esa-sceva/satcom-chunk-collection huggingface dataset:
# Research Needs for Realization of Zero-Carbon Power Grids with Selected Case Studies
## 2 Critical Research Agenda
### Inverters as VRE Interfaces with Grids
#### 2.2.5 Behind-the-Meter Units  
Although most of the focus is on VRE inverters, end-user loads must be similarly considered because power electronics converters are also widely used as the interfaces of building loads, such as light-emitting diodes, EV chargers, and HVAC units [48,49]. Under converter control, the penetration of constant-power loads has increased sharply in the past decades, affecting the damping ratios of the interfacing power grids. Therefore, more detailed modeling of building loads is required to analyze their effects on the dynamic responses of the interfacing grids comprehensively. Moreover, the increasing numbers of power electronics converters increase the complexity of load dynamic behaviors, particularly over very short timeframes. In other words, loads can no longer be treated using simple constant-power, -current, or -impedance assumptions during EMT-level modeling and analysis. Another important factor is the emergence of behind-the-meter generation and storage units, which are also interfaced using power electronics converters [48,49]. Such units significantly alter the characteristics of buildings' net load demands. As the numbers of behind-the-meter units continue to increase, improved modeling and analysis become important, facilitated by advanced metering infrastructure and Internet-of-Thing (IoT) sensing technologies.
# Mapping Atmospheric Exposure of the Intertidal Zone with Sentinel-1 CSAR in Northern Norway
## 3 Results  
### Percentage of Atmospheric Exposure Product  
Figure 12 shows the classification result into percentage of atmospheric exposure classes in the intertidal zone (ITZ_AtmExp) for the Tromso Municipality and in more detail at Langnes and Lille Grindoy. Obviously, at 10 m resolution, the ITZ needs to have a certain size to show details and patterns in percentage of atmospheric exposure which also directly reflects the topography of the ITZ. Single small areas and pixels of less than 5% atmospheric exposure surrounded by water can be ship traffic signs, noise from ship traffic, or strong winds. In harbors, the classification can reflect the harbor occupancy by ships.  
Figure 11: Aerial photograph at low tide from 2011 (© \(\copy\) norgeibilder.com, Kartverket, Terratec AS) with the mapped tidal stages.  
The atmospheric exposure classification at Langnes and Lille Grindoy was compared to the water line GPS tracks at tidal reference levels, as shown in Figures 13 and 14, respectively. The MSL line followed quite nicely the border between the 25-50% and 50-75% atmospheric exposure classes at Langnes. The ITZ area west of Lille Grindey had big stones and rocks that extended above water and were visible in aerial mosaics (Figure 11). The strong reflection of just the tip of such rocks above the water contributed enough to the radar backscatter in order to be mapped as higher atmospheric exposure areas. As the GPS track of the MSL water line was taken by walking along the water, single rocks offshore were not mapped. The water line at lowest spring tide (MLWS) was, in in this case, also well mapped in the classification at 10 m resolution. Figure 15 shows the classification results at Hillesey. The area between MLW and MLWS comprised very shallow mudflats with very few rocks and vegetation. These mudflats were completely water-saturated, and only small sand features were above water, as shown in the photo in Figure 15. The water line in such mudflats was diffuse and, therefore, difficult to define, being largely dependent on the subjective interpretation of the observer and, therefore, also difficult to map in the field, aerial, and satellite images. The ITZ definition of not being covered by water at some time would also exclude small water pools inside the ITZ.
## 3 Intersection Delay Fairness Analysis
### Delay Model Fairness Analysis
#### 3.1.2 Theory Analysis  
(1) Prevalence of unfairness  
Since the study is on low-saturation intersections, \(0<x_{i}<x<1\); therefore, Equation (8) < 0 is constant. From this, we can get that the phase vehicle average delay is monotonically decreasing, so there is only one case where each phase saturation is equal, i.e., \(x_{1}=x_{2}=\cdots=x_{i}=\cdots=x_{m}=\frac{x}{m}\). The conclusion can be illustrated that signalized intersections designed based on the Webster model can only appear in specific cases where the phase vehicle average delays are equal, i.e., the phenomenon of absolute fairness, whereas the phenomenon of inequality is universal.  
