esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Active Coating and its Control Algorithm
### Placement of Microphones
#### 6.5.2 Total Number of Microphones  
One can see, that in the areas of \(S_{p}\) with smaller curvature radius \(R_{p}\) the density of microphones is larger, than in the areas of \(S_{p}\) with larger curvature radius \(S_{p}\). It is the most obviously in the case of surface \(S_{p}\) (and \(S_{P}\)) of stretched shape (Figure 15) with length \(L_{P}\) and curvature radius \(R_{P}<\!<\!L_{P}\) at its ends. The microphones are absent on the side areas of surface \(S_{P}\) due to our assumption about plane incident waves. In other words, our active system protects _directions_ in space, unlike protection of _areas_ of space in the traditional systems. When our active system targets \(N_{w}\approx 4\pi/\theta_{w}^{2}>>\!1\) incident waves, using our approach, the active system needs \(N_{P}\approx 16\pi/\theta_{w}^{2}\) microphones. This amount is smaller by \(N_{P}\) / \(N_{PQ}\approx\!\kappa_{\min}^{2}/\theta_{w}^{2}L_{P}R_{P}\) times than when using the approach of [PERSON].  
Figure 15: An example of prominent hexagonal polyhedron surface \(S_{P}\) embracing the protected surface \(S_{P}\) of sufficiently stretched shape. Microphones are concentrated only near apexes of polyhedron \(S_{P}\) (number of bearing groups is equal to the number of apexes of \(S_{P}\)) in the end areas of \(S_{P}\). Microphones are absent in the lateral area of \(S_{P}\), where its curvature (along the longitudinal coordinate) is zero.
# An Adaptive Privacy Protection Method for Smart Home Environments Using Supervised Learning
## 2 Related Work
### Methods to Resist the FATS Attack  
In the ConstRate (sending messages in constant rate) model, all of the sensor nodes in the network maintain the same frequency when sending messages, whether or not actual events happen. When a real event occurs, it has to wait until the next transmission. Therefore, the model can effectively resist the static analysis by the attacker with a global listening ability. This model also has a congenital deficiency, i.e., the average delay is half of the transmission interval and it is difficult to determine the transmission interval. If the transmission rate is low, the delay will be very high; whereas, if the rate is high, the delay will decline, but the number of fake messages will increase significantly, resulting in an increased energy consumption.  
[PERSON] et al. proposed a probability-based model called the FitProbRate model, that aims at reducing the latency of a fixed frequency transmission [7]. The main idea is that every sensor in the network sends messages with an interval that follows the exponential distribution. When a node  
Figure 1: Four tiers in the FATS (Fingerprint And Timing-based Snooping) attack model.  
detects a real event, the algorithm needs to identify a minimum interval to obey the exponential distribution, and then waits to send the real event. Following this, all adjacent intervals will follow the same distribution, making the attacker unable to determine the real messages from the transmission sequence. This model can reduce the delay of transmission in some situations, e.g., when the time interval is relatively average and the sending interval is slightly longer. If the time interval is not uniform and is frequently triggered with small intervals, the delay will become high.  
[PERSON] et al. proposed a behavioral semantics model to generate a small amount of fake data, to protect the activity that will happen in the near future [7]. Firstly, the model predicts the activity from a long-term history and then presents the sensor nodes with the forecast of the activity sequence. If an attacker listens to a sequence in order to monitor the sensors, the attacker can only predict misbehavior. This model adds fake messages to disturb the FATS attack in Tier-3 and privacy protection depends on the accuracy of the prediction of future behaviors. Therefore, if the prediction is not accurate, the added fake messages will not make much difference. Generally, the reliability of this scheme is lower than the ConstRate and FitProbRate models.
# Recent Advances in Optical Detection of Aminoglycosodies
## 5 Amplification Strategies
### Discussion  
The detection sensitivity can be improved significantly by coupling the detection with amplification strategies. The ultrasensitive detection of kanamycin has been realized by using HRP as the label and Au NPs as the signal amplification carriers. Amounts of kanamycin as low as 1.7 aM could be sensitively detected. The proposed method also showed good selectivity and recovery in real sample detection. However, dual-functionalized Au NPs were employed. It is very difficult to ensure that thetwo kinds of DNA strands are immobilized on the surface of Au NPs uniformly. The inhomogeneous label may induce poor repeatability in the proposed method. We have noticed that enzymes are involved in various amplification strategies such as RCA and Exo III-assisted target recycling. The use of enzymes may increase the experimental cost. In addition, the activity of enzymes is sensitive to the environment and thus may also affect the repeatability of the proposed method. Although HCR shows a moderate signal amplification effect among these amplification strategies, the enzyme is not involved in the HCR technique. The presence of the target can trigger the hybridization of two hairpin DNAs automatically, which leads to amplified signals. By comparing these amplification technologies, we believe that HCR is a simple and convenient signal amplification technique. The careful design of hairpin DNA sequences is important to improve the assay performance, including sensitivity, selectivity, and reproducibility.
