esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Density Fluctuation Measurement  
To investigate the characteristics of the density fluctuations in the shear flow, measurements were taken at different positions in the flow. Figure 5a shows the schlieren image of the flow field. A shear flow with horizontal orientation can be observed clearly in the middle of the field of view. Here, the measurement points of density fluctuation are also marked red in the schlieren image. Figure 5b-i displays the power spectra of the density fluctuation at different measurement points. The peak frequency at point 1 is 84 kHz which is in good agreement with the simulation result in Figure 4d. Note that there are two peaks at 64 kHz and 128 kHz in all power spectra, which is attributed to the noise of the fast photodetector. Figure 5c shows the statistical histogram of the density fluctuation frequencies of different measurement points. The result shows that the frequencies of the density fluctuation are concentrated within the range from 50 kHz to 85 kHz. It is clear that the density fluctuation frequency decreases gradually as the measurement point is further from the nozzle. The variation of the frequency indicates the scale of the turbulence structure of shear flow gradually becomes larger as the shear flow propagates downstream [30]. This conclusion is consistent with the evolution of the vortex structures obtained in Figure 4c. Note that the intersection of shock wave and shear flow can be observed at point 3 as shown in Figure 5a. It can be seen from the time-varying voltage signal (not shown here) that the density fluctuation has a large amplitude due to the impact of shock waves; however, the frequency did not deviate significantly. The phenomenon above indicates the shock wave is stable in this shear flow device, and the interaction between the shear flow and shock wave has no effect on the density fluctuation frequency.  
Figure 5: (a) Schlieren image of the shear flow. (b–i) Power spectrum of density fluctuation at measurement points. (j) Peak frequencies of density fluctuation at different measurement points.
## 4 Three-Phase Inverters
### Modes of Operation
#### 4.2.2 Grid Feeding Inverters  
Grid feeding inverters are operated as power sources, and are mainly designed to deliver power to an energized grid [44; 45; 46; 47; 48; 49; 50; 34]. The active power \(P\) and reactive power \(Q\) are directly controlled, while the frequency \(\omega\) and voltage amplitude \(|E|\) are not directly controlled, and are determined by the interaction of the inverter with the grid. Typical applications are renewable energy systems and small grid-connected generators, which operate with specific active and reactive powers. As an example, in photovoltaic systems the active power is typically set by the source, and the reactive power is often set to zero. Grid feeding inverters are designed to operate in parallel to other inverters and generators. They cannot operate in isolation, and require additional units to set the voltage magnitude and frequency. In power flow studies, a grid feeding inverter is represented as a P-Q bus, with constant active power \(P\) and constant reactive power \(Q\). Grid feeding inverter are also called Grid Following Inverters, or inverters with P-Q control.
# Effective Strategies for Enhancing Real-Time Weapons Detection in Industry
## 1 Introduction
The dataset comprises intricate images containing weapons that are difficult to identify, simpler images with detectable weapons, and a substantial number of images showing objects that are not weapons but could be classified as such in both complex and simple scenarios. 2. Both the detection of small-aspect-ratio weapons (with an area less than or equal to 100 pixels2) and the reduction in false positives are improved using a method called Scale Match. Specifically, it obtains +27% average precision (AP) and \(-\)17% false positives (FP). 3. A new detection scheme is suggested to aid the base model in detecting weapons. By utilising this scheme, false positives are decreased from 70%, when relying solely on the second classifier, to 100%, when using either the time window or both modules, with only a minor impact on inference time of -13 frames per second (FPS). It is important to note that relying solely on the temporal window leads to a reduction in precision. 4. A comparative analysis is conducted on multiple versions of the You Only Look Once (YOLO) detectors, namely YOLOv5, YOLOv7, and YOLOv8, for real-time weapons detection. The study involves the execution of extensive experiments on both publicly available datasets, such as the _Gun-Dataset_[3], _YouTube-GDD_[4], _HOD Dataset_[5], and _usdataset_[1], as well as datasets generated specifically for this research. The remainder of the paper is structured as follows. Section 2 reviews the existing works related to weapons detection, small object detection, and real-time detection. In Section 3, the methodology used is explained. Both the experiments and their results are shown in Section 4. In Section 5, some key points are derived from the results. Finally, conclusions are drawn in Section 6.
