esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Input Data Level Ensemble in Pa-Ca Structure  
As mentioned in Section 3.1, the proposed methodology used five FCs (FC\({}_{1}\)-FC\({}_{5}\)) in the Pa structure. Figure 6 compares the performance of the Pa-Ca structure (without using PCA technique) for different FCs based on the validation dataset. It should be highlighted that based on the results of Sections 4.1 and 4.2, RF was selected as the base model and Meta-model in the Pa-Ca structure. Moreover, when there is only a single FC, there would be no Ca structure, and RF is directly implemented to obtain the results. For example, when FC\({}_{5}\) is used, only a single RF with optimized hyperparameters is used.  
As can be seen (Figure 6), when only a single FC is used, the lowest classification accuracies were obtained. Among the cases of using a single FC, FC\({}_{2}\) achieved the highest OA (81.87%) and Kappa (0.782). In contrast, the S1-related FC (FC\({}_{5}\)) achieved the lowest performance with an OA of 76.68% and a Kappa of 0.721. The results also indicate that among the single FCs, FCs of SIs (FC\({}_{2}\), FC\({}_{4}\)) generally yield better accuracies than FCs of raw spectral bands (FC\({}_{1}\), FC\({}_{3}\)). Additionally, S2-related FCs generally performed better than L8/9 FCs. The same results were also achieved in Figure 4.  
All the single FC cases only used the RF model for classification. It can be seen in Figure 6 that when more FCs are used in a Pa-Ca structure, the OA and Kappa values showed significant improvements. This proves the performance of the proposed Pa-Ca structure, which benefits from using different satellite data sources in each branch for crop type classification. By keeping FC\({}_{5}\) fixed and combining it with other FCs, the combination  
Figure 5: Performance of different ML models as the Meta-model in the Ca structure in terms of OA and Kappa using 5-fold cross validation of training data.  
Figure 6: Effect of using different data sources (without using PCA technique) based on the validation dataset. RF is selected as the ML model in Pa and Ca structures.  
of FC\({}_{2}\)+FC\({}_{5}\) in the Pa-Ca structure achieved the highest accuracy, with an OA of 87.58% and a Kappa of 0.851. The results also indicate that using the Pa-Ca approach with all five designed branches achieved the highest OA (91.13%) and Kappa (0.893). As a result, the best case (FC\({}_{1}\)+ FC\({}_{2}\)+ FC\({}_{3}\)+ FC\({}_{4}\)+ FC\({}_{5}\)) is selected as the input features of the proposed Pa-Ca ensemble structure in the rest of the paper. In the next step, the effect of employing the PCA technique on the outputs of the Pa section of the proposed methodology is further discussed.
# Implementation of the 3-30-300 Green City Concept: Warsaw Case Study
## 3 Results
### Three Conditions of the Concept  
The designation of buildings that meet the total of all three given criteria of the 3-30-300 concept was determined in two variants: for all forms of green areas and for park and forest only.  
Figure 13 shows the percentage of buildings in Warsaw that meet all three conditions for a green city, taking into account all forms of green areas. The chart shows that just over 22% of buildings in the Polish capital meet all the assumptions, while as many as 78% of buildings do not. The situation is even worse when only forest and park areas are taken into account. Here, the percentage of buildings that meet all three criteria falls to only 12.66% (Figure 14), making it possible to conclude that practically only 1 in 10 buildings in Warsaw meets the criteria of the 3-30-300 concept.  
Figure 14: Buildings that meet all 3 conditions for only parks and forests (green (yes)—percentage of buildings meeting 3 conditions simultaneously, red (no)—percentage of buildings not meeting 3 conditions simultaneously).  
Figure 13: Buildings that meet all 3 conditions for all forms of greenery (green (yes)—percentage of buildings meeting 3 conditions simultaneously, red (no)—percentage of buildings not meeting 3 conditions simultaneously).  
The spatial distribution of districts with the highest percentage of buildings meeting the three conditions of the concept is shown in Figure 15 (all forms of green areas) and Figure 16 (only forests and parks). Figure 15 shows that only a few districts in the northern and eastern parts of the city have more than 30% of buildings meeting all three conditions. The closer to the center, the lower the percentage of such buildings, mostly ranging between 0 and 15%. It is worth noting that half of the districts have more than 15% of buildings meeting the three criteria, while the other half fall between 0 and 15%. The districts with the lowest values are usually the oldest ones in Warsaw.  
