esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Noise Programmable Voltage Source
## 1 Introduction
Another realization [29] relies on filtering out the noise at the output of a conventional DAC and using a low-noise JFET transistor IF3601 as an output buffer, with auxiliary subsystems employed to insure reduced transient times and high accuracy and stability. This last system is characterized by a noise level of 4.5 nV/\(\surd\)Hz at 1 Hz and below 3.5 nV/\(\surd\)Hz above 10 Hz. The performances of these systems, together with the performances that can be obtained with commercial instrumentation, are summarized in Table 1. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Output Voltage (V)** & **Noise VSD** & **References** \\ \hline \multirow{2}{*}{0 to 5} & 14 nV/\(\surd\)Hz (0.1 Hz) & \multirow{2}{*}{[29]} \\  & \(<\)3 nV/\(\surd\)Hz (above 1 Hz) & \\ \hline \multirow{3}{*}{0 to 210} & \(\approx\)300 nV/\(\surd\)Hz (1 Hz) & \multirow{3}{*}{Keysight B2961A (values for 2 V range) [30]} \\  & 100 nV/\(\surd\)Hz (10 Hz) & \\  & 2 nV/\(\surd\)Hz (1 kHz) & \\ \hline \multirow{3}{*}{\(\pm\)20} & \(\approx\) 300 nV/\(\surd\)Hz (1 Hz) & \multirow{3}{*}{Stanford Research SIM928 [31]} \\  & \(<\)200 nV/\(\surd\)Hz (10 Hz to 100 Hz) & \\  & \(\approx\)70 nV/\(\surd\)Hz (1 kHz) & \\ \hline \multirow{3}{*}{\(\pm\)15} & \(<\)100 nV/\(\surd\)Hz (10 Hz) & \multirow{3}{*}{NF LP5393 [32]} \\  & \(\approx\)10 nV/\(\surd\)Hz (100 Hz) & \\ \hline \multirow{3}{*}{0 to 8} & 1 mV/\(\surd\)Hz (0.01 Hz) & \multirow{3}{*}{[25]} \\  & 31 nV/\(\surd\)Hz (0.1 Hz) & \\  & 10 nV/\(\surd\)Hz (1 Hz) & \\ \hline \multirow{3}{*}{0 to 8} & 4.5 nV/\(\surd\)Hz (1 Hz) & \multirow{3}{*}{[26]} \\  & 3.5 nV/\(\surd\)Hz (above few Hz) & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Noise performances of some programmable voltage sources. In this paper, a new design for the realization of a low-noise programmable voltage source is proposed. While the design is inspired from [29], a number of modifications are introduced that result in much lower levels of noise. While such a piece of instrumentation can be effective in many applications in the field of low frequency noise measurements, the design originated from the need for a low-noise bias source to be used for investigating the noise in a novel infrared detector at frequencies down to 1 Hz. Indeed, the effectiveness of the design we propose is demonstrated by the result of actual noise measurements performed on these devices. The design of the new programmable source will be discussed in detail in the next section.
## 4 Performance Validation
### Performance Metrics of Scheduling Algorithm
#### 4.1.2 Waiting Time  
Waiting time is defined as the time a process spends waiting in a queue before it is executed. It measures how long a process must wait before it can be executed and can determine the fairness of a scheduling algorithm. Waiting time is calculated by subtracting the submission time from the execution time of a process.
## 5 Conclusions  
The lateral excited vibration of a beam-like microstructure, resulting from time-dependent excitation and an axial elongation, has been examined based on the nonlocal theory. The viscoelastic damping parameter was involved in modeling the excited vibration of microbeams when an external harmonic excitation is considered. The perturbation method was used while solving the nonlinear equation of motion, and the excited vibration characteristics were analyzed. Our priority revolved around revealing the nonlocal and viscoelastic damping effects in the lateral excited vibration. The following conclusions were obtained:  
1. The nonlocal effect deflects the main vibration function and significantly affects the magnitude of the forced amplitude. There is a leap phenomenon that is similar to the classical vibration theory. The nonlocal parameter causes the displacement to draft along the time coordinate, and, hence, the hysteresis effect in nonlocal excited vibration is revealed.
2. The forced amplitude decreases with the increase of the viscoelastic damping, and the variation of the viscoelastic damping has a significant influence on the nonlocal effect in the forced amplitude. The nonlocal effects on the forced amplitude are different with different detuning parameters, because of the leap phenomenon. The softening and hardening effects were both observed in the microscale.
