esa-sceva/satcom-chunk-collection huggingface dataset:
# Two-Way Multi-Antenna Relaying with Simultaneous Wireless Information and Power Transfer
## 3 PSR Protocol
### PS-DF-STC Scheme
\begin{table}
\begin{tabular}{c c c c c c c} \hline \(M\) & \(N\) & \multicolumn{4}{c}{\(\mathbf{B}\)} & \multicolumn{4}{c}{\(\mathbf{H}\)} \\ \hline
2 & 2 & & \(\left[\begin{array}{cccc}b_{\rm r}[k]&-b_{\rm r}[k+1]\\ b_{\rm r}[k+1]&b_{\rm r}[k]\end{array}\right]\) & & & \(\left[\begin{array}{cccc}h_{i,1}&h_{i,2}\\ h_{i,2}&-h_{i,1}\end{array}\right]\) \\ \hline
3 & 4 & \(\left[\begin{array}{cccc}b_{\rm r}[k]&-b_{\rm r}[k+1]&-b_{\rm r}[k+2]&-b_{ \rm r}[k+3]\\ b_{\rm r}[k+1]&b_{\rm r}[k]&b_{\rm r}[k+3]&-b_{\rm r}[k+2]\\ b_{\rm r}[k+2]&-b_{\rm r}[k+3]&b_{\rm r}[k]&b_{\rm r}[k+1]\end{array}\right]\) & \(\left[\begin{array}{cccc}h_{i,1}&h_{i,2}&h_{i,3}&0\\ h_{i,2}&-h_{i,1}&0&h_{i,3}\\ h_{i,3}&0&-h_{i,1}&-h_{i,2}\\ 0&-h_{i,3}&h_{i,2}&-h_{i,1}\end{array}\right]\) \\ \hline
4 & 4 & \(\left[\begin{array}{cccc}b_{\rm r}[k]&-b_{\rm r}[k+1]&-b_{\rm r}[k+2]&-b_{ \rm r}[k+3]\\ b_{\rm r}[k+1]&b_{\rm r}[k]&b_{\rm r}[k+3]&-b_{\rm r}[k+2]\\ b_{\rm r}[k+2]&-b_{\rm r}[k+3]&b_{\rm r}[k]&b_{\rm r}[k+1]\\ b_{\rm r}[k+3]&b_{\rm r}[k+2]&-b_{\rm r}[k+1]&b_{\rm r}[k]\end{array}\right]\) & \(\left[\begin{array}{cccc}h_{i,1}&h_{i,2}&h_{i,3}&h_{i,4}\\ h_{i,2}&-h_{i,1}&-h_{i,4}&h_{i,3}\\ h_{i,3}&h_{i,4}&-h_{i,1}&-h_{i,2}\\ h_{i,4}&-h_{i,3}&h_{i,2}&-h_{i,1}\end{array}\right]\) \\ \hline \end{tabular}
\end{table} 
Table 1: Examples of \(\mathbf{B}\) and \(\mathbf{H}\) for space-time coding. Following the same procedure as in (9), the average BER associated with \(\gamma_{\text{ri}}\) is obtained as:  
\[ \begin{array}{ll}\overline{P}_{\text{e},\text{r}i}=&\frac{\sigma\ \Gamma(M+1/2)\big{(}\eta\rho M(P^{2}+\sigma_{2}^{2})\big{)}^{1/2}\big{(}\sigma_ {2}^{2}+\sigma_{2}^{2}\big{)}^{M}}{2\sqrt{\pi}\ \Gamma(M+1)\big{(}\eta\rho M \alpha^{2}\big{(}P^{2}+\sigma_{2}^{2}\big{)}+\sigma_{2}^{2}+\sigma_{2}^{2} \big{)}^{M+1/2}}\\ &\times_{2}\text{F}_{1}\bigg{(}1,M+\frac{1}{2};M+1;\frac{\sigma_{2}^{2}+\sigma _{2}^{2}}{\eta\rho M\alpha^{2}\big{(}P^{2}+\sigma_{2}^{2}\big{)}+\sigma_{2}^{2} +\sigma_{2}^{2}+\sigma_{2}^{2}}\bigg{)}.\end{array}  \tag{24}\]  
At the end node T\({}_{i}\), the intended signal \(x_{j}(j=1,2;j\
eq i)\) can be finally recovered by performing bit-level network decoding of \(\hat{x}_{\text{r}}\) with its own signal \(x_{i}\), and the corresponding end-to-end BER is obtained using Equation (17).
