esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Radar Observations of the Downslope Wind Storm and Turbulence
### Vertical and Horizontal Wind below Flight Level  
The WCR measured vertical velocity is shown in Figure 11 for the four along-wind flight tracks. No data are available near the flight level due to the radar blind zone; that zone is partly filled with flight-level vertical air velocity data, which match well with the WCR vertical velocity. The zonal wind derived from WCR vertical-plane dual-Doppler is shown in Figure 12, below flight level only, together with the gust probe zonal wind plotted at flight level. The curved nature of that flight level in Figures 11 and 12, especially on the far lee side, is testimony to the strong vertical drafts the aircraft experienced, resulting in departures from assigned flight altitude. The hydrometeor streamlines in Figure 12 are defined as being tangential to the 2D hydrometeor velocity vectors, i.e. the vertical component includes the actual particle fallspeed, as in [28].  
The flow over the windward side is weakly ascending and very smooth (the light-blue area, especially in Figure 11c). Terrain-driven vertical velocity dipoles are evident across the main crest and across a secondary crest in the lee. Turbulent rising and sinking eddies can be seen in the boundary layer, mainly in the lee, on account of the stronger winds there (Figure 12). Each transect shows a plunging, accelerating air current, starting above flight level just upwind of the crest and extending some distance in the lee, with wind speeds doubling to \(\sim\)25 m\(\cdot\)s\({}^{-1}\). In two transects, the second and the last ones, blowing snow is separated from the surface and lofted in a deep updraft reaching 8-9 m\(\cdot\)s\({}^{-1}\) at flight level. This updraft occurred at x = 24 km downwind of the crest in both transects (Figure 11b,d). In the third transect, no WCR are available that far east, but a strong updraft and saturated (cloudy) air was encountered at x = 25 km (Figure 9b). This persistent updraft was deep, lifting the cirrus cloud base aloft (Figure 8b,d). This updraft, referred to as a hydraulic jump, was associated with the separation of the downslope windstorm from the boundary layer, implying strong low-level convergence (Figure 12b,d). The same hydraulic jump may have been present in the first transect, but the aircraft did not extend far enough leeward.  
Figure 11: As in Figure 8, but for WCR measured hydrometeor vertical velocity. Also shown, at flight level, is the gust probe vertical air velocity. Note that the color bar is centered at 0 m\(\cdot\)s\({}^{-1}\) for the gust probe, yet at \(-\)1 m\(\cdot\)s\({}^{-1}\) for the WCR. This is intended to allow for interpretation of WCR data as air vertical motion.
## 3 Results  
### Analysis of Experimental Results for Path Loss  
After finishing with measurements, path loss \(P_{L}(d)\) was calculated, in dB, as the difference between the received power \(P_{r}(d)\) measured at the point of distance \(d\) from the transmitter and the received power \(P_{r}(d_{0})\) at the first point \(d=d_{0}=1\) m, that is used for reference distance:  
\[P_{L}(d)=P_{r}(d)-P_{r}(d_{0})\;[\text{dB}] \tag{4}\]  
The \(P_{L}(d)\) resulting for the combination of the three types of ground coverings--soil, short grass and tall grass--, three frequency bands--\(F_{1}\) = 868 MHz, \(F_{2}\) = 2.4 GHz and \(F_{3}\) = 5.8 GHz--and the two antenna heights--\(H_{1}\) = 20 cm and \(H_{2}\) = 40 cm--, are following shown in Figure 4. Results are compared to the free space path loss model. For better comparison, all the figures are depicted using the same range in the \(y\)-axis, from 0 to 50 dB.  
Figure 3: RSSI measurement system: (**a**) commercial Wireless Sensor Network (WSN) node used; (**b**) programming interface.  
Generally speaking, path loss values are increasing when lowering the antenna height. The difference between the free-space and measured path loss also decreases with the increase of the frequency range. This latter result is explained by the obstruction of the FFZ which presents a radio \(r_{1}=\sqrt{d\lambda}=2.94\) m, and remains permanently obstructed for all the cases. In Table 1 the clearance or obstruction of the FFZ is indicated in percentage with respect to the antenna height. The percentages  
Figure 4: Path loss experimental results and comparison to free-space model and proposed model, at (**a**) \(F_{1}\), \(H_{1}\); (**b**) \(F_{1}\), \(H_{2}\); (**c**) \(F_{2}\), \(H_{1}\); (**d**) \(F_{2}\), \(H_{2}\); (**e**) \(F_{3}\), \(H_{1}\); and (**f**) \(F_{3}\), \(H_{2}\).  
indicate the existence of near-ground scenario for both \(H_{1}\) and \(H_{2}\) with FFZ obstruction over \(>\)60% that is considered the limit for the existence of losses due to diffraction (OLoS).  
