esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Material and Methods
### Vapor-Phase Antibacterial Activity of the Tested Mixtures  
A group of Gram-negative and Gram-positive highly virulent and multidrug resistant bacterial pathogens was selected to be used as model organisms in testing the vapor-phase antibacterial activity of the studied mixtures. The selected pathogens are members of the \"ESKAPE pathogens\" that are attracting research attention, being the top leading causes of life-threatening infections [53].  
The vapor-phase antibacterial activity of the tested mixtures was investigated by determination of the vapor-phase minimum inhibitory concentration (VP-MIC) adopting the principles and approaches described before [12; 59]. The previously established methods for the determination of VP-MIC used either the disc volatilization assay or the airtight box assay [12; 59]. The disc volatilization assay was not applicable to our samples since we were dealing with mixtures of powders/solid particles instead of dealing with essential oils (liquid samples). The airtight box assay was thus found more convenient with our samples but with the drawback of consuming a large number of materials (culture media, inoculum, samples, etc.). The previously established method was thus slightly modified to develop a cost-efficient method that could test a large number of microorganisms using the least amount of samples using the conventional petri dish and without the need for any specialized tools. The VP-MIC of the tested mixtures was determined as follows: 15 mL of sterile Mueller-Hinton agar (MHA) was pipetted in a 10 cm diameter glass petri dish. The solidified MHA surface was then surface inoculated by spotting 10 \(\upmu\)L of the tested bacterial suspension (106 CFU/mL). Since nine microorganisms were tested in each assay, 10 \(\upmu\)L of each bacterial inoculum was spotted side by side in the same petri dish, as shown in Figure 2. The tested mixture was then placed on the cover of the petri dish, and the plate was kept inverted so that the inoculated agar was upward and the cover with the tested mixture was downward. Later, the petri dish was sealed by parafilm and incubated at 37 \({}^{\circ}\)C for 24 h. The control plate was prepared in the same way but keeping the cover of the petri dish empty without adding any samples. For each mixture, several plates were prepared with different concentrations (4.7, 2.4, 1.8, 0.6 mg/mL). The concentration of the tested mixture was calculated by dividing the weight (mg) of the mixture (placed on the cover of the petri dish) by the volume (mL) of the airspace in the petri dish. After incubation, the bacterial growth in the sample (mixture) plates and control plate was compared, and the VP-MIC was determined. The VP-MIC was identified as the least concentration of the tested mixture that caused apparent growth suppression of the tested microorganism when compared to the control (Figure 2). The assay was repeated at least three independent times, and the VP-MIC was reported as mean \(\pm\) SD (standard deviation).
## 3 Methodology
### CTG Signal Analysis
#### 3.2.2 FHR Signal Detrending  
After the preprocessing step, and before the CEEMDAN decomposition operation, a signal detrending operation is applied. For this purpose, we first compute a VLF signal trend denoted as the _floating-line_. The floating-line allows tracking the morphological behavior of FHR decelerations and accelerations, whose behavior involves nonlinear characteristics [5; 50]. Following [56], this floating-line is computed by a median filter with a sliding window of 10 s length, whose size was determined as follows:  
Figure 2: CTG recording number 1179 (last 1500 s) extracted from the CTU-UHB database. (**a**) Raw fetal heart rate (FHR) signal; (**b**) Raw uterine contraction (UC) signal.  
We randomly selected a set of ten CTG recordings from the CTU-UHB database.  
Each FHR signal was filtered by a median filter using different window lengths in the range of 6 to 12 s in steps of 1 s.  
The extracted traces (seven for each signal) were superimposed on the corresponding raw FHR signal in order to examine which one tracks the FHR decelerations and accelerations better.  
After a visual analysis, we selected the trace computed by a sliding window of 10 s length.  
The computed floating-line is shown in Figure 4a in red. Once the floating-line is computed, it is subtracted from the FHR signal, whose resulting signal (detrended FHR signal) is shown in Figure 4b. For the subsequent decomposition and spectral analysis, the detrended FHR signal is used.
