esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix B Target Hull Girder Loads
### Vertical Shear Force
#### 2.2.2 Vertical Wave Shear Force  
The vertical wave shear forces at any longitudinal position shall be taken as:  
\[Q_{wv-pos}=0.52f_{q-pos}f_{p}C_{w}LBC_{B} \tag{12}\]  
\[Q_{wv-neg}=-0.52f_{q-neg}f_{p}C_{w}LBC_{B} \tag{13}\]  
where  
\(f_{q-pos}\) = distribution factor along the ship length for positive wave shear force, shall be taken as:\[f_{q-pos}=\left\{\begin{array}{l}0.0\ for\ x\leq 0\\ 0.92f_{nl-vh}\ for\ 0.2L\leq x\leq 0.3L\\ 0.7f_{nl-vg}\ for\ 0.4L\leq x\leq 0.6L\\ 1.0f_{nl-vg}\ for\ 0.7L\leq x\leq 0.85L\\ 0.0\ for\ x\geq L\end{array}\right.\]  
Intermediate values of \(f_{q-pos}\) shall be obtained by linear interpolation.  
\(f_{q-neg}=\) distribution factor along the ship length for positive wave shear force, shall be taken as:  
\[f_{q-neg}=\left\{\begin{array}{l}0.0\ for\ x\leq 0\\ 0.92f_{nl-vg}\ for\ 0.2L\leq x\leq 0.3L\\ 0.7f_{nl-vg}\ for\ 0.4L\leq x\leq 0.6L\\ 1.0f_{nl-vh}\ for\ 0.7L\leq x\leq 0.85L\\ 0.0\ for\ x\geq L\end{array}\right.\]  
Intermediate values of \(f_{q-neg}\) shall be obtained by linear interpolation. The vertical wave shear force for each dynamic load case, \(Q_{uv-LC}\), is defined as follows.  
\[Q_{uv-LC}=\left\{\begin{array}{l}f_{\beta}C_{QW}Q_{uv-pos}\ for\ C_{QW}\geq 0 \\ f_{\beta}C_{QW}|Q_{uv-neg}\big{|}\ for\ C_{QW}<0\end{array}\right. \tag{10}\]  
where  
\(C_{QW}=\) load combination factor for vertical shear force;  
\(Q_{uv-pos}\), \(Q_{uv-neg}\) = positive and negative vertical wave shear force.  
Target hull girder vertical shear force  
The target hull girder vertical shear force, \(Q_{v-targ}\), at a longitudinal position for a given FE load combination is taken as:  
\[Q_{v-targ}=C_{SF-LC}Q_{sw}+Q_{uv-LC} \tag{11}\]  
where  
\(C_{SF-LC}\) = percentage of permissible still water shear force applied for the load combination under consideration.  
\(Q_{sw}\) = permissible still water shear force at the considered longitudinal position. \(Q_{sw}\) is either in positive (\(Q_{sw-pos}\)) or in negative (\(Q_{sw-neg}\)) shear force.  
\(Q_{w-LC}\) = vertical wave shear force for the dynamic load case under consideration.
# Evaluating Novel Speech Transcription Architectures on the Spanish RTVE2020 Database +
## 4 Systems Description and Configuration
### Novel ASR Architectures
#### 4.1.3 Wav2 vec2.0 Based System  
Wav2 vec2.0 [2] is a self-supervised E2E architecture based on CNN and Transformer layers schematically represented in Figure 3. The Wav2 vec2.0 model maps speech audio through a multi-layer convolutional feature encoder \(f:\chi\to Z\) to latent speech representations \(z_{1},\dots,z_{T}\), which are fed into a Transformer network \(g:Z\to C\) to output context representations \(c_{1},\dots,c_{T}\). These context representations are then quantised to \(q_{1},\dots,q_{T}\) to represent the targets in the self-supervised learning objective [2, 34]. The feature encoder contains seven blocks and the temporal convolutions in each block include 512 channels with strides \((5,2,2,2,2,2,2)\) and kernel widths \((10,3,3,3,3,2,2)\). The Transformer used was composed by 24 blocks, a model dimension of 1024, an inner dimension of 4096 and a total of 16 attention heads.  
