esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Elastic Nonlinearity Using Continuous Waves: Validation and Applications
## 2 Semianalytical Approach for the Determination of Modulus and Damping  
### The Method: Modulus and Damping Nonlinearities Evaluation (MoDaNE)  
The MoDaNE approach described in the following aims to allow separating velocity and attenuation nonlinearities. It takes advantage of an exact analytical solution of the 1D problem of monochromatic waves propagation in elastic bars. Of course, an analogous approach extended to 2D or 3D general cases would be more complete and also allow addressing issues which are not specific to a 1D problem, such as those connected to directionality of nonlinear effects [47]. Nevertheless, a 1D solution maintains its relevance, since a large number of samples and configurations used in laboratory experiments can be usually considered 1D (e.g., cylinders, rods or prisms) and also 1D geometries are often of interest in applications.
## 4 Trajectory Prediction Model Based on AIS Data and ACDE-SVM
### Results Analysis
#### 4.3.2 Comparison of Prediction Results from Different Optimization Algorithms
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter Optimization Method for** & \(\mathbf{E_{MAX}}\)**(°)** & **MAE_/(°)** & **RMSE_/(°)** & **Run Time/s** \\ \hline \multirow{2}{*}{ACDE} & Longitude & \(5.475414\times 10^{-5}\) & \(3.216385\times 10^{-5}\) & \(3.562460\times 10^{-5}\) & \multirow{2}{*}{\(38.14\)} \\  & Latitude & \(5.957327\times 10^{-5}\) & \(3.345339\times 10^{-5}\) & \(3.660051\times 10^{-5}\) & \\ \hline \multirow{2}{*}{DE} & Longitude & \(6.749311\times 10^{-5}\) & \(4.314214\times 10^{-5}\) & \(4.465213\times 10^{-5}\) & \multirow{2}{*}{\(45.21\)} \\  & Latitude & \(6.964512\times 10^{-5}\) & \(4.657491\times 10^{-5}\) & \(5.041521\times 10^{-5}\) & \\ \hline \multirow{2}{*}{PSO} & Longitude & \(7.495242\times 10^{-5}\) & \(5.745210\times 10^{-5}\) & \(5.954213\times 10^{-5}\) & \\  & Latitude & \(6.935740\times 10^{-5}\) & \(5.526796\times 10^{-5}\) & \(5.631453\times 10^{-5}\) & \\ \hline \multirow{2}{*}{Grid search} & Longitude & \(1.465458\times 10^{-4}\) & \(1.642193\times 10^{-4}\) & \(2.146923\times 10^{-4}\) & \multirow{2}{*}{\(24.71\)} \\  & Latitude & \(1.914526\times 10^{-5}\) & \(2.145237\times 10^{-4}\) & \(3.461430\times 10^{-4}\) & \\ \hline \multirow{2}{*}{GA} & Longitude & \(7.041523\times 10^{-5}\) & \(5.042750\times 10^{-5}\) & \(5.546311\times 10^{-5}\) & \\  & Latitude & \(7.352207\times 10^{-5}\) & \(6.004726\times 10^{-5}\) & \(6.462101\times 10^{-5}\) & \\ \hline \multirow{2}{*}{Default toolbox values} & Longitude & \(1.942751\times 10^{-4}\) & \(1.945221\times 10^{-4}\) & \(3.047522\times 10^{-4}\) & \\  & Latitude & \(2.545720\times 10^{-4}\) & \(2.541201\times 10^{-4}\) & \(3.948753\times 10^{-4}\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Influence of different parameter optimization methods on prediction accuracy of SVR model.
# A Web-Based Application for Smart City Data Analysis and Visualization
## 5 Results
### Use Cases and Data-Driven Insights  
The system's outputs support data-driven use cases relevant to urban management, mobility planning, and environmental monitoring. These applications are directly derived from aggregated statistics, time-series visualizations (see Figure 11), and the integration of spatial data. In the context of air quality, daily pollutant concentrations enable analysts to monitor localized pollution levels, detect peak periods, and compare sensor behavior across the city. Correlation analysis further helps identify consistent trends or anomalies between locations, while traffic and noise data provide insight into urban mobility and its environmental impacts (see Figure 12). By examining patterns in vehicle counts and decibel levels over time, officials identify congestion hotspots and areas exposed to noise (see Figure 13). Parking and EV charging logs reveal fluctuations in demand over time and space (see Figure 14), which helps optimize infrastructure, apply dynamic pricing, and plan expansions in response to the growing electric mobility. Likewise, the visual outputs also facilitate public engagement and transparency. The system encourages citizen awareness and informed participation in sustainability efforts by exposing pollution trends, parking usage, and environmental conditions through charts and maps.  
