esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Compensation of a Fluxgate Magnetometer Based on a Hybrid Optimizing Algorithm
## 3 Simulation and Signal Test
### Simulation Results and Discussion
#### 3.1.1 Comparison of Compensation Effects of Different Order Dynamic Compensators  
In order to explore the relationship between the order of the dynamic compensator and the dynamic compensation effect, the dynamic compensator is set to different orders, respectively, the GA-SA algorithm is utilized to calculate the parameters of the dynamic compensator, and the compensation effects of the dynamic compensator for different orders are compared. The results are shown in Figure 11 and Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Order** & **Overshoot/\%** & **Response Time/ms** & **Fitness** \\ \hline
1 & 0.03 & 3.35 & \(4.03\times 10^{-4}\) \\
2 & 0.27 & 1.95 & \(8.13\times 10^{-5}\) \\
3 & 32.39 & 0.80 & \(1.42\times 10^{-4}\) \\
4 & 62.66 & 6.20 & \(1.10\times 10^{-3}\) \\
5 & 1.11 & 9.40 & \(4.85\times 10^{-3}\) \\
6 & 1.01 & 9.10 & \(3.34\times 10^{-3}\) \\
7 & & divergency & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dynamic compensator’s performance for different orders.  
Figure 10: Step response curve of the magnetic sensor model.  
It can be seen that the second-order dynamic compensator can shorten the response time from 10.70 ms to 1.95 ms, and no high overshoot is introduced, which is only 0.17%. The response time could also be reduced by the third-order dynamic compensator, but 32.39% overshoot is generated as well. Subsequently, with the increase in the order, the performance of the compensator deteriorates and completely diverges at the 7 th order. Thus, the second-order compensator improves the response time and overshoot, and the output dynamic error after compensation is minimal.
# On the Impact of Multiple Access Interference in LTE-V2X and NR-V2X Sidelink Communications +
## 5 Results and Discussion
### Impact of Interfering Vehicles  
As a first investigation, we consider packet success probability as a function of the number of interfering vehicles in the platoon. In this case, the total number of resources in the selection window is set to 8 and the number of vehicles ranges from 8 to 16. Moreover, different distances (i.e., 20 m, 50 m, and 100 m) between the transmitter (the tagged vehicle) and the receiver (the platoon head) are considered. A comparison between the results obtained with our approximation procedure and simulations is shown in Figure 3 for the LTE-V2X and Figure 4 for the NR-V2X. It is possible to observe that the developed MMA approximation provides good accuracy for all the considered system parameter settings.  
Figure 5 provides a comparison between the packet success probability of LTE-V2X and NR-V2X. We can observe that the performance of the considered architecture is similar when the distance is set to 20 m and 50 m, with NR-V2X exhibiting a slight performance improvement for LTE-V2X. However, when the distance is increased to 100 m, the performance gap increases, showing a non-negligible performance improvement of NR-V2X over LTE-based systems. This may be due to the interplay of carrier sensing in SB-SPS and the different physical access parameters for NR-V2X in contrast to LTE-V2X. NR-V2X can more flexibly react to slot collisions due to hidden interfering vehicles at a larger distance with the semi-persi  
Figure 4: Packet success probability as a function of the number of vehicles.  
Figure 3: Packet success probability as a function of the number of vehicles.  
Moreover, the chance of successfully receiving packets decreases as the number of interfering vehicles increases, as expected. Nevertheless, the slope of performance degradation is not very pronounced, thus demonstrating the effectiveness of the SPS scheme in allocating resources among vehicles.
