esa-sceva/satcom-chunk-collection huggingface dataset:
# Object-Based Land Cover Classification of Cork Oak Woodlands using UAV Imagery and Orfeo ToolBox
## 2 Materials
### Dataset
#### 2.2.2 Flight Data Processing and Outputs  
Photogrammetric and image processing procedures were carried out and are synthetically depicted in the workflow (Figure 3). In this procedure, each image taken in-flight has the geographical position and elevation logged using the navigation system and the onboard computer. With this information, the raw dataset is processed using a structure-from-motion (SFM) technique during which the identification of common features in overlapping images is carried out. Subsequently, a 3D point cloud is generated from which a digital surface model (DSM) is interpolated, which in turn is used to orthorectify each image. Finally, all of these images are stitched together in an orthomosaic. Two orthomosaics were derived from two flights that were carried out in different seasons of 2017: spring (5 April at 13:00) and summer (13 July at 11:00) (Table A1). Moreover, each orthomosaic was radiometrically corrected in post-processing using a calibrated reflectance panel imaged before and after each of the flights. The use of the same scene in two different periods allowed for testing the classification algorithm's capacity to take into account the differences caused by variation in the phenological status as it changed throughout the various biological cycles of the main plant species. The resultant mosaics have an extension of 100 ha (a square of 1000 \(\times\) 1000 m) and a resampled GSD (ground sample distance) of 10 cm.  
\begin{table}
\begin{tabular}{l c} \hline \hline \multicolumn{2}{c}{**Technical Characteristics of S20**} \\ \hline Maximum take-off weight & 2.0 kg \\ Maximum weight of the payload (sensors) & 0.4 kg \\ Maximum flight autonomy & 2 h \\ Cruising speed & 60 km h\({}^{-1}\) \\ Maximum operating range drone- pilot station & 20 km \\ productivity (hectares per hour) & 200 ha h\({}^{-1}\) \\ Bands & Red, Green, NIR \\ Pixel resolution & 12.1 Mpx \\ Size of the sensor & 7.6 \(\times\) 5.7 mm \\ Field of view & 73.7 \(\times\) 53.1 degrees \\ Focus & 24 mm \\ Ground sample distance (GSD) & 5 cm pixel\({}^{-1}\) at 100 m of flight height \\ \hline \multicolumn{2}{c}{**Features of the two flights**} \\ \hline Covered area & 100 hectares \\ Flight height & 100 m \\ Sidelap and overlap & 60\% and 90\% \\ N\({}^{\circ}\) of photos & 1500 \\ Duration of flights & 41 min (spring), 28 min (summer) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technical characteristics of the S20 unmanned aerial vehicle (UAV) and the onboard camera [38], and features of the two flights (spring and summer).  
Figure 2: Flight plan (red) and flight path (yellow) (**A**). The fixed-wing UAV S20 before take-off (**B**).  
Figure 3: Workflow of the preprocessing, segmentation, classification, and validation steps implemented to derive an object-based land cover map for cork oak woodlands from UAV imagery.
# Grapevine Branch Recognition and Pruning Point Localization Technology Based on Image Processing
## 2 Materials and Methods
### Target-Detection Algorithms  
The YOLOv5 model, characterized as lightweight, demands fewer computational resources during training and possesses a compact average weight file while maintaining high detection accuracy, making it conducive for deployment on mobile devices. The model's architecture, depicted in Figure 7, comprises four components: Input, Backbone, Neck and Prediction. Firstly, the Input layer accepts images as input to the model. YOLOv5 adopts the mosaic data enhancement method and adaptive image scaling module for the input images, which can effectively improve the generalization ability and robustness of the model and make the model achieve good detection results on complex backgrounds and targets with different scales. Backbone consists of a series of convolutional and pooling layers, which are designed to capture low-level and high-level features from the input images. The backbone network of YOLOv5 uses CSPDarknet53, which is a modified Darknet53 architecture that utilizes Cross Stage Partial connections to improve the efficiency and performance of the network. The neck employs multiple feature fusion mechanisms to integrate information from multiple spatial scales. With Feature Pyramid Networks (FPNs) and PAN networks to fuse features at different levels and a CSP structure to enhance the feature fusion capability, the model is able to efficiently capture contextual information and spatial relationships between targets, improving its ability to accurately detect and localize objects of interest. Finally, the prediction header consists of a series of convolutional and linear layers for mapping the extracted features into the space of detection bounding boxes and categories. YOLOv5 uses the CIoU loss as a loss function for the bounding box to improve the speed and accuracy of the regression prediction box.  
