esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical and Experimental Process  
The investigated ACS was made of an aluminum skin (face) and a Nomex honeycomb core. The BVID was made by low energy impacts (4 and 10 J) from the Nomex core side. A BVID of diameter 1.4 cm (BVID 1) was made with an impactor by crushing the Nomex cells, and another BVID of diameter 1.8 cm (BVID 2) was also made with an impactor after removing the Nomex cells. The device used to create the BVID was a circular impactor of 10 cm diameter. The specimen is shown in Figure 0(a). The largest diameter BVID 2 is magnified in Figure 0(b), and Figure 0(c) shows the cross-section of the plate with the Nomex core and a one-sided aluminum facet. The Nomex core attached to the facet is shown in Figure 0(d) with an area where the BVIDs were made. BVIDs of diameters 1.4 cm (BVID 1) and 1.8 cm (BVID 2), and a hole of 0.5 cm diameter, constitute the damage. The SLDV is an NDT device largely used in industry to analyze the vibrations of structures. The SLDV instrument used was a PSV 400 from Polytec GmbH. Laser vibrometry uses the Doppler effect for non-contact optical vibration analysis. This uses light as a sensor and thus a contactless experimental process. This yields real-time measurements of temporal signatures and is based on the frequency of the laser shift between the reference beam and the incident beam from the laser. The interferometer inside the laser head analyzes this shift by propagating it to the photodetector. Thus, depending on the velocity, the backscattered light gets changed in frequency and phase.  
Figure 1: (**a**) ACS plate with damages; (**b**) zoomed image of BVID 2; (**c**) cross-section of the plate with the core; (**d**) Nomex core attached at the backside of the plate and region where a hole and BVIDs were made.  
Thus, the Doppler shift in frequency is analyzed based on the modulated detected output signals. Using inbuilt signal processing techniques, the velocity and displacement of the surface waves are detected. The PZT (SONOX P502) was bonded with a bonding glue to the plate. A sine tone burst signal with 10 cycles at 16 Vpp amplified 20 times produced the excitation. The measurement parameters are summarized in Table 1. During the experiment, the room was maintained with a constant temperature of 21 \({}^{\circ}\)C using air conditioning.  
For the spectral element method (SEM), only the hole of \(\mathcal{O}\) 0.5 cm was studied. The engineering constants used for the analysis are highlighted in Table 2[19]. The parameters used in SEM are explained in Tables 3-5.  
where \(\mathrm{p}-1\) and \(\mathrm{r}-1\) are the degrees of interpolating polynomial, and \(i,\ c\in[-1,\ 1]\) are element local coordinates. As a consequence of the orthogonality of shape functions, and the application of the GLL quadrature, the mass matrix is diagonal in the formulation. This property significantly reduces computation time for solving the equation of motion due to avoiding mass matrix inversion.
# InnCube--A Wireless Satellite Platform to Demonstrate Innovative Technologies
## 3 Spacecraft Overview
### Power  
The power system is composed of three main components, i.e., a battery pack, solar panels and a PCDU. The purchased battery is designed for the use in nano-satellites and is composed of high-capacity lithium ion cells in a 2S-4P configuration with a total capacity of 10.4 Ah. It is equipped with a heater system, expanding the operational temperature range. The 3U solar panels, which are radiation and temperature resilient, consist of Azur Space 3G30A triple-junction solar cells with up to 30% efficiency, comprising a temperature and sun sensor. The PCDU is a contribution by the Institute of Space Systems of DLR Bremen and follows a new approach of high modularity, giving the ability to be tailored for different mission requirements by use of design automation techniques. It is set up to be fault-tolerant, due to redundancy and includes standard features as maximum power point tracking (MPPT) for battery charging or latch up protection of the outputs. It delivers 19 switchable output channels, one for each module on the satellite. The power system fulfils the power budget requirements see Section 2.3.
# Hole Concealment Algorithm Using Camera Parameters in Stereo 360 Virtual Reality System
## 5 Conclusions  
We proposed an efficient algorithm to conceal the holes generated in stereo VR pictures, where the positions of the pixels to fill the hole were derived using the camera parameters. The camera parameters were predicted from their relationship with the homography matrix. Whereas conventional inpainting algorithms have constraints in the concealment of the hole because they were invented for non-VR images, the proposed method efficiently fills all of the pixels in the hole using the relationship between the left and right ERPs.  
