esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
We investigated the influence of RGB colored light on the growth and nutritional composition in tilapia in biofoc systems. We also measured the light effect on the biofoc nutritional composition. The results showed that the peak wavelength or color of light had a significant effect on growth performance and nutritional composition of Nile tilapia, which can be used to improve the sustainability and efficiency of tilapia production based on the needs of the consumer. Fish under blue light had the lowest growth performance, but blue light significantly improved the protein levels, had a low lipid level, and obtained the most homogeneous growth among the treatments. The effect of the red light on fish growth was significant. Tilapia under red light showed the best weight gain, body length, and survival rate, and also showed the highest lipid content. The fish also had a lower ash content under colored light compared to natural light. In addition, colored light influenced the lipid, protein, and ash content in biofocs.  
These results provide support for further studies in aquaculture to improve efficient, sustainable, and intensive fish production. For example, our results suggest that growth rates, feed utilization efficiency, survival, and nutritional body composition may be controlled using colored light, which can significantly modify the nutritional quality provided by Nile tilapia and biofocs. In addition, future research may include the effect of different intensity levels, photoperiods, and light spectra. For example a broad light spectrum with a designed special shape [50], resulting from the combination of LEDs of different colors and intensities, to obtain the best effects of each light wavelength and maximize the nutritional quality of tilapia and biofocs. This can be adjusted based on the needs of the consumer and, in the future, the scarcity of food resources.  
Conceptualization, D.L.-B., C.O.-O. and I.M.; Methodology, D.L.-B. and C.G.-M.; Validation, C.O.-O. and I.M.; Formal analysis, D.L.-B., and C.G.-M.; Investigation, D.L.-B. and C.G.-M.; Resources, C.O.-O., D.G.-M. and M.d.J.M.P.; Data curation, D.L.-B. and M.d.J.M.P.; Writing-original draft preparation, D.L.-B.; Writing--review and editing, I.M., and D.L.-B.; Visualization, C.G.-M. and D.G.-M.; Supervision, C.O.-O. and I.M.; Project administration, I.M., C.O.-O. and M.d.J.M.P. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
This research was supported by CONACYT (Mexican Council for Science and Technology) through financing scholarship granted to [PERSON].  
The authors declare no conflict of interest.
# A New Approach to Estimating the Path Loss in Underground Wireless Sensor Networks
## 2 RF Signals in Soil  
### Dielectric Properties of Soil  
Underground path loss estimation models commonly use complex dielectric permittivity, electrical conductivity and magnetic permeability of soil, which are the key dielectrics parameters, in their estimation calculations.  
Dielectric permittivity is a complex value, which signifies the ability of a dielectric medium (i.e., soil) to permit the transmission of an electric field. The complex permittivity is defined by Equation (1) [11; 16].  
\[\varepsilon^{*}=\varepsilon^{\prime}-je^{\prime\prime} \tag{1}\]  
where \(\varepsilon^{*}\) is the complex dielectric permittivity, \(\varepsilon^{\prime}\) is the real part of complex permittivity (storage of energy), \(\varepsilon^{\prime\prime}\) is the imaginary part of complex permittivity (relaxation and dispersive losses) and \(j=\sqrt{-1}\). The permittivity of a medium is commonly described by the relative permittivity, \(\varepsilon_{r}\), which is the ratio of the real part of the permittivity to the permittivity of free space [10; 11]. The permittivity of the soil can be affected by many factors. However, the main parameters affecting the permittivity are the water content, the frequency of the signal, the soil composition and the soil conductivity. Unfortunately, the complex permittivity of soil cannot be easily measured. Therefore, estimating the permittivity of soil has been the topic of numerous research in the literature, and various methods have been proposed to model the permittivity of the soil based on other, more easily measurable parameters (e.g., composition and water content) [16; 17; 18; 19]. These models are often known as mixing models. The existing estimation models for the attenuation of electromagnetic signals in soil rely heavily on the accuracy of the specific mixing model used for estimating the complex permittivity of soil.  
The static electrical conductivity of soil can be described as the ability of the material to conduct electrical currents [11]. The imaginary part of the complex permittivity is directly affected by the value of the conductivity, which in turn affects the attenuation of electromagnetic signals in soil [20]. The electrical conductivity of soil can be easily measured both in situ and in the laboratory via a variety of techniques (e.g., TDR) [11].  
