esa-sceva/satcom-chunk-collection huggingface dataset:
# An Investigation on Radiomics Feature Handling for HNSCC Staging Classification
## 2 Materials and Methods
### Statistical-Based Feature Selection Methods  
After the feature extraction step, a feature selection has been performed to reduce the high number of extracted features, applying different statistical measures to carry out the most appropriate statistical-based feature selection method. The high number of extracted  
Figure 1: Radiomics Head and Neck pipeline. Left part: CT and PET images have been collected and selected for the feature extraction. The red portion is the tumor ROI of the patient, from which the radiomics features have been calculated. Both CT and PET tumor ROI have been included. Middle part: the process of radiomics extraction schematizes our workflow in terms of feature extraction (first-order, shape and texture features, well described in Section 2.3), both from the original and the filtered (LoG and wavelet) images, except the shape 3D for the filtered image. Right part: the upper side synthesizes the feature selection process, while the bottom side resumes the classification task and the applied models; see Section 2.5.  
features leads to correlations and redundancies, which can degrade further analyses both in speed for the high dimensionality and in prediction accuracy for the irrelevant details [41]. Nevertheless, it is important to demonstrate which features give greater contributions in terms of classification after a feature selection, without excluding a priori features that can be pivotal in the clinical outcome. Among the statistical methods for selecting features, Pearson correlation-based selection has been used to eliminate the most correlated features; hence, only features with a Pearson coefficient less than 0.85, as a threshold, have been collected both for PET (SUV maps) images and CT ones [42; 43]. Moreover, a set of supervised methods for feature selection has been tested: Chi-square, Mutual Information, Fisher Score, Anova F-value and Recursive Feature Elimination using Logistic Regression [44]. After several experiments, Recursive Feature Elimination (RFE) using Logistic Regression (LR) has turned out to be the best method for selecting particularly significant features for classification purposes. RFE is a wrapped selection algorithm that reduces feature dimensionality, constructing recursively the model. Here, it trains an LR model and then computes the ranking criterion for all features and removes those with the smallest ranking criterion [45].  
A Leave-One-Out (LOO) cross-validation (CV) on \(DS_{1}\) has been conducted in order to determine the best subset of features selected by RFE-LR. For each patient in the test folder, a set of features \(S_{F_{k}}\) with \(k\) in \([5,10,20,50,100]\) was selected to train the LR model. Finally, at predefined cutoff values of the \(25^{th}\) and \(75^{th}\) percentile, two subsets of features were selected for each set \(S_{F_{k}}\), denoting them, respectively, \(S_{F1_{k}}\) and \(S_{F2_{k}}\). In conclusion, 10 subsets of features were selected for each patient.
## 2 Related Work
### VANET Based Real-Time Traffic Management  
The author of [26], presented a VANET-based Smart ITM system. A VANET is a type of mobile ad hoc network in which intelligent automobiles on highways were also regarded as the connection point to communicate to transfer congested roads' data. VANETs involve multiple network topologies, WLAN, an ad hoc network, and are adaptive. The authors of [31] proposed an advanced method to address this road traffic problem by using attributes of VANET. The mechanism is built and validated utilizing AODV procedures of mobile ad hoc networks to deal with vehicular road traffic in heterogeneous networks. The performance acted as an indicator through the number of transmissions distributed, the ratio of data packets, and a fraction of several vehicles rerouted and operational costs to handle the issues related to data congestion in-vehicle communication networks.  
The authors of [32] utilized a Vehicle-to-Infrastructure interaction architectural design to adapt and incorporate two innovative solutions of an innovative \"Intelligent Road Traffic Signaling\" system and the \"Predictive Road Traffic Signaling\" system. The authors of [33] presented a distinctive VANET-enabled transportation and traffic signal management framework that significantly enhances vehicles' movement, power efficiency, and the security of road users. In a research paper [34], a VANET-predicated technique was implemented utilizing a modular structure by integrating the interconnected connectivity characteristic. Research paper [35] recommends a distributed, interactive heavy-traffic detection and transmission system that utilizes VANET. A traffic app was installed on every one of the operators' mobile phones to identify one's position using the global positioning system.
# Exploration of Multi-Scale Reconstruction Framework in Dam Deformation Prediction
## 1 Introduction
This paper verifies the feasibility of the signal decomposition technology as well as the machine learning in dam deformation prediction, and predicts high frequency and low frequency separately, which reduces the data's complexity of data and greatly improves the accuracy of the dam's deformation prediction and has the obvious significance for actual project. The rest contents of this paper are as follows: the second part briefly introduces VMD, LSTM and RF-related theories, as well as the method of determining the K value of VMD; the third part introduces the two cases' research design, evaluation indicators, model realization, and the determination of relevant model parameters; the fourth part compares and analyzes the prediction performance of the proposed model and other models; the fifth part draws conclusions for the whole paper.
