esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study
### Performance of Instance Segmentation  
#### 4.2.1 Evaluation Metrics  
To evaluate the segmentation performance, the mean average precision (mAP) metric was utilized. Average precision (AP) is the area under the precision-recall curve for a specific class.  
\[Precision=\frac{TP}{TP+FP} \tag{7}\]  
\[recall=\frac{TP}{TP+FN} \tag{8}\]  
\[\text{AP}=\underset{n\rightarrow\infty}{lim}\sum_{k=1}^{n}{(R_{k}-R_{k-1})P_{ k}}=\int_{0}^{1}{p(r)dr} \tag{9}\]  
\[\text{mAP}=\frac{1}{N}\sum_{i=1}^{N}{AP_{i}} \tag{10}\]  
where \(R_{k}\) represents the k-th point on the precision-recall curve's horizontal axis, \(P_{k}\) represents the vertical axis at k. True positive (TP) refers to the count of instances correctly predicted as true by the model, false positive (FP) denotes instances wrongly predicted as true while they are actually false, and false negative (FN) represents instances incorrectly predicted as false that are actually true.  
Precision (Equation (7)) is the proportion of correct predictions among the results predicted by the model, while recall (Equation (8)) is the proportion of actual true instances that the model correctly predicts as true.  
mAP (Equation (10)) is the average of these AP values across all classes. Specifically, we used mAP@[IoU = 50:95] (mAP50:95), which averages the precision scores at IoU (Equation (11)) thresholds ranging from 50% to 95% in 5% increments. This provides a comprehensive measure of the model's accuracy across various levels of instance segmentation.  
\[IoU=\frac{Area\ of\ Overlap}{Area\ of\ Union} \tag{11}\]
# Design of a SIW Variable Phase Shifter for Beam Steering Antenna Systems
###### Abstract  
This paper proposes a new beam steering antenna system consisting of two variable reflection-type phase shifters, a 3 dB coupler, and a 90\({}^{\circ}\) phase transition. The entire structure is designed and fabricated on a single layer of substrate integrated waveguide (SIW), which makes it a low loss and low-profile antenna system. Surface mount tuning varactor diodes are chosen as electrical phase control elements. By changing the biasing voltage of the varactor diodes in the phase shifter circuits, the far-field radiation pattern of the antenna steers from \(-25^{\circ}\) to \(25^{\circ}\). The system has a reflection coefficient better than \(-10\) dB for a 2 GHz bandwidth centered at 17 GHz, a directive radiation pattern with a maximum of 10.7 dB gain at the mid-band frequency, and cross polarization better than 20 dB. A prototype is fabricated and measured for design verification. The measured far-field radiation patterns, co and cross polarization, and the reflection coefficient of the antenna system agree with simulated results.  
Keywords:beam steering antenna system; SIW technology; variable phase shifter; varactor diodes +
Footnote †: journal: Electronics Letters
###### Abstract
CS is also a powerful denoising technology, because signals can be preserved in sparse space, while the non-sparse noise can not be. The K-SVD algorithm combined with CS theory does a good job of removing Gaussian white noise from images [39; 40]. Inspired by the idea of prior learning in image denoising, we proposed an advanced approach based on compressive sensing to improve synchronization phase accuracy for LT-1 mission. The higher-order term of imaging phase difference is first input into the K-SVD algorithm for learning phase structure and prior information. After obtaining the over-complete dictionary, the noise in the synchronization compensation phase is eliminated by maximum a posteriori (MAP) estimation. Finally, the denoised compensation phase is employed in phase synchronization for BiSAR. Compared with the KF method in [37], the CS method presents a higher synchronization accuracy. This article is organized as follows. Section 2 introduces the non-interrupted synchronization scheme and its phase model of the LT-1 system. In Section 3, the phase denoise model based on compressive sensing, the K-SVD algorithm, and the processing flowchart are presented in detail. Then the effectiveness of the proposed method is confirmed by the denoising results of the data acquired in the ground validation system in Section 4. Finally, Section 5 concludes this article.
# Green Communication in Internet of Things: A Hybrid Bio-Inspired Intelligent Approach
## 3 Green Communication in the Internet of Things: A Hybrid Bio-Inspired Intelligent Approach
### Hybrid Intelligent Energy Optimization  
The primary objective of the proposed HIOA approach is to generate an optimal set of CHs to minimize the overall energy consumption and prolong IoT network lifetime, as shown in Figure 1. It consists of two optimization phases. First, a tentative set of CHs is selected through fuzzy logic inference system (FLIS) optimization. In the second optimization phase, the chicken swarm genetic optimization (CSGO) algorithm uses the output of the FLIS labeled as input and treated as the first initial population. Further, the CSGO algorithm optimizes the election process by choosing the appropriate IoT node as CH to successfully bring out optimum clustering operation.
