esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Analysis  
### Test Results and Analysis of Improved Loess with Single Index  
#### 3.1.1 Influence of Admixture on Permeability Coefficient  
Figures 4 and 5 illustrate the relationship between varying cement admixture content and the permeability coefficient, keeping particle properties, grading parameters, and dry density constant. The permeability coefficients of all three groups of soil samples decreased  
Figure 3: Preparation process and falling head permeability test apparatus.  
with increasing cement content. A substantial decline in permeability coefficients was observed in tests PL18, PL19, PL20, PL21, PL22, and PL23 when the cement content exceeded 9%. In contrast, tests PL27, PL28, PL29, and PL30 displayed curves with a more gradual slope, indicating a consistent and incremental decrease in permeability without distinct turning points. Notably, the effect of the cement admixture on the permeability coefficient was more significant for specimens with larger grain sizes and smaller Cu values.  
Furthermore, the permeability coefficient of the improved loess material, composed of a blend of Malan loess and Lishi loess with added cement, stabilized within the typical range of cement admixture ratios used in general engineering applications. For example, the cement admixture ratio commonly employed for constructing impermeable curtains using cement piles, typically ranging from 12% to 15%, resulted in a permeability coefficient of 7.6 \(\times\) 10\({}^{-8}\) cm/s, based on this study. Conversely, a mass ratio of 6:4 of Malan loess and Lishi loess (PL15) yielded a permeability coefficient of 7.12 \(\times\) 10\({}^{-7}\) cm/s at the same dry  
Figure 4: Relationship between cement content and permeability coefficient.  
Figure 5: Relationship between lime content and permeability coefficient.  
density, representing a 9.37-times higher value. Furthermore, an increase in lime content led to a decrease in the permeability coefficient, with a reduction factor of 1.3 observed as the lime content increased from 6% to 12%.  
The reason for such a change is that the soil used in the test has fewer particle clusters of clay particles. Therefore, the permeability coefficient of the cement soil exhibits a significant decrease as the cement admixture ratio increases. This decrease occurs due to the challenge faced by cement hydrate in fully filling all the pores within the fine particles initially. However, once the cement admixture ratio reaches a level where most of the pores in the fine particles are filled, further increases in the cement admixture ratio do not effectively reduce the permeability coefficient of the cement soil. This also explains the slight change in permeability coefficient after adding more than 12% of cement in Figure 4. It shows that the permeability coefficient of the improved loess is related to the pore space of the soil. Therefore, reducing the improved loess's void ratio and increasing the cemented soil's dry density can reduce the cemented soil's permeability coefficient more effectively with the same cement incorporation ratio.
## 2 Theoretical Background
### Present Study
#### 2.3.3 How Do Task and Image Type Interact?  
We hypothesized that any influences of task would strongly depend on image type. First, for objects, we expected human-CNN similarity to be consistently high across all three tasks with no clear differences between them. As the categories were defined by a locally restricted, salient and meaningful object, we assumed this object to attract human attention, both by focusing their gaze and constraining their manual selections. Thus, the risks of using eye movements should be negligible (i.e., low influence of task-irrelevant biases) and the relevant areas should be easy to select manually (i.e., simply drawing a polygon around the object).  
Second, for landscapes, we expected human-CNN similarity to be low across all three tasks, again with no differences between them. You probably cannot mark specific areas in any coherent manner for large, uniform areas of sand or grain, which should lead to a high variability of human attention maps in all tasks. Thus, not even the more intentional forms of selection (i.e., gaze-pointing, drawing) are likely to produce consistent results. Drawings might fall between two strategies, with some participants selecting large proportions of the scene, some selecting arbitrary patches and some selecting anything in between. For the two eye movement tasks, we expected participants to widely spread their gaze across the image. However, due to the powerful influence of scene guidance, eye movements were expected to be somewhat more systematic than drawings, perhaps even leading to higher similarity with CNN, which also rely global scene statistics.  
