esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Object Detection Application Domains
### Face Detection  
The oldest CV application is face detection. Earlier, the VJ detector [12] was used for face detection and is still playing a vital role in today's object detection. Face detection is used in everyday life, including in the attendance monitoring of students, smartphone authentication, and facial recognition for criminal investigations.  
In face detection, the main difficulties and challenges faced are: (a) occlusion; (b) multi-scale detection; (c) intra-class variation; (d) real-time detection. Face detection research history is divided into two time slots: traditional and deep learning periods. From 2000 to 2015, it was a traditional face detection period, and thereafter, it was a deep learning face-detection era.  
In traditional face detection methods [126; 127; 128; 129; 130; 131; 132; 133; 134] were constructed based on:  
* \"Boosted decision trees\" [11; 12; 135], as they are easy to compute, but for complex scenes they provide only low detection accuracy.
* CNNs which are used to speed-up detection where the computation of features is shared [136; 137].  
Deep learning-based face detection methods implement deep learning algorithms such as Faster RCNN and SSD. Table 10 describes possible directions to overcome the major challenges and difficulties faced in face detection.  
The widely used datasets for evaluating face detection performance are WIDERFACE [71], PASCAL-VOC [5], and Face Detection Dataset and Benchmark (FDDB) [72].  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Challenges** & **Methods** \\ \hline To improve speed up face detection & Cascaded detection [138; 139] \\ To improve speed up face detection & To predict the scale distribution of the faces in an image and then run algorithm on some selected scales’ [42; 140; 141] \\ \hline To improve multi-pose face detection & ‘Face calibration method’ using progressive calibration through multiple detection stages [142] \\ \hline \hline \end{tabular}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline  & ‘Detection based on parts’ [144; 145] \\ To improve accuracy of occluded faces & ‘Attention mechanism’ which highlights underlying face target features [146] \\ \cline{2-3}  & GAN is used for improving occluded objects by applying adversarial training which generate occlusion masks. [147] \\ \hline To improve multi-scale face detection & ‘Vote-based ensemble’ method’ \\ \cline{2-3}  & ‘Multi-resolution detection’ [152] \\ \cline{2-3}  & (dilated convolution) [153; 154; 155; 41] \\ \hline \hline \end{tabular}
\end{table}
Table 10: Remedies to improve challenges arising in face detection.
## 2 Materials and Methods
### Seismic Refraction  
The geophysical technique of seismic refraction is often conducted as a reconnaissance method in virgin areas. It is most useful where there is at least one high-seismic-velocity bed, having geological interest which extends without significant change over a wide area [22]. In addition, it has been extensively used in environmental, engineering, and hydrogeologic applications, due to the new developments in acquisition instrumentation, sources of sound waves, and updated interpretational computing techniques, which make data collection for site assessment and modeling investigations using seismic refraction methods more efficient and more applicable [23; 24; 25; 26].  
The method of seismic refraction depends on registering the trip time of the first arrivals for the refracted sound waves up to the ground surface by the inserted geophones on the ground surface and plotting them with the distances between the geophones to investigate the variation in seismic velocities for subsurface rocks with depth [23].  
In the present study, the main target for applying seismic refraction techniques is to delineate the site bedrock to determine the overburden thickness, which is essentially important in planning urbanization projects in the extension of Helwan city. The seismic field work was summarized by a total of 20 forward-split-reverse shootings, distributed all over the study area as shown in Figure 6, to perform and anatomize the areal extent of the compressional seismic wave velocity of the bedrock.  
Figure 5: Applied methodology for preparing the land-use plan of the investigated site.  
In order to generate compressional (P) waves, a sledgehammer is utilized to vertically hammer a steel plate on the ground surface. Vertically oriented geophones are used to pick up the ground motion due to generated seismic pulses. A 12-channel digital signal-enhancement seismograph (Strata-Vision-Geometrics) is used in recoding the generated seismic signals. It is known that the quality of seismic data depends on several parameters: the instrument used, topographic variations, geophone location, situation and location of the seismic source, and the surrounding noise. In the present study, the good manner of the collected seismic data is due to the location of the study area away from sources of noise such as surrounding traffic activities, machinery, human activities, and related parameters which greatly reduce the signal-to-noise rate.
