esa-sceva/satcom-chunk-collection huggingface dataset:
# Particle-Velocity-Based Mixed-Source Sound Field Translation for Binaural Reproduction
## 7 Conclusions  
We have proposed a new mixed-source expansion, which exploits particle velocity to enhance the performance of sound field translation for binaural reproduction. We describe spatial particle velocity using the velocity coefficients in the spherical harmonic domain, and expand the sound field in terms of the velocity coefficients. We compare the proposed method with the state-of-the-art expansion based on sound pressure. The simulation results show that the least squares solution of the particle-velocity-based expansion is parallel to the closed-form solution of the sound-pressure-based expansion, whereas the sparse solution of the particle-velocity-based expansion can reproduce the sound field over a larger area with less error, and has more accurate direction reproduction at the translated positions than the sparse solution of the sound-pressure-based expansion. Finally, the results of the MUSHRA experiment verify that the particle-velocity-based expansion outperforms the sound-pressure-based expansion for the sparse solution at the translated positions in terms of both source localization and audio quality for the single source scenario. The improvement for source localization is greater than audio quality.  
Conceptualization, H.Z., L.I.B., P.N.S., T.D.A. and V.T.; methodology, H.Z., P.N.S. and T.D.A.; software, H.Z. and L.I.B.; validation, H.Z.; formal analysis, H.Z., P.N.S. and T.D.A.; investigation, H.Z.; writing--original draft preparation, H.Z.; writing--review and editing, L.I.B., P.N.S., T.D.A. and V.T.; supervision, P.N.S. and T.D.A.; project administration, T.D.A.; funding acquisition, T.D.A. and P.N.S. All authors have read and agreed to the published version of the manuscript.  
This research is supported by Reality Labs Research at Meta.  
The ethical aspects of this research have been approved by the Australian National University Human Research Ethics Committee (Protocol 2019/767).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
AR/VR Augmented Reality/Virtual Reality  
MUSHRA Multiple Stimulus with Hidden Reference and Anchor  
HRTF Head-related transfer function  
LASSO Least absolute shrinkage and selection operator  
IRLS Iteratively reweighted least squares  
ITD Interaural time difference  
ILD Interaural level difference
# Evaluating the Efficacy of a Passive Exoskeleton for Enhancing Ergonomics in Manufacturing
## 3 Results and Discussion
### Analysis of the Survey
#### 3.5.1. Ease of Use  
The participants rated cognitive effort, movement freedom, physical effort, and overall ease of use. The cognitive effort required to use the exoskeleton was rated at 2.42 out of 10, suggesting that the mental demand of operating the device is relatively low. Movement freedom, or freeness, scored 5.09 out of 10, indicating a moderate level of autonomy  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Category** & **Min** & **Median** & **Mean** & **Max** & **Std Deviation** \\ \hline Safe with BExo & 1 & 7 & 6.91 & 10 & 1.93 \\ \hline Trust in BExo & 1 & 7 & 6.59 & 9 & 2.02 \\ \hline Importance of BExo & 1 & 8 & 6.45 & 10 & 2.94 \\ \hline Easy to Use & 1 & 7 & 6.32 & 10 & 2.48 \\ \hline Like BExo & 1 & 6.5 & 6.23 & 10 & 2.54 \\ \hline Ergonomic Assistance & 2 & 5.5 & 5.95 & 10 & 2.4 \\ \hline Comfort & 1 & 6.5 & 5.86 & 9 & 2.4 \\ \hline Posture Improvement & 2 & 5.5 & 5.55 & 9 & 1.74 \\ \hline Helpfulness & 1 & 6 & 5.41 & 9 & 2.28 \\ \hline Freeness & 2 & 4.5 & 5.09 & 9 & 1.93 \\ \hline Empowerment & 0 & 5 & 4.82 & 10 & 2.28 \\ \hline Intention to Reuse & 1 & 5 & 4.77 & 10 & 2.72 \\ \hline Initiating BExo Use & 1 & 1 & 4.73 & 10 & 4.19 \\ \hline Physical Effort & 0 & 3 & 3.27 & 7 & 2 \\ \hline Constraint & 0 & 3 & 3.09 & 8 & 1.85 \\ \hline Tiredness & 0 & 2.5 & 2.64 & 6 & 1.43 \\ \hline Loss of Autonomy & 0 & 2.5 & 2.45 & 5 & 1.84 \\ \hline Cognitive Effort & 0 & 2 & 2.41 & 6 & 1.99 \\ \hline Long Training Need & 0 & 1.5 & 2.41 & 8 & 2.22 \\ \hline Time Wasted & 0 & 2 & 1.86 & 5 & 1.46 \\ \hline \hline \end{tabular} \({}^{\star}\)Detailed scores per participants are reported in Appendix C, Table A3.  
