esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Cleft lip and palate are the most common congenital craniofacial malformations, with an incidence ranging from 1.0/1000 to 2.2/1000 [1; 2]. The prevalence is higher in the Asian population. Patients with cleft lip and palate could develop maxillary hypoplasia owing to intrinsic growth deficiency and extrinsic factors following surgical interventions. The maxillary deficiency causes dental malocclusion, skeletal discrepancy, maxillomandibular behavior asymmetry, or facial disproportion requiring orthognathic surgery (OGS), with an incidence ranging between 9% and 60% [3; 4; 5; 6; 7; 8].  
Traditional design of OGS for the correction of maxillary retrusion in patients with cleft has been LeFort I osteotomy and advancement, aiming to correct the anteroposterior discrepancy. However, patients with cleft could likely have facial asymmetry and disproportion issues in addition to midface retrusion. Because of the improvement in surgical techniquea, safety in general anesthesia, 3-dimensional (3D) imaging simulation, and patient demand of aesthetic outcomes, there has been an increasing use of two-jaw surgery in the surgical plan. It is still controversial whether to plan one-jaw or two-jawOGS, as each approach has its reasons and concerns [9; 10; 11; 12]. Surgical design could be based on an individual patient's maxillofacial condition, surgeon's preference, or operating room facility. It would be ideal to have a prospective clinical study comparing the two approaches, but this is impractical, and the current data are deficient in this regard.  
The trend has evolved over the past 40 years in our center, and two-jaw orthognathic surgery becomes the main approach for the correction of maxillofacial deformity in patients with cleft [13]. This study aimed to evaluate a series of consecutive patients with unilateral cleft lip and palate who had received orthognathic surgery and compare the two different designs (one-jaw versus two-jaw) using 3D imaging surgical simulation.
# Closing Connectivity Gap: An Overview of Mobile Coverage Solutions for Not-Spots in Rural Zones
## 3 Potential Solutions
### UAV-Assisted Networks
#### 3.1.1 The A2G Channel Modeling  
[PERSON] et al. comprehensively addressed the available A2G channel models-- also including the timeliness to be extended even for Air-to-Air (A2A)--in the study given by [56]. Throughout the paper, they stated the most relevant UAV's A2G schemes and thereby identified the limitations and future research directions for UAS-based communications scenarios. An outstanding classification of the analyzed literature includes deterministic methods, stochastic modeling, and their fusion.  
Even though the performed analysis provided by [PERSON] et al. remarkably spurred the development of a foremost deep-study on the A2G channel technical model, it is relevant to include a third slope into the classification: Machine Learning (ML) approach, which is scarcely investigated in the article [56]. To shed light on this end, Table 6 attempts to state some leading research studies to sketch out the aerial channel beneath two main strategies, ML and Geometry-based (GM) scheme, in addition to setting forth whether these strategies lie in stochastic (St) or non-stochastic (N-St) approaches.
## 3 \"Click\" Chemistry  
An emerging technique for _in situ_ post-polymerization modification of monolithic columns has been developed by a technique used originally in organic synthesis [81]. The technique has been used on silica and hybrid silica-polymeric monolithic columns [81, 82, 83, 84, 85, 86], however, this is beyond the scope of the current review. Click reactions include alkyne-azide, thiol-ene, and thiol-yne reactions [81] and may be conducted using elevated temperatures or at ambient room temperature. Click modification reactions occur in the presence of water and oxygen, unlike many polymer graft reactions, wherein molecular oxygen must be removed prior to polymerization [81]. For reactions occurring at room temperature, a metal catalyst is used. Some reactions are completed in the presence of an initiator such as AIBN or 2,2-dimethoxy-2-phenylacetophenone (DAP). In these techniques, a surface thiol is required. Preparation methods can be seen in Table 2.  