esa-sceva/satcom-chunk-collection huggingface dataset:
# An Adjusted Frequency-Domain Algorithm for Arc Array Bistatic SAR Data with One-Moving Transmitter
## 4 Resolution Analysis
### The Spatial Resolution of AA-BiSAR with Moving Receiver
 \tag{48}\]  
According to Equation  
Therefore, it can be deduced that the ground-range resolution along the gradient direction is:  
\[\rho_{r}=\frac{c/B_{r}}{|\
abla R_{b}|}=\frac{c/B_{r}}{\sqrt{\left(\frac{X_{n}}{ R_{pr}}+\frac{X_{n}-X_{r}}{R_{pr}}\right)^{2}+\left(\frac{Y_{n}}{R_{pt}}+\frac{Y_{n}-Y_{ r}-yt_{n}}{R_{pr}}\right)^{2}}} \tag{52}\]  
The simulation is carried out on the spatial resolution of AA-BiSAR with the moving receiver. The position of the receiver is set as (0 m, 0 m, 200 m), and the transmitter position is (0 m, 0 m, 1000 m), and the speed of the receiver is 50 m/s. Figure 7a is the simulation result of the azimuth resolution of different point targets. It can be seen that the azimuth resolution of the point targets of the same radius is basically the same. Figure 7b shows the ground-range resolution of different point targets in the imaging scene. It can be seen that the farther the point target is from the transmitter, the better the ground-range resolution.
## 5 Model Compilation Using Deep Learning Methods
### Results and Discussion
#### 5.4.3 Error Analysis
There was a noticeable difference between the results for misclassified Saudi and Jordanian articles, with 13 and 12 incorrect articles for both KSA and Jordan, respectively, as seen in Figure 14. These insights might indicate that training the model on smaller n-grams involved taking the text one unit at a time, compared to higher n-grams, which take more than one unit at a time. This finding was also found in studies for text classification [16]. However, these insights may not be generalizable, as we found, in contrast, that n-gram = 3 was more influential for the Brexit dataset, compared to the unigram (n-gram = 1) for the Hajj dataset. This was not the case in the Brexit dataset; we found that the number of misclassified Jordanian articles as Saudi articles were relatively high--34 out of 91 (see Figure 15). A possible explanation is that most of the articles on the topic of Hajj were distinctive between all three Arabic countries, Saudi Arabia, Egypt, and Jordan because the topic had a direct impact on them. This impact created a link between unique words that formulated the news articles for each country. For example, most Saudi articles presented senior political figures with their full titles, such as \"His Royal Highness Prince [PERSON]\"; however, some Jordanian articles would indicate him as \"Prince [PERSON]\". So, words such as \"highness\" would be linked mostly to articles originating from Saudi Arabia. However, since the topic of Brexit did not have the same impact on Arabic articles as the topic of Hajj did, and most of the articles described issues concerning foreign events related to the topic, unique words linking them to the three originating countries were not used. This would explain why the model needed to be trained on higher n-grams than 1--in this case, n-gram = 3. When further analyzing the misclassified articles, we observed that the most common feature of the false negative articles was the standard creation of journalistic terminologies written in modern standard Arabic (MSA). Because all news articles are written in a formal journalistic format, there was a need to analyze the text on a word level, assigning specific words to specific nations. In support of this idea, samples of the misclassified articles showed that most discussed topics related to other nations, including some of the misclassified Egyptian articles presenting a topic that happened in Saudi Arabia. Figure 16 shows examples of this.
## 5 Discussion  
The retrieval of daily mean RSF performs well when all orbits are used, demonstrated by a global RMSB below 7 Wm\({}^{-2}\) and a stable bias around 0 Wm\({}^{-2}\).  
The configuration without NOAA-18 results in several regions with pronounced biases (+/\(-\)30 Wm\({}^{-2}\)), often consisting of swirls with positive alongside negative bias, caused by the extrapolation of the mid-morning observation to the afternoon: this is problematic for fast moving small-scale or heterogeneous weather systems (e.g., fronts) or for large-scale weather phenomena with asymmetrical diurnal cycle (e.g., afternoon marine stratocumulus burn-off). The significantly degraded performance (RMSB of 12 Wm\({}^{-2}\)) stresses the important role of the early afternoon satellite in constructing the RSF diurnal cycle. A similar performance degradation is expected due to the orbital drift of NOAA-19 towards an evening orbit during 2016-2020. Even more important are the mid-morning orbits (NOAA-17 and MetOp-A), demonstrated by the even larger performance degradation when they are excluded (RMSB of 14.6 Wm\({}^{-2}\)). Loss of mid-morning AVHRR orbits is not expected in the near future, but should at some point occur after MetOp-C is decommissioned. Combined, these results demonstrate and quantify the added value of both early afternoon and mid-morning observations, proving them essential for capturing the diurnal variability and obtaining accurate daily mean RSF.  
