esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Current Status of the Circuit Component in the MMW Regime
### Voltage Control Oscillator (VCO)
#### 3.5.2 VCO in SiGe Technology  
Figure 17 illustrates the measured phase noise of the MMW SiGe VCOs. In [173], a 52 GHz VCO, which employs loop-ground transmission line, is proposed to enlarge the tuning range and output power without sacrificing phase noise. In [174], a high power efficiency inductive tuning VCO was reported. The base-degenerated inductor and resistor lead to a high quality factor and high tuning range. By controlling the bias current of the base-degenerated resistor, the tuning mechanism of the inductor is achieved. Also, the negative resistance of the cross-coupled pair is compensated. In [175], the tuning range and the output power are improved by utilizing the transformer. The primary winding of a transformer tank attracts the signal from the switching transistor bases, meanwhile, the output buffer is driven by the secondary winding. Thus, the tuning range and the output power will increase without leveling the supply voltage. In [176], the earliest broadband SiGe-based VCO/divider combination at 180 GHz is presented. With a divide by 32 prescaler, the total tuning range of the four fundamental VCOs is 56 GHz, which ranges from 125 to 181 GHz. In [177], the low phase noise common-collector Colpitts VCO is reported. With the fixed voltage swing, more current will flow into the tank due to the low impedance of the resonate tank. This results in a lower phase noise than the conventional MMW VCO.  
Figure 16: The circuit schematic of DCO [172].
## 3 Results and Discussion
### Growth of Sde 2-40, B. cereus, and Co-Culture on Agarose and Xylan
Cadherin and cadherin-like domains exhibit calcium dependent carbohydrate binding characteristics, which is very significant in resource-restricted aquatic environments [44]. [PERSON] et al. [45] described a commensal relationship between _B. cereus_ and _Cytoplas-Flavobacterium_ (CF) rhizosphere bacteria, where _B. cereus_ peptidoglycan encourages the CF bacterial growth. [PERSON] et al. [46] showed enhanced degradation of cypermethrin by co-culture of _Streptomyces aureus_ and _B. cereus_. In their study, cypermethrin mineralization in minimal medium was low by either _B. cereus_ or by _S. aureus_, but their combination consortium completely degraded cypermethrin. The authors correlated synergism with the partial and complete cypermethrin degradation abilities of _S. aureus_ and _B. cereus_, respectively. Enhanced degradation of cypermethrin was credited to the capability of strain _S. aureus_ to remove 3-phenoxyzaldehyde [46]. [PERSON] and [PERSON] [47] showed enhancement of soybean modulation by _B. cereus_. The authors suggested that _B. cereus_ seed treatments increased the acetylene reduction activity of soybeans by 25% to 73% compared with that of the untreated control. [PERSON] et al. [48] described that root colonization and promotion of growth can be affected due to root exudates prompting alterations in the cell surface of _B. cereus_. Dead microbial structures can possibly work as nutrient source for the growth of strains in the co-culture [49]. _Sde_ 2-40 and _B. cereus_ may have cell-to-cell interactions, which may help both to grow in the co-culture. The cadherin domains of _Sde_ 2-40 and cell components of _B. cereus_ may have active roles in the interactions. _B. cereus_ may be providing some nutrients or metabolizing waste products generated in the media, thereby keeping _Sde_ 2-40 in healthy and actively growing condition. This is an interesting observation which suggests that co-culture can be an innovative approach to increase the degradation rate of complex polysaccharides by _Sde_ 2-40. These observations also indicate that the media which are being used commonly at present for culturing _Sde_ 2-40 need proper optimization. The mechanism behind the enhancement of growth of _Sde_ 2-40 by _B. cereus_ needs further detailed investigation.
