esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The majority of the aforementioned algorithms only take into account single-channel scenarios; therefore, more research into multichannel control algorithms is necessary as control requirements increase. Although there has been some progress in the current research on active vibration control and some reports on the configuration method of sensors/actuators, the majority of the current research explores straightforward structures such as flat plates and cylinder shells with the primary goal of noise reduction [25; 26; 27; 28; 29]. The setup of active control sensors/actuators for sound quality is highly complex for the vibration mode and active control of the sound quality of irregular and complex structures in large spaces, such as the cab of commercial vehicles, due to the combination and allocation of modal vibration frequency and energy. Hence, the existing body of research in this area is comparatively sparse. This paper proposes an improved observability/controllability criterion for multimodal composite control using the cab body structure of commercial vehicles as an example, taking into account the characteristics of irregular large-space structures, based on structural vibration modal characteristics and the piezoelectric control principle. It also establishes a particle swarm optimization algorithm with a multimodal surface velocity vector as the core parameter to extract the position of the cab. An enhanced linear adaptive particle swarm optimization technique is suggested in order to satisfy the convergence and accuracy requirements of complicated structures.
# An Intelligent Condition Monitoring Approach for
## 3 Condition Monitoring Approach for Shearing Machines
### Training and Recognizing Process of the Hybrid HMM-ANN Model
#### Recognizing Process of the Hybrid Model  
The recognizing process of the hybrid HMM-ANN model is as follows:  
1. Construction of input vectors for ANN. The samples to be identified are preprocessed and feature extracted with the same method as used for training samples. Consequently, observed vectors can be obtained. Next, use the feature vectors as input to calculate the output probabilities of the samples to be identified for all trained HMMs. The input vectors of ANN are constructed with these probability values.
2. Probability prediction of samples to be identified for each health state. Using the input vectors to produce the outputs of the trained ANN model. As a result, the predicted probabilities of the samples to be identified for each health state are obtained.
3. Classification decision. Finally, considering the highest likehood, the health states of the samples to be identified are determined.
# Modeling of Vessel Traffic Flow for Waterway Design-Port of Swinoujscie Case Study
## 3 Results
### Regression Analysis of Traffic Flow Parameters
#### 3.3.1 The Model Including Drought T
As the draft increased, the standard deviation increased accordingly. These results confirmed the specificity of passenger ferry traffic in the selected area, and further analysis is required. The model is valid for PAS ships with \(T\geq\) 4 m.
# New Method to Estimate Rock Mass Deformation Modulus Based on BQ System
## 3 Engineering and Geological Background, Test Methods, and Results
### Zhen'an Pumped Storage Power Station  
The Zhen'an pumped storage power station is located in Yuehe Township, Shangluo City, Shaanxi Province, about 128 km away from Xi'an. The seismic intensity in the dam site area is level VI. Its key components include an upper reservoir, lower reservoir, water conveyance system, underground powerhouse, switch station, and other facilities. The lower reservoir, situated in the main channel of the Moon River, features a maximum dam height of 94 m and a normal water level of elevation 944 m. On the other hand, the upper reservoir, nestled in the Jinpen valley on the right bank of the Moon River, boasts a maximum dam height of 120 m and a normal water level of elevation 1386 m. The barrage is constructed as a concrete-faced rock-fill dam.  
The dam site of the upper reservoir primarily rests upon Middle Devonian strata (marble and crystallized limestone) and Indosinian intrusive granodiorite. The rock mass exhibits 3-4 sets of rock joints across all types, with the bedding plane of crystallized limestone featuring a low-angle dip, while others exhibit medium or high dip angles. Nearly all of the rock joints are straight and closed.  
The lower reservoir dam site is mainly composed of Indosinian intrusive granodiorite. Its joints have very similar characteristics to those in the upper reservoir rocks.  
