esa-sceva/satcom-chunk-collection huggingface dataset:
# NFV-Enabled Efficient Renewable and Non-Renewable Energy Management: Requirements and Algorithms
## 8 Energy Management Algorithmic Strategies a Service Function Chains  
Figure 11 illustrates an example of the deployment of the algorithmic strategies OptTs and FastTs decomposed as VNFs, forming Service Function Chains (SFCs), and coordinated by the NFV Orchestrator (NFVO). This implementation can exploit all the benefits that the NFV realm can offer i.e., the VNFs could be moved, migrated, shared, deployed in parallel mode, or used by other SFCs if necessary. In addition, the operation of algorithmic strategies through SFCs can be further improved by addressing aspects, such as optimal allocation of VNFs or the optimal composition and allocation of SFCs when considering energy efficiency, as discussed in [31; 32], respectively. These considerations are beyond the scope of this paper and they can be addressed in future work.  
Depending on the application scope (i.e., requirements for accuracy, scalability, or speed in the allocation of energy resources) and the amount of available physical resources (NFVI), the EM could choose the execution of OptTs and/or FastTs. The SFCs that are associated with the energy management strategies are listed below.  
In practical implementations, some VNFs can be concurrently executed, such as in the case of \(VNF_{1}\) and \(VNF_{2}\). In addition, certain VNFs may demand high processing and memory resources for their execution, such as \(VNF_{4}\) and \(VNF_{8}\). For instance, \(VNF_{4}\) requires high computational capabilities for computing all possible combinations, which can exceed tens or hundreds of millions of combinations for OptTs. Instead, \(VNF_{8}\) demands both high memory and processing capabilities to store the partial solutions and consolidate them in order to obtain the final service scheduling.  
Figure 11: Example of the deployment of OptTs and FastTs strategies as SFCs in the NFV-enabled Energy Manager (EM).  
Figure 10: Comparison of problem size between OptTs and FastTs. Parameters: \(N=\{1,\cdots,10\}\), \(T_{s}=3\) for all \(S_{k}\).
# GIS-Based Optimal Route Selection of Submarine Cables Considering Potential Seismic Fault Zones
## 3 Results
### Validation of the Smart Decision-Support Tool  
The reliability and effectiveness of the developed smart decision-support tool have been verified against a real engineering project, namely the EuroAsia Interconnector, which will be constructed in the near future. More specifically, a 1208 km long HVDC offshore cable is expected to transmit electricity between Israel, Cyprus and Greece. Herein, the optimal routing capabilities of the smart decision-support tool have been verified against the approximately 875 km long offshore cable (i.e., not considering the coastal and onshore parts) that will connect the island of Crete to Cyprus [68].  
In complex multi-criteria analyses, each of the adopted criteria is usually distinguished into categories or sub-criteria. Herein, the spatially distributed criterion of seabed inclination has been reclassified into five classes (sub-criteria). Moreover, a rating score ranging from 1 to 5 has been applied to all of the adopted criteria, where 5 denotes a class that has to be avoided during routing analysis and 1 represents a class that a CL could cross. Table 1 summarizes the selected criteria, their weight values, as well as the sub-criteria and their rating score for the verification case study. Figure 9 shows the proposed route that has been derived from the application of the smart decision-support tool, and the designed route of the EuroAsia Interconnector.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Criteria** & **Weight-Influence (\%)** & **Sub-Criteria** & **Rating Score** \\ \hline \hline \multirow{8}{*}{Seabed Inclination (\({}^{\circ}\))} & & 0–10 & 1 \\  & & 10–20 & 2 \\  & & 20–30 & 2 \\  & & 30–40 & 3 \\  & & \(>\)40 & 3 \\ \hline \multirow{8}{*}{Seismic Fault Zones} & & \
\begin{tabular}{c} Cretan–Rhodes ridge \\ Hellenic trench \\ East Mediterranean \\ ridge \\ Herodotus trough \\ West Cyprus arc \\ South Cyprus trench \\ \end{tabular} &
\begin{tabular}{c} 3 \\ 5 \\ 2 \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 1: Weight-influence and rating score of each of the adopted criteria and sub-criteria.  
Figure 9: Verification of the routing analysis of the smart decision-support tool.
