Miracle-dz/UniFAR-FTUs huggingface dataset:
Characteristics of equatorial plasma bubbles observed by TEC map based on ground-based GNSS receivers over South America

Abstract. A ground-based network of GNSS receivers has been used to monitor equatorial plasma bubbles (EPBs) by mapping the total electron content (TEC map). The large coverage of the TEC map allowed us to monitor several EPBs simultaneously and get characteristics of the dynamics, extension and longitudinal distributions of the EPBs from the onset time until their disappearance. These characteristics were obtained by using TEC map analysis and the keogram technique. TEC map databases analyzed were for the period between November 2012 and January 2016. The zonal drift velocities of the EPBs showed a clear latitudinal gradient varying from 123 m s −1 at the Equator to 65 m s −1 for 35∘ S latitude. Consequently, observed EPBs are inclined against the geomagnetic field lines. Both zonal drift velocity and the inclination of the EPBs were compared to the thermospheric neutral wind, which showed good agreement. Moreover, the large two-dimensional coverage of TEC maps allowed us to study periodic EPBs with a wide longitudinal distance. The averaged values observed for the inter-bubble distances also presented a clear latitudinal gradient varying from 920 km at the Equator to 640 km at 30 ∘ S. The latitudinal gradient in the inter-bubble distances seems to be related to the difference in the zonal drift velocity of the EPB from the Equator to middle latitudes and to the difference in the westward movement of the terminator. On several occasions, the distances reached more than 2000 km. Inter-bubble distances greater than 1000 km have not been reported in the literature. Keywords. Ionosphere (equatorial ionosphere; ionospheric irregularities) – meteorology and atmospheric dynamics (thermospheric dynamics)
Validity of Type D personality in Iceland

Type D personality has been associated with poor prognosis in cardiac patients. This study investigated the validity of the Type D construct in Iceland and its association with disease severity and health-related risk markers in cardiac patients. A sample of 1,452 cardiac patients completed the Type D scale (DS14), and a subgroup of 161 patients completed measurements for the ﬁve-factor model of personality, emotional control, anxiety, depression, stress and lifestyle factors. The Icelandic DS14 had good psychometric properties and its construct validity was conﬁrmed. Prevalence of Type D was 26–29%, and assessment of Type D personality was not confounded by severity of underlying coronary artery disease. Regarding risk markers, Type D patients reported more psychopharmacological medication use and smoking, but frequency of previous mental problems was similar across groups. Type D is a valid personality construct in Iceland, and is associated with health-related risk markers, but not cardiac disease severity.
Practical Predictability of Supercells: Exploring Ensemble Forecast Sensitivity to Initial Condition Spread

As convection-allowing ensembles are routinely used to forecast the evolution of severe thunderstorms, developing an understanding of storm-scale predictability is critical. Using a full-physics numerical weather prediction (NWP) framework, the sensitivity of ensemble forecasts of supercells to initial condition (IC) uncertainty is investigated using a perfect model assumption. Three cases are used from the real-time NSSL Experimental Warn-on-Forecast System for Ensembles (NEWS-e) from the 2016 NOAA Hazardous Weather Testbed Spring Forecasting Experiment. The forecast sensitivity to IC uncertainty is assessed by repeating the simulations with the initial ensemble perturbations reduced to 50% and 25% of their original magnitudes. The object-oriented analysis focuses on significant supercell features, including the mid- and low-level mesocyclone, and rainfall. For a comprehensive analysis, supercell location and amplitude predictability of the aforementioned features are evaluated separately. For all examined features and cases, forecast spread is greatly reduced by halving the IC spread. By reducing the IC spread from 50% to 25% of the original magnitude, forecast spread is still substantially reduced in two of the three cases. The practical predictability limit (PPL), or the lead time beyond which the forecast spread exceeds some prechosen threshold, is case and feature dependent. Comparing to past studies reveals that practical predictability of supercells is substantially improved by initializing once storms are well established in the ensemble analysis.
Comparing Maximum Likelihood and Generalized Method of Moments in Short Term Interest Rate Models

