esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Prediction of Ultimate Damage Load
#### 3.2.2 Prediction of Artificial Neural Network of Acoustic Emission Two-Parameter Data  
In this section, using the rise time (1) and amplitude (5) acoustic emission parameters, the damage ultimate loads of three different crumb rubber concretes were predicted by the acoustic emission two-parameter data artificial neural network. The prediction result was calculated by comparing the predicted result with its true value. The training tools and algorithms are the same as the prediction using AE single parameter.  
In the process of deriving the prediction results shown in Table 14, the input data consist of a two-parameter matrix composed of the rise time and amplitude column vectors of six specimens, and the hidden layer consisting of nine nerves. The composition of the element is the predicted value of the ultimate load of the crumb rubber concrete. The artificial neural network prediction result with rise time and amplitude double parameters provides an optimal number of 39 training iterations (Figure 17). At the same time, the maximum prediction error of the specimens with three different rubber contents is 2.11%, shown in Table 14. It can be concluded that the rise-time-amplitude two-parameter neural network is useful to predict the damage ultimate load of crumb rubber concrete.  
After using these two acoustic emission parameters to combine the two-parameter neural network prediction of the ultimate load of crumb rubber concrete, the error of prediction with this set of two parameters is the smallest and less than the respective prediction errors of these two parameters. Thus, the rise-time-amplitude two-parameter combination is the most suitable for the two-parameter neural network to determine the acoustic emission parameters of the crumb rubber concrete failure load. Furthermore, the prediction error of all acoustic emission two-parameter neural network models is significantly smaller than that of the corresponding acoustic emission single-parameter  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rubber Content** & **Actual Value (kN)** & **Predictive Value (kN)** & **Prediction Error** \\ \hline
5\% & 16.24 & 16.39 & 0.91\% \\
10\% & 15.27 & 15.53 & 1.71\% \\
15\% & 11.32 & 11.56 & 2.11\% \\ \hline \hline \end{tabular}
\end{table}
Table 14: Rise-time-amplitude (RA) two-parameter neural network prediction results.  
Figure 16: Prediction error of each single-parameter neural network.  
neural network parameter model. Figure 17 shows the two-parameter optimal combination rise-time-amplitude (RA) neural network training performance.
# A Network Device Identification Method Based on Packet Temporal Features and Machine Learning
## 3 Inception-TCN-Attention Model Design
### Feature Fusion and Classification  
In the previous section, we implemented the splicing of multiple variable features to construct a feature matrix \(G\). This matrix integrated multiple features to be able to comprehensively capture the information in the packet sequence. Next, to achieve effective feature fusion and further improve the performance of the model, we employed a new feature fusion strategy that introduced a multi-head self-attention mechanism to the feature matrix \(G\). The core of this mechanism is to dynamically assign weights to each feature in the feature matrix. In this way, the model can be made to focus more on those features that are crucial to the recognition task during the training process, improving the efficiency and accuracy of feature extraction. The introduction of the attention mechanism can significantly reduce the number of required parameters compared to traditional feature fusion methods, which not only reduces the complexity of the model but also makes the training process of the model more efficient. In addition, the attention mechanism can adaptively adjust the feature weights according to the characteristics of the data and the needs of the task, achieving the identification and exploitation of key features in the multi-feature matrix.  
Figure 6 represents the flow of the feature fusion and classification phase. The input of this phase was the feature matrix \(G\), which was the intermediate result obtained from the feature extraction phase. The feature matrix was computed by the multi-head self-attention mechanism to obtain the feature vector \(\delta\), and the output was the classification result.
