esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Satellite Data
#### 2.3.2 Sentinel 1 Pre-Processing  
For SAR data (Figure 4), the GEE Copernicus/S1_GRD (Ground Range Detected) dataset in IW (Interferometric Wide-swath) mode was utilized. Data were pre-processed with the Sentinel 1-Toolbox [72], including orthorectification, radiometric and thermal correction, expressed in decibels, and projected onto a regular 10 m grid. The acquired images over the Area of interest (AOI) for 12 months (from January 2022 to December 2022) were filtered by selecting the VV and VH polarization. Due to the considerably different viewing orientations between the ascending and descending satellite overpasses, only the ascending orbit was selected for this study to maintain data consistency. The resulting dataset consists of 73 images, preprocessed using the methodology of [66] with some adaptations. The data acquired from the different orbits were treated as distinct datasets, with a focus on selecting only the acquisitions with ascending orbits. To reduce speckle noise while preserving important image details, we applied the Lee Filter using a 3 \(\times\) 3 pixel configuration. To address the effects of incidence angle variations on the backscatter, we implemented the Square Cosine Correction based on the Lambert law. This correction is based on the mean incidence angle of each image cropped on the Area of Interest (AOI) and serves as a reference angle. The subsequent steps involve value conversion into the linear unit, followed by monthly averaging, and finally converting the data back to decibels.  
The choice to use data from the latest year (2022) aims to identify significant backscattering variations linked to recent events like extensive eradication activities. These changes might not be apparent when looking at median or average data over a four-year period. This process resulted in 12 average images, each with two frequency bands, totaling 24 features representing the SAR dataset.
## 2 Materials and Methods
### Methods
#### 2.3.3 Extraction of Disturbance Boundaries in a Single Mining Area  
The impact of coal mining on the surrounding ecological environment is confined to the mining area itself and exhibits spatial diffusion or transfer. In and around coal mining areas, some surface ecosystems experience \"disturbance\" due to coal mining activities. There exists a boundary between the \"disturbed\" and \"undisturbed\" areas. Therefore, this study masks the influence of buildings, farmland, and roads, focusing exclusively on the direct impact of open-pit mines on surface SM without considering indirect effects such as those related to groundwater changes or coal transportation. The TVDI, reflecting surface SM conditions, is employed as a direct monitoring indicator. The TVDI-CFS method is used to derive the distribution of TVDI disturbances caused by mining activities, which directly reflect changes in surface SM conditions. Additionally, the boundary of coal mining's impact on SM is defined as the distance from the open-pit mine boundary to the point where SM values gradually return to the surrounding normal levels, ultimately reaching a stable critical distance. A series of buffers with a step length of 30 m were established within 1000 m of the mining area boundary. Figure 4 presents the buffer zone setup and eight-directional analysis framework, which is used to define and quantify the mining disturbance boundaries systematically. The general equation for the TVDI-CFS fitting function is as follows:  
\[y=\frac{A_{1}-A_{2}}{1+\left(x/x_{0}\right)^{Np}}+A_{2} \tag{10}\]  
where \(x\) is the distance from the open-pit mine area, \(x_{0}\) is the inflection point, which is the midpoint of the transition from \(A_{1}\) to \(A_{2}\), \(y\) is the TVDI-CFS value, \(A_{1}\) and \(A_{2}\) represent the maximum and minimum values of the TVDI-CFS disturbances caused by mining activities, and \(p\) is the rate at which the influence of mining activities expands.  
Figure 3: TVDI-CFS extraction result diagram (using 2021 data as an example). **(a)** Predicted TVDI distribution, (**b**) remotely sensed TVDI distribution, and (**c**) TVDI-CFS.
