esa-sceva/satcom-chunk-collection huggingface dataset:
# A Design for a Wide-Band Antenna Pair Applied for Mobile Terminals at the Sub-6 GHz Band
## 4 Performance of the Eight-port MIMO System
This is enough to ensure that the proposed MIMO system has sufficient power to transmit information and meet the communication needs of the mobile terminals. The diversity gain (DG) reflects the ability to differentiate independent multipath signals on MIMO antenna systems, which can be calculated from the ECCs in Equation (1):  
\[DG(m,n)=10\sqrt{1-ECC(m,n)^{2}} \tag{1}\]  
It can be seen in Figure 14 that in the 3.4-5.2 GHz working bands, the value of DG is better than 9.99 dB. To verify the practicality and robustness of the proposed MIMO system as a mobile terminal antenna system, the MIMO system model, depicted for the right hand, is constructed as shown in Figure 15a. Among them, ports 5-8 are close to the inside of the hand, and radiation performance is more likely to be influenced by the hand. Figure 15b indicates that compared with the MIMO system model in the free space, the efficiency of handheld models is slightly reduced; the efficiency can be above 40% and the maximum value can reach 82%. The performances of the S-parameters are similar. Figure 15c,d indicates the \(-6\) dB impedance bandwidth is from 3.4 GHz-5.3 GHz and the isolation is  
Figure 12: Measured ECCs of the proposed MIMO system. Figure 13: Measured total efficiency and peak gain of the proposed MIMO system. (**a**) Total efficiency. (**b**) Peak gain. less than \(-9.8\) dB. The MIMO system can still achieve wide bandwidth and good coupling reduction under handheld conditions.
# How to Prevent Aseptic Loosening in Cementless Arthroplasty: A Review
## 4 Implant Properties
### Femoral Stem
#### 4.2.3 Design  
The femoral stems are divided into seven types, according to geometry, as following: type 1 (straight stem, single wedge), type 2 (straight stem, double wedge), type 3 (straight stem, tapered), type 4 (straight stem, cylindrical, fully coated), type 5 straight stem, (modular), type 6 (curved stem, anatomic stem) and type 7 (short stem) [19]. Type 1, type 2, type 6 and type 7 have an exclusively metaphyseal fixation, while type 3 and type 5 have a metaphyseal and diaphyseal fixation [19]. Type 4 has a primarily diaphyseal fixation. A study in 2011 by [PERSON] et al. showed that types 1, 2, 4 and 7 femoral stems have better survival, as follows: 98.8% at 17 years; 99.2% at 17 years, 97.9% at 15 years and 98.2% at 10 years, respectively [19]. Nevertheless, type 1, type 2 and type 4 are associated with a higher rate of stress shielding and thigh pain [19].  
Some shortened and shoulderless double-taper press-fit stems have a high rate of aseptic loosening (1,4% at five years), as shown by [PERSON] et al., in a study on 517 Dorr type A patients [20]. Straight stems had less bone loss compared to anatomical, tapered, and press-fit designs, according to a systematic analysis performed by [PERSON] et al. [15]. Regarding the femoral sizes, smaller Corail stem (sizes 8 and 9) have four times the rate of revision as compared to larger femoral sizes (10-20 stems) as found by a study on 42,265 primary THAs [21].  
According to a meta-analysis performed by [PERSON] et al., the lowest rate for aseptic loosening was found in the following stems: ABG I(r), Osteonics Cementless(r), R-B Interlok(r), Zweymuller Alloclassic(r), Freeman Cementless(r), Stanmore Custom Mare(r), MS-30(r), Corail(r) Profile Porous(r), Bi-Metric(r), Mallory-Head Cementless(r), Taperloc(r), Furlong(r), CLS Spotorno(r) and Titan(r) [14].
