esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Key Factors in Natural Grassland Fires  
Influence factors are critical in assessing grassland fire susceptibility, as their selection can significantly impact model accuracy. Table 3 details the contribution rates of various influence factors to the MaxEnt model. Distance from rivers, vegetation type, average annual precipitation, and average wind speed collectively contribute 86.9% to the model, identifying them as the primary factors influencing natural grassland fire susceptibility. Elevation and slope direction contribute 7.5% and 4.7%, respectively, and are considered secondary factors. In contrast, relative humidity contributes less than 1%, indicating a minimal impact on fire susceptibility.  
\begin{table}
\begin{tabular}{c c} \hline
**Impact Factor** & **Contribution (\%)** \\ \hline Distance to river & 33.6 \\ Vegetation type & 23.4 \\ Average multi-year precipitation & 16.9 \\ Average annual wind speed & 13 \\ Elevation & 7.5 \\ Slope direction & 4.7 \\ Relative humidity & 1 \\ \hline \end{tabular}
\end{table}
Table 3: Contribution of influence factors to the MaxEnt model.  
Figure 3: Distribution of natural grassland fire (**a**) and anthropogenic grassland fire (**b**) susceptibility in Qinghai province.  
This study analyzes the response curve (Figure 4) to clarify the relationships between grassland fires and key influencing factors. A probability exceeding 0.5 is widely recognized as indicative of a significant influence of the corresponding factor on grassland fire likelihood. Grassland fire susceptibility demonstrates an inverse correlation with distance from rivers, reaching a probability of 0.77 when closer to water sources. As the distance from rivers increases, the probability of grassland fires diminishes accordingly. This trend indicates that areas nearer rivers are characterized by higher soil moisture, lush vegetation, and a greater abundance of combustible material. Alpine meadows exhibit the highest susceptibility index due to robust vegetation, high belowground biomass, and substantial combustible material loads. In contrast, other vegetation types display reduced growth and lower combustible loads, leading to a decreased probability of grassland fires. Grassland fire susceptibility significantly increases when the multi-year average precipitation exceeds 480 mm. The probability of occurrence peaks at 0.99 when precipitation exceeds 756 mm, underscoring that higher precipitation levels correlate with increased combustible material loads. Furthermore, adequate water supply in swamps promotes peatification. Grassland fire susceptibility decreases with increasing wind speed; however, this effect becomes negligible at wind speeds exceeding 1.55 m/s.
## 4 Experiments
### Ablation Studies  
#### 4.4.1 Ablation Experiments on the Proposed Module  
To validate the effectiveness of the proposed method, we designed ablation experiments on four datasets, Oulu-CASIA (three different light conditions in near-infrared and visible light), CK+, RAF-DB, and FERPlus. The base model is a hybrid model of IRNet50  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Luminous Intensity** & **Pre-Processing Data** & **Post-Processing Data** \\ \hline strong & 1440 & 1440 \\ weak & 1440 & 1410 \\ dark & 1440 & 1380 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The specific number of Oulu-CASIA datasets in visible light.  
Figure 6: FERPlus dataset images of eight different facial expression images.  
Figure 7: Images that need to be discarded with severe detail loss. (**a**) Discarded image from dark; (**b**) Discarded image from weak; (**c**) Discarded image from dark.  
loaded with pre-trained weights and a ViT-small framework. Overall, both the CoT module and the adaptive approach can effectively improve the performance of the model. The combination of the two methods can further improve the performance of the model.  
Table 2 presents the results of our ablation experiments on the Oulu-CASIA dataset. The results show that our method exhibits excellent recognition performance under both NIR and natural light illumination conditions, showing strong robustness to different scenes. In the NIR dataset, the effects of different lighting variations on the images are relatively small, so we choose to perform the ablation experiments in a dark environment to compare the performance with other models. Compared to the base model, our method improves the accuracy by 2.398%. However, under natural light, the face expression image is greatly affected by lighting variations, which may result in less clear and complete expression features. To verify the adaptability of CoT_AdaptiveViT under different lighting environments, we conducted ablation experiments under the Oulu-CASIA dataset with three lighting conditions. Compared to the base model, we improved the accuracy by 3.31%, 2.66%, and 2.55% under strong, low, and dark lighting conditions, and 1.85% on the NIR dataset, which fully validates the robustness and adaptability of our method in complex scenes.  
