esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### High Throughput Sequencing (HTS) for Phytoplankton (and Bacterial) Species  
Although light microscopy is a powerful tool to identify and size taxonomic groups of phytoplankton species, and remains the primary technique used in most quantitative studies of ballast water, the method is very time consuming, requires highly skilled taxonomists, and will not identify rare species as only small volumes of water can be analyzed even following preconcentration. Molecular techniques have the capability of determining the relative abundance of all species in the sample. High throughput screening (HTS) approaches have been successfully applied to assess microbial diversity and phytoplankton community structure. For this study 250 mL water samples were filtered through PCTE filters (0.2 um and 1.0 um), then placed in 0.9 mL of Longmire's buffer (0.1 M Tris, 0.1 M EDTA, 10 mM NaCl, 0.5% (_w/v_) SDS) and shipped O/N on ice to the University of Maryland Center for Environmental Science, Institute for Marine Environmental Technology where DNA was extracted from the filter and amplified with 16S and 18S primers for Illumina sequencing. Although the focus of this effort is to determine phytoplankton occurrence through taxonomic recognition of nucleic acid sequences, data was also obtained on the bacterial species present and their relative abundance in the sample.  
Samples in DNA preservation solution were spun to collect material that had detached from the polycarbonate filter and the preservation solution was removed by aspiration. The lysis buffer from the Qiagen Power Soil DNA Extraction kit was added to each sample and any loose material was suspended by pipetting. The polycarbonate filter and the lysis buffer were then transferred back to the garnet bead containing tubes from the kit for lysis. Samples were split into subsets of twelve for processing using the Qiacube with the Powersoil kit protocol and an additional inhibitor removal step. The resultant nucleic acids were quantified by absorption at 260 nm using a Nanodrop ND-1000 spectrophotometer.  
The gene-specific sequences used in this protocol target the 16S V3 and V4 region followed the methods of [PERSON] et al. [19]. The full-length primer sequences, using standard IUPAC nucleotide nomenclature, to follow the protocol targeting this region were:  
16S Amplicon PCR Forward Primer = 5'  
TCGTCGGCACGGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCCWGCAG  
16S Amplicon PCR Reverse Primer = 5'  
GTCTCGTGGTCGGAGATGGTATAAGAGACAGACTACHVGGGATCTAATCC  
The algal specific primers were from the [PERSON] et al. [20]. vis.  
18S Algal Amplicon PCR Forward Primer-5'  
P73 (forward primer)--AAT CAG TTA TAG TTT ATT TGR TGG TACC  
18S Algal Amplicon PCR Reverse Primer-5'  
P47 (reverse primer)--TCT CAG GCT CCC TCT CCG GA
# Routing Schemes in FANETs: A Survey
## 1 Introduction
In Reference [25], UAVs embedded with solar panels, together with solar-powered charging stations, are deployed to overcome energy constraint in order to fulfill the energy, communication, and safety requirements of 5G. In Reference [26], Euclidean distance is measured and it is used to adjust the transmission power for hello messages in order to reduce energy consumption. The number of UAVs  
Figure 1: Two main scenarios in flying ad hoc networks (FANETs). required to achieve network performance requirements, such as throughput and packet delivery ratio, are also considered with the objective of minimizing energy consumption. In Reference [27], the secrecy outage probability and the average secrecy capacity of a multi-UAV swarm are derived in order to prevent eavesdropping.
## 4 Fault Current Limiting Devices
### Superconducting FCL
#### 4.2.2 Inductive Type FCL: Active SFCL with Reactive Power Injection  
An active SFCL, to compensate the voltage sag in the PCC and restrict the fault current level, has been studied in [35], and is shown in Figure 9b. An air-core superconductive transformer is placed in a series connection in the terminal of the DFIG. A three-phase converter with split DC-link capacitors has been connected to the secondary side of the transformer. The converter injects a controlled value of the current to compensate for the voltage drop on the PCC during the fault condition, and to surpass the current level. However, in this configuration, the cost of the superconductor and converter should be taken into account.
