esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Simulation of Underground Tunnel Vibration Waves
### The Vibration Velocity at Each Measurement Point in the Blasting Vibration Model  
In the blasting vibration simulation, when the explosive detonation in the fragmented zone generates shock waves, these waves are transmitted through the movement of particles to the surrounding rock mass. A concentration of stress occurs at locations such as the sudden fracture surface, the interface with air, and certain junctions. To investigate the effects of vibration waves under different conditions, a spectral characteristic analysis was conducted on the vibration signals collected from measurement points A to G. The variations in vibration velocity, stress, displacement, etc., with respect to time and distance from the blast center were observed. The blasting experiment involved five rows of explosives, each with a 50 ms delay in detonation, and the entire detonation process lasted for 2 s. Due to the large volume of simulation results, characteristic points were selected, and the results were visually processed for a clearer and more concise presentation. Vibration velocity history curves in various directions at measurement points A to G and the underground stress conditions of 1 MPa and 5 MPa were plotted using Origin software2021. The detailed results are shown in Figure 6.  
According to the data in Figure 6, within 0-2.5 s, as the blasting proceeds, the explosives detonate successively, resulting in severe fluctuations in the vibration velocity at various measurement points. When the blasting process ends within 2.5-3.5 s, the vibration velocity gradually decreases to 0 cm/s. Under a stress condition of 1 MPa, the vibration velocity in the X direction, perpendicular to the tunnel, is greater than the maximum vibration velocity in the Y and Z directions at each measurement point. In the X and Z directions, the maximum vibration velocity forms new peaks or valleys 0.02 s after the detonation of  
Figure 5: Stress state diagram of the model under initial in situ stress load.  
each row of explosives, followed by a gradual decrease in the maximum vibration velocity in a wave-like pattern. In the Y direction, the vibration velocity reaches its maximum value of 5.41 m/s after the detonation of the first row of explosives and then rapidly decreases. During the subsequent detonation of explosives, the maximum vibration velocity fluctuates within the range of 0 to 0.01 m/s. Under stress conditions of 5 MPa, the absolute values of the maximum vibration velocities at various measurement points are greater than those under 1 MPa conditions, but the pattern of vibration velocity change is the same as that under 1 MPa conditions.
# Evaluation of the User Adaptation in a BCI Game Environment +
## 5 Results and Discussion
### Game Testing
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Subjects** & **Average Score 1** & **Average Score 2** & **Improvement** &
\begin{tabular}{c} **Average Overall** \\ **Score** \\ \end{tabular} \\ \hline Sub2 & 65.20 & 77.40 & 8.13\% & 71.30 \\ Sub4 & 61.60 & 78.40 & 11.20\% & 70.00 \\ Sub15 & 62.60 & 83.30 & 13.8\% & 72.95 \\ Sub16 & 96.60 & 71.20 & \(-\)16.93\% & 83.90 \\ Sub17 & 68.40 & 73.20 & 3.20\% & 70.80 \\ Sub18 & 67.10 & 55.50 & \(-\)7.73\% & 61.30 \\ Sub20 & 43.20 & 58.90 & 10.46\% & 51.05 \\ Sub23 & 61.30 & 65.20 & 2.60\% & 63.25 \\ Sub24 & 82.10 & 84.30 & 1.46\% & 83.20 \\ Sub25 & 84.45 & 78.55 & \(-\)3.93\% & 81.50 \\ Sub29 & 73.20 & 70.00 & \(-\)2.13\% & 71.60 \\ Sub30 & 77.90 & 58.10 & \(-\)13.20\% & 68.00 \\ Sub31 & 70.20 & 64.20 & \(-\)4.00\% & 67.20 \\ Sub32 & 72.00 & 103.80 & 21.20\% & 87.90 \\ Sub33 & 51.40 & 61.10 & 6.46\% & 56.25 \\ Sub34 & 61.20 & 74.30 & 8.73\% & 67.75 \\ Sub35 & 69.40 & 67.80 & \(-\)1.06\% & 68.60 \\ Sub36 & 71.50 & 68.10 & \(-\)2.26\% & 69.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Level 2 game score results. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Authors** & **Subjects** & **EEG Device** & \begin{tabular}{c} **Mental** \\ **Commands** \\ \end{tabular} & **Reps per Subj** & **Evaluation Metrics** \\ \hline [PERSON] et al. [16] & 5 & NeuroSky Mindset & 2 & 1 & Avg mean meditation (49.4) \\ \hline [PERSON] et al. [17] & 24 & NeuroSky MindWave & 1 & - & Avg attention single player (53.49); \\  & & & & avg attention multiplayer (52.42) \\ \hline [PERSON] et al. [19] & 3 & Emotiv Insight & 2 & 1 & Not presented \\ \hline [PERSON] et al. [18] & 5 & NeuroSky MindWave & 2 & 1 & Avg maximum attention 1 (73.6) \\  & & Mobile & & 1 & Avg maximum attention 2 (76.4) \\  & & & & & Avg maximum meditation 1 (51) \\  & & & & & Avg maximum meditation 2 (47.4) \\  & & & & & Game duration 1 (34.6 s) \\ \hline [PERSON] et al. [20] & 4 & Emotiv Epoc+ & 2 & 2 & \
