esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**References** & \begin{tabular}{c} **Measurement** \\ **Period/Sampling Interval** \\ \end{tabular} & **Location** & \
\begin{tabular}{c} **Proposed Estimation** \\ **Method** \\ \end{tabular} &
\begin{tabular}{c} **Error Calculation** \\ **Technique** \\ \end{tabular} \\ \hline
[27] & June 2016–May 2017 & Brazil & SARIMA—NN & MAE, MAPE, RMSE \\
[28] & 15 min time interval & Liaoning, China & VMD—ANN & MAE, RMSE, R \\
[30] & 10 min time interval & China & PSO-SVR-NN & MAE, MAPE, RMSE \\
[31] & 2008–2016 & Bushehr, Iran & MLFFNN, SVR-RBF, & RMSE, R MSE \\
[26] & 2007–2016 & Sibu, Malaysia & Terrain-Based ANN & R, MAPE \\
[1] & 2013–2014 & Ankara, Turkiye & Weibull Function & - \\
[38] & 10 min periods, & Penglai, China & NN-Linear Combination & RMSE, MAE, MAPE \\
[24] & April 2006–March 2007 & Coimbatore, Tamil & MLPNN, RBFNN & R\({}^{2}\), MSE \\
[21] & May 2013-July 2016 & Saudi Arabia & ANN & R, RMSE \\
[6] & 2000–2016 & Incheon, South Korea & Weibull and Rayleigh & RMSE, R\({}^{2}\), X\({}^{2}\) \\
[8] & 1995–2002,2012–2013 & Giurlai, Lithuania & Weibull—MLM, & RMSE, R\({}^{2}\), X\({}^{2}\) \\
[22] & 4 years & Romania & Feedforward ANN & RMSE \\
[39] & - & - & ANN with BP, ANN & RMSE, R\({}^{2}\), R \\
[32] & - & - & ANFIS-Weibull & RMSE \\
[9] & 2004, 2005, 2006, 2009 & Florya, Yalova, Gebze, Biga & MUOM-Weibull & R\({}^{2}\), KS, RMSE, \\
[40] & 2014, 2016 & Xinjiang, China & SRA, Hybrid Laguerre NN & RMSE, MAE \\
[18] & 2016–2018 & Northern Pakistan & MOM, EML, EMJ, EPFM, & RMSE, R\({}^{2}\), MBE \\
[10] & 2009–2013 & Hatay, Osmaniye, Turkishye & Weibull with GM, MLM, & RMSE, R\({}^{2}\), MPE \\
[5] & January 2008–August 2011 & Osmaniye, Turkishye & Weibull and Rayleigh with GM, MOM & - \\
[19] & 2013 & Osmaniye, Turkishye & Weibull with MLM, LSR & RMSE, R\({}^{2}\), MAE, MBE \\
[11] & 2008–2012 & Jubail, Saudi Arabia & Weibull and Rayleigh & RMSE, R\({}^{2}\), MAE, MBE \\
[7] & 12 months & Northern Morocco & Weibull and Rayleigh & RMSE, R\({}^{2}\), MBE \\
[12] & September 2014 & Southern India & EMJ, EML, LSR, MLM, MMLM, PDM, AMLM & RMSE, MAPE, R\({}^{2}\), X\({}^{2}\) \\
[13] & Period of a year & Saudi Arabia & Weibull with MOM, LSR, MLM & MSE \\
[41] & 1971–2000 & Poland & Two and & R\({}^{2}\), SE \\
[42] & 2010–2014 & Bohawian, China & Webull-AI (GWO, PSO, CS, EMJ, EPL, EPFM, MLM) & RRMSE, R\({}^{2}\) \\
[43] & September 2014 & Kayathar, Tamil Nadu, India & Weibull with nine different methods & RMSE, MAPE, \({}^{2}\), X\({}^{2}\) \\
[44] & 1960–1978 & Faya-Largeau, Chad & Weibull & - \\
[33] & April 2016–December 2018 & Coastal region & Weibull-AI (GWO, BOA, GA, ABCOA) & RMSE, R\({}^{2}\), MBE \\
[45] & 2009–2013 & Türkaye(Adana, Osmaniye, Hatay) & Weibull with the PDM & R\({}^{2}\), MPE, RMSE \\
[14] & Period of a year & Hawke’s Bay region of Pakistan & Weibull with EM, MLM, MMLM, EPM, GM & RMSE, R\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the time granularity, estimation method, and error metrics of the studies on wind speed forecasting.