## 5 Results and Discussions
### Active and Reactive Power Sharing Assessment  
The active and reactive power sharing ability of the proposed controller-based converter can be influenced by variation of the virtual inertia and mechanical power. Firstly, the proposed controller-based converter is supplying a 3 kW + j2 kVar grid connected load and then an abrupt disconnection of the PV sources with 4 Kw + j3 kVar occurs at the PCC at t = 0.1 s which will be supplied by the proposed controller at t = 0.25s. Figure 9 shows the active power variations under different values for virtual inertia and mechanical power.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline dc-link Voltage (vdc) & 850 V & J1 & \(1\times 10^{3}\) s \\ Phase ac voltage & 220 V & Pm & 3.3 kW \\ Fundamental frequency & 50 Hz & P & 3 kW \\ Switching frequency & 10 kHz & Q & 2 kvar \\ Interface converter resistance & 0.1 Ohm & Interface converter inductance & 45 mH \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 8: The general structure of the model based on the proposed control technique. PWM: Pulse Width Modulation; PQ: Active and Reactive Power Coordination.  
As it can be seen from Figure 9a,b, the best response is due to the low-high values of virtual inertia as well as low VMP error. In contrast, the worst response can be achieved by very high values of both virtual inertia and mechanical power. Moreover, the active power with high VMP error is trying to follow its desired value that verifies the importance of choosing appropriately the VMP in the proposed controller. According to Figure 9a, for very low inertia, the active power reaches its reference value ultimately, however the transient takes very long time along with very high value of the transient magnitude. The reactive power of the proposed controller-based converter is illustrated in Figure 10. The same scenario associated with the best response happens for the reactive power. Also, the worst case of the reactive power response is belonging to very high value of the virtual inertia and mechanical power as shown in Figure 9a,b. In the cases of very low inertia and high VMP error, the reactive power is trying to make minimum divergence in its entire responses.  
Figure 10: Reactive power of the proposed controller-based converter under: (**a**) various inertia values, and (**b**) various VMPE values.  
Figure 9: Active power of the proposed controller-based converter under: (**a**) various inertia values, and (**b**) various VMPE values.
## 2 Description of IFGD Process and Modeling Problem
### Limestone Wet IFGD Process  
Limestone wet flue gas desulfurization, with the property of high SO2 removal efficiency, rapid reaction speed and operating reliability, has become the most widely used IFGD technology worldwide [18]. In the limestone wet IFGD process, the SO2-containing flue gas reacts with limestone reagent slurry (CaCO3), along with injected oxygen (O2); the intermediate product is calcium sulfite (CaSO3), and the final product gypsum (CaSO4) is produced. The above procedure can be expressed in a chemical reaction form,  
\[\text{SO}_{2}+\text{CaCO}_{3}(\text{s})\rightarrow\text{CaSO}_{3}+\text{CO}_{2} \tag{1}\]  
\[\text{CaCO}_{3}+0.5\text{O}_{2}\rightarrow\text{CaSO}_{4}(\text{s}) \tag{2}\]  
To further explain the IFGD process, Figure 1 shows a schematic of the overall desulfurization process. Having processed by electrostatic precipitator and heat exchanger, the cool and cleaned flue gas enters from the lower part of the spray tower. The entered flue gas is bottom-up, while the slurry is pumped from the reaction tank to spray headers and moves from up to bottom. In such a countercurrent contact, reactions (1) and (2) take place and gypsum is produced as one of reaction products. Then, the gypsum byproduct is transferred to the dehydration system for further processing, which mainly includes gypsum separation and dehydration with hydrocyclone and vacuum dehydtor, respectively. Lastly, the desulfurized flue gas is emitted from the stack after water elimination.  
