esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Determination of Ozone Rate Constants for the Selected Antibiotics
With increasing pH value, the decomposition rate constant of FF with ozone increases. Because the pKa value of FF is 9, FF has a neutral form at pH values lower than 9 and has an ionized form at pH values higher than 9. Therefore, the rate constants for the reaction of FF and OA with ozone increase with the pH value. The experimental and calculated values for the apparent rate constants for the ozonation of FF are shown in Figure 2a. The apparent rate constant for the second-order equation, was determined by the pseudo first-order method in the pH range 3-9. The red solid line represents the speciation of FF. The experimental values and calculated values matched well. From the calculated values, the species-specific rate constants k\({}_{1}\) and k\({}_{2}\) were determined. k\({}_{1}\) and k\({}_{2}\) were determined as 0.43 M\({}^{-1}\)s\({}^{-1}\) and 33.5 M\({}^{-1}\)s\({}^{-1}\), respectively. k\({}_{1}\) represents the neutral form of the reaction of FF with ozone and k\({}_{2}\) represents the ionized form of the reaction of FF with ozone. The k\({}_{2}\) value is approximately 80 times higher than the k1 value. That is, the ionized form of FF reacts with ozone faster than the neutral form of FF, because deprotonated species react faster with the electrophilic ozone. To determine the rate constant for the reaction of OA with ozone, the ozone exposure method was used. This method is necessary to measure the ozone decomposition, and the data were evaluated by plotting In ([OA]/[OA]\({}_{0}\)) versus ozone exposure. Figure 2b shows the reaction rate of OA with ozone, where ozonation experiments at different pH values from 3 to 8 were performed using the ozone exposure method. The slope of the represents the rate constant for the reaction of OA with ozone. In the case of OA, the apparent second-order rate constant was determined by the ozone exposure method conducted at a pH range of 3-9. The determined apparent rate constants for the reaction between ozone and OA are fitted with respect to their dependence on pH, in Figure 2b. Like FF, the species-specific rate constant of OA was also determined by the nonlinear least-squares regression of the experimental value of the apparent rate constant at each pH, for ozonation. The experimental values and calculated values (dotted line) agree well. Further, the red solid line represents the speciation of OA. The species-specific rate constants k\({}_{1}\) and k\({}_{2}\) were determined to be 120 M\({}^{-1}\)s\({}^{-1}\) and 2700 M\({}^{-1}\)s\({}^{-1}\), respectively. k\({}_{1}\) represents the neutral form of the reaction of OA with ozone, and k\({}_{2}\) represents the ionized form of the reaction of OA with ozone.
# Unsupervised Feature Selection for Outlier Detection on Streaming Data to Enhance Network Security
## 5 Evaluation
### Test Environment
In terms of evaluation metrics, for each measurement, we computed the typical parameters of the confusion matrix for binary classification, True Negatives (\(TN\)), False Negatives (\(FN\)), False Positives (\(FP\)), and True Positives (\(TP\)) to derive the harmonic mean of precision and recall, denoted as the \(F1\) score. It represents the classification performance of an algorithm and can be computed by Equation (7). Compared to other work that relies on the ROC (Receiver Operating Characteristic Curve), AUC (Area under the ROC), or only accuracy metric, e.g., [34; 36; 46], we see that the \(F1\) score is more appropriate for OD since, e.g., the false positive rate used in the ROC metric depends on the number of \(TN\), whose proportion in OD is typically quite large. Hence, the ROC curve tends to be near 1 when classifying imbalanced data and thus is not the best measure for examining OD algorithms. A good \(F1\) indicates low FP and FN and is therefore a better choice to reliably identify malicious activity in the network security domain without being negatively impacted by false alarms. \[F1=\frac{TP}{TP+\frac{1}{2}(FP+FN)} \tag{7}\]  
Furthermore, we have measured the average runtime per OD algorithm, denoted as \(avg\_t\), as a representative metric for the computational performance. Thus we accumulated the elapsed time for individual steps necessary to perform, e.g., partial fitting or prediction to derive the average runtime after multiple iterations for processing a particular dataset. Having a tradeoff between the classification and computational performance, the third metric is the ratio of \(F1/avg\_t\).
