esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Reformulating SVAT models to accommodate new data sources
This has also been called the problem of underdetermination, when a single data set can be used to support multiple competing hypotheses. Models that use remote sensing data are prone to this type of error because the same VIs, or transformations of VIs, are often used to estimate more than one model parameter, introducing co-linearity, and because the ground data by which they are calibrated or validated have errors or uncertainties on the order of 20-30%. The traditional approach to resolving problems of underdetermination is to prefer the simpler model or hypothesis over the more complex (Occam's Razor) [83], although this is a philosophical rather than a scientific principle. With the advent of flux towers and moderate resolution, daily return satellite imagery, a different approach has become feasible. Top-of-canopy measurements of fluxes can be scaled to larger landscape units using simple models developed from ground data and VIs. Tower data can then be used to invert SVAT models to test hypotheses about the controls on photosynthesis, growth and water consumption at the plot scale, and changes in foliage density over time revealed by satellite sensors can be used to scale these findings to the ecosystem or biome scales of measurement. Plant stress detected at the towers is detectable by satellites first as an anomaly in VI:surface temperature plots [75] and latter as a drop in VI as plants adjust their foliage density to match the limiting conditions [4]. A recent successful example of the combined approach was the use of flux tower and MODIS EVI data in the Amazon to show that ET and GPP increased, rather than decreased, in response to seasonal or interannual dry periods, contrary to model results [66, 67].Those papers concluded that more observational data and less reliance on models could lead to more accurate predictions of climate change effects on global ecosystems. To take advantage of the new tools, it might be necessary to reformulate traditional SVAT and SEB models that use VIs as proxies for LAI, f\({}_{\text{c}}\), and other canopy attributes that are only moderately well predicted by remote sensing measurements, as recommended in [37]. One approach is to use so-called Big Leaf models of carbon fixation and stomatal conductance [49]. These models, first suggested by [PERSON] in 1965 [cited in 49], treat the canopy and associated unvegetated areas as a single surface, and use average leaf and surface properties as scalars. Big Leaf models are especially well suited to satellite sensors and flux towers that provide top of the canopy measurements. Big Leaf models frequently perform as well as multilayer SVAT models in actual applications, e.g. [84]. Although they oversimplify actual canopies, they can produce accurate results when calibrated with flux tower or other ground data [16]. Current Big Leaf models still require an estimate of LAI, because they take average leaf properties then scale them over the canopy by multiplying by LAI [20, 49]. However, in principal LAI could be replaced by a VI in these models [37].
# A Systematic Review of Cyber Range Taxonomies: Trends, Gaps, and a Proposed Taxonomy
## 3 Proposed Taxonomy
### Environment
(2018) described user-facing interfaces and dashboards as part of the _Management_ layer, enabling remote access and the control of cyber range functionalities. Similarly, actions such as creating, modifying, and updating scenarios--often referred to as orchestration tasks--were also consistently framed as management activities in the literature [PERSON] et al. (2018); [PERSON] et al. (2018). We therefore did not treat these as stand-alone dimensions but as functional components of _Management_. We identified six functional areas that fell under this dimension, as shown in Table 10.  
