esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Analysis of Principal Components and Orthogonal Partial Least Squares  
PCA has identified two distinct groups based on the cultivars, where the biological replicates from each sample were grouped together (Figure 3a). Group 1, which consisted of the 'NuMex LotaLutein' samples, clustered on the left side, whereas Group 2, comprising 'NuMex Pumpkin Spice' samples, clustered on the right side of the PCA diagram. The first principal component (PC1) contributed to 47.19% of variation, whereas PC2 was associated with 15.95% of variation. Model prediction by OPLS-DA modeling revealed consistent clustering for the samples according to cultivars (Figure 3b). A significant model for partial LS discriminant analyses was supported by the high values of the explanatory rates, \(R^{2}X\) (0.67), \(R^{2}Y\) (1.0), and of the prediction ability of the model, \(Q^{2}\) (0.94). Excluding the QC  
Figure 2: Proportion of different classes of compounds for the metabolites detected from jalapeno and serrano chile peppers using a widely targeted metabolomics approach.  
samples, the overall contributions of the T-score and orthogonal T-score for the OPLS-DA were 48.3 and 19.1%, respectively. The results from correlation analyses further supported the differentiation observed from PCA. Mean Pearson correlation coefficients of 0.92 were observed within cultivars, whereas an average of 0.66 was observed across treatments (Figure 4). Coefficient of variation (CV) values across all the QC samples also demonstrated the stability of the experimental data, with the proportion of samples having a CV less than 0.3 and 0.5 being higher than 75% and 85%, respectively. The CV across all metabolites ranged between 1.34% and 115.54%, with a mean value of 36.50%.
## 3 DSPOT-Enhanced Self-Evolving Neural Networks
### DSPOT-Based Ramp Classifier
#### 3.1.2 DSPOT Algorithm  
Given a quantile \(q^{cat}\), the DSPOT algorithm determines the dynamic threshold \(z_{q^{cat}}(t)\) using the recent observations. Based on \(z_{q^{cat}}(t)\), wind generation difference \(\Delta P^{l}_{class}(t)\) will be labeled into ramp-up, ramp-down or non-ramp events, and the wind power of recent measurement \(P_{class}(t)\) will be added into the corresponding dataset \(\mathcal{X}^{class}_{i}\). The details of the DSPOT algorithm are provided in Algorithm 1.  
Specifically, Algorithm 1 will first initialize the thresholds \(z_{q^{up}}(t)\) and \(z_{q^{demn}}(t)\) using the first \(d+l\) wind power measurements. Then, Algorithm 1 will update \(z_{q^{up}}(t)\) and \(z_{q^{demn}}(t)\) using the new wind power measurement in the moving window with size \(d\) in an online manner, based on which the new wind power measurement will be added into the corresponding dataset \(X^{class}_{i}\). Algorithm 1 will be run for wind power measurements of each class of wind turbines.  
```
Input:\(\{P_{class}(t)\}\), \(d\), \(l\), \(q^{up}\), and \(q^{down}\). Output:\(X^{class}_{up}\), \(X^{class}_{down}\), and \(X^{class}_{non}\). Initialization: (1) Calculate initial thresholds \(z_{q^{up}}\), \(z_{q^{down}}\) based on Section 3.1.1 using \(\{P_{class}(t)|t=1,\ldots,d+l\}\). (2) Initialize \(X^{class}_{up}\), \(X^{class}_{down}\), and \(X^{class}_{non}\) based on \(z_{q^{up}}\) and \(z_{q^{down}}\). End Initialization For every\(t>d+l\) in \(\{P_{class}(t)\}\) (1) Update \(z_{q^{up}}(t)\) and \(z_{q^{demn}}(t)\) based on Section 3.1.1 using the recent observations \(\{\Delta P^{l}_{class}(t)\}_{d}\). (2) Classify \(\Delta P^{l}_{class}(t)\) based on \(z_{q^{up}}(t)\) and \(z_{q^{down}}(t)\), and add \(P_{class}(t)\) into the corresponding dataset \(\mathcal{X}^{class}_{i}\).