Conceptualization, [PERSON]; Data curation, [PERSON] and [PERSON]; Formal analysis, [PERSON], [PERSON] and [PERSON]; Funding acquisition, [PERSON]; Investigation, [PERSON] and [PERSON]; Methodology, [PERSON] and [PERSON]. [PERSON]; Resources, [PERSON]; Software, [PERSON]; Supervision, [PERSON]; Visualization, [PERSON]; Writing--original draft, [PERSON]; Writing--review & editing, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the National Research Foundation of Korea (NRF) under Grant NRF-2018R1A2A2A05023117 and partially by the Institute for Information & Communications Technology Promotion (IITP) under Grant 2017-0-00486 funded by the Korea government (MSIT).  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Width Ratio** & **SSIM** & **PSNR (dB)** & **Time (s)** \\ \hline
1.0 & 0.6031 & 15.8433 & 6.7485 \\
1.2 & 0.6476 & 18.3133 & 7.6910 \\
1.5 & 0.7721 & 23.8020 & 8.8516 \\
1.75 & 0.6505 & 17.6263 & 9.3063 \\
2.0 & 0.6656 & 18.7562 & 9.9974 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performances of proposed method according to size of neighboring blocks for indoor picture (Hallway) having a large hole.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **SSIM** & **PSNR (dB)** & **Time (s)** \\ \hline Bertalmio [16] & 0.7153 & 18.6928 & 15.7136 \\ Telea [17] & 0.7343 & 18.0315 & 16.3712 \\ Criminisi [18] & 0.5691 & 18.6113 & 62791 \\ Liu [19] & 0.3511 & 16.1611 & N/A \\ Gilisoft [33] & 0.7487 & 23.5176 & 0.5500 \\ Theinpaint [34] & 0.7585 & 22.5666 & 6.3600 \\ Simple copy & 0.6544 & 16.9261 & 0.2273 \\ Proposed algorithm & 0.7721 & 23.8020 & 8.8516 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison between performances of various methods for indoor pictures (Hallway) having a large hole.
# Minimizing Resource Waste in Heterogeneous Resource Allocation for Data Stream Processing on Clouds
## 4 Experimental Results
### Experimental Results
#### 4.2.2 Mixed Ratios of Application Resource Demands  
The performance of the MRG algorithm was evaluated when the resource demands of applications were more complex. A mixture of cases with the resource demands in [1:2], [1:4], and [1:6] was evaluated. Each set of the experiment consisted of six applications with the resource demand vector of CPU and memory by (1,2), (2,1), (1,4), (4,1), (1,6), and (6,1). In this experiment, the number of sets was increased from one to eight for each round of evaluation.  
Experimental results are depicted in Figure 6. As shown in Figure 6, the experimental result showed that the performance of MRG increased with an increase in resource demands. When the number of sets was two, both the load-balanced algorithm and the MRG algorithm could satisfy the low resource demand of applications. However, when the number of applications increased, the proposed MRG algorithm exhibited a lower completion time than the load-balanced algorithm. The results showed that the MRG approach outperform the load-balanced algorithm by up to 24.7% in terms of the completion time. This is because the degree of resource gap was higher for the load-balanced algorithm. By contrast, the MRG algorithm could prevent the resource gap and improve the resource utilization to reduce the overall completion time.  
Figure 5: Ratio of resource demand is [1:6] in which one application requires 1 CPU core and 6 GB RAM and another application requires 6 CPU cores and 1 GB memory.
## 3 Results
### The Effect of Aspect Ratio on Mean Average Precision  
The default aspect ratio of 320 \(\times\) 320 is set as the control dataset while 416 \(\times\) 416 and 512 \(\times\) 512 are set as test groups. Table 8 shows the mean Average Precision of the respective aspect ratio with 5000 steps and Table 9 is with 10,000 steps. The aspect ratio of 416 \(\times\) 416 is used for the subsequent detection scores test as it has the highest overall mAP for both 5000 steps and 10,000 steps.  
