esa-sceva/satcom-chunk-collection huggingface dataset:
# Durability of Mortars with Partial Cement Replacement by Recycled Brick Powder
## 3 Results
### Porosity Accessible to Water (Water Absorption)  
The results obtained from the porosity accessible to water tests are shown in Figure 7. In this figure, the thresholds established by different authors for classifying the material's durability are indicated [86,87].  
The results obtained exhibit a similar trend to that observed in the compressive strength tests. For cement replacements of up to 40%, porosity decreases as the replacement percentage increases, reaching a 43% reduction in mortar C40 compared to the reference mortar C00 (11.2% vs. 20% porosity, respectively). For higher replacement levels, the trend reverses, and porosity begins to increase. Nevertheless, even with a 45% replacement, the porosity of the mortar remains substantially lower than that of the reference mortar, showing a 34% reduction (13.2% vs. 20% porosity, respectively).  
The reduction in porosity observed in mortars with partial cement replacement is attributed to the denser cementitious matrix produced by the pozzolanic reaction of RBP, as previously described. This reaction leads to the formation of C-S-H gel while also consuming part of the calcium hydroxide generated during cement hydration. The crystals of calcium hydroxide (portlandite) negatively impact porosity, as they act like microcracks, generating additional voids within the matrix [88]. In fact, according to [78], a refinement of the porous network takes place, decreasing the percentage of larger capillary pores and increasing the percentage of pores smaller than 100 nm. However, as with compressive strength, an excess of cement substitution limits the production of CaOH\({}_{2}\) and therefore also the pozzolanic reaction of the RBP, which justifies that for cement substitutions higher than 40%, the porosity stops decreasing (Figure 7).  
Water-accessible porosity is a key parameter closely related to concrete's resistance to the ingress of aggressive agents and, therefore, serves as an indirect indicator of durability. In this regard, based on the thresholds established by some authors for classifying material durability [86,87], replacing 40% of cement with RBP improves durability, transforming a very low-durability mortar (mix C00) into a high-durability mortar. Even at 45% cement replacement (mix C45), the resulting mortars still fall within the average durability range (Figure 6).
# Icing Detection over East Asia from Geostationary Satellite Data Using Machine Learning Approaches
## 2 Study Area and Data
### Pilot Reports
Pixels that met the rules, except for rule 3, were excluded. A final decision as to whether an icing PIREP was valid or not was made using the remaining pixels within the buffer. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **COMS (April 2011–July 2017)** & **Himawari-8 (July 2015–July 2017)** \\ \cline{2-4}  & **Icing** & **Non-Icing** & **Icing** & **Non-Icing** \\ \hline KMA & 12 (18) & 219 & 6 (8) & 59 \\ HKO & 21 (36) & 0 & 10 (16) & 0 \\ Sum & 33 (54) & 219 & 16 (24) & 59 \\ \hline \hline \end{tabular}  
* FA = Flight Altitude (m); CTH = Cloud Top Height (m); CPH = Cloud Phase; COT = Cloud Optical Thickness; Tb = Brightness temperature (K); R = Reflectance.  
\end{table} 
Table 3: PIREP data used to develop icing detection models for COMS and Himawari-8. Values in parentheses are the number of icing PIREPs before conducting quality control, while the values in front of the parentheses are the number of icing PIREPs after the quality control.
# Two-Wire Power Line Microwave Communication Using Low-Loss Modes
## 4 Results  
### Pole Locations  
The first result (shown in Table 1) is a set of propagation constants for the SG (single wire) mode discussed in [22] and the mSG (two-wire common) and quasi-TEM (differential) modes for 1 and 10 GHz. The SG and mSG modes were found using a numerical solution of (31) set to zero without (for the SG mode) and with (for the mSG mode assuming the + sign) the second Hankel function. The quasi-TEM pole location has been determined using (31) set to zero with a minus sign or (36).  
It is clear that the mSG mode has slightly less loss than the SG mode. This is due to the fact that the current is spread out over a larger area of wire surface. However, the Quasi-TEM mode has a significantly larger loss factor. Hence, for communication systems, it is important to examine the excitation of each mode in the single- and two-wire cases.
