esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction: State of the Art  
Magnesium alloys are typically shaped in milling--the process that can produce a high-quality surface finish and is thus an ideal solution for both roughing and finishing operations. The analyzed metal exhibits very good specific strength, which is why it is widely used in the aerospace industry. Machining of low-rigidity components is problematic for several reasons; however, it is the occurrence of chatter that typically constitutes the biggest challenge. Vibrations tend to decrease the quality of the finish and the dimensional and shape accuracy of the workpiece. In order to counter the negative effects of machining, workpieces are subjected to finishing operations with the use of appropriate tools, strategies, and carefully adjusted machining parameters. One of the typical finishing operations is deburring, which can be performed with the use of dedicated ceramic brushes. These tools are applicable in fully automated machining centers and are known to generate minor force values during machining [1, 2].  
Surface roughness parameters and the energy state of the surface layer are particularly relevant in the case when the components are subsequently processed with the application of adhesion technologies [3].  
In addition to fostering adhesion, maintaining surface roughness is hugely important considering the increasingly strict tolerance limits demanded of machined products, particularly with respect to mating surfaces. The surface structure of components performing in cooperation is decisive of their utilitarian properties; therefore, knowledge on how to shape surface roughness parameters by modifying technological factors is essential. A precise determination of the impact of milling conditions on the workpiece surface requires extending the standard range of typically considered 2D surface roughness and 3D area roughness parameters (i.e., Ra or Rz and Sa or Sz). An in-depth analysis of 2D and 3D roughness parameters will contribute to enhancing the quality of the finish, the effectiveness of the process, and ultimately, the work of mating components [4; 5; 6].  
The experimental models provide the data for modern AI-based techniques employed for simulating different machining processes. Artificial neural networks (ANNs) are capable of predicting dimensional errors, machining performance, chatter, as well as surface roughness. While the simulations may not directly produce a ready-to-use set of machining parameters, they do, however, effectively approximate the desired range of cutting data and, therefore, support the work of technologists responsible for determining optimal machining conditions.
## 4 Results and Discussion
### Effect of LILC Change on Water Yield
They found that land-use changes in the watershed area directly affected surface runoff [62]. [PERSON] and [PERSON] applied the SWAT model to estimate water balance and water yield in the Reedy Fork-Buffalo Creek Watershed in North Carolina, USA. They found that the surface runoff and water yield at the watershed outlet were significantly increased due to the conversion of forest and grassland to impervious surfaces [41]. Likewise, [PERSON] et al. integrated GIS and remote sensing methods with the SCS-CN model to assess the impact of land-use change on surface runoff in Beijing, China. They found that changes in surface runoff were positively correlated with impervious land changes but negatively correlated with woodland, grassland, farmland, and water changes [63]. Similarly, [PERSON] et al. applied the SWAT model to determine the hydrologic responses to land cover and climate change in the Muleta watershed, Bukidnon, Philippines. Their results showed that urbanization influenced the increase in surface runoff, evapotranspiration, and baseflow. An increase in forest vegetation resulted in a minimal decrease in baseflow and surface runoff [64].
# Actuating Performance Analysis of a New Smart Aggregate Using Piezoceramic Stack
## 2 Basic Theory of SAPS  
### The Structure and Simplified Model of SAPS  
As shown in Figure 1, SAPS mainly consists of a protective shell, a PZT stack, two copper lids (lid I and lid II), epoxy, and a shielding cable. The PZT stack is comprised of PZT discs connected in series, and the epoxy is filled in the gap between the cylinder and stack. When the PZT stack is fabricated, the adjacent two PZT discs are bonded together by conductive adhesive under a constant pressure force of 150 N for at least 12 h. After the PZT stack is fabricated, the shielding cable is soldered to the lateral side of the PZT stack. Lastly, all the parts are assembled by epoxy resin under a constant force of 150 N for at least 24 h. The clearance between the PZT stack and the protective shell is filled with epoxy resin before the two lids are fixed.  
With the consideration that SAPS is usually used to excite an elastic stress wave in the axial direction, to study its performance, SAPS is simplified to a one-dimensional axial model, as shown in Figure 2. In this simplified model, \(n\) PZT discs are stacked in series. The polarization direction is along the \(z\)-axis, and its positive direction is along the positive \(z\)-axis. As shown in Figure 2, the thickness of the two lids is the same, as represented by \(h_{l}\); the inner and outer diameters of the protective shell are din and \(d_{o}\), respectively; the diameter and thickness of the PZT disc are \(d_{p}\) and h\({}_{p}\), respectively. The height of the cylinder varies with the number of the connected PZT discs. The input voltage of SAPS is \(U(t)\). It should be noted that the thickness and properties of the PZT discs in SAPS are the same.
