esa-sceva/satcom-chunk-collection huggingface dataset:
# A UAV-Assisted Stackelberg Game Model for Securing IoMT Healthcare Networks
## 4 Simulation Setup and Experimental Results
### Analysis of Results
#### 4.3.1 Packet Delivery Ratio (%) vs. No of Nodes  
Figure 6 shows the packet delivery rate (PDR%) results of different routing protocols in IoMT-assisted UAV networks with varying numbers of nodes (20, 40, 60, 80, and 100). The protocols tested include LPNRP, TPH, RPH, FDTRP, DADRP, and SBAODV. The PDR% was evaluated as the ratio of the total number of successfully delivered packets divided by the total number of packets sent. At the 20-node number, the SBAODV protocol achieved a PDR% of 97%, significantly higher than the other protocols, with DADRP being the closest with a PDR% of 92%. As the number of nodes increases, the performance of all protocols tends to decrease. However, the SBAODV protocol maintains its superiority, achieving a PDR% of 94.29% on 40 nodes, 91.99% on 60 nodes, 89% on 80 nodes, and 86% on 100 nodes. This can be attributed to its ability to adapt to network topology changes and dynamically select the best path to the destination based on the available network resources.  
Figure 5: Experimental scenario in NS2 environment.  
On the other hand, other protocols such as LPNRP, TPH, RPH, FDTRP, and DADRP show a decreasing trend in PDR% as the number of nodes increases. This may be because these protocols suffer due to the presence of black hole attackers, which destroys the paths and harms the devices as a result, the PDR% of other schemes decreases. In contrast, the proposed scheme adopted the Stackelberg game security model, efficiently detecting black hole attacks and maintaining the PDR% even at higher nodes.  
Moreover, the proposed SBAODV protocol utilized the Stackelberg game model, which can detect black hole attacks and recover affected nodes in case of any attempt because of its best strategies. This feature enhances the network's security and ensures safe and reliable data transmission in IoMT networks. The results of this study can guide the selection of appropriate routing protocols for specific applications based on their performance characteristics and requirements. Further, the PDR% results justify the effectiveness of SBAODV routing protocol in IoMT-assisted UAV networks in ensuring the efficient and reliable transmission of data critical for improving the quality of healthcare services.
## 2 The CNES PPP-WIZARD Demonstrator
### The User Side of the Demonstrator
#### 2.3.2 Instantaneous Ambiguity Resolution  
Undifferenced ambiguity resolution is now a well-known concept, at least in the dual-frequency GPS/Galileo context, and its performance is well established (see references [9; 10; 11; 39], for instance). Nevertheless, it suffers from an important drawback: the convergence time, which is intrinsic to the use of a Kalman filter to estimate the ambiguities and can be, in this case, in the order of tens of minutes.  
Multi-frequency ambiguity resolution is relatively new, in particular with the use of more recent constellations such as Galileo or Beidou. Recently, a new technique called OEUFS (Optimal Estimation using Uncombined Four-frequency Signals) has been proposed [30]. The idea is to form a consistent set of undifferenced and uncombined phase biases that can be processed optimally at the user level. Depending on the constellations and biases used, instantaneous widelane ambiguity resolution or even full ambiguity resolution can be achieved, leading to instantaneous centimeter-accuracy positioning. We recall here the main ingredients of the method and propose a way forward to further improve its robustness.  
First, we make a noise analysis for a single satellite. We assume that we can solve for all the widelane ambiguities, and we compute the noise of all the widelanes and extra-widelanes for each constellation, as done in [16; 40]. From this analysis, it is possible to select the extra-widelanes with the smallest noise in order to be able to fix their ambiguities. From this point, it is possible to add these ambiguities as constraints for the receiver position estimation (see [31]). Table 2 gives the expected noises of the receiver position using such noise-optimal extra-widelane fixed ambiguities. Using Galileo and Beidou is particularly interesting with an expected range noise of around 20 cm. We assume here a noise of 0.5 m for the code and 0.003 m for the phase.  
