esa-sceva/satcom-chunk-collection huggingface dataset:
# Wave-Shaped Microstructure Cancer Detection Sensor in Terahertz Band: Design and Analysis
## 1 Introduction
So, by examining the signal that was received, cell characteristics can be determined. There have been many PCF-based chemical and biological sensors developed thus far. As far as we know, [PERSON] et al. are the ones who first developed a biosensor that can identify glucose in blood [21]. Numerous techniques for biosensor applications improved as a result of this study. Researchers are now using photonics for cancer detection thanks to recent technological advances in the field. A unique spectroscopic optical sensor for cancer cell identification in diverse human body sections was proposed by [PERSON] et al. in 2021 (i.e., adrenal gland, blood, breast, cervical, and skin) [22]. It had the highest relative sensitivity of 95.51% for breast cancer at X-polarization. [PERSON] et al. (2021) proposed a new hollow core PCF to quickly identify various types of cancerous cells in the human body [23]. According to the simulation experiments, the recommended sensor operating at 2.5 THz has an astonishingly high relative sensitivity of over 98 percent with little loss (0.025 dB/cm). [PERSON] et al. presented a Mono Rectangular Core Photonic Crystal Fiber (MRC-PCF) for the efficient detection of skin and blood cancer, which had the highest sensitivity of 96.74% for blood cancer at 2 THz [24]. Another Hollow core PCF was designed by [PERSON] et al. (2022) to detect four types of cancer cells, namely, Jurkat (blood), HeLa (cervical), MCF-7 (breast cancer type-2), and Basal (skin) [25]. The relative sensitivity varied from 88.12% to 89.65% for the four cancer cells in this designed hollow-core PCF (HC-PCF). In 2022, a study by [PERSON] et al. [26] introduced a THz sensor utilizing HC-PCF to detect breast cancer cells. The sensor exhibited a relative sensitivity of 65.53% along the x-axis and 53.63% along the y-axis, while also possessing a high birefringence (0.0020), lower CL (\(17.33\times 10^{-9}\) cm\({}^{-1}\)), and effective area (\(3.04\times 10^{-20}\) m\({}^{2}\)). In order to further improve the sensitivity and other performances, we propose in this article a straightforward, highly sensitive HC-PCF for cancer cell detection. The central air hole of the HC-PCF is penetrated by a variety of cancer cells, including those from the adrenal gland, blood, breast cancer types 1 and 2, the cervical cavity, and the skin, and a numerical investigation of the comprehensive sensitivity is carried out with the aid of the FEM. These cancer cell samples have been monitored using the developed PCF geometry, which has a sensitivity of over 99.9% at 3 THz. The comparison of the different fundamental features also reveals extraordinarily high sensing capabilities, low CL, flattening dispersion, and low birefringence, which are superior to earlier prototypes [22; 23; 24; 25].
## 4 Image Semantic Segmentation Algorithm
### Backbone Network Comparison Experiments  
In image segmentation tasks, the backbone network is crucial for extracting features and plays a pivotal role in semantic segmentation models. To enhance the feature extraction capability, the original DeepLabV3+ model's backbone network was replaced with alternative architectures for evaluation. On the coal gangue dataset, experiments were conducted using five different backbone networks: Xception, ResNet, MobileNetV3, ResNeSt, and EfficientNetV2. The performance of these backbones was assessed based on three metrics: mean Intersection over Union (mIoU), parameter count, and frames per second (FPS). The experimental results are summarized in Table 1.  
As shown in Table 1, replacing the backbone network with EfficientNetV2 achieves a mean accuracy (mAcc) of 96.45% with a parameter count of 7.1 M. Although the MobileNetV3 backbone has fewer parameters, its mAcc is lower than that of EfficientNetV2, indicating reduced recognition accuracy. Both ResNet and ResNeSt have larger parameter counts compared to EfficientNetV2, but their mAcc values are only marginally different from EfficientNetV2. In contrast, the original Xception backbone performs worse than EfficientNetV2 in both parameter count and mAcc. Based on a comprehensive analysis, EfficientNetV2 was chosen as the backbone network for the model. It achieves higher accuracy while maintaining fewer parameters and lower computational resource requirements, making it more suitable for deployment in practical industrial scenarios.
