esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evaluation over Use Cases
### ICT Gateway
#### 4.1.5 Labeling  
The security label has been designed as a hexagonal radar diagram to support the visualization of the security level in a way that it could be understood by non-expert users. In this case, the SecurityScorer calculates the security level for each of the six security properties based on the information produced in the previous phase and the TP defined in the YAML file. As shown in Figure 5, the label is visualized in the GUI developed within the BIECO project. In particular, Figure 8 shows the label obtained for ICT GW after running the SecurityScorer, which integrates a QR code to deal with future updates of the label with the objective of providing additional information about the security evaluation process, enhancing transparency and reusing evidence.  
The label indicates that the ICT GW achieved a high level of security in terms of confidentiality, integrity, availability, and authorization. However, it highlights areas needing improvement, specifically in non-repudiation and authentication security properties. For a non-expert user, the label shows a hexagon with a large green area, suggesting that the product can be considered secure.
# 3D-Printed Accessories and Auxiliaries in Orthodontic Treatment
## 4 Discussion
### Contemporary Use of 3D-Printed Accessories in Orthodontics
#### 4.1.4 Palatal Expanders  
Rapid palatal expansion (RPE) is used in orthodontics for the treatment of maxilla transverse deficiency. It includes widening of the maxilla by rupture of the palatal suture, preferably without dental movement [44]. Bone-borne maxillary expansion [44; 45] appliances enable skeletal expansion while minimizing the side effects associated with tooth-anchored palatal expanders [46; 47; 48], especially undesired teeth movement [45]. RPE devices are traditionally produced using investment casting, also known as lost-wax casting. While this method is highly accurate, it is labor-intensive and costly. [PERSON] and [PERSON] aimed to lower the expenses by integrating fused filament fabrication (FFF) 3D printing using wax-based filaments with investment casting to manufacture orthodontic appliances [49]. However, another study claims high acquisition costs and the necessity of training needed for the digital workflow, incorporation of digital planning, and additive manufacturing promise of higher time-efficient treatment with better patient comfort [45]. The findings of [PERSON] et al. showed that mini implant-assisted palatal expansion could be achieved without traditional impressions or lab processes and successfully expanded the palate in an adult patient [44]. Another study also described the use of a digital laser-sintered palatal expander, with satisfying results [46].
## 5 Experiments and Results
### Data Collection  
We calculated the training and testing dataset from ARGoS simulations using randomly generated parameters for each UAV swarm. The training dataset consisted of 300 configurations for each swarm and their corresponding fitness value, which was calculated as shown in Equation (1). The testing dataset was obtained from the evaluation of 2700 configurations per case study. We removed the configurations that ended up in UAV collisions, usually happening when the swarm was misconfigured. They represent discontinuities in the fitness function which unnecessarily complicate the training process and it would have been unfair if we had used them later for testing the surrogates' accuracy.
# Denoising Generalization Performance of Channel Estimation in Multipath Time-Varying OFDM Systems
## 3 System Model
### LS Channel Estimation  
Since all positions of the OFDM system are considered as pilots in this paper, so we can directly use a simple LS estimate to get \(\hat{H}_{LS}\), by minimizing the cost function:  
\[J\Big{(}\hat{H}_{LS}\Big{)}=\Big{\|}Y-X\hat{H}_{LS}\Big{\|}^{2}=Y^{H}Y-Y^{H}X \hat{H}_{LS}-\hat{H}_{LS}{}^{H}X^{H}Y+\hat{H}_{LS}{}^{H}X^{H}X\hat{H}_{LS} \tag{7}\]  
Let the partial derivative of the above cost function concerning \(\hat{H}_{LS}\) be equal to 0:  
\[\frac{\partial J\Big{(}\hat{H}_{LS}\Big{)}}{\partial\hat{H}_{LS}}=-2\Big{(}X^{ H}Y\Big{)}^{H}+2\Big{(}X^{H}X\hat{H}_{LS}\Big{)}^{H}=0 \tag{8}\]  
Thus, the solution of LS channel estimation can be obtained as follows:  
\[\hat{H}_{LS}=\Big{(}X^{H}X\Big{)}^{-1}X^{H}Y=X^{-1}Y \tag{9}\]  
The LS algorithm is simple and has low complexity. However, it ignores the effect of noise, which leads to a large error in the estimation. Therefore, denoising is needed after LS estimation.
