esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Multipath Transmission Algorithm
#### 2.3.2 The Second Type of Simplification Operation  
The core idea of the second type of simplification operation is to delete every link on the first path that satisfies the multi-constraint requirement in the network topology. As illustrated in Figure 5, new links are obtained by deleting different subnetworks and links. The red, yellow, and purple clouds on the left represent different heterogeneous networks. The source and destination nodes are node S and node D, respectively. The first calculated path is S-E-I-K-N-D. Through the second type of graph deletion operation, we removed the nodes and links that the first path passed through in the red, yellow, and purple heterogeneous networks. Therefore, the second calculated path is S-B-J-L-D. The specific steps are as follows:  
Link deletion: In the network topology, delete each link on the first path that satisfies the multi-constraint requirement. This ensures that the new path does not intersect the first path in terms of physical links, thus improving the independence of the paths and the overall reliability of the system.  
Path computation: After performing the second type of simplification operation, obtain a new network topology map. At this point, the NSGA-II-based multi-constraint optimal path algorithm is used again to find a second path that satisfies the multi-constraint requirement. If the second path exists, the search succeeds; otherwise, the third type of simplification operation is used.
# Single Image Super-Resolution Method Based on an Improved Adversarial Generation Network
## 3 An Enhanced Generative Adversarial Network for SR
### Generative Network Structure
#### 3.1.2 Convolutional Skip-Connections  
The typical CNN algorithms have been proven to exhibit great advantages for SR tasks [33]. These CNN-based algorithms extract effective and robust features from LR information using convolutional layers such as SRCNN, VDSR, and DRRN. The convolution and ReLU operator can be viewed as:  
Figure 2: The sample of the Gaussian pyramid and the Laplacian pyramid, where the upward arrow denotes an up-sample operator, and the downward arrow represents a down-sample operator.  
\[f(x)=\max(w\otimes x+b,0) \tag{4}\]  
However, some GAN methods are not successfully applied to image super-resolution. The authors of DCGAN utilize a cascade convolutional network to generate image representations. Although the above algorithm extracts the image features using a convolutional neural network, the cascade architecture limits the generator to learn deep semantic features. We argue that feature maps from the intermediate layers may contain some latent information that are crucial for image representations. Therefore, it is important for our generator to employ some convolutional skip-connections and then to extract feature maps from the intermediate layers, which effectively avoids deep semantic loss. The deployed convolutional skip-convolutions can be represented by the following in Figure 3.  
The convolutional skip-connections play a significant role in both the forward propagation and the backward propagation. On the one hand, each convolutional skip-connection introduces a convolutional layer that is compared with traditional skip-connections, which slightly adjusts the feature maps that are extracted during the forward propagation. On the other hand, our convolutional skip-connections alleviate the gradient vanishing problem in the backward propagation, because the gradient is passed directly by our convolutional skip-connections.  
Specifically, we use small \(3\times 3\) kernels for the convolutional layers, as inspired by [PERSON] et al. [31], and we use the activation function ReLU as our first block. From the second to fourth blocks, a series of fractional-strided convolutions (FSC) are applied to all convolutional layers, as proposed by [PERSON]. Considering that batch normalization (BN) is usually utilized to counteract the internal covariate shift [34], the BN helps to bridge between the fractional-strided convolutions and ParametricReLU. For the activation function PReLU, the mathematical formulation can be expressed as:  
\[PReLU=\max(x,0)+a\max(0,x) \tag{5}\]  
where \(x\) denotes the input signal, and the parameter a is learnable for the PReLU in the negative portion, which effectively alleviates the \"dead feature\" in the zero gradients [35]. In the fifth block, the convolutional layer generates a plausible-looking image, with the goal of fooling the discriminative model \(D\).