Figure 6: The network structure of PSP-net.
# Mission-Oriented Real-Time Health Assessments of Microsatellite Swarm Orbits
## 3 Real-Time Health Assessment Algorithm
### Variational Power Synthesis Theory  
The weights calculated in the above assessment are constant, but the health status of each satellite in the swarm is dynamically changing, and the role of low-weight factors often becomes important when transient satellite failures occur. Therefore, it is necessary to introduce a variable weighting method to adjust the weights to overcome the limitations of the constant weight assessment.  
Combining the literature [20] and assuming that \(X=(x_{1}{}^{r},\ldots,x_{k}{}^{r})\) is the state vector of factor \(x_{i}(i=1,\ldots,k)\), \(W=(\omega_{1},\ldots,\omega_{k})\) is the constant weight vector of factor \(x_{i}(i=1,\ldots,k)\), and \(S(X)=(S_{1}(X),\ldots,S_{k}(X))\) is the state variable weight vector. The variable weight vector of factor \(x_{i}(i=1,\ldots,k)\) can be obtained from the Hadamard product as follows.  
\[W(X)=\frac{W\cdot S(X)}{\sum\limits_{i=1}^{k}\left(\omega_{i}S_{i}(X)\right)} =\frac{(\omega_{1}S_{1}(X),\cdots,\omega_{k}S_{k}(X))}{\sum\limits_{i=1}^{k} \left(\omega_{i}S_{i}(X)\right)} \tag{22}\]  
where \(S_{i}(X)=e^{-ax_{i}}\) is determined by the expert based on the actual situation.
# Identifying Growth Patterns in Arid-Zone Onion Crops (Alium Cepa) Using Digital Image Processing
## 3 Results  
The temperature and rain conditions obtained throughout the phenological cycle are shown in Figure 3. The minimum temperatures varied between 7 \({}^{\circ}\)C and 15 \({}^{\circ}\)C. The maximum temperatures fluctuated between 20 \({}^{\circ}\)C and 26 \({}^{\circ}\)C, remaining stable.  
Figure 3: Temperature and precipitation.  
Accumulated precipitation was nil for most of the period, except for weeks 10 (18 mm) and 16 (14 mm). Due to low sun conditions, an ideal environment for fungus propagation was generated, causing significant damage. Both graphs were created with data extracted from AccuWeather(r) [26].  
Figure 4 compares the crops' temporal development and the different processes applied to the selected representative images obtained from the plot, where the Original Image (OI), Vegetation Image (VI), Leaf Blight Image (LBI), Humidity Zones Image (HZI), Weed Images (WI), and Error Identification Images (EII) were analyzed. These terms represent the subtraction of OI and WI when analyzing the error between both.  
In this selection, the segmentation of the areas was applied in VI green using Equation (1), representing vegetation segmentation, including cultivation and weeds.  
In LBI, the RGB image is taken from the grayscale image to highlight the damage caused by white leaf blight, while the rest of the image contains grayer tones, making detection at low altitudes easy. At heights greater than 5 m, the damage caused by fungi is not visually detectable enough. HZI is the result of making a Jet-type mapping of the grayscale images, in which the dry areas are highlighted in reddish color while the humid areas are presented lighter colors, in blue passing through yellow.  
The RGB to CIELAB transformation was applied to detect weeds using the nearby neighbors' decision rule and Euclidean distance metric with erosion and dilation. Results were affected by image brightness, with correct results only obtained in certain lighting conditions [18], such as a cloudy day between 9:30 am to 11:00 am during week 16.  
