esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Seismic Analysis and Discussions
### Comparison with Test Results  
Figures 19-21 present the comparison results of the response spectrum obtained from tests and analyses for individual beam structures on the shaking table and the dummy mass. As shown in the figures, the calculated response spectrum from the seismic analyses is in good agreement with those of the tests. Overall, it can be seen that the used two analysis models of LRB give very similar results but in the case of the equivalent stiffness-damping model, the value of critical damping ratio used in the analysis is 30%, which is slightly higher than the 24.1% obtained from the quasi-static tests in Table 1.  
Figure 18: Seismic analysis model: (**a**) Schematics of the test mockup; (**b**) Finite element model.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & & & \multicolumn{6}{c}{**LRB Models**} \\ \cline{6-10}
**Structure** & & & & & & & & **Equiv.** & \\
**ID** & & & & & & & & **Stiffness-Damping** & \\ \cline{2-10}  & \(L\) **(mm)** & \(I_{\text{zz}}\) **(m\({}^{\ddagger}\))** & **ALPHA** & **BETA** & \(K_{\text{e}}\) **(kN/m)** & **(kN/m)** & **Q\({}_{\ddagger}\) (kN)** & **Keq** & **(kN/m)** & **\(\xi\) (\%)** \\ \hline S1 & 47.0 & 0.84E-11 & 0.863 & 1.349E-4 & 187.6 & 122 & 2.7 & 199.3 & 30 \\ S2 & 42.2 & 2.00E-11 & 3.247 & 4.569E-5 & 187.6 & 122 & 2.7 & 199.3 & 30 \\ S3 & 34.6 & 6.75E-11 & 1.626 & 2.059E-4 & 187.8 & 122 & 2.7 & 199.3 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Analysis input parameters for finite element modeling.  
Figure 22 presents the results for the LRB shear displacement responses. In this figure, Figure 22 shows comparison results of the maximum values between test and analysis. As shown in the figure, the maximum shear displacements of LRB in the analysis are 35.6 mm  
Figure 19: Comparison of response spectrum between test and analysis for S1 structure: (**a**) Non-isolated; (**b**) Seismically isolated.  
Figure 20: Comparison of response spectrum between test and analysis for S2 structure: (**a**) Non-isolated; (**b**) Seismically isolated.  
for the Bi-linear model and 37.3 mm for the equivalent stiffness-damping model. These results were slightly less than the 38.6 mm of the test result but were in good agreement with the 35 mm of the design target value presented in Table 1. Figure 22b shows the analysis result of the hysteretic behavior of LRB with the shear displacements vs. restoring forces. From the results, it could be confirmed that the Bi-linear characteristics of LRB identified from previous quasi-static tests can be used for the seismic analysis.
## 2 Materials and Methods  
### Conjugation of LHRH Peptides to MNPs  
PEG-coated magnetite nanoparticles (MNPs) were purchased from Ocean NanoTech, LLC (San Diego, CA, USA). The MNPs consisted of an Fe\({}_{3}\)O\({}_{4}\) core, a monolayer of oleic acid, and a polyethylene glycol (PEG) coating. The surfaces of MNPs were functionalized with amine groups (180 groups per nanoparticle, private communication, 2016) allowing for further conjugation. These MNPs were dispersed in deionized (DI) water with a concentration of 1 mg Fe\(\cdot\)mL\({}^{-1}\). Luteinizing hormone-releasing hormone (LHRH) peptides (Bachem, Bubendorf, Switzerland) were conjugated to MNPs using glutaraldehyde crosslinking chemistry, according to a previously established protocol [25; 26]. The sequence of LHRH is Pyr-His-Trp-Ser-Tyr-D-Trp-Leu-Arg-Pro-Gly-NH2.
# Multi-Objective Service Placement Scheme Based on Fuzzy-AHP System for Distributed Cloud Computing
## 2 Related Work
### Service Placement Metric  
The SP scheme helps reduce energy consumption and maximizes performance in CDCs. As shown in Table 2, we categorize related works according to the objective and metric.  
