esa-sceva/satcom-chunk-collection huggingface dataset:
# Concept of a 3D-Printed Voronoi Egg-Shaped Habitat for Permanent Lunar Outpost
## 2 Principal Concepts
### A Voronoi Structure Concept  
To create an egg-shaped light-weight structure, a Voronoi pattern was proposed. Thanks to its variability, it can be worked with in different ways with many additional parameters, which can be evaluated during future static assessment and design of its ideal density, size, etc.  
One of the ways is to apply the Voronoi pattern directly to the surface of the object. The achieved network enables to generate a structure with a thickness between individual cells and with a thickness in the third dimension (i.e., the thickness of the object's wall). Moreover, it is possible to round the generated shapes, which brings many manufacturing and mechanical advantages (see Figure 4a). The way to implement this shape for a regolith-filled structure is shown in Figure 5a (using a wall section). In order to prevent lunar soil from spilling through the openings into the interior space, a partition will be 3D-printed at the same time, which will withstand only this load. The resulting pockets, which are filled with regolith, serve as a lock for the settled object. On the inside there is an inflatable membrane, which is locked in place in the same way.  
Figure 4: Generated model of the Voronoi structure: (**a**) implemented single-layer Voronoi structure on an egg-shaped surface, (**b**) cut-out of the 3D Voronoi structure that can be used for the entire outer wall thickness envelope.  
Figure 3: Concept for grouping several habitats of different size in a crater: cross-section view.  
Another solution is to apply a 3D Voronoi structure in the wall thickness of the object (see Figure 4b). This creates a complex network system (Figure 5b) which can also be used extensively (but requires more computing power to generate the model). The fundamental difference in comparison to the previous variant is the possibility of directly filling its structure with regolith.  
For the future full scale design of the lattice cell structure, the principle of finite element analysis (FEA) should be used to read the properties of that design. FEA for a single-layer Voronoi structure on an egg-shaped surface, and a cut-out of the 3D Voronoi structure, can be used for the entire outer wall thickness envelope. The methodology proposed by [PERSON] and [PERSON] [28; 29], based on the principle of equivalent lattice cell structure idea to solve the problem, should be very useful.
## 2 Materials and Methods
### Stimuli  
All BCI tasks were presented on a 16'' laptop monitor cycling at 165 Hz and placed at an approximate viewing distance of 70 cm, though head position was not constrained. The presentation computer was a Lenovo Legion 5 Pro with an 11 th Gen Intel i7-11800H CPU @ 2.30 GHz and 16 GB of RAM running Windows 11 Home (64 bit). Application processes utilized an NVIDIA GeForce RTX 3050 laptop GPU to ensure presentation fidelity. All letter stimuli were rendered in Overpass-Mono font at the center of the screen. Letter stimuli were drawn in white against a solid black background, with the exceptions that targetprompts in calibrations were yellow, and all fixation crosses were drawn in red. Assuming a screen distance of 70 cm, the monospace font constrained letter stimuli to appear within an imaginary box that subtended 2.05\({}^{\circ}\) by 3.27\({}^{\circ}\) visual angle, or approximately 2.5 cm wide by 4.0 cm tall on the screen. Characters had a stroke width of approximately 0.4 cm, or roughly 0.33\({}^{\circ}\).
# Genetic Variation Affects the Anti-Melanogenic Efficacy of _Platycodon grandiflora_ Flowers
## 1 Introduction  
_Platycodon grandiflora_, commonly known as balloon flower, is a single species belonging to the family Campanulaceae and is extensively utilized for ornamental, edible, and medicinal purposes in East Asia [1]. Traditionally, the roots of _P. grandiflora_ have been employed for medicinal applications, particularly in the management of respiratory diseases including bronchitis, tonsillitis, asthma, and pneumonia [1; 2; 3]. Although considerable attention has been directed towards elucidating the pharmacological attributes of its root, recent investigations have begun to explore the utilization of the aerial parts of _P. grandiflora_ to repurpose by-products generated during harvesting [4; 5]. For example, extracts derived from the aerial parts of _P. grandiflora_ have demonstrated notable antioxidant, whitening, and anti-inflammatory properties [6; 7]. Furthermore, flower extracts exhibit skincare benefits, including anti-inflammatory properties and promotion of skin wound healing [8]. These properties indicate the potential of balloon flower by-products as valuable biomaterials for applications in the food, cosmetic, and medical industries.  
