esa-sceva/satcom-chunk-collection huggingface dataset:
# Discrimination of Spatial Distribution of Aquatic Organisms in a Coastal Ecosystem Using eDNA
## 2 Materials and Methods
### DNA Extraction and Metagenomic Sequencing  
Genomic DNA was extracted using PowerSoil(r) DNA Isolation Kit (Cat. No. 12888, Qiagen, Dusseldorf, Germany) according to the manufacturer's protocol. DNA extracted for sequencing was prepared according to Illumina 18S Metagenomic Sequencing Library protocols (San Diego, CA, USA). DNA quantity, quality, and integrity were measured by PicoGreen (Thermo Fisher Scientific, Waltham, MA, USA) and VICTOR Vivo Multimode Microplate Reader (PerkinElmer, Akron, OH, USA). The 18S rRNA gene was amplified using 18S V9 primers. The primer sequences are as follows: 18S V9 primer including adaptor sequence (Forward Primer: 5' TCGTCGGCACGGTCAGATGTGATAA GAGACCCCTGCHTTGTACACAC 3'/Reverse Primer: 5' GTCTCGTGGCCTCGGAGATG TGTATAAGAGACACACCCTTCYGCAGGTTACCTAC 3'). To amplify the target region attached with adapters, as a first PCR process, the extracted DNA was amplified by 18S V9 primers with one cycle of 3 min at 95 \({}^{\circ}\)C, 25 cycles of 30 s at 95 \({}^{\circ}\)C, 30 s at 55 \({}^{\circ}\)C, 30 s at 72 \({}^{\circ}\)C, and a final step of 5 min at 72 \({}^{\circ}\)C for  
Figure 1: Map of the study sites with division of zones referred to by [PERSON] et al. (2019) [25].  
-amplicon PCR product. As a second process, to produce indexing PCR, the first PCR product was subsequently amplified with one cycle of 3 min at 95 \({}^{\circ}\)C, eight cycles of 30 s at 95 \({}^{\circ}\)C, 30 s at 55 \({}^{\circ}\)C, 30 s at 72 \({}^{\circ}\)C, and a final step of 5 min at 72 \({}^{\circ}\)C. The final products were normalized and pooled using PicoGreen (Thermo Fisher Scientific, Waltham, MA, USA), and the size of libraries were verified using the LabChip GX HT DNA High Sensitivity Kit (PerkinElmer, Akron, OH, USA).  
The library was sequenced using the MiSeq(tm) NGS platform (Illumina, San Diego, CA, USA) provided as a commercial service (Macrogen Inc., Seoul, Korea). Raw reads were trimmed with CD-HIT-OTU and chimeras were identified and removed using rDnaTools. For paired-end merging, FLASH (Fast Length Adjustment of SHort reads) version 1.2.11 was used. Merged reads were processed using Qiime version 1.9 [31] and were clustered into operational taxonomic units (OTUs) with UCLUST [32], using a greedy algorithm with OTUs at a 97% OUT cutoff value. Taxonomic classifications were assigned to the obtained representative sequences using BLASTn [33] and UCLUST [32].
###### Abstract  
Plants of the Daucus genus (Apiaceae) are used worldwide as traditional medicines or culiary ingredients. In this work, in order to explore a new chemotype, the essential oil obtained from the aerial parts of _Daucus reboudii_ Coss., collected from the National Park of Gouraya (Bejia, Algeria), was analyzed by GC-MS. Twenty-eight compounds were identified, accounting for 96.6% of the total oil. (E)-anethol was the main constituent (59.4%), followed by estragol (21.2%) and dodecanl (4.4%). (E)-anethol is an uncommon constituent of Daucus genus, hence it could be considered as a marker that contributes to differentiating _D. reboudii_ from other species. Metal chelating, ABTS** and DPPH** assays were performed to determine the antioxidant activity. The highest activity was revealed by the DPPH** method, where _D. reboudii_ essential oil showed a significantly higher activity compared to the reference standard BHT at doses of 50 and 100 \(\mathrm{\SIUnitSymbolMicro m}\). Results suggest that the essential oil from _D. reboudii_ could have a potential use in the food industry as food preservative. Nevertheless, further studies are needed to assess its applicability, and to elucidate also the composition of non-volatile compounds of this plant.  
Daucus reboudii Coss.; essential oil; GC-MS; (E)-anethol; antioxidant +
Footnote †: journal: Applied Sciences  
the North-East of the country, in particular in Guelma and Kalylie regions [6]. _D. reboudii_ grows spontaneously in cork oak forests [6; 7], and it is characterized by a fennel savor. It is a perennial herbaceous plant, characterized by a small white down (1 mm in length) on the stems and along the petioles, and by 5-7 mm-long fruits [7].  
