esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In this context, many other analytical and numerical works have focused on the vibration and buckling response of nanocomposite materials and structures also in thermal conditions, see e.g., [11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30], among others. It is undoubted that the presence of damping within materials at different scales can affect significantly the overall structural response. For such a reason, the damping property must be carefully characterized and applied for the numerical study of actual nanomaterials and structures. Surprisingly, little attention has been devoted in the literature to this aspect, at least for nanoplates and nanoshells. As a result, [PERSON] et al. [20] analyzed a nanoplate in forced vibrational conditions, by assuming both an external and internal viscoelastic damping. The external damper was modeled as a visco-Pasternak foundation, whereby the internal damper was treated by a Kelvin-Voigt model. The Classical Plate Theory (CPT) in conjunction with the [PERSON]'s non-local continuum theory was successfully applied, whilst a rectangular nanoplate with simply-supported boundary conditions was examined according to the [PERSON]'s technique. [PERSON] et al. [21] investigated the eigen frequencies of a bi-layer viscoelastic nanoplate by means of a non-local elasticity theory. They applied the CPT on the non-linear strains, whereas the natural and resonant frequencies were computed by considering the Galerkin analytical method for clamped and simple boundaries. [PERSON] and [PERSON] [22] presented a free vibration analysis of a non-local viscoelastic nanoplate considering piezoelectricity placed on the viscoelastic medium in a hygrothermal environment. The Simple FSDT (S-FSDT) in a sinusoidal form was considered together with a non-local elasticity theory. In the present work, instead, we propose an original study on the vibrational response of nanoplates with sinusoidal corrugations. The structural damping is considered by applying a Kelvin-Voigt linear model, whereas a modified version of FSDT is proposed in a combined form with the non-local strain gradient theory to tackle the problem. Besides this, the numerical results are extracted according to the [PERSON]'s approach for simply supported nanoplates. For comparative purposes, the numerical results are verified against the outcomes of molecular dynamics-based simulations. Furthermore, the numerical results have been obtained for different geometrical and mechanical parameters, including a varying number of corrugations, as well as different viscoelastic coefficients and waviness configurations. The paper is organized as follows. First, we present the problem formulation and the governing equations in Section 2, whose solution procedure is proposedin Section 3. Next, the numerical parametric investigation is presented and discussed in Section 4, whereas the main conclusions are drawn in Section 5.
## 4 Experiment
### Confusion Matrix Analysis  
The confusion matrix reflects the extent to which an algorithm incorrectly predicts similar categories. In this experiment, three datasets, namely, THUCNews, AG's News, and arXiv-10, were selected to evaluate our method. The values on the main diagonal of the confusion matrix represent the prediction probability in the corresponding category (Figure 4). The darker the color, the higher the prediction probability. The true labels are on the \(y\)-axis, and the predicted labels are on the \(x\)-axis. We select the top three performance models on each dataset to draw the confusion matrix. The performance of the RQ-OSPTrans method on the corresponding dataset is located at the first of each row (Figure 4a,d,g). The baseline model will show higher prediction confusion on the Society and Education classes in the THUCNews dataset compared with our method. On the AG's News dataset, the confused categories are concentrated in \"Business\" and \"Technology\" categories. The RQ-OSPTrans's prediction confusion level in these two categories is equivalent to ELECTRA and better than MacBERT. The performance of the top three performance models on the arXiv-10 dataset is relatively close. In the two categories of \"cs\" and \"eess\", similar prediction confusion appears on the class labels. However, the prediction confusion of this method on the \"quant-ph\" and \"stat\" categories is better than the baseline model, and RQ-OSPTrans also has better classification accuracy on the \"hep-th\" category.  
Figure 4: Cont.
