esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Algorithm
### Hand Segmentation  
Many methods based on skin color have been proposed to detect hand, where the original RGB image is often converted to another color space such as \(\mathrm{Y}\mathrm{C}_{\mathrm{b}}\mathrm{C}_{\mathrm{r}}\), HSV, and UCS for further processing [32]. Since this is not our focus, we chose the experimental environment with a background color different from the skin, and adopted a simple method to segment hand region by thresholding on the \(\mathrm{Y}\mathrm{C}_{\mathrm{b}}\mathrm{C}_{\mathrm{r}}\) color space. First, the image is converted using Equation (1) [33]. Then, for each pixel, if all three of its color channels are between the corresponding lower and upper thresholds, this pixel belongs to the hand and is marked as white. Otherwise, it is marked as black.  
\[\left[\begin{array}{c}Y\\ C_{b}\\ C_{r}\end{array}\right]=\left[\begin{array}{c}16\\ 128\\ 128\end{array}\right]+\frac{1}{256}\left[\begin{array}{ccc}65.738&129.057&25.06\\ -37.945&-74.494&112.43\\ 112.439&-94.154&-18.28\end{array}\right]\left[\begin{array}{c}R\\ G\\ B\end{array}\right] \tag{1}\]  
After setting thresholds to obtain a binary image, we apply it with an erosion, dilation, and filling holes operations to eliminate noises. Among the remaining connected components, the one that contains the bottom middle point of the image is considered to be the hand. All subsequent operations are done within this region of interest. The procedures of segmentation, thresholds, and outcome are shown in Figure 3. Notice that the image we used has a small size of 120 \(\times\) 160 pixels, so figures of the hand shown in this paper might not look so smooth.
# Characterization of Tropical Cyclone Intensity Using the HY-2B Scatterometer Wind Data
###### Abstract  
The estimation of tropical cyclone (TC) intensity using Ku-band scatterometer data is challenging due to rain perturbation and signal saturation in the radar backscatter measurements. In this paper, an alternative approach to directly taking the maximum scatterometer-derived wind speed is proposed to assess the TC intensity. First, the TC center location is identified based on the unique characteristics of wind stress divergence/curl near the TC core. Then the radial extent of 17-m/s winds (i.e., R17) is calculated using the wind field data from the Haiyang-2B (HY-2B) scatterometer (HSCAT). The feasibility of HSCAT wind radii in determining TC intensity is evaluated using the maximum sustained wind speed (MSW) in the China Meteorological Administration best-track database. It shows that the HSCAT R17 value generally better correlates with the best-track MSW than the HSCAT maximum wind speed, therefore indicating the potential of using the HSCAT data to improve the TC nowcasting capabilities.  
s 2022141035 1 1
## 2 Knowledge Graph-Based Spacecraft Anomaly Detection Algorithm Architecture and Framework
### Knowledge Graph-Driven Spacecraft Anomaly Detection and Processing System Framework  
Following the establishment of the anomaly detection algorithm architecture based on a knowledge graph, as outlined in Section 2.1, we can now propose a more comprehensive system framework for knowledge graph-driven spacecraft anomaly detection and processing. It is crucial to note that our current research is still in its relatively early stages, and we actively encourage readers to closely follow the promising developments in this groundbreaking field.  
The proposed system framework, illustrated in Figure 2, comprises four primary layers: the basic data layer, the graph construction layer, the algorithm integration layer, and the graph application layer. It is designed to harness the potential of knowledge graphs for diagnosing and resolving spacecraft anomalies.  
The basic data layer serves as the foundation, containing data sources for various spacecraft system anomalies. It includes both structured and unstructured anomaly knowledge, which form the basic corpus for entity and relationship extraction in the spacecraft anomaly detection knowledge graph.  
The graph construction layer employs deep learning techniques and manual verification to extract knowledge from spacecraft anomaly text materials. Knowledge from multiple sources and various forms is synthesized and stored in a structured format of triples in the knowledge graph database.  
Figure 2: Knowledge graph-driven spacecraft anomaly detection and processing system framework.  
The algorithm integration layer builds on the constructed knowledge graph. It combines spacecraft anomaly detection task scenarios, processing experiences, and operation rules for knowledge logic association mining. The layer carries out analysis and computations through telemetry data feature extraction, similarity matching, and data statistical mining methods.  
