esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review
## 5 Challenges and Limitations
Some agricultural applications can be considered as time sensitive, such as those used in farm fire detection, detection of exposure of crops to pests, cow heat event detection during milking, and exposure of cattle to heat for a certain period. Such information must be transmitted as fast as possible if the critical issue is to be handled. In such a case, a tradeoff between energy consumption and data timelines is necessary. By contrast, some agricultural applications are delay tolerant (i.e., time insensitive), such as protein content, milk fat [177], gathering data from soil and grass monitoring. With delay tolerant approach, the short range low power wireless protocols can be used [178]. Where the information of the agricultural field can be collected and transferred to the master node or to a cloud computing node using \"messengers\" e.g., a mobile node (mobile robot or UAV). Consequently, the cost [179] and the complexity of the agriculture WSN will be reduced and the lifetime will be increased. * _Fault tolerance_: Fault tolerance is a crucial feature of WSNs for succeeding PA. Several faults may occur in PA based on the WSN system, which are (i) communication failure, (ii) faulty sensor setting, (iii) sensor component faults producing incorrect value, and (iv) node failure because of exhausted battery or any other cause. [PERSON] et al. [40] introduced communication failure and node fault tolerance for an irrigation system. If any fault occurs, the irrigation system tracks the default irrigation program. The node energy depletion failure was minimized in [1,40,180] by adopting solar-cell-powered nodes. Data aggregation and topology control schemes are probable to be fault tolerant for deploying sensor nodes in a vast area. * _Data management_: The data management in agriculture poses a challenge because the large amount of data that can be collected from several sensors spread in agricultural field, especially when the agricultural data are intended to be connected to the cloud. Determination of the (i) data analysis method, (ii) data collection schemes, (iii) sensor types, (iv) semantic sensor networking, (v) big data, and (vi) complex event processing enables the designer to manage these crucial aspects. The integration of IoT and software-defined network also introduces a promising and new methodology in deployment, monitoring, and design of network services and resources [181]. * _Heterogeneous sensors_: Integration of wired and wireless heterogeneous sensors into information platforms to perform interoperability pose challenges in PA. [PERSON] et al. [152] proposed \"web service-enabled cyber-physical infrastructure\" to solve this issue. The proposed system was able to integrate, process, acquire, and distribute surveillance data from different physical sensors spread in the PA system over the Internet. The infrastructure was executed to serve as an architecture middleware between PA clients and heterogeneous sensors.
# From Bottom of Sea to Space: Quo Vadis IoT? So What About Security?
## 5 Future Roadmap
### A Future Look-Ahead: The Promise of AI
#### 5.2.3 Quantum-Inspired AI  
Although AI has made much progress in recent years, there are still some challenges, including reasoning under uncertainty, adaptive ML, and neuromorphic cognitive models. Quantum-inspired AI, which means using quantum computing for ML and DL algorithms, is one of the solutions that can help AI level up and overcome these challenges. Adopting quantum computing concepts and techniques can improve AI by improving decision making capabilities in complex scenarios, optimizing ML algorithms, and speeding up the training phase of ML models [245; 246].  
Quantum-inspired AI has yielded promising results in several applications. For example, the authors of [247] have used Quantum-inspired Reinforcement Learning (QiRL) to plan the trajectory of Unmanned Aerial Vehicles (UAVs). QiRL method adopts a novel probabilistic action selection policy and a new reinforcement strategy, which are inspired by the collapse phenomenon and amplitude amplification in quantum computation theory, respectively. In another work, the authors have applied a quantum-inspired experience relay to traditional deep reinforcement learning [248]. They showed on Atari 2600 games that deep reinforcement learning with a quantum-inspired experience relay can improve training efficiency.  
The authors of [249] have developed two quantum-inspired multidirectional associative memories. The first uses a one-shot learning model, and the second uses a self-convergent iterative learning model. They used these models to show the advantages of quantum-inspired multidirectional associative memory, such as stability and reliability. Moreover, a QiRL algorithm for autonomous robot navigation has been proposed in [250]. Experiments show that this approach is more robust than traditional reinforcement learning algorithms.  
Finally, a quantum-inspired fuzzy neural network has been presented in [251] for two-class classification. Neurons are added to the hidden layer via fuzzy c-means clustering, with the fuzziness parameter being developed from quantum computing. Additionally, this network uses a modified step activation function. Experiments showed that this scheme outperforms state-of-the-art methods.  
