esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparative Analysis of Anomaly Detection Methods in IoT Networks: An Experimental Study
## 3 Methodology
### Datasets in IoT
#### 3.1.1 IoT-23 Dataset Characteristics for Evaluating Selected Machine Learning Algorithms  
The IoT-23 dataset is a comprehensive and valuable resource for researchers studying IoT security. It offers a comprehensive collection of real-world, labeled IoT network traffic, including both malicious (malware) and benign samples. Captured in a controlled environment using real IoT devices, this dataset provides a solid foundation for developing ML models aimed at detecting and mitigating IoT threats [34].  
Compressing 23 instances of IoT network traffic, the dataset is divided into 20 malicious and 3 benign categories. Each instance represents a specific scenario or event and is captured in the PCAP file format. The samples malicious were generated by executing various malware on Raspberry Pi devices whereas the benign samples were collected from real IoT devices such as a Philips HUE smart LED lamp, an Amazon Echo, and a Somfy smart door lock [34].  
To facilitate analysis and ML, each sample in the dataset was assigned descriptive labels indicating its network behavior. These labels were determined using a manual analysis process that combined defined rules and automated scripts [34].  
IoT-23 includes a wide range of attacks targeting IoT devices, allowing anomaly detection models to be tested in a variety of scenarios.
## 2 Research Object and Test Arrangement
### Setting of the Measuring Points  
The test focused on the diversion pipeline area of the Mount Changlong PSPS. The principles for arranging the measurement points were as follows: 1 In the diversion pipeline area, it was important to cover as many pipeline sections along the diversion pipeline as possible. Additionally, suitable locations needed to be selected to arrange monitoring points with intervals of 50 m or 100 m perpendicular to the direction of the pipeline. This facilitated the subsequent analysis of vibration propagation and ensured rationality in the analysis. If there were changes in the diversion pipeline material or cross-section, or if faults existed in the mountainous region, emphasis needed to be placed on testing in those areas. 2 Within the plant area, the measurement points needed to be positioned near the units and related equipment, with appropriate protective measures implemented for this region. 3 Vibration mainly affects residential areas; therefore, relevant measurement points needed to be arranged accordingly.  
The measurement points were positioned within the designated areas of the power station according to the description provided in the relevant geological data (please refer to Table 2 and Figure 2 for a detailed illustration of their specific arrangement). All measurement points were equipped with vibration acceleration sensors, and the sampling frequency was 8000 Hz.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & **Name** & **Remark** & **No.** & **Name** & **Remark** \\ \hline
1 & P1 & Upper reservoir & 5 & P5 & Near units 3\# and 4\# \\
2 & P2 & 6 & P6 & Near units 5\# and 6\# \\
3 & P3 & The measurement points near the horizontal & 7 & P7 & horizontal flat tunnel were influenced \\  & & flat tunnel were influenced by cars. & & by construction. \\
4 & P4 & 8 & P8 & Lower flat tunnel \\  & & 9 & P9 & \\ \hline \end{tabular}
\end{table}
Table 2: Positioning of the measure points.  
Figure 1: The top view and facility of the PSPS.  
Figure 2: Overview of measurement point locations.
## 5 Case Studies  
To validate the effectiveness of the proposed model, the test system, consisting of a 14-node gas network [35], Barry Island heating network [11], and IEEE 30-bus system [36], is established, as shown in Figure 7. The CHP, GB, and natural gas well serve as the balancing nodes of the corresponding subnetworks. More detailed parameters of the test system can be found in [37]. Hydrogen storage, SNG storage, and P2G constitute the alternative gas sources, and the properties of heterogeneous gas sources are listed in Table 3. In addition, the gas temperature is set to 288.15 K, and STP is defined as 1.01325 bar and 293.15 K. For the test system, the initial values of \(\mathbf{m}\) and \(\mathbf{f}_{\mathrm{E}}\) are specified as 100 kg/s and 2.5 MSCMD, respectively; \(\mathbf{T}_{\mathrm{sp}}^{\mathrm{G}}\) and \(\mathbf{T}_{\mathrm{sp}}^{\mathrm{R}}\) are valued as 100 \({}^{\circ}\)C and 30 \({}^{\circ}\)C, respectively; the convergence and iteration tolerances are set to 10\({}^{-8}\) and 200 times, respectively. All simulations are solved by MATLAB 2023a on a laptop with an Intel (R) Core (TM) i9-13900H CPU @2.60 GHz and 16.0 GB of memory.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Gas Property**} & \multicolumn{3}{c}{**Gas Category**} \\ \cline{2-4}  & **Natural Gas** & \(\mathbf{H}_{\mathrm{2}}\) & **SNG** \\ \hline Critical temperature (K) & 192.45 & 33.15 & 190.55 \\ Critical pressure (bar) & 46.37 & 13.10 & 46.5 \\ Heat capacity at constant & 1.69 & 10.19 & 1.71 \\ volume (k)-kg\({}^{-1}\).K\({}^{-1}\)) & 2.20 & 14.31 & 2.23 \\ Specific gravity & 0.6106 & 0.0696 & 0.58 \\ GCV (MJ/m\({}^{3}\)) & 41.04 & 12.75 & 37.04 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Properties of different types of gas sources [38].  
