esa-sceva/satcom-chunk-collection huggingface dataset:
# Protocol-Specific and Sensor Network-Inherited Attack Detection in IoT Using Machine Learning
## 3 Methodology
### SEMMA Process Model for ProSenAD Model Development  
SEMMA process model is adopted to define and explain the design and development stages of the ProSenAD model. Figure 2 illustrates the process model. The Sample stage in SEMMA includes data collection methods, functions, tools, and techniques used for gathering the required data from specific sources. The Explore stage of SEMMA is responsible for exploring the collected data and performing preliminary analysis to discover trends and patterns. In a labeled dataset, this stage helps in identifying the data points, instances, features, variables or attributes, and target classes. Descriptive statistical methods are used to find the percentage of data instances from different angles depending on the requirements analysis. In the Modify stage of SEMMA, the raw dataset is prepared, features are engineered, and selected. The data is cleaned by addressing missing values and duplicate features. Data points with incomprehensible values are replaced with suitable ones. For example, in this case, categorical features are converted to numerical using one hot encoding method. Next, the Model stage of SEMMA involves the baseline model development, parameter and hyperparameter tuning, ProSenAD optimization, and model training. This step is followed by the Assessment phase of SEMMA where the model performance is assessed using performance evaluation metrics. The model is then compared with the selected benchmarks.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Attack** & **Description** & **CIA Triad Impact** & **Impact on Network Performance** \\ \hline \multirow{4}{*}{PS-R attack} & The attacker node broadcasts fake credentials for attracting traffic, such as fake rank value, and shortest path to the sink node & Confidentiality, integrity & Resource consumption, network destabilization, end-to-end delay, \\ \hline \multirow{4}{*}{SN-W attack} & Malicious nodes form a connection and attract network traffic toward themselves by illegitimately displaying increased child and neighbor nodes using their path to communicate with the sink node. They do this by replaying the same messages at short intervals. Secondly, the malicious nodes selectively or completely drop the incoming victim packets & Confidentiality, availability & Increase in the illegitimate routing of packets, network, partial or complete packet loss \\ \cline{1-1}  & & & complete packet loss \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \end{tabular}
\end{table}
Table 2: PS-R and SN-W attacks, their impact on CIA triad and network performance.
# Energy Demand Forecasting Scenarios for Buildings Using Six AI Models
## 2 Methodology
### Model Development
#### 2.3.3 Mathematical Model Extreme Gradient Boosting (XGBOOST)  
XGBoost is an advanced energy prediction system well-suited for large datasets with complicated interactions. It produces Decision Trees sequentially, such that successive trees try to remedy the errors of the previous ones and the energy demand equation is given by Equations (23)-(28). Minimizing a loss function with gradient boosting maximizes its performance. It captures nonlinear relationships by registering variables like historical energy consumption, weather conditions, and temporal parameters (energy demand equation is shown in Table 4). Due to its internal regularization, it avoids overfitting, thus giving good grounds for energy companies to trust its estimations [47].
###### Abstract
This article designs a Fully Homomorphic Encryption with Optimal Key Generation Secure Group Communication (FHEOKG-SGC) technique in the IoT environment. The proposed FHEOKG-SGC algorithm mainly focuses on the encryption and routing of data securely in the IoT environment via group communication. To accomplish this, the presented FHEOKG-SGC technique initially designs an FHE-based encryption technique to secure the data in the IoT environment. Next, the keys in the FHE technique are chosen optimally using the sine cosine algorithm (SCA). At the same time, the plum tree algorithm (PTA) is applied for the identification of the routes in the IoT network. Finally, the FHEOKG-SGC technique employs a trust model to improve the secure communication process, and the key management center is used for optimal handling of the keys. The simulation analysis of the FHEOKG-SGC technique is tested using a series of experiments, and the outcomes are studied under various measures. The rest of the paper is organized as follows. Section 2 provides the related works, and Section 3 offers the proposed model. Then, Section 4 presents the results and analysis, and Section 5 concludes the paper.
