esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
In the last 20 years, the transportation paradigm has evolved in order to improve road safety, minimize energy consumption and pollutants emissions, and optimize traffic conditions [1, 2]. One of the key solutions is the development and deployment of automated mobility (level 3 to 5 of SAE (Society of Automotive Engineers) standard). With the expected functionalities of such automated systems, the mobility concepts will strongly and radically evolve. Technologies are a crucial issue in the development of this new generation of mobility means. Technology issues are not only focused on new powertrains, new hardware architecture, or new types of efficient embedded sensors for Autonomous Driving Systems (ADS) (i.e., radar [3], lidar, IR-neuromorphic-UHD-Polarized-Stereo-Fisheye cameras, GPS [4], etc.) but also on new methods, approaches and algorithms in order to process the data coming from information sources (sensors, HD Maps, communication). Artificial Intelligence (AI) takes an increasingly important role in the \"onboard intelligence\" [5]. Thus, sensors provide information to the perception layer which allows to assess the attributes of the road key components (obstacles, road, ego-vehicle, environment, and driver). This perception information allows building dynamic local perception maps, which can then be extended by sharing data using specific Vehicle to Vehicle or Vehicle to Infrastructure communication protocols. From these maps, decision module and path planning methods can be implemented in order to drive vehicles automatically and safely without any human active operation (see Figure 1).  
In a fully autonomous driving system, while perception modules are compared to human eyes and ears, decision-making and planning are the \"brains\" of autonomous driving, control/command stages being the feet and hands. In this architecture, the decision-making and planning modules represent the system's intelligence (reasoning layer). Like for human operating, after the brain receives various stimuli and perception information, the decision layer analyzes them to understand the current environment and then apply strategies to move according to constraints (safety, energy, comfort, etc.). Then, the decision-making module generates instructions (path, trajectory, orders, maneuvers, or advice) to the control module. The level of complexity that a decision-making planning module can handle is a core indicator for measuring and evaluating autonomous driving capabilities [6]. Another indicator is its ability to respond to unexpected events by rapidly re-planning its trajectory.
## 3 Two-Source Asymmetric Turbo-Coded Cooperative (ATCC) Scheme Based on CMI
### Two-Source Asymmetric Turbo-Coded Cooperative Spatial Modulation (ATCC-SM) Scheme Based on CMI
During the second time slot, the received matrices \(\overline{\mathbf{Z}}_{\text{S}_{2},\text{D}}\) and \(\overline{\mathbf{Z}}_{\text{S}_{1},\text{D}}\) at the receiving end are written as:  
\[\mathbf{v^{S_{1}}_{l_{1}r_{1}}}(\mathbf{m}_{1})\ =\ [0,\ \ldots,\ 0,\ \mathbf{v^{S_{1}}_{r_{1}}}( \mathbf{m}_{1})\,\ 0,\ \ldots,\ 0]^{\mathrm{T}},\ \ \ \ \mathbf{v^{S_{2}}_{l_{2}r_{2}}}(\mathbf{m}_{2})\ =\ [0,\ \ldots,\ 0,\ \mathbf{v^{S_{2}}_{r_{2}}}( \mathbf{m}_{2}),\ 0,\ldots,\ 0]^{\mathrm{T}} \tag{16}\]1  
