esa-sceva/satcom-chunk-collection huggingface dataset:
# Comprehensive Annual Ice Sheet Velocity Mapping Using Landsat-8, Sentinel-1, and RADARSAT-2 Data
## 2 Data and Methods
### Radarsat-2  
RADARSAT-2 is a synthetic aperture radar operating at the C-band frequency and launched on December, 14 2007 by MacDonald, Dettwiler and Associates Ltd. (MDA) and the Canadian Space Agency (CSA). The satellite has a 24-day repeat cycle, or twice that of Sentinel-1. RADARSAT-2 can observe in both right- and left-looking directions, allowing it to acquire data at both poles right up to the pole. Single look complex images provided by CSA have a resolution of 12 and 5 m in slant range and azimuth, respectively. The satellite is commercially owned and operated, however, CSA has a significant data quota and the Canadian Government uses the data in support of decision making and for science. CSA is supportive of ice sheet science and has agreed to conduct large scale acquisition campaigns in Greenland and Antarctica. In Greenland, the survey is often in mode conflict with interests from the government and commercial companies, so coverage for ice velocity mapping is limited. In Antarctica, two campaigns were carried out in 2013 to cover the entire visible area in right looking mode to provide a coastal mapping in order to offer data continuity following the end of several other SAR missions. After the launch of Sentinel-1, the focus of RADARSAT-2 shifted to left looking mode to cover the interior of Antarctica, i.e., south of 78\({}^{\circ}\) latitude over three years (2014-2016). RADARSAT-2 data are provided as single look complex scenes (100 \(\times\) 100 km) by CSA, which we concatenate into long tracks for easier calibration. Offset tracking is done with sub-images of 64 \(\times\) 256 pixels and a search window of 16 \(\times\) 16 in range and azimuth [27].
# Feasibility of a Helium Closed-Cycle Gas Turbine for UAV Propulsion
## 4 Discussion
### Component Design Constraints  
#### 4.2.1 Turbo Set  
The turbo set design relies on the technology available mainly in the area of the performance, material, design and fabrication method. The choice of the turbo set (compressor and turbine) type, and pressure ratio is an important design consideration for simplicity and cost reduction. In the proposed model, a compressor pressure ratio between 3 and 5 seems to be a modest value because of the design difficulties associated with the thermodynamic behaviour of the helium and the direct effect of the pressure ratio to compressor size. The relative engine length tends to increase in a regular manner with pressure ratio. However, the low molecular weight of helium causes a high acoustic velocity, hence, could eliminate the Mach number constraints on the aerodynamic design of the turbo-set components. Additionally, the enthalpy and specific heat change would allow for high blade velocity (m/s) design to reduce the number of stages. The challenge then becomes how to induce the highest possible velocities that the blade would allow since the stage loading factor is inversely proportional to the square of the blade velocity.  
Designing for a low noise propulsion system, one would prefer to slow the rotational speed of the shaft while maintaining power so that mechanical noise and propeller tip velocity are both decreased. The helium compressors design for the closed-cycle propulsion is characterized by high hub-to-tip ratios, low aspect ratios and small blade height. The overall design of the compressor uses a free vortex velocity distribution with constant inner diameter for all stages.  
The properties of the helium would also affect the turbine design characteristics, such as the compressor. Since it is desirable to have a high blade speed as possible to reduce the number stages, the problem will be on the turbine first stage due to the rotor blade temperature will be at a maximum. The overall design of the turbine is based on free vortex with constant mean diameter for all stages. With a turbine entry temperature between 700 and 850 \({}^{\circ}\)C, the turbine blade cooling requirement could be easily managed, and the blade life could be met with a bleed flow of \(\sim\)1%.  
Another design challenge will be on the seal to reduce leakage, accommodate nonuniformities between mating parts and to accommodate differential expansion and thermal distortion. The use of bearing stub shafts at each rotor end could eliminate unnecessary weight while maintaining bending stiffness. Table 4 shows the design characteristics for both the compressor and turbine.
