esa-sceva/satcom-chunk-collection huggingface dataset:
# A QoE-Oriented Uplink Allocation for Multi-UAV Video Streaming
## 7 Conclusions  
In this paper, the uplink channel allocation problem of multi-UAV video streaming is discussed. Not only the total video QoE, but also the cost for channel leasing and energy consumption is considered to formulate the utility function. Based on game theory, the distributed model is established, which further enhances the flexibility and robustness of the system. We have proved that the model converges to the correlation equilibria and have solved the model by the distributed self-learning algorithm. Simulation results show that the proposed mechanism can effectively solve the rate allocation problem of the UAV cluster.  
Conceptualization, C.T.; Data curation, C.H.; Formal analysis, C.H. and Z.X.; Funding acquisition, Z.X.; Software, C.H. and Z.X.; Supervision, C.T.; Writing--original draft, C.H. and Z.X.; Writing--review and editing, Z.X. and C.T.  
This work was funded by the Project of Natural Science Foundations of China (No. 91738201 and 61401507) and China Postdoctoral Science Foundation (No. 2017M613403).  
The authors would like to thank the editors and the reviewers for their great help and valuable suggestions.  
Figure 7: The influence of cost factor \(\eta\).
# FusionTrack: Multiple Object Tracking with Enhanced Information Utilization
## 3 Methodology
### FusionTrack  
We propose FusionTrack to combine the advantages of independent detectors in traditional track-by-detection methods and the integrated decoder in end-to-end track-by-attention methods. To address the issue of correlation between the detection and tracking branches, we propose a unique joint track-detection decoder. It performs detection and tracking tasks simultaneously and independently while allowing the tracking branch to take information from the detection branch. To better utilize the multi-level property of the decoder structure, we propose a score-guided multi-level query fuser. It extracts the input and intermediate query features from each level of the decoder layers and fuses them per object to obtain appearance features. These appearance features are saved per tracklet and then used to generate queries for the next frame. The in-frame and cross-frame target feature fusion are performed in the joint track-detection decoder and score-guided multi-level query fuser for better feature representation, respectively.  
In FusionTrack, the prediction is performed iteratively. We illustrate this process in Figure 2. At each frame, a backbone feature extractor and a transformer encoder first extract the basic features \(\mathcal{F}_{i}\) from the raw input image \(\mathcal{I}_{i}\). Object queries \(q_{d}\) are generated in the query initialization stage as a two-stage DETR [7] does. These queries, along with the track queries \(q_{t}\) generated by the states of currently active tracklets, are then sent to the joint track-detection decoder to iteratively refine the features. The output object and track queries are then used to generate two sets of predictions \(\mathcal{D}_{d}\) and \(\mathcal{D}_{t}\), representing all detected objects and tracking objects, respectively. Each prediction contains the classification \(c\) and localization \(b\) for an object candidate. The input and intermediate queries of the decoder are then fused in the score-guided multi-level query fuser to obtain the corresponding appearance feature for each object candidate. Finally, a tracklet manager filters and combines these results to updates the states of all present tracklets. These states are then used in the query propagation module to generate track queries for the next frame. The details of mentioned modules are discussed in execution order in the following sections.  
Figure 2: The overall architecture of FusionTrack. The entire model starts from the top-left image and tracks targets frame by frame in an autoregressive manner. To make the image simple, the figure only shows a model with two decoder layers.
## 2 Materials and Methods  
### Materials  
In this study, three different sizes of the same graphite particle type were used as conductive fillers: particles with an average size of 150 \(\upmu\)m (designated G150) were used as the main conductive filler up to 80 wt.%, and smaller average particle sizes of 75 \(\upmu\)m (G75) and 44 \(\upmu\)m (G44) were used as secondary fillers up to 10 wt.%. Graphite powder with a density of 2.19 g/cm\({}^{3}\) was purchased from Asbury Carbon, New Jersey. The binder (matrix) used was epoxy resin (1.1 g/cm\({}^{3}\) density) and hardener (635 thin epoxy resin) of 1.04 g/cm\({}^{3}\) density with a 4:1 ratio fast epoxy hardener, which had a low viscosity of 6 poise and was supplied by a US composites company. The SEM images of the different G particle sizes are shown in Figure 1. The G particles presented irregular plate-like shapes with slightly rough surfaces, and the particles were diverse in size around the average value for each type.
