esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Machine Learning Models for Estimation of Sailing Losses
#### 2.6.3 Artificial Neural Network Model  
An Artificial Neural Network (ANN) is a computational model composed of interconnected nodes called neurons which are organised into layers: an input layer, one or more hidden layers, and an output layer. These neurons receive inputs, process them and produce outputs passed to other neurons through weighted connections and apply activation functions to introduce non-linearities for learning complex patterns. The training consists in backpropagation, an algorithm that minimises the error between estimations and observations by iteratively updating weights and biases. Bias is an additional parameter in each neuron that adjusts the output alongside the weighted sum of inputs, enhancing the network's flexibility.  
In this work, an ANN was selected over more complex deep learning architectures because of the moderate dataset size (266 samples), where simpler models are less prone to overfitting. Prior studies applying ANNs to predict PV soiling losses have typically used a single hidden layer [22; 31]. However, to increase flexibility in capturing non-linear interactions between environmental variables, we used two hidden layers. The number of  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **Description** & **Value** \\ \hline iterations & Maximum number of iterations & 100 \\ depth & Depth of the trees & 6 \\ learning\_rate & Step size at each iteration & 0.1 \\ od\_pval & Threshold for overfitting detector & 0.05 \\ l2\_leaf\_reg & L2 regularisation term & 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Parameters for CatBoost model.  
Figure 9: Determination of the number of trees for the CatBoost model with early stopping.  
neurons in each hidden layer, along with the batch size, was optimised through grid search with five-fold cross-validation, systematically testing different configurations to minimise the validation error, using the mean absolute error as a loss metric. The final architecture consisted of 20 neurons in the first hidden layer and 4 in the second, offering a strong balance between model capacity and generalisation. The ANN takes seven standardised input features, has a single output neuron estimating soiling loss, and uses sigmoid activation throughout (Figure 10). Input data were standardised using Equation (3) to ensure consistent scaling.  
For training, early stopping with a validation split of 20% of the training set was used with mean square error as a loss metric. This means that 212 out of 266 samples were used for training, and the rest were used to calculate the loss metric for early stopping. The batch size was set to eight, which means that each batch (i.e., gradient update) was made with a subset of eight samples, thus completing an epoch (i.e., iteration) every 27 gradient updates. Setting the batch size relatively small compared to the training set helps prevent over-fitting, since each update is based on diverse samples, leading to better generalisation. Training was stopped after 143 epochs due to early stopping.
# Enhancing Resilience in Digital Twins: ASCON-Based Security Solutions for Industry 4.0
## 3 Proposed Solution
### Design Considerations and Requirements
#### 3.1.1 In-Scope Requirements  
Our in-scope requirements included one performance requirement and several requirements related to security. From the performance perspective, the underlying cryptographic algorithm of our proposed solution should be more efficient than the traditional algorithms in terms of power consumption, speed, and storage complexity. Furthermore, from the security perspective, the proposed solution should provide an adequate security level for typical data communication in an Industry 4.0 environment, including ensuring message/data confidentiality, integrity, and authenticity. In this regard, an encryption algorithm that provides a minimum 80-bit security level (size of key) should be used. The proposed solution encompasses two key requirements: performance and security. From a performance perspective, the solution must outperform traditional algorithms in terms of power consumption, speed, and storage complexity. In the realm of security, it should provide an adequate level of protection for data communication within an Industry 4.0 environment, necessitating the use of an encryption algorithm with a minimum 128-bit security level (key size). Moreover, the security requirement extends to specific services, including authentication, confidentiality, and data integrity.  
**Performance Requirement**: The proposed solution should be based on a cryptographic algorithm performing better than traditional algorithms in terms of power consumption, speed, and storage complexity.  
**Security Requirements**: the proposed solution should provide an adequate security level for typical data communication in an Industry 4.0 environment, including ensuring message/data confidentiality, integrity, and authenticity. In this regard, an encryption algorithm that provides a minimum 80-bit security level (size of key) should be used. In addition to the above general security requirement, the proposed solution should provide the following security services:  
* _Message authentication_: The solution should enable the message receiver to check the authenticity of the message.
* _Message Confidentiality_: The solution should provide message confidentiality by encrypting the message.
* _Data Integrity_: The solution should ensure the integrity of data transmission using checksums or other methods to detect message corruption.
* _Resilience_: The solution should be capable of detecting man-in-the-middle attacks that involve message modification and data injection.