Table 3 shows the results of our ablation experiments on the CK+, RAF-DB, and FERPlus datasets. Compared to the base model, our method improves by 1.232% on the CK+ dataset, 1.501% in RAF-DB, and 0.806% in FERPlus. The CK+ dataset is a lab-controlled dataset with complete information about the face expressions, and thus, the model achieves a high accuracy of 99.2% on this dataset. The RAF-DB and FERPlus, on the other hand, are field datasets, which face challenges such as light changes, face pose changes, and occlusion. The experimental results show that our model exhibits good solution capabilities in dealing with these problems.
## 3 Results and Discussion
### Data Acquisition  
We demonstrate our methodology using a shallow seismic survey acquired in eastern Saudi Arabia (Figure 3a) at the approximate GPS coordinates (26.351575 N, 49.829184 E). Figure 3b shows the survey location where the geological setting was a sand dune over Sabkha. The acquired data consisted of a 2D seismic line with 24 vertical receivers having a nominal spacing of 4 m. Five shots were produced by a 10 kg sledgehammer located at offsets of \(-4\), \(0\), \(46\), \(92\), and \(96\) m, where the first receiver was placed at offset 0 m. Positions and elevations of all receiver and shot points were measured using D-GPS.
## 2 Materials and Methods
### Analysis Methodology
#### 2.4.2 Methodology Application Feasibility
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Source**} & \multicolumn{2}{c}{_Villa Dafne_} & \multicolumn{2}{c}{_Bergi_} \\ \cline{3-6}  & & **\%** & **Toe/Year** & **\%** & **Toe/Year** \\ \hline \multirow{3}{*}{Domestic Hot Water (DHW)} & natural & & & & \\  & gas/diesel oil & 0 & 0.00 & 16 & 5.09 \\ \cline{2-6}  & electricity & 22 & 9.72 & 6 & 1.91 \\ \hline Lighting & electricity & 15 & 6.63 & 15 & 4.77 \\ \hline \multirow{3}{*}{Heating, Ventilation and  Air Conditioning (HVAC)} & natural & & & & \\  & gas/diesel oil & 25 & 11.05 & 15 & 4.77 \\ \cline{1-1}  & electricity & 14 & 6.19 & 24 & 7.64 \\ \hline \multirow{3}{*}{Other} & natural & & & & \\  & gas/diesel oil & 0 & 0.00 & 0 & 0.00 \\ \cline{1-1}  & electricity & 24 & 10.60 & 24 & 7.64 \\ \hline \multicolumn{2}{c}{**Total**} & **44.18** & & **31.82** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Energy sources and energy consumption breakdown for the two selected agitourisms. * \(R_{i}\) represent the energy savings obtained from Equations (A1) to (A8);
* _Tot. cons. ;_ is the total figure reported in Table 3;
* \(i\) and \(j\) represent the selected intervention and the considered agritourism, respectively. Regarding the pollutant emissions, an assessment of the CO\({}_{2}\) emissions' reduction was conducted assuming for the considered climatic context an emission factor equal to 2.30 tCO\({}_{2}\)eq/toe for the electrical supply [64; 65], while for natural gas and diesel oil an emission factor of 3.08 tCO\({}_{2}\)eq/toe and 2.34 tCO\({}_{2}\)eq/toe, respectively [66]. In order to single out the most convenient aforementioned \"environmental actions packages\", an economic estimation relative to the interventions suggested by the ARERA technical datasheets was also performed. To this purpose, the information relative to the costs of supply and installation for the materials, used to calculate the proposed interventions costs, were obtained from the current regional price list [67] and from local market surveys, as reported in Table 4.  
Successively, the economic savings, in terms of saved \(\xi\)/year, were obtained by multiplying the energy savings with the energy sources' average costs, according to the considered categories breakdown (Table 1). Furthermore, in order to select the optimal \"environmental actions packages\", for these the pay-back periods (not discounted) were also calculated and expressed in years.
