esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Model and Traffic Pattern on Determining the Self-Similarity in IP Networks
## 3 Self-Similarity Statistical Factor  
Time series can be used to detect operational anomalies in IoT networks. The time series represents the collection of items arranged along the timeline. The elements of classical time series are real numbers. It is a convenient and frequently used method for representing the dynamics of a wide variety of complex systems.  
When we aggregate self-similar time series, the new series has the same autocorrelation function as the original. Autocorrelation and autoregression are techniques for analyzing time series data that are characterized by fluctuations in which adjacent observations are generally of similar values, while the differences between distant observations can be quite large. If the fluctuations in the time series are seasonal, regression is applied using category flags or AR (higher order) models, or a classic time series decomposition is performed [1,24].  
The main advantage of using time series self-similarity models is the ability to determine the degree of self-similarity of a series through only one parameter--the Hurst exponent. This parameter expresses the decay rate of the series autocorrelation function.  
The dependence of the variance of the stochastic process \(X\)_(t)_ on time t for asymptotically longtime series is written by the following formula:  
\[\left\langle\,X(t)^{2}\,\right\rangle\,\sim\,\left\{\begin{array}{c}|t|\,\, \textit{Winer}^{\prime}\textit{s process}\\ |t|^{2}\,\,\textit{anomalous diffusion}\\ \,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\, \,\,\,\,  
of fractional Brownian motion obtained by letting the pointwise regularity parameter \(H\) vary over time [25; 26]. The Hurst exponent values of time series can be estimated using the rescaled range analysis (R/S) method. The method used for the Hurst exponent estimated calculation, using the R/S analysis, estimated by simple linear regression, follows the methodology developed by [PERSON] and [PERSON] [27; 28].  
Modern techniques for determining the Hurst exponent are most often used in fractal mathematics. Among the time series, the fractal series constitute a special class. This term is related to the chaos phenomena observed in systems, the dynamics of which are described by this type of time series. The article presents a detailed analysis of several methods of determining the \(H\) exponent in the detection of traffic anomalies in IoT, including estimation of R/S, corrected R/S estimation by [PERSON]'s exponent, Hurst empirical exponent, improved Hurst empirical exponent, and Hurst theoretical exponent [2; 20].
## 3 The Hardy Cross Method; Different Versions  
The Hardy Cross method is presented here: the original approach in Section 3.1, a version of the Hardy Cross method from Russian practice in Section 3.2, the modified Hardy Cross method in Section 3.3, and finally the method that uses multi-point iterative procedures instead the Newton-Raphson method and which can be implemented in all the aforementioned methods.
# Revealing Development Trends and Key 5G Photonic Technologies Using Patent Analysis
## 4 Empirical Study  
### Patent Search Results  
The patent search results were first analyzed before a technology network analysis was conducted to preliminarily understand the relevant technology development. Research indicates that a third-order patent technology classification code is sufficient to represent the technological characteristics of the patent in its relevant fields and is also close to industry classifications [43; 44; 45]. Table 1 shows the top five third-order CPCs of 5G photonic technology.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ranking** & **CPC Classification Number** & **Frequency of Appearance** & **Percentage** \\ \hline
1 & H04B & 19 & 25.33\% \\
2 & H04W & 12 & 16.00\% \\
3 & G02B & 8 & 10.67\% \\
4 & H01Q & 3 & 4.00\% \\
5 & H04Q & 3 & 4.00\% \\ \hline \hline \end{tabular} Note: Frequency of appearance denotes the number of times a patent appears in the CPC.  
\end{table}
Table 1: Distribution of top 10 Cooperative Patent Classification (CPC) third-order classification codes.  
A total of 30 third-order CPC technologies were observed in the third-order CPC distribution in this study. According to Table 1, 5G photonic technology is mostly concentrated technology in H04B, H04W, G02B, H01Q, and H04Q (definitions of each CPC code are presented in Appendix A). In the CPC system, H04B relates to data transmission; H04W relates to wireless communication networks; G02B relates to optical elements, systems, or apparatuses; H01Q pertains to antennas (i.e., radio aerials); and H04Q relates to details of selecting apparatuses or arrangements.  
The aforementioned analysis revealed that 5G photonic technology was mainly related to data transmission, wireless communication networks, and optical components. Table 2 shows the analysis results for the top five patentees. According to Table 2, the patentee with the most patents is CommScope Technologies LLC., which has benefited from the development of recent key industry trends such as fiber optics and mobile communications, 5G, and the Internet of Things.