## Appendix B Design of the S-SMC  
Define the synchronization sliding manifold and its time derivative as follows:  
\[\begin{array}{c}S_{S-SMC}=\epsilon_{\alpha}^{c}+z\dot{\epsilon}_{\alpha}^{c} \\ =\dot{\theta}_{\alpha}-\dot{\theta}_{R\alpha}\end{array} \tag{10}\]  
where \(z\) is a positive constant, \(\epsilon_{\alpha}^{s}\) is the cross-coupling error in Equation (9), \(\theta_{da}(t)\) is the prescribed trajectory, \(\dot{\theta}_{R\alpha}=\dot{\theta}_{da}-\Delta\epsilon^{s}\) is the reference velocity vector, and \(\ddot{\theta}_{R\alpha}=\ddot{\theta}_{da}-\dot{\Delta\epsilon}^{s}\) is the reference acceleration vector. \(\Delta\epsilon^{s}\) and \(\dot{\Delta\epsilon}^{s}\) were defined in Equation (9).  
Adding the robotic Equation (4) into Equation (10) gives  
\[\dot{S}_{S-SMC}=\tilde{M}_{\alpha}^{-1}\Big{[}\tau_{\alpha}-\dot{\bar{Q}}_{ \alpha}\dot{\theta}_{\alpha}\Big{]}-\ \Delta(\theta_{\alpha},\tau_{\alpha})-\ddot{\theta}_{R\alpha}(t) \tag{11}\]  
Then, to attain the desired tracking performance for the parallel robot manipulator (1), the torque command is designed as in Reference [63]:  
\[\tau_{\alpha}=\hat{M}_{\alpha}\ddot{\theta}_{R\alpha}(t)+\hat{Q}_{\alpha} \dot{\theta}_{R\alpha}-KS_{S-SMC}-(G+g)\text{sgn}(S_{S-SMC}) \tag{12}\]  
The BLA was applied to replace the discontinuous term in the torque command. Hence, the torque command of Equation (12) becomes  
\[\tau_{a}=\hat{M}_{\alpha}\ddot{\theta}_{R\alpha}(t)+\hat{Q}_{\alpha}\dot{ \theta}_{R\alpha}-KS_{S-SMC}-(G+g)sat(S_{S-SMC}/u) \tag{13}\]  
where \(G\), \(g\) and \(K\) are positive constants, \(sat(S_{S-SMC}/u)\) is a saturation vector [62], and \(u\) are the boundary layer thicknesses.
## 3 Metal-Organic Frameworks
### MOFs by Bionic Mineralization
The preparation of mechanochemical MOFs avoids the extensive use of organic solvents [127], and enzymes are more stable in powder form. He and colleagues successfully encapsulated thermophilic (QLM) spontaneously in ZIF-8 through grinding, obtaining Lipase@ZIF-8. This composite was effectively applied to the kinetic resolution of (_R_,_S_)-2-octanol, displaying good catalytic activity and enantioselectivity over 10 cycles of reactions [107]. In recent years, various enzymes have been individually encapsulated in MOFs, and the influence of MOF matrices on enzyme activity and stability has been studied. It is noteworthy that, so far, studies comparing the stability and activity of enzymes in MOFs with free enzymes in homogeneous media have mainly focused on individual enzymes rather than encapsulating multiple enzymes together [128]. [PERSON] et al. reported a one-step, convenient synthesis of MOF nanocrystals containing multiple enzymes (Gox and HRP) in aqueous solution. The rigid structure and confinement of the MOF framework significantly enhanced the thermal stability of the encapsulated enzymes. Furthermore, it protected the encapsulated enzymes from protein hydrolysis and chelation effects [104]. MOFs have emerged as a rapid and effective method for enzyme immobilization, improving the catalytic stability and recyclability of enzymes. Despite the many advantages of MOFs in enzyme immobilization, several challenges remain. Some of these challenges include the following: (1) Regulate the composition of composite metals in MOFs to alter morphological structures, microscopic structures, catalytic activities, etc. (2) Improvements are needed in Achieve more uniform particle sizes and enhance the catalytic efficiency of MOFs. (3) Some MOFs tend to degrade under acidic conditions, thereby losing their protective effect on enzymes. Enhancing the \"acid resistance\" of materials is another issue that needs to be addressed. Addressing these challenges will contribute to the further development and optimization of MOF-based enzyme immobilization strategies.
