esa-sceva/satcom-chunk-collection huggingface dataset:
# Clock Recovery Challenges in DSP-Based Coherent Single-Mode and Multi-Mode Optical Systems
## 3 Matrix Propagation Model for Optical Fibers
### Single-Mode Fibers with Strong Coupling between Polarizations  
Due to bends and stretches, the birefringence will vary throughout the fiber, making it impractical to design long optical fibers with no coupling between polarizations. Consequently, the differential group delay can be a limiting factor for optical communications. To overcome this issue, SMFs are fabricated with intentional birefringence rotations in order to average out the group delay [27]. Therefore, a common numerical approximation for the fiber model is to consider the optical fiber as the concatenation of shorter sections of fiber (Figure 3). Each of these sections is a piece of fiber that has length slightly longer than the length over which the complex polarization fields remain correlated [28] and has no coupling between polarizations, but both random rotations at the input and at the output occur. Moreover, the DGD is kept constant in each fiber section.  
Then, considering \(K_{sec}\) sections of a fiber with each of these sections having length \(L_{sec}\), and neglecting noise and nonlinearities, the signal at each fiber section output is related to the its input signal by  
\[\mathbf{E}_{out}^{(k)}(\Omega)=\mathbf{M}^{(k)}(\Omega)\mathbf{E}_{in}^{(k)}( \Omega), \tag{13}\]  
where \(k\) is the section index. The full fiber transfer characteristic is, then,  
\[\mathbf{M}(\Omega)=\prod_{k=1}^{K_{sec}}\mathbf{V}^{(k)}\mathbf{\Lambda}^{(k) }(\Omega)\mathbf{U}^{(k)\dagger}. \tag{14}\]
# Design Optimization of Reconfigurable Liquid Crystal Patch Antenna
## 2 Materials and Methods
### Liquid Crystal  
Liquid crystal is a material having dielectric constant anisotropy, in which the dielectric constant changes according to the movement of the polarization of liquid crystal molecules with the applied voltage. The liquid crystal used here is a nematic phase liquid crystal, having directional ordering, with a vertically aligned configuration. When the voltage is not applied, the polarization stands vertically on the surface of the alignment layer. They can be described in the form of a rod, as shown in Figure 2a. When the elongated polarization direction is parallel to the electric field direction, the permittivity is \(\varepsilon_{\parallel}\). When the elongated polarization direction is perpendicular to the electric field direction, the permittivity corresponds to \(\varepsilon_{\perp}\). The value of this permittivity continuously changes from \(\varepsilon_{\parallel}\) to \(\varepsilon_{\perp}\) according to the applied voltage, and this change in permittivity makes it possible to continuously change the resonant frequency of the patch antenna in Figure 2b. The estimated dielectric constants \(\varepsilon_{\parallel}\) and \(\varepsilon_{\perp}\) calculated from the refractive index values of the used liquid crystal are 2.19 and 2.43, respectively. Here, we propose a core model for patch antenna having a variable dielectric constant through the behavior of liquid crystal molecules that are changed by an external electric field. Therefore, VLC, which affects the behavior of the liquid crystal, and VRF, which is the signal component of the antenna, are applied to the liquid crystal cell, as shown in Figure 2a. According to the dielectric constant of the nematic phase liquid crystal used in this study, the resonance frequency is \(\sim\)0.3 GHz. The liquid crystal used in this numerical calculation used Merck's liquid crystal with a negative dielectric constant. The material parameters of the liquid crystal were optical anisotropy n = 0.1091, isotropic temperature 108 \({}^{\circ}\)C, splay elastic constant K11 = 13.8 pJ/m, and bend elastic constant K33 = 18.1 pJ/m.  
Figure 2: (**a**) Schematic diagram of dielectric anisotropy according to the voltage applied to the vertically aligned liquid crystal used as the substrate of the patch antenna. (**b**) Resonant frequency change according to a dielectric constant of liquid crystal.