Figure 2: Approaches for decision-making.  
We consider multi-metrics to find a good balance between metrics to minimize the trade-off. The performance degradations can increase the execution time, which, in turn, decreases the energy savings in order to define the balance between them.  
We further summarize and compare the SP scheme based on the decision method described in Table 2. Most of these SP schemes attempt to influence decision making when researchers try to include one or two metrics such as resource usage, response time, and cost. To ensure that their SP meets the specific performance requirements, the algorithms must be improved to be more flexible. The SP process introduces various trade-offs, such as that between delay and cost. However, it is difficult to develop an efficient, high-performance SP approach that is energy-efficient and optimizes the balance between allocated resource and SP overhead.  
There are many approaches for SP. Although these approaches address important and distinct aspects, to the best of our knowledge, each is insufficient for a scheme that applied current requirements considering conflicting goals.  
To find a good balance between metrics, in this paper, we categorize metrics according to the objective: performance, energy efficiency, and cost.  
The explanation for objectives is given in the following three sub sections.
## 4 Results
### Validation  
Testing the results, we assessed the importance score using SPSS 25 software for the analysis of significant factors. Presented in Table 7 are the normalized score of importance which predicted similar findings. The most important factor was Attitude among women which was followed by Perceived Risk, Subjective Norm, and Perceived Behavioral Control. Setting a threshold of 90% for the normalized score of importance, similar findings could be posited from the MLAs. For the succeeding factors, Self-Efficacy, followed by Perceived Vulnerability, Understanding of Safety, Buying Impulse, and Perceived Safety significantly affected purchasing intentions. A relatively high significance was seen among later latent variables, but the named factors still scored higher which posits that PMT and TPB factors  
Figure 4: Deep Learning Neural Network Model.  
Figure 5: Training and Validation Loss Rate.  
prompted the influential factors affecting purchasing intention of self-defense tools among women.  
Furthermore, the MLAs were tested for accuracy rates and output using the Taylor Diagram. With this analysis, the correlation, root mean square error, and standard deviations were related with a threshold of 90%, 20%, and 1.00 as acceptable results [26; 27]. Presented in Figure 6 is the Taylor Diagram which presents acceptable results appealing to the threshold set by related studies. In addition, the basic decision tree output was not considered because the resulting value was 0.00 which indicates that it was not a reliable classification model. Low significances were seen on factors such as buying impulse and perceived safety as their correlation results were below 90% but were still significant as their standard deviations were less than 1.
## 4 Discussion
Surprisingly, our clinical experience indicates a relatively good incidence of IZC mini-screws survival, even in the absence of bone of sufficient thickness (Figure 7 presents a CBCT scan of IZC mini-screw application in the case of small bone depth). Some authors attribute this high success rate to the penetration through two cortical layers, which guarantees more than 1 mm of the cortical bone and improves the primary stability of mini-implants [5; 7]. Nonetheless, insufficient bone depth might be attributed to the increased risk of maxillary sinus membrane rupture. As stated in the literature, sinus invasion during IZC mini-screws placement is relatively common. Although according to the Chinese study, 78% of mini-implants penetrated into the maxillary sinus [5], irritation of the Schneiderian membrane measured as the increase in its thickness was significant only when mini-implants penetrated into the sinus deeper than 1 mm. Minor, uncomplicated injuries of the maxillary sinus by miniscrews may regenerate spontaneously and orthodontic treatment interruption and mini-implant removal are not recommended [8; 21]. Although the findings of this study shed some light on IZC mini-screw placement, they have to be seen in light of certain limitations. One of them is the limited sample size coming from one center, so the results of the present research should not be extrapolated to the entire Polish population. Another limitation concerns the 380 \(\upmu\)m voxel size--a smaller voxel size might provide even more precise measurements.
