esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Evaluation
### Setup  
The Signals to be processed in this letter are synthetic reverberant signals created by convolving original AIRs measured in a real hall with reverberation time of 2 s (from an open database [12]) with a male speaker signal of 15 s length. Six AIRs (referred to as \(AIR_{1}\sim AIR_{6}\)) with different \(\kappa\) ranging from 0.12 to 1.54 are adopted. Figure 1 demonstrates the signal there was used in experiment with and without reverberation.  
Figure 1: Plot of signal used in experiment with and without reverberation. (**a**) with reverberation; (**b**) without reverberation.  
As mentioned in Section 4, the ground truth \(\kappa\)(fullband and \(1/3\)-octave subband) is calculated using the measured DRR and \(T_{60}\) via Equation (8). Besides, the reverberation time \(T_{60}\) is assumed to be known. Hence, the \(T_{60}\) used in this work is directly determined in \(1/3\)-octave subbands by applying Schroeder's method to AIRs. For evaluation purposes, the ground truth late reverberant speech component \(z_{l}(n)\) is defined as the anechoic male speaker signal convolved with the tail of AIR starting 50 ms after the direct sound. Other parameters used in this paper are chosen empirically as \(\kappa(0)=1\), \(\alpha_{\kappa}=0.75\), and \(\alpha_{\xi}=0.95\), similar to the reference [8]. All experiments are carried out in computer using MATLAB software.  
The Log Spectral Distortion (LSD) [4] is adopted to evaluate the LRSV estimator by computing the root mean square(RMS) value of the difference between the estimated LRSV \(\widehat{\lambda}_{l}(k,l)\) and the ground truth LRSV \(\lambda_{l}(k,l)\), which is defined as Equation (19)  
\[\begin{split} LSD_{late}(l)=\sqrt{\frac{1}{k}\sum\limits_{k=0}^{K -1}|e(k,l)|^{2}},\\ e(k,l)=L\big{\{}\widehat{\lambda}_{l}(k,l)\big{\}}-L\{\lambda_{l} (k,l)\},\end{split} \tag{19}\]  
where \(L\{\cdot\}=\max\{10\lg|\cdot|,\delta\}\) is the log spectrum confined to 50 dB dynamic range and \(\delta=\max_{k,l}\{10\lg|\cdot|\}-50\). The mean LSD (refered to as \(\overline{LSD}\)) is obtained by averaging Equation (19) over all frames. In addition, the lower and upper semi-variance of error \(e(k,l)\) were also calculated to evaluate the LRSV estimator [5] as Equation (20)  
\[\begin{split}\sigma_{l}=\sqrt{\frac{1}{|\overline{\tau}_{l}|}\sum \limits_{k,l\in\eta_{l}}\left(e(k,l)-\overline{e}\right)^{2}},\tau_{l}:e(k,l) \leq\overline{e}\\ \sigma_{u}=\sqrt{\frac{1}{|\overline{\tau}_{u}|}\sum\limits_{k,l \in\eta_{u}}\left(e(k,l)-\overline{e}\right)^{2}},\tau_{u}:e(k,l)>\overline{e }\end{split} \tag{20}\]  
where \(\overline{e}=\text{mean}_{k,l}\{e(k,l)\}\) is the mean value of \(e(k,l)\).  
In order to evaluate the robustness of the proposed estimator to noise, the white noise was added to synthetic reverberant signals with variable RSNR [5]  
\[\text{RSNR}=\frac{\sum\limits_{k,l}\lambda_{d}(k,l)+\lambda_{r}(k,l)}{\sum \limits_{k,l}\lambda_{p}(k,l)} \tag{21}\]  
where \(\lambda_{p}(k,l)\) is the additive noise spectral variance.
# Joint Resource Allocation in Secure OFDMA-Based Networks Taking a Base Station as a Two-Way Relay
## 4 Simulation Results and Discussion  
In order to verify the effectiveness of the proposed algorithm, computer simulations are carried out. The path-loss exponent is set to 3, and the adopted wireless broadband frequency selective fading channel model is Stanford University Interim-6, each channel of which is a six-tap channel. The first tap follows Ricean distribution, while the other five taps follow Rayleigh distribution. There is only one eavesdropper in one cell. All the users are deployed 40 m away from the base station and the eavesdropper is 200 m away from the base station. The noise power \(\sigma^{2}\) is \(-119\) dBm. There are two simulation scenarios.  