# An Adaptive Security Framework for Internet of Things Networks Leveraging SDN and Machine Learning
## 5 Enhanced SCAFFOLD Protocol
### SCAFFOLD Algorithm
#### 5.4.1 Ensemble Classifiers  
Enhanced SCAFFOLD utilizes an ensemble of machine-learning classifiers to analyze the network flows and identify potential attacks. An ensemble classifier combines the predictions of multiple individual models to make a final classification decision. This approach often yields better accuracy and robustness compared to using a single model.  
The ensemble used in Enhanced SCAFFOLD consists of three popular machine-learning algorithms as follows:  
Figure 5: Enhanced SCAFFOLD algorithm.  
Random Forests (RF): Random forests are an ensemble learning method that constructs multiple decision trees during training and outputs the class, which is the mode of the classes predicted by the individual trees. It is well known that random forests can handle high-dimensional data, minimize overfitting, and produce feature importance metrics.
2. Support Vector Machines (SVM): SVMs are supervised learning models that analyze data for classification and regression analysis. They construct a hyperplane or set of hyperplanes in a high-dimensional space to separate different classes. SVMs are effective in handling nonlinearly separable data by using kernel tricks.
3. A family of artificial neural networks called recurrent neural networks (RNNs) is made to process sequential data. They preserve an internal state that enables them to identify patterns and temporal connections in the supplied data. In applications involving time series data, RNNs--especially variations like long short-term memory (LSTM) and gated recurrent units (GRU)--have demonstrated remarkable performance.  
The Enhanced SCAFFOLD's ensemble classifier uses methods like majority voting and weighted averaging to integrate the predictions of these three models. Through the use of the benefits of each individual model, this ensemble approach provides a deeper analysis of the network flows.
# Innovative System for BIM/GIS Integration in the Context of Urban Sustainability
## 4 Results
### Cesium Platform  
Obviously, as mentioned, this technique exhibits drawbacks in the use of VR/AR reality. In this regard, to overcome the limitations of both the first and the second method, a method based on the use of the libraries and the Cesium platform is proposed. In particular, a software module was implemented for the conversion of the.ifc file into the.cityGML file. The result of this combination is emphasized in Figure 16, highlighting the characteristics present in the 3D model in the drop-down menu when the building of interest is queried.  
The advantages of this merging are evident, allowing to integrate GIS-like modeling and BIM data both in its three-dimensional representation and with regard to querying, also here in GIS style (identify, filter, etc.). Below, in the picture, it is possible to observe the presence of a time bar that allows for conducting temporal simulations. In fact, by updating the BIM data in real time (by also integrating different kinds of sensors), it is possible to make simulations over time of the structural behavior of the building, allowing for evaluations of the influence of the positioning of the building (for example, evaluating the exposure to the sun during the day) on the performance of the materials used (in  
Figure 16: Screenshot of three-dimensional model building view on the GIS map in Cesium.  
Figure 15: (**a**) App: virtual representation of one phase of the surveyed area; (**b**) app: visualization of BIM model in situ, showing the capacity to interact with it.  
the facade or structural of the facade renovation), and on the state of deterioration of the photovoltaic panels. To display temporal data, CesiumJS uses CZML, a JSON format, for describing dynamic graphics. CZML allows for representing moving objects, changes over time, and other temporal dynamics. CesiumJS can integrate dynamic data in real time using WebSockets for continuous two-way communication and REST APIs for periodic update requests, allowing users to update visualizations in real time with data from sensors, data feeds, or other dynamic sources. Real-time data, such as the location of vehicles and the state of infrastructure or weather conditions, can be visualized using data streams that continuously update the scene in CesiumJS. The possibility of geo-temporal analysis therefore guarantees the study of short-, medium-, and long-term phenomena.  
The data acquired can be used to simulate the impact of distinctive design solutions directly on the BIM model, reduce errors, and make data-driven decisions. It is possible to monitor infrastructures in real-time and to control the security and durability of structures thanks to more accurate inspections. Lastly, it can be used to strategize and oversee maintenance interventions using comprehensive data and forecast upcoming maintenance requirements, contributing to the realization of so-called digital twins.
