esa-sceva/satcom-chunk-collection huggingface dataset:
# Bird Detection on Power Transmission Lines Based on Improved YOLOv7
## 3 Enhanced YOLO v7 Model
### Alpha_GloU Loss Function  
\(Alpha\_GloU\) is a loss function employed in object detection tasks to assess the alignment between a predicted bounding box and a target bounding box, accounting for both positional disparities and size variations [26]. This loss function represents an enhanced iteration of the Intersection over Union (_IoU_) loss function and introduces a balancing parameter, alpha, to fine-tune the trade-off between _IoU_ and Generalized IoU (_GloU_). Utilizing the \(Alpha\_GloU\) loss function in the training of object detection models enhances the localization and classification accuracy of object bounding boxes.  
Here is the mathematical expression of the \(Alpha\_GloU\) loss function:  
\[Alpha\_GloU=IoU(C-U)/C \tag{3}\]  
where:  
* _IoU_ quantifies the relationship between the predicted bounding box and the target bounding box by typically representing the ratio of the intersection area of these bounding boxes to their union area.
* \(C\) represents the area of the smallest convex polygon encompassing both bounding boxes.
* \(U\) denotes the area encompassed by the union of the two bounding boxes.
* \(Alpha\_GloU\) signifies the value of the \(Alpha\_GloU\) loss function.  
A notable feature of the \(Alpha\_GloU\) loss function is its consideration of both positional and size disparities between the predicted and target bounding boxes. It penalizes size deviations through the term associated with \(C\). When alpha is set to 0, the \(Alpha\_GloU\) loss function simplifies to the traditional _IoU_ loss function. In contrast, when alpha is set to 1, the \(Alpha\_GloU\) loss function transforms into the \(GloU\) loss function, which strikes a balance between positional and size deviations. Thus, by adjusting the alpha value, one can achieve an appropriate trade-off between position and size, leading to improved object detection performance.  
When \(\alpha=1\), the \(Alpha\_GloU\) loss function is the original \(GloU\) loss function. Hence, in this investigation, the \(Alpha\_GloU\) loss function is selected to substitute the \(CloU\) loss function in the baseline model, with the power parameter \(\alpha\) set to 3.
## 3 Analysis of the Method
### Absolute Stability  
If errors introduced in one time step do not accumulate in successive time steps, the general stability of a numerical approach is confirmed. A region of absolute stability is defined as the set of points \(z\in\mathbb{C}\) such that the roots of the characteristic equation associated with the test equation \(y^{\prime}=\lambda y\) (\(\lambda<0\)) lie within a unit circle. For hybrid block methods, the absolute stability in the first non-overlapping interval is well established (see, for example, [5]). When this test problem is applied to the overlapping grid methods (11), the following matrix equation is obtained:  
\[Y_{n+1}=R(z)Y_{n} \tag{23}\]  
where \(z=\lambda h\) and \(R(z)\) is a matrix equation given by  
\[R(z)=(A_{1}-z\hat{B}_{1})^{-1}(A_{0}+z\hat{B}_{0}). \tag{24}\]  
By finding the eigenvalues of the matrix \(R(z)\), one can derive the stability function \(H(z)\). Figure 2 depicts, for illustrative purposes, the absolute stability region of the proposed overlapping grid hybrid block method created from optimally spaced and equally spaced intra-step points for \(m=3\). Given that the shaded area encompasses the entirety of the left half-plane, the absolute stability of the overlapping HBM is confirmed for both equally spaced and optimal intra-step points.  
Figure 2: Stability regions for the overlapping hybrid block method. (**a**) Equally spaced intra-step points. (**b**) Optimal intra-step points.
## 2 System
### Step Detection  
Step detection is performed based on the acceleration, angular velocity, and attitude estimation results. Previous studies have commonly used only the \(z\)-axis acceleration for estimation. However, because the magnitude of acceleration changes frequently depending on the way of walking, this introduces the possibility of a large error in the number of steps. Therefore, in this study estimation is performed using not only the acceleration along the \(z\)-axis but also the acceleration along the \(x,y\)-axis, walking interval, and magnitude of angular velocity. Based on the results of attitude estimation, we determine whether the subject is engaged in hand-held walking or hand-swinging walking. If the \(Roll\) axis (\(y\)-axis) is tilted by more than a certain angle, the user is judged to be engaged in hand-swinging walking; otherwise, they are judged to be engaged in hand-held walking. If the angular velocity around the \(Roll\) axis is larger than a certain value, this is considered to indicate a switch between hand-held walking and hand-swinging walking, and the number of steps is not counted.
