esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Comparative Analysis of Top-Down Method and Cut-and-Cover Method  
The top-down method, the cut-and-cover method (where excavation precedes the construction of the superstructure), the shallow tunneling method, and various combinations of these construction approaches are currently the predominant construction techniques employed worldwide for the construction of subway stations and similar infrastructure.  
The cut-and-cover method involves excavating soil from top to bottom. After the excavation is completed, the station building structure is constructed from the bottom to the top. The overall construction period is relatively long. The construction process is relatively simple, there are many working surfaces, the progress and quality are relatively easy to guarantee, and the cost is relatively low. However, during the construction period, a large area of land needs to be occupied for earthwork excavation and structural construction, and the interruption time of traffic is relatively long. The construction sequence of the top-down method is to dig the soil from top to bottom. After one layer is excavated, the station building structure is immediately constructed. Traffic can be restored after the roof is completed. The construction process is complex, and sometimes special equipment is required for soil disposal operations. In the early stage of construction, by quickly completing the roof construction of the main structure and restoring traffic, the interruption time for traffic can be greatly reduced. This section explored the advantages and disadvantages of the top-down and cut-and-cover methods for foundation pit deformation control through numerical simulations.
# Tree Based Approaches for Predicting Concrete Carbonation Coefficient
## 2 Tree Based Modelling Techniques
### Random Forest (RF)  
Random Forest is a non-parametric and semi-unsupervised algorithm within the decision tree family, encompassing a collection of uncorrelated trees to generate predictions for regression and classification purposes [26]. It combines bagging and ensemble learning theory with the random subspace method. This method allows the combination of multiple decision trees to obtain the output, instead of only depending on individual decision trees. Random Forest applies multiple decision trees as base learning models. One can randomly perform row and feature sampling from the dataset forming sample datasets for every model (bootstrapping [27]). Random Forest is a classifier involving a compendium of tree-structured classifiers: \(\{\mathsf{h}(\mathsf{x},\mathsf{\Theta}_{\mathsf{i}})\}\), \(\mathsf{i=1\dots}\), where \(\{\mathsf{\Theta}_{\mathsf{i}}\}\) are independent and similarly scattered random vectors and each tree forms a unit vote for the most popular class of input \(x\). The splitting continues until the error goal is met. A bootstrap re-sampling is applied to sample the initial data and create a set of training samples. Each training sample randomly includes the relevant attributes across random subspace techniques to build the decision tree. The optimal result is achieved by voting or averaging method. The analysis of the variable importance in modelling the behaviour of response variables is feasible with RF, using variable importance metrics in two stages [27; 28]. Figure 2 shows a Random Forest regression for a typical model.  
Feature importance analysis is also an interesting characteristic of RF. [PERSON] [27] also refers to permutation importance, which quantifies the significance of a variable based on the standard accuracy (classifier) or R\({}^{2}\) coefficient (explanatory variable) by evaluating the validation set or the out-of-bag (OOB) samples through the RF. The importance of that variable is the difference between the starting point and the decrease in overall accuracy or R\({}^{2}\) affected by transposing the column, and the results are more reliable [27; 28]. The higher the value of out-of-bag, the more important the input [29]. The relative position of a variable used as a decision node in a tree establishes the comparative significance of that variable for the prediction of the explanatory variable.
## 4 Simulations and Discussion
However, the average BER of the FSO communication system with DPSK modulation decreases more sharply than in the other modulation schemes. For instance, it can be observed that with DPSK modulation, the BER decreases from \(5\times 10^{-3}\) to \(7.9\times 10^{-6}\), while the average BER of the communication system with BPSK modulation and OOK modulation decreases from \(10^{-2}\) to \(5\times 10^{-4}\) and from \(10^{-2}\) to \(6.3\times 10^{-4}\), respectively, as the SNR increases from 20 to 30 dB. In addition, OOK modulation performs best when the SNR is less than 15 dB, while DPSK modulation performs best when the SNR is larger than 15 dB.