# When 5G Meets Deep Learning: A Systematic Review
## 3 Results
### What are the Main Problems Deep Learning Is Being Used to Solve?
#### 3.1.6 Traffic Prediction  
It is expected that Internet traffic will grow tenfold by 2027. This acts as a crucial anchor to create the new generation of cellular network architecture [42]. Predicting traffic for the next day, hour, or even the next minute can be used to optimize the available system resources, for example by reducing the energy consumption, applying opportunistic scheduling, or preventing problems in the infrastructure [42].  
In this systematic review, we found eight works that addressed traffic prediction using deep learning.  
The works presented in [43; 44] proposed a deep learning-based solution to predict traffic for network slicing mechanisms. Note that 5G relies on the use of network slicing in order to accommodate different services and tenants while virtually isolating them. In [43], a proactive network slice mechanism was proposed and a deep learning model was used to predict the traffic with high accuracy. In [44], a mechanism named DeepCog was proposed with a similar purpose. DeepCog can forecast the capacity needed to allocate future traffic demands in network slices while minimizing service request violations and resource overprovisioning.  
Three works considered both temporal and spatial dependence of cell traffic. In [6], the authors proposed a deep learning model to predict citywide traffic. The proposed model was able to capture the spatial dependency and two temporal dependencies: closeness and period. In [45], the authors proposed different deep learning models for mobile Internet traffic prediction. The authors used the different models to consider spatial and temporal aspects of the traffic. The maximum, average, and minimum traffic were predicted for the proposed models. In [46], the authors proposed a deep learning-based solution to allocate remote radio heads (RRHs) into baseband unit (BBU) pools in a cloud radio access network (C-RAN) architecture. The deep learning model was used to predict traffic demand of the RRHs considering the spatial and temporal aspects. The prediction was used to create RRH clusters and map them to BBU pools in order to maximize the average BBU capacity utility and minimize the overall deployment cost.  
In [47], the authors considered traffic prediction in ultra-dense networks, which is a complicated scenario due to the presence of beamforming and massive MIMO technologies. A deep learning model was used to predict the traffic in order to detect if a congestion will take place and then take decisions to avoid/alleviate such congestion.  
In [48], the authors addressed the benefits of cache offloading in small base stations considering the mobile edge computing (MEC). The offloading decision is based on the users' data rate, where the users with low data rates are offloaded first. Consequently, the authors proposed a deep learning model to predict the traffic data rate of the users in order to have a guide for the scheduling offloading mechanism.
# ZSM Framework for Autonomous Security Service Level Agreement Life-Cycle Management in B5G Networks
## 2 Related Work
### Network Slicing  
Network slicing presents itself as the centerpiece for splitting the physical infrastructure into multiple logical, isolated, and customized instances of network resources ([PERSON], 2017). When the slices span across multiple domains, encompassing different network segments and technologies, they are referred to as E2E network slices. The scientific community has focused on developing the intricacies of E2E network slicing as it is one of the main pillars of B5G networks. For instance, in [PERSON] et al. (2019), the authors present a reference architecture for network slicing end-to-end orchestration, using an ML approach to improve performance during the decision phases of the slicing life-cycle. However, it does not consider the use of ZSM during this process. Additionally in this line, [PERSON]. [PERSON] et al. (2019) offers a comprehensive study of network slicing security concerns, exploring the threats, challenges, and issues, highlighting the existing gaps in current research, with the aim to ease the development of a secure network slicing framework.  
Furthermore, in [PERSON] et al. (2019), a work with open-source tools such as OAI is presented, proposing a four-building-blocks classification that characterizes resource allocation, whereas various AI techniques are used in the orchestration to optimize each of these four blocks. Although the paper presents multiple useful insights, the presented network slicing orchestration only considers RAN slicing. In addition, it uses 5G Non-Standalone (NSA), which means that the slice has no logical management at the core, and security is not considered at all. In [PERSON] et al. (2019), [PERSON] et al. propose an architectural solution for E2E slice resource management composed of three different layers: network, orchestration, and intelligence based on Deep Reinforcement Learning (DRL). This work shows promising results in leveraging DRL as an enabling technology to enhance decision making in resource sharing. Additionally,its architectural proposal could be seamlessly integrated into the well-established and standardized ZSM framework.  
