esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results
### Experimental Timing Profile  
The total execution time (\(t_{exec}\)) is defined as the sum of the times demanded by the individual operations. The time required for the feature extraction is defined as \(t_{extraction}\) and represents the time needed to extract the feature set (Set 3) defined in Table 7. The average time required by the classifier to perform the sound classification using the feature vectors from all frames is defined as \(t_{classification}\). This value corresponds to the sum of the time required by each stage \(i\), in case of the cascade approach (\(t_{classification}^{stage_{i}}\)), to process the feature vectors from all frames, or, alternatively, it represents the time for the sound classification in case of a single classifier. Therefore, \(t_{exec}\) can be defined as follows:  
\[t_{exec}\ =t_{extraction}\ +\ t_{classification}, \tag{1}\]  
which becomes  
\[t_{exec}\ =t_{extraction}\ +\ t_{classification}^{stage_{1}}+\ t_{classification}^{stage _{2}} \tag{2}\]  
in case of a two-level cascade approach.  
Although the input audio is read from WAV files for our experiments, the time to read these files is not considered since the sound classification is assumed to be performed from the audio directly coming from a microphone.
## 5 Data Analysis and Results
### Analysis of PLS-SEM Results  
A variance-based technique (i.e., PLS-SEM) was used to analyze the structural model, and this decision was made for several reasons: firstly, the partial least squares (PLS) method is effective for small-to-moderately-sized samples, and it provides parameter estimates even at reduced sample sizes [183; 184]; secondly, PLS is viable for exploratory research [185], particularly when examining new structural paths in the context of incremental studies that extend previous models [186], or when the relationships and measures proposed are new or have not been extensively examined in prior literature [187; 188]; and thirdly, the variance-based approach in PLS is effective for predictive applications. Therefore, since the study's objective was to identify the factors underlying CC adoption, PLS was a suitable choice [189].
## 1 Introduction
reported the development of a piezoresistive sensor based on the combined effect of GNPs, flax fabrics, and biopolymers, such as chitosan and polyethylene glycol [6]. [PERSON] et al. also developed flexible and wearable strain sensors by the coating of flax fabrics with GNPs using the ultrasonication technique [25]. However, the incorporation of the GNPs into the microfiber membranes still represents a challenge. In some cases, it is difficult to ensure a proper distribution of the GNPs onto the membranes, which strongly influences their final electrical properties. Therefore, the use of the post-modification method for electrospun membrane impregnation appears to be an alternative to improve the homogeneity, adhesion, and stability of GNPs at the fibers surface. [26]. Hence, the main goal of this study was to develop a very flexible smart material based on GNPs and PCL electrospun membranes with excellent and stable electrical properties for potential application as piezoresistive sensors. First, the polymeric solutions and electrospinning parameters were optimized in order to produce defect-free PCL electrospun microfibers. In parallel, several GNPs solutions with different GNPs concentrations were optimized using ethanol as solvent. Then, the electrospun PCL membranes were immersed into the GNPs dispersions and dried after the impregnation. The developed systems were properly characterized through Raman Spectroscopy, Field Emission Scanning Electron Microscopy (FESEM), and Thermogravimetric Analysis (TGA). The electrical performance of the developed systems was studied, including the evaluation of the electrical conductivity properties, piezoresistive behavior (changes in the electrical resistance of the material caused by mechanical deformations), and their Gauge Factor (GF).
## 4 Results and Discussion
### The Influence of Air Inflow on the Oil Droplet Size Distribution  
The change of oil droplet size distribution under different air inflow is shown in Figure 10; when the oil inflow is 15 g/s, the initial oil droplet diameter is 28 \(\upmu\)m at \(t\) = 250 ms. It can be seen that the peak value of oil droplet diameter is mainly concentrated around 50-80 \(\upmu\)m. When the air flow is small, the proportion of oil droplet with 50 \(\upmu\)m is larger than that with diameter of 80 \(\upmu\)m. With air inflow increasing, the effect of turbulence intensity increases, and the strong disturbance in the flow field and the entrainment of vortices enhance the mutual collision between oil droplets. After the oil droplets collide with each other, the proportion of large diameter oil droplets increases in the oil droplet group due to the adhesion. Meanwhile, the increase of air inflow leads to the increase of oil droplet velocity, which promotes the coalescence between oil droplets. The smaller diameter oil droplet coalesces into larger diameter oil droplets. When the air inflow is 20 g/s, the peak value of oil droplet diameter distribution is mainly around 80 \(\upmu\)m. This shows that the air inflow has a significant effect on the oil droplet diameter distribution.  
