esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Deep Learning-Based Load Forecasting Model for Logical Range
## 2 Materials and Methods
### Proposed Load Forecasting Methodology
#### 2.2.1 Spatial Feature Extraction
Compute the global statistics \(z_{i,j}\) for each channel feature \(\mathbf{p}_{i,j}\) in \(\mathbf{p}_{i}\), where \(j=1,2,\ldots,c\), using Equation (7): \[z_{i,j}=\frac{1}{h\times l}\sum_{a=1}^{h}\sum_{b=1}^{l}p_{i,j}(a,b).\] (7) The global pooling operation aggregates the values of \(\mathbf{p}_{i,j}\) across its spatial dimensions to derive \(z_{i,j}\). 3. Compute the weight \(\beta_{i,j}\) for each channel feature \(\mathbf{p}_{i,j}\) in \(\mathbf{p}_{i}\) to obtain the weight vector \(\mathbf{\beta}_{i}\), where \(z_{i}=[z_{i,1},z_{i,2},\ldots,z_{i,c}]\), using Equation (8): \[\beta_{i}=\sigma(\mathbf{\omega}_{2}\delta(\mathbf{\omega}_{1}z_{i})),\] (8) where \(\sigma\) denotes the sigmoid function, \(\delta\) represents the ReLU function, and \(\mathbf{\omega}_{1}\) and \(\mathbf{\omega}_{2}\) are full layer matrices. 4. Compute the weighted feature map \(\mathbf{m}_{i}\) using Equation (9): \[\mathbf{m}_{i}=\mathbf{p}_{i}\cdot\mathbf{\beta}_{i}.\] (9)
5. Apply convolutional and transformation operations on \(\mathbf{m}_{i}\) to obtain the spatial feature output vector \(\mathbf{v}_{i}\) for \(\mathbf{x}_{i}^{\mathbf{v}}\).
# Antibiotic Resistance in the Farming Environment
## 1 Introduction  
Antibiotic resistance in bacteria is currently one of the key challenges in ensuring health security. Recent years have seen both the phenomenon of increasing microbial resistance to commonly used antibiotics and the emergence of resistance to new-generation antibiotics, including those considered last resort pharmaceuticals [1, 2]. This problem affects virtually every new group of antibiotics [3].  
Agricultural ecosystems are an important area of the spread of antibiotic resistance [4]. Discharges from agricultural practices are now seen as one of the major sources of antibiotic pollution in the environment [5]. Up to 90% of antibiotics used in livestock production ultimately end up in various forms in the environment, resulting in intensified selection for resistant microorganisms [4, 6, 7]. The specificity of the antibiotics used is reflected in the profile of antibiotic resistance genes detected in the environment [8]. The development of molecular methods, including functional metagenomics and pyrosequencing, has allowed more efficient detection and identification of antibiotic resistance genes also in agricultural ecosystems [9].  
The use of antibiotics in animal husbandry and livestock production has been practiced for a long time. As soon as the opportunity arose, breeders took advantage of measures that allowed them not only to avoid losses caused by massive disease in their animals or to intensify livestock production with a relatively small increase in costs but also to improve the welfare of their herds through widespread prevention. Nevertheless, in recent years, there has been an increase in public awareness regarding rational practices to limit the unjustified and excessive use of antibiotics, among other things, thanks to initiatives such as The Alliance for the Responsible Use of Medicines in Agriculture (RUMA) and the Food Industry Initiative on Antimicrobials (UK), which bring together variousstakeholders related to agricultural production, food production, environmental issues and legislation in this area, aim to develop standards for the responsible use of antibiotics in animal husbandry, including good husbandry practices in this aspect [10; 11]. In individual countries also at the level of the European Union, regulations are being introduced to reduce the use of antibiotics in various areas of farming activity [12]. However, there are still regions of the world where the use of antibiotics in animal production is poorly controlled, and awareness of the phenomenon of antibiotic resistance and the consequences of antibiotic overuse is still too low [13; 14]. It should be pointed out that antibiotic resistance is becoming an increasingly dynamic phenomenon; therefore, it is necessary to constantly update the state of knowledge in this area.  
This article presents the current state of research in the main aspects related to the issue of antibiotic resistance in the farming environment.