## 2 Related Works
### Weight Pruning  
The early work on weight pruning prunes the weights below the threshold using iterative connection pruning and fine-tuning. The pioneering work by [PERSON] et al. [33] utilizes the second derivatives (Hessian matrix) of the loss function to prune each non-essential weight. In the era of deep learning, the popular work on weight pruning by [PERSON] et al. [28] demonstrates that plenty of weights below the threshold can be simply removed from DNNs to achieve a significant compression ratio. The lottery ticket hypothesis (LTH), presented by [PERSON] et al. [11], is another popular approach in weight pruning that shows that dense, randomly-initialized neural network contains subnetworks (\"winning tickets\") when trained in isolation. Specifically, each pruned subnetwork is trained from scratch, but the weights are initialized as the same initial weights used in the original network. Several variants of LTH, such as Generalized LTH [34], Dual LTH [35], etc., are also developed for weight pruning. While [PERSON] et al. [10; 28] uses the constant global magnitude threshold for weight pruning in the whole network, [PERSON] et al. [12] uses the global compression ratio to find the appropriate per-layer sparsity ratio. [PERSON] et al. [36] formulated the connection pruning as a convex optimization problem.  
Although weight pruning can remove a significant number of connections without damaging the original capacity of the network, it requires specialized hardware or software to speed up inference. Due to irregular sparsity in weight matrices, it has limited applications on general-purpose hardware.
###### Abstract  
Cr-doped Al\({}_{2}\)O\({}_{3}\) spheres with strong red emission were produced using a simple laser processing approach with a 50 \(W\) continuous CO\({}_{2}\) laser. Structural characterization revealed that the produced spheres were monophasic, comprising the \(\alpha\)-Al\({}_{2}\)O\({}_{3}\) phase. Photoluminescence (PL) studies indicated that the observed red emission originates from multiple Cr\({}^{3+}\) optical centers being dominated by the \({}^{2}\)E \(\rightarrow\)\({}^{4}\)A\({}_{2}\) transition (R-lines), with a further contribution from the parity and spin forbidden \({}^{2}\)T\({}_{1}\)\(\rightarrow\)\({}^{4}\)A\({}_{2}\) transition (R'-lines). The identification of additional radiative recombination from chromium ion pairs (N-lines) evidences that the produced samples are heavily doped. As such, energy transfer processes between the different chromium optical centers are seen to take place, as suggested by the lifetime decay analysis. PL excitation revealed that the room temperature luminescence is preferentially populated via the spin-allowed \({}^{4}\)A\({}_{2}\)\(\rightarrow\)\({}^{4}\)T\({}_{2}\),\({}^{4}\)T\({}_{1}\) transitions and by the parity and spin-forbidden \({}^{4}\)A\({}_{2}\)\(\rightarrow\)\({}^{2}\)T\({}_{2}\) (B-lines), \({}^{2}\)T\({}_{1}\) (R'-lines), \({}^{2}\)E (R-lines). Such results demonstrate that the present synthesis method is able to deliver high-optical-quality Al\({}_{2}\)O\({}_{3}\):Cr crystals in a fast and simple way, with potential interest for optical, sensing, or lasing applications.  
Al\({}_{2}\)O\({}_{3}\); spheres; Cr\({}^{3+}\) doping; laser processing; photoluminescence +
Footnote †: journal: Applied Sciences  
Acticle Red-Emitting Cr\({}^{3+}\) on \(\alpha\)-Al\({}_{2}\)O\({}_{3}\):Cr Spheres Obtained in Seconds Using Laser Processing
## 4 Overview of Optimization Methods in Food Science: Linear, Non-Linear, and Evolutionary Approaches
### Linear Methods  
Linear optimization methods address problems where the relationships among variables are represented as linear equations or inequalities. In this approach, both the objective function and constraints are linear, making the problem relatively simple to model and solve using techniques such as linear programming (LP). Linear methods are highly effective for situations where the relationships between inputs and outputs are proportional and straightforward [4].  
