esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 LiCSAR System Architecture
### LiCSAR Processing Chain  
The aim of the LiCSAR processing chain is to generate interferometric products (see Section 4.1.1). The processing workflow to generate interferometric products by LiCSAR is outlined graphically in Figure 2. Prior to this workflow, each frame should be first defined and initialised within the LiCSInfo database. While frame definition means a logical linking of burst definitions within a frame unit, the initialisation of the frame means a status of having generated base frame data, including the primary epoch SLC and its multilooked intensity raster (MLI), height values based on a digital elevation model (DEM), and other frame-related derived products, such as the three components of the unit vector in the satellite line-of-sight (LOS) for each pixel, which define the sensitivity to motion in the East (E), North (N) and upward/vertical (U) directions [49]. We currently use a 1 arc-second void-filled version of the Shuttle Radar Topography Mission (SRTM) DEM as the basic DEM to derive the base frame data [50]. For cases of regions outside the SRTM DEM coverage, we have lately started to use the freely available WorldDEM product ([[https://geoservice.dlr.de/web/dataguide/tdm90](https://geoservice.dlr.de/web/dataguide/tdm90)]([https://geoservice.dlr.de/web/dataguide/tdm90](https://geoservice.dlr.de/web/dataguide/tdm90))) by Deutsches Zentrum fur Luft (DLR) based on TanDEM-X satellite measurements provided in 3 arc-seconds resolution. In exceptional cases, some frames are prepared using other topographic models. For example, most of the frames from Sentinel-1 Stripmap mode (SM) use the 1 arc-second ALOS World3D DEM, [[https://www.eorc.jaxa.jp/ALOS/en/aw3d](https://www.eorc.jaxa.jp/ALOS/en/aw3d)]([https://www.eorc.jaxa.jp/ALOS/en/aw3d](https://www.eorc.jaxa.jp/ALOS/en/aw3d)), by Japan Aerospace Exploration Agency (JAXA) based on Advanced Land Observing Satellite (ALOS) measurements; some older frames used 1 arc-second ASTER Global DEM product, [[https://asterweb.jpl.nasa.gov/gdem.asp](https://asterweb.jpl.nasa.gov/gdem.asp)]([https://asterweb.jpl.nasa.gov/gdem.asp](https://asterweb.jpl.nasa.gov/gdem.asp)), by NASA JPL and the Ministry of Economy, Trade and Industry of Japan (METI), based on data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite. The DEM-based frame data are key for geocoding of the products and estimation/removal of interferometric phase due to topography. Following subsections provide an overview on the four steps of our interferometric processing of Sentinel-1 data for each temporal epoch.  
Figure 1: General overview of Looking Into Continents from Space with Synthetic Aperture Radar (LiCSAR) system architecture: interconnections between storage and computing structures leading the path from Sentinel-1 SLC acquisition to publicly accessible geocoded interferometric products.
# Optimization of Buoy Shape for Wave Energy Converter Based on Particle Swarm Algorithm
## 3 Results
Figure 12: Motion response: (**a**) buoy pitch motion response; (**b**) swing-arm system pitch motion response. The common features were that \(\lambda\), which controlled the radius of the buoy, tended to reach the upper bound, \(\alpha=1\), which controlled the shape of the bottom of the buoy. Among the different features shown in Tables 3 and 4, \(\beta\), which controlled the buoy draft, converged to the upper limit of the constraint with the smallest buoy draft, while, shown in Table 5, \(\beta\) converged to the lower limit of the constraint with the largest buoy draft. In order to clarify the variation of wet surface area and drainage volume of the buoy, \(C=0\) in Table 3 was selected for the analysis. In Figure 13, (a) shows the variation of buoy draft volume \(V\) with the number of iterations in Table 3 and (b) shows the variation of wet surface area \(A\) of the buoy with the number of iterations in Table 3. It can be seen that, in general, the volume of the draft from the buoy, \(V\), decreased with the number of iterations, increased at step 6 of the iteration, and continued to decrease after step 6, while the wet surface area of the buoy, \(A\), increased with the number of iterations. In order to analyze the change in buoy shape during the iterative process, two dimensionless variables, \(\eta\) and \(\varphi\), were defined. \(\eta\) was the absolute value of the ratio of \(\beta\) to \(\lambda\), which characterized the flatness of the buoy, with a smaller \(\eta\) indicating a flatter buoy and vice versa. \(\varphi\) was the absolute value of the ratio of \(\alpha\) to \(\beta\), which characterized the taper of the buoy, with a smaller \(\varphi\) indicating a more tapered buoy. The expressions are as follows:  
\[\eta=\left|\frac{\beta}{\lambda}\right| \tag{14}\]  
\[\varphi=\left|\frac{\alpha}{\beta}\right| \tag{15}\]  
Figure 14 below shows the curves for \(\varphi\) and \(\eta\). As shown, the \(\eta\) curve showed a decreasing trend, indicating that the shape of the buoy became flatter and flatter with the number of iterations, while the \(\varphi\) curve showed a rapid increase after a small decrease at the \(5-th\) iterations, indicating that the shape of the buoy became less tapered with the number of iterations. Summing up the above patterns, the following can be concluded:  