## 3 Results and Discussion
### Immobilization of the AAB Cultures  
In Figure 2a, the SEM images of the immobilized species during ICW vinegar fermentation are presented. Bacterial cells with lengths below 2 \(\upmu\)m correspond with the morphology of the AAB species. According to [12], AAB cell length slightly decreases when ethanol is present, and significantly decreases (from an average of 1.4 to 1.0 \(\upmu\)m) when the acetic acid content in the culture medium reaches 1.7%. It remains stable during the rest of the acidification process. Ellipsoidal cells \(\sim\)5 \(\upmu\)m in diameter, correspond with the morphology of budding yeasts, such as _Saccharomyces cerevisiae_[13], while cylindrical rod-shaped cells with lengths above 7 \(\upmu\)m that divide by medial fission correspond with the morphology of _Schizosaccharomyces_ spp. [14].  
In Figure 2b,c the SEM images of the immobilized species during ICM vinegar fermentation are presented (_A. aceti_ and _K. europaeus_). In both cases, a large number of cells seem to be attached to the surface of the DCM carrier.  
The presence of species such as _Schizosaccharomyces_ yeasts in the vinegar produced by the selected culture can be explained by the fact that the FSS extract used to produce the wine was not previously pasteurized, neither SO2 was added, thus preserving its natural microflora. During the subsequent alcohol fermentation and acidification processes, this microflora was enriched and carried to the final products. Since, the study aimed at developing processes that are not very complex and are suitable for small to medium scale application, the work focused mainly on determining the acidification efficiency of the different biocatalysts as well as their effect on major vinegar attributes such as its volatilome, phenolic content, and antioxidant capacity. More detailed work, including microbiological analysis, should be done in future works based on vinegar production by specific cultures.
# A Wear Debris Signal Processing Method Based on Inductive Monitoring for Aero-Engine
## 5 Comparative Analysis  
In order to further verify the debris signature detection capability of this method, the oil debris simulation experiment in the laboratory is also carried out. The raw monitoring signal is illustrated as Figure 10(a), the energy value signal is obtained through the time domain analysis, illustrated as Figure 10(b). As illustrated in Figure 12, the data distribution of the debris signature and noise signature differs a lot. Furthermore, finally, the debris signature is identified as illustrated in Figure 13.  
Figure 11: Raw signal and its energy value signal: (**a**) raw signal and (**b**) energy value signal.  
Figure 10: The actual appearance times of debris: (**a**) actual times of each debris and (**b**) marks in the signal energy.  
To some extent, the debris detection is similar to the outlier detection, in which the energy value of the debris signature is the \"outlier\" should be detected. For comparison, a common outlier detection method, \(3\sigma\), is also applied on the same energy value signal, and the detection results are illustrated in Figure 14, in which a large amount of the noise signature is mistaken for the debris signatures. Frankly, the amplitude of noise is indeed high, which has broken the threshold of \(3\sigma\) after all. However, it is still not as high as the amplitude of debris, which shows a sharp contrast in Figure 14a. This shows the advantages of the debris detection method proposed in this paper. The key to this method is the combination of the histogram characteristic with the conversion to a relative threshold from the absolute threshold, and therefore, it has a satisfactory performance in the inhibition of false alarms. Additionally, the detection performance is only related to the signal itself, and therefore, it is applicable for various sensing principles, including but not limited to the inductive sensor signal.  
Figure 12: Typical histograms of debris signature and noise signature: (**a**) signal energy values of debris signature, (**b**) histogram of debris signature, (**c**) signal energy values of noise signature, and (**d**) histogram of noise signature.  
Figure 13: Detection results.
