esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Method for the Mechanical Testing of Sports Mouthguards
## 2 Field Study for Real-World Data Collection
### Methods  
The kinematics of the field hockey ball were measured as a part of a technical assessment using a high-speed camera. There are four known and calculated parameters needed to determine the speed of a shot: the distance moved by the ball between two frames; the frame number of the initial position; the frame number of the final position; the number of frames per second captured by the camera. The frame numbers are found by analysing the video, and the frames per second of the camera used in this work is 240 with a resolution of 1920 \(\times\) 1080 pixels. The difficult parameter to determine is the distance travelled by the ball.  
To determine the distance travelled by the ball, a reference distance of \(a\) = 0.8 m was plotted on a black board with two fiducial white lines, such that the ball passes between the board and the camera. Figure 1a illustrates this setup. The videos captured by the high-speed camera were processed and analysed in MATLAB (R2017a, Mathworks, Natick, MA, USA). To calculate the distance travelled in metres from the number of pixels travelled, the length of one pixel is used. This length is found by dividing the 0.8 m gap between the vertical lines on the board by the number of pixels between the white lines, which results in a pixel length of 0.0012 m. The MATLAB script identifies the number of pixels that the ball has travelled between two frames which, in conjunction with the pixel length, gives the distance in metres that the ball has travelled; however, due to the gap between the ball and the board, the pixel length on the board is different to the pixel length that the ball travels across. As a result, a parallax error is introduced as pixels cover a larger area when they are further from the camera. The parallax error is accounted for by using the size of the ball in the frame, which is used to determine the distance between the camera and the ball. This distance is known from a sample video in which the ball is captured at known distances and the size of the ball in pixels is recorded. Figure 1b demonstrates the distribution of the measured pixel size of the ball depending on the distance \(d\) to the camera with the exponential parameters \(m=116.75\) and \(n=0.992\). The script then combines the distance travelled by the ball with the corresponding frame numbers and frames per second of the camera to output the speed of the ball considering the parallax error and the known distance of the camera to the board, which is \(c=2.0\) m.  
The easiest way to collect many data points is to take shots from close to the board, thus, each player takes 100 shots from a distance of 3.2 m. Furthermore, to investigate the effect of distance from the board on the speed of the ball, the players perform 30 shots from a further distance of 4.8 m.
## 3 Results and Discussion
### Analysis on Cultural Perception-Interaction between Ethnic, Duration and Intensity  
A two-way MANOVA analysis was conducted on the different independent variables to investigate the effect of different combinations on perceiving emotions. The analysis revealed no significant interaction effect between intensity and duration or ethnic and duration variables, but did so between ethnic and intensity variables. A statistically significant ethnic x intensity multivariate main effect was also noted on perceiving the different emotions (F (24, 88.43) = 2.19, \(p\) = 0.004, Wilks' \(\Lambda\) = 0.196). The univariate analysis revealed that the significant effect was in perceiving _surprise_ (at \(p\) = 0.001) and _anger_ (\(p\) < 0.001). The means in Figure 5 indicate that although the Malaysians perceived _surprise_ correctly higher than the Vietnamese and statistically significantly higher than the Japanese (\(p\) < 0.05) and Chinese (\(p\) < 0.001) at intensity 0.5 and intensity 1 decibels, they perceived _surprise_ less at intensity 1.25 decibels. Other ethnic groups showed a direct proportion between the intensity and the mean of perceiving emotion.  
The results illustrated in Figure 5 show that intensity of speech could lead to inaccurate perception by out-group listeners. Experiments by [PERSON] et al. [41] found that, in Mandarin Chinese, syllables were lower in pitch and intensity. The Japanese language and similar languages such as Chinese and Vietnamese language, have vowel lengths that are phonemic such that shortening or lengthening spoken vowels changes word meanings [42]. Hence, the different features of utterances in Spanish may have influenced the perception rate among the different ethnic groups. The different melodic and acoustic patterns in languages spoken by Asians could be one of the main reasons of variation in results for the duration and intensity factors. These factors should be taken as a main consideration during the implementation of conversational artificial agents in any domain. Figure 5 shows the significant effect of intensity in perceiving anger by the Chinese ethnic group. Although the Chinese recorded the lowest perception mean of anger at intensity 0.5 decibel, they perceived the same emotion better than the other races at intensity 0.5 and intensity 1 decibels.  
