esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Review of Methods
### Convolutional Neural Networks
Besides Stacked Autoencoders and Recurrent Neural Networks, [PERSON] et al. [41] applied a CNN to a dataset that was collected using an FMCW radar system working at 5.8 GHz. The dataset was used to build features with dimensions of \(32\times 32\times 3\) based on the compressed range-time, Doppler-time amplitude and phase, and cadence velocity diagram data [41]. The data were collected from 83 participants performing six activities consisting of walking, sitting down, standing up, picking up an object, drinking, and falling, which were repeated thrice to deliver 1164 samples in total. An accuracy of 92.21% was achieved for the CNN using Bayes optimization, whereas the SAE achieved 91.23%. The SVM-based approach achieved 95.24% accuracy after feature adaptation using SBS, while the accuracy of the CNN was improved to 96.65% by selecting handcrafted features. [PERSON] et al. [63] used a combination of a CNN and a Recurrent Neural Network (LSTM) model as a feature extractor for point cloud-based data and a CNN to extract features from range-Doppler maps. The outputs from both models were merged and fed into an FCN-based classifier to classify the inputs into six activities consisting of in-place actions, e.g., boxing, jumping, squatting, walking, and high-knee lifting. The results showed a very high accuracy of 97.26%, which is higher than the results of the feature extraction methods used in other approaches. In [64], a CNN model was developed using two parallel CNN networks, whose outputs were fused into an FCN for classification (_DVCNN_). This approach along with an enhanced voxelization method led to high accuracies of 98% for fall detection and 97.71% for activity classification. [PERSON] et al. [65] used an open source pretrained DCNN, i.e., MobileNetV2, VGG19, ResNet-50, InceptionV3, DenseNet-201, and VGG16, to train with their own provided dataset (_DIAT- uRadHAR_) consisting of 3780 micro-Doppler images comprising different coarse-grained military-related activities, e.g., boxing, crawling, jogging, jumping with a gun, marching, and grenade throwing. An overall accuracy of 98% proved the suitability of transfer learning for HAR.
###### Abstract  
In a software-defined radar (SDR) system, most of the signal processing usually implemented in hardware is implemented by software, thus allowing for higher flexibility and modularity compared to conventional radar systems. However, the majority of SDR demonstrators and proofs of concept reported in the open literature so far have been based on simple antenna systems. As a result, the full potentialities of an SDR approach have not been completely exploited yet. In this work, we propose a flexible antenna module to be integrated into an active electronically scanning array (AESA) with controlled sidelobe level over a wide angular range, exhibiting polarization reconfigurability with a low cross-polarization level and high isolation. For this purpose, analytical and numerically efficient techniques for the synthesis of the aperture distribution and the correct evaluation of the radiating features (e.g., beamwidth, pointing angle, sidelobe levels, etc.) are presented in order to grant real-time control of the digital beamforming network. A sub-array module demonstrator is fabricated and measured to corroborate the concept.  
Keywords:phased arrays; planar arrays; antenna radiation patterns; beam steering; radar antennas; radar applications +
Footnote †: journal: Applied Sciences
## 2 Algorithm and Methodology  
### Model Description  
The proposed fatigue detection model utilizes a video stream to capture details of the driver's facial contours. The algorithm operates within the framework depicted in Figure 1. Initially, the video is captured at a frame rate of 30 frames per second (fps) and segmented into a series of image frames with a resolution of 640 \(\times\) 480 pixels. To reduce computational complexity, each frame is uniformly resized to 320 \(\times\) 240 pixels. The frames are then converted to grayscale using standard color space transformation techniques, followed by histogram equalization to enhance image contrast. A Gaussian blur with a kernel size of 5 \(\times\) 5 is subsequently applied to suppress high-frequency noise. Finally, all pixel values are normalized to the range [0, 1] to ensure consistency in illumination across frames. These preprocessing operations improve the robustness of the system by mitigating the effects of lighting variability and image noise, thereby enabling more reliable feature extraction and fatigue detection [32].The illustrations in Figure 1 depict the difference between the images before and after processing. Subsequently, the key facial points are extracted from the preprocessed image. The Dlib toolkit [33] is employed to extract the feature points of the driver's eyes and mouth. First, the head Euler angle \(\alpha\) (HEA\(\alpha\)) [34] values are calculated from the head features. Then, to distinguish the facial features of different drivers, the eye and mouth contour curves are fitted using the eye and mouth feature points, and the normalized evaluation indexes (\(\eta_{NAE}\), \(\eta_{NAM}\)) for the eyes and mouth are calculated from the open and closed normalization model [30]. Based on the obtained values of \(\eta_{NAE}\) and \(\eta_{NAM}\) personalized thresholds can be assigned to judge the states of the eyes and mouth for each driver. Subsequently, these values are input into the sliding window model t to further calculate the three evaluation indexes: blinking frequency (BF), yawning frequency (YF), and nodding frequency (NF) [1]. Finally, the obtained three evaluation indexes are input into the feature fusion model to derive the integrated features and assess the degree of fatigue.  
