Miracle-dz/UniFAR-FTUs huggingface dataset:
Field Measurements of Passive Earth Forces in Steep, Shallow, Landslide‐Prone Areas

Passive earth resistance plays an important role in slope stability analyses for predicting shallow landslide susceptibility. Three‐dimensional models estimate the contribution of this factor to slope stability using geotechnical theories designed for retaining structures and add it to the resistive forces. Systematic investigations have not been conducted to quantify this resistance in soils experiencing compression during the triggering of shallow landslides. This study presents field‐scale experimental data of passive earth force for cohesive and frictional clayey gravel evaluated at different combinations of soil depths and slopes. The experimental setup included a specialized device composed of a steel structure and a stiff plate that moved toward a mass of soil. In both dynamic and quasi‐static states, force‐displacement curves and maximum compression resistance were determined for several water content conditions induced by a rainfall simulator. The maximum dynamic force ranged from 8.49 to 31.67 kN for soil depths ranging between 0.36 and 0.50 m, whereas the quasi‐static force corresponded to 60% of the dynamic force. Furthermore, rainfall generated an additional decrease of compression resistance compared to that measured in the field. A comparison of measured data with theoretical models of passive earth force indicated that Rankine's solution provided the best estimate, whereas the logarithmic spiral approach significantly overestimated passive earth force by up to 70%. Therefore, the correct choice of geotechnical formulation or the direct use of field measurements to estimate passive earth force may significantly improve the accuracy of 3‐D limit equilibrium models for assessing slope stability over natural landscapes.
Research on Attention-Driven Unsupervised Underwater Image Enhancement

Images taken underwater often experience color discrepancies and diminished clarity due to the effects of light dispersion and absorption. Addressing this challenge, our study introduces an underwater image enhancement technique, VAUIR, leveraging an attention-driven approach. This technique is rooted in a refined Koschmieder model, segmenting each image into three distinct elements. By employing the attention mechanism, it minimizes noise interference and elevates image fidelity. The process involves treating original underwater photos with a scene brightness prediction network (J-Net), a transmission map prediction network (T-Net), and a universal background light estimation unit (GB). Subsequently, the images are reformed using the image segmentation model. The generated degraded image and the original underwater image are fed into J-Net and Feature Fusion Attention Network (A-Net) to get a clear image. In addition, we employ multi-layered supervisory mechanisms for supervising the model, namely: a reconstruction loss is employed to supervise the J-Net network to emphasize the ability of the potential components to reconstruct the input image; a consistency requirement is applied to guide the A-Net network, ensuring that both the initial underwater photo and the reprocessed image maintain analogous structural tiers. Additionally, a color correction loss is employed to rectify any color deviations in the restored image. Test outcomes indicate that the VAUIR technique delivers commendable results in restoring underwater images, showcasing efficient recovery quality and processing speed.
Low-Complexity GRAND by Segmentation

The recently introduced maximum-likelihood (ML) decoding scheme called guessing random additive noise decoding (GRAND) has demonstrated a remarkably low time complexity in high signal-to-noise ratio (SNR) regimes. However, the complexity is not as low at low SNR regimes and low code rates. To mitigate this concern, we propose a scheme for a near-ML variant of GRAND called ordered reliability bits GRAND (or ORBGRAND), which divides codewords into segments based on the properties of the underlying code, generates sub-patterns for each segment consistent with the syndrome (thus reducing the number of inconsistent error patterns generated), and combines them in a near-ML order using two-level integer partitions of logistic weight. The numerical evaluation demonstrates that the proposed scheme, called segmented ORBGRAND, reduces the average number of queries (time complexity/latency) to one-third at all SNR regimes. Moreover, the segmented ORBGRAND with abandonment also improves the error correction performance.
Vision Transformers with Augmented Dataset using DDPM to detect Parkinson's Disease

