esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimation of Optimal Speed Limits for Urban Roads Using Traffic Information Big Data
## 3 Data Collection and Model Equation Development
### Data Collection
#### 3.2.6 Variables Related to Roadside Speed  
The numerous (integrated) road variables affecting speed that have been investigated in related literature [34; 35] are listed in Table 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Variable (1 st Level)**} & \multirow{2}{*}{**Range**} & \multicolumn{3}{c}{**Variable Aggregation**} \\ \cline{3-4}  & & **2 nd Level** & **3 rd Level** & **4 th Level** \\ \hline Posted speed limit & & 40–70 km/h & \\ \hline Functional classification & 1 (major arterial, 44.5\%), 2 (minor arterial, 33.3\%), 3 (collector, 22.2\%) \\ \hline Land use & 1 (residential), 2 (commercial), 3 (industrial) \\ \hline Roadside development & 1 (high), 2 (mid), 3 (low) \\ \hline Median strip & 1 (divided), 0 (none) \\ \hline Number of lanes & & 4–10 lanes \\ \hline Lane width & & 1 (\(>\)3.3 m), 0 (\(<\)3.3 m) \\ \hline Parking density & & 1 (low), 2 (mid), 3 (high) \\ \hline Sidewalk width & & 1 (\(>\)3.0 m), 0 (\(<\)3.0 m), n/a (none) \\ \hline Number of signalized intersections & per km & Number of \\ \hline Number of signalized crosswalks & per km & signalized facilities & Number of \\ \hline Number of non-signalized intersections & per km & Number of \\ \hline Number of non-signalized crosswalks & per km & Number of \\ \hline Number of driveways & per km & non-signalized \\ \hline Number of entrances (a structure) & per km & \\ \hline Number of left turning bays & per km & \\ \hline Number of right turning bays & per km & \\ \hline Number of speed limit signs & per km & \\ \hline Number of other traffic signs & per km & \\ \hline Number of speed cameras & & per km \\ \hline Number of bus stops & & per km \\ \hline Bus lane & & 1 (existence), 0 (nothing) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Speed affecting variables investigated in the literature.  
Figure 12: Accident rate as a function of the average daily traffic volume.
# Estimating Forest Carbon Stock Using Enhanced ResNet and Sentinel-2 Imagery
## 3 Methodology
### Enhanced ResNet Architecture
#### 3.2.2 Channel Attention Mechanism  
In multispectral remote sensing imagery, not all spectral bands contribute equally to the estimation of forest carbon stock. Certain bands--such as the red-edge (B5-B7), near-infrared (B8, B8A), and shortwave infrared (B11, B12)--are particularly sensitive to vegetation structure, biomass, and moisture content. To enable the model to focus on these informative bands while suppressing irrelevant or noisy ones, we integrate a channel-wise attention mechanism based on the Squeeze-and-Excitation (SE) block [70] (see Figure 4) after each MSRB module.  
The SE block performs two main operations: squeeze and excitation. The squeeze operation aggregates spatial information from each channel using global average pooling (GAP), producing a compact channel descriptor that captures global contextual information:  
\[\mathbf{z}_{c}=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W}\mathbf{F}_{\text {ms}}^{(c)}(i,j),\quad\text{for }c=1,\dots,C \tag{3}\]  
where \(H\) and \(W\) denote the height and width of the feature map, and \(\mathbf{F}_{\text{ms}}^{(c)}\) is the \(c\)-th channel output from the MSRB. Next, the excitation step passes the squeezed descriptors through two fully connected (FC) layers with a ReLU activation followed by a sigmoid gating function:  
\[\mathbf{s}=\sigma(\mathbf{W}_{2}\cdot\text{ReLU}(\mathbf{W}_{1}\cdot\mathbf{ z})) \tag{4}\]  
Here, \(\mathbf{s}\in\mathbb{R}^{C}\) is the learned attention weight vector for all \(C\) channels, and \(\sigma(\cdot)\) ensures the weights are bounded in \([0,1]\). Finally, the attention weights are applied to the MSRB output through channel-wise multiplication:  
\[\mathbf{F}_{\text{att}}^{(c)}=\mathbf{F}_{\text{ms}}^{(c)}\cdot\mathbf{s}_{c} \tag{5}\]  
This mechanism allows the network to dynamically recalibrate the importance of each spectral band based on global contextual cues, thus enhancing the representation of critical biophysical signals while suppressing redundancy or noise. Importantly, this design preserves spatial information and introduces minimal computational overhead, making it well-suited for high-dimensional multispectral inputs. By combining spatial richness from MSRB with spectral selectivity from SE modules, the model is better equipped to extract meaningful vegetation characteristics for accurate and robust forest carbon stock estimation.
