esa-sceva/satcom-chunk-collection huggingface dataset:
# Free-Breathing and Ungated Cardiac MRI Reconstruction Using a Deep Kernel Representation
## 7 Conclusions  
In this work, a deep kernel method was introduced for real-time free-breathing and ungated cardiac MRI reconstruction. We implemented the deep kernel method using a cascade of two CNNs. We realized the feature extraction operator using a deep CNN, and a one-layer CNN is used for the kernel matrix calculation. The benefits of the proposed deep kernel methods are the elimination of the manual choices for the feature map and the kernel function, as well as the elimination of the manual tuning of the parameters in the kernel functions in the traditional kernel methods. This results in the improved performance of the deep kernel method. Moreover, the proposed scheme is unsupervised, which ideally fits the settings of free-breathing dynamic MRI. Comparisons with the competing state-of-the-art kernel method show improved reconstructions using the proposed deep kernel method.  
Methodology: Q.Z.; data acquisition: [PERSON] and A.H.A.; images rating: S.D. and T.H.; statistical analysis: [PERSON] and T.H.; writing--original draft preparation: [PERSON] and T.H.; writing--review and editing: [PERSON], S.D. and T.H. All authors have read and agreed to the published version of the manuscript.  
This work was conducted on an MRI instrument funded by NIH 1S10 OD025025-01.  
The Institutional Review Board at the University of Iowa approved the acquisition of the data.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author. The data has not been made publicly available due to the confidentiality of the data.  
Q.Z. would like to thank the Magnetic Resonance Research Facility (MRRF) at the University of Iowa for providing the resources for data acquisition, and the University of Iowa Research Services for providing the GPU and HPC resources for running the experiments.  
The authors declare no conflict of interest.
# Dist-YOLO: Fast Object Detection with Distance Estimation
## 5 Summary  
We started with YOLOv3, a fast one-stage object detector. In the literature overview, we showed that there were approaches modifying YOLOv3 to monocular absolute distance estimation, but no one fully integrated the distance estimation functionality into YOLO's architecture. To achieve that, we extended the output prediction vector, modified the loss function by the part responsible for absolute distance estimation, and described how to modify the training data. In the benchmark, we verified that distance estimation was complementary to bounding box estimation and, therefore, increasds the bounding box detection precision compared to the standard YOLOv3. We evaluated two versions of the designed Dist-YOLO, namely class-agnostic and class-aware, with the finding that only class-agnostic yielded satisfactory results. Benchmarking the KITTI dataset, the mean absolute distance error was 2.5 m, and the mean relative error was 11%, which support the claim that the full integration of distance estimation into YOLO's functionality can reach a significantly better result compared to solutions that build distance estimation on the top of the model's output. Finally, we reported that the computation speed was identical for both YOLOv3 and Dist-YOLOv3. The implementation is available at [[https://gitlab.com/EnginCZ/yolo-with-distance](https://gitlab.com/EnginCZ/yolo-with-distance)]([https://gitlab.com/EnginCZ/yolo-with-distance](https://gitlab.com/EnginCZ/yolo-with-distance)) (accessed on 8 October 2021).  
Conceptualization, M.V. and P.H.; Data curation, T.N.; formal analysis, M.V. and P.H. and T.N.; methodology, M.V. and T.N.; software, M.V.; validation, M.V. and P.H. and T.N.; visualisation, M.V.; writing, M.V. and P.H. All authors have read and agreed to the published version of the manuscript.  
The work was supported by ERDF/ESF \"Centre for the development of Artificial Intelligence Methods for the Automotive Industry of the region\" (No. CZ.02.1.01/0.0/0.0/17_049/0008414).  
Not applicable.  
