esa-sceva/satcom-chunk-collection huggingface dataset:
# Application Layer Protocol Identification Method Based on ResNet
## 4 Experimental Results and Discussion
### Datasets  
The experimental dataset comprises two datasets: the CIC-IDS2017 network communication dataset and an application layer protocol dataset obtained from the Shodan search engine. The CIC dataset accounts for 55%, while the Shodan dataset accounts for 45% of the experimental dataset.  
Due to the significant structural differences between character protocols and binary protocols, 20 common application layer protocols are selected from the dataset for experimentation. These protocols include BGP, DNS, FTP, HTTP, IMAP, LDAP, MQTT, MYSQL, NBNS, NTP, OCSP, POP3, SIP, SMB, SMB2, SMTP, SNMP, SSH, TELNET, and TLS.  
To ensure data balance, each protocol's dataset is divided into training, validation, and testing sets, with 800 samples in the training set, 200 samples in the validation set, and 200 samples in the testing set for each protocol.
# Memory Efficient Implementation of Modular Multiplication for 32-bit ARM Cortex-M4
## 2 Background  
### Multi-Precision Multiplication and Squaring  
A straightforward method to execute the large integer multiplication is row-wise multiplication (i.e., operand-scanning), which consists of a nested loop. One digit of an operand \(A\) (i.e., \(a[i]\)) of the inner loop is multiplied with all digits of the second operand \(B\), while the pointer of operand \(A\) points to the next digit of \(A\), in the outer loop. An alternative method is column-wise multiplication [16]. The product-scanning method consists of two inner loops. The first loop performs the lower part of the result. Afterward, the second loop performs the upper part of the result. An important operation in each inner loop is called the Multiply-ACcumulate (MAC) operation, which performs a form of \(C\gets C+a[i]\times b[j]\), where \(C\) is three digits of the intermediate results. The intermediate results are not stored or loaded to/from the memory. Carry propagation is simply achieved by a register-copy operation. In recent years, many works have improved the product-scanning method by re-ordering the inner loop and using new instructions [4; 17; 18].  
A squaring operation is a unique case of multi-precision multiplication, where the multiplier and multiplicand are the identical operand (e.g., \(A\times A\)). For multi-precision squaring, partial products of the form (\(a[i]\times a[j]\) with \(i\
eq j\)) are doubled based on the fact that \(a[i]\times a[j]=a[j]\times a[i]\), while \(a[j]\times a[j]\) appears just once for \(i=j\). Thus, squaring requires less \(\mathtt{mu}\) instructions or MAC operations than multiplication. Some previous implementations for squaring have been optimized based on this symmetric feature on embedded processors. The lazy-doubling method optimizes the doubling operation by performing the doubling column-wise [19]. Similarly, many works have focused on developing an optimized doubling method for target microcontrollers [20; 21].
# A Low-Profile HF Meandered Dipole Antenna with a Ferrite-Loaded Artificial Magnetic Conductor
## 2 Designs of the Artificial Magnetic Conductor and Antenna
### The Designed Antenna with the AMC  
The design concept of a meandered dipole antenna is depicted in Figure 4. This design method was introduced by [PERSON] et al. [27]. The optimization procedure begins by dividing each dipole arm into ten segments. Each segment is assigned such that it is on a column of the grid, denoted in green, with two adjacent segments connected by a vertical line, denoted in red. A binary genetic algorithm (GA) is applied to derive the optimized position of the horizontal segment which is encoded as three bits. Therefore, the meandered dipole antenna is designed by using 30 bits in total. The binary GA is conducted with MATLAB programming linked to HFSS through an HFSS scripting interface. The optimization process in conducted with one hundred iterations, a population of 20, and a mutation rate of 0.1 with the single-point crossover scheme. The horizontal segment and vertical segment are modeled by a rectangular patches \(82\times 85\) mm\({}^{2}\) and \(37\times 215\) mm\({}^{2}\) in size, respectively. The optimization process is conducted with the presence of the AMC.  
