esa-sceva/satcom-chunk-collection huggingface dataset:
# Wideband Printed Wide-Slot Antenna with Fork-Shaped Stub  
[PERSON]  
1 State Key Laboratory of Space-Ground Integrated Information Technology, Beijing Institute of Satellite Information Engineering, Beijing 100095, China; like.3714@163.  
The method presented in this paper can be classified as the third kind mentioned above, but it is different from the previous literature, which generally made modifications on a rectangular or circular patch. In this paper, the multi-branch method is applied to a fork-shaped stub. In [20], a printed WS antenna with a fork-shaped tuning stub was proposed and showed good BW enhancement, with an impedance BW of 46.48% for \(\text{VSWR}<1.5\) (\(\text{S}_{11}<-14\) dB). By recalculating this with the parameters provided in [20], it was found that the impedance BW for \(\text{S}_{11}<-10\) dB was 85.4%. This approach has caused significant progress in BW enhancement and has attracted the attention of many researchers. In [21], a rectangular slot antenna with a fork-shaped stub excited by CPW obtained an impedance BW of 110% for \(\text{S}_{11}<-10\) dB. In [22], a printed elliptical slot antenna using a tapered microstrip feeding line with a fork-shaped tuning stub achieved an impedance BW of 117% for \(\text{S}_{11}<-10\) dB. In [23], a printed WS antenna with a centered rectangular parasitic patch, fed by a fork-like tuning stub with rectangular parasitic elements is proposed, with an impedance BW of 148.4% for \(\text{S}_{11}<-10\) dB. It has been demonstrated that designs using a fork-shaped stub are an effective way to obtain broadband impedance matching.  
In this paper, a multiple-resonance technique is proposed to achieve a wider BW for a printed WS antenna with a fork-shaped stub. By adding an extra fork-shaped stub onto the main fork-shaped stub and rounding the rectangular corner of the stub and the rectangular slot, good impedance matching over a wide operating frequency range was realized. To validate this technique, two different antennas with stubs added at different positions of the main stub were studied. Details of the proposed antenna design and experimental results are provided and discussed.  
Compared with previously reported works, the advantages of the proposed antenna can be summarized as follows:  
1. The proposed antenna introduces an extra resonant stub to excite additional resonant modes and provides additional tuning capabilities, resulting in a large impedance bandwidth. In contrast to the previous literature, in which the multiple-resonance technique of adding an extra resonant stub was usually applied to the rectangular or circular patch, the proposed antenna embeds an extra resonant stub into the fork-shaped stub.
2. The measured impedance BW of the proposed antennas for \(\text{S}_{11}<-10\) dB reached 148.6%, which is wider than most referenced designs. Furthermore, the proposed antenna had a stable radiation pattern in a wide BW.
3. The proposed antenna has a relatively simple geometry, which releases the computation load in the optimization process. Moreover, the added variables of the extra stub increase the tuning degrees of freedom for the impedance matching.
# Analysis of the Relationship between Scintillation Parameters, Multipath and ROTI
## 2 Materials and Methods
### Structural Similarity (SSIM) and Pearson Correlation Coefficient (CC)  
The structural similarity index (SSIM) is an index for quantifying the similarity between two images. Three aspects are combined: luminance, contrast and structure [22]. These three components can show different characteristics of images. The comparison of luminance (\(l(m,n)\)), contrast (\(c(m,n)\)) show the difference in the means and variances respectively whereas structure (\(s(m,n)\)) quantifies the correlation. The similarities between images are higher with larger SSIM values. Therefore, SSIM is applied here for comparing maps of scintillation indices with those of MP and ROTI in order to describe spatial similarities. SSIM is calculated according to Equations (18)-(20) [22]:  
\[\begin{array}{l}l(m,n)=\frac{2\mu_{m}\mu_{n}+C_{1}}{\mu_{n}^{2}+\mu_{n}^{2}+ C_{1}},\\ c(m,n)=\frac{2\sigma_{\varphi}\mu_{n}+C_{2}}{\sigma_{n}^{2}+\sigma_{n}^{2}+C_{ 2}},\\ s(m,n)=\frac{\sigma_{\varphi}\mu_{n}+C_{3}}{\sigma_{n}\sigma_{n}+C_{3}}\end{array} \tag{18}\]  
\[SSIM(m,n)=l(m,n)\times c(m,n)\times s(m,n) \tag{19}\]  
\[\begin{array}{l}C_{1}=(K_{1}L)^{2},\\ C_{2}=(K_{2}L)^{2},\\ C_{3}=C_{2}/2\end{array} \tag{20}\]  
where \(m\) and \(n\) are two non-negative image signals, \(\mu_{m}\) is the mean of image m, \(\mu_{n}\) is the mean of image \(n\), \(\sigma_{m}^{2}\) is the variance of image m, \(\sigma_{n}^{2}\) is the variance of image n, \(\sigma_{mn}\) is the covariance between image \(m\) and \(n\). The parameters \(C_{1}\) and \(C_{2}\) were introduced to prevent instability if the value of the denominator is small. Following convention \(K_{1}=0.01\), \(K_{2}=0.03\) and \(L\) is the dynamic range of the pixel values [22]. In this research, the default SSIM command in MATLAB version 2019a was used.  
