esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Freight Traffic Efficiency at Urban Intersections Using Heavy Vehicle Platooning
## 4 Results
### Computational Efficiency  
Computational efficiency is a critical factor in determining the feasibility of implementing real-time traffic signal-optimization algorithms. The computational performance of the developed platoon-based algorithm was compared against the vehicle-based approach to assess its scalability and processing time under varying traffic conditions. Two Python-based implementations were developed to compute the optimal signal plans and advisory speed trajectories: one following a vehicle-based optimization and the other utilizing the platoon-based strategy. Both algorithms were executed on the same computing environment to ensure a fair comparison. The total computational time required for each algorithm was measured as a function of the total number of vehicles within the interest zone.  
Figure 9 illustrates the computational time required by both approaches as the number of vehicles increases. The results demonstrate that the vehicle-based algorithm experiences an exponential increase in computational time as the number of vehicles grows. This is attributed to the significant increase in possible departure sequences that need to be evaluated. For example, with just ten vehicles on each approach, the number of possible sequences exceeds 180,000, while increasing this to eleven vehicles results in more than 700,000 possible departure sequences. Consequently, the vehicle-based approach becomes computationally impractical for large-scale applications.  
In contrast, the platoon-based algorithm maintains a stable and low computational time, even as the number of vehicles within the interest zone increases. Since the platoon-based approach treats a group of vehicles as a single unit rather than individually optimizing each vehicle's movement, the total number of computations remains significantly lower. This characteristic makes the platoon-based algorithm highly scalable and suitable for real-time traffic-management applications.  
These findings confirm that the platoon-based algorithm not only enhances traffic efficiency but also ensures computational feasibility, making it a practical solution for real-time traffic signal control in connected vehicle environments.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Condition** & **Platoon-Based vs. Actuated (\%)** & \(p\)**-Value** & **Platoon-Based vs. Vehicle-Based (\%)** \\ \hline Under-Saturated & 55\% & \(<\)0.001 & 40\% \\ Over-Saturated & 35\% & \(<\)0.01 & 17\% \\ \hline \end{tabular}
\end{table}
Table 3: Reduction in the number of stops across CV penetration levels.
# Parameter Extraction of Photovoltaic Cells and Panels Using a PID-Based Metaheuristic Algorithm
## 3 Results and Discussion
### aSi Cell
With 900 iterations and a population size of 50, the optimum RMSE solution for the SDM was found, and the resultant calculation time was around 2.6704 s. For the DDM, the optimal solution took 50 population sizes and 1500 iterations, with an execution time of around 7.082 s. Table 8 displays the results of the experiment with the SDM, whereas Table 9 displays the experiment results with the DDM. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of** **Runs** & **Population** **Size** & **Number of** **Iterations** & **RMSE** & **Run Time (s)** \\ \hline \multirow{8}{*}{5} & 20 & 100 & 2.6833 \(\times\) 10\({}^{-4}\) & 0.1297 \\ \cline{2-5}  & 30 & 100 & 2.6592 \(\times\) 10\({}^{-4}\) & 0.1885 \\ \cline{2-5}  & 50 & 100 & 2.3619 \(\times\) 10\({}^{-4}\) & 0.3064 \\ \cline{2-5}  & 60 & 100 & 4.7753 \(\times\) 10\({}^{-5}\) & 0.3708 \\ \cline{2-5}  & 80 & 100 & 9.9107 \(\times\) 10\({}^{-5}\) & 0.4944 \\ \cline{2-5}  & 100 & 100 & 4.7163 \(\times\) 10\({}^{-5}\) & 0.6134 \\ \cline{2-5}  & 50 & 300 & 4.8050 \(\times\) 10\({}^{-5}\) & 0.9202 \\ \cline{2-5}  & 50 & 500 & 4.7721 \(\times\) 10\({}^{-5}\) & 1.4856 \\ \cline{2-5}  & 50 & 700 & 4.6683 \(\times\) 10\({}^{-5}\) & 2.0660 \\ \cline{2-5}  & 50 & 900 & 4.6123 \(\times\) 10\({}^{-5}\) & 2.6705 \\ \cline{2-5}  & 50 & 1100 & 4.6123 \(\times\) 10\({}^{-5}\) & 3.2334 \\ \cline{2-5}  & 50 & 1300 & 4.6123 \(\times\) 10\({}^{-5}\) & 3.8267 \\ \cline{2-5}  & 50 & 1500 & 4.6123 \(\times\) 10\({}^{-5}\) & 4.3954 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: aSi iteration vs. population size SDM case. Figure 11: aSi SDM and DDM convergence curve. In this experiment, the CPU execution times for the SDM and DDM were evaluated across two different processors: Intel Core i9 and AMD Ryzen 5 5600H. Table 10 presents the results, highlighting that the Intel Core i9 consistently outperformed the AMD Ryzen 5 5600H for both models in terms of execution speed. The SDM required less execution time than the DDM on both processors, indicating a simpler computational complexity. Specifically, the Intel Core i9 achieved 3.2214 s for the SDM and 6.2822 s for the DDM, while the AMD Ryzen 5 took 5.9455 s and 9.9611 s, respectively. These differences can be attributed to the Intel Core i9's superior architecture, clock speeds, and optimizations for single-threaded performance, which the algorithm likely benefits from. Conversely, the AMD Ryzen 5, while powerful, may not be as efficient for workloads with such specific computational demands.