# Automated Detection of Multi-Rotor UAVs Using a Machine-Learning Approach
## 2 Existing Methods for Detection and Identification of Objects in an Image
### Support Vector Machines (SVM)  
As mentioned in [10], support vector machines (SVMs) are suitable for assigning objects to N groups and their functionality is based on projecting data into multidimensional space. SVM search  
Figure 1: Contour searching applied to the edited image with a drone.  
Figure 2: Selective searching in the image with drones.  
for and determine the plane through which it splits data into groups. For example, if there is a vector of features with dimension 2500, SVM would represent this vector as a point in a space with 2500 dimensions [15]. For the sake of simplicity, let's imagine the SVM functionality for the vector of features with 2 dimensions. The visualization of SVM decomposition, in this case, can be seen in Figure 3.  
As can be seen in Figure 3, the line represents a classifier (for example a solid shape can represent the drone and shape with fill represents the background) and it divides the data successfully into two classes. Sometimes, when classifying with similar methods, the problem is that the superposition splitting data is too close to one of the classes. Therefore, SVM seeks a separator to reach the maximum distance between the data classes. If the classes are non-separable, the so-called non-linear SVM is used. In this case, the data are projected into a multidimensional space where they are separable [10].
# Cm-Level Photonic-Crystal-Like Subwavelength Waveguide Platform with High Integration Density
## 3 T-Shaped Splitter Based on the Subwavelength Waveguides
The fabrication process of the PhC-like waveguides can be conducted in the following five steps: (1) The EBL system is employed to pattern the ZEP520A spin-coated on a SOI wafer, to define the air holes and metal channels; (2) Reactive-ion etching (RIE) is used to transfer the defined pattern onto the top silicon layer of SOI with C4F8 and SF6 gases. RIE overetching is performed to etch away part of the buried oxide layer just beneath the waveguide core. Then, the remaining ZEP520A is etched away by oxygen plasma; (3) The fabricated wafer is spin coated with a thick layer (at least \(\sim\)1 \(\upmu\)m thicker than that of the metal sidewalls) of PMMA, which is defined by a second step of EBL with good alignment, and thus acts as the mask for protecting the waveguide core with air holes; (4) Silver is then evaporated into the metal channels; (5) Finally, the lift-off process is performed to remove the metal above the PMMA mask with acetone.
## 2 Materials and Test Methods
### Laboratory Testing  
#### 2.3.1 Rheological Property Tests  
The dynamic rheological properties of the SBSMA at different cycles of aging and rejuvenation were measured using the DSR test device (K Kinexus Pro+, Malvern, Wolverhampton, UK).  
Temperature sweep tests were carried out under the strain-controlled loading mode at a constant frequency of 10 rad\(\cdot\)s\({}^{-1}\) in accordance with the ASTM D7405 [31]. The temperature range was 58-82 \({}^{\circ}\)C for the rejuvenated asphalt-binders, and 22-34 \({}^{\circ}\)C for the aged asphalt-binders. The applied strain levels for the temperature sweep tests were controlled in the linear viscoelastic range. The rejuvenated asphalt-binder samples were molded to a diameter of 25 mm by 1 mm thick whilst the aged asphalt-binder samples to a diameter of 8 mm by 2 mm thick.  
Multiple Stress Creep Recover (MSCR) tests were conducted at 76 \({}^{\circ}\)C in line with the ASTM D7405 [31] test specification at standard stress levels of 0.1 kPa and 3.2 kPa, respectively. After subjecting the asphalt-binder samples to 10 loading cycles, each level with a 1 s loading period followed by a 9 s recovery period, two major rheological parameters, namely the percentage recovery (R) and non-recoverable creep compliance (\(I_{\text{nr}}\)), were calculated automatically at both stress levels of the DSR test.  