## 6 Open Challenges and Related Work
### Open Challenges
#### 6.1.3 Low Power Consumption  
Low-power transmission technology is the key to extending the life of household wireless sensors. The packet detection algorithms used on such sensors need to reduce the power consumption as much as possible while ensuring the detection rate. She [30] proposed an adaptive threshold algorithm combining shifting window difference (SWDT) and forward-backward (FBDT) difference in real-time R-wave detection. It can solve the problem of heavily loaded computation caused by the complicated algorithm of the traditional theory. [PERSON] [31] optimized the cross-correlation algorithm by quantizing the sampled data before performing correlation calculation. Because the raw data are analog signals, which are stored as floating-point numbers, it requires a lot of multiplication to perform correlation calculations. In hardware, multiplication is much more computationally intensive than addition since it requires more D-flip-flops. By contrast, the correlation calculation for quantified data can be achieved by addition. Therefore, they simplify the multiplication operations into addition and reduce system power consumption effectively. However, this method loses some data information and reduces detection accuracy. Therefore, high-accuracy algorithms for low-power systems are worth continuing to explore.  
It is difficult to achieve good performance in the above three aspects at the same time. Therefore, for future household wireless sensors, it is very important to take a trade-off on the above characteristics when designing the packet detection algorithms.
## 5 Results and Discussion
### Numerical Efficiency
#### 5.3.1 Successful Inversions  
This first criterion explores how often the selected model is actually able to fit the empirical evidence provided by remote sensing, or, equivalently, the sensitivity of the models to the temporal distribution of the input data. For this purpose, the entire record of 18 years of FAPAR data is used, over the 5 by 5 pixel target area around the Skukuza EC flux tower, so there are 450 possible cases. Table 4 provides the statistics, and it can be seen that the PDHyTgF (Hyperbolic Tangent) model provides the greatest proportion of successful model inversions for the 450 possible cases in the 5 by 5 pixel target area.
# Embedded Bayesian Network Contribution for a Safe Mission Planning of Autonomous Vehicles
## 4 Methodology for the BN Integration at the Mission Manager Level
### Decision Networks (DN)  
DN are directed acyclic graphs (DAG) with nodes belonging to three different categories: chance nodes (ellipses or circles in graphical notation) represent (as in BN) discrete random variables; decision nodes (rectangles) represent actions or decisions with a predefined set of states; value nodes (diamonds) represent utility (or cost) measures associated with random or decision variables. Edges represent direct (possibly causal) influence between connected objects. An example of DN (cf. Figure 14), linking the two BNs and the possible actions to activate by a utility table. The table (detailed in Table 4) gives a score for each action relative to the BNs, and the choice of the adequate action is given by computing a utility function (\(U_{f}\)) for each action.  
To compute the utility function, we need the probability of the UAV state and the battery state representing the correct functioning. These latter ones are provided by BN. The utility function of each action is equal to the sum of the products of the action with the adequate probability concerning the UAV state and the battery state. The action to be chosen is the action that has the highest utility function.  
Let us consider the example of Figure 14:  
Action_to_choose = \(Max(U\_f_{SU},U\_f_{SD},U\_f_{EL})\): the highest utility function where each utility function \(U\_f_{k}(k=\{SU,SD,EL\})\) is equal to  
\[U\_f_{k}= \sum_{i}\sum_{j}U(A=k,U\_A=i,H\_E=j)*P(U\_A=i)*P(H\_E=j)\] \[(i=\{inc,dec\}\quad and\quad j=\{ok,bad\})\]  
which represents the sum of the products of utility values from the Table 4 with the adequate probability concerning the UAV state (\(U\_A\)) and the battery (\(H\_E\)) from Figure 14.  
In this example, if we take the BN probabilities \(P(U\_A=inc)=0.9\), \(P(H\_E=ok)=0.8\) and the utility table of Table 4, then \(U\_f_{SU}=72\), \(U\_f_{SD}=8\) and \(U\_f_{EL}=20\) which means that the action chosen is \(SU\) (\"Speed up\") this case.
