esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Improved SSD Networks
### Feature Fusion  
The SSD algorithm uses the Conv4_3 layer to predict objects with a small proportion, deep convolutions Conv7, Conv8_2, Conv9_2, Conv10_2, and Conv11_2 predict large objects, SSD only uses one shallow convolution to extract the feature information of small objects, which has less semantic information for small objects, resulting in a poor overall detection effect. Refs. [30; 31; 32] use shallow feature maps to improve the quality of detection, because shallow features contain more detailed information. The improved algorithm uses feature fusion to merge shallow convolutions and deep convolutions. The outputs of the fusion convolutional layer contain not only more detailed information but also more semantic information. In the SSD network structure, the feature information extracted by the Conv5_3 layer covers more context information than the Conv4_3 layer. Therefore, we fuse the Conv4_3 and Conv5_3 layers. The new fusion layer is shown in Figure 4, Conv5_3 is deconvolved for upsampling so that the size of the extracted feature map is the same as the size of the feature map extracted by the Conv4_3 convolutional layer. The BN layer is added after the two convolutional layers to improve the convergence speed of the network, and the ReLU function is added after the BN layer. The ReLU function simplifies the network model, reduces the calculation, and improves the training speed of the network. By setting many parameter values less than 0 to 1, the generalization ability of the network is indirectly improved. We use the concate fusion method to splice the two feature maps into a 1024-channel feature map. The concate operation directly joins the two original feature maps to the channel, and the process of fusing the features is handed over to the network to learn because it is just simple. Therefore, the feature information of the original feature map is relatively complete.  
For other convolutional layers, the improved algorithm adopts the feature fusion method, as shown in Figure 5. Firstly, the original convolutional layer is processed by two 3 \(\times\) 3 convolutional layers to obtain more context information. The small-scale convolution uses deconvolution to keep the size of the large-scale convoluti  
Figure 4: Feature fusion module with Conv4_3 and Conv5_3. We use the deconvolution operation to enlarge the high-level feature maps and sum them in a concatenated way. Finally, a 1 \(\times\) 1 convolution is used for dimensionality reduction and feature reorganization.  
fuse it by the element-wise method. Finally, we use a 3 \(\times\) 3 convolution to enhance the distinguishability of features. For the last layer of convolution, the method is shown in Figure 5. There is no upsampling operation, and use three convolutional layers to improve the robustness of the model.
# Improving Time Study Methods Using Deep Learning-Based Action Segmentation Models
## 6 Evaluation  
Two metrics were selected for model evaluation on our dataset: frame-wise accuracy and a segmental F1@IoU [12] score with a defined minimum overlap threshold of 50%, in which the overlap is determined by calculating the ratio between intersection and union (IoU) of a true and predicted segment. Accuracy measures the proportion of accurately classified frames in a video relative to the total number of frames. The drawback of this metric is that models of similar accuracy can have large qualitative differences in terms of recognized segments in the video (a good example of this is given by Figure 5 in [13]). F1@IoU has three properties that make it suitable for evaluating models in the domain of action segmentation. The first property is that it penalizes over-segmentation errors, and the second property is that it does not penalize minor time shifts that may be due to variability and subjectivity in labeling, while the final property is that results depend on the number of activities rather than their duration. The accuracy metric and F1 metric are complementary because they penalize different model errors, which is why we selected both metrics as part of the procedure for the evaluation of models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Label 1** & **Hyperparameters** & **Set of Values Used** \\ \hline T & Number of Transformer encoder layers & \{1, 2, 8, 16, 32\} \\ NH & Number of heads in multihead attention & \{2, 4, 8\} \\ TN & Number of neurons in Transformer hidden layer & \{128, 256\} \\ FN & Number of neurons in two-layer feedforward network & \{128, 256, 512\} \\ DR & Dropout rate & \{0, 0.25, 0.5\} \\ \hline \hline \end{tabular} 1 Labels of hyperparameters used in Figure 12c.  