The optimized meandered dipole antenna is illustrated in Figure 5a. The Taconic RF-35 substrate, indicated with green in Figure 5a, with a dielectric constant of 3.5, a thickness of (\(h_{a}\)) of 1.52 mm, and a loss tangent of 0.0018 is used to design the antenna. The side length of the substrate of the meandered dipole antenna is set to \(W=1000\) mm, which is the length of the reference monopole antenna used here (OMNI-A0245, ALARISANTENNA[28]). Each meandered dipole element is symmetrically located on the top and bottom side of the substrate, as indicated in grey and white, respectively. The designed meandered dipole antenna is fed by a coaxial cable at the feeding point shown in Figure 5a. Figure 5b shows the configuration of the designed AMC, which consists of \(8\times 8\) unit cells. The number of unit cells is set to constitute a surface with a side length of 1000 mm equal to the length of the reference monopole antenna. There is a hole through which a coaxial cable for  
Figure 3: Simulated reflection phase of the proposed unit cell of the artificial magnetic conductor (AMC).  
excitation can pass through, as indicated by the orange circle in Figure 5b. A gap as large as \(r_{g}=1.9\) mm was added to prevent short circuits of the AMC patch and coaxial cable. The proposed antenna with the AMC is depicted in Figure 5c. The AMC is located beneath of the antenna with a distance of a gap of 18 mm.
# Study of Soot Deposits during Continuous Methane Pyrolysis in a Corundum Tube
###### Abstract  
Methane pyrolysis is one of the promising methods for producing low-carbon hydrogen, while one of the main problems of methane pyrolysis technology is soot clogging of the reactor space. In this work, soot deposits were studied during continuous methane pyrolysis in a corundum tube with an inner diameter of 50 mm. Experiments were carried out at temperatures of 1000 \({}^{\circ}\)C, 1050 \({}^{\circ}\)C, 1100 \({}^{\circ}\)C, 1200 \({}^{\circ}\)C and 1400 \({}^{\circ}\)C with methane flow rates of 1 L/min and 5 L/min. Each experiment lasted 1 h. The formed soot accumulated inside the reactor (corundum tube) and the connected filter, where the gaseous product of methane pyrolysis was separated from the soot. The gaseous product was studied by gas chromatography. The soot was studied by SEM, BET and ICP-MS. With an increase in the temperature of the pyrolysis process from 1000 to 1200 \({}^{\circ}\)C, the hydrogen yield increased from 28.64 to 92.74% and from 1.10% to 72.09% at a methane flow rate of 1 and 5 L/min, respectively. The yield of soot increased from 1.28 g at 1000 \({}^{\circ}\)C to 43.9 g at 1400 \({}^{\circ}\)C (at a methane flow rate of 1 L/min). With an increase in the flow rate of methane from 1 to 51/min, the yield of soot at 1200 \({}^{\circ}\)C increased by almost two times to 75.65 g. It was established that in the region of the reactor where maximum heating occurs, the accumulated soot sinters and forms dense growths. At 1050 \({}^{\circ}\)C, the particle size of soot varies from 155 to 650 nm, at 1200 \({}^{\circ}\)C--from 157 to 896 nm, and at 1400 \({}^{\circ}\)C--from 77 to 532 nm. The specific surface of soot was 3.5 m\({}^{2}\)/g at 1000 \({}^{\circ}\)C and 8.0 m\({}^{2}\)/g at 1400 \({}^{\circ}\)C. The purity of the produced carbon black was about 99.95%. This study is useful in the selection of materials and technical solutions for a pilot plant for methane pyrolysis.  
Keywords:hydrogen; carbon black; methane pyrolysis; thermal decomposition; soot deposits; corundum tube; natural gas decomposition +
Footnote †: journal: Applied sciences
# Low Discrepancy Sparse Phased Array Antennas
## 3 Sparse Planar Phased Array Antennas
### Radiation Patterns of the Sparse Phased Array Antennas  
The complete far-field radiation pattern of an antenna array with identical elements is given by  
\[F(u,v)=E(u,v)\sum\limits_{n=1}^{N}a_{n}e^{-jk[x_{n}u+y_{n}v]} \tag{15}\]  
where \(E(u,v)\) is the element pattern in u-v space, the summation represents the array factor, and \(F(u,v)\) is the far-field radiation pattern of the antenna array. In this expression, \(a_{n}\) is the element weight, and the other terms are as defined in (1). Here we consider elements that have unity amplitude and zero phase, so \(a_{n}=1\). The elements are also isotropic, so \(E(u,v)=1\).  
