esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This experimental paper has comprehensively characterized various nano-mechanical and thermal stability characteristics of biodegradable and sustainable hemp fiber-reinforced PCL biocomposites using nanoindentation, TGA and DSC techniques. Evidently, the effects of variable mean hemp fiber aspect ratios on these properties have been investigated.  
From the nanoindentation results obtained, a significant improvement in both hardness and reduced elastic modulus for the biocomposite samples was achieved, with sample AR_26 having the optimal values of hardness and elastic modulus, among other outstanding nanoindentation properties considered. This improvement can be attributed to the hemp fiber AR and the interfacial interaction.  
With regard to surface properties, the surface tension values for all samples using glycerol were less than the values obtained from water. This indicated that exposing the samples to glycerol exhibited more wettability than water. In addition, exposure of the samples to water demonstrated a higher hydrophobicity. Furthermore, it was evident that the thermal conductivity of the samples depended on their ARs, as the optimal HF/PCL biocomposite sample of AR_26 recorded the highest average threshold value of 0.2957 W/mK, while both neat (AR_00) and AR_38 samples possessed the lowest average value of 0.2539 W/mK.  
Hence, the addition of lignocellulosic hemp fiber into the biodegradable PCL matrix enhanced the properties of the HF/PCL biocomposite samples, when compared with the neat PCL sample. Conclusively, all the results obtained indicated hemp fiber as a promising reinforcing material for making biodegradable, renewable, low-cost and load-bearing thermoplastic biocomposites, significantly with optimal nano-mechanical and thermal properties of sustainable HF/PCL biocomposite sample AR_26. In summary, better knowledge of the properties obtained for these samples is very germane for materials' selection, design, and manufacturing of lightweight parts for various engineering applications.  
[PERSON] contributed in design, conceptualization, analysis and overall communication and writing, [PERSON] contributed in design, fabrication of samples and thermal analysis, [PERSON] contributed in the experimental work, analysis of results and writing, [PERSON] contributed in experimental and data analysis, [PERSON] contributed in review and editing. All authors have read and agreed to the published version of the manuscript.  
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.  
The assistance of the following colleagues is greatly and sincerely appreciated: [PERSON], [PERSON] and [PERSON], INRAE FARE, Reims, France.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Liquid** & **Total Surface Energy (mN/m)** \\ \hline Distilled water & 72 \\ Glycerol & 64 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Surface tension values of the liquids used.
# Bio-Corrosion Behavior of Ceramic Coatings Containing Hydroxyapatite on Mg-Zn-Ca Magnesium Alloy
## 2 Experimental  
### Preparation of the Coatings  
High-purity (>99.9 wt %) of Mg, Zn, and Ca were melted under an argon atmosphere in an induction furnace to achieve magnesium alloy with a composition of Mg\({}_{66}\)Zn\({}_{29}\)Ca\({}_{5}\) (in atomic percentage). The test specimen was a disc with a diameter of 14 mm and a thickness of 3 mm. Prior to MAO, the specimens were ground with SiC papers (from 150 to 2000 grits), and polished with Al\({}_{2}\)O\({}_{3}\) aqueous suspension to an average surface roughness of \(R\)a = 0.8 \(\upmu\)m. The specimens were then ultrasonically rinsed in acetone and ethanol, and finally dried using nitrogen.  
The electrolyte solution for MAO, which mainly consisted of Na\({}_{2}\)SiO\({}_{3}\) (10 g/L), Na\({}_{3}\)PO\({}_{4}\) (3 g/L), NaOH (2 g/L), and Glycerin (10 mL/L) was prepared with deionized water. And various concentrations of HA powder with mean size of 0.5 \(\upmu\)m were then added into the electrolyte.  
The concentrations of HA added into the electrolyte were selected as 0, 0.2, 0.4, and 0.6 g/L. Note that the electrolyte should be ultrasonically oscillated for 10 min before MAO to ensure that the HA particles are well dispersed throughout the electrolyte.  