Figure 16: Buildings that meet all 3 conditions (parks and forests only).  
Figure 15: Buildings that meet all 3 conditions (all forms of greenery).  
The situation taking into account only parks and forests is even worse. Figure 16 shows that most districts have a negligible proportion of buildings meeting these criteria (between 0 and 15%). Only four districts--Bielany, Rembertow, Wesola, and Wawer--have a higher percentage of buildings that meet all three conditions. The values here range from 15% to 100%. Interestingly, three of the districts--Rembertow, Wesola, and Wawer--have a percentage of over 50% of buildings meeting the set conditions. Again, Wawer is dominant here with over 70% of buildings meeting the 3-30-300 concept. The districts with the best results are the peripheral districts, which were the latest to be Incorporated Into the city. They are dominated by single-family housing and have a number of home gardens and forests.
# A Study of MANET Routing Protocols in Heterogeneous Networks: A Review and Performance Comparison
## 4 Results and Discussion
### Packet Delivery Ratio  
In this section, we present the packet delivery ratio (PDR) performance for all four MANET routing protocols that we considered in this study. The PDR is defined as the ratio of the number of packets successfully delivered to the destination to the total number of packets sent by the source. The UDP packet size was set to 512 bytes. Packet loss was primarily caused by wireless loss, which was modeled using a combination of free-space path loss and Rician fading to account for the mobility of the nodes. The buffer overflow was minimized by setting the buffer size to 64 KB per node. Figure 19 shows the PDR for the AODV routing protocol across various node types, including both fixed and mobile nodes. The chart highlights the consistency of the PDR between the sent and received packets for mobile hosts compared to fixed hosts, with only minor variations observed. AODV performs well in terms of maintaining a high PDR, particularly for mobile nodes, emphasizing its effectiveness in dynamic network environments.  
Figure 19: Packet delivery ratio for AODV.  
Figure 18: OLSR’s throughput for mobile nodes.  
Figure 20 focuses on the PDR for the BATMAN routing protocol. As with AODV, the mobile nodes generally show a slightly better PDR compared to the fixed nodes. However, the differences between the fixed and mobile nodes are more pronounced in the case of BATMAN, with the mobile nodes consistently achieving higher sent and received packet values. This result emphasizes the effectiveness of BATMAN's approach in managing packet delivery for varying mobility conditions, with the mobile nodes being better equipped to handle dynamic topology changes. Despite this, the overall PDR remains consistent across both the fixed and mobile nodes, highlighting BATMAN's robustness in diverse environments.  
Figure 21 shows the PDR for DYMO for various node types. The PDR is relatively consistent across both the fixed and mobile nodes, showing minimal variance in the sent and received packets. This indicates the reliability and efficiency of DYMO in maintaining data delivery across diverse network configurations.  
Figure 20: Packet delivery ratio for BATMAN.  
Figure 21: Packet delivery ratio for DYMO.  
Figure 22 illustrates the PDR for OLSR across various fixed and mobile nodes. The graph shows a stable PDR with minor variations between the sent and received packets for the different nodes, indicating OLSR's efficiency in maintaining reliable packet delivery across the network, especially for mobile hosts.
# A Review of Recent Advances in Roll Stability Control in On-Road and Off-Road Vehicles
## 4 Roll Stability Control in Road Vehicles  
### Roll State Monitoring and Risk Prediction  
Roll stability control for road vehicles mainly focuses on special scenarios such as high-speed cornering and emergency obstacle avoidance. To meet both the maneuverability and safety requirements of the vehicle, it is necessary to monitor the vehicle roll motion in real time and issue rollover warning signals in a timely manner, providing the control system with sufficient response time [84,85]. Figure 4 illustrates the typical prediction process of rollover risk [91]. Therefore, accurate state information must be first obtained to assess the vehicle roll dynamics and rollover risk in real time, serving as a crucial input for the control system to enhance roll stability and safety. Sensor-based state estimation is a straightforward and effective method for obtaining roll state variables, including IMUs, accelerometers, and gyroscopes. Numerous studies have focused on optimizing sensor placement, improving measurement accuracy, and reducing signal noise. [PERSON] [84], for example, used a combination of lateral accelerometers and gyroscopes to observe key roll parameters (such as roll angle and center of mass height) in real time. The stability of the observer was verified using the Lyapunov indirect method, significantly reducing hardware costs while maintaining accuracy.  
