esa-sceva/satcom-chunk-collection huggingface dataset:
# Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks
## 4 Methods
### Relative Distance Map
#### 4.2.2 RDM without Prior Segmentation  
Thus, we also investigated an approach that does not require any a priori knowledge about the retina position within the analyzed image. Two following solutions are evaluated:  
* Basic Map with Orientation--Firstly, we investigated if a map of linearly spaced values in the range of \(\langle 0,1\rangle\) would provide the network with sufficient information about the layers' hierarchy. Additionally, to account for retina orientation in the image and resulting rotation of the preretinal space, we propose to arrange the values according to the said retina orientation. For this purpose, the orientation is determined by first applying a Gaussian filter on the image (with \(\sigma=3\)) and then calculating _arctan_ of the vertical and horizontal image edges subsequently obtained with the use of Sobel edge detection. This map will be further called \"BasicOrient\".
* Cumulative Sum Map--The second method incorporates calculating a cumulative sum of intensity image values for each column of the image. This is based on the assumption that pixels in the vitreous and preretinal space region have very low-intensity values, as opposed to the retinal region. Additionally, the pixels below the retina have average intensity, hence providing lower variations in the cumulative sum. Furthermore, by performing a scaling operation, it is possible to obtain values similar to those produced by Equation (1), but with the significantly less computational expense (no need to use initial segmentation). Furthermore, this method is not subjected to error propagation (which may occur if the initial segmentation algorithm provides incorrect ILM and RPE borders). Thus, this map is further referred to as the \"CumSum\" map.  
Figure 4 illustrates prepared distance maps, for an example input OCT image, using four methods described above. The color in the images in the first row depicts values in the range given for each distance map. The images below represent the same map in greyscale, for which values are in the range of intensity of a B-scan image (i.e., values below 0 are represented by a black color, values in the range of \(\langle 0,1\rangle\) are in the shade of gray, values bigger than 1 are white).
## 4 Experimental Results and Discussions
### Experimental Results
#### 4.3.3 Extended Research on SSD300
The entries with the best APs for each object category are bold-faced. We also compared the proposed SSD300-FFAM with state-of-the-art two-stage detectors and one-stage detectors using different backbone networks. For the results in Table 5, we discuss as follows:  
* The proposed SSD300-FFAM has 300 more boxes than the baseline method SSD300 because the sizes of detection feature maps used are 38, 20, 10, 6, 3, 1, respectively. However, its detection accuracy is higher. * Two-stage methods like Faster R-CNN and R-FCN have a candidate frame extraction part, so the number of boxes is much less than that of one-stage methods YOLOv3 [9], SSD, or SSD300-FFAM. * Compared with the one-stage method YOLOv3 [9], the deeper the backbone network used, the higher the object detector's detection accuracy.
## 5 Malware Detection on Edge Platform
### Discussion  
The proposed method reveals several aspects of malware classification by incorporating sophisticated experiments. We illustrate how efficient feature learning with a straightforward training strategy can achieve state-of-the-art performance for malware classification. Despite there being a severe data imbalance in the benchmark dataset, the proposed method outperforms existing methods without exploiting any class information. Our FSAM, AB, and MHA help us learn salient features for malware classification. It is worth noting that attention guidance modules like MHA incorporate mobile-unfriendly normalization layers. Such layers can make the deployment of generic hardware impractical. We prune out such normalization layers to address the limitation while optimizing the edge devices.  
Apart from outperforming the existing method, we also illustrate the practicability of malware detection on edge devices. The proposed method is generic and specially optimized for mobile-friendly edge devices, including IoT and 5G infrastructures. Therefore, the proposed method can be deployed on any ARM power edge platform with traditional server or desktop-class hardware. Despite achieving a higher FPS on edge devices, our method drops 4 (%) accuracy as a trade-off between speed and accuracy. Such accuracy drops after optimization are shared among the research community [85]. Nevertheless, quantization-aware training can help recover the dropped accuracy by fine-tuning the optimized weights [86; 88]. We planned to study the quantization-aware training for malware detection in a future study. Apart from the dropped accuracy, our model enables inference with a speed of 545 FPS. Notably, such a higher inference speed confirms the practicability of an optimized malware detection algorithm in low-power hardware.  