# MB-OFDM-UWB Based Wireless Multimedia Sensor Networks for Underground Coalmine: A Survey
## 2 Overall Design of the WMSNs for Underground Coalmine and Related Works
### Main Challenges for Designing
In order to overcome the influence of different deep fading which is corresponding to different frequency, the whole channel is divided into several sub-channels in the frequency domain, turning the wide band frequency selective fading channel into different narrow band flat fading sub-channels. In Table 2, a comparison between MB-OFDM-UWB and the other two UWB techniques is summarized [36]. From the comparison, we can know that MB-OFDM-UWB outperforms in interference and also has great performance in other aspects. In the article [37; 38], through the in-depth analysis of underground coalmine channel environment and the characteristic of multi-carrier modulation, our group confirms that OFDM is a suitable ultra-wideband technique for communication in underground coalmine environment. Consequently, choosing the modulation scheme of MB-OFDM-UWB as the wireless transmission mode for wireless multimedia sensors in the underground coalmine environment (that is, constructing WMSNs for underground coalmine based on MB-OFDM-UWB), can realize the integration of video surveillance for coalmine safety, wireless voice communication, underground environment monitoring and personnel positioning. Moreover, it will become a great means to promote the abilities of the coalmine safety monitoring, the early warning, and the rescue and relief of disasters.
## 2 Materials and Methods
### Satellite Data
#### 2.3.1 Sentinel 2 Pre-Processing  
To acquire optical data (Figure 3), Sentinel-2 surface reflectance data in Google Earth Engine (GEE) were employed. The S2_SR collection comprises geometrically and atmospherically corrected (Level-2A) satellite images from Sentinels A and B, processed with the Sen2 Cor model [70]. The data selection focused solely on the Area of Interest (AOI), including images from 2019 to 2022, with a maximum 30% cloud cover threshold. The resulting image collection comprised 1182 images, either partially or entirely covering the AOI. The image collection was subdivided into monthly subsets, and median values were computed for RGB bands (B2, B3, B4), Red-Edge/NIR bands (B5, B6, B7, B8, B8A), and SWIR bands (B11, B12), all at a 10 m/pixel resolution. Nearest-neighbor interpolation was applied to bands B5, B6, B7, B8A, B11, and B12 for resampling to a 10 m resolution. Due to the coarse resolution of bands B1, B9, and B10, these bands were excluded from the analysis.  
The choice of a 4-year timeframe was driven by the dynamic nature of agricultural systems, where annual anomalies may arise due to localized or widespread environmental factors, attributed to natural causes or specific management practices (e.g., intensive pruning and drought).  
The use of median values for the monthly aggregation is particularly advantageous in mitigating the potential impact of outliers that might be present in the data. In the case of passive sensors such as Sentinel-2, these variations are often attributed to cloud cover [71], which can lead to anomalous pixel reflectance values. This approach can enhance the  
Figure 3: Sentinel-2 pre-processing and optical dataset in Google Earth Engine (GEE). (*) The images can fully or partially cover the AOI.  
reliability of the land-use classification process, providing a more accurate representation of the AOI temporal dynamics. However, it is worth noting that this approach may not capture young hazelnut orchards (<4 years old), which would be particularly challenging, given the spatial resolution limitations. The optical dataset thus consists of 12 monthly median images, each containing 10 bands, for a total of 120 features.
## 4 Delay and Energy Consumption Evaluation
### FSA-ACK and FSA-FBP Delay and Energy Evaluation  
For FSA-ACK and FSA-FBP, the delay and energy consumption of the coordinator are depicted in Figures 4a and 5a, respectively, whereas the energy consumption of the end-devices is depicted in Figure 5b. In both cases the results are reported according to \(m\) (number of slots per frame) and for \(m\in\{25,50,100\}\) nodes.  
For the network coordinator, the results show that the delay and energy consumption are minimal when the number of slots per frame is \(m=n/2\). In contrast, when \(m\) is below the optimum (i.e., \(m<n/2\)), the delay and the energy consumption tend to a very large number if \(m<n/4\). This is expected, as the shorter the frame length the higher the probability of collision, and the more frames are required to complete contention process. Otherwise, when the number of slots per frame is above the optimum (i.e., \(m>n/2\)), the delay and the energy consumption of the coordinator increase following a linear fashion and being proportional to \(m\). Here, a higher number of slots per frame leads the coordinator to listen to the channel during longer time periods, yielding greater delay and energy consumption.  
Thus, the number of slots in FSA-ACK and FSA-FBP has to be adjusted according to the number of end-devices in order to minimize the delay and the energy consumption of the coordinator. However, since the delay and the energy consumption of the coordinator increase with a small slope after the minimum, it is better to over-dimension the number of slots when the number of end-devices is unknown in order to ensure that the condition \(m>n/4\) holds.  