## 2 Methods Used for Integrated Monitoring and Management of Diet-Related Diseases
### Methods for the Development of the Tailored RHMS  
The shared methods employed in crafting our three tailored RHMS (the RO-SmartAgeing System, NeuroPredict Platform, and HepatoConect System) are presented as follows:  
* _Research-driven development_: the inception of our RHMS involves building upon a strong scientific foundation, aligning closely with research findings to create solutions grounded in evidence and innovation.
* _Common framework and elements_: RHMS development initiates by establishing an integrated insights framework, ensuring coherence and seamless integration of medical and technological insights throughout the design and development stages.
* _Multidisciplinary collaboration_: In the early stages, a collaborative effort brings together healthcare and IT experts, including the authors of this paper. This collaborative approach lays the groundwork for integrating medical insights with technological innovation.
* _Data-driven architecture_: Throughout development, a unified method employs cloud platforms for data storage and aggregation. This architecture guarantees scalability, flexibility, and secure access, maintaining consistency across RHMS.
* _Smart monitoring environment_: Emphasising the design process, we prioritize creating a smart monitoring environment tailored to individual patients and disease management. This comprehensive approach integrates wearables, ambient sensors, and cloud-based platforms for health monitoring.
* _Wearable technology integration_: implementation includes seamlessly integrating wearable noninvasive sensors for personalized and adaptive monitoring tailored to individual health needs.
* _AI and predictive modelling_: we incorporate advanced AI techniques for predictive modelling, ensuring a proactive and personalized healthcare approach across diverse health conditions.
* _Data analysis, storage, and security_: Real-time analysis and processing of collected data are implemented. Cloud-based storage acts as a central repository for organized and accessible health data, supporting advanced analytics for personalized insights, with robust security measures ensuring confidentiality, security, and integrity.
* _Ensuring remote healthcare access_: this involves facilitating efficient remote healthcare access and empowering healthcare professionals to monitor and engage with patients, especially in managing chronic conditions.
* _User interface design_: During development and implementation, a unified approach prioritizes an intuitive and accessible user interface. This ensures ease of use for patients, caregivers, and healthcare professionals, enhancing the overall user experience.  
In the context of our paper, the smart monitoring environment and data accuracy and evaluation are highlighted and presented considering the development of capabilities of the three tailored RHMS to be a novel evaluation method for diet-related disease management.
# Hardware Acceleration of Satellite Remote Sensing Image Object Detection Based on Channel Pruning
## 1 Introduction
The proposed deployment scheme addresses the challenges of high power consumption and real-time performance in hardware deployment platforms for object detection, enabling efficient implementation on resource-constrained hardware. * Superiority in Energy Efficiency and Real-Time Processing: Our lightweight algorithm model outperforms CPUs and GPUs in terms of both energy efficiency and real-time performance. This makes our proposed solution highly suitable for spaceborne remote sensing image processing systems, where the efficient utilization of resources is crucial. The remaining sections of this paper are structured as follows: Section 2 provides an overview of related work, including common lightweight network model structures, compression techniques for network models, and FPGA deployment of CNN models. Section 3 presents the experimental design for this study. It begins by introducing image enhancement techniques, followed by the detailed description of the improved YOLOv4-MobileNetv3 network model structure, then introduces the related experimental work of sparse training and channel pruning for YOLOv4-MobileNetv3. Finally, it covers the process of utilizing Vitis AI for quantizing the pruned network model and compiling it for deployment on FPGA platforms. Section 4 outlines the experimental setup and analyzes the results. It evaluates and compares the performance of the improved network model against the mainstream network model. Additionally, it analyzes and compares the performance of the network model deployed on different hardware platforms. Lastly, Section 5 provides a summary of the entire paper. It discusses the key findings and future research directions in this field.
