esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
A stainless steel fiber was made porous and adhesive by platinization and then coated by nanostructured polypyrrole (PPy), using an appropriate electrophoretic deposition (EPD) method. The morphological surface structure and functional groups of the PPy-coated fiber were studied using SEM (Scanning electron microscope) instrument. The prepared fiber was used for comparison of direct immersion (DI) and electroenhanced direct immersion solid-phase microextraction (EE-DI-SPME) of nicotine in human plasma and urine samples followed by gas chromatography flame ionization detector (GC-FID) determination. The effects of the influential experimental parameters on the efficiency of the DI-SPME and EE-DI-SPME methods, including the pH and ionic strength of the sample solution, applied Direct current (DC) voltage, extraction temperature and time and stirring rate, were optimized. Under the optimal conditions, the calibration curves for the DI-SPME-GC-FID and EE-DI-SPME-GC-FID methods were linear over the ranges of 0.1-10.0 \(\upmu\)g mL\({}^{-1}\) and 0.001-10.0 \(\upmu\)g mL\({}^{-1}\), respectively. The relative standard deviations (RSDs, \(n=6\)) were found to be 6.1% and 4.6% for the DI and EE strategies, respectively. The LODs (limit of detection) of the DI-SPME-GC-FID and EE-DI-SPME-GC-FID methods were found to be 10 and 0.3 ng mL\({}^{-1}\), respectively. The relative recovery values (for the analysis of 1 \(\upmu\)g mL\({}^{-1}\) nicotine) were found to be 91-110% for EE-DI-SPME and 75-105% for DI-SPME. The enrichment factors for DI-SPME and EE-DI-SPME sampling were obtained as 38,734 and 50,597, respectively. The results indicated that EE-SPME was more efficient for quantitation of nicotine in biological fluids. The developed procedure was successfully carried out for the extraction and measurement of nicotine in real plasma and urine samples.  
direct-immersion SPME; electroenhanced SPME; nicotine; plasma; urine +
Footnote †: journal: molecules
# Neural PD Controller for an Unmanned Aerial Vehicle Trained with Extended Kalman Filter
## 3 Multirotor Dynamic Model
Let the propeller rotation speed be proportional to the width of the arrow on the propeller; the movement of the UAV is denoted with the dashed arrow at the center of the quadrotor. **(a)**Upward movement, **(b)** horizontal movement, **(c)** downward movement, **(d)** left turning movement and **(e)** right turning movement. The quadrotor dynamics, as described in [4], are given by  
\[ \begin{array}{l}\ddot{x}=\left(\cos\left(\phi\right)sin\left(\theta\right)\sin \left(\psi\right)+\sin\left(\phi\right)\sin\left(\psi\right)\right)\frac{U_{1}}{ \theta n}\\ \ddot{y}=\left(\cos\left(\phi\right)sin\left(\theta\right)\sin\left(\psi\right)- \sin\left(\phi\right)\sin\left(\psi\right)\right)\frac{U_{1}}{m}\\ \ddot{z}=-g+\left(\cos\left(\phi\right)\cos\left(\theta\right)\right)\frac{U_{ 1}}{m}\\ \ddot{\phi}=\dot{\phi}\dot{y}\left(\frac{U_{1}-U_{2}}{I_{x}}\right)-\frac{U_{2} }{I_{x}}\dot{\theta}\Omega+\frac{U_{1}}{I_{x}}U_{2}\\ \ddot{\theta}=\dot{\phi}\dot{y}\left(\frac{U_{1}-U_{2}}{I_{y}}\right)+\frac{U_ {2}}{I_{y}}\dot{\phi}\Omega+\frac{U_{1}}{I_{y}}U_{3}\\ \ddot{\psi}=\dot{\phi}\dot{\phi}\left(\frac{U_{1}-U_{2}}{I_{x}}\right)+\frac{ U_{1}}{I_{x}}\end{array}  \tag{4}\]  
where \(\Omega\) represents the gyroscopic effects induced by the propellers, which are usually neglected [53], and they are given by  
\[\Omega=\Omega_{2}+\Omega_{4}-\Omega_{1}-\Omega_{3} \tag{5}\]  
\(U_{i}\) are the system inputs: \(U_{1}\) is related to its translation and \(U_{2}\) to \(U_{4}\) are related to its attitude and orientation. The relation between both subsystems can be seen in Figure 4. The inputs \(U_{i}\) for this specific configuration of multirotor are given by  
\[ \begin{array}{l}U_{1}=b\left(\Omega_{1}^{2}+\Omega_{2}^{2}+\Omega_{3}^{2}+ \Omega_{4}^{2}\right)\\ U_{2}=\frac{\sqrt{2}}{2}b\left(\Omega_{1}^{2}+\Omega_{2}^{2}-\Omega_{3}^{2}- \Omega_{4}^{2}\right)\\ U_{3}=\frac{\sqrt{2}}{2}b\left(\Omega_{1}^{2}+\Omega_{3}^{2}-\Omega_{2}^{2}- \Omega_{4}^{2}\right)\\ U_{4}=b\left(\Omega_{1}^{2}+\Omega_{4}^{2}-\Omega_{2}^{2}-\Omega_{3}^{2}\right) \end{array}  \tag{6}\]  
and they are calculated by the neural network. In this work, a Multilayer Perceptron trained with the Extended Kalman Filter is selected.