The fact that performance is only slightly affected by excluding only one of the two mid-morning orbits demonstrates that the added value of individual orbits is related to their temporal spread throughout the day. In other words, both orbits are \"interchangeable\" concerning the performance of the daily mean RSF. The negligible performance degradation following the exclusion of NOAA-15 or NOAA-16 shows that these very early and very late orbits provide little added value to the reconstruction of the diurnal cycle, which may be partly caused by their small impact on the diurnal cycle interpolation.  
The difference between Mean Absolute Bias calculated on a daily time scale and an hourly time scale is indicative of the intra-day compensating error. It is limited and systematic (about 4.5 Wm\({}^{-2}\)), meaning that it does not cause or influence the orbital-specific performance assessment on a daily time scale from which the results and conclusions remain valid.  
After the release of the full CLARA-A3 data record, the 42-year data record of TOA RSF will allow more in-depth analyses, including an assessment on the long term data series' stability and radiometric calibration, for example, by cross-comparison of monthly mean RSF from all existing reference data records.
## 3 Results
### Studies Implementing Vision Systems (VSS)
#### 3.2.2 Validation with Gold-Standard  
The in-depth analysis revealed that almost all VSS include a validation of the proposed solution/algorithm against traditional SHM techniques, based on one or more contact and non-contact sensors.  
The analysis revealed that several sensors had been used for validation purposes, including laser displacement sensors [32, 34, 41, 42, 49, 70, 74, 76, 82], accelerometers [32, 33, 34, 50, 51, 53, 55, 59, 61, 64, 65, 66, 67, 68, 64, 67, 68, 66], IMU [60], linear variable differential transformers [34, 61, 50, 54, 60, 66, 67, 73], laser Doppler vibrometers [38, 46], stationary cameras [42, 75, 83], actuators [71], laser trackers [81], infrared sensors [48], and marker-based systems [44, 73]. Marker-based systems are routinely used in other fields (such as medicine), where they are the traditional reference systems for motion capture and analysis. However, for specific measurements, such as displacement estimation, marker-based systems can also be successfully employed in SHM to compare the performance and accuracy of the proposed algorithms, thanks to retro-reflective markers applied to target locations on infrastructure-scale models. All the studies adopted the mentioned sensors as the gold standard with which to compare the performance, accuracy, and measurements of the proposed vision-based solutions. Figure 4 shows the percentage breakdown regarding the most common sensors used for validation purposes, as revealed by the analysis of the studies in Table 1. It is important to note that some studies adopted more than one type of sensor to validate their proposed solution. In addition, there is a high percentage related to accelerometers because many of the studies aimed to estimate infrastructure vibration, generally from displacement measurements.  
In other studies, however, validation with other sensors was not performed, but rather a performance comparison was conducted between algorithms or devices, indirect measurements, or actual observations. For example, in [37], estimated displacements were compared with actuator-generated movements and stationary targets. In [39, 47, 63], only a comparison between algorithms was performed. In [40, 52, 56, 57, 58, 62, 69, 77, 78, 79, 80, 85] a comparison with damage, deformation, and, in general, real-world manual measurements was performed. Finally, a proof of concept was provided in [84], with only qualitative results.  
Figure 4: Percentage distribution of sensors used for validation purposes, emerging from VSS studies (Table 1).