## 6 Discussion
### Proposed RNN/LSTM-Based Models  
Contrary to CNN models, very little automated sleep stage classification studies were done using RNN/LSTM models. The best performance was observed in a study by [PERSON] et al. [66] back in 2013, wherein a 4-layer RNN model was used. They had adopted the structure of an Elman network and had successfully classified various sleep stages with an overall accuracy score of 87.2%, as seen in Figure 12. A similar accuracy of 86.7% was achieved by [PERSON] et al. [67], where a cascaded RNN network was proposed, with 2 LSTM units. Two other studies explored a mixture of signals to train RNN-based models: [PERSON] et al. [83] proposed a mixed neural network where a RNN model was substituted for a multi-layer perception and an LSTM model was exchanged for an RNN model. The proposed final model achieved accuracies of 85.9% and 83.4% using F4-EOG and Fp2-EOG inputs, respectively. Both F4 and Fp2 were single channel EEG signals recorded at different electrode placements. Hence, F4-EOG and Fp2-EOG inputs were considered a mixture of signals (EEG + EOG). Subsequently, [PERSON] et al. [85] proposed an end-to-end hierarchical RNN model, known as SeqSleepNet, which consisted of an attention-based recurrent model and filter bank layers. Short-time Fourier transform was used to convert multiple PSG recordings (EEG, EOG, and EMG) into power spectra signals which were then fed to train the proposed model. The model achieved a high accuracy score of 87.1%, which was the highest amongst the RNN/LSTM-based models.
## 5 Experimental Results and Analysis
### The No-Load Condition with a Given 1 Rad Step Signal  
In Figure 15, it can be observed that when a 1 rad step signal is applied with the ADRC controller, the position aligns with the target curve at 0.1124 s. In contrast, the position reaches the desired curve at 0.0369 s when using the RBF-ADRC controller. The RBF-ADRC controller demonstrates enhanced speed and reduced chattering compared to the ADRC controller. Moreover, FRBF-ADRC is even more effective, achieving the desired curve at 0.0112 s with superior results.  
Figure 14: Block diagram of the hardware control system.  
Figure 13: Experimental platform.
## 5 Simulation Cases
### DFIG Participating in FR
In low WS region, \(\beta_{FRC}\) in Scenario 2 keeps at \(\beta_{2}=2.3^{o}\); in medium  
Figure 11: FR (frequency regulation) task of DFIG (double-fed induction aggregator). Figure 10: The Power output of the DFIG (double-fed induction generator) during FR (frequency regulation) process. and high WS regions, \(\beta_{FRC}\) will increase to keep power output at \(0.9P_{MPPT}(v_{w})\) and \(0.9P_{e}\), respectively. In Scenario 1, \(\beta\) changes to participate in both up-regulation and down-regulation. Figure 12: FR (frequency regulation) capacity of DFIG (double-fed induction generator). (**a**) DFIG’s FR capacity for up-regulation; (**b**) DFIG’s FR capacity for down-regulation. Figure 13: FR (frequency regulation) output of DFIG (double-fed induction generator). (**a**) DFIG’s output for FR; (**b**) DFIG’s output for up-regulation. The comparison of the FR effect in the two scenarios is shown in Figure 15. The root mean square (RMS) value of frequency deviation in Scenario 1 and Scenario 2 is 0.0346 Hz and 0.0386 Hz, respectively. It can be seen that the FR effect of DFIG participating in FR is obviously better than the situation without frequency support by DFIG, especially in medium and high WS regions. In low WS region, the FR effect is relatively worse as the FR output of DFIG is restrained by both the rotor speed protection and limit of FR capacity. In conclusion, the proposed FR model of DFIG can suppress system frequency deviation effectively under continuously varying WS.
