esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient One-Dimensional Texture Representation Approach for Lung Disease Diagnosis
## 3 Methods
### Local Binary Pattern Features  
The local binary pattern (LBP) feature extraction method was used to understand the characteristics of different classes better. The LBP initially used a 3 \(\times\) 3 window size to downsample the image with a stride number of 1, where it looks at the texture of the grayscale images by comparing each pixel with its neighboring pixels. It calculates the LBP using eight points in a circular pattern with a radius of one pixel around each center. The comparison is performed by checking whether the neighboring pixels are greater or smaller than the central pixel. If the pixel has a higher value than the central pixel, it is assigned as 1; if not, it is assigned as 0. Subsequently, these binary values for each pixel are arranged into a sequence to form a binary number. These values are then used to make a histogram with 19 bins representing different uniform LBP patterns. This histogram provides a statistical summary of the X-ray texture patterns across the image. The decision to use a histogram came from the idea that it allows for the compact and comprehensive representation of the texture distribution in a 1D form, reducing the data size in the storage and the training and testing time. Additionally, to achieve high classification performance, the LBP histogram provides a density estimate to help capture the relative frequency of several texture patterns of the lung with and without a disease, making it constant to scale and lung condition changes. Instead of directly using downsampled images, this approach facilitates the extraction of one-dimensional features essential for high classification performance. Figure 3 is a normalized histogram representing LBP values' frequency distribution. The values were normalized to focus on the texture distribution instead of the raw pixel count and to compare the textures across different image sizes.  
Figure 3: We normalized the histogram of LBP values to use it as a 1D feature. The \(x\)-axis represents the number of bins and the \(y\)-axis represents the frequency.
## 2 Literature Review
A system was designed based on the Internet of Things (IoT), which uses a mobile cloud computing platform. Further, the experimental results showed that the Crowd Lives Oriented Track and Help Optimization system (CLOTHO) could effectively help to improve the convergence rate, reduce evacuation time, and the route length [39]. An intelligent evacuation guidance system was proposed for guiding people to safety, and it was based on WSN, RFID, an IP camera, and image recognition along with digital signage [40]. A cloud-based smart evacuation system that estimated important information from multiple sources in real-time was developed. Integration of cloud and IoT-based systems has been proposed for the estimation of real-time evacuation paths during an emergency [41]. An intelligent evacuation route guidance system used Wireless Sensor Networks and digital signage for predicting safe routes during an emergency. This system used a wireless sensor network to monitor environmental conditions, and algorithms were used to estimate safe evacuation paths, which also trigger digital signage systems for safe evacuation routes [42]. An IoT technology-based emergency fire detection system framework was proposed and also provided the real-time information of human presence and the air quality in the vicinity of the fire location [43]. An intelligent integrated fire rescue system, using wireless sensors and visual guidance, was implemented to obtain the information on real-time fire status, alarming, and guidance in evacuation [44]. An intelligent smoke detector system based on LoRa and indoor positioning to achieve low cost and high maneuverability was developed [45]. A LoRa based wireless security alarm system was proposed, where the A star-shaped network topology was preferred for long-distance communication with an alarm system [46]. A geospatial data extraction (SPDA) approach was used to develop artificial intelligence modules for fire sensors to improve fire detection capabilities, and intelligent spaces provided reliable fire alarm detection, effective fire suppression, and adaptive evacuation strategies for passengers, increasing evacuees' chances of surviving building fires [47]. An intelligent fire detection system can predict the fire situation based on artificial intelligence technology and gas and temperature data collected from the fire scene through IoT devices [48]. From the literature review, it is concluded that a fire evacuation system requires an independent system that can display an evacuation path during an emergency. Moreover, the implementation of a hybrid communication protocol is required to implement a real-time system with IoT in fire evacuation.
