esa-sceva/satcom-chunk-collection huggingface dataset:
# Atmospheric GNSS RO 1D-Var in Use at UCAR: Description and Validation
## 3 Results
### Validation Using NWP Model Data  
Thanks to the remarkable advancements in numerical modeling, satellite observations, and data assimilation in the past five decades, NWP nowadays provides realistic descriptions of the atmosphere. Data assimilation systems combine model dynamics and physics with vast observational information from various observing platforms. Therefore, comparisons of RO data with NWP analyses greatly complement the comparisons to individual observing systems. For example, [PERSON] et al. (2018) used 10 different model data sets to estimate the random error statistics of RO observations using the three-cornered hat method. Because global NWP data are routinely available, one can always compare them with 1D-Var retrievals, which differs from the comparisons to individual observations (e.g., radiosondes) whose distribution in space and time is irregular and often sparse. This widespread availability of NWP data permits comparisons over large samples and across a broad spectrum of spatiotemporal scales. In particular, the comparison to NWP data is beneficial for understanding how the character of 1D-Var varies over different geographical regions and under disparate atmospheric conditions. The tests and comparisons shown in this section make use of NWP model data.
## 3 Results and Discussion
### Chemical Resistance of Cu\({}^{2+}\)-Bearing Geopolymer  
#### 3.4.1 Ion Exchange  
The ion exchange behavior of the Cu-GP materials is illustrated in Figure 4. We note that a small amount of K\({}^{+}\) (<0.2 mmol/L) was liberated to the experimental solutions from all Cu-GPs. This could represent either a mobile fraction of K\({}^{+}\) that occupied surface-accessible sites in Cu-GPs or dissolution of K\({}_{2}\)CO\({}_{3}\) impurities present in the raw GP [48]. However, no changes in BET-SSA of all ion-exchanged GPs were recognized within the analytical uncertainty of the BET measurements.  
As for the Cu:GP series, a minor proportion of Cu\({}^{2+}\) (0.03-0.74 mmol/L) was exchanged for Na\({}^{+}\) (0.17-1.39 mmol/L) upon reaction with a 100 mM NaCl solution for 24 h (Figure 4a). This indicates that some Cu\({}^{2+}\) ions occupied easily exchangeable (surface) sites in the Cu-GPs prepared under acidic conditions (Figure 2c). The Cu\({}^{2+}\)/Na\({}^{+}\) molar ratio was close to 0.5, which is needed to account for a charge compensation in the Na\({}^{+}\)-substituted Cu-GPs (Figure 4a). Such stoichiometric exchange of Cu\({}^{2+}\) for Na\({}^{+}\) suggests that the dissolution of gerhardtite was negligible and that a fraction of the initial Cu\({}^{2+}\) concentration added to the Cu:GP series was incorporated in the GP structure [57], corroborating the SEM-EDX results. The proportion of Cu\({}^{2+}\) dissolved from Cu-GPs and subsequently returned back to the solutions varied only between 0.4 and 2.4% (Figure 4b), which in turn demonstrates the low reactivity of the Cu-GPs during ion exchange reactions. Accordingly, SE images collected on ion-exchanged Cu-GPs show platy, well-crystallized gerhardtite (FWHM: 0.14-0.19\({}^{\circ}\) 20) particles that co-occur with the Na\({}^{+}\)-substituted Cu-GPs having a CuO/Al\({}_{2}\)O\({}_{3}\) ratio of 0.3 \(\pm\) 0.2 (Figure 4c).  