\begin{tabular}{c} Avg focus (0.38), Avg stress (0.49) \\ Avg relaxation (0.32) \\ Avg excitement (0.25) \\ Avg engagement (0.65) \\ \end{tabular} \\ \hline This work & 37 & Muse 2 Headband & 2 & 20 &
\begin{tabular}{c} Classification accuracy (98.75\%) \\ Game score 1 (52.70/100) \\ Game score 2 (70.35/150) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative study. of [PERSON] et al. [17] and [PERSON] et al. [18] mainly focused on the game duration and on attention-meditation level. [PERSON] et al. [20] used several metrics for their game, such as focus, stress, relaxation, interest, excitement and engagement, in order to help ADHD patients. In this work, a more thorough approach is employed, as the game is tested on 37 subjects, while in other studies, the average number of subjects is 8.2. Additionally, user adaptation and improvement are taken into consideration as a user evaluation metric. Lastly, classification accuracy is presented as an offline metric, and the game score is presented as an online evaluation metric.
## 3 Results and Analysis
### Characteristic of P-S Curves  
The loading end slip curve (P-S curve) can be simplified to the model shown in Figure 6. Here, The load is divided into two categories, each of them showing basically the same changes, which are divided into three parts: rising, sag, and gentle loads. Type (I) is characterized by a large initial load (65%-75% of the peak load), with a residual load that is slightly lower than the initial load. Type (II) is characterized by a small initial load (40%-65% of the peak load), with a residual load that is slightly higher than the initial load. The P-S curves of the specimens are shown in Figure 7.  
The following definitions of the characteristic points in Figure 6 are given:  
1. The initial load Ps: The load when obvious slippage occurred on specimens (point A)
2. The ultimate load Pu: The maximum value of the specimens (point B)
3. The residual load Pr: The load corresponding to the end of the descending stage (point C)  
Figure 5: Bursting failure of SRRC-1: (**a**) web; (**b**) flange.  
Figure 6: P–S curve models of the loading end: (**a**) Type (I); (**b**) Type (II).  
Figure 7: _Cont._  
In this paper, the P-S curves of the loading end are divided into four stages: nonslip, slip-crack, descending, and residual.  
1. OA in Figure 6 indicates the nonslip stage of the specimens. The key point is the initial bond load point, which determines the length of the section, and the main load is borne by the chemical adhesive force at this section. The composition of the bond stress in the section steel and RAC is similar to in the section steel and ordinary concrete. The bond stress is caused by chemical adhesion and frictional resistance in the article [33].
2. AB in Figure 6 indicates the slip-crack stage of the specimens, where the curve is basically a linear relationship and the slope is too large. When loading to 40%-65% of the ultimate load (the load is defined as the initial load P\({}_{s}\), the corresponding bond strength is the average initial bond strength \(\overline{\tau}_{s}\)), the loading end of the specimen begins to slip and developed rapidly.
3. The load drops sharply and the specimen has longitudinal through cracks when loading increases to the ultimate load P\({}_{u}\) (the corresponding bond strength is the average limited bond strength \(\overline{\tau}_{u}\)). The reason is that the chemical adhesion of the descending stage is suddenly broken and the friction is not sufficient to support the ultimate load.
4. The residual mainly depends on the residual load. The P-S curve is basically a horizontal line when the load falls to 50%-70% of the ultimate load (the load is defined as the residual load P\({}_{r}\), the corresponding bond strength is the average residual bond strength \(\overline{\tau}_{r}\)). It can be concluded that the determinants of each stage are the characteristic loads.  
Figure 7: P–S curves of each specimen: (**a**) SRRC-1; (**b**) SRRC-2; (**c**) SRRC-3; (**d**) SRRC-4; (**e**) SRRC-5; (**f**) SRRC-6; (**g**) SRRC-7; (**h**) SRRC-8; (**i**) SRRC-9.
