esa-sceva/satcom-chunk-collection huggingface dataset:
# Modelling Pollutant Dispersion in Urban Canyons to Enhance Air Quality and Urban Planning
## 1 Introduction
### Modelling Urban Airflow  
To investigate phenomena associated with fluid dynamics in an urban environment, two primary methods are employed: actual experimentation and numerical simulation. Experiments offer a more realistic environment, but the main handicap is that there is less control for external factors. For example, [PERSON] et al. [29] initially examined the thermal dynamics of a scaled-down street canyon model, followed by an outdoor ventilation study of the same model [30]. These studies obtained contours of different magnitudes of the fluid within the street but did not obtain numerical ventilation results for pollutant removal [31; 32]. However, although scaled models aim to provide a more faithful representation of airflow behaviour, they may not accurately replicate real-world conditions. This discrepancy can arise if the boundary parameters and air-inflow velocity are not properly adjusted, leading to inaccuracies primarily due to the Reynolds number. For instance, [PERSON] et al. [33] explored a broad spectrum of Reynolds numbers for a constant aspect ratio, noting that the airflow pattern is dependent on the Reynolds number, yet they did not extend their findings to other aspect ratio configurations. This highlights the necessity of conducting the experiments under a Reynolds number regime which resembles reality, by using full-scale models with feasible building heights and using common values of wind velocity in cities as evidenced by [PERSON] et al. [4].  
Apart from experimentation, numerical simulation through Computational Fluid Dynamics (CFD) offers another method to study this phenomenon. This approach is widely recommended and used by the scientific community as it can achieve good results with fewer resources than experimental methods. Previous research on this topic has provided valuable insights for setting up numerical models for simulations.  
Authors employ various approaches to address the turbulent nature of airflow across the urban landscape. Several authors have proposed the use of Reynolds-averaged Navier-Stokes (RANS) equations, specifically the k-\(\epsilon\) RNG turbulent model, as the most suitable solution for this problem [34; 35]. This model is considered advantageous because it can provide good results compared to LES or wind tunnel experimental data while requiring lower computational costs. For example, [PERSON] et al. [36] compared flow magnitudes in a street canyon using both methods, concluding that RANS equations are acceptable, although LES provides more accurate results. All this previous work has motivated the use of this specific set of options to model street canyon ventilation in further studies.
# Blind Source Separation of Intermittent Frequency Hopping Sources over LOS and NLOS Channels +
###### Abstract  
This paper studies blind source separation (BSS) for frequency hopping (FH) sources. These radio frequency (RF) signals are observed by a uniform linear array (ULA) over (i) line-of-sight (LOS), (ii) single-cluster, and (iii) multiple-cluster Spatial Channel Model (SCM) settings. The sources are stationary, spatially sparse, and their activity is intermittent and assumed to follow a hidden Markov model (HMM). BSS is achieved by leveraging direction of arrival (DOA) information through an FH estimation stage, a DOA estimation stage, and a pairing stage with the latter associating FH patterns with physical sources via their estimated DOAs. Current methods in the literature do not perform the association of multiple frequency hops to the sources they are transmitted from. We bridge this gap by pairing the FH estimates with DOA estimates and labeling signals to their sources, irrespective of their hopped frequencies. A state filtering technique, referred to as hidden state filtering (HSF), is developed to refine DOA estimates for sources that follow a HMM. Numerical results demonstrate that the proposed approach is capable of separating multiple intermittent FH sources.  
blind source separation; frequency hopping; direction of arrival; 3 GPP spatial channel model; hidden markov model +
Footnote †: This paper is an extended version of our paper published in the 57 th Annual Conference on Information Sciences and Systems (CISS), Baltimore, MA, USA, 2023.
