esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Spectral Decomposition  
In this analysis, three spectral decomposition methods were applied to the seismics to identify which methods could delineate the most prominent karst features. The frequencies were directly picked from the targeted high amplitude karst zone in seismics (Figure 6). Figure 7 shows the frequency picked window where the different methods of spectral decomposition techniques could be applied allowing the interpreter to pick frequencies of the targeted karst features. The three picked frequencies which represent low, mid and high are directly displayed as frequency decomposition by using RGB blending techniques (Figure 8). Co-rendering those frequencies with three colours i.e., red, green and blue, has enhanced the image of the karstic features.  
Figure 4: Modelling the relative geological time (RGT) modified from [23]. (**a**) Model-grid creation where each yellow node represents a small horizon patch with a limited size. (**b**) Links established in between nodes after propagation where the volume is composed of horizons with various size. (**c**) 3D continuous RGT model computed on the entire seismic volume based on the relationship of the nodes.  
Figure 5: **(a)** Horizon slicing (red line) and time slicing (blue line) shown in the vertical seismic section. (**b**) shows Horizon slice (**c**) shows time slice. Both are applied to image the karst dendritic patterns. Image (**b**) delineates more prominent and shows the continuation of the karst geobody compared to the time slice image (**c**) which could mislead karst interpretation that should be avoided during the well planning decision.  
Figure 6: Targeted dendritic pattern and high amplitude karst features shown in the red arrow for frequency picking.  
Figure 8: Red, green and blue (RGB) blending technique and co-rendering of extracted frequencies in real-time.  
Figure 7: The frequency picked window which allows **(a)** Several methods of spectral decomposition. A simple method of short-time Fourier transform (STFT) and two methods of continuous wavelet transform (CWT), using Morlet or Ricker wavelets **(b)** Associated frequencies of the targeted karst geobody which can be directly picked in the spectrogram viewer.  
Short-time Fourier transform (STFT) also known as fast Fourier transform (FFT) is one of the most commonly used spectral decomposition techniques to decompose non-stationary signals. STFT divides non-stationary signals into small segments (considered as stationary parts) and calculate Fourier transform for each segment [25]. The other spectral decomposition technique uses in this analysis known as continuous wavelet transform (CWT) convolutes the seismic trace with a defined wavelet at the frequency \(\omega\). Two types of wavelet are available, the Morlet wavelet and the Ricker wavelet. The Ricker wavelet results in a strong time resolution while the Morlet wavelet is more accurate in the frequency domain. Karst geobody in both Jintan and M1 platforms were then quantified in percentages manually by using grid paper to suggest which spectral decomposition technique could delineate the most karst features.
# Towards Understanding Neural Machine Translation with Attention Heads' Importance
## 3 Attention Heads' Importance
### Attention Head Analysis
Particularly, in encoder-decoder attention, we find that the higher layers (15, 16, 17) have a more significant impact on translation performance, indicating that higher layers in encoder-decoder attention are more important than lower layers. Figure 4 represents the distribution of important and unimportant attention heads in the model. We observe that important attention heads are primarily concentrated in encoder self-attention (0-5 in Figure 4a) and encoder-decoder attention (12-17 in Figure 4a), while unimportant attention heads are mainly found in decoder self-attention (6-11 in Figure 4b). This finding indicates that encoder self-attention and encoder-decoder attention are more crucial to the model than decoder self-attention. Moreover, from Figure 4a's 12-17, we can see that important attention heads are more concentrated in higher layers than in lower layers, implying that higher layers are more important than lower layers in the encoder-decoder attention mechanism. According to the results of two experiments, Figures 3 and 4, we find that the encoder self-attention layers (0-5) and the encoder-decoder attention layers (12-17) are important, while the decoder self-attention layers (6-11) are unimportant. And in the encoder-decoder attention layer (12-17) the higher layers are more important than the lower layers. These results support [PERSON]'s [8] findings, confirming the validity of the attention heads' importance we obtained. Figure 2: Evolution of BLEU score when heads are pruned. Respectively masking important head (yellow), unimportant head (blue), and random head (green).