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline \multicolumn{1}{c}{**Monolith**} & \multicolumn{1}{c}{**Reaction**} & \multicolumn{1}{c}{**\(\lambda\)**} & \multicolumn{1}{c}{**Temperature**} & \multicolumn{1}{c}{**Time**} & \multicolumn{1}{c}{**Reference**} \\  & & **(nm)** & **(C\({}^{\circ}\))** & **Time** & **Reference** \\ \hline \multirow{4}{*}{**Poly(NAS-_co_-EDMA)**} & Propargylamine modification to produce & & & & \\  & surface alkyne. Click grafting of & & & 30, 60, & \\  & 1-adamantanethiol using UV irradiation at & & & 120, and & \\  & 313 nm, using daroecure as initiator. & & & 180 min & \\ \hline \multirow{2}{*}{**Poly(GMA-_co_-EDMA),**} & Surface sulfhydryl groups clicked with (S)- & & & & \\  & N-(4-allyloxy-3,5-dichlorophenoxy)-2- & & & & \\  & **EDMA**, Poly(GMA-_co_-) & amino-3,3-dimethylbutanephosphonic acid & & & \\  & & in the presence of AIBN initiator. & & & \\  & & Surface thiol groups clicked with & & & \\  & & O-9-tert-butylcarbamoylquinoline in the & & & \\  & & presence of AIBN. & & & \\ \hline \multirow{4}{*}{**Poly(IEM-_co_-MMA-_co_-EGDMA)**} & Monoliths modified with 1-octanol, & & & & \\  & 1-decanol, 1-dodecanol, 1-octadecanol, & & & & \\  & n-decylamine and 1-decanethiol. & & & & \\ \hline \multirow{4}{*}{**Poly(GMA-_co_-EDMA)**} & Monolith thiolated with cysteamine, with & & & & \\  & cleavage of the disulphide thus exposing & & & & \\ \cline{1-1}  & thiols. MEDSA or LMA clicked using either & & & & \\ \cline{1-1}  & heat or UV initiation. & & & & \\ \hline \multirow{4}{*}{**Poly(NAS-_co_-EDMA)**} & Allylamine modified monolith surface & & & & \\ \cline{1-1}  & reacted with 1-octadecanethiol in the & & & & \\ \cline{1-1}  & presence of AIBN and UV initiation. & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Reactions used in “click” chemistry immobilization strategies.
# The Optimal Deployment Strategy of Mega-Constellation Based on Markov Decision Process
## 4 Case Study
### Parameters of Simulation Model
Given the boundary condition, the system could not provide a service in this stage. For the next deployment to progress successfully, 10 satellites would be launched at most, considering each cost item;
2. 20\(\sim\)27 satellites on-orbit operation. By reason of a malfunction such as collision and some faults, more satellites need to be launched into orbit for backups and the maximum capability of launching would be taken full advantage in this scenario;
3. 28\(\sim\)36 satellites on-orbit operation. Based on the last scenario, the optimal decision assures 37 satellites on-orbit. For maximizing the net profit function, there is an incremental reduction during the deployment stages;
4. 37\(\sim\)60 satellites on-orbit operation. Satellites could not be launched anymore because the system would provide services and launching costs would not be covered by the system benefits. The results of the upper limit of the launching capability are 15 and 20 satellites, as shown in Figures 9 and 10, respectively. As seen in Figures 9 and 10, the case of 15 satellites is similar to the case of 10 satellites. For the case of 20 satellites, 3 scenarios were divided and the analysis process was  
Figure 8: The optimal deployment decision of state variables with 10 satellites launched additionally. Figure 10: The optimal deployment decision with 20 additional satellites launched. Figure 9: The optimal deployment decision with 15 additional satellites launched. same as the example case. The results of tge deployment decisions under different launch capabilities are summarized in Table 9.
## 3 Methodology
### Selection of Samples from Clusters
#### 3.5.2 Probabilistic Sample Selection within Clusters  
The second strategy incorporates all clusters but selects samples from each cluster based on a sampling probability that depends on their distance from the cluster center. The rationale behind this approach is to prioritize examples that are more representative of the adversarial behavior captured by each cluster.  
This strategy operates under the assumption that samples closer to the cluster center are more \"typical\" adversarial examples for that cluster, while outliers might introduce noise or be less informative. Hence, samples closer to the center are selected with a higher probability, effectively serving as a strategy to remove unimportant or noisy samples.  
