esa-sceva/satcom-chunk-collection huggingface dataset:
techniques have been explored for designing single-band/dual-band reflection-type phase shifters [13; 14; 15] and wideband reflection-type phase shifters [16]. In [17; 18; 19], in-band phase deviation error minimization techniques have been explored using coupled lines. Meanwhile, a highly compact single-unit reflection-type phase shifter with large phase shifter range was presented in [20]. Additionally, the tunable reflection-type phase shifter was described in [21; 22; 23]. Although there are significant advancements in reflection-type phase shifter design, the conventional reflection-type phase shifter is designed for a fixed 50 \(\Omega\) termination impedance, which limits design flexibility, especially when integrating reflection-type phase shifter with other circuits. To overcome the limitations of conventional reflection-type phase shifters, this work proposes a new arbitrary termination impedance reflection-type phase shifter. This arbitrary termination impedance reflection-type phase shifter is then used as an input matching network of a microwave transistor in the co-design of the integrated microwave amplifier-phase shifter. The proposed phase shifter can provide not only phase control functionality but also offer impedance matching functionalities so that the optimum source impedance of the microwave transistor is directly matched with the arbitrary termination impedance reflection-type phase shifter. This co-design approach leads to improved gain, output power, reduced circuit size, and simplified design complexity compared to the traditional cascading approach. To validate the effectiveness of the proposed co-design methodology of the microwave amplifier-phase shifter, three prototypes of microwave amplifiers with different input matching networks were designed, fabricated, and measured.
## 2 DFIG Model and Vector Control  
The DFIG control operation is traditionally implemented by a vector control technique [12; 36; 37], which allows to control the stators \(P\) and \(Q\) separately, by means of the rotor current components (direct, \(i_{2d}\), and quadrature, \(i_{2q}\)) in the stator flux reference frame, obtained by:  
\[P=-\frac{3}{2}v_{1}\frac{L_{M}}{L_{1}}i_{2q},\text{ and} \tag{1}\]  
\[Q=\frac{3}{2}v_{1}\left(\frac{\lambda_{s}}{L_{1}}-\frac{L_{M}}{L_{1}}i_{2d} \right), \tag{2}\]  
where \(v_{1}=v_{1q}=|\vec{v}_{1 dq}|\), \(\lambda_{s}\) is the stator flux linkage, \(L_{1}\) and \(L_{M}\) are the proper and mutual inductances, respectively, and the subscripts \(1\) and \(2\) represent the stator and rotor parameters, respectively.  
Observing these equations, it is verified that the rotor currents will reflect at \(P\) and \(Q\), and, consequently (by using this principle), it is possible to implement DFIG power control. The detailed block diagram of the adopted DFIG controller, using PI controllers, is shown in Figure 2.  
In addition, the components of the rotor voltage vector are obtained by the PI controllers, in which the processing of rotor current errors occurs, being represented by  
\[v_{2d}=\left[\left(i_{2d_{ref}}-i_{2d}\right)\left(k_{p}+\frac{k_{i}}{s}\right) \right]-\underbrace{\left[\omega_{sl}\left(L_{2}+\frac{L_{M}^{2}}{L_{1}} \right)i_{2d}\right]}_{D_{d}} \tag{3}\]  
and  
\[v_{2q}=\left[\left(i_{2d_{ref}}-i_{2q}\right)\left(k_{p}+\frac{k_{i}}{s} \right)\right]+\underbrace{\left[\omega_{sl}\left(\left(L_{2}+\frac{L_{M}^{2} }{L_{1}}\right)i_{2d}+\frac{L_{M}}{L_{1}}\lambda_{s}\right)\right]}_{D_{q}}, \tag{4}\]  
where \(\omega_{sl}\) is the slip speed, \(k_{p}\) is the proportional gain, \(k_{i}\) is the integral gain, \(L_{2}\) is the rotor inductance, and \(s\) is the Laplace operator.  
