esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Phenological Validation  
The MODIS derived SOS, EOS, and LOS were validated with SOS, EOS, and LOS extracted from Sentinel NDVI data using the growing seasons of 2017/18 and 2018/19. ANOVA results confirmed a good agreement between phenological metrics derived from MODIS NDVI and Sentinel-2 NDVI. This is an indication that the MODIS derived phenological metrics are a reliable representation of rubber phenological events in the study area. Early SOS was seen in Sentinel-2 data for 2018/19 compared to MODIS. This was expected, as two studies have demonstrated that SOS for coarser-resolution image dataset is highly correlated to an early SOS identified in the fine-resolution image dataset [89; 90]. The NDVI from these satellites indicated a similar pattern, with Sentinel-2 being more sensitive in capturing rubber canopy changes (see Figure A3 in the Appendix A). The study conducted by [PERSON] et al. [68] has shown that fine-resolution data are better at retrieving phenological patterns, however due to limited data, we have used MODIS for the purpose of this study.  
The existence of minor discrepancies in the two datasets can be explained by missing data due to the cloud presence and different pixel data representation, even though the same configuration was set in TIMESAT. Both datasets have different cloud cover percentages, and the data pixels in MODIS represents the combined maximum of the best 8-day observations, while Sentinel-2 data represents daily data taken on a particular date. Another factor influencing differences in the two datasets is the difference in the yearly image numbers of each data used in TIMESAT. There are 46 (at 8-day interval) and 36 (at 10-day interval) images per year used for MODIS and Sentinel-2, respectively. These three factors affect the smoothing process and the calculation of phenological metrics extracted in TIMESAT. In addition to this, non-vegetational effects such as soil-wetness [91], viewing geometry, and illumination conditions [92; 93], and distorted signal under overcast conditions [94] may alter the received phenological signal and thus contribute to the observed data discrepancy.
# Performance of Sensor Data Process Offloading on 5G-Enabled UAVs
###### Abstract  
Recently, unmanned aerial vehicle (UAV)-oriented applications have been growing worldwide. Thus, there is a strong interest in using UAVs for applications requiring wide-area connectivity coverage. Such applications might be power line inspection, road inspection, offshore site monitoring, wind turbine inspections, and others. The utilization of cellular networks, such as the fifth-generation (5G) technology, is often considered to meet the requirement of wide-area connectivity. This study quantifies the performance of 5G-enabled UAVs when sensor data throughput requirements are within the 5G network's capability and when throughput requirements significantly exceed the capability of the 5G network, respectively. Our experimental results show that in the first case, the 5G network maintains bounded latency, and the application behaves as expected. In the latter case, the overloading of the 5G network results in increased latency, dropped packets, and overall degradation of the application performance. Our findings show that offloading processes requiring moderate sensor data rates work well, while transmitting all the raw data generated by the UAV's sensors is not possible. This study highlights and experimentally demonstrates the impact of critical parameters that affect real-life 5G-enabled UAVs that utilize the edge-offloading power of a 5G cellular network.  
U 2023238641 Ericsson Research, Laboratoriegrand 11, 977 53 Lulea, Sweden 1
# Implementation and Evaluation of Activity-Based Congestion Management Using P4 (P4-ABC)
## 4 Data Plane Programmability Using P4  
We give an overview of data plane programmability using the programming language P4 [25] and its processing pipeline. P4 allows the definition of new header fields and forwarding behaviour, which makes it attractive for the implementation of novel forwarding paradigms. P4 programs are compiled for so-called targets, i.e., P4-capable switches, and offer a program-specific application programming interface (API) for their configuration. This API serves for either manual configuration or automatic configuration using a controller. Due to the latter, P4 is often leveraged for software-defined networking (SDN).
