esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Graphene-to-Phoneme Conversion Methods
## 3 Strategies of G2P Conversion
### Pre-Training for G2P Conversion  
Pre-training is a process in which a model is initially trained on a large or general dataset. Then, the model for a specific task can be fine-tuned by the corresponding dataset. Ref. [33] fine-tuned a G2P conversion model from a pre-trained Text-to-Text Transfer Transformer (T5) model named T5G2P. This model carried out English and Czech tasks which is able to convert an input text sentence into a phoneme sequence. The subsequent works include [34; 35] that the researchers expanded their pre-trained G2P model to multiple languages. However, most of the pretraining models rely on large-scale datasets, in G2P field, these corpora may not apply to some languages. Inspired by the BERT language model, ref. [36] proposed a pre-trained grapheme model named \"grapheme BERT\" (GBERT), which is trained in a self-supervised manner on large word lists of specific languages. GBERT aims to capture the contextual relationships among graphemes within words. Experimental  
Figure 8: Transformer structure.  
results demonstrate that GBERT-based G2P models perform exceptionally well under both medium-resource and low-resource data conditions. The model structure is illustrated in Figure 9.
# Dynamic Simulation Model and Performance Optimization of a Pressurized Pulsed Water Jet Device
## 3 Simulation of Dynamic Characteristics of the PPWJ Device
### Governing Equations  
The construction of a PPWJ is complex, and the performance of the jet is affected by many factors. To reduce the calculation time of the model, some assumptions are provided as follows: the temperature and bulk modulus of the fluid are constant; the mechanical friction loss and oil pressure loss are ignored; the water inside the chamber is incompressible; all components are regarded as absolute rigid bodies except for the hydraulic oil delivery pipeline; and the fluid dynamic phenomenon related to the flows through the pipes are ignored.
## 3 Background
### Sequence-Level Knowledge Distillation  
The methods described by [1] can be used for word-level KD, since NMT models make use of multi-class prediction at the word level. However, these models also need to predict complete sequences that are dependent on previous predictions. [PERSON] and [PERSON] [2] introduced sequence-level KD, in which a new training set is created by translating a dataset with the teacher model using beam search. The new training set is then used to train a smaller student model. They demonstrated how the standard training objective for multi-class classifiers can be adapted to develop a function for KD, which can be extended further to support word-level and ultimately sequence-level KD.  
Assume that given a dataset \((x,y)\), we want to assign \(x\) into one of the classes \(V\). The aim is to minimize the cross-entropy between the data distribution and the model distribution \(p\) parameterized by \(\theta\). This can be accomplished by minimizing the negative log-likelihood (NLL) for each training example:  
\[\mathcal{L}_{NLL}(\theta)=-\sum_{k=1}^{|V|}\mathbb{1}\left\{y=k\right\}\log p (y=k|x;\theta) \tag{4}\]  
where \(\mathbb{1}\{\cdot\}\) is the indicator function. In the case of KD, we have a model distribution \(q(y=k|x,\theta_{T})\) learned by the teacher; therefore, Equation (4) can equivalently be written as Equation (5):  
\[\mathcal{L}_{KD}(\theta,\theta_{T})=-\sum_{k=1}^{|V|}q(y=k|x,\theta_{T})\log p (y=k|x;\theta). \tag{5}\]  
We can now use \(\mathcal{L}_{KD}\) to define functions for KD for NMT. Standard KD can be applied to NMT models because word-level NLL is minimized during training. The standard loss function is then defined as in Equation (6):  
\[\mathcal{L}_{WORD-KD}(\theta,\theta_{T})=-\sum_{j=1}^{J}\sum_{k=1}^{|V|}q(t_{j }=k|s,t_{<j})\log p(y=k|s,t_{<j}) \tag{6}\]  
where \(V\) is the target vocabulary and where \(t\) and \(s\) are the target and source sentences, respectively. Finally, a loss function for sequence-level KD is formulated, as word-level KD can often lead to the propagation of incorrect predictions during forward passes. Again, the teacher model's probability distribution can be used to define this loss function. Sequence distributions from the teacher model are used instead of word distributions, allowing Equation (5) to be rewritten as  
\[\mathcal{L}_{SEQ-KD}(\theta,\theta_{T})=-\sum_{t\in\mathcal{T}}q(t|s)\log p(t |s), \tag{7}\]  
where \(q(t|s)\) represents the sequence distribution over all possible sequences. This loss function is complex to handle, since it sums over an exponential number of terms. In [2], the authors suggested using beam search to approximate Equation (7), which reduces the complexity of \(\mathcal{L}_{SEQ-KD}\).
