esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Experimental Results
#### 4.4.1 Main Results  
The comparison results of our model BTDM versus thirteen baselines on the NYT and WebNLG datasets are shown in Table 2.  
As shown, our technique BTDM outperforms all thirteen baselines overall. Our model outperforms PRGC [19] on 9 of the 12 criteria and shows the second best performance, if not the best, on the other 3. In particular, BTDM outperforms baseline models over the F1 scores on all datasets. BTDM outperforms the strongest baseline with an 0.5% and 2% absolute gain in F1-score on datasets WebNLG and WebNLG*. Additionally, as shown in Table 1, WebNLG and WebNLG* are more challenging to improve than the NYT dataset because they contain fewer data but a greater variety of relation types (216 and 171, respectively). Nevertheless, not only did we achieve an improvement in both datasets, surpassing the previous best model in every metric, but for the first time ever, our model BTDM boosted the WebNLG dataset's 3 metrics by over 90%.  
In addition, we noticed two phenomena. CasRel is a typical one-way extraction framework. Our model BTM shows a superior performance compared to CasRel, demonstrating the correctness of our idea that bidirectional extraction avoids the propagation of errors due to subject identification failure. Additionally, the PRGC is the latest SOTA model, which first predicts the possible relations in the text. This leads to the omission of some possible relations, which leads to poor generalization of the model. Our model, the BDTM, performs better than the PRGC, especially in datasets with more relation types. This proves our idea that adding relation features can strengthen the interaction between entity pairs and relations so that the model can find the relationship between entity pairs more accurately and effectively. In addition, our bipartite graph-linking approach maintains a relationship matrix, allowing for each relation to make relation judgments and providing powerful classification and relation-mining capabilities.
## Materials and Methods  
### Testing Sample and Data Recorder Settings  
Four SAVER(tm) \(3\times 90\) field data recorders (Lansmont Corp., CA, USA) were used to collect the data in the field measurements. Vibration events in the vertical, lateral, and longitudinal directions were also recorded in a three-layer stacked unit for this study. One of the SAVERs was mounted to the platform directly, and others were in small corrugated boxes. The SAVERs were fixed in the boxes using rigid frames made of aluminum ITEM profile (Oitem Industrietechnik GmbH) with a cross section of \(20\times 20\) mm. Table 1 contains the specifications for the corrugated box units and SAVER settings used in this study. Figure 2 shows the elements of the measuring system for the boxes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Samples Used**} & \multicolumn{2}{c}{**SAVERs**} \\ \hline Corrugated board & 35 BC & Timer triggered data & 1 s \\ Weight of board & 742 g/m\({}^{2}\) & Wake-up interval & 1 s \\ ECT (Edge Crush Test) & 9.0 kN/m & Recording time & 1.000 s \\ BST (Bursting Strength Test) & 1685 kPa & Samples/sec & 500 Hz \\ Weight of box & 190 g & Sample size & 500 \\ Size of box (w \(\times\) d x h) & 180 \(\times\) 180 \(\times\) 195 mm & Frequency resolution (PSD) & 0.50 Hz \\ Weight of ALU frame & 1440 g & Anti-aliasing frequency & 200 Hz \\ Entire weight & 2630 g & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications for SAVER settings and samples used for this study.  
Figure 2: (**a**) Corrugated box, (**b**) aluminum profile, and (**c**) test sample used for this study.
## 3 Results and Discussion
### Build Orientation Effect
#### 3.1.1 Case 1: Adjusting the Volume Constraint  
The impact of build orientation on optimal geometry is significant, but other factors, such as volume fraction, loading conditions, and material properties, are sometimes overlooked. To comprehensively evaluate the effect of build orientation, the influence of volume fraction and boundary conditions was investigated using two L-shaped designs built in the \(-X\) and \(-Y\) directions. These specific designs were selected because they exhibited the highest deviation in target and convergence error, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Build Orientation** & **Cantilever Beam** & **Bridge Shape** & **L-Shaped Beam** \\ \hline +Z axis & 0.2147 & 0.003717 & 0.5392 \\ \(-Z\) axis & 0.2473 & 0.003710 & 0.5391 \\ +X axis & 0.1855 & 0.003714 & 0.5388  
Figure 6: Optimized bridge-shape structures in six different build orientations.  
