esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on the Damage of Optical Materials by out of Band Composite Laser
## 2 Laser Absorption in Optical Elements  
### Absorption and Scattering of Optical Film  
A flat window is composed of surface optical films and substrates. The following assumptions are used to solve the reflectivity of optical films: The film does not absorb incident light, its refractive index is uniform, and both interfaces are smooth. These assumptions accurately reflect the optical properties of general dielectric films. An optical film made of a single-layer film has a limited optical performance. To meet a variety of optical requirements, multi-layer films are needed. Suppose an optical film composed of an \(m\)-layer film--then, the \(j\)-layer has a refractive index of \(n_{j}\), and the feature matrix of this layer of film can be expressed as:  
\[A_{j}=\left[\begin{array}{cc}\cos\delta_{j}&\mathrm{i}n_{i}/\sin\delta_{j}\\ \mathrm{i}n_{j}\sin\delta_{j}&\cos\delta_{j}\end{array}\right] \tag{1}\]where \(\delta_{j}=(2\text{r}/\lambda)n_{j}d_{j}\) is the phase thickness of the \(j\)-th layer film. The combined feature matrix of the \(m\)-layer film is:  
\[\left[\begin{array}{cc}m_{11}&m_{12}\\ m_{21}&m_{22}\end{array}\right]=\prod_{j=0}^{m}\left[\begin{array}{cc}\cos \delta_{j}&\text{i}n_{i}/\sin\delta_{j}\\ \text{i}n_{j}\sin\delta_{j}&\cos\delta_{j}\end{array}\right] \tag{2}\]  
where \(n_{0}\), \(n_{\text{s}}\), and \(\lambda\) are the refractive index of the incident medium, the refractive index of the substrate, and the laser wavelength, respectively. For transparent dielectric films, \(m_{12}\) and \(m_{21}\) are pure imaginary numbers, and \(m_{1}\) and \(m_{22}\) are pure real numbers. The reflection coefficient r and reflectance \(R\) of the multilayer film are:  
\[r=\frac{(n_{0}m_{11}-n_{s}m_{22})+(n_{0}n_{s}m_{12}-m_{21})}{(n_{0}m_{11}+n_{s }m_{22})+(n_{0}n_{s}m_{12}+m_{21})} \tag{3}\]  
\[R=\frac{(n_{0}m_{11}-n_{s}m_{22})^{2}+(n_{0}n_{s}m_{21})^{2}}{(n_{0}m_{11}+n_{ s}m_{22})+(n_{0}n_{s}m_{12}+m_{21})^{2}} \tag{4}\]  
The reflectivity of the optical film can be solved by the above two equations [15; 16; 17].
# Review of Power System Resilience Concept, Assessment, and Enhancement Measures
## 2 Impacts of Extreme Events on Power Systems
### Snowstorms  
Snowstorms and ice events have severe impacts on the power grid, especially on overhead transmission lines and conductors. Apart from the possibility of towers and conductors being damaged due to icing, these events can also result in icing flashovers and sleet jumping. Power outages caused by snowstorms can thus lead to long-term service disruptions and significant economic losses [41; 42]. In 2020, a snowstorm hit the coastal areas of the Costa Brava in Spain, causing unexpected additional icing loads on transmission towers and resulting in damages to the power grid [13]. In February 2021, a heavy snowstorm caused power outages during a peak-demand period in Texas, affecting over 4.5 million customers and resulting in an estimated $130 billion in economic losses [43].