Figure 9: The influence of initial diameter on oil droplet size distribution when the air inflow is 12 g/s and the oil inflow is 15 g/s at \(t\) = 250 ms.
# Bit Error Rate of Multi-Gaussian Correlated Asymmetric Bessel Beam Through Turbulent Ocean
###### Abstract
demonstrated the higher received power and lower BER of the quasi-ring Airy vortex beam in atmospheric turbulence when 16-QAM discrete multi-tone signals were utilized [22]. Based on the investigations on traditional circular symmetric beams above, asymmetric vortex beams are developed gradually, such as the asymmetric Laguerre-Gaussian beam, the asymmetric Bessel-Gaussian beam, and the Pearcey beam [23; 24; 25; 26; 27]. As one typical asymmetric vortex beam, the asymmetric Bessel-Gaussian beam can have integral or fractional orbital angular momentum and diverse transverse intensity patterns by changing the asymmetry factor [23]. In this way, the adjustment of the beam field becomes more flexible. [PERSON] et al. demonstrated the generation of the asymmetric Bessel-Gaussian beam and its field distribution in free space [28]. [PERSON] et al. analyzed the channel capacity of the asymmetric Bessel-Gaussian beam transmitted in turbulent atmosphere [29]. Although source coherence and signal modulation are both important factors affecting beam propagation performance, relevant investigations on the asymmetric Bessel-Gaussian beam have not been reported. As a generalized extension of the common Bessel-Gaussian beam by considering source coherence and asymmetry simultaneously, the multi-Gaussian correlated asymmetric Bessel (MGCAB) beam provides more flexibility for field adjustment and application. Despite the fact that some research progress has been made on the propagation characteristics and field regulations of several multi-Gaussian correlated beams mentioned above, the discussions on BER under signal modulation are relatively scarce. Therefore, exploring the BER characteristics of the MGCAB beam can help to enrich the investigations on underwater communication using vortex beams. This paper combines the factors of source coherence, asymmetry, and modulation strategy together and explores their effects on signal transmission, which is expected to provide a theoretical basis for optimizing optical communication systems in turbulent ocean environments. In this paper, the propagation of the MGCAB beam with partial coherence is investigated under modulation of QAM. The spectrum of OTOPS is adopted to characterize oceanic turbulence, which quantifies the influences of ocean temperature and salinity concentration on turbulence. By using Rytov approximation and spiral harmonic decomposition, we derive the expressions of signal reception probability and then BER of the MGCAB beam. Based on the established theoretical model, numerical simulations are conducted in different oceanic turbulence conditions. The influences of signal modulation and beam features on BER are analyzed, from which some instructive conclusions are given.
## 7 Limitations and Mitigations  
The study results should be validated and applied to other types of contents and performed on other types of head-mounted displays. This experiment was conducted on an Oculus Rift S head-mounted display with gaming content.  
The differences between the low action and high action modes of the game could be argued. Our general intention was to use different modes of the game, low action being slow and \"boring\" and the high action with highly reactive psychophysiology, so that the participants experienced different VR scenarios to easier examine their responses to VR stimuli. A review of previous studies showed us that the selection of possible actions, speed of translation, and rotations were suitable for assessing the participants' VRISE, user experiences, and presence.  
To ensure that the participants adjusted and centered the HMD and picture optimally, the experimenters carefully managed the HMDs.  
The sample size was relatively small, especially because of the relatively large dropout rate (22.73%), and only completed experiments could be included in the analysis. However, the sample size offered adequate statistical power for the performance analyses, as compared to other relevant works and studies.  
