esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
These results are promising,providing users with accurate information on the rice cultivated areas and even the estimation of potential rice yields was demonstrated. These earlier demonstrations were based on ERS-1, ERS-2, Radarsat-1, and Envisat SAR data, but these satellites are not operational any longer. At the moment, possible SAR data sources are TerraSAR-X/TanDEM-X, COSMO-SkyMed, and Radarsat-2. The satellite constellations of several satellites enable shorter revisit times and more frequent monitoring of agricultural areas. However, considering the operational rice crop monitoring an important data source in the future is the Sentinel-1 SAR satellites (ESA), which is a continuation to the C-band ERS and Envisat SAR data series. ESA's free-of-charge data policy is also an obvious advantage of the Sentinel-1 system compared to other SAR satellite data. Therefore, this study exploits the verified, well-known, ESA Envisat C-band SAR data in crop type monitoring. The objective of this study is to obtain up-to-date land-use information and identify long term trends in changes in land use in Tra Vinh and Ben Tre provinces, which are the main interest areas to IFAD in the Mekong Delta region. The specific interest was put on the rice cultivated areas due to the salinity intrusion in the area; however, other crops (sugarcane, coconut, and orchard) and aquaculture were also included in the study. The hypothesis was that this information can be obtained using historical EO data and a combination of all available radar and optical images. The goal was to use standard automatic image classification procedures and validate the results using ground truth data and historical changes based on interviews with local farmers. The contribution of this study is that it combines SAR and optical imagery into a long-term time series of land use change. This information has been sparsely available in the area.
## 4 Introduction to the Visualization Tool Based on Graphical Information Systems
### Enhancing Grape Quality Assessment Through Web Mapping
#### 4.1.2 Introduction to SIGPAC  
The System for Agricultural Parcel Information (SIGPAC) stands as a pivotal tool within the agricultural domain, providing a comprehensive perspective on the distribution and characteristics of agricultural parcels. Particularly significant in the European context, this system empowers farmers, agricultural authorities, and industry professionals with access to detailed geospatial information, thereby facilitating informed decision-making and compliance with specific regulations.  
The SIGPAC allows users to explore precise data on land use, parcel locations, and agricultural-related specifics. This information is derived through the integration of remote sensing technologies and cadastral data, contributing to efficient land and agricultural resource management.  
Prior to all these systems where the visual information of maps and layers is displayed, a preprocessing of all this type of spatial information must be conducted so that it can be used correctly; all this previous work is carried out with multiple programs and applications developed for these purposes.  
Open-source geospatial software includes a broad range of libraries, tools, applications, and platforms developed and released under different Open-Source Initiative (OSI) licenses. Examples are the GDAL and PROJ libraries, designed for managing geospatial data and coordinating reference systems, and PostGIS and SpatiALite for storing data. This collaborative approach enables developers from both open- and closed-source software communities to work together on the fundamental software layer, leading to shared efforts and reduced development time. With this established software infrastructure, the emphasis can shift towards creating novel and innovative services that derive insights from geospatial data. Figure 9 depicts a flowchart detailing the process from obtaining plot data to map visualization.  
Figure 9: A visual workflow diagram for obtaining parcel data, transforming it into a database, building a web application, processing ticket calculations, and visualizing it on a map.
# A Multi-Scale Deep Back-Projection Backbone for Face Super-Resolution with Diffusion Models
## 3 Image Super-Resolution via Repeated Refinement
### Inference Process and Optimization Model
Initially, the LR image is interpolated to the target size for reconstruction, such as scaling up a 64 \(\times\) 64 \(LR\) image by a factor of four to obtain a 512 \(\times\) 512 image through interpolation. This interpolated and scaled image is referred to as \"_Ref_\" and serves as auxiliary information to guide image generation. The reference HR image, denoted as \(HR_{0}\), is combined with _Ref_ to generate the initial six image features. These features are then fed into the first base network, \(Net^{1}\), which produces the noise-added image \(HR_{1}\). _Ref_ is spliced with \(HR_{1}\) and the resulting image is then fed into \(Net^{2}\), generating the noise-added image \(HR_{2}\). This process is repeated for \(T\) steps, resulting in a sequence of noise-added images \(HR_{1}\), \(HR_{2}\), and so on, until reaching \(HR_{T}\), which represents a pure Gaussian noise image. During the inference process, _Ref_ is utilized to reconstruct \(HR_{0}\) from the same pure Gaussian noise image by backtracking step by step. As this paper is concerned with the underlying network in the diffusion model, readers who wish to investigate the mathematical principles of the diffusion model further can refer to the original SR3 [23].