## 4 Case Study--Safety Assessment of the Rockfill Specim in Triaxial Test  
### Problem Description  
Due to the simple and fully controlled boundary conditions, the triaxial test provides a suitable example for demonstrating the presented methodology. The core idea is to treat the triaxial test specimen as an analogue of the real structure whose safety is under investigation; thus, the complex three-dimensional stress distribution within a real structure (e.g., an embankment dam) is reduced to a single, fully controlled stress path that is easy to track and interpret. It should be noted that the real dams may exhibit complex interactions that cannot be captured by this analogy.  
The aim is to assess the safety of the _reference specimen_ subjected to a consolidated-drained (CD) triaxial test under the assumption that the test is interrupted at the chosen moment before the material specimen reaches its peak strength (in order to simulate the real structure's operating conditions). The stress-strain behavior of the specimen is known (monitored) up to this moment and provides the basis for incorporating monitoring data into the probabilistic analysis.  
The safety of the reference specimen is evaluated through a Monte Carlo analysis on a FEM model that reproduces the reference specimen test. In each simulation, the factor of safety (FS) is computed using the shear-strength-reduction (SSR) method once the simulated test loading sequence is complete.  
Model material parameters are calibrated from the preliminary investigation, using a series of twelve independent triaxial tests. These tests were carried out at different confining stress levels because the eventual confinement for the reference specimen is unknown. The variability observed in the test results is used to define the statistical distributions of the input parameters for the MCS.  
Within this compact yet representative example, the reference specimen's safety at the stoppage point is assessed using two probabilistic approaches for comparison:  
* Baseline probabilistic analysis: FS distribution based on nominal (_prior_) distributions of the input parameters.
* Weighted probabilistic analysis: FS distribution based on the baseline distributions adjusted in accordance with the available monitoring data (_posterior_).
## 4 Experiments
### The Result of Defect Detection
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method** & **mAP** & **Cr** & **In** & **Pa** & **Ps** & **RS** & **Sc** & **FPS** \\ \hline RetinaNet [32] & 76.45\% & 48.98\% & 80.41\% & 91.39\% & 84.87\% & 64.91\% & 88.15\% & 30.00 \\ DETR [41] & 76.82\% & 44.64\% & 86.19\% & 91.67\% & 80.90\% & 66.54\% & 90.99\% & 28.42 \\ YOLO-V5 & 76.94\% & 45.80\% & 82.04\% & 91.93\% & 83.93\% & 65.67\% & 92.28\% & 54.00 \\ Faster R-CNN [29] & 77.41\% & 53.68\% & 79.34\% & 91.16\% & 79.42\% & 70.94\% & 89.89\% & 16.88 \\ Faster R-CNN + FPN [11] & 78.32\% & 53.38\% & 83.98\% & 90.91\% & 80.65\% & 70.11\% & 90.88\% & 23.48 \\ Cascade R-CNN [40] & 78.71\% & 54.28\% & 83.02\% & 90.93\% & 81.86\% & 69.58\% & 92.61\% & 19.29 \\ Faster R-CNN + DPN & 79.67\% & 53.47\% & 84.06\% & 92.06\% & 85.91\% & 71.56\% & 90.98\% & 20.77 \\ DPN-Detector & 80.93\% & 55.33\% & 86.45\% & 92.09\% & 85.03\% & 73.54\% & 93.16\% & 18.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Defect detection results on NEU-DET. tection speed, it can still meet actual production needs. In terms of detection performance, it is superior to other detectors, which is very important for industrial applications. From the above evaluation results, the proposed DPN-Detector has better performance in the classification and detection tasks of the defect detection on the steel strip surface. The reason is that the proposed DPN module can enrich multi-scale information, expand the receptive field, and give good guidance to feature fusion. At the same time, double head takes full advantage of different inspection heads and high-lights defect features. It is very beneficial for the detection of surface defects of steel strips with various scales and strong background noise interference. Figure 8 shows some detection results sample of DPN-Detector on NEU-DET. Figure 8: Example of DPN-Detector detection results on NEU-DET: (**a**) Cr; (**b**) In; (**c**) Pa; (**d**) PS; (**e**) RS and (**f**) Sc. Figure 7: The PR curves for six defect types.
