esa-sceva/satcom-chunk-collection huggingface dataset:
# Systematic Literature Review of Predictive Analysis Tools in Higher Education +
## 3 Results  
This section covers the most relevant aspects of the selected studies, comparing them with each other: type of application developed, prediction goal, data used as input, algorithms used for analysis, and scenarios where they were applied. Appendix A contains a short summary for each paper, organized as a table.  
First, the selected papers were classified depending on whether they present a _predictor_ or an _early warning system_. For the sake of this review, a _predictor_ in an educational context is defined as an application that, given a specific set of input data, aims to anticipate the outcome of a course or degree, normally in terms of either grades or a passing/failure classification. An _early warning system_ performs the same tasks as a predictor, but, on top of that, it reports its findings to a teacher and/or students at an early enough stage so that measures can be taken to avoid or mitigate potentially negative outcomes. This means that EWS often have tighter timing requirements than predictors, as they need to perform analyses early in a course. Additionally, EWS must present analysis results in such a way that teachers and/or students are able to easily comprehend them. For this reason, EWS make use of tools for visual representation, such as dashboards, in order to deliver information to the end user.  
Figure 1: PRISMA flow diagram summarizing the results of the search and selection processes.  
Among the 26 selected documents, there was an even split regarding their classification as predictors or EWS: 13 studies of each type. Table A1 in Appendix A includes the category that was assigned to each one of the articles.  
To make establishing comparisons easier, predictors and EWS are described in their own subsections up next.
# Review on Quality Control Methods in Metal Additive Manufacturing
## 4 Experimental Quality Monitoring Methods in Metal AM and Welding
### Discussion on Experimental Quality Monitoring Methods in Metal AM and Welding  
Table 2 summarizes the classification of the experimental quality monitoring methods of metal AM and welding. Various data types have been used, such as images, plasma spectra, and acoustic signals for metal AM monitoring. Throughout the review, note  
that image data are commonly used for experimental quality monitoring in metal AM. Because the metal AM process involves the gradual addition of thin metal layers during manufacturing, the images of each layer or cladding track geometry provide intuitive information for quality monitoring. By collecting and analyzing images from the process, anomalies, such as melt pool, plume, spatter, and keyhole, can be identified, and quality-related features can be extracted from the anomalies. Plasma spectra are commonly used to quantify the elemental compositions during the metal AM process and sometimes to identify the cladding track formation in the DED process [86]. Acoustic signals have also been used for detecting anomalies, however, few cases have been reported in metal AM processes. In welding, similar to metal AM, images, plasma spectra, and acoustic signals have been introduced for experimental quality monitoring. Anomalies such as melt pool, keyhole, and distortion were identified through the images. Plasma spectra were also used to quantify the elemental composition of the welded area and identify the keyhole occurrences. Unlike metal AM, it was noted that acoustic signals were actively utilized for monitoring the welding process. By utilizing acoustic signals for the welding process, researchers can identify anomalies, defects in the welded area, and process temperatures. Several experimental quality monitoring methods for welding have the potential to be introduced to metal AM. For example, [PERSON] and [PERSON] [83] examined the possibility of using acoustic signals for both PBF and DED defect monitoring. The authors found that other types of metal manufacturing, such as machining and welding, have used acoustic signals for defect monitoring. Based on the review of these existing cases, the authors recommended that applying acoustic techniques for the real-time and nondestructive monitoring of metal AM can show promising results. It was found that several quality monitoring methods in welding can be directly applied to the DED process with some adjustment [3; 52; 57; 77; 79]. For example, [PERSON] et al. [3] focused on the possibility of using the plasma spectrum for real-time monitoring in the DED process. The applicability of using the plasma spectrum to quantify the elemental composition of metal alloys has already been demonstrated in welding [2]. To apply real-time spectrum monitoring to the DED process, the authors concentrated mainly on developing a compact coaxial spectrum sensor that can be attached to the nozzle. The results showed that using a spectrum sensor is also feasible for identifying the elemental compositions of DED products in real time.
