esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Photocatalytic Tests  
The photocatalytic activity of the synthesized Co\({}_{3}\)O\({}_{4}\) NPs was evaluated by degrading methylene blue dye. One hundred milliliters of 10 ppm methylene blue (M.B.) aqueous solution was used for the photocatalytic experiment. A total of 1 g/L of the Co\({}_{3}\)O\({}_{4}\) NPs was added to M.B. aqueous solution and stirred with a magnetic stirrer in the dark for 30 min to establish adsorption-desorption equilibrium between the solution and catalyst before irradiation from the 400 W high-pressure mercury lamp (\(\lambda\geq\) 420 nm). The lamp was turned on, and after particular time intervals (0, 25, 50, 75, 100, 125, 150, and 175 min), the samples were filtered using centrifugation to settle down the suspended catalyst and then examined by a UV-visible spectrophotometer in the range of 400-800 nm. The absorbance measurements estimated the degradation degree of M.B. in the aqueous solution at about 665 nm. All the aqueous samples were at neutral pH, and all experiments were carried out at room temperature.
# A Comprehensive Survey on Knowledge-Defined Networking
## 7 Application Plane
### Application Scenarios
#### 7.3.9 Configuration  
The application layer has network administrator-defined policies for network configuration. Note that, in KDN architecture, these policies can be dynamically updated based on the current network status or performance received by the application plane in the form of descriptive knowledge or rules and based on current network configuration information received from the management plane. This configuration application defines policies for network backup and restore, compliance, logging and auditing, error handling, firmware configuration, authentication, authorization, encryption, etc. The backup and restore policy defines the frequency of backups, the location of backups, and the procedures for restoring a switch configuration [549]. The compliance policy specifies the set of rules and regulations that protocols such as OF-CONFIG should comply with, such as the OpenFlow specification, security best practices, and industry specific-regulations [550]. The logging and auditing policy specifies the types and events to be logged, the format of logs, the retention period for log files, etc. The error handling policy specifies the procedures for handling errors, such as error codes and messages that are used to indicate failures and errors during configuration and management, retrying operations, reporting errors to the administrator, etc. The authentication policy specifies the allowed authentication mechanisms and the required credentials for providing access. The authorization policy specifies the types of operations that are allowed or denied based on user roles, privileges, and permissions [551]. The encryption policy specifies the encryption algorithm and the key length to be used for securing communication. The firmware configuration policy specifies the firmware version that each device should run; the firmware upgrade policy specifies the schedule for upgrades, target devices for upgrades, and the criteria for determining whether an upgrade is necessary, as well as the policies for rollbacking firmware upgrades in case of compatibility issues [552]. These policies will help configure the network swiftly to provide better network management with minimal configuration problems. Table 9 summarizes the details of different applications of the application layer.
## 3 Data analysis and BP Neural Network Model Building
In addition, in order to evaluate model training accuracy, Mean Square Error (MSE) and coefficient of determination (\(R^{2}\)) of the output results are applied and MSE can be calculated according to Equation (2). \[\mathrm{MSE}=\frac{1}{n}\sum_{i=1}^{n}(T_{i}-t_{i})^{2} \tag{2}\]  
where \(T_{i}\) represents the test value and \(t_{i}\) represents the real value. Meanwhile, \(n\) refers to the test set sample number. In \"newff\" function, there are not too many parameters to be determined. Hence, all of them are listed in the following Table 8. \begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Value/Function** \\ \hline net\_bp.trainParam.epochs & 100 \\ net\_bp.trainParam.goal & \(1\times 10^{-6}\); \\ net\_bp.trainParam.show & 10 \\ net\_bp.trainParam.lr & 0.001 \\ Number of neuron nodes & Gradation-13:4; Gradation-16:5 \\ TF (Transfer function) & tansig (hidden layer); purelin (output layer) \\ BTF (Backprop network training function) & trainlm \\ BLF (Backprop weight/bias learning function\_ & learngdm \\ PF (Performance function) & MSE (Mean Square Error) \\ \hline \end{tabular}
\end{table} 
Table 8: Parameters needed in “newff” function. Figure 4: BP neural network model structure. Because of the high relativity between gradation and mixture porosity, the prediction accuracy was great enough when the default functions were applied. In order to simplify the procedures of building the prediction model, the default transfer functions were finally determined. It should be noted that the determination of other key parameters is illustrated in the following Chapter 3.1, Input and output variables processing.