Although direct observation methods provide high-precision data, sensor noise often leads to deviations from the true vehicle state, limiting rollover warning accuracy. To address this, many studies have adopted state estimation approaches that fuse dynamic  
Figure 4: Typical prediction process of rollover risk.  
models with sensor data to enhance robustness and accuracy. [PERSON] et al. [92] developed an IMU-based warning system using adaptive extended Kalman filtering and LTR indicators, while [PERSON] et al. [61] proposed a CNN-LSTM method combining UKF and deep learning for early rollover prediction. [PERSON] et al. [72; 79] modeled acceleration thresholds using multi-body dynamics, and [PERSON] et al. [73] enhanced LTR sensitivity with an 18-DOF model by considering suspension and unsprung mass forces. In control applications, [PERSON] et al. [82] designed an adaptive anti-rollover algorithm based on road adhesion identification and sliding mode control. [PERSON] et al. [78] introduced a neural network-MPC hybrid system using the TTR indicator for coordinated control. For off-road scenarios, [PERSON] et al. [93] developed a warning system for forest fire trucks, validated through simulation and real tests. These studies highlight the diverse advantages of different prediction methods, with future work focusing on adaptive integration based on application needs.
# Design and Evaluation of a Web- and Mobile-Based Binaural Audio Platform for Cultural Heritage
## 4 PlugSonic Suite
### PlugSonic Soundscape Experience Mobile  
The Soundscape Experience Mobile application has been designed with two main goals: first, to enable navigating soundscapes using a natural, touch-based interface at home or on the go. Second, to allow users to explore soundscapes in an immersive virtual experience delivered in real-world environments. These goals are accomplished by providing two separate interaction modes.  
In the first mode (Figure 4a), users can explore the soundscape by moving the listener's icon using their finger and can change orientation by rotating the device. The 3D audio simulation is updated in real-time according to the listener's icon current position.  
The second interaction mode aims to provide an immersive experience by enabling users to explore a soundscape according to their movements in the real-world. In order to achieve such goal, we use ARkit [43], a technology developed by Apple to easily support detecting and tracking planar surfaces by analysing video frames captured by a device's camera and data collected by inertial sensors. The framework provides anchors in a real-world environment that can be used to determine the relative position of the device with respect to the detected plane. Such position is used to update the 3D audio simulation in real-time (Figure 4b).  
Soundscapes can be loaded from the PLUGGY social platform using the QR code reader included in the app or importing the metadata. The app includes buttons to reset the  
Figure 3: PlugSonic Soundscape Create dismissable menus: (**A**) Sound source search tab, (**B**) Sound sources settings tab, (**C**) Room settings tab, (**D**) Listener settings tab, (**E**) Exhibition tablistener's orientation, play and stop, and choose the HRTFs. It also allows some control over the convolutional reverb settings. The reverb uses impulse responses for three standard room sizes available from the 3D TuneIn Toolkit [24] but it could be extended by allowing to import other impulse responses.
# Spinel Magnetic Iron Oxide Nanoparticles: Properties, Synthesis and Washing Methods
## 4 Main Washing and Size Selection Methods for Nanoparticles
### Magnetic Separation  
Magnetic separation is by definition only used for magnetic nanoparticles. It attracts nanoparticles within a solution via the application of a magnetic field (often via a permanent magnet) [(67; 77; 80; 81)]. The largest nanoparticles will be easy to attract with a magnetic field, especially if they are agglomerated in the form of a cluster. The smallest nanoparticles which are totally isolated and stabilized in the solution by surface ligands will be more difficult to attract. The size sorting of nanoparticles by this principle can be done by modulation of exposure time of the sample to the magnetic field (for example, a short exposition of a few seconds will aim only to remove the big aggregates of the solution). In the case where the field will not be provoked by a permanent magnet but by an electromagnet, a modulation of the power of the magnetic field is possible to attract nanoparticles with a given minimum size [(80; 82)]. One of the advantages of this separation and washing method is it is very easy to implement and effective to eliminate free ions and ligands in excess. It also allows the elimination of any nonmagnetic by-products which can appear during the synthesis of iron oxide nanoparticles in production conditions (such as hydroxides or oxohydroxides). In a recent work, [PERSON] et al. [(71)] showed that during washing by repeated magnetic separation of the same sample of nanoparticles, the ligands ratio in the sample will tend to decrease significantly during the first washing, sign of the elimination of free ligands. However, unlike washing by dialysis and filtration that they have experienced too, the ligands ratio in the sample stabilizes after the first washing at a sufficiently high value to keep the stability of nanoparticles in the solution and avoid the aggregation.
