esa-sceva/satcom-chunk-collection huggingface dataset:
# Full-Field Mode Shape Identification Based on Subpixel Edge Detection and Tracking
## 4 Application on a Real Bridge  
The proposed method was applied for the modal identification of the Humen Bridge in China. A wind-induced vortex vibration occurred in 2020, and a video clip [47] was taken at a frame rate of 30 fps, containing 1080 frames with a pixel resolution of 1920 \(\times\) 1080. Due to the large number of edge points of the bridge girder, only a few edge points depicted in Figure 12a,b were selected for measurement.  
The lower edge of the bridge girder was extracted for the above positions. The position near the mid-span (i.e., ROI shown in Figure 13) was selected as an example for demonstration, as shown in Figure 13. The ROI was processed using the Canny operator to obtain the rough edge coordinates and the ZOM-based algorithm was used to precisely relocate the edge coordinates that were close to the true edge.  
The vertical displacement time histories at positions 1, 6, and 11 in Figure 12a were extracted using the proposed method after obtaining the subpixel position of the bridge edge, as shown in Figure 14. The resonant frequency corresponding to the displacements of these three positions is 0.3611 Hz, which validates the repeatability of the proposed method. Compared with the measured frequency of Humen Bridge when it was built [48], it is found that the extracted frequency is in good agreement with the third-order frequency of vertical bending vibration, with an error of 2.06%. It is noted that for a long-span structure, the vibration can still be treated as small-amplitude vibration because its displacement is small with 2-4-pixel displacements in the image due to a long shooting distance.  
Figure 15a,b show the identified 2D mode shapes of the bridge girder using 11 points and 51 points. From Figure 15a, it is difficult to determine which mode the mode shape belongs to, while it can be seen in Figure 15b that the identified mode shape is similar to the third-order mode shape of vertical bending vibration, which matches the frequency result. Thus, the proposed method can effectively determine the full-field mode shape of large-scale structures such as the Humen Bridge. It is noted that the public video used for this study has poor quality as a non-specialized test, and the proposed method can obtain more precise and complete mode shapes with higher resolution when conditions permit.  
Figure 12: Selected positions for measurement: (a) 11 edge points; (b) 51 edge points.  
Figure 14: Displacement time history and natural frequency of girder: (**a**) position 1; (**b**) position 6; (**c**) position 11; (**d**) natural frequency.  
Figure 13: Edge point extraction of the bridge girder.
## 3 Results and Discussion  
The implementation, namely EXO Data, is based on existing commercial products [27], but with important evolutions. The solution links the on-board drone hardware and the ground segment, composed by the GCS (link A) and the novelty of the data server with extra processing capacities for the traffic monitoring (link B), both with different requirements (Figure 6).  
Figure 5: Logical block diagram of drone elements and connections. Links description. E: First Person View (FPV) video (medium bandwidth). F: Downwards HD video (high bandwidth). G: Telemetry control (low bandwidth). H/I: Telemetry data (low bandwidth). J: Control commands (low bandwidth). K: Local bidirectional configuration/testing link. L: Local bidirectional configuration/testing link + FPV video (medium bandwidth) + Downwards HD video (high bandwidth) + Telemetry data (low bandwidth).
# Research on Synthetic Aperture Radar Processing for the Spaceborne Sliding Spotlight Mode
## 5 PFA Algorithm for Sliding Spotlight SAR
### PFA Algorithm Based on Image Segmentation
Under certain parameter conditions, the effective imaging scene radius of PFA is determined by the following formula [23]:  
\[r_{0}\leq\frac{2\rho_{a}}{K_{a}}\sqrt{\frac{R_{ac}}{\lambda_{c}}}. \tag{36}\]  
According to the parameters of GF-3 SAR, it only needs Quadtree segmentation for only one time to satisfy the sub-image without defocus. Similar to the CBP algorithm, the flowchart of PFA algorithm based on Quadtree sub-image segmentation is shown in Figure 14.  
Figure 14: Flowchart of PFA algorithm based on sub-image segmentation.