# Vibration Velocity Prediction with Regression and Forecasting Techniques for Axial Piston Pump
## 3 Results and Discussion
### Complexity
#### 3.1.2 Ensemble Learning
It outperforms other ensembles in all experiments except the reference tests with check valve, where GB Dense performed slightly better. In addition, simple strategies, stacking and GB Dense perform comparably well on all experiments, while GB RNN and GB LSTM perform significantly worse. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Metric** & **GB Dense** & **GB RNN** & **GB LSTM** & **Median** & **Mean** & **Linear Regression** \\ \hline \multicolumn{8}{c}{Experiment: Reference tests v1} \\ \hline MAE & 0.0711 & 0.0918 & 0.0983 & 0.067 & 0.0685 & 0.0764 \\ MAPE & 6.02 & 7.89 & 8.67 & 5.66 & 5.83 & 6.61 \\ MSE & 0.0101 & 0.0149 & 0.018 & 0.0094 & 0.0095 & 0.0101 \\ \hline \multicolumn{8}{c}{Experiment: Reassembling v1} \\ \hline MAE & 0.0773 & 0.1048 & 0.0997 & 0.0754 & 0.0764 & 0.0771 \\ MAPE & 5.27 & 7.03 & 6.71 & 5.05 & 5.12 & 5.26 \\ MSE & 0.0122 & 0.0227 & 0.0203 & 0.0127 & 0.013 & 0.0122 \\ \hline \multicolumn{8}{c}{Experiment: Reference tests v2} \\ \hline MAE & 0.086 & 0.1193 & 0.1021 & 0.0825 & 0.0833 & 0.0824 \\ MAPE & 6.98 & 9.94 & 8.56 & 6.64 & 6.71 & 6.82 \\ MSE & 0.0158 & 0.0245 & 0.0199 & 0.0156 & 0.0159 & 0.0134 \\ \hline \multicolumn{8}{c}{Experiment: Reference tests with check valve} \\ \hline MAE & 0.0957 & 0.114 & 0.1393 & 0.0982 & 0.098 & 0.1002 \\ MAPE & 8.6 & 10.2 & 13.29 & 8.89 & 8.94 & 9.25 \\ MSE & 0.017 & 0.0238 & 0.0271 & 0.017 & 0.0165 & 0.0163 \\ \hline \multicolumn{8}{c}{Experiment: Axial play fault} \\ \hline MAE & 0.6196 & 0.799 & 0.9081 & 0.7705 & 0.75 & 0.5925 \\ MAPE & 28.65 & 37.06 & 42.21 & 35.79 & 34.78 & 27.29 \\ MSE & 0.4165 & 0.6816 & 0.8713 & 0.6275 & 0.5984 & 0.3873 \\ \hline \multicolumn{8}{c}{Experiment: Timing plate fault} \\ \hline MAE & 0.3519 & 0.3407 & 0.3406 & 0.3131 & 0.3258 & 0.3646 \\ MAPE & 19.79 & 19.11 & 19.12 & 17.51 & 18.25 & 20.52 \\ MSE & 0.1493 & 0.1407 & 0.1418 & 0.1198 & 0.1287 & 0.1575 \\ \hline \end{tabular}
\end{table} 
Table 9: Metrics for simple strategies and gradient boosting ensembles for each evaluated on both training and test data for ensembles. In terms of implementation complexity, the easiest ones to implement are simple strategies: mean- and median-ensemble. The only thing to do in that case is to take outputs of the ensemble models and compute mean or median from them, depending on the type of ensemble used. This approach does not involve training any additional model. More complex strategies, such as stacking and GB, require training one additional model per ensemble. Additional effort is required in both cases to construct separate training and test datasets. Therefore, for stacking and GB, the implementation complexity is comparable. As a conclusion, taking complexity of implementation, impact on computational resources and MAPE metric value, the GB dense ensemble is the best choice. It does not have the best MAPE value, nor the simplest implementation, but it has the lowest impact on computational resources. While considering such a compromise, that choice is justified.
## 7 Implementation and Evaluation
### Multimedia Workflows
#### 7.4.4 Service Recovery  
The aim of the last evaluation was to measure the time needed to replace a failed service with a new one. We simulated the failure of the transcoding service and measured the time from the failure until the new service started sending out new data to the next neighbor in the workflow.  
Figure 20 shows the results (in ms) for each recovery from a failure, simulating 180 service failures in total. The average time for recovery is about 3.8 s, including network delays and latency. Considering 1 MB (6 seconds) chunks our PUMA framework still can ensure a smooth playback (for more details see [11]).