In Korea, _P. grandiflora_ is found in two prevalent varieties: those with purple flowers (PFs) and those with white flowers (WFs), each distinguished by unique phytochemical compositions, notably anthocyanins [9]. Additionally, distinctive triterpene saponins, including platycoside E (PE), platycodin D3 (PD3), and platycodin D (PD), have been identified as critical factors for discriminating between the varieties of _P. grandiflora_[10]. Notably, PD has a broad spectrum of pharmaceutical activities, including anti-inflammatory, anti-melanogenic, antiviral, anti-obesity, anti-coagulant, and anti-tumor properties [4; 11]. These observations suggest that variations in the content and composition of phytochemicals, depending on the variety, may lead to differences in pharmacological properties.  
Consequently, the selection of genotypes assumes paramount importance for facilitating the potential utilization of specific genotypes across various industries, such as cosmetics and pharmaceuticals. However, variations in bioactivities between PFs and WFs are poorly known.  
In this study, we compared and analyzed the differences in anti-melanogenic activity between two common varieties of _P. grandiflora_ to highlight the value of flowers as functional ingredients rather than merely as agricultural by-products. Based on HPLC and molecular docking analyses, we demonstrated that the variation in the anti-melanogenic activities of _P. grandiflora_ varieties is derived from different levels of several triterpene saponins including PD3 and PD. Our findings suggest that understanding genetic diversity is crucial for ensuring the quality control of medicinal plant-derived products.
## 10 Conclusions and Future Perspective  
Styrene-based polymers have emerged as promising candidates for PEMs and AEMs in fuel cell applications due to their tunable chemical structure, cost effectiveness, and ease of functionalization. Their hydrophobic backbone combined with the ability to introduce hydrophilic sulfonic acid groups enables these materials to achieve the critical balance between proton conductivity, mechanical stability, and fuel barrier properties. Advances in copolymerization techniques, such as the incorporation of fluorinated and aromatic units, have further improved their performance in terms of proton conductivity, oxidative stability, and durability under operating conditions. Despite these advantages, challenges such as water management, thermal stability, and susceptibility to oxidative degradation hinder the large-scale adoption of styrene-based PEMs and AEMs. Styrene starts melting at a temperature in the range of 100-125 \({}^{\circ}\)C which makes it unsuitable for HTPEMFC or SOFC operating temperature greater than 600 \({}^{\circ}\)C. At high temperatures, styrene-based membrane not only loses structural stability but also creates pollution due to toxic and corrosive environments such as the presence of hydrogen, oxygen, or steam. Ultimately, after the chain break of styrene, it pollutes the fuel cells which deteriorates the overall performance of the fuel cell. Moreover, achieving high proton conductivity at low relative humidity and elevated temperatures remains an area of ongoing research. While styrene-based PEMs and AEMs exhibit significant potential, their performance still lags behind commercially available membranes like Nafion in critical parameters, particularly in terms of long-term durability and operational efficiency under demanding conditions. Moreover, AEMs face low hydroxide ion conductivity, high ohmic losses, and poor chemical stability due to the degradation of cationic groups (e.g., quaternary ammonium) in high-pH environments.  
Future improvements for styrene-based PEMs and AEMs of fuel cells are given below.
# Evaluation of the Robustness for Integrated Production Scheduling and Maintenance Planning Problem
## 4 Robustness Measure for JSP with Preventive Maintenance
### Surrogate Robustness Measures  
Three surrogate robustness measures based on slack-time are investigated. First, [PERSON] and [PERSON] [6] applied the average total slack time of all operations to measure the quality robustness of a schedule, as shown in the following:  
\[RM_{1}=-\frac{\sum_{i=1}^{m}\sum_{j=1}^{n}t_{i,j}}{n\cdot m} \tag{11}\]  
where \(t_{i,j}\) indicates total slack time of the operation, \(O_{i,j}\).  
