esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results  
In this section, we make a comprehensive evaluation for the PCFL algorithm and the ACFL algorithm through simulation experiments on Matlab 7.0. Three localization schemes, Anchor-based [20], AUV-aid [19] and AFLA [8] are compared with PCFL and ACFL. In the simulation experiments, the localization area is defined as 1000 m \(\times\) 1000 m \(\times\) 20 m where anchors are randomly deployed. The depth information of mobile nodes and anchors can be obtained by the pressure sensors at each time instant \(t\). The original speed of mobile nodes which move in the ocean water is assumed to be 0.1 m/s. The maximum communication radius \(R\) is assumed to be 100 m. The localization error \(E_{i}^{\prime}\) for the mobile node \(n_{i}\),(\(i=p+1,...p+q\)),\(n_{i}\in M\) at time instant \(t\) can be calculated by [13]:  
\[E_{i}^{\prime}=\sum_{k=1}^{T}\sqrt{(\tilde{x}_{i}^{\prime}-x_{i}^{\prime})+( \tilde{y}_{i}^{\prime}-y_{i}^{\prime})+(\tilde{z}_{i}^{\prime}-z_{i}^{\prime}) }\ /\ T \tag{29}\]  
(\(\tilde{x}_{i}^{\prime},\tilde{y}_{i}^{\prime},\tilde{z}_{i}^{\prime}\)) and (\(x_{i}^{\prime},y_{i}^{\prime},z_{i}^{\prime}\)) are the real coordinate and the estimated coordinate of the mobile node \(n_{i}\), respectively. We run the simulation 50 times for each group of data, namely \(T=50\).  
Simulation parameters in this paper are summarized in Table 3.
## 3 Results
### Curve of Breakthrough Modeling  
#### 3.6.1 Yoon-Nelson Model Application  
An ordinary hypothetical model established by [PERSON] was utilized to examine the phenol's breakthrough behavior within a fixed-bed column. The \(k_{YN}\) (a constant for rate) and \(\tau\) (required time for 50% phenol breakthrough) values were acquired using Equation (5). Such values were implemented to determine the curve of breakthrough. The \(k_{YN}\) and \(\tau\) values are presented in Table 3. As expected, \(\tau\) decreased and \(k_{YN}\) increased as the concentration in phenol inlet, along with the rate of flow, increased. Furthermore, as the bed depth increased, \(\tau\) increased and \(k_{YN}\) decreased. The notional curves were matched against their equivalent experimental ones in Figure 9. It can be noticed that the resulting curves of breakthrough from the experimental data are similar to the those examined by the Yoon-Nelson model at different rates of flow, primary concentrations, and bed depths. The model provided higher (\(R^{2}>0.977\)) and lower APE values varying from 4.09% to 12.99% (Table 3).  
Figure 8: Curves of multiple breakthrough for phenol adsorption (1000 mg/L) at a constant 8 mL/min rate of flow & 8.5 cm bed depth.
## 4 Materials and Method
### Application of Statistical Quality Control Methods
#### 4.1.1 Anderson-Darling Test for Normality Assessment  
To verify whether the observed data follow a normal distribution, the Anderson-Darling test was applied.  
1. The significance level was set at \(\alpha\) = 0.05.
2. Establishing the null hypothesis H0: The data are normally distributed.
3. The test statistic (AD) is calculated using the formula [25; 26]  
\[AD=-\frac{1}{n}\cdot\left[\sum_{i=1}^{n}\left(2\cdot i-1\right)\cdot\left\{ln \Phi\cdot\left(\frac{x_{(i)-\overline{x}}}{s}\right)+ln\left(1-\Phi\cdot \left(\frac{x_{n+1-(i)-\overline{x}}}{s}\right)\right)\right\}\right]-n \tag{1}\]  
where  
\(\overline{x}\): arithmetic mean of the sample,  
\(s\): standard deviation,  
\(\Phi\): cumulative distribution function of the standard normal distribution NORM.DIST,  
\(x(i)\): i-th ordered observation.  
4. The critical value for the test is determined by the \(p\)-value:  
* If AD > 0.05, the null hypothesis is not rejected, and the data are considered to follow a normal distribution;
* If AD < 0.05, the null hypothesis is rejected, indicating that the data do not follow a normal distribution.