As mentioned in [17], the mAP on the COCO 2017 validation set is 22.2%. Because our overall mAP is higher than 22.2%, we can conclude that our detector has achieved good performance. In comparison with other works using SSD Mobilenet V2 such as [PERSON] et al. 10 on Nvidia Jetson (another low-powered embedded device), our mAP for a quantized model with a Learning Rate of 0.08 is 46.63%, whereas theirs is 45%. [PERSON] et al. [32] conducted a study on Raspberry Pi and their mAP was only 23.4%. Based on this, our results are better than those of the other two works.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classes** & **320 \(\times\) 320** & **416 \(\times\) 416** & **512 \(\times\) 512** \\ \hline Blue cube & 24.32 & 25.63 & 23.36 \\ Blue cylinder & 26.21 & 29.61 & 26.86 \\ Red cube & 46.36 & 53.62 & 38.22 \\ Red cylinder & 39.42 & 48.30 & 39.77 \\ Yellow cube & 44.30 & 54.81 & 38.93 \\ Yellow cylinder & 44.30 & 34.07 & 36.62 \\ \hline Overall & 37.48 & 41.01 & 33.96 \\ Comparison with default & - & +3.53 & \(-\)3.52 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Mean Average Precision with different aspect ratios (5000 training steps).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classes** & **320 \(\times\) 320** & **416 \(\times\) 416** & **512 \(\times\) 512** \\ \hline Blue cube & 29.98 & 36.91 & 31.61 \\ Blue cylinder & 37.80 & 30.76 & 31.00 \\ Red cube & 27.90 & 47.36 & 47.19 \\ Red cylinder & 29.06 & 31.35 & 37.95 \\ Yellow cube & 44.16 & 45.57 & 41.05 \\ Yellow cylinder & 40.94 & 48.73 & 32.05 \\ \hline Overall & 34.97 & 40.11 & 36.81 \\ Comparison with default & - & +5.14 & +1.84 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Mean Average Precision with different aspect ratios (10000 training steps).  
Figure 7: Comparison of Learning Rate and quantization.
# Gold Nanoparticles and Nanorods in Nuclear Medicine: A Mini Review
## 4 Gold Nanoparticle and Nanorods: Clinical Aspects
### Monoclonal Antibody-Labeled AuNPs
#### 4.5.2 Radionuclides \({}^{131}\)I and \({}^{123}\)I  
\(\gamma\) and \(\beta\)- emitter and \(\gamma\) emitter, respectively.\({}^{131}\)I-labeled immuno-AuNP (\({}^{131}\)I-C225-AuNPs-PEG) for EGFR-expressed A549 human lung cancer was evaluated in [PERSON] et al.'s study as a promising therapasostic agent [135]. MicroSPECT/CT imaging showed a significant uptake of the compound in tumor-bearing mice. In this paper, the authors concluded that \({}^{131}\)I-C225-AuNPs-PEG might be a valid agent for radioimmunotherapy in EGFR-expressing cancers.
# Current Sensorless MPPT Control for PV Systems Based on Robust Observer
## 4 Design of Luenberger Robust Observer Based on AEM
#### Incremental Conductance MPPT Algorithm  
The incremental conductance algorithm is based on detection of the slope of the P-V curve, and the MPP is tracked by searching the peak of the P-V curve. As the name implies, this algorithm makes use of the incremental conductance \(dI/dV\) and the instantaneous conductance \(I/V\) to perform MPPT. The operating point location of the PV module in the P-V curve can be determined based on the relationship between conductance values as follows  
\[\frac{dI}{dV} =-\frac{I}{V} \tag{31}\] \[\frac{dI}{dV} >-\frac{I}{V}\] (32) \[\frac{dI}{dV} <-\frac{I}{V} \tag{33}\]  
where (31) corresponds to PV module operating at the MPP, while (32) and (33) indicate the PV module operation is located at the left and right side of the MPP in the P-V curve, respectively.  
Equations (31)-(33) are obtained from the fact that slope of the P-V curve at MPP is equal to zero, that is  
\[\frac{dP}{dV}=0 \tag{34}\]  
and since \(P=VI\), (34) can be rewritten with the following form  
\[I+V\frac{dI}{dV}=0 \tag{35}\]  
The conventional INC algorithm achieves the MPP detection using (35), and the PV module voltage and current are measured by the MPPT controller. If (32) is fulfilled, the duty cycle of the DC-DC converter is decreased while if (33) is satisfied the duty cycle is increased. On the other hand, duty cycle keep the same value for the case where (35) is satisfied [33]. The overall operation of INC algorithm is depicted in Figure 3.
# Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants
## 3 Materials and Methods
### Machine Learning Models
#### 3.4.2 Support Vector Regression (SVR)  
SVR is reliable with non-linear data. As the study's data was non-linear, an SVR model was used to evaluate the performance capabilities of predicting EPA NO\({}_{2}\) emissions and to compare it to the performance of other models in this study.  
A radial basis kernel function was selected for this model. The RBF kernel is typically used to find a non-linear classifier or regression line and function for two points \(X_{1}\) and \(X_{2}\) that computes the similarity or how close they are to each other. Separate models were created for each scenario. Each utilized a grid search for a range of values to optimize hyperparameters for each experiment's scenarios and set of features.