Figure 1: Model framework diagram.
## 6 Evaluation of the Metric  
The results show that the metric and visualization of data set completeness is able to identify the inner structure of a multi-dimensional data set. Furthermore, the random forest regression in the preceding section proves the influence of data set completeness on model accuracy. Besides the evaluation of the metric based on the specific example \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Original** & **Data Set A** & **Data Set B** \\ \hline \(R^{2}\) & 0.999 & 0.999 & 0.970 \\ \(RMSE\) & 0.00268 & 0.00283 & 0.04520 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Coefficient of determination and RMSE of the random forest regression of the original data set, data set A and B.  
of condition monitoring of control valves, the metric should be discussed based on the requirements [42] given in Section 1. The following table (Table 4) gives a short explanation and a rating if the requirement is fulfilled or not. In order to increase the informative value of the metric, a further aspect should be considered: Even though the example of condition monitoring of control valves proves the impact of the data set completeness on the accuracy of prediction models, it is not possible to quantify the impact. The aim would be to provide information about the height of losses in accuracy based on a decreased data set completeness, e.g., a decrease of data set completeness of 0.1 causes a loss of accuracy of \(X\) percent. However, this is not easily done due to the variety of different algorithms and models. Modeling complex, highly nonlinear dependencies require a high data set completeness to receive a valid data-driven model. In contrast, a simple linear dependency can be modeled based on a few observations without high utilization of the feature space. The linear model can extrapolate to sections with less or no data. The impact of the data set completeness on model accuracy is also affected by the dependencies between variables and model output. Low data set completeness for a variable with low influence on the model output, e.g., the pressure in the example above, will not affect the model accuracy as much as low data set completeness for a variable with strong influence. Consequently, the expert's opinion is still required to derive conclusions from the metric and visualization of the data set completeness. The metric of the data set completeness offers the possibility to identify the structure of the data set and the utilization of the feature space indicating deficiencies in data quality. The metric enables to derive focused measures, e.g., the generation of data in specific
## 3 Results
### Functionality Tests  
#### 3.2.1 Colony-Forming Unit-Fibroblast (CFU-F) Assay  
All cell lines grew well in both culture conditions (normal and HA-containing medium), as demonstrated by their ability to form CFUs. On day 10, the intragroup comparisons  
Figure 1: Differentiation of MSCss grown in normal medium. Positive Oil Red O staining of adipogenic induced cells from (**A**) hMSCs, (**E**) SScqMSCs, (**I**) SScqMSCs; positive Alzarin red staining of osteoblasts derived from (**B**) hMSCs, (**F**) SScqMSCs, (**J**) SScqMSCs; Alican Blue staining of chondoblasts derived from (**C**) hMSCs, (**G**) SScqMSCs, (**K**) SSCqMSCs; embryonic bodies derived from (**D**) hMSCs, (**H**) SScqMSCs, (**L**) SScqMSCs. MSCs = mesenchymal stromal cells, hMSCs = healthy MSCs, SScqMSCs = granulation tissue MSCs, SSc = systemic sclerosis.  
Figure 2: Differentiation of MSCs grown in HA-containing medium. Positive Oil Red O staining of adipogenic induced cells from (**A**) hMSCs, (**E**) SScqMSCs, (**I**) SScqMSCs; positive Alzarin red staining of osteoblasts derived from (**B**) hMSCs, (**F**) SScqMSCs, (**J**) SScqMSCs; Alican Blue staining of chondroblasts derived from (**C**) hMSCs, (**G**) SScqMSCs, (**K**) SScqMSCs; embryonic bodies derived from (**D**) hMSCs, (**H**) SScqMSCs, (**L**) SScqMSCs. MSCs = mesenchymal stromal cells, hMSCs = healthy MSCs, SScqMSCs = gingival MSCs, SScqMSCs = granulation tissue MSCs, SSc = systemic sclerosis.  
showed no statistical differences in the frequency of colony-forming units for all cell lines grown in the normal medium (control) as compared to the HA-containing medium: hMSCs--77.67 (\(\pm\)4.1633) (control), 74.67 (\(\pm\)5.8594) (HA) (\(p\) = 0.51); SScgMSCs--78.67 (\(\pm\)2.517) (control), 83.67 (\(\pm\)3.786) (HA (\(p\) = 0.129); SScgMSCs--61.33 (\(\pm\)4.5092) (control), 65.33 (\(\pm\)4.7258) (HA) (\(p\) = 0.349).  