Limestone flow rate, as the input variable of IFGD process in our study, is measured via the flowmeter installed on the duct between the slurry pump and the slurry tank in the absorber (see Figure 1), which can record the flow rate of limestone slurry in real time. On the other hand, pH meter, which is installed on the duct between circulating pump and spray headers, is used to measure the pH value of the circulating slurry (see Figure 1).
## 4 Experiment and Results  
### Experiment Environment and Dataset  
For the experiment and evaluation of the system proposed in this paper, implementation and experiments were carried out, and the environment is shown in Table 2. This study previously explained the system with \(n\) voters and \(m\) node models. In this section, an environment was configured with 100 voters and three blockchain peer nodes to implement and experiment with the proposed system.  
In this study, the experiment through the node model consisted of three peer nodes. The pretrained deep-learning models of \(\alpha\), \(\beta\), and \(\gamma\) in Table 3 were assigned to each node model. The official AUC of the assigned deep-learning model was 0.919, 0.951, and 0.944, but an experiment was conducted to confirm the difference in AUC due to variations, such as differences in hardware.  
The dataset for the experiment was composed, as shown in Table 4. The dataset was sliced into units of 1000 and composed of feedback epochs to check the real-time nature of node reliability evaluation. Feedback epoch refers to a unit of performing one feedback 1000 times, which refers to a series of processes including proposing voting, voting, aggregation, integration, and node reliability reflection, considering the integrated voting process for contents. This study assumes a node model that can use different deep-learning models. Since each model may have a difference in accuracy for the dataset, each model can derive fair results by using the datasets in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Voting Result**} \\ \hline  & **Positive** & **Negative** \\ \hline
**Reward** & Flagger & \\
**recipient** & Positive Voter * & Negative Voter \\ \hline \hline \end{tabular}  
* Voters who voted that the disputed content was indeed problematic.  
\end{table}
Table 1: Compensation policy according to the result of the integrated vote.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & Blockchain \\ \hline \hline \multirow{3}{*}{Network Environment} & Virtual Machine \\  & Docker \\  & Ubuntu 20.04 LTS \\  & Hyperledger Fabric 2.1.0 * \\ \hline Chaincode Language & Golang \\ \hline \hline \multicolumn{2}{c}{Node Model} \\ \hline \hline \multirow{3}{*}{Machine Environment} & i9-10980 XE, RTX 3090 \\  & Anaconda \\ \cline{1-1}  & Ubuntu 18.04.5 LTS \\ \cline{1-1}  & cuda 11.1.1, pytorch 1.8.0 \\ \hline Language & python3 \\ \hline \hline \end{tabular}  
* [[https://github.com/hyperledger/fabric/releases/tag/v2.1.0](https://github.com/hyperledger/fabric/releases/tag/v2.1.0)]([https://github.com/hyperledger/fabric/releases/tag/v2.1.0](https://github.com/hyperledger/fabric/releases/tag/v2.1.0)) (accessed on 5 January 2023).  
\end{table}
Table 2: Experiment and implementation environment.
# Design and Performance Analysis of BDS-3 Integrity Concept
## 6 Conclusions  
This contribution focuses on the integrity concept design and construction of China's BDS-3. Both the B1I signal and the B1C/B2a signal of BDS-3 have integrity functions. Among them, the B1I signal uses 4.42 times the URE as the NTE, and uses the SatH1 parameter in the D1 navigation message as the health status indicator; the B1C/B2a signal uses 4.42 times the SISA as the NTE, and uses the HS, SIF, and DIF parameters in the B-CNAV1/B- CNNV2 navigation message as the health status indicators. According to the nine integrity risk bottom events in the BDS-3 satellite SIS fault tree model, the Psat design values of the B1I and B1C/B2a signals are analyzed and determined to be 0.9 \(\times\) 10\({}^{-5}\) and 0.8 \(\times\) 10\({}^{-5}\), respectively, which both meet the ICAO's performance requirement for the en-route flight operation (less than 1 \(\times\) 10\({}^{-5}\)). The Pconst design value of BDS-3 is 6 \(\times\) 10\({}^{-5}\).  
