esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Implementation Details
### FIR Filter, Decimator and Demodulator  
#### 4.3.1 Adaptive FIR Filter  
FIR filter in the receiver chain is a matched filter to the baseband half-sine pulse. Frequency response of the filter is shown in Figure 9(b). Figure 12 shows the structure of the adaptive FIR filter. As shown, the adaptive FIR structure has one CORDIC unit for coefficient generation, a theta generator for CORDIC unit, one central controller, and \(30\) taps (corresponding to maximum sampling frequency). The CORDIC unit generates FIR coefficients that are input to multipliers. The theta generator supplies phase values to CORDIC unit to generate coefficients. Generating FIR coefficients with CORDIC makes it more amenable to adaptive architecture. The phase values depend on \(f_{s}\). Resolution of coefficients are controlled based on \(Q_{dig}\).  
Figure 12: Adaptive FIR Filter. Controller controls the multiplexer to select which input to pass on to next delay element. A filter tap is deactivated by feeding a zero to its delay elements. Based on \(Q_{dig}\) controller controls the word length of FIR coefficients. Multipliers are Baugh–Wooley multipliers.  
Tap coefficients are sampled half sinusoid. Frequency response of filter is shown in Figure 9(b). Since the tap coefficients are symmetric, the filter has folded architecture with number of taps equal to half of number of coefficients. Number of taps need to be adjusted with change in sampling frequency to keep it matched. Each tap in the filter consists of a multiplexed delay element, an adder and a multiplier. The multiplexer is a \(3\times 1\) multiplexer. Depending on the sampling frequency, either a zero or output of the preceding delay element or input to the FIR filter is multiplexed to the input of delay element. As shown in the figure, when the sampling frequency is \(3\) Msps, delay elements numbered 14 and 15 are active and all other delay elements have zero inputs. Multipliers corresponding to inactive taps get zeros at its input and hence have no dynamic power. The carry save adder adds outputs of the multipliers.
# Research on Vehicle-to-Vehicle MIMO Wireless Channels in Various Tunnels
## 1 Introduction
### Prior Works
However, in principle, it is difficult, almost impossible, to determine the position of interfering objects in the propagation characteristics of the V2V channels in the time, space, and frequency domains, which makes it difficult to accurately evaluate the performance of the V2V tunnel communication systems.
# Low Ionosphere under Influence of Strong Solar Radiation: Diagnostics and Modeling
## 3 Results
### Comparison of Obtained Ionospheric Parameters
Excluding the results from [PERSON] et al., we obtain for the \(\beta\) a linear dependence, as in Equation (4), where now \(k_{1}\) = 0.89756 and \(k_{2}\) = 0.09654. Generally, the results show that electron density can be obtained by processing the observed amplitude and phase perturbations with a specially developed method. More precisely, we obtained parameters \(H^{\prime}\) and \(\beta\) which were further used to obtain the electron density for different classes of SF starting from usual to extreme. The parameters are in good agreement with the most of the existing data from the literature. As expected, the electron density time variation during solar-induced SIDs follows the variation of the registered solar GOES flux. As a conclusion, \(N\)e varies in time and height (few orders of magnitude) during SFs.
## 3 Control Strategy Approach  
The proposed control strategy deals with system voltages, currents, and powers of the transmission lines given in Figure 2.  
The active and reactive powers of the system are given by Equations (2) and (3):  
\[P_{S}=V_{1d}I_{1d}+V_{1q}I_{1q} \tag{2}\]  
\[Q_{S}=V_{1d}I_{1q}-V_{1q}I_{1d} \tag{3}\]  
where \(V_{1d}\) and \(V_{1q}\) are the direct and quadrature of the system terminal voltages, the equations of which are given in Equations (4) and (5).  
