esa-sceva/satcom-chunk-collection huggingface dataset:
and environmental factors which can drive down the CE. In particular, the wavefront phase distortion caused by atmospheric turbulence has the most serious impact on the CE. It is important to understand the influence of different wavefront aberrations on the laser beam to achieve optimal SMF coupling. Researchers have made many notable achievements in this field. [PERSON] made an in-depth study of coupling efficiency that includes the effects of aberrations, fiber misalignments, and fiber-mode mismatch [22]. Based on the mode-field matching theory, [PERSON] determined the influence of the first three orders of optical aberrations on CE [23]. [PERSON] analyzed the impact of random angular jitter and localized wavefront aberrations on fiber coupling for FSOC system [24; 25]. Adaptive optics (AO) technology has been shown to effectively improve the CE of SMF in FSOC systems under the influence of atmospheric turbulence [29; 30], while the AO system design and optimization method for FSOC still needs further study.  
Extant theories generally center on the statistical effects of atmospheric turbulence on CE. Statistical methods, such as power in the bucket (PIB) and the Strehl ratio (SR) approximation, concentrate on the relationship between turbulence and CE. However, these methods come with certain limitations in regards to the FSOC AO system design, as they do not solve how each single aberration affects CE, which is very important for some AO systems based on aberration mode detection, such as the holographic AO system. What's more, there have been few studies on the influence of higher-order aberrations. In addition, aberrations caused by optical systems are also important factors affecting CE and should be strictly controlled, which is also related to analyzing the effect of each single aberration on the CE.  
In this study, based on the mode-field matching principle of SMF, we analyzed the principle of spatial optical coupling into SMF and built a new pupil mode-field matching model. The method we provide analyzed how each single aberration affects the CE, whether it is intruded by optical systems or atmosphere turbulence. The statistical effect of aberrations could also be analyzed based on the model we built. The method provides a way to optimize the residual margin of AO system and make it working at best frequency, which will finally improve the CE and the overall system performance.  
We provide a brief introduction to the basic theories relevant to this study in Section 2, and discuss the effects of aberration spatial frequency characteristics and corrected residuals of the AO system on CE in Section 3. Our experimental verification and results analysis is shown in Section 4, and Section 5 provides a brief summary and conclusion.
# A Hybrid-Cryptography Engine for Securing Intra-Vehicle Communications
## 2 Intra-Vehicle Security
### Hardware Security Modules  
A key challenge is to provide cryptographic features in such a way that security is achieved without relevant impact on computational and communication performances. An alternative to a classical software implementation of cryptographic features is to use application-specific hardware solutions that can provide better performance. This approach is often adopted by using HSMs in the form of _Cryptographic Hardware Accelerators_. An example can be found in [51] where the authors propose a hardware accelerator for AES encryption in automotive scenarios. Our solution supports the adoption of HSMs and HW accelerators, in particular for ECC computation, which exhibits an increased computational cost with respect to AES. As described in Section 3.2, we made use of the available enhanced cryptographic features of the target HW platforms for AES. While this led toinferior performances in comparison with [51], it grants us increasing flexibility when no HW acceleration is present.  
Trusted Platform Module  
Establishing the so-called _root-of-trust_ is a critical problem in cryptographic implementations. A common solution is to move the root-of-trust in hardware (e.g., a separate chip). A well-known solution in this sense is the _Trusted Platform Module_ (TPM) [52]. The TPM allows for the secure generation and storing of secrets (e.g., cryptographic keys, certificates, nonces, etc.) so that they are kept confidential (via _wrapping_) and strongly tied to the host platform by specific unique keys and certificates created at manufacturing time. Given the platform-independent nature of the TPM specifications, it can be adopted in any computational platform, including intra-vehicle networks. In fact, many automotive-related micro-controllers provide support to TPM specifications and implementation (e.g., specific pin headers (An example is the Infineon OPTIGA TPM product family [[https://www.infineon.com/cms/en/product/security-smart-card-solutions/opt-iga-embedded-security-solutions/optiga-tpm](https://www.infineon.com/cms/en/product/security-smart-card-solutions/opt-iga-embedded-security-solutions/optiga-tpm)]([https://www.infineon.com/cms/en/product/security-smart-card-solutions/opt-iga-embedded-security-solutions/optiga-tpm](https://www.infineon.com/cms/en/product/security-smart-card-solutions/opt-iga-embedded-security-solutions/optiga-tpm)) (accessed on 27 November 2023) )). In comparison with the TPM, the Crypto-Engine proposed in this paper is designed not only to generate and store keys, but it also acts as an active cryptographic accelerator. While using a TPM requires software (e.g., operating systems, cryptographic libraries, etc.) to ask the TPM for a key/signature, the Crypto-Engine is able to perform encryption and signature-related operations by itself in a transparent manner.