# Acoustic Rocket Signatures Collected by Smartphones
## 3 Results
### Space Launch System B1  
NASA's Space Launch System B1 (SLS-B1) is a super-heavy-lift launch vehicle and the most powerful operational rocket to date. Its first stage is powered by four RS-25 liquid-fueled engines and two solid rocket boosters. It is represented in ASTRA by a single launch (Artemis I), from which six stations collected acoustic signals. The waveforms of all six signals can be seen in Figure 5. The waveforms collected less than 30 km from the source are characterized by a spindle-like shape, high chronological symmetry about the peak amplitude, short duration of the highest amplitude segment (\(\sim\)60 s) compared to longer-range signals, and the presence of a small transient signal preceding the high amplitude segment. The waveform collected at a 37.6 km range shows significant elongation but maintains some similarity in shape with the shorter-range data despite early signs of peak separation. In contrast, the shapes of the waveforms collected at a greater than 50 km range are significantly different, with all three showing two chronologically distinct high-energy regions separated by approximately 150 s, both of which appear to further separate into two sub-regions each.  
Multiresolution time-frequency analysis was performed on all six signals, and one signal from each of the range categories (10-30 km, 30-50 km, 50-70 km) was selected to illustrate the identified characteristics, as high similarity was observed within each range category. In signals collected between 10 and 30 km from the source, we observe a sharp increase in power across nearly the whole spectrum (see Figure 6), but particularly near 10, 20, 45, and 100 Hz. As the signal propagates and the contribution of higher-frequency components is reduced, the peak power band near 10 Hz becomes more dominant. This phenomenon can be seen in Figures 6 and 7, in which spectrograms of the continuous wavelet transform (CWT) power of the three selected signals are displayed. As the rocket ascends, moving away from the stationary sensor, an apparent decrease in the frequency of the signal is observed. This observed shift in frequency, an example of the Doppler effect, can be seen most clearly in Figures 6c and 7e.  
Figure 5: Normalized, start-aligned waveforms of all signals collected from the Artemis I launch. Labels on the left-hand \(y\)-axis indicate the estimated propagation distance of each signal.  
Figure 6: Continuous wavelet transform (CWT) power of start-aligned signals collected from the Artemis I launch at 24.4 km (**a**), 37.6 km (**b**), and 56.9 km (**c**) from the launch pad.  
Figure 7: CWT power of peak-aligned signals collected from the Artemis I launch at 24.4 km (**a,b**), 37.6 km (**c,d**), and 56.9 km (**e,f**). All panels show the CWT power of the normalized signals, with the left-hand panels showing frequency on the \(y\)-axis and time on the \(x\)-axis, and the right-hand panels showing time on the \(y\)-axis and frequency on the \(x\)-axis.
## 3 Results
### Case II. Application with a Frequency Modulation \(m_{f}=48\)
The THD analysis acquired is presented in Figure 10, indicating that the most significant harmonics now appear on the opposite sides of the carrier frequency 50, specifically at harmonics 49 and 47, with 22% and 16%, respectively. Notably, the harmonics before these exhibit a practically null percentage, around 0%. One way to reduce harmonic distortion is to incorporate a filter at the output, which reduces the amplitude of these harmonics [21], ultimately enhancing system efficiency. The  
Figure 10: Odd harmonics and their percentage concerning the fundamental harmonic. Figure 9: Output signal with frequency modulation \(\mathrm{m}_{\mathrm{f}}=48\) (blue signal), fundamental harmonic (red signal). THD obtained for this case was 34.20%, significantly improving over the previous scenario with fewer angles. This indicates a successful algorithm adaptation to comply with IEEE 519 and provide favorable signal quality and system efficiency results.
