esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Virtual Sensor Design
### Diagnostic Principles  
As it was already mentioned in the introductory part of this paper, the main objective was to develop virtual sensors providing:  
\[\hat{\mathbf{d}}_{k}=[\hat{f}_{x,f_{1}k},\hat{F}_{x,f_{r}},\hat{F}_{x,f_{1}},\hat {F}_{x,rr},\hat{T}]^{T}. \tag{68}\]  
Having the above estimates, the primary residual signal is formed:  
\[z_{T,k}=T_{k}-\hat{T}_{k}, \tag{69}\]  
which is used as a source of knowledge concerning the desired torque distribution within AGV. The remaining set of residuals concerns longitudinal forces. The general idea starts with defining the longitudinal slip ratio, which for all wheels is given by [17]:  
\[\sigma_{i,j}=\frac{\omega_{i,j}R_{e}}{v_{x}}-1. \tag{70}\]  
As can be observed in Figure 1, apart from the fact that the wheels are identical, they consist of a metal rim that is sealed with a thin rubber strip. Moreover, it is assumed that the AGV is operating on a level stiff surface. Thus, without loss of generality, it is possible to assume that \(\sigma_{i,j,k}=0\). This leads to the following relation:  
\[\omega_{i,j}=\frac{v_{x}}{R_{e}}. \tag{71}\]  
Since the actual \(v_{x}\) of AGV is available, it is possible to use (71) to calculate desired \(\omega_{i,j}\). Subsequently, the desired \(\omega_{i,j}\) along with \(T\) are employed to calculate the reference longitudinal forces \(F_{x,i,j,k}\) using (7)-(10). Note that both \(\omega_{i,j}\) and \(F_{x,i,j,k}\) are perceived as fault-free as they are generated solely with the AGV model, while the real AGV is exposed to various faults of mechanical nature as well as unexpected working conditions like sliding surfaces, which are also perceived as faults. As a result, the following set of residuals is formed:  
\[z_{F,x,i,j,k}=F_{x,i,j,k}-\hat{F}_{x,i,j,k}. \tag{72}\]
# Meander Line Super-Wideband Radiator for Fifth-Generation (5G) Vehicles
## 2 Meander Line Antenna Formula
Substrate thickness, h, is the thickness of the substrate material; \(\epsilon_{r}\) is the relative dielectric constant of the substrate material; and c is the speed of light. The substrate thickness in a patch antenna is crucial for determining the antenna's resonant frequency, bandwidth, and radiation pattern. By adjusting the substrate thickness, we can fine-tune the antenna's performance to meet specific frequency requirements. Optimal substrate thickness is also essential for suppressing surface waves, improving mechanical stability, and ensuring efficient energy transfer, and it influences the overall effectiveness of the antenna design. \[\lambda_{g}=\frac{c}{f\times\sqrt{\epsilon_{r}}} \tag{2}\]  
Width of the patch:  
\[W_{\text{P}}=\frac{c}{2\times f\sqrt{\frac{2}{\epsilon_{r}+1}}} \tag{3}\]  
Fringing field length:  
\[\Delta L=0.5\times W_{\text{P}} \tag{4}\]  
Total length of meander:  
\[L_{m}=6.5h_{s}\times w \tag{5}\]  
where w = width of meander lines. Length of the substrate:  
\[L_{s}=L_{m}+h \tag{6}\]  
Width of the substrate:  
\[W_{s}=2.5\times L_{m} \tag{7}\]  
Listed are the 5G FR1 and FR2 key mathematical expressions used in the design of meander line antennas [37; 38; 39]. (7)  
Height of meander:  
\[h=0.42\lambda_{g} \tag{8}\]  
Width of meander:  
Table 1.0  
Distance of meander:  
Table 1.1  
Listed are the 5G FR2 meander line key mathematical expressions that can be modified for broader bandwidth [40,41]. Height of meander:  
Table 1.2  
Distance of meander:  
Table 1.3  
Width of meander:  
Table 1.4  
Listed are the 5G FR2 meander line key mathematical expressions modified for super wideband [40,42,43]. Height of meander:  
Table 1.5  
Distance of meander:  
Table 1.6  
Width of meander:  
Table 1.7  
Table 1.8 Meander line formula structure [38].
