esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Evaluation of Antifungal Activity  
Figure 1 presents the cell counts of _Zygosaccharomyces baili_ in two batches of mayonnaise (MY B_0 and MY C), both incubated at 25 \({}^{\circ}\)C. In the mayonnaise control samples (MY C) prepared with the unflavored EVOO, the _Zygosaccharomyces baili_ cell counts had a steady upward trend, and during the incubation period of 20 days, they increased by approximately 1 log cycle compared to the initial inoculum. In contrast, the mayonnaise samples prepared with the EVOO flavored with 40 g L\({}^{-1}\) dry majoram (MY B_0) showed no yeast growth for two weeks. Yeast colonies appeared at the beginning of the third week of incubation at 25 \({}^{\circ}\)C. Some of the polyphenic compounds extracted from the marjoram dry leaves in the olive oil may be the cause of the partial inhibition of _Zygosaccharomyces baili_ cell growth in the mayonnaise prepared with the olive oil flavored with 40 g L\({}^{-1}\) dry marjoram (MY B_0). According to [PERSON] et al. (2015) [46], p-coumaric acid has shown limited inhibitory activity (15%) against _Zygosaccharomyces baili_. It has also been reported that luteolin exhibits activity against _Zygosaccharomyces baili_ and _Zygosaccharomyces rouxii_[47]. The pH value of the samples of both mayonnaise batches was 4.0 during the incubation period of 20 days. The evaluation of the antifungal activity of the flavored EVOO could be applied to the preparation of various foods, such as bakery products, spreadable fats, sauces, condiments and confectionaries, and enhance their microbiological shelf life by partially replacing synthetic preservatives.  
Figure 1: Yeast counts of _Zygosaccharomyces baili_ in samples of two batches of mayonnaise over an incubation period of 20 days at 25 \({}^{\circ}\)C. Yeast colonies were measured on YPD agar plates incubated at 25 \({}^{\circ}\)C for 48 h. MY B_0: mayonnaise samples prepared with EVOO flavored with 40 g L\({}^{-1}\) dry marjoram; MY C: mayonnaise samples prepared with the unflavored EVOO.
## 4 Results and Discussion
### Overview of Flow Characteristics
#### 4.1.2 Cross-Sectional Distribution of the Solid Volume Fraction
Nevertheless, at the impeller outlet, high-velocity elements shedding from the blade trailing edges are distributed regularly in the circumferential direction, as indicated in Figure 8b. In the volute chamber, intensive mixing is induced by the interaction between high-velocity streams of the mixture and water. The volute chamber is spacious enough, facilitating the development of the vortices involved. Four cross-sections, i.e., cross-sections II, IV, VI, and VIII, are selected to describe the velocity distributions in the volute chamber. The VIII cross-section is located at the connection between the divergent segment and the spiral segment. Along the circumferential direction opposite to the rotation of the impellers, cross-sections VI, IV, and II are separated from cross-section VIII by 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\), respectively. Both the velocity distribution and streamlines in the four sections are shown in Figure 9. The four sections are unanimously dominated by large vortices, and the sizes of the most dominant vortices are slightly different. Regarding the position of the core of the largest vortex in each section, the discrepancy is evident, as is related to the position of the  
Figure 8: Cross-sectional velocity distribution in the flow passage of the pump: (**a**) lower part and (**b**) upper part. section. In the solute chamber, downward streams containing sand and water interact with upward streams of pure water, and the multiphase patterns are significantly influenced by the wall of the solute chamber. Therefore, vortices with different sizes and profiles are produced. It is conceivable that sand particles exhibit diverse dynamic behaviors. Furthermore, the generation and evolution of these large vortices incur energy loss but promote the mixing of sand and water.
