esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Simulation Analysis
### Bit Error Rate Comparison of Different Systems  
Figure 12 illustrates the relationship between the error rates of the MC\(-\)ODBR\(-\)DCSK, SR\(-\)ODBR\(-\)DCSK, ODBR\(-\)DCSK, and DCSK systems under the same testing conditions. The systems were evaluated using an improved Logistic map and a semi-spread spectrum factor of 200. Based on the experimental results, the SR\(-\)ODBR\(-\)DCSK system exhibited the best error rate performance when the signal-to-noise ratio (SNR) was less than 11 dB, while the MC\(-\)ODBR\(-\)DCSK system demonstrated the best error rate performance when the SNR was greater than 11 dB.  
Figure 13 depicts the correlation between the number of semi\(-\)spread spectrum factors and the bit error rate of four systems, namely MC\(-\)ODBR\(-\)DCSK, SR\(-\)ODBR\(-\)DCSK, ODBR\(-\)DCSK, and DCSK, when the signal-to-noise ratio is 10 dB in an AWGN channel  
Figure 11: Relationship between semi\(-\)spread spectrum factor and bit error rate.  
Figure 12: Comparison of bit error rates of DCSK, ODBR\(-\)DCSK, SR\(-\)ODBR\(-\)DCSK, and MC\(-\)ODBR-DCSK systems in Gaussian and Rician channels.  
using the 2D-Logistic map. Generally, as the semi-spread spectrum factor increases, the error rate performance of the MC\(-\)ODBR\(-\)DCSK system changes slowly at first and then gradually deteriorates. The error rate performance of the SR\(-\)ODBR\(-\)DCSK system, ODBR\(-\)DCSK system, and DCSK system initially increases and then decreases.
###### Abstract  
**Objective:** Despite long simulation times, recently developed finite element analysis (FEA) models of knee joints have demonstrated their suitability for predicting individual risk of onset and progression of knee osteoarthritis. Therefore, the objective of this study was to assess the feasibility of machine learning (ML) to replicate outcomes obtained from FEA when simulating mechanical responses and predicting cartilage degeneration within the knee joint. **Design:** Two ML models based on the Gaussian Process Regression (GPR) algorithms were developed. The first model (GPR1) utilized age, weight, and anatomical joint dimensions as predictor variables to predict tissue mechanical responses and cartilage degeneration based on FEA data. The second model (GPR2) utilized age, weight, height, and gender to predict anatomical joint dimensions, which were then used as inputs in the GPR1 model. Finally, the GPR1 and combined GPR1+GPR2 models were used to investigate the importance of clinical imaging when making personalized predictions for knees from healthy subjects with no history of knee injuries. **Results:** In the GPR1 model, R\({}^{2}\) of 0.9 was exceeded for most of the predicted mechanical parameters. The GPR2 model was able to predict knee shape with R\({}^{2}\) of 0.67-0.9. Both GPR1 and combined GPR1+GPR2 models offered equally good performances (AUC = 0.73-0.74) in classifying patients at high risk for the onset and development of knee osteoarthritis. **Conclusions:** In the future, real-time and easy-to-use GPR models may provide a rapid technology to evaluate mechanical responses within the knee for researchers or clinicians who have no former knowledge of FEA.  
k _et al._ Article
## 4 Conclusions  
SAR images do not show a direct correlation with soybean yield; thus, using SAR data alone is not yet a viable alternative for predicting soybean yield within the fields in a harvest when optical data is not available.  
The filters evaluated for reducing speckle noise revealed that the Refined Lee filter performed the worst compared to the others. However, evaluating the different filters did not result in significant improvements in the correlation with yield. Therefore, choosing the Boxcar filter is advantageous, since it has the lowest operational cost among all the tested options.  
Testing prediction scenarios that include SAR data alongside EVI showed improved performance. Nonetheless, this difference was minimal in some situations (3-49%), but the computational effort required to process SAR data must be considered.  
Using only the DPSVI index and EVI after the vegetative peak resulted in superior predictive performance compared to other stages or SAR variables, compared to the use of optical data alone. This finding underscores the potential of SAR data to significantly improve the accuracy of yield predictions. SAR data offers valuable complementary insights that enhance those obtained from optical sensors, particularly in scenarios where acquiring optical images before and during peak vegetative growth is difficult due to high cloud cover.  
