esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Security Requirement and S-Box Design Methods
### S-Box Properties
#### 2.1.4 Linear Approximation (LA)  
The LA of an S-box is defined as follows:  
\[\text{LA}(F)=\max_{a,b\in\mathbb{F}_{2^{n}},a\
eq 0}\{\#\{X\in\mathbb{F}_{2^{n}}| \alpha\cdot X=\beta\cdot f(X)\}-2^{n-1}\}\]  
where \(\alpha\in\mathbb{F}_{2^{n}}\), \(\beta\in\{\mathbb{F}_{2^{n}}\setminus 0\}\) and \(\alpha\cdot X\), \(\beta\cdot f(X)\) are evaluated over \(\mathbb{F}_{2}\). The operation \(\alpha\cdot X\) denotes the inner product of vectors \(\alpha\) and input X of the S-box. \(\beta\cdot f(X)\) denotes the product of vectors \(\beta\) and output \(f(X)\) of the S-box.  
By omitting the trivial case \(\alpha=\beta=0\), the LA can be determined through the maximum entry in linear approximation table of the S-box. The lower value of LA is preferable in resistance against linear cryptanalysis [3]. Usually, the value of LA must be less than 28 (i.e., LA \(<\) 28).
## 3 Results and Discussion
### Unconfined Compressive Strength (UCS) Test
#### 3.1.3 Effects of M-GGBS-RHA on UCS  
As shown in Figures 7 and 8, all ternary binder compositions improved the UCS values in the stabilized gypseud soil. The highest values of UCS obtained by the M-GGBS-RHA stabilizer ratio of 1:2.5:0.5 were 2050, 5232, and 9142 kPa after 7, 28, and 90 days of curing, respectively. However, the lowest enhancements after 7, 28, and 90 days of curing were 563.67, 758.67, and 975 kPa, respectively, when a stabilizer-binder ratio of 1:0.5:0.5 was used.  
Figure 6: UCS of soils stabilized with 20% of L, M, and M-GGBS before and after soaking in the presence of sulfate: (**a**) after 7 days of curing, (**b**) after 28 days of curing, (**c**) after 90 days of curing.  
After soaking, the M-GGBS-RHA ratio of 1:2.5:0.5 produced the highest UCS of 9721 kPa among all the M-GGBS-RHA-stabilized soils, an increase of 6.33%; the UCS decreased with the reduction in the GGBS ratio. This enhancement in the UCS values might have been due to the hydration process following the prolonging of the curing time from 7 days to 90 days; this should ensure proper hydration, improved strength, and resistance against sulfate attacks [8]. Furthermore, the production of more cementitious gels, such as CSH, CAH, and CSAH, contributed to consumption of the available calcium, leading to the inhibition of et  
Figure 7: UCS of soils stabilized with 10% of L, M, and M-GGBS-RHA before and after soaking in the presence of sulfate: (a) after 7 days of curing, (b) after 28 days of curing, (c) after 90 days of curing.
# A New Approach to Identifying Crash Hotspot Intersections (CHIs) Using Spatial Weights Matrices
## 2 Methodology  
### Process Map  
This paper presents a three-step approach to directly identifying crash hotspot intersections (CHIs) by using (1) construction of the inverse network distance-band spatial weights matrix of intersection (INDSWMI), (2) construction of the k-nearest distance-band spatial weights matrix between crash and intersection (KDSWMCI), and (3) intersection hotspot analysis (IHA). The process map for the approach is shown in Figure 1.  
(1) INDSWMI construction  
The first step (INDSWMI construction) includes the following three substeps:  
1. Road network construction. In this research, we apply osm2 grouping [35], an open source software product, to build the road network including road junction and segment tables from the open street map (OSM) [36] road spatial dataset.
2. Intersection extraction. Note that a road junction that links with three or more road segments is considered an intersection, which is a junction of traffic flow and pedestrian flow [37, 38] in this research. We developed an intersection extraction algorithm to extract intersections, such as T-intersection, Y-intersections, cross-intersections, and X-intersections [39], from the road junction table and create the intersection table. The intersection extraction algorithm can be described as the following structured query language (SQL) script:\"create table public.intersection as select * from public.road_junction a where a.degree> 2\".
