esa-sceva/satcom-chunk-collection huggingface dataset:
# Field Test Study on Vertical Bearing Characteristics of Coal Rock
###### Abstract  
Coal rock is rarely used as the foundation bearing strata. For buildings located on coal rock, it is a reasonable and feasible way to obtain the vertical bearing characteristics of coal rock through field tests. Three plate load tests and two bi-directional load tests were carried out, and the design parameters of the vertical bearing capacity of the shallow foundation and the deep foundation were obtained, respectively. The plate load test results show that the pressure-settlement curve of coal rock is a steep-drop-type, and the deformation modulus decreases with the increase of the pressure. The bi-directional load test results show that the unit tip resistance-displacement curve of coal rock is a steep-drop-type, and the load transfer function of the unit tip resistance is an ideal elastic-plastic curve, and the displacement value corresponding to the ultimate bearing capacity is about 1% of the pile diameter. The load transfer function of the unit side resistance of coal rock is a hyperbolic curve. The equivalent pile top load-settlement curve obtained according to the load transfer functions of the unit tip resistance and the unit side resistance is consistent with the equivalent conversion results of the bi-directional load test method.  
Keywords:coal rock; vertical bearing characteristics; plate load test; bi-directional load test; deformation modulus; load transfer function; tip resistance; side resistance +
Footnote †: journal: Applied Sciences
# A Nested UNet Based on Multi-Scale Feature Extraction for Mixed Gaussian-Impulse Removal
## 5 Experiment and Analysis
### Discussion  
As mentioned in Section 5.2, the proposed MSNUNet algorithm demonstrates powerful performance in removing mixed noise, specifically AWGN+RVIN and AWGN + RVIN + SPIN. It surpasses state-of-the-art methods in terms of quality metrics and the visual appearance of the images. The effectiveness of MSNUNet in denoising can be attributed to its efficient extraction of multi-scale features. MSU-Subnet generates multiple receptive fields, providing rich local and global features for extraction. Given that mixed noise has a more complex distribution than a single source of noise, it is crucial to incorporate both local and global image information in the process of removing mixed noise. On the other hand, MSCAM utilizes a CAB to dynamically adjust the weights of individual channels in the global feature space. This helps in aggregating multi-scale features and allocating weights to relevant features based on their channel-wise correlations, thereby preserving more image details.  
In the task of removing mixed noise, the network's primary objective is to learn the mapping relationship between clean and noisy images. DEGAN [18] used a generative adversarial network for this purpose. DeGAN [18] utilizes a generator to learn the direct mappings between clean and noisy images, generating new images with a distribution similar to clean images. The generated images are then evaluated by a discriminator, providing feedback to the generator for generating more realistic clean images. However, the training process of GANs is unstable, leading to convergence difficulties for the generator and discriminator. Consequently, DeGAN [18] performs poorly when image details are severely corrupted. In our approach, the symmetric UNet structure effectively extracts image features, and the residual connections stabilize the training process, preventing gradient vanishing.  
In SACNN [20], local image features are extracted using Conv, and a hybrid attention mechanism is employed to learn weights for the image, incorporating SA and CA. With the powerful feature extraction capability of Conv, SACNN [20] effectively extracts local features and assigns weights through the hybrid attention mechanism in both the channel and spatial dimensions. This empowers the network to fully utilize valuable features and restore the corrupted details of the image. In our approach, we also employ CA to learn weights for feature information in the channel dimension. However, our method goes beyond SACNN [20] by extracting multi-scale features from high-resolution images using MSU-Subnet, which includes both local and global features. Throughout the denoising process of MSNUNet, MSCAM is utilized to effectively aggregate multi-scale features. As a result, MSNUNet outperforms SACNN [20] in denoising performance.
# Detecting Banana Plantations in the Wet Tropics, Australia, Using Aerial Photography and U-Net
## 2 Materials and Methods
### Remote Sensing Imagery  
Two aerial imagery captures were used for this study (Figure 1). The 2015 data were acquired between 17 th July and 14 th October 2015, and the 2018 data were acquired between 1 st and 27 th August 2018, by AEROmetrex. At the time of this study, the full extent of the 2018 Wet Tropics imagery capture was not available so the training was restricted to the middle and southern sections of the region.  