Mathematically, if \(d(x,C_{k})\) denotes the distance of a sample \(x\) from the center of cluster \(C_{k}\), the probability of selecting a sample \(x\) from cluster \(C_{k}\) for adversarial training, denoted as \(p(x,C_{k})\), can be defined as  
\[p(x,C_{k})=\frac{1}{d(x,C_{k})+\epsilon} \tag{9}\]  
Here, \(\epsilon\) is a small constant to prevent division by zero. This inverse relationship ensures that samples closer to the cluster center (smaller \(d(x,C_{k})\)) have a higher selection probability.  
Both strategies allow us to capitalize on the information provided by adversarial examples in a more targeted and efficient manner, leading to enhanced model robustness with reduced computational overhead.
## 3 Results
### Identification of Key Aroma Generation Substrates  
To further clarify the pivotal aroma metabolites among development and storage stages of pear, we measured the contents of four soluble sugars, four organic acids and seventeen amino acids (Figure 3A,B). The contents of fructose and glucose continuously increased, while the concentrations of sucrose constantly declined from stage S1 to Z5. However, the contents of sorbitol, citric acid, shikimic acid, malic acid and quinic acid were not affected by the pear's situation (Figure 3A). In addition, only Met content rose progressively from stage S1 to Z5, whereas four amino acids, namely Tyr, Gly, Thr and Lys, were reduced gradually (Figure 3B). Remarkably, Phe was up-regulated in the development stages but down-regulated in the storage stages. These results indicate that three sugars (e.g., fructose, glucose and sucrose) and six amino acids (e.g., Met, Tyr, Gly, Thr, Lys and Phe) are the substrates for the biosynthesis of pear aroma substances.  
Next, regarding the unique aroma metabolites in the 'Panguxiang' pear, we further measured the significant correlations between 4 sugars or 4 organic acids and 119 metabolites (Figure 3C). Results showed the changes in the concentrations of 113 metabolites were highly synchronized with these 9 aroma substrates. To identify the key substances in pear aroma, we found thirty hub nodes, including twelve esters, nine oxides, seven hydrocarbons and two terpenoids, in this network (Figure 3C; Table S2). Moreover, the neighbors/degree number of glucose, sucrose and fructose were all more than 30, indicating these sugars play key roles in pear aroma biosynthesis. Only the degree of Tyr was more than 30, and the Gly was significantly correlated with 26 metabolites, implying that Tyr and Gly contributed to pear aroma production. In total, these findings reveal that glucose, sucrose, fructose, Tyr and Gly are the synthetic substrates in the pear aroma synthesis pathway; in total, 65 metabolites correlated with these three sugars and two amino acids, which are the key ingredients of the 'Panguxiang' aroma (Table S3).  
Figure 3: Three sugars and two amino acids are primarily involved in aroma substances in pear fruits. (**A**) Concentrations of soluble sugars and acids from stage S1 to Z5. (**B**) Concentrations of amino acids from stage S1 to Z5. (**C**) Correlation network between soluble sugars or soluble acids and 119 key metabolites. The degree represents the neighbors per nodes.
## 3 Stamping Simulation and Property Mapping
### Property Mapping  
The process of mapping the stamping simulation results in the structural FEM model is illustrated in Figure 7. The process began with the use of the Result Mapper tool in Hypercash software of Altair Hyperworks 2021.1, which automatically maps thicknesses, stresses, and strains directly onto the FEM mesh. To simplify the process, the same FEM mesh was used for both stamping and structural models, ensuring seamless compatibility. The stamping simulation outputs were exported into two separate files: the first, in OptiStruct format (.fem), contained nodal and element thickness data, while the second, in Radioss format (.sta), held the strain data for each element. These files were processed using a custom Octave/Matlab script, referred to as the Mesh program. The script extracted the element IDs, thicknesses, and strain results from the stamping simulation data and calculated the corresponding stress vector based on the elastoplastic hardening model. This allowed for the adjustment of yield strength at the element level, accounting for localized hardening effects induced during stamping. To ensure accurate mapping, a comparison mechanism was incorporated into the script, analyzing individual elements and assigning material properties through Property Cards and Mat Cards. These cards defined the mechanical behavior of each element, including parameters such as Young's modulus, Poisson's Ratio, material density, hardening slope, and yield stress. By organizing material properties in ascending order and visually inspecting the results, the mapping ensured that each element accurately reflected the stamping-induced changes in geometry and material behavior. The complete workflow is represented in Figure 7.  
