esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review
## 6 Tsunamis
### Iot Architectures for Tsunami EW Systems  
Table 6 lists the main technologies used in Tsunami EW reviewed literature for each layer of the reference IoT architecture.  
Detection of wave heights and anomalies on the sea surface can also be used for early detection of tsunami waves. Wave measurements can be obtained from offshore buoys, but while they are very accurate, they can also be very costly. Low-cost, low-power MEMS sensors, such as accelerometers and gyroscopes, can also be used to obtain wave characteristics such as height, period, direction and speed by deploying them on floating buoys.  
The use of sensor networks in underwater environments comes with associated challenges related to the communication layer. Because of the harsher conditions and related higher costs, but also because this environment comes with propagation scenarios that make radio-frequency communications difficult to use, the acoustic channel is preferred compared to radio communication [76; 78], with typical data rates from 31 kbps to 125 kbps [88], or even up to 300 kbps in the solution by [PERSON] [79]. Optical communications are also a solution, and they provide faster, low energy communications with lower delays, but they require a line-of-sight unlike acoustic sensors. Some existing underwater sensor networks, such as S-Net in Japan, use wired optic communications. The most used communication choice for underwater wireless solutions is certainly the acoustic channel. Reviewed papers that used UWSN did not deal with 'onland' communications, which is usually carried out by an IP network such as a cellular network. Only [83] took into account the possibility of terrestrial communications failing in case of disaster, and as such considered the use of satellite communications to deal with that event. Satellite communication is also particularly suited to cover the large distances required in some solutions. Solutions such as LoRa and GSM have also been employed for tsunami warning devices.  
Most TEWs based on a UWSN use a basic architecture which uses a central server connected to the sink nodes through an IP network. As such the main computing is done at said server, and there are different methods to detect the tsunami and its point of origin (inversion, Machine Learning, underwater sensors classification, etc.). Non-UWSN systems can also use simple threshold methods or other algorithms such as the Fuzzy Logic algorithm developed by [86]. These relatively simple systems also allow to move the prediction at the edge of the network and closer to the monitored area, making alerts faster.
# Biotin Auxotrophy and Biotin Enhanced Germ Tube Formation in _Candida albicans_
## 3 Results
### Effects of Biotin on \(\Delta\)efg1, \(\Delta\)cph1, and \(\Delta\)cdc35 Deletion Mutants  
The previous series of experiments showed that biotin enhanced GTF in all growth media not already maximally stimulated by something else. However, as yet we do not know biotin's mode of action. One approach to this question tests a series of mutants defective in hyphal formation to see if high biotin restored their hyphal forming ability. Accordingly, we examined the \(\Delta\)efg1, \(\Delta\)cph1, and \(\Delta\)cdc35 (adenylyl cyclase) deletion mutants of _C. albicans_. However, none of these mutants exhibited GTF in 2.5 mM GlcNAc, either with or without 4 uM biotin. These results are consistent with earlier reports that these mutants did not form germ tubes in [PERSON]'s medium. [PERSON] et al. [23] showed that _C. albicans_ mutants defective in adenylyl cyclase were unable to switch from yeasts to hyphae under all in vitro conditions analyzed, including [PERSON]'s medium. Similarly, the hyphal defective mutants _cph1/cph1_[36] and _efg1/efg1_[22] were unable to form hyphae under all six of the hypha promoting conditions tested (not including [PERSON]'s medium) but subsequently [PERSON] et al. [37] showed that the single mutants _efg1/efg1_ and _cph1/cph1_, and the double mutant _efg1 cph1/efg1 cph1_ did not form hyphae in [PERSON]'s medium at 37 \({}^{\circ}\)C. These experiments collectively show that high biotin acts before adenylyl cyclase and the two transcription factors for hyphal associated genes, Efg1p and Cph1p.  