## 2 Methodology
### Spatial Variability Maps  
Spatial variability occurs when a quantity at different locations throughout an area exhibits different values. Spatial variability maps are raster images generated to display those variations across the surface. In this study, pressure and flow spatial variability maps were generated in ArcGIS using the inverse distance weighting (IDW) interpolation to spot the locations with relatively low readings. Pressure and flow dips within the distribution system are often caused by leaks, which deteriorate water quality as a result. To improve the accuracy of the results, chlorine residual values were examined at the suspected locations. The three parameters were combined using spatial variability maps to direct the user to the areas suspected of leaks. Using these maps, a customized GIS interface was developed to automate the proposed method which will enable the identification of possible leak locations during its execution.
## 2 Materials and Methods  
### HyPlant System  
The HyPlant system is a combination of three pushbroom imaging line scanners, aligned and attached to a single mounting plate (Figure 1a). The two line scanners covering the visible/near infrared (VNIR) and shortwave infrared (SWIR) range from 380-2500 nm share the same optic and, together, form the DUAL module. This part of HyPlant is an established airborne imaging platform similar to the Specim AisaFENIX system. The second module (named FLUO and available commercially under the name AisaIBIS) only covers the red and the NIR spectral range between 670 and 780 nm. Due to its high spectral sampling interval (SSI) of 0.11 nm, and fine spectral resolution, of 0.28 nm for the O\({}_{2}\)-A and 0.29 nm for the O\({}_{2}\)-B absorption feature, respectively, as well as its high signal-to-noise ratio (O\({}_{2}\)-A: 296; O\({}_{2}\)-B: 442) the sensor enables the retrieval of SIF. Both sensors are connected to an Oxford 3052 GPS/INS (Global Positioning System/inertial navigation system) unit (Oxford Technical Solutions Ltd., Oxford, UK), which collects precise and temporal highly resolved navigation and sensor orientation information, which is needed for the geometric correction of the HyPlant image cubes. The HyPlant system was first employed in 2012 and has been continuously upgraded and optimized for accurate SIF retrieval. Table 1 gives an overview of the major technical improvements made in previous years. The system has reached maturity with the version HyPlant 3 and has been purchased by various institutions. Table 1 summarizes the current and consolidated HyPlant 3 system and gives details of its main characteristics.  
Figure 1: HyPlant airborne imaging spectrometer: (**a**) installation of the sensor system in the aircraft, consisting of the broadband DUAL module (A), high-resolution FLUO module (B) and GPS/INS unit (C); (**b**) HyPlant DUAL (A) and FLUO (B) module installed in the hatch of the aircraft (image taken from below the aircraft); (**c**) HyPlant FLUO at-sensor radiance; (**d**) HyPlant DUAL at-sensor radiance; (**e**) HyPlant DUAL top-of-canopy radiance; (**f**) HyPlant DUAL top-of-canopy reflectance of selected surfaces.
## 3 Technologies for Treating Landfill Leachate
### Physical-Chemical Method  
#### 3.1.1 Adsorption  
The adsorption (ADS) method has been extensively applied in landfill leachate treatment due to its high efficiency in removing organic compounds and ammonia [102]. Among the diverse range of adsorbents, activated carbon remains the most efficient and promising material for this purpose [103; 104]. Activated carbon is primarily categorized into powdered activated carbon (PAC) and granular activated carbon (GAC) based on its physical  
Figure 1: Treatment technology for landfill leachate.  
morphology. In particular, stabilized leachate from the Goslar landfill, Germany, was firstly evaluated using a GAC column in 1995, illustrating a COD removal of 91% with an initial concentration of 940 mg/L [11]. [PERSON] [105] demonstrated the efficacy of PAC in removing low-molecular-weight organic pollutants from river water. However, it is important to note that the ADS method primarily functions through phase separation and pollutant transfer mechanisms rather than chemical degradation, which limits its ability to fully mineralize contaminants.  
To investigate the interaction of different PACs with DOM in membrane bioreactor (MBR) effluent at the molecular level, [PERSON] [104] respectively compared DOM adsorption efficiency over wood-based PAC (WoAC) and coal-based PAC (CoAC), and found the 89.2% removal of DOC by CoAC was significantly higher than the 80.9% of WoAC. CoAC's superiority is likely due to its Fe component, as iron oxides on the surface of CoAC may interact with certain functional groups (e.g., -OH groups) of unsaturated compounds, thereby enhancing DOM adsorption efficiency.  