A total of 24 in situ plate loading rock mass deformation tests were carried out in two types of rock: marble and granodiorite. Ten were in slightly weathered rock, and fourteen in moderately weathered rock. Table 7 lists the results of the tests.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Test Number** & **Test Position** & **Rock Types** & **Degree of** & **Deformation** & **Loading** & **BQ Value** \\ \hline E35-1-94-3 & PD35-1 (40.1 m) & moderately & 5.87 & vertical & 477 \\ E24-2-94-7 & PD24-2 (3.5 m) & intensely & 2.62 & vertical & 284 \\ E8-3-1 & PD8-3 (15.0 m) & moderately & 8.34 & horizon & 341 \\ E6-1 & PD6 (41.25 m) & moderately & 3.23 & vertical & 424 \\ E35-1-2 & PD35-1 (7.0 m) & slightly & 24.05 & & 423 \\ E5-2 & PD5-2(55.0 m) & moderately & 15.96 & & 441 \\ E8-3-2 & PD8-3 (30.0 m) & & 17.86 & horizon & 438 \\ E31-1 & PD31 (48.5 m) & & 22.4 & & 581 \\ E6-2 & PD6 (89.3 m) & & slightly & 10.72 & & 583 \\ E6-3 & PD6 (97.0 m) & & 48.99 & vertical & 573 \\ E32-2 & PD32 (40.0 m) & moderately & 6.05 & & 476 \\ E5-3 & PD5-2 (55.0 m) & & slightly & 45.30 & horizon & 573 \\ E31-2 & PD31 (70.0 m) & & 19.23 & vertical & 588 \\ E28-1 & PD28 (30.0 m) & moderately & 1.75 & & 356 \\ E7-2-1 & PD7-2 (40.5 m) & slightly & 14.24 & horizon & 549 \\ E28-94-10 & PD28 (47.2 m) & moderately & 0.9 & vertical & 380 \\ \hline \end{tabular}
\end{table}
Table 6: Deformation modulus of rock mass at Lasiwa power station.
## 4 Discussion  
Based on the obtained results (Figures 9-11), it can be concluded that the addition of SiO\({}_{2}\) (S1 series) and Al\({}_{2}\)O\({}_{3}\) (S3 series) nanoparticles increased the creep coefficient \(C_{IT}\) and the addition of TiO\({}_{2}\) nanoparticles (S2 series) reduced the creep coefficient \(C_{IT}\) in comparison to the reference series S0, which did not contain nanoparticles. It should be noted, however, that the obtained average values of the creep coefficient \(\mu_{CIT}\) for the series S1, S2, and S3 were in each case within the range of the standard deviation \(\sigma c_{IT}\) that was obtained for the series S0.  
Moreover, the difference between the mean values of the creep coefficient \(\mu_{CIT}\) for the tested series was not more than 6.7%. Therefore, it can be said that the addition of nanoparticles did not significantly affect the creep of the cement matrix of self-compacting concretes. This is a surprising result with regard to the hardness test results that were obtained for the same series of concrete by [PERSON] et al. (2012). In this paper, the authors stated that the addition of nanoparticles caused an increase in the hardness of a cement matrix, which was measured at the micro-scale. However, it should be noted that the two tests performed in that study were focused on two non-identical properties of hardened cement matrix (creep and hardness), and the parameters of the tests differed qualitatively.  
When considering the above research results, it can be said that the impact of nanoparticles on the rheological parameters of a cement matrix is insignificant. In addition, when assuming that nanoparticles do not significantly affect the interfacial transition zone ITZ between the cement matrix and the aggregate (the analysis of the ITZ of the composite was conducted in [24]), it can be concluded that nanoparticles also have a slight effect on the rheological parameters of hardened composites based on a cement matrix, such as self-compacting concrete.  
Nevertheless, it is worth emphasizing that the results provided in this article improve the state of knowledge concerning the impact of nanoparticles on the physical and mechanical properties of both hardened self-compacting concrete and its cement matrix.  
It is worth considering further directions of research regarding the impact of nanoparticles on the properties of cement matrices. Interference at the micro, or even nano level, in the structure of cement composites can be an important issue that allows concrete to be modified for a specific purpose.
## 2 Design Method, Study Case, Objectives and Assumptions
### Specific Objectives and Assumptions  
Two objectives were pursued in setting up the PHPV; these are the minimization of the relative error and the minimization of the Root Mean Square Error (RMSE). Two objectives were also targeted for the HEBP; the first was to maximize the number of active WSs. The second objective was to increase the quantity of data transferred by each needy WS. To achieve these results, the following reasonable assumptions are considered throughout this work.  
* Each WS in the network is equipped with a vibratory energy harvesting system and a wireless energy transfer/reception system [36].
* Data storage memory is assumed to be sufficient [7].
* The shaping of the energy harvested from the vibrations is not processed; it will be assumed that the WSs are equipped with maximum power point tracking [37].
* Linear behavior of piezoelectric transducers is assumed given the low vibration levels [38].
* The time division multiple access is considered to avoid interference when accessing the common radio channel [39].
* The gain of the channel between a needy WS and a self-sufficient WS is assumed to be unity.
