esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 System Model and Assumptions
### Adversary Model  
In a decentralized blockchain system, we must account for the potential of malicious actors.  
* Control Fraction (_f_): An adversary is assumed to be able to control up to a fraction \(f\) of the network's total resources, whether computational power, stake, or nodes.
* Behavior of Malicious Nodes: These adversaries may collude, delay messages, forge transactions, or attempt to disrupt the consensus process. Types of Attacks Considered:
* Sybil Attacks: Where an adversary creates multiple identities to gain disproportionate influence [29] (see Figure 9).
* Majority Attacks: Where an adversary gains control of more than _fmax_, allowing them to manipulate the blockchain ledger [30] (see Figure 10).
* Eclipse Attacks: Where adversaries isolate specific nodes by controlling their peer connections [31] (see Figure 11).  
Figure 10: Example of a Majority Attack in a graph of 11 nodes.  
Figure 9: Example of a Sybil Attack in a graph of nine nodes.  
We assume that the adversary's control fraction \(f\) remains below the security threshold \(f_{max}\) of the consensus protocol. This ensures that the system remains secure as long as \(f<f_{max}\). Additionally, both honest and malicious nodes are assumed to act in their self-interest, which may include economic incentives, in alignment with game-theoretic models of blockchain security. This aspect was already addressed by [PERSON] et al. [32], who proposed a new consensus mechanism that applies both game theory and graph theory to determine the Nash equilibrium of the network.
## 4 Results
### Scenario 2
#### 4.2.2 Second Example Test  
The results of a different test from Scenario 2 (with parameters: \(K=5,N=34,L=4\), \(QBER=7\)) are presented in Table 4 and Figure 6. The results related to the average number of victims' updates in the second test from Scenario 2 are presented in second column of Table 4 and in Figure 6a. In this test, the trend continues, as the number of updates between the victims is much more comparable comparable across all rule combinations (\(\approx\)88.5 on average). At the same time, the observations related to the attacker's average synchronization percentage-presented in third column of Table 4 and in Figure 6b-remain consistent: dynamic approaches provide a higher level of security compared to hebbian-only synchronization (\(\approx\)61% compared to \(\approx\)69% of attacker's synchronization) and the attacker's synchronization percentage in all of the random walk-oriented tests is higher than in tests where victims were using dynamic_rows or dynamic_matrix while the attacker chose the update algorithm randomly (\(\approx\)71.5% compared to \(\approx\)65%).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Update Rules** & **Victims’ Updates** & **Attacker’s Synchronization** \\ \hline all parties: hebbian & 78.94 \(\pm\) 2.06 & 69.32 \(\pm\) 0.22 \\ \hline victims: dynamic\_rows, attacker: hebbian & 85.05 \(\pm\) 1.89 & 61.42 \(\pm\) 0.18 \\ \hline victims: dynamic\_matrix, attacker: hebbian & 91.08 \(\pm\) 1.95 & 61.28 \(\pm\) 0.18 \\ \hline all parties: random walk & 92.15 \(\pm\) 2.17 & 71.5 \(\pm\) 0.2 \\ \hline victims: dynamic\_rows, attacker: random walk & 93.71 \(\pm\) 2.23 & 71.17 \(\pm\) 0.2 \\ \hline victims: dynamic\_matrix, attacker: random walk & 89.12 \(\pm\) 2.06 & 71.13 \(\pm\) 0.21 \\ \hline victims: dynamic\_rows, attacker: random rule & 87.02 \(\pm\) 2 & 65.85 \(\pm\) 0.21 \\ \hline victims: dynamic\_matrix, attacker: random rule & 90.76 \(\pm\) 2.17 & 65.41 \(\pm\) 0.22 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Scenario 2–\(K=5,N=34,L=4,QBER=7\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Update Rules** & **Victims’ Updates** & **Attacker’s Synchronization** \\ \hline all parties: hebbian & 33.06 \(\pm\) 1.19 & 68.41 \(\pm\) 0.24 \\ \hline victims: dynamic\_rows, attacker: hebbian & 53.95 \(\pm\) 1.69 & 64.42 \(\pm\) 0.21 \\ \hline victims: dynamic\_matrix, attacker: hebbian & 56.16 \(\pm\) 1.84 & 64.08 \(\pm\) 0.21 \\ \hline all parties: random walk & 53.95 \(\pm\) 1.64 & 71.39 \(\pm\) 0.23 \\ \hline victims: dynamic\_rows, attacker: random walk & 55.65 \(\pm\) 1.71 & 71.38 \(\pm\) 0.22 \\ \hline victims: dynamic\_matrix, attacker: random walk & 55.26 \(\pm\) 1.69 & 71.35 \(\pm\) 0.21 \\ \hline victims: dynamic\_
## 5 Cavity Analysis in CW Operation
### Basic Configuration of a Ti:sapphire Oscillator  
Figure 1 illustrates the basic configuration of a linear X-shaped, or Z-shaped cavity, which are commonly used for Ti:sapphire lasers. The feedback end mirrors, \(EM1\) and \(EM2\), are the output-coupler (OC) and the high-reflector (HR), respectively. Two curved mirrors, \(M1\) and \(M2\), with foci \(f_{1}\) and \(f_{2}\) are used to focus the laser mode into the crystal. The distance between the curved mirrors controls the spatial mode and stability range of the cavity. The cavity has two arms: from \(M1\) to \(EM1\) with a length, \(L_{1}\), and from \(M2\) to \(EM2\) with a length, \(L_{2}\). The most useful technique to analyze light beams in an optical cavity is using ABCD matrices. The ABCD matrices (formulated within the paraxial approximation) are used to represent an optical system as a 2 \(\times\) 2 matrix, so one can calculate how the optical system affects the path and properties of a beam of light passing through it. The beam at the system input is represented by a vector, \(\mathbf{V}_{in}\), containing the distance, \(x_{in}\), above the optical axis and the beam angle, \(\theta_{in}\), with respect to the optical axis. The beam at the output of the system can be calculated by \(\mathbf{V}_{out}=\mathbf{M}\mathbf{V}_{in}\), where \(\mathbf{M}\) is the optical system ABCD matrix:  
\[\left[\begin{array}{c}x_{out}\\ \theta_{out}\end{array}\right]=\begin{bmatrix}A&B\\ C&D\end{bmatrix}\left[\begin{array}{c}x_{in}\\ \theta_{in}\end{array}\right] \tag{4}\]  
A detailed description of this method along with explicit expressions for the ABCD matrices of the optical elements in a cavity can be found in [40]. For the cavity illustrated in Figure 1, we can define the distance between the curved mirrors as: \(f_{1}+f_{2}+\delta\) (\(\delta\) measures the shift from a perfect telescope) and use the ABCD matrix method to find the range of values for \(\delta\) where the cavity is spatially stable. The condition for resonator stability is \(|A+D|<2\), where \(A\) and \(D\) are the elements of an ABCD matrix that represents a single round trip in the cavity with respect to an arbitrary reference plane. Another optional cavity configuration is a ring configuration [41] with the benefit of a shorter cavity length and a higher repetition rate. The same ABCD matrix technique and stability condition hold for a ring cavity.
