esa-sceva/satcom-chunk-collection huggingface dataset:
# Stain Defect Classification by Gabor Filter and Dual-Stream Convolutional Neural Network
## 2 Stain Defect Classification System  
The structure of the stain defect classification system proposed in this paper is shown in Figure 1. The proposed method comprises gray-scale transform, Gabor image generation, Gabor image selection, and CNN. The general process is as follows. The input inspection image \(I_{RGB}\) is converted to a gray-scale inspection image \(I_{Gray}\) through gray-scale transform. After this, the Gabor image was generated and selected from the input image. Finally, the classification result \(O\) was obtained using a CNN with the selected Gabor images and gray-scale inspection image.  
Let \(R_{mn}\), \(G_{mn}\), and \(B_{mn}\) are red channel, green channel, and blue channel of the image, respectively, while \(M\) and \(N\) are the height and width of the image, respectively.  
\[\begin{split}\mathrm{I_{RGB}}=\begin{bmatrix}i_{11}&i_{12}& \cdots&i_{1N}\\ i_{21}&i_{22}&\cdots&i_{2N}\\ \vdots&\vdots&\ddots&\vdots\\ i_{M1}&i_{M2}&\cdots&i_{MN}\\ \end{bmatrix}\\ \text{where, }i_{mn}\left[R_{mn}\quad G_{mn}\quad B_{mn}\right]\\ i_{mn}\in\mathbb{R}^{1X3}(m=1,\cdots,M,n=1,\cdots,N)\end{split} \tag{1}\]  
\(I_{Gray}\) can be obtained as follows.  
\[\begin{split}\mathrm{I_{Gray}}=\begin{bmatrix}j_{11}&j_{12}& \cdots&j_{1N}\\ j_{21}&j_{22}&\cdots&j_{2N}\\ \vdots&\vdots&\ddots&\vdots\\ j_{M1}&j_{M2}&\cdots&j_{MN}\\ \end{bmatrix}\\ \text{where, }j_{mn}=\frac{0.229R_{mn}+0.587G_{mn}+0.114B_{mn}}{3}\\ j_{mn}\in\mathbb{R}^{1X1}(m=1,\cdots,M,n=1,\cdots,N)\end{split} \tag{2}\]  
The Gabor image generation step is a step to obtain the Gabor filter image \(G(k)\) for each phase. The Gabor filter transform uses a complex kernel with real and imaginary parts.  
\[G=\{G(k)\mid k=1,2,\cdots,K\} \tag{3}\]  
In the Gabor filter image selection step, a Gabor filter image with features useful for stain defects is selected among the generated Gabor filter images. By inputting the  
Figure 1: Stain defect classification system.  
gray-scale inspection image \(I_{Gray}\) and the selected Gabor filter image \(\hat{G}\) into the CNN \(E(*)\), the classification result \(O\) for the inspection image is the output.  
\[O=E(I_{Gray},\hat{G}) \tag{4}\]
## 2 Materials and Methodology
### Numerical Simulation
#### 2.3.1 Phase Change Modeling  
An implicit enthalpy-porosity approach employing lever rule [42] is applied to model the phase change. The thermos-physical properties of the PCM are assumed to be independent of temperature. Porosity values at each iteration estimate the melt fraction which is 0 for fully solid, 1 for fully liquid and between 0 and 1 for mushy PCM. The three-dimensional energy Equations (1)-(3) governing phase change are explained below:  
\[\frac{\partial}{\partial t}\left(\rho c_{p}T\right)=\frac{\partial}{\partial x }\left(K\frac{\partial T}{\partial x}\right)+\frac{\partial}{\partial y}\left( K\frac{\partial T}{\partial y}\right)+\frac{\partial}{\partial z}\left(K \frac{\partial T}{\partial z}\right)+S_{h} \tag{1}\]  
The change in enthalpy (\(H\)) is given by the Equation (2).  
\[\frac{d}{dt}\left(\rho H\right)=-\frac{d}{d_{x_{j}}}\left(\rho\ \mathbf{u}_{j}c_{p}T\right)+\frac{d}{d_{x_{j}}}\left[K\frac{dT}{d_{x_{j}}} \right]+S_{h} \tag{2}\]  
The parameters \(\rho\), \(\mathbf{u}_{j}\), \(c_{\text{p}}\) and \(K\) denote the density, velocity vector, specific heat capacity and thermal conductivity of the PCM, respectively, whereas \(S_{h}\) is the source term.  
