esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### RQ3: How Should Psychometrics Assess Emotional Experience?
#### 3.5.4 Classification Strategies  
Classification methods have played a significant role in emotion characterization in several studies. In this section, only the results from the methods that reached the highest performances for emotion classification are presented.  
The use of Support Vector Machines (SVM) reported a 73.35% accuracy classifying emotions in terms of arousal and 68.54% in terms of valence when using Independent Component Analysis (ICA) features of EEG [59]. An accuracy of 73.08% was achieved with categories of features of EDA and ECG in valence recognition [27]. Other classification methods documented in one study was auto-encoder neural network combining Decision Tree, K-Nearest Neighbors (KNN) and Random Forest (RF) [26]. Sections of EEG data from DEAP and SEED databases were used, and accuracies achieved were 62.63% (DEAP) and 74.85% (SEED). Logistic Regression (LR), Repeated Incremental Pruning to Produce Error Reduction (RIPPER) and Multilayer Perceptron (MLP) were reported in a study validating EEG activity as a good indicator of self-regulation. Groups were categorized according to EDA and reported the following best accuracies: 40.91% in non-self-regulated group using LR, 42.70% in self-regulated group using MLP, and 42.46% with RIPPER as best overall accuracy [32]. Table 7 details information on classifiers and features introduced in machine learning algorithms.  
\begin{table}
\begin{tabular}{c c c} \hline
**Test** & **Variables** \\ \hline Bootstrapping & Emotional behavior, SCR, and dynamic causal modeling connectivity parameters \\ \hline \(\chi^{2}\) test & Pathological conditions and discrete emotions \\ \hline \end{tabular}
\end{table}
Table 6: _Cont._  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Classes** & **Electrophysiological** & **Validation** & **Selection Method** & **Classifier** & **Performance** \\ \hline Positive, neutral, and negative & EDA, HRV & Leave-one-subject out & Recursive feature elimination & SVM & 73.08\% \\ \hline Pleasant, unpleasant and neutral & EEG, EDA & 10-fold cross-validation & Data divided in self-regulated and non-self-regulated groups & SVM, Logistic, RF, LR, RIPPER, MLP, KNN, Naive Bayes, C4.5 and Radial Basis Function & Best accuracy for the non-self-regulated group: RFPER, 42.4606\% \\ \hline \end{tabular}
\end{table}
Table 7: Technical information of four studies that used machine learning algorithms for emotion classification.
## 3 Experimental Results
### Measurements of Mat Pressure Sensor
#### 3.1.4 Fourth Experimental Test
The measuring system connected to the mat sensor consists of a power supply, a multiplexer, an Arduino board, and cables (Figure 2b). The voltage and resistance measurements were conducted connecting a resistor R of 2 k\(\Omega\) in series to the mat, as shown in the schematic electric circuit of Figure 3c. The supply voltage \(V_{g}\) is 4.2 V for the mat with 14 \(\times\) 14 active sensing elements.
## 2 Materials and Methods
### FTIR Measurements  
FTIR spectra were measured in the transfection mode by using a FTIR Microscope HYPERION 2000 (Bruker Optik GmbH, Ettlingen, Germany), where the IR radiation beam came from a Vertex 70 Bruker interferometer (Bruker Optik GmbH). The IR signal was detected by a mercury cadmium telluride (MCT) device, cooled at liquid N\({}_{2}\) temperature. Each spectrum was measured in the 1000-4000 cm\({}^{-1}\) spectral range by averaging the signal of 64 scans, with a resolution of 4 cm\({}^{-1}\). Then, the 1000-1760 cm\({}^{-1}\) (LWR) and 2700-3700 cm\({}^{-1}\) (HWR) spectral ranges were selected and analysed for each spectrum. The IR radiation was focused with a 15X objective onto a few cells included in the sampling area with a size of about 80 \(\upmu\)m \(\times\) 80 \(\upmu\)m. The background signal was detected within a slide area without any cells. The numbers of measured cells were 50 and 60 for the healthy and cancerous types, respectively. The spectra were normalised using the standard normal variate (SNV) method, which decreases the spectrum baseline shifts related to scattering effects [41] and minimises the differences in absorption intensity due to cells having different thicknesses. The SNV normalization was performed independently for the LWR and HWR of each FTIR spectrum. The t-test analysis was performed using SigmaPlot software (version 12.5, Systat Software, San Jose, CA, USA).
