esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion  
In this study, various GS configurations were implemented (see Figure 1). These configurations were chosen mainly because they make it possible to compare measured GR values with calculated GR values. Such a comparison is very useful because the GR performance can be assessed by installing LRMs in each of the vertical rods. In the literature there are field tests applying LRM in different configurations, but on a reduced scale, where the GR results could not be related to the existing formulation when another type of grounding mesh is required, even more so when it is required calculate the GR by modifying the formulation variables (see the formulation and its respective variables in Section 3.4); for example, Ref. [30] installed a 2 m \(\times\) 2 m grid in three soils with different resistivities. Ref. [31] installed five individual rods in each of five soils with different resistivities, and Ref. [32] used three cylindrical rods with the same length (l = 50 cm) but different diameters.  
In the present study, the results show that as the soil resistivity increased, the decrease in the GR of the LRMs decreased. This correlation matches the results of other studies, such as [14; 17; 33], which reported different GR reduction percentages because each study used different LRMs, GS designs and soil resistivities. Likewise in [20] showed that using an LRM with a 2.4-m ground rod reduced the GR by approximately 40% when the resistivity of the surrounding soil was higher than 250 \(\Omega\)\(\cdot\)m and by approximately 50% when the resistivity of the surrounding soil was higher than 300 \(\Omega\)\(\cdot\)m. Conversely, in this study, the results show that the GR of the LRMs for the 1.8-m rod decreased by 45% when the resistivity of the surrounding soil was 195 \(\Omega\)\(\cdot\)m and by 81% when the resistivity of the surrounding soil was 302 \(\Omega\)\(\cdot\)m. In [34] the GR decrease with LRM on average is 42.05% and in [35] the LRM reduction is 20% for a 3 m vertical rod in a 103.6 \(\Omega\)\(\cdot\)m soil from the nearest layer.  
As mentioned above, most LRM studies are isolated and small-scale, that is, they present results for a single configuration, in a single field of a given resistivity, and do not relate the results to the theoretical calculation. Single GR reduction percentages with LRM may confuse the reader, implying that these percentages are given for any configuration and in any soil of different resistivity. On the other hand, this study presents different configurations, terrains with different resistivity values, relationship with the theoretical calculation, and above all, field tests and measurements are carried out; resulting in different values of GR reduction (see Table 12).
## 3 Case Study
### Effects of Applying an Energy-Dissipation Support
#### 3.3.2. Support Deformation  
To study the construction deformation of the energy-dissipation support, the aforementioned 3D laser scanning system was used to monitor the holographic deformation of the initial support, with the corresponding monitoring grayscale diagram shown in Figure 16. Limited by space, the section with the largest deformation was selected for analysis, and the support time-displacement curve of key points was drawn, as shown in Figure 17.  
It can be seen from Figure 15 that the supporting deformation rate is relatively large from 0 to 10 days. With the deformation of the steel plate composite structure, the deformation rate of the support decreases gradually and eventually stabilizes after 25 days. Lastly,  
Figure 14: The design scheme of an energy-dissipation support.  
the deformation of the vault was 289.25 mm; that of the upper border of the steel plate composite structure was 404.38 mm; and that of the lower border of the same was 373.76 mm. The convergence deformation of the lower bench excavation line was 424.55 mm.
