esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
These functional traits highlight that the primary cause for tree mortality during drought is hydraulic failure, caused by carbon starvation or embolism [38; 39]. Methods or techniques that help to obtain functional data on a large scale can contribute to the understanding of the ecosystem. Therefore, mappings based on multispectral and hyperspectral images have sought to fill this information gap [40; 41; 42; 43; 44]. Satellite observations to study the phenology of tropical forests using vegetation indices as a proxy for photosynthetic activity and leaf renewal (leaf flush or leaf flushing), combined with in situ data, can help to minimize the uncertainties raised and improve the biophysical understanding of forest dynamics in wet and dry periods. Most of the in situ studies used for orbital validations were carried out in the eastern portion of the Brazilian Amazon, with an information gap in the southwest part of the basin. In addition, most of the in situ studies have not considered the ability of the forest to deal with droughts, measured by the plant hydraulic traits. Considering that the impact caused by climate changes does not depend exclusively on extreme events, but also on the ability of plants to resist the changes they are exposed and, due to limited knowledge of the vulnerability of tropical forests to droughts (extreme or seasonal) [45; 46], we evaluated ecophysiological attributes of drought tolerance. To better understand the climate-vegetation dynamics, this research was based on the following questions: How is the seasonal dynamics of the forest in the southwest portion of the Amazon? What are the forest mechanisms to resist seasonal drought? Does remote sensing have the potential to represent phenological and physiological changes in the forest? From these questions, this research aimed to evaluate the seasonal response to drought of a \"terra firme\" forest fragment located in the Jaru Biological Reserve--Rebio Jaru (RJA site), southwest of the Amazon. Here, we combine satellite with in situ data to analyze: (i) the phenological pattern of the canopy, using the enhanced vegetation index (EVI) and the green chromatic coordinate (Gcc) as a proxy of photosynthetic activity and production of new leaves, respectively, derived from the Moderate-Resolution Imaging Spectroradiometer (MODIS) from Terra/Aqua satellites, corrected by Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC); (ii) physiological behavior, using measures of stem growth and total litter production, (iii) ecophysiological behavior, using functional attributes indicative of drought tolerance (P50, \(\pi\)TLP, HSM and isohydricity); and (iv) soil moisture, water table depth and climatic data.
## 4 Results and Discussion
### Evaluation of the Trap Static Sealing Capacity
#### 4.3.2 Evaluation of Static Sealing of Faults  
For the lateral sealing of the underground storage in the depleted gas reserve, there are a total of nine faults within the trap structure. The main faults affecting the sealing of the gas storage reservoir are \(3\) faults: F2, F5, and F11, all of which pass through the reservoir of the Yingcheng formation and the upper cap rock of the D2 formation.  
The sealing of fault F2 is shown in Figure 19, where, on the left side of the top reservoir, there is a mudstone juxtaposition with good lateral sealing. The thickness of mudstone in the fault area is \(26.1\) m, with a vertical displacement of \(31.11\) m. The calculated mud  
Figure 18: Characterization of the cap rock of the underground storage in depleted gas reserve.  
content rate is 84%, and the mudstone smearing coefficient is 1.26, resulting in good lateral fault sealing.  
The sealing of fault F5 is shown in Figure 20, where, on the right side of the top reservoir, there is a mudstone juxtaposition with good lateral sealing. The thickness of mudstone in the fault area is 29.58 m, with a vertical displacement of 31.66 m. The calculated mud content rate is 93%, and the mudstone smearing coefficient is 1.09, resulting in good lateral fault sealing.  
The sealing of fault F11 is shown in Figure 21, which on the right side of the top reservoir, there is a mudstone juxtaposition with good lateral sealing. The thickness of mudstone in the fault area is 21.67 m, with a vertical displacement of 25.86 m. The calculated mud content rate is 84%, and the mudstone smearing coefficient is 1.29, resulting in good lateral fault sealing.  