# Investigation of Energy Harvesting Capabilities of Metglas 2605 SA1
###### Abstract  
In the framework of electromechanical energy conversion devices for vibrational energy harvesting, magnetostrictive materials are an attractive alternative solution to the brittleness of piezoelectric materials. Electromagnetic systems have low voltage output at a low frequency while magnetostrictive materials are suitable for a larger frequency bandwidth. In this work, a special experimental emphasis is placed on Fe\({}_{80}\)Si\({}_{9}\)B\({}_{11}\) (also known as Metglas 2605 SA1) alloy. The ultimate energy conversion abilities are investigated by performing experimental Ericsson cycles as well as through theoretical predictions using a dedicated model for the magnetic curves at the material scale. Typical output magnetic energy densities ranged between 0.1 and 1 mJ/cm\({}^{3}\)/cycle under moderate stress (<100 MPa) and magnetic excitation (up to 4 kA/m). Apart from its energy conversion abilities, Metglas 2605 SA1 also features attractive characteristics for realistic applications in microgenerators, such as a low price, which is an important advantage for the mass production and cost-effectiveness of the harvester. Furthermore, its soft magnetic property reduces the need for high magnetic fields and yields a well-adapted solution from a system point of view. It is therefore shown that this material is a suitable conversion material according to the available stress and magnetic excitation magnitudes, in addition to economic considerations.  
energy harvesting; magnetostrictive; Metglas 2605 SA1; Ericsson cycle +
Footnote †: journal: Applied Sciences
# What Advanced Treatments Can Be Used to Minimize the Production of Sewage Sludge in WWTPs?
## 3 Advanced Treatments and Technologies
### Biological Treatments
#### 3.2.1 Membrane Biological Reactor (MBR)
There is little knowledge about microbiological species in sludge subject to thermophilic processes. In fact, only some species like _Bacillus_ have been identified by researchers [78; 79]. Moreover, only in recent years has the impact of the operational parameters on the process under thermophilic conditions been investigated [80]. The membrane fouling represents a significant issue in this treatment. For instance, [PERSON] et al. [81] found that increasing the SRT, the membrane fouling rate decreased. The operative conditions can be different: (i) aerated, (ii) non-aerated or (iii) alternate [11; 81]. In order to reduce the production of SS, this system can be applied both in the water line (replacing the CAS) [80] and sludge line [11; 81]. Generally, aerobic conditions are applied if this treatment is used in the water line to treat COD-rich liquid waste (specific lower sludge production) [82; 83; 84], while anaerobic or alternate conditions are applied in the sludge line treatment [11]. To date, the application to the real scale concerns two plants in Italy. The reduction in the sludge production varies from 70% to 90% in terms of VS for the treatment of municipal sludge, while from 50% to 65% for those with a strong industrial component [11; 25]. This reduction is due to a low specific production of biomass (0.05-0.3 kgvss produced per kgCOD removed) [83].
# Infrared Nanosecond Laser Texturing of Cu-Doped Bioresorbable Calcium Phosphate Glasses
## 3 Results and Discussion
### Optimal Parameters for Surface Processing  
After characterizing the surface modification for laser parameters of average power and scan speed, it was necessary to define an optimal processing regime for a combination of the studied laser parameters. This provided a more complete characterization and also a reference for fabricating clean surface processing, devoid of cracks or other undesired surface features. For studies related to laser processing, a general parameter of importance is the laser fluence (\(F\)), defined according to Equation (1) as the energy delivered by pulse per unit area:  
\[F=\frac{E_{pulse}}{FA}, \tag{1}\]  
\[E_{pulse}=\frac{P_{arg}}{PRR}, \tag{2}\]  
Figure 7: (a) Height and width of the modified lines at different scan speeds at 19 W average power. Micrographs of laser modifications at scan speeds of (b) 1, (c) 3, and (d) 5 mm/s at an average power of 19 W. Scale bar represents 50 μm.  
where \(E_{pulse}\) is the pulse energy, \(FA\) the focal area, \(P_{avg}\) the average laser power, and \(PRR\) the pulse repetition rate. Here, the fluence is given in J/cm\({}^{2}\), where J represents Joules.  
Figure 8 displays the plot of the selected optimal process parameters, introducing laser fluence as a parameter.  