## 2 Data Processing and Network Description
### Signal Model  
In communication systems, signals are subject to various interferences such as channel fading, multipath interference, and noise during transmission. To accurately simulate the real communication environment, noise \(n(t)\) is typically introduced into the communication model. The signal model is commonly represented as follows:  
\[y(t)=x(t)+n(t), \tag{1}\]  
where \(y(t)\) represents the received complex envelope, \(x(t)\) denotes the actual modulated signal, which is influenced by various unknown parameters, expressed as follows:  
\[x(t,\text{u}_{i})=a\sum_{n=0}^{N-1}s_{n}^{k,i}e^{|(\omega t+\theta)}e^{\phi_{n }}g(t), \tag{2}\]  
where \(\text{u}_{i}\) represents the multidimensional parameter space of unknown quantities that affect the signal under the modulation mode \(i\).  
\[\text{u}_{i}=\bigg{\{}a,\theta,\varepsilon,h(t),\{\varphi_{n}\}_{n=0}^{N-1}, \Big{\{}s^{k,i}\Big{\}}_{k=1}^{M_{i}},\omega\bigg{\}}, \tag{3}\]  
where \(a\) represents the unknown signal amplitude, \(\theta\) represents the carrier phase and phase offset caused by propagation, \(\varepsilon\) represents the time offset between the transmitter and receiver, \(h(t)\) represents the channel impulse response, \(\{\varphi_{n}\}\) represents the phase offset parameters which are a set of continuous random variables, \(\Big{\{}s^{k,i}\Big{\}}\) represents \(K\) data symbols for the \(i\)-th modulation scheme, and \(\omega\) represents the carrier frequency parameter.
## 3 Finite Element Modeling and Parametrization
### Discretization  
Finite element modeling, meshing, and solving has been performed using ANSYS(r) MechanicalTM 18.1. The finite element models of the butt joint with different parameter combinations have been meshed with quadratic PLANE183 elements with plane strain condition. A preliminary convergence study showed that an adequate resolution of notch stresses is achieved with element edge lengths of \(0.05\cdot r\) in the notch region. This is five times finer than typical recommendations for notch stress approaches (\(0.25\cdot r\)), see [47; 48; 49; 50].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Symbol** & **[Lower Bound/Upper Bound]** & **Unit** \\ \hline Plate thickness & \(t\) & [1/100] & [mm] \\ Weld toe angle & \(\beta\) & [5/80] & [\({}^{\circ}\)] \\ Weld toe radius & \(r\) & [0.05/5] & [mm] \\ Weld seam width to & \(\frac{m}{t}\) & [0.2/4] & [\(-\)] \\ plate thickness ratio & \(u\) & [0/1] & [mm] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter ranges of the Finite Element Models.  
Figure 2: Notch geometry and mesh refinement at the notch in case of: (**a**) the existence of an undercut; (**b**) the absence of an undercut.  
The mesh is additionally refined perpendicular to the notch radius line up to a depth of 0.4 - \(r\). Exemplary meshes in the notch region are given in Figure 2a in case of the existence of an undercut \(u\) > 0 and in Figure 2b in case of the absence of an undercut \(u\) = 0.  
A global rougher element edge length of 0.05 - \(t\) is applied outside of the refined mesh. A complete finite element mesh of one specific design alternative with an undercut depth \(u\) > 0 is given in Figure 3.
## 3 Methodology
### Metrics for Framework Performance Assessment  
Maintaining business operations and long-term sustainability significantly depends on cloud performance. Cloud performance metrics enable effective monitoring of cloud resources, operational efficiency, and service delivery. There are a variety of metrics that can assist in monitoring and assessing the performance of cloud computing services. Typically, cloud performance indicators include VM capacity (refer to Equation (1)), multiple VM instances' capacities (refer Equation (2)), task execution time (refer to Equation (4)), energy consumption (refer Equation (5)), resource utilization (refer to Equation (6)), and power consumption (refer Equation (7)). Details of some of the notations from the literature are listed below:  
Capacity of a Virtual Machine (VM): A virtual machine's capacity is the product of the number of cores allotted to every virtual machine as well as the size of each core. Let \(V\) be the set of VM instances represented as \(V_{1}\), \(V_{2}\), \(V_{3}\),..., \(V_{n}\). Then the capacity of a virtual machine \(CP_{V}\) is defined as:  
\[\text{CP}_{\text{V}}=\text{C}_{\text{num}}(\text{VM}_{\text{V}})\;*\;\text{C}_{ \text{units}} \tag{1}\]  
Capacity of Multiple VM Instances: The VM instances' capacity is defined as the total of the active VM instances on the server \(S\):  
\[\text{CP}_{\text{sum}}=\sum_{i=1}^{n}\text{CP}_{\text{i}} \tag{2}\]  
Note: The overall volume of active virtual machine instances should also not exceed the server's (\(S_{p}\)) capacities and therefore can be represented in Equation (3):  
\[\text{CP}_{\text{sum}}\leq\text{S}_{\text{p}};1\  
Resource Utilization: It is represented as the ratio of the execution time of a workload executed by a particular resource and the total uptime of the resource:  
\[\mathrm{RU}=\sum_{i=1}^{n}\frac{\text{execution time of a workload executed on resource i}}{\text{total uptime of resource i}} \tag{6}\]  
Power Consumption: Power utilization can be defined as:  
\[\mathrm{P(u)}=\mathrm{k}\cdot\mathrm{P_{max}}+(1-\mathrm{k})\cdot\mathrm{P_{ max}}\cdot\mathrm{u} \tag{7}\]  
where \(P_{max}\) is the maximum power consumed when the server is fully utilized; \(k\) is the fraction of power consumed by the idle server, and \(u\) is the CPU utilization.