## 4 Modern Imaging Technologies
### Recent Advances in Radiation Protection in Dental Imaging  
Radiation protection remains a main priority in dental imaging. The transition from film-based systems to digital radiography has significantly reduced radiation exposure, largely due to the use of high-sensitivity sensors such as CCD and CMOS, which produce high-quality images at lower doses [18].  
Further improvements have been achieved through techniques like low-dose CBCT protocols, adaptive image filtering, and size-specific exposure modulation, which allow modern CBCT systems to automatically tailor radiation levels based on clinical requirements, minimizing unnecessary exposure [19]. In addition, deep learning algorithms now support noise removal from low-dose CT images, helping to maintain diagnostic accuracy while further reducing radiation [20].  
Innovations in patient protection have also contributed to safety. Nanocomposite-coated protective garments offer improved comfort without compromising radiation attenuation. Meanwhile, traditional lead aprons and thyroid collars continue to play a vital role in protecting sensitive tissues during radiographic procedures [21].
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**RN**} & \multirow{2}{*}{**No.**} & \(D\) & \(\theta\) & \(\alpha\) & \(R\) & \(\beta\) & \(\omega\) & \(SSQ\) & \(RMSE\) \\  & & **(cm2/min)** & **(-)** & **(cm)** & & & & & & \\ \hline \multirow{3}{*}{HTO} & Fit 1-2 & 0.0351 & 0.46 & 0.16 & 1.00 & 0.60 & 0.05 & \(6.89\times 10^{-3}\) & \(2.22\times 10^{-2}\) \\  & Fit 2-2 & 0.0378 & 0.46 & 0.17 & 1.00 & 0.52 & 0.08 & \(3.34\times 10^{-1}\) & \(9.77\times 10^{-2}\) \\  & Fit 3-2 & 0.0348 & 0.46 & 0.16 & 1.00 & 0.60 & 0.02 & \(2.77\times 10^{-2}\) & \(3.26\times 10^{-2}\) \\ \hline \multirow{3}{*}{Se(IV)} & Fit 4-2 & 0.0351 & 0.46 & 0.16 & 3.53 & 0.37 & 0.32 & \(3.47\times 10^{-2}\) & \(2.66\times 10^{-2}\) \\  & Fit 5-2 & 0.0378 & 0.46 & 0.17 & 2.44 & 0.58 & 0.29 & \(2.36\times 10^{-2}\) & \(2.19\times 10^{-2}\) \\  & Fit 6-2 & 0.0348 & 0.46 & 0.16 & 2.21 & 0.45 & 0.66 & \(6.44\times 10^{-2}\) & \(3.28\times 10^{-2}\) \\ \hline \multirow{3}{*}{Cs} & Fit 7-2 & 0.1175 & 0.46 & 0.54 & 14.30 & 0.07 & 0.40 & \(2.41\times 10^{-2}\) & \(2.22\times 10^{-2}\) \\  & Fit 8-2 & 0.1011 & 0.46 & 0.47 & 15.50 & 0.06 & 0.45 & \(1.34\times 10^{-2}\) & \(1.58\times 10^{-2}\) \\ \cline{1-1}  & Fit 9-2 & 0.0820 & 0.46 & 0.38 & 13.00 & 0.03 & 0.84 & \(3.01\times 10^{-2}\) & \(2.43\times 10^{-2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The fitting parameters for HTO, Se(IV), and Cs for the physical non-equilibrium model.
## 5 Performance Analysis and Simulation  
We compare our proposed schemes with RPBMP [16], EPUSBRF [18] and the shortest path algorithm from two aspects of security performance and communication overhead. According to References [16; 18], EPUSBRF and RPBMP only include the PN determination phase, and they do not consider the lasting time of the PN and the number of PN. Therefore, we analyze the case of single PN based on the descriptions in References [16; 18]. In order to prove the performance of our proposed schemes more intuitively, the shortest path algorithm with the lowest energy consumption is selected as one of the comparison schemes. Since the shortest path algorithm is locally optimal in terms of communication overhead, it does not consider security performance, so no further analysis is done below. For the convenience of description, in the following performance analysis, we assume that the source node visible area radius is the same as the node communication radius.
