esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Evaluating Force Transfer of FCS  
In order to investigate the sensor response of the FCS a fiber optic Mach-Zehnder (MZ) interferometer was applied [25]. The previously developed fiber optic MZ interferometer enables the investigation of the force transfer between the FCS and the optical glass fiber (fiber optic sensors) and thus the characterization of the bond between these two elements of the FCS. As illustrated in Figure 4, the applied fiber optic MZ interferometer consists of a broadband light source (BBS) (Opto-Link C-Band ASE), two fiber optic 3 dB couplers, two fiber arms, and a spectrometer (OSA) (Ando AQ6317B). The fiber optic MZ interferometer was set-up with only SM fiber components and all fiber components were spliced together. Moreover, the FCS was spliced to one arm of the developed interferometer in order to determine the strain transfer between the optical fiber and the FCS. When force is applied to the FCS using the tensile testing machine, the related length change of the optical fiber of the FCS causes a phase difference (\(\Delta\varphi\)) between the light (of wavelength \(\lambda\)) travelling in both fiber arms. This in turn results in a change of the interference pattern (displayed in the subset of Figure 4) from which the length change \(\Delta L\) of the optical fiber inside the FCS can be determined as follows:  
\[L=\frac{\lambda_{c}^{2}}{n_{\text{corg-}}\Delta\lambda} \tag{1}\]  
Figure 3: Experimental setup to evaluate the sensitivity to micro- and macro-bending effects on the applied optical glass fibers.  
In Equation (1), \(n_{core}\), \(\lambda_{c}\), and \(\Delta\lambda\) are the refractive index of the fiber core, the central wavelength of the light source, and spectral difference between two adjacent maximums of the measured transmission spectrum of the Mach-Zehnder interferometer, respectively. The refractive index value for the fiber core was taken as 1.45.
# Analysis of Flow Characteristics and Effects of Turbulence Models for the Butterfly Valve
## 2 Theory
### Turbulence Model
#### 2.1.3 Shear Stress Transport (SST) Model  
In the SST model, the eddy viscosity is expressed with the function of the turbulence kinetic energy and the specific dissipation rate as follows [40]:  
\[\mu_{t}=\rho\frac{k/\omega}{\max(1;\Omega F_{2}/a_{d}\omega)} \tag{7}\]  
The eddy viscosity value was influenced by the specific dissipation rate and \(a_{l}\). The specific dissipation rate can be obtained with an auxiliary function. The auxiliary function is defined as a function of wall distance of \(d\) as follows [40]:  
\[F_{2}=\tan\left\{\left(\max\left[2\frac{\sqrt{k}}{0.09\omega d};\frac{500\mu} {\rho d^{2}\omega}\right]\right)^{2}\right\} \tag{8}\]  
Two transport equations for the SST model can be defined with a blending function of \(F_{1}\) for the model coefficients of turbulence dissipation rate, \(\varepsilon\) and specific dissipation rate, \(\omega\). The transport equation for the turbulent kinetic energy is expressed, as the following [42]:  
\[\frac{\partial(\rho k)}{\partial t}+\frac{\partial}{\partial x_{j}}\left(\rho uj k-\left(\mu+\frac{\mu_{t}}{\sigma_{k}}\right)\frac{\partial k}{ \partial x_{j}}\right)=\tau_{ij}S_{ij}-\beta^{\prime}\rho k\omega \tag{9}\]  