For \(F_{1}\) = 868 MHz, the path loss is higher than that one due to the free-space model, and over the whole radio link distance. For \(F_{2}\) = 2.4 GHz, the path loss seems to modify the trend with the antenna height likely due to a lesser FFZ obstruction. Finally, for \(F_{3}\) = 5.8 GHz, the path loss is the case more similar to the free-space curve due to the fact that the FFZ is obstructed in a lower percentage \(\sim\)65% for \(H_{2}\) close to an obstruction-free condition (LoS).  
The type of ground covering also seems to influence the path loss trend. Soil and short grass present almost the same path loss in most cases. This may be due to the short height of the grass.  
However, the highest level of attenuation was experienced in tall grass field due to the length of the grass and the existence of more leafs that is reducing the clearance between the transmitter and the receiver, so lowering the effective antenna height to a value lower than \(H_{1}\) and \(H_{2}\).
# Retrieving Freeze/Thaw Cycles Using Sentinel-1 Data in Eastern Nunavik (Quebec, Canada)
## 4 Discussion
### Threshold Aspect  
Additionally, the effective threshold, on which the highest accuracy of a test site was reached, was further compared with the land-cover types (Figure 11). Observing from the average value alone, there was a clear distinction between the various categories. When the standard deviation and intuitive data distribution were taken into consideration (Table 2), the difference within a single category was relatively large, and there was no fundamental difference between categories.  
Figure 11: Best-performing threshold at different soil depths, clustered by four land-cover categories.  
Figure 10: Accuracy of different test soil depths: comparison between two polarizations, clustered by four land cover categories.  
In the practical optimization process, the distribution of the accuracies corresponding to thresholds of each site showed different forms. In the value range of 0 to 1, some sites had a clear and unique optimal threshold, some sites had multiple well-performing thresholds, some sites had continuous effective thresholds, and some sites had relatively smooth fluctuations in accuracy with the threshold. This situation increases the difficulty of finding a common useful optimal threshold directly based on land-cover categories.  
In general, optimizing the threshold helps to achieve significantly better accuracy. The attempt to explore the optimal threshold with consideration of land cover is not meaningless; however, to obtain more effective results, it is necessary to perform more comprehensive statistics with larger data sizes, where more land-cover information, especially vegetation information, should be included.
# Time Series Analysis of Acoustic Emissions in the Asinelli Tower during Local Seismic Activity
## 1 Introduction  
Fracture precursors in metallic [1; 2] and quasi-brittle materials like rocks [3; 4], concrete [5; 6; 7; 8; 9], and masonry [10; 11] can be experimentally investigated focusing on the statistical properties of an acoustic emission (AE) time series from growing micro-fractures, where the discovery of underlying scaling laws suggests a description of fracture as a critical phenomenon [12; 13; 14; 15; 16; 17; 18; 19; 20; 21]. Within this context, finding fracture precursors means identifying critical scaling exponents and early indicators of the approach to a critical state [22].  
The relevance of the AE applications in civil engineering for structural health monitoring is widely recognized. In this regard, it is increasingly necessary to give the highest priority to seismic risk mitigation in large portions of the Italian territory. Minor and light earthquakes can drive invisible damage processes in buildings and monuments, which eventually result in catastrophic collapses during stronger earthquakes. It is worth noting that AE can be exploited as a diagnostic tool in geophysics as well, since recent experimental evidences and theoretical studies support the hypothesis that increased AE and electromagnetic activities may be signature of crustal stresses redistribution in a large zone during the preparation of a seismic event [23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34]. Laboratory studies have been motivated by the need to provide tools for the earthquake prediction [35].  
Therefore, the AE structural monitoring might potentially provide twofold information: one concerning the structural damage and the other concerning widespread micro-seismic activity, propagating across the ground-building foundation interface, for which the building foundation represents a sort of extended underground probe [10; 11; 21; 23; 24; 30; 33].  