Data Availability Statement:Publicly available datasets were analyzed in this study. These data can be found here: [[http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d)]([http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d)) (accessed on 14 August 2021). The source codes implementing the presented approach are freely available at: [[https://gitlab.com/EnginCZ/yolo-with-distance](https://gitlab.com/EnginCZ/yolo-with-distance)]([https://gitlab.com/EnginCZ/yolo-with-distance](https://gitlab.com/EnginCZ/yolo-with-distance)) (accessed 14 August 2021).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
YOLO
# Error Detection for Arabic Text Using Neural Sequence Labeling
## 2 Background and Related Work
### Grammar Detection and Correction Approaches  
Grammar error detection and correction has been done for various languages [14], including Chinese [15], Greek [16], and Swedish [17]. Different approaches have been exploited for automatically detecting and correcting text. These include rule-based, syntax-based, and statistical-based classification and neural networks approaches. The rule-based technique has been applied to languages such as Amharic [18] and Latvian [19]. Additionally, a syntax-based technique has been used for Danish [20] and a statistical-based technique has been used for Amharic [18], as well as Filipino [2].  
Moreover, classification and Neural Networks have been employed for automatically detecting and correcting grammatical errors. The most common classifiers used in previous work include maximum entropy models, naive Bayes and support vector machines (SVM) such as in [10; 21] for English. On the other hand, neural networks have been used in [12; 22] by applying Recurrent Neural Networks (RNNs) on various English corpora to perform Neural Machine Translation (NMT) for error correction.  
Other works, such as in [1; 23; 24; 25], focused solely on identifying grammar errors in English without correcting them by employing sequence labeling to generate the probability of each word in the sentence being correct or not.
## 2 Background on ML-Based Optimisation of Wireless Networks
In addition, small discrepancies between the actual impairment and its model used for training may significantly degrade the performance during testing. 3. It is usually difficult to understand the relation between the topology of the NN, e.g., the number of layers, the activation and loss functions, and the performance of the transceiver. The explainability is a common problem hindering the adoption of AI/ML techniques in some application areas, though it may not be a significant obstacle in transceiver optimisation. Thus, efforts are needed not only to design new AI/ML models, but also to explain the working principle of the models. Fortunately, such research works are slowly emerging, for example, that of [25], which develops a parallel model to explain the behaviour of a recurrent neural network (RNN). Due to the above limitations, many studies have already adopted a third approach for optimising the design of wireless systems, where ML and mathematical modelling work together and mutually benefit from each other. The main idea behind model-aided ML-based design is to keep the modular structure of the transceiver and use ML to optimise only some of the signal processing blocks, especially those involving complicated computations,  
Figure 1: Block diagram of a channel auto-encoder with the transmitter and receiver implemented as fully-connected NNs and the channel incorporating linear effects, e.g., AWGN and inter-symbol interference, and non-linear distortions, e.g., power amplifier and quantisation. The output layer at the decoder applies a softmax activation function for soft symbol detection. or for which some simplifications are assumed to make them mathematically tractable. See, for instance, in Figure 2, the model-assisted NN-based implementation of Viterbi decoding, where a small-sized NN is used to estimate the log-likelihood ratios at the receiver, given the channel output, while the rest of the detector follows the traditional Viterbi algorithm [26]. In the same direction, we list in Tables 1 and 2 some key studies, which are separated on the basis of operation timescales for RAN intelligent control (RIC), following the breakdown suggested by the O-RAN Alliance. Every research study comes along with a one-sentence summary of its main contribution.
# Flying Ad Hoc Networks: A New Domain for Network Communications
## 4 Conclusions  
Along this work, we reviewed the state of the art related to mobility, positioning, and propagation models in flying ad hoc networks. While we detected a wide variety of contributions in mobility, positioning and propagation models still require further work from the research community to face the imposed challenges. One of these necessary improvements comprehends the use of FANETs to extend telecommunications coverage. The network communication capability of UAVs is usually provided by on-board communication devices that include one or several wireless technologies (e.g., WiFi, WiMAX, LTE, etc.). Even though these technologies are well known, quantifying the influence of the UAV on the radiation pattern of the on-board communication devices is still under study. Therefore, we also presented a case study in which we measured this impact in a fully-controlled environment, an RF anechoic chamber. First, we obtained the best position of the antennas for a WiFi communication device taking into account that it will be an aerial node. Whereas communications are usually focused in a 2-D plane, this new type of aerial communication must offer a reliable 3-D reliable coverage. Specifically, we obtained the best configuration of antennas for a U2U link and for a U2G link working at the 2.4 GHz and 5 GHz bands, respectively. Afterwards, we evaluated the influence of the UAV device on the radiation patterns, concluding that the influence of the UAV chassis represented a decrease in the transmitted power of around 10 dB for the U2G link, which meant that the power fell to a tenth of the original value. Finally, we demonstrated that the radiation pattern of a communication device on-board a UAV should not be approximated by an isotropic diagram unless (i) the correct orientation of the antennas is used and (ii) ensuring that, if the radiation diagram is not isotropic, at least the regions of interest have a constant radiation as we showed in this study.  