The locations and number of places where the FMS scores were collected during the gameplay could also be argued. However, given that FMS was collected after finishing each scene of the scenario and before and at the end of each scenario, the selection of locations seemed appropriable.  
A limitation related to this study was the sampling of the participants. The majority of the sample was gathered from the University of Ljubljana--Faculty of Electrical Engineering and web pages dedicated to gaming and virtual reality and invitations on social media. Therefore, the majority of participants who attended the experiment were interested in VR technology. Therefore, generalizing the results to other groups should be addressed in future studies.  
Another limitation associated with this study was that the increase in the familywise error rate across the reported statistical analyses was not controlled. However, considering related studies, there is a disagreement on whether those multiple comparisons are really necessary, whether the increase of the familywise error rate is important or not, and if the Bonferroni correction is too strict [40; 41; 42].
# Airborne and Dermal Collection Methods of Gunshot Residue for Toxicity Studies
## 3 Results/Discussion
### Inhalation Considerations (Filter and DST)  
The use of filter and DST sampling allowed for the detection of GSR released into the air, identifying a potential inhalation exposure scenario from the firing of a weapon. The filter samples represented GSR particles that were \(\leq\)2.5 \(\upmu\)m in aerodynamic diameter (PM\({}_{2.5}\)). PM\({}_{2.5}\) can not only be inhaled but can also travel deep into the lungs and potentially into the bloodstream [36]. Importantly, the chemical composition of PM\({}_{2.5}\) could increase the negative impacts of acute and chronic exposure, with the GSR elements, Pb [23,24] and Ba [26,27], having known health effects, including kidney diseases, cardiovascular, neurological, metabolic, and mental disorders. This study was able to quantify Pb and Ba from a single shot of a 0.22 caliber revolver. In our study, concentrations of Pb ranged from 1.81 to 76.37 ppb in the airborne samples, which is over 12 times higher than the National Ambient Air Quality Standards (NAAQS) for Pb [65]. In future toxicological studies, PM\({}_{2.5}\) should be collected to assess the potential for GSR particles to enter the lungs and bloodstream. The size-selective filter samples in this work were able to detect and quantify elements consistent with GSR using ICP-MS. Filter samples have been used in toxicological studies to investigate health impacts from environmental contaminants [66,67] and similar methods can be used to assess potential toxicity of inhalation exposures from GSR. The sample type also exhibited the ability to determine oxidative potential of GSR samples. The main concern for filter samples is the inability to acquire morphology data
# Power Dissipation and Wear Modeling in Wheel-Rail Contact
## 3 Numerical Solution Methods
The finite element discretization allows us to transform the original mechanical system (4)-(8) into a system of algebraic Equations (19)-(22). Therefore, the original mechanical system is replaced with the equivalent algebraic matrix system of equations. Its solution consists in finding, for a given temperature \(\theta^{j}\), at each iteration \(j\), the displacement, wear depth, generalized stress and normal traction \((u^{j},w^{j},(\sigma^{j},\chi^{j}),F_{2}^{j})\) satisfying:  
\[Ku^{j}+L_{p}^{j}+L_{c}F_{2}^{j}-M_{2}\theta^{j}=0, \tag{19}\] \[w^{j}=K_{w}u^{j},\] (20) \[N_{p}(u^{j},w^{j},(\sigma^{j},\chi^{j}))=0,\] (21) \[N_{c}(u^{j},w^{j},F_{2}^{j})=0, \tag{22}\]  
where \(K,K_{w},L_{c},L_{p},M_{2},N_{p},N_{c}\) are given matrices resulting from finite element analysis [17; 32]. Due to the semi-smoothness of the complementarity functions, this algebraic system is also semi-smooth. Therefore, a generalized rather than standard Newton method is applied to solve the system (19)-(22). Recall from [17; 28] that the use of nonlinear complementarity functions in (21) and (22) makes this approach close to an active set strategy-based primal-dual optimization method. In discretized form, the thermal system (9) and (10) is  
