esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Optimization of Lattice Structures: A Review
## 2 Lattice Structures
### Properties and Applications of Lattice Structures
#### 2.2.1 Properties
These studies provide a good theoretical basis for the application of lattice structures in vibration damping and isolation. (4) Dissipate heat [27; 44; 45; 46; 78; 97; 98; 99; 100]. [PERSON] and [PERSON] [45] compared copper textile-based lattice structures with selected heat dissipation media including stochastic open-cell metal foams, periodic Kagome and lattice frame materials, corrugated ducts and louvered fins. [PERSON] and [PERSON] [45] pointed out that although the copper textile-based lattice structures performed as well as the stochastic open-cell metal foams (aluminum and copper based), the pumping power required was significantly lower for the textile-based structures, because of the periodic topology. Therefore, their overall thermal efficiency was about three times greater than that of comparable weight open-cell copper foams. In addition. [PERSON] et al. also investigated the thermal conductivity of lattice structures [98]. Results showed that thermal conductivity was primarily a function of the material properties and volume fraction of the sample. The Schwarz primitive unit cell consistently gave the highest conductivity, with diamond and gyroid unit cells being marginally lower (as shown in Figure 8). On heat exchange and heat transfer properties of lattice structures, [PERSON] et al. [99] and [PERSON] et al. [100] studied fluid dynamics of flow through microscale lattice structures, and verified the heat exchange and heat transfer performance of lattice structure. The mechanical properties of lattice structure are the fundamental aspects for the design of lattices. Only when the lattice structure with good mechanical properties is designed, it will be more widely used in various industries. For example, the fatigue behavior of lattice structure, is frequently investigated [101; 102; 103; 104; 105; 106; 107]. By controlling and designing the unit cell shape, surface properties, post treatment and graded porosity, the fatigue behavior can be significantly improved, which may guarantee the long-term application [104]. Therefore, the premise of designing lattice structure is to consider its application field and performance, which is the basis of designing lattice structure and determines the application period of lattice structure. Figure 7: FEA simulations to estimate displacement of six lattice structures [92]: (**a**) model 1; (**b**) model 2; (**c**) model 3; (**d**) model 4; (**e**) model 5; (**f**) model 6.
## 2 Materials and Methods
### 3D Data Modelling
#### 2.4.3 3D Modelling of Natural Surface  
The 3D mesh modelling was performed using the software: QGIS 3.22.8, CloudCompare and Meshlab (Visual Computing Lab-ISTI-CNR, Pisa, Italy) [40]. The bathymic grids of Section 2.4.2. (grid FCR, grid OK and grid IDW) were imported into QGIS and transformed into punctual shapefiles. Each point was associated with the depth value of the cell and the coordinates in the WGS84/UTM 33N system (ID algorithms: 'native:pixelstoppoints' and 'native:addxyfields'). Subsequently, the layers were exported in text format (\".csv). During this step, it was possible to add additional information to the files in order to generate 3D models with depth exaltation, a type of representation used to visualise large surfaces (or lengths) with small variations in height (some modelling examples are shown in Figure 7). Then the files were converted into Polygon File Format (\".ply, a specific format for 3D object representation) via CloudCompare, generating the point clouds (Figure 7a-c). After, the files were imported into Meshlab for 3D modelling. In Meshlab, point cloud normals were generated and then the point clouds were modelled into 3D models tri-mesh using the Poisson surface reconstruction method (Figure 7d-f). Finally, the textures of the bathymic grids of Section 2.4.2. were applied in Meshlab to improve the readability of the models (Figure 7g-i). In order to facilitate exposition, the 3D model created using the grid FCR was called 3D model FCR, the 3D model created using the grid OK was called 3D model OK and the 3D model created using the grid IDW was called 3D model IDW.  
Figure 6: Bathymetric grid models produced by interpolation: (**a**) grid produced by Forest-based Classification and Regression method (grid FCR); (**b**) grid produced by Ordinary Kriging method (grid OK); (**c**) grid produced by IDW method (grid IDW).
