esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Emulsion Activity and Stability of Protein Extracts  
The emulsion activity and stability of the protein extracts generated from _Spirulina_ sp. and _Isochrysis_ sp. at five different pH values using four different oils are shown in Figure 6. The _Spirulina_ sp. protein extract suspended in olive oil showed the highest emulsion activity (22.41%), and rapeseed oil showed the lowest (21.00%). The _Isochrysis_ sp. protein extract suspended in olive oil showed the highest emulsion activity (11.36%), and rapeseed oil showed the lowest (10.01%). Both microalgal protein extracts displayed poor emulsion activity and stability percentage values compared with the commercial controls of defatted flaxseed protein and whey protein isolate (Figure 5). However, the _Spirulina_ sp. protein extract did display excellent emulsion stability values (85.91%) when assessed in olive oil. The EA values obtained here are in contrast to results reporting the EA values of seaweed derived protein extracts in oils (75.68-99.67%). In general, emulsion stability was more effective in the olive and groundnut oils. Emulsion stability can be affected by several factors, including the pH, droplet size, net charge, interfacial tension, viscosity and protein conformation. The high emulsion stability observed previously for the _Spirulina_ sp. protein extract in olive oil after heating at 85 \({}^{\circ}\)C could be due to the dissociation of some proteins, resulting in the formation of subunits with more hydrophobic groups and stronger interactions with the lipid phase [36].  
Figure 3: Tris-Tricine SDS-PAGE, performed using a Mini-PROTEAN® electrophoresis unit using Mini-PROTEAN® 10–20% Tris-Tricine precast gels (Bio-Rad laboratories, USA) for _Spirulina_ sp. protein extract (**A**) and _Isochrysis_ sp. protein extract (**B**).  
Figure 4: Solubility of extracted proteins at different pH values (n = 3).  
Figure 5: ES (**A**) and EA (**B**) of soluble protein extracts (n = 3). Values calculated against whey.
## 4 In Vitro Susceptibility Testing Methods for _B. burgdorferi_ s.l.
### Approaches for MBC Determination Using Macro- and Microdilution Methods  
_Borrelia burgdorferi_ s.l. is a fastidious organism that can evade the immune system. Its persistence in the host can lead to late-stage manifestations. Similarly, the survival of a small number of bacteria may result in persistence or a clinical relapse [7; 60; 61; 62; 63]. This is why, in contrast to testing of common rapid growers, there is good reason to apply more stringent test conditions and longer periods of subculture when investigating the borreliacid capacity of widely used antimicrobial agents so as to understand the possible regrowth of the pathogen after exposure to the antibiotic [17; 52]. Moreover, it appears that most antimicrobial agents need a longer length of time to kill _B. burgdorferi_ s.l., even when compared to other spirochetal infections like _Treponema pallidum_[17; 52; 64]. Likewise, it must be taken into account, that a 3 log reduction (99.9%), as commonly used to define the MBC in rapid growers, also means the survival of 10 to 10\({}^{4}\) bacteria per mL at two to four log\({}_{2}\) unit dilutions above the MIC, depending on the final inoculum (10\({}^{4}\)-10\({}^{7}\) per mL) and the substance being tested [51; 55]. Many studies on the in vitro susceptibility of borreliae, therefore, tend to use variations of micro- and macrodilution methods; however, the MBC criteria and the subculture periods for the detection of possible regrowth of the antibiotically treated borreliae tend to vary widely (seven days to three weeks) (Table 1). Usually, MBC values are determined after 72 h of incubation with the antibiotic by taking fresh aliquots from all vials lacking detectable growth and diluting them at a ratio of 1:75 with fresh BSK to achieve a sample dilution below the MIC. Incubation then continues at 33 \({}^{\circ}\)C in 5% CO\({}_{2}\) for an additional 3 weeks [17; 52] to assess the drug concentrations that provide a 100% killing of the initial inoculum under stringent conditions. The MBC is therefore commonly defined as the lowest concentration of the antimicrobial agent where no spirochetes can be detected after 5 to 10 high-power fields are examined by dark-field microscopy for the presence or absence of spirochetes after 3 weeks of subculture, i.e., 100% killing under rigorous conditions [52; 55; 65]. The efficacy of the tested antimicrobial substances in killing 100% of the inoculated microorganisms after 72 h is thus a very stringent criterion for any antibiotic and, as outlined above, leads to higher MBC values than those usually obtained by conventional time-kill studies and investigations using less restrictive MBC definitions. Such an approach also clearly differs from the traditional testing methodology commonly used in non-fastidious microorganisms but can be helpful in identifying substances that are more appropriate for the antimicrobial chemotherapy of LB [17; 52].
