esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Model Validation
This agreement was largely ensured by the preliminary calibration of the mathematical model (selection of the fan operating point and the heat transfer coefficient through the wall of the channel). The similarity in the variation of the studied flow parameters across the channel's cross-sections near the corresponding average sections further validated the accuracy of the selected mathematical model. The conclusion drawn demonstrates the suitability of RANS turbulence models for analyzing the patterns of steady non-isothermal flows in inclined channels with flow stratification induced by intense heat sources. This conclusion is justified as we analyzed time-averaged flow characteristics at a distance from the heat source, which are consistent with those in the existing literature [50]. However, it is important to note that RANS models may not accurately capture some small-scale structural features of convective flows in inclined channels and can lead to overestimated air temperatures in the vicinity of heat sources [51]. In contrast, LES can offer higher accuracy by resolving a broader range of turbulent scales [52; 53]. Nevertheless, we consider the accuracy provided by RANS models to be sufficient for the purposes of our study.
## 4 Experimental Datasets and Evaluation
### Model Comparisons  
To evaluate our rural building extraction method, we selected the typical encoder-decoder architecture extraction model SegNet, AGs-UNet with a structure similar to our building extraction model, C\({}^{3}\)Net, and CSA-UNet, also applied to rural building extraction for comparative experiments. A brief introduction of these models is as follows:  
Figure 8: Image and label selected from Varied Drone Dataset.  
(1) SegNet is an image semantic segmentation network based on the encoder-decoder architecture proposed by [PERSON] et al. in 2015 [47]. Compared with FCNs, the difference is that the decoder end reuses the max-pooling indices of the encoder pooling layers to help restore segmentation results with rich details [48]. SegNet shows good real-time processing capabilities and semantic segmentation accuracy and is especially suitable for accurate pixel-level building extraction, so it is selected as the benchmark model.  
(2) AGs-UNet is an improved version of UNet with multiple AG modules embedded to achieve enhanced representation of building-related features [21]. Its attention mechanism can suppress irrelevant features, thereby improving the model's ability to identify key building areas [49]. AGs-UNet has verified the improvement of extraction performance on multiple high-resolution urban remote sensing image datasets and is selected to evaluate the effect of the attention module on this task.  
(3) C\({}^{3}\)Net adopts an encoder-decoder structure. The decoder integrates context-aware and edge residual refinement modules to achieve a balance between extraction accuracy and integrity [23]. This model has been proven to make progress on multiple open datasets and is considered to provide comprehensive comparative extraction capabilities for this task.  
(4) CSA-UNet is based on the encoder-decoder structure, using channel spatial attention mechanisms to focus on key building features [50]. Compared with other datasets, its specifically constructed rural building sample dataset using UAV images is more consistent with the research objectives of this study. Considering the impact of dataset differences on model generalization capabilities, CSA-UNet is selected to evaluate the adaptability of models under new sample distributions.  
In summary, these models have different focuses on building extraction tasks and can serve as comparative benchmarks to analyze the strengths and weaknesses of the AGSC-Net model in the application of extracting buildings in typical rural areas of China, evaluating the extraction capabilities of the model. To evaluate the extraction capabilities of the proposed model, all networks are tested on two datasets: the Building Image Dataset of Typical Rural Areas in China and the complementary China Rural Building UAV Imagery Dataset. The experimental results from these datasets are utilized to verify the extraction potential of the AGSC-Net model. The empirical results of these comparative experiments are detailed in the subsequent sections.
## 3 Results and Analysis  
As aforesaid, 70% of heartbeats were randomly chosen from the sample set as the training dataset of the classifier and the other 30% of heartbeats were used as the test pattern for performance evaluation. Table 4 shows the confusion matrix for the testing beats of a one-time simulation. Regarding the test dataset, the accuracy of the normal beat reached 99.7%. However, the accuracy of the atrial premature beat was only 71.24%. The average of the accuracy of the six type of beats for one-time simulation was calculated, and the accuracy of the classification for the proposed method reached up to 94%.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Main Operating** & **Method 1** & **Method 2** & **Proposed Method** \\ \hline Fixed-size heartbeat & Y & Y & N \\ CNNs & Y & Y & Y \\ SPP layer & N & Y & Y \\ \hline \hline \end{tabular}
\end{table}
Table 3: Three network structures.  