# Intelligent Detection Method for Satellite TT&C Signals under Restricted Conditions Based on TATR
## 2 Related Works  
### Traditional Signal Detection  
Traditional signal detection methods can be broadly categorized into four types: feature-based detection, energy-based algorithms, matched filtering, and cyclostationary detection. Energy-based algorithms [12; 13] quantify the overall energy of the received signal over a specific time interval, estimate the noise variance, and compare it to a pre-determined decision threshold to ascertain signal presence. Although the energy-based method operates without requiring prior knowledge and exhibits rapid detection, it falls short in accurately detecting signals amid substantial noise or interference. The basic idea of feature-based detection [14] is to calculate the eigenvalues of the signal's covariance matrix and use the ratio of the maximum and minimum eigenvalues as the threshold of the test statistic for signal detection. The accurate determination of the threshold is crucial for the success of feature-based detection algorithms. Matched filtering [15; 16] constructs a matched filter based on the amplitude-frequency response of the signal to be detected. In cases where prior information about the signal is accessible, and the background noise adheres to a Gaussian distribution, matched filtering frequently demonstrates enhanced detection capabilities. Cyclostationary detection [17] utilizes the cyclostationary characteristics of a signal, such as symbol rate, carrier frequency, and sampling frequency, to detect the presence of a signal. It can also estimate certain modulation parameters [18]. However, this method has high computational complexity and poor real-time capabilities. Feature-based detection, matched filtering, and cyclostationary detection methods achieve pleasing results under conditions of complete information. However, the performance of these time-domain signal-processing-based methods sharply declines under restricted conditions. Although energy detection can alleviate this problem under certain conditions [19; 20]. As the variety of interference and clutter types grows, devising appropriate threshold levels becomes intricate, especially when the signal energy is weaker than those of interference and noise. Consequently, there is a pressing necessity to investigate intelligent detection methods.
# Efficient Construction of the Equation Automaton
## 3 Equation Automaton  
The equation automaton has been introduced for the first time by [PERSON] in [1]. In 1996, [PERSON] introduced the notion of partial derivatives and used it to define the equation automaton [2]. [PERSON] and [PERSON] [6] defined the notion of canonical derivatives of a linear expression and constructed a new automaton called the c-continuation automaton. They also proved that this automaton is isomorphic to the position automaton in the sense that the two automata have identical sets of states, identical initial and final states, and transitions Theorem 6 in [6]. Using an equivalence relation over the set of states of the c-continuation automaton, they derive the equation automaton in quadratic time. The definition of the equation automaton of a regular expression is based on that of the partial derivatives of regular expressions, which are multisets of regular expressions over \(A\). The partial derivative of E with respect to \(a\in A\) is defined recursively on the structure of E as follows:  
\[\partial_{a}(0) = \partial_{a}(1)=\emptyset,\] \[\partial_{a}(x) = \textbf{if }a=x\textbf{ then }\{1\}\textbf{ else }\emptyset,\] \[\partial_{a}(\mathrm{F}+\mathrm{G}) = \partial_{a}(\mathrm{F})\cup\partial_{a}(\mathrm{G}),\] \[\partial_{a}(\mathrm{F}\cdot\mathrm{G}) = \partial_{a}(\mathrm{F})\cdot\mathrm{G}\cup\lambda(\mathrm{F}) \cdot\partial_{a}(\mathrm{G}),\] \[\partial_{a}(\mathrm{F}^{*}) = \partial_{a}(\mathrm{F})\cdot\mathrm{F}^{*}\,.\]  
The partial derivative of E with respect to the string \(u\in A^{*}\) is denoted by \(\partial_{u}(\mathrm{E})\) and recursively defined by \(\partial_{\epsilon}(\mathrm{E})=\mathrm{E}\) and \(\partial_{ua}(\mathrm{E})=\partial_{a}(\partial_{u}(\mathrm{E}))\). Let \(D(\mathrm{E})=\{\mathrm{E}\}\cup\{\mathrm{E}^{\prime}\ |\ \mathrm{E}^{\prime}\in \partial_{u}(\mathrm{E})\text{ with }u\in A^{*}\}\). **Theorem 1**. : _(Antimirov [2]). The cardinality of the set \(D(\mathrm{E})\) of all partial derivatives of a regular expression \(\mathrm{E}\) is less than or equal to \(||\,||\,||+1\)._  
The equation automaton \(\mathcal{E}_{\mathrm{E}}=\langle D(\mathrm{E}),A_{\mathrm{E}},\mathrm{E}, \delta,F\rangle\) of E is defined by:  
* \(\delta(\mathrm{E}^{\prime},a)=\{\mathrm{E}^{\prime\prime}\in D(\mathrm{E})\ |\ \mathrm{E}^{\prime\prime}\in \partial_{a}(\mathrm{E}^{\prime})\}\),
* \(F=\{\mathrm{E}^{\prime}\in D(\mathrm{E})\ |\ \lambda(\mathrm{E}^{\prime})=1\}\)_._  