In contract, [PERSON] and [PERSON] [17] proposed using the sum of free slack times to measure the quality robustness, as shown in the following:  
\[RM_{2}=-\sum_{i=1}^{m}\sum_{j=1}^{n}tf_{i,j} \tag{12}\]  
where \(tf_{i,j}\) indicates free slack time of the operation \(O_{i,j}\).  
[PERSON] and [PERSON] [15] took the weighted total slack time of all operations as the surrogate robustness measure:  
\[RM_{3}=-\sum_{i=1}^{m}\sum_{j=1}^{n}\frac{w_{i,j}}{w_{tot}}t_{i,j} \tag{13}\]  
where \(w_{i,j}\) is the workload of the machine on which operation \(O_{i,j}\) is processed and \(w_{tot}\) is the sum of workloads of all machines.
## 3 Efficient Multi-Parent Hierarchical Routing Protocol
This situation can occur in the network initialization transient stage when there are nodes that still have zero hierarchy; (3) A node updates its neighbors, and possibly its hierarchy, at regularly specified intervals. Thus, the neighbor discovering process, sending an \(ND\) packet, is always done not long after a possible topology change. The purpose of this mechanism is to reduce the time in which nodes update their hierarchies, especially when extreme conditions occur due to the loss of critical links in the logical topology. This will diminish auto-configuration network delays and reduce the packet loss problem caused by the disconnection of links; (4) In order to prevent unreliable changes, caused by node faults, MPH uses a scheme that employs a slow erasure of nodes from the neighbor table, for which purpose, a variable called neighbor persistence, is used. If an NDRpacket is received from a neighbor as a response to an \(ND\) packet, and persistence takes a maximum value \(p\). Afterwards, prior to the transmission of an \(ND\) packet, persistence is reduced by one for every registered node in the neighbor table. Therefore, if after successive \(p\)\(ND\) packet emissions the origin node did not receive any \(NDR\) packets from the node that initially had persistence \(p\), then the route to this node is erased from the table. This mechanism solves the problem of inconstant changes in the neighbor tables, which can be caused by the momentary disconnection of the nodes. For example, occasionally, due to interference or packet collisions, the nodes might not respond in time to their neighbors, which, without the node persistence mechanism, would modify the neighbor tables. Thus, this mechanism gives more opportunities for weak links to keep an association with the routing tables and avoids abrupt changes; (5) The coordinator node builds the whole topology of the network after it gathers the neighbor tables from the nodes. Then, the coordinator node is able to specify the complete path to be followed by the packet to reach its destination, and it has the capacity to ask for statistics of the nodes in the network [13]. Figure 1 describes the link formation process followed to assign hierarchies to the nodes, so forming node generations. For the network formation process, each node establishes connections with other nodes within its coverage radio; these nodes are its neighbors and will belong to its neighbor table. Starting from the coordinator node, which is the node that has the highest hierarchy level, the nodes connected to it are assigned a lower hierarchy level (one level below the coordinator's hierarchy). Then, the next lower hierarchy is assigned to the nodes connected to the second highest hierarchy level nodes, and so on, until the hierarchical topology is established. For a given node, among the nodes in its neighbor table, those with higher hierarchy are parent nodes, and those with lower hierarchy are child nodes. Nodes having the same hierarchy level will belong to the same generation.
## 3 Optimization Methodology
### Objective Function  
The optimization model formulation is performed in a way for keeping the level of the road performance at an acceptable level by using strategies of cost-effective maintenance. This constitutes using a multi-objective function in decision making to obtain the optimal maintenance plan.  
As aforementioned, the PCR was used to evaluate the pavement condition in this study then one of the objective functions is maximizing the PCR value as shown in Equation (4). The other function is minimizing the cost to attain the optimal solution with minimum cost as possible as shown in Equation (5). In this study, the establishment of pavement maintenance strategies were constrained by the available budget for maintenance and the desired level of performance. The annual budget \(Bt\) constrains the objective function as in Equation (6). The Equation (7) is the constraint of the pavement performance. Only applying of one treatment is taken into account for each road section in each year as in Equation (8).  
