esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Squeezed vacuum states at audio-band frequencies are important quantum resources for practical applications. We demonstrated the generation of squeezed vacuum states at the audio-band frequencies from a subthreshold optical parametric oscillator with a periodically poled KTiOPO\({}_{4}\) crystal pumped by a homemade continuous wave single-frequency dual-wavelength laser. To detect squeezed vacuum states at audio-band frequencies, the influences of the local oscillator (LO) power, the common mode rejection ratio (CMRR) of balanced homodyne detectors, and the phase jitter between the LO and squeezed vacuum field on the measurement of squeezed vacuum states at audio-band frequencies were considered. By optimizing the LO power, improving the CMRR of photodetectors to 67 dB based on the design of differential fine-tuning circuit and adjustable bias voltage, and reducing the phase jitter between the LO and squeezed vacuum field to 1.7\({}^{\circ}\) with the help of the coherent locking technique, 6.1 \(\pm\) 0.3 dB squeezed vacuum states at audio frequencies from 5 kHz to 20 kHz were generated. A 3.0 \(\pm\) 0.3 dB phase squeezed vacuum state was obtained at the audio frequency of 3.5 kHz.  
squeezed vacuum states; audio-band frequencies; subthreshold optical parametric oscillator
## 4 Simulation Models
### Bipolar Charge Transport Model for Insulating Paper  
The insulation structure of the oil-paper interface of the bushing is distributed according to the paper-oil-foil layer structure, with traps uniformly distributed in the paper. Free electrons reaching the solid insulating paper after discharge in the foil layer oil will be captured by the traps in the medium and will also break away from the traps. Free electrons (\(e\mu\)), trapped electrons (\(et\)), free holes (\(h\mu\)), and trapped holes (\(ht\)) are the four carriers in the solid insulating paper composite, and the migrating charge transfer is as follows.  
\[\frac{\partial\rho_{e\mu}}{\partial t}-\
abla\cdot\left(\rho_{e\mu}\mu_{e\mu} \stackrel{{\rightarrow}}{{E}}\right)=D_{e}\rho_{e\mu}-B_{e}\rho _{e\mu}\left(1+\frac{\rho_{e\mu}}{N_{e0}}\right)-S_{1}\rho_{p}\rho_{e}-S_{3} \rho_{ht\mu}\rho_{e\mu} \tag{12}\]  
\[\frac{\partial\rho_{ht\mu}}{\partial t}+\
abla\cdot\left(\rho_{ht\mu}\mu_{ht} \stackrel{{\rightarrow}}{{E}}\right)=D_{h}\rho_{ht}-B_{h}\rho_{ ht\mu}\left(1-\frac{\rho_{ht\mu}}{N_{ht0}}\right)+S_{2}\rho_{et}\rho_{ ht\mu}+S_{3}\rho_{e\mu}\rho_{ht\mu} \tag{13}\]  
\[\frac{\partial\rho_{et}}{\partial t}=B_{e}\rho_{e\mu}\left(1+\frac{\rho_{et}} {N_{e0}}\right)-D_{e}\rho_{et}-S_{s}\rho_{et}\rho_{ht\mu}-S_{0}\rho_{et}\rho_ {ht} \tag{14}\]  
\[\frac{\partial\rho_{ht}}{\partial t}=B_{h}\rho_{ht\mu}\left(1-\frac{\rho_{ht} }{N_{ht0}}\right)-D_{h}\rho_{ht}+S_{1}\rho_{e\mu}\rho_{ht}+S_{0}\rho_{et}\rho _{ht} \tag{15}\]  
\[-\
abla\cdot\left(\varepsilon_{rp}\varepsilon_{0}\
abla\phi\right)=\rho_{hp}+ \rho_{ht}+\rho_{e\mu} \tag{16}\]  
where \(p_{e\mu}\), \(p_{ht}\), \(p_{et}\), \(p_{ht}\) are the charge densities of free electrons, trapped electrons, free holes, and trapped holes, respectively; \(S_{0}\), \(S_{1}\), \(S_{2}\), \(S_{3}\) are different complex coefficients, respectively; \(B_{e}\), \(B_{h}\) are free electron and free hole entry coefficients, respectively; \(D_{e}\), \(D_{h}\) are trapped electron and trapped hole exit coefficients, respectively.  
