esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Synthetic Approaches to Miktoarm Star Polymers  
Given the asymmetric nature of miktoarm stars, their construction requires careful selection of high yield methodologies for the build-up of individual arms on a branched core. Akin to the divergent and convergent synthetic methodologies pioneered by [PERSON] and [PERSON] for the synthesis of hyperbranched dendrimers [79; 80], the construction of branched miktoarm polymers can be mostly broken down to two methods: arm-first and core-first (Figure 2). These involve the independent synthesis of separate polymeric arms, before attachment to a core molecule, or polymerization initiated on a hetero-multifunctional core, respectively [21]. Depending on the necessary reaction conditions, cores may be functionalized separately, or have certain moieties blocked, in order to initiate polymerization from specific locations. Alternatively, in arm-first approaches, pre-synthesized polymers with conjugating end moieties can be coupled to these cores using esterification/amidation-based coupling, or, more recently, \"click\" chemistry [81]. Both arm-first and core-first methods present their own advantages depending on whether a miktoarm polymer is densely branched, requires precise arm lengths, needs to be synthesized with ease, etc. However, it has become more common recently to assemble miktoarm stars using a mixture of the arm- and core-first approaches. Such an approach can best accommodate varied conditions required for the construction of desired branched architectures. For example, considering that one of the most widely studied applications of miktoarm polymers is in biology, and especially as soft nanoparticle-based drug delivery, PEG has become an increasingly featured component of miktoarm polymers. Thus, it is often much simpler to purchase PEG of a desired molecular weight, modify it to contain a reactive end group, and couple it to cores that have been used for the initiation of hydrophobic polymer polymerization [32; 33; 34; 35; 36; 37; 40; 41].
# A New Method for Calculating Prediction Parameters of Surface Deformation in the Mining Area
## 3 Methodology
### Construction of Combined Model  
#### 3.3.1 ELM Neural Network  
The ELM neural network is a supervised machine learning algorithm, a single hidden layer feed-forward neural network which overcomes the defect that the BP neural network gradient algorithm is prone to falling into local extrema, and has the characteristics of high  
Figure 5: Fitting curve of tangent of major influence angle and thickness of unconsolidated layer.  
Figure 6: Fitting curve of propagation angle and coal seam dip angle.  
classification and regression efficiency and easy programming [34,35]. Based on advantages of the ELM neural network in regression analysis, the geological and mining conditions can be used as input and parameters of PIM as output to establish a regression analysis model. Figure 7 shows the network structure of the ELM neural network, which is similar to that of the BP neural network. Assuming that there are \(n\) neurons in the input layer, m neurons in the output layer, and one neuron in the hidden layer, and the connection weight between the input layer and the hidden layer is w, the mathematical model can be expressed as Equation (5):  
\[\sum_{i=1}^{l}\beta_{i}g(w_{i}x_{j}+b_{i})=t_{j},j=1,2,\cdots,n \tag{5}\]  
where \(w_{i}\) is the input weight connecting the \(ith\) hidden layer node and the input layer node; \(\beta_{i}\) is the output weight connecting the \(ith\) hidden layer node and the output layer node; \(b_{i}\) is the offset of the \(ith\) hidden layer node; \(x_{j}\) and \(t_{j}\) represent the input eigenvector and output eigenvector of the \(j\)th sample, respectively.  
Let \(\beta=[\beta_{1},\beta_{2},\cdots,\beta_{l}]^{\mathrm{T}},T=[t_{1},t_{2}\cdots t _{n}]^{\mathrm{T}}\) and  
\[H=\begin{bmatrix}g(w_{1}x_{1}+b_{1})&\cdots&g(w_{l}x_{1}+b_{1})\\ \vdots&\vdots&\vdots\\ g(w_{1}x_{n}+b_{1})&\cdots&g(w_{n}x_{l}+b_{1})\end{bmatrix}_{n\times l} \tag{6}\]  
Then, Equation (5) could be simplified as Equation (7):  
\[H\beta=T \tag{7}\]  
where \(H\) is the output matrix of the hidden layer, and the least square solution of the output weight could be easily obtained through the linear Equation (7):  
\[\hat{\beta}=H^{+}T \tag{8}\]  
wherein \(H+\) is the Moore-Penrose pseudoinverse of the hidden layer output matrix \(H\).  
The above analysis indicates that the network randomly generates weights and thresholds before training, so only the number of hidden layers and activation functions need to be determined, and the output \(\hat{\beta}\) can be calculated.
