esa-sceva/satcom-chunk-collection huggingface dataset:
# Automated Vision-Based Crack Detection on Concrete Surfaces Using Deep Learning
## 2 Methodology
### AlexNet CNN Model
#### 2.3.1 Transfer Learning  
The network analyzer was applied to display interactive visualizations of network architectures and detailed information about network layers. The network architecture is shown in Table 3. The first layer was an image input layer with an input image size of \(227\times 227\times 3\), where the 3 is the number of color channels. Additionally, the CNN consisted of convolution layers, pooling layers, fully connected layers, and the SoftMax layer. It also included other operations such as ReLU, cross-channel normalization, and dropout layers. The last three layers, fully connected, SoftMax, and the classification output layer of the pretrained network, were configured for 1000 classes. These three layers were fine-tuned by the transfer learning for the two classes (no-crack and crack) as shown in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Layer** & **Output** \\ \hline
1 & Image Input & \(227\times 227\times 3\) \\
2 & Convolution & \(55\times 55\times 96\) \\
3 & ReLU & \(55\times 55\times 96\) \\
4 & Cross Channel Normalization & \(55\times 55\times 96\) \\
5 & Max Pooling & \(27\times 27\times 96\) \\
6 & Grouped Convolution & \(27\times 27\times 256\) \\
7 & ReLU & \(27\times 27\times 256\) \\
8 & Cross Channel Normalization & \(27\times 27\times 256\) \\
9 & Max Pooling & \(13\times 13\times 256\) \\
10 & Convolution & \(13\times 13\times 384\) \\
11 & ReLU & \(13\times 13\times 384\) \\
12 & Grouped Convolution & \(13\times 13\times 384\) \\
13 & ReLU & \(13\times 13\times 384\) \\
14 & Grouped Convolution & \(13\times 13\times 256\) \\
15 & ReLU & \(13\times 13\times 256\) \\
16 & Max Pooling & \(6\times 6\times 256\) \\
17 & Fully Connected & \(1\times 1\times 4096\) \\
18 & ReLU & \(1\times 1\times 4096\) \\
19 & Dropout & \(1\times 1\times 4096\) \\
20 & Fully Connected & \(1\times 1\times 4096\) \\
21 & ReLU & \(1\times 1\times 4096\) \\
22 & Dropout & \(1\times 1\times 4096\) \\
23 & Fully Connected & \(1\times 1\times 1000\) \\
24 & SoftMax & \(1\times 1\times 1000\) \\
25 & Classification Output & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Network architecture of AlexNet.
###### Abstract  
The amount of sludge produced in wastewater treatment plants (WWTPs) has increased over the years, and the methods used to reduce this waste, such as incineration, agricultural use, or disposal in landfills, cause problems of secondary pollution. For this reason, it is necessary to find sustainable and low-cost solutions to manage this waste. Additionally, emerging and priority pollutants are attracting intention from the scientific community as they can generate health problems due to inadequate removal in conventional WWTPs. In this work, a pharmaceutical industry sludge was used as a precursor in the synthesis of four activated carbons (ACs) using different activating agents (ZnCl\({}_{2}\), FeCl\({}_{3}\cdot\)6H\({}_{2}\)O, Fe(NO\({}_{3}\))\({}_{3}\cdot\)9H\({}_{2}\)O, and Fe(SO\({}_{4}\))\({}_{3}\cdot\)H\({}_{2}\)O), to be used for the removal by adsorption of three neocicotinoid pesticides included in latest EU Watch List (Decision 2018/840): acetaminophen (ACT), thiamethoxam (THM), and imidalcoprid (IMD). The prepared ACs showed micro-mesoporous properties, obtaining relatively slow adsorption kinetics to reach equilibrium, but despite this, high values of adsorption capacity (q\({}_{\text{e}}\)) were obtained. For example, for AC-ZnCl\({}_{2}\) (S\({}_{\text{BET}}\) = 558 m\({}^{2}\)/g), high adsorption capacities of q\({}_{\text{e}}\) = 128.9, 126.8, and 166.1 mg/g for ACT, THM, and IMD, respectively, were found. In most cases, the adsorption isotherms showed a multilayer profile, indicating an important contribution of the mesoporousity of the activated carbons in the adsorption process.  
