esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Datasets and Metrics
#### 4.3.2 Comparison with State-of-the-Art Action Recognition Methods  
On the FPHA dataset, we compared the proposed method with a number of cutting-edge methods. These methods are based on different network architectures and feature fusion strategies and have a high impact in the field of first-view hand movement recognition. Among the 17 baselines, Two-stream [26], Collaborative [5] and Joule-color [43] are based on CNNs. HAN-2S [44], ResGCNeXt [45], SBI-DHGR [46], [PERSON] et al. [47], GCN-BL [48] are based on the skeletons. FPHA [1] and H+O [3] are based on the RNN. [PERSON] et al. [49] is based on the GCN and LSTM. EffHandEgoNet-Transformer [50], HTT [4], and Treat-depth [51] are based on CNN-Transformer architecture, in which EffHandEgoNet-Transformer [50] and HTT [4] use backbone based on CNN architecture for feature extraction.  
In addition, several representative methods in the more challenging H2O dataset are also selected for comparison, including H+O [3], H2O [2], SlowFast [25], C2D [52], I3D [24], HTT [4], PoseConv3D [53]. These methods cover the current mainstream action recognition frameworks and can better reflect the overall state of the art. Tables 2 and 3 summarize the comparison results of action recognition accuracy on the FPHA dataset and H2O dataset, respectively. As can be seen from the tables, the methods proposed in this study achieve recognition performance better than existing methods on both datasets, which verifies its powerful ability to recognize the action of object interaction in complex scenes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Left** & **Right** \\ \hline H+O [3] & 41.42 & 38.86 \\ LPC [41] & 39.56 & 41.87 \\ H2O [2] & 41.45 & 37.21 \\ HTT [4] & 35.02 & 35.63 \\ THOR-NET [42] & 36.8 & 36.5 \\
**Ours** & **31.16** & **35.21** \\ \hline \hline \end{tabular}
\end{table}
Table 1: The quantitative results of manual pose estimation in the H2O test set, MEPE, measured in millimeters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Modality** & **Acc.** \\ \hline Joule-color-all [43] & RGB + Depth + Skeleton & 78.78 \\ Two stream-color [26] & RGB & 61.56 \\ Two stream-flow [26] & RGB & 69.91 \\ Two stream-all [26] & RGB & 75.30 \\ FFHA [1] & Pose & 78.73 \\ H+O [3] & RGB & 82.43 \\ HAN-2S [44] & Skeleton & 89.04 \\ Collaborative [5] & RGB & 85.22 \\ ResGCNeXt [45] & Skeleton & 89.04 \\ SBI-DHGR [46] & Skeleton & 92.48 \\ [PERSON] et al. [49] & RGB + Depth + Skeleton & 91.95 \\ HTT [4] & RGB & 94.09 \\ EffHandEgoNet-Transformer [50] & RGB & 94.43 \\ Treat-depth [51] & Depth & 92.17 \\ [PERSON]. [PERSON] et al. [47] & Skeleton & 92.52 \\ SymNet-v2 [54] & RGB & 82.96 \\ GCN-BL [48] & Skeleton & 80.52 \\
**Ours** & RGB & **94.82** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of action recognition accuracy on FPHA set.
## 4 Result and Discussion
### Measurement Errors and Methods to Reduce them  
#### 4.4.1 Systematic Errors  
Measurement errors may be caused by source from the GMR system noise. In principle, this bias can be calibrated using the Tipping curve calibration and LN2 calibration. These calibrations can provide an absolute accuracy of at least 0.5 K [15; 18; 27]. The radiometer calibration is fundamental work to determine the link between the TB and output voltage. During this observation, the LN2 calibrations were twice a year and the Tipping curve calibration was applied to clear-sky condition. Thus, the impact of the system error can always be reduced.  
In addition, a radiometer is able to point in any direction. During the Tipping calibration and the solar observation, the pointing errors cause larger uncertainties. To calibrate the pointing errors, the difference between the antenna pointing and the calculated position of the sun is the calibration value of the antenna alignment. According to our method in Section 4.1, the calibration error is better than 0.2\({}^{\circ}\) and the effect of the pointing error can often be reduced significantly.
