esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods  
### Extraction of Water-Surface Area  
Many studies show that the object-oriented classification is preferred over traditional pixel-based classification method [20; 21; 22; 23; 24]. The object-oriented method fully utilizes the spectrum, spatial information, texture and other characteristics provided by remote sensing images to carry out classification. Therefore, object-oriented method is selected to extract water-surface area in this study. In the process of extracting water-surface area, the steps followed are image multi-scale segmentation, application of remote sensing indexes, setting of the indexes' threshold and establishment of classification rule sets based on eCognition8.64 software. The remote sensing indexes are used to  
Figure 1: Location of Honghu Lake, Hubei Province, China.  
delineate the water-surface area based on the difference of the absorption and reflection of light between water and other features on different frequency bands. In this study, the normalized difference water index (NDWI) [25], modification of normalized difference water index (MNDWI) [26], normalized difference of RVI and NDWI (NDRW) [27] and NDVI-NDWI [28] are compared with each other to determine the optimal water extraction index. The results indicate that the best index for Landsat and HJ1A/B data is MNDWI and NDRW, respectively, which can be used to distinguish water body and artificial construction land more accurately. It should be noted that water-surface area of Honghu Lake in this study does not include the area of enclosures aquiculture. The MNDWI and NDRW are calculated with the following equations:  
\[MNDWI=\frac{(\rho_{Green}-\rho_{MR})}{(\rho_{Green}-\rho_{MR})} \tag{1}\]  
\[NDRW=\frac{RVI-NDWI}{RVI+NDWI},RVI=\frac{\rho_{NIR}}{\rho_{Red}},NDWI=\frac{( \rho_{Green}-\rho_{NR})}{(\rho_{Green}-\rho_{NR})} \tag{2}\]  
where \(\rho_{Green}\), \(\rho_{NIR}\), \(\rho_{Red}\) and \(\rho_{MR}\) are green band, near-infrared band, red band and middle-infrared band reflectance, respectively.
## 3 LPBF Sensing Techniques
### Acoustic Techniques
Due to the change in the grating pitch and refractive index through the strain optic effect, the Bragg wavelength exhibits a change when an external strain is applied [177]. FBG sensors are more compact and lighter than standard AE detectors, although they are known to be thermally sensitive, so they have difficulty discriminating wavelength shifts from temperature and strain changes. [PERSON] et al. [178] quantified the levels of porosity concentration in SS316L cuboid-shaped samples using a fiber Bragg grating (FBG) optoacoustic sensor. [PERSON] et al. [179] used an FBG sensor to detect airborne AE signals generated during the LPBF process. Tubular defects were prevalent at the most considerable energy input, and the lack of fusion defects was dominant at the lowest energy inputs (see Figure 31). [PERSON] et al. [180] presented a novel solution for in situ and real-time quality monitoring based on detecting the AE signals emitted during the LPBF printing process. **Nonlinear reverberation spectroscopy** (NRS) exploits the amplitude-dependent changes in the resonance frequency of a sample. Once the sample is excited near resonance, the immediate vibration frequency of the decaying reverberation signal decays with decreasing amplitude. The nonlinearity of the material that defects can cause can be determined by  
Figure 30: Example provided by [171] showing (**a**) a \(\upmu\)-CT scan of an LPBF-printed part with a large crack, (**b**) AE results indicating the density of cracks, and (**c**) baseplate temperature. frequency-amplitude dependence. NRS is a non-destructive measurement technique that can rapidly measure AE so that thermal drift is primarily reduced. However, similar to other AE detectors, it cannot describe the defect's characteristics. [PERSON] et al. [181] demonstrated the sensitivity of acoustic nonlinearity and loss to small changes in porosity of LPBF-printed custom alloyed Al (0.2 at. % Zn) and commercial Al/075 cylinders. The acoustic signals were generated using non-contacting electromagnetic-acoustic transduction. The relative frequency shift measurements collected from the NRS method exceeded the best-reported precision of nonlinear resonant ultrasound spectroscopy (NRUS) by two orders of magnitude.