\[V_{1d}=\sqrt{\frac{2}{3}}\Big{(}V_{1a}\sin\theta+V_{1b}\sin\Big{(}\theta-120^{ \circ}\Big{)}\ +\ V_{1c}\sin\Big{(}\theta+120^{\circ}\Big{)}\Big{)} \tag{4}\]  
\[V_{1q}=\sqrt{\frac{2}{3}}\Big{(}V_{1a}\cos\theta+V_{1b}\cos\Big{(}\theta-120^{ \circ}\Big{)}\ +\ V_{1c}\cos\Big{(}\theta+120^{\circ}\Big{)}\Big{)} \tag{5}\]  
\(I_{1d}\) and \(I_{1q}\) are the direct and quadrature of the system terminal currents, given by Equations (6) and (7).  
\[I_{1d}=\sqrt{\frac{2}{3}}\Big{(}I_{1a}\sin\theta+I_{1b}\sin\Big{(}\theta-120^{ \circ}\Big{)}\ +\ I_{1c}\sin\Big{(}\theta+120^{\circ}\Big{)}\Big{)} \tag{6}\]  
\[I_{1q}=\sqrt{\frac{2}{3}}\Big{(}I_{1a}\cos\theta+I_{1b}\cos\Big{(}\theta-120^{ \circ}\Big{)}\ +\ I_{1c}\cos\Big{(}\theta+120^{\circ}\Big{)}\Big{)} \tag{7}\]  
PLL is used to obtain \(\sin\theta\) and \(\cos\theta\) in the voltage Equations (4) and (5), as well as the current Equations (6) and (7). The dq transformation is used for obtaining the direct axis (as well as the quadrature axis) of the voltages and currents in Equations (4)-(7).
# Wind Source Localization System Based on a Palm-Sized Quadcopter
## 4 Wind Source Localization Experiment
### Experimental Results
The blue and black lines represent successful and failed localization, respectively. The red area indicates the goal area. (**a**) Scenario 1. (**b**) Scenario 2. (**c**) Scenario 3. Figure 6: Statistical properties of experimental results. (**a**) Localization success rate ([PERSON]’s exact test, \(p<0.05\)). (**b**) Localization time (Wilcoxon rank-sum test, \(p<0.05\)). (**c**) Upwind rate (Steel–Dwass test, \(p<0.05\)). with red indicating the mean value. The 0-degree direction represents the upwind direction. Although a bias exists toward the rear direction in the windless condition, almost every azimuth has a value. Conversely, more wind directions were detected around the 0-degree direction under windy conditions. Moreover, in Scenario 3, with higher wind speeds, the rose plot shows a sharper distribution toward the upwind direction. These findings confirm that even with a palm-sized quadcopter, minimizing the wake effects allows for the accurate detection of wind direction and speed. It also underscores the importance of combining wind speed information to determine movement direction, particularly in windless environments with a higher risk of moving in incorrect directions due to wake effects.
## 3 Materials and Methods
### Third-Party Perspective
#### 3.4.1. QRCode-Based Third-Party View Device Identification Method  
There are two methods for third-party perspective device identification, namely computer vision-based and QRCode-based. The computer vision-based approach requires modeling all known ARCore-enabled devices and identifying the specific model and location of the device through a neural network. This method entails a large amount of manually labeled data for training and has poor recognition stability and complex implementation, as well as poor ease of use. It also involves retraining a new model for recognition if a new device appears. The QRCode-based method does not require a large amount of data to train the model and has high recognition efficiency and stability. By contrast, this approach makes it very simple to be ported to new devices in the future. The QRCode-based approach is therefore more suitable for the recognition of third-party viewpoint devices, and is implemented using the special toolkit. Each QRCode corresponds to a 3D coordinate system, and the special toolkit identifies the position of the QRCode in space and returns this 3D coordinate system. The position information and the coordinate system information are used to calculate the coordinates of the third-party perspective device in real-world space, as well as the device's rotation angle and its corresponding quaternion. At this point, the identification of the third-party view device is completed. The coordinate information, rotation angle information, and the corresponding quaternion information will be used in the subsequent spatial coordinate alignment algorithm. The QRCode identification steps are as follows.  
1. The recognition module is invoked by clicking the recognition button in HoloLens and sending a request to the server to start recognition.
2. The third-party viewpoint device displays the QRCode on the screen after receiving the start recognition request distributed by the server.