Free electrons and holes are injected from the oil into the solid insulating paper. The injection current density is related to the temperature of the insulating paper and the electric field strength, which can be described by Schottky thermal ion emission [29].  
\[J_{ein}=AT\exp\left(-\frac{E_{ein}}{k_{B}T}\right)\exp\left(\frac{q}{k_{B}T }\sqrt{\frac{q\left|\stackrel{{\rightarrow}}{{E}}\right|}{4\pi \epsilon_{0}\varepsilon_{rp}}}\right) \tag{17}\]\[J_{hin}=AT\exp\left(-\frac{E_{hin}}{k_{B}T}\right)\exp\left(\frac{q}{k_{B}T}\sqrt{ \frac{q\left|\vec{E}\right|}{4\pi\epsilon_{0}\epsilon_{rp}}}\right) \tag{18}\]  
The specific parameters in the formulas are shown in Table 1.
# A Novel Hybrid Elementary Cellular Automata and Its Application in a Stream Cipher
## 1 Introduction
The driving part includes two ECAs with different chaotic rules, \(r1\) and \(r0\), and the mask part is a chaotic ECA with the rule \(rn\). It should be emphasized that \(r1\), \(r0\), and \(rn\) belong to the global chaotic rules, and the total number is 34. The choice of the cell's transition rule (\(r1\) or \(r0\)) in the driving part is determined by the state value of the cell at the same position in the mask part. The HECA-Ms with different rule groups (\(r1\), \(r0\), and \(rn\)) exhibit different dynamical properties. Hence, the information entropy (IE), a powerful tool in information theory that quantifies the uncertainty or information content of a system, is introduced to analyze the HECA-Ms with different group rules, the number of which reaches 34 \(\times\) 34 \(\times\) 34 (39304). Then, we select 17 rule groups with outstanding IE and statistical properties from them to establish the look-up table (LUT). Secondly, a novel stream cipher based on HECA-M and the HASH function is proposed in this paper. Moreover, a buffer used for controlling the output of the keystream is also included in the proposed stream cipher. A secret key called seed and a constant binary vector IV are used as the input of the stream cipher. The SHA-512 belonging to the provable secure HASH function is chosen for acquiring the initial value of the HECA-M from the input. The existence of IV and the HASH function can avoid the weak key effectively. Meanwhile, good diffusion can be provided by the SHA-512. Furthermore, the Hamming weight of the input is utilized to choose the rule group for the HECA-M from LUT, enhancing the stream cipher's security. Then, the original keystream is generated by the iteration of the HECA-M. The buffer related to the initial value of the HECA-M determines the initially removed iteration and the final keystream generated by the cells with odd or even indexes. This design can hide the initial state values of the HECA-M and resist the GDA. Lastly, plenty of statistics and secure analyses are introduced in this paper. The NIST test estimates the randomness and other statistical properties of the keystream generated by the proposed stream cipher. The traditional cryptanalyses, such as B-M synthesis, distinguishing attack, and correlation analysis, are included in this paper. In addition to the conventional cryptanalyses, some novel attacks to the proposed stream cipher are discussed thoroughly, including time-memory-data tradeoff and guess-and-determine attacks. The results show that the proposed stream cipher possesses good randomness and can resist the abovementioned cryptanalyses. The remaining part of this paper is organized as follows. Section 2 introduces preliminary knowledge about the ECA and HECA. The HECA-M and its properties are presented in Section 3. Subsequently, the proposed stream cipher based on the HECA-M and SHA-512 is described in Section 4. Meanwhile, the statistical and secure analyses of the proposed stream cipher are also listed in Section 4. Finally, the conclusion is drawn in Section 5.
# Particle Swarm Optimization of a Passivity-Based Controller for Dynamic Positioning of Ships
## 2 The Port-Hamiltonian Modeling of the Surface Ship
### The DP System Overview  
Conventional DP systems for vessels are usually designed to achieve reliable and accurate position/station keeping capabilities. It compromises the following main components being a set of dedicated modules which are a combination of propulsion and maneuvering devices and controllers [1,3]:  
1. Power System. It compromises all components and units responsible to supply the DP system with its power requirements including: Prime movers, Generators, Switchboards, Electrical distribution system (cabling and piping), transformers. It may also include more advanced subsystems for energy storage and power management purposes.
