esa-sceva/satcom-chunk-collection huggingface dataset:
# Compacted Area with Effective Links (CAEL) for Data Dissemination in VANETs
## 4 Proposed Protocol
### Route Selection  
When the very first RREQ packet is received at the target node H within the trilateral zone, a timer is started that waits for a specified amount of time. During this time, the target node begins the collection of all other RREQ packets that attempt to reach it. When the timeout event occurs, the target node computes the RF value for each path gathered from the source to the target and chooses the routing path with the highest value of RF.  
To understand how CAEL's route selection process works, consider the network design shown in Figure 6 where multiple paths are available to reach from the source to the target node. The calculation of the best possible RF is shown in Figure 7 and it can be observed that \(RF_{Route1}\) has the highest calculated RF value among all other viable routes. Therefore, Route#1 will be selected for the data transfer.
# An Evaluation Survey of Knowledge-Based Approaches in Telecommunication Applications
## 3 Evaluation of Different Knowledge-Based Approaches
### Assessment Results
#### 3.3.6 Simulation-Based Formalisms  
Simulation-based models, often using mathematical or computational models, may not offer the expressiveness found in logic-based or graph-based representations. However, they aim for high accuracy by reflecting real-world behavior and providing precise predictions based on well-defined models. Simulations excel in precision, capturing fine-grained nuances and behaviors of the modeled systems comprehensively. These representations strive to cover a broad spectrum of behaviors and scenarios, although achieving this may require thorough modeling of each aspect. While they generally generalize within modeled domains, they might face limitations when applied to vastly different scenarios.  
Simulations generate results according to certain distributions and input parameters, maintaining consistency by adhering to defined physical laws or constraints within simulated environments. However, they might not explicitly capture uncertainty inherent in real-world scenarios, hindering certainty representation. Interpreting complex interrelationships within simulations might require specific expertise, impacting their explainability. Confidence estimation relies on input data accuracy and model validation, but lacks inherent methods to quantify confidence levels directly. Expanding simulations often involves modifying underlying models or parameters, posing challenges in accommodating additional or new knowledge seamlessly. Scaling simulations might be computationally complex, especially for real-time, large-scale, or dynamic systems.  
In summary, simulation-based representations offer accurate, precise, and comprehensive representations of modeled systems. However, challenges in scalability, interpretability, and adaptability to new knowledge may limit their expandability and generalizability across diverse domains.
## 2 State-of-the-Art Smart Transport Infrastructure
### Vehicular Network  
Machines are meant to do specific tasks in a distributed mechanism [49]. In such phenomena, immense duties may be accomplished faster by employing more machines instead of a reduced number of machines. Optimization is accomplished by adjusting the ratio of the advantages acquired from early completion in relation to the cost of machine operations. Machines were initially designed to be static and serve only a single function. Afterward, advancements were made to these machines in the second generation, allowing machines to tackle comparable tasks with a single instance. Electronic advancements and the advent of chips have greatly enhanced the current lifestyle, and the third generation of machines has increased control, accuracy, and automation. Design, awareness, and intelligence are all significantly improved in fourth-generation machines. 3D printing, nanotechnology, and artificial intelligence make it easier for people to be replaced by machines in activities that demand much effort and/or are conducted in hazardous situations. The interconnected, context-aware, decision-making intelligent machines that will drive the progress into the fifth generation are the emerging trend in this sector [50; 51]. Figure 3 shows the overview of the evolution of technology in machines.  
Decision support systems operate as the brain of the STI. They acquire data from many sources, including automobiles, roadside sensors, roadside monitors, smart cameras,  
Figure 2: Smart transport infrastructure.  
and weather sensors. One of the systems exploiting the powers and empowerment of the fifth-generation machine is the smart vehicular network. In the situation of smart cities, smart transportation is the critical aspect of the total infrastructure [52]. The smart transportation infrastructure is a complex mesh of network devices, smart cars, monitoring systems, sensors, image and vision recognition, peer-to-peer communication, and risk management techniques to accomplish a shared goal.  