# A Real-Time On-Orbit Signal Tracking Algorithm for GNSS Surface Observations
## 4 Method, Part 2: Open Loop GNSS-R Delay and Doppler Tracking Algorithms  
After the reflection geometry has been adequately defined (as per Figure 1) it is then possible to calculate the reflected signal code phase delay and Doppler frequency needed to initialize the DDM observation processing hardware. However, the delay estimation relies on a an important assumption of the instrument hardware configuration. This assumption allows for a much simpler calculation of relative code phase delay with respect to the directly tracked navigation signal from the same GPS transmitter.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\[\begin{split}\text{CR}&\text{Cognitive radio}\\ \text{OMA}&\text{Orthogonal multiple access}\\ \text{NOMA}&\text{Non-orthogonal multiple access}\\ \text{MISO}&\text{Multiple-input single-output}\\ \text{SISO}&\text{Single-input single-output}\\ \text{BS}&\text{Base station}\\ \text{TAS}&\text{Transmit antenna selection}\\ \text{ST}&\text{Space-time transmission}\\ \text{MCC}&\text{Maximum channel capacity}\\ \text{SA}&\text{Simulated-annealing}\\ \text{SOP}&\text{Secrecy outage probability}\\ \text{CDF}&\text{Cumulative distribution function}\\ \text{PDF}&\text{Probability density function}\\ \text{PLS}&\text{Physical layer security}\\ \text{QoS}&\text{Quality-of-service}\\ \text{PA}&\text{Power allocation}\\ \text{CSI}&\text{Channel state information}\\ \text{SINR}&\text{Signal-to-noise-plus-interference ratio}\\ \text{SNR}&\text{Signal-to-noise ratio}\\ \text{AWGN}&\text{Additive white Gaussian noise}\end{split} \tag{1}\]
## 3 Factors Influencing U-WSN Design
### Power Consumption and Network Lifetime
#### 3.4.2 Network Lifetime  
Network lifetime is a key design consideration for battery-powered U-WSNs. There exist number of research focus on the lifetime maximization issues [49; 50] or on the prediction of the remaining lifetime [51; 52]. Other research focus on the energy-efficiency issues so as to extend the network lifetime [53; 54; 55; 56], and the trajectory planning of the UAV is one of the most consideration among these schemes [30; 57; 58]. Although a number of studies have been done for network lifetime, U-WSN still face some issues, e.g., the collisions and re-transmissions during communications, which cannot fully avoided in applications that degrade the lifetime.
## 2 Method Description
### Finding the Feed Motion Variation Law  
Let us suppose that the feed increment of the rolling motion corresponds to the workpiece rotation of \(\Delta\varphi_{0}\) angle. If we intend to smooth the detached chip area to \(Ac\) value for any \(\varphi\in[\varphi_{min}\ \dots\ \varphi_{max}]\), then the following relations are obtained:  
\[\Delta\varphi=\Delta\varphi_{0}\cdot A_{c}/A(\varphi)\cong\Delta\varphi_{0} \cdot A_{c}/f(\varphi). \tag{2}\]  
In Equation (2), \(\Delta\varphi\) means the rolling motion increment (instead of \(\Delta\varphi_{0}\)) required to obtain the \(Ac\) value for the detached chip area (instead of \(A(\varphi)\)). Obviously, as \(f\) varies with \(\varphi\), \(\Delta\varphi\) also varies, so one can notice that smoothing the detached chip area means working with variable federate after an appropriate variation law. We have developed an original MatLab application to find this law in discrete form, the input data being:  
* The limits of the variation domain for the workpiece position angle \(\varphi\), \(\varphi_{min}\) and \(\varphi_{max}\), corresponding to the machining of a gear tooth interval.
* The analytical expression of the \(f\) function, meaning the variation law for the detached chip area and the rolling motion increment \(\Delta\varphi_{0}\), based on which this law was determined.
* The imposed value for the detached chip area, after variation smoothening, \(Ac\).
* Some auxiliary variables, namely the incremental parameter \(d\varphi\) (\(\Delta\varphi\) following to be determined as \(\Delta\varphi=i\cdot d\varphi\), \(i\) = 1, 2, \(\ldots\)), and the maximum admissible deviation from \(Ac\) for the value actually obtained of detached chip area \(Aca\) during the smoothened process, \(\varepsilon\).  
The application result consists of a series of (variable) values for the federate of the rolling motion, \(\Delta\varphi_{1}\), \(\Delta\varphi_{2}\), \(\ldots\), \(\Delta\varphi_{ns}\), such as  
\[A_{ca_{1}},A_{ca_{2}},\ \ldots\ A_{ca_{m}}\in[A_{c}-\varepsilon,A_{c}+ \varepsilon\,]. \tag{3}\]  
In Equation (3), \(ns\) means the number of kinematical steps needed for the complete machining of a gear tooth interval, which is obviously different from \(n\) number of steps completed when machining the same gear tooth interval with constant increments of the rolling motion, and \(\varepsilon\) means a quantitative parameter defining the width of the interval within which the detached chip area may vary. These parameters are not specifically related to any toothing method. The parameter \(ns\) is related to process productivity, while \(\varepsilon\) mainly impacts the computing speed of the software used in the method application. The parameters \(ns\) and \(\varepsilon\) should be set according to the manufacturer option.
