esa-sceva/satcom-chunk-collection huggingface dataset:
# Fabric Defect Detection Based on Improved Lightweight YOLOv8n
## 1 Introduction
[12] integrated new convolutional operators into the Extended Efficient Layer Aggregation Network to optimize feature extraction, effectively capturing spatial features while reducing computation. The experiments demonstrated that this fabric defect detection model reduced model parameters and the computational load by 18.03% and 20.53%. Although the aforementioned researchers have made significant progress in lightweight YOLO network detection methods, the complex background texture and numerous small targets in fabric defects pose challenges. The feature extraction capabilities of these networks remain limited, indicating that there is still room for improvement in the model's lightweight design. To address the aforementioned issues, this study proposes an efficient and accurate lightweight YOLOv8n-based fabric defect detection algorithm, GSL-YOLOv8n, suitable for textile production lines. The algorithm includes the following improvements:  
* Ghost Network Integration: The Ghost network is used to enhance the standard convolution (Conv) modules and C2f in the YOLOv8n network, significantly reducing the model's parameter count. * Semantic Information Extraction: To address the loss of semantic information between different features and the loss of small target features in the C2f module of YOLOv8, the parameter-free attention mechanism SimAM is embedded at the end of the backbone network. * Lightweight Detection Head: To further achieve model lightweight, a lightweight detection head (LSCDH) is designed by combining the GroupNorm and shared convolution concepts. The experimental results on fabric defect datasets demonstrate the effectiveness of the proposed algorithm. It successfully balances detection accuracy and speed while reducing model complexity, making it suitable for deployment on devices with limited computational resources. The remaining sections of this paper are organized as follows: The second section elaborates on the YOLOv8 algorithm and the proposed improvements. The third section introduces the fabric dataset, experimental setup, and detailed experimental results. The final section presents the conclusions and future work outlook of this study.
# Intelligent Prediction of Ore Block Shapes Based on Novel View Synthesis Technology
## 3 Experimental Verification  
### Ore Block Dataset Creation Process  
Since there is currently no publicly available large-scale dataset of ore block views and pose data in the fields of mining engineering and computer vision, and collecting diverse ore block views and calculating their corresponding poses in the field is time-consuming and insufficient to meet the training data scale requirements, this paper uses the simulation graphics software Blender 2.90 to create simulated ore block model data that mimics the shapes and textures of real ore blocks. Blender is an open-source 3D graphics software capable of constructing 3D models of any shape [37] and generating static images and corresponding poses using its built-in renderer. It also produces good results in generating details and textures [38]. To enhance the diversity of the dataset views, two sampling trajectories were used: \"hemispherical uniform distribution\" and \"Archimedean spiral\". Additionally, bounded random noise was added to the camera positions to further increase the diversity of the training data [39]. These approaches not only ensure that the training data has a high degree of coverage across different viewpoints and conditions but also effectively prevent the model from overfitting to specific scenarios, thereby enhancing the model's generalization ability. Moreover, this diversified data generation strategy better captures subtle variations in ore block morphology, enabling the model to recognize and distinguish more complex morphological features, ultimately improving the model's robustness. Illustrations of the two sampling trajectories are shown in Figure 4.  
In this study, 10,000 images and corresponding poses were rendered from 200 simulated ore blocks with various shapes using the aforementioned sampling methods. The data were also augmented to enhance its diversity and the model's accuracy. A portion of the data are shown in Figure 5.  
All dataset images were resized to 128 pixels \(\times\) 128 pixels for input and were split into training sets, validation sets, and test sets in a ratio of 8.0:1.2:0.8.
## 2 Structural Model, Wind-Loading Model, and Analysis Procedure
### Analysis Procedure  
The influence of the instantaneous load eccentricities on the response of the structural models evaluated in this study can be measured by employing the response ratios of the maximum peak responses of the structure with the instantaneous load eccentricities (A-\(\Delta\) effect), and the maximum peak responses without the influence of the instantaneous load eccentricities. Further, it is noted that the excessive wind-induced response could lead to various problems associated with human comfort [6; 7; 20]; for this reason, Equation (10) defines the response ratios in terms of displacement and acceleration.  