## 4 Results
### Exploration of Recommendation Rate Influencing Factors
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \begin{tabular}{c} **Doctor** \\ **Recommendation Rate** \\ \end{tabular} & \begin{tabular}{c} **Doctor** \\ **Online Work** \\ **Quantity** \\ \end{tabular} & \begin{tabular}{c} **Personal** \\ **Webpage** \\ **Visits** \\ \end{tabular} & \begin{tabular}{c} **Article** \\ **Count** \\ **Coun** \\ \end{tabular} & \begin{tabular}{c} **Total Post-** \\ **Consultation** \\ **Evaluation** \\ \end{tabular} & \begin{tabular}{c} **Post-** \\ **Center** \\ **Count** \\ \end{tabular} & \
\begin{tabular}{c} **Thank-You** \\ **Cetter** \\ **Count** \\ \end{tabular} &
\begin{tabular}{c} **Gratitude** \\ **Gift Count** \\ \end{tabular} \\ \hline Doctor Recommendation & 1 & 0.459 & 0.369 & 0.122 & 0.526 & 0.602 & 0.554 & 0.42 \\ Rate & & & & & & & & \\ Doctor Online Work Quantity & 0.459 & 1 & 0.263 & 0.274 & 0.717 & 0.743 & 0.706 & 0.724 \\ Personal Webpage Visits & 0.369 & 0.263 & 1 & 0.151 & 0.273 & 0.278 & 0.259 & 0.176 \\ Article Count & 0.122 & 0.274 & 0.151 & 1 & 0.163 & 0.148 & 0.137 & 0.148 \\ Total & & & & & & & & \\ Post-Consultation & 0.562 & 0.717 & 0.273 & 0.163 & 1 & 0.807 & 0.743 & 0.696 \\ Patient Count & & & & & & & & \\ Post-Consultation & 0.602 & 0.743 & 0.278 & 0.148 & 0.807 & 1 & 0.981 & 0.787 \\ Evaluation & & & & & & & & \\ Thank-You Letter Count & 0.554 & 0.706 & 0.259 & 0.137 & 0.743 & 0.981 & 1 & 0.755 \\ Gratitude Gift Count & 0.420 & 0.724 & 0.176 & 0.148 & 0.696 & 0.787 & 0.755 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Pearson Correlation Coefficients r Between Variables. Python was employed to generate a box plot illustrating the regression relationship between the primary recommendation rate influencing factor (post-consultation evaluation) and the recommendation rate (Figure 5). The horizontal axis represents the doctor recommendation rate, progressively increasing from 2.3 to 5, arranged sequentially; the vertical axis represents post-consultation evaluation. The upper and lower box edges correspond to the third (Q3) and first (Q1) quartiles, respectively. The horizontal line within the box represents the median, reflecting the central tendency of sample data. Whiskers above and below the box indicate maximum and minimum values, with dispersed points marked as outliers. Box positioning demonstrates the distribution of 50% of the data, collectively reflecting the centralized trend across different groups. The results explicitly indicate a significant positive correlation between doctor recommendation rate and post-consultation evaluation levels. These findings provide comprehensive insights into the complex mechanisms underlying doctor recommendation rates in online health communities. By systematically analyzing the intricate relationships between various professional and interactive indicators, the study illuminates the multi-dimensional nature of recommendation rate generation, emphasizing the critical role of post-consultation evaluation in shaping patient trust and service perception. Figure 4: Correlation Heatmap.
## 4 Comparison between Two Models
### Frequency Response of the Models
#### 4.2.2 Acceleration Power Spectral Density  
Since the harmonic frequency has a single characteristic, the test results of the harmonic excitations are easy to analyze the spectrum. The acceleration power spectral density (PSD) was used as an evaluation indicator [36], which defines the energy distribution of seismic excitation at different frequencies. The PSD at the top of the models (AS1 and AS2) and at the bottom of the models (AS0) are shown in Figure 9. The Welch method [31] was used to calculate the PSD at the measurement points. Overall, the PSD of AS0 at the bottom of the models decreased as the harmonic frequency increased. However, the PSD at the top of the models exhibited a different pattern.  
Under transverse excitation, the maximum amplitudes of the HRM model and the JRM model appeared at 12 Hz and 1  
of the \"free field\" (AS2/AS0). When the difference between the PSD ratios of the rock-structure and the \"free field\" was larger, it indicated that more input energy was consumed by the buried station, especially when the input frequency was close to the fundamental of the models. The variation in the PSD amplitude of AS0 with the frequency and the effect of the underground structure on the PSD ratio of the surface were in accordance with the conclusions of reference [37].