3. The forced amplitude increases with the increase in excitation amplitude, but the rate of increase is influenced by the detuning parameter and viscoelastic damping parameter. The effects of the nonlocal parameter and viscoelastic damping parameter on the excited vibration of microbeams are coupled.  
Figure 8: The evolvement of lateral displacement: (**a**) the real-part; (**b**) the imaginary-part.  
**Author Contributions:** C.L.: Conceptualization, Simulation, Investigation, and Writing--Original draft preparation. C.Z.: Data curation, Software, Methodology, and Validation. S.S.: Formal analysis and Writing--Reviewing and Editing. J.Y.: Resources, Project administration, and Supervision. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (Nos. 11972240 and 12072112), China Postdoctoral Science Foundation (Nos. 2020M671574 and 2021M700306), and Natural Science Foundation of Jiangxi Province (Grant No. 2020 ACBL214014).  
**Infrared Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the present article. The data presented in this study are available in each figure.  
The authors declare no conflict of interest.
# Towards an Efficient Chipless RFID System for Modern Applications in IoT Networks
## 4 RFID System Validation
Typically, a Friss free space transmission equation is used for this purpose, which can effectively quantify the loss in received power as a function of distance, frequency, and gain of the antennas. For the case of a retransmission based chipless RFID system, while considering line-of-sight power transfer, the Friss equation can be written as:  
\[P_{Rx}\ =\ \frac{P_{Tx}\ G^{2}_{Tx}\ G^{2}_{Rx}\ \lambda^{4}\ \tau^{2}}{\left(4 \pi d\right)^{4}} \tag{1}\]  
where \(P_{Rx}\) is the received power at the reader's receiving (\(R_{x}\)) antenna and \(G_{Rx}\) is the gain; \(G_{Tx}\) represents the gain of the reader's transmitting (\(T_{x}\)) antenna (the gains should both be equal because symmetrical antennas are used); \(\lambda\) is the free space wavelength; \(\tau\) is the mismatch coefficient between the tag and its antenna, reflecting the insertion loss; \(d\) is the separation between the tag and reader modules. One of the important parameters in Equation (1) is the tag-reader separation \(d\), which defines the range of the system. Ideally, an efficient RFID system should be capable of decoding the tags' information at a greater range, without an error and significant power drop. Table 2 summarizes the comparison between the range achieved for our proposed chipless RFID system and some reported works under arbitrary transmitting power conditions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Frequency Bandwidth (GHz)** & **Antenna Type** & **Max. Gain (dB)** & **Range Achieved (cm)** \\ \hline
[31] & 2 to 10 and 2 to 2.5 & Circular UWB monopole & 1 and 5.5 & 5–40 \\  & & and LPDA & & \\ \hline
[30] & 4.28 to 9 & fingertip-shaped antenna (FSA) & - & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Range comparison for chipless RFID systems. Figure 13: Simulated and measured \(S_{21}\) for the proposed RFID system. From the comparison in Table 2, it is evident that the proposed chipless RFID system can offer a greater detection range while operating in a high frequency range capable of producing high bit information density.
# Research Progress on the Influence of Thermo-Chemical Effects on the Swelling Pressure of Bentonite
## 2 Test Methods for Swelling Pressure
On the other hand, the disadvantage is that two samples are needed at the same time. The principle is that the response of one specimen is used to refine the load step of the second specimen and that a small incremental load-controlled swelling does not cause excessive water absorption in the soil specimens [14]. Many optimization test schemes were proposed according to the different properties of the above-mentioned test methods. For example, as early as in 1975, [PERSON] et al. [23] proposed a triaxial instrument that could directly measure the vertical swelling of a sample in a hydraulic triaxial stress path cell; it could predict the swelling potential of the soil under test better than the consolidation test method [11]. A new constant-volume sensor, developed by [PERSON] et al. [9], could measure the swelling pressure without both any strain adjustment and the influence of test equipment stiffness, thereby reducing the measurement errors (Figure 2). To reduce diaphragm deformation under external pressure, a mercury-filled pore gap is introduced below the diaphragm in the BER-A-58S pressure sensor utilized in the upper portion. The upper-pressure transducer is in contact with the upper surface of the sample for the direct measurement of the swelling pressure. Figure 3 shows the diagram equipped with this pressure transducer used with the constant volume method without manual load adjustment, which makes it very convenient for monitoring the long-term changes in swelling pressure. Figure 1: Schematic view of the experimental setup using a constant-volume cell, according to [14]. Figure 2: Schematic view of the developed constant-volume cell [9]. Accuracy, directness, simplicity, and long-term monitoring have become important criteria for selecting a test method. In addition, the different loading and wetting conditions used by a method will also cause the achieved swelling pressure to behave differently [11]. For instance, at constant volume, the swelling pressure is surprisingly reduced after wetting in soil subjected to a high initial vertical stress [9]. In recent years, microstructural investigations of the bentonite swelling pressure have clarified the causes of swelling pressure variation in relation to the test method. It was shown that similar swelling pressures obtained by different tests depended on their similar micro- or macro-porosity. The swelling pressure will show a good consistency, independent of the test method, when the samples are wetted under a sufficiently high vertical stress [21]. In general, the constant volume test is still the most commonly used method in current research [24; 25; 26].
