esa-sceva/satcom-chunk-collection huggingface dataset:
# Conductive Ink Printed Fabric Antenna with Aperture Feeding Technique
## 2 Material Selection and Pre-Processing
### Base Fabric Materials  
There is no base fabric for the direct printed antennas since the conductive inks are directly applied to the target fabric. However, for screen printing, several considerations were undertaken in the choice of base fabric materials to use. One such consideration is the ink absorption of the base fabric which is directly related to the yarn-weaving technique and a fabric pre-processing called calendering. Yarns with a flat cross-sectional area (flat yarns) were reported in our previous findings in [4] to be superior in absorbing silver-based inks better than round yarns (yarns with a rounded cross-section). In addition, the calendering process of compressing the fabric through heated rollers offers the flat yarns a smoother and even surface roughness (after ink application) compared to round yarns. Surface roughness is a key characteristic that determines the conductivity of a material [11]. Other considerations for the base fabric include the ability to withstand mechanical stress, ease of production, and availability as well as stretchability.  
The base material used for the screen printing was thus flat yarn-based polyethylene terephthalate (PET) fabric or polyester as it met most of the mechanical, economical, and electrical properties suitable for ink absorption and mass printing.
## 3 Pile Driving Formula
### Hiley Formula  
The Hiley formula is one of the oldest and most widely recognized pile driving formulas. It takes into account the efficiency of the hammer blow, the temporary compression of the pile, and the set per blow [23]. Over time, researchers have refined this formula, and the most accurate version of the Hiley formula to date is presented below [24]:  
\[Q_{u}=\frac{EMX}{S+q/2} \tag{1}\]  
where \(EMX\) = Maximum energy delivered to the pile during driving (ton \(\times\) m), \(S\) = penetration of pile per blow (m), \(q\) = rebound (m).  
This formula enhances reliability by using measured values from a Pile Driving Analyzer (PDA) as input variables. While this makes the updated Hiley formula more reliable than the original, it also imposes certain limitations for use in the field without a PDA. Specifically, accurate values for hammer weight, hammer drop height, hammer efficiency, penetration per blow, and rebound must be known in order to apply the formula effectively.
## 3 Methodology
### Recurrent Neural Network
#### 3.3.1 Standard RNN  
A standard RNN is a simple and powerful RNN. Figure 5a shows the typical structure of a standard RNN. \(x_{t}\) is the input vector at time step \(t\) and \(h_{t}\) is the hidden state of RNN cell at time step \(t\), which is computed based on the hidden state (\(h_{t-1}\)) at the previous time step \(t\)-1 and the input vector (\(x_{t}\)) at the current time step \(t\). Formally, the output of the hidden units of the standard RNN can be formulated as follows:  
\[h_{t}=\tan\h(W_{x}x_{t}+W_{h}h_{t-1}+b) \tag{6}\]  
The final output of RNN depends on not only the input of the current time step but also the calculated of the hidden layer in the previous time step. Theoretically, RNN can take advantage of all information no matter how long the sequences are. However, according to previous studies, because of the vanishing gradient problem, standard RNNs are suitable only for short-term dependencies [39; 40].
# A Phase Error Correction System for Bioimpedance Measurement Circuits
###### Abstract  
Bioimpedance sensing is widely used across a spectrum of biomedical applications. Among the different system architectures for measuring tissue impedance, synchronous detection or demodulation (SD) stands out for its lock-in amplifier utilising in-phase (I) and quadrature (Q) demodulation signals to derive real and imaginary impedance components. Typically, the current injected into the tissue is controlled by a voltage-controlled current source (VCCS). However, the VCCS can introduce phase shifts leading to discrepancies in real/imaginary outputs, especially at the highest end of the operating frequency bandwidth. Such discrepancies can significantly impact diagnostic accuracy in applications reliant on precise tissue phase profiling, such as cancer and neuromuscular evaluations. In the present work, we propose an automatic phase error compensation stage for bioimpedance measurement systems to minimise this systematic error. Our experimental findings demonstrated a considerable reduction in phase error, with the Phase Error Compensated Synchronous Detection (PECSD) system exhibiting a maximum phase error of \(2^{\circ}\) (\(\leq\)5% error) compared with the uncompensated SD system where error exceeded 20%. The improvements made by our proposed SD system hold great potential for enhancing the accuracy of impedance measurements, particularly in clinical diagnosis and disease detection.  
