esa-sceva/satcom-chunk-collection huggingface dataset:
# A Reconfigurable Single-/Dual-Bandstop Filter with Controllable Equal-Ripple Performance
###### Abstract  
Bandstop filters (BSFs) have many applications in suppressing interference signals and shielding specific bands. Among them, reconfigurable BSFs that cover more frequency bands by switching modes have great research value. In this paper, a novel synthesis approach for compact reconfigurable BSFs with controllable equal-ripple performance is proposed. By switching the ON/OFF state of the positive intrinsic negative (PIN) diode, the proposed reconfigurable BSF can be switched between single-BSF (S-BSF) and dual-BSF (D-BSF). Based on the synthesis approach, the equal-ripple levels of stopband and passband for S-BSF and D-BSF can be independently controlled. To verify, the equal-ripple levels of stopband and passband for S-BSF state and D-BSF state are independently controlled in four groups of examples. Finally, a reconfigurable BSF with a miniaturized size is designed and fabricated. The fabricated reconfigurable BSF behaves with 15 dB and 10 dB return loss (RL) in two different passbands of the D-BSF state, and 25 dB bandpass RL and controllable stopband rejection (SR) for the S-BSF state. The measured results are in good agreement with the electromagnetic (EM) simulation results.  
microwave passive devices; reconfigurable bandstop filters; miniaturization of filters; PIN diodes; controllable equal-ripple level +
Footnote †: journal: Applied sciences
## 1 Introduction  
Tuberculosis (TB) as both an acute and chronic infectious disease is still unbeaten and represents an important social, medical, and biological challenge for the healthcare worldwide [1]. The infection caused by _Mycobacterium tuberculosis_ strains is most often located in the respiratory tract from which it is transmitted in the population by aerosols. At the same time, mycobacteria can be disseminated also to other organs. However, it is now well acknowledged that even in its primary location, the TB pathogen is present in different microenvironments and thus in various metabolic states, which substantially complicates the treatment [2]. According to the last Global Tuberculosis Report issued by the World Health Organization (WHO) [3], in 2018 the death rate was estimated to be about 1.45 million people. Among the estimated 10 million new cases of TB, 3.4% had multidrug-resistant TB or rifampicin-resistant TB (MDR/RR-TB). This number increased to 18% in previously treated cases, with the highest proportions (>50% in previously treated cases) in countries of the former Soviet Union. Although efficient TB treatment is available, only 7 million new cases were treated for the disease in 2018, while an estimated 3 million patients did not have access to quality care or were not reported, and only one in three people with drug-resistant TB accessed care [3].  
In addition to interventions in the social area and point-of-care management of TB, one of the key challenges in making the fight against TB more effective is to improve treatment options. The current chemotherapy for the drug-sensitive TB takes about six months and requires administration of four different medicines (isoniazid, rifampicin, ethambutol, pyrazinamide) to avoid development of drug resistance. The length of the treatment and its adverse effects result in bad compliance and thus it is imperative to find novel drugs and regimens that would be less demanding on patients [4].  
During recent years, three novel drugs against TB were approved. These are bedaquiline [5], delamamid [6] and pretomamid [7], which are currently undergoing several clinical trials aimed at treatment of the drug-resistant TB, as well as at shortening standard TB treatment [8]. The efforts and hope for better TB drugs are exemplified also by the pipeline of several new molecules, that are currently at different stages of clinical development. Among them there are inhibitors of protein synthesis from the oxazolidinone family (delpazolid, sutezolid, contezolid, TBI-223), inhibitor of leucyl-tRNA synthetase (GSK3036656), inhibitor of DNA gyrase B (SPR720), compounds interfering with the respiratory chain of a TB pathogen (telacebec, TBI-166), a cell wall inhibitor targeting MmpL3 transporter (SQ109) and four compounds targeting another cell wall target, DprE1. These are BTZ043, TBA-7371, OPC-167832, and macozinone (Figure 1) [4; 8]. In this review we will present the key findings towards the discovery and development of macozinone, an optimized benzothiazinone, which currently appears to be the most advanced DprE1 inhibitor.
