esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Dynamic Derivatives Identification Based on the Forced Oscillation Method
### Unsteady Aerodynamic Model Based on the LSTM Network  
Recurrent neural networks (RNNs) have been applied in many fields and have achieved remarkable results in recent years [9; 10; 11]. However, traditional RNNs have two defects: gradient explosion and gradient vanishing [12]. Adding gradient truncation and regularization terms to RNNs can avoid gradient explosion. Gradient vanishing is also referred to as long-term dependencies. This mainly refers to how when the sequence input is long or the network structure is deep, the relevance of the data information in the sequence decreases or even disappears, resulting in the network being unable to learn the important information at the front of the sequence. The structure of RNNs is shown in Figure 3.  
LSTM is a derivative of RNNs that can overcome the problem of long-term dependencies. Through extensive research, it has been proven that LSTM has advantages in dealing with time series problems [13]. LSTM adds a structure called a memory cell to the neurons in the hidden layer of an RNN to remember past information [14], and adds three gate structures (input gate, forgetting gate, and output gate) to control the use of historical information. The structure of LSTM is shown in Figure 4.  
Figure 3: Structure of RNN.  
Figure 2: Flow chart of dynamic derivatives identification based on intelligent aerodynamic model.  
The input sequence is \((x_{1},x_{2},\cdots,x_{T})\) and the hidden layer state is \((h_{1},h_{2},\cdots,h_{T})\), at time \(t\):  
\[i_{t}=\sigma(W_{hi}h_{t-1}+W_{xi}x_{t}+b_{i}) \tag{5}\]  
\[c_{t}=f_{t}\bullet c_{t-1}+i_{t}\bullet g(W_{hc}h_{t-1}+W_{xc}x_{t}+b_{c}) \tag{6}\]  
\[o_{t}=\sigma(W_{ho}h_{t-1}+W_{ox}x_{t}+W_{co}c_{t}+b_{o}) \tag{7}\]  
\[h_{t}=o_{t}\bullet g(c_{t}) \tag{8}\]  
In the formula, \(i_{t},f_{t},o_{t}\) are input gate, forgetting gate, and output gate, respectively. \(c_{t}\) is a cell unit. \(W_{h}\) is the weight of a recursive connection. \(W_{x}\) is the weight from the input layer to the hidden layer. \(b_{i},b_{f},b_{c},b_{o}\) are the thresholds for each function. \(\sigma(\bullet)\) and \(g(\bullet)\) are the sigmoid function and tanh function, respectively. \(\bullet\) represents the inner product of a vector.  
To achieve the prediction purpose of LSTM, a linear regression layer needs to be added:  
\[y_{t}=W_{yo}h_{t}+b_{y} \tag{9}\]  
In the formula: \(y_{t}\) is the prediction result. \(b_{y}\) is the threshold of the linear regression layer.  
Select the Mach number, angle of attack, motion parameters, and corresponding aerodynamic data (lift drag and moment coefficient, etc.) as training data to train the neural network. Use a small number of known aerodynamic data as testing data to conduct real-time testing on the model, ultimately forming a stable and reliable intelligent aerodynamic model.
## 4 Results and Discussion
### Results: Pattern Extraction
#### 4.3.3 UMLS Patterns  
The UMLS patterns, given in Table 7, classified recommendation sentences with the accuracy of 74.27%, 82.57%, and 87.67% for asthma, rhinosinusitis, and hypertension guidelines, respectively, as shown in Figure 6d. The reason for the improvement of accuracy was the UMLS concepts used in the recommendation sentences. Mostly, the recommendation sentences use tags of \"Population Group\", and \"Pharmacologic Substance\"; therefore, UMLS-based patterns can easily recognize these sentences and increase the accuracy of the systems' classification.  