Finally, we did expect task differences for indoor scenes. Due to the risks described above, spontaneous gaze and drawing might be less similar to CNN, while gaze-pointing might lead to comparably high similarity. This is because we expected indoor scenes to result in a pattern somewhere in between objects and landscapes. Similar to landscape images, the broad distribution of scene-defining contents might make it hard to select relevant areas. Similar to object images, object-based scene guidance might direct participants' gaze to particular indoor scene objects. However, the benefit should be higher for gaze-pointing as it is assumed to compensate for systematic viewing biases. Taken together, we expected the influence of task to be strongest for indoor scenes.
## 4 Simulation Results
\(P_{s}=1.0\) W, \(P_{av}=2.0\) W.  
Figure 8: Learning episodes of five different PA algorithms for five optical channels: (**a**) the average system capacity and (**b**) the constraint function. \(P_{s}=1.0\) W, \(P_{av}=4.0\) W.  
Figure 6: Learning episodes of five different PA algorithms for five optical channels: (**a**) the average system capacity and (**b**) the constraint function. \(P_{s}=1.0\) W, \(P_{av}=3.0\) W.  
Figure 7: The average channel powers of five different PA algorithms for five optical channels during 5000 episodes. \(P_{s}=1.0\) W, \(P_{av}=3.0\) W.  
The supervised algorithm was trained using the exact solution taken from the water-filling algorithm as if the channel statistics had been known to the transmitter. That scenario is expressed in Section 3.1 and incorporates (2), (8), and (10). In Figures 4, 6 and 8, we observe that the proposed model-free DRL-aided solution outperforms the three baseline algorithms, showing a predicted average capacity advantage (18% better than Equal Power, 16% better than DQN, and 53% better than Random Power for \(P_{av}=2.0\) W). Additionally, it approximates the supervised method with excellent accuracy (2% worse performance for \(P_{av}=2.0\) W). The episode duration of \(T=100\) steps causes some waveform fluctuation, which can be resolved by using more time-steps at the cost of inference time. In Figures 5, 7 and 9, the average channel powers of the proposed algorithm match very well with the supervised ones, i.e., more power is assigned to the channels with better conditions, only differing by 0.1-0.2 W. The allocated channel powers of the other schemes differ significantly more, about 0.1-0.5 W. The inference time of an episode is 0.7-0.8 s including recorded data sampling, cumulative reward calculation, gradient computation, and back-propagation. In Figure 10, the means and standard deviations of the TN distributions for ch.0 and ch.3 are depicted. Gradually, the values are stabilized according to their conditions in Table 2. The mean value of ch.0 (\(\sim\)1.2) is greater than that of ch.3 (\(\sim\)0.85), and the standard deviation of ch.0 (\(\sim\)0.2) is smaller than that of ch.3 (\(\sim\)0.5). Thus, ch.0 has a much higher probability of being allocated with \(P_{s}=1.0\) W than the weaker ch.3. Figure 10: The mean and standard deviation of the TN distributions for two allocated channel powers corresponding to ch.0 and ch.3. Figure 9: The average channel powers of five different PA algorithms for five optical channels during 5000 episodes. \(P_{s}=1.0\) W, \(P_{av}=4.0\) W.  
Finally, in Figure 11 the loss function is plotted for a training (two-thirds of original dataset) and a validation set (one-third of original dataset), and for two fixed hyperparameter \(\lambda\) values. The \(\lambda\) can be selected to adjust the trade-off between the power constraint violation and the loss function. The training loss is on the same level as the validation loss; therefore, no overfitting is observed.
# Rock Mass Structure Classification of Caves Based on the 3D Rock Block Index
## 4 Probability Model of Joint Parameters
### Estimation of Joint Density  
The overall number of joints in the flat cave is determined by the joint volume density, which can be extrapolated from the joint line density. Joint line density \(\lambda_{d}\) refers to the number of joints intersecting the measurement line per unit measurement line length. In general, the joint line density is obtained directly via the line measurement method when the joints are counted on site. The joint shape is assumed to be a thin disk, and the formula \(\lambda_{\wp}\) is as follows [33]:  
\[\lambda_{d}=\pi\lambda_{\wp}\int_{0}^{\infty}R\int_{R}^{\infty}f_{r}(r)drdR \tag{6}\]  
where \(R\) is the distance from the intersection point between the measuring line and the disk to the outer edge of the joint; \(f_{r}(r)\) is the probability distribution of the joint diameter.  
