esa-sceva/satcom-chunk-collection huggingface dataset:
# Research Developments of Aerostatic Thrust Bearings: A Review
## 2 Theoretical Models and Experiment Methods
### Experimental Methods
#### 2.2.1 Measurement of Pressure Distribution  
The gas film clearance is extremely tiny, usually at the level of 10 \(\upmu\)m. It is relatively difficult to obtain accurate measurements of pressure distribution in the bearing clearance. There are two main methods of pressure distribution measurement.  
The first method is to connect the pressure sensor to the orifice to measure the pressure distribution in the gas film. The orifice is drilled on one of the pad's surfaces [30; 31]. This small hole is called the flow intake (d\({}_{\text{p}}\) = 0.2 mm) in the literature [32]. The detail of flow intake is shown in Figure 4a. The pressure distribution using this method shows a good agreement with the numerical simulation result, as shown in Figure 4b. However, the method of connecting the pressure sensor to the orifice plate still has some disadvantages. Firstly, non-negligible disturbances to the airflow in the thin gap appear and considerable measurement errors occur. In addition, the amount of experimental data obtained by this method is limited, both in terms of location and quantity. This method has been improved in the literature [33; 34; 35; 36]. The supporting element has been replaced by a linear guideway with position control capability. More pressure data of bearing clearance can be obtained in one single experiment.  
Figure 4: Schematic of flow intake and pressure distribution: (**a**) details of the flow intake, (**b**) pressure distribution of simulation and experiment [32].  
Another measurement method of pressure distribution was proposed by [PERSON] [37], named the pressure-sensitive film measurement method. With the application of pressure, the pressure-sensitive films produce a visible response on the surface in the form of a stain or imprint, as shown in Figure 5. It consists of the A-film and the C-film. The A-film is attached with a micro-encapsulated color-forming layer, and the C-film is made of a color-developing layer. There are numerous microscopic bubbles (2-26 \(\upmu\)m diameter) in the color-forming layer, which enclose the colorless liquid. A number of these bubbles burst depending on the magnitude of the applied pressure. Then, these bubbles react with the color-developing layer, producing a pink stain. With increasing pressure, more bubbles burst and a deeper stain occurs. This property is very conducive to the measurement of stain intensity and 2D pressure distribution. In addition, this experimental measurement method has the merits of easy operation, high spatial resolution, and high accuracy. This method demonstrates effectiveness when applied to different forms of bearings. However, this method has strict requirements for calibration, process, and recognition of the image. The roughness of the film surface is strictly required to avoid additional errors.
## 3 Experimental Results  
The experimental results retrieved from the data measured by XFROG and OSA are shown in Figures 4-9, where the data measured by OSA were calibrated during the retrieval of data in the experiments, the pulse energy was chosen discretely, i.e., 1.1, 2.7, 5.3, 13.3, 26.7, and 35.2 pJ, respectively, and the temperature was set to 12, 25, and 52.8 \({}^{\circ}\)C for every pulse energy measurement. The central wavelength of the input femtosecond pulse for measurements was 1550 nm, the temporal and spectral profiles of which are shown in Figure 3. The input pulse contains double temporal peaks (as shown in Figure 3a), with FWHM 113.4 fs and 89.3 fs, and a hollow down to 0.4 existing in the normalized wavelength profile to perform double spectral peaks, which will help us to analyze the dynamics further.  
Figures 4 and 5 illustrate that the pulse will show pulse acceleration, splitting, and trailing, which become more obvious when the input pulse energy increases at room temperature (25 \({}^{\circ}\)C) and low temperature (12 \({}^{\circ}\)C). Furthermore, the front sub-pulse shows pulse acceleration with low input pulse energies, and shows broadening and multiple tiny spikes splitting with higher input pulse energies. Furthermore, from the spectral lines, we found that the output pulse had a sharp peak in a long wavelength with higher input pulse energy.  
Figure 3: Temporal intensity profiles (**a**) and spectral intensity profiles (**b**) of the input pulses.  
