esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Effect of Combined Magnetic Fields on ROS Generation by Neutrophils  
#### 3.2.1 Direct Neutrophil Exposure to the Magnetic Field  
According to the results described in the above section, the GMF and SMF with 60 uT induction demonstrated comparable effect on chemiluminescence of neutrophils.  
The purpose of this section was to study the influence of the variable component of the magnetic field, which was used in addition to the static component. The combination of static (60 uT) and alternating magnetic field (100 nT), which is formed according to the sinusoidal regime at 12.6 or 48.5 Hz, generates combined collinear magnetic field (CMF).  
The results of these measurements are presented in Figures 4 and 5 and in Table 2.  
According to the results presented in Table 2, pre-incubation of neutrophil suspension in the CMF with alternating component of 12.6 Hz induced significant activation of respiratory burst in neutrophils compared to control. Intensity of chemiluminescence of neutrophil suspension after such exposure increased by more than 36%. In contrast, at 48.5 Hz, statistically significant inhibitory effect of the CMF on neutrophil suspension was observed compared to control (reduced cell chemiluminescence by almost 20%).
## 3 Phage-Mediated Bacterial Detection Approaches  
### Bacterial \(\beta\)-D-Galactosidase  
Lytic phages have been used for the detection of bacteria relying on the cytoplasmic contents (cell markers) released from the lysed cells (Figure 2). [PERSON] and co-workers developed an amperometric assay based on bacterial \(\beta\)-D-galactosidase activity to detect _E. coli_ at a concentration of 1 CFU/100 mL within 6 to 8 h [33]. In this assay, \(\beta\)-D-galactosidase was released from the phage-infected host cell following lysis, and an externally added substrate, p-aminophenyl-\(\beta\)-D-galactopyranoside, was converted into p-aminophen, whose successive oxidation could be sensed by a potentiostat-based device. Sample filtration and pre-incubation before phage infection have improved the sensitivity of the test. [PERSON] and co-workers reported two cytoplasmic markers for the detection of _Bacillus cereus_ (_B. cereus_) and _Mycobacterium smegmatis_ (_M. snegmatis_) with a detection limit of 10 CFU/mL using \(\alpha\)- and \(\beta\)-glucosidase, respectively, within 8 h [34]. Similarly, the presence of _E. coli_ in water has been detected after phage lysis with a detection limit of 40 CFU/mL in 8 h [35].  
Figure 2: Schematic diagram showing the various intracellular cytoplasmic factors (biomarkers) released following phage infection. Abbr.: AK, adenosine kinase; ATP, adenosine triphosphate.
## 2 Materials and Methods
### Satellite Observation Equation  
In spaceborne GNSS receiver orbit determination, pseudorange and carrier phase are the most important observables for locating ([PERSON] et al., 2022) [29]. After thoroughly evaluating all sources of inaccuracy, the observation models for pseudorange and carrier phase may be developed. The pseudorange observation equation is shown in Equation (1).  
\[P_{i,Leo}^{s}=\rho_{Leo}^{s}+c(\delta t_{Leo}-\delta t^{s}(t))+I_{i,Leo}^{s}(t )+T_{i,Leo}^{s}(t)+B_{Li,Lo}(t)+B_{Li}^{s}(t)+\varepsilon_{Li,Lo}^{s}(t) \tag{1}\]  
In this equation, \(P\) represents the pseudorange observation value, where the superscript \"\(s\)\" refers to GPS satellites, the subscript \"\(L_{i}\)\" indicates the \(L_{1}\) and \(L_{2}\) frequencies (with \(i=1\) for \(L_{1}\)), and the subscript \"\(Leo\)\" denotes the spaceborne GNSS receiver. \(\rho\) represents the geometric distance between the LEO satellite's GNSS receiver and the GPS satellite. \(c\) is the speed of light in a vacuum, \(\delta t\) represents the clock bias, \(I\) represents the ionospheric delay, \(T\) represents the tropospheric delay, \(B\) represents hardware delay, and \(\varepsilon\) represents the observation phase noise. The carrier phase observation model is:  
\[L_{i,Leo}^{s}=\rho_{Leo}^{s}+c(\delta t_{Leo}-\delta t^{s}(t))+I_{i,Leo}^{s}(t )+T_{i,Leo}^{s}(t)+\lambda_{i}.N_{i,Leo}^{s}(t)+B_{Li,Leo}(t)+B_{Li}^{s}(t)+ \varepsilon_{Li,Leo}^{s}(t) \tag{2}\]  
In this equation, \(L\) represents the carrier phase observation value, \(\lambda\) represents the GPS signal wavelength, and \(N\) represents the integer cycle carrier phase ambiguity. The meanings of the other symbols are the same as those defined above.  