# Packet Injection Exploiting Attack and Mitigation in Software-Defined Networks
## 4 Countermeasures
### Simplified DoS Detection
The equation of \(PFSL\) is as follows:  
\[PFSL=\frac{\sum\limits_{i}^{FlowNum}Flow_{i}(Lifetime_{i}<T_{FL})}{FlowNum} \tag{5}\]  
where \(Lifetime_{i}\) represents the duration of the flow rule for \(i_{th}\) flow and \(T_{FL}\) denotes the threshold value of a time duration. The DoS detection module implements a detection scheme based on machine learning (ML) using the above five-tuple feature. At present, ML has become an effective technology to provide system security and is widely used [14; 15; 27; 34]. Unlike traditional solutions, ML enables the network to identify attacks automatically. It can not only detect known attacks but also identify unknown threats. At the same time, considering the number of features, complexity, etc., we use support vector machine (SVM) as the classifier. SVM is a robust supervised learning method used for classification, regression, and outliers detection, being little affected by noisy data. The simplified attack detection mechanism is shown in Algorithm 3. When the classification result indicates that an attack occurs, it will notify the injection detection and flow rule management modules. The injection detection will clear and rebuild the MT, and the flow rule management module installs blocking rules to prevent malicious traffic from entering the SDN network. ```
1:\(S\): set of switches, \(H\): set of hosts, \(\Delta T1\): time interval, \(\Delta PN\): packets number threshold of per flow, \(\Delta FL\): lifetime threshold of per flow
2:\(SV\): set of vulnerable hosts
3: Initialize \(SV\) = \(\varnothing\) and store vulnerable hosts
4:For each switch \(s_{j}\) in \(S\):
5: Collect dataset \(ds\) in \(T_{TI}\) time window based on \(s_{j}\)
6:End For
7:For each host \(h_{i}\) in \(H\):
8: Extract dataset \(ds_{i}\) about \(h_{i}\) from \(ds\)
9: Compute \(feature_{i}\) based on \(ds_{i}\), \(\Delta T1\), \(\Delta PN\), \(\Delta FL\)
10: Detect attack with Classifier and \(feature_{i}\)
11:If detection result is **ATTACK**:
12: Add the vulnerable host \(h_{i}\) to \(SV\)
13:End For
```  
**Algorithm 3** Attack Detection
## 2 Basic Concepts
### Feature Selection
Finally, statistical feature and information-based selection algorithms can be further classified into two subcategories: univariate and multivariate techniques. Univariate feature selection algorithms detect redundant features by ranking them individually, for example, based on their association with a class variable or information gain. However, multivariate feature selection techniques handle features jointly rather than individually. Multivariate methods are used to detect redundant and irrelevant features [25]. Nowadays, feature selection plays an important role in machine learning projects because a good feature selection algorithm influences overfitting, improves accuracy, and minimizes training time [15]. Therefore, this paper focuses on multivariate filter based feature selection because the set of human activities has class labels and the other features have numerical values. Each record in the dataset has a triaxial acceleration from the accelerometer (total acceleration) and the estimated body acceleration, a triaxial angular velocity from the gyroscope, a 561-feature vector with time and frequency domain variables, and its activity label.
# 400 GbE Technology Demonstration Using CFP8 Pluggable Modules
## 4 Link Performance
### Back-to-Back Performance  
We first measure the performance of the 400 GE CFP8-LR8 module in a back-to-back scenario. Here we bypass the SMF spool as shown in Figure 9. We first adjust the VOA1 to test the interoperation between the transmitter (Tx) of the first module to the receiver (Rx) of the second module. Meanwhile, the attenuation of VOA2 is set to zero so that there is minimum error introduced on the backward path from the PIC to the ONT. As seen, even with 14 dB attenuation between the transmitter and the receiver, the BER is still below the FEC's UCW threshold of 2.2 \(\times\) 10\({}^{-4}\). For the BER performance of 16 ONT electrical lanes, we note that some lanes perform better than others. This is potentially because of the non-ideal combination of the PAM4 signal from two individual NRZ OOK signals.  
