esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Egress Model for Multiplex Buildings under Fire Based on Artificial Neural Network
## 2 Fire Simulation for Multiplex Building
### Description of Fire Simulation
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Parameters**} & \multicolumn{2}{c}{**Locations**} & \multicolumn{4}{c}{**Slope at the Distance from Fire Compartment**} \\ \cline{3-8} \multicolumn{2}{c}{} & **1 m** & **15 m** & **16 m** & **25 m** & **26 m** & **50 m** \\ \hline \multirow{4}{*}{O2} & Polyurethane & \(-\)0.0013 & \(-\)0.0009 & \(-\)0.0021 & \(-\)0.003 & \(-\)0.0024 & \(-\)0.0036 \\ \cline{2-8}  & Polystyrene & \(-\)0.0013 & \(-\)0.0009 & \(-\)0.0022 & \(-\)0.0010 & \(-\)0.0014 & \(-\)0.0026 \\ \cline{2-8}  & Ethanol & \(-\)0.0012 & \(-\)0.0008 & \(-\)0.0023 & 0.0011 & \(-\)0.0005 & \(-\)0.0011 \\ \cline{2-8}  & Kerosene & \(-\)0.0011 & \(-\)0.0009 & \(-\)0.0017 & \(-\)0.0009 & \(-\)0.0013 & \(-\)0.0033 \\ \cline{2-8}  & Wood & \(-\)0.0009 & \(-\)0.0006 & \(-\)0.0014 & \(-\)0.0008 & \(-\)0.0009 & \(-\)0.0012 \\ \cline{2-8}  & AVG & \(-\)0.0012 & \(-\)0.0008 & \(-\)0.0019 & \(-\)0.0009 & \(-\)0.0013 & \(-\)0.0024 \\ \hline \multirow{4}{*}{CO2} & Polyurethane & 0.0099 & 0.0303 & 0.0898 & 0.0932 & 0.2054 & 0.2995 \\ \cline{2-8}  & Polystyrene & 0.0120 & 0.0295 & 0.0530 & 0.0284 & 0.0985 & 0.1935 \\ \cline{2-8}  & Ethanol & 0.0140 & 0.0287 & 0.0351 & 0.0518 & 0.0759 & 0.0993 \\ \cline{2-8}  & Kerosene & 0.0086 & 0.0280 & 0.0427 & 0.0313 & 0.0789 & 0.0896 \\ \cline{2-8}  & Wood & 0.0084 & 0.0271 & 0.0419 & 0.0940 & 0.0818 & 0.0948 \\ \cline{2-8}  & AVG & 0.0106 & 0.0287 & 0.0525 & 0.0598 & 0.1081 & 0.1942 \\ \hline \multirow{4}{*}{Visibility} & Polyurethane & \(-\)0.0223 & \(-\)0.1811 & \(-\)0.2979 & \(-\)0.4640 & \(-\)0.4251 & \(-\)0.5966 \\ \cline{2-8}  & Polystyrene & \(-\)0.1509 & \(-\)0.2027 & \(-\)0.2428 & \(-\)0.1969 & \(-\)0.3507 & \(-\)0.4523 \\ \cline{2-8}  & Ethanol & \(-\)0.1467 & \(-\)0.2560 & \(-\)0.3668 & \(-\)0.3668 & \(-\)0.9863 & \(-\)0.6662 \\ \cline{2-8}  & Kerosene & \(-\)0.0658 & \(-\)0.1050 & \(-\)0.2857 & \(-\)0.1663 & \(-\)0.3998 & \(-\)0.5133 \\ \cline{2-8}  & Wood & \(-\)0.0450 & \(-\)0.8123 & \(-\)0.2292 & \(-\)0.1306 & \(-\)0.1054 & \(-\)0.2966 \\ \cline{2-8}  & AVG & \(-\)0.0861 & \(-\)0.3114 & \(-\)0.2845 & \(-\)0.2649 & \(-\)0.4535 & \(-\)0.505 \\ \hline \multirow{4}{*}{CO} & Polyurethane & 0.0102 & 0.0340 & 0.1191 & 0.1353 & 0.3443 & 0.1287 \\ \cline{2-8}  & Polystyrene & 0.0123 & 0.0327 & 0.0581 & 0.0309 & 0.1221 & 0.1942 \\ \cline{2-8}  & Ethanol & 0.0976 & 0.0251 & 0.0474 & 0.0541 & 0.2433 & 0.2093 \\ \cline{2-8}  & Kerosene & 0.0488 & 0.0311 & 0.0472 & 0.0352 & 0.0980 & 0.1081 \\ \cline{2-8}  & Wood & 0.0386 & 0.0303 & 0.0462 & 0.1503 & 0.1058 & 0.1488 \\ \cline{2-8}  & AVG & 0.0415 & 0.0306 & 0.0636 & 0.0812 & 0.1827 & 0.1578 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Slope with respect to fuel type and distance from the fire compartment measured in the nonfire area.