where \(\overline{\mathbf{H}}_{\text{S}_{2},\text{D}}^{\prime}\) and \(\overline{\mathbf{H}}_{\text{S}_{1},\text{D}}^{\prime}\) represent the channel matrices of \(\text{S}_{2}\) to destination and \(\text{S}_{1}\) to destination, respectively. \(\overline{\mathbf{H}}_{\text{S}_{2},\text{D}}^{\prime}\) and \(\overline{\mathbf{H}}_{\text{S}_{1},\text{D}}^{\prime}\) are defined similarly to \(\mathbf{H}\) in Equation (13). \(\overline{\mathbf{N}}_{\text{S}_{2},\text{D}}\) and \(\overline{\mathbf{N}}_{\text{S}_{1},\text{D}}\) denote the complex additive Gaussian noise of \(\text{S}_{2}\) to destination and \(\text{S}_{1}\) to destination, respectively. They are defined like \(\mathbf{N}\) in Equation (15). The destination node gets the matrices \(\text{Z}_{\text{S}_{1},\text{D}}\) and \(\text{Z}_{\text{S}_{2},\text{D}}\) in the first time slot. During the second time slot, the destination accepts \(\overline{Z}_{\text{S}_{2},\text{D}}\) and \(\overline{Z}_{\text{S}_{1},\text{D}}\). \(\text{Z}_{\text{S}_{1},\text{D}}\) and \(\overline{Z}_{\text{S}_{2},\text{D}}\) are given to the VBLAST detector and the joint ATC decoder to produce the estimated sequences \(\hat{\mathbf{a}}^{\prime}1_{1}\), \(\hat{\mathbf{a}}_{1}^{\prime 2},\ldots,\) and \(\hat{\mathbf{a}}_{1}^{\prime\text{N}_{\text{T}}}\). These sequences are combined into sequence \(\hat{\mathbf{a}}_{1}^{\prime}\) through the P/S converter. Similarly, the remaining \(\text{Z}_{\text{S}_{2},\text{D}}\) and \(\overline{Z}_{\text{S}_{1},\text{D}}\) are fed to the VBLAST detector and the joint ATC decoder to generate the estimations \(\hat{\mathbf{a}}_{2}^{\prime 1}\), \(\hat{\mathbf{a}}_{2}^{\prime 2}\),..., and \(\hat{\mathbf{a}}_{2}^{\prime\text{N}_{\text{T}}}\), which are merged into \(\hat{\mathbf{a}}_{2}^{\prime}\) by the P/S converter. The description of the joint ATC decoder is given in the next section.
## 2 Displacement Reconstruction Using Measured Acceleration Data  
### Mathematic Model for Acceleration Measurement  
Interference terms, such as random interferences and direct current (DC) components, generally exist in tested acceleration signals. Random interferences vary with time, and DC components are the drifts of an accelerometer relative to the baseline under the influence of environmental factors [26]. Therefore, the measured acceleration can be expressed as:  
\[a(t)=a_{s}(t)+a_{n}(t)+C \tag{1}\]  
where \(a_{s}(t)\) is the structural information of the measured acceleration, \(a_{n}(t)\) is the random interference part of the measured acceleration, and \(C\) is the DC component of the measured acceleration.  
The structural displacement signal can be obtained by the quadratic integration of the measured acceleration under the influence of the initial velocity and displacement:  
\[x(t)=\int v(t)+x(0)=\iint a_{s}(t)dtdt+\iint a_{n}(t)dtdt+\frac{1}{2}Ct^{2}+ \lambda t+v(0)t+\eta+x(0) \tag{2}\]  
where \(v(0)t\) is the first integral of the initial velocity, \(x(0)\) is the initial displacement, \(\frac{1}{2}Ct^{2}\) is the integral term of DC components, and \(\lambda\) and \(\eta\) are constant terms of the first and second integrals, respectively.  
The obtained displacement can be divided into three parts:  
\[\widetilde{x}(t)=\iint a_{s}(t)dtdt,\;\hat{x}(t)=\iint a_{n}(t)dtdt,\;\alpha( t)=\frac{1}{2}Ct^{2}+\lambda t+v(0)t+\eta+x(0) \tag{3}\]  
where the first part represents the real displacement of the structure, and the second part represents the influence of environmental noise and measurement, which is small in the practical tests and can be removed by multiple smoothing and filtering. The third part represents the combined influence of DC components, initial conditions, and integral constant terms (it can be removed by subtracting the average value and fitting the polynomial by the least square method). Therefore, the main goal of the following analysis is the removal of the second and third parts of Equation (3).
## 5 Modelling Based on EPR Method
### Comparing the Developed Model with Other Models  
In order to evaluate the ability of the EPR, the results obtained were compared and contrasted with those of other models. To this end, the results of the ANN model developed by [PERSON] et al. (2018) and those of the MGGP model developed by [PERSON] et al. (2018) were used. Table 11 compares the R\({}^{2}\) results from the EPR with those of the ANN and MGGP.  
