esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Multilayer Detection Algorithm (Feature Extraction)
### Beamforming and Signal Processing  
Sensor data from cylindrical hull-mounted arrays were beamformed using a Hanning window and match filtered. Delay-and-sum beamforming and polyphase filtration with an interpolation rate of 32 were performed in the time domain. In order to reduce the effect of ship movement in the beamforming stage, compensation for sensor position change was performed. The spatial position of the sensor array was recalculated from ownship pitch and roll data. Then, beamforming was performed with the new position of the sensor as beam stabilization. The sound speed value was also used for beamforming in real time.  
In the signal processing stage, relative Doppler compensation, pulse replica correlation, max Doppler picking, and two-dimensional normalization were performed. In order to remove the transmitting and receiving Doppler effect, ownship's Doppler nullification in the received beam output was processed with the course and speed data of ownship by considering the maximum speed of the target candidate. The correlation of the transmitted pulse replica was performed for continuous wave (CW) and linearly frequency-modulated (LFM) signals. The CW signal is a continuous wave signal, with the pulse length based on the center frequency. The LFM signal has a bandwidth of several hundred Hz with respect to the center frequency, and linearly modulates it by the pulse length. The CW signal is useful for deriving Doppler information from a target and the LFM signal has good range resolution. From Doppler data in 1-kt steps, bearing and range data with max Doppler were extracted. Then, the split two pass mean (S2 PM) algorithm [24], which has a good performance with simple calculations, was used for noise normalization in two dimensions. The gap and window size of S2 PM were selected by considering the ambiguity function of the transmitted pulse, the echo energy areas, and the resolution of the bearing and range in the matched filter output. Echo energy area was considered to be the area from the peak to the local minimum. For each ping, the beam and range for the peak echo were acquired by using target position data. Only one echo datum for each ping was acquired as the target because, in this experiment, there is only one target. The other echoes, including target multipath echoes, were used as clutters because a geographically multipath echo is not an accurate target locator. In order to investigate the classification performance of the proposed algorithm, we acquired all echoes higher than the noise level without a threshold for each ping.
# Doped Epoxy Resins as an Alternative to Luminescent Optical Sensors
## 3 Results and Discussion
### Double-Bond Conversion
At this point, it is worth explaining why the influence of the active solvent on the double-bond conversion values is more pronounced in the case of compositions based on EB600 resin. Of course, this is related to the difference in the viscosity of the resins. EB150 resin is characterized by relatively low viscosity. The addition of NVP increases the diffusion of photoinitiator radicals and monomer molecules in the structure of the resulting polymer, but not to the same extent as for compositions based on EB600. There is a very significant increase in the conversion rate already after 15 and 30 s of UV exposure (for example, comparing samples No.10-15 with No.13-15). The addition of both photosensitive agents to the composition containing the smallest amount of NVP worsens the conversion of double bonds (by approximately 10% in the case of 2.7-NAF.EP, samples No.11-60, and by approximately 20% in the case of fluorescein, sample No.12-60). However, in the case of 33% of the solvent content, the conversion of double bonds remains at a similar level (samples No.13-15), and in the case of 50% of the solvent content it increases slightly (samples No.16-18). The highest values of double-bond conversion (100%, Table 3) were obtained for samples containing 50 wt.% NVP and the addition of 2.7-NAF.EP hardened for 30 (samples No.17-30) and 60 s (samples No.8-60 and No.17-60), and for the sample containing fluorescein, hardened for 30 (samples No.18-30) and 60 s (samples No.18-60). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **Conversion/\%** & **Sample** & **Conversion/\%** \\ \hline Sample No.1-15 & 64.1 & Sample No.10-15 & 73.9 \\ Sample No.1-30 & 86.5 & Sample No.10-30 & 82.7 \\ Sample No.1-60 & 86.2 & Sample No.10-60 & 92.1 \\ Sample No.2-15 & 76.1 & Sample No.11-15 & 75.4 \\ Sample No.2-30 & 85.9 & Sample No.11-30 & 80.4 \\ Sample No.2-60 & 88.5 & Sample No.11-60 & 84.0 \\ Sample No.3-15 & 38.6 & Sample No.12-15 & 46.9 \\ Sample No.3-30 & 75.2 & Sample No.12-30 & 66.3 \\ Sample No.3-60 & 75.9 & Sample No.12-60 & 70.3 \\ Sample No.4-15 & 75.4 & Sample No.13-15 & 90.6 \\ Sample No.4-30 & 98.4 & Sample No.13-30 & 92.4 \\ Sample No.4-60 & 96.9 & Sample No.13-60 & 95.0 \\ Sample No.5-15 & 54.2 & Sample No.14-15 & 91.7 \\ Sample No.5-30 & 72.4 & Sample No.14-30 & 94.2 \\ Sample No.5-60 & 86.4 & Sample No.14-60 & 90.4 \\ Sample No.6-15 & 79.4 & Sample No.15-15 & 92.6 \\ Sample No.6-30 & 93.5 & Sample No.15-30 & 95.3 \\ Sample No.6-60 & 95.5 & Sample No.15-60 & 93.5 \\ Sample No.7-15 & 31.6 & Sample No.16-15 & 81.5 \\ Sample No.7-30 & 84.3 & Sample No.16-30 & 94.3 \\ Sample No.7-60 & 94.1 & Sample No.16-60 & 96.2 \\ Sample No.8-15 & 23.7 & Sample No.17-15 & 97.5 \\ Sample No.8-30 & 92.9 & Sample No.17-30 & 100.0 \\ Sample No.8-60 & 100.0 & Sample No.17-60 & 100.0 \\ Sample No.9-15 & 23.3 & Sample No.18-15 & 83.3 \\ Sample No.9-30 & 60.7 & Sample No.18-30 & 100.0 \\ Sample No.9-60 & 96.5 & Sample No.18-60 & 100.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Double-bond conversion.
## 1 Background
### Biosynthesis of Monoterpenes  
The methylerythritol 4-phosphate (MEP) pathway is a series of enzymatically mediated steps that was identified in bacteria that yields both of these activated derivatives as shown in Figure 1[11]. The first committed step of this pathway is the decarboxylation reaction between glyceraldyhyde-3-phosphate (G3P) and pyruvate catalyzed by the enzyme  
Figure 1: Simplified detailing the convergent biosynthesis of acyclic, monocyclic, and bicyclic monoterpenes highlighting the mevalonate and MEP pathways.  
deoxy-d-xylulose-5-phosphate synthase (DXS) [12]. This step results in the production of 1-deoxy-\(d\)-xylulose 5-phosphate (DXP). Subsequent enzymatic reactions will produce (\(E\))-4-Hydroxy-3-methyl-but-2-enyl pyrophosphate (HMBPP) which then undergoes a catalytic dehydration to leave DMAPP and IPP. The mevalonate (MVA) pathway begins with the condensation of three molecules of acetyl CoA to produce mevalonate. Mevalonate is then phosphorylated in two catalytic steps to give mevalonate pyrophosphate. This compound is then decarboxylated to provide IPP which can be isomerized further to produce DMAPP [13].  
Following the biosynthesis of the activated isoprene units, a key polymerization reaction takes place between DMAPP and IPP yielding geranyl pyrophosphate (GPP). The enzyme responsible for this reaction, GPP synthase (GPPS), is a highly regulated enzyme that is responsible for the first committed step towards production of all monoterpene compounds [14]. Through subsequent catalyzed reactions two important intermediates emerge as the starting point for further derivatization of monoterrenes. The first, geranyl cation, serves as the substrate for production of most acyclic monoterrenes. From this cationic intermediate a variety of reactions can occur leading to the monoterrenes. The second transition state, \(\alpha\)-terpinyl cation, is thought to be the branch point for the synthesis of mono- and bi-cyclic monoterrenes [15].
## 3 Methodology
### Frequency Ratio Methods
#### 3.2.3 Weighting Classes for Continuous Factors  
The slope factor is the continuous variable, and, therefore, the factor class weights have been calculated with the FR methods shown in Section 2.1. The ranges have been defined according to the methodologies indicated and are summarised in Table 5:  
* GFR: constant intervals every \(10^{\circ}\).
* EFR: intervals defined for the ELSUS v2 map (see Table 2).
* CFR: intervals obtained by cluster analysis for the three provinces: Castellon (CST); Valencia (VLC) and Alicante (ALC).  
