esa-sceva/satcom-chunk-collection huggingface dataset:
# Remote Sensing and Bio-Geochemical Modeling of Forest Carbon Storage in Spain
## 4 Data Processing
### Simulation of Forest NPP in Future Condition  
The simulation of GPP, \(R_{\text{G}}\), and \(R_{\text{M}}\) through the use of Biome-BGC (as indicated in Section 4.1) was driven by different daily precipitation and minimum and maximum air temperature datasets, which led to different daylight average partial pressure of water vapor, and daily average air temperature through the use of MT-CLIM.  
A large dataset of future climate projections over the study area is available through [[https://www.adaptecca.es](https://www.adaptecca.es)]([https://www.adaptecca.es](https://www.adaptecca.es)). It contains, among others, projections of daily maximum and minimum air temperature and precipitation for different climate change scenarios (RCP 4.5 and RCP 8.5) at both spatial and station levels. However, they are obtained from different models resulting in big differences among the produced datasets. Moreover, the spatial resolution of the spatialized projections (11 km) is too coarse compared to the one used in the present study (1 km), while projections at station-level are restricted to the locations where meteorological stations from the Spanish Meteorological Agency are placed. Furthermore, climatic projections generally present great uncertainties [47], especially for precipitation. Therefore, to address the range of variation of the climate projections corresponding to the different climate change scenarios (e.g., no mitigation, soft mitigation, and hard mitigation) that can be found in the literature, a simplified dataset of possible future changes in meteorological variables (i.e., daily precipitation and maximum and minimum air temperature) was generated for use in the present study.  
The original 2005-2012 series were taken as reference and the same variation was applied to each value in each series. In the case of air temperature, absolute increments (\(\Delta T\)) of 1 \({}^{\circ}\)C, 2 \({}^{\circ}\)C, 3 \({}^{\circ}\)C, and 4 \({}^{\circ}\)C were applied to both minimum and maximum series. In the case of precipitation, relative variations were applied in the form of reductions (\(\delta\)PRE) of 10%, 20%, 30%, and 40% of its reference value. These variations were chosen to take into account climatic projections over the study area from both global and regional climatic models for different CO\({}_{2}\) emission scenarios [47]. The four modified air temperature series, the four modified precipitation series, and the reference series were combined to obtain 24 new 8-year sets of simulated daily GPP, \(R_{\text{G}}\), and \(R_{\text{M}}\) through Biome-BGC. Finally, the corresponding NPP series were calculated for each set using Equation (4) fed with the same GSV as in the current condition, i.e., from the GSV map.
## 4 Receiver Implementation Approaches
### Vector Tracking Receiver  
Unlike a scalar-based receiver, a vector-based receiver exploits a centralized approach as it combines both tracking and navigation into a single estimation problem. This tracking technique is based on the idea that each channel's parameters, including code phase and carrier frequency/phase, are linked to the dynamics of the receiver by the line-of-sight (LOS) vectors [16]. The navigation solution is fed back to the tracking loops to drive the code and carrier numerically controlled oscillator (NCO) to exploit the coupling between the tracking and receiver's position and velocity [17]. This VT architecture has some advantages over its scalar counterpart, including [17; 18; 19]:  
* Improved tracking performance, as the positioning solution is assisting the tracking,
* Robustness to disturbances such as multipath, high dynamics and degradation in signal strength,
* Some immunity to jamming,
* Bridging momentary outages of one or more satellites.  
However, these advantages come at the penalty of more complexity in the implementation of a vector-based receiver. In addition, a faulty channel will potentially affect all other channels due to inter-channel aiding, which might result in an overall deterioration in the performance of the receiver [15].
## 4 Experiments and Results
### Ablation Studies  
To prove the necessity of the modules we designed for the PcBD network, we design an ablation study to examine each of the proposed essential blocks. We replace one of the PcBD's innovative blocks with traditional units at a time and train these versions of the  
Figure 16: The attempts of PcBD to extract 3D point cloud boundaries.  