\[\textbf{Max }\sum_{s=1}^{S}\sum_{j=1}^{m}PCR_{s,t}(X_{s,j}) \tag{4}\]  
\[\textbf{Min }\sum_{s=1}^{S}\sum_{j=1}^{m}C(X_{s,j})L_{s}W_{S} \tag{5}\]  
Subjected to  
\[\sum_{s=1}^{S}C\big{(}X_{s,j}\big{)}\times L_{s}\times W_{s}\leq B_{t},\qquad \quad\forall\;s=1\;\;\text{to}\;S \tag{6}\]  
\[\sum_{s=1}^{S}C\big{(}X_{s,j}\big{)}\times L_{s}\times W_{s}\leq B_{t},\qquad \quad\forall\;s=1\;\;\text{to}\;S \tag{7}\]  
\[\sum_{j=1}^{m}X_{s,j}=1,\qquad\qquad\qquad\forall\;s=1\;\;\text{to}\;S \tag{8}\]  
where, \(X_{s,j}\) maintenance application (option) for section \(s\) and treatment \(j\), \(C\) is the cost of maintenance activity, \(L_{s}\), and \(W_{s}\) are the length and width of the roadway section. The total maintenance cost of all sections is calculated as in (6).
# DDL R-CNN: Dynamic Direction Learning R-CNN for Rotated Object Detection
## 2 Related Works
### Research on the Representation of Rotating Boxes  
RROI pooling [17] enhances the generation of proposal boxes in various orientations by rotating anchor points, capturing the directional features of targets, and improving the precision of bounding box localization. This concept is then expanded to a rotated RoI pooling layer, which allows for the extraction of more distinct directional features from targets that are inclined or rotated, significantly boosting the accuracy and robustness of target detection in RS imagery.  
The Dynamic Refinement Network (DRN) [18] introduces two innovative elements: the Feature Selection Module (FSM) and the Dynamic Refinement Head (DRH). The FSM is designed to relay accurate and noise-reduced target shape and directional features to the detector. Meanwhile, the DRH dynamically adjusts its internal parameters based on the input target characteristics to better accommodate objects of different shapes and sizes and refine the detection of object locations and categories.  
R3 Det [19] enhances its detection capabilities by integrating rotated anchor points with horizontal anchor frames and addresses the issue of feature misalignment prevalent in single-stage detectors through the introduction of the Feature Refinement Module (FRM). The FRM has been integrated to determine position information for the refinement anchor via feature interpolation, with the entire feature map being reconstructed on a pixel-by-pixel basis to ensure feature alignment. Moreover, the FRM also reduces the number of bounding boxes in the refinement phase, speeding up the model training process.
## 2 Study Region and Data Source
### Data Source  
Landsat TM/ETM/OLI and HJ-1 A/B images were selected to extract water-surface area and land use changes for their high spatial resolution (30 m). Landsat data collected from 1990s to 2013 and HJ-1 A/B data collected from 2008 to 2013 were freely acquired from United States Geological Survey (USGS) [16] and China Center for Resources Satellite Data and Application [17], respectively. HJ-1A/B satellites are two small environment satellites equipped with the same CCD camera, which were launched on 6 September 2008 by China [18]. The low revisiting cycles every 48 h provided by the two satellites' constellation has great advantages in monitoring water area and land use changes. HJ-1 A/B have similar spectral range in the first four bands of Landsat TM/ETM+ image, but have much wider images reaching 700 km. The NIR-R-G \"false-color\" images were examined visually to choose cloud-free data in this study. In addition, 135 cloud free scenes (including 97 Landsat and 38 HJ-1A/B scenes) were selected. To obtain the real reflectivity data from land surface, Landsat data were processed by radiometric calibration and atmospheric correction processing, and HJ-1 A/B data needed geometric correction based on Landsat5 TM, radiometric calibration and atmospheric correction.  
The daily precipitation, average temperature, daylight hours, and wind speed data sets measured at seven meteorological stations located around the Honghu Lake were provided by the Meteorological Bureau in Hubei Province. In addition, the relative humidity data were downloaded from the China Meteorological Data Sharing Service System [19].