###### Abstract  
This inter-lab study aimed to evaluate the comparability of heavy metal concentrations in the same samples using three X-ray fluorescence spectrometers (XRFs) in three different global research institutions, namely a collaboration lab between Soonchunhyang University (Asan, Korea). and PAN (a branch of Malvern PANalytical, Seoul, Korea), RTI (Research Triangle Institute, NC, U.S.A), and Aerosol laboratory in Harvard University, Boston, U.S.A. Indoor air filter samples were collected from 8 homes using 3 filters in each household (n = 24) of individuals with asthma, and the same filter samples were sequentially analyzed separately in the collaboration lab Soonchunhyang-PAN, Harvard University, and RTI. Results showed the detection rates of most heavy metals (n = 25 metals) across the three institutions to be approximately 90%. Of the 25 metals, 16 showed coefficient of determination (R\({}^{2}\)) 0.7 or higher (10 components had 0.9 or higher) implying high correlation among institutions. Therefore, this study demonstrated XRF as a useful device, ensuring reproducibility and compatibility in the measurement of heavy metals in PM2.5, collected from indoor air filters of asthmatics' residents.  
heavy metals; X-ray fluorescence; PM2.5; indoor air quality Article
# Microseismic Data-Direct Velocity Modeling Method Based on a Modified Attention U-Net Architecture
## 2 Methods  
### U-Net  
U-Net is a type of convolutional neural network (CNN) that was initially proposed by [PERSON] et al. in 2015 [34]. The U-Net's design is inspired by a 'U' shape, featuring symmetric down-sampling and up-sampling paths. The down-sampling path captures image information and generates feature maps at different scales. In contrast, the up-sampling path combines features from different levels and produces high-resolution results. Specifically, it consists of the following components:  
* Encoder: This primarily consists of convolutional and pooling layers and captures contextual information in the input images through a series of successive convolution and pooling operations. With an increasing number of layers, the dimensions of the feature maps decrease, enabling the extraction of information at various scales and achieving global awareness. Furthermore, this process substantially reduces computational requirements and memory usage, thereby enhancing training efficiency.
* Skip connections: This is used to retain more input information in the network. After each pooling operation, the results are concatenated with the corresponding layers in the decoder. This approach facilitates the network in capturing features from different levels.
* Decoder: Composed of convolutional layers and up-sampling, systematically restores the fine-grained details that were lost during the encoding process. By mapping the extracted features back to high-resolution representations, the decoder ultimately recovers the original size of the input image.
* Output: The ultimate prediction results are generated by the final layer using a 1 \(\times\) 1 convolutional kernel in conjunction with the sigmoid function.
## 3 DC/DC Buck Converter Design  
The DC/DC buck converter was carefully designed to interface with the PV panel while maintaining maximum power point tracking. The complete design process is presented below, with the circuit topology shown in Figure 4[29; 30].  
Figure 4 illustrates the buck converter configuration, which includes a control switch, a diode, an inductor, and an output capacitor. The converter steps down the PV array voltage to the desired load level (20 V) while maintaining operation at the MPP, as dictated by the MPPT algorithm.  
\begin{table}
\begin{tabular}{l c} \hline
**Parameter** & **Value** \\ \hline Maximum power (\(P_{max}\)) & 29.88 W \\ Number of cells per module (\(N_{cell}\)) & 72 \\ Open-circuit voltage (\(V_{oc}\)) & 43.20 V \\ Short-circuit current (\(I_{sc}\)) & 0.92 A \\ Voltage at maximum power point (\(V_{mpp}\)) & 36.00 V \\ Current at maximum power point (\(I_{mpp}\)) & 0.83 A \\ Temperature coefficient of voltage (\(\beta_{Voc}\)) & \(-\)0.27 \%/\({}^{\circ}\)C \\ Temperature coefficient of current (\(\alpha_{Isc}\)) & \(+\)0.05 \%/\({}^{\circ}\)C \\ Temperature coefficient of power (\(\alpha_{Isc}\)) & \(-\)0.35 \%/\({}^{\circ}\)C \\ \hline \end{tabular}
\end{table}
Table 1: Specifications of the SOLARPOWER-XUNZEL-30W-24V photovoltaic panel [28].  
