esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions and Future Work  
We presented an authoring system with the idea of generating multiple experiences from a single 360\({}^{\circ}\) video. We introduced arbitrary object tracking in 360\({}^{\circ}\) videos and an HMD-based system for authoring multiple experiences in immersive VR. Moreover, we developed an adaptive focus assistance technique to guide the user efficiently in VR for transferring of 360\({}^{\circ}\) video experience. Our proposed system ensured the generation of a variety of experiences from a single 360\({}^{\circ}\) video. Overall, the proposed system performed very well in generating and transferring 360\({}^{\circ}\) video experiences. At the end of the user tests and survey, we analyzed each user's experience that was produced based on the visual content watched by that user. It was noticed by users that the experiences generated from a 360\({}^{\circ}\) video held different interesting information. Therefore, it resulted in multiple interesting experiences from one 360\({}^{\circ}\) visual content source. The possible number of experiences solely depends on the content presented in a 360\({}^{\circ}\) video. This system provided efficient transferring of the experiences to prospective users by using our focus assistance technique. The proposed arbitrary object tracking technique for panoramic videos also produced the best results with average precision of 0.95. The responses obtained from the user survey were very positive. They showed that overall, the audience wants to use our system for getting multiple experiences from one 360\({}^{\circ}\) visual content source. In future work, we will enhance the object re-identification module of our object tracker for timely re-identification of the desired object in case of loss of tracking. We will also create a better mechanism to track the partial object at boundaries of the 360\({}^{\circ}\) frame. Furthermore, we will work on further enhancement of our focus assistance method for transferring 360\({}^{\circ}\) experiences in an even more efficient way. We will also investigate how to record a better 360\({}^{\circ}\) video to make it capable of generating multiple interesting experiences from a single content source.  
Conceptualization, J.W.L., S.H.H.S., and K.H.; methodology, S.H.H.S.; investigation, S.H.H.S.; formal analysis, S.H.H.S. and K.H.; validation, S.H.H.S. and K.H.; writing--original draft preparation, S.H.H.S.; writing--review and editing, S.H.H.S., K.H., and J.W.L.; supervision, J.W.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2020-2016-0-00312) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation).  
The authors declare no conflict of interest.
# FrameAugment: A Simple Data Augmentation Method for Encoder-Decoder Speech Recognition
## 4 Results
### Discussion  
Figure 8 shows the attention alignment after learning. When speed perturbation is applied as shown in Figure 8b, the overall slope increases compared to the attention alignment of the original utterance shown in Figure 8a. However, since FrameAugment changes the speed of selected random sections of the original utterance, the slope of non-augmented sections remains unchanged, but the slope of the selected random section is changed, as shown in Figure 8c,d. The selected random section is marked with a box with red lines. We can notice that the slope increases because the frame rate is decreased in Figure 8c, and the slope decreases because the frame rate is increased in Figure 8d.  
Figure 8: Comparison of the attention alignments with the encoder feature index on \(x\)-axis and the outputs on \(y\)-axis for the same utterance. We note that speech frames are subsampled by 4:1 for encoder input of Transformer. (**a**) Original data; (**b**) speed perturbation (speed = 1.1); (**c**) FrameAugment (\(s=0.5\), \(n=300\), \(p=500\)); (**d**) FrameAugment (\(s=1.5\), \(n=500\), \(p=100\)).  
Figure 9 shows the original spectrogram and the augmented spectrogram when FrameAugment is applied.  
Figure 10 shows the attention loss on the training data and the validation data of the LibriSpeech database using FrameAugment and other existing data augmentation methods. Figure 10a shows the loss graph for the training data. If the amount of training data for each epoch is scaled to be equal in the baseline and the speed perturbation method (i.e., 90 epochs in the baseline is scaled to 30 epochs in the speed perturbation method), the training loss curve of every data augmentation method shows convergence slower than that of the baseline.  
Figure 10b shows the loss graph for the validation data, where the loss graph of every data augmentation method converged to a lower loss value than that of the baseline. SpecAugment, which augments data by masking on a random section of the spectrogram, converged to the lowest validation loss. Between the two data augmentation methods that change speed, FrameAugment converges to a lower loss value than speed perturbation. It can be expected that FrameAugment performs worse than SpecAugment but better than speed perturbation. In the experiments of the previous subsection, it was shown that FrameAugment can be constructively combined with SpecAugment and speed perturbation for the best performance.  
