esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Investigation of Erosion Wear in the Hydraulic Amplifier of the Deflector Jet Servo Valve
## 4 Results and Discussion  
### Distribution of Erosion in Valve  
As already mentioned, it is speculated that significant erosion wear is likely to happen at some places confronted with high-speed flow or jet impact. According to the CFD-based simulation, an overall profile of the erosion for oil of NAS 7 is represented in Figure 6, and the particle trajectories are shown in Figure 7. The erosion wear in the hydraulic amplifier can be graded into four levels. The wear of Level 1, with an erosion rate level of \(10^{-8}\) kg/(m\({}^{2}\cdot\)s), appears on the shunt wedge sustaining the major jet impact and some of the receiver surfaces close to the shunt wedge (see Figures 8-10). What it means is that the erosion in the vicinity of the shunt wedge will be much more severe than elsewhere and becomes our main concern.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Densities of oil \(\rho\) (kg/m\({}^{3}\)) & 850 \\ Oil viscosity \(\mu\) (kg\(\cdot\)s/m) & data \\ Densities of wall \(\rho_{w}\) (kg/m\({}^{3}\)) & 0.0085 \\ Densities of particles \(\rho_{p}\) (kg/m\({}^{3}\)) & 8030 \\ Average diameter of particles \(\delta_{m}\) (mm) & 1550 \\ Pressure of inlet \(P_{int}\) (MPa) & 21 \\ Pressure of outlet \(P_{out}\) (MPa) & 0 \\ Total flow rate \(Q_{l}\) (kg/s) & \(4.1858\times 10^{-11}\) \\ Shape factor \(\phi\) & 0.6 \\ Reference erosion rate \(K(H_{ww})^{k_{1}}\) (kg/kg) & \(3.124\times 10^{-4}\) \\ Wall material Vickers hardness \(H_{ww}\) (GPa) & 4.0 \\ Material hardness exponents \(n_{1}\) & 0.8 \\ Particle properties exponents \(n_{2}\) & 1.3 \\ Velocity exponent \(k_{2}\) & 2.35 \\ Diameter exponent \(k_{3}\) & 0.19 \\ Reference diameter \(d^{*}\) (\(\mu\)m) & 0.326 \\ Reference velocity \(u^{*}\) (m/s) & 104 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Calculation conditions of erosion wear simulation.  
Figure 6: Overall erosion rate distribution in deflector jet mechanism.  
Figure 8: Erosion rates on shunt wedge.  
Figure 7: Particle trajectories.  
Figure 9: Erosion rates of lower surface of the receiver.  
The wear of Level 2 has the erosion rates with an order of magnitude of 10\({}^{-9}\) kg/(m\({}^{2}\cdot\)s), happening at locations illustrated in Figures 11-14. It follows that the effects of erosion on the jet orifice, the side walls of the deflector and the guide curve of the receiver are much less than that on the shunt wedge, although these effects have conformed to previous expectations. Weaker wear is defined as Level 3, with the erosion rate level 10\({}^{-10}\) kg/(m\({}^{2}\cdot\)s), and happens mainly near the jet orifice, as depicted in Figure 15. In addition, the wear with erosion rates less than 10\({}^{-10}\) kg/(m\({}^{2}\cdot\)s) is classified as Level 4 and has a minimal effect on the valve.  
Figure 11: Erosion rates of jet orifice.  
Figure 12: Erosion rates of the receiver’s guide curve.