\[\frac{\partial(\rho\omega)}{\partial t}+\frac{\partial}{\partial x_{j}}\left( \rho uj\omega-\left(\mu+\frac{\mu_{t}}{\sigma_{\omega}}\right)\frac{\partial \omega}{\partial x_{j}}\right)=P_{\omega}-\beta\rho\omega^{2}+2(1-F_{1})\frac {\rho\omega_{\omega 2}}{\omega}\frac{\partial k}{\partial x_{j}}\frac{\partial\omega}{ \partial x_{j}} \tag{10}\]  
where \(P_{\omega}\) is the production term of \(\omega\) which can be expressed with vorticity as follows:  
\[P_{\omega}\equiv 2\gamma\rho\left(S_{ij}-\frac{\omega S_{nn}\delta_{ij}}{3} \right)S_{ij}=\gamma\rho\Omega^{2} \tag{11}\]  
The blending function \(F_{1}\) expressed formally can be defined as  
\[F_{1}=\tanh\left\{\left(\min\left[\max\left[\max\left[\frac{\sqrt{k}}{0.09 \omega d};\frac{500\mu}{\rho d^{2}\omega}\right];\frac{4\rho\sigma_{\omega 2}k}{CD_{k\omega}d^{2}}\right]\right)^{4}\right\} \tag{12}\]  
where \(\mathrm{CD_{k\omega}}\) is cross-diffusion term in the \(k-\varepsilon\) model as follows:  
\[CD_{k\omega}=\max\left[\frac{2\rho\sigma_{\omega 2}}{\omega}\frac{\partial k}{ \partial x_{j}}\frac{\partial\omega}{\partial x_{j}};10^{-20}\right] \tag{13}\]The turbulence constants in this model expressed with the combination of symbol \(\varphi\) defined by blending the coefficients in the \(k-\omega\) model and \(k-\varepsilon\) model, denoted as \(\varphi_{1}\) and \(\varphi_{2}\), respectively [43], as follows:  
\[\varphi=F_{1}\varphi_{1}+(1-F_{1})\varphi_{2},\ \varphi=\{\sigma_{k},\sigma_{ \omega},\beta,\gamma\} \tag{14}\]
# Edge and Cloud Computing in Smart Cities
## 5 Challenges, Open Issues, and Future Research Directions
### Task Offloading Strategies  
Task offloading in edge-cloud environments must be dynamically optimized to balance latency, energy consumption, and computational efficiency. A major challenge is determining the optimal execution location for each task, as edge devices, edge servers, and cloud platforms have distinct processing capabilities. The decision-making process must account for fluctuating network bandwidth, computation delays, and real-time user mobility [187; 188].  
Existing approaches to task offloading rely on static thresholds or heuristic models, which fail to adapt to dynamic workloads. More recently, Deep RL (DRL)-based adaptive offloading has been proposed, where an AI model continuously learns optimal offloading policies by analyzing network conditions and device capabilities. However, training efficiency, generalization to unseen network states, and scalability to large-scale deployments remain open issues [189; 190; 191].  
Another challenge is joint optimization of task partitioning and offloading. Many tasks in smart-city applications are computationally intensive and require partial execution at different layers. Traditional binary offloading (executing a task entirely at one location) is often suboptimal, leading to high transmission delays. Partition-aware offloading models, which split tasks dynamically across cloud and edge layers, need further exploration [192; 193].  
AI-based offloading policies should be adaptive and context-aware, considering network variability, device constraints, and service deadlines. Future studies should explore hybrid offloading models, combining cloud-based and edge-based decision-making mechanisms. Dynamic learning techniques can be employed to adjust offloading decisions in real-time based on evolving network conditions [194; 195; 196].