As can be, the EPR model predicted the coefficient of stress release (CSR) with higher precision compared to the ANN model, while the ANN model showed higher precision compared to the EPR model in predicting the collapse settlement (\(\Delta\)H) of sandy gravel soil. However, using the EPR model over the ANN model is preferred due to its simplicity and transparency. In fact, EPR-based models can easily be implemented with manual calculations. As shown in Table 11, the EPR model was more capable compared to the MGGP model in predicting the \(\Delta\)H and CSR of sandy gravel soil. Further, when predicting \(\Delta\)H with the MGGP model, the R\({}^{2}\) of the testing data was lower than that for the training  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Models** & \multicolumn{2}{c}{**EPR**} & \multicolumn{2}{c}{**ANN ([PERSON] et al., 2018)**} & \multicolumn{2}{c}{**MGGP ([PERSON] et al., 2018)**} \\ \hline
**Dataset** & **Training** & **Testing** & **Training** & **Testing** & **Training** & **Testing** \\ \hline \(\Delta\)H & 0.9759 & 0.9759 & 0.9828 & 0.9830 & 0.9780 & 0.9580 \\ CSR & 0.9833 & 0.9820 & 0.9806 & 0.9810 & 0.9610 & 0.9820 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Comparing the R\({}^{2}\) values of the EPR model with other models.  
Figure 12: Comparing the results of the predicted and measured coefficient of stress release based on the exponential function. (**a**) Training data, (**b**) Testing data.  
Figure 13: Comparing the results of the predicted and measured coefficient of stress release based on the sinusoidal function. (**a**) Training data, (**b**) Testing data.  
data, which indicates the over-fitting of the MGGP model in this case. Further, regarding the prediction of CSR using the MGGP model, the R\({}^{2}\) of the training data was lower than for the testing data, which could have been due to the improper partitioning of the data or the under-fitting of the developed MGGP model. Compared to the MGGP model, the EPR models presented in this research gave almost the same values for the coefficient of determination (R\({}^{2}\)) for the training and the testing data, which indicates that the EPR models did not over-fit.
## 3 Results and Discussion
### Wbhe Configurations
Using the fixed inlet working fluid temperature (50 \({}^{\circ}\)C) and the estimated maximum fluid temperature at the outlet for the different configurations, thermal power values were evaluated for 616.7 KW (98.6 \({}^{\circ}\)C-Coaxial WBHE) and 427.9 KW (84 \({}^{\circ}\)C-U-tube WBHE) (Figure 9a,b). Considering a cascading exploitation mode of the heat accumulated by the working fluid in Trecate4 WBHEs, it is possible to hypothesize a multi-variant and comprehensive use of the resource. The outflow temperatures of working fluids at the wellhead for Coaxial and U-tube WBHEs are 98.6 \({}^{\circ}\)C and 84 \({}^{\circ}\)C, respectively, which allows it to progressively be used for greenhouse heating (100-80 \({}^{\circ}\)C), food industry (80-70 \({}^{\circ}\)C), animal breeding (60 \({}^{\circ}\)C), biomass and agricultural cultures (<50 \({}^{\circ}\)C). The utilization of the geothermal resources at cascade levels is indeed identified as a series of sequential operations by integrating different technologies for distribution and use of thermal energy, drying and dehydration processes, recreational uses and any other direct use [40]. Figure 8: Wellhead temperature as the flow rate value changes: (**a**) Coaxial WBHE; (**b**) U-tube WBHE. By analyzing the characteristics of modern production plants in the agricultural field of central Italy regions, according to what was reported in [41], the average daily temperature values indicated for plants in greenhouses cultivations (horticultural and flower species) are in the range of 30-15 \({}^{\circ}\)C, with variations at daily and seasonal scales. Geothermal energy derived from a converted geothermal well can contribute to reducing the consumption of fuel used for heating and is strongly dependent on the production techniques adopted, the species and varieties cultivated and the control strategies of the climatic conditions adopted.