## 2 Materials and Methods  
### Experimental Setup  
The experiment (variety: Lingliangyou 268) was conducted during 2016-2017 in the Agricultural Meteorological Experiment Station (118.70\({}^{\circ}\) E, 32.20\({}^{\circ}\) N) of Nanjing University of Information Science and Technology. The climate of the region where the station is located belongs to the subtropical monsoon climate, with an average multi-year precipitation of 1100.0 mm and an average multi-year temperature of 15.6 \({}^{\circ}\)C. The maximum temperature in this area is 39.7 \({}^{\circ}\)C, and the minimum temperature is \(-\)13.1 \({}^{\circ}\)C. The annual average sunshine hours in this station exceeds 1900 h, and the frost-free period is 237 days.  
Figure 1 shows the variations in meteorological elements during the rice season of 2016 and 2017, respectively. From May to September 2016, the total precipitation was 815.0 mm, of which the precipitation time was mainly concentrated in the first 10 days of July, and the total radiation and average temperature were 1202.8 MJ/m\({}^{2}\) and 24.6 \({}^{\circ}\)C, respectively. From May to September 2017, the total precipitation was 485.5 mm, and the total radiation and average temperature were 1472.2 MJ/m\({}^{2}\) and 26.7 \({}^{\circ}\)C, respectively.  
Two shading treatments (T1 and T2) were set up in the experiment. After transplanting, two different shading materials were used to shade the rice until harvest. Each treatment was repeated 3 times with a random block design. The area of each experimental plot was 3 m \(\times\) 3 m, and the interval between plots was 2 m. In each treatment, the shading material effectively covered 4 m\({}^{2}\) and was dynamically adjusted to keep 0.6 m above the canopy. The radiation transmittance rate and diffuse radiation for each treatment are shown in Table 1. The diffuse radiation and total radiation were measured by the solar radiation monitor SPN1 (SPN1-MS1, Dalta-T, Inc., Cambridge, UK). The diffuse radiation and total radiation of each treatment were repeatedly measured 10 times, and the measurement data were tested for significance with a least significant difference (LSD) test (\(p<0.05\)). The test results are outlined in Table 1. It is shown that, for the total radiation, CK was significantly different from T1 and T2, but there was no difference between T1 and T2, and for the diffuse radiation fraction, there were significant differences between CK, T1, and T2. Under these treatments, the rice experienced distinct radiation conditions, which helped to effectively simulate the growth of rice under an environment with changing radiation, as well as to determine the changes in LAI, SPAD, and hyperspectral reflectance characteristics.
## 3 Personalized Waiting Time
### Central Subsystem
#### 4.4.2 Attraction Reservation Management Module  
This module provides kernel handling to attraction reservation and booking ticket verification. The former handles attraction reservation or booking requests from the mobile app subsystem, while the latter verifies the validity of booking tickets.  
* Attraction Reservation Handling  
When receiving a booking-attraction request of a designated attraction from the mobile app subsystem, the central subsystem will proceed with the following steps. Figure 3 illustrates the detailed message flow chart between the mobile app subsystem and the central subsystem regarding attraction reservation.  
1. Draw out all bookable sessions of this attraction and the current bookable quotas of these sessions from the database.
2. Return the information drawn in Step 1 to the mobile app subsystem via a booking-attraction response.
3. When receiving the booking-session-amount request from the mobile app subsystem, the central subsystem will insert a new booking record of the designated attraction into the database and update related field(s) in the database.
4. Send a booking confirmation message to the mobile app subsystem.  
* Booking Ticket Verification  
When receiving a ticket verification request from the ticket-scanning subsystem, the central subsystem will proceed with the following steps. The detailed message flow chart between the ticket-scanning subsystem and the central subsystem is shown in Figure 4.  
1. Compare the data in the ticket verification request with the booking records in the database; if no corresponding record exists, the central subsystem will return a ticket verification response with answer \"invalid\" to the ticket-scanning subsystem and end the verification process; otherwise, go to Step 2.  
* Check if the tourist arrives during the appointed period (e.g., within 15 min before the reserved session starts); if not, the central subsystem will return a ticket verification response with answer \"invalid\" to the ticket-scanning subsystem and end the verification process; otherwise, go to Step 3.
* Recognize this ticket as valid, update related fields in the database, and return a ticket verification response with answer \"valid\" to the ticket-scanning subsystem.  
Figure 4: Message flow chart of verification of a booking ticket.  
Figure 3: Message flow chart of attraction reservation.