## 3 Results and Discussion
### Evaluation of the Ability of Strain Q3-6 to Decolorize Dyes  
#### 3.7.1 Decolorization of Brilliant Blue G-250 by Strain Q3-6  
As shown in Figure 4, after 5 days, the decolorization rate of Brilliant blue G-250 was up to 76.2%. As evident from the figure, during the initial 0-1 d period, the optical density (OD) value undergoes a more pronounced and rapid decline, signifying an excellent decolorization efficiency. Subsequently, the OD value decreases at a slower pace and to a lesser extent, marking a significant reduction in decolorization efficacy. This phenomenon might be attributed to the robust growth of the strain at the onset, characterized by a high population density and, consequently, a stronger capacity for dye removal. Over time, however, the microbial population in the solution approaches saturation, coupled with the depletion of nutrients and the toxic influence of the dyes [74], which ultimately leads to a decline in the vitality of the strains or even their demise, thereby diminishing the decolorization efficiency.
# Research on On-Line Detection Method and System for Nitrate in Groundwater
## 3 Results and Discussion
### Parameter Optimization
Figure 4: Chronoamperometry scan voltammograms of calibration samples: time–current curve of the microelectrode in the testing solution when E\({}_{2}\) = \(-\)0.3 V, E\({}_{3}\) = \(-\)0.6 V: (**a**) E\({}_{1}\) from curve a to curve e is 0.1, 0.2, 0.3, 0.4, 0.5 V, respectively; (**b**) E\({}_{1}\) from curve f to curve l is 0.10, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16 V, respectively. Figure 5: Chronoamperometry scan voltammograms of calibration samples: time–current curve of the microelectrode in the test solution. NaNO\({}_{3}\) concentrations in the test solution are as follows: 0, 5, 10, 40, 100, 200, 400, 700, 1000, 1500, 2000, 2500, 3000, 3500 \(\upmu\)mol/L: (a) complete time–current curve; (b) amplification of the part of the figure with time of 4 s to 5 s.  
Figure 6: Corresponding calibration plots of current values: fitting lines between the electrode response current value and the NO\({}_{3}\)– concentration under different values of deposition potential E\({}_{2}\); NaNO\({}_{3}\) concentrations in testing solutions are as follows: 0, 5, 10, 40, 100, 200, 400, 700, 1000, 1500, 2000, 2500, 3000, 3500 \(\upmu\)mol/L. When E\({}_{2}\) = \(-\)0.4 V, the electrode response sensitivity is the highest, so the deposition potential value is selected as E\({}_{2}\) = \(-\)0.4 V in subsequent experiments. 3. Deposition potential E\({}_{3}\) As can be seen from Figure 3, under the detection system selected in this study, the reduction peak potential of nitrate is \(-\)597 mV. In order to obtain the maximum response current signal, the detection potential E\({}_{3}\) is selected to be \(-\)597 mV. In summary, through systematic optimization experiments, the optimal parameter settings of modified microelectrodes for nitrate ion detection were determined and are shown in Table 1:
## 3 Results and Discussion
### SEM and EDS Analysis Results
The increase in the deposition power achieved similar coating thickness between the two HEA targets, suggesting that the Cu content has less effect on thickness as power increases (Table 4). The deposition power of 300 W seems to be the optimal value, having the fastest deposition rate of 37 nm/min, and similar thickness obtained regardless of the Cu content of the target.
# Dimension Reduction Using New Bond Graph Algorithm and Deep Learning Pooling on EEG Signals for BCI
## 5 Results and Discussion
### Strength and Weakness of the Bond Graph Algorithm  
Based on our experiments for the new BGA, BGA could achieve a very fast convergence compared to GA and PSO on eight benchmarks with models 3 and 4. It means that the formulation of two domains is suitable for converging, whereas the convergence of models 1 and 2 was worse than the others. Based on our experience, introducing a formula structure is very effective for improving the convergence.  
