esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Sliding-Window Segmentation Methods  
The collected dataset has a variable temporal duration of the sequence since the data collection time period for each subject is different. Although most research papers [15; 16; 36] use a more than 2-s sliding-window segmentation in their method, real-time fall-detection applications should respond in at least less than 0.2 s. Therefore, we implement competing sliding-window segmentation techniques to detect falls in near-real time, anticipating that improvement in the hardware efficiencies will ultimately compress this response time. We compare three methods of window segmentation: conventional sliding window, peak-detection sliding window, and dynamic multi-window sliding.
## 5 Discussion
### Surrounding Rock Deterioration  
The water-rock interaction is a problem faced in almost all tunneling projects [41]. A large number of studies have shown that long-term water-rock interactions in soft rock tunnels are an important cause of tunnel damage during their construction and operation, with many tunnels suffering from water softening of mudstone [42]. The softening coefficient test has shown that the surrounding rock of the station is sensitive to water. In the seasonally precipitation-rich area, the shallow buried surrounding rock will be subjected to the repeated action of wetting and drying for a long time, which will reduce the mechanical properties of the surrounding rock. The Laifosi Tunnel in Chongqing, with its geological conditions very similar to those of Guobo Station, was found to have a number of serious to severe defects approximately 4 years after opening for operation. Then, the first remediation of the tunnel defects was carried out in 2010. Unfortunately, after approximately 10 years of treatment, tunnel defects continued to develop, and disease treatment had to be resumed again in 2020. One of the main reasons for the continuous development of the tunnel defects was the continuous deterioration of the mudstone caused by long-term water-rock interactions. Lessons learned from the past can guide one in the future. This reminds us that when building the Guobo station with a larger section, we must reinforce the surrounding rock more effectively. Furthermore, we should also take more careful waterproofing and drainage measures, not only during the construction period but also during the entire service life of the station, to ensure that the station will not suffer from damage.
# Isothiocyanates: An Overview of Their Antimicrobial Activity against Human Infections
## 4 Isothiocyanates Antimicrobial Properties against Human Pathogens
### Pseudomonas Aeruginosa
aeruginosa_ QS system inhibition induced by ITCs was indirectly confirmed evaluatingthe effects of the ITCs on biofilm formation and on the production of pyocyanin--a virulence factor of _P. aeruginosa_--both processes being controlled by the QS system. The authors [44] indicated that both SFN and ERN showed to be very effective to reduce the production of pyocyanin and interestingly showed good imanti-biofilm activity, although SFN displayed a greater efficacy compared to ERN. To explain this result [PERSON] et al. [44] hypothesized that the differences between SFN and ERN could be attributed to the different hydrophobicities of those ITCs, resulting in different permeability and distribution capacity during the growth of the biofilm. An alternative explanation may be a slower oxidation of ERN into SFN in the biofilm environment. Furthermore the effect of iberin (IBN) on _P. aeruginosa_ QS was also investigated by [PERSON] et al. [58]. The authors have shown that IBN, extracted from horseradish, was able to down-regulate the expression of the RhII/RhIR QS network in _P. aeruginosa_. IBN was also able to downregulate the QS-controlled virulence factors by activating the genes involved in the production of pyocyanin and genes (rhIA and rhIB) involved in the production of rhamolipid [58]. The latter contributes to _P. aeruginosa_ infection through their surfactant capacities leading to killing polymorphonuclear leukocytes. In particular, the RhiIR (PA3477) part of the _P. aeruginosa_ QS network appeared to be significant (>5-fold) down-regulated in the bacterial culture treated with IBN (64 \(\upmu\)g/mL). RhII was only moderately (about 4-fold) down-regulated, while LasR (<2-fold downregulated) and LasI (<2-fold upregulated) seemed to be practically unaffected [58].