activated carbon; adsorption; neocicotinoid pesticides; sludge-based adsorbents +
Footnote †: journal: Applied Sciences
###### Abstract  
Because of rain attenuation, the equivalent baseband transfer function of large bandwidth radio-links will not be ideal. We report the results concerning radio links to/from satellites orbiting in GeoSurf satellite constellations located at Spino d'Adda, Prague, Madrid, and Tampa, which are all sites in different climatic regions. By calculating rain attenuation and phase delay with the Synthetic Storm Technique, we have found that in a 10-GHz bandwidth centered at 80 GHz (W-Band)--to which we refer to as \"ultra-wideband-, both direct and orthogonal channels will introduce significant amplitude and phase distortions, which increase with rain attenuation. Only \"narrow-band\" channels (100-200 MHz) will not be affected. The ratio between the probability of bit error with rain attenuation and the probability of bit error with no rain attenuation increases with rain attenuation. The estimated loss in the signal-to-noise ratio can reach 3-4 dB. All results depend on the site, Tampa being the worst. To confirm these findings, future work will need a full Monte Carlo digital simulation.  
baseband; GeoSurf constellation; linear distortions; passband; phase delay; rain attenuation; synthetic storm technique +
Footnote †: journal:
# Advancements in Gaze Coordinate Prediction Using Deep Learning: A Novel Ensemble Loss Approach
## 3 Materials and Methods
### Ensemble Loss Function  
We applied the results of each loss function to the x- and y-axis. This is to reduce the impact of the increase and decrease on each axis on the other, since they are independent from each other. The formula for the loss function for each axis is shown in Equation (1) below. Furthermore, unlike the previous studies that used cm values of X and Y as label values, we used pixel values as input. This was chosen because they have a larger range than cm, so the gradient to reduce loss was larger, and the accuracy upon convergence was confirmed to be higher than when cm was used. Through experiments, we confirmed that the use of pixel values is useful. Finally, we did not end the loss by predicting the X and Y coordinate points. We additionally learned the loss for the category prediction of where the predicted coordinates are located in the width and height zones, each divided into 40 columns (a total of 1600 zones). This helps improve performance in that it adds a larger penalty loss if the coordinate prediction is not located in the correct answer area among the 1600 areas. The overall loss is the cross-entropy loss plus the mean square error value, and the learning is performed by calculating the loss for the x and y axes, respectively.  
\[TotalLoss(gt,pred)=-\sum_{i=0}^{N}gt_{i}\mathbf{log}pred_{i}+\frac{1}{N}\sum_ {i=0}^{N}(gt_{i}-pred_{i})^{2} \tag{1}\]  
In Equation (1), \(gt\) means the correct answer and \(pred\) means the predicted value. The first term in the right hand side is the cross-entropy loss, and the second term is the mean square error. For every prediction, learning progressed in the direction of decreasing the cross-entropy loss for the correct answer and discovering which cell the prediction was located in, as well as the mean square error value for the actual prediction value.
## 3 Results
### Segmentation  
[PERSON] et al. [49] evaluated segmentation methods for DF monitoring using multimodal imaging such as infrared and depth for 37 participants. The researchers analyzed three segmentation models: UNet with Depth (UPD), Skin with Depth (SPD), and SegNet. UPD employs a CNN based on UNet architecture, which uses an encoder derived from VGG11 and an expanding decoder for pixel segmentation. The depth channel enhances accuracy by applying Random Sample Consensus to remove noise at boundaries. SPD is an unsupervised method that identifies skin pixels using thresholds across RGB, HSV, and YCbCr color spaces, optimized with depth data to remove outliers. SegNet uses a deep
# Study on Microwave Deicing of Carbon-Fiber-Modified Concrete under Multi-Factor Coupling Effect
## 5 Discussion
**(a)** An external electric field acts on a conductor; **(b)** conductor internal displacement before polarization; **(c)** after internal displacement polarization of the conductor; **(d)** the conductor is oriented before polarization; **(e)** after the conductor is oriented and polarized. The intensity of the electric field decays exponentially with depth in the medium. The decay rate depends on the complex dielectric constant and complex permeability of the dielectric material. The dissipated power of microwave absorption by a medium in a given space is related to the distribution of the electric field and the magnetic field in the space. The average dissipated power is usually calculated by the effective values of the electrical field and magnetic field, as shown in Formulas (2) and (3):  
\[P_{av}=\omega\varepsilon_{0}\varepsilon_{eff}^{{}^{\prime\prime}}E_{rms}^{2}V+ \omega\mu_{0}\varepsilon_{eff}^{{}^{\prime\prime}}H_{rms}^{2}V \tag{2}\]  