# Adaptive MPC Based Real-Time Energy Management Strategy of the Electric Sanitation Vehicles
## 2 Modeling and Energy Consumption Analysis on an Electric Sanitation Vehicle
### Analysis of the Energy Consumption  
Figure 3 shows the typical velocity profiles of the sanitation vehicle, which could be divided into the scenario transition condition part and the working condition part. The former part occurred in the interim when the vehicle performed such manipulations as adding water, emptying the garbage, and moving at high speed. Herein, concentrating on the working part, it is indicated that the velocity was maintained at about 8 km/h most of the time. A slightly higher speed ranging from 10 km/h to 22 km/h was adopted by the vehicle when moving to the target field or turning at the road's end were required, which correspond to the segment from 2000 s to 2300 s.  
Although the scenario transition condition part was not of concern, the energy consumption analysis covered the whole driving cycle to reasonably evaluate the principal power components. Generally speaking, the energy stored in the battery was consumed by the driving motor (DM), the working motor (WM), an air compressor (AC) used for braking, a steering motor vehicle (SM), and a DCDC (DC-to-DC converter) connected with low-voltage accessories. Figure 4 illustrates the power curves of these components sampled from the cleaning routine. Specifically, under the driving mode, the working motor was kept off. The driving motor consumed power varying from \(-\)120 kW to 120 kW, in which being positive means that battery energy was consumed by the driving motor and being negative means that the electricity was regenerated by the driving motor from braking and restored to the battery. While under the working mode, the working motor steadily consumed power at about 50 kW, and extremely low operating power was consumed by the driving motor due to the low velocity. In Figure 5, the operating states consisting of the rotating speed and the torque of the working motor are displayed.  
In Figure 4, even though the peak power overpassed 20 kW, it is indicated that the steering motor and air compressor merely occasionally consumed that power for a relatively short time. The energy consumption proportions of these devices are shown in Figure 6. Taking the whole driving cycle into consideration, the sum of energy consumed by the two motors took a proportion up to 89%. Consequently, aiming at a better energy economy of the sanitation vehicle, only these two motors' operating states were analyzed and targeted in the optimization method to acquire optimal control sequences.  
Figure 3: Typical driving cycles of the sanitation vehicle.  
Figure 4: Electricity consumption of principal devices.  
Figure 5: Operating states of the working motor.  
Figure 6: Energy consumption proportions of the devices onboard.
## 2 Materials and Methods
### Remote Sensing Data Pre-Processing  
HJ-1A and HJ-1B satellites were launched by the China Center for Resources Satellite Data and Application (CRESDA) on 6 September 2008. They are sun-synchronous circular orbit satellites with an orbital altitude of 649 km. Charge-coupled device (CCD) sensors onboard the two satellites have similar spectral range and spatial resolution to the first four bands of Landsat TM/ETM+ (Table 2). However, the HJ-1A and HJ-1B satellite constellation provides a more frequent revisit time (2 days), and a wider swath width (360 km), which are considered of great importance for vegetation monitoring  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type** & **Dominant Species** \\ \hline Emergent vegetation & _Phragmites communis_, _Zizania latifolia_ \\ \hline Submerged vegetation & _Vallisneria spiralis_, _Ceratophyllum demersum_, _Potamogeton malaianus_, _P. maackianus_, _Hydrilla varticillata_, _Potamogeton maackianus_ \\ \hline Floating-leaved vegetation & _Eichhornia crassipes_, _Lenna minor_, _Nymphoides peltata_, _Trapa bicornis_ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Aquatic vegetation types and dominant species in the grass-type zone of Taihu Lake.  
and observation, especially for wet summers (_i.e._, between June and September) in southern China. We used three cloudless HJ-CCD images acquired on 11 July, 18 August, and 26 September 2013.  
All processing of remote sensing images were conducted with ENVI 4.5 software. Radiometric calibrations were made using coefficients provided with the image (e.g., gains and offsets). The FLAASH module in ENVI software was applied for atmospheric correction [25]. The input parameters were set based on the location, sensor type and ground weather conditions observed on the day the image was acquired. The atmospherically corrected images were geometrically corrected against a historical Landsat(tm) image with geometric accuracy of \(<\)0.5 pixel. Considering no aquatic macrophyte vegetation occurs in the algae-type zone except for emergent vegetation (mostly reeds) near the shore, the algae-type zone, including sections I and VI, was masked.