## 6 Conclusions and Future Perspectives
### Directions for Future Research
#### 6.3.3 Quantifying Model Uncertainty  
To improve the trustworthiness and reliability of DL models in real-world applications, future research could incorporate uncertainty quantification in their developed DL models. Techniques such as Bayesian Neural Networks, Monte Carlo dropout, and ensemble methods can be employed to estimate epistemic and aleatoric uncertainty. Taking the SST prediction task as an example, conventional CNN or Transformer-based models can be extended to output not only a predicted temperature but also an associated uncertainty estimate. To compute aleatoric uncertainty, the model can be trained using a heteroscedastic loss function. Monte Carlo dropout can be applied to estimate epistemic uncertainty by enabling dropout layers during inference and performing multiple forward processes to calculate prediction variance. In addition, BNNs are able to quantify model uncertainty by treating network weights as probability distributions rather than fixed values. Integrating such techniques can provide valuable insights into the reliability of DL predictions, supporting more informed decision making in ocean and climate research.
## Materials and Methods
### Capability Gap Based Value Decision
#### 2.1.2 Value Model Construction
To mathematically model the relations with universal forms, a formula is defined to fit the linear line above, shown in Equation (4). \[G(c_{i},cr_{i})=\left\{ \begin{array}{ll}-c_{i}/\left(2a\right)+1,0\leq c_{i} <a\\ \left(-c_{i}+u\right)/\left(2b\right),a\leq c_{i}<u\\ 0,u<c_{i}\end{array} ;benefit\ type\right.\end{array}\right., \tag{4}\]  
where \(G(c_{i},cr_{i})\) represents the capability gap between the \(i\)th capability and corresponding capability requirement interval \([l,u]\). Two special notation \(a\) and \(b\) are defined to simplify the formula, where \(a=(l+u)/2\), \(b=(u-l)/2\). The notation _worst_ denotes the requirement threshold of cost-type capability that the capability gap will be judged to be 1 if a cost-type capability value exceeds _worst_. Then, the capability gaps of all capabilities should be aggregated to compute the total capability gap of a system portfolio as \(\sum_{i=1}^{k}w_{i}G(c_{i},cr_{i})\), where the \(w_{i}\) denote the weight of the \(i\)th capability and is determined according to the method in Section 2.1. Then, according to capability gaps, the value is defined as Equation (5), which indicates that the bigger the total capability gap, the smaller the value of a system portfolio. \[V\left(x_{j}\right)=\frac{\sum\limits_{i=1}^{k}w_{i}(1-G(c_{i},cr_{i}))}{k}, \sum\limits_{i=1}^{k}w_{i}=1, \tag{5}\]  
Different to general \"capability-based\" evaluation methods, which are usually based on hierarchy structures such as a tree structure that the capabilities of all systems in bottom level are up-aggregatedto an integrated value, capability gap is a criterion denoting the gap between capabilities and capability requirements. For general methods, an inevitable defect is that an extremely high capability will pull up the integrated capability value, which is obviously unreasonable. Whereas for the capability gap, it can mitigate the effects of extreme capability values. For example, even an infinitely high benefit-type capability can only result in corresponding capability gap of 0, instead of unreasonably extreme value.
## 15 Thickness of soil layer  
[PERSON] et al. [51] analyzed the city of Dagupan after the 1990 Luzon event and revealed that the observed damage to the building was very small or absent in the presence of a sufficiently thick un-liquefied surface layer; especially in the case where the thickness exceeded 3 m. According to a survey conducted by the Japanese Society of Civil Engineers, when the thickness of the soil layer-sand exceeds 3 m, liquefaction should only occur with larger earthquake magnitude. However, when the thickness is between 1.0 m and 3.0 m, the soil is susceptible to liquefaction.  
