esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper presents a prototype of an auto-ranging phase-locked loop (PLL) with low jitter noise over a wide operating frequency range using the multiband programmable voltage-controlled oscillator (VCO) gain stage with automatic band selection. We successfully reduce the VCO gain (Kvco) and retain the desired frequency band. The proposed PLL comprises a prescaler, phase frequency detector (PFD), charge pump (CP), programmable VCO and automatic band selection circuit. The PLL prototype with all subblocks was implemented using the TSMC 0.18 \(\upmu\)m 1P6M process. Contrary to conventional PLL architectures, the proposed architecture incorporates a real-time check and automatic band selection circuit in the secondary loop. A high-performance dual-loop PLL wide tuning range was realized using an ASIC digital control circuit. A suitable way to maintain the Kvco low is to use multiple discrete frequency bands to accommodate the required frequency range. To maintain a low Kvco and fast locking, the automatic frequency band selection circuit also has two indigenous, most probable voltage levels. The proposed architecture provides the flexibility of not only band hopping but also band twisting to obtain an optimized Kvco for the desired output range, with the minimum jitter and spurs. The proposed programmable VCO was designed using a voltage-to-current-converter circuit and current DAC followed by a four-stage differential ring oscillator with a cross-coupled pair. The VCO frequency output range is 150-400 MHz, while the input frequency is 25 MHz. A sequential phase detection loop with a negligible dead zone was designed to adjust fine phase errors between the reference and feedback clocks. All circuit blocks of the proposed PLL were simulated using the EDA tool HSPICE and layout generation by Laker. The simulation and measured results of the proposed PLL show high linearity, with a dead zone of less than 10 pV. The differential VCO was used to improve the linearity and phase noise of the PLL. The chip measured results show rms jitter of 19.10 ps. The PLL prototype also has an additional safety feature of a power down mode. The automatic band selection PLL has good immunity for possible frequency drifting due to temperature, process and supply voltage variations. The proposed PLL is designed for \(-40\) to \(+85\,^{\circ}\)C, a wide temperature range.  
phase-locked loop (PLL); voltage-controlled oscillator (VCO); digital-to-analog converter (DAC); fast locking; jitter; phase detector; frequency synthesizer
## 4 The Study on Gas Control Technology of High Gas-Drainage Roadway
### Result and Discussion
#### 4.3.2 Extraction Effect at Different Horizontal Positions  
Figure 12 shows the variation in gas concentration in the high gas-drainage roadway and return air flow when the high gas-drainage roadway is 28 m away from the coal seam roof and at different horizontal distances from the return air roadway. As shown in Figure 12, it can be seen that when the vertical distance of the high gas-drainage roadway is fixed and the horizontal distance increases from 15 m to 90 m, the gas concentration in the high gas-drainage roadway initially increases and then decreases, reaching a peak at 30 m. This indicates that when the horizontal distance from the return air roadway is 15 m, the proximity to the return air roadway, combined with the negative pressure effect of the high gas-drainage roadway, causes a portion of the fresh airflow in the gob-side entry retaining to be drawn toward the high gas-drainage roadway, thereby reducing the local gas concentration. Meanwhile, the short horizontal distance also limits the drainage range of high-concentration gas. When the horizontal distance is 30 m, the gas concentration in the high gas-drainage roadway reaches its maximum value of 4.32%. At this position, the roadway can extract a large volume of high-concentration gas. As the horizontal distance increases from 45 m to 90 m, the extracted gas concentration gradually decreases. This trend indicates that with increasing horizontal distance, the high gas-drainage roadway progressively approaches the compaction area of the 'O'-shaped ring formed by mining-induced fractures. As a result, the difficulty of extracting high-concentration gas increases, leading to a reduction in extraction efficiency.  
As shown in Figure 12, when the horizontal distance increases from 15 m to 90 m, the gas concentration in the return airflow of the gob-side entry retaining initially decreases and then increases, exhibiting a trend opposite to that of the extraction concentration. The  
Figure 12: The gas extraction effects of the high gas-drainage roadway at different horizontal positions.  
maximum gas concentration in the return airflow reaches 0.87%, approaching 1%, while the minimum concentration occurs at 30 m, reaching as low as 0.44%.  