## 4 Methodological Approach
### ANN Training and Accuracy Improvement
The ensemble approach is the calculation of the predicted value as the average of the predicted values calculated by a set of DFANNs instead of as just the predicted value given by one single DFANN. In fact, given the very same model complexity, a number k of DFANNs can be achieved, giving accurate results. This may happen, for example, when their training and testing datasets partly differ. For this purpose, the so-called k-fold cross-test was used, and the initial dataset was split into k sub-datasets: each DFANN used k-1 sub-datasets for the training, and one exclusive sub-dataset was used for the testing. As a consequence, for epoch after epoch in the iterative process, the DFANNs converged toward similar overall results but while using slightly different DFANN architectures, since they were trained over slightly different datasets. Consequently, some DFANNs would overestimate the output value, while others would underestimate it. Therefore, the output value, calculated as an average of the predictions of all of the produced DFANNs, was generally much closer to the true value than any of the single DFANNs if taken separately.
# Vertical Force Monitoring of Racing Tires: A Novel Deep Neural Network-Based Estimation Method
## 1 Introduction
This approach integrates the feature extraction capability of deep learning methods with the nonlinear relationship processing capability of the Random Forest Algorithm. The proposed hybrid method uses a mixture of various neural network layers to extract complex features from raw data. These features are then fed into a Random Forest model for tire force estimation. The necessary training and test data were obtained using a test rig equipped with a 'drive-hard' (DHF) racing tire rated as Shore A hardness 71 according to ASTM D2240 measurement standards in the established experimental environment. The contributions of the proposed study to the literature are as follows:  
* A novel combination of the strengths of deep neural networks and Random Forests has been used to estimate vertical tire forces better than existing related studies. * A highly reliable dataset of racing tires with a specific hardness level has been obtained. The tests were performed using an original test rig setup in a racing tire manufacturer's factory to simulate the original behavior of the racing tire. * One of the important contributions is the use of inputs directly related to the vertical force for more realistic estimation. Acceleration, speed, and tire internal pressure values were used instead of irrelevant values used in the literature. * The labeling of analog data representing the acceleration acting on the tire was automated using deep learning to reduce labeling effort and enhance reliability. The rest of the paper is organized as follows: Section 2 reviews related works. Section 3 outlines the design steps of the proposed method. Section 4 introduces the data acquisition setup, data preprocessing stage, corresponding datasets, and data labeling. In Section 5, performance metrics are exclusively used to compare the proposed estimation method with others, and the comparative results are presented in a table. Additionally, estimation graphs illustrating the method's performance are included. Finally, Section 6 offers concluding remarks.
# Sampling Based on Kalman Filter for Shape from Focus in the Presence of Noise
## 6 Results and Discussion
### Experimental Results
Figures 15 and 16 provide the qualitative performance of 3D shape reconstruction of the experimented objects using three focus measures, SML, GLV, and TEN, before and after filtering in the presence of speckle noise. In Before Filtering and After Particle Filtering, it can be seen that the performance of 3D shape reconstruction was very poor due to unremoved or poorly removed jitter noise. However, in After Bayesian Filtering and After Kalman Filtering, it is evident that the performance of the 3D shape recovery was greatly improved due to the elimination of most of the jitter noise. It is proved from these experimental results that filtering the jitter noise using the Kalman filter improves the 3D shape reconstruction faster and more accurately. \begin{table}
\begin{tabular}{c c c c} \hline
**Experimented Objects** & **Particle Filter** & **Bayes Filter** & **Kalman Filter** \\ \hline Simulated cone & 0.637766 & 0.113022 & 0.008112 \\ Coin & 0.677874 & 0.124471 & 0.009683 \\ LCD-TFT filter & 0.625544 & 0.116263 & 0.007738 \\ Letter-I & 0.636709 & 0.112466 & 0.009179 \\ \hline \end{tabular}
\end{table} 
Table 9: Computation time of filters for the experimented objects in the presence of speckle noise. Figure 13: 3D shape recovery of coin, before and after filtering, using SML, GLV, and TEN in the presence of Gaussian noise. (**a**) Before filtering for SML; (**b**) Before filtering for GLV; (**c**) Before filtering for TEN; (**d**) After particle filtering for SML; (**e**) After particle filtering for GLV; (**f**) After particle filtering for TEN; (**g**) After Bayesian filtering for SML; (**h**) After Bayesian filtering for GLV; (**i**) After Bayesian filtering for TEN; (**j**) After Kalman filtering for SML; (**k**) After Kalman filtering for GLV; (**l**) After Kalman filtering for TEN. Figure 14: 3D shape recovery of LCD-TFT filter, before and after filtering, using SML, GLV, and TEN in the presence of Gaussian noise. (**a**) Before filtering for SML; (**b**) Before filtering for GLV; (**c**) Before filtering for TEN; (**d**) After particle filtering for SML; (**e**) After particle filtering for GLV; (**f**) After particle filtering for TEN; (**g**) After Bayesian filtering for SML; (**h**) After Bayesian filtering for GLV; (**i**) After Bayesian filtering for TEN; (**j**) After Kalman filtering for SML; (**k**) After Kalman filtering for GLV; (**l**) After Kalman filtering for TEN.
