esa-sceva/satcom-chunk-collection huggingface dataset:
# Complex Systems, Emergence, and Multiscale Analysis: A Tutorial and Brief Survey
## 2 Basics of Complex Systems and Emergence
### Power Law and Heavy-Tailed Distributions
#### 2.2.2 Simulation and Parameter Estimation  
To simulate a Pareto distributed random variable \(U\), we can associate \(U\) with an outcome of a random experiment. The same outcome may also be represented by the value of another random variable \(X\). The probability of an event of the experiment is then either \(dF_{U}(u)=f_{U}(u)du\) or \(dF_{X}(x)=f_{X}(x)dx\), where \(F_{U}(u)\) and \(F_{X}(x)\) are the cumulative distribution functions (CDFs) for the \(U\) and \(X\), while \(f_{U}(u)\) and \(f_{X}(x)\) are the PDFs. Then we have  
\[\int_{a}^{X}dF_{X}(x)=\int_{0}^{U}du. \tag{14}\]  
Since \(F_{X}(x)\) is monotonically nondecreasing, its inverse function exists. We then have  
\[X=F_{X}^{-1}(U). \tag{15}\]  
Now suppose \(U\) is a uniform \([0,1]\) random variable, while \(X\) is a Pareto random variable, then  
\[X=bU^{-\frac{1}{\alpha}}. \tag{16}\]  
The most important parameter of the Pareto distribution is the exponent \(\alpha\). To estimate it, we only need to notice that \(\ln P[X\geq x]\) vs. \(\ln x\) is a linear function, with the slope being \(-\alpha\). When estimating \(\alpha\) from a finite set of data points, it is important to first take the logarithm of \(x\), then estimate the CCDF for \(\ln x\), and finally check if the logarithm of CCDF has a linear relation with \(\ln x\). If one straightforwardly estimates a PDF or CCDF for the original data, then take log-log of both axes to estimate \(\alpha\), one will often get a very inaccurate or even wrong estimation. The reason is many of the small intervals used for counting the number of data points \(x\) falling within them will be empty.
## 2 Kinematics Analysis  
### Configuration and Coordinate System of SGP  
According to different connection modes of upper and lower hinge points, the configuration of SGP can be divided into 6-6 (six upper jungles and six lower jungles), 6-3 (six upper jungles and three lower jungles), and 3-3 (three upper jungles and three lower jungles). In this paper, the classical structure of the 6-6 form is used as the research object. It is worth mentioned that the idea proposed in this paper is also applicable to other configurations of parallel platforms. As shown in Figure 1a, 6-6 SGP consists of top platform A, base platform B, and six actuator outriggers which are respectively linked to the top and base platforms by twelve upper and lower hinges.  
Given a moving coordinate system \(o^{\prime}x^{\prime}y^{\prime}z^{\prime}\), \(o^{\prime}\) is the center of the top plate, \(\mathbf{A}_{i}\) is the center of top hinge \(i\) (Figure 1b). The global coordinate system \(oxyz\) describes the base plate, \(o\) is the center of the base plate, \(\mathbf{B}_{i}\) is the center of the base hinge \(i\). The posture of the top plate is composed of position vector \((x,y,z)\) and rotation vector \((\alpha,\beta,\gamma)\). The points described in the moving coordinate can be transformed to the global coordinate by rotation operator \(\mathbf{R}(\alpha,\beta,\gamma)\) and translation operator \(\mathbf{L}(x,y,z)\).
## 3 Results and Discussion
### Results of Rheological Properties at Ambient Temperature  
The rheological properties at room temperature are assessed through a fatigue resistance test, using the parameter G*.sin \(\delta\) (in kPa). Higher values indicate a greater vulnerability to fatigue cracking. All tested binders underwent PAV aging, which replicates the oxidative aging process that takes place throughout the lifespan of the pavement. Beginning with the original binder, PG64-22, which displayed a G*.sin \(\delta\) value of 3400 kPa, we can observe a fundamental level of fatigue resistance (as shown in Figure 4). The implementation of a 10% TLA led to a substantial increase in the G*.sin \(\delta\) value, reaching 5090 kPa. This indicates that the binder became more rigid and susceptible to fatigue cracking. The trend persists with a 20% modification in TLA, resulting in a G*.sin \(\delta\) value of 6548 kPa. Although the greater rigidity resulting from a higher TLA content can be beneficial in preventing rutting, it also raises concerns regarding the possibility of fatigue cracking, particularly in binders that have been aged over a long period of time.  