# Material Flow Optimization as a Tool for Improving Logistics Processes in the Company
## 5 Results
### Variant 1--Replacement of External Operator for SF Transport with In-House Company Transport  
One of the ways to reduce material flow costs is to lower transportation expenses for transfers. Since negotiating lower prices with external operators has not been successful,  
Figure 5: Material flow before optimization.  
we consider the possibility of acquiring two transport units through an operational lease and hiring two drivers with a C + E driving license.  
To assess the cost-effectiveness of this change, we calculated the current transport costs per kilometer. At present, the transport cost for SF transfers amounts to EUR 19.91 per kilometer.  
Table 9 presents the projected costs of this investment. The acquisition of two transport units would result in an annual expense of EUR 40,250.77, with a monthly lease cost of EUR 1672.77 per unit. The operating costs are calculated based on the estimated annual mileage of 13,186 km and the current fuel price of EUR 1.28 per liter, as reported by the Czech Statistical Office. Additionally, personnel costs for two drivers are estimated at EUR 51,234.67.  
The cost per kilometer is calculated using Formula (4), as follows:  
\[c_{km}=\frac{95,802.13}{13,186}=7.27\ \upc\]  
Based on the cost per kilometer data, we can now determine the price of a single transport to Fruta. The savings per transport are calculated using Formula (5), as follows:  
\[S_{transp}=39.83-14.54=25.29\ \upc\]  
The savings per transport amount to EUR 25.29, resulting in daily savings of EUR 480.52 for 19 transports. Annually, this leads to a total potential savings of EUR 166,741.88 on transport costs. Table 10 presents an overview of the savings on SF transport.  
Subsequently, we deduct these savings from SF transport and recalculate the total material handling costs. The costs after optimization are shown in Table 11.  
After recalculating the costs, the handling expenses were reduced. However, when comparing data from Tables 8 and 12, the cost reduction did not affect the size, intensity, or distances of the material flow. Thus, despite the lower costs, the overall flow volume remained unchanged.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**SF Transport Costs** & **Number of Transfers** & **Total Costs (EUR)** \\ \hline IN–OUT transfers per day (original state) & 19 & 756.73 \\ IN–OUT transfers per year (original state) & 6593 & 262,585.63 \\ IN–OUT transfers per day (proposed state) & 19 & 276.21 \\ IN–OUT transfers per year (proposed state) & 6593 & 95,843.75 \\ Daily savings & 19 & 480.52 \\ Annual savings & 6593 & 166,741.88 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Overview of savings on SF transport.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Lease for 48 Months** & **Costs (EUR)** \\ \hline
2x Road freight vehicle & 40,146.57 \\ Operating costs & 4420.90 \\ Personnel costs for 2x driver & 51,234.67 \\ Total & 95,802.13 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Costs of in-house transport.
## 4 Multi-Objectives Optimization of the Frame Based on Interval Analysis Method
### Determination of Optimization Function and Parameters  
According to the estimated results mentioned above, the strength under braking and the stiffness related to bending and torsion need to be improved. In order to avoid the conflict between optimized mechanical properties, all of them were considered as optimization objectives. In full consideration of uncertain factors during manufacturing and design stage, the thicknesses of top longitudinal beam, stiffener, lateral longitudinal beam and hanging ear in the frame were considered as design variables, and [PERSON]'s modulus \(x_{5}\) and Poisson's ratio were taken as uncertain variables [26; 27]. Moreover, the constraint of design and uncertain variables was within the range of \(0.8x_{i}\leq X_{i}\leq 1.2x_{i}\), while the range of variation for the optimization objective was ascertained from the interval allowable values.  
The relationship between design variables and optimization objectives could be acquired through the response surface method. The 30 sample data for the four design variables and the six sample data for the uncertain variables were obtained by the Latin Hypercube sampling method. Some of them and their corresponding responses are listed in Table 8. The \(y_{1}\), \(y_{2}\), \(y_{3}\), \(y_{4}\) and \(y_{5}\) represent the stress under dynamic bending, related to dynamic braking, connected with dynamic torsion, bending stiffness and torsional stiffness, respectively.  