## 2 Data and Methodology
### CPR Measurements and Cloud Data  
The A-Train satellite constellation is in a sun-synchronous orbit, observing a certain geographic location at a fixed local time, 1:30 p.m. and 1:30 a.m. for ascending and descending orbits, respectively. CloudSat is one of the key components of the A-Train constellation, launched in April 2006. The main scientific mission of CloudSat is collecting measurements of global clouds, especially their vertical structures and radiative properties. As many kinds of sensors are loaded on multiple satellite platforms of the A-Train constellation, such as radiometers, radars, and lidars, these joint measurements are useful for advancing knowledge on clouds, precipitation, and aerosols.  
CPR on the CloudSat platform is the first spaceborne millimeter-wavelength radar, working at 3.2 mm wavelength (or 94 GHz frequency). When compared with weather radar usually working at the centimeter band, CPR is advantageous for detecting small-sized hydrometeors, which are mainly cloud droplets or ice crystals, rather than large-sized raindrops or ice crystals [22; 23]. This detecting capability is further strengthened by its high signal sensitivity of approximately \(-30\) dBZ. The high spatial resolution also helps CPR to sample sufficient cloud targets, in particular for effectively resolving the cloud layers or inner-cloud structures. CPR steadily operates at the nadir-viewing mode without cross-track scanning, and the successive profiles generate a vertical section in the atmosphere. For a certain profile, it represents a horizontal effective field of view of about 1.7 km along-track and 1.4 km across-track. The vertical resolution is 480 m, resulting in 125 gate bins along the 30 km range. Note that, in the processed profile data, the nominal vertical interval has been enhanced to 240 m by using the oversampling technique. This forms the elementary unit in the CPR profile data and it is called a bin for convenience. Thus, the minimum geometric thickness of a cloud layer or a gap between vertically adjacent cloud layers is 240 m.  
It is noteworthy that CPR seriously suffers from ground clutter, and accordingly the lowest several bins above surface are invalid. This leads to the truncation of the profiles at about 1 km elevation over oceans [24]. The sampling on terrestrial warm clouds has considerable uncertainties since the truncation level would depend on complicated topography over lands. For sampling consistency, the present study is restricted to only considering oceanic warm cloud layers. Note that warm clouds occur much more frequently over oceans than over land, and oceanic warm clouds account for about 80% of the total warm clouds throughout the globe [25].
# Real-Time Statistical Measurement of Wideband Signals Based on Software Defined Radio Technology
## 3 Materials and Methods
### System Validation in Realistic Wireless Environments  
After parameterization, the designed system was applied to signals present in realistic and challenging wireless environments. LTE-A, 5G and IEEE 802.11 ax signals emitted by a mobile communication device were chosen to demonstrate the system capabilities in performing CP and CCDF measurements for different channel types, bandwidths (20/40/80/100/160 MHz) and types of services carried in the channels. The device used was a Huawei Mate xs2 mobile phone capable of supporting all three selected communication standards.  
For this validation phase, the measurements were performed over the air with the OmniLOG 70600 Aaronia manufactured antenna connected to the SDR RF channel. During the measurements, the mobile device was placed in proximity (-40 cm) to the receiving antenna.  
The channel center frequency assigned to the base station was 1750 MHz with 20 MHz channel bandwidth for the LTE-A standard and 3580 MHz with 100 MHz channel bandwidth for the 5G standard. The measurements were performed in a laboratory environment without the exclusion of possible interfering signals.  
The experimental CP calculation and CCDF statistical measurements targeted different services specific to the LTE-A and 5G standards. To include more diverse usage profiles, the mobile device was connected to a base station and set to perform different experimental scenarios:  
* video call;
* voice over IP;
* file download;
* upload;
* video streaming.  
WhatsApp, an application commonly used by smartphone users, was selected to perform the video call and VoIP measurement scenarios; a large game was downloaded for the download service; YouTube with 4K UHD 2160p fidelity was used for the streaming scenario and a free speed-testing application was used for the upload service.  
For 802.11 ax-specific measurements, a Wi-Fi 6 router model TP-Link Archer AX10 was used for two experimental scenarios: downloading and uploading files on the same Huawei mobile device. The Wi-Fi channels were configured in the 2.4 GHz Wi-Fi frequency band and set to 40/80/160 MHz bandwidth. Other Wi-Fi networks were also visible in the 2.4 GHz bandwidth, enabling the system validation in realistic electromagnetic environments, with external sources of interference.
