esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods
### Analysis of CECs  
#### 2.3.1 Chemicals and Reagents  
Pharmaceuticals including antibiotics, nonsteroidal anti-inflammatory drugs (NSAIDs), hormones, antiepileptic carbamazepine, endocrine disruptor bisphenol, and five widely spread azole antifungals were selected as target compounds. Analytical standards of selected compounds were supplied from Sigma-Aldrich-Merck (Darmstadt, Germany). The stock standard solutions were prepared in methanol at 1 mg/L. Working standards were made by diluting the stock solutions in ultrapure water to concentrations ranging from 2.5\(-\)50 ng/mL. Both stock and working standards were stored at 4 \({}^{\circ}\)C until further use. For UHPLC-HRMS analysis, LC-MS-grade methanol and water were purchased from Merck (Darmstadt, Germany) and acetic acid and formic acid LC grade from Fisher Scientific (Loughborough, UK). Solid phase extraction (SPE) Strata-X cartridges (500 mg/6 mL) from Phenomenex (Torrance, CA, USA) were used for pollutants' extraction from water samples.
## 4 Results and Discussion
### Two-Sensor Configurations
#### 4.3.3 Two-Sensors: Model Performance with 50% Overlapping Data  
In this configuration, the same two sensors--accelerometer and gyroscope--are utilized, but with a 50% overlapping data segmentation strategy. This method enhances the ability of models to capture contextual transitions between activities by retaining more temporal information, which is crucial for improved classification accuracy.  
The evaluation results in Table 17 indicate that the **TCN** model achieves the highest performance across the majority of metrics, with an F1 Score of **0.9765** on the test set. This model exhibits strong consistency throughout training, validation, and test phases, emphasizing its robustness in addressing overlapping configurations for human activity recognition.  
The Decision Tree (DT) and K-Nearest Neighbors (KNN) models serve as reliable baseline performance indicators, with DT attaining an F1 Score of 0.9707 on the test set. However, these simpler models typically fall short compared to their more advanced counterparts, particularly when processing high-dimensional sensor data [33; 34].  
Ensemble models such as Random Forest (RF) and XGBoost demonstrate commendable performance, effectively utilizing ensemble learning techniques to enhance generalization. RF achieves a test F1 Score of 0.9730, while XGBoost performs slightly better with an F1 Score of **0.9749**. This improvement underscores the advantages of boosting methods in managing intricate data interactions [26; 27].  
Support Vector Classifier (SVC) also shows strong adaptability to non-linear data, reaching a test F1 Score of 0.9745. This positions SVC as a strong candidate for activity recognition, particularly in configurations that require efficient handling of overlapping data. Additionally, the deep learning models GRU and LSTM yield consistent outcomes, with F1 Scores of 0.9750 and 0.9762, respectively, on the test set, demonstrating their effectiveness in processing sequential data [37; 38].  
The Transformer model exhibits robust capabilities, achieving an F1 Score of 0.9746 on the test set. Although it shows promise with its self-attention mechanism for capturing complex relationships, it falls slightly short of TCN's performance, indicating that while effective, TCN may be better suited for handling structured, sequential data in overlapping contexts.  
In conclusion, the **TCN** model stands out as the best performer in this configuration, exhibiting high accuracy, precision, recall, and F1 Score across all evaluation sets. Itssuperior generalization capabilities further reinforce its robustness for human activity recognition in settings utilizing overlapping data.
# Effect of Holes on Dynamic Crack Propagation under Impact Loading
## 3 Numerical Method
### Numerical Simulation Results  
The crack propagation patterns calculated using the numerical simulation are illustrated in Figure 11. The numerical simulation results agree well with the experiments, as the crack paths present a universal appearance with a straight line after initiation, while branching cracks occurred in the holes' zone in the B12-D15 and B12-30 specimens, as observed in the actual experiment. However, it is hard to identify the transverse cracks in the specimens. As mentioned, the transverse cracks in the experiment were induced by secondary impact, but there was no chance for secondary impact in this numerical simulation, as a single full period of dynamic loading was applied in the simulation.  