Figure 4: General processing stages; images of crop development at 20 m in different weeks: (**a–c**) in week 4, (**d**) in week 9 (**g,h**) in week 16; (**e**) image at 2 m in week 13, (**f**) image at 5 m in week 15.  
In the first weeks, abrupt changes affecting onion development were undetected. At the same time, it was not possible to detect the crop at heights greater than 20 m. Therefore, there were no significant results in the first four weeks.  
On the right side of the images, a sample of the analysis is presented at different heights (2, 5, and 20 m). The ideal height to detect vegetation areas, even with visible damage, was determined to be 5 m. However, the humid areas are visible at 20 m since they do not depend on the height of the plant but on water leaks or excessive irrigation.
# State-of-the-Art DC-DC Converters for Satellite Applications: A Comprehensive Review
## 3 Review of DC-DC Converters for Satellites
### Isolated Topologies
#### 3.2.2 Full-Bridge Converter  
Full-bridge converters are popular due to their features like good transformer core utilization, simple circuits, low-voltage switch stress, and good power-handling capacity [95]. Ref. [96] studied the full-bridge converter for high-power satellite applications. The converter operates over a wide output voltage range with good efficiency. Nonetheless, the output diodes' speed determines the switching frequency. Also, the converter is prone to shoot-through problems. A comparison of the phase-shifted full-bridge converter with the PWM full-bridge converter showed that the phase-shifted PWM has an inherent soft-switching capability and offers higher efficiency. However, it also faces similar problems with switching frequency and shoot-through. Nevertheless, its control strategy is more complex compared to the PWM full-bridge converter, making it a poor choice for high-power satellite applications. The circuit topology of a basic full-bridge converter is shown in Figure 13a.
# Pt-Based Nanostructures for Observing Genuine SERS Spectra of _p_-Aminothiophenol (PATP) Molecules
## Materials and Methods  
All raw materials in the experiment, such as K\({}_{2}\)PtCl\({}_{6}\) (99.8%), PVP (99.5%), CoCl\({}_{2}\)-6H\({}_{2}\)O (25%), and sodium borohydride (98%) which were purchased from LANYI REAGENT Ltd. (Beijing, China), were used as-supplied, without further purification. Pt-based hollow NCs were obtained by one-pot wet chemical method. Typically, CoCl\({}_{2}\)-6H\({}_{2}\)O (8.5 mg) and PVP (polyvinylpyrrolidone K30, 20 mg) were dissolved in 43 mL of water. After the dissolved solution was stirred for 20 min, a freshly prepared NaBH\({}_{4}\) solution (20 mg in 20 mL deionized water) was then added quickly with vigorous stirring. Then, 10 mL K\({}_{2}\)PtCl\({}_{6}\) (6 mM) was added into the mixture solution, followed by a stirring process for 20 min. Pt-based hollow nanostructures were collected by further centrifugal separation (16,000 rpm) and washed several times with water and ethanol. The final black powder CoPt sample was then obtained by oven drying at 60 \({}^{\circ}\)C for 20 h.  
The morphology of the products was characterised by Hitachi S4800 scanning electron microscope (SEM, Hitachi Ltd., Tokyo, Japan) and JEOL 2200F transmission electron microscope (TEM, JEOL Ltd., Tokyo, Japan). The crystal structure and chemical composition of the products were characterised by energy-dispersive X-ray spectroscopy (EDS, EDAX Inc., Mahwah, NJ, USA) and electron energy loss spectroscopy (ELLs) with the TEM. For X-ray diffraction (XRD, PANalytical B.V., Almelo, Netherlands) measurements, powder samples were analysed with an X'Pert Pro MPD system (Cu K\(\alpha\) X-ray source). The emission spectrum was observed with a Raman spectrometer (Horiba, Ltd., Kyoto, Japan), using a 504 nm wavelength excitation laser.  