# An Integrated Dictionary-Learning Entropy-Based Medical Image Fusion Framework
## 4 Experiments and Analysis
### Image Quality Comparison  
To show the efficiency of proposed method, the quality comparison of fused medical images is demonstrated. The proposed method and other five existing methods are implemented in CT-MRI, MRI-PET, and MRI-SPECT medical image fusion. The qualities of fused images are compared based on visual effect and following five objective evaluations.  
* AG computes the average gradients of each pixel in fused image, that shows the obvious degree of objects in the image. When AG gets larger, the difference between object and background increases [75].
* EI measures the strength of image local changes [76]. A higher EI value implies the fused image contains more intensive edges.
* \(Q^{AB/F}\) edge strength represents the edge information associated with the fused image and visually supported by human visual system [77]. A higher value of \(Q^{AB/F}\) value implies fused image contains better edge information.  
Figure 5: Nine Image Fusion Comparative Experiments, (**a**)–(**c**) are CT-MRI source image pairs; **(d)**–(**f**) are MRI-PET source image pairs; and **(g**)–(**i**) are MRI-SPECT source image pairs.  
* MI computes the information transformed from source images to fused images. When MI gets larger, the fused image gets more information from source images [78].
* VIF is a novel full-reference image quality metric [79]. VIF quantifies the information shared between the test and reference images based on the Natural Scene Statistics (NSS) theory and Human Visual System (HVS) model.  
According to existing papers, objective evaluation metrics are always used to evaluate the performance of the fused image objectively [15; 22; 23]. Doctors actually use image information to diagnose the disease. The high-quality fused images are helpful for doctors to make the accurate diagnosis. The above five objective evaluation metrics (AG, EI, \(Q^{AB/F}\), MI, VIF) used in this paper are the internationally common methods to evaluate the performance of medical image fusion [80; 81; 82].
## 3 Results and Discussion
### Thermal Stability of Electro-Sprayed mPCM
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**First Step**} & \multicolumn{4}{c}{**Second Step**} \\ \cline{3-10}
**Sample** & \begin{tabular}{c} **Initial** \\ **Depadation** \\ **Temperature** (°C) \\ \end{tabular} & \begin{tabular}{c} **Weight Loss** \\ **(100-350 °C) (\%)** \\ \end{tabular} & \begin{tabular}{c} **Maximum** \\ **Degadation** \\ **Temperature** (°C) \\ \end{tabular} & \begin{tabular}{c} **Maximum** \\ **Degadation Rate** \\ **(5\%/°C)** \\ \end{tabular} & \begin{tabular}{c} **Weight Loss** \\ **(350-500 °C) (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Maximum** \\ **Degadation Rate** \\ **Temperature** (°C) \\ \end{tabular} &
\begin{tabular}{c} **Maximum** \\ **Degadation Rate** \\ **(\%/°C)** \\ \end{tabular} \\ \hline PCL-EA & \(358.9\pm 3.1\) & \(4.2\pm 0.9\) & - & - & \(95.0\pm 1.1\) & \(411.4\pm 0.4\) & \(2.2\pm 0.1\) \\ \hline PCL-ChI & \(369.2\pm 3.6\) & \(3.0\pm 0.4\) & - & - & \(96.1\pm 0.4\) & \(413.4\pm 1.6\) & \(2.3\pm 0.02\) \\ \hline raw \(n\)-ecosane & \(178.8\pm 1.3\) & \(99.9\pm 0.1\) & \(269.1\pm 0.01\) & \(2.6\pm 0.1\) & - & - & - \\ \hline \(n\)-ecosane/PCL & \(196.4\pm 9.7\) & \(26.9\pm 3.0\) & \(236.1\pm 6.1\) & \(0.4\pm 0.1\) & \(72.4\pm 5.4\) & \(414.9\pm 0.6\) & \(1.7\pm 0.1\) \\ \hline \(n\)-ecosane/PCL & \(194.0\pm 13.6\) & \(44.1\pm 4.9\) & \(254.8\pm 3.5\) & \(0.9\pm 0.04\) & \(55.3\pm 5.4\) & \(413.6\pm 2.3\) & \(1.3\pm 0.2\) \\ \hline \(n\)-ecosane/PCL & \(188.8\pm 14.0\) & \(58.1\pm 6.8\) & \(248.2\pm 12.9\) & \(1.2\pm 0.2\) & \(41.6\pm 6.8\) & \(407.0\pm 5.1\) & \(1.0\pm 0.3\) \\ \hline \(n\)-ecosane/PCL & \(194.3\pm 11.8\) & \(30.6\pm 4.4\) & \(237.3\pm 4.9\) & \(0.4\pm 0.1\) & \(68.8\pm 4.3\) & \(414.4\pm 1.8\) & \(1.6\pm 0.1\) \\ \hline \(n\)-ecosane/PCL & \(195.9\pm 7.8\) & \(49.5\pm 4.0\) & \(268.1\pm 1.0\) & \(1.0\pm 0.1\) & \(49.8\pm 3.7\) & \(414.1\pm 2.2\) & \(1.2\pm 0.1\) \\ \hline \(n\)-ecosane/PCL & \(186.8\pm 10.5\) & \(69.3\pm 1.1\) & \(269.5\pm 5.6\) & \(1.3\pm 0.2\) & \(30.2\pm 0.8\) & \(413.2\pm 3.5\) & \(0.7\pm 0.04\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Thermogravimetric data for of \(n\)-eicosane, neat PCL microsphere and \(n\)-eicosane microcapsules in inert atmosphere at a heating rate of 10 °C min\({}^{-1}\).