The hierarchy structure in the present study of the significant seismic soil liquefaction susceptibility factors is shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Factors (Fi) Code** & **Significant Factors** & **Source(s)** \\ \hline F1 & Earthquake magnitude, (M) & [13; 24; 25; 26; 27; 28] \\ F2 & Epicentral distance, (S) & [29] \\ F3 & Duration of earthquake, (t) & [13; 30] \\ F4 & Peak ground acceleration, (a\({}_{\mathrm{max}}\)) & [26; 27; 30; 31; 32] \\ F5 & Fines content, (F\({}_{\mathrm{c}}\)) & [33; 34; 35; 36] \\ F6 & Soil type, (Cu\({}_{\mathrm{u}}\) and C\({}_{\mathrm{c}}\)) & [13; 37; 38] \\ F7 & Mean particle size, (D\({}_{\mathrm{S}0}\)) & [39; 40; 41] \\ F8 & Relative density, (D\({}_{\mathrm{T}}\)) & [39; 42] \\ F9 & Drainage condition, (K) & [43] \\ F10 & Degree of consolidation, (OCR) & [44] \\ F11 & Standard penetration test blow counts, (SPT N-value) & [45] \\ F12 & Effective vertical stress, (\(\sigma^{{}^{\prime}}_{\mathrm{v}}\)) & [26; 27; 46] \\ F13 & Groundwater table, (D\({}_{\mathrm{w}}\)) & [47; 48] \\ F14 & Depth of soil deposit, (D\({}_{\mathrm{s}}\)) & [49; 50] \\ F15 & Thickness of soil layer, (h) & [51] \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of significant factors identified from literature review.
## 7 Experimental Setup
### Evaluation Metrics  
Following metrics has been evaluated to validate our proposed scheme.  
* Packet Delivery Ratio (PDR): The ratio of the total number of data packets successfully delivered to the total number of data packets sent out by a source node.
* Packet Loss Ratio: It occurs when certain data packets traveling across a network fail to reach their destination. Packet loss is typically caused by network congestion. Packet loss is measured as a percentage of packets lost with respect to packets sent.
* Throughput: This value is calculated by dividing the overall number of messages received at destination node by the total messages sent from source nodes according to the following equation: \[\text{Throughput}=\frac{\sum\text{Total packets received}}{\sum\text{Total packets sent}}\]
* Delay: It is very important factor to measure the efficiency of any communication system. Delay represents the time period that needs to route a packet from the source to the desired destination which depends on PDR value in the system and can be calculated using the following equation \[\text{Delay}=\frac{Number\ of\ sending\ bits\ in\ the\ packet}{Throughput}\]
* Probability of Detection: It is the ratio between number of malicious nodes to the actual nodes present in the network. The malicious nodes are unable to make trust score up to threshold level, therefore 3 VSR has high probability to detect these nodes.
## 3 System Model
### Bidding Price Model  
In order to maintain bidding market stability, it is necessary for DR purchaser to design a reasonable bidding price model. Assume that bidding price of grade \(i\) of DR resource is \(p_{i}^{t}\) in time slot \(t\), and bidding amount of all community operator is \(x_{i}^{t}\) in time slot \(t\) for grade \(i\). Considering that market price generally has a significant linear relation with load demand level, the bidding price can also be designed as a linear function  
\[p_{i}^{t}=a_{i}^{t}x_{i}^{t}+b_{i}^{t} \tag{3}\]  
where \(a_{i}^{t}<0\) and \(b_{i}^{t}>0\) are coefficients of grade \(i\) of DR resource. It needs to point that, when community operator \(n\) has the \(i\)th grade of DR resource, then it can participate in the bidding of corresponding grade of DR resource with other same type of operators, otherwise the bidding amount is set to 0.