The correlation between \(m\) and \(n\) is defined as \(\sigma_{mn}/\sigma_{m}\sigma_{n}\). This is estimated using the standard Pearson correlation coefficient (CC). Note from Equation (18) that this is the same as \(s(m,\ n)\) except for the inclusion of \(C_{3}\). CC and \(s(m,\ n)\) can differ substantially when CC is small. This can also lead to high values of SSIM.
## 3 Results and Discussions
### Vertical Release Through the Leakages on the Floor
#### 3.1.2 Iso-Surface of 1% Male Fraction  
Standards _ISO/DIS 19880-1, NFPA 2, and IEC (60079-10)_ require that the ventilation rate should ensure a maximum hydrogen mole fraction at 1% for enclosures and buildings containing hydrogen equipment. Therefore, the iso-surface of 1% mole fraction should be investigated to provide guidance for the layout of hydrogen sensors.  
Iso-surfaces of 1% mole fraction rising to the ceiling for Model 1, Model 2, and Model 3 are illustrated in Figure 4. During the first phase, the time required for Model 1, Model 2, and Model 3 to rise to the ceiling was 10 s, 13 s, and 10 s, respectively, and the concentration envelope was a cylinder shape. It is evident that the time for Model 2 was the longest.  
Figure 3: Velocity vectors for Model 1 (left), Model 2 (middle), and Model 3 (right) whilst the envelope of 4% mole fraction of hydrogen was rising to the ceiling (up) and filling the whole ceiling (down) (m/s).  
The velocity vectors in the median z = 0.37 plane while the envelope of the iso-surfaces of 1% mole fraction was rising to the ceiling and extending horizontally to the end of the ceiling is illustrated in Figure 5. It can be seen from the figure that it takes almost the same amount of time for Model 1, Model 2, and Model 3 during the first diffusion phase.  
Like the aforementioned iso-surface of 4% mole fraction, the envelope of iso-surfaces of 1% mole fraction also extended along the ceiling during the second phase after getting to the ceiling. The time required for Model 1, Model 2, and Model 3 in the second phase was 15 s, 27 s, and 50 s, respectively. It was the shortest for Model 1, which might be due to the distance between the leakage position and the bottom vent being longer than those for Model 2 and Model 3. The result indicates that the time required for the iso-surface of 1% mole fraction to diffuse horizontally along the ceiling decreases with the increase of the distance between the leakage position and the bottom vent, and hydrogen tends to accumulate.  
Figure 4: Iso-surface of 1% hydrogen mole fraction for Model 1 (left), Model 2 (middle), and Model 3 (right).
## 2 Materials and Methods
### Acoustic Waveguide Simulation  
Experiments conducted on the samples of dissimilar welded joints allow us to estimate the AE activity and the values of the AE hit amplitudes, but the signal waveform is distorted. Due to multiple reflections from the specimen boundaries, the rise time and duration of the signal are several times higher than in the case of extended objects. In order to obtain the correct signal waveform and ensure the possibility of determining the signal waveform at a point remote from the AE source, the authors use a finite element model of the acoustic waveguide of the testing object.  