# Continuous Control Set Predictive Current Control for Induction Machine
## 3 Control Structure
### Outer Control Loop Algorithm  
To produce stator current reference values, methods from the classical rotor field oriented control in \(dq\)-reference frame can be applied. In other words, one can calculate \(d\)-component of the reference current from the rotor flux reference by using Equation (18) and \(q\)-component from the torque reference by using Equation (19). To transform these values from \(dq\) into \(\alpha\beta\)-reference frame, Equation (20) is used, where \(\theta_{r}\) represents angle between rotor flux vector and \(\alpha\)-axis.  
\[i_{sd}^{*}=\frac{\left|\psi_{r}\right|^{*}}{L_{m}} \tag{18}\]  
\[i_{sq}^{*}=\frac{2 pL_{r}T_{e}^{*}}{3L_{m}|\psi_{r}|^{*}} \tag{19}\]  
\[\begin{bmatrix}i_{sq}\\ i_{s\beta}\end{bmatrix}=\begin{bmatrix}cos(\theta_{r})&-sin(\theta_{r})\\ sin(\theta_{r})&\cos(\theta_{r})\end{bmatrix}\begin{bmatrix}i_{sd}\\ i_{sq}\end{bmatrix} \tag{20}\]  
In this paper, optimal selection of the rotor flux is not considered so the flux reference \(|\psi_{r}^{*}|\) will be set to constant value, while torque reference \(T_{e}^{*}\) is going to be generated from rotor speed error using \(PI\) regulator as shown in Equation (21). The purpose of the regulator is to reduce the impact of the disturbance on the system, which is in this case the load torque.  
\[T_{e}^{*}=(\omega_{r}-\hat{\omega}_{r})(K_{Pu}-\frac{K_{l\omega}}{s}) \tag{21}\]  
where \(s\) represents the integration in Laplace domain, \(K_{Pu}\) and \(K_{l\omega}\) represent speed \(PI\) regulator gains and \(\hat{\omega}_{r}\) represents estimated rotor speed value.
# Hybrid Representation of Sensor Data for the Classification of Driving Behaviour
## 4 Experimental Evaluation
### Experimental Setup  
For the CNN-based variant of the proposed approach, dropout is set to 0.4, whereas batch size was set to 32. In the case of the RNN-based variants dropout, recurrent dropout [28] and sample length were set to 0.2, 0.2, and 50, respectively, whereas the batch size for RNNs was set to 1914. The SVM used for route level classification employs an RBF kernel with C = 1000. When considering 20% ranges centered on these parameter settings, the classification accuracy variance did not exceeding 5%, for all parameters. In the case of event-based detection component, the thresholds used are the ones validated by [PERSON] et al. [1] for identifying accelerations, braking, and turnings, with three different levels of intensity: low, medium, and high. These threshold values are provided in Table 1, where a\({}_{\text{y}}\) and a\({}_{\text{z}}\), are the accelerations in lateral and longitudinal direction, respectively. Sixty percent of the samples have been used for training, 30% for validation, and 10% for testing. For the training stage, all time slices of a route inherit its label (i.e., all time slices of a normal or aggressive route are labelled as normal or aggressive, respectively). Accordingly, this component is trained using a broad labelling at the route level and provides classification at the time slice level.  
The experiments were performed on a workstation with AMD Ryzen 5 1400 quad core processor (8 CPUs) on 3.4 GHz and 8 GB RAM, using NVIDIA GeForce GTX 1060 GPU with 6 GB and Microsoft Windows 10 Pro (64 bit). All pipelines were implemented in Python, using Keras [29] with the Tensorflow [30] backend.