The liquid fraction (\(\beta\)) of the PCM during phase change is calculated from Equation (3) [43]:  
\[\beta=\left\{\begin{array}{cl}0,&\text{if }T<T_{sol}\\ \frac{T-T_{sol}}{T_{liquid}-T_{sol}},&\text{if }T_{sol}<T<T_{liq}\\ 1,&\text{if }T>T_{liq}\end{array}\right. \tag{3}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Sizing Criteria** & **Description** \\ \hline Size function & Curvature \\ Relevance Center & Fine \\ Smoothing & High \\ Growth rate & 1.2 \\ Nodes & 79,745 \\ \hline \end{tabular}
\end{table}
Table 3: Sizing criteria for the heat sink model.  
Figure 2: Meshing of the heat sink-phase change material (HS-PCM) system showing HS base plate, fins, PCM and side walls.  
where \(T_{\text{sol}}\) and \(T_{\text{liq}}\) represent the solidus and liquidus temperatures of the PCM, respectively. \(\beta\) is computed from the total enthalpy of the PCM given in Equation (4):  
\[H=H_{ref}+\int_{T_{ref}}^{T}c_{p}dT+\beta L \tag{4}\]  
where \(L\) is the latent heat of fusion and \(T_{\text{ref}}\) is the reference temperature.  
The phase change problem is modeled and solved in ANSYS platform (Version 15, Computer software company, Cecil Township, PA, USA, 2015). The solver constantly iterates Equations (2)-(4) to determine the temperature at each node. The solution is updated every one-second time step with 20 iterations per time step for a total flow time of 35 min.  
The enthalpy-porosity approach is employed due to its single domain formulation, which eliminates computationally expensive explicit tracking of solid-liquid interface [44]. However, the limitation is that the solver does not distinguish between melting and solidification temperatures, which may cause discrepancies, especially during solidification [45,46].
# Open Collaborative Platform for Multi-Drones to Support Search and Rescue Operations
## 2 Related Works
Since the size and weight of the Krypto module are small and light, it can be easily mounted on most drones. An example of the Krypto module is shown in Figure 1. An additional control wire (shown on the red, orange, and yellow ribbon cable in the left figure) can be connected to DJI's CAN port (circled in the middle figure) which allows the module to control the drone's flight path and download drone's status information (e.g., battery power, speed, location, etc.,). Note that DJI will prevent drones to fly over restricted areas (i.e., no-fly zones) such as cities or near the airport during normal use. However, the GEO No-Fly Zones can be unlocked by applying with DJI Flight Planner [36] for search and rescue operations. Once, the Krypto module is connected, two Wi-Fi cards are used with one care a wireless access point (AP) and one to monitor the received signal strength (i.e., RSSI). Figure 2 shows the main components of the Krypto module on different drones. In Figure 2a, the Krypto is mounted on DJI's Phantom 2's right-side landing gear and the portable charger on the left-side landing gear. Additional weight is added to balance both sides of the drone. In Figure 2b, the Krypto module is mounted on DJI's M100 with a ribbon cable connected to M100's COM port to control the flight path and download the status of the drone. The total weight of Krypto is 264 g which included all Krypto's components, wiring, mounting brackets, and balance weights. Figure 1: Prototype of the Krypto module. (**a**) Krypto module consist of Raspberry PI B+ and two Wi-Fi cards. (**b**) CAN port on DJI M100 drone. (**c**) Control wire (shown on the red, orange, and yellow ribbon cable in the left figure) for connecting to DJI’s CAN port. Figure 2: Krypto module on different drones. (**a**) DJI Phantom 2 with the Krypto module. (**b**) DJI M100 with the Krypto module.