Technology  
Finally, _Technology_ underpins all other dimensions by providing the infrastructure, simulation platforms, automation tools, and emerging solutions like AI or machine learning. We treated _Technology_ as its own dimension so it could capture vital aspects such as simulation platforms for realistic environments, automation tools for scenario execution, scoring, system management, as well as AI, machine learning, or augmented reality solutions that enhance realism or interactivity. As a result of Phase I, we compiled the baseline taxonomy, shown in Figure 4. \begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Characteristic** & **Sub-Characteristic** & **Description** & **Refs** \\ \hline Resource & Data Storage & Storing scenario models, logs, and backups. & [35; 76] \\ \hline Resource & Metadata & Configuration details about environments and exercises. & [77; 78] \\ \hline Lifecycle Management & Creation & Building or cloning infrastructures, including digital twins. & [79] \\ \hline Lifecycle Management & Editing & Modifying existing scenarios, though less commonly detailed in the literature. & [80] \\ \hline Lifecycle Management & Deployment & Automating the rollout of scenarios or network nodes. & [12; 81] \\ \hline Lifecycle Management & Execution & Starting, pausing, and stopping exercises. & [82] \\ \hline Lifecycle Management & Recovery & Cleaning up or resetting systems post-exercise. & [12; 83] \\ \hline Lifecycle Management & Generation & Orchestrating attacks, traffic, and events. Emphasizes _when_ and _how_ these are triggered. & [11] \\ \hline Remote Access & — & Enables administrative or authorized user control of the range infrastructure remotely. & [11; 12; 84] \\ \hline Role & — & Defines user types and permissions (e.g., Instructor, Student, Admin). & [57; 85; 86; 81] \\ \hline Management Interface & — & Dashboards and portals used to manage and monitor the range. & [87; 88] \\ \hline Command and Control & — & Low-level engines or interfaces that allow direct command execution within the range. & [11; 75] \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Characteristics of _Management_ dimension.
## 6 Sensitive Analysis
### ANN Model  
In this particular study, [PERSON]'s algorithm [92] was used to investigate the effect of various input variables on the predicted component dynamic amplification factors (CDAFs) using the ANN model. [PERSON]'s algorithm or [PERSON]'s method is a technique used to estimate the importance of input variables in a neural network model. It provides a way to assess the relative contribution of each input variable to the output of the network. The [PERSON] method offers easily interpretable results, facilitating the understanding of the relative importance of input variables. It is suitable for sensitivity analysis with models involving both continuous and categorical variables and is computationally efficient, particularly with numerous input variables. Additionally, the [PERSON] method is model-agnostic, enabling its application to various modeling techniques, including both linear and nonlinear models. According to [PERSON]'s algorithm, the connection weights between the input and hidden layers, as well as between the hidden and output layers, are divided into partitions. The absolute values of these partitioned weights are then used to calculate the relative importance of the input variables, as described in Equation (8). In this algorithm, the partitioning of the connection weights helps to assess the contribution of each input variable separately, based on the magnitude of the weights. By considering the absolute values of the partitioned  
Tables 19 and 20 show the relative importance and ranking of the various input parameters for the five- and ten-story models, respectively. The analysis of relative importance percentages and importance ranks reveals that the CDAF is significantly influenced by the tuning ratio (\(T_{\text{s}}/T_{1}\)), the damping ratio (\(\xi_{s}\)), and the position of the non-structural component, i.e., floor level. These variables demonstrate a strong dependency on the CDAF. The excitation direction and the soft-story level has relatively lesser influence. These findings can guide decision-makers and engineers in prioritizing their efforts to enhance a building's seismic resilience and readiness, focusing on the parameters that have the most significant impact on the CDAF.
# Analysis of the Near-Ground Wind Field Characteristics during Typhoon Soulik
## 4 Quantitative Analysis of Fluctuating Wind Properties
### Variations in the Gust Factor  
The gust factor is strongly associated with the intensity of turbulence and serves as a crucial measure of gust strength. It is defined as the ratio of the average peak wind velocity to the average wind velocity over a specified time period \(t_{g}\). Its expression is as follows:  
\[G_{u}=1+\frac{max\overline{u(t_{g})}}{U} \tag{13}\]  
\[G_{v}=1+\frac{max\overline{v(t_{g})}}{U} \tag{14}\]  
\[G_{w}=1+\frac{max\overline{v(t_{g})}}{U} \tag{15}\]  
where \(max\overline{u(t_{g})}\), \(max\overline{v(t_{g})}\), and \(max\overline{v(t_{g})}\), respectively, represent the average highest wind speed of longitudinal, transverse, and vertical fluctuating winds.  
