esa-sceva/satcom-chunk-collection huggingface dataset:
# Quality of Experience in 6G Networks: Outlook and Challenges
## 3 Enabling Technologies of Sixth Generation Networks
### Intelligence and Autonomy  
The softwarization of 5G, coupled with the introduction of analytics (through NWDAF), points to a network where intelligence is more finely fused with all aspects of network operation. This has been previously envisioned (e.g., in [17, 18]), whereby a hierarchical structure of intelligence that can address different operational tasks is introduced.  
In 6G networks, distributed forms of learning, e.g., federated learning, seem to hold strong potential. This learning distributes the processing aspects of learning in a networked structure, i.e., parallelizing the computational requirement of learning across multiple nodes. The neural or Bayesian network would be mapped over the physical processors, either IoT devices or edge-computing cores [58, 59].  
A view of the coupling between computing and intelligence here allows for another view. As will be discussed in the next section, certain services will rely on localized computing. This \"split\" or \"fog\" computing can be exploited to accelerate specific learning and decisions at the network peripherals [7]. Computing assignments at different levels of the intelligence hierarchy can then be made depending on the nature of the problem. For example, long-term, \"deep-thought\" processing can be made in the cloud, and real-time, reflex-like processing can be allocated to the fog tiers.
## 5 Results and Discussions
### Numerical Analysis on Benchmark Functions
#### 5.3.4 Population Sizes Change
The COBRA-fas method with the first problem could not make a decision regarding which component was the best during the first \(150,000\) calculations, but later the population of the WPS algorithm started to increase gradually and the population of the FSS algorithm became three times greater than it was initially. For the second stated problem, the PSO component appeared to be the most successful at the beginning of the optimization process. However, its population size did not change after \(20,000\) calculations. On the other hand, the population size of the FSS component increased after \(50,000\) calculations and it had the largest amount of resources by the end of the optimization process. For the last problem, as with the Discuss function, COBRA-fas could not determine the winner component algorithm at first, but then it increased the population of the FFA approach and minimized the sizes of all other populations down to zero simultaneously. As for the COBRA-SHA modification, it did not minimize population sizes down to zero, thus, provided a more diverse set of potential solutions. Regarding the Discuss problem, even though the FFA component gave better results than other biology-inspired algorithms at first, the WPS component started to outperform it quite early. Therefore, by the end of the optimization process the WPS component had the largest amount of resources, and the FFA approach the second largest, while the populations of other components had at least 20 individuals. A similar situation can be observed for the HGBat Function with FSS and PSO components as winners. As for the last benchmark problem, during the first \(70,000\) calculations the population of the FFA algorithm increased and consisted of more than 100 individuals, while at the same time, the population sizes of other algorithms were close to 20 and did not change. Nevertheless, the population size of the WPS component then started to increase significantly, still delivering goal function improvements. By the end of the optimization  
Figure 11: Population size changes for SET-2 with 30 variables. process, the WPS component algorithm had the largest number of individuals, yet that number was close to the number of individuals of the FFA's population. Thus, the demonstrated cases represent different scenarios where the resource tuning is helpful, as it is able to change the algorithm structure in accordance with the current requirements.
# A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model
## 4 Experiments Results  
### Dataset Generation  
At present, low-light image enhancement methods based on deep learning still need development and improvement. As, deep learning requires a large number of training samples, it is difficult to take low-light image and ground truth in the same scene. Therefore, we propose a generation method of training samples, which is simple for implementation and also less time-consuming.  
Figure 7: The feature maps learned in the nonlinear mapping part.  
According to Equation (1), the low-light image is the product of the illumination and the reflection component (namely, ground truth). In order to cover as many low-light images as possible, we have collected 500 images which captured by high quality sensors from the BSD dataset [25] as reflective component, then a total of 256,000 image patches with size 40 \(\times\) 40 are randomly selected for training. Assume the illumination component \(L\) with the uniform distribute, \(L\sim U(0,1)\), then the synthetic low-light image patch can be represented as \(J(x,y)=L\cdot R(x,y)\).
