esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Analysis
### Results for the Simulated Data
For the most complicated case, presented in Figure 5, i.e., for the signal with a large non-cyclic to cyclic impulses amplitude ratio (signal \(s_{4}\)), we expect to find information about the cyclic impulses with possibly suppressed information about non-cyclic impulses. Surprisingly, only the CVB selector correctly identified the frequency band corresponding to the cyclic impulses, based on the distribution of their amplitudes. The Alpha selector, spectral Gini index and infogram indicate both cyclic and non-cyclic impulses frequency ranges. The advantage of the first two ones is that the values  
Figure 4: Results of the informative frequency band selection for 7 different methods for the signal \(s_{3}\). of them do not have to be normalized before data filtration, because their values are between 0-1 (according to the definition). In particular, it is interesting that the [PERSON]'s infogram method, see [48], which takes into consideration both impulsiveness of the signal in the time domain and periodicity in the envelope spectrum, did not provide clear information for the last type of the signal, i.e., the mixture of the cyclic and non-cyclic impulses (signal \(s_{4}\)). As it was mentioned, the infogram is the averaged entropy for a given level of decomposition. In case the cyclic impulses appear in the signal, averaging of high entropy related to high impulses in the time domain, and smaller entropy related to smaller amplitudes of the components in the envelope spectrum, provides some information that favors  
Figure 5: Results of the informative frequency band selection for 7 different methods for the signal \(s_{4}\). impulsiveness more than periodicity. We have analyzed separately SE (envelope-based) and SES (spectral envelope-based) parts of the infogram, see Figure 5(a),b respectively. As it was mentioned, for the signal with non-cyclic impulses, the SES infogram contained small values and did not highlight any frequency band. On the other hand, for the mixture of cyclic and non-cyclic impulses, the IFB was correctly recognized but final information was dominated by the SE infogram. It should be mentioned that in [68] the authors suggested the extended version of the infogram with increased accuracy of the IFB selection, i.e., so-called clustered gray infogram. However, there is still place for enhancements and a weighted infogram could be proposed. Let us note that the normalized version of the selectors' results (normalization using its maximum value) has been added to the Appendix A to enable the comparison in the same scale. In the main part of the paper, the original scale of the selectors has been proposed in order to obtain the clear information if the given selector properly indicates the informative frequency band.
## 1 Introduction
Environmental impact assessment-based research aimed at identifying the potential environmental impact of each aggregate in advance must also be conducted to determine the applicability of recycled and by-product aggregates [16; 17; 18]. [PERSON] et al. [19] evaluated different kinds of concrete containing waste as an aggregate based on life cycle assessment (LCA) and concluded the recycled aggregate that had the least adverse impact on the environment was blast furnace waste. [PERSON] et al. [20] conducted a LCA of aggregate production from recycled waste materials and virgin sources and, concluded that compared with natural coarse aggregates, recycled coarse aggregates produced from construction and demolition waste reduce 65% greenhouse gases (GHGs) emission with a saving of 58% non-renewable energy consumption. Meanwhile, compared with the production of natural fine aggregates from river sand, producing recycled fine aggregates from waste glass saves 54% energy consumption and reduces GHGs and SO\({}_{\text{2 eq}}\) emissions by 61% and 46%, respectively. [PERSON] et al. [21] evaluated the sustainability, including the economic impact, the social impact, and the environmental impact, of a RCA-based portland cement concrete (RCA-PCC) pavement and a plain portland cement concrete (PCC) pavement, and concluded that although the RCA-PCC pavement is slightly less sustainable in the use phase due to its rougher pavement surface compared to plain PCC pavement, the RCA-PCC pavement is generally more environmentally and socially friendly due to less consumption of virgin aggregate in the materials production and construction phase. They also evaluated the sustainability of reclaimed asphalt pavement (RAP) in PCC, and among three pavements, including a single-lift pavement made of plain PCC slab, a single-lift pavement made of RAP-PCC slab, and a two-lift concrete pavement, the two-lift construction using RAP-PCC in the bottom lift demonstrated could have the highest positive impacts from environmental and social perspectives [22]. These previous studies are essential in that they evaluated the environmental impacts and sustainability to utilize recycled and by-product aggregates into concrete. Studies involving environmental impact assessments of various recycled and by-product aggregates, however, are still insufficient in terms of scale and depth [23; 24; 25; 26]. In particular, research involving LCAs of concrete has become more sophisticated and its scope has been expanding of late. Therefore, research involving the environmental impact assessment of recycled and by-product aggregates, which has usually been conducted using simplified methods or not sufficiently considered in previous studies, is even more urgently required [27; 28; 29; 30; 31]. This study thus aims to compare the potential environmental impact of recycled and by-product aggregates based on LCA, and to evaluate the environmental impact and cost when they are used as aggregates in concrete.