# Bilinear CNN Model for Fine-Grained Classification Based on Subcategory-Similarity Measurement
## 4 Results
### Model-Configuration Study  
#### 4.2.1 Weakly Supervised Localization  
Accuracy is widely used in fine-grained image classification [9; 19; 31] to measure the effectiveness of the method. Therefore, we adopt accuracy to measure the classification performance of our method to make it consistent with previous work, and its definition is as follows:  
\[Accuracy=\frac{Ra}{R} \tag{11}\]  
where \(R\) denotes the number of images in a testing set, and \(Ra\) denotes the number of images that are correctly classified. Mean average precision (mAP) [33] is also used to measure the effectiveness of our method in multiclass classification.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **\#Category** & **\#Training** & **\#Testing** \\ \hline CUB-200-2011 & 200 & 5994 & 5794 \\ Cars-196 & 196 & 8144 & 8041 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of used fine-grained datasets.  
Effective object localization can remove the influence of background noise on fine-grained classification; we obtain bounding box of the object by selecting convolution descriptors. Intersection over Union (IoU) is widely used to evaluate whether the object has been correctly localized [34]. Its formula is expressed as:  
\[IoU=\frac{A\cap B}{A\cup B} \tag{12}\]  
where \(A\) represents the predicted bounding box of the object, \(B\) denotes the ground truth that can be obtained from additional annotations of the dataset, \(A\cup B\) denotes the union of the predicted and ground truth bounding boxes, while \(A\cap B\) represents their intersection. If the IoU exceeds 0.5, we consider that the weakly supervised localization is correct. Since many localization methods use additional annotation, or do not give the localization accuracy of the whole object but only the parts. Results are compared with four typical weakly supervised localization algorithms, that is, WSDL [35], MEAN [36], Unsupervised Object Discovery [37], and SCDA [21]. The accuracy of localization is shown in Table 2. From the table, we can see that the proposed method can achieve better or almost the same localization results than other methods.
## 4 Baselines comparison
Because as parts of the DKT-STDRL, other models should keep the same settings to make only the model structure different in each experiment. In order to more intuitively compare the prediction effect of these models, Figure 3 displays the prediction results of all models on the ASSISTment2009 dataset in the bar charts. Figure 4 displays the prediction results of all models on the ASSISTment2015 dataset in the bar charts. Figure 5 displays the prediction results of all models on the Synthetic-5 dataset in the bar charts. Figure 6 displays the prediction results of all models on the ASSISTChall dataset in the bar charts. Figure 7 displays the prediction results of all models on the Statics2011 dataset in the bar charts. From these figures, it can be seen, obviously, that the DKT-STDRL had a higher AUC, ACC, and \(r^{2}\), and a lower RMSE, than CKT, DKT, DKT-SDRL1, and DKT-STDRRJ. Moreover, the AUC, ACC, and \(r^{2}\) of the DKT-STDRL were a little higher than DKT-TDRL and DKT-STDRRP, and the RMSE of DKT-STDRL was a little lower than DKT-TDRL and DKT-STDRRP. The model with higher AUC, ACC, and \(r^{2}\), and lower RMSE, is better. So, DKT-STDRL is significantly better than CKT, DKT-SDRL1, and DKT-STDRRJ. Moreover, DKT-STDRL is only a little better than DKT-TDRL and DKT-STDRRP. Firstly, DKT-TDRL, regardless of extracting spatial features, raised the RMSE no more than 2% and dropped the AUC, ACC, \(r^{2}\) no more than 2%, 0.7%, and 5%, respectively. So, the spatial features have little benefit in improving prediction results. Secondly, different from DKT-STDRL, DKT-SDRL1 learns unidirectional temporal features instead of bidirectional features. As the results show, DKT-SDRL1 is significantly worse than DKT-STDRL. Thus, the extracted bidirectional temporal features are very important for DKT-STDRL. Thirdly, DKT-STDRL has many advantages compared with DKT-STDRRP. Changing from DKT-STDRRP to DKT-STDRL can decrease the RMSE by over 2% and increase AUC, ACC, and \(r^{2}\) by over 2%, 1.8%, 6.8%, repsectively. So, prior learning features are also meaningful for obtaining a better model. Fourthly, DKT-STDRRJ has obvious weaknesses in all the metrics. It can be inferred that merging exercise performance history information with spatial features is necessary for the DKT-STDRL because the extracted spatial features have information errors compared with the original data and cannot be directly input into BiLSTM. So, the spatial features, temporal features, prior features, and joint features of DKT-STDRL play various roles in improving prediction.