## 3 On-Chip Circuit Implementation
### Symmetrically Matched Bandgap Reference  
To minimize the quiescent current, a symmetrically matched (SM) bandgap reference, as shown in Figure 6, is used [19]. Two transistors are symmetrically matched if they have essentially the same V\({}_{\rm gs}\) (MOSFET gate-to-source voltage) and V\({}_{\rm ds}\) (MOSFET drain-to-source voltage). In this topology, only two branches are needed to generate the bandgap voltage V\({}_{\rm ref}\) (around 1.2 V). The currents I\({}_{\rm X}\) and I\({}_{\rm Y}\) are well matched with SM transistor-pairs (M\({}_{\rm N1}\), M\({}_{\rm N2}\)) and (M\({}_{\rm P1}\), M\({}_{\rm P2}\)). If V\({}_{\rm gs3}\approx\) V\({}_{\rm R2}\), then M\({}_{\rm P3}\) and M\({}_{\rm P4}\) are also symmetrically matched. In this way, the error factor of the mismatch current is reduced. The startup circuit consists of M\({}_{\rm N4}\), M\({}_{\rm N5}\) and M\({}_{\rm P6}\). The reference voltage is given by  
\[\rm V_{ref}=V_{EB2}+\frac{R_{2}}{R_{1}}\frac{K}{K+1}\ln(N)\times V_{T} \tag{1}\]  
where N and K are the size ratios of Q\({}_{1}\):Q\({}_{2}\) and M\({}_{\rm P3}\):M\({}_{\rm P1}\), respectively. In this design, K = 3 and N = 8 for matching considerations.  
The total current of the reference is less than 2 uA, excluding the bias currents provided to other circuit blocks. A 3 pF n-type metal-oxide-semiconductor (NMOS) load capacitor C\({}_{\rm L}\) is connected to V\({}_{\rm ref}\) to improve its power supply rejection (PSR) at around 80 MHz.
###### Abstract  
Tool fault diagnosis in numerical control (NC) machines plays a significant role in ensuring manufacturing quality. However, current methods of tool fault diagnosis lack accuracy. Therefore, in the present paper, a fault diagnosis method was proposed based on stationary subspace analysis (SSA) and least squares support vector machine (LS-SVM) using only a single sensor. First, SSA was used to extract stationary and non-stationary sources from multi-dimensional signals without the need for independency and without prior information of the source signals, after the dimensionality of the vibration signal observed by a single sensor was expanded by phase space reconstruction technique. Subsequently, 10 dimensionless parameters in the time-frequency domain for non-stationary sources were calculated to generate samples to train the LS-SVM. Finally, the measured vibration signals from tools of an unknown state and their non-stationary sources were separated by SSA to serve as test samples for the trained SVM. The experimental validation demonstrated that the proposed method has better diagnosis accuracy than three previous methods based on LS-SVM alone, Principal component analysis and LS-SVM or on SSA and Linear discriminant analysis.  
Keywords:stationary subspace analysis; least squares support vector machine; NC machine; tool fault diagnosis +
Footnote †: journal: Applied Sciences
# A Ka-Band Circular Polarized Waveguide Slot Antenna with a Cross Iris
###### Abstract  
A Ka-band circularly polarized (CP) waveguide slot antenna with a cross iris is proposed. To achieve the CP characteristic (LHCP) in the antenna, a hexagonal shaped cavity is used. The impedance matching is improved by adjusting the sizes of the hexagonal cavity and a rectangular matching cavity. To enhance the axial ratio bandwidth, a cross iris is used with two arms of different lengths. From experimental results, the proposed antenna has a 3 dB axial ratio bandwidth of 6810 MHz (31.72 GHz-38.53 GHz). The \(-10\) dB reflection coefficient bandwidth of the proposed antenna ranges from 31.51 GHz to 39.21 GHz (7700 MHz).  
waveguide antenna; slot antenna; circular polarization; axial ratio enhancement Article
# Decellularized Scaffolds for Skin Repair and Regeneration
## 2 Tissue Sources for Preparation of ECM-Based Biomaterials for Skin Repair
### Acellular Dermal Matrix (ADM)
#### 2.1.4 GraftJacket(r) RTM (Wright Medical Technology, Inc., Licensed to KCl)  
GraftJacket(r) RTM is an intact human ADM containing a basement membrane (Figure 2), processed with a patented method to minimize damage to the scaffold and freeze-dried to remove moisture. GraftJacket(r) RTM is intended for replacement of damaged or inadequate integumental tissue, such as DFUs, venous leg ulcers, pressure ulcers, or other homologous uses on human integument [43]. A quantitative analysis of comparative literature including randomized, prospective controlled clinical studies with full-thickness DFU evaluated the efficacy of this ADM compared to standard moist wound care. The results demonstrated improved healing of DFUs in a significantly reduced time with this ADM. The authors highlighted the fact that a single application achieved this outcome, whereas multiple applications are necessary with other commercialized ADMs [44].  