Figure 6: Flowchart of the proposed NR-based EFC solution strategy.
# Security Analysis of the Symmetric Cryptosystem Tiny Jambu
## 4 Reducing the Security Margin of TinyJambu against Differential Cryptanalysis
### Security Margin with 384 Rounds
Table 5 is a much improved version of the first numerical values obtained by the authors of this work in [17], as the number of differential trails has significantly increased. In addition, the interval of the values of X is also greater than that in [17]. Table 6 shows a comparison between the results of Tables 3 and 5. It can be noticed that our best probability is \(2^{-71}\), while that of [PERSON] et al. is just \(2^{-74}\). Moreover, in this work, the number of trails associated with each probability is greater than the number of trails found in [7] for the same probabilities. Considering the scores of the previous differential trails X = 80 for the TinyJambu designers, X = 74 for the authors of reference [7] and X = 71 for our optimal trail, we can compare the success probabilities \(P_{succ. }\) for a differential attack in all these cases,  
\[P_{succ.}=\frac{1}{2^{71}}>\frac{1}{2^{74}}>\frac{1}{2^{80}}\, \tag{11}\]  
as well as the differential probabilities \(P_{dif. }\) in all these cases,  
\[P_{dif.}=\frac{1}{2^{65.9487}}>\frac{1}{2^{70.68}}>\frac{1}{2^{80}}. \tag{12}\]  
Our final differential probability is much greater than the probability of value \(p=2^{-80}\) obtained by the designers and rather greater than the probability \(p=2^{-70.68}\) obtained by the authors of [7]. The new differential probability dramatically increments the success probability for a cryptanalytical attack. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline [PERSON] et al. [7] & & & & & & & \\ \hline Probability & \(2^{-74}\) & \(2^{-75}\) & \(2^{-76}\) & \(2^{-77}\) & \(2^{-78}\) & \(2^{-79}\) & \(2^{-80}\) \\ \hline \#Trails & 1 & 5 & 9 & 14 & 20 & 24 & 30 \\ \hline This work & & & & & & & \\ \hline Probability & \(2^{-71}\) & \(2^{-72}\) & \(2^{-73}\) & \(2^{-74}\) & \(2^{-75}\) & \(2^{-76}\) & \(2^{-77}\) \\ \hline \#Trails & 9 & 24 & 27 & 28 & 18 & 14 & 22 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison between results given in reference [7] and those provided in this work. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline Probability & \(2^{-71}\) & \(2^{-72}\) & \(2^{-73}\) & \(2^{-74}\) & \(2^{-75}\) & \(2^{-76}\) & \(2^{-77}\) \\ \hline \#Trails & 9 & 24 & 27 & 28 & 18 & 14 & 22 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Differential trails with the same input/output differential masks defined in Table 4.  
In this subsection, we provided Tables 4 and 5 with their corresponding numerical results. We can say that we found much more differential trials with higher success probabilities for a differential cryptanalysis of type 3 than those ones obtained by previous authors. These numerical results are the best we can obtain at this point with our computational resources, that is, with the PC for which its specifications are described in Section 1. Better computational facilities would allow us to find more differential trails with the optimal value X = 71 and, consequently, to increase our final differential probability.
# An Improved Bi-LSTM-Based Missing Value Imputation Approach for Pregnancy Examination Data
## 1 Introduction
### Contributions  
All of the above research results are worthy of study. Pregnancy examination data is of high dimension, has a high missing data rate, is in a time series, and often has many non-linear relations, but the above research work has not enabled a richer and more complete extraction of pregnancy examination data in the data pre-processing stage, which in turn has led to poor results regarding missing value filling.  