# Application of Compact Folded-Arms Square Open-Loop Resonator to Bandpass Filter Design
## 6 Results Discussion  
The proposed fifth-order bandpass filter EM simulation results and practical filter measurement results are jointly captured in Figure 11 for clear evaluation. The performance comparison of the designed fifth-order bandpass filter, when contrasted with the current state-of-the-art reported in some recently published related research, is presented in Table 1. Based on the comparison in Table 1, the designed filter is of small size as its overall footprint is relatively smaller than those reported in the other papers chosen for the comparison. The practical filter performance also shows that it is relatively less susceptible to both the loss factors compared (that is, the insertion and return loss factors). Looking at Figure 11, it can be seen that the simulation and measurement results are closely matched. However,  
Figure 10: Image of the practical fifth–order filter and the measurement results.  
Figure 9: Fifth–order bandpass filter layout and the simulation responses for the folded-arms square open-loop resonator (all dimensions in mm).  
though the measurement return loss is about 19.2 dB, the simulation return loss is better than 20 dB. Both the simulation and the measurement insertion losses are approximately 1.0 dB, with a slightly better simulation insertion loss of 0.9 dB. The simulated filter roll-off is slightly sharper than the measured filter roll-off, as shown in Figure 11.
## 6 Effectiveness and Deficiencies of RLS  
Our proposed localization scheme has the following deficiencies:  
Figure 10: Average response time of localization.  
* RLS requires synchronization due to one-way ranging in TOA calculation like other existing range-based methods which utilize TOA such as, LSHL [31], DNR [19] and SLMP [8]. For time synchronization, more message exchange is required and higher energy is consumed. The nodes may be assumed to be synchronized for several weeks after initial deployment.
* We need to carefully plan the anchor node placement or increase the number of surface anchors to ensure that all nodes are always able to reach at least three anchor nodes in their communication range to avoid packet loss.  
Some of effectiveness of the proposed approach is address as follows:  
* It is an event-based localization method which can match duty-cycle environments. There is little research on UWSNs' localization problem for duty-cycle environment [30]. Sensor nodes can go to sleep according to some sleep scheduling mechanism. After detecting a phenomenon they can wake-up and launch localization tasks, so it is not required that all sensor nodes wake-up all the time.
* We can significantly reduce the packet size compared to other methods like ML [7] (96 bit), ARTL [50] (400 bit), and efficient localization method [31] (16 byte). Moreover, the number of messages exchanged is decreased by establishing an event-driven reverse localization where ordinary nodes do not have to wait to receive anchors' location information. The localization process is launched by detector sensors deployed at various water depths.
## 3 Basic Principles of Pulsed Operation in a Laser
### Formation of Ultrashort Pulses  
An optical cavity can support a set of longitudinal modes that satisfy its boundary conditions: \(L=m\lambda/2\), where \(L\) is the cavity length, \(\lambda\) is the mode wavelength and \(m\) is an integer number. The modes that can actively oscillate in the cavity are only modes whose frequency lies within the emission spectrum of the active medium. When a laser operates in CW, multiple longitudinal modes can oscillate in the cavity simultaneously, whose relative phases are random. Therefore, the modes are randomly interfering with one another, and the laser output in time is a noisy continuous intensity, which is periodic in the cavity round trip time. A laser is said to be mode-locked if many longitudinal modes are oscillating together with a well defined phase relation between them, as opposed to CW operation, where the phases are random. This phase relation causes the modes to constructively interfere only within a short period of time, while destructively interfering at all other times, forming a pulse with a high peak intensity. In other words, the mode-locking mechanism contracts the electro-magnetic energy, which would otherwise spread over a long period, into an extremely short pulse, which oscillates back and forth in the cavity. Eventually, ML reaches a steady state (due to limiting mechanisms, such as dispersion, gain bandwidth, _etc._), producing a stable pulse that circulates in the resonator. Pulse formation can be qualitatively modeled based on the Kuizenga-Siegman theory [19], describing the interplay between two mechanisms: first, pulse shortening due to some nonlinear effect, which monotonically decreases in magnitude as the pulse becomes shorter and shorter; and second, the finite gain bandwidth of the active medium, which acts as a pulse lengthening mechanism in time, which is monotonically enhanced as the pulse becomes shorter and shorter. When these two mechanisms are in balance, the pulse reaches a steady state.
