esa-sceva/satcom-chunk-collection huggingface dataset:
# Line Scan Spatial Speckle Contrast Imaging and Its Application in Blood Flow Imaging
## Materials and Methods
### Theory  
The speckle contrast is computed by the ratio of the standard deviation to the mean intensity, is expressed as,  
\[K=\frac{\sigma}{\langle I\rangle} \tag{1}\]  
where \(\sigma_{t}\) refers to the temporal standard deviation and \(K_{t}\) is the temporal speckle contrast. Similarly, the spatial speckle contrast (\(K_{s}\)) can be expressed as the ratio of spatial standard deviation (\(\sigma_{s}\)) to the mean intensity \(<\)\(l\)\(>\). The speckle contrast (\(K\)) is a function of the correlation time \(\tau\) and the exposure time \(T\) of the camera as [28].  
\[K^{2}=\frac{1}{T}\int\limits_{0}^{T}\beta|g_{1}(\tau)|^{2}\Big{[}2(1-\frac{ \tau}{T})\Big{]}d\tau \tag{2}\]  
where \(\beta\leq 1\) is a parameter related to the loss of correlation and \(g_{1}(\tau)\) is the field autocorrelation function. For Brownian motion (unordered flow), the field autocorrelation function \(g_{1}(\tau)\) corresponds to a Lorentzian line shape, which can be expressed as [30]  
\[|g_{1,Brownian}(\tau)|_{2}=\exp(-2\tau/\tau_{c,Brownian}) \tag{3}\]  
For ordered flow, the field autocorrelation function \(g_{1}(\tau)\) corresponds to a Gaussian line shape, which can be expressed as [30]  
\[\Big{|}g_{1,ordered}(\tau)\Big{|}_{2}=\exp(-2(\tau/\tau_{c,ordered})^{2}) \tag{4}\]  
[PERSON] and [PERSON] have pointed out that the actual distribution is some combination of the two models [30]. In this paper, we assume that the field autocorrelation function \(g_{1}(\tau)\) can be expressed as:  
\[\Big{|}g_{1}(\tau)\Big{|}_{2}=\exp(-2\tau/\tau_{c,Brownian})\times\exp(-2(\tau /\tau_{c,ordered})^{2}) \tag{5}\]  
Therefore, In the case of only Brownian motion, using the Equations (3) and (2), speckle contrast is given by  
Figure 1: Schematic of the experimental setup for laser speckle imaging: HWP, half-wave plate; BE, beam expander (3\(\times\)); CL, cylindrical lens; PBS, polarizing beam splitter; GV, 1-D galvo mirror; P, polarizer. TS, translational stage. The focal lengths of CL, L1, L2, L3, and L4 were 50, 50, 75, 40, and 100 mm, respectively.  
\[K=\sqrt{\frac{1}{T}\int\limits_{0}^{T}\beta\exp(-2\tau/\tau_{c,Brownian})\Big{[}2(1- \frac{\tau}{T})\Big{]}d\tau} \tag{6}\]  
In the case of both Brownian motion and ordered flow, using the Equations (5) and (2), speckle contrast is given by  
\[K=\sqrt{\frac{1}{T}\int\limits_{0}^{T}\beta\exp(-2\tau/\tau_{c,Brownian})\times \exp(-2(\tau/\tau_{c,ordered})^{2})\Big{[}2(1-\frac{\tau}{T})\Big{]}d\tau} \tag{7}\]
## 5 Results
### Pressure Losses of the RECs  
The improvement of the acoustic performance of a duct system cannot be at the expense of flow efficiency. A CFD model [32] (Figure 11), using the standard \(k\)-\(\varepsilon\) turbulence model, is adopted to calculate the difference (pressure loss) between the area-weighted average pressures at the inlet and outlet of the RECs. The model is discretized by about one million unstructured tetrahedral meshes with a mesh size of 3 mm. The inlet has a velocity of 10 m/s and the outlet has a zero gauge pressure.  
Table 5 shows the pressure losses of the RECs with one baffle or double baffles. It can be seen that the pressure losses of the Case 1-0 and Case 2a-0 are higher than the Case 2t-0. Therefore, the REC with double baffles distributed transversely (Type 2t) has good performance in both flow efficiency and TL.  
