esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[18] proposed an automatic detection method based on RetinaNet and the model for nest detection was established by adjusting the appropriate network structure and parameters. The recognition accuracy can reach 94.1%, which is higher than YOLO and SDD, but the recognition is slow. [PERSON] et al. [19] adopted an improved YOLOv3 to shorten the time for foreign object detection by replacing Darknet-53 with lightweight MobileNet on the skeleton of the original YOLOv3. [PERSON] et al. [20] proposed an insulator recognition method for power distribution lines based on modified YOLOv3. The K-means algorithm was used to cluster the bounding boxes, and the DenseNet was introduced to replace the DarkNet53. The accuracy was increased by 10% than the original YOLOv3. [PERSON] et al. [21] added K-means clustering and DIoU-NMS to YOLOv4, and the average accuracy was 8.39% higher than that of the original YOLOv4, which can provide early warning about the intrusion of foreign objects around transmission lines. Practical applications usually require timely feedback on the detection results, most of the current detection methods sacrifice accuracy to improve speed; thus, to meet the requirements of real-time detection. Moreover, the object detection method based on deep learning requires a large number of input images and high-performance inference equipment, which is obviously a challenge for practical deployment. Due to the automatic feature extraction procedure, the deep learning-based visual trackers have recently achieved a great success in challenging scenarios [22]. Using pre-trained CNN as feature extraction networks (FEN) and fusing deep representations of different CNNs [23] to detect the foreign objects on transmission lines can not only reduce the manual resources, but also have a high detection accuracy in the absence of high-performance equipment. In order to overcome the problem of insufficient samples and improve the detection performance, this paper proposes a method combining multi-network feature fusion and random forest for detection of foreign objects on transmission lines. A foreign object image dataset composed of balloons, kites, nests and plastic was established. The target region in the image was extracted by Otus binarization threshold segmentation and morphology processing. Five types of convolutional neural network (CNN) including GoogLeNet, DenseNet-201, EfficientNet-B0, ResNet-101 and AlexNet, were used to extract the features of the target region, and then they were fused by concatenation fusion strategy. The fused features of different schemes were input to train and test the random forest model. The gradient-weighted class activation mapping (Grad-CAM) was applied to offer visual explanations and verify the effectiveness of the optimal scheme. The results show that the detection accuracy reaches 95.88%.
# Effect of Processing on Volatile Organic Compounds Formation of Meat--Review
## 3 Non-Thermal Processing
### Cold Plasma (Cp)  
CP as a method of food decontamination is being used increasingly often in the food industry. Apart from the influence of this process on microorganisms, the influence of CP treatment on the quality characteristics of meat, including the VOCs profile, is also important [46; 47]. Unfortunately, treatment with the use of plasma is associated with the intensification of the fat oxidation process in the raw material, which results in the formation of secondary volatile and non-volatile compounds. These include VOCs such as alcohols, aldehydes, carbonyls, furans, and hydrocarbons [48; 49]. In addition, [PERSON] et al. [50] conducted experiments to demonstrate the effect of plasma treatment of water used to cure pork loin. Based on their experience, it was shown that water treatment with plasma not only affects the physicochemical properties of the final product, but also changes the profile of VOCs. The intensity of the process was strongly correlated with the formation of free amino acids, and it also influenced the formation of new volatile compounds in the product. Among them were 3-methylbutanol, hexanal, 2,3-hydroxy-butanone, 2,3-octanedione, and about 20 other volatile compounds. Nevertheless, some volatile compounds have also been shown to be lost through plasma treatment. These changes are most likely related tochanges caused by lipid oxidation, Strecker degradation, and Maillard reactions [50]. On this basis, they showed that plasma treatment not only contributed to the decrease in the number of microorganisms on the surface of the meat, but also increased the absorption of oils constituting a component of the protective coating. The beef-coating process itself contributed to the inhibition of fat oxidation and, as a consequence, the decrease in the content of some volatile compounds, including alcohols, esters, ketones, and aldehydes. CP has also found application in removing VOCs from the food industry that may be toxic and/or harmful. The main advantage of this method is relatively low consumption of energy and overall reasonable cost compared to traditional air-consuming methods. Moreover, this technology provides clean air with low concentrations of VOCs at relatively low operating temperatures [51].