## 2 Immunomodulatory Function of Extracellular Hsp60, Hsp70, and Hsp90
### Huntington's Disease  
HD is a progressive neurodegenerative disease caused by excess repeats of glutamine residues, called polyQ repeats, in the huntingtin (Htt) protein, causing protein misfolding [91]. The accumulation of misfolded Htt is associated with cognitive decline and motor defects [92]. Few studies have investigated the involvement of Hsp60 in HD. For instance, Hsp60 plays a protective role in HD by inhibiting polyglutamine aggregate formation and toxicity in vitro [93]. In HD, using confocal microscopy, it was observed that exogenous Hsp70 helps to reduce the number and size of polyQ inclusions [94]. Under normal conditions, the Htt proteins are under the quality control of the chaperone system, particularly Hsp90 and Hsp70. Hsp90 co-immunoprecipitates with both mutant and wild-type forms of Htt, and its inhibition blocks the interaction [95]. Hsp90 preferentially binds the mutant huntingtin (mHtt) rather than normal Htt proteins and also binds other proteins, such as the transcriptional repressor RE1-silencing transcription factor (REST) [96]. REST is normally quiescent in differentiated neurons, but its levels and activity increase as a consequence of neuronal damage [97]. Under physiological conditions, Htt indirectly regulates REST nuclear traffic through the formation of a complex that causes REST retention in the cytoplasm, whereas under pathological conditions, the binding of mHtt to this complex induces a conformational change that leads to the release of REST and its subsequent translocation to the nucleus [96]. A direct effect of this pathological transport of REST is the repression of neuronal genes containing RE1 sequences, including the brain-derived neurotrophic factor (BDNF), a survival factor for striatal neurons. Hsp90-specific inhibitors dramatically reduce Htt stability and REST levels, providing neuroprotective activity [96]. Given the complexity of the mechanism regulating REST expression and the way by which REST modulates the expression of its target genes, further studies are needed to understand the Hsp90-mHtt and Hsp90-REST interactions. Other studies report the involvement of Hsp70 in the pathogenesis of HD. Hsp70 has not only a neuroprotective role as an intracellular chaperone but it has also important extracellular functions. Extracellular Hsp70 can reach, for example, the hippocampus, leading to the initiation and propagation of generalized tonic-clonic seizures [98; 99].
# Advanced SDR-Based Custom OFDM Protocol for Improved Data Rates in HF-NVIS Links
## 3 Results and Discussion  
The tests, both under laboratory conditions and in real environments, were performed with SNR levels that meet minimum requirements, ensuring full information reception with minimum or without losses. In laboratory conditions the SNR was set at 20 dB. In the real test, measurements were taken at different frequencies to identify the frequency that provided the best SNR. Once the optimal frequency with the highest SNR was identified (greater then 20 dB), the final tests were performed and all subsequent measurements in this study were performed in this manner. Focusing the tests on the frequency with the best SNR ensured a fair and accurate evaluation of the transmission performance. This approach also allowed representative packet error rate (PER) values to be obtained that accurately reflect optimal transmission conditions. The settings of the OFDM protocol and the transmission parameters were adapted based on the results of previous studies carried out by the authors in order to guarantee an optimal system performance [25; 26; 29]. The goal of these tests was to demonstrate a working system composed of hardware system, digital processing implementation, and a graphical user messaging interface, allowing it to be tested and demonstrated in an accessible and intuitive manner. This approach facilitates the understanding of the system by users without advanced technical knowledge and represents an important initial step in demonstrating the capabilities and potential for the further extension of this technology.
# Performance Investigation of Active, Semi-Active and Passive Suspension Using Quarter Car Model
###### Abstract  
In this paper, a semi-active and fully active suspension system using a PID controller were designed and tuned in MATLAB/Simulink to achieve simultaneous optimisation of comfort and road holding ability. This was performed in order to quantify and observe the trends of both the semi-active and active suspension, which can then influence the choice of controlled suspension systems used for different applications. The response of the controlled suspensions was compared to a traditional passive setup in terms of the sprung mass displacement and acceleration, tyre deflection, and suspension working space for three different road profile inputs. It was found that across all road profiles, the usage of a semi-active or fully active suspension system offered notable improvements over a passive suspension in terms of comfort and road-holding ability. Specifically, the rms sprung mass displacement was reduced by a maximum of 44% and 56% over the passive suspension when using the semi-active and fully active suspension, respectively. Notably, in terms of sprung mass acceleration, the semi-active suspension offered better performance with a 65% reduction in the passive rms sprung mass acceleration compared to a 40% reduction for the fully active suspension. The tyre deflection of the passive suspension was also reduced by a maximum of 6% when using either the semi-active or fully active suspension. Furthermore, both the semi-active and fully active suspensions increased the suspension working space by 17% and 9%, respectively, over the passive suspension system, which represents a decreased level of performance. In summary, the choice between a semi-active or fully active suspension should be carefully considered based on the level of ride comfort and handling performance that is needed and the suspension working space that is available in the particular application. However, the results of this paper show that the performance gap between the semi-active and fully active suspension is quite small, and the semi-active suspension is mostly able to match and sometimes outperform the fully active suspension n in certain metrics. When considering other factors, such as weight, power requirements, and complexity, the semi-active suspension represents a better choice over the fully active suspension, in the author's opinion. As such, future work will look at utilising more robust control methods and tuning procedures that may further improve the performance of the semi-active suspension.  