3. HoloLens recognizes the QRCode on the screen of the third-party view device.
4. To complete the recognition of the device, calculate the coordinates of the third-party view device in the real-world space as well as the device's rotation angle and its corresponding quaternion.
# A Review on LiFi Network Research: Open Issues, Applications and Future Directions
## 4 Applications of the LiFi Network
### Transportation  
LiFi can be integrated into many areas. One that has gained momentum is transportation. Research application topics in this context included Vehicle-To- Vehicle communication (V2V) [53], Vehicle-To-Person communication (V2P) [4], Vehicle-To-Infrastructure (V2I) [150], and Vehicle-To-Network communication (V2N) [150]. Based on the nature of these applications, the role of LiFi technology from the literature has shown to be among the most critical ones, mainly because of the capabilities of LiFi to function as a real-time communication technology. Information can also be shared through car headlights. Therefore, contact can be generated between the driver and the vehicle. Furthermore, LiFi has massive potential in other ranges of car applications because it enables extensive collaboration and crash avoidance [17]. In traffic signals, LiFi can also be attached to vehicles via LED lights, thereby helping in handling traffic in geographical areas which has high density of cars more effectively. This technology can assist in the continuous regulation of traffic and reduce pollution and injury frequency. For example, LED car lights with LiFi sensors can warn drivers when other cars come too close. Radio waves interfere with PCs and cell phones. The interfering signals could also be unsafe and might affect the safety of a patient [17].  
Furthermore, Wi-Fi is unauthorized in certain domains because of the interaction among signals such as those in radiation operating rooms. Wireless Internet in several hospitals can obstruct the signals of control devices. LiFi solves these issues, often in operating rooms when the network is constant rather than flickering. LiFi is ideally used in modern medical facilities. Thus, addressing the challenges, including interference, which occur when using wireless technologies, such as IR and RF. Thus, LiFi is a substantial complementary approach to radio, being particularly attractive in scenarios where RF and/or IR fail to provide security, privacy, and zero electromagnetic interference [151]. This avoidance of electromagnetic radiation challenges which occurs with other wireless technologies in locations like aircraft, surgical facilities, and the oil and petrochemical industries enables many new opportunities [22; 152].
# Tailhook Recognition for Carrier-Based Aircraft Based on YOLO with Bi-Level Routing Attention
## 4 Experiments
### Datasets  
#### 4.2.1 CATH-DET  
The carrier-based aircraft tailhook recognition dataset (CATHR-DET) [27], constructed by [PERSON] and his team, is a dataset specifically designed for the task of carrier-based aircraft tailhook recognition. It is used to evaluate and test the performance of models aimed at recognizing carrier-based aircraft tailhooks. Combining real-world samples with high-quality visual simulation samples provides a diverse and comprehensive testing platform for tailhook recognition models. The dataset contains videos and images of various types of carrier-based aircraft landing operations collected from public sources, which are generated through visual modeling and the simulation of carrier-based aircraft landing operations. It covers tailhook images with different color and texture characteristics, as well as images under different meteorological conditions such as sunny, cloudy, and rainy days and under two lighting conditions: bright light and dark field. For the samples containing carrier-based aircraft tailhooks in the dataset, the bounding boxes are manually annotated to provide accurate positioning information for the targets. By combining real and simulated samples, CATHR-DET not only provides a wealth of scenarios and environmental conditions but also simulates various factors that may impact recognition performance, such as different lighting and meteorological conditions. This dataset design facilitates the training and evaluation of more robust and accurate carrier-based aircraft tailhook recognition models, which is crucial for enhancing the safety and efficiency of carrier-based aircraft landing operations. The dataset comprises a total of 4000 images, each with a size of 1300 \(\times\) 600. In this study, the dataset was randomly split into a training set and a test set in an 8:2 ratio. Some examples from the dataset are illustrated in Figure 9.