Regarding solutions that could be integrated into ZSM, [27] introduces a large-scale E2E network slice facility offering network slices as a service. This initiative effectively coordinates several domains via a federation approach. However, despite mentioning that the challenges associated with the project are being addressed by the ZSM standard, they do not adopt the reference architecture to integrate their solution. In our study, we expanded the concept of E2E slices, customizing them to address security requirements, resulting in the creation of E2E security slices. In particular, we have devised a ZSM-compliant approach to manage these E2E security slices within the context of B5G Networks.
## 4 Methodology
### Model Training
#### 4.2.2 Training Setup  
The neural network weights were initialized using the default Xavier uniform initializer provided by Keras. The dataset was partitioned into a 70% training set and a 30% hold-out evaluation set to assess the generalization performance of the trained RL agent. Details regarding the architecture of the actor and critic networks are provided in Section 4.1.2. The Adam optimizer was employed for training due to its combined advantages of adaptive learning rates and momentum, making it well-suited for deep learning scenarios involving sparse gradients or noisy observations, as is typical in RL settings. All training was conducted on a workstation running Ubuntu 22.04, equipped with an NVIDIA RTX 2080 Ti GPU.  
To rigorously evaluate the performance of the trained RL optimization model, a 30% hold-out split was further analyzed to identify edge cases. Specifically, samples in the evaluation set were ranked according to the deviation between their objective values and known optima. The top 100 samples exhibiting the highest errors, hereafter referred to as faulty samples, were selected as inputs to the RL agent. These samples represent scenarios where conventional optimization techniques often perform poorly, and thus serve as a challenging benchmark to evaluate the agent's post-optimization corrective capabilities. During evaluation, the RL agent was tasked with recommending optimal control parameter values for these faulty samples, thereby guiding the system toward improved output quality. The performance of the model was assessed based on the following criteria:  
* Mean reward: The average reward obtained across all optimized faulty samples.
* Stability: The consistency of predicted outputs across multiple evaluation trials.  
The RL agent consistently demonstrated the ability to reduce error in these difficult cases, validating its effectiveness as a robust post-optimization correction mechanism. The complete set of hyperparameters used for training the actor and critic networks is summarized in Table 3.
## 4 Joint Decoding-Complexity and Rate Control
### Determining the Model Parameters and Trade-Off Factors  
Having established the target bit rates and decoding-complexities at the CTU-level, the remaining modeling parameters in (6), QP and the trade-off factors for the bit rate and decoding-complexity must be determined at the CTU-level in order to apply the optimization function in (2) to determine the most appropriate coding structure for that CTU.
# Reconstructing Polarization Multiplexing Terahertz Holographic Images with Transmissive Metasurface
## 5 Conclusions  
In conclusion, polarization multiplexing THz holograms are realized by utilizing multi-foci metalens composed of all-dielectric meta-atoms with specific orientation angles, which is different from traditional metasurface holograms that were achieved based on iterative optimization algorithms. The focal points of the metalens are regarded as the pixel of a holographic image in our design. We analyze the effects of focal length and phase pixel number of metalens on the focal point, which shows that the holographic images with subwavelength resolution could be obtained at the optimized parameters. Moreover, by switching the polarization of incident lights, holographic images with different patterns are reconstructed on the focal plane. Such high-resolution and polarization multiplexing metasurface holograms have potential applications in THz communications, information engineering, and encryption.  
Figure 6: The corresponding reconstructed images with half of focal points when illuminated by (a) LCP, (b) RCP, and (c) LP lights for working frequency of 0.85 THz.  
