esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Seismic Performance Parameters of RCS Composite Joints Under Floor Effect Are Studied
### The Influence of Axial Compression Ratio on Seismic Performance of Nodes
#### 4.1.3 Energy Consumption Capacity and Stiffness Degradation  
The research shown in Figure 17 indicates that the energy dissipation coefficient does not vary significantly under different axial compression ratios. The energy dissipation coefficients of the three nodes range from 3.02 to 3.09, while the equivalent viscous coefficients range from 0.481 to 0.491, with a variation of less than 2.32%, all meeting the standard requirements. This suggests that the axial compression ratio has a limited impact on the energy dissipation performance of the nodes. In terms of stiffness degradation, a higher axial pressure ratio significantly enhances the initial stiffness of the joint, demonstrating a stronger resistance to deformation overall. As the load increases, the stiffness of the joint gradually diminishes. However, joints with high axial pressure ratios can still maintain a satisfactory stiffness level, which contributes to an improved seismic performance. This phenomenon is attributed to the increased vertical axial force at the top of the column, leading to an elevation of compressive stress in the core region of the joint. Consequently, this suppresses lateral displacement during cyclic loading, thereby enhancing its deformation resistance.  
Figure 16: (**a**) Comparison of hysteresis curves of different axial compression ratio nodes; (**b**) Comparison of skeleton curves of different axial compression ratio nodes.  
Figure 17: (**a**) Comparison of energy dissipation coefficients of different axial compression ratio nodes; (**b**) Comparison of stiffness degradation of different axial compression ratio nodes.
# Porous Organic Polymers-Supported Metallocene Catalysts for Ethylene/1-Hexene Copolymerization
## 2 Results and Discussion
### Surface Morphology  
The morphology of the PEwas characterized by scanning electron microscopy (SEM). The SEM image in Figure 6 clearly shows that the ethylene/1-hexene copolymers have different morphology due to the different pore structure of their catalyst supports. PE-1 had high porous fabric consisting of  
Figure 5: **(a)** TREF analysis of PE copolymers produced from POPs- and silica-supported metallocene catalysts; **(b)** DSC analysis of PE-1 and PE-4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Entry** & **Support** & & **Soluble Fraction (SF)** & **Peak 1** & **Peak 2** \\ \hline PE-1 & POP-1 & T/\({}^{\circ}\)C & & 52.6 & 94.2 \\  & & Area/\% & 1.8 & 0.7 & 96.3 \\ PE-2 & POP-2 & T/\({}^{\circ}\)C & & 63.7 & 94.5 \\  & & Area/\% & 0.3 & 0.5 & 98.6 \\ PE-3 & POP-3 & T/\({}^{\circ}\)C & & 61.8 & 94.7 \\  & & Area/\% & 0.6 & 1.0 & 96.8 \\ PE-4 & \multirow{2}{*}{Sylopol 2408} & T/\({}^{\circ}\)C & & 53.1 & 94.2 \\  & & Area/\% & 0.2 & 0.4 & 98.9 \\ \hline \end{tabular}
\end{table}
Table 3: Temperature rising elution fractionation (TREF) analysis results.  
fiber and spherical beads of micron dimension, PE-2 had porous structure aggregated mainly by spherical beads, and PE-4 was aggregated by spherical beads with glia around the beads. The different morphologies of these PE samples can be explained reasonably by the replicating effect of the PE product on the porous POP and silica in the olefin polymerization process [36; 37].The porous support played as a template on two levels [36], taking the overall morphology of product polymer into account. One level is the whole support beads, which resulted in the porous polyethylene particles. Another level is the porous structure in the support beads, which played as nanoreactors and resulted in the fiber structure in the polyethylene particles. According to this particle growth mechanism, the morphology of PE would be related to the polymerization time and activity, besides the porous structure of the support and the morphology of the support itself. With the continuous chain growth, the catalyst beads gradually became larger because of the expansion in the catalyst beads and external growth by the active sites in the outer surface; the morphology of PE-1 would be more like that of PE-2 if polymerization continued.
## 11 Analysis Reactive-Power Balance  
Figures 16 and 17 highlight system characteristics at the maximum and minimum demand levels, respectively. Bus-specific voltage profiles, reactive power balances, changes in voltage phase angles, are depicted in the abovementioned figures. In both situations, it can be observed that there is an association between reactive injection and the voltage increment at the bus, where the present power generation compensates for reactive powers. The reactive powers of systems are observed using Bus8 having diesel generators, and Bus13 having external reactive power. Nevertheless, as depicted in Figure 16, the maximum demand situation indicates the transfer of reactive power to buses 9 and 14.  