## 1 Introduction
### Research Background and Purpose
Thus, thermal imaging cameras can replace or supplement CCTV according to service characteristics and are essential for preventing civil safety accidents that are difficult to detect on CCTV. In addition, to monitor the safety of more citizens, it should be possible to operate them at the CCTV control center of the local government. Local government officials, who are major consumers, need decision-making support when selecting thermal camera-based services. Local government officials who monitor civil safety have abundant experience and know-how in operating services on civil safety but lack expertise in the technology itself. To this end, it is necessary to reflect the opinions of developers who specialize in understanding the specificity of thermal cameras along with local governments. Academically, related studies have mainly focused on the experimentation and advancement of individual technologies. However, research from a comprehensive perspective for practical urban control is insufficient. This study aimed to evaluate the preference for abnormal detection services using a thermal camera to control threat situations that are difficult to grasp immediately in general CCTV images. The service model presented in this study refers to a control service that detects abnormal situations that threaten the safety of citizens' lives using images captured by a thermal camera as materials for image analysis. While numerous studies have examined individual technology/services based on Thermography/IR images observed by thermal cameras, few studies examine comprehensive decision-making support to introduce practical thermal camera-based image analysis technologies. Meanwhile, this study conducts in-depth interviews and questionnaires with an expert group comprising operating personnel in local governments' control centersand Thermography/IR image analysis developers. Through this, we intend to support decision-making related to the selection and preference evaluation of abnormal situation detection services via thermal cameras. Comparing the differences in perceptions between the two groups implies service preference differences between providers (i.e., developers) and consumers (e.g., local governments). The findings can help inform the selection of core services in crime prevention, traffic, and disasters that are related to the safety of citizens within a city. To this end, in the remainder of Section 1, we review the research scope and methods, followed by a literature review. In Section 2, we suggest a methodology for service preferences. In Section 3, we set up abnormal situation detection services based on Thermography/IR images. In Section 4, we conduct an empirical analysis of this model, including evaluation criteria and service evaluation. Based on this analysis, in Sections 5 and 6, we discuss the findings and their implications.
# Study on Nitrogen-Doped Graphene Ink and Its Effects on the Heat Dissipation for the LED Lamps
## 3 Experiment Description
### Experiment of Nitrogen-Doped Graphene
#### 3.1.1 Atomic Force Microscopy (AFM)  
In this study, AFM (Innova produced by Bruker) was used for the surface measurement of NDG. The etched silicon probe (ESP), had a specification of 0.2 N/m, 13 kHz, and Al reflective coating. The tip of the probe had a radius of 8 nm, height of 12.5 um, and a set back from the end of the cantilever beam of 15 um. The front angle, back angle, and side angel of the tip were 25\({}^{\circ}\), 15\({}^{\circ}\), and 22.5\({}^{\circ}\), respectively.
###### Abstract  
The present research aimed to evaluate the antibacterial activity of volatile organic compounds (VOCs) produced by octocoral-associated bacteria _Bacillus_ sp. BO53 and _Pseudoalteromonas_ sp. GA327. The volatilome bioactivity of both bacteria species was evaluated against human pathogenic antibiotic-resistant bacteria, methicillin-resistant _Staphylococcus aureus_, _Acinetobacter baumannii_, and _Pseudomonas aeruginosa_. In this regard, the in vitro tests showed that _Bacillus_ sp. BO53 VOCs inhibited the growth of _P. aeruginosa_ and reduced the growth of _S. aureus_ and _A. baumannii_. Furthermore, _Pseudoalteromonas_ sp. GA327 strongly inhibited the growth of _A. baumannii_, and _P. aeruginosa_. VOCs were analyzed by headspace solid-phase microextraction (HS-SPME) joined to gas chromatography-mass spectrometry (GC-MS) methodology. Nineteen VOCs were identified, where 5-acetyl-2-methylpyridine, 2-butanone, and 2-nonanone were the major compounds identified on _Bacillus_ sp. BO53 VOCs; while 1-pentanol, 2-butanone, and butyl formate were the primary volatile compounds detected in _Pseudoalteromonas_ sp. GA327. We proposed that the observed bioactivity is mainly due to the efficient inhibitory biochemical mechanisms of alcohols and ketones upon antibiotic-resistant bacteria. This is the first report which describes the antibacterial activity of VOCs emitted by octocoral-associated bacteria.  