As for the Cu:GP\({}_{\mathrm{PH}}\) series, a small fraction of K\({}^{+}\) was exchanged for Na\({}^{+}\) (<0.2 mmol/L) to balance the charge in the Cu-GPs, similar to the materials of the Cu:GP series. In contrast, the Cu\({}^{2+}\)/Na\({}^{+}\) molar ratio was determined to be only \(\sim\)0.1, so that no correlation between exchanged Na\({}^{+}\) and Cu\({}^{2+}\) ions was recognized (Figure 4d), which underlines the low reactivity of Cu\({}^{2+}\) bound to geopolymers under highly alkaline conditions [52; 60]. Consequently, the fraction of Cu\({}^{2+}\) mobilized from the materials of the Cu:GP\({}_{\mathrm{PH}}\) series upon Na\({}^{+}\) exchange was very low (<0.04%; Figure 4e), which again demonstrates the immobility of Cu\({}^{2+}\) in the Cu:GP\({}_{\mathrm{PH}}\) series. The SE images collected show no microstructural modifications compared to the untreated Cu-GPs (Figure 4f) and the K-A-S-H matrices had a CuO/Al\({}_{2}\)O\({}_{3}\) ratio of 0.2 \(\pm\) 0.1.
# C1 DR-Net: Channel-Time Dense Residual Network for Detecting Crew Overboard Behavior
## 3 DR-Net and CTAM
### Activation Function  
The Dropout layer in DR-Net randomly sets the output of some neurons to zero to reduce the risk of overfitting. Considering the influence of the activation function on these neurons, the model adopts the LeakyReLU activation function. This function does not output zero in the negative interval and retains the modified nonlinear characteristics, which can effectively solve the problem of neuron \"death\" in the ReLU activation function. In addition, the additional nonlinear characteristics introduced by the LeakyReLU activation function can effectively enhance the nonlinear modeling ability and network expression ability, and improve the robustness and generalization ability of the network.
# Upscaling Gross Primary Production in Corn-Soybean Rotation Systems in the Midwest
## 2 Material and Methods
### NDVI Ground-Based Measurements  
Reflectance data on the ground were measured with a radiometer (CropScan, CROPSCAN, Inc., Rochester, MN, USA) in the corn and soybean field in Story County during the 2016 growing season. A total of 33 and 31 measurements were made on soybean and corn, respectively, in observation intervals of 2-14 days between DOY 124 and 308. Measurements were constrained to clear-sky conditions or with little cirrus clouds and with sun angles \(>\) 45\({}^{\circ}\) (i.e., 10.00 and 14.00 CST). The radiometer measured reflected and incoming radiation at 16 wavebands between 460 and 1700 nm. Each measurement consisted of 30 individual scans, and an average is calculated by the unit. Measurements were taken above the crop at five locations north of the EC stations, and the measurement height was adjusted to yield a surface pixel size of 1.5 m. The radiometer was calibrated with a reflectance panel and a dark measurement prior to field observations. The Normalized Difference Vegetation Index (NDVI) was calculated as:  
\[NDVI=\ \frac{\rho_{760}\ -\ \rho_{660}}{\rho_{760}+\ \rho_{660}} \tag{2}\]  
where: \(\rho_{660}\) and \(\rho_{760}\) were the surface reflectance of the red (660 nm) and near-infrared (760 nm) wavelength bands, respectively.  
The individual observations at each location were averaged, and the averaged values interpolated with an inverse weighted time average procedure to yield a daily NDVI dataset for the 2016 growing season. Further information on CropScan measurements can be found in literature [34; 35]. In addition to NDVI measurement, plant development and plant height was recorded (corn n = 21, soybean n = 40). The development stages are categorized in vegetative and reproductive growth as V and R Stages, respectively. The V-Stages in corn categorize plant emergence (VE), number of fully developed leaves (e.g., V1 for first leaf) and tasseling (VT), while the six R-Stages categorize corn flowering (R1), and ear development (R2-5) until maturation and senescence (R6) [36]. The V-stages in soybean categorize plant emergence (VE), first unifoliate leaf (VC), and number of fully developed trifoliate leaves on the main stem. In contrast to corn, reproductive stages occur during plant growth (V-stages), and categorize flowering (R1-2), pod development (R3-6), and maturation and senescence (R7-8) [37]. Plant height was measured from the ground to the top of the canopy, and daily height was calculated by fitting a three-parameter sigmoidal model. Due to resource restrictions, NDVI and intensive crop measurements were limited to the site in Story County.