\[\frac{d}{dx_{i}}(d_{i})=\frac{C(1-x_{i})\big{(}\frac{x_{i}}{x}-1\big{)}}{2x \big{(}1-\frac{x_{i}^{2}}{x}\big{)}^{2}} \tag{8}\]  
(2) The less saturated, the less fair  
It is not difficult to find that the numerator part of Equation (8) monotonically decreases with \(x\), so the denominator part can be simplified to the following Formula (9). Due to \(0<x_{i}<x<1\), it can be known as monotonically increasing with \(x\), so Equation (8) monotonically decreases with \(x\), i.e., the smaller the degree of saturation, the greater the change in the phase car average and the more unfair.  
\[x+\frac{x_{i}^{4}}{x}-2x_{i}^{2} \tag{9}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Entrance**} & \multicolumn{3}{c}{**East Entrance**} & \multicolumn{3}{c}{**West Entrance**} & \multicolumn{3}{c}{**South Entrance**} & \multicolumn{3}{c}{**North Entrance**} \\ \cline{2-11}  & **Left** & **Straight** & **Right** & **Left** & **Straight** & **Right** & **Left** & **Straight** & **Right** & **Left** & **Straight** & **Right** \\ \hline Number of lanes & 1 & 2 & 1 & 1 & 2 & 1 & 1 & 2 & 1 & 1 & 2 & 1 \\ \hline
\begin{tabular}{c} Saturated traffic \\ flow(veh/h) \\ \end{tabular} & 1990 & 6828 & 2155 & 1990 & 6828 & 2155 & 2010 & 6780 & 2168 & 2020 & 6652 & 2168 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Intersection non-peak hour traffic flow.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Type**} & **Name** & **Symbol** & **Value** & **Unit** \\ \hline \multirow{4}{*}{Vehicle Parameters} & Free Flow Speed & \(v_{f}\) & \(11.1\) & m/s \\ \cline{2-6}  & Average Deceleration & \(a_{d}\) & \(-2.5\) & m/s\({}^{2}\) \\ \cline{2-6}  & Average Acceleration & \(a_{a}\) & \(1.5\) & m/s\({}^{2}\) \\ \hline \multirow{4}{*}{Signal Parameters} & Number of Phases & \(M\) & 4 & \\ \cline{2-6}  & Saturation Flow Rate (Phase 1) & \(s_{1}\) & 6652 & veh/h \\ \cline{2-6}  & Saturation Flow Rate (Phase 2) & \(s_{2}\) & 2010 & veh/h \\ \cline{2-6}  & Saturation Flow Rate (Phase 3) & \(s_{3}\) & 6379 & veh/h \\ \cline{2-6}  & Saturation Flow Rate (Phase 4) & \(s_{4}\) & 2005 & veh/h \\ \cline{2-6}  & Start-Up Loss Time & \(l\) & 3 & s \\ \cline{2-6}  & Yellow Light Time & \(Y_{A}\) & 3 & s \\ \cline{2-6}  & Green Light Interval Time & \(l\) & 3 & s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Vehicle driving parameters and signal parameters.
# A Single RF Emitter-Based Indoor Navigation Method for Autonomous Service Robots
## 7 Navigation Testing
### Results and Discussions  
Each benchmark test was performed three times to ensure repeatability of the experiments and averaged, as shown in Table 1. In each experiment, the range of the results was negligibly small. In general, the hypothesis could be formulated that the preliminary system performs better in scenarios where there is a straight line to the emitter, and the full system performs better in all other cases. For four of the five tests, results were as expected. The unexpected result was that the preliminary system outperformed the full system in the blocked 4 m straight-line test (scenario B). This result may be due to small fluctuations in the estimate of the target location causing variations in the predicted path in the full system. In the blocked straight-line test, these small changes in the target position estimate caused the path-planning module to switch the path to go around the other side of the  
Figure 13: Diagrams showing the five test scenarios, with the expected paths of the preliminary system (green, dashed arrow) and the full system (blue, solid arrow).  
obstacle. If this fluctuation happens repeatedly, the robot may drive back and forth behind the obstacle without making any forward progress.  
Overall, these results support the claim that the full system is able to navigate relatively complex environments, as it significantly outperforms the preliminary system in the two final tests. In particular, for the fully blocked 4 m right angle test (scenario D), the robot could only complete it successfully one time (i.e., 391 s for that single test rather than an average) when using the preliminary system, and the preliminary system could not complete the U-turn test at all. Without long-term path planning, the robot was not able to move away from the target emitter to find an alternative route that eventually led back to the emitter.  