# Clinical Applications of Nano-Hydroxapatite in Dentistry
###### Abstract  
Nano-hydroxxapatite (nano-HAp) is a biomaterial which is finding increasing application in dentistry due to its osteogenic properties and biocompatibility. The aim of the present study is to review the clinical applications of nano-HAp in dentistry. A literature search was performed in PubMeD and Scopus. In total, 154 articles were retrieved. Following title and abstract analysis, 122 articles were excluded. Further 6 articles were excluded through full-text analysis. The systematic review was conducted on 26 articles. In 3 studies, nano-HAp was employed for tissue remineralization, 8 studies applied nano-HAp for the treatment of dentin hypersensitivity, 2 studies evaluated the role of nano-HAp in orthodonties, 9 studies applied nano-HAp as a graft material, and 4 studies applied nano-HAp to periodontology and implantology. The available evidence supports the use of nano-HAp in the prevention of caries, as a desensitizing agent in the treatment of dentin hypersensitivity and as a graft material. It should be, however, highlighted that further research appears mandatory in assessing and validating the full potential of nano-HAp in clinical dentistry.  
nano-hydroxxapatite; hydroxyapatite; dentistry; operative dentistry +
Footnote †: journal:  
by acting as a mineralizing agent [10]. Moreover, nano-HAp is employed for implant coating as it improves bone to implant contact and promotes bone adhesion, along with having bacteriostatic properties [10]. Such characteristics account for the favourable outcomes of nano-HAp use as graft material, enhancing angiogenesis and bone healing [10].  
The aim of the present study is to review the current applications of nano-HAp in dentistry.
## 5 Experimental Results and Discussion
### Analysis of the Estimated Area and Time Complexities

Table 1: Comparison between different word-serial field multipliers. By observing the latency and CPD formulas in Table 1, we notice that the design of Pan (2017) has lower latency and the design of Hua (2017) has the most considerable latency compared to the remaining designs, including the proposed one. The quantitative analysis presented in the following subsection shows that the developed multiplier-squarer structure has lower latency than the multiplier structures of [PERSON] (2017) and [PERSON] (2018) and higher latency compared to the multiplier structures of [PERSON] (2017) and [PERSON] (2017) for field size \(n=409\). We notice also from Table 1 that as the word size \(k\) increases, the latency of all designs significantly decreases as the latency is inversely proportional to \(k\). By examining the formulas of the CPD, we notice that the multiplier structures of [PERSON] (2017), [PERSON] (2017), and [PERSON] (2018) have a constant and lower CPD for all values of word size \(k\). In contrast, the CPD values of [PERSON] (2017) and the proposed design mainly increase as \(k\) increases. Since the latency of all designs significantly reduces as \(k\) increases and the CPD has constant values or increases as \(k\) increases, we cannot precisely conclude from the qualitative analysis which design should have the best execution time. The quantitative analysis provided in the following subsection will show which multiplier structure provides better execution time complexity.
## 1 Introduction
However, by now there is a paucity of studies on the use of amorphous alloys as core materials in micro-fluxgate sensor applications because amorphous soft magnetic alloys are incompatible with the microfabrication process. Studies on Fe-Co-B amorphous alloy-based micro-fluxgate sensors are thus virtually nonexistent. The goal of the current study was to develop a high-performance micro-fabricated fluxgate sensor associated with the advantages of a Fe-Co-B-based amorphous alloy. Thus, a novel MEMS-micro-fluxgate sensor based on a Fe\({}_{\text{58.1}}\)Co\({}_{\text{24.9}}\)B\({}_{\text{16}}\)Si\({}_{\text{1}}\) amorphous ribbon was designed, fabricated, and tested. First, a simple melt-spinning technique was carried out to obtain a Fe-Co-B-based amorphous ribbon composite of Fe\({}_{\text{58.1}}\)Co\({}_{\text{24.9}}\)B\({}_{\text{16}}\)Si\({}_{\text{1}}\). Then, the obtained amorphous ribbon was annealed at 595 K for 1 h to achieve soft magnetic properties. The prepared ribbon showed excellent soft magnetic behavior with a high saturated magnetic intensity (\(B_{\text{s}}\)) of 1.74 T and a coercivity (\(H_{\text{c}}\)) of less than 0.2 Oe. Afterward, a micro-fluxgate sensor based on the prepared amorphous ribbon was fabricated via MEMS technology combined with chemical wet etching. In addition to the properties of core materials, [PERSON] et al. [22] found that a higher cross-sectional area of the sensor core can improve the sensitivity of a fluxgate sensor. Furthermore, [PERSON] et al. [23] and [PERSON] et al. [24] proved that the larger cross-sectional area of the magnetic core not only increases sensitivity but also lowers noise. However, the demagnetization effect and eddy current effect limited our ability to increase the width and thickness of the sensor core. Thus, in the current study, a double-layer core structure was designed in order to increase the cross-sectional area and improve the sensitivity of our sensor. The resulting sensor exhibited a sensitivity of 1985 V/T and a linearity range of \(\pm\)1.05 mT under optimal excitation conditions. As compared to previously reported similar fluxgate sensors, our sensor showed significantly improved sensitivity and linearity range. Moreover, the sensor performance in terms of offset stability, perming error, and output noise was deeply investigated.