## 2 Theory and Experimental Methods  
### Extinction Cross-Section and Attenuation Coefficient  
Sound attenuates while propagating underwater, and its attenuation can be divided into sound scattering and absorption. In general, extinction cross-section (\(\sigma_{e}\)) is a measure of the effect of an individual finite object on taking energy out of forward propagatingacoustic waves in accordance with scattering and absorption [22]. \(\sigma_{e}\) is the sum of the scattering cross-section (\(\sigma_{s}\)) and the absorption cross-section (\(\sigma_{a}\)) [23].  
\[\sigma_{e}=\sigma_{s}+\sigma_{a}, \tag{1}\]  
where \(\sigma_{e}\) per unit distance in volume in water is denoted by the attenuation coefficient (\(\alpha\)), which is expressed as Equation (2) [23]:  
\[\alpha(\mathrm{Np}/\mathrm{m})\ =\ \sigma_{e}\cdot n/2, \tag{2}\]  
where \(n\) is the density per unit volume of a fish school, and \(\alpha\) describes the sound attenuation per unit distance. Given the difference between the theoretical and experimental \(\sigma_{e}\) values [3], \(\sigma_{e}\) must be quantified according to the measured \(\alpha\).
# Internet of Things and Distributed Computing Systems in Business Models
## 4 Theoretical Framework
### Discussion
#### 4.5.3 Requires Extensive Resources  
Implementing and maintaining IoT and Distributed Computing Systems often require extensive resources, both in terms of financial investment and technical expertise. The deployment of IoT devices involves significant upfront costs for purchasing and installing sensors, communication modules, and other hardware [92]. Additionally, the infrastructure needed to support data collection, storage, and processing, including servers, networking equipment, and cloud services, represents a substantial financial commitment. The operational costs associated with these technologies also add to the resource requirements for management [93]. Managing, maintaining, and updating IoT devices and distributed systems involves ongoing expenses for system administration, cyber security measures, and technical support. As these systems generate large volumes of data, businesses need to invest in scalable data storage solutions and high-performance computing resources to handle data processing and analytics effectively, which could be problematic for Small and Medium Enterprises that fall short of resources.
# Wireless Power Transfer Approaches for Medical Implants: A Review
## 2 Different Approaches for a Wireless Power Transfer System
### Ultrasonic-Based Wireless Power Transfer
The advantage of ultrasound compared to magnetic resonance and induction coupling is that these methods are restricted to a short transfer distance, misalignment issues may occur [94], and the magnetic field intensity must be under specified limitations for the safety of the body exposure. In the ultrasonic method, the operating frequency needs to be changed according to sound radiation and pressure distribution to obtain the optimum energy transition situation [93]. In the range of frequencies individuals hear, [PERSON] et al. [94] have developed an implantable pressure-sensing system driven by mechanical vibration. The pressure inductor has a planar coil with a center of ferrite in which their distance differs from the involved stress. An implantable pressure sensor prototype was designed, as shown in Figure 8, and examined in vitro and in vivo. The acoustic receiver is a piezoelectric cantilever and charges a capacitor by converting sound vibration harmonics into electrical energy. The stored electric charge is discharged across an LC tank with an inductor sensitive to pressure during the period that the cantilever is not shaking. [PERSON] et al. [95] investigated omnidirectional ultrasonic powering for deep implantable microdevices. When testing the omnidirectionality and outcome of the power transmission under the acoustic Food and Drug Administration (FDA) regulations, the piezoelectric devices with distinct geometries were examined. The receivers were able to produce power in a range of milliwatts with a matched load located 200 mm away from them. The receivers had symmetric geometry of \(2\times 2\times 2\) mm\({}^{3}\) and were insensitive to misalignment. Recent works of ultrasonic-based WPT systems are shown in Table 4. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Year** & **Frequency** & \begin{tabular}{c} **Output** \\ **Power** \\ **(mW)** \\ \end{tabular} & \begin{tabular}{c} **Efficiency** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Active** \\ **Range** \\ **(mm)** \\ \end{tabular} & \
\begin{tabular}{c} **Transmitter** \\ **Dimension** \\ **(mm)** \\ \end{tabular} &
\begin{tabular}{c} **Receiver** \\ **Dimension** \\ **(mm)** \\ \end{tabular} \\ \hline
[96] & 2020 & 700 kHz & - & - & 200 & - & d\({}_{\text{outR}}=10\) \\
[97] & 2017 & 1 MHz & 0.1 & - & 85 & d\({}_{\text{outT}}=0.55\) & - \\
[98] & 2017 & 1.8 MHz & - & 2.11 & 30 & d\({}_{\text{outT}}=10.8\), & d\({}_{\text{outR}}=1.1\), \\
[16] & 2016 & 1 MHz & 0.184 & - & - & - & - \\
[99] & 2016 & 1 MHz & - & 25 & 3–7 & d\({}_{\text{outT}}=8\) & - \\
[100] & 2015 & 280 kHz & 2.6 & 18 & 18 & d\({}_{\text{outT}}=20\) & d\({}_{\text{outR}}=20\) \\
[101] & 2015 & 3.4 MHz & 0.001 & - & 100 & - & - \\
[17] & 2015 & 30 MHz & 0.1 & - & \(<\)100 & - & d\({}_{\text{outR}}=0.7\),1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Existing ultrasonic-based WPT approaches for implantable power applications. Figure 8: An implantable pressure-sensing system.