The magnetic permeability of soil is commonly expressed as the relative magnetic permeability, which is defined as the ratio between the magnetic permeability of soil and free space [11]. For most common types of soil (i.e., non-iron oxide rich soils) the relative magnetic permeability of soil can be assumed to be 1 [21; 22].
## 3 Fluctuation Correlations and Thermodynamics of Binary Symmetric Electrolyte Solutions
### Thermodynamic Relationships
#### 3.3.3 Radial Distribution Functions and Pair Interaction Energies  
The starting point for determining the radial distribution functions are the concentration-fluctuation correlation functions \(\langle\delta c_{m}\mathbf{r}_{+},t\rangle\delta c_{n}(\mathbf{r}_{+},t)\rangle\), \(\langle\delta c_{m}\mathbf{r}_{-},t\rangle\delta c_{n}(\mathbf{r}_{-},t)\rangle\), and \(\langle\delta c_{m}\mathbf{r}_{+},t\rangle\delta c_{n}(\mathbf{r}_{-},t)\rangle\), where the indices \(+\) and \(-\) correspond to the two ionic species (positive and negative) in the solution. Combining Equations (29) and (30) leads to  
\[\begin{split}\frac{\partial\delta c_{+}}{\partial t}& =D\Bigl{(}\
abla^{2}\delta c_{+}-\frac{\kappa}{2 ze}\delta\rho_{e} \Bigr{)}-\
abla\cdot\delta\mathbf{j}_{+}\\ \frac{\partial\delta c_{-}}{\partial t}&=D\Bigl{(} \
abla^{2}\delta c_{-}+\frac{\kappa}{2 ze}\delta\rho_{e}\Bigr{)}-\
abla\cdot \delta\mathbf{j}_{-}.\end{split} \tag{60}\]  
Transforming Equation (60) and solving for the concentration fluctuation yields  
\[\begin{split}\delta\mathcal{E}_{+}(\mathbf{k},\omega)& =\frac{i\mathbf{k}\cdot\delta\mathbf{j}_{+}(\mathbf{k},\omega)}{i \omega+Dk^{2}}-\frac{D\kappa^{2}}{2 ze}\frac{\delta\hat{\rho}_{e}(\mathbf{k}, \omega)}{(i\omega+Dk^{2})}\\ \delta\mathcal{E}_{-}(\mathbf{k},\omega)&=\frac{i \mathbf{k}\cdot\delta\mathbf{j}_{-}^{\dagger}(\mathbf{k},\omega)}{i\omega+Dk^{ 2}}+\frac{D\kappa^{2}}{2 ze}\frac{\delta\hat{\rho}_{e}(\mathbf{k},\omega)}{(i \omega+Dk^{2})}\end{split} \tag{61}\]  
Equation (61) can be used to find the Fourier transform if the concentration-fluctuations correlations for positive-positive negative-negative and positive-negative ionic combinations. These are then used to obtain the radial distribution functions and the potentials of mean force for all ionic interactions in the mean field (i.e., Debye-Huckel) limit (see also Ref. [8]).
## 3 Materials and Methods
### Weather Observation Data  
We collected weather observation data to construct highly reliable learning/training data. Based on previous studies on influencing factors on PM, the weather observation elements were set as the PM (PM2.5 and PM10), temperature, humidity, wind direction, and wind velocity [33]. Regarding the meteorological observation equipment, PM and environmental observation equipment were installed at each ROI. As for the PM observation equipment, six conditions such as performance rating evaluation (Grade 1), accuracy (higher than 90%), mobility, convenience (retention of own power), data acquisition (automatic transmission and reception of communication network), and others (simultaneous observation of weather conditions, such as temperature/humidity, and illuminance) were set, and simple PM observation equipment (AM100) satisfying the conditions was created. The simple PM observation equipment is a light-scattering method using optical characteristics, such as rotation and reflection, that appear by shining light on fine dust particles, and it has the advantage of being easy to move because it can be attached and detached.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Category** & **Data Name** & **Data Type** & **Data Value** & **Collection Methods** \\ \hline \multirow{4}{*}{CCTV image data for an experiment} & ID & DeviceID & Varchar & KICT\_1 & \\ \cline{2-5}  & Position coordinates (latitude) & Latitude & Float8 & 37.67102804 & \\ \cline{2-5}  & Position coordinates (longitude) & Longitude & Float8 & 126.7394964 & NVR \\ \cline{2-5}  & Image data & Video & Video & Image data & \\ \cline{2-5}  & Date/time & Date\_Time & Date & 21 April 2022 20:17:05 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Meta-information on CCTV images.  
