esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Strutless Double-Row Structure for Deep Excavation: Working Mechanism Study and Analysis
## 5 Discussion  
Our findings align with prior research [13; 45] in demonstrating the superior deformation control of double-row pile systems compared to single-row systems. However, the ARPW system exhibits even lower lateral displacements and bending moments than DRPW systems, particularly at wider row spacings (4D to 8D). This improvement can be attributed to the enhanced stiffness of the back wall and the larger soil-pile contact area in the ARPW system. In contrast to [PERSON] et al. [18], who reported an optimal row spacing of 2D to 6D for DRPW systems, our results suggest that the ARPW system performs best within a slightly wider range (4D to 8D). This divergence highlights the importance of considering structural modifications when optimizing row spacing. From the above experimental results, it is evident that the superior support ability of double-row structures is attributed to their effective utilization of the space effect. The comprehensive stress analysis schematic of the double-row supporting structure and the soil is depicted in Figure 32. For the ARPW structure, considering the interaction between the piles and the surrounding soil is crucial. The significant contact area between the back wall and the soil between piles contributes to substantial friction resistance. As the row spacing increases, the weight of the soil between piles (Gs) becomes a key factor in enhancing the structure's anti-overturning moment. In DRPW structures, due to the smaller soil stiffness between piles and the smaller contact area between piles and soil, the contribution of soil weight and active friction to the anti-overturning resistance is lower. Consequently, while both the ARPW and DRPW structures show an increased load-bearing capacity within a specific range as the row spacing widens, the ARPW structure piles rely more on the soil between piles and friction in the active earth pressure for anti-overturning resistance. Conversely, the DRPW structure derives a greater portion of its anti-overturning moment from the passive earth pressure, leading to higher passive earth pressure values under equivalent row spacing conditions.  
In conclusion, the proposed ARPW system represents a novel unsupported deep excavation support structure compared with traditional SPWs and DRPW structures, achieving two major innovations, firstly, by maximizing the contact area between the back wall and the soil between piles, optimizing the spatial effect and significantly enhancing the friction resistance and the gravitational force of the soil (Gs), contributing to the overturning resistance. Secondly, ARPW adopts a hybrid structure combining front piles, rear walls, and top beams, completely abandoning traditional internal supports or anchor systems. Through the top beams coordinating the deformation of front and rear structures, combined  
Figure 32: Schematic diagram of overall stress analysis of soil between piles.  
with the pile-soil stiffness effect, a more balanced stress distribution is formed, thus greatly improving the support performance.
# Investigation of the Influence of Splitter Blades on the Resonance Conditions of Impellers
## 3 Numerical Analysis
### Tuned Case
#### 3.2.2 Resonance Identification by the Proposed Resonance Conditions  
From Section 2, we know that after the spatial harmonic content analysis of the target mode, the resonance of the impeller with splitter blades can be identified by the proposed resonance conditions.  
As for the 17-sector impeller used here, the relationship between the nodal diameter index and the harmonic index for a vibration mode is shown in Figure 5, which is obtained by Equation (17). It indicates the harmonic contents of mode 94 contain the 4 th harmonic and the 13 th harmonic. In order to clearly get the respective contribution of mode 94, spatial harmonic contents analysis of the blade-based RMV is required.  
In Section 2.2, the blade-based RMV is extracted from the degrees of freedom of blades. This is easily obtained by the lumped parameter model. As for the prototype impeller shown in Figure 2, the blade-based RMV can be formed by the axial modal displacements extracted from nodes with relatively similar positions on all blades' trailing edge regions. Here, the trailing edge nodes of all blades' tips are used for sampling. This is based on the following considerations: (1) the main and splitter blades have identical geometric characteristics in the trailing edge regions; (2) the diffuser-induced excitations mainly act near the impeller exit; (3) for the typical mode shape of impellers, the axial vibration at the outer regions of the impeller manifests as mode 94.  
The harmonic contents analysis is performed on the blade-based RMV and the result is shown in Figure 6. It indicates the 13 th harmonic is apparently dominant and the 4 th harmonic, in contrast, is negligible. That is exactly why the ND4 mode exhibits 13 nodal lines.  