## 4 Results and Discussion
### Effect of the Spacing of Adjacent Sealing Sheets  
As the relative position of the sealing sheets is determined by the arrangement of the radial segmental plates in the RAPH, the spacing between adjacent sealing sheets would gradually increase in the radial direction. Figure 8a shows the effect of the spacing on air leakage under the condition that the number, the total pressure difference, the gap size, and the bending angle are 2, 5 kPa, 25 mm, and \(-25^{\circ}\), respectively. According to the ideal calculation results, the spacing value does not affect the air leakage, whereas the air leakage and the orifice coefficient decrease dramatically with the spacing value. In Figure 8b, when fluid passes through a slit, part of the streamlines will curve under the velocity gradient perpendicular to the mainstream direction. The vortex in the flow boundary layer will also diffuse into the mainstream region under the action of a velocity gradient perpendicular to the mainstream direction. The loss of mainstream energy would be enlarged by the streamline curvature, the recirculation zone and vortexes [27]. The vortex converts the kinetic energy of the gas into other forms of energy, resulting in a reduction of the leakage amount.
# Reduced Complexity Detection in MIMO Systems with SC-FDE Modulations and Iterative DFE Receivers
Consequently, it is possible to reduce the signaling requirements between the receiving antennas and central unit that performs the multi-user separation, which is done through the backhaul, which has limited capacity. This work is organized as follows: in Section 2, we describe the cellular architecture considered in this paper, and Section 3 is concerned with the receiver design and the signals' detection. A set of performance results is presented in Section 4, and Section 5 concludes the paper. In this paper, we adopt the following notations: bold upper case letters denote matrices or vectors; \(\mathbf{I}_{N}\) denote the \(N\times N\) identity matrix; \(\mathbf{x}^{*}\), \(\mathbf{x}^{T}\) and \(\mathbf{x}^{H}\) denote the complex conjugate, transpose and Hermitian (complex conjugate transpose) of \(\mathbf{x}\), respectively. In general, lower case letters denote time-domain variables, and upper case letters denote frequency-domain variables; \(\bar{x}\), \(\hat{x}\) and \(\bar{x}\) denote sample, \"hard decision\" and \"soft decision\" estimates of \(x\), respectively. The expectation of \(x\) is denoted by \(\mathbb{E}\left[x\right]\).
# A Quantum-Resistant FHE Framework for Privacy-Preserving Image Processing in the Cloud
## 5 Discussion and Implications
Organizations subject to GDPR, HIPAA, or financial services regulations increasingly view privacy-preserving computation as a competitive necessity rather than a luxury. Beyond compliance, forward-thinking companies recognize \"zero-knowledge\" processing capabilities as a significant differentiator in markets where data sensitivity is paramount. The framework also provides tangible risk mitigation benefits, protecting against both traditional data breaches and the emerging quantum threat landscape. However, several technical barriers may slow adoption. The complexity of FHE requires specialized cryptographic knowledge that many development teams currently lack, creating a skills gap that must be addressed through training or specialized hiring. Performance expectations also present challenges, as stakeholders accustomed to near-instantaneous plaintext processing must adjust to systems that operate 10-100 times slower. Perhaps most significantly, integrating our framework into existing systems requires substantial architectural changes, often necessitating a ground-up redesign of data processing pipelines rather than simple drop-in replacements. It is also important to contextualize the 33.83 MB size of the FHE public materials. This size should not be directly compared to the key sizes of PQC KEMs (like Kyber's 0.8 KB public key) or signature schemes. The FHE public materials are significantly larger because they contain the rich mathematical structure (including evaluation keys) necessary to enable computation on encrypted data. This large size is a fundamental trade-off for achieving the powerful functionality of FHE, whereas PQC KEMs and signatures are designed only for key establishment and authentication, respectively. It is also important to consider the trade-offs involved when selecting security parameters for the FHE scheme. While our main evaluation focuses on a practical 128-bit security level, preliminary analysis shows that increasing the security level by using a larger polynomial modulus degree has significant performance implications, as illustrated in Figure 3. Increasing the security parameter leads to a corresponding rise in both data overhead for the public context and computational latency for server-side operations. This highlights a critical decision for practitioners, who must balance the need for long-term data confidentiality against the practical constraints of system performance and bandwidth.