\[R_{X}=\max_{t\in[0,T_{r}]}\big{|}u_{xAD}(t)\big{|}/\max_{t\in[0,T_{r}]}|u_{x} (t)|, \tag{10a}\] \[R_{Y}=\max_{t\in[0,T_{r}]}\big{|}u_{yAD}(t)\big{|}/\max_{t\in[0,T_{r}]}\big{|}u_ {y}(t)\big{|},\] (10b) \[R_{\widehat{X}}=\max_{t\in[0,T_{r}]}\big{|}\tilde{u}_{xAD}(t)\mp\tilde{\theta}_ {AD}(t)r\big{|}/\max_{t\in[0,T_{r}]}\big{|}\tilde{u}_{x}(t)\mp\tilde{\theta}(t )r\big{|},\] (10c) \[R_{\widehat{Y}}=\max_{t\in[0,T_{r}]}\big{|}\tilde{u}_{yAD}(t)\mp\tilde{\theta}_ {AD}(t)r\big{|}/\max_{t\in[0,T_{r}]}\big{|}\tilde{u}_{y}(t)\mp\tilde{\theta}(t )r\big{|}, \tag{10d}\]  
where \(T_{r}\) denotes the duration of the numerical analysis (10 min in this study). In Equation (10), symbols with the additional subscript \(A\)D indicate that the structural response includes the A-\(\Delta\) effect. For a given structure and predefined parameters to characterize the wind-loading model, the analysis procedure for assessing the influence of the instantaneous load eccentricities and correlation of wind forces and torsional moment on the response can be summarized as follows:1. [label=(0)]
2. Simulate \(F_{x}(t)\), \(F_{y}(t)\) and \(M(t)\) by using Equation (8);
3. Solve Equation (5) for the structure without and with the A-\(\Delta\) effect to obtain the time history of the responses using Gear's method [12];
4. Evaluate the response ratios (i.e., \(R_{X}\), \(R_{Y}\), \(R_{\dot{X}}\), and \(R_{\dot{Y}}\)) defined in Equation (10);
5. Repeat steps 1 to 3 for all the structures considered.  
Furthermore, the previous procedure can also be used to calculate the statistics of the response with and without the A-\(\Delta\) effect.
###### Abstract
(2021) investigated the effects of straw mulching on soil total N, available N, and available P contents [15]. [PERSON] et al. (2019) examined the impacts of straw deep ploughing on SOC, total N, available N, and available P [14]. [PERSON] et al. (2019) reported the influence of deep plowing with straw on SOC and TN contents [16]. However, there is little research that has compared the differences between the two straw return modes. Also, the relationship between the distribution patterns of SOC and nutrient contents with soil depths as affected by straw return modes is not well known. Furthermore, the relationships between straw return modes, soil depths, SOC and nutrient contents, and maize yield were still unclear. The purpose of the present study was to explore the influences of different maize straw return modes on SOC and nutrients (including total nitrogen (N), available N, total phosphorus (P), and available P) contents at 0-40 cm soil depths in a Mollisol of Northeast China. The relationships between straw return modes, soil layer depths, SOC and nutrient contents, and maize yield were evaluated. Because different straw return modes have contrasting influences on the distribution of straw residues among soil layers, we thus hypothesized that the responses of SOC and nutrient contents to straw return modes depended on soil layer depths. Moreover, we also hypothesized that straw return modes indirectly affected maize yield by directly affecting SOC and nutrient contents.
## 4 Discussion
### Lipid Production in Phosphorus-Limited Media
When yeast _R. toruloides_ Y4 was cultivated in the medium with 3.6 g/L of KH\({}_{2}\)PO\({}_{4}\) and 70 g/dm\({}^{3}\) initial glucose content, which corresponded to a C/P molar ratio of 72:1, after 96 h of batch shaking the flask biomass yield was 18.6 g\({}_{\text{d.m. }}\)/dm\({}^{3}\) with a lipid content of 21.2%. Moreover, enhancing the limitation of phosphorus source to C/P= 9552:1 resulted in improved SCO production to 62.1% and better biomass growth of 19.4 g\({}_{\text{d.m.}}\)/dm\({}^{3}\). Surprisingly, when the nitrogen source was additionally restricted (C/N = 22.3:1), the accumulation efficiency was 63.3% with a biomass yield of 19.9 g\({}_{\text{d.m.}}\)/dm\({}^{3}\). Under phosphorus-limiting conditions, lipid efficiency was consistently close to 60%, even when C/N = 6.1:1 [31]. In the present experiments, the results of the flask-shaken cultures show that the level of KH\({}_{2}\)PO\({}_{4}\) supplementation appeared to be a significant factor affecting the lipid accumulation efficiency, in contrast to the addition of nitrogen source in the form of (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\), for which no significant relationship was found over the concentration range studied. In a batch culture in a bioreactor, the highest efficiency of cellular lipid production by _Y. lipolytica_ yeast has been observed for M1 medium, in which C/P was 114.9:1 and C/N = 71.8:1. However, in bioreactor culture mode, a comparable relationship was not found for the impact of the molar ratio C/P. At a C/P of 114.9, both the highest and lowest microbial lipid production among all substrates was obtained. Under the established conditions, this nitrogen addition was significant. In the experiment of [PERSON] et al. [2], _Y. lipolytica_ yeast cultured in YPG medium with high nitrogen content and therefore low C/N ratio did not produce SCO. This is in contrast to mineral media with glucose (MG7) and olive oil (MO7), where the conversion yield of storage lipids per biomass was 0.116 and 0.207 g/g\({}_{\text{d.m.}}\). These could be further examples that confirm the role of nitrogen limitation in culture medium.