Figure 6 illustrates the future of secure IoT extensions as suggested by the trends discussed above.
## 4 Results
### Fuel Consumption Model  
The value of the fuel consumption depends mainly on the torque and engine revolutions. When implementing the data into the Matlab environment, the three-dimensional definition of the specific fuel consumption model was created. The input vectors speed \(v\), torque \(T_{e}\) and brake-specific fuel consumption (BSFC) were connected to the engine block.  
After converting vectors from the general engine block of the Simscape module environment to the common Matlab workspace, it is possible to visualize the BSFC model in the form of a contour plot, as it can be seen in Figure 25.  
The lowest fuel consumption is achieved by the engine at high torques and lower to medium engine speeds, while the highest specific fuel consumption is achieved by these engines at high speeds and low torques.  
Figure 24: Gearshift logic.  
Figure 25: Contour representation of the model’s fuel consumption values.  
Subsequently, it was possible to create a dependence of the effective engine power \(P_{e}\) and fuel mass flow rate FC on the engine revolutions and torque values. The obtained characteristics are shown in Figure 26.  
The graphs shown in Figure 26 confirm the assumption that the areas of maximum engine power utilization of the IVECO FICE0489C are in the opposite area to the values of the lowest fuel consumption of the vehicle. On the contrary, the graphical representation showed that there is a direct proportion between the curves of the values of the effective engine power Pe and the fuel mass flow rate FC--the higher the power, the higher the fuel consumption--or an inverse proportion: with increasing efficiency of the driving mode from the point of view of the engine power utilization, the efficiency of the driving mode from the point of view of current fuel consumption decreases. To verify this dependence, it was necessary to analyze the intersection of these curves, and the resulting 3D graphs of the dependence of the fuel consumption on the effective torque and engine revolutions are shown in Figure 27.  
These graphical representations prove that vehicle operation in the area of maximum utilized effective power and minimum actual fuel consumption is simultaneously impossible. The three-dimensional graph of specific fuel consumption obtained by intersecting the waveforms of these two quantities gives us a better idea of the area with the most efficient operation of the vehicle, which is located around the area of the global minimum. The global minimum was localized around the position with the largest positive differences between the effective engine power and fuel mass flow rate with the following conditions: \(n_{e}\) = 2000 rpm, \(T_{e}\) = 440 Nm, and BSFC = 208 g/kWh. From the point of view of sufficient use of engine power while maintaining appropriate fuel consumption, it is optimal to keep the engine velocity and torque in this range.  
Figure 27: Fuel consumption dependence on engine revolution and torque values.  
Figure 26: Dependence of effective engine power and fuel mass flow rate on engine speed and torque values.
## 3 Results and Discussions  
### Experimental Setup  
In this paper, an experimental platform for radial artery ultrasound imaging based on FPGA was built, as shown in Figure 7. The experiment used a 128-channel ultrasound probe with a center frequency of 3.5 MHz, with a MAX4940 chip as the transmitter module and an AFE5805 chip as the receiver module. The FPGA was used as a controller and signal processor for the transmission, acquisition, storage, processing, and display of the radial artery ultrasound signal. The processed ultrasound data were finally read out from the hardware via the USB driver, and the radial artery ultrasound images were displayed on the host computer for analysis. A list of the main parameter settings for the ultrasound system is given in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Parameter** \\ \hline Digital frequency & 100 MHz \\ Array elements center spacing & 0.8 mm \\ Array elements length & 10 mm \\ Transmitting focus delay accuracy & 2.5 ns \\ Maximum detection depth & 10 mm \\ Detection width & 8 cm \\ Scanning method & Receive/transmit interval scanning \\ Transmitting pulse width & 190 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: The main parameter settings for the ultrasound system.  
Figure 7: Experimental platform of digital radial artery ultrasound imaging system based on FPGA.