Figure 10 depicts the variations in longitudinal, transverse, and vertical gust components in relation to average wind velocity at different heights. From the figure, it can be seen that there is a progressive drop in each direction gust factor as the average wind velocity increases, aligning with the research outcomes of [PERSON] et al. [13]. As the average wind velocity is below 12 m/s, the gust factor diminishes more rapidly. Conversely, as the average wind velocity exceeds 12 m/s, the relationship with the average wind velocity tends to remain constant.  
Figure 11 depicts the observed variations in the gust factor of height and fitting curves. From the figure, it can be observed that the gust components in \(u\), \(v\), and \(w\) orientations diminish as the height rises. Since the expression of the gust factor profile is not yet clear, this study fits the various gust factors in the form of \(G_{i}=a\left(\frac{z}{\Omega}\right)^{\beta}\). The study demonstrates that the changes observed in the gust factors for \(u\), \(v\), and \(w\) orientations with height align with the fitting results. This suggests that the equation can accurately describe the changes in Typhoon Soulik's gust factor with height. The results of the fitting expression are presented in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Researcher** & **Direction** & **Expression** \\ \hline \multirow{3}{*}{Present Results} & Longitudinal & \(G_{u}=2.17\left(\frac{z}{\Omega}\right)^{-6.67}\) \\  & Transverse & \(G_{v}=3.76\left(\frac{z}{\Omega}\right)^{-5.03}\) \\  & Vertical & \(G_{w}=6.4\left(\frac{z}{\Omega}\right)^{-6.52}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Formula for fitting gust factor.  
Figure 10: Relationship between gust factor and average wind velocity: (**a**) Longitudinal, (**b**) Transverse, and (**c**) Vertical.
# Quantifying Sub-Elite Youth Football Weekly Training Load and Recovery Variation
## 1 Introduction
To the best of our knowledge, there is a lack of literature about training load and recovery variations across a competitive season in sub-elite youth football. More specifically, there are few studies in sub-elite youth football, concerning the research topic of training load and recovery. Building upon that, training load variations in sub-elite players may depend on socio-economics, demographics, experience and competitive levels [3; 4; 5; 6; 7; 8; 9; 10]. As far as we are concerned, there has not been any study assessing training load and recovery status in Portuguese sub-elite football players yet. Additionally, previous studies were conducted in elite youth football academies; wherefore, this is the first study in a sub-elite academy. Another research gap is the recovery status assessment. There is only one study addressing a recovery scale in youth football and it is important to understand the recovery variations across a competitive season [18]. Moreover, this study will allow us to determine if the sub-elite players have significant training load variations by age groups, weekly microcyles, training days and positional roles. Based on the above-mentioned information, the aims of this study were to: (i) to quantify the weekly training load and recovery variations performed by U15, U17 and U19 young players in a sub-elite football academy; and (ii) to analyze the influence of age, training day, weekly microcycle, training and playing position on the training load and recovery status. It was hypothesized that there are significant differences in weekly training load and recovery variations according to age group, weekly microcycle, training day and playing position.
# A Novel TLS-Based Fingerprinting Approach That Combines Feature Expansion and Similarity Mapping
## 2 Related Work  
The first version of the TLS protocol was published in 1999 by the Internet Engineering Task Force (IETF) and has been under constant review and development since, with the latest version, TLS 1.3 [3], being released in 2018. Despite such a lengthy period of adoption, it remains in the Open Web Application Security Projects (OWASP) top ten, and as recently as 2021 cryptographic failures were promoted to the number two slot [4]. According to the OSWAP report, underlying issues can range from a simple lack of understanding of the TLS mechanisms resulting in weak cipher suite choices to invalid certificate chains and depreciated TLS versions.  
TLS 1.3 is increasingly being adopted for its enhanced security and privacy features, with 63% of the global top 1 million web sites now supporting it [4]. Despite the modernity of the version, however, there remained gaps in the privacy aspect of the protocol, and in 2022 the TLS working group released a further amendment to the protocol [3], to introduce the Encrypted ClientHello (ECH). The introduction of ECH has far-reaching implications for TLS fingerprinting methods that have traditionally relied on metadata contained within the unencrypted ClientHello.  