As the main baseline Wav2 vec2.0 model, in this work, we selected the pretrained Wav2 Vec2-XLS-R-300M model ([[https://huggingface.co/facebook/wav2](https://huggingface.co/facebook/wav2) vec2-xls-r-300m]([https://huggingface.co/facebook/wav2](https://huggingface.co/facebook/wav2) vec2-xls-r-300m), accessed on 28 December 2021), which was self-supervised pre-trained with 436k h of unlabelled speech data in 128 languages from the VoxPopuli [35], MLS [36], CommonVoice [25], BABEL (corpus collected under the IARPA BABEL research program), and  
Figure 3: Wav2 vec2.0 architecture representation. The raw audio signal is mapped to speech representations that are fed into a transformer network to output context representations. Context representations are then quantised to represent targets in the self-supervised task. This figure is based on the one presented in [2].  
VoxLingua107 [37] corpora. The Wav2 Vec2-XLS-R-300M is one of the different versions of the Facebook AI's XLS-R multilingual model [38] composed by 300 million of parameters.  
With the aim of adapting this pre-trained model to the domain, we fine-tuned the Wav2 Vec2-XLS-R-300M model for a total of 50 epochs in two steps, and, using the acoustic corpus defined above, augmented dynamically during training through the SpecAugment technique. First, we evolved the pre-trained model for 30 epochs with a maximum learning rate of \(10^{-4}\). We used the cosine function as the learning rate annealing function with a warm up during the initial 10% of the training, whilst the batch-size was set to 256. This model was later fine-tuned for 20 additional epochs by modifying the maximum learning rate to \(5\times 10^{-5}\).  
Finally, the decoding was performed with the same 5-gram language model employed for the decoding of the previous Q15\(\times\)5 based system, but with a _beam-width_ of 256, \(\alpha=0.3\) and \(\beta=1.6\).
## 4 Results and Discussion  
### Membrane Damage Characterization  
In theory, the damage is transformable due to the flexibility of the membrane, so gas chamber volume \(V_{g}\) and the damage equivalent diameter \(d_{OR}\) may change with changing applied pressures.  
The membrane dynamic behavior was investigated experimentally, and it was found that the membrane obviously inflated when the pressurized gas was applied in the vessel. However, due to the limited area (750 mm\({}^{2}\)) of the membrane in the vessel, the gas chamber volume \(V_{g}\) of the intact membrane enlarged only about 4.5 mL when the pressurized gas (80 kPa) was pumped into the gas chamber.  
In order to investigate the influence of applied pressurized gas on the damage size, the defect diameters were measured using image acquisition systems depicted previously at different applied pressure of 40, 60, 80, 100, and 120 kPa. The relationship between equivalent diameter and original damage size is shown in Figure 3. It can be seen that, with increasing applied pressure, the equivalent diameter did not obviously change. It was difficult to say with certainty whether the damage size obviously varied with the pressure applied at a limited scope. Therefore, the modified coefficient \(\omega\) in Equation (4b) was assumed to be equivalent to 0. [PERSON] et al. [20] had come up with similar results and given a reasonable explanation. Therefore, in the simulation in this study, the effect of the membrane deformation properties on defect size was ignored.
## 2 Study Area
### Climate--Hydrological Data  
Lefkas lies within the Western Central Water District of Greece, one of the country's rainiest regions. Coastal areas receive 800-1000 mm of rainfall annually, while precipitation in the plains can reach up to 1400 mm, and mountainous regions up to 1800 mm [13]. The island follows a typical Mediterranean climate, with most rainfall occurring from November to February and the driest months being July and August. Based on Gaussen's Xerographic Index [14], the island's climate ranges from mild to strong Mid-Mediterranean, depending on altitude.  
Using rainfall data from local meteorological stations, a Precipitation Map was generated using the Thiessen polygon method (Figure 3). The average annual rainfall ranges from 845 mm to 918 mm, with the driest areas located in the southeast.  