\end{table}
Table 3: Hyperparameters tuned in experiments with Transformer encoder architecture.
# Information Fusion for Multi-Source Material Data: Progress and Challenges
## 3 Progress of Information Fusion for Multi-Source Material Data
### Integration of Big Material Data  
The differences between data integration and data fusion are hard to describe. In fact, some researchers acknowledge that these two concepts are almost the same under most circumstances. Nevertheless, data integration is also regarded as the primary phase of data fusion by many researchers. In this paper, we recognize data integration as a procedure of integrating material data sources, data disambiguation, and data storage in a uniform platform. There are some existing integration techniques, and some typical techniques include multi-level data integration, full-vectorization data integration, and knowledge-graph-based data integration. The existing truth-finding techniques used in data integration are analyzed and discussed in the literature [22]. Due to the continuity of material data, certain data ranges and heterogeneity, it is preliminarily judged that CRH (Conflict Resolution on Heterogeneous data) [23] may be a suitable framework for material data. Other researchers proposed to perform material data integration based upon semantic recognition [24; 25] and ontology technologies [26], such as lexical semantic similarity calculation, ontology alignment, semantics recognition with models like CRF (Conditional Random Field), and rule-based ontology matching. Data integration with semantic-index-enabled knowledge bases was also proposed in the literature [27]. Material ontology can be indexed semantically with the help of knowledge bases. Knowledge base generation consists of manual work [28] and algorithms like entity matching [29; 30] and extracting semantic relations [31; 32].
# Design and Accuracy Analysis of a Micromachine Tool with a Co-Planar Driving Mechanism
###### Abstract  
Due to the requirements of manufacturing miniaturized high-tech products, micromachining with micromachine tools has come to be regarded as an important technology. The main goal of this study is to build up the key technologies, including optimal structure and configuration design, synchronous driving control, analysis of optimal accuracy, in order to develop a low-cost and high-accuracy micromachine tool with a multi-degrees of freedom (DOF) platform with a co-plane synchronous driving mechanism. Due to the advantages of such a mechanism, the machine is able to possess a high feed resolution and high accuracy without the use of expensive drive components and high-end CNC controllers. Because of the no pile-up structure, the machine has less movement inertia effect, as well as the merits of light weight, high stiffness, and increased stability. Furthermore, the machine has more DOF, resulting in a better cutting performance than that of 3-DOF machine tools. To better understand the characteristics of major error sources of the machine in order to further enhance its accuracy, hybrid error analysis, kinematics analysis, and a volumetric error model were conducted. Finally, a prototype of the designed micromachine tool was built, and cutting experiments for accuracy calibration and verification were carried out using this machine. The results showed that the machine was able to effectively execute 4-DOF microcutting with positioning accuracy of 800 nm.  
micromachine tool; high resolution; volumetric error; error analysis; co-planar driving mechanism +
Footnote †: journal: Applied
## 2 Study Area and Data Sources
### Data Sources
#### 2.2.4 Ancillary Data  
Non-arable land was excluded using the Dynamic World v1.2 dataset from GEE, which provides near-real-time global land cover classification at 10 m resolution based on Sentinel-2 imagery. Among its nine land cover classes, the 'cultivated terrestrial vegetation' category was used to extract cropland [44]. The dataset was filtered by study region and date range (January 1 to December 31), and each image was converted to a binary mask, where cropland pixels (label = 4) were assigned a value of 1. These masks were summed to calculate the frequency of cropland classification per pixel. Pixels identified as cropland in at least 60% of valid observations were defined as stable cropland. This threshold was chosen to retain consistently cultivated areas while minimizing the influence of transient vegetation and classification noise. Similar thresholds have been widely adopted in cropland mapping studies, supporting the reliability of this approach [45]. To validate spatial-temporal accuracy, county-level crop area statistics (2019-2022) from the Zhangjiakou Statistical Yearbook were compared against satellite-derived estimates from linear regression analysis. Finally, the morphometric analysis of cultivation patterns was conducted using GEE, quantifying crop distribution dependencies on elevation and slope (Table 1).  