Figure 15: Test results of PcBD on the WHU-TLS dataset. (**a**) Pedestrians and a vehicle. (**b**) Trees. We change the number of points and projection direction within a scene to test the adaptability of PcBD.  
network on the proposed Bound57 dataset. The ablation studies follow the order of the processing flow of our network: Firstly, we replace the weighted calculation of normal vectors from SHOT to traditional PCA method, as in many geometry-based point cloud de-noising approaches. Then, we relace the PAConv blocks in the Set Abstraction block to regular convolutional layers while the kNN distances, normal vectors and angles are directly concatenated. We replace the proposed Point Transformers with normal vectors with the original ones in the Feature-Extracting block next. To verify the rationality of the Boundary-Detecting block, we firstly remove the part of extracting the relationship between 2D and 3D point clouds, and, in another version, we directly detect the boundary from the 2D projection point cloud. We do similar adjustments in the Smoothing block as we directly predict the moving vector on the detected boundary in 3D coordinates. The test results of these versions of PcBD on the Bound57 dataset are listed in Table 4.  
The results show that our proposed design of the Feature-Extracting block, Boundary-Detecting block and Smoothing block outperforms the widely-used blocks, and that the position of employment we choose is proper. Furthermore, the connection of the 2D and 3D features enables the network to learn structures and shapes with greater complexity.
## 4 Experimental
### Simulation Experiments with MAC Protocols and the Wake-Up Modem
#### 4.3.2 With Acknowledgment  
When acknowledgments come into play, the number of correctly received packets is reduced, since more time is needed to send the acknowledgments. Also, as shown in Figure 10(a), the difference in the number of correctly received packets is greatly reduced between ALOHA-CS and FAMA x2. Moreover, in the 100-node scenario, FAMA x2 received more packets than ALOHA-CS.  
In terms of packet delay, Figure 10(b) shows some interesting results. The delay when using T-LOHI increases slightly, and the larger increment comes with the ALOHA-CS protocol. The remaining protocol delays are fairly stable and do not vary when acknowledgments are introduced.  
Figure 10(c) shows how the lowest energy consumption is achieved by FAMA x2 and ALOHA-CS. MACA learning and T-LOHI tone have the highest energy consumptions, as they use the wake-up tone mode.  
Figure 10: Results without acknowledgment. (**a**) Normalized received packets; (**b**) delay; (**c**) energy per data packet—node; (**d**) Energy per data packet—sink.  
Finally, sink energy consumption per data packet is depicted in Figure 10(d), and as expected, the protocols without the CTS overhead have the lowest energy consumption. However, since the sink has to transmit and issue acknowledgments, the difference between these protocols and those with CTS is reduced. In addition, it can be seen how FAMA and FAMA x2 have a stable energy consumption, independently of the number of nodes--and this feature may be useful when trying to implement energy-neutral operations at the sink node.
## 6 Novel Techniques for Viscosity Analysis  
### Tomographic Velocity Profiling  
Tomographic velocity profiling is one of the emerging technologies. The working principle lie in the measurement of the velocity profile, which is specific to a cross-section of pipe and thereby determines the drop in pressure for a specific length. Unlike regular tube viscometry, whose theory is based on volumetric formulas for measuring viscosity data points, tomographic viscometry involves shear rate data points from the viscosity profile [47; 48]. The non-invasive and non-destructive manner of operation, as well as the in-line measurement of rheological parameters (e.g., slip velocity, yield stress, and shear viscosity) are the advantageous features of the process. In most liquid food, scattering particles are present that determine the flow characteristics. For food materials where scattering particles are more abundant, a portion of the velocity profile may be recorded instead of profiling the entire velocity [49].
# Toward Sustainable Mars Exploration: A Perspective on Collaborative Intelligent Systems
## 5 Collaborative Energy-Aware Computing
### Energy-Aware Task Scheduling and Distributed Computing
This feature makes these methods appealing because they can identify effective solutions, even when the search for the absolute best solution would require an impractical amount of time or computational resources [64]. These advanced algorithms, inspired by natural processes like genetic evolution and particle swarm behavior, optimize the sequence of tasks in time and the distribution of tasks among robots, while considering energy constraints. Furthermore, adaptive AI models, like reinforcement learning (RL), embedded within the swarm's architecture would allow vehicles to learn and adapt to Mars' unpredictable energy patterns, making the allocation process more efficient over time by incorporating accurate energy prediction. When considering the architecture for managing the complex interplay of tasks and energy allocation in UAV swarms, the choice between centralized and decentralized approaches becomes pivotal. In a centralized architecture, a central control unit makesdecisions for the entire swarm, whereas in a decentralized system, individual UAVs are empowered with decision-making capabilities, creating a more distributed and adaptive structure. On Mars, the advantages of a decentralized architecture are particularly pronounced due to the lack of immediate communication infrastructure and the vast, unpredictable environment, which make centralized control difficult to implement. This decentralized approach enables UAVs to operate autonomously, adapt to changing conditions, and recover from failures, ensuring continued functionality despite the challenges posed by the Martian environment [65]. A decentralized approach allows each vehicle to autonomously assess its energy levels, prioritize tasks, and collaborate with others when necessary--ensuring adaptability to the unique energy dynamics of Mars. Decentralization also reduces the risk of single points of failure, enhancing the system's overall robustness and scalability [66]. Furthermore, these intelligent robots, guided by algorithms that continuously learn and evolve, can be deployed in a collaborative human-robot environment, which is known as one of the industry 5.0 principles [67].