As a device used for MAO, the equipment (WHD-30) was composed of a pulsed power supply, electrolytic bath, and stirring and cooling system that can automatically control electrolyte temperature to <40 \({}^{\circ}\)C. In our experiment, constant voltage mode was adopted and the electric parameters were fixed as follows: 400 V anodic voltage, 80 V cathodic voltage, 1500 Hz frequency, and 50% duty cycle. The MAO coatings were deposited on the magnesium alloy specimens for 5 min.
###### Abstract  
The last few years, the scientific field of optical wireless communications (OWC) has witnessed tremendous progress, as reflected in the continuous emergence of new successful high data rate services and variable sophisticated applications. One such development of vital research importance and interest is the employment of high speed, robust, and energy-effective transdermal optical wireless (TOW) links for telemetry with implantable medical devices (IMDs) that also have made considerable progress lately for a variety of medical applications, mainly including neural recording and prostheses. However, the outage performance of such TOW links is significantly degraded due to the strong attenuation that affects the propagating information-bearing optical signal through the skin, along with random misalignments between transmitter and receiver terminals, commonly known as pointing error effect. In order to anticipate this, in this work we introduce a SIMO TOW reception diversity system that employs either OOK or more power-effective \(L\)-PPM schemes. Taking into account the joint impact of skin-induced attenuation and non-zero boresight pointing errors, modeled through the suitable Beckmann distribution, novel closed-form mathematical expressions for the average BER of the total TOW system are derived. Thus, the possibility of enhancing the TOW availability by using reception diversity configurations along with the appropriate modulation format is investigated. Finally, the corresponding numerical results are presented using the new derived theoretical outcomes.  
Keywords:optical wireless communications (OWC); transdermal optical wireless (TOW) links; implantable medical devices (IMDs); skin-induced attenuation; non-zero boresight (NZB) pointing errors; single input-multiple output (SIMO) system; average bit error rate (ABER) +
Footnote †: journal: computation
## 3 Results and Analysis
### Wear Depth Analysis of the DPRS
#### 3.3.3 Influence of the Pitch at Different Ambient Temperatures  
Figure 19 illustrates the pattern of the DPRS wear depth with pitch when the axial force is 30 kN, the speed is 30,000 rpm, and the running time is 38,000 h. When the pitch varies, the wear depth distribution of the roller is similar, and the wear depth increases with increasing pitch because the axial movement of the roller increases with the pitch.  
Figure 18: Influence of roughness at different ambient temperatures. (**a**) Ideal state (without thermal deformation); (**b**) 20 \({}^{\circ}\)C; (**c**) 70 \({}^{\circ}\)C; (**d**) 120 \({}^{\circ}\)C.  
This results in an increase in the relative sliding distance of the DPRS and an increase in the wear depth of the roller. Comparison of the different ambient temperature graphs shows that the pitch and ambient temperature have similar influences on the depth of wear in the first six sections of the DPRS and the opposite effects on the depth of wear on the threaded teeth in the later sections. Therefore, factors such as the operating temperature and pitch need to be considered when predicting DPRS wear.  
In [21], [PERSON] et al. use a sliding distance model to predict the wear depth of the roller and the screw, and when considering different P, their obtained wear depth change curves are similar to those of the change rule obtained in this paper.