## 4 Experiment and Results
### Exp. #5: Comparison of BPNN and PNN Classifiers
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **ANN Type** & **Time (s)** & **Recognition Rate (\%)** & **Sensitivity** & **Specificity** & **PPV** \\ \hline \multirow{3}{*}{Arousal} & BPNN & 448.35 & 74.45 & 0.74 & 0.75 & 0.75 \\  & PNN & 2.09 & 76.09 & 0.89 & 0.63 & 0.71 \\ \cline{1-1}  & BPNN & 448.21 & 74.06 & 0.75 & 0.74 & 0.74 \\ \cline{1-1}  & PNN & 2.09 & 75.31 & 0.93 & 0.58 & 0.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of results from BPNN and PNN. Figure 6: PNN results for various smoothing parameter values for (**a**) arousal and (**b**) valence emotion. ones; thus, the far-left handed side of the ROC graph becomes more interesting; thus, from this point of view, the BPNN performed better than the PNN. Although the maximum value achieved was not significantly different, the computing time of the PNN for a single run was about 200-times faster than that of the BPNN. However, PNN requires an optimization method for finding the best sigma value, whereas the BPNN does not require prior optimization; thus, the iterative training for BPNN can be conducted only once. Nevertheless, for tthis experiment, the total time for running 60 time trials of PNN is still faster (60 \(\times\) 2.09 = 125.4 s) compared to a single run of BPNN (448 s), which is about 3.5-times faster. Using one-way ANOVA, the time difference between BPNN and PNN shows a significant difference with \(p<10^{-4}\).
# Swin Transformer Assisted Prior Attention Network for Medical Image Segmentation
## 2 State of the Art and Related Work
### Vision Transformers  
Transformer [26] was initially proposed for solving the problems of machine translation and made eminent contributions in nature language processing (NLP). Inspired by the design of Transformer, researchers proposed an advanced vision transformer (ViT) [27], which considered an image as a sequence of patches and models' long-range dependencies by using self-attention mechanisms [27; 28; 29]. Vision transformer had achieved top-1 performance on ImageNet classification tasks by utilizing Transformer self-attention mechanism to process images. However, ViT has its disadvantages in comparison with CNN-based methods because it needs to be pre-trained on large dataset before formal training. To address this problem in training ViT, [PERSON] [30] proposed that applying several training strategies to help ViT better be trained on ImageNet such as integrating it with a distillation method for enhancing the robust and scalability of the network. Recently, there are several excellent researches that have been done based on vision transformer [6; 31; 32]. Particularly, Swin Transformer, an efficient and effective hierarchical vision Transformer, adopted a hierarchical window scheme that imitates the process of CNN to enlarge the receptive field, and being designed as a backbone in vision tasks such as ImageNet classification, objection detection, and semantic segmentation based on its shifted windows mechanism, which outperforms the state-of-the-art methods such as ViT/DeiT [27; 30], ResNet models [12], and importantly, with similar parameters and latency on these tasks. Swin Transformer obtains 53.5 mIoU on ADE20K and gains improvement of +3.2 mIoU over the state-of-the-art methods such as SETR, and it achieves a 87.3% top-1 accuracy on image classification task ImageNet-1K. Different from the previous works in the field of vision transformer, Swin Transformer has the following two outstanding contributions [6] that distinguish it from other works: (1) Swin Transformer constructs the hierarchical feature maps of the image on the basis of linear computational complexity. These hierarchical features can make Swin Transformer suitable as a general backbone for kinds of computer vision tasks. (2) Swin Transformer proposes a key design where shifted windows are equipped between consecutive attention layers, which can enhance modeling power while performing computation-efficient strategy. Motivated by Swin Transformer's tremendous success in various vision tasks, Swin-Unet [3] is firstly proposed to apply pure Transformer-based U-shaped architecture to replace the traditional U-shaped composed with convolution blocks.