Figure 2: The experimental setup with cross-correlation frequency-resolved optical gating (XFROG). BS: Beam splitter; FL: Focusing lens; TEC: Thermo electric cooler; DUT: SPNW chip; TED 4015: TEC temperature controller; FM: Flip mirror; RF: Reflector; OSA: optical spectrum analyzer; DM: D-shaped mirror; RM: Retro reflector mirror; CL: Bi-convex lens; BBO: barium borate crystal; SM: Spectrometer with charge-coupled detector (CCD).  
As shown in Figure 6, we found that, under high temperature and low pulse energy, the pulse will have a temporal profile where the acceleration will dominate the transmission, and the corresponding blue shift is clearly noticeable in the spectral lines. With the increase of pulse energy, the relative intensity of long wavelength sub-pulse increases, while the blue shift sub-pulse gradually weakens, which was the result of the output of red shift spectral peak and trailing temporal pulse.  
Figure 4: Temporal and spectral intensity profiles of the output pulses retrieved from the XFROG measurements for the coupled pulse energies that increased from 1.1 pJ to 35.2 pJ at the temperature of 12 \({}^{\circ}\)C.  
Figure 5: Temporal and spectral intensity profiles of the output pulses retrieved from the XFROG measurements for the coupled pulse energies that increased from 1.1 pJ to 35.2 pJ at the temperature of 25 \({}^{\circ}\)C.  
Figure 6: Temporal and spectral intensity profiles of the output pulses retrieved from the XFROG measurements for the coupled pulse energies that increased from 1.1 pJ to 35.2 pJ at the temperature of 52.8 \({}^{\circ}\)C.
# How Environmental Factors Affect Forest Fire Occurrence in Yunnan Forest Region
## 2 Materials and Methods  
### Study Area  
Yunnan is a province in Southwest China and the provincial capital is Kunming. Its administrative area includes eight prefecture-level cities and eight autonomous prefectures, with a total of 129 county-level divisions (Figure 1). Its geographical scope is 97\({}^{\circ}\)31\({}^{\prime}\) E-106\({}^{\circ}\)11\({}^{\prime}\) E, 21\({}^{\circ}\)8\({}^{\prime}\) N-29\({}^{\circ}\)15\({}^{\prime}\) N, and the overall terrain is generally high in the northwest and low in the southeast, with altitudes ranging from 76.4 m in the southeast to 6740 m in the northwest, dominated by plateau and mountainous areas, with typical vertical geo graphic features and rich species diversity, making it one of the top 25 biodiversity hotspots in the world [33; 34].  
Yunnan has a subtropical and tropical monsoon climate, influenced by monsoons from the Western Pacific Ocean and the Indian Ocean, with distinct wet and dry seasons. The dry season lasts from November to April, which is the high-frequency period of forest fires. Additionally, Yunnan has abundant forest resources; the National Bureau of Statistics of China reports that, as of 2017, Yunnan has 19.1419 million \(\mathrm{hm}^{2}\) of forest area, a forest coverage rate of 50.03%, and a forest volume of 169.30919 million \(\mathrm{m}^{3}\). The main tree species in the Yunnan Forest Zone are _Pinus yunmanensis, Pinus armanditi, Abies ernestii_, and _Quercus_ L.  
According to the records of the Yunnan Provincial Fire Brigade, Yunnan had the most forest fire events in the country [35]. For this study, we downloaded the entire MODIS Collection 6.1 thermal anomaly dataset covering all of Yunnan from 2005 to 2019, which was obtained from the NASA Fire Information Resource Management System (NASA FIRMS) ([[https://earthdata.nasa.gov](https://earthdata.nasa.gov)]([https://earthdata.nasa.gov](https://earthdata.nasa.gov)) (accessed on 13 September2021)). The coordinates with 95% confidence level or higher were selected because the hotspots collected by satellites were not necessarily the actual fire points. A total of 8542 fire points were further confirmed by the actual ground fire points. Upon considering the long duration of large-scale wildfire, which was recorded by satellites, 4649 fire points (Figure 1) were finally confirmed by comparing their longitude and latitude range and time span and identifying points with a time span of no more than 24 h and a distance of no more than 0.01 in longitude or latitude coordinates as the same wildfire.