## 5 Method Comparison Validation and Parameter Analysis  
### Method Comparison Validation  
To verify the accuracy of the established calculation method, comparisons were made with the results of [PERSON] et al. [28], as shown in Figure 11. For ease of comparison, the normalized ultimate support pressure \(\sigma_{t}/\gamma_{sat}D\) was defined. It was observed that in terms of computational accuracy, when the tunnel face water pressure coefficient is small, the results in this study are slightly lower than those of [PERSON] et al. [28], whereas they are opposite when the water pressure coefficient is large. This discrepancy may be attributed to neglecting the variation in water head in the \(x\)-direction in this study. As \(x\) increases, the aquifer ahead of the tunnel face will recover the water head, increasing pore water pressure and resulting in higher calculated values. As \(\eta\) increases, the influence of seepage diminishes, reducing this gap over time.  
In terms of computational efficiency, the two-dimensional analytical approach is more advantageous. Overall, the three-dimensional analytical water head equation is more accurate, while the two-dimensional analytical approach is more efficient.  
Figure 11: Comparison with other studies [28].
## 3 Results and Discussion
### Oxidative Status of Mayonnaises
Thus, rosemary extract did not exhibit protective effects towards the primary changes of oxidation in mayonnaise. The formation of free fatty acids determined as AV due to the hydrolysis of triacylglyceryloreols and hydroperoxide decomposition in the oil phase of emulsions during storage time is one of the most critical indicators of their deterioration. Consequently, AV results of the studied mayonnaise samples insignificantly increased with increasing storage duration, irrespective of the antioxidant type used (Duncan test, Table 2). However, BCCE added directly to mayonnaise and active film inhibited the generation of free fatty acids the most effectively. For this reason, at the end of the shelf life test, the AV of BCCM0.1%, BCCM0.3%, AFM, and CM did not differ significantly. However, the addition of OC to mayonnaise consistently increased its acidity from 0.13 to 0.23 mg KOH/g after 28 days of storage. Similar tendencies were observed in our previous work after fortification of rapesed oil with phenolipids (AV = 0.029-0.085 mg NaOH/g and 0.077-0.503 mg NaOH/g, for control rapeseed oil and rapeseed oil with phenolipids, respectively) [36]. It can be explained by the fact that OC, which contains the -OH group, can react with alkali and increase acidity expressed as AV. Nevertheless, the AV (0.23 mg KOH/g) of OCM was 6 times lower in comparison with the AV of CPBM (1.38 mg KOH/g) after 4 weeks of storage. Interestingly, the studied mayonnaise samples had lower AV results (0.07-1.38 mg KOH/g) than the AV (0.85-1.44 mg KOH/g) of mayonnaies with beetroot peels powder in different concentrations (1.5-7%) [7] and the AV (0.1-2.9 mg KOH/g) of mayonnaise samples enriched with different ginger concentrations and were stored for 4 weeks [6]. The proposed formula of aquafaba-based mayonnaise enriched with BCCE and OC and stored contacting with active film incorporating BCCE was characterized by lower oxidative parameters after 4 weeks of storage compared to the oxidative status of CPBM. At the end of storage, the TOTOX values of CM, BCCM0.1%, BCCM3%, and AFM (5.52, 4.09, 3.74, and 5.50, respectively) were lower than the TOTOX of CPBM at the beginning of shelf-life (7.53). Only the AnV of OCM from 2 to 4 weeks of storage was higher than AnV of CPBM (4.43-4.78 and 2.21-2.53, for OCM and CPBM, respectively). These results encourage the use of aquafaba-based formulation and active packaging to extend the shelf-life of egg-free mayonnaies from 2 to 4 weeks.
## 3 Methods
### Experimental Details
#### 3.5.3 Evaluation Metrics  
In this experiment, commonly used segmentation evaluation metrics for medical image segmentation are employed to assess the model's performance, including _Accuracy_, Dice Similarity Coefficient (_Dice_), Specificity, Sensitivity (_SE_), and mean Intersection over Union (_mIoU_), calculated as follows:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{23}\]  
\[Dice=\frac{2\cdot TP}{2\cdot TP+FP+FN} \tag{24}\]  
\[Specificity=\frac{TN}{TN+TP} \tag{25}\]  
\[Sensitivity=\frac{TP}{TP+FN} \tag{26}\]  
\[mIoU=\frac{1}{N}{\sum_{i=1}^{N}\frac{TP_{i}}{TP_{i}+FP_{i}+FN_{i}}} \tag{27}\]  
In the formulation, _TP_ denotes the number of positive samples correctly predicted as positive, _FP_ represents the number of negative samples incorrectly predicted as positive, _FN_ indicates the number of positive samples incorrectly predicted as negative, and _TN_ refers to the number of negative samples correctly predicted as negative.