# Recent Progress and Challenges Regarding Magnetic-Based Nanoparticles for Targeted Drug Delivery
###### Abstract
\begin{table}
\begin{tabular}{c c} \hline \hline  & \multicolumn{1}{c}{Comprecipitation} \\  & Hydrothermal synthesis \\  & Thermal parsing \\ Fabrication & Sol–gel synthesis \\  & Sonochemical method \\  & Laser ablation \\  & Ball milling \\ \hline  & FTIR, NMR \\  & SEM, TEM \\  & EDX \\  & XPS \\  & XRD \\ Characterization & N2 adsorption–desorption \\  & BET, BjH \\  & DLS \\  & TGA \\  & VSM, SQUID \\ \hline  & Biomedical \\  & Wastewater treatment \\ Application & Energy \\  & Electronics \\  & Agriculture \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A collection of the most commonly used methods for obtaining and characterizing magnetite nanoparticles and the main directions of their application. Magnetite nanoparticles can be characterized using different physicochemical techniques (Table 1) [6; 8]. Fourier transform infrared (FTIR) spectroscopy generally analyzes the chemical structural properties, while solid-state nuclear magnetic resonance (NMR) is much less frequently used. The morphology is investigated using scanning electron (SEM) and transmission electron (TEM) microscopies, while elemental analysis is achieved through energy-dispersive X-ray spectroscopy (EDX). X-ray photoelectron spectroscopy (XPS) is also a technique used to measure the material surface's elemental composition and formation of chemical bonds. X-ray diffraction (XRD) data are used for crystal structure characterization, while textural properties are described based on nitrogen adsorption-desorption isotherm analysis. The Brunauer-Emmett-Teller (BET) method is used to calculate specific surface area, while the Barrett-Joyner-Halenda (BJH) method is used to calculate pore size distribution and pore volume. The hydrodynamic diameter of nanoparticles is measured with the dynamic light scattering (DLS) technique. Moreover, thermogravimetric analysis (TGA) is often applied to evaluate the thermal degradation of the material. Finally, the magnetization of nanoparticles can be verified by using a vibrating sample magnetometer (VSM) or super quantum interference device magnetometer (SQUID). The global market is increasingly interested in using magnetite nanoparticles in various industrial fields [6]. The main sectors of interest are biomedical and environmental applications. The positive impact of magnetite nanoparticles on the environment could be applied for water treatment using different processes (adsorption, ion exchange, oxidation) or in the catalytic process of alternative energies (biofuels, hydrocarbon cracking) [9]. Other highly developed sectors for the use of magnetite nanoparticles include electronics (data storage, battery electrodes), energy (solar cells, batteries), agriculture (fertilizers, food packaging), and others (e.g., pigments, magnetic ink) [10].
# Multilevel Regularization Method for Building Outlines
## 4 Results and Analysis
### Analyzing Applicability to Complex Types of Building Outlines  
To more comprehensively analyze the applicability and superiority of our proposed method, we selected the regularization results of different types of complex buildings throughout the study area and demonstrated them in comparison with other methods, as shown in Figure 19. It can be seen that for complex types of building outlines, Method A tends to delete the feature points in denser node regions by judging them as interference points, resulting in the loss of part of the outlines--and at the same time--owing to the regularization with respect to only one principal direction, which leads to a large error in regularizing part of the outlines; Method B relies too much on the computational accuracy of MABR, and when the MABR direction is shifted, the building orientation of the regularization result is shifted, and some of the outline details are lost. Method C is good for regularizing the outlines of complex buildings that consist of multiple rectangles with a consistent orientation. However, for complex buildings consisting of multiple rectangles with inconsistent orientation or multiple different shapes, some details of the building will be lost after regularization. Our proposed method uses the processing idea of \"whole  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Resolution** & **Method** & **P (\%)** & **R(\%)** & **IOU (\%)** & **F1(\%)** \\ \hline \multirow{4}{*}{0.075 m} & A & 92.43 & 94.31 & 87.55 & 93.36 \\ \cline{2-5}  & B & 98.06 & 97.61 & 95.76 & 97.83 \\ \cline{2-5}  & C & 96.71 & 90.16 & 87.47 & 93.32 \\ \cline{2-5}  & Proposed method & 98.27 & 98.43 & 96.75 & 98.35 \\ \hline \multirow{4}{*}{0.5 m} & A & 88.07 & 94.62 & 83.87 & 91.23 \\ \cline{2-5}  & B & 94.41 & 91.58 & 86.87 & 92.97 \\ \cline{1-1} \cline{2-5}  & C & 91.28 & 94.28 & 86.49 & 92.76 \\ \cline{1-1} \cline{2-5}  & Proposed method & 92.57 & 94.82 & 88.11 & 93.68 \\ \hline \hline \end{tabular} \(\mathbb{P}\): precision; R: recall; IOU: intersection over union; F1:F1 score.  