The influence of the baffle angles and lengths on the pressure losses of the RECs are shown in Figures 12 and 13. Figure 12 shows that the pressure losses of Type 1 and Type 2t  
Figure 11: CFD model of the REC with one baffle.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Case 1-0** & **Case 2a-0** & **Case 2t-0** \\ \hline
141 Pa & 140.2 Pa & 117.7 Pa \\ \hline \hline \end{tabular}
\end{table}
Table 5: The pressure losses of the RECs with different baffle configurations.  
Figure 10: TLs of the RECs with different baffle lengths.  
increase as \(\theta\) increases, while the pressure loss of Type 2a decreases as \(\theta\) increases from 40\({}^{\circ}\) to 60\({}^{\circ}\). Figure 13 shows that Type 1 and Type 2a have much higher pressure losses than Type 2t, while the pressure loss of Type 2t increases faster than Type 1 and Type 2a as \(l_{b}\) increases. In general, Type 2t has better performance in flow efficiency than the other types, especially at small values of \(\theta\) and \(l_{b}\).
# The Influence of Motion Data Low-Pass Filtering Methods in Machine-Learning Models
Although a few studies have compared the effects of filters with different parameters on calculated gait kinematics [5; 6], there is limited knowledge on how these parameters influence the performance of machine-learning models that utilize filtered gait characteristics as inputs. A proper low-pass filter could help to remove unwanted noise from gait data while retaining the true physiological signals, leading to cleaner and more consistent input data and potentially enhancing model accuracy. However, inadequate filtering might cause the model to learn from noise patterns instead of meaningful data, thus degrading the model performance, especially when the noise levels are high. Alternatively, a strict filter might remove important details and critical components in the gait data, thus also adversely impacting model performance. Therefore, the appropriate filter parameters that preserve relevant gait features for input into machine-learning models need to be considered. This study aimed to evaluate how different filter parameters affect the performance of machine-learning models that utilize gait data and develop gait type identification models using the same gait features by different filters. By analyzing the effect of variant low-pass filters and cutoff values on outputted gait features and the models built upon them, we sought to determine which filter could effectively reduce noise without altering the real data, thereby enhancing the model performance. Additionally, since different machine-learning models might require different low-pass filtering methods, this study also investigated the effects of filter parameters across multiple models.
# Modified Pseudo-Dynamic Bearing Capacity of Shallow Strip Foundation near Slope on Non-Uniform Soil
## 4 Results and Analysis
### Parameter Analysis
#### 4.3.4 Influence of \(\lambda\)  
In this subsection, the effect of non-uniformity of soil on seismic bearing capacity is investigated. As mentioned above, \(\lambda\) is the non-uniformity coefficient. The trend of foundation seismic bearing capacity \(p\) with non-uniformity coefficient \(\lambda\) at different \(\varphi\) is  
Figure 11: The trend of \(p\) with \(L/B_{0}\) at different \(\varphi\).  
Figure 12: The trend of \(p\) with \(\beta\) at different \(\varphi\).  
given in Figure 13. \(p\) increases with increase in \(\lambda\). For \(\varphi=25^{\circ}\), the bearing capacity \(p\) raises by about 7% as \(\lambda\) increases from 0 to 1.4.  
For \(\varphi=35^{\circ}\), the bearing capacity \(p\) also increases by about 7%. It can be seen that the non-uniformity of the soil will have certain influence on the bearing capacity, but in this paper, in order to simplify the calculation, the linear growth of the cohesion distribution model is selected, although this does not match with the reality, but shows the non-uniformity of the soil cannot be ignored.
# A Novel Method for Constructing the S-Box Based on Spatiotemporal Chaotic Dynamics
## 5 Performance Analysis of the Constructed S-Box
### Nonlinearity  
Suppose a Boolean function is \(g(x)\). Its nonlinearity \(N_{g}\)[34] can be presented as:  
\[N_{g}=2^{n-1}(1-2^{-n}max_{\varphi\in GF(2^{n})}|S_{(g)}(\varphi)|), \tag{7}\]  
\[S_{(g)}(\varphi)=\sum_{\varphi\in GF(2^{n})}(-1)^{x\cdot\varphi\oplus g(x)}, \tag{8}\]  
where \(x\cdot\varphi=x_{1}\oplus\varphi_{1}+x_{2}\oplus\varphi_{2}+\ldots+x_{n}\oplus \varphi_{n}\). The higher the value of \(N_{g}\), the stronger \(g(x)\)'s ability to resist linear attacks.  