## 4 PFAC Calibration Results
### Cal/Val Results at Crossovers
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \multicolumn{3}{c}{**Sentinel-3A**} & \multicolumn{3}{c}{**Sentinel-3B**} & \multicolumn{3}{c}{**Jason-3**} \\ \hline  & & \multicolumn{3}{c}{**Nominal Orbit**} & & & \\
**Product** & **PB2.61** & **PB2.61** & **PB2.61** & **PB2.61** & **PB2.61** & **PB2.61** & **GRD-D** & **GRD-D** \\ Cycles & 2.57 & 2.52 & 1.56 & 20.34 & 20.38 & 21.35 & 1.150 & 1.150 \\ Pass & No. 14 & No. 278 & No.335 & No. 14 & No.71 & No.335 & No. 18 & No. 109 \\ Range & +5 mm & - & - & - & -4.51 mm & +5.60 mm & - \\ SSH(GVD/CRS1) & -12 mm & \(-\)4 mm & \(-\)6 mm & +4.55 mm & +4.85 mm & - & \(-\)4.34 mm & \(-\)2.78 mm \\ Mean SSH & & **\(-\)7.93 mm** & & **+4.70 mm** & & **\(-\)3.56 mm** & \\ \hline \multicolumn{7}{c}{**Tandem Phase**} \\  & \multicolumn{3}{c}{**Sentinel-3A**} & \multicolumn{3}{c}{**Sentinel-3B**} & \multicolumn{3}{c}{**S3B-SSA Offset**} \\ \hline Product & \multicolumn{3}{c}{PB2.61} & \multicolumn{3}{c}{PB2.61} \\ Pass & P14 & P278 & P335 & P14 & P278 & P335 & P14 & P278 & P335 \\ SSH & \(-\)1.62 mm & \(-\)8.40 mm & \(-\)14.96 mm & +18.39 mm & +2.40 mm & +16.23 mm & +20.01 mm & +10.80 mm & +31.19 mm \\ Mean & & & & & & **+20.66 mm** & \\ \hline \multicolumn{7}{c}{**Crossover Analysis**} \\  & \multicolumn{3}{c}{**Sentinel-3A Pass No. 14**} & \multicolumn{3}{c}{**Jason-3 Pass No. 109**} & \multicolumn{3}{c}{**SAA-Jason-3 Offset**} \\ \hline Cycles & 1-56 & \multicolumn{3}{c}{1-149} \\ SSH & & & & & & **+17 mm \(\pm\) 49 mm** \\ \hline \end{tabular}
\end{table} 
Table 7: Absolute and relative calibration results for Sentinel-3A, Sentinel-3B and Jason-3 based on sea-surface, transponder techniques, as well as crossover analysis and S3B tandem phase analysis at the Permanent Facility for Altimetry Calibration. All values are in mm.
# Anti-Recompression Video Watermarking Algorithm Based on H.264/AVC
## 3 Watermark Embedding and Extraction Algorithm
### Video Watermark Embedding Algorithm
#### 3.1.2 Watermark Embedding Process  
The detailed steps for watermark embedding are shown in Algorithm 1, as follows:  
1. The watermarked image of size \(32\times 32\) is encrypted by Arnold scrambling, and then it is converted into a watermark sequence \(W(k)\)\((k=1,2,\cdots,1024)\) of size \(1\times 1024\).
2. The H.264/AVC encoder is used to encode the video to the end of quantization, exclude the third I frame chroma subblock with non-zero quantization factor of 2 or \(-2\), calculate the \(C\) value of the remaining chroma subblocks, select the 1024 subblocks with the largest \(C\) value as the watermark subblocks to be embedded, and record the position of the subblocks in the \(D\) array.  
3. The \(S_{i}\) of each subblock to be embedded is judged, to determine the modulation mode according to their minimum value and to modify the value of \(Q3_{k}\) to achieve watermark embedding, and embed 1-bit watermark information in each subblock.
4. After embedding all the watermarks, the subblocks are put back to their original positions and video coding continued to obtain the H.264 video stream with watermarks.
# Machine Failure Prediction Using Survival Analysis
## 3 Background
### Feature Analysis  
Feature analysis is a process of examining the input features in a prediction model to gain insights into their predictive power and their relationship with the target variable. It is an important step in the development of prediction models, as it can help to identify the most important features for predicting the target variable and to understand the underlying patterns and relationships in the data. By analyzing the feature importance scores and/or combining them with other techniques, one can gain insights into the factors that are most strongly associated with the, in our case, time-to-event outcome and develop more accurate prediction models.