\end{table}
Table 5: Comparison of regularization results for different methods with two data resolutions.  
local-detail\". As a whole, we use the improved MBR method for direction control, which maintains the direction better than the other three methods; locally, we take the original building outline to be split into several for separate processing, which ensures the accuracy of each part; in detail, we simulate the idea of manual outlining and carry out the repair processing, which effectively removes the unreasonable details. Through comparative analysis, we know that our proposed method effectively improves the problems of other methods, such as insufficiently good overall or local orientation maintenance, poor outline detailing and partial outline loss. For the regularization of complex types of building outlines, our proposed method is more applicable.
## 3 Results  
### Point-to-Pixel Analysis  
The IMERG data were downloaded and then entered into the ArcGis 10.8 GIS software (GeoScene Information Technology Co. Ltd., Beijing, China), which allowed the correct coordinates of the three analysed weather stations to be identified and the satellite measured value to be extracted. The point-to-pixel procedure resulted in 10 years of daily IMERG precipitation data, also divided into rainfall and snowfall. The correlation of the daily IMERG data with the rain gauge data was then assessed using the Pearson correlation coefficient. The daily correlation coefficient between the IMERG data and rain gauge data showed a not very high correlation between them over 10 years. At the same time, the correlation between the monthly data and the annual data was also evaluated, as was that between the daily data for each individual year.  
It is evident that the correlation is not very high in general; however, there are some situations where there is a very good correlation, as in the case of daily data of Monte Bove Sud (MB) in 2018, and situations where the correlation is particularly low, as in the case of Monte Prata (MP) in 2016 or Pintura di Bolognola (PB) in 2019 (Table 1). These correlation coefficients must be taken into account to deduce possible differences in the conditions that allowed these fluctuations to occur. In particular, these differences could be due to particular humidity conditions that make the measurement of satellite data less accurate, or there could be important winds along certain directions that affect the measurement by rain gauges, or large amount of snow, etc. The next step was to investigate both monthly and annual mean differences between IMERG and rain gauge data.  
First of all, from a graphical point of view, the annual data were analysed, which tended to show for the two weather stations at higher altitude (MB and MP) lower rainfall values than IMERG (Figure 1). On the other hand, in the case of PB, rainfall and satellite data were in better agreement, with very similar years and years alternating between higher rainfall measured either by rain gauges or by satellites (Figure 2). In particular, there is substantial agreement in the fluctuations between rain gauges and satellite data, with few discrepancies, probably signifying systematic background underestimation (Figure 2).
## 6 Real Data  
In addition to the simulation study, cross-validation schemes were applied to the real dataset previously studied by [PERSON] et al. [30] and [PERSON] and [PERSON] [31], from whose work we attained the main description of the data: \"Red wines, 44 samples, produced from the same grape (Cabernet Sauvignon) were collected. A Foss WineScan instrument was used to measure 14 characteristic parameters of the wines such as the ethanol content, pH, etc. Hence a dataset consists of 44 samples and 14 variables. The actual measurements can be arranged in a table or a matrix of size (44 \(\times\) 14)\". The data are available at [[http://www.models.life.ku.dk/Wine_GCMS_FTIR](http://www.models.life.ku.dk/Wine_GCMS_FTIR)]([http://www.models.life.ku.dk/Wine_GCMS_FTIR](http://www.models.life.ku.dk/Wine_GCMS_FTIR)) (accessed on 1 April 2022) and, as it is a multivariate matrix with different measurement scales, we used the standardized matrix in all our analyses.  
A difficulty in evaluating schemas on real data is the lack of a priori knowledge of what should be \"good behaviour\". Because of this, and following the recommendation of [PERSON] and [PERSON] [29], we added outliers in the data set following the same procedure described in the simulation study and using the same comparison criteria, but the difference in the simulations occurred only once.
## 5 Model Validation
### Test Setup  
The test setup is illustrated in Figure 8. Since we want to compare predicted and measured tracking standard deviations, we have to collect live signal records to measure the code tracking standard deviation using a software receiver. Without loss of generality, we choose two IGSO satellites, PRN 13 and 14 instead of the whole current constellation to test the developed model. This is because: (i) In validation, only two satellites (desired and undesired signal) are necessary; (ii) PRN 13 & 14 are the two of the four currently operational IGSO satellites in BDS, and since MEO and IGSO in BDS use the same signal modulation scheme; IGSO signal will suffice to represent a combination of MEO and IGSO signals. Therefore, first we collect IF data containing both PRN 13 and PRN 14 signals and then we collect IF data containing only the PRN 13 signal. Finally, we obtain the measured code tracking standard deviation of PRN 13 using the first dataset, which includes the interference effect imposed by PRN 14. This interference effect is predicted using the estimated power (\(C_{s}\)) from the second dataset (free of interfering signal PRN 14) and our proposed model. As a result, a hardware signal simulator and not live signals from satellites, must be used as the signal source.  