###### Abstract  
In this study, the influence of window opening sizes and positions on wind-induced cross ventilation performance in an isolated gable roof building was numerically investigated using the k-\(\omega\) SST turbulence model. The results obtained from numerical analyses to evaluate the ventilation efficiency of different configurations show that larger inlet openings significantly increase the ventilation rates and the WO5 model reaches the highest ventilation rate of 0.004089 m\({}^{3}\)/s with an improvement of 37.27% compared to the reference model. As with the WO1 model, smaller inlet openings limited the air intake, reducing ventilation efficiency and indoor air quality. In terms of outlet window opening sizes, the LO5 model showed the highest ventilation efficiency, improving ventilation by 28% compared to reference model, while smaller outlet openings, as in the LO1 model, were associated with significantly lower performance. Additionally, when evaluating window opening locations, configurations with higher exit openings generally exhibited superior ventilation rates. The best overall ventilation performance was achieved in the Upper-Lower configuration at 0.003129 m\({}^{3}\)/s. The findings emphasized the critical role of window design in natural ventilation performance. Larger and strategically located window openings optimize airflow, increase ventilation efficiency and improve indoor air quality, providing valuable information for energy-efficient building design.  
Keywords:natural ventilation; cross-ventilation; ventilation rate; computational fluid dynamics (CFD); numerical simulation; wind; atmospheric boundary layer +
Footnote †: journal: Applied Sciences
# An Enhanced Time Series Analysis to Improve the Performance of 5G Communication Systems +
## 4 Conclusions  
Time series analysis is also used to identify system anomalies, optimize system controls, detect abnormal networking events, identify outages, and detect malicious activity. By identifying patterns in past data, 5G communication systems can use time series analysis to better understand how their networks are performing in the present and take steps to improve performance in the future. Time series analysis techniques can be used to identify trends, make predictions, and optimize the network performance by devising strategic interventions. This can, in turn, lead to faster connection establishment, reduced latency, and better user experience. Time series analysis could also be used to understand and identify problems in the 5G system quickly by tracking the performance of key components in real time. This could be used to troubleshoot problems more efficiently and help maintain the high availability of the 5G networks.  
Conceptualization, S.H.K. and A.P.; methodology, V.K.; software, T.R.V.L.; validation, S.H.K., A.P. and V.K.; formal analysis, V.K.; investigation, I.K.; resources, T.R.V.L.; data curation, T.R.V.L.; writing--original draft preparation, S.H.K.; writing--review and editing, A.P.;  
Figure 5: Throughput administration.
## 4 Experimental Setup and Results Analysis
### Experimental Method
#### 4.3.3 Prediction  
After training the K-means++ model and obtaining the clustering labels for the training samples, we can now use this trained model to make predictions on new or unseen samples.  
To predict on new samples, we apply the same preprocessing steps as before to obtain the binary image data. We then pass these data to the model's prediction method. The model will assign each new sample a clustering label based on the similarity to the existing clusters.  
For example, in the case of the 2D image shown in Figure 10d, after performing dimensionality reduction and passing it to the trained model, we obtain the predicted clustering labels. The visualization of the labels representing the vertical lines in the new samples is displayed in Figure 11a. Additionally, the predicted results can be observed in Figure 11b.
# Multi-Source Cooperative Data Collection with a Mobile Sink for the Wireless Sensor Network
###### Abstract  
The multi-source cooperation integrating distributed low-density parity-check codes is investigated to jointly collect data from multiple sensor nodes to the mobile sink in the wireless sensor network. The one-round and two-round cooperative data collection schemes are proposed according to the moving trajectories of the sink node. Specifically, two sparse cooperation models are firstly formed based on geographical locations of sensor source nodes, the impairment of inter-node wireless channels and moving trajectories of the mobile sink. Then, distributed low-density parity-check codes are devised to match the directed graphs and cooperation matrices related with the cooperation models. In the proposed schemes, each source node has quite low complexity attributed to the sparse cooperation and the distributed processing. Simulation results reveal that the proposed cooperative data collection schemes obtain significant bit error rate performance and the two-round cooperation exhibits better performance compared with the one-round scheme. The performance can be further improved when more source nodes participate in the sparse cooperation. For the two-round data collection schemes, the performance is evaluated for the wireless sensor networks with different moving trajectories and the variant data sizes.  
wireless sensor networks; mobile sink node; cooperative communications; sparse cooperation; distributed low-density parity-check codes +
Footnote †: journal: Sensors
## 5 Results and Discussion
### Effects of using Different Numbers of SAMs  
For the purpose of evaluating the effect of using different numbers of Supplementary Attention Modules (SAMs), we conducted an experiment.  
