esa-sceva/satcom-chunk-collection huggingface dataset:
# Satellite Image Super-Resolution by 2D RRDB and Edge-Enhanced Generative Adversarial Network +
## 2 Related Works
### Image SR  
CNN is widely used in image SR, and the early image SR model was proposed by [PERSON] et al. [15]. They use CNNs to achieve the end-to-end mapping between LR and HR images. Then [PERSON] et al. improved the previous SRCNN and proposed FSRCNN [16], which does not need to enlarge the image size outside the network. By adding a shrinkage layer and an expansion layer on the network, some small layers can be used to replace a large layer at the same time, and FSRCNN has a greater speed increase than SRCNN. Inspired by this work, other researchers proposed to adopt other deep learning architectures, such as RNN, Residual CNNs [12] and GANs [2] to solve single image super-resolution (SISR). Among them, [PERSON] et al. [3] introduced an architecture \"SRResNet\" inspired by ResNet [17], which preserves the batch normalization within the original residual block. This makes their model significantly reduce memory and allow adaptation to several ideas introduced for image deblurring. Similarly, [PERSON] et al. [12] proposed their EDSR (Enhanced Depth Super Resolution) model. This model effectively reduces the memory space by removing the batch normalization (BN) layer in the residual block in SRResNet, and uses this space to expand the size of the model, thereby achieving significant performance improvements.  
Because of the specialty (e.g., the large size of spatial dimension) of satellite images, some SR methods are specifically developed for satellite images. In [18], [PERSON] et al. indicate the characteristics of training data have a large impact on the accuracy of a reconstructed image. In [19], [PERSON] et al. investigate the application of SR techniques to satellite images and the effects on object detection performance. [PERSON] et al. [20] employ a deep segmented residual CNN approach to analyze the SR performance of a single satellite image. [PERSON] et al. [21] report considerable improvements in SR of remote sensing imagery are achieved by using supervised models in a reinforcement learning framework. In [22], [PERSON] et al. claimed using a simple down-sampling approach with a fixed kernel to create training images works fine on synthetic data, but does not perform well on real satellite images. In addition, some examples for recent SR methods based on CNN and Generative Adversarial Network (GAN) are [23; 24], respectively. Recently, [PERSON] et al. [25] introduced a unique loss function and a new image reconstruction method to enable the SR model to be executed on a low-power device for satellite environments.
## 4 Losses Analysis
### Copper Loss  
AC copper loss considering skin and proximity effects can be expressed by [21]:  
\[P_{ac}\ =\ k_{sp}l^{2}R, \tag{2}\]  
where \(k_{sp}\) is the coefficient accounting for skin and proximity effects, \(l\) is the effective value of the winding current, and \(R\) is the winding DC resistance.  
For form winding, skin and proximity effects significantly affect copper loss at high frequencies, as winding resistance increases as working frequency increases due to the skin and proximity effects [22]. In this paper, tooth-wound winding was used due to its winding end being short. For a given current, the skin effect is affected by winding size. Skin depth \(\delta\) can be calculated by  
\[\delta\ =\ \sqrt{1/\pi\sigma_{c}f\mu_{0}\mu_{r}}, \tag{3}\]  
where \(\sigma_{c}\) is the electrical conductivity of the conductor, \(\mu_{0}\) is the permeability of the internal space, and \(\mu_{r}\) is the relative permeability of the conductor. At a frequency of 2600 Hz, the calculated skin depth was 4.04 mm based on Equation (3).  
