esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Detection of Quasi-Periodic Emissions from Satellite Observations by Using DETR Method
## 4 Results
### Experimental Environment  
The study utilized Ubuntu 18.04.6 LTS as the operating system model. The CPU employed was an Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40 GHz. The system had running memory of 256 GB, and the GPU model used was the NVIDIA Corporation GV100 GL (Tesla V100 PCIe 32 GB) (rev a1), with a memory size of 32 GB. For the model hyperparameters, the epoch number was set to 300, the initial learning rate to 0.0002, and the batch size to 4. Stochastic Gradient Descent (SGD) was selected as the optimizer for model training, and the optimal weight file was saved as the final model during training [31].  
The dataset used in this study consisted of 827 QP events. We screened and randomly divided these images into a training set and a validation set, following a ratio of 8:2. The size of each image was 1295 \(\times\) 620 pixels.
# A Systematic Review of Accessibility Techniques for Online Platforms: Current Trends and Challenges
## 3 Results of the Systematic Review
### Solutions Based on Speech-to-Text and Text-to-Speech
The review highlights the significant advancements in TTS due to deep learning, compared to traditional machine learning methods. The paper introduces a taxonomy of popular deep learning architectures and models used in speech synthesis, reviews various datasets utilized in TTS, and describes common evaluation metrics for assessing synthesized speech quality [15]. It concludes by outlining the challenges and future directions for TTS systems. [PERSON], [PERSON] et al. present an almost unsupervised learning method for TTS and automatic speech recognition (ASR) aimed at low-resource languages, addressing the challenge of limited aligned data. The approach combines several components: a denoising auto-encoder for language modeling; dual transformation to enhance accuracy between TTS and ASR; bidirectional sequence modeling to reduce error propagation; and a unified transformer-based model structure. The method achieves 99.84% word-level intelligibility, a 2.68 MOS for TTS, and an 11.7% phoneme error rate for ASR on the LJSpeech dataset, using only 200 paired data points (about 20 min of audio) and additional unpaired data [16]. The trend lines shown in Figures 4 and 5 for the keywords \"speech to text\" and \"text to speech\" appear quite similar over the past 15 years. Both terms show relatively low and stable interest in the earlier years, followed by an increase in interest around 2021. This similarity suggests a correlated rise in the relevance of both technologies, possibly due to the increasing adoption of voice-enabled applications, digital assistants, and accessibility solutions. Figure 4: The trending of the keywords “speech to text” over the last 15 years (November 2009–November 2024) from Google Trends. Figure 5: The trending of the keywords “text to speech” over the last 15 years (November 2009–November 2024) from Google Trends. The simultaneous increase in search interest might indicate that users are increasingly seeking or using both \"speech to text\" and \"text to speech\" functionalities, potentially driven by advancements in AI, improvements in accessibility technologies, and the rise of remote work and online communication. This parallel growth could reflect a broader trend toward integrating voice-based technologies in online platforms and other digital environments.