## 3 Results
### Analysis of Heading Precision Estimates for All Flights
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Flight**} & \multirow{2}{*}{\begin{tabular}{c} **Mean** \\ **Horizontal** \\ **Velocity * \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Single-Antenna Heading** \\ **Velocity * \\ \end{tabular} } & \multicolumn{4}{c}{**Dual-Antenna Heading**} & \multirow{2}{*}{
\begin{tabular}{c} **Improvement** \\ **in Heading** \\ **Velocity \\ \end{tabular} } \\ \cline{3-3} \cline{5-8}  & & & & & & & & & \\ \hline A & 2.5 m/s & 0.28\({}^{\circ}\) & 0.45\({}^{\circ}\) & 0.369\({}^{\circ}\) & 0.07\({}^{\circ}\) & 0.11\({}^{\circ}\) & 0.082\({}^{\circ}\) & 4.5\(\times\) \\ B & 5.1 m/s & 0.21\({}^{\circ}\) & 0.35\({}^{\circ}\) & 0.251\({}^{\circ}\) & 0.07\({}^{\circ}\) & 0.11\({}^{\circ}\) & 0.086\({}^{\circ}\) & 2.9\(\times\) \\ C & 10.2 m/s & 0.16\({}^{\circ}\) & 0.20\({}^{\circ}\) & 0.175\({}^{\circ}\) & 0.08\({}^{\circ}\) & 0.12\({}^{\circ}\) & 0.093\({}^{\circ}\) & 1.9\(\times\) \\ D & 15.4 m/s & 0.12\({}^{\circ}\) & 0.16\({}^{\circ}\) & 0.141\({}^{\circ}\) & 0.08\({}^{\circ}\) & 0.11\({}^{\circ}\) & 0.094\({}^{\circ}\) & 1.5\(\times\) \\ \hline \hline \end{tabular}  
* Observations used within the GNSS velocity model analysis as discussed below.  
\end{table} 
Table 4: Quantitative measures for single and dual-antenna GNSS/INS heading precisions. Figure 9: Single and dual-antenna GNSS/INS heading precisions for all flights of the experiment.
# A Simple Water Retention Model Based on Grain Size Distribution
## 2 Key Phenomena for Water Retention Curve Models  
There are numerous approaches in the literature to model soil water retention behavior. A common approach is the conversion of a grain size distribution (GSD) to a pore size distribution (PSD), with each pore in the PSD associated with a volume of water it contains and a suction at which it will drain [11; 12; 13; 14; 15; 16; 17]. This information allows a water retention curve to be built. The draining suction of a pore is determined from a capillarity law (e.g., Jurin's equation), and adsorption mechanisms are generally ignored. In the context of PAW, ignoring adsorption is not detrimental as the wilting point is less than the suction at which adsorptive suction becomes significant (typically considered to be around 2 MPa) [18; 19]. This type of modelling approach will be referred to here as the grain size distribution approach to water retention curve estimation (noted GSD-WRC). The GSD-WRC approach was adopted as the starting point in this study, as the primary input parameter of a GSD is relatively simple to measure, and the predictions can be accurate for the suction ranges required. However, the approach, as has been implemented in previous research, suffers from a number of limitations arising from complicated phenomena that need to be simplified, which affects the prediction accuracy. The following two subsections will highlight some of the limitations identified, which are pore filling and contact angle.
## 3 Secretory Profile of Exosomes in Metabolic Tissues
### Skeletal Muscle  
Skeletal muscle is an essential structure in the human organism, possessing multiple functions, both endocrine and mechanical, with its primary function being movement [60; 68]. Exosomes play multiple roles in skeletal muscle, primarily contributing to the cellular repair of muscle fibers damaged by overexertion. They promote myogenesis, enhance angiogenic mechanisms within the muscle, and regulate proinflammatory factors, thus providing benefits at the immune level [61; 63].  
Communication between skeletal muscle and adipocytes has also been described. Exosomes released by adipocytes induce insulin resistance in skeletal muscle due to miR27a targeting PPAR\(\gamma\) in addition to the regulation of genomic expression [62; 69].  
Likewise, concerning skeletal muscle repair, it has been shown that exosomes derived from C2C12 myoblasts help form and heal this tissue [70; 71]. In this regard, it has been reported that these muscle-derived exosomes are linked to myogenic cascades, enhancing muscle differentiation and facilitating muscle repair following injury. It is proposed that this beneficial effect is mediated by these exosomes containing molecules of the Wnt cascade and other myogenic proteins [64; 72]. Significantly, it has been described that skeletal muscle exosomes secrete miRNAs, especially miR133a and miR133b, which aid in skeletal muscle myogenesis and local communication in muscle tissue [65; 73].  
Exosomes derived from mesenchymal cells help prevent muscle atrophy and degeneration in the rotator cuff muscles of rats subjected to surgery and treated with exosome injections [66; 74].  
In skeletal muscle angiogenesis, exosomes produced by adipose-derived stem cells can influence muscle's angiogenic proliferation by increasing the content of NRG1 [67; 75]. This protein promotes the proliferation of endothelial cells, contributing to improved vascularization. Additionally, genes involved in the production of growth factors, such as FN1, MMP2, and VEGF1, were found to be upregulated, helping to prevent ischemic muscle damage. These findings underscore the potential of exosomes in promoting tissue repair and regeneration, particularly in response to ischemic injuries [67; 75].