Linear methods are particularly advantageous in scenarios that require fast and reliable solutions with minimal computational resources. For instance, they are commonly used in resource allocation problems, where constraints and objectives can be clearly defined in linear terms. However, their applicability diminishes in cases involving non-linear dependencies, discontinuities, or multiple local optima. Such limitations make linear methods unsuitable for modeling complex phenomena such as chemical reactions or biological processes, which often exhibit non-linear behavior [40].
## 2 Experimental Section  
\(\beta\)-CD, 4-ATP and ethanol were purchased from Sigma Aldrich and used without further purification. IR spectra were recorded on a Perkin Elmer ATR-FTIR spectrometer, \({}^{1}\)H NMR was recorded on a 300 MHz Varian spectrometer. Crystallographic data was collected on a Rigaku instrument at 100 (\(\pm\)2) K temperature.  
Figure 1: Hydrogen bonded assembly of cyclodext in solid state. Head-to-head assembly of \(\beta\)-CD with a repeat distance of \(15.7\pm 0.3\) Å (**top**) and head-to-tail assembly of \(\alpha\)-CD with repeat distance of \(8.2\pm 0.1\) Å (**bottom**).  
_Appl. Sci._**2012**, \(2\)  
_Assembling \(\beta\)-Cyclodextrin4-ATP Inclusion Complex_  
A clear saturated solution of \(\beta\)-CD was prepared by dissolving 0.567 g \(\beta\)-CD hydrate in 40 mL water. In a separate container, a clear solution of ATP was made by dissolving 0.0640 g (0.511 mmol) of ATP in 10 mL of ethanol. The ethanolic ATP solution was slowly added to the aqueous \(\beta\)-CD solution. This mixture was left in the container overnight until crystals of the inclusion complex were noticed. Solvent was removed. The solid remaining was then washed three times with acetone and dried under atmosphere. Suitable crystals for diffraction studies were isolated from this sample.  
IR (\(\mathrm{v}\), \(\mathrm{cm}^{-1}\)): 3253 (m, br), 2929 (w, br), 1623 (w, sh), 1596 (w, sh), 1494 (w, sh), 1413 (w, sh), 1370 (w, sh), 1330 (w, sh), 1297 (w, sh), 1205(w, sh), 1155 (m, sh), 1079 (s, sh), 1024 (s, sh), 1000 (s, sh).  
\({}^{1}\)H NMR (D\({}_{2}\)O) \(\delta\) (ppm): 0.94 (t), 2.0 (s), 6.6 (d) and 7.0 (d), region 4-5 was noisy and no signal was detectable. This is because of disordered water molecules in the matrix of the inclusion complex which is released in D\({}_{2}\)O upon dissolution.
# Mental Workload Assessment Using Machine Learning Techniques Based on EEG and Eye Tracking Data
## 3 Materials and Methods
### Brief Overview of Machine Learning Algorithms
#### 3.5.3 Artificial Neural Networks (ANNs)  
Artificial neural network (ANNs) is a mathematical technique that utilizes the principles of the human brain's nervous system to analyze data and make predictions [52; 53; 54; 55]. These networks are capable of efficiently processing large and intricate datasets [56; 57]. The complexity of a neural network is greatly influenced by the number of hidden layers it possesses. Typically, a neural network architecture consists of an input layer to receive initial data, one or more hidden layers to process the data, and an output layer to generate the final prediction or output [58]. To ensure accurate predictions, it is crucial to select the appropriate design for the neural network [59]. For further explanations on ANN, there are additional resources available in the literature [58; 60; 61].
# Nucleotides as Bacterial Second Messengers
## 7 Clinical Application of di-Nucleotides  
Drug design to inhibit c-di-nucleotide signaling is an alternative to control bacterial biofilm formation. The approach is to use small molecules that act on the c-di-AMP-mediated transition from planktonic to sessile lifestyles [104]. Decreased biofilm formation and virulence factors were achieved by inhibiting c-di-GMP synthesis; for example, N-4-anilinophenyl-benzamide (DI-3) reduces c-di-GMP synthesis in _P. aeruginosa_ and _V. cholerae_. Similarly, N-2-henylethyl-aminocarbonoyl-benzamide (DI-8), inhibits _V. cholerae_[105]. Another mechanism is the global reduction of c-di-GMP levels, for which azathioprine, 6-mercaptoethanol, and terrein are effective on _E. coli_ and _P. aeruginosa_[106; 107; 108]. Other molecules affect di-nucleotide signaling by competition with c-di-GMP to binding or sequestration sites are thiol-benzo-triazoleole-quinazolinones, ebselen, and the proline-rich tetrapeptide Gup-Gup-Nap-Arg; these molecules were tested on _P. aeruginosa_[109; 110; 111].  