# Energy-Balanced Routing Algorithm Based on Ant Colony Optimization for Mobile Ad Hoc Networks
## 3 IEMACO Routing Algorithm
### Generation of Pheromone
#### 3.2.3 Generation of Pheromone  
The remaining lifetime of nodes and links proposed in this section are based on predictions of network status, while residual energy and average energy are assessments of the current status of the network. The pheromone initialization indicates the quality of each path obtained by the method of randomly selecting the _forward ants_ and updating the state to the _backward ants_ after the end of the route discovery phase. The pheromone is generated by the following equation:  
\[\tau_{0}=\frac{E_{min}\cdot E_{avg}}{L_{path}}\cdot T, \tag{12}\]  
where \(E_{min}\) represents the minimum residual energy of all nodes to be accessed on the path, \(E_{min}=\min E_{i}(i\in path)\). \(T\) represents the minimum remaining lifetime of all nodes and links to be accessed on the path, \(T=\min(\min(\mathrm{RLT}n),\min\left(\mathrm{RLT}l\right))\). \(E_{avg}\) is defined as the total energy remaining in the path to be accessed divided by the path length, \(E_{avg}=\sum_{i\in path}E_{i}/L_{path}\). The path length \(L_{path}\) is defined as the sum of the length of each hop to be accessed.
## 2 Materials and Methods
### Calculation of Surface Free Energy  
[PERSON] [43] gave an equation for the relationship between interfacial tension and the contact angle:  
\[\gamma_{S}=\gamma_{L}cos\theta+\gamma_{SL} \tag{1}\]  
where \(\gamma_{L}\) is the surface free energy of the liquid. \(\gamma_{S}\) is the solid surface free energy. \(\gamma_{SL}\) is the solid-liquid interfacial energy. \(\theta\) is the solid-liquid contact angle.  
[PERSON] [44] considered the surface free energy to be the sum of many components, which can be divided into a polar component and a dispersion component:  
\[\gamma_{L}=\gamma_{L}^{d}+\gamma_{L}^{p} \tag{2}\]  
\[\gamma_{S}=\gamma_{S}^{d}+\gamma_{S}^{p} \tag{3}\]  
where \(\gamma_{L}^{d}\) is the liquid dispersion component. \(\gamma_{L}^{p}\) is the liquid polar component. \(\gamma_{s}^{d}\) is the solid dispersion component. \(\gamma_{s}^{p}\) is the solid polar component.  
The Owen-Wendt equation [45] gave an expression for the surface free energy calculated from the dispersion and polar components:  
\[\gamma_{SL}=\gamma_{S}+\gamma_{L}-2(\sqrt{\gamma_{S}^{d}\gamma_{L}^{d}}+\sqrt {\gamma_{S}^{p}\gamma_{L}^{p}}) \tag{4}\]  
Based on the [PERSON], [PERSON], and Owen-Wendt equations, the relationship between surface free energy and the contact angle can be further derived as follows:  
\[\frac{\gamma_{L}(1+cos\theta)}{2}\frac{1}{\sqrt{\gamma_{L}^{d}}}=\sqrt{\gamma _{S}^{d}}+\frac{\sqrt{\gamma_{L}^{p}}}{\sqrt{\gamma_{L}^{d}}}\sqrt{\gamma_{S }^{p}} \tag{5}\]  
A linear fit is made to \(\sqrt{\gamma_{L}^{d}\gamma_{S}^{p}}\) and \(\gamma_{L}(1+cos\theta)/2\sqrt{\gamma_{L}^{d}}\). The goodness of fit determines how reliable the data are. The square of the middle slope of the resulting fitted line is the polar component \(\gamma_{s}^{p}\) and the square of the intercept is the dispersion component \(\gamma_{s}^{d}\).