# A Study on Aeroengine Conceptual Design Considering Multi-Mission Performance Reliability
## 1 Introduction
For instance, an aircraft equipped with a long-term used engine with uncertain performance degradation might be incapable to reach the desired flight range or combat radius in increment weather conditions. Moreover, its actual taking-off running distance might exceed the safety expectation due to the insufficient net thrust in hot days. On the other hand, excessive performance redundancy results in manufacturing cost increasing and brings extra troubles for turbomachinery component design as well. The reason is that the aerodynamic and structural design of turbomachinery are strictly in accordance with the aeroengine overall performance requirements, which are substantially determined by the key design parameters. For example, the high-pressure turbine needs to enhance its cooling system or require more advanced high temperature resistant materials for blades to withstand the hotter gas flow from combustor outlet. Without new technical breakthroughs in axial compressor aerodynamic design, further boosting always means extra pressurization stages. These not only challenge the structural strength design due to the increase of weight and size, but also escalate the costs of producing a new engine and even the aircraft. To sum up, the aeroengine conceptual design is already an extremely complicated problem, which involves the interaction of each component and the coupling of multiple disciplines. When the existence of uncertainty factors cannot be ignored, solving this problem becomes more difficult. Therefore, the traditional conceptual design method is facing challenges and it is worthwhile devoting much effort to this. To overcome the above challenge, the reliability-based multi-design point methodology is proposed for the conceptual design of aeroengine. Employing the artificial neural network (ANN) surrogate models, this unconventional approach integrates performance reliability analysis under multiple working conditions, and facilitates the optimization design procedure. The proposed methodology could efficiently obtain the feasible design scheme, which precisely creates the expected performance redundancy and also conduces to the control of technical risk and manufacturing cost. This paper is organized as follows. Section 2 presents a literature review. Section 3, the studies on artificial neural network for performance reliability prediction, including the generation and preprocessing of training samples and a hybrid algorithm for neural network training with the pre-training technique. In Section 4, the modeling of component-level aeroengine with uncertainty component performance is introduced, and the reliability-based multi-design point methodology is illustrated to determine the key design parameters. Section 5 validates the proposed methodology by the application of reliability-based aircraft engine thermodynamic cycle design. Conclusions and perspectives are given in Section 6.
## 2 Methodology  
### Generalized Asymmetric Correntropy  
First, a brief review of the correntropy and the asymmetric correntropy is given.  
Correntropy in information-theoretic learning (ITL) [8] is used to measure the similarity of two random variables, and is defined as:  
\[V(D,Y)=E[\kappa(D,Y)]=\int\kappa(d,y)dF_{DY}(d,y), \tag{1}\]  
where \(D,Y\) denote two random variables; \(E[\cdot]\) is the expectation operator; \(F_{DY}(d,y)\) represents the joint distribution function of \((D,Y)\); and \(\kappa(\cdot)\) stands for a Mercer kernel which is in general the Gaussian kernel defined as:  
\[\kappa_{\sigma}(d,y)=G_{\sigma}(\epsilon)=\frac{1}{\sqrt{2\pi\sigma}}\exp \left(-\frac{|\epsilon|^{2}}{2\sigma^{2}}\right), \tag{2}\]  
where \(\sigma\) is the Gaussian kernel width, \(\epsilon=d-y\), and \(1/\sqrt{2\pi\sigma}\) is the normalization parameter.  
In practical adaptive filtering, the available data \(\{d_{k},y_{k}\}_{k=1}^{N}\) of \(D,Y\) are discrete and the joint distribution \(F_{DY}(d,y)\) is unknown. So the cost function based on correntropy can be presented as the sample mean estimator:  
\[\hat{V}_{N}^{\sigma}(D,Y)=\frac{1}{N}\sum_{k=1}^{N}\kappa_{\sigma}(d_{k}-y_{k }). \tag{3}\]  
For some asymmetric noise, the maximum correlation entropy criterion based on a Gaussian kernel is no longer applicable. MACC replaces the Gaussian kernel in the maximum correntropy criterion with the asymmetric Gaussian kernel, in which different kernel widths are used to deal with the different distributions on both sides of the asymmetric noise peak.  