Figure 4: G*sin \(\delta\) at 25 \({}^{\circ}\)C of the modified binders in RTFO + PAV-aged condition.  
SIS, a thermoplastic elastomer, is added to asphalt binders to enhance their elasticity and fatigue resistance. This addition is important in counteracting the stiffening effects of TLA. Upon adding 5% SIS to the 10% TLA-modified binder, the G*.sin \(\delta\) value decreases to 3980 kPa. This exemplifies the capability of SIS to enhance flexibility and enhance the binder's durability against fatigue cracking. Furthermore, the inclusion of 5% SIS in the 20% TLA-modified binder results in a decrease in the G*.sin \(\delta\) value to 5480 kPa. Increasing the SIS content to 10% in both TLA-modified binders leads to lower G*.sin \(\delta\) values. The 10% TLA binder has a value of 2530 kPa, while the 20% TLA binder has a value of 4970 kPa. This demonstrates the significant effect of SIS in improving fatigue resistance, and Table 6 shows that all values are significantly different from each other.  
From a scientific standpoint, the findings emphasize the significance of SIS in contemporary pavement engineering. SIS reduces the likelihood of fatigue cracking by improving the viscoelastic properties of the binder, which counteracts the increased stiffness caused by TLA. The equilibrium between rigidity and adaptability is crucial for the durability of pavements, especially in older binders that have experienced substantial oxidative aging. SIS enhances the flexibility of the asphalt matrix, enabling it to efficiently absorb and disperse strain energy. This is essential for preventing the formation and spread of cracks throughout the lifespan of the pavement.
## 5 Seismic Performance Analysis
### Ground Motion Input  
Near-fault ground motions are significantly different from the far-fault ground motions due to the effects of the fling-step and forward-directivity [40]. The ground motions are characterized by pulse-type wave shape, small fault distance \(d\) (i.e., less than 20 km), large peak ground velocity (PGV) and peak ground acceleration (PGA), and sometimes large permanent ground displacement. Based on the characteristics, near-fault pulse-type ground motion records are selected from the PEER database [41] and presented in Table 4. All the records contain the ground motions in longitudinal, transverse, and vertical directions. In the Eurocode 8 [42], the site is classified as A, B, C and D, according  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Mode**} & \multicolumn{2}{c}{**Period (s)**} & \multirow{2}{*}{**Vibration Mode**} \\ \cline{2-2} \cline{4-4}  & **Prototype Bridge** & & & \\ \hline
1 & 2.191 & 2.072 & 1 st-order transverse symmetrical bending \\
2 & 1.676 & 1.635 & 1 st-order vertical bending \\
3 & 1.262 & 1.243 & 1 st-order transverse antisymmetric bending \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the vibration modes.  
Figure 6: Comparison of the vibration modes.  
to the value of the average shear wave velocity, \(\
u_{\text{S,30}}\). Besides, the structures in seismic regions should be designed and constructed based on the following requirements: no-collapse requirement under the DBEs and damage limitation requirement under SLEs. The no-collapse requirement means the bridge should retain its structural integrity and a residual load-carrying capacity after the DBEs. Similarly, the occurrence of damage and the associated limitations of use is not allowed in the damage limitation requirement when the structure subjects to SLEs.  
Therefore, the largest peak ground accelerations among three directions of the selected ground motions are scaled to 0.1 g and 0.4 g, to consider the PGA of the SLE and DBE, respectively. Besides, the PGAs in the other directions are scaled in proportion. The ground motion records in three directions are input simultaneously.
## 2 Results
### Experimental Validation of Predicted sRNAs and Prediction of Target mRNA  
Eighteen putative sRNAs predicted using sequence data in Table 1 were selected for validation by qRT-PCR analysis. Among these sRNAs, 10 sRNAs were confirmed, and the high \(C_{\mathrm{t}}\) value exhibited a low expression level (Table 1). To obtain preliminary information regarding the function target of  
Figure 2: Annotation and length distribution of sequenced data. (**A**) Annotation of the constituent ratio from sequenced data; and (**B**) the length distribution of small RNA libraries in three samples. The nt lengths of sRNAs are shown on the _x_-axis; the proportion of sRNAs accounting for total unique reads are shown on the _y_-axis. sRNAs (\(\geq\)37 nt in length) only account for a small proportion of the total reads and are thus shown together.  
these sRNAs, we predicted the target mRNAs using _RNAhybrid_ for the two most highly expressed sRNAs (_srn_884837 and _srn_133480) in the current study. Surprisingly, the results suggested that the reported virulence genes responsible for the acid tolerance of _S. mutans_ were putative targets of sRNAs (_glnQ, glnM, brpA_, and _relA_ for _srn_884837; _fflh, brpA_, and _relA_ for _srn_133480).
