esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Influence of biochar Incorporation on Plant Phytoremediation Potential  
Analysing the uptake of HCH isomers by _P. tomentosa_, noticeable accumulation was solely observed in the plant roots (Figure 4). Furthermore, \(\beta\)- and \(\delta\)-isomers were only found in plant roots among the five isomers detected in the DS. In the 5.0 and 10% biochar-amended sediments, the root BCFs for \(\beta\)- and \(\delta\)-isomers increased by up to 119%.  
Figure 3: Specific and phenomenological parameters of chlorophyll fluorescence of _P. tomentosa_ grown in biochar-amended sediments. Presented values normalised referring to control. Note: _ABS_—absorption; _RC_—reaction centre; _TR_—trapped energy; _ET_—electron transport; _DI_—dissipation; _CS_—cross-section; _PI_—performance index [89; 90].  
Figure 2: Influence of biochar incorporation on _P. tomentosa_ physiological parameters. (**a**) Plant height; (**b**) stem diameter; (**c**) aboveground biomass DW; (**d**) root biomass DW. Different letters within the parameter indicate a statistically significant difference (\(p<0.05\)).  
Analysing the phytoremediation potential of _P. tomentosa_ concerning elements in DS revealed distinct accumulation trends as biochar application rates rose (Table S4): Mg, Al, Si, Ti, Cr, Mn, Fe, Sr, Zr, and Pb decreased in AGB but increased in roots; P, S, Ca, and Zn decreased in both plant tissues; K increased in both plant tissues; Cu and Rb increased in AGB till 5.0% biochar with a subsequent decrease at 10% biochar.  
Hence, upon evaluating the CBCI values separately for EEs and PTEs, it is evident that biochar incorporation into sediments prompted a shift in the primary accumulation site from AGB to roots, resulting in an overall decrease in TE accumulation within _P. tomentosa_ (Figures 4 and S2). Specifically, a notable reduction in EE accumulation occurred in both AGB (0.81-0.40) and roots (0.32-0.09) when plants were grown in 0-5.0% biochar-amended sediments. However, in 10% biochar-amended sediments, EE accumulation in roots increased to 0.45 (Figure S2).  
Unexpectedly, at 2.5% biochar, the translocation of S and K to AGB increased, while Ca, Zn, and Pb translocation remained unchanged (Figure S3). However, in 5.0% biochar-amended sediments, the translocation of P, S, K, Ca, Cu, Zn, Rb, Zr, and Pb to AGB increased, while Mg, Al, and Mn translocation remained constant. Interestingly, in sediments with 10% biochar, translocation decreased for all elements except K and Pb.
###### Abstract
Furthermore, the use of inhibitors such as sulfathiazole has been investigated for their effectiveness in inhibiting aluminum corrosion in specific environments, highlighting the importance of corrosion inhibition strategies in maintaining the integrity of aluminum materials [20]. Furthermore, the presence of ceramic particles as additives has been shown to impact the electrochemical behavior of aluminum, potentially affecting its corrosion resistance as a galvanic anode in Al-air batteries [21]. Incorporating an aluminum alloy significantly lowers the corrosion rate, thereby prolonging the operational life of aluminum-air batteries. In addition, the introduction of small amounts of certain metals into an alkaline medium has been explored and found to harness synergistic effects that improve the performance of aluminum alloys [16; 22; 23; 24; 25]. However, aluminum reacts rapidly and irreversibly with oxygen, forming a stable oxide layer that critically affects its electrochemical behavior in aqueous electrolytes. Introducing specific metals into aluminum alloys and using a 2M NaCl electrolyte can improve voltage performance [26]. The corrosion resistance of these aluminum alloys has been evaluated in different electrolytic media to determine their inhibitory effects [27; 28; 29]. The integration of small metal concentrations into aluminum alloys has been shown to enhance current capacity and corrosion inhibition [8]. However, investigations on the corrosion behavior of metal-coated aluminum plates in the NaCl electrolyte of 3.5%, a surrogate for seawater, have been limited and have not been applied as anode material in aluminum-air batteries [30; 31]. In addition to utilizing aluminum metal alloys to enhance anode characteristics, the deposition of metals onto pure aluminum or its alloys has also been explored. Specifically, the deposition of copper on the anode has been shown to improve the efficiency of the anode. Furthermore, copper deposited in the 70/75 aluminum alloy through electrochemical and chemical deposition processes exhibits greater stability than copper deposited on pure aluminum. In particular, the electrochemical deposition process has been found to be more efficient, economical, and practical compared to the chemical deposition method, and in some cases, chemical deposition may not even be feasible [32; 33; 34]. Other metals, such as zinc, have been identified as potential corrosion inhibitors by forming a protective coating on the substrate surface, thus improving the performance of aluminum as an anode in metal-air batteries [35]. Therefore, this research intends to study and contrast the electrochemical behavior of two varieties of aluminum--the unalloyed alloy and the 6061 alloy--in Al-air batteries, deposited with zinc and employing a 3.5% sodium chloride solution as an alternative to seawater. Furthermore, the procedure of depositioning zinc on the aluminum surface was investigated with and without the incorporation of the EDTA complexing agent during electrodeposition to assess its influence on the performance of the anode.