## 2 Algorithms for Data Encoding to Minimize the File Size
Notably, the actual range of a DAQ device is often wider than its nominal range. For example, National Instruments' NI-9223 DAQ device has a nominal analog input range of \(\pm\)10 V but can actually measure up to \(\pm\)10.7 V. If the acquired value falls outside the configuration range, the conversion algorithm encounters errors. Several solutions are available to address this issue, including:  
**Solution 1: Using the nominal range; that is, \(\pm\)10 V.** If a value exceeds this range, such as \(-\)10.2 or 10.3, it is clipped to a limit value of \(-\)10 or 10, respectively. However, this solution requires additional signal processing. Condition-monitoring programs must check every value in real time, whether the value is in or out of range, and clip it if necessary. This task is insignificant for one or even thousands of values. However, with large amounts of data (tens of millions of values per second), it can become a significant workload that requires a long time to process. This process may require tens of milliseconds and should be considered carefully because the monitoring system must complete several other tasks within a limited time. **Solution 2: Using the actual range of the DAQ device. ** In this solution, test engineers must manually enter the actual analog input range of the connected DAQ devices or load them from a database of condition-monitoring programs. This approach ensures that all acquired values are within the configured range, thus eliminating the need for a monitoring program to check every value. Hence, the sensor reading values can be directly converted into binary formats, thereby reducing the processing time and optimizing performance.
# Research on the UAV Sound Recognition Method Based on Frequency Band Feature Extraction
## 1 Introduction
The mel-filter bank was innovatively replaced with a frequency band filter bank. The comparative experiments verified that the proposed method has advantages in the recognition of UAV sounds. A CNN with a small model was used for feature recognition, which has low requirements for computing power and can be deployed on more devices.
# An Embedding-Based Approach to Repairing OWL Ontologies
## 7 Conclusions and Future Works  
In this paper, we presented an embedding-based approach to repairing ontologies by translating OWL axioms into natural language sentences. Specifically, the translation was inspired by the ideas from NaturalOWL, and then various pre-trained models suchas Sentence-BERT and CoSENT were employed to the obtained sentences for computing embeddings. After that, the similarities between sentences could be calculated by encoded vectors. Benefiting from these similarities, three definitions were provided to compute a degree for an axiom based on the embeddings of axioms in the same semantic space. Afterwards, a threshold-based repair algorithm and a signature-based one were proposed to instantiate our embedding-based approach. Finally, we conducted abundant experiments over 20 real-life incoherent ontologies varying in size of axioms and number of unsatisfiable concepts. Experimental results indicated that our embedding approach could provide promising results, especially the threshold-based algorithm with the fourth model is able to differentiate axioms according to their semantics and remove fewer axioms.  
As for future works, we plan to explore how to choose a threshold dynamically according to similarity distributions and characteristics of various distance metrics. In addition, the combination of different single-ranking strategies and employing external knowledge will be considered in order to improve the effectiveness and efficiency of our approach. We also will consider the parallel diagnosing process to deal with the cases where an ontology contains too many diagnoses by borrowing the idea from the work [61]. Last but not least, we will study how to provide a friendly user interface to facilitate users to repair an ontology like the work given in [62].  
Conceptualization, Q.J. and W.L.; Funding acquisition, Q.J.; Methodology, Q.J. and G.Q.; Software, Q.J., Y.Y. and Y.S.; Supervision, G.Q.; Writing--original draft, S.H.; Writing--review & editing, Q.J. and G.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of China, grants (61602259, 62006125, and U21A20488), and the Foundation of Jiangsu Provincial Double-Innovation Doctor Program (JSCSB520210532).  
Not applicable.  
The authors declare no conflict of interest.