bioelectrical impedance; bioimpedance; synchronous demodulation; synchronous detection; phase error; PECSD; electrical impedance; electrical impedance spectroscopy Article
# Query Recommendation Using Hybrid Query Relevance
## 4 Results
### Selection of Parameters \(\alpha,\beta\)
#### 4.2.2 Selection of Parameter \(\beta\)  
Parameter \(\beta\) is the weight to balance the query information. A large number of experiments have been done by changing their value with interval of 0.1. \(\beta\) is 1, which means we only use query information. We also use the precision, the recall, and the F1 measure to evaluate results. The queries are recommended from Top 5 to Top 50 with intervals of 5. We calculate the average precision, the recall, and the F1 measure. Figure 6 shows the results.  
Figure 5(a) shows that the precision is not greatly improved when we add the URL information. Due to the data noise, the precision is lower than when only using query information sometimes. However, we can see the recall is greatly improved in Figure 5(b). URL information can be used as complementary features to better understand user search intention. We can find the relevance of queries more widely by clicked URLs. That is why the recall is greatly improved. Based on an overall consideration of precision and recall, we can see that the recommendation efficiency is improved in Figure 5(c).  
Figure 4: Selection of Parameter \(\alpha\). (**a**) Description of Precision in the first panel. (**b**) Description of recall in the second panel. (**c**) Description of F1 in the third panel.  
Figure 5: Selection of Parameter \(\alpha\). (**a**) Description of precision in the first panel. (**b**) Description of recall in the second panel. (**c**) Description of F1 in the third panel.  
To get the optimal values of the \(\beta\), we also change the range of parameter to 0.02 and conduct multiple experiments. The results are shown in Figure 7.  
In Figure 7, we can observe that precision, recall, and F1 declined rapidly beyond 0.9. This is because the query submitted by the user is the best way to reflect the query intention, and the clicked URL can be the supplementary condition to better understand the query intention in the process of query recommendation. Therefore, the weight of query information is relatively large. \(\beta=0.9\) is the best result. Therefore, we set parameter \(\beta\) to 0.9 in the later experiments.
## 5 Design of the Antenna Pointing Control System
### Equation of Motion for the Fixed-Wing UAV and Two-Axis Gimbals  
To design the proposed control system, it is necessary to clarify the rotational equation of motion for both the UAV and the two-axis gimbals. The reaction torques between the antenna driving control system and the flight control system are explicitly expressed in the equations and can be used to calculate the interference factor.  
Equation (5) shows the rotational motion around each axis in the body-fixed coordinates of the UAV and the two-axis gimbals. The equation clarifies that the attitude angular acceleration of the two-axis gimbals multiplied by the inertia moment of the UAV works as a torque on the two-axis gimbals, while the drive of the angle angular acceleration of the two-axis gimbals multiplied by their inertia moment works as a torque on the UAV.  
Because the two-axis gimbal structure for the antennas continues to rotate in the azimuth direction, the coordinate system does not match the aircraft axis of the fixed-wing UAV. As a result, the motion in the roll and pitch directions of the aircraft interferes with the driving of the elevation angle of the two-axis gimbals, and the motion in the yaw direction interferes with the driving of the azimuth angle.  