Figure 7: Optimized L-shaped beams in six different build orientations.  
To investigate the cause of the structural redundancy observed in the L-shaped beam when built in the \(-X\) and \(-Y\) directions, a reanalysis was performed by adjusting the volume constraints. Specifically, the overhang constraint remained constant for both directions, while the volume fraction was changed from 30% to (1) 50% and (2) 80% to examine the convergence process. The analysis revealed that when analyzing the \(-Y\) direction, attempts to achieve an optimal structure were unsuccessful due to persistent redundancy, as shown in Figure 8. This redundancy may have occurred because the outer portion of the leg did not fall within the convergence limit of the overhang with an angle greater than or equal to 45 degrees during the part build along the \(-Y\) direction. In the \(-X\) build direction for the L-shaped design, increasing the volume fraction to 50% and 80% also led to a similar result.
## 1 Introduction
The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was used in the study [27] to select the Pareto optimal solution by measuring the distance between the solution and the optimal worst-case point. The gray relation analysis (GRA) was used as a basis for ranking the Pareto solution set in ref. [28]. Selection methods based on information entropy have received much attention. The information entropy (Shannon entropy) was firstly proposed by [PERSON] in [29]. On this basis, various types of algorithms based on Shannon entropy have gained wide extension and application. Ref. [30] proposed a differential entropy-based compactness index to measure compactness in scenarios such as transportation systems. Based on a large amount of data and theoretical analysis, ref. [31] analyzes the effect of entropy weight on TOPSIS and proposes an entropy-based TOPSIS with adjustable weight coefficients. This article proposes a surrogate assisted multi-objective optimization framework with limited sample size, and the core work includes the following:  
1. Variable importance analysis using RF + ANOVA, which focuses on both the contribution of design variables on model prediction and the sensitivity of each design variable on optimization objectives, has combined machine learning and statistical methods. Variables selected in this way possess both model predictive utility and high sensitivity. 2. A surrogate model is established using a GRNN with excellent results under limited samples, whose mathematical foundation has a fixed architecture and is controlled by a single hyperparameter, simplifying parameter adjustment. An objective selection method of Pareto's optimal solution based on \"entropy weight distance\" is proposed, which distinguishes the weights of different performance indexes and avoids the dependence on experts' experience in the selection. In this paper, Section 2 describes the structure, electromagnetic performance, design variables and optimization variables of the IPMSM prototype; Section 3 carries out the importance analysis of each design variable; Section 4 describes the multi-objective optimization problem and constructs the surrogate model by a GRNN; Section 5 obtains the Pareto frontier through the MOPSO algorithm, and selects the Pareto optimal solution by the \"entropy weight distance\" method to select the Pareto optimal solution; and Section 6 concludes this article. The flowchart of this study is delineated in Figure 1.  
Figure 1: Flowchart of this study.
## 2 Materials and Methods
### Volatile Compound Analysis  
The volatile compounds in soft cheeses were extracted by solid-phase microextraction (SPME) after heating to 37 \({}^{\circ}\)C for 30 min, using a divinylbenzene-carboxen-polydimethylsiloxane (DVB/CAR/PDMS) 50/30 \(\upmu\)m fiber (Merck; Darmstadt, Germany). They were then analyzed by gas chromatography-mass spectrometry (GC-MS) in a Gas Chromatograph 6890 GC (Agilent Technologies; Santa Clara, CA, USA) equipped with a HP-5 column (5% phenyl\(-\)95% dimethylpolysiloxane) and coupled to a mass spectrometer (MS) detector, 5975C (Agilent Technologies). Oven temperature started at 40 \({}^{\circ}\)C for 5 min and was increased to 280 \({}^{\circ}\)C, with a rate of 7 \({}^{\circ}\)C/min. The desorption time was 30 min at 250 \({}^{\circ}\)C. The transfer line temperature was established at 280 \({}^{\circ}\)C. The carrier gas was helium (Air Liquide, Madrid, Spain) with a flow rate of 1.2 mL/min. MS detection was performed in full scan (50-350 amu). Automated peaks search and spectral deconvolution were used for data treatment, and the identification of the volatile compounds was achieved by comparing their mass spectra with the NIST/EPA/NIH library (Institute of Standards and Technology, Gaithersburg, MD, USA). Volatile compound identifications were confirmed based on comparisons of the linear retention index of standards of a series of n-alkanes analyzed under identical conditions to the samples.