# Agent-Based Virtual Machine Migration for Load Balancing and Co-Resident Attack in Cloud Computing
## 4 Agent-Based VM Migration Architecture  
The present study describes an agent-based architecture designed for VM application, allocation and migration, as illustrated in Figure 2.  
The architecture comprises three kinds of agents: Balancer Agents (BA), Server Agents (SAs), and Virtual Machine Agents (VMAs).  
The BA is responsible for forwarding VM allocation requests from users to the SAs according to a predefined set of VM allocation policies, as detailed in Section 5.5. Whenever the BA receives a VM application request, it broadcasts a request to the SAs, with the goal of identifying the optimal server for hosting the VM, based on a VM migration heuristic. If no host has sufficient computational power and matching security level, the BA recommends retaining the VM, as described in Section 5.1. The BA is intended for deployment on the balancer servers of cloud computing facilities.  
The SAs use server-centric migration policies, including workload thresholds and security level mismatch, to initiate VM migrations, with the objective of optimizing the allocation of resources and security risk mitigation. The SAs use migration heuristics to identify the optimal destination host and the most appropriate VM to migrate, as outlined in Sections 5.1 and 5.4. Additionally, SAs are equipped with VM acceptance policies to determine whether to accept VMs that are being migrated by other SAs, as elaborated in Section 5.3. When an SA is unable to migrate a VM due to conflicts with the VM acceptance policies of other SAs, the SA waits for a predefined interval before reinitiating the proposal. SAs are also responsive to VM allocation requests from the BA. The SAs are deployed on each host.  
The VMAs are responsible for monitoring the resource utilization of VMs and transmitting monitoring reports to the SAs, which manage the physical hosts. The SAs coordinate and collaborate to distribute workloads evenly across a heterogeneous set of hosts using a multi-agent protocol for VM migration, as detailed in Section 5.1.
# A Novel Geo-Localization Method for UAV and Satellite Images Using Cross-View Consistent Attention
## 4 Experiments
### Implementation Details
#### 4.1.4 Computing Resource  
In the experiment, due to limitations in computing resources, some batch sizes are tested to select the parameter with the best performance. The model can complete training on 2080 ti with a batch size of 32 and an image size of \(256\times 256\). The results illustrate that the proposed method reaches the highest accuracy compared to existing methods, even though some previous methods used a higher image resolution. Moreover, the proposed method with the original image size is also tested, i.e., \(512\times 512\). The batch size is kept the same to ensure that the comparison experiment results are only affected by the image size. The batch size can be adjusted for model training on a GPU with a smaller memory.
## 4 Discussion
This finding is particularly useful for medical physicists, radiologists, and radiological technologists, as it enables efficient assessment of the radiation dose received by pediatric patients, specifically the SSDE\({}_{\text{Dw}}\), for the justification and optimization of CT brain imaging protocols. The limitations of our study include the manual contouring of patient size, measured as the Dw at the middle slice of the brain. Although manual measurement benefits clinical users (physicians and radiological technologists) by reducing costs compared with purchasing commercial software, it may introduce variability if performed by less experienced personnel and may be time-consuming. Automatic segmentation is recommended for drawing ROIs. Additionally, this study used data from pediatric patients scanned with a single helical CT brain scan protocol without AEC. This study categorized patient data into five age groups, encompassing broad age ranges among pediatric patients. This approach may not fully account for the rapid changes in head size that occur during early childhood. Subdividing the age groups into narrower intervals could potentially yield a more robust correlation between age and head size as a function of the Dw.