In this thesis we will look at some different continuous models for predicting the short term interest rate, and focus on the method of parameter estimation in such models. A particular focus will be placed on the method of Maximum Likelihood Estimation (MLE), which has not been the most common method of estimation in this context. Furthermore, we will compare MLE to the Generalized Method of Moments (GMM), and both methods will be used in simulation experiments and on different sets of real data. Our starting point for investigation will be the model used in Chan, Karolyi, Longstaff, and Sanders [1992], a model in which several of the most popular short rate models can be nested. We will use a discrete-valued approximation of this model to facilitate the estimation of parameters. In conclusion, this thesis argues that the MLE method of parameter estimation in short term interest rate models deserves more attention than it is currently given. In simulation experiments, the MLE method produced more accurate and precise estimates than the GMM method. Specifically, the bias in estimating the mean-reversion parameter is smaller using the MLE method. These results are particularly interesting as the GMM method is currently the common approach to parameter estimation in short term interest rate models. However, as will be shown in this thesis, it may very well be that the MLE method constitutes a better approach than GMM for such estimations. The results in this thesis may therefore contribute to the theoretical perspective as well as the real-world applications of methods of parameter estimation in predicting the short term interest rate.
Adapting Unsigned Signals Between Triaxial Antennas For Use In Magnetic Induction Localization

Magnetic induction localization is an inverse problem that determines the relative position and orientation (pose) between transmitting and receiving coils by analyzing the received signals. Related work has established methods to resolve the localization into two candidate poses. However, these methods require having signed signals, or periodic signals whose starting point is unambiguously determined with respect to an absolute reference (the transmitted signal). For distributed systems, the signal signs are difficult to resolve. This paper presents a method to extract partial information about the signs from unsigned signals, The method is tested in a hardware experiment.
Evde Enteral Tüple Beslenen Hastaların Beslenmeye İlişkin Yaşadığı Sorunlar ve Yakınlarının Bakım Yükü

Giris: Gunumuzde teknolojinin hizla gelismesi ile enteral tuple beslenme, hastanin kendi ev ortaminda ailesi ile birlikte yasamini surdurebilmesine olanak saglayan bir yontemdir. Bu hastalarin bakim sorumlulugu da hasta yakinlarina dusmektedir. Amac: Bu calisma evde enteral tuple beslenen hastalarin beslenmeye iliskin yasadigi sorunlarin ve yakinlarinin bakim yuklerinin belirlenmesi amaciyla yapilmistir. Yontem: Kesitsel ve tanimlayici nitelikteki bu arastirmanin orneklemini hastanede enteral tup yerlestirilen ve evde beslenmesini bu yolla surdurmeye devam eden, ilk bir ayini tamamlamis olan 49 yetiskin hasta yakini olusturmustur. Veriler, Hasta ve Bakim Verici Bilgi Formu, Evde Enteral Tuple Besleme’ye (EETB) yonelik yasanilan sorunlar formu ve Zarit Bakim Verme Yuku Olcegi (ZBYO) ile toplanmistir. Veriler Mann- Whitney U testi ve Kruskall Wallis onemlilik testi ile degerlendirilmistir. Bulgular: Calismamizda hasta yakinlarinin bakim yukunun orta duzeyde (35.18±12.45) oldugu, gunluk yasam aktivitelerinin surdurulmesinde baskasina bagimli hastalarin yakinlarinin bakim yukunun daha fazla oldugu bulunmustur. Calismamizda gastrostomi bolgesinde enfeksiyon komplikasyonu ile karsilasan hasta yakinlarinin bakim yukunun istatistiksel olarak onemli duzeyde arttigi belirlenmistir (p .05). Sonuc: Hasta yakinlarinin EETB iliskin minor sorunlarla karsilastiklari, bununla birlikte hastasinda sorun gelisen hasta yakinlarinin bakim yukunun daha yuksek oldugu soylenebilir. Anahtar Kelimeler: Evde Enteral Beslenme, Hasta Yakini, Bakim Yuku.
Predicting the Sale Price of Pre-Owned Vehicles with the Ensemble ML Model