## 3 Data and Methods  
### Radiometric Calibration  
The calibration equations (horizontal and vertical channels) are determined from two points: a hot and a cold targets. In this study, the sky was used as \"cold target\", and an ambient temperature blackbody target as the \"hot target\". The \"hot target\" consists of pyramidal open-cell blackbody foam in which two precision temperature sensors are embedded (Figure 3). Since the \"hot target\" is seen as a near-blackbody target, the value of the emitted brightness temperature is equal to its physical temperature. The brightness temperature for \"cold target\" is nominally 3.5 K, provided that the field of view of the radiometer is away from the galactic center and the sky is clear. To accomplish this, according to the galactic noise map [(34; 35)], the antenna was pointed to the north, and at an elevation angle approximately 65\({}^{\circ}\) above the horizon.  
Then the radiometer measurements were performed at different observation angles from 16\({}^{\circ}\) to 60\({}^{\circ}\) (\(\theta_{i}=16^{\circ},26^{\circ},36^{\circ},45^{\circ}\)and 60\({}^{\circ}\)). The total number of the measurements was 101 for each polarization with five different incidence angles. The data possibly influenced by the RFI effect were first sorted out, and the measurements corresponding to the incidence angles outside the range of 1\({}^{\circ}\) larger or smaller than the incidence angles were also removed. For example, the measured brightness temperature exceeding the range of 35\({}^{\circ}\) to 37\({}^{\circ}\) for a 36\({}^{\circ}\) incidence angle would be rejected. After data quality control by the RFI effect and incidence angle abnormal, 18 measurements were rejected for each polarization locating at six stations, and the remain 83 high quality measurements were used for further analysis (Table 1).
# ISAC towards 6G Satellite-Terrestrial Communications: Principles, Status, and Prospects
## 4 Development Trend and Key Technology of ISAC towards 6G ISTNs
### Critical Techniques in Integrated Design
#### 4.2.1 Interference Cancellation Technique
With the development of communication technology, the existing interference cancellation technology based on terrestrial ISAC is still applicable in the future 6G ISTN scenario, but some technical optimization needs to be carried out in satellite and airspace. The interference signal of the ground mainly comes from the internal interference of the system and the clutter signal in the surrounding environment, while the interference of the satellite signal includes interference within the system, radio interference scattered by the cosmic environment, and interference between satellites. For high-throughput satellites, which have multiple directional beams pointing to the ground, inter-beam anti-interference is a key issue, and it is necessary to ensure that different frequency bands are used in adjacent areas as much as possible. In addition, as the number of satellites in earth orbit increases, the distance between satellites will decrease, and the difference in elevation angle between adjacent satellites will gradually decrease. As a result, if the beam of the ground uplink signal is shifted and pointed to the wrong neighboring satellite, the target satellite will have a low SNR, and the spectrum sensing technology will be used to identify the spectrum of the interfering signal. In addition, considering the attenuation of the signal by the atmosphere and environment between satellites and ground, reasonable power allocation combined with the sensing environment is an effective way to make up for the interference of the natural environment.
## 3 Results
### Structure and Catalogue of Scenarios  
Figure 9 presents an organigram of the structure of the compiled scenarios of scenarios, organized in use case clusters and use cases. The _central interactions_ can be considered the main body of the scenario collection. They are structured in a way that enables an interaction of one actor with any of the remaining ones. In addition, use case clusters (UCCs) regarding the remote operator's state, contextual factors, and technical malfunctions related to _peripheral factors_ that are not directly based on interactions.  
Table 1 is a comprehensive list of scenarios in remote operation compiled. It is structured as follows: the overarching classification categories use case cluster and use case, the defining elements of the scenario consisting of cause, event, and consequence, a column for every of the five actor categories, and the mode of remote operation.  
Figure 8: The chain of cause, event, and consequence provides the scaffolding for each scenario. In future iterations of the scenario catalogue, required measures may be added.  
Figure 9: Structure of use case clusters (UCCs, top row) and use cases (UCs, rows below top row). The core of the scenario collection is made up of interactions between different actors (central interactions). In addition, UCCs regarding the remote operator’s state, contextual factors, and technical malfunctions related to peripheral factors that are not directly based on interactions. RO = Remote Operator, AV = Highly Automated Vehicle, P = Passengers, I = Infrastructure, OA = Other Actors.  