## 2 O-RAN Architecture and Development
### O-RAN Architecture
The E2 interface is the interface between the nRT RIC and the radio units;
* WG4: Open Fronthaul Interfaces. This WG is responsible for defining the specifications for the open fronthaul interfaces. The fronthaul is the interface between the baseband unit and the radio units;* WG5: Open F1/W1/E1/X2/Xn Interfaces. This WG is responsible for defining the specifications for the open F1/W1/E1/X2/Xn interfaces. These interfaces are used to communicate between different parts of the RAN;
* WG6: Cloudification and Orchestration. This WG is responsible for defining the specifications for cloudification and orchestration of the RAN. Cloudification is the process of moving the RAN to the cloud. Orchestration is the process of managing the RAN;
* WG7: White-box Hardware. This WG is responsible for defining the specifications for white-box hardware for the RAN. White-box hardware is hardware that is not proprietary to a specific vendor;
* WG8: Stack Reference Design. This WG is responsible for defining the stack reference design for the RAN. The stack reference design is a model of the RAN that can be used to develop and test different RAN implementations;
* WG9: Open X-haul Transport. This WG is responsible for defining the specifications for open X-haul transport for the RAN. X-haul transport is the transport of data between the baseband units and the radio units;
* WG10: OAM. This WG is responsible for defining the specifications for operation, administration, and maintenance (OAM) for the RAN. OAM is the process of monitoring and managing the RAN;
* WG11: Security. This WG is responsible for defining the specifications for security for the RAN. Security is a critical part of the RAN, and this WG is responsible for ensuring that the RAN is secure. Figure 3 provides further detail of the O-RAN architecture. As can be seen, O-RAN consists of O-RU, O-DU, O-CU, Near-RT RIC, Non-RT RIC, and SMO. The Uu interface between UE and O-RAN components inside the green dashed area, as well as the UE and O-eNB, denote all the O-RAN functions required to support the Uu interface NR. On the other hand, the O-eNB terminates the Uu interface for LTE. The 3 GPP defined and maintained interfaces and is considered part of the O-RAN architecture includes the E1, F1-c, F1-u, NG-c, NG-u, X2-c, X2-u, Xn-c, and Xn-u, as depicted in Figure 3 [20]. In the following, we shall elaborate these O-RAN components. Figure 2: High-level architecture of O-RAN showing internal, 3 GPP, and external system interfaces [20].
## 4 Modified Functional Observer with Unknown Input
### 5 Observer-Kalman-Filter Identification for the Unknown Singular System
Step 4: Design the optimal linear quadratic tracker with an appropriate weighting matrix pair \(Q_{c}>>R_{c}\) to determine the OKID-based optimal control law [18] as  
\[u_{fa}(t)=-K_{c}\hat{x}_{e}(t)+E_{c}r(t), \tag{75}\]  
where \(K_{c}=\overline{R}_{c}^{-1}\Big{(}B_{\omega k,a}^{T}P+N_{c}^{T}\Big{)}\), \(E_{c}=-\overline{R}_{c}^{-1}\Big{[}\Big{(}C_{\omega k}-D_{\omega k,a}K_{c} \Big{)}\Big{(}A_{\omega k}-B_{\omega k,a}K_{c}\Big{)}^{-1}B_{\omega k,a}+D_{ \omega k,a}\Big{]}^{T}Q_{c}\), in which \(\overline{R}_{c}=R_{c}+D_{\omega k,a}^{T}Q_{c}D_{\omega k,a}\), and \(N_{c}=C_{\omega k,a}^{T}Q_{c}D_{\omega k,a}\), and \(P>0\) satisfies the algebraic Riccati equation  
\[A_{\omega k}^{T}P+PA_{\omega k}-\Big{(}PB_{\omega k,a}+N_{c}\Big{)}\overline{R }_{c}^{-1}\Big{(}B_{\omega k,a}^{T}P+N_{c}^{T}\Big{)}+C_{\omega k}^{T}Q_{c}C_{ \omega k}=0. \tag{76}\]  
Then, implement the controller  
\[u_{c}(t)=\eta u_{a}(t)=\eta\Big{(}u_{fa}(t)-\overline{d}_{\alpha}(t)\Big{)}. \tag{77}\]  
The architecture of the compensation improvement OKID-based LQAT disturbance estimator with a low-pass filter for the singular system is depicted in Figure 1. If the corresponding slow subsystem of the given singular system \((A_{\omega k},B_{\omega k},C_{\omega k},D_{\omega k})\) is square, then set \(\eta=I_{m}\) in Equation (77) and Figure 1.  