## 3 Results and Analysis
### Effect of Water Content on Peak Penetration Force  
As shown in Figure 12a, when the water content was 0, the peak penetration force was 340 kN during the first indentation into the granite specimen. As the water content increased, the peak penetration force decreased gradually. When the water content increased to 0.11%, the corresponding peak penetration force was 312 kN. Under other water content conditions, the peak penetration forces in the first indentation process also showed a decreasing trend. When the water content increased to 0.43%, the peak penetration force was only 257 kN. In the second indentation process, the variation trend of the peak penetration force with the water content was similar to that of the peak penetration force in the first indentation process. When the water content was 0, the corresponding peak penetration force was 292 kN, and when the water content was increased to 0.43%, the peak penetration force was only 196 kN. At the same time, it can be seen from Figure 6 that under the same water content conditions, the peak penetration force in the second indentation was smaller than that in the first indentation. For example, when the water content was 0, the peak penetration force in the first indentation was 340 kN; when the water content was 0.43%, the peak penetration force in the second indentation was only 200 kN. As the water content gradually increased, the peak penetration force  
Figure 11: Surface crack distributions of the granite specimens at different water contents: (**a**) 0; (**b**) 0.11%; (**c**) 0.22%; (**d**) 0.32%; and (**e**) 0.43%.  
gradually decreased. This was because the water content increase reduced the fracture toughness and the strength of the rock, making the rock more susceptible to breakage.
## 4 Results
### Static Results
#### 4.1.4 TTFF in an Ideal Position in a Vehicle  
The last static measurement was an average of 10 TTFF measurements performed at the front of the car, specifically at the front dashboard-_Face up_ position, as shown in Figure 3a. In this case, we decided to conduct the TTFF measurements for the static position inside the car when it was parked in an open area because it would provide a preview of the results when conducting the dynamic measurements. The TTFF test was not deemed applicable inside the building since IoT devices are often operated outside. As shown in Figure 8, although both results were very similar, the average of the linearly polarized antenna was slightly better than the average obtained with the circularly polarized antenna. Regarding the maximum time it takes to obtain the fix, both devices showed similar performance, but the linearly polarized antenna was 0.9 s better (less seconds to connect). The last comparison in Figure 8 shows the minimum time it takes to perform the fix, as we can see, both devices behaved identically, with a slight difference of fewer than 0.2 s in favor of the linear polarization.  
Figure 7: Average dilution of precision or DOP for each device in a static position inside a building with large windows. The position of this device was moved toward the interior of the building. The lower the DOP, the better.
# Performance Analysis of P2P Networks with Light Communication Links: The Static Managed Case
## 2 Related Work
In addition, all these analyses can be potentially applied in static scenarios such as hospitals, robotized processes, and museums [23]. On the other hand, an important branch has emerged in the use of optical and radio frequency technologies; although there are currently no works related to the comparison of their performances in many metrics, major studies focus on the formation and development of hybrid networks between these technologies, as can be seen in works about LiFi and WiFi hybrid networks [24], which is based on the AP selection of the most convenient technology in energy consumption, taking into account the number of nodes in IoT networks and preferably looking for static APs. Likewise, the work presented by [PERSON] [25] describes load-balancing processes in LiFi and WiFi channels for the handover process (processes approached with various techniques like self-adaptive medium access control (SA-MAC) protocols [26] or hidden Markov models (HMMs) [27]). In addition to focusing on LiFi and WiFi networks, some other works model and measure metrics such as permanence times (performing simulations with Monte Carlo, also capturing the mobility of users in the system [28]), throughput, delay (using the enhanced distributed channel access (EDCA) protocol for channel selection [29; 30]), and packet drop rate (analyzing the MAC layer to improve throughput across the network and calculating collision probability and packet drop rate [31]), among others. In our work, since nodes are static, and the download procedure is fully controlled by the administrator, we do not consider mobility or packet drop rate. Conversely, we provide average file download times in different architectures and scenarios. Finally, and keeping in mind that there are not many works about LiFi performance and use cases, this article proposes a novel comparison between pre-existing scenarios, such as those in which WiFi is used, and unprecedented scenarios, which use LiFi in P2P and centralized environments, describing the characteristics and considerations of each of these systems and providing guidance to enable the use of new alternatives for the exchange of large amounts of information. Consequently, new concepts of LiFi networks are added to the literature, supported by quantitative results.