For \(P\) control, the rotor current reference, using (1), is given by  
\[i_{2q_{ref}}=-\frac{2\hat{P}_{ref}L_{1}}{3v_{1}L_{M}}, \tag{5}\]  
and for \(Q\) control, by using (2), the rotor current reference is given by  
\[i_{2d_{ref}}=-\frac{2\hat{Q}_{ref}L_{1}}{3v_{1}L_{M}}+\frac{\lambda_{s}}{L_{M }}, \tag{6}\]  
where \(\hat{P}_{ref}\) and \(\hat{Q}_{ref}\) are the respective power references at the output of the wireless receiver on the wind generator.
## 4 Comparative Analysis
### Correlation Analysis Between Algorithms  
The Pearson correlation coefficient is a statistical measure that evaluates the strength and direction of a linear relationship between two continuous variables. The purpose is to quantify how strongly two variables are related in a linear fashion. It is one of the most widely used tools in statistics for measuring correlations.  
In this case, a Pearson correlation analysis was conducted to evaluate the linear relationship between the outputs of Apriori and FP-Growth across all parameter settings.  
* Overall correlation: r = 0.97, \(p\) < 0.001
* At support = 0.10: r = 0.963, \(p\) < 0.001  
These values indicate a strong positive correlation: when Apriori produces more rules, FP-Growth produces even more under similar conditions.  
A linear regression model further quantified this relationship.  
* Equations:  
\[\text{FP-Growth}=39.334\times\text{Apriori}+108.991\text{ (overall)}\]  
\[\text{FP-Growth}=31.007\times\text{Apriori}+289.004\text{ (at support = 0.10)}\]  
These results demonstrate that Apriori outputs can be used to approximate FP-Growth performance, though FP-Growth tends to scale more rapidly with relaxed thresholds.  
The strong linear relationship observed between the rule counts of Apriori and FP-Growth (\(r\approx 0.97\)) suggests that the output of one algorithm can serve as a reliable predictor of the other under similar parameter configurations. Specifically, the regression model derived from the data (e.g., FP-Growth = 39.334 \(\times\) Apriori + 108.991) offers a quantitative basis for estimating the FP-Growth rule output without executing the algorithm directly, potentially saving time in an early-stage analysis or parameter tuning. However, this predictive capability should be interpreted with caution. The regression is data-dependent and reflects the characteristics of the tested e-commerce dataset; its generalizability to other domains or data distributions is not guaranteed. Nonetheless, for practitioners constrained by computational resources or needing rapid approximations, the model provides a practical heuristic for anticipating algorithmic behavior and adjusting support/confidence thresholds accordingly.
## 3 Methods
### Generation of the Priority of Attributes Improvement
TOPSIS determines a score for each alternative by calculating the distance between the true alternative and two ideal alternatives: the positive ideal alternative (a dummy alternative constructed with the best value of each attribute of all alternatives) and the negative ideal alternative (a dummy alternative constructed with the worst value of each attribute of all alternatives). The calculation process for TOPSIS has been detailed in our previous work [70]. The TOPSIS method requires the weights of various indicators to be determined beforehand. Therefore, we adopted the entropy weight method (EWM) to determine the weights of each indicator. Since the entropy weight method is an objective weight calculation method that determines the weight of an indicator based on the amount of information, it can reduce the deviation caused by subjective factors when determining the weight of indicators and make the results more practical. It has been widely used [71]. The calculation steps are as follows:  
Step 1: Data standardization. To eliminate the influence of varying scales and magnitudes, the original data must be normalized. The normalization formula is as follows:  
\[z_{ij}=\frac{x_{ij}-\min\bigl{(}x_{j}\bigr{)}}{\max\bigl{(}x_{j}\bigr{)}-\min \bigl{(}x_{j}\bigr{)}} \tag{11}\]  