# A Psychoacoustic-Based Multiple Audio Object Coding Approach via Intra-Object Sparsity
## 4 Conclusions  
In this paper, an efficiently encoding approach for multiple audio objects based on intra-object sparsity was presented. Unlike the existing STFT-based compression framework, statistical analysis validated that for the case of tonal solo instruments audio objects possess better energy concentration property in the MDCT domain so that MDCT is selected as basic transform in our encoding scheme. In order to achieve a balanced perceptual quality for all object signals, both psychoacoustic-based and energy balanced _NPTF_ allocation strategy algorithm is proposed for obtaining the optimal MDCT coefficients of each object. Moreover, SQVH is utilized to further encode downmix signal at variable bitrates. Objective and subjective evaluations shows that the proposed approach outperforms the existing intra-object based approach and achieves a more balanced perceptual quality when eight simultaneously occurring audio objects were encoded jointly. The results also confirmed that the proposed framework attained higher perceptual quality compared to SAOC. Further research could include the investigation of relative auditory masking threshold, in order to acquire a better perceptual quality amongst all objects.  
The authors would like to thank the reviewers for their helpful comments. This work has been supported by China Postdoctoral Science Foundation funded project (No. 2017M610731), the Project supported by Beijing Postdoctoral Research Foundation, \"Ri xin\" Training Programme Foundation for the Talents by Beijing University of Technology.  
[PERSON] and [PERSON] contributed equally in conceiving the whole proposed codec architecture, designing and performing the experiments, collecting and analyzing the data, and writing the paper. [PERSON] corrected some syntax mistake. [PERSON] critically reviewed and implemented final revisions. [PERSON] supervised all aspects of this research.  
The authors declare no conflict of interest.  
Figure 9: MUSHRA test results with 95% confidence intervals for the soundfield rendering using separate AAC encoding at 30 kbps, SAOC, SPA and our proposed approach at 105 kbps.
## 3 Results
### Analysis of Current Modular Design and Prosumer Methods
#### 3.1.1 Definition of the Modular Design and Prosumer Processes  
A diagram has been drawn up that defines in detail the process of modular design and product creation for a prosumer (Figure 1). Some models have been taken as a reference that divide a product's life cycle into various phases [23; 24; 25; 26; 27]. Specifically, the eight phases defined by [PERSON] [25; 27] have been used because they provide a greater precision within the process than the sub-phases of the general stages (preliminary design, production and consumption) defined by other authors [23; 24; 26]. Specific objectives of both design processes have been assigned to each phase. This allows us to differentiate between the use of the methods within each general phase based on identified objectives. Thereby, Figure 1 is the basis used to classify the modular design and prosumer methods according to the different phases of the product life cycle in the next sections. This can facilitate the combination of methods from different phases to create a model, but not all sequences are valid. The feasibility of each model must be analyzed since the diagram is only a first step.  
As can be seen in the modular design product life cycle (Figure 1), the phases with most design objectives are the detailed design and the manufacturing plan phases. We also found goals in the feasibility study, the consumption plan and the removal plan, while there are no objectives for the primary needs and the preliminary design. On the other hand, in the process of creating products for prosumers, we detected that there is a large variety of objectives and tools in all phases of the design process due to their ability to intervene at all levels. However, its presence is greater in the manufacture and transformation of the product. Specifically, in the pre-design phase, most objectives focus on studying the feasibility of developing a product in order to ensure its success rather than meeting the real needs of the prosumer. This is because these analyses seek to obtain an economic benefit rather than optimize the emotional and social aspect of the product.  
Figure 1: Phases of the product life cycle and the relationship to modular design and prosumer goals.
# Parallel Mechanism of Spectral Feature-Enhanced Maps in EEG-Based Cognitive Workload Classification
###### Abstract  
Electroencephalography (EEG) provides a non-invasive, portable and low-cost way to convert neural signals into electrical signals. Using EEG to monitor people's cognitive workload means a lot, especially for tasks demanding high attention. Before deep neural networks became a research hotspot, the use of spectrum information and the common spatial pattern algorithm (CSP) was the most popular method to classify EEG-based cognitive workloads. Recently, spectral maps have been combined with deep neural networks to achieve a final accuracy of 91.1% across four levels of cognitive workload. In this study, a parallel mechanism of spectral feature-enhanced maps is proposed which enhances the expression of structural information that may be compressed by inter- and intra-subject differences. A public dataset and milestone neural networks, such as AlexNet, VGGNet, ResNet, DenseNet are used to measure the effectiveness of this approach. As a result, the classification accuracy is improved from 91.10% to 93.71%.  
