esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Definition and Application of Haptic Feedback Technologies
### Haptic Feedback  
Under the skin, Merkel disks, Pacinian corpuscles, Meissner corpuscles, Ruffini endings, Krause bulbs, and free nerve endings all contribute to the human sense of touch. Every type of mechanoreceptor has distinct purposes and sensing characteristics (Figure 3). The normal threshold for the tactile sensation on human fingers is 10-100 mN, with displacements of 10-100 mm, and it varies from person to person, closely correlated with the frequency of actuation. Low-frequency vibrations (10 Hz) affect the sensitivity of Merkel cells and Ruffini terminals. However, Meissner and Pacinian corpuscles react to high-frequency vibrations between 10 Hz and 800 Hz, with 30 Hz and 250 Hz as the most sensitive frequencies. To appropriately stimulate the receptors and optimize the dimension of the haptic device, these parameters are crucial for building haptic interfaces.  
Figure 3: Kineshtetic (**left**) and tactile feedback (**right**)are shown in a schematic depiction of haptic feedback. The awareness of muscles and joints in response to a gesture, stretch, weight, etc., is known as kinesthetic feedback. The sense of surface hardness, temperature, and other properties of the skin’s surface derived through mechanoreceptors is known as tactile feedback.  
Another aspect of tactile feedback is temperature. Skin's thermoreceptors gather various thermal data, enabling accurate temperature sensing. The hand's skin commonly ranges in temperature from 25 to 36 \({}^{\circ}\)C when at rest. Thermoreceptors typically respond to temperature changes between 5 and 45 \({}^{\circ}\)C, and a sudden shift in temperature can make people experience discomfort. One of the human body's most thermally sensitive areas is the skin at the base of the thumb, which can detect temperature differences of 0.02 to 0.07 \({}^{\circ}\)C for chilling pulses and 0.03 to 0.09 \({}^{\circ}\)C for heating pulses.
# Short Text Classification Based on Enhanced Word Embedding and Hybrid Neural Networks
###### Abstract  
In recent years, text classification has found wide application in diverse real-world scenarios. In Chinese news classification tasks, limitations such as sparse contextual information and semantic ambiguity exist in the title text. To improve the performance of short text classification, this paper proposes a Word2 Vec-based enhanced word embedding method and exhibits the design of a dual-channel hybrid neural network architecture to effectively extract semantic features. Specifically, we introduce a novel weighting scheme, Term Frequency-Document Frequency Category-Distribution Weight (TF-IDF-CDW), where Category Distribution Weight (CDW) reflects the distribution pattern of words across different categories. By weighting the pretrained Word2 Vec vectors with TF-IDF-CDW and concatenating them with part-of-speech (POS) feature vectors, semantically enriched and more discriminative word embedding vectors are generated. Furthermore, we propose a dual-channel hybrid model based on a Gated Convolutional Neural Network (GCNN) and Bidirectional Long Short-Term Memory (BiLSTM), which jointly captures local features and long-range global dependencies. To evaluate the overall performance of the model, experiments were conducted on the Chinese short text datasets THUCNews and TNews. The proposed model achieved classification accuracies of 91.85% and 87.70%, respectively, outperforming several comparative models and demonstrating the effectiveness of the proposed method.  