3. INDSWMI construction. We developed the INDSWMI generation algorithm to construct the INDSWMI, which can conceptualize the spatial relationships between intersections, with road  
Figure 1: A process map for the identification approach to detect crash hotspot intersections (CHIs).  
network constraints based on the intersection table and road segment table. The INDSWMI is saved in the swm file format which is compatible with ArcGIS.  
(2) KDSWMCI construction  
The second step (KDSWMCI construction) is to conceptualize the crash-intersection spatial relationships. We developed the KDSWMCI generation algorithm to calculate the number of crashes and adjacency crashes of each intersection and save the crash-intersection spatial relationships in the KDSWMCI table.  
(3) Intersection hotspot analysis  
The third step, intersection hotspot analysis, is to identify CHIs using a statistical variable--Getis-Ord Gi*. The intersection hotspot analysis (IHA) generates the intersection hotspots shapefile using the standardized Getis-Ord Gi* of each intersection under randomization null hypothesis [40] computation. We can detect CHIs through geographic information system (GIS) visualization of the intersection hotspots shapefile. The Intersection Prediction Accuracy Index (IPAI) was calculated to quantitatively evaluate the prediction performance of IHA.
# Using Optimisation Meta-Heuristics for the Roughness Estimation Problem in River Flow Analysis
## 5 Experimental Set-up
### Case Study 2--The River Aniene  
The second case study presents a stronger focus on risk mitigation practises and their implementation through the proposed computational approach, thus posing the attention on a different hydrodynamic aspect.  
More specifically, in the objective function the observed water surface elevation value \(h_{\text{obs}}\) is the outcome of a previous simulation performed with the standard approach where the Manning's \(n\) values are taken from the empirical tables). Such value is then compared to the 'expected' target limit value \(\text{HR}(\mathbf{x})\) imposed by the geomorphological shape of the riverbed. The latter is calculated to prevent flooding.  
This time, we consider the reach of the Aniene river forming a significant portion of the catchment of the river Tiber. This is an important stream crossing six different Italian regions and, even if only marginally, Vatican City. Its length is 99 km and it drains a catchment of 1414 km\({}^{2}\).  
We focus our analysis on the section of this river surrounding the town of Tivoli, as shown in Figure 5. This area features an ancient roman bridge crossing the course of the river thus forming, from a hydrological standpoint, a discontinuity in the computational field. This makes this case study more interesting but also more challenging due to the need of a more complex computational model.  
Note that the riverbed characteristics are more homogeneous with respect to those of the previous case study, with short, high permeable grassy plains depending of course on the seasons. The different feature here is represented by the presence of a few man-made constructions right near the river banks: this occurrence, together with a deeper shaped riverbed, generally guarantees a more controlled water flow and decreases the risk of flooding in the entire basin.  
Figure 4: HEC-RAS visualisation of the of the cross section placed in the same position of the gauge station.  
Not having an actual real data station, the cross-section selected to be evaluated in the objective function in this case is the first one, labelled with \"2\" (see Figure 1b)and the overall discharge considered is \(Q=400\) m\({}^{3}\)/s. Evidence of the simulation results are reported in Figure 6.  
Figure 5: Bird eye view of ‘Ponte Lucano’ and ‘Tomba dei Plauzi’ in Tivoli, showing the industrial and flood-prone downstream area of the river [retrieved from Google Maps 20 July 2020].  
Figure 6: HEC-RAS representation of the water surface elevation of the Aniene river after the simulation with optimised Manning’s \(n\).  
It has to be noted how in this second analysis, where the geometry, hence the computational area, is smoother, we were able to perform a steady hybrid 1D-2D simulation, thus improving the quality of the obtained results.
## 4 System Design
### System Organization  
Most of the functionality of the system will rely on server implementation while the client will act mostly as a presentation layer for the end-user. Both sides of the system can be delivered in multiple ways. The next decision to make is how the entities should be organized. Such a system can be delivered using two main ways: monolithic and distributed microservices.  