# A Novel Bi-Dual Inference Approach for Detecting Six-Element Emotions
## 4 Experiment
### Experimental Results and Analysis
#### 4.4.1 Macro Comparison Analysis of the Model with the Baseline Model  
The effects of both our proposed model and the baseline model are demonstrated in Tables 2 and 3. The results indicate that our proposed model improves precision (\(P\)) and \(F1\) scores compared to the baseline model to a certain extent. In the two datasets, our proposed model achieves the highest \(F1\) scores of 74.64% and 82.66% respectively. This represents an improvement of more than 5% over the other baseline model. Although the recall (\(R\)) is slightly lower in the NLPCC2013 dataset due to the mutual learning between the largest volume of training data and the unemotional text pairs, it increases significantly as the amount of training data for each sentiment label increases. The recall (\(R\)) increases from 67.37% to 80.22%.  
Based on the information provided, we can draw the following conclusions from the two tables. Firstly, as the size of the training data increases from small to large, the overall performance on the NLPCC2014 dataset is better than that on the NLPCC2013 dataset. This can be attributed to the fact that more training data leads to the generation of more proxy pairs (original-pair pairs), resulting in increased feedback signals that enhance the learning of the original model with the pairwise models, ultimately leading to improved  
\begin{table}
\begin{tabular}{c c c c} \hline
**Models** & **Macro P\%** & **Macro R\%** & **Macro F1\%** \\ \hline SVM & 52.41 & 47.12 & 49.63 \\ LSTM & 56.88 & 58.02 & 57.54 \\ Bi-LSTM & 56.39 & 62.15 & 60.01 \\ CNN-BiLSTM & 78.15 & 65.22 & 71.10 \\ C-GRU & 60.03 & **79.96** & 69.06 \\ MF-CSEL & 53.05 & 60.11 & 56.35 \\
**Bi-Dual Inference** & **83.67** & 67.37 & **74.64** \\ \hline \end{tabular}
\end{table}
Table 2: Overall performance of different models on NLPCC2013 data.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Models** & **Macro P\%** & **Macro R\%** & **Macro F1\%** \\ \hline SVM & 55.14 & 47.16 & 50.59 \\ LSTM & 59.02 & 58.47 & 58.74 \\ Bi-LSTM & 56.57 & 72.44 & 63.53 \\ CNN-BiLSTM & 81.87 & 66.79 & 73.57 \\ C-GRU & 63.95 & 77.43 & 70.05 \\ MF-CSEL & 59.39 & 62.55 & 60.92 \\
**Bi-Dual Inference** & **85.26** & **80.22** & **82.66** \\ \hline \end{tabular}
\end{table}
Table 3: Overall performance of different models on NLPCC2014 data.  
model results. Furthermore, the scores of the other baseline models indicate that deep-learning-based models (LSTM, Bi-LSTM, CNN-BiLSTM, C-GRU) generally outperform machine-learning-based models (SVM). However, the model MF-CSEL, which is based on an integrated learning model and designed for sentiment classification in multilingual texts, does not perform well on the monolingual dataset. Our proposed model shares some similarities with the integrated model, and the specific differences between them will be further explained in subsequent experiments. Figure 6 provides a visual representation of the variations between the models.
# Deep Learning Methods in Image Matting: A Survey
## 1 Introduction
### Distinguishing Image Matting from Image Semantic Segmentation  
The results generated by image matting may appear similar to those of semantic segmentation; however, in reality, they are fundamentally different techniques. Semantic  
Figure 1: Specific process of image matting. \(\oplus\) indicates the foreground extraction operation, and \(\otimes\) indicates the image composition operation.  
Figure 2: The image and its corresponding trimap are presented, where the foreground, background, and unknown regions of the trimap are denoted by white, black, and gray colors, respectively.  
segmentation is a classification task that extracts the semantic information in the input image and then classifies the pixels individually to obtain the semantic mask of the input image. When semantic segmentation only segments the foreground and background, the binary nature of segmentation leads to a strict boundary near the foreground edge. Image matting is a regression task that involves estimating the opacity of each pixel in an input image, which results in the extraction of the foreground via the alpha matte. A comparison of the results of semantic segmentation and image matting is shown in Figure 3.
# Supervoxels-Based Histon as a New Alzheimer's Disease Imaging Biomarker
## 4 Results
### Classification  
Table 4 shows the means of the results obtained with the dataset of subjects aged 60 and over (upper table) as well as with the reduced subset (lower table) using both the Clinica baseline and the proposed histon-based feature classification methods. Individual evaluation results for each split can be located in Appendix B.  
