esa-sceva/satcom-chunk-collection huggingface dataset:
# An Indoor Room Classification System for Social Robots via Integration of CNN and ECOC
## 4 Dataset & Simulation Experiments
### Scene Dataset  
There are several scene datasets proposed in the literature for addressing object/scene detection or classification problems. Some of them are small-scale datasets such as the 15-scene dataset, UIUC Sports, and CMU 300, and some are large-scale datasets such as 80 Million Tiny Image Dataset, PASCAL, ImageNet, LabelMe, SUN, and Places [39]. The dataset can be 3D scene, such as SUNCG [40], or it can be images for a particular environment with geo-referenced pose information for each image, such as TUM and NavVis [41]. These datasets can be classified into two view types: object-centric datasets, e.g., ImageNet, and scene-centric datasets, e.g., Places [8]. Places is the latest and largest scene-centric dataset, which is provided by MIT Computer Science and Artificial Intelligence Laboratory for the purpose of CNN training. It has a repository of around 2.5 million images classified into 205 categories, and for this reason it is called the Places205 dataset. This dataset is updated and extended with more images classified into 365 categories in Reference [42], which is called Places365.  
Since this project is within the scope of household robotics applications, five categories of images were selected to be downloaded from Places205 for addressing room-scene classification problems for social robots using CNN models. The five categories are: bedroom, dining-room, kitchen, living-room, and bathroom, which most, if not all, houses have. It should be noted that the corridor category is not available in Places205 and Places365 at the time of this work. This category is important in this research, and will be incorporated in the design once it is available. 11,600 images/category were used to train the CNN model, where 20% of images were used for validation, i.e., 2320 images/category.
## Appendix A
\begin{array}{rl}\int_{t_{1}}^{t_{2}}\delta V\mathrm{d}t&=\int_{t_{1}}^{t_{2}}u^{ \mathrm{T}}\mathcal{K}\delta u\mathrm{d}t=\sum\limits_{j=1}^{N}C_{j}(\mathbf{U}_{j}) ^{\mathrm{T}}\mathcal{K}\delta(\mathbf{U}_{j})\\ &=\left[\begin{array}{cc}(\mathbf{U}_{1})^{\mathrm{T}}&(\mathbf{U}_{2})^{\mathrm{T}}& \cdots&(\mathbf{U}_{N})^{\mathrm{T}}\end{array}\right]\left[\begin{array}{cc}C_ {1}\mathbf{K}&&&\\ &C_{2}\mathbf{K}&&&\\ &&C_{3}\mathbf{K}&&&\\ &&C_{4}\mathbf{K}\end{array}\right]\delta\left[\begin{array}{c}\mathbf{U}_{1}\\ \mathbf{U}_{2}\\ \vdots\\ \mathbf{U}_{N}\end{array}\right]\\ &=\overline{\mathbf{U}}^{\mathrm{T}}(\mathbf{C}_{\mathrm{G}}\otimes\mathbf{K})\delta \overline{\mathbf{U}}\\ \int_{t_{1}}^{t_{2}}\delta\mathcal{W}\mathrm{d}t&=\int_{t_{1}}^{t_{2}}(\tilde {\mathbf{u}}^{\mathrm{T}}\mathcal{K}u+\tilde{\mathbf{f}}^{\mathrm{T}}\delta\mathbf{u}) \mathrm{d}t\\ &=\sum\limits_{j=1}^{N}C_{j}\Big{\{}(\mathbf{D}_{j})^{\mathrm{T}}\mathbf{C}+(\mathbf{F}_{j })^{\mathrm{T}}\Big{\}}\delta(\mathbf{U}_{j})\\ &=\left[\begin{array}{cc}(\mathbf{D}_{1})^{\mathrm{T}}&(\mathbf{D}_{2})^{\mathrm{T}}& \cdots&(\mathbf{D}_{N})^{\mathrm{T}}\end{array}\right]\left[\begin{array}{cc}C_ {1}\mathbf{C}&&&\\ &C_{2}\mathbf{C}&&&\\ &&C_{3}\mathbf{C}&&\\ &&C_{4}\mathbf{C}\end{array}\right]\delta\left[\begin{array}{c}\mathbf{U}_{1}\\ \mathbf{U}_{2}\\ \vdots\\ \mathbf{U}_{N}\end{array}\right]+\\ &\left[\begin{array}{cc}C_{1}(\mathbf{F}_{1})^{\mathrm{T}}&C_{2}(\mathbf{F}_{2})^{ \mathrm{T}}&\cdots&C_{N}(\mathbf{F}_{N})^{\mathrm{T}}\end{array}\right]\delta \left[\begin{array}{c}\mathbf{U}_{1}\\ \mathbf{U}_{2}\\ \vdots\\ \mathbf{U}_{N}\end{array}\right]\\ &=\overline{\mathbf{U}}^{\mathrm{T}}\Big{(}\mathbf{A}^{\mathrm{T}}\otimes\mathbf{E})( \mathbf{C}_{\mathrm{G}}\otimes\mathbf{C})\delta\overline{\mathbf{U}}+(\mathrm{cs}(\mathbf{F}))^ {\mathrm{T}}(\mathbf{C}_{\mathrm{G}}\otimes\mathbf{E})\delta\overline{\mathbf{U}}\\ &=\overline{\mathbf{U}}^{\mathrm{T}}\big{\{}(\mathbf{A}^{\mathrm{T}}\mathbf{C}_{\mathrm{G}}) \otimes(\mathbf{E}\mathbf{C})\big{\}}\delta\overline{\mathbf{U}}+(\mathrm{cs}(\mathbf{F}))^{ \mathrm{T}}(\mathbf{C}_{\mathrm{G}}\otimes\mathbf{E})\delta\overline{\mathbf{U}}\\ &=\overline{\mathbf{U}}^{\mathrm{T}}\big{\{}(\mathbf{A}^{\mathrm{T}}\mathbf{C}_{\mathrm{G}}) \otimes\mathbf{C}\big{\}}\delta\overline{\mathbf{U}}+(\mathrm{cs}(\mathbf{F}))^{\mathrm{T }}(\mathbf{C}_{\mathrm{G}}\otimes\mathbf{E})\delta\overline{\mathbf{U}}\end{array}  \tag{10}\]  
where  
\[(\mathbf{D}_{j})^{\mathrm{T}}=\left[ \begin{array}{cccc}\sum\limits_{k=1}^{N}A_{ jk}u_{1k}&\sum\limits_{k=1}^{N}A_{jk}u_{2k}&\cdots&\sum\limits_{k=1}^{N}A_{jk}u_{nk} \end{array} \right]\,(j=1,\,2,\,\cdots,\,N) \tag{11}\]  
\[(\mathbf{U}_{j})^{\mathrm{T}}=\left[ \begin{array}{cccc}u_{1j}&u_{2j}&\cdots&u_{nj }\end{array} \right] \tag{12}\]
## 5 Performance Analysis and Optimization
### Average BEP/SEP  
The ABEP/ASEP is an alternative important measure for performance analysis of wireless digital communications systems operating in fading environments. In this section, by using the previously derived MGF formulas for SSC, SEC, and SECps, we consider the ABEP/ASEP including both coherent and non-coherent modulation schemes over Fisher-Snedecor \(\mathcal{F}\) fading channels.
## 7 Testing of the Algorithm to Discriminate Switching Events from Faults
### Inductive Switching  
The algorithm is tested for switching of the inductive load having a rating of 20 MVAR. The switching on the load event is realized at the fourth cycle, and the switching off the load event is realized at the eighth cycle. The respective FI for these events is presented in Figure 12. It is clear that the peak of the FI is less compared to the pre-set threshold, which is \(5\times 10^{9}\). Hence, the proposed technique is efficient in the differentiation of the switching (inductive load) events from the faults.  
Figure 11: FI at the moments of capacitor switching operations.
## 2 Specim Preparation and CT Image Acquisition
### CT Image Processing and 3D Reconstruction  
In the detection process of CT scanned objects, it can be seen from the Lambert-Beer law [13] theorem that the attenuation of the intensity of X-rays when passing through a homogeneous object can be expressed by Equation (1), and the decay law of energy intensity when X-rays penetrate the detected object is shown in Figure 2:  
\[I_{1}=I_{0}\ \mathrm{exp}(-\mu L) \tag{1}\]  
where \(I_{0}\) is the incident X-ray intensity, \(I_{1}\) is the attenuated ray intensity, \(\mu\) is the absorption density of the object per unit mass, and \(L\) is the projection thickness of the detected object.  