# A Moment-Fitted Extended Spectral Cell Method for Structural Health Monitoring Applications
## 3 Moment Fitting for Enriched Spectral Elements
### Discretized Equilibrium Equations  
Upon the substitution of Equation (15) in the weak form (Equation (1)), the discretized equilibrium equations are obtained:  
\[\mathbf{M}\;\ddot{\mathbf{u}}_{\mathrm{s}}(t)+\mathbf{K}\;\mathbf{u}_{\mathrm{s} }(t)=\mathbf{f}_{\mathrm{s}}(t). \tag{17}\]  
The mass matrix \(\mathbf{M}\), the stiffness matrix \(\mathbf{K}\), and the system's force vector \(\mathbf{f}_{\mathrm{s}}\) are assembled form the respective element contributions [17]:  
\[\mathbf{M}_{\mathrm{e}}=\int_{\mathbf{\Omega}_{\mathrm{e}}}\rho\mathbf{N}^{ \mathrm{T}}\mathbf{N}\;\mathrm{d}\Omega_{\mathrm{e}} \tag{18}\]  
\[\mathbf{K}_{\mathrm{e}}=\int_{\mathbf{\Omega}_{\mathrm{e}}}\mathbf{B}^{ \mathrm{T}}\mathbf{D}\mathbf{B}\;\mathrm{d}\Omega_{\mathrm{e}} \tag{19}\]  
\[\mathbf{f}_{\mathrm{e}}(t)=\int_{\Gamma_{\mathrm{s}}}\mathbf{N}^{\mathrm{T}} \mathbf{p}_{\mathrm{s}}(t)\;\mathrm{d}\Gamma_{\mathrm{s}}+\int_{\Gamma_{ \mathrm{cs}}}\mathbf{N}^{\mathrm{T}}\mathbf{p}_{\mathrm{c}}(t)\;\mathrm{d} \Gamma_{\mathrm{cs}} \tag{20}\]  
where the expression in Equation (19) implies the application of [PERSON]'s constitutive law, with \(\mathbf{B}\) collecting the nodal displacement derivatives and matrix \(\mathbf{D}\) governing the stress-strain relations. Finally, the element loads result from integrating tractions \(\mathbf{p}_{\mathrm{s}}\), \(\mathbf{p}_{\mathrm{c}}\) on domain boundaries \(\Gamma_{\mathrm{s}}\) and implicitly defined loading surfaces \(\Gamma_{\mathrm{cs}}\), respectively.  
For materials such as composites, a damping term \(\mathbf{C}\;\ddot{\mathbf{u}}_{\mathrm{s}}(t)\) might be added to the equations of motion:  
\[\mathbf{M}\;\ddot{\mathbf{u}}_{\mathrm{s}}(t)+\mathbf{C}\;\dot{\mathbf{u}}_{ \mathrm{s}}(t)+\mathbf{K}\;\mathbf{u}_{\mathrm{s}}(t)=\mathbf{f}_{\mathrm{s}} (t). \tag{21}\]  
As the physics described by the damping matrix \(\mathbf{C}\) is complex, [PERSON]'s assumption [101; 102] is often applied in order to simplfy the computations:  
\[\mathbf{C}=\alpha\mathbf{M}+\beta\mathbf{K} \tag{22}\]  
where \(\alpha,\beta\in\mathbb{R}^{+}\) are material-dependent constants. Since matrix \(\mathbf{C}\) has the same sparsity structure as \(\mathbf{K}\), a substantial increase in the memory requirements of the model and the number of operations necessary for time integration ensues. In explicit dynamics, this can be overcome by performing mass-proportional damping, i.e., setting \(\beta=0\), thus producing a diagonal damping matrix.
# Melody Extraction Using Chroma-Level Note Tracking and Pitch Mapping
## 2 Chroma-Level Note Tracking and Pitch Mapping-Based Melody Extraction  
Melody originates from the lead voice or the most prominent instrument. Generally, the main energy of the human voice concentrates at the first few harmonics, and the spectral slope of music instruments decays 3 dB to 12 dB per octave [16], so it can be deduced that melody has the most prominent chroma, though concealed by some strong accompaniments occasionally. Motivated by the aforementioned facts, a chroma-level note tracking and pitch mapping-based method is proposed to extract main melody from polyphonic music. Block diagram of the proposed method is shown in Figure 1. The polyphonic music recording is first chopped into frames, then chromagram is obtained by the HPCP calculation. After that, dynamic programming is applied to obtain the chroma sequences taking into account of both salience and temporal smoothness constraints. Next, coarse melody contour is roughly constructed from the chroma sequence by a salience-based pitch matching scheme, and then fine tuned by the modified Euclidean algorithm. Finally, the average salience of fine tuned pitch is utilized for voicing detection.  