Finally, the graph application layer translates these functional modules into intelligent applications, which are tailored according to spacecraft anomaly detection application requirements and scenarios. This layer represents the practical application of spacecraft anomaly knowledge in specific situations.  
By building on the architecture of the anomaly detection algorithm based on a knowledge graph, this system framework presents an innovative approach to spacecraft anomaly detection and resolution. As our research progresses, we anticipate that this framework will play a pivotal role in enhancing the reliability and efficiency of future space missions.
## 2 Constellation Dynamics (Walker)
[7] elaborated the target to satellite angular relations and emphasized maintaining thorough pattern designs using the same. The references suggest that the instance angle will be an addition of the elevation angle and 90 degrees exactly and can be expressed as below:  
\[\vartheta=90^{o}+\phi \tag{8}\]  
where \(\vartheta\) is the instance angle and \(\phi\) is the elevation angle. The inclination is fixed; hence, even for walker constellation, the angles and their orientation play a vital role in aligning the satellites specific to certain boundary conditions. A special Napier's rule was used for right spherical triangles as below:  
Latitude:  
\[\sin(\Psi)=\sin(\theta)\sin(i) \tag{9}\]  
Similarly, a satellite's longitude is determined as:  
\[\tan(\varphi)=\tan(\theta)\cos(i) \tag{10}\]Similarly, a similar equation in the celestial sphere is given as:  
\[ \begin{array}{l}\Psi_{E}=\Psi_{0}\\ \varphi_{E}=\varphi_{0}+\frac{\theta}{Q}\end{array}  \tag{11}\]  
The angles operating between the satellites in a walker pattern constellation also constitute a dynamic approach to build it in such a way that a robust formation in each plane can be observed, as well sighted in Ref. [7], and is thoroughly followed for the simulations. The angles among the planes and satellites are defined as follows:  
\(\kappa\), \(\zeta\) and \(\zeta\) are the radial spacing plane to plane in celestial sphere, angle between satellites in same plane, and angular offset between satellites from one plane to another. The respective formulation then framed is given as follows:  
\[\kappa=\frac{360}{P},\zeta=\frac{360}{\frac{T}{P}},\zeta=360*\frac{F}{T} \tag{12}\]  
where \(P\), \(F\) and \(T\) represent the same as mentioned before (no. of planes, relative spacing, no. of satellites). Finally, the RAAN spacing for each plane was also set as below:  
\[\varphi_{E}=\varphi_{0}+\kappa+\frac{\theta}{Q} \tag{13}\]  
where \(\theta\) is the true anomaly and \(Q\) is the number of orbits over a single rotation of Earth.
## 4 Proposed System Model
### Appliances' Categorization and Their Energy Models
#### 4.2.1 The Fixed Load  
These are those regular appliances whose starting time remains fixed. That is, a consumer can start and stop these appliances any time. Refrigerator and interior lights are examples of Fixed Load (FL). The energy consumed by all fixed appliances in the total time interval of 24 h can be found as in Equation (1) [24].  
\[E_{f}=\sum_{ap_{f}\in AP_{f}}\left(\sum_{n=1}^{48}\left(\rho_{f} \times X_{f,n}\times\tau_{f}\right)\right) \tag{1}\] \[\text{subject to:}\qquad\alpha_{f}\leq\tau_{f}\leq\beta_{f}\]  
where \"\(ap_{f}\)\" denotes each fixed appliance, \(AP_{f}\) is the set of fixed appliances, \(\rho_{f}\) is the power rating, \(X_{f,n}\) is the ON (1) and OFF (0) states of the nth fixed appliance, \(\tau_{f}\) is the LOT, \(\alpha_{f}\) is the earliest starting time and \(\beta_{f}\) is the latest ending time of fixed appliances, respectively.  
Most of the research works have considered 24 intervals for their energy calculations, which are usually not applicable to all appliances. Usually, an appliance, for example a microwave oven, a clothes dryer, etc., operates for less than an hour. Hence, further dividing an hour into two sub-intervals of thirty minutes each generates a total of forty-eight (48) time slots, which results in accurate manipulation. However, this slightly increased the manipulation time, but that can be ignored.