The presented research study was initially motivated by the debate about the alleged incompatibility between heavy vehicle traffic and Bologna's historical center, which concerns also the structural stability of two medieval towers, the Garisenda and the Asinelli (the taller) [36]. As reportedin a previous publication [30], the influence of environmental phenomena on the AE activity in the Asinelli tower, such as the flux of surrounding vehicle traffic, unusual anthropogenic activities, and wind action, has been excluded. Actually, the nearby seismicity apparently has some influence on the AE activity detected in a masonry wall of the tower [30]. Here, the focus is on the time correlation observed between the largest AE cluster and the strongest (magnitude 4.1) local earthquake, with an epicenter 100 km far from the monitoring site.
## 7 Experimental Results  
We simulate and analyze the proposed scheme by implementing it in the NS-2.30 network simulator [34]. In this section, we compare the proposed scheme with two other schemes: (1) SPEED [4], which is the most popular real-time protocol based on the relay speed information of one-hop neighbor nodes, and (2) THVR [6], which is another real-time protocol that uses the information of two-hop neighbor nodes. However, since the two schemes do not support sink mobility, a footprint chaining technique [9] is added to the schemes. The simulation network space consists of 1000 sensors randomly deployed in a 500 m \(\times\) 500 m squared area. The model of sensor nodes is followed by the specification of MICA2 [39; 40]. The radio range of each sensor node is 20 m. The source generates a CBR flow with an interval of 0.05 s, and a packet frame size is fixed at 30 bytes. The simulation time is 100 s and IEEE 802.15.4 standard [41] is exploited as the MAC protocol.
## 2 Materials and Methods
### Statistical Analysis  
The main aim of this study was to explore the various metal and sulfur pathways within the bottom sediment that contribute to enhancing the water quality from S1 to S3. Each measurement and analysis was considered a potential pathway or receptor for metals and/or sulfur compounds to achieve this. Triplicate results from each site were averaged to obtain the mean values, utilizing Excel and XLSTAT software (Microsoft 365 MSO-Version 2407 Build 16.0.17830.20056) for the calculations. These mean values were then systematically organized into tables and graphically represented to compare the changes occurring across the wetland.  
To identify the most significant differences in the substrate concentrations within the wetland, a comparison was performed between the inflow site (S1) and the far middle site (S3) using Student's \(t\)-test. This \(t\)-test is a powerful statistical tool used to determine whether there are meaningful differences between the means of two groups. This study allowed for the detection of significant variations in the concentrations of metals and sulfur compounds between the different sites. A probability value (\(p\)-value) of less than 0.05 was considered statistically significant, following the criteria set by [53]. These significance levels were employed to evaluate the strength of the associations between specific pathways and receptors across the different sites.  
In summary, the statistical analysis was crucial for interpreting the data and drawing significant conclusions about the behavior of metal and sulfur compounds within the wetland. This analysis revealed important differences between the sites and provided a thorough understanding of the interactions and patterns among the metal and sulfur pathways and receptors. As a result, it facilitated a more detailed evaluation of the wetland's effectiveness as an ecologically engineered treatment system.
## 4 Results and Discussion  
Effect of Solar Water and Normal Tap Water on the Temperature Difference across a 15 \(\times\) 10 TEG Panel  
A TEG module relies on the temperature difference between its surfaces to generate electric power using the Seebeck effect. In the proposed system, two methods are used to provide this temperature difference, depending on the renewable energy source. The first method, directs the passage of normal tap water through the TEG panel, as this water performs the task of dissipating heat on the surface of the cold panel. This is usually at a temperature ranging from 25 to 34 \({}^{\circ}\)C. The other way is by flowing this same water after it leaves the panel to the solar water heater, and then the water is used to heat the hot surface of the TEG. After that, the water is used for domestic purposes. In this way, a temperature differential is formed, and thus electrical energy can be generated by the panel. The proposed TEG panel was tested experimentally on a July day in Iraq. The hot (\(T_{h}\)) and cold side temperatures (\(T_{c}\)) are given in Figure 9.  
