esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Selection of Optimum Culture Medium  
Six culture media containing combinations of different carbon sources were used to identify the most suitable laboratory conditions for controlled bacterial growth. The fastest bacteria growth was observed when using culture medium A (Figure 2). A very fast exponential growth started after a short lag phase (6.5 h) and the stationary phase was established quickly.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Fraction (\(\mu\)m)**} & **Porosity** & **Bulk Density** & \begin{tabular}{c} **PSD Index** \\ **van** \\ **Genuchten (\(\downarrow\))** \\ \end{tabular} & \begin{tabular}{c} **PSD Index** \\ **Posed Index** \\ **Brooks/Corey** \\ **(\(\downarrow\))** \\ \end{tabular} & \
\begin{tabular}{c} **Mean Pore** \\ **Diameter** \\ **(\(\downarrow\))** \\ \end{tabular} &
\begin{tabular}{c} **pH Value** \\ **(\(\downarrow\))** \\ \end{tabular} \\ \hline
125–200 & 44.3 & 1.48 & 8.80 & 4.13 & 47 & 6.49 \\
200–630 & 41.5 & 1.55 & 5.71 & 2.50 & 95 & 6.36 \\
630–1000 & 38.5 & 1.63 & 4.91 & 2.47 & 230 & 6.38 \\
1000–2000 & 37.0 & 1.66 & 5.86 & 2.70 & 425 & 6.52 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characterization of the grain size fractions.  
Figure 2: Bacterial growth curves in different culture media.  
The bacteria grew at similar rates in culture media B and D but with a lower maximum cell concentration. The fast growth of bacteria in these three media was caused by the very good utilization of the available carbon sources. In culture media E and F, the development of bacterial cultures was strongly inhibited by the absence of an appropriate utilizable carbon source and thus no growth could be observed over the duration of the experiment. Culture media C was a mix of several organic and inorganic substances used to reproduce synthetic wastewater. In this case, the bacterial growth curve indicates that the available glucose was sufficient to stimulate growth while the two acids were utilized by bacteria after glucose consumption. The conversion of the cell metabolism from glucose to the two acids is reflected in a temporary slowdown of bacterial cell growth, so that a steady growth was observed over the duration of the experiment (173.5 h). The addition of trace elements and vitamin solution in all culture media did not exert a decisive influence on the growth of bacteria; therefore, they were not used in the column experiments.  
Based on these results, culture media C was used in the column experiments, which was crucial for the quantification of the degradation rates of the different grain size fractions. The decisive reason for selection was the inhibited excess growth of bacteria over the duration of the experiment compared to the rapid growth in culture media A, B and D, and the non-existing growth in solutions E and F.
## 3 ARIMA-ML Framework  
SHM for damage detection can be considered as a classification problem within the scope of supervised learning. In this study, the major contribution is to integrate both TS modeling and ML recognition to form the two-stage framework ARIMA-ML as schematically illustrated in Figure 1. Starting from stage 1: TS modeling, one arbitrary TS data is fed to the pre-processing module. Through smoothing-segmentation-normalization-differencing (SSND) operations with the \(I\)-th differencing and stationarity check, multiple stationary TS segments are generated. These segments are further passed to the model parameter determination module, and then a small group of candidate \((p,q)\) pairs is determined.  
In stage 2: ML recognition, with filtered TS segments and one pair \((p,q)\) from the candidates, an ARMA\((p,q)\) is applied to those segments in the feature extraction module. Subsequently, the extracted features are fed into the classification module to run multiple ML classifiers with a voting mechanism for damage state identification or pattern recognition for the SHM decision-making. The candidate models are evaluated by the _average segment accuracy_ (the ratio between the number of correct predicted segments and the total number of the input segments). Stage 2 is repeated for each \((p,q)\) candidate pair, and the best model is determined with the highest average segment accuracy. From the definition of the ARIMA model, fitting the \(I\) differentiated segments by ARMA\((p,q)\) models is equivalent to fitting an ARIMA\((p,I,q)\) model. The details of each module are described in the following subsections.