## 2 Numerical Method
### Fluid Solver  
In the MRT-based IB-LBM, the evolution equation of the velocity distribution function \(g_{i}\) along the \(i\)-th direction at position \(\mathbf{x}\) with BGK approximation is expressed as [35]  
\[g_{i}(\mathbf{x}+\mathbf{e}_{i}\Delta t,t+\Delta t)=g_{i}(\mathbf{x},t)-\Omega_{i}(\mathbf{x},t )+\Delta tG_{i},\quad i=0,1,...,8 \tag{1}\]  
where \(\Delta t\) is the time step. The collision operator \(\Omega_{i}\) and \(G_{i}\), which represent the body force effects on the distribution function, are defined as  
\[\Omega_{i} = -(M^{-1}SM)_{ij}[g_{j}(\mathbf{x},t)-g_{j}^{eq}(\mathbf{x},t)] \tag{2}\] \[G_{i} = [M^{-1}(I-S/2)M]_{ij}F_{j} \tag{3}\]  
where \(M\) is a \(9\times 9\) transform matrix for the D2Q9 model (two dimensional with 9 directions) used here, and \(S\) is a non-negative diagonal matrix related to the fluid viscosity. The details for the determination of \(S\) can be found in [36]. The lattice speed \(\mathbf{e}_{i}\) is defined as  
\[\mathbf{e}_{i}=\begin{cases}(0,0),&i=0\\ (\cos(\pi(i-1)/2),\sin(\pi(i-1)/2))\frac{\Delta x}{\Delta t},&i=1,2,3,4\\ \sqrt{2}(\cos(\pi(i-9/2)/2),\sin(\pi(i-9/2)/2))\frac{\Delta x}{\Delta t},&i=5,6,7,8\end{cases} \tag{4}\]  
where \(\Delta x\) is the lattice spacing. Then, the macro density and momentum are given as follows,  
\[\rho=\sum_{i=1}^{8}g_{i},\quad\rho\mathbf{u}=\sum_{i=1}^{8}g_{i}\mathbf{e}_{i}+\frac{ 1}{2}f\Delta t. \tag{5}\]  
The local equilibrium distribution function \(g_{i}^{eq}\) and the force term \(G_{i}\) are calculated by  
\[g_{i}^{eq}=\omega_{i}\rho[1+\frac{\mathbf{e}_{i}\cdot\mathbf{u}}{c_{s}^{2}}+\frac\mathbf{{u}\cdot(\mathbf{e}_{i}\mathbf{e}_{i}-c_{s}^{2}\mathbf{I})}{c_{s}^{4}}] \tag{6}\]  
\[G_{i}=\omega_{i}[\frac{\mathbf{e}_{i}-\mathbf{u}}{c_{s}^{2}}+\frac{\mathbf{e}_{i}\cdot\bm {u}}{c_{s}^{4}}\mathbf{e}_{i}]\cdot\mathbf{f} \tag{7}\]  
where the weights \(\omega_{i}\) are given by \(\omega_{0}=4/9\), \(\omega_{i}=1/9\) for \(i=1,2,3,4\), and \(\omega_{i}=1/36\) for \(i=5,6,7,8\). The sound speed \(c_{s}=\Delta x/(\sqrt{3}\Delta t)\), and \(\mathbf{f}\) is the force acting on the fluid. The relaxation time is related to the kinematic viscosity \(\
u\) in the Navier-Stokes equations in terms of \(\
u=(\tau-0.5)c_{s}^{2}\Delta t\). The rheological equation of state for power-law non-Newtonian fluid considered in this paper is defined by [27]  
\[\
u=m\left(\frac{l_{2}}{2}\right)^{(n-1)/2} \tag{8}\]  
where \(m\) is the power-law consistency index, \(n\) is the power-law fluid behaviour index, and \(l_{2}\) is the second invariant of the rate of strain tensor. Additionally, the multi-block technique developed by [PERSON] et al. [14] is combined with the geometry-adaptive method to enhance the computational efficiency.
