esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Prompt Dose Rates  
To assess if the shielding design meets the dose rate limits during irradiation, the USRBIN card was used to score the ambient dose equivalent rate inside and outside the experimental terminal when a 12 kW proton beam bombards the \({}^{100}\)Mo target. As shown in different sections of the dose rate's 3D distribution (Figure 11), the ambient dose equivalent rate is the highest near the beam spot, which is 2.6 \(\times\) 10\({}^{5}\) Sv/h and decreases to 100 Sv/h at the inner wall. With the shielding layout, the dose rates outside and above the terminal are about 0.1 \(\upmu\)Sv/h, thus meeting the dose rate limit requirements.  
Figure 11: Prompt ambient dose equivalent rate distribution (\(\upmu\)Sv/h) in the target terminal.  
The exposure dose rates of the workers in NINE regions of interest during irradiation were also scored. The regions were arranged 1 m from the outer shielding wall with the size of 30 cm (width) \(\times\) 60 cm (length) \(\times\) 170 cm (height), whose positions are illustrated in Figure 12. The average ambient dose equivalent rates of these regions are less than 0.15 \(\upmu\)Sv/h.  
In this work, a 3D visualization method of the FLUKA's radiation fields was developed as a supplement to the built-in visualization function of Flair, as detailed in Figure 13. It is mainly based on data-processing scripts and ParaView. ParaView is an open-source tool supported by Visualization Toolkit and the Qt cross-platform application framework [21]. The raw simulation data from FLUKA can be processed and imported into ParaView and overlaid with the geometry model in STL. Customized data processing and visualization functions can be added through plug-ins (e.g., Calculator, Contour, Clip, and Delaunay3D).  
Figure 12: Location and ambient dose equivalent rate distributions (\(\upmu\)Sv/h) in the regions of interest. (**a**) Top view at beamline level, (**b**) Side view at beamline level. The materials are indicated by different colors corresponding to Figure 4. Distances are given in cm.  
With this method, the 3D radiation field can be displayed interactively and intuitively, making it suitable for presentation and demonstrations.
# Monitoring and Forecasting Green Tide in the Yellow Sea Using Satellite Imagery
## 4 Results
### Semi-Automated Green Tide Extraction
#### 4.1.2 Accuracy Assessment
This was because the _NDVI_ value of the main background pixel (seawater pixel) was mostly negative, and the threshold value we automatically selected was generally positive. Therefore, it was rare to misclassify the background pixel as a green tide pixel (FP). The Recall varied little under different water depths, but it was significantly higher than that in muddy water areas (Zone C) and under thin cloud cover (C3). There were still some differences in accuracy results within the same zone with a similar environment, such as A1 and A2. We think this was related to the proportion and distribution of green tide in the selected area and the amount of sporadic floating green algae. When the amount of green tide in a one-pixel grid was small (the case of sporadic floating green algae pixel), it could easily be misclassified as a background pixel (FN). Therefore, we used the Coverage rate to roughly reflect the distribution of green tide in the selected area; the larger the value, the higher the quantity and aggregation degree of green tide in the area. As we can see in Table 6, the higher the value, the higher the accuracy. In general, the results of the comprehensive evaluation index F1-score were all above 90%, except C3, which is covered by thin clouds. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Zone**} & **Serial** & **Precision** & **Recall** & **F1-Score** & **Coverage Rate** \\ \cline{2-6}  & **Number** & \(\frac{TP}{TP+FP}\) & \(\frac{TP}{TP+FN}\) & **2\(\times\)**Precision\(\times\)Recall** & **TP** \\ \hline A & A1 & 99.6\% & 90.0\% & 94.6\% & 7.79\% \\ (Depth of & A2 & 99.8\% & 96.5\% & 98.1\% & 10.4\% \\ \(<\)\(-\)35 m) & A3 & 99.9\% & 99.0\% & 99.5\% & 18.9\% \\ \hline B & B1 & 99.9\% & 98.3\% & 99.1\% & 13.1\% \\ (Depth of & B2 & 99.9\% & 98.4\% & 99.1\% & 29.6\% \\ \(\geq\)\(-\)35 m) & B3 & 99.9\% & 94.6\% & 97.2\% & 8.59\% \\ \hline C & C1 & 99.9\% & 84.2\% & 91.4\% & 5.70\% \\ C & C2 & 99.9\% & 93.4\% & 96.6\% & 12.6\% \\ (Muddy & C3 & 99.8\% & 69.5\% & 82.0\% & 5.67\% \\ water area) & (Think cloud) & 99.8\% & 69.5\% & 82.0\% & 5.67\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracy assessment results of the extraction effect in different environments. Figure 9: Accuracy assessment of extraction results with different orders of magnitude of pixel numbers (RS data: Landsat 8, 23 June 2021, Worldwide Reference System: path 119 and row 35). (**a**) is a schematic diagram of extraction regions of different orders of magnitude pixel numbers and (**b**) is the corresponding accuracy of different orders of magnitude of pixel numbers.