Next, we adjust the VOA2 to test the interoperation between the Tx of the second module and the Rx of the first module. Meanwhile, the attenuation of VOA1 is set to zero so that there is minimum error introduced on the forward path from the ONT to the PIC. As seen in Figure 10, even with 12 dB attenuation between the transmitter and the receiver, the BER is still below the FEC's UCW threshold of 2.2 \(\times\) 10\({}^{-4}\).  
We also measure the receiver optical power (ROP) at the different values of attenuation. There is a difference of 2.3 dB between the launching powers of eight optical lanes. At 12 dB attenuation, the average ROP is \(-\)12.4 dBm.  
Figure 8: Optical spectra of two CFP8-LR8 modules: (**a**) first sample module; (**b**) second sample module. Top: spectrum at the output of the transmitter; Bottom: spectra of the individual lanes after going through a de-multiplexer.
# Asynchronous Chirp Slope Keying for Underwater Acoustic Communication
## 6 Conclusions and Future Works
**Abbreviations**  
The following abbreviations are used in this manuscript:  
AF Active Filter  
AWGN Additive White Gaussian Noise  
BB Baseband  
BER Bit Error Rate  
BPF Bandpass Filter  
CSS Chirp-Spread Spectrum  
CSK Chirp Slope Keying  
DDC Digital Down-Converter  
DUC Digital Up-Converter  
FDDS Frame Detect & Downsample  
FrFT Fractional Fourier Transform  
FHX Fast Hilbert Cross-Correlator  
FSK Frequency Shift Keying  
JDS Join & Downsample  
LFM Linear Frequency Modulation  
LPF Lowpass Filter  
MUX Multiplexer  
PA Power Amplifier  
PER Packet Error Rate  
RX Received or Receiver  
SNR Signal-to-Noise Ratio  
TB Time-Bandwidth Product  
TX Transmitted or Transmitter  
UAV Underwater Autonomous Vehicle  
XOR Exclusive Or Operation  
XCorr Cross-Correlation
## 3 Material and Methods
### Data Preparation
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Property**} & \multicolumn{6}{c}{**Variable**} \\ \cline{2-7}  & \multirow{2}{*}{**Slope Height (m)**} & **Angle of** & **Cohesion (kPa)** & **Friction Angle (\({}^{\circ}\))** & **Peak Ground** & **Factor of** \\  & & **Inclination (\({}^{\circ}\))** & **Cohesion (kPa)** & **Friction Angle (\({}^{\circ}\))** & **Acceleration** & **Safety** \\ \hline Symbol & H & \(\beta\) & c & \(\phi\) & PGA (m/s\({}^{2}\)) & FOS \\ \hline Category & Input & Input & Input & Input & Input & Output \\ \hline Min & 15 & 20 & 20 & 20 & 0 & 0.78 \\ \hline Max & 30 & 35 & 50 & 40 & 3.92 & 2.46 \\ \hline Average & 22.33 & 25.18 & 35.3 & 34.07 & 1.18 & 1.20 \\ \hline Std. Deviation & 5.6 & 5 & 11.18 & 5.88 & 1.07 & 0.35 \\ \hline Variance & 31.37 & 26 & 124.96 & 34.59 & 1.15 & 0.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Input and output variables for slope stability classification. and output (FOS). The sensitivity analysis results showed that the input parameters have a great influence on the FOS. Parameter \(\phi\) had the highest impact on FOS values followed by H, \(\beta\), C, and PGA. The results obtained were in line with previous studies [60; 61].
# High-Precision Pseudo-Noise Ranging Based on BOC Signal: Zero-Bias Mitigation Methods
## 4 Zero-Bias Mitigation Method  
The sampling distortion of the BOC signal causes the resolution errors and zero-bias of phase estimation. If a noncommensurate sampling frequency is used, the phase resolution error can be effectively suppressed, and as a result, the dominating sampling distortion error becomes zero-bias. According to the analysis in Section 2, In spite that the zero-bias of the digital subcarrier phase discriminator is very small, the zero-bias of the digital code phase discriminator in double estimation loop is transmitted to the subcarrier loop and causes pseudo-noise ranging. Section 3 analyzes the statistical characteristics of the zero-bias of the BOC signal phase discriminator and obtains quantitative description results. Based on the statistical standard deviation of zero-bias, the mitigation method of BOC signal zero-bias is explored in two aspects: Correlation interval and sampling frequency.