# New Flame Retardant Systems Based on Expanded Graphite for Rigid Polyurethane Foams
## 3 Results and Discussion
### Thermogravimetric Analysis (TGA) of PU Foams  
To better understand the role of EG and silica to the flame-retardant behavior of PU composite, the thermal stability of these composites was studied by TGA. Thermogravimetric analysis (TGA) and differential thermogravimetry (DTG) analysis of neat RPUF (PU-0) and RPUF filled EG and EG with silica or silica in presence IL on-air atmosphere are illustrated in Figures 8 and 9. Table 6 shows the individual temperatures with a specific weight loss, namely 5% (T5%), 10% (T10%), 50% (T50%), 70% (T70%) as well as the char residue at 600 \({}^{\circ}\)C.  
surface. EG pyrolysis consists of SO\({}_{2}\), CO\({}_{2}\) and water that is trapped when escaping near the edge of the graphite structure, which leads to the irreversible expansion of the graphene structure and the formation of a graphite carbonized layer on the foam surface. This layer prevents heat transfer through the solid phase and stops the fire. This effect can be seen when the foam surface expands during combustion [32]. This is followed by the third degradation stage, during which the fragments formed during the second stage are degraded at 500 \({}^{\circ}\)C, corresponding to a 70% weight loss [31; 33] and is the result of further breakdown of the RPUF structure and charring. At this stage, also other products that come from isocyanate groups (e.g., amines or benzene alkyl) decompose into volatile products (HCN, CO\({}_{2}\) or NO\({}_{2}\)) [34]. Compared to the PU-0 reference foam, foams containing only EG and EG with the addition of Si showed slightly less weight loss, suggesting increased carbon stability. In turn, the ionic liquid did not affect and even reduced the thermal stability of the foams compared to foams containing EG or EG+SiO\({}_{2}\), in which the thermal stability of PU foams is observed to increase with increasing temperature.
# An Adaptive Dempster-Shafer Theory of Evidence Based Trust Model in Multiagent Systems
## 4 Proposed Trust Model
### Indirect Reputation
#### 4.2.4 Model Indirect Reputation  
In this subsection, we explain how to learn the final indirect reputation. As explained, indirect reputation consists of two aspects, namely, the quality evaluation, which is based on performance, and the confidence of the witness. Three sub-factors determine the value of confidence, i.e., the consistency of the provided information (Equation (15)), the credibility of agents (Section 4.2.2), and the certainty (Equation (16)). These three sub-factors are used to revise the generated evidence and \(Conf(c_{i},w_{k})\) in Equation (13). Here, we use \(Conf(c_{i},w_{k})_{t}\), \(Sim(m_{kj})_{t}\), \(Cred(c_{i},w_{k})_{t}\), and \(Cer(m_{kj})_{t}\) to represent the value of confidence, the consistency, the credibility, and the certainty for pretreatment of the \(t\)th interaction, respectively. Detailed definition is given as follows:  
\[Conf(c_{i},w_{k})_{t}=Sim(m_{kj})_{t}*Cred(c_{i},w_{k})_{t}*[(Cer(m_{kj})_{t}) ] \tag{17}\]  
where \(\triangle=min\{0,Sim(m_{kj}^{dir})_{t}*Cred(c_{i},w_{k})_{t}-\frac{1}{L}\sum( Sim(m_{kj}^{dir})_{t}*Cred(c_{i},w_{k})_{t})\}\) is the smaller value between \(0\) and the difference of confidence obtained from consistency and credibility. In more detail, the confidence of a piece of evidence provided by the witness is directly proportional to the credibility of a witness, quality, and certainty of the evidence. In terms of certainty, as \(\triangle\) defines, the confidence \(Conf(c_{i},w_{k},s)_{t}\) of the evidence provided by the witness \(w_{k}\) decreases with the certainty \(Cer(m_{kj}^{dir})_{t}\) if its credibility \(Cred(c_{i},w_{k})_{t}\) and quality \(Sim(m_{kj}^{dir})_{t}\) are below the average level, which is calculated by \(Sim(m_{kj}^{dir})_{t}*Cred(c_{i},w_{k})_{t}-\frac{1}{L}\sum(Sim(m_{kj}^{dir}) _{t}*Cred(c_{i},w_{k})_{t})\). Otherwise, the confidence is determined by only the credibility and quality. That is to say, the resource customer agent is more confident in the good-quality indirect experience provided by credibility agents, as presented in Figure 4. Those experiences that originate from unreliable agents must be discounted and are no longer reliable. After modification, agents' indirect reputations are fused by [PERSON]'s combination rule, shown in Equation (4).  
Figure 3: The entropy when frame of discernent is \(\Omega=\{T,nT\}\). As is shown, entropy is in the range of \([0,2]\).