Therefore, we propose an improved bi-LSTM-based missing value imputation approach for pregnancy examination data. The combined model uses the random forest (RF) model principal component analysis (PCA) and undersampling algorithm in the data preprocessing stage, which can analyze the relationships of the features, balance the dataset and filter out abnormal data. Then, bi-LSTM fills the processed data and significantly improves the already good results from other models. This model, compared with the traditional LSTM model, has forward and backward factors to jointly determine the results of filling missing data, which makes the accuracy of filling missing data effectively improved.
## 3 Results and Discussion  
### Interface Structures  
The structures in Figure 1 represent the lowest-energy configurations after the geometry optimization. The interface atomic layers were planar before the optimization, and the optimized atomic layers were uneven due to the different atomic interactions. As can be seen in Figure 1a-e, in the alloy-Mg terminations, all interface atoms, especially the Co atoms, demonstrated an inward movement, which was similar to their surface behaviors in Heusler alloys [14; 34]. In the Heusler terminations where the interface atoms came in contact with the top Mg atoms, the Heusler and MgO atomic layers were repelled very far. For the alloy-O terminations, the distance between the Heusler and MgO layers was reduced due to the atomic bonding interaction at the interface layers (see Figure 1f-j). The shrinking phenomenon of interface atoms could still be observed.  
Table 1 lists the bond types and lengths at different interfaces. The Co-Mg bond in the CoCo-Mg termination had the longest length, while the Al-O bond in the AlAl-O termination had the shortest length. The bond lengths in the alloy-Mg terminations were obviously longer than those in the alloy-O terminations because of the forceful repulsion interactions between the Heusler interface atoms and the Mg atoms, especially those between the metal and Mg atoms. Moreover, the interface repulsion interactions of metal-Mg or metal-O were more vigorous than those of Al-Mg or Al-O. In the TiCo-Mg and TiCo-O terminations, the bond lengths of Co-Mg and Co-O were longer than those of Ti-Mg and Ti-O, thereby creating an uneven interface atomic layer that led to further spin electronic scattering from the mechanical mismatch interface layers to weaken the spin polarization [37].  
Figure 1: 10 lowest energy terminations in the Ti\({}_{2}\)CoAl/MgO(100) heterojunction after geometry optimization. (**a**) TiAl–Mg termination; (**b**) TiCo–Mg termination; (**c**) CoCo–Mg termination; (**d**) TiTi–Mg termination; (**e**) AlAl–Mg termination; (**f**) TiAl–O termination; (**g**) TiCo–O termination; (**h**) CoCo–O termination; (**i**) TiTi–O termination; and (**j**) AlAl–O termination. Color code: O (red), Mg (green), Ti (gray), Co (blue) and Al (pink).
## 2 Materials and Methods
### Biophysical Assessment of Loss of Ecosystem Services Flows
#### 2.3.4 Habitat Quality  
This is a regulation service which, in supplying different types of habitats, contributes to human well-being by guaranteeing ecosystem functionality, biodiversity maintenance, and environmental resilience. Various anthropogenic factors have an impact on habitat quality, causing degradation and alteration of ecological processes [62]. With a view to assessing the loss of ES flow due to land consumption in the period under investigation,the Habitat Quality model of the InVEST software has been used. In this model, habitat quality is assessed in relation to different land use and land cover classes [47], on the basis of the premise that areas with higher habitat quality host a greater number of species and that habitat size and quality decrease lead to a decline in the persistence of species [63]. After having associated the land cover classes with habitat types (according to the EUNIS classification [64]), a habitat suitability value for each land cover class, for the purpose of assessing its ability to provide a habitat for biodiversity, has been assessed, ranging from 0 to 1, where 1 indicates habitat with the highest suitability for species biodiversity [65]. Three types of threats affecting habitat suitability have been taken into account: the habitat's sensitivity to being influenced by different types of threats, the impact (weight) of different threats on each habitat type, and the maximum distance at which the threats can affect the habitat. A threats impact value has been assessed, ranging from 0 (absence of threat) to 1 (presence of threat). The habitat suitability values and the threats parameters, which have been used as input data in the InVEST model, have been set through an expert-based approach [66] (Tables 1 and 2), using a questionnaire put to over 41 national experts, whose results are described in [67]. The result is a map of habitat quality in which the cell values range from 0 to 1, where 1 indicates high habitat quality. The ES was estimated ranking the range in five classes (class 1 poor habitat quality, class 5 high habitat quality) and assessing the surface variation in the period 2012-2020 for each class. \begin{table}
\begin{tabular}{c c} \hline \hline
**Habitat Type** & **Suitability** \\ \hline Beaches, dune and, sands & 0.74 \\ Water bodies & 0.83 \\ Wetlands & 0.96 \\ Grasslands & 0.86 \\ Shrublands & 0.81 \\ Broadleaves forests & 0.93 \\ Coniter forests & 0.82 \\ Inland unvegetated or sparsely vegetated areas & 0.55 \\ Intensive agricultural lands & 0.26 \\ Extensive agricultural lands & 0.52 \\ Buildings and other artificial areas or impervious soils & 0.09 \\ Open urban areas & 0.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Habitat suitability values generated by the survey (adapted from [67]).