# Multi-Scale Features for Transformer Model to Improve the Performance of Sound Event Detection
## 2 Transformer Encoder
### Multi-Head Attention  
The attention mechanism used in the Transformer encoder is referred to as \"Scaled Dot-Product Attention\" and is defined as follows:  
\[\text{Attention}(Q,K,V)=\text{softmax}\bigg{(}\frac{QK^{T}}{\sqrt{d_{k}}} \bigg{)}V \tag{2}\]  
where the attention output is computed as the weighted sum of the values, \(V\). The weights assigned to the values are computed by the dot products of the queries, \(Q\) with the corresponding keys, \(K\). \(d_{k}\) is the dimension of the key. When \(Q=K=V\), as is in the Transformer encoder used in this study, the attention is called \"self-attention\".  
Instead of the single attention output, as in (2), multi-head attention (MHA) that performs multiple attention is used in the Transformer encoder. MHA projects the queries, keys, and values with \(h\) different linear transformations and then performs the attention function for each of them. The \(h\) different attention outputs are then concatenated and projected again to obtain the final output. The MHA is defined as follows:  
\[\begin{split}\text{MultiHead}(Q,K,V)=\text{Concat}(H_{1},H_{2}, \cdots,H_{h})W^{O}\\ H_{i}=\text{Attention}\Big{(}QW^{Q}_{i},KW^{K}_{i},VW^{V}_{i }\Big{)}\end{split} \tag{3}\]  
where the learnable projections are \(W^{Q}_{i}\in\mathbb{R}^{d_{model}\times d_{k}}\), \(W^{K}_{i}\in\mathbb{R}^{d_{model}\times d_{k}}\), \(W^{V}_{i}\in\mathbb{R}^{d_{model}\times d_{p}}\), and \(W^{O}\in\mathbb{R}^{hd_{p}\times d_{model}}\). In addition, we have \(d_{k}=d_{p}=d_{model}/h\).
###### Abstract  
Radio beacons enable measurements of ionospheric radio scintillations and total electron content (TEC). These beacons transmit unmodulated, phase-coherent waves in S-band frequencies. Many satellite applications require circularly polarized (CP) wideband antennas. Their compact size, lightweight, and simple fabrication method make CP antennas suitable for small satellite systems. The slot antenna has wideband impedance, but the 3 dB axial ratio bandwidth (ARBW) is narrower compared to the impedance bandwidth (IBW). In this paper, a circularly polarized circular-slotted antenna (CSA) is proposed to enhance the ARBW and the antenna gain. A pair of asymmetrical rectangular slots, a simple 50 \(\Omega\) feedline and a parasitic patch were introduced to a CSA to enhance the 3 dB ARBW and the antenna gain. Rectangular slots were inserted on the diagonal axis of the CSA, the feedline was shifted to the left side of the \(x\)-axis, and a parasitic patch was attached to the circular slot. The lengths of the rectangular slots correspond to the resonant frequency, and the parasitic patch width corresponds to the higher frequency of the 3 dB ARBW. The asymmetrical rectangular slots, the shifted feedline, and the parasitic patch successfully improved the measured 3 dB ARBW of the antenna by 787.5 MHz or 35.79%. The measured gain of a CSA with left-hand circular polarization (LHCP) was also improved by shifting the feedline and the rectangular slot, achieving a peak gain of 5 dBic.  
nanosatellite; microstrip antenna; slot antenna; axial ratio enhancement; circular polarization +
Footnote †: journal: Accepted: 12 March 2019; Published: 28 March 2019
# A MAC Protocol to Support Monitoring of Underwater Spaces +
## 3 Description of the UWSO-TDMA Protocol
### Data Transmission Phase  
Figure 9 shows the flowchart for the data transmission phase. In this phase, nodes transmit their data in the selected slot, together with the delay to the SB and depth. As all nodes within range overhear this information, they are able to keep an updated version of the network topology. As underwater transmission is prone to have data corruption, each message should be acknowledged by the synchronizing nodes in the previous sub-frames. If a message is not acknowledged, the sender retransmits it in the next frame. In the case a new message is generated, the information is aggregated or the old message is discarded, but this situation is flagged, so upstream nodes may notice the missing one. At the beginning of every frame, nodes wait for the synchronization message. If this message is not received with the acknowledgments of previous messages, the nodes in the sub-frame should assume that the synchronization node is out of work, and the backup synchronization node should be used if available. If there is not a backup, the node goes into the initialization phase.  