It is possible to change parameters to find out the most suitable parameters for convergence. However, it may not have a significant effect on their performance, because their structure remains unchanged.  
We obtained the best results for feature selection in one or two channels with model 3 when the selected features are sent directly to the ELM classifier. In addition, we obtained the best results for feature selection in two or three channels with model 3 when the selected features are sent directly to the CSP feature extraction. Then, the extracted features are sentto the ELM classifier. In both cases, the BGA converged significantly faster to the global optimum compared to the other algorithms.  
However, the BGA converges slower and may fall in local optimum when the feature selection is done based on four different parts, i.e., time domain, frequency domain, autoregression, and wavelet coefficients. In this case, if some parts are selected based on the binary model and some other parts are selected based on the random mode, the BGA performs poorly compared to the other algorithms. However, if all of the parts are selected based on either binary or random models, the results are better than the other algorithms. So, to solve this problem, two different models for feature selection should not be used simultaneously.
## 3 Development of the Hole Diameter Measuring Machine
### Structural Design of the Measuring Machine  
The structure of the HDMM is shown in Figure 5. A photo of the HDMM is shown in Figure 6. The machine had seven degrees of freedom of precision movement and positioning. The probe was mounted on a probe holder with two degrees of freedom (2D), which was mounted on an aerostatic linear slider. The 2D probe holder was used to precisely adjust the axis of the probe stylus to be parallel to the Z axis of the HDMM. The aerostatic linear slider could move horizontally in the X direction, which was also the direction of the measurement line of the hole diameter. The slider stroke in the X direction was 50 mm. A linear glass encoder with a resolution of 5 nm and an accuracy of ~1 \(\upmu\)m after calibration was provided for the X slider. A laser interferometer was used to measure the macro displacement of the probe, i.e., \(I\) in Equation (1). The part to be measured was mounted on a four-degrees-of-freedom (4D) worktable. The worktable could be controlled to move in the Y and Z directions, as well as to rotate in \(\theta\)x (about the X axis) and \(\theta\)y (about the Y axis) directions. The position and attitude of the hole to be measured could therefore be precisely adjusted with the worktable, which is further described in Section 3.3. The slider in the Z direction had a stroke of 150 mm. A linear glass encoder with a resolution of 5 nm and an accuracy of ~1 \(\upmu\)m after calibration was also provided for the Z slider. The tilting angle of the worktable was adjusted with fine thread and servo motors. The tilting angle was evaluated according to pulse counts of the rotary encoders of the motors, and one count corresponded to ~0.005\". The maximum error of the tilting angle adjustment of the worktable was ~0.2\", well meeting the measurement requirement.  
The measuring machine was supported by four air-floated vibration isolators. Environmental sensors were used to monitor the worktable temperature, air temperature, and air humidity to allow for compensation of the thermal expansion of the material and change of the air refractive index.  
Figure 4: Sensitivity characteristics of probing balls of different diameters.
# SCMC: An Efficient Scheme for Minimizing Energy in WSNs Using a Set Cover Approach
## 2 Related Works
### Redundancy Management  
Redundancy plays an important role in maintaining the reliability of a WSN by handling unforeseen failures and boosting the accuracy of calculated computations, due to the infeasibility of replacing the failed sensors. Due to this infeasibility, prolonging the network lifetime to ensure that measurements are taken for as long as possible is another desired trait. The Location-Unaware Coverage (LUC) algorithm [24] addresses the issue of finding a balance between efficient redundancy and network lifetime. The LUC algorithm determines whether a sensory node will be actively receiving measurements or sleeping to preserve its energy. To carry this out, each sensor node determines its neighbors and their relative distance in a two-hop neighborhood. LUC carries out a series of geometric and heuristic tests to determine the'strength' of the nodes, with the weaker ones put to sleep initially. The stronger nodes possess a higher reliability, and hence are kept active for a longer duration. The LUC algorithm has two protocols: Iterative LUC (LUC-I) and Probabilistic LUC (LUC-P), which determine whether nodes will be set in Active or Sleep mode through a series of hops. The protocols bring about low operating costs and may greatly minimize the number of nodes that are active.  