## 2 Materials and Methods
### Evaluation  
Special data sets are used to obtain the IR images in this article. Based on these image sequences, we conduct many simulations with our method and compare the denoising effect with traditional algorithms. The IR image sequence of denoising has many kinds of evaluation standards. As per usual, the mean square error(MSE) is shown in (23). It represents the difference between the original image and the denoising image. The PSNR is defined by the division between the biggest gray and the MSE. A larger PSNR means more similarity to the original images [38].  
\[MSE=\frac{\sum_{i=0}^{M}\sum_{j=0}^{N}((I(i,j)-I_{0}(i,j))^{2}}{M*N}. \tag{23}\]  
\[PSNR=10*Log\frac{255^{2}}{MSE}. \tag{24}\]  
Another evaluation metric is the structure similarity index measurement (SSIM). It is defined by the product of the brightness factor, contrast factor, and structure factor. A bigger SSIM means a higher similarity between the images [38].  
\[SSIM(X,Y)=l(X,Y)^{\alpha}*c(X,Y)^{\beta}*s(X,Y)^{\gamma}. \tag{25}\]  
\[l(X,Y)=\frac{2u_{x}u_{y}+C_{1}}{u_{x}^{2}+u_{y}^{2}+C_{1}}. \tag{26}\]  
\[c(X,Y)=\frac{\delta_{x}\delta_{y}+C_{2}}{\delta_{x}^{2}+\delta_{y}^{2}+C_{2}}. \tag{27}\]  
\[s(X,Y)=\frac{\delta(xy)+C_{3}}{\delta_{x}\delta_{y}^{2}+C_{3}}. \tag{28}\]  
\(I(X,Y)\) is the brightness factor in (26), and \(c(X,Y)\) is the contrast factor in (27). \(s(X,Y)\) is the structure factor in (28). The \(u_{x},u_{y}\) separately represent the average of the original image and the denoising image. \(\delta_{x},\delta_{y}\) are the std of the image. \(\delta_{xy}\) means the covariance. To simplify the analysis, we regard the \(\alpha,\beta,\gamma\) as 1. The SSIM still stands for structure similarity. Certainly, a bigger SSIM demonstrates a better denoising result.  
Finally, we use the recovered image as an input for target detection. Inspired by [39], we define the local SCRG of the small target as  
\[SCRG=\frac{(S/C)_{d}}{(S/C)_{n}} \tag{29}\]  
\(S\) is the mean difference between the local image and the small target. \(C\) is the standard deviation of the local image. \(()_{n}\) and \(()_{d}\) represent the parameters of input images with noise and output denoising images separately. Higher local SCRG is helpful for us to detect the IR small target for remote sensing.
### UAV Image Processing  
UAV image processing followed the flowchart of Figure 3. The DJI Phantom 4 RTK images taken in April over each field's bare soil were mosaicked in Pix4 Dmapper software to generate a DEM GeoTiff. Using the System for Automated Geoscientific Analysis (SAGA), a free, open-source spatial data analysis software package, topographic metrics were computed from the DEM [41]. Using the \"Terrain Analysis--Morphometry\" tool, the metrics generated include slope, aspect, profile curvature, and plan curvature. Creating topographic wetness indices (TWI) required several steps. First, using the \"Terrain Analysis--Hydrology\" tool, a flow accumulation layer of the field was created. Using the \"Flow Width and Specific Catchment Areas (SCA)\" tool, two SCAs were created using different algorithms: Deterministic 8 and Multiple Flow Direction. The \"Topographic Wetness Index (TWI)\" tool was then used to create two maps, TWI #1 and TWI #2, based on the respective algorithm SCAs. The final topographic metrics were exported as GeoTiff files.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Band** & **Name** & **Band Range (nm)** & **Centre Wavelength (nm)** & **Bandwidth (nm)** \\ \hline
1 & Blue & 465–485 & 475 & 20 \\
2 & Green & 550–570 & 560 & 20 \\
3 & Red & 663–673 & 668 & 10 \\
4 & Red-Edge & 712–722 & 717 & 10 \\
5 & NIR & 820–860 & 840 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Spectral characteristics of the five MicaSense bands.  