Figure 1: The CCTV installation building and camera locations.  
Figure 2: Differences in CCTV images based on PM deviations.  
The PM observation equipment consists of PM2.5 and PM10 data, and collecting the data through API connections at 30 s intervals is possible.  
The weather observation devices can collect temperature, humidity, and wind speed data. To ensure the reliability of the equipment, the GMX500 (integrated sensor) and CR300 (data logger) have been installed, which are products that have been awarded the CE DoC (Declaration of Conformity) certification for data loggers by the EU and have passed ISO9001 certification-based tests. The weather condition monitoring equipment consists of air temperature, humidity, illuminance, wind direction, and wind velocity data that can be collected via API connections with a data collection cycle of 1 min. Table 2 is the meta-information of the weather condition observation data.
### TF Image Analysis in Epilepsy  
Several algorithms for the analysis of iEEG signals in the time, frequency, and TF domain have been proposed for localizing the EZ. TF approaches are based on a time-compressed visual representation of the EEG signal. These TF images are sensitive and efficient screening tools that have been mostly used by expert reviewers to analyze ictal signals for the identification of seizures [2]. In the context of seizure detection, these spectrogram patterns (or TF images) can be classified as solid flames, irregular flames, broadband monotonous, narrowband monotonous, stripes, low power, and artifacts [2]. Based on these guidelines, clinicians are able to identify seizures with a sensitivity ranging between 70% and 90% (estimates based on several studies) [3; 4; 5; 6; 7; 8; 9; 10].  
In patients implanted with iEEG prior to surgery, the visual inspection of the TF images is time consuming (up to about 150 iEEG electrodes may be placed in the brain, and thus up to 150 images must be visually inspected) and subjective to interobserver variability. The level of expertise of the neurophysiologist greatly influences decision making. Algorithms have been proposed to automate the process of reading the iEEG signal as a TF image using advanced signal and image processing techniques. Most studies have focused on the ictal or the pre/post-ictal interval to locate the EZ. An index was proposed by [PERSON] et al. (2008) to predict the EZ [11] by combining the time and frequency information in the signal to assess the seizure dynamics. In other studies, TF images were able to characterize the suppression of beta and increase in gamma activity at the SOZ [12] or to predict the area that was removed during surgery [11; 12; 13]. Studies have also reported that TF patterns can be used to identify the underlying pathology causing seizures [14; 15; 16]. Several studies have also looked at the transition to seizure periods and compared them with the interictal regions using TF images.
## 2 Status of Smart Grids
### Popularization of the Smart Grid
#### 2.1.2 North America  
In the United States, private companies are leading the way in technical development with regulations, laws and finances from the government. Various research and development projects including IntelliGrid and the Modern Grid Initiative have been led by the Electric Power Research Institute (EPRI).  
In 2003, for the first time in the world, Grid 2030, driven by Department of Energy (DOE), planned a next-generation 100-year investment starting with $4.5 billion in a power network. It was a declarative start for the smart grid industry, with about 50 electric power companies and research institutes participating, aiming at replacing old facilities and improving the infrastructure for two-way facilities. Since replacing the utility mesh with a smart meter communicating over a wireless mesh network in 2003, Texas has worked to build a smart grid that can manage 200,000 devices (sensors in smart meters, smart thermostats, and sensor service areas). It supports 50,000 devices in real-time, serving one million consumers and 43,000 companies in 2009.  
The largest deployment programs in the world to date are also run by the DOE through the Smart Grid Program, which was funded by the American Recovery and Reinvestment Act (ARRA) of 2009 with matching funds from individual utilities. This program includes equipment for an advanced metering infrastructure (AMI) of more than 65 million smart meters, customer interface systems, distribution and substation automation, volt/VAR optimization systems with more than 1000 synchrophasors, dynamic line rating, cyber security projects, advanced distribution management systems, energy storage systems, and renewable energy integration projects that have been completed by 2015.  