To assess whether the proposed method performs significantly better than the Clinica baseline classifier, we used [PERSON]'s chi-square tests. The use of histones as features represents an improvement in all the proposed evaluation metrics ([PERSON] test \(p<0.05\) for all splits, except split 5). As can be appreciated from both the confusion matrix (see Table 4, upper row) and the negative and positive prediction values (NPV and PPV), the errors of classification in the Clinica baseline have a slight bias towards false negatives, whereas, for the proposed method, errors are mainly associated with false positives. It should be noted the remarkable differences between the results presented in Tables 3 and 4 for the aged 60 and over dataset. This is the result of the different evaluation strategies between the two cases. Cross-validation not only has a pessimistic bias (see [57]), but the cross-validation folds are completely random, not respecting the age or gender distribution of the original dataset.  
As expected, using the reduced dataset without the bias imposed by significant differences in gender between AD and CN groups and less variability in age, the results improve for both the Clinica baseline and the proposed method, as can be seen in Table 4 (lower table). In this case, the proposed method provides better results for all evaluation metrics compared to the results with the aged 60 and over dataset. The distribution of errors does not differ from the previous scenario (see Table 5, lower row). In this case, we can not claim significance for the results using the McNemar test.It should be noted that the [PERSON]'s chi-square test may be inadequate for small sample sizes [61].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Accuracy** & **B. Accu.** & **NPV** & **PPV** & **Sensit.** & **Specif.** & **F-Score** \\ \hline Histon: SVM linear kernel & **0.6918** & **0.6913** & **0.7473** & **0.6562** & **0.8012** & **0.5813** & **0.7214** \\ Histon: SVM polynomial kernel & 0.5995 & 0.5989 & 0.6181 & 0.5897 & 0.6760 & 0.5217 & 0.6266 \\ Histon: SVM radial kernel & 0.6503 & 0.65004 & 0.7034 & 0.6187 & 0.7738 & 0.5262 & 0.6874 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation results showing a comparison between the mean evaluation metrics proposed from the use of different kernels (linear, polynomial and radial) for the SVM classifier for the dataset of aged 60 and over, for the 10 repetitions. The best results are highlighted in bold.
## 2 Background
The simplex gradient of \(f\) at \(x^{0}\) over \(T\) is denoted by \(\
abla_{s}f(x^{0};T)\) and defined by_  
\[\
abla_{s}f(x^{0};T)=T^{-\top}\delta f(x^{0};T)\]  
_where_  
\[\delta f(x^{0};T)=\left[ \begin{array}{c}f(x^{0}+t^{1})-f(x^{0})\\ f(x^{0}+t^{2})-f(x^{0})\\ \vdots\\ f(x^{0}+t^{n})-f(x^{0})\end{array} \right]\in\mathbb{R}^{n}\,.\]  
Definition 2 ([59]): _(Simplex Hessian). Let \(f:\operatorname{dom}f\subseteq\mathbb{R}^{n}\to\mathbb{R}\) and let \(x^{0}\in\operatorname{dom}f\) be the point of interest. Let \(S= \begin{bmatrix}s^{1}&s^{2}&\cdots&s^{n}\end{bmatrix} \in\mathbb{R}^{n\times n}\) and \(T\in\mathbb{R}^{n\times n}\) with \(x^{0}\oplus S,x^{0}\oplus T,x^{0}\oplus S\oplus T\in\operatorname{dom}f\). The simplex Hessian of \(f\) at \(x^{0}\) over \(S\) and \(T\) is denoted by \(\
abla_{s}^{2}f(x^{0};S;T)\) and defined by_  
\[\
abla_{s}^{2}f(x^{0};S;T)=S^{-\top}\delta\
abla_{s}f(x^{0};S;T),\]  
_where_  
\[\delta\
abla_{s}f(x^{0};S;T)=\left[ \begin{array}{c}(\
abla_{s}f(x^{0}+s^{1} ;T)-\
abla_{s}f(x^{0};T))^{\top}\\ (\
abla_{s}f(x^{0}+s^{2};T)-\
abla_{s}f(x^{0};T))^{\top}\\ \vdots\\ (\
abla_{s}f(x^{0}+s^{n};T)-\
abla_{s}f(x^{0};T))^{\top}\end{array} \right]\in \mathbb{R}^{n\times n}\,.\]  
The simplex gradient and simplex Hessian do not necessarily require the matrices \(S\) and \(T\) to be square matrices with a full rank. These two definitions may be generalized by using the Moore-Penrose pseudoinverse of a matrix [55; 59]. Last, we recall the definition of a _fully linear class of models_. Definition 3 ([36]): _(Class of fully linear models). Given \(f\in\mathcal{C}^{1},x\in\mathbb{R}^{n}\) and \(\tilde{\Delta}>0\), we say that \(\{\widetilde{f}_{\Delta}:\Delta\in(0,\tilde{\Delta}]\}\) is a class of fully linear models of \(f\) at \(x\) parametrized by \(\Delta\) if there exists a pair of scalars \(\kappa_{f}\geq 0\) and \(\kappa_{g}\geq 0\) such that, given any \(\Delta\in(0,\tilde{\Delta}]\), the model \(\widetilde{f}_{\Delta}\) satisfies_  
1. \(|f(x+s)-\widetilde{f}_{\Delta}(x+s)|\leq\kappa_{f}(x)\Delta^{2}\) _for all_ \(s\in B(0;\Delta)\)_,_
2. \(\|\
abla f(x+s)-\
abla\widetilde{f}_{\Delta}(x+s)\leq\kappa_{g}(x)\Delta\) _for all_ \(s\in B(0;\Delta)\)_._  
Note that a fully linear model is equivalent to a model that is _order-1 gradient accurate_ and _order-2 function accurate_ based on the definitions introduced in [56]. We are now ready to introduce the pseudo-code of the DFTR algorithm that will be used to compare the calculus-based approach with the non-calculus-based approach.