Then, according to the simulated results, the response surface model of optimization objective function was fitted by quadratic polynomial equation. Due to limited space, only model of strength in braking case was shown here:  
\[\begin{array}{l}y_{2}=592390176.322597-6440001960.7152\times x_{1}-387018782.5 31165\times x_{2}-11501567583.2294\times x_{3}+340714204.106319\times x_{4}\\ +28383524629.0277\times x_{1}^{2}-19085871274.882\times x_{2}^{2}+53697949817.20 65\times x_{3}^{2}-3305903983.20956\times x_{4}^{2}\\ +5395879013.34058\times x_{1}x_{2}+80316087305.8226\times x_{1}x_{3}+34976216. 0475603\times x_{1}x_{4}\\ +18318993227.7762\times x_{2}x_{3}-942114760.33154\times x_{2}x_{4}-1111863944. 0763\times x_{3}x_{4}\end{array} \tag{14}\]  
The Pareto diagram analyzed from Equation (14) is shown in Figure 13. The design variable \(x_{3}\) and \(x_{1}\) had the most effect on optimization objective \(y_{2}\), and their influence ratios came to be \(-49.2\%\) and \(-31.9\%\), respectively, which implied that they had negative correlation. The source item \(x_{1}-x_{2}\) had the most positive influence ratio. The main effect on optimization objective \(y_{2}\) is displayed in Figure 14, which shows that the variation of design variable \(x_{3}\) and \(x_{1}\) have the most influence on optimization objective \(y_{2}\). Then, the three-dimensional response surface between some of the design variables and optimization objective \(y_{1}\) is presented in Figures 15 and 16. It is clearly seen that there is an approximately linear relationship between certain stress due to dynamic loading \(y_{1}\) and design variable \(x_{1
## 2 Position-Based Repetitive Torque Observer and Compensation Strategy
### Disturbance Torque Observer
\tag{8}\]  
For example, consider a system with \(\hat{f}=1\times 10^{-2},\ \ \text{B}^{*}=\)\(1\times 10^{-3}\) and the observer bandwidth \(\omega_{TOB}\) to be \(100\times 2\pi\), for \(n=0.1\); then, the resulting parameters from Equations (8) and (9) are \(\overline{K}_{D\_TOB}=5.6617\) and \(\overline{K}_{P\_TOB}=355.7333\). The Bode plot of \(H(s)\) is shown in Figure 3, which approximates to the first-order system. Since the cogging torque is periodic, the classical PD or PI controller cannot guarantee that the observer error \(E_{\theta}\) in steady state could ever approach zero, which means that the difference between \(T_{Cog}\) and \(T_{Cog}\) will always exist. Therefore, the position-based repetitive controller (PBRC) is employed in the observer design, as shown in Figure 4. Similar to the cogging torque model, the PBRC offers an internal model that learns the cogging torque as a function of the rotor position; in other words, it updates the spatial lookup table iteratively and eventually minimizes the observer error \(E_{\theta}\). In practice, the position-based (PB) delay can be realized by the memory array read/write process, which is triggered by encoder position pulses. The principle and a detailed realization method of PBRC will be addressed in the following sections. Figure 4: Proposed position-based repetitive control torque observer structure (PBR-TOB). Figure 3: Observer Bode plot for \(\hat{f}=0.01,\ \hat{B}=0.001,\ \omega_{TOB}=200\pi\ and\ n=0.1\).
## 4 Experiments and Results
### Efficiency Analysis  
The proposed Wet-ConViT model exhibits a remarkable performance in accurately classifying various wetland land covers. One critical factor to be considered, particularly in large-scale applications, is the efficiency of deep learning (DL) models in terms of memory usage and processing time. This challenge was taken into account during the design of the Wet-ConViT architecture, with the aim of achieving the highest accuracy while minimizing the number of parameters and optimizing processing time.  
To assess the efficiency of the proposed model, a comparative analysis was conducted between the proposed model and other state-of-the-art models in terms of four factors: the number of parameters (in millions), memory size (in MB), average training time per epoch (in seconds), and inference time (in seconds). The results are summarized in Table 4.  