Specific to this scenario, the power flow direction indicates negative power at these buses. Tables 7 and 8 provide details specific to these situations. The analysis of reactive power compensation requires these crucial scenarios as the starting points to obtain voltage optimisation. Concerning maximum demand, the compensation values can be validated using an analysis of the contributions during minimum demand. Figure 17 highlights the voltage characteristics concerning the specified case. Values are appropriate, and for certain buses, they exceed one p.u. In such cases, analyses should be performed during reactive power injection increase to optimise maximum demand situations  
Figure 16: (**a**) The reactive power balance at maximum demand and7 (**b**) the voltage at maximum demand.
## 2 Materials and Methods
### Measurement of the Operating Conditions  
Temperature (\({}^{\circ}\)C) and relative humidity (%) in the ISD were measured using USB data loggers (Voltcraft DL-220 THP, Voltcraft(r), Hirschau, Germany) laid out on the surface of the paddy along the centre line of the dryer at 0.5, 1.1, 7.4, 12.9 and 22.1 m distance from the inlets of the ISD. For conventional sun drying, a similar USB data logger (Voltcraft DL-181 THP, Voltcraft(r), Hirschau, Germany) was placed on the top layer of paddy rice, and for ambient meteorological conditions, a data logger of the same type was placed on a post 1.8 m above the ground. Solar radiation (W/m\({}^{2}\)) and photovoltaic (PV) voltage (V) were measured using USB data loggers (Voltcraft DL-131, DL-191V, Voltcraft(r), Hirschau, Germany). All measurements by USB data loggers were taken at a 1-min time interval, and the data was stored at the end of each drying batch. As additional information, the spatial temperature pattern in the ISD was visualized by taking thermal images using a hand-held thermal imaging camera (Testo 870-2, Testo SE & Co. KGaA, Lenzkirch, Germany). The thermal camera simultaneously records the true-colour (real) image to facilitate the spatial allocation. During drying, occasionally the thermal camera was held in one of the ISD outlets and directed to the entire length of the dryer. Air velocity (m/s) at the ISD outlet was measured using a digital anemometer (Voltcraft BL-30 AN, Voltcraft(r), Hirschau, Germany). In each batch, the total mass of paddy rice before and after drying was measured using a heavy-duty spring scale (NT-100 kg, Camry Electronic Ltd, Guangdong, China).
## 2 Method  
Given a dataset \(D=\left\{D^{T},~{}D^{Ts}\right\}\) split into two subsets, there are a training set \(D^{T\!r}\) and a testing set \(D^{Ts}\) with \(\mathcal{L}_{T\!r}=\left\{\ell_{i}\right\}_{i=1}^{N}\) and \(\mathcal{L}_{Ts}=\left\{\ell_{i}\right\}_{i=1}^{M}\) where \(N\) and \(M\) are \(\left|D^{T\!r}\right|\) and \(\left|D^{Ts}\right|\), respectively. Typically, the given dataset is a small dataset or a dataset with very small number of labeled examples compared to the number of unlabeled examples \(N\ll M\). The aim for our model is to learn a transformation function \(f\left(X^{k}\right)=Y^{k}\mid Y^{k}\cong\mu^{k},~{}k=0\ldots K-1\) where \(K\) is the number of classes in the dataset. The function maps each sample (\(x_{i}^{k}\in X^{k}\)) of the same class to a \(y_{i}^{k}\), where \(y_{i}^{k}\) approximates \(\mu^{k}\) centroid (mean sample) of the same class.  
As illustrated in Figure 1, our proposed framework consists of three main modules: feature extraction, feature mapping, and reconstruction. For feature extraction, we train an autoencoder [49]\(AE\), which is a generative model that reconstructs the available training samples in the dataset \(X^{k}\), through learning their latent representation. In the feature mapping module, we first pair the training samples of each class with all other samples of the same class. Second, we use the trained autoencoder to extract the features of the pairs. Then, we train a mapping network that consists of a set of multilayer perceptron (MLP) regressors \(\text{B}=\left\{MLP_{1},~{}MLP_{2},~{}\ldots,~{}MLP_{N_{f}}\right\}\), where \(N_{f}\) is the number of latent features in the embedding layer \(Z\) of the autoencoder \(AE\). Each \(MLP\) regressor in the network learns to output \(z_{i}^{s}\in Z,~{}s=1,~{}2,~{}\ldots,~{}N_{f}\). Finally, we take the mapped latent features \(Z\) from the feature mapping network B and feed it into the decoder of the trained autoencoder to reconstruct the mapped samples \(Y^{k}\).  