The integrity function of BDS-3 has two approaches, the ground monitoring and alarm, and the satellite monitoring and alarm, and the design TTA can reach 60 s and 6 s/300 s, respectively. Among them, the 6s rapid alert mechanism relies on the SAIM function, and BDS is still working hard to realize this capability as soon as possible.  
The integrity risk analysis and verification results show that the actual operation of the system is consistent with the conceptual design requirement, and it satisfies the integrity performance promised by BDS-3 in the ICAO SAPRs. The performance of the system in actual operation and service proved the effectiveness of the BDS-3 integrity concept design and system prevention and control measures.  
**Author Contributions:** Research design and original draft preparation, C.L. ([PERSON]); data analysis, Y.C. ([PERSON]) and G.Z.; conceptualization, W.G. and J.L.; methodology, Y.C. ([PERSON]), C.L. ([PERSON]), H.Z. and F.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China (Grant Nos. 42074044 and 41974041) and in part by the Young Talent Lifting Project of China Association for Science and Technology (Grant No. 2019 QNRC001).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Privacy-Friendly Datasets of Synthetic Fingerprints for Evaluation of Biometric Algorithms
## 3 Research Methodology  
The research presented in this paper comprises two parts: the training of generative models that will further assist us in generating fingerprints of virtual subjects (Section 3.1) and the compilation of two exemplary biometric datasets of synthetic fingerprints (Section 3.2). Note that the former part is mostly derived from our previous work [7] and replicated here to simplify the understanding of the complete approach.  
While defining the pipeline for the generation of datasets of synthetic images appropriate for the evaluation of fingerprint matching algorithms, we have in mind four objectives: (1) appropriate image resolution, (2) the realistic appearance of generated fingerprints, (3) the anonymity of generated fingerprints, and (4) the ability to produce mated fingerprints.  
The former two objectives are covered by the design of the pix2 pix models trained in our previous work [7], which allow for reliable fingerprint reconstruction from minutiae. To cover the latter two objectives, we propose to apply the Anguli tool to generate random non-realistic fingerprints with predefined basic patterns. These non-realistic fingerprints are used as a source of minutiae from which realistic fingerprints are generated using the pix2 pix models. In doing so, we ensure the anonymity of the resulting synthetic fingerprints. In order to produce several mated impressions for one finger, we propose to modify the minutiae extracted from Anguli fingerprints to preserve the identity of the finger and use these modified minutiae sets as an input for pix2 pix models. In doing so, we ensure that the outcomes of a pix2 pix model are the mated fingerprints, if the transformations applied to minutiae locations and orientations are in a certain range.
# Age Estimates from Name Characters
## 4 Experiment and Results
### Evaluation Method  
To evaluate the performance of the proposed model, the experiments were designed in three directions. The first direction compared the performance using different features and feature combinations for gender prediction and age-interval prediction. Through a series of experiments, how these features impacted the prediction results can be elaborated. The second way examined the generalizability using bilingual names--Chinese names and their corresponding English names. Finally, a different country dataset, Malaysian names, was applied to understand how our proposed model and features support cross-border situations.  