The findings indicate that positioning the high gas-drainage roadway 30 m horizontally from the return air roadway yields optimal performance. At this distance, the gas extraction concentration reaches a peak of 4.32%, while return airflow gas concentration is minimized to 0.44%, demonstrating effective gas control. Therefore, it is recommended that the high gas-drainage roadway be located 30 m horizontally from the return air roadway.
# Numerical Analysis of Cold Spray Process for Creation of Pin Fin Geometries
## 2 Numerical Modelling
### Study Parameters  
For the parametric study, four different masks were analyzed. The geometry specifications of the masks are shown in Table 1. The masks were modelled based on dimensions from commercially available products. For the study, two different mask rod diameters were analyzed, a smaller rod diameter and a larger rod diameter. Each mask also had a different opening area. It should be noted that the masks were modelled as a simplified mesh--the overlapping and weaving of the mesh rods were not considered in this analysis. To capture the effect of the substrate SOD, the analysis was performed at the following two substrate SODs: 10 mm and 20 mm. To analyze the effects of the mask SOD, the mask distance from the nozzle exit was increased at increments of 4 mm. The different test configurations and SODs are described in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Mask 1** & **Mask 2** & **Mask 3** & **Mask 4** \\ \hline
**Wire Diameter (mm)** & 0.89 & 0.89 & 0.46 & 0.46 \\ \hline
**Opening Size (mm)** & 1.22 & 1.65 & 1.14 & 0.81 \\ \hline
**\% Open Area** & 33 & 42 & 51 & 41 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mask properties.  
Figure 3: Flow domain and boundary conditions.  
The deposition of particles in a cold spray process is very sensitive to the inlet operating conditions of the nozzle. A higher pressure would allow particles to achieve greater velocities, as a result significantly increasing the chances of deposition. Therefore, to analyze the effect of the nozzle inlet conditions, the following two operating conditions were analyzed: a medium-pressure case and a high-pressure case. See Table 3 for the operating conditions, which are the same as those in the study performed by [PERSON] et al. [31]. A total of 48 simulations were performed for the parametric study. The analysis captured the effects of the mask geometry, the substrate SOD, the mask SOD, and the nozzle inlet operating conditions.
## 2 Materials and Methods
### Experimental Details
#### 2.2.3 Preparation of Injection Method  
In the soil improvement studies conducted using the injection method, extensive preparations were carried out in the laboratory to ensure the proper execution of the experimental program. Initially, the required quantities of soil for each type--S1, S2, and S3--were calculated for use in molds. Subsequently, appropriate molds were prepared, and the soil specimens were produced accordingly. These specimens, with dimensions of 38 mm in diameter and 76 mm in height, were compacted to achieve a relative density of about 50%. To ensure homogeneous distribution within the mold, Scotch-Brite filters were placed at both the bottom and top of the soil samples [34]. For the injection-based improvement, soil specimens were prepared using only MICP, only SA, and a combined SA-MICP approach, applied to the S1, S2, and S3 sand types. The nutrient medium and bacterial solution required for the microbial improvement process were prepared in advance. Additionally, SS was formulated for use as a biopolymer additive. Following this, the CS was prepared, completing the set of chemical components to be integrated into the injection system. In our experimental setup, the injection was performed from the top of the specimen by allowing the solution to percolate downward through the soil column. A peristaltic pump (Bimetron, Buca, Turkiye) was used to control the injection rate and ensure a uniform flow during the percolation process. A peristaltic pump, which plays a critical role in flow regulation, was used to calibrate the injection flow rate. After the setup was assembled, the injection process was carried out in accordance with the predetermined flow rate parameter. Upon completion of the injection procedure, the specimens were placed in an oven for the curing process. Visual representations of the prepared molds and soil specimens are presented in Figure 3a, while the injection test setup is shown in Figure 3b (See Figure 3).
# Experimental and Parametric Study on the Pull-Out Resistance of a Notched Perfobond Shear Connector
## 6 Analytical Work  
### Equations for Shear Resistance  
The pull-out and shear failure of perfobond shear connectors were both related to the shear of the concrete dowel, shear of the perforating rebar, and yield of the perfobond rib, but with different sequences and contributions. Since no existing equations have been reported in the literature for estimating the pull-out resistance of perfobond shear connectors, the available shear resistance equations were introduced to evaluate the tested and analyzed pull-out resistance of notched perfobond shear connectors in this study.  