After training the whole framework, we use it to map the test set examples to a more compact space for intra-class samples and separable for inter-class samples. Evaluating a classifier on the transformed samples gives better accuracy and loss than evaluating the same classifier on the untransformed examples. In the next three subsections, we explain the details of the three modules composing our framework.
## 4 Results and Discussion
### Data Refinement
#### 4.4.1 Selection
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **acc** & **prec** & **rec** & **f1** & **AUC** & **fpr** & **fnr** \\ \hline QCNN & **0.9706** & **0.9725** & **0.9685** & 0.9705 & **0.9834** & **0.0274** & **0.0315** \\ QMLP & 0.9689 & 0.9713 & 0.9663 & 0.9688 & 0.9814 & 0.0285 & 0.0337 \\ \hline \hline \end{tabular}
3. **Capping.** Data capping is performed by using different values (2, 4, 8, 16). For QMLP, there is no notable difference in metrics, but for QCNN, there is an improvement in FNR for all capping values (Table 11), no matter which capping value is chosen. The impact of the applied capping value on the model’s accuracy is shown in Figure 13(a) and on FPR in Figure 13(b).
4. **Reduction in longitudinal profile.** The complete HGCAL image contains 5 \(\times\) 5 \(\times\) 36 = 900 pixels per image, resulting in 2807 parameters for QCNN (respectively 7353 for QMLP). Here, QCNN is used as a classifier. As a result of reducing the longitudinal profile following procedures described in Section 4, the number of model parameters is drastically decreased. The described solutions are examined and results are shown in Table 12 :  
* The QCNN model using a full HGCAL image has 2807 parameters, and with this approach (E+Hf), the number of parameters is reduced to 1673.
* This approach (3 Eh+Hf) replaces the full HGCAL image (5 \(\times\) 5 \(\times\) 36) with a 5 \(\times\) 5 \(\times\) 4 image, decreasing the NN parameter numbers to 1079.  
\end{table} 
Table 10. The results for the keep layer maximum approach for QCNN and QMLP. Figure 14. (a) Model accuracy and (b) FNR as a function of capping value. As is clear from the above description, in approaches 1, 2, and 3, the HGCAL layer structure image is retained. The global picture of particle decay is kept but simplified, emphasizing the importance of high pt points above very small pt deposits. Theoretically, instead of sending the complete image, just the position and value kept after the applied selection could be sent, notably cutting back the number of parameters in the NN's input layer. Using approach 4, the number of layers can be drastically decreased, ultimately significantly reducing the number of neural network parameters.
# Multi-Pooled Inception Features for No-Reference Image Quality Assessment
## 1 Introduction
### Contributions  
Convolutional neural networks (CNNs) have demonstrated great success in a wide range of computer vision tasks [26; 27; 28], including NR-IQA [14; 15; 16; 29]. Furthermore, pretrained CNNs can also provide a useful feature representation for a variety of tasks [30]. In contrast, employing pretrained CNNs is not straightforward. One major challenge is that CNNs require a fixed input size. To overcome this constraint, previous methods for NR-IQA [14; 15; 16; 18] take patches from the input image. Furthermore, the evaluation of perceptual quality was based on these image patches or on the features extracted from them. In this paper, we make the following contributions. We introduce a unified and content-preserving architecture that relies on the Inception modules of pretrained CNNs, such as GoogLeNet [31] or Inception-V3 [32]. Specifically, this novel architecture applies visual features extracted from multiple Inception modules of pretrained CNNs and pooled by global average pooling (GAP) layers. In this manner, we obtain both intermediate-level and high-level representation from CNNs and each level of representation is considered to predict image quality. Due to this architecture, we do not take patches from the input image as in previous methods [14; 15; 16; 18]. Unlike previous deep architectures [15; 18; 22], we do not utilize only the deep features of the last layer of a pretrained CNN. Instead, we carefully examine the effect of different features extracted from different layers on the prediction performance and we point out that the combination of deep features from mid- and high-level layers results in significant prediction performance increase. With experiments on three publicly available benchmark databases, we demonstrate that the proposed method is able to outperform other state-of-the-art methods. Specifically, we utilized KonIQ-10k [33], KADID-10k [34], and TID2013 [35] databases. KonIQ-10k [33] is the largest publicly available database containing 10,073 images with authentic distortions, while KADID-10k [34] consists of 81 reference images and 10,125 distorted ones (81 reference images \(\times\) 25 types of distortions \(\times\) 5 levels of distortions). TID2013 [35] is significantly smaller than KonIQ-10k [33] or KADID-10k [34] because it contains 25 reference images and 3000 distorted ones (25 reference images \(\times\) 24 types of distortions \(\times\) 5 levels of distortions). For a cross database test, LIVE In the Wild Image Quality Challenge Database [36] was applied, which contains 1162 images with authentic distortions evaluated by over 8100 unique human observers.  