# Isothiocyanates: An Overview of Their Antimicrobial Activity against Human Infections
## 1 Introduction  
Since ancient times, many plants have shown marked beneficial/therapeutic properties for human health [1]. This is mostly due to their content of secondary metabolites, which show strong biological activity, particularly against cancer, cardiovascular and neurodegenerative diseases [2; 3; 4; 5]. It is known that plant metabolites act as naturally-produced antimicrobial agents, inducing self-defense mechanisms to prevent the development of plant infections and to ensure food preservation [6; 7; 8; 9; 10; 11]. The antimicrobial activity of isothiances (ITCs) against plant and foodborne pathogens has been particularly well documented, together with ITCs' beneficial properties for human health [8; 12; 13]. In the plant kingdom, ITCs are produced via enzymatic hydrolysis of glucosinolates (GLs), a class of sulfur-containing secondary metabolites occurring exclusively in the botanical order Brassicales, by the enzyme myrosinase [14; 15]. In the intact plant myrosinase is stored separately from GLs. When plant tissue damage occurs by alteration or disruption of tissues caused by chopping or chewing, the myrosinase comes into contact with GLs and effects their hydrolysis and consequently the production of ITCs [12]. It is known that ITCs exert their beneficial properties on human health after the regular consumption in the diet of the edible parts of _Brassica_ vegetables rich in GLs.  
Although the antimicrobial ability of ITCs to control plant and foodborne pathogen proliferation has been thoroughly investigated [8; 12; 13], only recently has the action of these compounds to counteract human infections been assessed [12]. Although several studies have described the in vitro antimicrobial activity against bacterial pathogens, little is known about in vivo antimicrobial effects of ITCs. The majority of the investigations refer to the antimicrobial activity of sulforaphane (SFN) against _H. pylori_. Concerning this topic, [PERSON] et al. used a model of mice implanted with human gastric xenografts, and assessed the antimicrobial activity of SFN after _H. pylori_ infection. The results indicated that SFN showed the ability to eradicate acute and recurrent _H. pylori_ infections [16]. Moreover, [PERSON] et al. used a mouse high salt diet model to evaluate the effect of SFN on gastric atrophy induced by a diet rich in salt after _H. pylori_ infection. Results showed that SFN exerted direct antimicrobial activity against _H. pylori_ colonization and also induced anti-inflammatory effects protecting the host from gastric mucosal atrophy [17].  
The remarkable natural properties of ITCs explain the interest in studying these compounds as potential antimicrobial agents against human infections. This statement takes on a greater significance nowadays where the development of new bacteria antibiotic resistance phenotypes is a pressing problem and the pipeline for the development of new antibacterial drugs is now virtually empty.
## 2 Materials and Methods
### Data Preprocessing and Quantification  
Raw GC-MS data were processed using MassHunter Workstation software (v. B.08.00, Agilent Technologies, Santa Clara, CA, USA) for peak integration and compound identification. Metabolite quantification was performed using a semi-quantitative internal standard method. For solid samples, relative concentrations (\(\mu\)g/g) were calculated based on the ratio of analyte to internal standard peak areas, adjusted by the amount and concentration of the internal standard. Missing values were imputed with one-fifth of the minimum value for each metabolite. Metabolites with a coefficient of variation (CV) < 0.5 across quality control (QC) samples were retained for downstream analysis. QC samples were prepared by pooling equal aliquots of all biological samples and injected periodically throughout the analytical run to monitor instrument stability. Relative odor activity values (rOAVs) were calculated using the equation: rOAVi = C_i/T_i, where C_i is the relative concentration of metabolite i, and T_i is its odor threshold obtained from public databases or the literature [17; 18]. Metabolites with rOAV \(\geq\) 1 were considered aroma-active.
# Integrated Sensing and Communication Using Random Padded OTFS with Reduced Interferences
## 3 The Random Padded OTFS  
The RP-OTFS is an OTFS variant that is designed for a more efficient use of the resources of the DD grid--see Figure 2. In the case of RP-OTFS, the DD grid consists of two zones: the pilot zone and the data zone. The RP-OTFS grid structure is very similar to that of other OTFS variants. The main difference is the absence of the guard zone (inactive carriers with values set to zero) between the pilot zone and the data zone of the grid. These carriers in the RP-OTFS are used as additional carriers in the pilot zone.  
The pilot zone of the RP-OTFS is \(2L\) samples long in the delay direction and \(N\) in the Doppler direction of the DD grid. It is filled with very short OFDM symbols of length \(L\) (the length of the cyclic prefix (CP) is also \(L\)) that are used as pilots. They do not carry any data and they are filled only with pseudo-random patterns known to both the transmitter and the receiver.
