esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Optimal Design Solutions and Comparative Analysis of Different Regions
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{5}{c}{**Xi’an**} & \multicolumn{5}{c}{**Yulin**} \\ \cline{2-9}  & **Case-1** & **Case-2/ Case-5** & **Case-3** & **Case-4** & **Case-1** & **Case-2/ Case-5** & **Case-3** & **Case-4** \\ \hline O & 25 & 30 & 25 & 25 & 10 & 5 & 5 & 0 \\ Swwr & 0.3 & 0.3 & 0.3 & 0.3 & 0.3 & 0.3 & 0.4 & 0.3 \\ Nwwr & 0.15 & 0.15 & 0.15 & 0.15 & 0.15 & 0.15 & 0.15 & 0.15 \\ Swin & A & C & C & A & A & C & C & A \\ Nwin & B & C & C & A & C & C & C & A \\ St & 0.2 & 0.2 & 0.18 & 0.02 & 0.17 & 0.17 & 0.17 & 0.02 \\ Nt & 0.1 & 0.17 & 0.16 & 0.05 & 0.15 & 0.2 & 0.2 & 0 \\ Et & 0.12 & 0.17 & 0.17 & 0.04 & 0.15 & 0.16 & 0.16 & 0.07 \\ Wt & 0.1 & 0.17 & 0.14 & 0.01 & 0.14 & 0.2 & 0.17 & 0.06 \\ Rt & 0.19 & 0.18 & 0.19 & 0.17 & 0.2 & 0.2 & 0.2 & 0.2 \\ L & 0.1 & 0 & 0.8 & 0.6 & 0.1 & 0 & 0 & 0.1 \\ \(f_{1}\) (EC) & 16.8 & 15.6 & 15.9 & 21.7 & 16.2 & 15.2 & 17.0 & 20.8 \\ \(f_{2}\) (TDHP) & 39.2\% & 38.6\% & 38.5\% & 40.5\% & 40.2\% & 39.8\% & 39.3\% & 42.1\% \\ \(f_{3}\) (LCC) & 254.8 & 314.4 & 305.8 & 199.3 & 230.2 & 272.7 & 291.8 & 165.2 \\ \(f_{4}\) (LCCO2) & 72.3 & 66.8 & 69.4 & 120.8 & 0 & \(-7.0\) & 45.2 & 44.5 \\ \hline \hline \end{tabular} Note: case-2 and case-5 are combined into a single column because they have the same solution.  
\end{table} 
Table 4: Optimal values of the objective functions and design parameters for Xi’an and Yulin. different preference guidance, such as in the optimal schemes of EC, LCCO\({}_{2}\), or TDHP, where the thermal performance of the south and north exterior windows and external walls is better than that of case-1. For the optimal LCC scheme, the thermal performance of the north exterior window and exterior wall is lower than that of case-1.
# Practical Work of Three-Phased Induction Motor Modeling in a Classical Spreadsheet Software
## 2 Three-Phased Induction Motor
### Composition and General Principles
In operation, the slip is always low, on an order of a few percent: from 2% for the largest machines to 6 or 7% for small three-phase machines (though it may reach 10% for small single-phase machines). The Joule effect losses in the rotor are proportional to the slip, so a quality machine must operate with a low slip. Hyper-synchronous operation: If the machine is driven above the synchronous speed, it operates as an AC generator. In this hyper-synchronous operation, the stator must necessarily be connected to the network because it alone can create the magnetic field necessary for the generation of rotor currents.
## 5 Stress State Improvement and Analysis
### Analysis of Calculation Results  
Through the FSI simulation of four blade models, the change rule of the maximum dynamic stress of four blade models over time within a cycle of spatial rotation in designed working conditions is figured out, as plotted in Figure 12. It is found that the maximum dynamic stress of the blade gradually reduces with the thickening of the blade root, and the maximum dynamic stress of Model 4 is only 108.41 MPa. Additionally, the local thickening effect on the stress-gathered area (Model 2) is slightly better than that for the whole root (Model 3).  