As this paper focused on future ability to detect and fingerprint malware and C2 domains under TLS, the background and exposition work focused on the latest released version, 1.3, including ECH[3], with previous versions only referenced for historical clarity.
# Survey of Network Coding Based P2P File Sharing in Large Scale Networks
## 2 BitTorrent-Like Systems and Network Coding Background  
### How BitTorrent-Like Systems Work  
Initially, a seeder, who owns the full content of a file, divides the file into equal size segments called pieces, chunks, or blocks. For the rest of the paper, the piece terminology is used. As new peers join the swarm, i.e., P2P network, the seeder randomly selects and distributes pieces to leechers which are nodes that need some pieces or all the pieces to complete the file downloading. When a peer finishes downloading a piece, it can upload this piece to other peers who request it. As shown in Figure 1a, the peer maintains a structure called a bitmap which indicates whether the peer has a certain piece or not. The original specification [4] of the protocol requires that clients download pieces in a purely random way, but subsequently new techniques were introduced to improve the performance [11; 19]. An example of an improved technique is the local rarest first, which is called the rarest first piece selection algorithm as well. In such technique, a piece that is the least common among all the neighbor peers is selected to be distributed with high priority. The target is to ensure a uniform distribution of the pieces. In practical systems, the distribution of the rare pieces is still a big issue--since the local rarest piece among neighbors is not necessary, the same global rarest piece among the entire network [14; 18].  
In the network coding BitTorrent as shown in Figure 1b, a peer first encodes the pieces that it has by multiplying each piece with a coefficient (c), which is randomly chosen from a finite field, known as Galois Field (GF). Then, the pieces are linearly combined as one encoded piece. The encoded piece is distributed to other downstream peers. At the receiver peer, the decoding process is performed when a sufficient number of encoded pieces are received.
## 4 Result and Discussion
### Three-Round Delphi Study Result
This grouping hierarchy was then included in the second and third rounds of Delphi questionnaires to obtain respondents' opinions based on 5-point Likert scales. In addition to opinions, a comment column was provided, allowing respondents to provide comments and other suggestions related to this proposal. Second-round questionnaires were sent to 14 respondents, who were asked to complete them within two weeks. However, only 12 experts participated in this round, with a return rate of 86%. Two participants could not participate was because of a time conflict. The results of the second round of questionnaires were analyzed using statistical tests to determine the convergence of the respondents' answers. The statistical test used was the Kendall Concordance coefficient (W coefficient) employing IBM SPSS version 26 software [56]. The significance level was set as \(p\)-value = 0.000, and this value was used to evaluate the level of convergence of respondents' answers [57]. The value of the concordance coefficient (W) was between 0 and 1; the value of 0 indicated no agreement and 1 indicated that all respondents' answers were identical or in perfect agreement. The concordance coefficient test showed a \(p\)-value of 0.000, which indicates that the respondents' answers converged. Kendall's W value for the second round of questionnaires was 0.542, indicating strong agreement among respondents [57]. In the third round, total questionnaires were sent to 14 respondents, and only 12 respondents participated, giving a return rate of 86% as in the second round of surveys. Two did not complete it because they were busy with work and did not have time to participate in the questionnaire. A statistical convergence test using IBM SPSS version 26 was performed to determine the concordance coefficient value. Kendall's W value was 0.688 with a \(p\)-value of 0.000. The statistical test indicated that the opinions of the respondents on the metric converged (strong convergence) because the Kendall W value above 0.6 indicated a strong convergence [57]. The statistical analysis of the third round of the Delphi survey indicated that all six potential metrics were valid as metrics to measure the effectiveness of coal transportation. The statistical analysis of three rounds of Delphi is described in Table 7. From the three-round Delphi study, we can conclude that the selected metrics in Table 6 are valid and can be used as coal mining transportation metrics. Based on the three three-round Delphi survey we established physical availability, use of availability, productivity, fuel efficiency, coal losses, and quality of service as the final metrics. Figure 3: Proposed Mining Transportation Overall Vehicle Effectiveness (MTOVE) framework.