## 1 Introduction
Building on this foundation, the main contribution of this work is the development of an enhanced error-adaptive error state Kalman Filter Model Predictive Control (EEA-ESKF-MPC) framework, which integrates a transfer function-based predictive model with an innovative adaptive error correction mechanism, combined with ESKF's robust interference estimation. The EA-ESKF-MPC's adaptive mechanism goes beyond standard filtering by dynamically adjusting control priorities based on real-time deviations and using exponential smoothing to reduce overreaction to transient disturbances and ensure smooth and stable control actions. This adaptability significantly improves the tracking accuracy and system stability compared to fixed-parameter KF or ESKF methods, providing a tailored solution to the multivariable coupling and large inertia challenges inherent in supercritical power plants.
## 2 Background Theory
### Open Connectivity Foundation (OCF) and IoTivity  
The OCF is one of the organizations that contribute to the IoT standardization to provide interoperability. The OCF is also an industry group that delivers an IoT software framework specification and a product certification program. The organization includes well-known companies such as Intel, Cisco, General Electric, Samsung, Microsoft, Qualcomm, and IBM in its membership. The OCF specification has adopted a resource-oriented architecture (ROA), implying that information and concepts are represented as resources. Resources are specified in RESTful API Modelling Language (RAML). Each resource definition contains a unique identifier, an indication of whether the resource is an actuator or a sensor or another type, and a list of supported methods a JSON schema for input and output for each method [3; 15].  
IoTivity is an open-source framework that implements the OCF protocol for easy and secure device-to-device communication for IoT devices [16; 17]. It runs as middleware and aims to provide connectivity devices to the cloud and device to device. The software framework is licensed under the Apache license version 2.0.
## 3 Results
### Heat Stress Visualization  
Pixel-based chemical images with increasing temperature were acquired to visualize the spatial distribution of heat stress in Chinese cabbage samples. As mentioned above, using the spectral data acquired from the Chinese cabbage grown for four and eight days under different temperature levels, the beta coefficients of the developed PLS model were obtained and are shown in Figure 4.  
Figure 5 shows the red, green, and blue (RGB), PLS-DA, and LS-SVM images of the Chinese cabbage samples grown at 20 \({}^{\circ}\)C, 28 \({}^{\circ}\)C, and 36 \({}^{\circ}\)C for four and eight days. The total pixel number of the PLS-DA and LS-SVM models was 400 \(\times\) 200 pixels in size. With increasing temperature, more pixels gradually changed from blue to red, and the color change index is described in the color bar.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multirow{2}{*}{**Day**} & \multirow{2}{*}{**Temperature**} & \multicolumn{6}{c}{**Model Accuracy (\%)**} \\ \cline{3-8}  & & (\({}^{\circ}\)**C**) & & **NDVI** & **RE/R** & **PRI** & **PLS-DA** & **LS-SVM** \\ \hline A \({}^{1}\) & & 20 vs 28 & 65.3 & 42.8 & 58.1 & 70 & 85.2 \\ B \({}^{2}\) & 4 & 28 vs 36 & 72.5 & 62.4 & 48.1 & 75.2 & 87.2 \\ C \({}^{3}\) & & 20 vs 36 & 57.2 & 69.6 & 56.2 & 92.4 & 93.6 \\ D \({}^{4}\) & & 20 vs 28 & 42.1 & 47.0 & 52.7 & 55.5 & 71.2 \\ E \({}^{5}\) & 8 & 28 vs 36 & 71.4 & 55.0 & 51.1 & 75.1 & 82.8 \\ F \({}^{6}\) & & 20 vs 36 & 63.5 & 52.2 & 53.8 & 84.5 & 92.4 \\ \hline \hline \end{tabular} \({}^{(-3)}\) Group A, B, and C: Chinese cabbage grown at 20 \({}^{\circ}\)C and 28 \({}^{\circ}\)C during 4 days, Chinese cabbage grown at 28 \({}^{\circ}\)C and 36 \({}^{\circ}\)C during 4 days; \({}^{(-4)}\) Group D, E, and F: Chinese cabbage grown at 20 \({}^{\circ}\)C and 28 \({}^{\circ}\)C during 8 days, Chinese cabbage grown at 28 \({}^{\circ}\)C and 36 \({}^{\circ}\)C during 8 days, and Chinese cabbage grown at 20 \({}^{\circ}\)C and 36 \({}^{\circ}\)C during 8 days.  