# Improving Roadside Unit Deployment in Vehicular Networks by Exploiting Genetic Algorithms
## 5 Simulation Results
### Performance Comparison between GARSUD and Other Existing Approaches
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of Vehicles** & **Vehicle** & **Geographic** & **D-RSU** & **GARSUD** \\ \hline \multirow{4}{*}{100} & \#1 & 15.984 s & 5.320 s & 3.979 s \\  & \#2 & 0.647 s & 0.524 s & 0.993 s \\  & \#3 & 6.989 s & 7.987 s & 3.647 s \\ \hline \multirow{4}{*}{200} & \#1 & 7.314 s & 11.651 s & 3.324 s \\  & \#2 & 15.688 s & 6.328 s & 0.993 s \\  & \#3 & 0.653 s & 0.652 s & 0.654 s \\ \hline \multirow{4}{*}{300} & \#1 & 0.650 s & 0.654 s & 0.656 s \\  & \#2 & 0.645 s & 0.646 s & 0.642 s \\ \cline{1-1}  & \#3 & 0.649 s & 0.654 s & 0.647 s \\ \hline \multirow{4}{*}{400} & \#1 & 0.655 s & 0.649 s & 0.650 s \\  & \#1 & 0.655 s & 0.649 s & 0.650 s \\ \cline{1-1}  & \#2 & 0.647 s & 0.650 s & 0.656 s \\ \cline{1-1}  & \#3 & 0.655 s & 0.645 s & 0.654 s \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Warning notification time (in seconds) for each warning vehicle when deploying 9 RSUs in Madrid considering a uniform traffic flow. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of Vehicles** & **Vehicle** & **Geographic** & **D-RSU** & **GARSUD** \\ \hline \multirow{4}{*}{100} & \#1 & 6.664 s & 15.993 s & 4.335 s \\  & \#2 & 11.340 s & 0.650 s & 3.653 s \\  & \#3 & 6.863 s & 3.983 s & 0.839 s \\ \hline \multirow{4}{*}{200} & \#1 & 13.978 s & 20.988 s & 8.313 s \\  & \#2 & 6.320 s & 0.868 s & 2.672 s \\  & \#3 & 0.650 s & 0.658 s & 0.648 s \\ \hline \multirow{4}{*}{300} & \#1 & 0.648 s & 0.652 s & 0.651 s \\  & \#2 & 0.643 s & 0.648 s & 0.647 s \\  & \#3 & 0.652 s & 0.648 s & 0.649 s \\ \hline \multirow{4}{*}{400} & \#1 & 0.650 s & 0.649 s & 0.647 s \\  & \#2 & 0.704 s & 0.652 s & 0.650 s \\ \cline{1-1}  & \#3 & 0.903 s & 0.912 s & 0.655 s \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Warning notification time (in seconds) for each warning vehicle when deploying 4 RSUs in Madrid considering a uniform traffic flow.
# Analysis and Modelling of Shrinkage and Creep of Reactive Powder Concrete
###### Abstract  
The objective of this study was to examine the shrinkage and creep of reactive power concrete (RPC) with different steel fibre contents (0%, 1% and 2% by volume). A total of 37 RPC specimens were prepared and tested for compression strength, elastic modulus, shrinkage, and creep. In addition, different axial stress ratios (0.2, 0.3 and 0.4) were used in the creep tests. Furthermore, the accuracy of the ACI 209-82 model, CEB-FIP 90 model, B3 model, and GL 2000 model for predicting the shrinkage and creep of RPC was evaluated and new numerical shrinkage and creep models were developed. The experimental results revealed that the compressive strength and elastic modulus increase with increasing steel fibre content. The shrinkage and creep decreased with increasing addition of steel fibre from 0% to 2%. A good linear relationship was found between the axial stress ratios and creep strain. All four existing models were unable to accurately predict the shrinkage and creep of RPC. A good agreement between the experimental results and proposed shrinkage and creep numerical models was observed. Therefore, it is suggested that the proposed shrinkage and creep models can be used to calculate the shrinkage and creep of RPC.  
reactive power concrete; shrinkage; creep; steel fibre; model +
Footnote †: journal: Applied Sciences
## 5 Experimental Verification
The results demonstrate that reducing the radial dimensions of the inner annular chamber can increase the fluid momentum at the non-throttling valve port, thereby suppressing the steady flow force of the slide valve. Figure 29 compares the experimental and simulated curves of steady flow force for the original spool and the optimized spool. The experimental results first validate the asymmetry in steady flow force characteristics under the bidirectional throttling mode, with a maximum deviation of 15.83% between the experiments and simulations. For outlet throttling, the average compensation rate for hydraulic dynamics within the experimental range was 15.78%, with a maximum compensation rate of 34.45%. For the inlet throttling mode, the average and maximum compensation rates were 42.58% and 76.47%, respectively.