Table 3 gives the results of the nonlinearity analysis. The maximum nonlinearity was \(108\), the minimum \(102\), and the average \(104.5\). For the average values of nonlinearity in Table 3, our S-box accords with other S-boxes. Hence, the obtained S-box had good nonlinearity property.
## 3 Materials and Research Methods  
The research process can be presented algorithmically, through several successive steps, as follows:  
1. In the first step, we performed the analysis of various factors that affect the quality of user experience and created an interactive \(QoE_{i}\) model;
2. In the next step, the research process was implemented in accordance with a subjective approach to the assessment of the level of user experience and the survey method, on the basis of which a research instrument was created--a \(QoE\) questionnaire. A correlation amongst the influencing factors on \(QoE_{i}\) in formulated questions and research-independent, transition and dependent variables was established.
3. The third step was the process of online surveying of users of network services and applications about the level of certain indicators of the quality of subjective-user experience in interactions with communication services performed by professional companies--providers of telecommunication services in certain locations;
4. Data obtained by surveying users was prepared for processing in the fourth step;
5. The statistical analysis of the research sample was performed in the fifth step, where basic statistical indicators related to the responses to individual questions and the structure of respondents were given;
6. In the sixth step, a mathematical model was created to assess the subjective-user \(QoE_{i}\) based on the responses to the questions from the \(QoE\) questionnaire as input variables;
7. In the seventh step, a \(QoE_{i}\) probability model was created;
8. Correlation analysis of research variables was performed in the eighth step;
9. The last step represents the special focus of the research and refers to the results of \(QoE_{i}\) modeling. Within this step, the results of the \(QoE_{i}\) prediction and classification model based on machine learning techniques are particularly important.  
A more detailed description of each of the aforementioned steps with applied methods is given in the next part of this section.
# Internet of Things (IoT) Cybersecurity: Literature Review and IoT Cyber Risk Management
## 3 The Four-Layer IoT Cyber Risk Management Framework
### IoT Cyber Ecosystem Layer
#### 3.1.1 IoT Cybersecurity Technology Developers  
Many IoT managers do not have sufficient expertise to develop IoT cybersecurity solutions. Hence, IoT managers need to be aware of the trends of IoT cyber technology developers' communities. As the IoT market grows, so does the market for the IoT cybersecurity technology developers. Cybersecurity technology developers are outfitting IoT devices with the latest tools in order to secure the transfer of data, prevent hacking, and keep privacy standards [59].  
New technology developments such as 5G, serverless, and edge/fog computing IoT systems should be able to provide better protection against various security cyberattacks. Machine learning and AI will also show great potential for providing real-time prevention, detection, and recovery measures for IoT systems with higher accuracies than traditional methods. The IoT security platforms need to provide end-to-end solutions to deliver secure IoT systems unifying heterogeneous devices and applications from multiple vendors.
# Cross-Technology Interference-Aware Rate Adaptation in Time-Triggered Wireless Local Area Networks
## 5 Results and Analysis
### Performance of CTINet based on the \(\alpha\).  
#### 5.2.1 Packet Error Rate  
The comparison results for PER across the algorithms are shown in Figure 5. Packet errors are defined as cases where the bit error rate of a transmitted frame exceeds \(10^{-5}\). In the result graph, \"Ideal\" represents the outcome under ideal rate control, where the type and power of CTI-causing devices are accurately known for each slot, allowing rate selection based on the calculated SINR to mitigate interference effectively.  
Overall, algorithms incorporating the CARA framework demonstrate superior reliability by adaptively performing rate adaptation in response to CTI situations. M-CARA shows a decreasing PER trend as WLAN SNR increases, presenting a graph shape that is nearly identical to the ideal case. In contrast, M-Base exhibits a tendency where PER does not decrease in the SNR range of 18-30. This is likely because it fails to reduce the transmission rate appropriately in the presence of CTI, resulting in error accumulation.  