Generally, when the total cross-sectional area of stator slot and slot fill factor are kept the same, the skin effect is higher when the single wire is thicker. Figure 8 shows \(k_{sp}\) versus the ratio of wire thickness to skin depth at different frequencies. When the wire thickness increased to 0.8 times of skin depth, \(k_{sp}\) quickly increased. Therefore, a wire of 5 mm width and 1.2 mm thickness was adopted for this research, and \(k_{sp}\) was set to be 3.55 to calculate copper loss.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BPSK & Binary Phase Shift Keying \\ CSMA/CA & Carrier Sense Multiple Access/Collision Avoidance \\ DSSS & Direct Sequence Spread Spectrum \\ DUT & Device Under Test \\ ED & Energy Detection \\ FCS & Frame Check Sequence \\ FDMA & Frequency Division Multiple Access \\ FSK & Frequency Shift Keying \\ IEEE & Institute of Electrical and Electronic Engineers \\ ISM & Industrial, Scientific and Medical \\ LoRa & Long Range \\ LP-WAN & Low-Power Wide-Area Network \\ MAC & Medium Access Control \\ MCS & Modulation and Coding Scheme \\ MCU & Micro-Controller Unit \\ OFDM & Orthogonal Frequency Division Multiplexing \\ \end{tabular}
## 3 Results and Discussion
### Environmental Inspections Module  
The monitoring of industrial processes, their emissions and impact on the environment are key elements of regulatory control. Such monitoring may be undertaken by the competent authorities responsible for inspection duties. Industrial process operators may also be required to carry out inspections themselves and report their results to the competent authorities [37]. This self-monitoring by operators cannot replace government supervision, but it provides additional information based on which the competent authorities can judge whether an operator is compliant with the relevant legislation and license conditions [38]. In fact, environmental inspection means carrying out all actions, including site visits, monitoring of emissions and checks of internal reports and follow-up documents, verification of self-monitoring, checking the techniques used and adequacy of the environment management of the installation, undertaken by or on behalf of the competent authority to check and promote the compliance of installations with their permit conditions and, where necessary, to monitor their environmental impact [10].  
Comparable emission data ought to be used when reviewing the compliance of industrial installations with the requirements set out in the environmental authorization provisions [39].  
In the web platform, users can consult the results of self-monitoring and inspections, enter new analytical data and download reports.  
These data could also be made available for research bodies involved in the analyses, modelling and assessment of the dispersion and impact of pollutants on the quality status of environmental matrices. For this purpose, a specific section is dedicated to the sharing of results of the analytical and statistical evaluations.
###### Abstract  
Workflows are largely used to orchestrate complex sets of operations required to handle and process huge amounts of data. Parallel processing is often vital to reduce execution time when complex data-intensive workflows must be run efficiently, and at the same time, in-memory processing can bring important benefits to accelerate execution. However, optimization techniques are necessary to fully exploit in-memory processing, avoiding performance drops due to memory saturation events. This paper proposed a novel solution, called the Intelligent In-memory Workflow Manager (IIWM), for optimizing the in-memory execution of data-intensive workflows on parallel machines. IIWM is based on two complementary strategies: (1) a machine learning strategy for predicting the memory occupancy and execution time of workflow tasks; (2) a scheduling strategy that allocates tasks to a computing node, taking into account the (predicted) memory occupancy and execution time of each task and the memory available on that node. The effectiveness of the machine learning-based predictor and the scheduling strategy were demonstrated experimentally using as a testbed, Spark, a high-performance Big Data processing framework that exploits in-memory computing to speed up the execution of large-scale applications. In particular, two synthetic workflows were prepared for testing the robustness of the IIWM in scenarios characterized by a high level of parallelism and a limited amount of memory reserved for execution. Furthermore, a real data analysis workflow was used as a case study, for better assessing the benefits of the proposed approach. Thanks to high accuracy in predicting resources used at runtime, the IIWM was able to avoid disk writes caused by memory saturation, outperforming a traditional strategy in which only dependencies among tasks are taken into account. Specifically, the IIWM achieved up to a 31% and a 40% reduction of makespan and a performance improvement up to 1.45\(\times\) and 1.66\(\times\) on the synthetic workflows and the real case study, respectively.  
