esa-sceva/satcom-chunk-collection huggingface dataset:
# Ground Deformation Associated with Deep Excavations in Beijing, China
###### Abstract  
A performance study is considered to be reliable method for comprehending deformations associated with deep excavation. To gain insight into the laws governing ground deformations that are associated with deep excavation, details of 88 cases were collected and analyzed in Beijing. The results were compared with worldwide case histories. Field data were selected to survey the ground behavior and to examine the correlation between deformation and excavation. The position and magnitude of the final ground deformation (\(\delta v\)), as well as the maximum deformation (\(\delta vm\)), the correlations between \(\delta vm\) and excavation depth (\(H\)), the length-width ratio, embedded depth ratio (EDR), and the stiffness of the support system, were assessed. The clear evolution process, influence zone, and final deformation pattern are illustrated. Our study revealed the following: (1) the groove pattern is detected in the final deformation of the ground surface, \(\delta vm\) occurred when positioned approximately 0.42\(H\)-0.62\(H\) off the wall, when the 1 st-2 nd supports on the bottom were removed; (2) \(\delta vm\) increases with an increase in \(H\), and it ranges from 0.04% to 0.12% when \(H\) has an average value of approximately 0.089%; (3) EDR has an observable effect on reducing the \(\delta vm\), as there a slight impact was observed until the ratios exceeded 0.4; (4) the deformation value of the 75% monitoring points ranged from \(-25\) mm to 0 mm; (5) excavation could cause minor upheaval in some areas, but the upheaval reduces with increasing levels of excavation, so both deformation magnitude and the number of points are low; (6) deformation exhibits clear temporal-spatial characteristics, the settlement rate gradually increased over time, especially after drainage started or consolidation appeared, and when the internal structure is completed, \(\delta vm\) decreases with the rise in support system stiffness, ranging from 7000 to 11,000, and deformation becomes stable; and (7) transverse sections near the excavation center experienced larger deformations than others and the smallest deformations were near the corners, a significant increase occurs with the removal of the lowest 1-2 struts, particularly on the long side where \(\Delta\)sv reaches 2.8 \(\pm\) 0.75 mm, and the influence zones extend from approximately 2.5\(H\) to 3\(H\) beyond the excavated face. These findings have valuable implications for designing and constructing similar projects in Beijing and other regions, as they can help prevent accidents and minimize resource wastage.  
deep excavation; field monitoring; ground surface deformation; Beijing subway 2023
# Harnessing Odorant Receptor Activation to Suppress Real Malodor
## 2 Results and Discussions
### Identification of Effective Antagonists That Suppress Cat-Urine-Responsive Receptors  
Not only mouse TAAR5 but also human TAAR5 responded to volatile compounds from cat urine (Figure 1H,J). Considering that human TAAR5 shares 86.6% sequence homology with mouse TAAR5 (Figure S1), we hypothesized that by screening for antagonists using mouse TAAR5, we could identify fragrances that also suppress human perception. Therefore, we screened for the antagonists of mouse TAAR5 to explore whether they could suppress cat urine odor. Since the amount of cat urine sample is limited, we used TMA-induced TAAR5 responses as a reference to screen for potential antagonists from a pool of 420 fragrance compounds (Figure 3A,B). In the primary screening of the 420 fragrance compounds, we selected the top 24 compounds with the strongest effects for a secondary screening using cat urine. The 24 candidate compounds were classified as follows: five alcohols (ALC), nine aldehydes (ALD), one carboxylic acid (CAR), one essential oil (ESS), five esters (EST), two ketones (KET), and one