As shown in Table 5, we could see that adjusting the numbers of SAMs in our proposed PCHNet would affect the final segmentation accuracy. The accuracy of the model gradually increases and then slowly decreases, indicating that, by continuously removing crack features that have already been extracted by the PCHNet from the high-level output, the SAMs could compel the PCHNet to learn new crack features, allowing the PCHNet to automatically and continuously learn supplementary detailed features. These supplementary detailed features could effectively enhance the feature expression of the PCHNet. However, using too many SAMs would bring redundant features. Thus, in the experiment, we used only three SAMs in our PCHNet.
## 4 Results and Analysis
### Minimum Temperature  
Since T\({}_{\rm min}\) is not the direct evaluation criterion, it is not as important as other indicators such as T\({}_{\rm max}\) and \(\Delta\)T. The T\({}_{\rm min}\) of seven designs at five flow rates are shown in Figure 7. Design 5 consistently showed the lowest T\({}_{\rm min}\) (307.55 K, 303.74 K, 301.14 K, 299.60 K, and 298.55 K) at all five flow rates, towering above the other designs in terms of the temperature reduction capacity. The deduction of T\({}_{\rm max}\) and T\({}_{\rm min}\), i.e., \(\Delta\)T, is a direct evaluation criterion which should be lower than 5 K. Therefore, higher T\({}_{\rm min}\) is better since lower T\({}_{\rm min}\) could result in higher \(\Delta\)T adversely, triggering the violation of the evaluation criterion of \(\Delta\)T (\(\leq\)5 K). Design 2 and Design 3 were both proven to deliver higher T\({}_{\rm min}\) values at five flow rates, showing the temperature reduction incapacity of the CVSG designs with larger vertical spacings (31.5 mm and 33 mm). On the other hand, the GVSG designs with smaller spacings (40/30/24 mm and 42/31.5/25.2 mm) delivered lower T\({}_{\rm min}\) values at five  
Figure 6: Optimal design (lowest T\({}_{\rm max}\)) at each flow rate.  
flow rates, which could also insinuate the poor temperature uniformity performances of Design 5 and 6.
## 5 Conclusions  
We compared LAI as simulated by 11 Earth-System Models against satellite data and uncoupled models [73], for the Northern Hemisphere during the 1986-2005 period. We compared the mean annual LAI, the spatial pattern of LAI and the onset, dormancy and length of the growing season. Our results show that models consistently overestimate the mean value of LAI, although considering the error of the reference data estimated by comparing the satellite data with ground measurements the model-data misfit is significantly reduced. In addition models have an increased growing season, mostly due to a later dormancy, compared to the satellite data. This is consistent with the finding on the uncoupled models.  
We conclude that validating LAI in each model against satellite observations should be a fundamental step for all modelling groups. This is essential since LAI is a fundamental variable in all models, required to correctly calculate the hydrological, energetic and carbon fluxes.  
We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table 1 of this paper) for producing and making available their model output. For CMIP the US Department of Energy's Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. We also thank [PERSON] for his valuable contribution and comments on the development of the paper, [PERSON] for his help on the methodology, and the four anonymous reviewers who helped to improve the paper.  
This work was funded by the European Commission's 7 th Framework Programme under Grant Agreements number 238366 (GREENCYCLESII project) and 282672 (EMBRACE project).
# Probing Collisional Plasmas with MCRS: Opportunities and Challenges
## 2 Theory
### Brief Summary  
By resolving the changes in resonant behavior of an electromagnetic standing wave, properties of the material inside the cavity volume and the cavity itself can be determined. For most low-pressure plasmas, it is sufficient to measure the change in the spectral position of a resonance peak. However, with the transition towards probing atmospheric-pressure plasmas, the change of the peak's width also becomes equally relevant. Moreover, these collisional plasmas induce much smaller changes in the resonance frequency and therefore require a very good resolution in monitoring the resonant behavior. One of the disadvantages of studying plasmas that only induce a very small change in resonant behavior is that it requires to take a large variety of physical phenomena into account, which can affect the measurements and separating these contributions is not trivial.
# Indoor Signal Strength Evaluation of the Orbcomm Low Earth Orbit Satellite Constellation +
## 3 Material and Methodology
### Receiver Setup  
To measure the signal power of Orbcomm satellites in different environments, we constructed a receiver, which consists of a Software Defined Radio (SDR) and an antenna connected via a one-meter coaxial cable. In what follows, we will further describe these components.  