# An Adaptive Delay-Tolerant Routing Algorithm for Data Transmission in Opportunistic Social Networks
## Appendix A The Details of Curve-Trapezoid Mamdani Fuzzy Inference System  
The remaining cache and energy evaluate the capacity of a node to store and process information shared from message carriers. However, these two factors are also influenced by other elements, such as the total number of tasks in the node, the way to supply the energy or the caching protocol of the node. To this end, the levels of remaining cache and remaining energy may change over time. If the transmission evaluation value between nodes is computed via the constantly variational index, the message carrier may make an inaccurate data transmission decision. Accordingly, we construct the transmission evaluation value by adopting a strategy of fuzzy inference, which assesses the special relationship between the two factor and data transmission from the perspective of the level of ambiguity, rather than the determined value, and more importantly, greatly reduces the interference of other uncertain factors to the final data transmission decision. A fuzzy inference system can be developed to evaluate the impact of two-factor on data transmission and to compute the transmission evaluation value. The Mamdani fuzzy inference system [13; 23] will be regarded as the system model used in the proposed algorithm due to its high extensibility and applicability, and it includes three components: a fuzzy component, a fuzzy inference component, and a De-Blurring component [13; 24; 25]. Next, we will introduce the construction process of each component proposed in this paper in detail.
# Adaptive Multi-Grained Buffer Management for Database Systems
## 3 Adaptive Multi-Grained Buffer Management
### Operations of AMG-Buffer
#### 3.2.1 Read-Tuple Operation  
Algorithm 1 shows the read-tuple operation of AMG-Buffer. We first get the page descriptor via the mapping table. If not found, we load the original page from SSDs and place it into the F-Buffer. Otherwise, we determine which sub-buffer the tuple is placed in by checking _PageType_. If the page is in the F-Buffer or the P-Buffer, the _PageType_ is _Full_Page_ and we read the tuple from the intact page and update _VisitBitmap_. If not, the _PageType_ is _Sparse_Page_ and we need to check the existence of the tuple through _VisitBitmap_, i.e., if the bit corresponding to the tuple is 1, we can get the memory address of the tuple by looking up in _SlotArray_; if not, we reload the original page into the F-Buffer and read the target tuple.
# Topp-Leone Modified Weibull Model: Theory and Applications to Medical and Engineering Data
## 1 Introduction and Motivation
For example, the \(TL\) Weibull distribution, \(TL\) inverse Weibull distribution, \(TL\) Burr XII distribution, \(TL\) geometric distribution, the transmuted \(TL\)-\(G\) family of distributions, \(TL\) odd log-logistic family, \(TL\) Lomax distribution, \(TL\)-\(G\) family of distributions, the \(TL\)-\(G\) generalized exponential power series distribution, \(TL\) power series family of distributions, the type II \(TL\)-\(G\) family, the type II generalized \(TL\)-\(G\) family, the Frechet \(TL\)-\(G\) family, among others. See [29] for elaborate references. In this article, the Topp-Leone modified Weibull (TLMW) distribution, which has four parameters are introduced by connecting \(G(x)\) in Equation (1) to the cdf in Equation (5). The TLMW distribution encompasses the behavior of and provides better fits than some well known lifetime distributions, such as Kumaraswamy Weibull, generalized modified Weibull, extend odd Weibull Lomax, Weibull-Lomax, Marshall-Olkin alpha power extended Weibull and exponentiated generalized alpha power exponential distributions, distributions. We are motivated to introduce the (\(TLMW\)) distribution because it is capable of modeling decreasing, increasing, J-shaped, U-shaped and bathtub hrf which are common in reliability studies and lifetime data analysis. It might be a useful option for fitting skewed data that other common distributions may not be able to correctly fit, and it may also be utilized in a range of challenges in diverse sectors such as biological investigations, industrial dependability, the suggested family have also an important utilisation in the insurance industry see; [30; 31; 32] and survival analysis. The reminder of the paper is organized as follows: In Section 2, the Topp-Leone modified Weibull \((TLMW)\) distribution is defined. Also, an useful expansion of the \((TLMW)\) distribution and some sub-models are studied in the same section. Some statistical features of the \(TLMW\) distribution are discussed in Section 3. In Section 4, the maximum likelihood (ML), maximum product spacing (MPS) and Bayesian estimators of the unknown parameters are obtained. Section 5 concerned with simulation results. Four illustrative applications based on real data sets related to medical and engineering sciences are investigated in Section 6. Finally, some conclusions are provided in Section 7.