## 3 Results
### Study of the Effect of Temperature on the Primary Separation of ABE-Fermentation Broth
Figure 10: Temperature dependences of permeate flow for butanol (**a**), ethanol (**b**), acetone (**c**), and water (**d**) for PDecMS, PHeptMS, and MDK-3 membranes after contact with the fermentation broth. In order to normalize the results obtained to the driving force of the process, the obtained results were recalculated using such quantities for permeability and selectivity. The permeability of n-butanol, acetone, and ethanol for PHeptMS and PDecMS membranes after contact with the ABE-fermentation broth tends to decrease with increasing temperature and then to increase (Figure 11). The increase in the permeability coefficient of the components at 60 \({}^{\circ}\)C correlates well with the increase in the partial fluxes at this temperature. A similar tendency can also be caused by entrainment of contaminants by raw material flow. The MDK-3 membrane was characterized by the opposite tendency: increase in permeability at 40 \({}^{\circ}\)C and then decrease at 60 \({}^{\circ}\)C.  
Acetone was the most permeable of the separated components of the mixture. However, the permeability of water was higher for PDecMS and MDK-3 membranes with increasing temperature. This result indicates the predominant diffusive contribution to the total transport through the membrane at a given temperature. The permeabilities of acetone and butanol were fairly similar for the PHeptMS membrane, while the permeability of water, in turn, was lower and closer in value to the permeability of ethanol. Due to the observed changes in the permeability of the components, we can note the highest selectivity for acetone, and then for butanol and ethanol. Along with the increase in permeability at 60 \({}^{\circ}\)C, an increase in selectivity was also observed for the PHeptMS membrane, apparently caused by desorption of protein compounds and a decrease in membrane hydrophilicity. Such low permeability values in the series of all temperatures studied, 54 mol/m\({}^{2}\)-h-bar for butanol, 53 mol/m\({}^{2}\)-h-bar for acetone, and 24 mol/m\({}^{2}\)-h-bar for ethanol at 30 \({}^{\circ}\)C, and the decrease in selectivity with increasing temperature for the PDecMS membrane, may be due to the strongest fouling of this membrane (Figure 12). Figure 11: Temperature dependences of permeability for PHeptMS (**a**), PDecMS (**b**), and MDK-3 (**c**).
## 4 Numerical Results and Discussion
### Analysis of Numerical Simulation Results of Main Tunnel Excavation
#### 4.3.2 Lining Deformation Analysis  
The vertical displacement of the lining structure was an important monitoring object in the construction of a double-arch tunnel. This indicator can intuitively reflect whether there was a greater risk in the tunnel excavation process. Figure 17 shows the vertical deformation of the vault lining corresponding to different excavation distances. The excavation distance shown in Figure 17a-i is that the left tunnel face advances to 20 m, 40 m, 60 m, 80 m, 100 m, 120 m and 140 m, the right tunnel excavates to 130 m and all main tunnel excavation ends. The settlement of the tunnel vault continued to increase with the progress of the main tunnel excavation. The maximum settlement values of the vault on the nine statistical nodes were 6.17 mm, 9.61 mm, 11.4 mm, 11.0 mm, 18.2 mm, 23.7 mm, 29.5 mm, 30.6 mm and 31.3 mm, respectively. When the left tunnel was excavated into the fault fracture zone, the vertical deformation of the vault lining increased greatly, which was 7.2 mm. Therefore, in the actual construction, the existing orientation of the fault fracture zone shall be accurately given in combination with the advanced geological prediction. When crossing the distribution area of the fault fracture zone, the construction process shall be adjusted to reduce the excavation footage. The primary support shall be carried out as soon as possible to minimize the deformation of the lining structure.  
During the construction process, due to the different excavation sequences of the left and right caves, there may be some differences in the vertical deformation of the vaults of the left and right main tunnels. Figure 18 shows the settlement deformation of the left and right main tunnel after the excavation of the main tunnel is completed. With the increase of the distance from the portal, the settlement displacement of the vault of the left main tunnel and the right main tunnel first increased and then decreased. In the fault fracture zone, the vault settlement displacement was large, and the maximum vault settlement of the left main tunnel and the right main tunnel was 28.2 mm and 31.6 mm respectively. The maximum settlement of the vault of the right main tunnel was greater than the maximum settlement of the vault of the left main tunnel. Controlling tunnel vault deformation was the key step to controlling the deformation of the whole tunnel [31]. Therefore, in the actual construction, the monitoring of vault settlement in the fault fracture zone should be strengthened and special attention should be paid to the change of vault settlement during the excavation of the post excavated main tunnel.  
Figure 16: Surface settlement during excavation of the main tunnel.