```  
**Algorithm 1** DSPOT
## 3 Results
### Contributions and Sensitivity of GPP and ET to WUE  
Variations in WUE are mainly attributed to changes in GPP and ET. Figure 7 shows the spatial patterns of the sensitivity coefficients of GPP and ET on WUE variations, with each pixel significant at the 0.05 significance level in F-statistics. A positive (negative) sensitivity coefficient shows that WUE increases (decreases) as GPP (ET) increases. WUE showed a sensitivity coefficient of 0.866 for GPP, with higher sensitivity in the eastern and southwestern regions. The sensitivity of WUE to ET was \(-\)0.606, with high sensitivity in the Da Hinggan Ling Mountains and low sensitivity in the western region. As shown in Figure 8c, the change in WUE in Inner Mongolia from 2001 to 2020 is mainly driven by GPP. The contribution of GPP to WUE was 59.36%, accounting for 83.82% of all pixels, widely distributed in most of the central and western parts of Inner Mongolia. The contribution of ET to WUE was 40.64%, accounting for 16.18% of the total pixels, mainly distributed in the Da Hinggan Ling Mountains in the northeast, the northern part of the Horqin sandy land, and the northern part of the Mu Us sandy land (Figure 8a,b). Figure 8 shows the spatial patterns of the relative contributions of GPP and ET to WUE variations, it exhibited opposite spatial pattern characteristics. Areas with a high contribution of GPP (>60%) were widely distributed in the central and western regions, accounting for 49.47% of the total pixels, whereas areas with a low contribution of GPP (<40%) were scattered in the north of the Mu Us sandy land and the south foot of the Da Hinggan Ling Mountains, accounting  
Figure 5: The mean values of each vegetation type and their contribution to (**a**) GPP, (**b**) ET, and (**c**) WUE IAV in Inner Mongolia from 2001 to 2020.
## 1 Introduction
### Background  
Recently, many attempts have been provided to realise collaborative environment for information management via artificial intelligent technologies [8]; these attempts should be applied in very different fields such as Cultural Heritage [9], Medicine [10; 11; 12], IoT [13; 14], Internet of Vehicles [15; 16], Cyber Security [17] and Smart Cities [18].  
One of the main tasks in information management is document management, which makes it possible to extract relevant information from a set of documents, in particular scientific papers information management (see Section 1.1.1) makes it possible to extract relevant information from a set of scientific papers.  
In literature, many approaches for documents management such as clustering, classification, Topic Modelling and the most recently neural networks approaches (see Section 1.1.1) have been defined.  
All of these approaches are artificial intelligence tasks and in particular they are Natural Language Processing tasks (NLP from now).  
The document clustering is an unsupervised model able to analyse a set of documents and to divide them into a set of clusters composed by similar documents (see Section 1.1.2). The document classification is a supervised method able to assign to each document a predefined class (see Section 1.1.3). The Topic Modelling is an approach able to define topics related to the analysed documents (see Section 1.1.4). The Word Embedding is a neural network approach based on a vector representation with variable length and it is able to capture the semantic and syntactic correlation between the words contained in a document (see Section 1.1.5).  
In our work we aim to address the goal of human-readable labelling of scientific papers exploiting the above mentioned approaches. In the following, further details about scientific paper information management and relative approaches will be provided.
# Advances in Three Dimensional Bioprinting for Wound Healing: A Comprehensive Review
## 4 3D Bioprinting Techniques
### Other Techniques  
#### 4.4.1 Magnetic Bioprinting Techniques  
An alternative method for 3D bioprinting is the magnetic bioprinting technique, which is used to combine cells in different ways without contact [208]. In this method, two different approaches have been tested. In the first method, label-free diamagnetophoretic printing, the cell medium is mixed with a paramagnetic buffer and exposed to an external magnetic field. Cells floating in the air are directed towards a region with a lower magnetic field strength. The shape of 3D cell assemblies is controlled by changing the magnet configuration. In the second approach, the cells are magnetized by incubating with magnetic nanoparticles (NPs) overnight. The magnetized cells are planted on a low-adhesive plate and cell aggregates are formed through air elevation. Then, the magnetized cell clusters are put back into suspension. Spatial modeling of the magnetic template used to create the desired morphology of cell aggregates is performed using the method of variable variations in shape. However, the cytotoxicity of the magnetic NPs taken into the cells and possible internal stresses can lead to negative effects that can damage the cells [208].  
[PERSON] et al. pioneered magnetic bioprinting technology. Using magnetite NPs and magnetic force, structures were created from biological materials (diameter 3 mm; length 10 mm; magnetic induction 1300 G). Magnetic NPs were produced within cationic liposomes, delivered to target cells and consequently magnetically labeled. Labeled cells were then placed on ultra-low-binding plates with a magnet underneath, and post-culture cells formed a surface layer. The magnet was placed in a silicone tube with an outer diameter of 5 mm, an inner diameter of 3 mm and a length of 25 mm. When the cell layer was wrapped around a cylindrical magnet, the magnetic force attracted the cells, forming a tubular structure. Finally, the magnet was removed, and a tubular structure remained as a result [209]. In another study, [PERSON] et al. used magnetic levitation and bioprinting methods to create 3D cancer cell spheroids. After incubating cells with magnetic NPs overnight, cells were placed on top of the spheroid drive, thus allowing them to form a spheroid shape [210]. Conclusively, this approach is effective for sculpting and organizing biological materials [209].