[PERSON] et al. [13] suggested Equation (5) to predict the shear resistance of conventional perfobond shear connectors with circular holes. This equation was developed based on push-out test results without considering the contribution of the rebar in the hole.  
\[V_{u}=1.4d_{p}^{2}f_{cu} \tag{5}\]  
where \(V_{u}\) is the shear resistance per hole (N); \(d_{p}\) is the hole diameter (mm); and \(f_{cu}\) is the concrete cube strength (MPa).  
[PERSON] et al. [15] proposed Equation (6) to evaluate the shear resistance of circular-hole perfobond shear connectors. The influence of the perforating rebar in the hole was considered by using two formulas with different variables.  
\[V_{u}=\left\{\begin{array}{ll}3.38\sqrt{t_{p}/d_{p}}\cdot d_{p}^{2}f_{c}-39.0 \times 10^{3}&\text{no rebar in hole}\\ 1.45\Big{[}\Big{(}d_{p}^{2}-d_{r}^{2}\Big{)}f_{c}+d_{r}^{2}f_{ru}\Big{]}-26.1 \times 10^{3}&\text{rebar in hole}\end{array}\right. \tag{6}\]  
where \(t_{p}\) is the thickness of the perfobond rib; \(f_{c}\) is the concrete compressive strength (MPa); \(d_{r}\) is the diameter of the rebar in the hole (mm); and \(f_{ru}\) is the ultimate tensile strength of rebar (MPa).
## 2 Materials and Methods
### Land Cover Classification
#### 2.3.2 Image Pre-Processing  
We used PlanetScope surface reflectance products, which were already atmospherically corrected to bottom-of-atmosphere reflectance. We generated the mosaic of these scenes in ENVI 5.3 and applied a 20-pixel edge feathering between images. Sentinel-2 images were atmospherically corrected in SNAP 6.0 using the Sen2 Cor plugin with a Maritime setting. Lastly, we enhanced Sentinel-2's 20 m and 60 m bands to 10 meters using Sen2 Res [58]. The Sen2 Res algorithm separates band-dependent information (reflectance) from information that is common to all bands (geometry of image elements), then unmixes lower resolution bands, maintaining their reflectance while propagating the geometry of scene elements to keep sub-pixel details [58]. All multispectral image mosaics, the DTM, and slope layers were clipped using a mask of the study area. The Sentinel-2 images were used as a reference for orthorectification of the PlanetScope images.
## 5 Experimental Analysis
### Result Analysis  
After applying the preprocessing approaches, the training sets and validation sets were prepared. After that, the proposed BengaliNet architecture was applied to all the datasets. Table 3 presents the number of classes and number of parameters for all 8 dataset formations. Tables 4 and 5 represent the overview of overall accuracies for BengaliNet architecture applied on the above-mentioned datasets and other independent datasets i.e., Ekush, BanglaLekha, and NumtaDB. When testing with Ekush, 15% data were taken under consideration and while testing with BanglaLekha and NumtaDB, the whole dataset was considered. For each dataset, we also calculated the class-wise accuracy, precision, recall, and f1-score which can be located in the supplementary file. Kaggle GPU environment i.e., 4 CPU cores, 16 Gigabytes of RAM, 20 Gigabytes of disk space, was used while calculating the test results. In that environment, the average recognition time per sample for 8 datasets using BengaliNet was 3.6, 4.1, 10.5, 10.5, 11.0, 1.7, 27.3, and 28.6 ms per sample respectively. The training times required for this research for the 8 considered datasets are 1.24, 6.07, 8.36, 8.42, 9.56, 0.95, 25.23, and 26.01 h respectively
# Dealing with Gender Bias Issues in Data-Algorithmic Processes: A Social-Statistical Perspective
## 8 Recommendations to Prevent, Identify, and Mitigate Gender Bias  
The importance of ethics in algorithm processes has been explored and discussed for a long time. However, the topic of integrating the gender dimension in data-algorithmic processes is increasingly getting more attention as the number of AI applications grows exponentially as well as relevant examples of gender bias in AI are identified in the scientific literature. In addition, the training datasets do not always contain representative public demographics limiting the integration of vulnerable groups amplifying the gender biases. Aiming at contributing to reduce the gender bias, we provide a list of recommendations for  
Figure 10: Adjusted model for _P(YYes)_ vs _Score_ by group.  
Figure 9: Adjusted logistic regression model.  