## 4 Computational Efficiency  
Regarding the description of computational efficiency, according to [PERSON] and [PERSON] (2011) [31], (Section 3.3, the first paragraph of that section), where it is explained as a standard way to quantify the time and space complexity of algorithms, \(O\) notation is used to describe the time and space complexity of algorithms, providing a standard method for evaluating algorithm performance. According to [PERSON] et al. (2015), the time complexity of HDBSCAN can be expressed as \(O(n\cdot logn)\). While the worst-case complexity is \(O(n^{2})\), it can achieve \(O(n\cdot logn)\) when spatial indexing is used. The time complexity of K-means is \(O(n\cdot k\cdot i)\), where \(k\) is the number of clusters and \(i\) is the number of iterations. When the dataset is very large and the number of clusters increases, the computational cost rises rapidly. In addition, the calculation of similar trajectories, such as the Mahalanobis distance, involves vector multiplication and summing squares, which requires covariance matrix computation. For \(n\) samples and \(p\) features, the time complexity is \(O(n^{2}\cdot p^{2})\), which clearly requires more. The Douglas-Peucker algorithm assumes the curve has n points. In the worst case, its time complexity is \(O(n\cdot logn)\), and in the best case, it is \(O(n)\). Therefore, it is generally expressed as \(O(n\cdot logn)\). The OPTICS algorithm's main time cost comes from calculating core distances and sorting. Assuming quicksort is used, the time complexity is \(O(n\cdot logn)\). The SQUISH algorithm has a time complexity of \(O(n)\) for interpolation, but if shape fitting or data compression is involved, it can reach \(O(n^{2})\) or higher.  
We understand that execution time is a key indicator in big data applications. However, we also recognize that execution time is significantly influenced by hardware specifications, data size, and parameter settings. In this experiment, the data volume is 293,000 records. By performing multiple clustering algorithm tests, we obtained the average execution times. Specifically, the average time for HDBSCAN over 10 runs was 38.37 s, DBSCAN was 213.16 s, K-means was 336.74 s, and OPTICS took the longest, with only 2 runs yielding an average time of 3091.87 s, as shown in Table 3. The execution time differences are substantial and deviate from the theoretical explanations of time complexity, which may be related to threshold settings. As we did not delve deeply into this, we plan to consider supplementing more experimental data in the future to further explore and more comprehensively evaluate the execution time of the algorithms.  
The hardware used in this study includes an HP Z6 G4 Workstation, Intel Xeon Silver 4208 CPU 2.10 GHz (11 M Cache, 2.10 GHz) 8 cores, 64 Gb RAM, SSD HDD, and NVIDIA Quadro RTX4000. OS and coding environments are Win10, MiniConda, Python v3.11 (Panda s1.5.3, Numpy 1.25.0 and Scikit-learn 1.0.2, Basemap modules, etc.).
# Rotating Lorentz Force Magnetic Bearings' Dynamics Modeling and Adaptive Controller Design
## 4 RLFMB Online Controller Design  
### Controller Performance Requirements Analysis  
The following three requirements serve as the foundation for the design of the RLFMB controller:  
1. The RLFMB's internal and exterior magnets are not designed to close, and the rotor swings back and forth within the operating air gap. To prevent rotor and stator collisions, we design the RLFMB's angular displacement output equation such that the angular velocity at the end point is 0.
2. RLFMB is oriented steadily to prevent needless shaking of the load compartment and frame compartment. We created an adaptive controller based on an RBF network, and we minimized input signal overshoot and static error. Additionally, the impact of any random system disturbances should be swiftly minimized.
3. We introduced a current feedback inner loop to improve the system's response time and control bandwidth while preventing excitation vibration of the input signal.  
The current widely used technique is to provide the input equation and construct the actuator as a program control system in order to limit the speed and position of the actuator at a specific point or over a specific period of time.  
The relevant rotor motion equation is determined from the input signal.  
For RLFMB, there are  
\[Bi(t)L_{a}D=J\alpha, \tag{9}\]  
The angular acceleration is expressed as  
\[\alpha(t)=\frac{Bi(t)L_{a}D}{J}=ki(t), \tag{10}\]  
In the above formula, \(k\) is the angular acceleration coefficient. It can be seen that the control of the current signal can realize the control of the diagonal acceleration. This paper uses the sinusoidal function as the rotor motion equation for RLFMB:  
\[\theta(t)=\frac{A\sin\omega_{e}t}{\omega_{e}k}, \tag{11}\]  
After seeking guidance, there is  
\[\omega(t)=\frac{A\sin\omega_{e}t}{k}, \tag{12}\]Let \(\omega_{t}\) be the ordinate and \(\theta(t)\) be the abscissa, so there is  
\[(\frac{\omega(t)k}{A})^{2}+(\frac{\theta(t)\omega_{e}k}{A})^{2}=1. \tag{13}\]  
This is an elliptical line's upper half. When using the highest value, it comes to zero. As a result, the angular velocity is zero when the rotor is at the magnet's edge, preventing it from touching the stator.