The data were captured with a fixed-wing mounted three-band true-color A3 Edge camera, at a spatial resolution of 25 cm and 20 cm for 2015 and 2018 respectively. The data were provided orthorectified by AEROmetrex based on a digital terrain model from LiDAR and stereo aerial imagery. The quality of the imagery is not consistent across the study area with some blurred areas and some discoloration. These artefacts are likely a result of post-processing of the imagery and appear to be located along tile boundaries where stitching the tiles and color balancing was not perfect. Unfortunately, the metadata supplied with the data does not list specific processing details. However the imagery is the best data available for the project area at a resolution suitable for this type of application. The Queensland Government has a large archive of aerial photography and it is likely these data, along with future captures will contain similar artefacts and any model developed will need to be robust enough to account for these inconsistencies.
# Multi-Scale Deep Neural Network Based on Dilated Convolution for Spacecraft Image Segmentation
###### Abstract  
In recent years, image segmentation techniques based on deep learning have achieved many applications in remote sensing, medical, and autonomous driving fields. In space exploration, the segmentation of spacecraft objects by monocular images can support space station on-orbit assembly tasks and space target position and attitude estimation tasks, which has essential research value and broad application prospects. However, there is no segmentation network designed for spacecraft targets. This paper proposes an end-to-end spacecraft image segmentation network using the semantic segmentation network DeepLabv3+ as the basic framework. We develop a multi-scale neural network based on sparse convolution. First, the feature extraction capability is improved by the dilated convolutional network. Second, we introduce the channel attention mechanism into the network to recalibrate the feature responses. Finally, we design a parallel atrous spatial pyramid pooling (ASPP) structure that enhances the contextual information of the network. To verify the effectiveness of the method, we built a spacecraft segmentation dataset on which we conduct experiments on the segmentation algorithm. The experimental results show that the encoder+ attention+ decoder structure proposed in this paper, which focuses on high-level and low-level features, can obtain clear and complete masks of spacecraft targets with high segmentation accuracy. Compared with DeepLabv3+, our method is a significant improvement. We also conduct an ablation study to research the effectiveness of our network framework.  
semantic segmentation; deep learning; dilated convolution; multi-scale; DeepLabv3+ +
## 6 Conclusions  
In this paper, we study the residual energy maximization problem at a WP-MEC system in FDMA offloading manner. By jointly optimizing the number of offloading bits, energy harvesting time, and the transmission bandwidth of mobile device, we aim to maximize residual energy by studying multiuser dynamic joint optimization of computation and wireless resource allocation under multiple time blocks. To solve this problem, we adopt convex optimization technology, combine with KKT conditions, and finally transform the problem into a univariate constrained convex optimization problem, aiming at obtaining  
Figure 8: Performance vs. AP Time Block. (**a**) Residual Energy; (**b**) Time Cost.  
Figure 7: Performance vs. Task Size. (**a**) Residual Energy; (**b**) Time Cost.  
lower time cost and higher accuracy. Furthermore, in order to improve the efficiency of solving the problem, we propose a combined method of Bisection method and sequential unconstrained minimization based on RLT. The simulation results show that our joint optimization scheme outperforms the other benchmark schemes. Moreover, the algorithm can maximize the residual energy, reduce the computation complexity, and improve computation efficiency. It is worth noting that compared with other work to maximize the residual energy, our work increases the possibility of the practical implementation of the WPT-MEC system. In the future, we will experiment in the real environment. We will simulate the wireless charging environment based on existing wireless charging devices, such as Xiaomi, Apple, and Samsung to verify the effectiveness of the proposed scheme in this paper. Besides, we will explore more efficient algorithms to build an energy efficient MEC system.  