## 3 Optically Pumped Lasers
### Distributed Feedback (DFB) Lasers
In particular, a DFB laser exploiting a bidimensional pattern of nanopillars (see Figure 11f) was obtained in MAPbI\({}_{3}\) thin films both by [PERSON] et al. [75] and by [PERSON] et al. [68], obtaining single mode emission around 785 nm, with a linewidth of below 0.4 nm. Furthermore, the last group also realized the only, to date, perovskite 2D DFB laser operating in the green by exploiting MAPbBr\({}_{3}\) as active material. After a proper optimization of the film realization procedure by solution, in order to minimize the ASE threshold, single mode lasing was demonstrated at about 555 nm, with a remarkably low threshold of 6 \(\upmu\)Jcm\({}^{-2}\) under 1 ns pulsed pumping at 355 nm. Moreover, by acting on the grating period the authors demonstrated lasing from both TE and TM modes (see Figure 11d,e) [18]. Dual mode lasing was also obtained in 3D inverse opals of MAPbBr\({}_{3}\), realized by infiltration of a polystyrene opal with the perovskite precursor solution, followed by polystyrene dissolution in toluene [69] (see Figure 11g). In this case, a rather high threshold of 1.6 mJcm\({}^{-2}\) was found, under 500 ps pumping at 532 nm, thus much higher than the typical values of DFB perovskite lasers. This was probably due to a lower effective gain resulting from the low perovskite fraction in the inverse opal structure, or to higher scattering caused by the irregularities of the all-solution processed 3D photonic crystal structure, or to higher non radiative relaxation rate related to the high surface area of the inverse opal structure. An interesting hybrid between DFB structures and WGM lasersg was demonstrated by depositing MAPbBr\({}_{3}\) microwires on a silicon grating [70] (see Figure 11h). In this case, single mode lasing was ascribed to the light confinement only in the microwires region above the void region, thus making the structure an array of microcavities, emitting along the substrate patterning direction. Finally, DFB lasers with CW operation was recently reported in a device exploiting a MAPbI\({}_{3}\) thin film deposited on a patterned thin alumina layer on a high thermal conductivity sapphire substrate [7]. Linearly polarized single mode lasing was obtained at the specific temperature of 102 K, and ascribed to the gain from tetragonal phase inclusions that are photogenerated by the pump source within the normally existing orthorhombic matrix, with a clear threshold at 17 KWcm\({}^{-2}\), under CW pumping at 445 nm. More recently room temperature lasing was claimed in a directly imprinted MAPbI3 film showing the appearance of a narrow peak with a linewidth of 0.7 nm at high excitation density, ascribed to lasing with an extremely low threshold of only 13 Wcm\({}^{-2}\)[76; 77], under CW pumping at 355 nm.
# A Wideband Dielectric Waveguide-Based 160-GHz Radar Target Generator
## 5 Measurements
For distances larger than 2.5 m the noise level  
Figure 11: Mean value (—) and standard deviation (- - -) of the range spectrum of a free-space radiating radar with an extended metal plate target (**a**), the RTG with an metal cuboid target (**b**), and an metal cylinder target (**c**). decreases, since the clutter is reflected again at the target. Due to the increased noise floor, the distance accuracy decreases with a proportionality of \(1/\sqrt{\mathrm{SNR}}\). The measured standard deviation of the target distance for the free-space radiating radar was \(13.5\,\mathrm{\SIUnitSymbolMicro m}\). In comparison, the standard deviation of the target distance for the RTG with a metal cuboid target was \(170.2\,\mathrm{\SIUnitSymbolMicro m}\). According to the Cramer-Rao lower bound [18], the higher standard deviation corresponds approximately to the measured noise level difference. Thus, the RTG only increased the noise level, but had no further influence on the distance accuracy. The minimum measurable distance of the RTG was limited by the mechanical construction of the transition from MMIC to dielectric waveguide and the target brackets. The range spectra with the metal cuboid brackets in contact with the mode transformer and without a target are shown in Figure 11(a). The target at \(13\,\mathrm{cm}\) is clearly distinguishable from the reflection of the dielectric waveguide in the mode transformer. A more compact construction would even increase the minimum distance that could be simulated by the RTG. A range spectrum with the two targets at measured distances of \(38.8\,\mathrm{cm}\) and \(40.8\,\mathrm{cm}\) is shown in Figure 11(b). To measure this minimum resolution, the targets were clamped around the waveguide in a distance of \(1.5\,\mathrm{cm}\) from each other. By increasing the target range, the minimum resolution deteriorates slightly due to the broadened peaks.