## 3 Modelling and Parameterisation
### Multiscale Geographically Weighted Regression (MGWR) Model
#### Mgwr  
The multiscale geographically weighted regression (MGWR) model was proposed by Fotheringham in 2017 [19] and further refined by [PERSON] et al. in 2019 for statistical inference [20]. MGWR is an improvement on the GWR model. MGWR relaxes the single-scale assumption in all modelling processes and allows each parameter surface to have its own bandwidth. The bandwidth is estimated during model calibration and interpreted as the spatial scale at which conditional relationships (or spatial processes) operate. The smaller the bandwidth,the more localised the spatial process and vice versa [21]. MGWR runs with both local and global variables. The specific formula for MGWR is as shown in Equation (3) below:  
\[y_{i}=\beta_{0}+\sum_{j=1}^{p}\beta_{ij}x_{ij}+\sum_{j=p+1}^{m}\beta_{ij}(u_{i}, v_{i})x_{ij}+\varepsilon_{i} \tag{3}\]  
where \(y_{i}\) is the dependent variable for the \(i\)-th station; (\(u_{i}\),\(v_{i}\)) are the spatial geographic coordinates of the \(i\)-th station; \(U_{i}\) is the longitude coordinate of the \(i\)-th sample point, and \(v_{i}\) is the latitude coordinate of the \(i\)-th sample point. \(X_{ij}\) is the individual independent variable; \(\varepsilon_{i}\) is the random error term; P represents the number of global independent variables; \(\beta_{ij}\) represents the regression coefficients for each variable.  
Figure 1: Research framework.  
In this study, MGWR 4.0 software was used to obtain the regression results of MGWR [22]. Model parameter selection was determined by sensitivity analysis, as shown in Table 1.  
The sensitivity analysis of Table 1 on the MGWR model resulted in the finalisation of the operation as follows: The MGWR model was used as the base estimation in this study. The finalised operations are as follows: In this study, the MGWR model is used as the base estimation. For bandwidth optimisation, a dynamic bandwidth determination algorithm based on sample point adaptation is introduced, in which the bandwidth search uses the golden section optimisation algorithm for efficient optimisation, the spatial kernel function is selected as a quadratic function, and the bandwidth selection criterion is based on the Corrected Akaike Information Criterion (AIC\({}_{\text{C}}\)). To address the convergence problem of the model, by comparing the strictness of different convergence criteria, it is found that SOC-f has stronger convergence constraints than SOC-RSS, so the SOC-f criterion is preferred as the convergence criterion. In order to improve the prediction accuracy of the model, the convergence threshold is set to \(10^{-5}\) orders of magnitude, and the model is judged to have reached the convergence state when the root mean square change of the regression coefficients is lower than this threshold during successive iterations.
## 4 Simulation Evaluation
### Comparison with Other Methods
#### 4.3.1 Result in the Fixed Sink Scenario
The total energy consumption of SC-Sched(simple) is lower than that of SC-Sched, because in times of a reception failure of a WuC, the sensor nodes remain in the sleep state. This is achieved at the cost of a low frame delivery ratio. However, as shown in Figure 9, the data collection time of SC-Sched, after using multiple channels, is still longer than that of the other methods. In SC-Sched, the sender node and the receiver node  
Figure 8: Total energy consumption in the four methods under different numbers of sensor nodes. Figure 7: Frame delivery ratio in the four methods under different numbers of sensor nodes. are woken up by a WuC per hop, and the WuC detection time and wake-up latency of the data communication module occur at each hop. By using multiple channels, the transmission (and detection) of a WuC and wake up of the data communication module for the next hop can be done in parallel with the data transmission of the current hop. For the pairs of leaf nodes in the routing tree there is only one data packet, and its transmission time is shorter than the overall time of wake-up latency of the data communication module and the WuC detection time in the presence of consecutive WuC transmissions. Therefore, the data collection time of SC-Sched is prolonged. In ContikiMAC, WuC detection time does not exist and wake-up latency does not influence the data collection time by early wake up. However, there is still a wait time due to CSMA/CA and duty cycling. In contrast, in WuR-TDMA, all sensor nodes are woken up at once. Therefore, the WuC detection time and wake-up latency occur only once, and the data collection time of WuR-TDMA is the shortest among the four methods. However, this is achieved at the cost of more energy consumption in receiving the routing and transmission scheduling information from the sink node. The data collection time of SC-Sched(simple) is lower than that of SC-Sched because SC-Sched (simple) has no retransmissions for WuCs and data frames.