## 3 Experimental Assessment of Mixed Metal Oxide Systems for TCES
### Mn-Fe-O Mixed Oxide System  
The cycling stability and reversibility of the redox reaction of Mn\({}_{2}\)O\({}_{3}\)/Mn\({}_{3}\)O\({}_{4}\) was greatly improved by the incorporation of iron from x(Fe) = 0.15 to x(Fe) = 0.5 (Figure 4a). This improvement concerning compositions above x(Fe) = 0.15 was attributed to the transition into a cubic spinel phase with increased re-oxidation capability, whereas for smaller Fe content the presence of the tetragonal spinel phase (hausmannite) is responsible for the poor re-oxidation [35; 47]. Very good cycling stability was also reported in other studies with the incorporation of Fe contents higher than x(Fe) = 0.15 in manganese oxide. The mixed oxide with the composition (Mn\({}_{0.8}\)Fe\({}_{0.2}\))\({}_{2}\)O\({}_{3}\)/(Mn\({}_{0.8}\)Fe\({}_{0.2}\))\({}_{3}\)O\({}_{4}\) has shown good stability over 75 redox cycles due to the incorporation of Fe modifying the structure and enhancing the oxidation rate [44]. The mixed oxide composition (Mn\({}_{0.75}\)Fe\({}_{0.25}\))\({}_{2}\)O\({}_{3}\) also showed cycling stability and reversibility over the course of a hundred redox cycles, although a change in the particles' morphology was noted [47].  
Concerning the overall OSC of the material, there was no change and it remained similar to that of pure Mn\({}_{2}\)O\({}_{3}\) and stable for all the studied compositions (Figure 4a).  
The reaction enthalpy also remained somewhat similar for all the studied compositions (0 \(\leq\) x(Fe) \(\leq\) 0.5), with a slight increase noticeable for x(Fe) = 0.2 (Figure 4b). However, when compared with the reaction enthalpies measured by [PERSON] et al. (2015), it is difficult to conclude on a clear variation in the reaction enthalpy for compositions between 0 \(<\) x(Fe) \(<\) 0.2. [PERSON] et al. (2016) reported a measured enthalpy up to 232.7 kJ/kg for x(Fe) = 0.33 [41].  
The temperature hysteresis of the Mn\({}_{2}\)O\({}_{3}\)/Mn\({}_{3}\)O\({}_{4}\) system is a concern which could be addressed with the Fe addition in an amount superior to x(Fe) = 0.15, from which a clear decrease in the temperature gap is evidenced between the reduction and the oxidation temperatures (Figure 4c).  
Figure 4: Mass variation (OSC), enthalpy change and onset temperatures of redox reactions measured for the Mn-Fe-O system.
## 3 Results and Discussion
### Rheological Properties of PBBs and PBSs  
The viscoelastic properties of batter are presented in Figure 1, whereas the corresponding data for sausages are presented in Figure 2. All analyzed samples were characterized by a dominance of storage over the loss modulus (G\({}^{\prime}\) > G\({}^{\prime\prime}\)) typical of solid food products. The extent of changes in the mechanical properties in the analyzed frequency range was also similar for all products. This observation was true regardless of the composition and processing, as indicated by the similar pattern of changes in the phase angle tangent (8). However, the effect of processing can be clearly observed when the data for batters and sausages are compared. This is reflected by the increase in both moduli and, consequently, in the complex viscosity (\(\eta^{\star}\)) observed for the sausages versus the batters. This is consistent with the LF NMR data (see Section 3.3), especially considering the spin-lattice relaxation time, which was shorter for the sausages. These results are also in line with the stiffer texture of the processed products.  
Figure 1: Viscoelastic properties of PBBs.  
Figure 2: \(Cont\).  