Figure 6: Germ tube formation at 2.5 mM _N_-acetylglucosamine (GlcNAcNAc) is not affected by the addition of biotin. Three times washed inocula were incubated at 37 \({}^{\circ}\)C with rotary agitation at 220 rpm. Cultures were observed for 5 h, and plotted as: \(\blacktriangledown\), no addition; \(\blacklozenge\), 100 nM biotin; \(\blacksquare\), 400 nM biotin; and \(\blacktriangle\), 1 uM biotin. Values shown are the average of triplicate experiments \(\pm\) SD. Virtually identical results were obtained using Yeast Extract-Peptone-Dextrose (YPD)-grown or defined Glucose-Salts-Biotin (GSB)-grown inoculum.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Percent Germ Tube Formation 1**} \\ \hline \# **Cell Washes** & **0.01** & **0.1** & **1.0** & **5.0\% Serum** \\ \hline
0 & 3 & 0 & 52 & 90 \\
3 & 0 & 14 & 71 & 80 \\
6 & 7 & 44 & 93 & 98 \\
8 & 0 & 80 & 95 & 98 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Effect of cell washings on serum-induced germ tube formation in _C. albicans_.
# Fragile Watermarking for Image Authentication Using the Characteristic of SVD
## 5 Conclusions  
In this paper, we present a pixel-based fragile watermarking algorithm for image authentication. According to the characteristic of SVD, the binary authentication information is produced and inserted into the host image. Experimental results and analysis demonstrate that the watermarked images obtained via the proposed method have a satisfactory visual effect. In addition, the Arnold transform is used twice in the watermark embedding process, which guarantees the security of the suggested algorithm. Compared with the recently developed watermarking schemes, it not only achieves blind detection but also has higher tamper detection and localization resolution for various attacks. In future work, we hope to further improve detection accuracy and research the effects of different parameters on tamper detection.  
This work was supported by the National Natural Science Foundation of China (No. 61201371), the Research Award Fund for Outstanding Young and Middle-Aged Scientists of Shandong Province, China (No. BS2013 DX022), and the Natural Science Foundation of Shandong Province, China (No. ZR2015 PF004).  
**Author Contributions:** [PERSON] and [PERSON] conceived the algorithm and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the results; [PERSON] drafted the manuscript; [PERSON] revised the manuscript. All authors have read and approved the final manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Items** & **[PERSON] et al. [(10)]** & **[PERSON] et al. [(12)]** & **The Proposed** \\ \hline PSNR/dB & 51.14 & 51.14 & 51.16 \\ Blind & No & Yes & Yes \\ Image cropping & Yes & Yes & Yes \\ Copy–paste attack & Yes & Yes & Yes \\ Text addition & Yes & Yes & Yes \\ Collage attack & No & Yes & Yes \\ Content-based attack & No & Yes & Yes \\ Constant-average attack & No & No & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 2: All comparisons among the proposed scheme and references [(10; 12)].  
Figure 11: False positive rate (FPR) and false negative rate (FNR) comparisons under different cropping sizes: (**a**) FPR; (**b**) FNR.
## 3 Results
### Impact of C-Rate on the Discharge Voltage  
Further discharge cycles were investigated in a C-rate range from 1/24\(C\) to 1/2\(C\) and subsequently compared with the experimental data. The results are plotted in Figure 5a. In general, the transition between the two voltage plateaus is found to become increasingly blurred for higher C-rates. For 1/2C in particular, there is almost no apparent plateau or transition and the voltage level drops significantly for small DoDs. The experimental data also reflects this. Especially for higher C-rates, there is a discrepancy between the modeled and experimental data for \(DoD=0\). In the model, the initial voltage drop is significantly overestimated. Furthermore, the model reproduces the respective transition region over DoD for the different C-rates well, but an overall shift in the transition region to smaller DoD can be observed compared to the experiment. The reason for this shift can be found again in the unknown exact composition of the commercial cell, since the ratio of \(NiCl_{2}\) to \(FeCl_{2}\) was estimated for the model initialization. Comparing the model results for different C-rates shows that the model is capable of reflecting the increasing voltage losses for higher C-rates.  
Figure 4: Model validation with 1/8C (\(I=5.125\ A\)): (**a**) discharge (dch) and (**b**) charge (chg). Highlighted are three different segments: **1 and can be removed. The following highlights are the same.** refers to the voltage plateau according to Equation (1); **2** refers to the transition towards the next plateau; and **3** is related to the voltage plateau according to Equation (2).  
Figure 5: (**a**) Simulated and measured discharge voltages for four different C-rates over DoD. (**b**) Modelled charge and discharged voltage for 1/8C showing a hysteresis.