# Marine Internet for Internetworking in Oceans: A Tutorial
## 2 Overview of Marine Internet
### Best-Effort Networking Principle  
As illustrated in Figure 1, marine Internet tries to exploit all possible networking resources available in oceans to maximize opportunities for the users to have cost-effective network services [1; 19; 20]. It is based on an open network architecture to accommodate any existing as well as newly developed communication systems in the future. The network resources can be either long-term established or short-term setup networks. An adaptive scheme is used to adjust selecting them for networking on real-time in order to find the most cost-effective services to the user. The operation of marine Internet does not rely on any particular network resources with a best-effort networking capability, which makes it to have strong survivability in harsh networking environments in oceans. More discussion can be found in Section 5.
## 3 Results and Discussion
### Effects of Surfactants on the UV-Blocking Efficacy of TiO\({}_{2}\) Dispersion  
The UV-ray-blocking performance depends on the degree of dispersion stability of the W/O formulation [23]. To evaluate the UV-blocking efficacy of the TiO\({}_{2}\) dispersion, a standard assessment was utilized [39] as follows: the TiO\({}_{2}\) dispersions in the corresponding non-polar oil (DC3456) and polar oil (CEH) were addressed and scrubbed on plates, and the absorption intensities were measured at 300 nm. Absorption gradually increased with increasing surfactant concentration (Figure 4). Overall, the TiO\({}_{2}\) dispersion using anionic SDS treatment had higher UV-absorption or UV-blocking efficacy than those using CTAB, DDAPS, and Tween 20.  
We further compared the optical microscopy images of the TiO\({}_{2}\) dispersion after anionic SDS treatment (0.001 wt% vs. 1 wt% addition). For 0.001 wt% SDS, aggregates and clusters of TiO\({}_{2}\) (dark part: TiO\({}_{2}\)) were observed. However, evenly spread particle-like TiO\({}_{2}\) was observed in the case of 1 wt% SDS. These results suggested that the well-dispersed and stable TiO\({}_{2}\) formulation contributed to the UV-blocking efficacy (Figure S9). Because the UV-blocking efficacy of the TiO\({}_{2}\) dispersion by anionic SDS treatment was the most effective, we further investigated the effect of different alkyl chains of anionic fatty acids as surfactants on the TiO\({}_{2}\) dispersion quality in subsequent experiments.
# IBA myQA SRS Detector for CyberKnife Robotic Radiosurgery Quality Assurance
## 4 Discussion
### Limitations and Future Perspectives  
The main limit of the study is the restricted number of measurements used to test the detector's performance. In fact, in order to make the prototype accessible to other centers, it was only available for a limited time at the author's Institute.  
Future studies should, therefore, deepen the investigation into the accuracy of patient-specific QA verification by introducing further test scenarios (miscalibration, dose distribution shifts, etc.).  
In this work, the left-right angular response was investigated by rotating clockwise and counterclockwise the detector with respect to the horizontal plane. We assumed a symmetrical behavior of the CMOS matrix in the antero-posterior direction with respect to the left-right direction, ignoring the array's non-squared shape, electronics, and hemispheric cap contributions. Further investigation into this topic can also be conducted.  
As far as the dosimetry system is concerned, one problem is the absence of the radiologically-detectable markers that the CyberKnife tracking system necessarily requires for QA plan delivery. In this study, lead pallets were manually placed by the authors onto the myQA SRS phantom surface. However, this procedure is cumbersome and potentially prone to errors related to possible pallet shifts between the CT acquisition and the treatment time. A marker set should be included in the future phantom structure in order to allow more reliable tracking during routine pre-treatment QA delivery.  