In a separate test, we tried to determine the maximum range of our transmitter/receiver in ideal conditions, and to determine if the system was still effective over long distances. This involved placing the transmitter at the end of a long corridor, with a transmission power of 0 dBm. The transmitter was able to be detected at a range of over 50 m, and the system was able to successfully navigate to the target over a period of approximately 20 min. However, this maximum distance could still be increased by using a higher transmission power.
## 4 Results
### Evaluation of the AGB Upscaling Model at a 1 km to 5 km Spatial Resolution  
To upscale the AGB from a 1 km to 5 km spatial resolution using the GBRT, RF, and ERT models, the MODIS-derived reference AGB during 2009-2011 was aggregated from 1 km to 5 km using the simple average method and then used to train the models. Figure 4a-c presents the scatter plots for the MODIS-derived reference AGB and training results using the three models. RF yields the best training performance with the highest R\({}^{2}\) (0.76) and the lowest RMSE (54.7 g C m\({}^{-2}\)) of the AGB training results against the MODIS-derived reference AGB among the three models, followed by GBRT with an R\({}^{2}\) of 0.76 and RMSE of 55.3 g C m\({}^{-2}\). The ERT model yields the lowest R\({}^{2}\) (0.72) and the highest RMSE (59.7 g C m\({}^{-2}\)).  
To evaluate the performance of the AGB upscaling model based on the GBRT model and the other two models, we validated the three models based on the MODIS-derived reference AGB estimation with a 5 km spatial resolution during 2012-2013. Figure 4d-f shows the scatter plots for the MODIS-derived reference AGB and AVHRR-derived estimated AGB by the three models with a 5 km spatial resolution. The results show that the GBRT model demonstrated the best performance with the highest R\({}^{2}\) (0.74) and lowest RMSE (57.8 g C m\({}^{-2}\)), and bias (\(-\)0.1 g C m\({}^{-2}\)) of the AVHRR-derived estimated AGB against the MODIS-derived reference AGB among the three models in the validation set. Therefore, we selected the GBRT model to predict the AGB based on AVHRR NDVI product, elevation, Ta, P and Rs with a 5 km spatial resolution to generate AGB datasets during 1982-2018.  
Figure 4: Scatter plots for MODIS-derived reference and AVHRR-derived estimated AGB with 5 km spatial resolution from (**a**) GBRT, (**b**) RF, (**c**) ERT in training set and (**d**) GBRT, (**e**) RF, (**f**) ERT in validation set.  
Figure 5 shows the MODIS-derived reference and AVHRR-derived estimated average AGB maps with a 5 km spatial resolution determined by the GBRT model in the validation set during 2012-2013. The results confirmed that the spatial pattern of the estimated AGB was consistent with that of the reference AGB, so it could reflect the spatial distribution of actual grassland AGB, which could confirm the reliability of the estimated AGB datasets based on AVHRR data. Therefore, the GBRT model is selected to produce the AGB dataset during 1982-2018 using the AVHRR NDVI product, Ta, P, Rs, and elevation with a 5 km spatial resolution.
## 3 Scheme Design
### Experimental Scheme  
The testers developed corresponding test schemes based on the measured flow method and isotope geochemistry, respectively.  
1. Experimental scheme of the measured flow method  
The determination site was selected on the 13# bottom lane, where CBM extraction had not been carried out and the conditions of the coalbed and gas storage were representative. Two locations with similar conditions were set up on both sides of the 13# bottom lane, namely, Zone A and Zone B, and four groups were assigned for each zone. The first group drilled through the 6u coalbed, sealed to the 6l coalbed and extracted CBM from the 6u + 6m + 6l coalbeds. The second group drilled through the 6u coalbed, sealed to the 6m coalbed and extracted CBM from the 6u + 6m coalbed. The third group drilled through the 6m coalbed, sealed to the 6m coalbed and extracted CBM from the 6m coalbed alone. The fourth group drilled through the 6l coalbed, sealed to 6l coalbed and extracted CBM from the 6l coalbed alone.  
The number of boreholes for each group is five; the distance between each group is 20 m, and the spacing of boreholes within groups is 5 m. The diameter of the boreholes is 94 mm; the orientation of the boreholes is 301\({}^{\circ}\) and 121\({}^{\circ}\), and the inclination of the boreholes is 45\({}^{\circ}\). The layout and parameters of boreholes are shown in Figure 5.  