Figure 10: Numerical (AUTODYN) simulation specimen. (a) Location of gauge points on the specimen. (b) Mesh of the specimen.  
Therefore, the reliability of the numerical simulation could be verified, and numerical analysis was conducted to investigate the holes' effect on crack propagation. The fractured time of each gauge point in the simulation was detected and stored as data; using these data, the crack propagating velocity could be obtained. Thus, the crack propagation velocity curves collected from numerical simulation are illustrated in Figure 12, together with the experimental results as a comparison.  
Figure 11: Numerical (AUTODYN) results of crack propagation paths.  
Figure 12: _Cont._  
It is distinct that the holes' effect on crack propagation behavior was remarkable and dependent on the distance B and D, which means that, when B and D changed, so did the influence of the holes. The crack propagating velocity was sharply reduced when the crack approached the holes, which could be used to determine the strength of the holes' function. For holes with constant D = 15 mm, the decay rates of the crack propagating velocity were different, with a smaller distance between the holes resulting in a stronger influence. These influences were magnified when distance D was increased  
Figure 12: Crack propagation velocity curves of different types of specimens using AUTODYN. (**a**) Specim with constant D = 15 mm. (**b**) Specim with constant D = 30 mm. (**c**) Specim with constant D = 45 mm.  
from 15 mm to 30 mm due to the decrease in impact energy released by the crack. Additionally, the fracture performances differed as distance B changed. When holes had B = 12, the branching cracks connected with the holes, stopping the crack in the holes' zone. On the other hand, when holes had B = 16, the holes could only transitorily restrain the extending crack, after which the crack regained its high speed, as shown in Figure 12b, with no branched cracks appearing.
# Formulation Strategies to Improve Oral Bioavailability of Ellagic Acid
## 4 Formulation Strategies for Improving EA Oral Bioavailability
### Inclusion Complexes
#### 4.3.2 EA Inclusion in Metalla-Cages  
A recent strategy for allowing EA administration in a more bioavailable form consisted of a self-assembled metallo-supramolecular architecture able to host EA inside three-dimensional cages [69]. These vectors, engineered via coordination-driven self-assembly, present several advantages. In particular, the metal-guest interactions are very strong and highly directed, leading to the formation of a stable rigid complex. In this regard, EA was encapsulated in arene-ruthenium metalla-prism cages with a yield of about 90% and its anticancer activity was investigated. The cytotoxic effect against A549 human lung cancer cells was significant, overall considering that free EA lacked any activity. In an attempt to investigate if EA effect was macrophage-mediated, a study on RAW264.7 murine leukemia monocytes was performed, highlighting that EA encapsulated in metalla-cages exhibited an inhibitory effect for cancer cells via G-CSF induction and Rantes inhibition at both mRNA and protein levels.
# Landslide Displacement Prediction during the Sliding Process Using XGBoost, SVR and RNNs
## 4 Support-Vector Machine Regression Algorithm (SVR)  
SVM is a machine-learning method used in the fields of classification, forecasting, and pattern identification [45]. The purpose of SVM is to find a plane that can divide training sets into two sets with the largest interval.  
For the sample set \(D\), SVR hopes to obtain a fitting relation such as Formula (15) so that the error between \(f(\mathbf{x})\) and \(y\) satisfies a certain condition, where \(f(\mathbf{x})\) is predicted value and \(y\) is sample value.  
\[\min_{\mathbf{\omega},b}\frac{1}{2}\|\mathbf{\omega}\|^{2}+C\sum_{i=1}^{m}l_{e}(f(\mathbf{ x}_{i})-y_{i}) \tag{15}\]  
where \(C\) is the penalty coefficient, termed as regularization constant; \(l_{e}\) is insensitive loss function.  
SVR gives certain tolerance \(\varepsilon\) for the error between \(f(\mathbf{x})\) and \(y\), as long as the error does not exceed \(\varepsilon\); that is, the distance from sample point to \(f(\mathbf{x})=\mathbf{\omega}^{\mathrm{T}}\mathbf{x}+b\) is less than \(\varepsilon\), the regression model denoted by Formula (15) is considered correct.  