## 3 Wireless Communication Schemes for Aircraft
### DF Scheme  
The DF communication consists of two phases for the source node to send the packet to the destination node by cooperation with the relay node. In the first phase, the source node transmits a packet to the relay node and destination node, in the second phase the relay node transmits the packet from the source node to the destination node. Thus, the total energy consumption [42] on three nodes in DF communication can be obtained by:  
\[\begin{split} E_{DF}=&\rho_{SR}\frac{\left(P_{AMP}+P_ {TX}+2P_{RX}\right)\kappa}{\theta R_{b}}\\ &+\left(1-\rho_{SR}\right)\frac{\left(2P_{AMP}+2P_{TX}+3P_{RX} \right)\kappa}{\theta R_{b}}.\end{split} \tag{24}\]where the first term means the energy consumption when the relay node fails to decode the packet from source node, and the second term means the energy consumption when the relay node succeeds to decode the packet and to carry out cooperative communication.  
In the case of cooperative schemes, a multi-hop scheme finding the roots of the polynomial, the optimal transmitted power can be obtained by:  
\[\rho_{0}\left(P_{DF}\right)^{3m}-\left(k_{1}k_{3}+k_{2}k_{3}\right)\left(P_{DF} \right)^{m}+\left(k_{1}k_{2}k_{3}\right)=0. \tag{25}\]  
where \(k_{1}\) and \(k_{2}\) are the same as mentioned above in multi-hop scheme, and \(k_{3}=\frac{\left(mN\mu\right)^{m}}{\Gamma\left(m+1\right)\gamma_{SD}^{2}}\).  
The DF throughput [34] can be expressed by:  
\[T_{DF}=\frac{R}{2}\left(1-\rho_{SD}\right)+\frac{R}{2}\rho_{SD}\left(1-\rho_{ SR}\right)\left(1-\rho_{RD}\right) \tag{26}\]  
where there are two terms, the first term means the throughput between the source node and the destination node, the second term means the throughput when communication is carried out by using relay node. The spectral efficiency of both terms is half due to the half-duplex constraint.
# Survey Solutions for 3D Acquisition and Representation of Artificial and Natural Caves
## 4 Discussion  
This paper analyzed and compared the possible methodologies for acquiring a 3D model of an underground structure. The realization of three-dimensional point clouds of indoor and underground structures has been a critical activity that now was facilitated thanks to innovative solutions. In the previous chapter, we presented the compared use of TLS, PLS, and SfM on the same test site. The PLS is a recently available solution, with a lower cost if compared to the TLS. PLS can be adopted for the acquisition of indoor and underground elements, where it can be very useful because it works dynamically [41]. Thanks to the embedded sensors and processing algorithms, these sensors can progressively cover the entire area of interest with a progressive merging of a sequence of surveys that have overlapping sectors. The PLS software uses common sectors to link the different acquisitions. TLS and PLS show how active sensors can be often considered the best solution because they do not have problems with different conditions of illumination that characterize passive sensors like photo cameras. On the contrary, the combined use of photo cameras and structure from motion has had an explosive diffusion in the last years. This use is often supported by the growing deployment of RPAS in the field of engineering geology [47] and natural hazards [53], even if the use of an image sequence for the acquisition of a 3D model based on SfM is not exclusively linked to the use of RPAS. In many cases, this approach can be adopted using different image sequence sources, like historical aerial images or terrestrial photos taken by smartphones or standard photo  
Figure 9: (**a**) photo of the stalagmite; (**b**) scheme of the high-resolution survey; (**c**) stalagmite 3D model.  
cameras [6; 61]. The use of photo cameras becomes particularly useful if we have to consider the infrastructure's price and the easiness of use.  
Table 8 presents a brief comparison between the three categories of systems able to acquire a 3D point cloud considered in this paper.
# Safe Path Planning of Mobile Robot Based on Improved A* Algorithm in Complex Terrains
## 3 Improvement of A* Algorithm
### Improved Environment Model  
When the conventional A* algorithm is applied for path planning, the environment is divided into two states: passable regions and forbidden regions. In the path searching process, the states in the forbidden regions would not be traversed and all states in the passable regions are equivalent to each other for the robot. That is to say, no matter how far or close a state is from the obstacles, the robot would spend the same safety cost to pass through the state. However, practically, the safety threat of a robot from the obstacles is inversely proportional to the distance between them. What is more, the expanding obstacles would cover some passable regions and may block the optimal path. For solving this problem, we may introduce the safety cost into the evaluation function of the conventional A* algorithm, but the computation amount is large and the efficiency of the algorithm would be reduced greatly. Therefore, we take the safety threat into account in environment modeling so that the danger coefficient calculation of each state is not required in the path searching process.  