# Modeling of Satellite-to-Underwater Integrated FSO-PON System Using NOMA-VLC
## 4 Results and Discussion
In addition to this, the proposed work's complexity highly depends on the OCDMA code sequence length, internet of underwater communication and multi-beam satellite communication. Thus, to reduce the system's complexity, reasonable input laser sources with modulators, 1D-based MNZCC code with suitable code length, line of sight single beam underwater and satellite communication are considered. Table 11 shows the complexity level of the proposed work ranges from moderate to high depending on various factors stated above. For the proposed work, stable low power consumption and cost-effective design are realized from sea-to-space communication. Table 12 illustrates the comparisons of the integrated FSO-PON system using NOMA-VLC, considering gain (\(G\)), noise figure (\(NF\)), \(P_{i}\), output power (\(P_{o}\)), input OSNR (\(OSNR_{i}\)) and output OSNR (\(OSNR_{o}\)) for distinct DN/UP wavelengths.
# A Review of Time-Scale Modification of Music Signals +
## 3 TSM Based on Overlap-Add (OLA)
### Tricks of the Trade  
While OLA is unsuited for modifying audio signals with harmonic content, it delivers high quality results for purely percussive signals, as is the case with drum or castanet recordings [8]. This is because audio signals with percussive content seldom have local periodic structures. The phase jump artifacts introduced by OLA are therefore not noticeable in the output signals. In this scenario, it is important to choose a very small frame length \(N\) (corresponding to roughly 10 milliseconds of audio material) in order to reduce the effect of _transient doubling_, an artifact that we discuss at length in Section 4.2.
## 3 Construction of Tower Mechanical Failure Dataset Based on SMOTE Algorithm
In the validation process, the proportion of test samples was 30% of all samples, and the number of the test samples of the original dataset was 36, among which the number of positive samples was 6 and the number of negative samples was 30. The number of test samples of the new dataset after SMOTE algorithm processing was 60, among which the number of both positive and negative samples was 30. Without normalization, as shown in Figure 6, the validation accuracy of the original dataset was 88.89%, and misclassification occurred four times, all of which occurred in the positive samples. As shown in Figure 7, the validation accuracy of the new dataset was 91.67% and misclassification occurred five times. It is concluded that in unbalanced samples, the classifier is more likely to classify the samples to the side with a larger number, and the dataset obtained with the SMOTE algorithm is better balanced and has higher prediction accuracy. Figure 5: (a) Comparison of echocardiogram dataset before applying SMOTE algorithm; (b) comparison of echocardiogram dataset after applying SMOTE algorithm. Figure 6: Echocardiogram raw dataset validation accuracy. In practice, due to the randomness of the SMOTE algorithm, it leads to the problem that the generated new samples and the original samples will overlap or nearly overlap, especially in a dataset with a small number of features. At this time, the number of effective samples that really work is still lower than the actual number of samples, thus in low-dimensional data, the value of parameter C should be changed appropriately so that the number of new samples of a certain category is slightly larger than the expected number, which can eliminate the influence of duplicate samples on the classification results to some extent and compensate for the number of effective samples to some extent.