MicaSense camera multispectral images were processed in Pix4 Dmapper to create an orthomosaic image per band with 4 cm \(\times\) 4 cm resolution. An important step in producing a high-quality final image is radiometric calibration considering the sensor influence and scene illumination of the UAV flight. Prior to each flight over a field, the MicaSense camera was positioned above a MicaSense Calibrated Reflectance Panel to take a minimum of five white reference images for each band. From the Exchangeable Image File Format (EXIF) metadata of the images, Pix4 Dmapper can read the sensor settings, properties, and geolocation at the time the images were taken. Before starting the mosaic processing, setting options include inputting the white reference images and manufacturer-provided panel reflectance values to calibrate and correct image reflectance for each of the bands. Then, Pix4 Dmapper uses the Structure from Motion (SfM) algorithm to correct image perspectives and recognize where to stitch images together [22]. The high image overlap parameters set in the flight plan enable the software to recognize greater, similar areas of each image for a higher chance of mosaic success. Pix4 Dmapper processing options include georeferencing with ground control points (GCP) to improve the absolute location accuracy. Following Pix4D's recommended number of five GCPs, black and white checkered boards were placed around the wheat fields and their coordinates recorded using a Global Positioning System (GPS) connected to the RTK. The output orthomosaic images are GeoTiff files with reflectance values for each MicaSense band.
# Generative Model Using Knowledge Graph for Document-Grounded Conversations
## 3 Document-Grounded Generative Model Using Knowledge Graph
### Fine-Tuning the Model  
We adopt BART as a pretrained language model to fine-tune the proposed model using knowledge graphs with a dialogue history. The proposed model is a transformer-based encoder-decoder model. Figure 6 illustrates the architecture of the proposed model.  
In the encoder layer, given the dialogue contexts \(X=\{x_{1},x_{2},\ldots,x_{n}\}\), we concatenate \(BOS\), \(X\), and \(EOS\) tokens. We then use a transformer-based BART encoder to convert the contexts into hidden states \(H=\{h_{1},h_{2},\ldots,h_{n}\}\), as shown in the following equation:  
\[H=BARTEncoder(BOS;X;EOS) \tag{1}\]  
where \(BOS\) denotes the token to present the beginning of a sequence, and \(EOS\) denotes the token to present the end of a sequence. The knowledge graphs constructed as described in Section 3.1 are encoded using a graph attention network (GAT) [18] to leverage the relevant document(s), as shown in the following equation:  
\[\vec{g}_{i}^{\prime} =\sigma\left(\frac{1}{K}\sum_{k=1}^{k}\sum_{j\in N_{i}}\alpha_{ij} ^{k}W^{k}\vec{g}_{j}\right), \tag{2}\] \[G =\left[\vec{g}_{1}^{\prime},\vec{g}_{2}^{\prime},\ldots,\vec{g}_ {N}^{\prime}\right]\]  
where \(\vec{g}_{i}^{\prime}\) denotes the final output representation of the \(i\)-th node \((\vec{g}_{i}^{\prime}\in\mathbb{R}^{N\times d_{k}}\), where \(d_{k}\) denotes the BART embedding size), \(K\) is the number of heads, \(W^{k}\) is the corresponding input linear transformation's weight matrix, \(N_{i}\) are neighbor nodes of the \(i\)-th node in the graph, and \(\alpha_{ij}^{k}\) is normalized attention coefficients). In the attention layer, matrices \(Attention(H,G,G)\) to the hidden states \(H\) use a residual connection [19] to obtain matrix \(F\), which reflects background knowledge, as shown in the following equation:  
Figure 6: The overall architecture of the proposed model. The model comprises two parts; constructing knowledge graph and fine-tuning the model. The red dotted blocks indicate the attention scores that are calculated by using a scaled-dot product.  