# LDPC Decoder Design Using Compensation Scheme of Group Comparison for 5G Communication Systems
## 4 Proposed Compensation Scheme for NPMSA
### Difference in Extrinsic Messages  
The NMSA derived from the simplified SPA leads to larger values due to the simplified process. Larger values cause the accuracy of the algorithm to decrease and directly affect the decoding capability. Therefore, NMSA combines the larger values with the normalized scaling factor \(\gamma\) to reduce the difference in the check node process. Similarly, the NPMSA derived from the simplified NMSA leads to a loss in accuracy of the accurate second minimum due to the adoption of a grouping search. In a grouping search, 2-to-1 comparators are used to search for the first minimum and the probabilistic second minimum. Because the probabilistic second minimum in the NPMSA would be greater than the second minimum in the NMSA, the gap between the first and second minimum in the NPMSA is greater than that gap in the NMSA. This gap in the NPMSA causes the accuracy of the algorithm to decrease and directly affects the decoding capability. Thus, the basic idea of the proposed improved normalized probabilistic min-sum algorithm (INPMSA) [10] is to reduce the gap or close the gap between the first minimum and the second minimum in the NMSA, thus enabling the decoding capability to be recovered. With regard to hardware implementation, the INPMSA maintains its advantages by reducing the area cost and complexity of routing, and accelerates the overall operation speed.
## 3 Research Status of the Multipath Error Data Processing Algorithm
### Multipath Error Mitigation Method Based on the Function Model
#### 3.2.3 Summary  
The precise extraction of multipath models from coordinate sequences or observations is the most important step in multipath error data processing. This can be effectively achieved using filtering methods. According to the spatiotemporal repeatability of multipath errors, the core point of SF is the acquisition of the satellite orbit repeat period, and MHM focuses the fitting of the grid surface and the actual site multipath errors. In the utilization of SF in the coordinate domain, it is advisable to employ satellites with similar repetition periods. Notably, SF in the coordinate domain becomes impractical when dealing with satellites that have significantly divergent repeat periods, which represents a limitation. It can be observed in Table 5 that many filtering methods are used for multipath error correction. Taking into account the satellite constellation structure, the coordinate domain method is suitable for GPS with a simple structure, and the observation value domain method is more suitable for BDS with a complex structure. Most of the methods employ a combination of the filtering method and the SF or MHM models, which can effectively weaken the multipath error.
## 4 Simulation Results and Analysis  
In this section, IBES algorithm, Local algorithm, MEC algorithm, BES algorithm, and PSO algorithm are simulated and compared, respectively.  
Local algorithm [21]: All tasks are left in the Local vehicle for processing.  
MEC algorithm [22]: Offload all tasks onto RSUs with the MEC server for processing.  
PSO algorithm [23]: Offloading algorithm based on particle swarm optimization.  
BES algorithm: Based on the bald eagle search optimization algorithm of computational offload algorithm.  
The simulation scene of this paper is at the crossroads of the highway, with two lanes, each lane 3.75 m wide. The mobile vehicles will be randomly distributed on the road, setting the vehicles to travel at speeds between 30 and 50 km/h. It is assumed that all edge servers have the same computing power, 40 vehicle end devices, 10 edge servers, and one cloud server. In this paper, the simulation is performed by MATLAB 2020A, and the communication transmission between vehicles in the vehicle network scenario follows the IEEE 802.11p protocol with some adjustments according to the simulation environment. We simplified the model by considering the presence of only Gaussian white noise. Specific simulation parameters are shown in Table 2.