octocoral-associated bacteria; antibacterial activity; volatilome; _Bacillus_; _Pseudoalteromonas_; bacteria volatile organic compounds; antibiotic-resistant bacteria +
Footnote †: journal:  
_Article_
# Focus Estimation Methods for Use in Industrial SFF Imaging Systems
###### Abstract  
A Shape-From-Focus (SFF) is a three-dimensional imaging method based on focus information. It is not yet widely used for in-line industrial inspection or measurement tasks. The main reasons are the time it takes to capture a 3D image of the inspected product and the presence of interference affecting image quality. This paper compares operators for estimating focus in source images in the scope of their use in constructing an industrial 3D scanner. Interferences were introduced by using additional illuminators and changing the acquisition parameters. The use of industrial-grade cameras, industrial-grade illuminators, and electrically controlled optics are discussed. A novel approach in the research is using an electrically tunable lens to move the position of the image plane during 3D image acquisition. The research was conducted on various surfaces found on typical industrial products. The research showed which focus estimation operators can be applied to SFF imaging within the range of interference considered. It was also confirmed that using the centre of gravity method for scene reconstruction allows for an increase in resolution compared to the maximum method.  
Shape-From-Focus; imaging; focus estimation; depth information; focus tunable lenses; image processing +
Footnote †: journal: Applied  
0000-0001-552-227][PERSON] *
## 4 Experimental Results
### Estimation of SoH with Deep Learning-Based Approaches  
The hyperparameters used in the study conducted with Bi-LSTM [20], one of the deep learning models, are listed in Table 8. Charge capacity data in the dataset were considered as a time series and were employed for the deep learning model.  
In the Bi-LSTM deep learning algorithm, each data was considered as a consecutive time series. Charge capacity data were used for training and testing. The next charge capacity, evaluated as a time series, revealed that the next data are important. According to the results obtained, it yielded a successful result, with an MSE value of 0.019618 and an MSE value of 0.14006. Bi-LSTM is illustrated to give better results than machine learning, regression models, and ANN models.  
Graphs of the results obtained from the Bi-LSTM deep learning algorithm are pointed out in Figure 6. These are the graphs showing targets and forecasts. Also, the shown graphs of the metrics were used to measure model performance.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Model** & **Model** & **Preset** & **RMSE** & **MSE** & **R-Squared** & **MAE** & **RMSE** & **MSE** & **R-Squared** & **MAE** \\
**Number** & **Type** & & **(VAL)** & **(VAL)** & **(VAL)** & **(VAL)** & **(Test)** & **(Test)** & **(Test)** \\ \hline \hline \multirow{3}{*}{1} & Neural & Narrow & \multirow{3}{*}{0.207} & \multirow{3}{*}{0.043} & \multirow{3}{*}{0.772} & \multirow{3}{*}{0.122} & \multirow{3}{*}{0.192} & \multirow{3}{*}{0.037} & \multirow{3}{*}{0.808} & \multirow{3}{*}{0.118} \\  & Network & Neural Network & & & & & & & \\ \cline{1-1}  & Neural & Medium & & & & & & & \\ \cline{1-1}  & Network & Neural Network & & & & & & & \\ \cline{1-1}
**3** & **Neural** & **Wide Neural** & & & & & & & \\ \cline{1-1}  & **Network** & **Network** & & & & & & & \\ \cline{1-1}  & Neural & Blayered & & & & & & & \\ \cline{1-1}  & Network & Neural Network & & & & & & & \\ \cline{1-1}  & Neural & Tralyered & & & & & & & \\ \cline{1-1}  & Network & Neural Network & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results obtained from the neural network.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Number** & **Model Type** & **Hyperparameters** & **Optimizer Options** \\ \hline \hline \multirow{8}{*}{1} & Epoch: 400 & \multirow{8}{*}{Adam} \\  & & MiniBatchSize: 64 \\  & & GradientThreshold: 1 \\ \cline{1-1}  & LSTM Bayesian & LearnRateDropPeriod: 125 \\ \cline{1-1}  & & LearnRateDropFactor: 0.2 \\ \cline{1-1}  & & Verbose: 1 \\ \cline{1-1}  & & Training: 80\% Test: 20\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Hyperparameters used for LSTM.