# Intelligent Video Surveillance Platform for Wireless Multimedia Sensor Networks
## 4 Design Implementation
### Congestion Detection
#### 4.3.1 Normal State  
Initially, the buffer of a sensor node is empty. When a packet arrives, a sensor node immediately switches to normal state. The buffer occupancy during this state is within [0, \(T_{1}\)] and buffer occupancy change rate is also less than \(\varrho\). \(\varrho\) is predefined limit of \(\delta\). All packets are delivered successfully; therefore, queue utilization is low.
## 2 Materials and Methods
### Data and Preprocessing  
#### 2.3.1 Meteorological Data  
The five meteorological data required for BEPS model are from the half-hour step data measured by the flux tower of Saihanba in 2020, which are temperature, radiation  
Figure 1: Location of the study area and the flux tower.  
precipitation, wind speed and relative humidity. Because the study area is a small and medium-sized area, and there is only one flux tower for meteorological observation, the meteorological input data of the study area model is only from the flux tower. Due to extreme weather, routine instrument maintenance and special reasons (power failure, etc.), the original data usually needs to remove outliers and data interpolation.
## 4 Conclusions  
The PCA data-driven algorithm takes advantage of PCA to eliminate data redundancy and represent complex atmospheric information in a succinct way thus simplifying the cumbersome retrieval process. In this study, two main parameters required in the implementation of the PCA retrieval algorithm--PC numbers and band regions--were analyzed, and the results of SFM algorithm inversion were utilized for cross-verification. The results demonstrated that three PCs could best illustrate information for SIF retrieval, and that the number of PCs severely interferes with the accuracy of SIF extraction, causing anomalously high values when encountering higher polynomial orders, and the NIR bands could boost the accuracy for fluorescence extraction better than the R-NIR regions. Thus, this study can provide a reference for the application of PCA algorithms in SIF retrieval. In addition, the  
Figure 6: The correlation between the values of SIF retrieval based on the SFM and PCA algorithms.  
reliability of PCA algorithms needs to be further researched by using more datasets in the future.  
Conceptualization, Z.S., S.Y., J.Y. and S.S.; methodology, S.Y. and J.Y.; software, S.Y.; validation, Z.S., S.Y. and J.Y.; formal analysis, S.Y.; investigation, S.Y. and J.Y.; resources, Z.S.; data curation, Z.S.; writing--original draft preparation, S.Y. and J.Y.; writing--review and editing, S.Y., J.Y. and S.S.; visualization, S.Y.; supervision, Z.S. and J.Y.; project administration, Z.S.; funding acquisition, S.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (41971307, 41801268), the independent research and development plan of the National Forestry and Grassland Administration (2020Z-3), the China Postdoctoral Science Foundation (2017T100582), and the Natural Science Foundation of Hubei province (2019 CFB532).  
Not applicable.  
Data Availability Statement: Data available upon request.  
The authors declare no conflict of interest.
## 2 Materials and Methods
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Model** & **MAPE** & **MAE** \\ \hline
1 & Lasso & 19.57 & 9.05 \\
2 & Extra Tree & 20.27 & 9.55 \\
3 & Random Forest & 20.44 & 9.66 \\
4 & LightGBM & 20.96 & 9.80 \\
5 & SVM Linear & 21.11 & 9.83 \\
6 & AdaBoost & 20.90 & 9.93 \\
7 & KNN & 22.46 & 10.23 \\
8 & MLP2 & 22.95 & 10.66 \\
9 & Ridge Regression & 23.73 & 10.78 \\
10 & XGBoost & 22.72 & 11.10 \\
11 & SVM.RBF & 23.82 & 11.39 \\
12 & LogisticReg & 23.89 & 11.73 \\
13 & MLP1 & 25.33 & 11.84 \\
14 & Meta-model & 18.64 & 8.93 \\ \hline \hline \end{tabular} The baseline models used for meta-model are highlighted.