The processing of digital images consists of two parts: pre-processing and threshold segmentation. The purpose of pre-processing is to eliminate the noise part of the experimental process and retain some small details in the original image, so as to improve the clarity of image restoration. Threshold segmentation is based on the fact that different phases will correspond to different gray value ranges, so as to divide their corresponding voxel sets to obtain different regions, and these regions have the same properties inside, so as to extract the part we need. The brightness difference method and binarization are used to output all the data within the selected threshold range as 1, and the rest of the data are output as 0. As shown in Figure 3, the binarized black and white image data and the corresponding 3D model can be obtained, in which the black dots are pores and the white is the matrix.  
The binary image after digital image processing was imported into the AVIZO-2022 software to reconstruct the image to realize the three-dimensional visual characterization of the tailings sample. Because the upper and lower ends are closely attached to the acrylic material during scanning, there are areas at the ends that are too bright or too dark for 3D representation, so in order to better reconstruct the image, only the middle part of the slice with better imaging is reconstructed in 3D. Figure 4 shows the three-dimensional visualization space stacking reconstruction model of a CLT specimen reconstruction.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Category** & **Parametric Indicators** \\ \hline Spatial resolution & 0.7 \(\upmu\)m \\ Tube voltage range & 30–160 kv \\ Sample loading capacity & 15 kg \\ Stage stroke (x, y, z) & 45 mm, 100 mm, 50 mm \\ \(Z\)-direction light source travel & 190 mm \\ Detector stroke (z) & 290 mm \\ \hline \hline \end{tabular}
\end{table}
Table 2: The main parameters of the instrument.  
Figure 2: The attenuation of energy as X-rays penetrate the object being inspected.
## 3 Results and Discussion
### Hugoniot Relationships  
From the measured particle velocity profile, the shock velocity can be calculated using the longitudinal elastic wave velocity (\(c_{L}\)), taking the amount of deformation as a reference value, and using the time of the plastic part of the shock wave front between the elastic limit and the peak free surface velocity, by:  
\[U_{s}=\frac{d}{\frac{d}{c_{L}}+\Delta t} \tag{4}\]  
where \(d\) is the thickness of the specimen. According to the free surface reflection multiplication law [27], the particle velocity inside the material is half of the back-free surface velocity. The Hugoniot data for HSLA in the impact velocity (\(U_{S}\))-particle velocity (\(u_{p}\)) space are shown in Figure 7. The obvious linear relationship was fitted to a linear equation given by:  
\[U_{s}=c_{0}+Su_{p} \tag{5}\]  
where \(c_{0}\) is the zero-pressure bulk sound velocity and \(S\) is the slope, which is related to the first derivative of the bulk modulus with pressure [28].  
As shown in Figure 7, the shock parameters \(c_{0}\) = 3961 m/s and \(S\) = 1.415 for the HSLA were determined. Data on pure iron obtained from references were also provided [10]. Compared to pure iron, the \(S\) value in our data is smaller, with a deviation of 15.7%. Considering that a lower value of \(S\) means greater resistance to compressive deformation [28], the improvement in stiffness of the HSLA may be due to the addition of other metal elements.  
Figure 6: Calculated values of spallation strength and tensile strain rate at different impact velocities for the HSLA.
## 4 MODECP Approach  
In this section, to address this issue, we propose a heuristic-based multi-objective controller placement approach, MODECP, which is divided into the network-partitioning stage and the controller placement stage. Specifically, the network-partitioning stage aims to determine the number of controllers that should be placed reasonably by relying on the characteristics of the network through an improved affinity propagation algorithm. In the controller placement stage, we propose an improved multi-objective differential evolution algorithm to determine the placement position of the controllers in the network and the ownership relationship between the switches and the controllers. Based on the network partition stage and the controller placement stage, the MODECP approach can produce a distributed network management scheme that makes the network performance approximately optimal. Under different network requirements, various design objectives can be optimized, including minimizing network transmission delay, maximizing network security performance, minimizing controller load-balancing rate, and minimizing link overhead cost. In addition, for clarity, the research approach in this section is represented in Figure 1.
