esa-sceva/satcom-chunk-collection huggingface dataset:
# Validation of a Wind Tunnel Propeller Dynamometer for Group 2 Unmanned Aircraft
### Propeller Theory
#### 1.4.1 Performance Characterization  
Propellers are characterized by the amount of torque and thrust they produce at a given shaft speed, and by the ratio of the power transferred to the air versus the mechanical power supplied, known as propeller efficiency [10; 11; 14]. As is typical in aerodynamics applications, the dimensional thrust and power are not typically specified; rather, non-dimensional coefficients are presented to allow the end-user of the data to adapt the results to their application (i.e., operating with a different atmospheric density or at a different velocity). Unlike aircraft wing aerodynamics, which are non-dimensionalized using freestream velocity, propeller performance coefficients are based in the propeller frame of reference, using chord-wise velocity at a given radial location as a function of both freestream and rotational velocities.  
Reynolds number is defined as the ratio of momentum force to viscous shear force. For propellers, Reynolds number is based on chord length (\(c\)), relative velocity (\(V_{rel}\)), air density (\(\rho\)), and dynamic viscosity (\(\mu\)). In order to satisfy the objectives of this research for validating a wind tunnel propeller dynamometer for Group 2 UAS, testing was conducted at low Reynolds numbers.  
\[Re=\frac{\rho cV_{rel}}{\mu} \tag{1}\]  
Propeller characteristics are typically cataloged as a function of the ratio between freestream and angular velocity to allow for translation to arbitrary operating speeds. This ratio is known as the advance ratio (\(J\)), and is shown symbolically in Equation (2), where \(V\) is freestream velocity, \(n\) is the rotational speed in revolutions per second, and \(D\) is propeller diameter.  
\[J=\frac{V}{nD} \tag{2}\]  
Thrust coefficient, defined as shown in Equation (3), is a non-dimensional quantity that relates thrust produced (\(T\)) to the rotational velocity (\(n\)) and propeller diameter (\(D\)), where \(\rho\) is the density of the air the propeller is acting on.  
\[C_{T}=\frac{T}{\rho n^{2}D^{4}} \tag{3}\]Similarly, power and torque coefficients are non-dimensional quantities that relate power (\(P\)) and torque (\(Q\)), respectively, to the rotational velocity and propeller diameter, as in Equation (4).  
\[C_{p}=\frac{P}{\rho n^{3}D^{5}} \tag{4}\]  
\[C_{Q}=\frac{Q}{\rho n^{2}D^{5}} \tag{5}\]  
Finally, propeller efficiency (\(\eta_{p}\)) is the ratio of power transferred to the air by the propeller to the mechanical power required to turn the propeller, as shown in Equation (6).  
\[\eta_{p}=\frac{JC_{T}}{C_{p}} \tag{6}\]
# Aminated Covalent Organic Polymers for Anionic Dye Adsorption in Aqueous Systems
## 3 Results and Discussion
### Adsorption Kinetics  
Anionic dye adsorption kinetic experiments were conducted to evaluate the adsorption rate of the prepared ACOP (Figure 5). Three ACOP doses, 0.25, 0.5, and 1.0 g/L, were used in the kinetic adsorption experiments. As displayed in Figure 5, the experimental kinetic results did not fit well with the pseudo-first- and pseudo-second-order kinetics owing to rapid dye adsorption, even in 1 min (approx. 68-86% adsorption capacity after 180 min). The adsorption capacity of the ACOP for MO and OG increased gradually as a function of the dosing amount. The intraparticle diffusion model was applied in order to understand the adsorption mechanism of anionic dye molecules on ACOP. As shown in Figure S1, ACOP tended to follow a single-step adsorption process for MO and OG. This single-step rapid adsorption indicating the adsorption of MO and OG on ACOP was attributed to the high specific area of ACOP and its dispersibility in an aqueous system. These advantages of ACOP make it suitable for adsorbing dye molecules due to the high availability of active adsorption sites on the surface [56]. The intraparticle diffusion rate constant (\(K_{i}\)) increased gradually as the amount of ACOP increased (Table S1). Based on the kinetic results, ACOP tended to rapidly absorb both MO and OG, and the MO showed a higher adsorbed amount than that of OG, owing to hydrophobic interaction. The adsorption efficacy of ACOP for anionic dyes increased in proportion to its dosage, and the adsorption process might have proceeded as a single adsorption step.  
