esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Method for Constructing the S-Box Based on Spatiotemporal Chaotic Dynamics
## 5 Performance Analysis of the Constructed S-Box
### Linear Approximation Probability  
[PERSON] [9] initially defined the LP. For an S-box holding the LP property, the value of its LP is the maximum unbalance value, which is presented as:  
\[LP=max_{\gamma_{1},\gamma_{2}\
eq 0}\left(\frac{\sharp\{z\in Z\mid z\cdot\gamma_{1}= g(z)\cdot\gamma_{2}\}}{2^{n}}-\frac{1}{2}\right), \tag{11}\]  
where \(Z\) contains all the input values, \(2^{n}\) is a count of all the elements in the S-box, \(\gamma_{1}\) denotes the input mask, and \(\gamma_{2}\) denotes the output mask. The parity of the input bits chosen by the mask \(\gamma_{1}\) is equal to the parity of the output bits chosen by the mask \(\gamma_{2}\). The smaller the value of LP, the stronger the ability to resist linear password attacks. Table 11 gives an analysis of LP for different S-boxes. Compared with other S-boxes, the obtained S-box had a minimum value of LP. Therefore, the obtained S-box had a better ability to resist linear password attacks than other S-boxes.  
\begin{table}
\begin{tabular}{c c} \hline
**S-Boxes** & **MaxDP** \\ \hline The obtained S-box & 0.0469 \\ S-box based on CML & 0.0391 \\ S-box proposed in [11] & 0.0469 \\ S-box proposed in [13] & 0.0391 \\ S-box proposed in [14] & 0.0547 \\ S-box proposed in [16] & 0.0391 \\ S-box proposed in [17] & 0.0469 \\ S-box proposed in [18] & 0.0469 \\ S-box proposed in [20] & 0.0391 \\ S-box proposed in [21] & 0.0468 \\ S-box proposed in [22] & 0.0391 \\ S-box 2 proposed in [23] & 0.0469 \\ S-box proposed in [24] & 0.0391 \\ \hline \end{tabular}
\end{table}
Table 10: The maximum differential probability (DP) analysis of different S-boxes.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline
8 & 6 & 10 & 6 & 8 & 6 & 6 & 6 & 8 & 6 & 8 & 6 & 6 & 6 \\
8 & 6 & 8 & 6 & 8 & 6 & 6 & 6 & 8 & 6 & 8 & 8 & 6 & - \\ \hline \end{tabular}
\end{table}
Table 9: Differential approximation table of our S-box.
# A CMOS-Based Terahertz Reconfigurable Reflectarray with Amplitude Control: Design and Validation
## 3 Element Design
### CMOS-Based Switch Design and Parameter Extraction
#### 3.1.3 Switch Parameter Extraction  
Terahertz switches applied on metasurfaces are equivalent to single-port devices, as shown in Figure 4. The parasitic capacitance values of the transistor are named \(C_{\text{on}}\) and \(C_{\text{off}}\), which are in a parallel configuration with the channel resistances \(R_{\text{on}}\) and \(R_{\text{off}}\). Since the transistor is located under the bottom of the metal interconnect layer in a silicon-based process, while the inductor is at the top layer, the inductance value of the metal via is also equivalent to an inductance \(L_{eq}\). Additionally, a custom-designed inductive loop with an inductance value of \(L\) is connected in parallel between the source and drain of the transistor.  
The terahertz switch can obtain a two-port S-parameter matrix through probe testing and TRL calibration testing. The two ground nodes of this two-port matrix are connected to a common ground. By extracting the equivalent impedance parameters between the two signal nodes, the single-port equivalent parameters of the terahertz switch can be obtained. The inductance value \(L\) of the inductive loop can be extracted by comparing the parameters with and without the inductive loop. The via inductance \(L_{eq}\) can be determined by performing full-wave simulations using electromagnetic simulation software (ANSYS HFSS 2020 R2) to find the ratio between the via inductance and the inductive loop inductance, followed by proportional calculations based on the extracted inductance values from the test results. After subtracting the inductance portion from the switch's single-port parameters, the remaining impedance parameters' real and imaginary parts can be used to calculate the values of the parasitic capacitance and channel resistance, respectively.  