The classification performance was influenced by the training dataset, which was randomly chosen for the classification. To avoid the influence of randomness, the simulation for each network structure was repeated 10 times, and the comparison of the accuracies for three network structures is shown in Figure 6. The accuracies of Method 1 and Method 2 were reduced by nearly 3.6% and 1.5% resulting from the data loss during the sampling process. In addition, the relatively lower accuracy of Method 1 was also derived from the removal of the SPP network structure as compared with the accuracy of Method 2. The two-sided Wilcoxon rank sum test [29] was also employed to evaluate whether the results between different methods had a significant difference, and the \(p\)-value shown in Table 5 manifests that the result of the proposed method had a significant difference when compared with the other two methods. Therefore, building an SPP structure into the traditional CNN allowed the input of different-sized heartbeats and could extract better features and improve the classification performance.
## 4 Performance Analysis
### Outage Probability
#### 4.2.3 Diversity Order Analysis  
To deeply study the outage performance of the two methods, we analyze the diversity order which is defined as \(D=-\lim_{\delta\rightarrow\infty}\frac{\log(\Pr^{\alpha}(\delta))}{\log\delta}\).  
As \(e^{-x}\approx 1-x\) when \(x\to 0\), (28) of the co-carrier-based DC method can be further derived as  
\[\Pr_{k}=1-(1/|g_{k}|^{2})^{M}(\frac{\alpha\Gamma_{k}}{M|g_{k}|^{2 }}+\frac{1}{|g_{k}|^{2}})^{-M}e^{-\frac{\Gamma_{k}}{\delta M|g_{k}|^{2}}}\] \[\stackrel{{\delta\rightarrow\infty}}{{\approx}}1-(1/ |g_{k}|^{2})^{M}(\frac{\alpha\Gamma_{k}}{M|g_{k}|^{2}}+\frac{1}{|g_{k}|^{2}}) ^{-M}(1-\frac{\Gamma_{k}}{\delta M|g_{k}|^{2}}). \tag{38}\]  
In this case, the diversity order can be found as \(D=0\).  
For the co-carrier-based selection combination, \((1-e^{-\frac{\Gamma_{k}}{|g_{k}|^{2}\delta(1-\pi k)}})^{M}\stackrel{{ \delta\rightarrow\infty}}{{\approx}}(\frac{\Gamma_{k}}{|g_{k}|^{2} \delta(1-\pi k)})^{M}\). Then, \(D^{\prime}=M\).  
Consequently, we can clearly find out that co-carrier-based SC is more reliable compared to the co-carrier-based DC.
## 3 Results
### Results of Sensitivity Analyses
#### 3.2.4 Sensitivity to Magnitude of Solar Irradiation  
For this analysis, the solar irradiation on the surface of the PV panels was multiplied by factors from 0.6 to 1.4.  
Figure 18a shows the NPC of the PV optimal systems for the pig farm case. In it, the line corresponding to the system with Li-ion batteries has a greater slope than that with lead-acid batteries, i.e., the system with Li-ion batteries was more sensitive to variations in the solar irradiation. Thus, in this case and in the interval studied, higher solar irradiation favored the presence of Li-ion batteries over lead-acid batteries in the absolute optimum, and vice versa. On the contrary, Figure 18b shows that in the hybrid systems, higher solar irradiation favored the presence of lead-acid batteries. However, the sensitivity to the variation of solar irradiation was not very high, so that in the interval studied, neither the crossing of the lines nor the consequent change in the type of battery present in the absolute optimum occurs. All other cases had the same behavior. This sensitivity analysis bears  
Figure 16: Sensitivity analysis of the NPC of the optimal hybrid systems with lead-acid and Li-ion batteries, based on the inflation of diesel: (**a**) Pig farm case; (**b**) Winery case.  
Figure 17: Sensitivity analysis on optimal hybrid systems for winery case, based on the inflation of diesel: (**a**) Battery capacity; (**b**) PV power and energy from diesel in the Li-ion system.  
some similarity to that of the acquisition cost of the elements of the PV generator, since both affect the cost of the PV generator necessary for a given energy production. However, their magnitudes and ranges are different.