# A Usable Encryption Solution for File-Based Geospatial Data within a Database File System
## 4 Automated and Transparent Encryption and Decryption
### Encryption Key Storage  
The issue of passwords is one of the most researched topics in usable security [26]. In our approach, the keys stored in the database are further protected using the _Transparent Data Encryption_ (TDE) facility provided by Oracle Database 11g Release 2 as part of the Oracle Advanced Security option [73]. The TDE option enables automatic encryption and decryption of database table columns (with small-scaled data), while the associated key is securely stored in an encrypted container called a wallet (_Oracle Wallet_). Using an external security module such as this wallet separates ordinary application functions from encryption operations, making it possible to separate duties between DBAs and security administrators [74]. Security is boosted since the DBA would not know the wallet password, as it would be necessary for the security administrator to input it.  
Using this approach, the cryptographic keys for single-user and multi-user spatial files are stored in a table in encrypted form and are decrypted transparently using TDE when the decryption procedure queries the table. A description of this table as well as the SQL commands that the DBA can use to create or delete single-user keys for the DBFS users are part of Supplementary Materials online. The Oracle Wallet can be initialised by issuing the following command, which creates a wallet, creates a master key for the entire database, and opens the wallet: \"ALTER SYSTEM SET encryption key identified by 'password'''. To prevent the entire encryption scheme from being dependent on one set of keys, a unique set of encryption and decryption keys is used to encrypt and decrypt each user's files (stored in a database table).
# Prediction of Ground Surface Settlements Induced by EPB Shield Tunneling in Water-Rich Soft Strata
## 2 Project Overview  
### Description of the Tunnel Project  
Practical tunnel engineering in Changzhou, China, was considered to investigate the ground surface settlements induced by EPB shield tunneling. Changzhou city lies northwest of Shanghai and has a total area of 4373 km2. Figure 1 describes the location and distribution of the rail track system in Changzhou. Nowadays, Changzhou Metro Lines No. 1 and No. 2 have opened with a total mileage of about 54 km in operation. EPB shield machines were employed to construct the tunnel section of the two opened metro lines. The shield tunnel length of Changzhou Metro is about 26 km in Line No. 1 and about 15 km in Line No. 2. Most shield tunnels were built with a buried depth in the range of 10 to 20 m, and the maximum depth reached was 27.5 m. Shield driving has an inevitable influence on the soft soils in Changzhou, resulting in ground loss and movement. A reliable prediction of tunneling-induced ground movements is of great importance for construction safety management.  
Figure 1: Location and distribution of the rail track system in Changzhou.
## Materials and Methods
### Characterization of the Hydrolysates
#### 2.3.3 Molecular Weight Distribution  
Molecular weight distribution of the hydrolysate (0.5%, \(w/v\)) was estimated with size exclusion chromatography. The hydrolysates were dispersed in 0.1 mol/L Na\({}_{2}\)SO\({}_{4}\) in 0.1 mol/L phosphate buffer (pH 6.7) and filtered through cellulose acetate membranes of 0.45 \(\upmu\)m (MilliporeSigma, Darmstadt, Germany, 2017) to remove insoluble particles before analysis. A Shimadzu HPLC (high performance liquid chromatography) system (Shimadzu China, Suzhou, Jiangsu, China, 2011) equipped with a TSK-gel G3000 PWXL column (Tosoh Corporation, Yamaguchi, Japan, 2017) and a Shimadzu ultraviolet detector (Shimadzu China, Suzhou, Jiangsu, China, 2011) were used. The hydrolysates were eluted by 0.1 mol/L Na\({}_{2}\)SO\({}_{4}\) in 0.1 mol/L phosphate buffer (pH 6.7) at a flow rate of 1 mL/min and monitored at 220 nm at 25 \({}^{\circ}\)C. Average retention time of five standards (bovine serum albumin, peroxidase, ribonuclease A, glycine tetramer and p-Aminobenzoic acid) (Merck KGaA, Darmstadt, Germany, 2017) was used to obtain the molecular weight calibration curve of the column.