The drainage network of the island (Figure 4) is denser in the southeast and reaches up to the 5 th order, according to [PERSON]'s classification covering an area of 365 km\({}^{2}\). Although the island has no lakes, its geology supports a major underground karst system, which supplies springs throughout the island. These water sources are used for drinking, irrigation, and limited industrial purposes. Annual water withdrawals total approximately 5.7 km\({}^{3}\), most of which is used for drinking water (3.5 km\({}^{3}\)), followed by irrigation (2.1 km\({}^{3}\)), with only small amounts for industrial (0.1 km\({}^{3}\)) and livestock use (0.01 h\({}^{3}\)) (Figure 4).  
Figure 4: Drainage network classification map of Lefkas Island according to [PERSON]. The chart shows the distribution of annual water withdrawals in the LAP Lefkados (EL0444) according to [15].  
Figure 3: Precipitation Map of Lefkas Island that was constructed with rainfall data from the meteorological stations across the region of interest using the Thiessen polygon methodology. The island experiences an average amount of precipitation of 845–918 mm, with the lowest amount occurring in the south-east part of the island.
###### Abstract  
We present analytical results on the average probability of error (PER) performance of an optically pre-amplified pulse-position modulation (PPM) receiver under Malaga-\(\mathcal{M}\) fading. The results are in the form of a finite sum whose number of terms depends on the PPM modulation order and the noise modes of the amplifier, enabling the efficient calculation of the average PER. In addition, we utilized the presented analysis to evaluate the performance of a equal-gain-combining (EGC) diversity receiver that operates in conjunction with optical amplification and PPM. The results show that the utilization of diverse and relatively low PPM orders achieves a drastic reduction in the average PER.  
s 2  
fading distribution [26], but no work has addressed more general fading distributions that have been proposed recently [27,28].  
In this work, we consider for the first time, to our knowledge, the impact of generalized Malaga-\(\mathcal{M}\) fading on the performance of an optically pre-amplified PPM receiver. To this end, we first express the PPM probability of error (PER) as a finite sum over the modulation order and the amplification noise modes. The sum is computationally efficient and enables the derivation of a similar finite sum for the average PER in Malaga-\(\mathcal{M}\) fading. The system with a single receiver was studied analytically in weak, moderate and strong fading, and it was shown that the modulation order has a significant impact in the weak fading regime, but additional reception branches are required in moderated and strong fading. Diversity reception is also studied analytically in this work, and we demonstrate that it significantly improves the average PER despite the additional noise that is introduced from using multiple amplifiers.  
In comparison with our previous works that focused on numerical integration rather than analytical investigation, the efficiency of the current analytical approach allows for (a) the study of generalized fading and (b) the assessment of receivers with wide optical filters. In both cases, the numerical approach proves time consuming and limits the analysis to relatively simple fading distributions (\(\gamma-\gamma\), negative exponential) and narrow-band receivers that are not always desirable [21]. Moreover, the current approach does not require knowledge of the combined signal fading statistics at the equal-gain-combining (EGC) output, which can be challenging to derive and is not currently available for Malaga-\(\mathcal{M}\) fading. Instead, it provides exact results for the multi-receiver from the fading distribution of a single branch.