## 2 General State-of-the-Art
### Point Cloud Processing
#### 2.1.2 Algorithmic Approach  
The algorithmic approach employs geometric rules and mathematical functions to perform point cloud segmentation [40]. This approach is often heuristic in nature, but maybe enough for certain purposes as they are fast and simple to implement [26]. Algorithmic segmentation uses mathematical rules and functions as constraints during the segmentation (and possibly also classification). These rules may range from simple rules (e.g., \"floors are flat and located below each storey\" or \"pillars are cylinders\") [25] to the implementation of ontological relations [41; 42; 43].  
The rules and constraints in this type of method are often determined differently according to the encountered case. The authors in [44] employ a type of multi-scalar approach by sub-dividing the point cloud into floors, rooms, and thence walls. The authors in [45] similarly use geometric constraints to segment the walls of bridges. In [41], relational ontology was used as constraints in determining the classes of point clouds segmented by connected component segmentation.  
It is worth noting that most of the examples seen in the literature address a particular scale level for the point cloud. For example, the authors in [39] focuse on small scale point clouds or larger areas mainly done to support surveying purposes, while the authors in [26] perform the point cloud processing at the larger scale of a building. Many algorithms were also developed with modern objects in mind [44; 45] even though forays into the heritage domain is becoming more and more numerous in recent years [36; 38]. The goal of this research is to develop a toolbox which enables the processing of multi-scalar heritage point cloud, from the scale of a neighborhood (heritage complexes) up to that of architectural elements. This is encouraged by the increasing trend towards multi-sensor and multi-scalar recording missions for heritage sites [22; 46].
## 3 The Machine Learning Swarm Intelligence Algorithm
### Whale Optimization Algorithm (WOA)  
The Whale Optimization Algorithm (WOA) is a bio-inspired metaheuristic based on the hunting behavior of humpback whales, particularly their use of \"bubble-net feeding\". Proposed by [PERSON] and [PERSON] in [11], WOA mimics the whales' strategies for searching, encircling, and attacking prey. These behaviors are mathematically modeled to solve optimization problems.  
The algorithm initializes with a randomly generated population of solutions (whales). During each iteration, the position of each whale is updated based on either the best solution found so far or a randomly chosen whale. This ensures a balance between exploration and exploitation of the search space.  
1. Identifying the Prey. The prey in WOA represents the current best solution. Whales adjust their position towards this solution using coefficient vectors \(\vec{A}\) and \(\vec{C}\), which are defined as follows:  
\[\vec{A}=2\cdot\vec{a}\cdot\vec{r}-\vec{a},\quad\vec{C}=2\cdot\vec{r}\]  
where \(\vec{a}\) decreases linearly from 2 to 0 over the iterations, while \(\vec{r}\) is a random vector in \([0,1]\). These coefficients enable WOA to dynamically shift between the exploration (\(|\vec{A}|\geq 1\)) and exploitation (\(|\vec{A}|<1\)) phases.  
2. Encircling the Prey. Once the prey is identified, whales move closer, mimicking the encircling behavior. This is modeled as follows:  
\[\vec{X}_{i}^{t+1}=\vec{X}^{*}-\vec{A}\cdot|\vec{C}\cdot\vec{X}^{*}-\vec{X}_{i} ^{t}|\]  
where \(\vec{X}^{*}\) represents the best solution so far, and \(\vec{X}_{i}^{t}\) is the position of the \(i\)-th whale at iteration \(t\).  