## 3 The AgroTRACE Infrastructure
### Event Capturing and IoT Application Platform
#### 3.1.2 IoT Applications
Waste treatment points are equipped with RFID readers at the entry points undertaking the continuation of traceability at the by-products' production level (bior fuels, compost, etc.). As an alternative solution, special brown organic waste bins can be installed in accordance with international standards. The brown bins, which will be marked with RFID, are weighed and identified via intelligent waste management systems (Waste Logistics) in garbage trucks. The information waste quantity is also integrated in the AgroTRACE System. For the above applications, the necessary Application Programming Interfaces (APIs) have been developed that are connected to the middle-tier of the information management platform. They are also connected to the middle-tier APIs of Google Maps and Google Surveys for evaluating several stages of the supply chain, with the intention of integrating the functions of the applications in the use cases.
# Designing and Implementing Weather Generators as Web Services
## 4 Case Study
### Performance Testing  
The average of response time to obtain the URL list of the realizations in both case studies is 46.2 s, which is efficient for practical applications. The response time depends on data preparation processes. A larger file of long-term climatological data needs more time to parse than the smaller one before the weather generator services.
## 4 Review of Existing Methods and Capabilities
### Safety Controls
#### 4.2.2 Collision Avoidance
Numerous methods have been proposed and explored as ways to prevent collisions between UASs and other aircraft. Considering collision avoidance an optimization problem and implementing geometry-based solutions has been proposed by multiple sources [124; 125; 126]. [PERSON] and [PERSON] [127]proposed using reachable sets, a collection of locations that can be reached at a given instant in time, for collision avoidance. [PERSON] and [PERSON] [128] also proposed using a variation of a rapidly expanding random trees approach with 3D Dubins Curves to avoid both stationary and moving targets. Optimal control methods were proposed by [PERSON] and [PERSON] [129] and [PERSON] [130] that require accurate system models to be implemented. Methods utilizing Markov decision processes were proposed in [131; 132; 133] but can be limiting due to the large state space requirements. [PERSON] _et al._[134] propose a sensor suite of both onboard and offboard sensors with sensor fusion as a method to detect threats. Numerous methods of integrated sensor solutions exist for identifying air collision threats including Traffic-alert and Collision Avoidance System (TCAS) [135], mobile radar [136], electro-optical/infrared [137], and Laser & Light Detection and Ranging (LIDAR) [138]. [PERSON] [139] provides a significant review of the work being performed in this area. In 2009, the US Office of Naval Research performed a detailed study of sensor solutions for sense and avoid [140].
## 2 Materials and Methods
### Data Pre-Processing  
The OCT data were exported from the OCT workstation available onsite. All OCT slices are RGB images, and the exported data contains the automatically detected coronary lumen, which is overlaid on the image and depicted in green. The spacing between the slices is 0.2 mm, and the number of slices per acquisition is constant at 376. Figure 1 displays the data pre-processing workflow starting from the exported OCT images with automatically detected lumen contour. First, the contours are automatically extracted by processing the green channel as follows: a threshold representing 90% of the maximum intensity value is used to create a binary image, and all the contours are extracted [49]. We then retain the contour which surrounds the center of the image: if there are multiple such contours, we pick the one with the largest area. Next, we use an in-house developed application to collect manual input that is provided by the clinical expert:  
* selection of the proximal start and distal end slice, which define the coronary artery region of interest. Slices representing the catheter are excluded, alongside other slices with sub-optimal image quality (e.g., blood artifacts);
* rejecting/correcting erroneous contours within the selected slice-range: the automatically detected contours may be incorrect on certain slices, typically in bifurcation regions and/or if the lumen has a profoundly non-circular shape (e.g., concave shape). Erroneous bifurcation contours are rejected, while erroneous contours in the stenosis region are corrected (required in less than 10% of the OCT acquisitions).  