## 3 MIMO System Model and the Implementation Steps of Channel Estimation
### Computation Complexity Analysis on the Offered Approach  
The computation complexity of the recommended channel estimation techniques in MIMO systems is given in Table 6. Here, the variable \(MaxIter\) indicates the maximum iteration and \(N_{pop}\) denotes the number of populations.  
Evaluation of the Performance of the Designed Channel Estimation Scheme in the MIMO System Using Recent Approaches  
Evaluation of the designed channel estimation scheme in MIMO systems against recent approaches is given in Table 7. Thus, the simulation outcome of the proposed model attains better results than the other baseline approaches.
## 3 Microalgae Microbial Fuel Cell (mMFC)
### Electron Transfer Mechanisms
transfer during photosynthetic energy conversion. Investigating this mechanism under different conditions may help elucidate microalgal processes at the biocathode [87]. Although there is not enough information to elucidate mechanisms of microalgae interaction with the cathode, recent studies have suggested an association of Reactive Oxygen Species (ROS) levels, such as H\({}_{2}\)O\({}_{2}\), with electric stimulation and cellular lipid accumulation [155; 156]. When H\({}_{2}\)O\({}_{2}\) was applied to _Dunaliella salina_, the concentration of the oxidative stress marker, malondialdehydedehyde, increases as an indicator of membrane peroxidation implying an increase in ROS levels. Microalgae can modify its photosynthetic system under stress, resulting in a decrease in the gene expression of various proteins forming up the photosystem complexes I and II. As a result of decreased photosynthesis rate, overall anabolic reaction flux is severely constrained. In this context, algae cells may favor storage of energetic molecules, such as lipids under stress that could be caused by the interaction with electrons in the cathode [157]. The electrical treatment of _E. gracilis_ by the application of direct current power supply at different current intensities in a thin platinum plate electrode induced the generation of ROS, indicating response to stress, stimulating the paramylon production by enhancing the glucose uptakes and by increasing the stress level [158]. Figure 8 explains a possible mechanism of interaction of _C. sorokiniana_ with the cathode of an mMFC [159; 160]. After identifying the metabolic pathways and key enzymes in the microalgae-electrode interaction, genetic engineering has the potential to improve lipid, biomass productivity and energy generation in mMFCs [161]. Metabolic engineering has been applied in microalgae production technology in several lines:  
* Improve carbon fixation through the modification of key enzymes such as RUbisco, SBPase, pseudoheptulose 1.7 bisphosphatase and Phosphoenolpyruvate carboxy-lase [162]. * Improving the use of light, by the modification and regulation of the synthesis of antenna pigments [162] and modification of plastoquinone (PQ) involved in the coupling of protons and electrons in electron transfer [9]. Figure 8: Scheme of the possible cathodic mechanism of interaction of _C. sorokiniana_. * Improving downstream processes, by auto flocculation through the expression of glycoproteins located in the extracellular matrix [162; 163]. * Modify specific enzymes of the fatty acid synthesis pathway by the overexpression of enzymes [164] such as Acetyl-CoA [72; 161], diacylglycerollycerol acyl transferase (DGAT) [162]. * Blocking competitive pathways such as carbohydrate synthesis to enhance lipid accumulation in microalgae [165; 166]. In this sense, research on genetically modified microalgae for enhanced electron transfer would provide valuable insights into the future development of mMFC technology.
# Pilot Studies of Vibrations Induced in Perambulators When Moving on Different Surfaces
## 3 Results and Discussion  
### Time Domain  
The time domain results for each of the research cases, including the point vibration total values (\(\mathbf{a}\)) and maximal values (\(\mathbf{a_{\max}}\)) calculated in accordance with Equations (1)-(2), are shown in Table 2 and Figure 4.  