3. Bubble-Net Attacking Technique. WOA simulates the spiral motion and shrinking circle of bubble-net feeding. The position update combines spiral motion and encircling with a 50% probability:  
\[\vec{X}_{i}^{t+1}=\begin{cases}\vec{X}^{*}-\vec{A}\cdot|\vec{C}\cdot\vec{X}^{* }-\vec{X}_{i}^{t}|,&\text{if $p<0.5$}\\ |\vec{X}^{*}-\vec{X}_{i}^{t}|\cdot e^{b\cdot l}\cdot\cos(2\pi l)+\vec{X}^{*},& \text{if $p\geq 0.5$}\end{cases}\]  
where \(b\) defines the spiral shape, \(l\) is a random number in \([-1,1]\), and \(p\) is a random probability.
# Dynamic Modeling and Simulation of a Robotic Lander Based on Variable Radius Drums
## 2 Dynamic Modeling of the Robotic Lander
### Soil Characterization
At this point, the internal angle of the soil is determined as can be seen in Figure 4a and is found to be \(\phi=(38.82\pm 2.02)^{\circ}\). We elected the angle to be the mean value, consequently \(\mu_{s}\approx 0.804\). Kinetic friction coefficient \(\mu_{k}\) on the other hand is assumed to be a fraction of the measured static friction coefficient. In this case, \(\mu_{k}=0.7\mu_{s}=0.563\). All the required lander design parameters are summarized in Table 1. Considering that every part of the lander is fabricated in Polylactic Acid (PLA) polymer, the inertial parameters can be computed, and it is possible to solve the ordinary differential equation resulting from Equation (9), and then evaluate the contact force, which is required for the synthesis of the VRD. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(r\) & \(5.00\times 10^{-2}\) m & \(g\) & \(9.81\,\mathrm{m\,s^{-2}}\) \\ \(r_{1}\) & \(3.20\times 10^{-3}\) m & \(H\) & \(0.450\,\mathrm{m}\) \\ \(m\) & \(0.500\,\mathrm{kg}\) & \(f\) & \(1.00\times 10^{4}\) \\ \(m_{a}\) & \(3.70\times 10^{-2}\) kg & \(M_{0}\) & \(1.54\,\mathrm{N\,m}\) \\ \(l_{c}\) & \(25.3\,\mathrm{cm}\) & \(l_{m}\) & \(27.1\,\mathrm{cm}\) \\ \(k_{s}\) & \(50.0\times 10^{3}\)\(\mathrm{N\,m^{-1}}\) & Material & PLA \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters used in the design of the lander. Figure 4: Experimental characterization of the soil: (**a**) measure of the internal angle \(\phi\); (**b**) measure of the penetration depth. The scale unit is centimeters in both images.
# In Vivo Evaluation of Decellularized Human Tooth Scaffold for Dental Tissue Regeneration
## 3 Results  
Histological and Immunohistochemical Analysis of PDLSC Recellularization after In Vivo Transplantation  
PDLSCs were repopulated on dHPDLs and cell viability was observed after nine weeks of transplantation in mouse pockets. Cells in the control group were not inoculated, but repopulation on dHPDL ECMs was observed through HE and MT staining for the PDLSC group (Figure 1A,B,D,E). HN staining confirmed that the cells were of human origin (Figure 1C,F). Immunohistochemical staining identified anti-human Col XII, CP23, and OC expression. The PDLSC group was stained while the control group was not (Figure 2).  