Given that soybean yield does not correlate well with remote sensing data, further studies with other crops may provide better predictive results using SAR imagery for yield estimation. It is important to consider conducting experiments in more areas and testing different cultivars to obtain a more comprehensive and accurate understanding of the potential of SAR images in predicting soybean yield.  
**Author Contributions:** Conceptualization, I.A.C. and L.R.A.; methodology, I.A.C., G.M.M.B. and L.R.A.; validation, V.H.R.P. and G.M.M.B.; formal analysis, I.A.C.; investigation, I.A.C.; resources, L.R.A.; data curation, I.A.C. and D.D.M.; writing--original draft preparation, I.A.C. and D.D.M.; writing--review and editing, I.A.C., G.M.M.B., V.H.R.P., D.D.M. and L.R.A.; visualization, L.R.A.; supervision, L.R.A. and G.M.M.B.; project administration, L.R.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Coordination for the Improvement of Higher Education Personnel--Brazil (CAPES)--Finance Code 001 and the Sao Paulo Research Foundation--FAPESP (Process number 2022/03160-8).  
**Institutional Review Board Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy concerns regarding yield data.  
**Acknowledgments:** The authors thank everyone who collaborated in the data collection and analysis and the field experiment.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Low-Profile Harmonic Transponder for IoT Applications
## 4 Experimental Results
### Link Budget Power and Interference Analysis
On the receiving path, HPF is also necessary to minimize the leakage of fundamental frequency, where the HPF insertion loss together with the cable is \(\approx\)5.5 dB at the harmonic frequency. Therefore, the second harmonic at 4.9 GHz can be detected up to \(-100\) dBm spectrum sensitivity. The range measurement verifies the link budget calculations, where a coverage of 3 m is achieved as explained in Figure 12. Table 1 shows the comparison of the developed transponder versus the previously reported ones from the printability, electrical size, and coverage aspects. In comparison to transponders, which are also single layer, as listed in Table 1, specifically, in references [15; 17], the introduced transponder outperforms the previously mentioned contributions in terms of the electrical size and coverage point of views. Further, the other advancements compared to those in the literature are listed above. Figure 11: Link budget measurement setup.
###### Abstract  
Efforts to enhance the utility of photodynamic therapy as a non-invasive method for treating certain cancers have often involved the design of dye sensitizers with increased singlet oxygen efficiency. More recently, however, sensitizers with greater selectivity for tumor cells than surrounding tissue have been targeted. The present study provides an approach to the modification of the known photosensitizer zinc phthalocyanine (ZnPc), to enhance its solubility and delivery to cancer cells. Targeting a photosensitizer to the site of action improves the efficacy of the sensitizer in photodynamic therapy. In this work we used PLGA-b-PEG to encapsulate a new zinc phthalocyanine derivative, 2(3), 9(10), 16(17), 23(24)-tetrakis-(4'-methyl-benzyloxy) phthalocyanine zinc(II) (ZnPcBCH\({}_{3}\)), to enhance uptake into A549 cells, a human lung cancer cell line. ZnPcBCH\({}_{3}\) exhibited the same photochemical properties as the parent compound ZnPc but gave increased solubility in organic solvents, which allowed for efficient encapsulation. In addition, the encapsulated dye showed a near 500-fold increase in phototoxicity for A549 cancer cells compared to free dye.  
PDT photosensitizer; phthalocyanine derivative; PLGA-b-PEG nanoparticles; phototoxicity 1
# Typhoon Track Prediction Based on Deep Learning
###### Abstract  
China is located in the northwest Pacific region where typhoons occur frequently, and every year typhoons make landfall and cause large or small economic losses or even casualties. Therefore, how to predict typhoon paths more accurately has undoubtedly become an important research topic nowadays. Therefore, this paper predicts the path of typhoons formed in the South China Sea based on deep learning. This paper combines the CNN network and the LSTM network to build a C-LSTM typhoon path prediction model, using the typhoon paths and related meteorological variables formed in the South China Sea from 1949 to 2021 as the data set, and using the Granger causality test to select multiple features for the data set to achieve data dimensionality reduction. Finally, by comparing the experiments with the LSTM typhoon path prediction model, it is proved that the prediction results of the model have smaller errors.  
t  
typhoon track prediction; granger causality test; deep learning; C-LSTM
# Current Context and Research Trends in Linear DC-DC Converters
## 4 Dominant Characteristics of LDOs
### Better Transient Performances  
LDOs can overcome transient fluctuations without needing any additional components. Regardless of the sudden variations in the input side, LDOs are capable of keeping the output voltage stable. There are specially designed LDOs for fast input variation applications, such as TPS57140-Q1 [22], where the slew rate is 1.2 V/ us, and the better transient performance is a very important factor when supplying power for microprocessors.
