esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
This study establishes an HCNG-CCS-P2G system and combines day-ahead DRO and intraday MPC rolling optimization methods to explore the adjustment effects of DHB in the day-ahead-intraday scheduling of the IES, aiming for the robust and economic operation of the IES. The simulation results verify the effectiveness of the proposed strategies, with the findings summarized as follows:  
* HCNG-CHP, as an integration of electricity, heat, gas, and hydrogen, possesses strong adjustment capabilities. For the day-ahead system DRO scheduling, the DHB strategy achieved a 0.87% reduction in total cost compared to the traditional fixed hydrogen blending ratio. This provides system operators with a direct and quantifiable method to improve the profitability and flexibility of complex, multi-energy systems.
* The THB strategy, proposed based on dynamic hydrogen blending with step characteristics, simplifies the secondary constraints of the dynamic hydrogen blending model in intraday scheduling. This not only reduces costs but also reduces the solving time by 80%, making it a good engineering approximation of CHB. This significant reduction in computational demand bridges the gap between theoretically optimal models and the fast-response requirements of live industrial control systems, making real-world implementation feasible.
* In improving the tracking performance of intraday MPC rolling optimization, further adjustment of the hydrogen blending ratio during the day led to a decrease in upper-layer and lower-layer tracking errors by 4.25% and 2.37%, respectively. This indicates that dynamic hydrogen blending can more effectively track the day-ahead scheduling plan, achieving a balance between economy and robustness. This leads to a more robust and stable energy system, minimizing costly deviations from dispatch plans and enhancing the reliability of energy delivery.
* Compared to CHB, THB demonstrates a more pronounced effect on power regulation at shorter timescales, leading to a 2.43% reduction in the lower-layer tracking error. This suggests that THB can effectively minimize the tracking error propagated from the upper layer to the lower layer within the MPC framework. Conversely, CHB is better suited for adjusting energy types over longer time horizons. This finding serves as a functional user guide for system operators, enabling them to select the optimal control strategy that effectively balances real-time stability with long-term economic efficiency.  
Conceptualization, Y.X. and X.Y.; methodology, Y.X.; software, Y.X.; validation, Y.X., Q.X. and K.W.; formal analysis, K.W.; investigation, Y.Z.; resources, X.Y.; data curation, K.W.; writing--original draft preparation, Y.X.; writing--review and editing, Q.X.; visualization, K.W.; supervision, X.Y.; project administration, Q.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
## 4 Conclusions  
High-capacity magnetic EDA-MPMs functionalized with amino groups were successfully synthesized and characterized by SEM, VSM, XRD and FT-IR. The EDA-MPMs were then employed for the removal of Cr(VI) from an aqueous solution. The results demonstrated that the initial solution's pH had an apparent effect on the adsorption capacity and the optimum pH value for Cr(VI) adsorption was found at pH = 2. It was revealed that the adsorption process was a pseudo-second-order reaction and the equilibrium was established within 120 min. Langmuir isotherm model was in good agreement with the experimental data and the maximum adsorption capacity was evaluated to be 236.9 mg/g at 298 K, which was higher than those of other adsorbents reported in the literatures. Thermodynamic results indicated that the adsorption was a spontaneous and exothermic process. In addition, the reusability data during the consecutive four times adsorption-desorption processes illustrates that of EDA-MPMs is a promising absorbent for application. The above result indicates that the EDA-MPMs could be employed as a low cost adsorbent for the removal of both Cr(VI) from the aqueous solution.  
Supplementary Materials: Supplementary materials can be found at www.mdpi.com/1996-1944/8/12/5461/s1.  
**Acknowledgments:** This work was supported by the National Natural Science Foundation of China (51263013), the Science and Technology Foundation of Higher Education of Inner Mongolia (NJZC14273) and the Natural Science Foundation of Inner Mongolia (2014 MS0215, 990301-5).  