## 4 Theoretical Optimization of Converter Inductor
### Frequency and Ripple Optimization for One Inductor Value
#### 4.1.1 Losses in the Main Inductors  
The following operating point is considered for the design optimization: \(V_{in}\) = 30 V, \(V_{out}\) = 20 V, \(\langle I_{out}\rangle\)= 2.5 A. For a given inductor value of 47 \(\mu\)H, the losses in the output inductors with respect to frequency are provided in Figure 15 below. One can see in the plots the losses due to the DC current (left), the losses due to HF current harmonics (centre) and the total losses in the inductors (right). It appears that no matter the type of losses, they all decrease with respect to the frequency. Indeed, increasing the switching frequency reduces the ripple current magnitude, which in turn reduces the RMS current value. This has a direct impact on the DC losses. Looking at HF losses, on one side, increasing the switching frequency increases ESR resistance (representing core losses and skin effects) but it also reduces the magnitude of the current harmonics. All in all, the higher the switching frequency, the lower the HF losses. Total losses are therefore decreasing with respect to switching frequency.
## 3 AMoGA-GIS
### Ammo  
The encoding primarily consists of two main components: the task allocation sequence and the task allocation quantity. Figures 4 and 5 represent the mutation processes for the task allocation sequence and the task allocation quantity, respectively. Task allocation sequence mutation comprises three primary operations: swap; insert; and group insert. Figure 4a,b illustrate the swap operation, where it can occur within the same allocation, representing changes in task execution order, or between different allocations, signifying changes in task assignments.  
Figure 4: The mutation operations for allocation sequence. The orange represents the tasks of the first vehicle V1, yellow represents the tasks of the second vehicle V2, and green represents the tasks of the third vehicle V3. The arrows looping between two blocks indicate the exchange of the blocks, while an arrow pointing from one block to a gap signifies the insertion of the block into that gap.  
Figure 3: The crossover operation. The gene positions selected for crossover under probabilistic selection are highlighted with an orange background, where relevant genes from \(x_{2}\) are assigned to \(x_{1}\). The positions marked in red font indicate the genes to be legalized and the missing genes in \(x_{1}\) are identified in \(x_{2}\) and assigned to \(x_{1}\), resulting in the final legalized offspring \(c_{1}\).  
Insert involves placing a block into any position, while group insert maintains the relative positions within the exchanged block when inserted into another position. Both insert and group insert operations may alter the task sequence and allocation. Task allocation quantity encompasses two operations: task quantity swap and task quantity redistribution. To minimize the disruption caused by redistribution, the maximum difference between the quantity before and after the disturbance is set to 1, as depicted in Figure 4(b).  
During the early stages of evolution, emphasis is often placed on global exploration and probing of unknown solution spaces. Higher intensity perturbations may be employed to explore a broader search space, reduce selection pressure, increase population diversity, and aid the algorithm in escaping local optima. As evolution progresses, the algorithm tends to focus more on local exploration and a finer exploration of the regions surrounding local optima. Different mutation strategies impart varying levels of disturbance at different evolutionary stages. Hence, an adaptive selection of mutation operations is proposed in our AMoGA-GIS algorithm. Initially, the selection probability for each mutation strategy is uniform. In each generation, the frequency of selecting a strategy and the dominant relationship between the generated solution and the original solution are recorded. A strategy with a higher probability, which produces a solution that dominates the original solution, is more likely to be chosen and accelerate the evolution process.
# Alternative Pedicle Screw Design via Biomechanical Evaluation
## 3 Mechanical Strength of the New Design  
The main loads due to human activity are due to bending and tension, and failure of the pedicle screws caused by them is due to fracturing by bending and pull-out by tensile loading. To investigate the strength of the screw, special experiments were conducted to analyze bending conditions and FEA simulations were performed to assess tensile loading. Experimental and FEA tests on the conventional single- and double-threaded screws and the proposed pedicle screw were performed. The detailed dimensions of the tested screws are reported in Table 1.  
The pedicle screw in this study had a diameter of 4.5 mm. In general, the outer diameter of pedicle screws ranges from 4 to 7 mm, and the selection of a proper pedicle screw diameter must be based on the morphology of the spinal pedicle. Therefore, it is difficult to define a representative pedicle screw diameter. In fact, the mechanical behavior of screws with other diameters is easily predictable because screws with different diameters perform analogously. In the present study, the effect of screw insertion was not addressed because pedicle screw insertion was assumed to be accomplished appropriately and the osseointegration was perfect even though the insertion could affect the clinical outcome.