For end-devices, results show that the energy consumption is minimal when \(m\geq n\). As expected, a higher frame length leads to a lower average number of frames where an end-device has to contend and, thus, a minimum in the energy consumption. In addition, although a higher number of slots leads to longer frames and longer periods in sleep, the use of a very low power sleep mode yields reduced energy consumption when the value of \(m\) increases. Of course, there is a trade-off between the coordinator and the end-device delay and energy consumption. When the number of slots per frame increases above \(n/2\), the end-devices consume less energy in a DCR, at the cost of increasing the delay and the energy consumption of the coordinator.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline MAC header & 8 bytes & Data-rate & 250 kbps \\ Data payload & 114 bytes & \(T_{data}\) & 4.1 ms \\ \(T_{preamble}\) & 160 \(\upmu\)s & \(T_{IFS}\) & 192 \(\upmu\)s \\ \(\rho_{tx}\) & 100.8 mW & \(T_{ACK}\) & 512 \(\upmu\)s \\ \(\rho_{rx}=\rho_{\sigma}\) & 66.9 mW & \(\rho_{sleep}\) & 60 mW \\ \hline \hline \end{tabular}
\end{table}
Table 1: System Parameters for FSA-ACK, FSA-FBP and DFSA.
## 4 Discussion
Merging three criteria (viewsheds, accuracy, and the number of the transceivers), the optimal model is No. 11, with nine transceivers, a coverage of 95.5%, and maximal standard deviations of 6.7 mm for the horizontal position and 11.5 mm for the vertical component.
## 4 Results  
This section provides a detailed outline of the experiments performed and their corresponding outcome. The experimental setup involved implementing the experiments primarily using Python, with most of the computations performed on computer system with 16 GB Ram, utilizing a Tesla T4 GPU. To optimize model performance, we utilized a dataset comprising 43,101 training samples. This dataset was then divided into 90% training (38,790 samples) and 10% validation (4311 samples) sets to facilitate model training and evaluation. For testing, two categories of datasets were provided: 6630 known and disclosed records, and 10,000 unknown and undisclosed records, totaling 16,630. However, for this work, we utilized the undisclosed hidden dataset of 10,000 recordings provided by the CinC 2020 organizers to ensure unbiased testing of the proposed model. Throughout the experiment, a number of libraries were used: for data visualization, Matplotlib, Seaborn, and Ecg-plot; for data processing, NumPy and Pandas; and for modeling, TensorFlow and Keras. For the assessment of the models, Sklearn was used, along with other libraries like SciPy and WFDB. The following hyperparameters were used for training all the DL models including the proposed model: an Adam optimizer, a batch size of 32, min_delta: 0.0001, a dropout rate of 0.2, and a filter size of 5 \(\times\) 5. A learning rate decay mechanism was used as a callback function depending on the AUC score during the model's training. The learning rate was degraded by multiplying it by 0.1 in the optimizer if the AUC score did not show improvement every epoch, suggesting a lack of convergence.  
We showcased the experimental outcomes derived from the models employed in this investigation. LeNet, AlexNet, VGG-16, ResNet-50, Inception, and LSTM are among the models. To ensure a fair comparison with the proposed HRIDM model, all SOTA models were trained from scratch on the same dataset rather than utilizing pre-trained weights. This approach allowed for a direct evaluation of model performance under identical conditions. The experimental setup previously described was used for training and evaluating these models on the dataset. The outcomes offer valuable perspectives on how well each model performs in appropriately classifying cardiac disorders. To evaluate the models' performance, evaluation criteria including accuracy, precision, recall, and F1-score were used. To further illustrate the trade-off between the true positive rate and false positive rate for various categorization levels, ROC curves were calculated. The experimental results provide insight into how well each model detects and classifies different heart problems. In order to satisfy the research objectives, we analyzed each model's performance, compared the outcomes, and expressed the advantages and disadvantages of each.
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 5 Studies toward Clinical Applications of TD-NIRS
### Time-Domain Fluorescence Diffuse Optical Spectroscopy (TD-FS) and Tomography (TD-FT)
#### Analytical Solutions of the Equations for TD-FS  
[PERSON] and [PERSON] derived analytical formulations of fluorescence emission light intensities measured at surfaces of homogeneously fluorescing semi-infinite media for TD-FS and FD-FS [197]. The emission light intensities are analytically given using the product of the fluence rate in the medium and the escape function, which is equivalent to the sensitivity of reflectance to absorption changes expressed by Equation (17). [PERSON] et al. provided analytical solutions of Equations (55) and (56) for cuboidal, spherical, and cylindrical media using eigen functions, and TOF distributions of the fluorescence emission were compared with those obtained by phantom experiments [198].