Another approach is to inhibit enzymes that synthesize or degrade c-di-GMP; raffinose inhibits biofilm matrix formation by activating c-di-GMP-specific PDE to reduce the level of c-di-GMP [112]; inhibitors of DGC are D-di-GMP and GTP; an anti-c-di-GMP peptide binds to the DGC YfiN of _P. aeruginosa_, which impacts the signaling function of c-di-GMP, interfering with motility and biofilm formation [113].  
Cyclic-di-AMP has been used to modulate the immune response. The c-di-AMP worked as an adjuvant to promote an immune response to against the influenza virus [114]; this strategy also worked against the hepatitis C virus [115] and simian immunodeficiency virus [116]. Regarding vaccines, c-di-AMP has been used for vaccination against tuberculosis [117] and _Tympanosoma cruzi_[118]. An oral vaccine prepared with the vaccine antigen and c-di-AMP with the probiotic lactobacillus has also been used [119].
# Single Image Reflection Removal Based on Residual Attention Mechanism
## 1 Introduction
Both channel attention and spatial attention can be used to improve the performance of the neural network by allowing it to focus on the most important parts of the input data. In addition, thanks to the introduction of the residual network and ConvLSTM [5], this module partially deepens the network depth while accelerating the convergence rate, without causing network degradation. In this paper, RABRRN consists of an encoder with a residual attention mechanism to extract semantic features and two decoders as two branches to separately predict the transmission and reflection layers. Our network has a simpler structure and can be trained more efficiently than a cascaded network that requires two encoder-decoders. The encoder with a residual attention mechanism can focus on the background information to be recovered and avoid problems such as color distortion. The iterative training network uses the output of the previous network as the input for the next training to improve the quality of the predicted transmission layer images through continuous iterations. However, due to the multiple iterations of training, the model convergence training becomes difficult with the occurrence of the gradient vanishing problem. Therefore, to avoid the vanishing gradients in iterative training, we introduced two convolutional LSTM (convolutional LSTM) modules. One is used in the transmission layer prediction branch and the other in the reflection layer prediction branch, which preserves the information from the previous iterations and allows the gradient to remain constant. The related design is described in detail in Section 3. The contributions of this paper include:  
(1) We propose the RABRRN, an iterative deep neural network based on a residual attention mechanism. Compared with state-of-the-art studies on the same problem, our method is composed of fewer parameters, which can improve the training efficiency while ensuring the reflection removal effect. (2) We add a residual attention mechanism to the encoder, which enables the network to enhance the channel and spatial representation, focus on the key information of the background that needs to be recovered, and prevent problems such as color distortion and deformation of the main area. (3) We have established a dataset, named SCAU-RID, of image pairs with reflection kept or removed for further research on reflection removal.
# Long-Term Forecasting Potential of Photo-Voltaic Electricity Generation and Demand Using R
## 3 Results  
### Data Cleansing (See SM5: R Code for Data Rows Graph)  
Both 2015 and 2017 had 365 (days)*24 (hours) = 8,760 rows of parameters for hourly data. However, 2016 was a leap year and therefore included February 29, which led to 366 (days)*24 (hours) = 8,784 rows of parameters. After data cleansing the amount of data remaining from 2015, 2016, and 2017 was 98.54%, 98.36%, and 97.93% respectively. (See Figure 3, based on code from [18]).  
The differences in the standard deviation (SD), mean, minimum and maximum values of five parameters before outlier removal and after the final removal of uncharacteristic values are shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & & & **2015** & & **2016** & & **2017** \\ \hline \multicolumn{7}{c}{**Pre-Cleansing**} & **Post-Cleansing** & **Pre-Cleansing** & **Post-Cleansing** & **Pre-Cleansing** & **Post-Cleansing** \\ \hline \multirow{2}{*}{Wind speed} & SD & 1.304764 & 1.188131 & 1.266143 & 1.167836 & 1.267896 & 1.131123 \\  & Mean & 2.285958 & 2.233074 & 2.265922 & 2.226386 & 2.235808 & 2.174749 \\  & Min & 0 & 0 & 0 & 0 & 0 \\  & Max & 10 & 6.9 & 10.5 & 6.9 & 11.1 & 6.6 \\ \hline \multirow{2}{*}{Temperature} & SD & 8.567907 & 8.599083 & 8.737433 & 8.760915 & 9.043145 & 9.069151 \\  & Mean & 14.9803 & 14.98214 & 14.953223 & 14.96025 & 14.28081 & 14.32673 \\  & Min & -6.3 & -6.3 & -6.0 & -6.0 & -6.5 & -6.5 \\  & Max & 35.6 & 35.6 & 35.5 & 35.5 & 34.4 & 34.4 \\ \hline \multirow{2}{*}{Solar irradiation} & SD & 0.9289794 & 0.9274603 & 0.9168617 & 0.9157871 & 0.9353226 & 0.9366301 \\  & Mean & 1.085775 & 1.094586 & 1.058818 & 1.056414 & 1.103574 & 1.094547 \\  & Min & 0.01 & 0.01 & 0.01 & 0.01 & 0.01 & 0.01 \\  & Max & 3.54 & 3 These results show that removing outliers of wind speed, total pv ac and purchased electricity does not greatly affect the standard deviation of all five parameters. It is interesting to note that whereas the maximum of solar irradiation has increased from 2015 to 2016 and stayed nearly the same between 2016 and 2017, the maximum solar power generated (total pv ac) is decreasing every year.
