esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of the Quality of Consumer Headphones in the Perception of Spatial Audio
###### Abstract  
High quality headphones can generate a realistic sound immersion reproducing binaural recordings. However, most people commonly use consumer headphones of inferior quality, as the ones provided with smartphones or music players. Factors, such as weak frequency response, distortion and the sensitivity disparity between the left and right transducers could be some of the degrading factors. In this work, we are studying how these factors affect spatial perception. To this purpose, a series or perceptual tests have been carried out with a virtual headphone listening test methodology. The first experiment focuses on the analysis of how the disparity of sensitivity between the two transducers affects the final result. The second test studies the influence of the frequency response relating quality and spatial impression. The third test analyzes the effects of distortion using a Volterra kernels scheme for the simulation of the distortion using convolutions. Finally, the fourth tries to relate the quality of the frequency response with the accuracy on azimuth localization. The conclusions of the experiments are: the disparity between both transducers can affect the localization of the source; the perception of quality and spatial impression has a high correlation; the distortion produced by the range of headphones tested at a fixed level does not affect the perception of binaural sound; and that some frequency bands have an important role in the front-back confusions.  
Keywords:headphones; spatial sound; quality; perception; binaural; subjective test; distortion; frequency response; front-back confusion : 43.66.Pn, 43.66.Yw +
Footnote †: journal: Applied Sciences
# Autonomous Vehicles in Rural Areas: A Review of Challenges, Opportunities, and Solutions
## 3 Results
### Future of AV Deployment in Rural Areas
#### 3.3.2 Challenges
[PERSON] et al. (2017) [10] aimed to \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Technological** & **Environmental** & **Infrastructure** & **Policy and Demographic** \\ \hline Sensor misdetections, & Extreme weather & Faded or low-contrast & Financial constraints \\ Unrelaible responses to & conditions & markings & \\ \hline GPS and localization & Unpaved roads, steep & & \\ failures & gradients & & \\ \hline Battery performance issues & Sharp curves with limited & & \\ visibility & High infrastructure costs & & \\ \hline Miscommunication at & Dynamic lighting & Unregulated intersections & Lack of local expertise and \\ intersections & stanlight) & (roundabouts, stop signs) & operational data \\ \hline Software malfunctions & Obstacles (animals, & & \\  & farming equipment, & & \\  & pedestrians) & & \\ \hline Object detection failures & Vegetation near roads & Need for frequent HD & \\  & & mapping updates & \\ \hline Difficulty reversing in & Active construction zones & Limited connectivity-ready & \\ automated parking & with confusing markings & roads & \\ \hline Frequent disengagements & Roadside variability & & \\ of automation systems & (curves, hills, dynamic & & \\  & lanes) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Challenges for AV deployment in rural areas. ensure that rural communities benefit from CV advancements in road safety and freight movement. The methodology involved a workshop at the National Rural Intelligent Transportation System meeting, expert interviews, and a literature review on rural transportation needs. Key challenges identified include limited resources, infrastructure constraints, high costs, inadequate communication networks, and cultural skepticism toward new technologies. Despite these barriers, CVs offer potential benefits such as improved road safety, better data collection, and enhanced freight movement. The study recommended sustainability plans, trial deployments in high-impact areas, innovative funding mechanisms, and outreach programs to build awareness and acceptance of CV technologies in rural communities.
## 4 Optimization  
### Particle Swarm Optimization  
In this study, the PSO method is employed to find the optimal updating parameters for constructing an accurate FE model. The optimization steps are as follows:  
_Step 1._ Randomly initialize the velocity and position of each particle.  
_Step 2._ Evaluate the fitness of each particle; record the best position of each particle \(p_{\rm best}\) and the best global position \(g_{\rm best}\).  
Figure 10: Schematic diagrams of the support springs. (**a**) x- and y-direction springs and (**b**) z-direction springs.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **First Approach** & **Second Approach** \\ \hline \multirow{4}{*}{Updating variables} & Perfect rigid constraint & \(k_{\rm H}\) \\  & Young’s modulus & Young’s modulus \\  & Poisson’s ratio & Poisson’s ratio \\  & Density & Density \\  & Length & Length \\  & Thickness & Thickness \\ \hline \hline \end{tabular}
\end{table}
Table 5: Updating variables for the different model-updating approaches.  
Step 3 Determine whether the conditions for the end of the iteration are met; if yes, terminate the calculation and output the optimal result; otherwise, continue to the following step.  