# Enhanced YOLO Network for Improving the Efficiency of Traffic Sign Detection
## 3 Method
### Space Pyramid Module  
The basic YOLOv5s SPPF module uses a spatial pyramid module with maximum pooling, and the extraction of global features also heavily relies on average pooling. In this research, we present the hybrid spatial pyramid pooling (H-SPPF) module, which further improves the extraction of network contextual information by fusing three distinct sizes of average pooling operations into the SPPF module. The specific structure of H-SPPF is displayed in Figure 2.  
As shown in the picture, the H-SPPF module is composed of an average pooling layer and a maximum pooling layer with a pooling kernel size of 5 \(\times\) 5. This feature map is then input into three sequentially connected maximum pooling layers with a 5 \(\times\) 5 pooling kernel size to generate six separate feature maps. These six generated feature maps are subsequently combined with \(F_{o}\) to complete the fusion of global and local features, resulting in a feature map with a richer perceptual field. Finally, a Conv module is used to obtain the final output \(F_{out}(W\times H\times C_{2})\). The whole calculation process can be expressed by Equation (1).  
\[\text{F}_{out}=\text{Conv}(\text{F}_{o}+\text{M}(\text{F}_{o})+\text{M}(\text {M}(\text{F}_{o}))+\text{M}(\text{M}(\text{M}(\text{F}_{o})))+\text{A}(\text {F}_{o})+\text{A}(\text{A}(\text{A}(\text{F}_{o})))) \tag{1}\]  
Figure 1: Improved multi-scale feature fusion structure (The bottom convolution module, the C3 module in the backbone network, and the 19 \(\times\) 19 feature fusion module in the feature fusion layer are deleted. The 32\(\times\) downsampling is then replaced with 16\(\times\) downsampling, and a new feature fusion module measuring 152 \(\times\) 152 is subsequently added to the feature layer.).  
where \(F_{o}\) denotes the result of the first Conv output, \(\mathbf{F}_{o}\) = \"Conv\"(\(\mathbf{F}_{\text{in}}\)); the symbol M() represents the largest pooling operation when using a \(5\times 5\) pooling kernel size; and the average pooling operation with a \(5\times 5\) pooling kernel size is indicated by the symbol A().  
The H-SPPF module improves the network's capacity to gather global and local features and allows it to obtain more contextual information by combining the maximum pooling layer and the average pooling layer. This further increases the network model's accuracy.
# Online Estimation of Short-Circuit Fault Level in Active Distribution Network
## 3 Implementation and Simulation Results
### Test Case and Results
The results are quite similar to Figure 8c, so they are not depicted. The overall numerical results, including confidence level, are given in Table 2. It can be concluded from the table that the errors between the predicted fault level and the truth value are less than 5%. In addition, it can also be observed that both the accuracy and the confidence increase with the magnitudes of disturbance increasing. The proposed method in this paper is mainly designed to online monitor the breaking capacities of the circuit breakers and disclose the potential risks if the fault level goes beyond or reaches the breaking limit. The existence of such risks usually means that the circuit breakers should be replaced or upgraded. This kind of estimation is desirable for power-grid planning, which normally does not require extreme accuracy. Hence, the results in Table 2 are accurate enough. Certainly, the estimation accuracy is critical to the performance of the algorithm. If the disturbances are too small, the estimation errors might be large, as measurement errors always exist. On the other hand, if the disturbances are large enough, the estimation errors could be decreased. However, the latter scenarios occur less frequently than the former ones in the practical distribution networks, so the chances to activate the proposed method are also reduced in the latter scenarios. Therefore, there should be a compromise between the accuracy and the time efficiency. The choice of the disturbances to produce the results with errors less than 5% is determined on the basis of trial-and-error on a number of simulations for different kinds of disturbances. This threshold ensures a sound performance, balancing the accuracy and the acceptable time intervals, to produce the results. Figure 7: The testing process of the proposed method.