When one is working jointly with discrete variables, it is not possible to increase the number of classes in the factors (as these are predefined). This requires the number of classes defined in the continuous variables to be in accordance with the discrete variables. In the example, slope has been distributed in 8 classes, lithology in 7, and land use in 9.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Class** & **Land Use and Land Cover (LULC)** & **CST** & \multicolumn{2}{c}{**FR**} & \\
1 & Artificial urban residential coverage. & 27 & 26 & 14 \\ \hline
2 & Tertiary artificial cover (services). Not urbanised. & 68 & 40 & 37 \\ \hline
3 & Primary artificial cover (industrial) & 22 & 55 & 61 \\ \hline
4 & Irrigated arable crops (market garden). & 9 & 8 & 8 \\ \hline
5 & Irrigated orchard (citrus, fruit trees) & 25 & 20 & 23 \\ \hline
6 & Non-irrigated arable and tree crops. & 34 & 33 & 46 \\ \hline
7 & Forest (coniferous and broad-leaved) & 389 & 239 & 223 \\ \hline
8 & Shrub lands. Burnt areas and rocky areas & 244 & 341 & 354 \\ \hline
9 & Grasslands and bare soil & 182 & 237 & 234 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classes defined for LULC, according to SIOSE (2006). Provinces of Castellon (CST), Valencia (VLC) and Alicante (ALC).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **Lithology (IGME)** & **CST** & \multicolumn{2}{c}{**FR**} & \\
1 &
\begin{tabular}{c} Very hard and/or massive rocks \\ (dolomites, limestones, and quartzites) \\ \end{tabular} & 120 & 130 & 145 \\ \hline
2 & Hard rocks with soft alternations & 113 & 120 & 140 \\ \hline
3 &
\begin{tabular}{c} Medium hard rocks (calaceous \\ sandstones, alternations) \\ \end{tabular} & 215 & 220 & 330 \\ \hline
4 & Fairly soft rocks & 160 & 190 & 189 \\ \hline
5 &
\begin{tabular}{c} Soft rocks (marls, gypsum, gypsum \\ with clays) \\ \end{tabular} & 335 & 235 & 100 \\ \hline
6 &
\begin{tabular}{c} Loose, poorly consolidated materials \\ and alluvial deposits. \\ \end{tabular} & 40 & 80 & 80 \\ \hline
7 & Very loose Quaternary materials, soils & 17 & 25 & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Lithology classes defined by the authors according to Geological and Mining Institute (IGME) data. Provinces of Castellon (CST), Valencia (VLC), and Alicante (ALC).  
For the RFR method, two regression adjustment curves with FR are used as the dependent variable for each province (as shown in Figure 2 for the example of Castellon). Each slope value presents a value of FR according to these regression formulae.
## 4 Experimental Verification
To further demonstrate the effectiveness of ICEEMDAN-LMS in reducing pipeline defect signals, ICEEMDAN was first performed on pipeline signals to obtain nine IMF components and residuals. Figure 9 shows the IMF components obtained from the pipeline signal decomposition using EMD and ICEEMDAN. The decomposition of ICEEMDAN is critical because the decomposition results affect the subsequent denoising steps. Figure 7: _Cont._  
According to the decomposition results, the low-frequency components are relatively stable. The fluctuation frequency of each high-frequency component is different, but it also has obvious regularity and small fluctuation in the whole time period. However, the trend of IMF9 generated by EMD is close to the residual component, which indicates that EMD has an over decomposition phenomenon and that the reconstruction error is large. Therefore, ICEEMDAN is more effective in suppressing residual noise and mode-aliasing problems that may exist in the IMF component. Figure 7: Decomposition results obtained using (**a**) EMD, (**b**) EEMD, (**c**) CEEMDAN, and (**d**) ICEEMDAN. Figure 8: _Cont._  
In order to verify the validity of the model proposed in this paper, ICEEMDAN decomposed data were input into the improved LMS model and the noise reduction process of LMS was developed to obtain the noise IMF components and true signal IMF components. In LMS noise reduction, the output of the filter is optimal when the mean squared error is minimized. The optimal output of each frequency band can be combined to reconstruct the denoised signal. The noise reduction results of ICEEMDAN-LMS for pipeline defects are shown in Figure 10 and reveal that the ICEEMDAN-LMS noise reduction method makes the waveform of the pipeline defect signal smoother and more realistic, and most of the high-frequency noise signals have been removed. As presented in Table 3, ICEEMDAN-LMS yields the highest SNR, indicating that ICEEMDAN-LMS has the largest effective  
Figure 8: Comparison of traditional and ICEEMDAN-LMS noise reduction methods. (**a**) EMD, (**b**) EEMD, (**c**) CEEMDAN, (**d**) ICEEMDAN-LMS. signal proportion and the best noise reduction performance. The minimum mean squared error indicates that the reconstructed and original signals are the most similar. Compared with the CEEMDAN method, ICEEMDAN-LMS improves the SNR by 6.74% and reduces the RMSE by 4.36%. Therefore, ICEEMDAN-LMS improves the SNR and RMSE and hence reduces noise better.