Figure 3: I–V and P–V curves of the SOLARPOWER 30W 24V module.  
The key parameters of the converter, such as duty cycle (\(D\)), inductance (\(L_{o}\)), and capacitance (\(C_{o}\)), were calculated using classical design equations to ensure continuous conduction mode (CCM) and minimize ripple in both current and voltage. These calculations are based on the following expressions, respectively:  
\[D=\frac{V_{out}}{V_{in}} \tag{5}\]  
\[L_{o}\geq\frac{(V_{in}-V_{out})\times D}{f_{\mathrm{s}}\times\Delta I} \tag{6}\]  
\[C_{o}\geq\frac{\Delta I}{8\times f_{\mathrm{s}}\times\Delta V} \tag{7}\]  
Table 3 summarizes the theoretical values obtained and used in the simulations.
## 5 Conclusions  
Several potential scenarios of the transmission of several diseases (including COVID-19) by droplets via talking, coughing, sneezing, and aerosols produced during clinical procedures are predictable. Concerns regarding the rapid spread of COVID-19 have become even more critical in dentistry since the oropharyngeal airway and droplets have been confirmed as routes for infection. Hence, the pandemic has severely disrupted dental and healthcare services and created a transient global shortage of basic personal protective equipment.  
The D-DART has been developed as an engineering control to contain and safely extract the aerosols generated during dental treatment. The results show that the D-DART can effectively contain and extract aerosols via an extraction valve and therefore protect clinicians from direct exposure to aerosols. Moreover, it prevents the spread of microorganisms and viruses onto clinical surfaces, hence decreasing the chances of potential infections and cross-contamination in clinics. The design can be used broadly to protect clinical staff when patients test positive for COVID-19 or other critical transmissible infectious diseases. Likewise, it can create a safer environment and decrease the anxiety and psychological distress imposed by highly infectious diseases on all parties involved in the dental clinic setting.  
Conceptualization, V.R., M.S.D., S.A. and F.Y.C.B.; Methodology, V.R., M.S.D., B.L.T. and K.S.T.; Investigation, S.V.A., S.Y.C. and Y.F.S.; Resources, V.R. and F.Y.C.B.; Writing--Original Draft Preparation, V.R. and S.V.A.; Writing--Review and Editing, V.R.; Supervision, V.R.; Project Administration, S.Y.C.; Funding Acquisition, V.R., M.S.D. and F.Y.C.B. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the Ministry of Education Singapore (AcRF Tier 1 Grant, A-0002958-00-00). The authors thank the National University Centre for Oral Health Singapore (NUCOHS) and its staff for the support provided during the design stage and clinical study.  
**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Domain Specific Review Board, National Healthcare Group (NHG DSRB Ref.: 2021/00059).  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author, GS, upon reasonable request.  
**Conflicts of Interest:** Design certificates have been issued to VR, MSG, FB, and S in Singapore, and patent applications have been filed in Brazil, China, Indonesia, Europe, and the United States.
# Steganalysis of Quantization Index Modulation Steganography in G.723.1 Codec
## 5 Experiment and Results Analysis
### Results Analysis
#### 5.3.3 Voice Duration  
In order to test the detection effect for different duration speech, speech segments with 100% embedding rate are prepared for each type of sample. Experiments are carried out for different durations. The average detection rate of ACD and SCD is shown in Table 5[21].  
In order to more intuitively compare the performance of the two types of detection, Figure 9 shows the relationship between the average detection rate of the five data sets and the duration of the speech segment. It can be seen from Figure 9 that the longer the duration, the higher the average detection rate of ACD and SCD. The longer the speech duration, the more embedded secret information. This results in a significant change in \(A\) and \(M\), and thus the detection rate is higher. When the length of the test reached 3 s or more, the average accuracy of ACD reached more than 85%. When the length reached 2.4 s or more, the average accuracy of SCD reached more than 85%. It shows that ACD is more sensitive to different duration, and SCD is more suitable for detection of different durations.
## 2 Elastoplastic Constitutive Relationship Theory  
### General Elastoplastic Constitutive Relationship  
In the elastoplastic constitutive relationship of soil, the elastic part is generally described by Hooke's Law, while the plastic part consists of the yield condition, flow rule, and hardening law.  