# Risk Prediction for Winter Road Accidents on Expressways
## 3 Methods
### Random Forest Model  
The random forest model, proposed by [PERSON], is an advanced technique to improve the predictive performance and overfitting problems of the decision tree model, and is  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No** & **Source** & **Lon.** & **Lat.** & **Accident** & **Road\_Type** & **Max\_Angle** & **Min\_Radius** \\ \hline \(1\) & & 128.686 & 35.869 & Weather & Road & 24.864 & 56.889 \\ \hline \(2\) & & 127.109 & 37.614 & Accident & Road & 32.075 & 42.691 \\ \hline \(3\) & MOLIT & 126.820 & 37.584 & Accident & Bridge & 11.396 & 14.961 \\ \hline \(\vdots\) & & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\ \hline \(8683\) & & 126.634 & 37.46475 & Accident & Tunnel & 9.300 & 147.142 \\ \hline \(\mathbf{No}\) & **Source** & **Temp** & **Ws** & **Prec** & **Hpa** & **Rh** & \\ \hline \(1\) & & 3.3 & 0.115 & 0 & 1019.6 & 77.338 & \\ \hline \(2\) & & \(-2.5\) & 3.116 & 0 & 1016.2 & 52.477 & \\ \hline \(\vdots\) & & 1.0 & 1.383 & 0 & 1027.4 & 73.974 & \\ \hline \(\vdots\) & & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \\ \hline \(8683\) & & 4.1 & 1.125 & 0 & 1027.8 & 56.885 & \\ \hline \(\mathbf{No}\) & **Source** & **Altitude** & **diff\_north** & **diff\_south** & **diff\_west** & **diff\_east** & **cov\_west** & **cov\_east** \\ \hline \(1\) & & 39 & 1 & 1 & \(-1\) & 1 & 0 & 0 \\ \hline \(2\) & & 38 & 1 & \(-1\) & 1 & 0 & 0 & 0 \\ \hline \(\vdots\) & & 10 & \(-4\) & 0 & \(-28\) & 0 & 0 & 28 \\ \hline \(\vdots\) & & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\ \hline \(8683\) & & 9 & \(-1\) & 1 & \(-1\) & \(-7\) & 0 & 11 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structure of the combined data.  
an ensemble technique that uses resampling-based bagging [30]. Here, multiple data are constructed by bootstrap sampling, and a decision tree grows from each bootstrap data with a limited number of variables. In the process of aggregating the prediction results of the decision tree, the majority vote is used for classification purposes, and the average of each tree is used for regression problems [31]. In this study, a model was created using the randomForest function built in the randomForest package of R, and the hyper-parameter used for the setting, was the number of explanatory variables (mtry) [32]. The number of trees was set to 500.
## 2 Materials and Methods
### Data Collection and Analysis Methods
#### 2.4.2 Measurement of Canopy Interception Capacity for Rainwater
The procedure is as follows: Firstly, select representative locations within the study area. Next, use a mallet to sequentially insert the inner and outer rings into the soil, ensuring their alignment at the exact center point. Typically, the rings are inserted to a depth of 10 cm, maintaining a level opening. Add a predetermined amount of water to the inner ring while simultaneously adding water to the outer ring without precise measurement, ensuring a roughly equal water level in both rings. Employ a stopwatch to record the time taken for water infiltration. Prepare the subsequent water application during this observation period. Following complete water infiltration, restart the timer and add water to the rings. Repeat this sequence until the consistent infiltration time for a fixed water quantity is achieved. 2.4.8. Measurement of Canopy Cover  
To determine the coverage percentage (%), begin by selecting a sample area of 1 m\({}^{2}\). Utilize steel tape and quadrat rope to create markings at 10 cm intervals. Using a needle with a thickness of approximately 2 mm, establish a sequence of 100 points spaced 10 cm apart horizontally. For each point, insert the needle vertically downward from above the vegetation until it makes contact, indicating its 'presence'. If there is no contact, record it as 'absent' and note it on the form provided. Finally, tally up the total count of 'presence' instances to calculate the coverage percentage. This calculation is performed as follows:  
\[C=\frac{T-N}{T}\times 100\% \tag{2}\]  
In the formula, \(C\) is the coverage (%); \(T\) is the total number of times; \(N\) is the number of no contact (\"none\").