# Feasibility Assessment of Acid Gas Injection in an Iranian Offshore Aquifer
## 4 Dynamic Modelling
Figure 13 shows the behavior of CO\({}_{2}\) in the presence of water when increasing the pressure. This information serves as a crucial input for the simulation processes. Figure 11: Vertical cross-section of reservoirs showing gas saturation in a homogenous model. Figure 12: Vertical cross-section of reservoirs showing gas saturation in a heterogenous model. An important remark is that both CO\({}_{2}\) and H\({}_{2}\)S exhibit similar phase equilibria. The critical points, as regards CO\({}_{2}\), are at T = 31.1 \({}^{\circ}\)C and P = 1070 psi, and for H\({}_{2}\)S at T = 100.2 \({}^{\circ}\)C and P = 1300 psi [4]. It is worth noting that CO\({}_{2}\) can form hydrates at temperatures up to 10 \({}^{\circ}\)C and H\({}_{2}\)S above 30 \({}^{\circ}\)C, even if there is no free water. Due to the lack of experimental data, a decision was taken to consider and simulate only one component, which led to the selection of CO\({}_{2}\). This choice was due to the extensive research that was conducted on the behavior and properties of CO\({}_{2}\) in various studies [22; 27; 29]. In the project plan, about 2 TCF of acid gas should be injected from the source field into the Surmeh formation. The source field consists of three production platforms, each with a capacity of 500 MMSCFD. Therefore, the total daily production from these platforms will be 1500 MMSCFD, with 12% of this gas being acid. This results in an average daily acid gas rate of 180 MMSCFD. Considering this acid gas rate and taking into account factors such as fluctuating flow rates, it is estimated that it will take about 30-35 years to inject the 2 TCF of acid gas into the formation. Simulation models were run over a 100-year lifetime to observe pressure changes throughout the injection period. This long simulation period allows a complete understanding of pressure dynamics throughout the injection process.
# Effect of Wheel Path in Raster Grinding on Surface Accuracy of an Off-Axis Parabolic Mirror
## 4 Discussion
### The Correlation between Scallop Height and Surface Shape Error
surface in the vertical direction is the surface shape error value, which is represented by the black line in Figure 15.  
In the XOZ plane, the surface shape error can be approximated as follows:  
\[\sigma=H(x)/\sin\beta(x) \tag{31}\]  
The calculation of the variation of the surface error with \(x\):  
\[y_{t}=y_{d}+H\cos\beta \tag{27}\]0  
In the off-axis parabolic with a large off-axis amount, the scallop height changes very little with \(x\), \(H^{\prime}\approx 0\), and \(\beta\) is positively correlated with \(x\):  
\[y_{t}=y_{d}+H\cos\beta \tag{27}\]1  
Therefore, in the meridian direction, with the increase in the \(x\) value, the surface shape error gradually decreases. In the YOZ plane, the feed path is an arc, and the wheel maintains a fixed angle. The distance \(I_{2}\) between the scallop height and the tool contact point in the vertical direction remains unchanged. As the \(y\) value increases on the arc, \(I_{1}\) decreases in the \(-Y\) direction and increases in the \(+Y\) direction. The calculation of the surface error value is as follows:  
\[\left\{ \begin{array}{rl}\sigma=I_{2}-I_{1}&y<0\\ \sigma=I_{2}+I_{1}&y\geq 0\end{array} \right. \tag{34}\]  
Therefore, the surface error gradually increases as the \(y\) value increases. The scallop height distribution along the arc vector direction is shown in Figure 16. The blue part represents the scallop height, the red points represent the vertex and bottom point of the scallop height, and the surface error is represented by black lines. In the YOZ plane, the surface shape error can be approximated as follows:  
\[\sigma=H(y)/\sin\beta(y) \tag{35}\]  
Figure 15: Diagram of the spatial distribution of the scallop height stepping along the meridian direction: (**a**) XOZ plane; (**b**) YOZ plane. When stepping on the same arc, the curvature radius of the workpiece is equal, so the scallop height \(H(y)\) is equal, and \(\beta\) increases first, and then, decreases. Therefore, the surface error value decreases first, and then, increases in the arc vector direction, and is symmetrically distributed on both sides of \(y=0\). In the XOZ plane, the feed path is a parabola, and the calculation of the surface error is as follows. As \(x\) increases, the scallop height gradually increases, but the increase is very small, as shown in Figure 10, the wheel maintains a fixed angle, so the distance between the scallop height and the tool contact in the vertical direction \(I_{2}\) increases very little. \(I_{1}\) decreases with the increase in the \(x\) value, and the decrease is much larger than the increase in scallop height, so the surface error decreases with the increase in the \(x\) value. \[\sigma=I_{2}+I_{1} \tag{36}\]