# Logistics Space: A Literature Review from the Sustainability Perspective
## 3 Methods and Results  
### Search Strategy  
This paper adopts several channels to identify relevant literature regarding of logistics space. Academic databases such as Web of Science, Science Direct, Springer Link and Compendex were applied to search for academic papers. Furthermore, Google Scholar was used to search for broader scope of the literature including books and conference proceedings. The starting keywords of the search included the two most widely-used phrases for the subject: \"logistics space\" and \"freight space\". Later, at least one term from each of the three categories of search terms is used to combine: (1) Logistics, freight, warehousing, distribution center; (2) Space, cluster, sprawl, distribution, locations, evolution, factors and sustainability. After the first search, a second search was made, combining with the new keywords \"sustainability\" and \"green\". The search for papers were limited to the past 15 years (2003-2018) and adapted to match the specific structure of each database. References within identified papers are also reviewed for further studies.
## 3 PAPR Reduction with Clipping and Modified Iterative Detection Scheme
### Iterative Soft Compensation
In general, such a clipping noise estimation method is blended with the original iterative detection process, so only a few estimation calculations are added to each iteration of the process, which does not increase the complexity much, as shown in Figure 2(a).
# Edge-Driven Multiple Trajectory Attention Model for Vehicle Routing Problems
## 3 Methodology  
### Edge-Driven Diverse Trajectory Attention Model  
We present the proposed E-MTAM model in the context of TSP. The model is also applicable to other routing problems, requiring only adjustments to the input, masks, and decoder context vectors [25, 41]. TSP is defined on an undirected graph \(G=(V,E,W)\), where node \(i\) is represented by the two-dimensional coordinates \(x_{i}\), for \(i\in V=\{1,\ldots,m\}\), with \(m\) being the total number of nodes. A link from node \(i\) to \(j\) is denoted as \(g_{ij}\in E\), \(i,j\in V\), and the associated distance information is represented as \(e_{ij}\in W\).  
We construct a solution \(\mathbf{a}=(a_{1},a_{2},\ldots,a_{m})\) to represent a permutation of all the nodes, where \(a_{i}\) denotes the index of the selected node at step \(i\). Given a problem instance \(G\), the goal of routing problems is to find a solution \(\mathbf{a}\) such that each node is visited exactly once, and the total tour length \(L(\mathbf{a}|G)\) is minimized.  
\[L(\mathbf{a}|G)=\left\|x_{a_{m}}-x_{a_{1}}\right\|_{2}+\sum_{t=1}^{m-1}\left\| x_{a_{t+1}}-x_{a_{t}}\right\|_{2} \tag{1}\]  
where \(\|\cdot\|_{2}\) denotes the \(L_{2}\) norm. Our E-MTAM model constructs a stochastic policy \(p_{\theta}(\mathbf{a}|G)\) for a given instance \(G\). Using the chain rule of probability, the selection probability for a tour \(\mathbf{a}\) is parameterized by \(\theta\) and is expressed as follows:  
\[p_{\theta}(\mathbf{a}|G)=\prod_{t=1}^{m}p_{\theta}(a_{t}|G,a_{1:t-1}) \tag{2}\]  
Our model leverages the encoder-decoder architecture, similar to the milestone AM [25]. To fully exploit edge information and better capture the dynamics of state transitions, we design an Edge-Driven Multiple Trajectory Attention Model (E-MTAM). Specifically, our E-MTAM incorporates three key modifications: (1) we embed edge information into the multi-head attention layer to effectively learn a representation of the underlying graph topology, which we refer to as edge-driven multi-head attention (EDMHA); (2) we integrate dynamic visitation information into the graph embeddings to be input into the decoder, enabling the decoder to perceive real-time changes in the visited graph; (3) we adopt a multi-decoder structure and introduce a regularization to encourage the decoders to output diverse trajectories, thereby learning distinct routing strategies.  