In this research, different methods were adopted to evaluate the performance of gender and age-interval prediction. Gender prediction was treated as a binary classification task; therefore, prediction accuracy and the F1 score were applied. The age-interval prediction was regarded as a multi-label classification task with multiple answers. Thus, the metrics of average year error and multi-answer accuracy were utilized to evaluate the performance. The year error calculates the gap between the predicted age interval and the exact birth year of the given name. For example, the model predicts that the age interval of \"B-
# Electrospun Porous PDLLA Fiber Membrane Coated with nHA
## 3 Results and Discussion
### FTIR of PDLLA/nHA Composite Fibrous Membrane  
FTIR analysis could provide some insights into the formation of PDLLA/nHA composite fibrous membrane; the results are presented in Figure 5. Compared with the spectrum of porous PDLLA membrane (Figure 5a) and the porous PDLLA membrane shrunken in absolute ethyl alcohol without immersing into nHA dispersions (Figure 5b), the presence of the same absorption bands, indicated that the shrinking process of the PDLLA porous membrane is a physical process. The characteristic adsorption peaks of PDLLA at 1750 cm\({}^{-1}\), which were attributed to a strong C=O peak in carboxylic acid and carbonyl moieties; the band at 1089 and 1180 cm\({}^{-1}\) was assigned to C-O stretching vibration bands. The bands at 1451 and 1381 cm\({}^{-1}\) corresponded to the C-H scissor bending vibrations [32; 33]. Compared with the spectrum of PDLLA/nHA composite fibrous membranes, seen from Figure 5b,c, the specific adsorption peaks of PO4\({}^{3-}\) at 1033, 603 and 564 cm\({}^{-1}\) were found [32]. And the typical adsorption peaks of PDLLA were not changed, demonstrating that the adsorption of the nHA particles and the shrinking of the mats occurred via all physical processes. The PDLLA/nHA composite fibrous membrane was formed by physical adsorption.  
Figure 4: SEM of PDLLA/nHA composite fibers and partially enlarged detail.  
Figure 5: FTIR of (**a**) porous PDLLA membrane; (**b**) porous PDLLA membrane shrank in absolute ethyl alcohol; (**c**) PDLLA/nHA composite fibrous membrane.
# Misalignment Fault Prediction of Wind Turbines Based on Combined Forecasting Model
## 3 Signal Acquisition and Feature Extraction
### Feature Vectors and Normalization  
The input speed of the dynamic simulation model of the wind turbine drive system is set to be \(81.3^{\circ}\)/s. Afterwards, 60 segments of vibration and current signals of parallel misalignment faults are collected. The time-domain, frequency-domain and time-frequency domain feature parameters of each signal segment are extracted to construct feature vectors. After extracting the time-frequency domain feature parameters of the vibration signal, the correlation coefficients of the first three IMFs of the vibration signal decomposed by IEMD are all greater than 0.1. Therefore, the first three IMFs are selected as effective components in this paper. The feature parameters of vibration and current signal are listed in Tables 3 and 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature Vector** & **Feature** & **Index** \\ \hline Vibration features library (9 dimensions) & Time domain & root mean square, kurtosis, kurtosis index \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Vibration features library \\ (9 dimensions) \\ \end{tabular} } & Frequency domain & mean square frequency, center of gravity \\  & Frequency domain & frequency, frequency variance \\  & Time-frequency domain & energy entropy of the first 3 IMF \\  & & components of IEMD \\ \hline \hline \end{tabular}
\end{table}
Table 3: The indexes of feature vector for vibration signal.  
The fault feature parameters have different orders of magnitude. In order to facilitate the processing of data and ensure that the program runs faster when it converges, the Mapminmax function is used to normalize the time-domain, frequency-domain and time-frequency domain feature parameters in this paper. The equation of Mapminmax function is as follows:  
\[y=\frac{y_{\max}-y_{\min}}{x_{\max}-x_{\min}}.(x-x_{\min})+y_{\min} \tag{31}\]  
where, \(x\) is an unnormalized matrix, \(y_{\min}\) is a minimum value of the normalization interval, and \(y_{\max}\) is a maximum value of the normalization interval. The feature parameters are normalized to the range [0, 1] in this paper.  
For the vibration and current signals, multivariate feature parameters with 9 dimensions and 11 dimensions are used as input vectors in this paper. Because the kurtosis is sensitive to early faults [43,44], the kurtosis is selected as the output of the prediction model.