## 4 Results and Discussion
### Error Analysis  
The measurement error of the Developed-UVP was analyzed. Basically, it is a combination of systematic error and random error. Various sources of systematic error are investigated and listed in Table 7. These errors are mainly influenced by equipment and methodology. Firstly, a basic frequency at 4 MHz that was emitted by the pulser/receiver. Secondly, the sound velocity which was affected by temperature variation. Thirdly, the incident angle of transducer installation. Lastly, Doppler frequency which was mainly affected by pulse repetition frequency, time lapse and frequency estimation of the developed algorithm. However, the error of pulse repetition frequency and time-lapse could be ignored due to the error magnitude was very small when compared with the error of frequency estimation. Consequently, the error of Doppler frequency was mostly involved by the frequency estimation of the developed algorithm. Therefore, the systematic error can be obtained by combining each error component as represented in equation 16 which is 3.02%. The main factor of systematic error is the incident angle.  
\[\%\text{ error of }V_{i}=\left(\frac{\Delta V_{i}}{V_{i}}\right)\times 100= \sqrt{\left(\frac{\Delta c}{c}\right)^{2}+\left(\frac{\Delta f_{Di}}{f_{Di}} \right)^{2}+\left(\frac{\Delta f_{0}}{f_{0}}\right)^{2}+\left(\frac{\Delta \sin\theta}{\sin\theta}\right)^{2}}\times 100 \tag{19}\]  
where \(\Delta\) is error magnitude of each parameter.  
The random error also affects the measurement accuracy. This error was unpredictable due to fluctuation or instability of the experimental condition occurred randomly that influenced by some phenomena such as the multi-dimensional motion of bubble and particle, and noises which exclude the influences of measuring equipment and methodology. Therefore, if these errors do not occur, the measurement error can be most affected by the systematic error.  
Considerably, within the near wall region (at a radius ratio between 0.8 and 1.0), the error occurred which caused by the near-wall effect. This is an effect of the wave overlapping between the wall region and the measuring fluid. The effect cannot be avoided. It is included in systematic error. However, the error from other sources can be ignored owing to the error magnitude was significantly lower than the error obtained from this effect. Therefore, within this zone, the measurement error is mainly influenced by near-wall effect.
# Measurement of Contrast and Spatial Resolution for the Photothermal Imaging Method
## 3 Numerical and Experimental Analysis  
This study numerically analyzed the effect of parameters of the measuring system (namely, the pump beam modulation frequency and pump beam radius) on the contrast and spatial resolution, specifically in relation to imaging a copper-resin double-layered structure that contains slits. This study also experimentally validated the representative result of numerical analysis.  
The specimens of the copper-resin double-layered structure that were used in the numerical analysis and experiment are illustrated in Figure 4.
## 2 Theoretical Model and Numerical Methods
### Model Implementation  
In this study, the COMSOL Multiphysics software (v.5.3a) was used for numerical implementation and simulation. A 1-D numerical model was developed based on the field study in a loess-gravel capillary barrier cover reported by [PERSON] et al. [9] (Figure 1a). From the bottom to the surface (Figure 1b), the capillary barrier cover consists of a 30 cm thick gravel layer, a 60 cm thick compacted loess layer, and a 30 cm thick vegetative layer composed of compacted loess mixed with 3% compost. The top of the cover is connected to the atmospheric environment and influenced by ambient conditions. The bottom, in contact with the landfill waste, is subjected to high temperature, high humidity, and the infiltration of LFG. The input material properties are displayed in Table 1. The temperature and relative humidity of the air are set to 20 \({}^{\circ}\)C and 0.5, respectively. The initial gas concentration and temperature in the cover are consistent with those of the ambient air (i.e., the molar concentration of CH\({}_{4}\), CO\({}_{2}\), O\({}_{2}\), and N\({}_{2}\) are 0 mol m\({}^{-3}\), 0.014 mol m\({}^{-3}\), 8.7 mol m\({}^{-3}\), and 32.5 mol m\({}^{-3}\), respectively). The transient numerical simulation runs for 30 days with a calculation step of 0.25 days.  