Denoising Diffusion Probabilistic Models (DDPM) have demonstrated their prowess in generating photorealistic and high-fidelity images, primarily in the context of images and videos. However, their potential to enhance downstream tasks like medical image classification remains underexplored. This study investigates the potential of utilizing synthetic data generated by DDPM for Parkinson's disease detection from MRI data. By training a range of Transformer models – ViT, LeViT, MaxViT, Swin Transformer, and DeiT using the synthetic data, we aim to elevate the performance of image classifiers. Our research addresses the gap between the potent image generation capabilities of DDPM and the enhancement of image classifiers. Integrating synthetic data, our aim is to enhance the classification accuracy of advanced Vision Transformer models, bolstering Parkinson's disease detection. This exploration reveals the symbiotic potential between generative models and classifiers, promising improved disease detection outcomes from medical images. Conducting a comprehensive evaluation across various Transformer architectures, we evaluate the impact of synthetic data on classifier performance. These findings illuminate the feasibility of integrating DDPM-generated data into the image classifier training process, advancing both image generation and classification realms in medical imaging.
The significance and management of natural carbon stores in the open ocean

This report sets out the importance of carbon in the open ocean and, through examples, illustrates the significance and values of some of its major carbon pools and sinks. It has been produced to promote better understanding of how atmospheric carbon is captured, stored and mobilized in the ocean, and how this has a significant bearing on sustainability, the welfare of people, and the future scale and intensity of climate change and ocean acidification. The core message is that the significance of carbon processes, pools and sinks in teh open ocean now need to be centrally focused into decision making at all scales. This report demonstrates from multiple angles that continuing with excessive carbon emissions from human activities, whilst ignoring carbon management through the various ways we use and interact with the ocean, is imperiling the ocean system that shields us from more rapid dangerous climate change impacts.
On the Performance of NOMA Assisted Semi-Grant-Free Transmission in Satellite Systems

This paper investigates a semi-grant-free (SGF) access scheme for satellite communication systems, where one earth station (ES) and many mobile terminals (MTs) desire to access the satellite network simultaneously via uplink non-orthogonal multiple access (NOMA). We first propose two NOMA assisted SGF transmission schemes according to the availability of different channel state information (CSI). When perfect CSI is available, the satellite broadcasts an instantaneous access threshold to all MTs to enable dynamic power control (DPC), thus guaranteeing the quality of service of the ES. Otherwise, with the introduction of a statistical access threshold, a novel DPC with probability constraint is proposed to fulfill a satisfactory transmission. Next, we propose a new probability density function to characterize the shadowed-Rician distribution with estimation error, with which closed-form system throughput expressions under the proposed schemes are derived. Finally, computer simulations validate the effectiveness and superiority of our analysis and reveal the impact of CSI errors on the system performance.
ReSkipNet: Skip Connected Convolutional Autoencoder for Original Document Denoising

Data pre-processing, data analysis, and Optical Character Recognition need a huge amount of clean data, and document images are usually a good source for this. However, document images frequently exhibit blurring and various other forms of noise, which can pose challenges in their manipulation and analysis. To denoise and deblur such document images, autoencoders have been used for a long time. For this task, we propose a novel Convolutional Autoencoder Network which is composed of multiple skip-connected residual blocks and other layers for supporting the encoder and decoder parts. This model not only uses less computational power to denoise existing document image datasets but also performs well. While prior research primarily concentrates on optimizing evaluation metrics, our approach additionally prioritizes larger resolution input sizes. This characteristic of using larger image sizes enhances its practicality and usability as real-world documents are typically characterized by a higher word density. Moreover, in order to further advance the development of our model, we produced an original dataset and proceeded to train our model on this dataset, resulting in satisfactory outcomes.
Performance Evaluation of Network Intrusion Detection Using Machine Learning

