esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 The Challenge of the Bolo Rendezvous and Catching
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{**Mode**} & **Existing** & **Existing** & **10 W/kg** & **Double-** \\  & **steel tether** & **PE tether** & **hub and PE** & **strength PE** \\ \hline Catch mass & 246 kg & 247 kg & 247 kg & 243 kg \\ Tether rotation period & 52 s & 73 s. & 73 s & 152 s \\ Tether length & 4 km & 9 km & 9 km & 32 km \\ Tether tip speed & 242 m/s & 387 m/s & 387 m/s & 661 m/s \\ Tether tip acceleration & 29.2 m/s\({}^{2}\) & 33.3 m/s\({}^{2}\) & 33.3 m/s\({}^{2}\) & 27.3 m/s\({}^{2}\) \\ Tether tension without payload & 6.2 kHz & 6.8 kHz & 6.8 kHz & 23.3 kHz \\ Tether tension with payload & 13.4 kHz & 15 kHz & 15 kHz & 30 kHz \\ \(\Delta\)v for payload & 474 m/s & 759 m/s & 691 m/s & 1310 m/s \\ \(\Delta\)v for tether \& & 13.8 m/s & 16.4 m/s & 88.7 m/s & 15.3 m/s \\ Tether mass (with shield) & 852 kg & 810 kg & 810 kg & 3416 kg \\ Tether MMOD shield weight & 190 kg & 651 kg & 651 kg & 3257 kg \\ Tether MMOD shield blocking class & 2 mm & 2.5 mm & 2.5 mm & 3 mm \\ Tether effective drag area & 20.6 m\({}^{2}\) & 56.7 m\({}^{2}\) & 56.7 m\({}^{2}\) & 238 m\({}^{2}\) \\ Total effective drag area (night glider mode) & 30.3 m\({}^{2}\) & 70.3 m\({}^{2}\) & 70.1 m\({}^{2}\) & 260.5 m\({}^{2}\) \\ Tether hub power & 7.6 kW & 10.7 kW & 11.2 kW & 17.4 kW \\ Tether hub mass & 7.6 ton & 10.7 ton & 1.1 ton & 17.4 ton \\ Tether catch orbit, perigee \(\times\) apogee (km) & 299 \(\times\)321 & 357 \(\times\)383 & 322 \(\times\)458 & 420 \(\times\)444 \\ Post-release (circular) tether orbit altitude & 299 km* & 357 km* & 322 km* & 420 km* \\ Pre-capture payload orbit, perigee \(\times\) apogee (km) & 111 \(\times\)299 & \(-\)218 \(\times\)365 & \(-\)214 \(\times\)317 & \(-\)591 \(\times\)404 \\ Post-release payload orbit, perigee \(\times\) apogee (km) & 301 \(\times\)487 & 375 \(\times\)958 & 327 \(\times\)858 & 448 \(\times\)1455 \\ Payload increase * & 25 kg & 42 kg & 34 kg & 79 kg \\ Base payload & 246 kg & 247 kg & 247 kg & 243 kg \\ Tether hub thrust for post-release orbit departure & 60 mH & 45 mH & 90 mH & 53 mH \\ Thrust necessary for tether 1 week-long re-boost & 193 mH & 311 mH & 283 mH & 527 mH \\ Refueling frequency, every Nth payload & N = 24 & N = 17 & N = 17 & N = 10 \\ Lifetime required to break-even mass on LEO & 10.89 years & 7.95 years & 1.91 years & 7.32 years \\ Average time to break due micrometeorites & 115 years & 97 years & 97 years & 44 years \\ \hline \hline \end{tabular} * Not taking into account terminal guidance \(\Delta\)v loss (about 100 m/s); * Polar orbit was assumed for all cases;
* Compared orbital delivery to 185 \(\times\)185 km parking orbit without momentum transfer tethers.  
\end{table} 
Table 3: The space launch mission budgets using momentum transfer tethers (bolo) on LEO. An alternative solution may be to spin a secondary tether around the tip of the primary tether, nullifying tether tip acceleration at the moment of capture and therefore allowing low-acceleration rendezvous. That solution was first proposed in 2004 by [PERSON] [44], but a dedicated research program may be necessary to develop it.