## 1 Introduction
### Vibration-Based Fault Diagnosis  
In [8], an online fault diagnosis system was developed using random forest (RF) and Extreme Gradient Boosting (XGBoost) algorithms, demonstrating a 15% performance improvement for XGBoost with optimized hyperparameters and a 1107% rise in training time. The issue of feature selection was discussed in [1; 2; 4; 9]. In [1], an IM fault diagnosis framework based on the Light Gradient Boosting Machine (LightGBM) was developed. The study employed vibration signals and Recursive Feature Elimination (RFE), in addition to Bayesian hyperparameter optimization, which resulted in an accuracy of 100% across varying operating conditions. In [4], fault identification using acoustic entropy feature selection and the Hierarchical Adaptive Neuro-Fuzzy Inference System (HANFIS) was proposed. The study in [9] used deep features extracted from vibration spectrograms, combined with Ensemble Deep Models Features Extraction (EDMFE) and optimized via ensemble feature selection (EFS), achieving 100% accuracy in less than 70 s. Similarly, adaptive feature selection for rolling bearing fault diagnosis was addressed in [2]. A total of 240 features were extracted. Then, a three-layer feature selection algorithm was applied to filter and optimize the feature set. The suggested three-layer algorithm comprised the Chi-square test, Variance-Relief-F, and hierarchical clustering algorithms. Lastly, optimized features were then classified using Fuzzy C-Means (FCM) clustering, resulting in a more complex and demanding fault diagnosis process compared to traditional methods.  
Moreover, in [10], a hybrid multi-model feature fusion approach was investigated for detecting multiple faults, including rotor and bearing defects. Similarly, in [11], the study employed fourteen dimensionality reduction methods, significantly reducing computational time by only 21%. The results of applying mutual information and Analysis of Variance (ANOVA) F-tests for feature selection and Logistic Regression (LR) and SVC for fault detection were reported in [12]. The study emphasized the superiority of the optimized hyperparameters SVC model compared to LR.  
Similarly, a combination of the Hilbert spectral envelope with RF for detecting and locating bearing defects was presented in [13]. The proposed combination achieved an accuracy of 99.94%. In [14], a Convolutional Long Short-Term Memory (CLSTM) architecture for fault detection and diagnosis (FDD) of rotating machinery was introduced. The proposed model achieved 100% accuracy with short input data, making it suitable for real-time monitoring. Studies [15; 16] addressed fault diagnosis for Variable Frequency Drive (VFD)-fed IMs. In [15], tri-axial vibration signals were used for fault diagnosis across varying operating frequencies, with discrete wavelet transform (DWT)-based feature extraction improving diagnostic accuracy by 12%. Meanwhile, the integration of Internet of Things (IoT) technology for online fault diagnosis of IMs was explored in [16].
## 4 Conclusions  
The removal and recovery of phosphate has attracted growing interest because of the two issues of aquatic eutrophication and global phosphate rock scarcity. Here, biochar functionalized with layered double hydroxides (BC-LDHs) was developed for the previous abatement of excess phosphate in water bodies, and post-adsorption BC-LDHs (P-BC-LDHs) were sequentially assessed for their application potential as a phosphate fertilizer. The lettuce seed germination treatments included four levels of P-BC-LDHs (0.5, 1.0, 2.5, and 5.0 g). The effects of P-BC-LDHs on seedling emergence during the first 24 h were more pronounced with increasing P-BC-LDHs. Treated and non-treated samples did not significantly differ on the last day of germination (\(p>0.05\)), in contrast with the subsequent experiments, in which lettuce crops were grown in growing media with 0.5%, 1.0%, 2.5%, and 5.0% (_w/w_) of P-BC-LDHs. The high input of P-BC-LDHs led to the best nutrient accumulation levels within plant tissues and the best growth features, particularly in terms of the root characteristics, where the length was significantly improved by up to 45% in the 2.5% (_w/w_) treatment compared with that of the control (\(p<0.05\)). The fact that P-BC-LDHs improved nutrition and created desirable conditions for lettuce growth could explain these phenomena. A comparison of the leaching behaviors among phosphorus, magnesium, and aluminum suggested that P-BC-LDHs achieved a slower release of phosphorus, which could extend the time of nutrient availability for plant uptake. The transformation of phosphate-loaded adsorbents into fertilizers offers an appealing alternative method of resource recycling from waste streams, which promotes new sustainable and economic strategies for improving the management of phosphate utilization. Aluminum and PAHs are likely potential contaminants found in P-BC-LDHs and may pose toxicological risks to plants and soil microbial activity. Hence, future studies directed toward the precise assessment of the impacts of P-BC-LDHs on the long-term persistence or bioavailability of these contaminants in the environment will provide valuable information and provide insights into the application of P-BC-LDHs as fertilizers.  