lactone (LAC) (Table S1). We identified several novel antagonists--ALC4 (santol pentenol), ALD2 (citral), ALD3 (muguet butanal), ALD5 (magnolia decadienal), ALD6 (trans-2-hexenal), ALD7 (cinnamaldehyde), ALD8 (floral butanal), ALD9 (helional), ESS1 (litsea cubeba fruit oil), and EST2 (ethyl safanate)--that inhibited TAAR5 responses to cat urine odor in a concentration-dependent manner. These antagonists outperformed the known TAAR5 antagonist timberol (1-(2,2,6-trimethylcyclohexyl)hexan-3-ol, designated ALC5 in this study) [29] (Figure 3C). Although there were relatively numerous molecules with aldehyde groups, we found no particular structural commonalities among the identified suppressors. Some of the antagonists also significantly suppressed the response of TAAR5 to TMA (Figure S3). Since TAAR5 exhibits high sensitivity to TMA, a higher concentration of inhibitor was expected to be required for suppression tests using pure TMA compared to the concentration of TMA present in cat urine. Among the identified
## 2 Materials and Method  
### Preparation of Activated Carbon  
The leftover leaves of \"_Lantana canara_\" and \"Olive trees\" (6 g) were collected. To eliminate dust from the leaves' surfaces, they are rinsed three times with distilled water (500 mL). To eliminate moisture from the leaves, they were kept in the open air for 3 to 4 days in the presence of sunlight before being crushed. The dried leaves were crushed and sieved to a depth of 250 \(\upmu\)m. After that, the fine biomass powder (4.345 g) was activated in a H\({}_{3}\)PO\({}_{4}\) (wt 50%) solution. The H\({}_{3}\)PO\({}_{4}\) solution's initial concentration and density were 85% and 1.685 g/mL, respectively. For a (1:1) impregnation ratio (the impregnation ratio was determined as the ratio of the weight of H\({}_{3}\)PO\({}_{4}\) to the weight of the dried biomass from olive trees and _Lantana canara_) the needed H\({}_{3}\)PO\({}_{4}\) volumes per (4.345-g) dry raw material were 3.03 mL [20]. The phosphoric acid activating agent took 24 h to absorb. H\({}_{3}\)PO\({}_{4}\) acts as an activator, causing the number of defects to rise [21]. The slurry-based material was calcined for 2 h at 550 \({}^{\circ}\)C in a box resistance furnace. The excess H\({}_{3}\)PO\({}_{4}\) was rinsed out of the char with distilled water until it reached a neutral pH. After that, it was dried in a 105 \({}^{\circ}\)C oven for 1 h to get a 4-g activated carbon [22]. Figure 1 shows the steps involved in the preparation of activated carbon.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Biomass Source**} & \multicolumn{3}{c}{**Proximate Analysis (\%)**} & \multicolumn{3}{c}{**Ultimate Analysis (\%)**} & \multirow{2}{*}{**Reference**} \\ \cline{2-2} \cline{4-9}  & **VM** & **FC** & **Ash** & **C** & **O** & **H** & **N** & **S** \\ \hline Oak Wood & 78.1 & 21.4 & 0.5 & 50.6 & 42.9 & 6.1 & 0.3 & 0.10 \\ \hline Christmas tree & 74.2 & 20.7 & 5.1 & 54.5 & 38.7 & 5.9 & 0.5 & 0.42 \\ \hline Corn Straw & 73.1 & 19.2 & 7.7 & 48.7 & 44.1 & 6.4 & 0.7 & 0.08 \\ \hline Almond Shell & 74.9 & 21.8 & 3.3 & 50.3 & 42.5 & 6.2 & 1.0 & 0.05 \\ \hline Cocont Shell & 73.8 & 23.0 & 3.2 & 51.1 & 43.1 & 5.6 & 0.1 & 0.10 \\ \hline Olive Wood & 79.6 & 17.2 & 3.2 & 49.0 & 44.9 & 5.4 & 0.7 & 0.03 \\ \hline Olive husk & 79.0 & 18.7 & 2.3 & 50.0 & 42.1 & 6.2 & 1.6 & 0.05 \\ \hline Olive Pits & 77.0 & 19.9 & 3.1 & 52.8 & 39.4 & 6.6 & 1.1 & 0.07 \\ \hline Olive Residue & 67.3 & 25.5 & 7.2 & 58.4 & 34.2 & 5.8 & 1.4 & 0.23 \\ \hline _Lantana_ (Stem) & 74.42 & 17.71 & 0.85 & 48.10 & 43.66 & 6.22 & 1.04 & 0.13 \\ \hline _Lantana_ (Twig) & 73.49 & 17.36 & 1.34 & 45.90 & 44.65 & 6.92 & 1.05 & 0.14 \\ \hline _Lantana_ (Leaves) & 67.45 & 17.51 & 7.55 & 43.00 & 42.57 & 5.69 & 1.05 & 0.14 \\ \hline \hline \end{tabular} \(\text{VM}=\text{Volatile Matter}\); \(\text{FC}=\text{Fixed Carbon C}=\text{Carbon}\); \(\text{O}=\text{Oxygen}\); \(\text{H}=\text{Hydroogen}\); \(\text{N}=\text{Nitrogen}\); \(\text{S}=\text{Sulphur}\).  