Hydro One, the largest smart grid service provider in Canada, deployed a communications infrastructure from Trilliant complying with a standard and that served 1.3 million customers in the province of Ontario at the end of 2010. Ontario has begun to build one of the most advanced power grids in the world supported by the Smart Grid Fund from the Ministry of Energy, which has been supporting innovative projects and the development of modern and intelligent electrical systems since 2011. Moreover, SmartGrid Canada promotes a modern and efficient electricity grid for its residents [18].
## 3 Research Methodology
### Sample Size
#### 3.1.2 Perceptual Vocabulary Extraction  
The collection of emotional vocabulary was conducted through an online survey. Non-targeted users were first excluded, and the final selection of 100 participants was approximately evenly divided between males and females, accounting for about 50 percent each. In terms of age distribution, 30 percent of participants were in the 18-25 age group, 40 percent in the 26-35 age group, and 30 percent in the 36-and-over age group. The study intentionally focused on the 18-35 demographic, as this cohort represents the most active user base in the context of digital product consumption and generative AI interaction, especially in areas such as e-commerce, packaging design preferences, and AI-enabled interfaces. While the 36-and-over group was not excluded, their smaller representation reflects the study's emphasis on user groups most likely to engage with AI-augmented design processes. Future studies may further explore intergenerational differences in perception and engagement to provide a more comprehensive view.  
In terms of geographical distribution, this study ensured that the proportion of urban and rural participants was relatively balanced, with a certain proportion of representative participants from each province to reflect diverse cultural backgrounds. Next, the testers were arranged to fill in the evaluation vocabulary on the 15 NRH product packaging test samples screened with regard to the stylistic, functional, material, and color aspects and to elaborate on their intuitive feelings towards the products, with no less than five perceptual vocabularies for describing the NRH product packaging required for each aspect [59]. The collected words were then transformed into adjectives to describe NRH product packaging, and focus group discussions were organized to eliminate words that were not suitable for describing NRH product packaging, such as worldly, wasteful, and useful, or those with repetitive semantics and ambiguous meanings. Then, 10 words for each of the aspects of shape, function, material, and color were obtained, making a total of 40 suitable perceptual vocabularies [60]. The focus group continued to be organized to score each of the 40 perceptual vocabularies using a 1-7 scale, with higher scores meaning that the perceptual vocabulary is more suitable. Finally, the 12 perceptual vocabularies with the highest mean scores were selected for the study [61]. The finalized 12 words, in order, are \"Innovative\", \"Straight\", \"Stable\", \"Practical\", \"Versatile\", \"Safe\", \"Sustainable\", \"Lightweight\", \"Soft\", \"Personalized\", \"Cutting-edge\", \"Interesting\".
# _Bacillus_ sp. GLN Laccase Characterization: Industry and Biotechnology Implications
###### Abstract  
Laccases are multicopper oxidases that exhibit a broad substrate spectrum, making them excellent biocatalysts for clean technological processes. The study isolated novel lacase-producing bacteria from decomposed wood samples and characterized the enzyme for potential industrial and biotechnological applications. The results showed that three bacteria, SP-2, SP-1, and WP-2, out of the eight isolated bacteria, oxidized both guaiacol and \(\alpha\)-naphthol in the plate assay and exhibited extracellular lacase activity of 7.0 \(\pm\) 0.01, 6.67 \(\pm\) 0.02, and 7.40 \(\pm\) 0.04 (U/mL), respectively. WP-2 was selected for further study and was identified as _Bacillus_ sp. GLN (accessioned number: MK290989). Strain GLN maximally secreted lacase 48 h post-fermentation, with an enzyme yield of 36.83 \(\pm\) 2.47 U/mL in optimized conditions. The crude lacase was optimally active at pH 9.0 and 90 \({}^{\circ}\)C and showed excellent pH and thermal stability, retaining approximately 65% residual activity at 100 \({}^{\circ}\)C for 270 min. GLN lacase demonstrated remarkable stability after treatment with organic solvents and metal ions, retaining more than 50% of its original activity in the presence of 100 mM inhibitors. The data from this study highlight the relevance of _Bacillus_ sp. GLN and its lacase in promoting clean technology.  
