esa-sceva/satcom-chunk-collection huggingface dataset:
# Local Stiffness Assessment of Geogrid-Stabilized Unbound Aggregates in a Large-Scale Testbed
## 5 Results and Discussion
### DCP Testing  
DCP tests were performed on the unbound aggregates as per the ASTM D6951 test method. Figure 11a shows the California Bearing Ratio (CBR) profiles from the surface to the geogrid location for Tests 1 to 3, estimated from the DCP tests. The DCP penetration rate was converted to CBR using the equation suggested by US Army Corps of Engineers [61]:  
\[\log(\text{CBR})=2.46-1.12\log\left(DCP_{(mm)}\right) \tag{10}\]  
where \(DCP\) is the penetration rate (mm/blow). In vicinity of the geogrid at a depth of 35.6 cm (14 in.) from the surface, the CBR values of the geogrid-stabilized specimen are higher than those of the unstabilized specimens, showing consistency with the BE modulus results.  
Figure 11b visualizes the number of DCP blows needed to penetrate through the unbound aggregate layer in the testbed. A higher blow count required in the geogrid-stabilized test section indicate that higher stiffnesses were achieved in the unbound aggregates of the geogrid-stabilized sections [12]. The number of DCP blows to reach the geogrid location was 29, 26, and 23 for the GG2-stabilized, GG1-stabilized, and non-stabilized sections, respectively. The higher blow count in the GG2-stabilized section indicates that the GG2 was more effective in enhancing aggregate stiffness, which is in accordance with the BE field-sensor test results. The horizontal dashed lines in Figure 11b represent the range of the potential upper bound of the geogrid-stiffened zone concluded from the BE field sensor tests, which is between the middle and the top sensor locations. Note that the slope of the accumulated blow count for the GG2-stabilized section increased in the geogrid-stiffened zone. Changes in slopes could indicate changes in strength properties. Therefore, the stiffness and strength profiles from the BE field-sensor tests and the DCP tests clearly indicate the existence of a geogrid-stiffened zone in the proximity of the geogrid, which becomes attenuated moving away from the geogrid.
## 4 Expedition Cruising in Svalbard and the Application of ICT
### ICT Tools Developed by AECO to Perform Collective Action
#### 4.1.1 The AECO Cruise Database
So, the longer you are in the business and the more people of course you know, the easier it is to solve those problems\"_ (research participant 10). With good communication, vessels at sea can inform each other about current conditions, notable wildlife sightings, or the activities of non-AECO vessels operating in the area. For instance, during the 2011 sailing season, a dead whale on a beach attracted many bears to the area. This information was quickly shared between operators via satellite communication, and many ships visited the area as a result. At the same time, network members organically agreed on a new rule that increased the minimum distance to the bears in the area to minimize adverse environmental impacts through overuse.
# Characterization of Chirality in Diffractive Metasurfaces by Photothermal Deflection Technique
## 2 Materials and Methods  
### PDT Characterization of Metasurfaces  
We investigated the same Ag-based metasurface from [34]; a photograph is shown in the top left inset of Figure 1. It was fabricated using low-cost nanosphere lithography [35], and a tilted deposition of 50 nm of Ag at 45\({}^{\circ}\)[20; 36; 37]. The sample was based on an hexagonal array of polystyrene nanospheres; the periodicity of the array and the diameter of the nanospheres were 522 nm, and 370 nm, respectively, and the metasurface lay on a 1 mm glass substrate. The tilted Ag deposition defined the asymmetric Ag shell, as well as the elliptical nanohole array (ENHA) [38] on the substrate. The geometry of such a metasurface led to diffraction orders for the wavelengths below its periodicity. Therefore,if such sample gets excited with an Ar pump beam, the light will be diffracted into well-defined orders. The intensity of these orders depends on the pump beam polarization, as the metasurface has asymmetric nanoshells and ENHA.  