# Ship Detection in Sentinel 2 Multi-Spectral Images with Self-Supervised Learning
## 2 Materials and Methods
### Baselines  
1. ImageNet transfer learning: Instead of pre-training the backbone with SSL, this baseline uses a ResNet-50 encoder pretrained on ImageNet as implemented by the torchvision package. Since these encoders are trained on RGB images, we copy the weights of the first 3 channels of the first layer in order to initialize the channels corresponding to spectral bands B8, B11, and B12. Both the TL and FT ship detector training approach can be applied to this baseline;
2. Random initialization: Instead of using a trained backbone network, we initialize the ResNet-50 encoder randomly following the standard Kaiming initialization. Only the fine-tuning (FT) detector training mode is applied when initializing the weights randomly;
3. BL-NDWI--Water segmentation baseline with NDWI: We develop a simple baseline which is based on classical image processing techniques. We use the NDWI spectral index \(NDWI=\frac{B03-B08}{B03+B08}\) and we threshold its value to segment water and non-water pixels. The threshold for the NDWI segmentation is chosen to obtain the best performance on the whole dataset, which may lead to suboptimal choices for some images.  
Next, we eliminate land pixels using the water/land segmentation (CO) (Section 2.5.1) map, giving a ship proposal map. We consider non-water pixels in what are normally water regions to potentially be ships. We extract connected components and we eliminate those that have a width and height greater than 50 pixels (500 m) since no ships larger than this size exist. These are due to islands or sandbanks not correctly mapped in OpenStreetMap layers or thin water banks where the coastline annotation in OpenStreetMap is imprecise.  
Finally, we do several filtering passes on the resulting proposal map: morphological opening and we apply watershed segmentation on the resulting map to identify individual ships.
# A Method for the Calibration of a LiDAR and Fisheye Camera System
## 1 Introduction
### State of the Art  
Calibration between camera and LiDAR systems is essential to ensure accurate and reliable perception of the environment in autonomous navigation and robotics applications. Thus, we are referring to methods that permit obtaining the following:  
* The intrinsic calibration parameters of the fisheye camera that enable the projection of the world points (in the camera coordinate system) to the image plane.
* The relative transformation matrix \(T\) between the camera and LiDAR coordinate systems.  
The first part is considered to be known since numerous methods can be found in the related literature to perform this calibration [6; 7; 8]. The proposed method focuses on the second part so that it permits obtaining the three-dimensional relationship between these sensors, which is the ultimate goal of performing the calibration. In the state of the art, calibration methods can be classified into two main categories: target-based methods and targetless methods. The subsequent subsections present some of the most relevant works in the literature.
# A Single Motor-Driven Focusing Mechanism with Flexure Hinges for Small Satellite Optical Systems
## 2 Materials and Methods
### Concept Design and Working Principle of SMFH  
Based on the requirements of the Schmidt-Cassegrain optics system, the focusing mechanism is conceptualized. As shown in Figure 3, the proposed SMFH consists of a. Secondary mirror dummy, b. Secondary mirror supporter, c. Flexure hinge, d. Rotation body, e. Motor screw, f. Linear screw, g. Motor, h. Roller, and i. Rotation shaft. Once the DC motor rotates in CW or CCW, the rotation body induces a deformation, and then each FlexHe is elongated. The detailed working principle is as follows:  
1. Once the motor screw that connects the motor shaft rotates CW, it advances the linear screw, enabling the movement of the \(x\)-axis (\(-\)).
2. The rotation body attached to the end of the linear screw rotates CW.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Optical Alignment** & **Requirement** \\ \hline De-space stroke range & \(\pm\)10 \(\upmu\)m \\ Maximum De-center & 8.2 \(\upmu\)m \\ Maximum Tilt & 200 \(\upmu\)rad \\ \hline \hline \end{tabular}
\end{table}
Table 3: The requirements of the optical alignments; De-space, De-center, and Tilt.  
Figure 2: Relation of MTF to De-space, with respect to the Tilt.  
A roller that is mounted at the tip of the rotation body positively displaces the secondary mirror along the \(z\)-axis (+), where it contacts the secondary mirror supporter.
4. The three flexure hinges are elongated along the \(+z\)-axis, and then it generates the De-spacewith a restoring force.
5. The motor rotates CCW, and then steps 1 to 4 repeat in the opposite direction/order. This process causes the secondary mirror to negatively displace in the \(z\)-axis (-).