We use a HWA-RNSS-7400, which is a multi-constellation GNSS signal simulator by HWA Create Co., Ltd. (Beijing, China) [21]. It features BDS B1, B2, and B3 signal simulation and is widely used within BDS industries. We also use a SAS6862A, which is a wide-band, dual channel (GPS L1+BDS B1 and BDS B3) radio frequency front end (RFFE) with a highly stable (better than 0.005 ppm) oven controlled crystal oscillator (OCXO) [22]. We use a multi-constellation software receiver called UNIversal COmmunications and Radio Navigation receiver (UNICORN), developed by the authors at the Centre for Transport Studies (CTS), Imperial College London and  
Figure 8: Test setup for model validation.  
certificate for the \"First 50 Successful Galileo Position Fixes\". The receiver has been adapted to process the B1 signal.  
Parameters used to configure UNICORN and drive the analytical model are the same and are provided in Table 3. The simulation starts at UTC 00:10:00, 27 Feb 27, 2014 and two 37-s IF data was recorded using SAS6862A. The receiver is assumed to be located at 31\({}^{\circ}\) N, 121.5\({}^{\circ}\) E, at an altitude of 5 m in Shanghai, China.  
UNICORN has to produce two measurements for the analytical model: \(C/N_{0}\) and code loop gain, which is indispensable for successful evaluation of tracking error variance through the model. Within UNICORN, several lines of code were inserted to calculate tracking error standard deviation.
# New Local Drug Delivery with Antibiotic in the Nonsurgical Treatment of Periodontitis--Pilot Study
###### Abstract  
Combination of the classical subgingival instrumentation (scaling and root planing procedure, SRP) with an antibiotic administered to periodontal pockets in a suitable medium is a promising alternative protocol of nonsurgical periodontal treatment. It enables obtaining the long-term minimum drug concentration inhibiting the development of period pathogens. Objectives: Clinical and microbiological evaluation of periodontal pockets two months after single application of a gel containing piperacillin and taxobactam (Gelcide)@in relation to the nonsurgical treatment procedure (SRP). Materials and methods: Ten patients aged 24-56 years (mean 39.5) with chronic periodontitis, nonsmokers with acceptable oral hygiene and no classical exclusion criteria were qualified for treatment. In the maxilla area, SRP was performed and the assessed gel was inserted to two randomly selected adjacent periodontal pockets. Clinical evaluation included the assessment of bleeding on probing (BoP), pocket depth (PD), and clinical attachment loss (CAL) at six measurement points. A microbiological examination with the use of PET deluxe diagnostic kit in the drug-administered pockets and symmetrically in two pockets on the other side of the dental arch was performed. The examination was conducted before the treatment and two months later. Results: Two months after the treatment, a significant improvement in all analyzed clinical parameters was observed. However, the extent of this improvement did not differ significantly between the compared treatment methods. No statistically significant differences were found in the number of bacteria before and after the treatment, except for a significant decrease in the number of Micromonas micros (2957 vs. 589, p = 0.028) and a higher number of the green complex bacteria Capnocytophaga gingivalis (5439 vs. 2050, p = 0.041) after antibiotic had been used. Conclusion: No significant clinical and microbiological differences were found after additional administration of gel with piperacillin and taxobactam in relation to SRP in the preliminary study.  
**Keywords:** periodontitis; SRP; LDD; gelcide
## 4 Conclusions  
The isolated strain _L. salivarius_ AS22 had significant probiotic potentials as evident; AS22 showed low pH and high bile salt tolerant features without hemolytic activity and possessed intense antagonistic activity against various intestinal bacteria and phyto fungi with good antioxidant activity. AS22 produced a significant level of lactic acid in MRS broth (an essential acid). _L. salivarius_ AS22 exert potential effects on silage fermentation by increasing lactic acid concentration which enhanced acidification of silage and this condition inhibited undesirable microbial growth at ensiled condition. Overall data suggest that the AS22 showed significant probiotics characteristics with antagonistic activities with great potential to improve the silage quality with enriched nutrients and can be preserved for long time storage. It is an in vitro laboratory-scale level experiment. However, in-depth investigation is essential to confirm the efficacy of the isolated LAB on silage production and preservation of different legumes and grass plants with an animal performance study.  