###### Abstract
[7] first conducted finite element analysis to compute the stress generated in pavement under moving vehicle load, which was simulated by using the local displacement shape functions in the finite-element formulation, considering the surface profiles produced by faulting, day- and night-time warping, and breaks of different levels of severity. Their analysis indicated that dynamic analysis is generally not needed for the design of rigid pavements and that it usually leads to decreased pavement response. On the other hand, [PERSON] et al. [10] found that dynamic vehicle load doubled the stress, which reached 0.65 MPa compared with the static stress of 0.31 MPa, when a semitrailer truck moved on a new PCC pavement at a speed of 47 km/h. The deflection of jointed plain concrete pavement (JPCP) was compared with the case of static load through field measurements as well as finite element analysis, which adopted a similar dynamic load application method to that of [PERSON] et al. However, it should be noted that in [PERSON] et al. 's method, the moving axle load was not generated by the 1/4 vehicle model and neither the effect of pavement surface roughness level indexed by the power spectrum density of the vertical road profile displacement as A, B, C, and D nor the variation of vehicle speed were considered. In other words, the dynamic effects were not investigated by including the road roughness as extra excitation between wheels and pavements. [PERSON] et al. [11] input the measured road surface profile to their designed quarter-car-pavement coupled model and found that higher vehicle speed caused higher stress in the pavement. However, [PERSON] et al. treated the pavement as an equivalent lump model which did not take the slab size and dowel bars into account. In review of the above discussion, there are different opinions on how rigid pavements respond to dynamic vehicle loads, due to the facts that many factors are involved during analysis and computing, including the definition of surface profiles, the vehicle model and parameters, the pavement structures, materials properties, the dynamic load generation method, etc., which deserve further comprehensive investigation. Altogether, it is very rare to find research analyzing the dynamic response of JPCPs considering the combined effects of pavement roughness, dynamic vehicle loads, and pavement structure. JPCP does not contain any steel reinforcement. However, there may be load transfer devices (e.g., dowel bars) at transverse joints and deformed steel bars (e.g., tie bars) at longitudinal joints [12]. In this paper, a numerical model is established to investigate the dynamic response of JPCPs under moving axle load, considering the effects of the pavement roughness level, the dynamic load parameters, and the pavement structure parameters. The result of this study can benefit future pavement design by providing useful dynamic factor values for various conditions. Moreover, preventive maintenance to avoid excessive fatigue stress, particularly the improvement of pavement surface roughness, can be planned by referring to the results of this study.
## 3 Results and Discussion
### Correlation Analysis
#### 3.4.1 Simple Linear Regression  
The results indicated that all variables showed weak linear relationships with AGB even though the correlations were significant at 95% confidence level. Table 7 summarized the correlation strength of all variables derived from the polarizations of PALSAR-2 and Sentinel-1A. The corresponding scatter plots listed in the table are shown in Figure 9. The results proved that the manipulation of  
Figure 8: Backscatter values from all polarizations at sample plots of PALSAR-2 (HH and HV) and Sentinel-1 (VV and VH).  