## 3 Experiment and Application
### Engineering Application: Multi-UAVs Cooperative Path Planning
#### 3.2.4 Simulation Validation
Fair comparison was ensured by setting the same evaluation approach for all common parameters such as the population size, dimension, and maximum iteration of the different algorithms. The relevant parameters of these algorithms are shown in Table 17. Such values represent the best parameter sets for these algorithms according to the original paper of GWO [8], HGWO [71], IGWO [50], PSO [2], and DE [68]. The convergence curve of the 3D-integrated path cost of each case is shown in Figure 12, and the distribution of the operation results (30 times of testing) is shown in Figure 13. The average time of each algorithm in each scenario are shown in Table 18.  
The results show that the cost functions of each algorithm can converge to a certain value, which meets the actual requirements of planning. As can be seen from Figure 11, the final convergence value of APO in each case was smaller than other algorithms, and the convergence speed was also faster. The comparison shows that the convergence speed and the convergence accuracy of the method proposed in this study was faster than those of the other algorithms. In addition, it can be seen from Table 17 that the calculation time of APO was better than other algorithms in case 1, 2, and 4. In case 3, APO was only 0.25 s worse than the best performing GWO. Thus, APO's advantage in computing time was verified. The results of multiple runs in various scenarios in Figure 12 show that APO can find a better solution than other algorithms in multiple runs, and then verifies the stability of the algorithm. In summary, the APO algorithm solves the problems of slow convergence speed and low convergence accuracy of other algorithms. The calculation time is obviously better than other algorithms, and it has good stability. Therefore, the APO was superior in solving the problem of multi-UAVs cooperative path planning. Furthermore, the successful application of APO algorithm in the path planning problem provides new ideas for other practical engineering problems. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Case** & **APO** & **GWO** & **HGWO** & **IGWO** & **PSO** & **DE** \\ \hline
1 & 5.1268 & 5.6972 & 7.1175 & 56.6774 & 14.0171 & 27.2034 \\ \hline
2 & 7.5716 & 8.1782 & 9.9407 & 64.2276 & 19.2131 & 38.6358 \\ \hline
3 & 12.7386 & 12.4842 & 15.829 & 65.7687 & 33.3886 & 61.1923 \\ \hline
4 & 18.7229 & 18.8581 & 21.8224 & 81.9172 & 45.57 & 81.6728 \\ \hline \end{tabular}
\end{table} 
Table 18: The average time of each algorithm in each case (unit: s). Figure 13: The distribution of the operation results (30 times of testing) of each case. (**a**) case 1; (**b**) case 2; (**c**) case 3; (**d**) case 4.
# Potential of Plant Stem Cells as Helpful Agents for Skin Disorders--A Narrative Review
## 4 Mechanistic Insights into the Skin Aging of Plant-Derived Stem Cells
### Antioxidant Activity
The cell extract of _Dolichos biflorus_ was evaluated in vitro against fibroblasts and keratinocytes. It exhibits the ability to mitigate cellular damage caused by UV-B radiation by reducing interleukin expression. Furthermore, it decreases the production of MMP-1 and MMP-3 caused by UV-A radiation [56]. A similar result was also obtained from _Aster yomena_ and _Tiarella polyphyllla_ in the vitro fibroblast assay [48; 70].
# Effect of Compressive Strain Rate on Auxetic Foam
###### Abstract  
Auxetic foams have previously been shown to have benefits including higher indentation resistance than their conventional counterparts, due to their negative Poisson's ratio, making them better at resisting penetration by concentrated loads. The Poisson's ratio and Young's modulus of auxetic open cell foams have rarely been measured at the high compressive strain rates typical during impacts of energy absorbing material in sporting protective equipment. Auxetic closed cell foams are less common than their open cell counterparts, and only their quasi-static characteristics have been previously reported. It is, therefore, unclear how the Poisson's ratio of auxetic foam, and associated benefits such as increased indentation resistance shown at low strain rates, would transfer to the high strain rates expected under impact. The aim of this study was to measure the effect of strain rate on the stiffness and Poisson's ratio of auxetic and conventional foam. Auxetic open cell and closed cell polymer foams were fabricated, then compression tested to \(\sim\)80% strain at applied rates up to 200 s\({}^{-1}\), with Poisson's ratios obtained from optical full-field strain mapping. Open cell foam quasi-static Poisson's ratios ranged from \(-\)2.0 to 0.4, with a narrower range of \(-\)0.1 to 0.3 for closed cell foam. Poisson's ratios of auxetic foams approximately halved in magnitude between the minimum and maximum strain rates. Open cell foam quasi-static Young's moduli were between 0.02 and 0.09 MPa, whereas closed cell foams Young's moduli were \(\sim\)1 MPa, which is like foam in protective equipment. The Young's moduli of the auxetic foams approximately doubled at the highest applied strain rate of 200 s\({}^{-1}\).  