_Applied_  
_sciences_  
_Article_  
**Numerical and One-Dimensional Studies of Supersonic**  
**Ejectors for Refrigeration Application: The Significance of Wall**  
**Pressure Variation in the Converging Mixing Section**  
**[PERSON] \({}^{1}\)\({}^{\copyright}\), [PERSON] \({}^{1}\), [PERSON] \({}^{1}\)\({}^{\copyright}\), [PERSON] \({}^{1,2}\) and [PERSON] \({}^{1,2,*}\)**  
\({}^{1}\) Mechanical Engineering Department, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia;  
[PERSON]DRESS] (E.D.); [EMAIL_ADDRESS] (S.S.); [PERSON]S] (S.A.);  
[EMAIL_ADDRESS] (J.O.)  
\({}^{2}\) K.A.CARE Energy Research and Innovation Center at Riyadh, Riyadh 11451, Saudi Arabia  
\({}^{*}\) Correspondence: [EMAIL_ADDRESS]  
**Abstract** This paper studies the pressure variation that exists on the converging mixing section wall of a supersonic ejector for refrigeration application. The objective is to show that the ejector one-dimensional model can be improved by considering this wall's pressure variation which is typically assumed constant. Computational Fluid Dynamics (CFD) simulations were used to obtain the pressure variation on the aforementioned wall. Four different ejectors were simulated. An ejector was obtained from a published experimental work and used to validate the CFD simulations. The other three ejectors were a modification of the first ejector and used for the parametric study. The secondary mass flow rate, \(\dot{m}_{s}\) was the main parameter to compare. The CFD validation results indicate that the transition SST turbulence model is better than the k-omega SST model in predicting the \(\dot{m}_{s}\). The results of the ejector one-dimensional model were compared before and after incorporating the wall pressure variation. The comparison shows that the effect of the pressure variation is significant at certain operating conditions. Even around 2% change in the average pressure can give around 32% difference in the prediction of \(\dot{m}_{s}\). For the least sensitive case, around 2% change in the average pressure can give around 7% difference in the prediction.  
**Keywords:** ejector refrigeration; one-dimensional model; computational fluid dynamics (CFD); transition SST turbulence model; converging mixing section; wall pressure variation
# Collaborative Channel Perception of UAV Data Link Network Based on Data Fusion
## 3 Node Weight Allocation Strategy Based on Data Fusion  
This section begins with an introduction to the basic idea of the node weight allocation strategy. It then employs the data fusion theory to analyze the credibility and falsity of a node's characteristic parameters. These parameters serve as a measure for detecting discrepancies and conflicts in node data. Subsequently, based on the corrected credibility and falsity of the node data, the geometric mean method is applied to calculate the channel state perception weight of the node, leading to the formulation of a weight allocation strategy.
## 4 Fault Diagnosis Model
### Fault Diagnosis Process  
In this article, rolling bearing fault diagnosis based on infrared images is studied. Firstly, the infrared videos of nine kinds of faulty bearings are acquired, so as to crop the infrared images. After that, a series of pre-processing work such as RoI acquisition, grayscale image conversion, image denoising and image segmentation are performed. After completing these tasks, GLCM is applied to extract features. Finally, the acquired feature set is fault diagnosed using RFR-GA-BLS. The steps are as follows:  
Step 1: Acquisition of IR images. IR video of nine types of rolling bearings were acquired for 15 min after temperature stabilization: i.e., HE, HO, IN05, IN10, IN15, OU05, OU10, OU15 and RO. IR images were cropped at 9 s intervals. Some 900 images can be obtained.  
Step 2: Cropping images. The temperature of the bearing and the axis affected by the bearing temperature can be acquired as RoI. The 280*150 pixel area is set.  
Figure 4: RFR-GA-BLS fault diagnosis model.  
Step 3: Grayscale image conversion.  
Step 4: Median filtering to remove noise. The size of the filter is set to 3*3, and the median is taken as the value of a certain pixel point.  
Step 5: Adaptive thresholding segmentation. The image is transformed into a binary image. Firstly, a Gaussian weighted average is applied to set the threshold value, and the filter size is set to 5*5. Each pixel point is processed according to the obtained threshold value; pixels larger than the threshold value are 255, and pixels smaller than the threshold value are 0.  