# Optimizing Energy Forecasting Using ANN and RF Models for HVAC and Heating Predictions
## 3 Results and Discussions
### Evaluation of Performance Metrics
#### 3.2.1 Correlations of Heatmap  
The close correspondence between the actual HVAC demand and both predicted values would imply that the models capture the major determinants of energy use. Both the Neural Network and Random Forest models showed the effective prediction and management of HVAC (Heating, Ventilation and Air Conditioning) systems, as shown in Figure 9a. In the correlation heat map, each variable had self-correlation with correlation equal to 1.0, which was represented by the parallel entries on the diagonal. The off-diagonal entries demonstrated how the different variables relate to each other, and the value of the correlation coefficient described the strength and direction of the linear (or quasi-linear) relationship. The analysis demonstrated that the actual HVAC demand correlated very well with the Neural Network predictions of 0.9627, which demonstrates that the model managed the actual demand well. This relationship strength and direction reflects the Neural Network's ability to replicate the actual real-world dynamics. The actual demand also correlated with the Random Forest predictions of 0.9445, to a slightly lesser extent than the Neural Network but also indicating a solid relationship.  
Figure 9: Correlations of heatmap: (**a**) (LUCIA Building, nZEB-HVAC), (**b**) (FUHEM Building-Heating).  
Figure 9 presents the correlation heatmap for the FUHEM Madrid dataset, which indicates the relationship between actual demand, the predicted demand by the Neural Network, and the predicted demand by the Random Forest. The correlation of the actual demand to the Neural-Network-predicted demand is perfect (1.0), indicating a very strong linear relationship, where increases in actual demand are followed by increases in predicted demand. The correlation of actual demand to the Random-Forest-predicted demand is also very close to one at 0.9948, which still indicates a positive and strong association. Finally, the correlation between both predicted models--Neural Network and Random Forest--is approximately equal to 0.9951, which suggests that the models were producing the same or very similar predictions for the FUHEM project. Values close to one such as what we see here indicate a very strong relationship, which further affirms these models' corresponding performance and reliability as a predictive model.
# Chemical Characterization of Cider Produced in Hardanger--From Juice to Finished Cider
## 3 Results and Discussion
### Chemical Composition of Apple Jices and End Fermented Cider of Different Apple Fruit Cultivars
#### 3.1.2 Total Phenols and Individual Polypphenols
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Variety**} & \multicolumn{2}{c}{**Juice (mg L\({}^{-1}\))**} & \multicolumn{2}{c}{**Cider (mg L\({}^{-1}\))**} \\  & & **N** & **Mean (Min-Max)** & **N** & **Mean (Min-Max)** \\ \hline \multirow{4}{*}{Ferulic acid} & Aroma & 5 & 0.75 (ND–3.00) & 7 & 0.43 (ND–3.00) \\  & Discovery & 3 & ND & 3 & ND \\  & Graenstein & 5 & ND & 3 & ND \\  & Summerred & 4 & ND & 4 & 0.23 (ND–0.90) \\ \hline \multirow{4}{*}{Phloridzin} & Aroma & 5 & 4.25 (ND–11.0) & 7 & 4.91 (ND–7.00) \\  & Discovery & 3 & 3.67 (ND–11.0) & 3 & 4.00 (ND–12.0) \\  & Gravenstein & 5 & 4.10 (ND–12.0) & 3 & 8.03 (ND–14.0) \\  & Summerred & 4 & 3.33 (ND–10.0) & 4 & 4.00 (ND–11.0) \\ \hline \multirow{4}{*}{Total Polypphenols} & Aroma & 5 & 236 (68.0–401) & 7 & 186 (49.8–414) \\  & Discovery & 3 & 269 (238–287) & 3 & 248 (179–360) \\ \cline{1-1}  & Gravenstein & 5 & 174 (106–238) & 3 & 289 (209–382) \\ \cline{1-1}  & Summerred & 4 & 77.0 (23.0–124) & 4 & 79.2 (11.0–193) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._final 15.0 mg L\({}^{-1}\). A slightly higher concentration of epicatechin in comparison to catechchin was observed in all ciders, but only for 'Aroma' can we see the trends of increase during fermentation from the initial 25.7 to 34.2 mg L\({}^{-1}\)[1; 4].