## 1 Introduction
### Structure of the Article  
The rest of the article is organised as follows. Section 2 presents the literature review relevant to the NOMA scheme used in the network. Section 3 describes the proposed system model for the downlink PD SISO-NOMA communication scenario for TI. This section also includes the derivation and analysis of SINR, achievable sum rate, fair PA and latency. The performance evaluation is presented in Section 4. The simulation results are also presented in this section. Section 5 concludes our work with future directions. Finally, Table 11 lists the abbreviations and their explanation (see Appendix A).
## 3 Model Implementation
### Control Plane Implementation: CUC and CNC  
The control plane is responsible for collecting stream requirements, computing the transmission schedule (GCL), and configuring the network devices. It consists of two primary logical entities: the Centralized User Configuration (CUC) and the Centralized Network Configuration (CNC).  
* Centralized User Configuration (CUC): The CUC serves as the interface for specifying the requirements of various data streams, particularly the Time-Triggered (TT) streams which demand deterministic transmission. For each TT stream \(F_{i}\), users (or higher-level application planners) provide its characteristic parameters as defined in Section 2 (Table 1). These include its length \(L_{i}\) (or transmission time \(t_{i}\)), period \(T_{i}\), deadline \(D_{i}\), intended transmission path \(P_{i}\) through the network, and its Priority Code Point (\(PCPi\)).
* Centralized Network Configuration (CNC): The CNC is the core intelligence of the control plane. It receives the stream requirements gathered from the CUC and the network topology information. The CNC then executes the scheduling logic to generate the GCL. In this work, the CNC implements the Baseline Scheduling Algorithm with Sub-Flow Division (detailed in Section 2.4) to compute the GCL. This involves stream preprocessing and filtering, sub-flow division for high-bandwidth streams (using Algorithm 1), iterative determination of constraint boundaries, and finally, invoking the Z3 SMT solver to compute the precise start times (\(\varphi_{i,j,\mu}\) or \(\phi_{i,\mu}\)) for all scheduled transmissions. Alternatively, for scenarios requiring faster computation, the CNC can employ the Scheduling Computation Acceleration Algorithm (detailed in Section 2.5). Once the GCL is computed, the CNC is responsible for translating this schedule into a format understood by the TSN switches and distributing it to the respective switches in the data plane to configure their gate control mechanisms.  
Figure 2: In-vehicle network architecture based on the IEEE 802.1 Qcc centralized model. The diagram shows the interaction between the control plane (CUC, CNC) and data plane components (end-nodes, TSN switch). Solid lines represent data plane traffic flows, while dashed lines indicate control plane communication. The red dot on the ‘LeftFrontWheel’ icon signifies a sensor data source.
# Development of Navigation Network Models for Indoor Path Planning Using 3D Semantic Point Clouds
## 2 Methodology
### JSON-Encoded Navigation Network  
The final extracted data structure is saved in JSON [30] format, as illustrated in Figure 9. JSON is a lightweight data exchange format that is easy for people to read and write, and easy for machines to parse and generate. This format facilitates easy manipulation and integration with various applications, making JSON an ideal choice for data exchange, especially in web applications.  
In this work, the designed JSON format data structure includes two main components: nodes and edges, as shown in Figure 9. Each node in the JSON structure represents a specific point (e.g., office, hallway, door, or staircase) in the indoor navigation network. The attributes of each node include the following:  
* ID: A unique identifier for each node.
* Name: The name assigned to the node (e.g., \"door\", \"staircase\").
* Navigable Type: The type of space the node represents (e.g., general space, connection space, or transition space).
* Affiliation: The specific floor to which the node belongs in a multi-floor building.  
* Coordinates: The x, y, and z coordinates that specify the node's position in the 3D space.
* Accessibility: A Boolean attribute indicating whether the node is wheelchair accessible. Edges define the connections between nodes, forming the basis of the indoor navigation network. Each edge includes the following:
* From: The ID of the starting node.
* To: The ID of the ending node.  
This approach enhances the interoperability of the extracted indoor navigation network data, allowing it to be seamlessly used in further computational analyses and real-world navigation applications.