# Modeling and Analysis of High-Power Ti:sapphire Laser Amplifiers-A Review
## 3 Phase Distortion Analysis
### Thermal Lens Model
Figure 15: Wavefront shapes induced by the thermal effect (**a**) by the temperature-dependent refractive index change, (**b**) by the thermal expansion, and (**c**) by the PE effect of the Ti:sapphire crystal, in case of [62]. \[f_{e}^{-1}=\alpha\left(n-1\right)\left[1+\
u-\Omega\left(\theta\right)\right]\frac{ \partial^{2}T\left(r,\theta\right)}{\partial r^{2}} \tag{34}\]  
\[\Omega\left(\theta\right)=\frac{\left(1-\
u^{2}\right)-n\left[1-\
u^{2}+S \left(\theta\right)E\right]}{\left(1-n\right)\left(1-\
u\right)} \tag{35}\]  
\[S\left(\theta\right)=\frac{1}{8}\left(3s_{11}+3s_{12}-4s_{13}\right)\cos \left(2\theta\right)-\frac{1}{4}\left[5s_{11}+5s_{12}+4s_{13}+2\sqrt{3}s_{14} \sin\left(2\theta\right)\right] \tag{36}\]  
where \(\alpha\) is the coefficient of thermal expansion, \(\
u\) is [PERSON]'s ratio, \(\Omega\) is the resistance of thermal expansion, \(E\) is [PERSON]'s modulus and \(S\) is the compliance tensor. Last, the PE effect can be derived with Equation (32). If we use only the second term for the simplification of the thermal lens expression, the simple lens shape by the PE effect can be expressed as Equation (37). Then finally, we can summarize the thermal lens effect as Equations (38) and (39). \[f_{PE}^{-1}=-\frac{\alpha E\ n^{3}}{16\left(1-\
u\right)}\left[\left(10\pi_{31 }+4\pi_{33}\right)+\left(-3\pi_{31}+2\pi_{33}\right)\cos\left(2\theta\right) \right]\frac{\partial^{2}T\left(r,\theta\right)}{\partial r^{2}} \tag{37}\]  
\[f_{x}^{-1}=\left\{\frac{dn}{dT}+\alpha\left(n-1\right)\left[1+\
u-\Omega\left( 0\right)\right]-\frac{\alpha En^{3}}{16\left(1-\
u\right)}\left(7\pi_{31}+6 \pi_{33}\right)\right\}\frac{\partial^{2}T\left(r,\theta\right)}{\partial r^{ 2}} \tag{38}\]  
\[f_{y}^{-1}=\left\{\frac{dn}{dT}+\alpha\left(n-1\right)\left[1+\
u-\Omega\left( \pi/2\right)\right]-\frac{\alpha En^{3}}{16\left(1-\
u\right)}\left(13\pi_{31 }+2\pi_{33}\right)\right\}\frac{\partial^{2}T\left(r,\theta\right)}{\partial r ^{2}} \tag{39}\]  
The thermal lens focal length used in the design process of the existing ultra-high-power Ti:sapphire systems only considers the change in the refractive index depending on the temperature. Using the method, we exhibit that more accurate predictions are possible. Figure 16 compares the focal length of another ultra-high-power laser system. The blue and red lines are the estimate that is obtained using a conventional thermal lens focal length equation and using our model, respectively. The triangular markers are the measured thermal focal lengths. The prediction using our model is much more accurate to the measured thermal lens focal length. Knowing the thermal lens focal length easily in the design process of the system is expected to assist in the design of the optical compression system and target chamber behind the amplifier.
## 2 Preliminaries  
Let \(\mathbb{R}:=(-\infty,\infty)\) denote the set of real numbers, \(\mathbb{R}_{\geq 0}:=[0,\infty)\) the set of real positive numbers, and \(\mathbb{N}:=\{0,1,2,\ldots\}\) the set of integer numbers. For a real symmetric matrix \(A\), we denote by \(\lambda_{\min}(A)\) the minimum eigenvalue and by \(\lambda_{\max}(A)\) the maximum eigenvalue. The transpose of \(A\) is written as \(A^{T}\) and the identity matrix of dimension \(n\) is denoted by \(\mathbb{I}_{n}\). We write \((x,y)\in\mathbb{R}^{n_{x}+n_{y}}\) to express the vector \([x^{T},y^{T}]^{T}\) for \(x\in\mathbb{R}^{n_{x}}\) and \(y\in\mathbb{R}^{n_{y}}\). The Euclidean norm of a vector \(x\in\mathbb{R}^{n_{x}}\) is defined as \(|x|:=\sqrt{x^{T}x}\), and if a matrix \(A\in\mathbb{R}^{n\times m}\), we define \(|A|:=\sqrt{\lambda_{\max}(A^{T}A)}\).  
