esa-sceva/satcom-chunk-collection huggingface dataset:
# A Bimodal EMG/FMG System Using Machine Learning Techniques for Gesture Recognition Optimization
## 2 Materials and Methods
### Data Acquisition  
EMG and FMG signals were collected simultaneously from each participant, using the BITalino platform with four acquisition channels: two for EMG and two for FMG. One pair of EMG/FMG sensors was placed in the extensor muscle group of the forearm and the other in the flexor muscle group.  
BITalino transmits the data via Bluetooth to a PC, where the data that is being acquired is visualized in real time and stored for further processing using OpenSignals software. Participants were instructed to perform five gestures: open, close, pinch, point, and thumb-up. Each collected data file contains approximately ten activations of each gesture.  
The implementation of signal acquisition went through the following steps:  
1. For each acquisition session, EMG sensors (in bipolar configuration) were positioned in the flexor and extensor muscle groups, with a separation of approximately 2 cm;
2. Between the two active electrodes, an FSR sensor (on a rigid PVC base) was fixed with an adhesive;
3. A velcro tape was applied to the forearm over the two FMG sensors simultaneously to stabilize the sensors in place;
4. Each participant was instructed to perform a series of activations of a specific type of gesture, with durations and rest intervals between activations ranging from 1 to 3 s to ensure the representativeness of the data collected. During data collection, the participant was asked to remain as relaxed as possible between activations and to keep the elbow joint still to minimize the influence of residual muscle strains on the collected data;
5. Each series of activations was recorded in a separate file with the name of the gesture performed, using the OpenSignals software. The sampling rate was 1000 Hz. Figure 3 shows images of signal acquisition for two different gestures. 4-Channels of BITalino  
\begin{table}
\begin{tabular}{l l} \hline \hline Gain & 1009 \\ Range & \(\pm\)1.64 mV (com VCC = 3.3 V) \\ Bandwidth & 25–480 Hz \\ Power Voltage & 2.0–3.5 V \\ Input Impedance & 7.5 G\(\Omega\) \\ CMRR & 86 dB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Technical specifications of the EMG module sensor.  
made the acquisition of the signals from two pairs of EMG and FMG sensors in flexor and extensor muscles.
# Dynamic Single-Electron Transistor Modeling for High-Frequency Capacitance Characterization
## 3 Simulation Results
### Frequency Dependence of Capacitances  
Figure 5 shows the gate input capacitance \(C_{\rm gg}\) as a function of frequency at low and high drain voltages of \(e/C_{\rm\Sigma}/100\) and \(e/C_{\rm\Sigma}/2\), respectively. It can be seen that the \(C_{\rm gg}\) starts to decrease at a frequency about an order of magnitude lower than the cutoff frequency (1 GHz) and approaches the dashed line at higher frequencies, which can be explained again as the approach to the condition of passive capacitance circuit consisting of \(C_{\rm g}\), \(C_{\rm d}\) and \(C_{\rm s}\). From such an analysis, we can quantitatively understand the frequency-dependent behaviors of the capacitances inside the SET.  
Figure 4: (a) SET input capacitance \(C_{\rm gg}\) (b) drain-to-gate capacitance \(C_{\rm gd}\) and (c) source-to-gate capacitance \(C_{\rm gs}\) as a function of gate voltage \(V_{\rm gs}\) for a high drain voltage \(V_{\rm ds}=26.7\) mV [0.5] at \(T=15.5\) K [0.05].  
Figure 5: SET input capacitance \(C_{\rm gg}\) as a function of frequency at drain voltages \(V_{\rm ds}=0.534\) mV [0.01] and 26.7 mV [0.5], gate voltages \(V_{\rm gs}=80.1\) mV [0.5] and 93.5 mV [0.6] and \(T=15.5\) K [0.05].
# Design of a Fixed IF Down-Conversion Double-Balanced Mixer for UHF Band Applications
## 3 Proposed Down-Conversion Mixer Design
### Local Oscillator: Square Wave Characteristics Generation  
In the ideal case, we assume the LO differential pair operates in a saturation region with square wave characteristics. At the collector current output, we force the sine wave to look like a square wave, ensuring that the differential pair operates near a saturation  
Figure 4: Collector current of RF stage (Q1 and Q4).  