###### Abstract  
Although hearing devices based on cartilage conduction have become more widely used in Japan, methods for evaluating the output volume of such devices have not yet been established. Although the output of air-conduction-based sound-generating devices (e.g., earphones and hearing aids) can be standardized via the head and torso simulator (HATS), this is not applicable to cartilage conduction devices because the simulated pinna is too soft (hardness: A5) compared with human aural cartilage. In this study, we developed polyurethane pinna that had the same shape but different degrees of hardness (A40, A20, and A10). We then compared the HATS results for the new pinna simulators with data from human ears. We found that the spectral shapes of the outputs increasingly approximated those of human ears as the simulated pinna hardness decreased. When a durometer was pressed against the ear tragus of a human ear, the hardness value ranged from A10 to A20. Accordingly, cartilage-conduction-based sound information could be obtained using a HATS that had a simulated pinna with a similar hardness value.  
cartilage conduction; pinna simulator; head-and-torso simulator (HATS); hearing aid; sound pressure level Article  
Manipulating the Hardness of HATS-Mounted Ear Simulators to Reproduce Cartilage Sound Conduction.
## 4 Reinforcement Learning Combined with ANN
### Q learning Combined with ANN Application in Energy Routing Design
#### 4.3.3 Reward Function  
In Section 3, the operating cost, environmental cost, and power losses are integrated and converted into the weights of the connection lines. A routing path with smaller weights is preferred in this paper, thus when the power is transmitted through such a path, the learning agent should receive a larger immediate reward. Therefore, the reward function is defined as follows:  
\[r=\left\{\begin{array}{ll}\frac{1}{W_{i-j}},&\text{within limits}\\ 0\,,&\text{beyond limits}\end{array}\right.. \tag{39}\]  
Considering the transmission capability of connection lines and secure operation of the energy system, upper limits should be satisfied. If the power transmitted is beyond the upper bound, the immediate reward will decrease to zero.
# Numerical Study of Wave Effect on Aircraft Water-Landing Performance
###### Abstract
[25] presented time-history results of the AW101 helicopter wave ditching in sea state 4, including acceleration, displacement and velocity. In 2020, [PERSON] et al. [26] proved that the numerical wave-making method can be used in the seaplane wave-landing study through an ALE-based framework. More inspired works [27; 28] were provided to further study the influence rules of wave height and initial wave position on the characteristics of the aircraft wave-landing. [PERSON] et al. [27] studied the extreme impact load of a high wing-tail regional aircraft during the sea state 4 landing process. The effect of wave height on the impact load and peak values are detailed. They concluded that the vertical and horizontal impact peaks are monotonously related to the wave height. In 2021, [PERSON] et al. [28] investigated the effects of initial wave-landing position and pitch angle on a helicopter. The general conclusions are that the dynamic motion and hydrodynamic behaviour of the helicopter ditching on wavy water are significantly different from those of ditching on calm water; ditching on the crest position is the best option with different initial pitch angles. However, the present knowledge is still insufficient for the aircraft wave landing problem. Particularly, the behavior and mechanisms of aircraft water-landing with regular waves are not fully explored. Therefore, in the present study, we primarily aim to provide a comprehensive understanding of the aircraft water-landing process with different wave surfaces and to further investigate the mechanism of wave height effect on the aircraft regular wave-landing performance. A three-dimensional (3D) numerical non-breaking wave is generated by combining the velocity-inlet/static boundary wavemaker and the momentum-absorbing method. The incompressible RANS equations, volume of fluid (VOF) function, turbulence model and six-degrees-of-freedom model are solved by the CFD packages STAR CCM+. Based on the results by [PERSON] et al. [29] that the compressibility effects are not expected to affect the general trends of the impact load and play non-dominated roles in the whole ditching process. Moreover, the ditching speed of the aircraft in this study is in the range of \(Ma\leq 0.11\), thus the compressible fluids are not considered. The remainder of the paper is organized as follows. In Section 2, the FVM-RANS-based computational method, wavemaker and case setup for the amphibious aircraft water-landing simulation are detailed. Numerical validation cases are then presented in Section 3 to verify the correct implementation and accuracy of the wavemaking method and numerical model. In Section 4, several case studies are performed to investigate the aircraft landing performance with different wave surfaces. The conclusions of this study are presented in Section 5.