# Design and Experimental Development of
## 2 Working Mechanism of the Stiffness-Adjustable Foot System  
### Overview of the Stiffness-Adjustable Foot System  
The central idea of the new foot system is the variable stiffness. This is achieved by attaching some pneumatic variable stiffness units (PVSUs) on the foot sole separately, as shown in Figure 1. Each PVSU mainly consists of a cylindrical pneumatic bladder and a stiffness-adjusting mechanism. In the default state, for example, when the foot is off the ground, the pneumatic bladders are set to high stiffness, being inflated with some compressed air. When the biped robot walks on rough ground with small-sized bumps and obstacles, the stiffness values of the pneumatic bladders may change accordingly with their contact conditions with the ground. Since there are some spaces among the PVSUs, if the bumps happen to be located in the spaces, all pneumatic bladders are just compressed slightly and still remain rigid. If some PVSUs come into contact with the bumps, the corresponding stiffness adjusting mechanisms are triggered to expel the air, making the pneumatic bladders soft, so that more deformation can be generated to comply with the shape of the bumps; meanwhile, the other pneumatic bladders which are in contact with the flat surface remain rigid. It should be noted that in order to reduce the control difficulty, the adjustment from high stiffness to low stiffness is purely passive, depending on the interactions between the pneumatic bladders and the bumps. Figure 2 shows the comparisons between a conventional rigid flat foot and the new foot when there are bumps and obstacles of various sizes and shapes on the ground (the sole of the new foot is set as transparent for clarity). For the flat foot, the posture around the three coordinate axes is greatly changed, which has to be compensated by the movement of at least three joints of other body parts to maintain multi-point contact. However, the new foot has the ability to adapt to the bumps and maintain sufficient contact area by means of its own adaptability.  
Figure 1: Schematic diagram of the stiffness adjustable foot system.  
Figure 2: Comparisons between a conventional rigid flat foot and the new foot on the ground with various bumps and obstacles.
## 4 Conclusions  
A simple technique for the designing and analysis of the birdcage RF coil based upon dominant resonance path and a novel analytical solution was demonstrated in this paper. We introduced a new concept of the dominant resonance path in the birdcage RF coil that identifies the specific closed current loop which is responsible of causing the dominant resonance frequency that is desired for NMR imaging. The concept is used to determine the numerical values of the lumped capacitances for the leg and/or end-ring segment of the birdcage RF coil using simple mathematical formulations. We also provided the analytical solutions for the birdcage RF coil in terms of its input impedance \(Z_{in}\) by converting each of its section into a two-port network. The transmission parameters are used to determine the final analytical solution with the help of an equivalent \(T_{e}\) matrix for the \(N\)-1 identical cascaded segments of the birdcage RF coil. Two separate analytical solution were developed by considering the feed port or external circuit location in the end-ring segment and leg segment. Both analytical solutions efficiently explain the characteristics of the birdcage RF coil, however the commonly used end-ring feed based analytical solution was mainly discussed in this paper. We implemented the birdcage RF coil in low pass, high pass and band pass configurations with FPCB etched conductor pattern technique for small volume NMR imaging applications at 1.5 T and 3.0 T MRI system. The numerical values of the lumped capacitance for the different configurations of the birdcage RF coil provided by the dominant resonance path method were found more accurate in comparison to those which were obtained through the conventional methods. The analysis of the implemented birdcage RF coils performed with the proposed analytical solution were also in complete agreement with the conventional methods.  