## 3 Experimental Results
A number of control theories of HAPFs such as instantaneous reactive power theory, synchronously rotating frame theory, and many more with several low-pass, high-pass, and band-pass digital filters along with several closed-loop controllers such as the proportional-integral (PI) controller, the adaptive fuzzy neural network (AFNN), etc., have been employed to implement HAPFs. Moreover, many manufacturers are developing HAPFs even at quite large power ratings to improve the power quality of a vast range of non-linear loads [30]. However, in this way, the most appropriate and easy to implement control approach are the main factors. The significant reasons why we use SMC are listed below in bullet and numbers:  
1. The SMC coefficients are easy to regulate based on the Lyapunov function and its first-order derivative, while the other approaches have some disadvantages such as: \[\bigcirc\] Proportional-integrator (PI): cannot be used in wide range of operation, and it is not able to eliminate the dynamic errors perfectly. \[\bigcirc\] Fuzzy neural network (FNN) or adaptive FNN: uses large training data volume to calculate the rules and implement the membership functions, the numbers of rules may be increased, there are errors in the de-fuzzier block, and it uses high processing time for large systems. Figure 6:0 Kalman filter integrated with wavelet transform: error in estimations and detections, error in uncorrelated and correlated data selection, error in calculation DWT coefficients. Figure 6:1 Source current THD in terms of load voltage. Genetic algorithm (GA)-based PI: hard to find the global optimum, certain optimization problems cannot be solved due to poorly known fitness functions, which generate bad chromosomes. Particle swarm optimization (PSO)-based PI: sometimes, the PSO algorithm fails to find the required optimas when the problem to be solved is highly complicated. Bacteria foraging optimization (BFO)-based PI: although the convergence speed is faster than GA and PSO, the drawback of the BFO algorithm is its fixed step size, which reduces its average rate. 2. The SMC convergence used in this work is exponentially obtainable, resulting in a high-speed response for load current tracking. 3. No extra complicated transform or membership function or neural network is needed to run the simulation. 4. Proper sliding surface selection has a powerful capability for signal tracking. 5. Good dynamic response can be achieved by SMC as proved in many studies in the literature. The only current research gap is that the load current is not completely compensated with a precise sinusoidal-shaped waveform. Although the presented strategy achieves better results than the others mentioned above, still, the complete harmonic current compensation (\(THD_{I_{Source}}=0\)) is a major research topic for those scholars interested in APF. The experimental and theoretical verifications of system efficiency are also added to the manuscript due to the output _rms_ load current variation, as shown in Figure 6:2 This verification implies that the proposed prototype is working successfully with high efficiency and reliable performance.
# A Survey on Intrusion Detection Systems for Fog and Cloud Computing
## 5 Intrusion Detection in Fog, Edge, and Cloud Computing
### Fog Computing
#### 5.1.1 Fog Computing Applications  
Many strategies of fog computing devices are focused on analyzing the device status and other time-critical data, such as false alerts, alarm status, and others. It can minimize latency and improve efficiency, whilst preventing major damage from taking place. Successes of the fog computing technology can be noticed primarily in critical Internet of Things (IoT) applications that are particularly time-sensitive or have a requirement for real-time responses, which can be the following: data acquisition and pre-processing, short-term data storage needs, condition monitoring, and rule-based decision making.
# On End-to-End Intelligent Automation of 6G Networks
## 2 6G: Preliminaries and Enabling Technologies
### Enabling Technologies
#### 2.2.2 Next-Generation Antenna and RF Technologies  
Another component that will be vital in 6G networks is the underlying physical device architectures and corresponding environments. These play a crucial role in the actual transmission and reception of the wirelessly communicated data. As such, technologies such as massive MIMO and intelligent reflective surfaces will be key enablers of 6G networks.  
* Massive MIMO: Massive Multiple Input Multiple Output (mMIMO) is a concept first proposed by Marzetta as part of the efforts to meet the exponential growth of traffic demand [46]. The concept refers to the use of \"physically small, individually controlled antennas to perform multiplexing and demultiplexing for all active users by utilizing directly measured channel characteristics\" [46]. Thus, it makes use of spatial multiplexing to allow the various users to occupy the same frequency and time slots  
Figure 3: The key 6G enabling technologies.  
without interference. Based on this fact, mMIMO has already been proposed as part of the development of 5G networks [47]. However, to meet the requirements of 6G, the concept of ultra mMIMO is emerging, in which the large arrangement of antennas is deployed over a larger surface area (e.g., the side of a building). This allows for higher levels of connectivity and throughput [48].