###### Abstract  
This research studies the effects of both Daubechies wavelet basis function (DWBF) and decomposition level (DL) on the performance of detecting atrial fibrillation (AF) based on electrocardiograms (ECGs). ECG signals (consisting of 23 AF data and 18 normal data from MIT-BIH) were decomposed at various levels using several types of DWBF to obtain four wavelet coefficient features (WCFs), namely, minimum (min), maximum (max), mean, and standard deviation (stdev). These features were then classified to detect the presence of AF using a support vector machine (SVM) classifier. Distribution of training and testing data for the SVM uses the 5-fold cross-validation (CV) principle to produce optimum detection performance. In this study, AF detection performance is measured and analyzed based on accuracy, sensitivity, and specificity metrics. The results of the analysis show that accuracy tends to decrease with increases in the decomposition level. In addition, it becomes stable in various types of DWBF. For both sensitivity and specificity, the results of the analysis show that increasing the decomposition level also causes a decrease in both sensitivity and specificity. However, unlike the accuracy, changing the DWBF type causes both two metrics to fluctuate over a wider range. The statistical results also indicate that the highest AF accuracy detection (i.e., 94.17%) is obtained at the Daubechies 2 (DB\({}_{2}\)) function with a decomposition level of 4, whereas the highest sensitivity, 97.57%, occurs when the AF detection uses DB\({}_{6}\) with a decomposition level of 2. Finally, DB\({}_{2}\) with decomposition level 4 results in 96.750% for specificity. The finding of this study is that selecting the appropriate DL has a more significant effect than DWBF on AF detection using WCF.  
atrial fibrillation detection; feature extraction; wavelet coefficient; Daubechies wavelet basis function; artificial intelligence
# Analysis and Design of Functional Device for Vehicular Cloud Computing
## 3 System Model
### V2I Communication  
All vehicles communications in vehicular networks can be regard as a VC for processing some complicated service applications. In addition, all vehicles can upload data to the VCs through RSUs in their respective region. Meanwhile, each RSU also provides wireless access services within its communication range for the vehicles. For complex service applications and computation, vehicles transmit their data to RSUs by a V2I communication mode as of limited computation resource. Based on the V2I communication mode, the vehicle can only access the corresponding RSU located on the road when it runs within a given segment.  
The communication between the adjacent RSUs through wireless backhauls. The RSUs establish a communication connection with the VCs. Vehicles within the communication range of each RSU cannot transmit the received data to the other RSU. In other words, the vehicle in the coverage area of \(RSU_{a}\) cannot communicate with \(RSU_{b}\) directly. The same applies to vehicles in the coverage area of \(RSU_{b}\). The vehicle cloud formed by all vehicles in networks can communicate with any device in the entire network. This communication mechanism can improve the transmission efficiency of wireless backhauls. Vehicles also receive data/commands from the corresponding RSU for VC.
###### Abstract  
Three-dimensional (3D)-printed models have been extensively applied in operative planning and medical education to directly visualize anatomical structures and gain tactile experiences. Although studies are available on the use of 3D printing technology in the gastrointestinal tract, there is a lack of detailed analyses of its current applications, particularly in the context of 3D-printed gastrointestinal tract models for surgical planning and education. Therefore, this systematic review aims to analyze the current application of 3D printing technology in gastrointestinal tract diseases, focusing on the techniques, materials, anatomical structures, and the impact of its use. A systematic search was conducted across the PubMed/Medline, Scopus, and Embase databases adhering to the PRISMA 2020 protocols. A total of 25 articles were identified as eligible for review. The findings revealed that 3D-printed gastrointestinal tract models can enhance technical skills, knowledge, and confidence in performing gastrointestinal surgery or other procedures in a risk-free environment. However, most studies (76%) were limited by their small sample size, with only 1-3 models printed, and lacked comparative analysis. The influence of this procedure on actual patients was not followed up; hence, the impact of this simulator on clinical practice outcomes remains unknown. Most of the 3D-printed models were designed for a single procedure, limiting their widespread application. Future research should focus on developing more realistic printed materials to accurately simulate real organs, including large sample sizes; comparing 3D-printed models with other simulators or other visualization modalities such as virtual reality and mixed reality; and investigating their impact on actual gastrointestinal procedures.  
Keywords:three-dimensional printing; gastrointestinal tract; medical education; surgical planning; additive manufacturing +
Footnote †: journal: Applied sciences
## 2 Data and Methodology
### Data
#### 2.1.3 Data Merging and Cross-Validation  
Since the reanalysis data's time period totally overlaps the period in which the measurements were taken, it is possible to merge both data sets (ERA5 data and the measurements) to form another one that covers that time period. It was done in a way that every data point corresponded to a timestamp and had the wind speed modules and directions of the anemometer and the reanalysis. Since ERA5 data had the lowest sampling frequency, every measured speed and direction was matched with the ERA5 data point that was closest in time, and all other measurements were discarded.  
The anemometer took a sample every 7 min when working properly, so, in most of the final data set, the ERA5's and the anemometer's timestamps of each data point were no more than 3.5 min apart. However, the gaps in the anemometer's time series could theoretically mean that some ERA5 data points could be matched with measurements taken a long time apart, or that various of them were matched with the same measurement. Those mismatched data points could disrupt the calibration, but luckily none of the gaps was large enough to do so, as the biggest difference between the anemometer's and ERA5'd timestamp for the same data point was around 5 min. This meant that, in this particular case, no data points were discarded.  