\[\begin{bmatrix}I_{\rm xx}&0&0&-I_{\rm GY}\text{sin}\,\psi_{\rm AZ}&0\\ 0&I_{\rm yy}&0&I_{\rm GY}\text{cos}\,\psi_{\rm AZ}&0\\ 0&0&I_{\rm zz}&0&I_{\rm GZ}\\ -I_{\rm GY}\text{sin}\,\psi_{\rm AZ}&I_{\rm GY}\text{cos}\,\psi_{\rm AZ}&0&I_{ \rm GY}&0\\ 0&0&I_{\rm GZ}&0&I_{\rm GZ}\\ \end{bmatrix}\begin{bmatrix}\dot{p}\\ \dot{q}\\ \dot{r}\\ \dot{\theta}_{\rm G}\\ \dot{\psi}_{\rm G}\\ \dot{\psi}_{\rm G}\\ \end{bmatrix}=\begin{bmatrix}L\\ M\\ N\\ T_{\rm GY}\\ T_{\rm GZ}\\ \end{bmatrix} \tag{16}\] \[+\begin{bmatrix}I_{\rm xz}\dot{r}+I_{\rm xz}pq+\left(I_{\rm zz}-I _{yy}\right)qr+I_{\rm GY}\left(r\dot{\theta}_{\rm G}cos\psi_{\rm G}+\ddot{ \theta}_{\rm G}\dot{\psi}_{\rm G}cos\psi_{\rm C}\right)-I_{\rm GZ}\dot{\psi}_ {\rm G}Q\\ -(I_{\rm xx}-I_{zz})pr-I_{\rm zz}\left(P^{2}-R^{2}\right)+I_{\rm GY}\left( \dot{\theta}_{\rm G}r\dot{s}n\psi_{\rm G}+\dot{\theta}_{\rm G}\dot{\psi}_{AZ} n\psi_{\rm G}\right)+I_{\rm GZ}p\dot{\psi}_{\rm G}\\ I_{\rm xz}\dot{p}-\left(I_{yy}-I_{\rm xx}\right)pq-I_{\rm xz}qr-I_{\rm GY}\left( p\dot{\theta}_{\rm G}cos\psi_{\rm G}+q\dot{\theta}_{\rm G}sin\psi_{\rm C}\right)\\ 0\\ 0\\ \end{bmatrix}\]  
Figure 14: Antenna driving control system for elevation angle.  
Figure 15: Antenna driving control system for azimuth angle.
## 3 System Architecture  
This paper proposes the technologies introduced in Section 2 as the basis for the development of an IoT platform that allows for the real-time monitoring of common areas in a community of neighbors, using iTV as a medium of interaction. As proposed in [18], this architecture consists of three layers.  
Firstly, a first physical layer formed by sensors, actuators, and control units with communication hardware. Secondly, a logic layer constituted by a storage system and computer tools for data analysis and, finally, a presentation or application layer that allows for the visualization and interpretation of data across different platforms. Following the criteria proposed in [19], we have considered the following aspects in the design of the proposed architecture:  
1. Integration of a heterogeneous infrastructure: the heterogeneity of devices and networks used should be homogenized for its integration into the platform and so expose its functionality in a standard way.
2. Infrastructure based on IoT: a great variety of devices should be integrated into the platform so that the access from it and to it can be done uniformly. Furthermore, it is necessary to expose all the chances of detection or performance transparently through well-defined interfaces to facilitate application development.
3. Services Cloud: It should be possible to take advantage of the capabilities of cloud computing to provide tools and functionality to application developers, enabling the setting up of applications as far as possible from existing software artifacts, creating new services from others that already exist.  
The proposed architecture can be seen in the Figure 2, in which you can see the three different layers: physical, logical, and application.