text classification; word embedding; gated convolutional neural network; bidirectional long short-term memory Article
## 3 Results  
This study analyzed the forces and moments in four experimental CA groups categorized by the presence or absence of attachments and reinforcement structures. Statistically significant differences in measured forces and moments were observed among all groups (\(p<0.001\); Table 2, Figure 3). Effect sizes (n\({}^{2}\)) were calculated for each Kruskal-Wallis test and are presented in Table 2 to indicate the magnitude of differences between groups.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Measurements**} & **Group 1** & **Group 2** & **Group 3** & **Group 4** & \multirow{2}{*}{\(p\)**-Value**} & **Effect Size** \\  & **(Median, IQR)** & **(Median, IQR)** & **(Median, IQR)** & **(Median, IQR)** & **(Median, IQR)** & **(n\({}^{2}\))** \\ \hline \multirow{2}{*}{Fx (N)} & \multirow{2}{*}{0.31, 0.30 a  } & \multirow{2}{*}{0.57, 0.13 b  } & \multirow{2}{*}{0.56, 0.26 b  } & & & H = 30.086 & \multirow{2}{*}{0.284} \\  & & & & & \(<\)0.001 *** & \\ \multirow{2}{*}{Fy} & \multirow{2}{*}{2.01, 1.80 a  } & \multirow{2}{*}{1.56, 1.01 a  } & & & H = 46.654 & \multirow{2}{*}{0.194} \\  & & & & \(<\)0.001 *** & & \\ \multirow{2}{*}{Fz} & \multirow{2}{*}{\(-\)0.60, 0.32 a  } & \multirow{2}{*}{\(-\)0.88, 0.66 a  } & \multirow{2}{*}{\(-\)1.23, 0.57 b  } & & & \(<\)0.001 *** & \\ \multirow{2}{*}{Mx (Nmm)} & \multirow{2}{*}{12.38, 6.76 a  } & \multirow{2}{*}{10.27, 7.58 b  } & & & & H = 65.121 & \\  & & & & \(<\)0.001 *** & & \\ \multirow{2}{*}{My} & \multirow{2}{*}{\(-\)3.23, 2.95 a  } & \multirow{2}{*}{\(-\)2.92, 2.55 a  } & \multirow{2}{*}{\(-\)6.06, 2.52 b  } & & & \(<\)0.001 *** & \\ \multirow{2}{*}{Mz} & \multirow{2}{*}{1.02, 2.51 a  } & \multirow{2}{*}{0.07, 1.78 a  } & & & \(>\)0.39 b  & \(-\)0.57, 2.11 c  & & & & H = 43.935 & \\  & & & & \(<\)0.001 *** & & \\ \multirow{2}{*}{Mx/Fy ratio} & \multirow{2}{*}{6.21, 3.55 a  } & \multirow{2}{*}{5.77, 1.72 b  } & & & & \(<\)0.001 *** & \\ \hline \end{tabular}
\end{table}
Table 2: Comparative analysis of the forces and moments generated by different clear aligner groups.
# Node-Based QoS-Aware Security Framework for Sinkhole Attacks in Mobile Ad-Hoc Networks
## 5 Results and Discussion
### Simulation 2: Performance Evaluation under Average Density Nodes Scenario (49 Nodes)
#### 5.2.2 Delay Performance Analysis  
The results of the scenarios show a relatively high delay value for the framework as compared to the other scenarios in Figure 15. This margin is proportional to the throughput of the network which is higher than the other scenario. The presented result proved that the employed practices were very effective. In the malicious scenario, all the other nodes show a constant delay of 2,137,687.396 microseconds except node 14, which has a delay of 2,771,719.28 microseconds. The margin shown in Figure 15 is extremely high compared to the framework whose delay for almost all the nodes was around 40,000 microseconds. The delay increases as many nodes are included in the network, but the framework was able to sustain the network performance and provide an acceptable level of QoS. The two scenarios have an opposite outlook in terms of their delay propagation. The growth in the delay is, however, normal as the network grows but the optimization of OLSR and the cross-layer interaction between the network, physical, and MAC layers play a huge role in making the network more delay-sensitive and efficient.
## 3 Results
### Results of the Analysis of the Influence of the Prediction Algorithm Parameters
Increasing \(T_{horizon}\) improved performance to a certain level (\(T_{horizon}\approx\) 0.14 s), after which the gains plateaued. This is consistent with MPC theory, as a longer prediction horizon and a deeper search allow for better control actions to be found. _LineSearchMax_ is a critical convergence parameter, i.e., a too small _LineSearchMax_ value significantly worsened all metrics, while _LineSearchMax_ = 40 provided nearly perfect results, without a significant increase in computation time. Adjusting the \(P_{Cost}\) weighting coefficients revealed a balance between two objectives: with a high weighting of speed, the profile accuracy increased to almost 100%, but at the same time, the energy consumption increased, and the accuracy of the desired magnetisation current waveform decreased slightly.