The equivalent parameters of the switch extracted from the measured results at 300 GHz are shown in Table 1. Using the switch parameters, the insertion loss and isolation of the switch are calculated, and the fitting results are compared with the measured results, as shown in Figure 5. A relatively good fitting effect is achieved within a wide frequency band ranging from 260 GHz to 310 GHz. These switch parameters can offer a precise basis for the design of terahertz elements.  
Figure 4: The single port equivalent circuit model of the THz switch.
## 3 Methodology
### Experimental Environment: NaviMoon Spaceborne Receiver
#### 3.3.1 User Segment  
The user segment made up of the NaviMoon receiver is then set up to run an aided GNSS scenario fed with an RF cable from the Spinent Radio Frequency Constellation Simulator (RFCS) generated signals. The NaviMoon is now a Technology Readiness Level (TRL) 8 technology developed by SpacePnt as its Protoflight Model has been delivered at SSTL for integration into the Lunar Pathfinder [74]. An engineering model version of the receiver is in the Radio Navigation Laboratory of ESA and it was used to perform the present tests. The NaviMoon receiver is a high-sensitivity spaceborne receiver with an acquisition engine sensitivity as low as 18 dB-Hz and a tracking threshold of 15 dB-Hz on L1/E1 and 12 dB-Hz on L5/E5a [75]. Aided data can be provided to the receiver to search only geometrically visible satellites, predict Doppler and prioritize satellites with a high \(C/N_{0}\). To simulate a GNSS receiver in lunar orbit both for VMMO and Pathfinder, the following data has to be retrieved and provided to the NaviMoon as inputs:  
* The initial position state of the user trajectory in Earth Centered Earth Fixed (ECEF) coordinates acting as the coarse PVT solution,
* the initial estimated clock drift of the NaviMoon clock after prior calibration on a terrestrial GNSS scenario,
* the Receiver Independent Exchange Format (RINEX) Navigation file including the GNSS constellations parameters after extraction from the RFCS.  
The receiver antenna pattern is an omnidirectional pattern with a 0 dB antenna gain. The necessary power gain to complete the link budget to meet descent visibility is set on the system side. It is assumed that this would not cause a significant difference in the performances with respect to the real scenario given the fact that the intended high-gain 14 dBi Harp antenna will be steerable and have a relatively flat antenna gain over the beamwith covering the Earth GNSS satellites directions as seen by the lunar orbiter [76].
# Design of a Tri-Band Wearable Antenna for Millimeter-Wave 5G Applications
## 3 Experimental Results and Discussions
### Radiation Pattern  
Figure 8a-c illustrates the radiation pattern of the proposed antenna at various resonance frequencies. The omnidirectional pattern in the H-plane and the elliptical pattern in the E-plane can be seen in both the 28 GHz and 38 GHz radiation patterns. For frequencies between 28 GHz and 38 GHz, a high correlation is observed between simulated and measured results. An omnidirectional pattern along the H-plane and a broadsided pattern along the E-plane is observed for the 60 GHz frequency because of the 40 GHz measurement constraint.  
Figure 7: (**a**) The prototype of the proposed antenna; (**b**) comparison between the simulated and measured return loss.