# Automatic Speech Recognition Advancements for Indigenous Languages of the Americas
###### Abstract
Despite the difficulty of the task, ASR for low-resource languages has attracted the attention of the scientific community, who have proposed new approaches to this issue, including speech augmentation techniques [21; 22; 23], semi-supervised models [24; 25; 26; 27; 28], fully unsupervised learning methods [29], transfer learning techniques [30; 31; 32], and specialized neural network architectures [33], among other solutions. In this paper, we present our winning approach in the ASR subtask of the America's Challenge competition of NeurIPS 2022 [34]. For this purpose, we trained and optimized an ASR system for each of the following languages: Bribri, Guarani, Kotiria, Wa'ikhana, and Quechua. This marks the first time an ASR model has been developed for the Wa'ikhana and Kotiria languages and we report the first results for these languages in the literature. We addressed the challenge of limited training data by leveraging a semi-supervised model and subsequent fine-tuning using the Wav2 vec2.0 framework and applying speed augmentation techniques. The training phase involved meticulous model selection based on the optimization of performance metric hyperparameters. Additionally, we constructed comprehensive n-gram language models using text corpora for the decoding, but the Greedy Search algorithm, supplemented with heuristic corrections, showed better preciseness. Our ASR system showed an average character error rate (CER) of 26.85, thereby achieving the best solution in the competition. The rest of the paper is organized as follows: Section 2 reviews previous research carried out in ASR for Indigenous languages. Section 3 details the experimental setup adopted for the architecture, the techniques applied during training, and the dataset creation. Section 4 shows the experimental results for our best models with the most suitable hyperparameters and configurations. Finally, in Section 5, we discuss the results and extract the highlights of this research.
## 3 Results and Discussion
### Yttrium Ions Desorption  
The desorption process of loaded Y(III) from AHIBC adsorbent was performed to reuse the adsorbent many times. Then, 1.0 M HCl solution was used to regenerate the AHIBC adsorbent, and 1:50 as S:L phase ratio for agitation time of 60 min at 25 \({}^{\circ}\)C temperature. The adsorption-desorption process occurred many times, till the efficiency of desorption decreased from 97% to 80% after eight consecutive rounds, proving the notable adsorption constancy of the AHIBC in the recovery of Y(III).
# Fundamental Matrix Computing Based on 3D Metrical Distance
## 3 Traditional Algorithm
### Essential Matrix Computation and Decomposition  
The relationship between \(F\) and essential matrix \(E\) is as listed by Equation (4).  
\[E=K^{\prime T}FK \tag{4}\]  
If \(K\), \(K^{{}^{\prime}}\) are intrinsic parameter matrices of two cameras. \(E\) implies the relative position of a two-camera pair if \(R\) and \(t\) mean the disposition of two cameras. Thus \(E\) can be represented as Equation (5).  
\[E=[t]_{\times}R \tag{5}\]  
For a given essential matrix \(E\) which can be decomposed by, Equation (6)  
\[E=U~{}diag(1,1,0)~{}VT \tag{6}\]  
and the first camera matrix \(P_{E}=[~{}I~{}|~{}0~{}]\), there are four possible choices for the second camera matrix \(P_{E}^{\prime}\), namely  
\[P_{E}^{\prime}=\begin{bmatrix}UWV^{T}|u_{3}]&[UWV^{T}|-u_{3}]\\ [UW^{T}V^{T}|u_{3}]&[UW^{T}V^{T}|-u_{3}]\end{bmatrix} \tag{7}\]  
where \(u_{3}\) is the third column of the \(U\), but only one of the four choices is correct. As we know, reconstructed \(X\) by the correct \(P_{E}^{\prime}\) should locate in front of two cameras. Choosing one or more \(X_{i}\) to check if they are in front of two cameras is the way to select the correct \(P_{E}^{\prime}\) out. This is the key point that our method can show the 3D metric reconstructed scene because our method can estimate \(E\) to obtain \(P_{E}^{\prime}\) through the given \(F\).