In order to avoid excessive amount of calculation, some agreements have been made, as follows:  
1. The danger coefficient of each state is calculated according to the shortest distance from the state to obstacles. If there is more than one nearest obstacle, only one time computation is needed.
2. The danger coefficient of a state is inversely proportional to the minimum distance between the state and obstacles.
3. If the minimum distance between a state and obstacles is not less than the given safe distance, the danger coefficient of the state is set to zero.  
According to the above rules, the environment model shown in Figure 1 is rebuilt and the danger coefficient of each state is computed and stored in the new improved environment model, as shown in Figure 3.  
In Figure 3, the black regions represent the actual obstacles while the grey regions around the obstacles represent the threatened regions. The darker the color of a state is, the greater the danger  
Figure 2: Simulation result of A* algorithm.  
coefficient is and vice versa. The safe distance between the robot and obstacles is 3 m; if the minimum distance between a state and obstacles is not less than 3 m, the danger coefficient of the state is set to zero.
## 3 Drone Routing Algorithms
### Charging  
Owing to the limited battery capacity and extra payload of delivery products, the flight time of the drone is short. The optimal clustering of targeted customers, the drone route planning according to customer order or priority of releasing payload, and the trajectory optimizations can minimize the drones' flight time and energy consumption. However, to efficiently cover a large area with many customers, drones require a battery charging strategy. Additionally, it is challenging to provide multiple deliveries simultaneously over long distances to support the maximum number of customers within a constrained timeframe by avoiding uncertainties of drone failure due to sudden energy depletion. Since drone-based delivery is mostly considered for delivering goods to remote areas, the drone flight time should be sufficiently large. Additionally, if a single drone can support multiple nearby customers in a single mission with extended flight time, it can deliver the desired product within the given time, which may help achieve customer satisfaction and successfully execute the delivery mission. Therefore, a suitable charging strategy is required to extend the flight time of drones. The charging strategy mainly considers the placement of optimal number of CSs to maximize coverage and drone flight time, and minimize the delivery time and system deployment cost [43].  
In order to achieve the above objective, several research ideas have been proposed to charge drone battery while it is executing the delivery mission. Most designs consider the optimal number of CSs and their placement according to the location of customers and hot spot areas [44]. Based on the flight time constraint, drones can recharge their battery, or their battery can be replaced automatically or manually at the CSs [21].  
The locations of CSs can be fixed [45] or mobile [46]. Moreover, they may have multiple charging pads to serve multiple drones simultaneously via wireless power transfer [33; 47]. Some designs consider collaboration between public transport, dedicated truck-based drone-enabled delivery, and CSs to execute drone-based delivery mission in remote areas [11; 48]. To plan a charging strategy, the drone flight time must be calculated precisely by adopting a suitable energy consumption model [12]. Notably, the energy consumption of a drone depends on its type, mechanical structure, weather conditions, payload, drone dynamics, and traveled trajectory. An insightful discussion about drone energy consumption model is given in [34; 49; 50]. Some innovative research ideas regarding drone charging in a drone-based delivery mission are discussed below, considering their designed model, optimization objective, mathematical model, and constraints.