A constant temperature and LFG influx were specified at the base of the cover, while the vapor influx considering saturated vapor flux carried by LFG, and vapor flux driven by vapor pressure gradient, were specified for moisture influx. At the surface of the cover, a constant gas molar concentration boundary was specified according to the gas  
Figure 1: Field loess–gravel capillary barrier cover (**a**) and schematic of the model (**b**).  
concentration in the atmosphere. The evaporation rate [26] and heat flux [36; 37] were determined as follows:  
\[AE=\left(0.027+0.0156V_{wind}^{2}+0.0025(T-T_{a})\right)^{2}\frac{P_{\text{{\tiny PS }}}H_{r}-P_{\text{{\tiny PSa}}}H_{a}}{100} \tag{6}\]  
\[Q_{h}=h_{a}(T-T_{a})+L\frac{AE}{86400} \tag{7}\]  
where \(AE\) represents actual evaporation; \(V_{\text{wind}}\) is wind velocity, which is ignored herein; \(T_{a}\) is atmospheric temperature; \(P_{\text{{\tiny PS}}}\) and \(P_{\text{{\tiny PSa}}}\) are saturated vapor pressures of the soil surface and air, respectively; \(H_{r}\) and \(H_{a}\) are the relative humidity of soil and air, respectively; \(Q_{h}\) is heat flux; and \(h_{a}\) is the heat exchange coefficient between solid surfaces and air. When wind is negligible, [PERSON] et al. [36] recommends that \(h\) equals 5.7 J m\({}^{-2}\) s\({}^{-1}\) K\({}^{-1}\), which is adopted in this paper.
## 3 Nonlinear Model Predictive Control Formulation
### Two-Link Robot Arm
Regarding the range \(\pm\)1.5/\(\pm\)0.3\(\cdot\)m for each joint, the results are shown in Figure 13. The results show that the input constraints of all control schemes are active, so the input signals reach the constraint bounds. Besides this, the output signals of the SDRE and MPC-TSF cannot track the reference signals, and the proposed MPC-FL has smaller overshoot. Table 7 summarizes the performance indices with the three input constraint ranges, and the results show that the LQT-FL has the smallest rising and settling time, and the proposed MPC-FL has the smallest ranges of inputs and RMSE. Figure 12: Two-link robot arm by applying the four control schemes with input constraints \(\pm\)6.0 N\(\cdot\)m: (**a**) output responses of joint 1; (**b**) output responses of joint 2; (**c**) input signals of joint 1; (**d**) input signals of joint 2. Figure 13: Two-link robot arm by applying the four control schemes with input constraints \(\pm\)1.5 and \(\pm\)0.3 N-m respectively for joints 1 and 2: (**a**) output responses of joint 1; (**b**) output responses of joint 2; (**c**) input signals of joint 1; (**d**) input signals of joint 2. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Input Constraint** & **Controllers** & **Joints** & **Rising Time (s)** & **Settling Time (s)** & **Max. Overshoot** & **RMSE (Rad)** \\ \hline \multirow{8}{*}{\(\pm\)6.1} & \multirow{3}{*}{SDRE} & 1 & 28.1920 & 127.3500 & 0.5094 & 0.5005 \\  & & 2 & 7.8480 & 10.6800 & 0.0059 & 0.4903 \\  & & 1 & 10.14408 & 138.7900 & 0.0000 & 0.1884 \\  & MPC-TSF & 2 & 88.1920 & 119.3350 & 0.0000 & 0.1253 \\  & & 1 & 0.2560 & 2.0900 & 0.4310 & 0.1785 \\  & & 2 & 0.6720 & 2.0200 & 0.0327 & 0.6997 \\  & MPC-FL (proposed) & 1 & 3.9240 & 5.4250 & 0.0028 & 0.0409 \\  & & 2 & 3.7480 & 5.3320 & 0.0028 & 0.0407 \\ \hline \multirow{8}{*}{\(\pm\)6.0} & \multirow{3}{*}{LQT-FL} & 1 & N/A & N/A & N/A & N/A \\  & & 2 & N/A & N/A & N/A & N/A \\  & & 1 & 125.3890 & 156.7362 & 0.0000 & 0.4993 \\  & MPC-TSF & 2 & 104.8225 & 131.0281 & 0.0000 & 0.4740 \\  & & 1 & 0.2160 & 2.3000 & 0.4383 & 0.1789 \\  & & 2 & 0.6640 & 2.1300 & 0.5421 & 0.7034 \\  & MPC-FL (proposed) & 1 & 3.9240 & 5.4250 & 0.0028 & 0.0409 \\  & & 2 & 3.7480 & 5.3320 & 0.0028 & 0.0407 \\ \hline \multirow{8}{*}{\(\pm\)1.5/\(\pm\)0.3} & \multirow{3}{*}{SDRE} & 1 & N/A & N/A & N/A & N/A \\  & & 2 & N/A & N/A & N/A & N/A \\ \cline{1-1}  & MPC-TSF & 1 & N/A & N/A & N/A & N/A \\ \cline{1-1}  & & 2 & N/A & N/A & N/A & N/A \\ \cline{1-1}  & & LQT-FL & 1 & 0.8480 & 2.1700 & 0.0379 & 0.1539 \\