The mapped model was validated through visual representation, where each element was color-coded based on its assigned material properties. This visualization facilitated quick checks to confirm the correct assignment of properties.  
Figure 7: Workflow for stress calculation, property mapping, and FEM model generation.
# Effect of Wind Direction and Velocity on PV Panels Cooling with Perforated Heat Sinks
###### Abstract  
The numerical modeling of the effect of wind direction and velocity over the air cooling of PV panels with heat sinks is realized. During the study, a random PV panel with typical characteristics was analyzed for three different wind directions--towards its back, towards its front and from the side. The analysis was realized on a fixed PV panel, oriented to the south, with an inclination of 45 degrees from the horizontal position. The accuracy of the numerical simulation was achieved by comparison with the experimental studies presented in the literature and by comparing the NOCT conditions. The numerical study is focused on different types of heat sinks attached to a typical PV panel. The fins were distributed both horizontally and vertically. A challenging task consisted in simulation of the real wind conditions around the PV panel by taking into account the entire air domain. The simulations were realized for air velocity \(v_{air}\) from 1 m/s to 5 m/s, solar radiation of \(G=1000\) W/m\({}^{2}\) and ambient temperature \(t_{air}=35\)\({}^{\circ}\)C. The output parameters analyzed were the average temperature of PV panels and their power production. Although the lowest temperatures were achieved for the back wind, the cooling effect was more intense for the side wind. The other direction studied also determined the cooling of PV panels. The passive cooling solutions analyzed introduced a rise of maximum power production between 1.85% and 7.71% above the base case, depending on the wind direction and velocity.  
CFD analysis; wind direction; PV temperature; air cooling; perforated heat sinks; NOCT validation 2022129665. [[https://doi.org/10.3390/app12199665](https://doi.org/10.3390/app12199665)]([https://doi.org/10.3390/app12199665](https://doi.org/10.3390/app12199665))
## 3 Method
### Retriever Consensus Confidence Fusion (RCCF)  
To address the identified limitations of traditional fusion methods, we propose the retriever consensus confidence fusion (RCCF) approach, which represents the first core innovation of the AttenFlow architecture. RCCF dynamically adjusts fusion strategies based on inter-retriever consensus and provides confidence estimates for retrieval results, directly addressing the gaps identified in existing approaches.
## 2 Structural Principle, Detection Mechanism, and Performance Indicators  
### Structural Principle of Optical Waveguide Refractive Index Sensing  
Refractive index sensing uses optical phenomena, such as light interference, diffraction, absorption, and dispersion, to demonstrate the different properties of different concentrations of target detection substances. When the detection substance flows through the sensor, it causes a shift in the resonant peak frequencies or a change in the optical power, leading to a change in the refractive index. The aim of detecting different components or substances with different concentrations can be achieved by detecting the change in the refractive index. At present, the common implementation methods for optical waveguide refractive index sensing are based on MRRs and MZIs. The structure and principles of these two optical waveguide refractive index sensors are described below.