Two main categories can be used to describe the forces operating on an artificial satellite while it is in orbit around the Earth:  
1. Conservative forces: These include the gravitational pull from third bodies like the Sun and Moon, the perturbations from the Earth's non-spherical gravity field because of its ellipsoidal shape and heterogeneous mass distribution, and the central gravitational field produced by the Earth's mass center--assuming that the Earth's mass is concentrated at a point at the geocenter. These forces are the main and most important elements in orbit determination models, and they are distinguished by well-defined potential functions.
2. Non-conservative forces: These include atmospheric drag caused by interactions with rarefied atmospheric particles at orbital altitudes, solar radiation pressure acting on the satellite's surface, recoil forces caused by the satellite's thermal emissions, interactions with the geomagnetic field, tidal effects, and other non-conservative disturbances. Accurate modeling of these forces necessitates precise information about the satellite's surface material qualities, geometric configuration, real-time attitude data, and surrounding environmental variables.  
The combined impact of the aforementioned conservative and nonconservative forces regulates the satellite's orbital dynamics [30]. The solution strategy is presented in Table 1 below [31; 32; 33; 34; 35]:
## 3 Signal Processing Method for a Precise Velocity Extraction
The rate of the parameter T and the power of the echo signal mainly influence the depth of the zero positions in the function \(R_{sg}^{(\tau_{D})}(T)\) from which, as a last step in the schematic, the Doppler frequency of the target at a given distance \(D\) is extracted. In practical applications, the zero-points are indicated by local minima of (22) instead of exact zero values. The method only requires that the target needs to be the sole Doppler-shifted echo in its range cell. Static echoes like stationary clutter are expected to limit the depth of the local minima of (22) which is less damaging to the evaluation method as would be two significantly different Doppler modulations in the same range-cell, a situation being quite unlikely to endure for a longer period. Section 4 presents an experiment creating suitable data to test the described signal processing method. The scheme is particularly suitable for (pseudo-)random signals where the signal bandwidth is not affected by the choice of the segment duration. It is important to emphasize again that the different matched filters, applied in this method, use the same set of data.
## Appendix B Detailed Description and Background Data of the Calculation Model and Hourly Simulation
### Electricity Consumption and Production
Space heating SEC\({}_{\text{B}}\) in the Near Future scenario for both residential and services buildings is aconversion of the Present Situation SEC with its fuel mix [54; 55] and corresponding useful thermal energy fractions [127] and a heat pump Coefficient of Performance (COP) of 3.5 [309; 310; 311; 312] into the electrical equivalent energy. For the Mid Century scenario, savings for, respectively, the residential and the services sector of 95% and 85% are achieved, based on [313]. Space cooling SEC\({}_{\text{B}}\) in the Near Future remains equivalent to the Present SEC [50; 51], whereas, in the Mid Century scenario, savings of 70% are realized for both sectors [309; 310; 313]. In the Near Future scenario, water heating and cooking SEC is realized by electrification of the Present SEC fuel mix [54; 55], with the useful thermal energy fractions [127] into the electrical equivalent energy [127]. Only for water heating, savings of 50% are used in the Mid Century [313], due to the combined application of electrification, heat pump usage, solar thermal heating, and other heat recuperation techniques. By extensive use of LED technology for lighting and LED efficiency increase, savings of 20% and 80% are assumed for, respectively, the Near Future and the Mid Century scenario in both sectors [314; 315; 316]. A total of 0% of net savings are assumed for the SEC\({}_{\text{B}}\) of electrical appliances. Although energy savings will be significant, the net savings will be zero due to an increased number and use of electrical appliances, home automation, and IT services [317; 318; 319]. Hamburg residential and services total SEC\({}_{\text{B}}\) values in the Near Future are 83.2 and 103.3 kWh/m\({}^{2}\)/year, respectively, resulting in total energy consumption of 24,838 MWh/year. In the Mid Century scenario, SEC\({}_{\text{B}}\) values decrease to 49.8 and 74.2 kWh/m\({}^{2}\)/year, and the energy consumption to 16,020 MWh/year. Murcia residential and services total SEC\({}_{\text{B}}\) values in the Near Future are 82.9 and 170.5 kWh/m\({}^{2}\)/year, resulting in total energy consumption of 21,760 MWh/year. In the Mid Century scenario, SEC\({}_{\text{B}}\) values decrease to 51.3 and 90.8 kWh/m\({}^{2}\)/year, and the energy consumption to 12,901 MWh/year.
