esa-sceva/satcom-chunk-collection huggingface dataset:
# FPGA Implementation of ECT Digital System for Imaging Conductive Materials
## 6 Experimental Setup and Results  
Our ECT system consists of six capacitance sensors evenly distributed around a rectangular foam pattern centered in circular flask a shown in Figure 1. The rectangular imaging area is where the metal will exist. The mutual capacitance between the electrodes corresponding to each metal distribution were wirelessly collected. The Finite Element Method (FEM) is applied to create a mesh consisting of 720 linear triangular elements inside the flask. The image area of size 16 \(\times\) 16 pixel is meshed in the center of the FEM model to reduce the complexity of the computation. Therefore, the sensitivity matrix \(S\) of size 6 \(\times\) 256 and a measurement electrodes capacitance \(C\) vector of size 6 elements were applied during the experiments. Since the solution of the forward problem requires solving nonlinear differential equations and it happens once, it is faster to calculate it using Matlab on a PC. The sensitivity matrix is calculated by FEM and saved on the SDRAM. Simulink modules representing the proposed system are developed for testing and verification purposes. The HDL Workflow Advisor generates the bitstream file which is downloaded on the FPGA fabric through the JTAG USB interface. The mathematical matrix operations library coded in C is compiled and built to an executable file. The generated file is deployed to the ARM processor in the Altera Qsys FPGA platform.  
The image reconstruction subsystem of the ECT system is coded with HDL-synthesizable; while the main processing unit is modeled with C-compatible. The system-level simulation of the cycle-accurate model allows functional verification as well as cycle-based performance measurement.  
The ILBP reconstruction algorithm implemented on the FPGA platform is applied to generate the final tomographic images. In order to test the performance of the developed system, different distributions of metal pieces inside the imaging area are used. Figure 6 illustrates the actual metal distributions in the first column, while the results of the reconstructed images are shown in the second column. Two metal pieces shown in Figure 5(a) are positioned in front of sensor \(S1\) and \(S4\) simultaneously. The reconstructed image shown in Figure 5(b) proves how the proposed system is able to identify the position of the two pieces correctly. Another metal distribution of three metals located in front of sensors S2, S4, and S6 is shown in Figure 5(c), while the result in Figure 5(d) from the algorithm deployed on the FPGA accurately detected the locations of the three pieces.  
Regarding the FPGA (Altera Cyclone V) resources, about 11% of the total logic elements is used on the integrated prototype and only 15% of its memory. The maximum propagation delay captured for the system is 12.04 ns lesser than the 50 MHz clock period of 20 ns. The power consumption of the Cyclone-V FPGA is low about 12 mW.
# IpDFT-Tuned Estimation Algorithms for PMUs: Overview and Performance Comparison
## 3 Estimation Results and Performance Analysis
### Results in Transient Conditions
* As far as the measurement delay times are concerned, the tuned TWLS and the IpDFTc estimators exhibit similar performances, quite better than those of the DCF method. Moreover, they are well below the limits reported in the Standard [10].
###### Abstract  
The need to deliver accurate predictions of renewable energy generation has long been recognized by stakeholders in the field and has propelled recent improvements in more precise wind speed prediction (WSP) methods. Models such as Weibull-probability-density-based WSP (WEB), Rayleigh-probability-density-based WSP (RYM), autoregressive integrated moving average (ARIMA), Kalman filter and support vector machines (SVR), artificial neural network (ANN), and hybrid models have been used for accurate prediction of wind speed with various forecast horizons. This study intends to incorporate all these methods to achieve a higher WSP accuracy as, thus far, hybrid wind speed predictions are mainly made by using multivariate time series data. To do so, an error correction algorithm for the probability-density-based wind speed prediction model is introduced. Moreover, a comparative analysis of the performance of each method for accurately predicting wind speed for each time step of short-term forecast horizons is performed. All the models studied are used to form the prediction model by optimizing the weight function for each time step of a forecast horizon for each model that contributed to forming the proposed hybrid prediction model. The National Oceanic and Atmospheric Administration (NOAA) and System Advisory Module (SAM) databases were used to demonstrate the accuracy of the proposed models and conduct a comparative analysis. The results of the study show the significant improvement on the performance of wind speed prediction models through the development of a proposed hybrid prediction model.  