## 5 Results
### Topology 1: Results
#### 5.1.4 Scenario 4  
In Scenario 4, the links on the right path h26->29->h27 were emulated with less delay and no packet loss, whereas the left path h26->h30->h27 had more delay including rate limiting.  
Figure 9a shows that the average response time with snnr was slightly inferior to snndp and snndp performed 1-11% worse than snnd and 1-20% better than snnd and 1-8 bundles/s. snndp and snnr showed 11-24% and 4-24% better performance than static at bundle rates of 9-10 bundles/s.  
Figure 9b shows the bundle loss percent was the same for all methods for bundle rates up to 8 bundles/s. Figure 9c shows that all routing methods showed similar throughput at all bundle rates.  
Figure 10 depicts the link selection at the node h26 by the SNN algorithms according to their optimization goals in Scenario 4.  
Figure 8: Link selection at Node h26: Scenario 3.  
Figure 9: Routing performance: Scenario 4. (**a**) Average response time; (**b**) Packet loss ratio (%); (**c**) Throughput.
# Multiobjective Optimization Design for a MR Damper Based on EBFNN and MOPSO
## 4 Optimization Design
### Optimization Design Based on MOPSO  
Herein, the structural parameters, the first shear effective length \(L_{1}\), the second shear effective length \(L_{2}\), and the nonmagnetic length \(t_{a}\) were selected as the design variables for this optimal design based on the discussion, while the volume constraint and design space for the optimal solution were incorporated into the optimization process. Hence, the mathematical expression of the multiobjective optimization problem can be written as  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{_RMSE_} & \multicolumn{2}{c}{_R\({}^{2}\)_} \\ \cline{2-5}  & \(\mathbf{F}\) & \(\mathbf{D}\) & \(\mathbf{F}\) & \(\mathbf{D}\) \\ \hline Quadratic RSM & 0.02193 & 0.03441 & 0.99317 & 0.98101 \\ Orthogonal Polynomial Model & 0.01733 & 0.02701 & 0.99574 & 0.9883 \\ Kriging Model & 0.08491 & 0.08033 & 0.897/71 & 0.89649 \\ Radial Basis Function Model & 0.01524 & 0.0223 & 0.9967 & 0.99202 \\ Elliptical Basis Function Model & **0.01253** & **0.01997** & **0.99777** & **0.9936** \\ \hline \end{tabular}
\end{table}
Table 3: Prediction comparison of different methods.  
Figure 10: Damping force and dynamic range via the elliptical basis function (EBF) model: (**a**) damping force (with \(L_{1}\) and \(L_{2}\)); (**b**) dynamic range (with \(L_{1}\) and \(L_{2}\)); (**c**) damping force (with \(t_{a}\) and \(L_{1}\)); (**d**) dynamic range (with \(t_{a}\) and \(L_{1}\)).  
\[\left\{\begin{array}{l}\max[F(L_{1},L_{2},t_{a}),D(L_{1},L_{2},t_{a})]\\ s.t.\left\{\begin{array}{l}L\leq 50\;\text{mm}\\ 4\;\text{mm}\leq L_{1}\leq 12\;\text{mm}\\ 4\;\text{mm}\leq L_{2}\leq 12\;\text{mm}\\ 2\;\text{mm}\leq t_{a}\leq 19\;\text{mm}\end{array}\right\}\end{array}\right. \tag{10}\]  
The MOPSO algorithm was selected in ISIGHT software to perform multiobjective optimization on the constructed approximate model. In the optimization problem, the objective is to maximize the damping force and the damping range. Moreover, the number of particles was set as 20; the maximum evolution iteration was set as 50; and the inertia, global increment index, and maximum velocity were set as 0.9, 0.9, and 0.1, respectively. Finally, 502 Pareto optimal solutions could be obtained by running the solution, and the distribution of the Pareto fronts is displayed in Figure 11. The optimal result achieved by the MOPSO was rounded, as shown in Table 4.
# Near-Real-Time Global Thermospheric Density Variations Unveiled by Starlink Ephemeris
## 2 Data and Methods
The term \(C_{d}A\) is estimated using the Sentman model [45] combined with Swarm satellite specifications documented in ESA's technical report ([[https://earth.esa.int/eogateway/documents/20142/37627/swarm-thermo-optical-properties-and-external-geometry.pdf](https://earth.esa.int/eogateway/documents/20142/37627/swarm-thermo-optical-properties-and-external-geometry.pdf)]([https://earth.esa.int/eogateway/documents/20142/37627/swarm-thermo-optical-properties-and-external-geometry.pdf](https://earth.esa.int/eogateway/documents/20142/37627/swarm-thermo-optical-properties-and-external-geometry.pdf)), accessed on 13 April 2025). This methodology enables direct comparison between the Swarm-derived results and those calculated from Starlink ephemeris data.