# Bioinspired Algorithms for Multiple Sequence Alignment:
## 4 Methods
### Synthesis Results
#### 5.2.1 RQ1: What Are the Common Bioinspired Algorithms Used for MSA? This section attempts to answer research question 1 by exploring the popular bioinspired algorithms used for the MSA. Based on the reviewed articles, many bioinspired approaches have been proposed to solve the MSA problem. These approaches include Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Memetic Optimization (MO), and Biography-Based Optimization (BBO). The summary of the bioinspired algorithms for MSA identified in the selected studies is summarized in Table 5 and given below. Genetic Algorithm (GA)  
Natural selection, mutation, and crossover processes that individuals experience in evolution theory serve as the foundation for this method [3; 70; 71]. Each individual in the GA when solving the MSA problem is a potential alignment, and the optimization process is provided by the rearrangement of the sequence based on recombination and mutation operators [29]. Generally, the first operator changes an existing gap position, but the second generates new ones by joining two individuals. Several papers that used GA-based methods were identified based on the selected studies in this SLR. [PERSON] [47] designed a GA centered on the mutation and crossover process. The starting population is created randomly, and each individual's quality is assessed using the sum-of-pairs objective function. Operators are put into action after a tournament selection process. The author used various mutation operators, which are empirically executed and dynamically selected, as well as vertical and recombination crossover. This approach was evaluated using the BAliBASE benchmark, with a maximum of 27 sequences assessed. In contrast to other popular GA techniques for MSA, such as SAGA [30] and MSA-GA [72], this methodology produced good results in most situations. This approach was modified in [21] to use the COFFEE objective function [73] for DNA alignments. The Needleman-Wunsch algorithm is used to construct the GA's initial population, and fitness
## 4 Discussion
### The Difficulty and Further Improvement  
However, it remains difficult to extend the experimental results to the wider scale for satellite RS purposes. The following aspects should be considered:  
1. The cyclic loading mode and the relatively high loading rate in this study are just one type of crustal stress condition. However, the actual crustal stress conditions are complex and variable, and the loading rate is uncontrollable in reality. Additionally, the wavelet method is not suitable for extracting the TIR signal related to the aperiodic crustal stress from a relatively strong background.
2. In addition, most of IR radiation recorded by satellite sensors is emitted by vegetation, soil, and unconsolidated sediments on the Earth's surface. Moreover, the received radiation is influenced by many nonseismic factors including climate, topography and landscape, geophysics, and geography. It is difficult to observe the TIR signal from the loaded rock on the ground directly.
3. The distance between the spectrometer and the specimen is at the level of meters in the experimental condition, which is much less than the orbit height at the level of hundreds of kilometres for satellite observations. The influence of the atmosphere effect is inevitable.  
Therefore, it remains challenging to detect the crustal stress from satellite RS observations. The following additional exploration and investigations in the experimental condition and method are considered:1. For the experimental design, the cyclic loading mode at a relative slow rate can be considered to simulate the actual crustal stress condition [46]. In addition, more types of rock samples can be selected to confirm the magnitude of the stress-related TIR signal.
2. For the TIR observations, the wavelength features in the spectral radiance change and the emissivity change for loaded rocks were observed [33; 34; 35; 36; 37; 38]. It is reasonable to conduct the experimental studies based on the TIR signal at different wavebands.
3. Considering the limitations of the wavelet method, the new methods for weak signal extraction from strong backgrounds can be adopted in further studies [63; 64].
## 4 Experimental Results
### Experimental Results Analysis
#### 4.2.1 Informer Experimental Results  
In the performance evaluation of the Informer (see Table 6 and Figure 5), the INF-RB-UB combination exhibits the best prediction results, with an \(R^{2}\) of 76.77%. This significantly outperforms the INF combination, which lacks regional and usage features and achieves an \(R^{2}\) of only 70.46%. This improvement highlights the substantial impact of incorporating regional and usage features on the model's explanatory power and prediction accuracy. Furthermore, the INF-RB-UB combination achieves lower MRE and SMAPE values of 0.0789 and 0.0811, respectively, further validating the effectiveness of this feature combination in reducing prediction errors.  
Figure 5 illustrates the comparison between the predicted values of the four base models of INF and the actual natural gas purchase volumes. The data clearly exhibit significant seasonal patterns, with natural gas demand peaking in the winter and declining in the summer, reflecting increased heating needs and low-demand periods, respectively. Overall, the models perform better during low-demand periods compared to high-demand periods. Models incorporating regional and usage features demonstrate better alignment with actual values, particularly in capturing demand peaks. Similar patterns are also observed in MLR and SVR models, which exhibit consistent behavior across seasonal variations. For instance, the feature combination in the INF-RB model enables it to better track the substantial increase in demand caused by low temperatures.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Approach** & **MRE** & **MAE** & **SMAPE** & \(R^{2}\) **(\%)** & **RMSE** & **MAPE (\%)** \\ \hline INF & 0.0851 & 1,094,138 & 0.0881 & 70.46 & 1,593,979 & 8.51 \\ INF-RB & 0.0957 & 1,117,435 & 0.1013 & 77.19 & 1,400,524 & 9.57 \\ INF-UB & 0.0846 & 1,056,106 & 0.0861 & 76.34 & 1,426,436 & 8.46 \\ INF-RB-UB & 0.0789 & 1,000,462 & 0.0811 & 76.77 & 1,413,591 & 7.89 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance evaluation results of INF.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Approach** & **With Weather Features (\(R^{2}\), \%)** & **Without Weather Features (\(R^{2}\), \%)** \\ \hline INF & 70.46 & 65.15 \\ SVR & 67.06 & 57.29 \\ MLR & 79.18 & 71.83 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison of models with and without weather features (\(R^{2}\), \(\%\)).