## 2 Description of Flywheel Energy Storage Systems  
### Background  
The flywheel is one of the most primitive mechanical energy storage systems which has been in existence for thousands of years. The potter's wheel was one of the earliest forms of rotary objects using its own inertia to maintain energy [22]. The name \"flywheel\" emerged as a result of the industrial revolution, where flywheels were used to operate as energy accumulators, as well as in steam engine trains and boats (namely in 1784) [23]. Despite its existence for millennia and implementation of numerous designs and shapes, there have not been major developments on flywheels until very recently. It was only at the beginning of the 20 th century that flywheels appeared as potential energy storage systems as a result of major developments on rotor shapes and thorough analysis of the rotational stress [24]. In 1960s and 1970s, FESS was proposed for electric vehicles, stationary power backup, and space missions [9; 10]. In the following immediate years, fibre composite rotors were built and tested. In 1980s, relatively low speed magnetic bearings started to appear [25].  
Although flywheels have been improved for use in different areas, their utilisation as energy storage systems deteriorated with the expansion and improvements of the electrical grid. However, recent major developments in materials, bearing systems, power electronics, and development of high speed electric machines have enabled flywheels to perform energy storage applications and emerge as a promising technology competing with many other storage systems [7; 8; 9; 26; 27].  
A flywheel is a mechanical storage system operating on the principal of a rotating mass to store energy. It converts the electrical energy into mechanical energy and stores it as rotational kinetic energy. The flywheel is charged by extracting electrical energy from an available source to accelerate the rotor speed and accumulate energy. It is discharged by delivering the stored energy back into its electrical form. The operation of the flywheel is controlled by an electrical machine functioning as a motor-generator (MG) to perform the energy conversion between electrical and mechanical forms [28; 29]. The flywheel and the MG are coupled on the same shaft, which enables the FESS to be controlled by the operation of the MG [30; 31; 32; 33].
## 4 Dataset Details  
The UT probe acquiring the data is situated on a rotating mechanical head which when centered inside an empty PT, and pushed very slowly down its length as the head spins, allows data acquisition over the entire inner diameter surface of the PT in a tight corkscrew pattern. Our dataset contains 3600 _ascans_ per head rotation, on an axial raster of \(\sim\)0.2 mm. In this dataset the subject under test is a calibration fixture, which is a mock PT, scanned before every real PT inspection. The signal response from notches in the fixture is used to verify and tune UT probes to meet inspection quality requirements. Figure 1 provides a detailed look at a portion of our dataset. Each _ascan_ in the dataset is associated with the tool position (axial and rotary) and time of data acquisition. This allows the set to be ordered (and visualized) in time, or position, of acquisition as desired.  
For our purposes we reduce the dimensionality of the _ascan_ dataset, summarizing each _ascan_ by the time of flight of its maximum value \(t_{pa}\), which we refer to as peak amplitude response time of flight. This is simply the time in \(\upmu\)s at which the peak value of a given _ascan_ (such as those in the top right column of Figure 1) occurs. Multiplying \(t_{pa}\) by the speed of sound \(v\) in \(D_{2}O\) (deuterium oxide, or heavy water, is the nuclear fuel coolant in a CANDU reactor) gives the round-trip distance between the UT probe and the primary reflector, in this case the calibration fixture inner diameter pipe wall.  
We can extract \(t_{pa}\) from a given _ascan_ using an \(argmax(\cdot)\) function (scientific computing software libraries generally include an \(argmax()\) function that will return an array's maximum value and index) operating on the _ascan_ data vector. So, given a set of _ascans_\(\mathcal{A}\) with elements \(a\), we construct dataset \(\mathcal{X}\) as follows:  
\[\mathcal{X}=\{argmax(a)|\forall a\in\mathcal{A}\} \tag{1}\]  
where \(argmax(a)\) yields \(t_{pa}\) for \(a\in\mathcal{A}\).