The essential oil composition of many Apiaceae species has been already reported. Among _Daucus_, the essential oil from the species _carota_ has been extensively studied, especially from the aerial parts [8; 9; 10] and seeds [11]. Nevertheless, to the best of our knowledge, only one study regarding the composition of essential oil from endemic _D. reboudii_ is present in the literature, reporting a high content of monoterenes hydrocarbons (69.3%) and oxygenated monoterenes (14.9%) [7].  
The present study aimed at investigating the composition of the essential oil obtained from the aerial parts of _D. reboudii_ growing in Northern Algeria and its antioxidant properties, measured using different techniques, namely metal chelating, 2,2\({}^{\prime}\)-azinobis(3-ethylbenzodiazep-6-sulfonic acid) (ABTS\({}^{\bullet+}\)) and 2,2-diphenyl-1-picryl-hydraz (DPPH\({}^{\bullet}\)) assays.
# Fault-Tolerant Optimal Consensus Control for Heterogeneous Multi-Agent Systems
## 1 Introduction
The aforementioned studies focus exclusively on homogeneous multi-agent systems, with limited research outcomes on achieving fault-tolerant consistency in heterogeneous multi-agent systems. Reference [25] investigated a collaborative fault-tolerant output regulation control approach for linear heterogeneous multi-agent systems operating under directed network topologies. The objective was to ensure system stability despite actuator failures. Reference [26] examines fault-tolerant consistency in heterogeneous multi-agent systems. The study introduces a sliding mode observer aimed at achieving consistent system control in the presence of uncertainties and actuator faults. While both references explored fault-tolerant control in heterogeneous multi-agent systems, they primarily discussed generic models rather than specific physical implementations. Building on the preceding analysis, this paper aims to investigate the consistency issues in leader-following heterogeneous UAV-UGV systems featuring faulty followers. Specifically, this paper designs a fault-tolerant consistency controller for leader-following heterogeneous multi-agent systems encountering partial actuator failures. The contributions of this study are outlined below:  
Firstly, this paper introduces dynamic models for intelligent agent systems and formulates them using Kronecker products in state space. It then constructs the Laplacian matrix of the communication topology. The paper proposes a fault-tolerant consistency controller for leader-following multi-agent systems in the presence of actuator failures, validated using Lyapunov stability theory. The algorithm shows robust fault tolerance to constant and time-varying actuator faults without needing to solve the Hamilton-Jacobi-Bellman equation. Performance index functions are designed for each UAV and UGV, aiming to minimize the performance index of each UAV and UGV pair to achieve overall minimization for the entire heterogeneous multi-agent system. The fault-tolerant consistency controller obtained through this optimization process is considered the optimal controller, ensuring leader-following consistency under conditions of actuator failures in both UAVs and UGVs. The remaining sections of this study are organized as follows. Necessary background and system models related to the problem of fault-tolerant consensus in heterogeneous multi-agent leader-following systems are provided in Section 2. Theorem designs that address the problem of designing control protocols for UAVs and UGVs are introduced in Section 3. Section 4 presents numerical simulations of multiple UAVs and UGVs, comparing them with existing algorithms. Section 5 summarizes the whole work.
# Subcritical Water Extraction of Ursolic Acid from _Hedyotis diffusa_
## Materials and Methods
### Ursolic Acid (UA) Purification  
After subcritical water extraction, the mixtures were centrifuged at 4000 rpm for 10 min to remove the insoluble materials. The supernatants were concentrated by rotary evaporation and then the resulting residue was purified with alkali-dissolution and the acid-precipitation method. Using sulphuric acid and sodium hydroxide, the pH value of extracted solution was adjusted repeatedly for purity of UA. The resulting precipitation was dissolved by ethanol, and the solution was passed through a 0.22 \(\upmu\)m membrane filter (Jinteng Experimental Equipment Inc., Tianjin, China) prior to HPLC analysis. The extraction yield of UA can be calculated using Equation (1):  
\[\text{Extraction yield }(\text{mg}/\text{g})=\frac{\text{content of UA }(\text{mg}/\text{g})}{\text{weight of sample powder }(\text{g})}. \tag{1}\]  
High Performance Liquid Chromatography (HPLC) and Electrospray Ionization Time-of-Flight Mass Spectrometry (ESI-TOF-MS) Analyses  
An HPLC method was developed for the detection and quantification of UA in extracts, which was performed on an Agilent 1200 HPLC system with a Venusil MP C18 column (4.6 mm \(\times\) 250 mm, 5 \(\upmu\)m, Bonna-Agela Technologies, Tianjin, China). The mobile phase consisted of methanol: 0.1% aqueous phosphoric acid (88:12 \(v/v\)). The flow rate was 1 mL/min, the separation temperature was 25 \({}^{\circ}\)C, UV detection was carried out at 210 nm, and injection volume was set up in 10 \(\upmu\)L. Calibration curves for standard UA were used to calculate the extraction yield that was expressed in units of mg/g material. Identification of target compounds was based on retention time and chromatographic behavior compared to the standard UA.  