# Biosynthesis Progress of High-Energy-Density Liquid Fuels Derived from Terpenes
## 3 Microbial Synthesis of Monoterpene and Sesquiterpene Fuels
### Microbial Synthesis of Monoterpenes  
Compared to sesquiterpenes, the titers of monoterpene compounds are relatively lower. [PERSON] et al. optimized the fermentation conditions to achieve high limonene production using glycerol as the carbon source in _E. coli_, and the final limonene yield reached 3.6 g/L in the fermenter with a rate of 0.15 g/L/h, which is the highest yield reported [45]. [PERSON] et al. screened efficient variants of pineene synthase to achieve the efficient synthesis of pineene. By co-expressing the variant with MVA-pathway-related genes and isopentenyl diphosphate isomerase in _E. coli_, the pinee production reached 140 mg/L in shake-flask fermentation [46]. In addition, researchers also synthesized pinee through whole-cell bio-catalysis. A titer of 166.5 mg/L pinee was achieved by co-cultivating _E. coli_ expressing the MVA pathway with the _E. coli_ expressing GPP synthase and pineinee synthase [47]. Our research group developed a strategy based on quorum sensing and the dynamic regulation of small RNAs to improve pineine synthesis in _E. coli_. By dynamically inhibiting the expression of multiple genes involved in competing pathways for pineinee synthesis without affecting cell growth, the pinee yield reached 165.3 mg/L in shake-flask fermentation, reaching an increase of 365.3% compared to the control strain [48]. Additionally, sabinene has also been successfully synthesized in _E. coli_. [PERSON] et al. introduced the sabinene synthase from _Salvia pomifera_ and the GPP synthase from _Abies grandis_ into _E. coli_, achieving a sabinene yield of 82.18 mg/L in shake-flask fermentation and 2.65 g/L in the fermenter [49].  
Furthermore, the synthesis of oxygen-containing monoterpenes has also received extensive attention. 1,8-cineole was synthesized in _Rhodosporidium toruloides_ with a lignocellulosic hydrolysate medium and reached a titer of 1.4 g/L [50]. [PERSON] et al. discovered that the (S)-linalool synthase (AaLINS) from _Actinidia arguta_ and the (R)-linalool synthase (ScLINS) from _Streptomyces clavuligerus_ exhibited the highest efficiency among 16 linalool synthases. When AaLINS and ScLINS were expressed simultaneously with a mutated FPP synthase (ispA*) in _E. coli_, the yields of (S)-linalool and (R)-linalool reached 5.6 g/L and 3.71 g/L, respectively, which are the highest reported yields so far [51].
# Towards Practical Applications in Modeling Blockchain System
## 4 Comparative Analysis of the Results  
Figure 4 shows that the values obtained by simulation modeling take into account the peculiarities of the data in packet networks, but globally do not contradict the analytical conclusion obtained by equations 7. Network nodes introduce latency, but the main latency in confirming a transaction will depend on the time spent in the memory pool and on the computational task being performed by the miners. It is worth noting that network latency plays a key role in propagating a transaction and block through the network, so a significant latency will affect the queuing in the memory pool, which will affect the time to add a transaction to the block, and the confirmation of the mining task since a node should stop working on its blocks as soon as a new block is announced to save computational resources, but nodes may receive newly created blocks too late due to propagation delay and continue working on the already obsolete block. Therefore, in terms of efficient transaction processing and overall system performance, the network parameters of the nodes should be of key importance. The experiment also showed that the network's performance depends on the intensity of requests, and the node intensity and buffer size values can be varied for the correct operation of the blockchain technology presented.  
It should be noted that modeling blockchain technology performance with AnyLogic is possible and convenient to analyze when various parameters change. However, for more accurate results and a more complete system response, more research into blockchain simulation is needed. The analysis of the models has shown the applicability of selected simulation systems for assessing the impact of blockchain technology on data networks.
# A Medical Image Encryption Scheme for Secure Fingerprint-Based Authenticated Transmission
## 3 Materials and Methods
### Simulation Results  
The proposed visually secure image encryption scheme is simulated using one medical image as an image to embed and one not significant image as a reference image for each test image. The wavelet used in 2D-DWT is 'haar', and the public and private keys for the key protection scheme are generated using Brainpool elliptic curves of 256 bits.  