\[Attention(H,G,G) = softmax\left(\frac{HG^{T}}{\sqrt{d_{k}}}\right)G, \tag{3}\] \[F =Attention(H,G,G)+H\]  
where \(Attention(H,G,G)\) is calculated using a scaled dot-product attention mechanism [11], and \(d_{k}\) is a normalization factor. In the decoder layer, matrix \(F\) is used as the input for a transformer-based BART decoder to generate the token \(y_{i}\).  
\[y_{i}=BARTDecoder(F,y_{i-1}) \tag{4}\]  
where \(y_{i}\) denotes the token generated at each decoding step, and \(y_{0}\) is the \(BOS\) token that is input into the decoder. Then, \(y_{i-1}\) is input differently into the decoder according to the training and inference phases. Because the BART decoder is an autoregressive model, the gold tokens are input in the training phase to generate the following tokens. The previously generated tokens are input in the inference phase.
# Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5
## 2 Materials and Methods  
### Selection of Algorithm  
The decision to use a convolutional neural networks was made due to their ease of use. There are a number of pre-trained models that can be tuned for a variety of tasks. They are also readily available, computationally inexpensive and show good performance metrics. Object recognition systems from the YOLO family [51; 52] are often used for vehicle recognition tasks, e.g., [27; 28; 29; 37] and have been shown to outperform other target recognition algorithms [53; 54]. YOLOv5 has proven to significantly improve the processing time of deeper networks [50]. This attribute will gain in importance when moving forward with the project to bigger datasets and real-time detection. YOLOv5 was pre-trained on the Common Objects in Context (COCO) dataset, an extensive dataset for object recognition, segmentation, and labelling. This dataset contains over 200,000 labelled images with 80 different classes, including the classes _car_ and _truck_[50; 55]. Therefore, YOLOv5 can be used as such to detect heavy goods vehicles and can be used as a starting point for an altered model to detect heavy goods vehicle features like their front and rear.
# Effects of Reflectance of Backsheets and Spacing between Cells on Photovoltaic Modules
###### Abstract  
In the photovoltaic (PV) module manufacturing process, cell-to-module (CTM) loss is inevitably caused by the optical loss, and it generally leads to the output power loss of about 2-3%. It is known that the CTM loss rate can be reduced by increasing the reflectance of a backsheet and reflective area through widening spaces between the PV cell strings. In this study, multi-busbars (MBB) and shingled PV cells were connected in series, and a mini-module composed of four cells was fabricated with a white and black backsheet to investigate the effects of reflectance of backsheets and space between the PV cells. Moreover, the MBB modules with cell gap spaces of 0.5 mm, 1.5 mm, and 2.5 mm were demonstrated with fixed 3 mm spaces between the strings. The shingled modules with varying spaces from 2 mm to 6 mm were also tested, and our results show that spacing between PV cells and strings should be well-balanced to minimize the CTM loss to maximize the output power (efficiency).  
photovoltaic modules; multi-busbar modules; shingled modules; backsheet reflectance; spaces between PV cells; CTM loss; output power +
Footnote †: journal: Applied  
the balance is important rather than high C2G and high reflectance. This may open a new direction of module technology for gaining optimized PV efficiency.