# PCCAU-Net: A Novel Road Extraction Method Based on Coord Convolution and a DCA Module
## 2 Methods
### Dual-Input Cross-Attention (DCA) Module  
In the intricate realm of remote sensing image analysis, developing ways of deftly and efficiently amalgamating features from disparate sources has become a cardinal research challenge. At the heart of PCCAU-Net is the Dual-Input Cross Attention (DCA) module, which answers this conundrum by adroitly harmonizing the strengths of deep learning and attention mechanisms. During the encoding phase, this module focuses on extracting and integrating features from two heterogenous pyramid pathways, ensuring a cohesive amalgamation of deep features. Even more critically, at the decoding phase, the DCA module serves as a skip connection, elegantly bridging deep and shallow features, and thereby achieving spatial and semantic coherence between them. The architecture of the DCA module is depicted in Figure 3.  
The intellectual underpinnings of the DCA module can be summarized as follows:  
1. Feature encoding and interaction: the DCA module initially performs specialized convolution operations on both input features, generating query (Q), key (K), and value (V) representations. Ingeniously concatenated, these features complement and interact with each other in the subsequent attention stage.
2. Cross- Attention fusion: the module dynamically determines the importance of each feature by computing weights based on the juxtaposed Q and K. This process ensures that DCA can pinpoint and integrate salient information from varied origins.
3. Feature harmonization and dynamic fusion: the resultant features are first optimized via residual connections and subsequently subjected to layer normalization to ensure the continuity and stability of the information flow. Ultimately, the fusion attention mechanism calculates fusion weights and meticulously integrates Out_Input1 and Out_Input2, guaranteeing seamless spatial and semantic alignment.  
In summary, the DCA module endows PCCAU-Net with an unprecedented capability for the effective interaction and integration of features across multiple scales and origins within a unified framework. This design ethos not only augments the model's feature representation capabilities but also brings about hitherto unmatched precision and efficiency in complex remote sensing image analysis tasks.
## 4 Results
### Scenario 1--Series of Simulation Experiments with Different Initial WIP  
The experimental scenario includes different initial parameters of WIP size from the range of 0-8 pieces for each queue. The results are presented in Table 3 and show the mean number of good quality products (OK), standard deviation of the mean, minimal and maximal production volumes, and bad quality products (NOK).  
The average WIP size for each queue according to different initial WIP sizes is presented in Table 4. The capacity of Queue 1 was set to 10.  
Figure 11 shows the replication box and whisker plot of the value of good quality production for each scenario.  
Obtained results show that there is high variability of WIP, which is related to system instability due to random failures, especially when the system starts from an empty state (Scenario 0 with initial WIP = 0). However, the next scenarios show an increasing trend of mean production and decreasing spread between extreme values. Therefore, increasing the initial WIP gives better efficiency and stability to the whole system, and an initial value of WIP = 7 is recommended in this case in order to achieve the planned production volume.  
Figure 10: Chart with WIP changes in each queue during an 8 h shift.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{tabular}{c} **WIP** \\ **Scenario** \\ \end{tabular} & \begin{tabular}{c} **Mean** \\ **Production** \\ **[OK Pieces]** \\ \end{tabular} & **Std. Dev.** & \begin{tabular}{c} **Min.** \\ **[Pieces]** \\ \end{tabular} & \
\begin{tabular}{c} **Max.** \\ **[Pieces]** \\ \end{tabular} &
\begin{tabular}{c} **NOK** \\ **[Pieces]** \\ \end{tabular} \\ \hline
0 & 156.75 \(\pm\) 4.20 & 8.97 & 143.00 & 172.00 & 9.00 \(\pm\) 1.50 \\ \hline
1 & 159.75 \(\pm\) 4.14 & 8.84 & 147.00 & 173.00 & 9.25 \(\pm\) 1.54 \\ \hline
2 & 162.95 \(\pm\) 4.29 & 9.16 & 149.00 & 179.00 & 9.35 \(\pm\) 1.52 \\ \hline
3 & 165.55 \(\pm\) 4.13 & 8.83 & 153.00 & 180.00 & 9.60 \(\pm\) 1.54 \\ \hline
4 & 167.00 \(\pm\) 4.01 & 8.56 & 154.00 & 181.00 & 9.65 \(\pm\) 1.54 \\ \hline
5 & 168.15 \(\pm\) 3.93 & 8.40 & 156.00 & 182.00 & 9.65 \(\pm\) 1.54 \\ \hline
6 & 169.35 \(\pm\) 3.71 & 7.94 & 158.00 & 182.00 & 9.75 \(\pm\) 1.52 \\ \hline
7 & 170.65 \(\pm\) 3.65 & 7.80 & 159.00 & 184.00 & 9.75 \(\pm\) 1.52 \\ \hline
8 & 171.75 \(\pm\) 3.54 & 7.57 & 160.00 & 184.00 & 9.00 \(\pm\) 1.50 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The values of mean, minimal, and maximal productions according to different initial WIP sizes for one shift (for 20 simulation runs with a 95% Confidence Interval).