## 1 Introduction
**Dual formal security verification:** A rigorous formal security analysis using both Burrows-Abadi-Needham (BAN) logic [14] to verify mutual authentication and shared belief establishment, and the Bellare-Pointcheval-Rogaway (BPR) model [15] for Authenticated Key Exchange (AKE) to prove resilience against a wide range of active adversarial attacks. 3. **Comprehensive performance evaluation:** A detailed performance evaluation, comparing Bio-2 FA-IoD with several contemporary IoT authentication protocols in terms of computational costs, communication overhead, and qualitative energy efficiency. Furthermore, a simulation-based performance analysis conducted using the Contiki OS and Cooja simulator [16; 17] illustrates Bio-2 FA-IoD's superior efficiency in computational and communication costs, alongside very low latency, high packet delivery rate, and minimal energy consumption. This positions it as a highly viable and lightweight solution for resource-constrained IoD environments. The remainder of this paper is organized as follows: Section 2 reviews related work in IoD authentication, focusing on biometric and lightweight approaches. Section 3 details the system and threat models, crucial security requirements for IoD, the cryptographic primitives employed (including fuzzy extractors), and the formal security models (BAN logic and BPR model) used for analysis. Section 4 presents the Bio-2 FA-IoD protocol in detail, outlining the registration and authentication phases. Section 5 provides a comprehensive security assessment, encompassing an informal analysis against various attacks, the BAN logic verification, and the BPR model proof sketch. Section 6 evaluates and discusses the performance of the proposed protocol, including a detailed simulation analysis. Section 7 explores conceptual extensions to the Bio-2 FA-IoD protocol for enhanced perfect forward secrecy and user anonymity. Finally, Section 8 concludes the paper and outlines potential directions for future research, followed by the list of references in the References section.
## 4 Results  
From the point of view of the results, two main aspects must be taken into account. On the one hand, the integration and use of DER. This proof of concept only includes two solar panels, whose generation has been estimated. As an example, the estimation for each hour during the period from February 17 th 2018 to February 25 th 2018 can be observed in Figure 12. The mean power obtained for these days (summer days in Chile) is approximately 174 W.  
On the other hand, the main purpose of this system is to allow a better integration of the local system (the microgrid) with the utility, which is done through a more controllable power system using the homeostatic control system. In this line, an example of results obtained from the simulation can be seen in Figure 13, where all the powers implied in the microgrid can be seen. The first blackout event has been considered, as can be seen when the line \"Utility\" drops to zero (between 8:57 and 11:26). After that, between 18:00 and 23:00 a high-price event has been considered, this is why the microgrid is automatically disconnected from the utility grid, causing consumption to drop to zero again. During these events, the control system connects the batteries, whose charge feeds the whole system. While the utility grid is available, the batteries are being charged, albeit without exceeding the maximum power consumption that the control system establishes as a limit. For the described situation, the Figure 14 shows how the batteries SoC change during the day.  
Of course, other types of events could also be applied to study different cases dealing with the use of energy under various conditions and circumstances, even considering demand response programs. This is part of several cutting edge smart grid concepts and technology trends being advanced by ENEL Distribution's Smartcity Santiago in Santiago, Chile [12], and it is conceived as a system that integrates state-of-the-art technologies to optimize the production of energy and its intelligent use in the functions of an urban laboratory, much in the same way that the microgrid installed in the Lab at Universidad Tecnica Federico Santa Maria's San Joaquin campus operates, such as lighting, HVAC, energy distribution, citizen security and communication system.  
Figure 12: Estimated solar generation power from February 17 th to 25 th 2018.