Figure 13 presents the dynamic stress distribution near the leading edge of blades with different thicknesses of roots at \(\varphi\) = 66\({}^{\circ}\). The stress concentration phenomenon of the blade is alleviated after the root thickening. In addition, the more the root thickens, the lower the stress concentration of the blades. However, the root thickening has no obvious influence on stress distribution beyond the stress concentration. Simultaneously, the fluid velocity and the torque generated by the work between the blade and fluid are smaller than those of other parts due to the blade root being close to the shaft; thus, the root thickening has little effect on the energy performance of the pump. In the design stage of axial-flow pump impeller blades, the thickening of the root, namely the asymmetric structure of stress concentration regions on the roots of leading edges, can be considered, which could effectively reduce the maximum dynamic stress of blades and improve the safety and stability of impeller blades.  
Figure 12: Maximum dynamic stress change rules of different root thickness blades.  
Figure 13: Maximum dynamic stress distribution of blades with different root thicknesses when \(\varphi\) = 66\({}^{\circ}\): (**a**) Model 1, (**b**) Model 2, (**c**) Model 3, and (**d**) Model 4.
# Distributed Routing and Channel Selection for Multi-Channel Wireless Sensor Networks +
###### Abstract  
We propose a joint channel selection and quality aware routing scheme for multi-channel wireless sensor networks that apply asynchronous duty cycling to conserve energy, which is common in many environmental monitoring applications. Energy resources may vary from node to node due to differential consumption as well as availability, as observed in rechargeable sensor networks. A data collection traffic pattern is assumed, where all sensor nodes periodically forward sensor data to a centralized base station (sink). Under these assumptions, the effect of overhearing dominates the energy consumption of the nodes. The proposed scheme achieves lifetime improvement by reducing the energy consumed in overhearing and also by dynamically balancing the lifetimes of nodes. Performance evaluations are presented from experimental tests as well as from extensive simulation studies, which show that the proposed scheme reduces overhearing by \(\sim\)60% with just 2 channels without significantly affecting the network performance.  
wireless sensor networks; multi-channel routing; distributed algorithms +
Footnote †: _Sens. Actuator Netw._**2017**, 6, 10; 10.3390/jsan6030010
###### Abstract
These studies have indicated that modulation frequency components ranging from 4 to 16 Hz are key in speech recognition [8; 11], which suggests that people can utilize the TAE of speech signals as a primary temporal modulation cue for the successful perception of linguistic information. Our previous experiments in which we systematically varied the number of channels in the NVS scheme and the upper limitation of the modulation frequency components [11; 12] demonstrated that temporal modulation cues play a key role in the recognition of both vocal emotion and speaker individuality, particularly at modulation frequencies lower than 8 Hz. Our research has also demonstrated that these features are robust to noise and reverberation [13; 14]. However, we have still not clarified the influence of temporal modulation cues in non-linguistic information such as that used in emergency situations. We have performed preliminary and experimental investigations to determine whether or not the TAE of speech affects the perception of urgency [15]. To ensure the results are exhaustive, in the current work, we performed four psychoacoustical experiments in which we investigated (1) whether the TAE of speech contributes to the perception of urgency when original and noise-vocoded stimuli are used individually, (2) whether the TAE of speech contributes to urgency perception when these stimuli are used simultaneously, (3) which temporal modulation component in a TEA of speech is a temporal modulation cue of urgency perception, where we restricted the lower or upper modulation frequency of the TAE, and (4) whether a temporal modulation cue in the TAE of time-reversal speech also contributes to the perception of urgency.
## 3 Bridge Type, SNR and Multipath in Geodetic Deflection Measurements
### The Threat of Dynamic Multipath
Second, the signal of some of the satellites before it arrives at the GNSS antenna reflects to a passing reflective surface of a vehicle and produces a corrupted signal during the passage of the vehicle; this _instantaneous_ effect represents a dynamic multipath effect, different from the long-period conventional multipath. Unfortunately, in most cases it is not known with certainty which satellites are corrupted, and a possible remedy is to exclude possibly infected satellites, especially those in low altitude.