The effect of composition on the complex viscosity of the products was not direct, both in the case of PBBs and PBSs. The viscosity of the batter was similar for all formulations with the exception of PBB1/F, which showed a considerably higher value of this parameter. This was caused by both an increase in the storage and loss moduli. The other observed outlier was the PBB1/IS batter with a relatively low G\({}^{\prime\prime}\) value corresponding to the low energy used for the initiation of the flow. This phenomenon was accompanied by a lower phase angle tangent value. This indicates that the incorporation of sprouts into the formula may significantly change the viscoelastic properties of the sample; however, the course of the change is influenced by interactions with other proteins.  
The specific values of the parameters describing the mechanical properties of the sausages were more diverse than those of the batters. However, the sausages did not follow the same pattern as the batters--indicating that the processing significantly changes the effectiveness of the used protein in binding water. In terms of the response to the applied stress, the investigated sausages could be divided into two groups. The samples PBS3/IS, PBS2/IS, and PBS3/F were characterized by a higher phase angle tangent, while PBS1/F, PBS2/F, and PBS1/IS showed lower values of this parameter. These differences were also noticeable in the case of other mechanical parameters, including the storage and loss moduli. The only exception was observed for PBS1/IS with the highest G\({}^{\prime}\) and G\({}^{\prime\prime}\) as well as low values of the phase angle tangent.
# Applications of GNSS-RO to Numerical Weather Prediction and Tropical Cyclone Forecast
## 4 Outlook
### Trends Toward Increasing the Amount of RO Soundings
#### 4.1.2 Commercial Cube Satellite Constellation  
As discussed in Section 2.2, 16,000 soundings can be a closer target. For such a substantial number, at least one hundred satellites with RO receivers are required. Fortunately, RO receivers are inexpensive, light, small, and less power consuming. RO receivers have been additional payloads on many satellites such as CHAMP, SAC-C/D, and GRACE. This approach has been commercially applied to cube satellites (10 cm \(\times\) 10 cm \(\times\) 30 cm and 4.7 \(\pm\) 0.1 kg) by companies, such as Spire, GeoOptics, and PlanetIQ, to establish a constellation network for providing critical weather data to NWP, ship, and aircraft tracking. Here, only the part regarding NWP of our interest is introduced.  
Spire is one of the largest commercial producers of RO measurement systems. Up to 2019, Spire launched more than 80 nanosatellites in LEOs for full global sampling [78], and the sounder Lemur-2 on these cube satellites can receive signals from GPS, GLONASS, QZSS, and Galileo, providing approximately 6000 RO profiles per day. According to the initial assessment and test run in the Met office NWP system, as reported in [79], Spire's data are as good as the operational GNSS-RO data below 30 km. The research also showed that assimilating Spire data as a replacement of MetOp-C data into the NWP system can further improve the forecast.  
The Climate Community Initiative for Continuing Earth Radio Occultation (CICERO) is another cube satellite constellation project designed as a follow-up and augmentation for COSMIC by GeoOptics Cooperation. Up to 2017, 48 cube satellites have been deployed in orbit [80]. If the data from these projects can be incorporated into the operational NWP assimilation systems, a considerable improvement will be seen in the accuracy of the NWP and TC forecast.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sonder** & **GNSS Tracked** & **Soundings/Day** & **Satellite** & **Status** \\ \hline TriG & GPS, GALILEO & \(\sim\)500 & COSMIC-2 & In orbit \\  & & \(\sim\)600 & GRACE-FO & \\ \hline TriG (JASON-CS) & GPS, GALILEO, & & & \\  & GLONASS & \(\sim\)1000 & JASON-CS-A/B & Planned \\ \hline GNOS & GPS, BeiDou & \(\sim\)1000 & FY-3D & In orbit \\  & & & & \\  & & & & \\  & & & & \\ \hline GRAS & GPS & \(\sim\)650 & MetOp-A/B/C & In orbit \\ \hline RO & GPS, Galileo, GLONASS, & & & \\  & Beidou (2 or 3 of them) & \(\sim\)1100-1500 & MetOp-SG-A/B & Planned \\ \hline  & & & \(\sim\)100 & TanDEM-X & In orbit \\  & & & & TerraSAR-X & Planned \\ \hline ARMA-MP & GPS, Galileo, GLONASS & \(\sim\)2000 & Meteor-MP & Planned \\ \hline TriG: Triple G (GPS, Galileo, GLONASS); GRAS: GNSS Receiver for Atmospheric Sounding: IGOR: Integrated GPS & Occultation Receiver; ARMA: Radio occultation instrument for Meteor-MP. & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Global navigation satellite system (GNSS) sonders (adapted from OSCAR website).