In Figure 1 we show a sketch of the PDT experiment, based on bringing the diffracted orders back to the surface. Namely, if the sample is placed on a mirror, the metasurface diffracts the beams to the glass, where they get reflected from the mirror, travel back to the sample surface, and are finally absorbed by the metal. Six diffracted orders and the zeroth order get absorbed by the metal on the surface, where they create heat sources. Each heat source creates a local gradient of temperature, which deflects a probe-beam. Therefore, the deflected signal is a measure of the induced heat, which depends on the intensity of the diffracted order, and, thus, on the chirality of the pump beam. The bottom left inset in Figure 1 shows a simple photograph of the diffracted orders of the sample, where the pump beam at 514 nm excited the sample at a normal incidence. Six clear orders exit in the positions of the hexagon vertices, while the back-reflected, zeroth order exits at the center. In Figure 1, this beam is omitted as it is superimposed on the pump beam.  
PDT exhibits a very high absorption sensitivity in solids, of the order of \(10^{-7}\) cm\({}^{-1}\)[28, 39]. In this work, we measured diffraction-induced absorption at different wavelengths using an argon beam (Coherent Innova 70): at 458 nm, 496 nm, and 514 nm. The pump beam's intensity was mechanically modulated at low modulation frequencies. In this work, we reported the PDT data obtained at 16 Hz and 36 Hz. This beam passed through a linear polarizer and a quarter wave plate, thus becoming circularly polarized with LCP or RCP. For the probe beam, we used a He-Ne laser (Spectra Physics). This beam passed through the air above the surface, traveling in the y-direction, where it was deflected by the thermal gradients arising from the aforementioned diffracted orders. A 2D position sensor detected this deflection and connected it to a lock-in amplifier; thus, giving both the PDT amplitude and phase as a result. Moreover, we controlled the pump-probe distance using a motorized translation stage.
## 3 Proposed System
### PMIPv6/AAND with Multiple Virtual Interfaces  
In the PMIPv6/AAND, every interface has its own neighbor cache (NC). Every IP address in the connection, along with its VIs and concerned LL-ID in the concerned network, is maintained by the NC. By setting the flag value as' 1' when the VI receives a neighbor solicitation (NS) message, it is received by the VI because the new field value is assigned by '1'. The neighbour advertising (NA) is responded to by the concerned VI with its LL-ID. Figure 11 explains the proposed method virtual interface concept.  
Figure 10: PMIPv6/AAND signal flow.  
Figure 9: Topology 3—Multi-Homing of PMIPv6/AAND.
# A Joining Procedure and Synchronization for TSCH-RPL Wireless Sensor Networks
## 5 Simulations and Testbeds
\begin{table}
\begin{tabular}{l l c} \hline  & **Parameters** & **Values** \\ \hline \multicolumn{3}{c}{**Global Parameters**} \\ \hline \multirow{3}{*}{**TSCH**} & Scan Duration (TSCAN) & 1 s \\  & Timeslot duration (Tslot) & 10 ms \\  & TSCH Slotframe Schedule & EBs: 397 slots in length with only \\  & Default Orchestra Schedule [10] with multiple slotframes, & two enabled (TX and RX) \\  & each one for a particular traffic plane. & RPL: 31 slots in length with only \\  & & one enabled (Shared) \\ \hline \multirow{3}{*}{**RPL**} & Trickle Timer Interval Min (Imin) & 12 \\  & Trickle Timer Interval Doublings (ID) & 8 \\  & Trickle Timer Redundancy Constant (RC) & 10 \\  & DIS interval (DISTT) & 60 s \\ \hline \multicolumn{3}{c}{**Test 1: Different numbers of channels in steady-state**} \\ \hline \multirow{3}{*}{**TSCH**} & Number of Channels (NC) & \{1,2,4,16\} channels \\  & Number of Scan Channels (NSC) & Equal to NC \\ \hline \multicolumn{3}{c}{**Test 2: Different numbers of scan channels**} \\ \hline \multirow{3}{*}{**TSCH**} & Number of Channels (NC) & 4 channels \\  & Number of Scan Channels (NSC) & [1,2,4] channels \\ \hline \multicolumn{3}{c}{**Test 3: Different EB period configurations**} \\ \hline \multirow{3}{*}{**TSCH**} & Fixed EB Transmission Time (EBTT) & \{16,4\} s \\  & EB Transmission Time based on Trickle Timer & RPL Trickle Timer \\  & Custom Trickle Timer & A 4-s period during the first two \\  & Number of Channels and Scan Channels (NC, NSC) & minutes and then a 16-s period \\  & Number of Channels and Scan Channels (NC, NSC) & 4 channels \\ \hline \multicolumn{3}{c}{**Extra Test: Different DIS interval values**} \\ \hline \multicolumn{3}{c}{**RPL**} & DIS interval (DISTT) & \{60,45,30,15,5\} s \\ \hline \end{tabular}
\end{table} 
Table 2: The configuration parameters that were simulated during the experiments.