## 4 _C. trachomatis_ Sensitivity Surveillance Efforts
A multicenter study that involved seven Spanish National Health Service hospitals aimed to assess the phenotypic and genotypic susceptibility of _C. trachomatis_ to key antimicrobials (macrolides, doxycycline, and quinolones) in isolates from patients around the country with treatment failure, excluding reinfection [76]. From 2018 to 2019, 73 isolates were collected. Phenotypic testing was conducted on 69 specimens using McCoy cell monolayers, while genotypic analysis involved sequencing the _23S rRNA gene_ (positions 2057, 2058, 2059, and 2611) for macrolide resistance, the _gyrA gene_ (G248T mutation) for quinolone resistance, and RT-PCR for the _tet(C)_ gene for tetracycline resistance [76]. Phenotypic testing succeeded for 10 isolates, all sensitive (MICs \(\leq\) 0.125 mg/L for azithromycin; \(\leq\)0.064 mg/L for doxycycline). Among 66 sequenced isolates, no resistance mutations were detected in _23S rRNA_, _gyrA_, or _tet(C)_. More specifically, no evidence of genomic resistance was found. A very recent national surveillance study by the Portuguese National Reference Laboratory for STIs examined 502 _C. trachomatis_-positive samples, primarily anorectal exudates, to assess genetic markers of AMR to macrolides and fluoroquinolones, with the use of multiplex PCR and next-generation sequencing (NGS) [64]. No samples showed 23S rRNA mutations linked to macrolide resistance, but three (0.6%) had a GyrA Ser83 Ile mutation potentially conferring fluoroquinolone resistance. The study also successfully differentiated LGV from non-LGV strains and identified L2/L2b genomic backbones, with a notable insertion upstream of CT105 showing promise as a rapid LGV diagnostic target [64]. Overall, it suggests low dissemination of AMR genetic determinants in clinical _C. trachomatis_ strains in Portugal.
# Study of the Effect of Exploiting 3D Semantic Segmentation in LiDAR Odometry
## 3 Methodology
### Evaluation Metrics  
After obtaining the output odometry data for each input configuration, these estimated poses are compared with the ground-truth poses. To simplify the evaluation process, an open-source tool, evo [17], was used. This tool allows performing an insightful analysis of the generated odometry data while also providing several metrics and plotting functions.  
Choosing a suitable evaluation metric has always been a subject of study. Since most LiDAR odometry algorithms are actually SLAM algorithms, we adopted two common metrics used to evaluate the estimated poses of SLAM methods. On the one hand, the Absolute Pose Error (APE) measures the absolute pose differences directly. This is computed in global coordinates and provides meaningful information about the consistency of the SLAM method because the error increases as the estimation gets farther from the reference trajectory. On the other hand, the APE metric is sensitive to errors at the beginning of the trajectory, because those early errors affect the whole estimation. For that reason, in [18], the Relative Pose Error (RPE) is proposed. This metric does not compare the poses directly; instead, it compares the measurement deltas between each pose. Therefore, RPE is more suitable for evaluating the drift of the algorithm.  
Let \(x_{i}\) and \(\hat{x}_{i}\) be the reference and estimated poses of the vehicle trajectory, respectively, where \(i\in 1:T\) is the time step of each pose. Considering \(\odot\) the inverse of the compositional operator [19], \(\delta_{i,j}\) is the relative transformation from pose \(x_{i}\) to \(x_{j}\). Accordingly, \(\hat{\delta}_{i,j}\) is defined as the estimated transformation from \(\hat{x}_{i}\) to \(\hat{x}_{j}\). Given this, it is possible to formulate APE and RPE metrics with (1) and (2).  
\[APE_{i}=\hat{x}_{i}\odot x_{i}\,,\quad\forall\ i\ \in 1:T \tag{1}\]  
\[RPE_{i,j}=\hat{\delta}_{i,j}\odot\delta_{i,j} \tag{2}\]  
Note that, although all poses in the trajectory are considered for computing the APE metric, this is not the case for RPE. In this case, the set of pairs \(\{i,j\}\) is obtained from all contiguous sub-sequences of a specific length inside the trajectory, in an analogous way as KITTI evaluation does.  