\end{table}
Table 1: The chemical composition of the biomass sources on a dry basis.
## 2 Materials and Methods
### Design of the DC-DC Controllers
#### 2.2.1 Incremental Conductance MPPT  
The incremental conductance algorithm (INCC) shown in Figure 9 takes the voltage \(V_{pv}\) and the current \(i_{pv}\) of the panel as inputs. Then, it calculates the differentials \(dV\) and \(dI\). Its operation is based on the fact that the slope of the solar panel power curve is zero at the maximum power point (MPP), positive to the left of the MPP, and negative to the right [27]. From the above, it can be deduced that in the MPP:  
\[\frac{dP}{dV}=\frac{d(VI)}{dV}=0, \tag{33}\]  
\[I+V\frac{dI}{dV}=0. \tag{34}\]  
Consequently, the following conditions are obtained:  
\[\frac{dI}{dV}=-\frac{I}{V},\text{ in MPP;} \tag{35}\]  
\[\frac{dI}{dV}<-\frac{I}{V},\text{ to the right of MPP;} \tag{36}\]  
Figure 8: DC-DC stage control loop; the current-based incremental conductance MPPT is regulated by a 2P2Z controller.  
Figure 7: Characteristic curve of a solar panel with irradiance values from 100 to 1000 W/m\({}^{2}\).  
\[\frac{dI}{dV}>-\frac{I}{V},\text{ to the left of MPP.} \tag{37}\]  
Therefore, the MPP can be traced by comparing the instantaneous conductance \((I/V)\) with the incremental conductance \((dI/dV)\)[19]. Once the MPP is reached, the solar panel's operation remains at this point unless a change is observed. The algorithm decrements or increments \(I_{ref}\) to track the new MPP.
## 3 Results and Discussion
### Sensory Analysis
[59] did not observe significant differences in beef lipids, based on fatty acids analysis between raw beef and beef cooked at 70 \({}^{\circ}\)C. Therefore, the limited extent of the above mentioned reactions at temperatures applied in our study presumably was the reason that no significant differences were observed between samples with regard to their aroma intensity. Moreover, [PERSON] et al. [6] did not observe a change in the free glutamate content in beef tenderloin sous vide cooked at 54 and 64 \({}^{\circ}\)C, which together with aroma volatile compounds delivering reactions might account for only small differences in sample flavour acceptability in the present study. On the other hand, [PERSON] et al. [60] reported lower glutamic and aspartic acid concentrations in pork loins sous vide cooked at 70 \({}^{\circ}\)C than at 60 and 80 \({}^{\circ}\)C, no effect of cooking parameters on inosine monophosphate and increased concentration of adenosine monophosphate with increased temperature and time of treatment. The lowest juiciness of the samples cooked at 75 \({}^{\circ}\)C in our experiment confirms the findings of [PERSON] et al. [11] and agrees with the hypothesis verified by [PERSON] et al. [14]. The hypothesis implied that transversal and longitudinal meat shrinkages, which take place at 45-60 \({}^{\circ}\)C and 60-90 \({}^{\circ}\)C, resp., decrease the water holding capacity of meat and as a result reduce its juiciness. In our study, cooking at 75 \({}^{\circ}\)C produced a less tender meat than cooking at lower temperatures. These results correspond to those of TPA hardness, cohesiveness and chewiness. According to [PERSON] [12], meat tenderness increases when cooked between 50 \({}^{\circ}\)C and 65 \({}^{\circ}\)C and then declines with temperature increases up to 80 \({}^{\circ}\)C. [PERSON] et al. [6] observed a slight decrease in beef tenderloin tenderness with longer cooking time both at 54 and 64 \({}^{\circ}\)C, and increased temperature. In the experiment of [PERSON] et al. [11] tenderness of pork increased both with temperature and cooking time.