###### Abstract  
Premature seal failure induced by the unevenness of interstage pressure distribution in multi-stage brush seals significantly compromises the sealing efficiency of Air-Turbo Rocket (ATR) engines operating under high-pressure (megapascal-level) differential conditions. Conventional pressure equalization designs for such seals often result in significant leakage rate increases. This study addresses the pressure imbalance phenomenon in four-tooth three-stage brush composite seals through a novel fractal-geometric porous-media model, rigorously validated against experimental data. Systematic investigations were conducted to elucidate the effects of structural parameters and operational conditions on both sealing performance and pressure distribution characteristics. Key findings reveal that, under the prototype structure parameter, the first-, second-, and third-stage brush bundles account for 18.3%, 30.0%, and 43.3% of the total pressure drop, respectively, with grate teeth contributing 8.4%, demonstrating an inherent pressure imbalance. Axial brush spacing exhibits a minimal impact on the pressure distribution, while the gradient thickness settings of the brush bundles show limited influence. Radial clearance optimization and gradient backplate height adjustment effectively regulate pressure distribution, albeit with associated leakage rate increases. Structural modifications based on these principles achieved only a 5.8% leakage increment while reducing the maximum bundle pressure drop by 23%, demonstrating effective pressure balancing. A simplified analysis of entropy reveals that the fundamental mechanism governing the pressure imbalance stems from non-uniform entropy generation caused by aerodynamic damping dissipation across sequential brush stages. These findings establish a dampened dissipation-based theoretical framework for designing high-performance multistage brush seals in aerospace applications, providing critical insights for achieving an optimal balance between leakage control and pressure equalization in extreme-pressure environments.  
**Keywords:** multistage brush combination seal; interstage pressure drop imbalance characteristics; inhomogeneous entropy change; fractal geometry porous media model; ATR engine
# Application of the KINEROS 2 Model to Natural Basin for Estimation of Erosion
## 3 Erosion Estimation Models Used
### Brief Introduction tokineros 2
#### 3.4.1 Model Functioning  
Basins are represented by spatially distributed elements: flat elements connected by channels up to basin outlet. KINEROS 2 model estimates hydrological losses taking into account the spatial variability. For that reason, KINEROS 2 considers rainfall, interception of soil vegetation cover, and infiltration.
## 2 Materials and Methods  
The aim of this study was to elucidate the fundamental variables at various time points in order to evaluate the future changes in water quality in the Huangyang Reservoir.  
The Huangyang Reservoir is situated in Gansu Province. The dam is specifically located in Liangzhou District of Wuwei City, which is in the eastern part of the West Corridor. The selected dam site is located on the Huangyang River, about 50 km southeast of Wuwei City. The reservoir control basin covers an area of 828,000 square meters and serves as a medium-sized annual regulation reservoir primarily utilized for irrigation purposes. It also provides benefits such as flood control, power generation, and tourism, making it a comprehensive and multifunctional facility. In recent years, the problem of water quality pollution in the Huangyang Reservoir has worsened significantly because of the increase in urban pollution. Water pollution originates from three primary sources: firstly, domestic sewage discharged by urban areas along rivers, secondly, industrial effluents released by nearby factories; and thirdly, rainwater that carries air and surface pollution into the river.