## 2 Materials and Methods  
### Materials  
#### 2.1.1 Raw Materials  
_Porphyna umbilicalis_ was collected at Praia da Tamargueira, Buarcos, Figueira da Foz (40\({}^{\circ}\)10\({}^{\prime}\)18.6\({}^{\prime\prime}\) N, 8\({}^{\circ}\)53\({}^{\prime}\)44.4\({}^{\prime\prime}\) W), Portugal. Samples were collected from areas with well-established macroalgae specie patches and no obvious epiphyytes or deterioration, during the morning low tide (Figure 1a), being packed in plastic bags and kept in thermal boxes. The specimens were transported to the Marine Algae Laboratory (Dep. Life Sciences) at the University of Coimbra, where they were washed with filtered seawater (harvested at the same location) to remove sand and macroscopic epiphytes. The samples were selected by hand, discarding the non-standard specimens, and placed on sieves to remove excess moisture. Then, they were weighed (300 g), stored in plastic bags under partial vacuum (~80%), and kept under refrigeration (8 \({}^{\circ}\)C). After 24 h, the samples were transported in thermal boxes to Faculty of Science and Technology (FCT NOVA), where they were processed.  
Due to _Ulva lactuca_ being an estuarine macroalgae, it was cultivated on a prototype system developed by Lusalage Lda. (Figueira da Foz, Portugal) and the Marine Algae Laboratory (Dep. Life Sciences) at the University of Coimbra.  
The method was based on [PERSON] et al. [36] in which _U. lactuca_ was grown in a water tank exposed to direct sunlight with aeration during the day (normally 14 h) with the goal of obtaining enough biomass for the assays. To minimize differences in biochemical composition caused by the macroalgae growth condition, the cultured species were identical to those of the indoor culture described by [PERSON] et al. [37]--i.e., collected in the same pools and dates, with a length of less than 5 cm. As a result, the culture technique was the primary factor affecting the biochemical profile.  
The culture medium was estuarine saltwater (23-34 PSU) taken from the Mondego River estuary in Figueira da Foz, Portugal, with no fertilizers added. The culture tank was 1000 L in volume and contained 800 L of mechanically filtered estuarine seawater [32].  
The cultivation began with an initial amount of 600 g of wet biomass in a single tank. Three times each week, about 75% of the amount of water in the tank was changed, and after three weeks, all the biomass was collected for subsequent examination.
# UAV-Derived Himalayan Topography: Hazard Assessments and Comparison with Global DEM Products
## 4 Discussion
### DEMs for Hazard and Disaster Assessments  
Quantifying contemporary topographic change in a high-relief environment at scales relevant to human populations requires up-to-date fine-resolution DEMs, such as those derived using UAV imagery. HMA DEM products (released December 2017) provide an important baseline dataset that is comparable to UAV-derived DEMs; however, gaps still exist, especially along topographically confined river reaches (e.g., along the Marsyangdi River close to Tal village). These gaps are likely to shrink as the archive of stereo imagery grows; however, local-scale UAV surveys still offer the highest elevation precision, tailored surveying, and orthophoto generation capabilities. Additionally, the 3D point cloud generated using UAV imagery can resolve vertical and overhanging topography (e.g., Figure 3), which is not possible using gridded DEM products. Greater emphasis can therefore be placed quantifying topographic change in 3D along the direction of surface normal (e.g., [54; 55]), which is more appropriate than vertical DEM differencing on steep slopes.  
The main limitation to acquiring UAV data for disaster management or hazard assessment in Nepal is the time required to obtain relevant permissions, which, following tighter regulations after the 2015 Gorkha earthquake, can now take a month or more [56]. Scientists from the Japanese J-RAPID disaster response program reported a wait exceeding six months to survey building damage from the Gorkha earthquake with a UAV and that helicopter flights were a faster alternative with fewer permission requirements [57]. Rapid humanitarian response with UAVs in Nepal would therefore benefit from a streamlined permission procedure. As UAVs become increasingly valuable in disaster response and hazard assessment workflows (Figure 10), improved regulation of their deployment and integration of UAV capabilities into governmental and nongovernment organisations will enhance short- and long-term analytical capabilities to minimise damages. A streamlined or expedited  
Figure 10: Using human-portable UAVs for hazard assessments and in disaster situations.  
permitting process for disaster response agencies would therefore be beneficial. Helicopters or (ultra) light aircrafts could collect imagery of comparable quality to generate DEMs and orthophotos for the landslide and flood modelling examples presented in this study. However, their ability or availability for deployment to remote sites may be limited, especially during times of large-scale humanitarian disasters.