# Adaptive Thresholding of CNN Features for Maize Leaf Disease Classification and Severity Estimation
## 3 Materials and Methods
### Proposed Methodology
#### 3.2.4 Visualizing the Class Activation Map (CAM)  
Class activation mapping (CAM) is a technique for identifying the discriminative regions used by a particular class [54]. GradCam is a modification of CAM that seeks to address the limitations of CAM, particularly (i), the need for CAM to have a Global Average Pooling layer in the architecture, and (ii) the drawback of visualizing only the heatmap of the final layer. We need to compute the gradient of \(y^{c}\) with respect to the feature maps \(A\) of the last convolutional layer, i.e., \(\frac{\delta y^{c}}{\delta A_{ij}^{(k)}}\). These gradients flowing back are global-average-pooled to obtain weights \(w_{k}^{c}\), as shown in Equation 9.  
\[w_{k}^{c}=\frac{1}{Z}\sum i\sum j\frac{\delta y^{(c)}}{\delta A_{ij}^{(k)}} \tag{9}\]  
where \(w_{k}^{c}\) captures the importance of feature map \(K\) for target class \(c\).  
The GradCam heatmap in Figure 7, is a weighted combination of feature maps with a ReLu.  
\[L_{GradCam}^{c}=ReLu\left(\sum_{k=1}^{K}w_{K}^{c}A^{K}\right) \tag{10}\]  
Figure 7: GradCam visualization. The last Conv layer can be thought of as the features of a classification model: \(y^{c}=f(A^{1},\dots,A^{512})\).
## 4 Discussion
### Future Research Directions  
In future work, it would be beneficial to explore model optimization algorithms and feature selection techniques that could not be applied in this study. Adapting the performance of algorithms through other state-of-the-art meta-heuristic algorithms [37] and feature selection methods [38] could provide further insight into their effectiveness. Moreover, investigating the impact of different parameter settings and incorporating adaptive mechanisms could potentially enhance the robustness and efficiency of the proposed model in this study.  
The results, significance, and limitations of this study are discussed above. This study proposed a new approach of angle estimation by CNN using lumbar spine X-ray images pseudo-generated from CT images and suggested its usefulness. Future work should take into account the limitations of this study while conducting a larger-scale, multifaceted validation.
## 4 Numerical Results  
### Numerical Demonstration of C-Property  
The first verification of our method was in simulating the lake at rest or C-property (a scheme that is exact in the stationary case is said to satisfy the C-property). This property was introduced by [29] to check the balance between convective flow and flow due to bed variations. This means that the scheme is exact when applied to the stationary case \(h+z=constant\). Numerical schemes satisfying this property are called balanced schemes. We model the bed of the lake as a channel of length 10 m, described by the following function:  
\[z(0,x)=5e^{-\frac{2}{5}(x-5)^{2}} \tag{23}\]  
We compute the numerical solution for 200 points within 1 s under the following initial conditions:  
\[h(0,x)=10-z(x)\ \ ;\ \ q(0,x)=0 \tag{24}\]In Figure 1, we present the numerical results obtained in terms of water level \(h\) and free surface \(h+z\), as well as the variation in error \(|10-(h+z)|\) along the free surface. The results show that the maximum error is always lower than \(10^{-5}\), satisfying the C-property.
###### Abstract  
Studies of polyphenols in various natural products have shown their variability and often low amounts. Thus, we aimed to investigate the bioaccessibility of the main polyphenolic groups extracted from two poplar types of propolis collected in continental Croatia. The propolis complex, in the form of water-soluble powder of propolis extract with the carrier 2-hydroxpropyl-\(\beta\)-cyclodextrinetrin (HP-\(\beta\)-CD), was obtained by applying ultrasonic-assisted extraction followed by lyophilization. The in vitro gastrointestinal digestion was conducted on the propolis complex extracts and a mixture of standards (pinoemphrin, galangi, chrysin, caffeic acid, and caffeic acidphenylethyl) ster. Fractions from the oral, gastric, and intestinal phase samples were analyzed spectrophotometrically by standard methods used to determine the content of total phenols (TPs), flavonoids,/flavones (TFFs), and flavanones/dihydroflavonols (TFDs). The small-intestinal phase was performed by two models: centrifugation and dialysis. The results of propolis-HP-\(\beta\)-CD extracts demonstrated high polyphenolic bioaccessibility during all three phases of digestion. The bioaccessibility of TPs, TFFs, and TFDs after the intestinal phase with centrifugation was from 0.02 to 8.86 times higher than in the dialysis phase, stressing that it is a better model than dialysis. Our findings show that the inclusion complex of propolis with HP-\(\beta\)-CD enables the good solubility of polyphenol groups.  