# A Study on the Analysis of the Effects of Passenger Ship Abandonment Training Using VR
## 2 Literature Review
However, are only a few studies that quantitatively determine how effective VR-based training is for seafarers in the maritime sector. In particular, it has been even more difficult to find cases where various drills are performed on a regular basis onboard ships. According to the International Standards of Training, Certification and Watchkeeping for Seafarers (STCW 1978), as amended, there are a number of training and educational courses that seafarers are obliged to complete [35]. Among these courses, in relation to requirements for safety training of personnel providing a direct service to passengers and passenger ship crowd management training--as specified in the STCW Code Part A/Section A-V/2--the IMO Model Course 1.41 (Passenger Ship Crowd Management Training) specifies the requirements for passenger ship crew to learn emergency passenger management skills through education and training. As can be seen from the Costa Concordia accident in 2012 and the Sewol ferry accident in 2014, passenger ship crews need to be trained to respond to emergencies at all times [12; 36; 37]. Unlike ship abandonment training on other types of ship, the importance of crew training with passengers has been emphasized [38]. There are, however, a number of practical difficulties in mobilizing passengers to conduct training during an actual passenger ship voyage--as well as the need to consider the safety of training participants during training--which lead to limitations in conducting training onboard a ship. Moreover, passenger evacuation and ship abandonment training actually conducted on passenger ships have been shown to have a reduced training effect due to the practical difficulties of mobilizing passengers and the use of life-saving appliances such as marine evacuation systems (MES) and life-rafts during training [39]. It can be anticipated--based on previous studies--that passenger ship abandonment training using VR will be effective in terms of safety [32]. However, the impact of VR-based training used in this case needs to be examined in more detail. Furthermore, it would be necessary to identify which items should be emphasized in the implementation of such training.
# Study on Frost Heave and Thaw Settlement Characteristics of Sanya Estuary Channel Soil Layer
## 1 Introduction
Numerous scholars [18; 19; 20] have demonstrated through experiments that the movement of the freezing front and the formation of ice lenses are major factors affecting soil frost heave and thaw settlement displacement. Frost heave and thaw settlement performance tests can also be combined with actual working conditions or regional characteristics to provide references for inhibiting frost heave and thaw settlement. [PERSON] et al. [21] studied the frost heave rate, thaw settlement coefficient, and moisture field evolution of typical red clay layers in Jiangxi under different freezing temperatures. [PERSON] et al. [22] explored the changes in frost heave and thaw settlement characteristics of silt subgrade soil in western China from the perspective of fiber reinforcement under different moisture contents and fiber parameters. [PERSON] et al. [23] studied the effects of cement content, curing time, and freezing temperature on the frost heave and thaw settlement performance of cement-improved soil based on typical soil layers crossed by the Fuzhou Metro Line 4 tunnel. [PERSON] et al. [24] studied peat soil in the Dunhua area of Jilin Province, conducting frost heave and thaw settlement investigations on undisturbed soil samples. They analyzed the effects of soil moisture content, dry density, and other factors on frost heave rate and thaw settlement coefficient, and developed a hierarchical evaluation model for the freeze-thaw characteristics of peat soil. This study utilizes closed unidirectional freeze-thaw tests to conduct freeze-thaw experiments on typical soil layers traversed by the Sanya River Estuary Tunnel Project to obtain the frost heave rate and thaw settlement coefficient. Comparative analysis identifies the most unfavorable soil layer for frost heave and thaw settlement. Subsequently, the evolution of frost heave and thaw settlement performance of the most unfavorable soil layer under different moisture contents is explored, revealing the impact of moisture content changes on the frost heave and thaw settlement performance of the soil layer. This study provides a comprehensive analysis of the frost heave and thaw settlement characteristics of typical soil layers traversed by the Sanya River Estuary Tunnel Project, offering references for inhibiting frost heave and thaw settlement in pipe-roof freezing construction for the tunnel project.