## 2 Materials and Methods
* Regularisation: The ANNS were regularised using dropout regularisation [45], with one dropout layer for each hidden layer (except the output layer). Several dropout rates were tested: None, 0.0625, 0.125, 0.25, 0.375, and 0.5, where None indicates that no dropout was used. * Layer weight constraints: A maximum norm constraint on the model parameters was set during training to avoid overfitting, i.e., constraints were put on the weight's incident to each hidden unit to have a norm less than or equal to the desired value. The values None, 1, 3, and 5 were tested, where None indicates no constraint. * Learning rate (lr): The values 0.00001, 0.0001, 0.001, and 0.01 were tested for the learning rate. Although 0.01 is usually considered too large a learning rate, large values are suggested in the original dropout paper [45]. * Batch size: The datasets have several thousand examples, so several batch size values were tested to speed up the training process (the dataset is divided into smaller batches, and the network weights will be updated after processing each batch). The None, 100, 500, and 1000 values were tested, where None indicates that the whole dataset is to be processed before updating the model's parameters (weights). As previously explained, the wave overtopping dataset is a moderate-extreme imbalanced dataset. In an imbalanced dataset, regular k-fold cross-validation could potentially give incorrect results [35]. This potential problem was avoided using stratified k-fold, so all the folds used during cross-validation have similar class proportions. In this problem, we are most interested in obtaining a model that detects as many overtopping events as possible (high recall) to increase the port's safety while having low false positives (high precision) to maximize the port's overativity. Precision and recall are increased and decreased by varying the decision boundary of the ANN's raw output sigmoid function, but usually, increasing one decreases the other. The port's stakeholders are still determining which decision boundary (threshold) is the one that gives the best tradeoff from a practical perspective of the port's safety and performance. Some metrics, such as the balanced accuracy and the F-score, use a 0.5 decision threshold. We used the average precision to choose the best model in cross-validation (although we calculated several metrics to see the models' performance). As previously explained, the average precision calculates the model's overall performance when varying this threshold by obtaining the precision and recall for each threshold and calculating the area under the curve this creates. Once we trained all the models using cross-validation and the iterative process previously explained, the best model for each dataset was selected and retrained on the whole train dataset (each model in its corresponding dataset). Once the final best three models were obtained, their performance was evaluated using the corresponding testing datasets. The whole process of creating the ANNs overtopping prediction models is summarised in Algorithm 1.
# Challenges and Potential in Increasing Lutein Content in Microalgae
## 2 Lutein Sequestration Versus Structural Function
### Estimation Of Lutein Storage Capacity in LHCs  
As understanding of photosystems (PS) structure in plants and microalgae advances, the amount of lutein and other carotenoid associated with the PS and LHC protein complex becomes clearer [55; 56; 51]. Most carotenoids involved in photosynthesis are bound to the light-harvesting chlorophyll a/b-binding (Lhcb) protein. The core of photosynthetic systems and cytochrome b6f complex can also attach carotenoids. Lhcb proteins have a similar structure and conserved binding sites for 11-14 chlorophyll molecules and  
Figure 1: The sequestration of lutein in photosynthetic organisms. PS: photosystems. UV: ultraviolet light. ROS: reactive oxygen species. CCD: carotenoid cleavage dioxygenases.  
2-4 carotenoid molecules through plants and microalgae. Here, the Lhcb protein is assumed as the direct lutein container for the purpose of estimating the storage capacity of lutein in structural form. Lhcb protein exhibits a 28.1 kDa mass weight and four carotenoid binding sites, L1, L2, N1, and V1. Lutein can bond to the L1 and L2 sites of Lhcb. Neoxanthin and violaxanthin can bond to the N1 and V1 sites, respectively. Thus, based on the 568.87 Da mass of lutein, lutein's storage capacity on Lhcb proteins is estimated as 4.1%. In addition, biochemical data suggested that these bonding sites can hold several types of carotenoids due to the similarity of their chemical structures [56]. Therefore, assuming that lutein can bond to all of these sites, the storage capacity of lutein on Lhcb protein is estimated at 4.1-8.1% in terms of mass ratio.  
Most lutein/carotenoids involved in the photosynthetic and the nonphotochemical-quenching (NPQ) reaction are bound to Lhc protein-based antenna complexes. However, other photosynthetic proteins such as the core of photosynthetic systems and cytochrome b6f complex can bind carotenoids. Hence, the storage capacity of lutein throughout the photosynthetic system should be addressed.  
Unfortunately, the amount of carotenoid associated with the entire photosynthetic system of algal species is not yet clear. Thus, the lutein storage capacity of the photosynthetic system can only be estimated. Since the core complexes of PSII and PSI are largely conserved from cyanobacteria to plants, the amount of carotenoids associated with PSII-LHCII complex in the model organism _Arabidopsis thaliana_ was taken into account [57]. In _A. thaliana_, the basic unit of the PSII-LHCII complex is the protein complex C2S2M2. The protein mass of C2S2M2 is around 1.1 MDa, and in this complex it binds 75 carotenoid molecules [58]. Therefore, assuming that the 75 carotenoids are all lutein, the storage capacity of lutein in the PSII-LHCII complex is around 3.9%. Since the antenna size and composition may vary in diverse organisms, the 4% of lutein storage capacity on this \"structural form\" is estimated based on the aforementioned knowledge.