The final form of the support-vector regression machine is denoted by Formula (16):  
\[f(\mathbf{x})=\sum_{i=1}^{m}\big{(}\alpha_{i}-\alpha_{i}^{\prime}\big{)}\kappa( \mathbf{x}_{i},\mathbf{x}_{j})+b \tag{16}\]  
where \(\kappa(\mathbf{x}_{i},\mathbf{x}_{j})=\varphi(\mathbf{x}_{i})^{\mathrm{T}}\varphi(\mathbf{x}_ {j})\) represents a kernel function for a support=-vector machine. In the paper, \(\kappa(\mathbf{x}_{i},\mathbf{x}_{j})=\exp\Bigl{(}-\frac{\|\mathbf{x}_{i}-\mathbf{x}_{j}\|}{2 \sigma^{2}}\Bigr{)}\), and \(\sigma\) is the bandwidth of the Gaussian kernel, \(\sigma>0\). \(\alpha_{i},\alpha_{i}^{\prime}\) are obtained by Formula (17).  
\[\left\{\begin{array}{c}\alpha_{i}(f(x_{i})-y_{i}-\varepsilon-\xi_{i})=0\\ \alpha_{i}^{\prime}\big{(}y_{i}-f(x_{i})-\varepsilon-\xi_{i}^{\prime}\big{)}= 0\\ \alpha_{i}\alpha_{i}^{\prime}=0,\ \xi_{i}\xi_{i}^{\prime}=0\\ (C-\alpha_{i})\xi_{i}^{\prime}=0,\ \bigl{(}C-\alpha_{i}^{\prime}\big{)}\xi_{i}^{ \prime}=0\end{array}\right. \tag{17}\]  
where \(\xi_{i}\) and \(\xi_{i}^{\prime}\) are slack-variable.
## 3 Results and Discussion
### Test Plan
#### 3.2.2 Test 2 Plan  
The initial conditions for the ownship and intruder of interest are shown in Figure 8 and reported in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline
**Test \#** & **Addressed ASACAS Functionality** & **Encounter Geometry** \\ \hline
1 & Separation Assurance & Head-on \\
2 & Collision Avoidance & Lateral \\
3 & Collision Avoidance & Head-on \\ \hline \end{tabular}
\end{table}
Table 1: Summary of the test cases described in the paper.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **x [m]** & **y [m]** & **z [m]** & **TAS [m/s]** & **Track wrt x [deg]** \\ \hline
**Ownship** & 0 & 0 & 0 & 35 & 0 \\
**Intruder** & 16,000 & 500 & 0 & 35 & 175 \\ \hline \end{tabular}
\end{table}
Table 2: Ownship RPAS and intruder starting position in local reference frame for test 1.  
Figure 7: Test 1: starting positions in local reference frame.  
Both aircraft performed straight and level flight towards their selected destination waypoints, so the geometry considered here led to a loss of separation and then to a collision condition, according to a beam encounter, while the ownship moved from WP2 to WP3.  
It is worth noticing that, in order to allow for the activation of the collision avoidance functionality, the maneuver proposed by the separation assurance functionality was voluntarily ignored by the remote pilot, thus allowing for the ownship RPAS to continue its flight plan until the distance threshold set for the activation of the collision avoidance functionality was reached.
## 2 Materials and Methods
### Analysis Instruments  
NMR (Bruker Avance 500, Billerica, MA, USA), EA (Heraeus varioIII-NCH, Hanau, Germany), and LC/MS (Waters Micromass Q-Tof, Milford, MA, USA) were used to identify the chemical structure of the ionic liquids. The dissolution process of cellulose was observed by polarized light microscopy (PLM, Microtech DC-218w, HK, China). X-ray diffractometry (XRD, Bruker D2 Phaser, Billerica, MA, USA) and Fourier transform infrared (FTIR) spectroscopy (Bio-Rad Digilab FTS-3500, Hercules, CA, USA) were applied to analyze the degree of crystallinity and molecular structure of regenerated cellulose samples, respectively. The degree of polymerization (DP) and thermal stability of the cellulose materials were identified using an Ostwald viscometer in cupriethylenediamine hydroxide (CUEN) solution (DIN 54270, Deutsches Institut fur Normung E.V., Germany) and a thermogravimetric analyzer (TGA, NETZSCH TG 209 F1 Iris, Burlington, MA, USA), respectively. Scanning electron microscopy (SEM, JEOL JSM6500F, Tokyo, Japan) was used to observe the surface and the cross-sectional morphologies of the regenerated cellulose films.