## 1 Introduction
This was usually implemented via dedicated multiprotocol label switching (MPLS) links [27; 28; 29; 30], as shown in Figure 1.  
Figure 1: Interconnectivity in enterprise network using MLPS. However, the current reality of network demands in terms of security has rendered traditional network connectivity unsuitable for the fast-paced needs of modern organizations. Many applications described earlier are currently being combined as a form of cloud migration to address the new security challenges of businesses and organizations [30; 31]. Therefore, this paper seeks to provide a comprehensive survey of various state-of-the-art technologies that have contributed to the evolutionary trends in enterprise networking. To the best of our knowledge, there is no existing literature that provides a detailed review of enterprise network evolution in the context of challenges, solutions, and new trends as presented in this paper. Furthermore, it is essential to present an updated overview of enterprise networking, considering its importance in supporting businesses to run highly secure and reliable networking systems. Our specific contributions in this paper are summarized as follows:  
1. Summary of traditional and state-of-the-art enterprise networking technologies and the impact on organizational transformation. 2. Discussion of the challenges and opportunities of enterprise networks, with special focus on security challenges and their limitations towards achieving seamless and reliable network connectivity. 3. Presentation of new trends and potential future research directions in the deployment of enterprise networks for next-generation business solutions. The rest of the paper is organized as follows. Section 2 introduces the main composition of enterprise networking architecture, which consists of access, distribution, and core layers, in addition to the corresponding sub-structures. Section 3 presents the benefits of an optimized enterprise networking system to herald the significance of functionalities discussed in the subsequent sections. Section 4 discusses various network and security protocols that are aggregated to support proper functioning of enterprise networks. Major state-of-the-art technological innovations in enterprise networking are discussed as new trends and focus for future research directions in Section 5. Finally, the paper is concluded in Section 6. An overview of the organization and structure of the paper is shown in Figure 2.  
Figure 2: Organization and Structure of the paper.
## 2 Fundamental of Phase Space Reconstruction and Deep Neural Network
### Modelling Steps of Prediction Model  
For a series of net load time series \(\mathbf{p}\ =\ \{p_{1},p_{2}\cdots p_{N}\}\) considering the PV power generation, the modelling, and forecasting steps of ultrashort-term forecasting model based on phase space reconstruction and DNN are as follows:  
* Step 1: The load time series is linearly normalized to facilitate the training of DNN. The maximum and minimum values of the data are saved for the reverse normalization of the load forecasting value to restore the actual value.
* Step 2: The C-C method is used to process the load time series, and the optimal embedded dimension \(m_{opt}\) and optimal delay \(t_{opt}\) of the time series are obtained.  
* Step 3: The load time series are reconstructed according to the embedding dimension \(m\) and delay \(t\) obtained in Step 2. The phase space matrix of the reconstructed load time series is as follows. In Formula (15), \(M\ =\ N-(m-1)t\). \[\left[\begin{array}{c}\mathbf{p}_{1}\\ \mathbf{p}_{2}\\ \vdots\\ \mathbf{p}_{M}\end{array}\right]\ =\ \left[\begin{array}{cccc}p_{1}&p_{1+t}&\cdots&p_{1+(m-1)t}\\ p_{2}&p_{2+t}&\cdots&p_{2+(m-1)t}\\ \vdots&\vdots&\cdots&\vdots\\ p_{M}&p_{M+t}&\cdots&p_{N}\end{array}\right].\] (15)
* Step 4: The p neural network is constructed, and the phase space matrix of load time series reconstructed in Step 3 is used as the training set to train the DNN. The trained DNN is used to predict the load value immediately after training.
* Step 5: Using the maximum and minimum values stored in Step 1, the load prediction values returned by the DNN are inverse-normalized to obtain the actual load prediction values.
* The model workflow chart is shown in Figure 2.