## 3 Results and Discussion
### Synthesis and Characterization
#### 3.1.3 SEM Characterizations  
The morphology of GO, zeolite, and the nanocomposites was analyzed using scanning electron microscopy (SEM). Figure 3a displays a micrograph of GO, revealing layers exceeding 10 \(\upmu\)m in size, arranged as wrinkled veils stacked atop one another. This wrinkled appearance is a distinctive characteristic of GO, resulting from the presence of functional groups that induce deformation as they strive to achieve electronic configuration stability. Additionally, this feature signifies a transition from sp\({}^{2}\) to sp\({}^{3}\) hybridization, highlighting structural changes in the material.  
Figure 2: FTIR spectra of GO, zeolite, and the ZGO2.5, ZGO5, and ZGO10 nanocomposites.  
Figure 3b reveals the morphology of natural zeolite, where the presence of pores is evident at high magnification (bar, 20 \(\upmu\)m). Zeolite exhibited a pellet-like morphology, with pellet dimensions ranging from 1.4 mm to 400 \(\upmu\)m. The whitish color was attributed to its aluminosilicate composition. Additionally, some grains were measured, obtaining a size range between 69.51 \(\upmu\)m and 1631.65 \(\upmu\)m (Figure S1), in concordance with the technical specifications.  
Figure 3c illustrates the ZGO nanocomposite with a GO concentration of 10 mg mL\({}^{-1}\) at the same magnification as that of GO (bar, 10 \(\upmu\)m). It is evident that the morphology underwent significant changes. Meanwhile, the wrinkled veils of GO remain visible, and zeolite can also be observed deposited onto the material. The 3D morphology of zeolite was altered due to the incorporation of the GO layers within the zeolitic domains. This is seen in several areas of the image that exhibit higher contrast and brightness--characteristic of heavier elements compared to carbon, such as aluminosilicates. Additionally, the characteristic whitish color shifted to a grayish tone, indicating ionic exchange due to the interaction with GO.  
Figure 3: (**a**) Graphene oxide (GO) morphology; bar, 10 \(\upmu\)m; (**b**) natural zeolite; bar, 20 \(\upmu\)m; (**c**) nanocomposite of zeolite and graphene oxide, bar, 10 \(\upmu\)m.
# The Carbon Footprint of Milk Production on a Farm
###### Abstract
Despite advances in research, gaps continue to exist, especially in low- and middle-income countries, where dairy systems are often dispersed and difficult to inventory. It is also necessary to better integrate the CF with other sustainable development indicators, such as biodiversity, soil health, animal welfare, and social justice. [PERSON] [25] pointed out that a single CF indicator, although useful, can blur the trade-offs between various environmental and social aspects and should be part of broader multi-criterial analyses. The aim of the present study was to comprehensively analyse the CF of milk production at the farm level, taking into account technological, environmental and systemic factors. Based on a review of scientific publications and environmental reports, databases and legal acts [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57], the stages of calculating the product carbon footprint, the application of LCA methodology, and the calculation approaches were identified (Section 3.1.). The main sources of GHG emissions, including the sources in milk production, were presented (Section 3.2). The CF range in milk production (Section 3.3), the effect of feed, its origin and feeding system on the CF (Section 3.4), and the production efficiency and CF (Section 3.5) were also presented. This paper also includes the farm scale and its characteristics (Section 3.6), as well as methods for reducing GHG emissions and the CF, technological innovations and future development directions (Section 3.7). A particular focus was placed on the relationships between the CF and production intensity, local environmental conditions, and possible decarbonisation pathways for the dairy sector. The aim of this study was not only to identify the emission factors but also to provide useful conclusions for farmers, advisors, decision-makers and institutions implementing climate policy in agriculture, also at the local/regional level.
