esa-sceva/satcom-chunk-collection huggingface dataset:
# CF-mMIMO-Based Computational Offloading for UAV Swarms: System Design and Experimental Results
## 2 System Design
### Interference Detection and Mitigation  
To ensure robust communication under interference-prone environments, the CF-mMIMO communication layer incorporates several adaptive mechanisms.  
First, the system continuously monitors the downlink transmission quality using PDCCH error statistics and ACK/NACK feedback. Based on these indicators, the base-band unit dynamically identifies affected sub-bands in the frequency domain and avoids scheduling data transmissions over heavily interfered channels.  
Second, when the system detects persistent synchronization degradation in the current SSB sub-band (e.g., due to narrowband jamming), a seamless sub-band switching mechanism is triggered to reassign SSB transmission to a cleaner frequency region, maintaining synchronization reliability without physical handover.  
Third, the system adopts interference-aware DCI mapping strategies. For narrowband interference, non-interleaved mapping is used to concentrate DCI transmission in clean spectrum regions. For comb-type multi-narrowband interference, interleaved mapping is employed to disperse the DCI content across available sub-band gaps, improving the probability of successful decoding.  
These mechanisms collectively enhance the system's anti-jamming capability and ensure reliable control signaling and synchronization in dynamic or hostile RF environments.
## 5 Improved VSG Control Strategy Based on Active Transient Feedback Compensation
### APFBC-VSG Small-Signal Model Downscaling and Parameter Design
\begin{array}{l}\xi_{\text{FBC}}=\frac{D_{\omega}\omega_{0}+K_{\omega}+T _{\text{FB}}K_{\text{FB}}}{2\sqrt{K_{\text{T}}}|_{\omega}\omega_{0}}\\ \omega_{\text{nFBC}}=\sqrt{\frac{K_{\text{T}}}{I\omega\omega_{0}}}\end{array} \right. \tag{21}\]  
Using Equation (21), the expressions for the phase margin and cutoff frequency corresponding to \(G_{\text{PFBC}}\)(s) can be further derived as follows:  
\[\left\{ \begin{array}{l}\gamma_{\text{FBC}}=\arctan\frac{2\xi_{\text{FBC}}}{ \sqrt{\sqrt{1+4\xi_{\text{FBC}}^{4}-2\xi_{\text{FBC}}^{2}}}}\\ \omega_{\text{cFBC}}=\omega_{\text{nFBC}}\sqrt{\sqrt{1+4\xi_{\text{FBC}}^{4}-2 \xi_{\text{FBC}}^{2}}}\end{array} \right. \tag{22}\]  
In accordance with classical control theory, to guarantee that the APFBC-VSG system exhibits robust stability and swift response during grid operation, \(\xi_{\text{FBC}}\) should exceed a threshold value to ensure stability, typically within the 0.707 < \(\xi_{\text{FBC}}\) < 1 range. Furthermore, the phase margin should be maintained within \(\gamma_{\text{FBC}}\) > 45\({}^{\circ}\). Utilizing these parameter selection methodologies and substituting the derived characteristic parameter values into Equations (21) and (22), an optimal design for the compensation coefficient \(K_{\text{FB}}\) and the decay time constant \(T_{\text{FB}}\) can be realized.
# Data-Free Area Detection and Evaluation for Marine Satellite Data Products
## 2 Materials and Methods
### Date Collection
#### 2.2.2 GOCI, MODIS and OLCI Products  
The Yellow Sea and Bohai Sea are often covered by clouds, and only a few clear and reliable pixels can be collected in the range of interest. This means that there are serious limitations in collecting various information for all seasons and different atmospheric states [42]. A statistical analysis of the missing data of the Yellow Sea and Bohai Sea over the years have found that green tide algae outbreaks were frequent in 2019. The coastal eutrophication was serious, and the marine ecological environment was complex. These conditions render the data-free area significantly more representative.  
We selected standard spectral samples in June (in the period of green tide outbreak and more serious coastal pollution) and January (in the period of sea ice outbreak). Moreover, we adopted the images of 23 June 2019 and 20 January 2019, with less cloudiness, and used them as the standard images for selecting the spectral samples. In this manner, the temporal phase of the data could be unified, and the effect of green tide algae and cloud movement could be reduced.  