Bacillus sp.; bioremediation; clean technology; microbial lacase; thermal stability Article
# Effect of Adding Waste Polyethylene and GGBFS on the Engineering Properties of Cement Mortar
## 3 Results and Discussions
### Setting Time  
As shown in Table 4 and Figure 5, when the W/B was 0.5, the cement mortar's initial and final setting times with 10% added waste PE and GGBFS were 368 min to 318 min and 496 min to 447 min, respectively. This indicated that the setting time shortened as the added waste PE increased. Since the texture of waste PE could accommodate the free water of the mortar, the water absorption amount of mortar grew as the volume of waste PE was added, reducing the free water of the mortar and accelerating the reaction of the cement's hydrothermal chemistry, leading to a short setting time of the mortar. If we added 2% waste PE with 0%, 10%, and 20% GGBFS to replace the cement, the initial setting times were 291 min, 346 min, and 375 min, respectively, and the final setting times were 407 min, 473 min, and 496 min, respectively. It shows that with the increase in the replaced volume of GGBFS, the setting time of the cement mortar increased. The GGBFS as a cement replacement had lesser water demand because of its glassy texture, and the glassy surface of the GGBFS particles did not absorb water onto its surface. At a fixed water-to-binder ratio, the volume of the floating water increase led to a prolonged setting time. The reduced total volume of the cement was due to the replacement of the cement by GGBFS, and the heat of hydration decreased. Therefore, the hydration reaction activity of the mortar declined, which delayed the setting time.  
Figure 4: The flow of cement mortars with the addition of different ratios of waste PE and GGBFS replacement.  
Overall, with the added waste PE, the setting time showed a shortened trend, while the cement replacement by GGBFS resulted in a lengthened setting time.
## 3 Dataset Overview
### Target Variable  
The target variable represents the wildfire probability (\(P\)), signifying the likelihood of wildfires.  
* \(P<0.5\): Lower probability, indicating minimal wildfire risk.
* \(P\geq 0.5\): Higher probability, suggesting an increased likelihood of wildfire occurrence.  
The dataset exhibited a considerable class imbalance, with positive cases (wildfire occurrences, \(P\geq 0.5\)) constituting 78.3% of the records, posing methodological challenges for predictive modeling and evaluation. Across the 24 years analyzed, annual wildfire occurrences ranged between 1200 and 6800 events. The most critical years were 2017 and 2023, which corresponded to national emergencies with extensive wildfire damage.
# Collision-Aware Routing Using Multi-Objective Seagull Optimization Algorithm for WSN-Based IoT
## 6 Results and Discussion
### Performance of Energy Consumption  
The performance analysis of the proposed CAR-MOSOA and correlated systems, for example, FDEAM [30], EOMR [31], TSGWO [32], and CoCoA [33], in the situation for consumption of energy, is presented in Figure 4. From Figure 4, it is indicated that a smaller amount of energy is consumed in the proposed CAR-MOSOA than that in the remaining three conventional methods. In the CAR-MOSOA method, fewer nodes are convoluted in the packet-promoting progression. Furthermore, a greater amount of energy is kept back as nodes through the maximum optimality factor that continually acquires significance to progress the data packets; quite the opposite, in conventional methods, an extra node is required in dispatching the equivalent data packet; accordingly, more energy is expended in the existing FDEAM [30], EOMR [31], TSGWO [32], and CoCoA [33] methods.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Constraints** & **Value** \\ \hline Transmission power (W) & 1.4 \\ Traffic type & CBR \\ Simulation time (s) & 100 \\ Receiving power (W) & 1.0 \\ Packet size (bytes) & 512 \\ Number of nodes & 400 \\ Network size (m) & 500 \(\times\) 500 \\ MAC protocol & IEEE 802.15.4 \\ Initial energy (J) & 100 \\ Data transfer rate (Kb/s) & 250 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Table 2 shows the comparative analysis of the energy consumption performance. The table shows that the proposed CAR-MOSOA achieves less consumption of energy when compared with existing techniques. In Figure 4, it witnesses that the CAR-MOSOA method has an outstanding reduction in the consumption of energy in comparison with the remaining three methods, as it exhibits consistency and consumes the smallest amount of energy to establish the ideal route from base to endpoint. Moreover, Figure 4 shows that consumption of energy rises as soon as node count rises in the setup.