The testing object was a dissimilar section of a process pipeline welded from austenitic and pearlitic steels, with an external diameter of 100 mm and a wall thickness of 3 mm. The model was calculated using the finite element method (FEM) in the Comsol Multiphysics software (version 5.5) Solid Mechanics module. The modeling was carried out in the time domain. The parameters of the steels used in the modeling are given in Table 4.  
The weld width of 10 mm and the reinforcement bead height of 3 mm for FEM were selected in accordance with the parameters of the real welded joint made of grade 20 and 12 Kh18N10T steels. For the filler material with an austenitic microstructure, the same properties were specified for 12 Kh18N10T steel. To prevent multiple reflections, the length of the FEM pipeline model was chosen to be 5 m. The defect was specified as a point source generating a single displacement oriented out-of-plane. The form of the source was a \"cosine bell\" with 1 \(\upmu\)s duration. The location of the source corresponded to the location of the diffusion interlayers--along the fusion line on the side of grade 20 steel.  
Figure 5a shows a general view of the model with a finite element mesh that was set finer in the area of the welded joint and coarser at a distance from it; Figure 5b shows the area of the welded joint in more detail.  
Since the occurrence of AE impulses is caused by the action of microscopic sources associated with the movement of dislocations, the emission of an AE impulse can occur at any point of the deformed volume. Therefore, the location of the AE source relative to the surface of the object varied; it is located on the surface of the pipeline, in the area of the weld root and at a depth equal to half the wall thickness.  
Also, for each of the models, AE signals were calculated at different distances from the source in the axial and azimuthal directions; the general list of variable model parameters is given in Table 5. The limited number of signals corresponding to the in-plane and oblique orientation of the AE source were also calculated.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Steel Grade** & **Density, g/cm\({}^{3}\)** & **Young’s Modulus, GPa** & **Poisson’s Ratio** \\ \hline
12 Kh18N10T steel & 7.9 & 200 & 0.3 \\ Grade 20 steel & 7.8 & 210 & 0.3 \\ \hline \end{tabular}
\end{table}
Table 4: Material parameters used when modeling.  
Figure 5: General view of the finite element model (**a**) and welded joint area (**b**).
## 3 Proposed Methods
### Improved Selection Rule Strategy  
The APL method was proposed based on the traditional priority list (PL) method. The traditional PL method uses the average full load cost as its criterion to prioritize units operating at full load according to fuel cost [19; 20]. However, this dispatch method is relatively conservative and unable to fully reflect the priorities of various units. In addition, when the units stop operating at full load or deviate from full-load operation, the obtained solution will deviate from the optimal solution substantially.  
In this study, the proposed APL method focused on the dispatch of units within a short time. Each unit undergoes modulation according to the ramp rate when its power output must be increased or decreased. Therefore, the cost calculation should consider the extent of the unit's ramp-up process. The ramp rate is defined as the power change of a unit per min (MW/min). The equation is derived as follows:  
\[C_{up}=\alpha+\beta*(P_{initial}+R_{up}*t)+\gamma*(P_{initial}+R_{up}*t)^{2}, \tag{9}\]  
\[C_{initial}=\alpha+\beta*P_{initial}+\gamma*P_{initial}^{2}. \tag{10}\]  
Equation (10) is subtracted from Equation (9) to obtain Equations (11) and (12):  
\[C_{Rampup}=C_{up}-C_{initial}, \tag{11}\]  
\[C_{Rampup}=\beta*\left(R_{up}*t\right)+\gamma*\left(R_{up}*t\right)^{2}+2* \gamma*\left(R_{up}*t\right)*P_{initial}. \tag{12}\]  
Equation (11) is divided by \(R_{up}*t\) to obtain the average cost per increased MW, as shown in Equation (13).  
\[C_{Rampup\_cost}=\beta+\gamma*\left(R_{up}*t\right)+2*\gamma*P_{initial}( \$/\text{MW}) \tag{13}\]  
\(C_{initial}\): Initial power generating cost  
\(C_{up}\): Power generating cost after the ramp-up phase  
\(C_{rampup}\): Cost during the ramp-up process  
\(R_{up}\): Ramp rate (MW/min)  
\(P_{initial}\): Initial power  
\(C_{rampup\_cost}\): Average cost per MW during the ramp-up process
# Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles
## 2 Traffic Sign Detection  
The road traffic images are captured by vehicle-mounted cameras installed on the smart cars, and the traffic sign detection aims to extract the interested traffic sign regions from the current road traffic images sufficiently. However, in different external conditions, the qualities of the acquired images are uneven, and these qualities must be effectively detected following the inherent characteristics of traffic signs, such as color and shape. In this section, it mainly includes two parts: traffic sign segmentation based on the color space and traffic sign detection based on shape features.