**Author Contributions:** [PERSON] used the ethylenediamine-functionalized magnetic microspheres (EDA-MPMs) for removal of Cr(VI) from aqueous solution. [PERSON] conducted the synthesis of EDA-MPMs and the adsorption. [PERSON] and [PERSON] contributed to characterization of the materials and revised the manuscript. All authors contributed to the discussions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Burst Detection in Water Distribution Systems:
###### Abstract  
Developing data-driven models for bursts detection is currently a demanding challenge for efficient and sustainable management of water supply systems. The main limit in the progress of these models lies in the large amount of accurate data required. The aim is to present a methodology for the generation of reliable data, which are fundamental to train anomaly detection models and set alarms. Thus, the results of the proposed methodology is to provide suitable water consumption data. The presented procedure consists of stochastic modelling of water request and hydraulic pipes bursts simulation to yield suitable synthetic time series of flow rates, for instance, inlet flows of district metered areas and small water supply systems. The water request is obtained through the superimposition of different components, such as the daily, the weekly, and the yearly trends jointly with a random normal distributed component based on the consumption mean and variance, and the number of users aggregation. The resulting request is implemented into the hydraulic model of the distribution system, also embedding background leaks and bursts using a pressure-driven approach with both concentrated and distributed demand schemes. This work seeks to close the gap in the field of synthetic generation of drinking water consumption data, by establishing a proper dedicated methodology that aims to support future water smart grids.  
a 2020 10 8219 10.3390/app10228219
# An Efficient Explainability of Deep Models on Medical Images
## 6 Conclusions  
This research work is part of the explainability of deep learning results on chest X-ray images. In this context, we used pre-trained deep learning models such as MobileNetV2, VGG16 and others, as well as two separate datasets, to evaluate the performance of these models. The results revealed that MobileNetV2 was the best model for the first dataset, while VGG16 was the best for the second dataset. These models demonstrated significant classification performance, highlighting the effectiveness of convolutional neural networks (CNNs) in classifying chest X-ray images.  
Then the visual explainability methods are applied such as GradCAM and LIME to better understand the decisions made by these models. The results showed that GradCAM was the most effective visual explainability method, closely followed by LIME, while Vanilla Gradient also demonstrated some usefulness. Doctors rated these methods as more understandable, satisfying, detailed, Exhaustiveness and accurate. These findings are of great importance in the healthcare field, as they provide clear, interpretable explanations of the results obtained by deep learning models. Explainability boosts doctors' confidence in the use of these models and facilitates their adoption in critical medical applications.  
This study opens up vast scientific prospects in the short and long term. Below are the main perspectives that deserve to be explored as a result of this study:  
Identifying and correcting biases in deep learning models to ensure more accurate and fair results.  
Exploring alternative methods of explainability such as text-based explanation through examples and others. Investigation of these alternative methods can provide additional information on the decisions made by deep learning models, offering an even more complete and interpretable picture.  
Using larger datasets enables the performance of deep learning models and explainability methods to be evaluated on a larger scale, offering more robust validation of the results obtained.  
Conceptualization, methodology, S.K., S.A.M., S.S., L.B. and B.S.; software, validation, formal analysis, investigation, resources, data curation, S.K., S.A.M. and L.B.; writing--original draft preparation, writing--review and editing, visualization, S.K., S.A.M. and S.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This study uses the following openly available datasets: COVID-19 Radiography and COVID CCR Image Dataset Research available on Kaggle Dataset [6].  
The authors declare no conflicts of interest.
# Metasurface Source Antenna Gain Improvement Using Simple Side Metal Structure
###### Abstract  
As metasurfaces are in the spotlight, research is being conducted to incorporate them into transmitarray (TA) antennas. Among these, as an attempt to create a low-profile design, a patch antenna classified as low-gain can be utilized as an appropriate source antenna. However, for high efficiency of the TA, the gain of the source antenna must be fundamentally improved. For this, a simple side metal structure was applied to a metallic cross-type slot transmitarray. This acts as a resonant element and reflector by utilizing the electromagnetic wave radiated from the source antenna. The changes in the center frequency and gain due to the application of the side metal structure to the source antenna were analyzed. The gain of the source antenna was improved by a total of 4.63 dB. This is expected to be applied to create various source waves and to conduct future research on improving the gain in transmitarray antennas.  