Keywords:polar propolis; phenolics; flavones; flavonols; flavanones; cyclic oligosaccharidesacharide; UV/Vis spectrophotometry; gastostinestinal digestion; bioaccessibility +
Footnote †: journal: Applied Sciences
_Article_  
A Robust Maximum Power Point Tracking Control  
Method for a PEM Fuel Cell Power System  
[PERSON] 1,2,*(), [PERSON] 1() and [PERSON] 2  
1 Departament of Automatic Control and System Engineering, Engineering School, University of the Basque Country UPV/EHU, Nieves Cano 12, 1006 Vitoria, Spain; [EMAIL_ADDRESS]  
2 Research Unit of Photovoltaic, Wind and Geothermal Systems SPEG/UR11 ES82, National Engineering School of Gabes, University of Gabes, Omar Ibn-Elkhattab, Zrig, 6029 Gabes, Tunisia; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +34-617-916-276 or +216-54-104-924  
Received: 2 November 2018; Accepted: 29 November 2018; Published: 1 December 2018  
Taking into account the limited capability of proton exchange membrane fuel cells (PEMFCs) to produce energy, it is mandatory to provide solutions, in which an efficient power produced by PEMFCs can be attained. The maximum power point tracker (MPPT) plays a considerable role in the performance improvement of the PEMFCs. Conventional MPPT algorithms showed good performances due to their simplicity and easy implementation. However, oscillations around the maximum power point and inefficiency in the case of rapid change in operating conditions are their main drawbacks. To this end, a new MPPT scheme based on a current reference estimator is presented. The main goal of this work is to keep the PEMFCs functioning at an efficient power point. This goal is achieved using the backstepping technique, which drives the DC-DC boost converter inserted between the PEMFC and the load. The stability of the proposed algorithm is demonstrated by means of Lyapunov analysis. To verify the ability of the proposed method, an extensive simulation test is executed in a Matlab-SimulinkTM environment. Compared with the well-known proportional-integral (PI) controller, results indicate that the proposed backstepping technique offers rapid and adequate converging to the operating power point.  
PEM fuel cells (PEMFCs); DC-DC boost converter; MPPT; backstepping technique
# Enhanced Bug Priority Prediction via Priority-Sensitive Long Short-Term Memory-Attention Mechanism
## 3 Our Approach  
In this study, we propose a method to predict the priority of bugs by integrating priority-based feature selection with an LSTM-Attention mechanism [7]. This approach aims to improve the accuracy and efficiency of bug prioritization. An overview of the process is illustrated in Figure 2.  
* Preprocessing: Bug reports from the bug repository are preprocessed to ensure consistency and remove irrelevant or noisy data, following established preprocessing techniques [12]. This step standardizes the input data for the subsequent stages of the workflow.
* Feature Extraction: Features relevant to each bug report, such as the product and its components, are extracted from the preprocessed data. This extraction process is guided by the priority of the bug, ensuring that features most indicative of the priority level are selected.
* Priority-Based Feature Selection: The extracted features undergo a priority-based selection process [8], where attributes particularly relevant to distinguishing between priority levels are retained. This step ensures that the model focuses on the most informative features for priority prediction.
* LSTM and Attention Mechanism: The selected features are input into a Long Short-Term Memory (LSTM) network, which captures temporal and sequential patterns in the data. The output of the LSTM network is then passed through an Attention mechanism, which identifies and emphasizes the most critical features for predicting the priority level of the bug.  
Figure 2: Overview of our approach.  
By combining LSTM's capability to analyze sequential dependencies with the Attention mechanism's ability to focus on relevant features, this approach enhances the accuracy of bug priority predictions. This methodology not only reduces the reliance on manual prioritization but also provides a scalable and consistent solution for managing bug reports in large-scale software projects.
## 4 Confirmation of Conventional Parameters in the DIC for an Optimized Measurement
### OPFPM Mode (Advanced Function in DIC)  
The calibration work presented above can be viewed as a conventional process for a standard DIC test. In what follows, we are going to show an advanced treatment for the revision of discontinuity error, which is realized via the OPFPM algorithm as introduced in the former section.  