# Effects of Velocity Profiles on Measuring Accuracy of Transit-Time Ultrasonic Flowmeter
## 2 Methodology
### Laminar Flow  
For laminar flow in a pipe, the velocity profile will be  
\[u=u_{m}(1-\frac{r^{2}}{R^{2}})=u_{m}(1-s^{2}) \tag{10}\]  
Substitute into Equation (9), we get  
\[\Delta T=\frac{2R}{u_{m}\sin\theta\cos\theta}\int_{0}^{1}\big{[}\frac{1}{K-(1- s^{2})}-\frac{1}{K+(1-s^{2})}\big{]}ds \tag{11}\]  
then  
\[\Delta T=\frac{2R}{u_{m}\sin\theta\cos\theta}\int_{0}^{1}\frac{2-2s^{2}}{K^{2 }-\left(1-s^{2}\right)^{2}}ds \tag{12}\]  
Because \(K^{2}>>\left(1-s^{2}\right)^{2}\), then  
\[\Delta T=\frac{2R}{u_{m}\sin\theta\cos\theta}\int_{0}^{1}\frac{2-2s^{2}}{K^{2 }}ds \tag{13}\]  
Finally, we have  
\[\Delta T=\frac{8 Ru_{m}\cos\theta}{3c^{2}\sin\theta} \tag{14}\]  
For a laminar flow, the flowrate can be expressed as \(Q=\pi R^{2}\frac{u_{m}}{2}\). Then, it is easy to get the flowrate for laminar flow from Equation (14)  
\[Q=\frac{3\pi Rc^{2}\tan\theta}{16}\cdot\Delta T \tag{15}\]  
Equation (15) clearly indicates the effect of velocity profile on the flowrate measured.  
If the effect of velocity distribution is neglected, the velocity u in Equation (4) is considered to be a constant, then  
\[\Delta T=\frac{L}{c-u\cos\theta}-\frac{L}{c+u\cos\theta}=\frac{2 Lu\cos\theta} {c^{2}-u^{2}\cos^{2}\theta} \tag{16}\]  
Since \(c^{2}>>u^{2}\cos^{2}\theta\) and \(L=\frac{2R}{\sin\theta}\), then  
\[\Delta T=\frac{2 Lu\cos\theta}{c^{2}}=\frac{4 Ru\cos\theta}{c^{2}\sin\theta} \tag{17}\]Therefore, the flowrate will be  
\[Q_{0}=\pi R^{2}u=\frac{\pi Rc^{2}}{4}\tan\theta\cdot\Delta T \tag{18}\]  
Comparing Equation (15) with (18), it is found that \(Q=\frac{3}{4}Q_{0}\). That is to say, the flowrate Q\({}_{0}\) measured with an ultrasonic flowmeter without considering the effect of velocity profile in the pipe should be multiplied by 3/4, in order to get the real flowrate.
## 5 Discussion  
The results of our study demonstrate that the DLCTC model outperforms traditional machine learning models and standard deep learning architectures on the collected X dataset. Achieving an F1 score of 87.64% and an accuracy of 85.14%, the DLCTC model effectively addresses the challenges inherent in classifying short, informal texts like tweets.  
The strengths of the DLCTC model are evident in its ability to integrate multi-level embeddings (character, word, and concept embeddings) with advanced neural architectures like BiLSTM and TextCNN, which allows for superior handling of informal and ambiguous social media texts. This integration enables the model to capture linguistic patterns at various levels, improving classification performance on short texts. Additionally, the attention mechanism incorporated in the model ensures that the most relevant features are weighted more heavily, further enhancing the model's ability to correctly classify traffic-related messages.  
In evaluating the model's performance, training and validation loss were monitored as indicators of how well the model fit the training data and its ability to generalize to new data. Train loss represents the error between the model's predictions and actual labels on the training dataset, showing how well the model learns from data it has seen. Validation loss, on the other hand, measures this error on a separate, unseen validation dataset, which helps assess the model's performance on data outside the training set.  
In this study, the final training results showed an accuracy of 85.14% and a train loss of 0.2071, indicating that the model was able to learn and fit the training data well and the validation loss of 0.2011. The close alignment between training and validation loss suggests a good generalization ability, with no significant overfitting. The slightly lower validation accuracy indicates a minor performance drop when encountering new data, which is typical but well within acceptable bounds for robust model performance. Furthermore, the precision of 79.90% and recall of 99.02% suggest the model is highly effective at capturing relevant instances (high recall), though slightly less precise.  
