esa-sceva/satcom-chunk-collection huggingface dataset:
# Firefly Optimized Resource Control and Routing Stability in MANET +
## 5 Simulation Results and Analysis
### Impact of Throughput  
The throughput is the ratio of the number of DPs sent to the total number of DPs received. It is evaluated as a percentage (%).  
Table 4 illustrates the throughput of the number of DPs sent, ranging from 10 to 100 during the routing in MANET. From the Table 4 value, it is clear that the proposed FFROR technique has achieved higher throughput than the existing works. From Table 4, it is clear that the proposed FFROR technique provides higher throughput results compared to existing the protocol. When the number of DPs sent increases, throughput also increases correspondingly. Nevertheless, the throughput in the proposed FFROR technique is high when sending the data packets through the resource-optimized route path. The resource-optimized route path is selected using FFRO algorithm. From the results, it is evident that the proposed FFROR technique has increased the throughput level by 20% when compared to the EAER Protocol [1], 41% when compared to the MECOR Protocol [4], 8% when compared to the EESMR technique [25], and 28% when compared to the OEERA scheme [26].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No, of Mobiles**} & \multicolumn{5}{c}{**Energy Consumption (Joules)**} \\ \cline{2-6}  & **Proposed FFROR** & **EESMR** & **EAER Protocol [1]** & **OEERA Scheme** & **MECOR Protocol** \\  & **Technique** & **Technique [25]** & **EAER Protocol [1]** & **[26]** & **[4]** \\ \hline
50 & 32 & 39 & 48 & 55 & 68 \\
100 & 35 & 42 & 51 & 60 & 70 \\
150 & 39 & 46 & 52 & 62 & 72 \\
200 & 43 & 49 & 54 & 68 & 75 \\
250 & 46 & 52 & 57 & 70 & 79 \\
300 & 48 & 53 & 59 & 72 & 80 \\
350 & 49 & 55 & 61 & 74 & 81 \\
400 & 51 & 57 & 63 & 76 & 85 \\
450 & 52 & 60 & 64 & 77 & 86 \\
500 & 53 & 61 & 65 & 80 & 87 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Calculation of energy consumption.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No, of Mobiles**} & \multicolumn{5}{c}{**Bandwidth Utilization Rate (\%)**} \\ \cline{2-6}  & **Proposed FFROR** & **EESMR** & **EAER Protocol [1]** & **OEERA Scheme** & **MECOR Protocol** \\  & **Technique** & **Technique [25]** & **EAER Protocol [1]** & **[26]** & **[4]** \\ \hline
50 & 45 & 52 & 60 & 65 & 75 \\
100 & 47 & 55 & 63 & 70 & 77 \\
150 & 49 & 56 & 67 & 73 & 80 \\
200 & 52 & 60 & 70 & 75 & 83 \\
250 & 55 & 62 & 73 & 77 &
# Smart Grid Theft Detection Based on Hybrid Multi-Time Scale Neural Network
## 3 Proposed Method
### Dataset Pre-Processing  
The process of collecting data from smart meters may be affected by equipment collection errors, special events, and other complex factors, thus mixing the electricity consumption data with a large amount of noise and other irrelevant factors. Additionally, the neural network model is susceptible to changes in the data, so it is necessary to carry out appropriate pre-processing of the data [27]. The fundamental measures of pre-processing include missing value, abnormal value, and normalization processing.
# A Novel Continuous-Discontinuous Multi-Field Numerical Model for Rock Blasting
## 4 Conclusions  
A coupling method is proposed to effectively simulate explosive cracks in rock. The plate flow equation is used to describe the process of gas expansion and pressure propagation in explosion; CDEM is used to describe the process of blast stress wave shocking and crushing rock. By combining the dynamic and quasi-static loads, the progressive failure process of rock from continuous to discontinuous is reproduced. Compared to previous studies, the role of explosive gas in the explosion is considered, providing more reliable results of damage regions. The effectiveness and accuracy of the method in blasting are proven by numerical examples. The main conclusions of this paper are as follows:  
1. The whole process of rock blasting including the blasting stress wave, explosive gas permeation, and fracking processes are reproduced by the proposed model.
2. For single-hole PMMA plate blasting, the established numerical model captures the crush and fracture zones. By comparing the two cases with blocked and unblocked blasting holes, it is found that the crushing zone in the explosion is mainly caused by the explosion stress wave. The tiny and dense cracks in edge of the fracture zone are caused by the reflection of the stress wave at the boundary. Meanwhile, the radial main cracks developed in the specimens are caused by the explosive gas. By comparing the fracture degrees, it can be found that the explosive gas can account for around 30% of the total damage.