Second, we work at the user level and we assume that the code and phase biases are available for each signal. The position of the user is estimated along with the phase ambiguities, and we obtain the following results:  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Constellation** & **Ranging Noise of Optimal Widelanes Combination (cm)** \\ \hline GPS (L1, L2, L5) & 30 \\ GAL (E1, E5a, E5b, E6) & 19 \\ BEI-2 (B1I, B2I, B3I) & 33 \\ BEI-3 (B1I, B1C, B2a, B2b, B3I) & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Single satellite noise analysis.  
1. all the widelanes can easily be solved;
2. the obtained solution is accurate enough to enter the narrowlane convergence domain, leading to centimeter accuracy.  
If the ambiguity resolution for the widelanes step can easily be achieved using any method, a partial ambiguity resolution method such as the BIE is preferred to solve for the narrowlane (but can also be applied to solve for the widelanes). The entire process is summarized in Figure 3.
## 6 Conclusions  
This paper has presented a GA-based optimization method for a multi-vector power generation system with hydrogen storage. The analytical model was developed based on actual experimental tests. By the GA algorithm, the energy vectors were regulated to achieve high renewable consumption and better use of energy storage components. The findings can be summarized as follows:1. In this study, actual demands fluctuated over the four days and the wind power was also intermittent. By using the energy storage system with hydrogen fuel cells, the demand was balanced.
2. During the operating period, the loads were levelled by the energy storage system to consume wind power locally.
3. By the GA optimization scheme, the wind energy is converted into either chemical (in hydrogen) or electrical forms with relatively high conversion efficiency.
4. The developed technologies provide a new methods to operate a grid-tied power network where intermittent renewables can be effectively used. This will encourage the uptake of wind, hydrogen, and fuel cells in power generation.  
This study has also proved that the proposed GA method is an effective way to search for the best solution when multi-objectives or variables are applied in the optimization problem. Moreover, it is superior to conventional methods in non-linear optimization problems. However, similar to the swarm optimization, the GA may also be trapped in local optimums while a global optimum may not be found.  
[PERSON] applied the GA as the optimization strategy on the determination of the operational scheme for the multi-vector energy system. [PERSON] proposed the integrated energy system consisting of wind turbine generator, water electrolyzer, PEM fuel cell module, DC/DC, DC/AC and AC/DC power converters. [PERSON] calculated the energy output and efficiency of the PEM fuel cell module and the energy flow of the system.  
This work is supported by National Natural Science Foundation of China under the grants NSFC 51867007 and 51867005.  
The authors declare no conflict of interest.
## 1 Introduction
### Main Contributions  
This research makes significant contributions to both the academic field and the electrical power sector. From an academic perspective, it introduces the PGA to the coordinated active and reactive power control of WTs within MGs while optimizing economic, technical, and environmental performance indicators. This novel application addresses clear gaps in the existing literature, as most studies have primarily focused on active power management or on isolated objectives, often neglecting a comprehensive assessment of system performance. The proposed optimization methodology is designed to be flexible and adaptive to changing operational priorities. As previously mentioned, its effectiveness is validated through testing on 33- and 69-node MGs, performing statistical analyses regarding solution quality, repeatability, and computational performance. A comparative study against six well-established metaheuristic algorithms is also conducted under standardized evaluation conditions, ensuring fairness and reliability in the benchmarking process.  
From the perspective of the electrical power sector, this study provides a practical and intelligent tool for improving MG operation under real-world conditions while integrating wind energy. The proposed strategy enhances decision-making in dynamic environments by addressing key challenges such as renewable energy curtailment and the need for reactive power support. By simultaneously minimizing operating costs, technical losses, and \(CO_{2}\) emissions, our methodology supports sustainability goals and the global transition to cleaner energy systems. In addition, the optimization framework accounts for realistic technical constraints (e.g., voltage, current, and generation limits), as well as for specific considerations regarding islanded operation. These characteristics ensure its applicability within regulatory and operational settings, contributing to the advancement of next-generation energy management solutions for distributed renewable systems.