## 4 Simulation Results  
To evaluate the resolution methods with respect to the formulations considered, we carry out extended statistical analyses by varying the number of neighboring nodes and the parameters of the algorithms. The simulation environment is a simulated 3D space of 100 m \(\times\) 100 m \(\times\) 50 m (\(x,y,z\) dimensions, respectively) centered on a global reference frame with the origin \((0,0,0)\). It contains a large population of randomly distributed fixed nodes (anchors) and a set of drones with predefined trajectories. For each test scenario, multilateration localization is solved for 100 randomly selected nodes with unknown positions, using \(k\in[4,10]\) randomly selected neighboring nodes of known coordinates and 100 random initial guesses to start the execution of the algorithm. The random character also makes it possible to evaluate the impact of the distribution and the relative positioning of the nodes. They are only constrained to have a minimum relative measurement distance of 1 m. This leads, for each test scenario, to 10,000 simulation tests, run under Matlab on a Linux Ubuntu 16.04 OS with 16 GB of memory and an Intel Core i7-4710 MQ processor (using a single 2.5 Ghz core for the simulations).  
For the estimation problems (5) and (6) expressing uncertainties, the errors on the assumed known positions of the nodes are modeled by a Gaussian noise centered on the  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Methods evaluated** & GD using a fixed rate step, a variable rate step, and a variable rate step including a damping parameter. \\ \hline
**Problem formulation** & Cost functions (3)–(6) without and with explicitly including positioning and measurement errors. \\ \hline
**Anchor nodes** & 100 randomly selected sample points in a 3D map. \\ \hline
**Algo. initialization** & 100 randomly chosen initial points. \\ \hline
**Number of relative measurements** & Varying from 4 to 10 neighboring points and relative distance measurements. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of the evaluated methods and the varied parameters in the simulations.  
real position of the simulated nodes with a standard deviation \(\sigma_{\mathbf{x_{n}}}\) determined empirically. Measurement errors are modeled based on the approximation given in (10) [38] and confirmed by our experimental study using UWB sensors in indoor and outdoor environments:  
\[\mathbf{e}_{pos}=\mathcal{N}(0;\sigma_{\mathbf{x_{n}}}) \tag{9}\]  
\[e_{d}=e_{unvb}=e_{0}+e_{1}d_{i,j}+e_{2}d_{i,j}^{2}+e_{3}d_{i,j}^{3} \tag{10}\]  
with the following experimentally identified parameters:  
\[\begin{array}{ll}e_{0}=2.361e^{-3},&e_{1}=1.805e^{-3},\\ e_{2}=4.808e^{-5},&e_{3}=4.655e^{-7}.\end{array}\]
# A Multi-Degree of Freedom Tuned Mass Damper Design for Vibration Mitigation of a Suspension Bridge
## 5 Experimental Verification  
### Experimental Setup  
The laboratory mock-up of the suspension bridge (in Figure 14) is used to investigate the performance of the 2 DOFs TMDs. An impact hammer is used to excite the structure. Prior to vibration measurements, the data acquisition system is established, which involves a single-axial accelerometer, positioned to measure vertical accelerations. The position of the accelerometer is illustrated in Figure 14. The output data are obtained by successive hammering all positions on the deck (Figure 15). The modal parameters of the bridge are estimated by hammering method. The natural frequencies and mode shapes of the mock-up are shown in Table 1.  
Figure 14: Laboratory mock-up of the suspension bridge.  
Figure 13: Mode shapes of TMD2. (**a**) The 1 st mode; (**b**) the 2 nd mode.