# Remote Sensing in Urban Forestry:
## 4 Concluding Remarks
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Spatial Resolution** & **Number of Bands** & **Revisit Time** & **Thermal Data** & **Free of Charge** & **Temporal Coverage** & **Spatial Coverage** \\ \hline ALOS & 2.5 m panchromatic & 5 & 46 days & No & No & 2006-2011 & Global \\  & 10 m multispectral & & & & & & \\ \hline ASTER & 15 m visible near infrared & & & & & & \\ \multirow{2}{*}{ASTER} & 30 m shortwave infrared & & & & & & \\  & 30 m thermal infrared & & & & & \\ \hline IKONOS & 0.82 m panchromatic & 5 & 3 days & No & No & 1999–present & Global \\  & 3.2 m multispectral & & & & & \\ \hline Landsat-5 (TM) & 30 m multispectral & 7 & 16 days & Yes & Yes & 1984–2012 & Global \\  & 120 m thermal & & & & & \\ \hline Landsat-7 (ETM+) & 30 m multispectral & 8 & 16 days & Yes & Yes & 1999–present & Global \\  & 160 m thermal & & & & & \\ \hline Landsat-8 (OLL/TIRS) & \
\begin{tabular}{} \end{tabular} & 11 & 16 days & Yes & Yes & 2013–present & Global \\ \hline MODIS & 250/50/1000 m multispectral & 36 & 1/8/16 days & Yes & Yes & 2000–present & Global \\  & 500/1000 m thermal & & & & & \\ \hline NAIP aerial imagery & 1 m multispectral & 34 & Sub-annual & No & Yes & 2003–2015 & CONUS \\ \hline RapidEye & 6.5 m multispectral & 5 & 1 day & No & No & 2008–present & Global \\ \hline Sentinel-2A (MSI) & 10/20/60 m multispectral & 13 & 10 days & No & Yes & 2015–present & Global \\ \hline SPOT-5 & 5/10 m panchromatic & 6 & 3 days & No & Yes & 2002–present & Global \\  & 10/20 m multispectral & & & & & \\ \hline WorldView-2 & 0.5 m panchromatic & 9 & 1 day & No & No & 2009–present & Global \\ \hline WorldView-3 &
\begin{tabular}{} \end{tabular} & 9 & 1 day & No & No & 2014–present & Global \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Description of commonly used remote sensing products/platforms for urban forest studies. Figure 1: Examples of satellite imagery and LiDAR point-cloud data: (**a**) WorldView-2 NDVI at city-regional level, (**b**) LiDAR point-cloud Canopy Height Model (CHM) at city- regional level, (**c**) WorldView-2 NDVI at local-district-city level, (**d**) LiDAR point-cloud CHM local-district-city level. **Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2072-4292/11/10/1144/s1](http://www.mdpi.com/2072-4292/11/10/1144/s1)]([http://www.mdpi.com/2072-4292/11/10/1144/s1](http://www.mdpi.com/2072-4292/11/10/1144/s1)). * **Author Contributions:** Conceptualisation, R.L., X.L. and W.Y.C. ; sources of review studies, X.L. ; writing--original draft preparation, X.L. ; writing--review and editing, W.Y.C., R.L. and G.S. ; visualisation, X.L. ; supervision, R.L. * **Funding:** This research was funded by The University of Hong Kong, Seed Fund for Basic Research, 2018-2020. * **Acknowledgments:** The authors also wish to acknowledge [PERSON] for contributing to the review of this work and for editing the language of the manuscript. * **Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Estimation of VF Material Properties
### Parameterization of the FE Model  
The silicone VFs were numerically modeled with three layers: the body, cover (which combines the SLP and epithelium), and ligament. Employing the same dimensions as the silicone VFs (see Section 2.1), a FE representation was generated comprising 205 triangular elements and 120 nodes, see Figure 2a. The sensitivity of the results to the triangularization is discussed in Section 5.3.  
All material properties are modeled as uniform across all layers except for the Young's modulus, which differs from layer to layer, but is constant within a layer. As a result, the FE model is parameterized by: \(\rho\), \(\eta\), \(E\) (for each layer), \(\
u\), and \(p_{\text{sub}}\) and \(p_{\text{sup}}\). The density, viscosity, Young's modulus for each layer, and subglottal pressure were estimated while all other parameters were treated as fixed and  
Figure 2: (**a**) Finite element triangulation used to simulate the silicone vocal folds; and (**b**) mesh deformation occurring as a result of medial compression. Red region: body; green region: ligament; and blue region: cover.  
known. Specifically, we assumed \(\
u=0.4995\)[31] and \(p_{\rm sup}=0\,\)Pa. This parameterization results in low dimensionality for the estimation problem while still employing a high degree-of-freedom VF model.  