3. The explosion crushing zone is mainly formed by the action of the explosion stress wave, while the crack zone is formed by the combined action of the explosion stress wave and explosive gas.  
Conceptualization, Y.L.; literature search, Y.L.; Funding acquisition, C.F. and Y.Z.; method feasibility analysis, Y.L. and C.F.; algorithm development, Y.L.; validation, Y.L. and C.D.; Writing--original draft, Y.L.; Writing--review and editing, Y.L. and Y.Z.All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 52178324), the National Key Research and Development Project of China, and the Ministry of Science and Technology of China (Project No. 2018 YFC1505504). The APC was funded by [PERSON].  
Not applicable.  
The data used to support the findings of this study are included within the article.  
Figure 9: Fracture degree.  
**Acknowledgments:** The authors would like to acknowledge the financial support of the National Natural Science Foundation of China (Grant No. 52178324), the National Key Research and Development Project of China, and the Ministry of Science and Technology of China (Project No. 2018 YFC1505504).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Analysis of Rewetting Characteristics and Process Parameters in Tobacco Strip Redrying Stage
## 2 Model Construction
### Initial and Boundary Conditions  
When the local factory carries out the process, the local temperature, humidity, and meteorological conditions are combined to make a range of processing conditions and parameters. The initial moisture content of the heaped tobacco strips is 8.5%, the temperature is 40 \({}^{\circ}\)C, the initial vapor mass fraction \(\omega_{v}\) is 0.85, and the initial pressure is the atmospheric pressure.  
\[c_{l(t=0)}=\rho_{l}\varphi S_{l(t=0)} \tag{16}\]  
where \(c_{l(t=0)}\) indicates the initial concentration of the liquid phase (kg/m\({}^{3}\)).  
The pressure at the boundary of the environment is equal to the environmental pressure \(P_{amb}\), and thus, the boundary conditions for the liquid phase and gas phase can be written as:  
\[\overrightarrow{n}_{w}=h_{nnv}\varphi S_{l}\frac{(p_{v}-p_{v,a})}{RT} \tag{17}\]  
\[\overrightarrow{n}_{v}=h_{nnv}\varphi S_{g}\frac{(p_{v}-p_{v,a})}{RT} \tag{18}\]  
where \(h_{nnv}\) indicates the mass transfer coefficient (m/s), 0.079 (m/s); \(p_{v,a}\) indicates the ambient air and vapor pressure (Pa); \(n_{w}\) indicates the liquid-phase flux; and \(n_{v}\) indicates the vapor flux.  
The convection and condensation heat boundary conditions are expressed as:  
\[q_{surf}=h_{T}(T-T_{air})+h_{nnv}\varphi S_{l}\frac{(p_{v}-p_{v,a})}{RT}h_{fg} \tag{19}\]  
where \(q_{surf}\) indicates the surface heat (J), \(h_{T}\) indicates the heat transfer coefficient (W/m\({}^{2}\)/K), and \(h_{fg}\) indicates the latent heat of evaporation (J/kg).  
The dependence of the effective moisture diffusivity on the temperature is usually described by an Arrhenius equation [28]:  
\[D_{eff}=D_{0}\exp(\frac{-E_{a}}{R(T+273.15)}) \tag{20}\]  
where \(D_{0}\) indicates the pre-exponential factor (m\({}^{2}\)/s) of the Arrhenius equation and \(E_{a}\) indicates the activation energy (kJ/mol).
## 2 Results and Discussion  
### Basic Quality Parameters of Investigated Virgin Olive Oils  
The basic quality parameters such as acidity, peroxide value (PV), K\({}_{232}\) and K\({}_{270}\) values of the investigated VOOs were assessed according to EU method and the results are presented in Table 1.  
