esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Monte Carlo Simulations of Fluorescence Imaging  
In order to approve the relevance of the obtained fluorescence images, Monte Carlo simulations were performed for the geometry corresponding to that of the agar mixture phantom. Figure 7 depicts the results of the simulations for the phantom with an embedded BPD tube for the probing wavelength of 690 nm. The top row of Figure 7 demonstrates the distribution of the probing radiation absorption within the medium. The maps demonstrate photon fraction absorbed per unit volume, normalized by the number of launched photons per unit area. One can see that the absorption within the BPD tube was quite uniform, confirming that the registered fluorescence response originated from the entire labeled volume. Figure 7c demonstrates the simulated fluorescence image, while Figure 7d shows the simulated and experimentally registered central cross-sections of the fluorescence profile. The calculated fluorescence map shows the ratio of densities of fluorescence photons that reached the top surface of the medium to the number of launched photons; fluorescence quantum yield was taken to be equal to 1. Similar results were obtained for the FL of the phantom with the embedded IRDye800 tube for the probing wavelength of 785 nm (not shown).  
Figure 8 demonstrates the widths of the fluorescence profile central cross-sections for different diameters of the labeled tubes. The demonstrated monotonous dependence of FWHM on the tube size confirms that complementary bimodal imaging allows to obtain additional information on the size of the nanoconstruct-containing volume based on a given depth which can be determined from OA measurements. Note that the value of the fluorescence profile FWHM calculated for the tube with the  
Figure 7: Monte Carlo simulations: absorption maps within the agar mixture phantom with embedded BPD tube at the wavelength of 690 nm in two projections (**a**,**b**), corresponding fluorescence image (**c**), and comparison of experimental and simulated fluorescence profiles (**d**). All maps are normalized for the number of launched photons per unit area. The absorption values in (**a**,**b**) are shown in mm\({}^{-1}\), fluorescence intensity is dimensionless (**c**). Color map values in (**a**–**c**) are shown in logarithmic scale.  
inner diameter of 0.5 mm and located in a turbid phantom at the depth of 1 mm is FWHM = 3.3 mm which completely agrees with the corresponding value given in [18].
## 3 Results
### Frameworks for Robust and Decentralized Energy Management
#### 3.5.3 Future Perspectives  
The future of robust and decentralized energy management lies in integrating advanced predictive control and AI to enhance system adaptability. Predictive models, such as those combining CNN with LSTM algorithms, have already shown promise in forecasting load demands and coordinating distributed resources [94]. Expanding these methods to include real-time data integration and adaptive learning could significantly improve the dynamic response capabilities of decentralized systems [85]. AI-driven optimization techniques, such as hybrid grey-wolf and whale optimization algorithms, have demonstrated potential in multi-microgrid systems by addressing renewable energy intermittencies and demand-side variability. Incorporating such methods into decentralized frameworks could further enhance their scalability and efficiency in managing complex, interconnected grids [16].  
Additionally, emerging technologies like blockchain and smart contracts are expected to play a crucial role in ensuring transparency and security in decentralized markets. By integrating trust-based consensus protocols and human-centric metrics, future frameworks could address challenges related to privacy and trust in P2P energy trading systems, enabling broader adoption and acceptance [92]. On the other hand, decentralized energy management frameworks must increasingly focus on resilience to disruptions in dynamic markets. Adaptive control mechanisms that leverage real-time analytics and scenario-based planning will be essential for mitigating the impacts of market volatility and renewable energy fluctuations. These advancements will ensure that decentralized systems remain reliable and efficient even under rapidly changing conditions, paving the way for sustainable and robust energy networks.
## 5 Conclusions  
The purpose of this study was to explore the response characteristics of vegetation coverage changes in coastal regions to LULC changes. NDVI and LULC data from 2000, 2005, 2010, 2015, and 2020 were divided into six categories and the distribution characteristics of the NDVI and LULC were analyzed in sequence. The change characteristics during the transition period were analyzed using a Sankey diagram and dynamic analysis. Finally, the spatial response characteristics and contribution index were used to quantify the response of vegetation coverage changes to LULC changes. The main results are as follows.  