Figure 2: Phenological calendar for four crop types. “E”, “M”, and “L” represent the early, middle, and last 10-day periods of each month. DOY indicates the day of the year.
## 4 Results
### Basic Characteristics of Tight Gas Layer
#### 4.1.3 Reservoir Physical Features  
The porosity distribution of the first member of the Lower Stone Box Formation in the Hangjinqi Prospect area ranges from 4% to 16%, with an average value of about 9.8% [30]. The average porosity of the full-diameter core CT scan experiments of the studied section is 9.15%. The average porosity of the gravelly coarse-grained clastic quartz sandstones from CT scan experiments of cut samples within 25 mm diameter samples is 10.53% and the average permeability is 0.73 mD; the average porosity of the sandy conglomerates is 13.40% and the average permeability is 1 mD. Based on helium porosimetry and gas permeability meter test results, the porosity distribution of the studied section ranged from 8.4% to 17.9%, with an average porosity of 13.60%. The permeability distribution is 0.22-1.88 mD, with an average permeability of 0.85 mD. The overall porosity and permeability of the study section showed a significant positive correlation with a good correlation (Figure 7). The reservoir's physical characteristics indicate that the studied section belongs to the dominant reservoir class of dense sandstone.
# Design of a Laparoscopic Robot System Based on Spherical Magnetic Field
## 3 Structural Design of Laparoscopic Robot System  
### Main Structure of the Laparoscopic Robot System  
As shown in Figure 2a, the laparoscopic robot system consists of a stator and a rotor. The stator consists of five electromagnets, while the rotor consists of five IPMs. These electromagnets and IPMs are distributed on concentric hemispheres. For the rotor, one of its IPMs are distributed on the \"north\" side of sphere, the rest of the four IPMs are distributed at 45\({}^{\circ}\) north latitude of the sphere, and the latitude difference between adjacent IPMs is 90\({}^{\circ}\). The distribution of the electromagnets in the stator is similar to that of the IPMs but in a different concentric hemisphere.  
Electromagnets are adopted for the stator rather than permanent magnets because the combination of electromagnets is more flexible in controlling magnetic fields. Additionally, electromagnets can create the desired magnetic field by changing the input currents of electromagnets, which can simplify the position and orientation control of the stator.  
In theory, the configuration of three or four electromagnets can achieve the position and orientation control of the stator, and the symmetric configuration of four electromagnets are more stable in the deflection control of the robot than the configuration of three. However, these two configurations will also lead to the strong coupling between gravity compensation and orientation control of the rotor and, therefore, limit the driving capacity to the stator. Thus, a vertically-distributed electromagnet is added in the configuration of four electromagnets to achieve the gravity compensation of the stator, which can weaken the coupling between gravity compensation and deflection control, and then achieve better controlability.
# Smart Grid Data Management in a Heterogeneous Environment with a Hybrid Load Forecasting Model
## 1 Introduction
Our research proposes a novel hybrid load forecasting model combining fuzzy C-means for data clustering and auto-regressive integrated moving average (ARIMA) for historical load data analytics. Additionally, the system is complemented with Gradient Boosted Tree Learner external factors weather conditions analytics. The suggested PIAS system database is integrated with the Knime analytics platform [20]. The Knime platform can perform real-time analytics of high-volume data and predict power consumption through its many plugged-in machine learning and artificial intelligence algorithms, depending on users' specific purposes. As a result, the system can enhance the load forecasting modeling, and this will contribute positively to helping make the right determinations for the decision maker to save time and costs in the energy sector in Iraq. The rest of the paper is structured as follows. Section 2 elaborates on Iraq's energy sector as a case study and Section 3 discusses the most relevant related works. Section 4 explains the proposed PIAS throughout many sub-sections such as data, analytics, application, and presentation. Sections 5 and 6 discuss the results obtained through two case studies. Finally, Section 7 concludes the paper.