# Locally Scaled and Stochastic Volatility Metropolis-Hastings Algorithms
## 3 Experiments
### Real World Application
#### 3.4.1 Merton Jump Diffusion Model  
It is well documented that financial asset returns do not follow a normal distribution but instead have fat tails [42; 43]. Stochastic volatility models, Levy models, and combinations these models have been utilised to capture the leptokurtic nature of asset return distributions [26; 44; 45; 46]. Jump diffusion processes were introduced into the financial markets literature by [PERSON] and [PERSON] in the 1970s [26; 43; 47]. In this paper, we study the jump diffusion model of [PERSON] [26], which is a one-dimensional Markov process \(\{S_{t},t\geq 0\}\) with the following dynamics [43]:  
\[d\ln S_{t}=\left(\mu-\frac{1}{2}\sigma^{2}\right)dt+\sigma dB_{t}+d\left(\sum_ {i=1}^{N_{t}}Y_{i}\right) \tag{13}\]where \(\mu\) is the drift coefficient, \(\sigma\) is the diffusion coefficient, \(B_{t}\) and \(N_{t}\) are the standard Brownian motion process and Poisson process with intensity \(\lambda\), respectively, and \(Y_{i}\sim\mathcal{N}(\mu_{jump},\sigma_{jump}^{2})\) is the size of the \(i\)th jump. As shown in [PERSON] [43], the transition density of the returns of the jump diffusion in Equation (13) is given as:  
\[\mathbb{P}(\ln S(t+\tau)=w|\ln S(t)=x)=\sum_{n=0}^{\infty}\frac{e^{-\lambda\tau }(\lambda\tau)^{n}}{n!}\frac{\phi\Bigg{(}\frac{w-x-(\mu\tau+\mu\mu_{jump})}{ \sqrt{\sigma^{2}\tau+n\sigma_{jump}^{2}}}\Bigg{)}}{\sqrt{\sigma^{2}\tau+n \sigma_{jump}^{2}}} \tag{14}\]  
where \(w\) and \(x\) are realisations of \(S(t)\) at times \(t+\tau\) and \(t\), respectively, and \(\phi\) is the probability density function of a standard normal random variable. The likelihood function is the given as:  
\[L(\mu,\sigma,\lambda,\mu_{jump},\sigma_{jump})=\prod_{k=1}^{N}\mathbb{P}(\ln S (t+\tau)=w_{k}|\ln S(t)=x_{k}) \tag{15}\]  
where \(N\) is the sample size. The likelihood is multi-modal as it is an infinite mixture of normally distributed stochastic variables with the mixing weights being probabilities being from a Poisson distribution [43]. In this article, we truncate the infinite summation in Equation (14) to the first ten terms as done in [43]. Furthermore, the jump diffusion model is calibrated to historical financial market returns data as outlined in Table 1.  
In this work, we use MCMC methods to calibrate the jump diffusion process with the likelihood function in Equation (15) to data across different financial markets. A total of 1000 samples were generated after dropping 500 samples as burn-in. Thirty independent paths were run for both these targets and each value of \(D\).
# 5G-Enabled E-Textiles Based on a Low-Profile Millimeter-Wave Textile Antenna +
###### Abstract  
Wireless Body Area Networks (WBANs) are a key application underpinned by advances in electronic textiles (e-textiles). Achieving higher throughput, data-rate, network capacity, and delivering wireless power to miniaturized devices requires WBANs to operate at millimeter-wave 5G+ frequencies. This, however, imposes significant challenges on the antenna design to cope with the additional losses introduced by textile substrates. In this paper, the performance of a novel, high-efficiency, textile-based millimeter-wave antenna is investigated for wireless links with a wearable device. Indoor \"real-world\" channel gain measurements are used to evaluate the antenna's performance compared to anechoic gain measurements. Based on the measured channel gain between textile antennas, it is concluded that high-speed wireless links in the 24-30 GHz 5G+ spectrum could be realized with over one meter range using e-textile antennas.  
wearable sensor; antenna sensor; RF breathing sensor; wireless sensors; textile sensors; vital sign monitoring +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/))) in Electronic Textiles (E-Textiles 2021), Manchester, UK, 3-4 November 2021.