The remainder of this paper is organized as follows. Section 2 introduces our proposed approach. In Section 3, the experimental results and analysis are presented. A conclusion is drawn in Section 4.
## 4 Results and Discussion  
MB and MF robust control strategies were designed, and the simulation results are presented in the previous sections. The challenge to design MB and MF robust control strategies for achieving the desired performance in terms of transient behavior and steady-state error for step and impulse inputs, and minimizing the tracking error for a time-varying sinusoidal signal, was addressed successfully.  
The step response of both the MB- and MF-controlled systems is robust as the system settles down in the presence of disturbances. However, the MF-controlled system settles faster as shown in Table 2. The same trend is followed in case of the impulse input. The slower system in the case of MB closed loop control is due to absence of a lead compensator. A lead compensator was not designed for these inputs because the objective to reach the desired final value was achieved without the effect of disturbances in these cases.  
Figure 8: Model-free robust control design-closed loop responses of link (parallel to femur) and tracking error: (**a**) step response-displacement, velocity, and acceleration; (**b**) tracking error displacement step response; (**c**) impulse response-displacement, velocity, and acceleration; (**d**) tracking error displacement impulse response; (**e**) sinusoidal response-displacement, velocity, and acceleration; (**f**) tracking error displacement sinusoidal response.  
In the case of a time-varying sinusoidal input, the response of the MB controller is relatively faster. This is because of the lead compensator integrated with the \(H_{\infty}\) reference gain controller. The lead compensator accounted for the delay in the reference input signal and the output signal. The sinusoidal input response of the MF-controlled system is faster than that of the MB controller. The results are summarized in Table 2.  
The results show that, for both MB and MF controllers, the desired results, i.e., robustness and minimal steady-state/tracking error in the case of reference inputs, were achieved but the performance of the MF controller is better than that of the MB controller. The rise time and settling time are less in the case of MF control than that of MB control, indicating faster response. This work focused on achieving robustness for time-varying signals to achieve robustness in gait pattern tracking through the LLRE. The next step is to check the performance of the designed controllers for time-varying human gait patterns.
# Detailed Design and Aerodynamic Performance Analysis of a Radial-Inflow Turbine
## 5 Numerical Analysis  
The CFD solver ANSYS-CFX has been used to solve the three-dimensional compressible unsteady Reynolds-averaged Navier-Stokes (URANS) equation. The high resolution discretization was employed for momentum and turbulence equations. The standard \(k\)-\(\epsilon\) has been chosen to model the turbulent flow. The pressure inlet and pressure outlet boundary conditions were set for the volute inlet and the rotor outlet. A frozen rotor interface was set between the volute and the nozzle and a mixing plane condition was set at the interface between the stationary (nozzle) and rotational (rotor) frames. Therefore, the circumferential variation of the flow has no important effects on the stage efficiency and mass flow rate. The stage interface employs time-averaging to estimate the parameters in the interface between the rotor and nozzle stages. The physical time step is considered equal to \(1/\omega\), where \(\omega\) is the revolution speed in rad/sec. Due to the difference in the number of blades in the rotor and nozzle stages, a pitch angle change, which is equal to the rotor-nozzle blade ratio, is considered for the flow.
# A Line Segment Detector for Space Target Images Robust to Complex Illumination
## 3 Results  
We have conducted extensive and detailed experiments, and both quantitative and qualitative experiments are employed for the evaluation of the ST_LSD line segment detection method. The performance of ST_LSD is also compared to LSD, EDLines, Linelet, and AG3 line. Among them, LSD and EDLines are the most widely used line segment detectors in recent years, Linelet is a relatively new detector, and the AG3 line detector has better effectiveness and faster speed.