## 2 Energy Market Structure  
The energy market structure of a DN with an MMG is shown in Figure 1. There are three MGs connected to the DN from different nodes. Each MG consists of a PV system, energy storage and transferable loads. The involved entities are explained as follows:  
1. DNO: It is mainly responsible for the stable operation of the system and for providing a reliable power supply. It sets the reasonable selling price (\(P_{cs}\)) and buying price (\(P_{cb}\)) to encourage an MG to participate in the DN transaction. When there is an internal mismatch between power generation and power demand, the DNO is required to trade with the utility grid. The price set by the DNO needs to meet the following constraints: \[P_{cs}\triangleq\left[{P_{cs}}^{1},{P_{cs}}^{2},{P_{cs}}^{3},\cdots{P_{cs}}^{T }\right]\] (1) \[P_{cb}\triangleq\left[{P_{cb}}^{1},{P_{cb}}^{2},{P_{cb}}^{3},\cdots{P_{cb}}^{T }\right]\] (2) \[P_{cs}^{t}>P_{cb}^{t},\,t\in[1,2,3,\cdots T]\] (3) \[\left(P_{cb}^{t},P_{cs}^{t}\right)\in\left[P_{gb^{t}}^{t},P_{gs}^{t}\right],t\in[1,2,3,\cdots T]\] (4) where \(P_{cs}^{t}\), \(P_{cb}^{t}\) are the selling price and buying price of the DNO at time \(t\), respectively, and \(P_{gs}^{t}\), \(P_{gb}^{t}\) are the selling price and buying price of the DN at time \(t\), respectively.
2. MGO: It is mainly responsible for the local optimization. Each MGO adjusts its own energy storage and transferable load based on the price information of the DNO. When there is an internal mismatch between power generation and power demand, the MGO is required to trade with the DNO to balance supply and demand.  
The structure of energy trading is summarized in Figure 2. Both the DNO and the MGO want to minimize daily operation costs, and through the electricity price incentive mechanism, the economic cost and safety of the system are improved.  
Figure 1: Energy market structure of distribution network (DN) with multi-microgrid (MMG).
## Appendix C Model Parameter  
### Model Parameter Calibration  
The Parameterization for the species-specific allometric relationships between biomass and dbh as well as between dbh and height, crown diameter and mortality rate for _Fagus sylatica_, _Quercus petraea_ and _Betula pendula_ are based on [PERSON] et al. (2016) (Table A3) except for \(p_{max}\) and \(\alpha\) (Table A2). The parameterization for _Betula pendula_ was also taken for _Carpinus betulus_ due to equal growth strategies.  
All adjusted parameters (Table A2) were calibrated against measured _NEP_, _GPP_ and \(R_{exo}\). The productivity parameters \(p_{max}\) and \(\alpha\) needs to be adjusted upwards compared to [PERSON] 2014 to reach the measured carbon fluxes. The parameters that are influencing the carbon fluxes based on climate variability such as \(T_{opt}\), \(T_{sig}\), \(Q_{10}\), \(T_{ref}\) and the water stress related parameters \(SW_{pup}\), \(SW_{fc}\) were calibrated against filtered EC fluxes for optimal climate condition according to [PERSON] et al. (2016). Model functions were directly fitted through filtered data.  
Other soil parameters such as porosity and pore size distribution. where set according ([PERSON] et al., 2016) classification of a silt loam soil type.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Parameter** & _Fagus sylatica_ & _Quercus petraea_ & _Betula pendula_ & _Carpinus betulus_ \\ \hline  & Productivity & & & & \\ \(p_{max}\) & max. photoproductivity of & & & & \\  & leaf (\(\mu\)mol (\(\text{CO}_{2}\)) s\({}^{-1}\) m\({}^{-2}\)) & 15.768 & 20.244 & 22.672 & 15.768 \\  & slope of light response & & & & \\ \(\alpha\) & curve (\(\mu\)mol (\(\text{CO}_{2}\)) & 0.1288 & 0.0736 & 0.0728 & 0.1288 \\  & \(\mu\)mol(\(\text{photons}\))\({}^{-1}\) & & & & \\ \hline  & Temperature & & & & \\ \(T_{opt}\) & optimal temperature for & & & & \\  & photosynthesis (\({}^{\circ}\)C) & & & & \\ \(T_{sig}\) & width of new temperature & & & & \\  & curve (\({}^{\circ}\)C) & & & & \\  & constant for & & & & \\ \(Q_{10}\) & temperature-dependent & & & & \\  & respiration & & & & \\ \(T_{ref}\) & Reference temperature & & & & \\  & (\({}^{\circ}\)C) & & & & \\ \hline \end{tabular}
\end{table}
Table 2: Calibrated Model Parameter.  