1. The overall vegetation coverage in the coastal regions of the Guangdong Province is relatively high, with higher coverage and above being the main categories (accounting for 85.37% to 89.48%) and high coverage mainly distributed in the east and west. From 2000 to 2020, the vegetation coverage in this region gradually increased overall, but decreased in the central region. There were spatiotemporal differences in the transfer of different vegetation coverage levels, and over time, the transfer became increasingly frequent;
2. The LULC structure mainly comprises forest land (46.5% to 47.5%) and cultivated land (30.7% to 33.4%), with forest land mainly distributed in relatively high-altitude regions and cultivated land mainly distributed in the plains. In terms of space, forest land and cultivated land remain dominant. In terms of time, the changes in LULC from 2015 to 2020 were relatively significant, especially the mutual transfer of cultivated land and forest land. The built-up land continued to increase from 2000 to 2020, but the speed gradually slowed down;
3. The change in vegetation coverage showed a trend of improvement on the whole, especially in the western and central mountainous and hilly regions, but in the central the Pearl River Delta there was a large regional deterioration. The NDVI change caused by the transfer of forest land to other land was the most significant. The net contribution rate of forest land change to vegetation coverage change had the largest range (\(-38.903\%\) to 23.144%), but the average was the lowest (\(-6.179\%\)).  
Z.C.: conceptualization, data curation, methodology, writing--original draft preparation; S.X.: writing--review and editing, supervision, funding. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grants 42376226 and 41877411).  
Not applicable.  
All data supporting the findings of this study are included within the article. Data will be made available from authors on reasonable request.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Rtp  
When the magnetic RTP map was examined (Figure 12), a number of intersecting linear and circular structures with NNW-SSE drift and variable amplitudes were discovered. These features are related to dykes, faults, shear zones, and geological formations. We see a string of positive and negative anomalies located in the inlier's south central, western, and eastern regions. Additionally, we highlight significant positive anomalies with WNW-ESE trend mostly associated with the Bou Azzer inlier ophiolitic complex and a NE-SW trend primarily associated with the Foum Zguid dyke.  
The interactions between the disclosed rock components and the negative and positive anomalies are complete, and these interactions can be used to identify possible mineraliza  
Figure 11: Geological fault density map of Bou Azzer inlier.  
Figure 12: Residual magnetic anomaly map of Bou Azzer inlier and adjacent regions reduced to the magnetic pole.  
tion zones. Basanites, trachyandesites, trachyte, and rhyolites that are intruded by a svenitic pluton are revealed as geologic objects (Figures 2 and 12) by the processed aeromagnetic survey (RTP). The lack of linkage is particularly noticeable in the extent's southwest, where a strong negative anomaly is related to sedimentary overburden. Additionally, the majority of the WNW-ESE lineaments seen on the RTP map were absent on the geology map. The most significant relationship between the magnetic and geological framework can be summed up as follows:  
1. Linear magnetic anomaly (LA1): runs along the inlier central part, corresponds to the ophiolitic complex of the Bou Azzer inlier with a WNW-ESE trend, and presents continuity towards to the west part, which can be considered as prospective areas for mineralization;
2. Linear magnetic anomaly (LA2): corresponds to the Foum Zguid dyke with a NE-SW trend, predominantly constituted by dolerite which matches to the high magnetic susceptibility;
3. Circular anomaly (CA1): corresponds to the Bleida copper deposit located at the south-eastern part of the inlier;
4. Circular anomaly (CA2): located at the southern part of the ophiolitic complex, the anomaly corresponds to the alkaline complex of Jbel Boho formed mainly by basanites, trachyandesites, trachyte, and rhyolites that are intruded by a svenitic pluton.
### Contributions Statement  
Inspired by the above research, a linear active disturbance rejection control (LADRC) scheme based on the diagonal recurrent neural network (DRNN) [25; 26; 27; 28] has been designed for adaptive control of the radar position servo system facing dead zone and friction nonlinearities in this paper. For one thing, the LADRC is used for real-time estimates and compensates for disturbance with unknown nondeterminacies. Moreover, an adaptive tracking differentiator (ATD) was also designed and introduced into the LADRC in order to eliminate the peak value of the control signal and reduce the spike impact of the control signal on the actuator. For another thing, DRNN is utilized to identify the sensitivity of the plant with respect to its input on-line and optimize parameters of the LSEF in the LADRC in real time. Moreover, the compound controller DRNN-LADRC can accomplish high accuracy control; additionally, it can also improve the resistance interference ability of the controller, forming an adaptive form to attain the optimal control result. The effectiveness of this method was proven by MATLAB simulation.  