# Seasonal Monitoring of Volatiles and Antioxidant Activity of Brown Alga _Cladosteplus spongiosus_
###### Abstract  
_Cladosteplus spongiosus_ was harvested once a month during its growing season (from May to August) from the Adriatic Sea. Algal volatile organic compounds (VOCs) were obtained by headspace solid-phase microextraction (HS-SPME) and hydrodistillation (HD) and analysed by gas chromatography and mass spectrometry (GC-MS). The effects of air drying and growing season on VOCs were determined. Two different extraction methods (ultrasound-assisted extraction (UAE) and microwave-assisted extraction (MAE)) were used to obtain ethanolic extracts of _C. spongiosus_. In addition, the seasonal antioxidant potential of the extracts was determined, and non-volatile compounds were identified from the most potent antioxidant extract. Aliphatic compounds (e.g., pentadecane) were predominantly found by HS-SPME/GC-MS. Hydrocarbons were more than twice as abundant in the dry samples (except in May). Aliphatic alcohols (e.g., hexan-1-ol, octan-1-ol, and oct-1-en-3-ol) were present in high percentages and were more abundant in the fresh samples. Hexanal, heptanal, nonanal, and tridecanal were also found. Aliphatic ketones (octan-3-one, 6-methylhept-5-en-2-one, and (_E,Z_)-octa-3,5-dien-2-one) were more abundant in the fresh samples. Benzene derivatives (e.g., benzyl alcohol and benzaldehyde) were dominant in the fresh samples from May and August. (_E_)-Verbenol and _p_-cymen-8-ol were the most abundant in dry samples in May. HD revealed aliphatic compounds (e.g., heptadecane, pentadecanal, (_E_)-heptadec-8-ene, (_Z_)-heptadec-3-ene), sesquiterpenenes (germacrene D, epi-bicyclosesquiphellandrene, gleenol), dipterpenes (phytol, pachydicyl O, (_E_)-geranyl geraniol, cembra-4,7,11,15-tetraen-3-ol), and others. Among them, terpen were the most abundant (except for July). Seasonal variations in the antioxidant activity of the ethanolic extracts were evaluated via different assays. MAE extracts showed higher peroxyl radical inhibition activity from 55.1 to 74.2 \(\upmu\)M TE (Trolox equivalents). The highest reducing activity (293.8 \(\upmu\)M TE) was observed for the May sample. Therefore, the May MAE extract was analysed via high-performance liquid chromatography with high-resolution mass spectrometry and electrospray ionisation (UHPLC-ESI-HRMS). In total, 17 fatty acid derivatives, 9 pigments and derivatives, and 2 steroid derivatives were found. The highest content of pheophorbide \(a\) and fucoxanthin, as well as the presence of other pigment derivatives, could be related to the observed antioxidant activity.  
brown algae; GC-MS; HS-SPME; hydrodistillate; aliphatic compounds; terpen; UHPLC-ESI-HRMS; fatty acid amides; xanthophylls; antioxidant activity; UAE; MAE
## 3 Results and Discussion
### S-Parameters Analysis  
The KAYI shape UWB-MIMO antenna is fabricated with an overall size of \(30\times 30\) mm\({}^{2}\) and a substrate thickness of 1.6 mm. Figure 6 shows the antenna's simulated and measured S\({}_{11}\) result. The result shows that the antenna has a good impedance matching profile for the UWB characteristic, along with implementation of three band stop characteristics. The band stop range at the WiMax is from 2.7 to 3.3 GHz, the WLAN band stop characteristic is from 5.1 to 5.3 GHz, and the notch band for the satellite provider network is from 8.6 to 9.1 GHz. The S\({}_{11}\) parameters are almost same for the three ports due to the symmetry of the antenna. The simulated and measured results are in good agreement with each other.  
Figure 5: (a) Geometry of the inverted L-shaped slit and U-shaped slot; (b) S\({}_{11}\) parameter of combine \(L=11\) mm and \(U_{u}=8\) mm along with the creation of another rejection band at X-band for satellite communication.  
Figure 6: (a) Addition of L-shaped notching element with \(L_{u}=11\) mm to the radiating patch; (b) Addition of U-shaped notching element with \(U_{u}=8\) mm to the radiating patch; (c) Addition of both L-shaped and U-shaped notching elements \(L_{u}+U_{u}\) to the radiating patch; and (d) Effect of coupling of notching elements (\(L_{u}\) and \(U_{u}\)) on band rejection at WiMax and WLAN and the creation of third band rejection at X-band for satellite communication.
## 5 Conclusions  
In this paper, a general method that would obtain the FRF from the disturbance source to the sensitive payload was proposed. Firstly, the integrated model of a high-precision optical remote sensing satellite was developed, including the disturbance mode, structural dynamic mode, and optical mode, while the effect of AOCS was ignored. Secondly, a general method based on the \(\mathbf{H}_{\rm v}\) algorithm was presented to obtain the FRF, which does not require an additional excitation source, except for the RWA itself. Finally, a microvi-bration test of the satellite using different RWAs was carried out. The experimental results demonstrate that the coherence is high, and the peak positions of the FRF obtained by different RWAs are basically the same, while the amplitudes are slightly different, especially at 23 Hz-30 Hz, which indicates that the method proposed in this paper is effective. Therefore, the image motion of the sensitive payload can be obtained by multiplying the disturbance of the RWA measured at a fixed interface with the FRF, and without extensive testing when changing different RWAs in the same series.  