AI practitioners. They are classified into 3 categories, depending on whether they aim to prevent, identify, or mitigate gender bias.
* Preventing gender bias: (i) configure a reasonable representation of both genders among each category of experts working in the design, implementation, validation, and documentation of algorithms; (ii) set a reasonable gender distribution among each category of experts working in the extraction/collection, pre-processing, and analysis of data; (iii) incorporate at least one expert in data-algorithmic bias to the group; and (iv) train all staff (male/female/non-bi) in gender bias (and approaches to prevent, avoid, detect, and correct it).
* Identifying gender bias: (i) be transparent regarding the composition of the working group (gender distribution and expertise in ethics and data-algorithmic bias), the strategies implemented to mitigate bias, and the results of the tests implemented to detect potential bias; (ii) assess and publish the limitations regarding gender bias; (iii) improve interpretability of 'black-box' models; and (iv) analyze periodically the use and results of the algorithms employed.
* Mitigating gender bias: (i) avoid to reuse data and pre-trained models with gender bias that cannot be corrected; (ii) apply methods to get a balanced dataset if needed [49], as well as to measure accuracy levels separately for each gender; (iii) assess different fairness-based measures to choose which ones are more suitable in a particular case; (iv) test different algorithms (and configurations of parameters) to find which one outperforms the others (benchmark instances or datasets with biases are available in the literature to assess new algorithms); (v) modify the dataset to mitigate gender bias relying on specific-domain experts; (vi) document and store previous experiences where bias has been detected in a dataset and how it has been mitigated (as commented before, gender bias tend to be recurrent in some specific fields); and (vii) implement approaches to remove unwanted features related to gender from intermediate representations in deep learning models.
# Intrinsic Interference Use for FBMC-IOTA Systems
## 4 Evaluation of IIU
### Effect of Imperfect Channel Estimation  
Conventional channel estimation methods used for OFDM cannot be directly applied to FBMC-IOTA due to the intrinsic interference. To address this problem several preamble-based algorithms have been proposed in the literature [15]. We consider that the estimated channel is  
Figure 2: Effect of CFO.  
correlated with the true channel with a correlation factor \(\rho_{e}=E[\hat{H}_{k,l},H_{k,l}]\), where \(H_{k,l}\) represents the channel coefficient at the \(l\)th symbol and the \(k\)th subcarrier frequency after the IOTA filtering and FFT operation. The estimated channel can be modeled as  
\[\hat{H}_{m,n}=\rho_{e}H_{m,n}+\sqrt{1-\rho_{e}^{2}}E_{m,n} \tag{24}\]  
where \(E_{m,n}\) represents the Gaussian error signal and independent of the real channel. Figure 3 shows that imperfect channel estimation will change parity symbols, consequently the IIU algorithm and its gain are slightly affected. Any discrepancy in the performance is due to the fundamental dependence of the schemes on channel state information (CSI), rather than the particular channel estimation procedures employed. In future we aim to study a scattered pilot-based channel estimation scheme which benefits from the intrinsic interference calculations we carried out in this paper.
# On the Local and String Stability Analysis of Traffic Collision Risk
## 2 Preliminary on Conventional Traffic Stability Analysis  
This section offers an overview of the fundamentals of conventional traffic stability analysis, serving as a concise primer to facilitate the understanding of the proposed risk stability concepts in the following section. For a more detailed and comprehensive review of conventional traffic stability analysis, readers may refer to [PERSON] et al. [28] and [PERSON] et al. [29].  
In the microscopic modeling of traffic flow, the longitudinal driving behavior of individual vehicles is captured using car-following models. A representative form of a general car-following model is as follows:  
\[\ddot{x}_{n}(t)=f(\Delta x_{n}(t),\Delta\dot{x}_{n}(t),\dot{x}_{n}(t)) \tag{1}\]  
where \(x_{n}(t)\), \(\dot{x}_{n}(t)\), and \(\ddot{x}_{n}(t)\) denote the position, speed, and acceleration of vehicle \(n\) at time \(t\), respectively; \(\Delta x_{n}(t)\) and \(\Delta\dot{x}_{n}(t)\) denote the spacing difference and speed difference between the following vehicle \(n\) and the leading vehicle \(n-1\) (as demonstrated in Figure 1), i.e., \(\Delta x_{n}(t)=x_{n-1}(t)-x_{n}(t)\), \(\Delta\dot{x}_{n}(t)=\dot{x}_{n-1}(t)-\dot{x}_{n}(t)\); and \(f(\cdot)\) denotes a general form of a car-following model.  
