esa-sceva/satcom-chunk-collection huggingface dataset:
# A Lightweight Entropy-Curvature-Based Attention Mechanism for Meningioma Segmentation in MRI Images
## 4 Experiments
### State-of-the-Art Comparison
#### 4.2.2 Performance on the BraTS 2021 Dataset  
To verify the robustness of ECA on 3D medical images, we performed experiments comparing ECA with other SOTA models on the BraTS2021 dataset; the results are shown in Table 3. The main evaluation metrics include DSC and HD95 for different tumor regions. In addition, to verify the efficiency of ECA, we also performed experiments comparing ECA with other 3D image attention on FLOPs and Params. For the WT, TC, and ET regions, ECA achieved values of 83.07, 87.04, and 87.15 for the DSC metrics, respectively, which were higher than the other models. In both WT and ET regions, ECA achieved the lowest HD95 values of 3.69 and 2.26, respectively, which means that it demonstrated good performance in terms of boundary accuracy, but in the TC region--because of the uncertainty in the shape of the TC region and its distribution being relatively scattered--the ECA could not  
\begin{table}
\begin{tabular}{c l l l l l} \hline \hline  & **Model** & **DSC** & **HD95** & **FLOPs** & **Params** \\ \hline \multirow{4}{*}{M} & UNETR & 61.20 & 6.41 & - & - \\  & SwinUNETR & 78.21 & 4.60 & - & - \\  & nnFormer & 84.88 & 9.21 & - & - \\  & UNETR++ & 79.35 & 18.79 & - & - \\  & dResU-Net & 84.92 & 8.23 & - & - \\  & CU-Net & 84.27 & 7.69 & - & - \\  & nnUNet & 86.81 & 3.36 & - & - \\ \hline \multirow{4}{*}{A} & 3D CBAM & 77.70 & 10.24 & 973.57 & 22.48 \\  & 3D SA & 84.82 & 7.22 & 968.83 & 22.38 \\ \cline{1-1}  & HDC & 80.72 & 6.17 & 1274.00 & 26.87 \\ \cline{1-1}  & TDPC & 71.96 & 8.64 & 1269.00 & 26.86 \\ \hline \hline  & ECA & **89.22** & **2.88** & 977.02 & 22.38 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparisons with the SOTA methods and other attention mechanisms on the meningioma test set. M represents ‘method’ and A represents ‘attention’. The bold is the best result.  
learn the accurate curvature of the TC shape well. In terms of FLOPs and Params, similar to the meningioma dataset, ECA achieved the best performance in DSC and HD95, with FLOPs of 979.01 and Params of 23.02, while consuming relatively moderate computational resources, showing high segmentation accuracy and boundary processing capabilities, reflecting its superiority and efficiency.
## 2 Related Works
In their study, they demonstrated that the proposed method achieved over 99% accuracy on the test dataset and that this method outperformed other algorithms for bearing fault diagnosis. [PERSON] et al. [7] investigated the use of machine learning techniques, specifically SVMs, to detect ten different failure states in rotating machinery using publicly available datasets obtained from a tachometer, two accelerometers, and a microphone. A total of fifty-eight features were extracted from the time and frequency domains using the short-time Fourier transform, with a window size of 1000 samples and 50% overlap. They achieved a failure classification accuracy of 99.8% using all 58 features with the SVM model. They also aimed to reduce the dimensionality by sorting the features with a decision tree model to rank the important predictors, allowing for the selection of eleven key features. They demonstrated that their approach reduced the training complexity while maintaining a high classification accuracy of 99.7%, achieved in less than half of the training time. [PERSON] et al. [40] investigated the performance of different classifiers using statistical, histogram, and ARMA features extracted from vibration data using machine learning techniques for early fault diagnosis in dry friction clutch systems. In the evaluations made with classifiers such as the SVM, k-Nearest Neighbor (kNN), and multilayer perceptron (MLP), the highest accuracy was achieved with methods such as the Random Forest (RF) and kNN. Ensembles consisting of three classifiers combined with the voting strategy achieved higher accuracy rates than the classifiers alone. As a result of the studiesconducted, they revealed that the correct feature selection and classifier combination are important in fault identification. [PERSON] et al. [41] used semi-supervised anomaly detection (AD) techniques to overcome the limitations of supervised learning methods due to the lack of labeled fault data. The study compared the effectiveness of the AD method between traditional techniques and deep learning-based methods and revealed that traditional methods exhibited similar or sometimes superior performance when compared to deep learning techniques. The results obtained indicated that the simplicity and low computational requirements of traditional methods provide advantages and suggested that future research should start with traditional approaches. Table 1 presents a comparative summary of selected studies on motor fault detection, highlighting the use of both real and synthetic datasets. These works differ in terms of data sources, fault types, and applied methods.
