esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Antibacterial Activity  
The antibacterial activity of uncoated plant extracts was evaluated against four reference bacteria: _Brochotrix thermosphacta_ DSM20171, _Listeria Innocua_ DSM20640 (two Grampositive bacteria), _Escherichia coli_ DSM613 and _Salmonella enterica_ DSM11320 (two Gramnegative bacteria). The strains were stored at \(-20\)\({}^{\circ}\)C in tryptone soy broth (TSB) (Biokar diagnostics, Beauvais, France) with glycerol (15 mL/100 mL). Then, 1 mL of the stock culture was transferred to 9 mL of TSB and incubated for 8 h at 37 \({}^{\circ}\)C. One mL of this pre-culture was transferred in 9 mL of TSB and incubated at 37 \({}^{\circ}\)C for 16 h. The pre-cultured bacteria were then diluted in TSB to a final concentration of \(10^{6}\) CFU/mL. Briefly, 1 mL of the bacterial suspension was incorporated into melted tryptone soy agar (TSA) in an amount of 5 mL/100 mL and cooled in Petri dishes. A sterilized glass pipette (6 mm diameter, Fisher Scientific, Hampton, NH, USA) was used to inoculate a well in the TSA Petri dishes. Under aseptic conditions, the wells were impregnated with 100 uL of uncoated phenolic compounds (10 mg/mL) and placed on the culture plates after removing the solvent via evaporation. Physiological water was used as a negative control. The Petri dishes were then kept at 37 \({}^{\circ}\)C for 24 h. Antibacterial activity was assessed by measuring the zone of growth inhibition (mm) around the well. All experiments were performed in triplicate.  
Similarly, the antibacterial activity of all microcapsules against four bacteria was carried out using the agar well diffusion method. The antibacterial effect of the microparticles was determined using the protocol previously used [32]. An amount of 12 mg of microcapsule powder was added to each well. The Petri dishes were then incubated at 4 \({}^{\circ}\)C for 2 h and then at 37 \({}^{\circ}\)C. Finally, the diameter of the inhibition zone was measured after 24 h and each experiment was repeated three times.
# Background Subtraction for Dynamic Scenes Using Gabor Filter Bank and Statistical Moments
## 2 Theoretical Considerations  
### Texture Index  
An essential part of background modeling is understanding what a texture is and how to quantify it. Although a formal definition has not yet been reported in the literature, authors have classified it as regions composed of points, edges, ellipses, circles, or lines called primitives. It is also defined as the intersection of random and possibly periodic areas [55; 56]. It is also defined as the color or intensity distribution [57]. According to [58], variations in intensity, perspective, uniformity, directionality, or scale changes must also be considered. So dealing with texture is a complex issue since it involves the characterization of density, thickness, roughness, or intensity, both in micro and macro textures, irregular or regular and periodical or quasi-periodic [59]. The dataset used in the experiments are explained in Section 3.1.  
Another crucial aspect to consider is acquisition noise. When the image is digitized or sampled, noise is generated in the analog-to-digital converter due to an insufficient quantization level. Generally, the camera sensor is 8-bit, which reduces the effective dynamic range of the sensor, thereby producing false contours in the image that are detected as textures.  
The question is how to identify the edges that represent the texture. Because randomness leads to subtle changes in intensity levels, detecting these changes can lead to orientation measurements, in which sudden or discontinuous changes can be detected. Generally, the texture depends on the frequency of pixel tones, directionality, and contrast.  
In this work, we consider that the texture is the variability of the color pixel intensity. This is determined by the frequency and size of the area affected by the Gaussian function of the Gabor filter.