## 4 Materials and Methods
### Heterologous Protein Detection  
Protein detection in the positively transformed strains of _P. tricornutum_ and the negative control (wild type, transformed with pPtGE30) was performed on 50 mL (OD\({}_{680}\) nm 0.1 a.u.) cultures that were 6 days old with OD\({}_{680}\) nm values between 0.9 and 1.7 a.u. The cells were centrifuged at 4000 rpm for 20 min at 4 \({}^{\circ}\)C. The resulting pellets were weighed and resuspended in loading buffer 1\(\times\) (0.8% sodium dodecyl sulfate, 0.05 M Tris-HCl pH 6.8, 6% glycerol, bromophenol blue, and 3.2% \(\beta\)-mercaptoethanolhanol) to a final concentration of 500 mg FW\(\cdot\)mL\({}^{-1}\). The cell lysates were boiled at 95 \({}^{\circ}\)C for 5 min and were then centrifuged at maximum speed for 5 min at room temperature. A volume of 35 \(\upmu\)L of the supernatant was loaded into 12% SDS-PAGE gel and migrated at 80 volts until the proteins passed through to the stacking gel, then the voltage was raised to 120 volts. The gel was then transferred using the BioRad Trans-Blot Turbo Transfer system at 2.5 A for 15 min. The blot was equilibrated in 1\(\times\) TBS solution and blocked with 5% milk for 1 h at room temperature, before washing it three times with TBST solution and adding the primary antibody overnight at 4 \({}^{\circ}\)C. In this study, the various primary antibodies used for the T2A sequence were purchased from Sigma Aldrich (cat. #ABS31, Boston, MA, USA), the 6X-His Tag Monoclonal antibody from Thermofisher (cat. #MA1-21315, Waltham, MA, USA), and anti-GFP/CFP/YFP from Cedarlane (cat. #CLH106 AP, Burlington, ON, Canada), all at a 1:1000 dilution in 3% BSA. After three washes with TBST solution, the _Pt_OA1 sample blot was incubated for 1 h in a 1:20,000 dilution in 5% milk of the Immun-Star Goat Anti-Rabbit(GAR)-HRP conjugate from Bio-Rad (Mississauga, ON, Canada, cat. #1705046). Meanwhile, the _Pt_OA2 and _Pt_OA3 blots were incubated under the same conditions with Immun-Star Goat Anti-Mouse (GAM)-HRP conjugate from BioRad (cat. #1705047, Hercules, CA, USA). The multiple Tag protein (GenScript, cat. # M0101, Piscataway, NJ, USA) and the recombinant YFP purified protein (10 ng) from _E. coli_ were used as the positive controls. After three washes with TBST solution, protein detection was performed using the Clarity Max Western ECL Substrate-Luminol solution from Bio-Rad (cat #1705062S, CA, USA). The blots and Coomassie-stained gels were visualized using a ChemiDoc Imaging System with Image Lab Touch software 2.4 (Bio-Rad, cat #12003153, Hercules, CA, USA) and Image Lab(tm) 5.2 software (Bio-Rad, cat # 1709690, Hercules, CA, USA).
# A Multi-Input Fusion Model for Privacy and Semantic Preservation in Facial Image Datasets
## 4 Method
### Architecture and Working
#### 4.2.2 Facial Clustering in Dataset  
The principal goal of this stage is to divide \(V\) into 15 different categories such that the features in one group are very similar, while the difference among different groups is quite large. Firstly, we randomly selected 15 face features \(V_{\mu}=\{v_{\mu_{1}},v_{\mu_{2}},\ldots,v_{\mu_{n}}\}\) as centroids. Then, to assign other face features to the closest cluster, we calculated the square errors \(J\) between every element in \(V\) and every centroid. Facial features in the same cluster are more homogeneous when the value of \(J\) is lower. Equation (11) defines the objective function \(J\).  
\[J=\sum_{i=1}^{N}\sum_{k=1}^{15}\omega_{ik}\
orm{v_{i}-v_{\mu_{k}}}^{2} \tag{11}\]  
where \(\omega_{ik}\) is set to 0 when \(v_{i}\) is not in one cluster or 1 when \(v_{i}\) is in one cluster.  
Figure 1: The overall structure of the Facial Privacy and Semantic Preservation (FPSP) model.  
By controlling the value of \(\omega_{ik}\), we obtained the lowest square error based on the random centroids. Furthermore, the belonging of different clusters of face embedding is defined by \(\omega_{ik}\). The next step is to update the centroid in each cluster by computing the average value among all face embeddings in the cluster. To obtain the global lowest variation within clusters, we kept iterating the above steps until there was no change to the centroids. The whole faces were then split into 15 clusters.