Pt-based hollow nanostructures in ethanol solution were dropped onto a silicon wafer and dried. Afterward, the substrate was immersed in 10\({}^{-4}\) mol/L PATP ethanol solution for 6 h. Then the Si substrate, covered with the sample, was taken out and constantly rinsed by ethanol. After being dried in air, the substrate was characterised by SERS measurement. A Horiba Jobin Yvon Raman spectrometer (Horiba, Ltd., Kyoto, Japan) with 532 nm laser (laser spot size \(\sim\)1.5 \(\upmu\)m and beam power 320 \(\upmu\)W) was used to measure the Raman spectra. SERS spectra were collected with a 50\(\times\) objective lens. A total of 10 or 100 spectra were measured for an integration time of 20 s each, giving a total SERS measurement time of 200 s or 2000 s, respectively.
# Error Averaging Effect in Parallel Mechanism Coordinate Measuring Machine
## 4 Property of Slider Motion Errors in PCMM
### Abbe Error and Angular Motion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Selected Points/mm** & \multicolumn{3}{c}{**Corresponding Slider Position/mm**} & \multicolumn{3}{c}{**Error Value/\(\upmu\)m**} \\ \hline \((X,Y,Z)\) & \(x_{1}\) & \(x_{2}\) & \(x_{3}\) & \(\delta_{xA}\) & \(\delta_{yA}\) & \(\delta_{xA}\) \\ \hline
750, 150, 200 & 193.58 & 1167.44 & 1319.58 & \(-7.60\) & \(-21.96\) & \(-12.13\) \\
750, 150, 300 & 240.49 & 1047.23 & 1285.55 & 2.53 & 19.79 & 6.84 \\
750, 150, 400 & 314.56 & 1074.49 & 1211.45 & 4.59 & 16.38 & 11.68 \\
750, 300, 200 & 257.96 & 1357.98 & 1468.03 & 2.55 & \(-15.23\) & 5.77 \\
750, 300, 300 & 311.70 & 1308.28 & 1414.32 & 5.90 & 17.06 & 15.68 \\
750, 300, 400 & 400.57 & 1228.27 & 1325.46 & \(-5.69\) & \(-20.05\) & 1.74 \\
750, 450, 200 & 390.01 & 1371.35 & 1486.67 & \(-2.79\) & \(-18.34\) & \(-1.57\) \\
750, 450, 300 & 467.86 & 1310.48 & 1458.19 & 1.28 & 7.87 & 1.40 \\
750, 450, 400 & 652.02 & 1203.11 & 1374.04 & \(-5.36\) & 14.45 & 1.97 \\
1000, 150, 200 & 443.58 & 1403.64 & 1582.45 & \(-3.57\) & \(-14.40\) & 4.15 \\
1000, 150, 300 & 490.49 & 1397.26 & 1735.50 & \(-3.52\) & \(-11.86\) & \(-0.25\) \\ \(\cdot\cdot\cdot\cdot\) & \(\cdot\cdot\cdot\) & \(\cdot\cdot\cdot\) & \(\cdot\cdot\cdot\) & \(\cdot\cdot\cdot\) & \(\cdot\cdot\cdot\) \\ \(\cdot\cdot\cdot\) & \(\cdot\cdot\cdot\) & \(\cdot\cdot\cdot\) & \(\cdot\cdot\cdot\) & \(\cdot\cdot\cdot\) & \(\cdot\cdot\cdot\) \\
1250, 450, 200 & 890.00 & 1671.33 & 1836.56 & 3.11 & 17.34 & 4.51 \\
1250, 450, 300 & 967.86 & 1610.46 & 1858.18 & 2.62 & 17.07 & 3.33 \\
1250, 450, 400 & 1152.0 & 1653.10 & 1874.07 & 2.27 & \(-11.78\) & \(-2.10\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Error contributed by Abbe error of three sliders. Figure 5: Autocollimator detection. Figure 6: Pitch and yaw angle error of slider \(1\). ranging from \(-21.9\) to \(\sim\)+22.3 \(\upmu\)m. The error variation can be observed in Figure 7. Compared with the errors in \(Y\) and \(Z\) directions, the Abbe error has a minimal effect in the \(X\) direction because the error averaging effect in \(X\) direction is strongest. It is similar to the results of our previous reports on reading head error analysis [21].