## 3 Results
### Functional Annotation of the Host Genes of DEcircRNAs  
GO enrichment analysis revealed that the host genes of DEcircRNAs mainly participated in protein ubiquitination (GO terms: protein ubiquitination, \(p\) = 2.38 \(\times\) 10\({}^{-6}\); protein polyubiquitin, \(p\) = 7.94 \(\times\) 10\({}^{-6}\); and regulation of protein ubiquitination, \(p\) = 2.72 \(\times\) 10\({}^{-4}\)), neuronal morphology (GO terms: neuron projection morphogenesis, \(p\) = 1.01 \(\times\) 10\({}^{-3}\); cell projection morphogenesis, \(p\) = 1.69 \(\times\) 10\({}^{-3}\); and cell part morphogenesis, \(p\) = 1.99 \(\times\) 10\({}^{-3}\)), and histone modification (GO terms: regulation of histone modification, \(p\) = 4.26 \(\times\) 10\({}^{-5}\); and histone modification, \(p\) = 1.29 \(\times\) 10\({}^{-3}\)) (Figures 2C and S3). KEGG annotation and enrichment revealed that the host genes of DEcircRNAs mainly participated in pathways of ubiquitin-mediated proteolysis (\(p\) = 3.99 \(\times\) 10\({}^{-4}\)) (Figures 2D and S4).  
\begin{table}
\begin{tabular}{c c c c} \hline
**ID** & **Type** & **Fold Change** & **Regulation** \\ \hline rno-Ick\_0003 & exon & 13.13123 & UP \\ rno-N4 bp1\_0001 & exon & 11.10113 & UP \\ rno-Ell2\_0005 & exon & 10.88539 & UP \\ rno-Rims2\_0060 & exon & 9.183134 & UP \\ rno-Stk39\_0001 & exon & 9.045527 & UP \\ rno-Rsf1\_0012 & exon & 8.201328 & UP \\ rno-Chd2\_0001 & exon & 4.98735 & UP \\ rno-Trip12\_0024 & exon & 4.913605 & UP \\ rno-Arl8b\_0001 & exon & 4.636859 & UP \\ rno-Dmd\_0004 & exon & 4.296785 & UP \\ rno-Ralgps2\_0004 & exon & 6.565114 & DOWN \\ rno-Susd1\_0002 & exon & 7.722112 & DOWN \\ circRNA.15164 & intergenic region & 8.034623 & DOWN \\ rno-Sergef\_0005 & exon & 8.351556 & DOWN \\ circRNA.4818 & exon & 8.367079 & DOWN \\ rno-Kdm4c\_0013 & exon & 8.40449 & DOWN \\ rno-Pesk5\_0002 & exon & 8.931029 & DOWN \\ rno-Prex2\_0027 & exon & 9.574243 & DOWN \\ rno-Rps6 ka5\_0004 & exon & 10.93379 & DOWN \\ rno-Slc16a10\_0001 & exon & 12.47348 & DOWN \\ \hline \end{tabular}
\end{table}
Table 2: Top 10 up- and down-regulated DEcircRNAs obtained by sequencing.  
Figure 2: Expression of DEcircRNAs. (**A**) Hierarchical clustering of DEcircRNAs in LID (levodopa-induced dyskinesia) rats compared to non-LID (NLID) rats. The expression values are denoted using a color scale. The intensity increases from red (relatively higher expression) to blue (relatively lower expression). Different columns represent different samples (\(n=6\)), and each row represents a single circRNA. (**B**) Volcano plot of circRNAs. A total of 39 up-regulated and 60 down-regulated circRNAs were screened out. Up-regulated and down-regulated circRNAs are denoted in red and blue, respectively. (**C**) Gene Ontology (GO) enrichment analysis. The abscissa is the rich factor, and the ordinate represents the GO terms. The size of the dot represents the number of genes annotated to the GO term. The color of the dot represents the q value. (**D**) Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The size of the dot represents the number of genes annotated to the KEGG term. The color of the dot represents the q value.