# Provably Secure ECC-Based Anonymous Authentication and Key Agreement for IoT
## 5 Security Analysis
### Automatic Formal Verification  
The security of the proposal is formally validated with ProVerif [5]. Table 3 illustrates the codes of \(S\), where \(schs\) is a secret channel used for \(S\) registration, and \(ch\) is a public channel used for \(S\) and \(SP\) authentication. Based on the following results, it can be concluded that both the authentication process and the session key are secure from adversary attacks.  
Here are the results of the queries in ProVerif:  
\begin{table}
\begin{tabular}{l} \hline \hline let S = \\ new rs:bitstring; \\ let Rs = Mul(rs, P) in \\ out (schs, (IDs, Rs)); \\ in (schs, (vIDsp:bitstring,vPKsp: bitstring, vPKs: bitstring,vrtas:bitstring)); \\ let ks = add (rs, vrtas) in \\ let PKs= Mul (ks, P) in \\ if PKs = vPKs then \\ let WSsp = Mul (ks, vPKsp) in \\ ( \\ event startAuthsp; \\ let As = Mul(xs,PKs) in \\ let Bs = Mul(xs, WSSp) in \\ let EDDs = xor (IDs, Bs) in \\ new Tseeds:bitstring; \\ let Ts = generate. Timeline(TSeeds) in \\ let Vs = Hash(con (con (WSSp, Ts), IDs),Bs))in \\ out (ch, (As, EIDs, Ts, Vs)); \\ in (ch, (vAsp: bitstring, vTyp: bitstring, vVsp:bitstring)); \\ let Cs = Mul (mul (vs, ks), vAsp) in \\ let SSRs = Hash(con (con (lDs,lDSp), Bs), Cs)) in \\ let Vsp = Hash(con (con (on (WSSp,vIsp),lDSp), SSKs)) in \\ if Vsp = vVsp then \\ event endAuths; \\ 0 \\ ). \\ \hline \hline \end{tabular}
\end{table}
Table 3: Codes for end device \(S\).
## 3 Results and Discussion
### Root and Shoot Dry Matter  
Figure 4 shows the impact of different salinity levels on shoot and root dry matter of three _Chloris gayana_ genotypes. The highest shoot dry matter (71.2 g/pot) was obtained in ILRI-6633 genotype whereas the lowest (60.3 g/pot) was recorded in ILRI-7384. The reduction in shoot dry matter was more pronounced at salinity levels of 15 dSbm\({}^{-1}\) and 20 dSbm\({}^{-1}\). The overall reduction in shoot dry matter from 0 to 20 dSbm\({}^{-1}\) was found to be 18.0 g/pot for ILRI-6633, 21.5 g/pot for ILRI-7384, and 27.5 g/pot for CV-massaba. This demonstrates that ILRI-6633 genotype has the lowest reduction in shoot dry matter with the increasing salinity.  
The root dry matter ranged between 10.4-17.4 g/pot at 0 dS m\({}^{-1}\), 14.2-18.4 g/pot at 5 dS m\({}^{-1}\), 12.5-18.3 g/pot at 10 dS m\({}^{-1}\), 12.2-16.4 g/pot at 15 dS m\({}^{-1}\), and 10.0-12.2 g/pot at 20 dS m\({}^{-1}\) (Figure 4b). For all salinity levels, the highest root dry matter was achieved for ILRI-6633 followed by CV-massaba. The maximum root dry matter for ILRI-6633 and CV-massaba was obtained at low to medium salinity levels (5-10 dS m\({}^{-1}\)). This suggests that the performance of ILRI-6633 with regard to root dry matter was superior to the other two genotypes. The average reduction in root dry matter ranged from 11.3-16.3 g/pot with an increase in salinity from 5-20 dS m\({}^{-1}\). The declining trends in the root dry matter with increasing salinity levels can be ascribed by the fact that in saline soils,  
Figure 4: Effect of different salinity levels on (**a**) shoot dry matter (LSD = NS; CV = 23.75) and (**b**) root dry matter (LSD = 3.53; CV = 26.15) of three selected Chloris gayana genotypes.  
plants spend more energy to get water and nutrients from the soil. This situation negatively affects the yield and the quality of the plant [3; 35; 36; 37; 38].