A monolithic system usually represents a system where all the building blocks are delivered under the same process [35; 36]. From a development point of view, it is the easiest way to build and deliver software since all the resources are accessible by all the application blocks. To evaluate if a monolithic architecture is the proper solution for collaborative document editing software, there are two fundamental questions to be addressed: What are the scaling needs of the system? and What are the scaling capabilities of the architecture?
# Intelligent Resource Orchestration for 5G Edge Infrastructures
## 7 Results
### Resource Optimization Results  
As stated previously, the optimization problem addressed in this work consists of finding the optimal mapping of virtual resources (VMs or containers) to edge servers using different optimization criteria and constraints. The solution proposed for this problem ise based on the ILP formulation, as shown in Section 6. These kinds of problems can be effectively solved using standard solvers for linear programming, such as COIN CBC [78], CPLEX [79], and SCIP [80]. In particular, our implementation was built upon the OR Tools library for Python [81], leveraging the SCIP solver integrated within the Pywraplp module [82].  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Model** & **RMSE (Avg.)** & **Exec. Time (Fit + Predict)** \\ \hline Naive seasonal (K = 24) & 0.0498 & 17 min. \\ Bayesian Regression & 0.0419 & 21 min. \\ FB Prophet & 0.0504 & 40 min. \\ Auto-ARIMA & 0.0508 & 5.9 h \\ RNN-LSTM & 0.0491 & 12.7 h \\ \hline Adaptive (oracle) & 0.0314 & – \\ Adaptive (realistic) & 0.0414 & – \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of prediction accuracy (average RMSE) for Azure traces.  
Figure 6: Prediction accuracy (RMSE) of various time-series forecasting models applied to Azure VM traces.  
Our experimental test bed consisted of 500 VMs randomly chosen from the Azure dataset, which had to be distributed across 12 different 5G edge locations. Each edge location housed a physical server equipped with 64 cores and 192 GB of RAM. The parameters defining each VM included the assigned number of cores, the predicted CPU usage percentage for the next 24 h period (based on hourly predictions derived from the Bayesian regression model), and the preferred edge location. These preferred edge locations were also chosen randomly, following the distribution illustrated in Figure 7.
# Resilience Evaluation of Multi-Path Routing against Network Attacks and Failures
## 1 Introduction
### Contributions  
The contributions of our study are threefold:  
1. We classify multi-path routing to be of two types--FMP and MMP--based on path control. Because SP routing lacks rapid mitigation mechanisms to counter Internet hazards, FMP routing can mitigate these problems by selecting a connection link to the first hop beyond its control. However, it cannot deal with a problem in another hop. To mitigate this, MMP routing can take advantage of controlling all paths available between a source and a destination.
2. We verify that the effectiveness of multi-path with real networks deployed globally, especially MMP routing, shows more resilience than FMP used currently. Using the global network, we show that FMP mitigates certain cases of network problems, but MMP gains impressively lower loss rate by comparison.
3. When the global evaluation is under a transit-link DDoS attack, the loss rates of FMP that pass the transit-link are affected significantly by a transit-link DDoS attack, but the other alternative MMP paths show stable status under the DDoS attack with proper operation under 1% loss rates. Meanwhile, the impact to loss rate of FMP is over 40% by a bandwidth of 700 Mbps.
## 2 Materials and Methods
### Architectures
#### 2.3.1 Atrous Net  
The Atrous-Net is inspired by the work by [25]. Dilated convolutions--also called atrous convolutions--are convolutional operations that are performed on non-contiguous voxels instead of being performed on adjacent voxels. The distance between voxels in a convolution is called dilation. Therefore, spatial information can be better preserved each time a filter is applied. This way, the Atrous net performs a succession of convolutions without pooling to avoid the reduction of the spatial resolution of the feature maps. It uses dilated convolutions in order to achieve enough receptive field to compute complex features. Dilated convolutions have been used with quite successful results in other works [34; 35]. These convolutions are used as an alternative to the pooling operation to calculate multi-scale features without reducing the shape of the input. In this work, dilation 1 was used for the first and last layer and dilation 2 was implemented for all other layers. After every convolution, a batch normalization and a Rectified Linear Unit (ReLU) as non-linear activation are applied. Figure 3 shows a scheme of the architecture and the number of filters used in every convolution. This network performs 10 convolution operations and has 3.2 and 10.6 million parameters in the 2D and 3D implementations, respectively.