metasurface; transmitarray; side metal structure; sidewall; side plate; gain enhancement; patch source +
Footnote †: journal: _Sensors_  
0
Footnote 0: Copyright** © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## 7 Future Research Directions
### Security and Privacy  
The integrated network may be vulnerable to malicious attacks due to open connections and congested topologies that span a mission-critical range via purposeful jamming or disruptions. Since UAVs are constantly unattended, they can be easily seized or assaulted. Security is a critical issue in UAV-assisted systems. A secure and lightweight component is required to avoid malicious modification, such as eavesdropping, man-in-the-middle attacks, etc. To address cyber-physical security gaps in UAV communication systems, a zero-sum network interdiction game was created. The system considers the case of a vendor and an attacker trying to move UAVs from one point to another. This game can successfully ensure the cyber security of the UAV delivery system. Fake signal solutions were also suggested to keep UAVs safe in cellular-connected applications. A spoofer strategy can be used to create fake GPS signals that are almost indistinguishable from original GPS signals, making it more difficult for cyber attackers to hack into the system. Within the vast scope of space-air-ground coordinate systems, Software-defined networking (SDN) controllers are capable of overseeing assets and controlling operations. It is critical to protect SDN controllers from various cyber-attacks that allow adversaries to wiretap data and control signals transmitted through UAV framework radio connections. Cyber-attacks on UAV frameworks have been documented. Cyber-security is still a major issue in the real-world application of UAVs. Convenient tactics and counter-mechanisms must be planned ahead of time to counteract dangerous cyber-attacks. The important point to emphasize here is that the mobility of UAVs must be controlled by security. This keeps the routing positions under network management. Furthermore, the deployment points of the deployed UAV as UEs or BSs must be secure to prevent any attacks aimed at stealing users' communication data.
# Fault Diagnosis of a Solenoid Valve Based on Multi-Feature Fusion
## 5 Fault Diagnosis Example of an Electromagnetic Reversing Valve
### Result Analysis  
The research group used the RBF-based C-SVC to identify and classify the fused feature vectors, with the corresponding recognition results illustrated in Figure 13. The results indicate that, when the feature vector is constructed by fusing the time-frequency analysis features and the time-domain parameter features, the training accuracy rate is 100%, the testing accuracy rate is 100% and the five-fold cross-validation accuracy rate is 92.11%. The training process of the SVM is a process of continuous optimization, where the feature vector samples are input into the SVM for training, and after 1000 epochs, the accuracy rate tends to be stable. The trials show that using C-SVC on the basis of RBF can accurately identify the four states of the electromagnetic directional valve (normal valve, spring broken, spool stuck and slightly stuck spool).  
To support the findings, the research group challenged the performance of the method against [1], which employed a BP neural network, and [27], which utilized time-frequency analysis to identify the failure mode. The training target error and training times of BP neural network were set by the grid search method. The corresponding results are presented in Figure 14. The latter figure reveals that the BP network recognition accuracy rate was 84.78%, and the C-SVC recognition accuracy rates on the basis of time-frequency analysis and on the combination of time-frequency analysis and time-domain analysis were 91.3% and 100%, respectively.  
Figure 13: Multi-feature fusion prediction results. (**a**) Multi-feature fusion training set test results. (**b**) Multi-feature fusion test set test results.