The relation depicted in Figure 8 between the laser power and the laser scan speed for surface processing is coherent with thermal-based effects resulting in the surface texturing of the Cu-doped calcium phosphate glass. Having an idea of the optimal processing parameter window paves the way towards more advanced patterning to obtain more complex and functional surface textures without thermally induced cracks. As a test to demonstrate the patterning capabilities of the laser texturing on the glass, we defined a grid pattern at a power of 17 W and a scan speed of 2 mm/s. Figure 9 shows the SEM and profilometer analyses of an optimal surface modification. Such a grid texture with an average height of 7 \(\upmu\)m can be employed as a surface for scaffolding cells. Even more geometrically complex structures can be generated and transferred with the laser controller, thus providing a powerful tool to easily explore the effect of different geometrical textures of this bioglass on the growth of cells and other biomedical applications.  
Figure 8: Optimal processing region (green) for IR ns-laser processing of the Cu-doped calcium phosphate glass.  
Figure 9: (a) SEM image of designed patterns written at a fixed average power of 17 W and at 2 mm/s. (b) Profilometer analysis of the cross patterns.
## 4 Previously Proposed Error Control Method  
In a previous study, an optimal QoS control scheme that employs decomposable error control coding and [PERSON]'s ARQ scheme was proposed [20; 21; 22]. As an example of the decomposable code, punctured convolutional code (constraint length \(K=3\); coding rates \(r_{c}\) are \(8/9\) to \(1/16\)) is used. The \(r_{c}=8/9\) punctured code patterns (codeword 1 and codeword 1') are generated from a convolutional code whose generator polynomial is [5; 7] and the coding rate is \(r_{c}=1/2\) as shown in Figure 3.  
Figure 2: Example smart health care monitoring system.  
In our proposed method, retransmission is performed as follows (Figure 4) [20; 21; 22]:  
1. Firstly, the information bit sequence \(m\) is encoded via the punctured convolutional code, and codeword 1 is transmitted.
2. If bit errors are detected after decoding codeword \(1\), the receiver stores the transmitted codeword \(1\), and the transmitter re-sends the sub-codeword of codeword \(1^{\prime}\)\(n_{i}\) times if \(1\ \leq i\ \leq 3\). At the receiver, the received sub-codeword and stored codeword are combined, and the reconstructed codeword is decoded.
3. After the third retransmission, codeword \(1\) is sent \(n_{4}\) times and combined with a buffered codeword at the receiver. If bit errors are detected after decoding reconstructed codeword, the \(n_{4}\) codeword \(1\) is buffered in the receiver, and codeword \(1^{\prime}\) is transmitted \(n_{5}\) times and combined with a stored codeword.
4. After that, codeword \(1\) and \(1^{\prime}\) are sent alternately \(n_{i}\) times and stored. Then, a receiver reconstructs and decodes low-rate decomposable codes by changing the number of data copies \(n_{i}\) in Weldon's ARQ protocol. At this time, a buffered old codeword is updated to a transmitted new codeword.
5. This operation continues until no bit errors are detected or the maximum number of transmissions \(q\) is achieved.  
Figure 5 shows a flowchart of the protocol of our proposed error correcting scheme.  
This scheme has the following advantages [21; 22]. The first one is that the coding rate is very wide. Hence, bit (or packet) errors can be sufficiently eliminated by the coding rate of \(r_{c}=8/9\) under very good channel conditions, while very low coding rates can remove bit errors under bad channel conditions. As for the second advantage, in the case of a small number of retransmissions, it is sufficient to transmit a small number of redundant bits. This characteristic leads to improvement of energy efficiency and reduction of transmission delay on retransmission. Finally, combining characteristics of Weldon's ARQ protocol makes it possible to perform wider QoS control. That is, by controlling the number of data copies \(n_{i}\) in Weldon's ARQ protocol for transmission, the error correcting capability can be changed even if an error correcting code with the same coding rate is used.  
Figure 3: Punctured convolutional codes \(r_{c}=8/9\).  