Among the deployed DL models, the proposed model comprises a relatively low number of parameters and memory size, the second after WMF. The proposed model's number of parameters and memory size are approximately half of the second-best model, CoAtNet, along with a 2% improvement in terms of overall accuracy. The memory size is a crucial factor for evaluating the efficiency of models, especially for deployment in resource-constrained environments. The relatively low size of the proposed model (36.3 MB) makes it more suitable for real-time applications and scenarios where computational resources are limited.  
Training time and inference time are crucial factors for evaluating the efficiency of models in practical applications. The proposed Wet-ConViT model has an average training time of 77 s per epoch, which is competitive compared to other models such as CoAtNet (75 s) and significantly faster than transformer models. Although ResNet50 demonstrates a relatively short training time per epoch (63 s), it demands considerably more memory and falls behind the Wet-ConViT model in terms of overall accuracy metrics (\(\sim\)5%). Regarding inference time, the proposed model has an inference time of 9.16 s, which, although not the fastest, strikes a balance between speed and accuracy. WMF exhibits the fastest inference time (3.07 s) but is less accurate compared to the proposed model (\(\sim\)5%). The proposed model's inference time is acceptable for real-time applications while offering superior classification performance.
## 3 Fundamental Properties of RP
### Particle Size Distribution of RP  
The particle size gradation was generally obtained using a particle size laser analyzer, image analyzer, and mercury intrusion porosimeter (MIP) in accordance with ASTM C204-11 [111] and EN 196-6 [112]. Figure 1 summarizes the median particle size (D50) of RP reported in the literature compared to PC, FA, and GGBS. RP presents greater data dispersion, with maximum and minimum values of 142 \(\upmu\)m [113] and 0.25 \(\upmu\)m [114], respectively, and an average of 17.8 \(\upmu\)m. Atypical points for RP are 9.19 \(\upmu\)m and 30 \(\upmu\)m. PC, FA, and GGBS present lower D50 value dispersion. The average D50s of cement and FA are 19.7 \(\upmu\)m and 18.6 \(\upmu\)m, respectively, which are similar to RP. Atypical points for cement are 14 \(\upmu\)m and 19.97 \(\upmu\)m and atypical points for FA are 12 \(\upmu\)m and 30 \(\upmu\)m. On the other hand, the D50 average of GGBS (13.6 \(\upmu\)m) is smaller than RP. Atypical points for GGBS are 11 \(\upmu\)m and 15.6 \(\upmu\)m. When utilizing RP as an SCM, it is advisable to ensure it has a high reactivity and has a small particle size, comparable to or smaller than cement [9,115,116,117,118]. These properties contribute to the hydration reaction by promoting nucleation, creating new hydrated products, and enhancing material compactness through a filling effect [9,38,115].  
Figure 1: Median particle size (D50, \(\upmu\)m) for RP, cement, FA, and GGBS.
# Recent Advancements in Non-Destructive Testing Techniques for Structural Health Monitoring
## 4 Infrared Method
Both captured vision and IR images are first distortion-calibrated and then go through the time-spatial-integrated (TSI) coordinate transformation. After the transformation process, IR images are further processed via phase mapping and spatial derivative method for precise multiple crack visualization, whilst vision images go through a deep convolutional neural network (CNN) process for the automatic crack extraction. Then, the images are matched and evaluated by the deep CNN process. The system was experimentally tested and validated using concrete specimens with various cracks in laboratory conditions. The results demonstrated the potential of the system to automatically visualize macro- and microcracks by minimizing false alarms.
# An Internet of Things (IoT) Application on Volcano Monitoring
## 2 IoT for Volcano Monitoring
### LoRa for the Thermometers Network  
LoRa is a patented [70; 71] digital wireless data communication technology developed by Cycleo (Grenoble, France) and acquired by Semtech in 2012. Thanks to the unique chirp modulation, the wireless link can achieve sensitivity up to \(-148\) dBm and up to \(168\) dB of link budget. It covers a wide range of frequencies, including some license-free ISM bands, and provides data rates in the range of kilobits per second, which is suitable for IoT and Machine-to-Machine applications.  
Characteristics of LoRa modulation depend on different parameters [60] configured to tune link performance and energy consumption: transmission power (TP), carrier frequency (CF), bandwidth (BW), coding rate (CR), and spreading factor (SF) (see Appendix A for more detail).  
