esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Plaque Classification
#### 4.2.1 Four-Way Plaque Classification  
Very few studies which attempt an automatic classification of tissue in OCT images discriminate four plaque types. To the best of our knowledge, there is no study for A-line classification that distinguishes four plaques. We performed five runs of four-way classification with different test sets of attenuation images. This procedure was repeated for original images. Cumulative confusion matrices were calculated for original images and attenuation images by adding the confusion matrices of five test runs. The accuracy was calculated from the cumulative confusion matrices. The overall accuracy was 53.47% for the attenuation images and 47.68% for the original images. In Figure 8, the cumulative confusion matrix of the five runs is presented for the attenuation images.  
It can be observed that lipid and fibrous plaque are more often mistaken for each other. This is because A-lines often include both plaques. Fibrous plaque starts on the surface of the arterial wall and is followed by lipid plaque, which is located deeper. In fact, A-line classification methods refer to their combination as fibrolipidic plaques. Mixed and calcified plaques are also mistaken for each other because they present similar textures, and their tissue types share common properties. However, the borders of calcified plaque are sharply delineated. Border information is not captured by A-line classification. Therefore, A-line classification methods [22; 23] probably refer to mixed and calcified plaques as one group, i.e., fibrolcific plaque.  
Figure 7: Detection of the atherosclerotic plaque position (purple line) in the (**a–c**) images for three different plaque types. (**a**) mixed plaque (green area as labeled by the manual segmentation), (**b**) calcified plaque (white area), and (**c**) fibrous plaque (blue area). Yellow line in (**a–c**) images shows the detected as normal parts of the arterial wall (lumen border position). In the three images the purple line shows the position of the atherosclerotic plaque in high agreement with the manual segmentation.
# Research on the Aerodynamic-Propulsion Coupling Characteristics of a Distributed Propulsion System
## 2 Materials and Methods
### Validation of Body Force Model
The aerodynamic characteristics of the fan stage at design rotation speed (100% RPM) are presented in Figure 5. The experimental results were obtained from the test rig at the Civil Aviation University of China, with a relative error band of 3%. It can be found that the results obtained using the BFM method are in good agreement with the numerical simulation results (RANS) and experimental results (EXP). The isentropic efficiency obtained using the BFM method is slightly higher than that obtained using the RANS method. The maximum deviation is not more than 2 percentage points. This can be explained by the fact that only the work and deflection of the blade acting on the airflow are considered, and three-dimensional flow losses, such as tip leakage flow loss and secondary flow loss near the endwall region, are neglected. The mass flow rates at the near-choke point obtained using the numerical simulation method (both the BFM and RANS methods) are slightly higher than the experimental results. This can be attributed to the blockage effects of the centrifugal compressor, combustion chamber, and turbine in the DGEN 380 engine test, which lead to a reduction in the mass flow rate through the fan. Therefore, the experimental result of the mass flow rate at the near-choke point is slightly lower than that of the numerical simulations. The Mach number contours in the meridional plane are illustrated in Figure 6. It can be observed that the results obtained using the BFM method agree well with the RANS results. The body force model can accurately simulate the work performed by the fan stage  
Figure 4: Full-annulus computational domain and mesh for RANS method: (**a**) computational domain; (**b**) computational mesh. Figure 5: Fan stage aerodynamic characteristics at design rotation speed: (**a**) total pressure ratio vs. mass flow rate; (**b**) isentropic efficiency vs. mass flow rate. and the flow loss in the boundary layer near the endwall region. In the rotor and stator regions, the Mach numbers obtained by the BFM method are lower than those obtained by the RANS method. This is because the BFM method does not consider the effects of secondary flows, such as tip leakage flow and endwall passage vortex. In general, the numerical simulation results obtained using the BFM method agree well with the RANS results, and the computational efficiency is markedly improved. Therefore, it is feasible to use the BFM method to simulate the blade force acting on the airflow.