# A Study on the Optimisation of Tennis Players' Match Strategies from the Perspective of Momentum
## 3 The Mechanism of How Player Momentum Affects Match Trends
### Consistency Test for the Correlation Between Player Momentum and Match Trends  
The Spearman correlation coefficient is a non-parametric statistical method used to measure the correlation between two variables. It is applicable to all types of data, including both ordinal and non-ordinal data. In this study, the Spearman correlation coefficient formula was employed to calculate and test the correlation results obtained from the Eta coefficient. The calculation results and significance test results are presented in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Match**} & \multicolumn{4}{c}{**Each Game**} & \multicolumn{4}{c}{**Each Point**} \\ \cline{3-10}  & & **P1** & **P2** & **P2** & **P1** & **P1** & **P2** & **P2** \\  & & **Spearman** & **Sig.** & **Spearman** & **Sig.** & **Spearman** & **Sig.** & **Spearman** & **Sig.** \\ \hline
1 & 1304 & 0.600 & 0.000 & 0.619 & 0.000 & 0.208 & 0.000 & 0.206 & 0.000 \\
2 & 1406 & 0.782 & 0.000 & 0.807 & 0.000 & 0.308 & 0.000 & 0.347 & 0.000 \\
3 & 1502 & 0.521 & 0.000 & 0.764 & 0.000 & 0.135 & 0.023 & 0.315 & 0.000 \\
4 & 1601 & 0.768 & 0.000 & 0.699 & 0.000 & 0.293 & 0.000 & 0.229 & 0.004 \\
5 & 1701 & 0.725 & 0.000 & 0.611 & 0.000 & 0.202 & 0.000 & 0.214 & 0.000 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Spearman correlation results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Match**} & \multicolumn{2}{c}{**Each Game**} & \multicolumn{2}{c}{**Each Point**} \\ \cline{3-5}  & & **P1** & **Eta** & **P2** & **P1** & **Eta** & **P2** \\ \hline
1 & 1304 & 0.470 & 0.505 & 0.209 & 0.206 \\
2 & 1406 & 0.752 & 0.800 & 0.310 & 0.338 \\
3 & 1502 & 0.544 & 0.731 & 0.144 & 0.311 \\
4 & 1601 & 0.780 & 0.752 & 0.274 & 0.265 \\
5 & 1701 & 0.717 & 0.491 & 0.208 & 0.202 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Eta correlation results.  
As demonstrated in Table 4, the results of the significance test indicate that \(p<0.05\), signifying that the null hypothesis is rejected at the 95% confidence level, and the significance test is passed. The magnitude of the Spearman correlation coefficient is indicative of the strength of the correlation between the two variables. The calculation results in the table demonstrate a strong correlation between the cumulative momentum score for each game and the player's win/loss changes in that game, while the momentum score for each point exhibits a weaker correlation with the player's win/loss changes at that point. This finding is consistent with the results obtained from the Eta correlation analysis, thus validating the accuracy of the Eta correlation analysis.  
Consequently, based on the aforementioned correlation analyses, it can be concluded that cumulative momentum can reflect the match trends to a certain extent. The success of players in a match is not entirely random and can be optimised through momentum-related strategies.