# High Voltage Ride through Strategy of Wind Farm Considering Generator Terminal Voltage Distribution
## 1 Introduction
The application of the synchronous condenser in wind farms was discussed in References [18; 19]; however, the developed HVRT strategy, with the participation of a synchronous condenser, did not consider the voltage distribution in wind farms [19]. This article takes the practical project of wind power transmission through HVDC transmission technology in Northwest China as the background. Aiming to solve the problem of voltage swell caused by DC blocking, this article put forwards a HVRT strategy considering the voltage distribution in the wind farm. The proposed strategy combines the reactive power compensation characteristics of the synchronous condenser and DFIG. The typical topology and parameters of a practical wind farm are investigated, then the effectiveness and feasibility of the proposed strategy are verified by simulation results. The contributions of the article can be summarized:  
1. A key voltage node extraction technology based on generator terminal voltage distribution curves is proposed. Compared with the grid-connected node, the extracted node can better reveal the generator terminal voltage distribution characteristics of the wind farm. The stable control of key nodes can ensure the stable operation of all wind turbines during the HVRT period. 2. Based on the reactive power compensation characteristics of the synchronous condenser and the DFIG, an improved HVRT strategy is proposed. By means of controlling the voltage of the extracted node, the proposed strategy coordinates the reactive power output of the synchronous condenser and DFIGs, effectively avoiding trip-off accidents during the voltage swell period. 3. The evaluation index voltage swell index (VSI) is proposed to evaluate the ride-through performance of the HVRT strategy. Based on the practical topology and parameters of the wind farm, the proposed HVRT strategy was used to test the model built in MATLAB/SIMULINK, and simulation results showed the effectiveness and feasibility of the proposed strategy. This article is organized as follows: Section 2 presents the DC blocking fault in HVDC transmission, and the voltage distribution characteristics inside the wind farm. Section 3 presents the reactive power output characteristics of the synchronous condenser and DFIG. Section 4 put forward a method of extracting the key voltage node, and an improved HVRT strategy. Section 5 simulates and verifies the proposed HVRT strategy. Section 6 concludes this article.
# Estimating Degradation Costs for Non-Cyclic Usage of Lithium-Ion Batteries
## 2 Materials and Methods
### Degradation
#### 2.2.2 Degradation of LFP Cells  
The cell studied by [PERSON] et al. [11] and [PERSON] et al. [33] is an LFP/graphite 26650-type cell of 2.3 Ah and 3.3 V.  
The calendar degradation model is described by [PERSON] et al. [11, 12, 14]. The value of calendar capacity loss of the LFP technology from the beginning of life normalized with \(C_{BOL}\) (\(C_{loss,cal}^{LFP}\)), a function that depends on \(SoC\), \(T\) and \(t\), can be calculated using Equation (17).  
\[C_{loss,cal}^{LFP}\ =\ S\cdot\exp(\alpha\cdot 100\cdot SoC)\cdot\exp\left(- \frac{\beta}{T}\right)t^{\gamma}, \tag{17}\]  
where \(S\), \(\alpha\), \(\beta\), and \(\gamma\) values are defined by [11] (\(S=165400\), \(\alpha=0.01\), \(\beta=4148\) K, and \(\gamma=0.5\)). All these parameters were experimentally determined by [PERSON] et al. [11].  
[PERSON] et al. [11] defined a cyclic degradation model that depends on time. Moreover, [PERSON] et al. [32] modified this model. The latter study provides an expression for cyclic degradation of capacity while maintaining the same coefficients as those used by [PERSON] et al. [11]. The dependency on time is changed to dependency on total Ah-throughput. The cycles used are constant current (CC) cycles.  
The value of cyclic capacity loss of LFP cells from the beginning of life normalized with \(C_{BOL}\) (\(C_{loss,cyc}^{LFP}\)) as a function of \(DoD\), \(CR\), and \(Q\), can be performed using Equations (18)-(20).  
\[C_{loss,cyc}^{LFP}\ =\ B\cdot K_{1}\cdot K_{2}\cdot Q^{z}, \tag{18}\]  
\[K_{1}\ =\ \alpha_{1}+\alpha_{2}\cdot 100\cdot DoD+\alpha_{3}\cdot\left(100\cdot DoD \right)^{0.5}+\alpha_{4}\cdot\ln(100\cdot DoD), \tag{19}\]  
\[K_{2}\ =\ \beta_{1}\cdot CR^{2}+\beta_{2}\cdot CR+\beta_{3}, \tag{20}\]  
where the coefficients \(B\), \(\alpha_{1}\), \(\alpha_{2}\), \(\alpha_{3}\), \(\alpha_{4}\), \(\beta_{1}\), \(\beta_{2}\), \(\beta_{3}\), and \(z\) are extracted from [PERSON] et al. [11] and [PERSON] et al. [32], this model is valid for \(CR\) values above 0.77 and \(DoD\) above 3.6%.