Figure 17: Daily (**left**, (**a**) _GPP_, (**c**) _R\({}_{coo}\)_, (**e**) _NEP_) and monthly (**right**, (**b**) _GPP_, (**d**) _R\({}_{coo}\)_, (**f**) _NEP_) mean simulated vs. observed ecosystem carbon fluxes (_NEP_, _GPP_ and _R\({}_{coo}\)_) at Hohes Holz for the calibrated parameterization for three years (2015–2017).
### Factors Leading to UHI  
The UHI effect can be attributed to various factors, and its characteristics depend on both intrinsic city parameters such as building size, geometry, density, and population, as well as external factors such as topography, regional climate, and meteorological conditions [7]. UHI formation takes place due to dense building structures in urban areas absorbing a substantial amount of solar radiation in the daytime leading to nighttime irradiative cooling, and, thus, delaying the rate of decrease in air temperature, compared with rural areas where cooling occurs more rapidly. This variation in surface energy balance leads to a marked difference in the surface and near-surface temperatures between urban and rural regions, developing the UHI [11].  
The significant variances in the thermal characteristics of radiating surfaces in urban and rural areas (urban areas substitute natural land cover with surfaces such as pavements and buildings that absorb and retain heat at elevated levels), lack of green space leading to decreased evapotranspiration, the 'urban canyon effect' leading to increased absorption of shortwave radiation, a reduced sky view that diminishes outgoing longwave radiation, decreased local air circulation due to increased surface roughness reducing boundary layer winds resulting in less discharge of accumulated heat, anthropogenic heat dissipation, and high levels of pollution, all lead to the UHI effect [12].
# A Novel Dual Mixing Attention Network for UAV-Based Vehicle Re-Identification
## 2 Related Work
### Attention Mechanism  
The attention mechanism uses deep neural networks to imitate human cognitive processes. The method has been widely applied in computer vision, with the characteristic of learning more skills to express [8; 9; 33] [PERSON] et al. [33] constructed a motion attention transfer (MATNet) attention framework based on human visual attention behavior for semantic segmentation tasks, solving the problem of insufficient datasets of basic facts. In addition, a new weakly supervised semantic segmentation group learning framework [8] is proposed. The attention mechanism can also solve the problem of the detection and identification of human interaction in images (HOI) [9].  
According to [34; 35], for vehicle ReID, the attention mechanism concentrates on regions that correlate to delicate and distinct image areas, including windshield stickers and custom pains. The attention mechanism automatically extracts the characteristics of the distinct regions, increasing the vehicle re-identification task's accuracy. [PERSON] et al. [34] found that most re-identification methods focus on the critical point locations. However, these point locations weigh differently in distinguishing cars. As a result, they created a dual-path adaptive attention model for the two-path vehicle ReID. The oriented conditional component appearance path learns to capture local discriminant traits by concentrating on the most instructive critical spots, whereas the global appearance path catches macroscopic vehicle features. [PERSON] et al. [35] presented a spatial and channel attention network based on diffusion-convolution neural network (DCNN). This attention model includes a spatial attention branch and a channel attention branch, which adjusts the output weights of different locations and channels separately to highlight the outputs in the distinguished regions and channels. The attention model refines the feature map and can automatically extract more discriminant features.  
Although these attention mechanisms fuse attention to important areas or channels by combining different attention branches, mixed attention tends to learn along a single dimension, ignoring the features of the remaining dimensions. With significant space, perspective, and scene changes, the UAV perspective has excellent challenges for the attention mechanism.
## 2 Image Reconstruction  
Image reconstruction for an array system and for a synthetic aperture system differ in the sensor arrangement and the reconstruction method. For an array system, the transmitting device is formulated as a single transducer, which sends an omnidirectional pulse. In contrast to the typical SAR wavefront reconstruction [1], the data is recorded such that the transmitting and receiving devices are in the same position while traversing an aperture path. The corresponding interpolation to a uniform linear grid is also known as a Stolt interpolation [23]. Conventional beamforming, sometimes also denoted as data-independent beamforming, is usually used for imaging systems.