## 5 Results and Discussion
However, the study showcases the importance of closely monitoring wetland data in Sri Lanka for sustainable water resources management. It is well understood that traditional data monitoring process can be cost consuming; however, water level measuring and weather sensors can be effectively used for long-term data recording without much human intervention. \begin{table}
\begin{tabular}{c c c c} \hline
**Disaggregation Studies** & \(\mathbf{R^{2}}\)**Score** & \(\mathbf{MSE}\) & \(\mathbf{MAE}\) \\ \hline [PERSON], [PERSON], \& [PERSON], 2016 [53] & 0.989 & - & - \\ [PERSON], [PERSON], \& [PERSON], 2015 [54] & 0.985 & - & 1.27 \\ [PERSON] et al., 2017 [55] & 0.853 & - & 0.039 \\ [PERSON], [PERSON], \& [PERSON], 2005 [56] & 0.985 & - & - \\ Current study & 0.8786 & 0.0004 & 0.0155 \\ \hline \end{tabular}
\end{table} 
Table 6: Comparison of partition performance of the LSTM model with existing studies. Figure 8: Loss curves of LSTM-based model for **(a)** Kimbulawala, **(b)** Kotte Canal, **(c)** Kotte North, and **(d)** Parliament Lake.
## 7 Experimental Data and Model Validation
### Validation Method of the Area Metric for the Drag and Lift Forces  
The area metric is the method recommended by ASME in their validation guidelines as being well-suited for the experimental research [48; 49]. This method does conclude that a model is accepted or rejected, and it also allows the model validation to be quantified. The area metric method uses the cumulative probability of the observed data and the simulated values and calculates the integral (using the trapezium method) obtained for the surface of the observed values and the simulated values. The difference in the surfaces (between the curves) gives the value of the quantified metric in the same units as the analyzed data.  
Comparing the curves obtained from the physical experiments with those given by the mathematical models is an important technique used by researchers to determine whether a model adequately represents the physical phenomena. This method allows the validation of a simulation model when only a limited number of experimental values can be measured, which is an advantage over the other validation methods.  
Due to its quantitative measure of the discrepancy between all the data available (predicted data and observed data), the area metric offers an important advantage compared to classical validation methods; it is considered objective and robust [49] in producing a graphical representation of the discrepancy in the same physical units as the experimental data, and this makes it possible to evaluate the differences across the full range of prediction results while considering simulation uncertainties.  
The area metric is calculated with the cumulative distribution function (CDF) of the experimental data and that of the model data. Both CDFs are step functions. Figures 25 and 26 illustrate the mismatch between the prediction distribution (shown in the \"red\" line) and the experimental data (shown in the \"blue\" line), with the area metric shown as the grey shaded area. This metric response has a value of 0.056N for the drag force. With the area metric, it is possible to check the adequacy of the model locally (on a specific point of the experimental test) or globally, by evaluating the difference value of 1.44N, for the lift forces.  
The model validation process was conducted using three methods recommended by the ASME. The residuals, the adjusted R\({}^{2}\), and the area metric validation methods agree that the CFD model accurately estimates the lift and drag forces of the adaptive leading edge wing prototype. The model gave a 98% prediction rate, which is above the prediction rate of 85% recommended by ASME.
# ENSeg: A Novel Dataset and Method for the Segmentation of Enteric Neuron Cells on Microscopy Images
## 4 Proposed Approach  
In this work, we propose an approach for the ENC segmentation task designed to merge the efficiency of instance segmentation models, with the adaptability of prompt-guided segmentation. The goal of such an approach is to enable humans (in our case, researchers of the ENS) to interact with the final system and control the segmentation results. The semi-automatic approach is essential in various medical applications, as it allows the end user to actively engage with the system, providing critical guidance and intervention to shape and refine the final outcomes. Keeping that in mind, our approach is based on the following:  
1. Generating candidate cells from the input image, by using a fine-tuned instance segmentation model;
2. Obtaining a single point prompt per target neuron cell, inputted by the user from the same image;
3. Selecting the candidates, based on the intersection of the point prompts and the outputs of the instance segmentation model (more details on Section 4.3);
4. If they intersect, apply a fusion protocol that can use the selected candidates' coordinates to generate bounding box prompts (enhance the prompt from a click to a box), perform the prompt-guided segmentation, and combine both outputs to obtain the final segmentation mask (more details on Section 4.3);
5. If they do not intersect, perform a prompt-guided segmentation, using the point prompt.  
In addition to selecting the cells to be segmented, our approach also takes advantage of the zero-shot capabilities of SAM-like architectures, using them to further enhance the final segmentation masks through the fusion/combination of both outputs. More details about each one of the steps of the pipeline may be seen in the following subsections.  