# Network Coding for Efficient Video Multicast in Device-to-Device Communications
## 4 System Evaluation
### PSNR Video Quality Analysis  
The actual performance of the three schemes in this paper is shown in Figure 8. The PSNR of the replication-based scheme is 24.6, and the PSNR of the other two schemes is 100. The image quality transmitted by the two schemes based on network coding is higher than that based on the replication. The transmission quality of the proposed scheme is similar to the traditional RLNC-based scheme.  
The overall result of video transmission is shown in Table 2. In the table there are the number of sent frames and the number of decoded frames and the quality of the transmitted picture.  
The quality of the video is an essential criterion for testing the reliability of transmission, so it needs to be focused on experiments. In the experiment, we analyze the video quality of the three schemes with PSNR as the criterion, as shown in Figure 9. PSNR is a primary video quality evaluation indicators, and its value ranges from 0 to infinity. The PSNR value indicates the degree of distortion of the picture. The larger the value is, the smaller the distortion is. Generally, when the PSNR value is higher than 50, it means that the picture quality is excellent. We consider that the picture is almost completely restored when the PSNR value exceeds 100. So we set all values higher than 100 to 100. Moreover, when the data frame is lost, the PSNR value is set to 0. Therefore, in the figure, the value of PSNR  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Replication-Based** & **RLNC-Based** & **Our Scheme** \\ \hline Total & 960 & 960 & 960 \\ Decoding quantity & 730 & 926 & 897 \\ Average PSNR value & 45 & 84 & 85 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Transmission situation.  
Figure 8: Experimental result diagram: **(a)** Description of Replication-based scheme; **(b)** Description of Proposed scheme; **(c)** Description of Random Linear Network Coding (RLNC)-based scheme.  
According to the diagram, the replication-based scheme has the most inadequate video decoding quality and the least successfully decoded frames, and the other two schemes have similar decoding and recovery performance. Experimental results show that the average PSNR value of the replication-based recovery rate is only 45. However, the recovery rate of the proposed scheme and the RLNC-based scheme are 85 and 84. Compared with the replication-based scheme, the proposed scheme can significantly improve the frame quality. Moreover, the PSNR value of the decoded frames is slightly better than that of the traditional RLNC scheme, but the number of decoded frames in the proposed scheme is less than that of the RLNC scheme. Therefore, we consider that the video quality of the proposed scheme is comparable to that of the traditional RLNC scheme.