# Monitoring the Dynamics of Ephemeral Rivers from Space: An Example of the Kuiseb River in Namibia
## 3 Data Used for This Study
### In Situ Data
#### 3.3.2 River Discharges  
Daily river discharges measured at Gobabeb, Rooibank, and Schlesien, which were available until 2018, were downloaded freely from the GRDC (Global Runoff Data Cen  
Figure 2: Flowchart of the processing steps for analysing both amplitude and phase of Sentinel-1 data. All the processing was done using SNAP (SeNtinel Applications Platform) software, version 8.0.0.  
tre) website ([[https://www.bafg.de/GRDC/EN/01_GRDC/grdc_node.html](https://www.bafg.de/GRDC/EN/01_GRDC/grdc_node.html)]([https://www.bafg.de/GRDC/EN/01_GRDC/grdc_node.html](https://www.bafg.de/GRDC/EN/01_GRDC/grdc_node.html)) (accessed on 18 March 2022).
# Performance Analysis of a WPCN-Based Underwater Acoustic Communication System
## 1 Introduction
Then an amount of about 23 mW of output power can be harvested at the receiver with a proper EH circuit for the terminal node [6; 7].
## 4 Verification of the Artificial Neural Network
This is evidenced by the low average absolute error \(|\Delta H|\) value of 1.16% and the average relative error \(|RE|\) of 19.02% (not very high for an in-situ study). It is also worth noting that the average \(U_{mc}\) humidity value of 5.59% identified by the ANN is close to the \(U_{m}\) humidity value of 5.68% obtained by means of a test using the gravimetric method. According to the authors, the obtained results can be considered satisfactory. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Moisture Content**} & \multicolumn{2}{c}{**Moisture Content**} & \multicolumn{2}{c}{**Absolute Relative**} \\ \multirow{2}{*}{**Designation of Measuring Points**} & **Obtained during Tests** & **Identified by the ANN** & **Error** & **Error** \\ \cline{2-5}  & \(U_{m}\) & \(U_{mc}\) & \(|\Delta H|\) & \(|\Delta H|\) \\ \cline{2-5}  & \% & \% & \% & \% \\ \hline
1 & 8.32 & 10.49 & 2.17 & 20.71 \\ \hline
2 & 9.75 & _7.77_ & 1.98 & 25.46 \\ \hline
3 & 6.87 & 5.25 & 1.62 & 30.82 \\ \hline
4 & 4.57 & 5.67 & 1.10 & 19.40 \\ \hline
5 & 5.50 & 4.72 & 0.78 & 16.42 \\ \hline
6 & 3.00 & 2.67 & 0.33 & 12.53 \\ \hline
7 & 7.21 & 5.68 & 1.53 & 26.93 \\ \hline
8 & 6.18 & 5.34 & 0.84 & 15.80 \\ \hline
9 & 4.00 & 4.04 & 0.04 & 0.87 \\ \hline
10 & 4.95 & 4.93 & 0.02 & 0.31 \\ \hline
11 & 4.17 & 3.25 & 0.92 & 28.25 \\ \hline
12 & 7.14 & 10.41 & 3.27 & 31.43 \\ \hline
13 & 2.01 & 2.45 & 0.44 & 18.03 \\ \hline
14 & 5.09 & 5.16 & 1.01 & 17.38 \\ \hline
15 & 6.43 & 5.95 & 1.29 & 21.01 \\ \hline
**Mean value** & **5.68** & **5.59** & **1.16** & **19.02** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative list of selected \(U_{m}\) and \(U_{mc}\) humidity values determined using the gravimetric method and artificial neural network, respectively. Figure 6: Relationship between \(U_{m}\) mass humidity obtained on the basis of gravimetric tests and \(U_{mc}\) humidity identified by the neural network for experimental verification. Figure 7 presents the relative error histogram \(|RE|\) of \(U_{m}\) mass humidity obtained using the gravimetric method, as well as the \(U_{mc}\) humidity identified by means of an artificial neural network for experimental verification. The figure also shows that the relative error values \(|RE|\) are mostly in the range of 15% to 25%, which indicates a satisfactory distribution of the obtained results.