\end{table}
Table 6: Comparing descriptive statistics in BExo evaluation (sorted by mean score) \({}^{\star}\).  
in movement while using the exoskeleton. The physical effort required to operate the exoskeleton was rated 3.27 out of 10, reflecting that the physical exertion needed was manageable but could be improved. Overall, the exoskeleton's ease of use was rated 6.32 out of 10, suggesting that while the device is generally user-friendly, there are opportunities to further reduce the cognitive and physical effort required to use it effectively.
## 3 Results
### In vitro Release Studies
It was observed that the release from 7.5-10 wt% GA-loaded electrospun CA fiber mats in phosphate buffer medium at room temperature was also rather rapid during the first 2 h, reaching cumulative release values of 35-45% after 100 h. The release profile reported was related to relative high content of GA, suggesting that the polyphenol was mainly located on the surface of electrospun fibers due to its limited solubility in the CA solution for electrospinninging. Oppositely, the multilayer PLA films exhibited a highly sustained release profile of GA to the selected simulant, that is, the saline medium. It can be observed that the multilayer PLA films smoothly delivered the bioactive and also still retained high amounts of GA during the whole tested period, that is, 10 weeks. In particular, at 375 h, when the release rates of bilayer films were already stabilized, the cumulative GA release values were only 7.4%, 10.8%, and 17.3% for the PLA films based on interlayers electrospun for 1 h, 2 h, and 3 h, respectively. Then, the three multilayers started to stabilize  
Figure 5: Gallic acid (GA) release from the GA-containing polylactide (PLA) films as a function to the immersion time in saline medium: (**a**) Total amount released; and (**b**) cumulative release. at approximately 1000 h, nearly reaching a plateau. The final cumulative GA values for the multilayers containing the electrospun interlayers processed for 1 h, 2 h, and 3 h were approximately 30%, 38%, and 46%, respectively. It can be then considered that the loaded GA was highly confined in the electrospun PLA interlayer due to the presence of the 10-um top PLA layer. Therefore, although the release rate was stabilized, a slow and prolonged release is expected to occur for longer immersion times (no more data was collected). Since the GA release is the result of the diffusion and equilibrium process involving its transfer from the electrospun interlayer into the food simulant, the top PLA layer acted as an efficient barrier against the GA diffusion due to its intrinsic hydrophobic behavior. In this regard, it has been reported that the water absorption of PLA is only 0.7 wt% [46]. Therefore, the release mechanism in the fully PLA-based multilayer system can be easily further controlled and then tuned by not only adjusting the deposition time of the electrospun interlayer but also by selecting the appropriate thickness of the top layer prior to the thermal post-treatment.
# Land Suitability Mapping Using Geochemical and Spatial Analysis Methods
## 5 Conclusions  
Human activities have strongly influenced the land uses of the study area. Thus, the urban and agricultural land covers 92% of the total area. Cadmium and Co are enriched in the Ioannina basin's topsoil since their median value is higher than that of European soil. The content of Cd in the topsoil of the area studied can be attributed to various anthropogenic sources (traffic emissions, aircraft operations, vehicle crushing and dismantling activities). The observed Cd and Co content in soil at sampling sites located in both the eastern and western edge of the Ioannina plain presented similar spatial distribution, which can be attributed to a common origin or similar dispersion mechanism. The aircraft operations (engine wear), traffic emission and vehicle crushing activities are the most likely anthropogenic source of the high content of Cd and Co in the topsoil of the sampling sites located parallel to the western and eastern edge of the Ioannina plain. This study demonstrates that topsoil in the study area contains toxic elements whose concentration exceeds soil quality guidelines for agricultural land use. Moreover, topsoil contains Co concentration, which may pose a threat to residents and terrestrial ecological receptors. Distribution and delivery of weathering material enriched in Cd and Co from the Ioannina basin to receiving Pamvoti's lake may severely affect the lake water and sediments.  