## 2 Description of the Problem
### Modelling of WT and PV Generation
#### 2.3.2 Modelling of PV Output Power  
The output electrical power of a PV generating unit can be formulated as follows, which depends on irradiance [19]:  
\[P_{PV}(S)=\left\{\begin{array}{ll}P_{pvn}\Big{(}\frac{S^{2}}{K_{C}S_{ctc}} \Big{)}S\leq R_{C}\\ P_{pvn}\Big{(}\frac{S}{S_{ctc}}\Big{)}S\geq R_{C}\end{array}\right. \tag{27}\]  
\(P_{pvn}\) shows the nominal power generation by the PV unit, S denotes the irradiance or amount of solar power hit on the surface of a PV module (W/m2), \(S_{stc}\) expresses the irradiance at normal conditions (STC), and \(R_{c}\) shows a specific irradiance point.  
Intermittent irradiance is generally modeled using the Beta PDF (\(f_{s}(S)\)) as follows [53]:  
\[f_{s}(S)=\left\{\begin{array}{ll}\frac{\Gamma(\alpha+\beta)}{\Gamma(\alpha )\Gamma(\beta)}S^{\alpha+1}(1-S)^{(\beta-1)};&0\leq S\leq 1,\alpha\geq 0, \beta\geq 0\\ 0;&\text{Otherwise}\end{array}\right. \tag{28}\]  
where \(S\) is the irradiance (kW/m2), whereas \(\alpha\) and \(\beta\) are the shape variables of the Beta function, also \(\Gamma\) is the Gamma function.  
The output power generation by a given PV unit can finally be calculated as follows [19; 53],  
\[P_{PV}=\frac{\sum_{g=1}^{N_{s}}P_{PVg}f_{s}\Big{(}S_{g}^{t}\Big{)}}{\sum_{g=1}^{ N_{s}}f_{s}\Big{(}S_{g}^{t}\Big{)}} \tag{29}\]  
In this equation, \(S_{g}^{t}\) is the \(g\)th state of solar irradiance at period \(t\), \(P_{PVg}\) gives the output power of the PV unit found from (27) for \(S=S_{g}^{t}\).
# A Study on the Calibrated Confidence of Text Classification Using a Variational Bayes
## 4 Experimental Results  
### Dataset and Experimental Setup  
Experiments are conducted to examine the proposed method's applicability. The data used in the experiment were 11,444 US patents. The patents were collected from the WipsOn database. Table 1 shows the predictors extracted from the collected data. The table lists 10 (\(=p_{1}\)) variables--from the number of claims to the number of family patents (\(\text{fam}_{\text{E}}\))--that are quantitative indicators. \(\text{Emb}_{\text{p2}}\) is a 384 (\(=p_{2}\))-dimensional vector in which the text in the patent document is embedded. In the experiment, we used a transformer-based document embedding model to process the natural language of the patent data [68].  
The experiments have three target variables. The first is _Litigation_, which indicates whether a patent is litigated. Patent litigation is a process for claiming the prohibition of sale, compensation for damages, and return of unreasonable profits from a defendant accused of infringing on the rights of the plaintiff [69]. Thus, patent litigation could inflict huge damage on a company, and they need to predict patent litigation risks.  