Step 4 Update the velocity and position of each particle according to Equation (2) and Equation (3), and return to Step 2.  
\[v_{\rm i}(t+1)=\omega v_{\rm i}(t)+c_{1}r_{1}(p_{\rm best}(t)-x_{\rm i}(t))+c_{2} r_{2}(g_{\rm best}(t)-x_{\rm i}(t))\quad{\rm i}=1,2,\ldots,N \tag{2}\]  
\[x_{\rm i}(t+1)=x_{\rm i}(t)+v_{\rm i}(t+1) \tag{3}\]  
where i is the particle number, \(N\) is the total number of particles, \(t\) is the \(t-\) th iteration, \(\omega\) is the inertia weight, \(r_{1}\) and \(r_{2}\) are random values from 0 to 1, and \(c_{1}\) and \(c_{2}\) denote the self-learning factor of the particles and the group learning factor, respectively. Before using the PSO method for model-updating, the parameters in the PSO algorithm are set as presented in Table 6. In this method, the larger the number of particles, the easier it is to find the optimal value in the full design domain, but the higher the time consumption. In this work, \(N\) was set to 20, 30, and 50 to find a balance between calculation cost and accuracy of the result. In addition, in order to ensure the full convergence of the process of finding the optimal value, the maximum number of iterations \(t_{\rm max}=20\) was set as the termination condition.
# Study on Passenger Comfort Based on Human-Bus-Road Coupled Vibration
## 3 Comfort Evaluation of Human Body
### Comfort Evaluation Based on Anoyance Rate  
In order to consider the uncertainty caused by individual diversity, the annoyance rate model was adopted [21]. The model uses the psychophysical signal detection theory to analyze the randomness of the human perception of vibration. The model creates the relationship between the fuzzy membership and the human acceleration and analyzes the fuzziness of human perception [22; 23]. The annoyance rate model is then used to evaluate passenger comfort, considering the human variety.  
The annoyance rate refers to the percentage of humans who feel unacceptably annoyed. The human subjective response to vibration includes two steps: perceive the vibration signal (randomness) and make judgments on the sensed signal (fuzziness). According to the centralization statistical method and the principle of the psychological annoyance rate, the annoyance rate of any vibration acceleration can be expressed as  
\[A(x)=\int\limits_{u_{\min}}^{\infty}w(x|u)v(u)du \tag{15}\]  
where \(w(x|u)\) characterizes the sensitivity variation of the human body; \(v(u)\) is the fuzzy membership function; \(A(x)\) is the annoyance rate.  
The sensitivity varies from person to person, which can be described in the form of a lognormal distribution.  
\[w(x|u)=\frac{1}{\sqrt{2\pi}u\sigma}\exp\left\{\frac{-(\ln\frac{u}{x}+0.5\sigma ^{2})^{2}}{2\sigma^{2}}\right\} \tag{16}\]  
where \(u\) is the vibration intensity; \(x\) represents a statistical average of \(u\); \(\delta\) is the coefficient of variation; \(\sigma^{2}\) is the variance, \(\sigma^{2}=ln\Big{(}1+\delta^{2}\Big{)}\). Equation (16) gives out the vibration acceleration felt by a person under a certain vibration stimulation.  
The [PERSON]'s law given in psychophysics reveals that the membership of subjective response \(v(u)\) is proportional to the logarithm of the vibration acceleration rate \(u\)[24; 25], such that  
\[v(u)=a\ln u+b \tag{17}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Weighted RMS Accelerations/m-s\({}^{-2}\)** & **Reactions** \\ \hline \(<\)0.315 & not uncomfortable \\
0.315-0.63 & a little uncomfortable \\
0.5–1 & fairly uncomfortable \\
0.8–1.6 & uncomfortable \\
1.25–2.5 & very uncomfortable \\ \(>\)2.0 & extremely uncomfortable \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comfort evaluation—ISO 2631 standard.  
where Equation (17) establishes a relationship between the subjective human response and the objective vibration acceleration.