# Metrics-Based Comparison of OWL and XML for Representing and Querying Cognitive Radio Capabilities
## 5 Evaluation
### Experiments
#### 5.5.1 Experimental Setup  
All experiments were run on the MacBook Pro 2016 computer with the following parameters: Processor, 2.6 GHz quad-core Intel Core i7, Turbo Boost up to 3.5 GHz, with 6 MB shared L3 cache; Memory, 256 GB PCIe-based onboard SSD; and Storage, 16 GB of 2133 MHz LPDDR3 onboard memory.  
In the experiments, the SDR ontology ([[https://github.com/YankeeChen/evaluator/blob/master/ontologies/SDROntology/BenchmarkOntology/SDR.owl](https://github.com/YankeeChen/evaluator/blob/master/ontologies/SDROntology/BenchmarkOntology/SDR.owl)]([https://github.com/YankeeChen/evaluator/blob/master/ontologies/SDROntology/BenchmarkOntology/SDR.owl](https://github.com/YankeeChen/evaluator/blob/master/ontologies/SDROntology/BenchmarkOntology/SDR.owl)), accessed on 15 November 2022) was selected as the background knowledge of the RF devices and set as the most complete knowledge representation, \(B_{0}\). In order to assess the effect of the richness of the ontology on the quality of the matching process and performance in the OWL-based approach, we developed five progressively less complete cases of background knowledge, \(\{B_{1},B_{2},B_{3},B_{4},B_{5}\}\), as the test ontologies ([[https://github.com/YankeeChen/evaluator/tree/master/ontologies/](https://github.com/YankeeChen/evaluator/tree/master/ontologies/)]([https://github.com/YankeeChen/evaluator/tree/master/ontologies/](https://github.com/YankeeChen/evaluator/tree/master/ontologies/))\(\backslash\)SDROntology/TestOntology, accessed on 15 November 2022). The smaller (less complete) ontologies were created by removing some of the defining axioms from the previous ontology. Table 4 shows the axiom coverage of the ontologies. For simplicity, only the axioms that \(B_{0}\) includes are listed. \(B_{5}\) is not included in this table since it did not cover any of the axioms. More axioms defining the radio capabilities add more constraints, and thus, the OWL reasoner is expected to be more precise when selecting devices that satisfy queries. The less complete definition implies that OWL reasoning can result in false negative classifications. This aspect of completeness is not directly correlated with the results of the XML-based approach, since it does not make use of logical inference.  
In the experiments, 1000, 2000, 3000, and 4000 device descriptions in RDF/XML generated by RODG [8] with the most complete knowledge base \(B_{0}\) were selected as the test datasets for the two approaches. Four batches of 5000 queries in SPARQL and XQuery were selected as the test query sets for matching device capabilities against the test datasets. For each batch of queries, SPARQL queries were generated by SQG [9] with the most complete ontology \(B_{0}\) and respective test datasets. The corresponding XQuery queries were generated by converting the SPARQL queries with xql2 xquery [32; 33]. Note that queries in each batch may not be the same since they are for different datasets.
## 5 Performance Evaluation
### Verification of the Theorems  
In this subsection, the three theorems are verified by simulations. Three picocells with radii of 100, 200 and 300 m are randomly deployed, and their PSNE are obtained by the Milchtaich method. Then, we take the PSNE into the theorems to verify their correctness.  
For Theorem 1, by increasing \(B_{i}\), we can see that both \(g_{1}(n_{i})\) and \(A_{1,i}\) increase, and \(g_{1}(n_{i})\) is perfectly located between \(A_{1,i}-1\) and \(A_{1,i}\). When \(B_{i}\) is larger than a certain threshold for each cell radius, all of the devices under the coverage of this picocell select it, so the curve of \(g_{1}(n_{i})\) leaves \(A_{1,i}-1\) by keeping \(n_{i}^{*}=N_{i}\) for the PSNE, as shown in Figure 5a.  