# Towards Understanding Neural Machine Translation with Attention Heads' Importance
## 4 Linguistic Knowledge
### Part-of-Speech
However, due to the different baseline values for each part of speech, we cannot intuitively determine which part of speech experiences the greatest decrease in correct translation accuracy. Therefore, we have presented in table format the correct translation accuracies of words for each part of speech generated by the complete model, as well as the decrease in these accuracies when important attention heads are masked, as shown in Table 6. This method allows for an effective observation of the changes in correct translation accuracy when multiple attention heads are masked [14]. We observed that the attention head learned more about adjectives and nouns when the important attention head was masked. This is presumably due to the reason that nouns and adjectives play a more significant part in sentence structure in English. When masking the most important attention heads (e.g., five), we found that the accuracy of prepositions reduced the fastest, indicating that preposition-related knowledge primarily exists in the most important attention heads. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Complete** & \multicolumn{6}{c}{**Pruned Model**} \\ \cline{3-8}  & **Model** & **5** & **10** & **15** & **20** & **25** & **30** \\ \hline N & 55.07 & 12.54 & 13.19 & 20.61 & 27.74 & 32.79 & 34.46 \\ V & 40.05 & 6.39 & 7.22 & 12.84 & 16.56 & 19.01 & 21.01 \\ ADJ & 53.85 & 14.27 & 15.15 & 35.83 & 38.63 & 39.77 & 40.23 \\ ADV & 43.79 & 18.69 & 20.07 & 30.03 & 30.59 & 31.01 & 31.92 \\ IN & 58.87 & 18.61 & 20.33 & 21.08 & 22.38 & 25.30 & 28.41 \\ DT & 65.01 & 13.55 & 15.74 & 11.69 & 10.15 & 11.93 & 18.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The changes in the accuracy of correct translations for words of each POS between the complete model and the pruned model. All data in the table are represented as percentages. The leftmost column lists the POS. The “Complete Model” column shows each part’s translation accuracy, while the “Pruned Model” column, labeled “5-30”, displays the percentage decrease in accuracy after masking 5 to 30 attention heads. Figure 6: Masking important attention heads on each piece of POS information.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Mean** & & & & & & & \\
**(95\% CIs)** & **Condition** & **Pre-Run** & **Post-Run** & **Post-Run** & **Post-Run** & **Post-Run** & **Post-Run** \\  & & & **0 Min** & **5 Min** & **10 Min** & **20 Min** & **30 Min** \\ \hline \multirow{4}{*}{Blood lactate (mmol/L) ICC: 0.75} & IBP & 1.3 & 9.8 & 6.7 & 5.4 & 4.2 & 3.3 \\  & & (0.2) & (1.1) & (1.2) & (1.1) & (0.8) & (0.5) \\  & & 1.3 & 9.4 & 6.9 & 4.4 & 3.3 & 2.7 \\  & & (0.2) & (1.0) & (1.1) & (0.8) & (0.6) & (0.4) \\  & Passive & 1.3 & 9.3 & 8.6 \(\dagger\) & 6.0 \(\ddagger\) & 4.3 & 3.5 \\  & & (0.3) & (1.2) & (1.2) & (0.9) & (0.7) & (0.5) \\ \hline \multirow{4}{*}{Heart rate (bpm) ICC: 0.91} & IBP & 72.3 & 185.0 & 95.9 & 110.2 & 95.3 & 90.1 \\  & & (6.2) & (2.0) & (5.7) & (7.6) & (6.9) & (5.1) \\  & & 73.8 & 184.9 & 123.3 \(\#\) & 117.9 & 96.3 & 91.1 \\  & & (5.2) & (1.5) & (5.3) & (8.2) & (4.8) & (5.0) \\  & Passive & 72.8 & 185.5 & 102.9 & 114.0 & 94.2 & 90.9 \\  & & (5.8) & (1.1) & (5.5) & (7.3) & (5.4) & (4.9) \\ \hline \multirow{4}{*}{Energy expenditure (kcal) ICC: N/A} & IBP & - & 74.5 & 20.5 & 29.9 & 33.2 & 30.6 \\  & & & (10.9) & (4.6) & (7.1) & (7.9) & (6.8) \\  & & & 76.3 & 38.8 & 34.1 & 37.3 & 31.9 \\  & & & (12.5) & (5.1) & (6.2) & (8.1) & (7.1) \\  & Passive & - & 75.7 & 24.7 & 31.9 & 36.0 & 31.9 \\  & & & (12.0) & (5.5) & (6.5) & (8.0) & (7.3) \\ \hline \multirow{4}{*}{Fatigue perception (cm) ICC: 0.76} & IBP & 1.1 & 6.8 & 3.4 & 2.1 & 1.3 & 0.8 \\  & & (0.4) & (0.8) & (0.9) & (0.5) & (0.4) & (0.2) \\  & Active & 0.7 & 6.6 & 3.3 & 1.9 & 1.2 & 0.7 \\  & & (0.2) & (0.9) & (0.7) & (0.5) & (0.4) & (0.2) \\  & Passive & 0.9 & 7.0 & 3.3 & 2.2 & 1.2 & 0.8 \\  & & (0.3) & (0.9) & (0.6) & (0.8) & (0.4) & (0.4) \\ \hline \end{tabular}  
* IBP: inverted body position; ICC: intraclass correlation coefficient, \(\dagger\) Different from the IBP (23%, \(p\) = 0.001, ES = 0.82) and active (20%, \(p\) = 0.01, ES = 0.75) conditions at post-run 5 min; \(\ddagger\) Different from the active condition (27%, \(p\) = 0.02, ES = 0.90) at post-run 10 min; \(\sharp\) Different from the IBP (22%, \(p\) = 0.001, ES = 2.52) and passive (17%, \(p\) = 0.002, ES = 1.92) conditions at post-run 5 min.  
\end{table} 
Table 1: Changes in heart rate, energy expenditure, blood lactate, and fatigue perception. Figure 4: Changes in total energy expenditure regardless of time, IBP: inverted body position; Error bars are upper limits of 95% confidence intervals, \(\ddagger\) Condition effect (F228 = 4.81, \(p\) = 0.01): Active was recorded at higher total energy expenditure values compared with the IBP (219 vs. 189 kcal, 14%, \(p\) = 0.01, ES = 0.48). Fatigue perception did not differ among the three cool-down conditions at each time point (no condition \(\times\) time interaction: F\({}_{10,238}\) = 0.13, \(p\) = 0.99; no condition effect: F\({}_{2,238}\) = 0.87, \(p\) = 0.42; time effect: F\({}_{5,238}\) = 245.80, \(p\) < 0.0001; Table 1). Regardless of the condition, fatigue perception was 87% higher after running (6.8 cm, \(p\) < 0.0001, ES = 4.63) and returned to the pre-run level (0.9 cm) at post-run 20 min (1.2 cm, \(p\) = 0.55; Figure 3A).
# Monoterpenoids Evolution and MEP Pathway Gene Expression Profiles in Seven Table Grape Varieties
## 2 Results and Discussion
### The Correlation of the Genotype sup1822 in VvDXS1 to the Composition of Monoterpenoids  
According to the report of [PERSON] (2010), the gain of function mutation of _VvDXS_ gene caused the accumulation difference of monoterrepenoids in grape berry, the snp: 1822 G/T and T/T lead to the muscat phenotype while G/G leads to the neutral flavor [30]. This snp site in all the seven varieties studied here was genotyped by Sanger sequencing (Figure S1). The results showed that 'Christmas rose' and 'Moldova' carried the G/G loci, which was corresponded to the very low content of total monoterrepenoids, 'Muscat of Alexandria', 'Italia', 'Tamina' and 'Xiangfei' carried the G/T loci, while 'Zaomeiguxiiang' carry the T/T loci. [PERSON] investigated the monoterrepenoids contents of core collection in FEM to conclude that the homozygous mutants (T/T) had higher contents than heterozygous mutants (G/T) [30]. It was interesting to notice that 'Xiangfei' with G/T type had the highest total contents, more than 'Zaomeiguxiiang' with T/T type.  
The expressions of _VvDXS1_ and _VvDXS3_ were compared in the two varieties. It was worthy to notice that the expression of _VvDXS1_ in the two varieties showed opposite trends during the developmental stage. In 'Xiangfei', it decreased and then increased sharply with a high expression at grape ripening, while in 'Zaomeiguxiiang', it showed an increasing trend and then a continuous decrease with a very low expression in the ripening stage. The expression of _VvDXS3_ was similar in both varieties, but had a slightly higher expression in 'Xiangfei'at maturity. The expression of these _VvDXSs_ might affect monoterrepenoids accumulation within the two varieties.