The second is _Valuation_, which is graded in accordance with the technology's future value. Since the number of patents being filed has rapidly increased recently, it takes a lot of time and expense to search for prior art or vacant technology for TM. To improve this, experts provide a grade that evaluates the future value of a patent. Then, researchers can utilize high-grade technology to analyze patent data. Thus, we use the grades provided by the WipsOn database. The _Valuation_ variable used in the experiment is a binary category that denotes whether the grade of a patent is high or low.  
Finally, _Transfer_ is a target variable that indicates whether technology is transferred. Technology transfer, a strategy that can rapidly increase the technological competitiveness of a company or research institute, means transferring patents [70; 71; 72]. The target variables used in the experiment, _Litigation_, _Valuation_, and _Transfer_, often have imbalanced labels due to TM. Therefore, this study applies the proposed method to confirm the practical applicability of the three TM tasks.  
In Sections 4.2-4.4, we present the statistical tests performed for the three hypotheses in this study. Experiments were conducted individually, in accordance with the purpose of document classification. All statistical hypotheses were tested at the 0.05 significance level.
# Changes in Spinal-Reflex Excitability during Static Stretch and/or Explosive Contraction
## 4 Discussion
### Quadriceps Spinal-Reflex Excitability after Static Stretch and/or Explosive Contraction  
Our study was the first attempt to observe changes in quadriceps spinal-reflex excitability in responding to static stretch and/or explosive contraction. The H-reflex is a monosynaptic response that is modulated by the magnitude of Ia sensory input and the sensitivity of muscle spindle activity [46]. The endings of muscle spindles respond to change in muscle length (e.g., speed and size) such as a quick muscle stretch or artificial electrical stimulation [47]. Stimulation of Golgi tendon organ after a certain period of static stretch (e.g., >6 s) overrides the impulses from the muscle spindles [48]. A decreased H-reflex after static stretch (e.g., via autogenic inhibition), therefore, could be interpreted as an inhibition of muscle spindle activity. This could be further indicative of a reduction in muscle activation and force development due to the innervation of \(\alpha\)-\(\gamma\) coactivation system to muscle spindles [46]. Our hypotheses were based on previous studies that reported changes in the soleus H-reflex after static stretch [15,16] and isometric contraction [17,18]. We, however, did not observe such change (e.g., a statistical interaction on condition by time) in terms of quadriceps spinal-reflex excitability. We are unsure if changes in quadriceps spinal-reflex excitability are dose-dependent, and the volume of our stretch or contraction protocol did not reach the threshold point when excitability begins to alter significantly from baseline. Future studies should attempt to find this threshold, in terms of the volume and intensity, on quadriceps spinal-reflex excitability.
## 3 Test Methodologies
### Test Procedure
\tag{1}\]where \(A_{s}\) is the end area of the specimen; \(L_{s}\) is the thickness of the specimen; \(F_{1}(t)\) and \(V_{1}(t)\) are the pressure and velocity at the input terminal of the specimen, respectively; and \(F_{2}(t)\) and \(V_{2}(t)\) are the pressure and velocity at the output terminal of the specimen. According to one-dimensional elastic wave theory, the pressure and velocity of the incident rod and the transmission rod can be deduced from the following Equations (2) and (3), respectively:  
\[\left\{ \begin{array}{l}F_{1}(t)=E[\varepsilon_{i}(t)+\varepsilon_{r}(t)]A\\ V_{1}(t)=C_{0}[\varepsilon_{i}(t)-\varepsilon_{r}(t)],\end{array} \right. \tag{2}\]  
\[\left\{ \begin{array}{l}F_{2}(t)=E\varepsilon_{t}(t)A\\ V_{2}(t)=C_{0}\varepsilon_{t}(t),\end{array} \right. \tag{3}\]  