# Adaptive Stacking Ensemble Techniques for Early Severity Classification of COVID-19 Patients
## 3 Proposed Method
### Feature Selection  
In this phase, we propose a feature selection algorithm to improve the performance of our severity prediction model by using only selected features of high importance and removing variables of low relevance. Feature selection is an important process aimed at identifying a subset of relevant features to improve the ability to predict clinical severity and enhance interpretability. Feature selection methods can be classified into three main types: filters, wrappers, and embedded methods [45]. Filter methods use statistical techniques to evaluate the correlation or dependence between each input feature and the target outcome. These methods are efficient and fast; however, they can select redundant variables because they do not consider the interactions between the features. Moreover, a high correlation coefficient does not always indicate model suitability. In contrast, embedded methods select features based on the feature importance results of learning-based models, such as the Gradient Boosting Machine (GBM) or random forest (RF). Although these methods consider feature interactions, they may be overly biased toward specific models. To address these limitations, we propose a feature selection algorithm that uses wrapper methods to select a subset of features based on various models with high accuracies. To identify the important features, we employed greedy search techniques, including forward selection, backward elimination, and RFECV. We used all three feature selection methods to determine the optimal feature set. The drawback of wrapper methods is that the selected features are heavily dependent on the specific model used for the feature selection. This implies that a feature set that is optimized for one model may not perform effectively with another model. To address the model dependency issue inherent in wrapper methods, our algorithm utilizes various tree-based ensemble models, such as Light Gradient Boosting Machine (LGBM), XGBoost, and CatBoost. Instead of relying on a single model, the proposed algorithm evaluates the performance of each feature across these models. It then compiles a feature set containing features that have high importance across multiple models, thereby reducing the reliance on feature selection for any single model. We also employed cross-validation methods to train these models iteratively and eliminated features deemed to have low importance. Algorithm 2 briefly describes the feature selection method.
## 2 Design and Configuration of DPMS
### Mathematical Model of Scanning Locus  
The main purpose of the system is to obtain the total surface information of polishing pads. Because the system is set up on the polishing machine with a rotating platen, the sensor's scanning locus is combined with platen rotating and swing motion of the dressing arm [17]. The scanning locus of height sensor needs to be calculated with the actual position of the sensor, as the thickness of the pad changes with the radius during the CMP process. After combining the sensor's location and measurement data, the distribution of height and groove depth of pad are shown in the results. The motion locus is expressed as a spiral line; the diagram is shown in Figure 2.  
The equation of locus can be expressed as:  
\[\left[\frac{X(t)}{Y(t)}\right]=D\left[\frac{coscos\ (\alpha-\omega\times t)}{sinsin \ (\alpha-\omega\times t)}\right] \tag{1}\]  
\[D=\sqrt{\left(d_{x}+L(\beta-\omega_{d}\times t)\right)\ ^{2}+\left(d_{y}+L(\beta- \omega_{d}\times t)\right)\ ^{2}} \tag{2}\]  
where \(D\) means the distance between pad center and sensor location, \(d_{x}\)_and_\(d_{y}\) are the distance between pad center and arm's rotating center, \(L\) is the length of swing arm, \(\alpha\)_and_\(\beta\) mean the initial angle of pad and swing arm, \(\omega\)_and_\(\omega_{d}\) mean the rotating speed of pad and swing arm.
# Postextraction Ridge Width Alterations Following Socket Seal Surgery--A Retrospective Study
## 3 Results
### Postextraction Ridge Width Alterations at 6 Months  
The final ridge width varied from 74% to 121% of the original value. Although considerable inter-site variation was seen in the degree of ridge width alterations, the overall mean loss of width was 5.3% (SD = 13.4%) (Table 1). Site location had a significant effect on socket width change (\(p<0.005\)). Incisor sites did not lose width (1.5% \(\pm\) 13.1 change), while loss of width was present in the canine area (17.0% \(\pm\) 2.1, \(p<0.01\) vs. incisors) and premolar area (10.5% \(\pm\) 11.2, \(p<0.01\) vs. incisors).
## 3 Experimental Results and Analysis  
### Shear Stress-Shear Displacement Curve  
Figure 2 shows the shear stress-shear displacement curve. It can be observed that the shear curve shapes of non-persistent joint specimens with different working conditions are similar. According to the shear curve corresponding to the shear displacement, the shear process can be divided into four stages: the elastic stage, damage stage, strength recovery stage, and residual stress stage.  