The overall framework of our E-MTAM is illustrated in Figure 1. E-MTAM adopts a transformer-based architecture. The encoder first embeds the initial node features and edge features into node embeddings. Then, each decoder computes \(m\) steps and selects the optimal node to visit at each time step, based on the node embeddings and the current context embedding. The entire model is optimized using a policy gradient.
## 3 Materials and Methods
### Machine Learning Models  
We considered two publicly available machine learning models, namely MalConv and LGBM. These pre-trained models were produced for Windows PE malware detection using the labelled PE files recorded in the EMBER dataset [10]. The MalConv model was trained from the raw byte-based representation of PE files, while the LGBM model was trained from the engineered features extracted using the static analysis of binary PE files. Although the binary version of EMBER PE files used to yield the pre-trained MalConv model is not publicly available, the tabular representation of the engineered features used to yield the pre-trained LGBM model is publicly available with the machine learning method code to retrain the model on any new dataset.  
MalConv [9] is an end-to-end deep neural model that takes the raw bytes of a PE file as input and predicts the malicious behaviour of the input file. The architecture includes both an embedding layer and a convolution layer. The embedding layer maps each input sequence of 1-dimensional byte values into the sequence of 8-dimensional real embeddings. This is done by handling every byte value as a categorical value and mapping it into a distinct 8-dimensional real point of the embedded space. This embedding represents bytes that have semantically similar behaviour as closer points in this space. The convolutional layer includes 128 filters to iterate over disjoint windows of 500 bytes each. The gated outputs of the convolutional layer integrate the global max pooling to select 128 features, achieving the largest activation. The selected features are subsequently used to feed a fully connected layer in charge of the final soft-max classification. In this study, we used the pre-trained MalConv model described in [10]. This model was trained using 1 Mb bytes of input Windows PE files. Specifically, the model was trained from the EMBER PE files by appending zeros to the end of PE files smaller than 1 Mb and removing the last bytes of PE files greater than 1 Mb. Notably, this model is also used in [5; 11].  
LGBM is a Light Gradient-Boosted Decision Tree Model trained with parameter set-up described in [45]. The pre-trained model was learned by processing 2381 features extracted through the static analysis of EMBER binary Windows PE files [10]. The static analysis was performed using the LIEF PE parsing library. The processed feature space included imports,byte histograms, byte-entropy histograms, printable character histograms, header properties and section properties. Both the original study [10] and the subsequent study of [5] verified that LGBM outperforms sophisticated MalConv, highlighting the difficulty in outperforming the performance of domain knowledge via parsed features with featureless deep learning.
# An Efficient Data Retrieval Parallel Reeb Graph Algorithm
## 4 Reeb Graph Sequential Algorithm on Triangulated Surfaces  
In this section, we assume that we are given a PL function \(f:M\longrightarrow\mathbb{R}\) defined on a triangulated surface \(M\) possibly with a non-empty boundary \(\partial M\). This includes the case when \(f\) is a Morse function. Subsequently, we discuss the degenerate case when \(f\) has non-simple saddles.  
The algorithm above relies on Morse theory to find a finite set of paths traced concurrently inside parts of the manifold where the topology of the manifold with respect to a given scalar function does not change. The sequential algorithm that we present here is similar to the Reeb graph algorithm that is given in [26], where tracing paths inside cylinders were used to construct the Reeb graph. We provide the sequential version of the algorithm because it has some key differences from the algorithm given [26] that will be utilized later in our parallel algorithm.  
The main idea of the algorithm is the construction of a sub-simplicial complex \(X\) of \(M\), such that the Reeb graph \(R(X,f|_{X})\) of \(X\) with respect to \(f|_{X}\), the restriction of \(f\) on \(X\), is identical to the Reeb graph \(R(M,f)\). The constructed simplicial complex \(X\) does not only provide us with a Reeb graph of \((M,f)\), but also immediately implies an algorithm to compute the map \(F:R(M,f)\longrightarrow M\) that allows for us to extract the manifold data given the corresponding Reeb graph points. The main two ingredients of the algorithm are the critical sets and the ascending paths. We introduce these two concepts next.