## 5 Discussion
The spatial distribution function of the scatterers \(z(t)\) is also dependent on the frequency because the scattering cross-sections of the scatterers are proportional to the fourth power of ultrasound frequency [25].The above issues imply that the IER is a frequency-dependent parameter, as supported by the results obtained from the tissue-mimicking phantom(See Figure 10). The \(C_{1}\), \(C_{2}\), and IER values were inversely proportional to the ultrasound frequency. The difference is that the IER performed better than the \(C_{1}\) and \(C_{2}\) image intensities in reducing the mean decreasing rate with frequency, although in principle its frequency dependence is an unavoidable property. According to observations on the results in Figure 10, the IER value dropped down when the frequency was larger than 5.5 MHz. In order to allow the IER characterizing tissues with a larger dynamic range, ultrasound frequencies lower than 5.5 MHz may be used. On the other hand, noise is the other potential issue to influence the IER. The phantom results indicated that the gain independence of IER performs worse when the gain is small, as indicated by the results in Figure 8. This may be explained by the noise effect on EMD that has been clarified in the previous study [12]. In general, the sources of external noise are numerous and complex, which mainly originate from the environment and user operation such as electromagnetic fields, instrument switching, or personnel contacting. Internal noises come from the electronic components and conduction wires within a system that may be a summation of various noises including thermal noise, shot noise, flicker noise, partition noise, and burst noise [26]. Due to those external and internal noises respectively exhibiting different frequency characteristics, the white noise, the summation of noises from all aspects, is the most commonly encountered electrical noise that affects the characteristics of input and output signals [27]. Smaller gains easily generate significant white noise interference to affect the EMD outcomes. White noise has a large bandwidth (a uniform distribution for an ideal condition) and contains fluctuations at higher frequencies compared with those in the signal. Therefore, noise provides the signal waveform with additional local extrema, thereby altering the probability of detecting local extrema for large- and small-amplitude waveforms in the signals [12]. The waveforms for the large-amplitude oscillations exhibit a relatively steeper slope compared with the small-amplitude waveforms; thus, the extrema of noise may not be conspicuous in the signal. By contrast, for the small-amplitude waveforms, the extrema from noise may be conspicuous and detectable. In this condition, IMF contains severe mode-mixing effects to affect EMD of data [12; 28]. As supported by the results in Figure 7, the IER decreased with decreasing the SNR (i.e., equivalently increasing the noise interference). Thus, a larger gain and any strategiesfornoise reductionare suggested to improve the measurement error of the IER.
## 3 Methodologies
### Trajectory Generation
#### 3.2.3 Spatial Mapper  
Although the pedestrian detector and tracker modules permit the extraction of each pedestrian's position in each CCTV video frame, these positions are relative to the picture plane coordinates of the video. The pedestrian's position needs to map onto the physical space for performing scenario analysis of crowds. To extract the spatial and temporal position of each pedestrian relative to the physical space, it is necessary to find a projection from the image plane coordinates of the CCTV to the physical space's plane coordinates.  
The process of mapping a pedestrian's position from the image plane to the physical space plane can be viewed as mapping a point from one plane to another, as shown in Figure 3. The original plane containing the point is in the \(X-Y\) coordinate system, whereas we want to find its mapped position on a plane in the \(X^{\prime}-Y^{\prime}\) coordinate system. We, therefore, introduce the spatial mapper module, which finds the homography matrix, or the transformation matrix between the two coordinate planes [35]. Formally, a homography transformation is defined as:  
\[\begin{bmatrix}x^{\prime}\\ y^{\prime}\\ 1\end{bmatrix}=H\begin{bmatrix}x\\ y\\ 1\end{bmatrix}=\begin{bmatrix}h_{11}&h_{12}&h_{13}\\ h_{21}&h_{22}&h_{23}\\ h_{31}&h_{32}&h_{33}\end{bmatrix}\begin{bmatrix}x\\ y\\ 1\end{bmatrix} \tag{2}\]  
where the homography matrix \(H\in\mathbb{R}^{3\times 3}\) has 8 degrees of freedom, generally normalized such that \(h_{33}=1\); The vector \(\{x,~{}y,~{}1\}\) represents a point in the image coordinate plane, whereas the vector \(\{x^{\prime},~{}y^{\prime},~{}1\}\) represents a point in the physical space coordinate plane.  