After individual evaluation, we combined all three techniques and evaluated asthma, Rhinosinusitis, and Hypertension guidelines before providing salient terms and after providing salient terms. Before using salient terms the extracted patterns achieved the accuracy of 76.92%, 84.63%, and 89.16%, respectively, as shown in Figure 7a. However, after using salient terms the pattern accuracy increased to 78.89%, 85.32%, and 92.07%,  
Figure 6: Extracted patterns accuracy (**a**) Heuristic patterns without salient terms accuracy (**b**) Heuristic patterns with Salient terms accuracy (**c**) POS based patterns accuracy (**d**) UMLS based pattern accuracy.  
respectively, as shown in Figure 7b. Here each sentence was evaluated by the three patterns and tagged independently. A sentence tagged by one or more techniques was finally considered to be an RS sentence otherwise NRS.  
As shown in Figures 5-7 the feature selection, salient terms, and combined patterns increased the classification accuracy, respectively. However, we performed a non-parametric \(p\)-value test to check the significance of the improvements [39]. The improvement shown in Figure 5 via feature selection (hereafter Model FS) compared to without feature selection (hereafter Model WFS) is evaluated with a threshold value of 0.05 under the following hypothesis.  
* Null hypothesis \(H_{0}\): Model FS is not better than Model WFS
* Alternate hypothesis \(H_{1}\): FS is better than WFS  
The calculated \(p\)-value for the above hypothesis is 0.035, which is less than the threshold value of 0.05. Therefore, it rejects the null hypothesis \(H_{0}\) and conclude that model FS is better than WFS. Similarly, we calculated the p-vale for other two cases, with and without salient terms Figure 6, and combined vs individual patterns Figure 7 with resulted value of 0.038 and 0.040, respectively. Hence the \(p\)-values showed the improvement caused by feature selection, salient terms, and combination of heuristics, POS, and UMLS patterns are statistically significant.
# A Linear-Time Algorithm for the Isometric Reconciliation of Unrooted Trees
## 1 Introduction
### Notation  
A gene tree will be in the rest of the text denoted \(G\), species tree \(S\). Nodes of \(G\) are usually denoted as \(u\) and \(v\), nodes of \(S\) as \(x\) and \(y\), the root of \(G\) as \(q\), the root of \(S\) as \(r\) and leaves in both trees as \(a\) and \(b\).  
A point in a tree is one of its nodes or a point inside an edge where this edge can be subdivided by a new node. By \(d_{T}(u,v)\), we denote the distance between points \(u\) and \(v\) in tree \(T\). For a tree \(T\) with nodes \(u\) and \(v\) and real value \(\alpha\), such that \(0\leq\alpha\leq d_{T}(u,v)\), let \(T(u,v,\alpha)\) denote the point located at distance \(\alpha\) from node \(u\) on the path connecting points \(u\) and \(v\). By \(\mathsf{lca}_{T}(u,v)\), we denote the lowest common ancestor of \(u\) and \(v\) in a rooted tree \(T\).  
Let \(\{u,v\}\) be an edge in an unrooted tree \(T\). By \(\mathsf{sub}(T,u,v)\) we will denote a rooted subtree of \(T\) obtained by removing edge \(\{u,v\}\) from \(T\), taking the resulting connected component containing \(u\) and rooting it in \(u\). A tree with \(m\) edges thus has \(2m\) rooted subtrees. In rooted trees, we will consider subtrees in the conventional sense, consisting of a node and all its descendants.  
For a tree \(T\), let \(L(T)\) be the set of its leaves and \(V(T)\) the set of its nodes. For a tree \(T\) and a set of leaves \(A\), let \(T_{A}\) denote the subtree of \(T\) induced by \(A\) and all paths connecting pairs of leaves from \(A\). In particular, we will often consider the subtree \(G_{L(T)}\) for some rooted subtree \(T\) of \(S\); this is the part of \(G\) that naturally corresponds to subtree \(T\) of \(S\).