## 2 Materials and Methods
### Model Evaluation Criteria  
This paper evaluates the model's detection accuracy and image segmentation capabilities through metrics such as precision, recall, and mean average precision (mAP). Frames per second (FPS) measures the inference speed on hardware, i.e., the number of images processed per second by the device.  
_Precision_ (P) and _Recall_ (R) are calculated using a confusion matrix, which includes _True Positives_ (TP), _True Negatives_ (TN), _False Positives_ (FP), and _False Negatives_ (FN). Precision is the proportion of actual targets in the total predicted targets by the network, representing the classification accuracy of the network, whereas recall is the ratio of true targets correctly predicted by the network to the actual number of true targets. The corresponding formulas are as follows:  
\[Precision=\frac{True\ Positive}{True\ Positive+False\ Positive} \tag{9}\]  
\[Recall=\frac{TruePositive}{True\ Positive+False\ Negative} \tag{10}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Configuration** & **Allocation** \\ \hline CUDA version & 11.3 \\ Python version & 3.8 \\ PyTorch version & 1.12 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Training and testing environment.  
Figure 6: (**a**) The method for calculating the coordinates of DSConv. (**b**) The receptive field of DSConv [30].  
Intersection over union (_IoU_) represents the ratio of intersection to union in object detection, used to measure the degree of overlap between the model-generated boundaries and the original annotated boundaries. When _IoU_ exceeds 0.5, it is considered that the object is detectable. If the true region is \(A\) and the annotated region is \(B\), then  
\[IoU=\frac{A\cap B}{A\cup B} \tag{11}\]  
The average precision (_AP_) for a single category is determined by ranking the model's predictions based on their confidence scores and calculating the area under the precision-recall (_PR_) curve, as follows:  
\[AP=\int_{0}^{1}P(R)d(R) \tag{12}\]  
Mean average precision (_mAP_) indicates the average precision across multiple categories, with _mAP_50 representing the _mAP_ at a 50% _IoU_ threshold. _mAP_50 - 95 is a stricter evaluation metric, calculating the average of the _AP_ at each _IoU_ threshold from 50% to 95%, with increments of 0.05, allowing for a more accurate assessment of model performance at various _IoU_ thresholds.  
\[mAP50-95=\frac{AP_{\textit{IoU}=0.5+}+AP_{\textit{IoU}=0.55}+AP_{\textit{IoU}= 0.6}+...+AP_{\textit{IoU}=0.95}}{n} \tag{13}\]
# Virtual Reality-Based Interface for Advanced Assisted Mobile Robot Teleoperation
## 1 Introduction
### Proposal  
This paper proposes a new virtual reality-based interface for the teleoperation of mobile robots in unknown scenarios. The proposed interface is designed to be natural and immersive to the user, reducing the learning process of the interface. The proposed interface is fully described in the article, and its efficacy is experimentally demonstrated using the mobile robot Turtlebot3 Burger. In addition, a complete study with users of different ages and backgrounds is detailed to determine the quality and usability of the proposed interface.  
Concretely, this work presents several contributions as highlighted below:  
* Unlike the works mentioned above, this work presents an intuitive interface designed to teleoperate mobile robots in totally unknown environments. To do this, the user is able to guide the robot through the environment in order to benefit from the intelligence and adaptability of the human, whereas the robot is able to automatically avoid collisions with the objects in the environment in order to benefit from its fast response.
* Contrary to the aforementioned works, the proposed interface does not seek the realism of the virtual environment but provides all the minimum necessary elements that allow the user to carry out the teleoperation task in a more natural and intuitive way. Hence, the proposed interface establishes different virtual elements (e.g., mobile robot, user reference, 2D map of the environment, information related to the robot or task, and the 3D position of the objects detected in real-time, among others) that allow the user to quickly interact with the interface and successfully perform the robot teleoperation task.