Consequently, we selected a total of 33 views of GOCI, MODIS and OLCI image data of the Yellow Sea and Bohai Sea taken in all seasons of 2019 (i.e., two views per sensor data in winter and three views per sensor data for each of the other seasons). The selected data included Level 1 data without calibration processing and Level 2 product data with data-free areas under the same spatiotemporal conditions (Table 2). The data-free area is present in the vast majority of the product data for the same sensor, and it is spatially located in the same place. Here, the Level 2 product data were only used to determine the spatial location of the data-free area and the size of the area. Consequently, the chlorophyll concentration product, which is commonly used in ocean color products, was selected as the image data for evaluation.
## 2 Cardiovascular Disease
### Coronary Artery Disease
#### 2.1.7. Current Treatments  
The development of atherosclerotic lesions within the coronary inner wall causes narrowing to the lumen, and thus, restricts the blood flow to distal parts of the heart. To alleviate the symptoms of CAD, patients can undergo either PCI, coronary artery bypass \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Risk Factors** & **Comments** \\ \hline \multirow{4}{*}{Modifiable} & \multirow{2}{*}{Obesity} & Obesity is an independent risk factor that contributes to CAD. \\  & & Epidemiologic studies showed that there is a strong association \\  & & between CAD and obesity [57]. \\ \cline{2-3}  & Hyperlipidaemia & Excessive LDLs in the blood enable their adherence to the arterial wall, \\  & (ex, LDL) & followed by atherosclerosis [58]. \\ \cline{2-3}  & Lifestyle & Lifestyle behaviours are the main cause behind the development of \\  & & other risk factors. For example, unhealthy diets lead to type II diabetes, \\  & & obesity, hypertension, or high cholesterol (LDL) [59]. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._graft (CABG), or medications for minor conditions. PCI is a non-surgical procedure where a well-trained physician obtains vascular access at patients' groin area (femoral artery) or at the arm wrist (radial artery). Through this vascular access, a flexible thin hollow tube is inserted and threaded up to the heart. The catheter tip is then engaged into the LCA or RCA ostia (the opening of the vessels). Materials such as balloons, stents, and medications are delivered through the catheter to the injured site. Commonly, PCIs start by the insertion of a small balloon and inflate it to squash fatty deposits against the CA wall and thereby widen the lumen. After ballooning the vessel, a stent is then delivered and inflated to keep the lumen open. Stents act like a scaffold and stay in the heart permanently. CABG, also called open heart, is a surgical procedure to allow blood to reach ischemic tissues when PCI becomes not ideal. During CABG procedures, a surgeon uses healthy blood vessels from other body parts to bypass severely stenotic or clogged areas. This procedure is essentially recommended in individuals with severe stenosis or multiple coronary arteries. The left internal mammary artery (LIMA) from the chest and the saphenous vein from the leg are the most commonly used grafts. The LIMA, termed arterial graft, has a better survival rate, whereas the saphenous vein, termed vein graft, has poor outcomes in long-term follow-up [71; 72]. In addition to lifestyle changes, treating coronary artery disease with medications to alleviate symptoms and lower the risk is possible. Generally, medications include antiplatelets, antihypertensive, cholesterol-lowering, and antiarrhythmic drugs. See Table 2 that summaries the commonly used medications for CAD.
# ChroSegNet: An Attention-Based Model for Chromosome Segmentation with Enhanced Processing
## 3 Method  
In this work, our main focuses are on (1) the construction of a chromosome segmentation dataset and (2) the design of our ChroSegNet model with an effective attention mechanism.  
We work with genetic disease laboratory professionals to obtain raw chromosome data. High-quality biomarker slides are prepared by professionals. Then, we use optical microscope camera (including high-resolution camera, optical microscope, and image frame storage board), three-dimensional object loading platform, three-dimensional platform automatic controller, objective lens switching controller, slide glass replacement controller, and computer platform to form a high-level view. The precise three-dimensional optical platform is used to capture and photograph the metaphase chromosomes on the film, and the image data is stored by computer. Again, we used the annotation tool to annotate the 46 chromosomes in each image one by one to obtain label data. Finally, we obtained about 430 RGB microscopic images with a resolution of 1280 \(\times\) 1024 and the corresponding label data in json format.  