## 3 Mathematical Model and Thermodynamics Analysis
### Desiccant Cooling System (DCS)  
The desiccant wheel is the main component of the DCS, and the model established by [PERSON] et al. [25] is used to simulate the desiccant wheel in this study. The following are the associated energy balances and governing equations for the DCS components:  
* The combined potential of the desiccant wheel \[F_{1,i}=\left[-\frac{2865}{\left(t_{i}+273.15\right)^{1,49}}\right]+4.344[w_{i }/1000]^{0.8624}\] (12) \[F_{2,i}=\left[\frac{\left(t_{i}+273.15\right)^{1,49}}{6360}\right]-1.127[w_{i }/1000]^{0.07969}\] (13)
* Desiccant wheel's efficiency \[\eta_{F1}=\frac{F_{1,2}-F_{1,1}}{F_{1,8}-F_{1,1}}\] (14) \[\eta_{F2}=\frac{F_{2,2}-F_{2,1}}{F_{2,8}-F_{2,1}}\] (15)
* Energy and mass balances of the desiccant wheel \[m_{P,a}^{\bullet}(h_{a2}-h_{a1})=m_{R,a}^{\bullet}(h_{a8}-h_{a9})\] (16) \[m_{P,a}^{\bullet}(w_{a1}-w_{a2})=m_{R,a}^{\bullet}(w_{a9}-w_{a8})\] (17)
* Heat exchanger \[m_{P,a}^{\bullet}c_{p,ma}(t_{a2}-t_{a3})=m_{R,a}^{\bullet}c_{p,ma}(t_{a7}-t_{ a6})\] (18) \[\varepsilon_{HE-1}=\frac{m_{P,a}^{\bullet}c_{p,ma}(t_{a2}-t_{a3})}{C(t_{a2}- t_{a6})_{min}}\] (19) where, \[C_{min}=min\big{\{}m_{P,a}^{\bullet}c_{p,ma},\,m_{R,a}^{\bullet}c_{p,ma}\big{\}}\]
* Direct evaporative coolers: \[\eta_{DEC-1}=\frac{t_{a3}-t_{a4}}{t_{a3}-t_{a3,mb}}\] (20)\[\eta_{DEC-1}=\frac{w_{a4}-w_{a3}}{w_{a4,Idel}-w_{a3}} \tag{21}\] \[\eta_{DEC-2}=\frac{t_{a5}-t_{a6}}{t_{a5}-t_{a5,wb}}\] (22) \[\eta_{DEC-2}=\frac{w_{a6}-w_{a5}}{w_{a6,Idel}-w_{a5}} \tag{23}\]
* Regeneration energy, space cooling capacity and coefficient of performance \[Q^{\bullet}_{in,DCS}=m^{\bullet}_{R,a}(h_{a8}-h_{a7})\] (24) \[Q^{\bullet}_{cooling}=m^{\bullet}_{P,a}(h_{a5}-h_{a4})\] (25) \[COP_{DCS}=\frac{Q^{\bullet}_{cooling}}{Q^{\bullet}_{in-DCS}}\] (26)
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 3 Preliminaries
### Elliptic Curve Signcryption  
Signcryption is a well-established cryptographic technique which logically enables the functionality of authentication and confidentiality in a single step. The technique of signcryption becomes more efficient when combined with elliptic curve cryptography. [PERSON] and [PERSON] [59] have shown that it saves 58% time in computation and 40% overhead in communication, when compared with the traditional signature-then-encryption approach, when used along with elliptic curve cryptography. Moreover, the combined functionality of signcryption and elliptic curve cryptography can satisfy many security attributes, at the same time resisting many attacks. Due to this, it will be wise to use elliptic curve-based signcryption for securing a resource constraint device such as a smart card.  
In order to provide a preview of how the elliptic curve based signcryption scheme can be developed, a brief overview of the very first elliptic curve signcryption scheme developed by [PERSON] and [PERSON] [59] has been presented here. The two variants of Shortened Elliptic Curve Digital Signature Scheme (SEDSS) used in the elliptic curve signcryption scheme of [PERSON] and [PERSON] have been shown in Table 1. The three phases of this approach are the initialization phase, the signcryption phase, and the unsigncryption phase.