Figure 9: Comparison of the spectrograms with frame index on \(x\)-axis and feature index on \(y\)-axis. The length of the utterance increases due to FrameAugment. (**a**) Original spectrogram; (**b**) spectrogram with FrameAugment (\(s=1.3\), \(n=500\), \(p=100\)).
# Overcoming Limitations of LoRa Physical Layer in Image Transmission
## 3 Background
### Comparison of LoRa and Other LPWAN Technologies
#### 3.3.2 LoRa versus IEEE 802.15.4  
The IEEE 802.15.4 standard offers data rates approaching 250 kbit/s. However, the transmission range greatly depends on the environment. While, for a clear line-of-sight this can reach 1000 m, the range is measured in tens of meters in most cases. Three unlicensed frequency bands are supported by the IEEE 802.15.4 standard: 868 MHz in Europe, 928 MHz in North America, and 2.4 GHz worldwide [19].  
In general, LoRa is less sensitive with respect to interference than IEEE 802.15.4g. Research on channelization has proven that it is resilient enough to obtain high packet reception rates (PRR), even with strong interference from IEEE 802.15.4g signals, which are communicated in the same frequency band. The impact of these signals depends significantly on the configuration of the LoRa network (i.e., the \(SF\) and \(BW\)). At high data rates, LoRa tolerates interference from signals with amplitudes that are 6 dB higher than the power of the actual LoRa signal being received. At low data rates, this tolerance goes up to 16 dB, as defined by the acceptable PRR [20].
# Detection and Localization of Multiple Damages through Entropy in Information Theory
## 2 Wiener Entropy Measurements for SHM Purposes
### Uses for Damage Detection  
The values of WE can be computed at any point and compared with the known baseline computed from the pristine system. This approach is in accordance with the classic statistical framework of pattern recognition-based SHM [20].  
However, a deviation from the normality model is not necessarily an indicator of damage per se. Such deviations could happen due to damage-unrelated confounding influences, collectively known as environmental and operational variations (EOVs, [21; 22]). For this reason, the pipeline is here investigated only without any internal pressure to avoid changes in the stress field induced by the changing operating conditions. The rationale is that SHM is rarely performed online and in real-time but rather periodically, during a phase of inactivity (e.g., at night, see for instance [23]). For unchanged external conditions, any statistically relevant variation can be then pinpointed to a change in the internal structure of the target system, i.e., to damage.
## 4 Discussion  
We have shown that the MS2E method is able to estimate PV output with high accuracy. In this section, we discuss the effect of atmospheric parameters, cloud conditions, air mass, and incident angle modifier on PV output. Figure 10 shows the seasonal PR fluctuation estimated by each PV output forecasting model as shown in Table 1. The air mass variations were considered in all models. The residual errors, defined as the difference between actual PR and predicted PR, were calculated using a statistical analysis. The purpose of our statistical analysis is to ensure that the error in MS2E does not contain bias and skew, and that the residual error distribution is accidentally random, i.e., the normal distribution is centered on zero. Figure 11 shows the comparison of each prediction model to the normal distribution of residual errors. For the IMM module, apparently, both Case-3 and Case-4 showed highly skewed and highly biased error distributions. This situation was significantly improved upon the introduction of atmospheric parameters (Case-2) and further improved using the MS2E (Case-1). Figure 12 shows the external quantum efficiency (EQE) of Si and IMM solar cells over the reference solar spectrum in AM 1.5G. It can be seen that the bottom cell in IMM mainly absorbs the wavelength spectrum that gets absorbed by precipitable water. On the other hand, the normal distribution of the residual error of the Si module showed the same tendency in all cases, because of the broader EQE of the Si solar cell, as shown in Figure 12a. Therefore, the Si modules were almost unaffected by spectral changes, due to the parameters that were set. It is not important to estimate the output considering the spectral changes. However, for solar cells with a narrow EQE, such as double-junction amorphous Si and organic cells, the effect of the spectral change on the characteristics of the output could be observed using our MS2E method [38].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Cloud Conditions** & \
\begin{tabular}{c} **Atmospheric** \\ **Parameters** \\ \end{tabular} &
\begin{tabular}{c} **Incident Angle** \\ **Modifier** \\ \end{tabular} & **Air Mass** \\ \hline Case-1 (MS2E) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Case-2 & & \(\surd\) & \(\surd\) & \(\surd\) \\ Case-3 & & & \(\surd\) & \(\surd\) \\ Case-4 & & & & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter-set of the PV output forecasting model.  