In addition, we evaluated our optimized weight on a single-edge board due to hardware limitations. It would be an interesting future scope for the malware detection research community to study the performance of deep learning-based malware detection algorithms on numerous edge hardware from different manufacturers.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Acceleration** & \multicolumn{2}{c}{**Unoptimized**} & \multicolumn{2}{c}{**Optimized**} \\ \hline
**Architecture** & **CPU (V64)** & **GPU (GTX 3060)** & **Jetson (ARM64)** & **Jetson (ARM64)** \\ \hline Weight & & Float32 & Float32 & Float16 \\ Precision & & 99.36 & 95.19 & 94.97 \\ Accuracy & & 0.9892 & 0.95187 & 0.9497 \\ Precision & & 0.9879 & 0.95187 & 0.9497 \\ Recall & & 0.9924 & 0.95187 & 0.9497 \\ F1 & & 0.9924 & 0.95187 & 0.9497 \\ \hline FPS & 49.93 & 103.83 &
## 4 Experimental Setup
### SLDV Setup  
For the excitation of the specimen, in the SLDV as well as the ULT setup, a conventional piezoelectric element by Farnell is glued to the surface close to the area of interest (some cm next to the rivet line). The piezoelectric transducer is a circular plate with a thickness \(d=0.5\,\mathrm{mm}\) and a radius \(r=35\,\mathrm{mm}\) and has a nominal resonance frequency of \(f_{res}=2.8\,\mathrm{kHz}\). It is worth mentioning that this is the designed resonance frequency according to the datasheet provided by the manufacturer.  
The SLDV measurements of the fuselage panel were performed with a short wavelength infrared (SWIR) SLDV head from the company Optomet GmbH from Germany. This scanning head has an integrated function generator which simplifies the synchronization between the excitation and data acquisition. With a wavelength of \(1550\,\mathrm{nm}\), this scanning head operates in the _short-wave infrared range_ (SWIR) which allows an output power of \(10\,\mathrm{mW}\) while eye safety laser class 1 is still fulfilled. As mentioned before, SLDV performs point-wise measurements on a predefined grid. The total grid area and points density are linearly influencing the measurement time. In this work, the focus lies on a high spatial accuracy which leads to a high point density of 12 points per mm.  
The input signal of the piezoelectric wafer is a \(\tau=23\,\mathrm{ms}\) long linear chirp signal with a frequency of \(1\,\mathrm{kHz}\) to \(350\,\mathrm{kHz}\). For every point in the grid, three measurements with a sampling rate of \(1.4\,\mathrm{MS/s}\) are recorded and averaged. For each time signals \(v_{x,y}\), the FFT for each point (\(\mathrm{FFT}_{(x,y)}\)) is calculated and exported from the operating software for further  
Figure 4: A sketch of the cross section of area I and II.  
processing and visualization with Python 3.1. For optimal feature extraction, the data is then frequency filtered before the root mean square (RMS) of each FFT\({}_{(x,y)}\) is calculated and normalized to the maximum RMS in the respective measurement. Additionally, the color bar for the representation of the individual frequency bands is optimized for good visual feature recognition. The lowest 0.8 % of measurement points are shown red and the highest 0.5 % of measurement points are in saturation. This promotes an intuitive judgment of the features like loose or damaged fasteners.