The smart vehicles of the modern day have connectivity and compute engines that process the data and transmit/receive information inside and outside the vehicle. The On-Board Computer (OBC) interconnects major components/systems inside the vehicle and manages information exchanges. However, the foremost impacting ability of today's smart cars is to communicate and coordinate with vehicles and other road infrastructure components surrounding them. The new genre of vehicles is smart and linked [53]. This smartness and connected approach eliminate many concerns and obstacles in advancing towards completely autonomous smart vehicle operations, yet they confront numerous open challenges. These challenges include unstable network nodes (vehicles), high throughput requirements to be real-time operations, the common language of communication among vehicles from different vendors, privacy and security challenges, and integrity of the data being shared over these networks [54].
###### Abstract  
Many remote sensing metrics have been applied in large-scale animal species monitoring and conservation. However, the capabilities of these metrics have not been well compared and assessed. In this study, we investigated the correlation of 21 remote sensing metrics in three categories with the global species richness of three different animal classes using several statistical methods. As a result, we developed a new index by integrating several highly correlated metrics. Of the 21 remote sensing metrics analyzed, evapotranspiration (ET) had the greatest impact on species richness on a global scale (explained variance: 52%). The metrics with a high explained variance on the global scale were mainly in the energy/productivity category. The metrics in the texture category exhibited higher correlation with species richness at regional scales. We found that radiance and temperature had a larger impact on the distribution of bird richness, compared to their impacts on the distributions of both amphibians and mammals. Three machine learning models (i.e., support vector machine, random forests, and neural networks) were evaluated for metric integration, and the random forest model showed the best performance. Our newly developed index exhibited a 0.7 explained variance for the three animal classes' species richness on a global scale, with an explained variance that was 20% higher than any of the univariate metrics.  
b
# A 12 dBm B1 dB N-Path Notch Filter for Transmitter Leakage Suppression in Wideband Receiver
## 4 Circuit Implementation
### Eight-Phase 12.5% Duty Cycle Clock Generator  
The eight-phase 12.5% duty cycle clock generator schematic is shown in Figure 10. A single-ended sinusoidal clock input clock balun with \(4\times f_{LO}\) inputs is used to generate a symmetrical differential clock, LO+ and LO\(-\). The clock balun is realized by a same-size inverter with a transmission gate that compensates for delays to minimize differential signal phase errors. The divide-by-four ring counter circuit, composed of D flip-flops, generates eight phase-shifted signals, each with a 50% duty cycle and a 12.5% phase shift between successive paths. The eight-phase, 12.5% duty cycle clock is derived from eight NAND gates [19]. Preceding the eight-phase clock, a duty cycle calibration module and a buffer circuit ensure accurate signal generation. The large filter switch size leads to larger output buffer size and increased clock dynamic power consumption.
## 1 Introduction
_Distribution 3_: Asymmetric heavy-tailed distribution with probability density function \[g(r)=\tfrac{1}{c}\left[\left(1+\tfrac{\alpha}{2}\right)-\tfrac{ \alpha}{2}r^{2}\right],f(r)=\tfrac{1}{c}\left[\left(1+\tfrac{\alpha}{2}\right) +\left(\tfrac{1}{2}-\alpha\right)r^{2}+\left(\tfrac{\alpha-1}{2}\right)r^{3} \right],0\ \leq r\leq 1\] (8a) \[g(r)=\tfrac{1}{c}r^{-\alpha}\,,f(r)=\tfrac{1}{c}r^{-(1+\alpha)/2}\,,1<r<\infty\] (8b) (right tail heavier than left tail), where \[B(a):=\ \sum_{i}\lvert x_{i}-a\rvert^{p} \tag{2}\]0 (8c) (For Distribution 3, the \(\beta\) of condition (3b) is \((1+\alpha)/2\).) In Distributions 2 and 3, \(\alpha\) is a real number \(>1\). Gaussian Distribution 1 is used to show that the results discussed here are applicable not only to heavy-tailed distributions but also to light-tailed distributions. These results are applicable _a fortiori_ to compact distributions with no tails at all (tails uniformly 0). (Analysis and computations were carried out with the uniform distribution and with a pyramidal distribution, two distributions with no tails, but these results will not be discussed here.) While \(L^{p}\) and \(l^{p}\) averages can be calculated for light-tailed and no-tailed distributions, there are more meaningful and more efficient ways, for example, arithmetic averaging, to calculate central points of light-tailed and no-tailed distributions. \(L^{p}\) and \(l^{p}\) averages are most meaningful for heavy-tailed distributions.