In order to cross-validate the results, this data set was randomly split in two equal-size sets: one for training and the other one for testing. The splitting was made just once for the whole study; that means that all the quantile mapping calibrations, the classification via random forest, and the estimation of produced energy done on the 1-year data set were trained and tested with the same training and testing data sets.  
By this point, we have three different data sets that contain timestamps and wind speed and direction data. Table 1 below shows the scope of each data set for clarification purposes.
###### Abstract  
The growing energy demand around the world has increased the usage of renewable energy sources (RES) such as photovoltaic and wind energies. The combination of traditional power systems and RESs has generated diverse problems due especially to the stochastic nature of RESs. Microgrids (MG) arise to address these types of problems and to increase the penetration of RES to the utility network. A microgrid includes an energy management system (EMS) to operate its components and energy sources efficiently. The objectives pursued by the EMS are usually economically related to minimizing the operating costs of the MG or maximizing its income. However, due to new regulations of the network operators, a new objective related to the minimization of power peaks and fluctuations in the power profile exchanged with the utility network has taken great interest in recent years. In this regard, EMSs based on off-line trained fuzzy logic control (FLC) have been proposed as an alternative approach to those based on-line optimization mixed-integer linear (or nonlinear) programming to reduce computational efforts. However, the procedure to adjust the FLC parameters has been barely addressed. This parameter adjustment is an optimization problem itself that can be formulated in terms of a cost/objective function and is susceptible to being solved by metaheuristic nature-inspired algorithms. In particular, this paper evaluates a methodology for adjusting the FLC parameters of the EMS of a residential microgrid that aims to minimize the power peaks and fluctuations on the power profile exchanged with the utility network through two nature-inspired algorithms, namely particle swarm optimization and differential evolution. The methodology is based on the definition of a cost function to be optimized. Numerical simulations on a specific microgrid example are presented to compare and evaluate the performances of these algorithms, also including a comparison with other ones addressed in previous works such as the Cuckoo search approach. These simulations are further used to extract useful conclusions for the FLC parameters adjustment for off-line-trained EMS based designs.  
microgrid; energy management system; fuzzy logic control; particle swarm optimization; differential evolution; cuckoo search algorithm; nature-inspired algorithms +
Footnote †: journal: Applied Sciences  
**C**** (2021)**  
**A Comparison of Fuzzy-Based Energy Management Systems Adjusted by Nature-Inspired Algorithms**  
**[PERSON], 1,2,*+, [PERSON] 1,2, [PERSON] 1,2, [PERSON] 1,2, [PERSON] 1,3,*, [PERSON] 1,2, [PERSON] 1,2, [PERSON] 4,5 and [PERSON] 6(c)**  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
**C**** (2021)**
# Trusted GNSS-Based Time Synchronization for Industry 4.0 Applications
## 4 Trusted Computing
### Integrity Measurement Architecture (IMA)
Available online: [[https://www.septentrio.com/en/products/gnss-receivers/rover-base-receivers/receivers-module/mosaic](https://www.septentrio.com/en/products/gnss-receivers/rover-base-receivers/receivers-module/mosaic)]([https://www.septentrio.com/en/products/gnss-receivers/rover-base-receivers/receivers-module/mosaic](https://www.septentrio.com/en/products/gnss-receivers/rover-base-receivers/receivers-module/mosaic)) (accessed on 6 September 2021). * (56) Tallysman(tm). VSP6037L VeroStar(tm) Full GNSS Precision Antenna Plus L-Band. [Online]. 2021. Available online: [[https://www.tallysman.com/product/vsp6037l-verostar-full-gnss-antenna-l-band/](https://www.tallysman.com/product/vsp6037l-verostar-full-gnss-antenna-l-band/)]([https://www.tallysman.com/product/vsp6037l-verostar-full-gnss-antenna-l-band/](https://www.tallysman.com/product/vsp6037l-verostar-full-gnss-antenna-l-band/)) (accessed on 6 September 2021). * (57) Uptronics(tm). Raspberry Pi GPS/RTC Expansion Board Datasheet, Revision 2.3. [Online]. 