Based on the framework of [40], we consider the following hybrid systems:  
\[\dot{x}=F(x,w)\quad x\in\mathcal{C},\qquad x^{+}\in G(x)\quad x\in\mathcal{D}, \tag{1}\]  
where \(x\in\mathbb{R}^{n_{x}}\) is the state, \(w\in\mathbb{R}^{n_{w}}\) is the external disturbance, \(\mathcal{C}\) is the flow set, \(F\) is the flow map, \(\mathcal{D}\) is the jump set, and \(G\) is the jump map.  
**Definition 1** ([44]).: _System (1) is said to achieve \(\mathcal{L}_{2}\)-gain stability from the input \(w\) to an output \(y=f(x,w)\) if the following holds:_  
\[||y||_{2}\leq\alpha(|x(0,0)|)+\eta||w||_{2}, \tag{2}\]  
_where \(\alpha\in\mathcal{K}_{\infty}\) and \(\eta\geq 0\), and in this case, the achieved \(\mathcal{L}_{2}\)-gain is less than or equal to \(\eta\)._
## 5 Simulation Study
### Impact of Varying Sensor Node Density  
Figure 8 illustrates the impact of sensor node density by varying the total number of people equipped with sensor nodes in the network.  
Figure 6: data delivery _vs._ value of _Th._  
Figure 7: Data delivery _vs._ value of \(\lambda\).  
We see that the ARP scheme outperforms the other two protocols in terms of delivery ratio. With the increase of sensor node density, the delivery ratios of all three schemes increase. This is reasonable since the number of neighboring sensors of each sensor node increases, and more sensor nodes help relaying messages and messages have a better chance of reaching sink nodes. Meanwhile, we also notice that flooding protocol achieves the highest data delivery ratio when a low node density is deployed. This can be explained as follows: the number of neighboring sensors for each sensor node decreases when fewer nodes are deployed, thus fewer message copies are generated in the network. Since the buffer queue may be large enough for these copies to fit in, the data delivery ratio increases as a result. Figure 8(b) shows that the number of average copies increases in all protocols with higher node density, because the number of neighboring sensors of each sensor node increase then. As more copies can increase the opportunity to deliver the matched message to sink nodes, the average data delivery delay decreases in ARP, LABEL and flooding protocols with the increase of sensor node density.
# SemABC: Semantic-Guided Adaptive Bias Calibration for Generative Zero-Shot Point Cloud Segmentation
## 3 Methods
### Adaptive Bias Calibration Module  
We introduce an adaptive bias calibration method based on the confidence of the predicted segmentation probabilities to improve the model's adaptability in open scenarios. That is, we estimate a confidence that indicates the likelihood of a successful prediction and use it as a weight to integrate the predictions of two branches. Intuitively, when the confidence of the primary segmentation branch is high, we assign a smaller weight to the prediction from the visual-semantic fusion branch, and conversely, when the confidence is low, we give more weight to the visual-semantic fusion branch's prediction.  
Specifically, we adopt the kurtosis measure to estimate the predicted confidence, denoted as \(\kappa\) (see Equation (9)), which is a statistical measure that describes the shape of the data distribution. Kurtosis reflects the steepness or flatness of the distribution, particularly indicating the height of the peak in the probability density curve at the average value. A higher kurtosis indicates that the distribution has a more pronounced peak, representing a higher degree of prediction confidence. Given the predicted point-wise segmentation probabilities \(P_{main}\), we adopt \(\kappa\) to balance the effect of the two branches:  
\[\kappa=E[(\frac{P_{main}-\mu}{\sigma})^{4}]^{\rho}, \tag{9}\]  
\[P=P_{main}+\frac{\rho}{\kappa}P_{aux}, \tag{10}\]  
where \(\mu\) and \(\sigma\) are the mean and standard deviation of \(P_{main}\), and \(\rho\) is a parameter that modulates the strength of the kurtosis influence. \(\beta\) is a hyperparameter that controls the confidence weight value.
