esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analytical Model and Evaluation
### Analytical Model  
For modeling the radio and network, we try to resemble the simulations that are explained in the next section. The received signal strength at distance \(d\) can be estimated using Equation (7) with \(\sigma\) being the standard deviation in RSSI measurements due to shadowing. We consider a scenario in which an MN moves from the proximity of static node A, approaching its future parent, static node B that resides in a 10-meter distance, with a constant speed of \(V\) horizontally as depicted in Figure 4(a). The transmission range of the nodes is 5 m. The upward control beacons are assumed to be delivered to the controller, and the particle filter tracks the MN (with positioning error = 0). The downward packet containing the new routing rule from the controller (route-projection packet) is assumed to be received at the MN at time \(T_{r}x\) which follows a normal distribution. The expected signal quality (\(\overline{R_{A}(t)}\)) from node A deteriorates exponentially in time with increasing distance and contrarily rises for node B (see Figure 4(b)).  
Since the distribution of the noise (\(X_{\sigma}\)) follows the Gaussian distribution, the probability of the RSSI to be below a certain threshold (\(T_{\ell}\)) can be derived by:  
\[P(R_{a}(i)<T_{\ell})=Q(\frac{-T_{\ell}+\overline{R_{a}(i)}}{\sigma})\]  
where \(T_{\ell}\) corresponds to the lower threshold of RSSI and Q function is the complementary distribution function of the standard Gaussian distribution.  
\[Q(x)=\frac{1}{\sqrt{2\pi}}\int_{x}^{\infty}\frac{exp(u^{2})}{2}du \tag{10}\]  
Accordingly, the probability of packet loss at time \(t\) can be estimated. If the route-projection packet is not received at time \(t\) (\(T_{rx}>t\)), probability of packet loss equals (\(R_{a}(t)<T_{\ell}\)) meaning that if the link to the old parent gets disconnected. After reception of the route-projection packet, it is the link to parent B that matters and packets can get lost only if \(R_{b}(t)<T_{\ell}\). We assume that the probability of receiving the route projection packet in time follows a normal distribution. Since the reception of the route-projection packet is  
Figure 5: The illustration of the scenario considered for the analytical evaluation—(**a**) mobile node M moves from vicinity of its current parent (S1) to the future parent (S2); (**b**) expected RSSI values with respect to the lower threshold of RSSI when M starts moving from vicinity of \(AP_{a}\) to \(AP_{b}\) (right).  
independent of the link quality, we can derive the packet loss probability by multiplying these two as formulated in Equation (11).  
\[\begin{split} P_{Loss}(t)&=P(R_{a}(t)<T_{\ell})\times P (T_{rx}<t)\\ +& P(R_{b}(t)<T_{\ell})\times P(T_{rx}>t))\end{split} \tag{11}\]  
where the \(P(T_{rx}<t))\) is simply the integral of the probability distribution function of \(T_{rx}\):  
\[P(T_{rx}<t)=\int_{0}^{t}P(T_{rx=\theta})d\theta) \tag{12}\]