Figure 3: Spatial mapping from the image coordinate plane to the physical space coordinate plane.  
To obtain the homography matrix \(H\), the four points identified in the spatial alignment step (Section 3.1 step (3)) are used. The least square method proposed by [PERSON] et al. [PERSON] et al. (2019) and implemented in OpenCV is adopted, where given \(n\)\((x,y)\) points and corresponding \((x^{\prime},y^{\prime})\) points, the following back-projection error term is minimized:  
\[error=\sum_{i}^{n}\biggl{(}x_{i}^{\prime}-\frac{h_{11}x_{i}+h_{12}y_{i}+h_{13 }}{h_{31}x_{i}+h_{32}y_{i}+h_{33}}\biggr{)}^{2}+\biggl{(}y_{i}^{\prime}-\frac{ h_{21}x_{i}+h_{22}y_{i}+h_{23}}{h_{31}x_{i}+h_{32}y_{i}+h_{33}}\biggr{)}^{2} \tag{3}\]  
The back-projection error term can be interpreted as the sum of the errors between each point \(\left(x_{i}^{\prime},\ y_{i}^{\prime}\right)\) on the physical coordinate plane and the corresponding point \(\left(x_{i},\ y_{i}\right)\) mapped from the image coordinate plane via homography transformation. With the homography matrix \(H\) derived, the video frame position of any pedestrian generated from the pedestrian tracker can be mapped to the physical space with respect to the floor plan using simple matrix multiplication.
# Carbon Balance in Salt Marsh and Mangrove Ecosystems: A Global Synthesis
###### Abstract  
Mangroves and salt marshes are among the most productive ecosystems in the global coastal ocean. Mangroves store more carbon (739 Mg C\({}_{\text{ORG}}\) ha\({}^{-1}\)) than salt marshes (334 Mg C\({}_{\text{ORG}}\) ha\({}^{-1}\)), but the latter sequester proportionally more (24%) net primary production (NPP) than mangroves (12%). Mangroves exhibit greater rates of gross primary production (GPP), aboveground net primary production (AGNPP) and plant respiration (R\({}_{\text{C}}\)), with higher P\({}_{\text{GPP}}\)/R\({}_{\text{C}}\) ratios, but salt marshes exhibit greater rates of below-ground NPP (BGNPP). Mangroves have greater rates of subsurface DIC production and, unlike salt marshes, exhibit active microbial decomposition to a soil depth of 1 m. Salt marshes release more CH\({}_{4}\) from soil and Creek waters and export more dissolved CH\({}_{4}\), but mangroves release more CO\({}_{2}\) from tidal waters and export greater amounts of particulate organic carbon (POC), dissolved organic carbon (DOC) and dissolved inorganic carbon (DIC), to adjacent waters. Both ecosystems contribute only a small proportion of GPP, R\({}_{\text{E}}\) (ecosystem respiration) and NEP (net ecosystem production) to the global coastal ocean due to their small global area, but contribute 72% of air-sea CO\({}_{2}\) exchange of the world's wetlands and estuaries and contribute 34% of DIC export and 17% of DOC + POC export to the world's coastal ocean. Thus, both wetland ecosystems contribute disproportionately to carbon flow of the global coastal ocean.  
biogeochemistry; carbon; carbon balance; ecosystem; ecosystem processes; mangrove; salt marsh; wetland +
Footnote †: journal: Marine Science and Engineering
## 4 Satellite Precision Orbit Determination and Accuracy Analysis  
### Orbit Determination Strategy  
In this study, we utilized Bernese 5.2 to perform precision orbit determination for HY-2B/2C/2D satellites [27], and the orbit determination arc was set to 24 h. The reduced dynamic method can combine the advantages of dynamic method and kinematic method [28; 29; 30], and adding the dynamic model weakens the influence of conservative and non-conservative forces. This method employs empirical parameters and pseudo-random pulses, enabling it to better accommodate dynamic model errors and residual perturbation effects [31; 32; 33; 34].  