**Author Contributions:** Conceptualization, X.W.; methodology, X.W. and R.W.; software, X.W.; validation, X.W.; formal analysis, X.W.; investigation, X.W., J.W., R.W., L.L. and Y.J.; resources, X.W.; data curation, X.W.; writing--original draft preparation, X.W. and J.W.; writing--review and editing, X.W. and J.W.; supervision, L.L. and Y.J.; funding acquisition, Y.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (Grant Nos. 62075048, 620205106), and the Natural Science Foundation of Shandong Province (Grant No. ZR2020 MF129).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Methodology
### Satellite Image Processing
#### 3.1.2 Atmospheric Corrections  
The main focus of this research was to assess the suitability of a number of spatial models to predict relative water depth (WD) using an already tested band ratio algorithm and calibration data. Atmospheric corrections may pose unnecessary bias in accurately assessing the spatial models due to cumulative  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **Name** & **Description** & **Topography** &
\begin{tabular}{c} **MBES** \\ **Backscatter** \\ \end{tabular} \\ \hline
1 & Hardground & Rock outcrops and mixed gravelly sediments & Rough & High \\
2 & Channel & Topographically controlled class with mixed & Rough & High \\  & & fine-grained sediments & & \\
3 & Fine sediments I & Featureless fine sediments & Smooth & Moderate \\
4 & Fine sediments II & Featureless fine sediments & Smooth & Low \\  & & Fine sediments with bedforms and possible & & \\
5 & Fine sediments III & & tidal control & Smooth & Very Low \\ \hline \hline \end{tabular}
\end{table}
Table 1: Seabed type and descriptions.  
increase in error uncertainty. Several studies focusing on measuring water quality using Landsat sensors have questioned the advantage of using atmospherically corrected data when attempting to describe the variability of optical water properties [44, 45]. Additionally, by using a single satellite image, and with the atmospheric conditions present, it is a reasonable assumption that they will not be noticeably different across the test area.
## 5 Application of Multilayer Architecture
### Architectural Support Using Docker and Kubernetes  
With the application of this multilayer architecture in dynamic and heterogeneous environments, the use of container technologies like Docker and Kubernetes (k3s) plays a crucial role in mitigating privacy leakage:  
* Isolation and Consistency: Containers provide isolated environments for each participating node, ensuring that computations are consistent and reproducible. This isolation limits the potential for privacy leakage between nodes.
* Orchestration and Scaling: Kubernetes automates the deployment, scaling, and operation of containers. This ensures efficient resource management and helps in dynamically adapting to changing computational demands, which is essential in large-scale FL systems.
* Secure Communication: Containers can be configured to enforce secure communication protocols, ensuring that data in transit are protected. Kubernetes can manage and automate the deployment of these secure channels.
* Automated Updates: Integration with Continuous Integration/Continuous Deployment (CI/CD) tools ensure that security patches and updates are consistently applied across all nodes, reducing vulnerabilities.
# The Impact of the 4.0 Paradigm in the Italian Agricultural Sector: A Descriptive Survey
## 3 Methodology  
Survey research is useful for obtaining information about a specific phenomenon concerning large populations, allowing for an adequate level of accuracy [36; 37]. The current research adopts descriptive survey research because the objective is to understand the significance of a phenomenon and describe its occurrence in a population [38; 39]. Indeed, descriptive surveys are highly valuable for gathering data from diverse populations because the researcher can extract the attitudes and features of respondents accurately [40]. Moreover, it is possible to provide an effective \"picture\" of the phenomenon being investigated from which evidence can be drawn. Thus, a descriptive survey is a convenient method when knowledge of a phenomenon is not too poorly underdeveloped, the variables and context can be described in detail, and the objective is to understand to what extent a given relationship is present. The intent of descriptive surveys is not necessarily to determine causal relationships, but they do provide an effective method for investigating a representative sample and enabling data regarding particular issues to be collected, which may be used to form the basis of decision-making activities in the future [41].  
Therefore, the primary research objective is not theory development but rather the investigation of the impacts of the Agri 4.0 paradigm in the Italian primary sector by describing the knowledge levels, achieved benefits, and perceived challenges.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Cluster of Challenges** & **Challenges** & **References** \\ \hline \multirow{3}{*}{Technological challenge} & Limited or absent interoperability & \\ \cline{2-3}  & Limited or absent flexibility & [27; 34] \\ \cline{2-3}  & Lack of connectivity & \\ \hline Economic challenge & Low return of investment & [35] \\ \hline \multirow{3}{*}{Implementation challenge} & Insufficient assistance & \\ \cline{2-3}  & Usage difficulty & \\ \hline Cultural and organizational challenge & Lack of key digital skills in the farm & [25] \\ \hline \hline \end{tabular}
\end{table}
Table 3: List of challenges.  
To obtain the above-mentioned objectives, a survey research process consisting of three steps was adopted: survey design, pilot testing, and data collection and analysis.