**Example 2**. : _Let us consider the regular expression \(\mathrm{E}=(a^{*}+ba^{*}+b^{*})^{*}\).
## 2 Materials and Methods
### Workflow Description
#### 2.3.3 Scenarios Definition  
After defining the datasets to be used, we evaluated different scenarios while maintaining spatial resolutions of 5 m (RF1-RF6) and 10 m (RF7-RF12). This analysis aimed to determine whether spatial resolution influenced the outcomes, as summarized in Table 4. The datasets are represented as follows: \"TOPO\" refers to Digital Elevation Model data from Sigtierras, including slope and aspect derivatives. \"PS\" represents PlanetScope imagery and indexes, while \"GLMC\" denotes textures derived from the near-infrared band of PS. \"S2\" represents Sentinel-2 data and indexes, and \"S1\" represents Sentinel-1 data. In the proposed scenarios, the multiclass or binary target approach will also be evaluated to distinguish _Polylepis_ from other shrub classes.  
Variable Reduction  
GEE provides a variety of supervised classification algorithms suitable for satellite image classification. This study employed the Random Forest (RF) algorithm, implemented in GEE as smileRandomForest (SRF). RF is a nonparametric statistical method of decision tree assembly widely used in land cover classification from remote sensing data [21]. Each tree is trained with different data so that by combining the results, some errors are compensated by others. This algorithm is used by several authors in forestry domains based on pixel classification of satellite images [22; 45; 59; 60; 61] and with object approach as in [36; 40]. For this study, the number of trees was set to 50, while other parameters were left at their default values to facilitate scenario reliability comparison.  
From the former, the Recursive Feature Elimination (RFE) technique has been selected for its ability to eliminate noise and irrelevant features, using a Machine Learning algorithm to rank features by importance, discard the least important features, and re-fit the model to improve the performance of the classification model [62]. In GEE, the SRF algorithm was employed to assess the importance of variables in each classification scenario. The variable reduction with RFE was performed by being the first iteration run with all variables, and, in the following runs, the features of lesser importance were removed. After eliminating the least significant variables, the classification process was run again, and the proposed metrics were evaluated.
## 3 Data and Methods
### Vegetation Indices
#### 3.2.2 Gndvi  
The GNDVI is very similar to the NDVI, with the difference that GNDVI uses the green band (Green, 0.5 \(\upmu\)m) instead of the red (Table 2). It is believed to be more sensitive to chlorophyll--a concentration as compared to NDVI [39]--and thus, may be more accurate than NDVI over areas with dense vegetation cover. The GNDVI was also shown to be better correlated to burn severity than NDVI in several Mediterranean sites [40].
# Self-Organizing Networks for 5G and Beyond: A View from the Top
## 4 Machine Learning Algorithms for SON
### ML Algorithms Taxonomy
#### 4.1.2 Unsupervised Learning (UL)  
Unsupervised learning does not involve any training input or sample contrary to supervised learning. The key rationale of unsupervised learning is to recognize a pattern among the input data that the algorithms receive so that future inputs can be predicted. Typical examples include social networks analysis while in SON applications. Unsupervised learning algorithms are used mainly for self-healing and self-optimizing use cases [9]. The most common cases are the following:  
Figure 9: ML Algorithms taxonomy.  
* Self-organizing maps (SoM): A SoM intends to identify groups of data so that a representation of the input data is created. This technique is called clustering. The main idea behind SoM is to group cells with similar network parameter settings and trigger changes for the specific group uniformly.