## Appendix A Throughput under Changing Data Traffic  
Under less data traffic, buffer overflows are less frequent due to less buffer occupancy. Consequently, PRoPHET without a replication technique records relatively high throughput: since most messages remain in node buffers and are replicated whenever a more suitable relay node is encountered. However, as data traffic increases, buffer overflows become more frequent and the lack of a replication technique causes throughput to reduce more drastically. PRoPHET without a replication technique records the sharpest drop in throughput while incorporating a replication technique records more stable throughput values. From the lowest to the highest data traffic, PRoPHET without a replication technique experiences a 40.8% drop in throughput as compared with 16.5%, 19% and 21.2% for fixed-quota, LARep and ADS, respectively.  
While controlling replication with a fixed quota results in the most stable throughput under different data traffic, delivery guarantees are limited and relatively low. ADS records slightly less throughput since the replication quota does not exceed the fixed value. LARep records the highest throughput among the replication techniques as data traffic increases. The results suggest that incorporating LAMeD into PRoPHET improves throughput while our proposed replication mechanisms regulate generated copies to sustain delivery guarantees.
# Machine Learning-Based Methods for Enhancement of UAV-NOMA and D2D Cooperative Networks
## 1 Introduction  
Undoubtedly, the utilization of unmanned aerial vehicles (UAVs) as UAV flying base stations (UFBSs) is of potential interest in the context of new-generation wireless communication systems. UAV-enabled wireless communication systems can provide wireless coverage extension, capacity enhancement, communication restoration during disaster events, and aerial data collection within the framework of Internet of Things (IoT) applications [1, 2]. In contrast to conventional wireless communication systems that depend on fixed terrestrial infrastructures, UFBSs are dynamic and simple to deploy and reconfigure. Thus, their use introduces several degrees of freedom in terms of flexibility, wide coverage, and communication restoration during a disaster and temporary events. However, the anticipated advantages of deploying UFBSs are heavily contingent on their precise location within the region of interest to offer terrestrial users reliable and high-quality communication [3].
# Novelty Detection with Autoencoders for System Health Monitoring in Industrial Environments
## 6 Discussion
Similar results are obtained even considering a sudden fault in one of the components (See Table 5). In particular, if the fault (C4) is included in the training set, i.e., if the fault mode has already occurred in the past, both known and novel operating conditions are correctly detected, and the autoencoder-based approach provides better results than traditional approaches. However, in cases where the fault is not known at the moment of training, the ability to detect the condition C4 decreases as more operating conditions are considered in the training phase (look at the last scenario of Table 4). This result suggests that the proposed approach is able to determine the operating condition of the system, but has to be improved in order to perform a system-level analysis and component-level analysis simultaneously. Improvements in this direction will be the objective of further research. From the experiment conducted in Section 5.1.2, it emerges that the window size does not affect the performance of autoencoder-based approaches, which implies a lower dependency of the model from the parameters set by the user. In Section 5.2, the performance of the classification of the operating conditions are evaluated. The results show that the classification accuracy is high for all conditions except for condition C4. This means that when only considering a system-level analysis, the proposed approach recognizes exactly the current operating condition. In other words, a dataset collected during the machinery functioning in its actual environment is correctly and automatically labeled with the operating condition it refers to. Finally, the performance of the localization task is evaluated. The goal of this analysis is to determine whether there exists a signal, or a subset of signals, that mostly contributes to the novel condition detection. This evaluation is important for two reasons. First, selecting a smaller subset of input signals would reduce the quantity of data to transfer and analyze. Second, it helps to identify which signals are more relevant for the setting recognition problem, which is particularly important in making the results of deep learning approaches easier to interpret. However, considering the results summarized in Table 10, it emerges that the most relevant signals depend on both the model used and the condition implemented. Hence, the localization task is useful in identifying the signals that determine the change of the setting, but cannot be used as a feature selection method.