Figure 4: The overall mean for emotion perception by Asian ethnics based on speech intensity.
# High-Reliability Underwater Acoustic Communication Using an M-ary Cyclic Spread Spectrum
## 2 Proposed Method
### Symbol Synchronization and Doppler estimation with MSS  
The method proposed in this paper is the calculation of the sample change at the peak position, the calculation of the approximate Doppler based on the change amount, and the calculation of \(f_{c\mp\Delta}\) accurately within the range. Within the approximately calculated Doppler range (\(f_{c-\Delta}\)\(\sim\)\(f_{c+\Delta}\)), the step size is established and the (\(\Delta_{i}\)) with the highest correlation result is found and compensated. It compensates \(\Delta_{i}\) and compensates for the starting point of the next symbol as follows:  
\[len(i)=(1+\Delta_{i})\text{Chip}_{\text{ns}}\times L \tag{17}\]  
The number of samples per \(i\)-th symbol is denoted by \(len(i)\). We can compensate for the start synchronization of the next symbol for each of the symbols by adjusting the number of samples after Doppler compensation [26].  
Following the MSS method, the step size is determined within the range of \(-\Delta\) to \(+\Delta\) using the previously determined \(\Delta\), and the largest value is found to compensate for precise Doppler, as shown in Figure 4. The peak is then found using the CSK's correlation in the Doppler-compensated symbol, and the value of n is determined by comparing it to the peak position of the \(MSS_{f_{c}}^{q}\) correlation. The derived \(q\) and n are then decoded into binary numbers.  
In the conventional M-ary cyclic shift keying (MCSK) method, if \(q\) sequences are used for MSS and N cyclic shifts are performed on the original MCSK signal, (\(\log q+\log N\)) bits can be expressed in one symbol. The MCSK method, on the other hand, creates a number of cases by cyclic shifting each MSS sequence, whereas the MCSS method proposed by us overlaps the MSS sequences and the CSK sequence so that the receiver can demodulate it using only MF. Furthermore, because MSS and CSK overlap, MSS serves as a reference for the midpoint of CSK's cyclic shift, allowing the cyclic shift value to be demodulated more consistently.
## 1 Introduction
### Principal Contributions  
The principal contributions of this paper are as follows:  
* Hybrid Model Development : Proposes the STL-DCSInformer-ETS hybrid model, which combines STL decomposition, enhanced DCSInformer, and ETS models to address the unique challenges of FMCG sales forecasting.
* Feature Selection: Introduces the RF-RFE method to enhance feature selection precision, improving model reliability and predictive accuracy.
* Temporal Dependency Modeling: Incorporates dilated causal convolution and multi-scale feature extraction in the DCSInformer component to simultaneously capture short-term fluctuations and long-term trends.
* Seasonal Pattern Modeling: Integrates ETS to effectively model seasonal variations, enhancing the ability to handle periodic patterns in time-series data.
## 6 Analysis and Conclusions
### Tuning Network Parameters  
The hybrid network requires partitioning the available network channels between the stationary mesh and MAV communications. With one unique path to the base station, the optimal scenario involves the ability of the MAV to transmit a packet every other slot. If not enough slot offsets are available, the paths to the base station will fill the transmission schedule in a way that does not permit the MAV to transmit a packet every other slot. This is considered sub-optimal.  
As a rule of thumb, the number of hops (paths to the base station) can only be twice the size of the reserved channels to ensure that the MAV can transmit a packet every other slot. As a MAV gets more hops away from the base station, more channels should be allocated accordingly to ensure that burst mode is carried out at the fastest possible rate. As such, to achieve high throughput, the number of allocated MAV channels must be at least half the number of hops that the data is expected to traverse (see Figure 6).  
With half-duplex radios, each node along the route must spend half its time receiving and half its time retransmitting the data along the pipe, thus limiting the throughput to half the available data bandwidth. However, with a sufficiently dense network, this limitation could be lifted. If two completely independent paths can be found from the MAV to the base station without overlapping intermediate nodes, data can be alternately sent along both routes, further doubling the throughput to the full available data rate. In this case, the number of reserved MAV channels would need to be at least half the number of hops from MAV to base station.  