# Analysis of Flow Characteristics and Effects of Turbulence Models for the Butterfly Valve
## 3 Numerical Implementation
### Calculation Conditions  
The three different sizes of butterfly valves were modeled with different opening percentages of disc from 10% to 100%, for the disc modeling with solid body. The illustrations of calculation domain are shown in Figure 1. According to research by [PERSON] [9], the distance from the valve required for the flow to fully develop upstream and downstream lengths was determined to be five and 10 times the valve size. The flow characteristics around the butterfly valve were investigated with water as the working fluid with a density of 999 kg/m\({}^{3}\) and dynamic viscosity of 1.12 \(\times\) 10\({}^{-3}\) N\(\cdot\)S/m\({}^{2}\). The constant used in the valve flow coefficient was adopted according to the ISA standard [45; 46]. The flow was assumed to be incompressible and steady. The pressure difference and corresponding flow rates were used to calculate the valve flow coefficient using Equation (15). The pressure difference between the inlet and outlet boundary conditions were set at uniform pressure of 1 bar to satisfy the valve flow coefficient conditions. With regard to the constants for the two-equation turbulence models of \(k-\varepsilon\), \(k-\omega\), and SST, the nominal values are used as following: \(\mathrm{C}_{\varepsilon 1}=1.44\), \(\mathrm{C}_{\varepsilon 2}=1.92\), \(\mathrm{C}_{\mu}=0.09\), \(\sigma_{k}=1.00\), \(\sigma_{\varepsilon}=1.30\) for the \(k-\varepsilon\) model, \(\alpha=0.556\), \(\beta=0.075\), \(\beta^{\prime}=0.090\), \(\sigma_{k}=2.00\), \(\sigma_{\omega}=2.00\) for the \(k-\omega\) model, respectively. The scalable wall function and automatic wall function approach was applied. A structured and non-adaptive grid (hexahedral grid) was generated. For the pipe wall, a no-slip boundary condition was applied. The turbulence intensity was set to 5-10% at the inlet [23]. For the near wall flow model, a maximum value of 20 for y+ was considered close to the valve sur face and pipe wall, based on the research by [PERSON] and [PERSON] [31]. A high-resolution advection scheme was used and a convergence criterion for maximum root-mean-square residual of 10\({}^{-6}\) was set, which is two orders of magnitude below the default convergence level of the solver [8; 51].
# Sentence Representation Method Based on Multi-Layer Semantic Network
## 4 Experiment
### Experimental Steps  
For the task of text implication recognition, to avoid the interference caused by the reasoning process, to judge the performance of a multi-layer semantic network, a complete sentence representation reasoning model is formed by combining the general reasoning model [38] and multi-layer semantic representation network. The inference module uses a simple heuristic matching method. The inference information includes the embedded representation of the premise sentence and the hypothetical sentence, the difference between the premise sentence and the hypothetical sentence, and the product of the two.  
The prediction module comprises a fully connected neural network and a classifier used to predict and classify the implication relationship. The specific experimental steps are as follows:  
1. Firstly, the multi-layer semantic representation network is used to obtain the sentence representation of the premise statement and the hypothetical sentence as \(u\) and \(v\);
2. Then, the inference information \(|u-v|\) and \(u*v\) between sentences are calculated by the inference module;
3. Then, the inference information \(|u-v|\), \(u*v\), premise sentence representation \(u\), and hypothetical sentence representation \(v\) are spliced and input into the fully connected network [26] to predict the classification implication relationship.  
This paper uses Yelp data to do an emotion classification task [29]. According to their comments, this paper judges users' star ratings. It divides them into one star to five stars, from negative evaluation to positive evaluation. The basic experimental steps are the same as above. The only difference is that the final three classifications are replaced by the five.
## 4 Method Validation
### Multimodal Test Function Experiments
#### 4.2.2 Analysis of the Results  
Convergence accuracy analysis: As shown in Table 5, the HLSSA successfully yielded the optimal values for test functions \(F_{8}\), \(F_{9}\), and \(F_{11}\), and its optimization performance was significantly better than the PSO, MVO, and WOA. Although the HLSSA did not yield the optimal values for \(F_{10}\) and \(F_{12}\), its performance in terms of the obtained optimal values, averages, and standard deviations was still better than other algorithms. For \(F_{9}\), \(F_{10}\), and \(F_{11}\), the SSA performed similarly to the HLSSA, but regarding the other two functions, the SSA was more prone to becoming entrapped in local optima. Overall, the HLSSA exhibited stronger global search capabilities when solving multimodal test functions, confirming the effectiveness of LX perturbation in helping the SSA escape local optima.  