The radiation patterns of the arrays in the u-v space are computed using (15) and are given in Figure 5. We note that in these graphs the visible region is a circle with \(\sqrt{u^{2}+v^{2}}\leq 1\). It can be seen that the uniform case has the worst performance and four grating lobes with sidelobe level of 0 dB appear in the visible space. Random sampling completely removes the grating lobes, however as we saw in Section 2, it places many elements too close to each other. Random sampling with jitter reduces the grating lobes but cannot break the grating lobes completely because its element spacing is too regular. On the other hand, all LDS methods are effective in reducing the SLLs while maintaining low discrepancy. In comparison between these methods, Hammersley sampling has the poorest performance due to its hierarchical problem. For Hammersley sampling, performance degrades as the prime base number is increased. Halton sampling on the other hand shows a much better performance, and all 6 cases studied here show that they can break the grating lobes. Due to its binary implementation, Sobol sampling shows a similar performance to Hammersley sampling with a prime base of 2. Poisson sampling appears to outperform all other LDS methods as well as the random distribution. The 2D u-v graphs in Figure 5, allow one to visually compare the radiation performance of all the sampling techniques, however, for better comparison, a quantitative analysis is also provided in the next section.
# Steganalysis of Quantization Index Modulation Steganography in G.723.1 Codec
## 5 Experiment and Results Analysis
### Results Analysis
#### 5.3.1 Performance  
This paper uses TPR, FPR, and \(Pr\) to evaluate the accuracy and reliability of the steganalysis algorithm. The higher the TPR, the lower the FPR, and the higher the Pr, the better the accuracy of the steganalysis method.  
CNV-QIM is a hidden method with good concealment. In order to test the detection and analysis performance of the CNV-QIM steganography algorithm, Table 2 shows the results of PCA pretreatment. The dimensionality feature vector is reduced from 256 to 120, and the reduction in the number of features simplifies the design of the classifier. We choose a voice message with an embedding rate of 100%. In this paper, the probability distribution matrix and the transfer probability matrix of G.723.1 speech were calculated to obtain the average TPR, FPR and \(Pr\) of CM, CW, EM, EW and Hybrid, respectively. As is shown in Table 3. It can be seen that the ACD and SCD were all over 93%, and the FPR was all less than 8%. This proves that the method proposed in this paper is very effective. For the five types of samples, \(Pr\) is all over 93%, which proves that the algorithm has good accuracy and reliability [23; 24].  
Figure 6: Steganalysis process.  
Steganalysis method based on Fourier spectral statistics and Mel cepstrum coefficients for second derivatives of audio signals is proposed in the literature [8] (MFCC). This kind of steganalysis method is of great importance in recent years. The method can achieve better detection results for many speech steganography algorithms. CNV-QIM is a hidden method with good concealment. In order to show the superiority of the method in this paper, this paper makes an experimental comparison of these two algorithms. The comparison results are shown in Figure 7.  
Figure 7: Comparison of detection algorithms.  
We can see from the above figure that the TPR and \(Pr\) of both ACD and SCD are higher than the MFCC method, and the TPR of ACD and SCD is significantly lower than the MFCC method, it is indicated that the method proposed in this paper is better in accuracy and reliability. Comparing the above three figures, another advantage of the method of this paper is that it can adapt to different languages. We know that our method is superior to MFCC in terms of performance in the face of different sets of voice data.
## 2 Materials and Methods
### Manufacturing Process
#### 2.1.1 Cad  
The calibration piece design was conducted using CAD software (Solidworks 2021), considering the contour profile and dimensions depicted in Figure 1. The contour profile is composed of a curve, defined as a quarter of a circle with a radius of 20 mm, a horizontal line with a length of 25 mm, and a slope represented by a line segment with a length of 12.5 mm inclined at an angle of 33.69\({}^{\circ}\). The contour profile was designed to be contended in a \(75\times 75\times 12\) mm rectangular workpiece within a line profile tolerance of 0.8 mm to obtain the ideal profile shown in Figure 2. The definition of the calibration piece guarantees the accuracy and relevance required for the NIIM to inspect the contour profiles, facilitating a thorough assessment of the machining processes.  
Material selection was guided by optical and contrast properties, considering delrin (black), nylon (white), aluminum (silver), and AISI 1018 (gray) due to their interaction with light and impact on image acquisition quality ([PERSON] et al., 2018). Additionally, the choice aligns with findings from ([PERSON] et al., 2018), highlighting these materials as commonly used in industrial applications. Refer to Table 1 for detailed properties.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Materials** & \(\sigma_{u}\) & **H** & **E** \\ \hline AISI 1018 (Gray) & 440 Mpa & 89 HRB & 205 GPa \\ Aluminum 1060 & 95 MPa & 20 HRB & 69 GPa \\ Delrin (Black) & 85 MPa & 110 HRC & 2.6 GPa \\ Nylon (White) & 85 MPa & 70 HRC & 3.0 GPa \\ \hline \hline \end{tabular} \(\sigma_{u}\): Tensile strength; HRB: Hardness Rockwell B. HRC: Hardness Rockwell C; E: Modulus of Elasticity.  