## 3 Results  
### Sieve Analysis of Quartz Sands  
Table 1 displays the results of the sieve analysis, while Table 2 outlines the fundamental parameters of the grain matrix employed in the experiments. According to Table 1, the quartz sands utilized in the tests exhibit variations in their primary fractions. In the case of medium sand (QS1), the primary fraction, collected on three adjacent sieves, falls within the range of sieve openings of 0.32/0.20/0.16 mm, with the 0.20 fraction representing over 60%. For the QS2 sand, the main fraction is collected on 0.40/0.32/0.20 mm sieves, and the predominant grains are sized at 0.40 mm, accounting for approximately 48% of the total sample weight. On the other hand, the coarsest sand (QS3) is characterized by the main fraction collected on 0.63/0.40/0.32 mm sieves, with sifting on a 0.63 mm sieve constituting 52.65% of the sample weight. As indicated in Table 2, the average D50 grain size for the tested quartz sands is as follows: QS1--0.25, QS2--0.43, QS3--0.66. Notably, the arithmetic and geometric averages of the grain sizes are very close. In the case of the sand with the largest average grain size (QS3), the primary fraction constitutes over 95% of the totalsample, resulting in the highest homogeneity index of 84%. The remaining sands share a similar proportion of the main fraction within the entire sample volume, with QS2 being the least homogeneous at 63%.
## 5 Scoring Privacy Policies  
Once trained, the SVM provides for each statement a set of tags and a confidence score (classification threshold) that measures the relative distance of a sample with respect to the hyperplane separating the classes learnt by the SVM. We consider a tag to be reliable if the confidence score is 60% or higher. Specifically, by fixing a value above 50% we focus on tags that exhibit a dominant class membership.  
To score the degree of policy compliance \(p\) for a given goal \(i\) we measured the difference between the number of positive and negative tags obtained for that goal \(i\). If the difference is positive (negative) for a given goal, we consider that the policy is compliant (non-compliant) with such goal in a degree proportional to the magnitude of the difference. Scores were normalized by the number of tags associated with the policy for all goals (\(G\)) and expressed as percentages. In this way, one can directly compare policies regardless the number of tags they have associated.  
\[Score\_goal_{i}(p)=\frac{\#positive\_tags_{i}(p)-\#negative\_tags_{i}(p)}{ \sum_{g\in G}(\#positive\_tags_{g}(p)+\#negative\_tags_{g}(p))}\times 100 \tag{1}\]  
After normalization, a high positive score for a certain goal means that statements in the policy related to that goal are mostly on the compliant side; therefore, the service is respectful with that goal. Inversely, a large negative score implies that the policy mostly contains non-compliant statements for that goal; therefore, the service entails serious privacy risks. Finally, a value near 0% means that the number of positive and negative statements for the goal are balanced. Per goal scores give a clear and intuitive picture on how the service provider deals with the different perspectives of data protection.  
\begin{table}
\begin{tabular}{c c} \hline
**Tag** & **Number (Percentage)** \\ \hline data minimization compliant & 14 (1.9\%) \\ data minimization non-compliant & 38 (5.17\%) \\ \hline availability compliant & 29 (3.95\%) \\ availability non-compliant & 0 (0\%) \\ \hline integrity compliant & 19 (2.59\%) \\ integrity non-compliant & 0 (0\%) \\ \hline confidentiality compliant & 68 (9.25\%) \\ confidentiality non-compliant & 109 (14.83\%) \\ \hline unlinkability compliant & 12 (1.63\%) \\ unlinkability non-compliant & 83 (11.29\%) \\ \hline transparency compliant & 150 (20.41\%) \\ transparency non-compliant & 45 (6.12\%) \\ \hline intervenability compliant & 107 (14.56\%) \\ intervenability non-compliant & 61 (8.3\%) \\ \hline \end{tabular}
\end{table}
Table 1: Distribution of tags in the training data set To assess policies in global, we also quantified the relative difference between positive and negative tags for all goals.  
\[Global_{score(p)}=\frac{\sum_{g\in G}\left(\#positive\_tags_{g}(p)-\#negative\_ tags_{g}(p)\right)}{\sum_{g\in G}\left(\#positive\_tags_{g}(p)+\#negative\_tags_{g}(p) \right)}\times 100 \tag{2}\]  
In this way, it is possible to immediately compare and rank policies of different companies or service providers.