Conceptualization, Y.H.; data curation, Y.H., C.W., P.D. and H.Y.; formal analysis, Y.H.; funding acquisition, L.Z.; investigation, Y.H.; methodology, Y.H. and S.C.; project administration, L.Z.; resources, S.C. and L.Z.; software, Y.H. and H.Y.; validation, Y.H. and P.D.; visualization, X.Z. and L.Z.; writing--original draft, Y.H.; writing--review and editing, Y.H. and C.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Jilin Province Science and technology development plan project of China, Grant No. 20210508038 RQ.  
Not applicable.  
The authors declare no conflict of interest.
## 1 Introduction
Nevertheless, they have a slow convergence rate and require a free parameter, namely the step size. The fixed-point iteration technique has a smaller step size and comparatively quick speed of convergence for obtaining system performance estimation in noisy environments [26]. In this regard, [PERSON] et al. [27] provided a necessary condition for the fixed-point MCC algorithm's convergence properties. The recurrence of covariance matrices is ignored by maximum correntropy-based KFs, which can affect the precision of their estimation. The maximum correntropy Kalman filter (MCCKF) for linear non-Gaussian systems was proposed by [PERSON] et al. [5] as a solution for redesigning the KF while implementing the linear regression issues. The MCKF was then expanded to include nonlinear non-Gaussian systems and several MCKF distinctions have since been developed [28, 29]. Further, the studies based on robust filters show the computation complexity for a large noise environment. There are numerous real-world uses for heavy-tailed noises, such as GPS and radar measuring systems, among others [30]. In this present study, we have proposed a new nonlinear Kalman filter technique based on the maximum correntropy criterion known as MCCEKF. The MCCEKF also uses the state mean and covariance matrix dispersion equations to estimate the state and covariance matrix before updating its subsequent estimates based on a fixed-point algorithm [31, 32]. The primary benefits of EKF over UKF are its theoretical and analytical simplification. As a result, extensive use in the field of applied engineering has been achieved in recent years [33]. We present a new type of maximum correntropy EKF for GPS navigation, i.e., maximum correntropy EKF based on nonlinear regression (MCCEKF-NR). In this work, we studied the nonlinear extension of the maximum correntropy-based KF to improve the estimation accuracy by the iterated EKF (IEKF). Further, the well-known fixed-point recursive procedure is applied for updating the posterior estimations. The recently developed filters are calculated in real time, and also include iterative resolutions for the estimated state. It is worth noting that for a basic form of MCCEKF, it only took the linear measurement equation into account [34]. However, we studied the nonlinear measurement with nonlinear regression and a mixture of linear regression calculations. Because of its ease of use along with the minimal computational expense, the approach presented in this paper is the refinement of the linear KFs and can be implemented in nonlinear estimated state systems. The remaining section of the work is structured as follows. We briefly discuss the MCC and the EKF in Section 1. We generate the MCCEKF-NR algorithm in Section 2. The results of the simulation are then discussed in Section 3. Section 4 provides the conclusion at the end.
## 4 Calibration Comparison of Landsat 8 and Sentinel 2
### Vicarious Calibration Based on In Situ Surface Measurements  
Two teams in the United States have been acquiring reflectance measurements of the surface under OLI and MSI: University of Arizona (UAz) at the Railroad Valley Playa, Nevada (RRV), and South Dakota State University (SDSU) at the Brookings, SD, site [24; 25]. In both cases, the software MODTRAN (Moderate Resolution Atmospheric Transmission) is used to predict top-of-atmosphere reflectance from the surface measurements, which are then compared to the reflectance measured by OLI and MSI. See Figure 10. Results of these calibration efforts suggest, once again, that the two instruments are calibrated consistently with respect to each other in the order of 2% or better. However, absolute calibration uncertainties are somewhat larger, though consistent within their respective methodologies.