## 2 Materials and Methods
### Theoretical Modeling  
Homogeneity is quantified using  
Homogeneity Index (H) based on density standard deviation.  
Density gradient (\(\
abla\rho\)) for vertical stratification.  
Fick's second law for diffusion-based mixing.  
Kinetic energy and viscous resistance for vibration modeling.  
Assuming a 50 mL aqueous solution:  
Vibration-induced energy: \(\sim\)0.39 mJ per cycle.  
Power requirement: \(\sim\)3.95 mW per axis.  
IR laser power requirement: \(\sim\)1.4 W (realistic with 3 \(\times\) 500 mW NIR diodes).  
To validate our hypothesis, we developed a mathematical model describing the diffusion and mixing dynamics of injectable drugs under continuous vibration. Our model accounts for:  
The interplay between Brownian motion and forced mechanical agitation.  
The relationship between vibration frequency, amplitude, and homogeneity over time.  
Drug-specific properties, such as viscosity and molecular weight, influence the effectiveness of the homogenization process.  
Next, we will present the calculation methods for homogenizing solutions depending on the viscosity and volume gradient.  
Homogeneity Index (\(H\)) Based on Density Variation A solution is considered homogeneous if the density (\(\rho\)) is uniform throughout the volume. The homogeneity index can be defined as  
\[H=1-\frac{\sigma\rho}{\rho^{-}} \tag{1}\]  
where  
\(H\) = homogeneity index (0 to 1, where 1 is perfectly homogeneous)  
\(\sigma\rho\) = standard deviation of density in different regions of the solution  
\(\rho^{-}\) = mean density of the solution  
If \(H\approx 1\)  
\(H\approx 1\), the administered drug solution is highly homogeneous; if \(H<1H<1\), the solution is heterogeneous.  
Density gradient (\(\
abla\rho\)) as a measure of drug solution mixing in the intensive care unit (ICU)  
Another way to assess homogeneity is to analyze the spatial density gradient in a drug solution formed by  
\[\
abla\rho=\frac{dp}{dx} \tag{2}\]where \"\(x\)\" represents the spatial coordinate in the direction of mixing. A smaller gradient (\(\
abla\rho\approx 0\)) indicates better homogeneity.  
The author's interest is in the law of Diffusion-Based Homogeneity Over Time. If diffusion is the primary mixing mechanism, the density variation follows [PERSON]'s second law:  
\[D\
abla^{2}\rho=\frac{dp}{dt} \tag{3}\]  
where \(D\) represents the diffusion coefficient. Over time, (something the author is interested in since the preparation of drugs) density differences (\(\rho\)) are reduced, improving homogeneity.
# Clinical Brain Monitoring with Time Domain NIRS: A Review and Future Perspectives
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Main Characteristics**} & \multicolumn{2}{c}{**Methodology**} \\ \cline{2-3}  & **CW** & **FD** & **TD** \\ \hline Practicalities & & Depends on number of channels, from low to & High & Very high \\ Instrument cost & & high & High & Very high \\ Instrument size & Depends on number of channels, wearable & & Bulky but transportable & Bulky but transportable \\ Availability of commercial systems & Yes, multiple companies & Yes, but only one company & Yes, but only one company \\ Technical considerations & & Can be very high (more than 1000) with the & Can be high. Commercial & Lowest. Usually between 1 and 30 \\ Number of channels & latest HD-NIRS systems. Commercial systems & system with up to 500 channels & channels. Commercial system with up to 2 channels \\ Sampling rate & Highest: 100 Hz & Middle: 50 Hz & Lowest: 1-10 Hz (can be higher, \\ Non-contact scanning possibilities & Yes & No & depends on number of channels, \\ Depth sensitivity & Low, depends on source detector distance & Deep, depends on source & wavelengths) \\ Measurement parameters & & & \\ [HOO], [HHb], [HbT] & Yes, changes & Yes, absolute but requires & Yes, absolute, even with a single \\ Absolute values of absorption and & & Yes, but requires multi-distance & Channel \\ scattering coefficients, and path length & No & Yes, but requires multi-distance & Yes \\ SiO & Yes, but requires multi-distance & Yes \\ DOT possible & Yes & Yes & Yes \\ \hline \hline \end{tabular} Abbreviations: CW: continuous-wave; FD: frequency-domain; TD: time domain; SO\({}_{2}\): tissue saturation in oxygen; DOT: diffuse optical tomography; HD-NIRS: high-density NIRS.  