2. Thruster system. The vessel's horizontal motion is influenced and controlled by means of thrusters and propellers which are also the sole source of position/heading keeping. They supply the DP system with thrust force and direction and it includes thrusters with electronic drive units, propellers and rudders, cabling and piping and other associated auxiliary subsystems.  
Control System. it compromises all hardware and software components including processors and computers, joystick control unit, Sensors and instrumentation and signal processing units, Position and heading reference systems (navigation, acoustic, microwaves and laser systems), operator and display panels.
## 3 Results
### HS-SPME-GC-MS Analysis of VOCs Produced by Strain CT32  
The total ion chromatogram of VOCs emitted by strain CT32 is shown in Figure 3. A total of 30 volatile compounds derived from strain CT32, i.e., eight alkanes, six ketones, five alcohols, four aldehydes, two phenols, one alkene, one thiazole, one ester, one nitrogen compound and one biphenyl, were identified by GC-MS (Table S1). Alkanes, ketones, alcohols, and aldehydes were found to be the predominant VOCs emitted by strain CT32, which constituted 76.67% of the 30 detected compounds. The relative amounts of aldehydes, alcohols, ketones, and alkanes were calculated to account for 50.32%, 24.31%, 11.28% and 2.75% of the total volatile components, respectively. The most abundant volatile compound was dodecanal (48.56%), followed by 1-dodecanol (10.27%), 2-undecanol (8.46%), 2-undecanone (3.37%) and 2-dodecanol (3.24%).  
Figure 2: Effect of VOCs emitted by strain CT32 on the growth of 8 phytopathogenic fungi in vitro. In the sealed plates test, fungi in the control groups were cultured on potato dextrose agar (PDA) medium. The mycelial growth of fungi in the treatment groups was suppressed upon exposure to volatiles emitted by strain CT32. (**a**) _V. dahliae_; (**b**) _F. oxysporum_ f. sp. _fragariae_; (**c**) _F. oxysporum_ f. sp. _niveum_; (**d**) _F. oxysporum_ f. sp. _cucumerium_; (**e**) _B. cinerea_; (**f**) _T. cucumeris_; (**g**) _G. cingulata_; (**h**) _B. dothidea_.
# Drone Ground Impact Footprints with Importance Sampling: Estimation and Sensitivity Analysis +
## 2 Ground Impact Simulation
### UAV Dynamics
The dynamic model of the UAV can be summarized with the following state-space representation  
\[\hat{\chi}=f(\chi,u,V_{w}) \tag{6}\]  
The simulation of the UAV descent trajectory is performed from an initial condition \(\chi_{0}\), defined at the engine failure instant \(t_{0}\), to ground impact, that corresponds to instant \(t_{f}\) such that the altitude \(h(t_{f})=-z(t_{f})=0\). During a steady flight (coordinated turn, straight flight, pull-up/pull-over etc. ), the accessible space through the initial condition \((\chi_{0},u_{0})\) is considerably reduced. Defining the initial condition then consists of zeroing numerically the dynamic part of Equation (6), while simultaneously considering kinematic constraints related to flight mode [14]. In this case, the control vectors and the dynamic part of the state vector are entirely defined by these constraints. This method is called trim algorithm. A simple way to represent a trajectory is to consider the two following parameters:  
* the turn rate \(R=d\psi/dt\), where \(\psi\) is the heading angle
* the flight path angle \(\gamma=\dot{z}/V_{a}\), where \(V_{a}\) is the aerodynamic speed of the aircraft. Therefore, the trim algorithm can be run to determine the initial condition \((\chi_{0},u_{0})\), by assigning values \(R_{0}\), \(\gamma_{0}\), \(V_{a_{0}}\) and \(h_{0}\) to the turn rate, flight path angle, aerodynamic speed and altitude, which are representative of the UAV flight conditions. A straight cruise flight mode at constant altitude can, for example, be considered by choosing \(R_{0}=0\) and \(\gamma_{0}=0\). Note that the wind is not considered in the trim algorithm. From this initial condition \((\chi_{0},u_{0})\), the trajectory of the UAV is simulated until the impact time \(t_{f}\), by considering zero thrust (main engine failure) and taking into account the wind speed \(V_{w}\). The complete simulation process is represented in Figure 1.  