# Analytical Study of a Circular Thin Plate Contacting with an Elastic Sphere
## 1 Introduction  
Many researchers have adopted [PERSON] theory [1; 2] to deal with different problems of contact. [PERSON] et al. [3; 4] utilized [PERSON] theory for the contact problem of revolute joints with clearance and presented an impact pair model to represent the contact force at the contact surfaces. [PERSON] and [PERSON] [5] used the [PERSON] contact approach to evaluate the contact force which develops in the patellofemoral and tibofemoral connections within a dynamic knee simulator for modeling total knee prosthesis. [PERSON] et al. [6] proposed an analytical approach for the stress analysis of gear drives with localized bearing contact based on the Hertz theory. [PERSON] and [PERSON] [7] examined the hypothesis that the contact dimensions of edge-loaded ceramic and metal hip bearings can be accurately estimated using the Hertz theory. [PERSON] et al. [8] verified the applicability of the Hertz theory by comparing the theoretical results from the Hertz contact model and the simulated results from the quasicontinuum method.  
All the above works indicate that the Hertz theory is good enough for the case of small deformation contact. However, it is not applicable to the elastic sphere in contact with the thin plate with large deformation. This is due to the fact that the elastic strain energy is determined by the membrane stresses that result from the large out-of-plane deflections of the thin plate. Consequently, geometrical nonlinearity has to be considered, and detailed analysis need to be performed. Many researchers have performed theoretical, numerical, and experimental studies on the behavior of thin plates subjected to impulsive loadings [9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19]. This research primarily focused on the behavior of thin plates subjected to impulsive loadings. However, there is no literature at present investigating the exact closed-form solution to the problem of an elastic sphere in contact with a circular thin plate.  
In this paper, a circular thin plate clamped peripherally with frictionless contact to an elastic sphere is studied. Firstly, a set of equations is established to predict the relationship between the contact force and the relative penetration. On this basis, analysis of the contact stress and surface displacements of the circular thin plate and the elastic sphere is carried out. Then, comparison of the results with the Hertz theory is presented. Finally, the validity of the methodology developed is examined by comparing the theoretical results with the finite element method.
## 3 Research on Sunshine Shadow Occlusion Algorithm
### Sunshine Shadow Recognition Technology Based on Ray Tracing  
#### 3.2.1 Sunshine Shadow Recognition Method  
The sunshine shadow recognition method based on ray tracing in this paper adopts the process shown in Figure 6. The specific implementation steps are as follows:  
1. Because the number of meshes in the structural solid model was generally relatively large and the calculation workload was relatively large, the Python programming language was used to write a script tool for Abaqus software Version 6.10. to extract the surface mesh information (node coordinates, surface element nodes, and normal vectors) of the calculation model during the research of the sun shadow algorithm, to avoid a large number of invalid calculations in the calculation process of sunlight and shadow recognition.
2. The relative position relationship between the sun and the structure at this time was determined according to the calculation theory of the position parameters of the sun described in Section 2.1.1 after the extraction of mesh information from the surface of the model.
3. We determined the incident angle of the sunlight projected onto the grid surface of the node to be detected, which was located through the cross multiplication operation, and judged the size of the incident angle. When the incident angle was greater than 90\({}^{\circ}\), we directly determined that the node to be detected was in the self-occlusion state; otherwise, the subsequent judgment continued.
4. The face mesh of the 3D model was projected into a 2D plane mesh along the direction of the sunlight rays with the ground as the projection surface, The nodes to be detected were also projected in the same way.
5. The inclusion detection of the projection point and the projection mesh were carried out through a triangular barycentric coordinate method. The node to be detected was determined to be in a non-occluded state when the projection point was outside the projection mesh area; otherwise, the subsequent projection depth detection was carried out.