* Intelligent Reflective Surfaces: As a promising paradigm for 6G to achieve a reconfigurable wireless propagation environment, an intelligent reflective surface (IRSs) is a planar surface composed of a large number of passive reflecting elements [49]. Each element of this surface can induce a controllable amplitude and/or phase change to the incident signal independently [49]. As a result, the propagation channel between the transmitter and receiver can be flexibly reconfigured, allowing for potentially lower interference and better reliability [49]. Due to its characteristics, IRS is being proposed as a key solution for various challenges and applications. For example, IRS can be used to communicate with users located in dead zones or at cell edges by creating virtual line-of-sight (LoS) links between the users and the corresponding base stations [50]. Similarly, IRSs can enable the massive D2D communication while simultaneously mitigating the corresponding interference [50]. Lastly, IRSs can facilitate simultaneous wireless information and power transfer (SWIPT) to multiple IoT devices [50]. Accordingly, IRSs are expected to play a pivotal role in offering extreme coverage and extreme massive connectivity in 6G networks.
## Article
### _Application_  
**Abstract:** The construction of a reasonable and reliable deformation prediction model is of great practical significance for dam safety assessment and risk decision-making. Traditional dam deformation prediction models are susceptible to interference from redundant features, weak generalization ability, and a lack of model interpretation. Based on this, a deformation prediction model that considers the lag effect of environmental quantities is proposed. The model first constructs a new deformation lag influence factor based on the plain HST model through the lag quantization algorithm. Secondly, the attention and memory capacity of the model is improved by introducing a multi-head attention mechanism to the features of the long-time domain deformation influence factor, and finally, the extracted dynamic features are transferred to the ConvLSTM for learning, training, and prediction. The results of the simulation tests based on the measured deformation data of an active dam show that the introduction of the deformation lag factor not only improves the interpretation of the prediction model for deformation but also makes the prediction of deformation more accurate, and it can improve the evaluation indexes such as RMSE by 50%, the nMAPE by 40%, and R\({}^{2}\) by 10% compared with the traditional prediction model. The combined prediction model is more capable of mining the hidden features of the data and has a deeper picture of the overall peak and local extremes of the deformation data, which provides a new way of thinking for the dam deformation prediction model.  
**Keywords:** hysteresis; ConvLSTM; attention mechanism; prediction model; dam deformation
# Managing Redundancy in a Multiple-Robot Orienteering Problem for Equilibrium and Robustness
## 3 Results
### Simulation Results

Table 3: Performance comparison of different methods in terms of total reward, total budget spent, and workload balancing value in the City (Figure 2) and Maze (Figure 6) scenarios. Figure 10 presents the analysis of the impact of the number of attackers. As the number of attackers increases, the number of available robots decreases. This creates an experimental condition analogous to that in Figure 8a, and thus, the results show a similar trend. Figure 8: Performance analysis of the robotic system under varying parameters. (**a**) The influence of the number of robots. (**b**) The influence of the individual budget allocated to each robot. Figure 9: Impact of environmental parameters on algorithm performance. (**a**) Performance as a function of the number of vertices in the graph. (**b**) Performance as a function of the number of edges, which reflects graph density. Taken together, these results lead to a clear conclusion: our RMOPWB algorithm is most impactful in dense, confined, or resource-limited scenarios where it can significantly reduce system redundancy and improve workload balancing while ensuring mission rewards are not compromised. However, it is important to critically acknowledge the associated trade-offs. The two additional optimization stages in RMOPWB mean it is computationally more intensive than the simpler RMOP algorithm. While this makes RMOP inherently faster, the results presented in Table 3 demonstrate that our parallelized implementation of RMOPWB yields acceptable computation times for practical applications. This makes RMOPWB a highly effective solution, balancing superior planning quality with manageable computational demands.