## 3 Methodology
### Identification and Representation of Key Parts  
The vital issue in 3D action recognition tasks is efficiently capturing and representing dynamic features within point cloud sequences. For now, estimation methods based on scene flow [37; 38] can help to understand 3D motion, but it is very time-consuming. Some studies use temporal rank pooling [3; 39] to preserve motion processes in 3D space by dividing time segments. These methods can capture more temporal information but often only divide a small number of intervals, resulting in coarse-grained dynamic features. We propose a crucial part identification and encoding module to better focus on the critical dynamics during the motion. It can extract the major parts from the global 3D voxel space according to the space occupancy times and encode the action details through fine-stream. Specifically, we first analyze the space occupancy by constructing a 3D space \(U\) with the exact boundaries as point cloud sequences for each spatial location, and the initial valuesof \(U\) are set to 0. We process the \(m\) uniform groups of sequence \(\hat{D}\) in order. Then, the 3D space usage can be calculated according to Equation (4):  
\[U_{voxel}(x,y,z)=\begin{cases}U_{voxel}(x,y,z)+1,v_{i}\
eq 0\\ U_{voxel}(x,y,z),otherwise\end{cases} \tag{4}\]  
The total space occupation \(u\) for each position can be obtained after counting all \(m\) point sets. In addition, since the point sets are naturally ordered in time, we can easily record the first and last time taken, \(f\) and \(l\), respectively, for each spatial location. Then, we define the threshold \(\theta\) to partition the prominent local space. The locations occupied less than \(\theta\) are treated as incidental noise, and those that record more than and less than \(m\) constitute the critical motion parts \(S\) as Equation (5):  
\[S_{voxel}(x,y,z)=\begin{cases}0,&U_{voxel}(x,y,z)<\theta\\ 1,&\theta\leq U_{voxel}(x,y,z)\leq m\end{cases} \tag{5}\]  
If the value of \(S_{voxel}(x,y,z)\) equals 0, this denotes that the voxel belongs to the global space \(\mathcal{G}\); otherwise, it belongs to the local space \(\mathcal{L}\). Compared with the point cloud processing methods [10; 40], which commonly adopt uniform downsampling operations, the proposed method is more effective, especially for actions involving only a tiny number of limb parts, because dividing the local space can not only also overcome the background effects to a certain extent, but also effectively enhance the gold content of the sampled point data. As shown in Figure 2, the local space \(\mathcal{L}\) fully preserves the detailed information of the main body parts, which provides critical cues for 3D action recognition while substantially reducing redundancy.
# Accelerated Inference of Face Detection under Edge-Cloud Collaboration
## 3 Proposed Method  
### Research Motivation  
The CenterNet object detection model is based on the keypoint estimation network, finds the center of the object, and obtains other properties of the object through regression. As a typical representative of anchor-free, this model has achieved good results in both speed and accuracy and is end-to-end differentiable. In addition, there is no need to perform any subsequent processing of non-maximum suppression (NMS), which saves computing resources. This paper performs further research on the CenterNet model. Through experiments, it is found that, as shown in Figure 2, the amount of computation in each layer is different, and the most important thing is that the main amount of computation is concentrated in the deconvolution layer [26] (deconv). The previous work is to prune the convolutional layer or other model compression processing, and rarely prune the deconvolutional layer. Based on this, this paper analyzes the redundant features of the convolutional and deconvolutional layers in CenterNet, and finds that these layers can be further pruned and compressed to reduce the amount of computation and parameters of the model.  
Interestingly, as the network deepens, the output size of each layer is different. As shown in Figure 3, the smallest output is reached before the deconvolution layer. Combined with the research in Figure 2, it can be found that there is a possibility of partition in the CenterNet because the output data of the shallow layer are large but the amount of computation is small, and the output data of the deep layer are small but the amount of computation is large; that is to say, the neural network model is divided into two parts. The shallow part with a small amount of calculation and a large output is placed on the edge device for calculation, and the deep part with a large amount of calculation and a small output is placed on the server for calculation. The computing resources of the edge device and the server are combined to minimal network overhead speeding up inference for face recognition in object detection tasks. Combining the findings of Figures 2 and 3, this paper will adopt a double-layer acceleration structure. The first step of acceleration is through pruning the convolutional layer and deconvolution, and the second step of acceleration is through the best split point, joint cloud-edge computing resources to reduce end-to-end delay.