When comparing cell lines plated in the control medium, significant statistical differences (\(p\) = 0.002) between CFU values were calculated. SScgtMSCs formed less CFU than hMSCs (\(p\) = 0.05) and SScgMSCs (\(p\) = 0.004), respectively. hMSCs and SScgMSCs plated in the control medium showed similar CFU values (\(p\) = 0.946).  
When cell lines were cultivated in HA-containing medium, the frequency of colony-forming cells of SScgtMSCs was significantly lower than for SScgMSCs (\(p\) = 0.009). No significant differences were recorded when hMSC CFU values were compared to SScgMSCs (\(p\) = 0.138) and SScgtMSCs (\(p\) = 0.124) values under similar plating conditions (Figure 3A).
# Characterization of the 2014 Indus River Flood Using Hydraulic Simulations and Satellite Images
## 3 Results
### Damage Assessment  
This study shows that the 'crop/agricultural land' was the most damaged LULC class because of the 2014 flood. Approximately 371.2 km\({}^{2}\) of crop/agricultural land was inundated compared to 65.98 km\({}^{2}\) for the built-up area. Furthermore, the results highlight that deposited material damaged about 45.91 km\({}^{2}\) of agricultural/cropland and 11.57 km\({}^{2}\) of built-up area (Figure 5).
## 3 Results
### Pore/Grain Morphology Evolution during CSP
#### 3.1.3 Grain Morphology Changes during CSP  
Figure 8 shows SEM pictures of the sample surfaces, both intact exterior surfaces and fracture surfaces for samples subjected to the same CSP conditions as shown in the photographs of Figure 1. Some of the sintered grain dimensions for KDP subjected to CSP with p = 50 MPa at 60 \({}^{\circ}\)C appear as small as 10 \(\upmu\)m, but there is clear evidence of grain coarsening to 20 \(\upmu\)m to 30 \(\upmu\)m, and beyond. While these exact conditions were not used in the in situ USAXS/SAXS/WAXS studies, the SEM observations are consistent with expectations based on the macropore median diameter results for CSP with p = 100 MPa at 60 \({}^{\circ}\)C, shown in Figure 5. By contrast, the sintered grain dimensions discernible for KDP subjected to CSP with p = 150 MPa at 60 \({}^{\circ}\)C, do not appear to have coarsened to the same degree, and indeed appear broadly consistent with the macropore median diameter remaining below 10 \(\upmu\)m for this condition, as indicated in Figure 5. The SEM pictures of the fracture surfaces confirm the grain coarsening for the p = 50 MPa at 60 \({}^{\circ}\)C case, but also reveal the multiple facetted fracture interfaces opened up between the KDP grains, especially in the p = 150 MPa at 60 \({}^{\circ}\)C case. This observation is consistent with such fracture, even at a local level, resulting in a dramatic increase in the USAXS/SAXS scattering intensity, and a departure from the progressively decreasing scattering intensity associated with densification during CSP--as shown for the p = 150 MPa at 60 \({}^{\circ}\)C case presented in Figure S5 of the Supplementary Materials.  
Figure 7: Nanoscale pore median diameter versus cold sintering time for 40 \({}^{\circ}\)C, 50 \({}^{\circ}\)C, 60 \({}^{\circ}\)C with p = 100 MPa and 125 MPa, and for p = 100 MPa, 125 MPa, 150 MPa at 60 \({}^{\circ}\)C. Straight-line fits are guides to the eye. The 60 \({}^{\circ}\)C data for all 3 compressive stresses are plotted out to just 50 min, in this and future real-time plots to show more clearly the different variations with time.