\[\varepsilon_{eff}^{{}^{\prime\prime}}=\varepsilon^{{}^{\prime\prime}}+\frac{ \sigma}{\omega\varepsilon_{0}} \tag{3}\]  
where \(\varepsilon^{{}^{\prime\prime}}\) is the imaginary part of the complex dielectric constant, representing the loss term in the polarization of the medium, reflecting the loss capacity of the medium, also known as the loss factor; \(\sigma\) is the conductivity of the medium; and \(\omega\) is the circular frequency of the electromagnetic wave. Therefore, a micro-element on the concrete surface is taken as the research object, and the heat generation rate of microwave absorption inside the micro-element is:  
\[P_{av}=\frac{\mathrm{d}Q}{\mathrm{d}\tau}=cm\frac{\mathrm{d}T}{\mathrm{d}\tau }=c\rho V\frac{\mathrm{d}T}{\mathrm{d}\tau} \tag{4}\]  
By substituting Formula (4) into Formula (2), Formula (5) is obtained as follows:  
\[\frac{\mathrm{d}T_{c}}{\mathrm{d}\tau}=\frac{0.566\times 10^{-10}}{c\rho}fe_{ eff}^{{}^{\prime\prime}}E_{rms}^{2} \tag{5}\]  
The temperature increase rate of a concrete surface under microwave irradiation is proportional to that of the concrete. Under the same conditions, a material with higher conductivity and lower reflectivity has a more significant wave-absorbing heating effect.
## 2 Methods
### Data Analyses
Apparently, false positive and true positive rates vary with the value of the decision threshold. By sweeping the threshold value from the lowest to the highest values of the tested feature, we generated the ROC curve by plotting the true positive rate as a function of the false positive rate. From the ROC curve, we determined the optimal value for the decision threshold that results in the highest classification accuracy. To assess the effectiveness of the tested features, one can use the entire dataset to determine the ROC curve and the corresponding optimal decision threshold. However, with a limited number of participants, whether such results can be accurately generalized to the general population is questionable. To address this issue, this study used a generalization test procedure to assess the effectiveness of the tested features. Specifically, this generalization test procedure randomly selected 80% of the participants (8 men and 8 women for each age group) as the training subset and assigned the remaining 20% of the participants (2 men and 2 women for each age group) to the testing subset. For each of the tested features, this generalization procedure performed the following steps. First, based on the classification method described in the previous paragraph, this procedure used the training subset to determine the optimal decision threshold. Next, this decision threshold was used to classify the testing subset samples. With such a randomly divided training and testing subsets, this procedure was repeated 1000 times for each of the tested features. To characterize the effectiveness of a tested feature, the final step determined the average accuracy of these 1000 generalization test results. One of the limitations of the three-trial averaging technique is that each of the participants needed to have at least three trial results. Therefore, the effectiveness of the proposed approach becomes unclear in dealing with subjects with less than three trial measurements. To address this problem, we repeated the generalization tests by revising the testing subset samples. Specifically, the decision thresholds were still determined by using training samples generated from the three-trial averaging technique. However, the testing subset used the single-trial measurements of the testing subset participants to characterize the effectiveness of the tested features. Therefore, the number of samples associated with each of the testing subset participants reduced from 220 (the number of combinations of selecting 3 trails from 12 trials) to 12 (each participant has 12 trial results). Note that by repeating this procedure 1000 times with randomly divided training and testing subsets, this method tested 48,000 samples (12 trials/participants * 4 testing subset participants/test * 1000 generalization tests) for each age group.