* For the transfer of energy between the WS, time switching is considered; that is, the needy WS receives power for a fraction of time \(\tau T\) and transfers data during the other fraction of time (\(1-\tau\)) \(T\). \(T\) being the duration of a measurement cycle [40].
* QoS is characterized by the number of active WSs and the amount of data transferred to the BS.
## 1 Introduction
In contrast to previous works, we do not use contrastive learning for large-scale model pretraining; rather, we formulate a joint task and contrastive objective during regular model training. Our key contributions can be summarized as follows:  
* We present an overview of the data-driven approaches in the literature geared towards OSH applications. We summarize the currently studied applications and datasets and present an overview of the current trends. * To our knowledge, most of the works on the data-driven analysis of OSH observations are based on text-based analysis. The multimodal analysis of textual and visual observations is a novel and not widely explored field. In this paper, we demonstrate that the inclusion of on-site photographs can provide complementary information to the observation description that can significantly improve performance of data-driven OSH applications. * We construct and curate the Safety4 All dataset, comprising of 5344 safety observations, gathered from 86 safety officers (SO) across 486 sites. The observations consist of pairs of on-site photographs and issue descriptions, accompanied with relevant metadata. We present an extensive analysis on this dataset and define three tasks for the automatic analysis of OSH observations, i.e., (a) priority classification of observations, (b) observation assessment and (c) observation categorization. * We perform extensive ablations regarding the proposed shared-space fusion approach, using all combinations of two state-of-the-art text and visual encoders, and compare it to a simple late-fusion baseline. Our analysis shows that shared-space representations extracted with the use of the joint loss yield a superior performance to the representations extracted through the baseline fusion approach for all experimental settings. * We train and publish GR-Electra, a large pretrained language model for the Greek language based on the ELECTRA architecture [18], created for the analysis of the issue descriptions. GR-Electra yields comparable performance to GreekBERT [19], given less training data, due to the aggressive cleaning of the pretraining corpus. * Code for all trained models will be made open source ([[https://gitlab.com/ilsp-spmd-all/public/s4a-models-public](https://gitlab.com/ilsp-spmd-all/public/s4a-models-public)]([https://gitlab.com/ilsp-spmd-all/public/s4a-models-public](https://gitlab.com/ilsp-spmd-all/public/s4a-models-public)), accessed on 31 May 2022).
## 2 Proposed Method
### Missing Value Imputation
#### 2.2.2 WGAN  
The GAN model is inspired by the two-player zero-sum game [26]. The typical structure of the GAN includes a discriminator and a generator. The goal of the discriminator is to correctly distinguish between actual data sampled from the input data set and fake data generated by the generator. The purpose of the generator is to produce fake data that can deceive the discriminator. GAN is trained by alternating adversarial learning between  
Figure 3: Schematic of the GRUD.  
discriminator and generator, and the optimum objective is to achieve Nash equilibrium. Finally, the generator can accurately estimate the distribution of data samples. However, the original GAN has some problems, such as training difficulty and mode collapse. Therefore, [PERSON] et al. proposed the WGAN model, which adopts the Wasserstein distance instead of the Jensen-Shannon divergence to indicate the difference between the actual samples and the generated samples [32]. Specifically, the Wasserstein distance of the WGAN can be simplistically expressed as:  
\[W=max(E[f(x_{r})]-E[f(x_{g})]) \tag{10}\]  
where \(E[\ ]\) represents the mathematical expectation, \(x_{r}\) and \(x_{g}\) represent samples of the real data and the generated data, respectively, and \(f()\) indicates a neural network model for which the last layer is not a nonlinear activation layer.  
The introduction of the Wasserstein distance solves the problem of gradient extinction. To minimize the Wasserstein distance, the loss function of the generator \(G()\) and the discriminator \(D()\) of WGAN can be expressed as:  
\[L_{G}=-D(G(x)) \tag{11}\]  
\[L_{D}=D(G(x))-D(x_{r}) \tag{12}\]  
In addition, to satisfy the Lipschitz continuity condition, the parameters of the discriminator network need to be clipped to \([-c,c]\). \(c\) is a fixed constant, and its value does not affect the direction of the gradient.
## 3 Experimental Study  
This section aims to demonstrate the effectiveness of the feature engineering approach and 1D-CNN-LSTM hybrid neural network for RUL prediction. Experiments were conducted on the subset FD001 of the C-MAPSS datasets injected with additive noise. In model training, the MSE of the cross-validation set was used to evaluate the performance of the trained neural networks. Tests were conducted on a separate dataset to analyze the results using the two evaluation metrics, i.e., the RMSE and score function. Also, prediction results were compared with similar works of other researchers.