## 3 Optical Controlled Phased Sub-Array in Multi-Hop Bent-Pipe Payload
### Optically Controlled Beam Steering
Assume the first receiver signal is \(s(t)=e^{i\phi(t)}\) The \(i^{th}\) subarray synthesis signal is:  
\[y_{i}(t)=s(t)\times exp(-j(\alpha_{B}(l)+\alpha_{\lambda_{0}}(i))),i=1,2, \dots,m;l=1,2,\dots,M \tag{7}\]  
The total array synthesis signal is:  
\[y(t)=\sum_{i=0}^{M-1}y_{i}[t-mi\tau_{B}] \tag{8}\]  
Large phase differences are compensated for each sub-array by adjusting the delay amount to \(\tau_{B}\) in \(\alpha_{s}\) by AWGR. Ultra-high precision beam scanning is then achieved by fine-tuning the high-precision digital phase shifter \(\alpha_{B}\) in \(\varphi_{B}\). With such a two-stage phase adjustment, the final array synthesized signal allows both large-angle beam switching and ultra-high accuracy scanning and target tracking.
# EPTlib: An Open-Source Extensible Collection of Electric Properties Tomography Techniques
## 4 Examples of Application  
The implemented Helmholtz-EPT and convection-reaction-EPT techniques were applied on virtual data obtained from the simulation of a 1.5 T low-pass 16-leg birdcage body-coil (radius = 36 cm, height = 45 cm). The body-coil was used both in transmission and in reception with polarization switch. The gradient-EPT technique, instead, was applied on data from the simulation of a 7 T 8-channel head-coil made of eight loops arranged elliptically [47] (height = 16 cm, semi-axes = 11.2 cm and 13.2 cm). The same head-coil was used in transmission and reception and the raw data acquired by the eight channels were combined according to a circular polarization. Both the coil models were used to perform head imaging of the anatomical human model [PERSON] [48], as illustrated in Figure 4.  
In order to test the EPT techniques in realistic conditions, the simulated sensitivities were sampled on an isotropic Cartesian mesh with sides of 2 mm and were corrupted with additive spatially-uncorrelated Gaussian noise, with a uniform standard deviation and an average signal-to-noise ratio (SNR) of 200 and 100. For the 1.5 T case, the simulated distributions of Tx sensitivity \(|B_{1}^{+}|\) and TRx phase \(\varphi^{\pm}\) are shown in Figure 5, as well as the noisy samples.
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 2 Wireless Sensor Node: An Overview
### Sensing Sub-System  
Sensors convert physical phenomena into electrical signals [5]. Therefore, the sensing sub-system links the node to the environment. Digital or analogue signals are obtainable from the sensors. The sensors' interfacing circuit enables the microcontroller to process the detected signals. Some of the existing node platforms have embedded or built-in sensors while others do not. Several sensor nodes have sockets capable of accommodating a variety of sensors to be connected thus provisioning for flexibility.