## 2 Power Converter Principle
### Designs of Output Capacitor and Inductor
#### 2.3.1 SBBC Inductor for Buck Mode  
In accordance with the study [14], the inductor for the SBBC's buck mode operation can be estimated as follows:  
\[L_{bk}=D_{bk}(V_{in(max)}-V_{o(min)})/(2f_{s}\Delta i_{L}). \tag{1}\]  
The notation, \(L_{1}\), (Figure 3) can be replaced with \(L_{bk}\). \(\Delta i_{L}\) is the inductor peak current that can be estimated using the \(\beta l_{o}\), \(\beta\) is the percent from the peak-to-peak inductor current, and \(l_{o}\) is the HPLDDC output current. \(D_{bk}\) is the duty cycle of the buck mode that can be estimated using the \(V_{o(min)}/V_{in(max)}\), \(V_{o(min)}\) is the HPLDDDC minimum output voltage, and \(V_{o(max)}\) is the HPLDDC maximum output voltage. \(f_{s}\) is the switching frequency of \(Q_{1}\)-\(Q_{4}\). Substitution of \(\Delta i_{L}=\beta l_{o}\) and \(D_{bk}=V_{o(min)}/V_{in(max)}\) into (1) can rewrite the equation as follows:  
\[L_{bk}=V_{o(min)}(V_{in(max)}-V_{o(min)})/(2V_{in(max)}f_{s}l_{o}\beta). \tag{2}\]  
Figure 5: Boost mode principle: (**a**) \(v_{gs1}\)–\(v_{gs4}\) and \(i_{L}\) timing charts; (**b**) operating circuit in \(t_{t1}\)–\(t_{t2}\); (**c**) operating circuit in \(t_{t3}\)–\(t_{t4}\).
## 4 Results and Discussion
### Performances of the BET Consensus Controller  
#### 4.1.1 Time and Iteration  
The average time the agent took to reach the rendezvous point for the desired target point in 10 time runs was an average of 84.3413 s and 676.6 iterations as shown in Table 3 (Proof of Theorem 2 and Proof 4). The shortest time and iterations taken by an agent to converge was at least 35.996 s with 312 iterations when the state measurement error, which indicates the performance index, showed a reading of 0 after a certain number of iterations as shown in Figure 7.
# Precipitation Dominates the Distribution of Species Richness on the Kunlun-Pamir Plateau
## 3 Results  
### Distribution Patterns of DHIs  
In the Kunlun-Pamir Plateau region, the spatial changes in the distribution of the cumulative DHI, minimum DHI and variation DHI values are rather insignificant. However, various MODIS products demonstrated different distribution patterns (Figure 1a-f). The DHIs of the same MODIS vegetation dataset indicated positive correlations. The correlation between the cumulative DHIs and the minimum DHI was moderately intense, except in the case of GPP, in which the correlation between the cumulative DHI and minimum DHI was rather small (0.23). The Spearman rank correlation ranged from 0.50 to 1. The correlations of variation DHIs with a cumulative DHI and minimal DHI were 0.43 to 1 and 0.21 to 0.81, respectively. The correlation between the GPP variation DHI and minimal DHI was 0.21 (Figure 2).  
The DHI correlations from different vegetation datasets were uneven. Some of correlations were also nonlinear (Figure 2). The Spearman rank correlations of the same  
Figure 1: The spatial patterns of three DHIs derived from (**a–c**) the NDVI, (**d–f**) LAI, (**g–i**) GPP, (**j–l**) FPAR and (**m–o**) EVI.  
Figure 2: Spearman rank correlations and scatter plots among the three DHIs derived from the five MODIS products. It represents a correlation matrix for cum–min–var with scatter plots on one side of the diagonal and correlations on the other side. Significant relations: *** \(p<0.001\).  
vegetation products (e.g., NDVI- and EVI-based DHIs) were higher than the ones between the DHIs based on different vegetation products (e.g., FPAR- and GPP-based DHIs). The highest correlations values were noticed between the FPAR- and LAI-based DHIs (from 0.78 for the minimum DHI to 1 for the cumulative and variation DHI). The correlations between the cumulative and variation DHIs based on the GPP, FPAR and LAI were also substantial (Figures 2 and 3). The GPP-based DHI correlated the least with the DHI based on other vegetation products, except the cumulative and variation LAI, with a minimum DHI correlation of 0.11.  
Due to the high correlation between FPAR-based DHIs and species richness, we selected FPAR-DHIs for the subsequent work analysis (Tables 3 and S1-S4). Most of the correlations between the FPAR-based DHIs and the climate dataset were weak (Figure 4), indicating that the DHIs possibly provide various information. The highest Spearman rank correlation value with the energy factor was \(-\)0.13 (BIO4, Temperature Seasonality) during the cumulative DHIs. Regarding the water factor, the highest Spearman rank correlation measured 0.07 (BIO13, the wettest month).