# Retrieval and Comparison of Multi-Satellite Polar Ozone Data from the EMI Series Instruments
## 1 Introduction
The first-generation EMI ozone products and EMI-2 ozone products were retrieved and validated using the World Ozone and Ultraviolet Radiation Data Centre (WOUDC) dataset, with an average deviation of less than 10% [38; 39]. However, the performances of EMI-DQ01 and EMI-GF5(01A) have not yet been verified, and the calibration of differences between the EMI series instruments is still required to obtain higher-quality TOC products. In this study, the DOAS method was used to obtain TOC results from the EMI series instruments. Reliable ground-based observations were used for comparison and cross-verification between EMI ozone products. Next, weighted fusion correction was performed. In addition, we analyzed the changes in the TOCs in the morning and afternoon and improved the spatial resolution and precision of the EMI ozone products via weighted fusion. This approach is suitable for accurate long-term ground-based observations of the Antarctic atmosphere. The aim of this study was to evaluate the ozone retrieval performance of a new generation of EMI series instruments and to assess the consistency, accuracy, and stability of the different ozone data products generated using those EMI instruments. By doing that, we can use long-term stable EMI data to better monitor the diurnal change of ozone and analyze the mechanism of ozone change and the healing of ozone holes. The rest of this article is organized as follows. Section 2 describes the EMI data used in this study. Section 3 explains the steps followed in the retrieval algorithm, which focuses on the parameter settings and the sources of different retrieval parameters for the EMI series sensors. Section 4 compares the inter-satellite consistency and stability of the EMI observations with those of the TROPOspheric Monitoring Instrument (TROPOMI). Based on the validation results of the ground-based data, the diurnal variation in the polar ozone TOC results was analyzed by comparing the retrieval results for different overpass times of the EMI instruments. Section 5 presents the conclusions of the study and discusses future work.
# 3D Photoacoustic Tomography System Based on Full-View Illumination and Ultrasound Detection
## 1 Introduction
In addition, in order to improve the resolution of the photoacoustic imaging system, a negative lens can be mounted on the surface of the ultrasonic transducer to increase the receiving angle [32; 33]. Due to the large field-of-view of the virtual point ultrasound transducer, some research has applied virtual point ultrasonic transducers to a photoacoustic imaging system [34; 35; 36]. [PERSON] et al. developed a virtual point ultrasonic transducer with a large receiving angle and applied it to the photoacoustic tomography system. The signal-to-noise ratio of the system is greatly improved compared with the negative-lens transducers [35]. [PERSON] et al. use a synthetic-aperture focusing technique based on the virtual point detector to obtain a high-resolution photoacoustic image of hemoglobin oxygen saturation in living mice [36]. In this paper, a 3D photoacoustic imaging system based on \"dual-element\" ultrasonic transducer is proposed under the premise of full-view imaging and uniform illumination. The system contains 1-to-8 fan-out fiber bundles and an optical cage design, which delivers approximately uniform laser radiation toward the imaging phantom and provides an exciting source for completing the photoacoustic tomography of each B-scan (B-scan refers to a cross section of photoacoustic tomography in this paper). The rotary motor carries two customized dual-foci virtual point ultrasonic transducers for photoacoustic data acquisition in the process of rotary motion, while the precise vertical stepper motor drives the imaging target to do vertical movement to complete the 3D scanning of it. The dual-foci virtual point ultrasonic transducer can expand the signal acceptance angle in the short focusing direction because of its large field of view, such that a relatively complete photoacoustic signal can be acquired with fewer detection units, which reduces the cost of the photoacoustic imaging system and accelerates the photoacoustic imaging speed. In the long focusing direction, the narrow sound beam and the annular illumination beam are coupled to form a photoacoustic coplanar mode, which improves the signal to noise ratio of the photoacoustic signal. The pattern of 360\({}^{\circ}\) circular laser radiation provides uniform illumination at all angles around the imaging cross section, which facilitates photoacoustic tomography of imaging phantom with irregular surfaces. Furthermore, the feasibility and operability of the 3D-PACT system is validated through systematic testing and 3D photoacoustic experiments on a variety of phantoms.