Despite their effectiveness, particularly toward wound bed vascularization, skin allografts present some drawbacks. These include limited availability, potential for pathogen transmission, and cost [33].
## 3 Human Activity Recognition Techniques
### AI-Driven HAR Algorithms
#### 3.2.2 Deep Learning Approaches  
While traditional models have proven effective for simple tasks, their reliance on feature engineering limits their scalability and generalizability to diverse activities and sensor modalities. Deep learning techniques are known for their ability to extract high-level features directly from raw data. Neural networks, particularly CNNs and RNNs, excel in capturing spatial and temporal patterns in activity data and have become the backbone of modern HAR systems. In [69], [PERSON] et al. discussed the performances, strengths, and hyperparameters of CNN architectures for HAR along with data sources and challenges. The study in [70] evaluated a pretrained CNN feature extractor for HAR using an inertial measurement unit (IMU) and audio data. While CNNs are particularly effective in capturing spatial dependencies in inertial data, RNNs and long short-term memory networks (LSTMs) are widely used for modeling temporal sequences in human motion signals [71]. [PERSON] et al. [72] demonstrated using an LSTM-RNN model for HAR using the WISDM dataset [73]. The dataset includes activities such as jogging, walking, sitting, standing, ascending stairs, and descending stairs captured using accelerometer data on smartphones. The advent of multimodal sensor fusion has further amplified the potential of these techniques by integrating data from diverse sources, such as accelerometers, gyroscopes, and cameras, to improve recognition accuracy.  
Multimodal sensor fusion, enabled by deep learning, plays a pivotal role in addressing the limitations of single-modality systems, such as sensor noise and modality-specific failures. Techniques like attention mechanisms and graph neural networks (GNNs) are increasingly employed to model the relationships and complementarity between modalities. For example, attention-based architectures selectively emphasize informative signals across modalities, thus mitigating the impact of unreliable or missing data. [PERSON] et al. proposed AttnSense [74], a neural network model incorporating a multi-level attention mechanism. The study utilizes data from accelerometers and gyroscopes to identify activities, including running, walking, and sitting. The model's combination of the CNN and gated recurrent units (GRUs) network captures dependencies in spatial and temporal domains, leading to improved performance. However, GNNs provide a framework for representing multimodal data as a graph structure, capturing complex interdependencies between sensors. [PERSON] et al. proposed a graph-inspired deep learning approach to HAR tasks [75]. The GNN framework shows good meta-learning ability and transferability for activities like walking, running, and transitional movements.
# Mechanisms of Noise Transmission in Velocity Broad-Band Seismometers: Modeling and Analysis
## 2 Theoretical Model of VBB Seismometer
### Noise Source Models of Seismometer
#### 2.2.2 Electrical Noise  
Electronic noise primarily consists of stationary noise and non-stationary noise. Stationary noise includes thermal noise, shot noise, and \(1/f\) noise, while non-stationary noise mainly refers to burst noise, which is caused by impurities in semiconductor materials [29]. By selecting components with mature manufacturing processes, burst noise can be minimized or even eliminated, and thus, it is not considered in this paper.  
Both thermal and shot noise can be treated as Gaussian white noise. In real applications, circuit modules operate within specific bandwidths, so the combined thermal and shot noise effects can be modeled as band-limited Gaussian white noise. The PSD of band-limited Gaussian white noise \(S_{n}^{w}(f)\), is constant within the system's bandwidth \(B\) and zero outside this range[30]:  
\[S_{n}^{w}(f)=\begin{cases}\frac{N_{0}}{2},&|f|\leq B\\ 0,&|f|>B\end{cases}. \tag{20}\]  
In addition to thermal and shot noise, \(1/f\) noise is another form of stationary noise. The PSD of \(1/f\) noise, \(S_{n}^{f}(f)\), follows an inverse relationship with frequency and is expressed as follows:  
\[S_{n}^{f}(f)=\frac{A_{f}}{f}, \tag{21}\]  
where \(A_{f}\) is the coefficient of the \(1/f\) noise.  