# Latest Advances in the Global Navigation Satellite System--Reflectometry (GNSS-R) Field
## 6 New Science Investigations
### Microplastics  
The authors in [147] demonstrated the effectiveness of GNSS-R in detecting and imaging ocean microplastics using CYGNSS data. Microplastic concentrations in the ocean vary significantly across different locations, with particularly high levels found in the North Atlantic and North Pacific gyres. This new application met the need for the global measurement of microplastic distribution and its temporal variability. The method relied on CYGNSS GNSS-R measurements of ocean surface roughness and assumed that surfactants, which acted as tracers for microplastics near the surface, reduced responsiveness to wind-driven roughening. The study found that the annual mean microplastic distributions estimated by CYGNSS were generally consistent with model predictions, and that the spaceborne observations were able to detect temporal changes that the models could not resolve. The authors observed seasonal dependencies in mid-latitudes in both the Northern and Southern Hemispheres, with lower concentrations in the winter months. In addition, time-lapse images at finer spatial and temporal scales revealed episodic bursts of microplastic tracers in the outflow from major river discharges into the sea. In [148], authors conducted an experimental study to investigate the feasibility of using GNSS-R observations to detect marine plastic litter in a water flume. The experiment included different wave conditions, wave heights, and types of plastics and marine litter collected from the Dutch coast. The results showed that detecting marine plastic litter based on a change in reflected power is challenging. However, the authors noted that it might be possible to detect large accumulations of certain types of marine litter, such as nets, bottles in a net, food wraps, and bags, through the statistical analysis of the GNSS-R estimated reflectivity at very short integration times (coherent integration time \(\mathrm{Tcoh}=1\) ms). These types of litter have the ability to reduce waves and, in turn, reduce ocean surface roughness. In a subsequent study published in [149], the researchers investigated the underlying flow physics of the remote sensing method for detecting oceanic microplastics that was developed in [147]. The focus of the study was to determine whether the reduction in surface roughness observed in the data was caused by the microplastics themselves or by surfactants that travel along similar paths as microplastics. The authors found that the damping effect of surfactants on both mechanically generated waves and wind waves was much more significant than that of microplastics.
## 5 Results and Discussion
### Year-Long Results  
For this analysis, three types of metrics are employed: (1) _sustainability_ in the form of increased SC and SS; (2) _economical_, measured in total savings in Euros, and the profit per Euro invested; and (3) _technical_, in the form of battery cycles and SOH The values of the different performance metrics are presented in Table 10. Table 11 shows the hourly median of the SOC for each UPAC and control strategy for selected storage combinations. A heatmap from blue to red indicates the best options for each UPAC (dark blue, best; dark red, worst). The bold face in the SC column indicates the best control strategy.
## 3 State of the Art
### Proposed Coexistence and Coordination Techniques
#### LTE CSAT Approach  
In regions such as the U.S., China and South Korea, where an LBT procedure is not required by the local regulations, different types of coexistence techniques can be applied. Carrier Sensing Adaptive Transmission (CSAT) [17], proposed by Qualcomm, is a technique that can enable coexistence among LTE and Wi-Fi based on minor modifications of the 3 GPP LTE Release 10/11/12 Carrier Aggregation protocols [4]. CSAT introduces the use of duty cycle periods and divides the time into LTE \"ON\" and LTE \"OFF\" slots. During the LTE \"OFF\" period, also known as the \"mute\" period, LTE remains silent, giving the opportunity to other coexistent networks, such as Wi-Fi, to transmit. During the LTE \"ON\" period, LTE accesses the channel without sensing it before a transmission. Moreover, CSAT allows short transmission gaps during the LTE \"ON\" period to allow for latency sensitive applications, such as VoIP in co-located networks. In CSAT, the eNB senses the medium for a time period ranging from tens of ms up to 100 ms and according to the observed channel utilization (based on the estimated number of Wi-Fi Access Points (APs)) defines the duration of the LTE \"ON\" and LTE \"OFF\" periods [17]. Figure 6 depicts the CSAT duty cycle periods.
## 2 Results and Discussion
### Analysis of the Unknown Elements Recognized by ANAC074  
To determine the borders of the two unknown elements, we serially deleted the sequences of \"CCCTTAGTTTCGGG\" and \"CCCTGAAATCCGGG\" (insertion is underlined) from both 5' and 3' terminals, and cloned the sequences into pHIS2 to drive the _HIS3_ gene. The interaction between these deleted insertion sequences and ANAC074 was studied using Y1H. The sequences of \"AGTT\" and \"TAGT\" abolished the binding to ANAC074 while the sequence of \"TAGTT\" restored the binding activity (Figure 1A). This result suggested that the DNA sequence recognized by ANAC074 was \"TAGTT\" (named NRS1). Similarly, the sequences of \"AATC\" and \"GAAT\" disrupted binding to ANAC074 while the sequence of \"GAATC\" restored the binding activity (Figure 1B). Therefore, the sequences recognized by ANAC074 were \"GAATC\" (named NRS2).