# RETRACTED: Using Data Mining Methods for Predicting Sequential Maintenance Activities
## 3 The Proposed Model
### Generation of Frequent Spare Parts Groups
Sequences having a single maintenance activity are ignored from our model as they do not indicate the next maintenance activity information. This will only be used while computing the confidence value of the sequential association rules. In this analysis, we assumed that there are no occurrences of parallel maintenance activities performed for the conveyor belt. In our analysis, the symbol '\(\longrightarrow\)' indicates the sequence direction and the symbol '==>' indicates the rule direction. \(\bullet\) Collection of maintenance data:  
Using the GSP algorithm, 58 sequential models were generated with a 30% support threshold value and, likewise, 27 models were generated with 40% support threshold. The results are given in Table 7. \begin{table}
\begin{tabular}{c c} \hline
**Maintenance** & **Spare Part Code** & **Part Description** \\ \hline  & 490000092 (S61) & HYDRAULIC CYLINDERS—ALUMINIUM ALLOY FO \\  & 490001251 (S62) & HYDRAULIC CLAMP TOWER JACKS CYLINDER (IT) \\  & HYDRAULIC JACK OIL SEAL: 315 X 355 X 33.3 FOR \\  & 490010217 (S63) & WAGNER PRESS \\ HYDROULIC SYSTEM & 490030045 (S64) & HYDRAULIC OIL PRESSURE GAUGE (1) RANGE \\ MAINTENANCE (M06) & 490030045 (S65) & PRESSURE FOOT FOR NILOS HYDRAULIC JACK O \\  & 499050201 (S66) & HYDRAULIC HOSE CONFIRMS TO SAE 100 R2 ID \\  & 499050297 (S67) & HYDRAULIC HOSE OF ID5/16 INCHES \\  & AND LENGTH 3000 MM \\  & 499120101 (S68) & END BOLD FOR HYDRAULIC TRAVERSEOR 1800 \\  & 499120304 (S69) & NILOS HYDRAULIC CYLINDERS WITH “T” JOINT \\ \hline  & 4990000122 (S71) & BEARING HOUSINGS FOR PROUGHING AND RETURN \\  & 491020009 (S72) & BEARING COVER INNERANDOUTER AND LARYRINTH \\ BEARING AND & 499130007 (S73) & GREASE SEAL RING FOR IMPACT IDLERS \\ LUBRIFICATION (M07) & 499130215 (S74) & OF 1800 MM CONVEVOR \\  & 499130401 (S75) & BEARRE WITH BEARING HOUSINGS WELDED ON B \\  & 499130416 (S76) & BEARING HOUSING FOR IMPACT IDLER OF 1800 \\ \hline \end{tabular}
\end{table} 
Table 5: _Cont._One can remark that the sequences have the maximum support values as follows:  
For a combination of 2: M04\(\rightarrow\)M03\(\rightarrow\)  
For a combination of 3:  
One can note that the second sequence has the maximum support value, however, the chosen sequence is M06\(\rightarrow\)M03\(\rightarrow\)M02 because of its lower average interval. Generation of sequential rules  
Since the sequences are identified, one can calculate the following 4 measures: rule support, rule confidence, lift, and Chi-square measure. From these 4 measurements, one can deduce if the sequences are significant. Classification by rules of maintenance activities  
After determining the rules, it was necessary to test them and calculate the coverage and accuracy rates. The results are as follows (i.e., see Table 8).
# Protection against Shiga Toxins
## 2 Compounds that Protect Cells against Stx
### Manganese  
Mn\({}^{2+}\)-ions have been described by several groups to protect cells against Shiga toxin [105] and Stx1 [103]. [PERSON] and [PERSON] reported that the protection was due to redirection of the toxin to the degradative pathway; they also reported that mice injected with a lethal dose of Stx1 could be rescued by a nontoxic dose of Mn\({}^{2+}\) that was injected five days before the toxin [103]. However, Mn\({}^{2+}\) failed to protect cells against Stx2 under the same conditions [65]. These authors also showedthat elevated levels of Mn\({}^{2+}\) resulted in a down-regulation of GPP130, which in turn led to inhibition of retrograde transport of Stx1, but not Stx2 [65]. Furthermore, this group later showed that the increased Mn\({}^{2+}\) levels induced GPP130 oligomerization and its sorting to lysosomes for degradation [102].  
The protective effect of Mn\({}^{2+}\) against Stx toxicity was also studied by another group, and there the authors failed to see any protective effect of Mn\({}^{2+}\) against Stx1 and Stx2 in cultured Vero cells and CD-1 outbred mice at Mn\({}^{2+}\)-doses that were not toxic to the cells and the animals [104]. They concluded that the ability of Mn\({}^{2+}\) to protect against Stx toxicity might be dependent on the cell line and mouse strain, and that protection may be observed only at potentially toxic concentrations of Mn\({}^{2+}\). These authors and others have discussed that Mn\({}^{2+}\)-ions are neurotoxic; for reviews see [155; 156]. Due to this well-known toxic effect of Mn\({}^{2+}\), we believe that it is unlikely that Mn\({}^{2+}\) can be developed as a drug against Stx toxicity.