Conceptualization, D.E.A., G.D.B., H.D.S. and D.E.G.; methodology, D.E.A., G.D.B. and H.D.S.; software, H.D.S.; validation, D.E.A., G.D.B. and D.E.G.; formal analysis, D.E.A.; investigation, G.D.B.; resources, H.D.S.; data curation, D.E.G.; writing--original draft preparation, D.E.A. and H.D.S.; writing--review and editing, D.E.A.; visualization, D.E.G.; supervision, D.E.A., G.D.B., H.D.S. and D.E.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors gratefully acknowledge the staff of ICME for the help rendered during the sampling campaign and laboratory work.  
The authors declare no conflict of interest.  
Figure 9: Suitability map comparing Co content in soil of the study area to: (**A**) CCME (A); and (**B**) CCME (R).
# A Mathematical-Based Model for Estimating the Path Duration of the DSDV Routing Protocol in MANETs
## 2 Related Work
Moreover, existing mathematical models targeted a small set of factors that directly affect the process of estimating the path duration; this is because including all factors increases the complexity of constructing the mathematical models. Therefore, in this research study, we proposed a mathematical-based model for estimating the link and path durations and studied their effects on the performance of the DSDV routing protocol--one of the main highly deployed proactive routing protocols. We thoroughly described this model on a step-by-step basis,and verified its validity by doing a comparative analysis between the simulation results and the predicted values derived from the mathematical model.
# The Application of Satellite Image Analysis in Oil Spill Detection
###### Abstract  
In recent years, there has been an increasing use of satellite sensors to detect and track oil spills. The satellite bands, namely visible, short, medium infrared, and microwave radar bands, are used for this purpose. The use of satellite images is extremely valuable for oil spill analysis. With satellite images, we can identify the source of leakage and assess the extent of potential damage. However, it is not yet clear how to approach a specific leakage case methodologically. The aim of this study is the remote sensing analysis of environmental changes with the development of oil spill detection processing methods. Innovative elements of the work, in addition to methodological proposals, include the long-term analysis of surface water changes. This is very important because oil is very likely to enter the soil when water levels change. The classification result was satisfactory and accurate by 85%. The study was carried out using images from Landsat 5, Landsat 7, Landsat 8, Sentinel-1, and Sentinel-2 satellites. The results of the classification of the oil stains in active and passive technologies differ. This difference affects the methodology for selecting processing methods in similar fields. In the case of this article, the oil spill that occurred on 29 May 2020 in Norisk was investigated and compared with data from other years to determine the extent of biodegradation. Due to the tank failure that occurred at the Nornickel power plant on that day, a large amount of crude oil leaked into the environment, contaminating the waters and soil of local areas. Research shows that oil spills may be caused by human error or may be the effect of climate change, particularly global warming.  
oil spill detection; satellite imagery; spatial data; environmental analysis +
Footnote †: journal: Applied Sciences
###### Abstract  
Structural health monitoring to assess the safety, durability and performance of structures can be performed by non-destructive methods such as the measurement of impedance in self-sensing cement-based elements. Cement-based materials, like mortars and concretes, generally have high electrical resistivity but the addition of carbon-based fillers and fibres decreases their electrical resistivity and thus enhances their self-sensing capabilities. In this study, two waste carbon-based fillers, namely, used foundry sand and gasification char were compared to commercial graphene nanoplatelets and used to produce self-sensing cement mortars, both with and without recycled or virgin carbon fibres. The mortars were tested in terms of their mechanical and electrical properties as well as their propensity to capillary water absorption. The results demonstrate that gasification char alone is the best carbonaceous waste for decreasing the electrical resistivity (\(-42\%\)) and water absorption (\(-17\%\)) of mortars, while their compressive strength remains unaltered. Moreover, although there is a slight reduction in compressive strength and an increase in water suction when gasification char is coupled with fibres, the combination of fillers and fibres has a synergistic effect in decreasing mortars' electrical resistivity, especially when recycled carbon fibres are used (\(-80\%\)).  
self-sensing; carbon-based additions; fibres; fillers; gasification char; used foundry sand; graphene nanoplatelets; mechanical strength; electrical resistivity; capillary water absorption +
Footnote †: journal: Applied Sciences
# A Hybrid Learning Framework for Enhancing Bridge Damage Prediction
## 2 Materials and Methods
### Preprocessing  
Depending on the purpose, nature, quality, and use of the data, one can employ various strategies, as data preparation is a crucial step in preparing for model training. Preprocessing the model might also boost model inference and reduce training time. Reducing the size of the input images can significantly reduce the model's training time while still preserving its performance, especially if the images are very large. This paper employs the following preprocessing techniques:  
* Image resizing works the model correctly; images must be resized to a consistent size. A large image is difficult to process effectively. Therefore, we resized the different sizes of the used datasets to 100 \(\times\) 100.