## 1 Introduction
In spite of the success of these latter methods, there are still a great deal of areas that might be improved, such as making anonymized speech more distinctive and handling more potent assault scenarios [13; 29; 30; 31; 32]. Notably, the forthcoming VoicePrivacy Challenge will focus on developing attack models against speaker anonymization systems ([[https://www.voiceprivacychallenge.org/](https://www.voiceprivacychallenge.org/)]([https://www.voiceprivacychallenge.org/](https://www.voiceprivacychallenge.org/)) (accessed on 1 March 2024)). A further limitation of the current approaches is the trade-off between the amount of anonymization and the quality of the processed speech. As a matter of fact, if the speech embeddings carry a great deal of acoustic information to allow for high-quality speech synthesis, they inevitably include information about the speaker. Refs. [33; 34; 35] discussed some of the aspects related to the residual speaker information preserved in anonymization mechanisms. As a follow up to our prior effort, as presented in [17], this work proposes a novel strategy for anonymization via voice conversion, which, instead of manipulating the x-vectors, leverages the approach of ContentVec [36] to obtain speaker-independent speech representations and starts from pre-trained models within the S3 PRL toolkit [37]. The proposed strategy is evaluated on a public dataset and compared against a variety of neural and signal-processing-based voice conversion methods.
# Hierarchical Parallel Evaluation of a Hamming Code
## 1 Introduction and Background  
The present work concentrates on the Hamming code [1], one of the best known error correction codes, which is able to correct any single bit error in a block of size \(2^{m}-1\) bits of which \(2^{m}-m-1\) carry information, for \(m\geq 2\). We show how to exploit a hierarchical structure of connected processors to parallelize--and thereby accelerate--the evaluation of a Hamming code. We use the term _evaluation_ to stand for both encoding and decoding, as the same structure proposed hereafter can be used for both, as well as for error detection. This research was inspired by the work of [PERSON] et al. [2] on the parallelization of remainder calculations that are needed for the fast evaluation of hash functions in large de-duplication systems.  
Parallel implementations of Hamming codes have been considered before [3, 4]. [PERSON] and [PERSON] [3] refer to a hardware architecture in which several input bits are transformed simultaneously into a number of output bits using some integrated circuit logic. The authors show how to substitute these circuits by ROM modules. Our model of parallelism is different: we assume the availability of a number of processors that do not all work in parallel, but are rather partitioned into a number of _layers_. Within each layer, all the processors work simultaneously, but between layers, the work is sequential, as the output of one layer is the input of the next. The assumption is that each of the processors has a limited amount of internal memory, and that they all perform essentially the same task, similarly to graphics processing units (GPUs) and their general-purpose variants (GPGPU).  
[PERSON] et al. [5] present a parallel implementation of a Hamming code on a GPU, but rather focus on decoding. The identification of the redundancy bits as well as error detection and correction and redundancy removal are all done in parallel. A preprocessing stage is applied to the bit stream in order to rearrange the data into clusters of adjacent bits. Not only does our proposed method work with no preprocessing stage, but it also uses the same mechanism to compute the Hamming encoding, rather than concentrating on decoding only.  
The paper is organized as follows: In the next section, we recall the necessary details of the Hamming code and present the computational model. Section 3 brings the details of the suggested method. Section 4 shows how to keep all of the processors busy all of the time, after some short initialization phase. Section 5 extends the Hamming scheme also to double error detection; and Section 6 concludes.
# End-to-End Delay Model for Train Messaging over Public Land Mobile Networks
## 6 Results
### Example of Model Application and Validation
In Figure 15, we compare the CDF of the E2E delays obtained from the model after having collected the E2E delays of each generated sample. Even in these cases, we have obtained a good agreement between experimental and simulated CDFs, thus confirming the effectiveness of the proposed model.