Mass spectrometric (MS) analysis was performed on a Liquid Chromatography Mass Spectrometry (Shimadzu, Kyoto, Japan) with an ESI source in order to further identify unsolic acid. The MS conditions for detection were as follows: Negative ion mode; detector voltage, 1.58 kV; curved desolvation line temperature, 200 \({}^{\circ}\)C; heat block temperature, 200 \({}^{\circ}\)C; 1.5 L/min of nebulizing gas (N\({}_{2}\)) flow rate; 1.5 \(\times\) 10\({}^{-2}\) Pa of IT area vacuum; 1.6 \(\times\) 10\({}^{-4}\) Pa of TOF area vacuum; 149 kPa of drying gas pressure. The spectra were recorded from m/z 100 to 600 and the data for the negative ion mode were acquired for [M\(-\)H] \({}^{-}\) at m/z 455.3631 for UA.  
Figure 1: Schematic diagram of subcritical water extraction apparatus. IR: impounding reservoir; PG: pressure gauge; H: preheater; BR: batch reactor; T: thermocouple.
## 4 Software and Hardware Evaluation System
### Software Design  
The PUF platform can be fully controlled by the user from a PC via a serial communication interface. Similar to existing measurement instruments, the Standard Commands for Programmable Instruments (SCPI) protocol [31] is used to send commands to the evaluation platform. The user can configure the microcontroller software and set the PUF's operating conditions (\(VDD_{core}\) and \(V_{in}\)). Additionally, the timing of the PUF response generation procedure can be defined through the \(t_{s,1}\) and \(t_{s,2}\) parameters, which also affect the ADC sampling time, as shown in Figure 2b.  
Figure 3a shows the program flow as a state machine for sending PUF challenges including ADC measurements. The software on the microcontroller behaves as shown in Figure 3b and is fully sensitive regarding incoming SCPI commands. The microcontroller subsequently controls the DEMUX/MUX circuits according to the addresses included in the sub-challenge \(c_{k}\). The corresponding inverter input biasing voltage \(V_{in}\) is routed towards the two selected inverter input terminals. Additionally, the two addressed inverter outputs are routed towards the comparator input terminals, as shown in Figure 1. The voltages at both comparator input terminals are tracked via an ADC. Finally, the ADC values and the sub-response \(r_{k}\) are sampled by the microcontroller. This procedure is repeated until all sub-challenges are processed and the full-length PUF response is generated. The microcontroller sends the binary PUF response and the corresponding ADC values to the PC.  
Figure 2: PUF core bit generation using a comparator and corresponding timing diagram for software-based sampling. (**a**) PUF core output voltages to comparator terminals; (**b**) exemplary timing diagram showing the bit generation procedure.
# Task Scheduling Mechanism Based on Reinforcement Learning in Cloud Computing
## 6 Conclusions  
In this paper, we propose a reinforcement learning task scheduling framework called QMTSF based on the UCB strategy for the task scheduling problem in cloud computing. When tasks are allocated in the cloud data center, they are first assigned to servers that are more proficient in handling certain types of task based on the task type. In task scheduling on servers, the tasks in the queue are sorted by their respective deadlines, then the UQRL algorithm is used to schedule each task in the queue and allocate it to a VM. Using the UQRL algorithm minimizes the makespan while ensuring that task deadlines are reached, reducing the average task processing time, and improving CPU utilization, thereby  
Figure 7: CPU utilization: comparison of different scheduling methods.  
lowering the overall energy consumption of the cloud data center. Our experimental results demonstrate that the QMTSF method performs better than other task scheduling strategies.  
In the future, we plan to apply this framework to task scheduling scenarios involving unmanned aerial vehicles (UAVs). For this, it will be necessary to explore ways of better classifying and mapping tasks while considering their varying resource requirements, duration, and service quality in order to validate the effectiveness of this framework in real-world environments. Additionally, it will be necessary to address the implementation challenges of cloud clusters in scenarios with larger-scale servers within the cloud environment. In addition, we intend to further explore the application of reinforcement learning in multi-objective task scheduling problems.  