## 1 Introduction
Genes encoding relevant enzyme activities are _fbaA_ (fructose 1,6-bisphosphate aldolase A), _pfkA_ (phosphorturokokinase A), _pfkB_ (phosphofructokinase B), _pgi_ (phosphglucose isomerase), _tpiA_ (triosephosphate isomerase), _zwf_ (glucose 6-phosphate dehydrogenase), EII\({}^{\text{Glc}}\) (glucose-specific enzyme II of the PEP-dependent sugar:phosphotransferase system, PTS), FRT = FLP recognition site. glycerol via DHA [29], and a new pathway for the synthesis of glycolic acid [30] were accomplished. As well, a novel road to the formation of 1-deoxyxylulose from hydroxyacetone was opened which allows for a novel access to the methylerythmitrolphosphate biosynthesis pathway of isoprenoids in _E. coli_[31]. From theoretical considerations, the FSA retro-aldol reaction has already been introduced as a constituent in several genome scale metabolic models for _E. coli_[32; 33; 34] but, to the best of our knowledge, experimental proof for such a role in cleavage of F6P has not been provided so far. We wanted to find out whether the wt_fsaA_ gene (recombinantly expressed either from a plasmid vector or after insertion into the chromosome, in both cases under the control of the IPTG-inducible promoter P\({}_{\text{tac}}\)) would reinstall growth on glucose in a triple mutant of _E. coli_ K-12 which lacks PFK and ZWF activities. We show here that, while protein formation and sufficient enzyme activity was detectable in cell-free extracts, no growth was observed with the recombinant wild type gene. Albeit, a mutant allele (_fsaA_A1298) [24; 27; 35] conferred a glucose-positive phenotype, both when expressed from a plasmid or as single copy in the chromosome. DHA is an ingredient in many self-tanning compounds and glycerol is a carbon source which has received much attention as it can be used for production of many added-value compounds (for references see [36]). In contrast to other microorganisms such as yeast [37], cells of _E. coli_ are not known to form and excrete glycerol when growing on glucose [38; 39]. By introduction of genes from yeast which encode enzymes for the conversion of DHAP to glycerol, however, _E. coli_ glycerol producers had been constructed [40; 41; 42; 43]; eventually this has led to recombinant _E. coli_ cells forming 1,3-propanediol from glucose via the intermediate glycerol. 1,3-propanediol is now used as monomer for the industrial production of the biobased polymer polytrimethylene terephthalate (PTT) [36; 41]. As we observed transient formation of DHA from glucose with the newly developed strains described here, we decided to aim for a novel pathway to DHA and glycerol from glucose in _E. coli_ mutants. This pathway is completely independent of heterologous genes.
# EMF Assessment Utilizing Low-Cost Mobile Applications
## 2 Materials and Methods
### Measured Data Processing
#### 2.3.4 Conversion Factor  
Since the aim of this study was to estimate the actual electric field strength, it was essential to extract both the power flux density and, by extension, the electric field strength from the smartphone's parameters.  
Given that all measurements were taken in the far field, the power flux density was related to the received power, as shown in Equation (1) [21].  
\[S=\frac{P_{R}}{A_{eff}} \tag{1}\]  
where \(S\) is the power flux density in Watts per square meter, \(P_{R}\) is the received power in Watts, and \(A_{eff}\) is the effective aperture in square meters.  