1. With the shape vector constraint, the wet surface area of the buoy became larger and the drainage volume decreased as the buoy shape was optimized iteratively. 2. With the shape vector constraint, the buoy shape tended to be flat with a small taper as the buoy shape was optimized iteratively.
## 3 Methods  
### Bayesian Network  
Bayesian networks are acyclic directed graphs that represent the conditional dependencies between a set of variables [13]. Bayesian networks are also considered to be a graphical inference technique used to express the causal relationship among the variables. Bayesian networks are able to perform both predictive analysis and diagnostic analysis [14]. Hence, they are widely used in modeling of complex systems and risk analysis of a wide variety of accidents based on probabilistic and uncertain knowledge thanks to its flexible structure and reasoning engine.  
Many authors have investigated different techniques based on Bayesian network to analyze risks in logistics or port scenarios, but there is a lack of analyzing the risk of cargo theft using real operational data. For instance, an expert system for maritime safety management based on Bayesian network was introduced in [15]. The study [16] provides a Bayesian network-based solution to the problem of offshore piracy ranging from the detection of a potential threat to the implementation of a response. A method to generate a Bayesian network from a process model is proposed in [17] to analyze the lateness risk of container handling. [18] presents a Bayesian network-based model to predict accident consequences in the Tianjin port.  
Clearly, Bayesian networks have been used extensively for port risk analysis where the model parameters are based on expert elicitation and learning from historical data. It is a powerful technique for analyzing relations between risk components, visualizing specific risk structures and reasoning risk potential as well. As a result, Bayesian networks are used in this paper for the risk analysis of bulk cargo theft based on real theft records from a port security bureau and operational data from a bulk cargo port. The constructed model can be utilized to help cargo interests and security bureau to understand the inter-relations between risk components of bulk cargo theft in ports and further support the risk early detection and risk control strategy optimization.
## 6 Conclusions
Moreover, to further improve the performance of OELs network, we will also research on the temporal properties of precipitation fields such as Hurst-Kolmogorov process [53]. According to the conclusions from this article, several issues need to be addressed in practice:  
1. Assessing the uncertainty caused by the representativeness of OEL inversed rain intensity. In the process of rainfall field reconstruction, we assume that the path-average rain intensity is equal to the value in the middle of link. This may be not reasonable for the cases when precipitation has significant spatial heterogeneity. The sophisticated tomography reconstruction techniques, such as simultaneous algebra reconstruction technique (SART) and compressed sensing (CS), can be considered. 2. Assessing the errors introduced by the melting layer attenuation. The frequency of OEL for rainfall inversion is usually in the Ku- or Ka-band. For the signal above 10 GHz, melting layer attenuation is comparable to path-integrated rain attenuation, especially for low elevation OEL whose propagation path in melting layer is relatively long. Therefore, the issue of distinguishing melting layer attenuation from that of precipitation, needs to be addressed. 3. Assessing the variance of the variogram model. It has been shown that the estimator of semi-variance often exhibits an obvious variability and so can give estimations with large uncertainty [54]. Therefore, it is necessary to find an advanced method to reduce this effect. For example, climacogram has a better performance than conventional autocovariance and variogram, which can give more accurate results by removing the bias from estimation [55]. **Author Contributions:** Conceptualization, M.X. and X.L. ; methodology, M.X. and X.L. ; resources, M.X. and K.S. ; investigation, K.S. ; supervision, T.G. ; writing-original draft preparation, M.X; writing-review and editing, M.Y. and X.L. ; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the National Natural Science Foundation of China, grant number 41975030, 41505135 and 41475020. **Acknowledgments:** The authors would like to thank the editor and anonymous reviewers for their helpful comments and suggestions. The data measured by our established oblique earth-space link and laser dishormeter OTT can be supplied via request to the corresponding author of this manuscript. The data used in the simulation experiment on rainfall reconstruction and nowcasting is provided by Climate Prediction Center of NOAA which can be download from an open access [[https://ftp.cpc.ncep.noaa.gov/precip/CMORPH_V1.0/](https://ftp.cpc.ncep.noaa.gov/precip/CMORPH_V1.0/)]([https://ftp.cpc.ncep.noaa.gov/precip/CMORPH_V1.0/](https://ftp.cpc.ncep.noaa.gov/precip/CMORPH_V1.0/)). **Conflicts of Interest:** The authors declare no conflict of interest.