# Automatic Spatial Audio Scene Classification in Binaural Recordings of Music +
## 2 Materials and Methods
### Feature Extraction
#### 2.3.4 Mel-Frequency Cepstral Coefficient (MFCC) Features  
Mel-frequency cepstral coefficients are commonly used as spectral descriptors in the area of music genre recognition [21] as well as in the ASC algorithms [30]. Since they are very effective in \"summarizing\" a shape of the spectrum (spectral envelope), it could be tentatively hypothesized that they might also be useful in capturing some information regarding the influence of the head-related transfer functions on the spectral characteristics of binaural recordings (peaks and deeps across the frequency response). Hence, they could also represent some cues responsible for the front-back discrimination of the auditory objects. Consequently, it could be speculated that in addition to being effective spectral descriptors, the MFCCs might help to discriminate between \(FB\) and \(BF\) scenes.  
In our study, 40 MFCCs were extracted from the \(m\) and \(s\) signals, respectively. Rank of the coefficients ranged from 1 to 40. The selection of such an unusually large number of coefficients was in line with the recent trend in speech and audio classification [55; 56]. The zeroth coefficient, representing signal energy, was omitted. In summary, the following MFCC-based metrics were computed:  
1. means and standard deviations of the 40 MFCCs derived from \(x\) signal (\(2\times 40\) features),
2. means and standard deviations of the 40 MFCCs derived from \(y\) signal (\(2\times 40\) features),
3. means and standard deviations of the 40 MFCCs derived from \(m\) signal (\(2\times 40\) features),
4. means and standard deviations of the 40 MFCCs derived from \(s\) signal (\(2\times 40\) features).  
Therefore, the above procedure produced 320 features. Similar calculations were also performed for the delta-MFCCs, yielding additional 320 features (640 metrics in total).  
The RMS-based metrics, binaural cues, spectral features, and MFCC-derived features were extracted separately from the training and testing binaural excerpts. They were stored in two datasets (spreadsheets), intended for training and testing, respectively. For research reproducibility purposes, both spreadsheets were included in the publicly available repository accompanying this paper.
## 3 Materials and Methods
### Silanisation of Fused Silica Capillary  
Fused silica capillaries were washed with acetone (5 min, 1 \(\upmu\)L\(\cdot\)min\({}^{-1}\)), dried with nitrogen (10 min) and then flushed sequentially with 0.2 M NaOH (30 min, 1 \(\upmu\)L\(\cdot\)min\({}^{-1}\)), water (5 min, 1 \(\upmu\)L\(\cdot\)min\({}^{-1}\)) and 0.2 M HCl (5 min, 1 \(\upmu\)L\(\cdot\)min\({}^{-1}\)) before further drying with a nitrogen flow for 20 min. The capillary was filled with 50% (\(v/v\)) 3-(trimethoxysilyl) propyl methacrylate in acetone, sealed and incubated at 60 \({}^{\circ}\)C in a water bath for 48 h. Finally, the capillary was rinsed with acetone (5 min, 1 \(\upmu\)L\(\cdot\)min\({}^{-1}\)) and dried with nitrogen.  
Figure 6: Isocratic alkylbenzene separation on 22.9 cm \(\times\) 200 \(\upmu\)m i.d. capillary columns of a reduced graphene oxide nanoparticle (rGONP) PS-\(co\)-DVB column (red) and PS-\(co\)-DVB polyHIPE (blue). Mobile phase 50:50 acetonitrile:water, sample concentration 0.5 mg\(\cdot\)L\({}^{-1}\), injection volume 0.1 \(\upmu\)L, flow rate 5 \(\upmu\)L\(\cdot\)min\({}^{-1}\), and detection wavelength 214 nm. analytes noted as ethylbenzene (1), propylbenzene (2), butylbenzene (3) and pentylbenzene (4). (\(t_{\text{void}}\) denotes time taken for void volume to reach the detector).