* Game theory: Known since the 1950s and applied later in biology, social science and economy/political sciences during 1970s game theory have been used for network systems modelling. According to game theory, several players, nodes or artificial agents (in the case of computer science) perform actions that affect each other while the target is to get a zero-sum result by subtracting the losses of one player and adding the gains of the winning player [83]. According to the authors of [15], operators can avoid zero human interaction by using game theory approaches in mobile networks but limited literature concerning game theory applicability in LTE networks exists.
## 4 Our Findings
### Analysis of Core Reasons for Vulnerabilities  
In our research, we discovered the primary causes of vulnerabilities throughout the included studies, as shown in Figure 5. The most common explanation was \"Software Vulnerability\", which had 35 instances, showing the ubiquity of faults in software systems as a key source of security hazards. This was followed by \"Access Control Weakness\" (23) and \"AI Model Bias\" (21), which emphasizes the difficulties in protecting access mechanisms and resolving biases in artificial intelligence models. Furthermore, \"IoT Security Weakness\" occurred 15 times, indicating rising worries about the security of Internet of Things (IoT) devices. With 10 instances apiece, \"Blockchain Security Issue\" and \"Cryptographic Flaw\" are two more noteworthy explanations that highlight how critical it is to fix flaws in blockchain systems and cryptographic implementations. Although they are less common, problems like \"Human Manipulation\" (3), \"Third-Party Risk\" (3), and \"Data Privacy Issues\" (8) also add to the overall vulnerability illustration.  
The category \"Other\" had the highest count (117), which represents a broad range of less frequently cited but equally critical reasons for vulnerabilities. These include emerging threats, such as novel attack techniques not yet widely studied; regulatory and compliance gaps, where security weaknesses arise from a lack of standardized regulations, particularly in emerging technologies like blockchain and AI; economic and incentive misalignment, where cost-cutting or misaligned incentives in decentralized finance (DeFi) lead to security flaws; legacy system weaknesses, where outdated or deprecated technologies remain susceptible to known exploits; interoperability and integration flaws, caused by poor integration between platforms or systems; lack of security culture and awareness, where organizational or user behavior leads to vulnerabilities, such as developers neglecting best practices or users falling for scams; and Protocol Design Flaws, where inherent weaknesses in system architectures, such as blockchain consensus mechanisms or smart contract frameworks, create exploitable vulnerabilities. These findings emphasize the diverse and  
Figure 4: Distribution of attack categories and their frequencies in the included studies.  
multifaceted nature of security weaknesses, ranging from technical flaws to human, organizational, and systemic factors, and highlight the need for comprehensive strategies to mitigate these risks.
## 3 Results and Discussion
### Load and Electricity Generation in July 2030
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Category** & \multicolumn{2}{c}{**Installed Capacity, MW**} & \multicolumn{2}{c}{**Total Generation, GWh**} & \multicolumn{2}{c}{**Total Generation, GWh**} \\  & **2024** & **2030** & **July 2024** & **July 2030** & **Year 2024** & **Year 2030** \\ \hline Nuclear & 348 & 348 & 255 & 255 & 2776 & 2776 \\ CCGT & 550 & 700 & 265 & 442 & 2878 & 3654 \\ Hard coal & 200 & 200 & 96 & 140 & 731 & 478 \\ Total non-renewables & 1098 & 1248 & 616 & 837 & 6385 & 6908 \\ \hline Hydro run-of-river & 430 & 430 & 161 & 161 & 1812 & 1812 \\ Hydro reservoir & 1700 & 2100 & 249 & 287 & 4228 & 4117 \\ Solar PV & 200 & 1500 & 27 & 250 & 239 & 1879 \\ Onshore wind & 1114 & 1500 & 191 & 277 & 2617 & 3807 \\ Biomass & 100 & 100 & 45 & 45 & 606 & 606 \\ Other renewables & 50 & 50 & 27 & 27 & 344 & 344 \\ Total renewables & 3594 & 5680 & 700 & 1047 & 9846 & 12,565 \\ \hline Total generation & 4692 & 6928 & 1316 & 1884 & 16,231 & 19,473 \\ \hline Total electricity demand, GWh & & & 1806 & 1913 & 18,291 & 18,778 \\ Renewable share,\(f_{\rm RES}\) (\%) & & & 38.8\% & 54.7\% & 53.8\% & 66.9\% \\ Non-renewable share,\(f_{\rm non-RES}\) (\%) & & & 34.1\% & 43.7\% & 34.9\% & 36.8\% \\ Fossil fuel share,\(f_{\rm fossil}\) (\%) & & & 20.0\% & 30.4\% & 19.7\% & 22.0\% \\ Net import dependency,\(f_{\rm import}\) (\%) & & & 27.1\% & 1.6\% & 11.3\% & \(-\)3.7\% \\ Share of VRE in load,\(f_{\rm VRE}\) (\%) & & 12.1\% & 28.4\% & 15.6\% & 30.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Power demand and electricity generation in Croatia: comparison between 2024 and 2030. Extreme heatwaves in South Europe are found to lead to record-high electricity demand while simultaneously reducing wind power generation [59]. Heatwave-driven power surges have also been recorded in China, the US, and India in the summer of 2024. Surges in electricity demand, driven by increased air conditioning use, were largely met by fossil fuel generation--mainly coal in China and India, and both coal and gas in the United States [60]. This is exactly what the model forecasts for Croatia in 2030, and potentially for other Southern European countries, as electricity demand and summer temperatures are expected to increase in the coming years. Renewable energy alone will be unable to meet the full electricity demand during heatwaves, making an increase in fossil fuel generation inevitable. Improved resilience measures, such as energy storage, flexible generation, and better climate forecasting, will be essential to reduce the reliance on fossil fuels during heatwaves.
# A MAP Overhead Aware Two-Dimensional OFDMA Burst Construction Algorithm
## 5 Simulations Results and Discussion  
### Simulation and Modeling  
The simulation scenario was an OFDMA system with a 20-MHz channel, which was comprised of 24 OFDMA symbols and 60 subchannels in a downlink subframe. Because the partial usage subchannelization technique allocated two OFDMA symbols for each slot, the total number of slots in the DL sub-frame was 720. According to the received signal-to-noise ratio (SNR), we adopt many quadrature amplitude modulations of QPSK1/2, QPSK3/4, 16 QAM1/2, 16 QAM3/4, 64 AQM2/3, and 64 QAM3/4. The SNR in each subchannel received by each SS followed the normal distribution. The mean SNR was set to 15 db, and the standard deviation was set to 5 db. The arriving traffic of each downlink connection followed the Poisson distribution. Furthermore, the simulation tool we used was the ns2 Worldwide Interoperability for Microwave Access (WiMAX) module with some modifications [15].  
The flow scheduling which estimated the appropriate number of slots to each connection applied channel quality and the Quality of Service (QoS) aware bandwidth allocation algorithm (CQQ) [16] in this paper. The CQQ examined channel quality, avoided wasting system bandwidth by dynamically modifying the DL/UL bandwidth ratio to match the DL/UL traffic ratio, and provided QoS guarantee in the OFDMA network. It applied a weighted fair queuing strategy to the connection which demanded a larger requested bandwidth and a higher average transmission rate was assigned higher weight and a larger allocation of slots.  
Figure 7: An Example of an enhanced burst indexing aware algorithm (EHA). (**a**) allocating bandwidth to \(C_{1}\); (**b**) allocating bandwidth to \(C_{2}\); (**c**) allocating bandwidth to \(C_{3}\); (**d**) splitting the burst for \(C_{3}\).  
In Section 5.2, we provide simulation results to illustrate the performance of the proposed algorithm, EHA, and compare it with other algorithms. The simulations investigate several performances including the requested bandwidth, the number of bursts, and the variation of channel quality between subchannels under the total throughput. In order to evaluate the total throughput of the subframe allocation alone, the results in this section were obtained without taking into account the DL-MAC overhead.