# Enhanced Tuna Detection and Automated Counting Method Utilizing Improved YOLOv7 and ByteTrack
## 5 Conclusions  
This study aims to enhance the accuracy of tuna detection and achieve automated statistical analysis of tuna catch quantities. To achieve this goal, a method for automated tuna detection and counting based on YOLOv7-Tuna and incorporating a specific region counting technique into ByteTrack is proposed. To address the characteristics of the tuna dataset, the YOLOv7 model is augmented with DySnakeConv, CoordConv, and DyHead modules. This augmentation enhances the model's ability to extract temporal features,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fish Species** & **Count Quantity** & **Actual Quantity** & **Miscounted Quantity** \\ \hline Albacore-day & 12 & 11 & +1 \\ Albacore-night & 8 & 7 & +1 \\ Yellowfin-day & 4 & 4 & 0 \\ Yellowfin-night & 2 & 3 & \(-1\) \\ Blackfin-day & 1 & 1 & 0 \\ Blackfin-night & 2 & 2 & 0 \\ Sum & 29 & 28 & +1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Tuna Counting Experiment.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Counting Method** & **Repeated Count** & **Missed Count** & **Total Discount** & **Total Count** & **Counting Error** \\ \hline \hline \�  
perceive spatial positions, and suppress complex backgrounds, thereby improving its accuracy. Furthermore, the inclusion of a specific region counting method effectively addresses issues such as occlusion and irregular movements during tuna counting. This method reduces counting errors and enables the possibility of automated statistical analysis of tuna catch quantities.  
Conceptualization, Y.L. and L.S.; methodology, Y.L. and L.S.; software, L.S., J.L. and Y.C.; validation, Y.L.; writing--original draft preparation, L.S.; writing--review and editing, Y.L., L.S., J.L. and Y.C.; project administration, Y.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key Research and Development Program for the 14 th Five-Year Plan. (Grant No.2023 YFD2401301 and No.2023 YFD2401305).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The Tuna datasets are not publicly available due to the data being part of ongoing research.  
The authors would like to express their gratitude for the support of the Fishery Engineering and Equipment Innovation Team of Shanghai High-level Local University.  
The authors declare no conflicts of interest.
## 3 Results
### Evaluation of Remote Sensing (RS)  
Table 4 shows the average PMC, CP, NDF, and PQI reference values of pasture samples and the NDVI\({}_{\text{RS}}\) and the NDWI\({}_{\text{RS}}\) at each of the eight experimental fields and each of the three sampling dates. The pattern, in general, showed a decrease in PMC and CP and an increase in NDF as the vegetative cycle of the pasture advanced (increase of the DOY). The PQI, being a ratio between CP and NDF, tended to decrease with the evolution of the vegetative cycle. This similarity of temporal patterns of the parameters related to pasture quality (PMC, CP, and the PQI) and spectral indices obtained by remote sensing (the NDVI\({}_{\text{RS}}\) and the NDWI\({}_{\text{RS}}\)) was reflected in the significant correlation between (a) the PQI and the NDVI\({}_{\text{RS}}\) (R\({}^{2}\) = 0.77; \(p\) < 0.01) and (b) between PMC and the NDWI\({}_{\text{RS}}\) (R\({}^{2}\) = 0.72; \(p\) < 0.01) (Figure 6). This evolution showed, however, the site-specific variability function of precipitation regimes, which determines the soil moisture availability, the true driver of productivity in dryland pastures of the Mediterranean region [2].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Field**} & \multicolumn{2}{c}{**DOY**} & \multirow{2}{*}{**PMC (\%)**} & \multirow{2}{*}{**CP (\%)**} & \multirow{2}{*}{**NDF (\%)**} & \multirow{2}{*
# Semi-CNN Architecture for Effective Spatio-Temporal Learning in Action Recognition
## 4 Experiments  
### Implementation Details  
We evaluated our framework using the popular action recognition dataset, UCF-101. It contained a total of 13320 video clips, with 101 action classes, and was divided into three training-validation splits. In our experiments, we only utilized the split-1 training and validation sets for evaluation. During training, 16 consecutive frames were randomly sampled from each video. Input frames were re-scaled and randomly cropped at multiple scales, before resizing them to the size of \(224\times 224\). They were also randomly flipped horizontally to allow data augmentation for better training. The input was normalized using the mean and standard deviation of the ImageNet dataset. For validation, the input frames from each validation video were sampled at fixed locations. The frames were rescaled and center cropped, without horizontal flipping.  