The yield condition can be quantitatively described using the yield surface equation, \(f\), which determines the stress condition at which plastic deformation begins. The yield surface equation, \(f\), is a function of stress, \(\sigma\), and the hardening parameter, \(H\):  
\[f(\sigma,H)=0 \tag{1}\]  
The flow rule can be quantitatively described using the plastic potential surface equation \(g\), which defines the direction of plastic strain increments. The plastic potential surface equation, \(g\), is generally expressed as follows:  
\[g(\sigma,C)=0 \tag{2}\]  
where \(C\) is a constant.  
The hardening law essentially determines the hardening parameter, \(H\), which governs the magnitude of plastic strain increments. The hardening parameter, \(H\), is generally a function of plastic strain:  
\[H(\epsilon^{\mathrm{P}}) \tag{3}\]  
where \(\epsilon^{\mathrm{P}}\) represents the plastic strain. Different constitutive models adopt different definitions of plastic strain. For example, the MCC model uses the plastic volumetric strain, \(\epsilon_{\mathrm{v}}^{\mathrm{P}}\), as the hardening parameter, i.e., \(H(\epsilon^{\mathrm{P}})=\epsilon_{\mathrm{v}}^{\mathrm{P}}\). The CSUH model, on the other hand, adopts a plastic volumetric strain incorporating a stress ratio coefficient as the hardening parameter, expressed as \(H\Big{(}\varepsilon_{\text{v}}^{\text{p}}\Big{)}=\int\frac{\big{(}M_{\text{v}}^{ \text{d}}-\eta^{4}\big{)}}{\big{(}M_{\text{v}}^{\text{d}}-\eta^{4}\big{)}}de_{ \text{v}}^{\text{p}}\), where \(\eta\) represents the stress ratio, while \(M_{\text{Y}}^{\text{d}}\) and \(M_{\text{c}}^{\text{d}}\) denote the potential failure stress ratio and characteristic state stress ratio, respectively. Further details will be introduced later.
## 2 Proposed PLL Architecture
### Programmable Current DAC for Wide Frequency Range Selection  
Kvco is a VCO frequency/voltage gain having the unit of Hz/V. It measures how much the VCO output frequency changes relative to the VCO input control voltage Vctr change. Kvco is the slope of the curve of frequency vs. Vctr. For a high Kvco, fast settling with higher jitter occurs. It also creates loop stability and accuracy degradation. On the other hand, a very small Kvco value results in a slow VCO response and a narrow output frequency range. Hence, by dividing the target frequency into several sub-bands, each band's Kvco can be reduced. This results in small jitter without sacrificing the required frequency range.  
The programmable VCO is optimized by a 3-bit current DAC, which contains a weighted current mirror in section B to generate multiple current values. By using the weighted current mirror, the Kvco gain can be controlled, as with Kvco-1, Kvco-1/4, Kvco-1/8 and Kvco-1/16. It also operates as a DC current into the V-I converter. The programmable iDAC is the most important circuit block of the VCO to obtain a low Kvco gain through multiple VCO bands. The automatic band selection circuit output moves into the iDAC and provides the required change in the bias voltage Vbp2. From Figure 7, Vbp2\(<\)0:3\(>\) along with Vbp1\(<\)0:3\(>\) optimizes the delay cell tail current. As we can see in Figure 8a, section B provides multiple band twisting options for retaining a low VCO Kvco gain such as Kvco-1, Kvco-1/4, Kvco-1/8 and Kvco-1/16, while section A further adjusts the Kvco by providing band hopping to obtain the desired centralized frequency operation range.
## 4 Numerical Simulation  
### Numerical Model and the Related Parameters  
The cubic numerical model was established in the PFC3D software (PFC3D 6.0), and the FJM3D (flat-jointed model) constitutive model was chosen (Figure 7). Because the macro-physical and micro-physical parameters of the PFC3D model do not show a linear relationship, the model parameters were defined by the trial-and-error method; the parameter calibration was based on uniaxial and tensile test data. Table 2 lists the related mechanical properties of the numerical model, and the experimental and numerical results  
Figure 6: Splitting failures of tunnel-surrounding rock after the true triaxial test: (**a**) failure patterns of the cavern-free sample; (**b**) failure patterns of the cavern-existing sample.  
present good coincidence. The loading rate for the numerical model was set to 0.01 to reflect the experimental loading rate (0.01 MPa/s).  