where \(x_{ij}\) is the original value of the \(j\)th evaluation criterion of the \(i\)th alternative, \(z_{ij}\) is the normalized value, and \(\min\bigl{(}x_{j}\bigr{)}\) and \(\max\bigl{(}x_{j}\bigr{)}\) are the minimum and maximum values of the \(j\)th evaluation criterion, respectively. Step 2: Calculate the entropy value of each criterion. Compute the entropy value for each criterion. The entropy value reflects the degree of dispersion in the criterion data. The formula is as follows:  
\[e_{j}=-\frac{1}{\ln\,n}{\sum_{i=1}^{n}p_{ij}\ln\,\,p_{ij}} \tag{12}\]  
where \(e_{j}\) is the entropy value of the \(j\)th evaluation criterion, \(n\) is the number of alternatives, \(p_{ij}\) is the proportion of the \(j\)th evaluation criterion value for the \(i\)th alternative to the sum of that criterion, which can be calculated by \(p_{ij}=\frac{z_{ij}}{\sum_{i=1}^{n}z_{ij}}\), and \(\ln\) is the natural logarithm. Step 3: Calculate the dispersion coefficient of each criterion. The dispersion coefficient is the reciprocal of the entropy value and reflects the ability of a criterion to distinguish between evaluation objects. The formula is as follows:  
\[d_{j}=1-e_{j} \tag{13}\]  
Step 4: Calculate the weight of each criterion. Determine the weight for each criterion based on its dispersion coefficient. The weights are proportional to the dispersion coefficients. The formula is as follows:  
\[w_{j}=\frac{d_{j}}{\sum_{j=1}^{m}d_{j}} \tag{14}\]  
where \(w_{j}\) is the weight of the \(j\)th evaluation criterion, and \(m\) is the number of criteria.
# A Novel Effective Multipath Routing Technique Providing High Availability in Wireless Networks
## 4 Performance Analysis
### Hop Counts  
Assume that messages are propagated between any two adjacent nodes in the wireless network with the same speed and delay. The shortest path between a source and a destination is the path with the least number of hop counts.
## 3 Results and Discussion
### Radioactivity Parameters of Drinking Water in the Studied Area
Recently, a radiological investigation of 64 samples of natural carbonated water originating from four Romanian counties, namely, Covasna, Harghita, Bistrita-Nasaud, and Maramures revealed that 53.5% and 26% of the investigated samples exceeded the allowed values for gross alpha activity and gross beta activity, respectively, which was attributed to the presence of volcanic rocks in the studied area [75]. Table 2 highlights the radioactivity parameters (mean values) measured in drinking water from different areas of Romania. In this respect, regional models of water radioactivity parameters relating to the water sources as the best predictor should support more action from the local public health authorities. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Area** & **Water Source** & \begin{tabular}{c} **Gross Alpha Activity** \\ **(Bq/L)** \\ \end{tabular} & \begin{tabular}{c} **Gross Beta Activity** \\ **(Bq/L)** \\ \end{tabular} & \
\begin{tabular}{c} **Rn-222** \\ **(Bq/L)** \\ \end{tabular} &
\begin{tabular}{c} **Ref.** \\ \end{tabular} \\ \hline Sibiu & mixed & 0.01 & 0.09 & 4.1 & Present study \\ (Southern Transylvania) & & mixed & 0.02 & 0.07 & Not measured & [74] \\ Galati (S-E region) & & 1.03 & 1.14 & Not measured & [75] \\ Eastern Carpathians & springs & & & 7 & [76] \\ Western Carpathians & & mixed & Not measured & 15.9 & [77] \\ N-W region of Transylvania & & & & & \\ \hline \end{tabular}
\end{table} 
Table 2: Drinking water radioactivity parameters (mean values) in different regions of Romania.
# Mixed-Integer-Based Path and Morphing Planning for a Tensegrity Drone
## 2 State of the Art
### Motion Planning for Rigid Drones  
UAV route planning has been done in a variety of ways, using methods such as the Voronoi diagram approach, rapidly-exploring random trees algorithm, evolutionary algorithm, particle swarm optimization, D* algorithm, vector field histogram algorithm, dynamic window technique, and artificial potential fields method [30; 31; 32; 33; 34]. Recent studies have focused on combining the static programming algorithm for global path planning with the dynamic path search algorithm for local path planning, as well as the aircraft's kinetic or dynamic constraints, to make the path feasible for a drone to track under specified performance conditions.  
