esa-sceva/satcom-chunk-collection huggingface dataset:
# Approach-Based Analysis on Wireless Power Transmission for Bio-Implantable Devices
## 3 Analysis of Various WPT Approaches
### WPT Systems Based on Power Control and Regulation Improvement
This convertor aims the requirements of a neural stimulator implant for the purpose of retinal reconstructive surgeries provided by an inductive link-based WPT. The SCC can dynamically self-reconstruct and self-adjust over the range of 1.3 V to 3.3 V input potential, attaining the required 5 V potential output with 57-74% of efficiency. Here, the Absolute switch size helps to maximize overall reconfigurable SCC efficiency. [PERSON] et al. [37] proposed a voltage resonance mode receiver (RVMRx) to charge the battery at receiver. An inductor-capacitor tank circuit at the receiver has been configured and separated from the obtained output by a resonance interleaving approach, resulting in optimum transfer of power without depending on the phase of operation. Therefore, the efficiency of energy transfer is insensitive to the charging conditions such as the variation of the battery voltage. A scheme combines of MDTL (minimum diode time tracking loop) and ODTL (optimum duty tracking loop) lead to energy conversion efficiently through adaptive operating state regulation. The RVMRx prototype module in a CMOS process of 0.18 \(\upmu\)m can function at a 13.56 MHz great resonant frequency with an RX coil of smaller size (7 mm \(\times\) 7 mm), while reaching a maximum efficiency in power received by the receiver of around 67.8% which is for charging of the battery. In addition to this, a new technique was developed for WPT [38] based on the duty cycle of the signal. Instead of sending waves continuously, they only send for a short particular time and then pause for some time. This allows increasing transmit power during active periods while maintaining average transmit power and specific absorption rate (SAR). The input voltage to the rectifier is then significantly increased, allowing the rectifier to rectify the input with a higher PCE, improving PTE.
## 2 Microalgae-Based Wastewater Treatment
### Removal of Medicinal/Pharmaceutical Substances
The efficiency of the removal of active pharmaceutical ingredients (APIs) depends on the type of microalgae, culture method, operational parameters, and physicochemical characteristics of the active substance, and varies from zero to complete removal [26]. In the study of [PERSON] et al. [26], eight green microalgae were selected from the collection of local (Swedish) microalgae grown in municipal waste, which showed excellent performance in nutrient removal and biomass production, and were compared to _S. obliquus_ RISE in their ability to remove APIs. The vaccines were prepared in 100 mL conical bottles with Bold's Basal Medium as the growth medium, and the time it took to develop them was from 7 to 9 days. Each crop was then inoculated in a photobioreactor with an initial biomass concentration of about 100 mg/L and an artificial growth medium. Each experiment lasted 12 days in the presence and absence of APIs. The photobioreactor used for the experiments was laboratory-constructed, with a flat usable volume of 1.2 L, illumination by LED lamps in the pattern of day and night every 12 h, air supply with 3% CO\({}_{2}\), and temperature regulation at 20 \({}^{\circ}\)C and pH at 7.2 \(\pm\) 0.5. The experiments were conducted on 19 APIs and indicatively showed that water-soluble active substances, such as caffeine, carbamazepine, oxazepam, tramadol, flucanazole, codeine, and trimetoprim, were persistent, as 60% of their initial amounts remained after 12 days in the microalgae cultures, while the strongly lipophilic Bieriden, Trihexyphenidyl, Clomipramine, and Amitriptyline accumulated in the biomass (88%), especially in the case of microalgae that showed increased biomass productivity. In particular, Chlorella vulgaris 13-1 and _Chlorella saccharophilia_ RNY had high biomass production (2.6 and 5.4 g/L on a dry basis, respectively) and the best removal yields for all 19 APIs after 12 days. It was found that the removal of APIs is strongly dependent on the microalgae species, while the total lipid content ranged from 13 to 30% on dry biomass, which was also dependent on each species. lipophilic substances were removed from the culture medium quickly and efficiently but accumulated in biomass more than polar substances. Only two of the eight microalgae were unable to respond satisfactorily, while all others were equal to or better in terms of efficacy than _Scenedesmus obliquus_ RISE, the reference strain [26].