## 3 Results and Discussion
### Selectivity and Assignment on the Characteristic SERS Band  
The SERS spectra of 18 essential amino acids in the human body were acquired to investigate the selectivity of the prepared ZIF-8@AuNPs substrate. As shown in Figure 4, the ZIF-8@AuNPs substrate demonstrates a selective adsorption capacity for Cys, Val, and Trp, which is attributed to its distinctive pore structure. In addition, only seven SERS spectra are shown in Figure 4 for conciseness. The SERS spectra of phenylalanine, glutamic acid, histidine, and serine were selected randomly from the acquired SERS silent spectra. Val showed strong Raman characteristic peaks at 537, 846, 944, 1349, and 1450 cm\({}^{-1}\), among which the peak at 537 cm\({}^{-1}\) corresponds to the stretching vibration of a C=O bond, and the peak of 1349 cm\({}^{-1}\) corresponds to the bending vibration of a saturated C-H bond [33]. Cys exhibits strong characteristic peaks at 679, 1134, and 1344 cm\({}^{-1}\), among which 679 cm\({}^{-1}\) and 1344 cm\({}^{-1}\) correspond to the out-of-plane bending of -C-S-H and the symmetric stretching vibration of -COO-, respectively [31]. In addition, strong Raman characteristic peaks of Trp were exhibited at 757, 1010, 1423, and 1542 cm\({}^{-1}\), among which the Raman peaks at 757 cm\({}^{-1}\) and 1423 cm\({}^{-1}\) are attributed to the atomic stretching of C-C and C-N [34].  
Figure 3: TEM images of AuNPs with different diameters (**a–d**), and their UV–Vis spectra with corresponding optical photos (**e**). UV–vis absorption spectra of AuNPs, ZIF-8, and ZIF-8@AuNPs (**f**).
###### Abstract  
All real-world systems are affected by the saturation phenomenon due to inherent physical limitations of actuators. These limitations should be taken into account in the controller's design to prevent a possibly severe deterioration of the system's performance, and may even lead to instability of the closed-loop system. Contrarily to most of the control strategies, which assume that the saturation limits are constant in time, this paper considers the problem of designing a state-feedback controller for a system affected by time-varying saturation limits with the objective to improve the performance. In order to tie variations of the saturation function to changes in the performance of the closed-loop system, the shifting paradigm is used, that is, some parameters scheduled by the time-varying saturations are introduced to schedule the performance criterion, which is considered to be the instantaneous guaranteed decay rate. The design conditions are obtained within the framework of linear parameter varying (LPV) systems using quadratic Lyapunov functions with constant Lyapunov matrices and they consist in a linear matrix inequality (LMI)-based feasibility problem, which can be solved efficiently using available solvers. Simulation results obtained using an illustrative example demonstrate the validity and the main characteristics of the proposed approach.  