[PERSON] and [PERSON] conceived the idea, developed the analytical solution and carried out simulations. [PERSON] designed the coils completed the experiments. [PERSON] developed the programming code for the implementation of the proposed analytical solution. [PERSON] analyzed the data and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the Ministry of Trade, Industry, and Energy (MOTIE), Korea, under the \"Regional industry based organization support program\" (reference number R0004072) and the BK21 Plus project funded by the Ministry of Education (21A20131600011).  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Oxidation Flavor (OF)
However, by looking at Figures 1 and 2 it is evident that the correlation models are best suited for the later stages of storage (after week 1), which can be explained by the fact that hexanal, being a secondary lipid oxidation product [28], is not present in large quantities at the early stages of oxidation. This is especially true for the phosphate containing samples where the oxidation levels are being kept especially low (Tables 2 and 3). Nevertheless, when using hexanal as an indicator of lipid oxidation and meat flavor deterioration, one must be aware of the further reaction of hexanal [39], which may make it a less optimal indicator compound at later stages of oxidation for some food products. Figure 1: Prediction plot for off-flavor (OF) from headspace hexanal when examining chicken filled at week 1 (\(R^{2}=0.332\); \(p=0.031\)) and week 7 (\(R^{2}=0.649\); \(p=0.001\)) of refrigerated storage. Dashed lines provide the 95% prediction limits. The shaded area is the 95% confidence limits of the fit. The finished product was thermally processed and modified atmosphere packaged in 75% N\({}_{2}\) and 25% CO\({}_{2}\). The off-flavor scale was 0 to 15 with 15 being highest off-flavor. Units for hexanal were mg per kg (weight/weight). Figure 2: _Cont._  
The results of these correlation studies are in line with previously reported results for sliced, uncured deli turkey with or without phosphate and with added RE [22], cooked ground pork [40], cooked beef [41], and a meat model system made from boneless pork joins [39].
# Optimizing Methanol Injection Quantity for Gas Hydrate Inhibition Using Machine Learning Models
## 3 Results and Discussion
### Feature Importance Analysis  
The feature importance analysis conducted for the XGBoost, RF, and DT models using the full dataset elucidated the relative contributions of the five input features of flow rate, temperature, pressure, gas specific gravity, and water content to predict the minimum methanol quantity required for hydrate inhibition (Figure 11). This analysis, derived from the models' intrinsic Gini importance scores via scikit-learn and XGBoost in PyCharm [33], provided critical insights into the key drivers of methanol dosing, aiding the operational optimization of gas pipelines.  
The most influential feature across all three models was found to be flow rate, with importance scores of approximately 0.34 (RF), 0.27 (XGBoost), and 0.31 (DT), highlighting the dominant role of flow rate in driving methanol distribution and hydrate inhibition requirements [26]. The next most important feature was temperature, with scores of 0.28 (RF), 0.27 (XGBoost), and 0.31 (DT) due to its influence on the phase equilibrium and overall hydrate stability according to different pipeline parameters [2]. Pressure fell as a moderate importance feature, with a static score of 0.20 across RF, XGBoost, and DT, which was reasonable, based on the influence it has over the gas-water phase [6]. Gas specific gravity was given importance scores of 0.14 (RF), 0.18 (XGBoost), and 0.14 (DT), emphasizing a secondary role in the methanol needs of lean gas systems [38]. Water content showed the least impact, with scores of 0.05 (RF), 0.08 (XGBoost), and 0.04 (DT), suggesting its minimal contribution to methanol prediction in this context [27].  
These results indicated that flow rate and temperature were the primary determinants of methanol dosing, aligning with the dynamic flow and thermal conditions critical for hydrate inhibition [4]. The consistency in feature rankings across models validated their reliability, although minor variations (e.g., slightly higher water-content importance for XGBoost and DT) suggested model-specific sensitivities, potentially due to differences in tree structures [29]. This analysis could enable engineers to prioritize optimizing flow rate and temperature control to enhance methanol efficiency, improving pipeline safety and cost-effectiveness in hydrate management.
## 3 Methodology
### Inception Model
#### 3.1.3 Comparative Models  
This study uses the Inception model to assess the susceptibility and risk of landslides in the study area. Additionally, it compares the fitting effects of four models, including BPNN, ResNet, CNN, and VGG-16.  