Packet error-based algorithms inherently adjust the MCS index based on the success or failure of packet transmission, which results in a relatively higher PER in the SNR range of 8-25 compared to measurement-based algorithms. Specifically, packet error-based algorithms, as shown in the experimental settings in Table 4, rely on success/failure thresholds for rate control. This makes them slower to react to channel state changes than measurement-based algorithms. Furthermore, because these algorithms operate implicitly, they tend to interpret the receipt of an ACK message as an indication of good channel  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Models** & **FLOPs (M)** & **Params (M)** & **MAE** & **CPU (ms)** \\ \hline CTINet-32-1.0 & 3.232 & 0.976 & 6.956 & 0.202 \\ CTINet-32-0.75 & 2.309 & 0.611 & 7.106 & 0.144 \\ CTINet-32-0.5 & 1.176 & 0.297 & 7.292 & 0.074 \\ CTINet-32-0.25 & 0.594 & 0.110 & 7.361 & 0.037 \\ \hline CTINet-64-1.0 & 9.821 & 0.976 & 3.041 & 0.614 \\ CTINet-64-0.75 & 7.293 & 0.611 & 3.109 & 0.456 \\ CTINet-64-0.5 & 3.760 & 0.297 & 3.230 & 0.235 \\ CTINet-64-0.25 & 2.032 & 0.109 & 3.256 & 0.127 \\ \hline CTINet-128-1.0 & 36.178 & 0.976 & 1.322 & 2.261 \\ CTINet-128-0.75 & 27.231 & 0.611 & 1.359 & 1.702 \\ CTINet-128-0.5 & 14.101 & 0.297 & 1.414 & 0.881 \\ CTINet-128-0.25 & 7.787 & 0.109 & 1.468 & 0.487 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparison of CTINet based on the \(\alpha\).  
quality. Consequently, they may attempt to select a higher rate, which leads to transmitting packets at rates exceeding the actual channel capacity, ultimately resulting in a worse PER (explained in more detail in Section 5.2.4).
# User-Perceived Capacity: Theory, Computation, and Achievable Policies
## 4 The Cross-Layer Approach
### The Probabilistic Scheduling Policy and Markov Chain  
We develop a probabilistic scheduling policy based on the queue state, the time index, and the channel state. To this end, we use \(f^{s}_{q,m,l}\) to characterize the probabilistic scheduling policy, which is given by  
\[f^{s}_{q,m,l}=\Pr\{s[t]=s|q[t]=q,m[t]=m,h[t]=h_{l}\}. \tag{13}\]  
Moreover, we have  
\[\sum\limits_{s\in\mathcal{S}}f^{s}_{q,m,l}=1,\quad\forall q\in\mathcal{Q},m \in\mathcal{M},h_{l}\in\mathcal{H}. \tag{14}\]  
Given a policy \(\boldsymbol{F}=\left\{f^{s}_{q,m,l}\right\}\), we denote by \(\boldsymbol{\pi_{\mathrm{F}}}\) the steady-state distribution of the Markov chain. Then, we have  
\[\boldsymbol{\pi_{\mathrm{F}}}=[\pi_{\mathrm{F}}(q,m)],\quad q\in\mathcal{Q},m \in\mathcal{M}, \tag{15}\]  
where \(\pi_{\mathrm{F}}(q,m)\) is the steady-state probability of \(q[t]=q\) and \(m[t]=m\). Considering the normalization condition, we have  
\[\sum\limits_{q\in\mathcal{Q}}\sum\limits_{m\in\mathcal{M}}\pi_{\mathrm{F}}(q, m)=1. \tag{16}\]  
For each given scheduling policy \(\boldsymbol{F}\), the following lemma presents the transition probability of the Markov chain, defined as  
\[\lambda_{(q,m),(q,m)}=\Pr\{(q[t+1],m[t+1])=(\tilde{q},\tilde{m})|(q[t],m[t])= (q,m)\}. \tag{17}\]  
**Lemma 1**.: _The transition probability is equal to_  
\[\lambda_{(q,m),(q,m)}=\begin{cases}\sum\limits_{h_{l}\in\mathcal{H}}\sum \limits_{s\in\mathcal{S}}\eta_{l}f^{s}_{q,m,l}\mathds{1}\{\tilde{q}=q-s\} \mathds{1}\{\hat{m}=m+1\}&\text{if }m<T-1,\\ \mathds{1}\{\tilde{q}=B\}\mathds{1}\{\hat{m}=0\}&\text{if }m=T-1,\end{cases} \tag{18}\]  
_where \(\mathds{1}\{\cdot\}\) is the indicator function._  
Proof.: When \(m<T-1\), the transition to the next state does not involve packet arrival. Thus, the queue state transition depends solely on the transmission rate, leading to a change from \(q\) to \(q-s\) with probability \(f^{s}_{q,m,l}\). Meanwhile, the time index will inevitably increase by \(1\). Thus, we have  
\[\lambda_{(q,m),(q,m)}=\sum\limits_{h_{l}\in\mathcal{H}}\sum\limits_{s\in \mathcal{S}}\eta_{l}f^{s}_{q,m,l}\mathds{1}\{\tilde{q}=q-s\}\mathds{1}\{\hat{ m}=m+1\},m<T-1. \tag{19}\]  
When \(m=T-1\), the queue state will definitely transition to \(B\), and the time index will be reset to zero.  