# Design and Application of New Aeration Device Based on Recirculating Aquaculture System
## 2 Materials and Methods
### Design and Parameter Selection
#### 2.2.2 Key Structures of Impeller Aeration Component: Impellers  
Impeller size and rigid strength are the primary parameters to be calculated in the design of the impeller component. The evaluation index comprises the device's total power and the average water flow rate, along with intermediate parameters such as drag coefficient, blade rotational speed, blade submergence depth, diameter, and number of blades. First, the resistance created by the water is reduced to a concentrated force operating at the centre of the submerged blade, which simplifies the combined resistance of the water against the blade. Then, geometric data such as the radius of the blade and the angle in the vertical direction are used to determine the moment. Lastly, by considering the instantaneous power, the average power that will be used by the blades overall is obtained. The impeller shaft may also be thought of as a spinning shaft. Stiffness and strength are calibrated based on the torsional stiffness conditions and the bending and torsion synthetic strength criteria of the shaft, respectively. The following are the primary specifications for the design of an impeller-type aerator, according to [PERSON] et al. [18]: impeller size of 0.5 m, shaft of 0.05 m, blade rotational speed of 100 r/min, maximum blade submergence depth of 0.125 m, blade width of 0.1 m, and 64 blades in total. This leads to the calculation of \(P_{z}\), the minimal total power needed for the impeller aeration component. Its specific design parameters are shown in Table 3.  
\[F=\frac{C_{D}A\rho{v_{i}}^{2}}{2} \tag{6}\]  
\[A=bx \tag{7}\]  
\[x=R-(R-H)/\cos\alpha \tag{8}\]  
\[l=\left(R+\frac{(R-H)}{\cos\alpha}\right)/2 \tag{9}\]  
\[P_{i}=M\omega=M\frac{2\pi n}{60} \tag{10}\] \begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Parameter Value** & **Unit** \\ \hline Machine size (length \(\times\) width \(\times\) height) & 3000 \(\times\) 5000 \(\times\) 2850 & mm \\ Number of aeration discs & 475 & / \\ Size of aeration holes & 3.2 & mm \\ Angle of inclined plate & 40-60 & \(\circ\) \\ Vertical height of inclined plate & 2 & m \\ Minimum boost pressure for fan & \(-\)290 & kPa \\ Theoretical outlet flow rate of fan & 230 & m\({}^{3}\)/h \\ Total motor power & 2.2 & kW \\ Standard oxygen transfer rate (SOTR) & 2.25 & Kg/h \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Main design parameters of microporous aeration component.
## 1 Introduction
### Related Work
#### 1.3.1 Adversarial Attacks  
In the past few years, many adversarial attacks have been proposed; the most common attack proposed by [1] is called the fast gradient sign method (FGSM). This attack adds a small perturbation to the target image in the direction of the gradient of the loss function with respect to the human-perception content in order to misclassify the trained targeted model. It is a white-box attack where the attacker fully knows the deep learning model, including its architecture, parameters, and training data. Later, a more efficient attack known as Deepfool [13] finds the smallest perturbation necessary to cause a DNN to misclassify an input image, which increases the attack success rate compared to FGSM.  
The potential of deceiving DNN models increased significantly over the past few years of adversarial attack development. Today, imperceptible perturbations can be added to input images with the flexibility of adjusting the attack goal to either a white box or a black box, such as the one proposed in [14] and named after its founders, [PERSON] (CW) attack. This attack uses an optimization algorithm in order to find the smallest perturbation that minimizes a loss function that balances the size of the perturbation with the misclassification success rate. Moreover, the attack has the ability to incorporate constraints on the perturbation, such as by limiting the magnitude of the perturbation or restricting the pixel values of the perturbed image. The power of this attack raises the challenge of defense solutions against multiple attacks at once.  
The white-box approach becomes more desirable for adversaries, as it was introduced by [15] and is known as the complete white-box adversary. Researchers found that the projected gradient descent (PGD) can lift any constraints on the amount of time and effort the attacker can put into finding the best attack. The iterative feature of the PGD attack makes it more effective than other attacks, such as FGSM, in finding imperceptible adversarial examples. The variety and effectiveness of adversarial attacks open a wide range of areas for researchers to develop different attacks, such as in [16; 17], and to find defense mechanisms on the other side.