## 4 Experimental Results and Discussion of IHSSAO Performance Verification
### Experiment I: Classical Benchmark Function
#### 4.1.1 Parameter Setting  
To validate the performance of the modified algorithm, the IHSSAO algorithm was compared with the original SSA, AO, and five advanced meta-heuristics, i.e., WOA, HHO, Multi-Verse Optimizer (MVO), Sooty Tern Optimization Algorithm (STOA), and Tunicate Swarm Algorithm (TSA). For all these algorithms, the population size and the maximum number of iterations are set as 30 and 500, respectively. We ran each algorithm independently for 30 times according to the parameter settings as shown in Table 4.
## 3 Problem Statement
### Preamble and Data Collision  
A collision can occur between the data and the preamble when nodes suddenly switch into sender-initiated mode. When a beacon is sent to Sender 1 and Sender 2 by Receiver 1, Sender 2 is able to find the beacon, but Sender 1 does not find the beacon because of an asymmetric link. Sender 1 will then go into sender-initiated mode and begin transmitting the preamble. Figure 3 shows how this can collide with the data being sent by Sender 2 at Receiver 1's end. [PERSON] et al. showed that, when the network has more than 15 nodes, the ASYM-MAC protocol introduces highest data collision rate [51]. However, in our proposed protocol, there is no transition between receiver initiated and sender initiated mode. Therefore, this type of collision never occurs in our proposed protocol.
## 2 Materials and Methods
### Cellular Toxicity Measurements  
A CellTiter-Glo Luminescent Cell Viability Assay (Promega, Madison, WI, USA) was employed to obtain the cellular toxicity of the aqueous nanoparticle suspension sample. A luminometer (Synergy HT, BioTek, Winooski, VT, USA) was used to quantify the intracellular adenosine triphosphate in this assay. Normal mouse hepatocyte (NCTC1469) and human prostate cancer (DU145) cell lines were employed for the tests. The cells were incubated for 24 h on a 24-well cell culture plate (5 \(\times\) 10\({}^{4}\) cell density, 500 \(\mu\)L of cells per well, 5% CO\({}_{2}\), and a temperature of 37 \({}^{\circ}\)C). RPMI-1640 and DMEM were employed as the culture media for the DU145 and NCTC1469 cells, respectively. The original concentrated nanoparticle suspension sample was diluted with a sterile phosphate-buffered saline (PBS) solution to make five test nanoparticle suspension samples, and 2 \(\mu\)L of each test nanoparticle suspension sample was added to the above-cultured cells to obtain Mn-concentrations of 10, 50, 100, and 200 \(\mu\)M in the cells. Thereafter, the treated cells with nanoparticle suspension samples were incubated for 48 h. A total of 200 \(\mu\)L of CellTiter-Glo reagent was added to 200 \(\mu\)L of the above incubated treated cells per well and the cells were lysed for 30 min on an orbital shaker. The viabilities of the cells were measured using the luminometer (300-700 nm), following which they were normalized with respect to those of the control cells with 0.0 M Mn. The average cell viabilities were estimated from three measurements.
# Low Temperature Chemoselective Hydrogenation of Aldehydes over a Magnetic Pd Catalyst
## 3 Results and Discussion
### Recycling of the \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)@HAP-Pd Catalyst  
Finally, the stability of the \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)@HAP-Pd was also studied, and the recycling experiments were conducted (Figure 5). The hydrogenation of benzaldehyde was used as the model reaction, which was performed at room temperature and 10 bar H\({}_{2}\) pressure. After reaction, the \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)@HAP-Pd catalyst was separated with an external magnet. The resulting solid was washed with water and ethanol, dried in a vacuum ovenand reused for the next time. They-Fe\({}_{2}\)O\({}_{3}\)@HAP-Pd was successfully recovered and reusedsixtimes without significant loss of catalytic activity (95.1% yield initially, 94.1% yield after the sixth run).Additionally, the stability of the catalyst wasassessed by carrying out two twin experiments in whichone of them the solid catalyst was separated with an external magnetat 20 min reaction time, when conversion was about 60%, and allowing the reaction to continue in the absence of catalyst (Figure 6). It can be seen that the experimentwithcatalyst reactedcompletely (blue line), while the other processwithout catalyst was very minor (red line), indicating that the catalyst was stable. The reaction of small amount ofaldehyde-decouldbe the result of the physical loss of catalytic materialduring separating. Furthermore, ICP-OES also showed the Pd content remain 2 wt.% after the catalystreusedsixtimes, indicating that the catalyst was stable.  