Figure 8: Fitting curve of joint trace length.  
The joint volume density is related to the joint line density and the average trace length. When the joint morphology conforms to the assumed thin disk, the approximate calculation can be obtained via the following:  
\[\lambda_{\text{\tiny p}}=\frac{2\lambda_{d}}{\pi^{2}\overline{L}^{2}} \tag{7}\]  
where \(\lambda_{\text{\tiny p}}\) is the joint volume density; \(\lambda_{d}\) is the joint line density, which can be obtained through statistical calculation; and \(\overline{L}\) is the average trace length of the joint.  
\(f_{r}(r)\) can substitute the probability distribution of joint trace length of different groups of each homogeneous section into Formula (4), and the volume density of each group of joints in each section can be calculated according to Formula (7). The results are shown in Table 3.
# Detection Based on Crack Key Point and Deep Convolutional Neural Network
###### Abstract
In terms of obtaining crack image data, the current information era ensures easy access to a huge number of surface crack images, so the crack detection methods based on deep learning are reasonably trustworthy. There are two main directions for crack detection algorithms. One regards crack detection as an object segmentation task by image segmentation algorithms to classify and predict pixels and finally output binary images [34]. This method can precisely predict the location and width of cracks but requires detailed and accurate annotation of crack images. However, crack detection in practical engineering works does not require pixel-level positioning. Another strategy is to simplify the problem. This method partitions the crack image and detects crack individually on each patch before stitching them together to locate the cracks [22]. This method simplifies the annotation and can use object sorting algorithms with high convergence. Nonetheless, this method causes two problems: the lack of integrity of cracks when partitioning and the loss of the global feature of the crack image, reducing the generalizability and noise immunity of the model. Compared with the usual visual computer tasks, crack detection poses three notable challenges. Firstly, cracks are typical linear objects. Secondly, the commonly existing environmental noise causes confusion, so the crack detection results are sensitive to global features. This crack detection problem is relatively severe, influencing the detection results and invalidating the traditional image processing algorithms. Third, the task is substantially a binary classification problem with only two options, having a crack or not. Therefore, only these specificities are considered when designing and modifying a conventional deep convolutional neural network model. The accuracy and suitability of the crack detection method are therefore called into question. Some studies have also shown that the crack feature information of different scales greatly influences the crack identification effect. For this purpose, we established a crack detection model that considers the cracks' linear characteristics and maximizes the use of their global characteristics. Based on the cracks' linear characteristics, this method can identify cracks as long as the key points on the cracks are identified. Considering the network advantages of traditional computer vision tasks, this research work uses the separation and fusion of the global and local features of cracks to construct a KP-CraNet model for crack detection. Finally, evaluation criteria are set to evaluate the effectiveness and suitability of the model. The numerical experiment has proven that our crack detection model, KP-CraNet, showed a relatively strong detection ability with great potential for further improvement.
## 5 ESTCube-Luna Preliminary Mission Requirements and Platform Design Solutions
### Communication (COM)
#### 5.4.1 Low-Speed communication (LSCOM)  
UT Tartu Observatory operates a UHF-band Yagi-Uda antenna in T\(\alpha\)vere, Estonia, which is continuously accessible and can be used for low-speed telemetry and command Tx and Rx of ESTCube-LuNa. The gain of the LSCOM ground station antenna is about 23 dBi in the 435 MHz frequency band. In case of insufficient data transfer, we plan to find more antennas across the globe.  
For LSCOM on board ESTCube-LuNa, we are assuming an isotropic antenna system: a turnstile antenna which does not have any blind spots, like other types of antennas do. A turnstile antenna is a near-isotropic-type antenna that consists of two crossed dipoles, oriented at right angles to each other (see Figure 12 on the right). For example, ISISPACE has a 1 U deployable antenna system that can be fitted on the 1 \(\times\) 2 U side, opposite to the E-sail payload.