* Grayscale image: A grayscale instead of color is used in this work to simplify the image's data and lower the processing requirements of the algorithms.
* Data augmentation techniques are used to produce new images from existing ones to increase the size of a dataset. It enhances the model's generalization and decreases overfitting.
* The data normalization technique sets pixel intensity values to a predefined range, usually between 0 and 1.
## 2 Methods
### HD-EEG Connectivity Analysis  
Using the software Matlab (Version 9.5.0) [25], data from EEG channels were averaged according to 10 pre-defined regions over both hemispheres: primary motor cortex (M1), primary somatosensory cortex (S1), premotor cortex (PMC), supplementary motor area (SMA) and posterior parietal cortex (PPC). A figure outlining the channels pertaining to each of these regions of interest is given in the Supplementary Materials (Table S1, Figure S1). Brain regions and respective electrodes were adapted from the recent literature [18; 30]. For each segment, autoregressive models were calculated using the mvfreqz.m and mvar.m function implemented within the BioSig toolbox [31]. The multivariate autoregressive models were calculated for all region x region combinations and for the two frequency bands of interest (alpha: 7-13 Hz, and beta: 13-39 Hz). The model order was chosen in adherence with two boundary criterions: first, the maximum model order was set at 200, thus guaranteeing the adherence with a proposed ratio of 3:1 between given samples and the number of estimates [32]. Second, given the aim to estimate measures for 34 frequency components and the spatial sampling of 10 regions, the model order needed to be above 6.8 to ensure valid mapping of respective components [33]. Based on these restrictions, we performed a model optimization procedure, calculating for each subject and trial an autoregressive model with model orders 7 to 200 and deriving the Schwarz Bayesian criterion for each model using the artif2.m function of the sta toolbox [34]. Following this, we calculated the optimal model order for each participant as an average from the respective trials and performed the final calculation of the multivariate autoregressive models based on this average of optimal model orders (see Table S2 in Supplementary Materials for individual averaged model order). From these coefficients, we derived the full frequency directed transfer function (ffDTF), a directed measure of interaction normalized with respect to all of the frequencies in the predefined frequency interval [35]. For each subject, the mean connectivity matrices over the trials were calculated. In order to remove activation due to the visual and auditory stimulation, we subtracted the connectivity of the resting condition from the movement and movement imagination conditions as follows:  
* Watching the movie + beep was subtracted from movement + beep.
* Watching the movie + music was subtracted from movement + music.
## 5 Discussion
### Influence of Intermediate Principal Stress on the Dilatancy of Surrounding Rock  
As previously mentioned, the plastic radius \(R_{p}\) decreased whereas the dilatancy coefficient \(K_{\psi}\) increased with increasing support pressure \(p_{i}\). Figure 8 shows the changes in the dilatancy coefficient \(K_{\psi}\) with changing surrounding rock depth \(r\) in the plastic region.  
Figure 8: Relationship between the dilatancy coefficient \(K_{\psi}\) and intermediate principal stress.  
Figure 7: Variation in the dilatancy coefficient at the tunnel opening with supporting pressure.  