## 4 A Fault Feature Extraction Method  
Because it is difficult to extract fault features of rolling elements under complex working conditions and strong interference, a new feature extraction method of rolling element fault based on KMVMD and PGMCKD, namely KMVMD-PGMCKD, is proposed here. Firstly, the KMVMD is employed to decompose the signal into IMFs and reconstruct the signal. Then, based on the parameter adaptive selection method of power spectrum entropy, the \(L\) and \(T\) of MCKD are selected adaptively, and the fault features of the reconstructed signal are effectively enhanced by PGMCKD. Finally, the power spectrum is selected to analyze the deconvoluted signal to diagnose faults.  
The realized steps of the KMVMD-PGMCKD are described as follows.  
Step 1: Determine the maximum number of modes and decompose the signal.  
Step 2: Based on the maximum kurtosis mean value, the mode number is determined, and IMFs with larger kurtosis values are selected for reconstructing the signal.  
Step 3: Set the values of \(L\) and \(T\) in MCKD. The interference of rotating frequency is considered to determine \(T\). Calculate the theoretical value of the search range, and obtain the search range.  
Step 4: Grid search is employed to determine two parameters in MCKD, and the power spectral entropy evaluation criterion is employed to obtain ideal values of parameters.  
Step 5: Hilbert transform is employed to transform the enhanced signal. The power spectrum is applied to analyze the enhanced signal to identify fault feature frequency.
## 4 Fast Initial Trajectory Setting and Distributed Grid Points Adjustment  
### Fast Initial Trajectory Setting  
In order to improve the solution seeking speed of the method and ensure that the initial trajectory meets a large number of constraints as much as possible, according to the landing points of the vehicle, a fast initial trajectory setting method that meets the dynamic models was designed. Since the terms of Coriolis acceleration and implicated acceleration caused by the earth rotation are small, the flight path of the re-entry glide vehicle in any direction, at any longitude and latitude coordinate can be approximated to the equatorial origin, and with the same initial speed and same control law in the longitudinal direction by rotating and moving, which is shown in Figure 2a,b. In this paper, the landing points map of the constant angle of attack and bank angle control strategy of the re-entry glide vehicle is established offline, and suitable control variables and initial mapping trajectory are obtained by interpolation, as shown in Figure 2b. Finally, the initial generated trajectory satisfying the constraint can be obtained by integrating the corresponding control law in the original state.  
Reachable landing point locations of the re-entry glide vehicle are shown in Figure 3a,b. Among them, the change of constant attack angle and constant bank angle has regularity. Generally, with the fixed attack angle and the different bank angle, the lateral landing points locations are mainly affected. With the same bank angle and different attack angle, the flight range is affected. When the setting of the attack angle is less than the maximum lift drag ratio attack angle, the flight range is proportional to the attack angle; when it is greater than the maximum lift drag ratio attack angle, the flight range is inversely proportional to the attack angle.
# Analyzing TCP Performance in High Bit Error Rate Using Simulation and Modeling
## 3 Results
### FTP Response Time  
Table 4 shows the time needed by the FTP client to download a 10 MB file from the FTP server for the five selected scenarios. Column 1 lists the five selected scenarios. The corresponding response times are shown in column 2. The rightmost column compares the result of each scenario with that of \(p_{drop}=0\%\). We observe that the download response time increases with increasing \(p_{drop}\), as expected.