As can be seen, at the same surface variants, both the point vibration total values (\(\mathbf{a}\)) and maximal values (\(\mathbf{a_{\max}}\)) were noticeably lower for the \"old type\" perambulator equipped with pneumatic types (old pram) in relation to the lightweight modern pram. The greatest difference can be found for the asphalt road (a): 70% lower point vibration total value and 77% lower maximal value. On average, their point vibration total value was 48% lower, and the maximal value was 59% lower for the old pram. Such a difference was most probably due to the lower weight of the modern pram and higher vibration transmissivity of both the frame and its polyurethane foam tyres.  
For the modern pram, the RMS accelerations in the x direction (\(\mathbf{a_{x}}\)), parallel to the gravity vector, on almost all the surfaces exceeded the value of 2.5 \(\frac{\overline{m}}{g^{2}}\). The mentioned value was never exceeded in the cited [7; 27] child safety seat tests (the accelerometer placed under the child's seat).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Modern Pram—Surface Variants**} & \multicolumn{5}{c}{**Old Pram—Surface Variants**} \\ \cline{2-13}  & **Model** & **Model** & **Model** & **Model** & **Model** & **Model** & **Model** & **Model** & **Model** & **Model** & **Model** & **Model** \\ \hline \(\mathbf{a_{x}}\left[\frac{\overline{m}}{g^{2}}\right]\) (RMS) & 1.82 & 2.63 & 4.06 & 3.97 & 4.36 & 4.74 & 0.45 & 0.92 & 2.04 & 2.20 & 3.37 & 3.24 \\ \hline \(\mathbf{a_{y}}\left[\frac{\overline{m}}{g^{2}}\right]\) (RMS) & 0.84 & 0.67 & 1.58 & 1.66 & 2.55 & 2.33 & 0.32 & 0.39 & 0.72 & 0.79 & 1.33 & 1.53 \\ \hline \(\mathbf{a_{z}}\left[\frac{\overline{m}}{g^{2}}\right]\) (RMS) & 0.70 & 1.12 & 2.26 & 1.71 & 1.64 & 1.48 & 0.32 & 0.42 & 0.92 & 0.80 & 1.58 & 1.30 \\ \hline \(\mathbf{a}\left[\frac{\overline{m}}{g^{2}}\right]\) (RMS) & 2.12 & 2.94 & 4.91 & 4.63 & 5.31 & 5.48 & 0.63 & 1.09 & 2.35 & 2.47 & 3.95 & 3.81 \\ \hline \(\mathbf{a_{\max}}\left[\frac{\overline{m}}{g^{2}}\right]\) & 6.86 & 11.63 & 19.02 & 18.91 & 20.11 & 20.07 & 1.58 & 3.01 & 7.87 & 7.34 & 13.79 & 9.86 \\ \hline \hline \end{tabular}
\end{table}
Table 2: RMS and maximum acceleration values (static acceleration component excluded) applied to the weight placed in the space provided for a child in the tested perambulators.  
Figure 4: Comparative bar graphs of the RMS and maximum acceleration values from Table 2.
## 3 Short-Circuit Current Calculation Considering Power Fluctuation
### Real-Time Short-Circuit Current Calculation Considering Fluctuation in DGs and Loads
According to the relationship between the output current and the terminal voltage in the LVRT strategies, the change in the output current of each accessed DG after the \(k\)-th iteration is given by \[\Delta I_{IIDG,j}(k)=I_{IIDG,j}(k)-I_{IIDG,j}(0)(t)\] (13) where \(I_{IIDG,j}(k)\) represent the output current of each DG accessing node j in the distribution network after the \(k\)-th iteration;
5. According to the change in the output current of DGs, the voltage at node \(i\) after \(k\)-th iteration is given by \[\dot{V}_{i,f}^{(k)}(t)=\dot{V}_{i,f}^{(0)}(t)+\sum_{j\in H}Z_{ij}\Delta I_{IIDG,j}(k)\] (14) where \(\mathbf{H}\) represent the set of all PCCs, \(\dot{V}_{i,f}^{(k)}(t)\) represents the voltage at node \(i\) after the \(k\)-th iteration when a fault occurred, and \(Z_{ij}\) is the \(i\)-th row and \(j\)-th column element of the distribution network impedance matrix \(\mathbf{\mathrm{Z}}\) formed in step 2;