# Building Scalable and Secure Multicast Delivery Infrastructure in a Local Area Network
###### Abstract  
Internet Protocol (IP) multicasting is a method for one-to-many and many-to-many communication between hosts in an IP network. This communication happens in a real-time synchronous fashion. It is a useful mechanism for distributing management data in a Local Area Network (LAN). Management data includes frequent updating of host Operating System (OS), security patches, OS update for network hardware, new configuration updates, etc. In the absence of any admission control or a source identification, any host with malicious intent can disseminate malicious codes or rootkits exploiting the underlying multicast framework. Routing protocols like RIPv2 and OSPF use a certain form of authentication to exchange routing information with their peer routers. However, their authentication and the distribution of routing information in its present form has several security and performance-related issues. Motivated through these problems, in this paper, we propose an efficient and scalable multicast architecture for distributing management and routing information in a LAN. We use Core-based Tree (CBT) for constructing the multicast delivery tree and the pseudo identity-based encryption of the underlying cryptosystem. We also demonstrate that our proposed multicast architecture is immune to a number of popular attacks.  
multicast; pseudo-identity based encryption; core based tree; secure routing Article
# Hybrid Oversampling and Undersampling Method (HOUM) via Safe-Level SMOTE and Support Vector Machine
## 8 Conclusions  
Imbalanced datasets were examined within the scope of the present study. The literature review revealed the importance of augmenting minority-class samples. It also highlighted the challenges created by reducing valuable data from the majority class. To facilitate comparison with the existing literature, the frequently utilized G-mean criterion was chosen as the performance evaluation metric. In this study, the majority-class data that may not provide information in the area distant to the decision boundary found by the SVM were reduced to balance the classes. If a balanced class was not achieved, the data of the minority classes were increased using SLS. These processes were cycled repeatedly until a balanced class was reached. Once the balanced dataset was achieved, some well-known classification algorithms (KNN, RF, SVM, and ANN) using datasets from similar studies in the literature were required in order to validate the developed methodology. The proposed HOUM algorithm was compared with the SMOTE, SMOTEENN, and SMOTETomek algorithms and the original dataset for validation. In five of eight datasets, variations of the HOUM obtained better performance metrics. The HOUM was also compared with two methodologies, W-SIMO and RusAda, utilizing common datasets, and the successful results validated the algorithm's performance. The aim of the DSS project, conducted in the weaving industry, was to bridge academic research with a real production environment. The purpose of the aforementioned project was to prevent yarn breakage during the weaving process on looms. One process was selected to achieve the objective, and the most important incoming material control parameters and production parameters were automatically collected; these data are called the TexYarn dataset. When the HOUM was applied to this dataset, nearly all yarns in the test set could be detected.  
[PERSON] developed and coded the algorithms introduced in the study. [PERSON] supervised the project and made important contributions to the study, the algorithms, and the manuscript. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.
# An FPGA-Based Four-Channel 128k-Point FFT Processor Suitable for Spaceborne SAR
## 1 Introduction
The remainder of this paper is arranged as follows: in Section 2, we analyze the proportion of FFT in the chirp-scaling (CS) algorithm and introduce the principle and implementation details of the radix-23 and the mixed-radix algorithms; in Section 3, we introduce the hardware structure of the FFT processor, the radix-23 SDF pipelined structure, the fixed-point word length configuration scheme, and we propose a single-channel, four-channel FFT structure; the experiments and results are shown in Section 4; and finally, our conclusions are presented in Section 5.