# ERS-1/2 and Sentinel-1 SAR Data Mining for Flood Hazard and Risk Assessment in Lima, Peru
## 3 Results
### No Flooding
* Archeological excavations: four changes in the radar backscatter show decrease in the signal in areas with archaeological remains. In one case, the decrease in the backscatter signal could be influenced by the decrease in vegetation in the archaeological area due to a fire (Figure 17). These changes are located on alluvial deposits, on a non-urban area with an A-class slope. Figure 16: Examples of NF3 change “Ephemeral changes” and NF9 change “New building constructions”. (**a**) ERS-2 RGB composite and (**b**) ratio map highlight patterns of increased radar backscatter, in one case matching with (**c**) vehicles parked near the principal wholesale fruit market in Lima, in the other with (**d**) the start of the construction activities of the Jockey Plaza shopping center (April 1996) [90]. Figure 15: Example of NF2 change “Vegetation cover—cropland changes”: (**a**) ERS-2 RGB composite and (**b**) ratio map of the cross-event pair 10/04/1996—20/05/1998 (see Appendix A, Table A1) show a clear pattern of radar backscatter decrease in Las Praderas de Pariachi on the left bank of the Rimac River that, until mid-1996, was occupied by agricultural fields and, afterwards, was subject to urban sprawl, in specific for the development of the residential district [48]. The land-cover transition is confirmed in the band combination 7-4-2 (“natural-like” [89]) of Landsat 4-5 Thematic Mapper (TM) images collected on (**c**) 14/04/1996 and (**d**) 22/05/1998.
## 3 Sliding Mode Controller Design
### Fuzzy Sliding Mode Controller Design  
#### 3.2.1 Power Reaching Law Based Sliding Mode Controller Design  
The following power-reaching law is employed in this research to achieve the fast approach of the sliding mode variable  
\[\dot{s}=-K_{1}s-K_{2}|s|^{\beta}\text{sign}(s) \tag{13}\]  
where \(K_{1},K_{2},\beta\) are the designed parameters. It should be noted that Equation (13) can be regarded as the dynamic description of the sliding variable. In view of its non-smooth  
Figure 2: Schematic of the proposed path tracking controller.  
nature, with the appropriate parameter adjustments, it may perform better than a constant reaching law during the reaching phase in terms of its disturbance rejection ability and reaching speed.  
By combining Equations (9) and (13), the sliding mode controller can be designed as  
\[u=\frac{L_{1}}{v(\beta_{1}L_{2}-\beta_{2}L_{3}+\beta_{2}L_{2})}\begin{pmatrix} \beta_{2}v\text{sin}\varphi-L_{2}v\text{asin}\theta-\\ L_{2}K_{1}s-L_{2}K_{2}|s|^{\beta}\text{sign}(s)\end{pmatrix} \tag{14}\]  
Similarly, through the inverse trigonometric function, one can obtain  
\[\delta=\arctan\left(\frac{L_{1}}{v(\beta_{1}L_{2}-\beta_{2}L_{3}+\beta_{2}L_{ 2})}\begin{pmatrix}\beta_{2}v\text{sin}\varphi-L_{2}v\text{asin}\theta-\\ L_{2}K_{1}s-L_{2}K_{2}|s|^{\beta}\text{sign}(s)\end{pmatrix}\right) \tag{15}\]  
Compared to the front wheel steering angle in (11), the front wheel steering angle in (15) has a continuous characteristic, which significantly reduces the chattering phenomenon. The anti-disturbance performance and reaching speed of the sliding mode variable in the reaching stage may both be significantly improved by utilizing the non-smooth dynamic feature.  
A Lyapunov function (\(V_{2}=\frac{1}{2}s^{2}\)) is selected to verify the stability of the closed system, and taking the derivate of the Lyapunov function, the authors have  
\[\begin{array}{rcl}\dot{V}_{2}&=&\dot{s}\dot{s}=s\left(-K_{1}|s|^{\beta}\text {sign}(s)-K_{2}s\right)\\ &=&-K_{1}|s|^{\beta+1}-K_{2}s^{2}\leq 0\end{array} \tag{16}\]  
According to [PERSON]'s theory, under controller (14) the closed system is stable. It should be observed that control parameters \(K_{1}\) and \(K_{2}\) are contained in the controller (14). When \(K_{1}/K_{2}\) increase, the straight-line tracking accuracy rises as well as the anti-disturbance capability of the vehicle path tracking, but the vehicle's online speed slows down. Conversely, when \(K_{1}/K_{2}\) decrease, the vehicle's online speed increases, but at the same time the straight-line tracking accuracy and path tracking anti-disturbance capability both decline. At the moment, controller parameters are often adjusted manually. Setting control criteria is challenging since there is no scientific justification for them. Therefore, in the next subsection, this study uses sliding mode variables and their derivatives to design corresponding fuzzy rules and optimize controller parameters.