In this figure, it can be observed that (\(T_{c}\)) is low at the start of the experiment. Then, it increased because (\(T_{h}\)) has an effect on (\(T_{c}\)) over time until an equilibrium condition is established. The \(\Delta T\) between both the hot and cold sides of the 10 \(\times\) 15 TEG Panel is reduced as a result of this impact. On the hot side, the TEG panel measured low and high temperatures of 67.22 \({}^{\circ}\)C and 67.88 \({}^{\circ}\)C, respectively. Despite this, the temperature averaged 67.62 \({}^{\circ}\)C. Low and high temperatures of 25.40 \({}^{\circ}\)C and 33.28 \({}^{\circ}\)C were, respectively, recorded on the cold side, with an average temperature of 29.95 \({}^{\circ}\)C. On the surface of the panel, microcontroller thermocouples detected a temperature differential of 37.67 \({}^{\circ}\)C. During the same time period, the largest and lowest temperature variations were 42.35 \({}^{\circ}\)C and 34.22 \({}^{\circ}\)C, respectively. These results were acquired employing thermocouples in direct contact with panel surfaces, which allowed for more exact results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measuring Instrument** & **Range** & **Accuracy** & **Uncertainty \%** \\ \hline AC/DC Digital Clamp Meter, V & \(-\)20\({}^{\circ}\)C & \(\pm\)0.01\% & 0.01 \\ \hline Digital and Temperature Humidity & \(-\)20\(-\)70 \({}^{\circ}\)C & \(\pm\)1.0 \({}^{\circ}\)C\% & 0.58 \\ \cline{2-4} Meter, \({}^{\circ}\)C, RH\% & 0\(-\)100\% RH & \(\pm\)3.0\% RH & 1.73 \\ \hline NTC sensor & \(-\)200\(-\)260 & \(\pm\)1\% & 0.58 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The accuracy and uncertainty of various instruments.
## 3 FHP Detection and Correction Using an IMU Sensor
### Identifying the Criteria of FHP  
A good metric for accurate FHP determination is measuring the CVA [29]. The CVA is the angle between the line connecting the tragus (center of the outer ear), C7 (seventh cervical vertebra) (Figure 1a), and the horizontal line passing through C7 [30; 31]. [PERSON] et al. [32] measured the CVA using the side photographs of 30 healthy individuals without cervical spine abnormalities, wherein a result of \(-\)53.2 \(\pm\) 5.85\({}^{\circ}\) was obtained. However, the CVA varies from person to person. Therefore, a clear reference for CVA has not been reported in the literature to determine FHP [33]. However, as shown in the actual CVA measurement presented in Figure 1b,c, the higher the CVA, the better the posture.  
Other methods of measuring the CVA include using an inclinometer [34] or cervical range of motion (CROM) [35; 36]. The inclinometer presented in Figure 2a can be tilted to create an incline equal to the imaginary line that connects the tragus and C7 to measure the CVA. When the CROM presented in Figure 2b is worn on the head, it displays the angle at which the head is tilted. However, because instrumental methods require such equipment, it is difficult to measure the real-time CVA for daily postures. Further, when taking side photos, CVA estimations vary depending on the position of the camera, and--even if the position of the camera is fixed--the angle at which the photo is taken will vary owing to the different heights of individuals.  
Using the relationship between the CVA and sensing data collected by an IMU sensor, this study proposes a system to determine CVA with the sensing data retrieved by only a singular IMU sensor, which is then used to help prevent and treat FHP. Moreover, in this study, the average angle obtained from the user's good posture in the first 10 s during the experiments is employed as the user's characteristic standard angle of CVA (_baseline CVA_). Then, the FHP is determined based on this standard angle to build a personalized system that changes the standard angle according to an individual's physical situation and build.