## 4 Quantitative descriptions of scale threshold and scale domain
### Fractals method (FM)  
The term \"fractals\", first proposed by [PERSON] [65], is now widely used in different science domains, such as biology, physics, chemistry, geography and so on. The reason why fractals attract more and more attention is that the real world is too regular to be measured or simulated by traditional methods [13]. Many curves or surfaces in the world may be statistically made up of copies of itself at a reduced scale. This statistical self-similar property is the key point to understanding the concept of fractals. In classical geometry, the dimension of a point is zero, a line is 1, a plane is 2, and a cube is 3. While, in fractal geometry, the fractal dimension D of an object can be any non-integer dimension [66]. For example, the fractal dimension of a curve can be any value between 1 and 2. The fractal dimension of a surface can be any value between 2 and 3. Empirical studies indicate that true fractals with strict self-similarity do not exist. However, the information that fractal dimensions provide with scale can be used to indicate the optimum measurement scale. There are many ways to determine fractal dimensions. [PERSON] and [PERSON] [67] introduced several frequently used definitions of fractal dimension, which could be used to find the process scale. The turning points of fractal dimension may contain some important information. For example, they should be those where new patterns may emerge or resolutions approach dominant operational scales. Generally speaking, the more irregular an object, the bigger the fractal dimension. The fractal dimension of an image is expected to be lower as the resolution becomes coarser due to coarser resolution corresponding to lower variability and vice versa. Therefore, the scale at which the highest fractal dimension is measured may be the scale at which most processes operate [13]. Although no agreement has been reached on the definition of fractal dimension, it is a promising research direction. Table 2 summarizes the advantages and disadvantages of the above mentioned methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Advantages** & **Disadvantages** & **References** \\ \hline \multirow{4}{*}{GVM} & - A hierarchical analysis to determine the relative variability and independent contribution at each level. & Its validity remains unclear and more analyses are needed. & [3] \\  & - The data set can be divided into any arbitrary nested scale. & & [59] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of different methods used to quantitatively describe scale threshold and scale domain.
# Mutated Channelrhodopsins with Increased Sodium and Calcium Permeability
## 3 Results
Reversal potential shift for Na\({}^{+}\) was calculated by the reversal potential differences in two outside buffers containing 120 mM NaCl pH 7.6 and 1 mM NaCl pH 7.6. Reversal potential shift for H\({}^{+}\) was determined by the reversal potential differences in two outside buffers of pH 7.6 and 9.6 containing 120 mM NaCl. Data points were presented as mean \(\pm\) SD, \(n\) = 4–6. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Expression \({}^{\star}\)**} & \multicolumn{2}{c}{**I\({}_{\text{s}}\)**} & \multicolumn{2}{c}{**Closing Time (ms)**} & \multicolumn{2}{c}{**EPD\({}_{\text{50}}\)**} \\  & & & **\(\tau_{\text{1}}\) (\%+)** & \(\tau_{\text{2}}\) (\%+)** & **(\(\mu\)W/mm\({}^{\text{2}}\))** \\ \hline ChR2 & 1\(\times\) \({}^{\dagger}\) & 1\(\times\) \({}^{\ddagger}\) & 7.5 \(\pm\) 0.5 (\(>\)98) & - & 710 \\ \hline XXM & 2.7 \(\times\) & 30 \(\times\) & 80 \(\pm\) 5.5 (8) & 1100 \(\pm\) 110 (92) & 90 \\ XXL & 3 \(\times\) & 48 \(\times\) & - & 71000 \(\pm\) 2900 (\(>\)96) & 5.4 \\ \hline Chronos & 1 \(\times\) \({}^{\ddagger}\) & 1 \(\times\) \({}^{\ddagger}\) & 2.8 \(\pm\) 0.3 (\(>\)99) & - & 500 \\ Chronos & 2.2 \(\times\) & 1.2 \(\times\) & 1400 \(\pm\) 160 (56) & - & 84 \\ Chronos D173C & 2.6 \(\times\) & 10 \(\times\) & 22 \(\pm\) 1.3 (85) & 130 \(\pm\) 16 (15) & 190 \\ \hline \(P\)sChR & 1 \(\times\) \({}^{\ddagger}\) & 1 \(\times\) \({}^{\ddagger}\) & 8.5 \(\pm\) 0.6 (\(>\)98) & - & 810 \\ \(P\)sChR D139H & 2.2 \(\times\) & 6 \(\times\) & 37 \(\pm\) 2.7 (14) & 810 \(\pm\) 54 (86) & 160 \\ \(P\)sChR D139C & 3.1 \(\times\) & 11 \(\times\) & 3.4 \(\pm\) 0.8 (4) & 74000 \(\pm\) 4300 (96) & 3.2 \\ \hline \hline \end{tabular} \({}^{\dagger}\), expression or photocurrent of corresponding wild type was normalized as 1\(\times\). Is, stationary (plateau) current. \({}^{\ddagger}\), as most ChRs exhibit biphasic off-kinetics which comprised a fast and a slow component, here the \(\%\) indicated the percentage of the amplitude of the fast (\(\tau_{\text{1}}\)) or slow (\(\tau_{\text{2}}\)) component to the whole photocurrent. Data are shown as mean \(\pm\) SEM, n = 4-5. Values are presented as approximates. \({}^{\star}\), expression level was calculated from the data of fluorescence value in Appendix A Figure A1.  