# How 5G Wireless (and Concomitant Technologies) Will Revolutionize Healthcare?
## 4 5G and Healthcare Opportunities
### Mobile Devices and Tablets can Help Leverage AI, Big Data and Connectivity  
The proliferation of mobile communication devices and associated wireless technologies has stimulated various innovative health interventions for personalized care. The advanced connectivity of mobile devices (like mobile phones, tablets, laptops, EHRs, sensors, PDAs, etc.) and applications provide enough health-related information that can be excellently utilized by big data and AI to provide smart healthcare solutions [7]. In addition, smartphone and tablets can also serve as IoT controllers to monitor health data in real time that can make necessary changes to physical sensors or components in the medical IoT network. When 5G becomes available with improved connectivity and cloud-based storage, a much wider mesh of devices, even the smallest sensors, will be able to communicate. Powerful cloud-based computations will bring up a mobile IoT that will provide a wide variety of opportunities, especially in healthcare.  
The world's persisting problems like the uneven distribution of healthcare resources, healthcare disparities, the growing number of patients with chronic diseases and increasing medical  
Figure 6: Some major goals articulated for 5G. Adapted from resources made available by the European Telecommunications Standards Institute (ETSI).  
expenses will be greatly mitigated with the blend of technologies like 5G, IoT, big data and cellular technology.
## 3 Analysis of the Electromagnetic Energy Harvester
### Voltage Response Analysis
For the proposed REMEH with the two-directional magnet array, the RMS value of the induced voltage increases proportionally as the rotation speed increases at the same gap diameter. As the data show in Figure 15, the RMS value of the induced voltage in the REMEH with the two-directional array is obviously larger than that with the Halbach array when the gap diameter is 1 mm. It can be seen from Figures 10-15 that the phase of the induced voltage does not affect the RMS value. The critical gap diameter was determined when the structural parameters were fixed. Figure 6:0 Two phases of the induced voltage in the coil of the REMEH with the two-directional magnet array versus time under a rotation speed of 10 r/s when the gap diameter is 2 mm. Figure 6:1 Four phases of the induced voltage in the coil of the REMEH with the Halbach array versus time under a rotation speed of 10 r/s when the gap diameter is 2 mm. As shown in Figure 16, the RMS value of the induced voltage in the coil decreases as the gap between the magnet and the coil increases. The RMS value of the voltage in the coil of the REMEH using the Halbach array decreases more slowly than the RMS value of the voltage in the coil of the REMEH using the two-directional array. When the gap reaches 10 mm, the RMS value of the voltage in the coil of the REMEH with the two-directional magnet array is almost zero while the RMS value of the voltage in the coil of the REMEH with the Halbach array remains considerable. The RMS value of the voltage in the coil of the REMEH formed by the two-directional magnet array or the Halbach array is same when the gap is 1.55 mm. The two-directional array has better performance in energy harvesting than the Halbach array when the gap is less than 1.55 mm. The Halbach array has better performance in energy harvesting than the two-directional array only when the gap is greater than 1.55 mm. Figure 6:2 Comparison of the RMS value of the voltage in the REMEH between the Halbach array and the two-directional array under different gaps when the rotation speed is 10 r/s. Figure 6:3 RMS value of the induced voltage of the REMEH with the two-directional magnet array and the REMEH with the Halbach array versus rotation speed at different gap diameters.
## 3 Results
### Satellite Data Used  
Advancements in remote sensing technology enabled researchers around the world to timely and accurately monitor vegetation. According to [22], the number of satellite systems for vegetation monitoring increased by 66% during 2016. Remote sensing enables the convenient collection of data dating back several years while providing reliable and accurate estimates of LAI and other biophysical attributes for different vegetation types.  
The majority of forest, crop and grassland studies analysed in this paper used Landsat, followed by Sentinel and MODIS sensors. Freely accessible satellite data such as Landsat, Sentinel and MODIS gained popularity compared to commercial satellite data sources, such as World-View, IKONOS and QuickBird. Other commonly used sensors for estimating LAI were WorldView-3, SPOT, QuickBird and IKONOS. LiDAR data were mostly used on forests, followed by crops. There were only four studies on grasslands that used LiDAR data (Figure 5). In general, forests have a complex canopy structure; therefore, observing forest parameters using LiDAR was shown to be efficient [23], especially for reducing the impact of LAI saturation [24].  
Figure 4: Spatial distribution and number of LAI studies conducted at country level.  
Figure 5: Sensors used to estimate LAI for forest, crop and grassland.  
