esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### TRESS01 Datasets  
The dataset used in this paper was collected in the first target recognition experiment of sound and seismic signals (TRESS01), which was organized by our team. The experimental site was on the campus of Hunan Normal University in Changsha, Hunan Province. The experiments were conducted over a period of two weeks. A signal acquisition device was placed on the east side of a concrete road on the campus. As people and vehicles moved along the road, people moved within 15 m from the UGS, and vehicles within 30 m.  
The detection distance of an unattended sensor is closely related to the noise intensity of its deployment area. Its alert distance is shorter in a noisy urban area than in a quiet rural area [26]. Vibration intensity (_VI_) and sound pressure level (_SPL_) are used to describe the respective noise levels of seismic and sound signals:  
\[VI=20\log_{10}\frac{S_{RMS}}{S_{pre}}, \tag{9}\]  
\[SPL=20\log_{10}\frac{P_{RMS}}{P_{ref}}, \tag{10}\]  
where \(S_{RMS}\) is the \(RMS\) of the seismic signal, \(S_{ref}\) and \(P_{ref}\) are the relative reference values of the _VI_ and _SPL_, both taken as \(10\times 10^{-5}\), and \(P_{RMS}\) is the \(RMS\) of the sound signal.  
Table 2 shows the _VI_ and _SPI_ indicators of ambient noise at each data acquisition, the temperature and humidity of the environment at the time of acquisition, and the data of the acquisition experiment. The datasets include a training set for model fitting, and a test set to evaluate the generalization ability of the final model. To ensure a more effective assessment, the training and test sets are composed of sound and seismic signals collected at different times. The target acoustic and seismic signals collected from the first to the fifth data acquisitions constitute the training set, and the target acoustic and seismic signals from the fifth data acquisition constitute the test set. Table 3 shows the size of the processed dataset.  
Figure 6: Experimental hardware.
## 3 Results and Discussion
### Apoptosis Induction Effect
#### 3.6.2 Caspase 3/7 Activity Assay  
To confirm the apoptosis-inducing effect of BGR, the caspase 3/7 activity was determined as the enzymes are activated during apoptosis. BGR at both 100 and 200 \(\upmu\)g/mL at 5-7, 10-15 and 20-15 days induced apoptosis at 24 h that indicated by the significantly increased activity of caspase 3/7 in Jurkat cells in comparison with the untreated cells or the control (100.00 \(\pm\) 9.26%) (\(p<0.05\)) (Figure 3b). The standard anticancer drug docetaxel at 10 \(\upmu\)M was used in this study as it is clinically used in leukemia [38; 39; 40]. Docetaxel strongly increased the caspase 3/7 activity at 281.56 \(\pm\) 7.26% compared to the control. A higher caspase 3/7 activity was found when Jurkat cells were treated with 200 \(\upmu\)g/mL BGR than those with 100 \(\upmu\)g/mL.  
At 100 \(\upmu\)g/mL, BGR at 5-7, 10-15 and 20-25 days increased caspase 3/7 activity to \(215.36\pm 14.54\%\), \(340.85\pm 11.29\%\), \(308.98\pm 28.94\%\), respectively compared to the untreated cells. And at 200 \(\upmu\)g/mL, BGR at 5-7, 10-15 and 20-25 days increased caspase 3/7 activity to \(267.79\pm 16.91\%\), \(407.13\pm 11.63\%\), \(382.57\pm 3.40\%\), respectively compared to the untreated cells (Figure 3b). BGR induced apoptosis via increasing caspase 3/7 activity in a concentration-dependent manner. Notably, 200 \(\upmu\)g/mL of BGR at 10-15 days increased caspase 3/7 activity at a 4 times higher than the control.