workflow; data-intensive; in-memory; machine learning; Apache Spark; scheduling Article  
Exploiting Machine Learning for Improving In-Memory Execution of Data-Intensive Workflows on Parallel Machines  
1
# The Mechanical Properties of Reinforced Concrete Columns with Longitudinal Pre-Embedded Holes
## 1 Introduction
[23] performed a comparative seismic analysis of beam-column joints strengthened with bonded steel plates and carbon fiber-reinforced polymer (CFRP) sheets using ABAQUS simulations. The results indicated that the externally wrapped prestressed steel strip method significantly improved joint energy dissipation and ductility, establishing it as a preferred reinforcement technique. [PERSON] et al. [24] developed a nomogram-based design tool for beam-column joints. This tool facilitates rapid Eurocode 8-compliant joint verification while optimizing joint dimensions and stirrup configurations, offering a practical approach to align structural and architectural design requirements. Although significant achievements have been made in the study of the mechanical performance of beams and columns with openings, there has been limited research on reinforced concrete columns with longitudinal pre-embedded holes. To systematically investigate the influence mechanism of longitudinal pre-embedded holes on the mechanical performance of reinforced concrete columns, this study employs a finite element numerical simulation approach. The entire loading process of reinforced concrete columns with pre-embedded holes under various parameter conditions is simulated, and key mechanical response indicators such as load-displacement curves, reinforcement stress distribution, and the development of concrete plastic strain are presented. This study focuses on the effects of critical parameters--including the pre-embedded hole diameter, concrete strength grade, stirrup ratio, and slenderness ratio--on the axial compressive load-bearing performance of these columns. It reveals the mechanisms by which these parameters influence the failure mode, load-bearing capacity degradation, and ductility performance of the components. The findings provide a theoretical foundation and design references for the engineering application of reinforced concrete columns with longitudinal pre-embedded holes.
## 3 Methodology  
### Data Envelopment Analysis (DEA)  
DEA can be roughly defined as a non-parametric method of measuring the efficiency of a decision-making unit (DMU) with multiple inputs and/or multiple outputs. DEA is used to measure the relative productivity of a DMU by comparing it with other homogeneous units, transforming the same group of measurable positive inputs into the same types of measurable positive outputs. [PERSON], [PERSON], and [PERSON] [49] introduced the DEA method to address the problem of efficiency measurement for DMUs with multiple inputs and multiple outputs in the absence of market prices.  
They coined the expression 'decision making units' (DMU) to include non-market agencies such as schools, hospitals, and courts, which produce identifiable and measurable outputs from measurable inputs but generally lack market prices for outputs (and often for some inputs as well). Supposing that there are \(N\) firms, each producing \(m\) outputs from \(n\) inputs, firm \(t\) uses the input bundle to produce the output bundle. As noted above, measurement of average productivity requires the aggregation of inputs and outputs. [PERSON] et al. [49] proposed a minimization problem to obtain the efficiency score, assuming a constant returns scale (CRS). This represents the global technical efficiency of a DMU, known as the CCR (Charnes-Cooper-Rhodes) model. They assume that there are \(n\) DMUs to be evaluated. Each DMU consumes varying amounts of \(m\) different inputs to yield \(s\) different outputs. More in particular, DMU \(j\) consumes amount \(x_{ij}\) of input \(i\) and produces amount \(y_{rj}\) of output \(r\). Equation (1) presents the multiplier CCR model in an output-oriented version displayed as a linear programming problem:  
\[\begin{array}{l}\min q=\sum\limits_{j=1}^{m}v_{i}\chi_{io}\\ \min q=\sum\limits_{i=1}^{m}v_{i}\chi_{io}\\ \text{subject to}\\ \sum\limits_{i=1}^{m}v_{i}\chi_{ij}-\sum\limits_{r=1}^{s}\mu_{r}y_{rj}\geq 0 \\ \sum\limits_{r=1}^{s}\mu_{r}y_{rj}=1\\ \mu_{r},v_{i}\geq\varepsilon>0,\end{array} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  
where \(\varepsilon>0\) is an element defined to be a negligible positive real number, and \(\mu,v\) are the weight vectors of output variables. If the constraint \(\sum_{j=1}^{n}\lambda_{j}=1\) holds, then the model is called BCC (Banker-Charnes-Cooper) proposed by [PERSON] et al. [50]. This model incorporates the property of variable returns to scale (VRS).
## 5 Use Cases
### Requesting Source Code to Obtain a Standalone Dashboard  
The services' independence allows the integration of the dashboard generator with other technologies of the educative domain. In this example, part of the Open University (OU) dataset has been used to show a dashboard request [44].  