# An Unsupervised Image Denoising Method Using a Nonconvex Low-Rank Model with TV Regularization
## 3 Proposed Method  
The system framework of the proposed method is presented in Figure 1. As discussed, the RPCA model based on nonconvex \(\gamma\)-norm can improve the approximation accuracy of the rank function, and the TV regularization can solve the smoothing problem of the image's structure edge information. This section introduces our proposed RPCA model based on the nonconvex \(\gamma\)-norm and anisotropic total variation technique. The mathematical model of the method is as follows:  
\[\min_{\mathbf{L},\mathbf{S}}\|\mathbf{L}\|_{\gamma}+\lambda\|\mathbf{S}\|_{1}+ \tau\|\mathbf{L}\|_{TV}\text{ s.t. }\mathbf{D}=\mathbf{L}+\mathbf{S} \tag{6}\]  
where \(\mathbf{D}\) is the data matrix of the observed image; \(\mathbf{L}\) is the low-rank matrix; \(\mathbf{S}\) is the sparse matrix; \(\|\mathbf{L}\|_{\gamma}\) is the \(\gamma\)-norm of the matrix \(\mathbf{L}\); \(\|\mathbf{S}\|_{1}\) represents the \(l_{1}\)-norm of sparse matrix \(\mathbf{S}\); \(\|\mathbf{L}\|_{TV}\) represents the TV regularization of matrix \(\mathbf{L}\); \(\tau\) is a trade-off parameter for controlling the TV norm; \(\lambda\) is also a trade-off parameter; and \(\lambda=1/\sqrt{\max(m,n)}\). m and n are the dimensions of the observed image matrix \(\mathrm{D}\). Considering the nonconvex property of the \(\gamma\)-norm and the introduction of an anisotropic TV regularization, the augmented Lagrange multiplier algorithm [29], also known as the alternating direction method of multipliers (ADMM), is used to solve the optimization problem. We first introduce a new auxiliary variable \(\mathbf{Z}\) into Model (6):  
\[\min_{\mathbf{L},\mathbf{S}}\|\mathbf{L}\|_{\gamma}+\lambda\|\mathbf{S}\|_{1} +\tau\|\mathbf{Z}\|_{TV}\text{ s.t. }\mathbf{D}=\mathbf{L}+\mathbf{S},\mathbf{Z}=\mathbf{L} \tag{7}\]  
By adding the Lagrange multiplier and a positive punishment scalar, Formula (7) can be further derived as the following augmented Lagrange function:  
\[ \begin{split} h(\mathbf{L},\mathbf{S},\mathbf{Z},\mathbf{\Lambda }_{1},\mathbf{\Lambda}_{2})=&\operatorname*{arg\,min}_{ \mathbf{L},\mathbf{S},\mathbf{Z}}\|\mathbf{L}\|_{\gamma}+\lambda\|\mathbf{S} \|_{1}+\tau\|\mathbf{Z}\|_{TV}\\ &+\langle\mathbf{\Lambda}_{1},\mathbf{D}-\mathbf{L}-\mathbf{S} \rangle+\langle\mathbf{\Lambda}_{2},\mathbf{L}-\mathbf{Z}\rangle\\ &+\frac{\mu}{2}\Big{(}\|\mathbf{D}-\mathbf{L}-\mathbf{S}\|_{ \mathrm{F}}^{2}+\|\mathbf{L}-\mathbf{Z}\|_{\mathrm{F}}^{2}\Big{)}\end{split}  \tag{8}\]  
Figure 1: Illustration of the proposed method. where \(\mu>0\) represents a penalty parameter; \(\tau>0\) represents a penalty parameter; \(\langle\cdot,\cdot\rangle\) represents the inner product of two matrices, while \(\mathbf{\Lambda}_{1}\) and \(\mathbf{\Lambda}_{2}\) represent Lagrange multipliers; \(\|\cdot\|_{\text{F}}\) is the Frobenius norm of a matrix. The variable \(\mathbf{L}\), \(\mathbf{S}\), \(\mathbf{Z}\), \(\mathbf{\Lambda}_{1}\), and \(\mathbf{\Lambda}_{2}\) are updated under alternate iterations to improve the solution accuracy. The optimization process is summarized in Algorithm 1.