suspension system; quarter car model; active vs. semi-active vs. passive suspension; PID controller; comfort ride
## 5 Simulation Details
### Implementations of Wake Models  
In this paper, wake models are implemented in a CFD framework Open-Source Field Operation And Manipulation (OpenFOAM) [55] for developing customized numerical solvers and pre-/postprocessing utilities for fluid flow problems using the finite-volume method. It allows developing user-defined boundary conditions, turbulence models, and solvers, or adding source terms in a convenient way by extending or modifying existing models. As shown in Figure 11, we implement all the test turbulence models (the proposed, Laan, El-Askary, and AM) and develop a large time-step transient solver using the PIMPLE (merged PISO-SIMPLE) algorithm to simulate wakes of wind turbines under stable conditions. At the beginning of simulations; this solver first initializes the flow field based on boundary conditions. During this period, similarity functions and their derivatives are also evaluated and then used in turbulence models to estimate \(S_{k,\mathrm{ABL}}\) and \(C_{\mathrm{c3}}\) if required. This solver then solves the continuity, momentum and energy equations based on the eddy viscosity \(\mu\) provided by the turbulence models before in iterations the finish of the simulation. During the iteration, source terms \(S_{u}\) and \(S_{c,\mathrm{wake}}\) are modeled based on local flow information via user-specified finite volume options (fvOptions). In simulations, we apply a second-order backward difference scheme for temporal discretization and a second-order central difference scheme for spatial discretization of other derivative terms.
## 3 Results and Discussion  
### Infrared Spectrum  
The infrared spectral analysis allows the determination of whether the final synthesized substance contains characteristic functional groups. The infrared spectrum of the oil-based nanoplugger poly (MMA-BA-ST) is shown in Figure 1. In Figure 1, the -OH stretching vibration peak of liquid water appears at 3355 cm\({}^{-1}\), which is because the solvent of the synthesized poly (MMA-BA-ST) is ultrapure water. The stretching vibration peak of the benzene ring skeleton appears at 1639 cm \({}^{-1}\), and the external deformation vibrational peak of C-H on the benzene ring appears at 690 cm\({}^{-1}\), proving the presence of the benzene ring in the structure, which is mainly provided by styrene. The structure contains ester group C=O, its stretching vibration peak is near 1720 cm\({}^{-1}\), covered by the peak at 1639 cm\({}^{-1}\), and the ester group is mainly provided by methyl methacrylate and butyl acrylate. The characteristic functional groups of poly (MMA-BA-ST) can be tentatively determined by IR spectroscopy. The benzene ring enhances the temperature resistance of poly (MMA-BA-ST), and the ester group enhances lipophilicity.