Two visualizations will be requested, one to display the scores obtained in different assessments by the students and the other to display the range of scores by assessment. To obtain the source code, we need to build an HTTP POST request containing the dashboard layout and each visualization's configuration to generate both the HTML and the JavaScript code (Figure 4).  
Figure 4: HTTP POST request’s payload, which contains the dashboard configuration.  
Figure 3: Schematic view of the data transformation service architecture.  
Once the HTTP request has been sent, the service gets the payload data (i.e., the dashboard configuration) and performs the application engineering process to yield the personalized source code. To do so, the input JSON object is processed by the dashboard generator process, which is in charge of filling the code templates with the specific information handed by the client.  
This process' outcomes will be the source code of the dashboard, which is included in text format inside the API call response to the client. The source code could be used standalone and embedded within other applications by injecting the HTML and dynamically loading the JavaScript code. Figure 5 shows an excerpt of one of the generated JavaScript files.  
Every generated JavaScript file follows the same structure:  
1. Creation of the SVG container;
2. Declaration of the scales;
3. Creation of the visual marks;
4. Addition of each visual mark's channels.  
The obtained source code can be deployed as a standalone web page: Figure 6 displays the rendered dashboard with the configured features at the beginning of the example.  
Figure 5: Excerpt of a generated JavaScript source file.  
Figure 6: Rendered dashboard.
## 5 Ethical Considerations
### Ethical Issues in the Use of Spatial Data  
Spatial data can be utilized in various fields such as urban planning, disaster response, and smart city development. However, its indiscriminate use may lead to privacy violations and legal issues. To ensure ethical data usage, this study exclusively utilized legally provided public data and open geographic information systems (GISs) while explicitly excluding any data containing personally identifiable information (PII) or sensitive location details Table 25.
# LIDAR Point Cloud Registration for Sensing and Reconstruction of Unstructured Terrain
## 4 Experimental Results and Analysis
### Necessity Validation of the Initial Registration  
To verify the validity and necessity of the initial registration, we take three point cloud pairs of the unstructured terrain (Figure 3b) to conduct experiments. Each point cloud pair consists of two \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Point Cloud Pair** & **Algorithm** & **Key Point** & **Iteration** & \
\begin{tabular}{c} **Average** \\ **Registration** \\ **Time/ms** \\ \end{tabular} &
\begin{tabular}{c} **Registration** \\ **Error/m** \\ \end{tabular} \\ \hline \multirow{4}{*}{PCPair1 (17456,17728)} & Our improved ICP & 677,561 & 8 & 632 & 0.004456 \\  & Traditional ICP & 677,561 & 7 & 1226 & 0.009770 \\  & 4 PC improved ICP & 677,561 & 6 & 956 & 0.008124 \\  & IPDA & 677,561 & 8 & 5120 & 0.004221 \\ \hline \multirow{4}{*}{PCPair2 (17920,17856)} & Our improved ICP & 580,576 & 12 & 762 & 0.003982 \\  & Traditional ICP & 580,576 & 15 & 1490 & 0.004877 \\ \cline{1-1}  & 4 PC improved ICP & 580,576 & 13 & 1162 & 0.004543 \\ \cline{1-1}  & IPDA & 580,576 & 12 & 6235 & 0.003542 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Result Comparison of Different Registration Algorithms. Figure 5: Comparison of different registration algorithms for PCPair2. IPDA: Integrated Probabilistic Data Association; PCP: point cloud pair. To fully verify the validity and universality of our method for unstructured terrains, we chose three types of unstructured terrains as the experiment sites, as shown in Figure 7. More specifically, Figure 7a shows an unstructured terrain with small gullies and stones; Figure 7c shows an unstructured terrain with deep pits and stones; and Figure 7e shows an unstructured terrain with big bulges and stones. The experiment results are shown in Figure 7b,d,f. The characteristics of the actual terrain are well expressed by the terrain point cloud being generated, such as ditch, ridge, pit, bulge, and stone. To further verify the quality of registration, we take three points A, B, and C that are easy to distinguish as shown in Figure 7. Firstly, we measure the distance from A to B in the actual terrain and terrain point cloud, denoted as \(d_{AB}\) and \(d^{\prime}_{AB}\) respectively. Then, we measure the distance from A to C in the actual terrain and terrain point cloud, denoted as \(d_{AC}\) and \(d^{\prime}_{AC}\), respectively. Finally, we calculate \(\tau=d_{AB}\cdot d^{\prime}_{AC}/d_{AC}\cdot d^{\prime}_{AB}\).