# NMR Analysis of Pulegone in Food Products
## 2 Results and Discussion
### Method Development and Optimization
#### 2.1.3 Extraction of Pulegone from Food  
In this study, an attempt was made to extract pulegone from tea, herbs, and confectionery using both ultrasonic-assisted extraction and steam distillation. In ultrasonic-assisted extraction, both CDCl\({}_{3}\) and MeOD were used as the extraction agents. The extraction method was used to examine peppermint teas and chewing candies. Despite an increase in the weight of the sample and the volume of the extraction agent, no pulegone was detected.  
In contrast, hydrodistillation was employed for the extraction of pulegone from teas, herbs, pastilles, and coated tablets. The hydrodistillation method was developed based on the ISO 6571 method [28]. Deuterated xylene-\(d_{10}\) was used as receiving solvent. As part of this study, the impact of reducing the amount of expensive xylene-\(d_{10}\) used on the yield of pulegone was examined in peppermint and pennyroyal. Furthermore, the potential impact of distillation time reduction on the yield of pulegone was investigated using pennyroyal samples. The test specification specifies a distillation time of 4 h, whereas peppermint requires a shorter distillation time of 3 h.  
In both pastilles and coated tablets, the ingredients may include hydrophilic substances such as sugar and hydrophobic substances such as magnesium salts of fatty acids and carnauba wax. To eliminate the possibility of hydrophobic ingredients influencing the yield of pulegone, a candy and a coated tablet solution were filtered and then distilled without insoluble ingredients. It was determined that filtration prior to hydrodistillation reduced the yield for both sample types: the filtered sample of the pastille exhibited a 37% reduction in the pulegone content compared to the unfiltered sample. The filtered dragee sample yielded only about half the amount of essential oil. Without knowledge of the exact volume of the essential oil, pulegone could only be identified qualitatively in the filtered dragee sample. The diminished yield of the two filtered samples can be attributed to the loss of volatile aroma components during the filtration process.
# Real-Time Safety Decision-Making Method for Multirotor Flight Strategies Based on TOPSIS Model
## 1 Introduction
A multirotor generally flies according to the established trajectory during flight, but when faced with a sudden failure, a sudden change in the weather, or an abnormal environment, the fixed flight control strategy may lead to mission interruption, a crash, or even destruction of the aircraft [37]. Therefore, real-time monitoring of the flight status of the aircraft is needed. When the safety degree decreases, it is necessary to dynamically adjust the flight safety strategy according to the current environment and airframe state to select the most reasonable flight control strategy. Therefore, this paper first proposes a manipulation strategy decision-making mechanism of the safe flight control strategy of a multirotor and puts forward the data processing and calculation method in the decision-making process. In the decision-making mechanism of the multirotor flight strategy, firstly, based on the Rflysim platform developed by the Reliable Flight Control Research Group, Beihang University, the flight of the multirotor was simulated, the flight status was monitored in real time, various fault injections were simulated, and the flight data of the multirotor under various fault conditions were collected. Based on the random forest algorithm, the failure level classification model of the multirotor was constructed, and the flight safety classification results of the multirotor were obtained. After judging the flight safety level of the multirotor, the safety decision calculation was performed. An attribute-strategy model was established, which covers alternative control strategies, real-time attributes, and other factors and is the basic framework of the subsequent decision-making calculation. The model proposes a flight strategy selection method under real-time state constraints, establishes the inherent state attribute weight matrix of the alternative control strategy, normalizes the real-time data, calculates the matching with the weight matrix, and obtains the selection result of the alternative control strategy which is most consistent with the current real-time state. According to the real-time attribute decision model of the multirotor based on TOPSIS, the flight decision of the multirotor under different fault conditions can be realized.