\end{table} 
Table 3: Performance (MAPE and MAE) of individual candidates for the baseline models obtained by 5-fold cross-validation and then averaged for all three prediction tasks: 1 st, 4 th, and 7 th days after surgery. The last row shows the corresponding performance of the meta-model, for the selected ten base regressors. On the fourth and seventh postoperative days, the gain was over two times, more precisely an average Gain_MAPE = 2.39 for the 4 th day and average Gain_MAPE = 2.21 for the 7 th day, i.e., Gain_MAE = 2.18 for the 4 th day and Gain_MAE = 2.10 for the 7 th day. For these days, [PERSON]'s method proved to be the best of the traditional methods, giving Gain_MAPE = 2.09 and Gain_MAPE = 1.91, for the 4 th and 7 th day, respectively. The corresponding values for Gain_MAE are 1.90 and 1.79, respectively. 3. The superior advantage of the meta-model in the first postoperative day has the greatest clinical value since it can be used to project optimal recovery during the hospital stay, which usually ends after 7 days. In evaluating the effectiveness of regression predictive models, residuals as the difference between predicted and actual values

\[e_{i}=y_{i}-\hat{y}_{i},i=1,2,\ldots,N \tag{11}\]

are also informative. Figures 3-5 show box plots of residuals for our meta-model and the three traditional methods, for the 1 st, 4 th, and 7 th day after operation. These results show that, unlike traditional methods, the proposed meta-model gave residuals whose expected value is close to zero. This property is an important characteristic in well-fitted regression models that capture the underlying relationship between input and output variables. However, it is important to note that having zero mean residuals is not always a strict requirement for a good regression model. What is more important is that the residuals are centered around zero and do not exhibit any systematic patterns, such as trends or heteroscedasticity. Analyzing the box plots from Figures 3-5, we concluded that our meta-model has good properties in this respect as well.
## 2 Materials and Methods
### Characterization
#### 2.3.3 Thermogravimetric Analysis (TGA)  
Thermogravimetric analysis (TGA), performed by a STA 449 F3 Jupiter (Netzsch, Selb, Germany), was used to study the dehydration/hydration cycles and stability. The Thermo-Gravimetric (TG) unit is coupled with an evaporation system for the water vapor supply. After a preliminary sample (\(\approx\)20 mg) drying at 125 \({}^{\circ}\)C in inert gas flow (N\({}_{2}\) flow rate: 100 mL/min) for 60 min, each dehydration/hydration cycle was performed by the following procedure: (i) Heating. The temperature was increased (10 \({}^{\circ}\)C/min) up to the chosen dehydration temperature (T\({}_{\text{d}}\) = 250 \({}^{\circ}\)C); (ii) Isothermal. The sample was held at these conditions for 120 min, allowing for the complete dehydration reaction; (iii) Cooling. The temperature was decreased (\(-\)10 \({}^{\circ}\)C/min) to the hydration temperature (T\({}_{\text{h}}\) = 125 \({}^{\circ}\)C); (iv) Stabilization step. Performed after cooling the temperature down to the preset value, and therefore, this interval is essential to stabilize the system temperature before the following step. (v) Hydration reaction. A gas mixture flow of water vapor (2.22 g/h) and N\({}_{2}\) (35 mL/min) was supplied (p\({}_{\text{H2O}}\)/p = 0.58), and the isothermal hydration reaction proceeded for 120 min; (vi) Release of adsorbed water. Finally, the water vapor supply was stopped, and the sample was kept at 125 \({}^{\circ}\)C for 30 min under a constant N\({}_{2}\) flow (100 mL/min) to remove the physically adsorbed water from the sample. Table 3 summarizes a single TG cycle.
## 3 Experiment Descriptions  
The performance of CALDAS was assessed based on three simulations: (1) the ARPS-SiB2 (hereafter ARPS) run, where a one-way nesting procedure employed the land-atmosphere mesoscale model without any assimilation; (2) the LDAS-A run, in which an ARPS run was accompanied by sequential land data (soil moisture) assimilation; and (3) the CALDAS run, in which the LDAS-A run was accompanied by the cloud microphysics data assimilation.
## 3 Analysis and Results
### Discussion  
This study analyzes the effectiveness of response surface methodology (RSM) and machine learning techniques in predicting stress levels under various axle configurations, including single, tandem, and tridem axles with dual tires. RSM is proven to be a reliable statistical approach for figuring out correlations between tire spacing, pavement thickness, and stress distribution. The investigation demonstrated that stress levels decrease dramatically with deeper evaluation points, highlighting the significance of depth in pavement design. Additionally, RSM emphasized how reduced tire spacing leads to higher stress levels due to stress overlap, establishing a significant relationship between these variables. Among the RSM models, the quadratic model stood out for its predictive strength, supported by high adjusted R\({}^{2}\) values and statistically significant \(p\)-values.  