Figure 5: (**a**) Gain and phase of two RF stages, and (**b**) amplitude and phase difference between two RF stages.  
region. The waveforms of the upper transistors' collector currents (Q2 and Q3, and Q5 and Q6 represented in Figure 2) are represented in Figure 6. We lose the device symmetry in the collector current at higher frequencies because of the phase shift of the transconductance \(Y_{21}\); the collector current waveforms at the lower side show ringing and limited voltage, while the upper side shows saturation.  
The double-balanced Gilbert-cell mixer, reported in Figure 2, consists of two identical single-balance mixers. Therefore, the circuit can be split in two for analytical reasons. Switching cores Q2 and Q3, and Q1 at the RF stage, are present in the first half circuit. Q5 and Q6 are the LO switching cores and Q4 at the RF stage is part of the second half of the circuit.
# IoTactileSim: A Virtual Testbed for Tactile Industrial Internet of Things Services
## 3 Result and Discussion
### Scenario I: Teleoperation with 3 DoF
Figure 10b-d, depicts the improvement in the communications network impairments (delay, jitter) with the number of packets = 100, 1000, 10,000. The reason behind this is that at the beginning, the proposed testbed IoTactile5 im understands and fine-tunes the simulation parameters to support delay-sensitive and loss-intolerant applications. The efficacy of the scenario I regarding reliability characterization is summarized in Table 3. The reliability of the transmitted haptic data packets is evaluated in terms of delayed/lost and out-of-ordered packets. \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Experiments**} & \multicolumn{3}{c}{**Packet Statistics**} \\ \cline{3-4}  & & **Dropped/Delayed (\%)** & **Out-of-Order (\%)** \\ \hline \hline \multirow{4}{*}{**Experiments**} & 10 & 20.0 (2 Packets) & 11.1 \\  & 100 & 2.00 (2 Packets) & 1.00 \\  & 1000 & 0.10 (1 Packets) & 0.00 \\ \cline{1-1}  & 10,000 & 0.10 (7 Packets) & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of the reliability characterization for haptic datset and real-time haptic drive teleoperation experiment. Figure 10: Packet latency histogram for scenario I haptic data transmission; (a) data packets = 10, (b) data packets = 100, (c) data packets = 1000, (d) data packets = 10,000.
# Non-Linear Creep-Relaxation Constitutive Damage Model for Aging Concrete
## 2 Theoretical Formulation
### Form of the Non-Aging Relaxation Functions  
Finally, the form of the non-aging relaxation functions is discussed. The mathematical form of the basic (non-aging) relaxation function can be obtained using functional equations. The form of the relaxation functions is determined through the second Cauchy equation. It is worth mentioning that other forms of equations can be used (such as the first Cauchy equation). However, those equations do not satisfy the thermodynamic restrictions expressed in Equations (43) and (44). If one recalls that \(v(t)\geq 0\) and \(\dot{v}(t)\geq 0\), the relaxation functions in Section 2.3.1 must also meet the thermodynamic restrictions in Equations (43) and (44), i.e.,  
\[\begin{split}\lambda(t-\tau)&\geq 0\\ \mu(t-\tau)&\geq 0\\ \chi_{I}(t-\tau)&\geq 0\text{ (with }I=1,2,\cdots,4 \text{)}\end{split} \tag{54}\]  
and  
\[\begin{split}\dot{\lambda}(t-\tau)&\leq 0\\ \dot{\mu}(t-\tau)&\leq 0\\ \dot{\chi}_{I}(t-\tau)&\leq 0\text{ (with }I=1,2,\cdots,4 \text{)}\end{split} \tag{55}\]  
From the first statement of the proof of the second Cauchy equation, i.e., \(f(t+\tau)=f(t)f(\tau)\), for all \(t,\tau\) that belongs to the real numbers, and where \(f(t)\) is a function of real numbers that maps to the real numbers, the similarity with Equations (54) and (55) is recognized. Hence, it is easy to prove [61] that \(f(t)=a^{t}\) or \(f(t)=e^{t}\), and thus, through the thermodynamic restrictions,  
\[\begin{split}\lambda(t)&=\sum\limits_{i=1}^{n}C_{ \lambda i}e^{-k_{i}^{\lambda}t}\\ \mu(t)&=\sum\limits_{i=1}^{n}C_{\mu i}e^{-k_{i}^{ \lambda}t}\\ \chi_{I}(t)&=\sum\limits_{i=1}^{n}C_{\chi_{Ii}}e^{- k_{i}^{\lambda}t}\left(\text{with }I=1,2,\cdots,4\right)\end{split} \tag{56}\]  
The summation was considered for a general solution to represent the Maxwell chain [41; 44; 59], where \(e\) is Euler's number; \(k_{i}^{\lambda}\), \(k_{i}^{\mu}\), and \(k_{i}^{\lambda}t\) are a set of positive experimental constants associated with the non-aging relaxation function \(\lambda\), \(\mu\), and \(\chi_{I}\) with \(I=1,2,\cdots,4\), respectively; while \(C_{\lambda i}\), \(C_{\mu i}\), and \(C_{\chi_{Ii}}\) are experimental coefficients associated to their corresponding relaxation function, added to shift the relaxation functions. Equation (56) is consistent with the Dirichlet series used in [41; 44]. The graphical representation of Equation (56) is consistent with recent experimental data obtained [62]. It should be mentioned that the variable \(t\) is replaced by the loading duration \((t-\tau)\) when the relaxation functions are substituted into the constitutive model, as shown in Equations (54) and (55).