Figure 6c shows the simulation results of the visually meaningful encrypted images obtained given Figure 6a,b images as input. After embedding the medical images (Figure 6a) into reference images (Figure 6b), the generated encrypted images (Figure 6c) have the same visual effects as the reference images. This reduces the attention of the attackers and their tentative attacks, thus protecting the medical images. Subsequently, the proposed visually secure image decryption scheme enables the reconstruction of medical images by ensuring high quality and the same visual effect as the original medical images, as shown in Figure 6d.  
To qualitatively assess the proposed scheme's performance, the PSNR and MSSIM have been computed. PSNR is a mathematical metric used to calculate the quality of the  
Figure 6: (**a**) medical images to be transmitted and protected; (**b**) not-significant reference images; (**c**) visually meaningful encrypted images; (**d**) reconstructed medical images.  
reconstructed image [48]. It is computed between an original image and the corresponding reconstructed image according to Equation (14).  
\[PSNR=10\times log_{10}\bigg{(}\frac{255^{2}}{MSE}\bigg{)} \tag{14}\]  
where \(MSE\) is the mean squared error, and it defines the average squared difference between the estimated values and the actual values. \(MSE\) between two images \(I\) and \(I^{\prime}\) is computed following Equation (15).  
\[MSE=\frac{\sum_{i=0}^{N-1}\sum_{j=0}^{N-1}\left(I(i,j)-I^{\prime}(i,j)\right)^ {2}}{N\times N} \tag{15}\]  
MSSIM is a metric used for measuring the structural similarity between two images [49]. MSSIM considers image degradation as the perceived change in structural information that is based on the high interdependencies between the pixels, especially when they are spatially close. MSSIM between two images \(I\) and \(I^{\prime}\) is computed according to Equation (16).  
\[MSSIM=\frac{1}{M}\ \sum_{k=1}^{M}\frac{(2\mu_{I}\mu_{I^{\prime}}+C_{1})(2\sigma_{ I^{\prime}}+C_{2})}{\big{(}\mu_{I}^{2}+\mu_{I^{\prime}}^{2}+C_{1}\big{)} \big{(}\sigma_{I}^{2}+\sigma_{I^{\prime}}^{2}+C_{2}\big{)}} \tag{16}\]  
where \(M\) is the number of image block, \(\mu_{I}\) and \(\mu_{I^{\prime}}\) are the pixel sample mean of image \(I\) and \(I^{\prime}\) respectively. \(\sigma_{I}^{2}\) and \(\sigma_{I^{\prime}}^{2}\) are the variance of \(I\) and \(I^{\prime}\), respectively. \(\sigma_{I^{\prime}}^{2}\) is the covariances of \(I\) and
## 5 Conclusions  
This study provides significant evidences that deep learning methods could be used for evaluating cardiovascular risk by using CAC as unique biomarker. Preliminary results show promising accuracies on a small sample sized database by using classical DL architectures. On the other hand, our findings show that there are clinical variables which also correlate positively with CAC \(>\) 400. A very simple preliminary study shows above chance accuracies, and more important, complementarity with the results obtained by image analysis. Based on that, we build two applications that optimize a precision or a recall criteria for a specific application.  
This is a preliminary work that proves that there exists discriminative information in the retinal images. Further research challenges are: data acquisition and model improvements. Results can be significantly improved gathering more clinical data (increasing the number of relevant variables) or the number of images (more patients). In addition, more efforts can be put in the specialization of the DL architecture, taking advantage of higher detailed information in the retinal images (thickness, dimension, tortuosity of the vessels, etc.) or increasing the model capacity with current developments in self-supervision (to mitigate the small sample size problem) and curriculum learning (training the machine learning models in a meaningful order [26]).  
Conceptualization, R.G.B., C.H., R.S. and D.M.; methodology, R.G.B., C.H., R.S. and D.M.; software, R.G.B.; validation, R.G.B. and D.M.; formal analysis, R.G.B., C.H., R.S. and D.M.; investigation, R.G.B., O.S.-S., A.P., C.H., R.S. and D.M.; resources, O.S.-S., A.P., C.H., R.S. and D.M.; data curation, O.S.-S., A.P., C.H. and R.S.; writing--original draft preparation, R.G.B. and D.M.; writing--review and editing, R.G.B., R.S., C.H. and D.M.; funding acquisition, R.S., C.H. and D.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by \"RTI2018-095232-B-C22\" grant from the Spanish Ministry of Science, Innovation and Universities (FEDER funds).  