## 4 Results and Discussion
### Fault Classification
Figure 15: Fault classification performance for the different classifiers with the WSN dataset: (**a**) random forest, (**b**) XGBoost, (**c**) SVM, (**d**) KNN, and (**e**) LightGBM. In Table 7, a brief comparison of our proposed model with some existing works on sensor fault analysis is presented. As multi-step prediction-based approach is not implemented for sensor fault classification and detection tasks; for that reason, we select works that considered the WSN dataset and common fault types as in this paper. The accuracy score provided for comparing the performance represents the highest accuracy reported in the paper for drift, bias, or stuck fault. The authors of [16; 21; 21] used the same WSN dataset with SVM and ExtraTree (ET) algorithm. It can be seen that our proposed model classifies  
Figure 16: Performance metrics for WSN dataset fault classifications. Figure 17: Fault classification performance from the different classifiers with the WSN dataset when considering multiple prediction horizons: (**a**) random forest, (**b**) XGBoost, (**c**) SVM, (**d**) KNN, and (**e**) LightGBM. the faults with higher accuracy than ET. The SVM model-based method achieves 100% accuracy, which is comparable with our work but they only considered the classification task. Compared with the results reported in [45], our proposed model achieved higher accuracy scores for both fault detection and classification. Although deep bidirectional Long Short-Term Memory recurrent-neural-network-based deep recurrent canonical correlation analysis (BLCCA) [49] model and Fuzzy Deep Neural Network (FDNN) [55] used different datasets, the classification accuracy for the fault types is significantly higher in our proposed model. The proposed methodology can be applied to anomalous behaviour detection for all real-world applications where sensors are deployed, for instance, self-driving cars, smart homes, smart energy meters, smart cities, and Internet-of-things. The challenge lies in the collection and use of sufficient realistic for training ML algorithms because of the infrequent occurrence of fault for short intervals of time. Nevertheless, we have designed the LSTM model to be small and identified only four features to work with the ML algorithm, thereby simplifying the approach. Consequently, our proposed method could be a good candidate for real-world implementation.
# GIS-Based Wind and Solar Power Assessment in Central Mexico
## 1 Introduction
[40] proposed a GIS-interval type-2-fuzzy AHP for wind farm location analysis in Nigeria, considering economic, environmental and social criteria. [PERSON] et al. [41] combined AHP-TOPSIS decision-making with GIS technology for spatial planning of suitable locations for installing wind farms in Eastern Macedonia and the Thrace region of Greece. [PERSON] et al. [42] developed a GIS-based model for site suitability analysis of WTB farms in Ecuador, by assessing the various meteorological, location, and environmental parameters, where the AHP method is executed to quantify the importance of each parameter. In addition, OWA, VIKOR, TOPSIS and occupational repetitive actions (OCRA) methods are utilized and compared to evaluate the alternatives. Recently, [PERSON] et al. [43] proposed a new approach integrating GIS, interval AHP and stochastic VIKOR to obtain the preferable sites for installing wind farms in the Wafangdian region of China. The relative weights of economic, environmental, and social criteria were determined using AHP, and the ranking of various alternatives was carried out through VIKOR. Further, [PERSON] et al. [44] assessed different economic, environmental and physiographic aspects using GIS-AHP to locate utility-scale WTB and SPV farms in Songkhla, Thailand. Various authors have evaluated the REs in Mexico integrating GIS capabilities, such as the case of [PERSON] et al. [45], which proposed a novel method for planning onshore wind energy in Mexico using a geospatial system of collective intelligence (SIGIC); [PERSON] et al. [46] evaluated potential zones for offshore wind development in the Gulf of Mexico. In this way, [PERSON] et al. [47] presented a particular study, based on GIS, of the offshore wind energy resources in the coast of Michoacan, Mexico, and [PERSON] [48] evaluated the offshore wind resource potential, assessing up to approximately 80 km off the coast of Mexico. The solar resource was studied by [PERSON] et al. [49], who performed an energy savings analysis for solar photo-voltaic technology implementation in rural and urban zones in Mexico, using GIS; [PERSON] et al. [50] developed a GIS analysis of the State of Nuevo Leon in Mexico to identify solar energy opportunities in this zone. However, despite this series of efforts, no study currently locates areas with the most significant wind and solar potential in the central zone of Mexico. The main objective and contribution of this work is to offer maps of solar irradiation and wind speed of Central Mexico to identify the areas with the greatest energy potential in this zone; thus far, there are not enough studies that present these values. Investors, government, society and academia can use these maps to gain a preliminary idea of the area they are interested in studying.
# Evaluation of Operator and Patient Doses after Irradiation with Handheld X-ray Devices
## 3 Results  
Background radiation of TLDs, which are not among other TLDs using mannequins and phantom heads in irradiation and are kept away from the irradiation area, was found to be 0.0132 Gy. Irradiation measurements in the gonad, thyroid, right-eye, left-eye, right-hand, and left-hand regions using conventional (C), phosphor (P), and RVG (R) devices in NOMAD Pro 2 TM (N), Rextar X (RX) and Diox 602 (D) devices at dose levels, both protected and unprotected, were performed in triplicate, and the mean values for these results are summarized in Table 2.  