Figure 11: Time-series representation of energy consumption per electric vehicle charging session. Each point corresponds to the unique sessions’ energy usage in kWh, with the red dashed line denoting the median value. Peaks and fluctuations illustrate diverse charging behaviors among users.  
Figure 10: This heatmap visualizes the degree of correlation between different sensors and pollutants (O\({}_{3}\), NO\({}_{2}\), PM\({}_{2}\),5). The color scale on the side reflects correlation values ranging from \(-1\) to \(+1\), with cooler tones (e.g., purple) indicating weaker or negative correlations and warmer tones (yellow) signifying strong positive correlations. Each cell represents the relationship between two specific measurements of the same type (e.g., “G107 NO\({}_{2}\)”, and “SfJohanns NO\({}_{2}\)”). Strong intra-pollutant clustering often reflects shared atmospheric behavior or familiar emission sources. Weak or negative correlations indicate local environmental differences or independent dynamics of pollutants. Overall, the heatmap provides a quick visual summary of which pollutant measurements tend to rise and fall together, highlighting meaningful relationships in urban air quality patterns.
# A Survey of Vehicle to Everything (V2X) Testing
## 7 Conclusions  
As new technology, V2X not only provide a more comfortable and safer traffic environment, but also are important for improving traffic efficiency, reducing pollution, and reducing accident rates. The main V2X communication technologies are DSRC and LTE-V2X. There are many V2X applications, and their application requirements mainly focus on latency/reliability and security. Latency/reliability is threatened by the network performance problems and multiple types of malicious attacks. Security faces mobile terminal security threats, V2X service platform security threats, V2X communication security threats, vehicle network data and privacy threats. Testing is an important part of V2X. First, we describe the abstract test system and then introduce test methods from three perspectives: function, performance, and conformance. We then focus on testing methods such as vehicle gateway testing, penetration testing, and accelerating testing, and analyze the requirements for field testing. Finally, we have proposed an end-to-end testing system combining virtual and real environments which can undertake the test task of the full protocol stack.  
Conceptualization, J.W. and Y.S.; investigation, Y.S.; resources, Y.G.; data curation, R.Y.; writing--original draft preparation, Y.S.; writing--review and editing, J.W.; project administration, J.W.  
This work was supported by the National Nature Science Foundation [61572229, and 6171101066]; Jilin Provincial Science and Technology Development Foundation [20170204074 GX, 20180201068 CX]; Jilin Provincial International Cooperation Foundation [20180414015 GH] and CERNET Innovation Project [NGII20170413].  
The authors declare no conflict of interest.
## 3 Results and Discussion  
### Production of Highly Filled Al\({}_{2}\)O\({}_{3}\) + GO Polymer Compositions  
Figure 1 shows the microstructure of the initial aluminum oxide powder at various magnifications and graphene oxide sheets obtained after drying an aqueous paste of graphene oxide.  
According to the microstructure of the alumina powder (Figure 1A,B), it can be seen that the initial powder is represented by small uneven particles in the order of 200 nm, forming small agglomerates in the order of 1-2 microns. The initial graphene oxide is presented in the form of one-dimensional sheets having a characteristic wrinkled shape (Figure 1C,D) with a sufficiently large size of over 500 microns.  
Figure 2A shows a photograph of samples of the obtained highly filled Al\({}_{2}\)O\({}_{3}\) + GO polymer compositions. It is visually noticeable that with an increase in the content of graphene oxide, the color of the composition changes from white (0 vol.% graphene oxide) to dark gray (0.50 vol.% graphene oxide).  