###### Abstract  
In this study, metallic nanoislands were prepared by thermal annealing of gold thin film produced by vacuum evaporation on a glass substrate to investigate the surface-enhanced Raman scattering (SERS) effect on them. The influence of the analyte on the enhancement factor of SERS was studied with riboflavin and rhodamine 6G dye. Two laser excitation sources at 532 and 633 nm wavelengths were used for SERS measurements. We found that the enhancement factors of the gold nanoisland SERS substrates were influenced by the analytes' adsorption tendency onto their surfaces. The SERS amplification was also found to be dependent on the electronic structure of the molecules; higher enhancement factors were obtained for rhodamine 6G with 532 nm excitation, while for riboflavin the 633 nm source performed better.  
gold nanoislands; plasmonics; surface-enhancement Raman scattering; sensors; photonic devices +
Footnote †: journal: Applied Sciences
## 3 Design of the Asymmetric Coupled Line Balun
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**f (GHz)**} & \multirow{2}{*}{**Technology**} & \multirow{2}{*}{**Topology**} & **Insertion Loss** & **Amplitude** & **Phase** & \multirow{2}{*}{**Ref.**} \\  & & & **(min.–max.)** & **Imbalance** & **Imbalance** & **Reference** \\ \hline \multirow{2}{*}{16.5–67} & 180-nm CMOS & \multirow{2}{*}{Asymm.stack} & (1.1–4) & \multirow{2}{*}{1} & \multirow{2}{*}{5} & [15] \\ \cline{1-1} \cline{5-6} \multirow{2}{*}{57–67} & 65-nm CMOS & & With grounded & & & \\  & CMOS & T-bars & (1.5–3.5) & \multirow{2}{*}{0.5} & \multirow{2}{*}{1} & [16] \\ \hline \multirow{2}{*}{27.6–65.5} & 65-nm CMOS & \multirow{2}{*}{Stack} & (2.8–4.2) & \multirow{2}{*}{0.8} & \multirow{2}{*}{8} & [17] \\ \cline{1-1} \cline{5-6} \multirow{2}{*}{20–67} & 130-nm CMOS & & & & & \\  & CMOS & offset stack & (1.4–3) & \multirow{2}{*}{1} & \multirow{2}{*}{4} & [18] \\ \hline \multirow{3}{*}{34–110} & 180-nm SiO-He BiCMOS & \multirow{2}{*}{Stack} & (1.7–4.7) & \multirow{2}{*}{1.5} & \multirow{2}{*}{7} & [19] \\ \cline{1-1} \cline{5-6} \multirow{2}{*}{200–325} & 130-nm SiO-He BiCMOS & & & & & \\  & SiO-He BiCMOS & & (2.5–3.3) & \multirow{2}{*}{1.5} & \multirow{2}{*}{10\({}^{(2)}\)} & [20] \\ \hline \multirow{3}{*}{88–224} & 130-nm SiO-He BiCMOS & 3-line asymm. edge coupled & (1.2–1.5) \({}^{(3)}\) & \multirow{2}{*}{\(<\)1} & \multirow{2}{*}{\(<\)7} & \multirow{2}{*}{This Work \#} \\  & SiO-He BiCMOS & & (1.2–3) \({}^{(4)}\) & & & \\ \hline \multirow{3}{*}{106–262} & 130-nm SiO-He BiCMOS & 3-line asymm. edge coupled & (1.2–1.9) \({}^{(3)}\) & \multirow{3}{*}{\(<\)1 \({}^{(3)}\)} & \multirow{3}{*}{\(<\)6.2} & \multirow{3}{*}{This Work \#} \\  & SiO-He BiCMOS & & (1.2–2.1) \({}^{(4)}\) & & & \\  & SiO-He BiCMOS & & (1.35–3.8) \({}^{(5)}\) & & & \\ \hline \hline \end{tabular}  
* Results using EM simulation of the baluns in ADS momentum. \({}^{(1)}\) The 3-dB loss due to splitting is not included. \({}^{(2)}\) Valid in the frequency band of 200–273 GHz. \({}^{(3)}\) Valid for the WR-6 band (110–170 GHz). \({}^{(4)}\) Valid for the WR-5 band (140–220 GHz). \({}^{(5)}\) Valid for the WR-4 band (170–260 GHz).  
\end{table} 
Table 1: State-of-the-art balun comparison. Figure 10: Simulated S\({}_{11}\), S\({}_{22}\) insertion loss (**a**) and amplitude and phase imbalance (**b**) of the re-scaled asymmetric coupled line balun.