The effective aperture is given by Equation (2) [21].  
\[A_{eff}=\frac{\lambda^{2}}{4\pi}*G \tag{2}\]  
where \(\lambda\) is the wavelength of the received electromagnetic wave in meters and \(G\) is the antenna gain.  
The wavelength can be calculated from the absolute radio-frequency channel number (ARFCN) provided by the app. The ARFCN can be converted to frequency, and thus to wavelength.  
We propose that the received power can be related to the \(RSRP\) value as shown in Equation (3):  
\[P_{R}=RSRP*Correction\ Factor \tag{3}\]  
where \(RSRP\) is the strength of the reference signal in Watts, and the _Correction Factor_ adjusts the reference signal power to represent the total power received by the smartphone. It is important to note that since our goal was to estimate the received power across the entire frequency band (not just the user-specific signal), the Correction Factor must consider network load and traffic. Thus, it is not a constant value but varies depending on network conditions.  
Combining Equations (2) and (3) into Equation (1), we can rewrite the expression for the power flux density as follows:  
\[S=\frac{4\pi}{\lambda^{2}}*RSRP*Conversion\ Factor \tag{4}\]  
where the _Conversion Factor_ is a linear term that includes both the antenna gain and the _Correction Factor_.  
This _Conversion Factor_ was calculated using a portion of the dataset via the least squares method, with no intercept forcing the fit through the origin. Specifically, the goal was to determine the value \(x\) that minimizes:  
\[min_{x}\sum_{i=1}^{n}{(S_{RSRP,i}*x-S_{i})^{2}} \tag{5}\]  
where \(S_{RSRP,i_{i}}\) is the estimated total received power from the app, including both the main cell and three neighbor cells, divided by the corresponding effective aperture and \(S_{i}\) is the power
# Joint Deployment of Sensors and Chargers in Wireless
## 5 Simulation Evaluation
### Solution of the Charger Deployment
#### 5.3.3 Varying the Perception Probability of Sensors  
Figure 9 shows the effect of the increase of sensor perception probability on the number of deployed chargers. The results show that with the increase of sensor perception probability, the number of deployed chargers increases, and the increasing trend slows down. In Figure 9a, the GHDSAE-based GH solution saves 9.99-19.39% (with an average of 13.43%) more chargers than the GHDS-based GH solution. In Figure 9b, the GHDSAE-based GH solution consumes \(-\)6.69-3.62% (with an average of 0.69%) more time than that of the GHDS-based GH solution. In Figure 9c, the GHDSAE-based PSO solution saves 8.98-20.79% (with an average of 12.98%) more chargers than the GHDS-based PSO solution. In consequence, In Figure 9d, the GHDSAE-based PSO solution saves 11.09-24.98% (with an average of 15.77%) less time than the GHDS-based PSO solution. As a result, the GHDSAE-based solution deployment chargers are superior to the GHDS-based solution deployment chargers with acceptable computing time. Likewise, both the PSO solutions based on the GHDSAE and the GHDS solutions can always deploy fewer chargers than the GH solution based on the GHDSAE and the GHDS solutions. In Figure 9a,c, the GHDS-based PSO solution saves 5.45-6.63% (with an average of 5.84%) chargers compared to the GHDS-based GH solution. The GHDSAE-based PSO solution saves 3.91-8.29% (with an average of 5.39%) more chargers than the GHDSAE-based GH solution. In Figure 9b,d, the GHDS-based PSO solution consumes 275.83-330.32% (with an average of 307.64%)  
Figure 8: Deployed chargers with the increase of \(L_{s}\). (**a**) Deployed chargers of GH; (**b**) Solving time of GH; (**c**) Deployed chargers of PSO; (**d**) Solving time of PSO.  
more solving time than the GHDS-based GH solution. In summary, both the GH and PSO solutions are scalable as the perception probability grows, and the PSO solution always outperforms the GH solution by deploying fewer chargers with the cost of acceptable calculation time expenses, and the aggregation effect of sensors can effectively reduce the deployment of chargers at the cost of slightly incremental computing time.