Since deep learning needs to be driven by large data, and the original image data has problems such as complex noise and insignificant chromosome features, we designed an enhanced processing to convert the limited original data into the expected Enhanced dataset to ensure that the obtained segmentation model has High precision and robustness (see Section 3.1). Based on the Enhanced dataset, we then propose our ChroSegNet for fine chromosome instance segmentation. On the one hand, the hybrid attention mechanism is introduced into ChroSegNet to achieve efficient extraction of chromosome characteristics and location information. On the other hand, we deepen the network structure on the basis of the baseline, in order to obtain more rich multiscale information, which allows the model to be more quickly adapted to microscopic image data with a large number of tiny chromosomes (see Section 3.2).
# Optimizing Healthcare Delivery: A Model for Staffing, Patient Assignment, and Resource Allocation
## 4 Methodology
### Use of Simulated Data: Overcoming Challenges in Obtaining Real-World Data  
During this research, acquiring real-world data that aligns precisely with the specified parameters was challenging owing to diverse practical limitations. Factors such as accessibility, availability, concerns about privacy, and the dynamic state of healthcare systems hindered the availability of suitable data. Nevertheless, it is crucial to address these challenges to guarantee the consistency of health research and decisions. Consequently, a simulated data procedure was employed to address these difficulties and offer a typical setting for carrying out experiments. Moreover, the simulated data enabled controlled experiments, plausible scenarios, and the generation of varied datasets with specific parameter arrangements. Despite circumstances in which collecting real-world data proves tough or impractical, simulated data enabled dependable and precise analysis.  
Data Selection for Real-World Healthcare Simulation  
The initial data utilized in the simulation model were carefully selected and simulated to represent or mimic real-world healthcare setting scenarios. The selection was guided by intuition and expert judgment and grounded in the following principles:  
* **Realistic Assumptions:** The chosen values for parameters such as working hours, resource utilization, patient demand, and capacity are aligned with typical healthcare practices, regulations, and operational constraints.
* **Expert Consultation:** Inputs from healthcare practitioners and administrators play a pivotal role. Moreover, experts have informed the selection of initial values, ensuring that they mirror the complexities and nuances of real-world healthcare delivery.
* **Sensitivity Analysis:** The model's responsiveness to variations in the initial values have been tested, confirming that the chosen values provide a robust and representative simulation of real-world scenarios.
## 5 Maximize Energy Production of Wind Farm
### Optimization for WT\({}_{2}\)  
In this subsection, the constant wind direction at 270\({}^{\circ}\) and constant wind speed across the whole year are assumed and the optimal pitch angle and tip speed ratio of WT\({}_{2}\) are selected from the cut-in wind speed to the cut-out wind speed. As mentioned before, the rotor speed limit of a minimum 6.9 rpm and a maximum 12.1 rpm, the rated power limit of 5 MW, and the maximum lifetime limit of 30 years are taken into account. As shown in Figure 14, the optimal pitch angle and tip speed ratio of each wind turbine can be selected separately from the downstream wind turbine to the upstream wind turbine. In this case, WT\({}_{2}\) can be treated as an isolated single wind turbine without any interaction with other wind turbines.  
For a single wind turbine, the active power, lifetime, and energy production in terms of pitch angle and tip speed ratio at the wind speed of 7 m/s, 9 m/s, and 11 m/s are shown in Figure 15a-c, respectively. In Figure 15a,b, the maximum lifetime is limited by 30 years. In Figure 15c, the tip speed ratio is limited by 7.2, because the maximum rotor speed of 12.1 rpm. It can be observed from Figure 15  
Figure 14: Flow chart to obtain the optimal pitch angle and tip speed ratio in the wind farm with two turbines.  
that, at the wind speed of 7 m/s, the maximum energy production is obtained at the same pitch angle and tip speed ratio with the maximum active power, because the lifetime at each set of pitch angles and tip speed ratios are all limited to 30 years. At the wind speed of 11 m/s, the maximum energy production is obtained at the same pitch angle and tip speed ratio with the maximum lifetime due to the higher difference of lifetime compared to the active power.  
Similarly, the pitch angle and tip speed ratio can be obtained from the cut-in to the cut-out wind speed at the maximum energy production of WT\({}_{2}\). As a result, the optimized pitch angle and tip speed ratio, the corresponding rotor speed, the active power, the lifetime, and the energy production of WT\({}_{2}\) are shown in Figure 16. Compared with the MPPT method, it can be seen that the energy production of the wind turbine can be increased a lot from the wind speed of 9 m/s to 11 m/s.  
Figure 16: _Cont._  
Figure 15: The active power, lifetime, and energy production in terms of the pitch angle and tip speed ratio at the wind speed of: (**a**) 7 m/s; (**b**) 9 m/s; (**c**) 11 m/s.