## 4 Simulation and Performance Evaluation
### Group keys
#### Performance Metrics  
In this section, we evaluate the performance of the proposed key management framework for underwater wireless sensor networks based on four key metrics: key distribution time, memory usage, energy consumption, and scalability. We compare the performance of the proposed framework for different key distribution mechanisms, namely pre-distributed keys, pairwise keys, and group keys.  
mechanism distributes keys to all sensor nodes in the network simultaneously, whereas the pairwise keys and group keys mechanisms require pairwise or group key establishment.  
Table 5 shows that the memory usage for the pre-distributed keys mechanism is also significantly lower than that of the pairwise keys and group keys mechanisms, which is expected since the pre-distributed keys mechanism requires each sensor node to store only one key.  
Table 6 shows that the energy consumption for the pre-distributed keys mechanism is also lower than that of the pairwise keys and group keys mechanisms. This is because the pre-distributed keys mechanism requires only a single message exchange for key distribution, whereas the pairwise keys and group keys mechanisms require multiple message exchanges.  
As Figure 7 shows that the proposed key management framework exhibits good scalability for up to 150 sensor nodes in the network for all three key distribution mechanisms. The circles in the figures represent the energy consumption. The energy consumption increases with the increase of sensor nodes.  
Overall, the results indicate that the pre-distributed keys mechanism is the most efficient in terms of key distribution time, memory usage, and energy consumption and that the proposed key management framework is scalable for small to medium-sized underwater wireless sensor networks.  
From Table 7, we can observe that the pre-distributed keys mechanism has the lowest energy consumption, lowest memory usage, and fastest key distribution time among the three mechanisms evaluated. The pairwise keys mechanism has the highest energy consumption and memory usage, and the slowest key distribution time. The group keys mechanism has intermediate energy consumption and memory usage, but a relatively fast key distribution time.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Key Distribution Mechanism** & **Energy Consumption (Joules)** & **Memory Usage (Bytes)** & **Key Distribution Time (s)** \\ \hline Pre-distributed keys & 0.72 & 128 & 0.2 \\ Pairwise keys & 1.25 & 256 & 1.5 \\ Group keys & 1.68 & 192 & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison with models.  
Figure 7: Performance evaluation for group keys mechanism.  
Table 8 Compare our proposed Framework with the existing state-of-the-art Approaches. We can observe that our proposed pairwise keys mechanism outperforms all the other techniques in terms of energy consumption, key distribution time, and memory usage. The proposed framework performs well in terms of key distribution time and energy consumption, while the memory usage of PKPS is slightly better than ours.
## 5 Practical Applications
### Limitations and Directions for Further Research  
The current study features both strengths and limitations that must be considered when interpreting the findings. One of the key strengths of this study is that it focuses on basketball players, a specific cohort that can provide normative values to help athletes and coaches to improve sprint performance in a sport where speed and explosive power are critical. These values can be particularly useful for guiding training regimens tailored to the needs of basketball players. However, it is important to note that correlation does not imply causation. While improvements in vertical jump or maximal strength may be associated with enhanced sprint performance, it cannot be assumed that such gains directly lead to improvements in sprinting speed. Further studies are needed to explore the causal relationships between these variables. Although the regression models revealed strong predictive values (e.g., R\({}^{2}\) > 0.85), these results may overestimate the true predictive capacity due to potential model overfitting, particularly given the small sample size.  
However, there are several limitations to this study. First, the relatively small sample size is a significant constraint. Small sample sizes can lead to the overestimation of effect sizes, making it more difficult to generalize the results for a larger population. This also affects the replicability of the findings, as results based on a small number of participants may not hold true in different settings or with different cohorts. Although the sample rep resents a high level of competition in female basketball, access to elite players is inherently challenging, which made recruiting a larger sample difficult.  
Another limitation is the anthropometric variation within the cohort. Basketball players, even within the same team, exhibit considerable diversity in terms of height, weight, body composition, and other physical attributes. These differences may influence the interpretation of sprinting and explosive strength data, as physical characteristics such as leg length, muscle mass, and overall body composition can affect performance outcomes. Thus, while the findings provide valuable insights, they may not be fully applicable to all basketball players, especially those at different levels of competition or with varying anthropometric profiles.  