Afterwards, each of the APE and RPE errors is decomposed into their translation and rotation components using (3) and (4), where \(\angle\left[\cdot\right]\) is the rotation angle [7].  
\[E_{\text{trans}}=\left\|E\right\|_{2} \tag{3}\]  
\[E_{\text{rot}}=\angle\left[E\right] \tag{4}\]
# Pulsed Thermography Applied to the Study of Cultural Heritage
## 3 Results
### Recovery of Hidden or Lost Texts in Ancient Books/Documents
#### 3.2.2 Damaged Surface Texts  
Ancient manuscripts and documents are often affected by different forms of deterioration and physical degradation phenomena, due to a combination of factors--such as environmental conditions, dirt, effects of natural disasters, handling, and biological attacks--all causing the loss of readability of their textual elements [74]. IRT has proved to be able to recover many of the elements belonging to the surface of the artefacts, like texts on parchment or leather, that have become invisible to a simple optical inspection. As an example, the results obtained by the analysis performed on the XIII century manuscript \"ms 509/D813\", hosted at the _Biblioteca Angelica_ of Rome, are reported. The manuscript, an Expositio about the Old Testament, is composed of 127 written parchment folios in part affected by water damage. The last pages are also characterized by several extended water stains that caused the reduction of the readability of the writing, which appears totally deleted around the edge of the water stains (Figure 8). In the case of _folio_ 124 _vsco_, the thermograms performed on different damaged areas (Figure 8B,C) show a partial recovery of the inked text in all the investigated parts. Such thermograms have been obtained by illuminating the leaves with two flash lamps whose power output was set at 1 kW. The possibility to recover the ink contrast by means of IRT relies on the local heating produced on the ink residues which effectively absorb parts of the incident light spectrum (UV-VIS-NIR) and by the different emission properties characterizing such residues and the surrounding support. In particular,  
Figure 7: XVI century book, _Biblioteca Angelica_ of Rome. (**A**) Photograph and (**B**) corresponding thermogram; (**C**) magnification of the dashed area in 7B [50].  
two basic mechanisms can be proposed to explain the recovery of the text. Concerning the first possible mechanism, once the loss of the pigmental component of the ink occurs, traces of some other lighter components associated with the ink solvent (often not at all visible) are left in the support in correspondence of the lost text. Their presence can generate specific local absorption different from that of the surroundings, particularly in the UV and NIR spectral ranges. As a consequence, the photothermal perturbation induces different temperature values between the text area and the surroundings originating from the thermal contrast that make the text readable in the thermograms. A possible additional contribution to the contrast enhancement arises from the difference of the IR emissivity values between the areas containing the residual light components of the original ink and that of the surroundings, resulting in a different amount of locally emitted IR radiation, independently of their temperature difference.
# Automatic Transmission Fluids in Electrified Transmissions: Compatibility with Elastomers
## 2 Materials and Methods  
### Materials  
The compatibility of four commercial ATFs with three elastomeric materials was studied. Both the ATFs and elastomers are commonly used by car manufacturers at the time of writing. Table 1 shows the base oils which are composed each ATF and their concentration, as well as some physicochemical properties of the ATFs.  