## 3 Materials and Methods
### Characterization of Silica Particles
#### 3.5.3 TGA  
TGA was used to characterize the grafted mPEG groups on the surface of silica particles. A thermogravimetric analyzer, SDTA 851 (Mettler Toledo, Columbus, OH, USA) equipped with STARe data analysis software was used. Approximately 4 mg dried sample was heated under nitrogen flow with a heating ramp of 50 \({}^{\circ}\)C/minute from room temperature to 120 \({}^{\circ}\)C. The sample was maintained at 120 \({}^{\circ}\)C for 3 min to ensure the removal of remaining water/solvent in the sample prior to a new 100 \({}^{\circ}\)C/minute heating ramp to 950 \({}^{\circ}\)C, where the sample was kept for 5 min. The percent mass loss between 120 \({}^{\circ}\)C to 950 \({}^{\circ}\)C was calculated for both bare and mPEG-grafted silica particles.
## 5 Performance Evaluation  
In this section, in order to show the feasibility of the proposed solution and the superiority of our algorithm, we first trained our model on a smaller scale and conducted numerical simulation experiments. Afterwards, we expanded the size of the target range and conducted further experiments using a dataset from the Melbourne CBD, Australia. Considering the actual application situation, all UAVs are set to take off from the same location in the numerical simulation experiments. All users are randomly placed at the beginning of each training session and are distributed within a square area of 100 \(\times\) 100 m\({}^{2}\). Table 3 lists the important parameters used in this paper.  
Where parameters \(v_{vmax}\), \(v_{lmax}\), \(P_{0}\), \(P_{b}\), \(v_{0}\), \(\varepsilon_{v}\), and \(\varepsilon_{h}\) related to UAV flight are taken from references [31; 33] and fine-tuned according to the actual situation. \(d_{min}\) and \(d_{comm}\) are taken from references [34; 35] and fine-tuned according to the actual situation. Parameters \(\eta_{s}\), \(S\), \(G_{s}\), \(\alpha_{s}\), \(\beta_{s}\), and \(\delta_{s}\) related to solar charging are taken from reference [10].  
The initial flight height of all UAVs was set to 50 m, and the maximum and minimum flight heights were set to 100 m and 50 m, respectively. Within 400 time slots, a UAV at its maximum flight altitude received an additional 37.87 W of energy compared to a UAV at its lowest flight altitude. When the maximum flight altitude was extended to 500 m, the UAV at the maximum flight altitude gained an additional 333.19 W of energy compared to the UAV at the lowest flight altitude. Figure 5 shows a set of flight trajectories from our simulation experiment. Due to its long flight distance, the UAV 2 increased flight altitude to collect more solar energy. However, UAVs 0 and 1 had shorter flight distances and remained at lower altitudes to expand their coverage area.  
\begin{table}
\begin{tabular}{c c c} \hline
**Notation** & **Explanation** & **Value** \\ \hline \(v_{vmax}\) & Maximum horizontal flight speed of UAV & 6 m/s \\ \(v_{lmax}\) & Maximum vertical flight speed of UAV & 10 m/s \\ \(d_{min}\) & Minimum safety distance between UAVs & 1 m \\ \(d_{comm}\) & Maximum communication distance of UAVs & 102 m \\ \(P_{0}\) & Inductive power of UAV in hover & 29.09 W \\ \(v_{0}\) & Average speed of UAV rotor & 3.6 m/s \\ \(\eta_{s}\) & Energy conversion efficiency of solar panels & 0.4 \\ \(S\) & Area of solar panels & 0.1 m\({}^{2}\) \\ \(G_{s}\) & Average solar radiation of the Earth & 1367 W/m\({}^{2}\) \\ \(\alpha_{s}\) & Maximum value of atmospheric transmittance & 0.8978 \\ \(\beta_{s}\) & Atmospheric extinction coefficient & 0.2804 \\ \(\delta_{s}\) & The uniform height of the Earth’s atmosphere & 8000 \\ \(P_{b}\) & Disturbance parameters of UAV speed & 1.8 \\ \(\varepsilon_{v}\) & Horizontal flight power of UAVs & 10 W \\ \(\varepsilon_{h}\) & Vertical flight power of UAVs & 15 W \\ \hline \end{tabular}
\end{table}
Table 3: List of important parameters in this article.