Figure 4: Time course of the deoxygenation of benzylaldehyde into toluene. Reaction conditions: benzylaldehyde (1 mmol),2 wt.% \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)@HAP-Pd catalyst (10 mg), H\({}_{2}\)O (10 mL), 2.5 bar H\({}_{2}\), 70 \({}^{\circ}\)C.  
Figure 5: The results of the recycling experiments of the \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)@HAP-Pd catalyst. Reaction conditions: Benzaldehyde (1 mmol), \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)@HAP-Pd catalyst (10 mg), H\({}_{2}\)O (10 mL), 10 bar H\({}_{2}\), 25 \({}^{\circ}\)C and 1 h.
## 3 Experimental Results and Discussion
### Strengths and Critical Strain  
The strengths and critical strain data are summarized in Table 6. In this study, critical strain is defined as the strain measured at the ultimate compressive strength of the CA mortar. In detail, the values of the compressive strengths are fluctuated between 14.09 MPa and 17.65 MPa, with a mean value of 15.23 MPa and standard deviation of 1.83 MPa.
## 2 Theoretical Basis of Time-Varying Target Formation
### Stability Analysis of Time-Varying Topology Formation  
In this section, based on the second-order integrator model, it is provided how the control law of the swarm reaches a steady state in segment interval, and finally realizes affine formation maneuver control. That is, the distributed control laws \(u_{si}(t)\) for the follower makes the final realization of \(\delta_{s_{pf}}(t)\to 0,\delta_{s_{pf}}(t)\to 0\). The following are the stability analysis of the control laws (15).  
Multiplying \(\gamma_{si}\) on both sides of (15) gives  
\[\begin{array}{l}\underset{j\in N_{i}}{\sum}w_{s_{lj}}(\dot{v}_{si}-\dot{v}_ {si})\\ =\underset{j\in N_{i}}{\sum}w_{s_{lj}}[-k_{p}(p_{si}-p_{sj})-k_{v}(v_{si}-v_{ si})]\end{array} \tag{18}\]  
The matrix-vector forms are expressed as  
\[\begin{array}{l}\overline{\Omega}_{s_{ff}}\dot{v}_{sf}+\overline{\Omega}_{s _{fl}}\dot{v}_{sl}^{*}\\ =-k_{p}(\overline{\Omega}_{s_{ff}}p_{sf}+\overline{\Omega}_{s_{fl}}p_{sl}^{*}) -k_{v}(\overline{\Omega}_{s_{ff}}v_{sf}+\overline{\Omega}_{s_{fl}}p_{sl}^{*}) \end{array} \tag{19}\]  
Because of \(\delta_{s_{pf}}(t)=p_{sf}(t)+\overline{\Omega}_{s_{ff}}^{-1}\overline{\Omega }_{s_{fp}}p_{sl}^{*}(t)\), \(\delta_{s_{pf}}(t)=v_{sf}(t)+\overline{\Omega}_{s_{ff}}^{-1}\overline{\Omega }_{s_{fl}}v_{sl}^{*}(t)\), so  
\[\overline{\Omega}_{s_{ff}}\dot{v}_{f}+\overline{\Omega}_{s_{fl}}\dot{v}_{sl}^{ *}=-k_{p}\overline{\Omega}_{s_{ff}}\delta_{s_{pf}}-k_{v}\overline{\Omega}_{s _{ff}}\delta_{s_{pf}} \tag{20}\]  
Multiplying both sides by \(\overline{\Omega}_{s_{ff}}^{-1}\), after shifting the term, we get \(\dot{v}_{sf}=-k_{p}\delta_{s_{pf}}-k_{v}\delta_{s_{pf}}-\overline{\Omega}_{s_ {ff}}^{-1}\overline{\Omega}_{s_{fl}}v_{sl}^{*}\), and Formula (20) can be denoted as  
\[\dot{\delta}_{s_{pf}}=\dot{v}_{sf}+\overline{\Omega}_{s_{ff}}^{-1}\overline{ \Omega}_{s_{fl}}\dot{v}_{sl}^{*}=-k_{p}\delta_{s_{pf}}-k_{v}\delta_{s_{pf}} \tag{21}\]  
As a result, the error dynamics can be written as  
\[\begin{array}{l}\left[\begin{array}{cc}\dot{\delta}_{s_{pf}}\\ \dot{\delta}_{s_{ef}}\end{array}\right]=\begin{pmatrix}0&I\\ -k_{p}I&-k_{p}I\end{pmatrix}\left[\begin{array}{c}\delta_{s_{pf}}\\ \delta_{s_{pf}}\end{array}\right]\end{array} \tag{22}\]  
Define \(\varphi_{s}=\begin{bmatrix}\delta_{s_{pf}}^{T},\delta_{s_{pf}}^{T}\end{bmatrix}^ {T}\), and \(W_{s}\) is the state matrix containing the control gain \(k_{p},k_{v}\).  