The four ATFs are each composed of two base oils, which may vary from one to another, and an additive package which is also different for each ATF (Table 1). Oil A is composed of a mixture of two base oils from Group I and 11.7 wt.% of additives. Oils B, C, and D are composed of mixtures of two base oils from Group III and an additive content of 11, 17, and 27 wt.%, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**ATF**} & \multirow{2}{*}{**Base Oils (wt.\%)**} & \multicolumn{2}{c}{**Density**} & \multirow{2}{*}{**Kin. Viscosity (cSt) 1**} & \multirow{2}{*}{**VI 2**} \\ \cline{3-3} \cline{5-6}  & & & **15 \({}^{\circ}\)C** & & **100 \({}^{\circ}\)C** & & \\ \hline \multirow{2}{*}{Oil A} & Group I, 4.9 cSt/100 \({}^{\circ}\)C (77.7) & \multirow{2}{*}{869} & \multirow{2}{*}{7.8} & \multirow{2}{*}{43} & \multirow{2}{*}{161} \\  & Group I, 2.7 cSt/100 \({}^{\circ}\)C (10.6) & & & & & \\ \hline \multirow{2}{*}{Oil B} & Group III, 3.0 cSt/100 \({}^{\circ}\)C (38.3) & \multirow{2}{*}{847} & \multirow{2}{*}{6} & \multirow{2}{*}{30} & \multirow{2}{*}{151} \\  & Group III, 6.5 cSt/100 \({}^{\circ}\)C (50.7) & & & & & \\ \hline \multirow{2}{*}{Oil C} & Group III, 3.0 cSt/100 \({}^{\circ}\)C (45.0) & \multirow{2}{*}{854} & \multirow{2}{*}{7.2} & \multirow{2}{*}{35} & \multirow{2}{*}{181} \\  & Group III, 6.5 cSt/100 \({}^{\circ}\)C (38.5) & & & & & \\ \hline \multirow{2}{*}{Oil D} & Group III, 3.0 cSt/100 \({}^{\circ}\)C (36.5) & \multirow{2}{*}{854} & \multirow{2}{*}{6.9} & \multirow{2}{*}{34} & \multirow{2}{*}{165} \\  & Group III, 4.2 cSt/100 \({}^{\circ}\)C (36.5) & & & & & \\ \hline \hline \end{tabular}  
* 1 [cSt] = 1 \(\times\) 10\({}^{-6}\) [m\({}^{2}\)/s].  
\end{table}
Table 1: Properties of the ATFs.  
Table 2 shows the main properties of the studied elastomers. These elastomers (FKM, silicone, and EPDM) are commonly used as seals and gaskets in transmissions and also as insulator of electrical wires [15]. Although the EPDM is considered as non-compatible with mineral oils, it was included in this study in order to determine if the base oils from Group III, also considered as synthetics, are compatible with this material [16]. Table 2 shows the main characteristics of the elastomers as given by the provider. The specimens for measurements of mechanical properties were prepared with the dimensions specified (type V) in the ASTM D638 standard and were extracted from sheets of each material.
## 2 Model Construction and Algorithm Enhancement
### Validation and Evaluation
#### 2.3.2 Decision Tree Model  
The decision tree [27] model is a prediction model based on a tree-like structure. It recursively splits the data to construct the tree, where each node represents a feature, each branch represents a feature value, and the leaf nodes represent the prediction results. The advantages of decision trees are their ease of understanding and interpretability, as well as their ability to handle nonlinear relationships and missing values. However, they are prone to overfitting.  
The principal formula for the decision tree model is as follows:  
\[y=f(x) \tag{2}\]  
where \(y\) is the predicted output, \(x\) is the input feature vector, and \(f(x)\) is the function learned by the decision tree.
## 4 Conclusions  
We used a CW laser to in-situ reduce silver ink film on flexible PI substrates for MS transmission lines. The laser-introduced photothermal effects, which can trigger the sintering and melting of laser produced silver nanostructures, could also increase the conduction passes. Therefore, we were able to tune the sheet resistance of the strip conductors via a variation of the laser power. The RF characteristics of laser-scribed MS transmission lines were investigated. The experimental results match well with the simulation data. When the sheet resistance was not above 0.32 \(\Omega/\square\), the transmission characteristics of the MS lines were insensitive to the sheet resistance and similar to an ideal condition. Moreover, the transmission and reflection characteristics of the MS lines could be simply modified by accommodating the resistance of the strip conductors. LDW provides a simplified method of integrating the fabrication of resistive components and other functional structures into a single step. This simplification promotes flexible and low-cost manufacturing, and may be beneficial for the device's design.  
Figure 6: S\({}_{11}\) spectra at various sheet resistances: (**a**) simulation results; (**b**) experiment results.  