## 4 The SECOM Standard
### Data Integrity and Confidentiality  
The data protection scheme of the SECOM standard is provided by a suite of microservices, first generating a shared key between the ship and the SCC, then using this key to encrypt the information and ensure confidentiality by adding a tag for integrity and authenticity of the message. In this schema, each entity authenticates with the authentication server through the API Gateway and then obtains an ID token as well as an access token. Then, each side establishes a WebSocket with the support of the WebSocket microservice for dual communication between the parties. The ship side first requests to the CA the certificate of the other party; it then requests the key generator microservice to generate a pair of Diffie-Hellman (DH) private and public keys. It obtains the public key of the shore side, encrypts the message with its public key, generates a signature with the asymmetric encryption and signature microservice, and forwards it to the SCC together with its CA-signed public key. At this point, the SCC side also requests the key generator microservice to generate a DH private and public keys pair. First, it retrieves the ship-side certificate from the previous message and verifies and decrypts the received message through the asymmetric decryption and signature verification microservice. Then, the shore side signs its public key and the message with its private key and forwards it back, concatenating with its certificate. From now on, the SCC can generate the shared key by combining the ship's public DH key that was received and its generated DH private key, through the key generator microservice. Since the ship has also received the message, it first decrypts and verifies it, retrieves the DH public key of the SCC, combines it with its own DH private key, and generates the shared key. At this point, both parties have the same shared key that can be used to encrypt the messages and generate integrity tags to send the information from one side to the other through the symmetric encryption microservice. Through these interactions, the message is confidential, and its integrity is guaranteed. The other side uses a Symmetric Decryption microservice to verify the tag and decrypt the message by using the shared key.  
Figure 4: High-level view of the SECOM implementation between the ship and the SCC.  
To establish a secure communication channel, we have implemented mutual authentication on each side, as both sides act as a service provider and a consumer at the same time. Each microservice has its certificate and private key stored in a key store and a CA certificate in a trust store. Whenever a microservice wants to serve a request, it has to provide the certificate to authenticate itself but, on the other hand, it needs to authenticate the service requester by asking for the certificate and verifying it against the CA certificate in the trust store. This allows the system to establish a secure communication channel, providing data integrity and confidentiality capabilities.
# A Novel CA-RegNet Model for Macau Wetlands Auto Segmentation Based on GF-2 Remote Sensing Images
## 3 Data and Methodology
### Deep Learning Models  
#### 3.2.1 ResNet Model  
This study employs the ResNet model for benchmarking, leveraging its renowned effectiveness in various vision tasks due to its distinctive hierarchical residual connections [23]. Given the complex landscape of wetlands, comprising heterogeneous elements like water, vegetation, and artificial structures, ResNet's superior multi-scale feature extraction and contextual comprehension provide detailed perspectives on these elaborate attributes. The selection of ResNet for wetland image segmentation in prior research is attributed to its demonstrated proficiency in deciphering complex spectral-spatial relationships, rendering it optimal for analyzing the intricate characteristics of wetlands [24]. Consequently, ResNet is utilized in this research as the benchmark for comparison.  
In this study, the ResNet model is initiated with a 2D convolutional layer for spectral-spatial feature extraction, followed by a max pooling layer to reduce spatial dimensions. Core to the model is cascading ResNet blocks, each adept at efficient multi-scale feature extraction [25]. These blocks contain parallel convolutional towers designed to capture intricate spectral-spatial relationships. A shortcut connection amalgamates the resulting feature maps, mitigating the deep learning issue of gradient vanishing. Key parameters include a primary convolutional layer with 64 filters and a \(7\times 7\) kernel, followed by a max pooling layer with a \(3\times 3\) kernel size. The design ensures efficient backpropagation and addresses deep learning challenges. In this study, the number of epochs is set at 180, ascertained through cross-validation to assess model complexity and learning rates to avoid the presence of overfitting. To further mitigate overfitting, dropout layers are incorporated, and L2 regularization is applied. Figure 3 illustrates the model's architecture.  
Figure 2: Three different kinds of interpretation signs of Macau wetlands (**a**) Constructed Wetland; (**b**) Coastal Mudflat Wetland; (**c**) Coastal Mangrove Wetland.