# Measurement of Lower Extremity Alignment Using a Smartphone Application
###### Abstract  
Identification of lower extremity misalignment requires radiation exposure and complex imaging. We developed and tested a smartphone application to facilitate quick identification of misalignment using photographs. Lower extremity alignment was measured by two independent researchers using a proprietary smartphone application and conventional radiographs. The results were compared between the methods and evaluators for interrater and intranater reliability. Ninety datasets were obtained from 45 patients, with 90 lower extremity alignment angles measured via radiographs and the smartphone application. The intranater reliability of the hip-knee-ankle angle (HKAA), measured twice by evaluator A using the radiographic imaging program, was 0.985, whereas that measured by evaluator B was 0.995. The intranater reliability of the predicted lower extremity alignment angle (PLEAA) measured using the smartphone application was 0.970 and 0.968 for evaluators A and B, respectively. Thus, all results showed excellent reliability. In validity analysis, the correlation between PLEAA and HKAA measured twice by evaluators A and B was analyzed using Pearson's correlation coefficient. HKAA (A) and PLEAA (A) had a positive correlation coefficient of 0.608 (\(p<0.01\)), whereas HKAA (B) and PLEAA (B) had a positive correlation coefficient of 0.627 (\(p<0.01\)). Thus, our smartphone application can facilitate for self-diagnosis of lower extremity misalignment.  
Keywords:diagnosis; knee osteoarthritis; lower extremity; prevention; knee deformity; smartphone application +
Footnote †: journal:
## Appendix A The LoRaWAN Communication Solutions
### Understanding LoRaWAN Limitations  
The aim of this section is to briefly resume compromises and limitations resulting from the adoption of LoRaWAN (and alike) communication solutions. In few words, everything is related to the exchange of performance with low-power, low-cost and long-range requirements [57].  
For instance, the long-range is obtained increasing the sensitivity of the radio, which in turn is permitted by low bandwidth. As a consequence, the bitrate is poor (down to a fraction of kbps for higher SF values, with a maximum user payload as low as 51 bytes). Consequently, message length can be in the order of several seconds, thus suffering from possibly high collision rate with other interfering messages in dense environment with frequent updates. On the other hand, the low-cost needs for simplified protocol, which permits the use of resource constrained microprocessors. For this reason, simple but inefficient ALOHA medium access strategy is chosen, instead of scheduled transmissions, which require some kind of synchronization.  
Other limitations arise from the operation in sub-GHz unlicensed bands, where few channels are permitted, despite the aforementioned reduced bandwidth. Even if transmission of overlapping frames modulated using different SF typically results in the correct reception of all of them, such virtual channels are only quasi-orthogonal and implementing SF-multiplexing could be not so effective. Additionally, duty-cycle is bounded due to regional regulations; for example, when used in Europe in the EU 868 ISM band, the typical duty-cycle is 1 percent, therefore resulting in a maximum over-the-air time of 36 s/h in each sub-band for each device.  
Regarding the protocol stack, it has to be highlighted that LoRaWAN specifications only define a transport mechanism from end-devices to users. Nothing is said about the actual application level; profiles, which pave the way to interoperability, are not defined. Indeed, users are free to overlay LoRaWAN communications with their own protocol, that has pros and cons. Referring to Figure 2, LoRaWAN is limited to the first four layers and only partially provides some services of the fifth one. Accordingly, it is the user's responsibility to define and provide both application data and metadata.  
Regarding fault tolerance, a LoRaWAN message sent by a node can be received by multiple Gateways, thus ensuring some form of redundancy at the link level. Successively, the NS intrinsically performs a deduplication operation in order to reject multiple copies. On the other hand, redundancy of the backend is out of the LoRaWAN scope, but can be implemented if needed.
# A Study of MANET Routing Protocols in Heterogeneous Networks: A Review and Performance Comparison
## 1 Introduction
### Research Challenges  
In this study, we address the following three research questions/challenges.  
Research Question 1: How do the routing ad hoc on-demand distance vector (AODV), optimized link state routing (OLSR), better approach to mobile ad hoc networking (BATMAN), and dynamic MANET on-demand (DYMO) protocols affect the MANET performance in a heterogeneous environment?  
MANETs are increasingly popular due to their ability to be set up easily and provide communication support in areas lacking infrastructure. However, maintaining a high level of performance in MANETs is challenging due to factors such as the node mobility, battery limitations, and multi-hop routing structures. While extensive research has been conducted on MANET routing protocols [1], the performance implications of heterogeneous nodes--devices with varying capabilities--remain underexplored. This study aims to review and compare the performance of four selected MANET routing protocols in a heterogeneous setting, considering different types of nodes, including PDAs (fixed nodes), laptops (low mobility), and mobile phones (high mobility).  