# Brake Torque Sensor Calibration Device
## 6 Calibration Measurements That Were Performed  
After the theoretical calculations, calibration measurements were performed with the given calibration device (Figure 9). The torque sensor pulled from the measuring chain was inserted into the prepared calibration device together with the calibration wheel and weight carriers. After running the calibration program on the measuring computer, the sensor was loaded three times to its maximum value in the calibrated direction using a set of calibrated weights to ensure its mobility throughout the measurement range. Calibration was performed gradually from the smallest required value by a suitable combination of calibration weights that were added to the weight carrier. A torque was created on the arm of the calibration wheel, which was subsequently compared with the value displayed by the program on the measuring device. Calibration consisted of loading the sensor to preselected values with ten consecutive repetitions. Figures 10-16 show the measured values for a torque of 1-5, 7 and 9 Nm.  
During calibration, the measured values were read from the display of the measuring device to which the calibrated sensor was connected. The settings of the given measuring device made it possible to display the measured values in three decimal places.  
After finishing the measurement by this method, the analysis of the measurement was carried out and the measurement was carried out with the original calibration device (Figure 17) using the standard torque sensor loaded by means of a lever. In this type of calibration, a Lorenz DR-12 torque sensor with a measuring range of \(\pm\)25 Nm and an accuracy class of 0.1 was used as the standard. By carrying out both compared calibration measurements in a short time span, the same conditions were ensured for both measurements.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Required Torque Value [Nm]** & **Calibrated Weight Value [N]** &
\begin{tabular}{c} **Standard Uncertainty of the** \\ **Considered Source [Nm]** \\ \end{tabular} \\ \hline
0.5 & 2.5 & 0.0000065298 \\
1 & 5 & 0.0000073987 \\
2 & 10 & 0.0000147975 \\
3 & 15 & 0.0000165439 \\
4 & 20 & 0.0000209261 \\
5 & 25 & 0.0000221962 \\
7 & 35 & 0.0000266793 \\
9 & 45 & 0.0000305043 \\
10 & 50 & 0.0000739788 \\
15 & 75 & 0.000077235 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Calculated values of the standard uncertainty of the considered source.  
Figure 10: Graph of measured values for the required torque value of 1 Nm.  
Figure 9: Calibration device with weights and the calibrated torque sensor.  
Figure 11: Graph of measured values for the required torque value of 2 Nm.  
Figure 12: Graph of measured values for the required torque value of 3 Nm.  
Figure 13: Graph of measured values for the required torque value of 4 Nm.  
Figure 16: Graph of measured values for the required torque value of 9 Nm.  
Figure 14: Graph of measured values for the required torque value of 5 Nm.  
Figure 15: Graph of measured values for the required torque value of 7 Nm.
## 3 Results
### Volatile Compounds  
The amount and type of volatile compounds are also indicators for the degradation suffered by lipid compounds during oxidation processes. As with the rest of the secondary products, the type of volatile compound formed depends on the fatty acid substrate [2,6]. Our study identified a total of 35 compounds including hydrocarbons, aldehydes, ketones, acids, alcohols, furans and terpen in the different oil samples (Tables 3-5 for coconut, rapeseed and grape seed oil, respectively). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Volatile** & **KI** & **T0** & **T1** & **T2** & **T3** \\ \hline \multicolumn{2}{c}{**Hydrocarbons**} & & & & \\ \hline