Finally, the absence of a change-of-direction (COD) test is a notable limitation. In basketball, athletes frequently engage in rapid directional changes, and sprint performance alone does not fully capture the demands of the sport. A COD test would have provided additional insight into the agility and explosive movements critical for basketball performance, offering a more comprehensive understanding of how physical characteristics translate to on-court success. Future studies could incorporate this type of test to better assess the multidimensional aspects of athletic performance in basketball.
# A Spatio-Temporal Building Exposure Database and Information Life-Cycle Management Solution
## 6 Application Scenarios
### Scenario I: Information Integration and Value Updating  
The aim of the first scenario is to integrate three different data sources to a unified dataset that covers the whole Area of Interest (AOI) while improving the overall value for the final inventory. The location and geometries considered in the scenario are taken from different sources over a district of a German city (Figure 5). Geometries are iteratively inserted, updated, and deleted with the aim to simulate both real-world changes to the building objects (construction, modification, destruction) and information updates, as result of better information becoming available about existing objects during their life-time. Attribute values for updates are randomly chosen from a set of allowed values according to the GEM Building Taxonomy for randomly selected objects.  
The data sources used for this application scenario are 422 building footprints extracted from Earth Observation (EO) data using remote sensing tools [43], 1539 buildings from OpenStreetMap (OSM), and cadastral data from an Official source (OF) that holds 541 buildings. The value of the sources (here defined by the map accuracy) increases from EO to OSM to OF.  
During the integration phase, the database transactions shall be logged and changes shall be archived for possible later retrieval. At each critical stage of the integration, a release is issued via Git and GeoGig and pushed to a remote repository. The following database transactions were performed in order to integrate the available data:  
* Transaction tt1: Insert object geometries from EO data analysis.
* Release R1: EO data.
* Transaction tt2: Update EO geometries with geometries from OSM where (ValueOSM > ValueEO) \(\wedge\) (GeomOSM \(\cap\) GeomEO).
* Transaction tt3: Insert OSM objects where not GeomOSM \(\cap\) GeomEO.
* Release R2: EO and OSM data.
* Transaction tt4: Update attributes of 10 buildings with values following the standards for RRVS data entry.  
Figure 5: Scenario Area of Interest (AOI) with the different sources for creating a unified building inventory.  
* Transaction tt5: Update EO and OSM geometries (EOSM) with geometries from OF where (ValueOF > ValueEOSM) \(\wedge\) (GeomOF \(\cap\) GeomEOSM).
* Transaction tt6: Insert OF objects where not GeomOF \(\cap\) GeomEOSM.
* Realease R3: EO, OSM and OF data enriched with RRVS attributes.  
Table 1 shows the evolution of the average value for selected attributes as the dataset evolves over time. Figure 6a shows the integrated building inventory at release 3 (R3), with colors indicating the different sources of the building geometries. Figure 6b shows the database state at transaction time 2 after the update of EO geometries with geometries from OSM. This state lies in between two releases (R1 and R2) and has been recovered using the transaction time history query (time_history()) of the database model.
## 3 Proposed Method
### Moving-Object Extraction  
Background subtraction is the basis of subsequent abnormal detection. In the training phase, the segmentation result obtained by background subtraction is used as a label mask. In the detecting phase, the foreground object obtained by background subtraction is used as the input of subsequent recognition and classification. In this way, it is only needed to judge and classify the foreground object rather than to recognize the whole image with a sliding window. Therefore, computation can be reduced and processing speed can be improved. The advantage of this method is that it is unsupervised; hence, the performance of background subtraction directly affects classification accuracy. In this paper, SuBSENSE [20], a state-of-the-art unsupervised background-subtraction method, was adopted for extracting the foreground object in the video. SuBSENSE is a pixel-level background-subtraction algorithm, and its basic idea is to use color and texture features to first detect moving objects, then introduce the idea of feedback control to adaptively update the parameters in the background model with the obtained rough segmentation results, so as to achieve better detection results. Foreground \(F\) can be obtained after video frame I is processed by SuBSENS:  
\[F(i,j)=\left\{\begin{array}{ll}1,&if\ SuBSENSE(I(i,j))\ is\ foreground\\ 0,&if\ SuBSENSE(I(i,j))\ is\ background\end{array}\right. \tag{1}\]  
where \(i\) and \(j\) are the position coordinates of the pixels. After obtaining the foreground pixels, the connected component-labeling method is used to locate and mark each connected region in the image, so as to obtain foreground target \(\mathbf{O}\)[21]:  
\[\mathbf{O}=blob(F)>n \tag{2}\]  
where \(n\) is the least number of pixels in a connected region; in this paper, we set n = 150, namely, only the connected regions with more than 150 pixels were regarded as foreground objects.  