Figure 7: Relationship between tree size, number of trees, and AGB in a hectare of dipterocarp forest.  
polarizations from a single sensor slightly improve the correlation strength. Further improvement was attained when the polarizations are combined together, from one either single sensor or integration of both sensors.
# A Compact Triple Band Antenna Based on Multiple Split-Ring Resonators for Wireless Applications
## 3 Results and Discussion

Table 6: Comparative analysis showing trade\(-\)offs between a conventional patch antenna and the proposed miniaturized multiband antenna. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & **Frequency** & **Max Return** & \multirow{2}{*}{**Fr (GHz)**} & **Bandwidth in** \\  **(mm)** & **Band (GHz)** & **Loss (dB)** & & **\%** \\ \hline W4 = 0.14 & 4.75–6.5 & \(-\)45 & 5.3 & 26.43 \\ \hline W4 = 0.24 & 4.5–6.75 & \(-\)37/\(-\)36 & 4.78/6.1 & 40.03 \\ \hline W2 = 0.34 & 4.5–7.84 & \(-\)34/25 & 4.8 /6.8 & 54.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Effect of W4 on proposed antenna frequency band, return loss, resonant frequencies and bandwidth.
# Dimensional Reduction-Based Moment Model for Probabilistic Slope Stability Analysis
## 6 Conclusions  
In this study, a practical reliability analysis approach was developed to estimate the stability of slope under uncertainties through combining MDRM and FOSM. MDRM was used to approximate the limit state function. The mean value and standard deviation of the safety factor were estimated by numerical quadrature based on the MDRM model. FOSM was adopted to compute the reliability index and failure probability. The proposed method was applied to two slopes, one with an analytical solution and one with a numerical solution. The proposed method improved the efficiency of reliability analysis with higher accuracy. It provided a practical way to estimate and qualify the uncertainty of slope.  
Figure 12: The relationship of running times and number samples. (I, II, III denote three different soil layers).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Soil Layer**} & \multicolumn{2}{c}{_c (kN/m2)_} & \multicolumn{2}{c}{_\(\varphi\) (\({}^{\circ}\))_} \\ \cline{2-5}  & **Mean** & **Standard Deviation** & **Mean** & **Standard Deviation** \\ \hline I & 0 & 0 & 38 & 5 \\ II & 5.3 & 0.7 & 23 & 3 \\ III & 7.2 & 0.2 & 20 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The statistics property of random variables.  
Figure 13: The comparison of reliability index using different method.  
1. MDRM model approximated the complexity, nonlinearity, and high dimensional relationship between the safety factor and its influence factor using low dimension function. MDRM can characterize the mechanical behavior of slope well.
2. It is simple and practical to estimate the statistics moment based on MDRM without any gradient information of limit state function. The MDRM based reliability analysis does not need iteration to obtain the reliability index in contrast to the response surface method. This is important to reliability-based design optimization of a slope.
3. The probability distribution of the safety factor was estimated based on the MDRM model. It was in good agreement with the results of MCS. It showed that MDRM-based reliability analysis can represent the uncertainty of slope well. It is feasible to deal with uncertainty using the MDRM model.
4. The reliability index obtained by the proposed method showed excellent agreement with those obtained by FORM and the traditional RSM methods when used for reliability analysis in slope engineering. The proposed method can also be applied in other rock engineering contexts.  
Investigation, H.Z.; methodology, H.Z., M.W.; software, H.Z.; validation, H.Z., Z.H. and M.W.; writing--original draft, H.Z.; writing--review and editing, M.W. and Z.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Experimental Method  
The compressive testing of concrete specimens was performed according to ASTM C39 [46]. A cylindrical specimen was used, and the test was performed with the base leaning against the bottom loading plate in the compression equipment. An unbonded capping system comprising two neoprene pads was placed at the bottom and top of each specimen to evenly distribute the loads. After that, the loading piston moved downwards until it contacted the specimen. The load application rate was 15 MPa/min until reaching specimen failure. According to ASTM C39, compressive strength is the maximum load applied to the specimen between its cross-sectional area. Therefore, it can be calculated using Equation (1), where \(f_{cm}\) is the compressive strength in MPa; \(P_{m\underline{\alpha}x}\) is the maximum load in N; and \(D\) is the average diameter in mm.  