\end{table}
Table 1: Mechanical properties of the materials employed.  
Figure 3: NIIM stages.
# A Review on the Use of Microalgae for Sustainable Aquaculture
## 4 Problems and Prospects
### Prospects  
As the eco-friendly aquaculture and the nutrients recovery from wastewater are becoming more important to the global sustainability, microalgae-assisted aquaculture bringing great beneficial benefits to natural environment merits more attention from both academia and industry. With the solution of aforementioned problems, microalgae-assisted aquaculture will move forward from lab research to industrial application. Generally, the great advancement brought to aquaculture by the wide use of microalgae biotechnology can be classified into environmental and economic aspects.  
Firstly, the threats of aquaculture to the environmental sustainability can be controlled to some extent. Owing to the great performance of microalgae in carbon dioxide fixation and wastewater remediation, pollution caused by aquaculture can be alleviated. In addition, since microalgae feed has beneficial effects on the health of aquatic animals, the abuse of antibiotics or medicines could be prohibited in microalgae-assisted aquaculture. Thus, the antibiotics-resistance, which is becoming more serious in aquaculture, may be controlled. Therefore, with the wide application of microalgae-assisted aquaculture, its positive effects on the environmental safety and sustainability will contribute to the global sustainability.  
Secondly, the cost of aquaculture can be reduced by the nutrients recovery and biomass production. The cost of feed accounts for a large portion of the total aquaculture cost in a real-world application, so the financial burden is a barrier to the development of aquaculture. By microalgae-assisted aquaculture,nutrients in wastewater are converted to value-added biomass, which can be further exploited to produce aquaculture feed. Thus, the nutrients recovery in aquaculture by microalgae biotechnology can reduce material input and the total cost of the whole system.  
Considering the advancement in environmental and economic aspects, with the wide application of microalgae-assisted aquaculture, it will make great contribution to the aquaculture sustainability and global sustainability.
# Image Interpolation Based on Spiking Neural Network Model
## 3 Conductance-Based Integrate-and-Fire Neuron Model  
In 1952, [PERSON] and [PERSON] [34] introduced a model for simulating actions in neurons with the help of differential equations. However, the HH neuron model has a high computational cost since it has many differential equations and parameters. Therefore, different neuron models such as integrate-and-fire (IF), FitzHugh-Nagumo (FHN), and Izhikevich have been presented [37; 38; 39]. Among these models, the CIF neuron model [40] stands out with its lower computational cost advantage in large-scale networks [41]. In the CIF model, the membrane potential is calculated by Equation (2).  
\[c_{m}\frac{dv(t)}{dt}=g_{l}(E_{l}-v(t))+\frac{w_{ex}g_{ex}(t)}{A_{ex}}(E_{ex} -v(t))+\frac{w_{in}g_{in}(t)}{A_{ih}}(E_{ih}-v(t)) \tag{2}\]  
where \(c_{m}\) is the membrane capacitance. \(g_{l}\) is the membrane conductance and \(E_{l}\) is the reverse potential of the membrane. \(E_{ex}\) and \(E_{ih}\) are the reverse potentials of excitatory and inhibitory synapses, respectively. \(w_{ex}\) and \(w_{ih}\) are the weights of the synapses, and \(A_{ex}\) and \(A_{ih}\) are the membrane surface areas. \(g_{ex}\) and \(g_{ih}\) represent the time-varying conductance of excitatory and inhibitory synapses. If a neuron's excitatory connections send a higher signal than its inhibitory connections, the neuron's membrane potential exceeds the threshold voltage \(\upnu_{th}\) at time \(t\), and the neuron generates a spike. Immediately after this, from the moment \(t+1\), the membrane potential of the neuron remains constant at its initial value \(\upnu_{reset}\) for a time \(\uptau_{ref}\), called the refractory time. For the simplicity of the model and ease of calculation, the \(\uptau_{ref}\) value was accepted as 0 in this study as in similar studies [27; 31; 32]. Figure 3 illustrates the spike sequence produced by a neuron (Neuron \(i\)) according to signals from synaptically connected neurons.