# Service-Driven Dynamic Beam Hopping with Resource Allocation for LEO Satellites
## 3 A Cooperative Multi-Agent RL for Single-Satellite Beam Hopping
### MDP Model
The joint action in time slot \(t\) is expressed as  
\[A_{t}=(a_{1}^{1},a_{t}^{2},\cdots,a_{t}^{k},\cdots,a_{t}^{k}) \tag{19}\]  
\[a_{t}^{k}=(P_{t}^{k},\mathbf{X}_{t}^{k}) \tag{20}\]  
\[P_{t}^{k}=\{p_{n}|n=1,2,\cdots,N\} \tag{21}\]  
\[\mathbf{X}_{t}^{k}=[x_{t}^{1},x_{t}^{2},\cdots,x_{t}^{m},\cdots,x_{t}^{M}]^{T},x_{ t}^{m}\in\{0,1\},\sum_{m\in\mathcal{V}}x_{t}^{m}=1 \tag{22}\]  
where \(A_{t}\) represents the joint action of all cooperative multi-agents in time slot \(t\). \(a_{t}^{k}\) represents the action of the \(k\)-th agent in time slot \(t\). \(P_{t}^{k}\) represents the \(N\) power allocation schemes of the \(k\)-th agent in time slot \(t\), and \(X_{t}^{k}\) represents the \(M\) beam position illumination schemes of the \(k\)-th agent in time slot \(t\). According to the above definition of the action space, it can be seen that the size of the cooperative multi-agent's overall action space is \(M\times N\). Compared with the overall action space size \(C_{M}^{K}\times N\) of the single agent, the multi-agent architecture can effectively reduce the on-board memory. Figure 4: Cooperative multi-agent VDN algorithm framework for multi-beam satellite. Reward: Agents receive rewards after taking actions. To minimize the long-term optimization target, we use user satisfaction and the total packet delay as immediate rewards. The reward value after the agent takes an action in time slot \(t\) is defined as  
\[R_{t}=r_{t}^{k}=(1-\alpha)\sum_{m\in\psi}\frac{C_{t}^{m}}{D_{t}^{m}}-adelay_{t} \tag{23}\]  
where \(R_{t}\) represents the shared reward of all cooperative multi-agents in time slot \(t\), and \(\alpha\) is the weight coefficient between user satisfaction and delay. From the above text, we can see that there are action constraints among agents, and service beams that operate simultaneously cannot illuminate adjacent beam positions. Otherwise, serious co-channel interference problems will occur. Therefore, the trained agents will avoid illuminating adjacent beam positions when making decisions.
# Shear Resistance Assessment of the Y-Type Perfobond Rib Shear Connector under Repeated Loadings
## 1 Introduction
In addition, the cyclic loads are applied to be fully reversed cyclic loadings to simulate the loading conditions during earthquakes. This study investigates the deteriorating shear strength capacities of Y-type perfobond rib shear connectors under the one-directional repeated loadings, in which the rib height is designed to be tall and 100 mm high (the total height is 160 mm including the root and dowel hole) to provide high shear resistances. Therefore, the embedded concrete slab in test specimen is 280 mm deep and the concrete compressive strength is designed to be about 60 MPa. The applied testing loads are designed to be one directional without any reversal loads, which may be experienced in the anchor systems of tension cables. It is planned to suggest how to predict the residual shear strength of the shear connectors after repeated one directional loads. The design formula for the residual shear strength after the repeated loads is proposed based on the probabilistic characteristics of the nominal shear strength as well as the strength degradation with the energy dissipations due to the repeated loads. The residual shear strength formula is proposed according to the target reliability levels. The proposed procedure to generate the design formula of the residual shear strength may be applied to various types of shear connectors under the repeated loads.
# Rain Monitoring with Polarimetric GNSS Signals: Ground-Based Experimental Research
## 3 Results and Discussion
### Statistical Results of G22 Satellite in 2015  
#### 3.2.1 Phase Shift in No-Rain and Rainy Days  
Data from G22 is also analyzed. For the GPS antenna, this satellite's elevation fluctuates mainly between 0\({}^{\circ}\) and 3\({}^{\circ}\), which makes it difficult to translate its time series into elevation series. Since its azimuth varies significantly, we consider converting time series into azimuth series. Here, data with azimuth from 44\({}^{\circ}\) to 72\({}^{\circ}\) is selected. By calculation, data of 67 days are available, of which five days have rain. In addition, daily data lasts 73 min in this azimuth range. By computing no-rain days' data, the average \(\overline{\Delta m}\) and standard deviation \(\sigma_{\Delta m}\) can be obtained, shown in Figure 9.  