\end{table}
Table 4: Classification accuracy of normalized vegetation index (NDVI), red-edge ratio (RE/R), photochemical reflectance index (PRI), partial least squares-discriminant analysis (PLS-DA), and least-squares support vector machine (LS-SVM) models.  
Figure 4: Beta-coefficient plot of the partial-least squares model acquired from snapshot-based multispectral images of Chinese cabbage grown for 4 and 8 days.
## 1 Introduction  
In 1977, [PERSON] [1] and [PERSON] and [PERSON] [2] independently pioneered the smoothed particle hydrodynamics (SPH) method for modeling astrophysical phenomena in three-dimensional space. As a Lagrangian, meshfree method, the SPH method can handle fluid dynamic problems with complicated and time-variant domain topologies, large density ranges and object separation, as shown in recent reviews by [PERSON] [3], [PERSON] and [PERSON] [4] and [PERSON] [5]. Like other meshless methods being used in computational acoustics [6; 7], the SPH method has the potential to be used to solve complicated acoustic problems, such as combustion noise, bubble acoustics, sound propagation in multiphase flows, and so on [8].  
Recently, [PERSON] [9] and [PERSON] [10] directly solved the fluid dynamic equations by the SPH method to obtain pressure perturbations in the computational domain. Subsequently, [PERSON] _et al._[8; 11] proposed using the SPH method to solve linearized acoustic wave equations in the quiescent fluid. However, the appearance of unphysical oscillations leads to the instability of SPH simulation results.  
In order to eliminate the unphysical oscillations phenomena, [PERSON] and [PERSON] [12] proposed the methodology of artificial viscosity for simulating one-dimensional shock waves. [PERSON] [13] subsequently added a linear artificial viscosity term to the Von Neumann-Richtmyer artificial viscosity, which could further smooth the oscillations. On this basis, [PERSON] and [PERSON] [14], [PERSON] [15] and [PERSON] [16] developed another type of artificial viscosity (Monaghan-type artificial viscosity) for simulating shocks. After this, other modifications to the Monaghan-type artificial viscosity have also been proposed [17], but the effects of the modified Monaghan-type artificial viscosity are still being investigated. In recent years, many research fields [18; 19; 20; 21; 22] have adopted various forms of artificial viscosity to capture shock or model a physical Navier-Stokes viscosity. So far, Monaghan-type artificial viscosity [14; 15; 16] is the most widely used artificial viscosity in the SPH simulations. Therefore, this paper focuses on introducing the Monaghan-type artificial viscosity [14; 15; 16] into the SPH simulation of sound propagation and interference.  
The present paper is organized as follows. In Section 2, the SPH algorithm is built for solving the linearized acoustic wave equations, and a Monaghan-type artificial viscosity [14; 15; 16] term is added to the momentum equation. In Section 3, SPH algorithms with or without artificial viscosity are both used to simulate sound propagation and interference, and the effects of artificial viscosity are discussed. In Section 4, the numerical errors of sound pressure obtained by SPH algorithms with different kinds of smoothing kernel functions, different particle spacings and Courant numbers are calculated and analyzed. On this basis, suitable computational parameters of SPH algorithms for simulating sound waves are recommended. Finally, the conclusion of the paper is drawn in Section 5.