# A Blockchain and IoT-Enabled Framework for Ethical and Secure Coffee Supply Chains
## 2 Existing Studies and Literature Review
### IoT Sensor Technologies for Coffee Environment Monitoring  
Environmental monitoring constitutes a fundamental component in modern coffee supply chains. [PERSON] et al. (2017) pioneered an architecture utilizing pH, moisture, temperature, and humidity sensors connected to WiFi microcontrollers, demonstrating effective environmental data transmission with minimal power consumption [17]. Building on this foundation, [PERSON] et al. (2021) implemented DHT22 temperature and humidity sensors with Raspberry Pi 4 IoT gateways and LoRaWAN modules to achieve transmission ranges exceeding 10 km while maintaining 24-month battery life, which is particularly valuable for remote coffee-growing regions [18].  
Addressing sensor calibration challenges, [PERSON] and [PERSON] (2019) employed XBee-Pro S2C modules on ZigBee protocols, achieving measurement accuracy between 96-99% compared to laboratory-grade instruments while implementing self-healing mesh networks that are essential for dense plantation environments [19].
## 4 Engineering Application and Analysis
### Characteristics of Rock Mass and Lining Deformation
#### 4.2.2 Deformation Characteristics of the Tunnel Wall  
The tunnel wall displacement curve following excavation is jointly influenced by the rock mass mechanical properties and the tunnel excavation radius. To elucidate the variation in tunnel wall displacement \(u_{\rm w}\) with support pressure \(p_{\rm i}\) under different excavation radii \(R_{0}\), this study conducted computational analyses based on Equation (16). Specifically, for scenarios where \(\Delta u=0\) (i.e., \(R_{0}=7.18\) m) and \(\Delta u=u_{\rm wmax}=26.9\) mm (i.e., \(R_{0}=7.2069\) m), the radial displacement \(u_{\rm w}\) of the tunnel wall was calculated for various support pressure \(p_{\rm i}\). The results were plotted and fitted in a Cartesian coordinate system, as shown in Figure 10.  
Figure 10 clearly presents the fitting results of the deformation in the fault section's surrounding rock tunnel walls. Under the two extreme conditions (i.e., maximum and minimum \(\Delta u\)), the deformation curves exhibit minimal variation, suggesting that the tunnel wall deformation curves for other excavation radii \(R_{0}\) will lie between these two. To provide a representative displacement pattern, the tunnel wall deformation curve at \(\Delta u=0\) (i.e., \(R_{0}=7.18\) m) is selected as the baseline (green curve), reflecting the displacement trends across different \(\Delta u\) scenarios.  
Figure 9: Effect of excavation radius on the dimensions of the plastic zone.
## 3 Results and Discussion
### Discussion
It is important to solve the problem of fairly comparing the models [24; 25]. For VIS-NIR model analysis, the common evaluation metrics are the coefficient of determination, root mean square error, mean absolute error, residual predictive deviation, and the ratio of performance to IQ [7; 8; 9; 10; 29; 30]. The accuracy of the models can be weighed by \(R^{2}\), RMSE, and MAE. These models with the same RPD level represent consistent stability; therefore, the stability can be analyzed by RPD and RPIQ [23]. Meanwhile, the sum of the ranked differences [24; 25] was calculated for a fair comparison of models. Table 9 summarizes these metrics of the regression models with the prediction of the concentration of soil-available potassium by VIS-NIR. The RPDs of the four regression models were all level A. Considering the SRD, \(R^{2}\), RMSE, and RPIQ shown in Table 9, the boosting algorithms of GBRT and AdaBoost were significantly superior to SVR and PLS. GBRT is better than AdaBoost according to SRD and MAE, but AdaBoost is better than GBRT according to \(R^{2}\) and RPIQ. Therefore, the two boosting algorithms had their own advantage for the prediction of VIS-NIR regression. To yield a more robust and trustworthy model, Figure 9 shows the prediction of two sub-ranges with boosting algorithms and all pretreatment methods. The models with low and high concentrations exhibited better performance than those with all samples but showed only minor improvement. These reasons are analyzed as follows:  
1. The samples were collected from two places that are far apart. Therefore, two sub-range samples both have some outliers that would affect the performance of these models. 2. The number of samples with a sub-range was not enough for training. The regression algorithm is better if there are more samples. Although the performance did not improve, the pretreatment and boosting methods have a positive influence on the quantitative model. Therefore, future studies should focus on outlier analysis methods and collect more samples to build a more robust and trustworthy model that can be used across industries for NIR quantification. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Evaluation Methods** & **SVR\_RBF** & **PLS\_RBF** & **GBRT** & **AdaBoost** \\ \hline SRD & 180 & 200 & 110 & 144 \\ R\({}^{2}\) & 0.923 & 0.928 & 0.939 & 0.941 \\ RMSE & 35.5 & 34.3 & 31.5 & 31.2 \\ MAE & 23.4 & 25.2 & 17.9 & 18.8 \\ Level of RPD & A & A & A & A \\ RPIQ & 2.75 & 2.84 & 3.10 & 3.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Evaluation results of the regression models with the testing dataset.