## 3 Results and Discussion
### Properties of the Nanocomposite Films
#### 3.2.5 WVP and WCA  
The WVP and surface wettability (hydrophilicity or hydrophobicity) of the gelatin films are presented in Table 3. The WVP of the neat gelatin film was (2.26 \(\pm\) 0.37) \(\times\) 10\({}^{-9}\) g\(\cdot\)m/m\({}^{2}\cdot\)Pa\(\cdot\)s and was significantly decreased by adding CuSNP depending on the CuSNP concentra  
Figure 5: TGA and DTG thermograms of Gel/CuSNP films (in a nitrogen atmosphere and a heating rate of 5 \({}^{\circ}\)C/min).  
tion, which decreasesed to (1.64 \(\pm\) 0.14) \(\times\) 10\({}^{-9}\) g\(\cdot\)m/m\({}^{2}\cdot\)Pa\(\cdot\)s (reduction of 27%) when 2 wt % of CuSNP was added. The improved water vapor barrier (or reduced WVP) of the gelatin/CuSNP films was most likely due to forming a tortuous path for water vapor dissemination by the homogeneously dispersed nanoparticles in the polymer [40]. A similar result was found in the agar film incorporated with a low concentration of CuSNP [40]. Meanwhile, it was reported that the effect of WVP on the gelatin-based film varied depending on the nanoparticles used. For example, AgNP and TiO\({}_{2}\) increased, but ZnONP reduced the water vapor barrier property of gelatin film [17; 19; 24]. This difference seems to be due to the difference in the wettability of these nanoparticles.  
The WCA of the neat gelatin film was 63.4 \(\pm\) 3.5\({}^{\circ}\), representing that it has a wettable surface [48]. Though the change was not statistically substantial (\(p\) > 0.05), the addition of CuSNP slightly reduced the WCA of the gelatin film. Previously, it has been reported that the addition of CuSNP, AgNP, and ZnONP increases the surface hydrophobicity of agar and gelatin films [19; 20]. The wettability of the composite films can be influenced by the type and compatibility of nanofillers and biopolymers.
## 4 Spatial Knowledge
### Atomic Gesture Identifier  
In order to perform this task, a recursive algorithm, named Atomic Gesture IDentifier (AtomGID), was created by our team. The algorithm takes as input a dataset of position to extract the atomic gestures. A description of this algorithm can be seen below (Algorithm 1). AtomGID takes two inputs: the average error \(\varepsilon\) and a list of positions \(L_{p}=[(x_{n},\ y_{n}),\dots,(x_{m},\ y_{m})]\). In that list, the symbol \(n\) is the beginning of the list, and \(m\) is the ending number in terms of iterations. The algorithm can take an input list of varying size and deals with the data stream. As for the output, the algorithm creates a list of atomic gestures \(L_{\alpha}=[\dots]\). Gesture \(\alpha\) inside that list is composed of the pair of value directions (\(D_{x}\)) and distance (\(\varepsilon_{y}\)). Those values are described in qualitative terms according to the model defined previously. Supplementary information is needed in order for the algorithm to work. This element, \(\varphi\), is a correlation coefficient and is used to evaluate the certainty of the recognition. Therefore, an atomic gesture \(\alpha\) is a structure \(<\left(D_{x},\varepsilon_{y}\right),\ \varphi>\). As a side note, in the actual implementation of the spatial datamining algorithm, AtomGID is only called on the sequence of positions from an active object (as opposed to all objects). An ad hoc algorithm was implemented in order to classify, in real-time, whether an object is active or idle from the changes in all the objects. Only the varying positions of the active object are streamed to AtomGID.  