# Monitoring Changes in Water Use Efficiency to Understand Drought Induced Tree Mortality
## 2 Materials and Methods
### Tree Mortality  
The USDA Forest Service Forest Health and Monitoring (FHM) program conducts aerial detection surveys for California forest (USFS Region 5) annually and data is available from 1978 to the present ([[https://www.fs.usda.gov/detail/r5/forest-grasslandhealth/?cid=fsbdev3_046696](https://www.fs.usda.gov/detail/r5/forest-grasslandhealth/?cid=fsbdev3_046696)]([https://www.fs.usda.gov/detail/r5/forest-grasslandhealth/?cid=fsbdev3_046696](https://www.fs.usda.gov/detail/r5/forest-grasslandhealth/?cid=fsbdev3_046696))). Surveys were done by flying over the forests and sketch mapping areas of mortality, recording the mortality agent, forest type, and affected trees per hectare (TPH). Mortality was detected by identifying yellow to reddish brown tree crowns. Areas identified by discolored tree crowns were categorized by (a) damage type (i.e., mortality, top kill, defoliation); (b) the number of trees affected (TPH); and (c) the affected tree species. Surveys also recorded the probable damage-causing agent (i.e., fire, bark beetles, etc.). If the damage-causing agent was unknown at the time of the flight, the local Forest Service entomologist and/or pathologist determined the likely agent. Generally, areas with <2.47 TPH of mortality were considered to have \"background\" or \"normal\" levels of mortality and were not mapped. Although, if low levels of mortality were indicative of a localized pest-related event then areas were mapped. Annual mortality surveys were compiled (2013-2016) and the maximum TPH was used to develop a gridded mortality layer. This layer was designed to match the resolution of WUE layers (1 km; Table 1).  
The maximum tree mortality layer is an estimate of maximum tree mortality rates within a 1 km pixel. While appropriate for examining large-scale patterns in tree mortality, it is useful to understand patterns in tree mortality that exist at finer scales. Forest Inventory and Analysis (FIA) data will be used to evaluate plot level patterns in tree mortality. We will compare annual FHP data to FIA plots (\(n\) = 1390) that were measured in 2013-2016. Although there are 6000 FIA plots in California, only a portion of plots are measured in a given year (\(\sim\)10%). Mortality rates observed on a plot level (TPH year\({}^{-1}\).) are unlikely to reflect landscape mortality patterns recorded in the FHP survey. In areaswhere tree mortality was sketch mapped, plots are likely to have higher than background (>2.47 TPH) mortality rates.
## 3 Results and Discussion
### Particulate Sizing
#### 3.2.2 High-Altitude Samples  
Figure 5 shows the SEM images of the airborne \"as-trapped\" particulates collected on disks at the HAS. It should be noted that, as seen in Figure 5(a2,a3,b2), these larger particles often had depositions of other particulates below these thresholds.  
A comparison between the two sampling altitudes (SEM images in Figures 4 and 5), reveals some distinct aspects of the particulates. One notable difference is the difference in size. That is, the particulates from HAS are much larger in size. A second observation is that the particulates from HAS appear to have a more uniform shape. That is, the particles from this type of plumes (professional fireworks) have a spherical or near-spherical shape. The particulates sometimes were still compound, featuring smaller particulates deposited onto the larger surface as seen through the various arrows indicating regions of interest with higher magnification imaging of these regions (Figure 5a-c,e). These deposited particulates were also often spherical in shape but did not exclusively maintain this shape profile. For instance, Figure 5a shows a particulate that is approximately 20 \(\upmu\)m in diameter with a smooth surface. Higher resolution SEM imaging on a selected area, the boxed area in Figure 5a, shows surface depositions that appear to be spherical in nature (Figure 5(a2,a3)).  
Other particulates from the HAS appear to be roughened (Figure 5(b,b2)). Figure 5c shows two large particles from HAS that are bonded (Figure 5c). The larger particle is approximately 150 \(\upmu\)m in diameter while the smaller is approximately 85 \(\upmu\)m in diameter. High-resolution SEM imaging, the selected area on the larger particle, shows that the deposits have a platelet-like structure (Figure 5(c2)). Meanwhile, the deposits on the smaller particles are more spherical (Figure 5(c3)). Other large particles in the HAS are coarse and have prominent irregularities or larger protrusions on its surface (Figure 5(e,e2)).