###### Abstract  
In this work, Aspen Plus was used to simulate a sorption-enhanced steam methane reforming (SE-SMR) process in a fluidized bed reformer using a Ni-based catalyst and CaO as a sorbent for CO\({}_{2}\) removal from the reaction environment. The performances of the process in terms of the outlet gas hydrogen purity (y\({}_{\rm H2}\)), methane conversion (X\({}_{\rm CH4}\)), and hydrogen yield (\(\eta_{\rm H2}\)) were investigated. The process was simulated by varying the following different reformer operating parameters: pressure, temperature, steam/methane (S/C) feed ratio, and CaO/CH\({}_{4}\) feed ratio. A clear sorption-enhanced effect occurred, where CaO was fed to the reformer, compared with traditional SMR, resulting in improvements of y\({}_{\rm H2}\), X\({}_{\rm CH4}\), and \(\eta_{\rm H2}\). This effect, in percentage terms, was more relevant, as expected, in conditions where the traditional process was unfavorable at higher pressures. The presence of CaO could only partially balance the negative effect of a pressure increase. This partial compensation of the negative pressure effect demonstrated that the intensification process has the potential to produce blue hydrogen while allowing for less severe operating conditions. Indeed, when moving traditional SMR from 1 to 10 bar, an average decrease of y\({}_{\rm H2}\), X, and \(\eta\) by \(-16\%\), \(-44\%\), and \(-41\%\), respectively, was recorded, while when moving from 1 bar SMR to 10 bar SE-SMR, y\({}_{\rm H2}\) showed an increase of \(+20\%\), while X\({}_{\rm CH4}\) and \(\eta_{\rm H2}\) still showed a decrease of \(-14\%\) and \(-4\%\).  
sorption-enhanced steam methane reforming; fluidized bed; CaO; CO\({}_{2}\) capture; Ni-based catalyst; Aspen Plus simulation +
Footnote †: journal: Applied Sciences
# Effect of Soil Reinforcement on Tunnel Deformation as a Result of Stress Relief
## 3 Results and Analysis
### Effect of Reinforced Soil's Young's Modulus on Tunnel Diameter Change  
Figure 7 shows the measured and computed change in the tunnel diameter with different soil reinforcement moduli. The positive and negative values denote the elongation and compression of the tunnel, respectively. It is noted that the described tunnel deformation was located in the tunnel section in a circumferential direction. All three cases predicted that the tunnel lining was vertically elongated and horizontally compressed and that the magnitude of the elongation (\(\Delta D_{V}\)) and compression (\(\Delta D_{H}\)) increased with the excavation depth. All of the computed values underestimated the tunnel diameter change in comparison to the experimental measurements. This can be attributed to the fact that, in the transverse direction, the computed soil stiffness around the tunnel was larger than that in the centrifuge model test. According to the British Tunnelling Society [26], the maximum distortion of a tunnel \((\left(\Delta D_{V}+\Delta D_{H}\right)/D)\) should not exceed 2%. The maximum distortion of the existing tunnel (i.e., 0.09% \(D\)), which was induced by the basement excavation in this study, was within the abovementioned allowable limit. The increase of the reinforced soil's Young's modulus varied from \(E_{\text{s}}\) to \(5E_{\text{s}}\), which led to the maximum elongation of the tunnel lining of 9%. Significant changes were not observed when the reinforced soil's Young's modulus increased from \(5E_{\text{s}}\) to \(10E_{\text{s}}\). Thus, it was concluded that only a slight influence was exerted by the reinforced soil's Young's modulus on the change of the tunnel diameter.
# Routing Protocols for Mobile Internet of Things (IoT): A Survey on Challenges and Solutions
## 2 Background and Literature Review
### Background
RPL builds a logical topology by using three control messages and bi-directional paths are established between nodes and sinks with minimal overheads.
## 3 Theoretical Foundation and Numerical Model Construction
### Numerical Model
#### 3.2.1 Construction of Numerical Model  
Based on the three-dimensional geological model of the mine, high-precision mesh discretization was performed using HyperMesh 2023 software. Considering the lithological diversity and multi-scale characteristics of the mining area, unstructured tetrahedral meshes were employed to accommodate the complex and irregular geological structures, such as multiple faults and folds.  
The modeling process was as follows: first, stratigraphic boundaries of lithologically similar layers, faults and secondary structures in non-critical study areas, as well as cleanup benches and safety platforms, were appropriately simplified and merged. This allowed for the construction of a geometric model suitable for 3D numerical simulation while preserving the essential geological features of the open-pit mine. After solid geometry repair (it refers to the necessary checking and repair operations on the imported 3D solid model during the numerical simulation modeling process to ensure the integrity and correctness of the model geometry. Specifically, this includes repairing cracks in the geometry, eliminating overlapping surfaces, closing openings, and correcting unreasonable boundaries to ensure the quality of finite element or finite difference meshing and the stability of subsequent calculations), coplanarity adjustment of stratigraphic layers, and edge smoothing, a surface mesh was generated. The minimum mesh size was controlled at one-fourth of the thickness of the corresponding stratum. In areas with intense stratigraphic undulations or fault intrusions, control lines were manually added, and sub-blocks were divided to optimize the mesh. Mesh quality was checked, and issues such as distorted elements, degenerated cells, and free edges were corrected.  