protective equipment; negative Poisson's ratio; foam; impact; PPE +
Footnote †: journal: Applied Sciences  
1
Footnote 1: email: {https:////www.mdpi.com/journal/applsci}
# Optimization of Offshore Saline Aquifer CO2 Storage in Smeaheia Using Surrogate Reservoir Models
###### Abstract  
Machine learning-based Surrogate Reservoir Models (SRMs) can replace/augment multi-physics numerical simulations by replicating the reservoir simulation results with reduced computational effort while maintaining accuracy compared with numerical simulations. This research will demonstrate SRMs' potential in long-term simulations and optimization of geological carbon storage in a real-world geological setting and address challenges in big data curation and model training. The present study focuses on CO2 storage in the Smeaheia saline aquifer. Two SRMs were created using Deep Neural Networks (DNNs) to predict CO2 saturation and pressure over all grid blocks for 50 years. 18 million samples and 31 features, including reservoir static and dynamic properties, build the input data. Models comprise 3-5 hidden layers with 128-512 units apiece. SRMs showed a runtime improvement of 300 times and an accuracy of 99% compared to the 3D numerical simulator. The genetic algorithm was then employed to determine the optimal rate and duration of CO2 injection, which maximizes the volume of injected CO2 while ensuring storage operations' safety through constraints. The optimization continued for the reproduction of 100 generations, each containing 100 individuals, without any hyperparameter tuning. Finally, the optimization results confirm the significant potential of Smeaheia for storing 170 Mt CO2.  
e +
Footnote †: journal: _Algorithms_  
1  
eological carbon storage; surrogate reservoir model; artificial intelligence; deep learning: optimization
## 3 Results and Discussion
### Improving Fragment Terpenoid Production via Enzyme Fusion
coli_ resulted in a strain with a relatively low titer of 38\(-\)42 ug/mL [47]. As indicated above, to our knowledge, this is the first demonstration of \(\beta\)-eudesmol production in the yeast system. [PERSON] and coworkers previously engineered _E. coli_ to produce \(\beta\)-eudesmol by overexpressing \(Z\)zBES2 and the mevalonate pathway enzymes [48]. Their engineered _E. coli_ strain produced \(\beta\)-eudesmol at a titer of 100 mg/L in a culture medium supplemented with 500 mg/L of the intermediate mevalonate. While the reported \(\beta\)-eudesmol titer of 100 mg/L is well above the titer achieved in our yeast system (23.9 \(\pm\) 1.1 mg/L), we contend that the _E. coli_ system's requirement of exogenous mevalonate supplementation will likely increase the production cost which may make the bioprocess difficult to scale up. Several strategies exist for improving the product titer of these high-value terpenoids [4; 5]. For example, as alluded to earlier, compartmentalization of pathway enzymes can create a spatial constraint between the substrate and pathway intermediates and the enzymes which results in improved efficiency of the metabolic pathway. [PERSON] and coworkers introduced a heterologous mevalonate pathway into the yeast peroxisomes to supplement the endogenous pathway in the cytosol [50]. Their engineered strains produced terpenids at up to 125-fold yield over cytosolic production. An additional benefit of targeting terpenoid production to the yeast peroxisome is that many terpenoids require further decoration by cytochrome P450 enzymes, which reside at the endoplasmic reticulum (ER). The proximity of the ER to the peroxisomes has been suggested to improve the P450-mediated oxidation of terpenoids [50; 51]. Another strategy that has shown significant promise in improving the terpenoid titer is to increase the availability of the precursor acetyl-CoA [52]. Overexpression of the mevalonate pathway, as employed in our study, may drain the pool of cytosolic acetyl-CoA, which can adversely affect yeast growth, which in turn decreases terpenoid production. [PERSON] and coworkers have created a pyruvate dehydrogenase bypass to increase cytosolic acetyl-CoA [53]. More recently, [PERSON] and coworkers devised a synthetic metabolic pathway to increase the cytosolic acetyl-CoA levels for farnesene production [54]. Their pathway has a reduced ATP requirement, reduced carbon loss, and improved pathway redox balance. The engineered strain produced 25% more farnesene than the control strain. We contend that these strategies could be utilized to improve the production titer of fragrant terpenids in our strains.