# A Deep Learning Approach to Estimate Soil Organic Carbon from Remote Sensing
## 3 Materials and Methods
### Stage 2--The SOC Estimator  
Once the LC-classifier is trained, one can use the output of any of its layers as features for any transfer learning task related to LC classification. We opted to use the output of the last layer of the network that has the same dimensions as the output of the network, i.e., the first deconvolutional layer of the network (bottleneck), located some 16 layers before the output in our model. Its output is the latent high-level features for each pixel, and we hypothesized that these are relevant for SOC estimation. Each pixel is represented by a vector of 194 values. For a patch, we, therefore, have 794,624 (64 px \(\times\) 64 px \(\times\) 194) values.  
We, thus, constructed an encoder-only U-Net, which, at inference time, provides us the features for all pixels in the ROI in a single pass.  
To create the datasets used to train and evaluate our SOC estimators, Sentinel-2 imagery was downloaded for the the geographical locations for which the SOC ground truth is available. The images downloaded were taken as close as possible to the date of sampling, with the condition that cloud cover was less than 10%. A 64 \(\times\) 64 px patch is then extracted from these images, centered on each of the locations with the known SOC values and fed into our feature extractor, providing us with a 194-feature vector for each SOC measurement.  
This dataset was then used to train and evaluate a number of different machine learning models and approaches listed in Table 3.  
All 13 spectral bands of Sentinel-2 were used in our pipeline. At inference time, the model processes each input image and produces an output of the same 64 \(\times\) 64 px size. However, to account for the model's decreasing capacity to maintain context as it approaches the borders of the input image, we discarded a frame 8 px-wide on all sides, resulting in an effective output image size of 48 \(\times\) 48 px.  
To form the final continuous SOC map, we employed a sliding window technique.
# Design and Test of Real-Time Detection System for Cleaning Loss of Rapeseed Harvester
## 3 Test and Discussion
### Calibration Test of Loss Detection System  
In order to verify the accuracy of the loss detection system, a calibration test with different kernel counts and different mixtures was conducted by selecting an impact velocity of 4 m/s and a C1 and C2 of 1.100 and 3.490, and each group of tests was repeated three times. The number of kernels was set at 50 to 200, and four different calibrated mixtures were prepared depending on the ratio of carob shells, short stems and kernels in the mixture. The results are shown in Table 4.  
Figure 13: The fitting model of the distinction threshold and fan speed: (**a**) a fitting model distinguishing between the threshold C1 and fan speed; (**b**) a fitting model distinguishing between the threshold C2 and fan speed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Impact Velocity (m/s)** & **C1 (V)** & **C2 (V)** & **Distinction Precision** \\ \hline
2.5 & 0.840 & 2.690 & 0.904 \\
3 & 1.000 & 2.970 & 0.910 \\
3.5 & 1.040 & 3.280 & 0.919 \\
4 & 1.100 & 3.490 & 0.939 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Optimal distinction threshold under different impact velocities.  
As can be seen from Table 4, the loss detection system has a good ability to distinguish impurities in general, in which the carob shells and short stems have a certain impact on the accuracy of the loss detection system. For pure kernels, the detection accuracy of the loss detection system could reach 98%. When the number of kernels was 50-150, the impact strength of the kernels was low, the interaction between the kernels was small and the detection accuracy was higher, reaching more than 95%. When the kernel flow exceeded 150 grains and reached 200 grains, the accuracy was reduced, which was due to a large number of kernels colliding with the sensor surface in a short time, so that two or more kernels hit the inductive plate at the same time, resulting in a situation where the kernel was missed, but the error was still 10%.  
In the test of only the impurities, it could be seen from a single impact test on 10 g carob shells that a small amount of carob shells would be misdetected as kernels, while 10 g short stems would not be misdetected as kernels. An analysis of the biological characteristics for the carob shells and the short stems showed that the weight of the carob shells was generally smaller and the weight of the kernels was not much different, while the weight of the short stems was generally larger and the weight of the kernels was much different. For mixed materials with different proportions of kernels and impurities, the detection accuracy could reach 95.2-96.8%. With the reduction in the proportion of impurities in the mixed materials, the identification accuracy of the loss detection system was constantly improved.