Among the LoRa devices available in the market, the Ra-02 module from Ai-Thinker [72] was chosen to implement communications of the proposed network because of (i) the configuration options,(ii) the price, and (iii) the ease of purchase, given its high availability in the market. This module comes with a Semtech SX1278 [73; 74] integrated circuit working on a 433-MHz frequency. This transceiver provides ultra-long range spread spectrum communication and high interference immunity while minimizing current consumption.  
To test the selected module, two communication links were carried out in Las Car\(\acute{\text{a}}\)adas del Teide caldera, Tenerife (Figure 1): the first one from the southern rim of the Teide volcano crater to the Parador (5.35 km), and the second one from the northern rim of the crater to Diego Hernandez caldera (8.48 km). The crater was chosen because it presents areas of thermal anomalies, the Paradox was chosen because there is access to the Internet provided by a station owned by the Spanish National Geographic Institute (IGN), and the point of Diego Hernandez caldera was chosen because it allows the largest link within Las Car\(\acute{\text{a}}\)adas del Teide caldera. In both links, a 2-dBi dipole antenna in the crater unit and a 9-dBi Yagi antenna in the other two places were used. For a 12-byte message size every 10 s over 15 min, a \(-\)123 dBm sensitivity limit value, TP = 17 dBm, CF = 433 MHz, SF = 7, BW = 125 kHz, and CR = 4/5; all messages were received in both links. The received signal strength indicators (RSSI) were \(-\)100 dB and \(-\)105 dB, respectively: more than 15 dB above the noise level. Therefore, LoRa protocol and the selected module were appropriated to implement long-range links for the proposed network.
## 4 Experimental Verification
### Experimental Results and Analysis  
The DM was controlled to generate single aberrations of different amplitudes as we measured the influence curve of each single aberration on the CE. We used the normalized CE to express our experimental results to reflect the similarity between the measured data and theoretical calculated value, which is aimed to reduce the influence of errors in components and optical path. The theoretical calculated value in the figures below takes into account the Fresnel reflection and transmission loss, which was calculated as the energy loss by 4%, and the theoretical calculated curve of the SR approximation method is also added for comparison.  
As shown in Figure 11, the measured CE curves of the three aberrations Z4, Z11, and Z22 are almost the same as the theoretical values in the small aberration range. Although the values in the large aberration range show some deviation, the maximum error is less than 0.12, and the measured data are consistent with the curve trends of the theoretical values.  
The relation between tilt-coma items and CE is shown in Figure 12. Figure 12a shows the influence of tilt (Z2) on CE, where the measured CE has a local maximum value (between 2-3 rad) formed by the secondary diffraction ring of the Airy disk. As shown in Figure 12b,c, the measured CE curves of Z16 and Z30 deviated from theoretical calculations, which can be possibly attributed to the crosstalk of the same terms and the error of aberration generation. The maximum error between measured and calculated values is no more than 0.076.  
Figure 13 shows the influence curve of astigmatic aberrations on CE. Although the experimentally measured CE is lower than the theoretically calculated value with the maximum error less than 0.2, the trend of measured data curve is the same as the theoretical curve. More importantly, as the spatial frequency of the aberration increases, the CE in the large aberration regime also increases, which is fully consistent with the theoretical analysis results.  
Under small aberration (RMS \(<\) 1.5 rad) conditions, the experimental data is almost completely consistent with the theoretical curve. However, when RMS \(>\) 1.5 rad, the influence of DM fitting ability and Hartmann WFS detection accuracy introduced other aberrations in addition to the desired single aberration; this caused deviation between the measured and theoretical CE values. The experimental  
Figure 11: Relation between circularly symmetric aberrations and CE. (**a**) \(\varphi\) = Z4; (**b**) \(\varphi\) = Z11; (**c**) \(\varphi\) = Z22.  
Figure 12: Relation between tilt-coma terms and CE. (**a**) \(\varphi\) = Z2; (**b**) \(\varphi\) = Z16; (**c**) \(\varphi\) = Z30.  
results validate the proposed CE calculation method as well as our analysis of the influence of spatial frequency and other factors on CE.