\end{table} 
Table 1: Characteristic of the three NIRS methodologies.
## 2 Data Collections and Method
### Description of the Study Sites and Data Collection Method
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Data**} & \multirow{2}{*}{**Statistics**} & **B** & **D** & **L** & **T** & **PF** & **Q** & **DIS** & **RMR** & **AOp** \\  & & **(m)** & **S (m)** & **(m)** & **(m)** & **(kg/m\({}^{3}\))** & **(kg)** & **(m)** & **RMR** & **(dB)** \\ \hline Training & Count & 81 & 81 & 81 & 81 & 81 & 81 & 81 & 81 & 81 & 81 & 81 \\  & Mean & 0.99 & 1.22 & 60.57 & 2.61 & 0.69 & 0.39 & 1.47 & 190.09 & 57.79 & 67.64 \\  & Min & 0.40 & 0.40 & 51 & 1.20 & 0.28 & 0.21 & 0.10 & 14 & 40 & 51.5 \\  & Max & 2 & 2.50 & 76 & 6.20 & 1.40 & 0.78 & 8.00 & 1120 & 73 & 81.9 \\  & Std & 0.42 & 0.55 & 12.23 & 1.04 & 0.29 & 0.12 & 1.65 & 179.45 & 7.36 & 7.52 \\ \hline Test & Count & 34 & 34 & 34 & 34 & 34 & 34 & 34 & 34 & 34 & 34 \\  & Mean & 0.91 & 1.14 & 56.15 & 2.53 & 0.64 & 0.40 & 1.24 & 213.88 & 58.53 & 67.37 \\  & Min & 0.40 & 0.40 & 51 & 1.2 & 0.28 & 0.27 & 0.13 & 37 & 44 & 53.8 \\  & Max & 2 & 2.5 & 76 & 6.2 & 1.40 & 0.78 & 8.00 & 1344 & 73 & 84.6 \\  & Std & 0.35 & 0.48 & 10.26 & 0.99 & 0.25 & 0.11 & 1.62 & 280.41 & 7.62 & 7.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Brief statistical description of AOp model training and test datasets. Figure 2: (**a**) Geological distributions of the Korean peninsula (adapted from [38]). (**b**) Location of the construction blasting sites in this study.
## 2 Radio Planning Aspects
### Quality of Service (QoS)  
The signal received in mobile communication presents wide random variations that can be modeled by introducing a statistical correction. This statistical correction defines a fade margin (FM). If the FM is equal to zero, radioelectric coverage is only guaranteed in 50% of the locations in the cell (defined in the DTM). The micro scalar quality is defined by the percentage of locations in the cell in which radioelectric coverage is guaranteed. This quality sets the fade margin that is considered in the link budget.  
Assuming a log-normal distribution, the FM can be estimated by:  
\[FM\;(\mathrm{dB})=K(\mathrm{L})\cdot\sigma_{\mathrm{L}} \tag{1}\]  
where \(\sigma_{\mathrm{L}}\) (dB) is the variability of locations in the cell and \(K(\mathrm{L})\) is the normalized distribution abscissas for the percentage of locations (L). The \(K\) parameter is related with the inverse function of Gauss \(G^{-1}(\mathrm{L})\) by the expression:  
\[K(\mathrm{L})=G^{-1}\;(1-\mathrm{L}/100) \tag{2}\]For calculations, a typical value for the UHF band in rural environments is a shadowing standard deviation with locations at 6 dB. If we fix a micro scalar quality of 90%, the fade margin is:  
\[\mathit{FM}\left(\mathrm{dB}\right)=1.28\;6=7.7\;\mathrm{dB} \tag{3}\]  
The macro scalar quality is the percentage of cells with radioelectric coverage within an area. This area could be delimited by a polygon, that represents a municipal or a regional area in our case. In our calculations, we assumed a macro scalar quality of 90% for the regional term and 85% for the municipal term.