An SDR provides a flexible and versatile radio platform [12]. Previously, these physical layers were implemented in hardware, but hardware redesign can be time-consuming and costly. The SDR, by contrast, has the possibility to adapt to different protocols, bandwidths, as well as frequencies [13]. Furthermore, the data can easily be saved and post-processed. This makes it a great choice for this type of research. For this paper, the Ettus B210 was used which has the ability to receive signals between \(70\,\mathrm{MHz}\) and \(6\,\mathrm{GHz}\) and with a maximum bandwidth of up to \(56\,\mathrm{MHz}\). This SDR utilizes the AD9361 chip which is a high-performance transceiver [14]. The two receivers onboard feature a 12-bit resolution and a configurable gain between \(0\) and \(74.5\,\mathrm{dB}\). When the center frequency is set to \(800\,\mathrm{MHz}\), the Noise Figure (NF) is equal to \(2\,\mathrm{dB}\) and the accuracy is \(\pm 2\,\mathrm{dB}\)[15]. The NF is not indicated for the frequency range that we utilize, therefore, this is the closest available value.  
Two main factors should be taken into account when selecting a receiving antenna from the wide range of available options. First, the polarization of the transmitting antenna on the satellite should ideally match the polarization of the GRS antenna, otherwise, this will result in polarization losses. Second, the gain of the antenna should be taken into account. A high-gain antenna results in a stronger received signal but requires tracking the satellites, whereas an omnidirectional antenna does not require tracking [3,16].  
For our setup, we selected an antenna which was optimal for receiving the Orbcomm constellation. This constellation utilizes a Right Hand Circular Polarized (RHCP) signal with downlink frequencies between \(137\,\mathrm{MHz}\) and \(138\,\mathrm{MHz}\). Therefore, we selected the Diamond DPKE137 antenna, as shown in Figure 1. This antenna is a RHCP quadriffilarhelix antenna designed to receive this frequency band [17]. The quadrifilar helix antenna is an improved version of the regular helical antenna. Adding extra windings tightens the radiation pattern and minimizes sidelobes. Moreover, the quadrifilar helix antenna is more omnidirectional than the helical antenna [18].
###### Abstract  
Chlorophyll fluorescence (ChIF) information offers a deep insight into the plant physiological status by reason of the close relationship it has with the photosynthetic activity. The unmanned aerial systems (UAS)-based assessment of solar induced ChIP (SIF) using non-imaging spectrometers and radiance-based retrieval methods, has the potential to provide spatio-temporal photosynthetic performance information at field scale. The objective of this manuscript is to report the main advances in the development of UAS-based methods for SIF retrieval with non-imaging spectrometers through the latest scientific contributions, some of which are being developed within the frame of the Training on Remote Sensing for Ecosystem Modelling (TRuStEE) program. Investigations from the Universities of Edinburgh (School of Geosciences) and Tasmania (School of Technology, Environments and Design) are first presented, both sharing the principle of the spectroradiometer optical path bifurcation throughout, the so called 'Piccolo-Doppio' and 'AirSIF' systems, respectively. Furthermore, JB Hyperspectral Devices' ongoing investigations towards the closest possible characterization of the atmospheric interference suffered by orbital platforms are outlined. The latest approach focuses on the observation of one single ground point across a multiple-kilometer atmosphere vertical column using the high altitude UAS named as AirFloX, mounted on a specifically designed and manufactured fixed wing platform: 'FloXPlane'. We present technical details and preliminary results obtainedfrom each instrument, a summary of their main characteristics, and finally the remaining challenges and open research questions are addressed. On the basis of the presented findings, the consensus is that SIF can be retrieved from low altitude spectroscopy. However, the UAS-based methods for SIF retrieval still present uncertainties associated with the current sensor characteristics and the spatio-temporal mismatching between aerial and ground measurements, which complicate robust validations. Complementary studies regarding the standardization of calibration methods and the characterization of spectroradiometers and data processing workflows are also required. Moreover, other open research questions such as those related to the implementation of atmospheric correction, bidirectional reflectance distribution function (BRDF) correction, and accurate surface elevation models remain to be addressed.  
Keywords:hyperspectral remote sensing; light weight spectroradiometer; telluric bands; ESA-FLEX; VNIR; SIF; UAS +
Footnote †: journal: Remote Sens.