# The Bimodal Neutron and X-ray Imaging Driven by a Single Electron Linear Accelerator
## Appendix A Electron Linear Accelerator
### Fusion of Images  
Benefiting from the almost same imaging beam geometries for neutrons and photons, the pixel-wise matching between the neutron image and photon image can be conducted directly, without additional linear translation, rotation, scaling, or skew. For the same pixel of the inspected object's images, the corresponding values of neutron attenuation and photon attenuation can be extracted from the two images compared with the images of \"air\" of neutrons or photons, respectively. The value for neutron attenuation and that for photon attenuation of the same pixel determine a point in the coordinate system whose \(x\)-axis is the photon attenuation, and \(y\)-axis is the neutron attenuation. By analyzing all the pixels present in the two images, a bivariate histogram, in which different materials with various mass thicknesses can be identified, is formed.  
For both neutrons and photons, the attenuation can be calculated as follows:  
\[Att=\log\frac{I_{0}}{I}=\mu_{m}t_{m} \tag{1}\]  
where \(Att\) is the attenuation of neutrons or photons, equal to the product of \(\mu_{m}\) and \(t_{m}\), which are the mass attenuation coefficients for neutrons or photons and the mass thickness of the inspected objects, respectively; \(I_{0}\) and \(I\) are the measured counts for neutrons or photons penetrating \"air\" or the inspected object, respectively. The ratio that determines a slope in the bivariate histograms to identify different materials is given by the following:  
\[Slope=\frac{Att_{n}}{Att_{X}}=\frac{\mu_{m,n}}{\mu_{m,X}}\] (A2)  
where subscript \(n\) and \(X\) stand for neutrons and photons, respectively; and the \(t_{m}\) cancels because it is the same for neutron penetration and photon penetration. Therefore, when the hardening effect is not severe (the energy spectra of neutrons or photons are not significantly influenced when the inspected object's mass thickness is varied), in the bivariate histogram, a specific material will have a certain slope. In the case that several different materials are successively penetrated by neutrons and photons, the slope might be modified as follows:  
\[Slope=\frac{\sum\limits_{i=1}^{N}\mu_{m,n,i}\cdot t_{m,i}}{\sum\limits_{i=1}^ {N}\mu_{m,X,i}\cdot t_{m,i}}\] (A3)  
where \(N\) is the number of materials. In general, we cannot separate three or more materials with the information provided by the bivariate histogram. However, considering that the pulse working mode of the e-LINAC-driven system enables energy-resolving neutron imaging, a multivariate histogram can be formed for the further identification of materials. We have successfully conducted an isotope identification experiment, which realizes the energy-resolving neutron measurement, demonstrating the possibility of the multivariate image data acquisition within the framework of the system in this study.
## 4 Experiment  
To evaluate our network, we compared it with four advanced methods in this field: the Gaussian Mixture Model (GMM [9]), Unidirectional Global Sparse Model (UGSM [8]), Deep Convolutional Neural Network (DCNN [16]), and Feature Supervised GAN (FS-GAN [28]).  