Overall, 74 scenarios were compiled. These were subsumed under 15 use cases, which in turn were grouped into eight use case clusters, five of which are central interactions, and the remaining three are regarded as peripheral factors. Both use cases and use case clusters are stated in Figure 8. A sequential number (\(N\)) was given to each use case cluster (UCC), use case (UC), and Is Scenario (Sc), in the following fashion:  
\[<\!\!N_{\text{UCC}}\!\!>.<\!\!N_{\text{UC}}\!\!>.<\!\!N_{\text{Sc}}\!\!>\]  
From left to right, Table 1 includes the following columns: Use case cluster, Use case, Cause, Event, Consequence, and the descriptive Is Scenario. In the section \"Actors\", each actor involved is checked with an \"X\" if involved. If not involved, the respective column remains blank. The same system is used to indicate the Mode of Remote Operation. Here, an \"X\" indicated that the scenario is valid for Remote Driving, Remote Operation, or both.
## 3 Motivation
### Protocol-Response Layer  
The protocol-response layer contains response data such as HTTP and SSH protocols. Obtaining protocol responses requires sending probe packets to the target device. If the target device opens the particular service, it will return a response packet containing a device fingerprint. For example, as shown in Figure 1, a Cisco device opens the 80_HTTP and 23_Telnet ports. The Telnet protocol response contains information about the device model and firmware version, while the information about the device brand appears in the HTTP protocol response. Therefore, both 80_HTTP and 23_Telnet responses should be captured to obtain the complete device fingerprint.
## 2 Materials and Methods
### Data Processing  
Sequence data from patients were aligned to hg38 as a reference using STAR (version 2.7.0a) [16] with default parameters. Relative gene expression was determined by high-throughput sequencing (HTseq) [17]. To normalize gene expression levels, transcripts per million (TPM) (referenced by hg38) [18] were used. The selected data had a TPM value of greater than 1. To detect the differences in gene expression between metastatic and non-metastatic breast cancer, the DESeq2 package (version 1.24.0) in R was used [19]. Differentially expressed genes exceeding the cut off (\(p\)-value \(<\) 0.05 and \(\mid\)log2 fold-change \(\mid\)\(>\) 1) were selected and their functions in metastasis were analyzed. Information on gene functions in metastasis was taken from the Human Cancer Metastasis Database (HCMDB) [20].
## 5 Computational Experiments
### Computational Results  
The numerical results for the first experiment are shown in Table 3. Columns 1-3 consist of the instance no, name, and a number of parcel locker facility in the corresponding instance. The following columns 4 and 5 are the objective and number of vehicle being used for the CVRP solution. The next columns are the results related to 2 EVRP and 2 Phases approaches. Those columns use header Echelon1 (objective value of the first echelon), Echelon2 (objective value of the first echelon), NV (number of the vehicle used for the routing in the first echelon), and the CPU solving time to obtain the results. The last two columns show the saving of routing cost using 2 EVRP-LF compared to the CVRP and the comparison between the total cost of using 2 EVRP-LF and 2 Phases optimization approach. The results show that using 2 EVRP-LF can give benefits by having on average 70.4% less cost compared to conducting the last mile using HD concept or in this case modelled as CVRP. However, in order to make this saving the user needs to travel to the designated pickup station that on average is 2.623 times of the routing distance. Therefore, a common approach of using CP is to give some compensation to make it interesting to the customers to use the system. This compensation can use the saving obtained from the less routing cost in the first echelon. Further analysis shows that the two-phase optimization generates a worse solution compared to the 2 EVRP-LF. The average difference between the two approaches is 18.95%. The situation is expected because the sub-optimal result is generated at each phase of the 2 Phases approach. However, the 2 EVRP-LF still has drawbacks in solving instances with a large number of \(m\). This exposes the limitation of the solver to solve the problem which belongs to the class NP-hard problem. The results from the second experiment, as shown in Table 4, are compared with the previous results of 2 EVRPLF generated using GUROBI solver with the proposed SA. The results show that using SA the average distance travelled in the first and second echelon are 322.46 and 826.88, respectively. Furthermore, the average percentage different between GUROBI and SA shows that SA can give a better result with an average of 2.01%. The computational time of SA is also faster with the average of 2.57. As suspected earlier, exact solution approach such as using GUROBI has limitation on larger and more complex instances. It is indicated by the result that SA can better perform mostly on the instances where GUROBI was not able to generate the optimal solution. Meanwhile, for the result that already solved until optimality, the percentage difference is zero. Furthermore, Table 5 shows the detail experimental results of the 2 EVRPLF dataset in terms of the best objective function found and the average computational results over five replications. The result description can be used by the future researchers to conduct and repeat the experiment. Figure 3: SA parameters setting.