Figure 4: Method of reconstructing decomposable codes. (**a**) \(1/2\leq r_{i}\leq 8/9\); (**b**) \(r_{i}\leq 1/2\)[20; 21; 22].
# Research and Development of Automatic Monitoring System for Livestock Farms
## 3 Research Process
### Planning and Implementation of the Hardware for the Automatic Monitoring System of Livestock Farms  
In the past, traditional practices utilized manual controls for dairy and pig barns to adjust the environmental parameters by using conventional timers to configure fixed time settings daily for controlling the switches of the water valves or water curtains, and manually activated or deactivated the fuseless switches for the fans to lower down the temperature in the dairy and pig barns. As the temperature was manually measured and recorded without setting up the devices for detecting wind speed, illuminance, carbon dioxide and so forth, there was no complete and instant information for the environmental status in the livestock farms. Due to financial and wire-erecting considerations and the environmental parameters needed in this study, the corresponding sensor types and wireless technology for data transmission have been adapted after the discussions with the livestock administrators. The design and installation of hardware devices are shown in Figure 5. For the sake of staff and vehicle access requirements, we reconfigured new power distribution boxes to replace the old ones, one was at the backside of the dairy barn, and the other was outside the pig barn. As to the water valves, water curtains, and fans, we used existing devices.
## 2 Non-Parameterized Time-Frequency Analysis
### Short-Time Fourier Transform
#### 2.1.1 Definition  
The short-time Fourier transform (STFT) provides a local time-frequency analysis by sliding a window function over the signal and performing Fourier transforms segment by segment. The definition is as follows:  
1. Continuous-Time Signal: For a continuous signal \(x(t)\), the STFT is defined as \[X(\tau,f)=\int_{-\infty}^{\infty}x(t)\,w(t-\tau)\,e^{-j2\pi ft}\,dt\] (1) where \(\tau\) is the time position parameter, indicating the center of the window function; \(f\) is the frequency variable; and \(w(t-\tau)\) is the window function (such as a rectangular window or Hamming window), used to extract a local segment of the signal around time \(\tau\)[3].
2. Discrete-Time Signal: For a discrete signal \(x[n]\), the discrete form of the STFT is \[X[m,k]=\sum_{n=-\infty}^{\infty}x[n]\,w[n-m]\,e^{-j\frac{2\pi kt}{N}}\] (2) where \(m\) is the discrete time frame index. \(k\) is the discrete frequency index. \(N\) is the length of the window function or the number of FFT points. \(w[n-m]\) is the discrete window function, which slides with \(m\).  
Key Points  
* Time-Frequency Analysis: The STFT divides the signal into short segments, and each segment is transformed into the frequency domain via Fourier transform to reveal how frequency components vary over time.  
Figure 1: Key signal processing techniques in structural health monitoring.  
* Role of the Window Function: It restricts the signal to a local time segment, balancing time resolution and frequency resolution (the narrower the window, the higher the time resolution and the lower the frequency resolution).
## 2 Literature Review
### Current Challenges in 5G Network Slice Orchestration  
#### 2.1.1 Dynamic Resource Allocation Complexity  
Fifth-generation networks are designed to handle highly diverse services like high-speed video streaming, massive IoT connectivity, and ultra-reliable low-latency communications, all running on the same infrastructure. This presents a challenge of increasing the complexity of dynamic resource allocation.  
The constant fluctuation of resource demand by competing services makes real-time resource allocation a complex problem. Traditional methods, such as static or manual allocation, are too slow and inefficient to handle these dynamic needs.