The boreholes of each group are connected to the same manifold (\(\Phi\) 108 mm) individually and then connected to CBM extraction pipeline. The temperature, pressure difference, negative pressure, concentration and mixed flow of each group are monitored once a day from the date of connection. The observation is concluded when the measured values change rate of all groups is less than 10% or stable.
2. Experimental scheme of isotope geochemistry  
Measurement sites were selected on the 13# bottom lane, the 11# transportation lane and the 14# transportation lane, where the coalbed and gas storage were representative. It was designed that the teams were arranged in different roadways to collect the gas samples extracted from 6u, 6m, 6l, 6u + 6m, and 6u + 6m + 6l, respectively. The arrangement of the sampling boreholes is shown in Figure 6.  
Figure 5: Borehole layout for measured flow method.
## 2 Initiation of the Project
### Satellite Registration and Frequency Allocation
During the following months the development team should expect to receive formal letters of \"request for coordination\" from international telecommunication authorities. These requests must be replied with formal letters, which are again handled by the local (national) authorities. Finally, in case of using amateur frequency range for the CubeSat, the development team must notify the IARU with the allocated frequency no earlier than six months after the publication of the pertinent case in the IFIC. The notification process is similar to the satellite registration process, but the forms must be filled with the definitive and accurate data.
## 3 Bioactivity of _Chlorella vulgaris_ Compounds
### Neuoprotective Effects  
The neuroprotective effects of _C. vulgaris_ are largely due to its rich content of bioactive compounds, including carotenoids, polyphenols, PUFA, and polysaccharides. Carotenoids like lutein, along with polyphenols, enhance the body's antioxidant defense mechanisms, protecting neurons from oxidative stress and reducing the risk of neurodegenerative diseases [107]. These compounds also have strong anti-inflammatory effects, which further shield neurons from damage. Polyunsaturated fatty acids, especially omega-3s, contribute to neuron protection by maintaining cell membrane integrity and reducing inflammation.  
Polysaccharides, such as beta-glucans, play a role in modulating the immune response and lowering neuroinflammation, while _Chlorella_ growth factor supports neuronal repair and regeneration [9]. Tryptophan, an amino acid found in _C. vulgaris_, boosts serotonin production, improving mood and cognitive function. According to Panahi et al. [108], C. _vulgaris_ extract, an antioxidant-rich algal product, was found to have beneficial effects when used as an adjunctive therapy for patients with depression. In a 6-week exploratorytrial, _C. vulgaris_ extract supplementation significantly improved anxiety, as well as physical and cognitive symptoms of depression. Clinical reports also show increased serum concentrations of antioxidants following short-term supplementation with extracts from this microcalga [64; 99; 108]. The reduction of oxidative stress could be regarded as a plausible mechanism contributing to its observed antidepressant properties. Depression is often accompanied by a depletion in total antioxidant status and deregulated activity of antioxidant enzymes, such as glutathione peroxidase and superoxide dismutase. _C. vulgaris_ extract contains a complex mixture of antioxidants, including chlorophyll, \(\beta\)-carotene, \(\alpha\)-carotene, ascorbic acid, \(\alpha\)-tocopherol, lutein, lycopene, and zeaxanthin. Additionally, it provides trace elements such as zinc, copper, and magnesium, which are crucial for the activity of antioxidant metalloenzymes.  
The synergistic effect of these bioactive compounds, combined with essential vitamins and minerals like B vitamins, magnesium, and zinc, make _C. vulgaris_ a potent neuroprotective agent, offering antioxidant, anti-inflammatory, and regenerative benefits for brain health [9; 107; 109; 110].