## 6 Conclusions and Future Work  
The findings of this study demonstrate the vital role of biogas and biomethane as a renewable fuel in decarbonizing the energy and water systems within the GCC region, together with highlighting the importance of diverting organic substrates from landfills to avoid CH4 release. The biomethane potential of sludge for the region ranges between 232-334 \(\times\) 10\({}^{6}\) m\({}^{3}\), with a total heat-value up to 10.7 trillion BTUs annually. The produced biomethane can generate up to 1665 GWh of electric energy, an equivalent amount to the current levels of electricity generation from wind and solar power combined. The findings from the case study on Kuwait's indicate that biomethane could displace 13 \(\times\) 10\({}^{6}\) m\({}^{3}\) of natural gas, or approximately 86,000 barrels of crude oil, together with reducing greenhouse gas emissions by 86% when compared to the base-scenario. In addition, biomethane can serve as a flexible and renewable source of energy to maintain a reliable power supply with minimum carbon emissions, allowing larger shares of variable energy sources (wind and solar) to be integrated into the power systems. Despite its benefits, the widespread deployment of biogas generation and recovery from organic substrates in the GCC region is hindered by several economic and policy barriers. The fossil fuels production industries are mostly state owned and receive political and financial support from the governments in the region. Thus, low carbon energy targets, energy and environmental policies, and economic incentives remain the major drivers for deployment of the biogas and biomethane industries. The scope of this paper was limited to the theoretical estimation of biomethane potential, using publicly available data on wastewater, sludge characteristics and chemical composition. Further research is required on biomethane potential using experimental methods which mimic anaerobic conditions in practice and assess the impact of operational factors (such as biodegradation, incubation times, macronutrients requirements, and temperature) on CH4 yields.  
Conceptualization, M.A. ([PERSON]) and A.A.; Data curation, H.A.; Investigation, M.A. ([PERSON]), A.A. and M.A. ([PERSON]); Methodology, M.A. ([PERSON]); Re-sources, A.A. and H.A.; Software, M.A. ([PERSON]); Validation, A.A. and M.A. ([PERSON]); Writing--original draft, M.A. ([PERSON]); Writing--review & editing, A.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# A Novel Network Protocol Syntax Extracting Method for Grammar-Based Fuzzing
## 4 Design and Implementation
### Main-Parsing Module
#### 4.2.1 Standard Fields Extraction  
In this step, the parsing process will extract all fields owned by the target protocol, which are defined in the static data structure \"hf_register_info\" in the protocol registration function. We call the field information extracted in this step which contains the name, identifier, data type, length, and optional value information of the fields as standard fields. In the subsequent parsing process, standard fields are used to verify the extracted per packet type field information and supplement them.
## Materials and Methods
### Data  
#### 2.2.1 Landsat Imagery  
Landsat Enhanced Thematic Mapper (ETM+) and Thematic Mapper (TM) imagery were obtained from the USGS website ([[http://glovis.usgs.gov](http://glovis.usgs.gov)]([http://glovis.usgs.gov](http://glovis.usgs.gov))). In the study area, as in other tropical regions, cloud  
Figure 1: Study area “Menda 1” in the geographical context of the Yucatan Peninsula, Mexico.  
cover limited the choice of imagery available per year; however, the probability of acquiring at least one cloud-free Landsat image per season is relatively high [30]. In total, 155 Landsat scenes were downloaded. The images were in a L1T geometrically corrected format and were atmospherically corrected using the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiative transfer approach [31].  
Landsat 7 ETM+ images acquired with the SLC-off (i.e., SLC failure in 2003) were corrected using the Geostatistical Neighbour Spatial Pixel Interpolator algorithm, GNSPI, [32]. The GNSPI has proven to efficiently estimate pixels missing due to SLC failure, and its outputs are suitable for forest monitoring applications [33]. Landsat Images were separated according to their recording date (i.e., wet or dry season), and a first cloud filter was applied. Imagery with more than 10% cloud cover was avoided and was not included in the analysis.