The development of 5G network and beyond has led to an explosion of data generation. It is therefore crucial to have an intrusion detection system (IDS) to detect and remove malicious packets from entering network. This paper therefore presents an IDS based on a Feature Selection approach which applies the Recursive Feature Elimination and Random Forest Classifier with 10 -fold Cross Validation to classify malicious and benign traffic on a publicly available UNSW-NB15 dataset. Most existing Feature Selection approaches on this dataset directed to enhance the performance of a limited number of algorithms used. Our proposed Feature Selection approach was tested on six well-known supervised machine learning (ML) algorithms including Artificial Neural Network (ANN), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machines (SVM) and Logistic Regression (LR) performing binary classification. In addition, we performed hyperparameter tuning to get the best possible parameters for each ML algorithm. Unlike hyperparameter tuning in most studies, we perform both Manual Search and Grid Search. The performance of the selected ML algorithms are evaluated based on Accuracy, Recall, Precision, and F1 score. The results from our experiments indicate that the most robust algorithm is ANN whereas the weakest performing algorithm is LR. RF is the second-best performing algorithm, however, its runtime is much lower than that of ANN. In particular, ANN excels with (testing accuracy, F1 score) of (88.62%, 96.473%), RF with (87.40%, 89.60%), DT with (87.266%, 89.414%), KNN with (87.11%, 88.7%), SVM with (81.835%, 86.959%) and LR with (81.835%, 85.632%). In addition, the over-fitting problems are eliminated based on our proposed Feature Selection and Hyperparameter turning. Compared with existing works with the same ML algorithms on UNSW-NB15 dataset, our proposed Feature Selection approach achieved better results in most cases and more stable among different ML algorithms.
Mean platelet volume and D-dimer as predictors for complicated community-acquired pneumonia in hospitalized children

Background Community-acquired pneumonia (CAP) is one of the primary causes of child mortality and morbidity. The primary objective of our research was to assess the value of mean platelet volume (MPV) and D-dimer levels in predicting complicated community-acquired pneumonia in hospitalized children. Methods This observational retrospective study gathered medical data from the electronic medical records of children diagnosed with CAP who were admitted to the Pediatric Pulmonology Unit between December 2021 and December 2022. Results This study included 154 pediatric patients. Their age at presentation was 4.15 ± 3.60 years. A comparison of patients with complicated CAP and non-complicated CAP revealed a statistically significant decrease of MPV in the complicated CAP group than in the non-complicated group ( p = 0.016). The D-dimer level was significantly higher in the complicated CAP 3.42 ± 3.02 µg/ml compared than in the non-complicated 1.63 ± 2.04 µg/ml, p = 0.002). Low MPV and increased D-dimer were powerful indicators of complicated CAP (OR 0.577, p = 0.021, OR 1.419, p = 0.003). Conclusion The current study highlights that low MPV and high D-dimer levels can be useful predictors of pulmonary complications of CAP in children. However, prospective observational studies are needed to evaluate the changes in these predictors during the disease and assess the time needed for normalization.
Detection of Coffee Leaf Diseases using DenseNet-264 and SLIC Segmentation

Coffee leaf disease (CLD) identification and management is critical to the coffee industry since it can lead to reduced yields and poor-quality coffee. We offer a new method for recognizing CLD in this work that makes use of the Simple Linear Iterative Clustering (SLIC) segmentation algorithm and the Densenet-264 deep learning model. Our method involves SLIC segmentation to extract diseased areas from a collection of coffee leaf images, which are then used to train a Densenet-264 model to classify images as infected or healthy. Experiments findings demonstrate that our suggested strategy outperforms existing methods for CLD identification with an accuracy of 97.17% in correlation with Resnet-50 with accuracy 96.54%. This innovation has significant ramifications for the coffee industry because it gives a more accurate and efficient method of measuring.
Oxygen ion precipitation in the Martian atmosphere and its relation with the crustal magnetic fields