Humic acid poses a significant challenge in landfill leachate treatment. To address this issue, [PERSON] [106] conducted a comprehensive study on the selective adsorption of humic acid using graphitic carbon nitride (g-CN) across leachate samples from 10 different landfills. The research demonstrated that g-CN achieved a UV\({}_{254}\) removal rate of 30-70% across all leachate samples, significantly surpassing TOC removal efficiency. This innovative selective adsorption technique not only effectively addresses UV quenching substances (UVQSs) in leachate treatment but also enables the recovery of humic acid for potential reuse as liquid fertilizer in agroforestry applications. Furthermore, the adsorption method has proven effective in reducing key leachate parameters, including COD, suspended solids, and color intensity, thereby offering a promising solution for leachate treatment challenges. However, the adsorption process can only be used as a pretreatment of landfill leachate, and the effective purification of landfill leachate requires an integrated approach combining biological degradation and chemical treatment processes.
## 4 Materials and Methods
### Principal Component Analysis  
Principal component analysis (PCA) is a classical unsupervised dimensionality reduction method whose core idea is to transform the original high-dimensional features into linearly independent low-dimensional variables (principal components) by orthogonal transformations while retaining the maximum variance information in the data [35]. The method is widely used in eliminating redundant information among features, improving the computational efficiency of models, and visualizing high-dimensional data.
## 5 Results and Discussion
### F1-Score Analysis  
The F1-scores, representing the harmonic mean of precision and recall, remained exceptionally high across all models (Figure 7). The Transformer model achieved the highest mean F1-score (0.9997), followed by the CNN (0.9994) and Actor-Critic (0.9993). The observed minor fluctuations across experiments (standard deviation \(\leq\) 0.0003) indicate robust model performance across different data partitions. A one-way ANOVA with post hoc Tukey HSD test confirmed the statistical significance of these differences (\(p<0.01\)).
# A Method to Determine BeiDou GEO/IGSO Orbital Maneuver Time Periods
###### Abstract
analyzed the spatial debris orbit determination algorithm using space-based optical observation data [16]. [PERSON] et al. detected the start time of orbital maneuvers by using single point positioning (SPP) technology and residual values of a pseudo-range [17; 18]. Although some research has been done to detect the orbit maneuvering of Global Navigation Satellite System (GNSS) satellites, prior complex mechanical models and additional measurement data are both restricted for the common user, and the time period of orbit maneuvering has not been determined. Moreover, the maneuver period and the initial orbit could not be detected by publicly available observation data. In order to solve the above problems, this paper proposes a backward integration method to calculate the positions of maneuvering satellites by measured orbits on the day after orbital maneuvering, and subsequently calculate the residual value of the pseudo-range to determine the period of orbital maneuvers.
# Positioning-Based Uplink Synchronization Method for NB-IoT in LEO Satellite Networks
## 3 Position-Aided Uplink Synchronization Method
### NPSS Detection
#### 3.1.3 Frequency Offset Estimation Algorithm  
After completing time synchronization, frequency synchronization needs to be performed to complete the entire downlink synchronization process. Given the known starting position of the NPSS, we begin by performing an FFT on the signal starting from the fine synchronization point, using a length equivalent to one OFDM symbol, to convert it into the frequency domain:  
\[y(k)=FFT[y(n)] \tag{12}\]  
where \(y(k)\) represents the FFT output based on the correctly aligned sampling point after timing synchronization has been completed. \(y(k)\) is correlated in parallel with the 11 local sequences \(zc_{1},zc_{2},\ldots\ldots zc_{11}\) to estimate the integer multiple frequency offset. \(\varepsilon_{i}\) is the integer part of the Doppler frequency offset, and \(\varepsilon_{f}\) is the fractional part. In the algorithm, these two components are calculated separately. The local sequence that produces the maximum correlation value is identified, and the corresponding integer frequency offset \(\varepsilon_{i}\) is eliminated. This results in the integer frequency offset compensation factor \(b\):  
\[b=e^{-2\pi\varepsilon_{i}n/N} \tag{13}\]  
After multiplying \(b\) with the received signal, the remaining signal with only the fractional frequency offset \(\varepsilon_{i}\) is expressed as:  
\[\begin{array}{ll}y_{i}(n)&=x(n)e^{2\pi n\varepsilon/N}\cdot e^{-|2\pi \varepsilon_{i}\cdot n/N}\\ &=x(n)e^{2\pi n\varepsilon_{f}/N}\end{array} \tag{14}\]  
The fractional frequency offset \(\hat{\varepsilon}_{f}\) is estimated by performing correlation calculations in the time domain:  
\[\begin{array}{ll}\chi&=\sum\limits_{n=0}^{N-1}y_{i}(n)\cdot y_{i}(n+N)\\ &=\sum\limits_{n=0}^{N-1}x(n)e^{2\pi n\varepsilon_{i}/N}\cdot x(n+N)e^{-|2\pi \varepsilon_{i}n/N}\\ &=\sum\limits_{n=0}^{N-1}|x(n)|^{2}e^{-|2\pi\varepsilon_{i}}\end{array} \tag{15}\]  
The result of the fractional frequency offset estimation is given by:  
\[\hat{\varepsilon}_{f}=-\text{arg}(\chi)/2\pi \tag{16}\]  
All steps of the NPSS detection process are provided in Algorithm 1.  