Conceptualization, Y.W.; methodology, Y.W. and S.D.; software, Y.W. and W.F.; validation, Y.W.; formal analysis, Y.W.; investigation, S.D.; resources, S.D.; data curation, Y.W. and S.D.; writing--original draft preparation, Y.W.; writing--review and editing, S.D.; visualization, W.F.; supervision, W.F.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Natural Science Foundation of China (Grant Nos. 62102196, 62102195, 62172291), Natural Science Foundation of Jiangsu Province (No. BK20200753), Jiangsu Postdoctoral Science Foundation Funded Project (No. 2021K096A), and Postgraduate Research and Practice Innovation Program of Jiangsu Province (KYCX23_1087).  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors would like to thank the anonymous reviewers and the editor for their careful reviews and constructive suggestions to help us improve the quality of this paper.  
The authors declare no conflict of interest.
# 10 Gb/s Bidirectional Transmission with an Optimized SOA and a SOA-EAM Based ONU
## 2 Optimizing the Performance of an SOA
Finally, NF remains stable with bias current since the stimulated radiation tends to be unaltered at the adequate bias current. Figure 1c,d shows that the gain flatness of the SOA meets the standard between 1540 nm and 1560 nm. The output performance can basically meet the requirements of various applications, and different applications can be selected according to the performance of gain and NF in different operating regions, as summarized in Table 2.  
Figure 1: Characteristic analysis diagram of an SOA: (**a**) Gain and NF with input power; (**b**) Gain and NF with bias current; (**c**,**d**) Gain and NF with wavelength (the left input power is \(-25\) dBm, the right input power is \(-5\) dBm). To sum up, we get the optimum operating point of the SOA. Under the circumstances where the bias current is 200 mA and the input power is \(-25\) dBm or \(-5\) dBm of the SOA, the output optical power can keep constant on the premise of ensuring low noise relatively. The input power of \(-25\) dBm is suitable for phase modulation, while \(-5\) dBm is suitable for amplitude modulation.
# A Novel Adaptive Gain of Optimal Sliding Mode Controller for Linear Time-Varying Systems
## 2 Design of Optimal Control Law
The group \(-\gamma G_{L\xi}{}^{T}Q_{\Delta L}(t)x_{L\xi}(t)\) can improve the choice of the optimized value of \(Q_{\Delta L}(t)\) in the linear time-varying system, which belongs to the signal variation of \(x_{L\xi}(t)\)._  
**Theorem 1**. : _The control system (1) with the cost function (2) is integrated with the control law (8) and the derivative of the gain matrix function (11). Then, the optimal control state of the linear system (1) is re-written by the system (10)._  
**Proof. ** To prove the stability of the proposed optimal controller, the candidate of Hamiltonian function is firstly established below:  
\[u_{L\xi}^{*}(t)=-M_{L\xi}^{-1}(t){H_{L\xi}}^{T}(t)q_{L\xi}(t) \tag{3}\]2  
The maximum principle \(\frac{\partial H_{L}}{\partial u_{L\xi}}=0\) is used to find the optimal control law as follows:  
\[u_{L\xi}^{*}(t)=-M_{L\xi}^{-1}(t){H_{L\xi}}^{T}(t)q_{L\xi}(t) \tag{3}\]3  
Now, substituting Equation (4) into Equation (14) yields the following main input control:  
\[u_{L\xi}^{*}(t)=-M_{L\xi}^{-1}(t){H_{L\xi}}^{T}(t)q_{L\xi}(t) \tag{3}\]4  
Continuously, using the definition of co-state equation given by \(\frac{dq_{L\xi}(t)}{dt}=-\frac{\partial H_{M}}{\partial x_{L\xi}}\), the following equation is obtained:\[u_{L\xi}^{*}(t)=-M_{L\xi}^{-1}(t){H_{L\xi}}^{T}(t)q_{L\xi}(t) \tag{3}\]5  
Substituting Equation (4) into Equation (16) yields the following equation:  
\[u_{L\xi}^{*}(t)=-M_{L\xi}^{-1}(t){H_{L\xi}}^{T}(t)q_{L\xi}(t) \tag{3}\]6  
Then, using the property of \(\frac{d(sign(x_{L\xi}(t)))}{dt}=0\), Equation (17) is rewritten as follows:  
\[u_{L\xi}^{*}(t)=-M_{L\xi}^{-1}(t){H_{L\xi}}^{T}(t)q_{L\xi}(t) \tag{3}\]7  
Equation (17) has two cases. The first case is \(x_{L\xi}(t)=0\), then \(u_{L\xi}(t)=0\), and the value of \(Q_{L\xi}(t)\) is not required. The second case is \(Q_{L\xi}(t)\), which is a solution of Equation (7). Thus, in this case, Equation (9) is satisfied. On the other hand, Equation (4) is written at time \(t=T\) as follows:  