After the kinetic experiments, changes in the surface charge of the ACOP were investigated. As displayed in Figure S2, the characteristic positive surface charge of ACOP shifted toward the negative region, and the average zeta potentials of the MO- and OG-adsorbed ACOPs were \(-\)29.02 and \(-\)25.52 mV, respectively. These results indicated that the adsorption of negatively charged dyes on the ACOP altered the surface charge of ACOP, and this is consistent with the higher adsorption capacity of ACOP for MO than for OG (Figure 5).  
Figure 4: Point of zero charge (PZC) of the prepared ACOP.
## 3 Results and Discussion
### Two-Dimensional Models
#### 3.1.3 Model 2D-TBC  
Figure 10 shows the difference in the z-direction displacement of Model-TBC with and without a crack. The observed behavior is the same as that of Model 2D and Model 2D-Anisotropy. Therefore, it is considered that cracks can be detected by using the difference in the top surface height of the materials with and without a crack, even in materials with a heat coating. In addition, the displacement of Model 2D-TBC is found to be slightly less than that of Model 2D. Figure 11 shows the \(z\)-direction strain distribution of Model 2D-TBC. It is found that the part where the compressive strain in the \(z\)-direction is large is slightly smaller compared to Figure 7. This may be because the applied load was the same as the other models, and the applied stress was reduced by the thickness of the coating.  
Figure 10: The difference in the \(z\)-displacement between the models with and without a crack of Model 2D-TBC.  
Figure 9: The strain distribution (\(\varepsilon_{z}\)) of Model 2D-Anisotropy, \(a=0,\,0.5,\,1.0,\,1.5\,\mathrm{mm}\).
# Design and Implementation of a Two-Wheeled Self-Balancing Car Using a Fuzzy Kalman Filter
## 3 Fuzzy Kalman Filtering Algorithm Design  
In optimal conditions, when a dual-wheel self-balancing car operates under normal conditions, and with known system and measurement noise parameters, fixed complementary and Kalman filters effectively mitigate measurement errors. However, in scenarios where the vehicle's measurement sensors deviate due to environmental disturbances or when external noise is uncertain, relying solely on a Kalman filter with predetermined noise parameters may fail to accurately ascertain the vehicle's genuine angle and velocity information. This paper proposes a fusion of fuzzy control theory and the Kalman filtering method, leveraging information derived from observation data to continuously estimate and rectify the model and noise statistical characteristics in real time. Such an approach promises to enhance filtering performance and bolster the stability of the vehicle.  
To compare with other filtering algorithms, this section first introduces the fuzzy Kalman filter algorithm proposed by the author, and then briefly introduces the classical Kalman filtering theory without fuzzy theory and the widely used complementary filtering theory.
# Automated Design of CubeSats using Evolutionary Algorithm for Trade Space Selection
## 5 Results
Figure 12: Rendering of the evolution of the design of the CubeSat done in MATLAB VRML. Each figure shows the best design among the population in each generation: (**a**) generation 1; (**b**) generation 12; (**c**) generation 15; (**d**) generation 20; (**e**) generation 30; (**f**) generation 80; (**g**) generation 100; (**h**) generation 150. Finally, we present a comparative analysis between three stochastic algorithms, evolutionary algorithm (EA), particle swarm optimization (PSO) [37], and simulated annealing (SA) [38], in terms of performance on the CubeSat design MMKP problem. The theory behind the application of the EA to our design problem has already been discussed in the preceding sections. We now provide a brief explanation on how PSO and SA are implemented.
## 3 Results and Discussion
### Water Infiltration and Application of the BEST Algorithm
These results imply that macropore flow is likely prominent in the topsoil, facilitating rapid drainage of excess water towards the underlying limestone layer. Furthermore, the observed funneling infiltration mechanism through the restrictive limestone layer (Figure 2) and the contrast in hydraulic conductivity between the two layers (Figure 3) imply that ponding on the surface and the generation of overland flow primarily result from a saturation-excess mechanism [54]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & **N** & **Min** & **Max** & **Mean** & **CV** \\ \hline \(n\) [\(-\)] & 5 & 2.116 & 2.139 & 2.127 & 0.5 \\ \(m\) [\(-\)] & 5 & 0.055 & 0.065 & 0.060 & 7.6 \\ \(K_{-30}\) [mm h\({}^{-1}\)] & 3 & 33.3 & 70.1 & 53.8 & 34.9 \\ \({}^{\dagger}K_{\mathrm{s}}\) [mm h\({}^{-1}\)] & 9 & 192.2 & 1031.0 & 424.4 & 63.8 \\ \(\alpha\) [mm\({}^{-1}\)] & 9 & 0.060 & 0.244 & 0.117 & 54.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Sample size (N), minimum (min), maximum (max), mean, and coefficient of variation (CV, %) of the BEST and TI estimations. Figure 3: Example of the procedure adopted for detecting flow impedance owing to the hydraulic resistance exerted by the underlying limestone layer. (**a**): Entire cumulative infiltration curve [\(I(t)\) vs. \(t\)]. (**b**): Data linearized according to the cumulative linearization ([PERSON], [PERSON] and [PERSON], 1976) method (\(I\surd t\) vs. \(\surd t\)). The abscissa (\(\surd t\)) of the intersection point of the two straight lines splits the infiltration data into two subsets. (**c**): Cumulative infiltration data representative of the first stage when water infiltrates into the upper layer.