## 3 Results and Discussion
### Catalytic Application in the Solvent-Free Friedel-Crafts Alkylation Reaction
#### 3.2.6 Catalyst Recycling and Stability
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Catalyst** & \begin{tabular}{c} **Reaction** \\ **Temperature (\({}^{\circ}\)C)** \\ \end{tabular} & **Reaction Time (h)** & **Catalyst Amount** & \
\begin{tabular}{c} **Molar Ratio of** \\ **Reactants** \\ \end{tabular} &
\begin{tabular}{c} **Re-Used Cycle** \\ **Number** \\ \end{tabular} & **Product Yield (\%)** & **Reference** \\ \hline
5c \({}^{\circ}\)a & 80 & 0.5 & 1 mole \% & 6:1 & 6 & 95.0 & This work \\ AIC3 & 130 & 1 & 2 wt \% & 6:1 & n.r.4 & 96.5 & 52 \\ InCl3 & 50 & 16 & 10 mole \% & 10:1 & n.r. & 75.0 & 53 \\ PrimmCl-FcCl3-MCM-41 & 80 & 0.75 & 15 wt \% & 10:1 & n.r. & 100.0 & 11 \\ MZ2-T1 & 75 & 24 & 1 wt \% & 10:1 & n.r. & 92.3 & 54 \\ (HS0-primm[OT]) & 40 & 0.5 & 20 mole \% & 2:1 & 5 & 93-97 & 29 \\ [PyroJ][H(CF2)\({}_{0}\)(0\({}^{\circ}\)(F2)\({}_{2}\))SO\({}_{3}\)] & 50 & 24 & 2 mL & 2:1 & n.r. & 90.0 & 30 \\ [EMMM]-AlCl3 [b] & 20 & 1 & n.r. & n.r. & n.r. & 89.0 & 15 \\ [EMMM][B[TB3]\({}_{0}\)(4)] & 50 & 1 & 5 mole \% & 5:1 & n.r. & 51.0 & 16 \\ Bi(O7)\({}_{2}\)[EMMM][TB3]\({}_{2}\)] & 150 & 3 & 5 mole \% & 2:1 & 4 & 91.0 & 17 \\ Cu(O7)\({}_{2}\)[MMMM][BF4] & 80 & 24 & 5 mole \% & 5:1 & n.r. & 81.0 & 18 \\ Sc(O7)\({}_{2}\)[MMMM][SB6]\({}_{2}\)] & 20 & 12 & 20 mole \% & 2:1 & n.r. & 92.0 & 19 \\ [BMIMM]-ClCl3 [b]\({}_{c}\)] & 80 & 2 & 1 mL & 10:1 & 1 & 100.0 & 47 \\ [BMIMM]-Cl-FcCl3 [b]\({}_{c}\)] & 80 & 2 & 1 mL & 10:1 & 3 & 97.1 & 47,48 \\ [BMIMM]-FcCl3 [b]\({}_{c}\)] & 80 & 2 & 1 mL & 10:1 & 2 & 99.4 & 47 \\ [BMIMM]-ZnCl2 [b] & 80 & 2 & 1 mL & 10:1 & 8 & 98.6 & 47 \\ \hline \hline \end{tabular}  
* Solvent-free condition in this study. \({}^{\circ}\)Molar fraction of metal chloride salt must be more than 0.67. \({}^{\circ}\)The product yield has been drastically reduced in the net recyclable test because of the loss of metal chloride salt in each run. \({}^{\circ}\)n.r. not reported.  
\end{table} 
Table 2: Comparison of reaction conditions between B-L MIL 5c and previously reported catalysts.
