assess different aspects of the _S_Gs that cannot be solved with conventional data processing techniques [14].
# Review of Ka-Band Power Amplifier
## 3 Topologies
### Power Combining
#### 3.4.3 Differential Amplifier
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Ref.** & **Tech** & \begin{tabular}{c} **Frequency** \\ **(GHz)** \\ \end{tabular} & \begin{tabular}{c} **Gain** \\ **(dB)** \\ \end{tabular} & \begin{tabular}{c} **P\({}_{\text{sat}}\)** \\ **(dBm)** \\ \end{tabular} & \begin{tabular}{c} **P\({}_{\text{1 dB}}\)** \\ **(dBm)** \\ \end{tabular} & \
\begin{tabular}{c} **PAE\({}_{\text{peak}}\)** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Area** \\ **(mm\({}^{2}\))** \\ \end{tabular} & **Topology** \\ \hline
[74] & 350 nm GaAs & 26.5–28.5 & / & 34.8 & / & / & 84.0 &
\begin{tabular}{c} 4-MMICs \\ parallel \\ combined \\ \end{tabular} \\ \hline  
8 & 14.2 & 33.6 & 0.31 &
\begin{tabular}{c} 2-stage CS \\ differential \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the measurement results with the power combining PA.  
basically all PA in III-V technologies reached 35 dBm, and the highest reached 40 dBm. Although higher power can be obtained, PA has a large area and is widely used in 5G communication technology. In particular, the performance of power amplifier in GaN/SiC was significantly improved. Despite the high cost and limited availability of SiC substrates, they can achieve complete modules with superior performance. It is suitable for applications where performance is more important than cost, such as military and satellite. The corporate combiner power amplifier selects III-V technologies to alleviate the reduction in efficiency and PAE with the increase in synthesis number, and its effect is obvious. However, we also find that the area of the corporate combiner power amplifier is large and changes sharply with the increase in the synthesis order. The differential power combining technique is normally used in bulk CMOS due to its multilayer metal layer. Transformers are one of the most common methods to combine power in CMOS. Multiple metal layers can realize 3D wide side transformer and reduce parasitic capacitance for better performance. As can be seen from the data in [88; 89; 90; 92], PA can maintain a high PAE while maintaining a not low output power and gain.