## 7 Accuracy of the Forecasting Modules Used in the Proposed Preventive Control Strategy  
To identify whether the system can tolerate \(N\)-1 contingency events, frequency nadir must be forecast accurately. Figure 11 plots the distribution of \(\mathbf{f_{\min}}\) forecasting errors. The \(\mathbf{f_{\min}}\) forecasting errors are distributed between \(\pm\)0.06 Hz, with 93.74% of the errors in the \(\pm\)0.02 Hz range, which is acceptable in the Penghu Island power system.  
Figure 10: The single line diagram of the power system in Penghu Island.  
To evaluate the effectiveness of the proposed preventive control methods, the frequency nadir with the application of the BESS, DLC or EDRP method must be forecast accurately as well. For the BESS method, the error distribution of predicted frequency nadir is plotted in Figure 12. The \(\mathbf{f_{\min,BESS}}\) forecasting errors are distributed between \(\pm 0.06\) Hz, with 90.84% of the errors in the \(\pm 0.02\) Hz range.  
Using the DLC method, the error distribution of the predicted frequency nadir is plotted in Figure 13. The \(\mathbf{f_{\min,DLC}}\) forecasting errors are distributed between \(\pm 0.06\) Hz, with 91.07% of the errors in the \(\pm 0.02\) Hz range.  
Figure 11: Distribution of \(\mathbf{f_{\min}}\) forecasting errors without preventive controls.  
Figure 12: Distribution of \(\mathbf{f_{\min,BESS}}\) forecasting errors by the BESS method.  
Figure 13: Distribution of \(\mathbf{f_{\min,DLC}}\) forecasting errors by the DLC method.  
Lastly, by utilizing the EDRP method, the error distribution of predicted frequency nadir is plotted in Figure 14. The \(\mathbf{f_{\min,EDRP}}\) forecasting errors are distributed between \(\pm 0.06\) Hz, with 91.02% of the errors in the \(\pm 0.02\). Hz range.  
Based on the statistical results, it demonstrates that the developed forecasting modules of \(\mathbf{f_{\min}}\), \(\mathbf{k_{\textit{BESS}}}\), \(\mathbf{k_{\textit{DLC}}}\) and \(\mathbf{k_{\textit{EDRP}}}\) are capable to provide accurate forecasting results.
## 3 Results and Discussion
### Effects of Different Extraction Conditions on the Yield of PSP
#### 3.1.2 Extraction Temperature  
The effects of different temperatures from 30 to 70 \({}^{\circ}\)C on the yield were studied and other extraction conditions were fixed as follows: Extraction time of 40 min, enzyme concentration of 2.5%, and pH of 6.0. Figure 1b showed that the yield of PSP continued to increase with the extraction temperature and was enhanced to a highest value 2.94 \(\pm\) 0.13% at the temperature of 60 \({}^{\circ}\)C, then began to decline owning to the inactivation of enzyme at higher temperatures [21]. According to this result, 60 \({}^{\circ}\)C was chosen for the next extraction process.