# Physical Side-Channel Attacks on Embedded Neural Networks: A Survey
###### Abstract  
During the last decade, Deep Neural Networks (DNN) have progressively been integrated on all types of platforms, from data centers to embedded systems including low-power processors and, recently, FPGAs. Neural Networks (NN) are expected to become ubiquitous in IoT systems by transforming all sorts of real-world applications, including applications in the safety-critical and security-sensitive domains. However, the underlying hardware security vulnerabilities of embedded NN implementations remain unaddressed. In particular, embedded DNN implementations are vulnerable to Side-Channel Analysis (SCA) attacks, which are especially important in the IoT and edge computing contexts where an attacker can usually gain physical access to the targeted device. A research field has therefore emerged and is rapidly growing in terms of the use of SCA including timing, electromagnetic attacks and power attacks to target NN embedded implementations. Since 2018, research papers have shown that SCA enables an attacker to recover inference models architectures and parameters, to expose industrial IP and endangers data confidentiality and privacy. Without a complete review of this emerging field in the literature so far, this paper surveys state-of-the-art physical SCA attacks relative to the implementation of embedded DNNs on micro-controllers and FPGAs in order to provide a thorough analysis on the current landscape. It provides a taxonomy and a detailed classification of current attacks. It first discusses mitigation techniques and then provides insights for future research leads.  
physical side-channel attacks; Side-Channel Analysis; hardware security; machine learning; Embedded Neural Networks; Deep Neural Networks +
Footnote †: journal: 25 June 2021  
11 Univ Nantes, CNRS, IETR UMR 6164, F-44000 Nantes, France 1  
2 CentraleSupelec, CNRS, IETR UMR 6164, F-35576 Cesson-Sevigne, France; [EMAIL_ADDRESS] 2
## 3 Results and Discussion
### Range Analysis and Analysis of Variance
#### 3.3.1 Range Analysis and ANOVA of Compressive Strength
This correlation led to the conclusion that although the void ratio was an influencing factor, the void ratio alone was relatively insufficient to predict the compressive strength of the LPC. In fact, the influence of aggregate size on the compressive strength of PC shows great differences in the literature. [PERSON] et al. [73] studied the compressive strength of permeable concrete prepared with eight different aggregate sizes, but their porosity was 20%. Their results showed that the compressive strength of PC with an aggregate size between 2.36 and 4.76 mm was the lowest, while the compressive strength of PC with an aggregate size between 10 and 15 mm was the highest. A study by [PERSON] et al. [74] showed that under constant porosity, the compressive strength of PC with 60% 8-16 mm and 30% 4-8 mm aggregates was 21% higher than that of PC with 60% 4-8 mm and 30% 8-16 mm aggregates. [PERSON] and [PERSON] [75] tested PC specimens made of single-sized aggregates (i.e., 2.38, 4.76, and 9.51 mm). Their results showed that due to the larger number of pores, smaller aggregate sizes lead to lower strength. However, some studies showed the opposite results [76,77], that is, the use of smaller aggregate sizes leads to a decrease in porosity and an increase in compressive strength. It can be seen from the above that the influence of aggregate size on the compressive strength of PC is still inconclusive in the literature, mainly due to its influence on porosity, which is negatively correlated with strength [78]. On the other hand, the ANOVA and \(F\) test results of the compressive strength in Table 14 confirmed that the lightweight coarse aggregate size was the most important factor that affected the compressive strength of the LPC. The contribution percentages of these factors were coarse aggregate size (69.63%), matrix type (26.53%), and fine aggregate content (3.84%). From the above results of the S/N ratio and analysis of variance analyses, the optimal combination level of the experimental control factors for achieving maximum compressive strength was A\({}_{2}\)B\({}_{3}\)C\({}_{3}\)D\({}_{2}\), i.e., the coarse aggregate size at level 2, fine aggregate content at level 3, matrix type at level 3, and aggregate to binder ratio at level 2.
## 3 Results and Discussion
### Effects of Reynolds Number and Surface Roughness on Flow Characteristics  
The flow characteristics will be discussed in this subsection to analyze the reasons that \(C_{\text{f}}\) is influenced by \(Re\) and \(Ra\). Figure 9 shows the streamlines and velocity magnitude at the meridional plane with \(Re\) of \(10^{2}\), \(10^{4}\), and \(10^{7}\) for \(Ra\) = 0.8-6.3 \(\upmu\)m. It is worth mentioning that since the flow was highly similar at the axial direction in the whole gap, Figure 9 only shows the contours near one end wall.  