Conceptualization, A.G.-P. and M.-D.C.; Data curation, A.G.-P. and M.-D.C.; Formal analysis, A.G.-P. and M.-D.C.; Funding acquisition, A.G.-P. and M.-D.C.; Investigation, A.G.-P. and M.-D.C.; Methodology, A.G.-P. and M.-D.C.; Supervision, M.-D.C.; Validation, A.G.-P. and M.-D.C.; Visualization, A.G.-P. and M.-D.C.; Writing-original draft, A.G.-P. and M.-D.C.; Writing-review & editing, A.G.-P. and M.-D.C.  
This work was supported by the AEI/FEDER UE project grant TEC2016-76465-C2-1-R (AIM) and the program \"Becas de Iniciacion en la Actividad Investigadora UPCT en colaboracion con el Banco Santander\" 2017.  
The authors declare no conflicts of interest.
## 7 Conclusions  
In this research, we designed a two-stage dual-branch UNet-based network for the tasks of building localization and damage assessment after natural disasters. Pre-trained feature extraction backbones, along with the proposed SKM for the UNets, were utilized to improve the localization and damage assessment performances. For this purpose, the xBD dataset was used as a benchmark to develop models and compare different networks. The results verified that compared with state-of-the-art UNet-based architectures, the proposed strategy could achieve significant improvements in both stages. Then, in order to study the transferability capability of our model, we evaluated it on a completely unseen dataset from another region in the world. The Bam dataset was extremely challenging not only because of the limited number of images that banned the fine-tuning step of the models but also because of the completely different type and density of buildings in the city compared to buildings that existed in the xBD dataset. All the experiments and transferability analysis demonstrated the applicability of the SKM-UNets for building localization and damage assessment. Further investigations using the xBD dataset revealed the relative importance of GSDs, Sun elevation angles, and satellite-looking angles in building localization and damage assessment.  
Conceptualization, S.A.A., A.M. and N.Y.; methodology, S.A.A.; software, S.A.A.; validation, S.A.A. and A.G.; formal analysis, S.A.A.; investigation, S.A.A.; resources, S.A.A. and A.G.; data curation, S.A.A.; writing--original draft preparation, S.A.A.; writing--review and editing, A.M., N.Y. and A.G.; visualization, S.A.A.; supervision, A.M. and N.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The xBD dataset is openly accessible through [[https://xview2.org/](https://xview2.org/)]([https://xview2.org/](https://xview2.org/)), accessed on 15 March 2023, while due to commercial restrictions, the QuickBird images acquired over Bam city are only accessible upon request from the corresponding author. The codes of our methodology are available at [[https://github.com/[PERSON]/BDA_SKUNet](https://github.com/[PERSON]/BDA_SKUNet)]([https://github.com/[PERSON]/BDA_SKUNet](https://github.com/[PERSON]/BDA_SKUNet)).  
The authors declare no conflict of interest.
# Predictive Analytics for Sucker Rod Pump Failures in Kazakhstan Oil Wells Using Machine Learning
## 3 Data
### Data Structure  
The uninterrupted operation of downhole pumping equipment is influenced by various factors such as well operation mode, geological conditions, and wellbore curvature. This study focuses on wells operated by the SRP system, which consists of surface and downhole equipment. The emphasis is on predicting downhole equipment failures, including tubing strings, sucker rods, and sucker rod pumps.  