# Recent Advances in Nanoparticle-Mediated Treatment of Inflammatory Bowel Diseases
## 8 Hydrophilic Nanocarriers  
Clodronate-loaded nanoparticles comprising cationic polymethacrylate (Eudragit RL) were developed to test their efficacy in an experimental model of colitis and compared to clodronate alone [26]. For the negatively charged drug, the authors employed cationic NPs in order to obtain smaller nanoparticles (\(\sim\)120 nm). As a free drug, clodronate does not demonstrate considerable cytokine inhibitory potency, so it is efficiently encapsulated. The authors used two mouse models that mimic the two major subtypes of IBD ([PERSON]'s disease and ulcerative colitis) for clodronate-loaded cationic nanoparticles and free clodronate, the controls being treated with saline solution or blank nanoparticles. While the free drug was not effective in the murine model of colitis, clodronate-loaded cationic nanoparticles achieved the inhibition of the inflammatory process in both models. Due to the high-encapsulation efficiency and high hydrophilicity of clodronate, the authors concluded that the encapsulation of the drug inside NPs is optimal for non-selective intracellular delivery [26].
## 3 Results and Discussion
### Foundability  
Figure 2 shows the values of the parameters HM (maximum foam height) and HS (height of foam stability) for each of the studied cavas (CH and XA). In general, as can be seen, CH cava presented slightly higher HM values than XA, as was observed in the previous studies by [PERSON] et al. [24] and [PERSON] et al. [25], in which Chardonnay cavas showed higher HM values than Macabeo, Xarel.lo, and Parellada cava wines. However, the HS values were similar in all varieties.  
Figure 2: Foamability measurements of the different Chardonnay (CH) and Xarel.lo (XA) base wines made with different yeasts strains, _Saccharomyces cerevisiae_ (Y1, Y2, Y3) and non-_Saccharomyces_, _M. pulcherrima_ (Y4) and _T. delbrueckii_ (Y5), using the Mosalux procedure. HM indicates the maximum height reached by the foam and HS the height in a steady state. Different letters indicate significant differences between samples with different yeasts. Uppercase letters for HM and lowercase letters for HS. Samples (n = 2).  
By comparison, when the possible influence of the different yeasts on foamability was studied, it was observed that the highest HM and HS measurements were obtained when using Y1 and Y4 for the Chardonnay variety and Y3 and Y4 for the Xarel.lo variety. As can be seen, the samples that provided the highest HM values coincided with those with the highest values of protein content, so it can be concluded that there is a direct relationship between both parameters. These results agree with previous studies which reported that proteins and mannoproteins released by yeasts exert a positive effect on the foam [26], and that an increase in the amount of protein could lead to an increase in foamability [26] and lower velocities of foam dissipation [27]. Therefore, in this study, the results indicate that the wines examined with the addition of _M. pulcherrima_ at first fermentation presented good foamability. Furthermore, this may be related to the high protein content, which can stabilize the bubble film due to its surface properties [26].
## 3 Predictive Analysis Using Machine Learning Methods  
This study utilizes a dataset comprising approximately 7500 reviews sourced from Skytrax, a renowned platform for airline and airport reviews. It leverages the SOAP API in Python 3.1 to gather data spanning mainly from 2015 to 2024, with a small proportion extending back to even earlier years. In the data, each review has a variety of features including terminal cleanliness, terminal seating, terminal signs, food and beverages, airport shopping, WiFi connectivity, and airport staff. Additionally, each review is associated with a binary classification target variable, indicating whether the reviewer recommends the airport. The predictive analysis pipeline is shown in Figure 1.  
From the figure, the Skytrax online reviews scraped are first pre-processed by removing numbers, special characters and stop words and correcting typos to ensure consistency and accuracy. Then, the Exploratory Data Analysis (EDA) is performed based on descriptive, correlation and ANOVA techniques to understand the distribution and relationships within the data. The time series analysis in descriptive analysis studies the temporal patterns and trends, while the correlation analysis and ANOVA tests examine the relationships among features and the differences in the airport service ratings across years, respectively.  