## 4 Multi-Objective Hybrid Resource Consolidation Algorithm
### Mathematical Modelling of Objectives Functions
#### 4.1.2 Resource Consolidation Model  
The aim of multi-objective resource consolidation is to simultaneously minimize energy consumption and SLA violation because of migrating VMs from one PM to another. Therefore, an index has been assigned to the consolidation technique as the evaluation parameters as follows:  
Y is a consolidation parameter and \(1-\)Y is the lack of a consolidation parameter.  
The index shows the amount of resource energy consumption (\(EU(t)_{j}\)) and SLA violation (\(SVM\big{(}A_{i},B_{j}\big{)}\)) due to resource consolidation (\(R^{c}\)). Therefore, the resource consolidation index in the CC environment is as defined above, in which their coefficients (\(\Phi\), \(\Omega\), \(\Psi\)) are subject to change. According to consolidation importance, Y has the higher value and coefficients, \(\Omega\) and \(\Psi\), according to the type of system, and the importance of energy consumption and SLA violation will change. Eventually, the aim is to minimize the index \(R^{c}\). When the number of \(UR^{n}\) increases, the complexity rises, and while the allocated resources reach to their maximum utilization a lot of resources need to be relocated to improve performance. Consequently, \(EU(t)_{j}\) and \(SVM\big{(}A_{i},B_{j}\big{)}\) increase. To overcome this limitation, the number of active PM resources is reduced by consolidating VMs, and \(EU(t)_{j}\) is reduced drastically in the Cloud environment. Consolidation causes the reduction of \(SVM\big{(}A_{i},B_{j}\big{)}\) and then existing resources by allocation techniques as much as possible. Therefore, in principle, data centers' resource management requires consolidation in resource allocation to improve the overall data center resource utilization that reduces energy consumption and SLA violation.  
The combined objectives' function of resource consolidation denoted as \(R^{c}\), which minimizes energy consumption and SLA violation of the Cloud data center, can be mathematically formulated as follows in Equation (5).  
\[R^{c}=\Phi(1-conflict)+\Omega EU(t)_{j}+\Psi\ SVM\big{(}A_{i},B_{j}\big{)} \tag{5}\]  
where \(\Phi\), \(\Omega\), and \(\Psi\) are the coefficient of the consolidation with the conflicting objectives, while \((1-conflict)\) signifies that there is no consolidation.  
\[\text{Min}\big{(}R^{c}=\Phi\big{(}1-conflict_{Bj}\big{)}+\Omega EU(t)_{Bj}+ \Psi\ SVM_{Bj}\big{)} \tag{6}\]  
Min \(R^{c}\) is the objective function to minimize the overall energy consumption and SLA violation.  
where:  
\[EU(t)_{j}=\left\{\begin{array}{rl}1\ if\ UR\text{ {\it assigned to }}A_{i}\\ 0\ else\end{array}\right.\]  
\[SVM_{Bj}=\left\{\begin{array}{rl}1\ if\ VM_{i}\ migrate\ to\ PM_{j}\\ 0\ else\end{array}\right.\]
## 5 Conclusions and Further Research  
This paper proposed a network orchestration system based on network virtualization technology. Actual performance testing used open-source hardware as a network and set Docker-based virtual network function. This showed maintenance of service availability by repositioning network function based on the CPU usage of the nodes. Moreover, in order to solve the restart problem when the network is reset, this paper proposed a faster way of recovering to the environment before the reset. This improved the container structure, which, unlike virtual machines, could not provide continued service and allowed the container to enjoy the same continuity in the system.  