# Key Quality Indicators Prediction for Web Browsing with Embedded Filter Feature Selection
## 4 Experimental Results and Analysis
### Experiments of KQI Prediction
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No. of Features** & **Hamming Loss** & **Macro-F1** & **Micro-F1** & **Instance-F1** \\ \hline ML-ReliefF & 1 & 0.299 \(\pm\) 0.007 & 0.817 \(\pm\) 0.006 & 0.824 \(\pm\) 0.005 & 0.800 \(\pm\) 0.005 \\  & 4 & 0.176 \(\pm\) 0.004 & 0.877 \(\pm\) 0.005 & 0.881 \(\pm\) 0.004 & 0.868 \(\pm\) 0.003 \\ ML-ReliefF & 7 & 0.161 \(\pm\) 0.001 & 0.887 \(\pm\) 0.004 & 0.891 \(\pm\) 0.002 & 0.878 \(\pm\) 0.001 \\  & 10 & 0.164 \(\pm\) 0.005 & 0.886 \(\pm\) 0.005 & 0.889 \(\pm\) 0.004 & 0.875 \(\pm\) 0.004 \\  & 13 & 0.162 \(\pm\) 0.003 & 0.887 \(\pm\) 0.004 & 0.890 \(\pm\) 0.003 & 0.876 \(\pm\) 0.002 \\ \hline \hline \multirow{4}{*}{RF-ML} & 1 & 0.299 \(\pm\) 0.007 & 0.817 \(\pm\) 0.006 & 0.824 \(\pm\) 0.005 & 0.800 \(\pm\) 0.005 \\  & 4 & 0.287 \(\pm\) 0.004 & 0.817 \(\pm\) 0.005 & 0.825 \(\pm\) 0.004 & 0.802 \(\pm\) 0.004 \\  & 7 & 0.181 \(\pm\) 0.011 & 0.874 \(\pm\) 0.010 & 0.879 \(\pm\) 0.008 & 0.864 \(\pm\) 0.008 \\  & 10 & 0.183 \(\pm\) 0.013 & 0.873 \(\pm\) 0.012 & 0.878 \(\pm\) 0.009 & 0.863 \(\pm\) 0.009 \\  & 13 & 0.162 \(\pm\) 0.003 & 0.887 \(\pm\) 0.004 & 0.890 \(\pm\) 0.003 & 0.876 \(\pm\) 0.002 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results of KQI prediction with unweighted ML-kNN (\(k\) = 15, L = 14). (3) Influence of window length L on KQI prediction  
As the prediction window length is an important parameter in feature-weighted KQI prediction, we need to find its optimal value. Here, we divide the dataset according to different \(L\) and compare its influence on the KQI prediction. According to the experimental results (as shown in Figure 5), except that RF-ML is not sensitive to the window length, the other two FS algorithms reach the best performance with a window length of 14. Therefore, in practice the KQI prediction with \(L=14\) is suggested.
# Can We Exploit Machine Learning to Predict Congestion over mmWave 5G Channels?
###### Abstract  
It is well known that transport protocol performance is severely hindered by wireless channel impairments. We study the applicability of Machine Learning (ML) techniques to predict congestion status of 5G access networks, in particular mmWave links. We use realistic traces, using the 3 GPP channel models, without being affected using legacy congestion-control solutions. We start by identifying the metrics that might be exploited from the transport layer to learn the congestion state: delay and inter-arrival time. We formally study their correlation with the perceived congestion, which we ascertain based on buffer length variation. Then, we conduct an extensive analysis of various unsupervised and supervised solutions, which are used as a benchmark. The results yield that unsupervised ML solutions can detect a large percentage of congestion situations and they could thus bring interesting possibilities when designing congestion-control solutions for next-generation transport protocols.  