On the other hand, machine learning models, especially gradient boosting, displayed superior prediction accuracy, obtaining the lowest mean squared error (MSE) and root mean squared error (RMSE) across all axle types. However, SHAP value analysis provides a deeper insight on variable relevance. While RSM emphasized the considerable importance of tire spacing on stress levels, machine learning models suggested a lesser function for spacing, with pavement thickness appearing as the most influential element in stress forecasts. This finding highlights how machine learning excels at uncovering complex relationships between variables and pinpointing other factors that influence stress distribution.  
While some machine learning models, like decision tree, showed a tendency to overfit, gradient boosting and random forest consistently produced reliable and accurate results. SHAP analysis also provided valuable insights into the importance of various features, showing that, while tire spacing does have an impact, it is not the main factor driving stress effects. Residuals plots offered further validation for the machine learning models by showing that they satisfied crucial assumptions, such as residual independence and consistency.  
In conclusion, RSM gives a structured and methodical approach to predicting stress, while machine learning provides the flexibility and precision needed to capture the dynamic nature of pavement behavior. Through implementing these methodologies, researchers can acquire a deeper understanding of pavement performance. RSM can develop initial  
Figure 11: SHAP value analysis for tridem axle model features.  
hypotheses, and machine learning can enhance these predictions with real-world data. Therefore, this strategy has the potential to customize pavement design, leading to stronger, more durable solutions for a variety of situations while encouraging innovation in engineering techniques.
# An ANN-Based Adaptive Predistorter for LED Nonlinearity in Indoor Visible Light Communications
## 3 The Proposed ANN-Based Predistorter
The main contributions of the proposed ANN predistorter in comparison with the methods in previous literatures are summarized as follows. First, the nonlinear ANN was studied to establish function approximation to the inverse function of the nonlinear characteristic curve of LED. Based on universal approximation theorem, ANN can approximate any continuous function and thus it can prevent the predistorter from quantization error as in [11; 12]. Moreover, based on the relationship between the inputs and the corresponding outputs of the LED device, to adopt a real nonlinear characteristic curve of LED truly represents a more realistic LED nonlinear effect to the DCO-OFDM VLC transmission. Second, in the study, laboratory experiments utilizing physical devices were conducted to verify the theoretical analysis of the predistortion algorithm. In view of most literatures related to LED linearization only presenting their results using computer simulations, the experimental results based on the self-built DCO-OFDM VLC testbed presented in this study would make a constructive contribution for the design of the DCO-OFDM VLC transmission.
## 3 Experiments
### Experiments on Inria Dataset
#### 3.3.1. _TernausNet_ on Inria  
TernausNet is the improved version of the U-Net architecture (for more details about U-Net, refer to Section 3.3.2). This amended version is composed of encoder and decoder networks, where VGG11 without a fully connected layer was adopted as its encoder. The fully connected layer of the VGG11 network was replaced with a single convolutional layer of 512 filter channels. In the decoder, transposed convolutional layers are concatenated with information from the encoder. The pre-trained TeranusNet (baseline model) has 32.3 billion (B) FLOPs and 22.9 million (M) parameters with a 96% Val. Acc. As reported in Table 1, a reduction in the number of FLOPs and the number of parameters was observed with the proposed approach across all initialization strategies. For instance, when compressing TeranusNet with the proposed approach using random initialization, the FLOPs were reduced by 84.98% and the number of parameters decreased by 83.54%, while the drop in accuracy was negligible. Moreover, using He and Xavier initializations, our approach reduced the FLOPs by 64.18% and the number of parameters by 73.20% with even a 0.04% improvement in accuracy. However, the reduction in FLOPs was 20.8% less than with a random initialization strategy. Overall, the FLOPs were reduced by more than 64% while maintaining the baseline performance, which indicates that the proposed approach is able to reduce the FLOPs by a large amount with all initialization strategies, while maintaining the Val. Acc. almost intact.