2-E-Octene & 809 & ND & 6691 \(\pm\) 4688 & ND & ND \\
2-Z-Octene & 816 & ND & 4685 \(\pm\) 3378 & ND & ND \\
**Total hydrocarbons** (\(\Sigma\)) & & ND & 11,376 \(\pm\) 8066 & ND & ND \\ \hline \multicolumn{2}{c}{**Aldehydes**} & & & & \\ \hline (E)-2-Pentenal & 748 & ND & 3983 \(\pm\) 2086 b  & ND & 1850 \(\pm\) 530 a  \\ Hexanal & 803 & 2200 \(\pm\) 749 a  & 15,749 \(\pm\) 2794 b  & 21,685 \(\pm\) 1020 c  & 94,823 \(\pm\) 7218 d  \\ (E)-2-Hexanal & 851 & ND & 1797 \(\pm\) 803 a  & ND & 14,301 \(\pm\) 901 b  \\ Hepanal & 901 & ND & 1800 \(\pm\) 387 a  & 2726 \(\pm\) 894 a  & 28,217 \(\pm\) 3317 b  \\ (E)-2-Hepfenal & 954 & ND & 5055 \(\pm\) 306 a  & 8054 \(\pm\) 934 a  & 150,112 \(\pm\) 5215 b  \\ (E,E)-2-Hepfenal & 995 & ND & ND & ND & 18,217 \(\pm\) 1604  & Octanal & 1002 & ND & 5462 \(\pm\) 3111 a  \\
2,4-Heptaional & 1009 & ND & 2225 \(\pm\) 267 a  & 2796 \(\pm\) 278 a  & 4968 \(\pm\) 1704 b  \\ Nonanal & 1103 & ND & 1122 \(\pm\) 395 a  & 1176 \(\pm\) 424 a  & 106,442 \(\pm\) 15,688 b  \\ _Total aldehydes_ (\(\Sigma\)) & & 2200 \(\pm\) 749 a  & 37,196 \(\pm\) 12,884 a  & 41,965 \(\pm\) 5580 a  & 626,692 \(\pm\) 59,654 b  \\
**Ketones** & & & & & \\ \hline
2-Hexanone & 792 & ND & ND & ND & 13,732 \(\pm\) 1592 \\
2-Heptanone & 890 & ND & 1833 \(\pm\) 627 a  & 2108 \(\pm\) 146 a  & 30,275 \(\pm\) 3422 b  \\
4-Octanone & 972 & ND & ND & ND & 1957 \(\pm\) 443 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Volatile compound content (*) detected for coconut oil: unheated (T0), after 10 and 20 days of treatment at 65 \({}^{\circ}\)C (T1 and T2, respectively) and after 90 min at 180 \({}^{\circ}\)C (T3).
# No-Reference Image Quality Assessment Based on Image Multi-Scale Contour Prediction
## 3 Approach
### Multi-Scale Contour Features  
We believe that the sharpness of the edge contour of the image is an important feature that affects the image quality. At the same time, the same distortion type of the distorted images have different degrees of distortion, and the contour features of all images in the image scale space can simulate images with different degrees of distortion. Multi-scale features are used to simulate the contour response of the retina to images at different distances, so we train the model in the first stage to predict the contour features of images at different scales.  
The scale-space of an image is the convolution of the image and the Gaussian function of the variable scale. The two-dimensional Gaussian function is:  
\[G(x,y,\sigma)=\frac{1}{2\pi\sigma^{2}}\exp\biggl{[}-\frac{(x^{2}+y^{2})}{2 \sigma^{2}}\biggr{]} \tag{1}\]  
The scale-space of an image \(I(x,y)\) is:  
\[L(x,y,\sigma_{i})=\{l_{0}(x,y),I_{1}(x,y),\dots,I_{i}(x,y)\} \tag{2}\]  
where  
\[I_{i}(x,y)=\left\{\begin{array}{c}I(x,y),i=0\\ G(x,y,\sigma_{i})*I(x,y),i\
eq 0\end{array}\right. \tag{3}\]  
The image subtraction of adjacent scales obtains the multi-scale contour features. Therefore, the contour feature ground truth is defined as:  
\[\begin{array}{l}D_{i}(x,y)=I_{i}(x,y)-I_{i+1}(x,y)\\ =G(x,y,\sigma_{i})*I(x,y)-G(x,y,\sigma_{i+1})*I(x,y)\\ =[G(x,y,\sigma_{i})-G(x,y,\sigma_{i+1})]*I(x,y)\end{array} \tag{4}\]  
Figure 2 shows the generation of the multi-scale contour features ground truth. The images shown in Figure 3a-e are an image scale space of Figure 3a. The images shown in Figure 3f-i are contour features of different scales of the image in Figure 3a. Figure 4 is the same as Figure 3.  