# New Approach of Blind Adaptive Equalizer Based on Genetic Algorithms
## 3 Methods
### Adaptive Blind Equalization
#### 3.3.1 Channel Model
The discrete time signals \(r(k)\), \(u(k)\), and \(x(k)\) are expressed by Equations (6), (7), and (8), respectively. The linear adaptive equalizer (Figure 5) is a linear digital filter (Figure 4), which relies on an algorithm to adapt its parameters, \(\mathbf{w}(k)\), according to the time-varying properties of  
Figure 4: Structure of a linear equalizer. Variable \(N\) represents the length of the equalizer, \(z^{-1}\) indicates a register, \(u(k)\) indicates the equalizer input, and \(w(k)\) indicates the parameters of the equalizer. \[a(k)=a^{I}(k)+ja^{Q}(k), \tag{4}\]0 Illustration of a discrete base-band communication system with ISI and AWGN. The arrows indicate the transmitted signal (\(a(k)\)), channel response (\(h(k)\)), the output of the channel (\(x(k)\)), the AWGN (\(r(k)\)), the equalizer input (\(u(k)\)), and the equalizer output (\(a(k)\)). the communication channel. The adaptation is performed by optimizing a cost function, \(J(\mathbf{w}(k))\), in which  
\[\mathbf{w}(k) = \begin{bmatrix}w_{0}(k)\\ \vdots\\ w_{N-1}(k)\end{bmatrix} = \begin{bmatrix}w_{0}^{I}(k)\\ \vdots\\ w_{N-1}^{I}(k)\end{bmatrix} +j \begin{bmatrix}w_{0}^{Q}(k)\\ \vdots\\ w_{N-1}^{Q}(k)\end{bmatrix}  \tag{10}\] \[= \mathbf{w}^{I}(k)+j\mathbf{w}^{Q}(k),\]  
where \(\mathbf{w}^{I}(k)\) and \(\mathbf{w}^{Q}(k)\) are, respectively, real and imaginary parameters of the equalizer.
## 3 Results
### Localization Accuracy Results
The outcomes concerning the horizontal starting angle indicate that there is no statistically significant difference in absolute deviation from the true angle between one's own individual HRTF and the individual HRTF of another person. However, a statistically significant difference is observed between both of these cases and the case when generic HRTF is used. For the horizontal ending angle, there was no statistically significant difference between HRTFs. The results obtained from the pairwise comparison indicate a significantly lower absolute deviation from true vertical starting and ending angles when one's own individual HRTF is used compared to the remaining two HRTFs. Additionally, the measurement results acquired from the participants were analyzed for the correct determination of the direction in which the virtual sound source moved. If the angular value of the sound source was increasing from the starting to the ending angle, it was assigned the value \"1\", and if it was decreasing, it was assigned the value \"0\". This was performed for both the programmed values to determine the correct movement direction and the measurement results. The results are presented in Table 2.  
Although the missing data did not affect the absolute deviation calculations because of the independent data point averaging, it should be considered for the correct determination of the direction calculations. When the missing data arose, there were two possible ways of handling it: defining the direction determination as incorrect or excluding the missing data from further analysis. In the case of a participant's unsureness about where the sound source started or ended (missing data), the direction could not be defined with absolute certainty; therefore, the exclusion of the missing data is a more pertinent approach. There was no missing data for the horizontal plane. A chi-square (X\({}^{2}\)) test was conducted to test whether the scores obtained for correct and incorrect determination of the direction of movement in the vertical plane carry any statistically significant difference from the values expected from purely guessing the direction by random chance. This was particularly important regarding the generic HRTF since the correct determination percentage was slightly below 50%. The results of the chi-square test (\(p\) = 0.08 for missing data included in incorrect determinations, \(p\) = 0.33 for missing data excluded from incorrect determinations) imply that no statistically significant difference exists in this case, i.e., the determination of direction in the vertical plane using generic HRTFs is essentially based on guessing. A binomial test was also conducted, and it confirms the results of the chi-square test (\(p\) = 0.3961). The presented results demonstrate that using one's own individual HRTF leads to considerably higher accuracy when determining the direction in which the sound source moves in the vertical plane.