## 4 Enabling Technologies for FSO Communication
### Mode Diversity Reception
The second scheme achieves few-mode KK detection by employing a few-mode local oscillator and a single PD, thereby simplifying the MDM system to an extreme level. Experimental results indicate that an FSO communication system utilizing six-mode fiber demonstrates an average received power gain of approximately 6 dB over a system based on SMF under moderate-to-strong turbulence conditions. The two proposed MDRs exhibit similar performance, with an improvement of 5 dB to 6 dB compared to SMF-based heterodyne detection in the same turbulence conditions. Moreover, the simplified receiver schemes reduce both the volume and cost of the optical receiver, offering practical benefits for FSO communication systems.
# NOMA-Based VLC Systems: A Comprehensive Review
## 3 Integration of NOMA-Based VLC with Emerging Technologies
### NOMA-Based VLC System with IRS  
The idea of \"intelligent reflecting surfaces\" (IRSs) has recently become a hot topic in the wireless communication industry. This is because IRS offers a spectrum-, power-, and cost-efficient way for wireless networks to evolve over the long term. An IRS is made up of a number of reflecting elements (REs) that may be intentionally created to alter the way they react to incident light rays. Based on this, it is feasible to efficiently manage light signal propagation to obtain desired performance benefits. The researchers [106] give a full analysis of the pros and cons of using IRS technology in VLC and LiFi systems. Ongoing studies have concentrated on evaluating and improving VLC performance in IRS-enabled systems. For instance, [107] explored IRS-enabled indoor VLC systems, whereas [108] addressed the optimization of the IRS reflection coefficients with the goal of sum-rate maximization. The authors of [109] describe a framework for an IRS-assisted NOMA-VLC system with the goal of improving connection dependability. The authors look into how adding IRSs to NOMA-based VLC systems could improve their performance. In traditional NOMA-aided VLC, the decoding order of users and, by extension, power allocation may be determined by the LoS channel gain. However, in IRS-aided systems, this is not always the case. As a result, tweaking the IRS allows you to gain control over the apparent overall channel at the receiver. With the goal of improving the BER, the authors present a structure for the coordinated design of the IRS reflection coefficients, NOMA decoding order, and power allocation. In addition, they prove that this multi-dimensional optimization issue is NP-hard and present an adaptive-restart genetic algorithm (GA) to solve it efficiently. The compelling integration of IRS and NOMA has drawn significant research attention in an effort to exploit the new DoFs empowered to NOMA through IRSs [110].  
In another recent work [111], the authors provide a PLS method for NOMA-VLC systems with IRS support. PLS is shorthand for \"physical layer security,\" which describes methods that use the inherently unbreakable nature of the optical channel to protect data transmission from snoopers. They propose a challenge to maximize the secrecy capacity of a trusted user by preserving minimal rate limitations for the untrusted user, under the premise that users in the network are given a trust score, i.e., based on their recent behavior. To accomplish this, the optimal PA of NOMA and IRS setup are collectively optimized according to the available system characteristics, user locations, and desired rates. To achieve computational efficiency, the authors offer a novel PLS approach and an alternating optimization technique that employs the adaptive-restart genetic algorithm (GA). IRS-aided NOMA-VLC system for two users is shown in Figure 7. The system is based on a trusted user and an untrusted user. The users can get data from LoS and reflected path from IRS.