The following are available online at [[https://www.mdpi.com/article/10_3390/app11146489/s1](https://www.mdpi.com/article/10_3390/app11146489/s1)]([https://www.mdpi.com/article/10_3390/app11146489/s1](https://www.mdpi.com/article/10_3390/app11146489/s1)).  
Conceptualization, J.B.; methodology, J.B.; investigation, J.B.; writing--original draft preparation, J.B.; writing--review and editing, T.I.; supervision, T.I. Both authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors thank [PERSON] for kindly providing a horizontal electric tube furnace for the biochar preparation used in this study. The authors also thank Editage (www.editage.com) for English language editing.  
The authors declare no conflict of interest.
# Detection of Large-Scale Floods Using Google Earth Engine and Google Colab
## 2 Materials and Methods
### Processing Chain  
To make everything less computer-intensive, processing will be applied only to flood-prone zones predetermined according to topography. This is performed by thresholding the HAND model. The processing chain developed is based on detecting changes between two Sentinel-1 radar images using the normalised difference ratio: images acquired before floods for reference and images acquired during or after a flood has peaked are required. Before attempting to detect flooded areas, it is also crucial to filter out speckle noise from radar images. The choice of reference Sentinel-1 images will have a significant impact on results and their interpretation. The procedure for selecting these images is outlined in a dedicated subsection.
# Combined Improved CEEMDAN and Wavelet Transform Sea Wave Interference Suppression
## 2 Cross-Media Communication Model  
In this section, we analyze the signal transmission of the cross-media communication system, which contains the underwater speaker that excites the WSAW, and the radar emits a frequency-modulated continuous wave (FMCW) and processes the reflected signals. The overall communication system includes two parts: water-air interface and radio frequency (RF) model.
# Application-Aware Network Traffic Management in MEC-Integrated Industrial Environments
## 7 Conclusions and Future Work  
In this work, we explored application-aware network traffic management to deal with the unprecedented volume of data traversing industrial networks nowadays, while satisfying the requirements of network-intensive industrial applications. To this end, we designed an architecture based on the SDN-eINP interplay for enabling application-aware network traffic management in MEC-integrated industrial environments, where on-premises and off-premises edge computing resources are used in a coordinated way.  
The rationale of eINP is to provide the network with additional capabilities (e.g., data aggregation, data filtering, data encryption, message duplication, and pattern-matching detection) for packet payload inspection and processing through edge computing. Because this implies additional delays, we developed a proof-of-concept prototype to quantify the performance of our solution while providing D&A, SE, HE, HA, and HD. We performed extensive in-the-field experimentation of the proposed solution: the reported performance results show the feasibility of our proposed approach and its tradeoffs in terms of performance and confidentiality. In particular, horizontal scaling revealed a promising mechanism for coping with CPU-intensive features, such as SE.  
Such promising results foster the research activity towards more sophisticated orchestration strategies using predictive algorithms to activate eINP modules proactively based on movement patterns of mobile industrial components. We also plan to investigate how to make P4-enabled programmable switches and ENs work together.  
Conceptualization, M.F. and C.G.; methodology, M.F. and C.G.; software, M.F.; validation, M.F. and C.G.; formal analysis, M.F.; investigation, M.F.; resources, C.S.; data curation, M.F.; writing--original draft preparation, M.F. and C.G.; writing--review and editing, P.B., M.F., C.G. and C.S.; visualization, M.F. and C.G.; supervision, P.B. and C.S.; project administration, P.B. and C.S. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the European Union under the Italian National Recovery and Resilience Plan (NRRP) of NextGenerationEU, partnership on \"Telecommunications of the Future\" (PE000001--program \"RESTART\").  
The data presented in this study are contained within the article.
## 5 3 GPP Standardization for URLLC
### Handover  
Handover, which is one of the most crucial prominent issues in supporting URLLC requirements, was also a part of the discussion in Release 16 of 3 GPP. A way to support handover while keeping low latency and jitter remains a significant concern. In 3 GPP TS 23.502 [50], the handover process requires a lossless handover. The source RAN node forwards data directly or indirectly to the target RAN node. The use of tunnel is the current approach, but it introduces additional jitter and latency. With this current approach, the issue remains open, and further studies were suggested by 3 GPP on the matter.