Linear Amplitude Sweep (LAS) tests were performed using the DSR test device with 8 mm parallel plates and 2 mm gap to determine the asphalt-binders' resistance to fatigue damage in accordance with the AASHTO T391-20 [32] test specification. The LAS test procedure consisted of two phases. Firstly, the frequency sweep test was performed at 0.1% strain level with a frequency range of 0.1-30 Hz to obtain the undamaged property of the samples (\(\alpha\)). Then, in the amplitude sweep, the load was applied from 0 to 30% strain at 10 Hz for 300 s under strain-controlled loading mode. The LAS test data were processed using a Visco-Elastic Continuum Damage (VECD) model approach to determine the fatigue parameters A and B. In this study, all the LAS tests were performed at a room temperature of 25 \({}^{\circ}\)C.
## 2 Experimental Setup
### Table-Top Water-Window Microscope  
The sketch of the microscope is shown in Figure 2, and Figure 3 illustrates a real photo of the microscopic system. The emitted SXR radiation is filtered through a titanium filter (Lebow, Goleta, CA, USA; 200 nm) to achieve monochromatic radiation with wavelength \(\lambda\) = 2.88 nm. The filtered radiation is focused by an ellipsoidal, grazing incidence, nickel-coated condenser mirror (Rigaku Inc., Auburn Hills, MI, USA; focal length 200 mm, mirror length 100 mm) into the sample plane. The focal spot has a circular shape with a diameter of 200 \(\upmu\)m at FWHM, observed by YAG scintillator, and recorded onto a CCD camera (Chameleon, Point Grey Research Inc., Richmond, BC, Canada). A Fresnel zone plate (ZonePlates Ltd., London, UK; width of the outer-most zone 30 nm, diameter 180 \(\upmu\)m, focal length 1.87 mm) was employed to create an image of a specimen transmission on an SXR-sensitive back-illuminated CCD camera (Greateyes GmbH, Berlin, Germany; 2048 \(\times\) 2048 pixels, \(13.5\times 13.5\)\(\upmu\)m pixel size, quantum efficiency at 2.88 nm 83%). The images were acquired with a magnification of up to 400\(\times\), and the CCD camera was cooled down to minus 30 \({}^{\circ}\)C to minimize its thermal noise.  
Figure 1: Schematic of capillary plasma driver and charging circuit.
## 4 Flow Field Characteristics behind the WGB
### Turbulence Characteristics  
Figure 16 shows the effect of various WGB cases on the turbulence intensity around the bridge deck wind field. It can be observed from Figure 16 that the WGB causes higher turbulence intensity in the wind field compared to the vertical wind barrier. The larger zones of turbulence occur near the bridge deck, the shear layer near the middle jet, and the flow around the top of the barrier after the installation of WGB. The height of the shear layer is 4.5 m, and the zone below it is considered the effective windproof zone.  
Figure 15: Effect of various WGB cases on vertical wind velocity for the bridge deck: (**a**) Case 2; (**b**) Case 3; (**c**) Case 4; (**d**) Case 5; (**e**) Case 6; (**f**) Case 7.  
Regarding the effect of \(\theta_{1}\), the test results of Cases 3 and 4 indicate that changing \(\theta_{1}\) does not significantly affect the turbulence intensity of the bridge wind field, but it will change the position of the stronger turbulence zone. Specifically, increasing \(\theta_{1}\) will shift the position of the stronger turbulence zone upward in the middle jet of the WGB. Regarding the effect of \(h_{1}\), the test results of Cases 3 and 5 show that the max turbulence intensity occurs near the surface layer of the bridge and the bottom of the shear layer around the top of the barrier when \(h_{1}\) is changed. When \(h_{1}=500\) mm, the turbulence intensity near the downstream side of the bridge surface reaches 32.5%, and the wind velocity ratio is also higher than when \(h_{1}=350\) mm, which is not beneficial to windproofing. Regarding the effect of \(B_{1}\), the test results of Cases 4, 6, and 7 show that for the upstream track, the turbulence intensity near the bridge surface increases with the increase in \(B_{1}\), and the turbulence intensity at the top of the WGB reaches a max at 300 mm. For the downstream track, within the shear layer range, the turbulence intensity gradually decreases as \(B_{1}\) increases, and when the size increases from 300 mm to 350 mm, the height of the turbulence boundary layer decreases to 5 m.