## 3 Results and Discussion
### Content of TPC Using the Folin-Ciocalteu Reagent Method
#### 3.1.4 Comparison of the Extracts Tested
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Type of Solvent**} & \multirow{2}{*}{**Apple Cultivar**} & \multicolumn{6}{c}{**Content of Phenolic Compounds in Extracts (mg/1 g of DW)**} \\ \cline{3-8}  & & **Epicatechin** & **Rutin** & **Hyperoside** & **Isoquercitrin** & **Phlridzin** & **Phloretin** \\ \hline \multirow{4}{*}{Water} & Ligol & 0.132 \(\pm\) 0.070 & 0.080 \(\pm\) 0.050 & 0.220 \(\pm\) 0.006 & 0.931 \(\pm\) 0.020 & 3.391 \(\pm\) 0.550 & 0.021 \(\pm\) 0.006 \\ \cline{2-8}  & Gala & 0.076 \(\pm\) 0.005 & 0.083 \(\pm\) 0.004 & 0.204 \(\pm\) 0.290 & 0.647 \(\pm\) 0.010 & ND & ND \\ \cline{2-8}  & Gloster & 0.140 \(\pm\) 0.006 & 0.079 \(\pm\) 0.006 & 0.196 \(\pm\) 0.070 & 0.672 \(\pm\) 0.020 & ND & ND \\ \hline \multirow{4}{*}{Water-methanol (20:80, \(v/v\))} & Ligol & 0.698 \(\pm\) 0.040 & 0.078 \(\pm\) 0.004 & 0.288 \(\pm\) 0.060 & 2.587 \(\pm\) 0.090 & 103.08 \(\pm\) 2.940 & 3.134 \(\pm\) 0.060 \\ \cline{2-8}  & Gala & 0.429 \(\pm\) 0.007 & 0.077 \(\pm\) 0.003 & 0.282 \(\pm\) 0.005 & 2.827 \(\pm\) 0.060 & 79.527 \(\pm\) 1.510 & 1.252 \(\pm\) 0.040 \\ \cline{2-8}  & Gloster & 0.380 \(\pm\) 0.150 & 0.074 \(\pm\) 0.020 & 0.280 \(\pm\) 0.007 & 2.563 \(\pm\) 0.120 & 65.471 \(\pm\) 0.890 & 1.915 \(\pm\) 0.060 \\ \cline{2-8}  & Ligol & 0.258 \(\pm\) 0.090 & 0.069 \(\pm\) 0.005 & 0.287 \(\pm\) 0.004 & 2.552 \(\pm\) 0.090 & 53.337 \(\pm\) 0.380 & 1.614 \(\pm\) 0.070 \\ \cline{2-8}  & Gala & 0.376 \(\pm\) 0.070 & 0.063 \(\pm\) 0.020 & 0.303 \(\pm\) 0.020 & 2.763 \(\pm\) 0.040 & 27.325 \(\pm\) 0.500 & 0.439 \(\pm\) 0.090 \\ \cline{2-8}  & Gloster & 0.114 \(\pm\) 0.090 & 0.065 \(\pm\) 0.007 & 0.270 \(\pm\) 0.006 & 2.504 \(\pm\) 0.140 & 26.601 \(\pm\) 0.110 & 0.895 \(\pm\) 0.050 \\ \hline \hline \end{tabular}  
* Mean values for triplicates \(\pm\) SD. Abbreviations: DW: dry weight; ND: Not Detected.  
\end{table} 
Table 5: Content of polyphenolic compounds identified with HPLC in apple leaf extracts obtained by shaking them with a solvent (SSE).
## 4 Discussion
### Tracking Time and Dynamic Efficiency  
Tracking time is a metric that indicates how quickly the system can adapt to changing conditions. Dynamic efficiency, on the other hand, reflects how effectively and sustainably energy production is optimized following the adaptation process. These two metrics are critical for evaluating the performance of the proposed method in real-time applications. In our study, these concepts were analyzed through indirect measures such as the convergence time of the genetic algorithm, adaptation to dynamic conditions, and energy efficiency. Tracking time was assessed based on the convergence time, which represents the duration required by the genetic algorithm to find the optimal solution. As shown in Table 8, the convergence times under different scenarios were kept notably low, demonstrating the rapid responsiveness of our method. For instance, in Scenario 1, the best convergence time was recorded as only 0.0002152 s, while in the more complex solution space of Scenario 3, it was measured at 0.6772 s. These values confirm the suitability of the method for real-time applications.  
Dynamic efficiency, on the other hand, relates to the system's ability to optimize energy production under varying shading conditions. Despite a large solution space (e.g., 4.19 billion possibilities for a four-row PV configuration as noted in Table 1), the proposed method stands out with its fast convergence time and ability to minimize energy losses. As illustrated in Figure 8, the algorithm successfully adapted to dynamic conditions and achieved maximum efficiency in energy production. This demonstrates the system's stability and ability to adapt effectively to changing environmental conditions. Additionally, the success rate and solution quality presented in Tables 6 and 7 validate the stability and reliability of the proposed method. For example, optimal solutions have been achieved with a 100% success rate in Scenarios 1 and 2, while even in the more complex configurations of Scenarios 3 and 4, success rates have been maintained at high levels of 88.8% and 93.65%, respectively. Furthermore, the low standard deviation values in Table 7 indicate that the algorithm consistently delivers reliable and stable energy production results.  
As a result, the proposed method demonstrated superior performance in terms of both tracking time and dynamic efficiency. The algorithm's fast convergence time facilitates adaptation to dynamic conditions in real-time applications, while ensuring continuous optimization in energy production. These findings highlight the practical applicability and reliability of the proposed method.