## 6 Results and Comparison
### Results Comparison
#### 6.4.1 Equalizer Module
The recorded throughput is 2.6-times less than that achieved by the implementation in [24], knowing that the latter uses five-times more multipliers and almost 2.5-times less FPGA registers and LUTs. When comparing to our proposed architecture, _EquASIP_[26] almost requires 1.6-times more registers, 1.7-times more LUTs and 1.2-times more dedicated multipliers to be implemented. The comparison is conducted targeting the same device (Xilinx Virtex-5 LX330 FPGA) and using the same synthesis options and tools. Moreover, its throughput is less for all system configurations.
## 2 Related Studies
For images that were subjected to feature extraction through the image encoder, a long short-term memory (LSTM) network was used to perform the process of the image decoder to derive a hierarchical diagnosis. The final accuracy was 78.0%, and the classification accuracy between melanocytic and nonmelanocytic lesions was 92.2%. Similarly, two models were used in the present study as the image encoder, and an RNN-based LSTM model was used as the decoder. LSTM requires a longer training time and inference time than CNN-based models, owing to its recurrent architecture. By contrast, the skin disease diagnosis method proposed herein uses only a single classification model, which is achieved by extracting multiple types of features from a single image. By reducing the computational load required for visual feature extraction through dimensionality reduction, computation can be performed effectively using simple equipment. Many types of features require the segmentation of the edge image, color spaces (RGB, HSV, and YCrCb), and texture features, and each information must be converted into new one-dimensional (1D) vector information through line-segment-type analysis and histograms. In this study, 50 features in 1D vector format were generated and, after conversion, a two-dimensional (2D) matrix (50 \(\times\) 256) was generated. The generated 2D matrix was used as an input for a 1D convolution-based feature analysis model. The methods for detecting each type of feature are relatively simple, and a diagnosis of a skin disease was performed by combining existing well-established methods and the line-segment-type analysis algorithm proposed by the authors in a previous study [22; 23]. The proposed processing method allows the problem-solving framework of skin disease  
Figure 1: Example of skin lesions images; (**a**) actinic keratosis, (**b**) basal cell carcinoma, (**c**) benign keratosis-like, (**d**) dermatofibroma, (**e**) malignant melanoma, (**f**) melanocytic nevi, (**g**) vascular lesions. diagnosis to evolve from a classification model-centered diagnostic method (used in the past) to a feature analysis-centered diagnostic method.
## 4 Conclusions  
Two techniques to characterize the morphology of supported gold nanoparticles prepared by Volmer-Weber growth have been applied and the extracted results have been compared. First, we applied simple AFM measurements in combination with a rigorous modeling of the inhomogeneously broadened extinction spectrum of the nanoparticle ensemble. With AFM we have measured the nanoparticle density and extracted the mean equivalent radius of the nanoparticles. This data has been afterwards used as input parameter in our new model to determine the functional dependence between the size and shape of the nanoparticles. Using this functional dependence, we have determined fundamental morphological parameters of the nanoparticle ensemble.  
In addition, we have demonstrated that the morphological data can be related to the optical spectrum. Hence, theoretical modeling of the inhomogeneously broadened extinction spectrum using the mean equivalent radius and the nanoparticle density as free parameters can be performed. By obtaining the best fit to the experimental data, we have extracted again the nanoparticle density and the mean nanoparticle size, but now based on the extinction spectrum. The comparison of the modeled data with the experimentally determined data was in excellent agreement.  
Second, we performed extensive TEM measurements to extract again the nanoparticle density and the mean lateral diameter of the nanoparticles. Afterwards, we have compared the morphological data measured by TEM with data extracted from AFM analysis and rigorous modeling of the inhomogeneously broadened extinction spectrum. The comparison reveals that the results obtained with both methods show a perfect agreement and demonstrates that our new theoretical model can precisely characterize the morphology of noble metal nanoparticles ensembles. For example, the lateral size of the nanoparticles has been determined to be \(2b=(13\pm 2)\) nm by modeling the extinction spectrum in combination with AFM, while it amounts to \(2b=(12\pm 2)\) nm if measured with TEM. However, our modeling technique allows to extract much more morphological parameters that are not easily accessible by TEM or AFM measurements. Most importantly, the functional dependence between the shape and the size has been extracted. This functional dependence has been used to determine the most probable axial ratio of the supported nanoparticles, which amounts to \(a/b=0.61\). Hence, rigorous theoretical modeling of the extinction spectra in combination with AFM reveals important morphological data of noble metal nanoparticle ensembles with high precision, which can not or, at least, can not easily be extracted by other techniques.