When \(Re=10^{2}\), it can be seen that the streamlines were considerably similar than when \(Ra\) ranged from 0.8 to 6.3 \(\upmu\)m, which further validated that \(Ra\) does not influence the flow in the gap and leads to \(C_{\text{f}}\) being highly close for different values of \(Ra\), as shown in Figures 7 and 8. Moreover, except for the vortex near the end wall, which is due to the combined effect of the boundary layer of all walls, including the two end walls, the flow in whole gap was laminar and no vortexes occurred. Under \(Re=10^{4}\) and \(Re=10^{7}\) conditions, it can be seen that the flow transitioned from laminar flow to TC flow and periodic Taylor vortexes appeared, the number of which increased with \(Ra\), resulting in increased \(C_{\text{f}}\), as described in Figure 7. Additionally, when \(Re\geq 10^{4}\), the size of the Taylor vortexes increased as \(Re\) increased. This is because a higher \(Re\) reflected a higher rotational speed and greater centrifugal force was applied to the fluid, leading to more complex and unstable flow.  
In addition, it was found that the velocity was higher near the inner walls under higher \(Re\) conditions, and it decreased to zero near the outer walls along the radial direction. The reason is that a higher \(Re\) indicated a higher rotational speed, combined with the no-slip wall conditions, which resulted in the fluid velocity near the walls being the same as the linear velocity of the walls. To analyze the velocity distribution in the gap in more detail, Figure 10 presents the velocity in the radial direction with \(Re=10^{4}\) and \(Ra=0.8\)-6.3 \(\upmu\)m.  
According to Figure 10, the velocity was divided into three regions. Moreover, the fluid velocity in the speed-stable region that increased with \(Ra\) was approximately half of the inner walls' linear speed, which was consistent with the velocity distribution shown in Figure 9. In addition, the velocity gradient in the speed-drop regions, which also increased with \(Ra\), was significantly larger than that in the speed-stable region. These results indicated that \(Ra\) influenced the thickness of the boundary layer and flow near the walls, but the flow in the center of the gap was more stable.  
Figure 9: Streamlines and velocity magnitude at meridional plane with \(Re\) of \(10^{2}\), \(10^{4}\), and \(10^{7}\) for \(Ra\), \(Ra\) = 0.8–6.3 \(\upmu\)m.
# SOC Estimation with an Adaptive Unscented Kalman Filter Based on Model Parameter Optimization
## 2 Establishment of Battery Model and Parameter Identification
### Identification and Verification of Battery Model Parameters
#### 2.2.2 Model Parameter Identification  
Figure 6 is a schematic diagram of the terminal voltage response curve at the end of lithium battery discharge.  
\((V_{1}-V_{0})\) is the process in which the voltage drop generated on the ohmic resistance inside the battery disappears at the end of discharge. Thus, the ohmic resistance of the battery can be obtained as \(R=\frac{V_{1}-V_{0}}{I}\). The battery model adopted in this paper simulates the polarization process of the battery by superposing two RC links. An RC parallel circuit with a small time constant is used to simulate the process of rapid voltage change \((V_{2}-V_{1})\) when the current of the battery changes abruptly. An RC parallel circuit with a large time constant is used to simulate the process of slow voltage change \((V_{3}-V_{2})\) when the current of the battery changes abruptly.  
The battery is assumed to discharge for a period of time during \((t_{0}-t_{r})\) and then stay in a resting state for the remaining time. Here, \(t_{0}\), \(t_{d}\), and \(t_{r}\) are the discharge start time, the discharge stop time and the static stop time, respectively. In this process, the RC network voltages are:  
\[U_{s}=\left\{\begin{array}{ll}R_{s}i(t)\bigg{[}1-e^{-\frac{-(t-t_{0})}{t_{s}} }\bigg{]}&t_{0}<t\leq t_{d}\\ U_{s}(t_{d})e^{-\frac{-(t-t_{d})}{t_{s}}}&t_{d}<t<t_{r}\end{array}\right. \tag{7}\]  
Figure 5: The calibration curve results.  