2021. Available online: [[https://store.uptronics.com/files/Uptronics%20](https://store.uptronics.com/files/Uptronics%20) Raspberry%20 PI%20 GPS%20 RTC%20 Board%20 Datasheet.pdf]([https://store.uptronics.com/files/Uptronics%20](https://store.uptronics.com/files/Uptronics%20) Raspberry%20 PI%20 GPS%20 RTC%20 Board%20 Datasheet.pdf) (accessed on 6 September 2021). * (58) Adafruit Industries. Ultimate GPS HAT for Raspberry Pi. [Online]. 2018. Available online: [[https://cdn-learn.adafruit.com/downloads/pdf/adafruit-ultimate-gps-hat-for-raspberry-pi:pdf?timestamp=1627027424](https://cdn-learn.adafruit.com/downloads/pdf/adafruit-ultimate-gps-hat-for-raspberry-pi:pdf?timestamp=1627027424)]([https://cdn-learn.adafruit.com/downloads/pdf/adafruit-ultimate-gps-hat-for-raspberry-pi:pdf?timestamp=1627027424](https://cdn-learn.adafruit.com/downloads/pdf/adafruit-ultimate-gps-hat-for-raspberry-pi:pdf?timestamp=1627027424)) (accessed on 6 September 2021). * (59) The NTP (R&D) Project. ntpq--Standard NTP Query Program. [Online]. 2018. Available online: [[http://doc.ntp.org/archives/4.2.8-series/ntpq/](http://doc.ntp.org/archives/4.2.8-series/ntpq/)]([http://doc.ntp.org/archives/4.2.8-series/ntpq/](http://doc.ntp.org/archives/4.2.8-series/ntpq/)) (accessed on 6 September 2021). * (60) The GPSD Project. gpsmon(1) Manual Page. [Online]. 2021. Available online: [[https://gpsd.io/gpsmon.html](https://gpsd.io/gpsmon.html)]([https://gpsd.io/gpsmon.html](https://gpsd.io/gpsmon.html)) (accessed on 6 September 2021).
# High-Reliability Wireless Pressure Measurement System Based on FEC Algorithm
## 5 Test Results and Analysis
### Upper Computer Testing  
To verify the reliability of the overall functions of the designed system, it was applied to a pressure parameter testing experiment of a certain type of aircraft engine. First, the engine was installed on the test bench. Then, the sensors to be tested were installed on the outer wall of the engine through threads according to the set direction and position. A professional aviation cable was used to connect the front-end memory with the main part of the testing system. A digital trigger cable was introduced to the memory to receive the trigger signal for the start of the test. The front-end memory was placed on a special shock-absorbing pad near the test bench, and the back-end testing station was arranged at a certain safe distance from the test bench. The entire testing system was powered on, and the back-end testing station was connected to the upper computer via an Ethernet cable. The upper computer sent commands to start monitoring and storage to initiate the experiment. After the experiment, the data in the flash storage module was read back. After being processed by the upper computer software, the curve was plotted as shown in Figure 15. The y-axis represents the pressure in MPa, and the x-axis represents time. From 0 to 13 s, the aero-engine is in the startup phase. After the engine ignites, the pressure in the combustion chamber gradually builds up, and the fan and compressor start to operate. From 13 to 26 s, the engine enters a stable idle state, and the pressure in the combustion chamber rises slightly. Subsequently, the engine thrust gradually increases, and the engine is in the medium-load stage, with a significant increase in the combustion chamber pressure. From 67 to 93 s, the engine is in the full-load stage, gradually reaching the maximum thrust state, and the combustion chamber pressure reaches its peak. Finally, the engine is shut down for cooling, and the pressure in the combustion chamber is gradually released.
## 2 Theory
### Optigrating Simulation
#### 2.1.2 Transmission Spectrum  
We also simulated the transmission spectrum of the FP structure in both 125 \(\upmu\)m cladding diameter and 70 \(\upmu\)m cladding diameter fiber, as shown in Figure 5. The spectrum possesses two transmission dips within the FBG main spectrum.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Core radius & 4.07 \(\upmu\)m \\ Cladding radius & 62.5 \(\upmu\)m \\ Surrounding medium radius & 20 \(\upmu\)m \\ Core RI & 1.46 \\ Cladding RI & 1.44681 \\ Surrounding medium RI & 1.0 \\ Design wavelength & 1550 nm \\ Total number of LP\({}_{l,m}\) modes & 110 [\(l\) = 0,1,2...10; \(m\) = 1,2,3...10] \\ Grating shape & Rectangle \\ Apodization & Uniform \\ Grating length & 3 mm \\ Structure length & 7 mm \\ RI modulation & \(3\times 10^{-4}\) \\ Number of segments & 80 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 4: Variation of effective refractive index.  
However, the aim is to fabricate the FP structure in an adiabatic fiber taper whose diameter has been tapered down to 70 \(\upmu\)m.