# Relationship between CO2 Fertilization Effects, and Stand Age, Stand Type, and Site Conditions
###### Abstract  
The CO2 fertilization effect (CFE) plays a crucial role in the amelioration of climate change. Many physiological and environmental factors, such as stand age, stand type, and site conditions, may affect the extent of the CFE. However, the relationship between the CFE and these factors remains elusive. In this study, we used the emerging gross primary production (GPP) remote sensing products, with GPP predicted using eddy covariance-light use efficiency models (EC-LUE GPP) based on satellite near-infrared reflectance of vegetation (NIRv GPP) and assessed with a random forest model to explore the CFE trends with stand age in a coniferous forest and a broad-leaved forest in Heilophagiano Province, China. We additionally compared the differences among the CFEs under different site conditions. The CFEs in coniferous forests and broad-leaved forests both showed a rapid increase in stands of 10 to 20 years of age, followed by a decline after reaching a maximum, with the rate of decline reducing with age. Eventually, CFE remained stable in stands near 100 years of age. However, the CFE in coniferous forests exhibited more extended periods of rapid increase and a higher maximum than in broad-leaved forests. Moreover, in this study, we used the site class index (SCI) to grade site conditions. The results demonstrate that the CFE differed significantly under different levels of site conditions, and these differences gradually decreased with age. The site with the highest SCI had fewer environmental restrictions on the CFE, and consequently, the CFE rate of decline was faster. Our results are of significance in understanding the CFE and adapting to future changes in atmospheric CO2 concentration.  
CO2 fertilization effect; gross primary productivity; stand age; stand type; site class index index (SCI) to grade site conditions. The results demonstrate that the CFE differed significantly under different levels of site conditions, and these differences gradually decreased with age. The site with the highest SCI had fewer environmental restrictions on the CFE, and consequently, the CFE rate of decline was faster. Our results are of significance in understanding the CFE and adapting to future changes in atmospheric CO2 concentration.  
2023 15 4197. [[https://doi.org/10.3390/rs15174197](https://doi.org/10.3390/rs15174197)]([https://doi.org/10.3390/rs15174197](https://doi.org/10.3390/rs15174197))
## 3 Information and Communication Technology Solutions for Precision Agriculture
### Data Analysis and Artificial Intelligence  
Various data analysis and AI tools and technologies supporting PA were reviewed for the purposes of this paper, encompassing big data analytics, computer vision, data fusion/reconstruction, recommender systems, evolutionary algorithms, fuzzy logic, granulation/interpolation techniques, machine learning, heuristic/decomposition/denoising/pattern-matching algorithms, semantic processing, predictive models, simulation/visualization tools, and others.
# CrossDeFi: A Novel Cross-Chain Communication Protocol
## 5 Proposed Work Overview
### Asset-Specific Bridge Selection (ABS)
#### 5.2.4 Quantifying Bridge Suitability  
The ABS considers multiple criteria to select the most suitable bridge between the source and destination. A miner is involved in selecting the suitable bridge for the transactions. Multiple bridge information is stored in the bridge hub, and the miner node estimates the bridge score \(B_{\text{Score}}\) for each bridge for suitable bridge selection. Based on  
Figure 4: Operational flow of bridge selection in CrossDeFi.  
the real-time DeFi characteristics, the bridge hub information is varied. This selection is quantified through the following equation:  
\[B_{\text{Score}}=\alpha(C_{\text{Size}})+\beta(A_{\text{Type}})+\gamma(L_{\text{ Depth}})+\delta(S_{\text{level}})+\omega(A_{\text{level}}) \tag{6}\]  
The variable \(B_{\text{Score}}\) represents the bridge's score value, estimated using criteria such as community size \((C_{\text{Size}})\), asset type \((A_{\text{Type}})\), liquidity depth \((L_{\text{Depth}})\), security level \((S_{\text{level}})\) and adaptability level \((A_{\text{level}})\). The coefficients \(\alpha\), \(\beta\), \(\gamma\), \(\delta\), and \(\omega\) are weighting factors for each criterion, summing to one.