Using this approach, one can simulate a single trajectory and compute the coordinates of the ground impact point. An example of the trajectory is provided on Figure 2, corresponding to \(\gamma_{0}=0\) deg, \(R_{0}=0.15\) rad/s, \(V_{a_{0}}=30\) m/s, \(h_{0}=150\) m and no wind. Figure 1: Simulation flowchart used to generate ground impact points. Nevertheless, impact point coordinates are not deterministic in the sense that their computation will suffer from different sources of uncertainties on the parameters of the problem. The next section covers the characterization of these uncertainties, as well as the Monte Carlo approach to handle them.
## 3 Results
### NDWI and FWP Analysis Results  
The NDWI band ratio indices calculated for all RGB images are compiled in Figure 3. The Wheal Maid DEM is included as the base layer. NDWI index value ranges differed amongst the instruments due to radiometric nuances in the spectral bands (as seen in Figure 3). The FWP analysis helped to identify maximum water and land limits, and these were plotted as histograms (Figure 3e-g). These charts descend in order of increasing spatial resolution, moving from multispectral to hyperspectral sensors. Each of the sensor's NDWI rasters is accompanied by its corresponding FWP\({}_{total}\) from a-c, starting with Sentinel-2 (simulated) (a) and ending with the UAV raster (c). FWP\({}_{total}\) exhibits an inverse relationship, as FWP\({}_{total}\) increased as the instrument's spatial resolution decreased. The simulated Sentinel-2 image had an FWP\({}_{total}\) of 8.5%; PlanetScope FWP\({}_{total}\): 3.7%; and UAV Nano-Hyperspec FWP\({}_{total}\): 2.4%.  
NDWI histograms (Figure 3e-g) exhibited weakly bimodal distributions with land peaks being more prominent due to greater pixel count, as nearby water bodies were relatively small. The identified water and land limits differed amongst the instruments. The Sentinel-2 (simulated) and PlanetScope FWP plateau ranged from 0.17 to 0.25 and from 0.16 to 0.19, respectively, whereas UAV Nano-Hyperspec exhibited a more commonly reported NDWI range of 0.5 to 0.75, where the water limit maximum was closer to 1. The histograms exhibited a weak multimodal distribution, where a third lesser peak would arise left of the land peak.
## 3 Methodology
### Maximal Information Coefficient (MIC)  
Compared with the traditional statistical indexes such as the Pearson coefficient, MIC allows to detect various correlation relationships including linear, non-linear, functional, and non-functional relationships. Secondly, the MIC is designed to maintain similar results even in presence of equal levels noise of different types [34; 35].  
For continuous variables \(x\) and \(y\), the MIC between \(x\) and \(y\) is described by the following formula:  
\[\begin{array}{c}\text{MIC}(x,y)=\max\bigl{\{}I(x,y)/\log_{2}\min\bigl{\{}n_{x },n_{y}\bigr{\}}\bigr{\}}\\ \text{where}\\ I(x,y)=H(x)+H(y)-H(x,y)\\ =\sum\limits_{i=1}^{n_{x}}p(x_{i})\log_{2}\frac{1}{p(x_{i})}+\sum\limits_{j=1} ^{n_{y}}p(y_{j})\log_{2}\frac{1}{p(y_{j})}-\sum\limits_{i=1}^{n_{x}}\sum \limits_{j=1}^{n_{y}}p(x_{i}y_{j})\log_{2}\frac{1}{p(x_{i}y_{j})}\end{array} \tag{5}\]  
where \(P(x_{i})\) presents the marginal probability of \(x\), \(P(y_{j})\) presents the marginal probability of \(y\), \(P(x_{i},y_{j})\) presents the joint probability density function of \(x\) and \(y\), and \(n_{x}\), \(n_{y}\) is the number of bins of the partition of the \(x\)- and \(y\)-axis. An MIC of zero indicates that there is no dependence between the concerned variables, while MIC of one implies a stronger relationship [36]. Based on previous research, the final input variables with MICs greater than 0.1 [37; 38] were selected from input candidates for model training.