* In contrast to the works about virtual reality interfaces mentioned above, where virtual reality controllers are used for interacting with the virtual environment, this work proposes the use of gamepads to carry out this interaction. Thus, this work aims to improve the ergonomics of the user, allowing them to teleoperate the robots in a natural way for long periods of time.
* This work is focused on improving the interaction between human users and interfaces for the teleoperation of mobile robots. In this sense, in addition to conventional studies, similar to those carried out in the abovementioned works to establish the viability and efficiency of the proposed interface, this work also carries out a study of the experience lived by users of different ages, gender and backgrounds when using the proposed interface in order to establish its degree of naturalness and intuition.
# Sizing Hybrid Energy Storage Systems for Distributed Power Systems under Multi-Time Scales
## 4 Case Study  
In this paper, a small community in China with a peak demand of 4.5 MW is analyzed. In this community, a distributed PV generation system, a wind turbine system, and diesel generators are installed. Additionally, this community works on the off-grid operation mode. Moreover, in order to meet the basic requirements of the operational cost of the community, smooth the renewable generation and the loads curves, reduce the carbon emissions, and improve the system security (reduce the LOLP rate), this paper focuses on optimizing the installation capacity of a battery super-capacitor energy storage system. Figure 3 shows the average yearly load curve of the proposed community on an hour-based time scale. Figure 4 is the meteorological data of this region, which includes the average yearly wind speed and solar irradiance. It is worth noting that the main body of Figure 4 (hour-based) shows the variation tendency of wind speed and solar irradiance, and two peripheral diagrams in Figure 4 (minute-based) are used to illustrate the uncertainty of renewable energy.  
Figure 4: The average yearly wind speed and solar irradiance.  
Figure 3: The average yearly load curve of the proposed community.  
According to the LCOE published by State Electricity Regulatory Commission, \(C_{W}\), \(C_{PV}\), and \(C_{d}\) are assigned the value of 10 US cents/kWh, 14 US cents/kWh, and 6 US cents/kWh in this paper, respectively [36]. Considering the constraint of space, the installation capacity of the PV and wind turbine systems should be no more than 2 MW and 1 MW, respectively. In addition, in order to reduce the LOLP rate, the \(LOLP_{\text{max}}\) is set as 5%. Moreover, in order to extend the battery lifespan, the maximum discharge current coefficient of the battery \(\beta\) is assigned to 1, the nominal cycle efficiency of the battery system is set as 90% [27], SDR is set as 0.1%, and the nominal efficiency of the power conversion system is set as 99%. Finally, the energy efficiency of the diesel generators is 40% [37]. The following results are acquired by implementing MATLAB (R2016b, MathWorks company, Nettie, MA, USA) Simulation.
# The Effect of Silica-Filler on Polyurethane Adhesives Based on Renewable Resource for Wood Bonding
## 3 Results and Discussion
### Thermal Properties  
Thermogravimetric experiments revealed (Table 5, Figure S3) that the presence of silica affects thermal degradation of the polymers. In all cases temperatures of a indicated weight loss were shifted to a lower value. It is surprising as the increase in PUR thermal stability in the presence of silica has been recognized in the literature [54; 55].  
DTA curves shown in Figure 7 indicate that the endothermic peaks in region 110-125 \({}^{\circ}\)C correspond to dehydration of polymers while exothermic peaks around 554.2-576.3 \({}^{\circ}\)C are associated with thermooxidation. MS results coupled with TG presented in Figure S3 proved that the main thermooxidation products are water (_m_/_z_ 18) and CO\({}_{2}\) (_m_/_z_ 44). Maximum water evolution was found at 390.2 \({}^{\circ}\)C and 400.5 \({}^{\circ}\)C for the A0% SiO\({}_{2}\) and A2% SiO\({}_{2}\) PURs, respectively, and at 380.3 \({}^{\circ}\)C and 359.2 \({}^{\circ}\)C for the C0% SiO\({}_{2}\) and C6% SiO\({}_{2}\) PURs. Maxima for carbon dioxide evolution were observed at 597.4 \({}^{\circ}\)C for C0% SiO\({}_{2}\) PUR and at 566.9 \({}^{\circ}\)C for C6% SiO\({}_{2}\) PUR.  