###### Abstract  
A system for the automatic classification of cardiac sounds can be of great help for doctors in the diagnosis of cardiac diseases. Generally speaking, the main stages of such systems are (i) the pre-processing of the heart sound signal, (ii) the segmentation of the cardiac cycles, (iii) feature extraction and (iv) classification. In this paper, we propose methods for each of these stages. The modified empirical wavelet transform (EWT) and the normalized Shannon average energy are used in pre-processing and automatic segmentation to identify the systolic and diastolic intervals in a heart sound recording; then, six power characteristics are extracted (three for the systole and three for the diastole)--the motivation behind using power features is to achieve a low computational cost to facilitate eventual real-time implementations. Finally, different models of machine learning (support vector machine (SVM), k-nearest neighbor (KNN), random forest and multilayer perceptron) are used to determine the classifier with the best performance. The automatic segmentation method was tested with the heart sounds from the Pascal Challenge database. The results indicated an error (computed as the sum of the differences between manual segmentation labels from the database and the segmentation labels obtained by the proposed algorithm) of 843,440.8 for dataset A and 17,074.1 for dataset B, which are better values than those reported with the state-of-the-art methods. For automatic classification, 805 sample recordings from different databases were used. The best accuracy result was 99.26% using the KNN classifier, with a specificity of 100% and a sensitivity of 98.57%. These results compare favorably with similar works using the state-of-the-art methods.  
EWT; Shannon energy; power features; automatic segmentation; feature extraction; machine learning; heart sound; e-health +
Footnote †: journal: Applied
# AI-Assisted Security Alert Data Analysis with Imbalanced Learning Methods
## 4 Experiments
### Performance Evaluation
#### 4.4.3 Ensemble Resampled Data  
The proposed approach leveraged MAS and a data-subsampling algorithm to maintain a positive alert detection performance while simultaneously improving the FPR. A low sampling rate for minority class data augmentation algorithms was selected to reduce the FPR caused by distorted distributions at the class boundaries. The sampling rate, represented by the _sampling_strategy_ parameter, is considered high when it surpasses 40% and low when it is less than 40%. The _sampling_strategy_ for each oversampling method was set to 30% to ensure a low sampling rate.  
The results presented in Table 4 demonstrate that the proposed approach enhances the FPR reduction capabilities of the algorithms while maintaining positive alert detection performance. The improved FPR was reflected in most algorithms tested. XGBoost yielded the best FPR of 0.119% when using MAS-OSS. Table 5 presents the results on the ensemble resampled benchmark dataset, which further validates the improvement in the FPR performance brought about by the proposed approach. The FPR of XGBoost of 0.251% when using SMOTE oversampled data improved to 0.232% when using MAS-OSS. XGBoost excels because of its ability to leverage the power of ensembles of DTs to create more accurate and robust models with improved generalization performance. XGBoost-MAS-OSS was adopted in the proposed system, resulting in superior performance on all seven evaluation metrics for the enterprise and benchmark datasets. Notably, the high recall rate of the system indicates its effectiveness in detecting positive alerts. Simultaneously, the improved FPR suggests that the number of non-critical security alerts investigated by security analysts in the SOC has been reduced, thus addressing the alert fatigue issue, which has been a persistent challenge in the security industry for several years.