The main contributions of this paper are summarized as follows:  
a. It is well known that increasing the speed factor in the traditional tracking differentiator (TD) will reduce its filtering effect, reproduce the noise of the input signal in the output signal, and produce the peak error of the output differential signal [29]. Then, it is necessary to increase the filtering factor, but this will increase the lag of the system output. The ATD, which can adaptively adjust the speed factor and filter factor according to the change rate of the input signal, is designed in this paper to deal with the contradiction between filtering performance and tracking speed.  
b. The LADRC has self-learning ability and better control performances by introducing the DRNN to tune the parameters of the LSEF in real time, adapting well to the changes of the plant such as parameter and disturbance changes, etc. Moreover, it has higher tracking accuracy for the radar position servo system than the LADRC based on the back propagation neural network (BPNN-LADRC) [30], since DRNN is found to be more suitable to identify dynamic nonlinear systems than those static neural networks such as BPNN, radical basis function neural network (RBFNN) and so on, that contains internal feedback loops which can store the plant information and utilize it at the later stage [25; 26].  
c. The LADRC containing the proposed ATD consumes less energy than the LADRC [22; 31]; the peak value of the control signal is removed, thus effectively suppressing the influence on the final control element, and it is of great significance in practical application.  
d. In the whole control system, only one parameter (controller bandwidth, denoted as \(w_{0}\)) needs to be adjusted; other arguments are automatically adjusted by the algorithm proposed in this paper.
# The Role of an Industrial Alkaline Wastewater in the Alkali Activation of Biomass Fly Ash
## 3 Results and Discussion
### Fresh Paste Characterisation
#### 3.2.2 Dissolution Tests  
Figure 4 presents the Al, Si, and Ca leaching from the precursors for the two studied compositions (reference and 25_Ef). The results show a much higher dissolution of aluminium and silicon from the precursors compared with calcium, and this regardless of the incorporation of the effluent. In the reference, and after 5 min, the Al dissolution concentration reached 33 mg/L, this being 4.4 times higher than the Si (7 mg/L) and 33 times higher than the Ca (1 mg/L). Longer contact time further enhanced the dissolution of Al and Si, these elements reaching 81 mg/L and 17 mg/L after 60 min. Interestingly, the incorporation of the alkaline effluent affected the Al and the Ca dissolution, while the Si concentration in solution was mostly unaffected in the first 20 min. A twofold increase was observed in the Ca dissolution after the first 5 min in the effluent-containing assay compared with the reference batch. The differences in the Ca dissolution concentration were further extended with the use of longer contact times. On the contrary, after 60 min lower dissolution of Al and Si was seen in the effluent-containing assay, the values were roughly 22% lower than those seen when using the NaOH activator (reference composition). These differences in the Al, Si, and Ca dissolution are attributed to the lower pH of the effluent (pH\(\sim\)11) compared with the 10 M NaOH (pH\(\sim\)14.5). When 25 vol.% of the NaOH is replaced by the effluent, a decrease in the pH is to be forecasted and this hinders the dissolution of Al and Si. In fact, previous studies have demonstrated that a decrease in the activator pH decreases the solubility of Al and Si [38]. Our results also show that the calcium concentration in solution increases substantially when the effluent is employed as activator, and this explains the faster setting and lower spread values seen in Figures 2 and 3.  
Figure 4: Dissolution of Al (**a**), Si (**b**), and Ca (**c**) for the studied compositions.
## 1 Introduction
Two GNN variants, Graph Convolutional Network (GCN) [21] and Graph Attention Network (GAT) [22], have been investigated in our model (GNNSDP: GNN -based Semantic Dependency Parser). GNNSDP has been evaluated on the SemEval 2015 Task 18 dataset which covers three languages (Chinese, English, and Czech) and contains three semantic dependency formalisms (DM, PAS, and PSD). Compared to the previous best one, our parser yields 0.3%  
Figure 1: Second-order, third-order, and fourth-order parts that appear in the example sentence. and 2.2% improvement in average labeled F1-score on English in-domain (ID) and out-of-domain (OOD) test sets, 2.6% improvement on Chinese ID test set, and 2.0% and 1.8% improvement on Czech ID and OOD test sets. Experimental results show that GNNSDP outperforms the previous state-of-the-art parser in three languages. In addition, GNNSDP shows greater advantage over the baseline in the longer sentence and PSD formalism (appearing linguistically most fine grained). To the best of our knowledge, this work is the first study to apply GNNs in semantic dependency parsing. Our code is publicly available at [[https://github.com/LiBinNLP/GNNSDP](https://github.com/LiBinNLP/GNNSDP)]([https://github.com/LiBinNLP/GNNSDP](https://github.com/LiBinNLP/GNNSDP)) (accessed on 16 April 2022).