In [PERSON] and [PERSON]'s work, dynamic path planners employ a memory-less representation of the environment, and closed-form primitives are used for planning [35; 36]. In complicated, cluttered environments, these tactics can fail. Map-based static planners, on the other hand, often depict the environment with occupancy grids or distance fields. These planners may either plan the entire trajectory at once or use a Receding Horizon Planning Framework to optimize trajectories locally while still using a global planner. In [37; 38] so-called optimistic planners (assuming unknown space to be free) are employed, whereas in the work [39], an optimistic global planner is paired with a conservative local planner.  
In [40] a planning framework that uses multi-fidelity models to bridge the gap between local and global planners is suggested. They consider sensor data not yet fused into the map during the collision check to address the relationship between a fast planner and a slower mapper [40]. A FASTER (Fast and Safe Trajectory Planner) is proposed to achieve agile flying in unknown crowded surroundings with high velocity. Their work also uses mixed-integer quadratic program formulation for further performance improvements.  
The study [41] makes use of the KD-tree fast nearest neighbor search characteristic and constructs a flight corridor with a safety guarantee in 3D space using a sampling-based path-finding algorithm based on an incrementally built point cloud map. They employ a quadratically constrained quadratic programming (QCQP)-based trajectory generating method to construct trajectories that are totally limited within the corridor [41].
# Ginger: Panacea or Consumer's Hype?
## 2 Pharmacology of Ginger
### Biological Activity
#### 2.3.3. Ginger in Dysmenorrhea  
Dysmenorrhea is a perimenstrual female dysfunction characterised by low abdominal or pelvic pain and often accompanied by back pain and nausea. As described in the previous subsections, ginger combines analgesic and anti-emetic activities, thus being very suited for the management of this condition. The beneficial action of ginger in dysmenorrhea is demonstrated by six clinical trials [64] that evidence its overall effectiveness, at different doses, in significantly reducing pain when compared with a placebo. In one of these trials, the tested daily dose was quite low--150 mg per day of ginger powder, taken for five consecutive days--and still there was a reduction of both severity and duration of pain in the test group (\(n=59\)) [65]. Another two trials reported that 1 g of ginger powder (taken along the day in four doses of 250 mg) reduced the severity of pain as effectively as NSAIDs, namely nefenamic acid (3-4 \(\times\) 250 mg per day) [66; 67] and ibuprofen (4 \(\times\) 400 mg per day) [67]. Finally, another trial reports a wide set of benefits from the intake of 2g of ginger per day, which include relief of cramping, colicky pain in lower abdomen, nausea, and even diarrhoea [68].
## 1 Introduction
Table 1 presents a comparative summary of recent UAV-based A2G communication channel measurement campaigns, highlighting key aspects such as frequency, sampling intervals, UAV speed, flight altitude, channel modeling methods, and channel characteristics [32; 33; 34; 35; 36; 37; 38; 39; 40]. In these studies, the measurement frequencies ranged from 1.2 to 38 GHz, whilst the sampling interval of the measurement data ranged from nanoseconds to milliseconds. UAV speeds ranged from 0.7 to 6.1 m/s and included cases of acceleration as high as 5.6 m/s2. Flight altitudes vary from near-ground up to 105 m. Channel modeling methods included ray tracing and clustering, focusing on metrics for evaluating communication performance such as path loss and Rician K-factor, with some using advanced techniques such as the space-alternating generalized expectation-maximization (SAGE) for detailed analysis. This paper evaluates communication stability at a frequency of 2.5 GHz in a university environment. By flying the UAV at a very low speed to minimize the Doppler effect, the estimated Rician K-factor from the measured data at a low sampling rate allows for a more accurate evaluation of different propagation environments than previous studies. This paper presents the measurement and analysis of the Rician K-factor, which indicates the stability of communication in an LOS environment, i.e., the Nakagami-Rice environment, at any point during low altitude A2G communications. In our previous work, we evaluated the Rician K-factor in a complex propagation environment [41]. To discuss the mechanism of change in the Rician K-factor, in this paper we extended the evaluation of that by performing the radio wave propagation measurements in a simple propagation environment. We conducted a detailed analysis of the changes in the Rician K-factor due to the complexity of the surrounding buildings and differences in the directivity and polarization of the transmitting and receiving antennas. The remainder of this paper is structured as follows: Section 2 describes the measurement environment and methodology and the directivity measurements of the antenna used in the anechoic chamber. Section 3 describes the method of generating dummy fading data and estimating the Rician K-factor. Section 4 shows the results of the Rician K-factor in various real radio propagation environments. Section 5 concludes this paper.