# Semi-Supervised Semantic Segmentation Network for Point Clouds Based on 3D Shape
## 4 Results
### Experimental Results and Analysis  
#### 4.2.1 Classification Experiment  
To verify the effectiveness of the proposed classification model, the same evaluation criteria and metrics as those for other algorithms were adopted, including mean class accuracy (MAcc) and overall accuracy (OA). MAcc is useful for evaluating classification models, especially when the dataset is imbalanced and the classes have different sizes. It provides a more comprehensive evaluation of a model's performance by taking into account the accuracy of each class rather than only the overall accuracy. It is calculated using Equations (13) and (14). OA evaluates the performance of a classification model by calculating the percentage of correctly classified instances over the total number of instances in the dataset. It is calculated using Equation (15).  
\[acc_{i}=\frac{M_{i}}{n_{i}}. \tag{13}\]  
\[mAcc=\frac{1}{C}\sum_{i=1}^{C}acc_{i}=\frac{1}{C}\sum_{i=1}^{C}\frac{M_{i}}{n_ {i}}. \tag{14}\]In Equations (13) and (14), \(M_{i}\) represents the number of correctly classified samples in the \(i\)th category, \(n_{i}\) represents the number of samples in the \(i\)th category, \(acc_{i}\) represents the quasi-curvature of the \(i\)th category, and \(C\) represents the number of classified categories.  
\[OA=\frac{TP+TN}{TP+FP+TN+FN}. \tag{15}\]  
In Equation (15), \(TP\) denotes the number of true positive predictions, \(TN\) denotes the number of true negative predictions, \(FP\) denotes the number of false positive predictions, and \(FN\) denotes the number of false negative predictions.  
The proposed model was compared with several recent deep learning-based point cloud classification methods, including PointNet, PointNet++, 3D-GCN, DGCNN, PointCNN, MANet, GCN3D, and DTNet. The comparison results are presented in Table 1.  
As shown in Table 1, the proposed method achieved 90.6% MAcc and 93.1% OA in the ModelNet40 dataset. Its OA was higher than that of several deep learning-based methods, including 3D-GCN (by 1%), MANet (by 0.6%), GCN3D (by 0.1%), and DTNet (by 0.2%). It was also significantly higher than that of the classical PointNet (by 3.9%) and PointNet++ algorithms (by 2.4%).  
This paper also compared the time needed to train an epoch with various methods on the ModelNet40 dataset. In order to ensure the fairness of the experiment, the methods involved in the comparison adopted the same settings--the training period was 300, the batch size was 10, the initial learning rate was 0.1, the momentum coefficient was 0.9, and the interval used to adjust the learning rate had a step size of 50 and the gamma was set to 0.3, as shown in Table 2. Our method consumed more time, but it was not much different from other mainstream methods. Although it took longer, our method had higher accuracy.
# A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation
## 1 Introduction  
Client churn is a significant problem for telecommunication companies as it results in decreased profit [1]. Moreover, this is particularly relevant since telecommunication companies operate in a saturated global market, meaning it is increasingly challenging to retain customers. Although such companies make considerable marketing investments to acquire new users, retaining a customer is usually less expensive than acquiring a new one [2]. For these reasons, avoiding customer churn has become a significant concern for telecommunications companies.  