# Comparison of Dimensionality Reduction Approaches and Logistic Regression for ECG Classification
## 3 Materials and Methods
### Logistic Regression (LR)
#### 3.2.2 Bayesian Logistic Regression  
To incorporate parameter uncertainty and prior beliefs, we implemented two Bayesian logistic regression models. The first uses a non-informative Jeffreys prior [17], which is invariant to scale and suitable in the absence of expert knowledge. The second model (LR-BN) assumes a weakly informative multivariate normal prior, where large variance reflects prior neutrality. Both models were estimated using Metropolis-Hastings MCMC sampling [18], allowing posterior distributions over all 11 coefficients to be inferred.  
The Bayesian framework proved useful in assessing the robustness of variable contributions and improved classification stability in low-prevalence classes. In particular, posterior summaries offered interpretable insights into the importance of individual ECG features across rhythm types. Though not novel methods per se, their use in this study highlights how Bayesian inference adds value to ECG analysis by quantifying uncertainty, an often-overlooked aspect in clinical decision systems.
## 3 Results and Discussion
### Thermal Conductivity  
Figure 9 shows the conductivity of fiber-cement flat sheets with the addition of YCF and MCF. The thermal conductivity of cement sheets decreased when the addition of YCF and MCF content increased over time. The conductivity coefficient of cement board made with YCF decreased from 0.334 W/mK to 0.224 W/mK with the addition of fiber from 5% to 9.5% by weight of binder. The thermal conductivity of fiber-cement flat sheets mixed with MCF was reduced from 0.354 W/mK to 0.245 W/mK with the addition of fiber ranging from 5% to 9.5% by weight of binder. Typically, thermal conductivity properties increase because of the decreasing unit weight of concrete [22].  
Figures 10 and 11 illustrate the relationship between the bulk density and thermal conductivity of fiber-cement flat sheets with YCF and MCF. There was a strong relationship between two parameters, with correlation coefficients (\(R^{2}\)) of 0.90 and 0.84. The relation is expressed by Equations (4) and (5):  
\[K=0.0002D-0.1033,\,\,R^{2}\,\,=\,\,0.90 \tag{4}\]  
\[K=0.0002D-0.0852,\,\,R^{2}\,\,=\,\,0.84 \tag{5}\]  
where \(K\) is thermal conductivity (W/mK) and \(D\) is bulk density (kg/m\({}^{3}\)).  
Figure 8: The modulus of elasticity and modulus of rupture in fiber–cement flat sheets [19,20].  
Figure 11: The bulk density and thermal conductivity of flat sheets with MCF.  
Figure 10: The bulk density and thermal conductivity of flat sheets with YCF.  
Figure 9: The conductivity of fiber–cement flat sheets.