# Localized Necking Model for Punching Fracture Simulation in Unstiffened and Stiffened Panels
## 2 Plasticity and Fracture Models
### Fracture Model  
#### 2.3.1 Hosford-Coulomb Fracture Model  
[PERSON] and [PERSON] [5] proposed a micromechanically motivated HC model to predict ductile fracture initiation under proportional loading. This model is motivated by the  
Figure 1: Geometric representation of the associated flow rule in principle stress space.  
Mohr-Coulomb criterion, which is expressed by combining the Hosford equivalent stress, \(\overline{\sigma}_{HC}\), and normal stress acting on the plane of maximum shear stress:  
\[\sigma_{HC}+c(\sigma_{1}+\sigma_{3})=b, \tag{16}\]  
where \(b\) is the critical value, and \(c\) is the friction coefficient in the Mohr-Coulomb criterion. The Hosford equivalent stress is expressed as follows:  
\[\sigma_{HC}=\left\{\frac{1}{2}\big{(}(\sigma_{1}-\sigma_{2})^{a}+(\sigma_{2}- \sigma_{3})^{a}+(\sigma_{1}-\sigma_{3})^{a}\big{)}\right\}^{\frac{1}{a}}. \tag{17}\]  
Equation (17) can be transformed to Haigh-Westergaard coordinates, \(\big{(}\overline{\sigma},\ \eta,\ \overline{\theta}\big{)}\):  
\[\sigma_{f}\big{[}\eta,\overline{\theta}\big{]}=\frac{b}{\left\{\frac{1}{2} \big{(}(f_{1}-f_{2})^{a}+(f_{2}-f_{3})^{a}+(f_{1}-f_{3})^{a}\big{)}\right\}^ {\frac{1}{2}}+c(2\eta+f_{1}+f_{3})}. \tag{18}\]  
Finally, the fracture strain for proportional loading, \(\overline{\varepsilon}_{f}^{pr}\), can be expressed in a mixed stress/strain space \(\Big{(}\eta,\ \overline{\theta},\ \overline{\varepsilon}_{f}\Big{)}\) using the inverse of the isotropic power-law type hardening law:  
\[\overline{\varepsilon}_{HC}^{pr}\big{[}\eta,\overline{\theta}\big{]}=b(1+c)^ {1/n_{HC}}g\big{[}\eta,\overline{\theta}\big{]}, \tag{19}\]  
\[g\big{[}\eta,\overline{\theta}\big{]}=\left(\left\{\frac{1}{2}\big{(}(f_{1}- f_{2})^{a}+(f_{2}-f_{3})^{a}+(f_{1}-f_{3})^{a}\big{)}\right\}^{\frac{1}{2}}+c(2 \eta+f_{1}+f_{3})\right)^{\frac{1}{n_{HC}}}. \tag{20}\]  
The HC model has four parameters: \(a,\ b,\ c,\) and \(n_{f}\). The transformation coefficient, \(n_{f}\), is set to 0.1, for general steel [5].  
Fracture models based on equivalent plastic strain are known to be affected by the loading paths for materials and structures. The linear damage accumulation law was adopted for considering the stress history. For non-proportional loading paths, the HC fracture model is written as follows:  
\[D_{HC}=\int_{0}^{\overline{\varepsilon}_{f}}\frac{\overline{\varepsilon}_{p} }{\overline{\varepsilon}_{HC}^{pr}\big{[}\eta,\overline{\theta}\big{]}}d \overline{\varepsilon}_{p}, \tag{21}\]  
where \(\overline{\varepsilon}_{f}\) is the fracture strain. Fracture initiation occurs when the damage indicator \(D\) reaches unity.