forecasting; machine learning; Weibull distribution; wind speed Article  
A Novel Hybrid Method for Short-Term Wind Speed Prediction Based on Wind Probability Distribution Function and Machine Learning Models  
2022
## 3 Numerical Model
### Material Parameters  
To take into account the generation of dynamic pore pressure, the Finn model in FLAC3D was chosen as the soil constitutive model. The Finn model contains two optional formulations: the [PERSON] et al. formulation and the Byrne formulation. The Byrne formulation is a modified and simplified formula based on the [PERSON] et al. formulation [21] and was adopted in this paper. The applied soil parameters are shown in Table 1. To simulate the material damping, a local damping ratio of 20% was used during vibratory penetration.  
A hydro-mechanically coupled model requires many parameters, including the void ratio, the hydraulic conductivity, and the fluid bulk modulus, which were not reported in their experiment [8], unfortunately. In this paper, the void ratio and the hydraulic conductivity were assigned as 0.72 and \(5\times 10^{-3}\) cm/s, respectively. As shown in Figure 2, the fully saturated condition is actually hard to assure. Thus, the sand saturation was assumed to be 99%, approximately corresponding to a fluid bulk modulus of 20 MPa [12; 13].  
In other studies of vibratory pile driving [7; 8; 9; 10; 12; 13; 14; 15; 16; 17; 18; 19], piles are often regarded as rigid piles. However, when piles and tubes are composed of zone elements in FLAC3D, rigidity is not applicable for piles and tubes. The elastic model is thus applied for the pile and the tube, and the related parameter is shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Material** & **Constitutive Model** & **Density \(\rho\) [kg/m\({}^{3}\)]** & **Elastic Modulus \(E\) [GPa]** & **Poisson’s Ratio** & **Internal Friction Angle** \(\varphi\) [\({}^{\circ}\)] & \(N_{1}\) * \\ \hline Sand & Finn & 1924 & 0.02 & 0.28 & 28 & 6 \\ Pile & Elastic & 7850 & 206 & 0.26 & & \\ Tube & Elastic & 7850 & 206 & 0.26 & & \\ \hline \hline \end{tabular}  
* \(N_{1}\) is the normalized standard penetration test value.  
\end{table}
Table 1: Material parameters in the numerical model.  
Figure 3: Axisymmetric model for simulating the vibratory open-ended pile driving.
# An Architecture for the Performance Management of Smart Healthcare Applications
## 4 The PRIMUS Architecture
### PRIMUS Architecture
#### 4.2.1 Decision-Making Module  
This module takes decisions on slice configuration, slice admission, and slice scheduling following three steps: (i) Slice-Aware Context, (ii) Admission Control, and (iii) Scheduling. These steps define the creation of a new slice or the allocation of a pre-configured slice. There are pre-configured slices for s-health since urgent care application supports only 1 ms of latency. Thus, the slice-aware context step gets information about the application context and its requirements. As aforementioned, our previous work [21] identifies the application that is generating network traffic. Hence, through announce control messages, PRIMUS informs the requirements demanded by each application (we detail control messages in Table 3 from Resource Management Module). Thus, the first step analyzes and stores this information to make future decisions. The information about the requirements enhances the network resources utilization efficiency, once the infrastructure provider can get the network slice demands on the slice-aware context. The slice-aware context stores data regarding applications, requirements, and end-users. It stores information about identical applications but from different end-users since several users may generate traffic, simultaneously. In such situations, PRIMUS allocates equal or the same network slice to meet the same application needs. Each slice allows up to eight users per time. The resources are further pre-allocated following the slice-aware to provide the admission control policy.  