## 4 Conclusions  
FOV is one of the important performance indicators of space-based detection systems. The selection of the FOV should not only ensure the indicator requirements of the coverage rate and the revisit period of the satellite, but also consider the existing technical conditions. Therefore, it is very important to choose a reasonable sensor FOV. Based on the observation strategy and orbit characteristics of satellites, we studied the detection FOV of the DDSSO satellites used to observe GEO targets, and verified the correctness of the theoretical derivation through simulation experiments. The innovations of the research results of this paper are as follows:  
1. By analyzing the evolution law of the GEO targets, we obtained the characteristics that the trajectory of the GEO target presents a band-like distribution of sine equal width. We improved the passive observation mode and proposed an observation mode suitable for observing the distribution characteristics of the current GEO target trajectory.
2. In the improved observation mode, the sensor's field of view was divided into COFOV and AOFOV, and their thresholds were determined respectively. On the premise that the coverage rate of the observation satellite to the GEO target reaches 95%, when the revisit period of the satellite is one day, the threshold of COFOV is 15\({}^{\circ}\), and the  
Figure 7: Simulation results of the minimum FOV in the along-orbit direction that meets the requirements when the revisit period of the satellite ranges from 1 day to 5 days.  
threshold of AOFOV is \(12^{\circ}\); when the revisit period of the satellite is two days, the threshold of COFOV is \(15^{\circ}\), and the threshold of AOFOV is \(6^{\circ}\). Additionally, according to the orbit theory, the orbital height and inclination angle of the satellite in two cases are determined respectively.  
For the space-based space target detection system, given the clear system detection requirements, the method in this paper can provide the threshold FOV of the sensor that meets the detection index. We provided some constructive guidance for the selection of the FOV of the detection system, and the method for determining the FOV can be more widely applicable to the space-based space target detection system, providing a reference for their FOV selection.  
Conceptualization: D.X.; methodology: D.X. and Y.H.; software: D.X. and Y.H.; validation: C.Y.; formal analysis: D.X.; investigation: D.X. and Y.H.; resources: D.X.; writing--original draft preparation: D.X.; writing--review and editing: Y.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (NSFC), grant number 62105331.  
The authors declare no conflict of interest.
## 3 Materials and Methods
### Collection and Processing of Data from Log Files  
Subsequently, the activities performed by each specialist were analyzed through the content of the log files, which also allowed identifying certain behavioral patterns regarding pauses in modeling activity or dependencies among the various specialties in the design process. Autodesk Revit records activities in a text file that describes how the designer uses the software [23]. Log files are generated for each session with the corresponding date and time of each action. These files include information about the project name, current user, session, commands used, and computer characteristics (see Figure 3), thus enabling the extraction of information about all the operations performed within the model.  
During this experiment, the log files were collected from each computer after the end of each session and subsequently stored and organized into folders per day of work to correctly identify the data corresponding to each session. A session is defined as the set of activities carried out by a user from the moment the BIM software is loaded until the moment it is closed [30].  
Data registered in the server generated a large number of files, thereby making it necessary to clean and organize the information to obtain a structured, reliable, and integrated database to study the frequency of execution of each command. These cleaning and organization processes were carried out as follows:  
1. _Data cleaning_: In this first step, a simple program was developed to parse the information from the log files and exclude a large number of records related to the configuration and monitoring of operations, which are not relevant for the characterization of tasks. The result was a text file containing all the commands executed by each user and indicating their ID (command identification code), access route, a short description, and the date and time of execution.
2. _Data organization_: After the previous data cleaning process, the information was transferred to a spreadsheet and organized into tables for each designer, which made it possible to count the number of executions of each command for an initial analysis.  