# A Statistical Performance Analysis of Named Data Ultra Dense Networks
## 5 Results
### Model Selection
#### 5.3.1 Initial Model  
In the following initial model (Equation (7)), all predictors (NN, NI, RB, and RD) and all of their two-way interactions were included.  
\[ISR =\beta_{0}+\beta_{NN}NN+\beta_{NI}NI+\beta_{RB}RB+\beta_{RD}RD+ \beta_{NN\times NI}NN\times NI+\beta_{NN\times RB}NN\times RB\] \[+\beta_{NN\times RD}NN\times RD+\beta_{NI\times RB}NI\times RB+ \beta_{NI\times RD}NI\times RD+\beta_{RB\times RD}RB\times RD+\epsilon \tag{7}\]  
The coefficient estimates and their corresponding \(p\)-values are presented in Table 3. The coefficients in the MLR were the intercept and slopes. The intercept was the grand mean of the ISR, which became the baseline of all observations. The F-statistic for the model accuracy was significant (\(p<2.2\times 10^{-16}\) ), implying that there was at least one predictor that had a significant effect on the ISR.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline  & **Levels** & 250–500 & 250–750 & 250–1000 & 500–750 & 500–1000 & 750–1000 \\ \multirow{2}{*}{**NI**} & _p-values_ & 0 & 0 & 0 & \(8\times 10^{-7}\) & 0 & \(1\times 10^{-7}\) \\  & **estimates** & \(-18.01813\) & \(-28.85063\) & \(-40.51750\) & \(-10.83250\) & \(-22.49938\) & \(-11.66688\) \\ \hline \multirow{2}{*}{**NN**} & **Levels** & 9–25 & 9–41 & 9–49 & 25–41 & 25–49 & 41–49 \\  & **\(p\)-values** & 0.0000134 & 0.000029 & 0.0000003 & 0.9881607 & 0.8683795 & 0.9706484 \\  & **estimates** & \(-9.6742188\) & \(-10.3223438\) & \(-11.2096875\) & \(-0.6481250\) & \(-1.5354687\) & \(-0.8873437\) \\ \hline \multirow{2}{*}{**RB**} & **Levels** & 1–5 & 1–10 & 1–20 & 5–10 & 5–20 & 10–20 \\  & **\(p\)-values** & 0 & 0 & 0 & 0 & 0 & 0 \\  & **estimates** & 34.59859 & 57.21875 & 82.51203 & 22.62016 & 47.91344 & 25.29328 \\ \hline \multirow{2}{*}{**RD**} & **Levels** & 10–50 & 10–100 & 10–200 & 50–100 & 50–200 & 100–200 \\  & **\(p\)-values** & 0.99995204 & 0.9997671 & 0.9952438 & 0.9999952 & 0.9992516 & 0.9987582 \\ \cline{1-1}  & **estimates** & \(-0.2200000\) & \(-0.1728125\) & \(-0.4753125\) & 0.0471875 & \(-0.2553125\) & \(-0.3025000\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: [PERSON]’s honestly significant difference (HSD) test.  
Model \(1\) included all candidate predictors with four levels and their interactions chosen from mean plots, interaction plots, and multiple comparison tests. In Model 1, the only significant predictors were NI and RB; this did not agree with the result from the previous mean plots and multiple comparison tests. An NN of nine is supposed to have a significantly different effect on the ISR compared to an NN of 25, 41, and 49. This phenomenon occurs when nuisance predictors with multicollinearity are included in the model. Therefore, stepAIC was employed to select significant predictors and remove unnecessary ones from the model.
# A Survey on Underwater Acoustic Sensor Network Routing Protocols
## 3 The Knowledge of Underwater Acoustic Sensor Networks
### The Propagation Delay  
Due to the fact the propagation delay varies with different scenarios and applications, up to now, there is no uniform model or standard for the propagation delay of underwater sensor networks. Therefore proposing an available model or standard for underwater acoustic sensor network propagation delay is an open issue and a challenge for further research. The propagation delay of underwater sensor networks is mainly related to the communication media properties and the network scenario environment. The propagation speed changes frequently with temperature, depth, and salt density. In addition, considering the moving speed of AUVs and sensor nodes, this propagation delay will have great effect on the routing protocol performance; sometimes, it can disable the routing, so how to calculate the propagation delay in underwater acoustic sensor networks is important and necessary for routing protocol design.