## 3 Results and Discussion  
### Flow and Irrigation Intensity of Segments  
Eleven segments were determined. The measured (actual) flows of each segment were between 11% and 18% higher than the designed (theoretical) ones, whereas the CV of the initial segment was much higher than that of the final segments (Table 2).  
Wetted width showed an increasing trend in the initial segments (1 to 3), reaching 14 and 15 m in the final segments (Table 3), in agreement with the theoretical wetted diameter of sprinklers. Wetted length depends on the number of sprinklers grouped; segment 5 is the greatest (55.2 m, Table 3), with 20 sprinklers grouped. Likewise, the coarseness indices (CI) were 7 for all the segments since CI was lower than 7 (Equation (3)). Irrigation intensity increased from 5.7 to 77.4 mm h\({}^{-1}\) in the successive segments (Table 3), which allowed us to represent changes in irrigation intensity along the center pivot system.
## 4 Autopilot System  
### Line-of-Sight Guidance Algorithm  
It is demonstrated that the line-of-sight (LOS) guidance algorithm is suitably applied to the surface ships [29]. In addition, the LOS guidance algorithm plays an important role in accurate path tracking control of the AUV [30,31]. Since the path tracking cannot be achieved due to lack of lateral control for the under-actuated AUV, using the LOS guidance algorithm converts the lateral error to the heading movement, which means that the desired path tracking is indirectly achieved by heading control [32].  
In this study, the LOS guidance algorithm as shown in Figure 4 was adopted to estimate the desired displacements of the SFRM in the yaw and the pitch modes from the current position to the next waypoint in the earth-fixed coordinate system. The desired yaw and pitch angles can be represented as below:  
\[\Psi_{d}=tan^{-1}(\frac{Y_{k}-Y_{C}}{X_{k}-X_{C}}),k=1,2,\ldots,n \tag{21}\]  
\[\theta_{d}=tan^{-1}(\frac{Z_{k}-Z_{C}}{X_{k}-X_{C}}),k=1,2,\ldots,n \tag{22}\]  
where both the SFRM's current position \((X_{C}\), \(Y_{C},Z_{C})\) and the next waypoint position \((X_{k},Y_{k},Z_{k})\) refer to the earth-fixed coordinate system; \(\Psi_{d}\) and \(\theta_{d}\) denote the desired yaw angle and pitch angle, respectively; and \(n\) is the number of waypoints. Subsequently,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(Z_{w}^{\prime}\) & \(-0.013910\) & \(Y_{v}^{\prime}\) & \(-0.027834\) \\ \hline \(M_{w}^{\prime}\) & \(0.010324\) & \(N_{v}^{\prime}\) & \(-0.013648\) \\ \hline \(Z_{q}^{\prime}\) & \(-0.007545\) & \(K_{v}^{\prime}\) & \(-0.000584\) \\ \hline \(M_{q}^{\prime}\) & \(-0.003702\) & \(Y_{r}^{\prime}\) & \(0.005251\) \\ \hline \(Z^{\prime}{}_{\dot{w}}\) & \(-0.014529\) & \(N_{r}^{\prime}\) & \(-0.004444\) \\ \hline \(M^{\prime}{}_{\dot{w}}\) & \(-0.000561\) & \(Y^{\prime}{}_{\dot{v}}\) & \(-0.016186\) \\ \hline \(Z^{\prime}{}_{\dot{q}}\) & \(-0.000633\) & \(N^{\prime}{}_{\dot{v}}\) & \(0.000396\) \\ \hline \(M^{\prime}{}_{\dot{q}}\) & \(-0.000860\) & \(Y^{\prime}{}_{\dot{r}}\) & \(0.000398\) \\ \hline \(Z^{\prime}{}_{\dot{s}}\) & \(-0.005603\) & \(N^{\prime}{}_{\dot{r}}\) & \(-0.000897\) \\ \hline \(M^{\prime}{}_{\dot{s}}\) & \(-0.002409\) & \(Y^{\prime}{}_{\dot{s}}\) & \(0.005929\) \\ \hline \multicolumn{3}{c}{\(N^{\prime}{}_{\dot{s}}\)} & \(-0.002217\) \\ \hline \multicolumn{3}{c}{\(K^{\prime}{}_{\dot{s}}\)} & \(-0.000005\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hydrodynamic coefficients of the DARPA SUBOFF model.  