Additionally, each session was divided into five periods of equal duration in order to generate more data for the analysis of distributions and to better allocate isolated events that might arise related to users' behavior. Two main categories, contributory and non-contributory commands, with three sub-categories each (described in Table 1), were created to classify the commands executed by each designer, in this way producing a more precise analysis.  
Figure 3: An example of a Building Information Modeling (BIM) log file and its content.
## 4 Materials and Methods
### Reaction Procedure and Analysis of Reaction Mixture  
Testing of Pd-containing HPS-based samples in hydrogenation of alkynols was carried out in toluene medium at 90 \({}^{\circ}\)C and ambient hydrogen pressure in a 60 mL isothermal glass-batch reactor installed in a shaker at vigorous stirring (more than 800 two-sided sharing per minute). The total volume of the liquid phase was 30 mL. A recirculating bath (LOIP LT 100, Saint-Petersburg, Russia) was used to stabilize the reaction temperature within \(\pm 1\)\({}^{\circ}\)C, with water as a heating medium. The reactor was connected to a gasometrical burette for _on-line_ hydrogen consumption control. At the beginning of each experiment the temperature was set and allowed to stabilize, so the reactor was charged with the catalyst,and hydrogen was then introduced and the catalyst was kept under hydrogen for 60 min. Then, substrate was added and the reaction started.  
Samples of the reaction mixture were analyzed via GC (Kristallux 4000M, Yoshkar-Ola, Russia) equipped with FID and capillary column ZB-WAX (60 m \(\times\) 0.53 mm i.d., 1 \(\upmu\)m film thickness). Helium was used as a carrier gas. The concentrations of the reaction mixture components were calculated using the absolute _calibration_ method using chemically pure components.  
Conversion of substrates was defined as:  
\[X\left(\%\right)=(C_{sub,0}-C_{sub,i})\times C_{sub,0}{}^{-1}\times 100. \tag{1}\]  
Selectivity with respect to the target product was given as:  
\[S\left(\%\right)=C_{prod,i}\times(C_{sub,0}-C_{sub,i})^{-1}\times 100. \tag{2}\]  
Reaction rate was designated as \(R_{0}\), \([\mathrm{mol_{sub}\cdot mol_{Me}^{-1}\cdot s^{-1}}]\):  
\[R_{0}=(C_{sub,0}-C_{sub,i})\times(C_{Me}\times\tau_{i})^{-1}. \tag{3}\]  
It is noteworthy that \(R_{0}\) was calculated in the range of concentrations, corresponding to the linear part of the kinetic curves of alkynols' conversion vs. time (induction period was excluded).
## 4 Experimental Results and Discussion  
### Experimental Settings  
We evaluated our proposal on an Intel Xeon E5 v3 computer with 128 GB of RAM using an NVIDIA Titan Xp GPU for both training and testing. All of the CNN architectures were implemented using Python3 under the Keras library [41] and using Tensorflow as the backend.  
To measure the performance of both our approaches, we used the F1-score and the accuracy metrics, as can be seen in Equations (3) and (4). We chose these metrics to represent the robustness of our classifiers. The F1-score is the harmonic mean of the precision and recall measures, which are detailed in Equations (1) and (2), respectively. In these equations, TP and FP represent true and false positives, respectively, while TN and FN indicate true and false negatives.  
\[P=\frac{TP}{TP+FP} \tag{1}\]  
\[R=\frac{TP}{TP+FN} \tag{2}\]  
\[F1=2\cdot\frac{P\cdot R}{P+R} \tag{3}\]  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{4}\]  
In image classification problems, a true positive is when an image is assigned its correct label. For a particular class, a false positive is considered for each image that has been labeled as belonging to a given class, but belongs to a different one. At the same time, false negatives are considered as the images from the class that have been assigned other labels.  
We selected the 0.9 value for the confidence threshold of the pipeline empirically. Following the requirements of the monitoring service where the proposal would be integrated, we initially evaluated high threshold values of \(-0.8\) and \(0.9-\) over a small set of images. We visually inspected the results of the classification under both thresholds and concluded that 0.9 obtains a higher generalization capability with a lower number of failures, meeting the requirements of the monitoring tool.  
We also measured the time needed for feature extraction and classification in both the CPU and GPU, retrieving both the mean and standard deviation in each task.