Conceptualization and methodology: M.V.A., N.A.A.-D., B.B. and P.V. Investigation, validation formal analysisand writing--original draft preparation: I.S., K.B. and P.V. Data curation, writing--review and editing, visualization, and supervision: M.V.A., N.A.A.-D. and K.C.C. Project administration: M.V.A. and N.A.A.-D. All authors have read and agreed to the published version of the manuscript.  
Authors extend their appreciation to the Researchers Supporting Project number (RSP-2021/20), King Saud University, Riyadh, Saudi Arabia.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Control** & **AS22 Treated** & **_p-Value** \\ \hline LAB (Log CFU/g) & 5.3 \(\pm\) 0.29 & 6.80 \(\pm\) 0.14 & 0.0005 ** \\ Fungi (Log CFU/g) & 1.2 \(\pm\) 0.36 & 0.90 \(\pm\) 0.29 & 0.0156 * \\ Actinomycetes(Log CFU/g) & 0.72 \(\pm\) 0.28 & 0.43 \(\pm\) 0.19 & 0000 *** \\ pH & 5.29 \(\pm\) 0.03 & 3.84 \(\pm\) 0.07 & 0.00262 ** \\ Lactic acid (\(\mathrm{\SIUnitSymbolMicro g}\)/g) & 22.3 \(\pm\) 3.9 & 38.7 \(\pm\) 1.40 & 0.0002 *** \\ \hline \hline \end{tabular} Total LAB, fungi, and actinomycetes populations were determined and the result was expressed as Log CFU/g. Lactic acid concentration was assayed and expressed as \(\mathrm{\SIUnitSymbolMicro g}\)/g. (* statistically significant at \textless{}0.05 (\text{\textSymbolMicro g}\), at \textless{}0.005 (\text{\textSymbolMicro g}\)), \textless{}0.0005 (***) and \textless{}0.0001 (***) level, respectively).  
\end{table}
Table 3: Microbial population and lactic acid content of experimental silages.
## 2 Related Work
Secondly, the selected CHs verifiedthe identities of the mobile sink nodes and then forwarded the multimedia data. The results showed significant performance when compared to other work. The authors in [34] proposed a resource scheduling and secure data transmission of IIoT data using SoftMax-DNN and improved RSA techniques. The authors validated and evaluated the proposed techniques and existing techniques, using simulation in JAVA and NS3 platforms, by evaluating various performance metrics in terms of latency and throughput. The proposed scheduling algorithm uses the NDRF-SSA clustering and SHA-512 algorithm; compared with existing techniques, it attains the lowest latency, the lowest energy consumption, and the highest network lifetime. In [35], a smart collaborative routing protocol with low delay and high reliability was proposed, contributing to the mixed link scenarios. The researchers constructed a one-hop delay model and analyzed the possible effects of the media access control (MAC) layer. Moreover, data forwarding, maintenance, and efficient policies were made to improve the performance of the routing protocol. Based on the experimental results, it was observed that the ratio of latency decreased compared to the existing solution. In [36], the authors proposed a mobile cloud-based scheduling strategy for the IIoT. Different computing solutions, i.e., fog, mobile, and edge computing could be combined in IIoT, allowing offloading of the execution of any task on the cloud system. The proposed solution models the problem of task scheduling to optimize the energy consumption issue. It uses genetic algorithms while taking into account task dependency, data transmission, and resource constraints. The experiments were conducted; the results showed significant improvement of the proposed solution when compared to the existing work. The technology of the IoT and mobile edges are broadly utilized for data sensing and support efficient network structure. Such systems facilitate many network applications, i.e., healthcare, military, farming, multimedia, agriculture, etc. However, the devices and network sensors are restricted in various operations and resources. Such limitations impose many difficulties in managing the network stability and are not able to fulfill the users' demands. Nowadays, traditional solutions are not able to support real-time data collection with a high amount of risky threats. Although some solutions are proposed in the literature, they are not fully accurate in terms of delay tolerance and delivery performance, especially when the network grows rapidly. Moreover, it was also observed that many proposals have failed to provide light cost authentication.