We present a series of experiments on a synthetic dataset (on the basis of the truth image, the rainy image is synthesized by artificially adding rain streaks through related operations) and a real-world dataset (image containing rain streaks captured with camera on rainy day). The former contained images from the Rain100 L dataset provided in [18], and the latter contained images from the dataset provided in [9]. We trained and tested the proposed algorithm using TensorFlow in the Python environment on an NVIDIA GeForce GTX 1070 with 8 GB of GPU memory. During training, the filtering window of the bilateral filter was 50, and the standard variance of the color space and the coordinate space was set to 30. The h\({}_{1}\) and h\({}_{2}\) in the H-G discriminator were set to 0.2 and 0.8, respectively. The weight in (16) was set to 0.005. Both the rain streaks removal network and the H-G discriminator used the Adam algorithm, and the initial momentum was set to 0.9. The batch size and patch size were set to 2 and 64, respectively. The initial learning rate was 0.01, and the number of iterations was 150 k. After 100 k iterations, the learning rate was reduced to 1/10 of the original. Training stopped once the maximum number of iterations was reached.  
The synthetic dataset consisted of 1800 images and was labeled with rain-density level. Three rain-density labels were present in the dataset (as shown in Figure 4 light, medium and heavy). There were approximately 600 images of rain streaks with different orientations and scales per rain density level in the dataset. The images with different backgrounds of the synthetic dataset are shown in Figure 4.  
The real dataset consisted of 59 images obtained on a rainy day, shown in Figure 5.  
Figure 4: The partial images of the synthetic dataset.
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 2 Fundamentals of Dense Video Captioning
### Key Subprocesses  
Video Feature Extraction (VFE): VFE constitutes the initial critical subprocess in DVC [16; 17]. It entails extracting meaningful representations from raw video data for subsequent tasks like event localization and caption generation. This typically involves employing deep learning models, such as Convolutional Neural Networks (CNNs) for spatial feature extraction and Recurrent Neural Networks (RNNs) or Transformers for temporal feature encoding. The aim of VFE is to capture both the visual content and the temporal dynamics of the video.  
Temporal Event Localization (TEL): TEL pertains to identifying the start and end times of events within a video. This subprocess necessitates precise temporal segmentation of the video into meaningful event segments. TEL can be tackled using diverse techniques, including sliding window methods, proposal-based methods, and end-to-end detection frameworks. The challenge resides in accurately detecting events, particularly when they overlap or occur in rapid succession.  
Dense Caption Generation (DCG): DCG is the culminating subprocess in DVC, where natural language descriptions are crafted for each localized event. This involves encoding the visual and temporal features of the event into a format that can be decoded into a coherent sentence or phrase. DCG often harnesses encoder-decoder architectures, such as sequence-to-sequence models or Transformer-based models, trained on extensive datasets of video-caption pairs. The objective is to generate captions that are both precise and fluent, encapsulating the essential details of the event.
# A Systematic Literature Review of Autonomous and Connected Vehicles in Traffic Management
## 3 Research Analysis
### Traffic Management Methodologies Consisiting of Primary Goals
#### 3.2.1 Efficiency
[42] studied and created a variable speed limit (VSL) based on Q-Learning (QL) using CAVs as operators and mobile sensors in combination with SpeedTransition Matrices (STMs) for prediction. it was found that the created algorithm was able to figure out the best strategy for each situation that was put to the test and enhance travel speeds. The rule-based techniques were categorized among those aiming for increased effectiveness (e.g., [55; 57; 89]), then optimization (e.g., [56; 88; 90]) hybrid (e.g., [11]) and machine learning (e.g., [39; 40]). These various studies primarily focused on roundabouts and intersections. Most of the suggested techniques and base cases were examined in a simulated setting.