## 3 Materials and Methods  
The main objective of this study was to investigate whether the use of an application based on augmented reality is appropriate for learning to play basic chords on the guitar without the assistance of a teacher. Using the goal question metric (GQM) template [37], the specific objective can be defined as follows: _To evaluate the effects of using augmented reality on learning processes in guitar playing by comparing the time taken to learn seven basic major chords using a traditional methodology against the proposed methodology._ It was decided that participants would be selected from university students, and that they should have no prior experience or knowledge of how to play the guitar.  
The goal was to establish which methodology (traditional instructor led class vs. AR-assisted class) promoted improved learning of seven major chords on the guitar, based on the interpretation of symbols indicating finger placement [38].  
Study participants were randomly assigned to one of two groups: one group followed the AR-assisted methodology, while the other followed the traditional teacher-led methodology. In the self-taught group, participants were instructed to learn seven chords using the ad-hoc AR application designed for this study. Technical assistance was provided to explain to participants how the app worked before commencing self-taught lessons. In the teacher-led group, participants were instructed to learn the same seven chords following instructions provided by an expert guitar player. As with traditional music lessons, training was delivered one-on-one.  
In addition to comparing effects between groups, researchers also analyzed the effect of gender on learning. This decision was based on it being assessed by other studies [39] and the idea that it may influence findings given its importance in the development of cognitive abilities and skills [40; 41]. As such, the two independent variables taken into account in this study were \"methodology\" and \"gender\", and the dependent variable was the time taken by participants to perform the proposed tasks.  
Upon finishing the study, each participant from the group using the AR-assisted methodology completed a system usability scale (SUS) questionnaire to establish the quality of the application and measure the usability of the app.  
The following null hypotheses were defined:  
* Ho1: The time taken to learn the basic musical chords on the guitar does not depend on the methodology used.
* Ho2: The gender of participants does not affect the time taken. In contrast, the alternative hypotheses were defined as:
* Ha1: The methodology used does affect the time taken.
* Ha2: The gender of participants does affect the time taken.
# Time Evolution of CO2 Diffusivity of Carbonated Concrete
## 2 Modeling of CO\({}_{2}\) Diffusivity
### Microstructure Characterization of Cement Paste
#### 2.3.3 Pore Blocking Due to Moisture  
Diffusivity is significantly influenced by relative humidity in cementitious materials [17]. Because gas cannot penetrate through pore water, amount of capillary water should be reflected in estimating effective diffusivity. During the hydration process, capillary pores are gradually emptied until a thermodynamic equilibrium is reached. The effective pore volume (\(V_{p}^{eff}\)) of the gaseous inflow is equivalent to the volumetric fraction of vapor, which means the remaining air pore fraction depending on the degree of saturation (\(S_{r}\)) in system [18]:  
\[V_{p}^{eff}=V_{p}\cdot V_{g}=V_{p}(1-S_{r}) \tag{7}\]  
The pore system vs. degree of saturation is illustrated in Figure 3. The remaining amount of the pore water and volume of pore system were calculated with hydration process. The pore structure system of the material is filled with vapor and pore water. If concrete is assumed to be an infinite material, moisture from outdoor environment is less easy to mobilize and moisture evaporation due to external heat should be ignored. Thus, the amount of pore water can be regarded as the amount of capillary water.  
Pore system of cementitious materials consists of space filled with pore water (\(V_{w}\)) and remaining space with vapor (\(V_{g}\)):  
\[V_{p}=V_{w}+V_{g} \tag{8}\]  
Following capillary water consumed due to ongoing cement hydration [15], amount of pore water also can be expressed as:  
\[V_{w}(\alpha)\ =\ \frac{\rho_{\text{ce}}}{\rho_{w}\ +\ \rho_{\text{ce}}w/c}(w/c\ - 0.4\cdot\alpha)V_{cp} \tag{9}\]  
in which, \(\rho_{i}\): specific mass of \(i\), \(\alpha\): degree of hydration.  