According to the proposed resonance condition (Equation (27)), we know the 13 th harmonic component of the mode can be excited by the excitations with \(n_{1}=13,21\ldots\) and the 4 th harmonic component can be excited by the excitations with \(n_{2}=4,30\ldots\). Thus, the excitation generated from 21 vanes can arouse the resonance of mode 94 by exciting the 13 th harmonic component. Additionally, due to the higher amplitude of the 13 th harmonic component, we conclude that the resonant amplitude aroused by the excitations with \(n_{1}\) should be larger.  
Figure 4: (**a**) Front view of mode shape of mode 94; (**b**) Back view of mode shape of mode 94.  
Figure 5: Relationship between nodal diameter index and harmonic content for a 17-sector impeller with splitter blades.
## 3 Research Results--Analysis and Discussion
### Modulus of Compressibility (M\({}_{v}\), E\({}_{\text{oed}}\))  
The modulus of compressibility (M\({}_{v}\), E\({}_{\text{oed}}\)) was determined for four load levels (50 kPa, 100 kPa, 200 kPa, and 400 kPa) in the oedometric test. The obtained values of E\({}_{\text{oed}}\) at the maximum load level of 400 kPa are shown in Figure 8. The samples were tested at time intervals of 3, 7, and 28 days after the treatment of the clayey soil with the optimal content of each of the considered stabilisers.  
Based on the given graphical interpretation of the results, it can be concluded that each of the considered stabilisers contributed to the increase in the value of E\({}_{\text{oed}}\). The greatest increase in the value of E\({}_{\text{oed}}\) is achieved by adding a **conventional stabiliser**--time (38.392 MPa) or cement (44.07 MPa), whereby the value increased by about 3 times compared to the natural soil condition (12.945 MPa).  
Figure 8: Change in the soil E\({}_{\text{oed}}\) value over time given the optimal content of the considered conventional, waste, and alternative materials as stabilisers.  
With regard to the **waste material as a stabiliser**, the effect on improving this soil property is somewhat smaller, with an increase in the E\({}_{\text{oed}}\) value of about 1.5 times in the case of rock flour and about 2 times in the case of fly ash.  
Considering the **alternative materials** as stabilisers, the highest value was obtained with the addition of calcium chloride (an increase of about 2 times), with the addition of ferric chloride the value was increased about 1.5 times, whereas potassium chloride had a minor contribution to the improvement of the natural soil property.  
It is also noticeable that in the cases of lime and calcium chloride, the value of E\({}_{\text{oed}}\) achieved 28 days after the treatment was lower than the value obtained after 7 days.  
Lastly, it can be concluded that from the aspect of this soil property, conventional stabilisers resulted in the greatest effect, between which cement stands out slightly. However, given the optimal stabiliser content, the use of lime is more rational than cement.
# Multi-Band Unmanned Aerial Vehicle Antenna for Integrated 5G and GNSS Connectivity
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Dimensions** & **Material** & **Frequency** & **Gain (dBi)** & **Antenna Type** & **Polarization** \\ \hline
[1] & 80 \(\times\) 80 \(\times\) 1.6 & FR4 & WIFI-6E, 5G, n77, & 4.56, 2.28, 4.26, & Single & CP \\  & & & n78, and GNSS & and 4.30 & & \\ \hline
[2] & 60 \(\times\) 60 \(\times\) 1.6 & FR4 & GNSS, WLAN, X & 5.7, 3.79, and 5.4 & Single & - \\ \hline
[3] & 58 \(\times\) 37 \(\times\) 17 & FR4 & GNSS & 3 & Single & - \\ \hline
[5] & 150 \(\times\) 75 \(\times\) 7.8 & FR4 & (n77/n78/n79) and WLAN & - & MIMO & - \\ \hline
[6] & 50 \(\times\) 50 \(\times\) 0.8 & - & 6 GHz & 4.52 & Single & RHCP \\ \hline
[8] & 40 \(\times\) 40 & FR4 & 5G n78 & 2.8 & MIMO & - \\ \hline
[9] & 75 \(\times\) 50 \(\times\) 30 & FR4 & GNSS L1, 5G & 5.2 and 3.9 & Single & RHCP and LP \\ \hline
[10] & 125(R) \(\times\) 5.08 & Rogers TMM10i & GNSS & 6 & MIMO & RHCP \\ \hline
[12] & 28 \(\times\) 6 & FR4 & 5G-n77/n78/n79 & 4.2–8.2 & MIMO & - \\ \hline
[13] & 16 \(\times\) 28 \(\times\) 1.6 & FR4 & 5.5, 5.5, and & 2.24, 3.49, and 3.41 & MIMO & - \\  & & & 6.5 GHz & & \\ \hline
[14] & 61 \(\times\) 61 \(\times\) 1.6 & FR4 & 3.1, 4.7, 6.9 and & 5.6 & MIMO & - \\  & & & 9.5 GHz & & \\ \hline
[15] & 60 \(\times\) 60 \(\times\) 1.6 & FR4 & 3–11 GHz & 3.4 & MIMO & - \\ \hline
[16] & 312 \(\times\) 312 \(\times\) 157 & Rogers RT-5880 & 25.2 GHz & 8.7 & MIMO & - \\ \hline
[17] & 40 \(\times\) 50 \(\times\) 0.8 & Rogers RT-5880 & 4.17 GHz & 7.01 & MIMO & - \\ \hline Proposed & 40 \(\times\) 80 \(\times\) 3.2 & TUC803B & GNSS, 5G n79 & 5.44 and 6.43 & MIMO & CP and LP \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of this work with the literature.