* BPNN (Backpropagation Neural Network) is a neural network model that uses the \"backpropagation algorithm\" for learning and optimization. After the model completes a forward propagation calculation to obtain the output result, it compares the actual target output to calculate the error between them. Each layer of neurons adjusts its weights and biases based on the received error signal to minimize the loss function of the entire network. This weight update method based on the gradient descent strategy ensures that the network continuously optimizes and gradually improves the model's prediction accuracy with each iteration.
* ResNet (Residual Neural Network) introduces the concept of residual blocks. Instead of directly mapping the input signal to the output through a series of layers in a residual block, it learns a residual mapping from the input to the production. Therefore, even if the network structure is intense in ResNet, it can effectively alleviate the gradient vanishing problem and avoid degradation in network performance as the depth increases [33].
* CNN (Convolutional Neural Network) is a deep learning model designed for processing image data. It is widely used in computer vision tasks, such as image classification and object recognition. Its core idea is to simulate certain characteristics of the human eye's retina and brain cortex, automatically extracting features from raw pixel information [34].  
Figure 6: The structure of the Inception model.  
Figure 7: Structure of Inception for landslide susceptibility assessment.  
VGG-16 (Visual Geometry Group-16) is a deep convolutional neural network model proposed by researchers from the Oxford University Visual Geometry Group in 2014, characterized by its deep network architecture. All convolutional layers in VGG-16 use \(3\times 3\) convolutional kernels, a design choice that maintains a relatively small parameter while achieving a receptive field equivalent to larger kernels by stacking multiple \(3\times 3\) convolutions. This design increases the depth and non-linear expressive power of the network. Stacking small convolutional kernels is akin to performing multiple acceptable local feature extractions, which helps capture richer detail information. It has shown excellent accuracy in some landslide susceptibility studies [35].
# Spatial Data-Based Automatic and Quantitative Approach in Analyzing Maintenance Reachability
## 3 Materials and Methods
### Maintenance Reachability Analysis-Oriented 3D Reconstruction of Maintenance Scene
#### 3.2.2 Geometric Characteristic Data Acquisition of Product  
If the reconstructed product models are regular shapes, then they will be represented by bounding spheres, bounding boxes, cylinders, and capsules. Otherwise, they will be meshed and represented by triangular patches. The necessary geometric characteristic data of each product model should be obtained for subsequent calculation, as shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Product Model**} & \multicolumn{3}{c}{**Geometric Characteristic Data**} \\ \cline{2-4}  & **Position Class** & **Range Class** & **Direction Class** \\ \hline Sphere & Center Coordinate & Radius & — \\ OBB & Center Coordinate & Range Vector & Local Axes \\  & (Half Length of Three Sides) & (Direction Vector of Three Sides) \\ Cylinder & Axis Ends’ Coordinates & Radius & — \\ Capsule & Axis Ends’ Coordinates & Radius & — \\ Triangular Patch & Coordinates for Vertexes & — & Face’s Vertexes Connection \\  & & Order \\ \hline \hline \end{tabular}
\end{table}
Table 2: Necessary Geometric Characteristic Data of Different Product Models.  
Figure 4: Arm Model Simplification Diagram.
## 1 Introduction  
Experimental characterization and theoretical modeling of materials assumes a growing importance not only for the verification of their performance but also for the ever-greater diffusion of simulation systems and analytical forecasting methods for the design of complex systems that include these materials. The behavior of a material can be modeled using different physical, mechanical, and acoustic parameters that summarize its properties to improve the vibro-acoustic performance of the systems analyzed [1; 2; 3].  
Sound absorbing materials have a dual function. They are used in acoustic insulation, that is, to attenuate the sound pressure level in noisy environments. They are also used for acoustic correction, that is, to control the reflections and the reverberation of the rooms. There has always been considerable interest in the materials and combined solutions used in sound absorption. Today, many efforts are made to design and produce new materials obtained from different industrial recycling methods, or from vegetable or natural fibers [4; 5; 6].  