where \(E\) is the elastic modulus of the rod; A is the area of the terminal of the rod; \(C_{0}\) is the wave velocity in the rod; \(\varepsilon_{i}(t)\) is the measured incident wave signal; \(\varepsilon_{r}(t)\) is the measured reflected wave signal; and \(\varepsilon_{t}(t)\) is the measured transmitted wave signal. Substituting Equations (2) and (3) into Equation (1), Equation (4) was obtained as follows:  
\[\left\{ \begin{array}{l}\sigma_{s}(t)=\frac{E\epsilon}{L_{s}}[\varepsilon_{i }(t)+\varepsilon_{r}(t)+\varepsilon_{t}(t)]\\ \dot{\varepsilon}_{s}(t)=\frac{C_{0}}{L_{s}}[\varepsilon_{i}(t)-\varepsilon_{ r}(t)-\varepsilon_{t}(t)]\\ \varepsilon_{s}(t)=\frac{C_{0}}{L_{s}}\int_{0}^{t}[\varepsilon_{i}(t)- \varepsilon_{r}(t)-\varepsilon_{t}(t)]dt.\end{array} \right. \tag{4}\]  
Equation (4) is the formula of the three-wave method in SHPB. If the assumption of stress uniformity is used, (\(F_{1}(t)=F_{2}(t)\)), Equation (5) can be derived according to the one-dimensional stress wave theory as follows:  
\[\varepsilon_{i}(t)+\varepsilon_{r}(t)=\varepsilon_{t}(t). \tag{5}\]  
Substituting Equation (5) into Equation (4), the formula of the two-wave method is obtained as follows (Equation (6)):  
\[\left\{ \begin{array}{l}\sigma_{s}(t)=\frac{\varepsilon_{t}(t)EA}{L_{s}}\\ \dot{\varepsilon}_{s}(t)=\frac{-2\varepsilon_{r}C_{0}}{L_{s}}\\ \varepsilon_{s}(t)=\frac{-2C_{0}}{L_{s}}\int_{0}^{t}\varepsilon_{r}(t)dt.\end{array} \right. \tag{6}\]  
From Equation (6), it can be seen that if the transmissive wave strain, elastic modulus, diameter, and specimen diameter are known, the axial stress of the specimen can be obtained. The instantaneous axial strain rate of the specimens can be deduced according to the strain value, the velocity of the  
Figure 2: Typical shaped waveforms during SHPB tests (no specimen). reflected wave, and the thickness of the specimens. Finally, the axial strain of the specimen can be obtained by integrating the instantaneous axial strain rate with time.
## 2 Materials and Methods
### Statistical Analysis
#### 2.6.2 Extraction of Structural Features and Analysis of the Prediction Models  
After MRI data preprocessing, a total of 377 structural features were extracted based on the atlas. Among of them, 170 GMV were extracted from the automated anatomical labelling 3 (AAL3) atlas [19]; 69 cortical thickness, sulcal depth, and gyrification values were obtained from the Desikan-Killiany atlas (DK40), [20] (Figure 1). For each structural feature, a linear regression method was conducted to regress out the effect of age, gender, and education. Further, to extract the features, a Pearson correlation was computed between the Y\(-\)BOCS reduction rate and all the structural features including GMV, cortical thickness, sulcal depth, and gyrification values. The features were ranked from maximum to minimum according the correlation values; then, the top ten features were selected to construct the prediction model. Thus, 10 comprehensive variables were selected from all the structural features, and 10 variables were selected from the GMV, cortical thickness, sulcal depth, and gyrification values, respectively.  
Five prediction models were constructed using a linear SVR model with the leave-one-out cross validation (LOOCV) method based on the selected features, using GMV, cortical thickness, sulcal depth, gyrification value, and comprehensive variables. The reduction rate of Y-BOCS score at 12 weeks (reduction rate = (Y\(-\)BOCS score at baseline \(-\) Y\(-\)BOCS score at 12-week)/Y\(-\)BOCS score at baseline) was the dependent variable, whereas the selected features were the independent variables. To estimate the model performance, the mean absolute error (MAE), mean squared error (MSE), coefficient of determination (R\({}^{2}\)) and Pearson correlation (R) were calculated. Furthermore, permutation tests were performed 1000 times to verify the generalizability of the model. The whole process of the model building was performed in Python using the scikit-learn toolkit [11].  
Further, to explore the correlation between the reduction rate of Y\(-\)BOCS and the features with best performance of the prediction model, the Pearson correlation method was conducted.