###### Abstract  
As the increasing penetration of inverter-based generation (IBG) and the consequent displacement of traditional synchronous generators (SGs), the system stability and reliability deteriorate for two reasons: first, the overall inertia decreases since the power electronic interfaces (PEIs) are almost inertia-less; second, renewable generation profiles are largely influenced by stochastic meteorological conditions. To strengthen power systems, the concept of the virtual synchronous generator (VSG) has been proposed, which controls the external characteristics of PEIs to emulate those of SGs. Currently, PEIs could perform short-term inertial and primary frequency responses through the VSG algorithm. For renewable energy sources (RES), deloading strategies enable the generation units to possess active power reserves for system frequency responses. Additionally, the deloading strategies could provide the potential for renewable generation to possess long-term frequency regulation abilities. This paper focuses on emulation strategies and economic dispatch for IBG units to perform multiple temporal frequency control. By referring to the well-established knowledge systems of generation and operation in conventional power systems, the current VSG algorithm is extended and complemented by the emulation of secondary and tertiary regulations. The reliability criteria are proposed, considering the loss of load probability (LOLP) and renewable spillage probability (RSP). The reliability criteria are presented in two scenarios, including the renewable units operated in maximum power point tracking (MPPT) and VSG modes. A LOLP-based economic dispatch (ED) approach is solved to acquire the generation and reserve schemes. The emulation strategies and the proposed approach are verified by simulation.  
economic dispatch; inverter-based generation; loss-of-load probability; reserve; virtual synchronous generator +
Footnote †: journal: Applied Soft. 2020, 10, 1303; doi:10.3390/app10041303
## 5 SRM Simulation Model  
The implemented control system of the SRG model in Simulink(r) is shown in Figure 3, where the model includes the SRG, position sensor, AHB converter, controller, and load. Simulations and analyses are carried out on the assumption of a constant rated speed. The output of the load is compared with the reference signal. The error signal is the primary input to the PID and fitness function of PSO. PSO optimizes the required PID parameters. The output of the PID could be a turn-on angle or turn-off angle, which depends on the required action. The SRG parameters are listed in Table 1, where the magnetization flux linkage/current curve at different positions is shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline number of phases & 3 & \\ number of stator poles & 6 & \\ number of rotor poles & 4 & \\ stator resistance & 0.05 & \(\Omega\) \\ machine inertia & 0.05 & N m s\({}^{2}\)/rad \\ friction & 0.02 & N m s/rad \\ unaligned Inductance & \(0.67\times 10^{-3}\) & H \\ aligned Inductance & \(23.6\times 10^{-3}\) & H \\ maximum Current & 450 & A \\ maximum Flux Linkage & 0.486 & V s \\ rated Speed & 3000 & RPM \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulated SRG parameters.  
Figure 3: Block diagram of the proposed control strategy.