# Improved YOLOv7 Algorithm for Small Object Detection in Unmanned Aerial Vehicle Image Scenarios
## 6 Conclusions  
UAV images are characterized by a large proportion of small objects and dense object regions. In response to these challenges, this paper proposes an efficient YOLOv7-UAV algorithm to detect small objects in UAV image scenarios. YOLOv7-UAV adds an extra low-level prediction head (P2) to extract the detailed information of small objects, removes a deep-level prediction head (P5) to reduce the effect of multiple down-sampling operations, employs the BiFPN-like weighted cross-level connection structure to improve the fusion performance of multi-scale feature maps, and adopts the SIoU loss function as the regression loss function to improve the convergence efficiency, which improves the algorithm's ability to detect small object features. The final experimental results demonstrate that our proposed YOLOv7-UAV algorithm is highly applicable in UAV image scenarios, achieving a mAP of 45.3% on the VisDrone2019 dataset. Finally, YOLOv7-UAV is quantified and compiled by Vitis-AI and deployed on the FPGA (AXU3 EG) platform to achieve higher energy efficiency, which is improved by 12 times compared to the GPU platform. The proposed YOLOv7-UAV algorithm in this paper is designed for the specific application of UAV image scenarios, but the additional P2 prediction head and the removed P5 prediction head focus on detecting small objects, which can be extended to other small object detection domains in the future.  
Conceptualization, X.L.; methodology, X.L. and Y.W.; software, Y.W.; validation, J.L. and W.D.; investigation, J.L., W.D. and Y.W; resources, X.Z. and Y.H.; writing--original draft preparation, X.L. and Y.W; writing--review and editing. Y.W., X.Z. and Y.H. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China under Grant 62201479, in part by the Natural Science Foundation of Sichuan Province under Grant 2023 NSPSC1388, in part by the National Natural Science Foundation of China under Grant 62101386, in part by the Shanghai Sailing Program under Grant 21 YF1450000, in part by the Fundamental Research Funds for the Central Universities under Grant 22120230311, in part by the Guangdong Provincial Key Laboratory of Future Networks of Intelligence, the Chinese University of Hong Kong, Shenzhen, under Grant 2022B1212010001-OF04, and in part by the Key Laboratory of Medicinal and Edible Plant Resources Development of Sichuan Education Department, the Chengdu University, under Grant 10Y202201.  
Not applicable.
## 3 The Proposed FMEA Model  
In this section, we propose a new hybrid FMEA model, FMEA-ROC-CoCoSo, which not only improves the accuracy and robustness of risk prioritization, but is also characterized by its ease of use, which can facilitate its acceptance in the industry. The implementation of hybrid models, such as the use of fuzzy logic or neural networks, often requires advanced technical knowledge and high computing power, which is an obstacle for many organizations [34]. However, by integrating techniques such as Rank Order Centroid (ROC) and Combined Compromise Solution (CoCoSo), the new model simplifies the assignment of weights and prioritization of failure modes.  
The ROC provides a simple and objective method for calculating weights and removes subjectivity in the weighting of severity, occurrence, and detection criteria. CoCoSo, in turn, allows different trade-off solutions to be combined without the need for complex mathematical operations, providing a clearer and easier-to-understand risk assessment.  
This simplification makes the model more accessible and requires less training and technical infrastructure. Therefore, it has a greater potential to be accepted and implemented by industries that want to improve their risk analysis without investing in overly complex models. The fact that the model is more intuitive and less dependent on experts in specific areas of data analysis or advanced mathematics also increases its feasibility and makes it a practical and efficient option for companies looking to modernize their risk management.