The SRP well operation is characterized by several parameters, categorized as follows:  
1. Statistical parameters--do not change during well operation;
2. Dynamic parameters--change over time during well operation.  
Table 1 presents the parameter breakdown.  
For model building, necessary data were prepared. Detailed data analytics are crucial for predictive modeling. High-quality data preparation is indicative of prediction model success. This study used data on well operation mode (flow rate, water cut), well curvature (rate of deviation change), failure data (failure date, reason), and pump operating conditions (pump set depth, size).  
The pilot field for this study consists of approximately 1000 artificial lift wells. There were over 1600 failures per year, with an average time between failures of 149 days.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Statistical Parameters** & **Dynamic Parameters** \\ \hline reservoir properties & production data (well regime) \\ well completion data & SRP exploration data \\ well inclinometer & well repair data \\ \hline \hline \end{tabular}
\end{table}
Table 1: SRP well’s data.  
Figure 2: Main equipment’s failure reasons; data were collected in the national company’s “KazMunayGas” in 2023.  
Three datasets were used:  
1. Workover dataset--1 year data of wellbore intervention (monthly well failures), including number of wells, failure date, production method, liquid flow, water cut, type of repair, pump set depth, pump size;
2. Well operating parameters--monthly well operation mode, including number of wells, production method, pump set depth, pump size, pumpdown performance (stroke length, pump rate); actual operating mode;
3. Directional survey--dataset of well hole curvature, including inclination angle, drift direction, rate of deviation.
# Weak Edge Target Segmentation Network Based on Dual Attention Mechanism
## 2 Theoretical Basis
### U-Net Semantic Segmentation Network
In the design of the loss function, U-Net utilizes pixel-wise cross-entropy to measure the difference between the predicted results and the ground truth labels [26]. This loss function effectively penalizes misclassified pixels while encouraging accurate segmentation results. However, in practical medical image segmentation tasks, there is often a significant imbalance in the number of pixels between foreground and background regions. This imbalance may lead the model to bias optimization towards the larger background class, neglecting the relatively smaller foreground regions. To address this issue, U-Net introduces weight coefficients in the loss function, assigning different importance to the contributions of pixels from different classes. This weighted cross-entropy loss function balances the optimization efforts among classes, enhancing the accuracy and balance of segmentation results. In conclusion, the U-Net semantic segmentation network achieves efficient and accurate medical image segmentation through its structured encoder-decoder design, feature fusion mechanism via skip connections, application of data augmentation strategies, and end-to-end training approach. Its innovations not only drive advancements in medical image analysis but also offer valuable insights and inspiration for image segmentation tasks in other fields. Subsequent research has widely applied and enhanced U-Net, leading to a series of semantic segmentation models based on the encoder-decoder structure, such as V-Net [27], U2-Net [28], and others. These developments further extend the application scope and performance boundaries of U-Net, contributing to the continuous evolution of semantic segmentation models.
###### Abstract
These issues highlight the need for further advancements in the field, in the efficient fusion of global and local contextual information to improve model performance in real-time applications. In response to these challenges, this paper proposes a novel DAPONet: A Dual Attention and Partially Overparameterized Network for road damage detection. DAPONet is designed to achieve efficient processing of multi-scale features and enhanced fusion of global and local information. The proposed model significantly improves detection speed and efficiency while maintaining high accuracy, making it suitable for real-time road damage detection tasks in complex scenarios. The main contributions of this study are outlined as follows:  
1. A novel Global Localization and Context Attention (GLCA) mechanism is proposed, enhancing the model's ability to handle complex backgrounds and multi-scale targets through the integration of both local and global attention mechanisms. 2. The proposed Cross-Stage Partial Depthwise Overparameterized Attention module is proposed, which combines partial overparameterized convolution with global and local Context Attention mechanisms to achieve efficient processing of multi-scale features, significantly improving the detection accuracy and computational efficiency of the model. 3. This study propose the Mixed Convolutional Downsampling module, which downsamples and processes feature maps via multiple parallel paths, enhancing both the diversity and efficiency of feature extraction. A real-time detection model, a dual attention and partially overparameterized network, is designed for road damage detection tasks in complex scenes, which significantly improves the model's performance in multi-scale feature extraction and fusion by incorporating the dual attention mechanism and partially overparameterized convolution, as well as parallel downsampling. The model is validated on SVRDD public dataset and MS COCO dataset, which demonstrates the superiority of the proposed model. The paper is organized as follows. Section 2 provides an overview of related work in object detection and road damage detection. Section 3 details the architecture and components of the proposed DAPONet model, including its dual attention mechanism, multi-scale overparameterization, and downsampling techniques. Section 4 outlining the datasets used, describes the experimental environment, and evaluation metrics. Section 5 presents the experimental results and evaluates the performance of DAPONet in relation to existing models. Finally, Section 6 concludes the paper and suggests directions for future research.