## 4 Conclusions  
This investigation confirmed the efficacy of hydrothermal pretreatment (HTP) for improving the solubility of lignocellulosic biomass and facilitating the production of total reducing sugars (TRS), proteins (PR), and volatile fatty acids (VFAs). Orange peel (OP) and sugar beet pulp (SBP) showed the highest solubilization yields after 30 min of pretreatment versus the other tested biomasses. However, in the case of OP, the shown high solubilization did not result in increased hydrogen production for 18.6 mL H\({}_{2}\) (n.c.)/g (d.m.). On the contrary, SBP and brewer spent grain (BSG), when pretreated for 20-30 min, obtained the highest hydrogen production during dark fermentation, as follows: 97.5 and 34.1 mL H\({}_{2}\) (n.c.)/g (d.m.), respectively. Rice husk (RH) showed limited solubilization due to its high lignin content, though hydrogen production was still enhanced after pretreatment of 14.1 mL H\({}_{2}\) (n.c.)/g (d.m.). These results underscore the importance of biomass characteristics and optimized pretreatment conditions in improving the bioproduct recovery. The combination of hydrothermal pretreatment with dark fermentation, as a two-stage approach, has proven to be an effective biorefinery strategy for converting lignocellulosic biomass into biofuels and high-value by-products.  
A.R.: writing-original draft, investigation, formal analysis, data curation, and visualization. C.J.A.-G.: supervision, methodology, conceptualization, writing--review and editing, and visualization.  
editing, funding acquisition, and visualization. All authors have read and agreed to the published version of the manuscript.  
This work is part of the R+D+i project CTM2016-79071-R, financed by the Spanish ministry MCIN/AEI/10.13039/501100011033/ and the European Regional Development Fund (ERDF)--\"A way to make Europe\"--and by a grant from the Program for the Promotion and Impulse of Research and Transfer of the University of Cadiz (Ref: IRTP04_UCA). Additionally, this publication has been co-financed by the \"Plan Propio-UCA 2025-2027\".  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BSG & Brewers spent grain \\ DF & Dark fermentation \\ d.m. & Dry matter \\ DOC & Dissolved organic carbon \\ ESBP & exhausted sugar beet pulp \\ HMF & Hydroxymethylfurfurral \\ HRT & Hydraulic retention time \\ HTP & Hydrothermal pretreatment. \\ LHW & Liquid hot water \\ n.c. & At normal conditions of pressure and temperature \\ OP & Orange peel \\ PR & Total proteins \\ RH & Rice husk \\ SBP & Sugar beet pulp \\ sCOD & Soluble chemical oxygen demand \\ TP & Total phenols \\ TS & Total solids \\ TRS & Total reducing sugar \\ VFA & Volatile fatty acids \\ VS & Volatile solids \\ \end{tabular}
# Coordinated Multi-UAV Reconnaissance Scheme for Multiple Targets
## 3 Problem Model
### Model Establishment
Based on the analysis above, we can establish the mathematical model for the multi-UAV multi-target coordinated reconnaissance problem as follows:  
\[\max f_{1}=\sum_{j=1}^{N_{t}}c_{j}\cdot(1-e^{-\beta_{(}u^{j}_{j})^{\cdot t_{j} }}), \tag{5}\]  
\[\min f_{2}=\sum_{i=1}^{N_{u}}D_{i}, \tag{6}\]  
The constraints are  
\[G_{min}\leq c_{j}\cdot(1-e^{-\beta_{(}u^{j}_{j)}\cdot t_{j}}),j=1,2,...,N_{t}, \tag{7}\]  
\[\frac{D_{i}}{v_{i}}+\sum_{k=1}^{|q_{i}|}t_{q_{(i,k)}}\leq T_{i},i=1,2,...,N_{ u}. \tag{8}\]  
In Formula (5), \(f_{1}\) is the total revenue of the multi-UAV cooperative reconnaissance. \(N_{t}\) represents the total number of task areas. \(c_{j}\) refers to the reconnaissance value coefficient of task area \(j\), \(U^{j}\) stands for the UAV assigned to the \(j\)-th task area, \(\beta_{(U^{j},j)}\) is the reconnaissance ability of the UAV \(U^{j}\), to task area \(j\). \(t_{j}\) means the reconnaissance time assigned to task area \(j\) by the UAV \(U^{j}\). In Formula (6), \(f_{2}\) represents the total flight distance of all UAVs, and \(N_{u}\) is the total number of UAVs. Formula (7) indicates that the reconnaissance revenue of each UAV in a task area must be greater than the minimum reconnaissance revenue \(G_{min}\) of the corresponding task area. This ensures that the information obtained by the UAV after reconnaissance of the task area is not too little or nothing and prevents the reconnaissance of the task area from losing its significance. Formula (8) is a constraint which means the sum of the time spent on the road and the reconnaissance of each UAV cannot exceed the endurance time. It ensures that the UAV can return to the base smoothly after performing the task. In Formula (8), \(v_{i}\) refers the flight speed of UAV\(i\), \(t_{q_{(i,k)}}\) represents the reconnaissance time allocated by the \(i\)-th UAV to the \(k\)-th task area, and \(T_{i}\) is the endurance time of UAV\({}_{i}\).