## 1 Introduction
### Iterative Clustering Algorithm  
Based on Equation (10), the similarity measurement with entropy-based weighting applied to the k-means framework can be conducted as Algorithm 2.  
Steps 1-3 utilize the automatic categorization process to obtain transformed categorical datasets based on Algorithm 1. Since the attributes of the transformed dataset are all categorical, the weight of each attribute can be calculated with the entropy-based weighting strategy, and Steps 4-9 show the process. Steps 10-21 are the iterative process that applies the similarity measurement based on Equation (10) into the k-means algorithm framework to address the transformed dataset.  
```
0:\(X=\{x_{1},x_{2},\ldots,x_{m}\}\) (dataset to cluster with \(d_{c}\) categorical attributes and \(d_{u}\) numerical attributes); \(k\) (number of clusters); \(f_{q}\) (a clustering function that partitions \(X^{l}\) into \(q\) clusters and returns the corresponding assignments); \(q_{max}\) (maximum number of categories to examine); Output:\(idx=\{idx_{1},idx_{2},\ldots,idx_{m}\}\) (an assignment of each point in \(X\) to one of \(k\) clusters);
1:for\(l=1\) to \(d_{u}\)do
2:\(\hat{X}^{l}=Categorize(X^{l},f_{q},q_{max})\) (automatic categorization of \(X^{l}\));
3:for\(r=1\) to \(d_{c}\)do
4:\(w^{c}_{r}=\frac{H(A^{l}_{r})}{\sum_{r=1}^{d_{c}}H(A^{l}_{r})+\sum_{r=1}^{d_{u} }H(A^{l}_{r})}\) (calculate the importance of each categorical attribute);
5:for\(r=1\) to \(d_{u}\)do
6:\(w^{u}_{r}=\frac{H(A^{l}_{r})}{\sum_{r=1}^{d_{c}}H(A^{l}_{r})+\sum_{r=1}^{d_{u} }H(A^{l}_{r})}\) (calculate the importance of each numerical attribute);
7: Set \(idx=\{0,0,\ldots,0\}\) and select \(k\) initial objects randomly as \(k\) initial centroids for each cluster
8:\(noChange=true\);
9:repeat
10:for\(i=1\) to \(m\)do
11:\(idx^{(new)}_{i}=argmax_{j\in\{1,2,\ldots,k\}}[s(x_{i},C_{j})]\);
12:if\(idx^{(new)}_{i}\
eq idx^{(old)}_{i}\)then
13:\(noChange=false\);
14: Update the information of clusters \(C^{(new)}_{idx_{i}}\) and \(C^{(old)}_{idx_{i}}\), including the frequency of each categorical value.
15:until (\(noChange=true\))
16:return\(idx\)