# STHarDNet: Swin Transformer with HarDNet for MRI Segmentation
## 5 Experiments
### Speed Comparison Experiments of Models  
The golden time for strokes from the onset to diagnosis and treatment was less than one hour. Therefore, not only the segmentation performance, but also the image process speed, are critically important for stroke diagnosis models. Therefore, comparative experiments of image processing speed were conducted with the STHarDNet and the models used in the performance comparison experiments. In the experiments, the time consumed and frames per second (FPS) were recorded when 100,000 images were processed, respectively, in the same environment. Table 4 shows the results of the experiments on the speed comparison of the models.  
In Table 4, HarDNet showed the fastest speed with 307.58 FPS when processing 100,000 images, whilst STHarDNet was the second fastest with FPS of 299.943 FPS. STHarDNet was 2.48% slower than HarDNet when performing calculations with 100,000 images,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model Name**} & \multirow{2}{*}{**Input Image Size**} & \multicolumn{3}{c}{**100,000 Frames**} \\ \cline{3-4}  & & **Seconds** & **FPS** \\ \hline U-Net & 224 \(\times\) 224 & 475.109 & 210.477 \\ SegNet & 224 \(\times\) 224 & 415.869 & 240.459 \\ PSPNet & 224 \(\times\) 224 & 637.318 & 156.907 \\ HarDNet & 224 \(\times\) 224 & 325.117 & 307.58 \\ TransHarDNet & 256 \(\times\) 256 & 352.066 & 284.036 \\ Swin Transformer & 224 \(\times\) 224 & 1353.657 & 73.873 \\ Swin UNet & 224 \(\times\) 224 & 414.798 & 241.081 \\ X-Net & 224 \(\times\) 192 & 982.462 & 101.785 \\ D-UNet & 192 \(\times\) 192 & 572.719 & 174.605 \\ STHarDNet (Proposed) & 224 \(\times\) 224 & 333.395 & 299.943 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Speed comparison of models.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model Name** & **Input Type** & **Output Type** & **Dice** & **IoU** & **PrecisionRecall** \\ \hline U-Net [4] & 2D & 2D & 0.4517 & 0.3333 & 0.4831 & 0.5118 \\ SegNet [23] & 2D & 2D & 0.3751 & 0.2675 & 0.4418 & 0.3767 \\ PSPNet [24] & 2D & 2D & 0.4465 & 0.3282 & 0.5653 & 0.4200 \\ HarDNet [14] & 2D & 2D & 0.5066 & 0.3774 & 0.7358 & 0.4331 \\ TransHarDNet & 2D & 2D & 0.5051 & 0.3816 & 0.5785 & 0.5176 \\ Swin Transformer [15] & 2D & 2D & 0.1640 & 0.1053 & 0.7306 & 0.1247 \\ Swin UNet [16] & 2D & 2D & 0.4034 & 0.2883 & 0.5871 & 0.3402 \\ X-Net [17] & 2D & 2D & 0.4859 & 0.3670 & 0.6277 & 0.4391 \\ D-UNet [18] & 2D+3D & 2D & 0.4759 & 0.3570 & 0.4780 & 0.5248 \\ STHarDNet & 2D & 2D & 0.5170 & 0.3866 & 0.6222 & 0.4979 \\ STHarDNet (Proposed) & 3D & 2D & 0.5547 & 0.4184 & 0.6764 & 0.5286 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of models in the ATLAS dataset.  
but the Dice and IoU performances were 9.49% and 10.89% better, respectively. Furthermore, when 100,000 images were calculated, the FPS was 42.5% faster than that of the U-Net, 71.78% faster than that of the D-UNet, and 5.6% faster than that of the TransHarDNet that used the transformer in the bottleneck of the HarDNet.