# Intelligent Traffic Management in Next-Generation Networks
## 3 DL/ML Applications in Software-Defined Networking
### Traffic Classification
#### 3.1.1 Existing Solutions
This may be attributed to the limitation of the processing speed leading to the dropping of statistics packets. * **Other Approaches for Traffic Classification** Video surveillance traffic increased sevenfold between 2016 and 2021 [98]. Due to these changes, multimedia traffic must be managed as efficiently as possible. SDN and ML can be promising solutions to manage video surveillance traffic. Therefore, by applying SDN and ML, we are able to increase efficiency and reduce the cost of management. In this context, [PERSON] et al. [99] proposed an intelligent system for guaranteeing QoS of video traffic in surveillance IoT environments connected through SDN using the AI module, which is integrated into SDN. The traffic classification that detects whether the incoming flow is critical or not is based on the packets sent by the SDN controller to the AI system. This detection is performed through the use of SVM as a classifier and is able to detect critical traffic with 77% accuracy, which is better than other tested methods (i.e., NN or KNN). [PERSON] et al. [60] proposed a real-time elephant flow detection system which includes two main stages. In the first one, suspicious elephant flows are distinguished from mice flows using head packet measurement. In the second stage, the correlation-based filter (CFS) is used to select the optimal features to build a robust classifier. Then, elephant flows are used for improving the classification accuracy, and the decision tree (C4.5) classifies them as real elephant flows or suspicious flows. To maximize the detection rates and minimize the misclassification costs of elephant flows, they used the cost-sensitive decision trees as classifiers. [PERSON] et al. [100] proposed a traffic classification method using a deep neural network (DNN) which classifies the packets based on the action (accept/reject). As a proof concept, the performance of DNN is compared with two classifiers, which are SVM and KNN. In addition, [PERSON] et al. [101] proposed a network slicing classification framework. Given network features, the proposed framework tries to find the network slices, such as enhanced mobile broadband (eMBB), massive machine-type communication (mMTC), or ultra-reliable low-latency communication (URLLC) by combined deep belief network and neural network models as well as meta-heuristic algorithms in order to enhance the classification performance.
## 5 Results and Discussion
### Transient Engine Modelling
#### 5.2.3 Baseline Transient Response  
The baseline transient results are shown in Figure 24. The results are realistic compared with literature experimental and numerical results, with 3 points in excess of 2 s and one point well above what was expected [3; 9]. At 2000 rpm full load, the time to reach maximum shaft speed is 5.16 s. However, as previously discussed in the model validation (Section 4.3), the performance at this speed was the only exception for the entire model but the ratio of power to fuel mass flow rate was matched to that of the original engine. Looking at the trend of each speed at their part loading, the results show a consistent percentage change for all engine speeds, with 2000 rpm being the exception.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Turbocharger** & **Turbine** & **Compressor** \\ \hline Moment of Inertia (kg/m\({}^{2}\)) & Moment of Inertia (kg/m\({}^{2}\)) & Moment of Inertia (kg/m\({}^{2}\)) \\ \(5.54\times 10^{-6}\) & \(4.72\times 10^{-6}\) & \(8.22\times 10^{-7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Experimentally obtained mass moment of inertia for GT1548 turbocharger.  
Figure 24: Baseline time to reach maximum shaft speed at various engine speeds and loads.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameter** & **GT 1548 Turbocharger** & **GT1548 Scaled Turbocharger** & **Scale Factor** \\ \hline Compressor diameter & 0.048 m & 0.04902 m & 1.02125 \\ Turbine diameter & 0.0412 m & 0.0543 m & 1.29684 \\ Compressor inertia & \(8.22\times 10^{-7}\) kg m\({}^{2}\) & \(9.13\times 10^{-7}\) kg m\({}^{2}\) & (1.02125)\({}^{5}\) \\ Turbine inertia & \(4.72\times 10^{-6}\) kg m\({}^{2}\) & \(1.73\times 10^{-5}\) kg m\({}^{2}\) & (1.29684)\({}^{5}\) \\ Turbocharger inertia & \(5.54\times 10^{-6}\) kg m\({}^{2}\) & \(1.82\times 10^{-5}\) kg m\({}^{2}\) & n/a \\ \hline \hline \end{tabular}
\end{table}
Table 9: Results of inertia testing of GT1548 turbocharger.
## 2 Materials and Methods
### Residual Networks  
Residual networks were first introduced by [PERSON] et al. [39]. Theoretically, neural network depth is important, especially for image recognition tasks. However, once reaching a certain threshold for the network depth, adding more layers will lead to problems, such as vanishing gradients, where the network becomes unable to learn from the data and gets saturated. Additionally, the computational complexity associated with optimizing many network parameters makes the training of the network much more difficult. Residual networks provide a new way of connecting neurons using skip connections. These connections are added every two or more layers. Figure 7 shows the normal CNN connections (left) and residual networks or \"skip connections\" (right). The principal is as follows: Let x be the input and H(x) be the \"underlying mapping\" representing the function that fits two or more layers (two in the present study). Instead of H(x), we try to approximate a function F(x) = H(x) \(-\) x. Before computing the output of the second layer, x is added to F(x) and passed to the ReLU activation. We obtain H(x), which is composed of x (the input). This procedure can transfer important information (x) from any layer to the following layers and prevent the vanishing gradient problem [39].  