## 3 Research Background and Hypotheses  
Logistics plays a crucial role in supply chain management, encompassing planning, control, data storage, and product distribution from origin to consumption, catering to consumer needs. While logistics activities typically focus on procurement, distribution, maintenance, and inventory management, they usually exclude marketing activities, new product development, and finance ([PERSON] et al., 2022). Green logistics, on the other hand, encompasses production, distribution, packaging, and product management, emphasizing material and waste management, energy conservation, and fuel-efficient transportation, aligning with environmental and social responsibility ([PERSON] et al., 2022; [PERSON] et al., 2022). The primary goals of green logistics include minimizing the environmental impact of logistics activities, reducing energy and waste, enhancing product brand value, improving operational efficiency, and cutting costs through optimal energy use [29; 30; 31]. Additionally, green logistics aims to decrease carbon emissions, waste production, and environmental impacts in the production supply chain while promoting eco-friendly containers, packaging, and transportation [32]. Environmentally friendly logistics is a vital component of the green supply chain, serving as an effective strategy for sustainable development [26]. It aids businesses in the supply chain in becoming more efficient by selecting eco-friendly material suppliers, proposing sustainable solutions, and utilizing green transport for customer delivery. Implementing environmentally friendly logistics also enhances the competitiveness of logistics companies in global provision. Research on green logistics encompasses various factors, including green design [30; 31], procurement [32; 33; 34], manufacturing [31; 33], marketing [33], consumption [35], reverse logistics [30; 36], transportation [30; 32; 37; 38], and communication [39]. These factors are predominantly applied in the manufacturing industry and have been extensively tested, proving highly reliable in creating eco-production management efficiency that surpasses customer expectations and impacts the supply chain [40; 41; 42; 43]. In the realm of Green Logistics adoption, the literature delves into both external and internal factors that play a pivotal role. External factors encompass consumer/supplier and third-party influences, including regulatory and institutional pressures, international environmental regulations, competition, reputation, and social responsibility, with government policies and legal regulations often emerging as dominant forces [44]. The literature extensively explores external and internal factors influencing Green Logistics' adoption. External factors include consumer/supplier and third-party influences, such as regulatory and institutional pressures, international environmental regulations, competition, reputation, and social responsibility, with government policies and legal regulations often identified as dominant forces.
# Detection of Household Furniture Storage Space in Depth Images
## 2 Related Research  
The research presented in this paper focuses on the detection of empty and cluttered storage space above furniture shelves in depth images. We investigated solutions to this problem by applying methods commonly used for object detection--more specifically, neural network-based 3D object detectors. In recent literature, researchers have proposed solutions for object detection in 2D and 3D visual sensor data [4; 5; 6]. We focus on obtaining 3D information to facilitate robotic manipulation. Robots used for autonomous manipulation typically rely on depth-view sensors such as RGB-D cameras and LiDARs to provide point-cloud representations of 3D scenes. Newer 3D object detectors that use point clouds are typically based on deep learning methods that require large datasets with annotated objects of interest for training [2; 3; 7; 8; 9; 10].  
In our work, we investigated the application of neural network detectors for storage volume detection in household furniture. We considered VoteNet [2] and FCAF3D [3]. VoteNet, similarly to H3 DNet [7], uses a voting scheme for generating object proposals. This approach is applied to many object recognition problems. For example, 3D object recognition in point clouds using the Hough voting model is proposed in [5]. VoteNet is used to vote on the centers of objects and learn to aggregate the votes according to their features and local geometry to generate high quality object proposals. The importance of multi-level context information for 3D object recognition from point cloud data was investigated in [11; 12], where the Multi-Level Context VoteNet was used to recognize 3D objects based on three context modules in the voting and classification stages of VoteNet. FCAF3D is a fully convolutional, anchor-free, indoor 3D object detection method based on a GSDN-like sparse convolutional network. It uses a voxel representation of a point cloud and processes voxels with sparse convolutions. The neural network accepts RGB-colored points and outputs a set of 3D object bounding boxes.  
There are some other methods which, like ours, detect bounding boxes in 3D images. In [13], a method for cuboid detection and tracking using a multi RGB-D cameras is presented. This approach is designed for applications in the manufacturing industry, and detects boxes and other cuboid objects. Similarly to our method, this method also deals with rectangular geometric structures and returns 3D bounding boxes, but instead of detecting cuboid objects, our method detects cuboid empty space. The authors of [14] proposed an approach, based on 2D object detection, for 3D objects bounding box detection--without training. This method does not detect storage space.