Their results showed that the moisture content and moisture activity production of fruits decreased by 20% and 0.50% after drying for 51.5-55 min under a microwave power of 5.7-6 kW and a fruit load of 10-9.75 kg. However, they did not discuss the influences of dry control parameters on the drying quality of berries. [PERSON] et al. [10] studied the effects of microwave processing variables on the quality of microwave drying products through the central composite rotatable design and response surface method, whereby they constructed a regression model of the hardness, adhesivity, chewiness, microwave power, drying time, and fruit loads of dried berries. However, they did not optimize the drying quality factors and parameters of the berries. [PERSON] et al. [11] explored the influences of microwave vacuum drying technological variables on the moisture content, rehydration ratio, hardness, L value, and total chromatic aberration of berries by combining the response surface method and central composite rotatable design. The results showed that the moisture content of fruit declines quickly when the microwave power and drying time increase and the vacuum degree decreases. However, the rehydration rate, hardness, L value, and chromatic aberration was found to increase. None of the abovementioned studies considered the influences of the material surface temperature on the quality of berries under drying conditions. Moreover, the influence laws of different parameters on the nutrient quality of mulberry must be further investigated. In this study, a safe moisture content of mulberry products was chosen according to changes in the total plate count in mulberry under different moisture contents in storage,and the influences of the moisture content of mulberry on texture characteristics were discussed. Moreover, the influences of material surface temperature, power, and vacuum degree on the quality of instant mulberry were discussed using the membership function model. The optimal microwave drying technological parameters of mulberry were discussed by combining factor analysis and response surface optimization to enrich the processing techniques of mulberry and increase the economic value of mulberry products.
## 5 Deployment of PLC for Smart Metering and Smart Grid Systems
### Grid Description
If the distance between transmitters and receivers is too large (in terms of attenuation) for a specific implementation of a technology, or if the density of the PoS is low enough to favor repetition of the PLC signals (according to the switching concept in [119]), or if the number of PoS per subnetwork is not high enough to allow for a high enough economic return on investment, PLC might not be an attractive technology. In a scenario where PLC turns outs to be a viable and adequate technology for any of the MV and/or LV segments (this is the case according to the deployment status of PLC systems for Smart Metering networks, due to economic reasons, e.g., [127], once PLC systems have been technically proven on field at a massive scale [128,129]), these aspects mentioned are the ones that need to be solved by the technology and the implementation of the PLC devices. However, there are a number of other aspects that need to be solved regarding the architecture of the system, and at operational system level. These other aspects are oriented to bridge the gap among the technology and the devices themselves, and the deployment on the grid to guaranty that the PLC network performs in such a way that it is capable of supporting the requirements of Smart Metering and Smart Grid.
# An Asymmetric Encryption-Based Key Distribution Method for Wireless Sensor Networks
## 2 Design Constraints and Security Issues in WSNs
### Asymmetric Cryptography in WSNs  
Asymmetric encryption uses key pairs to encrypt and decrypt data for both sides of communication. Any message encrypted with the public key can only be decrypted by that containing the private key. The private key is secretly held by its holder, and the public key can be obtained by the required communication entity through a public channel. Asymmetric cryptography can provide confidentiality, integrity, and authentication for different kinds of networks. Although information encryption based on asymmetric key has been proved to be applicable to sensor networks, its application is still limited by its complex computation. Furthermore, taking the actual sensor chip as an example, the time taken for asymmetric encryption is still in the order of seconds, which may not be suitable for those applications with strict real-time performance.  
Fortunately, in recent years, the new cryptographic algorithms have shown great energy efficiency and reached the same security level as traditional algorithms. For example, the elliptic-curve cryptography (ECC) [35] method is the representative version of those algorithms. ECC is a cryptographic regime built on the discrete logarithm problem of elliptic curves. Using point \(G\) on an elliptic curve and integer \(k\), it is easy to find \(K=kG\). Conversely, using the points \(K\) and \(G\) on an elliptic curve, finding the integer \(k\) is a difficult task. The main advantage of ECC is that it uses smaller keys and provides a considerably higher level of security. The 164-bit key in the ECC algorithm can provide a level of security equivalent to the strength of secrecy provided by the 1024-bit key in the RSA algorithm. The ECC algorithm is less computationally intensive, is faster to process, and takes up less storage space and transmission bandwidth. Therefore, Bitcoin has also chosen ECC as its encryption algorithm.  