# A Novel Top-Down Fabrication Process for Vertically-Stacked Silicon-Nanowire Array
## 2 Fabrication Process
In a conventional Bosch process, polymer deposition, polymer etching, and isotropic-silicon etching are conducted for five seconds, three seconds, and five seconds, respectively, in order to configure a high aspect-ratio structure. However, the process developed in this paper controls the final silicon etching time to fabricate the protrusion structures, which are in a location where the silicon nanowires are formed. The detailed DRIE process is summarized in Table 1. Several experiments are performed to optimize the adequate scallop size by tuning the duration of the isotropic-silicon etching step. As shown in Figure 2f, a thermal wet-oxidation process is conducted to define the width and thickness of the silicon nanowire, both of which can be precisely controlled according to the wet-oxidation time. The process is performed under a temperature of 1000 \({}^{\circ}\)C with an H2 flow rate of 7000 sccm and an O2 flow rate of 6500 sccm. In essence, the wet-oxidation process consumes silicon to produce thermal oxide; hence, the silicon nanowires are fabricated under a protrusion structure, while the vertical layers are isolated by the insulation layers. In this paper, Bosch 5/3/15 process is used for DRIE, and the purpose is to fabricate nanowires with a width of approximately 300 nm. The wet oxidation rate of the furnace used in this process is approximately 50 A/min to form nanostructures, and the oxidation process is performed for about 70 min to isolate each layer by completely oxidizing  
Figure 2: Fabrication process flow. (**a**) TEOS deposition; (**b**) PR patterning using UV lithography; (**c**) TEOS patterning; (**d**) PR removal; (**e**) DRIE for fabricating protrusion and support structure; (**f**) wet oxidation for nanowire fabrication and layer-to-layer isolation. the support structure. The additional oxidation is 20-25% more than the width of the support structure because the rate of oxidation is slower than the surface. After removing the TEOS and thermal oxide layers by hydrofluoric (HF)-acid dipping, the vertically-stacked silicon-nanowire arrays are formed. The number of vertical layers can be adjusted by the number of DRIE-process cycles without additional masking or photolithography processes.
# A Power Processing Circuit for Indoor Wi-Fi Energy Harvesting for Ultra-Low Power Wireless Sensors
## 3 Analytical Analysis  
### The Equivalent Circuit  
An equivalent circuit simplifying the schematic circuit is presented in Figure 3. Here, the receiving antenna is represented by an AC source with an internal resistance \(R_{g}\). \(V_{in}\) is the input voltage of the matching circuit, \(V_{\textit{rf}}\) is the input voltage of the rectifier/switching circuit, and \(V_{dc}\) is the direct current voltage across \(C_{2}\). \(V_{out}\) is the voltage measured at \(C_{7}\). \(Z_{in}\) is the input impedance looking from the receiving antenna, and \(Z_{\textit{rf}}\) is the rectifier/switching input impedance. The impedance of the push-pull circuit is denoted as \(Z_{\textit{pp}}\). The passive component in the equivalent circuit is transformed into complex impedances for simplifying the analysis as shown in Figure 4. At all input cycles, it is assumed that the diodes are acting as an ideal switch and the matching circuit is lossless.  
The rectifier and the filter capacitor \(C_{2}\) are combined as a single load admittance \(Y_{R}=G_{L}+jB_{L}\).  
The input power of the circuit \(P_{in}\) can be calculated by  
\[P_{in}=\frac{1}{2}v_{in}^{2}\Re\{Y_{in}\} \tag{1}\]  
where \(Y_{in}=1/Z_{in}\) and \(\Re\{x\}\) stands for the real part of complex variable \(X\) and \(V_{in}\) as shows in Figure 4. By applying [PERSON]'s voltage and current laws, \(V_{ac}-I_{Rg}=V_{in}=I/Y_{in}\), where \(I\) is the current flowing through \(R_{g}\), then Equation (1) can be represented as  
\[P_{in}=\frac{1}{2}\bigg{|}\frac{1}{1+Y_{in}R_{g}}\bigg{|}^{2}v_{ac}^{2}\Re\{Y _{in}\} \tag{2}\]  
The output power of the rectenna or switching circuit is given by  
\[P_{in}=\frac{1}{2}v_{rf}^{2}\Re\{Y_{L}\} \tag{3}\]  
Figure 4: The simplified circuit.  
Figure 3: The equivalent circuit.  
where \(v_{rf}\) is as indicated in Figure 4, and \(Y_{L}\) is the admittance as seen at the input of the rectifier/switching circuit.