machine learning mmWave; 5G; congestion control; ns-3; network simulation; unsupervised learning +
Footnote †: journal: Applied
# A Unified Nonlinear Elastic Model for Rock Material
## 6 Application of the New Model  
According to the data in Table 1, the uniaxial compressive strength (\(UCS\)) values and the initial elastic modulus \(E_{ni}\) values for the different rocks were counted, and the scatter diagrams are shown in Figure 13a-d. The results of the linear fitting of the data are also shown in these diagrams. The coefficients of correlation \(\mathrm{R}^{2}\) values for the four rock types are greater than 0.90, indicating that the \(UCS\) values and the \(E_{ni}\) values have a good linear relationship [39]. The slope and intercept of different linear fittings and the \(\mathrm{R}^{2}\) values are listed in Table 4. Granite and limestone have the highest \(\mathrm{R}^{2}\) values, followed by dolomite and sandstone. The slopes of the linear formula for the different rocks were close, whereas the intercepts were different. When marble was added as another rock type, the scatter plots of the \(UCS\) and \(E_{ni}\) values for the five rock types were determined and are listed in Figure 14. The results of the linear fitting of the data are also shown in Figure 14. In order to ascertain whether the experimental values were, in fact, unique to the present model, the 95% prediction band for the present model was determined and is represented by the red band. Almost all the experimental data fall within the 95% prediction band of the present  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type of Rock** & **Best-Fit Range** & \
\begin{tabular}{c} **Range When \(\mathrm{R}^{2}\)** \\ **Values Exceed 0.95** \\ \end{tabular} &
\begin{tabular}{c} **Range When \(\mathrm{R}^{2}\)** \\ **Values Exceed 0.80** \\ \end{tabular} \\ \hline Dolomite & 0.86\(\sim\)0.96 & 0.58–1.20 & 0.08–1.46 \\ Granite & 0.65\(\sim\)0.99 & 0.28–1.23 & 0.00–1.47 \\ Limestone & 0.80–1.03 & 0.51–1.22 & 0.04–1.52 \\ Sandstone & 0.82–1.05 & 0.49–1.26 & 0.00–1.32 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Range of \(m\) values with different \(\mathrm{R}^{2}\) values.  
model, which means that the _UCS_ values can be predicted by the \(E_{ni}\) values. The linear fitting formula is listed below:  
\[UCS=0.927E_{ni}+11.26 \tag{24}\]  
Figure 14: Comprehensive relationship between _UCS_ and \(E_{ni}\) for all the rock types.  
Figure 13: Correlations between _UCS_ and \(E_{ni}\) for different rock types. (**a**—Dolomite; **b**—Granite; **c**—Limestone; **d**—Sandstone).
# Design of Edge-IoMT Network Architecture with Weight-Based Scheduling
## 2 Related Works
### Algorithms for Transmission Scheduling of Physiological Signals
#### 3.4.1 Method 1: Priority Queueing  
Each element in the priority queue (PQ) has its own priority, and the higher-priority queue has absolute priority over the lower-priority queue. Packets with a lower priority can be delivered only after the packets of a higher priority are transmitted, and if they have the same priority, they are served by the order of their arrival. This queuing mechanism ensures the delivery of high-priority packets in the event of network congestion. PQ is an unquantified Differentiated services (DiffServ) that only defines pre-defined high-priority packets with priority forwarding rights, but its potential drawback is that a high percentage of priority traffic will cause ordinary traffic to suffer extreme performance degradation, which may make lower priority tasks never be allocated with resources, resulting in a starvation phenomenon.  
The three physiological signals we used are assigned their own priorities. Since ECG is a vital signal that can be used to identify many signs of disease, ECG is given the highest priority. Consequently, EEG and EMG are the medium and the lowest priority, respectively. As shown in Figure 11, the received physiological signals are placed in a queue for transmission according to their priorities, but when a higher priority signal is added during the waiting period, the lower priority signal can be transmitted only after the higher priority packet has been transmitted. If the sampling rate is greater than the transmission rate, the low-priority physiological signals may have a long waiting time or even fail to be transmitted.