## 2 Fundamental Theory
### SCSSR Algorithm Flow  
Based on the small parameter condition of the input of the SR system, the frequency compression ratio (R) is introduced here to make the actual vibration large frequency signal meet it. For the selection of system parameters, this paper uses SOA [56; 57] to optimize the parameters \(a\), \(b\), \(r\), \(k\), and \(R\) synchronously, with the output SNR as its fitness function. Figure 2 shows the system flow chart.  
The specific implementation steps are given below:  
* Signal preprocessing: The obtained vibration signal is preprocessed; The resonance band of the vibration signal is found through power spectrum analysis; And band-pass filtering and Hilbert transform (HT) are carried out; Finally we can get the envelope signal \(S_{1}\), \(S_{2}=S_{1}-mean(S_{1})\), \(S_{3}=\max(abs(S_{2}))\), and \(S_{4}=S_{2}/(2*(S_{3}))\); \(S_{4}\) is the input signal. The above operations ensure that \(S_{4}\) is the signal with amplitude less than 1.
* System parameters initialization: Determine the maximum number of iterations and population size of SOA. Set the optimization range of five parameters.
* Take the output SNR as the fitness function of SOA.
* Record the number of iterations. If it reaches the maximum number of iterations, proceed to the next step; if it does not, return to the previous step.
* Record the maximum output SNR and get the values of the five parameters at this time.
* Weak signal detection: The preprocessed signal is introduced into the determined parameter SCSSR system to get the output. Recombine the frequency and amplitude to complete the detection of a weak signal.
# Research on On-Line Detection Method and System for Nitrate in Groundwater
## 4 On-Line Monitoring System  
In order to use sensors to monitor the nitrate concentration of groundwater on-line, an on-line monitoring system is adopted. This system consists of an on-line detection system (DS) and a wireless transmission system (WS), as shown in Figure 9. The on-line detection system consists of a MPS430 microcontroller, sensors, signal processing circuits and AD conversion circuits. The AD is a 16-bit high-precision converter AD7705 (Analog Devices, Inc., Wilmington, MA, USA), which collect nitrate concentration signals in groundwater through sensors. Each on-line detection system is fixed on anchor rods and is inserted into the groundwater to detect quality.  
The main function of wireless transmission systems is to transmit nitrate concentration signals over long distances. The power supply of each system is powered by batteries. The wireless transmission system is connected to the on-line detection system through wired means.  
The wireless transmission module is composed of CC1101 (TI Company in Dallas, TX, USA) and a microcontroller; the signal is transmitted through the chip and antenna.  
The receiving end is also composed of CC1101 (TI Company in USA) and a microcontroller. After processing has been performed by the microcontroller, it is connected to the upper computer through a serial port circuit and displayed on the upper computer.  
In order to reduce energy consumption in the network and extend the entire monitoring network lifecycle, the LEACH (low-energy adaptive clustering hierarchy) protocol is adopted for wireless transmission [32]. This protocol has the characteristics of simple implementation, autonomous cluster head election and convenient expansion.  
In order to collect nitrate concentration from multiple points, the whole system sets up multiple on-line monitoring points, each of which is assigned an identity code. The data transmission format of each monitoring point includes both identity code and nitrate concentration information, and the data are received by terminal system (TS).
# RF Frequency Selective Switch by Multiple PMIM Conversions
## 3 Results
### Flexible RF Channel Control Capability of the RF FSS  
Next, through the joint control of multiple ports of optical WSS, we can realize the RF FSS function. The following three examples show the flexible RF channel control capability of our proposed RF FSS. In the first example, as shown in Figure 5a, we set the first 10 comb teeth of the optical frequency comb to output through port 1 of the optical WSS, and their residual lights are all output from port 2. In our scheme, according to the principle, the output path of the RF channel completely depends on the corresponding optical channel path, so we can observe the flat frequency response in the range of 9.5 GHz-10 GHz at port 1, while we can observe the output of the RF signal in the range of 10 GHz-10.5 GHz at port 2. In the second RF FSS example, we use five channels of the optical WSS, each of which outputs four adjacent teeth of the optical frequency comb in turn. Correspondingly, in the RF domain, we obtain a 1 to 5 frequency division multiplexer, which divides 9.5 GHz-10.5 GHz evenly into five channels, each channel has a bandwidth of 0.2 GHz, and outputs from five ports of the optical WSS. We can see, from Figure 5b, that the five output frequency responses obtained from the experiment have a good consistency. In the third example, we use two ports of optical WSS to divide the 1 GHz RF bandwidth into 10 channels with 0.1 GHz granularity. All odd channels are output from port 1 of optical WSS, while all even channels are output from port 2 of optical WSS. In this way, we only need to control the  
Figure 4: **(a)** The frequency response when the RF channels are turned on randomly. **(b)** The response when the channel amplitudes are set to linearly increase (in dB).  
direction of the optical frequency comb, and we can obtain an RF interleaver, as shown in Figure 5c.  