# Overview of FEM-Based Resistance Models for Local Buckling of Welded Steel Box Section Columns
## 3 Finite Element Model
### Material Model
#### 3.2.2 Ramberg-Osgood-Type Material Model Used for High Strength Steel (HSS)  
To determine the buckling capacity of high strength steel structures, Ramberg-Osgood-type material model is applied, which is commonly accepted to represent the material characteristics of HSS. The specialty of HSS material is that usually it has no clear yield plateau; after the first yielding occurs, the plastic deformations increase by increasing stresses until the ultimate stresses are reached and fracture of the material occurs. The Ramberg-Osgood-type material model considers both elastic and plastic deformation in all material behaviors. In low-stress states, plastic deformation is minimal in comparison to elastic deformation. However, as the stress level increases, plastic deformation plays a prominent role in the material's behavior. To define the mathematical relationship of this model, the following parameters are required. The first value is the yield offset \(\left(\alpha\times\frac{\sigma_{0}}{\rm E}\right)\). This strain appears at the beginning of the material's behavior, where the elastic deformation is dominant. The original author's research determined this to be 0.2%. The second is the exponential value n; this value fits the relationship to the experimental data, and the literature recommends values greater than 5 [41]. This parameter can be obtained by taking the logarithm of the strain at any two points and dividing it by the logarithm of the stresses at those same points in various coupon tests [42]; this equation is presented in (7):  
\[\rm n=\frac{\log\left(\frac{\varepsilon_{1}}{\varepsilon_{2}}\right)}{\log \left(\frac{\sigma_{1}}{\sigma_{2}}\right)} \tag{7}\]  
The mathematical expression for the Ramberg-Osgood material model is shown in Equation (8). The general shape of the stress-strain curve using this model is shown in Figure 3:  
\[\varepsilon=\frac{\sigma}{\rm E}+0.002\left(\frac{\sigma}{\rm f_{y}}\right)^{ \rm n} \tag{8}\]  
where  
\(\rm f_{y}\) yield stress [N/mm\({}^{2}\)];  
[PERSON]'s modulus of steel [N/mm\({}^{2}\)];  
\(\varepsilon\) strain[mm/mm];  
\(\sigma\) stress [N/mm\({}^{2}\)].  
Figure 3: General shape of Ramberg–Osgood-type material model based on [PERSON] [41].
# Study on Effects of Energy Deposition and Thermal Shock Wave by Electron Beam Irradiation
## 3 Energy Deposition of Electrons in Materials  
When electrons are transported in a substance, they interact with the substance through a series of different paths [12; 25] to deposit energy in the material. When a substance absorbs a large amount of sedimentary energy in a short period of time, it will produce extremely complex thermo-mechanical phenomena, such as high temperature and high pressure, solid-state, liquid-state, and gaseous-state phase changes, thermal shock wave propagation, and spallation damage [26]. Calculating the energy deposition of electrons in materials is the basis and premise of studying the thermo-mechanical effects. Electrons lose energy and deposit in materials through interactions with electrons and nuclei in substances in the forms including (1) inelastic collisions with electrons in atoms; (2) inelastic collision with the nucleus; (3) elastic collision with electrons in atoms; (4) elastic collision with the nucleus. Inelastic collision is the main mechanism of the energy loss of electrons.  
In this paper, the analytical method and the Monte Carlo method for calculating the energy deposition of electrons in materials are presented.  
Figure 3: Photos of PVDF sensor and sample before and after experiment No. 21040.  
Figure 2: Pictures of the REB device. Test circuit of PVDF charge mode.
## 4 Conclusions  
In this paper, microcapsules with nano-CaCO\({}_{3}\)/ceresine wax composite shell and E-44 epoxy resin healing agent were prepared via the melt condensation method. We measured the core content, compactness, particle size distribution, morphology, chemical structure and micromechanical properties of microcapsules. The microcapsules were then mixed into the mortar, and the self-healing ability of the mortar containing microcapsules was investigated. The following conclusions can be drawn from this study:  
1. The results indicated that the addition of nano-CaCO\({}_{3}\) to cereosine wax could further improve the encapsulation ability, mechanical properties and compactness of microcapsules. The core content, elastic modulus, hardness and weight loss rate (60 days) of WM2 were 80.6%, 2.02 GPA, 72.54 MPa and 1.6%, respectively.
2. The particle size distribution of WM2 mainly ranged from 40 \(\upmu\)m to 60 \(\upmu\)m. It could be seen from the SEM images that the surface of WM2 is rough and can form a good bond with the cement matrix. FTIR spectra shows that TDI has been successfully encapsulated in the nano-CaCO\({}_{3}\)/ceresine wax composite shell.
3. After 14 days of self-healing, the compressive strength recovery rate, proportion of harmful pores and chloride ion diffusion coefficient recovery rate of CCM2 were 90.1%, 45.54% and 79.8%, respectively. The surface crack with an initial width of 0.35 mm in CCM2 self-haeled after 3 days. The results indicate that the microcapsules can rapidly self-heal the surface cracks of the mortar and fill its internal harmful pores, thus recovering the mechanical properties and impermeability of the pre-damaged mortar.  
Figure 13: Surface crack self-healing of mortars. (**a**) CCM0 before self-healing, (**b**) CCM0 after 3 days of self-healing, (**c**) CCM1 before self-healing, (**d**) CCM1 after 3 days of self-healing, (**e**) CCM2 before self-healing, (**f**) CCM2 after 3 days of self-healing.  