Stability analysis: According to the standard deviation (STD) data in Table 5, the HLSSA exhibited comparable performance to the SSA considering \(F_{9}\), \(F_{10}\), and \(F_{11}\). However, in terms of the remaining multimodal test functions, the HLSSA revealed consistently lower STD values, indicating better stability on multimodal test functions.  
Convergence speed analysis: As shown in Figures 9-13, the HLSSA had an absolute advantage in convergence speed on the five multimodal test functions, and the initial solutions obtained were very close to the optimal values. This suggests that the HLSSA demonstrates accelerated convergence when addressing high-dimensional complex problems.  
In summary, the HLSSA exhibited enhanced convergence accuracy, accelerated convergence speed, and improved stability when dealing with high-dimensional multimodal problems. Compared to the basic SSA, the HLSSA achieved significant improvements in performance.
## 3 Results
### Comparison with Experimentally Measured Values  
Received signal was measured experimentally to compare with the simulation results. The transmitter was powered by a small battery (CR2032) and the circuit board and the electrodes had the same dimensions used in the simulation. The frequency was 10 MHz and the output signal amplitude was 744 mV. The receiver consisted of a preamplifier and a battery-powered oscilloscope to measure the output of the preamplifier. The preamplifier and the oscilloscope were placed on a tall box that had the same dimensions as the receiver frame in the simulation. The surface of the box was covered with aluminum foil, which was connected to the ground of the receiver circuit. Although it is often mentioned in the literatures that the measuring apparatuses affected the capacitive coupling in HBC, we assumed that the battery-powered oscilloscope acted as part of the receiver ground and had little effect on the overall signal path in this case. Received signal was measured for five times by a single subject. The results are given in Table 3. The transmission characteristic was evaluated by the ratio between the amplitudes of the received signal and the transmitter output. The difference between the one- and two-electrode transmitters was 18.3 dB for the experiment and 26.3 dB for the simulation. It was concluded that these similarities verified the reliability of the simulation.
# _Applied Sciences_
## 4 Discussion
### Fidelity  
Fidelity (the agreement rate between the explanations' consequent and the reference model's output), is given in Table 4. Only gtb-HIPS and Anchors are guaranteed to be locally accurate by means of their algorithmic steps. Unfortunately, it was not possible to collect the fidelity scores for the LORE method. The computation time of the (very long-running) method, which makes it prohibitive to re-run the experiments. However, the fidelity of LORE is listed in the originating paper (2018) as \(0.959\pm 0.17\) for the adult data set, \(0.988\pm 0.07\) for the german data set, and \(0.992\pm 0.03\) for a third data set not used in this investigation. LORE is assumed to reach this level of fidelity for other data sets used in these experiments.  
It must be noted that poor fidelity with the black box model is a critical flaw. An explanation in the form of a classification rule is not fit for purpose if the consequent does not match the target class. On this point, there can be little debate because a key requirement, local accuracy, is violated. On the other hand, the tolerance for anything less than perfect fidelity is a domain-specific question. This tolerance will depend on the cost or inconvenience of failing to explain any given instance. So, we make only the following assertion as to what is an acceptable score: it would be surprising to find levels as low as 0.90 permissible in critical applications. At this level, one in ten explanations is unusable.
TextNetTopics employs a topic-scoring mechanism that can become a computational burden as the number of topics increases. This mechanism involves training a Random Forest model on each topic independently using the Monte Carlo cross-validation (MCCV) approach and assigning a score based on a specific mean performance metric (e.g., accuracy, f1-score, etc.). Moreover, the score does not account for interactions between all features residing in all topics. * Explicit Semantic Modeling. LDA does not explicitly model semantic relationships between words; it indirectly captures semantic information by grouping words that frequently co-occur in similar contexts within the dataset based on statistical patterns. The main objective of this study is to propose faSTNT, an enhancement of TextNetTopics based on the G-S-M approach, to overcome the mentioned limitations. The main contributions of this research paper can be summarized as follows:  
1. This study addresses the issue of feature redundancy and relevancy within topics by introducing a robust filtering component that leverages Random Forest feature importance values. By systematically removing the least informative features from LDA topics, the proposed method retains only the most relevant features for the classification task. This selective feature retention ensures that the resulting feature set is both concise and highly discriminative, leading to more efficient computations and improving overall classification performance and generalization capabilities. 2. Our study introduces a more efficient approach to the scoring component that allows for the simultaneous training of all topics and scoring via leveraging the feature importance-based Random Forest. This novel approach considers all topic interactions while scoring, providing a significant advancement over the one used in TextNetTopics. 3. In this study, we enhance LDA topics by explicitly leveraging semantic relationships through pre-trained word embeddings. This enhancement is integrated into the grouping component, where the embeddings provide a dense, continuous representation of words that preserves their semantic similarities. By incorporating word embeddings, we refine the LDA topics to form more semantically coherent groups of words, thus improving the overall quality of the topics. The paper is structured into the following sections: Section 2 covers related work, including relevant studies and concepts. Section 3 outlines the proposed methodology in detail. Section 4 describes the experimental work. Section 5 presents the experimental results. Finally, Section 6 discusses the conclusion and future work.