For the fault F2, F5, and F11, the smearing coefficient of mudstone is less than four, and the mud content rate is greater than 80%, indicating that the underground storage in the depleted gas reserve has good lateral fault sealing.  
Figure 19: Seismic profile of the F2 fault and on both sides juxtaposition relationship.  
Figure 20: Seismic profile of the F5 fault and on both sides juxtaposition relationship.  
The vertical sealing performance of the underground storage in depleted gas reserve is mainly evaluated from the aspects of the normal stress on the fault surface, mud content, and displacement pressure. According to the pilot test results, the evaluation indicators for the vertical sealing performance of faults F2, F5, and F11 are shown in Table 4. The results indicate that the normal stress on the fault zone is around 33 MPa to 45 MPa, the mud content in the fault zone is maintained at 80% or above, and the displacement pressure of the fault rock is around 5 MPa, demonstrating the strong vertical sealing performance of the fault zone in the underground gas storage of the depleted gas reservoir.
## 6 Experiments and Discussion
### Field Experiments
#### 6.3.3 Segment Analysis  
Four segments marked by yellow circles in Figure 16 were selected to elaborate on the comparison results. Figure 17 presents the trajectories provided by the different schemes. The trajectories obtained by RMNCE-TC reside the closest to the reference trajectories. But the performance of STC5 is not as good as might be expected when introducing a redundant satellite, and is at times even inferior to STC4 (e.g., see the results for S3 presented in Figure 17c). This indicates that merely increasing the number of satellites without introducing additional error handling methods can lead to unexpected degradation in the navigation performance. ATC provides an improved positioning accuracy due to the RAE-UKF. MATC and CNE-TC have an accuracy improvement over ATC but are still inferior to RMNCE-TC.  
The GDOP values obtained by STC4, STC5 (with an equivalent satellite selection as that of ATC), RMNCE-TC (with an equivalent satellite selection as that of MATC) and CNE-TC are plotted in Figure 18. The results show: the GDOP values of STC5(ATC) calculated using five satellites are lower than STC4 and RMNCE-TC (MATC); CNE-TC always provides a minimum GDOP value due to its loosely excluding condition; the GDOP of RMNCE-TC is relatively large at many epochs. We calculated the single point positioning (SPP) errors of the selected satellite measurements, which are shown in Figure 19. Unexpectedly, the SPP error of STC5(ATC) is very large, and RMNCE-based satellite selection presents the best performance. This further indicates that adding redundant satellites always decreases the GDOP value, but may not always improve the positioning accuracy. Increasing the quantity of observations has the risk of unexpectedly introducing large measurement errors into the system. Therefore, the measurement quality should be fully considered when selecting an appropriate satellite combination.  
Figure 16: Four segments in the vehicle trajectory selected for detailed analyses.  
Figure 15: Histogram of the positioning errors obtained for the test schemes.  
Finally, the 3D positioning errors during segments S1, S2, S3, and S4 are presented in Figure 20. CNE-TC has the ability to control the large errors based on the weighted pseudo-range variance, but it is still inferior to RMNCE-TC. RMNCE-TC can adaptively eliminate the satellites with poor measurement quality, and effectively estimate **R** to obtain a robust solution.  
Figure 17: Local reference trajectories and those provided by different test schemes: (**a**) trajectories in segment S1; (**b**) trajectories in segment S2; (**c**) trajectories in segment S3; (**d**) trajectories in segment S4.  
Figure 18: GDOP values of the different satellite selection algorithms during segments S1, S2, S3, and S4.
# Application of a Coupled Eulerian-Lagrangian Approach to the Shape and Force of Scientific Balloons
## 4 Dynamic Fluid-Structure Coupling Shape and Force Analysis  
The flight state and the environment of a scientific balloon are constantly changing. When the ascent speed of the balloon is relatively slow (\(\approx\)5 m/s), the ascent shape could be treated as a quasi-static process. So, applying hydrostatic pressure differences to the balloon  
Figure 6: (**a**) The sphere is divided using a sweeping mesh. (**b**) Upward load is applied to the internal helium cluster.  