For Theorem 2, we set the transmission power of the devices selecting the three picocells as \(-9\), \(-3\) and 0.5 dBm, so that their coverage increases proportionally. The calculation of \(g_{2}(n_{i})\) is not based on the ICI obtained by Equation (12), which is unstable due to the instantaneous distribution of the devices. Instead, Equation (A15) obtained in the Appendix, as an average value, is used. Note that this average value is derived under the condition \(\gamma=2\), so that a closed-form expression as an analytical result can be obtained to compare to the simulation results and to validate the theorems. Based on our experience, when \(w\) is relatively small, the result of CGI is quite similar to that of TCG. By contrast, when \(w\) is larger than a threshold, the PSNE collapses to all zeros, _i.e._, all of the devices select the macrocell. Therefore,  
Figure 5: Verification of the theorems. (**a**) Theorem 1; (**b**) Theorem 2; (**c**) Theorem 3.  
we choose \(w=10^{6}\) to show this phenomenon in Figure 5b. We can see that \(A_{2,i}\) is not within \(g_{2}(n_{i})\) and \(g_{2}(n_{i}+1)\), except for the three collapse points, one for each picocell. In detail, before each collapse point (_i.e._, on the right side), the PSNE have \(n_{i}^{*}=N_{i}\). While after each collapse point (_i.e._, on the left side), the PSNE collapse to all zeros, so the curves leave \(A_{2,i}\) immediately.  
For Theorem 3, the same configuration as above is used. To show clearly the relationship between \(g_{3}(n_{i})\) and \(A_{3,i}\), the total bandwidth of picocells (_i.e._, the x-axis) is set to [0.2, 3] MHz. We can see that the curves for \(A_{3,i}\) are smooth, while the curves for \(g_{3}(n_{i})\) and \(g_{3}(n_{i}+1)\), as functions of \(n_{i}\), are slightly fluctuating. \(\forall i\in\{1,2,3\}\), \(A_{3,i}\) is perfectly located between \(g_{3}(n_{i})\) and \(g_{3}(n_{i}+1)\), indicating the correctness of Theorem 3.
## 2 Methodologies
### Extended Kalman Filter  
The basic methodology for the proposed novel nonlinear Kalman filter is discussed in this section. The state model and the measurement model for this study are  
\[\textbf{x}_{k}=\textbf{f}(\textbf{x}_{k-1})+\textbf{q}_{k-1} \tag{5}\]  
\[\textbf{y}_{k}=h(\textbf{x}_{k})+\textbf{r}_{k} \tag{6}\]where the state and system noise vectors are \(\mathbf{x}_{k}\in\Re^{n}\) and \(\mathbf{q}_{k-1}\in\Re^{m}\), measurement vector and measurement noise vectors are \(\mathbf{y}_{k}\in\Re^{m}\) and \(\mathbf{r}_{k}\in\Re^{m}\), the system noise and the measurement noise covariance matrices are \(\mathbf{Q}_{k}\) and \(\mathbf{R}_{k}\), respectively. The nonlinear measurement function is represented by \(\mathrm{h}\left(\cdot\right)\) and the continuously differentiable nonlinear state function by \(\mathrm{f}\left(\cdot\right)\). The noise vectors \(\mathbf{q}_{k-1}\) and \(\mathbf{r}_{k}\) in Equations (5) and (6) are mutually uncorrelated with zero means and the covariance matrices are  
\[E[\mathbf{q}_{k-1}\mathbf{q}_{k-1}^{T}]=\mathbf{Q}_{k-1};\;E[\mathbf{r}_{k} \mathbf{r}_{k}^{T}]=\mathbf{R}_{k};\;E[\mathbf{q}_{k}\mathbf{r}_{k}^{T}]=0\; \;\text{for all values of $k$} \tag{7}\]  
where \(E\left[\cdot\right]\) represents expectation, and the matrix transpose is denoted by superscript \"T\".  
There are two steps involved in EKF.  
Step 1: Prediction for state vector and covariance matrix.  