Commonly, the smaller the average particle size of the model, the more accurate the corresponding results. Previous numerical works prove that particle size shows a slight influence on a sample's compressive strength when the particle size is small enough [30; 31]. To balance the calculation efficiency and model precision, the minimum and maximum particle diameters were set as 1 mm and 1.66 mm, respectively. The ratio of maximum particle diameter to the cavern diameter was 8.3% (good model precision), and the total particle number was almost 1,100,000 (this quantity of particles only needs to occupy the workstation for 3-4 weeks).  
\begin{table}
\begin{tabular}{l c} \hline
**Micro-Parameters** & **Value** \\ \hline Maximum to minimum grain diameter ratio, \(d_{\text{max}}/d_{\text{min}}\) & 1.66 \\ Minimum grain diameter & 1.0 \\ Installation gap ratio, \(g_{ratio}\) & 0.3 \\ Local damping factor, \(damp\) & 0.0 \\ Bonded element fraction, \(\varphi_{B}\) & 0.9 \\ Slit element fraction, \(\varphi_{S}\) & 0.1 \\ Radial element, \(N_{r}\) & 1 \\ Circumferential elements, \(N_{a}\) & 3 \\ Effective modulus of both particle and bond, \(E_{c}=\overline{E}_{c}\) (GPa) & 12.8 \\ The ratio of normal to shear stiffness of both particle and bond, \(k_{n}/k_{s}=\overline{k}_{n}/\overline{k}_{s}\) & 1.0 \\ Mean and standard deviation bond tensile strength, \(\phi_{b}\) (MPa) & (1.2, 0.1) \\ Mean and standard deviation bond cohesion strength, \(\phi_{c}\) (MPa) & (16.5, 1.5) \\ \hline Macro-parameters of the ISRM sample (real/numerical model) & Value \\ \hline Uniaxial compressive strength (UCS)/MPa & 26.63/26.58 \\ Elastic modulus (EM)/GPa & 17.19/17.15 \\ Tensile strength (TS)/MPa & 1.13/1.12 \\ \hline Related properties of the cubic model & Value \\ \hline Length of cubic model/mm & 100 \\ Total particle number & 1,098,824 \\ Interval between each saving file/steps & 5000 \\ \hline \end{tabular}
\end{table}
Table 2: Micro- and macro-parameters of the numerical model.  
Figure 7: Establishment of the numerical model.
# Designing a Conceptual Digital Twin Architecture for High-Temperature Heat Upgrade Systems
## 3 Digital Twin Architecture for Heat Upgrade Systems
### Digital Twin Process Modeling Perspective  
The twinning process, represented in Figure 2, involves a continuous bidirectional connection between physical and the corresponding virtual counterpart. While the physical to virtual connection creates and maintains a digital replica by observing and getting data from the physical world using the physical devices sensors, the virtual to physical provides optimized configuration and goal-oriented commands.  
Figure 1: Proposed concept of a digital twin architecture for a high-temperature heat upgrade system.  
Using the terminology described in [38], the physical environment represents the measurable'real-world' environment, within which the physical entity exists that influences its processes, state, or functionality. A physical entity refers to any man-made, tangible object that is twinned being defined by its properties, parameters, and physical characteristics. For example, while TES might not be heavily influenced by external temperature due to insulation, a renewable energy collection system such as solar panels or wind turbines will be directly influenced by external ambient factors. The virtual entity represents the virtually modelled physical entity that is used by virtual processes for analysis, simulation, and optimization. The results of these virtual processes are then sent back across the virtual to physical connection in the form of updates to the physical entity, modifying operational parameters or control strategies.  
An important challenging characteristic of digital twins is their fidelity to the real world [38]. Fidelity determines the complexity of the virtual counterpart in terms of modeled parts and sub-parts, parameters, precision, abstraction level, or simulation details. Choosing the proper fidelity level for a specific physical entity requires having well-defined use cases and requirements for the digital twin. Requirements and use cases that define the fidelity level include data quality and resolution of the measurements, physical entity modeling complexity, digital twin simulation capabilities. Integration with external systems parameters such as socio-economic indexes or environmental data will increase the digital twin complexity but also improve the predictive capabilities and realism. According to multiple studies [38; 39], very high-fidelity digital twin implementations are yet to be realized if not impractical to do, due to computational power limitations or development costs.