The admission control step needs information about the available resource and priority policy. Thus, it offers information to the scheduling step to analyze the priority levels and, jointly to resource management, to know the available and allocated resources. Moreover, for the admission control policy, we assume that the resource demands are accurate. Thus, in the first moment, the admission control analyzes the network slice type, i.e., URLLC, eMBB, or mIoT, to decide what network resource each slice will receive since URLLC has priority over eMBB and mIoT. The scheduling step stores and updates the priority information. As URLLC transmits critical network data, it receives the highest priority. eMBB and mIoT receive the second and third priorities, respectively. Hence, there are three priority queues: 1-URLLC, 2-eMBB, and 3-mIoT. Afterwards, to know the allocated and available resources, the admission control verifies resource management's resource block. For URLLC, there are pre-configured slices to not increase the latency since slice creation and configuration take time.  
Figure 3: PRIMUS architecture.
# Mobile Networks and Internet of Things Infrastructures to Characterize Smart Human Mobility
## 3 Human Mobility Research in Evolution Mobile Network
### 5G Mobile Network to Explore Human Mobility
#### 3.2.1 Components of 5G Physical Infrastructure  
Service-driven 5G network architecture aims to meet diversified mobile service requirements. It broadens cloud access, core networks, transport and support the underlying physical infrastructure using network functions virtualization (NFV) and software-defined networking (SDN). For better support of diversified 5G services the cloud adoption presents as an efficiently and flexibly solution. In addition, it enables the key technologies of E2E network slicing (E2 ENS) by the component-based network functions, and on-demand deployment of service anchors. The transport network consists of SDN controllers and underlying forwarding nodes. SDN controllers generate a series of specific data forwarding paths based on network topology and service requirements. This network architecture also manages network resources and implements E2E automatic slicing (E2 EAS).  
As Figure 5 shows, E2 ENS is a key to evolution and support in diversified 5G services. Based on SDN and NFV, physical infrastructure of 5G network architecture consists of sites and three-layer DCs, i.e, Central Office DC (CODC), Local DC (LDC), and Regional DC (RDC). Sites deploy cloud RAN real-time (RT) functions in the form of pico, micro, and macro base stations using multiple modes like 5G, LTE, and wi-fi. However, these functions require real-time and high-performance, computing capability, and inclusion of a specific dedicated hardware platform. In its turn, three layer cloud DC consists of storage and computing resources. The CODC is closest in relative proximity to the base station side. The second layer is the LDC, and the upper layer is the RDC. Each layer of cloud DCs is connected through transportation network infrastructure.
# GDPR and Large Language Models: Technical and Legal Obstacles
## 1 Introduction
### Right of Access and LLMs  
Article 15[6] of the GDPR grants individuals the right to obtain confirmation about whether their personal data is being processed, to access that data, and to receive additional information about the processing activities [6]. This right is crucial for transparency and for empowering individuals to understand how their data is used.  
For LLMs, the Right of Access poses significant challenges. Personal data used in training is embedded within the model's parameters in a non-transparent manner, which makes direct access difficult. Moreover, retrieving specific data points from models trained on vast datasets is not straightforward [31; 37]. Potential solutions include developing model auditing tools to detect personal data within the models, providing query-based access to allow individuals to receive outputs related to their data, or publishing transparency reports that detail data sources and processing activities without revealing individual data points [40; 41; 42].
# Smooth 3D Dubins Curves Based Mobile Data Gathering in Sparse Underwater Sensor Networks
## 4 Simulation Results
On the other hand, the energy consumption spent on the multi-hop acoustic transmission of underwater sensors is substantially reduced because sensor nodes only need to deliver sensory data to neighboring AUVs. As a result, the lifetime of entire USNs improves with the mobile data collection method. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Number of Sensors** & \(n=3\) & \(n=7\) & \(n=11\) & \(n=15\) & \(n=19\) \\ \hline LI algorithm (case 1) & 20.3 & 51.1 & 80.5 & 118.7 & 136.2 \\ BI algorithm (case 1) & 22.5 & 55.6 & 90.0 & 127.3 & 159.7 \\ LI algorithm (case 2) & 23.1 & 55.3 & 80.3 & 117.7 & 135.9 \\ BI algorithm (case 2) & 27.5 & 57.2 & 85.3 & 125.3 & 152.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average total cruising length of three-dimensional (3D) Dubins curves (unit). LI denotes linear-interpolation-based 3D Dubins path planning; BI denotes Bezier interpolation based 3D Dubins path planning. Figure 23: \(Z\)-axis of Bezier interpolation three-dimensional (3D) Dubins curves (case 1). Figure 24: First-order derivative of \(Z\)-axis coordinate (case 1).