## 1 Introduction  
Colloidal quantum dots (QDs) have attracted widespread attention as promising materials due to their tunable emission wavelength, high color purity, and near-unity photoluminescence quantum yield. In particular, quantum dot light-emitting diodes (QLEDs) are expected to provide the excellent emission characteristics of QDs and the benefits of solution processing techniques [1; 2; 3; 4]. [PERSON] and coworkers demonstrated highly efficient QLEDs with an external quantum efficiency (EQE) of 20.5% based on the optimization of the charge balance [5]. As both the emission layer (EML) and the charge transport layer in QLEDs can be fabricated using a solution process, this approach is more economical than the standard high-vacuum deposition systems.  
The most common solution process is the spin-coating technique. In the early stages of research on standard-structured QLEDs, both the hole transport layer (HTL) and EML were formed by the spin-coating process, and then the organic electron transport layer (ETL) was deposited by the thermal evaporation process [6; 7]. Now, however, chemically stable ZnO nanoparticles (NPs) have replaced the organic molecules for the ETL and the resultant device performance has been outstanding [8; 9; 10]. Despite the necessity of the spin-coating process for all the layers of QLEDs, the majority of research has focused on analyzing and improving the inorganic material itself, and a comparative study has not been conducted on the one-step and two-step deposition of ZnO NPs in terms of device performance.  
Herein, we report the significant changes in the luminance and efficiency of QLEDs according to the changed morphology. In the spin-coating process, the step schedule is set as a variable to analyze the generated morphology. Both the one-step- and two-step-coated QLEDs exhibit excellent optoelectronic performance, but the devices with the two-step deposition of the ZnO NPs show a current efficiency approximately 2.3 times higher than that with one-step deposition. The correlation between the surface morphology and device performance is analyzed through atomic force microscopy (AFM) images. The results indicate that, in addition to the material properties and the device structure, the surface morphology of each layer should also be considered for achieving highly efficient QLEDs.
## 2 Results and Discussion
### Antioxidant Activity of APTM Aqueous Extract  
Antioxidants can prevent the human body from producing the harmful effects of free radicals by administering certain drugs, including aminoglycosudes such as GM. They halt the course of specific acute and chronic disorders and the development of lipid peroxidation. Indeed, the aqueous extract of APTM may exhibit antioxidant properties against DPPH free radicals and lipid oxidation in biological systems.  
The diversity and complexity of secondary metabolites present in plant extracts requires several techniques to assess the antioxidant effects and evaluate the efficacy of these phytochemicals. Most of these techniques rely on a reagent's coloring or discoloration in the reaction mixture [12]. Usually, the methods of evaluation of antioxidant activity can be grouped into two classes: the tests used to evaluate the trapping power of free radicals, such as the DPPH test, and tests used to evaluate and estimate lipid peroxidation in biological systems while assessing the degree of inhibition of oxidation, such as the \(\beta\)-carotene/linoleic acid test. Our study used two antioxidant tests: the DPPH trapping test and the \(\beta\)-carotene bleaching test.  
The results of the antioxidant effect of the aqueous extract APTM against free radicals DPPH were presented in Figure 1. Indeed, the aqueous extract of APTM has a dose-dependent inhibition effect on the free radical DPPH. Results showed a moderate ability to trap free radicals in APTM aqueous extract with an IC\({}_{50}\) of 111 \(\pm\) 0.02 ug/mL, which is even lower than that of ascorbic acid (IC\({}_{50}\) = 30.70 \(\pm\) 0.06 ug/mL). These findings demonstrated that APTM's aqueous extract had moderate antioxidant activity against DPPH free radicals. The current antioxidative activity results from the aqueous APTM extract is consistent with those shown by [PERSON] et al. (2015) in an extract of _T. microphylla_'s leaves and flowers from Draa Elhadja region in M'sila, Algeria [12].  