## 3 Results and Discussion  
### Microbiological Analysis  
The main factor limiting the shelf life of largemouth bass fillets is the development of microbial groups during the preservation. Meat products are often easily degraded by microbial activities. Figure 1 describes the changing trend in the number of TVC of largemouth bass with different treatments during storage. The results show that the TVC of largemouth bass was significantly different with different storage times and sample treatments (\(p<0.05\)). The higher initial TVC for fresh largemouth bass was 4.5 log CFU/g (Figure 1). [PERSON] et al. [24] found that the initial TVC of fresh largemouth bass fillets was about 4.55 log CFU/g, and the initial value was high. The TVC value of largemouth bass fillets exceeded 7 log CFU/g, which is recognized to be the maximum acceptable limit of microorganisms [25]. The TVC of fresh largemouth bass fillets on the sixth day of storage was 7.41 log CFU/g. HHP-0- and HHP-10-treated fish fillets exceeded the maximum limit (7 log CFU/g) on day 18 and 24 of storage, respectively. HHP-0+SV- and HHP-10+SV-treated fish fillets exceeded 7 log CFU/g on day 30 of storage, as shown in Figure 1. The population of the TVC increasingly elevated with storage time. Throughout the same storage time, HHP and HHP combined with SV treatment significantly reduced the number of TVC (\(p<0.05\)). [PERSON] et al. [26] found that high hydrostatic pressure (HHP; 200 MPa, 15 min) could eliminate Listeria monocytogenes in trout. At the same time, the synergistic effect of HHP, liquid smoke and freezing can reduce its bacteria. Except for 20 min SV treatment at 65 \({}^{\circ}\)C, all temperature/time combinations used to cook turkey slices could greatly inactivate pathogenic microorganisms [27]. Compared with the control group, the shelf life of largemouth bass fillets treated with HHP-0 and HHP-10 may be extended by 12 days and 18 days, respectively, whereas the combination of HHP with SV was substantially more effective in extending the microbiological shelf life of largemouth bass fillets by 24 days.
## 5 Results
### Comparison of RL Algorithms in Quasi-Static Environment
#### 5.1.1 Performance with Varying Turbulence Severity  
In this section, we employ the PPO algorithm to evaluate its performance under low and high quasi-static turbulent conditions. The corresponding results are displayed in Figure 9.  
Figure 8: Comparison of algorithms applied on 20 randomly selected quasi-static atmospheric turbulences of \(D/r_{0}=5\). Note that the shaded regions extend to negative values of the Strehl ratio because the standard deviation may be larger than the mean, represented by the solid curves.  
Figure 9: Average reward of 20 randomly selected quasi-static atmospheres of different \(D/r_{0}\) ratios with 64 actuators and 4 observers on PPO algorithm and Shack–Hartmann wavefront sensor.  
As anticipated and illustrated in Figure 9, a decrease in the Fried parameter value results in a decline in the model's capacity to achieve a higher reward. If the agent's performance cannot significantly improve beyond the uncorrected 2 to 10% Strehl ratio (depending on \(D/r_{0}\) value), it can be considered impractical.
# A Learning-Based Methodology to Optimally Fit Short-Term Wind-Energy Bands
## 2 Wind-Power Uncertainty and the Use of Forecasts
### PSF-Like Forecast  
Pattern Sequence-based Forecasting algorithm (PSF) is a novel nonparametric approach to infer forecasts. This method has provided promising results when applied to an assortment of time series forecasting in several international markets, at a horizon of one or a few days ahead. The main idea of the PSF algorithm -and more recent variants- involves three parts working sequentially:  
1. The historical of time series is clustered into groups that have similar temporal daily profiles;
2. The time series of daily profiles is converted into a discrete sequence of labels for the cluster each day belongs to;
3. Given the label corresponding to the current day, a window of days of fixed length is used to seek along the whole past sequence of labels for those that match the window ending at present time. The profile for the day to be predicted is constructed averaging profiles that follow to each one of those identical windows in the past.  