As there are only 16 channels available in the IEEE 802.15.4 standard, allowing a more extensive network requires more channels to be allocated for MAV use, and fewer for the underlying stationary mesh. However, as shown in [27], the robustness and efficiency benefits of channel hopping become evident with only a small subset of channels; peak performance can be achieved by allocating six  
Figure 6: Given the expected maximum network distance (number of hops) from the MAV to the base station, a number of channels can be allocated to the MAV burst mode. As long as the number of channels is at least half the number of hops, the MAV will be able to communicate at the maximum supported throughput of the network given one unique path to the base station.  
channels to the stationary mesh. Ten channels can thus be available for MAV communications, allowing for a reliable network up to 20 hops deep.
## 5 Results and Discussion
### Ablation Studies on VRFormer  
To evaluate the individual contributions of key components of the VRFormer architecture, we conducted a series of ablation studies. These studies involved selectively removing or altering specific components of the model and then measuring the impact on the performance metrics. This enabled us to isolate the effects of various design choices and demonstrate their importance to the overall performance of the model. Specifically, we tested four variations of VRFormer as shown in Table 9: removing V2X data, replacing the Bayesian fusion approach with a basic averaging technique, using a standard Kalman filter instead of a hierarchical Kalman and particle filter approach, and finally replacing the LSTM with a simpler RNN model.  
Table 9 shows the ablation study results. Removing V2X data caused a 12% drop in mIoU, a 15% drop in APA, and a 15% drop in mAP. This shows that V2X provides a more comprehensive and robust view of the driving environment than onboard sensors alone and thus improve accident prediction. Similarly, using simple averaging instead of Bayesian inference caused a significant drop in all metrics (7% in mIoU, 8% in APA, and 8% in mAP), which confirms the latter is better at weighing inputs. The hierarchical Kalman and particle filter worked well; replacing it with a standard Kalman filter caused a drop in  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Training Dataset** & **APA (\%)** & **mIOU (\%)** & **mAP (\%)** \\ \hline VRDeepSafety (Ours) & 90 & 92 & 85 \\ \hline nuScenes & 73 & 78 & 65 \\ \hline V2X-Sim & 70 & 75 & 62 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparative performance with external datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Variation** & **(mIOU)** & **(APA)** & **(mAP)** \\ \hline VRFormer (Full Model) & 92\% & 90\% & 85\% \\ \hline No V2X Data & 80\% & 75\% & 70\% \\ \hline Deterministic Fusion (Averaging) & 85\% & 82\% & 77\% \\ \hline Standard Kalman Filter & 86\% & 80\% & 78\% \\ \hline Replaced LSTM with RNN & 83\% & 78\% & 74\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Ablation study results for VRFormer.  
performance (6% in mIoU, 10% in APA, and 7% in mAP). Finally, the limitations of simpler temporal models were exposed by replacing the LSTM with an RNN, causing a 9% drop in mIoU, a 12% drop in APA, and an 11% drop in mAP. In summary, these results underscore that each component significantly contributes to VRFormer's overall performance.
## 5 Conclusions  
Lab-scale planted and unplanted Constructed Wetland units were tested for the treatment of _P. radiata_ and _E. globulus_ leachates. In general, similar performances were found for both leachates. However, the unplanted CW units were more efficient in the removal for COD, BOD\({}_{5}\), and total phenolic compounds by approximately 10-20%, possibly due to the recalcitrant nature of the organic matter composition in the influent leachate and their inhibition activity to the microbial growth within the planted units. The highest CW performance was found at an OLR of 13 g COD/m\({}^{2}\)/day for the _P. radiata_ leachate and at 25 g COD/m\({}^{2}\)/day for the _E. globulus_ leachate. The CW performance in terms of organic matter and specific compound removal was decreased at higher OLR. Aromatic compounds contained in _P. radiata_ and _E. globulus_ leachates were removed by almost 95% _w/w_ of BOD\({}_{5}\), while COD removal ranged between 40-60% _w/w_. Furthermore, the low values of the UV\({}_{254}\)/UV\({}_{280}\) ratio between the influent (1.13) and the effluent (1.12) at an OLR of 13 g COD/m\({}^{2}\)/day for _P. radiata_ leachate or 25 g COD/m\({}^{2}\)/day for _E. globulus_ leachate indicated the recalcitrant behavior of lignin-derived compounds in the influent leachate.  