Figure 2: The generation of the multi-scale contour features.  
In the first stage of training, the contour prediction network learns to predict contour features, and the loss function is defined by the mean square error between the predicted value and the ground-truth:  
\[L1=\frac{1}{N}\sum_{i=1}^{N}\left(D_{i}^{m}-h_{\theta}(I_{i})\right)^{2} \tag{5}\]  
where \(h_{\theta}(I_{i})\) is the contour feature of the image \(I_{i}\) predicted by the model, \(\theta\) is the parameters of the contour prediction network, and \(m\) is the exponent number. In our experiment, we choose \(m=0.5\).
## 3 Results and Discussion
### Antibacterial Activity  
As shown in Table 4, antimicrobial screening against four oral pathogens revealed that compounds **1**, **3**, **4**, **12**, and **16** were inhibitory to the growth of _Streptococcus mutans_, _Streptococcus analis_, _Streptococcus sapuinis_, and _Phorphyromonas gingivalis_. Among these, compounds **1**, **3**, **4**, and **12**, were either equally or more potent than their respective crude extract of origin (Table 4). Earlier in vitro studies have shown that the Sonoran ethanolic extract of propolisis exhibited antibacterial activity against _E. coli_ (ATCC 25922) and _S. aureus_ (ATCC 6538P). The propolis constituents CAPE, pinochemrin, pinobanksin 3-O-acetate, and naringenin exhibited significant inhibitory activity on the growth of _S. aureus_. CAPE exhibited the maximum inhibitory effect on the bacterial growth (CAPE (MIC 0.1 mmol/L), pinochemrin (MIC 0.4 mmol/L), pinobanksin 3-O-acetate (MIC 0.8 mmol/mL), and naringenin (0.8 mmol/L)). None of the propolisis constituents influenced the growth of _E. coli_ at any of the tested concentrations ([PERSON] et al., 2018).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cell Line** & **IC\({}_{50}\)** & **Reference Drug** & **Reference Concentration** \\ \hline C6 & 92.2 ug/mL & Temozolomide & IC\({}_{30}\) 250 uM \\  & & & (50 ug/mL) \\ \hline HeLa & \(>\)100 ug/mL (357 ug/mL) & & IC\({}_{50}\) 46 uM (14 ug/mL) \\ \hline SiHa & \(>\)100 ug/mL (500 ug/mL) & & IC\({}_{50}\) 121 uM (36 ug/mL) \\ \hline CaSki & \(>\)100 ug/mL (538 ug/mL) & & IC\({}_{50}\) 163 uM (50 ug/mL) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Cytotoxic effect of EEP on several cancer cell lines.
## 3 Results and Discussion
### Effect of Flow on Coagulation Performance  
For continuous coagulation reaction, the flow determines the retention time of reactants in each reactor. The increase of flow indicates the decrease of retention time. In order to investigate the effect of flow on coagulation performance, continuous experiments were performed under magnesium ion 250 mg/L, rapid mixing speed 300 rpm with 30 L/h and 60 L/h. The retention time in the rapid mixing tank was 2 min and 1 min for flow of 30 L/h and 60 L/h, respectively. As shown in Table 4, the average floc size decreased with increasing influent flow for each operation unit. As for the rapid mixer, the average floc size was 8.06 and 7.25 \(\upmu\)m for 30L/h and 60 L/h, respectively. According to earlier results [14], in the stage of rapid mixing, magnesium hydroxide was formed and particles grew in a very short time. A suitable period of rapid mixing was necessary for good coagulation. Reactive orange removal efficiency reached 89% and 83% for 30 L/h and 60 L/h, respectively. The retention time in each unit for 60L/h was shorter than that of 30 L/h, especially in the rapid mixer of 1 min retention time. This is too short to form magnesium hydroxide-reactive orange flocs.  