# Enhancing Detection of Arabic Social Spam Using Data Augmentation and Machine Learning
## 3 Proposed Framework for Enhancing Arabic Spam Detection
### Data Preparation  
#### 3.1.1 Data Extraction  
During this phase, a dataset of around 1.6 M tweets was collected using the Twitter Streaming API from December 2019 to April 2020. The tweets were collected using the query \"lang Ar\" (language is Arabic) and the trending hashtag (#YEMEN). Next, each tweet was cleaned to obtain the distinct tweets by removing diacritics, removing repeating char, removing punctuations, normalizing Arabic, removing stop words, removing non-Arabic alphabets, Arabic light stemming, etc. Out of the 1.6 M tweets, there are 313 k distinct tweets. For experimental purposes, a set of 5k tweets was randomly selected for the labeling process, which is explained next.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Detecting Level** & **Features** & **Classifiers** & **Dataset** & **Results** \\  & & user-based & & & **(Accuracy-F1)** \\ \hline
[48] content-level & content-based, & AraBERT, SVM & 133,500 tweets. & F1 98\% \\
[49] content-level & content-based & NB, SMO, LR & 2224 Facebook comments & 91.73\% \\
[51] user-level & content-based, & NB, SVM & 500 accounts & 90\% \\
[52] content-level & content-based & SVM & - & 79\% \\
[53] content-level & content-based & LR, SVM, RNN & 6000 Arabic tweets & 79\% \\
[50] content-level & content-based & NB, SVM, DT & 3503 Arabic tweets & 87\% \\
[57] content-level & content-based & LR, SVM, NB, RF & 1600 hotels reviews & 95.25\% \\
[58] content-level & content-based, & NB, KNN, SVM & 2848 hotels reviews & F1 99.59\% \\
[59] content-level & content-based & KNN, SVM, NB & 3000 Facebook comments & 92.63\% \\
[60] user-level & content-based, & RF & 12,486 accounts & 90\% \\
[61] user-level & content-based, & NB, RF, SVM & 5000 tweets & 92.59\% \\  & user -based & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Arabic spam detection studies.
# Locating Image Sources from Multiple Spatial Room Impulse Responses
###### Abstract  
Measured spatial room impulse responses have been used to compare acoustic spaces. One way to analyze and render such responses is to apply parametric methods, yet those methods have been bound to single measurement locations. This paper introduces a method that locates image sources from spatial room impulse responses measured at multiple source and receiver positions. The method aligns the measurements to a common coordinate frame and groups stable direction-of-arrival estimates to find image source positions. The performance of the method is validated with three case studies--one small room and two concert halls. The studies show that the method is able to locate the most prominent image sources even in complex spaces, providing new insights into available Spatial Room Impulse Response (SRIR) data and a starting point for six degrees of freedom (6 DoF) acoustic rendering.  
Keywords:spatial room impulse responses; room acoustics; SDM; geometry calibration; early reflections; image sources +
Footnote †: journal:  
The article is organized as follows--we introduce all the processing stages of our method in Section 2. We then present the analysis of a medium sized room and two concert halls in Section 3. For a rectangular room, we show the results of the analysis with five source positions. Then, we extend the analysis to more complex cases and demonstrate that combining the directional estimates of several SRIR measurements can give new insights into available concert hall data. Finally, we discuss properties as well as applications of the proposed methods and draw conclusions.