Following some preliminary tests of several pretrained CNNs on the ImageNet challenge, such as the VGG16 and the VGG19 [37], we found that ResNet50 has a small advantage in terms of training accuracy and offers a considerable improvement in validation accuracy compared to normal CNNs. This was the main motivation behind the use of residual networks in this work.
# Distributed MIMO Measurements for Integrated Communication and Sensing in an Industrial Environment
## 4 System Calibration
### DC Component  
This step is not, in the strict sense, a calibration procedure but is performed because our hardware uses direct down-conversion (DDC) from radio frequency down to baseband. To avoid local oscillator (LO) leakage, the DC component is nulled by transmitting a 0 on the center subchannel (\(f_{\text{c}}\)). Hence, the (complex baseband) DC-component has to be interpolated by taking the average of the amplitude of the two neighboring complex coefficients and the average phase evolution as follows:  
\[\widehat{\left[\mathbf{r}_{n}^{(hh^{\prime})}\right]}_{f=0}=\frac{\left|\left[ \left(\mathbf{r}_{n}^{(hh^{\prime})}\right)_{f=\Delta f}\right|+\left|\left[ \mathbf{r}_{n}^{(hh^{\prime})}\right]_{f=-\Delta f}\right|\right.}{2}\cdot\exp \left\{\text{j}\left(\angle\left[\mathbf{r}_{n}^{(hh^{\prime})}\right]_{f=- \Delta f}+\hat{\phi}_{n}\right)\right\}\!, \tag{2}\]  
where \((\text{diff}([a_{1},a_{2},\ldots,a_{L}])=[a_{2}-a_{1},a_{3}-a_{2},\ldots,a_{L}-a _{L-1}]\) is a function that takes two consecutive values in the vector and their differences, with the resulting vector being one element smaller; \(\text{unwrap}(\cdot)\) is the phase unwrapping function of Matlab); and \(\hat{\phi}=\text{E}\Big{\{}\text{diff}\Big{(}\text{unwrap}\Big{(}\angle r_{ n}^{(hh^{\prime})}\Big{)}\Big{)}\Big{\}}\) is the average phase difference between two consecutive subcarriers after the phase has been unwrapped.
# Prototyping the Symmetry-Based Chaotic Communication System Using Microcontroller Unit
## 3 Results
### Noise Resistivity Analysis  
Let us perform classical BER analysis through direct error counting for the same ranges of symmetry coefficient and parameter values as in the previous subsection.  
It should be noted that, in this study, we used a small spread of parameters for the Lorenz system, which led to a relatively low noise immunity. The key reason for this issue is that we focused here on the secrecy of a chaotic communication system. Thus, to adequately compare the two investigated modulation methods, it is necessary to choose a relatively small spread of parameters in the case of PM, where noise immunity and secrecy are similar to SCM. At the same time, an increase in the spread of parameters leads to more significant changes in the dynamics of the signal, which makes message transmission more vulnerable, even to spectral analysis attacks.  
The results are shown in Figure 7. The BER analysis was performed for 10,000 bits.  
The results of the experiments show that two out of three experimental cases (namely, PM case 1 and SCM case 2, PM case 2 and SCM case 3) have similar noise immunity characteristics. Knowing about the similar noise immunity, one can conclude the advantage  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**SCM**} & \multicolumn{3}{c}{**PM**} \\ \cline{2-7}  & \(\mathbf{c_{1}}\) & \(\mathbf{c_{2}}\) & \(\mathbf{\Delta c}\) & \(\mathbf{s_{1}}\) & \(\mathbf{s_{2}}\) & \(\mathbf{\Delta s}\) \\ \hline Case 1 & 2.3 & 2.5 & 0.2 & \(-1\) & 2 & 3 \\ Case 2 & 2.3 & 2.6 & 0.3 & \(-1.5\) & 2.5 & 4 \\ Case 3 & 2.3 & 2.7 & 0.4 & \(-2\) & 3 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Set of modulation parameters for experimental cases.  
Figure 7: BER vs. SNR estimation.  
of the investigated modulation method by evaluating the secrecy of these approaches. The secrecy analysis is performed in the next subsection.