In summary, the total electronic noise affecting the seismometer can be modeled as the combination of band-limited Gaussian white noise and \(1/\text{f}\) noise:  
\[S_{n}(f)=S_{n}^{w}(f)+S_{n}^{f}(f). \tag{22}\]  
Various sources of electronic noise include preamplifier noise, filter noise, integrator noise from the integral controller, resistor thermal noise from the proportional controller, force transducer resistor thermal noise, and differential amplifier module noise. The time-domain representation of these noise sources are denoted as \(n_{c}(t)\), \(n_{v}(t)\), \(n_{int}(t)\), \(n_{R1}(t)\), \(n_{ft}(t)\) and \(n_{dd}(t)\), respectively. Their PSD are \(S_{n_{c}}(f)\), \(S_{n_{v}}(f)\), \(S_{n_{int}}(f)\), \(S_{n_{R1}}(f)\), \(S_{n_{f_{i}}}(f)\) and \(S_{n_{dd}}(f)\), all of which can be represented using the electronic noise model given by Equation (22). Each source of noise is injected into the input of its respective module.
###### Abstract  
Cosmic radiation has always been the most obvious barrier to planetary travels, especially in long-duration deep space exploration missions. Therefore, the reliability of satellite materials and the requirements of satellite miniaturization have received considerable attention. In this paper, the effect of \(\gamma\)-ray irradiation on the reliability of Sn50 Pb49 Sb1/Cu solder joints was investigated. It was found that the influence of \(\gamma\)-ray irradiation on the thickness and morphology of the intermetallic compound layer in Sn50 Pb49 Sb1/Cu was not obvious. However, the formation and growth of micro-voids and micro-cracks was observed in Pb-based solid solutions. Due to the Compton effect, the \(\gamma\)-ray photon could knock the electron out of its orbit, which created the energetic electron. The accumulation of dislocated atoms and lattice vacancies generated by energetic electrons could be the main factor that caused the formation of micro-voids and micro-cracks. The pull force of Sn50 Pb49 Sb1/Cu solder joints was reduced by 22% after being irradiated at the dose rate of 0.25 Gy(Si)/s for 960 h. Fractographic analysis showed that after irradiation, the fracture type of solder joints was still ductile but the ductility of the solder joints decreased with slightly inconspicuous dimples.  
Sn50 Pb49 Sb1 solder joint; \(\gamma\)-ray irradiation; fractographic analysis; pull force; microstructure 24 August 2018; Accepted: 13 September 2018; Published: 20 September 2018
## 3 Results and Discussion
### Comparison of Total Specific Energy Input of the Pretreatments  
About the pretreatments on fresh turmeric, specific energy input (W\({}_{\text{T}}\)) was ordered as: TP > UP > EP > PEF (Table 1). TP was considered as the conventional method to assist extraction. The thermal treatment has advantages such as fast and no modern equipment requirements; however, TP's energy consumption is too high. The main mechanism of UP is creating extremely high local temperature and pressure in the ultra-short time [42]. Moreover, this method has many industrial applications [48]. Nevertheless, the major disadvantage of UP is that it consumes much energy, ultrasonic waves are limited in penetration depth, may generate high temperatures, and cannot be completely controlled regarding a uniform distribution, resulting in the loss of some sensitive components.  
EP is a low energy consumption technique than other pretreatments (Table 1). According to [PERSON], et al. [49], enzymatic pretreatment is a highly selective and effective method. However, the disadvantage is that the processing time can be prolonged, and the investment and operation costs are expensive [50]. PEF was considered a non-thermal treatment widely investigated in the last two decades [51] and low energy consumption, high efficiency, no temperature increasing, so it is very suitable for the extraction of temperature-sensitive compounds. Table 1 shows that PEF is the least energy-consuming pretreatment compared to the other treatments used in this study.  
The moisture content of fresh and dried turmeric was 86.91% and 9.44%, respectively. To dry the fresh turmeric, the drying was last 72 h at 50 \({}^{\circ}\)C. Nonetheless, with dried material, the \(Z_{p}\) value reached a critical point, and TC did not increase anymore under different cell disintegration techniques.
# Mini-LED and Micro-LED: Promising Candidates for the Next Generation Display Technology
## 3 Micro-LED  
As [PERSON]'s group in Texas Technique University reported the first fabrication of micro-LED chip with diameter of 12 \(\upmu\)m in the year of 2000 [22; 23; 24], micro-LED has become a hot topic soon after the inception.  