# A Critical Review of Flood Risk Management and the Selection of Suitable Measures
## 2 Flood Management Practices
### Worldwide Practices  
In the USA, the Federal Emergency Management Agency (FEMA) identifies flood hazard areas, maps flood hazard areas, sets flood insurance rates, covers the risk, establishes design requirements for floodplain development, and provides funding for mitigation projects [7]. The 1/100-year flood is the de facto flood risk management norm in the USA. Local governments prohibit construction in a floodway of a 1/100-year flood zone. In addition, the ground floor of new or significantly renovated buildings in a 1/100-year flood zone must be elevated if a levee is not provided. Similarly, for both river and sea flooding, flood maps are developed in the U.K. \"River or sea flooding was mapped for 1 percent and 0.5 percent probability floods, respectively, not considering defenses because if an extreme event flood occurs, defenses can be overtopped or fail\" [8]. The Netherlands has a long history of flood management. At the moment, about 55% of the Netherlands' total surface area is vulnerable to flooding, and about 25% is below the mean sea level [9]. The country follows different standards, including the highest safety standard (1/10,000-year flood) [10]. At the same time, Germany's Parliament adopted the \"Flood Control Act\" in July 2004 as a consequence of severe flood damage in August 2002; however, no formal and uniform nomenclature or accepted flood management practices are available [11]. Flood safety standards along the coast are based on a 100-year flood return period. Flood protection safety standards along the main courses of the river differ greatly, and range from 1/30 years to 1/1000 years along the upper Rhine [12]. Approximately one-fourth of the total French population is exposed to floods [13]. In France, only one return period is calculated for flood hazard [10]. The Federal Flood Commission (FFC) is responsible for all flood-related issues in Pakistan [14]. A 1/100-year flood is a widely accepted design standard of flood protection measures. However, flood management is dominated by political influence.
# Image-Based Structural Health Monitoring: A Systematic Review
## 4 Discussion
### Purpose and Applications of Image-Based Structural Health Monitoring
#### 4.1.1 To Identify and Discover  
Founded from the study of [6], CV-based methods were developed to go beyond the limits of the human eye to perform assessment and to detect structural impairment in pictures. However, these techniques can only detect certain forms of damage in concrete or steel. This study proposes the application of R-CNN may be used for technological vision tasking or object detection [6]. According to the methodology of their study, multiple types of damage may be detected and localized using the Faster R-CNN method for the quasi-real-time processing of pictures and videos. Another fascinating use of image-based SHM synthesized from included literature is DIC. The finding of [47] on SHM-based damage detection, which is focused on global vibration response, has proven helpful in expressing the existence of the damage as well as occasional coarse-grained location information, but it is severely restricted in reconstructing the 3D geometry of non-external damage. They proposed rebuilding the interior damage using DIC's full-field response data in a topology optimization framework. The optimization aims to reduce the differences between the actual full-field response obtained experimentally with DIC and the numerically estimated response using the model. Using steel coupon specimens with intentionally manufactured faults, the structure was tested in a variety of simulated and real-world studies. On simulated and real-world tests, the suggested approach could detect the location, size, and shape of the damage, with average F1 scores of 82.8% and 69.6%, respectively. This work has effectively recovered exact internal damage information that would otherwise be prohibitively expensive and difficult to get using innovative technology, and it may thus be used as a viable subsurface damage detection tool.  
CV-based structural dynamics approaches have evolved and proven considerable cost, speed, and spatial resolution improvements. A procedure for this application, as illustrated by [48], utilizes motion magnification that allows the magnification of small displacements  
Figure 2: Purpose and applications of image-based SHM.  
of large structures [49]. It is vital to highlight that motion magnification is a technique that reveals movements discovered from video records that are not discernible to the human eye, allowing for more effective frequency domain analysis [49].  
A region of interest must be defined before video recording and motion magnification [48]. As the magnification is carried out, additional noises are expected that results in the inaccuracy of findings. It can be eliminated and processed in adherence to phase-based video motion processing techniques presented by [50]. The application of motion magnification includes protection of cultural heritages [51], ancient constructions [49], identifying the dynamic range of motion, and modal frequencies for large infrastructures [52], which is a non-invasive methodology [53].