## 3 Problem Formulation
\begin{split}&\text{MSE}(\mathbf{R},\mathbf{F},\mathbf{W})=\sum_{k=1 }^{K}\text{MSE}_{k}\\ &=\sum_{k=1}^{K}\eta^{2}\mathbf{g}_{k}^{T}\mathbf{W}\mathbf{W}^{T }\mathbf{g}_{k}+\eta\mathbf{g}_{k}^{T}\mathbf{w}_{k}+\eta^{2}\mathbf{g}_{k}^{T }\mathbf{D}\mathbf{D}^{T}g_{k}+\eta(2n_{b}+1)\mathbf{g}_{k}^{T}\mathbf{D}+n_{ b}^{2}+n_{b}+1.\end{split}  \tag{24}\]  
Considering the practical application of optical communication, we need to add some inequality constraints to ensure the working effect of optical devices. Since the transmitted signal in optical communication is non-negative, we need to add the following DC bias constraint:  
\[\sum_{k=1}^{K}\mathbf{w}_{k}u_{k}+\mathbf{D}\geq\mathbf{0}. \tag{25}\]  
where \(\mathbf{0}\) stands for the \(M\)-dimensional vectors with all \(0\) elements. In addition, we must ensure that the signal transmit power cannot exceed the maximum power:  
\[\sum_{k=1}^{K}\mathbf{w}_{k}u_{k}+\mathbf{D}\leq\frac{P_{\text{max}}\tau}{h \
u}\mathbf{1}, \tag{26}\]  
where \(P_{\text{max}}\) is the maximum transmit power of the LEDs, and \(\mathbf{1}\) stands for the \(M\)-dimensional vectors with the whole one element. Constraints (25) and (26) ensure that the transmit power of each LED remains within physically feasible ranges. Based on the above analysis, we can construct the optimization problem, which aims to minimize the MSE of IRS-assisted photon-counting MIMO by jointly designing the transmitter precoding \(\mathbf{W}\) and IRS configurations \(\mathbf{F}\) and \(\mathbf{R}\). Specifically, the problem is expressed as follows:  
\[ \begin{split}\mathcal{P}1:\min_{\mathbf{R},\mathbf{F},\mathbf{W}} &\text{MSE}(\mathbf{R},\mathbf{F},\mathbf{W})\\ \text{s.t.}&\text{C1}:\sum_{k=1}^{K}\mathbf{w}_{k}u_ {k}+\mathbf{D}\geq\mathbf{0},\\ &\text{C2}:\sum_{k=1}^{K}\mathbf{w}_{k}u_{k}+\mathbf{D}\leq \frac{P_{\text{max}}\tau}{h\
u}\mathbf{1},\\ &\text{C3}:\sum_{m=1}^{M}r_{n,m}\leq 1,\ \forall n\in\mathcal{N},\\ &\text{C4}:\sum_{k=1}^{K}f_{n,k}\leq 1,\ \forall n\in\mathcal{N},\\ &\text{C5}:f_{n,k}\,\ r_{n,m}\in\{0,1\}\,\ \forall k\in\mathcal{K},m\in\mathcal{M},\end{split}  \tag{27}\]  
where constraints C1-C2 are derived from power constraints in optical communications, and constraints C3-C5 originate from the characteristics of IRS and the fact that one IRS can only be assigned to one LED/PD pair.
## 2 Experiment 1: The Refined Model
### Results  
Table 1 shows the results of F0 range estimation with the refined model. The reported best MAPE of the mean F0 in [14] was 2.3%. A comparison with that of the refined model, i.e., 2.15%, indicates that the refined model works better than the initial model.
# A Literature Survey on Offline Automatic Algorithm Configuration
## 2 Literature Review  
In recent years, parameter tuning has become commonly utilized in the industry and academic community, and there has been a significant advancement in this area. As a result of this research, there is a wide body of literature about the algorithm configuration problem, and a comprehensive review is an important requirement. In [PERSON] et al. (2020), authors reviewed two main topics, which are: the automatic configuration of multi-objective optimizers; and research about multi-objective configuration, which aims to optimize multiple performance metrics simultaneously to evaluate the algorithm performance, such as algorithm speed or quality of the solution. [PERSON] (2018) provided a review of only three parameter tuning methods for metaheuristics and gave information about the applications of these methods.  
