esa-sceva/satcom-chunk-collection huggingface dataset:
# B-Line Detection and Localization in Lung Ultrasound Videos Using Spatiotemporal Attention
## 5 Experiment Results
### B-Line Classification  
We compared three model architectures to investigate the potential benefit of using temporal information in the B-line detection task. Our baseline model (C2D+S+T) has 2D convolutions in the initial CNN subnet followed by spatial and temporal attention modules without LSTM. The second model (C3D+S+T) is a model with 3D convolutions in the CNN subnet followed by spatial and temporal attention and no LSTM. The last model (C2D+S+LSTM+T) has 2D CNN followed by spatial attention, LSTM and temporal attention.  
Table 3 shows the classification results on our LUS video dataset. The performance of the C2D+S+T model is least (59.9%), because without the LSTM model it cannot model the temporal part of the video. With the C3D+S+T model, the performance improves (78.3%), which indicates the ability of the C3D network for modelling the data temporal aspect but with a shorter context span over time compared to LSTM. However, when LSTM is added to the C2D model in C2D+S+LSTM+T, the \(F1\) score increases (83.2%), which indicates the importance of the LSTM model for the B-line classification as it considers long temporal progression of the LUS video data.  
Figure 6: Examples of spatial attention map for B-line localization task. Our spatial attention module can automatically highlight B-line regions (red areas) and avoid irrelevant regions corresponding to no-B-line regions or background. Yellow straight lines represent ground truth. Correlation coefficient values (\(r\)) are presented at the bottom of each attention map.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1** \\ \hline C2D+S+T & 58.1 & 61.9 & 59.9 \\ \hline C3D+S+T & 74.1 & 83.2 & 78.3 \\ \hline
**C2D+S+LSTM+T** & 77.2 & 90.3 & **83.2** \\ \hline \hline \end{tabular}
\end{table}
Table 3: B-line classification performance. All the values are in percentages.
## 2 Related Studies  
### CS-GAN and SentiGAN  
There are many variations of GANs. One line of research is based on integrating additional category information into GANs. There are two major approaches to handle category information in GANs. Auxiliary classifier GAN (ACGAN) [3] is one of the most popular architectures for categorical data generation. ACGAN deploys an additional classifier layer into a discriminator. A generator is trained to minimize losses calculated by the discriminator and the classifier in ACGAN. Another approach to categorical data generation is to adopt multiple generators, with each generator being dedicated to each category. SentiGAN [4] is a representative GAN with multiple generators.  
In text generation, category sentence GAN (CS-GAN) [5], which is an ACGAN, incorporates category information into the GAN to generate synthetic sentences with categories.  
A generator of CS-GAN exploits long short-term memory (LSTM) networks, which are one of the most common methods used for sentence generation. Category information is added at each generation step. As with ACGAN, the training of the generator is led by the discriminator and the classifier in CS-GAN. A policy-gradient method of reinforcement learning is used to update the parameters of the generator in CS-GAN. In [5], the CS-GAN model generated sentences using various categories of information,with 2 to 13 classes for several datasets. Experimental results have shown that generated sentences with categories can perform well in supervised learning, especially in multi-category datasets.  
SentiGAN [4] consists of multiple generators and one multi-class discriminator. The number of generators is determined by the number of categories. SentiGAN is designed to generate text with different sentiment labels. Owing to the multiple generators dedicated to a specific sentiment, SentiGAN can focus on accurately generating its own text with a specific sentiment label. SentiGAN's discriminator is a multi-class classifier with \(k\) classes and an additional fake class when there are \(k\) categories. This architecture allows the generators to focus on generating text with specific sentiment labels and avoid generating unacceptable sentiment text.
## 4 Proposed Model
### Open Challenges, Limitations, and Issues
#### 4.2.4 Accreditation Transparency for HED  
The major challenging aspect of accreditation is to provide candidates' certificate-sensitive credentials transparency in a distributed environment [36; 37]. The hyperledger sawtooth enables the provision of pre-defined information integrity and confidentiality policies [38]. In the proposed model, we have designed and created a customized protection mechanism using hash-encryption (SHA-256), which manages overall educational node transaction verification and validation, and implemented a consensus policy (PoET: range of parameter tuned) that allows for efficient performance in terms of security, privacy, and protection. Hyperledger sawtooth also provides a modular infrastructure to preserve records in a secure channel, which is completely transparent, provenanced, and immutable. This complete process can easily track records. No additional cost is required for managing accreditation records and reducing redundancy.