## 4 _P. gingivalis_ and Systemic Diseases  
### Cardiovascular Diseases  
According to the World Health Organization (WHO), cardiovascular diseases (CVDs) remain the leading cause of death worldwide [36]. These diseases encompass a range of cardiac and vascular conditions, including ischemia, atherosclerosis, peripheral arterial disease, infective endocarditis, and acute myocardial infarction. Numerous studies have highlighted a correlation between CVDs and periodontal disease, as both conditions share common risk factors and are multifactorial in nature.  
These studies [37] demonstrated that individuals with periodontitis are at a significantly higher risk of developing coronary heart disease compared to those without periodontitis. A 2023 meta-analysis confirmed a consistent association between periodontal disease and adverse cardiovascular outcomes, such as myocardial infarction and stroke, across different populations and genders [38].  
Similarly, [PERSON] et al. [39] reported that activities such as tooth brushing, debridement, or scaling could facilitate the entry of oral pathogens and their virulence factors into the bloodstream. It has also been shown that _P. gingivalis_ can be detected in atherosclerotic plaques [40; 41], and more recent studies indicate that the presence of periodontal bacteria in the bloodstream or in situ within vascular lesions is associated with an increased risk of aneurys disease [42]. Recent studies have shown that periodontal disease is linked to a significantly increased risk of developing coronary artery disease, stroke, and heart failure, with this association observed consistently in both males and females [38].  
The literature identifies two main mechanisms through which periodontitis may influence the development of cardiovascular diseases: direct and indirect mechanisms. The direct mechanism involves the bacterial invasion of blood vessels, whereas the indirect mechanism operates through the activation of excessive inflammatory responses in the host, which in turn may influence the onset of systemic diseases [43].  
The interaction between bacterial antigens and host cells can provoke both local and systemic immune responses. _P. gingivalis_ has been shown to invade endothelial cells, induce dysfunction, and trigger pro-atherogenic responses, promoting the development and progression of atherosclerotic lesions [44].
# Combination of Permanent and Variable Load Is Dependent
## 1 Introduction  
### Limitations and Terms  
This article addresses the combination of permanent and variable loads only. The Eurocodes and corresponding steel, timber and concrete codes are used as references [1, 2, 3, 4] and terms of these codes apply.  
Some further terms are cleared up:  
**Design load combination.** The load combination which is made in the structural design from the deterministic load table values of permanent and variable loads. The design load combination can be made in three alternative ways, independently, dependently, and semi-dependently. The semi-dependent combination is omitted here as it is not applied in any codes.  
1. Independent load combination, ILC. This load combination is realized by the ILC model where the combination load is constructed from single and random load pairs omitting load groups including a random permanent load and a random variable load. The probability of simultaneous occurrence of the high values of the loads is low and therefore a load reduction of approximately 10% is applied regarding the arithmetic sum of actual load table values. The load reduction is normally implemented by two safety factors of the permanent load, in the Eurocodes, \(\gamma_{\text{G}}\) = 1.15 and 1.35. In the Monte-Carlo (MC) analysis a random seed number is selected for the permanentload and for the variable load, the loads are computed from the distributions and the combination load is the arithmetic sum of these loads. The same result is obtained by using the convolution equation. The ILC is never applied in the serviceability state (SLS) while it is normally applied in the ultimate limit state (ULS). According to the governing hypotheses, it should always be applied in the ULS. However, it is not always applied, as in the combination rule (8.12) of the Eurocodes. Due to the load reduction, the ILC is in contradiction with physics and deterministic mechanics [5].
2. Dependent load combination DLC. The combination load is constructed as if the distributions were correlated. In the MC analysis one random seed number is selected, the same used for the permanent load and for the variable load, the loads are computed from the distributions and the combination load is the arithmetic sum of the loads. That is, the combination load distribution is obtained by adding up the partial loads by fractiles. The convolution equation can also be used given with a constraint that the combination load distribution crosses the crossing point of the partial distributions [6]. In the DLC no load reduction appears. The combination load is the arithmetic sum of the partial loads, i.e., the design load is obtained by adding up the load table values normally multiplied by load factors. The DLC matches with the physics and deterministic mechanics [5]. The DLC is always applied in the SLS and sometimes in the ULS.