Algorithm configuration methods have been classified in many different ways in the literature. [PERSON] and [PERSON] (2018) is the most detailed survey, in which they constructed three different taxonomies to distinguish and categorize algorithm configuration techniques. In their study, the parameter tuning methods are divided into four categories, such as: meta-EAs, sampling methods, screening, and model-based methods. In (2018), they classified tuning methods as tuning by analogy, hand-made tuning, hybrid tuning, experimental design-based tuning, and search-based tuning. [PERSON] et al. (2020) conducted a review of automatic algorithm configuration techniques and classified these methods as simple generate-evaluate techniques, iterative generate-evaluate techniques, and high-level generate-evaluate techniques based on the structure of these configuration methods. [PERSON] et al. (2018) provided classification schemes for algorithm configuration problems and for algorithm configurators. Additionally, they summarized the related literature on these topics.  
In this paper, we will use a more general distinction of automated parameter tuning methods made in the literature: model-free and model-based tuning approaches (see (2018)) with some modifications. We will describe racing methods and traditional experimental design-based methods as separate categories. In this paper, we examined the developed automatic algorithm configuration methods concerning this categorization. Additionally, for explaining each method (especially for widely used methods, due to the space limitation), we used some criteria such as search strategy, parameter type, expected output, stop criterion, and configuration objective (multi or single). Additionally, we also mentioned whether these methods use surrogate models or not.  
Figure 1 demonstrates the classification of algorithm configuration methods and an overview of the most widely used automated algorithm configuration methods in each class.  
Figure 1: An illustration of the classification of automated algorithm configuration methods.
# Data-Driven Health Assessment in Flight Control System
## 5 Certain Type Aircraft Flight Control System Health Assessment
### Discussions  
In the comparison of the above two methods, the AHP evaluation result has more flights that are close to or have reached functional degradation. However, even some flights whose channel function has not reached severe degradation are assessed as severely degraded (such as the 51 st flight in Table 7); this is obviously inaccurate. The reason for this is that when constructing the judgment matrix of the  
\begin{table}
\begin{tabular}{c l c} \hline \hline
**Flights** & **Channel** & **SCHI** \\ \hline \multirow{3}{*}{26} & flap channel & 0.9127 \\  & pitch channel & 0.5000 \\  & yaw channel & 0.3933 \\  & roll channel & 0.3449 \\ \hline \multirow{3}{*}{34} & flap channel & 0.7883 \\  & pitch channel & 0.2500 \\  & yaw channel & 0.2687 \\  & roll channel & 0.5138 \\ \hline \multirow{3}{*}{54} & flap channel & 0.9766 \\  & pitch channel & 02946 \\ \cline{1-1}  & yaw channel & 0.2500 \\ \cline{1-1}  & roll channel & 0.6456 \\ \hline \hline \end{tabular}
\end{table}
Table 6: SCHI of the 26 th, 34 th, and 54 th sorties.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Flights** & **SCHI of Pitch Channel** &
\begin{tabular}{c} **System CHI by the** \\ **Proposed Method** \\ \end{tabular} & **System CHI byAHP** \\ \hline
5 & 0.5548 & 0.6442 & 0.5896 \\
19 & 0.5310 & 0.6755 & 0.5353 \\
26 & 0.5000 & 0.5015 & 0.5106 \\
34 & 0.2500 & 0.4426 & 0.4060 \\
44 & 0.5400 & 0.7592 & 0.4275 \\
47 & 0.5909 & 0.6927 & 0.5194 \\
51 & 0.5890 & 0.7659 & 0.3876 \\
54 & 0.2946 & 0.4626 & 0.4813 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Pitch channel’s SCHI of the flights in functional degradation.  
AHP method, the weight of the pitch channel's SCHI in Table 7 is set too high, which results in the overall health of some flights whose function is close to severe functional degradation being evaluated as severe functional degradation; in other words, human subjective judgment magnifies the degree of actual failure.  
In contrast, the evaluation method based on fuzzy comprehensive evaluation and rough set reduction algorithm proposed in this paper is based on the membership function to solve the weight of each channel; it reduces the inaccurate evaluation caused by the bias of subjective judgment, which improves the reliability and robust of evaluation result and reduces the false alarm rate of the evaluation process.  
Furthermore, the evaluation method proposed in this article still has some shortcomings. For the selected characteristic parameters that characterize the health status of the flight control system, although these parameters are set in the standard range when the aircraft leaves the factory, they are constantly changing during the actual flight. Therefore, it is necessary to analyze this impact of uncertainty in future studies.