###### Abstract
The detection results are seriously affected by the defect location, shape, material and grain size, and other factors. The acoustic emission method is usually used to monitor the changes of parameters throughout the test process and is suitable for laboratory research to summarize the rules, but it is inconvenient to use for coal mine engineering and cannot be used to detect the state of bolts at a certain moment. The stress wave method has become an important method for the study of nondestructive testing of bolts because of its advantages of long measuring distance, low requirements for testing conditions, and easy operation, and a large number of scholars' studies have provided a rich theoretical basis and signal analysis methods, and its applicability has been widely proven by field engineering applications [14; 15; 16]. The stress wave detection method does not lead to any damage to the bolt. When an excitation force is applied to the end of the bolt, a stress wave is generated at the rod end, which propagates along the rod towards the rod end and generates reflected and transmitted waves when it encounters the wave impedance interface, enabling the state of the bolt to be checked and tested by the waveform characteristics. Most of the existing studies on nondestructive testing of bolts have focused on the detection of bolt length [17], anchorage force [18; 19; 20], and anchorage quality [21; 22], which provides data processing and interpretation methods for nonlinear and non-smooth signals [23; 24; 25; 26]. However, in practical engineering applications, the nondestructive testing method of using axial force to assess the quality of anchor support is applicable to newly constructed bolts. For bolts that have been in support for a long time, the test results show that the axial force of the bolts is small, but the bolts may still have safety hazards. During the tensioning process of bolts at low loading speeds, the axial force increases first and then decreases after reaching the necking fracture stage when a fracture is about to occur, which is ignored by the nondestructive testing of the bolt axial force. Based on the importance of bolt nondestructive testing to the safety and stability of coal mine roadways and the application of the stress wave reflection method in bolt nondestructive testing, this paper designs a bolt tensile and stress wave testing test system in order to solve the defects of the nondestructive testing technology of bolt axial force. The deformation characteristics of bolts under axial tensile stress and the propagation characteristics of stress waves in bolts were studied by means of experimental research and nonlinear stress wave data processing. Then, the connection between the two was found, and a nondestructive testing method based on the stress wave method to identify bolts in necking condition was proposed to provide a reference for the continued development of bolt nondestructive testing technology.
## 3 Static Analysis
### Fatigue Life Analysis  
#### Compilation of the Load Spectrum  
As an important factor affecting fatigue analysis, the load spectrum determines the accuracy of the final results of the fatigue analysis. The load spectrum in the fatigue analysis is divided into the time series load spectrum, time step load spectrum, constant amplitude load spectrum, etc. According to the actual working condition of the submarine rudder stock, the static fatigue analysis method is adopted, and the load spectrum is determined as the time series load spectrum.  