Furthermore, this paper proposed a network orchestration system user service that improved a two-dimensional QR code identification method and tested its performance. Firstly, the proposed QR code identification method is different from the existing method as it conducts an image-matching process which facilitates the advantages of using a massive number of images, such as in a cloud system. A Nexus 4 smart phone was used to test and compare performance with those of existing applications. Better performance for the proposed method was observed, as it can identify images at a maximum scanning angle of 60\({}^{\circ}\) while the existing system allows a maximum angle of only 40\({}^{\circ}\). Under the proposed system environment, an image database was set using different databases based on network device status information to improve processing speed. This showed that the information on a number of machines was used to set separate databases, allowing the best network conditions for using network status information to conduct network orchestration.  
In conclusion, as mobile data traffic has significantly improved, expanding the scale of networks and their complexity and making research on 5G networks possible, the proposed method provides advantages for network orchestration systems in devising a 5G mobile access network with low cost using cheaper machines and open-source networks. Moreover, it provides advantages in terms of constant network provision.  
In the future, this research could be used as a basis for measures to establish the most optimal virtual network function using network status information based on different requirements. In addition, with its low operating/development cost, such a system is expected to contribute to active establishment of computing environments for less economically developed countries.  
Figure 18: Test of QR code identification for different numbers of databases used.  
**Acknowledgments:** This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2015R1D1A1A01059147).  
**Author Contributions:** All the authors contributed equally to this work. All authors read and approved the final manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Results
### Dynamic versus Static Measurements Results and Validation  
#### 4.2.1 Results at Fixed Testing Inflow Conditions  
The comparison on the power coefficient and on the efficiency between the values obtained by dynamic and by static point-by-point measurement methods, for fixed inflow conditions (recirculating pumps' speed of N\({}_{\text{p}}\) = 1500/2000 min\({}^{-1}\)), is given in Figure 9. In the case of the discrete point-by-point method, the speed of the turbine runner(s) has been modified in steps of 250 min\({}^{-1}\) from 0 to 3000 min\({}^{-1}\), whilst is the case of the dynamic method, it has been continuously increased, and then decreased, from 0 to 3000 min\({}^{-1}\), and vice versa. A good agreement between the results obtained by the two methods, with a maximum error of 1% between the mean values, may be observed for both tested cases, overall operating range, including off-design conditions. In the case of the microturbine, the negative efficiency values are given by a negative mechanical torque of the runners, corresponding to the turbine brake operating mode.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Acceleration/Deceleration Ramp**} & \multicolumn{2}{c}{\(\mathbf{n^{\star}_{STD}}\) **[\%]**} \\ \cline{2-3}  & **Microturbine** & **PAT** \\ \hline
10 & 2.6467 & 2.2774 \\
25 & 1.1363 & - \\
30 & - & 1.0995 \\
40 & 0.6693 & - \\
60 & 0.5369 & 0.7863 \\
90 & 0.3167 & 0.6246 \\
120 & 0.3213 & - \\ \hline \hline \end{tabular}
\end{table}
Table 6: Resulting STD values of the efficiency fluctuation depending on the acceleration/deceleration ramp.  
Figure 9: Dynamic versus static power coefficient and efficiency measurements during one forth and back cycle at fixed testing inflow conditions: (**a**) variation of the counter-rotating microturbine power coefficient (N\({}_{\text{p1,2,3}}\) = 1500 min\({}^{-1}\)); (**b**) variation of the PAT power coefficient (N\({}_{\text{p1,2,3}}\) = 2000 min\({}^{-1}\)); (**c**) variation of the counter-rotating microturbine efficiency (N\({}_{\text{p1,2,3}}\) = 1500 min\({}^{-1}\)); (**d**) variation of the PAT efficiency (N\({}_{\text{p1,2,3}}\) = 2000 min\({}^{-1}\)).