# Experimental and Numerical Study on Dynamics of Two Footbridges with Different Shapes of Girders
## 2 Materials and Methods
### Theory of Experimental Mode Shapes  
The methods based on the classical input-output estimation of modal parameters are the well-known and widely used methods of modal analysis [34; 35]. The peak picking (PP) method is one of the most accessible because of its simplicity and speed [77; 78]. This method is based on the estimation of the frequency response function (FRF) in the frequency domain or the impulse response function (IRF) in the time domain. The experimental tests can be carried out under any excitation, including sinusoidal, impulse, random, or pseudorandom. The excitation is usually applied at one point while the signals are measured at many points. The second method is to place the sensor at one key point of the structure and then force it at subsequent points.  
The algorithm of the method assumes subjecting each force signals and structure responses signals to the Fourier transform. Based on the obtained Fourier transform, the transform function \(\mathbf{H}(\omega)\) is calculated. In order to receive one row of the matrix \(\mathbf{H}(\omega)\), the measurement is taken for the signal of excitation in particular points of the structure; however, the responses are measured in one point. For the matrix column \(\mathbf{H}(\omega)\), the signal of force is measured in one point, and the value of structure response is recorded from each measuring point. The determination of mode shapes is possible at any row or matrix column \(\mathbf{H}(\omega)\). The measured signals used in the PP method may concern accelerations, velocities, or displacements. The type of signal determines three types of transition functions: accelerance, mobility, and receptance (Table 1).  
The method's limitation is its use for structures that are lightly damped. For structures with very high damping or damping close to zero (infinite peak), the method does not give satisfactory results [78].  
The modified PP method can also be used for the operational modal analysis (OMA). In this case, FRF is replaced with power spectrum densities (PDS) from the output. Comparing the results of  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**FRF** & **Symbol** & **Signal** \\ \hline \multirow{2}{*}{Receptance} & \(\mathbf{H}^{r}(\omega)\) & \(\frac{Displacement}{Force}\) \\  & \(\mathbf{H}^{m}(\omega)\) & \(\frac{Volacity}{Force}\) \\ \multirow{2}{*}{Accelearance} & \(\mathbf{H}^{a}(\omega)\) & \(\frac{Accidental}{Force}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: FRFs used in PP method.  
Figure 8: Dynamic measurement scheme of the arch footbridge above the University Route in Bydgoszcz.  
PP method with the frequency domain decomposition (FDD) and stochastic subspace identification (SSI), very similar results are usually obtained [39]. Using this method, damping can be estimated by employing the half-power procedure [79].
## 3 Results and Discussion
### Effect of Model Configuration on Detection Performance
#### 3.2.2 Effect of Fine-Tuning on the Feature Extraction Layer  
A comparison of Figure 6a,c and Figure 6b,d suggests that fine-tuning improved the F1 score. Fine-tuning is effective regardless of the classifier; however, the improvement was slightly larger for FC. Consequently, when fine-tuning was applied, the difference in the classifiers slightly affected the performance of the model.  
Fine-tuning also improved the model performance when evaluated using recall (Figure 7). As in the case of the evaluation using the F1 score, the difference in model performance owing to differences in classifiers was smaller when fine-tuning was applied.  
These results indicate that fine-tuning almost always improved the model performance in this study. It could be explained that since the pre-trained Xception was adjusted to the ImageNet dataset, whose characteristics noticeably differ from the PCM images used in this study, the adjustment using fine-tuning showed a strong impact on the model performance.  
Figure 7: Each model’s recall for the validation dataset. Averaged performances and the standard deviation of five-fold cross-validation are shown. (**a**) Models with class weighting and fine-tuning. (**b**) Models with class weighting without fine-tuning. (**c**) Models without class weighting with fine-tuning. (**d**) Models without fine-tuning and class weighting.