###### Abstract  
This research developed a comprehensive computer model for a lab-scale Slurry Bubble Column Reactor (SBCR) (0.1 m D\({}_{t}\) and 2.5 m height) for Fischer-Tropsch (FT) synthesis under flexible operation of synthesis gas load flow rates. The variable loads of synthesis gas are set at 3.5, 5, 7.5 m\({}^{3}\)/h based on laboratory adjustments at three different operating temperatures (483, 493 and 503 K). A set of Partial Differential Equations (PDEs) in the form of mass transfer and chemical reaction are successfully coupled to predict the behavior of all the FT components in two phases (gas and liquid) over the reactor bed. In the gas phase, a single-bubble-class-diameter (SBCD) is adopted and the reduction of superficial gas velocity through the reactor length is incorporated into the model by the overall mass balance. Anderson Schulz Flory distribution is employed for reaction kinetics. The modeling results are in good agreement with experimental data. The results of dynamic modeling show that the steady state condition is attained within 10 min from start-up. Furthermore, they show that step-wise syngas flow rate does not have a detrimental influence on FT product selectivity and the dynamic modeling of the slurry reactor responds quite well to the load change conditions.  
Power to Liquid; Fischer-Tropsch; dynamic modeling; lab-scale +
Footnote †: journal: Applied Science and Technology
## 2 Materials and Methods
### Patch Sampling  
The proposed patch-based approach helps us to avoid severe modifications in input images (e.g., down-sampling and resizing), like in the original work of [14], which can significantly distort original images and lead to probable information loss. Nevertheless, the use of patch-based methods also has some issues to consider, e.g., they can suffer from class imbalance, resulting in models biased towards one class over another. Therefore, the process of patch extraction should be controlled and guided with some predefined set of rules. To achieve this, we used a modification of a balanced sampling strategy, where an equal number of patches indicating both classes (lesion and the rest of the brain) is usually extracted from each image. In our case, as we are specifically interested in HE prediction, we want to put more emphasis on the lesion expansion area; therefore, we increase the representation of this class up to 80% of all extracted samples. We define this hematoma growth area as the subtraction between follow-up and baseline lesion masks. This process is illustrated in Figure 2. In addition, we limit the non-lesion patch sampling within the brain mask, to avoid extracting patches from the image background, which  
Figure 1: Schematic process of image generation with synthetically induced lesion deformation. (**A**) Original baseline lesion mask. (**B**) The target lesion mask (shown in white) obtained after a binary erosion operation on the original lesion mask (shown in gray). (**C**) Deformation field produced after registration of the original mask to the target lesion mask. Different colors of the image represent values and directions of all 3D vectors of the DVF. (**D**) Original baseline CT image. (**E**) The resulting CT image obtained after applying the deformation field to the original CT image.  
does not support any valuable information for the network. In our experiments, we used patches of size 64 \(\times\) 64 \(\times\) 64, as a compromise between the ability of the input sample to fit into memory and the useful content of the patch, preserving more information about the lesion and the growth location together with its shape and size. Moreover, the selected patch size covers the full growth area in most of the cases.
## 2 Materials and Methods  
### Reagents and Materials  
Silica gel powder (Qingdao Guichuang Fine Chemical Industry Co., Ltd., Qingdao, China) was used as the silica source. Sodium meta aluminate (NaAlO\({}_{2}\), Sinopharm Chemical Reagent Co., Ltd., Shanghai, China) was used as the aluminum source. The compositions of Na\({}_{2}\)O and Al\({}_{2}\)O\({}_{3}\) in NaAlO\({}_{2}\) were 39.5% and 48.0%, respectively, which were tested with ICP-OES. Tetraethyl ammonium hydroxide (TEAOH, 25%), the structure-directing agent, was supplied by Kente Catalysts Inc. (Hangzhou, China) Sodium hydroxide (NaOH, Sinopharm Chemical Reagent Co. Ltd., Shanghai, China) was used as the alkali source. Ultrapure water with a resistance of 18.2 M\(\Omega\).cm (25 \({}^{\circ}\)C) was obtained by Milli-Q Preference (Molsheim, France). Eight standards of neonicotinoid insecticides, namely, dinotefuran, thiacloprid, thiamethoxam, flonicamid, imidaclothiz, clothianidin, imidacloprid, and acetamiprid at the concentration of 100 mg/L in acetonitrile, were purchased from Alta Scientific Co., Ltd. (Tianjin, China) and maintained at \(-\)18 \({}^{\circ}\)C, diluted with acetonitrile before analysis. HPLC-grade reagents purchased from Merck (Darmstadt, Germany), including acetonitrile and methanol, were used for HPLC-MS/MS analysis. Glass microfiber filter (Grade G/F) with a diameter of 47 mm was purchased from Whatman International Ltd. (Maidstone, UK).