# Real-Time Seismic Data from the Bottom Sea
## 2 Standalone Broadband Ocean Bottom System
### The Mooring and Inductive Communication System
#### 2.3.1 Mooring Configurations  
The proposed design consists of a seabed unit, an intermediate underwater buoy, and a surface buoy (see Figure 6). The intermediate buoy supports part of the weight of the cable minimizing displacements and tensions at the seabed anchorage. The three units are linked with a mooring/communication cable and two options, using a steel cable or a combination of steel cable plus elastomer, have been studied. Mooring cables have to support communication and data transfer. In Model 1 the mooring cable is a plastic-jacket steel wire rope. Their two ends are connected to seawater ground, closing the electrical circuit. The electrical circuit thus created allows direct injection of data or its transfer using a magnetic field. In Model 2 an additional electrical cable around the elastomer is needed. Keeping in mind that the purpose is to maintain the seabed anchorage as stably as possible, a long steel cable or the use of the elastomer in Line 1 are good options. The inverse catenary or elastomer in Line 1 absorbs part of the movements and tensions induced by the meteorological conditions (waves, winds, and currents). In particular, we evaluated three cases of each model where the length of Line 1 is varied (see Figure 6). The ratio of Line 1 is defined as the length of Line 1 in front the depth of the intermediate buoy in the static case (100 m or 20 m, depending on the Model).  
As stated above, in order to support the cable weight (Line 2 and partially Line 1), a cylindrical intermediate buoy of 40 kg mass with a buoyancy of 2000 N has been chosen.  
A 6 mm section steel cable line (6 \(\times\) 19) has been chosen. This is the minimum size that supports the loads in extreme conditions. The selected elastomer is made of natural rubber with cord diameter 35 mm, Shore A, with 300% elongation and hardness of 45 or 60 [12]. In the open sea, the environmental conditions of currents and waves are the main environmental loads acting on the surface buoy, but the load due to the wind cannot be ignored, mainly because of the resistance offered by the solar  
Figure 5: The surface buoy.  
panels. The wind rose of the \"Cabo de Gata\" and the maximum wave have been considered to design the system.
###### Abstract  
In recent years, with the reduction of the cost of microsatellites, the development of commercial rockets and the multi-satellite launching technology, the construction of large-scale constellations in low-Earth orbit (Mega-Constellations) has become a development trend. Since the motion of LEO satellites is affected by perturbations such as non-spherical gravitational fields and atmospheric drag, as well as the uncertainty of actuators, measurement systems, and dynamic models, it is easy to cause divergence of constellation configurations. The station-keeping control of the satellites is crucial for the stable operation of the mega-constellation. Aiming at this problem, this paper proposes an uncertainty propagation approach based on semi-analytical and Monte Carlo for LEO Mega-Constellations. Under the assumption that initial uncertainty on the osculating trajectory is Gaussian distribution, through hypothesis testing analysis, the uncertainty propagation simulations of a single satellite suggest that the satellite argument of latitude and the relative phase of co-plane satellites can be both considered as Gaussian distributions with zero means. Multi-group Monte Carlo simulations with product-based least-squares surface fitting establish an approximate mapping between initial and terminal errors. The mapping provides an efficient method for deviation prediction and can be used to design the station-keeping control strategy.  