A remarkable advantage of the PSF method is its reduced number of parameters. There are only two main parameters to adjust: the number of clusters \(K\) and the historical time window \(W\).  
Figure 6 shows the centroids computed over the actual wind-power data-set when using different numbers of clusters. For example purposes, assume \(K=3\) and \(W=4\); so, a sequence of \(D\) days translates into a sequence \(\{s_{d}\}\) (\(d\in D\)) of digits, with \(s_{d}\in\{1,2,3\}\). Here, \(s_{d}\) is the index of the closest centroid to the realization of the day \(d\).  
Within such sequence, we aim now on finding subsequences with \(W=4\) symbols, for instance, the sequence 1231 (we are assuming that current day belongs to cluster 1, given by the last symbol in this sequence). Figure 7 shows examples where that subsequence could be found. The outcome of this predictor (one day ahead) is the average of the actual PLF realizations, among those samples immediately following the subsequences registered. To extend the construction to a two-days ahead forecast, one could repeat the  
Figure 5: Histograms for total deviations of forecasts within a horizon of 72 h ahead. Red and blue areas concentrate 50% of probability in all cases [leftmost: Garrad Hassan, center: Meteológica, rightmost: PSF44].  
Figure 6: Centroids of actual wind-power samples for different number of clusters.  
process seeking for the subsequence composed of the previous \(W-1\) days plus the new forecasted one, and so on.  
The purpose behind the development of this forecast is not devaluating statistical methods. On the contrary, this work shows an example of how such a simple method, may contribute to the overall quality when combined with forecasts coming from complementary techniques.
# Water Body Mapping Using Long Time Series Sentinel-1 SAR Data in Poyang Lake
## 4 Discussion
### Comparisons with Other Studies
Recent studies have also optimized the CNN-based water body mapping models to improve the accuracy [46; 51; 54]. The drawbacks of CNN-based models are the need for a large number of training samples and its time-consuming quality  
Figure 19: The influence of wind-induced waves on water extraction. (**a**) is for the Sentinel-1 SAR data acquired on 15 February 2020, and displayed in VH/VV/(VV + VH), and (**b**) is for the classification result, with blue used for water and green for non-water. (**a**) Sentinel-1 SAR data; (**b**) classification result. during the training process. However, when the model is trained, it will take a short time to make predictions from new images [32]. Some studies have also provided IF maps in their works, and most of these maps were generated from MODIS, Landsat optical data [61; 75; 76; 77], ENVISAT ASAR data [13], ENVISAT Low and Medium Resolution data [78], or the simulation of hydrodynamic models [59]. Although the data sources are different, the IF from different studies have similar temporal-spatial distribution trend, with IF generally varying along the gradient of the elevation, exhibiting maximum values within the deep flow channels and permanently flooded sub-lakes. In addition, many more details are presented in the the proposed 10 m resolution IF distribution map. Some researchers have also studied the relationship between the water area and water level of Poyang Lake. The relationship during 2000-2020 was provided in [61] as \(water\;level=0.003\times water\;area+5.494\) with \(R^{2}=0.926\), where the water level data were from DAHITI [79]. The relationship during 2000-2015 was provided in [62] as \(water\;area=231.01\times water\;level-722.04\) (\(water\;level<15.0\;\mathrm{m}\)) and \(water\;area=38.493\times water\;level+2387.9\) (\(water\;level\geq 15.0\;\mathrm{m}\)), where the water level data were from Xingzi gauging station. The differences among these IF distribution maps and these relationships may be caused by the study periods, data sources, and the bathymetry of Poyang Lake [80]. The bathymetric changes in Poyang Lake were attributed to the combined effect of human activities (e.g., sand dredging [62; 80; 81; 82], levele construction [62; 81], and the impoundment of the Three-Gorges Dam [81]) and weather events (the excessive precipitation resulting in the increased bottom elevation [81] caused by the sediments).