Measurements of the inhibition of volatile organic molecules and the production of conjugated diene hydroperoxide from linoleic acid oxidation are used to determine the antioxidant activity in the \(\beta\)-carotene bleaching assay [20]. Figure 2 displays the outcomes of the aqueous APTM extract's \(\beta\)-carotene bleaching test. The findings of this test reveal that our extract significantly inhibited \(\beta\)-carotene oxidation as a function of concentration. The percentages of \(\beta\)-carotene that are oxidized in the presence of APTM aqueous extract are 40.43%; 33.02%; 24.38% and 15.02%, respectively, for the concentrations of 12.5, 25, 50 et 100 ug/mL, and those of butylated hydroxytoluene were respectively 3.40%, 1.85%, 0.31% and 0.01% for the concentrations of 12.5, 25, 50 and 100 g/mL. The aqueous extract of APTM's ability to prevent the oxidation of \(\beta\)-carotene may result from the extract's high concentration of polyphenols and flavonoids. This may be due to the correlation between polyphenols or flavonoid content in a plant and its antioxidant effects [21].
## 3 Recurrence Quantification Analysis
### Estimation of the Embedding Parameters  
#### 3.1.1 Time Delay  
Theoretically, the time delay \(\tau\) can be almost arbitrarily chosen if the observation \(y(n)\) is noise-free with an infinite number of samples. However, these conditions cannot be satisfied in real applications because of noise which can generate statistical dependence among the state vectors \(\mathbf{x}_{k}\). Therefore, the time delay \(\tau\) has to be wisely chosen in order to reduce this statistical dependence [20, 36, 38]. To determine the optimal time delay \(\tau_{0}\), one can use the autocorrelation function, Average Mutual Information (AMI), or phase portrait approach. The most appropriate method is the AMI because it measures the general dependence between two random variables [39]. Therefore, it could provide a better criterion for the optimal time delay \(\tau_{0}\). The concept of AMI consists, first of all, in estimating the mutual information \(I(\tau)\) between \(y(n)\) and its delay version \(y(n-\tau)\) by varying the value of \(\tau\) from \(0\) to \(N\); \(N\) denotes the number of samples contained in \(y(n)\). After that, the optimal \(\tau_{0}\) is chosen as the first \(\tau\) value that minimizes \(I(\tau)\)[20, 40, 41]. Based on Equation (1), we can conclude that \(I(\tau)\), and indirectly \(\tau_{0}\), depends on three parameters: the SNR value, the sampling rate, and the modulation scheme. Consequently, using the AMI method to determine \(\tau_{0}\) is not so suitable in the context of a noncooperative spectrum sensing algorithm.
# Heart Attack Detection in Colour Images Using Convolutional Neural Networks
###### Abstract  
Cardiovascular diseases are the leading cause of death worldwide. Therefore, getting help in time makes the difference between life and death. In many cases, help is not obtained in time when a person is alone and suffers a heart attack. This is mainly due to the fact that pain prevents him/her from asking for help. This article presents a novel proposal to identify people with an apparent heart attack in colour images by detecting characteristic postures of heart attack. The method of identifying infarcts makes use of convolutional neural networks. These have been trained with a specially prepared set of images that contain people simulating a heart attack. The promising results in the classification of infarcts show 91.75% accuracy and 92.85% sensitivity.  
image analysis; heart attack detection; convolutional neural networks +
Footnote †: journal: Medical  
This article proposes a method for detecting heart attack events using a non-invasive method. That is, it does not require a person to wear a series of devices, a specialist to directly monitor the patient, or the person to operate a system in some way. This work included the creation of a data set of images with people simulating infarction conditions for training the convolutional neural networks (CNNs) constructed. Our proposal has achieved 91.75% accuracy and 92.85% sensitivity in detecting people with a potential heart attack. It consists of a visual monitoring system that enables the identification of people with apparent postures of infarcts, using CNNs to analyse colour images. The proposed system is intended to be added to larger systems such as intelligent health environments [7; 8] or accompanying robots [9; 10], enriching their capabilities.  
The remainder of the paper is organised as follows. Section 2 presents several related works and their strategies in the recognition of human activity. Section 3 shows the methodology used to develop, train, and test the proposal. This section also presents the results obtained and a comparison with related works. Finally, some relevant conclusions and possible future works are described in Section 5.