# Splatters and Aerosols Contamination in Dental Aerosol Generating Procedures
## 2 Materials and Methods  
### Experimental Setup  
The experiment was carried out in a 20 m\({}^{2}\) PC2 laboratory. The mock dental procedures were performed on the left mandibular incisor tooth (tooth 31, FDI World Dental Federation notation), on a Columbia phantom head mannequin containing typodont model teeth in both jaws. Sodium fluorescein (Sigma-Aldrich, St Louis, MO, USA) was added into the coolant water to give a final concentration of 10 mg/mL. The following types of dental equipment were used: ultrasonic piezoelectric dental scaler (Dentsply Cavitron, Charlotte, NC, USA), air-water spray triplex syringe in a portable dental cart (Forest(tm), Hillsboro, OR, USA), a high-speed air turbine handpiece (300,000 rpm) fitted with a tapered fissure bur (TiMax, NSK, Kanuma, Japan), and a low-speed air turbine handpiece (W&H implantMED(tm), Burmoos, Austria) (1200 rpm) fitted with a round #5 dental bur. Representative images for each AGP are shown in Figure 1a. For the ultrasonic scaler and low-speed handpiece, the procedures were carried out for 15 s and 5 min, respectively. For the high-speed handpiece and triplex syringe, the procedures were performed for 5 s and 15 s, respectively. Water flow rates were adjusted as per the manufacturer's recommendations. The irrigation flow rate was 40 mL/min for the ultrasonic scaler, 140 mL/minfor the triple syringe, 15 mL/min for the high-speed handpiece, and 15 mL/min for the low-speed handpiece.  
Before each procedure, pieces of filter paper (retention size 2.00 \(\upmu\)m, Filtech), with a size of 160 (length) \(\times\) 100 mm (width), were placed in 10 different locations (refer to No. 1-10 in Figures 2-4), as illustrated in Figure 1b. To visualise the splatter pattern, the filter papers were visualised in a Gel Doc(tm) EZ Imager (Bio-Rad Laboratories Inc, USA). The aerosols retained in the filter paper fibres were imaged using a Leica DMi8 inverted fluorescent microscope (Leica Microsystems, Japan). Each experiment at each location was repeated four times. To prevent potential contamination between procedures, the whole area was cleaned between runs and 70% ethanol was used to wipe down surfaces.  
Figure 1: Representative images of liquid particles and experimental design for liquid particle collection. (**a**) Examples of the position of dental instruments. The fluorescein tracer is seen as green because of illumination with 405 nm violet light using an LED torch. The liquid generated by the ultrasonic scaler, triplex syringe, high-speed handpiece and low-speed handpiece can be seen. (**b**) A schematic diagram of the positions for splatter and aerosol collection at 10 different locations (No. 1–10). The angular positions in degrees were calculated relative to the sagittal plane of the mannequin.  
Figure 2: (**a**) Representative splatter images; (**b**) mean fluorescence intensity across 10 locations (after images were converted into grayscale); (**c**) particle distributions at location 10 after 15 s use of the ultrasonic scaler, triplex syringe, high-speed handpiece and low-speed handpiece.
## 2 Numerical Simulation Methods
### Rigid-Flexible Coupling Equation and Numerical Solving Method
\begin{array}{cc}S_{1}=\int_{V}\rho_{m}\phi dV&S_{2}=\int_{V}\rho_{m}\tilde{ \rho}\phi dV\\ S_{1}=\int_{V}\rho_{m}\phi dV&S_{2}=\int_{V}\rho_{m}\tilde{\rho}\phi dV\\ \sim&\\ Y_{3}=\int_{V}\rho_{m}\big{(}\phi\dot{\eta}\big{)}\left((\rho+\phi\eta)\right) dV&H_{1}=\int_{V}\rho_{m}\phi^{T}\tilde{\omega}\phi dV\\ H_{2}=\int_{V}\rho_{m}\tilde{\rho}^{T}\widetilde{\omega}\widetilde{\rho}dV&H_ {3}=\int_{V}\rho_{m}\tilde{\rho}^{T}\widetilde{\omega}^{T}\widetilde{\omega} \phi\phi dV\\ M_{c}=m_{c}I_{3}&J=\int_{V}\rho_{m}\left(\widetilde{\rho}+\widetilde{\phi} \eta\right)^{T}\left(\widetilde{\rho}+\widetilde{\phi}\eta\right)dV\end{array}  \tag{14}\]  