# Mean Estimation on the Diagonal of Product Manifolds
## 2 Background
### Weighted Frechet Mean  
The Euclidean mean has three defining properties: The _algebraic property_ states the uniqueness of the arithmetic mean as the mean with residuals summing to zero, the _geometric property_ defines the mean as the point that minimizes the variance, and the _probabilistic property_ adheres to a maximum likelihood principle given an i.i.d. assumption on the observations (see also [9] Chapter 2). Direct generalization of the arithmetic mean to non-linear spaces is not possible due to the lack of vector space structure. However, the properties above allow giving candidate definitions of mean values in non-linear spaces.  
The Frechet mean [1] uses the geometric property by generalizing the mean-squared distance minimization property to general metric spaces. Given a random variable \(X\) on a metric space \((E,d)\), the Frechet mean is defined by  
\[\mu=\operatorname*{arg\,min}_{p\in E}\mathbb{E}\Big{[}d(p,X)^{2}\Big{]}. \tag{1}\]  
In the present context, the metric space is a Riemannian manifold \(M\) with Riemannian distance function \(d\). Given realizations \(x_{1},\ldots,x_{n}\in M\) from a distribution on \(M\), the estimator of the weighted Frechet mean is defined as  
\[\hat{\mu}=\operatorname*{arg\,min}_{p\in M}\sum_{i=1}^{n}w_{i}d(p,x_{i})^{2}, \tag{2}\]  
where \(w_{1},\ldots,w_{n}\) are the corresponding weights satisfying \(w_{i}>0\) and \(\sum_{i}w_{i}=1\). When the weights are identical, (2) is an estimator of the Frechet mean. Throughout, we shall make no distinction between the estimator and the Frechet mean and will refer to both as the Frechet mean.  
In [6,10], the weighted Frechet mean was used to define a generalization of the Euclidean convolution to manifold-valued inputs. When closed-form solutions of geodesics are available, the weighted Frechet mean can be estimated efficiently with a recursive algorithm, also denoted an inductive estimator [6].
## 6 Conclusions  
1. The spatial variability of material properties in earth slopes may have a significant effect on their seismic performance. Accounting for this spatial variability using stochastic methods allows for a better evaluation of the expected permanent displacements of slopes due to strong ground motion, and a better design for mitigation of the expected seismic damage.  
2. It has been shown by using statistical analyses that the most suitable distributions for the description of permanent seismic displacements are the Gamma, Extreme Value, and Weibull distributions. Among them, the Weibull distribution provides the best fit of the numerical data.  
3. The results show, as far as the impact on spatial correlation length of the soil properties is concerned, that for a doubling of autocorrelation length in large values (e.g., from 20 m to 40 m), the effect on horizontal displacements is low, since both of these lengths indicate great spatial autocorrelation. On the contrary, for doubling of autocorrelation length in small values (e.g., from 2 m to 4 m), the effect is significant in that the spatial correlation with short distances is small but increases more intensely with the doubling of distance. Indeed, for vertical displacements, the doubling of the autocorrelation length significantly affects the dispersion of the results.  
4. For slopes with the same material strength, the results of the numerical simulations show that both the mean value and standard deviation of permanent seismic displacement increase substantially when slope inclination increases.  
5. The automated procedure of combing the Local Average Subdivision algorithm and the FLAC software through the program Mathematica allowed for the efficient execution of complex, time-consuming Monte Carlo simulations and significantly helped the investigation of the effects of the spatial variability of soil properties on seismic permanent displacements.  
Conceptualization, N.A. and P.D.; methodology, N.A. and P.D.; software, N.A. and P.D.; validation, N.A. and P.D.; formal analysis, N.A. and P.D.; investigation, N.A. and P.D.; resources, N.A. and P.D.; data curation, N.A. and P.D.; writing--original draft preparation, N.A.; writing--review and editing, P.D.; visualization, N.A. and P.D.; supervision, P.D.; project administration, N/A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
No additional data are available beyond those presented in the paper.  
The authors declare no conflict of interest.