Step 6: Extraction of features. Extract the image features using GLCM, which contains energy, contrast, entropy, sum average, variance and correlation, with designed pixel distances of 1, 2 and 3 and angles of 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\) and 135\({}^{\circ}\), respectively. There are 72 features in total.  
Step 7: Fault diagnosis. The obtained 72 features are input into the RFR-GA-BLS fault diagnosis model. Finally, the optimal features, the best parameters and the highest accuracy are obtained.
## 2 Materials and Methods
### Crop Modeling-Data Assimilation Framework  
Figure 3 provides an overall framework for the process of assimilating remotely sensed SM, retrieved by the water cloud model, into the WOFOST model to estimate the regional winter wheat yield with the EnKF algorithm. First, we ran the WOFOST model with the calibrated crop and soil parameters and meteorological data. Meanwhile, we set the ensemble members (in our case, the ensemble number was set as 50) for the output soil moisture (M\({}_{\text{SM}}\)1-M\({}_{\text{SM}}\)n) from the WOFOST model. Second, Processed Sentinel-1, Sentinel-2, and field measured data are used in the water cloud model to retrieve remotely sensed soil moisture, which are treated as the observation soil moisture (O\({}_{\text{SM}}\)1-O\({}_{\text{SM}}\)n). Third, the EnKF assimilation algorithm will be run at the date when a new observation soil moisture (O\({}_{\text{SM}}\)1-O\({}_{\text{SM}}\)n) becomes available and will generate the optimized soil moisture (SM1-SMn) data set. Finally, the optimum soil moisture is input into WOFOST model to simulate winter wheat yield, which will be validated by ground measured yield.
## 3 Mathematical Development
### Matrix Formulation of the Model

Table 2: Integral form of shape functions products. Figure 3: Deformed shape of the rigid plate under prescribed force at node 1. The consistent \(4\times 4\) DOF's mass matrix is obtained through solving the integrals with the shape function combination for all four nodes of the plate element, as demonstrated in Equations (15) and (16). \[\left[\boldsymbol{M}\right]_{4\times 4}=\frac{Lb}{144}\rho h \begin{bmatrix}4&2&1&2 \\ 2&4&2&1\\ 1&2&4&2\\ 2&1&2&4\end{bmatrix} , \tag{15}\]  
\[\sum_{i=1}^{3}\sum_{j=1}^{3}\text{M}ij=\rho Lb=m_{total}. \tag{16}\]  
The recently presented mass matrix is associated with a 4-node element defined by 4 nodal displacements. However, such a displacement field, as shown in Equation (1), can be condensed into a 3-DOF as:  
\[\left\{\boldsymbol{u}\right\}\ =\left[\boldsymbol{T}\right]_{4\times 3} \left\{ \begin{matrix}u_{C}\\ \left\{\theta_{x}\right\}\\ \theta_{y}\end{matrix} \right\}\ \Leftrightarrow\ \begin{matrix}u_{1}\\ u_{2}\\ u_{3}\\ u_{4}\end{matrix} =\frac{1}{2} \begin{bmatrix}2&-b&-L\\ 2&b&-L\\ 2&b&L\\ 2&-b&L\end{bmatrix}\begin{matrix}u_{C}\\ \theta_{x}\\ \theta_{y}\end{matrix} \right\}. \tag{17}\]  
The scenario is the same regarding the acceleration vector. It is possible to reformulate Equation (8) for previous transformations to achieve a \(3\times 3\) mass matrix. Previous algebraic operations lead to a useful \(3\times 3\) mass matrix that is diagonal as:  
\[\left[\boldsymbol{M}\right]_{3\times 3}=\frac{\rho h}{144}\frac{Lb}{2} \begin{bmatrix}2&2&2&2\\ -b&b&b&-b\\ -L&-L&L\end{bmatrix}\begin{bmatrix}4&2&1&2\\ 2&4&2&1\\ 1&2&4&2\\ 2&1&2&4\end{bmatrix} \left[\boldsymbol{T}\right]_{4\times 3}=\rho hLb \begin{bmatrix}1&0&0 \\ 0&\frac{b^{2}}{\Gamma}&0\\ 0&0&\frac{b^{2}}{\Gamma}\end{bmatrix} . \tag{18}\]  
Consequently, the stiffness matrix was formulated as a \(3\times 3\) matrix, which contributes to providing a simple and practical solution to the problem of natural frequencies. It is remarkable to note that, in this orthogonalized mass matrix, a mass is associated with a displacement DOF, while moments of inertia are associated with rotation DOFs, \(\theta_{x}\), and \(\theta_{y}\).