## 2 Materials and Methods
### Immunocytochemistry (ICC)  
MEF spheroids were picked at D15 and transferred onto a 4-well plate (Nunc, Rochester, NY, USA) coated with hESC-qualified matrigel (BD Biosciences, San Jose, CA, USA) at 37 \({}^{\circ}\)C for 2 h. Spheroids attached on a 4-well plate were fixed with 4% paraformaldehyde (Sigma-Aldrich, USA) for 15 min, and then washed twice with DPBS. The cells in spheroids were permeabilized with DPBS including 0.2% Tween 20 (Amresco, Solon, OH, USA) for 10 min at room temperature. After washing twice with DPBS, the spheroids were incubated with 10% bovine serum albumin (BSA) (Bio basic Canada Inc., Markham, ON, Canada) for 1 h at room temperature for blocking nonspecific binding. MEF spheroids were then incubated with each of the primary antibodies diluted in 10% BSA solution at 4 \({}^{\circ}\)C overnight. The primary antibodies used at 1:20 dilution were anti-Oct4 (Santa Cruz biotechnology, Santa Cruz, CA, USA), and anti-SESEA-1 (Abcam, Cambridge, MA, USA), and those used at 1:200 dilutions were anti-Neuronal Class III b-Tubulin (TUJ1) (Covance, Princeton, NJ, USA), anti-Sarcomeric Alpha Actin (Abcam, USA) and anti-alpha 1 feoprotein (NOVUS Biologicals, Salem, MA, USA). The spheroids were rinsed twice with DPBS and incubated with secondary antibodies diluted in 10% BSA solution at room temperature for 2 h. The secondary antibodies of Alexa Fluor 488 goat anti-mouse IgG (Life Technologies, Carlsbad, CA, USA) or Alexa Fluor 488 goat anti-rabbit IgG (Life Technologies, USA) used for pluripotent markers were diluted at 1:50 and those for differentiation markers at 1:400. The MEF spheroids were then washed twice with DPBS and 2 mg/mL of 4',6-diamidino-2-phenylindole (DAPI, Sigma-Aldrich, USA) was added for nuclear staining. After 10 min of incubation at room temperature and washing once with DPBS, the spheroids were placed on a slide glass and mounted using aqua poly/mount (Polysciences Inc., Warrington, PA, USA). Images of Oct4 and SSEA-1 stained spheroids were acquired by LSM710 (Carl Zeiss, Oberkochen, Germany) with Zen 2009 LE, and images of three germ layers stained in vitro were obtained by an ECLIPSE Ti (Nikon, Japan) with Zyla sCMOS (Andor Technology, Belfast, UK) camera and NIS-Elements AR. 4.20 program.