# Optical Design for Laser Diode Scanner Headlamp with
## 4 Experimental Results and Discussion
Similarly, the value in the phosphor region measured at 0.5 \(\pm\) 0.086 is indicated as 0. To compare the performance of the designed optics with that of the existing scanning system, the prism was removed and an experiment was performed, as shown in Figure 10a. Unlike previous experiments, we discovered that the detection time of the PD signal increased as the detection position approached the edge of the phosphor plane. Figure 10b shows the position of the beam spot in the phosphor plane along the scanning period in the scanning system without a prism; as shown, the time for the beam spot to pass through the edge of the phosphor was longer than the time to pass through its center. This means that the optical energy increased when the phosphor spot was at the edge. Figure 11 shows the normalized optical energy measured in the designed scanning system with a prism and a scanning system without a prism. The optical energy was calculated by integrating the voltage values measured from Figures 9 and 10 by the measured time. In the designed scanning system and the scanning system without a prism, the optical energies of light passing through the center were 5.4 and 0.35 times that at the edge, respectively. Figure 8: Configuration of laser diode scanner system using galvanometer and its experimental setup. LD—laser diode; EL—expanding lens; M—mirror; A—aperture; GM—galvanometer; P—prism; FL—focusing lens; PD—photo diode  
Figure 10: (**a**) Measured voltage by PD at phosphor plane in scanning system without prism. (**b**) Position of beam spot at phosphor plane according to scanning period in scanning system without prism. Figure 9: (**a**) Measured voltage by photodiode (PD) at phosphor plane in designed scanning system. (**b**) Position of beam spot at phosphor plane according to period of scanning in designed scanning system. The distributions of light illuminated on the phosphor plane by the designed scanning system and the scanning system without a prism are shown in Figure 12. The scanning velocities of the long and short axes were 100 and 1 Hz, respectively. For the designed scanning system, the center of the phosphor plane was brighter than the edge, as shown in Figure 12a. For the scanning system without a prism, the edge of the plane was brighter than the center, as shown in Figure 12b. In Figure 12a,b, the effective area of the phosphor plane is represented by the dashed yellow box. These experimental results imply that the optical power in the headlamps can be distributed more efficiently using the proposed scanning system.
## 6 Discussion and Comparison  
A number of techniques for controlling the motor at different speeds and torques have been described in the technical literature for the DFIM. As an example, consider the FOC control developed by [18], which is very sensitive to variations in the parameters of the motor. The torque ripples of this strategy are 2.7 Nm, and the response time is 0.56 s. Because of the drawbacks of the controls outlined above, the DTC emerges as the most suited option, which is known by its robustness, a response time of 0.0402 s, and 3.28 Nm for the torque ripples. In ref. [23], the authors replaced the torque and flux hysteresis comparators with three PI controllers in DTC in order to have a new control, named space  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Controller Parameters**} & \multirow{2}{*}{**Classic DTC**} & \multicolumn{4}{c}{**GA-DTC**} \\ \cline{3-6}  & & **ITAE** & **IAE** & **ISE** & **Weighted** \\ \hline K\({}_{\rm P}\) & 18 & 87.5096 & 48.5655 & 89.8234 & 72.8895 \\ \hline K\({}_{\rm I}\) & 0.8 & 0.0162 & 0.0141 & 0.2338 & 0.0729 \\ \hline K\({}_{\rm D}\) & 0 & 3.7311 & \(-0.8231\) & 5.5144 & 0.5262 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters of PID controller under DTC and GA-DTC.  
vector modulation (DTC-SVM), but this technique is not robust, due to linear PI controllers sensitive to parametric variations, which presents a response time of 0.16 s and 3.28 Nm for torque ripples. In ref. [40], the authors used a new optimization algorithm, named the rooted tree optimization algorithm (RTOA), to optimize PI gains in order to have a robust behavior in the DFIM, but the higher torque ripples remain the major drawbacks of this technique.However, in our proposed technique based on GA with DTC, by using combined objective functions, showed great DFIM behaviors, which were presented by a rapid response time of 0.0242 s, and medium and smallest torque ripples of 2.59 Nm. The performance of several techniques used in the DFIM is shown in the following table, which may be found below (Table 5).