## 5 Discussion: Limitations and Future Works
### Future Work  
The methodology applied and successfully validated in the current study, through engagement with various companies, promptly identified emerging needs that these companies requested us to integrate. Among the most noteworthy requests are those related to expanding the existing tool by incorporating elements aligned with the principles of the new Industry 5.0 paradigm. Despite the seemingly natural and linear nature of such integration, the twin transition introduces a new axis that is nearly orthogonal to the previous ones, thereby complicating both the mapping and explanation processes. While acknowledging the importance of considering this perspective, the authors are not inclined towards a three-dimensional roadmap. Instead, they are contemplating presenting distinct two-dimensional projections derived from a comprehensive 3D roadmap. This approach aims to focus independently on separate pairs within the three aspects of technology, digitalization, and sustainability.
## Appendix A Proof of Theorem 1  
Let \(D_{threshold}\) denote the threshold value of the URLLC load \(D\), at which equality holds in (9). Meanwhile, \(D_{threshold}\) always exists when \(\delta_{\text{CBG}}\) has a value between \(l-\varepsilon\) and \(l+\varepsilon\). When \(\delta_{\text{CBG}}\) is smaller than \(l-\varepsilon\), the throughput of an eMBB CBG is the same as that without puncturing. In contrast, when \(\delta_{\text{CBG}}\) exceeds \(l+\varepsilon\), the throughput of an eMBB CBG is zero. Therefore, the inequality in (9) can be re-written as  
\[\frac{1}{2}+\frac{1}{2\varepsilon}(l-\delta_{\text{CBG}})\leq 1-\frac{D}{b(l _{\text{MCS}}^{\text{URLLC}})}\cdot\frac{1}{\Phi} \tag{10}\]  
By substituting (3) into (10) and rearranging it for \(D\),  
\[D\leq(\varepsilon-l)\left(\frac{N_{\text{CBG}}\cdot\Phi\cdot b(l_{\text{MCS} }^{\text{URLLC}})}{2\varepsilon N_{\text{CBG}}-\phi_{u}\cdot b(l_{\text{ MCS}}^{(u)})}\right) \tag{11}\]  
As \(\varepsilon\) approaches zero, the right-side term of (11) is given as  
\[\lim_{\varepsilon\to 0}\left(\varepsilon-l\right)\left(\frac{N_{\text{CBG}} \cdot\Phi\cdot b(l_{\text{MCS}}^{\text{URLLC}})}{2\varepsilon N_{\text{CBG }}-\phi_{u}\cdot b(l_{\text{MCS}}^{(u)})}\right)=\frac{l\Phi b(l_{\text{MCS} }^{\text{URLLC}})N_{\text{CBG}}}{\phi_{u}b(l_{\text{MCS}}^{(u)})}=N_{\text{ CBG}}\cdot l\cdot\left(\frac{\Phi}{\phi_{u}}\right)\cdot\left(\frac{b(l_{\text{MCS} }^{\text{URLLC}})}{b(l_{\text{MCS}}^{(u)})}\right) \tag{12}\]  
Consequently, the switching threshold, \(D_{threshold}\), for URLLC traffic load is given by  
\[D_{threshold}=N_{\text{CBG}}\cdot l\cdot\left(\frac{\Phi}{\phi_{u}}\right) \cdot\left(\frac{b(l_{\text{MCS}}^{\text{URLLC}})}{b(l_{\text{MCS}}^{(u)})}\right) \tag{13}\]
## 2 Materials and Methods
### Abiotic Stress Treatments and AtTPR10 Expression Analysis  
Two-week-old seedlings of _Arabidopsis thaliana_ ecotype Columbia (Col-0; wild-type [WT]) were grown in Murashige and Skoog (MS) medium containing 3% sucrose. To perform the heat shock treatment, temperature was increased from 23 \({}^{\circ}\)C to 37 \({}^{\circ}\)C or 45 \({}^{\circ}\)C and then held constant for 3 h [24]. To conduct the cold treatment, the temperature of soil-grown plants was decreased from 23 \({}^{\circ}\)C to 4 \({}^{\circ}\)C and then held constant for 48 h [25]. To induce salt stress, soil-grown plants were incubated in a solution containing 250 mM NaCl for 48 h [26]. To induce drought stress, plants grown in MS medium for seven days were transferred to soil and grown for one week with sufficient watering; then, watering was withheld for two days [27,28].  