## Materials and Methods
### Mechanical and Piezoresistive Measurements  
Static compression tests were carried out by using a mechanical testing machine (Instron 5564 dynamometer, Norwood, MA, USA, 1997) and the 10 \(\times\) 10 \(\times\) 10 mm\({}^{3}\) cubic specimens were compressed at a strain rate of 3 mm/min. Electrical and compression tests were carried out simultaneously to evaluate the piezoresistive properties of the 3D printed structures. Thus, coupled to the mechanical testing machine a multimeter (Keysight 34401A \(6\frac{1}{2}\) Digit Multimeter, Agilent Technologies, Santa Clara, CA, USA, 2006), which was controlled by a homemade LabVIEW program, was used to measure the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Process Parameters** & **Value** \\ \hline Laser power (W) & 60 \\ Laser scan spacing (\(\upmu\)m) & 100 \\ Laser scan speed (m/s) & 7.6 \\ Part bed temperature (\({}^{\circ}\)C) & 95 \\ Powder feed temperature (\({}^{\circ}\)C) & 65 \\ Outline laser power (W) & 5 \\ Layer thickness (\(\upmu\)m) & 150 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sintering parameters adopted for processing thermoplastic polyurethane/graphene (TPU/GE) composite powders.  
change of electrical resistance with the applied load and induced deformation. Two electrodes, made of copper conductive tape, were glued on the top and on the bottom of the specimen and connected with the multimeter through copper wires. The mechanical properties were evaluated by submitting the samples to a cyclic compressive strain/unstrain up to 8% of initial value of the length of cubic sample, with a deformation rate of 3 mm/min, at 25 \({}^{\circ}\)C. Before measurement, the porous structures were pre-compressed to a strain value of 4%. The electrical resistance of the specimen was monitored simultaneously to compression testing. The strain sensitivity of the samples was expressed as Gauge Factor, GF = (\(\Delta\)R/R\({}_{0}\cdot\varepsilon\)), where \(\Delta\)R/R\({}_{0}\) is the resistance change rate and \(\varepsilon\) is the compression strain.
# Enhancing Street-Crossing Safety for Visually Impaired Pedestrians with Haptic and Visual Feedback
## 3 Study 1: Haptic and Visual Feedback for Visually Impaired Pedestrians Crossing at Traffic Signals
### Experimental Design
#### 4.1.2 Experimental Design  
Study 2 employed both haptic and visual feedback modes, maintaining the same periodic stimulation pattern as in Study 1 (see Figure 2), with each mode containing 32 parameter combinations (2 directions \(\times\) 2 states \(\times\) 4 frequencies \(\times\) 2 sizes). The experiment was divided into two stages: a pre-experiment phase and a formal experiment phase. Each phase consisted of 64 trials (32 haptic feedback; 32 visual feedback), resulting in a total of 128 trials per participant. The pre-experiment phase aimed to familiarize participants with the basic characteristics of the feedback modes. In the formal experiment phase, the presentation order of the variables feedback mode, state, and direction was randomized to avoid sequence effects. Meanwhile, within each feedback mode, the parameter combinations were presented in a fixed order (with the size gradually increasing first, followed by frequency gradually increasing). This design helped participants more accurately recall and evaluate their perceived urgency and decision confidence under each condition during subsequent questionnaire assessments and interviews.  
Figure 8: Overview of haptic and visual feedback systems with signal pattern types.
## 5 Joint Observations with Operational Passive MW Radiometers
To quantify what percentage could be captured using data from five current operational satellites plus one research satellite (i.e., GPM), the near-coincident passive MW coverage was assessed during the entire 40-day simulation period. This was done by checking for passive MW swath coverage from each of the passive MW satellites in succession (Figure 10 caption) until coverage (of all eight 200-km ray paths) was located within \(\pm\)15 min. The horizontal dashed lines in Figure 10b show the accumulated coverage when the passive MW satellites are considered in succession. For example, there are very few intersections between the RO ray paths and GPM, and with this satellite alone the total coverage is \(<\)1%. If GPM and the SSMIS onboard the DMSP F-17 satellite are considered, the total coverage rises to 5%. When all six satellites are considered, the fractional coverage is about 26%, which is nearly commensurate with the 1500-km radiometer swath.