## 2 Development of the Numerical Model
### Geometric Models  
The geometric model established in this work is shown in Figure 2. The size of the confined space is 4 m \(\times\) 2.8 m \(\times\) 2.8 m. The leakage hole appears on the gas phase pipeline connecting the indoor unit and the outdoor unit of the refrigeration system. The leakage hole is assumed to be circular with a diameter of 3 mm, the height of the leakage hole is 1.9 m, the distance from the leakage hole to the xy wall is 0.115 m, the distance from the leakage hole to the zy wall is 0.25 m, the confined space is a non-ventilated environment with closed doors and windows, and the refrigerant is leaking vertically upwards. In addition, to simplify the calculation, other assumptions given in this paper are as follows:  
(1)  
The refrigerant leakage process is regarded as the leakage of gaseous refrigerant, and the physical properties of the leaking refrigerant are calculated using the physical properties at room temperature (25 \({}^{\circ}\)C) and atmospheric pressure (0.1 MPa);  
(2)  
The leakage process is regarded as uniform leakage, and the leakage rate is the average rate of the working medium in the leakage process.  
The refrigerant leakage and diffusion processes were simulated using ANSYS 2021 R1 software. The study focused on four refrigerants: R717, R290, R32, and R1234 yf. For comparative analysis, R22 and R744 were also included. (The viscosity of R22 and R32 is close, and the density of R290 and R744 is close, which is conducive to better analysis of the influence of the refrigerant's physical parameters on the diffusion process and con centration distribution of the refrigerant after leakage into a limited space in the vertical upward direction). The physical parameters of the refrigerant are shown in Table 1.  
At the same time, the refrigerant is regarded as a compressible ideal gas, and the leakage rate of the refrigerant leakage process is further determined. Based on the real refrigerant leakage diffusion model established by [PERSON] et al. [18], the saturation pressure corresponding to different refrigerants at room temperature and the leakage rate at a 3 mm aperture were calculated. The calculation results are shown in Table 2. While the actual leakage rate varies slightly for different refrigerants under the same aperture size and room temperature conditions, the purpose of this paper is to investigate the differences in the diffusion and deposition characteristics of different refrigerants under the same leakage. A constant flow rate of 1.2 g/s that can be achieved by all the refrigerants in this paper during the leakage process was selected to facilitate the comparative analysis of the diffusion and deposition characteristics.
## 2 Preliminaries
### Definition of DSSE  
A DSSE scheme consists of three algorithms: **Setup**, **Update**, and **Search**. Let \(\lambda\) denote the security parameter, and it is implicit input for all algorithms. DB \(=\{(ind_{i},w_{i})\}\) refers to the database, where \(ind\) denotes file identifier and \(w\) denotes the keyword. EDB denotes an encrypted database, and \(\sigma\) denotes the state of the client. op denotes the type of update operation (addition or deletion). Each algorithm is defined as follows:  
* (EDB, \(\sigma\)) \(\leftarrow\)**Setup(\(1^{\lambda}\), DB): **Setup** takes as input the security parameter \(\lambda\) and the initial database DB.
* (\(\sigma^{\prime}\), EDB\({}^{\prime}\)) \(\leftarrow\)**Update**(\(\sigma\), op, ind; EDB): **Update** is a client-server protocol, where the client takes as input the state \(\sigma\), a file identifier ind, and type of operation op \(=\{add,del\}\); and the server takes as input the encrypted database EDB. The client outputs updated state \(\sigma^{\prime}\), and the server outputs an updated encrypted database EDB\({}^{\prime}\) as a result.
* (\(\sigma^{\prime}\), DB(\(w\)); EDB\({}^{\prime}\)) \(\leftarrow\)**Search**(\(\sigma,w\); EDB): **Search** is also a client-server protocol, where the client takes as input the state \(\sigma\) and search keyword \(w\); the server takes as input the encrypted database EDB. Then, the client outputs \(\sigma^{\prime}\) and DB(\(w\)) of the file identifiers matching keyword \(w\); the server outputs the updated encrypted database EDB\({}^{\prime}\).  
The adversary is only allowed to learn the information explicitly captured by the leakage function \(\mathcal{L}=(\mathcal{L}^{Setup},\mathcal{L}^{Scrch},\mathcal{L}^{Uplt})\).