## 3 Results and Discussion
### Spectral Synthesis
The extension of the panchromatic band affects the grey values of the panchromatic channel rendering some traditional pansharpening techniques less effective. The PANSHARP algorithm, for instance, is resilient to this challenge in that it is a statistics-based technique that uses the least-squares method to determine the best fit between the grey level values of the spectral bands being merged and adjusts the contribution of each band to the pansharpening result to minimize color distortions. [PERSON] [26; 27] also highlights that the statistics-based approach utilized in the PANSHARP algorithm lessens the influence of dataset discrepancy and automates the pansharpening process. This assertion is supported in this study as shown by the superior performance of the IMVM, RCS, and Bayer's fusion techniques. The high performance of the IMVM image fusion algorithm was confirmed in similar studies. [PERSON] [44] reported that the IMVM algorithm produced some of the best fusion results when compared to a range of pansharpening algorithms when evaluated using CC, RMSE, Deviation Index (DI), SD, and DIV metrics. [PERSON] and [PERSON] [43] compared fusion techniques and confirmed that the LMVM algorithm produced the best spectral consistency and synthesis when applied to Worldview-2 data. As in our case, other techniques that produced favorable spectral consistency and synthesis results included PANSHARP, MIHS, EHLERS, GRM, and wavelet principal components techniques [44; 45]. The shortcomings of traditional fusion techniques such as PCA, Brovey transform, and wavelet fusion are well described by [PERSON] [26]. To improve the quality of pansharpening results of traditional pansharpening methods some propositions recommended include stretching the principal components in PCA pansharpening to give them a spherical distribution. Alternatively, the first principal component could be cast-off. Modifications of traditional pansharpening techniques are necessary to deal with some of the limitations confronted in dealing with new satellite sensors. In a general sense, the quality of image geometric and radiometric rectifications done before the pansharpening directly impacts on the quality of all pansharpening results for all the image fusion techniques. Lastly, the spectral integrity of pansharpened images is an important requirement for most quantitative remote sensing applications. While this study used an array of reference-based metrics to assess the image quality of various pansharpened images in terms of spectral consistency, spatial consistency, and image synthesis, the information content within the images was not quantified. The use of image information metrics such as Shannon entropy and Boltzmann entropy [46; 47; 48; 49; 50] enables the quantification of the average amount of information in the fused images and could be used to effectively assess the efficacy of various pansharpening methods in terms of the ability to retain or enhance both spectral and spatial information.
# Advances in the Monitoring of Algal Blooms by Remote Sensing: A Bibliometric Analysis
## 5 Conclusions  
This study aimed to provide a systematic analysis of research on remote sensing of phytoplankton blooms during 1999-2019 via bibliometric technique. We built a database that included 1292 peer-reviewed articles. The number of publications increased since 2004 and originated from 47 countries, clearly dominated by the United States and followed by China. Coastal environments are 60% of total analysed studies, open ocean 25%, and inland environments 14%. Thus, coastal environments are the most studied along the entire study period. SeaWiFS was the most used sensor in phytoplankton blooms publications from 2000 to 2015 (49% of the studies), either as unique sensor or included in multisensor studies. MODIS (including MODIS Aqua and MODIS Terra) was the second most used satellite from 2005 to 2017 (27% of the analysed studies). Ocean colour is recognised as an Essential Climate Variable by the Global Climate Observing System, but most of the analysed publications conducted short time studies, and only 1% of publications studied a period longer than 30 years. It is important to highlight the CCI initiative that obtained a time series for the period 1997 to 2018 using a multisensor approach merging data from MERIS, SeaWiFS, MODIS-Aqua, and VIIRS. This type of approach and a consistent field database for validation are key to elucidate climate change effects on phytoplankton dynamics. The recent advancements in high spatio-temporal resolution sensors allow us to have a better knowledge of small water bodies such as inland waters (e.g., reservoirs) and to identify blooms with the typical patchy distribution. Though the use of Chl-\(a\) algorithms is very common, we encourage the use of RRS, especially in optically complex waters. According to our results, most of the reviewed studies focused on detecting high Chl-\(a\) levels, which can not necessarily be classified as HABs and do not report the causative species. Toxin concentration in water is only measured on a few studies and link to the anthropic use of water bodies (e.g., reservoirs for freshwater supply or coastal water with shellfish farming).  
Conceptualization, M.-T.S.-F., J.-A.-M. and I.H.-D.; Formal analysis, M.-T.S.-F., J.-A.-M., I.H.-D., E.S.-d.-A., S.M.-M. and J.E.; Investigation, M.-T.S.-F., J.-A.-M., I.H.-D., E.S.-d.-A., S.M.-M. and J.E.; Methodology, M.-T.S.-F., J.-A.-M. and I.H.-D.; Software, J.-A.-M., I.H.-D. and S.M.-M.; Supervision, M.-T.S.-F., Validation, E.S.-d.-A. and J.E.; Visualization, J.-A.-M., I.H.-D., E.S.-d.-A., S.M.-M. and J.E.; Writing--original draft, M.-T.S.-F., J.-A.-M. and E.S.-d.-A.; Writing--review & editing, M.-T.S.-F., E.S.-d.-A. and J.E. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# PFC Single-Phase AC/DC Boost Converters: Bridge, Semi-Bridgeless, and Bridgeless Topologies
## 3 Categorization of Semi-Bridgeless Boost Converter Topologies
### Semi-Bridgeless Boost Converter with Clamped Switches  
SBBC hybrid clamped topology or SBBC with active virtual ground is depicted in Figure 11[105]. The capacitor \(C_{1}\) and two clamped switches (\(Q_{3}\) and \(Q_{4}\)) are added while diodes are removed. As an advantage, it is possible to implement an LCL filter (with \(L_{1}\), \(L_{2}\) and \(C_{1}\)) between the SBBC and the AC power supply; then, this filter can reduce the DM noise. In addition, this topology requires less robust EMI filters compared to the SBBC with clamp diodes' topology. CM noise can be reduced using \(C_{1}\); however, the conduction losses are increased. In [106], this converter is controlled by cascade PI control for \(Q_{1}\) and \(Q_{2}\), operating as conventional SBBC while switches \(Q_{3}\) and \(Q_{4}\) have an independent control. This paper presents an application in low voltage and power with \(THD_{i}\) into a normative range for powers greater than 200 W. The active filter allows for reducing or eliminating (depending on the application) the passive EMI filter between the grid and converter; additionally, the switches increase conduction losses, control requirements, and implementation cost. In contrast, the SBBC topology with clamped diodes and passive EMI filter presents a similar behavior with lower design and implementation requirements.