\[u_{L\xi}^{*}(t)=-M_{L\xi}^{-1}(t){H_{L\xi}}^{T}(t)q_{L\xi}(t) \tag{3}\]8  
Hence, the value \(Q_{L\xi}(T)\) is found as follows:  
\[u_{L\xi}^{*}(t)=-M_{L\xi}^{-1}(t){H_{L\xi}}^{T}(t)q_{L\xi}(t) \tag{3}\]9  
The value \(\frac{\Xi_{L\xi}}{\gamma^{2}}\) of Equation (20) shows that the final value of \(Q_{L\xi}(T)\) is the positive value. The value \(\frac{\Xi_{L\xi}}{\gamma^{2}}\) can be treated as a reference value for the optimal control law. This completes the proof of Theorem 1. \(\Box\)
###### Abstract  
Plasma-liquid reactions represent an emerging green chemical process for nitrogen fixation; however, these processes generally exhibit low selectivity for ammonium (NH\({}_{4}\)+). This limitation highlights the need to explore simple methods to increase NH\({}_{4}\)+ selectivity. In this study, a catalyst-free falling film dielectric barrier discharge plasma system was employed for the selective synthesis of NH\({}_{4}\)+. By manipulating the flow state of the discharge gas, NH\({}_{4}\)+ selectivity was found to increase by 138.4% in the sealed gas flow state compared to the flowing gas state. Furthermore, an increase in the discharge voltage positively influenced the NH\({}_{4}\)+ selectivity. This phenomenon can be attributed to higher energy input and longer reaction times, which facilitate the formation of nitrogen molecular ions, a critical intermediate in ammonia synthesis. The reaction products were analyzed by UV spectrophotometry and emission spectroscopy to investigate the underlying mechanisms of ammonia synthesis. This study reveals the highest reaction rate reported to date for ammonia synthesis via single-system plasma gas-liquid reactions and offers a novel way to improve both the yield and selectivity of ammonium synthesis via non-thermal plasma gas-liquid interactions.  
Keywords:plasma gas-liquid interaction; selective synthesis; dielectric barrier discharge; sealed gas flow +
Footnote †: journal: Applied Sciences
## 4 Discussion  
### Comparison of Fracturing Methods  
For hydraulic fracturing, the resulting cracks are very narrow and accompanied by numerous localized damage zones, a feature absent in deflagration fracturing, which tends to completely separate the rock blocks. For the purpose of reservoir enhancement, deflagration fracturing undoubtedly produces larger-scale fractures and achieves superior results. To further compare the fracture spaces created by the two fracturing methods, rough modeling of the post-fracturing samples was conducted based on the exposed fracture traces and the morphologies of some fracture surfaces (Figure 11). The resulting fracture surface areas obtained from the models are presented in Table 3. Based on Figures 5, 6 and 10, if an average fracture width of 0.05 mm is assumed for hydraulic fracturing and 1.5 mm for deflagration fracturing, it can be observed that the fracture space generated by deflagration fracturing is hundreds of times larger than that of hydraulic fracturing. Additionally, based on the exposure of cracks and damage zones, it can be seen that nearly all the damage caused by hydraulic fracturing is distributed along a vertical plane oriented in the \"upper left-lower right\" direction, which is evidently independent of temperature and the number of boreholes. Experiments on hydraulic fracturing conducted by [PERSON] (2017) [37], [PERSON] (2024) [38], and  
Figure 10: The fracture distribution diagram of the sample after deflagration fracturing: (**a–d**) correspond successively to the four cases D1–D4.  
others on glutenite have demonstrated that geostress difference is the primary controlling factor for crack propagation. This is further corroborated by the numerical simulation results of [PERSON] (2013) [39], who found that the main crack propagates primarily along the direction of maximum principal stress. Therefore, it can be inferred that under the loading conditions of this study, the direction of maximum principal stress is approximately along the \"upper left-lower right\" direction, and it significantly controls the crack propagation and damage distribution in the four experiments. In contrast, during the deflagration fracturing, the crack propagation seems unaffected by the geostress difference caused by external loads.