The eigenvalue of the state matrix can be derived as \(\lambda_{s}=(-k_{v}\pm\sqrt{k_{v}^{2}-4k_{p}})/2\), which always has the negative real part for any \(k_{v},k_{p}>0\). Thus \(W_{s}\) is the Hurwitz matrix, and \(\varphi_{s}\) can converge to zero.  
The global and exponential convergence follows. Through the above analysis, it can be concluded that the number of agents can achieve the expected configuration under the condition of switching topology.
# Performance Evaluation of Graph Neural Network-Based RouteNet Model with Attention Mechanism
## 3 Methodology
### RouteNet Model Implementation  
The simplified architecture of the RouteNet model is depicted in Figure 4. It takes input features, processes them through link and path updates using GRU cells, and incorporates information exchange between links and paths. A readout neural network then produces the final output. The design highlights the integration of link and path information for effective information processing.  
Figure 5 presents the detailed architecture of the RouteNet model, emphasizing the exchange of information through message passing between links and paths. Following 't = 8' iterations, the model then progresses through the readout network.  
Figure 4: RouteNet model architecture.  
In the initial phase, known as \"Link and Path Update\", GRU cells are employed to iteratively update link and path states. In Link Update, the initial link state involves a vector where link capacity is concatenated with zeros, and a similar pattern is followed in Path Update with bandwidth concatenation. Each GRU cell, comprising 32 units, receives messages from respective paths or links, leading to state adjustments. The subsequent stage, \"Message Passing\", unfolds in two steps. Firstly, from links to paths, link states are aggregated and summed for each path, and the path GRU cell updates the path state using this aggregated link state. In the second step, from paths to links, path states are gathered and summed for each link, and the link GRU cell updates the link state using the accumulated path state.  
The final stage introduces the \"Readout Neural Network\", responsible for processing path states post the message-passing process and predicting delays for each path. This network comprises three layers: an initial Dense layer with 8 units using the Scaled Exponential Linear Unit activation function, L2 regularization, and dropout, a subsequent Dense layer with 8 units using the Rectified Linear Unit activation function, L2 regularization, and dropout, and a final Dense layer with a single output unit and L2 regularization.  
Finally, RouteNet underscores its effectiveness in predicting network delays through the interplay of message-passing mechanisms and subsequent neural network processing.
###### Abstract
While there are substantial datasets for large water bodies (for example [[https://dashboard.waterdata.usgs.gov/app/nwd/](https://dashboard.waterdata.usgs.gov/app/nwd/)]([https://dashboard.waterdata.usgs.gov/app/nwd/](https://dashboard.waterdata.usgs.gov/app/nwd/)) and [[http://www.bom.gov.au/waterdata/](http://www.bom.gov.au/waterdata/)]([http://www.bom.gov.au/waterdata/](http://www.bom.gov.au/waterdata/))) (accessed on 20 February 2022), there are essentially no observed data from on-farm irrigation reservoirs. Similarly, while there are substantial datasets on modelled evapotranspiration, there are very few direct observations of vegetation sapflow that could link fine scales to satellite observations [45]. As a result, there are significant uncertainties in the observation of finer-scale water-cycle processes, and these uncertainties would need to be quantified [21; 27]. More specifically, the focus should be on the lack of accurate spatial ET estimates, lack of understanding of the fine scale spatial variation in inputs and outputs, and lack of understanding of on-farm storage of water [7]. In summary, three major remaining challenges remain if detailed water accounting and water productivity tools need to be developed that can be scaled from the field and farm to the continent level:  
* The lack of detailed fine scale observations in space and time of water cycle components, particularly for soil moisture, farm dam volumes, and vegetation transpiration;
* The need for models linking satellite data and water cycle components that are transferable in space and time, most likely using physical relationships or through fast self-calibrating data-based models; and
* The need to quantify uncertainties in the satellite and water cycle relationships beyond reporting simple root mean square errors or bias. However, there are continued efforts to develop new tools, one of which is the EU-funded WaterSENSE project ([[https://www.watersense.eu/](https://www.watersense.eu/)]([https://www.watersense.eu/](https://www.watersense.eu/))) (accessed on 20 February 2022). The objectives of this paper are, therefore, to firstly show progress towards an operational water cycle observation system for water accounting and water footprints at the farm and field scale based on a case study in Australia and, secondly, to outline the challenges to extend this system to other places in the world and the projected uncertainties related to input data availability.