(i) Prediction of state vector  
\[\widetilde{\mathbf{x}}_{k}=\mathbf{f}(\hat{\mathbf{x}}_{k-1}) \tag{8}\]  
(ii) Prediction of state covariance matrix  
\[\widetilde{\mathbf{P}}_{k}=\mathbf{F}_{k-1}\hat{\mathbf{P}}_{k-1}\mathbf{F}_ {k-1}^{T}+\mathbf{Q}_{k-1} \tag{9}\]  
where \(\mathbf{F}_{k-1}\in\Re^{n\times n}\) is the Jacobian matrix and is given by  
\[\mathbf{F}_{k-1}=\left.\frac{\partial f}{\partial\mathbf{x}}\right|_{\mathbf{ x}=\hat{\mathbf{x}}_{k-1}}\]  
Step 2: Updating.  
(i) Calculation for the Kalman gain is  
\[\mathbf{K}_{k}=\widetilde{\mathbf{P}}_{k}\mathbf{H}_{k}^{T}[\mathbf{H}_{k} \widetilde{\mathbf{P}}_{k}\mathbf{H}_{k}^{T}+\mathbf{R}_{k}]^{-1} \tag{10}\]  
where \(H_{k}\in\Re^{m\times n}\) is the Jacobian matrix and is given by  
\[\mathbf{H}_{k}=\left.\frac{\partial h}{\partial\mathbf{x}}\right|_{\mathbf{ x}=\widetilde{\mathbf{x}}_{k}}\]  
(ii) The updated posterior state vector and the corresponding covariance matrix for this case are  
\[\hat{\mathbf{x}}_{k}=\widetilde{\mathbf{x}}_{k}+\mathbf{K}_{k}[\mathbf{z}_{k} -\mathbf{h}(\widetilde{\mathbf{x}}_{k})] \tag{11}\]  
\[\hat{\mathbf{P}}_{k}=[\mathbf{I}-\mathbf{K}_{k}\mathbf{H}_{k}]\widetilde{ \mathbf{P}}_{k}[\mathbf{I}-\mathbf{K}_{k}\mathbf{H}_{k}]^{T}+\mathbf{K}_{k} \mathbf{R}_{k}\mathbf{K}_{k}^{T} \tag{12}\]  
where the \(n\times n\) identity matrix is denoted by I.
## 2 Materials and Methods
### Chemistry
#### 2.1.3 Determination of Some Physicochemical Properties  
* Estimation of electronic properties  
The electronic properties of **6a-g** were defined by the values of the logarithms of molar absorption coefficients (log \(\varepsilon\)) following Lambert-Beer's law (Equation (S1)). The details regarding employed experimental procedures published in [31; 32] are also listed in the Supplementary Materials. The log \(\varepsilon\) values for the presently observed absorption maxima (\(\lambda\)) of **6a-g** (i.e., \(\lambda_{1}\) = 203.50-206.00, \(\lambda_{2}\) (Ch-T) = 238.50-243.00 (charge-transfer absorption maximum), and \(\lambda_{3}\) = 279.50-286.50 nm) are provided in Table 1.  
* Estimation of lipophilic properties  
Lipohydphilic characteristics of **6a-g** were estimated by reversed-phase high-performance liquid chromatography (RP-HPLC). Details on the measurements and calculation of retention factors (capacity factors; \(k\)) according to Equation (S2) can be found in the Supplementary Materials as well as in [31]. The calculations for the extrapolated retention (capacity) factor (\(k\)) logarithms for 100% water (isocratic RP-HPLC)--log \(k_{\text{w}}\) parameters (Table 2)--were made from intercepts of linear plots between log \(k\) and \(\varphi_{\text{M}}\) following Equation (2):  
\[log\,k=log\,k_{\text{w}}-S\times\varphi_{M} \tag{2}\]  
where the \(S\) parameter is the slope of a regression curve describing the solvent strength of a pure organic solvent, and \(\varphi_{\text{M}}\) is the volume fraction of MeOH [33; 34].  