# Performance Validation of Control Algorithm Considering Independent Generator Torque Control in PCS
## 4 Experimental Validation
### Wind Tunnel Test Result  
The control algorithm used in the simulation was converted into C (programming language)-based code and updated to the PLC for application to the scaled wind turbine.  
Figure 11: Comparison of the simulation results for the scaled wind turbine (6 m/s).  
condition. For the generator power, a tendency for a 6.15% increase was noted under the PCS torque control condition due to oscillations in the generator torque. Conversely, under the modified control switching logic condition, the generator power decreased by about 10.77%. While all of the comparisons yielded different absolute values, they exhibited similar trends to those observed in the simulations.
## 2 Nanodiamonds Synthesis Techniques
### Chemical Vapor Deposition (CVD)  
Chemical vapor deposition is one of the most popular methods for the deposition of thin film, and it has been used for the synthesis of nanocrystalline diamond film [33]. In detail, the deposition of carbon atoms occurs during decomposition of a gas mixture with a carbon containing species, usually methane CH4, in an excess of hydrogen. The gas phase is decomposed by using a hot filament or a microwave plasma to form radicals such as H\({}^{\bullet}\) and CH3\({}^{\bullet}\), which are essential for diamond growth. The ND film forms on a substrate, typically a silicon wafer coated with a micrometer sized diamond powder acting as a seed for ND nucleation [33], and eventually forms a continuous film. The typical set-up [34] for CVD synthesis is reported in Figure 3a. The size of the grains composing the film ranges to tens of micron down to a few nanometers, depending on CH4/H2 relative concentration [35].  
Figure 2: (**a**) Detonation process. A mixture of 60% TNT (C6H2(NO2)CH3) and 40% hexogen (C3H6N6O6) is detonated inside a metallic chamber filled either with water (liquid or ice) or gas (an atmosphere of N2, Ar, or CO2). Then, 4–5 nm sized nanodiamonds (NDs) agglomerated into sub-micrometer clusters are obtained. (**b**) Schematics of NDs synthesis upon shock wave propagation. The detonation (I) leads to chemical dissociation of the carbon precursor (II), which can be the explosive molecules or graphite. The dashed line (III) represents the region where the pressure required to form the diamond phase is reached. The detonation products expand (IV), and the carbon atoms condense and crystallize (V) to form nanoclusters (VI). Finally, NDs crystallize, starting from carbon nanoclusters, grow, and agglomerate (VII). Reproduced with permission from [1], [PERSON], 2012.  
A weak concentration of CH\({}_{4}\) produces microcrystalline diamonds, whereas a high value of the CH\({}_{4}\)/H\({}_{2}\) ratio decreases grain size to the order of tens of nanometers for 1-5% CH\({}_{4}\)/H\({}_{2}\). The standard diamond growth mechanism [35] is schematized in Figure 3b. Carbon atoms at the surface of diamond seeds are left with dangling bonds after hydrogen abstraction by H\({}^{\bullet}\) radicals. These bonds are then filled by CH\({}_{3}\)\({}^{\bullet}\) molecules. When this process takes place in two adjacent sites, the new carbons can bond together, and finally be locked into the diamond lattice.