The expression of an asymmetric Gaussian kernel is  
\[\kappa(d,y)=AG_{\sigma_{+}\sigma_{-}}(\epsilon_{k})=\left\{\begin{array}{ll} \exp\left(-\frac{\epsilon_{k}^{2}}{2\sigma_{+}^{2}}\right)&,&\epsilon_{k}\geq 0 \\ \exp\left(-\frac{\epsilon_{k}^{2}}{2\sigma_{-}^{2}}\right)&,&\epsilon_{k}<0 \end{array}\right., \tag{4}\]  
where \(k\) of \(\epsilon_{k}\) stands for the time subscript.  
To enhance the adaptability of the asymmetric correntropy-based robust adaptive filtering, this paper builds the cost function based on a new generalized maximum asymmetric correntropy criterion by adding a shape parameter to the asymmetric Gaussian kernel and extending the asymmetric Gaussian kernel to the generalized asymmetric Gaussian (GAG) kernel.  
By combining the definitions of the Gaussian kernel, generalized Gaussian kernel, and asymmetric Gaussian kernel, the generalized asymmetric Gaussian kernel is obtained as  
\[\kappa(d,y)=GAG_{\alpha,\sigma_{+},\sigma_{-}}(\epsilon_{k})=\left\{\begin{array} []{ll}\exp\left(-\left|\frac{\epsilon_{k}}{\sigma_{+}}\right|^{\alpha}\right) &,&\epsilon_{k}\geq 0\\ \exp\left(-\left|\frac{\epsilon_{k}}{\sigma_{-}}\right|^{\alpha}\right)&,& \epsilon_{k}<0\end{array}\right., \tag{5}\]  
where \(\alpha\) is the shape parameter of the kernel function.
# A Novel Cloud Approach for Connected Vehicles
## 3 GeoVCDN Approach
### Centralized Context Cloud Architecture
#### 3.1.1 Static Part  
The static part of the context cloud is composed of the roadside units (RSU) and a central station named C-ITS-C (cooperative intelligent transport system central). RSUs are the gateways between the two parts of the cloud. They communicate using ITS-G5 with the mobile node and are wired to the C-ITS-C, which is responsible for the computing and distribution of the data in the static part. We consider that in the connectivity between the RSUs and the C-ITS-C, the computing resources do not suffer from any constraints: the central station can be distributed regarding the need and the size of a context. This is a topic related to edge computing that we do not treat in this paper. This would also address the different issues related to centralization (bottleneck, delay, failures, etc.).  
Figure 2: GeoVCDN exchanges.
# Decentralized Robust Control of a Network of Inverter-Based Distributed Generation Systems
## 4 Simulation Results
### Performance of the Proposed Control Scheme and Independent Control Design  
The interconnection impedances, which consist of inductive and resistive components, are proportional to the distances among the inverters. They play an essential role in the strength of the interconnections among the inverters, and as a result in the transient and steady state behavior of the entire system. Figure 6 illustrates the time response of the proposed sequentially-designed decentralized controllers for inverter #4 (\(Inv_{4}\)). It is clear that the sequentially-designed controllers accurately follow their reference voltages. On the other hand, Figure 7 demonstrates that the independently-designed controllers (benchmark [35]) are not capable of damping the undesired oscillations caused by the strong coupling interconnections among the inverters. It should be noted that the results for the other five inverters in Figure 5 were similar to those of inverter #4; hence, they are not shown here.  