# Evaluation of a Newly Developed Device for Resonance Disorders: A Clinical Pilot Study
###### Abstract  
This clinical pilot study aimed to assess the clinical effectiveness of a newly developed device, Smart NasoTM, for resonance disorders and compare its performance with that of the Nasometer II 6450. A group of 18 total participants (nine each in normal and hypernasality groups) were examined using the VIP inspection and K SNAP Test while wearing Nasometer II 6450 or Smart NasoTM, and the reliability of the new device and similarity of both devices were evaluated. When the newly developed Smart NasoTM and Nasometer II 6450 were applied to the hypernasality group, similarity was observed (Smart NasoTM: 0.776-0.989, Nasometer II: 0.922-0.996). In the test-retest reliability evaluation, both devices were found to be highly effective and reliable in the hypernasality group (Smart NasoTM: 0.948, Nasometer II: 0.953); the Smart NasoTM was found to be similarly effective and reliable for clinical applications. Smart NasoTM exhibited comparable effectiveness to that of Nasometer II 6450 and exhibits clinical utility for patients with hypernasality and those diagnosed with cleft lip and palate, cleft palate, and submucosal cleft palate.  
hypernasality; resonance disorders; speech language therapy; rehabilitation  
word length. Consequently, establishing normal reference values presents considerable challenges [6]. A nasometer, which is non-invasive and exhibits the highest correlation with auditory perceptual investigation outcomes, is a widely used computer-based system [7] that quantitatively measures sound between the nasal and pharyngeal cavities [8; 9; 10]. The device captures oral and nasal consonants through a microphone, divides the nasal acoustic energy into the sum of the oral and nasal acoustic energy, multiplies this value by 100, and displays it as a percentage, thereby facilitating the assessment of nasal resonance for analyzing nasalance. Nasometers are utilized globally for nasalance research and inspection [11; 12; 13; 14; 15; 16]. Despite the numerous advantages, the clinical application of nasometers is limited owing to the following factors: non-differentiation of headgear sizes for adults and children; risk of cutaneous irritation when tightened; cumbersome and heavy design resulting in discomfort during prolonged use; significant transportation requirements of all associated programs; and high costs owing to international import [17; 18].  
Recently, Smart Naso(tm) was developed for patients with resonance disorders to address the aforementioned limitations [17; 19]. This study aimed to assess the clinical effectiveness of this device, verifying its efficacy and reliability compared to the existing device, Nasometer II 6450, by measuring the nasalance of patients with resonance disorders. We hypothesized that Smart Naso(tm) would be non-inferior to Nasometer II 6450 in terms of the reliability and similarity of nasalization measurements of participants in the normal and hypernasality groups.
## 2 Problem Description and Modeling
### Mathematical Formulation  
Let \(\boldsymbol{x}\) and \(\boldsymbol{y}\) be the decision vectors representing task assignment and task order on edge servers, respectively. The variable \(x_{p}^{k}\) in \(\boldsymbol{x}\) is a decision variable such that \(x_{p}^{k}=1\) if task \(m_{p}\) is assigned to server \(u_{k}\) and \(x_{p}^{k}=0\) otherwise. Another variable \(y_{p,j}^{k}\) in \(\boldsymbol{y}\), equal to 1 if \(m_{p}\) is assigned to sever \(u_{k}\) before \(m_{j}\), and \(y_{p,j}^{k}=0\) otherwise.  