Finally, the signal dependence upon delivered beam angle remains an unsolved issue since the provided angular sensor-correction factors pair is inadequate for CyberKnife(r) radiation delivery. Beam pruning could be a potential answer, but it allows only a partial verification of the patient's plan. The development of a different solution would be a significant improvement.
## 4 Discussion
### Comparison with Previous Studies
#### 4.1.1 Comparison with Previous Studies Based on Bonn Database
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Paper** & **Year** & **Methods** & **Sensitivity** & **Specificity** & **Precision** & **Accuracy** & **F1** \\ \hline
[4] & 2014 &
\begin{tabular}{c} DWT + Fuzzy Approximate \\ Entropy + SVM Classifier \\ \end{tabular} & 100\% & 100\% & 100\% & 100\% & 1 \\ \hline
[46] & 2015 &
\begin{tabular}{c} Discrete Short-Time Fourier \\ Transform (DSTFT) + Multilayer \\ Perceptron (MLP) \\ \end{tabular} & 99.9\% & 99.6\% & 99.6\% & 99.8\% & 0.9975 \\ \hline
[47] & 2016 &
\begin{tabular}{c} EMD-Based Temporal and Spectral \\ Features + SVM \\ \end{tabular} & 99\% & - & - & - & - \\ \hline
[48] & 2016 &
\begin{tabular}{c} Weighted Permutation Entropy \\ (WPE) + SVM \\ \end{tabular} & - & - & - & 99.5\% & - \\ \hline
[43] & 2016 &
\begin{tabular}{c} Dual-Tree Complex Wavelet \\ Transformation + Normal Inverse \\ Gaussian Parameters \\ \end{tabular} & 100\% & 100\% & 100\% & 100\% & 1 \\ \hline
[42] & 2017 &
\begin{tabular}{c} Wavelet Packet Transform (WPT) \\ Based Entropy + ANN Classifier \\ \end{tabular} & 99.40\% & 100\% & 100\% & 99.70\% & 0.9970 \\ \hline
[41] & 2017 &
\begin{tabular}{c} Tunable-Q Wavelet Transform Based \\ Multi-scale Entropy Features + SVM \\ \end{tabular} & 100\% & 100\% & 100\% & 100\% & 1 \\ \hline
[11] & 2017 &
\begin{tabular}{c} Genetic Algorithm-Based \\ Frequency-Domain Feature Search \\ (GAFDS) + KNN Classifier \\ \end{tabular} & - & - & - & 99.5\% & - \\ \hline
[49] & 2018 &
\begin{tabular}{c} Universum Twin Support Vector \\ Machine (UTSVM) \\ \end{tabular} & - & - & - & 99\% & - \\ \hline
[50] & 2018 &
\begin{tabular}{c} A Novel Signal Modeling Approach \\ Based on Discrete Cosine Transform \\ and Hurst Exponent + SVM \\ \end{tabular} & 95.40\% & 94.30\% & 94.36\% & 94.85\% & 0.9488 \\ \hline
[51] & 2018 &
\begin{tabular}{c} Local Binary Pattern (LBP) Based on \\ Wavelet Decomposition + SVM \\ \end{tabular} & - & - & - & 99.63\% & - \\ \hline
[35] & 2018 &
\begin{tabular}{c} Phase–Amplitude Cross-Frequency \\ Coupling + SVM \\ \end{tabular} & 100\% & 100\% & 100\% & 100\% & 1 \\ \hline
[36] & 2019 &
\begin{tabular}{c} A Hybrid Model with Genetic \\ Algorithm (GA) and Particle Swarm \\ Optimization (PSO) \\ \end{tabular} & 99.5\% & 99.25\% & 99.25\% & 99.38\% & 0.9938 \\ \hline
[44] & 2019 &
\begin{tabular}{c} DWT + Random Forest Classifier \\ \end{tabular} & 100\% & 91.66\% & 92.30\% & 99.95\% & 0.9600 \\ \hline
[6] & 2019 & \
\begin{tabular}{c} Hermite Transform + Permutation \\ Entropy, Histogram Features and \\ Statistical Features + SVM Classifier \\ \end{tabular} & - & - & - & 99.5\% & - \\ \hline This Work & - &
\begin{tabular}{c} The Proposed RR-DTDWT (DTDWT \\ + AGRM) + SVM \\ \end{tabular} & 100\% & 100\% & 100\% & 100\% & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison with previous studies on Bonn database (normal versus ictal).