\[f_{cm}=\frac{4\ P_{m\underline{\alpha}x}}{\pi D^{2}} \tag{1}\]  
The indirect tensile strength test was performed using the diametral compression of cylindrical specimens or the splitting test according to ASTM C496 [47]. Here, a cylindrical specimen was used. This specimen was tested on its side with its longitudinal axis parallel to the support points. Two neoprene pads were also placed in the load application zone to distribute the loads. The load piston moved downwards until it contacted the specimen. The load application rate was 1.2 MPa/min until the specimen failed. According to ASTM C496, the splitting-test tensile strength is defined as the load applied on the specimen between the failure area comprised of the load application lines. Therefore, it can be calculated using Equation (2), where \(f_{sp}\) is the splitting-test tensile strength in MPa; \(P_{m\underline{\alpha}x}\) is the maximum load in N; and \(D\) is the average diameter in mm.  
\[f_{sp}=\frac{2\ P_{m\underline{\alpha}x}}{\pi ID} \tag{2}\]  
Figure 3: The sequence of material placement during concrete mixing.  
The flexural test is an indirect tensile test that measures the ability of a concrete beam to withstand loads in flexural strength or modulus of rupture [51]. The four-point beam flexural test was performed according to ASTM C78 [48] for regular concrete and ASTM C1609 [49] for fiber concrete. A parallelepiped specimen simply supported at its ends with a spacing of 450 mm was used. Loads were applied to three beam sections at a spacing of 150 mm. The load was applied at a 1.2 MPa/min rate until the specimen failed. The flexural tensile strength of beams is defined as the flexural moment in the pure bending zone, i.e., in the central third within the section modulus of the beam. Therefore, it can be calculated using Equation (3), where \(f_{r}\) is the modulus of rupture in MPa; \(P_{max}\) is the maximum load in N displayed on the machine; \(L\) is the length of the beam in mm; \(b\) is the base of the cross-section; and \(h\) is the beam depth.  
\[f_{r}=\frac{P_{max}L}{bh^{2}} \tag{3}\]
## 2 Methods  
### VI for Estimating the Maximum Rate of Low-Stress GPP  
We defined the GPP under low-stress conditions as GPP capacity. The light-response curve under low-stress conditions using a rectangular hyperbolic function is described as:  
\[\text{GPP}_{\text{capacity}}(\text{PAR})=\frac{\text{P}_{\text{max },\text{capacity}}\times\alpha_{\text{slope}}\times\text{PAR}}{(1+\alpha_{ \text{slope}}\times\text{PAR})}, \tag{1}\]  
where \(\text{GPP}_{\text{capacity}}\) (mg\(\cdot\)CO\({}_{2}\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\)) is the low-stress GPP, P\({}_{\text{max},\text{capacity}}\) (mg\(\cdot\)CO\({}_{2}\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\)) is the maximum GPP\({}_{\text{capacity}}\) under light saturation, \(\alpha_{\text{slope}}\) is a photosynthetic quantum efficiency representingthe initial slope of the light-response curve, and PAR (\(\mu\)mol\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\)) is photosynthetically active radiation (Figure 1).  
To apply satellite data for estimating GPP capacity using Equation (1), \(\alpha_{\text{slope}}\) and Pmax_capacity should be determined. Leaf physiological research has revealed that the initial slope of the light-response curve depends on the efficiency of light conversion into fixed carbon [52] and the chlorophyll concentration. Furthermore, analysis of flux data has shown that the initial slope of a light-response curve derived from a non-rectangular hyperbolic function changes under different weather conditions; however, the initial slope and Pmax both exhibit a linear relationship with the same VI [46], suggesting that the initial slope and Pmax are linearly correlated. On the other hand, other studies have reported that Pmax is related to the amount of chloroplasts [53, 54]. From these findings, we in order to reduce the number of parameters, we assume that \(\alpha_{\text{slope}}\) is a constant for each plant functional type. In addition, we assume that Pmax_capacity is related to the amount of chlorophyll. From this perspective, we examined the method and VI for estimating Pmax_capacity from satellite data.  
First, we examined spectral reflectance to select potential candidate VIs that show linear correlations with chlorophyll content from a set of VIs. Second, we studied the relationship between candidate VIs and Pmax_capacity of canopy light-response curves under low-stress conditions. Finally, the selected VI was validated.