## 3 Power Consumption, Heat Fluxes, and Cooling in Data Centers  
Data centers are responsible for approximately 3% of global electricity consumption and contribute to around 4% of total greenhouse gas (GHG) emissions [37]. The electric demand has seen a significant upsurge, increasing by at least ten-fold [9]. Past estimates projected a demand of 8-15 kW/m\({}^{2}\) for the current decade [38]. Modern data centers feature servers with power densities ranging from 0.5 to 2 kW/m\({}^{2}\), leading to power requirements of up to 30 kW per rack. Interestingly, these servers consume at least 60% of their nominal power even to maintain standby mode. In the United States alone, energy consumption has grown from 28 billion kWh in 2000 to an estimated 140 billion kWh in 2020 [39]. High-density facilities reach consumption rates equal to 40 kW/m\({}^{2}\), while extremely dense communication equipment has seen rates as high as 96 kW/m\({}^{2}\)[40]. The primary energy consumers within these centers are information technology (IT) equipment and their associated heating, ventilation, and air conditioning (HVAC) systems. Operations lead to heat generation, whose management is one of the most critical challenges. Estimates indicate that cooling constitutes 30-50% of the total energy usage [41; 42]. Effective thermal management systems are essential to handle thermal loads while ensuring the electronic components are within safe operational temperatures. The potential for recovering and reusing waste heat energy in data centers presents a significant opportunity to reduce operational costs. The literature estimates that up to 70% of the waste heat can be recovered [1]. Despite the abundance of this thermal energy, its quality is relatively low. This low-grade source poses a challenge for reuse through conventional thermodynamic cycles and processes, requiring innovative approaches to harness this energy resource effectively. The temperature limits of the electronic components, typically around 85 \({}^{\circ}\)C, constrain the maximum temperature [12]. Within data centers, there is a notable variation in temperatures across different electronic components housed in racks, leading to diverse heat dissipation rates. These insights are in Table 1. It effectively compiles and displays key aspects, systematically organizing data from sources [12; 31; 37]. The table provides a concise overview, presenting the main characteristics of components and cooling systems. In addressing the challenge of the cooling process, studies have focused on air cooling, liquid (water) cooling, and two-phase fluid-based systems. Air cooling solutions are predominant due to their simplicity, accounting for about 70% of usage [42]. The design of these systems aims to circulate air and form distinct hot and cold a
## 3 Results
### Outcomes and Estimation  
The results of our work can be analyzed in the order consistent with the following research questions:  
1. Does the study design minimize carry-over effects?
2. Are there differences in the results between the first and second measurements in each of the group's A/B and B/A?
3. Will there be a significant difference in the measured parameters in the non-stabilized and stable position depending on the study group (tipstim(r) vs. placebo)?
4. Is there a relationship between the tipstim(r) parameters (median and ulnar sensation) and the parameters of motor coordination and handgrip strength?  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Group** & **Age** & **Height** & **Body Mass** & **BMI** \\ \hline A/B (n = 15) & 71.73 \(\pm\) 13.07 & 167.60 \(\pm\) 7.31 & 72.53 \(\pm\) 8.28 & 25.79 \(\pm\) 2.14 \\ \hline B/A (n = 14) & 63.00 \(\pm\) 8.18 & 174.64 \(\pm\) 9.58 & 80.86 \(\pm\) 7.03 & 26.55 \(\pm\) 1.84 \\ \hline Wilcoxon U & 52.50 & 62.00 & 47.00 & 72.00 \\ \hline Z & \(-\)2.29 & \(-\)1.88 & \(-\)2.54 & \(-\)1.44 \\ \hline \(p\) & 0.022 & 0.060 & 0.011 & 0.150 \\ \hline effect size & 0.43 & 0.35 & 0.47 & 0.27 \\ \hline \end{tabular}
\end{table}
Table 1: Biometric data of study population and clinical control group.  
\begin{table}
\begin{tabular}{c c c} \hline
**Participants** & **n = 29 (100\%)** \\ \hline Post-stroke group & A/B & B/A \\ \hline n/\% & 15 (51.72\%) & 14 (48.28\%) \\ \hline Female & 6 (40\%) & 6 (42.86\%) \\ \hline Male & 9 (60\%) & 8 (57.14\%) \\ \hline Right affected side & 8 (53.35\%) & 7 (50\%) \\ \hline Left affected side & 7 (46.67\%) & 7 (50\%) \\ \hline Dominant right hand & & 29 (100\%) \\ \hline Cerebral ischemic stroke & & 29 (100\%) \\ (thromboembolic) n/\% & & \\ \hline Time post stroke/episode (weeks) & & 5–7 \\ \hline \end{tabular}
\end{table}
Table 2: The basic epidemiological data of the study population and the clinical control group.