Figure 3: (a) Dry skull. (b) Conventional X-ray film. (c) Phosphor plate image. (d) RVG image. For the gonad region, the highest value was measured as 26.8 \(\upmu\)Gy in the unprotected C irradiation with NOMAD Pro 2 TM, and the lowest value was 7.3 \(\upmu\)Gy in the protected R irradiation with Rextar X. There is no statistically significant difference between devices N and D in C and P irradiations for the gonad region. Device RX produced a statistically lower radiation dose. There is no statistically significant difference between devices D in R irradiation of device N. Device RX produced a statistically lower radiation dose. The same is not statistically significant difference between devices D in R irradiation of device N. Device RX produced a statistically lower radiation dose than device D (Table 3). On the mannequin, the highest value for the thyroid region was 30.5 \(\upmu\)Gy in the unprotected C irradiation with NOMAD Pro 2 TM, and the lowest value was 7.8 \(\upmu\)Gy in the protected R irradiation with Rextar X. There is no statistically significant difference between devices RX and D in the C irradiation of the device N in the thyroid region. Device RX produced a statistically lower radiation dose than device D. While there was no statistically significant difference between devices N and D in P and R irradiation, device RX produced a statistically lower radiation dose (Table 3). The highest value for the right eye area was 31.9 \(\upmu\)Gy in unprotected C irradiation with NOMAD Pro 2 TM, and the lowest value was 7.8 \(\upmu\)Gy in protected R irradiation with Rextar X. There is no statistically significant difference between devices N and D in C irradiation for the right eye area. Device RX produced a statistically lower radiation dose than devices N and D. There is no statistically significant difference between the three devices in P irradiation. There is no statistically significant difference between devices N and RX in R irradiation. Device D produced a statistically higher radiation dose (Table 3).
## 5 Conclusions  
This study presented the characterization of land subsidence with the spatial variability of soil formation and groundwater withdrawals in the Choshui delta, Taiwan. Various factors, including the percentage of coarse-grained soil, percentage of fine-grained soil, length of maximum drainage path, percentage of agricultural land use, electricity consumption of wells, and accumulated depth of land subsidence, were adopted. The thematic maps based on these factors were established utilizing the GIS spatial analysis. The fundamental concepts and construct of the proposed method were addressed in detail. The findings are as follows:  
1. To examine the influence of different environmental factors on land subsidence, the correlation coefficient was calculated. The results obtained show that factor #5 (accumulation depth of land subsidence from 2015 to 2020) is the cumulative subsidence factor, which is positively correlated with factor #1 (percentage of agricultural land use), factor #2 (electricity consumption of wells), and factor #3 (percentage of fine-grained soil). Furthermore, it was found that the correlation with factor #2 (electricity consumption of wells) is the highest, which is 0.46, indicating the high electricity consumption of the wells increases the accumulated land subsidence.
2. The eigenvalues and contribution rates of factors demonstrate that the cumulative contribution of the first two principal components is 64.06%. It is found that the first two principal components can explain 64% of the data, which is representative to a certain extent. The contribution is 93.31%, which means that the first four principal components can explain almost all the data.
3. From the PCA results, the largest subsidence rate was observed within the region that has both a high electricity consumption of wells and a large percentage of fine-grained soil. It appears that the electricity consumption of wells is highly correlated with the accumulated depth of land subsidence. The first principal component is the acquired factor causing land subsidence, such as the excessive withdrawal of groundwater. The second principal component is the congenital factor causing land subsidence, which corresponds to fine sand, silty, and clayey soils. The findings observed in this study may provide authorities with more information for planning future mitigation measures in significant land subsidence areas.  
**Author Contributions:** Conceptualization, C.-Y.K.; Performed the analysis, C.-Y.L.; Data collection, H.-C.L.; Writing, C.-Y.K. and C.-Y.L.; All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Science Council, Taiwan, The Republic of China (MOST 111-MOEA-M-008-001).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets generated during the current study are available from the corresponding authors upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.