# Extracting Fingerprint Features Using Autoencoder Networks for Gender Classification
## 1 Introduction  
Fingerprint gender identification aims to extract gender-related features from an unidentified fingerprint to recognize one's gender information. It can be divided into two stages, namely extracting as well as classifying [1; 2; 3; 4; 5; 6; 7; 8], in which the first step is of great significance, since the effectiveness of gender identification is determined primarily by the sufficiency of gender-related features. Nowadays, classifying ridge-related features extracted manually has achieved fairly good results, reaching an overall accuracy of 90% for the average [8; 9; 10]. High performances, however, depend strongly on the manual extraction of features from well-selected regions [11]. These methods have major shortcomings, such as high error, weak robustness, and high labor consumption. So far, with the growing popularity of machine learning and deep learning, automatic feature extraction has become a major focus.  
To realize the automatic feature extraction, considerable work has been done. In machine learning, methods such as DWT, SVD, PCA, as well as FFT are extensivelyused [3; 12; 13]. For deep learning-based methods, including deep autoencoder, neural networks such as VGG and ResNet have been investigated [14].  
Although numerous algorithms have been proposed, there are still two major challenges. First, traditional approaches acquire excessive labor consumption and lack automation. Second, the automatic feature extraction method lacks robustness, which only concerns regions, instead of considering the global field. Figure 1 shows the difference between the normal automatic feature extraction method and the deep neural network with global reception consideration. The latter can divide the feature space normatively. In addition, private datasets with disparate data distributions and sizes will directly influence the accuracy, and fingerprint information contains a lot of noise, resulting in low recognition accuracy. Thus, because of the above challenges, we propose a global feature extraction method to improve the efficiency of gender classification.  
The main contributions of this study are as follows:  
* We propose a feature extraction method of fingerprint that takes global features into account. For existing typical automatic feature extraction methods and classification methods, we make comprehensive comparisons with fair implementation details.
* We make a comprehensive comparison to test the efficiency of our method. The finger with the richest gender-related characteristics is detected using the highest-performance method. Finally, we visualize the concentrations using the selected method, which indicates the regions with the highest contributions and their corresponding specific features in the gender identification task.
* We will finally open source the dataset since the open-source datasets in fingerprint gender classification are limited.
## 5 Validation Results and Analysis
### Air-Ground NLOS Identification Based on Ray Intersection Detection
If the air-ground swarm performs cooperative navigation for UGV 1 but does not perform NLOS signal identification, UGV 1 is subject to NLOS errors, and its position error will fluctuate several times. When the traditional angle-only NLOS identification method is used to co-navigate unmanned vehicle 1, unmanned vehicle 1 is not affected by NLOS error; however, because the traditional angle-only method mistakenly separates the navigation signals of the visible member, localization error of unmanned vehicle 1 still occurs in the simulation at 400-500 and 700-800 s. The inertial guidance system localization error of unmanned vehicle 1 is not affected by NLOS error. If the proposed NLOS identification method is used for the cooperative navigation of unmanned vehicle 1, the NLOS error of unmanned vehicle 1 is suppressed, the navigation signal of the visible member is preserved, and the inertial guidance system localization accuracy of unmanned vehicle 1 is higher at simulations of 400-500 s and 700-800 s.  
The visibility analysis algorithm based on the LOS direction plus LOS distance proposed in this paper can improve the localization accuracy in complex terrain environments by suppressing the NLOS error under multidomain cooperative navigation conditions and can retain LOS cooperative signals mistakenly separated by traditional methods based on the cover angle of the terrain.
# Indoor Localization for Augmented Reality Devices Using BIM, Point Clouds, and Template Matching
## 2 State of Technology
### Localization with External Sensors  
External sensors can be defined as all active or passive sensors that are considered static in the to-be-localized environment. This can be radio-frequency beacons such as Wi-Fi signals or RFID tags or global navigation satellite systems such as GPS. External sensors are used for estimating a position, which is usually obtained by triangulation of time signals or signal strength. The type of positioning precision can vary widely, from an exact 6D pose (location and rotation) of the device to general descriptors such as floor or room numbers.  