# Effect of Bioaugmentation on Biogas Yields and Kinetics in Anaerobic Digestion of Sewage Sludge
## 2 Materials and Methods
### Laboratory Installation and Operational Set-Up  
The study was performed in anaerobic reactors operating at a temperature of 35 \({}^{\circ}\)C in semi-flow mode. The laboratory installation consisted of three completely mixed digesters (with an active volume of 40 dm\({}^{3}\)) working in parallel, equipped with a gaseous installation (consisting of pipelines, pressure equalization unit and a mass flow meter), an influent peristaltic pump and storage vessels (Figure 2).  
An inoculum for the laboratory reactors was taken from Pulawy wastewater treatment plant as a collected digest from a mesophilic anaerobic digester with a volume of 2500 m\({}^{3}\) that was operated at 35-37 \({}^{\circ}\)C and a hydraulic retention time (HRT) of about 25 days. The adaptation of the digester biomass was achieved after 30 days.  
Two phases of the experiment were conducted, differing in the organic loading rates (OLRs). Each phase lasted 90 days (30 days for acclimatization and 60 days for measurements). In the first phase, three reactors were operated. The first reactor (R1--control one) was fed daily with 2 dm\({}^{3}\) of mixed sludge. The HRT reached 20 days, and the OLR was time-dependent with an average value of 1.55 kg VS m\({}^{-3}\) day\({}^{-1}\).  
Figure 1: Microbial genera present in the bioaugmenting substrate; “other” includes unclassified bacteria (1%) and Archaea (1%), and taxa with abundance below 1% (3%); sequencing results are present in the Sequence Read Archive SRA (National Center for Biotechnology Information NCBI, BioProject PRJNA431048).  
The second reactor (R2) was operated following the same schedule. However, the reactor was fed with sludge bioaugmented with microbial mixture at a volumetric ratio of 91:9 (the influent consisted of a mixture of 2 dm\({}^{3}\) sludge and 0.2 dm\({}^{3}\) of bioaugmenting mixture). The HRT was shortened to 18.2 days, and the average value of the OLR was 1.54 kg VS m\({}^{-3}\) day\({}^{-1}\).  
The third reactor (R3) arrangement was the same as in R2, but this time the volumetric ratio was set at 87:13 (influent consisted of a mixture of 2 dm\({}^{3}\) sludge and 0.3 dm\({}^{3}\) of bioaugmenting mixture). The HRT shortened to 17.4 days, and the average value of the OLR was 1.53 kg VS m\({}^{-3}\) day\({}^{-1}\).  
In the second phase, two reactors were operated. The fourth reactor (R4) was operated analogously to the R1 (as a control), although at OLR of only 1.30 kg VS m\({}^{-3}\) day\({}^{-1}\).  
Reactor R5 was operated also at low OLR of 1.33 kg VS m\({}^{-3}\) day, and the sludge to bioaugmenting mixture volumetric ratio was set at 83:17 (influent consisted of a mixture of 2 dm\({}^{3}\) sludge and 0.4 dm\({}^{3}\) of bioaugmenting mixture). The HRT was shortened to 16.7 days.  
The reactors were bioaugmented in continuous mode because of the long-term wide microbial growth (from 20 h to 20 days) as well as their possible wash out from the system.
## 3 Numerical Experiments
As shown in Figure 12 and Table 3, the differences in the misfits, reconstruction errors, structural fidelity, and calculation costs with different focusing factors are marginal, although the MSG-constrained method consistently enhances the convergence behavior, reducing both misfits and model reconstruction errors, and improves the model's structural fidelity compared to unconstrained inversion. This indicates that MSG methods are not sensitive to the selection of the focusing factors.