## 2 Related Works
### Uploading Pattern of Source Nodes
#### 2.2.2 Active Uploading Pattern  
In the active uploading case, sensory data are uploaded to sink by multi-hop as soon as sensory data is sensed.  
In [14], [PERSON] et al. propose Binary-Tree based Data Gathering Scheme (BTDGS). In this algorithm, a virtual binary-tree infrastructure is established. The whole network area is divided into many small sectors. There is a head node in each sector to collect data from this sector. The mobile sink moves along the boundary of network to collect data. Sensor nodes relay data packages in a greedy manner. When source nodes get data, they send the data toward the center area. That is because nodes in centerarea hold the position of sink. On the process of relaying to center area, the direction of relaying will change to sink upon meeting a node which has sink's position. Then sensory data is routed to sink. However, the way of updating sink's location is complexity, and sensory data is always routed to the center area firstly to find sink's location. In this way, the route path becomes longer and more energy is consumed.  
VGDRA [24], proposed by [PERSON] et al., decreases the complexity of sink location updating than BTDGS. In VGDRA, the network is divided into virtual grids, and the number of virtual grids is decided by the number of sensor nodes. Each grid has a head node. When nodes get sensory data, they transmit data to their own head node immediately. The head nodes can communicate with each other directly, and data is relayed by head nodes. That means head nodes form the routing path. Sink moves around the network boundary with a constant speed. When sink is closer to a different grid, the formed routing path changes according to rule. The difference between VGDRA with BTDGS is that the sensory data can be routed by a formed routing path in VGDRA. When nodes sensed data, they do not need to take time to search for the location of the sink. However, another problem appears. As the sink moves along the network boundary to collect data, sensory data is all routed to the nodes which are in boundary grids. These nodes consume energy fast, and the 'hot spots' problem occurs.  
Algorithms mentioned above adopt active uploading to achieve lower latency, as many application scenarios need fresh data. However, there are also some challenges with active uploading. The first challenge is how to update sink's location quickly with lower energy consumption when sink is moving. The second challenge is that nodes around the sink always consume energy quickly. This may leads to the 'hot spots' problem.
## 3 Results and Discussion
### Optical Properties by DRS-UV and Raman Spectroscopy  
We used a Shimadzu UV-2450 spectrophotometer to acquire UV-Vis diffuse reflection spectra (DRS). Band energy values for samples and detectable light for pollutant degradation via band gap determination are both determined using UV-diffused reflectance spectroscopy (DRS-UV). The high UV absorption of the as-synthesized sample suggests that UV light is an essential activator for photocatalysis in this system (Figure 4a). The Tauc plot was used to calculate the band gap of the generated samples. [PERSON]'s plot of photon energy (h\(\gamma\)) and (\(\alpha\)h\(\gamma\))\({}^{2}\) reveals values of 3.67 eV for DW, 3.22 eV for ZnO and 3.02 eV for the band energy of the DWZG composite (Figure 4b). The deterioration process is greatly influenced by the determination of the band energy value. Light degrades the pollutant by  
Figure 3: EDS spectrum and elemental mapping of DWZG nanocomposites and weight percentage of presenting elements.  
Figure 2: SEM images of (**a,b**) Dy\({}_{2}\)WO\({}_{6}\), (**c**) ZnO, (**d**) Dy\({}_{2}\)WO\({}_{6}\)-ZnO, (**e,f**) Dy\({}_{2}\)WO\({}_{6}\)-ZnO/GO (DWZG) nanocomposite at different magnification.  
causing the production of e\({}^{-}\)-h\({}^{+}\) pairs, which occur when electrons are stimulated from the valence band to the conduction band. The photocatalytic degradation reaction in the UV range has been facilitated by band gap value 3.02 eV, which has also increased the efficiency of the reaction.  
Raman spectroscopy was used to identify the GO layer material. The Raman spectra of DWZG materials are shown in Figure S2, where two distinct bands can be seen at 1345 and 1592 cm\({}^{-1}\), which correspond to the D and G bands. It is feasible to calculate the values of the I\({}_{\text{D}}\)/I\({}_{\text{G}}\) ratio, and they are 0.952 for GO and 1.17 for rGO [45], respectively. The G band is located at 1592 cm\({}^{-1}\) due to the sp\({}^{2}\) hybridization. In comparison to rGO, GO has a lower I\({}_{\text{D}}\)/I\({}_{\text{G}}\) ratio of 0.952. GO has formed entirely, as evidenced here [46,47].