## 2 Materials and Methods
### Methods
#### 2.2.2 Atrous-CNN  
In a conventional CNN, convolution and pooling operations extract data features. However, due to downsampling in the pooling layer, some feature information of the data needs to be recovered. This problem is especially serious in hyperspectral data, which contains rich information, and pooling causes some information loss.  
This paper proposes a new approach to solve this problem: using a null convolution layer instead of the conventional convolution pooling operation. The null convolution structure is very simple and based on the zero-padding principle in regular convolution. Compared with the regular convolutional layer, the hollow convolutional layer can maintain no loss of data information in the response layer and substantially increase the perceptual field of the convolutional computation.  
The formula for calculating atrous convolution is as follows:  
\[o[i]=\sum_{j=1}^{J}f[i+r\cdot j]w[i] \tag{1}\]  
In this equation, \(f[i]\) stands for the input vector, \(o[i]\) for the value of the output vector \(o\) at position \(i\). The dilation rate of the atrous convolution is \(r\), the convolution size is \(w\), and the total number of convolutions between the vectors \(f[i]\) and \(w\) is \(j\). Formula (1) shows that the hole convolution is filled with \(r-1\) times of 0 adjacent to the conventional convolution. When \(r\) is 1, the hole convolution is equivalent to the conventional convolution, indicating that there is no convolution expansion. The atrous convolution receptive field calculation formula is as follows:  
\[\begin{split} F[i]=F[i-1]+\left(k^{\prime}-1\right)\times S_{i-1 }\\ k^{\prime}=k+(k-1)\times(d-1)\\ S_{i}=\prod_{i=1}^{i}\text{Stride }_{i}\end{split} \tag{2}\]  
where \(F[i]\) is the receptive field of the convolution kernel in the current convolution layer and \(k\) is the size of the convolution kernel, \(k^{\prime}\) represents the actual size of the convolution kernel after expansion, and the number of holes is \(d\). The product of all previous layer steps is represented by \(S_{i}\), and the step size of each layer is represented by Stride.
## 4 Conclusions  
In this paper, an innovative and a general solution based on a combination of UAV and WSN to monitor variables from fragmented crop fields has been proposed. Problems related to fragmentation of the regions of interest in a wide area such as the impossibility of deploying a fully connected WSN, the large distances to cover and the continuous monitoring of the environment variables can be overcome with the proposed approach.  
Such problems have been tackled with the basic idea of a _mobile_ WSN node that can periodically recollect the information of the sub-networks deployed in each one of the regions of interest. Since the mobile part is an airborne node, problems related to the nature of the ground are easily overcome.  
The main contributions of this work are the use of UAVs as airborne communication nodes and the resolution of the problems regarding WSN dynamic configuration when a UAV is approaching to the target area. An _ad-hoc_ communication protocol and a communication workflow have been implemented in order to deal with such issues.  
In addition to this, a user-friendly operator interface that allows handling the system and monitoring data has been implemented. It should be highlighted that cheap and reliable UAV platforms are currently available and they can be easily used in this kind of applications. This work provided the opportunity to test the use of a commercial quadrotor in a real scenario. The UAV was able to successfully perform its missions, flying under different wind conditions.  