# Can Isotopes Be Used as Lead Tracers in Shooting-Range Soils?
###### Abstract  
Lead isotopes have been widely used to assess the sources of Pb in the soil environment since lead isotopes ratios have a specific signature that allows us to use them as environmental tracers. However, some lead sources are difficult to be identified. This article contains the lead isotope data from soils and shot pellets collected in an abandoned shooting range (NW Spain). Twelve soil and three-shot pellet samples were randomly collected and analyzed using MC-ICP-MS. The isotope ratios are shown, and analyses proved that Pb originated predominantly from the used shot pellets. Contaminated soils exhibited an isotopic composition (\({}^{206}\)Pb/\({}^{207}\)Pb, \({}^{208}\)Pb/\({}^{204}\)Pb, \({}^{206}\)Pb/\({}^{204}\)Pb and \({}^{208}\)Pb/\({}^{206}\)Pb) close to some shot pellets from different manufacturers. These results offer new, valuable data for other researchers working on lead contamination research and the identification sources of Pb for adjacent areas to shooting-range facilities and for wildlife ecotoxicology. Still, the use of several ammunitions derived from different sources, such as recycled Pb, showed that it is hard to identify the lead source and these kinds of facilities.  
civilian shooting; isotope ratio analysis; lead stable isotopes; lead ammunition; shot pellets; recycled lead; soil contamination; source identification +
Footnote †: journal: Medical  
ammunition origins, and sometimes, the isotope identification can be overlapped with topsoil signatures [5]. More data are needed to improve databases about soil contamination by lead ammunition and their potential risk for plants and wildlife fauna.  
This study reported lead isotopic data from an abandoned trap shooting range facility in NW Spain, with lead concentrations ranging from 80 to 720 mg kg\({}^{-1}\) as pseudototal amounts and significant contents of bioavailable lead due to the acidic soil pH [23]. Additionally, higher levels of polycyclic aromatic hydrocarbons (PAHs) were found as a result of the usage of clay pigeons for shooting activities [14], and lead contamination was seen throughout the soil profile at various soil depths [24]. Here, we showed the lead isotope ratios (\({}^{207}\)Pb/\({}^{204}\)Pb, \({}^{208}\)Pb/\({}^{204}\)Pb, \({}^{206}\)Pb/\({}^{204}\)Pb, \({}^{208}\)Pb/\({}^{206}\)Pb and \({}^{206}\)Pb/\({}^{207}\)Pb) from 3 shot pellets and 12 soil samples from various locations in the shooting range facility. Lead isotope ratios from soils and shots pellets were measured by MC-ICP-MS. They were compared with isotopic data from previously published ammunition debris (shot pellets, civilian and/or military bullets).
# WFO: Cloud-Edge Cooperative Data Offloading Strategy Akin to Water Flow
## 3 System Model and Problem Statement
### Overview of Framework  
WFO is designed to effectively use cloud and edge nodes to realize fast same data forwarding under the service architecture of cloud-edge collaboration. Specifically, the approach is meant for live streaming. This is a shift from a traditional one-to-one transmission to a one-to-many transmission. The design process of WFO consists of three steps. _First, grouping nodes._ Edge nodes are queued and numbered in chronological order of request. The bandwidth of the cloud is allocated based on the node ID and the actual bandwidth requirements. Until the cloud has no bandwidth left, the subsequent nodes are mounted on the earlier nodes within the user's tolerance delay. Multiple data forwarding links are formed. Data forwarding between groups does not affect each other. _Second, calculating priorities._ When grouping is performed, the flow of data between nodes is not determined. We must calculate the priority of nodes in a group and sort the data routing based on priority. _Finally, data offloading._ Data are transmitted in the permutation generated in the second step, passing through permutation nodes, akin to water flow, to the next node. When the first node receives the data, the first node and cloud offload the data to subsequent nodes until the last node receives the data. The overall framework is shown in Figure 2.