In the following subsections, more case studies are presented to demonstrate the acceptable performance of the proposed decentralized robust controllers in terms of transient overload, nonlinear load, and robustness.  
\begin{table}
\begin{tabular}{c l} \hline \hline \begin{tabular}{c} Inverter 1 \\ \end{tabular} & \begin{tabular}{c} \(M_{inv_{1}}=1\), \(L_{1}=3.0\) (mH), \(C_{1}=2.0\) (\(\mu\)F), \\ \(L_{load_{1}}=1.0\) (H), \(R_{load_{1}}=630\) (\(\Omega\)) \\ \end{tabular} \\ \hline \begin{tabular}{c} Inverter 2 \\ \end{tabular} & \begin{tabular}{c} \(M_{inv_{2}}=1\), \(L_{2}=2.8\) (mH), \(C_{2}=2.2\) (\(\mu\)F), \\ \(L_{load_{2}}=1.2\) (H), \(R_{load_{2}}=632\) (\(\Omega\)) \\ \end{tabular} \\ \hline \begin{tabular}{c} Inverter 3 \\ \end{tabular} & \begin{tabular}{c} \(M_{inv_{3}}=1\), \(L_{3}=2.9\) (mH), \(C_{3}=2.1\) (\(\mu\)F), \\ \(L_{load_{3}}=0.9\) (H), \(R_{load_{3}}=628\) (\(\Omega\)) \\ \end{tabular} \\ \hline \begin{tabular}{c} Inverter 4 \\ \end{tabular} & \begin{tabular}{c} \(M_{inv_{4}}=1\), \(L_{4}=3.2\) (mH), \(C_{4}=2.3\) (\(\mu\)F), \\ \(L_{load_{4}}=1.3\) (H), \(R_{load_{4}}=635\) (\(\Omega\)) \\ \end{tabular} \\ \hline \begin{tabular}{c} Inverter 5 \\ \end{tabular} & \begin{tabular}{c} \(M_{inv_{5}}=1\), \(L_{5}=3.1\) (mH), \(C_{5}=2.2\) (\(\mu\)F), \\ \(L_{load_{5}}=1.1\) (H), \(R_{load_{5}}=631\) (\(\Omega\)) \\ \end{tabular} \\ \hline \begin{tabular}{c} Inverter 6 \\ \end{tabular} & \begin{tabular}{c} \(M_{inv_{6}}=1\), \(L_{6}=2.7\) (mH), \(C_{6}=1.9\) (\(\mu\)F), \\ \(L_{load_{6}}=1.4\) (H), \(R_{load_{6}}=634\) (\(\Omega\)) \\ \end{tabular} \\ \hline \
\begin{tabular}{c} Interconnection Lines \\ \end{tabular} &
\begin{tabular}{c} \(R_{L}=0.642\) (\(\Omega\)/km), \\ \(L_{L}=2.2\times 10^{-4}\) (H/km) [39] \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Values of the system parameters.  
Figure 5: A network of IBDGs including six inverters.
# Two-Level Sharing and Extraction of Sensing Information with Hybrid V2X Communications
## 7 Conclusions  
This paper examines the extraction of sensing information from vehicles for the extended sensing-perception at V2X edge servers. The proposed method enables the combination of local cooperation among vehicles and vehicle-edge communications in  
Figure 16: Average resource consumption in Scenario 4.  
the extraction of sensing information to reduce wireless resource consumption and the traffic load at the V2X edge server. We employed the MinFactor of the sensing results as the main example of target information extraction. The MinFactor extraction can be applied to assess the appropriate broadcast time, channel condition, and driving safety. The evaluation results reveal that the number of global reports and the wireless resources consumption are significantly reduced by utilizing the proposed scheme in the extraction of sensing information from vehicles. The proposed method reduces the report traffic at the BS/AP by utilizing the local exchange and extraction of sensing results among vehicles before the global collection and extraction of sensing information at the edge server. This procedure allows the system to avoid the unnecessary reports to the edge server and highly reduces the traffic of sensing information reports. The proposed method is expected to be applied to reliable broadcasts in V2X and to maneuver coordination among vehicles. For instance, utilization of the proposed method facilitates the setup of the transmission power in broadcasts at the BS/AP according to the extracted RSSI or RSRP information of vehicles.  