Therefore, volumetric fraction of gas (\(V_{g}\)) vs. the volume of the cement paste (\(V_{cp}\)) can be expressed as:  
\[V_{g}(\alpha)=\frac{V_{p}-\frac{\rho_{\text{ce}}}{\rho_{w}+\rho_{\text{ce}}w/c }(w/c-0.4\,\alpha)\cdot V_{cp}}{V_{p}} \tag{10}\]  
Therefore, the CO\({}_{2}\) diffusivity can be divided into two diffusivities; (a) apparent diffusivity of the condition described in Figure 3a, ignoring the moisture and considering the total pores; (b) effective diffusivity of the condition described in Figure 3b, considering the effective pores through which moisture is excluded from the total pores and gas can penetrate. The calculation result was shown in Figure 4.  
Figure 3: Comparison of CO\({}_{2}\) apparent diffusivity and CO\({}_{2}\) effective diffusivity. (a) Apparent pore system; (b) Effective pore system.
# Utilizing Graphite Waste from the Acheson Furnace as Anode Material in Lithium-Ion Batteries
## 3 Results and Discussion
### Formatting of Mathematical Components  
\[2\times\mathrm{d}_{002}\mathrm{\ sin\ }\theta=\mathrm{n}\lambda, \tag{1}\]  
\(\mathrm{d}_{002}\) refers to the distance between adjacent graphene layers in graphite, which is calculated using Bragg's Law. This parameter is a critical indicator of the material's crystallinity and graphitization degree. The diffraction angle (\(\theta\)) is the angle at which X-rays are scattered by the crystal lattice, and it is used alongside the X-ray wavelength (\(\lambda\)) to determine \(\mathrm{d}_{002}\). The wavelength is a fixed property of the X-ray source, and \(\lambda\) is typically provided by the XRD instrument.  
\[\mathrm{d}_{002}=(\mathrm{n}\lambda)/(2\mathrm{\ sin\ }\theta), \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**OCV 1 (V)**} & \multirow{2}{*}{**ICE 2 (\%)**} & \multicolumn{4}{c}{**Capacity (mAh/g)**} \\ \cline{3-6}  & & & **1 st Charge** & **1 st Discharge** & **3 rd Discharge** \\ \hline SGW9 & 3.26 & 82.1 & 423.5 & 347.6 & 352.1 \\ SGW9@RCT & 3.20 & 85.1 & 403.6 & 343.5 & 348.9 \\ CAM & 3.29 & 93.9 & 372.1 & 349.3 & 348.3 \\ \hline \hline \end{tabular} \({}^{\mathrm{1}}\) Open circuit voltage; \({}^{\mathrm{2}}\) initial Coulombic efficiency.  
\end{table}
Table 4: Occup, ICE, and capacity results for LIB cells.  
Figure 8: First and third charge–discharge curves of prepared LIB cells: (**a,b**) SGW9, (**c,d**) SGW9@RCT, (**e,f**) CAM.  
\[\text{GD}=(0.3440-\text{d}_{002})/(0.3440-0.3354)\times 100(\%), \tag{3}\]  
The order of diffraction (n) indicates the diffraction plane being measured, with n = 1 commonly used for simplicity in XRD analysis. The graphitization degree (GD) quantifies the structural order of the graphite relative to ideal graphite and disordered carbon. This parameter is essential for understanding the material's suitability as an anode in LIBs, as higher graphitization correlates with better electrochemical performance.  
\[\text{ICE}=(\text{capacity \@1 st discharge})/(\text{capacity \@1 st charge})\times 100(\%), \tag{4}\]  
The initial Coulombic efficiency (ICE) represents the ratio of the discharge capacity to the charge capacity during the first charge-discharge cycle. Expressed as a percentage, this metric evaluates how efficiently lithium ions are stored and released, which is a critical measure of the anode material's practical performance. These parameters collectively provide a comprehensive understanding of the material's structural and functional characteristics.
## 2 Mathematical Model of the Horizontal System
### The Relationship Between the Horizontal Thrust and the Current of the Horizontal Control Coil  
Thrust and the Current of the Horizontal Control Coil Force sensors are respectively fixed on the front and rear sides of the MRC to measure a set of horizontal thrust values when the current of the horizontal control coils ranges from \(-\)2A to \(+\)2A. The relationship between the horizontal thrust and the current of the horizontal control coil is then plotted, as shown in Figure 3. In this paper, the forward thrust is defined as positive.  