# Towards a Heat-Resistant Tethered Micro-Aerial Vehicle for Structure Fire Sensing
## 4 Thermal Analysis
### Thermal Model Validation  
This model is validated in laboratory experiments using a test sample placed on top of an insulated oven with an opening on top. The test sample is manufactured following an identical procedure to the actual drone structure. The outer surface temperature is measured using a Mavic 2 Enterprise Advanced (DJI, Shenzhen, China) (DJI product website: [[https://www.dji.com/es/mavic-2-enterprise-advanced](https://www.dji.com/es/mavic-2-enterprise-advanced)]([https://www.dji.com/es/mavic-2-enterprise-advanced](https://www.dji.com/es/mavic-2-enterprise-advanced)) (accessed on 15 March 2024)) high-resolution thermal camera, adequately calibrated with the experimental conditions. The test sample is photographed multiple times to extract the temperatures from the initial condition up to the equilibrium temperature, as seen in Figure 7.  
The average measured temperature at each time is collected. Adapting the formulation proposed in Section 4.2 to the experimental setup, the results are compared in Figure 8.  
Figure 6: Detail of temperature evolution of different components over time, with threshold line.  
Figure 7: Thermal image of an experimental sample placed over the oven opening at 100 °C. Rainbow color map, red means hotter inside.  
The error bar is composed of both the DJI thermal camera and the oven temperature probe uncertainties published by the manufacturers. As seen in the figure, the model provides a reasonable prediction. Minor temperature differences may be associated with uncontrolled heat losses in the experimental setup.
## 4 Results and Analyses
### Displacement of the Preceding Tunnel  
The displacement of the preceding tunnel resulting from construction of the following tunnel is another major concern, which refers to the horizontal or vertical displacement of the tunnel along the excavation direction. According to a previous study on the influence mechanism of the following tunnel on the preceding tunnel, it was found that the maximum horizontal displacement of the preceding tunnel was located at the tunnel shoulder and the maximum vertical displacement was located at the tunnel crown [40; 41]. Therefore, after the excavation of the following tunnel was completed, the horizontal at the shoulder and the vertical displacement at the crown of the preceding tunnel would be monitored, and the maximum displacement would be extracted. In this paper, the horizontal displacement to the right and the vertical displacement in the upward direction is defined as positive.  
The maximum displacements under different cases as obtained from the numerical simulation are presented in Table 2. Explanation of the data, e.g., 27.25/17.96 is the maximum horizontal displacement/maximum vertical displacement at \(t=0.00D\) of the RR. In addition, decrease rate = (value \(t_{=0.00D}\) - value \(t_{=0.30D}\))/value \(t_{=0.00D}\). It can be seen that, with the increase in \(t\), the maximum value of horizontal and vertical displacement gradually decreases. In terms of the decrease rate, the LR and TR are more effective than RR and CR.  
Figure 6: Variation of the maximum surface settlement with \(t\).  
Figure 7 shows the variation of the maximum horizontal displacement at the shoulder and the vertical displacement at the crown of the preceding tunnel with the reinforcement thickness. It can clearly be seen that the trends of different curves are similar. During the excavation of the following tunnel, the LR and TR could effectively reduce both displacements of the preceding tunnel, while the CR could only significantly reduce the horizontal displacement and RR could only significantly reduce the vertical displacement.
###### Abstract  
Introduction: Apical periodontitis (AP) is an inflammatory disease of the periapical tissues that is often asymptomatic and diagnosed through radiographic examination. A challenge in traditional endodontics is disinfection of the root canal system (RCS), which anatomically presents numerous variations, often leading to persistent infections. Antimicrobial photodynamic therapy (aPDT) and photobiomodulation therapy (PBMT) offer promising adjucts, due to their antimicrobial and tissue-healing properties. Objective: The aim of this article was to report five cases of teeth with pulp necrosis and asymptomatic apical periodontitis (AAP) treated with aPDT and PBMT protocols. Materials and Methods: Five cases of pulp necrosis and AAP were treated with conventional endodontic therapy supplemented with aPDT and PBMT. The treatment protocol included chemomechanical preparation (CMP), aPDT using a 660 nm diode laser (DL) with methylene blue (MB) as a photosensitizer (5 min pre-irradiation time), and PBMT using a 940 nm DL. Treatment results were evaluated through cone-beam computed tomography (CBCT)-based evaluation over 1 year of clinical follow-up. Results: All cases showed significant bone regeneration and tissue healing, demonstrating the efficacy of the combination of aPDT and PBMT. Post-operative pain did not occur in any of the patients, suggesting a possible analgesic effect of PBMT. Conclusions: The combination of aPDT and PBMT in endodontic therapy promoted tissue recovery and improved the prognosis of AAP. Further research and randomized control trials are needed to optimize treatment protocols and evaluate the long-term effects.  
photobiomodulation therapy; antimicrobial photodynamic therapy; laser; bone regeneration; apical periodontitis; root canal system
## 3 Results and Discussion
### Photocatalytic Activity Tests
#### 3.1.4 Effect of pH on Photocatalytic Hydrogen Production  
The effect of initial pH on H\({}_{2}\) photocatalytic production was evaluated in the range of 2-10 using the 1.08 Cu_ZnO photocatalyst (Figure 5) with a glycerol initial concentration equal to 10 wt %.  