Since the content of graphene oxide in the obtained compositions is very insignificant, it was not possible to observe the GO in the compositions using scanning electron microscopy methods. Raman spectroscopy was performed to confirm its presence. The Raman spectra of graphene paste and the resulting highly filled Al\({}_{2}\)O\({}_{3}\) + GO polymer compositions with different 0.10, 0.25 and 0.50 vol.% GO contents are shown in Figure 2B. Two main peaks of the D-band and G-band can be observed on all spectra in the region of \(\approx\)1315 and \(\approx\)1580 cm\({}^{-1}\), respectively. These lines are characteristic of carbon-containing materials and are caused by differences in their molecular structure. The G-band is related to the ideal graphite structure, while the D-band shows structural defects. The position of the peaks obtained on the spectra of the samples is consistent with that for graphene oxide according to the literature [28; 29; 30; 31; 32]. Both peaks can be observed for all ceramic-filled polymer compositions, and it is seen that their intensity increases with increasing GO content. The results obtained suggest that graphene oxide retained its original structure in the obtained compositions after dispersion, freeze-drying with aluminum oxide and mixing with a polymer binder.
## 5 Experiment Results and Analysis
### Linearity of CH\({}_{4}\) PA Signal  
Different methane gas concentrations were obtained by diluting the standard methane gas (100 ppm) using two mass flow controllers (MFCs). One MFC was employed to control the pure nitrogen, while the other MFC was for methane. Figure 8 shows the recorded second-harmonic signal of different methane concentrations demodulated from the lock-in amplifier. Ten different concentrations of methane (from 10 ppm to 100 ppm) and pure nitrogen were measured. The linear regression result of the signal intensity versus the concentration of methane is shown in Figure 9. A good linear relationship between the signal intensity and the methane concentration (y = 0.0074\({}^{\circ}\)x \(-\) 0.0070, R\({}^{2}\) = 0.999) was evident.
## 3 Results
### Focus of the Studies
(2020) [190] developed an open-source, scalable method for fusion of very high-resolution imagery with multispectral SAR and thermal data, enabling detailed mapping across large areas and multi-year analysis for small-scale agriculture mapping. [PERSON] et al. (2023) [192] demonstrated the impact of climate change-induced flooding on crop failures in Burkina Faso. [PERSON] et al. (2017) [186] linked NDVI trends to changes in cultivated areas in the Sahel, specifically focusing on fallow fields. [PERSON] and [PERSON] (2019) [191] explored the relationship between wildfires and food security in Ghana. [PERSON] et al. (2004, 2006) [73; 74] used RS data in two studiesto identify malaria vector habitats in Mali's rice fields. [PERSON] et al. (2024) [76] mapped flood impacts, such as inundation extent and frequency, employing Landsat, Sentinel-1 SAR, and Sentinel-2 in the confluence region of rivers Niger and Benue to inform flood risk management.
## 2 Materials and Methods
### Data Sources and Preprocessing  
The digital elevation model (DEM) data were obtained from the Geospatial Data Cloud using SRTMDEM 90 m resolution data ([[https://www.gscloud.cn](https://www.gscloud.cn)]([https://www.gscloud.cn](https://www.gscloud.cn)), accessed on 15 July 2024). Slope data were derived from the digital elevation data using the \"Slope\" tool in ArcGIS 10.8. The boundaries of the TNSFP were sourced from the National Ecological Data Center Resource Sharing Service Platform ([[http://www.nesdc.org.cn/](http://www.nesdc.org.cn/)]([http://www.nesdc.org.cn/](http://www.nesdc.org.cn/)), accessed on 15 July  
Figure 1: Location of the study area.  
2024), while provincial, city, and county administrative boundaries, as well as national boundaries, were obtained from the National Center for Basic Geographic Information ([[https://cloudcenter.tianditu.gov.cn/administrativeDivision](https://cloudcenter.tianditu.gov.cn/administrativeDivision)]([https://cloudcenter.tianditu.gov.cn/administrativeDivision](https://cloudcenter.tianditu.gov.cn/administrativeDivision)), accessed on 17 July 2024). Data on the distribution of nature reserves were accessed via the China Nature Reserve Specimen Resource Sharing Platform ([[http://bhq.papc.cn/](http://bhq.papc.cn/)]([http://bhq.papc.cn/](http://bhq.papc.cn/)), accessed on 18 July 2024). Land use data, with a resolution of 1 km \(\times\) 1 km, were acquired from the Earth Resources Data Cloud ([[http://gi5g.com](http://gi5g.com)]([http://gi5g.com](http://gi5g.com)), accessed on 20 July 2024) and reclassified into six categories: arable land, forest land, grassland, water bodies, construction land, and unutilized land.  