# Propagation, Scattering and Defect Detection in a Circular Edge with Quasi-Edge Waves
## 3 Experimental Study  
This section presents an experimental study for the verification of the 3D FE approach described in Section 2.3. The specimen details and experimental setup for generating and sensing the \(QES_{0}\) wave mode is presented in Sections 3.1 and 3.2, respectively. Finally, in Section 3.3, a comparison between the experimentally measured and the corresponding FE simulation results is presented.  
Figure 10: Snapshot of \(QES_{0}\) (FTV = 2) propagation (**a**–**c**) before and (**d**–**f**) after encountering the through-thickness edge defect with depths of 2 (**a,d**), 8 (**b,e**) and 14 (**c,f**) mm.
## 3 Proposed Model
### Blade and Non-Blade Binary Classification  
After locating the position of the hub in the input image, the positioning information \(\{\hat{W}_{h},\hat{H}_{h},\hat{x}_{h},\hat{y}_{h}\}\) is estimated with the confidence scores. We select a random point with distance \(r\) and angle \(\theta\) relative to the hub center (\(\hat{x}_{h},\hat{y}_{h}\)). Distance is calculated based on Equations (8)-(10):  
\[r=\sqrt{x_{r}^{2}+y_{r}^{2}} \tag{8}\]  
\[x_{r}=\hat{x}_{h}+k_{x}\times\hat{W}_{h},k_{x}>0 \tag{9}\]  
\[y_{r}=\hat{y}_{+}k_{y}\times\hat{H}_{h},k_{y}>0 \tag{10}\]  
According to Equation (11), \(\theta\) is supposed to have a random uniform distribution, and it will be selected randomly between \(\theta\)1 and \(\theta\)2:  
\[f_{\theta}(\theta)=\left\{\begin{array}{ll}1/(\theta 2-\theta 1)&\theta 1\leq\theta\leq\theta 2\\ 0&\text{otherwise}\end{array}\right. \tag{11}\]  
Based on these equations the pair \((r,\theta)\) is estimated then a sub-image named \(I_{h}\) is masked at this point. Now, for the resulting image \(I_{h}\), with dimensions \((W_{i},H_{i})\), the blade/non-blade classification algorithm can be run. From the morphological point of view, a blade and consequently a random part of it are directional objects. In traditional approaches, there are a lot of methods such as Radon transform [37,45,50] and Gabor wavelet transform (GWT) [6,51,52,53] that can be used for directional pattern analysis. In our previous work [6], we applied GWT to solve the issue. However, these approaches have deficiencies and suffer from scale and illumination variations. Therefore, in this work, we develop our classification model based on deep neural networks [54]. To tackle this issue, we define a binary classification problem. We propose to deploy transfer learning and fulfill the task by fine-tuning some strong classification models such as _DenseNet_, _SqueezeNet_, _ResNet50_, and _InceptionV3_ for our developed dataset including 10,000 images with high precision. This dataset includes 5000 blade images with diverse angles (0 to 360 degrees), different thicknesses and lengths, as well as 5000 non-blade images. Some blade samples are depicted in Figure 5. In Figure 6, histograms of both height and width of the blade images in our dataset are shown.  
As said before, some of the more powerful methods are used and compared in our work.  
Figure 6: Distribution of height and width for 5000 blade sub-images in our dataset.
# Research Progress on Synergistic Technologies of Agricultural Multi-Robots
## 3 Research Progress on Synergistic Technologies of Agricultural Multi-Robots
### Communication
Figure 18: The computer as the center controller was used to send initial paths for UGVs and UAVs control units through communication. And perception data were exchanged between multi-robots and computers.UGV. In addition to the above wireless communication technologies, [PERSON] et al. adopted a mobile ad hoc (peer-to-peer) network [128; 129], which regarded the UAV as a communication node in the network and used three communication strategies (simple, flooding, geo-aware) to solve the communication problem of UAVs flying in the field. The simplest communication strategy is a single broadcast mode, that is, the source node sends information to the nearest node. Flooding constitutes a multi broadcast mode, that is, the source node sends information to multiple agents. Geo-aware employs a source node with the highest utilization rate, and this node sends the messages. All three communication strategies ignore communication errors and focus on the impact of the communication range and protocol on work efficiency. The simulation results show that the effective information of weed monitoring can be transmitted with a minimum number of UAVs under the geo-aware approach. However, the communication strategy discards new information obtained by UAVs of the distributed architecture, and messages cannot be effectively transmitted with a wide range of communication (such as over wide areas of farmland). Agricultural multi-robots working in the farmland often encounter signal occlusion, atypical weather, etc. Large agricultural multi-robots working in agricultural fields rarely encounter problems such as signal occlusion and atypical weather. However, in other agricultural products, such as greenhouse and orchard, when the size of the multi-robot is smaller than the height of the crop, its communication signal strength is extremely attenuated by factors such as crop planting, growth characteristics, planting scale, and weather (natural wind and rain). Previous references [126; 127; 130] showed that the test results of the WI-FI communication system of agricultural multi-robots suffered from WI-FI signal intensity attenuation largely because of the reflection and scattering effects of crops, and the effective communication distance was less than 50 m (far less than the theoretical communication distance of 300 m) in mature wheat fields, cornfields, and peach gardens. Therefore, it is a future research
## 3 Results
### NO Emissions  
The specific NO emissions of the engine in all simulation cases are presented in Figure 11. The simulation results showed a significant reduction in NO emissions in the DF mode, in comparison to the diesel mode. The DF mode reduced the NO emissions by up to 71%, compared to the diesel mode.  