Remark 1 Consider a class of linear strictly proper regular systems with an uncertain time-varying state delay and an unknown input vector (to be estimated). The system also has another unknown disturbance vector, which might contain either the parameter uncertainties or non-linearities, and can be treated as an additional unknown input vector to the system. As a result, two unknown input vectors exist that act on the system. The method developed in [11] consists of designing reduced-order functional observers to simultaneously estimate the states and the first unknown input vector (but not the unknown disturbance vector, which represents uncertainties or non-linearities) of the aforementioned systems (see Chapter 7 in [11]). As an alternative, in the approach proposed in this paper, the uncertainties or state-dependent non-linear disturbances have been theoretically merged into the first unknown input vector, such that the combined unknown input vector is estimated. From the point of view of the design methodology, it is important to point out that the proposed two-norm minimization approach performs well for the system addressed in this paper, however not for general uncertain systems or non-linear systems. Specifically, the H-infinity-norm minimization approach is suggested for uncertain systems and will be considered as a future research topic.
## 1 Introduction  
Standing posture requires good spinopelvic balance, obtained through a balanced pelvic and spinal shape [1, 2]. Sagittal profile spinal parameters play a crucial role in determining disability, health-related quality of life and therapeutic options [3, 4]. From a surgical point of view, restoring spinopelvic balance is fundamental to maximize functional outcomes, reduce pain, and avoid complications [5]. The pelvic and spinal parameters showed high correlations in healthy populations [6, 7] and significant individual variability [8]. The normative values of spinopelvic parameters in adolescents and children have been reported in healthy subjects [9, 10, 11] and patients with spinal deformities [12, 13, 14, 15], showing higher variability than in adults [9].  
With the practical aims of planning surgery and reducing failures, [PERSON] suggested four categories in healthy adults. Roussouly classification is based on the Sacral Slope (SS), the pelvic parameter best correlating with spinal sagittal curves [16]. This approach offers an easier and practical way to classify subjects before surgery, as shown by the number of studies based on the Roussouly classification and new classificationsproposed with the same purposes [17]. Contrarily, there are almost no studies in the field of conservative interventions, where current sagittal classification seems less valuable.  
Bracing is effective in controlling scoliosis evolution [18]. Scoliosis bracing aims to correct the frontal plane, while keeping the best possible sagittal balance [19]. Nevertheless, the sagittal plane constitutes a significant clinical problem with a frequent flat back induction [5; 20], and possible in-brace unbalance [21]. The immediate effects of bracing pose the same problems as surgery to the sagittal balance because it fixes the trunk and spine in a specific position for long durations. Nevertheless, bracing action aims at improving spinal balance at the end of growth. Moreover, children have different sagittal balance compensation mechanisms than adults because their spine and joints are less rigid, and their bodies constantly change toward adulthood. For this reason, we hypothesize that a classification of sagittal balance based on a reliable, anatomical parameter, such as Pelvic Incidence, could be more helpful in bracing children.  
The present study aims to (1) assess if the distribution of the Roussouly types differ in children from adults; (2) if so, identify the correct boundaries of Roussouly types in normal children of different age and sex; (3) provide a new classification to the sagittal balance of the growing spine, similar to the Roussouly, but based on the anatomical parameter PI instead of the positional SS; (4) compare the two classifications. The overall aim is to give researchers and clinicians in the field of conservative interventions a better understanding of sagittal balance in children.
## 4 Results and Discussion
### Performance Analysis
#### 4.5.3 Analysis of ROC  
The proposed method uses the SVM classifier to label the input obtained from the EDRL model. The classification performance is enhanced using the feature representations obtained from the DAE model.  
The receiver operating characteristic (RoC) curve of the proposed approach is presented in Figure 14. From the curve, it can be assured that the true positive rate (TPR) offered by the proposed approach is predominantly higher than its FPR. The area under the curve (AUC) value of the proposed approach is 0.997, which proves that the proposed approach provides better and more accurate classification results with minimum FPR. When the FPR is low, the proposed approach produces very low false classification and better accuracy. The proposed work uses the SVM model to classify normal and attack instances, and this model resulted in better TPR during the entire classification phase.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Matrices** & **RF** & **CNN** & **GGRU** & **DQN** & **Proposed** \\ \hline Accuracy (\%) & 97.4 & 97.7 & 97.8 & 98.5 & 99.6 \\ Precision (\%) & 97.2 & 97.4 & 97.5 & 98.2 & 99.4 \\ Recall (\%) & 97.3 & 97.5 & 97.6 & 98.3 & 99.1 \\ F1-score (\%) & 97.1 & 97.2 & 97.4 & 98.1 & 99.3 \\ FPR (\%) & 0.028 & 0.026 & 0.021 & 0.015 & 0.003 \\ Kappa (\%) & 94.3 & 94.0 & 95.7 & 96.8 & 99.2 \\ MCC (\%) & 93.1 & 94.57 & 95.12 & 96.43 & 99.21 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of the proposed and existing works.  