a sphere of acceptance of the LOS guidance algorithm [26] was required for selecting the next waypoint as follows:  
\[\left(X_{k}-X_{C}(t)\right)^{2}+\left(Y_{k}-Y_{C}(t)\right)^{2}+\left(Z_{k}-Z_{C }(t)\right)^{2}\leq R_{0}^{2} \tag{23}\]  
where the allowable radius \(R_{0}\) was set to be two times the SFRM's total length \(L\)[26].
# Wheat Ear Detection Algorithm Based on Improved YOLOv4
## 2 Materials and Methods
### Evaluation of Model Performance  
Some commonly used object detection metrics are calculated to evaluate the performance of the proposed model, including Precision, Recall, and Precision-Recall curve. Precision is the ratio of the number of true positives to the total number of positive predictions, which is defined in Equation (1).  
\[Precision=\frac{TP}{TP+FP}*100\% \tag{1}\]  
where _TP_ is True Positive number and _FP_ is False Positive number. Recall is the ratio of the number of true positives to the total number of actual objects, and it indicates how many positive cases the model could identify out of all positive cases, which is defined as  
\[Recall=\frac{TP}{TP+FN}*100\% \tag{2}\]  
where _FN_ is the False Negative number. The predicted bounding box is associated with a confidence score, which assess the probability that the object class appears in the bounding box. By setting a threshold, we can obtain a pair of _Precision_ and _Recall_ values. Because of the trade-off between Precision and Recall, it is unlikely to yield both peak _Precision_ and _Recall_ values at the same confidence threshold value. Commonly, _Precision_ and _Recall_ are combined to make a _Precision-Recall_ curve, where each point in the curve represents _Precision_ and _Recall_ for a certain confidence threshold value. Thus, the _Precision-Recall_ curve provides a model-wide evaluation based on the _Precision_ and _Recall_. With this curve, Average Precision (_AP_) is derived. _AP_ is the precision average across all recall values  
Figure 1: Improved YOLOv4 neural network architecture.  
between 0 and 1 at various IoU thresholds. By interpolating across all points, \(AP\) can be interpreted as the area under the curve. It serves as a better evaluation of the performance of the model, encapsulating both _Precision_ and _Recall_. Mean Average Precision (\(mAP\)) is an extension of \(AP\), which is defined as the mean of \(AP\) across all K classes, expressed in Equation (3). In this case, only one class is interested in the research, so \(mAP\) turns out to be \(AP\).  
\[mAP=\frac{\sum_{i=1}^{K}AP_{i}}{K} \tag{3}\]  
Another important performance metric is the \(F_{1}\) score. It is a weighted average of the Precision and Recall, whose value ranges from 0 to 1, where 1 means the highest accuracy, defined as  
\[F_{1}=\frac{Precision\times Recall}{\frac{(Precision+Recall)}{2}} \tag{4}\]  
Finally, in order to evaluate the performance of detection speed, Frames Per Second (FPS) is calculated. All of these metrics are calculated and compared with the state-of-the-art method. It is believed that these metrics are enough to evaluate the performance of the proposed model.
## 3 Methodological Framework
### Data Availability
#### 3.2.2 Data for Energy Demand and Performance
For example, yearly reconciliation aggregates surplus energy credits over the year to offset annual consumption costs. Conversely, monthly or daily reconciliation calculates balances over shorter periods, which can reduce the economic benefit for users when energy generation and consumption fluctuate significantly. The results highlight how these policies influence the building's energy costs under both low-usage conditions (using 2021 data during the COVID-19 period) and high-usage conditions (using 2024 data under stable operation). These policies align with established methodologies and are supported by the literature, such as the comparative assessment of net metering and net billing systems by [PERSON] and [PERSON] (2015) [56]. By considering different reconciliation strategies and energy export schemes, the analysis provides a comprehensive evaluation of the economic performance of the PV system across varying building usage scenarios, different socioeconomic conditions affecting energy prices, as well as different billing schemes. The large number of parameters and potential combinations to be considered highlights the advantages of scenario-based approaches over the traditional solutions outlined in Section 2.1.