## 3 Results and Discussion
### The Data Transmission to the Cloud  
Figures 13 and 14 illustrate the measured impedance values of concrete columns M1 and M2, respectively. The provided data for both mixtures correspond exclusively to the electrode pair E4-E5, chosen for its depth within the concrete, rendering the measured data less susceptible to factors such as the water evaporation rate on the surface of the concrete columns.  
In the case of mixture M1, an initial impedance of 372 Ohms was observed upon concrete pouring. Over a span of 30 h, this value gradually escalated to 639 Ohms. Subsequently, within one day, a peak value of 120 kOhms manifested--a peak intricately linked to sudden shifts in the internal concrete temperature following cement hydration. These temperature fluctuations saw a rise from 21.7 \({}^{\circ}\)C to 39.9 \({}^{\circ}\)C. A few hours later, the temperature decreased to 24.6 \({}^{\circ}\)C and consequently so did the impedance. Following this thermal upheaval, there was a swift decline in temperature to 24.6 \({}^{\circ}\)C, mirrored by a corresponding dip in impedance. Nonetheless, the impedance continued increasing due to the ongoing concrete-hardening process. Notably, the undulations in the impedance curve concurred with the oscillations in temperature. After the passage of 28 days, M1 exhibited an impedance of 126 kOhms.  
As for column M2, the initial impedance measured during concrete pouring stood at 570 Ohms. In a manner akin to M1, a temperature surge from 23 \({}^{\circ}\)C to 31.1 \({}^{\circ}\)C prompted a corresponding 2853 Ohms rise in impedance. Despite subsequent temperature diminution to 25.7 \({}^{\circ}\)C, the measured impedance reached 17 kOhms 46 h after the mixture was poured. Evidently, the fluctuations in the resistivity curve mirrored the temperature variations with remarkable fidelity. Upon the culmination of 28 days, the impedance reached 221 kOhms. Comparatively, M2 displayed a more rapid rate of impedance increase than M1. Impressively, after a span of 83 days, M2's impedance surpassed that of M1 by a factor of 6, which can be linked to the different dynamics of the cement hydration reaction given that M2 was produced with a blended cement type, in which a secondary chemical reaction (the pozzolanic reaction) is expected to take place at later ages, contributing to a densification of the matrix and further strength development.  
After a duration of 83 days, both devices remained operational without any reported issues for the batteries or in data transmission. This serves as a testament to the robustness  
Figure 14: Measured impedance of column M2 between 7 June and 29 August.  
Figure 13: Measured impedance of column M1 between 7 June and 29 August.  
and dependability of the wireless communication system in use, regardless of the presence of steel reinforcement and cement types. In addition, after 83 days, the batteries were still at their highest capacity, meaning that the current consumption of the nodes in the long term has been stable.
## 2 Materials and Methods
### Adsorption Column Modeling
#### 2.7.1 Model of Yoon-Nelson  
The Yoon-Nelson model is based on the assumption that the rate of decrease in the probability of adsorption of adsorbate molecule is proportional to the probability of the adsorbate adsorption and the adsorbate breakthrough on the adsorbent. The Yoon and Nelson model is not only less complicated than other models, but also requires no detailed data concerning the characteristics of adsorbate, type of adsorbent, and the physical properties of the adsorption bed [37]. The Yoon-Nelson equation's linear expression for a sole constituent can be shown in Equation (5), as follows [37]:  
\[ln\ \left(\frac{C_{eff}}{C_{0}-C_{eff}}\right)=k_{YN}\ t-\tau\ k_{YN} \tag{5}\]  
where \(k_{YN}\) represents a constant for the rate (given in min\({}^{-1}\)) in addition to \(\tau\), which is the time, required for 50% adsorbate development (given in min).
## 2 Materials and Methods
### Organic Acid and Sugar Analysis  
Two grams of each sample were diluted in double-distilled water and homogenized for 180 s at 4000 rpm. Afterward, 10 mL of the homogenate was treated with 12.5 mL of 0.01 N H\({}_{2}\)SO\({}_{4}\), vortexed for one minute, and the upper phase was collected in an Eppendorf tube, followed by centrifugation at 10,000\(\times\)\(g\) for 5 min. The supernatant was filtered using a 0.45 \(\upmu\)m filter (Merck, Millipore, Millex-LG). This pre-extraction process was applied for both organic acid and sugar analyses.  