# Degradation State Recognition of Piston Pump Based on ICEEMDAN and XGBoost
## 1 Introduction
[18] combined neighborhood preserving embedding (NPE) with Self-organizing Map  
Figure 1: Structure of slipper pair. 1 is the inner edge of the slipper. (SOM) in bearing degradation state recognition, and achieved good results. As classical classifiers, Support Vector Machine (SVM) [19; 20] and Artificial Neural Network (ANN) [21; 22] have been widely used in the field of mechanical fault diagnosis and condition recognition. For instance, an incomplete wavelet packet analysis (WPA) model composed of five-level discrete wavelet transform (DWT) and four-level WPA was established in the literature [23], and it was applied to multi-layered ANN engine failure classification. In Ref. [24], Laplace scoring algorithm is introduced to automatically select sensitive features according to the the importance of each feature, and the multi-state recognition of rolling bearing is realized based on particle swarm optimization-based support vector machine (PSO-SVM). However, SVM also has some shortcomings, that is, after selecting the appropriate kernel function, the quadratic programming of the function is required when dealing with the classification problem [25]. In this process, a lot of storage space is needed. Tree-based ensemble learning is an extensive and efficient method [26; 27; 28]. However, the traditional method is the model based on serial structure, which will inevitably increase the computational complexity and time [29]. XGBoost [30], as a new tree-based ensemble learning algorithm, can process data in parallel. It has minimal specification requirements for characteristic values, which can intelligently handle missing data and avoid overfitting [31]. Compared with SVM, the prediction accuracy is higher under relatively less parameter adjustment time [32]. Compared with deep learning, XGBoost is easier to classify small data sets [33]. In this paper, a new method for degradation state recognition of piston pump based on a further optimized decomposition method-improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) [34; 35; 36] and XGBoost is proposed. Firstly, ICEEMDAN is used to decompose the vibration signal to generate a series of IMFs. This method greatly suppresses the illusive components and mode mixing caused by the initial decomposition process, and has better decomposition results. According to the correlation coefficient method, the IMFs which have a great correlation with the original signal is extracted, and the different characteristic values of IMFs are analyzed and selected. Finally, the new characteristic values with low coincidence degree are selected by PCA as the input, and the XGBoost model is used for training to complete the degradation state identification. The superiority of this method is verified by comparing with different algorithms. An overview of the proposed methods and analysis steps in the paper are shown in Figure 2.  
Figure 2: An overview of the proposed methods and analysis steps.
## 3 Results and Discussion
### The Laser Power Effect on the PL Intensity  
Figure 7a,b shows the bright field and dark field optical microscope images of the perovskites (C\({}_{6}\)H\({}_{5}\)CH\({}_{2}\)NH\({}_{3}\))\({}_{2}\)PbBr\({}_{4}\) and (C\({}_{6}\)H\({}_{5}\)CH\({}_{2}\)NH\({}_{3}\))\({}_{2}\)PbI\({}_{4}\). It is possible to observe the regular and transparent crystal geometry of the perovskites (C\({}_{6}\)H\({}_{5}\)CH\({}_{2}\)NH\({}_{3}\))\({}_{2}\)PbBr\({}_{4}\) and (C\({}_{6}\)H\({}_{5}\)CH\({}_{2}\)NH\({}_{3}\))\({}_{2}\)PbI\({}_{4}\), which emit blue and green photoluminescence under the 800 nm femtosecond laser. Figure 7c,d shows the two-photon PL spectrum of the perovskites (C\({}_{6}\)H\({}_{5}\)CH\({}_{2}\)NH\({}_{3}\))\({}_{2}\)PbBr\({}_{4}\) and (C\({}_{6}\)H\({}_{5}\)CH\({}_{2}\)NH\({}_{3}\))\({}_{2}\)PbI\({}_{4}\). PL peaks are located at 415 nm (298 eV) and 540 nm (2.30 eV) respectively. PL intensity of the perovskite (C\({}_{6}\)H\({}_{5}\)CH\({}_{2}\)NH\({}_{3}\))\({}_{2}\)PbBr\({}_{4}\) increases from 117 photon counts to 2195 photon counts when laser power increases from 10 mW to 100 mW. In contrast, PL intensity of the perovskite (C\({}_{6}\)H\({}_{5}\)CH\({}_{2}\)NH\({}_{3}\))\({}_{2}\)PbI\({}_{4}\) increases from 158 photon counts to 2771 photon counts when laser power increases from 10 mW to 100 mW.  