# A Review of Magnetoelectric Composites Based on ZnO Nanostructures
## 3 ZnO Magnetoelectric Composites
### Thin Films
#### 3.1.1 Pure ZnO Thin Films
An analysis of the interface  
Figure 3: **(a)** Schematic representation of a magnetoelectric SAW device, showing the ZnO piezoelectric layer, the magnetostrictive Metglas substrate and the interdigitated electrodes; **(b)** effect of the external magnetic field on the central frequency of the ZnO/Metglas heterostructure, along with the calculated frequency sensitivity. Reproduced with permission from [52]. Copyright [2016], AIP Publishing. between ZnO and FeTb revealed the presence of substantial residual strain, which was attributed to the process of sample preparation and to the mismatch between the thermal expansion coefficients of FeTb and ZnO. The same procedure was used in order to evaluate the magnetostrictive induced strain in situ between 0 and 0.05 T (Figure 4b), showing an agreement with the cantilever bending measurements (Figure 4a) at a strain value of 3.7 \(\times\) 10\({}^{-4}\), validating the method for use in magnetoelectric characterization. In a similar fashion, the field-induced strain generated at the interface between ZnO and amorphous FeCoSiB was evaluated by GIXRD, showing tensile and compressive strains along the [110] and [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] of the ZnO crystal, as depicted in Figure 4d [61]. The magnetostriction measurements using the cantilever method (Figure 4c) were in good agreement with the magnetic-field-induced strain calculated by the shift of the ZnO (110) Bragg peak. GIXRD enables a more direct approach in residual and magnetic-induced strain in ME interfaces by providing the local average strain distribution (corresponding to the beam impinging area) and allowing strain mapping on the microscale.
# In-Orbit Attitude Determination of the UVSQ-SAT CubeSat Using TRIAD and MEKF Methods
## 3 Attitude Determination Methods
### Multiplicative Extended Kalman Method  
The MEKF method aims to improve the attitude determination accuracy by correcting instrument noise and calibrating the gyrometer in real-time. The Kalman filter was described by [PERSON] in 1958 or [PERSON] [27] and [PERSON] [28]. The principle is based on a two-step method that aims to correct noises and instrument uncertainties.  
Figure 7: Reference frames for TRIAD method.  
The state variables describe the system at each time. These variables provide information on the corrections to be made to the instruments as well as the orientation of the satellite. They are first estimated and then corrected based on the observation from the instruments. Although standard Kalman filters are truly efficient for linear systems, they cannot be accurate for non-linear systems. Therefore, in our case, we use an Extended Kalman Filter (EKF). This algorithm is a linearized Kalman filter at the point of reference using the Taylor series expansions principle.
To make a tradeoff between the performance and the computational complexity with different antenna configurations, the iterative discrete estimation (IDE) algorithm is integrated into the ADMM algorithm [25], which presents low computational complexity due to the avoidance of the calculation of the Gram matrix and LDL decomposition. Motivated by the aforementioned algorithms, we propose a modified IDE (MIDE) algorithm to present a better BER performance and lower computational complexity than the ADMM algorithms. To summarize, the main contributions of this work are listed as follows. \(\bullet\) A novel iterative data detection algorithm for uplink multiuser massive MIMO systems is designed by exploiting the IDE-based algorithmic framework. The proposed MIDE algorithm refactors the detection algorithm as a series of simpler subproblems with closed-form solutions. \(\bullet\) A heuristic damping factor is defined based on the Euclidean distance instead of a fixed factor. Compared with the fixed damping factor, this self-updated damping factor contributes to a faster convergence in the proposed MIDE algorithm. \(\bullet\) The computational complexity analysis indicates that the proposed algorithm has a lower computational complexity than the traditional approximated detection approximation algorithms (LMMSE, AltMin, and ADMM), under the same BER performance. Specifically, the complexity of the novel MIDE detection algorithm is only \(O\left(N_{t}^{2}\right)+O\left(N_{r}N_{t}\right)\). \(\bullet\) Simulation results reveal that for the typical independent and identically distributed (i.i.d.) frequency flat Rayleigh fading channel in massive MIMO systems, the proposed MIDE detection algorithm performs better than the ADMM and AltMin-based detection algorithms and the LMMSE detection algorithm in terms of BER performance with various system configurations. The rest of the paper is organized as follows. In Section 2, we briefly introduce the system model. Section 3 specifies the proposed low-complexity signal detection based on the IDE algorithm and performs the computational complexity analysis of the algorithms. In Section 4, the numerical simulation results of the BER performance are presented. Finally, Section 5 concludes the paper. Notation: Throughout the paper, the lowercase and uppercase boldface type is used for vectors (e.g., **a**) and matrices (e.g., **A**). The superscripts \(\left(\cdot\right)^{-1}\) and \(\left(\cdot\right)^{H}\) denote the matrix inversion and the conjugate transpose, respectively. The \(L_{2}\) norms of the vectors are represented by \(\left\|\cdot\right\|_{2}\). \(\Re\) and \(\Im\) denote the real part and the imaginary part of the complex-valued signal, respectively. The typical uplink massive MIMO system is considered in this work, as shown in Figure 1, where there are \(N_{t}\) single-transmitting antenna UE devices and \(N_{r}\) receiving antennas at the BS [26]. In general, \(N_{r}\) is larger than \(N_{t}\) for an uplink massive MIMO communication system [27].