Car price forecasting is a popular study topic because it requires a lot of work and knowledge. Used car pricing forecasting is a major auto industry concern. Machine learning can accurately predict used automobile prices based on many characteristics. Many distinct qualities are considered for accurate predictions. The suggested model uses a dataset that contains vehicle brand and model, year of production, mileage, condition, and other factors that affect used car prices. This study used linear regression, GBT regression, and random forest regression to estimate secondhand car prices. Then, algorithm performance was compared to find which method better fit the data set. Thus, these methods outperform others.
Diabetic Prediction using Feature Selection based Random Forest and Fine Tuned K-Nearest Neighbor Classifier Algorithm-A Design Thinking Approach

In low- and middle-income nations today, diabetes affects the majority of the population, according to a World Health organization (WHO) research. The WHO report suggested that 80% of the deaths would be due to the diabetes from 2016 to 2030. However, the current method continues to provide findings that are erroneous, which has a substantial negative impact on performance. To overcome the abovementioned issue, in this work, Random Forest (RF) algorithm and Fine tuned K-Nearest Neighbor (FKNN) classifier algorithm is proposed. Pre-processing, feature selection, and classification are the three primary stages of this project. Initially, preprocessing is performing for improving the final dataset results more accurately. Preprocessing is the process of cleaning the database into correct format. In order to choose more relevant and useful data from the dataset, the feature selection is then carried out utilizing the RF algorithm. It also minimizes the risk of over fitting with minimum features. Finally, diabetic prediction and classification is done by using FKNN classifier algorithm is used for categorizing items in the feature space based on training samples that are the most similar to the objects being classified. According to the experimental results, the suggested RF+FKNN method outperforms the current algorithms in accuracy, precision, recall, and f-measure.
Efficient Control-Channel Security for the Aeronautical Communications System LDACS

Since Air Traffic Management (ATM) is still conducted largely via analogue voice communications, the digitization of data transmission is crucial to automate and secure ATM in civil aviation. For that purpose, several new digital data links are developed. The continental, terrestrial long-range candidate is the L-band Digital Aeronautical Communications System (LDACS), which is a cellular, ground-based digital communications system for flight guidance and communications related to the safety and regularity of flight. The security of LDACS has been the focus of recent works, however, the problem to secure data on its small control-channels remains unsolved. The objective of this work is to propose slim and efficient security measures to protect data on the LDACS control-channels and evaluate their security and performance impact. First, via a 3-pass instancing of the ISO/IEC11770-3:2021 “Key agreement mechanism 7” protocol, keys to secure LDACS control-channels are established between air and ground radios. Second, via these point-to-point keys and point-to-multipoint group keys, the control-channels are secured. Thereby the limitations set by the limited bandwidth are respected and cryptographic overhead optimized. Finally, the security of our proposal is validated using a symbolic model with the Tamarin proof system. Also, via computer simulations, the LDACS performance impact of the control-channel security solutions is evaluated.
GenTAL: Generative Denoising Skip-gram Transformer for Unsupervised Binary Code Similarity Detection

Binary code similarity detection serves a critical role in cybersecurity. It alleviates the huge manual effort required in the reverse engineering process for malware analysis and vulnerability detection, where the original source code is often not available. Most of the existing solutions focus on a manual feature engineering process and customized code matching algorithms that are inefficient and inaccurate. Recent deep learning-based solutions embed the semantics of binary code into a latent space through supervised contrastive learning. However, one cannot cover all the possible forms in the training set to learn the variance of the same semantics. In this paper, we propose an unsupervised model aiming to learn the intrinsic representation of assembly code semantics. Specifically, we propose a Transformer-based auto-encoder like language model for the low-level assembly code grammar to capture the abstract semantic representation. By coupling a Transformer encoder and a skip-gram style loss design, it can learn a compact representation that is robust against different compilation options. We conduct experiments on four different block-level code similarity tasks. It shows that our method is more robust compared to the state-of-the-art solutions.
Automated Keyphrase Generation for Brazilian Legal Information Retrieval