# Personalized Online Live Video Streaming Using Softmax-Based Multinomial Classification
## 2 Related Work
### Dash  
The DASH method is able to provide video services with disparate features (e.g., adaptive bitrate switching) at different service types (e.g., on-demand, time-shifted viewing) to users, as illustrated in Figure 3[20]. The DASH method has been studied for contributing to seamless video streaming services on one's mobile device. That is, the DASH aims to provide high-quality video not only in the case of the traditional video streaming services (which do not have any interaction with user), but also in cases with user interaction. The Real-Time Streaming Protocol (RTSP) method, one of the traditional video streaming methods, uses a stateful protocol. Upon a user connecting to the streaming server, the server keeps track of the state of the user's environment. After that, the server transfers continuous streams  
Figure 2: Structure of the scalable video coding (SVC) method.  
of packets (video) over UDP or TCP [21]. Unfortunately, the Hypertext Transfer Protocol (HTTP) is stateless, and the RTSP method operates based on HTTP. On the other hand, the DASH method has media (video) for various bitrates on the host's HTTP server. In practice, the user requests the most suitable bitrate file for user's network state based on a reference by the media presentation description (MPD) file. This feature allows customized files to be sent. In addition, the progressive download (PD) streaming method has multiple weaknesses with respect to bandwidth, bitrate adaptation, and live streaming. The DASH method compensates for these kinds of RTP/RTSP-based defects of PD through the trick mode, including rewind, fast skipping, etc. In this way, the DASH method enables live streaming considering user interaction. However, DASH falls short of considering the whole spectrum of interaction, including the viewing patterns of users. Each user has different viewing patterns depending on genre, video length, etc., which need to be taken into account when designing a protocol. In other words, previous studies of DASH considered the devices (desktop computers, smartphones, tablets, etc.) that users use to watch media. Moreover, they enable adaptive-bitrate streaming according to users' network conditions. However, these studies focus on the users' environment rather than the users' direct action (e.g., skipping). Therefore, we propose a method to overcome this problem. Toward this end, in this paper we consider the prediction of users' viewing pattern, and incorporate this pattern in the operation of DASH, namely in its trick mode to make DASH faster.
# Quality Assessment by Region and Land Cover of Sharpening Approaches Applied to GF-2 Imagery
## 4 Discussion
### Impact of Pansharpening Methods in Different Regions and Imagries Acquired with Various Satellites
For forest region, the best result was achieved by the PCA pansharpening method. The CN method exhibited the best performance for shrub and grassland regions, farmland regions, and salt pan regions. For aquaculture, and sea regions, the best result was achieved by the mIHS method. The GS method was the most effective for reservoir, and canal regions. These results are consistent with those of [PERSON] et al., who reported that for GeoEye-1 urban images, the CN method exhibited the highest scores for spatial metrics, followed by the mIHS, GS, and PCA methods, although different spatial metrics, such as the canny edge detection filter, the high-pass correlation coefficient, and the RMSE of a Sobel-filtered edge image, were used [37]. The results are also consistent with previous studies in which the mIHS method achieved better scores for spatial information than the PC method [32; 52]. Regarding the EN and AG values (image quality analysis), for urban regions, the mIHS method resulted in the smallest changes to the EN and AG values, followed by the PCA, CN, and GS pansharpening methods. This is partly consistent with previous results indicating that the mIHS method resulted in the smallest changes to the EN values, followed by the GS, PCA, and CN methods, for the town of Killinocchi, Sri Lanka, and for the city of Rome, Italy [12]. The inconsistency may be due to the imagery from different sensors and urban regions composed of different land covers. With regard to the increasing EN and AG values, the PCA method was superior to the mIHS method, which is consistent with the results of [PERSON] et al. (2008) [48]. Generally, a larger EN value corresponds to a larger amount of information in the image, and a larger AG value corresponds to higher clarity of the image. However, these values are affected by the noise in a pansharpened image. Therefore, the results for the increase in the EN and AG values should be interpreted with caution. Overall, the quantitative assessment by regions demonstrated that independent of regions, the mIHS method produced better results than the GS, PCA, and the CN method by preserving the colors of the original MS image. The mIHS, GS, and PC methods produced all the smallest spectral variations in the shrub and grassland region, and the CN method produced the smallest spectral variations in the farmland region, which indicated that the different pansharpening methods had different effects in the different regions.