## 3 Materials and Methods  
### Raw Materials  
The raw materials used in this study include SBS (I-D) modified asphalt binder with a linear SBS content of 4.5 wt% (China Petroleum & Chemical Corporation Zhenhai Refining & Chemical Branch, Ningbo, China), KD-BEP epoxy resin and curing agent (KINDAI KASEI Co., Ltd., Osaka, Japan), TAFPACK-Super (TPS) (Taiyo Construction Co., Ltd., Nagoya, Japan) and lignin fibers (Guangzhou Shenchuang Chemical Co., Ltd., Guangzhou, China). Moreover, the aggregates used to prepare asphalt concrete consisted of coarse basalt aggregate, fine basalt aggregate, and limestone mineral powder, which were supplied by Zhenjiang Maodi Industrial Co., Ltd. (Zhenjiang, China). The properties of the raw materials satisfied the \"Technical specification for the construction of highway asphalt pavements\" (JTG F40-2004) and the \"Specifications for Design and Construction of Pavement on Highway Steel Deck Bridge\" (JTG/T3364-02-2019) [23,24]. The basic properties of SBS-modified asphalt binder and modifiers are shown in Tables 1 and 2.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Technical Index** & **Unit** & **Result** & **Specification** \\ \hline Penetration (25 \({}^{\circ}\)C, 100 g, 5 s) & 0.1 mm & 54.4 & 40–60 \\ Ductility (5 cm/min, 5 \({}^{\circ}\)C) & cm & 34 & \(\geq\)30 \\ Softening point & \({}^{\circ}\)C & 80.5 & \(\geq\)80 \\ Penetration index & / & 0.76 & \(\geq\)0 \\ Dynamic viscosity (135 \({}^{\circ}\)C) & Pa-s & 2.57 & \(\leq\)3.0 \\ Flash point & \({}^{\circ}\)C & 291 & \(\geq\)230 \\ Elastic restitution (25 \({}^{\circ}\)C) & \% & 88 & \(\geq\)80 \\ Solubility & \% & 99.5 & \(\geq\)99 \\ Density (15 \({}^{\circ}\)C) & g/cm\({}^{3}\) & 1.021 & / \\ Mass loss & \% & \(-\)0.16 & \(\leq\)\(\pm\)1.0 \\ RTFOT (163 \({}^{\circ}\)C, 5 h) & Penetration ratio & \% & 76 & \(\geq\)65 \\ Ductility (5 \({}^{\circ}\)C, 5 cm/min) & cm & 23 & \(\geq\)20 \\ \hline \end{tabular}
\end{table}
Table 1: Basic properties of SBS (I-D) modified asphalt binder.  
Figure 2: Steel bridge deck resurfacing scheme: (**a**) Chuanshan direction; (**b**) Ningbo direction.
## 5 Discussion
* Multifunctional applications for potential industrial applications, such as: high-performance wave absorption characteristics [175; 176; 177], electromagnetic interference shielding [178; 162; 179; 180], adsorption thermodynamics and dynamics characteristics [181], and can be used in medical care, satellite communications, electronic equipment,national defense security and other fields in the future [180; 182; 183; 184; 185]. And conduct more experiments to verify potential industrial applications.
## 4 Case Study
### Performance of Proposed Model
#### 4.3.3 Dab Distance  
The total error of dab distance was evaluated based on the error in the dab centroid location, and RMSE was used as a metric. The RMSE of the dab location was 6.9 mm; the RMSE of each type is shown in Table 3. Figure 10 is a dab centroids location scatter graph. The errors of type 1 and type 5 are high, measuring at 12.95 mm and 14.64 mm, respectively, which is likely due to the difficulty in extracting the centroid location caused by the irregularity of the dab.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Type**} & **Ribbon Width** & **Dab Distance** \\ \cline{2-3}  & **Error** & **RMSE** \\ \hline Type 1 & 7.55\% & 12.95 mm \\ Type 2 & 6.94\% & 4.13 mm \\ Type 3 & 5.70\% & 4.10 mm \\ Type 4 & 5.77\% & 6.16 mm \\ Type 5 & 9.29\% & 14.64 mm \\ Type 6 & 6.83\% & 3.95 mm \\ Type 7 & 7.97\% & 4.76 mm \\ Type 8 & 22.58\% & 7.17 mm \\ Type 9 & 12.11\% & 4.20 mm \\ \hline Total & 9.42\% & 6.90 mm \\ \hline \hline \end{tabular}
\end{table}
Table 3: Error of ribbon width and RMSE of dab distance.  
Figure 9: Graph comparing ground truth and predicted ribbon width values.