Despite these strengths, there are some limitations. For example, while the model performs well on the collected X dataset, the computational complexity introduced by multi-level embeddings and the attention mechanism also increases training time, which may pose a challenge for real-time applications. Further optimization of the model could be explored to address these concerns.
## 3 Results
### Potentiometric Head Classification  
The wells that were prioritized in PCA were used to obtain a potentiometric surface of the study area. The average head values of these 135 prioritized wells over three and half years were considered as points for IDW interpolation. IDW interpolation results consisted of a minimum GW head value of 22.89 m and a maximum head value of 41.37 m. The potentiometric head surface was classified into 19 classes with a variation of 0.97 m in each class, and three wells in each class (first at the head, second at the middle, and third at the tail) were selected as proposed locations for the installation of observation wells. In addition, a few additional wells were also selected based on the interpolation results, such as, a few pockets of water which were part of some classes but were not continuous with that class. If there were already three or fewer wells in any class, they were left unaltered. A total of 59 wells were selected as the critical locations of the study area.  
Potentiometric head surfaces of these 59 wells were generated using an IDW interpolation for different time series and compared against the base case scenario. The results showed that these 59 observation wells could represent the study area with an average error of 0.61 m and a standard deviation of 1.15 m (Table 1). The groundwater head classification helped in reducing the number of observation wells from 135 to 59, which was more than a 56% reduction, with an insignificant loss of information. The overall performance of the network was satisfactory as it could represent the area with an average error of 0.61 m.  
Figure 9: Hydrographs of simulated and observed water levels in meters for the model calibration period (i.e., October 2010–April 2014).
# Exploring Benefits of Using Blending Splines as Transition Curves
## 2 Preliminaries
### Blending Spline  
A blending spline is a collective term of the family of blending-type spline constructions originating from research conducted since 2003. The initial blending spline, called Expo-rational B-spline (ERBS), was presented for the first time in [23] and within two years published in [20; 24]. After that the family grew, with new splines derived from the ERBS, including generalized expo-rational B-splines [25] and logistic expo-rational B-splines [26]. The blending spline is a construction where local functions at the knots are blended together by \(C^{k}\)-smooth basis functions, see Figure 1. It is defined in [21] as  
\[f(t)=\sum_{k=1}^{n}l_{k}(t)B_{k}(t),\ t\in(t_{1},t_{n}], \tag{1}\]  
where the coefficients \(l_{k}(t)\) are scalar-, vector-, or point-valued local functions defined on \((t_{k-1},t_{k+1})\). \(t=\{t_{k}\}_{k=0}^{n+1}\) is an increasing knot vector, and \(B_{k}(t)\) are the blending functions (B-functions). The B-functions can be any function possessing the following set of properties:  
1. \(B:I\to I(I=[0,1]\subset\mathbb{R})\),
2. \(B(0)=0\),
3. \(B(1)=1\),
4. \(B^{\prime}(t)\geq 0,\ t\in I\),
5. \(B(t)+B(1-t)=1,\ t\in I\).  
The last property is optional and specifies the point symmetry around the point \((0.5,0.5)\). Figure 2 shows a plot of possible B-functions meeting the properties. In this paper, we considered a subset of ERBS called the scalable subset, proposed in [21], that used a specific B-function (the ERB-function), which is \(C^{\infty}\)-smooth,  
\[B_{k}(t)=\begin{cases}S_{k-1}\int_{0}^{\omega_{k-1}(t)}\psi_{k-1}(s)ds&\text {if }t_{k-1}<t\leq t_{k},\\ S_{k}\int_{\omega_{k}(t)}^{1}\psi_{k}(s)ds&\text{if }t_{k}<t<t_{k+1},\\ 0&\text{otherwise},\end{cases} \tag{2}\]  
where \(S_{k}=(\int_{0}^{1}\psi(s)ds)^{-1}\), \(\omega_{k}(t)=\frac{t-t_{k}}{t_{k+1}-t_{k}}\), and \(\psi_{k}(s)=e^{-\beta\frac{|s-\lambda|^{k}(1+\gamma)}{(\gamma(1-s)^{k})}}\) with the intrinsic parameters restricted to \(\alpha>0\), \(\beta>0\), \(\gamma>0\), and \(0\leq\lambda\leq 1\). We also note that the ERBS shares the minimal support and partition of unity properties of the linear B-spline, which means that only two functions are blended together in every knot interval (i.e., between two knots) and that the two B-functions in the interval sum up to \(1\) for a given \(t\). By applying the two previous properties, the simplified formula of (1), over one knot interval, becomes  
\[\begin{split} f(t)&=B_{k}(t)l_{k}(t)+B_{k+1}(t)l_{k +1}(t)\\ &=(1-B_{k+1}(t))l_{k}(t)+B_{k+1}(t)l_{k+1}(t)\\ &=l_{k}(t)+B_{k+1}(t)(l_{k+1}(t)-l_{k}(t)).\end{split} \tag{3}\]  
For the experiments in this paper, we use parametric curves as local functions \(l_{k}(t)\). The specific choices are presented and explained in Section 3.2.