The location of artificial boundaries is a factor to be considered in numerical modeling. Slopes are located in native rock within the site, and the slope model should include as much native rock as possible. However, this usually results in models that are too large and computationally time consuming to be feasible. In practical modeling, the location of artificial boundaries should follow the principle of not affecting the slope damage pattern [22]. The open pit quarry has an east-west length of 6.6 km, a north-south width of 2.2 km, and a height of 420 m. To minimize the boundary effect, the final model size is 15 km \(\times\) 10 km \(\times\) 1.5 km, as shown in Figure 4.  
After completing the aforementioned preprocessing steps, tetrahedral mesh discretization was carried out. The resulting mesh model consists of approximately 41.18 million nodes and 230 million tetrahedral elements. It incorporates major geological structures and topographic features, including granitic gneiss, Cretaceous sandstone, interbedded mudstone and shale, oil shale, coal seams, basalt, tuff, primary faults (F1, F1A, F5), the unloading-relaxation zone, and 19 major mining benches. The mesh model is shown in Figure 5.
# Design and Implementation of 24-GHz and 48-GHz VCOs Using Noise Filtering Technique in 90-nm CMOS
## 1 Introduction  
Voltage controlled oscillators (VCOs) play an important role in the design of microwave circuits. Its function is to provide a stable local oscillation (LO) signal. The VCOs are required to have low phase noise, low power consumption, wide tuning range and high output power from the design considerations. In radio frequency (RF) systems, low phase noise can improve the sensitivity and linearity of the receiver, which is crucial for some high-precision RF and digital signal processing systems. However, the tail current in the current mirror transistor has a nonlinear effect, which mixes the second harmonic signal with the fundamental signal to produce a signal with a frequency close to f\({}_{0}\). This new signal will shift the \(2\)f\({}_{0}\) noise to near the oscillation frequency. In order to reduce phase noise, using LC noise filtering circuit can effectively reduce phase noise [1, 2, 3] and eliminate the harmonic noise between the current source and the CMOS cross-coupling [4, 5, 6]. In Section 2 of this paper, we used a set of LC filters with a resonant frequency of 2f0 to filter out the noise at \(2\)f\({}_{0}\), through the capacitor to the ground, and reduce the phase noise of the circuit [7].  
However, the output signal of a single frequency oscillator is limited by the cut-off frequency of the transistor, which means that the output oscillator signal cannot reach a higher frequency band. Therefore, the push-push architecture used \"\(\lambda\)\"/4 transmission linecan improve the output signal limitation of the characteristics of the transistor itself [8], This paper extends the proposed VCO architecture, design a \"\(\uplambda\)\"/4 transmission line combined with an LC voltage-controlled oscillator to implement a Push-Push voltage-controlled oscillator [9; 10] and we also discuss how to use the slow wave factor (SWF) equation to design low-attenuation transmission lines [11]. The remainder of this paper is organized as follows. Section 2 describes the design and analysis of the two proposed VCOs. In Section 3, the implementation and measurements of the two VCOs detailed. Finally, Section 4 concludes this paper.
# Adversarial Machine Learning for NextG Covert Communications Using Multiple Antennas
## 5 Simulation Results
### Performance Evaluation of CJ with One Antenna for Signals with Different Modulations
The BER performance at the receiver for different modulation types and SNR values is compared in Figure 6 when \(d_{cr}=1.5\) and \(d_{ce}=0.5\). We observe that the BER gap between the Gaussian noise and adversarial perturbation for the same SNR value decreases due to the increased path loss effect between the CJ and the receiver. Thus, the CJ can create a perturbation signal that causes misclassification with higher success without increasing the BER further if the location of the CJ is closer to the eavesdropper. This result motivates the control of the CJ positions to fool a target classifier while protecting the BER performance of the intended receiver.