According to the International Olive Oil Council, acidity should not exceed 0.8% for extra virgin olive oils (IOC/T.15/NC n\({}^{\circ}\) 3/Rev.7, 2012 [18]). The percentual acidity for the olive oils tested ranged from 0.14% to 0.22%. _Krvavica_ oil showed the highest acidity, while _Masnjaca_ oil showed the lowest acidity. Peroxide value is a measure of the primary products of auto-oxidation (hydroperoxides) of an olive oil and should not exceed 20 mEq Q\({}_{2}\)/kg for extra VOO. PV for olive oils tested ranged from 3.32 to 5.37 mEq O\({}_{2}\)/kg. _Masnjaca_ oil showed the lowest PV (3.32 mEq O\({}_{2}\)/kg), while _Leccino_ oil showed the highest PV (5.37 mEq O\({}_{2}\)/kg). K\({}_{232}\) is a measure of conjugated dienes and their oxidation products that absorb at \(\lambda\) = 232 nm and K\({}_{270}\) is a measure of conjugated trienes and secondary oxidation products (carbonyl compounds) which absorb at \(\lambda\) = 270 nm. K\({}_{232}\) and K\({}_{270}\) should not exceed 2.50 and 0.22, respectively for extra VOO. K\({}_{232}\) and K\({}_{270}\) values for the olive oils tested ranged from 1.58 to 1.65 and from 0.11 to 0.14 respectively. _Masnjaca_ oil showed the lowest K\({}_{232}\) and K\({}_{270}\) value while _Leccino_ oil showed the highest K\({}_{232}\) and K\({}_{270}\) values. The parameters for all studied olive oils (Table 1) were within estimated limits of EC Reg. 1989/2003 (2003) [19], which place them in the category of extra VOOs.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**No.** & **Cultivar** & **Acidity \({}^{\text{a}}\) (\%)** & **Peroxide value \({}^{\text{b}}\) (mEq O\({}_{2}\)/kg)** & **K\({}_{232}\)\({}^{\text{c}}\)** & **K\({}_{270}\)\({}^{\text{d}}\)** \\ \hline
1. & _Krvavica_ & 0.22 \(\pm\) 0.01 & 3.35 \(\pm\) 0.12 & 1.62 \(\pm\) 0.1 & 0.12 \(\pm\) 0.02 \\
2. & _Masnjaca_ & 0.14 \(\pm\) 0.02 & 3.32 \(\pm\) 0.16 & 1.58 \(\pm\) 0.4 & 0.11 \(\pm\) 0.01 \\
3. & _Leccino_ & 0.15 \(\pm\) 0.01 & 5.37 \(\pm\) 0.10 & 1.65 \(\pm\) 0.8 & 0.14 \(\pm\) 0.02 \\ \hline \end{tabular}  
* All values are expressed as mean of triplicate determinations \(\pm\) SD; \({}^{\text{a}}\) threshold value for extra VOO is \(\leq\)0.8;
* threshold value for extra VOO is \(\leq\)20; \({}^{\text{c}}\) threshold value for extra VOO is \(\leq\)2.5; \({}^{\text{d}}\) threshold value for extra VOO is \(\leq\)0.22.  
\end{table}
Table 1: Mean values of basic quality indices (% acidity, peroxide value, K\({}_{232}\), K\({}_{270}\)) of _Masnjaca_, _Krvavica_ and _Leccino_ VOOs.
# A Multi-Scale Video Longformer Network for Action Recognition
## 4 Experiments
### Dataset
#### 4.1.2 Ucf101 Dataset  
UCF101 [38] was created by a research team from the University of Central Florida (UCF) and is composed of 13,320 video clips downloaded from YouTube, with approximately 70% serving as the training set and about 30% as the validation set. It covers a total of 101 action categories distributed across 5 major action types. UCF101 boasts approximately 27 h of video content, and each action category typically includes between 100 and 800 video examples.
## 6 Discussion
However, the Decision Trees and Ensemble algorithms understand this curvature as an outlier which is corrected or eliminated by those algorithms. On the contrary, the proposed ANFIS-based approach copes with this curvature. Consequently, its prediction performance is much better. Summing up, the proposed ANFIS-based approach for predicting the consequences of oil spills using small sets of data can be considered a model that produces an efficient prediction with a small dataset. Consequently, we have shown that none of the ML algorithms give such a predictive performance as ANFIS, which consists of FIS and ANN. We have learned that the proposed ANFIS-based approach performed the best. These results show us that the inclusion of fuzzy sets in the prediction algorithm allows us to flexibly describe the oil spill process and, as a result, obtain more accurate prediction results. Therefore, we assume that if the GPR algorithm was extended to include fuzzy sets, this could produce more accurate results. Moreover, we can assume that the proposed ANFIS-based approach will perform better than other algorithms with small datasets in general. However, in order to verify this hypothesis, we need additional research with small datasets from various application areas. As such, we leave these tasks for future work. In this vein, this paper has several limitations which at the same time provide future research opportunities. We will always have limitations associated with the quality of data, model configuration, algorithm assumptions, etc. With that in mind, it is possible to develop a model using better quality field data to help improve the performance and reliability of the prediction. In future work, we intend to extend the set of analysed fuzzy inference and ML methods and conduct new and more complex experiments that allow us to explore in more detail the prediction capabilities and to choose the most suitable approaches for predicting the consequences of oil spills. In addition, we plan to analyze the efficiency of the methods used for the prediction of the consequences of oil spills. This study will allow us to determine which prediction approach is most appropriate under certain conditions. For example: Which approach is more suitable for devices with limited resources? Which approach has less computational complexity? Which approach takes the least time to achieve a prediction? Summing up, the presented research has proposed a better understanding of the application of fuzzy inference and ML methods for prediction with small datasets. The obtained results show that ANFIS is suitable for prediction with a small dataset and provides sufficiently accurate prediction results. Moreover, it is useful for further research.