## 3 Performance Analysis of the MPS Beamformer
### Angular Scanning Performance of MPS Beamformer  
The previous analysis of the MPS beamformer has been conducted for the case where the mainlobe direction is set at \((0^{\circ},0^{\circ})\) to clearly identify the sidelobes in the radiation pattern. To optimize the array at any other mainlobe directions, we can utilize the same amplitude coefficients obtained at \((0^{\circ},0^{\circ})\) and provide the necessary phase shifts for the new mainlobe direction as in phased arrays. However, the SLL should be at the required level so that the array weights optimization is carried out one time and should not repeated if the mainlobe is changed. According to Equation (10), the resulting overall weighting matrix after executing the algorithm for \(I\) iterations at the broadside mainlobe direction \((0^{\circ},0^{\circ})\) is given by:  
Figure 9: MPS beamformer performance for rectangular planar arrays: (**a**) array structure of \(10\times 20\) elements (**b**) normalized array power gain in dB to achieve \(-45\) dB SLL, (**c**) the rounded-corners rectangular array structure at \(\delta_{T}=0.8\)\(\delta_{MAX}\), and (**d**) normalized array power gain in dB to achieve \(-45\) dB SLL for the array in (**c**).  
\[\mathbf{W_{o}}=\mathbf{S}(0^{\circ},0^{\circ})-\beta\sum_{i=1}^{I}\sum_{p=1}^{p}SUM(At_{i}^ {H}(\theta_{p},\phi_{p})|_{N}\mathbf{S}(\theta_{p},\phi_{p})) \tag{11}\]  
Therefore, the weighting matrix at any mainlobe direction \((\theta_{o},\phi_{o})\) is given by the following phasing equation:  
\[\mathbf{W}(\theta_{o},\phi_{o})=|\mathbf{W_{o}}|\circ\mathbf{S}(\theta_{o},\phi_{o}) \tag{12}\]  
where \(|\mathbf{W_{o}}|\) is the absolute value of the coefficients of \(\mathbf{W_{o}}\).  
Figure 10a displays the amplitude coefficients for an array formed by \(20\times 20\) elements using the first two major sidelobe groups (i.e., \(P=2(M+N-4)\)) at \(\beta=0.1\) to achieve \(-40\) dB SLL. The array weights in this figure exhibits tapered profile as expected for SLL reduction. Providing the proper phasing to this amplitude coefficients as in Equation (12) moves the mainlobe to the desired direction as shown in Figure 10b where the SLL remains at the same required level (\(\approx\)\(-40\) dB). Therefore, the proposed MPS beamformer has consistent and solid performance with the varying mainlobe direction which simplifies the optimization process to be carried out only one time according to the required SLL. In Figure 10b, and for \(\theta_{o}\geq 60^{\circ}\), the pattern has higher radiation levels near the array surface due to the array geometrical projection (reduced array effective area) which is known for all array planar structures, however the other side SLL values (towards the broadside direction) has the same required SLL even at directions near the endfire of the array (i.e., \(\theta_{o}=90^{\circ}\)) which is very important for scanning beam applications such as in radar systems.