1. Two normal users and two paired users with 32 subcarriers
2. Four normal users and four paired users with 64 subcarriersThe relation between secrecy spectral efficiency and base station transmitting power is shown in Figure 2 when all the users have the same transmitting power of 2 dBm over all subcarriers in the uplink. The equal power allocation method means to allocate equal power to all users, which is less complicated than the proposed scheme. For the conventional scheme, there is no subcarrier pairing or subcarrier assignment.  
It can be seen that the total secrecy spectral efficiency rises with the increase of transmitting power. The total secrecy spectral efficiency under the condition with 48 subcarriers is higher than the secrecy spectral efficiency under the condition with 32 subcarriers. This is because the second scenario with 48 subcarriers has more users, which can increase the signal-to-noise ratio. The relation between secrecy spectral efficiency and user transmitting power is shown in Figure 3, when the base station has a transmitting power of 20 dBm. It can be seen that the secrecy spectral efficiency rises with the increase of user signal-to-noise ratio. The proposed scheme has the largest secrecy spectral efficiency. The equal power allocation method almost has the same secrecy spectral efficiency as the proposed scheme when the number of subcarriers is small at a low signal-to-noise ratio. When the signal-to-noise ratio is about 43 dB, the secrecy spectral efficiency tends to be stable. This is because the capacity of the wiretap channel grows with the increase of user signal-to-noise ratio. From Equation (15), we can see that the mean value of the secrecy spectral efficiency is a constant for fixed power at the base station, which is relevant to the channel characteristics.  
Figure 3: Secrecy spectral efficiency versus user transmitting power.  
Figure 2: Secrecy spectral efficiency versus base station transmitting power.
## 5 Overview of 5G-Enabling Technologies
### Key 5G-Enabling Technologies
#### 5.1.21 Access Point Densification  
Densification of access points refers to the establishment of additional base stations and access points per unit of area. The success of the 5G rollout will surely be contingent on access point densification, which means boosting available network capacity through the deployment of more cell sites, such as radio access networks, in-building wireless, macro sites, and small cell deployments. Network densification will be most effective around metropolitan areas and major venues with a high density of digital users [118].  
Network densification is critical for 5G because digital device consumers continually demand better speeds and more connectivity. Driverless automobiles and remote surgeries are two examples of future digital services that rely on the enhanced connection and quicker speeds of 5G networks. Small cells are needed to improve 5G coverage and capacity, and certain vehicles can assist the public network by acting as self-deployed moving cells.  
Sector-splitting and large multiple input multiple output technologies can potentially increase the number of antennas and tiny cell sites. Network densification is crucial for 5G because digital device users always want faster speeds and more connection. It must be capable of transmitting data at ten times the pace of 4G while maintaining a consistent connection [118; 119; 120].
# A Review of Deep Learning Advancements in Road Analysis for Autonomous Driving
## 3 Road Damage Detection  
In this chapter, we delve into the methodologies that define the current SOTA in RDD. This field, crucial for maintaining road infrastructure and ensuring safety, has seen remarkable advancements thanks to the intersection of Computer Vision (CV), Machine Learning (ML), and sensor technology. Our focus will be on exploring and evaluating the diverse range of methods that have been developed and refined to address the challenges inherent in accurately detecting road damage.  
The traditional approach to RDD, largely manual and visually based, has been significantly overshadowed by the emergence of automated systems powered by advanced computational techniques. Among these, DL, particularly the use of CNNs, has emerged as a leading method.  
The approach to RDD aligns closely with Object Detection (OD), an established technique in CV. In this application, the primary targets for detection are various types of road damage, such as cracks and potholes. Figure 6 presents an example of this type of detection.  
Figure 5: Summary of the review methodology.  
Recognizing RDD as a facet of OD is essential, as it guides the application of specialized DL models adept at pointing and identifying objects within the visual data. Two of the most popular methods in OD are the one-stage detector and two-stage detector due to their effectiveness in balancing the critical trade-offs between speed and accuracy. Given this aspect, we analyzed the SOTA through these two methods.  