# A Cross-Layer Media Access Control Protocol for WBANs
## 3 System Model  
### Network Model  
We consider a typical WBAN system as consisting of a single coordinator and several sensors, as shown in Figure 1. All nodes are single-antenna devices that operate in a half-duplex scheme. Sensors are deployed in different positions on the body and have limited energy. Sensors package their remaining energy values in the header of the periodically uploaded packets, enabling the coordinator to understand the energy distribution status in the network and formulate targeted cooperative transmission schemes. Let \(T_{i}\) denote the lifetime of sensor node \(i\); the lifetime of the network \(T^{*}\) is defined as:  
\[T^{*}=  
Maximized \(T^{*}\) is desired on the basis of ensuring the QoS of the network. To prolong the lifetime, each sensor adjusts transmission power adptively. As shown in Figure 1, because node \(n_{1}\) is located closer to the coordinator compared with \(n_{2}\), sensor \(n_{1}\) can choose a smaller transmission power on the premise of ensuring network performance. Except for power control, a point-to-point relay transmission scheme is adopted. When the energy of sensor \(n_{i}\) is relatively low and potential relay nodes exist, the coordinator will assign a proper cooperator to \(n_{i}\) to relay packets generated by \(n_{i}\). Then, \(n_{i}\) can transmit its data to the relay instead of the coordinator, using a smaller transmission power. As illustrated in Figure 1, nodes \(n_{3}\) and \(n_{4}\) form a point-to-point collaborative group, \(n_{3}\) can transmit data to \(n_{4}\) using a small transmission power to further prolong its lifetime, and \(n_{4}\) needs to consume more energy to relay packets generated by \(n_{3}\). In this paper, the path loss model \(PL(d)\) with distance dependency can be described by Equation [30]  
\[PL(d)=PL(d_{0})+10 nlog_{10}(\frac{d}{d_{0}}), \tag{2}\]  
where \(d_{0}\) denotes the reference distance, \(PL(d_{0})\) denotes the path loss at the reference distance \(d_{0}\), \(n\) represents the path-loss exponent, and \(d\) is the distance between the sender and the receiver.
# High Cellulose Purity by Acid Hydrolysis Pretreatment on Kenaf Outer Bast
## 3 Result and Discussion
### Effects of Times  
Another variable considered in this hydrolysis treatment of kenaf outer bast fiber was the reaction time at the reaction temperature and acid concentration. All experiments were carried out with a hydrolysis rate that varied slightly with intervals of 30 min. The effect of reaction time was studied using sulfuric acid and temperature covering a wide range of reaction times, as shown in Figure 4. The total yield of hemicellulose decreased from 10.2 to 7.9% by increasing the reaction time from 60 min to 120 min. Hemicellulose yields showed that a reaction time could be expected in specific xylan and was hydrolyzed to xylose as monosaccharides. It also reported that 1% sulfuric acid at 140 C could remove hemicellulose from 5.23% in 30 min, 4.50% in 60 min to 1.47% in 120 min [45]. The short  
Figure 3: Different acid concentrations of sulfuric acid hydrolyze different xylan in the hemicellulose of kenaf outer bast by NMR spectrum.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \begin{tabular}{c} **Acid Con b** \\ **(N)** \\ \end{tabular} & \begin{tabular}{c} **Tem a (c C)** \\ \end{tabular} & \begin{tabular}{c} **Time** \\ **(min)** \\ \end{tabular} & \begin{tabular}{c} **Yield (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Chemical Composition** \\ **Cellulose (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Lignin (\%)** \\ \end{tabular} \\ \hline
0.25 & 130 & 120 & 52.9 \(\pm\) 0.2 & 50.2 \(\pm\) 0.1 & 2.2 \(\pm\) 0.1 & 0.5 \(\pm\) 0.4 \\ \hline
0.50 & 130 & 120 & 50.7 \(\pm\) 0.4 & 48.5 \(\pm\) 0.2 & 1.6 \(\pm\) 0.1 & 0.5 \(\pm\) 0.2 \\ \hline
1.0 & 130 & 120 & 49.6 \(\pm\) 0.1 & 47.9 \(\pm\) 0.1 & 0.8 \(\pm\) 0.2 & 0.8 \(\pm\) 0.1 \\ \hline \hline \end{tabular}  
* Acid concentration; b Temperature.  
\end{table}
Table 3: Chemical composition of kenaf outer bast with a different acid concentration of acid hydrolysis.  
times and higher temperatures were beneficial for decreasing hemicellulose, especially the increased purity of cellulose [46]. Furthermore, the reaction time dominates the degradation of soluble monosaccharides at reaction times over 100 min [47]. The reaction time does not appear to increase the hydrolysis efficiency significantly; its main effect was to estimate monosaccharides produced in hydrolyzing [47].