# Research on Joint Resource Allocation for Multibeam Satellite Based on Metaheuristic Algorithms
## 5 Conclusions  
In this paper, we proposed a non-dominated beam-coding (NDBC) method to solve the adjacent bandwidth overlap constraint in the HTS joint resource allocation problem, achieving full bandwidth flexibility in the evolution of the algorithm while decreasing computational complexity. By applying the NDBC method to a metaheuristic algorithm, it is possible to reduce the algorithm's execution time while simultaneously enhancing the quality of the resource allocation results. Finally, a general architecture for applying NDBC to metaheuristic algorithms was developed. Simulations demonstrated that NDBC can reduce the computational complexity of the bandwidth-constrained handling session by 75-80% and the overall algorithm execution time by 9-21% while improving the USC results by 9-33%. We also found that when NDBC was applied, the PSO and QPSO algorithms performed better than the GA and DE algorithms. Importantly, the model does not consider all aspects of a multibeam satellite communication system, as doing so would make the problem extremely complex and difficult to describe. Our conclusions remain unaffected by factors not accounted for in the model, such as the antenna pattern, satellite payload characteristics, overall frequency plan (feeder and user links), rain attenuation, and precoding. The results reported herein are instructive for the application of metaheuristic algorithms to multibeam satellite DRM problems.  
Conceptualization, W.G. and L.W.; methodology, W.G.; validation, W.G. and L.W.; formal analysis, W.G. and L.W.; investigation, L.W.; resources, L.Q.; data curation, W.G.; writing--original draft preparation, W.G.; writing--review and editing, L.W.; supervision, L.Q.; project administration, L.Q.; funding acquisition, L.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Defense Pre-research Foundation in Information and Communication College (No. 514010204-203).  
Not applicable.  
Data Availability Statement:Data are contained within the article.  
Conflicts of Interest:The authors declare no conflict of interest.  
Figure 12: The execution time and optimized solutions of the algorithms in continuous-time demand scenarios. (**a**) The red vertical line indicates where the USC of NDBC is better than that with UBA, and vice versa for grey; and (**b**) the actual offered capacity calculated by subtracting the USC value from the total required capacity at each instant (this value is usually not equal to the total allocated capacity).
# Chromium(III) Removal from Wastewater by Chitosan Flakes
## 3 Results
### Effect of pH  
Figure 3 shows the effect of pH on Cr(III) adsorption on chitosan. Notably, the pH of the solution strongly affected the adsorption of metal ions, with the latter increasing with the pH of the solution. Under acidic conditions, the amino groups (R-NH\({}_{3}\)+) and the hydroxyl groups (R-OH\({}_{2}\)+) are protonated and the molecule is a sort of polycation, with a reduced number of binding sites available for the adsorption of Cr(III); according to [30], the pKa of the amine groups is 6.3. In addition, the positive surface charge may hinder the adsorption of metal ions. On the contrary, a high pH will favor their adsorption since the nitrogen free electron doublet is responsible for cations coordination. Considering that K\({}_{\rm ps}\) = [Cr\({}^{3+}\)] \(\times\) [OH]\({}^{3}\) = 6.7 \(\times\) 10\({}^{-31}\), chromium(III) hydroxide begins to precipitate at pH \(\approx\) 6.5, for pH values higher than 3.8, there is a significant reduction of the Cr(III) fraction, with formation of Cr(OH)\({}^{2+}\) and Cr(OH)\({}_{2}\)+ hydrolyzed complex species [20]. The result is an increase in chromium adsorption due mainly to hydrolyzed forms. Therefore, the adsorption of metal ions is mainly due to the electrostatic interactions between counter ions.  
As previously reported [21], the final pH values of the equilibrated solutions were higher as the Cr(III) concentration became smaller (Table 2). This is probably due to the fact that Cr ions are Lewis acids; therefore, the lower the concentration, the higher the pH (Figure 3). Moreover, the chromium adsorption capacity increases by increasing the metal ions concentration (see Section 3.4), which causes a greater competition with H\({}^{+}\) protons.  
Figure 2: Effect of the grain size of the flakes on the adsorption of Cr(III) on chitosan (100 mg), at pH = 3.8, T = 20 \({}^{\circ}\)C, and C\({}_{0}\) = 100 and 500 mg L\({}^{-1}\) of Cr(III).  