The experimental details considering methods for the purity verification of **6a-g** are provided in [31] and the Supplementary Materials (see Tables S1 and S2).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Entry** & \(\lambda_{1}\) **(nm)** & **log \(\varepsilon_{1}\)** & \(\lambda_{2}\) (Ch-T) **(nm)** & **log \(\varepsilon_{2}\) (Ch-T)** & \(\lambda_{3}\) **(nm)** & **log \(\varepsilon_{3}\)** \\ \hline
6a & 203.50 & 4.53 & 238.50 & 4.30 & 280.00 & 3.25 \\
6b & 203.50 & 4.52 & 239.00 & 4.28 & 280.50 & 3.21 \\
6c & 204.00 & 4.24 & 239.00 & 4.27 & 281.00 & 3.24 \\
6d & 204.00 & 4.48 & 241.50 & 4.28 & 280.50 & 3.25 \\
6e & 206.00 & 4.55 & 240.00 & 4.32 & 286.50 & 3.34 \\
6f & 205.00 & 4.51 & 241.00 & 4.31 & 286.50 & 3.57 \\
6g & 204.00 & 4.47 & 243.00 & 4.33 & 279.50 & 3.07 \\ \hline \end{tabular}
\end{table}
Table 1: The absorption maxima (\(\lambda_{1}\), \(\lambda_{2}\) (Ch-T), and \(\lambda_{3}\), in nm) observed in the UV/Vis absorption spectrum from 190 to 820 nm and logarithms of molar absorption coefficients (log \(\varepsilon_{1}\), log \(\varepsilon_{2}\) (Ch-T) and log \(\varepsilon_{3}\)) of target compounds’ methanolic solutions (\(c\) = 5.0 \(\times\) 10\({}^{-5}\) M).
# An Earthquake Forecast Model Based on Multi-Station PCA Algorithm
## 4 PCA Extraction for Single Station
### PCA Algorithm of Single Station
The number of values is decided by K, and here are three: feature1, the first principal component; feature2, the second principal component; and feature3, the third principal component. These features will be used in the prediction model to represent each station itself. Another feature is the proportion of the related eigenvalues of each principal component in the sum of all eigenvalues. We find that before an earthquake, the proportion of the first principal component often decreased and the proportion of the second and third principal components increased, so it can become a feature [23; 24; 25]. The first principal component may be related to the background signals caused by the local environment and geological structure, so it is a general data form. On another side, the second and third principal components may be related to pre-anomalies of an earthquake, which may be caused by seismic activity.
## Proposed Method
### Comparative Analysis with the Existing Research
Based on the above characteristics, it is possible to highlight the difference from the general system. **Dispersibility**: The generally applied network has a single server and aims to provide a server-dependent service. In this way, error in the server can be solved at a single point, and since the service provider has authority for the server, it is free to modify. However, the integrity of the data cannot be guaranteed, and the data of a system for which integrity is not guaranteed cannot  
Figure 5: Process of verification for AI data. Figure 6: Comparing common AI learning models with the proposed AI learning model. be trusted. In this regard, a blockchain that applies a distributed network to a system where data integrity is prioritized over ownership of the system can provide improved services when compared to a general network. **Scalability**: In the case of the general network service, the axis, connection, and expansion of the service can be performed by the provider that provides the service. This may be more effective in services that do not need to be modified externally. Scalability through the use of open source in a distributed network can be proposed by various participants, and it is possible to provide an environment in which flexible services can be created, but a method to limit services by untrusted proposers is required. **Transparency**: Data of general network services depend only on the data recorded on the server and are not shared with participants. This method can be applied as a more effective method for personal information. However, in the case of blockchain, since the recorded data are shared by all participants, the shared data can be transparently provided to all participants; it is thus possible to guarantee transparency of the data, but it is necessary to study security measures to protect information. **Security**: General network services tend to rely on a single server, which presents a problem in that integrity can be compromised through forgery and alteration of the server. For such a problem, the strategy of the learning environment that applies blockchain is to ownership the data by a majority of users who share the data, and some errors can be corrected by having a large number of data. However, there is a possibility that the collected data may not have been provided in a reliable environment, and research on a mechanism for reliably collecting the data is required. **Stability**: In the case of a network service that relies on a single server, the service may be stopped due to a fatal error of the central server, which degrades system availability. In this regard, the learning environment using blockchain has a minimal impact on service due to fatal error of a single node in the distributed network and can thus provide stable services. Table 3 shows a comparative analysis of the learning model in the existing general learning environment and the learning environment model using blockchain.