## 3 Results  
### Statistical Analysis  
Twenty patients with AMI requiring revascularization were randomly assigned to receive either Vytorin 10/10 mg (n = 9) or Vytorin 10/40 mg (n = 11) between 8 June 2013, and 3 July 2017. Two patients in the Vytorin 10/40 mg group were excluded because of changing of Vytorin dose. During follow-up angiography with OCT, assessment was completely achieved in 18 subjects, nine of the Vytorin 10/10 mg group and nine of the Vytorin 10/40 mg group. The mean age of the total population was 60.2 \(\pm\) 10.3 years, 15 patients (83.3%) were men, and six patients with STEMI were included. Baseline clinical and procedural characteristics and discharge medication of these patients are summarized in Table 1 and show no differences between the two groups in terms of age, gender, incidence of cardiovascular risk factors, angiographic findings, or medication.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Variables** & **Vytorin 10/10 mg (n = 9)** & **Vytorin 10/40 mg (n = 9)** & \(p\)**Value** \\ \hline Demographic & & & \\ Age, mean \(\pm\) SD, y & 57.0 (8.3) & 63.4 (11.5) & 0.222 \\ Male & 8 (88.9) & 7 (77.8) & 0.527 \\ Cardiovascular risk factors & & & \\ Hypertension & 3 (33.3) & 6 (66.7) & 0.157 \\ Diabetes mellitus & 2 (22.2) & 4 (44.4) & 0.317 \\ Current smoking & 7 (77.8) & 4 (44.4) & 0.147 \\ STEMI & 4 (44.4) & 2 (22.2) & 0.317 \\ Culiprit lesion & & & \\ LAD & 5 (55.6) & 7 (77.8) & \\ LCx & 1 (11.1) & 1 (11.1) & \\ RCA & 3 (33.3) & 1 (11.1) & \\ Baseline QCA data, mean \(\pm\) SD & & & \\ Reference vessel diameter, mm & 3.40 \(\pm\) 0.42 & 3.37 \(\pm\) 0.38 & 0.931 \\ Minimum lumen diameter, mm & 0.12 \(\pm\) 0.13 & 0.23 \(\pm\) 0.21 & 0.297 \\ Diameter stenosis, \% & 96.7 \(\pm\) 3.5 & 92.8 \(\pm\) 6.7 & 0.222 \\ Implanted stent, median (IQR) & & & \\ No. of stents & 1.0 (1.0-1.0) & 1.0 (1.0-1.0) & 0.730 \\ Stent diameter, mm & 3.5 (0.3-3.6) & 3.5 (3.3-3.5) & 1.000 \\ Stent length, mm & 24 (18-28) & 18 (18-26) & 0.340 \\ Medication at discharge & & & \\ Aspirin & 9 (100) & 9 (100) & \\ Clopidogrel & 1 (11.1) & 3 (33.3) & 0.257 \\ Tscagnler & 4 (44.4) & 4 (44.4) & 4 (44.4) \\ Prasugrel & 4 (44.4) & 2 (22.2) & 0.317 \\ ACE1/ARB & 8 (88.9) & 9 (100) & 0.303 \\ Beta-blocker & 7 (77.8) & 7 (77.8) & \\ \hline \end{tabular}  
* Data are expressed as No. (%) unless otherwise indicated; Abbreviations: ACEi, angiotensin-converting enzyme inhibitor; ABB, angiotensin II receptor blocker; IQR, interquartile range; LAD, left anterior descending artery; LCx, left circumplex artery; QCA, quantitative coronary angiography; RCA, right coronary artery; STEMI, ST-elevation myocardial infarction.  
\end{table}
Table 1: Characteristics of baseline clinical and procedures and discharge medication between the two groups.