6. Calculate the current in branch \(mn\) between node \(m\) and node \(n\) after the \(k\)-th iteration based on the short-circuit voltage at each node after the \(k\)-th iteration via \[\dot{I}_{m,n,f}^{(k)}(t)=\frac{\dot{V}_{m,f}^{(k)}(t)-\dot{V}_{n,f}^{(k)}(t)} {Z_{mn}}\] (15) where \(\dot{I}_{m,n,f}^{(k)}(t)\) represent the branch current between node \(m\) and node \(n\) after the \(k\)-th iteration at time \(t\), \(Z_{mn}\) represent the impedance of the branch \(mn\), \(\dot{V}_{m,f}^{(k)}(t)\) and \(\dot{V}_{n,f}^{(k)}(t)\) represent the voltage at node \(m\) and node \(n\) after \(k\)-th iteration at time \(t\) when a fault occurred;
7. Perform the abovementioned steps (4)-(6) cyclically until the following convergence condition is satisfied: \[\left(\left|\dot{V}_{i,f}^{(k+1)}(t)-\dot{V}_{i,f}^{(k)}(t)\right|<\varepsilon \right)\left\|(k>K_{0}),\ \ i\in H\right.\] (16) where \(K_{0}\) represent the maximum iteration number, and \(\varepsilon\) represent the maximum voltage error. The convergence condition means that the voltage at node \(i\) remains the same after the \(k+1\) iteration even considering the change in the output current of DGs under LVRT strategies. According to the final calculated voltage at node \(m\) and \(n\) after the iteration, the current flowing through branch \(mn\) can finally be obtained based on Equation (7). Compared to traditional short-circuit calculation methods, segmented output current of DGs under LVRT strategies have been embedded into the power flow calculation in our proposed method, which can automatically calculate short-circuit current in real time under several typical daily curves after the clustering of historical output power data of high-penetration DGs.
## 3 Numerical Simulations  
### Establishment of Simulation Model  
A three-dimensional model was constructed based on the CB12 segmented tire mold, applicable to the majority of complex segmented tire molds. Figure 1 is the structural schematic diagram of the segmented tire mold. The basic structure of the segmented tire mold comprises the mold shell and forming components. The mold shell includes the upper cover, mounting ring, guide ring, guide strip, slide block, lower base, etc. The forming components, which directly contact the tire, include the upper side plate, tread segment and lower side plate. The upper heating plate and lower heating plate in the figure are components of the vulcanizer, serving as heat sources in direct contact with the mold.  
Figure 1: Structural schematic diagram of the segmented tire mold (1: Upper Heating Plate, 2: Upper Cover, 3: Upper Side Plate, 4: Upper Slide Block, 5: Top Cover Slide Plate, 6: Guide Ring, 7: Guide Ring Slide Plate, 8: Base Slide Plate, 9: Lower Base, 10: Lower Side Plate, 11: Lower Heating Plate, 12: Tread Segment, 13: Slide Block, 14: Mounting Ring).  
The workflow of the mold involves preheating, mold opening, placement of the green tire, mold closing, pressurization, vulcanization and subsequent mold opening for tire removal. The mold is preheated by injecting 180 \({}^{\circ}\)C steam into the cavities of the upper heating plate, lower heating plate and guide ring, transferring heat to internal structures via conduction.  
During preprocessing, fillets and chamfers were appropriately simplified to enhance computational efficiency without compromising accuracy.  
For meshing, since the forming components such as the upper side plate, tread segment and lower side plate directly contact the tire, they critically affect the tire's quality. And considering subsequent stress analysis requirements, structured grids were employed for these components to achieve precise meshing. The average element quality was 0.83, with an average aspect ratio of 2.5, meeting stress analysis requirements. Irregularly shaped internal components were meshed with unstructured grids. The hybrid meshing strategy ensured both precision and computational efficiency, yielding an average element quality of 0.81 (Figure 2).  