Normalized Difference Vegetation Index (NDVI) data were sourced from the U.S. Geological Survey (USGS) Distributed Data Archive Center for Land Processes MOD13A3 product ([[https://lpdaac.usgs.gov/](https://lpdaac.usgs.gov/)]([https://lpdaac.usgs.gov/](https://lpdaac.usgs.gov/)), accessed on 22 July 2024). Maximum NDVI data for the entire year, as well as for May to September, were obtained from the Earth Resources Data Cloud. Leaf Area Index (LAI) data were sourced from MODIS data products reprocessed by the Earth Resources Data Cloud. Gross Primary Productivity (GPP) and Net Primary Productivity (NPP) data for 2018-2022 were obtained from the USGS Distributed Data Archive Center for Terrestrial Processes MOD17A3H product, while NPP data for May to September were sourced from the Earth Resources Data Cloud. Key wildlife protection data were collected from the Ministry of Ecology and Environment, the Forestry Bureau, wildlife protection stations, the National Key Wildlife Protection Animal List, and the National Key Wildlife Protection Plant List. All data were for 2022 and were resampled to a 1 km \(\times\) 1 km scale.
###### Abstract  
The present paper concerns the problem of estimating the loading induced by the wind on a gantry crane standing in the open air. Sufficiently strong wind may cause the device to move or even tip over. Two different structures were studied, namely the box girder and truss girder. At the very beginning, the two sectional scaled parts of the main horizontal beam (box and truss girder) of the gantry were prepared. Next, experimental analysis using these models was carried out in an aerodynamic tunnel to estimate the horizontal forces induced by the airflow acting on them. The experimental values of the aerodynamic forces were exploited to verify the 3D computational model of the studied structure. Numerical computations were carried out using the ANSYS Fluent 2022R2 system for both sectional models of the gantry crane mentioned above. The standard k-epsilon model of the turbulent flow of the air is employed. Satisfactory agreement of the values between the experimental and numerical results was achieved. As a result of the performed computations, the magnitude of the critical wind velocity that can be dangerous for the studied gantry cranes was estimated. Finally, a model of the gantry crane with box girder at full scale was analyzed using CFD simulations for different Davenport wind profiles. The results obtained from the experimental and numerical analysis of the sectional models were compared with the appropriate standards. In the current work, attention is drawn to the importance of changing wind direction in the vertical plane since, as shown in the results of this work, even a small change in vertical angle, up to 6 deg, causes significant changes in the value of the force required to overturn the gantry crane.  
wind loading; gantry crane; box girder; truss girder; CFD; aerodynamic tunnel; tip-over
## 3 General Opportunities, Challenges, and Applications
### Opportunities
#### 3.1.1 Enhanced Security and Reduced Data Transfer Costs  
With increasing data breaches, protecting data privacy is crucial, especially in sensitive fields like healthcare and personal communications [14]. FL's decentralized approach ensures that raw data remain on users' devices, minimizing security risks and enhancingtrust. This infrastructure is particularly beneficial in sectors like healthcare and banking, where data-driven insights are needed without exposing raw data [15; 16; 17; 18]. FL also reduces the costs and environmental impacts by minimizing data transfer and processing data locally [19; 20].