6. We calculated the projection depth of each node in the surface mesh used for projection and the node to be judged. The point to be detected was in a non-occluded state when the projection depth of the point to be judged was greater than the projection depth of each node in the surface mesh; otherwise, the point to be detected was in a mutual occlusion state.  
Figure 6: Sunshine shadow recognition technology implementation process.
# The SXFEL Upgrade: From Test Facility to User Facility
## 4 Commissioning Status
### SASE FEL Line  
The commissioning of the SASE FEL line started by the end of March 2021. Matching was performed first by using the quadrupoles upstream of the undulator. The first lasing of SASE at 5.6 nm was achieved at the end of April 2021. After that, saturations of SASE at 5.6 nm, 3.5 nm, 2.4 nm and 2.0 nm were achieved successively.  
The radiation has been delivered to beamline and end-station since May 2021. After careful tuning and optimization, the FEL pulse energy reaches a few hundred micro-joules and the short-term stability can be better than 10%. The first set of test experiments have been performed at the coherent diffraction imaging end-station with 2.4 nm FEL radiation and the designed imaging resolution has been achieved.
# Cross-Domain Transfer Learning for Natural Scene Classification of Remote-Sensing Imagery
## 4 Results and Discussion
### Summary of Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Training Mode**} & \multirow{2}{*}{**Epochs**} & \multicolumn{2}{c}{**Overall Accuracy**} \\ \cline{3-4}  & & **ResNet-50** & **VGG-19** \\ \hline From scratch training & 100 & 82.67\% & 76.72\% \\ \hline Last 3 layers fine-tuned & 100 & 99.10\% & 97.28\% \\ \hline Entire model fine-tuned & 100 & 99.50\% & 98.02\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of ResNet-50 and VGG-19 performance on NaSC-TG2. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Training Mode** & **Trainable Parameters** & **Total Parameters** & **Inference Time** \\ \hline From scratch training & 23,555,082 & 23,587,712 & 4.65 s \\ \hline Last 3 layers fine-tuned & 20,490 & 23,587,712 & 4.78 s \\ \hline Entire model fine-tuned & 23,555,082 & 23,587,712 & 4.59 s \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Training parameters and inference time for ResNet-50. To conclude, the state-of-the-art ResNet-50 model deployed for cross-domain transfer learning achieved the highest accuracy compared to other methods in the literature, indicating that it was mostly able to correctly classify images belonging to each class while avoiding false positives and false negatives. Overall, our approach of fine-tuning a pre-trained ResNet-50 model and VGG-19 model on the NaSC-TG2 dataset resulted in a significant improvement in the classification accuracy. This illustrates the usefulness of transfer learning across different domains, resulting in a better deep-learning performance on challenging natural scenes datasets.
## 5 Technical and Economic Analysis of the Test Design
### Construction Performance Comparison  
Although the original steel girder bridge design is easy to process, assembling large segments of steel structures can lead to stability issues. It requires stiffening plates and transverse diaphragms, leading to high steel consumption and extensive welding work. Additionally, the steel structure is prone to corrosion and rust, resulting in high maintenance costs. The construction method involves temporary scaffolding, which significantly increases the workload. In contrast, the test design for the composite truss bridge featured a rectangular concrete-filled steel tube truss for the main upper structure. During construction, the steel tube can serve as a rigid framework to bear the construction loads and structural weight, while the concrete inside the steel tube effectively prevents local buckling of the steel tube [32; 33; 34; 35]. The composite structure allows for standardized prefabrication of segments in a factory, ensuring the controlled quality of the components. The upper structure can be entirely prefabricated, standardized, and assembled. When the standard prefabricated segments are transported to the site, they can be quickly and conveniently assembled using lifting or incremental launching methods. Only a small amount of onsite wet work is required to connect the prefabricated bridge deck panels to the steel truss using shear studs.