In the preparation of the load spectrum, the data relationship between the actual force and time of many mechanisms is not easy to collect, so many scholars use the method of multibody dynamics simulation to prepare the load spectrum. [PERSON] et al. carried out a dynamic simulation analysis for the transmission mechanism of the cross rudder of a sightseeing mini-submarine, and the variation tendency of the contact force between the tiller and piston rod was obtained through simulation results [22]. [PERSON] et al. used numerical models to simulate the dynamic ice-, wind-, and wave-structure interaction, which can then be used as a loading sequence for the fatigue assessment or fatigue testing of the welded joints of offshore wind turbine support structures [23].  
Since it is difficult to obtain data from the actual time history load spectra of the submarine rudder stock, the curves obtained in Section 2 are used as the time series load spectra in this section.
## 3 Results and Discussion
### Delay
#### 3.2.2 Computational Time
 } \\ \cline{3-10}  & & \multicolumn{2}{c}{**n = 1**} & \multicolumn{2}{c}{n = 2} & \multicolumn{2}{c}{n = 1} & \multicolumn{2}{c}{n = 2} & \multicolumn{2}{c}{n = 1} & \multicolumn{2}{c}{n = 2} \\ \hline \multirow{3}{*}{\(\mathrm{INV\_X1M}\)} & \(\mu\)(ps) & 4640 & 3740 & 4530 & 46.9 & 45.1 & 46.9 & 14.1 & 14.2 & 14.2 \\  & \(\sigma\)(ps) & 3470 & 2390 & 2670 & 11.8 & 10.1 & 10.5 & 1.08 & 1.09 & 1.09 \\  & \(\sigma\)/\(\mu\)(\%) & 74.8 & 63.9 & 58.9 & 25.1 & 22.4 & 22.3 & _7.67_ & _7.67_ & _7.67_ \\ \cline{2-10}  & \(\mathrm{MAPE}\) (\%) & - & 29.4 & 13.8 & - & 3.76 & 1.48 & - & 0.718 & 1.24 \\  & Runtime(s) & 13.2 & 0.191 & 0.33 & 13.55 & 0.255 & 0.413 & 14.5 & 0.192 & 0.347 \\ \hline \multirow{3}{*}{\(\mathrm{INV\_X2M}\)} & \(\mu\)(ps) & 2810 & 2230 & 2620 & 32.2 & 30.9 & 31.9 & 9.18 & 9.14 & 9.18 \\  & \(\sigma\)(ps) & 2060 & 1190 & 1320 & 7.74 & 5.99 & 6.19 & 0.604 & 0.574 & 0.577 \\  & \(\sigma\)/\(\mu\)(\%) & 73.4 & 53.1 & 50.4 & 24 & 19.4 & 19.4 & 6.58 & 6.28 & 6.28 \\ \cline{2-10}  & \(\mathrm{MAPE}\) (\%) & - & 21.9 & 19.3 & - & 3.65 & 3 & - & 0.77 & 0.602 \\  & Runtime(s) & 13.34 & 0.226 & 0.537 & 13.55 & 0.242 & 0.366 & 14.41 & 0.192 & 0.368 \\ \hline \multirow{3}{*}{\(\mathrm{INV\_X3M}\)} & \(\mu\)(ps) & 2350 & 1890 & 2310 & 25.1 & 24.1 & 25.1 & _7.67_ & _7.64_ & _7.66_ \\  & \(\sigma\)(ps) & 1760 & 1260 & 1400 & 6.19 & 5.44 & 5.63 & 0.247 & 0.252 & 0.253 \\  & \(\sigma\)/\(\mu\)(\%) & 74.9 & 66.5 & 60.8 & 24.7 & 22.5 & 22.4 & 3.22 & 3.3 & 3.31 \\ \cline{2-10}  & \(\mathrm{MAPE}\) (\%) & - & 31.7 & 12.4 & - & 3.75 & 1.41 & - & 0.73 & 0.567 \\  & Runtime(s) & 13.8 & 0.212 & 0.458 & 13.98 & 0.209 & 0.379 & 14.97 & 0.205 & 0.358 \\ \hline \multirow{3}{*}{\(\mathrm{INV\_X4M}\)} & \(\mu\)(ps) & 1920 & 1540 & 1840 & 21.5 & 20.4 & 21.2 & 6.89 & 6.91 & 6.93 \\  & \(\sigma\)(ps) & 1430 & 931 & 1040 & 5.21 & 4.37 & 4.52 & 0.235 & 0.233 & 0.234 \\ \cline{1-1}  & \(\sigma\)/\(\mu\)(\%) & 74.4 & 60.5 & 56.3 & 24.2 & 21.4 & 21.4 & 3.41 & 3.37 & 3.38 \\ \cline{1-1} \cline{2-10}  & \(\mathrm{MAPE}\) (\%) & - & 27.2 & 15.1 & - & 5.26 & 2.09 & - & 0.383 & 0.558 \\ \cline{1-1}  & Runtime(s) & 13.98 & 0.432 & 0.409 & 13.82 & 0.201 & 0.42 & 14.07 & 0.204 & 0.442 \\ \hline \multirow{3}{*}{\(\mathrm{INV\_X8M}\)} & \(\mu\)(ps) & 1310 & 1050 & 1270 & 15.7 & 15.5 & 16 & 5.37 & 5.35 & 5.35 \\ \cline{1-1}  & \(\sigma\)(ps) & 968 & 641 & 716 & 3.49 & 2.95 & 3.05 & 0.11 & 0.0817 & 0.0821 \\ \cline{1-1}  & \(\sigma\)/\(\mu\)(\%) & 74.1 & 60.8 & 56.5 & 22.3 & 19 & 19 & 2.05 & 1.53 & 1.53 \\ \cline{1-1} \cline{2-10}  & \(\mathrm{MAPE}\) (\%) & - & 26.8 & 14.8 & - & 2.