\begin{table}
\begin{tabular}{c c c} \hline
**Gene** & **Forward Primer Sequence (5/-3)** & **Reverse Primer Sequence (5/-3)** \\ \hline _ALP_ & GGACCATTCCCCACGTTTCAC & CCTTGTAGCCAGGCCCATG \\ _CD31_ & CCTCATTGTCTCCCCCCTTCCACCA & ACTTGTCTCTCCTCCTCCTCGCGCG \\ _CD34_ & CCTCCTCCTTGCAGGATTA & CCTCAGAGAGCCACCAGGT \\ _Col 1_ & CGATGGTCCAGGCTCACACAC & CAGCTTGGATGAGGAGGGTTTAC \\ _Cal XII_ & CGGCACAGAGCCTTACGGTCC & CTGCCCGGTCCG \\ _CP23_ & AACCATCATGCCTGGAACCTCAC & GGAATACCACCCTCTCTGCTTGC \\ _DMP1_ & GATCACACCACCCTCCTCCTCCTC & AGCACAATAGACCCTCCTCCTC \\ _DSPP_ & GGGATGTGTGGATGCA & CCAGCACTACTGTAGGTCCATTCTC \\ _NES_ & GCCCTGACCACCCTGATTTA & GGAGTCTGATGTTCTCCTC \\ _OC_ & CAAAGGTGCACCCTTGTGTGTCT & TCAAGCTGCTGATGATG \\ POSTN_ & CACACCACCGTGTAGACCTGGAC & TGTCTCTCTCTGTAGAGAG \\ _GAPDH_ & TCCTGACCACCACAACTCTT & TGGCAGATGGCATGGC \\ \hline \end{tabular} Abbreviations: _ALP_, gene encoding alkaline phosphatase; _CD31_, gene encoding cluster of differentiation 34; _CD34_, gene encoding collagen type I; _Col XII_, gene encoding collagen type XII; _CP23_, gene encoding cement-derived protein 23; _DMP1_, gene encoding dentin matrix acidic phosphoprotein 1; _DSPP_, gene encoding dentin sialophosphoprotein; _NES_, gene encoding nestin; _OC_, gene encoding osteocalcin; _POSTN_, gene encoding periostin; _GAPDH_, gene encoding glyceraldehyde-3-phosphate dehydrogenase.  
\end{table}
Table 2: qPCR forward and reverse primer sequences. The annealing procedures were performed at 60 \({}^{\circ}\)C for all primers.  
Figure 1: Histological characteristics of PDLSC recellularization on dHPDL transplants. HE staining (**A,D**), MT staining (**B,E**), HN staining (**C,F**), HE, MT, and HN staining showed that dHPDL transplants were contributed to recellularization of cells, indicated by red arrows. Scale bars: 20 \(\mu\)m. Abbreviations: PDLSC, periodontal ligament stem cells; dHPDL, decellularized human periodontal ligament; HE, hematoxylin and eosin; MT, Masson’s trichrome; HN, human nuclei.
## 1 Introduction
### Lifestyle Management in Space  
Based on microgravity simulation studies, NASA has proposed several potential biomedical countermeasures in space [33; 160; 161]. Mandatory exercise protocols in space are crucial and can be used to maintain physical fitness and counteract the effects of microgravity. While these protocols may be beneficial, exercise alone may not be enough to prevent certain effects of microgravity (e.g., an increase in arterial thickness/stiffness) [265; 266; 20; 267]. For example, a recent study found that resistive exercise alone could not suppress the increase in bone resorption that occurs in space [20]. Hence, a combination of resistance training and an antiresorptive medication (e.g., bisphosphonate) appears to be optimal for promotion of bone health [20; 21]. Further research is needed to identify the optimal exercise regimen including recommended exercises, duration, and frequency.  
In addition to exercise, dietary modification may be another potential area for optimization. The use of a diet based on caloric restriction (CR) in space remains up for debate. Based on data from terrestrial studies, caloric restriction may be useful for improving vascular health; however, this benefit may be offset by the associated muscle atrophy and osteoporosis [268; 269]. Given that NASA encourages astronauts to consume adequate energy to maintain body mass, there has been an attempt to mimic the positive effects of CR on vascular health while providing appropriate nutrition. Further research is needed this area to identify the ideal space diet.  
Based on current guidelines, only vitamin D supplementation during space travel is recommended. Supplementation of A, B6, B12, C, E, K, Biotin, folic acid are not generally recommended at this time due to insufficient evidence [64] (Table S3). The use of traditional prescription medications may not function as intended on Earth. Therefore, alternative methods such as synthetic biologic agents or probiotics may be considered [35; 38]. However, evidence in this area is extremely limited, and it is possible that the synthetic agents or probiotics may themselves be altered due to microgravity and radiation exposure. Further research is needed to investigate the relationship between these supplements and potential health benefits in space.  
Currently, most countermeasures are directed towards cardiovascular system and musculoskeletal pathologies but there is little data against issues like immune and sleep deprivation, SANS, skin, etc. Artificial Gravity (AG) has been postulated as adequate multi-system countermeasure especially the chronic exposure in a large radius systems. Previously, the main barrier is the huge increase in costs [270; 271; 272]. However, there are various studies that show the opposite and also the recent decrease in launch cost makes the budget issue nearly irrelevant especially when a huge effort is paid to counteract the lack of gravity. The use of AG especially long-radius chronic AG is feasible. Further studies are needed to determine the utilization of AG in long-duration space travel.