\end{table} 
Table 1: Basic properties of ChR2, \(P\)sChR, and Chronos variants. Figure 5: Calcium permeabilities of selected channelrhodopsin variants. (**a**) Photocurrent traces of different channelrhodopsins before (grey) and after (black) 1,2-bis(o-aminophenoxy)ethane-N,N,N\({}^{\prime}\),N\({}^{\prime}\)-tetraacetic acid (BAPTA) injection. Measurements were done in 80 mM CaCl\({}_{2}\) pH 9.0 at \(-\)100 mV. Blue bars indicate light illumination. (**b**) Comparison of calcium permeability of ChR2 (light grey), Chronos (dark grey), \(P\)sChR (black), CatCh (light grey), XXXM (dark grey), and \(P\)sChR D139H (black). All data points were plotted in the figure and mean \(\pm\) SEM was indicated.
# Diagenesis of Deep Low Permeability Reservoir in Huizhou Sag and Its Influence on Reservoirs
## 6 Conclusions  
1. It was found that the reservoirs of the Enping Formation have a high quartz content and are mainly composed of feldspar quartz sandstone (Q\({}_{76}\)F\({}_{16}\)L\({}_{8}\)) and lithic quartz sandstone (Q\({}_{67}\)F\({}_{15}\)L\({}_{18}\)). The reservoir space primarily includes intergranular pores, dissolution pores, and microfractures, but the primary pores and dissolution pores are the main ones, and the two kinds of reservoir space account for more than 70%. The porosity of the core ranges from 1.1 to 19.8%, while its permeability ranges from 0.0017 to 612.02 \(\times\) 10\({}^{-3}\)\(\mu\)m\({}^{2}\). The reservoir is characterized by low porosity and permeability.
2. The diagenetic evolution sequence of the Enping Formation reservoir can be summarized as follows: early diagenetic stage A (IA): mechanical compaction/chlorite film \(\rightarrow\) early diagenetic stage B (IB): mechanical compaction/tuffaceous alteration/feldspar dissolution \(\rightarrow\) middle diagenetic stage A (IIA): mechanical compaction/calcite cementation/feldspar dissolution/debris dissolution/kaolinite consolidation \(\rightarrow\) middle diagenetic stage B (IIB): chemical compaction/dolomite cement/pore chlorite/quartz enlargement.
3. The deep low-permeability Enping Formation reservoir space is significantly influenced by diagenesis, with compaction resulting in a porosity reduction ranging from 12.5 to 27.2% (an average of 18.9%). Cementation reduced the pore volume by 2.1-28.7% (average of 11.7%), while dissolution increased porosity by 1.4-25.6% (average of 10.1%).  
**Author Contributions:** Conceptualization, Methodology, Software, and Writing--Original Draft, S.J. ([PERSON]); Formal Analysis and Validation, R.G.; Data Curation, S.J. ([PERSON]); Investigation and Writing--Review and Editing, J.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was financially supported by the National Natural Science Foundation of China (No. 42002150), the Open Foundation of the Cooperative Innovation Center of Unconventional Oil and Gas, Yangtze University (Ministry of Education & Hubei Province) (No. UOG2024-12), the Open Foundation Project of the Key Laboratory of Polar Geology and Marine Mineral Resources (China University of Geosciences, Beijing, China), the Ministry of Education (No. PGMR-2023-201), and the National Key Research Program of China (No. 2017 ZX05032-002).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All data and materials are available upon request from the corresponding author. The data are not publicly available due to ongoing research using a part of the data.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Design and Analysis Methods
### Electromagnetic-Thermal Coupling  
Heat generation in electrical machines is caused by power losses such as ohmic (including eddy loss) and iron (hysteresis loss, eddy loss, and excess loss) present in the machine. It can cause its components to reach temperatures that affect their performance and life. Balancing cooling system efficiency with energy losses is essential to optimize motor performance [37]. Thermal analysis of electrical machines is a critical study for determining and optimizing the cooling method of the machine before production. Thanks to numerical-based analysis, machine temperatures can be reached very quickly, and the analysis of work that cannot be performed on real machines is possible. The thermal model required for thermal analysis must include the three modes of heat transfer: conduction, convection, and radiation.  