## 3 Results
### Temperature Distribution within the Process Zone
#### 3.4.3 Temporal Temperature Sequence  
So far, the spatial and global influence of beam oscillation on the temperature distribution was analyzed. However, the particularity of beam oscillation is its dynamics. For this reason, temporally resolved temperature sequences were created (Figure 18). The origin of the values corresponds to the location of the highest temperatures marked in Figure 16.  
The static beam shaping reveals low temperature volatility in Figure 18a. The temperature ranges from 1610 to 2185 \({}^{\circ}\)C. So, the temperature difference is calculated with \(\Delta\)T = 575 \({}^{\circ}\)C and an average value of 1890 \({}^{\circ}\)C. The temperature volatility significantly increases for beam oscillation (Figure 18b-d). That means that the material position alternately heats up and cools down. The temperatures of set #2 (Figure 18b) decreases to melting temperature and increase up to 2640 \({}^{\circ}\)C. The measured temperature difference is \(\Delta\)T = 1230 \({}^{\circ}\)C. Set #3 (Figure 18c) has a slightly higher temperature difference \(\Delta\)T = 1420 \({}^{\circ}\)C but drops partially below the melting temperature. The highest temperature range is in set #4 (Figure 18d), which reaches from below melting temperature up to a 2840 \({}^{\circ}\)C peak value. The measured temperature difference is \(\Delta\)T = 1700 \({}^{\circ}\)C.
## 4 Results  
As mentioned earlier in this study, concerning tests, in [23], it is described how this technological solution was tested to check the functioning of the web application concerning employee registration, the full information path, which includes data acquisition from temperature/humidity and light sensors by ESP32, the sending of encrypted data over the Internet through the MQTT protocol, the processing of the data on the server, storage in InfluxDB, and display graphs in Grafana. The tests regarding the suitability of the hardware resources available on the server (memory, disk, and CPU) are described. In the present work, the tests are focused on verifying the functionalities of the web interface for employee registration, especially regarding the provision of health information, the updating of this information, and the generation of corresponding alerts. All of the tests consider fictitious employees and health information, aiming to ensure that the system works according to what was planned. Usability tests with workers and employers will be carried out in a future stage of development of this system. The results are shown below.  
Figures 5-8 show the web pages for employee registration. The first step of employee registration is shown in Figure 5. This stage occurs after the choice of employee ID and monitoring device ID.  
Figure 6 shows the questions related to other professional activities that may be performed by the worker. This information is optional. On the other hand, it is mandatory to record for how long the worker has held similar positions in other companies.  
The web page \"Exposure data and alerts\" is presented in Figure 9. A list of the previously registered workers is shown.  
Clicking on the ID 8465, it is possible to view the alert generated for the employee who was registered with hypertension and skin lesions. All of the agents related to the disease and symptoms are considered according to Table 2. The alert presented in Figure 10 also highlights the total time worked in similar positions including the current job.  
Figure 11 shows the web page for updating employee information. In this case, \"Update employee's health conditions\" was selected, and skin diseases and cough (as symptoms) were chosen as the conditions previously recorded.  
Figure 8: Health information—symptoms.  
Figure 10: Alert considering hypertension and skin lesions.  
Figure 9: Exposure data and alerts web page.  
As explained, the web interface for registering employees and generating alerts worked properly. The complete system will be evaluated in at least one company. For this purpose, it will be necessary to assemble a few devices. They will be used by a group of workers who will be asked to access the mobile application once a day. The web application will be used by a manager and/or OSH professional. It is planned for participants to answer questionnaires about the usability of the solution.
## 3 Results and Discussion
### Geometry of the Title Molecule  
The optimized molecular structure of (\(2E\))-HIPC with labeled atoms is shown in Figure 3. Selected optimized theoretical parameters in the gas phase and X-ray diffraction geometrical parameters of (\(2E\))-HIPC are listed in Table S2. The optimized bond lengths of the C-C bond in the phenyl ring of (\(2E\))-HIPC fall in the range of 1.390-1.409 A and 1.380-1.391 A in the B3 LYP and HF methods, respectively. The observed XRD data for these bond lengths in (\(2E\))-HIPC fall in the range from 1.368 to 1.393 A, which are in good agreement with the values obtained using the HF method. The mean aromatic ring C-C bond distance is 1.385 A by the HF method, which is consistent with the experimental value (1.382 A). The optimized C-C bond length in the imidazole ring of (\(2E\))-HIPC is 1.353 A using the HF method, which is slightly longer than the observed XRD value (1.350 A).  