To better understand the effect of each solution presented, we have centralized the most used evaluation metrics in our analyzed papers in Table 1 regarding RDD. The brief description alongside the mathematical formula serves as a quick reference to the essential statistical tools for evaluating model predictions.
## 3 Results and Discussion
### Content Analysis  
To ensure a stronger link and relationship among publications, the frequency of keyword co-occurrence is a generally accepted reliable indicator [19; 25]. [PERSON] et al. assert that content analysis provides an effective medium for collecting valuable themes according to the research domains [26]. Similarly, [PERSON] et al. argue that keyword analysis alone is not sufficient to understand the current intellectual structure and its  
Figure 2: Word dynamics-growth.  
relations in detail [17]. Therefore, the thematic content analysis is required to thoroughly clarify the connections between each sub-topic [15]. We employed the thematic content analysis to address the second research question regarding \"the main themes-topics and clusters which emerge in the current intellectual structure\". This method is appropriate for analyzing the categorization of the selected articles during the review process [24; 27; 28].  
To detect the intellectual structure in the publication dataset, we conducted a thematic cluster analysis using the VOS viewer software [16]. As depicted in Figure 3, there are four main thematic clusters from 71 articles. According to its algorithm, the software automatically generates tag names (nodes) from Keyword Plus, Author's Keywords, Publication Titles, and Abstracts. The co-occurrences of these nodes are as follows: 258 single occurrences of 524 frequent Keyword Plus; 293 single occurrences of 446 frequent Author's Keywords; 328 single occurrences of 667 frequent Publication Titles; and 2439 single occurrences of 8898 frequent Abstracts. The illustration of the main thematic clusters is sorted by relevance and different colors in the diagram for the Internet of Things in relation to Business Model Innovation. The visualization of the research trends of the Internet of Things in relation to Business Model Innovation resulted in four co-occurrence clusters leading to some of the topic areas mentioned as follows: (1) The Internet of Things, (2) Business Model Innovation, (3) Technology infrastructure, and (4) Digital transformation and capabilities.
###### Abstract  
This paper presents the results of an environmental assessment of a Nickel-Manganese-Cobalt (NMC) Lithium-ion traction battery for Battery Electric Light-Duty Commercial Vehicles (BEV-LDCV) used for urban and regional freight haualage. A cradle-to-grave Life Cycle Inventory (LCI) of NMC111 is provided, operation and end-of-life stages are included, and insight is also given into a Life Cycle Assessment of different NMC chemistries. The environmental impacts of the manufacturing stages of the NMC111 battery are then compared with those of a Sodium-Nickel-Chloride (ZEBRA) battery. In the second part of the work, two electric-battery LDCVs (powered with NMC111 and ZEBRA batteries, respectively) and a diesel urban LDCV are analysed, considering a wide set of environmental impact categories. The results show that the NMC111 battery has the highest impacts from production in most of the impact categories. Active cathode material, Aluminium, Copper, and energy use for battery production are the main contributors to the environmental impact. However, when vehicle application is investigated, NMC111-BEV shows lower environmental impacts, in all the impact categories, than ZEBRA-BEV. This is mainly due to the greater efficiency of the NMC111 battery during vehicle operation. Finally, when comparing BEVs to a diesel LDCV, the electric powertrains show advantages over the diesel one as far as global warming, abiotic depletion potential-fossil fuels, photochemical oxidation, and ozone layer depletion are concerned. However, the diesel LDCV performs better in almost all the other investigated impact categories.  
life cycle assessment; Lithium-ion battery; ZEBRA battery; NMC battery; commercial electric-vehicle; commercial light-duty vehicle; urban freight transport +
Footnote †: journal: Applied Sciences
# Interpolation Methods with Phase Control for Backprojection of Complex-Valued SAR Data +
## 2 Problem Formulation  
In this section, we introduce a measurement setup and the image formation process based on the time-domain GBP algorithm. The novel part of the image-formation process is the phase control of estimated complex SAR data, which is performed under the interpolation stage of the backprojection algorithm to assign the range distance between the SAR imaging system and the given point of space in the defined image plane.