The HPCP calculation presented in [17] is introduced to extract the chroma features, and the modified Euclidean algorithm described in [12] is utilized to compute the pitch candidates for fine tuning. For the other steps, such as chroma-level note tracking formulated by DP, chroma-level note detection, pitch coarse mapping and voicing detection, are new in this method. The HPCP calculation, melodic chroma sequence tracking, melodic pitch mapping and voicing detection are presented in detail in the following subsections.
# The Study on the Text Classification Based on Graph Convolutional Network and BiLSTM +
## 3 Proposed Method  
Following in-depth research on text classification based on neural networks, this paper proposes IMGCN. The whole process of IMGCN can be divided into the steps of text composition, graph convolutional network, feature fusion, and final prediction. The architecture of IMGCN is shown in Figure 2. The colored continuous line in the IMGCN architecture is the output of each single model, and the intermittent line represents the modules composed of a single model. We construct two text graphs by establishing edges between word nodes. Build a vocabulary graph from word co-occurrence relations and semantic lexicon. Build a dependency graph from the inclusion and dependencies of documents and words. We use point-wise mutual information (NPMI) [27] and a semantic dictionary to assign association weights to edges between words to jointly build a text graph. Then, the dependency-based TF-IDF method is used to assign association weights to the edges between words and documents and use a matrix transformation to map them to the relations between words and words, thereby constructing another text graph. Then the word embedding of BERT is used as the input vector required by the graph convolutional network, and the two text graphs are respectively used as the adjacency matrix required by the graph convolutional network. Subsequently, they are separately fed into a two-layer graph convolutional network for training, and the two hidden output vectors are then summed. Next, the added word vector is fused with the feature vector of the pre-trained BERT and input to the first-layer BiLSTM model to extract the semantic dependency information between words within a single sentence. To further distinguish the importance of words, we add an attention mechanism to the first layer of BiLSTM to distinguish the importance of words by assigning different word weights. The word weights and the output of the first layer of BiLSTM are weighted and summed to form an overall representation of the current sentence. Here, the bidirectional word hidden features are combined as the context vector of the attention mechanism to guide the learning of the attention mechanism. The obtained sentence feature representation is then input to the second layer of the BiLSTM to extract semantic dependency information between sentences within the text. The learned sentence feature representation and the feature obtained by the context attention layer are subjected to residual operation to form an overall text representation. Finally, the overall text representation is input to the Softmax classification layer to obtain the final category of the text.
## 4 Comparison of ML Models
### Model Validation
Furthermore, it is essential to note that the metrics reported are derived from models that incorporate the extended feature set which includes additional variables that enhance the predictive capabilities of the models, thereby providing a more comprehensive understanding of the factors influencing player performance. Ridge regression emerged as a standout model, achieving a mean squared error (MSE) of 0.0005 and a mean absolute error (MAE) of 0.0094. This performance underscores its effectiveness in aligning predicted outcomes with actual player performance, showcasing its capacity to minimize prediction errors. Notably, the R\({}^{2}\) score for ridge regression reached an impressive 0.953, indicating that approximately 95% of the variance in player metrics can be explained by this model. This strong performance is on par with linear regression, which also achieved an MSE of 0.0005 and an R\({}^{2}\) score of 0.953. Such results highlight the reliability of both models in providing valuable insights into player performance. Conversely, lasso regression fell short with an MSE of 0.0066 and a significantly lower R\({}^{2}\) score of 0.396, indicating its limitations in capturing the complexity of player metrics. Similarly, models like K-nearest neighbors (KNN) and multi-layer perceptron (MLP) demonstrated less favorable outcomes, with MSE values of 0.0065 and a staggering 1.5914, respectively. These discrepancies illustrate how some models struggled to generalize effectively, especially in a domain characterized by inherent variability. Interestingly, the performance of decision trees and random forests also revealed a nuanced narrative. While decision trees recorded a low MSE of 0.0002 and an R\({}^{2}\) score of 0.985, random forests outperformed them with an even lower MSE of 0.0001 and an R\({}^{2}\) score of 0.995. However, their predictive performance did not consistently translate into practical application, as their complexity can lead to overfitting, which is a significant concern in sports analytics where data variability is high.