Figure 7: The zero-pressure spherical balloon filled with different volumes of helium gas.  
can aid in calculating a stable ascent shape. However, during the ground launching or further ascent after partial payload release, the internal helium gas experiences significant fluctuations, leading to rapid changes in the balloon shape, thus failing to satisfy the quasi-static hypothesis. At this point, both the internal helium gas flow field and the external air flow field interact with the balloon envelope, and the fluid-structure coupling effect becomes significant.  
In the past, balloon designers found this problem difficult to handle and could only rely on experience, such as thickening the balloon's head (multi-layered head structure), to ensure that the launching or release of the payload would not damage the balloon. However, there has been little research involving fluid-structure coupling simulations for this process, and no researcher has specifically analyzed this issue. In this section, the CEL method is used to solve for the mushroom shape observed during ground launching and the drag-induced shape caused by lateral wind flow, as well as the resulting forces on the balloon envelope [26; 27].
## 5 Discussion
### Summarized Tables on AI Techniques and Performance
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Process Stage** & **Key Sensors** & **Machine Learning Techniques** \\ \hline \multirow{4}{*}{Crushing} & Vibration sensors, hydraulic pressure sensors, motor temperature sensors, acoustic sensors, energy consumption meters [49; 53; 62]. & Predictive maintenance using Random Forest [53; 62], fault detection via CNNs, anomaly detection with Autoencoders [11], regression models for energy consumption forecasting [49]. \\ \hline \multirow{4}{*}{Material Transport (Conveyors)} & Belt misalignment sensors, load sensors (strain gauges), speed sensors, temperature sensors for rollers, optical particle size sensors [5; 1; 59; 1; 21]. & Support Vector Machines for belt wear classification [21], Reinforcement Learning for dynamic speed control [59], Bayesian Networks for failure prediction, and image-based particle size estimation with Deep Learning [18]. \\ \hline \multirow{2}{*}{Agglomeration} & Moisture sensors, flow meters for reagents, viscosity sensors, temperature sensors [36; 66]. & Decision Trees for reagent optimization, RNNs for moisture-level prediction [36], and Fuzzy Logic for viscosity control. \\ \hline \multirow{4}{*}{Leaching} & pH sensors, ORP sensors, flow meters for leaching solutions, pressure sensors in drip irrigation, conductivity sensors, and online spectrophotometers for metal concentration [6; 83]. & Gaussian Processes for real-time pH forecasting, Deep Reinforcement Learning for acid dosing optimization [6], PCA for metal concentration monitoring, and LSTM networks for solution flow control [83]. \\ \hline \multirow{4}{*}{Solvent Extraction (SV)} & pH sensors, ORP sensors, conductivity meters, flow meters for organic/aqueous phases, level sensors in separation tanks [23; 32; 58]. & Gradient Boosting for phase separation efficiency prediction [58], Self-Organizing Maps for anomaly detection in organic extraction, and Reinforcement Learning for dynamic solvent flow optimization [32]. \\ \hline \multirow{4}{*}{Electrowinning (EW)} & Voltage and current sensors, electrolyte temperature sensors, pH/ORP probes, level sensors in electrolytic cells, metal concentration sensors [38; 41; 44; 51]. & Neural Networks for predicting optimal current density [51], Reinforcement Learning for voltage control [38], time-series forecasting with LSTMs for electrolyte monitoring [41], and Decision Trees for cathode quality classification [44]. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Key sensors and machine learning techniques in hydrometallurgical processing. above 95% accuracy in critical applications, such as bearing fault detection in conveyors and mineral crushers. However, interpretability challenges remain, especially for black-box deep networks.