## 2 Requirements of 5G-Based V2X Use Cases
### Positioning  
The V2X applications usually cover low-latency, safety-critical use cases where the distance between the vehicles is relatively low. The use cases mostly investigate situations where the ego vehicle is/will be in some kind of relationship with the remote event or object [16; 21]. Such an event can be a traffic light change, an accident, or road works. The objects can be vehicles, emergency vehicles, or pedestrians, for example. Since the relation is the most crucial in these cases, the relative position is more important than the absolute position. This differs from most of the classical use cases, like conventional navigation applications or geo-fencing-based warning applications. In those applications, the absolute position of the client is the most important. When it comes to positioning with 5G and beyond technologies, there are various methods [21] available for V2X purposes as well.
# Identifying Misbehaving Greedy Nodes in IoT Networks
## 5 Simulation and Results
### Greedy Behaviour Analysis Results
As a result, these two parameters allow our detection algorithm to gain in accuracy. Figure 3: Packets sent by legitimate and greedy nodes. In Figure 5, we have represented the two states of the radio (on, and in transmission (Tx)) of the greedy node and the other nodes of the network. Both parameters are presented in percentage. We find that in each network, the greedy node keeps its radio on longer, and performs more radio transmission than other legitimate nodes, which is normal because it sent more than the other nodes of the network. According to Figure 5, this difference in value of the two parameters is very significant between the greedy node and the legitimate nodes; this is why we have added these two parameters in the detection algorithm. In Figure 6, APC represents Average Power Consumption in transmitting state (transmit power). ARDC represents the Average Radio Duty Cycle in transmitting state (transmit duty cycle). This latter parameter informs about the wake-up frequencies performed by the node to transmit its packets. We notice in this figure that the three parameters APC, ARDC, and power of the greedy node are clearly superior than the other legitimate nodes. These three parameters indicate that the greedy node consumes more energy because it carries out more transmission than the rest of the nodes, knowing well that a device in transmission or reception mode consumes five times more energy than in any other state [24]. Figure 4: Received packets and collision number of legitimate and greedy nodes. Figure 5: Radio on and radio Tx of greedy and legitimate nodes. According to Figure 6, these three parameters are indicative of a greedy behaviour, since the energy consumption increases drastically at the selfish node compared to the rest of the nodes of the network. For this reason, these three parameters were also chosen as metrics to increase the efficiency of the detection algorithm. We observe that as the number of parameters that identifies greedy behaviour increases, the detection of greedy nodes will be more efficient in terms of effective detection and reduction in false alarms. Thus, with our mechanism, it is now very difficult to confuse a legitimate node with a greedy one. Furthermore, for a false alarm to occur, the legitimate node must be able to satisfy all eight conditions of greedy behaviour, which is very difficult to achieve. Based on the analysis of the parameters previously carried out, we conclude that the parameters, number of packets sent, radio transmission rate (radio Tx), wake-up frequency rate (radio on, ARDC), as well as the power consumption (power, APC) of the greedy node are significantly higher compared to those of other legitimate nodes in the network. Concerning the level of the parameter, number of packets, received by the greedy node, it is low compared to the legitimate nodes. Finally, the greedy node causes a bit more collision than the rest of the nodes. The analysis results of the greedy node parameters are summarized in Table 1.
## 4 Proposed System Design
### FSO Channel  
The FSO channel is used in our proposed model. Various weather conditions will have impact on the channel due to the attenuation caused by them. Therefore, in this study, we focus on the effect of different fog levels and different dust storms on the proposed OAM/OCDMA-FSO system performance during transmission between the transmitter and receiver.  