# Parameter Study for the Ice Adhesion Centrifuge Test
## 4 Discussion
The roughness and chemical composition of the solid are the determining factors for the SFE and an optimum for the icephobic performance was observed for PUR C25 (18.0 mN/m). A further decrease in PTFE-tape (15.1 mN/m) is related to an increase in the roughness, resulting in a higher ice adhesion strength. This was already published for other polymeric coatings during the degradation process [13]. However, to our knowledge, this has not been reported by other research groups for icephobic properties. Furthermore, the often-highlighted parameter contact angle hysteresis (CAH), as a suitable correlating parameter for the icephobic performance [9; 11; 15], follows the same trend as the SFE. The identified findings might not be valid for superhydrophobic surfaces (e.g., [26; 27; 28; 29; 30]) or materials following or inspired by the slippery liquid-infused porous surfaces (SLIPS) approach (e.g., [24; 31; 32; 33]), as they can generate \"extreme\" values, constituting exceptions to the general rule. Moreover, the elastomeric properties of coating materials are discussed as relevant parameters for the ice adhesion strength [12] but should not be dominant in this study due to the low (<100 \(\upmu\)m) and comparable material thicknesses. In general, the data point distribution for ice adhesion tests is comparably high and standard deviations \(\geq 20\%\) are often reported (e.g., [8; 15; 25]). This also applies to the current study, specifically for the low ice adhesion surface PUR C25, with data points below 50 kPa. One reason for this is the limited data resolution in the lower measurement area. Further reasons, such as the limited material durability in repeated tests or disturbing factors (e.g., temperature fluctuation, mechanical disturbance of the ice-test surface interface, contaminations), were excluded in this study as much as possible. All measures to reduce the data scattering need to be considered, and the reduction in the acceleration speed of the centrifuge should be considered in order to increase the test resolution and improve the data quality.
## 4 Hydrogen Port Fuel Injection
### Pilot-Fuel-Ignited Engine with Port Hydrogen Injection  
Ignition of a port-injected hydrogen charge can be triggered by a DI of pilot diesel fuel, as commonly employed in converted CI engines. One of the main advantages of this mode is its flexibility of hydrogen share in the total energy input. Usually, the substitution rate of diesel fuel with hydrogen is limited by the excessive rate of pressure rise and end-gas knocking of the well premixed charge. Also, at high hydrogen energy ratio, the increased \(\phi\) raises the tendency for pre-ignition, similar to SI engine applications. As reviewed elsewhere, a majority of previous investigations were limited to a hydrogen energy share of \(\sim\)30-40% at low and medium loads and \(\sim\)6-25% at high load [60; 61]. While a study by [PERSON] et al. [62] demonstrated a utilization of 97% hydrogen energy share in this engine mode is possible in a single-cylinder compression-ignition engine at 1.9 bar BMEP, a trade-off in the engine efficiency was also reported with increased hydrogen share. In terms of pollutant emissions, most studies show that this working mode cannot significantly alleviate the emissions of CO and particulate matter, while the emissions of CO\({}_{2}\) in first order decrease proportionally to the energy substitution rate. Nevertheless, over 50% of CO and smoke emission reduction relative to diesel operation has been reported at substitution rate of 46% [63]. The effect of diesel substitution with hydrogen on \(\mathrm{NO_{x}}\) emissions is still not fully understood and contradictory engine testing results were reported [60]. For instance, [PERSON] et al. [64] claimed that \(\mathrm{NO_{x}}\) emissions can be lowered if hydrogen substitution is more than 30% of energy share, which was attributed to a reduced peak combustion temperature. To the contrary, [PERSON] et al. [63] reported that increased \(\mathrm{NO_{x}}\) emissions with hydrogen addition under all tested conditions, from 0-46% hydrogen energy fraction. This was attributed to the pathway of unburned hydrogen, oxidized to \(\mathrm{HO_{2}}\), boosting the conversion of NO to \(\mathrm{NO_{2}}\)[60]. This shows that the underlying mechanisms, governing the efficiency and pollutant formation in this combustion mode, are still not well understood. Undoubtedly, in the current state of technology, this combustion mode cannot fulfill the future emission legislation. The potential for the substitution of conventional fuel is also limited. Different injection strategies and charge cooling strategies should be further developed to improve \(\mathrm{NO_{x}}\) emissions and pre-ignition problem leading to a higher hydrogen share, respectively, for this engine mode [60]. While hydrogen PFI is relatively straightforward to implement with the current engine technology and can be used in short term to promote hydrogen ICE development, more advanced combustion strategies are required to overcome the limitations of PFI in achieving high engine loads and reducing emissions.