Without a global intrinsic magnetic field, oxygen ions in the Martian atmospheric corona can be picked up by the solar wind directly. The majority of the pickup ions escape, while some can precipitate into the atmosphere, producing sputtering of atmospheric constituents, which may play a significant role in the loss of neutral atmosphere. With its widely distributed crustal magnetic fields, Mars is unique. While it has been shown that Mars's crustal fields can alter the global distribution of escaping ions, little is known about the influence of the crustal fields on the spatial distribution and energy deposition of precipitating pickup ions. In this paper, the global distribution and energy spectrum of the precipitating pickup oxygen ions are calculated using the test particle method in the electric and magnetic fields set up by MHD simulation. Cases of different crustal field conditions are compared to show the influence of the crustal fields on the oxygen ion precipitation. We find, using a test particle code, that the crustal fields likely change the spatial distribution of the precipitation, resulting in irregularly distributed low-energy ion precipitation parches and wide high-energy ion precipitation belts on both the dayside and nightside. The crustal fields increase O(+) ion precipitation substantially, enhancing energy deposition especially on the nightside. Our results imply that atmospheric sputtering, and accompanying neutral escape, might happen globally and may be enhanced by a factor of almost 2 with the presence of the crustal magnetic fields.
FLDetect: An API-Based Ransomware Detection Using Federated Learning

Ransomware, a malicious piece of software responsible for several high-profile attacks in recent years, poses a significant threat to organizations of all sizes. Such attacks can cause significant operational and financial harm, including system interruptions and compromises of system integrity. By developing the ability to detect and prevent ransomware attacks, we can contribute to the creation of a more secure and safe digital ecosystem. In this research, we propose FLDetect, a unique Federated Learning (FL)-based method for identifying ransomware on Windows machines. Windows machines, integral to Internet of Things (loT) networks, can act as brokers to other sensor nodes, rendering them susceptible to such attacks. Our approach utilizes distributed computing to train a Machine Learning (ML) model using data from various devices without relying on centralized data storage. The API-call-pattern-based detection method is the preferred approach for detecting ransomware in this paper. We made use of an open-source dataset, known as ransomwaredataset2016, for a comparable objective. The global model's accuracy was 93.1% after we trained it with twenty different devices. Our results demonstrate that our method is effective in identifying ransomware while maintaining the privacy and security of the training data by utilizing FL.
The Effect of Tactile Stimulation and Imagery for a Brief Vibratory Stimulus on Fingertip

Tactile Imagery (TI) refers to the cognitive depiction of tactile sensation (TS), even in the absence of any physical stimuli. The potential applications of TI span across various fields, including rehabilitation, virtual reality, and brain-computer interfaces. Studies have shown that imagining vibrotactile stimuli has similar cortical activity to receiving an actual vibration. In the current study, brief vibrotactile stimulation was applied at the digit pads of participants’ left hand to investigate their cortical activity and compare the same with tactile imagery. The result showed an event-related desynchronization (ERD) for both TI and TS with TI showing a pronounced ERD compared to TS.
Development and Evaluation of an Atmospheric Water Generation System Utilizing Seawater as a Cooling Source

Artisanal fishing operations often encounter emergency situations at sea, resulting from problems such as boat breakdowns. It is crucial to ensure a stable supply of drinking water to safeguard the lives of the boat's crew in such scenarios. This research is dedicated to addressing this challenge by developing a prototype capable of generating water from atmospheric humidity. The prototype, comprising water generation and storage modules, aims to utilize seawater as a cooling source to condense atmospheric humidity. The performance of the prototype was evaluated through experimental tests conducted in the city of Arequipa, Peru, and via simulations under the environmental conditions of the coastal city of Camana, Peru. The results indicate that water production reached 3.83 mL/4h in Arequipa, with a relative humidity of 32.09% and a temperature of 20.95 °C. In Camana, it reached 11.56 mL/h, with a relative humidity of 89.15% and a temperature of 15.33 °C. The direct influence of relative humidity on the prototype's performance was clear. It was observed that in Camana, approximately three times more water is produced within the same time frame, thanks to its high relative humidity, as compared to the lower relative humidity experienced in Arequipa.
Regional Tectonic Setting of Pilgrim Hot Springs, Seward Peninsula, Alaska