As discussed in Section 2.2, medial compression of the silicone VFs is considered. Medial compression pre-stresses the VFs introducing more initial potential energy into the system. The compression is modeled via an initial position parameter, \(x_{0}\), which represents the maximum value that the \(x\) coordinate of any node can attain. As a result, having \(x_{0}=8.4\,\)mm indicates no medial compression and any value \(0\leq x_{0}<8.4\,\)mm indicates the presences of medial compression, as shown in Figure 2. The initial displacement of each node \(\mathbf{\theta}_{0}\) in cases involving medial compression was calculated from Equation (7) with time derivatives set to zero. This equation was solved iteratively by adjusting the \(x\) coordinate of the nodes in steps of \(10^{-4}\,\)mm while enforcing that no node cross the midline.  
The FE simulations used a time step of \(h=0.05\,\)ms [27], which is equivalent to a frame rate of 20,000 fps. Since the HSV was captured at 2000 fps, the simulated signal was downsampled to match that of the HSV. The first 250 ms of the simulated GAW were trimmed to ensure that the numerical model had reached stable oscillations and to avoid any initial numerical transients. This generally resulted in a phase shift between the measured and simulated signals which was corrected by cross correlating the first two cycles of the signals and phase shifting to align the remainder of the signals.
## 10 Conclusions and Roadmap Ahead
For the sake of a reduced complexity of simulations, the observation window used in our simulations was of 2 ms. Further investigative studies at a lower \(C/N_{0}\) than 100 dBHz should also increase the observation windows, in order to deal better with the high noise level typical in the pre-correlation domains. The equivalent block diagrams and the methodological approach presented here, as well as the initial pre-selection of relevant features and feature extractors can also serve the basis towards further studies in the post-correlation domain, where the noise levels are significantly lower than in the pre-correlation domain, especially for the long post-detection integration times. Conceptualization, W.W., I.A.S., G.C., E.S.L. ; methodology, W.W., I.A.S., G.C., E.S.L. ; software, W.W. and E.S.L. ; validation, W.W.; formal analysis, W.W., I.A.S., G.C., A.M., T.W., E.S.L. ; investigation, W.W., I.A.S., G.C., A.M., T.W., E.S.L. ; resources, W.W., I.A.S., G.C., A.M., T.W., E.S.L. ; writing--original draft preparation, W.W. and E.S.L. ; writing--review and editing, I.A.S., G.C., A.M., T.W. ; visualization, W.W.; supervision, E.S.L. ; project administration, I.A.S., T.W., and E.S.L. ; funding acquisition, E.S.L. and A.M., T.W. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was partly funded by the European Space Agency (ESA) in the project FINESSE (\"Fingerprinting GNSS for authentication\"). The opinions expressed herein reflect the authors' view only. Under no circumstances shall the ESA be responsible for any use that may be made of the information contained herein. This work was also partly supported by the Academy of Finland, under the project ULTRA (328226, 328214). **Institutional Review Board Statement**: Not applicable. **Informed Consent Statement**: Not applicable. **Data Availability Statement**: Not applicable. **Conflicts of Interest**: The authors declare no conflict of interest.
## 4 Analysis of Settlement Data and Discussion
### MDD Settlement Analysis  
The analysis of Multiple-Depth Deflectometer (MDD) data and their comparison with original data and smoothed curve generated by the LOESS trend model is presented in Figures 13-16. All MDD measurements for each laser module installed in the track foundation soil after applying the resampling method are presented in Figure 17. Standard deviation errors were calculated for each MDD laser module (#1, 2, 3, and 4) using the trend models, which found values of 0.872 mm, 0.6956 mm, 0.6295 mm, and 0.4327 mm, respectively. These very small errors indicate a close correspondence between MDD laser deformation data and the smoothed trend models. Similar standard deviations of Lasers #2 and #3 suggest their shared location in the similar foundation soil layer, while differences in deformation between Lasers #1 and #4 can be attributed to variations in soil depth and stress level differences. Notably, the higher standard deviation of Laser sensor module #1 implies its position located between the concrete slab layer (HSB; Hydraulically Stabilized Layer) and the existing lower compacted soil layer, potentially leading to increased random deformation due to railway traffic load.  