**Supplementary Materials:** The following are available online at [[https://www.mdpi.com/article/10.3390/nano12020197/s1](https://www.mdpi.com/article/10.3390/nano12020197/s1)]([https://www.mdpi.com/article/10.3390/nano12020197/s1](https://www.mdpi.com/article/10.3390/nano12020197/s1)). Nanoindentation test and Pore size Distribution.
* [1]**Author Contributions:** Conceptualization and writing--original draft preparation, W.D.; writing--review and editing, W.D. and R.I.; software, E.L. All authors have read and agreed to the published version of the manuscript.
* [2]**Funding:** The research was supported by the National Natural Science Foundation of China (52108221), the National Natural Science Foundation of Ningbo City (2021)092) and the State Key Laboratory of Silicate Materials for Architectures (Wuhan University of Technology) (SYSJ)2021-11).
* [3]**Data Availability Statement:** Data is contained within the article and Supplementary Materials.
* [4]**Conflicts of Interest:** The authors declare no conflict of interest.
# Advances in Ionospheric Space Weather by Using FORMOSAT-7/COSMIC-2 GNSS Radio Occultations
## 3 Equatorial and Low-Latitude Ionosphere  
This section provides an overview of the prominent features of equatorial and low-latitude ionosphere consisting of the evolution of EIA [38], plasma cave structures [39], longitudinal modulation of EIA by non-migrating tide of wave number four (wavenumber-4) [41; 42], and plasma depletion bays [40] by making use of the enhanced Tri-G measurements from the F7/C2 constellation. This denser sampling allows scientists to depict the 3D structures and features in the equatorial and low-latitude ionosphere and thus examine the detailed temporal features of the associated electrodynamic processes.
# Efficient Health Data Transmission Method in a Wireless Body Area Network for Rural Elderly
## 4 Opportunistic Routing Protocol for a WBAN Based on a Time-Varying Link  
By predicting the connection probability, reliable links can be selected to establish a network topology. However, in a WBAN, the channel will change dramatically with the change of human posture and environment. Many studies showed that the one-hop star topology cannot meet the reliability requirements of WBANs in a time-varying environment. It is necessary to design an efficient routing algorithm to realize a reliable and energy efficient data transmission. For the opportunistic routing, when the network link is in a good state, the nodes consume more energy because they need to transmit information to multiple candidate nodes. When the link connectivity is unstable, opportunistic routing can ensure a more stable transmission and reduce the energy consumption by reducing the number of data retransmissions. Therefore, we should avoid selecting too many candidate nodes to reduce the transmission energy consumption; however, with too few candidate nodes, it will be difficult to ensure transmission stability. To comprehensively optimize energy efficiency and reliability, we studied a WBAN opportunistic routing protocol based on probabilistic link prediction and link interaction.
## 3 Results and Discussion
### Comparison of Commercial CWR and Sputtered Carbon SPME Fibers with Our New Test Mix
In most cases, the PDMS-coated sputtered fiber performed better than the bare fiber. Figure 5: Comparison of DI-SPME–GC–MS results for 11.5 μm sputtered carbon SPME fibers and the commercial CWR SPME fiber. \(n=3\) for each compound, and y-error bars represent standard deviations. Figure 6: Volume normalized responses of DI-SPME–GC–MS results for 11.5 μm sputtered carbon SPME fibers and the commercial CWR SPME fiber. These are the same data as in Figure 5 in normalized form. The volume-normalized responses in Figure 6 are significantly (\(p\leq 0.05\)) higher for our sputtered carbon SPME fibers (with and without PDMS) than the commercial CWR SPME fibers, with the exception of those for n-pentane, chloroform, and linalool. However, it is not clear from these results whether the sputtered carbon fibers extract and/or desorb more efficiently. The extraction strength of our sputtered carbon fibers is consistent with our previous observations that the cauliflower morphology of the sputtered SPME fibers affords a great deal of surface area for extraction [19; 20]. In addition, our previous carry-over studies have demonstrated that sputtered SPME fibers desorb more efficiently [19; 20]. The implications of the current observations and those of previous studies are that both sputtering silicon and carbon (with and without functionalization, e.g., PDMS and 6-phenylhexylsilane) is a powerful approach to improving SPME fiber responses, especially if the sputtered surfaces were to be applied at volumes comparable to the commercially available SPME fibers, e.g., 95 \(\upmu\)m.