# Improved Hybrid A* Algorithm Based on Lemming Optimization for Path Planning of Autonomous Vehicles
## 2 Algorithm Overview  
### Hybrid A\({}^{*}\) Algorithm  
The hybrid A\({}^{*}\) algorithm effectively integrates a discrete search with a continuous state space, balancing path smoothness and computational efficiency through a grid discretization strategy while accounting for vehicle kinematic constraints and \(\theta\)-direction exploration. This approach generates paths that combine motion feasibility with obstacle avoidance capabilities. Its discretization mechanism not only ensures a bounded search space but also maintains algorithmic efficiency. The hybrid A\({}^{*}\) search process is illustrated in Figure 2.
# Breath Analysis: A Promising Tool for Disease Diagnosis--The Role of Sensors
## 3 Types of Nanomaterial-Based Sensors in Breath Analysis
### Cross-Reactive Applications
#### 3.5.2 Colorimetric  
Colorimetric sensors, usually classified as a sub-group of optical sensors [51], are based on environmentally dependent color changes [15]. Chemo-responsive indicators are able to chemically react and change color in a distinctive way upon exposure to different gas analytes [6], thus permitting analyte identification. In this case the response upon analyte exposure is based not on the physical properties but the chemical reactivity of the indicators [15].
## 5 Conclusions  
In this paper, a novel method that determines the optimal BESS capacity with considering auxiliary services compensation is proposed from the point view of wind-energy union system. By quantifying the auxiliary services cost that is caused by the variability and uncertainty of wind output and analyzing the effect of irregular charge/discharge process on the life cycle of BESS, both the auxiliary services compensation and the equivalent loss of the cycle life are introduced in this model, which is more reasonable and precise in economic and electrochemical sense. Simulation results shows that the auxiliary services compensation can encourage wind farm configuration BESS effectively.  
Moreover, effect of the on-gird price of wind power, investment cost of BESS, cycle life of BESS and BESS reserve level are assessed though sensitivity analyses. Results show that BESS can be early applied in large-scale wind farm with investment cost being less than 360 $/kW\(\cdot\)h or the cycle life being more than 2800 times with taking the auxiliary services compensation into account. It is noteworthy  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \({{\varepsilon}_{s}}\) & \({{c}_{g}}\) (\$/MW-h) & \(f\) (5) & \(q\) (\%) & _Pcap_ (MW-h) & _Scap_ (MW-h) \\ \hline
100\% & 21.238 & 2990 & 0.0\% & 58 & 122.4 \\
80\% & 21.324 & 3160 & 1.10\% & 44.8 & 95.2 \\
60\% & 21.323 & 3930 & 1.84\% & 34.8 & 79.6 \\
40\% & 21.312 & 3560 & 2.54\% & 31.2 & 70.35 \\
20\% & 21.307 & 2910 & 3.23\% & 29.5 & 61.25 \\
0\% & 21.290 & 1790 & 3.61\% & 27.75 & 60.25 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Optimal results under different reserve level of BESS.  
that the net income of wind-storage system reach maximum with the reserve interval of BESS being about 40%-60% of the wind power forecasting error.  
Conceptualization, Y.J.; Methodology and Software, X.J.; Validation, G.N.; Investigation, H.L.; Data Curation, Z.G.; Writing--Original Draft Preparation, X.J.; Writing--Review and Editing, Q.Z.  
This research was funded by National Natural Science Foundation of China (grant number 51807180).  
The authors declare no conflict of interest.
## 5 Biosensor Application of Electrodeposited Composite Nanostructures
### Composite Electrodes with Nafion  
Nafion is a perfluorinated and sulfonated polymeric ionomer that can be cast over electrode surfaces to stabilize and entrap components while allowing for good ion exchange [135]. As such, it is promising for being cast over complex composite nanostructures on electrode surfaces. Gold nanoparticles were shown to be able to be electrodeposited into a Nafion membrane coating on either ITO glass or GCE [136]. The electrode surfaces were first covered by a solvent cast film of Nafion; then, electrodeposition proceeded at \(-0.2\) V (versus Ag/AgCl) from \(1\) wt% HAuCl\({}_{4}\) in \(0.5\) M of H\({}_{2}\)SO\({}_{4}\) until a desired charge had passed. The deposition of the nanostructures was modified spectroelectrochemically following the emergence of the plasmon absorbance band. When a higher amount of charge passed, there were more nanoparticles that came closer together, and were often in contact with each other. The modified electrode was found to catalyze the oxidation of NO, which was generated in situ from NaNO\({}_{2}\), and a detection limit of one nM was observed.