The study was conducted according to the guidelines of the Declaration of Helsinki, and was approved by the local ethics committee (Ethical Committee for Clinical Research of the Vall d'Hebron University Hospital), with a reference number PR(AG)127/2014.  
Informed Consent Statement: Informed consent was obtained from all subjects involved.  
Authors acknowledge the support from the NVIDIA Hardware grant program.  
The authors declare no conflict of interest.The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
BMI Body Mass Index  
CAC Coronary Artery Calcium  
CNN Convolutional Neural Network  
CT Computed Tomography  
CVD Cardiovascular Diseases  
DL Deep Learning  
DR Diabetic Retinopathy  
FN False Negative  
FP False Positive
# Simple Compact UWB Vivaldi Antenna Arrays for Breast Cancer Detection
## 5 Conclusions  
As proof of concept in the application of the NTL and VNSPA theories for compactness, while maintaining or enhancing good performance, the novelty of this work is achieved by designing, fabricating, and testing simple 2 \(\times\) 1 linear compact VTSA and VNSA arrays at UWB frequency band (3.1-10.6 GHz). The compact VTSA array (Array 1) and VNSA array (Array 2) outperform the VTSA and VNSA single components in terms of enhanced impedance and gain by 24.39% and 15.11%, respectively. Array 2 achieves 52% compactness with 6.76% BW enhancement as compared to Array 1. The suggested compact arrays with high gain, wideband, stable radiation patterns, and low SAR are good candidates for high-resolution medical applications such as breast cancer detection (BCD), which is addressed in this paper using Array 2 based on the CST simulator. Detailed simulation and measurement setups for breast and brain cancer detection using N \(\times\) 1 VTSA or VNSA arrays are aimed to be considered in our future work.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Parameters** & **Skin** & **Fat** & **Fibroglandular** & **Tumor** \\ \hline Mass density (Kg/m\({}^{2}\)) & 1090 & 950 & 1000 & 440 \\ \hline \(\varepsilon\tau\) & 34.2 & 4.32 & 39.65 & 54.9 \\ \hline Conductivity (S/m) & 4.67 & 0.509 & 7.65 & 4 \\ \hline \end{tabular}
\end{table}
Table 7: Densities and electric properties of the breast phantom tissues.  
Figure 17: Simulated 3D SAR results over 10 g of the four elements of the proposed compact UWB Array 2 at (**a**) 3.35 GHz, (**b**) 4.35 GHz, (**c**) 8.86 GHz, (**d**) 12.72 GHz, and (**e**) 14.61 GHz.  
**Author Contributions:** Conceptualization, S.S.; validation, S.S.; formal analysis, S.S.; investigation, S.S.; resources, S.S.; data curation, S.S. and T.S.; writing--original draft preparation, S.S.; writing--review and editing, S.S., T.S. and N.T.; visualization, S.S., T.S. and N.T.; supervision, T.S. and N.T.; project administration, N.T.; funding acquisition, N.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by Enterprise Ireland, Ireland.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Model-Based Assessment of Energy Efficiency in Industrial Pump Systems: A Case Study Approach
## 2 Case Study on Water-Cooling System: Operation, Data Collection, and Efficiency Modelling
### On-Site Measurements of the Existing VSDP
The nominal data of the existing VSDP components, i.e., converter, IM, and pump, are listed in Table 1:  
Figure 2(a) shows the measurement results of the pressure difference and flow for each combination of pre-set valve opening and pre-set speeds. The measured operating points (OPs) are highlighted with large markers, each labelled with the corresponding \begin{table}
\begin{tabular}{c c c} \hline \hline
**Frequency Converter** & **Induction Motor** & **Water Pump** \\ \hline Danfoss \# VLT6027 HT & KSB * 1 LG4207-4 AA60-Z & KSB Etanorm 150–250 \\ \(U_{r}=380\ -\) 460 V & \(P_{r}=30\) kW & \(H_{r}=16\) m \\ \(I_{r}=61\) A & \(U_{r}=400/690\) V & \(Q_{r}=420\) m\({}^{3}\)/h \\  & \(I_{r}=56/31\)A & \(P_{r}=21.2\) kW \\  & cos\(\varphi=0.85\) & \(n_{r}=1450\) rpm \\  & \(n_{r}=1465\) rpm & \(\eta_{\text{BEP}}=86.4\%\) \\  & No. of poles: 4 & \\  & \(\eta_{r}=91\%\) (IE1) & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Rated data of the existing VSDP components. hydraulic power in kW. A cloud of scattered grey points represents the annual dataset of OPs. Figure 3b shows the input electrical power at the measured OPs with calculated system efficiency indicated next to each label. To accurately predict the system efficiency for each OP in the annual dataset, a precise VSDP efficiency model must be developed based on the measured results.