## 3 Proposed Approach
### Feature Extraction
#### 3.2.4 Grey Level Co-Occurrence Matrix (GLCM)  
[PERSON] et al. [89] presented the GLCM and textural features for image classification. GLCM matrix depicts the occurrence of two adjacent pixels with grey values of \(i\) and \(j\). For an M \(\times\) N image, GLCM is defined based on the following equation [90]:  
\[C_{d}(i,j)=\left|\left\{\begin{array}{cc}(p,q),&(p+\Delta x,q+\Delta y):I(p,q )=i,\\ I(p+\Delta x,q+\Delta y)=j\end{array}\right\}\right| \tag{11}\]  
where \((p,q)\), \((p+\Delta x,q+\Delta y)\in M\times N\), \(d=(\Delta x,\Delta y)\) and \(\mid.\mid\) represents the cardinality of a defined set. For a grey level \(i\), the probability of the grey level \(j\) with distance \(d\) is obtained by the following equation:  
\[P_{d}(i,j)=\frac{C_{d}(i,j)}{\sum C_{d}(i,j)} \tag{12}\]  
The matrix element \(P\)\((i,j\mid d,\theta)\) indicates the probability values for changes between grey levels '\(i\)' and '\(j\)', while \(d\) is the distance between pixels and \(\theta\) depicts the particular angle between the neighbours. Typically, \(d\) = 1,2 and the orientations \(\theta\) = 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), 135\({}^{\circ}\) are calculated [15].
# Collaborative Analysis of Learners' Emotional States Based on Cross-Modal Higher-Order Reasoning
## 4 Experiments
### Evaluation Metrics  
This model's output can be used for both classification and regression tasks. It categorizes cross-modal emotional expressions into predefined emotion categories while also quantifying the intensity or subtle differences in emotions to adapt to various application scenarios and analytical needs. Therefore, this research adopted evaluation metrics for both regression and classification tasks to comprehensively assess the model's performance,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Dropout & 0.1 \\ Batch size & 32 \\ Epoch & 180 \\ Optimizer & AdamW \\ Learning rate & \(1\times 10^{-4}\) \\ Weight decay & \(1\times 10^{-2}\) \\ Attention head & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: ACAM Parameter Configuration.  
including mean absolute error (_MAE_), correlation (_Corr_), accuracy (Acc), and F1 score, represented by the following formulas:  
\[MAE=\frac{1}{n}{\sum\limits_{i=1}^{n}}{\left|{y_{i}-\hat{y}_{i}}\right|} \tag{24}\]  
_MAE_ (mean absolute error) is one of the metrics used to evaluate the accuracy of prediction models. It measures the predictive performance of a model through calculating the average of the absolute errors between predicted values and true values. Here, \(\left|{y_{i}-\hat{y}_{i}}\right|\) represents the absolute difference between the true values and the predicted values, and \(n\) represents the number of samples:  
\[Corr=\frac{{\sum\limits_{i=1}^{n}{(y_{i}-\overline{y})\big{(}\hat{y}_{i}- \overline{y}\big{)}}}}{{\sqrt{\sum\limits_{i=1}^{n}{(y_{i}-\overline{y})^{2} \sqrt{\sum\limits_{i=1}^{n}{(\hat{y}_{i}-\overline{y})^{2}}}}}}} \tag{25}\]  
Correlation (_Corr_) measures the degree of association between the true values of data labels and the values predicted with the model. Here, \(y_{i}\) represents the true values of data labels, and \(\overline{y}\) represents the predicted values of data labels.  
\[\text{Accuracy}=\frac{{\text{TP}+\text{TN}}}{{\text{TP}+\text{TN}+\text{FP }+\text{FN}}} \tag{26}\]  
Accuracy (Acc) represents the proportion of samples correctly predicted by the classifier out of the total sample size and is a key metric for evaluating the accuracy of classification models. In this context, TP denotes the number of true positives, TN denotes the number of true negatives, FP denotes the number of false positives, and FN denotes the number of false negatives. High accuracy implies that the model has a high level of accuracy in classifying the data.  