# Customized Convolutional Neural Networks Technology for Machined Product Inspection
## 3 Experimental Method
### Accuracy of Customized Model  
The custom-designed convolutional neural network in this experiment (Figure 7) was trained with an NVIDIA GeForce RTX 2070 SUPER GPU; the Batch Size was set to 8, Learning Rate was set to 0.0001, Epoch was set to 1, and iteration was set to 119. This training took 1 min 14 s and achieved 99.36% Accuracy.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer Type** & **Filters** & **Kernel Size/Stride** & **Output Size** \\ \hline Convolutional 1 & 64 & 3 \(\times\) 3/1 & 189 \(\times\) 186 \(\times\) 64 \\ Batch Normalization/ReLU & & & 189 \(\times\) 186 \(\times\) 64 \\ Convolutional 2 & 64 & 3 \(\times\) 3/2 & 95 \(\times\) 93 \(\times\) 64 \\ Batch Normalization/ReLU & & & 95 \(\times\) 93 \(\times\) 64 \\ Convolutional 3 & 128 & 3 \(\times\) 3/1 & 95 \(\times\) 93 \(\times\) 128 \\ Batch Normalization/ReLU & & & 95 \(\times\) 93 \(\times\) 128 \\ Convolutional 4 & 128 & 3 \(\times\) 3/2 & 48 \(\times\) 47 \(\times\) 128 \\ Batch Normalization/ReLU & & & 48 \(\times\) 47 \(\times\) 128 \\ Convolutional 5 & 256 & 3 \(\times\) 3/1 & 48 \(\times\) 47 \(\times\) 256 \\ Batch Normalization/ReLU & & & 48 \(\times\) 47 \(\times\) 256 \\ Convolutional 6 & 256 & 3 \(\times\) 3/2 & 24 \(\times\) 24 \(\times\) 256 \\ Batch Normalization/ReLU & & & 24 \(\times\) 24 \(\times\) 256 \\ Convolutional 7 & 512 & 24 \(\times\) 24/1 & 1 \(\times\) 1 \(\times\) 512 \\ Convolutional 8 & 2 & 1 \(\times\) 1/1 & 1 \(\times\) 1 \(\times\) 2 \\ Softmax & & & 1 \(\times\) 1 \(\times\) 2 \\ Classoutput & & & 1 \(\times\) 1 \(\times\) 2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Customized design model architecture.  
Figure 7: \(Cont.\)  
From the training result graph (Figure 8), it can be found that, when the Iteration was around 10, the Accuracy value began to gradually increase, and when the Iteration was in the range of 20 to 80, the Accuracy value oscillated in the range of 80 to 100. Iteration gradually converged after 80 and the oscillation was less obvious.
## 1 Introduction  
Metal mixed oxides (ABO\({}_{3}\)), in particular RFeO\({}_{3}\) (R = La, Eu, Gd) structures, have been considered as one of the most attractive semiconductors for a wide range of applications such as catalysts, hazardous gases sensors, quantum electromagnets, and microelectronic devices due to their outstanding physical, chemical, mechanical, optical, and magnetic properties [1]. [PERSON] et al. [2] introduced LaFeO\({}_{3}\) perovskite as a potential candidate for using in photocatalytic applications because of its strong absorption in the visible light spectrum. GdFeO\({}_{3}\) possesses ultra-low-temperature thermal conductivity (\(\kappa\)) of 1W/Km at 1 K, which makes it a suitable candidate for the thermoelectric applications [3]. Moreover, it has been known that the EuFeO\({}_{3}\) perovskite has a great performance to use in the microelectronic industry due to its high dielectric constant of 315, and a low dielectric loss of 1.7 [4]. One of the most important applications for these materials is high-temperature electrochemical devices, where the thermal expansion and mechanical properties of the material are crucial [5]. Differences in the thermal and chemical expansion coefficients of the constituent materials introduce compressive or tensile stresses to the compound, which can potentially cause a destruction in the device. Therefore, the effect of such stresses should be investigated on the mechanical properties of the materials. This is our motivation to study the mechanical properties of RFeO\({}_{3}\) and their response to the external stresses. To predict the mechanical behavior of RFeO\({}_{3}\), it is important to calculate the parameters such as fracture strength, Young's and shear modulus, and fracture toughness under mechanical loading. In the current study, the mechanical properties of the LaFeO\({}_{3}\), EuFeO\({}_{3}\), and GdFeO\({}_{3}\) perovskites in the cubic symmetry are first studied by exerting first-principles calculations based on density functional theory (DFT). In the next step, the topological analysis of electron density is performed utilizing quantum theory of atoms in molecules (QTAIM) to investigate the role of chemical bonds on the mechanical properties of RFeO\({}_{3}\) perovskites.