The orbit determination strategy is shown in Table 4. The empirical parameters are used to absorb non-conservative forces such as solar radiation and atmospheric resistance. To address dynamic model errors, a set of pseudo-random pulses were introduced at intervals of 6 min in the R, T, and N directions. These pseudo-random pulses had a prior standard deviation of 5 nm/s\({}^{2}\)[35].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Satellite** & **MP1 (m)** & **MP2 (m)** & \
\begin{tabular}{c} **Mean Elevation Angle** \\ (\({}^{\circ}\)) \\ \end{tabular} &
\begin{tabular}{c} **Satellites** \\ **Visibility** \\ \end{tabular} \\ \hline HY-2B & 0.36 & 0.24 & 33.1 & 6.7 \\ HY-2C & 0.38 & 0.27 & 35.6 & 6.6 \\ HY-2D & 0.34 & 0.22 & 34.8 & 6.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Multipath error RMS value and mean elevation angle.
# Reliable and Energy-Efficient LEO Satellite Communication with IR-HARQ via Power Allocation
## 1 Introduction  
As a supplement to terrestrial networks, sixth generation (6G) non-terrestrial networks (NTNs) can serve more users in a wide area at a low cost [1]. In 6G NTNs, low earth orbit (LEO) satellites are often used to provide services for internet of things (IoT) terminals [2; 3] because of their advantages such as large coverage, low transmission delay and good environmental adaptability. In order to cope with the increasing user demand, satellite communication systems tend to operate at higher frequency bands such as Ku/Ka-band or Q/V-band, where a large number of uncongested spectrum resources are available. However, at higher frequency bands, the transmission loss of the signal will become larger and more seriously affected by the weather attenuation [4]. Rain can cause severe attenuation of more than 10 dB, resulting in signal interruption. It makes reliable transmission more challenging.  
However, the energy provided by LEO satellites is severely limited [5; 6]. Apart from this, in order to protect the satellite system from interference by other satellites, the maximum transmission power of the satellite is constrained [7]. Therefore, reliable transmission cannot be realized simply by increasing the transmission power. In order to improve the transmission reliability of satellite communication systems, hybrid automatic repeat request (HARQ) protocols are usually used to combat the channel fading.  
Most studies on HARQ protocols in satellite communication systems mainly pay attention to the reliability or the throughput of the system. However, as the payload of a satellite is severely limited, energy efficiency should also be improved as much aspossible when HARQ is adopted in the satellite communication system. In fact, many energy-efficient HARQ protocols for terrestrial communication systems have been widely studied. Inspired by this research, we focus on the study of energy-efficient HARQ in LEO satellite communication systems.
# RSS-Based Localization and Mobility Evaluation Using a Single NB-IoT Cell+
## 3 Outdoor NB-IoT Database  
The database used in this research consists of two data sets. The first data set contains NB-IoT messages collected during an outdoor measurement campaign in the city of Antwerp, Belgium. This data set is used to evaluate the performance of RSS-based localization algorithms in an urban environment. The second data set is smaller and is used to evaluate the mobility of NB-IoT.  
All NB-IoT messages are collected by sending uplink messages from a Ublox Sara N211 System on Chip (SoC) over a Release 13 NB-IoT network to a backend. Each message contains the Cell ID of and the RSS value to the responding eNB. By connecting a Global Positioning System (GPS) module to the UE, the current location (i.e., latitude, longitude, altitude) is added to the message. The GPS coordinate is used as a ground truth reference location. Therefore, in the following, we define the location estimation error as the geographical distance from the estimated location to the location provided by the GPS receiver. A picture of the battery-equipped UE with GPS is shown in Figure 1.  
Since we collected a large outdoor publicly available Sigfox and LoRaWAN data set in the city of Antwerp [27], we decided to perform the NB-IoT measurements in the same environment. In this way, we are able to compare the localization performance of the three most popular LPWAN technologies available on the market. The heterogeneous zone of interest covers an area of 53 km\({}^{2}\) and is fully covered by a public NB-IoT Release 13 network, consisting of 83 eNBs. During the measurement campaign, we collected 1307 NB-IoT messages within the predefined zone, as shown in Figure 2.  
A smaller data set is collected in northern Antwerp. In order to evaluate the mobility issues addressed in Section 1, we want to analyze the performance of cell selection and reselection in NB-IoT. The eNB centered around the track of our measurement campaign consists of three directional antennas, with each cell covering a sector of 120\({}^{\circ}\). Note that other configurations such as four antennas separated 90\({}^{\circ}\) exist. To evaluate the handover between two antennas or cells, we collected 95 messages by moving counterclockwise around the eNB at a constant speed of 7 km/h and an update rate of 5 s. Figure 3 depicts a map of the uplink messages, together with the cell sector boundaries.  
Figure 1: Composition of the User Equipment (UE) used to collect NB-IoT messages.