## 3 Results and Discussion
### Model Validation  
#### 3.1.1 Discharge Uniformity  
In this work, the APHeDBD is simplified with the 1D PFM under the assumption of uniform discharge. However, it is noted that APHeDBDs may not be uniform under various operating conditions [17; 18]. The discharge uniformity under current operating conditions is confirmed by the ICCD photograph taken during the breakdown of a typical cycle with the 1 \(\upmu\)s exposure time as shown in Figure 4. The uniform discharge irradiation occurs near the surface of the glass plate covering the grounded electrode due to excited species generated by energetic electrons in the cathode fall region. Uniform discharges generated in the planar reactors under similar operating conditions were reported by other groups with similar discharge structure presented [16; 17], supporting the uniform discharges observed in this work. The confirmed discharge uniformity justifies the application of the 1D PFM for modeling the APHeDBDs.
## 3 Results
### Self-Reported Overall Fatigue and Musculoskeletal Pains by the Degree of Hazard Exposure
#### 3.3.4 Self-Reported Lower Limb Pain by the Degree of Hazard Exposure  
Table 9 shows the distribution of self-reported lower limb pain by the degree of hazard exposure. There were significant differences between the lower limb pain complaining group and the non-complaining group in most of the hazards except vibration exposure (\(\chi^{2}=2.811\), \(p=0.245\)), standing posture exposure (\(\chi^{2}=5.604\), \(p=0.061\)) and infection exposure (\(\chi^{2}=2.593\), \(p=0.274\)). The proportion of the lower limb pain complaining group increases as the degree of hazard exposure increases. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Factor**} & \multirow{2}{*}{**Variables**} & \multicolumn{3}{c}{**ADHE**} \\ \cline{3-6}  & & **<2** & **2-4** & \(\geq\)**4** & \(\chi^{2}\) **Test** \\ \hline \multirow{4}{*}{Physical hazard} & Vibration & 24.8\% & 13.8\% & 33.6\% & \(\chi^{2}=12.425\), \(p=0.002\) * \\ \cline{2-6}  & Noise & 20.8\% & 23.0\% & 38.1\% & \(\chi^{2}=14.702\), \(p=0.001\) * \\ \cline{2-6}  & High temperature & 20.7\% & 19.6\% & 48.2\% & \(\chi^{2}=31.900\), \(p<0.001\) * \\ \cline{2-6}  & Low temperature & 24.9\% & 22.6\% & 43.8\% & \(\chi^{2}=10.228\), \(p=0.006\) * \\ \hline \multirow{4}{*}{Ergonomic hazard} & Awkward posture & 18.6\% & 19.5\% & 38.1\% & \(\chi^{2}=20.419\), \(p<0.001\) * \\ \cline{2-6}  & Manual material handling & 23.0\% & 26.5\% & 36.4\% & \(\chi^{2}=6.455\), \(p=0.040\) * \\ \cline{2-6}  & Standing posture & 25.0\% & 13.5\% & 32.0\% & \(\chi^{2}=11.992\), \(p=0.002\) * \\ \cline{2-6}  & Repetitive motion & 17.0\% & 6.0\% & 31.8\% & \(\chi^{2}=17.478\), \(p<0.001\) * \\ \hline \multirow{4}{*}{Chemical and biological} & Fumes and dust & 21.6\% & 22.5\% & 46.1\% & \(\chi^{2}=23.356\), \(p<0.001\) * \\ \cline{2-6}  & Vapor & 24.0\% & 33.3\% & 48.8\% & \(\chi^{2}=12.380\), \(p=0.002\) * \\ \cline{1-1} \cline{2-6}  & Skin contact with chemical & 24.3\% & 28.1\% & 51.2\% & \(\chi^{2}=13.329\), \(p=0.001\) * \\ \cline{1-1} \cline{2-6}  & Tobacco smoke & 25.5\% & 32.6\% & 52.6\% & \(\chi^{2}=7.437\), \(p=0.024\) * \\ \cline{1-1} \cline{2-6}  & Infection & 26.0\% & 34.8\% & 47.4\% & \(\chi^{2}=4.847\), \(p=0.089\) \\ \hline \hline \end{tabular}  
* Significant at 0.05. ADHE = adjusted daily hazard exposure time.  