## 4 Results and Discussion
### Generation Events
#### 4.1.1 What Is the Average Length of Generated Sentences for Each Category?  
This analysis studies the characteristic complexity of interactions according to their meanings. Table 10 shows the number of sentences generated with the application according to their category and their average lengths in characters. We obtained the average length of the **phrase** field from Table 4.  
Users tend to build longer sentences to communicate needs related to nutrition or care, where specificity prevails (e.g., _\"Quiero comer para desayunar galletas de chocolate\"_, I want to have chocolate cookies for breakfast). On the contrary, when interacting socially, they prefer to have shorter and more direct sentences (e.g., _\"Hola\"_, Hello, or _\"Adios\"_, Good by).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Samples** & **Average Length** \\ \hline Social interaction & 20,394 & 11.552 \\ Nutrition & 20,340 & 25.675 \\ Leisure & 14,038 & 17.811 \\ Care & 9273 & 19.083 \\ Emotions & 4513 & 13.293 \\ Health & 2861 & 13.852 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Test 1: Sentences in categories and their average lengths.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Frequency** & **Users** & **Phrases** & **New Pictograms** \\ \hline Daily & 6.67\% & 4.64\% & 9.54\% \\ Weekly & 10.99\% & 44.65\% & 28.72\% \\ Monthly & 82.35\% & 50.71\% & 60.54\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Distribution of users and samples by access frequency.
# A Deep Learning Method for DOA Estimation with Covariance Matrices in Reverberant Environments
## 5 Simulations and Evaluation
### Methods for Comparison  
Spherical harmonic MUSIC (SH-MUSIC) method [42], direct-path domain test method for MUSIC algorithm (DPD-MUSIC) [28; 43] and FOA [44] method are used for the performance comparison.
# Nature-Based Solutions in Urban Areas: A European Analysis
## 2 Sustainability in the Built Environment
### Nature-Based Solutions in the Built Environment
Furthermore, the implementation of NbS in an urban environment is a central element of European strategies [25] and is directly aligned with the objectives of the 2030 Agenda [28; 45] as a way to achieve sustainable cities and communities while playing an active role in the strategic implementation and fulfilment of the SDGs [28], specifically \"SDG 11--Sustainable cities and communities: make cities and human settlements inclusive, safe, resilient and sustainable\" and \"SDG 13--Climate action: take urgent action to combat climate change and its impacts\" [25; 45]. In the built environment, NbS implementation can be integrated into three levels [10] characterized by:  
1. Green building materials: raw natural materials taken from the biological cycle. Their processing must have minimal negative effects on the environment, with low incorporated consumption of energy, carbon, water, and chemicals. Optimal production and construction methods should allow for the safe return of nutrients to the ecosystem after the material use cycle. 2. Green building systems (systems for the greening of buildings), including green and living components integrated into structures and used for the afforestation of buildings, for example, green roofs, facade greenery, living walls, and house trees. 3. Green building sites (green urban sites): areas of land adjacent to buildings (e.g., pocket parks, urban plazas, and small community parks), which play a blue-green role in cities, emphasizing the value of open spaces with vegetation and water-sensitive urban design. These environments provide a variety of ecosystem services and reflect resilient and regenerative approaches to addressing diverse challenges such as reducing noise pollution and mitigating climate change. Inspired and based on nature and the adoption of ecosystem principles, NbS aim to recover and regenerate natural processes and flows in the urban environment at different scales (Table 2). NbS address social challenges at a range of spatial scales--local, regional, and global [26]--which can be classified according to the level of interventions [34], representing actions from buildings or plots, including districts or neighbourhoods, to cities and beyond (other larger interventions) [46; 47]. To differentiate the scale and scope of interventions, another approach used for NbS classification--for implementation in urban environments--is the proGIreg projects (funded by the EC under the Horizon 2020 programme). Specifically, NbS are classified into eight different types [48];(1) leisure activities and clean energy on former landfills, (2) new regenerated soil, (3) community-based urban farms and gardens, (4) aquaponics, (5) green walls and roofs, (6) accessible green corridors, (7) local environmental compensation processes, and (8) pollinator biodiversity.