The use of GPS is among the most common global localization techniques. The advantage of using the established global network of satellites for positioning is availability, as the infrastructure already exists. While availability is widespread, GPS lacks the precision and reliability needed for localization. [PERSON] et al. [5] have introduced an AR application for utility maintenance that visualizes hidden infrastructure elements. Underground infrastructure could be located directly by workers without having to refer to 2D plans. GPS was used in combination with an inertial measurement unit for pose estimation. The accuracy and reliability of the GPS tracking system was identified as the major shortcoming of the system. Especially in cities with urban canyons, the GPS signals are subject to multicasting as signals are reflected by surrounding buildings, causing the pose to deviate widely [6].  
In an indoor or underground environment, large-scale external sensors such as GPS prove to be unreliable. An alternative is resorting to localized sensors called beacons emit some form of signal that can be used by the device to estimate the distance to the beacon. One approach is using RFID emitters with a known global location and using the signal's strength as a distance estimator [7]. RFID-based localization can be very accurate but requires extensive deployment effort. As deployment effort is a major concern for widespread use, relying on preexisting beacons can be a major improvement. Especially Wi-Fi signals are commonplace in modern buildings and are usually set up to cover the whole building. [PERSON] et al. [8] are using Wi-Fi access points as beacons to localize devices, but accuracy is limited to a few meters due to multicasting. [PERSON] et al. [9] used data fusion to combine multiple radio-frequency sources into one model. Still, the localization depends entirely on the coverage of the beacons, which is not always a given. On a similar note, [PERSON] and [PERSON] [10] have achieved indoor localization through the use of magnetic field generators outside of buildings. While magnetic fields can, for the most part. ignore the problem of multicasting, their range is limited to 10-20 m. While beacons can give accurate results, their range is often limited and pre-deployment effort can be a deal breaker for most use cases.
## 5 Discussion
### The Effects of the Seven Audio Features  
In order to characterize the two program clusters, the group means of each feature are presented in Table 8. The profiles of the two program clusters described by these features can be summarized. Compared to the C2 group which contains programs with poor discrimination ability, the C1 group has a lower tempo and higher pulse clarity. This suggests that programs whose rhythm is slow and clear are possibly the right test signals for revealing the spectral irregularities in the low-frequency range, which supports previous research findings by [PERSON] [10].  
To more clearly show the effects of BE 400-800, BE 800-1600, and BE \(>\) 1600, the average one-third octave spectral curves of the two program clusters are presented in Figure 10. The C2 group has higher BE 400-800 and BE 800-1600 values and smaller BE \(>\) 1600 value, shown in Figure 10 as the more energy distribution in the band 400 Hz to 1600 Hz, and less high-frequency content. By contrast, the C1 group has a smoother and uniform spectrum in the whole band. The result is consistent with previous work [13] which demonstrates that programs with flat and extended-spectrum can reveal the spectral irregularities more easily.  
\begin{table}
\begin{tabular}{c c} \hline
**Variable** & **Discriminant Loading** \\ \hline Component 1 & 0.593 \\ Component 2 & 0.517 \\ \hline \end{tabular}
\end{table}
Table 7: Discriminant loadings of the two principal components.  
\begin{table}
\begin{tabular}{c c c} \hline
**Feature** & **C1** & **Program Cluster** & **C2** \\ \hline Tempo & 115.96 & 140.6 \\ Pulse clarity & 0.50 & 0.15 \\ BE 400–800 & 0.09 & 0.17 \\ BE 800–1600 & 0.10 & 0.14 \\ BE \(>\) 1600 & 0.51 & 0.38 \\ Skewness & 1.38 & 2.19 \\ Kurtosis & 4.20 & 8.74 \\ \hline \end{tabular}
\end{table}
Table 8: Group means of the seven features.  
Kurtosis and skewness describe the shape of the spectrum, and higher values suggest that there are more extreme values far away from the mean in the spectral distribution. As shown in Table 8, the C2 group has higher values on the two features, indicating that the programs in C2 have a leptokurtic and asymmetric spectral energy distribution.  
Therefore, the profile of the two program clusters based on the features are as follows: C1 (the discriminating program cluster) has a slower and clearer rhythmic feature, with a smooth and even spectrum.  
C2 (the undiscriminating program cluster) has a quicker tempo, lower pulse clarity, and uneven spectral energy distribution.  
The three research questions raised in Section 1 could now be answered.  
Question 1: Among the 20 programs analyzed, the programs in the C1 group are better at revealing the spectral differences in the low-frequency range.  
Question 2: The more discriminating programs have some common characteristics in the energy-related features and rhythmic features. To be specific, these programs have a slower and clearer rhythmic feature and a smooth and even spectrum.  