Elastic stage (I): In this stage, shear displacement and shear stress show a linear or nearly linear relationship. The specimen undergoes elastic deformation, and no significant damage occurs within the rock mass. If the load in the test is removed at this time, the specimen can still return to its initial state.  
Figure 1: Schematic diagram of the direct shear test process and equipment for non-persistent joints: (**a**) specimens preparation process and non-persistent joint specimens; (**b**) test instrument; (**c**) shear box.  
Damage stage (II): In this stage, the shear stress value increases rapidly to a peak as the shear displacement increases. As the shear displacement increases, damage begins to occur in the specimen. Cracks are gradually initiated within the specimen, and the cracks' initiation, propagation, and extension cause local fractures within the specimen, resulting in a shear stress curve that shows small, jagged fluctuations.  
Strength recovery stage (III): In this stage, the shear stress suddenly drops to a low point and then slowly increases. As the shear displacement increases, the shear stress reaches a value close to peak strength. The specimen then develops cracks that span the rock bridge and loses integrity. With increasing shear displacement, shear surfaces rub together and this friction gradually raises shear stress.  
Residual strength stage (IV): During this phase, as the shear displacement escalates, the shear stress curve exhibits slight fluctuation and gradually converges to a stable value or undergoes a sluggish decline. This is due to significant damage to the specimen during the previous shear phase, which resulted in stabilized fracture propagation. At this stage, the shear stress is predominantly due to friction at the shear surfaces. Despite the increase in shear displacement leading to phenomena such as minor wear and fragmentation at the shear surfaces, the shear stress trend remains relatively constant and tends to stabilize or gradually decrease overall.
# Heterodyne Angle Deviation Interferometry in Vibration and Bubble Measurements
## 2 Materials and Methods  
We use the common-path heterodyne interferometry (CPHI) to implement angle deviation measurements in order to obtain some parameters; for example, vibration frequency, the speed of water flow, and the bubble size, etc. The heterodyne light, with two orthogonal polarizations and a beat frequency, is produced by a laser and a two-frequency modulation system. The laser is divided into two polarizations and modulated with different frequencies by using two acousto-optic modulators (Intra Action Corp., Bellwood, IL, USA) (AOMs) and then combining these two beams to let them coincide with each other. Thus, these two polarizations with a beat frequency on the same path are in real anti-interference of the environment. The heterodyne light can be utilized to measure some parameters in the general environment. The base Heterodyne Angle Deviation Interferometry (HADI) [9] is shown in Figure 1. The frequencies of the s- and p-polarizations are \(f_{1}\) and \(f_{2}\), respectively. Thus, the beat frequency is \(f=f_{1}-f_{2}\). The reference signal from the analyzer \(AN_{\mathrm{r}}\) and photodetector \(D_{\mathrm{r}}\) is given as \(I_{\mathrm{r}}=I_{\mathrm{ro}}\left(1+A_{\mathrm{r}}\mathrm{cos}\left(2\pi ft \right)\right)\) and the test signal from the sample, angular sensor, analyzer \(AN_{\mathrm{r}}\), and the other photodetector \(D_{\mathrm{r}}\), is given as \(I_{\mathrm{t}}=I_{\mathrm{ro}}\left(1+A_{\mathrm{t}}\mathrm{cos}\left(2\pi ft +\phi\right)\right),\) where \(I_{\mathrm{ro}}\) and \(I_{\mathrm{to}}\) are the DC bias voltages and \(A_{\mathrm{r}}\) and \(A_{\mathrm{t}}\) are the visibilities of \(I_{\mathrm{r}}\) and \(I_{\mathrm{r}}\), respectively. The phase \(\phi\) is only the phase shift of the angular sensor if the sample has no induced phase shift. Comparing these two signals by using a lock-in amplifier, the phase signal \(\phi\left(t\right)\) from the output of phase channel of the lock-in amplifier can be achieved, and it represents the variation of the sample in real-time.  
Some angular sensors with high phase sensitivity can be used in the HADI; for example, the SPR sensor (self-made). The angle deviation of the test beam will induce a large phase shift when the beam passes through the SPR sensor at the resonant angle.