Figure 3: Framework of proposed method.
## 6 Conclusions  
In this paper, we introduced a novel UAV routing protocol to optimize the UAV network performance. Our protocol overcame the shortages of other deep-reinforcement-learning protocols. Meanwhile, we designed a reasonable link stability evaluation indicator to adapt high mobility, and utilized the ARIMA energy prediction model to balance energy. With numerous experiments, simulation results show that DSEGR significantly outperforms GPSR and QNGPSR in terms of PDR. The average E2E delay of DSEGR is close to QNGPSR. The classic GPSR has poor performance metrics. DSEGR can achieve a faster convergence speed. The time of the first dead node is postponed for DSEGR. In summary, our proposed routing algorithm has better robustness and reliability, and is more suitable for performing search and rescue tasks in high dynamic and energy-limited UANETs.  
In our work, we only considered a simple scenario. In the future, we will extend the UANET to a more complex integrated ground-air-space scenario. In addition, we will apply multi-agent reinforcement learning in a collaborative manner to obtain better network performance.  
Conceptualization,Y.Z.; Funding acquisition, H.Q.; methodology, Y.Z.; software, Y.Z.; formal analysis, Y.Z.; validation, Y.Z.; data curation, Y.Z.; formal analysis, Y.Z.; investigation, Y.Z.; resources, H.Q.; visualization, Y.Z.; supervision, Y.Z. and H.Q.; writing--original draft preparation, Y.Z.; writing--review and editing, Y.Z. and H.Q. All authors have read and agreed to the published version of the manuscript.  
This research work was supported by the National Science Foundation of Guangxi Province under grant 2022 GXNSFDA035070.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 16: Performance comparison of the time of the first dead node among GPSR, QNGPSR, DSEGR, and FEQSAI with different initial energy.
# A Fragile Watermarking Scheme for Authenticity Verification of 3D Models in GLB Format
## 3 Materials and Methods
### GLB Format  
Today, most personal computers can render extensive, complex, realistic 3D scenes. In addition, having a dedicated graphics card has become almost mandatory. Hence, the creation of individual models is increasingly being replaced by modelling the entire virtual scene at once. To facilitate the transfer of complex and sophisticated 3D scenes, which may contain not only models but also information such as lighting, materials or camera settings, between 3D graphics applications, the gITF standard was developed. The format structure consists of a JavaScript object notation (JSON)-formatted text file and a binary file containing the data, called a buffer, where the former describes the contents of the latter. In the current gITF 2.0, the elements of a virtual scene form a hierarchical structure (Figure 1). It is important to note that a mesh object can consist of several meshes called primitives; e.g., the gate.glb (Table 1) model contains two separate sets of vertices that form a coherent whole. In this case, the two sets result from using two 3D scanners to fix the physical model as a 3D object.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & **Parameters** & **Model** & **Parameters** \\ \hline \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } & Name: pinecone.glb & Name: trolloduck.glb \\  & Size: 13,070 KB & Size: 47,228 KB \\  & Vertices: 58,057 & Vertices: 60,940 \\  & Triangles: 100,000 & Triangles: 102,617 \\  & Extras: texcoord, & Extras: texcoord, \\  & normals & normals, tangents \\  & Source: comuter \\  & design & \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: Conceptual relationships between objects in the gITF 2.0 standard. Adapted from [[https://www.khronos.org/files/gltf20-reference-guide.pdf](https://www.khronos.org/files/gltf20-reference-guide.pdf)]([https://www.khronos.org/files/gltf20-reference-guide.pdf](https://www.khronos.org/files/gltf20-reference-guide.pdf)) (accessed on 18 June 2025).  
A file with a glb extension is one coherent set of bits. The first 12 bytes are the header, which contains information about the format, version and the length of the entire file. Next is the JSON section, followed by the data buffer area (Figure 2).