## 3 DCS Scheme Families
### Similarity Data Storage (SDS)  
[19] proposes an efficient spatial-temporal similarity search scheme for both static and dynamic WSN. Efficient data aggregation and query, similarity search for multi-attribute data and spatial temporal search are identified as three major challenges faced by DCS schemes. SDS is an important approach that utilizes spatial-temporal and similarity search functionalities and aims to reduce overhead, energy consumption and search latency. In SDS, a deployed large-scale WSN field is considered as a rectangular field. The entire field is divided into small rectangular zones. Each zone has a dedicated sensor node named as zone head. It is assumed that each node in the network is configured with three information elements: (1) Number of zones horizontally \(n_{x}\) and vertically \(n_{y\cdot}\), (2) the zone ID assignments scheme--IDs are assigned within the zone sequentially from left to right, and (3) the zone ID and geographical location. A node in the zone with \(ID_{i}\) can calculate its Euclidean distance from another zone \(ID_{j}\) using \(\left|ID_{i},ID_{j}\right|=\sqrt{{\delta{x_{i,j}}^{2}+{\delta{y_{i,j}}^{2}}}}\), where \(\delta{x_{i,j}}=(ID_{j}-ID_{i})\psi_{i}n_{x}\) and \(\delta{y_{i,j}}=(ID_{j}-ID_{i})n_{x}\). A head node in a zone is responsible for communication with other zones. All other nodes inside a zone are connected with the head node.
## 2 Materials and Methods
### Numerical Model of Wearable WIT Device  
The investigations were conducted for a wearable WIT device capable of establishing Wi-Fi and/or Bluetooth connections in the 2.45 GHz ISM band. A key element of such a device is the RCM (in our case, the commercially available, easily programmable ESP32-WROOM module (Espressif Systems, Shanghai, China)), equipped with a circularly polarized omnidirectional MIFA antenna (meandered inverted-F antenna) [1; 6]. This popular RCM has been developed and optimized for free-space operation in the 2.45 GHz ISM band, whereas the capacitive coupling between the body and the antenna changes the resonant frequency and input impedance of the body-antenna complex loading the RCM in considered exposure scenarios [7; 20].  
The investigations did not include any additional matching circuits or other structures that may optimize the parameters of its EMR emission or minimize SAR for the operation of the RCM near the body [10; 18; 29]. The numerical model of the considered RCM comprised an MIFA with external dimensions of 15.3 mm \(\times\) 5.9 mm and a thickness of 0.035 mm with the assigned parameters of copper, placed on a substrate with external dimensions of 18.0 mm \(\times\) 25.5 mm, a thickness of 0.7 mm, and the parameters of FR-4 (glass-reinforced epoxy laminate material) (Figure 1a, Table 2). Details of the numerical model are reported in [1; 6; 8]. The numerical model of the investigated antenna in free space at 2.45 GHzfrequency is characterized by 1.11 dBi gain, 1.74 dBi directivity, and radiation patterns (3D and 2D), as shown in Figures 1 and 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material/Tissue** & \(\varepsilon_{\mathbf{r}}\) & \(\sigma\), S/m & **Density, kg/m\({}^{3}\)** \\ \hline \multicolumn{4}{c}{The numerical model of the RCM equipped with MIFA} \\ \hline Copper & 1.0 & 5.8 \(\times\) 10\({}^{7}\) & 8930 \\ FR-4 & 4.0 & 3.8 \(\times\) 10\({}^{-3}\) & 1850 \\ \hline \multicolumn{4}{c}{The simplified numerical models of body parts} \\ \hline Bone & 11 & 0.39 & 1910 \\ Brain & 43 & 1.8 & 1040 \\ Fat & 11 & 0.27 & 910 \\ Internal organ & 55 & 2.5 & 1060 \\ Muscle & 53 & 2.2 & 1090 \\ Skin & 38 & 1.5 & 1110 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The dielectric properties of materials and tissues of simplified models of body parts used in investigations [1,6,8,10,29].  
Figure 1: A 3D model of the investigated 2.45 GHz ISM band antenna (**a**) and its 3D radiation pattern (in dB) in free space at 2.45 GHz (**b**).  
Figure 2: The 2D radiation patterns (antenna gain in dB) of the investigated 2.45 GHz ISM band antenna in free space at 2.45 GHz.