## 5 Results
### TDRS Orbit Determination Results
#### 5.1.2 Scenario #2  
This investigation bears a close resemblance to scenario 1, with one key deviation. In this instance, the initial position and velocity parameters were perturbed by 1000 m and 10 cm/s in all directions, respectively. Despite the perturbed initial conditions, all TT&C-only solutions managed to reproduce the results shown in Table 2. This occurred because the TT&C basis and its covariance were in alignment with the operational values. This implies that slight changes to the initial conditions would not hinder the TT&C-only solutions, provided that the TT&C basis and covariance values are precise.
# Design and Implementation of 24-GHz and 48-GHz VCOs Using Noise Filtering Technique in 90-nm CMOS
## 2 Design and Analysis
### The Proposed Push-Push 48-GHz VCO  
#### 2.2.1 The \(\lambda\)  
/4 Transmission Line  
With the development of communication systems, low-loss and high slow-wave transmission lines are required for improving circuit performances and miniaturization. In this design, a \(\lambda\)/4 transmission line was combined with an LC voltage-controlled oscillator to implement a push-push VCO. Firstly, the characteristic impedance value of the transmission line must be calculated, as shown in Equation (1).  
\[Z_{\text{C}}=Z_{0}\sqrt{\frac{\left(1+S_{11}\right)^{2}-{S_{21}}^{2}}{\left(1- S_{11}\right)^{2}-{S_{21}}^{2}}} \tag{1}\]  
Secondly, according to Equation (2), various electrical characteristics of the transmission line can be extracted. First, we need to extract the effective propagation constant \(\gamma\) of the transmission line, and the effective propagation constant is denoted by \(\gamma=\alpha+\beta\beta\), where \(\beta\) is the phase constant, \(\alpha\) is the attenuation constant, and L is the physical length of the transmission line.  
\[e^{\gamma L}=\frac{1-{S_{11}}^{2}+{S_{21}}^{2}\sqrt{\left(1+{S_{11}}^{2}-{S_{2 1}}^{2}\right)^{2}-\left(2\;S_{11}\right)^{2}}}{2\;S_{21}} \tag{2}\]  
Figure 4: Simulation of VCO tuning range.  
Figure 5: Phase noise comparison chart after adding noise filter circuit.  
In the design of transmission lines, the slow wave factor (SWF) plays an important part [8]. The slow wave factor refers to the measurement of the loss and distortion experienced by electromagnetic waves propagating in transmission lines. We can calculate the length of the \(\lambda/4\) transmission line at 48 GHz to be 672 um based on Equation (3), as shown in Figure 6a. The transmission line is winding according to this length. The slow wave factor and phase after the simulated winding are shown in Figure 6b, and the phase attenuation is shown in Figure 6c.  
\[SWF=\frac{\beta_{\mathrm{g}}}{\beta_{0}}\mathrm{=}\frac{\lambda_{0}}{\lambda_ {\mathrm{g}}}\mathrm{=}\sqrt{\varepsilon_{r}u_{r}}\approx\sqrt{\varepsilon_{r}} \tag{3}\]
## 4 Discussion
### Limitations and Future Research  
One notable limitation of the current study is that no markers were placed on the head or upper limbs during data collection. The upper body, particularly the head and arms, plays a critical role in balance and momentum control during running. By excluding these areas, the analysis was limited to the lower extremities and trunk, potentially missing a full picture of the interconnected biomechanics involved in locomotion tasks [1, 14]. In addition, the lack of markers on the head and upper limbs may lead to underestimation of the contributions of upper body dynamics to overall stability. Future studies should incorporate a full-body marker set for a more comprehensive analysis.  
Another limitation is the relatively short running duration, which may not fully reflect real-world running scenarios and may not fully capture long-term neuromuscular adaptations. Future studies should explore longer interventions and larger, more diverse samples. Additionally, as the current study is a secondary data analysis, it is constrained by the original dataset's participant selection criteria and the information available. This limits the ability to examine additional factors, such as detailed training history, running habits, or injury history. Future research could benefit from a more diverse sample, including participants with varying levels of running experience, from novice runners to more advanced athletes, to explore how gradient changes affect different populations.