Keywords:cognitive workload; electroencephalography; spectral map; feature enhancement; neural network; mental workload +
Footnote †: journal:
# Object Detection with Low Capacity GPU Systems Using Improved Faster R-CNN
## 2 Related Works
### Deformable Convolutional Network  
The regular convolutional unit samples the input property map at fixed locations and generates the output by calculating the weighted sum of the samples. Recently, deformable convolution has been proposed to overcome the limitations of standard convolution (Figure 2).  
Regular convolution is operated on a regular grid \(R\). Deformable convolution is operated on R but with each point augmented by a learnable offset \(\Delta Pn\). Convolution is used to generate \(2N\) number of feature maps corresponding to \(N\) 2D offsets \(\Delta Pn\) (x-direction and y-direction for each offset).  
Regular convolution calculated as follows:  
\[y(p0)=\sum_{Pn\in\mathcal{R}}w(Pn)\cdot x(Po+Pn) \tag{1}\]  
Deformable convolution calculated as follows:  
\[y(p0)=\sum_{Pn\in\mathcal{R}}w(Pn)\cdot x(Po+Pn+\Delta Pn) \tag{2}\]  
\(Pn\) is used to sort the R positions, \(w\) and \(Po\) denote the weight and pre-specified offset for output location, respectively. In Equation (1), the output property map is computed for each location of position \(p0\) in \(y\). In addition to Equation (1), in Equation (2), offset \(\Delta Pn\) is taken into account.  
Figure 1: Faster R-CNN structure.  
As shown in Figure 3, deformable convolution selects values from different locations for standard convolution in the input image or property maps. As a result of this, the deformable convolution which is fixed to larger objects selects more receptive areas and exposes more features related to objects. This makes it easy to detect small objects in remote sensing images.
# Intent Classification and Slot Filling Model for In-Vehicle Services in Korean
## 2 Related Work  
### Task-Oriented Dialogue System  
In a task-oriented dialogue system, the ability to understand users' requests is important. Extracting the useful information from the request is processed with two tasks: Intent Classification and Slot Filling. Previous studies are generally divided into the following two approaches: Multi-task framework and using intent information to guide the slot-filling task. In a multi-task framework, they share helpful information between two tasks [21]. For this, one applies a deep bi-directional recurrent neural network (RNN) [22] with long short-term memory (LSTM) [23] and gated recurrent unit (GRU) to learn the representations shared by the tasks [24]. An attention-based neural network model is also used for joint intent detection and slot filling [25]. Another approach is that the model train two tasks jointly using a framework that incorporates intent information to guide slot filling [26; 27]. Another study directly uses intent information as input for the slot filling task to make the model perform the slot filling based on intent semantic knowledge [28]. Because the two tasks are closely related and one task affects the other, they are trained jointly in previous approaches [28; 29].  
In the study of the Korean task-oriented dialogue system, studies on intent classification and slot-filling tasks are also conducted. For the study in [30], Korean intent classification data provided by AI Hub ([[https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=102](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=102)]([https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=102](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=102)), accessed on 3 November 2022) are used for intent classification model that matches the most similar intent using semantic textual similarity. Data in constructed on a calendar domain for slot-filling task research in [31], and the highest probability slot and value are predicted by utilizing the LSTM model and applying two methods, greedy-search decoding, and beam-search decoding. Another builds the Korean-based restaurant reservation and recommended conversation system [19] with the dataset provided by the research [32]. It also proposes a joint model that can predict intents and slot filling using KoBERT simultaneously. In Korean research, task-oriented dialogue system data from various domains are opened, but the data on in-vehicle services is still limited. In particular, joint models on intent classification and slot filling for mobility services are insufficient compared to the studies on English regarding the numbers of studies.