Based on Lemma 1, the balance equation is expressed as  
\[\sum_{q\in\mathbb{Q}}\sum_{m\in\mathcal{M}}\lambda_{(q,m),(q,\bar{m})}\pi_{\mathbf{ F}}(q,m)=\pi_{\mathbf{F}}(\bar{q},\bar{m}). \tag{20}\]
# Cascade Speech Translation for the Kazakh Language
###### Abstract  
Speech translation systems have become indispensable in facilitating seamless communication across language barriers. This paper presents a cascade speech translation system tailored specifically for translating speech from the Kazakh language to Russian. The system aims to enable effective cross-lingual communication between Kazakh and Russian speakers, addressing the unique challenges posed by these languages. To develop the cascade speech translation system, we first created a dedicated speech translation dataset ST-kk-ru based on the ISSAI Corpus. The ST-kk-ru dataset comprises a large collection of Kazakh speech recordings along with their corresponding Russian translations. The automatic speech recognition (ASR) module of the system utilizes deep learning techniques to convert spoken Kazakh input into text. The machine translation (MT) module employs state-of-the-art neural machine translation methods, leveraging the parallel Kazakh-Russian translations available in the dataset to generate accurate translations. By conducting extensive experiments and evaluations, we have thoroughly assessed the performance of the cascade speech translation system on the ST-kk-ru dataset. The outcomes of our evaluation highlight the effectiveness of incorporating additional datasets for both the ASR and MT modules. This augmentation leads to a significant improvement in the performance of the cascade speech translation system, increasing the BLEU score by approximately 2 points when translating from Kazakh to Russian. These findings underscore the importance of leveraging supplementary data to enhance the capabilities of speech translation systems.  
c 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).  
c 2023, 13, 8900. [[https://doi.org/10.3390/app13158900](https://doi.org/10.3390/app13158900)]([https://doi.org/10.3390/app13158900](https://doi.org/10.3390/app13158900))
## 2 Materials and Methods
### Numerical Simulation  
The response of six restored models was mechanically analysed with 3D- FEA (ANSYS 19.2, ANSYS Inc., Houston, TX, USA). Three models of a restored tooth for each cavity design and with a multilayer construction were created and investigated using different material combinations, as summarized in Table 1.  
All the volumes were discretized by 4-node tetrahedral elements with a total size extending from 0.06 mm to 0.2 mm. To minimalize the results mesh-dependency due to the small curvature radius and notch effects, mesh improvement techniques were used according to the mesh convergence test. 2. All the FEA analyses were focused on load during the closing phase of the chewing cycle [10]. The variability of chewing function was considered dependent on the contact between food and tooth surface [10]. Solid food apple pulp was displayed on the occlusal surface (Figure 2) and slide-type contact elements were used between the food and the tooth surface. For class I the total number of elements were 445,242 with 97,364 nodes in the model A, and 438,092 elements with 97,412 in models B and C. For class II the total number of elements were 404,766 with 88,513 nodes in the model C, and 395,712 elements with 86,333 in models E and F.  
It was stated that the shrinkage stresses in composite material restored teeth may be lower than those calculated on elastic model basis (Figure 3). Throughout the polymerization, kinetic stress relaxation goes together with the viscous flow of composites and the E modulus and viscosity rapidly upsurge. Seeing this behaviour, a basic approach has been used [29; 30]. Effective linear shrinkage sr = 0.001 was accepted in the analyses. The mechanical properties allocated to each material and the scales of linear shrinkage (%) of materials are potted in Table 2. Adhesive layers and shrinking materials polymerization shrinkage were simulated with the thermal expansion approach by transmission a one-degree drop in temperature [13; 29]. Physiological masticatory loads were simulated as an occlusal static load of 600 N and a transversal load of 20 N [10; 27; 28; 30]. These loads vertically and buccolingually applied on the food and simultaneously in combination with shrinkage effects [28].  
Figure 2: Loading condition with food bolus and meshing finite element model.  
The model's lower surfaces were constrained in all the directions. Statistically and linearly analyses were carried out. The analyses were performed considering a non-failure condition and all materials were assumed to present an elastic behaviour.