## 4 FTGC Design-Based LOS Angular Rate and the Target Motion Estimation Information
### LOS Angular Rate Reconfiguration and Target Acceleration Estimation
That is to say, there exists a small positive constant \(\varepsilon_{o}^{\text{max}}\) satisfying \(\max(\|\mathbf{e}_{o}\|,\|\mathbf{e}_{1}\|,\|\mathbf{e}_{2}\|)\leq\varepsilon_{o}^{\text{ max}}\). As a result, it can be concluded that the states of the ESO will converge to the LOS angles, LOS angular rates, and target acceleration, i.e., \(\lim_{t\to\infty}(\mathbf{z}_{0},\mathbf{z}_{1},\mathbf{z}_{2})=(\mathbf{x}_{0},\mathbf{x}_{1}, \mathbf{a}_{\text{Ts}})\). Assuming that \(\beta_{i}=diag(\beta_{i1},\beta_{i2})\), through simple calculation, the characteristic polynomial of \(A_{o}\) can be given by  
\[p(A_{o})=\bigg{(}\lambda^{3}+\beta_{01}\lambda^{2}+\beta_{11}\lambda+\frac{ \beta_{21}}{R}\bigg{)}\bigg{(}\lambda^{3}+\beta_{02}\lambda^{2}+\beta_{12} \lambda-\frac{\beta_{22}}{R\cos q_{z}}\bigg{)} \tag{26}\]  
Therefore, the selection of the observer gain matrices will determine the bandwidth frequency of the observer. If the desired bandwidth frequency of the observer is \(\omega_{o}\), the observer gains can be given by  
\[ \begin{array}{l}\beta_{01}=\beta_{02}=3\omega_{o}\\ \beta_{11}=\beta_{12}=3\omega_{o}^{2}\\ \beta_{21}=R\omega_{o}^{3},\beta_{22}=-R\cos q_{z}\omega_{o}^{3}\end{array}  \tag{27}\]  
Note that a larger \(\omega_{o}\) will result in a faster convergence rate, which, however, may enlarge the observer noise. Thus, the observer gains can be determined by selecting a proper bandwidth frequency.
## 2 Basic Notions and Preliminary Results
### Quantizer Description  
#### 2.2.1 Logarithmic Quantizer  
In this section, the logarithmic quantizer described in [34] is considered. The quantizer with bounded region satisfies the following properties:  
\[q_{l}(u)=\begin{cases}\begin{array}{cc}u_{i}&\frac{u_{i}}{1+\delta}<u<\frac{u_ {i}}{1-\delta}\\ 0&0\leq u<\frac{d}{1+\delta}\\ -q_{l}(-u)&u<0\end{array}\end{cases} \tag{1}\]  
\[|\Delta q_{l}(u)|\leq\delta|u|+(1-\delta)d \tag{2}\]  
where \(\Delta q_{l}(u)=q(u)-u\), \(q_{l}\) with quantization error \(\Delta q_{l}\), \(u_{i}=\rho^{(}1-i)d\), \(i=1,2,...\), \(N\). The parameters \(d>0\) and \(0<\rho<1\), \(\delta=\frac{1-\rho}{1+\rho}\) determine the quantization density of \(q_{l}(u)\). \(q_{l}(u)\) is in the set \(U=\{0,\pm u_{i}\}\). \(u_{min}=\frac{d}{1+\delta}\) determines the size of the deadzone for \(q_{l}(u)\). For this quantizer, some remarks about its range, number of quantization levels and quantization density are given in [35].
## 4 Probabilistic Analyses
### Non-Linear Finite Element Analyses
#### 4.8.2 Pre NLFEM-Modeling  
The IABSE Working Commission Group 1 organized a NLFEM round-robin modeling [4] for the previously discussed columns with the following process steps in the context of the amount of accessible information for non-linear modeling: 1 st Round-Robin Modeling Process Step: _Deterministic analyses based on the drawings without conformity test results_; 2 nd Round-Robin Modeling Process Step: _Deterministic analyses based on the drawings with conformity test results_; 3 rd Round-Robin Modeling Process Step: _Analyses based on the drawings with defined input parameters_; 4 th Round-Robin Modeling Process Step: _Deterministic analyses based on the drawings with conformity test results and the test results of the column_; 5 th Round-Robin Modeling Process Step: _Probabilistic analyses based on the drawings with conformity test results and the test results of the column_. For more details regarding these process steps and a list of the Round-Robin Modeling Experts see [4].  