# Harris Hawks Sparse Auto-Encoder Networks for Automatic Speech Recognition System
###### Abstract
The training process observes every change, variation, and speech modulation that reduces the difficulties in the noisy environment speech recognition process. Then, the deviation between the exact spoken speech and the predicted speech is minimized by network parameter analysis. Continuous examination of the network parameters and the fine-tuning process help to reduce the maximum error rate problem. Minimum-error problems directly improve the ASR system's precision and maintain the system flexibility and robustness of the system. The Mel-frequency cepstral coefficients (MFCCs) approach [20; 21] is applied to derive various features from the collected speech signal. The training and testing process is then initiated via the sparse autoencoder algorithm, which improves the overall recognition accuracy. Recognition decision is handled through the hidden Markov model (HMM) [19] and the network parameter fine-tuning process. Thus, the main contribution of the proposed system is reducing the maximum error rate problem and improving the precision of the ASR system. The rest of the paper is organized as follows. Section 2 analyzes the various researchers' opinions regarding the speech recognition process. Section 3 discusses the introduced meta-heuristic algorithm-based ASR process and the system effectiveness, which is evaluated in Section 4. Finally, the summarization of the entire work is discussed in Section 5.
## 4 Results
### Performance Comparison
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Missing Rate** & **ARIMA** & **Infomer** & **Informer-WGAN** \\ \hline
20\% & 2.711 & 1.791 & **1.119** \\
40\% & 4.247 & 1.830 & **1.157** \\
60\% & 4.612 & 2.019 & **1.188** \\
80\% & 12.609 & 2.062 & **1.283** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The PRMSE (the smaller, the better) results of the informer-WGAN and other imputation results on electricity dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Missing Rate** & **ARIMA** & **AST** & **Infomer** & **Informer-WGAN** \\ \hline
20\% & 2.709 & 2.477 & 2.311 & **2.309** \\
40\% & 3.036 & 7.487 & 2.339 & **2.338** \\
60\% & 3.156 & 9.963 & 2.438 & **2.437** \\
80\% & 3.736 & 19.567 & 2.574 & **2.569** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The PRMSE (the smaller, the better) results of the informer-WGAN and other imputation results on ETTh1 dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Imputation Model** & **mae** & **mse** & **rmse** & **mape** & **mspe** & **p\_corr** \\ \hline ARIMA & 2.48034 & 19.27467 & 4.35074 & 0.52469 & 4.69485 & 0.65881 \\ AST & 7.87295 & 94.89117 & 9.70722 & 2.44605 & 14.57877 & 0.38196 \\ Informer & 2.11488 & 11.82524 & 3.43685 & 0.59489 & 9.80083 & 0.74467 \\ ST-WGAN & 2.23207 & 21.70739 & 4.60613 & 0.69644 & 24.20782 & 0.65263 \\ Informer-WGAN & 2.01439 & 12.37496 & 3.51492 & 0.56910 & 10.93439 & 0.74725 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The imputation results of different model on electricity dataset at the missing rate of 60%.
# An Extremely Close Vibration Frequency Signal Recognition Using Deep Neural Networks
## 2 Vibration Signal Detections  
In vibration signal detection, sampling frequency and sampling rate differ, particularly when identifying extremely close frequencies. The sampling frequency, representing the number of samples taken within a given time frame, plays a pivotal role in determining the resolution of the frequency content within the signal. Differently, the sampling rate, delineating the number of samples acquired per unit of time, is a critical parameter influencing the accuracy of signal representation. Detailing the response of the signal analysis method for vibration signal detections, we choose an optimal sampling frequency. This research focuses on the vibration signal with various frequencies, fixed amplitude, and phase, and neglecting the signal noise level.  
Figure 2: The proposed configuration of vibration detection. (PC: Personal computer, OSC: Oscilloscope, TL: Tunable laser, PD: Photodetector, OC: Optical circulator, FBG: Fiber Bragg Grating, DP: Displacement platform, VG: Vibration generator, SG: Signal generator).