Conceptualization, Z.Y. and Y.L.; methodology, Z.Y. and G.X.; software, Z.Y. and Y.L.; validation, Z.Y. and Y.L.; investigation, Z.Y. and P.L.; resources, Z.Y.; data curation, Z.Y. and L.L.; writing--original draft preparation, Z.Y.; writing--review and editing, Z.Y., Y.L. and L.L.; visualization, Z.Y.; supervision, G.X. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by National Key R&D Plan of China under Grant 2017 YFA0604500, Jilin Province Science and Technology Development Plan Project under Grants 20190201180 JC and 20200401076 GX, Jilin Province Education Department 13 th Five-Year Science and Technology Project under Grant JJKH20191310 KJ, National Key R&D Program of China under Grant 2018 YFB1800302, Natural Science Foundation of China under Grant 61702013, Beijing Natural Science Foundation under Grants KZ201810009011, 4202020 and 19L2021.  
Not applicable, the study does not report any data.  
The authors declare no conflict of interest.
## 1 Introduction
The basic premise is as follows: To minimize the total energy consumption of WSN, we propose a new network structure model and derive the optimal number of clusters according to the former and the original energy consumption model [12]. The clusters generated by traditional clustering routing protocols tend to be of different sizes, resulting in unbalanced energy consumption (i.e., the energy consumption of CHs in large clusters is often much larger than that in small clusters.). To alleviate this problem, based on the distance, the variance is introduced to reduce the difference in the distance between the nodes within the clusters in the cluster setup phase. Then in the CHs election phase, we implement the D-CHs division of the energy balance strategy and the node dormancy mechanism for the large cluster area before and after the death of the first node, respectively. In terms of optimal CHs election, we take the position of the node into account apart from the residual energy, which is obviously different from traditional protocols considering only the residual energy. The main innovative points of this paper include the following:  
* The optimal number of clusters is derived to minimize the total energy consumption of WSN. * Variance introduction, the D-CHs division of the energy balance strategy and the node dormancy mechanism are necessary to enable the energy consumption balance. * A new CHs priority function can ensure the nodes with better positions and adequate residual energy could have higher probabilities to be CHs. * The new clustering routing protocol achieves good network performance, including lifetime, energy consumption, and throughput. As for the communication technology, the previous Bluetooth [27] has high system complexity, short transmission distance, and large power consumption, which is not popular in WSN. In contrast, ZigBee has a wide range of applications in WSN due to its simplicity, low power consumption, low cost, and long-distance transmission. In 2016, Bluetooth 5 [28; 29] came into being. Compared with the previous Bluetooth version, its maximum transmission distance is increased 3 times, the power is greatly reduced, and the transmission rate is significantly improved. In addition, it will increase the maximum data capacity to 255 bytes, while ZigBee has only 100 bytes in this aspect. Thus, Bluetooth 5 is gradually becoming a new generation of Internet of Things communication technology. In this paper, we choose Bluetooth 5 as the communication technology of WSN. The rest of this paper is organized as follows: Section 2.1 introduces a new network structure model, Section 2.2 describes the original energy consumption model, and Section 2.3 proposes a new method for determining the optimal number of clusters. Section 3 describes the details of the protocol. The simulation study is conducted in Section 4. Finally, Section 5 summarizes the research and prospects for the future.
## 5 Numerical Experiments  
The ARPO algorithm has been implemented as a Java application. Being an interpreted language, Java-based programs do not execute as fast as other compiled programs, such as those developed in C or C++. Nevertheless, Java permits a rapid, platform-independent, development of object-oriented prototypes that can be used to test the potential of an algorithm. Also, using Java allowed to integrate our code with ojAlgo ([[http://ojalgo.org](http://ojalgo.org)]([http://ojalgo.org](http://ojalgo.org))), an open-source quadratic programming solver developed in Java. A standard personal computer, Intel Core i5 CPU at 3.2 GHz and 4 GB RAM with Linux Ubuntu, was used to perform all tests.  
In this research, we experiment with stock market data already used in previous studies. It was retrieved from the repository ORlib, and it can be downloaded from the following website: [[http://people.brunel.ac.uk/](http://people.brunel.ac.uk/)]([http://people.brunel.ac.uk/](http://people.brunel.ac.uk/))\(\mathtt{-}\)mastjib/jeb/orlib/portinfo.html. These instances were proposed by [32]and were studied by [39; 40; 41; 58; 59]. The data set comprises constituents of five stock market indices, Hang Seng (Hong Kong), DAX 100 (Germany), FTSE 100 (United Kingdom), S&P 100 (United States) and NIKKEI 225 (Japan). These indices were extracted from DataStream and are measured at weekly frequency spanning the period from March 1992 to September 1997.  