Next, the data are pre-processed: the inside area of each non-rejected lumen contour in the selected slice-range is computed and the effective radius is determined (considering an equivalent circular contour with identical area). The radius of rejected contours is set using linear interpolation that is applied on the radiuses of the closest neighboring contours that have not been rejected. The radiuses are then arranged in a 1D sequence, starting with the proximal slice of the selected slice-range. Since the OCT slices are equidistant, only the radius values are used as input. For the further processing using deep neural networks, the 1D radius sequence is padded to a size of 376 (maximum length of an OCT sequence), and z-score normalization is performed [50]. The mean and standard deviation of each acquisition are computed, and then a global mean and global standard deviation are computed for the training set by averaging the mean and standard deviation values of the acquisitions that are included in the training set. The acquisitions in the validation/test split are normalized using the values that are employed for the training set. The 1D sequence of normalized radius values is used as input for the deep neural network predicting FFR.
## 3 Methodology
### Performance Metrics  
The effectiveness of the proposed models (the optimized versions of ANN and ELM models) in predicting the PV daily system conversion efficiency is evaluated by resorting to the following standard performance metrics in the literature for both sets of data, the training and validation datasets (i.e., 143 days) and the test dataset (i.e., 36 days) [54]:  
* The coefficient of determination (\(R^{2}\)) (Equation (4)) and adjusted coefficient of determination (\(R^{2}_{adjusted}\)) (Equation (5)), which describe the variability in the dependent (output) variable provided by the prediction models caused by the two independent (input) variables only. Specifically, 100% values of these metrics entail that the variability in the output variable can be fully explained by the two considered input variables (i.e., the dust accumulation and the ambient temperature), whereas values less than 100% entail that there are other independent variables that can affect the output variable but have not been taken into account during the development of the prediction models: \[R^{2}=\left[1-\frac{SS_{res}}{SS_{tot}}\right]\ \times\ 100\%=\left[1-\frac{\sum_{i=1}^{n}( \eta_{i}-\hat{\eta}_{i})^{2}}{\sum_{i=1}^{n}(\eta_{i}-\overline{\eta})^{2}} \right]\ \times\ 100\%,\] (4) \[R^{2}_{adjusted}=\left[1-\left(\frac{SS_{res}}{SS_{tot}}\right)\left(\frac{n-1} {n-2}\right)\right]\ \times\ 100\%,\] (5) where \(\eta_{i}\) and \(\hat{\eta}_{i}\) are the \(i\)-th true and predicted daily system conversion efficiency obtained by the prediction models, and \(i=1,\ldots,n\) and \(n\) is the overall data size (i.e., \(n=179\));
* Accuracy (_Accuracy_) (Equation (6)) describes the match between the true and the predicted daily system conversion efficiency obtained by the prediction models. Indeed, higher accuracy values entail that the predictions match the actual conversion efficiency and, thus, the prediction model is effectively capable of capturing the hidden mathematical relationship between the independent and dependent variables, and vice versa: \[Accuracy=\frac{\sum_{i=1}^{n}\left(1-\frac{\eta_{i}-\hat{\eta}_{i}}{\eta_{i}} \right)}{n}\ \times\ 100\%,\] (6)
* Mean square error (\(MSE\)) (Equation (7)) describes the mismatch between the true and the predicted daily system conversion efficiency obtained by the prediction models (i.e., opposite to the _Accuracy_ metric). Apparently, small \(MSE\) values are desired: \[MSE=\frac{\sum_{i=1}^{n}\left(\eta_{i}-\hat{\eta}_{i}\right)^{2}}{n}.\] (7)
# Patch-Wise-Based Diffusion Model with Uncertainty Guidance for Low-Light Image Enhancement
## 2 Related Work
### Diffusion Model for LLIE  
The diffusion model for low-light image enhancement (LLIE) leverages probabilistic generative techniques to restore images affected by noise, color distortion, and detail loss. By integrating physical models like Retinex with deep learning, this method enhances illumination and textures while generating missing details, effectively improving image quality even in challenging low-light conditions without the need for paired datasets. For instance, JoReS-Diff introduces a novel approach for LLIE by incorporating Retinex-based and semantic-based priors into the diffusion model, addressing the shortcomings of prior models that overlooked task-specific conditions, which often led to suboptimal results [51]. Similarly, Diff-Retinex combines Retinex decomposition with a generative diffusion model, effectively tackling challenges such as dark illumination, noise, and color distortion, yielding superior performance through detailed restoration of image nuances [6]. Additionally,ExposureDiffusion merges a diffusion model with a physics-based exposure model, offering improved performance, reduced inference time, and better generalization by utilizing an adaptive residual layer for iterative refinement [26]. Furthermore, PyDiff employs a pyramid diffusion method to speed up the process and mitigate degradation, incorporating a global corrector to ensure high-quality results, thus delivering superior efficiency and performance across benchmarks [27]. In summary, these works demonstrate the significant potential of diffusion models for LLIE, addressing various challenges related to noise, illumination, and computational efficiency, and providing highly effective solutions for real-world low-light scenarios.