Figure 3: Common noise types found on the images of the ENSeg dataset. (**a**) Blurred image; (**b**) tear of the tissue; (**c**) blood vessels; (**d**) overlapped neuron cells.  
A visual representation of the proposed approach may be seen in Figure 4. In this setup, both models operate within a two-step pipeline. Starting from the inputs (image and click/point prompts), the models integrate by forwarding the outputs of one model to the next after processing them through the prompt enhancement mechanism. Although the models function within the same system, this configuration allows the use of different computing resources during inference, such as allocating a separate GPU for each model. It is also worth noting that each individual model pre-processes the input image according to its expected shape and format prior to inference (further details are provided in Section 5.1).
## 2 Design of the Proposed Antenna Array
### Parasitic Patch Antenna Array Layer  
To improve the gain, the parasitic patch antenna array layer was designed based on the driven antenna element above; Figure 4 shows a schematic diagram of the multilayer antenna subarray, which ignores the bonding layer. The whole antenna subarray consists of three layers of metal plates and two layers of Rogers 5880 plates. From bottom to top, the first metal plate layer is the CSC feeding network, which also serves as the ground for the second layer of the driven antenna element; the third metal plate layer is the parasitic antenna array, and four patches form a subarray, which are couple-fed by a driven antenna element; each layer of the antenna subarray is connected by metal vias, which could reduce the mutual coupling between the driven antenna element and the parasitic antenna array, as well as suppressing surface waves [18]. Figure 5a shows the main view of the antenna subarray, and Table 1 gives each dimension parameter.  
Figure 4: Schematic diagram of the multilayer antenna subarray.  
Figure 3: (**a**) Photograph of the fabricated sample; (**b**) the reflection coefficients and matched impedance; (**c**) simulated and measured gain of the proposed antenna.  
As shown in Figure 5b, the radiation field of the antenna subarray (\(E_{T}\)) can be regarded as a superposition field under the mutual coupling between the driven antenna element, \(E_{d}=E_{1}\), and the parasitic antenna array, \(E_{P}=(E_{2}\mid\mid E_{3}\mid\mid E_{4}\mid\mid E_{5})\), which could be described by the mutual coupling coefficient, \(K_{C}\):  
\[E_{T}(\theta,\varphi)=E_{d}(\theta,\varphi)+K_{C}E_{P}(\theta,\varphi) \tag{1}\]  
We simulated the proposed antenna using the HFSS 2022 software. Figure 6 shows the radiation pattern of the proposed antenna subarray on the phi = 0\({}^{\circ}\) and phi = 90\({}^{\circ}\) planes at 30 GHz. It can be seen from the figure that the sidelobe is suppressed below \(-\)10 dB. Since neither the driven antenna element nor the parasitic array are centrosymmetric, the pattern of the 0\({}^{\circ}\) and 90\({}^{\circ}\) planes is not similar. The radiation pattern has better directivity in the 90\({}^{\circ}\) plane and a larger beamwidth in the 0\({}^{\circ}\) plane. The simulated peak gain of the proposed antenna subarray is 12.1 dBi at 30 GHz.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Symbol** & \(W\) & \(L\) & \(W_{p}\) & \(L_{p}\) & \(S_{x}\) & \(S_{y}\) \\ \hline
**Value** & 7.23 mm & 2.6 mm & 3.1 mm & 2.6 mm & 1.4 mm & 2 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical dimensions of the multilayer antenna array.  
Figure 5: Schematic diagram of radiation field analysis of the multilayer antenna subarray: (**a**) structure diagram; (**b**) radiation field diagram.  
Figure 6: Simulation results of antenna subarray radiation pattern at 30 GHz: (**a**) gain; (**b**) directional diagram.