# An Efficient Grid-Based Geocasting Scheme for Wireless Sensor Networks
## 3 The Proposed Scheme
### Cell Head Election  
In a cell, there may be a lot of sensor nodes available. A cell head is the node that is elected in the cell for data sending, data forwarding, and data fusion. This study uses the Cell Head Election Algorithm, as shown in Algorithm 1, for cell head election in a cell. If a node \(v\) is available, in line 1, it calculates the GID of node \(v\), called Cell(\(m\), \(n\)). In line 2, a node \(v\) must join a cell, and it broadcasts the _head_query_ message to all neighbor nodes. The _head_query_ message carries the information <_Type, SourceGID_, _GID_>. _Type_ is the packet type of message. _SourceGID_ is the GID of the source. In line 3, if node \(v\) receives a _head_available_ message from the cell head in Cell(\(m\), \(n\)), this state means there is a cell head available in Cell(\(m\), \(n\)). The _head_available_ message includes the information <_Type, HeadID_, _GID_>. _HeadID_ is the GID of the cell head. In line 4, node \(v\) joins the cell head \(h\) in Cell(\(m\), \(n\)), and sends the _head_joining_ message. In line 5, for the power saving, node \(v\) switches to the sleep mode after transmitting its data. The _head_joining_ message consists of the information <_Type, SourceID, GID_>. At system initialization, all the sensors have a uniform battery level, and no cell head is available in a cell. Therefore, node \(v\) cannot receive a _head_available_ message from the cell head in Cell(\(m\), \(n\)). In lines 8 and 9, it waits a random time, and then broadcasts a _head_available_ message. Due to the CSMA/CA method in the IEEE 802.11 based WLAN standard, a node transmits the sensing data only when the channel is sensed to be idle. Thus, if a node in the Cell(\(m\), \(n\)) is the first to broadcast a _head_available_ message, it is the cell head in Cell(\(m\), \(n\)). The flowchart of Cell Head Election Algorithm is shown in Figure 1.  
```
//Cell head election algorithm at node \(v\) in Cell(\(m\), \(n\))
1: Calculate the GID of node \(v\).
2: Broadcast _head_query_ message.
3:if (node \(v\) receives a _head_available_ message from the cell head in Cell(\(m\), \(n\))) {.
4: Send a _head_joining_ message to the cell head \(h\).
5: Switch to the _sleep_mode_ after transmitted their data.
6: }
7:else {
8: Wait a random time
9: Broadcast a _head_available_ message
10: }
```  
**Algorithm 1**Cell Head Election Algorithm  
In a cell, there is only one node that is a cell head to carry out the task for information sensing, forwarding messages, and data fusion. The others are all sleeping, and awakened if necessary.
## 4 The Proposed Watermarking Scheme
### The Extraction Process  
For data extraction, most of the steps described for embedding in Section 4.1 are followed:  
* Audio signal is segmented in \(N\)-samples blocks and intDCT transformed.
* Each transformed block is splitted into eight sub-bands.
* Transformed sub-bands are divided by \(2^{8}\).
* For each sub-band in a transformed block, entropy is computed; then, the average entropy of all sub-bands is computed.
* Data is extracted from sub-bands showing a higher entropy than threshold using the corresponding strategy as described in Section 3.  
Figure 4 shows the insertion process.
# Securing Network Traffic Classification Models against Adversarial Examples Using Derived Variables
## 4 DVars for Adversarial Attack Defense
### DVar Algorithms
#### 4.2.1 The Sum of Adjacent Values  
This process involves taking the sum of the adjacent values of each set of features in the baseline data. To ensure that the DVars remain within the same feature space as the baseline and preserve the validity of the network flow, a condition is set to retain the maximum value if any resulting value exceeds the maximum value in the baseline. This process is repeated for each adjacent feature value until the final feature value of the selected features is obtained. The new set of representative features obtained through this process are then combined with the baseline dataset. Representative feature \(Y_{ij}\) can be calculated using Equation (7).  
\[Y_{ij}=\begin{cases}X_{ij}+X_{i(j+1)},&\text{if }Y_{ij}>\max(X_{i,:})\\ \max(X_{i,:}),&\text{otherwise}\end{cases} \tag{7}\]  
where \(X\) is the baseline dataset, \(X_{ij}\) represents the value of the \(j\)-th feature in the \(i\)-th sample, \(Y\) is the matrix containing the representative features, \(Y_{ij}\) represents the value of the representative feature for the \(j\)-th feature in the \(i\)-th sample, and \(\max(X_{i,:})\) represents the maximum value of the features in the \(i\)-th sample.  
The process of deriving variables using the sum of adjacent feature values is shown in Algorithm 1.  