## 3 Materials and Methods
### Evaluation of the Antioxidant Properties  
#### 3.5.1 DPPH Free Radical-Scavenging Activity Test  
The capacity of the extract to trap the free radical DPPH of dark violet color, which transforms into a yellowish color (after reduction), is the basis of this test. Spectrophotometry is used to measure discoloration. The DPPH radical-scavenging test was carried out, as reported in [52]. Indeed, increasing doses of aqueous APTM extract (6, 12, 25, 50, 100, 200, and 400 \(\mu\)g/mL) were created in test tubes, then 0.1 mL from every tube was collected and combined with 2.5 mL of ethanolic DPPH solution (4%). The solutions were then kept in the dark for 30 min. The absorbance of the combinations was measured immediately against a blank (ethanol) after incubation, using spectrophotometry at 517 nm. Ascorbic acid was employed as an ordinary point. Every one of the tests was carried out in triplicate. The APTM extract trapping activity was determined using the following formula:  
\[\textbf{DPPH}\textbf{\leavevmode\
obreak\ trapping\leavevmode\
obreak\ activity\leavevmode\
obreak\ }(\%)=\frac{\textbf{A}_{0}-\textbf{A}_{1}}{\textbf{A}_{0}}*\textbf{100}\]  
The absorbance of the control reaction is \(\textbf{A}_{0}\), while the absorbance of all extract samples and standards is \(\textbf{A}_{1}\).
# Complex-Valued CNN-Based Defect Reconstruction of Carbon Steel from Eddy Current Signals
## 3 Materials and Methods  
### Experimental Setup  
In this study, an artificial electrical discharge machining (EDM) notch specimen was employed for eddy current testing. The specimen was made of carbon steel, as shown in Figure 1, with the surface treated to remove mill scale in order to reduce surface interference and improve the signal-to-noise ratio (SNR). The size of the indentation is shown schematically in Figure 2. The overall dimensions of the specimen were 400 mm \(\times\) 100 mm \(\times\) 20 mm (length \(\times\) width \(\times\) thickness). The length is denoted as L, the width as W, and the thickness as T in Figure 2. Multiple standardized EDM notches were fabricated on the surface to simulate cracks or grooves. Each notch had a fixed width of 0.1 mm, with depths of 0.5 mm, 1.0 mm, 2.0 mm, and 3.0 mm and lengths of 2 mm, 3 mm, 5 mm, 10 mm, and 20 mm.  
These defects were regularly arranged on the specimen surface to facilitate systematic data acquisition and classification experiments. The scanning area was defined as 100 mm \(\times\) 30 mm, and a two-dimensional grid scanning strategy was adopted. The X-direction had a scanning step of 0.5 mm for a total of 200 steps, while the Y-direction had an interval of 1.0 mm for 30 lines. A pancake coil was used for detection, with a diameter of 6 mm, consisting of 9 turns per layer and 4 layers, totaling 36 turns. The coil had a thickness of 1.4 mm. To obtain high-quality eddy current signals, the coil was positioned with nearly zero lift-off, meaning that the coil substrate was placed directly onto the surface of the flat specimen. This configuration provides a standardized and repeatable experimental basis for defect detection and characterization. In addition to the 3 mm length defects, the different lengths and depths of the other defects constituted the different types of defects, totaling 16 types, i.e., class 0 to class 15.