## 3 Results and Discussion
### Electrochemical Measurement Results of CDPC Electrode
Figure 5: (**a**) CV of CDPC electrode with different scanning rates (20–100 mV/S); (**b**) CV of CDPC after 10, 30, and 50 cycles and 100 mV/s scan rate; and (**c**) CV of CDPC after 1, 500, and 1000 cycles and 100 mV/s scan rate. The CDPC electrode discharge was measured by applying a constant current with a change in current density of 0.1, 0.2, and 0.3 A/g for 120 s, with voltage in the range from 0.20 V to 0.45 V (Figure 6a,b). As depicted in Figure 6a, the charge and discharge profiles manifest as symmetrical triangles, reflecting a high degree of symmetry. Notably, no discernible voltage drop occurs at elevated current densities, implying the material's robust electrochemical characteristics even under substantial discharge currents. Here, energy storage within the electrode primarily occurs through the formation of charged double layers via physical adsorption. This observation is in consonance with the findings deduced from the CV curve analyses. Figure 6b further illustrates that at the lowest current density (0.1 A/g) and the highest current density (0.3 A/g), the specific capacitance of the carbon-derived material is measured at 236 F/g and 156 F/g, respectively. This finding indicates a decline in specific capacitance in response to increasing current density, thus providing insights into the material's responsiveness to various current regimes. Table 2 demonstrates that the specific capacitance values align well with previous studies under equivalent conditions regarding electrolyte composition and charge/discharge current density. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Materials**} & \multirow{2}{*}{**Electrolyte**} & **Charge/Discharge** & **Specific** & \multirow{2}{*}{**Reference**} \\  & & **Current Density (A/g)** & & **Capacitance (F/g)** & \\ \hline Nanoporous gold & [EMIM]BF4 & 0.14 & 79.1 & [34] \\ \hline NiO nanosheet assemblies & 3 M KOH & 0.5 & 81.67 & [35] \\ \hline Active carbon (COS) & 1 M H2 SO4 & 0.2 & 374 & \\  & 6 M KOH & 0.2 & 266 & [36] \\ \hline Porous carbon nanoparticles (PCNs) & 6 M KOH & 0.06 & 309 & [37] \\ \hline Zn-HSC & 1M Zn(CF3 SO3)2 & 0.1 & 170 & [38] \\ \hline Nitrogen-doped graphene & 6 M KOH & 0.5 & 197 & [39] \\ \hline Graphene nanocapsules (from coal tar & & & \\ pitch using a nano-ZnO-template & & & \\ strategy coupled with in situ KOH & & & \\ activation) (GNCs) & & & \\ \hline Coal-derived porous & 6 M KOH & 1 & 170 & [41] \\ carbon fibers (CPCFs) & & & \\ \hline CDPC & 6 M KOH & 0.1 & 236 & This work \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparing specific capacitance with prior studies. Figure 6: **(a)** Galvanostatic charge–discharge curve of CDPC electrode in 6 M KOH at different current densities (0.1, 0.2, and 0.3 A/g) and (**b**) specific capacitance of CDPC plotted against current density.
# A Multi-Channel Uncertainty-Aware Multi-Resolution Network for MR to CT Synthesis
## 2 Methods
### Modelling Heteroscedastic Uncertainty  
Previous works on MR to CT synthesis have shown that residual errors are not homogeneously distributed throughout the entire image. It can be observed that they are largely concentrated around organ/tissue boundaries. Thus, the most suitable aleatoric uncertainty model for this task is a heteroscedastic one, which assumes that the data-dependent, or intrinsic, uncertainty is variable. Firstly, we model our task likelihood as a normal distribution with mean \(\mathbf{f^{W}}(\mathbf{x})\), the model output corresponding to the input \(\mathbf{x}\), parameterised by weights \(\mathbf{W}\), and voxel-wise standard deviation \(\sigma^{\mathbf{W}}(\mathbf{x})\), the data intrinsic noise can be described as:  
\[p(\mathbf{y}|\mathbf{f^{W}}(\mathbf{x}))=\mathcal{N}(\mathbf{f^{W}}(\mathbf{x}),\sigma^{\mathbf{W}}( \mathbf{x})). \tag{1}\]  
Figure 1: MultiRes\({}_{unc}\) network architecture. \(T_{1}\) and \(T_{2}\) magnetic resonance (MR) patches from the same subject are fed into each independent instance of the HighRes3 DNet architecture [20] at various levels of resolution and field of view. A downsampling operation followed by a central crop at every level ensures the patch size is maintained while obtaining patches with different fields of view and resolution. Lower level feature maps are concatenated to those at the next level until the full resolution level, where these concatenated feature maps are passed through two branches consisting of a series of \(1\times 1\times N\) convolutional layers: one resulting in a high-resolution synthesised pseudo computed tomography (pCT) patch and the other in the corresponding voxel-wise heteroscedastic uncertainty.  