The received deterministic structural responses (4 th process step) allowed an unambiguous comparability of the predictions with the experimentally obtained data as shown in Table 2.  
The results from the group investigations according to the 1 st paper of this paper series can be summarized as follows (see [4]): the 1 st Round-Robin Modeling Process Step revealed a modeling uncertainty based on the minimum and maximum values found by the partners for the C45/55 column of \(\varphi\)_EXP_,C45/55 = 1 + (_Nmax_,NLFEM_,C45/55 - _Nmin_,NLFEM_,C45/55)/_Nmean_,EXP_,C45/55 = 1.14. This uncertainty was reduced in the 4 th Process Step to \(\varphi\)_EXP_,C45/55 = 1.04, see [4]. For the C100/115 column, the uncertainty improved the \(\varphi\)_EXP_,C100/115 from 1.23 to 1.06 in the 4 th Process Step. Consequently, the optimized 4 th Round-Robin ATENA 3D NLFEM model from the University of Natural Resources and Life Sciences (BOKU) was used for the following probabilistic analyses.
## 3 Results
### Kinematics  
No significant interaction or midsole hardness effects were observed for the kinematic variables that were analyzed (Table 2). While platform height had no effect on knee or hip kinematics, the ankle was increasingly plantarflexed at initial ground contact as drop height increased (\(p<0.001\)), which was accompanied with a greater ankle ROM (\(p=0.002\)).  
\begin{table}
\begin{tabular}{l l l l l l l l l l l l l} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Drop** \\ **Height** \\ **[cm]** \\ \end{tabular} } & \multirow{2}{*}{**Midsole Hardness**} & \multicolumn{6}{c}{**ANOVA**} \\ \cline{3-11}  & & & & & & & & & & & & & \\ \cline{3-11}  & **50 Asker C** & **60 Asker C** & \(p\) & \(\eta_{p}{}^{2}\) & \(\beta\) & \(p\) & \(\eta_{p}{}^{2}\) & \(\beta\) & \(p\) & \(\eta_{p}{}^{2}\) & \(\beta\) \\ \hline \multicolumn{11}{c}{_Initial contact_} \\ \hline \multirow{3}{*}{\begin{tabular}{c} Ankle \\ [\({}^{\circ}\)] \\ \end{tabular} } & 31 & \(-\)20.4 (7.2) & \(-\)20.3 (6.9) & & & & & & & & & \\  & 46 & \(-\)22.6 (3.8) & \(-\)22.6 (5.9) & \(<\)**0.001 \(\times^{0.1}\)** & 0.459 & 0.982 & 0.775 & 0.005 & 0.059 & 0.953 & 0.009 & 0.057 \\  & 61 & \(-\)24.2 (5.9) & \(-\)24.0 (5.7) & & & & & & & & \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Knee \\ [\({}^{\circ}\)] \\ \end{tabular} } & 31 & 27.6 (8.9) & 28.7 (9.7) & & & & & & & & & & \\  & 46 & 28.7 (7.1) & 29.2 (8.4) & & & & & & & & \\  & 61 & 30.6 (6.8) & 29.3 (7.0) & & & & & & & & \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Hip \\ [\({}^{\circ}\)] \\ \end{tabular} } & 31 & 30.8 (9.4) & 31.2 (9.2) & & & & & & & & & & \\  & 46 & 29.8 (7.1) & 29.6 (8.1) & & & & & & & & \\  & 61 & 30.0 (7.7) & 29.8 (7.5) & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Platform height and midsole hardness effects on hip, knee, and ankle kinematics in the sagittal plane during the landing phase of drop vertical jumps.