## 1 Introduction
In onshore CAES systems, compressed air is generally stored in a constant volume, thereby contributing to fluctuating pressure and temperature in charging and discharging processes and the obvious off-design operations of compression plants, heat exchangers, and expansion plants [19]. Either throttling or sliding pressure operation is needed, which pulls down the round-trip energy efficiency. In contrast, the isobaric storage of compressed air can be achieved in UWCAES systems by taking advantage of hydrostatic pressure in deep water. This allows the system to be steadily operated at designed points and the throttling and sliding pressure operation are avoided, thereby contributing to a higher round-trip energy efficiency. On the other hand, many barriers hinder the development of UWCAES, such as the harsh marine environment, complex and expensive underwater systems, and lagging offshore renewable energy technologies. Overcoming these challenges would make UWCAES a promising solution for flexible-scale energy storage for coastal cities, islands, offshore platforms, offshore renewable energy farms, etc. Natural gas and hydrogen will play more important roles in the future energy landscape. Due to the similar physical properties of air, hydrogen, and natural gas, they can be stored in similar ways: small-scale artificial pressure vessels in the high-pressure gaseous state, thermally insulated containers in the liquid state, and large-scale underground caverns in the high-pressure gaseous state. Beyond this, natural gas and hydrogen possess much higher volume exergy density than compressed air with a ratio of about 70:20:1 [20]. Thus, in recent years, UWCAES has been expanded to underwater compressed gas (air, hydrogen, natural gas, carbon dioxide, etc.) energy storage (UWCGES) [21; 22]. Overall, UWCGES is still embryonic and faces many uncertainties. In this review, the latest studies and industrial progress of UWCGES in recent years are updated. Challenges are summarized and discussed. Finally, possible application modes and perspectives are presented.
# Ontology-Based Context Event Representation, Reasoning, and Enhancing in Academic Environments
## 3 Event-Driven Approach Based on an Ontology Model
### Event Enhancing  
An enrichment process is presented to extend the information about events with an inference process and then enhance the model. Figure 6 shows how the enrichment process is performed to return the inferred information to our ontological model for academic events.  
The enrichment process is used for inferring the corresponding physical place where an event has happened. Event enhancing is implemented by using SWRL rules since they allow it to express the inferences in terms of OWL concepts. It generates new knowledge in the ontological model.  
This process is carried out for the following purpose. If an event was detected by a node and that node is located in a certain physical place, then the event happened in that location. It is implemented with the SWRL rule, where the consequence part is inferred from the head part as follows.  
\begin{tabular}{l} AcademicEvent (?event) \(\land\) \\ PhysicialSpace (?Space) \(\land\) \\ Node(?node) \(\land\) isDetectedBy (?event,?node) \(\land\) \\ nodeIsLocatedIn (?node,?space) \(\rightarrow\) \\ happensIn (?event,?space) \\ \end{tabular}  
Figure 6: Enrichment process for event ontological model.  
The rule presented above associates the happensIn property with all OWL individuals of events that were detected by a node and assigns its name of physical spaces to those events. Therefore, it is used to discover new knowledge for academic events, which is returned to the ontological model to enrich it.
# Regeneration Performance of Activated Carbon for Desulfurization
## 2 Materials and Methods
### Recycle of AC  
Saturated AC was placed in a quartz boat and heated in a tube furnace at 420 \({}^{\circ}\)C for 1.5 h, under a gas flow rate of 1.65 L/min. The AC was weighed after cooling to room temperature. After that, the regenerated adsorbent was treated to remove SO\({}_{2}\) cyclically under the suitable experimental conditions, while the cycles were carried out ten times. The regenerated samples were marked as Reg-ACn, where n represents the number of cycles. After each regeneration, the weights of the adsorbent were recorded, and the regeneration loss and sulfur capacity calculated. The samples were characterized by several methods, and the desulfurization efficiency and adsorption capacity obtained. The adsorption capacity can be calculated as follows:  
\[q_{m}\ =F/m\left(\mathrm{C_{i}t-\int\limits_{0}^{t}C_{o}dt}\right) \tag{5}\]  
where \(q_{m}\) is the adsorption capacity of SO\({}_{2}\) (mg/g); F is the flue gas rate (L/min); Ci represents the SO\({}_{2}\) inlet concentration, and Co the SO\({}_{2}\) outlet concentration (ppm); m is the weight of AC (g); t is the time of removal of SO\({}_{2}\) (min).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**AC Dosage/g** & **Flue Gas Rate/L-min\({}^{-1}\)** & **Oxygen Content/\%** \\ \hline
2 & 1.65 & 5 \\
4 & 1.65 & 5 \\
6 & 1.65 & 5 \\
8 & 1.65 & 5 \\
10 & 1.65 & 5 \\
4 & 1 & 5 \\
4 & 2 & 5 \\
4 & 4 & 5 \\
4 & 1.65 & 0 \\
4 & 1.65 & 2.5 \\
4 & 1.65 & 5 \\
4 & 1.65 & 7.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental conditions of SO\({}_{2}\) adsorption.  
Figure 1: Schematic of the experimental apparatus.