Figure 4 clearly indicates floc formation and growth in the magnesium hydroxide-reactive orange continuous coagulation system. In the rapid mixer and flocculation basin, average floc size remained stable and flocs aggregated to relatively large flocs in the sedimentation tank. These two figures showed the same trends that the magnesium hydroxide coagulation process was similar to the precipitation process. When magnesium ion is added to the alkaline solution in rapid mixing period, the reaction crystallization process will happen rapidly. Magnesium hydroxide coagulation includes magnesium hydroxide nucleation and a combination of reactive orange into flocs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Flow (L/h)**} & \multicolumn{3}{c}{**Average Size (\(\upmu\)m)**} \\ \cline{2-4}  & **Rapid Mixer** & **Flocculation Basin** & **Sedimentation Tank** \\ \hline
30 & 8.06 & 7.89 & 11.21 \\
60 & 7.25 & 7.52 & 10.47 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average size in different process units.  
Figure 3: Effects of rapid mixing on removal efficiency and zeta potential.
# Survey on Power-Aware Optimization Solutions for MANETs
## 1 Introduction
### Motivation and Scope of this Survey  
The goal of this survey article is to synthesize the existing power-aware optimization solutions for MANETs from diverse viewpoints and to present a classification of them. This article extends the work done by previous surveys, presenting recent power-aware optimization solutions for MANETs. We present new techniques that do not just consider power-aware routing metrics and energy-efficient routing protocols, but we also consider other power-aware optimization solutions for MANETs. This survey analyzes state-of-the-art power-aware optimization solutions for MANETs. To the best of our knowledge, a comprehensive survey of such solutions for MANETs does not exist and is the goal of this article. This survey article comprises as follows:  
* It analyzes key-issues on power-aware optimization solutions for MANETs;
* It surveys the existing power-aware optimization solutions for MANETs;
* It discusses open research areas in MANETs, such as: (1) cross-layer designs; (2) hybrid optimization algorithms for topology management in cluster-based MANETs; (3) design of cooperative MAC protocols; (4) multipath routing based on hybrid modeling; (5) fuzzy-logic support in multicast routing schemes.  
The remainder of this article is structured as follows: Section 2 presents previous surveys that summarize research into energy-efficient routing in MANETs and its related power-aware routing metrics;Section 3 delineates the leading energy-efficiency related issues unsettling numerous routing-based proposals. It analyses all the categories of power-aware optimization solutions for MANETs, such as cross-layer optimization approaches for energy conservation in MANETs; Section 4 reflects upon some lessons that we have learnt to date from this research; The survey article presents design challenges and future research opportunities in Section 5.
# Data Interpretation Technology of GPR Survey Based on Variational Mode Decomposition
###### Abstract  
Data interpretation is the crucial scientific component that influences the inspection accuracy of ground penetrating radar (GPR). Developing algorithms for interpreting GPR data is a research focus of increasing interest. The problem of algorithms for interpreting GPR data is unresolved. To this end, this study proposes a sophisticated algorithm for interpreting GPR data with the aim of improving the inspection resolution. The algorithm is formulated by integrating variational mode decomposition (VMD) and Hilbert-Huang transform techniques. With this method, the intrinsic mode function of the GPR data is first produced using the VMD of the data, followed by obtaining the instantaneous frequency by using the Hilbert-Huang transform to analyze the intrinsic mode functions. The instantaneous frequency data can be decomposed into three frequency attributes, including frequency division section, time-frequency section, and space frequency section, which constitute a platform to gain insight into the nature of the GPR data, such that the inspected media components can be examined. The effectiveness of the proposed method on a synthetic signal from a GPR forward model was studied, with the multi-resolution performance being tested. Inspecting the media of a highroad by analyzing the GPR data, with the abnormal characteristics being designated, validated the applicability of the proposed method.  
Keywords:ground penetrating radar; Hilbert-Huang Transform; empirical mode decomposition; variational mode decomposition; intrinsic mode function +
Footnote †: journal: Applied