# Properties of Green Tea Waste as Cosmetics Ingredients and Rheology Enhancers
## 3 Result and Discussion
### Characterization of CNF as Cosmetic Raw Materials
#### 3.4.2 AS Rheology Modifiers  
Figure 4 shows the viscosity of different concentrations of CNF DS green tea waste as a function of shear rate. All GTW DS samples showed shear thinning behavior. The addition of carboxyl groups makes the viscosity of CNF green tea waste gradually increase from 168.9 mPa\(\cdot\)s of DS 0 to 12,414 mPa\(\cdot\)s of DS 0.4. Meanwhile, the viscosity of CNF DS 0.4 of hardwood pulp was 16,167 mPa\(\cdot\)s [33], much higher than CNF from green tea waste in the same DS was 12,514 mPa\(\cdot\)s (Figure 5). According to [PERSON] et al., 0.4 mL of 0.6% (\(w/v\)) CNF hydrogel has almost 255,000 mPa\(\cdot\)s [46]. The highest viscosity is CNF hydrogel (255,000 mPa\(\cdot\)s) > CNF DS 0.4 of hardwood pulp (16,167 mPa\(\cdot\)s) > CNF green tea waste DS 0.4 (12,514 mPa\(\cdot\)s). Interestingly, it is recommended that higher concentrations of cations have a strong shielding effect on CNF gels, and these studies strongly influence the viscosity of CNF gels. HA at low concentrations, leading to high viscosities--a 1% \(w/w\) HA solution has a viscosity comparable to that of a jelly (almost 106 Pa\(\cdot\)s) [46].  
Similar to what was observed for viscosity, adding carboxyl groups increases the storage and loss modulus of the CNF GTW gels (Figure 6a-c). The effect of ionic strength  
Figure 4: Different viscosity from different DS—treated green waste biomass.  
Figure 5: Viscosity from green tea waste DS 0.4 and CM CNF DS 0.4.  
on the storage and loss modulus of 2% CNF GTW caused by adding MCA is shown in Figure 6b,c. The G\({}^{\prime}\) value increased from 202.51 to 228.85 Pa as the concentration increased from 2.17 to 2.89 g MCA, confirming that a stronger network of CNF gel is created at 2.89 g MCA. Meanwhile, CNF GTW DS 0 (Figure 6a) without added MCA has a really low storage and loss modulus.  
The viscosity of CNF DS 0.4 of hardwood pulp was 256.72 Pa, much higher than CNF from green tea waste DS 0.4 was 227.73 Pa, but all DS from CNF from green tea waste was higher from 0.4 mL of 0.6% (\(w/v\)) CNF hydrogel that only has 84.1 Pa. The highest storage modulus is CNF DS 0.4 of hardwood pulp (256.72 Pa) > CNF DS 0.4 of green tea waste (228.85 Pa) > 0.4 mL of 0.6% (\(w/v\)) CNF hydrogel (84.1 Pa) [31, 46, 47]. However, the treated green tea waste had a significantly higher storage modulus G\({}^{\prime}\) than the untreated sample with a frequency of 0.1 s\({}^{-1}\). For treated green tea waste, the G\({}^{\prime}\) and G\({}^{\prime\prime}\) values were almost equal for all strains around 200-230 Pa.
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 6 Benefits of CR-LPWAN
### Technical Benefits to IoT Devices
#### 6.1.5 Improved Reliability  
With less hardware involvement and additional SDN usage for CR purposes, CR-LPWAN systems will provide reliable network operability [85]. This implies that channel conditions will be better with free bands to use and additional spectrum characterized by lower noise levels as against the ISM bands. With potential diversity in the available spectral pool for CR-LPWAN technologies, the chances of using reliable channels will increase, thereby improving the QoS of LPWAN users.