# Robust Space Time Adaptive Processing Methods for Synthetic Aperture Radar
## 1 Introduction
In this paper, we propose two STAP methods for SAR with piecewise sub-apertures and data constraints for non-stationary interference cancellation based on element space processing and the generalized sidelobe canceller (GSC), respectively. In these methods, the entire synthetic aperture time is divided into several sub-apertures so that the interference can be considered as stationary in each sub-aperture. At the same time, the consistency of the echo phase in the slow time domain is preserved by the data constraint to ensure the null depth of the antenna pattern for non-stationary interference cancellation in the entire synthetic aperture time and the performance of azimuth focusing in SAR. The proposed algorithms are validated through the model simulation and measured data.
# High-Resolution Digital-to-Time Converter Implemented in an FPGA Chip
## 4 Experiments
### Temperature and Voltage Stability Tests  
The temperature stability of the proposed DTC is tested on the evaluation board with the help of a thermal chamber. A constant time interval (5 ns) is generated under different temperature, in a range of 10-70 \({}^{\circ}\)C. The time interval is measured by the digital oscilloscope, and measurement under  
Figure 11: Nonlinearities of proposed DTC system. (**a**) DNL; (**b**) integral nonlinearity (INL).  
Figure 10: Differential nonlinearity (DNL) of delay cells in a PDL. (**a**) DNL of delay cells in PDLA237\(-\)PDLA239; (**b**) DNL of delay cells in PDLB237\(-\)PDLB239.  
a temperature is repeated 50 times. The standard deviation under different temperature is shown in Figure 12a, which indicates that the proposed DTC is less sensitive to variations of temperature when compared with other DTCs implemented in a similar FPGA device [10].  
The voltage tests are performed using the DC power supply RIGOL DP832A (RIGOL Technologies Inc., Beijing, China). The nominal operating voltage of the DTC board is 1 V, which is the internal supply voltage of the FPGA device. In the tests, the supply voltage of the FPGA is changed within the range of 0.95-1.05 V with an interval of 5 mV. A constant time interval (5 ns) is generated under different supply voltages at an ambient temperature of 25 \({}^{\circ}\)C. The time interval is measured by the digital oscilloscope, and measurement under a temperature is repeated 50 times. The standard deviation of the measurement results is shown in Figure 12b. In the figure, with the change of supply voltage, the largest differences in standard deviation is less than 0.5 ps, which indicates that the proposed DTC is not sensitive to variations of supply voltage.
## 4 Results and Discussion
### Influence of the Geometrical Parameters
#### 4.3.3 Effect of Coil Height (HC)
For instance, after 120 min, the temperature distribution in the top region of shell is more than case F so that the red region appears in the top region of case E.  
Figure 17: Contours of the liquid fraction and temperature for cases E and F at four different times in the plane \(x=0\).
# Novel MIA-LSTM Deep Learning Hybrid Model with Data Preprocessing for Forecasting of PM2.5
## 5 Results & Discussions
### Performance of Proposed Method  
In the experimental setup for all the cases, the multivariate LSTM model at the last stage of prediction has the same number of layers. The relu activation function was used. Based on the experiments performed, the epoch size is limited to 10 epochs in each case, as the error in 10 epochs is low. In Figure 3, a sample graph of the model training and validation curve (loss) is presented. This graph shows the training and validation loss for the Tiantan location dataset. From the graph, it is seen that the training loss and validation loss decrease. It can be concluded from the graph that the proposed model is a good fit for the used dataset. Table 4 shows the values for the performance evaluation parameters, namely MAE, MSE, RMSE, and R\({}^{2}\), for four different models, namely univariate LSTM, univariate 1D CNN, univariate GRU, and multivariate LSTM, for three different scenarios, i.e., with the original data with missing values removed, with IIET imputation, and finally with removed anomaly dataset, respectively. Each graph shown in Figures 4 and 5 is a visualization of the results and provides the following information: the MAE and RMSE values for each of the four models with the raw (blue), imputed (brown), and proposed method where anomalies were removed (green). Table 4 shows the results of all experimentations performed for all three locations of Aotizhonhxin, Gucheng, and Tiantan in Beijing. In the case of Aotizhonhxin, it is observed that the RMSE values for the raw data with the missing values removed, with IIET imputed data, and with the proposed data preprocessing method are 13.6125, 19.7891, and 9.8883, respectively; the same is the case with the MAE values which are 10.4696, 13.7667, and 7.4455, respectively. Here, it is observed that the RMSE values and MAE values for the MIA-LSTM method are much smaller compared to the state-of-the-art methods, such as using univariate LSTM, univariate 1D CNN, univariate GRU, and LSTM without data preprocessing. These stated methods with input data without preprocessing can be considered benchmark methods for comparison. These results show the importance of data preprocessing prior to the application of any prediction method. A similar variation is observed for Gucheng and Tiantan locations. Figure 3: Sample graph of training and validation curve for Tiantan location dataset.