In the aspect of experimental implementation, our scheme has many advantages over the existing techniques. For example, the phase modulator is easy to use and does not need complex bias point control, so the setup is more stable. Because the through end of ring resonator filter is used instead of the drop end, the optical insertion loss is small. In the experiment, we also find that the frequency response of RF filter is poor in the out-of- band suppression. This is because the notch optical filter uses a single resonant cavity device, so its frequency response is only a Lorentz type with a poor rectangular coefficient. This has been discussed in many existing reports, and therefore there are some solutions [33; 34]. We think that the more promising scheme is to make full use of the advantages of photonic integration and use multiple resonators to combine and approximate a notch frequency response with a better rectangular coefficient [35]. As long as the combined optical resonators have the same FSR, the combined frequency response can also appear periodically in the optical spectrum, which can be used in our multiple PMIM conversions.
# IoT-Portrait: Automatically Identifying IoT Devices via Transformer with Incremental Learning
## 4 System Design
### Active and Passive Information Collection  
We analyze real-world IoT devices and traffic in Section 3. Unlike computers and servers, IoT devices are more diverse in vendor, type, OS, etc., so the volume of information that can be collected is quite huge. Since existing active scanning tools cannot obtain much information, we design a comprehensive active scanning scheme. Moreover, we leverage active intrusive scanning and passive collection to gather IoT device information as accurately as possible.  
For passive collection, we collect device traffic through a central router. Then, the traffic is forwarded to the deep learning module to train the classifier. We use dumpcap [24], a tool  
Figure 3: The system overview of IoT-Portrait. The orange part represents the process of retraining the classifier when a new device joins the network.  
for network traffic dumping, to collect packets of IoT devices from the router and store them together. In the system pre-build phase, we need to collect as much device traffic as possible.  
For active collection, the device information is jointly obtained by Nmap [18], ARE [21], and cURL [25]. As shown in Figure 4, we use Nmap to perform routine scanning of the IP obtained from the collected traffic. This will collect basic information about the corresponding device. Then, we extract service-related information from the basic information obtained. After this, we simulate a client using cURL to send requests to obtain the responses of those services and forward the response to ARE for further analysis to generate richer device information like its manufacturer. The relevant information obtained from the devices will be saved, used as the traffic's label, and then displayed to users in the real-time identification phase.  
However, the information collected by the active scanning may not be sufficient since closing or blocking most ports is quite common in IoT devices. For those devices that active collection only collects fuzzy operating system information, we should label them manually.
# Design of CubeSat-Based Multi-Regional Positioning Navigation and Timing System in Low Earth Orbit
## 3 Results
### System Budgets
#### 3.6.1 Mass Budget  
In the mass budget shown in Table 6, the harness is assumed to have a mass equal to 5% of the total mass of all components. Finally, given that a deployer for 8U CubeSats could have a maximum mass capacity of 15 kg [16], this leaves a mass margin of the deployer pod of over 3.5 kg.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Subsystem** & **Mass (g)** & **Mass with Margin (g)** \\ \hline CDHS & 132 & 138.6 \\ EPS & 2564 & 2692.2 \\ TTC & 540 & 567 \\ ADCS & 819 & 860 \\ PRO & 900 & 945 \\ STR & 1873 & 2024 \\ PNT Payload & 1558 & 1821.4 \\ Harness & 374.3 & 393 \\ Total & 8760.3 & 9441.1 \\ Total mass with system margin (g) & 11,329.3 & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Mass budget—subsystem level.  
Figure 10: S/C internal (**a**) and external (**b**) view.