Following [41] we divided the portfolio frontier into 100 equidistant points on the vertical axis that represents the user-defined rate of expected portfolio return. Although the algorithm has been designed for the constrained case, it is initially tested on the unconstrained mean-variance optimization problem. The test results show that our solver is able to return solutions that are overlapping with the unconstrained efficient frontier (UEF) published at the OR Library, which contributes to validate the effectiveness of our approach.  
Next, we execute the algorithm on a constrained mean-variance frontier (the algorithm is executed 30 times and both the best and average results are recorded). The maximum time of execution for each instance is 20 s. The benchmark constraints are those imposed by the previous authors. Essentially, the constraints involve the following conditions: \(\epsilon_{i}=0.01\), \(\delta_{i}=1\), \(k_{min}=1\), \(k_{max}=10\), \(\forall i\in\{1,2,\ldots,n\}\). As in the aforementioned studies, pre-assignment constraints are not considered in these experiments, i.e., \(p_{i}=0\), \(\forall i\in\{1,2,\ldots,n\}\). Notice that, despite other authors claim that their approaches can solve the constrained problem with all the aforementioned constraints, this fact is not clearly showed neither in the description of their methods nor in the benchmarks they solve, since the parameter values they use in their benchmarks do not seem to impose a real challenge for their algorithms in terms of tight constraints.
# Corrosion Behavior of Pre-Rusted Rebars in Cement Mortar Exposed to Harsh Environments
## 2 Experimental Materials and Methods
### Specimen Preparation  
The rebar was cut into 160-mm-long pieces and coated with Devcon ceramic epoxy (titanium putty 10760) in the upper (70 mm) and lower (22 mm) areas, as shown in Figure 1a. Two solutions, CaCl\({}_{2}\) (10%) and HCl (3%), were applied using a brush at intervals of 24 h for 3 days, and the rebar was dried naturally to induce pre-rusting. The specimens were classified into three groups: A, pre-rust conditions (RE) for the standard reinforcement, B for CaCl\({}_{2}\) (10%), and C for HCl (3%). Each group included eight specimens. The average weights of the rebar before being embedded in the cement-mortar composite were 95.220 g (A), 95.888 g (B), and 96.034 g (C). An acrylic mold (D 50 x H 150 mm, as shown in Figure 1b, was used to prepare the cement-mortar specimen. A mortar having a W/C value of 0.485, in accordance with ASTM C109, was placed carefully into the acrylic mold with the rebar at the center, as shown in Figure 1c, and the formulation is shown in Table 3. The specimen was de-molded after 48 h and water-cured for 28 days before it was immersed in a CaCl\({}_{2}\) (3%) solution and stored at \(23\pm 3\)\({}^{\circ}\)C.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **C** & **Si** & **Mn** & **P** & **S** & **Cu** & **N** & **C Eq 1** \\ \hline SD400 & 0.27 & 0.13 & 0.49 & 0.014 & 0.013 & 0.33 & 0.01 & 0.40 \\ \hline \hline \end{tabular} \({}^{1}\)Carbon equivalent.  
\end{table}
Table 1: Chemical composition of steel bar (% mass).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **CaO** & **MgO** & **Fe\({}_{2}\)O\({}_{3}\)** & **SO\({}_{3}\)** & **K\({}_{2}\)O** & **Na\({}_{2}\)O** & **TiO\({}_{3}\)** & **Loss on Ignition** \\ \hline
18.2 & 4.87 & 70.5 & 1.32 & 3.32 & 0.27 & 0.53 & 0.02 & 0.32 & 0.65 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chemical composition of cement (%).  
Figure 1: Mortar specimen: (**a**) coating of steel bar, (**b**) an acrylic mold, (**c**) top view, and (**d**) side view.