###### Abstract  
With the advancement of logistics technology, Automated Guided Vehicles (AGVs) have been widely adopted in manufacturing enterprises due to their high flexibility and stability, particularly in flexible and discrete manufacturing domains such as tire production and electronic assembly. However, existing studies seldom systematically consider practical constraints such as limited AGV transport resources, AGV charging requirements, and charging station capacity limitations. To address this gap, this paper proposes a flexible job shop production-logistics collaborative scheduling model that incorporates transport and charging constraints, aiming to minimize the maximum makespan. To solve this problem, an improved PPO algorithm--CRGPPO-TKL--has been developed, which integrates candidate probability ratio calculations and a dynamic clipping mechanism based on target KL divergence to enhance the exploration capability and stability during policy updates. Experimental results demonstrate that the proposed method outperforms composite dispatching rules and mainstream DRL methods across multiple scheduling scenarios, achieving an average improvement of 8.2% and 10.5% in makespan, respectively. Finally, sensitivity analysis verifies the robustness of the proposed method with respect to parameter combinations.  
flexible job shop scheduling; AGVs; limited transportation resources; charging constraints; DRL; PPO; target KL divergence Article
## 4 Results  
### Data Description  
In this section, we present the performance of both the proposed method and an existing approach, along with a comparative analysis. We focus on the permutation-based group feature importance method introduced in Section 3, as well as the established group lasso method. For this evaluation, three distinct datasets ([[https://jundongl.github.io/scikit-feature/OLD/datasets_old.html](https://jundongl.github.io/scikit-feature/OLD/datasets_old.html)]([https://jundongl.github.io/scikit-feature/OLD/datasets_old.html](https://jundongl.github.io/scikit-feature/OLD/datasets_old.html)) (accessed on 15 February 2023)) were used: GLA-BRA-180, CLL-SUB-111, and TOX-171. These datasets consisted of microarray gene expression data sourced from the National Center for Biotechnology Information (NCBI). The datasets contained genes as predictors and included multiclass response variables. Specifically, the GLA-BRA-180 dataset comprised 49,151 features and 4 distinct classes, whereas CLL-SUB-111 included 11,340 features and 3 classes. The TOX-171 dataset encompassed 5749 features and 4 classes. Further insights into the characteristics of these datasets are presented in Table 1.  
The GLA-BRA-180 dataset encompasses the expression profiles of stem cell factors important for exploring tumor angiogenesis. This dataset is useful for analyzing gliomas of various grades. In total, 180 samples were categorized into distinct classes: 23 samples belonged to the brain oligodendoxia class, 26 samples corresponded to glioblastomas, 81 samples represented astrocytomas, and 50 samples belonged to the non-tumor class. The CLL-SUB-111 dataset, another gene expression dataset, comprises distinct clinically and genetically delineated subgroups of B-cell chronic lymphocytic leukemia (B-CLL). Within this dataset, the initial 11 samples were assigned to the first class, 49 to the second class, and the remaining samples to the third class. This dataset facilitated the investigation of nuanced characteristics within B-CLL subgroups. The TOX-171 dataset considers the effect of influenza A on plasmacytoid dendritic cells (PDC). It leverages toxicology to assimilate a diverse range of biological data, encompassing aspects such as expression and clinical chemistry. This dataset was characterized by profiles generated across three distinct types of toxicants. The underlying objective of this dataset involves discerning whether a given sample exhibits toxicity, non-toxicity, or control.