# Genomic and Metabolic Characteristics of the Pathogenicity in Pseudomonas aeruginosa
## 4 Metabolic Pathways Associated with Pathogenicity  
With new and robust results of genome sequencing, an increasing interest in the _P. aeruginosa_ genome has taken place, mainly with the purpose of better understanding its adaptive mechanisms and multi-omics interactions [46]. Studies have shown that, although the _P. aeruginosa_ genome is well conserved and shows moderate genetic variability, non-synonymous mutations, and recombination events play an important role in phenotypic diversification [47]. Genes that encode for aerobic respiration, denitrification, and anaerobic fermentation, found in the core genome, have been identified in all strains of _P. aeruginosa_, highlighting the metabolic versatility of this bacterium [48]. Proteins involved in regulation, transport, virulence and resistance also contribute to the high nutritional versatility and adaptive capacity of this bacterium [26]. Thus, the study of extracellular proteins is of high interest due to their essential roles in bacterial lifestyle [49]. Currently, although antibiotic pressure, airway heterogeneity and the natural versatility of _P. aeruginosa_ play an important role in its diversification, the exact mechanisms that drive adaptability and diversification in this species are still not fully understood [2; 50; 51].
# Cross-Corpus Training Strategy for Speech Emotion Recognition Using Self-Supervised Representations
## 3 Materials: Experimental Setup  
### Databases  
The experiments in this study utilize several datasets, namely EmoDb [18], RAVDESS [19], CREMA-D [20], and IEMOCAP [21]. All of these datasets are freely available for research purposes. EmoDb, RAVDESS, and CREMA-D are acted datasets, meaning that the audio recordings involve actors intentionally portraying different emotions. All of them were recorded in a studio, so the audio samples have high quality.  
The selection of appropriate datasets is of paramount importance in Speech Emotion Recognition (SER) research to ensure the validity and generalizability of the proposed models. In this study, we carefully curated four specific databases, namely EmoDb, RAVDESS, IEMOCAP, and CREMA-D, as they represent the most extensively utilized datasets in the SER community, providing a rich array of references for result comparison. Notably, EmoDb, RAVDESS, and IEMOCAP exhibit a comparable number of speakers, contributing to a controlled evaluation environment. In contrast, the CREMA-D dataset stands out by encompassing a significantly larger number of speakers compared to the cumulative speaker count of the other three datasets. The inclusion of CREMA-D enables a comprehensive analysis of the impact of heightened variability in the cross-corpus strategy.  
Table 1 shows further details about the datasets, where _Time_ is the duration in minutes of each database, _Lang_. is the language spoken in each database, and _Spk_ is the number of speakers. Then, #Neutral_, #Happy_, #Anger, and #5 ad indicate the number of utterances with the corresponding label of each emotion. _Text_ indicates how the database was recorded: _Read_ means the text has been read when acting an emotion, while _Improv_. indicates the actors improvise what they are saying based on general instructions provided by the creator of the database.