# Robust LQR Control for PWM Converters with Parameter-Dependent Lyapunov Functions
## 5 Simulation Results
Figure 6b shows a similar  
Figure 5: Simulated transient of the boost converter under a load step transient with the robust LQR controllers \(K\) (solid line), \(K_{\text{eq}}\) (thick dashed line), \(K_{perf}\) (dashed line) and \(K_{rob}\) (dotted line); (**a–c**) show the transient when \(\text{Vg}=12\) V and the operating point duty cycle is \(\text{D}^{\prime}=0.5\); (**d**) shows the transient when \(\text{Vg}=7.2\) V and the operating point duty cycle is \(\text{D}^{\prime}=0.3\). step in the input voltage, but now the input voltage decreases down to \(v_{g}(t)=2.4V\), such that the operating point duty cycle moves from D\({}^{\prime}\) = 0.3 to D\({}^{\prime}\) = 0.1. The method proposed in [7] did not allow us to consider such a large range of operating point uncertainty, whereas the proposed method resulted in controller \(K_{rob}\). As can be seen in the figure, \(K_{rob}\) is the only controller that successfully maintains stability under those conditions, exhibiting excellent stability properties. It is worth noting that the transient shown in Figure 6b shows the saturation of the duty cycle at 100% with the unstable controllers. Although the modeling of that nonlinearity is out of the scope of this paper, this aspect has been treated in the specific context of switched-mode power converters in [27]. Finally, Figure 7 depicts the waveforms of the converter startup, with the three controllers \(K\), \(K_{perf}\) and \(K_{rob}\). The input voltage is Vg = 12 V and the voltage reference ramps up from 12 V to 24 V at \(t\) = 0, with a rate of change of 2400 V/s. It can be observed that the three controllers operate inside the expected range of operation and stabilize the converter. Figure 6: Simulated transient of the boost converter, in the presence of an input voltage disturbance, with the robust LQR controllers \(K\) (solid line), \(K_{perf}\) (dashed line) and \(K_{rob}\) (dotted line). (**a**) The input voltage steps down to 7.2 V, which corresponds to D = 0.7. All controllers consider such a change of operating point and maintain the stability. (**b**) The input voltage steps down to 2.4 V, which corresponds to D = 0.9. Only controller \(K_{rob}\) maintains the stability of the regulation. Figure 7: Simulated transient of the boost converter during startup with Vg = 12 V, for the three controllers K (solid line), \(K_{perf}\) (dashed line) and \(K_{rob}\) (dotted line). Top waveforms: output voltage. Middle waveforms: duty cycle. Bottom waveform: input voltage.
## Appendix A Confusion Matrix on the NTU 60 Dataset  
Figure 11 visualizes the confusion matrix for action recognition in the NTU 60 dataset. It can be observed that the recognition accuracy of the EHC-GCN model is still relatively low for actions such as writing, reading, typing on a keyboard, and using a smartphone. Upon analysis, these misclassified actions are mainly performed through slight tremors of both hands, and their features are extremely similar in both spatial and temporal sequences. Due to the lack of sufficient finger joint data in the NTU RGB+D skeleton dataset, the information of both hands is generally insufficient, which has a significant impact on the recognition accuracy. Additionally, since the action characteristics of putting on shoes and taking off shoes are similar in the spatial dimension but differ in the temporal sequence, further improvement is needed in distinguishing these actions.