Conceptualization, R.T. and K.S.; investigation, R.T. and K.S.; validation, R.T. and K.S.; writing--review and editing, R.T. and K.S. All authors have read and agreed to the published version of the manuscript.  
This work was partly supported by JSPS KAKENHI Grant Number JP20H00589.  
Not applicable.  
The authors would like to thank members of the Dynamic Map 2.0 Consortium for advice.  
The authors declare that there is no conflict of interest.
## 4 Control System Design
### Yaw Rate Controller  
The yaw rate controller and surge speed controller are designed to achieve the control of the heading angle and speed of USV.  
Step 1: Define a heading tracking error \(\psi_{e}\) as:  
\[\psi_{e}=\psi-\psi_{d} \tag{35}\]  
From (3) and (9), the time derivative of \(\psi_{e}\) is given by:  
\[\dot{\psi}_{e}=r-\dot{\psi}_{d} \tag{36}\]  
In order for \(\psi_{e}\to 0\), \(\alpha_{r}\) is selected as the virtual control.  
\[\alpha_{r}=-k_{1}\psi_{e}+\dot{\psi}_{d} \tag{37}\]  
where \(k_{1}\) is a positive design parameter. Make \(\alpha_{r}\) pass through a neural shunting model, then one can obtain:  
\[\dot{\beta}_{r}=-A\beta_{r}+(B-\beta_{r})f(\alpha_{r})-(D+\beta_{r})g(\alpha_{ r}) \tag{38}\]The meaning of each symbol has the same meaning as those defined in (32). Define:  
\[r_{e}=r-\beta_{r} \tag{39}\]  
From (9) and (38), the time derivative of (39) is given by:  
\[\dot{r}_{e}=f_{r}+\frac{1}{m_{33}}\tau_{r}+\frac{1}{m_{33}}b_{r}+\Theta_{r} \tag{40}\]  
where \(\Theta_{r}=[A+f(\alpha_{r})+g(\alpha_{r})]\beta_{r}-[Bf(\alpha_{r})-Dg(\alpha_ {r})]\).  
The neural network minimum parameter learning method is used to estimate the uncertain function \(f_{r}+\frac{1}{m_{33}}b_{r}\). Its approximation error is \(\varepsilon_{r}\), and \(|\varepsilon_{r}|\leq\varepsilon_{r},\varepsilon_{r}>0\).  
The corresponding control law is chosen as:  
\[\tau_{0r}=-k_{re}\varepsilon_{e}-m_{33}\Theta_{r}-\psi_{e}-\frac{1}{2}r_{e} \phi_{r}h^{T}h \tag{41}\]  
where \(k_{re}\) is a positive design parameter and \(h\) is the shorthand for \(h(x)\).  
\[\dot{e}_{r}=\left\{\begin{array}{ll}-K_{cr}e_{r}-\frac{|r_{e}\cdot\Delta\tau _{r}|+0.5\Delta\tau_{r}^{2}}{|\sigma_{r}^{2}|}\cdot e_{r}+\Delta\tau_{r},| \varepsilon_{r}|\geq\zeta_{r}\\ 0,&|\varepsilon_{r}|<\zeta_{r}\end{array}\right. \tag{42}\]  
where the meaning of each symbol has the same meaning as those defined in (34). The ultimate control law for \(r\) is selected as:  
\[\tau_{r}=\left\{\begin{array}{ll}\tau_{r}\max,&\quad if\ \tau_{0r}>\tau_{r}\max\\ \tau_{0r},&\quad if\ \tau_{r}\min\leq\tau_{0r}\leq\tau_{r}\max\\ \tau_{r}\min,&\quad if\ \tau_{0r}<\tau_{r}\min\end{array}\right. \tag{43}\]  
where the meaning of each symbol has the same meaning as those defined in (33).  