###### Abstract  
With the decrease of oil and gas resources on land, increased attention has been paid to multiphase oil-gas exploitation and the transportation technology represented by oil-gas multiphase pumps. The helical-axial multiphase pump has become the focus of research on oil and gas mixed transmission technology due to its relatively high operating efficiency and adaptability to a wide range of gas volume fraction changes. In order to investigate the thickness variation in the air foil from the hub to the shroud of the blade on the mixture transportation characteristics of the gas-liquid two-phase flow in a helical-axial pump, the thickness ratio coefficient \(\xi\), was introduced, and the hydraulic performance of the single compression unit with different thickness ratio coefficients was investigated. A single compression unit including an impeller, diffuser, inlet section and outlet section of a helical-axial multiphase pump. The hydraulic performance including the hydraulic head and efficiency was investigated by numerical simulation with the Eulerian multiphase model and the shear stress transport (SST) \(k\)-\(w\) turbulence model. In order to demonstrate the validity of the numerical simulation approach, the hydraulic head and efficiency of the basic model was measured based on a gas-liquid two-phase flow pump performance test bench. The simulation results agreed well with the experimental results; the error between the simulation results and experimental results of different inlet gas volume fractions was within 10% at the design point, which indicated the numerical simulation method can be used in the research. The thickness ratio coefficient \(\xi\), which was taken as a variable, and the aggregation degree \(\lambda\) of the gas were introduced to analyze the gas-liquid mixture transportation characteristics of the pump. The thickness ratio coefficient was selected in a range from 0.8 to 1.8. The results showed that, for the same hub thickness, the head coefficient and efficiency increase, and the aggregation degree of gas decreases with the decreasing of the thickness ratio coefficient. The head coefficient of the modification multiphase pump was 5.8% higher in comparison to the base pump while the efficiency was 3.1% higher than that of the base pump, the aggregation degree of this model was the lowest, which was 30.3%; the optimal model in the research was the model of scheme 1 with \(\xi=0.8\). The accumulation of gas in the flow passage of the impeller could be delayed to the trailing edge of the blade by adjusting the thickness ratio coefficient, which produced a super-separated airfoil for helical-axial multiphase pumps and effectively ensured reliable operation under high gas volume fraction conditions. The accumulation area of gas was consistent with the area in which the gradient of turbulent kinetic energy was large.  
helical-axial multiphase pump; mixture transportation characteristics; thickness ratio coefficient; aggregation degree; super-separated airfoil
## 3 Testing Results Analysis
### Total Displacement Analysis
\begin{table}
\begin{tabular}{c c c} \hline \hline
**The Rupture Parameters of Rock Mass** & **Fitting Formula** & \(\mathbf{R^{2}}\) \\
**in Rockburst Disasters** & & \\ \hline Depth of left side \(d_{1}\) & \(d_{1}=38.98\times(1-\mathrm{e}^{-0.0066t})^{20.70}\) & 0.949 \\ Depth to right side \(d_{2}\) & \(d_{2}=111.80-110.13\times 0.9996^{t}\) & 0.858 \\ Width of left side \(b_{1}\) & \(b_{1}=-0.0360\times(1-\mathrm{e}^{)0.0075t}\) & 0.942 \\ Width of right side \(b_{2}\) & \(b_{2}=-2.34\times 10^{-6}\times(1-\mathrm{e}^{)0.0206t}\) & 0.974 \\ Height of left side \(h_{1}\) & \(h_{1}=44.7684\times(1-\mathrm{e}^{-0.0070t})^{48.1}\) & 0.944 \\ Height of right side \(h_{2}\) & \(h_{2}=111.4946\times(1-\mathrm{e}^{-0.0092t})^{219.85}\) & 0.985 \\ The fractured centre angle \(\theta\) & \(\theta=78.8593\times(1-\mathrm{e}^{-0.009t})^{23.8}\) & 0.982 \\ Total length of left crack \(l_{1}\) & \(l_{1}=-0.0754\times(1-\mathrm{e}^{)0.0102t}\) & 0.850 \\ Total length of right crack \(l_{2}\) & \(l_{2}=-0.0758\times(1-\mathrm{e}^{)0.0101t}\) & 0.960 \\ The area of rock mass in rockburst & \(S\)= \(-1.4993\times 10^{-11}\times(1-\mathrm{e}^{)0.0413t}\) & 0.980 \\ \(\mathrm{Number\ of\ cracks\ }n\) & \(n=-0.8751\times(1-\mathrm{e}^{)0.0043t}\) & 0.901 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Relationship expression between rupture parameters of rock mass in rockburst and time. Figure 10: Relationship curve between rupture parameters of rock mass in rockburst and time. (**a**) Depth and width of the rock mass in the rockburst disaster; (**b**) The height and angle of the fractured center angle of rock mass in rockburst disaster; (**c**) Total length and number of cracks and the area of the rock mass of the rockburst disaster. pits on site and the laboratory simulation experiment is \"pan-shaped\", which proves the rationality of results of this rockburst experiment.