## 1 Introduction
Both \(C_{dl}\) and \(R_{ct}\) in \(BHLC_{ACqu}\) do not distinguish charging/discharging, too [15,18,20]. The impedance, on a single (positive or negative) electrode-electrolyte interface, consisting of one \(R_{ct}\) and one \(C_{dl}\), characterizes both charging and discharging at the same time. The authors of [21] also suggest that the long-chain series of an \(R_{ct}\parallel C_{dl}\) circuit can approximate the Nyquist curve as close as possible, but long-chain series elements do not have practical physical and electrochemical meanings, and cannot distinguish charging/discharging impedances either. \(FPLC_{DCqu}\), describing DC steady-state and transient-state volt-ampere characteristics of batteries, typically includes \(L_{S}\), \(R_{C}\), \(C_{bulk}\) and impedances on electrode-electrolyte interfaces (\(z_{eei}\)) [20]. \(z_{eei}\) distinguishes \(R_{ct}\) into charging (\(R_{ct,pc}\)_and_\(R_{ct,nc}\))/discharging (\(R_{ct,pd}\)_and_\(R_{ct,nd}\)) [2,20,22]. \(R_{ct,pc}\), \(R_{ct,nc}\), \(R_{ct,pd}\) and \(R_{ct,nd}\) in \(FPLC_{DCqu}\) are a function of the surface area of the solid electrolyte [2,3,22]. Surface areas of charging and discharging for electrochemical processes are mutually exclusive and collectively exhaustive [2,3,22]. Generally, they are different; therefore, charging (\(R_{ct,pc}\)_and_\(R_{ct,nc}\)) and discharging (\(R_{ct,pd}\)_and_\(R_{ct,nd}\)) charge-transfer resistors are different. However, \(C_{dl}\) in \(FPLC_{DCqu}\) does not distinguish charging/discharging [3,20], and only one capacitor (\(C_{dl,p}\) or \(C_{dl,n}\)) combines both charging and discharging DC transient-state volt-ampere characteristics of each electrode. \(C_{dl}\) is generated on a solid electrolyte interface and always in parallel with \(R_{ct}\) in \(FPLC_{DCqu}\)[3,20]. Therefore, charging (\(C_{dl,pc}\) or \(C_{dl,nc}\)) and discharging (\(C_{dl,pd}\) or \(C_{dl,nd}\)) double-layer capacitors should depend on their individual electrochemical reaction surface areas, and they should be different in general. Existing \(BHLC_{ACqu}\) does not distinguish charging impedances (\(R_{ct,pc}\), \(C_{dl,pc}\), \(R_{ct,nc}\) and \(C_{dl,nc}\)) from discharging impedances (\(R_{ct,pd}\), \(C_{dl,pd}\), \(R_{ct,nd}\) and \(C_{dl,nd}\)) on electrode-electrolyte interfaces. Therefore, there is no physical relationship between the EIS Nyquist curve and \(FPLC_{DCqu}\). This paper proposes AC equivalent first principle linear circuit (\(FPLC_{ACqu}\)) by average switch modeling [23]. \(FPLC_{ACqu}\) accurately models the steady-state AC fundamental response as the same as \(FPLC_{DCqu}\), and average switch modeling calculates steady-state DC static response as the same as \(FPLC_{DCqu}\). \(FPLC_{ACqu}\) bridges the physical relationship between Nyquist and \(FPLC_{DCqu}\). \(FPLC_{ACqu}\), focused on AC-input, is an extension of \(FPLC_{DCqu}\). These two first principle linear circuits complement each other to guide lead-acid BMS design.