# Quality Assessment of Dry Soil Mixing Columns in Soft Soil Areas of Eastern China
###### Abstract  
To investigate the quality of dry soil mixing (DSM) columns in different soft soil areas of east China, a large number of laboratory test results and field test results of DSM columns were collected and analyzed statistically. Furthermore, a quality assessment method for DSM columns is proposed in this paper. The hardness description (HD), standard penetration test (SPT), uncombined compressive strength (UCS), and soil-cement column quality designation (SCQD) are used as assessment indexes. The statistical analysis showed that the test results of SPT, UCS, and SCQD were scattered, particularly in shallow ground. The mean values of the SPT blow count, UCS, and SCQD of the DSM columns decreased with depth: the greater the depth, the worse the quality of DSM columns. The quality assessment results showed that the proportion of the great quality columns was 64.84%, and the proportion of the unqualified columns was 1.4%. The proportion of DSM columns with great quality in the lagoon soft soil area was greater than in other areas. The proportion of unqualified columns in the lacustrine soft soil area was the largest. For all soft soil areas, the proportions of great quality, good quality, general quality, and unqualified soil decreased in that order. The quality assessment of 8627 DSM columns showed the proportions of great quality and unqualified were 64.84% and 1.4%, respectively. It was found that the greater the depth of the DSM column, the more unqualified DSM columns, and the more difficult it was to control the quality of DSM columns.  
d
## 4 Conclusions
Specifically, when the dip angle of the pre-existing crack is \(\alpha=45^{\circ}\), the growth rate of absorbed energy during the loading and failure process is the slowest; the energy evolution curve of specimens with dip angles \(\alpha=30^{\circ}\) and \(\alpha=60^{\circ}\) is almost the same; when the crack dip angle is \(\alpha=90^{\circ}\), the growth rate of absorbed energy is the fastest, followed by specimens with a dip angle of \(\alpha=0^{\circ}\). The presence of pre-existing cracks leads to considerable energy damage in the specimens. Notably, when the crack dip angle is \(\alpha=45^{\circ}\), the specimen experiences the smallest maximum absorbed energy of only 6.45 J, representing 40.56% of the intact specimen's energy absorption. On the other hand, when the dip angle is \(\alpha=90^{\circ}\), the specimen exhibits the largest maximum absorbed energy of 11.84 J, which corresponds to 74.4% of the intact specimen's energy absorption. The difference in maximum absorbed energy between specimens with crack dip angles of \(0^{\circ}\), \(45^{\circ}\), and \(60^{\circ}\) is relatively small, with a variation range of only 9%. The maximum absorbed energy of the specimen first decreases and thenincreases with the increase in crack dip angle, and the nonlinear correlation between the two is good. The maximum absorbed energy of the specimen also shows a corresponding nonlinear relationship with the tensile strength and acoustic emission energy. Moreover, the decrease in the slope of the curve indicates that as the maximum absorbed energy increases, the energy storage and consumption inside the rock sample will increase, and the corresponding increase in tensile strength will decrease. The authors made the following contributions to this study: Conceptualization, T.W. and L.L. ; methodology, T.W., W.Y., L.L. and A.L. ; software, T.W. and A.L. ; validation, T.W., W.Y., L.L. and S.Z. ; formal analysis, T.W., W.Y. and A.L. ; investigation, W.Y. and A.L. ; resources, T.W. and L.L. ; data curation, T.W., W.Y., L.L. and A.L. ; writing--original draft preparation, T.W. and W.Y. ; writing--review and editing. T.W., W.Y. and L.L. ; visualization, T.W., W.Y. and L.L. ; supervision, N.J., L.Z. and S.Z. ; project administration, L.L. ; funding acquisition, T.W. and L.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (Grant Nos. 51874014, 52004015, and 52311530070), the fellowship of China National Postdoctoral Program for Innovative Talents (Grant No. BX2021033), the fellowship of China Postdoctoral Science Foundation (Grant No. 2021M700389), the Fundamental Research Funds for the Central Universities of China (Grant Nos. FRF-IDRY-20-003 and QNXM20210001), and State Key Laboratory of Strata Intelligent Control and Green Mining Co-founded by Shandong Province and the Ministry of Science and Technology, China (Grant No. SIGGM202108). Not applicable. Data Availability Statement: Data are available upon reasonable request to the corresponding author. The authors declare no conflict of interest.