## 3 Results and Discussion
The residual distributions are also shown in Figure 10. The results for models trained on classes A and C are shown in Appendix A.  
Figure 9: Random forest generalization results—Actual vs. predicted blade tip edgewise deflections. The model follows the actual output almost identically for all turbulence classes, although it has been trained on one class only. The results show slightly higher accuracy for turbulence class B that it has been trained for; however, the results for the other two classes are accurate and could be used to estimate edgewise deflections subject to any inflow conditions. As a general observation, the generalization of the random forest regression model is more effective in the edgewise direction compared to the flapwise. This could result from the higher blade stiffness in the edgewise direction so that the deflections are within a limited range, and the model can predict it easily. Another reason is that the blade azimuth position plays a significant role in the edgewise deflections, according to the Pearson correlations in Figure 3. The blade deflects depending on its position on the rotor plane rather than the inflow conditions. Figure 5:0 Time-marching edgewise deflections and residuals distribution for the random forest model trained on turbulence class B. The blade root shear forces are also very important outputs to be predicted for the structural design phase. The random forest model is tested for generalization among different inflow conditions for the flapwise and edgewise shear forces. To avoid increasing the length of this paper, the results of the generalization of the random forest model are shown in Appendix B. The main trends in predicting the blade's root shear forces follow the same observations of the blade's tip deflection results. The model performs in the edgewise direction with a higher accuracy compared to the flapwise direction. The prediction accuracies in the flapwise direction shear force are between the values of 73 and 98%. Meanwhile, in the edgewise direction, the accuracies are above 99.6%.
## 3 Results
### De-Icing Test  
Figure 3 shows the result of the de-icing test that was performed for the specimen that showed the best results in terms of [PERSON]'s heating: the coating doped with 1.0% CNT + 0.5% GNP. First, Figure 3a shows the minimum temperature of the specimen, located in the ice, as a function of de-icing time. In addition, IR thermographs and pictures of Figure 3b show the progressive reduction of the ice layer. In this regard, the de-icing time of the specimen was around 188 s, which can be observed in Figure 3a, as the sudden increase in the minimum temperature of the specimen ranged from 0 to 20 \({}^{\circ}\)C.  
Figure 3: De-icing test of the specimen with better [PERSON]’s heating capability (1.0% CNT + 0.5% GNP). (**a**) Minimum temperature as a function of time, and (**b**) IR thermographs and real pictures of the specimen at different times: 1, 2, 3, and 4 min.
## 2 System Description
### Data Collection and Transmission Unit (Drone Mapper Device)
\begin{table}
\begin{tabular}{l r} \hline \hline
**Parameter** & **Value** \\ \hline Size & 96 mm \(\times\) 66 mm \(\times\) 40 mm \\ \hline Weight & 100 g \\ \hline Technical standard & ARIB STD T108 \\ \hline CPU & RaspberryPi4 \\ \hline Memory & 2 GB \\ \hline Modulation method & LoRa/FSK \\ \hline Operation frequency & 920.1 MHz\(\sim\)928.0 MHz \\ \hline Duplex & TDMA \\ \hline Transmission mode & Broadcast \\ \hline Synchronous method & GNSS-based \\ \hline Transmission power & 20 mW max \\ \hline Transmission rate & 293 bps\(\sim\)37.5 kbps (LoRa) \\  & 50 kbps\(\sim\)300 kbps (FSK) \\ \hline Transmission distance & 1 km\(\sim\)10 km (LoRa) \\  & 600 m\(\sim\)2 km (FSK) \\ \hline Transmissions per second & 4 times (FSK) \\  & 2 times (LoRa) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Drone Mapper device specifications. Figure 4: Hardware photograph of Drone Mapper device. process. As a result, each UAV shares its local information with a duration \(T=\tau N\), and interference among UAVs can be ignored under the assumption of no channel delay and perfect transmitting synchronization. Certainly, interference among slots generally occurs and results in packet loss, which reduces the reliability of information sharing among UAVs. Fortunately, in general cases, UAVs operate under good channel conditions, and the interference would not be a severe issue.