Figure 14: RoC curve analysis of the proposed work.  
Figure 13: MCC comparison of the proposed and existing works.
# Information-Theoretic Analysis of a Family of Improper Discrete Constellations
## 4 Information-Theoretic Analysis
### Asymptotic Results at Low SNR
: _Equation (21) shows that at low \(\operatorname{snr}\), the behavior of the proposed family of improper constellations \(\mathcal{A}(\phi,\kappa,M)\) only depends on the circularity coefficient \(\kappa\), but neither on the phase \(\phi\), nor on the cardinality \(M\) of \(V\)._  
A more elaborate proof derived directly on the complex field gives us the MI third-order expansion around \(\operatorname{snr}=0\). This is stated in the next theorem, which is proven in Appendix B. **Theorem 3**. : _Let \(V\) be a discrete improper random variable with circularity coefficient \(0\leq\kappa\leq 1\) taking values in \(\mathcal{A}(\phi,\kappa,M)\) and constructed as in (16) with \(X\) being a square proper \(M\)-QAM constellation. Then, the mutual information between \(Y\) and \(V\) in (5) admits the following third-order expansion around \(\operatorname{snr}=0\):_  
\[\operatorname{I}(\sqrt{\operatorname{snr}}\,V+N;V)=\operatorname{snr}-\frac{1} {2}(1+\kappa^{2})\operatorname{snr}^{2}+\frac{1}{3}(1-\kappa^{2}) \operatorname{snr}^{3}+O(\operatorname{snr}^{4}). \tag{28}\]  
Proof. : See Appendix B. **Remark 4**. : _The asymptotic expansion of the capacity (8) of the AWGN channel when transmitting improper Gaussian codewords is:_  
\[\frac{d}{d\,\mathrm{snr}}\mathrm{I}(\sqrt{\mathrm{snr}})=\mathrm{mmse}(\sqrt{ \mathrm{snr}}), \tag{24}\]0  
_Comparing Equations (28) and (29), we see that at low \(\operatorname{snr}\), the behavior of the mutual information for the discrete improper constellation \(V\) and the improper Gaussian capacity is the same up to the second-order term._
# Planar Delaunay Mesh Smoothing Method Based on Angle and a Deep Q-Network
## 4 Results and Discussion
Figure 20: Example 1—local comparison of each method after smoothing. (**a**) Laplacian; (**b**) angle-based; (**c**) ODT; (**d**) CVT; (**e**) unconstrained DQN; and (**f**) constrained DQN. As shown in Figure 22, the six methods have similar optimization effects for Example 3. As shown in Figure 23a-d, Example 4 still has many poor elements after smoothing through Laplacian, angle-based, ODT, and CVT. The polygon formed by these poor elements is marked with red lines. Due to the influence of geometric shapes, the improvement effect of these four methods on low-quality elements is limited. Comparing Figure 23e,f, both unconstrained and constrained DQN can improve the shape of these poor elements, but the quality of element 1 in Figure 23f is significantly higher than that in Figure 23e. The optimization effect for low-quality elements also needs to consider the number of poor elements after optimization. First, there are no elements with a quality of less than 0.5 in the initial mesh. Second, elements with aspect ratio values of less than 0.8 are called low-quality elements. In summary, to investigate the optimization effect of the proposed method for low-quality elements, all elements of the four examples were taken as a whole, and the distribution of element quality between 0.5 and 0.8 was counted; the results are shown in Figure 24. It can be seen that the proposed method has the lowest number of poor elements after optimization, which also shows the advantage of the algorithm in improving the quality of poor elements in this paper. Figure 23: Example 4—local comparison of each method after smoothing. (**a**) Laplacian; (**b**) angle-based; (**c**) ODT; (**d**) CVT; (**e**) unconstrained DQN; and (**f**) constrained DQN. Figure 22: Example 3—local comparison of each method after smoothing. (**a**) Laplacian; (**b**) angle-based; (**c**) ODT; (**d**) CVT; (**e**) unconstrained DQN; and (**f**) constrained DQN. ### Comparative Analysis of Experiments  
In this paper, finite element analysis is carried out using two arithmetic examples to verify the superiority of the proposed method. Example 1 is an L-shaped plate with 788 elements and 435 nodes; Example 2 is a gravity dam with 3178 elements and 1665 nodes.