In Figure 3, \(F_{rt}\) represents the thrust on the right-hand side of the MRC, and \(F_{lt}\) represents the thrust on the left-hand side. It can be observed from Figure 3a that the current of the horizontal control coils on both sides of the MRC is proportional to the thrust generated by the coils. However, due to the parallel installation and close proximity of Co1 and Co2, as well as Co3 and Co4, the mutual inductance coefficient between Co1 and Co2 is nearly 1, and the same is true for Co3 and Co4. As a result, when \(i_{\rm cr}\) changes from positive to negative, that is, during the process of \(i_{\rm c1}\) decreasing to zero, there is mutual  
Figure 3: (**a**) The relationship between the horizontal thrust and the current of the horizontal control coil. (**b**) Enlarged view of the center of the relationship between the horizontal thrust and the current of the horizontal control coil.  
inductance in Co2, and this mutual inductance does not disappear when \(i_{c1}\) reaches zero. During the process of \(i_{c2}\) gradually increasing, although the mutual inductance decreases, it still has an impact on the thrust. From Figure 3b, it can be seen that near the origin of the coordinate system, the thrust exhibits fluctuations. Given that the fluctuation range is very small, it can be disregarded.
# Recent Advances in Plasmonic Sensor-Based Fiber Optic Probes for Biological Applications
## 3 Resonance Conditions
### Surface Plasmon Resonance  
The resonant oscillation of the conduction electrons at the interface between the dielectric and metal stimulated by an electromagnetic wave is known as SPR, an underlying principle widely used in sensing applications nowadays. SPR can be excited by the photons or electrons [38]. It is known that SPs are excited by the evanescent waves under the resonance condition. When the propagation constant of an incident light is equal to that of the collective oscillation of surface electrons in the metal, their momenta can be matched, and hence resonance occurs. Thus, SPR is highly sensitive to variations in the refractive index (RI).
# Non Data-Aided SNR Estimation for UAV OFDM Systems
###### Abstract  
Signal-to-noise ratio (SNR) estimation is essential in the unmanned aerial vehicle (UAV) orthogonal frequency division multiplexing (OFDM) system for getting accurate channel estimation. In this paper, we propose a novel non-data-aided (NDA) SNR estimation method for UAV OFDM system to overcome the carrier interference caused by the frequency offset. First, an absolute value series is achieved which is based on the sampled received sequence, where each sampling point is validated by the data length apart. Second, by dividing absolute value series into the different series according to the total length of symbol, we obtain an output series by stacking each part. Third, the root mean squares of noise power and total power are estimated by utilizing the maximum and minimum platform in the characteristic curve of the output series after the wavelet denoising. Simulation results show that the proposed method performs better than other methods, especially in the low synchronization precision, and it has low computation complexity.  
unmanned aerial vehicle; orthogonal frequency division multiplexing; signal-to-noise ratio; parameter estimation; non data-aided +
Footnote †: journal:
## 2 Mechanism Introduction and Modeling Analysis
### Design of Key Components
#### 2.2.3 Drive Cam  
A drive cam was the key component in driving the rotation of the seedling plate, and its profile curve was divided into a horizontal section and a vertical section, corresponding to the two states of the seedling plate, and the corresponding center angles are \(\alpha_{1}\) and \(\alpha_{2}\), respectively, as shown in Figure 8. An installation auxiliary plane was also designed in the horizontal section to facilitate the cam installation to be adjusted the installation angle with an inclinometer.  
Assuming that the height of the pot seedling (including the pot body) is \(h_{0}\) and the length of the seedling plate is \(d_{0}\), the minimum time for a pot seedling to complete a safe fall is  
\[t_{\text{min}}=\sqrt{\frac{2(h_{0}+d_{0})}{g}} \tag{2}\]  
The rotation angular velocity of the drive cam is \(\omega_{0}\), then  
\[\alpha_{\text{1 min}}=\omega_{0}t_{\text{min}} \tag{3}\]  
Considering the influence of friction resistance between the blade and the inner wall of the seedling cylinder in the process of dropping seedlings in pots, the actual dropping acceleration is less than gravity acceleration g. Therefore, the corresponding circular angle \(\alpha_{1}\) of the horizontal section arc should be greater than \(\alpha_{\text{1 min}}\). The actual value was \(60^{\circ}\) after calculation with a rotational angular speed of \(180^{\circ}/\text{s}\) and the corresponding planting speed of \(150\text{ plants}/(\text{min}\cdot\text{row})\).