## 7 Results and Discussion
Each sensor node is made up of several sensors (temperature and relative humidity sensors, CO, methane, and solvent vapor sensors), as well as a Wi-Fi communication link. The proposed work tests the outdoor air quality with the help of sensor nodes PM\({}_{10}\), PM\({}_{2.5}\), NO\({}_{2}\), O\({}_{3}\), and CO which is set in vehicles. Long-Range gateway node is used to upload the data on the cloud server via Internet. The gateway converts RF transmissions into IP packets so that information can be communicated across the Internet protocol. In the previous studies, it has been identified that limited studies have implemented the customization \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**References**} & **Parameters** & **Customized** & **Gateway** & **Cloud** & **Server** & **Blockchain** \\  & **Considered** & **Communication** & **Hardware** & **Gateway** & **Server** & **Integration** \\ \hline
[39] & PM & NA & Not Implemented & NA & No & No \\ \hline
[40] & Fine PM\({}_{2-5}\), CO\({}_{2}\), VOCs, RH, and temperature & & & & & \\ \hline
[41] & O\({}_{3}\), CO, NO\({}_{2}\) & ZigBee + Wi-Fi & Not Implemented & Yes & Yes & No \\ \hline
[43] & CO, NO\({}_{2}\), O\({}_{3}\), CO\({}_{2}\) & ZigBee + Wi-Fi & Not Implemented & Yes & Yes & No \\ \hline Proposed Work & PM\({}_{10}\), PM\({}_{2.5}\), NO\({}_{2}\), O\({}_{3}\), CO & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Comparison of the previous literature survey with proposed work. of hardware for the implementation of wireless-based system. To overcome transmission range and power consumption, this study has implemented long-range communication protocol with Internet of things network. Along with this, the blockchain integration with hardware is missing in the previous studies. In the proposed study any architecture is proposed to interface the blockchain network with hardware. In this study, low-cost sensors such as PM\({}_{10}\), PM\({}_{2.5}\), NO\({}_{2}\), O\({}_{3}\), and CO were used to collect the data and integrated cloud platform with blockchain system. Here blockchain ensure to collect sensor data by setting the threshold value for collecting and storing the data. The smart contracts assist to pre-trigger the alerts when it exceeds the threshold value.
## 3 GNSS Signal Disruption Simulation  
The GNSS signal provides the position, velocity, and time (PVT) of the end user anywhere on the globe using the satellite measurements of at least four satellites. For this purpose, right-circularly polarized (RHCP) waves are continuously transmitted over three carrier frequencies, L1, L2, and L5 (1575.42 MHz, 1227.6 MHz, and 1176.45 MHz), respectively. The L1 and L2 carrier signals are modulated with binary phase shift keying (BPSK) modulation and pseudorandom noise (PRN) codes known as the C/A code (coarse and acquisition code) and the encrypted P(Y) code (precision code) [49]. The C/A codes are applicable for dual-purpose use in terms of civilian and military applications. In contrast, the P(Y) code is exclusively used by the US military and under the United States Department of Defense's authorization [49].
## 3 Materials and Methods
### Traffic Scenario Map Construction  
Traffic scene graph construction involves multiple steps. First, objects are identified and tracked across consecutive video frames. Next, these objects are oriented in a bird's-eye view to facilitate the identification of their relative positions. Finally, for each frame, the relative position information of the objects is encoded with a spatial map, as shown in Figure 3.
# A 3D-Printed Enclosed Twist Dielectric Resonator Antenna with Circular Polarization
## 2 Dielectric Resonator Antenna Design and Parametric Studies  
The proposed antenna design is based on a rectangular DRA fed with a single-aperture couple feeding. This feeding network will excite a TE mode [20], which gives a radiation pattern compatible with the intended UAV applications. The drawback of using this single feeding technique is that this configuration makes the antenna radiated with a linear polarization, which is not the best for the target application. As a CP polarization is needed, we proceed to modify the structure of the DRA in order to keep a simple feeding technique.