```
Data: Baseline dataset \(X\) Result: Matrix of representative features \(Y\) for each sample \(i\) in \(X\)do for\(j=1\)to number of featuresdo if\(j+1\leq number\) of featuresthen \(Y_{ij}=X_{ij}+X_{i(j+1)}\); if\(Y_{ij}>\max(X_{i,:})\)then \(Y_{ij}=\max(X_{i,:})\);  end if  end for else \(Y_{ij}=\max(X_{i,:})\);  end if  end for  end for
```  
**Algorithm 1**Derivation of representative features using the sum of adjacent values.
## 1 Introduction  
Spray flame synthesis (SFS) is a versatile technique for the fabrication of functional nanoparticulate metal oxides, such as maghemite (\(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)), which is advantageous for several medical and biomedical applications given its superparamagnetic properties attained at sufficient low domain/particle size, e.g., drug delivery, hyperthermia, and protein separation [1, 2, 3]. Corresponding particles can also be prepared by wet-phase chemistry [4, 5]. However, SFS offers several advances, such as simple scalability; wide access to low-budget, low-toxic precursors; and the fact that particles with higher (surface) purity are typically obtained [6, 7, 8].  
To fabricate superparamagnetic \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\) by SFS, a precursor composition composed of iron (III) nonahydrate (INN), ethanol (EOH), and 2-ethylhexanoic acid (EHA) can be utilized, which typically should yield a dominant gas-to-particle conversion [9; 10; 11; 12]. It should be mentioned here that in SFS, a precursor is dissolved in organic solvents, and the solution obtained is atomized by dispersion gas (typically oxygen), which becomes ignited by a self-sustaining gaseous flame (pilot flame) yielding a turbulent, reactive, multiphase flow. There, particles can either evolve from the gas phase (gas-to-particle conversion) or by precipitation processes taking place in the liquid phase (droplet-to-particle conversion) [6; 7; 8]. Given the size dependencies of these pathways, superparamagnetic \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\) can only be achieved from the gas phase, which has the potential to yield particles \(<\)10 nm. Droplet-to-particle conversions solely yield particles of several hundred nm size and above and are therefore not suitable to synthesize nanoscale superparamagnetic \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\). Additionally, the occurrence of droplet-to-particle conversions also yields bimodal size distributions, which is usually undesired.
## 4 Comparison and Discussion  
In this section, the model proposed in this paper is analyzed and discussed in two main parts. The first part is the ablation study, where several innovations of the prediction method in this paper are ablated for experiments to verify the effectiveness of the innovations. The second section compares the model in this paper with other prediction models. It should be noted that the curves in the pictures in this section are the average of all replicate experiments. In order to show the comparison effect more clearly, the pictures only show the part of the test set with a length of 600. The table shows the mean and standard deviation of the error of all replicate experiments.
## 3 Data Source and Methods
### Methods  
#### 3.2.1 MBMP (Multi-Band Multi-Pass)  
We use the MBMP method to retrieve methane column enhancements from the Sentinel-2 SWIR band measurements [34; 35; 36]. A clear-sky reference image is chosen for each location, with the MBMP image given by:  
\[MBMP=\frac{cR_{12-}R_{11}}{R_{11}}-\frac{c^{\prime}R^{\prime}_{12}-R^{\prime}_ {11}}{R^{\prime}_{11}} \tag{1}\]  
where \(R_{11}\) and \(R_{12}\) are the raw Sentinel-2 band 11 and 12 observations for the current image, \(R_{11}\) and \(R_{12}\) are the raw Sentinel-2 band 11 and 12 observations for the reference image, and \(c(c^{\prime})\) is calculated by the least-squares regression of \(R_{11}\) against \(R_{12}\) (\(R^{\prime}_{11}\) against \(R^{\prime}_{12}\)) for all pixels. These images are used to manually identify and label the extent of the methane plumes for each time step.  
Figure 2: Methane transmittance spectrum based on the HITRAN2020 and the spectral response functions of Sentinel-2A/B [32].
# Analysis of Weighted Factors Influencing Submarine Cable Laying Depth Using Random Forest Method
## 4 Conclusions  
This study employs machine learning algorithms to address the challenges associated with submarine cable burial depth during construction, such as the limited use of available cable data, high costs of depth detection equipment, and operational complexities. Factors such as geographical coordinates, cable tension, lateral forces, entry angles, cable length, water depth, and spatial deviations were analyzed using a backpropagation (BP) neural network optimized with the improved seagull optimization algorithm (ISOA).  
Key findings of the study are as follows:  
1. Random Forest Analysis: The random forest algorithm effectively captures the relationships between the burial depth and multiple influencing factors, identifying significant feature indicators and their impact weights. This demonstrates the applicability of machine learning in multi-faceted submarine cable projects.