```
Input: List of positions \(L_{p}=[(x_{n},\ y_{n}),\dots,(x_{m},\ y_{m})]\); Average error \(\varepsilon\) Output: List of atomic gesture \(L_{\alpha}=[\dots]\);  A gesture \(\alpha\) is a structure \(<\left(D_{x},\varepsilon_{y}\right),\ \varphi>\) Compute radius of the smallest enclosing circle \(L_{p}\to\delta\) If\(\delta\leq\epsilon\) or\(|L_{p}|<20\) Return\(L_{\alpha}=(<(Idle,\varepsilon_{0}),0>)\) Call AtomGID \((L_{p}([x_{n},y_{n}],\ [x_{q-1},y_{q-1}]),\ \varepsilon)\to R_{l}\) Call AtomGID \((L_{p}([x_{m/2},y_{m/2}],[x_{m},y_{m}]),\varepsilon)\to R_{r}\) Compute CorrelationCoefficient \((L_{p})\to\varphi\) Compute BestLinearRegression \((L_{p})\to\sigma\) Find QualitativeDirection\((\sigma,p_{n}^{-}p_{m})\to dir\) Call Combine \((Last(R_{l}),\ First(R_{r}),<dir,\varphi>)\to L_{c}\) Return\(L_{\alpha}=(R_{l}[1,\ Last(R_{l})-1]+L_{c}+R_{r}[2,\ Last(R_{r})])\)
```  
**Algorithm 1** Atomic Gestures Identifier.
## 5 Experiments
### Diagnosis by Wavelet-Based Envelope Analysis  
To show the advantages of the proposed method, the envelope demodulation method in the order domain, based on wavelet de-noising, is selected for comparison. The following steps can be used:  
1. Order tracking. Even angle resampling in the speed difference domain, a method proposed in this paper, is applied to the original signal, achieving a stationary signal in the angle domain instead of a nonstationary signal in the time domain.
2. Signal decomposition and reconstruction with a wavelet packet are used to remove noises. \"Sym8\" (the wavelet packet basis function) and level 5 (the decomposition level) are selected to decompose and reconstruct signals in order to obtain the signal in the order domain after wavelet de-noising.
3. The envelope demodulation method based on the Hilbert transform is applied to achieving the envelope order spectrum.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pitch Diameter D/mm** & **Ball Diameter d/mm** & **Contact Angle \(\alpha\)/\({}^{\circ}\)** & **Number of Rolling Elements z** \\ \hline
125 & 8 & 0 & 34 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometric parameters of intershaft bearing.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Outer Ring/Order** & **Inner Ring/Order** & **Rolling Elements/Order** & **Cage/Order** \\ \hline
15.9 & 18.1 & 7.8 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fault characteristic orders of intershaft bearing.  
Figure 17: Fault in inner ring: (**a**) waveform of vibration and (**b**) speed trends.  
The above envelope order demodulation algorithm can be applied to the fault data of the intershaft bearing, which are shown in Figures 16 and 17. Then, the envelope order spectrum is obtained, as shown in Figures 18 and 19.  
Figure 18 shows the envelope order spectrum of the fault signal of the intershaft bearing's outer ring. In this spectrum, the highest amplitude order is 2.8, and is the result of other impacts in the rotor system and not the FCFs of the bearing. However, an order of 15.9, corresponding to a fault in the outer ring, is completely buried by noise.  
Figure 19 shows the envelope order spectrum of the fault signal of the intershaft bearing's inner ring. In this spectrum, the dominant order is 1 and its harmonics are caused by rotational speed differences in the inner and outer rings of the intershaft bearing. However, an order of 18.1, corresponding to the fault of the inner ring, is completely buried by noise.  
The analysis results for the above two sets of data show that it is very difficult or even impossible for the envelope order demodulation algorithm to extract the weak fault features of intershaft bearing.