Figure 6: Terminal voltage response curve at the end of battery discharge.  
\[U_{p}=\left\{\begin{array}{ll}R_{p}i(t)\bigg{[}1-e^{\frac{-(t-t_{0})}{t_{p}}} \bigg{]}&t_{0}<t\leq t_{d}\\ U_{p}(t_{d})e^{\frac{-(t-t_{d})}{t_{p}}}&t_{d}<t<t_{r}\end{array}\right. \tag{8}\]  
Let \(\tau_{s}=R_{s}C_{s}\) and \(\tau_{p}=R_{p}C_{p}\) be the time constants of the two parallel circuits. The voltage change \((V_{3}-V_{1})\) is caused by the disappearance of the polarization effect of the battery. In this process, the battery voltage output is \(V=E-IR_{s}e^{\frac{-t}{t_{s}}}-IR_{p}e^{\frac{-t}{t_{p}}}\), which can be simply written as \(V=E-ae^{-ct}-be^{-dt}\). This form can be used to fit the coefficients of the double exponential terms with MATLAB. After calculating \(a\), \(b\), \(c\) and \(d\), according to \(R_{s}=\frac{a}{t}\), \(R_{p}=\frac{b}{t}\), \(C_{s}=\frac{1}{R_{s}c}\), and \(C_{p}=\frac{1}{R_{p}d}\), the values of \(R_{s},\ R_{p},\ C_{s},\ \text{and}\ C_{p}\) can be identified.
## 3 Design of Shaft Lining Model Parameters  
Based on the hoisting capacity of the construction equipment, each section of the drill shaft with a reasonable height was preliminarily designed to be no thicker than 800 mm. The ultimate thickness would be optimized based on the model test results obtained in this study. Thus, three thicknesses (600, 700, and 800 mm) were designed for the full-scale shaft lining, based on which simulation tests were conducted. Based on the raw material supply and field pumping conditions, three concrete strength grades (C60, C65, and C70) were used according to \"Code for design of concrete structures\" (GB50010-2010) [24]. Based on the machining conditions, three thicknesses (10, 16, and 20 mm) were designed for the inner steel plate, and two thicknesses (10 and 16 mm) were designed for the outer steel plate. The steel grade of the outer and inner plates, along with the anchor clamps, was Q345 according to \"Standard for design of steel structure\" (GB50017-2017) [25].  
Based on Formula (5), Table 1 summarizes the parameters of the model specimens determined for the model test, the parameters of the full-scale shaft lining, and the dimensions of the loading system for the shaft lining simulation test. Each specimen had an outer diameter of 925 mm and a height of 562.5 mm. Figure 1 shows the sizes of the shaft lining models. One U-shaped anchor was welded onto the inner steel plate surface for every 20 cm\({}^{2}\) (shown in Figure 2). The thickness and width of the steel plate for the anchor were 2 mm and 0.5 mm, respectively. The height and width of the anchor were half the thickness of the shaft lining model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Specimen** & **Specimen** & **Concrete** & **Inner Steel Plate** & **Outer Steel Plate** \\
**Number** & **Thickness (mm)** & **Strength Grade** & **Thickness (mm)** & **Thickness (mm)** \\ \hline ZJ1 & 56.1 & C60 & 0.93 & 0.93 \\ ZJ2 & 56.1 & C65 & 1.50 & 0.93 \\ ZJ3 & 56.1 & C70 & 1.86 & 1.50 \\ ZJ4 & 65.4 & C60 & 1.86 & 1.50 \\ ZJ5 & 65.4 & C65 & 0.93 & 0.93 \\ ZJ6 & 65.4 & C70 & 1.50 & 0.93 \\ ZJ7 & 74.8 & C60 & 1.50 & 0.93 \\ ZJ8 & 74.8 & C65 & 1.86 & 1.50 \\ ZJ9 & 74.8 & C70 & 0.93 & 0.93 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the shaft lining model specimens.