Question 3: It is possible to apply a predictive model to estimate the program's revealing ability. This method would be helpful in the selection of program material.
# Mapping Gaps in Sugarcane Fields in Unmanned Aerial Vehicle Imagery Using YOLOv5 and ImageJ
## 3 Materials and Methods
### Training the YOLO Model  
During the training phase of the convolutional neural network, it is crucial to have multiple input images of consistent dimensions, each containing samples of the objects that the neural network is designed to detect. These input images must have a resolution that is a multiple of 32 pixels. In this study, the input images were obtained by manually cropping sub-images from the orthomosaic. The chosen size for the input images was 416 \(\times\) 416 pixels2; a standard dimension for such purposes [26; 27]. The neural network in this study was tasked with identifying two classes: field gaps designated as class 0 and plants designated as class 1, as depicted in Figure 3.  
Annex A includes the command utilized for neural network training. Subsequently, image samples and corresponding annotations for the two classes were employed to train the YOLOv5 neural network using the Google Colab platform [28; 29] (Figure 4), negating the need to invest in computers equipped with GPUs for this endeavor. Labellmg software 1.8.6 [30; 31] was utilized for class annotations (Figure 5).  
Figure 2: Sugarcane fields location and the drone used to take images for orthomosaic generation.  
Figure 4: Google Colab platform used for training YOLOv5.  
Figure 3: YOLO training after image sampling and object annotation.
## 3 Results and Discussion
### The Cell Viability and Cytotoxicity Study in hFOB 1.19 Cells  
Cell viability and cytotoxicity in hFOB 1.19 cells in response to various concentrations (1 mM, 5 mM, 10 mM, and 50 mM) of MgO/Mg(OH)\({}_{2}\) NPs were studied through a live/dead assay, as represented in Figures 9 and 10, respectively. Fluorescence microscope results showed green (live), healthy, and viable hFOB 1.19 cells with a few red (dead) cells upon exposure to lower concentrations (5 mM or less) of MgO/Mg(OH)\({}_{2}\) NPs when compared to the higher concentrations (10 mM or more). The negative control group Figures 9b and 10b results showed green (live), healthy, and viable cells with no red (dead) cells. An increase in competitive red (dead) cells at the higher concentrations indicated cytotoxicity in the cells. Figure 10f illustrates the clustering of hFOB 1.19 cells in response to 50-mM concentrations of Mg(OH)\({}_{2}\) NPs. The clustering of red (dead) cells indicated an agglomeration of Mg(OH)\({}_{2}\) NPs within the cells, showing toxic effects on cell survival. Figures 11 and 12 represent a statistical analysis of the % live cells for MgO/Mg(OH)\({}_{2}\) NPs.  
Figure 8: SEM–EDX mapping result of 100-mM concentrations of Mg(OH)\({}_{2}\) NPs after incubation for seven days in DMEM. Various depositions of elements and (**a**) overlay mapping of the elements: (**b**) Mg, (**c**) O, (**d**) Na, (**e**) Ca, (**f**) P, (**g**) Cl, and (**h**) K.  
Figure 7: SEM–EDX mapping result of 100-mM concentrations of MgO NPs after incubation for seven days in DMEM. Various depositions of elements and (**a**) overlay mapping of the elements, (**b**) Mg, (**c**) O, (**d**) Na, (**e**) Ca, (**f**) P, (**g**) Cl, and (**h**) K.  
Figure 10: Live/dead assay results demonstrated live (green) and dead (red) hFOB 1.19 cells in response to various concentrations of Mg(OH)\({}_{2}\) NPs. (**a**) Media Only (-ve control group), (**b**) Cells + Media (-ve control group), (**c**) Cells + Media + 1 mM Mg(OH)\({}_{2}\) NPs, (**d**) Cells + Media + 5 mM Mg(OH)\({}_{2}\) NPs, (**e**) Cells + Media + 10 mM Mg(OH)\({}_{2}\) NPs, and (**f**) Cells + Media + 50 mM Mg(OH)\({}_{2}\) NPs.  
Figure 9: Live/dead assay results demonstrated live (green) and dead (red) human fetal osteoblast (hFOB) 1.19 cells in response to various concentrations of MgO NPs for 30 min. (**a**) Media Only (-ve control group), (**b**) Cells + Media (-ve control group), (**c**) Cells + Media + 1 mM MgO NPs, (**d**) Cells + Media + 5 mM MgO NPs, (**e**) Cells + Media + 10 mM MgO NPs, and (**f**) Cells + Media + 50 mM MgO NPs.