**Author Contributions:** [PERSON], [PERSON], and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON], and [PERSON] performed the simulations; [PERSON] and [PERSON] analyzed the data; and [PERSON] contributed materials. [PERSON] and [PERSON] wrote the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (61704090), the Natural Science Foundation of Jiangsu Province (BK20170908, BK20170903), the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province (17 KJB510041, 17 KJB510040, 17 KJA470005), the National and Local Joint Engineering Laboratory of RF Integration and Micro-Assembly Technology (KFJ20170307, KFJ)20170204, KFJJ20180201), and NUPTSF (NY217123, NY217124).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Hybrid Framework Description
### Foot-State Classifier  
In this section, we propose a multi-class FSC that is capable of recognizing the swing phase, stance phase, and still phase. Figure 2 shows that only the ZUPT algorithm is unable to constrain the position and heading drifts when the sensor stays stationary for a long time. The reason is that the ZUPT cannot completely correct the velocity error and cannot observe the heading error. Therefore, we introduce a state lock in the still phase to freeze the position and quaternion and reset the velocity to zero. Simultaneously, the ZARU algorithm is employed to update the gyro bias.  
In different states of foot movement, the average angular rate energy is different. The energy in the swing phase is the highest, while the energy in the still phase is the lowest. Moreover, in the still and stance phases, the variances in the angular rate are near zero, while the variances in the swing phase are greater. Therefore, an effective multi-class classifier can be designed as  
\[ZUPT=\left\{\begin{array}{ll}0&\quad\|\bar{\omega}\|>\gamma_{s}\text{ and }\bar{\sigma}_{\bar{g}}^{2}>\gamma_{gv}\\ 1&\gamma_{s}^{\prime}\leqslant\|\bar{\omega}\|\leqslant\gamma_{s}\text{ and }\bar{\sigma}_{\bar{g}}^{2}\leqslant\gamma_{gv}\\ 2&\quad\|\bar{\omega}\|<\gamma_{s}^{\prime}\text{ and }\bar{\sigma}_{\bar{g}}^{2} \leqslant\gamma_{gv}\end{array}\right., \tag{1}\]  
where 0 denotes the swing phase, 1 denotes the stance phase, and 2 denotes the still phase. \(\bar{\omega}=\frac{1}{W}\sum_{i=k}^{k+W-1}\omega_{i}\) refers to the average three-axis angular rate in the sample window with a size of \(W\), and \(k\) is a time instant. \(\gamma_{s}\) refers to the energy threshold between the still phase and the stance phase, and \(\gamma_{s}^{\prime}\) is the threshold between the stance phase and the swing phase. \(\bar{\sigma_{g}^{2}}\) refers to the average of the three-axis angular rate variances. \(\gamma_{gv}\) is the variance threshold. In our experiment, \(\gamma_{s}^{\prime}=0.07\), \(\gamma_{s}=0.5\), \(\gamma_{gv}=0.002\), and \(W=5\). Figure 3 shows that a detector that satisfies both conditions outperforms an energy- or variance-condition-based detector.  
Figure 2: Position and attitude computed by the ZUPT-aided pedestrian navigation system (PNS) when the sensor (Mtw Awinda) stays still for half an hour.
## 5 The ECOAL Project Implementation
### Field System Installation
This technique overcomes the conventional time-frequency trade-offs encountered in traditional BOTDA by subtracting the gain profiles obtained in the same position with slightly different pulse lengths. A resolution of 0.5 m is thus obtained, subtracting the results obtained with 52 and 47 ns pulses. For gas measurement, two different spots in the coal waste pile were selected (Figure 7C). In these spots metallic tube structures with a height of \(\sim\)1.6 m were installed to isolate the gas sensors from the nearby coal pile material (the structure permitted the gas emitted by this material to access its interior; views of these structures are shown in Figure 9B). In one of these spots (the left one in Figure 7C), besides gas sensing heads, a Fiber Bragg Grating (FBG)-based sensor system was installed with three temperature measurement points at different heights, the purpose being the characterization of the temperature profile of the metallic column aiming gas sensor calibration objectives. In this spot was also located a novel interferometric temperature sensor based on microstructured optical fibers to assess its performance in such a harsh environment. Figure 8: General architecture of the optical fiber sensing system for gas and temperature measurement in coal waste piles.