The proposed system has been designed in order to assess the viability of monitoring frost in vineyards during the spring season, but the same approach can be also adopted in other applications with similar characteristic related to agricultural issues or not.  
\begin{table}
\begin{tabular}{|c|c|c|c|c|} \hline
**Experiments** & **Wind speed** & **Aerial Robot velocity** & **Battery status** & **Mission duration** \\ \hline
1 & 4.2 m/s & 5 m/s & 10.23 V & 191 s \\
2 & 8.9 m/s & 5 m/s & 9.25 V & 276 s \\ \hline \end{tabular}
\end{table}
Table 4: The two experimental sets summed up focusing in the mission time and battery consumption.  
A set of experiments have revealed that both the user interface of the base station and the alert systems based on SMS have increased situation awareness, providing crop managers with crop health status in real-time. In particular, temperature and humidity status from vineyards parcels that are useful in order to prevent damage and loss due to frosts.  
Although the experimental results have been obtained using a three-parcel scenario, the system is completely scalable. Experimental results have shown that this approach provides many advantages considering ground WSN, such as improvement of the WSN communication workflow, improvement of the data routing to the base station, and a better monitoring of the crop.  
In conclusion, we believe that the proposed approach has a great potential for remote crop monitoring using wireless sensor networks technology. Moreover, it also overcomes many of its limitations in the kind of scenario at hand.
# Weigh-in-Motion Method Based on Modular Sensor System and Axle Recognition with Neural Networks
###### Abstract  
Weigh-in-motion systems can measure the number of axles to obtain a vehicle's type and upper limit of weight, which, combined with the weight measured by the system, can be used for highway toll collection and overload management. This paper proposes a new modular system based on multi-sensor fusion and neural network axle recognition to address issues concerning the high failure rate of axle recognition devices and low weighing accuracy. We use a modular system consisting of multiple weighing platforms, enabling whole-vehicle-load weighing with multiple vehicles traveling through platforms. In addition, we propose a sequential generation model based on a Transformer and Gated Recurrent Unit to calculate the weighing signal generated by the weighing sensors, and then obtain the number of axles and the gross vehicle weight. Finally, the axle recognition algorithm and modular systems are tested in multiple scenarios. The accuracy of the axle recognition is 99.51% and 99.84% in the test set and the toll station, respectively. The weighing error of the modular system in the test field is less than 0.5%, and 99.18% of vehicles had an error of less than 5% at the toll station. The modular system has the advantages of high accuracy, consistent performance, and high traffic efficiency.  
axle recognition; modular system; transformer; gated recurrent unit; weigh-in-motion Article
## 6 Conclusions and Outlook  
This paper proposes a comprehensive application of machine learning techniques for short-term solar radiation prediction. Firstly, aiming at the missing entries in solar radiation data, a matrix completion method is used to recover them. Then we denoise the completed data by robust principal component analysis. The denoised data is clustered into low, medium and high intensity types via fusing sparse subspace representation and k-nearest-neighbor. Subsequently, four commonly used neural networks (BP, RBF, ELM and LSTM) are adopted to predict the solar radiation. In order to quantitatively verify the performance of the prediction model, the RMSE and MAE indicators are applied for model evaluation. The experimental results show that the hybrid model can improve the solar radiation predication accuracy.  
In future research work, we will try to improve the model in the following respects to enhance its prediction ability. A multi-step forward prediction is necessary in practice, and it is urgent to develop the corresponding forecasting models by an ensemble of machine learning techniques and signal decomposition methods. In the procedure of establishing the prediction model, the input meteorological element used in this paper is only global horizontal irradiance. In fact, there are many other elements that affect solar radiation, such as the variation of daily temperature and precipitation. The influence of multiple elements on solar radiation will be considered and analyzed so as to improve the prediction ability of solar radiation. Furthermore, this paper only merges a few machine learning techniques into the forecast of solar radiation. In particular, deep learning models have a powerful representative ability and their further application in forecasting solar radiation will be very prospective.  
Conception and design of the experiments: [PERSON], [PERSON]; Performance of the experiments: [PERSON]; Writing--original draft preparation: [PERSON]; Writing--review and editing: [PERSON], [PERSON]; Supervision: [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to their confidentiality.  
This work is partially supported by the National Key R&D Program of China (2018 YFB1502902) and the Natural Science Basic Research Plan in Shaanxi Province of China (2021 JM-378).  
The authors declare that they have no conflict of interest.