Figure 8: SO-UWSN frame.  
Figure 6: Network model example.  
Figure 7: Frame.  
Data messages differ from synchronization messages. The first ones only contain information about the process, while the second ones are used during the node synchronization and slot allocation processes.
_Appl. Sci._**2012**, \(2\), 206-219; doi:10.3390/app2010206  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
**Oxyfluoride Chemistry of Layered Perovskite Compounds**  
**[PERSON] 1,*, [PERSON] 2,3 and [PERSON] 2,3**  
1 International Center for Young Scientists (ICYS), International Center for Materials  
Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS)/Namiki 1-1, Tsukuba, Ibaraki 305-0044, Japan  
2 Superconducting Properties Unit, NIMS/Namiki 1-1, Tsukuba, Ibaraki 305-0044, Japan;  
E-Mails: [EMAIL_ADDRESS] (K.Y.); [EMAIL_ADDRESS] (E.T.-M.)  
3 JST (Japan Science and Technol- ogy Agency), Transformative Research-Project on Iron Pnictides (TRIP)/Namiki 1-1, Tsukuba, Ibaraki 305-0044, Japan  
* Author to whom correspondence should be addressed;  
E-Mail: [EMAIL_ADDRESS]; Tel.: +81-29-851-3354; Fax: +81-29-860-4706.  
_Received: 14 February 2012; in revised form: 22 February 2012 / Accepted: 28 February 2012 / Published: 6 March 2012_  
**Abstract:** In this paper, we review recent progress and new challenges in the area of oxyfluoride perovskite, especially layered systems including Ruddlesden-Popper (RP), Dion-Jacobson (DJ) and Aurivillius (AV) type perovskite families. It is difficult to synthesize oxyfluoride perovskite using a conventional solid-state reaction because of the high chemical stability of the simple fluoride starting materials. Nevertheless, persistent efforts made by solid-state chemists have led to a major breakthrough in stabilizing such a mixed anion system. In particular, it is known that layered perovskite compounds exhibit a rich variety of O/F site occupation according to the synthesis used. We also present the synthetic strategies to further extend RP type perovskite compounds, with particular reference to newly synthesized oxyfluorides, Sr\({}_{2}\)CoO\({}_{3}\)F and Sr\({}_{3}\)Fe\({}_{2}\)O\({}_{5+x}\)F\({}_{2-x}\) (\(x\sim 0.44\)).  
**Keywords:** oxyfluoride; layered perovskite; high-pressure synthesis; low-temperature fluorination
# Hybrid Sensing Platform for IoT-Based Precision Agriculture
## 4 Results
### Data Calibration
#### 4.2.3 Aerial Spectrometer Data Calibration Using Ground-Based Data--Experiment 6
We apply the linear regression equation on morning cycles to obtain the intercept (a) and slope of linear regression line (b) for the calibration of the aerial spectrometer channels, as described in Table 9.  
Based on the obtained intercept and line slope values in Table 9, the calibration was performed on each aerial channel's data. As Figure 18 shows, the calibrated aerial data's Euclidean distance from the ground-based data is remarkably reduced. For channel E (green), the distance between calibrated aerial data and ground-based data is reduced to 65, which shows a 92% improvement in data accuracy. For channel H (yellow), the distance is reduced to 69, which shows a 90% improvement in data accuracy, and for channel O (NIR), the distance is reduced to 323, which also shows a 90% improvement in data accuracy. Therefore, it can be concluded that calibration using linear regression proves to be efficient in improving aerial spectrometer data quality. Figure 16: Comparison of aerial and ground-based spectrometer green, yellow, and NIR channel data from target plant. Figure 17: Comparison of aerial and ground-based spectrometer green, yellow, and NIR channel data from target plant during morning cycles before calibration. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Aerial and Ground-Based Data** & **Intercept** & **Line Slope** &
\begin{tabular}{c} **Euclidean Distance** \\ **before Calibration** \\ \end{tabular} \\ \hline Channel E (Green) & 1.46 & 0.13 & 823 \\ Channel H (Yellow) & 4.40 & 0.13 & 746 \\ Channel O (NIR) & 20.70 & 0.13 & 3407 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Aerial and ground-based spectrometer intercept, line slope, and Euclidean distance before calibration.