In [36], the author proposed a new SUA-WSN scheme based on elliptic-curve cryptography (ECC) and proved that it achieves user anonymity, as well as AKE security, in the extended model. [PERSON] et al. implemented ECC on the MSP430 microcontroller, which is a widely used microcontroller in WSNs, using Edwards curves for point arithmetic and the number theoretic transform for the underlying finite-field multiplication and squaring operations [37]. [PERSON]'s research shows better timing values and can be applied to ECC implementations.  
From the perspective of energy consumption and computational complexity, ECC has promising uses for data encryption in WSNs. It provides comparative security with a smaller key, which also reduces the energy of computation and communication in WSNs. Based on this method, a new security key management scheme and an authentication approach are proposed in Section 3.
## 4 Results
### General Practitioner Questionnaire  
Nine GPs consented to participate in the study but only six completed the questionnaire. All GPs held a spirometer in their surgery; three never used it or had not used it in the past 6 months, while two used it several times a week. Only two GPs were aware of the mobile lung function testing clinic. One GP had referred a patient and received a lung function test report. The GP reported that the information in the report was concise and easy to read, and that the report, and the results provided, were helpful for patient diagnosis or patient management. Five GPs advised they did not receive a report.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multicolumn{2}{c}{**Variable and Baseline, Follow-Up and**} & \multicolumn{2}{c}{**Metropolitan**} & \multicolumn{2}{c}{**Rural (Mean)**} & \multicolumn{1}{c}{\(p\)**} \\ \hline \multirow{4}{*}{ACT score} & Baseline & 16.12 & 17.23 & 0.517 \\ \cline{2-5}  & Follow-up & 16.88 & 19.43 & 0.160 \\ \cline{2-5}  & Change & 0.43 & 2.55 & 0.264 \\ \hline \multirow{4}{*}{CCQ total score} & Baseline & 2.23 & 1.46 & 0.240 \\ \cline{2-5}  & Follow-up & 2.61 & 2.30 & 0.401 \({}^{*}\) \\ \cline{2-5}  & Change & 0.40 & 0.53 & 0.796 \\ \hline \multirow{4}{*}{CCQ symptom score} & Baseline & 2.83 & 1.40 & 0.067 \\ \cline{2-5}  & Follow-up & 3.09 & 2.75 & 0.561 \\ \cline{2-5}  & Change & 0.46 & 1.13 & 0.289 \\ \hline \multirow{4}{*}{CCQ functional score} & Baseline & 1.92 & 1.25 & 0.446 \\ \cline{2-5}  & Follow-up & 2.22 & 1.70 & 0.308 \\ \cline{2-5}  & Change & 0.38 & 0.06 & 0.697 \\ \hline \multirow{4}{*}{CCQ mental score} & Baseline & 1.67 & 2.00 & 0.775 \\ \cline{2-5}  & Follow-up & 2.44 & 2.60 & 0.795 \\ \cline{2-5}  & Change & 0.33 & 0.25 & 0.909 \\ \hline \multirow{4}{*}{CAT Sscore} & Baseline & 12.33 & 7.80 & 0.298 \\ \cline{2-5}  & Follow-up & 16.67 & 12.40 & 0.261 \\ \cline{2-5}  & Change & 5.25 & 1.50 & 0.156 \\ \hline \multirow{4}{*}{St George score} & Baseline & 38.88 & 33.16 & 0.292 \\ \cline{2-5}  & Follow-up & 36.37 & 30.10 & 0.246 \\ \cline{2-5}  & Change & 0.82 & \(-1.66\) & 0.405 \\ \hline \multirow{4}{*}{St George symptom score} & Baseline & 52.46 & 48.97 & 0.641 \\ \cline{2-5}  & Follow-up & 45.56 & 40.07 & 0.521 \\ \cline{2-5}  & Change & \(-0.63\) & \(-8.84\) & 0.207 \\ \hline \multirow{4}{*}{St George activity score} & Baseline & 51.53 & 45.05 & 0.324 \\ \cline{2-5}  & Follow-up & 56.32 & 49.82 & 0.399 \\ \cline{2-5}  & Change & 5.37 & 4.86 & 0.857 \\ \hline \multirow{4}{*}{St George impacts score} & Baseline & 22.78 & 18.25 & 0.272 \\ \cline{2-5}  & Follow-up & 19.62 & 15.41 & 0.268 \\ \cline{1-1} \cline{2-5}  & Change & \(-2.66\) & \(-2.14\) & 0.828 \\ \hline \end{tabular} \({}^{*}\)Satterthwaite \(p\)-value.  
\end{table}
Table 4: Differences in various validated test scores at baseline and follow-up between metropolitan and rural areas.