# 1D Barcode Detection via Integrated Deep-Learning and Geometric Approach
## 4 Barcode Localization
### Line Angle Detection  
The 1D barcode can be regarded as a set of parallel line segments in a close distance. Since the 1D barcode already occupies the main region in every bounding box region, we can easily define the main angle of all the line segments to represent the rotation angle of the 1D barcode. The endpoints of the line segments have a further application for a particular region, as is proposed in Section 5.  
We use the LSD (line segment detection) algorithm to detect all the line segments in the bounding box region and generate a collection of \(L\), which consists of all the detected line segments. The LSD algorithm does not need to tune parameters manually. Then, we calculate the angle and length of every line segment using the endpoints. The line segments with the same angle are clustered, and each of these angles calculates the line segment length of the sum. The angle with the longest length represents the main rotation angle \(\theta(0\leq\theta<180)\) of the 1D barcode. Figure 2 illustrates the details.  
Figure 1: Overview of our approach.  
We use the above method to predict the rotation angle, which is dependent on the apparent geometric feature of the 1D barcode. [PERSON] et al. [6] solve this problem by training another neural network, which can bring unstable training results, unnecessary computational costs, and extra data preparation work; hereafter, we confirm that our approach is more efficient than [PERSON] et al. [6]. To test our hypothesis, we designed an experiment which shows our approach can predict the rotation angle with a tiny deviation compared to ground-truth. In addition, the rotation angle prediction time in our approach is less than that when using a neural network such as [PERSON] et al. [6]. [PERSON] et al. [6] take 17.3 ms to perform the same task, while we take only 14.3 ms in the first stage. See the details in Section 6.
# ReZNS: Energy and Performance-Optimal Mapping Mechanism for ZNS SSD
## 5 Related Work  
In this section, we introduce the prior efforts on performance and energy optimization in terms of datacenters [1; 2; 5; 6] and storage devices [19; 20; 21; 22; 23; 24; 26; 27; 28].  
**Energy efficiency:** Today, the problems caused by carbon emissions are extremely important; thus, many industries have started to mitigate the issues by designing their own optimization systems [1; 2]. For example, TESLA [1] proposed a new cooling system to minimize the power consumption that comes from cooling down the internal temperature of datacenters. Some researchers focused on the trade-off between performance and carbon emissions and introduced a software-centric approach [2]. Others proposed a novel energy-aware scheduling in the Linux operating system, and they observed the relationship between the process and energy consumption [5]. For a sustainable future, some researchers proposed Lovelock using high-performance-based smart NICs in data-heavy environments [6].  
Figure 14: Energy consumption of zone-reset commands on all workloads.  
Figure 13: Energy breakdown (in percentage) of eight workloads on the baseline.  
**Emerging ZNS SSD:** The rise of datacenter technologies has brought about innovative storage devices that can meet high-performance requirements, called ZNS SSDs. There are lots of studies that demonstrate the advantages of ZNS SSDs in various environments. [PERSON] et al. proposed a new ZNS interface, called ZNS+ to address the high storage-level reclaiming overhead; it optimizes the performance of ZNS SSDs by supporting in-storage zone compaction and sparse sequential overwrites [20]. [PERSON] et al. introduced ZMS, a new I/O stack, to apply the ZNS interface in mobile environments [27]. This approach addresses the critical issue of write amplification in mobile storage and improves both performance and write amplification compared to the conventional block-based I/O stack. Meanwhile, some researchers introduced ConfZNS and NVMeVirt [21; 28] that help to develop new policies and mechanisms inside the ZNS SSD and observe the internal behaviors and features in detail. Fair-ZNS [23] was proposed as an I/O scheduling that handles the slowdown caused by running multiple applications simultaneously.  
**Zone-reset in ZNS SSD:** The _zone-reset_ command plays a critical role in ZNS SSDs; thus, many researchers have recently focused on the command and its overhead. For example, WA-ZONE [22] was recently proposed to consider the ware-leveling on zones and to avoid unnecessary erase operations. Some researchers introduced a novel algorithm for _zone-reset_ command, called FAR, that dynamically adjusts zone resets based on the available free space in ZNS SSDs [24]. [PERSON] et al. proposed Hi-ZNS [26], which tunes the size of a zone to put a single SST or WAL file into the zone.  