The mathematical model for task offloading with data-dependent constraints in SEC is presented below:  
\[minimize\ \mathbf{F}=(Makespan,Cost,Energy) \tag{14}\]  
\[\text{s.t.}\ \sum_{u_{k}\in\mathbf{U}}x_{p}^{k}=1,\forall m_{p}\in\mathbf{W} \tag{15}\]\[\sum_{m_{p}\in\mathbf{W}}x_{p}^{k}\geq 0,\forall u_{k}\in\mathbf{U} \tag{16}\]  
\[\prod_{m_{p}\in\mathbf{W},x_{p}^{k}=1}T_{p}^{qui}=0,\forall u_{k}\in\mathbf{U} \tag{17}\]  
\[\sum_{u_{k}\in\mathbf{U}}(T_{j}^{qui}-T_{p}^{start}-T_{p}^{com})y_{p,j}^{k}=0, \forall m_{p},m_{j}\in\mathbf{W} \tag{18}\]  
\[y_{p,j}^{k}\Big{[}T_{j}^{start}-T_{p}^{start}-T_{p}^{com}\Big{]}\geq 0,\forall m _{p},m_{j}\in\mathbf{W},m_{p}\in pre_{j},u_{k}\in\mathbf{U} \tag{19}\]  
\[z_{p}-z_{j}+N_{m}\sum_{u_{k}\in\mathbf{U}}y_{p,j}^{k}+(N_{m}-2)\sum_{u_{k}\in \mathbf{U}}y_{j,p}^{k}\leq N_{m}-1,\forall m_{p},m_{j}\in\mathbf{W},m_{p}\
eq m _{j} \tag{20}\]  
\[x_{p}^{k}\in\{0,1\},y_{p,j}^{k}\in\{0,1\},\forall m_{p},m_{j}\in\mathbf{W},u_ {k}\in\mathbf{U} \tag{21}\]  
\[x_{p}^{k}x_{j}^{k}=y_{p,j}^{k},\forall m_{p},m_{j}\in\mathbf{W},u_{k}\in \mathbf{U} \tag{22}\]  
where Equation (14) is the objective function, and _Makespan_, _Cost_, and _Energy_ can be calculated by Equations (8), (9), and (13), respectively. Equation (15) indicates that each task is executed by a specific edge server. Equation (16) indicates that some edge servers may have no assigned tasks. Equations (17) state that all edge servers are available simultaneously at time 0, and Equations (18) dictate that an edge server will be ready for the next task immediately upon completing the current one. The task precedence constraints are specified in Equation (19). Let \(z_{p}\) be the number of tasks that an edge server has performed before task \(m_{p}\). Equation (20) is the traditional subtour elimination constraints. Equations (21) and (22) specify the domains of the involved variables.
## 2 Materials and Methods  
### Rice Samples  
Grain samples of ten rice varieties, comprising four improved varieties (IR-64, Pusa Basmati-1, Pusa Basmati-1509, and Pusa 1652 (Improved Kala Namak)) and six landraces (Jeera 32, Govind Bhog, Kala Jeera, Kala Nuniya, Kala Namak-1, and Kala Namak-2) were obtained from the Farm Seed Storage Facility of the International Rice Research Institute, South Asia Regional Centre (ISARC) (Figure S1). Based on aroma testing through the sensory approach, Basmati-1, Pusa Basmati-1509, Pusa 1652 (Improved Kala Namak), Kala Namak-2, Jeera 32, Kala Jeera, and Govind Bhog were confirmed as aromatic germplasm (data not shown). Although Kala Nuniya and Kalanamak-1 are considered as aromatic rice varieties, our sensory test revealed only mild-aromatic rice of these samples. All varieties were cultivated in the wet season for two consecutive years (2019 and 2020) in the ISARC farm, Varanasi, India (25.282\({}^{\circ}\) N latitude, 82.956\({}^{\circ}\) E longitude). The plot size was 1 m \(\times\) 5 m with 20 cm row spacing. The average accumulated precipitations in 2019 and 2020 were 1114.7 mm and 1164.5 mm, respectively. Initially, seeds were sown in a nursery in the last week of June, and twenty-one days old seedlings were transplanted manually in the main field. After maturity, the paddy was harvested and sun-dried until moisture content was reduced to about 13%. Paddy samples of all the above varieties were stored in cold conditions until further processing. All paddy samples were de-hulled and milled with a rice-dehulling machine (PAZ-1/DTA, Testing Rice Mill, Zaccaria S/A, Brazil) to obtain milled rice.