## 3 Distance-Aware Replica Adaptive Data Delivery Scheme
### Queue Management  
Due to the limited buffer size of each node in a DTMSN, the buffer queue management would greatly influence the transmission performance. In DRADG, the ticket indicates how many replicas are allowed to be propagated, and the survival time shows how long a certain message has existed in the network. Therefore the ticket and survival time together denote the importance of a message, and the queue management is just based on the two parameters.  
Messages are sorted in the routing queue based on a decreasing order of their tickets. For those with the same ticket value, they are further sorted according to an increasing order of survival time. Thus messages with larger tickets and shorter survival time are closer to the top of the queue, and can be transmitted with higher priorities. Moreover, messages will be dropped in the following two occasions: (a) when a message arrives and the queue is full, it is compared with that message at the end of the queue, and the one with less ticket is dropped among them. If the ticket values of the two messages are equal, then the one with a longer survival time is dropped; (b) whenever one message's survival time is longer than the delay tolerance of the network, it is dropped to avoid unnecessary resource consumption.
# Single-Cell Patterning Based on Immunocapture and a Surface Modified Substrate
## 2 Preparation of the Substrate for Single Cell Patterning
### Overall Process of Substrate Preparation for the Single-Cell Array  
To achieve the chip for the single-cell patterning, its substrate should be prepared first. Here, the fabrication process was based on micro fabrication, including photolithography, chemical vapor deposition (CVD), dry etching, wet etching, and polyethylene glycol (PEG) passivation. Meanwhile, three primary processes were employed to prepare the chip substrate, including piranha wet etching, oxygen plasma etching, and hexamethyldisilazane (HMDS) CVD, through which the patterned substrate was obtained with a clear pattern and boundary.  
The purpose of the substrate surface modification for the single-cell patterning is to prepare a lattice pattern of two different substrate properties, including the pattern and the background, which can be used for the selective adhesion of proteins. To be effectively bonded to the proteins, the target islands should exhibit the same hydrophobic properties as the proteins, while the background without the pattern should be treated with passivation agents to prevent the unwanted adhesion. HMDS, which is often used in integrated circuit processing to enhance adhesion, will react with the hydroxyl silicon structure on the surface of the SiO\({}_{2}\) glass substrate and form siloxane.  
The HMDS-modified surface of the glass slide will be hydrophobic, which can be a better adhesive to the photoresist. The chemical reaction can be expressed as:  
\[\begin{array}{c}2(\text{Si}_{(substrate)}-OH)+(CH_{3})_{3}Si-NH-Si(CH_{3})_{3}\\ \qquad\qquad\to 2(Si_{(substrate)}-O-Si(CH_{3})_{3})+NH_{3}\end{array}. \tag{1}\]  
Here, we used HMDS as the first processing material in the template generation. Our experiments showed that the islands after HMDS treatment exhibit good hydrophobic properties, which are a strong adhesive to proteins, and the surface of the siloxane group is nontoxic to the bioactive substances.  
To obtain the HMDS pattern, the CVD method was employed. First, the photoresist mask was formed by direct lithography. Next, the CVD process was employed, through which the areas not covered by the photoresist will react with HMDS, followed by photoresist release and passivation treatment. The whole surface modification process includes lithography, dry etching, wet etching, chemical vapor deposition, etc., as shown in Figure 1. The selection of the parameters of each process directly determines the quality of the pattern on the substrate.
## 2 System Model
### Communication Model  
#### 2.2.1 Non-Cooperative Communication Model  
Based on the non-cooperative communication model, the \(EU_{m}\) transmits the data directly to the BS. The attainable transmit rate \(R_{m}^{dir}\) of uplink transmission between the \(EU_{m}\) and the BS may be expressed as:  
\[R_{m}^{dir}=\log_{2}\left(1+\frac{h_{bm}P_{m}}{N_{0}}\right) \tag{2}\]  
where \(P_{m}\) denotes the transmit power of the \(EU_{m}\). Furthermore, \(h_{bm}\) represents the channel gain of the uplink from the \(EU_{m}\) to the BS and \(N_{0}\) represents the power of Additive White Gaussian Noise (AWGN).