To replicate actual conditions, gravity (9.8 m/s2) was applied to the internal flow field. The fluid domain's inlet was set as a pressure inlet (180 \({}^{\circ}\)C, 1.15 MPa gauge pressure), and the outlet as a pressure outlet. Fluid simulation results were used as boundary conditions for the mold's thermal analysis. Temperature field results were then imported as thermal loads into a linear static structural analysis. Fluid-solid coupling interfaces were defined on the inner surfaces of the upper heating plate, guide ring and lower heating plate. Molding force was applied to the upper surface of the mounting ring, while fixed supports were assigned to the lower surface of the lower heating plate (Figure 3). The material properties of key components are listed in Table 1.  
Figure 2: Meshing: (**a**) Global meshing of the mold (**b**) Meshing of the heating plates and tread segment.
## 2 Methodology
### Principle of GPR Detection  
GPR transmits high-frequency electromagnetic wave signals to underground media through transmitting antennas. When the electromagnetic wave propagates to the underground dielectric interface with different dielectric constants, corresponding reflections will be generated. Receiving antennas capture the electromagnetic wave signals reflected back from targets or strata. The echo data received by the GPR at each detection position is called the A-Scan, and multiple consecutive A-scans on the same detection line can form a B-Scan. B-Scan data are processed in a series of ways to display the subsurface vertical profile in grayscale or pseudo-color images. Figure 2 is the schematic diagram of the GPR detection principle. The underground propagation time of an electromagnetic pulse is shown in Equations (1) and (2):  
\[t=\frac{\sqrt{4z^{2}+x^{2}}}{v} \tag{1}\]  
\[v=\frac{c}{\sqrt{\varepsilon_{r}}} \tag{2}\]  
where \(z\) is the depth of the target, \(x\) is the distance between the transmitting antenna and the receiving antenna, \(v\) is the propagation speed of the electromagnetic wave in the medium, \(c\) is the speed of light, and \(\varepsilon_{r}\) is the relative dielectric constant of the medium.
# Cloud-Based Internet-of-Things System for Long-Term Bridge Bearing Monitoring Using Computer Vision
## 4 Discussion  
For validation of the results, the proposed system was compared to a displacement trajectory measurement device installed by the Seoul Facilities Corporation [44]. This analog device, consisting of a chalk and board mechanism, tracks bridge movement by leaving marks on a pad, thereby drawing the trajectory of displacement in both the axial and perpendicular directions of the bridge. Figure 14 shows the installed displacement trajectory measurement device and the trajectory it generated. The displacement measurements obtained from this analog system were similar in both form and magnitude to those measured by the proposed system. The comparison, illustrated in Figure 14, demonstrates the close alignment between the two methods, validating the accuracy and reliability of the proposed monitoring system.  
Figure 14: Displacement trajectory measurement device: (**a**) installed measurement device; (**b**) drawing board with displacement trajectory; (**c**) proposed sensing data (X vs. Z-displacement).  
Figure 13: \(\varepsilon\)-MR control chart of bearing displacement difference measurements for (**a**) \(x\)-axis; (**b**) \(y\)-axis; (**c**) \(z\)-axis.  
In addition to validating our proposed system, it is essential to highlight its advantages over traditional technologies. Among the existing options, the primary method for measuring bearing movement is the use of LVDTs. While LVDTs are known for their high precision in measuring displacement, they involve complex installation processes requiring jigs for installation, as well as Data Acquisition (DAQ), along with a constant power source for continuous operation and a network for data transmission. In contrast, our proposed system utilizes low-cost IoT sensors, making it a more economically feasible option for long-term monitoring. The reliance on LVDTs for a constant power supply can limit their applicability in remote areas. Our system, however, features an event-driven power mechanism that allows for extended battery power operation supplemented by solar energy. This approach significantly reduces power consumption and enhances sustainability. Installation complexity is another critical consideration. LVDTs necessitate precise calibration and extensive wiring, leading to increased labor costs and longer installation times. Conversely, our proposed system is designed for straightforward deployment, leveraging computer vision to simplify the setup process.