# A General Euler Angle Error Model of Strapdown Inertial Navigation Systems
## 3 Simulation Experiments
### Simulation and Analysis of General Euler Angle Errors  
In order to validate the proposed general Euler angle error models (Equations (43) and (44)), three group simulation experiments are carried out, where the initial attitude and initial Euler angle errors of the SINS are respectively supposed as \(\left[\begin{array}{cccc}\theta_{0}&\gamma_{0}&\varphi_{0}\\ \end{array}\right]=\left[\begin{array}{cccc}-45^{\circ}&-45^{\circ}&-45^{ \circ}\\ \end{array}\right]\), \(\left[\begin{array}{cccc}\delta\theta_{0}&\delta\gamma_{0}&\delta\varphi_{0} \\ \end{array}\right]=\left[\begin{array}{cccc}0.1^{\circ}&0.1^{\circ}&0.1^{ \circ}\\ \end{array}\right]\). In the three experiments, the SINS is driven to rotate with \(\hat{1}^{\circ}/s\) in turn along the \(X\), \(Y\), and \(Z\) axes.  
The results of three group simulation experiments are given in Figures 8-10. The general Euler angle error model can precisely describe the attitude error propagation rules of the SINS. When the SINS' attitude error is regarded as the reference, the RMSE of attitude angle errors calculated by the general Euler angle error model is shown in Figures 8-10 and Table 2. The RMSE of the proposed Euler angle error model is superior to 0.3195\", which is accurate enough. The simulation results prove the validity of the general Euler angle error model proposed.  
The general Euler angle error model can accurately describe attitude error propagation rules, and it can be applied in the attitude movements. The proposed Euler angle error model can direct the response attitude movement status, especially in large-angle attitude movements. The attitude movement optimization can also be analyzed.
# A Secure IoT-Based Authentication System in Cloud Computing Environment
## 2 Related Works
### Review of [PERSON] et al.'s Scheme
#### 2.2.1. [PERSON] et al.'s Scheme Cannot Achieve Mutual Authentication  
Mutual authentication refers to the mutual verification between two entities. In [PERSON] et al.'s scheme, _CS_ verifies \(U_{i}\) by checking \(D_{4}?=h(ID_{i}||PID_{i}||PID_{i},new||r_{u}||D_{3})\) in Step 3 of the authentication phase. We know \(D_{3}=C_{2}\oplus h(ID_{i}||HP_{i})\oplus PID_{i},new\oplus h(r_{u}||ID_{i})\) and \(C_{2}\)' = \(h(ID_{i}||x)\) from Step 1 of the authentication phase and the user registration. When _CS_ computes \(D_{3}\oplus h(ID_{i}||x)\oplus h(r_{u}||ID_{i})\), _CS_ only can obtain \(h(ID_{i}||HP_{i})\oplus PID_{i},new\), where the parameter \(HP_{i}\) is only known by \(U_{i}\). _CS_ cannot successfully calculate \(PID_{i},new\) from \(D_{3}\oplus h(ID_{i}||x)\oplus h(r_{u}||ID_{i})\), even if the message \(M_{1}=\{PID_{i}\), \(D_{1}\), \(D_{2}\), \(D_{3}\), \(D_{4}\}\) is sent from a legal user \(U_{i}\). Therefore, [PERSON] et al.'s scheme was unable to complete mutual authentication.