The organic acids of the samples were quantified using high-performance liquid chromatography (HPLC), equipped with a UV-visible detector and an Inertsil ODS-3V C18 column. The mobile phase consisted of a 5 mM H\({}_{2}\)SO\({}_{4}\) solution (pH adjusted to 3.0), flowing at 1.0 mL/min with a column temperature of 30 \({}^{\circ}\)C. Organic acids were monitored with a diode array detector at 210 nm, with results calculated via correlation coefficient (r values for lactic, acetic, citric, and formic acids were 0.999, 0.999, 0.999, and 0.999, respectively).  
Similarly, the sugar contents of the samples, including glucose, galactose, and lactose were measured. HPLC analyses of the compounds were performed using a refractive index detector (RID-20A, Shimadzu, Kyoto, Japan). The Transgenic CARBOSep COREGEL-87P column was used for qualitative and quantitative determination. Ultra-pure water was used as the mobile phase at a flow rate of 0.8 mL/min and a column temperature of 80 \({}^{\circ}\)C.  
The qualitative and quantitative determinations of organic acids and sugars were carried out using the HPLC method. The retention time (t\({}_{\text{R}}\)) value of each compounds in the method was determined three times, and when the repeatability data were evaluated, the relative standard deviation percentage (%RSD) was calculated below 2%. In addition, chromatographic retention parameters were calculated to determine the compounds quantitatively. Accordingly, it was determined that the capacity factor (k) values were in the range of 1 \(\leq\) k \(\leq\) 10, the selectivity factor (\(\alpha\)) was \(\geq\)1.15, and the separation factor (R\({}_{\text{s}}\)) value was \(\geq\)1.5.
## 1 Introduction
Figure 2 shows the distribution of longitudinal sleeve-grout bond stress in cross section of the sleeve, where the size of symbol \"\(\times\)\" and symbol \"\(\bullet\)\" indicate the relative magnitude of stress. In this way, the tensile load of the sleeve is small. Furthermore, the stress mechanism of the grouted sleeve lapping connector was studied in the literature [23]. The results showed that the sleeve was longitudinally stretched at the initial stage of loading, while the sleeve  
Figure 1: Schematic diagrams of grouted splice connector and grouted sleeve lapping connector. (**a**) Grouted sleeve splice connector, (**b**) Grouted sleeve lapping connector. was longitudinally compressed in the late period of the loading process. As a result, the requirements for the material properties of the sleeve are low and the inner wall does not need to be treated. The grouting material can be reinforced by C60 grouting material. In addition, the inner diameter of the sleeve is large, and the maximum particle size of the grouting material can be controlled to be smaller than 4.75 mm. All the above points can lower the cost of the connector. Comparative tests on the mechanical properties of grouted splice connectors and APC connectors were conducted by [PERSON] et al. [24]; it was shown that the constraint on the grouting material of an APC connector was stronger than that of a grouted splice connector, and the mechanical properties of the APC connector were better than those of the grouted splice connector. The APC connectors have mainly been studied using uniaxial tension tests [23; 25] to determine the bearing capacity, load-displacement curves, and hoop and longitudinal strains of the sleeves. Meanwhile, the ductility, residual deformation, and hoop and longitudinal strain of the APC connector under high-stress repeated tension and compression loads have largely gone uninvestigated. The force distribution and constraint mechanism under repeated tension and compression loads need to be studied to fully understand the mechanical properties of connectors. It is also significant to research the bonding performance of connectors under repeated tension and compression loads since bonding plays an important role in resisting earthquake forces and wind loads. In this study, 32 APC connectors were tested under uniaxial tension and high-stress repeated tension and compression loading. The failure mode, load-displacement curves, ductility, residual deformation, and hoop and longitudinal strain curves were analysed with the lapping length of the steel bar as a variable parameter. Moreover, the mechanical mechanism of the connector under compression was analysed, and an equation for calculating the average bonding stress and critical lapping length was proposed based on the defects of the connector.
# A Comprehensive Signal Quality Assessment for BDS/Galileo/GPS Satellites and Signals
## 2 Data and Methods
### Code Multipath Analysis

Table 3: Selection of signals in different multipath combinations.