# An Improved Shoulder Line Extraction Method Fusing Edge Detection and Regional Growing Algorithm
## 2 Materials and Methods
### Data  
In this study, high-resolution satellite images were used to produce higher-density spectral and textural information. Imagery of 3 m resolution downloaded from Planet Explorer ([[https://www.planet.com/explorer/](https://www.planet.com/explorer/)]([https://www.planet.com/explorer/](https://www.planet.com/explorer/)), (accessed on 1 September 2021)) was used. The image data were mainly captured between June and September 2021. During this period, the images had less cloud content, and crops were harvested on the land surface; therefore, the boundaries of objects with loess morphologies were more obvious, which aided image edge detection and shoulder line extraction. The Advanced Land-Observing Satellite (ALOS) Digital Elevation Model with a spatial resolution of 12.5 m was used to calculate the peak point and outlet point of the sample area. The DEM data were downloaded from the National Aeronautics and Space Administration (NASA, [[https://search.asf.alaska.edu/#/](https://search.asf.alaska.edu/#/)]([https://search.asf.alaska.edu/#/](https://search.asf.alaska.edu/#/)), (accessed on 1 October 2016)). The horizontal and vertical accuracies of the elevation data could reach 12.5 m. These were the most accurate data from open-source DEM data. More details on the data are shown in Table 1.
## 2 Materials and Experimental Methodology
### Asphalt High Temperature Aging: Mechanisms of Short-Term (RTFO) and Extended Aging (PAV)
#### 2.4.2 Long-Term PAV Thermal Aging  
As per ASTM D6521-19 [34], asphalt binders are subjected to extended aging in a Pressurized Aging Vessel (PAV) following the short-term aging simulated by the RTFO. Usually, covering five to ten years, the PAV test models the oxidative aging that takes place across the lifetime of a pavement. This test allows asphalt to be accelerated in the aging process by high temperatures and pressures, thus enabling the prediction of its long-term performance. The PAV approach replicates environmental stresses including heat, oxygen, and UV radiation, which helps asphalt to gradually harden, and embrittlement occurs. Assessing the resilience and durability of asphalt pavements depends on an awareness of long-term thermal aging, thus guaranteeing that they satisfy performance criteria over their expected lifetime.  
These tests taken together give a thorough knowledge of asphalt's thermal aging response. They greatly help to direct the choice and design of binders capable of meeting the demands of road service conditions as well as the difficulties of initial processing.
## 5 Experiments in Actual Maritime Area
### Leader-Follower Swarm-Control Algorithm Performance Test  
The performance test of the leader-follower swarm-control algorithm involved placing the leader and follower USVs at different locations and heading angles. Next, four arbitrary waypoint locations were input, and the swarm controller, as discussed earlier, was evaluated for its proper operation, specifically verifying whether the follower successfully tracked the leader. Testing included analysis of the acquired data.  
The results of the swarm control performance test, particularly the path-tracking outcome, are presented in Figure 10.  
Figure 9: Satellite picture of experimental area.  
The results showed that, after initially being positioned in the upper-right corner of the leader, the follower executed a turn to organize a lateral formation. Subsequently, the follower maintained a consistent distance from the leader while navigating through each waypoint. As was observed, particularly at waypoints 3 and 4, the leader marginally deviated from the intended path due to a northeastward current during the experiment. This deviation resulted in an increase in the turning radii of both the leader and follower. Despite errors, the follower successfully maintained a constant distance from the leader.  
The results of the swarm control performance test, specifically with respect to the heading angle, are presented in Figure 11.  
The results indicate that, as shown in the graph, the leader and follower USVs, initially facing different angles, commenced and completed the mission with similar patterns. In this case, because the swarm formation involves a follower positioned to the right of the  
Figure 11: Heading angle results of swarm control performance test.  
Figure 10: Path following the results of the swarm control performance test.  
leader in a linear formation, the turning radius of the follower is larger than that of the leader. Consequently, in the graph, it is evident that the slope of the heading angle for the follower is gentler than that of the leader. Finally, Figure 12 illustrates the distance error between the leader and follower USVs.  
The graph shows that the initial distance between the leader and the follower, which was approximately 12 m, was maintained at approximately 3.5 m during navigation. In each waypoint segment, the leader and follower turned, which caused the distance to increase by approximately 10 m. However, after the turning motion was completed, the distance was maintained at approximately 3-5 m during the navigation.