## 2 Database and Characteristics
### Database  
The database used in this paper was co-measured by Avic Huadong Photoelectric Co., LTD and our research group in Northwestern Polytechnical University. The measurement was conducted in a semi-anechoic room, with a size of 4.4 m \(\times\) 4 m \(\times\) 3.2 m. The cut-off frequency was 80 Hz, and the background noise was 10 dB. During the measurement, the loudspeaker was fixed at the arc slide track at a distance of 1.2 m to the head center. A pseudo-random signal was adopted as the measuring signal. The database contains 56 subjects aged between 25 and 45 with 50 anthropometric features of each subject and Head-Related Impulse Responses (HRIRs, the denotation of HRTFs in time domain) at 723 directions (see details in Table 1). All the HRIRs were in 800-sample length with a sampling frequency of 44.1 kHz.
## 4 Results and Discussion
### Absorption in Binary and Ternary Systems
#### 4.1.1 Binary Systems
It is noteworthy that, from thermodynamic studies on alkanol and diols, it was established that the hydrophilic character of the OH group extends to the first three neighboring carbon atoms [33]. This study was conducted by assuming the function \(\lambda_{max}(x_{D})\) to be analytic, i.e., it can be represented as a power series:  
\[\lambda_{max}=\lambda_{max}^{\infty}+\lambda_{xx}x_{D}+\ldots \tag{22}\]  
where \(\lambda_{max}^{\infty}\) is the value of \(\lambda_{max}\) at infinite dilution and represents the contribution to the shift due to the alkanol-NR interactions. The coefficient \(\lambda_{xx}=\frac{d\lambda_{max}}{d\lambda_{D}}\) is the contribution due to NR-NR interactions. Figure 4a shows that the first two terms of the series are sufficient to correctly describe the experimental results. Furthermore, the parameter \(\lambda_{max}^{\infty}\) is plotted as a function of \(n\) in Figure 4b. Figure 2: (**a**) Integral absorbance of NR in H\(-\)(CH\({}_{2}\))\({}_{n}-\)OH (\(n=1-8\)) solutions as a function of alkanol mole fraction. The number of carbon atoms is shown on each experimental curve. (**b**) The dimerization constant, calculated by Equation (20), as a function of the number of carbon atoms. Figure 4: **(a)** The wavelength at the maximum of the absorption band in binary systems \(\mathrm{NR-H-(CH_{2})_{n}-OH}\) (the \(n\) value is read directly on each line) as a function of mole fraction. (**b**) The wavelength of maximum absorption, extrapolated to infinite dilution as a function of the number of carbon atoms in the alkanol molecule. (**c**) Slope of the curve \(\lambda_{max}(x_{D})\) as a function of the number of carbon atoms squared. Figure 3: Chemical structures of NR. (**A1**) stable conformer without charge separation. (**A2**) conformer with charge separation stabilized by resonance between diamine nitrogen and oxygen. (**A3**) possible dimerization of NR molecules via H-bonding with alkanol. It decreases with \(n^{-0.5}\), according to the equation:  
\[\lambda_{max}^{\infty}=537.4+\frac{22.3}{\sqrt{n+1}} \tag{23}\]  
Equation (23) suggests that the medium may exert a dual effect on the spectrophotometric behavior of NR. For \(n\rightarrow\infty\), i.e., a perfectly hydrophobic environment, \(\lambda_{max}^{\infty}\)= 537.4 nm, and for \(n=0=\lambda_{max}^{\infty}\)= 559.7 nm. In other words, the change in medium produces a maximum shift of 22.3 nm. This behavior can be rationalized bearing in mind that short-chain alkan-lols have a purely hydrophilic (polar) character, on the contrary, long-chain alkan-lols are practically apolar. Results seem to show that the excited state is less stabilized by a hydrophobic environment, from which we can assume that maybe the excited state is more polar than the ground state. Concerning dye-dye interactions, the inspection of Figure 4c reveals that \(\lambda_{xx}\) > 0, for all the alkan-lols investigated, namely \(\lambda_{max}\) increases with the NR concentration.