\[F1=\frac{{2\times\text{Precision}\times\text{Recall}}}{{\text{Precision}+ \text{Recall}}} \tag{27}\]  
The \(F1\) score combines a model's precision and recall, where precision represents the proportion of correctly predicted positive samples out of all samples predicted as positive, and recall represents the proportion of correctly predicted positive samples out of all actual positive samples.
## 4 Discussion
One idea is to have a longer interstimulus interval (ISI) to obtain more robust auditory cortical responses to target speech that are less interfered with by relatively strong noise-onset evoked responses. Having distinct obligatory responses to target speech is even more important when measuring the neural SNR from individuals with hearing impairment whose perceptual performance may rely more on clear speech cues than noise itself [18; 34]. A longer ISI and more precise distinction between evoked responses to target speech and noise onsets would increase neural SNR's predictive power regarding the variability in NR outcomes among individuals with different noise tolerance characteristics. Nowadays, nearly all digital hearing aids are equipped with NR algorithms, and often offer multiple preset options of NR strength ranging from no NR to strong NR, to which users react very differently [85]. With the integration of artificial intelligence technology, more variations of hearing-aid signal processing schemes will be employed to reduce background noise at given auditory scenes, potentially leading to an even wider range of preferred signal processing in hearing aids. As technology progresses, it becomes increasingly crucial to accurately quantify the extent of reduced noise and speech distortions and understand how individuals with different noise tolerance characteristics would perceive such varying effects due to a range of signal processing. With precise quantification of NR effects, future research could improve our experimental design by incorporating advanced NR algorithms, widening the scope of application to contemporary hearing aid technologies.
## 3 Results and Discussion
Note: a solid line represents the results of laboratory tests, while the results of numerical simulations are marked with a dashed line. In the numerical and analytical models, this phenomenon was not considered because, in real conditions, the loads never change so quickly to such an extent. The best compliance of the results in terms of the history and the characteristic values was obtained for the pressure difference in the gap (see Figure 7). This confirms the correctness of the assumptions while building the numerical model. A difference can be noticed in the case of the history of displacements and stresses (Figure 7). The experimental tests showed a slower increase in these values for the case of withdrawing the gas from the gap compared to glass injection. This is because there is, in fact, a difference in the rotational rigidity of the panes' edges depending on the direction of rotation of the panes' edges. This phenomenon is related to the different positions (with the center of the pane) of the rigid aluminum frame and the secondary silicone sealant. In the numerical model, different rotational restraints were not considered; therefore, the values' history for gas injection and withdrawal are the same. It should also be noted that intersections between the laboratory test results for \(-100\) and \(-150\) mL can be observed. This is probably related to the different gas exchange rates executed with a manual syringe or temporary temperature, or atmospheric pressure changes during testing. The intersections, however, do not affect the stabilized values that are considered reliable. Table 3 provides a quantitative comparison of the results obtained from laboratory tests (EXP), numerical simulations (FEM), and the analytical model (AN). The table contains average, characteristic values of the pressure difference in the gap (\(p\)), displacement (\(w\)), and stress (\(\sigma\)) at selected points on the sample (Figure 2). In terms of the experiments, the table shows the maximum values ('max' index) and stabilized values ('stab' index). In contrast, only the stabilized values are given for the numerical and analytical results, which do not consider the adiabatic transformation of the gas. The percentage change in the absolute value of a given parameter after stabilization in relation to the immediate value is given in parentheses. As can be seen from the laboratory tests, the stabilized values of the pressure difference, displacements, and stresses are lower, in absolute terms, than the peak value by an average of 7.3%, 0.9%, and 4.6%, respectively. It should also be noted that the greater the amount of injected or withdrawn gas, the lower the percentage difference between the measured values.