As depicted in Figure 12, a crack in the target image generates the analysis error when the DIC is conducted through a conventional method. In this case, the subset radius is set as 10 pixels while the average opening value of cracks is measured as 4 pixels. Given the substantial size of the discontinuity, the extra color emanating from the crack could potentially lead to miscomputation in correlation analysis if it is included in the calculations of adjacent subsets. It is important to note that four subsets along the crack boundary encountered computational errors, resulting in an error rate of 21.1% among the 19 subsets that were distributed along the crack. As for case (c), the OPFPM scenario effectively mitigates the impact of cracks, leading to a refined calculation of the cracked region while leaving the rest of the area unchanged, aligning with the conventional solution. Last to be highlighted, due to the additional four blocks set for each subset, OPFPM mode takes at least five times the work to realize an optimized result. Hence, it should be activated thoughtfully based on specific needs.  
Two superiorities are indicated for analysis with the OPFPM. The first one is the revision of the measurement error arising from discontinuities (i.e., cracks) in samples. Another advantage lies in the time cost when it comes to duplicated trial tests in DIC. We know that a trial test on DIC parameters (i.e., subset radius) is essential for a better revision of discontinuity influence in conventional DIC. The OPFPM helps to avoid parametric studies on error revision and saves time in analysis.  
Figure 11: Comparison of volume strain diagrams varied with four selected mesh densities. Mesh spacing in four cases is set at (**a**) 20 pixels (**b**) 10 pixels (**c**) 5 pixels and (**d**) 2 pixels.
# Recent Web Platforms for Multi-Omics Integration Unlocking Biological Complexity
## 3 Discussion
### Case Studies in Multi-Omics Platforms
#### 3.1.3 Emerging Applications  
Beyond established platforms, newer tools such as Argonaut and Majorbio Cloud have supported interdisciplinary research. Argonaut's collaborative features enabled the integration of lipidomics and proteomics to study metabolic disorders, while Majorbio Cloud facilitated the identification of microbial biomarkers for gut health in a metagenomic study of inflammatory bowel disease (IBD).
# Analysis of BDS-3 Onboard Clocks Based on GFZ Precise Clock Products
## 3 Analysis of Long-Term Clock Bias, Frequency, Drift Rate, and Noise
### Fitting Precision Analysis of Clock Offset  
Figures 7 and 8 show the residual series of BDS-2 and BDS-3 onboard clocks by fitting and removing a daily second-order polynomial. The fitting residuals of BDS-2 are stable within the range of \(-5\)-\(5\) ns, while those of BDS-3 are stable within the range of \(-2\)-\(2\) ns. The fitting residuals of BDS-3 onboard clocks are overall smaller than those of BDS-2, regardless of clock type. Moreover, it can be seen from Figure 8 that the fitting residuals of clocks on BDS-3 IGSO (C38, C39, C40) and GEO (C59, C60) satellites are greater than those of other BDS-3 satellite onboard clocks subjected to the lower accuracies of orbit estimation of the IGSO and GEO satellites. Note that the residuals of C01, C03, and C04 from DOY 60 to 80, 2021 and from DOY 250 to 300, 2021 are much larger than those of other days.  
Figure 9 summarizes the RMS of the daily residuals of BDS-2 and BDS-3 clocks according to the types of orbits and clocks. In terms of BDS-2 onboard clocks, the fitting residual RMS values of the MEO, IGSO, and GEO satellites are 0.33, 0.53, and 0.58 ns, respectively. It is concluded that the daily model fitting residual accuracies of IGSO and GEO are magnitude comparable, while the MEO satellite is better than the other two types. As for the BDS-3 satellite onboard clocks, the RMS value is between 0.15 and 0.46 ns, which is smaller than for the clocks carried on the corresponding types of BDS-2 satellites. It can also be found that the residual of PHMs on BDS-3 MEO satellites is about 0.15 ns, which is slightly better than the residual of BDS-3 Rb clocks. However, the fitting residuals of the PHMs carried by GEO and IGSO satellites increase to 0.46 and 0.28 ns, respectively, which is probably a result of the lower accuracies of their clock products.