station-keeping; mega-constellations; uncertainty propagation; Monte Carlo 2022, 9, 128. [[https://doi.org/10.3390/aerospace9030128](https://doi.org/10.3390/aerospace9030128)]([https://doi.org/10.3390/aerospace9030128](https://doi.org/10.3390/aerospace9030128))
## 4 Experiments and Discussion
### Discussion
#### 4.5.2 The Necessity of Long-Term Dependencies  
As shown in Table 1, regardless of whether dynamic factors are used, RNN's prediction of bus arrival time is significantly better than that of other methods. This is because RNN can learn long-term dependencies, while other algorithms in the experiments only build short-term dependencies. Both DA-RNN and LSTM RNN contain the LSTM structure. The LSTM units can selectively \"forget\" old information, \"remember\" new information and determine output. So the RNN-based models can make full use of the information of the last several road segments, while other methods only use the information of the current road segment for prediction. In other words, all methods establish a mapping relationship from influencing factors to travel time. The difference is that the mapping relationship of other models exists only in a single road segment, while the RNN-based model can establish a mapping relationship in several road segments. Effective use of historical information is the reason why the RNN network can get better prediction results.  
Figure 6 shows that in offline mode, the MAE of the prediction results increase with the increase of the number of stations. Among them, the MAE of two RNN models is small and the growth rate is slow. The main reason for the growth of MAE is that the arrival time was not added, and the prediction result cannot be corrected according to the observations, so the absolute error of the prediction was accumulated in the iteration. The secondary reason is that dynamic factors cannot be utilized under offline conditions. In offline mode, RNN models exhibits greater robustness. This indicates that long-term dependency is indispensable in the bus arrival time prediction task. It is worth noting that the prediction MAE at the 10 th station of the proposed RNN model can still be kept within one minute (59.41 s). This proves that our approach also has high practical value in the offline state.
## 3 Results and discussion
### An Alternative Method to Estimate WUE Using MODIS EVI Data  
Considering that large uncertainties exist in the MODIS WUE estimates from GPP and ET, an alternative method needs to be developed in order to extrapolate tower-based WUE measurements to different time and space scales. The strong relationship between time series of MODIS EVI data and ecosystem WUE also indicated this potential. Here, we established a regression model that was solely based on the 8-day EVI data. For the corn-planting year and soybean-planting year, crop WUE can be calculated using the following Equations (4) and (5), respectively:  
\[WUE_{GR}=7.245\ \times EVI-0.871\ (R^{2}=0.82;\ \text{RMSE}=1.07) \tag{4}\]  
\[WUE_{GR}=6.601\ \times EVI-0.938\ (R^{2}=0.90;\ \text{RMSE}=0.69) \tag{5}\]  
Figure 4: Comparison of seasonal dynamics in gross primary production and evapotranspiration between flux tower measurements (GPP\({}_{\text{EC}}\), ET\({}_{\text{EC}}\)) and MODIS estimates (GPP\({}_{\text{RS}}\), ET\({}_{\text{RS}}\)) at the flux tower site US-Bo1 during 2001–2006.  
The performances of these empirical models (GR) was evaluated using scatter plots between the predicted (WUEGR) _vs._ measured WUE (WUEEC) and the seasonal variations between them in 2005 (corn-year) and 2006 (soybean-year). As shown in Figure 5 and 6, the seasonal patterns of WUE simulated by the GR model agreed reasonably well with the tower-based observations for both corn and soybean in 2005-2006, which also exhibited a nearly linear relationship. Compared with the indirect MODIS WUE estimates (WUERS) from GPP and ET in Figure 3, large discrepancies in ecosystem WUE at the beginning and ending of crop growing season and peak periods were generally reduced. This result may be explained by the underlying phenological information in the time-series of MODIS EVI data. A large  
Figure 5: Seasonal dynamics in ecosystem water use efficiency from flux tower measurements (WUEEC) and estimates based on MODIS EVI data (WUEGR) at the flux tower site US-Bo1 during 2005 (corn) and 2006 (soybean), with the growing seasons displayed in gray.  