# Smart Grid Theft Detection Based on Hybrid Multi-Time Scale Neural Network
## 1 Introduction
At the weekly time scale, a residual network (ResNet) was used to solve the gradient disappearance problem in deep extraction of customer electricity data. Additionally, at the monthly time scale, the Alex network (AlexNet) was used to obtain the seasonal variation of the underlying monthly electricity consumption law of the electricity consumption data and enhance the learning effect. Finally, the proposed method deployed the CatBoost (categorical features gradient boosting) classifier in the model for final classification. The main research contributions of this paper can be summarized as follows:  
* To address the problem of imbalanced datasets in current AI-based power theft detection methods, this study proposes the use of a time generative adversarial network (Time GAN) to synthesize datasets, thus effectively solving the issue of insufficient power theft user data. The validity of this method is demonstrated through experiments. * To address the issue of existing power theft detection methods only processing power consumption data in a singular manner, which does not fully consider the time series nature of consumption data, a hybrid multi-time scale neural network was designed to extract features, utilizing Bidirectional Long Short-Term Memory (Bi-LSTM), Residual Network (ResNet), and AlexNet to extract features at different time scales, thus improving detection accuracy and reducing the false positive rate. * Using CatBoost for classification, the experimental results showed that this method could detect power theft with 96.64% accuracy and 96.87% recall rate while significantly reducing the false positive rate. This is a significant achievement of this study. Therefore, the main contribution of this study is the proposal of a comprehensive power theft detection method that uses Time GAN for data synthesis, multi-time scale neural networks for feature extraction, and CatBoost for classification. The validity of this method is demonstrated through experiments.
# Assessment of Deep Learning Methodology for Self-Organizing 5G Networks
## 4 Enabling 5G with SON and Deep Learning
### Deep Learning-Based Anomaly Detection
#### 4.2.4 Phase 1--Training and Testing  
The machine learning framework is shown in Figure 3. The framework depicts that the model was first trained, as shown by the flow of the top graph in the left block, which is phase 1. Model training only require normal KPIs or non-anomalous data. Usually pre-processing must occur before training the autoencoder model. There could be missing values which need to be properly handled. We used the mean imputation method to address missing KPIs and then scaled the values between 0 and 1. The autoencoder was trained with the linear reconstruction cost; as a result, the parameters of the model were learned. The reconstruction cost for each sample was obtained, and this acted as the score. In the bottom graph in Figure 3, in phase 1, new unseen KPIs consisting of both anomalous and non-anomalous samples were used. Again, in this case, the KPIs were preprocessed in a similar way to the training KPIs. The trained model was then used to reconstruct the test samples, and as a result, the reconstruction error for each input sample of the test KPIs was obtained. Then, a decision functionwas formed based on the fact that small reconstruction values represent non-anomalous samples and high reconstruction values represent anomalous samples.
## 2 Preprocessing
### Features Selection
#### 2.2.3 Calculated Frequency of Signals  
Electroencephalography (EEG) measures electrical activity in the brain through electrodes placed on the scalp. The calculated frequency of EEG signals is vital for understanding brain state and mental processes. Here is an overview of EEG frequency bands and their significance: Delta (0.5-4 Hz): Associated with deep sleep and restorative processes. Common in infants and deep anesthetic states. Theta (4-8 Hz): Linked to light sleep, relaxation, and creativity. Often observed during meditation and daydreaming. Alpha (8-12 Hz): Indicates relaxed, yet alert states, often present when the eyes are closed. Attenuates with mental tasks or distraction. Beta (12-30 Hz): Related to active thinking, problem-solving, and focus. Prominent during active concentration and anxiety. Gamma (30 Hz and above): Associated with higher-level cognitive functions, including perception and consciousness. Involves memory recall and sensory processing. Description shown in Figure 3.