Nonlinear car-following models are usually linearized via Taylor expansion at the equilibrium state in traffic stability analysis. The linearized form of Equation (1) can be written as  
\[\ddot{x}_{n}(t)=f_{\Delta x}\Delta x_{n}(t)+f_{\Delta x}\Delta\dot{x}_{n}(t)+f _{\dot{x}}\dot{x}_{n}(t) \tag{2}\]  
where \(f_{\Delta x}=\frac{\partial f}{\partial\Delta x}|^{eq},f_{\Delta x}=\frac{ \partial f}{\partial\Delta x}|^{eq},f_{x}=\frac{\partial f}{\partial\Delta x}| ^{eq}\) are the first-order Taylor expansion coefficients at equilibrium state. In the frequency domain, the transfer function is formulated based on the car-following model linearized above as  
\[Q_{n}(s)=\frac{f_{\Delta x}+f_{\Delta x}s}{s^{2}+(f_{\Delta x}-f_{x})s+f_{ \Delta x}} \tag{3}\]  
which reflects the propagation of disturbance from vehicle \(n-1\) to vehicle \(n\) in the frequency domain.
## 3 Results and Discussion
### Effect of Drain Current on Appearance Probability and Amplitude  
Figure 19 shows the Gumbel plot of the V\({}_{\text{RMS}}\) for 18,048 MOSFETs. \(I_{\text{DS}}\) varied from 0.1 to 20 \(\mu\)A. The floor noise in this experiment was smaller than the others and was approximately 35 \(\mu\)V\({}_{\text{RMS}}\)[60]. In Figure 7, V\({}_{\text{RMS}}\) decreases with an increase in \(I_{\text{DS}}\) for all V\({}_{\text{RMS}}\). In Figure 19, larger V\({}_{\text{RMS}}\) can also be observed in small \(I_{\text{DS}}\) in relatively large V\({}_{\text{RMS}}\) regions. However, the higher appearance probability in large \(I_{\text{DS}}\) than that in small \(I_{\text{DS}}\) for the small V\({}_{\text{RMS}}\) region of less than 500 \(\mu\)V could not be observed in Figure 7 because the floor noise was approximately 1 mV in that experiment. The amplitude characteristics are the same as the V\({}_{\text{RMS}}\) characteristics, and the distribution of the time constants is the same for all conditions [60]. Figure 20 shows the frequency of RTN with two, three, and more than three states in 18,048 MOSFETs. The frequency of all states increases with \(I_{\text{DS}}\).  
Figure 17: (**a**) shows \(\tau_{\text{c}}/\tau_{\text{e}}\) and \(E_{\text{T}}\)-\(E_{\text{F}}\) as a function of \(I_{\text{DS}}\), and (**b**) band diagram of MOS structure for varying V\({}_{\text{GS}}\) (\(I_{\text{DS}}\)). \(\tau_{\text{c}}/\tau_{\text{e}}\) and \(E_{\text{T}}\)-\(E_{\text{F}}\) are calculated from the data in Figure 16 and Equation (12), respectively.  
Figure 18: (**a**) Amplitude and (**b**) transition frequency as a function of \(I_{\text{DS}}\).  
It is assumed that the probability that electrons and percolation paths are close to each other increases as \(\mathrm{I_{DS}}\) increases, increasing the number of electrons in the channel and the number of percolation paths. Figure 21 shows the Gumbel plot for the amplitude of two-state RTN. Notably, frequency sometimes increases with an increase in \(\mathrm{I_{DS}}\) even though the effect of trapped electrons on the channel decreases with an increase in the electron density in the channel.  
Figure 21: Relationship of time constants between continuous on-state and periodically switched conditions. (a) \(\tau_{c}\), (b) \(\tau_{e}\).  
Figure 19: Gumbel plot of the \(\mathrm{V_{RMS}}\) for 18,048 MOSFETs. \(\mathrm{I_{DS}}\) varied from 0.1 to 20 \(\upmu\)A. The floor noise was a 35-\(\upmu\)V\({}_{RMS}\).  
Figure 20: Frequency of RTN with two, three, and more than three states in 18,048 MOSFETs.