Despite these efforts, the wasted capacity inside zones and the costly _zone-reset_ command still remain unclear in the context of ZNS SSDs. In addition, they omit the issues of energy efficiency and carbon emissions even though it becomes extremely important as time passes.
# Study of Fast and Reliable Time Transfer Methods Using Low Earth Orbit Enhancement
## 5 Conclusions
Consequently, the PPP model enriched with LEO satellite data positively influences the reliability of time transfer. 4. Experiments involving varying quantities of LEO satellites demonstrated that an increase in LEO satellite data correspondingly improved the enhancement degree. This was mainly manifested as faster convergence speeds and higher stability in the convergence interval. In summary, the time transfer method proposed in this study can effectively enhance the performance of time transfer. This will be beneficial for further advancing the development of time-frequency application services and industries, especially for mobile time synchronization terminals; it can improve their application efficiency and product performance. [PERSON] and [PERSON] provided the initial idea for this work and wrote this manuscript; [PERSON] designed the algorithm. [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] contributed to the analyses of the results. All authors have read and agreed to the published version of the manuscript. The work is partly supported by the National Key R&D Program of China and Shandong Province (grant No: 2021 YFB3901300) and program of National Natural Science Foundation of China (grant No: 41974032, 42274019). The raw/processed data required to reproduce these findings cannot be shared at this time as the data also form part of an ongoing study. This work was supported by the Chinese Academy of Sciences (CAS) programs of \"High Level Talents\".
# Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges
## 3 Grid Operations
### Voltage and Reactive Power Control in Operation
#### 3.6.3 Automated Voltage Control Systems for Efficient Operation  
Automated voltage control within low-voltage distribution networks can leverage advanced digital techniques. It features a control algorithm embedded in a voltage source converter (VSC) for effective regulation, utilizing both reactive and minimal active power at the point of common coupling (PCC). This approach addresses traditional inefficiencies, with a digital signal controller (DSC) underscoring the pivotal role of digital technology in optimizing power flow and enhancing voltage stability and quality in the complex terrain of low-voltage networks [196].  
The integration of digital strategies, including MPC, volt-var controllers (VVCs), OLTCs, and DGs, signifies substantial progress in voltage and reactive power management. These developments indicate a transition from conventional to dynamic, real-time control mechanisms that is supported by SG technologies. This evolution focuses on maintaining grid stability and facilitating efficient RES integration.
# Beam-Hopping-Based Resource Allocation in Integrated Satellite-Terrestrial Networks
## 3 Problem Formulation
### Problem Formulation for MMCDR  
The capacity to demand ratio (CDR) is defined as the ratio of the transmission capacity to the average traffic demand of each satellite terminal or terrestrial system. To maximize the minimum CDR in the ISTN, the resource allocation problem can be formulated as  
\[\text{P2:}\ \max_{\{x_{i,t}\},\{y_{j,t}\}}\min\left(\min_{i\in N_{sat}}\left( \frac{C_{i}^{sat}}{D_{i}^{sat}}\right),\min_{j\in N_{ter}}\left(\frac{C_{j}^{ ter}}{D_{j}^{ter}}\right)\right) \tag{17}\]  
\[\text{s.t.}\ \ \eqref{P2},\ \eqref{P3},\ \eqref{P4},\ \eqref{P5},\]  
where \(D_{i}^{sat}\) denotes the average traffic demand of the \(i\)-th satellite terminal in a BH time window and \(D_{j}^{ter}\) denotes the average traffic demand of the \(j\)-th terrestrial system in a BH time window, where the average traffic demand can be obtained by analyzing the historical data of the capacity requirements. This problem aims to optimize the minimum CDR of the satellite terminals and the terrestrial systems so that the transmission resources and the traffic demands can achieve better matching and fairness can be guaranteed.  
Both the above two optimization problems, P1 and P2, are binary-integer nonlinear programming problems. Directly solving the optimal solutions of these problems leads to extremely high computational complexity. To obtain optimal solutions efficiently, we reformulate these problems as MILP problems and solve them via classical optimization algorithms. Furthermore, two greedy suboptimal algorithms are also proposed for P1 and P2, respectively, and can achieve desirable performance with much lower complexity.