## 2 Study Area and Data
### The GPM IMERG-E Satellite Precipitation Product  
The GPM mission, as an advanced successor to the TRMM, provides new global rainfall and snowfall observation products. The GPM Core Observatory satellite was launched in February 2014 by NASA and JAXA, equipped with a Dual-frequency Precipitation Radar (DPR) and a GPM Microwave Imager (GMI). The GPM mission has different products, organized using three levels: Level-1 (IMERG-E), Level-2 (IMERG-L), and Level-3 (IMERG-F). The IMERG system is run in near-real time and provides IMERG-E and IMERG-L with 4-h and 12-h latencies, respectively. The IMERG-F is calibrated with rain gauge analyses by the Global Precipitation Climatology Center (GPCC) and then would be available after 3.5 months. These products are provided at 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) spatial scale between 60\({}^{\circ}\) S-60\({}^{\circ}\) N and at half-hourly time scale. In this study, IMERG-E half-hourly data from 2011 to 2018 were downloaded from the NASA website ([[https://gpm.nasa.gov/data-access/downloads/gpm](https://gpm.nasa.gov/data-access/downloads/gpm)]([https://gpm.nasa.gov/data-access/downloads/gpm](https://gpm.nasa.gov/data-access/downloads/gpm))). This product was chosen due to its short time latency (4-h), making it interesting for the early warning of flood events. We extracted four pixels of GPM IMERG-E that cover the Ghdat basin and we interpolated observed precipitation data with the inverse distance method to represent areal precipitation over the studied basin.
# Comparison of DFN Modelled Microfracture Systems with Petrophysical Data in Excavation Damaged Zone
## 2 Materials and Methods
### Fracture Network Simulation
#### 2.4.1 Geometric Parameters  
Individual fractures are spatially finite and can be interpreted as multiply bent two-dimensional surfaces, which can be approximated as planes [18; 19; 20]. They appear circular in isotropic rocks and form ellipsoids (penny shape) in anisotropic rocks [21]. The length and aperture parameters define the size of the fracture. Natural fracture networks generated under a specific stress field show fractal behaviour, e.g., [22; 23; 24]. They are self-similar and self-affine [25], which allows scaling.  
It is generally agreed that the size distribution of fracture lengths is asymmetric, with small fractures significantly outnumbering larger ones [24]. It can be estimated that the relative number of short to long fractures is an invariant of the scale [24]. The length distribution of fractures in a fracture network is commonly described as:  
\[N(l>L)=F.L^{-E^{*}}, \tag{3}\]  
where \(N\) is the number of fractures longer than \(L\), \(F\) is a constant dependent on the size of the specimen, and \(E^{*}\) is the fracture length index [26; 27]. The fracture length index \(E^{*}\) characterizes the distribution of the lengths of a given fracture set: The greater the absolute value of \(E^{*}\), the more short fractures are present.  
Fracture length and aperture are often considered to be linearly correlated, e.g., [22; 28; 29]. Aperture \(A\) can be defined as:  
\[A=a\cdot l+B, \tag{4}\]  
where \(l\) is the fracture length and \(a\) and \(B\) are constants [29]. Constant \(a\) describes the ratio of the maximum aperture/length and has been estimated to be between \(2.1\times 10^{-4}\) and \(8.2\times 10^{-3}\)[29]. \(B\) describes the aperture of fractures with zero length and should thus be zero but can obtain non-zero values since the constants are typically defined using linear regression.  
The spatial density of fractures scales due to the fractal nature of the fracture systems and can be estimated using box-counting [22; 25; 30]. Here, the studied image is covered by boxes with varying sizes. Number \(N\) of boxes containing a part of the studied image is proportional to their size \(r\) as:  
\[N(r)\sim r^{-D}, \tag{5}\]  
where \(D\) is a constant. For fractal images, \(D\
eq 1\) and the fractal dimension \(D_{f}\) can be estimated as the slope of \(log(N(r))\) against \(log(r)\) as:  
\[D_{f}=\frac{log(N(r))}{log(r)}. \tag{6}\]  
The fractal dimension is a quantitative parameter of a fracture network, which defines the spatial density of fractures.  
Strike and dip define the orientation of an individual fracture, and the orientation of a group of fractures is approximated with a mathematical distribution generated from the strike and dip values of individual fractures.