## 4 Experimental Results
### Performance Analysis
#### 4.3.2 Quantitative Evaluation  
Cross validation (CV) is a common method to evaluate the performance of machine-learning-models on limited data samples [38]. Then, the holdout CV is adopted to evaluate the performance of the proposed scheme. For each holdout CV, these 400 psoriasis images were partitioned into training, validation, and testing sets. The ratio among the training, validation, and testing sets was 80:10:10, respectively. Here, we performed the holdout CV three times. Figure 9 illustrates the image segmentation results of the proposed scheme after CV. As shown in Figure 9, the average precision, recall, accuracy, and F1-score values of the proposed scheme were at least 94.74%, 93.17%, 96.12%, and 93.77%, respectively. These performance indexes were higher than 93% after CV. Since the F1-score values of the proposed scheme were at least 93.77%, the result shows that most pixels in the unconstrained psoriasis images were correctly classified using the proposed scheme. These experimental results demonstrate that the proposed scheme can not only detect the psoriasis and normal skin regions but also classify these pixels into three categories effectively for unconstrained psoriasis images.  
As for object localization, IoU was determined as 0.5 for computing the mAP rate. The mAP rates of the proposed scheme were 92.53%, 96.16%, and 85.9% after CV. The results show that the proposed scheme can locate and recognize the psoriasis regions and the normal skin areas successfully for unconstrained psoriasis skin images.  
During CV2, these images in the testing dataset were also used to perform robustness evaluation. Table 2 shows the experimental results of the proposed scheme for robustness evaluation. As shown in Table 2, the average precision, recall, accuracy, and F1-score values of the proposed scheme were at least 93.05%, 81.62%, 97.34%, and 84.34%, respectively, for psoriasis regions after noise insertion or blurring. Since these performance indices were at least more than 81.6%, the results show that the proposed scheme still located psoriasis regions effectively even when blurring or noise insertion occurred. For normal skin areas, the average precision, recall, accuracy, and F1-score values of the proposed scheme were at least 96.31%, 97.14%, 96.72%, and 96.57%, respectively, after noise insertion or blurring. These performance indices were at least more than 96.3%, so the results show that the proposed scheme located the normal skin areas correctly for blurred or noisy psoriasis images. For psoriasis regions and normal skin areas, the average precision, recall, accuracy, and F1-score values of the proposed scheme were 95.86%, 95.62%, 97.03%, and 95.73%, respectively. The F1_score reached up to 95.73% for psoriasis regions and normal skin areas  
Figure 9: Image segmentation results after cross validation.  
even when blurring or noise insertion occurred. These experimental results demonstrate that the proposed scheme can distinguish pixels in psoriasis and normal skin regions from the background pixels regardless of whether blurring or noise insertion exists.
## 2 Related Work  
Triples extraction is one of the core tasks in the field of information extraction. It has been continuously researched for more than 20 years. Feature engineering [3], kernel methods [4; 5; 6], and graph models [7] have been widely used, and some staged results have been achieved. With the development of deep learning, neural network models have achieved breakthroughs in triples extraction.  
Named entity recognition is a prerequisite for relation prediction. A series of results have been achieved using machine learning algorithms to automatically recognize named entities in sentences. For named entity recognition tasks, CRF [8] and Bi-LSTM-CRF [9] are two well-known methods.  