The point of intersection between the curve and the \(Y\)-axis represented the value of \(K_{\psi}=1\), indicating that plastic deformation of the rock mass was fully developed at the boundary of the tunnel opening. As shown in Figure 8, the dilatancy coefficient \(K_{\psi}\) gradually decreased from the elastic-plastic boundary to the tunnel opening wall under different intermediate principal stress. The peak dilatancy occurred in the elastic region and elasto-plastic critical region, along with the peaks in the strength parameters of surrounding rock. In addition, the dilatancy confident \(K_{\psi}\) under the intermediate principal stress coefficient \(b\) (0.5, 0.75, and 1) tended to be close in the plastic region. In contrast, the distribution of the dilatancy coefficient \(K_{\psi}\) in the plastic zone revealed obvious differences among three conditions (\(b\) = 0, 0.25 and 0.5), especially under the conditions of principal stress coefficient \(b\) = 0. Table 2 shows the influences of intermediate principal stress \(b\) on the dilatancy coefficient \(K_{\psi p}\). The dilatancy coefficient \(K_{\psi}\) initially increased and then decreased in the strain-softening zone with increasing intermediate principal stress coefficient \(b\). In particular, the plastic zone of surrounding rock calculated under different intermediate principal stress showed considerable differences, which resulted in the clearly different curve trajectories of the dilatancy coefficient. However, the differences in \(K_{\psi}\) were due to the increase and decrease in the intermediate principal stress coefficient \(b\) and plastic radius \(R_{p}\), respectively, which resulted in an increase in the slope of the dilatancy coefficient \(K_{\psi}\). As indicated in Equation (15) to Equation (16), there was exponential variation in \(\sigma_{r}\)\(K_{\psi}\) with \(\sigma_{r}\).  
In summary, the dilatancy coefficient \(K_{\psi}\) curves showed differences in the plastic region when the intermediate principal stress coefficient \(b\) < 0.5 and the dilatancy coefficient \(K_{\psi}\) curve presents a similar trend for \(b\geq 0.5\).
## 4 Selenium in Thyroid Bland
### Role of Selenoproteins in Thyroid Gland
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Selenoproteins** & **Localization** & **Functions** & **References** \\ \hline Deiodinases (DIO) & & & [80] \\ \hline DIO1 & Liver, kidney, thyroid gland, lung, eyes, & Conversion of T4 into T3 and rT3 & \\  & pituitary, CNS & and T3 into rT3 or T2 & \\ \hline DIO2 & Thyroid gland, pituitary gland, skeletal, & Conversion of T4 into T3 and of & \\  & heart muscles, brain, fat tissue, spinal cord, placenta & rT3 into T2 & \\ \hline DIO3 & Gravid uterus, placenta, fetus liver, fetal & Conversion of T4 into T3 and of & \\  & and neonatal brain, skin & rT3 into T2 & \\ \hline Glutathione peroxididase (GPx) & & & [82; 83] \\ \hline GPx1 & Cytoplasm, ubiquitous & Cytosol Antioxidant & \\ \hline GPx3 & Plasma and thyroid follicle & Plasma and extracellular & \\  & antioxidant & & \\ \hline GPx4 & Mitochondrial membrane & Membrane antioxidant & \\ \hline Thioredoxin reductase (TRx) & & [82; 84] \\ \hline TRx1 & Principally cytosolic, ubiquitous & Inhibition of apoptosis, redox state & \\  & & of transcription factors & \\ \hline TRx2 & Mitochondrial, ubiquitous & Reduce basal oxidative stress, & \\ \hline TRx3 & Principally mitochondrial, ubiquitous & Regulation of apoptosis and & \\  & & signaling pathway & \\ \hline Selenoprotein P (SePP) & Blood and thyroid & Transportation of selenium and & \\  & & storage, endothelial antioxidant & \\ \hline DIO = iodot hyronine deiodinase, T2 = diiodothyronine, T3 = triiodothyronine, rT3 = reverse T3, T4 = thyroxine, & \\ GPX = glutathione peroxidase, TRx = Thioredoxin reductase, SePP = selenoprotein P. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main selenoproteins in thyroid and their functions. The family of TRx play an important role in thyroid metabolism, mainly its members; TRx1 and TRx2 [84]. TRx1 is an intracellular enzyme while TRx2 is mitochondrial ensuring the reduction of oxidative stress in mitochondria [82]. Others selenoproteins are involved in the function of thyroid gland including SePP, Selenoprotein S and K provide quality control in endoplasmic reticulum [82]. The relationship between Se and thyroid diseases is complex. The hierarchy in the distribution of Se protects the thyroid from small fluctuations in Se intake [90]. Two isoforms of Se-Cys-tRNA[Ser]Se-Cys have been described. One of them is involved in the biosynthesis of the most important selenoproteins (TRx1, TRx2 and GPx4). The second ensures the synthesis of less essential proteins (GPx1, GPx3, selenoprotein S) [91]. Several studies have shown that Se supplementation has given positive results in certain autoimmune thyroid diseases such as [PERSON]'s thyroiditis and [PERSON] disease [90].