## 4 Simulation and Prediction Methods
### Prediction Methods of Concrete Shrinkage and Creep Effects  
The long-time influences brought by concrete S&C were predicted adopting the calculation method in the CEB-FIP 90 code [18; 19] and the age-adjusted effective modulus method [20]. The relationship between the final concrete creep deformation and initial instantaneous elastic deformation is approximately linear when the concrete stress does not exceed 40% of the ultimate strength. The superposition principle is suitable for the calculation of the strain that is caused by the stress applied step by step. The relationship between the strain increment \(\Delta\varepsilon_{c}(i,\,i-1)\) and stress increment \(\Delta\sigma_{c}(i,\,i-1)\) of concrete caused by creep during the time interval of (\(t_{i}\), \(t_{i-1}\)) is shown in Equation (1),  
\[\Delta\varepsilon_{c}(t_{i},\,t_{i-1})=\frac{\Delta\sigma_{c}(t_{i},\,t_{i-1} )}{E(t_{i-1})}[(1+\chi(t_{i},\,t_{i-1})\phi(t_{i},\,t_{i-1})]+\sum_{j=1}^{i-1} \frac{\Delta\sigma(t_{j})}{E(t_{j})}[\phi(t_{i},\,t_{j})-\phi(t_{i-1},\,t_{j})]\]  
\[\chi_{(i\mid-1)}=\frac{1}{1-e^{-\phi(i,i-1)}}-\frac{1}{\phi(i,\,i-1)} \tag{1}\]  
where, the \(c\) represents the creep effect, \(E\) is the elastic modulus, \(\chi(i,\,i-1)\) is the aging coefficient, and \(\phi(i,\,i-1)\) is the creep coefficient. The age-adjusted effective modulus \(E_{\phi}(t_{i},\,t_{i-1})\) can be defined as Equation (2),  
\[E_{\phi}(t_{i},\,t_{i-1})=\frac{E(t_{i},\,t_{i-1})}{1+\chi(t_{i},\,t_{i-1}) \phi(t_{i},\,t_{i-1})} \tag{2}\]  
Firstly, the concrete elastic modulus was replaced by the age-adjusted effective modulus \(E_{\phi}(t_{i},\,t_{i-1})\) during each time interval of (\(t_{i}\), \(t_{i-1}\)). Then, the structural stress changes and deformations that are caused by concrete creep of each step were obtained by the sequential calculation and gradual accumulation. The concrete shrinkage effects were calculated adopting the analogous means used in temperature effect analysis based on the shrinkage strain of each step. In addition, the prestress relaxation was considered through updating the applied initial strains in the prestressed tendons during each time interval (\(t_{i+1}\), \(t_{i}\)) based on the calculated prestresses of the former time interval (\(t_{i}\), \(t_{i-1}\)). Moreover, the changes in stress and geometry caused by the concrete S&C effects during the construction phases were considered in the bridge completion state. This part of stress and geometry evolution was calculated using the same method as the method described above, based on the actual field construction time from the cast of concrete to the completion state of the bridge.
# HP-YOLO: A Lightweight Real-Time Human Pose Estimation Method
## 3 HP-YOLO Method
### Model Pruning and OKS Integration  
To further optimize HP-YOLO's performance on keypoint-based tasks, we develop an L1 pruning algorithm guided by the Object Keypoint Similarity (OKS) [32]. The objective is to prune less significant weights while retaining high accuracy in keypoint localization. Such a balance between model compactness and accuracy is crucial for real-time applications (e.g., robotics or video surveillance) where processing speed is as important as detection performance.  
By combining OKS with L1 pruning, we ensure that critical weights for accurate keypoint localization are preserved [35]. Specifically, we compute the gradient of OKS with respect to each weight \(W_{j}\), and define an importance score \(S_{j}\) as follows:  
\[S_{j}\ =\ |W_{j}|\ \times\ \Big{|}\frac{\text{QKS}}{\delta W_{j}}\Big{|}. \tag{8}\]  
A higher \(S_{j}\) indicates a stronger contribution to maintaining OKS-based accuracy.  
Weights with \(S_{j}\) below a threshold \(\theta\) are pruned, as outlined in Algorithm 1. After pruning, the model is fine-tuned on the training set to adjust the remaining weights [36], thus minimizing any accuracy drop due to weight removal. Finally, the pruned model is evaluated again to ensure that keypoint detection precision is not significantly degraded.  