The application field of LEDs varies depending on the chip size. Due to the size difference, the traditional LED chip is mainly used in general lighting [25; 26; 27] and display backlight module [28]. The mini-LED is applied to backlight applications such as HDR and flexible displays [18], while the micro-LED is suitable for applications such as wearable watches, mobile phones, automotive head-up displays, AR/VR, micro projectors, and high-end televisions [29] (Table 2). Additionally, micro-LED can be combined with a flexible substrate to realize the flexible characteristics like OLED [30]. Therefore, micro-LED displays have the potential to match or exceed today's OLED displays, with its high contrast, low power consumption, high brightness, and especially the high life-span [31].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Auto Display** & **TV** & **Digital Display** \\ \cline{2-3}
**Application** & & & \\ \cline{2-3}
**Panel Size (inch)** & 6–12 & 32–100 & 150–220 \\ \hline PPI & 150–250 & 40–80 & 20–30 \\ \hline Chip volume (M) & 4.1 & 24.9 & 24.9 \\ \hline Chip Size (\(\upmu\)m) & 50–100 & 50–80 & 80–100 \\ \hline  & **AR** & **Watch** & **Mobile** \\ \cline{2-3}
**Application** & & & \\ \cline{2-3}
**Panel Size (inch)** & 0.5–1 & 1-1.5 & 4–6 \\ \hline PPI & 450–2000 & 200–300 & 300–800 \\ \hline Chip volume (M) & 49.8 & 0.4 & 6.2 \\ \hline Chip Size (\(\upmu\)m) & 1–5 & 10–30 & 30–50 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Requirements for mini-LED and micro-LED in typical applications.  
Figure 7: (**a**) The 16.7 inch curved automotive display with direct backlight solution of JDI. (**b**) Ultra-High Contrast UHD Display of BOE.  
At present, the micro-LED display technology still faces some challenges, like the transfer printing of micro-LED chips for mass production [32; 33; 34] and the full-color method for display applications [35; 36; 37]. With the rapid development of some transfer printing approaches summarized in Table 3, the first problem is expected to be effectively solved. Thus, we will focus on methods of fabricating the full-color display for micro-LEDs.
# Continuous-Service M/M/1 Queuing Systems
## 4 The System and Queue Time Distributions
### The System Time Distribution  
We begin with deriving the CDF of the system time for our continuous-service M/M/1 systems. Let \(T\) be a random variable representing the amount of time a _real_ customer spends in the system (system time). Recall that \(N\) represents the number of _real_ customers in the system. When \(N=0\), a new real customer who arrives into the system will have to spend \(T=S+S\) (service time for the imaginary customer currently under service plus service time for the said customer) in the system before the said customer leaves the system. When \(N=1\), an arriving real customer into the system will have to stay in the system for \(T=S+S\) (service time for the real customer currently under service plus service time for the said customer). If \(N=2\), then a real customer entering into the system stays for \(T=S+S+S\) (service time for the real customer currently under service plus service time for the real customer currently waiting in queue plus service time for the said customer). Then, when \(N=3\), we have \(T=S+S+S+S\); and so on. Now, we look into \(N=-1\), a new real customer arriving into the system will need to stay for \(T=S+S+S\) (service time for the imaginary customer currently under service plus service time for the real customer currently waiting in queue plus service time for the said customer) before leaving the system. Similarly, if \(N=-2\), we have \(T=S+S+S+S\); and so forth.  
We employ the Law of Total Probabilities for conditional probabilities to determine the probability that a real customer spends no more than a \(t\) amount of time in the system [27]. Employing the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{\(\rho\) = 0.25} & \multicolumn{2}{c}{\(\rho\) = 0.50} & \multicolumn{2}{c}{\(\rho\) = 0.75} \\ \cline{2-7}  & **Theoretical** & **Simulated** & **Theoretical** & **Simulated** & **Theoretical** & **Simulated** \\ \hline \(L\) & 0.5833 & 0.5832 & 1.5000 & 1.4998 & 3.7500 & 3.7498 \\ \(L_{q}\) & 0.3333 & 0.3332 & 1.0000 & 0.9997 & 3.0000 & 2.9998 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons between theoretical and simulated results for \(L\) and \(L_{q}\).  
Therefore, the average time a real customer spends in system, \(W\), is given by \(W=\frac{(2-\rho)\rho}{\lambda(1-\rho)}\). We would expect the average time in our systems to be greater than that for the standard M/M/1 system. When we compare our result to that for the standard system, which is \(W=\frac{\rho}{\lambda(1-\rho)}\)[25], we can see that our result again is that of the standard system multiplied by \((2-\rho)\). Our result is greater than that of the standard system since our \(L\) is greater than that of the standard system. Our result could readily be verified by Little's Law, \(L=\lambda W\)[25].