# Advances in the Neural Network Quantization: A Comprehensive Review
## 3 Quantization Techniques
### The Selection of Quantization Bitwidth  
Traditional model quantization methods involve quantizing the weight parameters and activation values of the whole model to a fixed bit-width. However, while high bit-width quantization ensures high accuracy, it also causes larger memory footprint and computation. Low bit-width quantization has lower accuracy but a smaller memory footprint and computation. Because different layers have different redundancies and computational effort, simply assigning the same bit-width does not guarantee optimal network performance. Therefore, mixed precision quantization (MPQ) is needed to achieve further efficient compression of the model. [PERSON] [50] proposes a search method using a differentiable neural network structure search for the bit-width of each layer with no consideration about the inference delay on hardware. [PERSON] [51] proposes the HAQ algorithm, which combines the hardware latency and energy consumption fed back from the hardware simulator to constrain the search for bit-widths at each layer, resulting in a hardware-aware hybrid accuracy policy. In order to quickly allocate the quantization bit-width of each layer, [PERSON] [52] proposes HAWQ, which uses the second-order information of the model parameters to assess the sensitivity of each layer of the model to quantization, and then allocates the bit-width of each layer based on this sensitivity to improve the search efficiency. [PERSON] [53] proposes the EMQ method, which automates the search for mixed-precision configurations with the help of evolutionary algorithms. [PERSON] [54] proposes the SEAM method using small agent datasets to perform a fast search in order to uncover effective MPQ strategies applicable to large-scale training datasets, thus improving the search efficiency and practicality. HAWQv2 [55] enhances model quantization by using second-order information (the Hessian) to determine optimal quantization levels, thereby preserving accuracy while significantly reducing computational and memory costs. [PERSON] [56] proposes the LIMPQ method that can speed up the indicator training process by parallelizing the original sequential training processes. With these learned importance indicators, the MPQ search problem is treated as a one-time integer linear programming (ILP) problem.  
Existing hybrid bit-width allocation methods suffer from the following challenges: Most of the methods do not consider the adaptability to hardware, and since the efficiency and accuracy of the quantized neural network model may vary significantly on different intelligent processors, it is necessary to incorporate hardware feedback on accuracy and speed into the optimization function of the bit-width search. Meanwhile, these methods either still require a lot of computational resources or are very sensitive to hyperparameters or even initialization, so it is important to propose a parsimonious and fast search technique.
## 3 Results and Discussion
### Effect of Different Calculation Approaches for Gas Flux Estimation
#### 3.3.1 Time Series Soil-Atmosphere Fluxes
In situ measurements of soil gas diffusivity would allow to assess the quality of the diffusivity models [69; 81] and improve the gradient based flux estimates. Additionally, chamber measurements next to the soil gas profiles would allow comparing and evaluating the gradient based flux estimates and also the diffusivity model [77].
# Second-Order Statistical Properties of Vehicle-to-Vehicle Rician Fading Channel
## 1 Introduction
[12; 13] restricted their studies to LSS and MSS scenarios, and provided thorough solutions on LCR and AFD with experimental validation for V2V Rician fading channels. The most intriguing methods for LCR and AFD in V2V Rician channels have been proposed in [14; 15]. However, these methods require extensive mathematical computation and utilize a combination of a two-ring and ellipse model. Consequently, there is a pressing need for new V2V channel models that can accommodate the requirements of 5G and 6G technologies, particularly in terms of higher operational frequencies (5-100 GHz), as evidenced by the recent measurement campaigns in [16]. Furthermore, there is also a demand to focus on improving the accuracy and reliability of channel models to reflect their real-world conditions. To build upon the existing research and the new challenges ahead, this paper presents a comprehensive analysis of the LCR and AFD for non-isotropic scattering V2V Rician flat fading channel leveraging the tractable simplified geometric two-ring (SGTR) model to meet the recent advancements in V2V channel modeling. The proposed model employs both single-bounce (SB) and double-bounce (DB) paths in its analysis, and is modeled with an operational frequency above 5 GHz. These features enable the realistic representation of signal paths and simplified analysis, and enhance the model's adaptability to diverse scenarios, unlike the previous models in [10; 17], and others. The derivation and analysis of LCR and AFD in this work are simplified and detailed, addressing the limitations of previous studies and thoroughly exploring the effects of Tx-Rx separation distance (LSS, MSS, and SSS) on the statistical properties. Additionally, the paper examines the impact of VTD and other critical environmental parameters based on the derived LCR and AFD models to reflect real-world conditions better. Furthermore, the numerical results from the theoretical analysis of LCR and AFD are in excellent agreement with measured data and thus show the model's accuracy. The subsequent sections of the paper are structured as follows. Section 2 introduces the SGTR for the 2D non-isotropic scattering narrow-band SISO V2V Rician fading channel. Section 3 details the derivation of the corresponding LCR and AFD based on the proposed model. The numerical results and analysis from simulation using the analytical LCR and AFD model are compared with measured data and presented in Section 4. Finally, Section 5 provides the conclusions drawn from the study.