# A Method of Discriminating Between Power Swings and Faults Based on Principal Component Analysis
## 4 Simulation Tests
### Test Results Based on Nine-Bus System  
Some simulation tests were also conducted based on a 400 kV system with three alternators and nine buses (WSCC system), as shown in Figure 10. The parameters of different components of this system are presented in Table A1 in Appendix A[24].  
For testing the performance of the proposed method under different fault conditions, some three-phase shorting faults on L78 are simulated. Figure 11 shows the waveform of the three-phase voltage under a fault, and the corresponding value of \(C\) under a fault is shown in Figure 12. The fault distance is 125 km. The fault occurs at 1.2 s, and the PSB scheme is enabled at 1.201 s (\"\(t_{1}\)\" in the figure). In such a case, the value of \(C_{\text{avr}}\) is 31.68, and the PSBU will not operate. More obtained results are presented in Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Distance (km)** & \(\delta\) & \(\Delta\mathbf{P}\) & **Result** \\ \hline
1 & 10 & 50\({}^{\circ}\) & 1605.52 & F \\
2 & 80 & 50\({}^{\circ}\) & 895.34 & F \\
3 & 150 & 50\({}^{\circ}\) & 1162.82 & F \\
4 & 220 & 50\({}^{\circ}\) & 1325.18 & F \\
5 & 290 & 50\({}^{\circ}\) & 1441.59 & F \\
6 & 150 & 80\({}^{\circ}\) & 770.14 & F \\
7 & 150 & 120\({}^{\circ}\) & 125.90 & F \\
8 & 150 & 150\({}^{\circ}\) & 276.45 & F \\
9 & 150 & 180\({}^{\circ}\) & 698.84 & F \\
10 & 150 & 220\({}^{\circ}\) & 1076.44 & F \\
11 & 150 & 260\({}^{\circ}\) & 1325.72 & F \\
12 & 150 & 330\({}^{\circ}\) & 593.41 & F \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test results of PSD for two-end system.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Distance (km)** & \(t_{0}\) **(s)** & \(C_{\text{avr}}\) & **Result** \\ \hline
1 & 10 & 1.200 & 1.57 & F \\
2 & 30 & 1.200 & 5.28 & F \\
3 & 70 & 1.200 & 16.90 & F \\
4 & 110 & 1.200 & 27.37 & F \\
5 & 150 & 1.200 & 36.10 & F \\
6 & 190 & 1.200 & 42.96 & F \\
7 & 230 & 1.200 & 49.32 & F \\
8 & 270 & 1.200 & 55.39 & F \\
9 & 290 & 1.200 & 58.05 & F \\
10 & 290 & 1.201 & 58.05 & F \\
11 & 290 & 1.202 & 58.05 & F \\
12 & 290 & 1.203 & 58.05 & F \\
13 & 290 & 1.204 & 58.05 & F \\
1 Figure 11: The waveform of the three-phase voltage under a fault.  
Figure 12: The value of \(C\) under a fault.  