## 4 Discussion
### Limitations and Future Research  
Since dynamic building simulation involves complex sets of building, environmental and occupancy information, the model adopted in this study is established with certain simplifications. For instance, the shading on the north of reference buildings is omitted. However, it would have limited effect on the energy use and thermal comfort state of the reference buildings since there is limited solar radiation coming from the north.  
Standard heating setpoint and occupant schedule from other context are used in this study due to insufficient empirical data or relevant local guidelines. Reliable experimental studies are so far rare, further studies are required to address this gap.
# Application of Neural Networks in Distribution of the Load in Cluster-Based Web Systems
## 3 Load Balancer  
The primary purpose of an intelligent load balancer is to redirect incoming HTTP requests \(r_{i},\ i=1,\dots,\ I\) to web servers that are able to service requests in the shortest time. It can be expressed as in Equation (1):  
\[z_{i}=\min_{w}\{tp_{i}^{w}:w\in\{1,2,\dots,W\}\} \tag{1}\]  
where  
\(z_{i}\)--the chosen executor out of \(W\) executors;  
\(tp_{i}^{w}\)--an estimated response time to request \(r_{i}\) for the \(w\)-th server;  
--the index of the request.  
The load balancer (Figure 2) consists of the following modules: classification module (\(CM\)), measurement module (\(MM\)), decision module (\(DM\)), redirection module (\(RM\)), and executor models (\(E^{w}\)), where \(w=1,\dots,W\). At first, an incoming HTTP request  
Figure 1: Web cloud system diagram.  
is assigned to the class \(k_{i}\) and it is determined as to whether it is a static or dynamic (\(d_{i}\)) request in CM. The extracted information is transferred to \(E^{\omega}\) modules.  
The classification of requests is needed in order to better estimate the response time of incoming requests. Each request can have the status \"dynamic\" or \"static\" (\(d_{i}\)) depending on whether it refers to a script executed on the server (e.g., PHP, Python, Java) or whether a static file is downloaded (e.g., an image file). Additionally, the request is also assigned to one of the classes, depending on the type of an object requested and its size. Requests belonging to the same class have similar request response times. More detailed classification is presented in Section 5.5.  
The \(MM\) measures the time of servicing requests on servers and their load, and then passes this information to \(E^{\omega}\) modules.  
The executor model estimates the \(tp_{i}^{w}\) response time of the corresponding server, taking into account its load \(M_{i}^{w}=\left[f_{i}^{w}.\delta_{i}^{w}\right]\) where:  
\(f_{i}^{w}\)--the number of static requests processed on the \(S^{w}\) at the time of arrival of the request \(r_{i}\);  
\(\delta_{i}^{w}\)--the number of dynamic requests processed on the \(S^{w}\) at the time of arrival of the request \(r_{i}\).  
The executor models \(E^{w}\) can adapt to the environment using previous response time \(tt_{i-1}^{w}\).  
The \(DM\) makes a decision according to Equation (1) using information from \(MM\) and \(CM\) modules. The \(RM\) module forwards the incoming request \(r_{i}\) to the selected server.
# Digital 3D Technologies for Humanities Research and Education: An Overview
## 4 Epistemics and Methodology
### Purpose of 3D Modelling
#### 4.3.1 Education  
In education, 3D models of cultural heritage are used in various settings:  
* The predominant purpose still is to illustrate and **teach history and heritage in informal settings** like museums, games, or television broadcasts [12, 233]. Especially, the role and effects of media like visualizations in museum education is the focus of scientific discourse (e.g., [78, 234, 235, 236, 237, 238]), as are various settings employ 3D models (e.g., [239, 240, 241, 242]). As one example, interactive applications for 4D city exploration [243, 244, 245] allow virtual visits and remote spatial learning [246], support guiding visitors through the city [245, 247, 248], provide access to additional information, and enable users to gather a virtual view of temporal change and historic spaces, buildings, and monuments or covered parts [249, 250, 251, 252, 253, 254].
* **Teaching heritage in formal education** is another highly important scenario. A multitude of projects employ 3D models in higher education facilities (e.g., [255, 256]) or evaluate the use of 3D models of cultural heritage in school education [257, 258].