Furthermore, the effect of weather conditions of two cities, one in Egypt that is Alexandria and the other in India that is Srinagar, are also investigated in this study so as to show the proof of concept of our proposed system in a real environment.
# Wireless Channel Models for Over-the-Sea Communication: A Comparative Study
## 4 Channel Models for Over-the-Sea Wave Propagation
### Modeling of Near-the-Sea Mobile Radio Wave Propagation at 5 GHz  
The paper presented in [11] investigated near-sea-surface LoS radio wave propagation at 5 GHz. The investigation was conducted using narrowband measurements. The results obtained for the received signal strength were compared with the FSL model [6] and the two-ray path loss model [6]. The results and analysis carried out in [7] show that in the maritime environment considered in [11] there is an evaporation duct layer. For this reason, a three-ray path loss model could predict the trend of the LoS signal strength variations at a larger propagation distance, by taking into account both the reflection from the sea surface and the refraction caused by the evaporation duct.  
The measurement system in [11] used 5.15 GHz for continuous wave transmission with no interfering signal. At an approximate height of 3.5m above sea level, an omni-directional antenna was set up in a speedboat. The antenna was connected to a high-power amplifier and a signal generator. The base station was on shore. The LoS condition between the transmitter and the receiver was ensured because the receiver consisted of an identical omni-directional antenna. The measurements in [11] were carried out in the open sea area, located at the southeast coast of Singapore.  
As discussed earlier, the results of the received signal strength, having a transmission distance of up to 10 km, were analyzed using the FSL model and the two-ray model in [11]. The PL can be predicted by the FSL model for radio wave propagation using equation (2). In addition, the two-ray path loss model can be obtained in equation (3). Both of these models (FSL and two-ray) were used in [11] for predicting the received signal strength in ideal conditions for the near-sea-surface radio wave propagation at 5.15 GHz. The results obtained from these two models were compared with the three-ray path loss model [7]. The authors in [11] assumed that the evaporation duct layer was horizontally homogeneous in their three-ray path loss model. The three-ray path loss model can be summarized by equations (4) and (5) in this paper.  
In [11], a three-ray path loss model was implemented in a realistic maritime environment, with real-time experimental results obtained that show superiority over the two-ray and FSL models, especially when the propagation distance exceeds 2000 or 3000 m. The only parameter authors have taken into account is the signal strength. The error rate of the received signal could have been considered as another parameter.
## 3 Mixed Integer Linear Programming (MILP)/Mixed Integer Quadratic Programming (MIQP) Models  
### Electric Boilers  
The optimization model for electric boilers considers the maximum heat load \(\dot{Q}^{B,max}\) as the cost driver for investment costs and the required power \(P_{el}^{B}\) as a driver for operating costs. The momentary heat load \(\dot{Q}_{t}^{B}\) and the power consumption \(P_{el,t}^{B}\) are linked through the boiler efficiency \(\eta^{B}\). The index \(t\) represents the operating periods and \(NOP\) is the set of all these time periods.  
\[\dot{Q}^{B,max}\geq\dot{Q}_{t}^{B},\hskip 14.226378 pt\forall\;t\in\;NOP \tag{1}\]  
\[\dot{Q}_{t}^{B}=P_{el,t}^{B}\;\eta^{B},\hskip 14.226378 pt\forall\;t\in\;NOP \tag{2}\]  
For simplicity, the investment costs for electric boilers \(C_{invest}^{B}\) are considered to be a linear function of the maximum heat load \(\dot{Q}^{B,max}\) with the cost coefficients \(c_{0}^{B}\) and \(c_{1}^{B}\).  
\[C_{invest}^{B}=c_{0}^{B}+c_{1}^{B}\;\dot{Q}^{B,max} \tag{3}\]  
Energy costs \(C_{energy}^{B}\) are modelled as the sum of the momentary power consumption \(P_{el,t}^{B}\) multiplied by the interval duration \(\Delta t\) and the momentary electricity price \(c_{el,t}\).  
\[C_{energy}^{B}=\sum_{t\in NOP}\left(P_{el,t}^{B}\;\Delta t\;c_{el,t}\right) \tag{4}\]