Figure 9: Results of the annual forecasting PV energy yield per module area (kWh/m\({}^{2}\)) in Japan.
## 4 Methodology
### Historical Trajectory Embedding  
The historical trajectory is a crucial part of our model. By aggregating a large number of GPS trajectories, we can identify implicit spatiotemporal relationships within the trajectory. These relationships are the focus of our model learning. By learning spatiotemporal features from historical trajectories, the model can make temporal predictions for the current spatial trajectory. To strengthen the weight of certain trajectories, multiple historical trajectories are defined to capture the spatiotemporal relationships within different trajectories. The aim is to reconstruct spatiotemporal trajectories more accurately. Therefore, the historical trajectory embedding module can be divided into four parts: historical trajectory aggregation, the aggregation of historical trajectories with the same period, the aggregation of historical trajectories with the same state, and spatiotemporal trajectory fusion.
# Investigation of the Effect of Capillary Barrier on Water-Oil Movement in Water Flooding
## 3 Capillary Barrier Phenomenon
### Effect of Different Contact Angles and Opening Angles on the Capillary Force
#### 3.4.2 Oil-Wet Conditions  
Figure 14 shows the influence of opening angles on the capillary force with different contact angles under oil-wet conditions. As can be seen from Figure 14: (1) The capillary pressure is negative under oil-wet conditions when the liquid is leaving the throat section; (2) When \(\theta-\beta<90^{\circ}\), there is a positive peak capillary pressure when the oil-water interface enters the throat from the pore; (3) Define \(\Delta=90^{\circ}-\beta+\theta\); the larger the value of \(\Delta\), the larger the displacement force in the process of liquid entering the throat section.  
Figure 15 shows the distribution of the maximum and minimum capillary force varying with contact angle \(\theta\) and opening angle \(\beta\) under oil-wet conditions. As can be seen from Figure 15, the maximum capillary pressure is obtained at the interface of liquid entering the throat under oil-wet conditions. Generally speaking, the capillary pressure is negative, but when \(\theta-\beta<90^{\circ}\), the capillary pressure is positive and can mobilize the fluid in the process of liquid entering the throat section.
## 5 Discussion
### Error in BRDF Correction
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Raw and BRDF Corrected Radiance std/ave (\%)**} \\ \cline{3-8}  & & \multicolumn{3}{c}{**29 June 2015**} & \multicolumn{3}{c}{**1 July 2015**} \\ \cline{3-8}  & & **0.768 \(\upmu\)m** & **1.606 \(\upmu\)m** & **2.061 \(\upmu\)m** & **0.768 \(\upmu\)m** & **1.606 \(\upmu\)m** & **2.061 \(\upmu\)m** \\ \hline \multirow{4}{*}{**OCO-2 (Averaged)**} & non-BRDF & 8.76 & 6.94 & 8.88 & 6.77 & 6.03 & 8.45 \\ \cline{2-8}  & BRDF-corr & 5.03 & 3.96 & 5.08 & 4.89 & 5.26 & 6.75 \\ \cline{2-8}  & BRDF-corr + & \multirow{2}{*}{4.49} & \multirow{2}{*}{3.75} & \multirow{2}{*}{4.27} & \multirow{2}{*}{4.46} & \multirow{2}{*}{4.89} & \multirow{2}{*}{5.61} \\  & ZN \textless{}55 deg & & & & & & \\ \cline{2-8}  & BRDF-corr + & \multirow{2}{*}{3.55} & \multirow{2}{*}{3.66} & \multirow{2}{*}{3.66} & \multirow{2}{*}{3.63} & \multirow{2}{*}{4.31} & \multirow{2}{*}{4.99} \\  & ZN \textless{}6 OSAT\_ZN & & & & & & \\ \hline \multirow{2}{*}{**GOSAT**} & non-BRDF & 1.49 & 1.43 & 1.07 & 0.62 & 0.78 & 0.72 \\ \cline{2-8}  & BRDF-corr & 1.49 & 1.43 & 1.07 & 0.62 & 0.78 & 0.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The same as Table 3 but with the coefficient of variation of radiance instead of averaged spectral radiance. The coefficient of variation of radiance is the sample standard deviation over the sample mean. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Raw and BRDF Corrected Radiance (W/m\({}^{2}\)/\(\upmu\)m/str)**} \\ \cline{3-8}  & & \multicolumn{3}{c}{**29 June 2015**} & \multicolumn{3}{c}{**1 July 2015**} \\ \cline{3-8}  & & **0.768 \(\upmu\)m** & **1.606 \(\upmu\)m** & **2.061 \(\upmu\)m** & **0.768 \(\upmu\)m** & **1.606 \(\upmu\)m** & **2.061 \(\upmu\)m** \\ \hline \multirow{4}{*}{**OCO-2 (Averaged)**} & non-BRDF & 164.02 & 36.41 & 12.07 & 151.99 & 33.60 & 11.03 \\ \cline{2-8}  & BRDF-corr & 164.89 & 36.64 & 12.20 & 161.85 & 35.78 & 11.93 \\ \cline{2-8}  & BRDF-corr + & \multirow{2}{*}{165.80} & \multirow{2}{*}{36.70} & \multirow{2}{*}{12.27} & \multirow{2}{*}{162.57} & \multirow{2}{*}{35.91} & \multirow{2}{*}{12.04} \\  & ZN \textless{}55 deg & & & & & & \\ \cline{2-8}  & BRDF-corr + & \multirow{2}{*}{165.58} & \multirow{2}{*}{36.52} & \multirow{2}{*}{12.23} & \multirow{2}{*}{162.33} & \multirow{2}{*}{35.71} & \multirow{2}{*}{12.01} \\  & ZN \textless{}55 deg & & & & & & \\ \cline{2-8}  & GOSAT\_ZN & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The averaged GOSAT and OCO-2 spectral radiance during the RRV target observation on 29 June 2015 and 1 July 2015 at wavelengths of (a) 0.768 \(\upmu\)m; (b) 1.606 \(\upmu\)m and (c) 2.061 \(\upmu\)m with the selected sensor zenith angles (55 degree and GOSAT satellite zenith angle). The GOSAT satellite zenith angles are 38 degrees on 29 June 2015 and 28 degrees on 1 July 2015. In this Table, ZN and non-BRDF, BRDF-corr are sensor zenith angle, radiance with BRDF correction and without BRDF correction, respectively.
## 4 Results
### Learning Effectiveness
#### 4.1.2 Comparison of the Differences in Learning Outcomes  
The descriptive statistics of the pre- and post-tests of different groups were shown in Table 9. The mean score and standard deviation of the control group in the pre-test were 80.875 and 10.794 respectively, while those of the experimental group were 82.195 and 9.490 respectively. The mean score and standard deviation of the control group in the post-test were 83.000 and 11.367 respectively, while those of the experimental group were 86.585 and 9.901 respectively. Note that, since most of the test questions are low-level questions, the difference in the mean of the corresponding pre-test and post-test scores is relatively small.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Source of Change**} & **Sum of Squared** & **Degree of** & **Mean Sum of** & **F-Test** & \(p\)**-Significance** \\  & **Deviation from the Mean** & **Freedom** & **Squares** & **F-Test** & \(p\)**-Significance** \\ \hline Pre-test results & 6859.834 & 1 & 6859.834 & 279.385 & 0.000 \\ \hline Group & 37.443 & 1 & 37.443 & 1.525 & 0.221 \\ \hline Interaction effect(pre-test results \(\times\) group) & 23.714 & 1 & 23.714 & 0.966 & 0.329 \\ \hline Error & 1890.607 & 77 & 24.553 & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Test of homogeneity of within-group regression coefficients.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Group**} & **Source of** & **Mean** & **Standard** & **Number of** \\  & **Change** & **Deviation** & **People** \\ \hline \multirow{2}{*}{Control group} & Pre-test & 80.875 & 10.794 & 40 \\ \cline{2-5}  & Post-test & 83.000 & 11.367 & 40 \\ \hline \multirow{2}{*}{Experimental group} & Pre-test & 82.195 & 9.490 & 41 \\ \cline{2-5}  & Post-test & 86.585 & 9.901 & 41 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Summary table of descriptive statistics.  
The results of the covariance analysis were shown in Table 10. The differences between the two groups were significant (F-value = 4.565, \(p\)-value = 0.036 < 0.05). The experimental group outperformed the control group in the post-test, indicating that there would be significant differences in the scores on the post-test learning effectiveness among students as a result of different methods of experimental teaching. It was deduced from Table 11 that the mean of the control group was 83.621 after adjustment, while that of the experimental group was 85.979 after adjustment. The mean of the experimental group in the post-test was higher than that of the control group in the same test; such results indicated that the experimental group, compared with the control group, was better in relation to student learning outcomes.