For traditional relation classification tasks, it is effective to use feature engineering and machine learning algorithms to achieve relation classification, such as bootstrapping [10] and SVM [11]. In recent years, with the development of deep learning, many studies based on neural networks [12] have achieved good performance. Among these, on public datasets for specific relation extraction tasks, the popular methods of recent years have been Att-based LSTM [13] and Att-based CNN [14; 15; 16].  
With further research, some scholars have combined named entity recognition and relation extraction to put forward some joint entity-relation extraction models [17]. Related research shows that the better-performing method uses a novel labeling strategy for jointly modeling of entity-relations [18; 19].  
In addition to the triple extraction of a specific relation, extraction of triples for open relations from unstructured text has also been recognized as a highly important task. In recent years, preliminary results have been obtained for the automatic extraction of triples within unstructured Chinese text. The more well-known methods are CORE [20], UnCORE [21], and DSNFs-based ORE [22]. In addition, with the advent of the attention mechanism [23] and large-scale pre-training language model BERT [24], many new records have been set for natural language processing tasks. Some studies have used these approaches to complete relation classification [25; 26; 27] and have achieved a higher accuracy.  
In addition, [PERSON] [28] proposed a multi-layered encoder-decoder framework to generate relation tuples. Inspired by the above studies, in this paper we propose an open domain Chinese triples hierarchical extraction method.
# Graphene-Based Cylindrical Pillar Gratings for Polarization-Insensitive Optical Absorbers
## 2 Materials and Methods  
Figure 1a shows the sketch of the proposed 2D dielectric grating. It consisted of a periodic array of polymethyl-methacrylate (PMMA) cylindrical pillars deposited on a tantalum pentoxide (Ta\({}_{2}\)O\({}_{5}\)) waveguide. A slab of silicon dioxide (SiO\({}_{2}\)) was used as a substrate. The monolayer graphene was placed on top of the waveguide under the PMMA pillars. The proposed 2D dielectric grating can be realized using nanoimprint lithography (NIL). This technology enables the faster fabrication of polymeric-based dielectric gratings at a low cost. The choice of the proposed 2D configuration was based on the possibility of an insensitive polarization behavior at normal incidence for the GMRs that are excited in the Ta\({}_{2}\)O\({}_{5}\) dielectric waveguide. A 20 nm thick buffer layer of PMMA was added on top of the graphene layer (and under the cylinder) to take into account the fabrication tolerances introduced by the NIL fabrication step.  
The thickness of the monolayer graphene was equal to 0.34 nm. A thin buffer layer of PMMA (20 nm thick) was also placed over the monolayer graphene considering the quality of graphene was not affected by pillar fabrication. The geometrical parameters of the device are detailed in Figure 1b. The thickness \(t_{Ta2O5}\) of the Ta\({}_{2}\)O\({}_{5}\) slab and the PMMA pillar height \(t_{PMMA}\) were initially set equal to 150 nm and 600 nm, respectively. The PMMA pillars have symmetric periodicity both in \(x\)- and \(y\)-directions and the value chosen was equal to 600 nm at the start. The radius \(r\) of the cylindrical PMMA pillars was chosen as 250 nm.  
The 2D graphene-based dielectric gratings were simulated by means of RSoft-DiffactMOD that implements a Fourier-space method, the rigorous coupled-wave analysis (RCWA), to solve scattering from periodic structures. The refractive indices for PMMA, Ta\({}_{2}\)O\({}_{5}\), and SiO\({}_{2}\) were retrieved from [PERSON] et al. [25]. For monolayer graphene, we used the complex refractive index \(n=3+\text{jC}_{1}\)\({}^{*}\)\(\uplambda\)/3 reported in [26] at visible frequencies where C\({}_{1}\) was equal to 5.446 \(\upmu\)m\({}^{-1}\) and \(\uplambda\) was the free space wavelength to take into account the losses.