## 3 Illustrative Numerical Results
### Waveguide Crossing  
There are three major impairment contributions related to waveguide crossing: Crossing Insertion Loss (CIL), Crossing Reflected Power (CRP) and Crossing XT (CXT) power, the latter due to adjacent waveguide power coupling [14]. Such impairments depend on the crossing angle. We consider values that are related to the orthogonal waveguide crossing, which represents the best-case, since such a condition can be achieved by a careful geometric design of the waveguides.  
CIL increases the variability of \(IL\) among channel resonance peaks since signals directed to different Output planes may traverse a different number of waveguide crossings. The gap between \(IL_{m}\) and \(IL_{M}\) increases from almost 3 to 4 dB when setting CIL up to 1 dB; nevertheless, XT level values scale down along with peak loss.  
CRP above 2% is highly detrimental. It causes high variability of channel \(IL\), resonance frequency shifting and severe distorsion. Moreover, high values of XT have been found, the worst case is \(X_{A}=-10\) dB and \(X_{NA}=-15\) dB. The most detrimental impairment is the waveguide CXT power. In addition, in this case, values higher than 2% may impair feasibility. \(<\)\(IL\)\(>\) varies from \(-15\) dB of the 0% case to \(-21\) dB of the 15% and the worst-case gap between \(IL_{m}\) and \(IL_{M}\) is 11 dB.
## 4 Discussion
### Computational Performance  
For this type of application, code execution acceleration is crucial for achieving results in moderate execution times, particularly when the size of the domain increases exponentially. Therefore, OpenACC and OpenMP directives were used to enable the code for GPU and multi-core CPUs, using the methodology described in [63].  
The implementation consists of incorporating OpenACC to accelerate the wave propagation, while using OpenMP to parallelise the rainfall (based on the Ziggurat algorithm). The NVIDIA compilers with OpenACC v23.1 were used, and the code was tested in two cards: RTX Titan and RTX 3060. The flow diagram of the parallel kernels' execution sequence is shown in Figure 18.  
In general, bi-dimensional FDTD code is of low computational intensity, which means that the number of float operations is not much more significant than the number of memory accesses. Thus, OpenMP is unsuitable for accelerating wave propagation in this case. Nevertheless, it is very useful to accelerate the Ziggurat algorithm.  
The main limitation of using the GPU is the amount of available NVRAM. For this reason, an efficient implementation of the application is performed, yielding a negligible difference in the use of NVRAM between the free space and the presence of rain scenarios. Table 6 shows the NVRAM consumption for the four scenarios analysed in this work.  
An optimised implementation of the convolutional variables used in the boundary condition is the key to reducing memory usage, following the algorithm presented in [53]. It should be stated that the memory usage in the rain simulations occupied only 2.28% more on average compared to the free space simulations, which is a consequence of the efficiency in coupling the FDTD method with the Ziggurat algorithm.  
Figure 18: Flowchart showing the parallel kernels’ execution sequence.  
On the other hand, execution times were measured by taking as the reference one CPU core (Xeon(R) CPU E5-2630 v4 @ 2.20 GHz); the results for the 5 GHz scenario are depicted in Figure 19. As can be observed, OpenACC reduces the computing serial time by 5.06X and 5.39X for the rain scenario (for RTX Titan and RTX 3060, respectively), as well as between 5.48X and 6.93X for the free space scenario (for RTX Titan and RTX 3060, respectively), considering standard double precision variables.  
For the 25 GHz experiments, a similar reduction in execution times was found, as can be observed in Figure 20. In this case, OpenACC directives reduce the execution time by 5.00X and 5.39X for the free space propagation scenario (for RTX Titan and RTX 3060, respectively), and 5.48X and 6.93X for the precipitation case (for RTX Titan and RTX 3060, respectively).  
Although the rain scenario introduces computational overload, it only represents between 12.4805% and 12.5490%. Due to the large number of pseudo-random numbers used in each stage of the numerical method, this percentage is reasonable. In other words, implementing the Ziggurat method using OpenMP is highly parallel.