# Towards Automatic Recognition of Wakes Generated by Dark Vessels in Sentinel-1 Images
###### Abstract
They are also characterized by high values of the deadrise angle (\"deep-V hull\") to improve seakeeping and maneuverability performances, even if observations in very calm and narrow water, such as the area between St. Lawrence in the Cornwall are of Ontario, between the United States and Canada, have found that the deep-V design is replaced by a flat-bottomed skiff version. Moreover, a boat with a broader beam and no chine, but with two engines to reach high speed, has been used by Somali pirates. The detection of GoFast ships in satellite images is a tremendous challenge. Due to their building materials, size and sea-wind conditions, their observation in radar images is almost unfeasible, and, operating typically during the nighttime, the optical sensors cannot be used. Nevertheless, thanks to their high velocity and narrow beam, the GoFast ships are likely to produce long visible wakes. Such wakes are very persistent, lasting for distances of several tens of kilometers [28; 29; 30], and can be observed by SAR imagery [31; 32]. Since during the unreported transits, unauthorized commodity transshipment, smuggling, illegal fishing, and other unlawful activities, the GoFast ships operate with their automatic identification system (AIS) and other identification systems switched off-against IMO regulations, they can be also called dark vessels. Preliminary results on the GoFast ships detection by wake pattern recognition are shown in [33], and no additional literature results are available, since in both [24; 25], no wakes generated by GoFast vessels have been detected. The algorithm in [33] has been applied over an X-band image gathered by TerraSAR-X over the Gulf of Naples, and the results confirm its potentialities, in terms of wake pattern recognition in SAR images and rejection of false detections. The present paper moves from those results and aims to:  
* Extend the technique to a different radar band (C-band) and lower resolution images (Sentinel-1);
* Get further insight into the capabilities and the limitations of the approach for wake pattern recognition in difference scenarios. The latter, and more important, aim paves the way towards an operative application of the technique to achieve an effective maritime surveillance. In fact, thanks to the availability of ground truth data for the analyzed dataset, the manuscript provides an understanding of wake detection performance in case of marine image characterized by (i) homogeneous sea clutter, (ii) the presence of natural surface film, and (iii) strong transition from low-speed areas to more windy zones.
## 3 ESTCube-Luna E-Sail Payload and Experiment Operations
### E-Sail Environment Characterisation  
The order of magnitude estimation of the current collected by the tether can be based on the orbital-motion-limited approach (see, e.g., [2; 50]). For the intended tether (assuming simplified, as compared with Figure 4, a 4-fold Hoytether made of 50 um diameter wires), the current per unit length is  
\[\frac{dI}{dz}=4.3d_{w}en_{0}\sqrt{\frac{2 eV_{0}}{m_{e}}} \tag{2}\]  
where \(e\) is the elementary charge, \(n_{0}\) is the solar wind density, \(V_{0}\) is the tether voltage, \(m_{e}\) is the electron mass and \(d_{w}\) is the wire diameter. The estimated constant of 4.3 comes from the 4-fold Hoytether, corrected for the fact that the length of the two X-shaped crossing middle wires (see Figure 4) is somewhat longer than the length of the parallel wires. For a wire of diameter 50 um, a typical solar wind density of about 7 cm\({}^{-3}\) and 10 kV tether voltage (conservative estimate, as compared with the planned 20 kV), the current is about 15 mA km\({}^{-1}\).  
The current collected by the tether should be balanced by the electron emitter, so that the latter is capable of providing currents in the range of tens of mA.  
The interaction of the spacecraft with the solar wind plasma can be rather complex for the E-sail system, given the presence of the tether, which is biased to high positive voltage, and an electron emitter. The reflection of solar wind ions by the charged tether sheath creates a region of counter-streaming ions, which may develop plasma instabilities. The sheath around the tether and the spacecraft depends on the interplay of all these parameters and affects the performance of the E-sail.  
A **Langmuir probe** instrument can be used to characterise the spacecraft-plasma interaction. A pair of probes can be implemented, with cylindrical current-collecting electrodes at the tips of rigid cylindrical booms extending from the edges of the solar panels, to bring the probes away from the spacecraft body. Each probe can be biased independently. Analysis of the current-voltage characteristics of the probes provides the parameters of the plasma environment and the spacecraft potential with respect to it. As the typical Debye length is larger than the boom dimensions, the Langmuir probes will in most cases be inside the E-sail's sheath, which can be characterised for different modes of the E-sail operations.
## 3 Results and Discussion
### CFD Simulations
#### 3.2.4 Statistical Validation  
As for the experimental results in the fresh state, the impact of the RCWTB content on the parameters of the Bingham model, yield stress, and plastic viscosity was validated through a one-way ANOVA at a 5% significance level and by calculating [PERSON]'s and Spearman's correlations. The results obtained are detailed in Table 7. These results show that the RCWTB content did not affect the yield stress of SCC, which resulted in very low correlations. However, the plastic viscosity was significantly affected by the content of this residue in SCC, which led to high correlation values, especially in linear terms.