## 3 Results and Discussion
### Ionic Conductivity  
Figure 4 reports the conductivity vs. temperature dependence (depicted as an Arrhenius plot) of the FTISI ionic liquids and their mixtures with the LiTFSI salt. No crystalline-liquid phase transition is shown within the whole temperature range investigated, which in agreement with previous DSC measurements. A Vogel\(-\)Tammann\(-\)Fulcher (VTF) behavior (i.e., absence of a linear dependence) typical of the IL electrolytes [40] is observed even in the presence of the LiTFSI salt, and these results are in good agreement with the DSC data reported in Figure 3. The incorporation of the LiTFSI lithium salt (full data markers in Figure 4) into the pure ILs (open data markers) leads only to a moderate decrease in conductivity. The high charge density on the Li\({}^{+}\) cations (much higher than that of the IL cations) results in the increase of the ion-ion interactions, in turn increasing the viscous drag and, therefore, depleting the ion transport properties [30]. A moderately lower conductivity is shown by the N122(ZO1)-FTSI electrolytes, due to the larger steric hindrance of the N122(ZO1)\({}^{+}\) cation with respect to N1114\({}^{+}\), (leading to higher viscosity). However, interesting conduction values (\(>\)10\({}^{-4}\) S cm\({}^{-1}\)) are observed already at \(-\)10 \({}^{\circ}\)C in both FTSI ionic liquid electrolytes, which approach 10\({}^{-3}\) S cm\({}^{-1}\) at 20 \({}^{\circ}\)C. This makes the FTISI-based electrolytes of interest even for low temperature applications.  
Figure 4: Ionic conductivity vs.temperature dependence, reported as Arrhenius plot, of pure FTISI ionic liquids and their electrolyte mixtures with the LiTFSI salt (mode fraction equal to 0.2). Panel (**A**): (N1114)\({}^{+}\) based electrolytes. Panel (**B**): (N122(ZO1))\({}^{+}\) based electrolytes. Heating scan rate: 1 C h\({}^{-1}\).  
Figure 3: Differential scanning calorimetry curves of the investigated ILs.
# Will Zero Vulnerability Computing (ZVC) Ever Be Possible? Testing the Hypothesis
## 2 Problem Statement  
Experts unanimously agree that fool-proof cybersecurity is \"practically impossible\". This is essentially because of the following two mandatory design attributes that legacy computers must comply with to render them usable:  
* **Permissions** that third-party applications need to run, but bad actors often exploit to introduce vulnerabilities, resulting in attack surfaces that can be exploited by malware [11].
* **In-computer data storage**, in absence of which, using, processing, or saving data is impossible. However, because most computers remain connected, stored data are continuously exposed to hackers. Hence, any data stored in a networked computing device are considered a priori at risk.  
Both **necessary evils** are the core enablers of cybersecurity breaches. State-of-the-art cybersecurity techniques are predominantly focused on strategies that reduce the attack surface and encrypt the data stored in online devices to counter these evils. However, these approaches have known limitations and are often complex, making cybersecurity experts conclude that fool-proof cybersecurity is impossible. Gene Spafford, a National Cybersecurity Hall of Famer asserts:  
_\"The only truly secure system is one that is powered off, cast in a lock of concrete and sealed in a lead-lined room with armed guards--and even then, I have my doubts.\"_[12]  
Cybersecurity experts unanimously agree (_\"this is a world in which the promise of secure digital technology turns out to be in many respects a poisoned chalice\".\"_) [13] that data within a connected device can never be entirely secure, because network exposure can never be risk-free [14].  
Since the birth of the Internet and the cybercrime industry, the attack surface has been growing. The severity of vulnerability or common vulnerabilities and exposures (CVEs) is also increasing. The National Vulnerability Database (NVD) [15] provides qualitative severity rankings in the scale [_Low-Medium-High_] depending on the Common Vulnerabilities Scoring System (CVSS) as defined in the CVSS specification [16], which scores from 0-10 in an increasing order of severity. Of the top 50 products [17] reporting the total number of distinct vulnerabilities in 2020, OSs were directly or indirectly responsible for almost all reported vulnerabilities.