```  
**Algorithm 2** Iterative clustering algorithm with entropy-based weighting.
## 4 Impact of Plastic Particles on Soil Properties
### Interaction of Plastic Particles with Soil Contaminants
These suggestions are based on the differences in crystallinity, specific surface area, and surface structure between MPs and MBPs, although more research is needed to support this finding [38]. Chemical contaminants and pathogens, when adhered to MPs, can be transported along with them, potentially leading to contamination of new environments. Only a limited number of studies have investigated the transport of contaminants associated with microplastics directly in soil; some authors involve simulations conducted under conditions that mimic porous media [47]. Experimentally, MPs of LDPE have been shown to increase the mobility of glyphosate in soils containing microplastics (MP) [78], MPs of PE to enhance the mobility of atrazine and 4-(2,4-dichlorophenoxyoxy) butyric acid [78], and MPs of PE, PET, PP, PS, PU, PES, and biodegradable PLA to improve the bioavailability of Cu, Pb, Cd, Fe, and Mn [79]. However, this effect appears to be compound-specific, as in the latter case, the PA MPs had no impact on the bioavailability of metal ions. However, [PERSON] and [PERSON] noted that due to the limited amount of adsorbed contaminants, MPs as a source of exogenous molecules are negligible compared to endogenous chemicals that are part of plastic composition (i.e., additives such as phthalates, biocides, etc.). Additionally, [PERSON] et al. [79] evaluated the diffusion and partitioning coefficients of agrochemicals on tire and PE MPs and found that the contaminants' desorption was too rapid, implying that MPs are unlikely to facilitate the transport of contaminants in soil unless under conditions with log K\({}_{\text{ow}}\)\(>\) 5 and preferential flow. The transport properties of biodegradable MPs have been less frequently studied, as this is a relatively new area of research. MBPs generally contain more polar binding groups; hence, in these materials, polar interactions such as hydrogen bonding, charge-charge, and electron donor-acceptor (EDA) interactions play a more dominant role [80]. Although the overall interaction of polar microplastics with environmental constituents has not been extensively studied, existing research indicates that polar microplastics exhibit a higher sorption rate of metals and some organic pollutants compared to their non-polar counterparts. Additionally, the transport processes will be significantly affected by MP aging, which increases the surface hydrophilicity [81], thereby affecting combined MP/contaminant transport dynamics within soil profile [82]. In conclusion, the interaction of plastic particles with soil contaminants is complex and multifaceted, involving adsorption, transport, and potential changes in contaminant bioavailability and toxicity.
# Deep Learning-Based Imagery Style Evaluation for Cross-Category Industrial Product Forms
### Aims of This Study  
This paper proposes an Imagery Style Evaluation (ISE) method for multi-type industrial product forms, oriented towards user emotional preferences. The ISE method addresses key limitations of traditional approaches, such as heavy reliance on manual labor, small sample prediction, and cumbersome data transformations. It enables rapid, end-to-end, and visualized evaluation, helping designers better understand user emotions, reduce cognitive biases, and shorten the design cycle. The main contributions of this paper are as follows:  
1. Construction of representative style paradigms: Naturalness, Technology, Toughness, Steadiness, Softness, and Dynamism (NTTSSD) for evaluating imagery styles across multi-category product forms. Based on Kansei Engineering (KE), we integrate four key visual morphological features, i.e., contour line, edge transition angle, visual direction, and visual curvature, to establish the NTTSSD paradigms. These paradigms enable a systematic mapping between product form features and user emotional preferences, allowing for the quantification of perceptual evaluations across diverse product categories. Accordingly, a dataset of 8604 images covering 20 industrial product types was constructed.
2. Development of an end-to-end intelligent ISE framework: This study integrates an enhanced You Only Look Once version 5 small (YOLOv5s) deep learning network with NTTSSD paradigms by incorporating a Convolutional Block Attention Module (CBAM) to improve efficient learning of key morphological features. A shallow-to-deep semantic feature fusion mechanism is also proposed to capture multi-scale morphological characteristics across diverse product types. Experimental results show that the mean Average Precision ([EMAIL_ADDRESS]) across all categories reaches 86.1%, representing a 1.4% improvement over the state-of-the-art baseline model, while increasing the processing speed by 40.7 Frames Per Second (FPS). This framework significantly reduces the dependence on costly user surveys and complex data transformations, enabling efficient and collaborative evaluation across product categories.  
3. Validation of the model's generalization and interpretability: We utilize key layer feature maps and Gradient-weighted Class Activation Mapping (Grad-CAM) technology to visualize the model's attention to critical morphological regions, thereby enhancing interpretability. In extended testing across 40 product types, the ISE model achieves a root mean square error (RMSE) of 0.26, demonstrating strong cross-category generalization. These results highlight the feasibility of data-driven, intelligent imagery style evaluation in cross-category industrial product design.  
This paper is structured as follows: Section 2 presents a review of related studies. The NTTSSD paradigms are constructed in Section 3. Section 4 presents the ISE method. Section 5 describes feature visualization of the ISE method. The conclusions and future work are discussed in Section 6.