# Panoramic Digital Image Correlation for 360-Deg Full-Field Displacement Measurement
## 2 Materials and Methods
### Three-Dimensional Shape Reconstruction and Full-Field Displacements Determination  
Once the intrinsic and extrinsic parameters and scale factor of each stereo camera pair have been calibrated, the local 3D shape (reconstructed 3D coordinates) of the test specimen is reconstructed from the stereo correspondences by using a linear triangulation algorithm [46; 47]. The local three-dimensional shapes obtained by each camera are independent and have no relationship with each other, but there is local overlap. In order to construct a continuous merged surface, the overlap between the three-dimensional points needs to be resolved and the adjacent three-dimensional points stitched together. In order to solve the overlapping problem, an algorithm is proposed. Based on the registration results of the reconstructed local three-dimensional points and laser point cloud, the algorithm starts from the boundary of the overlap regions, calculates the distance between the overlap points and the laser point cloud in the overlap regions, and removes the points with large distance to the laser point cloud until the overlap regions is spliced into a single continuous surface, as shown in Figure 2. The coordinates of the removed points are recorded for redundancy removal and merging of the overlapped three-dimensional points after deformation. Using the same intrinsic and extrinsic parameters of cameras and merging method, combined with the deformed point cloud data obtained by DIC, the three-dimensional surface of the deformed specimen is constructed. The three-dimensional displacement fields of the test specimen are finally estimated by subtracting the three-dimensional coordinates of all measurement points at initial state from those of deformed states. The relevant algorithm steps are presented in Algorithm 4.  
```
Input: correspondence points of each camera pair: \(I_{l}\), \(I_{r}\) intrinsic parameters of each camera: \(f_{x},f_{y},c_{x},c_{y}\), extrinsic parameters of each camera pair: \(s\), R, t. Output: full-field displacement component: \(u,v,w\). Local 3D shape construction
1 Reconstruct local 3D coordinates based linear triangulation algorithm. Global 3D shape merging
2 Merge a complete 3D shape in the current state based on the proposed distance-based three-dimensional point merging method with the aid of laser point cloud. Panoramic displacement fields measurement
3 Extract the panoramic displacements from the 3D position of the points under different states.
```  
**Algorithm 4** Determining full-field displacements from three-dimensional shape reconstruction.
Although the random noise can be reduced by filtering and smoothing, the non-random multipath errors cannot be effectively suppressed in a short time [24]. In addition to these above methods, many scholars also use other sensor observations to improve the performance of the detection of cycle slips, e.g., [PERSON] (2016) used inertial information to assist the Beidou tri-frequency to improve the success rate of detecting cycle slips and repair the rate in the case of serious multipath errors. This method overcomes the shortage of the accuracy of pseudorange observations, but its INS prediction error increases with time [25]. [PERSON] (2020) constructed a time difference carrier phase (TDCP)/INS system by using multiple constellations, which ignored system time differences, and judged whether the TDCP system performed abnormally for the cycle slips' detection and repair through the INS prediction error range [26]. Such INS-aided method surely shows quite a good detection ability, however, they require additional sensors. The above research either improved the MW + GF combination method or adopted different auxiliary methods to weaken the influence of pseudorange multipath effects dramatically, but in a severe multipath environment, even if a high sampling frequency is employed, the multipath effect cannot be completely eliminated in the case of an epoch difference, especially if the pseudorange multipath residual seriously affects the cycle slips' detection capability of the MW combination. At present, the reliable detection and repair of BDS3 (B1c + B2a) double-frequency cycle slips in complex environments is still worthy of further study. Therefore, for the carrier phase cycle slips detection and repair on BDS-3 B1c (hereinafter referred to as P1 and L1) and B2a (hereinafter referred to as P5 and L5) frequencies under a severe multipath, we propose an AR model-assisted MW + GF BDS dual-frequency combined detection method (AMG method). In this method, the satellite-to-ground distance observation data unaffected by the multipath were used as the training volume, an AR model was built based on the sliding-window method to predict the reliable satellite-to-ground distance observation data in the multipath environment, and then the cycle slips detection model of the AR combined with MW + GF method was derived. The completeness monitoring index was introduced to analyze the detection performance, and finally the cycle slips were repaired by the MW combination. The rest of this paper is structured as follows. Section 2 introduces the mathematical model of the cycle slips' detection and repair. Sections 3 and 4 introduces the experimental design and results.