2. Optimization of BP Neural Network: The ISOA was used to optimize the BP neural network, determining an optimal structure with six hidden layers and 41 neurons. The ISOA efficiently fine-tunes the network architecture, enhancing model performance as evidenced by the fitness curve variations.
3. Performance Comparison: The ISOA-BP model outperforms traditional machine learning models such as support vector regression (SVR), gradient boosting regression trees (GBRTs), and decision trees in predicting cable burial depth. It exhibits a root mean squared error (RMSE) of 0.0927, a mean absolute error (MAE) of 0.0708, and a coefficient of determination (R\({}^{2}\)) of 0.964, indicating a robust fit to the actual data.
4. Implications for Submarine Cable Installation: The results provide actionable insights for optimizing submarine cable burial processes in China, enhancing construction quality, and reducing operational risks.  
This research promotes the integration of big data and AI in the fields of submarine cables and wind power, enhancing prediction accuracy and supporting the digitization and optimization of marine engineering and offshore wind projects. Future studies will aim to integrate diverse data sources, including construction and real-time environmental data, to enhance real-time predictive capabilities and improve construction quality assessments through a combined approach of data-driven analysis and expert insights.  
Conceptualization, methodology, formal analysis, X.Z.; data curation, investigation, C.L.; writing--review and editing, visualization, S.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Not applicable.  
The data presented in this study are openly available in Zenodo at [[https://doi.org/10.5281/zenodo.13770550](https://doi.org/10.5281/zenodo.13770550)]([https://doi.org/10.5281/zenodo.13770550](https://doi.org/10.5281/zenodo.13770550)).  
The authors declare no conflicts of interest.
# Overflow Energy Loss Recovery System Based on Hydraulic Motor-Electric Generator
## 7 Conclusions  
In this paper, an overflow energy loss recovery system based on a hydraulic motor-electric generator is studied. Furthermore, a pressure difference control method of the relief valve based on pressure compensation is adopted. Through simulation analysis and experimental research, the effectiveness of the system is verified. The results show that the system has superior control characteristics. When the overflow pressure changes, the outlet pressure can respond quickly and achieve stability, and the system has strong anti-interference performance. After adding the energy recovery unit, the pressure regulating characteristics of the relief valve are improved, and the pressure regulating deviation of the relief valve is reduced from 10% to 2.8%, and the pressure control accuracy of the relief valve is improved. By reducing the pressure difference of the relief valve, more overflow energy can be recovered.  
[PERSON] proposed the idea of relief energy regeneration. [PERSON] developed the structure and working mode. [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
The authors acknowledge the support of National Natural Science Foundation of China (51875218), Excellent Outstanding Youth Foundation of Fujian Province of China (2018)06014), Natural Science Foundation of Fujian Province of China (2019)01060).  
Not applicable.  
All data are available in the public domain.  
The authors declare no conflict of interest.  
Figure 35: Power consumed in the throttle-governing system with the energy regeneration system.
## 2 Preliminaries
### Some Useful Theorems  
**Theorem 1** (Factorization Theorem [23]).: _Let \(N=pq\), where \(p\) and \(q\) are large primes. Let \(c\) be a cubic residue modulo \(N\), and \(r_{1}\) and \(r_{2}\) be \(c\)'s two cubic roots modulo \(N\); that is, \(r_{1}^{3}\equiv r_{2}^{3}\equiv c\pmod{N}\) and \(r_{1}\
eq r_{2}\pmod{N}\). \(N\) can be factored by taking \(\gcd(r_{1}-r_{2},N)\) in polynomial time, where \(\gcd(x,y)\) is the greatest common divisor of \(x\) and \(y\)._  
Theorem 1 is easily validated, as if \(r_{1}^{3}\equiv r_{2}^{3}\equiv c\pmod{N}\), we have \((r_{1}-r_{2})(r_{1}^{2}+r_{1}r_{2}+r_{2}^{2})\equiv 0\pmod{N}\). There must exist an integer \(k\) such that \((r_{1}-r_{2})(r_{1}^{2}+r_{1}r_{2}+r_{2}^{2})=kpq\). If \(r_{1}\
eq r_{2}\pmod{N}\), \(r_{1}-r_{2}\) cannot be a multiple of \(N\) at the same time; \(r_{1}-r_{2}\) must contain a non-trivial divisor of \(N\), which is \(p\) or \(q\). Therefore, the integer \(N\) can be factored by Theorem 1. However, the two cubic roots satisfying \(r_{1}\equiv r_{2}\pmod{N}\) cannot lead directly to factoring the integer \(N\).  