Figure 6: A scatterplot comparison between ecosystem WUE from flux tower measurements (WUEEC) and estimates based on MODIS EVI data (WUEGR) at the flux tower site US-Bo1 during the growing season of 2005 (corn) and 2006 (soybean).  
number of researchers have used time series of EVI data to extract phenology characteristics [36,58,59]. [PERSON] _et al._[60] also indicated that in order to precisely evaluate the land-surface mass (water and carbon fluxes) and energy budgets, we need to account for crop phenology in ecosystem models. _RMSE_ of WUEs and WUEGR with WUEEC over the growing season were 2.24 g C kg\({}^{-1}\) H\({}_{2}\)O _vs_ 0.79 g C kg\({}^{-1}\) H\({}_{2}\)O in 2005 for corn, and 1.58 g C kg\({}^{-1}\) H\({}_{2}\)O _vs_ 0.66 g C kg\({}^{-1}\) H\({}_{2}\)O in 2006 for soybean. Therefore, this simple empirical model provides a very reasonable estimate of ecosystem-level WUE.
# Extended-Reach Drilling (ERD)--The Main Problems and Current Achievements
## 3 Drilling Fluids for Extended-Reach Wells  
In the well-planning phase of ERW construction, special attention should be paid to the choice of drilling fluid. Selected drilling fluids for extended-reach wells should satisfy the same basic functions that are common to all drilling fluids, and they have to provide excellent reservoir protection [51]. When drilling extended-reach wells, the following critical factors should be considered: hole cleaning, torque and drag, borehole stability, equivalent circulating density (ECD) and lost circulation [51; 52; 53; 12; 18; 19; 24; 5].
# Review and Comparison of Emerging Routing Protocols in Flying Ad Hoc Networks
## 3 Communication Architecture of FANETs
### Multi-Layers UAV Ad Hoc Network  
Figure 3 shows the multi-layers UAV network structure. This communication system is composed of several different groups. As the UAVs are heterogeneous, each group has different functions, presenting a layered structure. In general, communication between UAVs takes place in the lower layer, and the higher layer is used for communication between all backbone groups and ground stations. The communication or information exchange between groups does not require the participation of the ground station, and the ground station only takes care of the information sent to the ground station, which greatly reduces the communication load and calculation amount of the ground station. This communication architecture is best suited to the mission scenarios of one-to-many UAV operations. In addition, this communication architecture has the advantage of having no single point of failure.  
In conclusion, decentralized communication architecture is the most suitable architecture for connecting UAVs [35] teams, whereas multi-layers UAVs ad hoc network is more suitable for FANET.
## 5 Simulation Results and Analysis
### Analysis and Comparison of GDOP
However, as shown in Figure 8b, at the points inside the green quadrilateral, GDOPs of the proposed hybrid positioning are not much different than GDOP of the TDOA-only positioning. Even when the ratio \(k\) is equal to 0.1, the minimum GDOP of the proposed hybrid positioning is 0.59. And the minimum GDOP of the TDOA-only positioning is just 0.79, which is only 0.20 higher. When the ratio \(k\) is equal to 1, 5 and 10, the GDOPs of the proposed hybrid positioning are approximate to the GDOP of the TDOA-only positioning. Analysis result shows that adding a TOA measurement to the TDOA measurements to achieve hybrid positioning have potential to greatly improve the positioning ability of the system in the edge area of the network. At the same time, in the core area of network coverage, although hybrid positioning has only a limited improvement in positioning ability, it is also superior to the TDOA-only positioning.