In Algorithm 1, if \(S_{j}<\theta\), the weight \(W_{j}\) is set to zero and effectively removed from the network. This ensures that weights crucial for keypoint localization (i.e., with higher scores) are retained. Excessive pruning may cause a notable accuracy drop, whereas insufficient pruning yields minimal speed gains; thus, selecting \(\theta\) is a key design choice. After fine-tuning, the model typically recovers or even surpasses its original accuracy, owing to the removal of redundant parameters.  
Figure 3: Network architecture of RepNCSPELAN4, illustrating multi-scale convolutions, CSP connections, and the ELAN mechanism for efficient feature fusion. This design captures features at various scales, essential for detecting objects of different sizes.  
```
0:\(N\) (number of keypoints), \(\{W_{j}\}\) (model weights), \(\theta\) (pruning threshold)
0: Pruned weights \(\{W^{\prime}_{j}\}\)
1: Compute OKS (Equation (9)) between predictions and ground truth.
2: Compute gradients \(G_{j}=\frac{\partial\text{OKS}}{\partial W_{j}}\) for all weights \(\{W_{j}\}\).
3: Compute scores \(S_{j}=|W_{j}|\times|G_{j}|\) (Equation (8)).
4:for each weight \(W_{j}\)do
5:if\(S_{j}<\theta\)then
6:\(W^{\prime}_{j}\gets 0\) // Prune weight
7:else
8:\(W^{\prime}_{j}\gets W_{j}\)
9:endif
10:endfor
11: Fine-tune the pruned model on training data.
12: Evaluate the final model performance (OKS, AP, etc.).
```  
**Algorithm 1** Prune Weights Using OKS
## 2 Problem Formulation
### Kinematic Model
#### 2.2.2 State Equation of the Mobile Platform  
(1) Ideal state equation without disturbance  
Converting the discrete kinematics model into the state equation, we redefine various variables of the mobile platform, where \(u(k)=\left[q_{cl}(k)\right)\), \(q_{cr}(k)\right]^{T}\) is the input control variable, \(y(k)=\left[p_{xc}(k),p_{yc}(k),q_{zc}(k)\right]^{T}\) is the output variable, and \(q(k)\) is the state variable of the system. Therefore, the discrete state equation of the mobile platform can be expressed as follows:  
\[\left\{\begin{array}{c}q(k+1)=q(k)+d_{k}\cdot B(q_{k})\cdot u(k)\\ y(k)=q(k)\end{array}\right. \tag{8}\]  
where \(B(q_{k})\) is the the control matrix of the mobile platform, which can be expressed as follows:  
\[B(q_{k})=\frac{R_{c}}{2}\left[\begin{array}{cc}\cos(q_{zc}(k))&\cos(q_{zc}( k))\\ \sin(q_{zc}(k))&\sin(q_{zc}(k))\\ 1/B_{c}&-1/B_{c}\end{array}\right] \tag{9}\]  
(2) Practical state equation with disturbance  
The primary parameters of the state equation include the wheel radius and the width of the mobile platform. However, the above parameters can only be obtained through actual measurement, and the specific error between the measured parameters and the actual parameter cannot be ignored. Therefore, the exact state equation of the mobile platform can be expressed as follows:  
\[\left\{\begin{array}{c}q(k+1)=q(k)+d_{k}\cdot\tilde{B}(q_{k})\cdot u(k)\\ y(k)=q(k)\end{array}\right. \tag{10}\]  
where \(\tilde{B}(q_{k})\) is the estimated parameter of the corresponding mobile platform.  
The external disturbances of various signals need to be added to the actual state equation,where \(\beta(k)\) is the random disturbance in the state parameters of the system, and \(\alpha(k)\) is the random disturbances in the initial parameters of the system. Considering the above disturbance parameters in Formula (10), we can express the practical state equation with disturbance as follows:\[\left\{\begin{array}{c}q(k+1)=q(k)+d_{k}\cdot B(q_{k})\cdot u(k)+\tilde{\beta}(k) \\ y(k)=q(k)+\tilde{\alpha}(k)\end{array}\right. \tag{11}\]  
where \(\tilde{\beta}(k)=\beta(k)+d_{k}\cdot(\tilde{B}(q_{k})-B(q_{k}))\cdot u(k)\) is the sum of the state disturbance and the system, and \(\tilde{\alpha}(k)=\alpha(k)\) is the combination of the output disturbance and the measurement error of the system.  
**Remark 2**: _The kinematics model is mainly used to construct the basic control model of the mobile platform. The control signal needs to be directly input to the actual mobile platform in trajectory tracking. Therefore, we did not need to perform high-precision tests on the above-mentioned physical structural parameters._