## 4 Conclusions  
A clock synchronization and syntonization system for satellites based on VC-OCXOs is described in this paper. Compared with the existed schemes, the proposed scheme behaves better under the similar simulated scenarios. A high-accuracy phase error extraction and correction method is proposed, including error analysis, correction methods and control signal prediction. The dedicated requirements  
Figure 14: Comparison of the proposed scheme and two existed schemes. The Allan deviations of a free run slave clock and related controlled clock are presented. Data from the 1 st h to the 24 th hour are truncated to evaluate the synchronization performance. The standard deviations of the free run clock and the stable loops are 1.2 microsecond, 0.15 nanosecond, 0.18 nanosecond and 0.12 nanosecond, respectively.  
Figure 13: Comparison of the Allan deviations of four free run slave clocks and related controlled clocks. Data from the 1 st h to the 24 th h are truncated to evaluate the synchronization performance. The standard deviations of the stable loops and the free run clocks are 0.004 nanosecond, 0.04 nanosecond, 0.4 nanosecond, 4.1 nanosecond, 35.14 microsecond, 1.19 microsecond, 0.16 microsecond and 0.01 microsecond, respectively.  
for different scenarios are proposed. The simulation results imply that the proposed system could achieve a relative good synchronization and syntonization performance under the premise of reducing overall costs. The proposed dynamic compensation scheme proves that the system could reduce the dynamic error to sub-nanosecond level in various space scenarios, such as formation flying scenarios, GNSS constellations or hybrid constellations. The slave clocks are recommended to be the same type because the synchronization loop has a limited disciplining capability for a particular VC-OCXO. Moreover, the bandwidth of the control loop relates to the convergent rate and synchronization precision of the closed loop. In addition, the residual error is also a critical element that will affect the synchronization loop, which must be considered carefully. Although the key features and techniques for this novel system have been described in this paper, there still remain many interesting open issues, such as distributed synchronization loop design in case of holdover mode.
## 3 Experimental Results and Analysis  
### Improved CCS-YOLOv8 Model  
#### 3.1.1 Changes in Losses  
Figure 7 shows the variation curves of box_loss, dfl_loss, and cls_loss of the proposed CCS-YOLOv8 model on the training and validation sets of common livestock detection datasets in the Qinghai region, which are used to validate the convergence performance of the CCS-YOLOv8 model.  
At first, the loss values drop off quickly, showing that the model is learning and adapting quickly. Nevertheless, the loss numbers tend to level out and vary within a small range as the exercise continues. This suggests that the optimization process has converged and the model has achieved a reasonably stable state. The loss values' oscillations imply that the model is adjusting its parameters to strike the best possible balance between generalization and precision. Overall, there are no overfitting or underfitting problems with the CCS-YOLOv8 model's generalization capabilities in the livestock target identification test.
# Multi-User Detection Based on Improved Cheetah Optimization Algorithm
## 2 Methods
### Improved Cheetah Optimization Algorithm
#### 2.4.2 Individual Location Update Strategy  
This paper addresses the multi-user detection problem, which is a binary data information individual search for the optimal problem. The usual bit information is in the form of a combination of 1 and \(-1\), but if the cheetah optimization algorithm is not modified and directly deals with the problem, it will lead to the algorithm search for optimal unity of the problem, thus restricting the algorithm to the scope of the search and increasing the solution error rate. Therefore, this paper aims to improve the position update strategy of the searching individual \(\overline{X}_{i,j}^{t+1}\) in the Cheetah optimization algorithm  
\[X_{i,j}^{t+1}=\frac{1}{1+\exp\Bigl{(}-\overline{X}_{i,j}^{t+1}\Bigr{)}} \tag{20}\]  
\[X_{i,j}^{t+1}=\begin{cases}-1,\ \ \text{rand}>X_{i,j}^{t+1,}\\ \ \ \ \ 1,\ \text{otherwise}\end{cases} \tag{21}\]  
where \(\overline{X}_{i,j}\) represents a random number selected from the range of values denoted by \([UB,LB]\). The function in Equation (21) serves to restrict the estimated position to [0, 1]. This is subsequently transformed into estimated information bits according to Equation (20). Improvements to the individual position update strategy can be used to solve problems such as local convergence due to the monolithic search for empty rooms when dealing with multi-user detection problems.