## 7 Evaluation and Discussion
### Experimental Results
#### 7.2.2 Space Overhead  
Our instrumentation adds a new section of code to the original program, which is on average 1.03 times the size of the original code. The new section contains more than one Dispatcher to assign Handlers to jump to the right addresses for execution. The Dispatchers are mostly like an address translation table for every Handler.  
Figure 8 shows different applications' average running memory costs. The blue bar-graph represents the memory costs when the original programs are running. Correspondingly, the red bar-graph shows the memory costs when the protected programs are running. In total, the space overhead for _VMGuards_ is 117% over the original program. We need to note that execution must be confined to the new code, although the file size has increased. Except for the programs that store read-only data in their code, other programs do not even access their code once. Hence, the runtime memory overhead is unaffected by the presence of the original copy of the file.  
Figure 7: Increment of different lengths of TPI.
## 4 Results and Discussion
### Pavement Performance Based on Rheological Tests  
#### 4.4.1 Workability  
In order to guarantee the service performance of asphalt mixture, the viscosity of an asphalt binder shall be controlled within a certain range. In the Technical Specification for Construction of Highway Asphalt Pavement (JTG F40-2004), it is recommended that the optimum viscosity in the mixing and compacting process of asphalt binder to be 170 mPa\(\cdot\)S and 280 mPa\(\cdot\)S, respectively. Figure 14 shows the viscosity-temperature curve of virgin asphalt, aged asphalt, and rejuvenated asphalt with different dosages of W-oil at 120-180 \({}^{\circ}\)C. The line of recommending compacting viscosity and mixing viscosity are drawn at 280 mPa\(\cdot\)S and 170 mPa\(\cdot\)S, respectively, in which the corresponding horizontal coordinate is the compacting and mixing temperature of the related asphalt. According to Figure 14, it can be seen that an increase of temperature makes the asphalt viscosity decrease, so the control on the optimum mixing and compacting viscosity of asphalt is realized by controlling the temperature. The lower the construction temperature, the less energy is required in the heating process, and the better the workability. The viscosity of virgin asphalt increases by an aging effect, and the mixing and compacting temperature of aged asphalt under the recommended mixing and compacting viscosity are 180 \({}^{\circ}\)C and 168 \({}^{\circ}\)C, respectively. Both are increased by around 25 \({}^{\circ}\)C in comparison with that of virgin asphalt, so aging effect lowers the workability of virgin asphalt. As the dosage of W-oil in aged asphalt increases, the viscosity of rejuvenated asphalt reduces rapidly, which demonstrates that W-oil can improve the workability of aged asphalt. When the dosage of W-oil is 20%, rejuvenated asphalt has a viscosity-temperature curve close to that of virgin asphalt, which demonstrates that 20% W-oil shall be added to aged asphalt for restoring its workability to the virgin level. Viewing from the viscosity-temperature curves of 20%WRA and virgin, the change in the viscosity of virgin asphalt is more sensitive to temperature, which demonstrates that the addition W-oil can recover the viscosity of aged asphalt, but the temperature susceptibility cannot be recovered to the level of virgin asphalt.  
Figure 15 shows the mixing and compacting temperature of virgin asphalt, aged asphalt, and rejuvenated asphalt with different dosages of W-oil. It can be seen that, as the dosage of W-oil increases, the mixing and compacting temperature keep on declining. The construction temperature of rejuvenated asphalt presents a linear decrease with the addition of W-oil; it can be concluded that W-oil improves the workability of aged asphalt. When the dosage of W-oil is 20%, the viscosity of rejuvenated asphalt is basically restored to the level of virgin asphalt.  
Figure 14: Viscosity-Temperature Curve of Virgin, Aged, and Rejuvenated Asphalt.