Conceptualization, G.V.; methodology, C.M.; software, I.V.-P.; validation, C.P.d.l.R. and A.I.S.; formal analysis, C.M.; investigation, C.M.; resources, G.V.; data curation, I.V.-P.; writing--original draft preparation, C.M. and G.V.; writing--review and editing, A.I.S.; visualization, G.G.; supervision, G.V.; project administration, G.V.; funding acquisition, G.V.  
This research was funded by the Comision Nacional de Ciencia y Tecnologia de Chile (National Commission of Science and Technology of Chile), grant number CONICYT/FONDAP/15130015.  
This work was partially supported by \"Comision Nacional de Ciencia y Tecnologia de Chile\" (National Commission of Science and Technology of Chile).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Results
### The Greendale Fault Case Study
The expected fracture systems (cleavages and extensional fractures) calculated using the package FRAP [59] are illustrated as well. (**D**) The orientation of the expected predominant extensional fractures (blue rose diagram, mean azimuth of N139\({}^{\circ}\)), together with the combined results of ground motion polarization at stations ND6 and ND7 (red rose diagram). frequency peak, the ones between 2 and 5 Hz are more strongly anisotropic, attaining amplitudes up to a factor of four in the N50-60\({}^{\circ}\) direction. The covariance matrix analysis led to better identification of the ground motion polarization direction. The two stations installed on the fault, Dar6 and Dar7, show that the narrow rose diagrams peaked in the N52\({}^{\circ}\)\(\pm\) 27\({}^{\circ}\) and N53\({}^{\circ}\)\(\pm\) 24\({}^{\circ}\) directions, respectively. The results are consistent between the analyzed clusters of seismic events, suggesting that it is not affected by the seismic path, but is rather ascribed to the site subsoil structure. All stations in the Canterbury Plains and at kilometers from the GF (Dar1, Dar2, Dar3, Dar4, Dar5, Cch1, Cch2, Cch4, CRLZ, and MQZ) show very scattered rose diagrams, with no horizontal polarization (see also [34]). Following the approach described in Section 3, this effect was interpreted in terms of the fracture pattern in the fault damage zone, which was modeled through the package FRAP [34; 59]. In Figure 3C we provide a sketch of the right-lateral fault movement in the N90\({}^{\circ}\) direction (black arrows), with the regional stress field (red arrows), and the kinematic components of the local stress field (K1 and K3). Modelling showed that extensional fractures are the expected predominant fracture cleavage in the shallow layers (<2 km), with an expected strike of N139\({}^{\circ}\) (the blue rose diagram). The fracture orientation is consistent with coseismic surface rupture observations, confirming the reliability of the model. Therefore, the horizontal polarization is orthogonal to the predominant fracture strike (Figure 3D). The ground motion polarization was also compared with the shear-wave anisotropy derived from the shear-wave splitting, confirming such inferences. The relationship between seismic anisotropy ground motion polarization will be discussed later on, in Section 4.
## 4 Results and Discussion
### Day-Ahead Forecast Results
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Polynomial Equations of the Day-Ahead DNN Model**} \\ \hline \(\boldsymbol{y}\) & \(\boldsymbol{x_{i}}\) & \(\boldsymbol{x_{j}}\) & \(\boldsymbol{a_{0}}\) & \(\boldsymbol{a_{1}}\) & \(\boldsymbol{a_{2}}\) & \(\boldsymbol{a_{3}}\) & \(\boldsymbol{-a_{4}}\) & \(\boldsymbol{-a_{5}}\) \\ \hline \(N_{3}\) & \(\mathcal{R}_{fl}\) & \(E_{w}\) & \(-6738.72\) & \(5.99\times 10^{7}\) & 1.08 & \(-0.46\) & \(5.96\times 10^{7}\) & \(8.01\times 10^{-8}\) \\ \(N_{6}\) & \(E_{d}\) & \(\mathcal{C}_{n}\) & 8369.54 & 1.03 & 0.81 & \(-2\times 10^{-6}\) & \(3.25\times 