Figure 2: Wear evaluation algorithm. transformed into the linear system of algebraic equations. This linear system uses the Cholesky method. Due to friction, material removal and wear, the geometry of the contacting surfaces is changed. The wear phenomenon is responsible for the increase in the gap between the bodies as well as the modification of the shape of the contacting surfaces. At the finite element level, wear or material removal occurs as a distortion of the mesh, i.e., the aspect ratio of surface finite elements does not satisfy constraints and the mesh becomes irregular. The wear box two-step procedure [17; 22] is used to take into account the worn material and to ensure the regularity of mesh. At a finite element level, this update is realized in the form of the so-called wear box procedure [17]. In this procedure, for each finite element on the contact patch, the average wear depth \(w_{a}^{\prime}\) is estimated using the calculated volume of the worn material and wear depth at time instant \(t\). These calculations are based on Formulas (11)-(16). The calculated average wear depth allows us to identify whether the volume of the wear debris is so large that it causes a finite element mesh distortion. This distortion is measured via the finite element aspect ratio. In this case, the worn material volume is removed not only from the finite element layer along the contact interface but also from the adjacent layers. Therefore, the distortion of the finite element mesh is avoided at the cost of the repositioning of finite elements nodes located close to a contact interface.
The approach has been reported to have the capability to produce ARD for Landsat 5-7-8 and Sentinel-2 for anywhere in the world [33]. Conventional models for ingesting data into a cube require design choices to be made for both the temporal and spatial resolution, cube dimensions and the coordinate reference system [34]. The process is often reliant on the re-sampling of data and, consequently, does not retain the original resolution and bit depth, which has been optimised for a specific application only. Furthermore, the fusion and inter-operability of heterogeneous data cubes acquired from different sensor systems are characterised by inherent challenges owing to the variability in (i) the spatial resolution of different satellites, leading to different pixel sizes, and (ii) the irregular revisit times of satellites, leading to different reference temporal durations [34]. The data cube also needs regular updating to ingest the most recent available data. Here, a Python open-source framework for the automatic generation of on-demand, temporally matched Sentinel-1 and Sentinel-2 ARD for any location on Earth is detailed. The platform does not require the archiving of ARD in the form of a data cube as all processing is executed for the region of interest (ROI) and a timespan dictated by the requirements of the target user only. SAR data from the Sentinel-1 constellation and optical images from the Multi-Spectral Instrument (MSI) on the Sentinel-2 constellation are temporally aligned to enable the mining of their complementary data. The framework can be utilised to generate collocated, multi-modal and multi-temporal Sentinel-1 (S1) and Sentinel-2 (S2) ARD for a user-defined ROI. In particular, the following are possible: (a) S1 time series, (b) S2 time series, (c) temporally matched S1 and S2 time series. The platform is an enabler for creating time series of multi-modal images with customised temporal resolution, the data foundation that facilitates the development of a variety of downstream applications and services, such as crop mapping [35], deforestation mapping [4], urban fabric mapping [36] and burned area mapping [37; 38]. The remainder of the article is organised as follows. Section 2 details the proposed framework, including its configurable parameters, the scene discovery algorithm for the selection of Sentinel tiles, the alternatives for data download, as well as the SAR and optical processing pipelines. Results for an ROI in the United Kingdom are presented in Section 3 as an illustration of the process flow, together with a discussion of the main challenges addressed by the proposed ARD framework and an example of its application to crop monitoring. Conclusions are drawn in Section 4.
## 4 Experimental Results
### Experimental Setup
#### 4.3.3 Comparison of Computational Complexity  
To measure the computational complexity, we define the encoding speed as the number of frames which can be encoded in one second on the hardware platform with processor Intel Core i5 4-core CPU 3.10 GHz. The computational complexity is considered as inverse value of the encoding  
Figure 8: Subjective visual quality comparisons of the 8 th reconstructed frame of “City” sequence at 896 kbps.  