Upon completion of the protocols, all nodes are set to either the Sleep or the Active mode. Active nodes are responsible for covering the entire target area, and the WSN's lifetime and reliability are both improved.
## 5 Conclusions  
The simulation and real-time experimental results prove the superiority of the optimization problem formulated as MILP and solved by CPLEX over the storage priority algorithm. The results also show the feasibility of the proposed supervisory control of the IIREVs, which contains the HMI and the energy management with power balancing and interacts with the smart grid. The proposed supervisory control executes efficiently with respect to the constraints and fulfilling the EV user demands. Furthermore, the EVs that charge in slow mode depend mainly on PV energy, while for average or fast charging, they depend on the PV, storage and grid power sources. The EV energy distribution is considered good compared to the storage priority; only in the case with high fluctuations was the EV energy distribution better in storage priority. In addition, selling energy to the grid returns profits to the IIREV operator and makes optimization better than the storage priority algorithm.  
The optimization takes into consideration the intermittent arrival and departure of EVs. Further works will concentrate on realizing optimization taking into consideration the intermittent arrival and departure of EVs with services such as vehicle-to-grid, vehicle-to-home, and infrastructure-to-home.  
Conceptualization, S.C.-M., M.S. and F.L.; methodology, S.C.-M., M.S. and F.L.; software, S.C.-M., M.S. and F.L.; validation, S.C.-M., M.S. and F.L.; formal analysis, S.C.-M., M.S. and F.L.; investigation, S.C.-M., M.S. and F.L.; resources, S.C.-M., M.S. and F.L.; data curation, S.C.-M., M.S. and F.L.; writing--original draft preparation, S.C.-M.; writing--review and editing, M.S. and F.L.; visualization, S.C.-M., M.S. and F.L.; supervision, M.S.; project administration, M.S.; funding acquisition, M.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by ADEME France, project PV2E_Mobility, grant number #1905C0043.  
Not applicable.  
The authors declare no conflict of interest.
## 5 Discussion
### Effect of Experimental and Simulation Parameters
#### 5.1.2 Effect of Deep Rolling Speeds  
The speed at which the rolling tool moves across the material surface plays a critical role in determining the uniformity and effectiveness of the induced stresses. In our study, we used an experimental rolling speed of 1800 mm/min (30 mm/s) and found that the FEA simulation closely matched this at 1796.52 mm/min (29.942 mm/s). The results showed that higher rolling speeds led to more dynamic pressure fluctuations during the rolling process, yet still maintained a uniform stress profile over the surface.  
Our findings are consistent with the work of [PERSON], [PERSON] et al. [31], who reported that increasing the rolling speed up to an optimal limit improved processing efficiency without compromising stress uniformity. However, speeds that were too high introduced inconsistent stress distributions and even led to microcracks in some cases. In our experiment, the chosen speed was within this optimal range, and no adverse effects were observed, confirming that maintaining an appropriate rolling speed is crucial for ensuring a balance between processing time and stress uniformity.
# Electrification in Maritime Vessels: Reviewing Storage Solutions and Long-Term Energy Management
## 3 Energy Storage System for the Marine Power System
### Lithium Nickel Cobalt Aluminum (NCA)  
An NCA battery is composed of approximately 80% nickel, 15% cobalt, and 5% aluminum. While its chemistry is similar to NMC's, NCA distinguishes itself by substituting manganese with aluminum. This modification contributes to a specific energy range of 200-260 Wh/kg. The inclusion of aluminum not only elevates the energy density but also imparts additional stability to the cathode material. Despite these advantages, NCA cathodes face reduced safety profiles and increased production costs. Nonetheless, due to its high energy density, this material is considered suitable for maritime applications where energy efficiency is critical [27]. In maritime environments, where fire suppression and ventilation are more challenging than in land-based settings, these risks are magnified. As a result, NCA batteries require more robust battery management systems (BMSs), active cooling mechanisms, and thermal insulation to be used safely on ships. Despite these drawbacks, NCA cells are still considered for marine use when high energy density is a priority--especially in space-constrained vessels--but only when paired with appropriate safety measures and system-level engineering.