## 3 Methodology
### Building Prediction Models
#### 3.3.3 Multilayer Perceptron (MLP)  
MLP [39] is a multi-layer structure composed of an input layer, a hidden layer, and an output layer, the input layer receives data, the hidden layer processes the data, and the output layer is responsible for the final output of the model. The MLP experimental process in this study consists of the following steps.  
1. Set the initial weight and deviation value
2. Input training data and target data
3. Calculate the error between the expected output and the target
4. Adjust the weight and update the network weight
5. Repeat step (3)-step (4) until the end of learning or convergence.
# Evaluation of Glottal Inverse Filtering Techniques on
## Appendix A  
Two tables are presented summarizing the ranges of parameters and the characteristics of the datasets used in the papers present in the literature review of this study.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline
**Paper** & **Pitch** & **Vowels** & **Type** & **Sample Freq.** & **Phonation** & **Other** \\ \hline
[17] & 100:5:240 Hz & 14 & Synthetic (LF) & \(16\) kHz &
\begin{tabular}{l} Q: 0.3:0.05:0.9 \\ \(\alpha_{m}\): 0.55:0.05:0.8 \\ \end{tabular} & SNR (dB): 10:10:80 \\ \hline
[20] & 60:20:180 Hz & \
\begin{tabular}{l} /a/ //\(\theta\)/ \\ /i // /y/ \\ \end{tabular} & Synthetic (LF) &
\begin{tabular}{l} Q: 0.4:0.05:0.9 \\ \(\alpha_{m}\): 0.6:0.05:0.9 \\ \end{tabular} & male \\ \hline
[20] & Flat pitch & /a/ & Real &
\begin{tabular}{l} Decreasing Oq \\ \end{tabular} & \\ \hline
[22] & \begin{tabular}{l} Two values for \\ the pitch period \\ for female \\ and male \\ \end{tabular} & /a/ & Synthetic [36] & 8 kHz & \
\begin{tabular}{l} Breathy \\ Normal \\ Pressed \\ \end{tabular} &
\begin{tabular}{l} 8 th order \\ All-pole filter \\ Male \\ Female \\ \end{tabular} \\ \hline
[23] & \
\begin{tabular}{l} F: 133–200 Hz \\ M: 67–100 Hz \\ \end{tabular} & /a/ / i/ & Synthetic [36] & 8 kHz &
\begin{tabular}{l} Male \\ Female \\ \end{tabular} \\ \hline
[24] & \begin{tabular}{l} /a/ \\ \end{tabular} & Real & \
\begin{tabular}{l} 44.1 kHz down \\ to 8 kHz \\ \end{tabular} &
\begin{tabular}{l} Weak, breathy \\ Breathy Modal \\ Loud, slightly tense \\ Shouted \& Tense \\ \end{tabular} & \\ \hline
[25] & 100:5:240 Hz & 10 & Synthetic (LF) & \(16\) kHz & Rd: 0.4-2.7 & \\ \hline
[25] & \begin{tabular}{l} \\ \end{tabular} & Real & \begin{tabular}{l} 48 kHz down \\ to 16 kHz \\ \end{tabular} & \
\begin{tabular}{l} Normal \\ Lombard \\ \end{tabular} &
\begin{tabular}{l} 11 sentences \\ 2 to 9 s \\ \end{tabular} \\ \hline
[28] & 75:10:405 & \begin{tabular}{l} /a/ /e/ /i/ \\ /i/ \\ \end{tabular} & Synthetic (LF) & 8 kHz & \
\begin{tabular}{l} 625 different \\ LF pulses \\ \end{tabular} &
\begin{tabular}{l} Optimize AME \\ 8 th order \\ All-pole filter \\ \end{tabular} \\ \hline
[28] & 80:10:400 &
\begin{tabular}{l} /e/ /o/ / /e/
# TEC and ROTI Measurements from a New GPS Receiver at BOWEN University, Nigeria
## 2 The CASES Hardware Platform
The receiver employed numerous novel techniques that make it suitable for space-weather studies compared to other nearby GPS such as the different methods for eliminating local clock effects, advanced triggering mechanism for determining scintillation onset, data buffering to permit observation of the prelude to scintillation, and data-bit prediction and wipe-off for robust tracking. More so, the CASES hardware is made up of a custom-built dual frequency, a digital signal processor board, and a \"single board computer\" with an ARM microcontroller.