As can be seen, the terms \(S_{1}\) and \(S_{2}\) are constant and they depend on the selected structural modes and the rigid-body mass distribution. On the other hand, \(X_{1}\) and \(X_{2}\) are time-dependent and vary as the aircraft structure deforms. Therefore, the inertia matrix of a flexible aircraft is time-varying and has to be updated each time step. The coupled equations of motion were solved numerically using the fourth-order Runge-Kutta method, which balances computational efficiency with implementation simplicity in coding; Equation (13) can be rewritten as  
\[\mathrm{M_{G}\dot{q}}+\mathrm{C_{G}q}+\mathrm{K_{G}w}=\mathrm{F_{G}} \tag{15}\]  
where q and w are, respectively,  
\[\mathrm{q}=\left( \begin{array}{c}V_{c}\\ \omega\\ \dot{\eta}\end{array} \right),w=\left( \begin{array}{c}R_{c}\\ \theta\\ \eta\end{array} \right) \tag{16}\]  
To facilitate expression, intermediate state variable \(E\) is introduced:  
\[E=\big{[}\dot{w},\dot{q}\big{]}^{T} \tag{17}\]  
Therefore, the equation can be solved using the Runge-Kutta method, and the iterative solving process is expressed as follows:  
\[ \begin{array}{l}E^{(0)}=E^{(n)}\\ E^{(1)}=E^{(0)}+\frac{\Delta t}{2}E^{(0)}\\ E^{(2)}=E^{(0)}+\frac{\Delta t}{2}E^{(1)}\\ E^{(3)}=E^{(0)}+\Delta tE^{(2)}\\ E^{(4)}=E^{(0)}+\frac{\Delta t}{6}\Big{(}E^{(0)}+2E^{(1)}+2E^{(2)}+E^{(3)} \Big{)}\\ E^{(n+1)}=E^{(4)}\end{array}  \tag{18}\]
# Suppressing Salt Transport through Composite Pervaporation Membranes for Brine Desalination
## Materials and Methods
### Pervaporation Desalination  
Pervaporation desalination experiments were conducted by using a laboratory scale pervaporation unit, shown in Figure 1 A membrane with an effective surface area of 40 cm\({}^{2}\) was placed in the middle of the module, where four thermocouples were installed to monitor the inlet and outlet temperature of feed and permeate, in order to have an accurate measurement of the temperature difference on both feed and permeate sides across the membrane. During the pervaporation test, the feed solution was preheated in a water bath and pumped to the membrane module with a cross-flow velocity (CFV) of 0.625 m/s, while maintaining feed inlet temperature to the module at 65 \(\pm\) 1 \({}^{\circ}\)C. The permeate water vapor was withdrawn by a pump on the permeate side with vacuum pressure around 24 kPa (unless otherwise stated), and collected in the beaker on the balance after condensing (cold water maintained at \(\sim\)10 \({}^{\circ}\)C). Both the feed pump and permeate pump used in this experiment were Masterflex L/Svariable speed digital peristaltic pump (Cole-Parmer, Vernon Hills, USA). Spacers (commercial RO module spacer, with filament thickness of 0.66 mm, mesh length of 3.1 mm and spacer thickness of 1.18 mm, Synderiltration, Vacaville, USA) were used in both of the membrane feed and permeate sides to support the membrane. During the operation, the feed tank was topped up using the solution with the same concentration as the original feed.  
The salt rejection (R) was calculated by the following equation:  
\[R=\frac{C_{f}-C_{p}}{C_{f}}\times 100\%=\frac{\kappa_{f}-\kappa_{p}}{\kappa_{f}} \times 100\% \tag{1}\]  
where \(C_{f}\) and \(C_{p}\) referred to the feed and permeate salt concentration, \(\kappa_{f}\) and \(\kappa_{p}\) referred to the feed and permeate conductivity.