# An Effective Scheduling Algorithm for Coverage Control in Underwater Acoustic Sensor Network
## 4 Performance Evaluations
### Convergence of the ESACC
In summary, experiments show that ESACC can effectively reduce the number of active nodes and reduce node utilization based on network status. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Number** & **Fitness** & **ESACC** & **Std.** & **GA** & **Std.** & **Number** & **Fitness** & **ESACC** & **Std.** & **GA** & **Std.** \\ \hline
50 & 0.62 & 0.35 & 0.08 & 0.09 & 0.02 & 150 & 0.79 & 3.03 & 0.07 & 8.53 & 3.54 \\
50 & 0.58 & 0.26 & 0.07 & 0.07 & 0.02 & 150 & 0.75 & 1.49 & 0.05 & 4.28 & 2.24 \\
50 & 0.37 & 0.01 & 0.01 & 0.01 & 0.01 & 150 & 0.36 & 0.03 & 0.01 & 0.05 & 0.01 \\
250 & 0.82 & 4.7 & 0.11 & 31.16 & 16.52 & 350 & 0.85 & 8.91 & 0.21 & 52.64 & 25.36 \\
250 & 0.81 & 3.91 & 0.11 & 18.56 & 10.23 & 350 & 0.83 & 7.37 & 0.20 & 40.52 & 21.23 \\
250 & 0.35 & 0.05 & 0.01 & 0.09 & 0.01 & 350 & 0.34 & 0.07 & 0.01 & 0.12 & 0.02 \\
450 & 0.87 & 16.61 & 0.28 & 74.52 & 36.14 & 550 & 0.88 & 20.07 & 0.21 & 118.11 & 55.21 \\
450 & 0.86 & 12.10 & 0.27 & 68.48 & 33.74 & 550 & 0.87 & 18.01 & 0.30 & 100.11 & 52.62 \\
450 & 0.34 & 0.09 & 0.01 & 0.15 & 0.01 & 550 & 0.34 & 0.12 & 0.01 & 0.19 & 0.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The statistic results of experiments. Each experiment was repeated 30 times to compare the average computation time (seconds) between ESACC and GA at different fitness values.
## 4 Laboratory Tests
### Phase 2: Materials and Physicochemical Characterisation
#### 4.1.1 Soils: Expansive Clayey Soil and Sandy Soil  
In this work, two types of soils have been analysed for the subsequent stabilisation and construction of the layers of the trial section: an expansive clayey soil (CS) and a quarry reject from the production of crushed gravel called sandy soil (SS).  
The clayey soil (CS) comes from the plot where the real-scale sections are built, in Villacarrillo (Jaen), Andalusia.  
The sandy soil comes as a reject from a crushed gravel production quarry located near Villacarrillo. The rejects are subjected to a sieving process to achieve a particle size within the parameters of Spanish regulations [34]. Table 1 shows the physicochemical properties of the two soils analysed, and Figure 2 shows the granulometric curves of the soils.  
Figure 2: Particle size distribution of clayey soil and sandy soil.  
As shown in Table 1, the clayey soil shows a very high plasticity, with a plasticity index greater than 30, a discontinuous granulometry in which 95% of the particles are fine clays and silts and a maximum particle size of 5 mm.  
In contrast, the sandy soil has a continuous grain size, composed mainly of particles larger than 4 mm, with a maximum aggregate size of 32 mm and a percentage of fine grains of less than 6%. The plasticity index of SS is 4.2, which indicates that it is not a very plastic material.  
Both materials have low percentages of organic matter content and water-soluble sulphates, which make them suitable for use in road layers in accordance with Spanish regulations.  
Regarding the composition of the soils, an analysis of the major compounds by X-ray fluorescence (XRF) and mineralogical analysis by X-ray diffraction (XRD) was carried out, as shown in Table 1 and Figures 3 and 4.  
The clayey soil presents a main composition of silicon, calcium and aluminium, a typical composition of clays, which is observed in a mineralogy composed of quartz, calcite and montmorillonite-type clay minerals.  
The sandy soil has a main composition of calcium and magnesium, a typical composition of dolomite minerals, as shown in the XRD of Figure 4.