Passive sensors were the most widely used sensors for forest, crop and grassland LAI studies. Alternatively, active sensors such as radar systems and multisource remote sensing had a smaller proportion of studies. This review showed that the majority of forest and crop studies were carried out using active and passive sensor types, while grassland LAI studies mostly used passive sensors. There were no grassland LAI studies carried out using active remote sensing ([PERSON] et al., 2020) [12]. The increase in freely available passive satellite data led to extensive use in forest, crop and grasslands. The number of LAI estimation studies using passive sensors was three times higher when compared to active remote sensing studies. The number of studies using unmanned aerial vehicles (AUV) increased since 2010, which enabled a quick turnaround time and real-time crop monitoring. There were a few forest and grassland LAI studies that used AUV compared to crop LAI studies. Figure 6 depicts the application of different sensor systems for forest, crop and grassland studies.  
In terms of commercial forests, according to Figure 5, most forest studies used passive sensors. LiDAR was the most used active remote sensing type for commercial forests. LiDAR was one of the most common active remote sensing systems, which detected canopy attributes by emitting light pulses to the target feature, measuring distance ranges and generating three-dimensional information about the canopy structure [25; 26; 27]. LiDAR was the most commonly used active remote sensing system for forestry [28; 29], there were fewer crop studies and no studies for grasslands that used LiDAR.
## 3 Results and Discussion
### Statistical Features in Different Terrains
#### 3.2.3 Ground Reaction Force  
From uphill to downhill, the features \"Start T\" and \"Heel ON\" are decreasing, while \"Hallux ON\" and \"End T\" are increasing, which means that the time interval between the front and rear foot landing is shorter during downhill, while the time taken for the front foot to grip the ground is longer. A similar discipline reporting a lower duty factor in foot strike was also observed in [74]. This can resist the tendency of slipping downhill during the stance phase and make the gait stable, whereas in uphill, the shorter burst of the front foot helps to achieve sufficient thrust to move forward.  
The significant differences (\(p<0.05\)) tested by the paired t-test of the features between each pair of terrains are also shown in Figure 6. The p-values of the KS test, Kruskal-Wallis test and paired t-test are shown in Table S1 in the Supplementary Materials and the distribution of all the features in each terrain were Gaussian (\(p>0.05\)). Here, all the features pass the Kruskal -Wallis test (\(p<0.05\)), and significant differences (\(p<0.05\)) between at least two pairs of terrains were observed in the features we selected, which indicates that all the features contributed to correctly classifying the terrains positively. In addition, significant differences between all the three pairs of the terrains were observed in the four features we proposed in the EMG feature set, i.e., the TA peak, SL peak, TA 80 and SL 25, as shown in Figure 6, which indicates that the features we proposed in this work were more correlated with the terrains compared with the conventional EMG time-domain features. The p-values presented here proved that the features selected in this work were highly correlated with the terrain labels, which potentially improved the performance of the classifier. The following section will evaluate the performance of the SVM model utilizing these features.
## 2 Proposed Framework and Experimental Methodology
### Proposed Encryption Workflow with Hybrid CPU-GPU Strategies
#### 2.5.2 I/O Bottleneck Reduction  
As shown in Algorithm 2, this method reduces GPU memory transfer bottlenecks by having the CPU pre-encrypt metadata and small data blocks, while the GPU handles bulk data encryption in parallel. Small blocks (\(S\)) are empirically defined as data chunks smaller than 4 MB based on benchmarking which indicates reduced GPU memory transfer overhead at this threshold. Data larger than 4 MB is considered normal or large, and processed directly by GPU encryption. The reduction in data transfer time, given by \(T_{transfer}=(TotalData-Pre EncryptedData)/Bandwidth\), improves overall encryption performance by minimizing GPU idle time. Recent studies on CPU-GPU heterogeneous platforms have shown that efficient workload partitioning significantly reduces data transfer overheads, thereby improving energy efficiency and overall encryption performance.  
```
This method reduces data transfer bottlenecks by encrypting metadata and smaller data blocks on the CPU in advance, minimizing GPU idle time and improving overall encryption throughput. 1. CPU_encrypt(metadata \(M\) and small blocks \(S\)) 2. GPU_encrypt(remaining data \(D-(M\cup S)\)) 3. Combine encrypted metadata, small blocks, and bulk data
```  
**Algorithm 2**I/O bottleneck reduction with CPU pre-encryption