## 3 Related Work  
### Literature Review on Retina Segmentation  
In particular, segmentation has received considerable attention recently as it is an important step in aiding diagnosis since it helps determine the RoI. [PERSON] et al. [10] proposed a method for the segmentation of eight retinal layers in Heidelberg spectralis SD-OCT images. Global segmentation algorithms such as active contours and Markov random fields are used. In addition, a Kalman filter was designed. It is used to model the approximate parallelism between the photoreceptor segments [10].  
A comparison between different edge detectors was conducted by [PERSON] et al. [11]. They studied and compared the performance of canny edge detector [12; 13], the two-pass method proposed by  
Figure 1: Illustration of abnormal class vs. normal class (**a**) CNV vs. NORMAL (**b**) DME vs. NORMAL (**c**) DRUSEN vs. NORMAL.  
[PERSON] et al. [14] and Edgeflow technique [15]. All of these techniques were used with retinal OCT images to delineate the retinal layer boundaries. From the evaluation of the results, it was shown that the two-pass method outperforms the Canny detector and the EdgeFlow technique that is used with OCT images to delineate the retinal layer boundaries. In addition, the mean localization deviation metrics show that the smallest edge shifting problem is caused by the two-pass method. The study suggests that the two-pass method is the best one for retinal OCT boundary detectors.  
In the work of [PERSON] et al. [16], a method that uses graph theory and the shortest path algorithm was presented to detect certain layers. The aim was to choose the RoI that could be used to distinguish normal cases from abnormal ones. They discovered that using changes in some parts, such as inner limiting membrane (ILM), retinal nerve fiber layer (RNFL) and retinal pigment epithelium (RPE), leads to separating these layers easily. The proposed technique was applied to all B-Scan images of 16 people, including low-quality images and some images with diseased eyes. The results were accurate according to manual segmentation of an expert.  
Others such as [PERSON] et al. [17] proposed a method to segment five layers (NFL, GCL+IPL, INL+OPL, IS, OS) in OCT retinal images. The layers were identified using constructed geometric graph and computed a minimum cost closed set. This graph is constructed from the edge/regional information and a priori determined surface smoothness and interaction constraints.
# Predicting Human Location Using Correlated Movements
## 7 Evaluation Results and Discussion
### Top-\(k\) Accuracy  
In this subsection, we evaluate the top-\(k\) accuracy of the proposed PLP model when \(k=\{1,2,3,4\}\). Other parameters are set to the default values shown in Table 5. For top-\(k\) accuracy, the model outputs a list of \(k\) location(s) with the highest probability. If the correct location belongs to the list of \(k\) element(s), the prediction is considered accurate.  
As expected, Figure 6 shows that the obtained accuracy increases from top-1 to top-4. For example, in case of the MIT dataset, the BSB approach results in 71.00%, 82.17%, 85.53%, and 87.59% accuracy corresponding to top-1, top-2, top-3, and top-4, respectively. In all examined PCM selection schemes, the steepest rising rate is observed when \(k\) changes from 1 to 2. Then, the outcomes tend to plateau.  
As can also be observed from Figure 6, there is higher prediction accuracy in the Dartmouth dataset than the MIT one. This observation is attributed to the fact that human movement in the MIT dataset was collected within a wider area than that in the Dartmouth traces. More specifically, all APs in the Dartmouth dataset are located in the university campus while movement trajectories of people in the MIT dataset are not bounded in the school area. Therefore, people mobility in the MIT dataset tends to be more divergent and more difficult to predict than that in the Dartmouth traces.  
Figure 6: Top-\(k\) accuracy of the prediction model in case of (**a**) the MIT dataset and (**b**) Dartmouth dataset.