## 5 Test Experiments
### Length Measurement Experiment
#### 5.5.4 Measurement Uncertainty  
The main components that significantly affect the measurement uncertainty of length measurement are the uncertainty of repeatability of the scale length measurements \(u_{1}\), the uncertainty of the scale bar calibration \(u_{2}\), the uncertainty of the transit offset error measurement \(u_{3}\), the uncertainty of the transit tilt error measurement \(u_{4}\), and the uncertainty of changes in the environmental temperature \(u_{5}\). The single measurement error of the length measurement caused by measurement repeatability was \(u_{1}=0.007\) mm. The uncertainty of the scale bar calibration \(u_{2}\) is given by the calibration certificate of the scale bar; the standard uncertainty \(u(L_{0})\) was 0.010 mm, and the uncertainty of \(u_{2}\) was  
Figure 15: The coordinates of two ends of points of scale bar measured by laser tracker  
\(u_{2}=|\frac{\partial\varepsilon}{\partial L_{0}}|\cdot u(L_{0})=0.010\) mm. The uncertainty of the transit offset error measurement \(u_{3}\) was 0.006 mm according to Section 5.4.1 The uncertainty of the transit tilt error measurement \(u_{4}\) was 0.006 mm according to Section 5.4.2 Due to the measurement equipment and scale bar placed in the laboratory constant temperature and humidity environment for a sufficient period of time, along with the fact that the laser tracker has a temperature compensation function, thus the component caused by environmental temperature could be considered negligible; thus, \(u_{5}=0\) mm.Therefore, the combined standard uncertainty came out to \(u_{c}=\sqrt{u_{1}^{2}+u_{2}^{2}+u_{3}^{2}+u_{4}^{2}+u_{5}^{2}}=\sqrt{0.007^{2 }+0.010^{2}+0.006^{2}+0.006^{2}+0^{2}}=0.014\) mm. So, the uncertainty evaluation of the length measurement was 0.014 mm.
## 3 Experimental Step
### Testing Conditions  
New superconducting tapes were used under transverse magnetic fields of 0, 42.4, 75.9, and 122.9 mT. Two kinds of tapes, SC_SH type (Shanghai Superconductor Technology Co., Ltd., Shanghai, China, type ST-12-L) and SC_8602 (American Superconductor Inc. Boston, MA, USA, type 8602), were tested under each magnetic field. The testing superconductor tape was 11 cm length each. Figure 3 shows the experiment's objective. The superconducting tape was welded with the copper current wire, which was 3 cm width. The voltage leads were connected to both ends of the superconducting tape, respectively. Transverse magnetic fields were generated by two pieces of permanent magnets (Nd2 Fe14B), which were put beside the superconductor tape. The magnetic fields applied to superconducting tapes were 0, 42.4, 75.9, and 122.9 mT, respectively. The supporting frame was used to prevent the two permanent magnets bonding together. The supporting frame and the superconducting tape are not in contact with each other. The distance between testing superconducting tapes and the supporting frame (Al material) was 3 cm. Liquid nitrogen was used to keep testing superconducting tape in low temperatures at 77 K. the four-probe method was used to measure the voltage of the testing tape and the transport current.  
\begin{table}
\begin{tabular}{c c c} \hline
**Sample** & **SC\_SH** & **SC\_8602** \\ \hline Length/cm & 11 & 11 \\ Width/mm & 12 & 12 \\ Critical Current Ic/A (77K, & 230 & 225 \\ self-magnetic field) & 0.125 & 0.117 \\ Room resistance per meter/\(\Omega\)/m & 0.125 & 0.117 \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of Superconductor Tapes.  
Figure 2: Test circuit and first half waveform of prospective current.