# Band-Pass Sampling in High-Order BOC Signal Acquisition
###### Abstract  
The binary offset carrier (BOC) modulation, which has been adopted in modern global navigation satellite systems (GNSS), provides a higher spectral compatibility with BPSK signals, and better tracking performance. However, the autocorrelation function (ACF) of BOC signals has multiple peaks. This feature complicates the acquisition process, since a smaller time searching step is required, which results in longer searching time or greater amounts of hardware resources. Another problem is the high Nyquist frequency, which leads to high computational complexity and power consumption. In this paper, to overcome these drawbacks, the band-pass sampling technique for multiple signals is introduced to BOC signals. The sampling frequency can be reduced significantly. Furthermore, the ACF of the sampled signal has only two secondary peaks, so that the code phase can be searched with a larger searching step. An acquisition structure base on dual-loop is proposed, to completely eliminate the ambiguity and compensate the subcarrier Doppler. The acquisition performance and the computational complexity are also analysed.  
Keywords:global navigation satellite system; binary offset carrier; band-pass sampling; subcarrier Doppler; acquisition +
Footnote †: journal: Applied Sciences
## 3 Software Lifecycle in the Remote Handling Environment  
Defining a software lifecycle plays a fundamental role in designing an architecture that blends all the aforementioned approaches. Moreover, reliability, availability, maintainability, and safety (RAMS) are eminent requirements of RH systems. The primary software lifecycle phases extracted from ISO-12207 [28] are as follows:  
* Software Requirement Analysis Process.
* Software Architectural Design Process.
* Software Detailed Design Process.  
- Module testing.  
- Integration testing.
* Software Qualification Testing Process.  
The development and operation of a system of this complexity require rigorous adherence to safety engineering, quality assurance, validation and verification, requirement management, and requirement traceability processes to ensure reliability and fail-safe operation.  
In short, an architecture that integrates multiple approaches must be designed, paying special attention to requirement gathering and the software lifecycle (Table 2). Both processes must be adapted to leverage the strengths of each approach and ensure that the resulting system meets the business needs and end-user's expectations.
## 5 Simulation Results
### Cluster Performance for Static Networks  
In order to exhibit the effectiveness of the designed clustering method for static networks, Figure 4 shows the clustering results of 50 UAVs with a total of four clusters formed in the 3D version and the XY axis version.  
In Figure 4, UAVs in the same color belong to the same group. In addition, UAVs with the same color circle around belong to the same cluster. The clustering results show that our clustering method can effectively control the number of clusters and divide UAVs belonging to the same task group into one identical cluster.
## 4 Applications  
### Adsorption  
MWCNTs are also being successfully explored in environmental applications, such as for water quality control and treatment. Two interesting reports [9; 10] are included in this Special Issue, both of which are related to the use of MWCNTs as adsorbents of contaminants, namely nonylphenol [9] and organochlorine pesticides [10] from source waters and agricultural irrigation water samples, respectively. The contributing authors [9; 10] characterized the main operational parameters and the type of adsorption demonstrating the applicability of MWCNTs for the removal and extraction of contaminants from water samples.  
[PERSON] [11] also characterized the possibility of applying MWCNTs as sorbents, but in this case, for gold(I) and gold(III) cations' adsorption from cyanide and chloride solutions. The reached data suggested that the recovery of the selected metal may be accomplished by subsequent elution with acidic thiourea solutions (for cyanide medium) or with aqua regia (for chloride solutions), with a further possibility of obtaining zero-valent gold nanoparticles.