The solutions are designed based on the sound absorption coefficient. This is the fundamental characteristic that defines the sound-absorbing quality, which is defined as the relationship between the sound energy absorbed by the surface and the incident energy. Designing new materials therefore means testing the product by measuring the absorption coefficient and modeling its behavior to reproduce its performance in different operating conditions [7; 8].  
In the present work, the results obtained from the phenomenological and numerical modeling of the acoustic behavior of giant reeds shredded fibers are compared. To start, the characteristics of the models widely used for the modeling of sound-absorbing materials are reported. Then, acoustic properties' measurements results are described. Lastly, phenomenological and numerical modeling of the acoustic coefficient are compared using the data obtained from the measurements made. The aim of this study was to produce an algorithm based on neural networks to predict the values of the acoustic absorption coefficient, known to be some characteristics of the material under examination. Algorithms based on artificial neural networks are widely used in various fields for the prediction of the behavior of nonlinear systems for which there is no rigorous mathematical model. In this research, these algorithms are used to simulate the acoustic behavior of a fibrous material obtained from giant shredded reeds for which there is no rigorous mathematical model, but only numerical models that must be adapted to this type of material.
## 5 Conclusions  
In view of the fact that the current single-manifold learning strategy cannot map the multi-manifold structure in hyperspectral data, we propose the model SSAMMA. Firstly, we propose a new representation based on SAWRM for HSIs. SAWRM chunks HSIs via the image segmentation algorithm and adaptively weights the samples in each super-pixel block to form a new image with more similar spectral lines for the same class of samples. Secondly, we propose a new way to construct a spatial-spectral multi-manifold map based on SSMMM. Finally, a spatial-spectral objective function is designed to adaptively obtain the optimal projection direction on each sub-manifold, which fuses the discriminative features on different sub-manifolds to improve the classification performance. We conduct an enormous number of experiments using two datasets compared to eight dimensionality reduction algorithms, demonstrating the superiority and stability of the proposed SSAMMA. The computational complexity of the algorithm may be high due to super-pixel segmentation and the construction of a multi-manifold map. Longer runtime and larger computational resources may be required when processing large-scale HSIs.  
Conceptualization, S.X., S.G., Q.Y. and H.G.; methodology, S.X., S.G., Q.Y. and H.G.; validation, S.G. and H.G.; formal analysis, S.G. and S.X.; investigation, S.X. and S.G.; writing--original draft preparation, S.G. and S.X.; writing--review and editing, S.G.; supervision, S.X.; project administration, S.X. and H.G.; funding acquisition, H.G. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 62071168, the Natural Science Foundation of Jiangsu Province under Grant BK20211201, and the China Postdoctoral Science Foundation under Grant 2021M690885.  
Not applicable.  
Data Availability Statement: Data are contained within in this article.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Module**} & \multicolumn{3}{c}{**Metrics**} \\ \cline{3-6}  & & **SAWRM** & **SSMMM** & **OA** & **AA** & **K** \\ \hline \multirow{4}{*}{IP} & M1 & \(\times\) & \(\times\) & 82.65 & 80.72 & 79.68 \\  & M2 & \(\surd\) & \(\times\) & 88.04 & 87.13 & 86.19 \\  & M3 & \(\times\) & \(\surd\) & 88.27 & 87.36 & 86.50 \\  & M4 & \(\surd\) & \(\surd\) & 90.56 & 89.71 & 88.02 \\ \hline \multirow{4}{*}{PU} & M1 & \(\times\) & \(\times\) & 92.87 & 91.48 & 90.16 \\  & M2 & \(\surd\) & \(\times\) & 95.11 & 94.00 & 93.14 \\ \cline{1-1}  & M3 & \(\times\) & \(\surd\) & 95.34 & 94.29 & 94.05 \\ \cline{1-1}  & M4 & \(\surd\) & \(\surd\) & 97.86 & 97.06 & 96.22 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification accuracy of different models using IP and PU datasets.