# A Deep Multi-Task Learning Model for OD Traffic Flow Prediction Between Highway Stations
## 3 Preliminaries and Problem Definitions  
### Basic Concepts  
The OD traffic flow between highway stations is the traffic demand from the entrance toll station to the exit toll station, express the OD traffic of all toll stations in the highway network through a matrix as follows:  
\[X^{t}=\begin{pmatrix}x^{t}_{1,1}&\cdots&x^{t}_{1,n}\\ \vdots&\cdots&\vdots\\ x^{t}_{n,1}&\cdots&x^{t}_{n,n}\end{pmatrix}\in\mathbb{R}^{N\times N} \tag{1}\]  
The entrance inflow of a highway station \(x^{t}_{i:}\) is defined as all traffic flow from the origin toll station \(s^{o}=s_{i}\) during the t time step.  
The exit outflow of highway stations \(x^{t}_{j}\) is defined as all traffic flow at the destination toll station \(s^{d}=s_{j}\) during the t time step.  
So for each \(x^{t}=(x^{t}_{i:},x^{t}_{j:},x^{t}_{i:j})\).  
The OD traffic flow prediction between highway stations is based on historical OD data and external factors, mining hidden spatio-temporal features to establish a model, and predicting the OD of the current road network at the next time step.  
\[X^{t}=f(X^{t-h},\cdots,X^{t-2},X^{t-1})E \tag{2}\]  
where \(f(\cdot)\) is a predictive function that needs to be fitted,  
\(t-h\) is the time step of historical data,\(E\) is an external environmental factor variable, and this study involves weather and radiation population factors.
# Fluctuation-Enhanced Sensing (FES): A Promising Sensing Technique
## 5 Practical Sources of Errors  
### Turbulence and Convection  
The flow of the carrier gas around the sensor can cause vortex/turbulence effects that are random in nature. Similarly, the convection of the hot air flying up from the heated sensors has random features. These phenomena contribute to the temperature fluctuations in heated sensors, which, in turn, cause resistance fluctuations because their resistance is usually strongly temperature dependent (they are typically semiconductors). These excess fluctuations are a nuisance because they interfere with the FES signals which are also fluctuations.  
There is a method of operation, the \"sampling-and-hold\" (or \"frozen-smell\") technique, that solves the above-mentioned problems (see details in Section 6.2.1 below).
## 2 Multi-Criteria Analysis--A Methodological Approach
### Promethee Method  
Multi-criteria optimization approaches that attempt to rank variants based on the degree to which certain criteria are met include methods for multi-criteria ranking of application solutions [39].  
[PERSON] and [PERSON] [42] developed the PROMETHEE method (preference-ranking organization approach for enrichment evaluations) for multi-criteria ranking of variations in 1983, which is one of the most important methods of multi-criteria analysis. This method is applied to a wide range of decision problems in transportation planning and design. [PERSON] et al. [43] present a list of PROMETHEE-related publications, research papers, applications, and debates.  
The main input of the PROMETHEE method is a matrix that consists of a set of potential alternatives (actions), A, where each element of A has its own evaluation, f(a). The PROMETHEE I method allows the partial ranking of variants, where different variants can have the same rating, which allows for the utilization of certain ranks. The PROMETHEE II method enables entire ranking, where variants are precisely rated in dependence on the function of preference [44].  
If the decision-maker requires a complete ranking of options, this means that two or more alternatives cannot have the same rank; each alternative must have its own rank.  
For each solution \(a\in A\), the net flow is:  
\[\phi(a)=\phi^{+}(a)-\phi^{-}(a), \tag{1}\]  
and for the solution ranking, it could be simply applied that \(a\) has a higher rank than \(b\):  
\[\left(aP^{(2)}b\right)\quad\text{if}\quad\phi(\text{a})\ \ >\phi(b) \tag{2}\]\(a\) is indifferent to \(b\)  
\[\left(aI^{(2)}b\right)\quad\text{if}\quad\phi(\text{a})\ =\phi(b) \tag{3}\]  
According to [45], the PROMETHEE method can be easily understood by decision-makers and can offer reasonable ranking of all alternatives. Therefore, the method is widely adopted in energy projects, tourism competitiveness, and transport location selection.  
The PROMETHEE II method is often used for traffic-planning and design purposes as it allows order determination. Based on an accurate data input, this method allows a partial or complete classification of several variants with respect to a larger number of criteria.