###### Abstract  
With the increasing requirements of precision agriculture for massive and various kinds of data, remote sensing technology has become indispensable in acquiring the necessary data for precision agriculture. Understanding the spatial variability of a target soil variable (i.e., soil mapping) is a critical issue in solving many agricultural problems. Field sampling is one of the most commonly used technologies for soil mapping, but sample sizes are restricted by resources, such as field labor, soil physicochemical analysis, and funding. In this paper, we proposed a sampling design method with both good spatial coverage and feature space coverage to achieve more precise spatial variability of farm field-level target soil variables for limited sample sizes. The proposed method used the super-grid to achieve good spatial coverage, and it took advantage of remote sensing products that were highly correlated with the target soil property (SOM content) to achieve good feature space coverage. For the experiments, we employed the ordinary kriging (OK) method to map the soil organic matter (SOM) content. The different sized super-grid comparison experiments showed that the 400 \(\times\) 400 m\({}^{2}\) super-grid had the highest SOM content mapping accuracy. Then, we compared the proposed method to regular grid sampling (good spatial coverage) and k-means sampling (good feature space coverage), and the experimental results indicated that the proposed method had greater potential in the selection of representative samples that could improve the SOM content mapping accuracy.  
soil sampling; spatial coverage; feature coverage; farm field-level soil mapping; super-grid; limited sample size +
Footnote †: journal: Remote Sensing, 2019, 11, 1946; doi:10.3390/rs11161946
# Human-AI Collaboration for Remote Sighted Assistance: Perspectives from the LLM Era +
## 3 Identifying Challenges in RSA: Literature Review
### Challenges in Localization and Orientation
#### 3.1.1 Unfamiliarity with the Environment  
In a previous study [101], the RSA agents expressed their frustrations with the users' expectations of the agents' quick start of assistance, which is usually not possible because most places are new to the agents, and thus, they need some time to process information to orient themselves. The fact that RSA agents have never been in the place physically and depend only on the limited map and the video feed is reported as a cause for the challenge in the research work [13; 42; 43].
## 4 Discussion
A false negative prediction (or missed detection of an emergency) means that the model failed to recognize a potential emergency, predicting a normal state when the situation is actually hazardous. The model's operation should be aimed at preventing emergencies at the station, even at the expense of overall accuracy (i.e., false positive predictions are acceptable). This approach prioritizes safety and environmental protection, ensuring that potential risks are not overlooked. The confusion matrix presented in Figure 9 indicates that no changes to the prediction thresholds are required, as the model successfully identifies all instances of filamentous bulking without producing false negatives. This result underscores the effectiveness of the model in emergency prediction. The residual analysis demonstrates favorable results; however, the presence of slight biases in the median suggests that incorporating a larger dataset of historical data could further enhance model performance [15]. Expanding the dataset would improve the model's generalizability and robustness, capturing a wider range of operating conditions and the variability inherent in wastewater treatment processes. Overall, this study demonstrates the feasibility and effectiveness of using advanced machine learning models, specifically CatBoost, for predicting effluent quality and potential emergency situations in wastewater treatment plants. The inclusion of operational parameters unique to this study contributes to a deeper understanding of process dynamics. Future research should explore the impact of external substrates and their introduction points on treatment efficiency, as well as the integration of larger, more diverse datasets to enhance model reliability.
## 6 Discussion
### The Impact of Increasing the Number of Variables on the Performance  
The aim of this section is to study the impact of high dimensionality on the reliability and accuracy of the solutions achieved by the tested optimization algorithms in solving computationally-expensive complex optimization problems. Figures 8-10 compare the performance of these six methods for the Sphere, Griewank and Dixon and Price functions. The horizontal axis shows the number of variables, and the vertical axis shows the objective function's error value at a fixed number of function evaluations. Similarly, the value obtained in each iteration serves as a performance indicator. As illustrated, increasing the number of variables leads to the degradation in the performance of BA and FPA. This is owing to the fact that the exploitation capability of BA is comparatively low-especially for high- dimensional problems. The results also reveal that FPA is only efficient in handling low-dimensional expensive black-box functions design problems (less than 10 design variables). On the other hand, GWO and ABC are not as sensitive as the other methods in handling high-dimensional problems regardless of the complexity and the surface topology of the tested problem. Figures 8-10 show that the performance of the GWO and ABC algorithms, which is related to capability and efficiency, is superior for high-dimensional problems. Figure 10 illustrates that the FFA and CS methods continue to provide comparable and acceptable solutions regardless of the complexity of the optimization problem. Furthermore, the results indicate that the performance of these algorithms is roughly similar for low-dimensional unimodal problems (\(D<10\)). In summary, the dimensionality strongly affects the performance of most algorithms; however, it seems that ABC is more stable as the dimension of the problems increases which is probably a result of the greater exploration ability of the ABC algorithm.  