Customer churn refers to the loss of a customer in favor of a competitor [3], reflecting the end of the relationship. Customer churn prediction allows one to identify the reasons for the end of the relationship and assemble a strategy that will minimize the churn rate, increasing profits. Thus, anticipating a customer's intention to end a relationship is instrumental for telecommunication companies and is considered a competitive advantage.  
Previous studies have attempted to understand customer churn. For instance, [PERSON] et al. [1] suggested a clustering and classification framework for churn management. [PERSON] et al. [4] proposed a new combined model based on ensemble and clustering classifiers. [PERSON] et al. [5] aimed to anticipate customer churn using the principles of data anonymization. Although multiple studies have aimed to explain and predict customer churn, no study has tried to predict telecom client churn through discriminant analysis and logistic regression.  
Facing this identified gap in the literature, this study aims to use factor analysis to investigate the business characteristics of telecom clients and to build a discriminant model and a logistic regression model to predict telecom client churn using customersegmentation data from three major Chinese telecommunication companies. Data are collected from China Mobile, China Unicom, and China Telecom and analyzed using a data mining approach to understand the factors that influence and allow one to predict telecom customer churn. Our study extends the previous work by [PERSON] [6] by innovatively showing how logistic regression analysis can be applied to build a telecom customer churn prediction model. Thus, we propose the following research questions: (1) Which factors will lead to customer loss? (2) How can one predict customer loss using the approach of data mining? (3) How can one develop a model to predict customer churn? It is expected that the results of this study will help telecommunications managers to identify the customer churn profile and create strategies to retain customers.
## 2 Materials and Methods  
### Overview of Research Area  
According to the Outline of the Integrated Development Plan for the Yangtze River Delta region issued by the State Council, the planning scope of the Yangtze River Delta (Figure 1) includes Shanghai, Jiangsu Province, Zhejiang Province, and Anhui Province of China (Area: 358,000 km\({}^{2}\), Population: 235 million people). Yangtze River Delta Urban Agglomerations are established with 27 cities as the central area (225,000 km\({}^{2}\)), including Shanghai Municipality, Nanjing, Wuxi, Changzhou, Suzhou, Nantong, Yangzhou, Zhenjiang, Yancheng, and Taizhou in Jiangsu Province of China; Hangzhou, Ningbo, Wenzhou, Huzhou, Jiaxing, Shaoxing, Jinhua, Zhoushan, and Taizhou in Zhejiang Province of China; Hefei, Wuhu, Ma'anshan, Tongling, Anqing, Chuzhou, Chizhou, and Xuancheng in Anhui Province of China, so as to radiate and drive the high-quality development of the Yangtze River Delta region. Qingpu District (Shanghai), Wujiang District (Jiangsu), and Jiashan County (Zhejiang, China) are taken as the Ecological and Green Integrated Development Demonstration Zone of Yangtze River Delta (area: about 2300 km\({}^{2}\)) to the demonstrate and lead the higher-quality integrated development of the Yangtze River Delta region. Shanghai Lingang and other areas are taken as the new areas of the China (Shanghai) Pilot Free Trade Zone to create special economic function zones that are in line with international general practices and have greater influence and competitiveness in the international market.  
The Yangtze River Delta region is in the lower reaches of the Yangtze River in China and is close to the Yellow Sea and the East China Sea. It is located at the intersection of the river and the sea and has many ports. It is an alluvial plain formed before the Yangtze River enters the sea, and has a vast water surface, significant tidal action, and a lot of sediment deposits. It is mainly dominated by subtropical monsoon climate. Since 2000, the annual mean temperature and the annual mean maximum and minimum temperatures have increased significantly, and the temperature's increasing rate is higher in winter and spring, and lowest in summer. The Yangtze River Delta is the region with the highest drainage density in China, with a mean length of 4.8-6.7 km per square kilometer. There are more than 200 lakes on the plain, with abundant water resources and a good natural water environment. There are complex types of ecosystems and diverse surface coverages. There are mainly six land use types, namely grasslands, croplands, forestlands, wetland, built-up lands, and unused lands. The Yangtze River Delta spans the north and middle subtropics, and consists of three types of forest tree communities, namely deciduous broad-leaved forests containing the evergreen broad-leaved tree species, deciduous and evergreen broad-leaved mixed forests, and evergreen broad-leaved forests.  
Figure 1: The location of the study area.