# Acoustic Shooting and Bounce Ray Method for Calculating Echoes of Complex Underwater Targets
## 3 Modeling of Time-Domain Scattered Sound Field  
Equation (9) can be used to calculate the sound field characteristics of a single frequency signal in the frequency domain, but it is not suitable for a wide band signal system. Fourier transform is applied to Equation (8) to obtain the time-domain sound field model [7; 8]:\[\begin{array}{ll}p_{s}(\mathbf{r},t)&=\frac{1}{2\pi}\int_{-\infty}^{\infty}P_{s}( \mathbf{r},\omega)e^{i\omega t}d\omega\\ &=\frac{1}{2\pi t}\int_{-\infty}^{\infty}\Biggl{\{}-\frac{ik}{2\pi r}e^{-ikr} \iint\limits_{S}\Gamma(\phi)e^{ikz^{\prime}}P_{i}(\mathbf{r}^{\prime},\omega)cos \phi dS\Biggr{\}}e^{i\omega t}d\omega\\ &=\frac{1}{2\pi rc}\iint\limits_{S}\Gamma(\phi)\biggl{\{}\frac{1}{2\pi}\int_{- \infty}^{\infty}P_{i}(\mathbf{r}^{\prime},\omega)\frac{\partial}{\partial t}e^{i \omega(t-\frac{r-2r^{\prime}}{c})}d\omega\Biggr{\}}cos\phi dS\\ &=-\frac{1}{2\pi rc}\iint\limits_{S}\Gamma(\phi)\frac{\partial}{\partial t} \biggl{\{}\frac{1}{2\pi}\int_{-\infty}^{\infty}P_{i}(\mathbf{r},\omega)e^{i\omega (t-\frac{r-2r^{\prime}}{c})}d\omega\biggr{\}}cos\phi dS\\ &=-\frac{1}{2\pi rc}\iint\limits_{S}\Gamma(\phi)\frac{\partial}{\partial t} \biggl{\{}\frac{1}{2\pi}\int_{-\infty}^{\infty}P_{i}(\mathbf{r}_{0},t-\frac{r-2r^{ \prime}}{c})\biggr{\}}cos\phi dS\end{array} \tag{10}\]  
The following formula can be used to calculate the partial time conductance of the time domain sound field:  
\[\frac{\partial}{\partial t}\{P_{i}(\mathbf{r}_{0},t)\}=\frac{P_{i}(\mathbf{r}_{0},t- \Delta t)-P_{i}(\mathbf{r}_{0},t+\Delta t)}{2\Delta t} \tag{11}\]  
where \(\Delta t\) represents the discretization of time.  
Similarly, the time domain scattering field is divided into the surface elements of the target surface for discrete integration:  
\[P_{ss}(\mathbf{r},t)=-\frac{1}{4\pi rc\Delta t}\sum\limits_{m=1}^{M}\Gamma_{m}( \phi_{m})\iint\limits_{S_{m}}\left(P_{i}(\mathbf{r}_{0},t-\frac{r-2z^{\prime}}{c} -\Delta t)-P_{i}(r_{0},t-\frac{r-2z^{\prime}}{c}+\Delta t)\right)cos\phi_{m}dS \tag{12}\]  
When using Equation (12) to calculate the time-domain signals of the sound field, time discrete sampling is required, and the frequency change process of the wideband signal is expressed by the time function. Therefore, time discrete sampling also realizes the solution of the time-domain sound field characteristics of the wide band signal, and the specific process is described in the following section.
## 2 Method
### Encoder Architecture
The benefit of this approach is that it fully utilizes the Sobel operator to extract edge information from the image, thereby enhancing the network's ability to learn edge features. Additionally, in the edge processing encoder, the average pooling layer is replaced with a max-pooling layer to better extract edge information. This results in higher edge detection accuracy and stronger generalization ability. To effectively utilize edge feature information, we constructed a Fusion Unit, as illustrated in Figure 5. The input consists of two parts: features from the edge extraction branch and those from the backbone network. The edge features are processed using a 2 \(\times\) 2 Max-Pool operation, while the backbone features undergo 2 \(\times\) 2 AvgPool. This choice of a 2 \(\times\) 2 pooling kernel effectively reduces dimensionality while retaining crucial local features, enhancing computational efficiency and preventing overfitting. The pooled feature tensors are then concatenated to combine both average and maximum information, enriching the feature representation. This fused tensor is further refined through two 3 \(\times\) 3 convolutional layers and one 1 \(\times\) 1 convolutional layer, which extract and enhance high-level features. These operations transform the original feature representations into more enriched and discriminative forms, laying a solid foundation for subsequent tasks. Finally, the refined features pass through a sigmoid activation function and are element-wise multiplied with another MaxPooled result from the backbone features, introducing an attention mechanism that amplifies important features while suppressing less relevant ones. This mechanism enhances the model's representation and generalization capabilities. Figure 4: CBAM structure diagram.
## 4 Discussion
### Transfer Learning Without Extra Labels  
One especially notable finding is that a model trained to predict partial churn (40-day inactivity) can also detect full churn (90-day inactivity) by simply adjusting its decision threshold. This means that organizations do not have to wait the full 90 days to label their training data; 40-day labels already capture enough churn-related information to be applied to 90-day churn predictions. Thus, a single model can cover both situations without a separate round of training being required for the longer inactivity labels, providing a practical example of transfer learning in churn prediction.