# A Novel Dynamic Transmission Power of Cluster Heads Based Clustering Scheme
## 3 Preliminaries
### Optimization Goal  
#### 3.3.1 The reliability of the FANET  
We can measure the reliability of the network by packet delivery ratio (PDR). The PDR is defined by  
\[PDR=\frac{P_{R}}{P_{S}}, \tag{1}\]  
where \(P_{R}\) represents the number of data packets successfully received by the destination nodes, and \(P_{S}\) represents the total number of data packets generated by the source nodes. The destination node are the CHs, and the CHs will upload data to the cloud.  
Figure 1: The system model.  
Figure 2: HELLO message structure.  
The higher the PDR, the better the communication quality. In this paper, the optimized cluster head election strategy and the cluster maintenance process increase the PDR. Using the cluster head election strategy, the stable CHs will be selected to forward packets so that the success rate of information transmission will be improved. Moreover, the effective cluster maintenance reduces the probability of link disconnection, which improves the connectivity of UAVs and the transmission success ratio.
# Combining Advanced Feature-Selection Methods to Uncover Atypical Energy-Consumption Patterns
## 4 The Feature-Selection Process
### Recursive Feature Elimination with Multinomial Logistic Regression  
The RFE-MLR determines the optimal number of features required to achieve the best model accuracy. Initially, the algorithm calculates the accuracy of all features and then identifies which feature must be removed to maintain or improve accuracy. This process is repeated until RFE indicates that no more features need to be removed. The algorithms were implemented in Python's scikit-learn library, and stratified k-fold cross-validation was used to enhance performance [21]. To address the potential biases introduced by the initial set of features, we conducted a thorough literature review and consulted with domain experts to identify the most relevant variables for household energy consumption. Additionally, we performed sensitivity analyses to evaluate the impact of including or excluding specific features on the overall model performance. These steps are crucial for ensuring that our feature-selection process remains robust and that the selected features genuinely contribute to understanding energy-consumption patterns.  
The initial accuracy of RFE-MLR with all eleven features, indicating an optimal number of ten features, was 68.6% using the k-means algorithm (Figure 5(a)). The algorithm suggested the removal of the U18 feature, after which the accuracy of 68.6% was maintained, and the algorithm then identified an optimal number of nine features (Figure 5(b)). After removing TYP and running the algorithm for the third time, RFE-MLR showed that the remaining nine characteristics were the optimal number of features (Figure 5(c)). This means that the k-means algorithm selected the following features as optimal: ARE, HAC, DHW, NPE, WPU, INC, COE, PET and BED, and the model finished with an accuracy of 69.7%.  
For the AHC target, we ran the RFE-MLR with all eleven features and obtained an accuracy of 60.1%. The algorithm suggested that only three features are optimal: HAC, DHW and NPE. After removing the non-relevant features, the accuracy increased to 64.1%, as depicted in Figure 5(b). Therefore, these three features are considered relevant for the prediction model.  
Figure 6: Model accuracy vs. the number of features with k-means. (**a**) All features, (**b**) After the 1 st removal, (**c**) Process ends.  
According to the RFE-MLR algorithm, only one feature is considered optimal when the target is SOM. This is shown in Figure 8. The model that includes all eleven features has an accuracy of 67.3%. However, when only the HAC feature is used, the accuracy increases to 68.7%.  
After analyzing the model accuracy with respect to the choice of factors and using the RFE-MLR algorithm, we found that the algorithm presents different optimal features with each target. Therefore, we have decided to consider the features selected with the k-means algorithm for further analysis. The final accuracy achieved was nearly 70%, and the optimal features selected were ARE, HAC, DHW, NPE, WPU, INC, COE, PET and BED.