Our network was trained end-to-end using stochastic gradient descent with learning rate 0.1, momentum 0.9, dampening 0.9 and weight decay of 0.0001. The learning rate was reduced by a factor of 10 when the validation loss value did not decrease for 10 epochs. The network architecture and training was implemented in PyTorch with CUDA, utilizing two GPUs of GeForce RTX2080 Ti. Due  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **2D-CNN** & **3D-CNN** & **Semi-CNN** \\ \hline VGG-16 & 13 & 8 & (9, 1, 3) \\ ResNet-18 & BasicBlock [2, 2, 2, 2] & BasicBlock [2, 2, 2, 2] & BasicBlock ([2, 1], [1], [1, 1, 2]) \\ ResNet-34 & BasicBlock [3, 4, 6, 3] & BasicBlock [3, 4, 6, 3] & BasicBlock ([3, 2], [2], [3, 3, 3]) \\ ResNet-50 & Bottleneck [3, 4, 6, 3] & Bottleneck [3, 4, 6, 3] & Bottleneck ([3, 2], [2], [3, 3, 3]) \\ ResNet-101 & Bottleneck [3, 4, 23, 3] & Bottleneck [3, 4, 23, 3] & Bottleneck ([3, 2], [2], [12, 11, 3]) \\ ResNet-152 & Bottleneck [3, 8, 36, 3] & Bottleneck [3, 8, 36, 3] & Bottleneck ([3, 4], [4], [18, 18, 3]) \\ DenseNet-121 & [6, 12, 24, 16] & [6, 12, 24, 16] & ([6, 6], [6], [12, 12, 16]) \\ DenseNet-169 & [6, 12, 32, 32] & [6, 12, 32, 32] & ([6, 6], [6], [16, 16, 32]) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configuration comparison for 2D, 3D and semi-CNN architecture, using base model VGG, ResNet and DenseNet.  
to memory constraints, we implemented accumulated gradients during training to allow processing of large batch sizes, while retaining the size of the computation graph. We used a mini batch size of 8 (or 4 or 2 depending on the network complexity) and accumulated the gradients for 32 iterations (effective batch size is 256) before back-propagation. The architecture was trained for 50 epochs and we presented the validation results for comparison.
## 4 Performance Analysis
### Engineering Applications  
The development of intelligent medical treatment cannot be separated from the support of medical data. With the popularization of information technology, more and more hospitals are exploiting electronic medical records in the medical system. Moreover, these electronic medical records not only record the medical data of patients but also make disease predictions for patients. In fact, the application of electronic medical record information in the medical field is a crucial section to promote the sharing of medical data. Because electronic medical records contain different types of data, such as text data and image data, and the text data are mainly exploited in deep learning models to complete the task of NLP. However, deep learning models heavily rely on a large amount of labeled data. In addition, electronic medical records require manual labeling and standardization of standards. In this regard, a medical text data augmentation method that relies on medical knowledge graph is proposed to generate a large number of label data without manual annotation, which is aimed at datasets with a small number of samples. As shown in Figure 5, we can employ a data-driven approach to import some of the electronic medical records data into our methods. Moreover, the MDA method is adopted to generate new synthetic data based on the type of task, which can promote the sharing of medical data.  
Figure 4: The results of the RC task in augmented data.  
Figure 3: The results of the RC task in original data.  
As mentioned above, to further enhance the validity of augmented data, our next step is to increase the diversity of sentences by extracting the features of words. Therefore, the language model can be introduced to identify the features of words, which can improve the efficiency of the model for keyword recognition. Furthermore, the MDA can promote the development of the intelligent hospital.