# STAVOS: A Medaka Larval Cardiac Video Segmentation Method Based on Deep Learning
## 4 Experiment
### Experimental Setup  
Following the widely adopted approach [24], we chose to pretrain our model using the DAVIS2016 dataset, which is a dense adaptive video segmentation dataset. The intention is to equip the model with the capability to extract features related to prominent moving objects in videos before initiating training on the medaka dataset.  
The reason for selecting DAVIS2016 is attributed to its annotation approach, which distinguishes only between foreground and background through binary labeling, aligning closely with the nature of our task. Moreover, the prominence of DAVIS lies in its high-quality, high-resolution characteristics, making it a mainstream benchmark dataset for VOS tasks. This choice is expected to enhance the credibility of our model, particularly given the dataset's reputation for being a leading evaluation dataset in the field. The dataset has a video frame rate of 24 fps, a resolution of 1080 p, and short video sequences lasting 2-4 s. It comprises a total of 50 video sequences (3455 frames), with 30 videos (2079 frames) in the training set and 20 videos (1376 frames) in the validation set. Detailed information about the DAVIS dataset can be found at [[https://davischallenge.org](https://davischallenge.org)]([https://davischallenge.org](https://davischallenge.org)) (accessed on 1 July 2017).  
Before conducting neural network training, a series of image augmentation operations were applied to enhance the model's generalization capability. Initially, we randomly selected a video from the dataset and extracted a consecutive sequence of 10 frames. For these 10 frames, we applied video inversion with a probability of 40%, horizontal and vertical flipping with a probability of 20%, and additional swapping with a probability of 20% for supplementary enhancement. Prior to inputting into the model, we computed the STA frame for these 10 frames, followed by random joint cropping to a size of 384 \(\times\) 384. In cases where the cropping excluded any objects, we repeated the process until the foreground object was included. Additionally, random affine transformations and random balanced cropping were introduced. Similar to CFBI (Collaborative Video Object Segmentation by Foreground-Background Integration) [25], sequences with insufficient foreground pixel counts after cropping were excluded to prevent the model from biasing towards background objects.  
During the training phase, the batch size was set to 8, the initial learning rate was set to \(1\times 10^{-4}\), and cross-entropy loss was utilized with the Adam optimizer [26]. Furthermore, all main encoders were frozen, and all batch normalization layers [27] were disabled.
# SQRT: A Secure Querying Scheme of Routing Table Based on Oblivious Transfer
## 2 System Model
### The Directory Server Stores and Updates the Status Information of the Routing Nodes  
The DS dynamically collects the status information of routing nodes through the network heartbeat mechanism [16; 17]. The DS regularly sends heartbeat detection packets to the routing node \(RN_{j}\), where \(j=1,\,2,\,\ldots,\,n\), and the present number of routing nodes in the network is \(n\). The DS waits for the responses of the routing nodes. If the responses of the routing nodes are not received within a certain time, it is considered that the current routing node has been offline and performed operation \(n=n-1\). The heartbeat response packet fed back by routing node \(j\) to the directory server contains the real-time network bandwidth \(BW_{j}\) of the current routing node. The DS counts the online time \(T_{j}\) of the routing node up to the current time based on the heartbeat response packet fed back by routing node \(j\). If the DS detects a newly added or re-online routing node, the real-time network bandwidth and online time of the routing node are also collected according to the above process, and the operation \(n=n+1\) is executed.  
The DS can establish a routing table database based on the routing node data collected through the heartbeat mechanism [18], which is expressed as \(\mathbf{DB}=[\mathbf{d}_{j}]\), \(j=1,\,2,\,\ldots,\,n\), where \(n\) is the number of nodes in the routing table; \(\mathbf{d}_{j}\) is the state information set of routing node \(j\), i.e., \(\mathbf{d}_{j}=(\,d_{1}^{j},\,d_{2}^{j},\,\ldots,\,d_{m}^{j})\), \(i=1,\,2,\,\ldots,\,m,j=1,\,2,\,\ldots,\,n\). For example, \(d_{1}^{j}\) represents the first type of status information of node \(j\), such as the network bandwidth \(BW_{j}\), and \(d_{2}^{j}\) represents the second type of status information of node \(j\), such as the online time \(T_{j}\) of the node \(j\). The DS stores and updates the status information set table \(\mathbf{d}_{j}\) of the routing node.