Due to the high volume of cases in transit in the Brazilian judicial system, it is of great interest to automate processes that help legal professionals. With the recent advances in text generation methods, there is potential to apply these techniques in tasks that require automation. In this article, we address the problem of automating the writing of keyphrases, a sequence of key terms present in documents used in courts throughout Brazil. For this, we are proposing the use of a text-to-text framework based on generative Transformers. We evaluated several generative Transformer models (such as PTT5, mT5, OPT, and BLOOM) for the proposal, and a comparison of their performance is presented. The best-performing model, PTT5, was adopted as the text generator since it achieved a BLEU score of 37.54% in the test set, outperforming other evaluated methods by up to 24.6%. Finally, to assess the influence of keyphrases and the quality of the generated ones, we performed several experiments based on a real case of legal information retrieval. By using traditional information retrieval methods, such as TF-IDF and BM25, in combination with the original, generated keyphrases, or both, we observed gains statistically significant (p-value ${<}$ 0.05) in all experiments.
A Comparative Analysis of Convolutional Neural Network Architectures for Coffee Leaf Rust Detection

Coffee plays a cultural part in the lives of people. Eight out of ten adults in the Philippines drink an average of 2.5 cups of coffee per day, and nine out of ten households have coffee in their pantries. In 2021, it is reported that coffee will be an approximately PHP 3.0 billion (USD 5 million) industry in the country. As such, much effort is placed into sustaining its economic production. Botanical studies reveal that some factors severely affect the quality of coffee plants. One of them is coffee leaf rust which is caused by the fungus known asHemileia vastatrix and affects many coffee-growing regions.If not detected early enough it can greatly reduce coffee yield or worse wipe out an entire plantation affecting the farmers. This study proposes a methodology for detecting coffee leaf rust disease using computer vision and employs multiple pre-trained architectures of Convolutional Neural Networks (CNNs). Specifically, this study used twenty-five convolutional neural networks, and the best-performing models are ResNet101V2, InceptionV3, ResNet50V2, Xception, and DenseNet169. They have a training accuracy of 92.62%, 96.90%, 93.45%, 98.45%, and 96.79% respectively. Meanwhile, their validation accuracy is 91.67%, 90%, 94.44%, 93.89%, and 95.56% respectively. Out of the top five CNNs, ResNet101V2 achieved the highest test accuracy with 95.56% and it also excels in other evaluation metrics such as Precision, Recall, and F1 – score. Although this study used a variety of CNNs, it is also recommended to use more types of algorithms as well as increase the number of epochs. Future studies can also consider the detection of other coffee diseases and a larger dataset for a wider scope. Overall, the result of this study is a step closer to achieving improved coffee production and the livelihoods of farmers.
Efficient Lung Nodule Detection via 3D Deep Learning with Shifted Convolutions

The high computational costs of deep convolutional neural networks hinder their deployment in real-world applications, including pulmonary nodule detection from CT scans where large 3D image sizes amplify the issue. This paper presents a novel 3D method to detect pulmonary nodules, based on anchor-free U-shaped networks, AFNet. A shifted convolution is further introduced to replace standard 3D convolutions, which reduces both the model sizes and FLOPs (floating-point operations). The shift operator is parameter-free, enabling 3D context fusion between CT slices using 2D convolutions. Extensive experiments on a large-scale lung nodule detection dataset validate the effectiveness of the proposed methods. The AFNet backbone is first proven to be comparable to the previous state of the art (e.g., NoduleNet). We then show that the proposed method with shifted convolutions balances model complexity and performance better than several lightweight methods, and generalizes well with different backbones. As an example, compared to the vanilla model, AFNet with shifted convolutions increases average FROC by 3.08% and reduces FLOPs (floating-point operations) and parameters by 62.40% and 66.62%, respectively.
The Role of Social Media Usage, E-WOM, and Perceived Enjoyment in Shaping Customer Attitudes and Blockchain Adoption Loyalty in UAE Banking: A Quantitative Investigation