Figure 7: Impact of increasing number of function dimensions vs. CPU time for Dixon and price function.  
Figure 8: Error vs. variable number for Sphere function.  
Figure 10: Error vs. variable number for Dixon and Price function (\(f_{13}\)).  
Figure 9: Error vs. variable number for Griewank function (\(f_{7}\)).
# A Lightweight and Partitioned CNN Algorithm for Multi-Landslide Detection in Remote Sensing Images
## 7 Conclusions  
This paper presented LP-YOLO, a new lightweight landslide-detection model based on the YOLO algorithm and a comprehensive landslide dataset. The proposed model addressed the challenges of accurate landslide detection in remote sensing images with varying spectral, texture, terrain, landslide type, and scale characteristics. LP-YOLO comprises several key components that contribute to its superior performance, including the PartitionNet backbone, the VH block, the new feature fusion structure, the CSPCrossStage module, the SIOU loss function, and the CBAM attention mechanism. The experimental results demonstrated that the proposed model outperformed YOLOv5 and the other models in terms of the AP, parameters, and FLOPs. LP-YOLO has the potential to be deployed on power-constrained devices for real-time automatic detection of landslides. However, further research is needed to improve the detection frame rate and model accuracy and expand the dataset to reduce error detection problems. Overall, this work provides a promising solution to the problem of real-time landslide detection and contributes to the development of lightweight detection models for landslides.  
Methodology, data curation, software, writing--original draft, validation, P.M., D.L. and M.L.; Formal analysis, writing--review and editing, J.J. and X.C. All authors have read and agreed to the published version of the manuscript.  
Figure 13: Real landslide and predicted results of the YOLOv5s and LP-YOLO models. **(Left)** Ground truth box for dense landslide; **(middle)** YOLOv5s model prediction; **(right)** LP-YOLO prediction.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.  
**Acknowledgments:** We extend our appreciation to [PERSON], [PERSON] and [PERSON] for their insightful guidance, constructive criticism, project coordination and support throughout the research project. Their expertise and suggestions have significantly contributed to the quality of this study. We would also like to acknowledge the contributions of our research team members who worked tirelessly to collect and analyze data. Their efforts have been indispensable to the successful completion of this project. We extend our sincere thanks to all those who have contributed to this research project.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Cyber Firefly Algorithm Based on Adaptive Memory Programming for Global Optimization
## 4 Experimental Results and Discussions
### Analysis of CFA Strategies
#### 4.2.1 Selection Strategy for Multiple Guiding Solutions  
In contrast to GSO and FA, the CFA selects multiple guiding solutions for performing the firefly movement. The firefly can circumvent the false peaks by relaxing the limitation that constrains the firefly to move toward the single best solution within neighborhood. To investigate the influence on performance of using various selection strategies for guiding solutions, we compare the variants of the CFA that employs the roulette-wheel selection, the tournament selection, and the rank selection, respectively. The comparative performance of the CFA variants is listed in Table 3. It is seen that for the test functions with fewer than 10 variables and the relatively simple functions (Sphere and Zakharov), all of the CFA variants work well and the mean best value obtained is very near the optimal value. For the harder and larger functions (Rosenbrock, Rastrigin, and Griewank with 10 or more variables), the CFA with the rank selection strategy finds a more effective solution for most of the functions than the CFA with the other two selection strategies. The rank selection outperforms the other selection strategies in tackling harder problems because it eliminates the fitness-scaling problem by working in the rank-value space instead of in the function-value space such that the firefly will not be severely misled by false but higher peaks.