# Can Blockchain Strengthen the Energy Internet?
## 2 Energy Sustainability through Heterogeneity
### Role of Blockchain  
Smart contracts can be utilized to ensure seamless integration of DERs through invocation of certificates and revocation of them upon request. Blockchain establishes a trusted environment in a decentralized marketplace, which provides confidence for the prosumers to engage in P2P energy trading while avoiding the risk of non-repudiation and double spending [12]. Microgrid implementations such as Brooklyn microgrid [26] and Power Ledger [27] in Australia could be identified as promising, decentralized blockchain-based solutions facilitating P2P trading. Furthermore, Share & Charge in Germany and Juice Net of North America are milestone projects in decentralized energy markets for P2P EV charging [13]. Inherent cryptographic encryption of blockchain would enable user authentication without third-party intervention. Blockchain could further function as a secure and reliable data communication link and storage platform for efficient management of large data sets associated with these diverse EI applications. Energy sustainability is assured with transparent exchange of heterogeneous forms of energy [12].  
However, mitigation of transient overvoltages and issues related to interoperability, which arise while the implementation of heterogeneous EI grids cannot be facilitated through a blockchain platform itself. Adequate standards need to be implemented with the intention of sustaining desirable grid operations.
## 4 Robot Localization Using Condition-Invariant Features  
After training the model, the encoding part of the proposed structure can be used to extract the condition-invariant feature \(\mathbf{z}_{p}\) from the image. If there are two image sequences \({}^{u}\mathbf{X}=\{{}^{u}\mathbf{x}^{(1)},{}^{u}\mathbf{x}^{(2)},...,{}^{u} \mathbf{x}^{(M)}\}\) and \({}^{v}\mathbf{X}=\{{}^{v}\mathbf{x}^{(1)},{}^{v}\mathbf{x}^{(2)},...,{}^{v} \mathbf{x}^{(N)}\}\) from different environments \(u\) and \(v\), we can extract feature sequence \({}^{u}\mathbf{Z}=\{{}^{u}\mathbf{z}_{p}^{(1)},{}^{u}\mathbf{z}_{p}^{(2)},...,{} ^{u}\mathbf{z}_{p}^{(M)}\}\) and \({}^{v}\mathbf{Z}=\{{}^{v}\mathbf{z}_{p}^{(1)},{}^{v}\mathbf{z}_{p}^{(2)},...,{} ^{v}\mathbf{z}_{p}^{(N)}\}\), respectively. Then, the similarity matrix \(S\in\mathbb{R}^{M\times N}\) can be  
Figure 5: The structure of the CP-VAE for feature extraction in changing environments.  
constructed from the affinity score between features. The component of the \(S\) is the affinity score \(s_{ij}\) between \({}^{u}\mathbf{x}_{p}^{(i)}\) and \({}^{v}\mathbf{x}_{p}^{(j)}\), where \(1\leq i\leq M\) and \(1\leq j\leq N\). It is calculated using the cosine similarity as follows:  
\[s_{ij}=\frac{{}^{u}\mathbf{z}_{p}^{(i)}\cdot{}^{v}\mathbf{z}_{p}^{(j)}}{\|{}^{u}\mathbf{z}_ {p}^{(i)}\|{}^{v}\mathbf{z}_{p}^{(j)}\|} \tag{6}\]  
The affinity score \(s_{ij}\) has a value between \([0,1]\), and the closer it is to 1, the higher the probability of the same place. From the similarity matrix \(S\), we can find the correspondence between the query sequence \({}^{v}\mathbf{X}\) and the database sequence \({}^{u}\mathbf{X}\), and the location of the mobile robot can be successfully recognized.