of 10 ms. Thus, the terminal only needs to detect the number of NPSSs received within one cycle to determine the number of visible satellites.  
TDOA is a positioning algorithm which requires only three satellite observation points and their corresponding signal arrival times at the terminal to establish the necessary equations for positioning. Our research focuses on the selection of satellite observation points and the estimation of signal arrival times.
## 3 Results  
### Comparison of C5 and C6 CI Products  
#### 3.1.1 Spatial Evaluation  
The worldwide spatial distribution of CIs in January and July 2015 retrieved from C5 and C6 MCD43 data is shown in Figure 3. Figure 3a-d present global maps of CIs, from green to yellow, indicating CI values from low (clumped distribution) to high (random distribution), and the white, blue, and gray colors denote excluded non-vegetation area, water, and missing value pixels, respectively. A comparison between the global maps of C5 and C6 CIs (Figure 3a-d) shows that these two data exhibit similar global spatial patterns that are somewhat related to the distribution of vegetation types [67; 69]. Tropical forest regions and middle-high latitude forests of the Northern Hemisphere have lower CI values, while the grass, shrub, crop, and savanna regions display higher CI values [24; 26; 99]. In the Northern Hemisphere, the January CI is generally higher than that in July, especially in middle-high latitudes, where the CI responds significantly to mature and senescent vegetation stages. In the Southern Hemisphere, the CI in January is lower than that in July, particularly in southern Africa, where the predominant vegetation is shrubs. These results are confirmed against the latitudinal distributions of these two versions of CI data.  
As shown in Figure 3e,f, the latitudinal distributions of the C5 and C6 CIs in January and July exhibit similar profiles. In the higher latitudes, both the C5 and C6 CIs present clear  
Figure 3: Global distribution of CI retrieved from MODIS C5 and C6 MCD43 products in January and July 2015. Panels (**a–d**) are color-coded global maps of the MODIS C6 and C5 CIs. The sinusoidal projection is used here. Panels (**e**) and (**f**) show the latitudinal distribution of the MODIS C5 and C6 CIs, respectively. The red and blue lines indicate C5 and C6 CIs, respectively, and the corresponding shaded area indicates the one standard deviation range.  
seasonal differences; in line with the vegetation phenology [45; 46], the CI in the mature stage is lower than the CI in the senescent stage [30; 47]. In the tropics, there is no obvious seasonal difference in these latitudinal belts for the year-around ample illumination, except that the C5 CI is slightly higher in January among 10\({}^{\circ}\)N-10\({}^{\circ}\)S, and the C6 CI is slightly higher in July within the 10\({}^{\circ}\)S latitude and the Tropic of Capricorn. Comparing the two data versions reveals that the C5 and C6 CI profiles in July match relatively well (R = 0.86 for July and R = 0.83 for January), while the C5 CI overestimates the C6 CI in January within the latitude range of 10\({}^{\circ}\)N-30\({}^{\circ}\)S. The higher C5 CI in January may result from the relatively large number of pixels retrieved when using the backup algorithm compared to the C6 data.
# Intelligent Traffic Management in Next-Generation Networks
## 3 DL/ML Applications in Software-Defined Networking
### Traffic Classification
#### 3.1.2 Public Datasets  
The evaluation and the success of ML/DL models depend on the dataset used. In this section, we present some well-known datasets used for network traffic classification.  