# A Machine Learning Framework for Assessing Seismic Hazard Safety of Reinforced Concrete Buildings
## 4 Methodology implementation
### Data Pre-Processing  
Data pre-processing is critical while feeding the data to the model for learning. There are various ways to manipulate the data as per the requirement; a few important ones are considered for shaping the datasets used in the study.  
* Re-scaling the data: To accommodate the data on the same scale, the data are standardized using _\"MinMaxScaler\"_ from _sklearn_ library. The standardization frames all the input features between 0 and 1, which brings robustness to minimal standard deviation of features and speeds up the algorithm's calculation.
* Strategy for missing data: The datasets contain very few missing attributes. Removal of data could cause the study of a critical loss of information like the seismic vulnerability test of buildings. Therefore, the missing values are replaced by _mean_ strategy using _SimpleImputer_ from _sci-kit learn_ library.
* Imbalanced data: The imbalanced data in the dataset are handled by Random Over-sampling using _SMOTE_. It randomly duplicates the minority class's attributes to match the majority class's sample number, but do not make any effective change in the class. Tables 5-8 shows how the imbalanced data get manipulate with each iteration for the dataset of Ecuador, Haiti, Nepal, and South Korea, respectively. The final iteration in all datasets has an equal number of data in each class.  
Figure 4: Ecuador’s histogram plots show that each parameter’s data or value in the input dataset spreads disorderly. The result affects the stability and predictability of the classifier. The other three input datasets follow the identical pattern.
# Flexural Strength and Stiffness of Donut-Type Voided Slab
###### Abstract  
The voided slab system has been known as an effective technique to replace a heavy reinforced concrete slab system without the decrease in flexural strength. However, according to the previous studies, the flexural capacities such as flexural strength, stiffness and ductility of the voided slabs were practically lower than that of the solid slabs depending on the void shapes and details. Therefore, in this study, an analytical and experimental study were conducted to derive the optimal void shape and details focused on the flexural capacities of voided slabs. Based on a finite element (FE) analysis, a donut-type void shaper, which was a hexahedron with rounded edges and a hole penetrating the center, was suggested as the optimal shape in voided slabs, and an experimental study was conducted to verify flexural capacities of the donut-type voided slab. The flexural strength, stiffness and deflection of the donut-type voided slab were investigated by void shape and fixing method of void shaper as variables. The ductility of voided slab was also evaluated, because ductility is as important as strength for the safe design of slab member. The test results showed that the flexural strength of the donut-type voided slabs was equivalent to 98% and 105% that of the solid RC specimen, and the donut-type voided slab specimens had enough ductility for the flexural member. The stiffness of the donut-type voided slab was decreased about 8-9% compared with the solid slab, but it was improved up to 7% compared to the non-donut-type voided slab. Based on test results, the flexural design method of the donut-type voided slab associated with the void shape and fixing device of void shaper was suggested, and it was confirmed that the donut-type voided slab is one of the efficient alternatives to replace heavy flat plate slabs.  
donut-type voided slab; void shape; flexural capacity; finite element analysis; experimental investigation +
Footnote †: journal: Applied  
Academic Editors [PERSON], [PERSON] and [PERSON]  
Received: 2 May 2022  
Accepted: 4 June 2022  
Published: 7 June 2022  
Published: **Note**: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
# Channel Characteristics and Link Adaption for Visible Light Communication in an Industrial Scenario
## 3 Analysis of Channel Characteristics
### Rms Ds
The light rays generated by the multiple LEDs will experience complicated reflections and be reflected in all directions, so that more MPCs reach the RX, resulting in the larger RMS DS for VLC channels in this paper.
# Line Matching Based on Viewpoint-Invariance for Stereo Wide-Baseline Aerial Images
## 3 Line Matching for Image Pair on the Rectified Image Space
### Back-Projection of MatchedLine Segments  
After the matched line segments on rectified images are obtained using the LJL matching method, they can be back-projected onto original images. Taking a line segment on the left rectified image as an example, it is assumed that \((x_{1}\prime,y_{1}\prime)\) and \((x_{2}\prime,y_{2}\prime)\) are two endpoints respectively, and then the line segment on the original image obtained by the back-projection is:  
\[l:[x_{i},y_{i}]=M_{S}\cdot[x_{i}\prime,y_{i}\prime],(i=1,2) \tag{11}\]