## 2 Literature Review
### Modeling of Building Energy Consumption  
Energy performance of a building is affected by many factors in different levels, such as climate zone, building structure and properties, and occupants and their behavior [25]. Because of the complexity of the energy systems of buildings and the necessity to obtain an accurate prediction of usage, a large number of approaches have been proposed to solve this complicated issue, including both elaborate and simple methods. These methods are used in different applications, such as designing new buildings and operations or retrofitting existing buildings. Engineering, statistics, and artificial intelligence are used to model and estimate the energy use of buildings [26]. Many software programs have been developed for assessing energy consumption in buildings, such as Energy Plus [27; 28]. Although these tools are accurate, they have some difficulties related to detailed parameters as inputs and require a high level of experience to ensure effective usage [29].  
Statistical models, such as regression method, are commonly be used for building energy assessments [30]. These models correlate energy consumption with effective variables and depend on historical data. While this is an easy method to use, it is less accurate.  
Artificial intelligence methods are widely used to solve nonlinear problems and complex applications. Artificial neural networks (ANNs) and support vector machines (SVMs) are the most widely used models applied to building energy predictions [31; 32].  
Recently, many studies have used ANNs to model different categories of building energy consumption for various categories of buildings in different climate zones [33; 34; 35; 36]. The input data require specific processing to remove noise and useless variables to achieve the best training results. Some research has focused on the preprocessing stage of input data [37]. The output of ANNs may not be close to expectations or not accurate. Therefore, some correction or calibration is needed to achieve high accuracy outputs [38].  
Some comparisons have been made between ANNs and other prediction models, such as regression models [39; 40; 41], or engineering models [42].
# Study on Fluctuating Wind Characteristics and Non-Stationarity at U-Shaped Canyon Bridge Site
## 3 Analysis of Fluctuating Wind Characteristics
### Fitting Results of Wind Speed Distribution  
The fitting results of the U-component wind speed probability density distribution in the canyon area are presented in Figure 2 and Table 1, with Table 2 detailing the specific functions of the equations used for fitting. The wind speed in this region exhibits a bimodal distribution, with the main data range spanning 0-8 m/s and peaks occurring at approximately 1 m/s and 5 m/s. Consequently, a mixed distribution model was introduced for fitting. In this mountainous terrain, the mixed distribution model (R\({}^{2}\) > 0.99) provides significantly more accurate fitting results compared to the single distribution model (R\({}^{2}\) = 0.804 and R\({}^{2}\) = 0.772). Based on the fitting results, the Weibull-Gamma mixed distribution model is recommended for describing wind speed characteristics.
# Attention-Based LSTM Algorithm for Audio Replay Detection in Noisy Environments
## 4 The Proposed Algorithm
### Network Architecture for Replay Attack Detection  
The proposed framework is shown in Figure 3. The network architecture is as follows: (1) the frame-level features are extracted from the input utterance, (2) the hidden states of the whole sequence are obtained by LSTM, (3) a batch-normalization layer is utilized to reduce overfitting and to improve the model's performance, (4) attentional weighting of the hidden state is transformed into a super vector, and (5) fully-connected layers followed by a softmax layer are used to make a final decision on whether the voice is genuine or a spoof.  
Figure 3: Network architecture for replay attack detection with attention mechanism.
## 3 Transcription Data  
The expert (counselor) provides counseling to the child (counselee), records the conversation, and generates transcription data by capturing the recorded data as text in line with the protocol. All utterances made by the expert and the child during the counseling procedure are included in the transcription data. Children's utterances should be gathered in the same setting and conditions as much as feasible in order to evaluate the level of language development with high reliability. As a consequence, the expert adheres to a standardized utterance collecting procedure (conversation protocol). The conversation protocol's primary function is to keep the expert from interfering with the flow of the discussion or the content of the children's replies as much as possible. Only when professional interaction is minimal can a child's pure level of language development be tested. Furthermore, properly delineating the boundaries between each speech uttered in counseling aids in determining the child's language development level [26, 27].  
Transcription data gathered by the Hallym Conversation Protocol [28] created by the Division of Speech Pathology and Audiology in Hallym University was used in this study. When collecting transcription data, the expert's participation is limited to asking questions to elicit replies from the child and passively reacting to the child's responses. Transcription data from children ages 2, 4, and 6 (which implies a 2-year gap) were employed in this study to discern developmental changes. Each child's transcription data comprised an average of 151 utterances and were gathered for a total of 111 children. Table 1 shows the simple statistics of transcription data obtained by the age group, and Table 2 shows transcription data samples.