## 3 Results
### Effect of Aggregate Size on Compressive Strength
From the close trend of the turning points of the two lines, it can be seen that there is a good correspondence between the total energy released by the acoustic emission and the compressive strength, \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline
**Particle Size Type** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **15** & **20** & **25** & **30** \\ \hline Aggregate size (mm) & 3000 & 1500 & 1000 & 750 & 600 & 500 & 428 & 375 & 333 & 300 & 200 & 150 & 120 & 100 \\ compressive strength (Mpa) & 22.23 & 20.40 & 21.10 & 22.30 & 24.03 & 23.43 & 22.87 & 24.63 & 24.10 & 24.60 & 24.17 & 25.80 & 25.93 & 27.03 \\ \hline \end{tabular}
\end{table} 
Table 4: Aggregate size and compressive strength of different types of RFC. Figure 12: Numerical model and element damage nephogram of type 6 to type 30 RFC. (**a**) Numerical model; (**b**) Element damage nephogram. that is, the higher the total energy released by acoustic emission, the greater the compressive strength. Figure 13b is a fitting of the data in Figure 13a, and its fitting function is as follows:  
\[ \begin{array}{l}f_{c}(t)=27.07-6.38\times e^{-0.08t}\\ E(t)=16.07-7.51\times e^{-0.07t}\end{array}  \tag{7}\]  
where \(t\) is the particle size type;\(f_{c}(t)\) is the corresponding compressive strength; and \(E(t)\) is the total energy released by the corresponding acoustic emission. The two dashed lines in Figure 13b represent the compressive strength of SCC and rock under the same specimen size (3000 mm). The strength of RFC with each particle size type is distributed between the strength of rock and SCC. From the fitted curve of compressive strength, it shows that, while keeping the proportion of aggregate unchanged, the compressive strength of RFC gradually increases with the increase in particle size type, i.e., the decrease in aggregate size, and the increasing trend gradually tends to flatten, with a maximum compressive strength of 27.07 Mpa.
# A Closed-Form Method for Simultaneous Target Localization and UAV Trajectory Optimization
## 4 Simulations, Experiments and Results Analysis  
To validate the localization performance obtained from the UAV trajectory optimized by the proposed algorithm, simulations and field experiments were conducted.
# Development of an L-Band SAR Microsatellite Antenna for Earth Observation
## 4 Validation of Parabolic Mesh Reflector  
### Verification of Surface Error  
Reflector surface errors, due to fabrication errors, deformations caused by gravity, or roughness of the reflective surfaces, reduce the antenna gain estimated. Antenna surface tolerances were limited to 4.22 mm following [PERSON]'s prediction for surface tolerance efficiency of 3 dB.  
A research model of the deployable parabolic mesh reflector was constructed, as depicted in Figure 8a. Two approaches to measure surface accuracy of the mesh reflector using laser and three-dimensional photogrammetric technique were performed.
# Research on Feature Fusion Method Based on Graph Convolutional Networks
## 3 Methods
### Interactive Output Layer  
The interactive output layer is realized by two linear layers which are used to map the fused features to the category space for classification.  
Specifically, the linear layer in linear fusion maps the fused features to a hidden layer, which then passes through a nonlinear activation function (ReLU). The output of this hidden layer passes through the first linear layer, which then passes through the ReLU activation function to obtain the first fused features. It then goes through an operation identical to the first linear layer (second fusion) to obtain the final fused features.  
First fusion: The output features of the BERT and the output features of the GCN are spliced according to specific dimensions and then mapped through a linear layer and passed through a nonlinear activation function (ReLU). The formula is as follows:  
\[\text{fused\_features\_1}=\text{ReLU}(\text{Linear}(\text{H}_{\text{BERT}} \oplus\text{H}_{\text{GCN}})) \tag{10}\]  
Second fusion: The results of the first fusion are again mapped through a linear layer and again passed through a nonlinear activation function (ReLU) to obtain the final fused features. The formula is as follows:  
\[\text{fused\_features\_2}=\text{ReLU}(\text{Linear}(\text{fused\_features\_1})) \tag{11}\]  
where \(\text{H}_{\text{BERT}}\) is the output feature of BERT, \(\text{H}_{\text{GCN}}\) is the output feature of GCN, \(\oplus\) denotes the splicing operation, Linear denotes the linear layer, and ReLU denotes the ReLU activation function.