# Fast Equipartition of Complex 2D Shapes with Minimal Boundaries +
## 7 Conclusions  
In this work, we introduced a novel methodology for the fast equipartition of complex 2D shapes while minimizing intrinsic boundary length. The proposed method, which is based on a fast balanced clustering algorithm, demonstrates superior performance in basic shapes and outperforms existing techniques in a large number of complex shapes. Additionally, our approach is combined with the PSO framework to improve partition quality and robustness, ensuring near-optimal subdivisions even in complex shapes. However, due to the high computation cost of SEP-PSO FBC, in real-time applications where the number of \(N\) is quite high (e.g., more than 100), SEP-FBC may be more preferable than the PSO framework due to its lower computational cost. Experimental results on various datasets of more than 2800 shapes with more than 25,000 segmentation instances in two standard datasets confirm the robustness and computational efficiency of our approach. Finally, we study the application of the proposed framework on the tree detection problem.  
In ongoing and future work, we plan to study optimal solutions of the 2D-SEP problem with higher values of \(N\) and under more complex basic shapes. Moreover, our aim is to apply 2D-SEP on real computer vision and pattern recognition problems, where the goal is to provide segmentation of a given 2D shape. Future work could also explore extensions to three-dimensional shapes and other real-world applications.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The associated code and datasets are developed for the project can be shared publicly after the paper acceptance. The sharing of parts of the code or datasets could be approved, at the author's discretion, upon request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
Figure 19: A promising result of the proposed (**a**) _SEP-PSO FBC_ and (**b**) _SEP-FBC_ methods on the tree detection problem under a low-quality dense forest image.
## 3 Materials and Methods
### Data Acquisition for the Experiment Testing and Evaluation  
MSStickC is a microcontroller that has a built-in 6-axis accelerometer MPU6886, WiFi module, LCD, and various other peripherals. The data presented in this article was collected using this integrated IMU accelerometer and written Winforms application (Microsoft.NET Framework Version 4.8.04084) in Visual Studio 2019 version 16.10.2.  
MSStickC was programmed in ArduinoIDE version 1.8.13. Data on 3 measured accelerations in the \(X\), \(Y\), \(Z\) axes are sent to the web server running on this microcontroller. In addition, gyroscopic data (also in the X, Y, Z axes), temperature values, current battery voltage, and electrical current consumption are also read. All this information is stored in a.csv file. Our testing data set consists of 4394 samples that were collected for algorithm testing purposes. The Figure 7 below shows a part of the measured data--specifically accelerations in X axis.  
The measured data was gathered by the external sensor only for the testing predictive module performance and the collected data were preprocessed in MATLAB R2020b. The imported data has been converted to timetable objects, which offer several useful functions. These data were cleared or filtered for outliers and erroneous values, respectively. Outlying data were replaced by a linear interpolation method. The measured data were resampled using the _retime()_ function. This measured and processed signal was then smoothed using a _smoothData()_ filter. In addition, it was necessary to create functions for creating training data from specific parameters.  
Figure 7: Measured accelerometer data in X-axis for experiment evaluation and MSStickC IMU device.
# Improved Proactive Routing Protocol Considering Node Density Using Game Theory in Dense Networks
## 4 Simulation Environment and Results
### Simulation Parameters  
We evaluated the performance of our improved protocol by doing simulations using the Riverbed Modeler 18.0 [21]. Riverbed Modeler simulates real network environments and analyzes the network. Furthermore, existing protocols can be enhanced and evaluated easily. With a bid to make this network suitable for unstable and dynamic environments, we included random behaviour in all nodes except the source and destination node. Due to the mobility of the nodes, the dynamic receiver group computation is set to refresh every 10 s [22]. The transmission range is set uniformly at 30 m to ensure a longer multihop from the source to the destination.  
At the beginning of the simulation, all nodes are randomly deployed in a 100 m \(\times\) 100 m area and are free to just within this area. We set the traffic packets to simulate a simple voice codec, G.711 at 8 KB/s. Thus, a 6400-bit packet is sent every 0.1 seconds from the source node to the destination node. The pause time is set to zero so that the node keeps on moving during the simulation. The simulation time is set to 600 s. The HELLO traffic and TC message intervals are set to 5 seconds and all nodes are set to WILL_DEFAULTULT willingness and then changed after the game is played. User datagram protocol (UDP) is used as the transport protocol. Every scenario simulates 10 minutes of network activity. The simulation is carried out 10 times using various random positon of the nodes. Table 5 summarizes the simulation settings.