The following theorem shows a solution to compute a \(3^{\ell}\)-th root of a cubic residue without factoring \(N\).  
**Theorem 2**.: _Let \(\omega\equiv 1\pmod{3}\), \(\ell>0\), \(c\) be a cubic residue modulo \(N\), and \(X\in Z_{N}^{*}\) satisfy_  
\[c^{\omega}\equiv X^{3^{\ell}}\pmod{N}\]  
_Then we can easily calculate the cubic root \(y\); that is, \(y^{3}\equiv c\pmod{N}\)._  
Because \(\omega\equiv 1\pmod{3}\), we can denote \(\omega=3^{\ell}(3\delta+1)\); following this,  
\[c^{\omega}  
Let \(y=X^{q^{\prime}-r-1}/c^{\delta}\); then we have \(y^{3}\equiv c\pmod{N}\)  
Theorem 2 can be used in the security proof for \(\mathbf{IBMS^{CR}-1}\). We introduce the following Theorem [24,29] regarding the cubic residue used in the security proof for \(\mathbf{IBMS^{CR}-2}\).  
**Theorem 3** (Cubic residue construction [24,29]).: _If \(p\) and \(q\) are two primes with \(p\equiv 2\pmod{3}\) and \(q\equiv 4\) or \(7\pmod{9}\), it is easy to produce a cubic residue modulo \(N\). Let nc be a non-cubic modulo \(q\), for any \(h\in\mathbb{Z}_{N}^{*}\); we can compute that \(\eta=\frac{(q-1)\pmod{9}}{3}\), \(\lambda=\eta\pmod{2}+1\), \(\beta=(q-1)/3\), \(\xi\equiv nc^{\eta\beta}\pmod{q}\), \(\tau\equiv h^{\lambda\beta}\pmod{q}\) and_  
\[b=\left\{\begin{array}{ll}0,&\text{if }\tau=1\\ 1,&\text{if }\tau=\xi\\ 2,&\text{if }\tau=\xi^{2}\end{array}\right.\]  
_We can construct a cubic residue C modulo \(N\); that is, \(C=nc^{b}\cdot h\pmod{N}\)._  
**Theorem 4**.: _Let \(p\), \(q\), \(N\), \(C\), and \(\eta\) be defined as in Theorem 3; we can calculate a cubic root \(s\) of \(C^{-1}\) by \(s\equiv C^{[2\eta^{-1}(p-1)(q-1)-3]/9}\pmod{N}\). Note that \(s^{3}\cdot C\equiv 1\pmod{N}\)._
## 3 Batch Authentication in the Vehicle-to-Grid (V2G) Network
### Existing Authentication Protocols in the Vehicle-to-Grid (V2G)
#### 3.2.3 Certificateless Public Key Infrastructure (PKI)-Based Protocols  
[PERSON] [85] introduced a certificateless PKI-based privacy-preserving protocol, which protects the identities of vehicle owners using a partially-blind signature scheme, and maintains identity and location privacy, confidentiality, and integrity of communications. Hwoever, authors have not discussed efficiency, cost, and generated overhead during authentication. Then, [PERSON] _et al._[86] proposed an identity blind signature-based framework for vehicle monitoring. However, it is not clear whether the protocol is resistant against security attacks.
# Remote Sensing Inversion and Mapping of Typical Forest Stand Age in the Loess Plateau
## 3 Results
### Establish of Forest Stand Age Inversion Model in Loess Plateau
#### 3.3.3 Establishment of Forest Stand Age Inversion Model Using RF Model  
The regression random forest model mainly considered the two processes of the training stage and the prediction stage to realize the forest stand age inversion based on remote sensing data. In MATLAB 2023a, 603 pairs of data were randomly divided into two datasets; about 70% (400 pairs of data) were used as regression samples, and the remaining samples were used for dataset validation. The model was trained 30 times, and the model with the largest \(R^{2}\) was selected; the maximum \(R^{2}\) was 0.63.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input Parameters** & **Model** & \(R^{2}\) & \(p\) \\ \hline \(SR\), \(NDVI\), \(B_{5}\) & \(Age\) = 12.03 \(\times\)\(SR\) + 13.25 \(\times\)\(NDVI\) + 26.19 \(\times\)\(B_{5}\) + 2 & 0.69 & \(p\) \(<\) 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Multiple linear regression model and parameters.  
Figure 7: Cumulative contribution rate of principal components of canopy spectral information.