* **Teaching digital competencies via heritage:** In the third usage scenario, the primary subjects of teaching are digital literacy and competencies as modelling techniques or VR technologies, while the historical object is used \"only\" as a training example. This closely relates to the concept of data literacy comprising data collection, exploration, management, analysis, and visualization skills [259]. Data literacy has been defined by various recommendations and standards [260, 261].
# Integrated Communication and Measurement System with BOC-Assisted OFDM
## 3 Reception of the Composite Signal  
In this section, we introduce the process of receiving the BOC signal and the OFDM signal. First, we introduce the correlation between the BOC signal and the OFDM signal. Then, we design the receiving system for the composite signal, which performs acquisition and tracking of the BOC signal and demodulation of the OFDM signal.
# Real-Time Simulation and Sensor Performance Evaluation of Space-Based Infrared Point Target Group
## 4 Simulation Experiments and Validation
### Simulation Analysis
#### 4.2.1 Constellation Analysis  
The results from sensor imaging are displayed in Figure 9. Upon analysis, the following characteristics were identified when the constellation imaged the targets:  
Figure 8: Distance between satellites and the main target.  
Figure 9: Image results in band A from different satellites to targets.  
Taking the image from Sat11 as an example, the target group was a bright spot at the beginning. As time progressed, each target consistently spread outward, and the images showed a general spreading trend. Because of the high-speed movement of both the satellites and targets, the relative angles between them underwent constant changes. For instance, images taken by Sat41 evolved from scattered to concentrated. With respect to observational efficacy, during the constellation imaging period, satellites (e.g., Sat27 and Sat47) that pass through the orbits plane of the target group have a lower probability of mutual occlusion during imaging which makes them suitable for the classification and tracking of target groups. In conclusion, observing from multiple perspectives using a constellation ensures that complete spatiotemporal distribution information of the target group is collected. Thus, it is essential to make targeted adjustments to the trajectory of potential observation targets when designing a satellite constellation to enhance the constellation's tracking ability.
# Assessment of Quality and Safety of Farm Level Produced Cheeses from Sheep and Goat Milk
## 2 Materials and Methods
### Microbial Analyses  
In addition to physicochemical analysis, microbiological analysis was performed on the obtained samples of sheep's and goat's milk and cheeses. Preparation of test samples, initial suspension and decimal dilutions were prepared according to STN EN ISO 6887-5 (2011) [35]. Subsequently, the presence of _Escherichia coli_, _Bacillus cereus_, _Listeria monocytogenes_, and _Staphylococcus_ sp. was detected in the examined samples of sheep's and goat's milk and cheese according to the relevant ISO standards. Detection of the number of bacteria of the family _Enterobacteriaceae_ was performed according to STN EN ISO 21528-1 (2019) [36] using the selective diagnostic medium Violet Red Bile Agar (VRBL; HiMedia, India). After incubation, suspected colonies were tested for glucose fermentation and oxidase reaction (OXItest; Erba-Lachema, Brno, Czech Republic) to confirm bacteria of the _Enterobacteriaceae_ family. Tryptone Bile X-glucuronide medium (TBX; Oxoid, Hampshire, UK) selective diagnostic medium was used to detect the number of \(\upbeta\)-glucuronidase-positive _Escherichia coli_ according to the procedure described in STN ISO 16649-2 (2001) [37]. The number of bacteria of the genus _Bacillus cereus_ was determined using the procedure described in STN EN ISO 7932 (2004) [38]. The detection and number of _Listeria monocytogenes_ was determined according to STN EN ISO 11290-2 (2019) [39]. The number of coagul-positive staphylococci (CoPS) was also determined in the examined samples according to STN EN ISO 6888-1 (1999) [40] using Baird-Parker selective-diagnostic medium (HiMedia, India). At the same time, the number of lactic acid bacteria (LAB) was determined in milk and cheese samples according to STN ISO 15214 (2002) [41]. Microbiological values were transported into logarithmic transformation (log\({}_{10}\)) to approximate the data to a normal frequency distribution in order to correctly apply statistical testing methods (one-way analysis of variance) [42].