As already stated, it is accepted that chitosan amino groups are the main reactive sites for metal ions and that hydroxyl groups (in particular in C-3 position) may contribute to sorption. Metal sorption may involve different mechanisms (chelating, electrostatic attraction) depending on the pH, the solution, and the metal (concentration, speciation, etc.). Protonation of the amino groups at acidic pH increases the adsorption of anionic species, while cations interactions increase with the pH due to deprotonation of amino/hydroxyl groups. Moreover, increasing the ions competition, due to increase in metal concentration, the pH decreases. The fraction of free (accessible) amine groups is the key parameter.
## Appendix 2 Computation of HCRF by Monte-Carlo Integration  
In a Cartesian coordinate system with the origin at the footprint ellipse center, we write the ellipse as \(\frac{x^{2}}{a^{2}}+\frac{y^{2}}{b^{2}}=1\), where \(a\) and \(b\) are semi-major and semi-minor axes respectively. It can be proved that \(a\) and \(b\) are functions of three variables of the observation geometry: sensor height \(H\), sensor field-of-view angle \(\beta\), and off-nadir angle \(\alpha\):  
\[a=H\cdot\frac{\tan\left(\alpha+\frac{\beta}{2}\right)-\tan\left(\alpha-\frac {\beta}{2}\right)}{2},\]  
\[b=H\cdot\sqrt{\left(\frac{\cos(\alpha)+\sin(\alpha)\cdot l/H}{\cos\left(\frac {\beta}{2}\right)}\right)^{2}-\left(\frac{l}{H}\right)^{2}-1}\,, \tag{11}\]  
where \(l=H[\tan\left(\alpha+\frac{\beta}{2}\right)+\tan\left(\alpha-\frac{\beta}{2} \right)]/2\). The integral in the HCRF formula in Equation (10) can be computed by Monte-Carlo integration as:  
\[\bar{\rho}=E\left[\frac{\pi f(\theta_{r})\cdot\cos(\theta_{r})\cdot\mathbb{1} \left(\frac{x^{2}}{a^{2}}+\frac{y^{2}}{b^{2}}\leq 1\right)}{f(x)f(y)\cdot r^{2}} \right]/E\left[\frac{\cos(\theta_{r})\cdot\mathbb{1}\left(\frac{x^{2}}{a^{2}} +\frac{y^{2}}{b^{2}}\leq 1\right)}{f(x)f(y)\cdot r^{2}}\right], \tag{12}\]  
where the indicator function \(\mathbb{1}(\cdot)=1\) if the random point \((x,y)\) falls in the ellipse (including border), and \(0\) otherwise. \(f(x)\) and \(f(y)\) are probability density functions of \(x\) and \(y\) respectively. \(E(\cdot)\) is the expectation of the integrand, which is approximated by the mean from N random samples, if N is large enough.  
It can be proved that:  
\[f(x)=\frac{1/H}{\beta}\cdot\frac{1}{1+\frac{(x-l)^{2}}{H^{2}}}\,, \tag{13}\] \[f(y)=\frac{0.5/\sqrt{l^{2}+H^{2}}}{\arctan\left(\frac{b}{\sqrt{ l^{2}+H^{2}}}\right)}\cdot\frac{1}{1+\frac{y^{2}}{l^{2}+H^{2}}}\,.\]  
Therefore the marginal distributions of \(x\) and \(y\) are Cauchy distributions. Their corresponding random variables can be generated by:  
\[x=l-H\cdot tan(\theta_{x}),\] \[y=\sqrt{l^{2}+H^{2}}\cdot\tan(\theta_{y}),\]  
where \(\theta_{x}\) and \(\theta_{y}\) are sampled from a uniform distribution:  
\[\theta_{x}\in U\left(\alpha-\frac{\beta}{2},\alpha+\frac{\beta}{2}\right),\text {and}\]Sensors_**2011**, _11_  
\[\theta_{y}\in U\left(-\arctan\left(\frac{b}{\sqrt{l^{2}+H^{2}}}\right),\arctan \left(\frac{b}{\sqrt{l^{2}+H^{2}}}\right)\right).\]