Technology is shaping the banking sector around the globe. Therefore, the prime objective of this research was to examine the relationship among social media use, e-WoM, perceived enjoyment and customer’s attitude towards loyalty to the use of Blockchain technology in the context of U.A. E. banks. This study used a cross-sectional research design and quantitative methods for data collection. Five-point Likert scale was used to develop the well-designed questionnaire for data collection. A simple random sampling technique was adopted to distribute the questionnaire among respondents. A sample of one hundred seventy-four observations was analyzed using Smart PLS 3.3. 9. The findings confirmed the significant and direct effect of social media use, e-WoM, and perceived enjoyment on customers’ attitudes. The direct effect of customer attitude on Blockchain adoption was also supported. The findings also revealed the important mediating role of customer attitude as well. The findings are helpful for policymakers and academicians.
Enhanced Deep Learning Satellite-based Model for Yield Forecasting and Quality Assurance Using Metamorphic Testing

Fresh produce (FP) yield forecasting is crucial for both: farmers to estimate fair prices for their crops and retailers to protect against highly priced FPs. To precisely forecast future yield, a deep learning forecasting model is proposed in this work which is trained and tested using relevant input parameters retrieved from satellite images and mapped to tabular yield data recorded for strawberry as an output parameter. To enhance the model performance, the preprocessing approaches and the set of input parameters are improved. The best satellite image preprocessing technique has to be found to represent the images with less data for efficiency. Therefore, a preprocessing approach based on averaging is proposed and implemented then compared with the literature approach which is based on histograms, where the proposed approach improved performance by 20%. The proposed Deep Feed Forward Neural Network with Embedded Gated Recurrent Units (DFNNGRU) ensembled with Attention Deep GRUs (ADGRU) is then tested against well-performing models of Stacked-AutoEncoder (SAE) ensembled with Convolution Neural Networks with Long-short term memory (CNNLSTM), where the proposed model is found to outperform the literature model by 12.5%. To have a better set of parameters, a Normalized Vegetation Difference Index (NDVI) is added to the input parameters which further enhances the performance by 2%. Finally, a quality assurance technique using metamorphic testing is applied and it is found that the model fulfills all expected metamorphic relations which proves the soundness and quality of the model as compared with other solutions.
The interplay of blocking properties with charge and potential redistribution in thin carbon-doped GaN on n-doped GaN layers

In carbon-doped GaN (GaN:C) buffers used in a GaN-on-Si technology, the buffer is embedded in between transition and channel layers. This makes the analysis of buffer properties difficult due to e.g., carrier injection from or potential drop at these adjacent layers. Here, we analyze capacitance- and current-voltage characteristics of 200–300 nm thick GaN:C ([C] = 1019 cm−3) layers which are embedded between a top metal electrode and bottom n-doped GaN (n-GaN). Such structures allow a better potential control in GaN:C and thus determination of the band diagram quantitatively. The accumulation of negative charge (concentration up to 6 × 1017 cm−3) with bias is observed in GaN:C at both polarities. For biases Vappl +1.7 V, accumulated negative charges in GaN:C raise an energy barrier of ∼1.1 eV for electron injection from n-GaN .
AI-Enabled Analysis of 3-D CT Scans for Diagnosis of COVID-19 & its Severity

This paper describes the 3rd COVID-19 Competition, taking place in the AI-enabled medical image analysis (AIMIA) Workshop of the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023). The 3rd COVID-19 Competition is a continuation of the Competitions held at ECCV 2022 and ICCV 2021 Conferences, and aims to tackle the challenges of whole slide image and CT/MRI/X-ray analysis/processing and to identify research opportunities in the context of Digital Pathology and Radiology/COVID19. The 3rd COVID-19 Competition consists of two Challenges targeting COVID19 detection and COVID19 severity detection. Both Challenges are based on an extended version of the database used in the 1st and 2nd COV19D Competitions, the COV19-CT-DB database, which includes chest CT scan series. A large part of the COV19-CT-DB database is annotated for COVID-19 detection and consists of 8,0003-D CT scans. About 1,0003-D CT scans of the database are also annotated with respect to four COVID-19 severity conditions. Both parts have been split in training, validation and test datasets. These are used for training and validation of machine learning models, as well as for evaluation. The paper further describes the baseline methods for the 3rd COVID-19 Competition, which are deep learning approaches, based on CNN-RNN networks. Their performance on detecting the existence and the severity of COVID-19 is reported.