Figure 10: Sketch of 400 kV system.  
The above test results also indicate the effectiveness of the proposed method in discriminating between faults and power swings.  
For testing the PSD function, L57 is still tripped because of a fault at 1.2 s. Several faults on L78 were simulated after observing the power swing on it. Table 6 shows the test results, from which it can again be seen that the proposed method can detect faults during power swings.
# A Novel Hybrid Unsupervised Learning Approach for Enhanced Cybersecurity in the IoT
## 4 Proposed Method for Intrusion Detection and Prevention
### Detection Stage 2: OCSVM + Deep Autoencoder (dAE)  
The deep autoencoder (dAE) is pivotal in the second stage of our detection process. Its design focuses on operating as a deep learning classifier, adept at handling lower-dimensional data, especially in clustered data scenarios. This section outlines how the dAE leverages its capabilities to effectively differentiate between normal and anomalous traffic.
## 2 A New Routing Architecture  
One of the general principles for a more scalable Internet is the _separation_ of core and edge networks [9] in terms of routing and address space. By removing the edge network prefixes from the routing tables in the DFZ, dynamic changes in the routing space of edge networks are hidden and remain local. The routing tables in the DFZ then grow with the small number of core networks and not with the number of edge networks. This separation principle requires a new addressing and routing architecture which can be realized in many different ways. We explain the basic design options for such an architecture and use them to classify GLI-Split. Then, we give an overview of related architectures and compare them to our approach.
## 2 Materials and Methods
### Sample Preparation and Analysis  
The distribution of aggregates was determined using dry and wet sieving methods. Initially, 300 g of soil sample was shaken for 3 min on an electric vibratory sieve to obtain aggregates of eight sizes: 7-10, 5-7, 3-5, 2-3, 1-2, 0.5-1, 0.25-0.5, and \(<\)0.25 mm [20]. The components were weighed to determine their proportion in the total dry soil sample weight. The separated samples were then combined into a composite soil and 50 g of this recombined composite soil was placed on a sieving machine (LM12-XSB-88, Zhetai Machinery Manufacturing Co., Shanghai, China) equipped with 2 mm, 1 mm, 0.5 mm, 0.25 mm, and 0.053 mm sieves. The samples were soaked in deionized water at room temperature for 10 min. Subsequently, the sieves were vibrated 250 times for 10 min with an amplitude of 30 mm. This process yielded aggregates of five sizes (\(>\)2 mm, 1-2 mm, 0.5-1 mm, 0.25-0.5 mm, 0.053-0.25 mm, and \(<\)0.053 mm). The aggregates were then dried to a constant weight in a 60 \({}^{\circ}\)C dryer and weighed [21].  
The POC was determined using hexametaphosphate extractions [22]. The SOC was measured using the K2 Cr2O7 oxidation method [23]. Soil bulk density (BD) was determined using the undisturbed core method. A subsample from each sampling location was dried at 105 \({}^{\circ}\)C to a constant mass and used to determine the remaining moisture for bulk density calculations. A laser-diffraction particle-size analyzer ([PERSON], 2000, Malvern, UK) was used to divide the soil into clay (\(<\)2 um), silt (2-20 um), and sand (20-200 um), according to the international soil texture classification standard. Soil pH was determined using a pH meter (MYRON L Ultrameter \(\Pi^{\text{TM}}\), Carlsbad, CA, USA) [24].  
The mean weight diameter (MWD) was used as an indicator to characterize the structure of large soil aggregates, calculated using the formula  
\[\text{MWD}=\sum_{i=1}^{n}\text{X}_{i}\text{W}_{i} \tag{1}\]  
where W\({}_{i}\) is the percentage weight of the sample on the sieves used for analysis and X\({}_{i}\) is the mean diameter of aggregate size classes (mm) [25].