10^{-8}\) & \(-10^{-6}\) \\ \(N_{2}\) & \(N_{3}\) & \(N_{6}\) & \(-7253.28\) & 0.31 & 0.67 & \(4.82\times 10^{-7}\) & \(1.78\times 10^{-7}\) & \(2.58\times 10^{-7}\) \\ \(N_{10}\) & \(E_{w}\) & \(\mathcal{C}_{n}\) & 10,302.22 & 1.07 & 1.04 & \(-5\times 10^{-6}\) & \(4.22\times 10^{-8}\) & \(-19\times 10^{-6}\) \\ \(N_{11}\) & \(E_{d}\) & \(d_{t}\) & \(-8129.99\) & 1.10 & \(6.69\times 10^{12}\) & \(-0.17\) & \(5.58\times 10^{-8}\) & \(6.69\times 10^{12}\) \\ \(N_{9}\) & \(N_{10}\) & \(N_{11}\) & \(-1056.66\) & 0.31 & 0.65 & \(6.72\times 10^{-8}\) & \(6.17\times 10^{-9}\) & \(1.80\times 10^{-8}\) \\ \(N_{1}\) & \(N_{2}\) & \(N_{9}\) & \(-3.113.15\) & 0.77 & 0.24 & \(-13\times 10^{-6}\) & \(-6\times 10^{-6}\) & \(-7\times 10^{-6}\) \\ \(N_{15}\) & \(d_{t}\) & \(h_{d}\) & 590,817.7 & \(-2.57\times 10^{15}\) & 44,555.06 & \(-1274.41\) & \(-2.57\times 10^{15}\) & 1782.64 \\ \(N_{17}\) & \(E_{w}\) & \(E_{d}\) & \(-6999.91\) & 0.36 & 0.68 & \(4.05\times 10^{-7}\) & \(1.82\times 10^{-7}\) & \(2.45\times 10^{-7}\) \\ \(N_{14}\) & \(N_{15}\) & \(N_{17}\) & 794,912.7 & \(-2.21\) & 0.81 & \(2.82\times 10^{-7}\) & \(-2\times 10^{-6}\) & \(2.25\times 10^{-8}\) \\ \(N_{18}\) & \(N_{11}\) & \(N_{17}\) & 7265.85 & \(5.96\times 10^{-4}\) & 0.97 & \(-2\times 10^{-6}\) & \(-10^{-6}\) & \(-8.23\times 10^{-7}\) \\ \(N_{13}\) & \(N_{14}\) & \(N_{18}\) & \(-239.39\) & 0.08 & 0.92 & \(-17\times 10^{-6}\) & \(-9\times 10^{-6}\) & \(-8\times 10^{-6}\) \\ \hline \(\boldsymbol{\hat{E}}\) & \(\boldsymbol{N_{1}}\) & \(\boldsymbol{N_{13}}\) & **6961.16** & **0.27** & **0.71** & \(\boldsymbol{2\times 10^{-6}}\) & \(\boldsymbol{8.95\times 10^{-7}}\) & \(\boldsymbol{10^{-6}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Polynomial representation of the best day-ahead DNN model. Figure 7: Expression tree of the best GEP model for an hour-ahead electricity demand forecasting.
# Efficient Computer-Generated Holography Based on Mixed Linear Convolutional Neural Networks
## 6 Conclusions  
In this paper, we proposed a non-iterative deep learning model, MLCNN, for generating ECGH images. Compared with the traditional U-net network [21, 22] and the GS algorithm [17], our method can achieve faster computation speeds in hologram generation. High-quality and stable computational holographic images were successfully obtained using the ECGH method. A major feature of the MLCNN network structure is that it can calculate the cross-region exchange of data. This is optimal for complex optical functions such as Fourier transforms that require manipulation of global information. The results show that the MLCNN network structure is more suitable for the optical domain than the classical U-net network, especially for holography generation and reconstruction work.  
Virtual reality (VR) and augmented reality (AR) are currently hot topics in display technology and application. However, conventional technologies in VR and AR employ micro-displays to load images, which can cause visual fatigue when they are used for extended periods of time. Benefiting from the ability to reproduce three-dimensional scenes perfectly, computational holography can prevent visual fatigue. This ECGH method is expected to ease the huge CGH computing load and to improve the quality of the computational holography images.  
Conceptualization, X.X.; Data curation, X.W.; Formal analysis, X.W.; Funding acquisition, H.W.; Investigation, X.W.; Validation, W.L. and Y.S.; Writing--original draft, X.W.; Writing--review & editing, X.X. All authors have read and agreed to the published version of the manuscript.  
Natural Science Foundation of Shandong Province, China (ZR2019 MD023) and Fundamental Research Funds for the Central Universities of China (15 CX05033A).  
Not applicable.  
The authors declare no conflict of interest.