Concerning scattered data in MDD laser sensor recordings, specifically at MDD position #1, we observed that this position experienced a different deformation pattern compared to other positions. Deformation values measured at MDD position #1 deviated significantly from the expected values, indicating an upward trend and divergence in settlement data when compared to lower MDD laser positions that exhibited a downward trend. One potential factor contributing to this difference is the cavity expansion resulting from cement grout pressure during the repair of track foundation soil that was settled and an unexpected void space existing between the slab and the compacted soil surface.  
Grouting pressure was applied for restoration of the slab track between the upper subgrade layer and the HSB layer, precisely at the location of MDD position #1. The presence of pressurized cement grout and subsequent cavity expansion introduced non  
Figure 13: Original deformation monitoring results and estimated displacement using the trend model at Laser module #1.  
uniform settlement patterns contributing to the observed divergence in settlement data at MDD position #1.  
It was necessary to consider factors such as non-uniform cavity expansion, weak or unstable subgrade conditions, and the loss of grout and formation of unknown voids as potential contributors to divergence of settlement types. However, cavity expansion resulting from grout pressure can be one of the major potential contributing factors among many others in this kind of repair task for the track slab.  
Figure 14: Original deformation monitoring results and estimated displacement using the trend model at Laser module #2.  
Figure 15: Original deformation monitoring results and estimated displacement using the trend model at Laser module #3.
# GNSS Positioning Using Mobile Devices with the Android Operating System
## 4 Results and Discussion  
Since different tests were conducted following both static and kinematic approaches, as described in the previous section, now the main interesting results are shown, also considering the two different software used for the post-processing analysis. To make a more readable paper, these results will  
Figure 2: Synthetic outline of the data acquisition and data processing procedures.  
be shown and commented on in two sub-sections, the first related to the behavior considering the two different environments and the second comparing the smartphones' results with those obtained with the u-blox receiver. In both cases, the results were obtained considering the post-processing approach, while in the third Section 4.3, the real-time results will be shown. We have considered two main statistical information for analyzing the obtained results: the precision estimated through the root mean square error (RMSE) and the accuracy as the difference between the mean estimated values and the reference ones.
# Application of Enhanced YOLOX for Debris Flow Detection in Remote Sensing Images
## 5 Discussion
### Overall Performance Analysis of RS-YOLOX-Nano
#### 5.1.1 Introducing Combined Data Augmentation Strategies of Mosaic and Mix-Up  
The network model trained using a combined data augmentation strategy of Mosaic and Mix-up demonstrated an average reduction of 8 ms in single-sample image detection time compared to before the improvements. Concurrently, its mean average precision (\(mAP\)) increased by 1.01%. This approach not only reduced the time required for detection inference but also enhanced the average detection accuracy for debris flow geological disasters. Thus, the introduction of the combined data augmentation strategy using Mosaic and Mix-up to expand the dataset evidently improves the efficiency of debris flow geological disaster detection, enabling more comprehensive model training and reduced inference time.
## 4 The Proposed Measuring Method
### Accurate Matching  
Since \(\Delta_{\varphi}\) was in the range \([-6^{\circ},\,+4^{\circ}]\), accurate matching was conducted in the range \([q_{FPDD_{max}}-10^{\circ},q_{FPDD_{max}}+10]\). The template was pre-intercepted from different angular spool positions at each degree from 0 to 360. Thus, there were 360 templates in total. The exact template used for accurate matching was that closest to \(q_{FPDD_{max}}\). The position where the matching metric reached the highest value was considered to be the drive bore location. The accurately matched spool angular position \(\varphi_{match}\) was then calculated by transforming the matched location to spool angular coordinate using Equation (6). The process is shown in Figure 9.  
\[\varphi_{match}=tan^{-1}\frac{x_{c}-x_{match}}{y_{match}-y_{c}} \tag{6}\]  
where \((x_{match},y_{match})\) is the position of the center point of the matched template, and \((x_{c},y_{c})\) is the position of the center of the main bore. The zero point is at the upper left corner of the image.  
Figure 8: Robustness investigation result for _FPDD:_\(\Delta_{\varphi}\) for \(\alpha=0^{\circ},\,1^{\circ},\,\ldots,\,360^{\circ}\).  
Figure 9: Accurate matching in pre-located area.