speed, which is measured without any use of assemblers, threads, or other program optimization techniques [6; 7; 15; 28]. Table 4 demonstrates the encoding speed for the proposed algorithm, ENH-MC-EZBC, RWTH-MC-EZBC, RPI-MC-EZBC, and MC-EZBC. As shown in Table 4, the encoding speed of the proposed algorithm is about 1.14, 3.65, 4.65 times faster than RWTH-MC-EZBC, RPI-MC-EZBC, and MC-EZBC, respectively. The reason is that for the sequences with strong inter-frame dependencies, more bits are allocated to the reference frames which lead to better prediction results. Thus, only small residual signals need to be processed in the following encoding steps, which further result in computational complexity reductions. However, the encoding speed of ENH-MC-EZBC is about 1.11 times faster than our algorithm. This is mainly due to the additional operations on the statistics for video content characteristics. In general, the experimental results demonstrate that the computing overhead brought by the proposed Lagrange multiplier selection algorithm can be negligible when compared to the other four methods. Although our algorithm encodes at average 3.07 frames per second, it is insufficient for real-time video processing applications.  
Since the ENH-MC-EZBC reference software is non-optimized C++ implementation, development of an efficient low-complexity 3-D wavelet-based scalable video coding scheme is an important practical problem, which is necessary to be considered in our future work.
## 4 Centralized Scheduling Algorithm for the IEEE802.15.4e TSCH Network
### Deepening of the Observed Scheduling Algorithm
#### 4.4.1 IRByTSA
The turn_now[i] variable is used to store information related to the turn. Thus, supposing we have turn_now[10] = 5 in a cycle, the child of node 10 that gets the transmit turn is node 5. If node i has more than one child (NCD [i] > 1) and the child of node i has a transmit turn (node z) and its queue is empty (qz = 0), the transmit turn will be given to another child of node i. If no child of node i has data to send, the scheduling process will continue for node (i + 1). The flowchart section in Figure 17 describes these steps. 6 If node i only has one child node and it has data to send, the child node (nz) will be scheduled to transmit data to its parent (ni), indicated by the command line matching_link[s] = matching_link[s] + [nz,ni]. Figure 18 shows the part of flowchart that illustrates that step. 7 The part of the flowchart in Figure 19 is used to find the node that has largest number of packets to be sent in a cycle (s), since the number of packets indicates the number of timeslots needed. Figure 11.4 Giving turns to child nodes (parents with more than one child and qz = 0). Figure 11.5 Process of giving a turn to a child node (parent has only one child node and qz > 0). IRByTSA looks for the largest timeslot value required by active nodes in each cycle (s) to ensure that the number of timeslots allocated meets the transmission needs of all active nodes. Figure 20 shows the application of IRByTSA on the 16-node network shown Figure 11.6 Figure 20 illustrates the steps, starting from the initial condition where each node has one packet waiting to be transmitted, and it continues with the process of sending packets gradually to the master node. IRByTSA requires eight cycles to collect all data in the master node. Based on Figure 20, we could determine the values of parameters C, \(\lambda\), and total channel offsets (ChOffs) required if we used IRByTSA as in the following:  
Figure 11.7 Process for determining TS (s) in IRByTSA. Figure 11.8 Application of IRByTSA on 16-node networks. \(\bullet\)\(\mathrm{C_{IRByTSA}}=8\) cycles;  
\(\bullet\)Total ChOff = \(\sum\limits_{s=1}^{C}ChOff(s)\) = ChOff(1) + ChOff(2) + ChOff(3) + ChOff(4) + ChOff(5) + ChOff(6) + ChOff(7) + ChOff(8) = 5 + 5 + 5 + 3 + 2 + 1 + 1 + 1 = 23 ChOffs;  
\(\bullet\)\(\lambda_{\mathrm{IRByTSA}}=\sum\limits_{s=1}^{C}\mathrm{TS(s)}=\mathrm{TS(1)}+\mathrm{TS(2)}+ \mathrm{TS(3)}+\mathrm{TS(4)}+\mathrm{TS(5)}+\mathrm{TS(6)}+\mathrm{TS(7)}+ \mathrm{TS(8)}=1+2+3+4+2+1+2+2=17\) timeslots.