# Efficient High-Dimensional Quantum Key Distribution with Hybrid Encoding
## 2 Schematic Description
\begin{pmatrix}1&1&1&1&\cdots&1\\ 1&\omega&\omega^{2}&\omega^{3}&\cdots&\omega^{d-1}\\ 1&\omega^{2}&\omega^{4}&\omega^{6}&\cdots&\omega^{2(d-1)}\\ 1&\omega^{3}&\omega^{6}&\omega^{9}&\cdots&\omega^{3(d-1)}\\ \vdots&\vdots&\vdots&\vdots&\ddots&\vdots\\ 1&\omega^{d-1}&\omega^{2(d-1)}&\omega^{3(d-1)}&\cdots&\omega^{(d-1)(d-1)} \end{pmatrix} . \tag{2}\]  
Subsequently, a OAM value of the single photon is measured in each output port of the tritter. The result of the measurement is obtained from click of a single photon detector. A click in one of the \(d^{2}\) detectors corresponds to a projection into one of the following two qudits encoded in a single photon written in Equation (3):  
\[\ket{\Phi_{di+j}}=\frac{1}{\sqrt{d}}\sum_{x=0}^{d-1}\omega^{jx}\ket{l_{x},p_{x +i}}, \tag{3}\]  
where \(i,j\in\{0,1,2,...,d-1\}\), and (mod \(d\)) is omitted in the subscript of \(p\). Since the states have the similar form to the \(d\)-dimensional Bell states, it is expected that the states can be used to distribute a secret key between [PERSON] and [PERSON]. The relation between two qudits encoded in a single photon that enters into the tritter and its corresponding detector click event is shown in Equation (4):  
\[\ket{\Phi_{di+j}}\to D(l_{d-i},p_{d-j}) \tag{4}\]  
where \(i,j\in\{0,1,2,...,d-1\}\), and we label a click event of a single photon detector as \(D(l_{x},p_{y})\) corresponding to the single photon whose OAM value is \(l_{x}\) and path mode is \(p_{y}\) after the tritter operation. Since there are \(d^{2}\) orthonormal states in Equation (3), the measurement setup should include \(d^{2}\) single photon detectors for one-to-one correspondence of the states and the detectors. In order to share a secret key, it is necessary to retrieve [PERSON]'s information based on the basis choice of [PERSON] and [PERSON], and the result of the measurement. For restoration, [PERSON] sends her basis choice to [PERSON]. The method of the restoration is shown in Table 1 as an example when \(3d\)-QKD with hybrid encoding is performed. [PERSON] announces only the basis matching information through classical communication, not the result of the measurement. [PERSON] does not need to know the measurement outcome, since [PERSON] already retrieved his encoded information by using the result. Figure 2: Schematic setups of [PERSON]’s two encoding systems. (**a**) [PERSON] chooses one path to encode his information by using optical switch; (**b**) [PERSON] encodes his information by control phase shifters, B1 and B2. Details are described in the maintext. BS1 : 50:50 beam splitter; BS2 : beam splitter of which transmissivity is \(1/3\); PS : phase shifter
# Utilizing Machine Learning for Detecting Harmful Situations by Audio and Text
## 4 Methodology Description
### Methods Used for Classification via Audio
#### 4.2.3 Machine Learning Classifiers  
The audio formats described above were examined using the following ML classifiers:  
1. **A fully connected neural network** with the first hidden layer identical to the input size, followed by a dropout layer of 0.3, a fully connected layer of size 100, an additional 0.3 dropout layer, and three additional fully connected layers of size 868, 100, and 50. Unless otherwise stated, the tanh activation function was used in all FC hidden layers. The model illustration is provided in Figure 7. The final output layer consisted of a sigmoid neuron for binary classification and three softmax neurons for the task of three types classification (neutral, insulting, or unsafe sentences). The loss function used was the weighted categorical cross entropy or binary-cross entropy, and training was performed using the ADAM optimizer with a learning rate of 0.0001.
2. **Bidirectional RNN classifier** An RNN model: This model was trained on the sub-framed MFCC, obtained from the audio samples. The model consisted of an LSTM cell with a ReLU activation function, followed by a dropout of 0.3, and a fully-connected layer with a sigmoid activation function (or softmax for three classes). It used a weighted cross-entropy loss function and ADAM optimizer with a learning rate of 0.0001.  
Note that the NLP classifier was examined on the different audio vector formats, namely, the eight emotion vectors, FSFM vector, and different variants of the Wav2 Vec vectors, while the bidirectional RNN classifier received the MFCC sub-frame data, presented as a sequence of audio signals over time, as inputs.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Sample frequency & 16 kHz \\ Learning rate & \(2\times 10^{-5}\) \\ Training epochs & 10 \\ Training batch size & 3 \\ Gradient accumulation steps & 2 \\ Total train batch size & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hyper-parameters used for fine-tuning.