_Applied_  
_sciences_  
Permeability and Leaching Properties of Recycled Concrete Aggregate as an Emerging Material in Civil Engineering  
**[PERSON] 1,*(r)**, **[PERSON] 1**, **[PERSON] 1**, **[PERSON] 2** and  
**[PERSON] 2**  
1 Water Centre Laboratory, Faculty of Civil and Environmental Engineering, Warsaw University of Life Sciences, 02-787 Warsaw, Poland; [PERSON]AIL_ADDRESS] (W.S.); [PERSON]J.D.)  
2 Department of Geotechnical Engineering, Faculty of Civil and Environmental Engineering, Warsaw University of Life Sciences, 02-787 Warsaw, Poland; [PERSON]RESS] (E.S.); [PERSON](A.S.)  
* Correspondence: [PERSON]el.: +48225-935-405  
Received: 26 October 2018; Accepted: 30 November 2018; Published: 26 December 2018  
Featured Application: Recycled concrete aggregate (RCA) is an emerging alternative material as a substitute for natural aggregate. The RCA blends test results in this paper has shown the appropriate coefficient of permeability for earth dam construction and for sub-base layers in road engineering. Moreover, the RCA is environmentally safe.  
**Abstract:** In this article, a study of the threshold gradient and leaching properties for recycled material, namely, recycled concrete aggregate (RCA), was conducted. The RCA in this study is a material that comes from recycling concrete debris. A series of tests in permeameter apparatus in a constant head manner were conducted. The test method has been improved to eliminate common mistakes, which occur when the constant head method is used. During the following study, aggregates with gradations equal to 0-8, 0-16, and 0.05-16 mm were tested. The tests were conducted on gradients ranging from 0.2 to 0.83. This range of tested gradients led to the evaluation of the flux velocity and indicated non-Darcian flow. For engineering applications, the threshold gradients for three RCA blends were calculated using a statistical analysis. The average coefficient of permeability, kavg, for linear flow was equal to 1.02 \(\times\) 10\({}^{-4}\)-1.89 \(\times\) 10\({}^{-4}\) m/s. In this paper, suffosion analysis was also conducted for the three blends in order to eliminate the possibility of particle movement. Moreover, for RCA blend 0-16 mm, leaching properties was examined. It was found that the concentration of chlorides, sulphates, and heavy metals in the water solution does not exceed the permissible standards. This paper ends with conclusions and proposals concerning the threshold gradients obtained from the statistical analysis, suffosion analysis, and flux velocity.  
**Keywords:** permeability; recycled concrete aggregate; threshold gradient; constant head method; coefficient of permeability; leaching properties
## 4 Engineering Application of the ASRB Parameter Design Method
### Analysis and Design Optimization of ASRB Parameters
#### 4.3.3 Correlation Analysis  
(1) Stress aspectAccording to the simulation results in Table 9, the average stress release rate results of various factors at different levels and locations are shown in Figure 16. The influence of the \"length\" factor on the average stress release rate has no obvious rule (Figure 16b). The level of \"inclination,\" \"included angle,\" and \"spacing\" factors were negatively correlated with the average stress release rate (Figure 16a,c,e), while \"aperture\" was positively correlated (Figure 16d). In addition, along the direction of the tunnel axis, the average stress release rate of each monitoring point showed a decreasing trend, and the average stress release rate of I and II was higher. After reaching III, the stress release effect was significantly reduced.  