## 2 Materials and Methods  
### Study Area  
The study area of this research is Cartagena Bay, Colombia, which encompasses the regions of Bocagrande, Isla Tierra Bomba, and Playa Blanca (Figure 1). This area had an estimated 2021 population of 1,043,926 inhabitants [30]. Cartagena Bay is characterizedby a humid tropical climate, with relatively high average annual temperatures of around 28 \({}^{\circ}\)C, in addition to an average monthly precipitation of 20.2 mm, in an area totaling 85.72 km\({}^{2}\)[8; 30]. Cartagena has three seasons throughout the year: strong winds/low runoff (January-April), weak winds/intermediate runoff (May-August), and weak winds/high runoff (September-December) [23; 29; 31; 32].  
Although the Colombian Caribbean region displays the typical crystalline color of its waters, pollution and contamination from highly polluting sources, such as coal extraction and transport to the port of Cartagena; the incineration of urban waste, a cultural disposition leading to relatively high rates of litter and lackadaisical efforts; ineffective waste management; livestock activities; and the use of agricultural pesticides and industrial activities on the shores of Cartagena Bay, raise concerns among Colombian governments [33; 8; 34]. For these reasons, and because it is one of the most industrialized Caribbean areas, this study seeks to deepen scientific knowledge about the contaminants in the area under study.  
The study area's soil characteristics are clayey, containing dispersive clays, and highly erosive due to natural precipitation. This can cause a high accumulation of sediment in the waters of the Cartagena Basin [35]. According to [PERSON] et al. [36], sediments are capable of coupling dangerous chemical elements in the form of NPs and ultrafine particles, which is highly harmful to human health and aquatic fauna and flora, in addition to compromising the livelihood of local fishermen and threatening tourist activities.  
Cartagena is a tourist city driven by its historical buildings from the medieval period, Cartagena Bay, and the Caribbean Sea. Approximately 4.5 million tourists visit annually [8; 30]. Consequently, Cartagena Bay is recognized as one of Colombia's leading tourist destinations. Therefore, environmental conservation becomes essential to ensure that the local population continues to enjoy Cartagena Bay and that the tourist market in Cartagena is not suppressed [31; 32].  
Figure 1: Location of study area in Cartagena Bay, with satellite photo and physical sediment collection points in Cartagena, Colombia.
## 2 Human Microbiome
### Microbiome Types
#### 2.1.4 Gastrointestinal Tract
Intestinal peristalsis, stomach-derived hydrochloric acid, and the high concentration of bile salts result in a low count of microorganisms in the small intestine. As one progresses from the duodenum to the ileum, bacterial density increases. At the beginning of the small intestine, the microbiota is very similar to that present in the stomach, while in the terminal part of the small intestine, the microbiota closely resembles that of the large intestine [77]. The bacteria most frequently found in the duodenum and jejunum belong to the genera _Streptococcus_ and _Lactobacillus_, which is very similar to the microbiome found in the stomach. This similarity is because these structures are located nearby, and in the duodenum, there are bile and pancreatic secretions responsible for the acidity [78]. The following portion, the ileum, already presents a moderate microbiome with a predominance of the genera _Enterococcus_, _Lactobacillus_, _Bacteroides_, and _Bifidobacterium_. In the colon, a larger and more-complex population of anaerobic microorganisms can be found, such as _Bacteroides_, _Bifidobacterium_, _Eubacterium_, and _Clostridium_. This is due to a progressive decrease in acidity, allowing for the existence of more microorganisms. Consequently, the colon exhibits greater microbial diversity among individuals, making it an ideal site for fermentation between the microbiome's microorganisms and nutrients from food digestion [75; 78]. In Table 1, the composition and concentrations of more-important microbial species in different locations of the gastrointestinal tract are depicted based on several authors [79; 80; 81; 82]. All these bacteria are usually commensal, but in immunocompromised or weakened individuals, they can become pathogenic, even in small quantities. It has been concluded that the origin of a disease is not a specific microorganism, but rather an imbalance in the intestinal microbiome, which may involve multiple microorganisms at the same time, referred to as dysbiosis [83]. This has implications for human health, leading to a variety of diseases such as inflammatory bowel diseases, cancers, metabolic disorders, and even cardiovascular diseases, which is the main subject of this review [84].