We then derive our loss function by calculating the negative log of the likelihood:  
\[\begin{split}\mathcal{L}(\mathbf{y},\mathbf{x};\mathbf{W})&=-\text{ log}\,p(\mathbf{y}|\mathbf{f}^{\mathbf{W}}(\mathbf{x}))\\ &\approx\frac{1}{2\sigma^{\mathbf{W}}(\mathbf{x})^{2}}\Big{(}\mathbf{y}-\mathbf{f }^{\mathbf{W}}(\mathbf{x})\Big{)}^{2}+\text{log}\,\sigma^{\mathbf{W}}(\mathbf{x})\\ &=\frac{1}{2\sigma^{\mathbf{W}}(\mathbf{x})}\mathcal{L}_{2}(\mathbf{y},\mathbf{f }^{\mathbf{W}}(\mathbf{x}))+\text{log}\,\sigma^{\mathbf{W}}(\mathbf{x}).\end{split} \tag{2}\]  
In regions where a high \(\mathcal{L}_{2}\) error is observed, the uncertainty should compensate while also increasing. The second term in the loss function prevents the network from collapsing to the trivial solution where a large uncertainty is assigned everywhere.
## 2 Modelling and Methods
### ANN Development Protocol
#### 2.5.2 Input Data Preprocessing  
Although data preprocessing does not appear explicitly as a separate step in the ANN development protocol, it was considered necessary to be mentioned here; it did form an integral part of the work method followed for this study. From the input data presentation, it became apparent that categorical variables have also been included in the dataset (i.e., insulation type, insulation position, etc.). These variables were encoded to ensure that, ultimately, the input offered to the ANN was consistently numerical. To prevent the negative effect of a \"dummy trap\" [33], some of the resulting encoded numerical variables were removed.  
Acknowledging that the range of the variable values was quite wide, taking values from 1 for the encoded variables to 21,600 sec for the timestamps, a scaling of the data was considered appropriate [34]. To prevent an implicit bias of the algorithm towards the higher values of the dataset, standardisation was applied throughout the dataset values. The default values of the \"StandardScaler\" function from the library sklearn.preprocessing were utilised [35]. Equation (6) describes the scaling method followed for both the input and output data. Once the predictions were made, the same scaling tool was used to reverse the scaling and allow for the inspection of the actual predicted temperature figures generated by the algorithm.  
\[z=\frac{(x-u)}{s} \tag{6}\]  
where \(z\) is the standard score of the sample being converted, \(x\) is the feature being converted, \(u\) is the mean, and \(s\) is the standard deviation.
## 3 Results and Discussion  
### Determination of the Significant Factors Affecting the Extraction Process  
The optimization of the process of bioactive compound extraction from spent coffee grounds (SCG) was performed in two stages. In the primary stage, the influence of several extraction parameters, namely, organic solvent concentration, solvent to matrix ratio, extraction time, ultrasound assistance and solvent type, on antioxidant activity and total phenolics content was evaluated. The results obtained in each of the runs are presented in Table 3. Ethanol, methanol and their hydroalcoholic mixtures were used at this stage. These solvents should ensure high levels of bioactive compound recovery from a plant-based matrix [35]. The highest level (above 100 mg Trolox/g SCG d.m.) of antioxidant activity was observed for the hydromethanolic extract in run 10. On the other hand, the hydroethanolic extract obtained in run 1 had the highest total phenolic content. Pareto charts are a type of bar chart for monitoring the effects of the used operational extraction variables. This chart allows researchers to verify the significance of each tested extraction process variable and to monitor the influence on the used response analysis. Each bar represents the standardized effect of the examined term. The bars passing the vertical line are statistically significant at a 95% confidence level. It can be observed from Figure 1 that the extraction process of antioxidant bioactive compounds was affected by organic solvent concentration, SMR and the application of ultrasound. The remaining variables, namely, extraction time and solvent type, proved to be non-significant and were eliminated from the model.