NO is responsible for more than 90% of NOx emissions emitted by ICEs. Chemically, there are two chemical mechanisms involved in the NO emission formation: (1) thermal NO mechanism (described by the extended Zeldovich mechanism); and (2) the prompt NO mechanism (described by the Fenimore mechanism) [41]. However, in ICEs, the thermal NO mechanism is prominent, so only this mechanism must be considered when analyzing the NO emissions. This is the reason why the extended Zeldovich mechanism was employed to model the NO formations in this study. In the thermal NO mechanism, NO formations are significantly influenced by the local peak temperature, the O\({}_{2}\) concentration inside the cylinder, and the reaction resident time. In the cylinder of ICEs, NO emissions are mainly formed in the regions where the temperature is equal to or above 1800 K. The rate of the formation dramatically increases with the increase of the local peak temperature [18; 33; 41; 42].  
In the DF mode, as shown in Figure 10, the local peak temperatures were significantly lower than those in the diesel mode. This resulted in a significant reduction in NO emissions, as shown in Figure 11. The injector SA of 155\({}^{\circ}\) and 150\({}^{\circ}\), respectively, produced the highest  
Figure 11: NO emissions in all simulation cases.  
local maximum temperature in the diesel and DF modes, leading to the highest NO emissions in these corresponding modes. Moreover, the injector SA of 145\({}^{\circ}\) produced the lowest NO emissions in both the diesel and DF modes. The reason is that because this SA of the injector generated the lowest local peak temperature in both the diesel and DF modes. Additionally, in the DF mode, the gaseous primary fuel was injected into the intake port to mix with the charge air, forming a homogeneous fuel-air premixed mixture, prior to being supplied to the engine cylinder. This leads to a significant reduction in the local O\({}_{2}\) concentration in the engine cylinder. This contributed to the NO emission reduction when burning gaseous fuel, compared to pure diesel. This tendency has been reported in the previous studies [8; 9; 19]. In conclusion, the injector SA of 145\({}^{\circ}\) is the optimal value to reduce NO emissions in both the diesel and DF modes.
###### Abstract  
We report on the room-temperature experimental measurement of THz absorption signatures in aqueous, double-stranded nucleic acid solutions confined to the submicron silica channels on fused quartz substrates using THz frequency-domain (photomixing) spectroscopy. Three sharp (i.e., strong and narrow) signatures, \(\sim\)10-20 GHz FWHM, are observed in the shortest base pair sample--small interfering, double-stranded (ds) RNA--in the range of 800 GHz to 1.1 THz. Three similar signatures are also observed in a 50-bp dsDNA ladder sample. For a 1-bp dsDNA ladder sample, the three are still evident, but are broadened and weakened. For a 48.5-bp sample (\(\lambda\)-DNA), no prominent signatures are observed, but rather a quasi-sinusoidal transmittance spectrum consistent with a substrate etalon effect. The division between sharp signatures and no signatures is consistent with the molecular length being shorter or longer than the persistence length.  
Keywords:THz spectroscopy; THz absorption signatures; nanofluidic chips; nanochannels; double-stranded RNA; double-stranded DNA; molecular persistence length +
Footnote †: journal: Applied