The adaptive law of the estimation function is:  
\[\dot{\phi}_{r}=\frac{1}{2}\gamma r\tau_{e}^{2}h^{T}h-\kappa_{r}\gamma_{r}\dot {\phi}_{r} \tag{44}\]  
where \(\gamma_{r}\) and \(\kappa_{r}\) are two positive design parameters.
## 5 Physical Explanation and Equivalent Circuit
Similarly, impedance corresponding to C\({}_{1}\), Z\(\_\)C\({}_{1}\) will be large in the first frequency band while it will become much smaller in the second frequency band. Figure 8: Electric field strength at (a) f\({}_{1}\) = 1.398 THz, (b) f\({}_{2}\) = 4.82 THz. Figure 10: Equivalent circuit for the proposed converter under incident (a) X polarization, (b) Y polarization. Figure 9: Magnetic field strength at (a) f\({}_{1}\) = 1.398 THz, (b) f\({}_{2}\) = 4.82 THz. Now it will be explained how dual polarizations exist within two bands. For this, assume Z_L1 (f1) and Z_L1 (f2) represent impedances corresponding to L1 at f1 and f2, respectively. Similarly, Z_C1 (f1) and Z_C1 (f2) represent impedances corresponding to C1 at f1 and f2, respectively, and Z_L2 (f1) and Z_L2 (f2) represent impedances corresponding to L2 at f1 and f2, respectively. f1 corresponds to any frequency within the first frequency band while f2 corresponds to any frequency within the second frequency band. Thus, \(\left|\text{Z\_L1}\right|\) (f1) \(\left|\text{<Z\_L1}\right|\) (f2), \(\left|\text{Z\_C1}\right.\)(f1) \(\left|\text{>>Z\_C1}\right.\)(f2), \(\left|\text{Z\_L2}\right.\)(f1) \(\left|\text{<Z\_L2}\right.\)(f2)\(\left|\text{U\_C1}\right.\)(f2). Overall impedance for the top layer can be calculated as \(\left|\text{Z\_L1}\right|\left|\left|\text{Z\_L1}\right|\right|\) (\(\text{2\times\left|\text{Z\_C1}\right|}+\left|\text{Z\_L2}\right|\)). At f1, \(\left|\text{Z\_C1}\right|\) will be larger than that at f2, \(\left|\text{Z\_C1}\right|\) (f2). Thus, the \(\text{2\times\left|\text{Z\_C1}\right|}+\left|\text{Z\_L2}\right|\) component will be larger. Hence, the overall effect for a parallel combination of large capacitive impedance and small inductive impedance will be small inductive impedance. For a frequency f2 in higher frequency band, \(\text{2\times\left|\text{Z\_C1}\right|}+\left|\text{Z\_L2}\right|\) will become small and the overall effect will be capacitive. At f2, the resultant impedance will be a parallel combination of large inductance and small capacitance, which will result in small capacitive effect. Thus, the overall change in impedance behavior at f2 explains the idea of dual polarization.
# Narrowband Internet of Things (NB-IoT):
## 5 Open Research Questions and Discussion
### Random Access  
Massive NB-IoT modules that try to request the radio channel resources at the same time for uplink data transmission may suffer from random access preamble collision. This is caused by several factors such as detection inaccuracy that may not satisfy the detection threshold, the high probability of false alarm, etc. Several works have proposed random access preamble detection algorithms (i.e., random access with differential barring etc.) and others have developed mathematical models to characterize the preamble transmissions in order to improve the NPRACH success rate and better time-of-arrival estimation and other NPRACH performance improvements. However, it is still unclear which scheme is effective for massive deployment, since most of the proposed schemes do not consider the heterogeneous network architecture, channel estimation impairments, or realistic channel conditions [98; 99].