## 3 Material & Methods
### Pentesting
#### 3.3.1 Equipment and Tools  
Software Defined Radio (SDR) technologies have become mainstream. These consist of radio communication systems in which components that have been traditionally implemented in hardware (e.g., mixers, filters, amplifiers, modulators/demodulators, detectors, etc.) are instead implemented using software on a computer. This allows for more flexibility in the design of the radio system and the ability to easily change its functionality. SDRs are used in a variety of applications, including wireless communication, navigation, and radio astronomy. In recent years, many new SDRs have been produced, the most well-known being HackRf, Ubertooth, or BladeRF, which we used (see Figure 5). The model we chose (BladeRFx40) cost us CHF 520 (\(\approx\)USD 565) with two quad band antennas and was able to perform all of the attacks that we implemented. To use this equipment efficiently, we also used multiple tools, listed hereafter:  
* BladeRF-cli [35]: tool required to program the BladeRF. * GNU radio [36]: widely used open-source SDR software. * GPS Test [37]: GNSS app for phone and tablet. * Gps-sdr-sim [38]: generates custom GPS data streams. * Gqrx [39]: radio waves visualization tool. * RfCat [40]: Python library for easier programming of the BladeRF. * Ubuntu [41]: main operating system. * YateBTS [42]: allows the creation of one's own GSM base station. * Wireshark [43]: open-source packet analyser.
## 4 Experimental Result and Evaluation
### Evaluation
#### 4.3.1 A Comparison of the Repetition Rate for Evaluation of the Methods
Further testing simulations for a robot faced with moving obstacles, of these methods in terms of the repetition rate of a robot (100-250 nodes), together with static obstacles are as follows: Regular obstacles, irregular obstacles, multiple obstacles, and multiple irregular obstacles. Table 5 demonstrates the reduction in duration time (in seconds) achieved using our proposed model over the alternative methods for all types of operating environment and obstacle type. Table 6 shows that the repetition rate (a robot to traverse an operating environment--effectively the search space) for our proposed model under all operating environments, massively outperforms the alternative methods considered; this is evidenced by the ability of the proposed method to achieve complete coverage of the operational environment with a very low repetitive coverage rate in the range 2% to 7%. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Duration (in Seconds)**} \\ \cline{2-5}  & **Regular** & **Irregular** & **Multiple Regular** & **Multiple Irregular** \\ \hline MRN & 142 & 162 & 148 & 151 \\ BA* & 102 & 141 & 129 & 135 \\ A*-CPP & 87 & 107 & 97 & 125 \\ Proposed model & **67** & **78** & **68** & **80** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: A comparative analysis of the duration time to visit the operational environment. Figure 10: The duration times and repetition rate for robot visits in dynamic environments.
# Ball Tracking Based on Multiscale Feature Enhancement and Cooperative Trajectory Matching
## 2 Related Work
### Ball Tracking by Detection  
Siamese networks [27; 28; 29] mainly perform similarity matching through Siamese networks. This single-object tracking method requires manually selecting the object in the initial image, traversing each position in subsequent video frames, and determining the position through similarity comparison. However, in sports competition videos, there is no information about the object in the first frame. It is necessary to automatically provide each frame, including the object information in the first frame of the video and the algorithm for reappearing after the object disappears in the middle frame. The tracking-by-detection algorithm [30] means that the object information in each image is obtained through the detection algorithm before tracking. It first detects the object and then matches it to the existing trajectory. The number of tracking objects and the type of tracking object are all determined by the results of the detection algorithm.  
The detection-based object tracking algorithm process mainly includes detecting the object position in each frame, predicting the position where the object will appear in the next frame, and then comparing and correlating the predicted position with the detection results of the next frame. When there are multiple tracking objects, it is also necessary to distinguish based on object characteristics to determine which existing trajectory the new tracking result is associated with. Another important feature of ball tracking is that the number of ball objects that need to be tracked in each frame does not fluctuate within a certain range like the number of players that need to be tracked. Therefore, it can be considered that the same object is tracked at any time, which avoids the identity ID allocation link in the tracking process and reduces the complexity of the tracking problem.