(2) Energy aspectFrom the perspective of average volume release energy, the simulation results in Table 9 are also plotted, and the average volume release energy results of each factor at different factor levels are discussed, as shown in Figure 17. The conclusions obtained from the analysis of the average volume release energy are basically consistent with the conclusions obtained from the analysis of the average stress release rate. The \"length\" factor had no significant impact on the average volume release energy (Figure 17b). The level of the \"inclination\", \"included angle\", and \"spacing\" factors were negatively correlated with the average volume release energy (Figure 17a,c,e), and \"aperture\" was positively correlated (Figure 17d). Along the tunnel axis, the average volume release energy of each monitoring point showed a decreasing trend.  
Figure 15: The range change of various factors at different monitoring positions: (**a**) average stress release rate; (**b**) average volume release energy.  
Figure 16: Effect of different levels of various factors on the average stress release rate: (**a**) inclination; (**b**) length; (**c**) included angle; (**d**) aperture; (**e**) spacing.
# Vibration Control of Multi-Modular VLFS in Random Sea Based on Stiffness-Adjustable Connectors
## 3 Vibration Control Method Based on the Stiffness-Adjustable Connector
### Feedback Control Gains and the Parameters of Hydraulic-Actuators
\begin{array}{l}p_{l}\frac{\pi D^{2}}{4}-f_{a}\lx@note{footnote}{{$ =$}}p_{r}\bigg{(}\frac{\pi D^{2}}{4}-\frac{\pi d^{2}}{4}\bigg{)}=\rho p_{l} \Big{(}v_{0}-\frac{d^{2}}{D^{2}}v_{l}\Big{)}\\ p_{r}\bigg{(}\frac{\pi D^{2}}{4}-\frac{\pi d^{2}}{4}\bigg{)}+f_{a}\lx@note{footnote}{{$ =$}}p_{l}\frac{\pi D^{2}}{4}=-\rho p_{r}\Big{(}v_{0}-\frac{d^{2}}{D^{2}-d^{2} }v_{r}\Big{)}\end{array}  \tag{30}\]  
where \(v_{0}\) is the velocity of the piston rod, and \(\rho\) is the density of the hydraulic fluid. From Equation (30), we can derive the relationship between the velocities of the hydraulic fluid flowing through the oil orifices and the pressures in the two chambers, which is  
\[\frac{{d_{l}}^{2}}{D^{2}}p_{l}v_{l}-\frac{{d_{r}}^{2}}{D^{2}-d^{2}}p_{r}v_{r}=0 \tag{31}\]  
From Equations (29) and (31), the proper chamber pressures for the required control force of the actuator can be determined. As for the vibration control method using the proposed connector, the actual stiffness is in fact adjustable because of the control force of the hydraulic actuator. As indicated in Equation (25), the last term of \(\mathbf{K}\) is the stiffness contribution from the actuators, which is a state-dependent matrix that is associated with the feedback gains adapted to the wave conditions. This term changes the flexibility of the connectors and thus makes the whole floating system avoid the resonances in critical wave frequencies. The control process can be summarized as follows: When the wave conditions change, such as the wave excitation angle and wave frequency, the corresponding control gains can be obtained using the SQP optimization process based on the feedback states of the floating structure. With the required control forces, the control parameters of the hydraulic actuators can be determined by Equations (29) and (31) to exercise the control. Since the feedback gains are adaptive to the wave conditions, the control scheme can keep the floating system away from resonant vibrations. Since the present work does not involve the prototype design, we cannot provide specific dimensions and parameters for the actuator in the numerical analysis.
## 5 Results and Analysis
### Deceleration-Acceleration Mode at Constant Torque  
To analyse the flywheel performance during acceleration and deceleration states, different charge-discharge operations are performed during the period of 20 min. A constant torque operation is performed here to show the system responsiveness due to step torque variations that can be accounted for the load variations in real life situations. Therefore, the flywheel is initially charged for 130.2 s, then switched to standby mode for 300 s before starting to discharge due to a negative torque of 90 N\(\cdot\)m applied at \(t=430\) s (Figure 8). In order to test the dynamic stability of the SCIM-FESS model, multiple operations where performed by accelerating and decelerating the flywheel. At \(t=530\) s, the flywheel starts charging for 100 seconds before the charging torque is reversed to decelerate the flywheel at \(t=630\) s. Then the flywheel goes into the standby mode (\(t=720\) s) and spins with 22% of SoC and 0.4 kWh of stored energy. Different charging and discharging states of the flywheel and corresponding three-phase currents and torque waveforms are shown in Figure 8. Magnified images of three-phase stator currents and torque are shown in Figure 9 where it can be seen that IM smoothly switches from generating to motoring mode in less than 5 m s without any spikes or transients in the current waveforms showing the compliance and better performance of the machine side control system (MSC).  