# Abnormal Ship Behavior Detection Based on AIS Data
## 3 Materials and Methods  
The research method presented in this study consisted of three parts: the general situation of the research area and preprocessing of the research data, the detection of abnormal behavior of a ship based on spatial attributes, and the detection of abnormal behavior of a ship based on thematic attributes. For the second part, there were two phases: (1) the extraction of maritime routes based on AIS data and (2) the detection of abnormal behavior for ships using spatial attributes based on [PERSON]'s criterion. For the third part, there were two phases: (1) nearest route matching and (2) the detection and scoring of the abnormal behavior characteristics of the ship's thematic attributes based on the isolation forest algorithm. The framework of this study is shown in Figure 2.  
Figure 2: Framework for abnormal detection.
# A Novel 3D Topography Stitching Algorithm Based on Reflectance and Multimap
## 2 Materials and Methods
### Description of the Evaluation Criteria for Stitching Algorithms
#### 2.3.3 Mean Repositioning Error (Quantitative Indicator)  
For each local stitching, the displacements to apply to each elementary map are determined in the registration steps. Offsets can be calculated as the difference between these displacements (along \(x\), \(y\) and \(z\) axes) and theoretical displacements on the stitching grid, which is defined by the measurement apparatus before the acquisition of elementary maps, depending on user inputs (such as overlap percentage, stitched topography desired dimensions, objective lens). For in-plane registration, offsets \(\Delta_{x,offset}\) and \(\Delta_{y,offset}\) can then be computed. Error for each local stitching can then be computed with Equation (2).  
\[Error=\sqrt{\Delta_{x,\ offset}{}^{2}+\Delta_{y,\ offset}{}^{2}} \tag{2}\]  
The hypothesis of linearity of cumulated errors in stitching is assumed, as statistically accumulated errors should be linear, given that measurement apparatuses have bounded uncertainties in displacement errors (also known as tolerance). In this hypothesis, it is then possible to assess stitching quality using a linear regression to compute how linear the error accumulation of each local stitching is. Sources of non-linearity are then mostly due to bad registration, sometimes resulting in stitching chain reaction failure, i.e., a bad local registration, which impedes the next local registration, makes the following ones increasingly difficult. Figure 7 shows the cumulative repositioning errors of the same set of elementary maps stitched with two different stitching algorithms: one that has failed (Figure 7a) and one that has successfully stitched the surface (Figure 7b). Linearity of the cumulative repositioning error is lower for the stitching algorithms that have failed registration of elementary maps. The numbers of the failed local stitching can be detected as the cumulated repositioning errors are locally higher, creating sharp edges on the cumulative repositioning error plot. The slope of the accumulated errors is then the mean repositioning error, which is used for the evaluation of the stitching quality in this paper.
# Joint Power and Time Allocation in Hybrid NoMA/OMA IoT Networks for Two-Way Communications
###### Abstract  
This article investigates two-way communications between an access point (AP) and multiple terminals in low-cost Internet of Things (IoT) networks. The main issues considered are the asymmetric transmission traffic on the uplink (UL) and downlink (DL), and the unbalanced receivers processing capability at the AP and the terminals. As a solution, a hybrid non-orthogonal multiple access/orthogonal multiple access (NoMA/OMA) scheme together with a joint power and time allocation method is proposed to address these issues. For the system design, we formulated the optimization problem with the aim of minimizing the system power and satisfying the UL and DL transmission rate constraints. Due to the coupling of power and time variables in the objective function and the multi-user interference (MUI) in the UL transmission rate constraints, the formulated problem is shown to be non-linear and non-convex and thus is hard to solve. To obtain a numerical, efficient solution, the original problem is first reformulated to be a convex one relying on the successive convex approximation (SCA) method, and then a numerical efficient solution is thus obtained by using an iterative routine. The proposed transmission scheme is shown to be not only physically feasible but also power-efficient.  
n +
Footnote †: journal: 2022, 24, 1756. [[https://doi.org/10.3390/e24121756](https://doi.org/10.3390/e24121756)]([https://doi.org/10.3390/e24121756](https://doi.org/10.3390/e24121756))