The optimized bond length for the C=O bond in the title compound is 1.221 A by the HF method, which is in a good agreement with the experimental value (1.227 A). It is interesting that the calculated N15-N16 bond length in the side chain of the title molecule is almost the same as the observed XRD value (1.384 A). Based on the above comparisons, although there are some differences between our values and the literature data, the optimized geometric parameters can reproduce the literature values well and are the basis for subsequent discussion. The theoretical optimized C-H bond lengths show large deviation from the experimental values, which may originate from the low scattering factors of hydrogen atoms in the XRD. The C-H bond lengths determined using the HF method lead to geometric parameters that are much closer to the experimental data. The ring C-N bond distances are in the range of 1.353 to 1.412 A using both the HF and B3 LYP methods, while the observed XRD values are in the range of 1.347 to 1.418 A. The longest bond length was found in C20-N18 due to the close C=O group. The influence of different groups in the title compound on the bond length and dihedral angle parameters seems to be negligibly small. The mean angle deviation of the phenyl ring carbon atoms C-C-C was found to be greater by 0.2\({}^{\circ}\) in the HF method and by 0.1\({}^{\circ}\) in the B3 LYP  
Figure 3: Optimized molecular structure of (\(2E\))-HIPC.  
method than the corresponding XRD values. A large deviation was observed between theoretical and experimental XRD values in the bond angles of H38-N16-N15 and H35-C11-C12. The hydrogen bonding geometrical parameters [14] are presented in Table S3. The N-H\(\cdots\)N and C-H\(\cdots\)O bonds are identified as the NH and CO groups of (\(2E\))-HIPC. These bonds are overestimated compared with the XRD values, whereas the H\(\cdots\)N and H\(\cdots\)O bonds are best fit with the HF and B3 LYP values. Additionally, most of the intramolecular bond angles are in good agreement with the experimental values.
# Measuring and Modeling the Polarized Upwelling Radiance Distribution in Clear and Coastal Waters
## 1 Introduction  
The in-water and water-leaving radiance in the ocean is partially polarized and this has implications for both biological activity [1] and for viewing the ocean from a satellite [2]. The biological implications of the polarization are a current topic of research [3] but the implications for ocean color remote sensing are just being exploited [2, 4]. Various studies have indicated that measurement of the polarization of the radiance leaving the ocean can be used to determine particle characteristics [5, 6, 7]. In addition to using the polarization of the water leaving radiance for studies of the water properties, polarization may also be important for processing data from ocean color sensors that have unintended polarization sensitivities [8, 9].  
Measurements of the scalar (without regard to polarization) spectral upwelling radiance distribution have occurred more frequently since the development of the radiance distribution camera, RADS [10], and then the upwelling radiance distribution camera, NuRADS [11]. These instruments use electro-optic camera systems combined with filter changers and fisheye cameras to image the complete upwelling radiance distribution, for a specific wavelength, in one image. The CCD (charge coupled device) resolution and optics allow measurement of the radiance distribution with a \(1^{\circ}\) angular resolution. These systems have been used in studies of the in-water light field [12] and studies of the angular radiance distribution variations in ocean color algorithms [13; 14]. Recently, two new cameras, polarized radiance camera (PolRADS) [15] and downwelling polarized camera (DPOL) [16], have been used to make measurements of the polarized spectral upwelling radiance distribution, as described below.  
Measurements of the upwelling, in situ, polarized radiance have typically been done with variations of a Gershun-tube radiometer, obtaining the angular distribution by changing the viewing direction of the radiometer [17; 18]. These instruments have advantages such as the simplicity of calibration and the ability to do hyperspectral measurements. Hyperspectral measurements of the polarized light field have been suggested as a method to measure in situ solar stimulated fluorescence [19]. Unfortunately, to obtain the full radiance distribution requires many measurements during which the illumination conditions can change, and spatially varying angular features resulting from the sea surface may not be captured. By making measurements using fisheye lenses, while limited to multi-spectral measurements rather than hyperspectral measurements, higher angular spatial resolution can be obtained nearly simultaneously (typical exposure times are less than 1 s).  
Models for the unpolarized bi-directional reflectance distribution function (BRDF) [20; 21] have been validated in Case I [13] and Case II [14] waters, but variations in the polarization of upwelling radiance due to sun angle, viewing geometry, dissolved material, and suspended particles have not been systematically documented.