The Central Alaska Hot Springs Belt (CAHSB), extending from eastern Alaska west to the Seward Peninsula, includes Pilgrim Hot Springs located 70 km north of Nome. The CAHSB continues west across Bering Strait to Chukotka, Russia, where over 30 more geothermal sites are known (Polyak et al., 2010; 2013). The faults and tectonic setting that control these geothermal resources are poorly known. We review the geologic and tectonic setting of geothermal sites nearest to the Bering Straits: the Pilgrim Hot Springs, Serpentine Hot Springs, those near the Kigluaik and Bendeleben Mountains, and the Lorino Hot Springs in Russia. Active faults close to these hot springs appear to re-activate older normal structures in today’s N-S extensional stress field. Limited young displacement occurs on these faults, but at their time of formation in the Late Cretaceous to Eocene, larger displacements occurred, making them re-activated zones of weakness today. Active faults on the Seward Peninsula include the E-W trending Kigluaik and Bendeleben normal faults that extend for 175 km with associated hot springs, including Pilgrim Springs. These faults uplift metamorphosed and intruded rocks and bound Cenozoic to Recent basins in their hanging wall. The parallel Bering Strait, Port Clarence and Norton Sound faults are active offshore. In Chukotka, the NW trending Kolyuchin-Mechigmen zone is likely a reactivated Cretaceous fault system that juxtaposed granitic and metamorphic rocks to the north with supracrustal rocks to the south (Bering Strait Geologic Field Party, 1997). First motions show normal and strike-slip and hot springs have variable isotopic evidence for mantle-derived He (
Cotton gravity is not predictive

It is well-known that the theory of Cotton gravity proposed by Harada is trivially solved by all isotropic and homogeneous cosmologies. We show that this under-determination is more general. More precisely, the degree of arbitrariness in the solutions increases with the degree of symmetry. We give two simple examples. The first is that of static spherically symmetric solutions, which depend on an arbitrary function of the radial coordinate. The second is that of anisotropic cosmologies, which depend on an arbitrary function of time.
Physics Informed Neural Networks for Baculovirus-Insect Cell System

The Baculovirus expression vector system (BEVS) is one of the widely utilized platforms for the development of recombinant proteins, virus-like particles (VLPs), and vaccines. A mathematical model tuned with data generated from such systems is critical for its optimization and control. In this work, a mathematical model driven by physics depicting such a cell behavior has been proposed and validated with experiments conducted and subsequently used to study the physics informed neural networks (PINNs). Since the governing equations are found as a set of stiff ordinary differential equations (ODEs), Stiff-PINN, a variant of PINN that is utilized to solve stiff ODEs, is implemented here. Assuming a quasi-steady state for the oxygen concentration, the equation responsible for stiffness, the results are found to be more accurate compared to regular PINN. Such Stiff-PINN models, once developed, can act as a replacement of regular PINN models having trouble to handle stiff equations of BEVS.
Object Detection Approach for Batch Detection of Cacao Bean Defects

For the longest time, cacao bean sorting has been a manual and subjective process that is incredibly time consuming where every bean is checked for defects. Given that this process is visual in nature, it is imperative that human error exacerbates its high subjectivity to the senses. Provided that the presence of computer vision and deep learning techniques is prominent in recent smart agriculture studies, this issue may be addressed as these provide an objective way to quantify the cacao beans’ defects and classify each bean batch. This study aims to aid this process by employing YOLOv4 alongside a counting function in the development of an object detection algorithm which shall objectively categorize each bean into specific defects and classify each batch’s grade. Upon the completion of the training, a robust detector was developed with a precision of 0.7267, a recall of 0.8134, and a mean average precision (mAP) of 88.2922% while operating at 14.8 frames per second. Given its high mAP value and the execution time, this suggests that the model can operate with relatively high precision at a practical inference time.