# Shipborne GNSS-Determined Sea Surface Heights Using Geoid Model and Realistic Dynamic Topography
## 6 Discussion
### Squat Estimation Function  
Ideally, squat information should be provided by a vessel-specific squat table. Such a table was not available for this study. Thus, the squat correction was estimated empirically as a direct data derivative (cf. Section 5.3). Since the squat is approximately a quadratic velocity function, a quadratic function was fitted to the empirical data in the least-squares sense. As an alternative, a least-squares fit of a linear function was also used to approximate squat correction (cf. Figure 13a). The resulting residuals from using these two functions are compared in Figure 21. Note that the offset term was not considered for the linear function, as was the case with the quadratic function (cf. Equation (21)).  
Although the use of the quadratic function is, in theory, more correct, the results in Figure 21 indicate a better performance for the linear function, as the residuals are significantly less biased toward negative. It, thus, appears that the quadratic function underestimates the squat correction. The result also implies that a portion of the 5.2 cm bias in the data originates from inaccurate squat correction.  
Figure 20: (**a**) Histograms of residuals (all campaigns combined): the impact of geoid model choice, and (**b**) histograms showing differences between the NKG2015 and EST-GEOID2017 models (along the vessel’s routes) and the SSH datasets derived using these models during data processing.
## 6 Conclusions  
A three-input majority voter is vital for implementing TMR that is widely used to improve the fault tolerance of mission- and safety-critical applications. Many designs of the three-input majority voter have been discussed in the literature. However, most of these correspond to the synchronous design paradigm, and just one corresponds to a bundled data asynchronous design paradigm, which is not delay insensitive and hence non-robust. Notably, robust QDI designs of the majority voter were not considered in the literature. In this context, this article presented the QDI designs of four three-input majority voters viz. Singh_MV, DIMS_MV, Toms_MV, and SI_MV, based on the corresponding strong indication logic synthesis methods. Example QDI TMR circuits were realized using an early output  
Figure 14: Split-up of power dissipation (in \(\mu\)W) between voters and the rest of the logic corresponding to different QDI TMR circuits based on: (**a**) RTZ handshaking, and (**b**) RTO handshaking.  
QDI full adder for the function modules and the QDI majority voters. The implementations used the popular DI dual rail code for data encoding, and four-phase RTZ and RTO handshake protocols for data communication. Based on the simulation results, it has been inferred that from the perspectives of speed (i.e., cycle time) and power, DIMS_MV is preferable to Singh_MV, Toms_MV, and SI_MV. From the area perspective, SI_MV is preferable.  
Conceptualization, P.B.; methodology, P.B.; validation, P.B.; formal analysis, P.B., N.E.M.; investigation, P.B.; resources, D.L.M., N.E.M.; data curation, P.B.; writing--original draft preparation, P.B.; writing--review & editing, P.B.; visualization, P.B.; supervision, D.L.M.; project administration, D.L.M., P.B.; funding acquisition, D.L.M.  
This research is supported by an Academic Research Fund Tier-2 research award of the Ministry of Education (MOE), Singapore under Grant MOE2018-T2-2-024.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Preparation of Freeze-Dried Porous Chitosan Microspheres for the Removal of Hexavalent Chromium
## 2 Materials and Methods
### Preparation of Porous Chitosan Microspheres  
Porous chitosan microspheres were prepared using a freeze-drying method [25]. Previously, homogeneous chitosan solution (1%, 2%, 3% \(w/v\)) was prepared by dissolving chitosan powder in 0.2 M acetic acid, stirring for 12 h at 60 \({}^{\circ}\)C, and then vacuuming at room temperature overnight to remove air bubbles. Next, chitosan hydrogel beads with a porous structure were obtained by dripping the chitosan solution droplets into 100 mL of 1.0 M NaOH aqueous solution using a syringe pump (LSP02-1B, Longer Precision Pump Co., Beijing, China) with micronelele diameters of 500 \(\upmu\)m or 800 \(\upmu\)m. The obtained hydrogel beads were rinsed with distilled water until neutral and then subjected to pre-cooling and freezing at \(-20\)\({}^{\circ}\)C for different times (24 h, 48 h, 72 h), followed by being dehydrated in a freeze dryer. The control group experiment was set to dehydrate directly in a freeze dryer without freezing at \(-20\)\({}^{\circ}\)C; that is, direct lyophilization without freezing. Freezing temperature fixed at \(-20\)\({}^{\circ}\)C and was based on previous works [22; 25]. This temperature is more energy-saving and can be much more easily achieved using a refrigerator that can reach \(-20\)\({}^{\circ}\)C, without the need for special devices.