## 3 Model Formulation and Scheme Design  
### Model Formulation  
As shown in Figure 2, energy-constrained sensor nodes are deployed in the monitoring area. The sensor nodes are clustered in a star-shaped topology. The common nodes in the cluster send the collected data to the cluster heads, which fuse the data. The wireless signal coverage of the cluster head nodes is circular. Just above the cluster heads are the possible data collection points. The flying height of the UAV is fixed, and the UAV only needs to communicate with the cluster heads to complete the data collection of the whole cluster. The dashed line is the initial flight path of the UAV, and the solid line is the flight path of the UAV dynamically adjusted according to the LoRa communication strategy. Obviously, it is a TSP problem and belongs to the NP-hard [34] class. We treat the cluster head as a city and the traveling salesman as a drone with sufficient capacity to collect data from all sensor nodes.  
We consider the following three steps to resolve how to reduce the UAV flight distance and data gathering task time. First, an improved LEACH algorithm is designed to cluster sensor nodes. The algorithm considers the location factor of the UAV take-off point, making  
Figure 2: System model formulation.  
the cluster head position closer to the UAV take-off point, which is conducive to subsequent path optimization. Then, based on the clustering results, the initial path planning of the UAV is performed with each cluster head node directly above as a possible data collection point. An improved genetic algorithm is designed to optimize it, and an optimal sequence of unmanned access cluster heads is obtained. Finally, according to the LoRa 2.4 GHz adaptive data rate strategy we designed, the communication between the UAV and the cluster head is carried out. The UAV can adjust moving status based on the data gathering completion status of the current cluster head, to reduce the time of the data gathering task. The optimization process is shown in Figure 3. In the paper, we assume that the positions of all sensor nodes are known. The improved LEACH and improved GA algorithms can be executed on the ground computer at the UAV take-off point, in the initial stage. When the planned path is obtained from the ground computer, the UAV starts to carry out the data-gathering task.  
Descriptions of main notations for the model are explained in Table 1.
## 5 Conclusions  
Compared to traditional segmentation methods in pixels, superpixel segmentation methods could well consider local regional correlation, and clustering on this basis could improve segmention efficiency and accuracy. However, most existing superpixel segmentation algorithms are presented for 2D images, which only depend on the color and pixel planar position of image to define the distance measure. For geological objects, they may also be more affected by other characteristics such as the spatial location and rock surface attitude. Such segmentation methods are not always appropriate for geological objects, and especially for those special circumstances with similar image features but various spatial or other features, it is difficult to guarantee reliable and accurate results. In this paper, a new hybrid superpixel segmentation algorithm, MFF-SLIC, for rock surface extraction by integrating the TLS point cloud and digital images is proposed. In order to take full use of characteristics of Multi-Source RS data, this algorithm constructs the 1-\(N\) mapping relationship between TLS point cloud and digital images based on the registration results, defines a new distance measure model integrating multiple features including the color and planar position of pixels in digital images as well as the spatial location and local rock surface attitude of 3D points in the TLS point cloud, and introduces a hierarchical and optimized clustering strategy of superpixels which defines a regional dissimilarity as adjacent superpixels clustering criterion and combines RAG and NNG to improve clustering efficiency. The algorithm was verified by use of the columnar basalt data from Guabushan Geoppark in Nanjing, China. The results demonstrate that the proposed method could achieve rock surface extraction with high precision and efficiency, the result of which would be prepared for further geological statistics and analysis.  
All the authors contributed to the development of proposed segmentation method and this manuscript. [PERSON] and [PERSON] proposed the methodology. [PERSON] and [PERSON] performed the experiments and analyzed the results; [PERSON] and [PERSON] was responsible for data acquisition. [PERSON] and [PERSON] wrote the draft of the manuscript. [PERSON] guided the research and revised the manuscript.  
The research is supported by Research Project of Surveying Mapping and Geoinformation of Jiangsu Province (No. JSCHKY201801), Research Project of Nanjing University of Posts and Telecommunications (No. NY218136), Natural Science Foundation of Jiangsu Provincial (No. BK20180765 and No. BK20180779), National Key R&D Program of China (2018 YFC1508603), and National Natural Science Foundation of China (41830110).  
The authors would like to thank the editors and anonymous reviewers who provided useful comments, which were incorporated into this manuscript.  
The authors declare no conflict of interest.