Figure 7: Three phase stator currents of IM during acceleration and standby mode.  
Figure 8: SCIM-FESS acceleration-deceleration mode: (**a**) %SoC of the flywheel; (**b**) Energy stored; (**c**) Mechanical torque; (**d**) Three phase stator currents.  
Figure 9: (**a**) Torque at \(t=530\) s; (**b**) Magnified three phase currents; (**c**) %SoC at \(t=530\) s; (**d**) stored energy in kWh.
# A Multimodal Affective Sensing Model for Constructing a Personality-Based Financial Advisor System
## 3 System Model
### Personality Feature Extraction
#### 3.3.2 Speech Analysis and Feature Extraction  
Time-domain features can be obtained from the original audio waveform by physical transformations, such as zero-crossing rate, maximum amplitude, or RMS energy, while frequency-domain features are usually converted from time to frequency domain by Fourier transform. Frequency-domain features, such as spectral centroid and spectral flux, are typically transformed from the time domain to the frequency domain using a Fourier transform. To extract emotional features from audio files, in this study, we used Librosa to extract personality features from audio. This method simulates the auditory nerve of the human ear, only paying attention to sounds of certain frequencies [60; 61] and adding zero-crossing rate (ZCR) and spectral centroid as input features to the model. Speech features were extracted by setting a sliding window of 50 ms.
## 1 Introduction
However, a lower tool tilt angle will reduce the flow of materials and a higher tool tilt angle will produce flashes; thus, an optimal tool tilt angle is required for a smooth weld surface [19; 20]. Various welding parameters have a remarkable impact on YS, UTS, and % E. Researchers have analyzed the mechanical behaviors of dissimilar FSW joints of Al and Cu. They concluded that the tensile properties of welded joints increase up to a maximum  
Figure 1: Schematic diagram of FSW [4]. value with an increase in weld speed, but that further increase in weld speed beyond the optimal value decreases the tensile properties. Tensile properties were also investigated at various pin offsets and rotational speeds. They concluded that the UTS increases with tool rotational speed and with the decrease in the probe offset [16; 18; 21]. Some researchers have investigated tensile-tested specimens to understand the failure behavior of fractured surfaces. Fractured surfaces have been studied using SEM by some researchers; they observed cavities and large dimples on the fractured surface. Under SEM they found cleavage fractures and layered fracture surfaces and they also reported that the failure mode was brittle in nature. Some researchers used FESEM for a more detailed study of fracture surfaces and they found microscopic voids and dimples on fractured surface. They also concluded that the fracture mode was not purely ductile [7; 17; 22; 23; 24]. Many researchers have worked on dissimilar FSW joints of aluminum and steel. They successfully joined aluminum to steel using the FSW process. [PERSON] et al. developed an innovative overlap joint concept for single pass and multi pass configurations of the joining of Al with steel using the FSW process. They concluded that with a two-pass weld, the mechanical properties of the joints were improved. Researchers have also investigated important properties, e.g., high cycle fatigue in FSW joints. [PERSON] et al. investigated the fatigue properties of dissimilar FSW joints of aluminum alloys and found a good correspondence in fatigue properties in a comparative analysis of base materials and welded joints [25; 26]. In this experimental work, FSW of dissimilar materials, Al and Cu alloys, was successfully carried out. A superior weld joint was obtained by maintaining the harder Cu base plate at the advancing side and a probe offset of 2.0 mm towards the softer Al base plate was used. The objectives of this research work are: (i) to conduct experiments on the FSW of Al-alloy and Cu-alloy using a full factorial design of the experiment; (ii) to establish a statistical model that will help in understanding the connections between the process variables and outcomes; (iii) to investigate the impact of process variables on tensile properties; and (iv) to analyze the fracture surface of the tensile specimens.
## 3 Materials and Methods
### Characterisation of the Membrane Surfaces
#### 3.3.2. _Contact_ Angle Measurement  
A contact angle measuring system G10 ([PERSON], Hamburg, Germany) was utilized to analyze the effect of the modification on the membrane hydrophilicity. A de-ionized water droplet of 2 \(\upmu\)L was placed on a dry flat homogeneous membrane surface and the contact angle was measured after 5 s. Each measurement was taken at least 15 times and the average value was reported. The error was controlled below 3\({}^{\circ}\).