## 2 Data and Methods
### Data  
Within this study, the data types utilized for modeling include borehole data, geological maps, and geological sections, originating from different geological departments and professional domains. Additionally, there are significant variations in the modeling data relied upon by different modeling objects. For instance, the 3D modeling of Quaternary strata primarily depends on 236,000 borehole records and DEM data, whereas bedrock modeling heavily relies on geological maps and geological section data (Table 1). This is primarily due to the fact that borehole data, primarily derived from engineering exploration, is designed to penetrate the Quaternary loose sedimentary strata and expose the bedrock as a load-bearing layer. Therefore, these borehole data often lack comprehensive information regarding bedrock strata, rendering them unsuitable for bedrock modeling and limiting their usage to verification or calibration purposes.  
Figure 1: Geographical location, topography, and borehole distribution of Wuhan’s MDA. (**a**) Location of Hubei province; (**b**) location of Wuhan’s MDA; (**c**) distribution of 236,000 drill holes in the study area; (**d**) topography of Wuhan’s MDA; (**e**) geological section A–B.  
More generally, the data for bedrock modeling primarily consists of geological maps and sections from various professional fields, such as basic geology, hydrogeology, tectonic  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Modeling Objects** & **Modeling Data Types** & \multicolumn{2}{c}{**Instruction**} \\ \hline \multirow{7}{*}{Quaternary loose strata} & \multirow{7}{*}{236,000 drillhole data} & \multirow{7}{*}{**Engineering geological boreholes** (about 95\% of the total drill holes); the depth is generally less than 50m, and more than 65\% of the holes are less than 30 m.**} \\  & & \multirow{7}{*}{**Hydrogeological boreholes** (about 4\% of the total drillholes); the depth is generally less than 50 m, and more than 76\% of the holes are less than 30 m.**} \\  & & \multirow{7}{*}{**Quaternary geological boreholes** (about 0.5\% of the total drillholes); the depth is generally less than 50 m**} \\  & & \multirow{7}{*}{**Basic geological survey boreholes** (about 0.4  
geology, and geophysical exploration. Table 1 outlines the specific types of modeling data relied upon by each modeling object in this study.  
In this study, the collected borehole geological data were divided into two categories: modeling data and validation data, in a ratio of 9:1. To enable an accuracy assessment of the bedrock model, the validation boreholes included 12,000 boreholes that exposed bedrock, accounting for approximately 75% of all boreholes that exposed bedrock.
# Verifiable Threshold Multi-Party Fully Homomorphic Encryption from Share Resharing
## 1 Introduction
### Related Work
However, their approach is limited by the server's ability to perform only a predetermined number of online non-interactive decryption operations, after which an offline interactive step is required. Subsequently, [PERSON] et al. [29] proposed an efficient homomorphic commitment scheme based on lattice hardness assumptions, and constructed the corresponding zero-knowledge proof of knowledge for opening. The scheme achieves significant compression in the sizes of commitments and proofs under the assumptions of computational hiding and binding, and it supports commitments to vectors with larger norms. This efficiency comes at the cost of the commitment size growing linearly with the dimension of the input vector. Building on [PERSON] et al. 's scheme and its improvements, [PERSON] et al. [30] developed an efficient zero-knowledge proof system for lattice-based matrix-vector relations, which supports the standard definition of soundness and achieves negligible soundness error. [PERSON] et al. [31] proposed a distributed key generation protocol, based on the BGV scheme [3], that provides active security; however, it is limited to the full-threshold scenario. [PERSON] et al. [32] built verifiable secret sharing based on the LWE problem. Nevertheless, in their construction, the discrete logarithm assumption underpins the zero-knowledge proofs, giving them non-resistant quantum resilience. [PERSON] et al. [33] constructed a threshold FHE scheme using general zero-knowledge proofs. [PERSON] et al. [34] constructed an MFHE scheme with threshold decryption based on the BGV [3] scheme, and implemented a verifiable distributed key generation protocol using the hash function, commitment, and NIZK. On this basis, they instantiated a variant of the signature scheme developed by [PERSON] et al. [26]. [PERSON] et al. [20] proposed a practically deployable multi-party fully homomorphic encryption scheme secure against malicious adversaries, which supports zero-knowledge verification of the correctness of key generation, decryption, and bootstrapping. However, their scheme does not support threshold settings and lacks flexibility in the configuration of participating parties. [PERSON] et al. [35] achieved malicious security for [PERSON] and [PERSON]'s scheme [22] based on commitment and NIZK, and constructed a verifiable distributed decryption protocol based on the RLWE problem. Although [PERSON] and [PERSON]'s scheme [22] supports an arbitrary threshold, [PERSON] et al. 's scheme [35] only supports the full-threshold setting, resulting in insufficient system reliability and flexibility, as well as higher communication overhead compared to arbitrary threshold settings.