## 2 Materials and Methods
### GPP Datasets  
GPP refers to the total amount of carbon dioxide (CO\({}_{2}\)) fixed from the atmosphere through photosynthesis and converted into organic matter by terrestrial ecosystems. It represents the upper limit of carbon input into the ecosystem and serves as a key indicator of vegetation productivity and the carbon cycle. Two GPP products are employed in this study. One is the GPP\({}_{\text{GPCOSIF}}\) product ([[http://data.globalecology.unh.edu/data/GOSIF-GPP_v2/](http://data.globalecology.unh.edu/data/GOSIF-GPP_v2/)]([http://data.globalecology.unh.edu/data/GOSIF-GPP_v2/](http://data.globalecology.unh.edu/data/GOSIF-GPP_v2/)), accessed on 12 June 2024), which was derived from Solar-induced chlorophyll fluorescence (SIF) observed by the Orbiting Carbon Observatory-2 (OCO-2) [41]. The GPP\({}_{\text{GPCOSIF}}\) product has been evaluated through comparisons with multiple tower observations and shows a good performance in producing global GPP estimations, both in terms of spatial distribution and seasonal cycle [42]. The other product is GPP\({}_{\text{FluxSat}}\) ([[https://avdc.gsfc.nasa.gov/pub/tmp/FluxSat_GPP/](https://avdc.gsfc.nasa.gov/pub/tmp/FluxSat_GPP/)]([https://avdc.gsfc.nasa.gov/pub/tmp/FluxSat_GPP/](https://avdc.gsfc.nasa.gov/pub/tmp/FluxSat_GPP/)), accessed on 12 June 2024), which was derived from the MODerate-resolution Imaging Spectroradiometer (MODIS) instruments on the NASA Terra and Aqua satellites [43]. This study used the latest version of GPP\({}_{\text{FluxSat}}\) (V2.2), which employs machine learning with MODIS reflectance, trained on FLUXNET 2015 and OneFlux data. Both GPP\({}_{\text{GPCOSIF}}\) and GPP\({}_{\text{FluxSat}}\) are available at a 0.05\({}^{\circ}\) resolution for the period 2000-03-01 to the recent past. We employed the two GPP products for the July-August-September season (JAS) and interpolated them to a 0.1\({}^{\circ}\) resolution.
## 1 Introduction
This encompasses various aspects such as the sampling strategies employed for collecting raw data, the synchronization techniques utilized for k subspaces, the processes of deblurring and denoising, the estimation of motion, and eventually, the reconstruction of super-resolution images. 3. This paper presents a novel model that utilizes a Generative Adversarial Network (GAN)-based super-resolution technique for the reconstruction of MR images. 4. The algorithm is specifically created to extract features from images at different scales. In general, it is common for other authors to employ a reductionist approach when addressing this specific issue. 5. The system demonstrates the ability to extract visual cues across various scales. The topic at hand is often excessively simplified by authors with contrasting perspectives. 6. The technology utilizes specific preprocessing phases to address the removal of motion blur and noise layers. 7. The solution being suggested utilizes a convolutional neural network-based methodology for the purpose of magnetic resonance image reconstruction. Its primary objective is to rebuild low-quality images that have been obtained from highly sparse k-space. 8. The methodology described above utilizes the compressed sensing framework in order to prioritize the reduction in data collecting times. 9. The author's GAN-based deformable motion estimation approach is integrated within the reconstruction layer of the procedure. 10. A novel approach is described for deformable motion registration, utilizing Generative Adversarial Networks. Pyramidal registration is a method that trains parameters for spatial transformation in order to correct image motion. The proposed registration system employs a Generative Adversarial Network (GAN) as its core architecture. The network is trained using multiple loss constraint functions to enable unsupervised training. Therefore, the proposed system does not depend on correct registration by ground-truth deformation. 11. The proposed approach has demonstrated its superiority over all competing algorithms. The algorithm presented achieved the highest Peak Signal-to-Noise Ratio (PSNR) values, with a 9 percent increase compared to the second best method. It also has the best Image Enhancement Measure (IEM) values, with the second best method achieving a 7.8 percent lower rate. Additionally, it has the lowest Mean Absolute Error (MAE) rates, with the second best method having a 2.1 percent greater error. * The suggested scanning strategy effectively reduced the scanning duration by a factor of three (112 s compared to 359 s). The organization of the manuscript is representative of the primary algorithm's flowchart. The proposed approach employs several networks to address various image processing tasks, including deblurring, denoising, registering low-quality images, and enhancing overall image resolution.