## 1 Introduction
### Calculation of the Frequency Response Functions of the Experimental Carbody Model  
For the calculation of the frequency response functions of the acceleration of the experimental model of the carbody with anti-bending bars under the action of the harmonic force \(F(t)=F_{o}\mathrm{cos}(\omega t)\), the following complex quantities were associated with the real ones:  
\[\overline{z}_{c}(t)=\overline{Z}_{co}e^{i\omega t},\ \overline{\theta}_{c}(t)= \overline{\Theta}_{co}e^{i\omega t},\ \overline{T}_{c}(t)=\overline{T}_{co}e^{i\omega t},\ \overline{F}(t)= \overline{F}_{o}e^{i\omega t}, \tag{41}\]  
where \(\overline{Z}_{co}\overline{\Theta}_{co}\),\(\overline{T}_{co}\overline{F}_{o}\) are complex amplitudes. Hysteretic damping specific to the rubber element was introduced  
\[\overline{k}_{zc}=k_{zc}(1+\mathrm{in}_{zc}), \tag{42}\]  
and hysteretic damping of the experimental carbody model and anti-bending bars  
\[\overline{k}_{mc}=k_{mc}(1+\mathrm{in}_{c}), \tag{43}\]  
\[\overline{k}_{xb}=k_{xb}(1+\mathrm{in}_{b}), \tag{44}\]  
where \(h_{c}\) and \(h_{b}\) were the corresponding loss factors. Equations of motion (15)-(17) became  
\[(-\omega^{2}M_{c}+4\overline{k}_{zc})\overline{Z}_{co}+4\overline{k}_{zc} \epsilon\overline{T}_{co}=\overline{F}_{o}, \tag{45}\]  
\[(-\omega^{2}f_{c}+4\overline{k}_{zc}a_{c}^{2})\overline{\Theta}_{co}= \overline{F}_{o}\bigg{(}x_{o}-\frac{l_{c}}{2}\bigg{)}, \tag{46}\]  
\[(-\omega^{2}M_{mc}+\overline{k}_{mc}+8\psi^{2}h_{b}^{2}\overline{k}_{xb}+4 \overline{k}_{zc}\epsilon^{2})\overline{T}_{co}+4\overline{k}_{zc}\epsilon \overline{Z}_{co}=\xi\overline{F}_{o}. \tag{47}\] \begin{table}
\begin{tabular}{l c c c c} \hline The diameter of the anti-bending bars [mm] & 1 & 2 & 3 & 4 \\ \hline Vertical bending frequency of the experimental & & & & \\ carbody model with anti-bending bars [Hz] & 13.07 & 14.64 & 16.20 & 17.71 \\ \hline \end{tabular}
\end{table} 
Table 4: Frequency of the experimental model of the carbody with anti-bending bars as a function of the minimum diameter of the anti-bending bars.
# Online Static Security Assessment of Power Systems Based on Lasso Algorithm
## 4 Online Static Security Assessment Method
### Solution Using Online Static Security Assessment Method  
Based on the above overall frame of OSSA and the introduction of the Lasso module, the proposed method is discussed in this section. The online static security assessment method is presented by two parts: the OSSA module and the Lasso module. The application of the proposed method is discussed as follows.  
In the OSSA module, the load condition randomly varies from 50% to 150% of the base load; thus, the load condition needs to be identified. According to the values of active loads \(P_{D}\) and reactive loads \(Q_{D}\), the power system data are divided into different types for representing different load conditions.  
For the determined load condition, the set is randomly generated in the post-contingency condition. The set contains the observations and responses of the Lasso algorithm, and the observations are active power outputs \(P_{G}\), reactive power outputs \(Q_{G}\), voltages \(U\), taps of transformers \(T\), switching capacities of reactive power compensation capacitors \(Q_{C}\), and the status of all of the lines that are represented by \"0\" and \"1\"; the responses are corresponding security indices \(PI_{c}\). The number of sets is based on the number of outage lines and types of load conditions. Meanwhile, 20% of the sets are employed as test sets, and the others are applied as training sets. Then, each training set can obtain a Lasso module after training.  
Once all of the Lasso modules are trained, the Lasso algorithm can be employed for predicting the security index \(PI_{c}\) of a certain load condition with the line outage. According to the value of \(PI_{c}\), the operating state of the power system can be identified, and the contingencies can be sorted and screened.