Research Question 2: What impacts do heterogeneous nodes have on the quality of service (QoS) parameters in MANET routing protocols?  
The heterogeneity of the nodes in MANETs introduces complexity into communications, potentially affecting the QoS metrics. Understanding how different types of nodes impact these metrics is crucial to optimizing the performance of MANETs. This research utilizes the OMNeT++ network simulator to measure and analyze the QoS metrics across the four selected routing protocols, providing insights into the performance variations based on the node capabilities and mobility. In fact, we stress the multimedia QoS performance metrics such as the packet delay and throughput that we measure by simulation and compare the performance among the four selected MANET routing protocols. This multimedia QoS aligns with the focus of the special issue.  
Research Question 3: How can the findings from the performance comparison of MANET routing protocols in heterogeneous environments inform the development of next-generation wireless networks?  
The insights gained from studying the performance of AODV, OLSR, BATMAN, and DYMO in a heterogeneous MANET can guide future network researchers and engineers in developing robust wireless networks. Identifying the strengths and weaknesses of each protocol under varying conditions will be essential for creating systems capable of efficiently managing heterogeneous nodes and enhancing the overall network performance.
# Effective Fabric Defect Detection Model for High-Resolution Images
## 3 Experiments
### Experiment IV: Improvement of the FPN Model  
To evaluate the performance of the proposed model, we compared the commonly used FPN model with the improved FPN model proposed in the study. We introduced two main improvements in the FPN module, i.e., CARAFE and network path optimization.  
The CARAFE module was employed in Experiment 4-1 and the two aforementioned improvements were employed in Experiment 4-2. In Figure 19, the blue, orange, and gray rectangles represent the detection results of Experiments 3, 4-1, and 4-2, respectively.  
Figure 17: Contrast experiment for data augmentation.  
Figure 18: Contrast experiments to improve ResNet.  
Comparing the detection results of Experiments 3 and 4-1, it was evident that the AP of each defect almost increased and the mAP increased from 71.78% to 74.67%. Then, in comparison to Experiment 4-1, the mAP of defects in Experiment 4-2 increased by 0.66%.  
Images in the top part of Figure 20 illustrate the detection results of the original FPN module in Experiment 3, while those in the bottom part show the detection results of Experiment 4-2. The analysis of detection results indicated that the improved FPN module exhibited higher detection and positioning accuracy for small defects. Moreover, the two improvements in the FPN module improved the detection accuracy of defects in fabric.
# A Review of Recent Advances in Roll Stability Control in On-Road and Off-Road Vehicles
## 4 Roll Stability Control in Road Vehicles
### Active Roll Stability Control Technologies
#### 4.2.1 Single-Actuator Control
However, the accuracy of this method depends on high-performance actuators and precise vehicle dynamic models. In extreme conditions, it may need to be combined with other control strategies, such as active braking or active suspension, to achieve optimal rollover prevention effects. Active suspension control directly adjusts suspension forces and roll stiffness to reduce body roll angle and optimize load distribution, making it a direct method for rollover prevention [98,99]. Compared to active braking and active steering, active suspension can provide additional control forces through suspension actuators to directly suppress roll dynamics, without significantly affecting vehicle handling and comfort. Therefore, it is considered one of the most direct methods for rollover prevention. In recent years, researchers have conducted in-depth studies on active suspension-based rollover control. For example, [PERSON] et al. [100] developed an adaptive interconnected suspension (AIS-ARS) using hydraulic links and two electromagnetic valves to replace traditional anti-roll bars, reducing costs by 60% and roll angles by 35%. [PERSON] et al. [59] combined rollover warning with fuzzy PID to enhance suspension-based rollover prevention, while [PERSON] et al. [64] introduced a wheelbase-preview MPC method that improves ride stability on uneven roads. [PERSON] et al. [65] proposed an NMPC strategy with adaptive weighting to coordinate the yaw, roll, pitch, and vertical motions, boosting the control accuracy by 40% and steering response by 25%. [PERSON] et al. [101] incorporated road preview into a 9-DOF model predictive controller to enhance comfort and safety on unstructured roads. [PERSON] et al. [102] used SOS-based polynomial tracking to reduce roll angle errors, improving control in semi-active suspension systems. Although active suspension control can dynamically adjust suspension forces and stiffness, this method places high demands on actuator performance and has higher implementation costs. Therefore, in practical applications, it is essential to balance the system complexity, energy consumption, and control effectiveness.