From the practical point of view the changes in thermal stability of the PURs can be neglected as in the area of their target application high thermal resistance is not required.  
Figure 6: Bondline strength in pull-off tests performed on medium density fiberboard (MDF) veneered with 0.6-mm thick oak veneer.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{2}{c}{**Temperature of Weight Loss [\({}^{\circ}\)C]**} & \multicolumn{2}{c}{**Residual Weight**} \\ \cline{2-5}  & **5\%** & **50\%** & **80\%** & **at 800 \({}^{\circ}\)C [\%]** \\ \hline A0\% SiO\({}_{2}\) & 207.8 & 421.2 & 592.4 & 1.21 \\ A2\% SiO\({}_{2}\) & 189.0 & 406.1 & 580.4 & 3.87 \\ C0\% SiO\({}_{2}\) & 163.3 & 387.3 & 567.3 & 1.20 \\ C6\% SiO\({}_{2}\) & 156.2 & 367.7 & 559.5 & 6.25 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Thermal stability of the studied PURs.
## 4 Tests
### In-Orbit Tests  
On 20 May 2022, the low-orbit communications test satellite 03/04 was put into orbit. On 20 July, the first chain was started, and on 21 July, the first error-free transmission was finished. A 145-min period of stable and error-free connection was accomplished on 27 July. By 27 July, 14 bidirectional captures had been completed after the first chain establishment, with 10 of those taking place in under 30 s (71.4%). The four capture times that were longer than 30 s all occurred early in the test, and after the test's full completion on 23 July, all capture times were within the required range of 30 s. Table 13 displays statistics regarding  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{3}{*}{**Test Scheme**}} & \multicolumn{8}{c}{**Laser Communication Load Prism (\({}^{\star}\))**} & \multicolumn{2}{c}{**Variation**} \\ \cline{3-10}  & \multirow{3}{*}{**Test Scheme**} & & \multicolumn{2}{c}{**The Satellite Is Not Powered up (20 \({}^{\circ}\)C)**} & \multicolumn{2}{c}{**The Satellite Is Heated**} & \multicolumn{1}{c}{**(\({}^{\circ}\))**} \\ \cline{3-10}  & & & **X** & **Y** & **Z** & **X** & **Y** & **Z** \\ \hline \multirow{4}{*}{Star-sensor prism} & Test & X & 57.45941 & 104.70392 & 36.49674 & 57.48653 & 104.69043 & 36.46410 & 117.74 \\  & \multirow{3}{*}{\begin{tabular}{c} Test \\ scheme 1 \\ \end{tabular} } & Y & 92.57665 & 19.33458 & 70.85150 & 92.593926 & 19.32406 & 70.86453 & 75.42 \\  & & Z & 147.33186 & 102.27354 & 60.25443 & 147.35272 & 102.72365 & 60.28129 & 97.40 \\ \cline{2-10}  & \multirow{3}{*}{\
\begin{tabular}{c} Test \\ scheme 2 \\ \end{tabular} } & X & 56.44269 & 104.43308 & 37.32726 & 56.44544 & 104.43352 & 37.32485 & 9.90 \\  & & Y & 93.42561 & 19.96337 & 70.35752 & 93.42671 & 19.96400 & 70.35709 & 4.28 \\  & & Z & 146.22100 & 103.49310 & 59.69107 & 146.22360 & 103.49353 & 59.69405 & 10.72 \\ \cline{2-10}  & \multirow{3}{*}{
\begin{tabular}{c} Test \\ scheme 3 \\ \end{tabular} } & X & 56.44269 & 104.43308 & 37.32726 & 56.44623 & 104.42694 & 37.32080 & 27.74 \\ \cline{2-10}  & & Y & 93.42561 & 19.96337 & 70.35752 & 93.43385 & 19.96175 & 70.36072 & 31.60 \\ \cline{2-10}  & & Z & 146.22100 & 103.49310 & 59.69107 & 146.22347 & 103.49733 & 59.69590 & 21.66 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Prism angle calibration results.  
Figure 23: Calibration site of low-orbit communication test satellite.  
capture times. The satellite bus's thermal deformation control optimization design was shown to be successful by the on-orbit acquisition time.