The highest H\({}_{2}\) production was obtained at pH equal to 6 (H\({}_{2}\) production equal to 4776 umol/L). It was evident that the change in pH had in any case worsened the overall production of hydrogen, which in acidic conditions was equal to 2771 umol/L and in basic conditions was only 1764 umol/L. Therefore, the best result was obtained by operating at the spontaneous pH of the glycerol aqueous solution (pH = 6). This can be seen as an advantage, particularly from an economic point of view, since it is not necessary to use additional chemicals to change the pH in order to improve the production of hydrogen. The increase of hydrogen production observed with the increase of initial pH from 2 up to 6 was consistent with the literature concerning glycerol photoreforming [17]. The best hydrogen evolution rate could be related to the adsorption of glycerol on the photocatalyst surface [17; 18], the charging behavior of the semiconductor surface, the size of the aggregates of the photocatalyst particles, as well as the positions of the valence and conduction band levels of the semiconductor with respect to those of the redox couples in solution [57].
## 4 Evaluation and Results
### Results  
We evaluated our proposed framework for three parameters. First, we calculated the traffic flow from the CDN, i.e., transit link load for both proposed VNF based multicast and unicast transmission. Second, the traffic load on RAN is calculated, i.e., bandwidth usage. Third, we examined the usage of the total number of Resource Blocks (RBs) on the last leg of the network. Fourth, we calculated the achieved QoE of each user in both unicast and multicast transmissions.
## 3 Results
### Results on RBC  
The parameter to be calculated in the RBC method is the number of projection angles. To do this, typical values 4, 8, 16, and 32, as considered in [53], have been chosen. The results of the carried tests are shown in Figure 9.  
As we can see in Figure 9, the best result is obtained with four projections, which presents the next confusion matrix \(CM_{RBC}\):  
\[CM_{RBC}=\begin{pmatrix}27&3\\ 3&90\end{pmatrix} \tag{6}\]  
It implies that there are three false positives and three false negatives, thus having an error rate of 4.88% and, therefore, a recognition rate of 95.12%.
# Improved MUSIC Method against Range Dimension Deceptive Jamming Based on FDA-MIMO
## 5 Simulation and Analysis
### Improve MUSIC Method against Range Dimension Deceptive Jamming  
The spatial spectra of the desired targets and the existing false targets are shown in Figure 9. From Figure 9, we can detect that the existing deceptive interference EFT1 and EFT2 have the same large output power as the real targets. The spatial spectra of the desired targets and the new deceptive jamming are shown in Figure 10. From Figure 10, we can see that the output power of the new deceptive jamming NFT1 and NFT2 has been severely suppressed. By performing the range-angle two-dimensional spatial spectrum search in the airspace, the beamforming gain of the desired targets has hardly changed at this time, and it can still obtain large output power, while the deceptive jamming NFT1 and NFT2 have been suppressed because of the mismatch of distance dimensional information.  
Figure 8: The range-angle two-dimensional adaptive matched filter output power of new deceptive jamming. (**a**) Three-dimensional beamforming gain; (**b**) Range bin analysis results.  
Figure 9: Spatial spectra of existing false targets and desired targets for FDA-MIMO. (**a**) Spatial spectral distribution of range-angle two-dimensional; (**b**) Range profile.  
Since the FDA-MIMO radar has range-angle two-dimensional freedom, the energy of deceptive jamming NFT1 and NFT2 is not lost when they influence the range bin of the FDA-MIMO radar, which causes a mismatch between the range displayed by the range bin and the equivalent range between the airspace where the target is located, so after the range-angle two-dimensional spatial spectrum search, it can be effective against the range dimension deceptive jamming.  
Figure 11 shows the spatial spectrum of four false targets and desired targets after passing through the adaptive beam. From Figure 11, we can observe that the beamforming gain of the real targets has hardly changed at this time, while the output power of the existing deceptive interference and the new deceptive interference has been severely suppressed, which can no longer develop an effective deceptive interference result. By combining the spatial spectrum search technology and the adaptive beamforming technology, it can be effective against the range dimension deceptive jamming, and the problem of range dimension deceptive jamming suppression can be solved.