# Secure Indoor Localization Based on Extracting Trusted Fingerprint
## 5 Experiment Analysis
### The Impact of Trust Threshold on Voting  
The trust threshold \(\Delta\) represents the tolerance of the RSSI, which directly affects the voting result of APs. The larger the value of \(\Delta\), the greater the change in RSSI that can be tolerated, and the  
Figure 7: Experimental scene.  
recognition rate for impaired AP and environment-affected APs is lower. Therefore, when the trust threshold \(\Delta\) is larger, the number of votes for impaired APs and environment-affected APs is lower. In the experiment, the set of environmental interference values is 0 to 10 dbm, the RSSI of the impaired AP decreases by 30 to 50 dbm and the RSSI value of the broken AP is 0. In this experiment, six APs were placed, the impaired AP was \(AP_{5}\) and the rest of APs were disturbed by the normal environment. Figure 8 shows the result of this algorithm voting when the AP is impaired. In the experiment, we convert the number of votes for each AP into percentiles, that is the maximum number of votes is 100. If there are large metal objects (e.g., coal trans) in the environment that would generate large disturbances in WiFi signals, according to the voting mechanism, the APs will get a higher number of votes. The smaller the weight based on the number of votes, the lower the APs' reference value. The abscissa indicates the trust threshold, and the ordinate indicates the number of APs votes. The higher the number of \(AP_{5}\) votes, the higher the recognition rate of the algorithm for impaired APs. The lower the number of votes from other APs, the better the tolerance of the algorithm for environmental change. Therefore, the greater the difference between the numbers of votes obtained by \(AP_{5}\) and other APs, the better the filtering performance of the algorithm.  
As can be seen from Figure 8, the greater the value of \(\Delta\), the lower the recognition rate of impaired AP, but the ability to tolerate environmental interference is strong. When the value of \(\Delta\) is small, the algorithm can absolutely identify impaired AP, but the algorithm's ability to tolerate the environment is very low. When the environment changes slightly, it can affect the AP. It can be seen from Figure 8 that, when \(\Delta=4\), the algorithm not only has a better recognition rate of impaired APs, but also has some tolerance to environmental interference.
## 5 Discussions and Future Works
### Future Work
#### 5.2.1 _k-Shot_ Prompts and Questions  
Throughout this paper, we used only a 0-shot prompt. It is obvious that most RegTech users will use 0-shot prompt questions. However, it is possible to train such users to use k-shot prompting as well. Such prompts can increase the reliability and performance accuracy of LLMs. Future work can lay out the _k-shot_ prompt design techniques for RegTech. The following are example scenarios on how and where few-shot prompts can be used in RegTech for agriculture.  
Scenario 1: Streamlining Pesticide Usage Reporting with Few-Shot Learning  
A farmer is required to submit monthly pesticide usage reports to the local agricultural regulatory authority. To simplify this process, a few-shot learning approach can be employed using a large language model (LLM). The farmer provides the LLM with a few examples of reports from previous months (the \"few shots\"). These examples demonstrate the required format, including specific data fields and their organization. The farmer then inputs the current month's pesticide usage data, such as the types of pesticides used, quantities applied, and application dates. Leveraging the few-shot examples, the LLM automatically generates the new monthly report, populating it with the current data and ensuring adherence to the regulatory authority's prescribed format.  
Scenario 2: Worker Safety Training Records  
Documenting worker safety training is essential for compliance. The LLM can be trained with examples of compliant training records, including training topics, dates, and attendees. The farmer inputs the details of recent training sessions, and the LLM generates the necessary documentation.  
Multi-Level Checking and Rule Relaxation  
LLMs can enable _multi-level compliance_ checking. It would involve a tiered approach for compliance verification, with non-linear dependencies of guidelines issued by different authorities. LLMs can keep track of these changes in rules in real time and alert relevant stakeholders if they are impacted.  
Another feature of LLM is _severity detection_ of non-compliance. If the producer meets the bare minimum of the regulatory requirements, they pass the compliance checks. However, when they fail to meet the compliance, it would be interesting for regulators to know how much they failed to comply. On the other hand, once they meet the minimum requirements, the regulator does not need to be concerned about the quality above the minimum standards. Severity detection is also helpful for early warning systems.  
There may be opportunities for _rule relaxation_ in certain circumstances. For example, less stringent regulations could be applied to small-scale, family-owned farms that pose minimal environmental or food safety risks. In emergencies, such as natural disasters or disease outbreaks, temporary regulation relaxation may be necessary to address urgent issues. With this, regulators can strike a balance between ensuring compliance and promoting agricultural innovation and sustainability.