To determine _AtTPR10_ expression under various abiotic stresses, samples were collected at the indicated time points, immediately frozen in liquid nitrogen, and used for RNA extraction and subsequent analysis. Real-time polymerase chain reaction (RT-PCR) was performed to amplify _AtTPR10_ using gene-specific primers (AtTPR10 RT_F, 5\({}^{\prime}\)-CTGTGAAGTCTTGTTAGAC-ACAATCC-3\({}^{\prime}\); AtTPR10 RT_R, 5\({}^{\prime}\)-GGTATGACCGTTGGCTAAAATCAATT-3\({}^{\prime}\)). Tubulin, which was used as the loading control, was amplified using the following primers: Tubulin F (5\({}^{\prime}\)-CTCAAGAGGTTCCAGTA-3\({}^{\prime}\)) and Tubulin R (5\({}^{\prime}\)-TCACCTTCTTCATCCGCAGTT-3\({}^{\prime}\)).
# A Mapping Model of Propeller RANS and LES Flow Fields Based on Deep Learning Methods
## 4 Experiments  
### Dataset and Model Training  
The dataset used in the experiments of this work was obtained using a simulation of RANS and LES flow fields. The computational mesh for the simulation employed a non-structural cut-body configuration and the simulation data were generated using Hexpress software (Version 6.2). The number of mesh elements was approximately 12 million. In the simulation, the flow was characterized via a Reynolds number of 1.4 \(\times\) 10\({}^{7}\), a CFL (Courant-Friedrichs-Lewy) number of 0.3, and a \(y+\) value of 24, and the propeller's advance speed was described using a coefficient of 0.925. In a comparison with experimental data using the model, the error for hull resistance calculated was 0.6%, the error for propeller thrust was \(-\)4.5%, and the error for torque was 1.6%. It comprises 1000 samples, with each sample containing simulation data of RANS and LES in the central region of the propeller. The flow field information in both cases is in CSV format and includes spatial coordinates (\(x,y,z\)), with \(x\) always being 0 for two-dimensional data and velocity (\(u,v,w\)). Each sample consists of approximately 30,000 points with three velocity components (\(V\_axial,V\_tangential,V\_radial\)). The points are distributed within the range of \((x=0,-1\leq y\leq 0,-0.7\leq z\leq 1)\). The data sampling was performed at intervals of 1\({}^{\circ}\) during the rotation of the propeller, with the speed of the propeller rotation fixed. As a result, there is an inherent regularity in the continuous samples regarding time intervals and angle variations of images.  
We split 70% of the dataset for training, 10% for validation, and 20% for testing. As we also conducted training for the two-stream network, feature extraction for optical flow was  
Figure 11: The structure of mapping module.  
Figure 10: The process of inference and mapping.  
performed by computing the flow between two consecutive images in time. The computed optical flow and the subsequent image were used as inputs for the two-stream network.  
The training process for various methods was conducted on two GPUs (3080 Ti). Each method was trained for 20 epochs by using the mean squared error (MSE) loss function, as defined by  
\[MSE=(x-y)^{2} \tag{2}\]  
where \(x\) represents the predicted value and \(y\) is the ground truth.
## 2 Results  
### Preliminary Analysis of SO Volatiles  
A preliminary analysis was performed with the aim of identifying the main volatile compounds and particularly the isothianates in our samples. Volatile profiles of SO plants were obtained by solid-phase micro extraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GCMS).  
Hydrolysis products of glucosinolates were extracted and analyzed following literature methods [26; 27].  
For SPME sampling, fresh leaves were put in a vial and rapidly mashed and crushed, had water added, stoppered with a screw cap equipped with septa, and then an SPME fiber was inserted into the sample headspace to be analyzed by GCMS. These conditions were adopted to simulate the mastication process of fresh leaves, releasing myrosinase from the tissues in the oral cavity and hydrolyzing the glucosinolates to the corresponding volatile isothianates. ITCs are then perceived in the oral cavity due to their pungency, mediated by the TRPA1 receptor. In our experimental conditions, the volatile ITCs were analyzed by GCMS and identified basing on their retention time, mass spectrum and by comparison with the commercially available standards.  
Results show that ITCs account for the major proportion in volatiles. Hydrolysis mainly leads to IPITC (compound **2**), followed by 2-BITC (compound **4**). The percentage of the two compounds varies in different samples, with compound **2** always being the main component in leaves, seeds and flowers; compound **4** is present in variable amounts, reaching 24% of total volatiles in a sample of fresh flowers [28]. The compound 2-BITC is the hydrolysis product of glucochoclearin **3**; it is abundant in plants of the genus Cochlearia and has been previously found also in SO [5]. The sum of allylisothiocyanate, methylisothiocyanate and other isothi cyanates accounts for only about 1% of total volatiles. This result confirms that sinigrin can be present in SO as a minor component.