Heat transfer by conduction is expressed with [PERSON]'s law equation as [38]  
\[q_{con}=-kA\
abla T\]  
where \(q\) is the heat flux density (W/m\({}^{2}\)), \(k\) is thermal conductivity W/(m\({}^{\circ}\)K) of the material, \(A\) is the surface area (m\({}^{2}\)), and \(\
abla T\) is the temperature gradient (\({}^{\circ}\)K/m).  
Convection is the heat transfer caused by fluid motion, as opposed to conduction. It can be accomplished by natural convection due to density differences in the fluid, or by forced convection by external devices such as fans. Convection heat transfer depends on the temperature difference between the surface and the fluid and is expressed by [PERSON]'s law.  
\[Q_{conv}=hA(T_{s}-T_{\infty}) \tag{33}\]  
Figure 9: Two-Dimensional FEM results; (**a**) phase currents, (**b**) back emf, (**c**) currents FFT, (**d**) cogging torque, and (**e**) output torque.  
where \(h\) is the convection heat transfer coefficient (W/m\({}^{2}\)K), \(T_{s}\) is the surface temperature, and \(T_{\infty}\) is the fluid temperature (\({}^{\circ}\)K or \({}^{\circ}\)C).  
Heat transfer by electromagnetic waves is defined as a radiation phenomenon. The heat flux rate for the radiation quad is derived from the Stefan-Boltzmann law, which determines the emissivity of radiation emitted by a surface:  
\[q_{rad}=\varepsilon\sigma A(T_{s}^{4}-T_{sur}^{4}) \tag{34}\]  
where \(\varepsilon\) is the surface emissivity, \(\sigma\) is the Stefan-Boltzmann constant (W/m\({}^{2}\)K\({}^{4}\)), and \(T_{sur}\) is the surrounding temperature.  
The principle diagram of the proposed analysis method is presented in Figure 10. This method provides the opportunity to perform direct coupling electromagnetic and thermal analyses in an integrated manner, as shown in Figure 11. The 1D machine model is converted into a 3D model in this proposed model. Numerical analysis determines the core and winding losses that create thermal loads and are directly connected to thermal analysis [13; 39]. The resulting thermal values are coupled as feedback to the electromagnetic component to change the permeability and conductivity of the material. The correct definition of material engineering data needs to be considered here.
## 4 Dataset Observation and Prepossessing
### Making Incomplete Trajectories
#### 4.3.1 Masking Strategy  
To simulate missing data in trajectory sequences, we employ a comprehensive masking strategy that involves randomly selecting and discarding segments of the trajectory. This method ensures that the simulated missing data reflect realistic scenarios of incomplete data in trajectory analysis. Let \(X\) represent a complete trajectory with \(T\) data points, where \(x_{t}\) denotes the data point at time step \(t\). We define the masking strategy as follows:  
* **Masking Ratio (\(\alpha\))**: We introduce a hyperparameter \(\alpha\) which represents the percentage of data points we want to mask in each trajectory. The value of \(\alpha\) is determined during training and can be fine-tuned for optimal performance.
* **Random Selection**: To simulate missing data, we randomly select a starting point within the trajectory and discard a segment with a length proportional to \(\alpha\cdot T\). This means that a contiguous portion of the trajectory is removed, simulating realistic missing data scenarios