Figure 8 shows the laser power dependence of the PL intensity of the perovskites (C\({}_{6}\)H\({}_{5}\)CH\({}_{2}\)NH\({}_{3}\))\({}_{2}\)PbBr\({}_{4}\) (red curve) and (C\({}_{6}\)H\({}_{5}\)CH\({}_{2}\)NH\({}_{3}\))\({}_{2}\)PbI\({}_{4}\) (blue curve). It can be observed the PL intensity increases with increasing laser power. PL intensity of the perovskite (C\({}_{6}\)H\({}_{5}\)CH\({}_{2}\)NH\({}_{3}\))\({}_{2}\)PbBr\({}_{4}\) is less than that of the perovskite (C\({}_{6}\)H\({}_{5}\)CH\({}_{2}\)NH\({}_{3}\))\({}_{2}\)PbI\({}_{4}\) at the same laser power. The radius of a Br atom is less than that of an I atom, and the lattice volume of (C\({}_{6}\)H\({}_{5}\)CH\({}_{2}\)NH\({}_{3}\))\({}_{2}\)PbBr\({}_{4}\) is less than that of (C\({}_{6}\)H\({}_{5}\)CH\({}_{2}\)NH\({}_{3}\))\({}_{2}\)PbI\({}_{4}\). Therefore the perovskite (C\({}_{6}\)H\({}_{5}\)CH\({}_{2}\)NH\({}_{3}\))\({}_{2}\)PbI\({}_{4}\) has a larger Bohr radius and smaller exciton binding energy, so the excitons of the perovskite (C\({}_{6}\)H\({}_{5}\)CH\({}_{2}\)NH\({}_{3}\))\({}_{2}\)PbI\({}_{4}\) will more easily separate and produce radiative recombination.  
Figure 6: The absorption spectra of the perovskites (C\({}_{6}\)H\({}_{5}\)CH\({}_{2}\)NH\({}_{3}\))\({}_{2}\)PbBr\({}_{4}\) (red curve) and (C\({}_{6}\)H\({}_{5}\)CH\({}_{2}\)NH\({}_{3}\))\({}_{2}\)PbI\({}_{4}\) (blue curve).
# Energy-Efficient LoRa Routing for Smart Grids
###### Abstract  
Energy-efficient routing protocols in Internet of Things (IoT) applications are always of colossal importance as they improve the network's longevity. The smart grid (SG) application of the IoT uses advanced metering infrastructure (AMI) to read and record power consumption periodically or on demand. The AMI sensor nodes in a smart grid network sense, process, and transmit information, which require energy, which is a limited resource and is an important parameter required to maintain the network for a longer duration. The present work discusses a novel energy-efficient routing criterion in an SG environment realised using LoRa nodes. Firstly, a modified LEACH protocol-cumulative low-energy adaptive clustering hierarchy (Cum_LEACH) is proposed for cluster head selection among the nodes. It uses the cumulative energy distribution of the nodes to select the cluster head. Furthermore, for test packet transmission, multiple optimal paths are created using the quadratic kernelised African-buffalo-optimisation-based LOADng (qAB_LOADng) algorithm. The best optimal path is selected from these multiple paths using a modified version of the MAX algorithm called the SMAx algorithm. This routing criterion showed an improved energy consumption profile of the nodes and the number of active nodes after running for 5000 iterations compared to standard routing protocols such as LEACH, SEP, and DEEC.  
advanced metering infrastructure; cluster head selection; energy efficiency; IoT; LEACH; long range (LoRa); network lifetime; smart grid; WSN; optimisation +
Footnote †: journal: Sensors
## 4 Implementation Details
### Parallelization
#### 4.2.1 Shared Memory Parallelization  
For shared memory systems with uniform memory access (UMA), two types of parallelization are utilized. First, an **implicit parallelization** may be applied by the interpreter for an additional acceleration of block operations. Second, **explicit parallelization** is available for the computation of different state and output trajectories, using parallel for-loops parfor (See [[http://mathworks.com/help/matlab/ref/parfor.html](http://mathworks.com/help/matlab/ref/parfor.html)]([http://mathworks.com/help/matlab/ref/parfor.html](http://mathworks.com/help/matlab/ref/parfor.html))), but deactivated by default to guarantee replicable results, as the use of parfor does not guarantee a unique order of execution.