_Applied_  
_sciences_  
_Article_  
**Numerical Analysis of the Localization of Pulmonary Nodules during Thoracoscopic Surgery by Ultra-Wideband**  
**Radio Technology**  
**[PERSON] \({}^{1,\star}\)\({}^{\copyright}\), [PERSON] \({}^{2}\)\({}^{\copyright}\) and [PERSON] \({}^{1}\)\({}^{\copyright}\)**  
\({}^{1}\) Institute of Technical Medicine (TheM), Furtwangen University (HFU), Jakob-Kienzle-Strasse 17,  
78054 Villingen-Schwenningen, Germany; [PERSON]SS]  
\({}^{2}\) Institute of Medical Device Technology, University of Stuttgart, Pfaffenwaldring 9, 70569 Stuttgart, Germany; [EMAIL_ADDRESS]  
\({}^{\star}\) Correspondence: [PERSON]**Abstract: Worldwide, lung cancer is one of the most common causes of cancer-related death. Detected by computer tomography, it is usually removed through thoracoscopic surgery. During the surgery the lung collapses requiring some strategies to track or localize the new position of the lesion. This is particularly challenging in the case of minimally invasive surgeries when mechanical palpation is not possible. Here we undertake a preliminary study with numerical analysis of an ultra-wideband (UWB) radio technology which can be employed directly during thoracoscopic surgery to localize deep solitary pulmonary nodules. This study was conducted through Finite Difference Time Domain (FDTD) simulations, where a spherical target mimicking a nodule located between 1 and 6 cm of depth and an UWB pulse at several frequencies between 0.5 and 5 GHz was used for localization. This investigation quantifies the influence of several parameters, such frequency, lesion depth, and number of acquisitions, on the final confocal image used to locate a cancer in the lung tissue. We also provide extensive discussion on several artifacts that appear in the images. The results show that the cancer localization was possible at operational frequencies below 1 GHz and for deep nodules (\(>\)5 cm), while at lower depths and higher frequencies several artifacts hindered its detection.**  
**Keywords: lung cancer; solitary pulmonary nodule (SPN); microwave imaging; ultra-wideband (UWB); finite difference time domain (FDTD)**
# Minimally-Invasive Neural Interface for Distributed Wireless Electrocorticogram Recording Systems
## 3 Integrated Circuits
### Intra-Skin Communication for Modular Expandable Systems  
Up to date, several attempts have been made to send and receive electrical signals over the human body due to their low-power operation [31; 32]. The body was treated as a transmission medium: a simple conductor, a dielectric material (capacitive coupling), or a waveguide (Galvanic coupling). We decided to utilize a Galvanic coupling since the high-frequency electromagnetic waves can propagate through the body from the transmitting terminals without external wires and the surrounding environment does not significantly affect the transmission quality [31]. There are mainly two safety issues when the current is injected into the human body: (1) The maximum allowable current (300 mA) and voltage (500 V in open circuit conditions) should be limited by the Section 51.104 of the IEC 601-2-10 standard [33], and (2) the charge-injection should be balanced because the injected charge may cause the charge accumulation inside the body and result in harmful effects such as pH shift and ionic charge induction near the implanted electrodes which may damage the neural tissues.  
We prepared a phantom brain model and conducted a few experiments with a custom PCB module which consists of V/I and I/V converters assembled using the off-the-shell components (AD620, Analog Devices Inc.) in order to set the design parameters. We found that the carrier frequency of \(<\)5 MHz can be well-balanced to provide a reasonable bit-error-rate (\(<\)10\({}^{-6}\)) and power consumption (\(\sim\)200 uW) for the ISCOM. The driver currents of \(<\)100 uA can guarantee to limit the target open circuit voltage. After the phantom brain experiments, we finalized the design of the on-chip low-power wireless ISCOM scheme as shown in Figure 11. We employed the Manchester coding technique, self-clocking, and biphasic balanced charge injection for the synchronization between the transmitter and receiver at low-power. The Manchester encoder can be easily implemented using an exclusive-OR gate with two inputs: data and clock. The encoded data is modulated by means of frequency shift keying (FSK) with a carrier frequency of 2-4 MHz. The modulated data is converted  
Figure 10: Schematic of the reference current and voltage generator.  
into a biphasic current by the ISCOM current driver which generates alternating currents to ensure no charge build-up inside the body. The output current polarity is determined by the current difference between the paths, \(P_{1}\) and \(P_{2}\), according to the modulated signal input. In addition, the proposed ISCOM cannot only transmit the data energy-effectively as a stand-alone system but also expand the recording coverage and sensing modalities for a distributed modular system by simply deploying multiple modules at different carrier frequencies.