# Reverse Time Migration Imaging Using SH Shear Wave Data
## 2 Application Background and Method Principles
### Principle of Combined Compact Difference Scheme  
The combined compact difference (CCD) to suppress the numerical dispersion caused by the spatial step size are enclosed in this paper to solve the problem of low SH shear wave velocity; the formula is as follows [35; 36]:  
\[\left\{\begin{array}{c}a_{1}\big{(}f^{\prime}_{i+1}+f^{\prime}_{i-1}\big{)}+ f^{\prime}_{i}+hb_{1}\Big{(}f^{\prime\prime}_{i+1}-f^{\prime\prime}_{i-1}\Big{)}= \frac{1}{h}\sum\limits_{m=1}^{n}c_{m}\big{(}f_{i+m}-f_{i-m}\big{)}\\ \frac{1}{h}a_{2}\big{(}f^{\prime}_{i+1}-f^{\prime}_{i-1}\big{)}+f^{\prime\prime }_{i}+b_{2}\Big{(}f^{\prime\prime}_{i+1}+f^{\prime\prime}_{i-1}\Big{)}=\frac{1} {h^{2}}\sum\limits_{m=1}^{n}d_{m}\big{(}f_{i+m}-2f_{i}+f_{i-m}\big{)}\end{array}\right. \tag{2}\]  
In Equation (2), \(h\) is the grid spacing, \(a,b,c,d\) are the difference coefficient matrices; \(f\) is the function value of node \(i\); \(f^{\prime}_{i}\) and \(f^{\prime\prime}_{i}\) represent the first- and second-order derivatives of node \(i\), respectively; \(f_{i+m},f_{i-m}\) represent the function values of node \(i\) successively m nodes forward and m nodes backward; \(f^{\prime}_{i+1},f^{\prime}_{i-1}\) represent the first-order derivatives of node \(i\) successively one node forward and one node backward, respectively; \(f^{\prime\prime}_{i+1},f^{\prime\prime}_{i-1}\) represent the second derivative of \(i\) node one node forward and one node backward. The wave field of the SH shear wave can be simulated with the above method applied to the numerical simulation of SH shear wave propagation under the condition of a two-dimensional medium.  
In this paper, we use the three-point sixth order format of Equation (2) for the SH shear wave reverse time migration, that is, Formula (3).  
\[\left\{\begin{array}{c}\frac{7}{6}\big{(}f^{\prime}_{i+1}+f^{\prime}_{i-1} \big{)}+f^{\prime}_{i}-\frac{h}{16}\Big{(}f^{\prime\prime}_{i+1}-f^{\prime \prime}_{i-1}\Big{)}=\frac{15}{16i}\big{(}f_{i+1}-f_{i-1}\big{)}\\ \frac{9}{8h}\big{(}f^{\prime}_{i+1}-f^{\prime}_{i-1}\big{)}+f^{\prime\prime}_{i }-\frac{1}{8}\Big{(}f^{\prime\prime}_{i+1}+f^{\prime\prime}_{i-1}\Big{)}=\frac {3}{h^{2}}\big{(}f_{i+1}-2f_{i}+f_{i-1}\big{)}\end{array}\right. \tag{3}\]
# Software Design and Experimental Evaluation of a Reduced AES for IoT Applications
## 3 Materials and Methods  
The design of LAES takes into consideration the following modifications: S-box arrangements, operation of finding the inverse in GF(24), affine transformation, and key expansion process. These changes allow the algorithm to operate on 64-bit plaintext input. This section presents an overview of the main components of the proposed 64-bit LAES cryptographic algorithm that uses a 128-bit encryption key. The LAES consists of ten rounds that use an expanded key generated from the initial key. The operation of LAES is described as follows, as illustrated in Figures 1 and 2 (the major components are described in the next section):  
1. The plaintext is divided into 64-bit blocks using a state matrix generator, which reshapes the 64 bits into 4 \(\times\) 4 matrix, whereby each element has 4 bits.
2. The encryption key is divided into two 64-bit parts. The left part of the key is XORed with the text generated from the state matrix generator, to obtain the state matrix text. The left part is fed to the key expander to generate the round keys for the first nine rounds.
3. The state matrix text is substituted to the S-box using the LSubNibble component, which enhances non-linearity during each round.
4. Each row in the resulting 4 \(\times\) 4 matrix text obtained from step 3 is rotated cyclically by offsets of 0, 1, 2, and 3. This step is inherited mainly from the original AES, to enhance the diffusion and confusion of the LAES.
5. The mixing column has the same objectives of the previous step, multiplying the resulting matrix from step 4 by a constant matrix that has entries from GF(24).
6. The key expander generates a round key based on the left part key. The generated round keys are dependent on each other. The matrix generated in step 5 is XORed with the round key.
7. Steps 3 to 6 are repeated nine times.
8. The resultant text after the ninth round is processed using steps 3, 4, and 6. The key expander generates the key round based on the right part of the key.