To predict the likelihood of an airport being recommended, a comparative approach using multiple machine learning algorithms [44; 45; 46] is applied, including logistic regression (LR), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbours (KNN),  
Figure 1: Predictive analysis pipeline for airport service quality evaluation based on online reviews.  
Gradient Boosting (GB), and Deep Neural Network (DNN). Each model's performance is thereafter assessed using mean accuracy and standard deviation of accuracy across multi-fold cross-validation, an approach used in machine learning to evaluate the performance of a model on unseen data. A logistic regression model is also employed in the pipeline to assess the impact of various airport features on the likelihood of an airport being recommended by passengers. Interaction terms between pairs of features are created and included in the model to assess the combined effects.
# RGB-D Camera for 3D Laser Point Cloud Hole Repair in Mine Access Shaft Roadway
## 4 Data Processing
### Data Processing of Ground 3D Laser Scanning
#### 4.1.2 Point Cloud Denoising  
The 3D laser scanner could not obtain the complete 3D point cloud information in one attempt due to its limited scanning perspective and the complex environment. It was necessary to splice the point cloud data of different stations into the same coordinate system [24], which is the process of point cloud registration. In the point cloud registration process, according to the different classification standards, the registration of point cloud data can be divided into two categories: point cloud registration based on features and point cloud registration without features [25].  
Due to the large and narrow slope of underground tunnel shaft, it was dangerous to install the total station and difficult to layout the target; thus, we selected the point cloud registration without a target. The most basic target-free point cloud registration algorithm is the Iterative Closest Point (ICP) algorithm [26]. Its processing flow is to collect samples from the original point cloud data, determine the point set corresponding to the initial matching, filter and screen the corresponding point pairs, and solve the transformation matrix. Maptek I-Site Studio software was used to register the cloud data of each scanning survey site of the underground shaft roadway based on the ICP algorithm. The results are shown in Figure 9. After registration in Figure 9, it is evident that the 3D laser point cloud is obviously missing.
## 6 Challenges and Future Trends in LEV Charging
### Standardization of LEV Charging  
A significant challenge in the expansion of LEV charging stations is the lack of standardization. In recent years, numerous standards have been enforced for conventional EVs [13; 128] by organizations such as IEC (IEC 61851) [129] and SAE (J1772) [130], which deal with AC and DC charging. Furthermore, standards are also in place for the type of connectors used [81]. Although efforts have been made to standardize charging stations for EVs such as BEVC-AC001/DC001 [131] and Type 3 A [81] for connectors, efforts must be made by LEV manufacturers to ensure compatibility with these standards. Battery swapping stations must also be standardised to ensure cross-compatibility.  
Another related concern is the lack of standards for LEV batteries. LEV batteries can range anywhere from 48 V to 72 V, and furthermore, they may also lack an active cooling mechanism [3]. This can potentially cause safety hazards. Hence, standardization of LEV batteries and charging features is a main priority for manufacturers to increase LEV adoption in the future.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Control Technique** & & **Salient Features** \\ \hline \multirow{4}{*}{1.} & & \(\bullet\) & Most popular control technique for EV charging applications. \\  & & \(\bullet\) & CC stage—increase in battery voltage up to certain state of charge. \\  & Constant Current/Constant & \(\bullet\) & CV stage—decrease in charging current. \\  & Voltage (CC-CV) & \(\bullet\) & Has limitations such as long charging time, lower efficiency, hence not suitable for fast charging applications. \\  & & \(\bullet\) & Allows for larger charging current to be injected in pulses. \\  & & \(\bullet\) & Additionally involves negative pulse and rest periods to preserve battery life. \\  & & \(\bullet\) & It has the limitation of lower average charging current due to negative pulse period. \\  & & \(\bullet\) & This can be overcome by choosing appropriate pulse widths and charging current amplitudes. \\  & & \(\bullet\) & Nascent charging technique, which allows charging within a limited temperature band. \\
3 & Constant temperature (CT) & \(\bullet\) & Charging current is dynamically set according to the battery temperature. \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of discussed control techniques.