## 2 Related Work
### Success Probability of the Concurrent Transmission  
We next derive the success probability of a transmission in the presence of other interfering transmissions. Suppose that there are \(N\) interfering transmitters around a tagged receiver, and that the distance between interfering Node \(i\) and the receiver is \(r_{i}\). The distance between the tagged transmitter and receiver is \(d\). Similar to (3), the interference by interfering Node \(i\), \(P_{i}\), is represented by:  
\[P_{i}=P_{0}10^{-\beta\log\left(\frac{r_{i}}{d_{0}}\right)+0.1X_{i}}=C\left( \frac{Y_{i}}{r_{i}^{\beta}}\right) \tag{4}\]where \(Y_{i}\) is a log-normal random variable with zero mean and standard deviation \(\sigma\). From (4), the cumulative interference \(P_{I}\) measured at the receiver amounts to:  
\[P_{I}=\sum_{i=1}^{N}P_{i}=\sum_{i=1}^{N}C\left(\frac{Y_{i}}{r_{i}^{\beta}}\right) \tag{5}\]  
From (3) and (5), we obtain the signal-to-interference ratio (SIR) at the tagged receiver as:  
\[\frac{1}{SIR}=\frac{P_{I}}{P_{r}}=\frac{\sum_{i=1}^{N}C\left(\frac{Y_{i}}{r_{i }^{\beta}}\right)}{C\left(\frac{Y_{r}}{d^{\beta}}\right)}=\sum_{i=1}^{N}\left( \frac{d}{r_{i}}\right)^{\beta}\left(\frac{Y_{i}}{Y_{r}}\right) \tag{6}\]  
In order to receive and decode the packet successfully, the SIR at the tagged receiver should be greater than a threshold \(T_{SIR}\). The probability of such an event is:  
\[P_{succ}=\Pr\{SIR>T_{SIR}\}=\Pr\left\{\frac{1}{Y_{r}}\sum_{i=1}^{N}\left( \frac{d}{r_{i}}\right)^{\beta}Y_{i}<\frac{1}{T_{SIR}}\right\} \tag{7}\]  
Let \(U_{i}=\left(d/r_{i}\right)^{\beta}Y_{i}\), which is a log-normal random variable with mean \(\mu_{i}=\beta\ln\left(d/r_{i}\right)\) and variance \(\sigma^{2}\). Furthermore, let \(V=\sum_{i=1}^{N}U_{i}\). Since \(U_{i}\)'s are independent, \(V\) can be approximated by a log-normal random variable \(W\) with the following mean \(\mu_{w}\) and variance \(\sigma_{w}^{2}\) according to the Fenton-Wilkinson approximation method [34]:  
\[\left\{\begin{array}{l}\mu_{w}=\log(\sum_{i=1}^{N}e^{\mu_{i}})+\sigma^{2}- \frac{\sigma_{w}^{2}}{2}\\ \sigma_{w}^{2}=\log\left[(e^{2\sigma^{2}}-1)\frac{\sum_{i=1}^{N}e^{2\mu_{i}} }{(\sum_{i=1}^{N}e^{\mu_{i}})^{2}}+1\right]\end{array}\right. \tag{8}\]  
Finally, \((W/Y_{r})\) is a log-normal random variable with mean \(\mu_{w}\) and variance \((\sigma_{w}^{2}+\sigma^{2})\).  
By utilizing the _logistic distribution_ for the cumulative distribution function (CDF) of the log-normal distribution [35], \(F(x;\mu,\sigma)=\left[\left(\frac{e^{\mu}}{x}\right)^{\pi/(\sigma\sqrt{3})}+1 \right]^{-1}\), we finally obtain the success probability of the transmission as:  
\[P_{succ}=\left[(T_{SIR}e^{\mu_{w}})^{\frac{\pi}{\sqrt{3(\sigma_{w}^{2}+\sigma ^{2})}}}+1\right]^{-1} \tag{9}\]
## 2 Materials and Methods  
### EIT System Structure  
The composition of the FPGA-based EIT system is shown in Figure 1. Firstly, the excitation electrical signal is applied to the electrode array on the sensor to obtain the electrical parameters of the tested field. The response electrical signal is measured through a data acquisition system and sent to the data processing system of the upper computer. The upper computer calculates and analyzes the measurement data through software algorithms, thereby achieving the estimation of key flow parameters within the measured field and achieving the integration of concentration and flow measurement.  
Figure 1: EIT system composition.  
According to the measuring process, the workflow of the EIT acquisition process in this study is shown in Table 1.  
The existing EIT system has a collection speed of 60 fps, an SNR of 52 dB, and a system signal frequency of 80 kHz [14]. Data acquisition is the core composition of any EIT system. Its performance directly determines the detection quality in dredging engineering.