9. Finally, the resulting matrix is reshaped into a 64-bit vector, which represents the ciphertext.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Cryptographic** & **Block Size (Bit)** & **Key Size (Bit)** & **Number of Rounds** & **Structure** & **Proposed Use** & **Limitation** \\ \hline AES & 128 & 28/192/256 & 10/12/14 & SPN & Conventional applications & Requires a large number of hardware resources \\ \hline Present & 64 & 80/128 & 31 & SPN & RFID tags and sensor networks & Less throughput than Celfia \\ \hline Celfia & 128 & 128/192/256 & 18/22/26 & Feistel network & Very limited resource environment & Provides less memory utilization compared with Present \\ \hline Mini-AES & 16 & 16 & 10 & SPN & Educational purposes & Not applicable for real IoT environments \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between cryptographic algorithms.
## 3 DFIG Model
### Grid-Side Controller  
The controller for the GSC is depicted in Figure 7. This controller consists of four PI controllers to compensate different error signals. The GSC reactive power (\(Q_{g}\)) and DC-link voltage (\(V_{dc}\)) are controlled through d-axis (\(I_{gd}\)) and q-axis (\(I_{gq}\)) current components, respectively. The reactive power reference is set to zero, and the DC-link voltage reference is set to 1.0 pu (1.2 kV).
## 5 In Situ Evaluation Methods
### In Situ Evaluation Model Based on Stress State  
Laboratory test data indicate that the soil pore ratio and permeability decrease with increasing consolidation pressure [141; 142]. Given that consolidation pressure is closely related to the in situ stress state of the soil, studying the in situ stress state of soil-bentonite backfill is particularly important. An accurate assessment of the hydraulic conductivity of in situ backfill necessitates proper calculation of effective stresses [143; 144].  
Figure 14: 6 CPTU pore pressure dissipation curves for SB vertical cut-off walls [133].  
Figure 15: Variation in permeability coefficient with depth for block samples after \(11\) years [140].  
[PERSON] et al. [145] developed an advanced model for predicting steady-state horizontal and vertical effective stresses in consolidated backfill. This model integrates the composite effects of lateral stress and lateral compression, demonstrating significant improvements in predicting the stresses in soil-bentonite slurry tank walls compared to conventional models [131; 146]. By utilizing the effective stresses calculated by the model, the variation in the hydraulic conductivity of the backfill with depth can be estimated. The results indicate that the hydraulic conductivity decreases with depth, as shown in Figure 16[143; 145], providing a new perspective for understanding the hydraulic conductivity of soil-bentonite backfill. Additionally, the model accounts for changes in pore ratio, which is crucial for accurately predicting the permeability properties of backfill, thereby offering a scientific basis for the design and construction of soil-bentonite cut-off walls.
## 3 Results  
### Numerical Simulation Model Generation  
To investigate the change in the SRM strength with increasing VBP, this paper refers to the laboratory test data by [PERSON] et al. (2014). In [PERSON]'s paper, there are two kinds of SRM samples with different sizes (150 \(\times\) 300 mm and 100 \(\times\) 200 mm) in order to study the possible scale effect on the mechanical behavior of the SRMs. Consequently, the result of this laboratory test shows that there is no significant scale effect on SRMs and confirms [PERSON]'s perspective that the SRM is a scale-independent material based on some field measurements performed on Franciscan melange (2012). Thus, in this paper, one specimen of the SRM with the size (100 \(\times\) 200 mm) is generated. The grading curve in [PERSON]'s indoor test is applied for this paper, the particle size distribution of rock block aggregates is shown in Table 1, and the grading curve is presented in Figure 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sieve Size (mm)** & **Cumulative Passing (\%)** \\ \hline
12.0 & 100 \\
11.2 & 92.0 \\
10.5 & 67.5 \\
9.0 & 19.6 \\
6.0 & 0.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The particle size distribution of rock block aggregates.  
Figure 4: SRM model with VBP of 60%: (**a**) Generate the boundary of rock block aggregates; (**b**) Create clumps; (**c**) Consider the border effect of the SRM and redistribute the aggregates; (**d**) Trim the geometry; (**e**) Generate the block-matrix interface; (**f**) Generation of numerical mesh model.  
According to the above grading curve, rock block aggregate size was set to 6.0 to 12 mm. Five models with a VBP of 50%, 55%, 60%, 65% and 70% were generated, shown in Figure 6.  
Figure 5: Size distribution of rock particles prepared for numerical simulation.  
Figure 6: SRM models with different VBP ranging from 50% to 70%.