# The Role of Elasticity in the Vortex Formation in Polymeric Flow around a Sharp Bend
## 2 Materials and Methods
### Computational Methodology
#### 2.2.2 Simulation Parameters  
The main goal of this research is to evaluate the role that elasticity plays in the formation of vortices in flow around a sharp corner. For that purpose, we performedmultiple simulations to create two phase diagrams in order to show the overall effects of four dimensionless parameters, Wi, Re, El, and \(\beta\). These parameters are defined as follows:  
* The Weissenberg number Wi = \(\lambda\cdot\)U/H, where \(\lambda\) is the relaxation time of the polymer, U is the characteristic velocity, and H is the characteristic length scale of the geometry (channel height). The Weissenberg number is a ratio of the polymeric timescale to a convective timescale, akin to an elastic to viscous forces ratio in the context of this work.
* The Reynolds number Re = \(\rho\cdot\)U\(\cdot\)H/\(\eta_{0}\), where \(\rho\) and \(\eta_{0}\) are, respectively, the density and the zero-shear rate viscosity of the solution. The Reynolds number is a measure of the ratio of inertia to viscous forces.
* The elasticity number, a derived parameter, characterizes the balance of elastic and inertial forces in the fluid and is defined as El = Wi/Re.
* The solvent viscosity ratio \(\beta=\eta_{\text{s}}/\eta_{0}\), where \(\eta_{\text{s}}\) is the Newtonian solvent viscosity and \(\eta_{0}\) is the zero-shear rate viscosity of the solution.  
An important note about the elasticity number El and the viscosity ratio \(\beta\) is that they are both material parameters that depend on \(\eta_{0}\), and are independent of the fluid velocity. The zero-shear rate viscosity, \(\eta_{0}\), physically corresponds to the concentration of polymers in the solution. A decreasing El indicates a decrease in the polymer concentration. Conversely, an increase in \(\beta\) corresponds also to a decrease in the polymer concentration, which is the parameter typically varied during experimental work.  
Two sets of simulations were conducted in order to meet this goal. The first group of simulations conducted during this research were used to create a Weissenberg number versus Reynolds number phase space diagram. The Weissenberg numbers evaluated were 0.5, 1, and 1.5. Simulations were run with viscosity ratios, \(\beta\) values, ranging from 0 to 1 in 0.1 increments. A \(\beta\) value of 0 represents a polymer melt. A \(\beta\) value of 1 represents the viscosity of a Newtonian fluid (solvent). A second group of simulations was conducted in order to create a viscosity ratio, \(\beta\), versus elasticity number, El, phase diagram. All of these simulations were performed with a constant value for the Weissenberg number (Wi = 1).
## 2 Related Work
### Virtual Network Mapping
In [29], the objective was to tackle data privacy and data areolation concerns, and the introduction of Federated Learning (FL) as a means to model VNE was presented for the first time. Building upon Horizontal Federated Learning (HFL), the researchers introduced a VNE architecture denoted as HFL-VNE. This architecture collectively optimizes decisions by leveraging distinct local and federated reward mechanisms and loss functions, leading to a substantial enhancement in learning efficiency. In summary, according to above situation, there are limited virtual network mapping algorithms that can fully take into account the QoS requirements. Furthermore, the metrics employed by previous algorithms are typically insufficient, potentially overlooking key factors influencing QoS in practical applications. Hence, the algorithm designed by us considers multiple QoS metrics for VNRs and provides different QoS guarantees for VNRs with different QoS requirements. This approach aims to enhance the request acceptance rate while diligently safeguarding the quality of service for VNRs to the greatest extent possible.