\end{table} 
Table 10: Distribution of subjective pain complaints in any one area by the degree of hazard exposure.
## 7 Simulation
### Effect of Random Early Dropping Method on Energy and Video Quality,  
Random early dropping is presented in Section 6.2.2 as an approach for extending the lifetime of the WVSN. Although this scheme has various benefits, it influences the video quality. In this section, two criteria are utilized for evaluating the performance of dropping scheme. The criteria are average bytes which are forwarded from source to Sink and average PSNR in receiver side. Video is transmitted first by using dropping scheme and then without using this scheme. It is assumed that all nodes have full batteries and the environment examined is a mixture of all four categories of environments. Furthermore, simulation lasted for _10_ seconds.  
The simulation results shown in Table 3, are evidence that the number of sent bytes in dropping scheme decreases. According to Equation (9), the optimization of the sent bytes in the dropping scheme is _24%_.  
Figure 21: Middle-variation environment frames  
Figure 22: High-variation environment frames\[\begin{split}\text{Performance}=\frac{\sum_{i=3}^{18}\big{(}\text{Average \ transmitted bytes without dropping}\big{)}_{i}-\sum_{i=3}^{18}\big{(}\text{Average \ transmitted bytes with dropping}\big{)}_{i}}{\sum_{i=3}^{18}\big{(}\text{Average \ transmitted bytes without dropping}\big{)}_{i}}\end{split} \tag{9}\]  
In this equation, \(i\) is the hierarchical level that video capture node is situated on.  
On the other hand, Figure 23 shows that average PSNR is reduced by approximately _1.51_ dB. The resulted value, however, is a satisfactory average PSNR for video transmission. Based on the achieved results, it is reasonable to use dropping method for achieving energy efficiency. Dropping scheme takes account of energy and hierarchical levels of nodes and data importance for dropping. Therefore, it drops low-priority data while having in view the level of source node. In short, average PSNR does not change a lot even when about one quarter of the data is dropped.
# Method for Select Best AIS Data in Prediction Vessel Movements and Route Estimation
## 2 Related Works
Once the anomaly is determined, the authors proposed to remove it and reconstruct the segment to standardize the data [14]. [PERSON] et al. [15] proposed a vessel trajectory prediction to avoid a collision that reduced the error by 40.85% using neural networks with LSTM. The selection of the historical routes' data is made considering only with those that show historical movements and are filtered based on the MMSI. Besides, a filter made on the historical data's similarity based on the current route is predicted. Data selection can be improved to estimate vessel movements, even maritime traffic. [PERSON] et al. [16] proposed a procedure based on predicting maritime traffic density using different time series models. They considered selecting the historical AIS data and the temporary labeling of the data for four distinct seasons in the year. [PERSON] [17] estimated the future vessel location, which was tested with the validation of experts. The prediction is done independently for latitude and longitude with neural networks. The data selection is through the area of interest, defining the minimum and maximum latitudes and longitudes. Its MMSI differentiates this block of data for each route. The port makes the selection of the routes of departure and arrival. Similar routesused for prediction are selected with a clustering process to locate the similar routes using the Partition Around the Medoids (PAM) algorithm. [PERSON] et al. [18] developed a system that detects predefined maritime anomalies to support the maritime surveillance system for tanker vessels and specifies the difficulty of analyzing large volumes of data. The filter targets vessels on a large volume of data containing historical and erroneous information. The trajectory is considered an anomaly in the study of circular movements over an area detected by changes in angle and speed. [PERSON] et al. [19] and [PERSON] et al. [20] proposed the generation of navigation simulations on a route using statistical analysis. The historical AIS selection is based on only the vessels that travel the route from start to finish and discards those ships that have no similarity in the trajectory. The method highlights using statistical analysis to identify the similarity of the routes that can simulate the trajectory. [PERSON] et al. [21] built a model that estimates a vessel's time to arrive in a port. It highlights the AIS historical data selection based on the routes with similarities to the starting location and the destination location related to the arrival time. Finally, [PERSON] and [PERSON] [22] proposed a model to predict the ships' movements by identifying patterns in the AIS historical data. It highlights the selection of routes by similar patterns using clustering analysis and statistical classification by samples or indicators in three different ways: using the complete routes, using segments of the routes, and using each of the routes' positions. They are highlighting as the best options for the segmentation of the routes for later selection by clustering.
# Remote Sensing of Ecosystem Water Use Efficiency in Different Ecozones of the North China Plain
## 2 Data and Methods
### Methods
#### 2.3.2 Water Use Efficiency  
The calculation formula of WUE is as follows:  
\[WUE=\frac{GPP}{ET} \tag{1}\]