* **VPN-nonVPN dataset**[96] is a popular encrypted traffic classification dataset. It contains only time-related features and regular traffic, as well as traffic captured over a virtual private network (VPN). Specifically, it consists of four scenarios. Scenario A is used for binary classification in order to indicate whether the traffic flow is VPN or not. Both scenario B and scenario C are classification tasks. Scenario B contains only seven non-VPN traffic services like audio, browsing, etc. Scenario C is similar to Scenario B, but it contains seven traffic services of the VPN version. Scenario D contains all fourteen classes of scenario B and scenario C to perform the 14-classification task;* **Tor-nonTor dataset[102]** contains only time-related features. This dataset has eight different labels, corresponding to the eight different types of traffic captured, which are browsing, audio streaming, chat, video streaming, mail, VoIP, P2P, and file transfers;
* **QUIC**[103] is released by the University of California at Davis. It contains five Google services: Google Drive, Youtube, Google Docs, Google Search, and Google Music.
* **Dataset-Unicauca**[88] is released by the Universidad Del Cauca, Popayan, Colombia. It was collected through packet captures at different hours during the morning and afternoon over 6 days in 2017. This dataset consists of 87 features, 3,577,296 observations, and 78 classes (Twitter, Google, Amazon, Dropbox, etc.)
# Analysing Land Cover Change in the Valencian Community through Landsat Imagery: From 1984 to 2022
## 5 Conclusions
The conceptualisation, project administration, and funding acquisition were spearheaded by J.A.S., while [PERSON] and [PERSON] handled data curation and methodology. The validation process and the initial drafting of the manuscript were collaboratively conducted by [PERSON], [PERSON] and [PERSON]G. All authors have read and agreed to the published version of the manuscript. This research was funded by the European Union and Valencian Community, who co-financed the project 'Estimacion de Indicadores Medioambientales en la Comunidad VALENCIANA usando datos de SATelite (VALENSAT)', part of INVESTIGO 2022 financing plan (INVEST/2022/411), and by the Ministerio de Ciencia e Innovacion PID2020-112494 RB-I00 IPL-LSTM. Data Availability Statement: Data are contained within the article. We are sincerely grateful to the European Space Agency (ESA) and the Copernicus program for the supply of images and validation products, which significantly contributed to the research efforts presented in this paper. We acknowledge Google Earth Engine for its indispensable data-processing capabilities, which greatly facilitated the handling of the substantial volume of data utilised in this study. **Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. Figure A1: Cont. Figure A1: Land cover classification obtained with the Random Forest algorithm, using Landsat 5–9 imagery for the period 1984–2023.
## 1 Introduction
### The Conceptual Framework of the Viewing Process
#### 1.3.4 Temporal Dynamics  
Temporal information not only reflects the dynamic nature of viewing behavior but also reveals the evolution of cognitive strategies over time, enabling us to capture how viewing structures emerge, develop, and transform throughout the exploration process. [PERSON] et al. [50] argued that although models based on retinal topography can explain the selection of many fixation points, they often neglect the temporal and sequential aspects of eye movement patterns. [PERSON] [51] further emphasized that understanding the temporal order of gaze behavior is essential for interpreting visual exploration strategies. Similarly, [PERSON] et al. [11] demonstrated that in naturalistic viewing environments, saliency alone does not determine gaze behavior; rather, gaze patterns emerge from the interaction of stimulus-driven saliency and observer-driven goals, shaped by time-dependent behavioral dynamics. These findings suggest that analyzing the temporal dynamics of gaze can complement structural analysis by illustrating how attention shifts and cognitive focus evolve during the viewing process, while also revealing how temporal changes interact with other dimensions--such as regional association and attentional weight--to form coherent visual strategies that support the real-time construction of visual understanding. Although their study focused on naturalistic scenes, this broader insight into the dynamic interaction of bottom-up and top-down influences offers a conceptual foundation for analyzing temporal gaze patterns even in static image viewing. Rather than treating time as a simple sequential variable, our framework integrates temporal sequencing with spatial structure to reveal the evolutionary dynamics of cognitive organization.  
Based on this four-dimensional theoretical framework, this study develops a computational approach to reveal the structural organization of visual exploration in design contexts. Through the proposed analytical dimensions--spatial clustering, associative relations, attentional weights, and temporal dynamics--we aim to demonstrate that the designers' viewing behavior exhibits systematic organizational patterns that can be computationally modeled and visualized. This framework moves beyond traditional statistical measures to provide new insights into the cognitive mechanisms underlying professional visual expertise, offering a foundation for understanding how design thinking manifests in structured viewing behaviors.