###### Abstract
Accordingly, the cluster nodes update their status in response to real-time events occurring within the network. This intra-round adaptability enables efficient energy conservation while maintaining the network's coverage and performance;
3. Utilize the INs distributed across the network, including those located near sleeping clusters, to collect and forward data to the BS. These INs also provide valuable feedback that supports future sleep decisions. By combining the dual-sleep mode design with real-time operational awareness, the EDMR protocol offers a more adaptable sleep management strategy than traditional protocols, which are typically based on fixed or non-responsive mechanisms;
4. Integrate the proposed sleeping mechanism with the dynamic routing technique to ensure consistent data flow and minimize energy usage. This paper is organized as follows: Section 2 introduces related data routing protocols and sleeping techniques proposed for WSNs. Section 3 presents a detailed explanation of all phases of the DMR protocol. Section 4 is dedicated to the new modification techniques proposed by the EDMR protocol. Section 5 discusses the results obtained from applying the EDMR protocol. Lastly, Section 6 concludes the paper.
## 3 Results and Discussion
### Human Element  
#### 3.1.1 Definition  
In Resolution A.849(20), the IMO defines the human element as follows [23]: \"The human element is a complex multidimensional issue that affects maritime safety and marine environmental protection. It involves the entire spectrum of human activities performed by ship's crews, shore-based management, regulatory bodies, recognized organizations, shipyards, legislators, and other relevant parties and they need to cooperate to address human element issues effectively\". However, this definition is specific and complex. The British Nautical Institute proposes a simpler and more concise definition [24]: \"In the maritime context, the term Human Element embraces anything that influences the interaction between a human and any system aboard ship\". This second definition focuses on entities aboard the ship, whereas the first has a broader scope.
## 4 Conclusions and Discussion  
Due to the influence of various error factors, pseudorange observations are not reliable and the accuracy and reliability of positioning cannot be guaranteed in indoor high-precision pseudolite positioning. Considering these problems, a holistic indoor pseudolite integrity monitoring algorithm PLRAIM is proposed and the specific steps are shown in Section 2.2. The feasibility of the whole algorithm is analyzed by static and dynamic experiments. The ambiguity fixed rate of both experiments is greatly improved when using PLRAIM and the reliability of AR is also guaranteed due to the stability of test statistics and HPL. In terms of positioning precision, the RMS of the zero-baseline static experiment reaches mm level when PLRAIM is applied and the maximum deviation in the dynamic experiment is within 5 cm, which ensures the positioning precision well.  
Figure 17: (**a**) The First Dataset in the Second Period of Experiment 2, (**b**) Horizontal protection level (HPL) of Experiment 2.  
The proposed method can be further improved. In fact, it is not reliable to choose the reference pseudolite of an indoor pseudolite system based on the elevation angle because of the fixed pseudolite constellation and the small scope of the indoor environment. Therefore, the elevation angles of all pseudolites are basically very close. Besides, whether the reference pseudolite is faulty is analyzed based on the stability of DD observations.A more suitable fault identification method for reference pseudolite is the next research focus. In addition, the threshold of test statistics is determined by a given \(P_{\beta}\), but it may not be the best way for an indoor pseudolite system. How to adjust the threshold dynamically according to the actual situation is also the next important issue.  
Furthermore, this set of pseudolite hardware and software devices will be applied to some actual engineering situations or more complex indoor environments in the future. More actual data will be collected for a longer period. The available state of pseudolites will be monitored in real-time to verify the robustness of our method.  
[PERSON] performed all data acquisition. [PERSON] and [PERSON] conceived the experiments and analyzed the data. [PERSON] and [PERSON] performed data processing and contributed to the manuscript. [PERSON] and [PERSON] provided critical comments and contributed to the final revision of the paper. [PERSON], [PERSON] and [PERSON] contributed to the expression and the design of programs. All authors contributed to the manuscript and discussed the results. All authors together developed the idea that led to this paper. All authors have read and agreed to the published version of the manuscript.  
This work was supported by \"National Nature Science Foundation of China\" (Grant Nos. 41604019, 41474004, 41871373) and Guangxi Key Laboratory of Spatial Information and Geomatics (Grant Nos. 19-050-11-10, 19-050-11-01).  
The authors declare no conflict of interest.