In Figure 9, its standard deviation ranges from 1.06 mm to 2.95 mm, with a mean of 1.78 mm, which is also quite small [24]. This reveals that the multipath effect on G22 is basically stable. Furthermore, by statistical analysis, the \(\Delta\phi_{+}(\beta)\) of G22 are all zero on no-rain days while not zero on rainy days.  
Figure 10 displays the variations of corrected phase shift and with azimuth (\"black+\" line), whose value can be seen according to left vertical axis. Also, the value of \(\Delta\phi_{+}(\beta)\) (red line) can be seen from right vertical axis. As shown in this figure, since their \(\Delta\phi_{C}(\beta)\) are both within \(\pm 2\sigma\), their \(\Delta\phi_{+}(\beta)\) are zero, which also coincides with the facts. Additionally, the other no-rain days' results are similar with these two cases.  
Figure 11 shows the results of two rainy days. It is obvious that their phase shift exists, whose maximum can be up to 6 mm. However, whether there are raindrops along the GPS rays still needs further research based on other data.  
Figure 9: Statistical characteristics of G22 data in all no-rain days.
# A Comprehensive Review of Machine Learning Models for Optimizing Wind Power Processes
## 4 ML Models Classification for Wind Power
### ML Models for Optimized Wind Power Concept  
The previous research on wind energy and the application of ML algorithms has raised the question of how many of these address the optimization of wind energy production processes. After adding the word \"optimization\" to WOS searches, new perspectives were obtained on the applicability of each ML model to optimizing wind energy systems. These results are summarized in Figure 6.  
Linear regression generated 19 articles, meaning it is less used for directly optimizing turbine performance. This result reflects that the linear regression model cannot capture the complexity of the phenomena behind the parameters influencing wind energy production and, therefore, its optimization. Logistic regression is identified with only three articles and is rarely applied in this context.  
KNN and decision tree models, each with 11 articles, show that they are incompatible with optimization in wind energy production. However, SVM recorded 68 articles in the last 5 years. This value demonstrates the usefulness of these algorithms in optimizing turbine performance and energy production.  
With 49 articles, random forest demonstrates the model's ability to handle large datasets. This model is suitable for optimizing wind turbine performance because it manages data complexity. Similarly, with 174 articles, ANN shows the ability to learn from complex data, being extensively studied in the problem of optimizing wind energy production. Likewise, with 83 articles, CNN demonstrates the model's suitability for analyzing wind turbine sensor signal data to improve optimization processes.  
RNN was identified in the literature with 52 articles. These address short-term forecasts and modeling of the dynamics of wind turbines. Regarding boosting models, AdaBoost (7 articles) and gradient boosting (37 articles) are less debated in the literature but help improve performance by combining multiple classifiers. The XGBoost, with 31 articles, is considered an efficient model for optimizing ML processes, and it is being applied to improve wind energy predictions. Clustering models such as K-Means, with 73 articles, allow for the grouping of data based on common characteristics, and they are used for  
Figure 6: ML models for wind energy optimization.  
segmenting and analyzing turbine behavior. DBSCAN (10 articles) is used to identify densely clustered data groups, and PCA, with 35 articles, helps reduce data dimensionality.  
Therefore, ANN, CNN, RNN, random forest, and SVM models are the most widely used in the field due to their ability to learn from complex data and optimize energy generation processes. This detailed analysis shows the superiority of the naive Bayes model in wind energy optimization, confirmed by the 265 articles identified in the WOS.
## 2 Materials and Methods
### Global Path Smoothing Optimization
#### 2.2.2 Improved Path Smoothing  
To eliminate the extra turning points in the paths and achieve simplified global paths, a path smoothing algorithm is proposed in this study. Based on the paths obtained by the traditional A* algorithm, the non-essential path points are eliminated, and the critical path points are retained to obtain smoothed and optimized critical paths. The specific steps of this algorithm are shown in Figure 4 below. After eliminating the non-essential path points, the number of points in the optimized path set is reduced. In subsequent steps of the fusion algorithm, optimized global paths are collected and added to the path point set according to the unit grid length of the A* algorithm.