# Multi-User Linear Equalizer and Precoder Scheme for Hybrid Sub-Connected Wideband Systems
## 2 System Model
### Transmitter Description  
We assume \(U\) UTs sharing the same radio resources, each equipped with \(N_{tx}\) transmit antennas and with a single RF chain. Figure 1 presents the general schematic of the \(u\)th user terminal. Firstly, the time domain \(N_{c}\)-length sequence \([s_{u,t}]_{t=0}^{N_{c}-1}\), with \(\mathbb{E}[|s_{u,t}|_{2}]=1\), is divided into \(R\) data blocks of size \(N_{s}=N_{c}/R\), where \(\{s_{u,t}\}_{t=(r-1)N_{s}}^{rN_{s}-1}\) represents the \(r\)th data block. Then, this time domain sequence is moved to the frequency domain and the resulting sequence denominated by \(\left\{c_{u,k}\right\}_{k=(r-1)N_{s}}^{rN_{s}-1}\), where \(\left\{c_{u,k}\right\}_{k=(r-1)N_{s}}^{rN_{s}-1}\) is the discrete Fourier transform (DFT) of the time domain sequence \(s_{u,t}\). After that, the frequency domain data is interleaved and mapped to the OFDM symbol. To simplify the formulation, we assume that \(N_{s}=N_{c}\), which means that only a single \(N_{c}\)-length block is considered and assume the identity mapping. Therefore, the frequency domain sequence \(\left\{c_{u,k}\right\}_{k=0}^{N_{c}-1}\) is the DFT of the full-time sequence \(\left\{s_{u,t}\right\}_{t=0}^{N_{c}-1}\). After the cyclic prefix (CP), an analog precoder \(\mathbf{f}_{u,u}\in\mathbb{C}^{N_{tx}}\) is employed. Due to the hardware constraints, we only consider analog phase shifters that force all coefficients of the precoder to have equal magnitude, i.e., \(\left|\mathbf{f}_{u,u}\right|^{2}=1/N_{tx}\), and furthermore, it is assumed that they are constant over the subcarriers. Therefore, the discrete transmit complex baseband signal \(\mathbf{x}_{u,k}\in\mathbb{C}^{N_{tx}}\) of the \(u\)th user at subcarrier \(k\) can be represented as  
\[\mathbf{x}_{u,k}=\mathbf{f}_{a,u}c_{u,k}\,, \tag{1}\]  
where \(c_{u,k}\in\mathbb{C}\). The design of the analog precoder coefficients will be presented in the next Section. We assume that the number of users is lower than the number of RF chains (\(N_{RF}\)) at the receiver, \(U\leq N_{RF}\).
## 1 Introduction
### Related Work  
The power allocation optimisation problem is formulated as a non-convex optimisation problem that is difficult to solve directly [5, 6, 7]. Furthermore, power allocation and user pairing are coupled problems, whilst the trade-off between throughput maximisation and fairness is an additional optimisation challenge. Moreover, the analysed work assumed that fairness can be affected by cell radius, number of users per cell, and path loss channel coefficient [8; 9; 10]. The literature review pays particular attention to methods that focus on fairness and throughput maximisation, as detailed in Table 1, and provides details about the objectives of and the algorithms applied in existing approaches in the literature. For example, the authors of [8] formulated a joint beamforming and power allocation problem to achieve a fair user data rate by utilising a maximum-minimum fairness scheme to maximise the minimum achievable user fairness based on a mm-wave NOMA system. The researchers first obtained closed-form optimal power allocation from the problem formulation and then designed the beamforming vector to reduce joint optimisation [8]. In ref. [12], [PERSON], [PERSON], and [PERSON] developed FBL to attain an optimal power allocation that can also ensure fair throughput for a downlink NOMA system with two users. Their investigation focused on achieving maximum-minimum fairness while enhancing total throughput. The authors of [15] developed hybrid beamforming to improve the rate of fairness and power consumption as multi-objectives. The optimisation scheme deployed an inner approximation algorithm and graph theory and considered power control and quality of service (QoS). In ref. [16], a fair NOMA scheme developed on the basis of a scheduling paradigm assumes that users can always achieve the data rate and compare it if they are using OMA. The authors also derived power allocation coefficient bounds as a channel gain function for two users.
## Materials and Methods  
### Site Description  
The study site was located at the Station for Measuring Forest Ecosystem-Atmosphere Relations (SMEAR II) in Hyytiala, Southern Finland (61.847 N, 24.294 E, elevation 181 m), which has been measuring ecosystem fluxes (e.g., CO\({}_{2}\), H\({}_{2}\)O), climatic and ecological variables continuously since 1995 [51]. The studied canopy was a Scots pine (_Pinus sylvestris_ L.) stand established in 1962, with stem density, mean tree height and mean diameter at breast height (1.3 m) of 755 stems ha\({}^{-1}\), 16 m and 18 cm, respectively [52]. Four years prior to our study, all-sided leaf area index (LAI) of pine trees around the eddy covariance tower was estimated at 6.5 [53], which corresponds to a projected LAI of approximately 3. Ground vegetation around SMEAR II is composed mostly of the ericaceous shrubs _Calluna vulgaris_ L., _Vaccinium vitis-idaea_ L. and _V. myrtillus_ L., and of the moss species _Dicranum undulatum_. The terrain at the site is relatively flat, with a thin soil (50-150 cm) classified as a Typic Haplocryod [51; 54]. The mean annual temperature is 4.3\({}^{\circ}\)C and the mean annual precipitation is 590 mm [52].