# Securing Wireless Sensor Networks Using Machine Learning and Blockchain: A Review
## 2 Existing Surveys on ML and BC in WSN  
This paper discusses ML and BC protection mechanisms in a comprehensive manner [10; 11; 12; 13; 14; 15]; however, the emphasis is on securing WSNs. In this regard, a few previous surveys have focused on presenting state-of-the-art ML and BC techniques for WSN cybersecurity. Key surveys tackling WSN security are tabulated in Table 1, which highlights the different subtopics covered, including ML, BC, attack taxonomy, and ML-BC integration, among others. The surveyed sources were collected from popular academic databases, such as IEEE Xplore, Elsevier, and Scopus, as per the most recent citation provided by Google Scholar, and are detailed in Figure 1.  
Table 1 reveals that research work on ML techniques is the primary subject of existing survey papers in the literature. A number of surveys that were published between 2012 and 2017, such as [16], did not examine WSN-related ML techniques, instead jointly discussing methods adopted in both IoT and WSN. On the other hand, surveys similar to [16; 17; 18; 19] focused primarily on WSN. The authors of [19] considered only Denial of Service (DoS) attacks over the five TCP/IP layers. The authors of [17] provided a generalized and comprehensive review of ML techniques adopted to support WSNs against their inherent limitations, including security. The paper specifically focused on ML methods used to detect outlying and misleading measurements. The authors of [14] discussed the different types of attacks targeting WSNs and associated proposed ML solutions. Protecting WSNs using several ML methods was discussed in [16]. The authors of [20] explored using ML  
techniques with WSNs, including anomaly detection, with a focus on Deep learning (DL) techniques. A different research direction was analyzed in [20; 21], where the authors focused on a specific type of WSN. The authors of [20] presented ML learning techniques targeting advanced WSN systems, and [21] reviewed ML techniques to secure industrial WSN systems. The authors of [22] reviewed ML algorithms and considered using software-defined networking (SDN) as a solution that can help enhance the node efficiency, creating a new foundation for using ML schemes to secure WSNs.  
Considering BC techniques, several reviews have been conducted on securing IoT through the use of BC, such as [7; 23; 24; 25; 26; 27; 28; 29; 30]; however, Ref. [31] is the only article addressing BC for mitigating cyberattacks in WSNs. The study concluded that integrating BC techniques within WSNs has limitations, as BC is demanding in terms of both energy and computational complexity and is not expandable. Thus, to the best of our knowledge, our paper is the first work to review the integration of both technologies to improve WSN security, which is confirmed by Table 1.  
Figure 1: Paper collection criteria flowchart.
## 4 Discussion  
As shown in Figure 4, gas discharge current in pulsed excitation is much shorter than in sine excitation. Short pulses of current flowing through plasma eliminate the overexcitation of gas discharge, while long pauses provide the relaxation of gas-discharge plasma. This method of excitation by short pulses makes it possible to obtain a diffuse plasma jet without contrasting channels and local plasma overheating (Figure 5). On the other hand, long excitation pulses lead to plasma contraction, plasma resistance decrease, and an increase in gas temperature.  
Unlike conventional interference methods of plasma observation, the digital holographic interferometry technique makes it possible to digitally evaluate the phase difference over the entire observation field, as well as to fully automate data acquisition and processing [10].  
The results do not allow us to definitely conclude that the recorded phase changes are completely related to plasma refraction, and that the neutral atoms, according to expression (4), have no effect on the phase shift. Thus, we have not yet been able to perform an accurate analysis of the electron concentration in the plasma. This method requires further improvements to increase its sensitivity, accuracy and reliability of synchronization. The sensitivity can be improved by switching to another spectral range, which will allow us to take into account the influence of neutral atoms on the resulting interferogram [25]. Increasing the accuracy of synchronization can provide precise locking of the image acquisition related to the beginning of plasma-pulse duration. It is also possible to use ultrahigh-speed digital cameras.  
Figure 9: Phase difference: (**a**)—phase difference corresponding to the initial state; (**b**)—phase difference with plasma; (**c**)—phase difference at 700 ns after the end of plasma jet.