LPV Systems; LPV control; Input saturation +
Footnote †: journal: Applied Sciences
## 2 Results
### Data Collection and Establishment of PTS-Pre for the Prediction of trans-Prenyltransferases
We firstly assigned a \"blocking score\" to each amino acid on the basis of its side-chain geometry volume and torsion capacity (Table S1) [23]; next, we calculated the \"blocking score\" for each of the \"floors\"; finally, chain lengths of the final products were predicted on the basis of the \"blocking score\" following the rules of the \"three floors\" model (Figure 2B). The weighting factors for each position of \"floor\" residues were trained using a series of experimental data including the 11 searching templates and 27 site-mutated _Arabidopsis trans_-PTs sequences (Dataset 1) [10]. The resulting trained weighting factors indicate that the following: (1) for the first floor, site_1 and site_2 have a similar contribution to the \"blocking score\", while site_3 has much less importance (1.1% contribution to the final \"blocking score\" of \"floor 1\"); (2) in the second floor, site_1, site_2, and site_3 contribute 20.0%, 55.3%, and 24.7% to the \"blocking score\", respectively; (3) in the third floor, similar with the first floor, site_1 and site_2 contribute 36.5% and 62.9%, respectively, while site_3 accounts for 0.6% contribution in the \"blocking score\" (Table S2). Next, a rough polyprenyl pyrophosphate elongation route could be traced on the basis of these statistics following the rule that a higher weighting factor of the \"floor\" site indicates a greater likelihood of the elongation prenyl group passing by it (Figure S3). Interestingly, the traced elongation route is consistent with a previous study, which used ligand docking or co-crystallization to determine the elongation route in _trans_-PTs [14; 24]. Taken together, the final equation was designed as an automatic algorithm and developed as a web tool named PTS-Pre ([[http://124.70.187.228:1080/](http://124.70.187.228:1080/)]([http://124.70.187.228:1080/](http://124.70.187.228:1080/)), accessed on 30 June 2022) for further testing and high-throughput functional predictions. Figure 2: Sequence similarity network map of the _trans_-PT family and flowchart of PTS-Pre tool. (**A**) Sequence similarity network of the _trans_-PTs family with BLAST e-value cutoff set to 1e\({}^{-50}\). Eleven representative _trans_-PTs (PDB IDs: 2 FOR, 5E8H, 1 WY0, 2E8W, 3 OYR, 3 AQ0, 3 QQV, IFPS, 3Q2Q, 3 PKO, and 1 WMW) are marked as large red circles. The incorrect and correct prediction cases from the experimentally determined testing dataset are marked as orange triangles and blue squares, respectively. (**B**) The general strategy followed by PTS-Pre for the functional prediction of _trans_-PTS. First, the target protein is aligned with each of the searching template sequences, and the best template with the highest sequence identity is selected as a reference for locating the six “floor” residues. Afterward, PTS-Pre calculates the “blocking score” for each “floor” on the basis of the types of amino acids and the parameter weights for each site. Finally, according to the “blocking scores”, PTS-Pre predicts the products’ chain length of target enzymes.
## 3 Results
### Abrasion Resistance  
Road pavement concrete may wear out due to continuous wheel load. In particular, the ACS aggregate has a larger number of pores than natural aggregates and thus can be affected by abrasion. The abrasion test results for rural-road pavement concrete are shown in Figure 7. The ACS aggregate had no significant effect on the abrasion resistance of the rural-road pavement concrete. The abrasion amount was 0.055, 0.055, and 0.053 g/cm\({}^{2}\) for plain, GG 100%, and GS 50%/GG 100% mixes, respectively, with associated abrasion ratios of 1.00, 1.00, and 0.96. Thus, the abrasion resistance increased for the GS 50%/GG 100% mix, showing a 4% reduction. This suggests that exposure of the aggregate to the surface for long periods may affect its abrasive action; thus, additional investigation may be necessary to resolve this effect. Overall, the application of the ACS aggregate did not present a significant issue in terms of abrasion resistance.  
Figure 6: Test results of repeated wetting and drying cycles.
# Cooperative Downloading for LEO Satellite Networks: A DRL-Based Approach
## 4 Performance Evaluation
### Effect of Number of Satellites  
Figure 6 shows the effect of the number of LEO satellites on the average contact-time utilization when 100 episodes are randomly generated and the G-fairness index is set to 0.2 or 0.8. From Figure 6, it can be found that the average contact-time utilization generally increases as the number of satellites increases. For example, in case of highly biased data distributions (i.e., \(\mathcal{I}(\mathbf{m})=0.2\)), the contact-time utilizations of DRL-CD, RANDOM, and No-COOP increase by 21.4%, 11.5%, and 18.4%, respectively, when the number of LEO satellites is changed from 2 to 5. This is because the amount of downloaded data in the cooperative downloading highly depends on the contact time not only with GS but also with other satellites. The higher the number of satellites, the higher the probability of providing opportunities for ISL offloading, which allows more data to be offloaded from over-burdened satellites to under-utilized ones. Consequently, DRL-CD becomes more effective by deploying more LEO satellites, which is promising since more and more LEO satellites will be launched in the future.