## 5 Forward Simulation Results of Different Salt Cavity Shapes
### Influence of Cavity Diameter  
In the production method of salt mining companies, the diameter of the brine extraction cavity is mainly influenced by the water injection time. The cavity at the injection  
Figure 12: Seismic record profiles of variable density at different cavity spacings.  
Figure 13: Congruent diagrams of waveform seismic record profiles and cavity models with different cavity spacing.  
end becomes more developed and thus larger in diameter with longer injection times. For this analysis, a symmetrical distribution model is employed, where the cavity diameters on both sides change synchronously, to conduct forward simulation analysis to assess the impact of cavity diameter.  
Figure 14 shows the seismic record profiles of variable density at different cavity diameters. When the cavity diameter is small, specifically 20 m, the seismic reflection effect from the top surface of the cavity is weak and indistinct. However, as the diameter increases to 40 m, the seismic reflection becomes progressively clearer. The top surface continues to exhibit a strong valley reflection feature, characterized by a pattern of \"two wave peaks sandwiching a strong wave valley\". Notably, the upper peak possesses greater amplitude energy compared to the lower peak. As the cavity diameter further increases, the width of the strong valley on the top surface gradually widens, making the \"two peaks sandwiching a strong valley\" even more pronounced. Inside the cavity, the reflection is chaotic and weak, with the entire cavity displaying distinct annular features. Additionally, the reflection characteristics of the horizontal channel are relatively clear, showing a medium-strong amplitude reflection pattern of \"one valley and one peak\", which is similar to the effect of cavity spacing.  
Figure 15 presents a superposition diagram of waveform and salt cavern models with different cavern diameters. The diagram distinctly highlights the prominent trough features on the top surface of the cavity, which remain consistent in their relative position irrespective of the cavity diameter. As the cavern diameter increases, the wave width depicted by the waveform expands accordingly, demonstrating an amplification effect. Notably, a smaller cavern diameter results in a larger magnification factor, whereas a larger cavern diameter causes the seismic waveform width to more closely align with the model width. This inverse relationship between the magnification factor and the salt cavity diameter is further illustrated in Figure 16. Such phenomena have also been documented in literatures [46; 47]. Additionally, the top surface of the insoluble residue within the cavity exhibits a downward deviation pattern. The seismic reflection characteristics of the horizontal channel are relatively distinct, with its bottom surface corresponding to the peak reflection features, and their relative position remains largely unaffected by changes in the cavity diameter.
# The Impact of Space-Based AIS Antenna Orientation on In-Orbit AIS Detection Performance
## Materials and Methods
### Analysis Method  
As mentioned in the introduction, the aim of this research is not intended to study the impact of different antenna orientations on the quality of space-based AIS messages, but rather the quantity of the received messages with valid status data integrity. Therefore, in this research, we did not perform AIS message correction. Necessary correction of AIS messages can be performed by removing the wrong location reported messages, re-used MMSI (Maritime Mobile Service Identity) identifier, and out of view message [20]. All of those corrections have to be done in case to assess the quality of the AIS dataset received by an AIS receiver. Hence, the valid and non-valid data portions need to be calculated.