# A Joint Positioning Algorithm in Industrial IoT Environments with mm-Wave Communications
## 3 Overview of NR PRS and SRS-Pos Design
### Resource Overhead Analysis of DL PRS and UL SRS-Pos  
According to the above description, the radio resource overhead for PRS and SRS-Pos, from the perspective of UE side, can be expressed as  
\[\left\{\begin{array}{l}\eta_{\text{PRS}}=\frac{L_{\text{PBS}}N_{\text{BB}}^{ \text{RS}}N_{\text{RP}}}{N_{\text{synb}}^{\text{system}}N_{\text{BW}}^{ \text{system}}N_{\text{comb}}^{\text{PSS}}}\\ \\ \eta_{\text{SRS-Pos}}=\frac{N_{\text{synb}}^{\text{SSS}}N_{\text{RW}}^{\text{SSS }}}{N_{\text{synb}}^{\text{system}}N_{\text{BW}}^{\text{system}}N_{\text{RC}}^ {\text{PSS}}}\end{array}\right., \tag{2}\]  
where \(N_{\text{synb}}^{\text{system}}\) denotes the system symbol number per slot and \(N_{\text{BW}}^{\text{system}}\) is the system bandwidth.  
From (2), it can be observed that resource overhead of DL positioning is a little bit larger than that of UL positioning. Another thing that should be noted is measurement reports in DL positioning will need additional UL radio resource, which requires corresponding resource allocation. The measurement port for DL positioning will also impact power consumption on the UE side. In case of DL-TDOA positioning method, the PRS overhead will be larger than that of UL, since at least three TRPs are required for exact positioning.
## 3 Overview of Rollups
### Zk-Rollups
#### 3.3.2 Zk-STARKs  
Zk-STARK stands for Zero-Knowledge Scalable Transparent Argument of Knowledge [16]. It is an evolution of zk-SNARK since it eliminates the need for a trusted setup, which enhances security and transparency. Indeed, while zk-SNARK relies on elliptic  
Figure 2: General architecture of Zk-Rollup.  
curve pairings and requires a trusted setup phase for generating public parameters, zk-STARK employs polynomial commitments and interactive oracle proofs, which not only ensure quantum-resistant proofs but also allow scalability for larger computations and transparency. In particular, zk-STARK leverages the Fast Fourier Transform (FFT) for efficient computation and verification of large datasets, while the collision-resistant hashing functions help to achieve transparency and quantum resistance and provide long-term cryptographic security. However, this results in larger proof sizes, in comparison to zk-SNARK, which can pose some problems in terms of transaction costs.  
The proof generation and verification of zk-STARK involves the usual three main steps described for zk-SNARK: Setup \((S)\) to initialize public parameters; Prove \((P)\) to generate proofs; and Verify \((V)\) to check the validity of the proofs. The main difference resides in the characteristics and assumptions made in the setup phase.
# A Novel Malware Detection Model in the Software Supply Chain Based on LSTM and SVMs
## 3 Technical Overview
### Long Short-Term Memory Network  
LSTM is a special kind of Recurrent Neural Network (RNN) architecture capable of learning long-term dependencies in data. Presented by [PERSON] and [PERSON] in 1997 [8], when working with lengthy sequences, traditional RNNs encounter issues with vanishing and exploding gradients. LSTM was created to solve these issues.  
The key innovation of LSTM lies in its use of three types of gates to control the flow of information:  
1. Forget Gate: Decides which data to discard from the cell state.
2. Input Gate: Determines what fresh data to store in the cell state.
3. Output Gate: Decides what data to output from the cell state.  
Each LSTM unit contains a cell state that remains constant across time steps until it is updated. This design allows LSTM to remember information over long periods and remain stable when processing long sequences. The inner cell state, depicted in Figure 1[23], is central to the functioning of the LSTM network.  
In Figure 1, \(C_{t-1}\) and \(h_{t-1}\) show the cell state and output of the LSTM cell layer at the previous time, respectively; the three \(\sigma\) correspond to the sigmoid activation function of the Forget, Input, and Output Gates; and the tanh layer is used to create new candidate cell states \(C_{t}\).  
In SSC, the temporal correlation is very significant between software entities. For software with the same name but different version numbers, the LSTM network can effectively capture and learn these temporal dependencies by virtue of its unique recurrent structure.