## 4 Discussion
### Limitations  
The present study has some limitations that need to be taken into consideration. First, the sample size comprised only 26 healthy reproductive-age women, potentially limiting the generalizability of the findings. Replicating the study with a more extensive and diverse population would enhance the external validity and robustness of the results. Second, while the research identifies differences in the electrical activity across the menstrual cycle sub-phases, it falls short of establishing direct links to specific conditions. Future investigations could delve deeper into the clinical relevance and implications of these variations in uterine electrical activity. Third, the insertion of a flexible electrode array into the endometrial cavity for EUG recordings raises concerns about invasiveness. Despite the positive feedback received from the participants for this painless procedure, evaluating the safety and acceptability of this procedure is important for its potential adoption. Additionally, as a single-center study, the research's applicability to diverse settings may be limited. Conducting replications in multiple centers would strengthen the generalizability and reliability of the findings. To overcome these limitations, subsequent investigations should emphasize larger and more diverse cohorts, establish clinical correlations, incorporate long-term monitoring, and thoroughly assess the acceptability of the EUG procedure across different populations.
# Satellite Measured Ionospheric Magnetic Field Variations over Natural Hazards Sites
## 6 Discussion  
By applying novel satellite magnetic field techniques and approaches on existing state-of-the-art best practieses it is possible to circumspectly answer some of the most prominent questions in natural hazards investigations. Magnetic field variations in the ionosphere can shed light on processes in the lithosphere, Earth's surface and above ground. In any configuration the orbital recurrence intervals are crucial points in order to get many crossing segments in the survey area of few degrees around a target. A constellation of satellites like ESA's _Swarm_ mission has inherently advantages, the CSES-2 expansion, together with CSES, will double the revisiting frequency and is very encouraging. The high-res 50 Hz sampling enables statistical analyses of each individual segment, either before or after an event. A 1 Hz temporal resolution, due to limited number of vectors per single crossing within a selected investigation area (box size), induces a merging of pre- and post-event periods (which could slightly differ), i.e., only two time spans are effectively available for the evaluation. An analysis to remove confounding effects is obligatory, among them spacecraft interferences and external sources like magnetospheric (mainly in high latitudes) and solar influences on the ionospheric F-region plasma. In that sense the practical experience of instrument teams and amassed evidence through real-time services in the field of near Earth space can provide important knowledge to detect spurious signals. Future research directions include a thorough characterisation of the AW- and  
Figure 10: Distribution of _Swarm_ A/B/C VFM magnetic field fluctuations after scalogram processing and grouped in individual frequency bands for an Ariane 5 launch, 25 July 2018, box size \(\pm 10\) deg, time span \(\pm 1\) day. The frequency band is in log-scale according to \(\ln(f_{n})=3.1469-0.069315\times n\), shown is the range n = \(\{1-40\}\), i.e., the left box plot (n = 1) is \(f_{1}=21.7062\) Hz (Nyquist frequency \(f_{s}/2=25\) Hz) and the right box plot (n = 40) is \(f_{40}=1.4540\) Hz. The colour coding is the same as in Figure 2, the geomagnetic activity is \(|\)Dst\(|\)\(\leq 174\) nT (start and main phase of a magnetic storm) for the selected time span.  
AGW-channel into the upper atmosphere and the coupling mechanisms between neutral particles and the ambient plasma population. By combining a variety of multi-parameter methods to describe the phenomena more details will show up. An outlook is to transform these results with an all-encompassing perspective into an essential space based service to leverage natural hazards evaluation and risk assessment.
## 4 Results
The output time-frequency matrix of WVD is \(N\times N\), the discrete points of \(\theta\) from 0 to \(2\pi\) are K. From Equation (10), the Hough transform needs two complex multiplications and one complex addition. Therefore, the total requirements of the Hough transform are \(2N^{2}K\) complex multiplications and \(N^{2}K\) complex additions. Similarly, the total requirements of the Hough transform in case of RSPWVD are \(2 NMK\) complex multiplications and \(NMK\) complex additions. From Table 3, the requirements of WVD are higher than those of RSPWVD because of \(N\gg M\) and \(N\gg L\). As a result, the total requirements of the Hough transform of WVD are higher than those of RSPWVD. Finally, the chirp signal is a continuous wave at any fixed time and can be mitigated by a notch filter based on the second-order direct form structure with two parameters named \(\alpha\) and \(\beta\) which are determined by the power of chirp interference and the instantaneous frequency estimated by proposed method, respectively. The receiver operating characteristic (ROC) curves are shown in Figure 7 by Monte Carlo simulations to analyze the performance of the notch filter to mitigate the chirp interference under the condition where the BDS B1I signals are simulated with chirp interferences. The JNR is set to 12 dB and the \(C/N_{0}\) is set to 43 dB-Hz. The integration time is set to 1 ms. The instantaneous frequency of one chirp ranges from 30 MHz to 50 MHz and the instantaneous frequency of the other ranges from 50 MHz to 30M Hz. The sweep period is 2.56 us. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sweep Period** & **Estimation of Initial Frequency** & **Estimation of Chirp Rate** \\
**(us)** & **RMSE (Normalized)** & **RMSE (Normalized)** \\ \hline
2.56 & 0.005438 & 0.005941 \\
5.12 & 0.015086 & 0.006581 \\
10.24 & 0.017463 & 0.007522 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Chirp detection for different sweep periods. \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Computational Requirements** \\ \hline WVD & \(2N^{2}+2N^{2}\log^{2N}\) complex multiplications \\ \(4N^{2}\log^{2N}\) complex additions \\ RSPWVD & \(NM(2+L)+NM/2\log^{M}\) complex multiplications \\ \(NM(4+L)+NM\log^{M}\) complex additions \\ WVD + Hough & \(2N^{2}+2N^{2}\log^{2N}+2N^{2}K\) complex multiplications \\ \(4N^{2}\log^{2N}+N^{2}K\) complex additions \\ RSPWVD + Hough & \(NM(2+L)+NM/2\log^{M}+2 NMK\) complex multiplications \\ \(NM(4+L)+NM\log^{M}+NMK\) complex additions \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Computational requirements of the Hough transform of WVD and RSPWVD. Figure 7 depicts that the ROC curve in the two chirp scenario presents a worse acquisition performance than that in the one chirp scenario. However, when the notch filter is adopted, the ROC curves present an improved acquisition performance. The results depicts that the proposed method can effectively detect the one or two chirp interferences, especially for those with crossed frequency without a priori knowledge.
## 2 Materials and Methods
### Satellite-Based Observations  
In this work, the satellite-based LAI from the GLASS product is merged within the land surface model. The GLASS LAI product is generated using a general regression neural network approach, which allows a spatially and temporally consistent long-term record of vegetation conditions [36]. Two sources, MODIS and AVHRR, provide input to this dataset. MODIS data are available for 2000-2018 and the AVHRR data are available for 1981-2018. The MODIS-based GLASS LAI V50 product is used in this work, which was preferred over the original MODIS LAI product because of the cloud obstruction gaps present in the latter [37].  
The SMAP mission provides direct sensing of soil moisture. The SMAP satellite was launched on 2015 by National Aeronautics and Space Administration (NASA). It provides soil moisture data in the top 5 cm of the soil column during 2015-present. The SMAP satellite is in a 6 AM/6 PM ascending and descending orbit, but retrieving soil moisture from the morning overpasses is the primary objective of SMAP [38]. SMAP Level 1 delivers  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Dataset** & \begin{tabular}{c} **Spatial** \\ **Resolution** \\ \end{tabular} & \begin{tabular}{c} **Temporal** \\ **Resolution** \\ \end{tabular} & \begin{tabular}{c} **Temporal Extent** \\ **Temporal Extent** \\ \end{tabular} \\ \hline Atmospheric forcing & MERRA-2 & \begin{tabular}{c} 0.50\({}^{\circ}\)/0.625\({}^{\circ}\), \\ lat/lon \\ \end{tabular} & Hourly & 1980–present \\ \hline Satellite observations & \begin{tabular}{c} GLASS \\ SMAP \\ \end{tabular} & \begin{tabular}{c} 0.05\({}^{\circ}\) \\ 36 km \\ \end{tabular} & \begin{tabular}{c} 8 days \\ Daily \\ \end{tabular} & \begin{tabular}{c} 2000–2018 \\ April 2015–present \\ \end{tabular} \\ \hline Validation & \begin{tabular}{c} GLEAM \\ FLUXCOM \\ ISMN \\ \end{tabular} & \begin{tabular}{c} 0.25\({}^{\circ}\) \\ 0.50\({}^{\circ}\) \\ Point data \\ \end{tabular} & \
\begin{tabular}{c} Daily \\ Daily \\ \end{tabular} &
\begin{tabular}{c} 2003–2018 \\ 1980–2018 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the datasets used in this study as input to the land surface model, satellite observations, and reference for validation. All data are available at the global scale.  
Figure 1: Land surface classification over the study region by MODIS in the Noah-MP model. The percentage area for each class is shown in the legend (EN: evergreen needleleaf; EB: evergreen broadleaf; DN = deciduous needleleaf; DB: deciduous broadleaf).  
products from its instrument measurements and Level 2 delivers SMAP's Swath based geographical retrievals [38]. The SMAP Level 3 (SPL3 SMP) product, which is used in this work, is a composite based on daily passive radiometer estimates of global land SM at the top 5 cm of the soil that is resampled to a global, cylindrical \(\sim\)36 km Equal-Area Scalable Earth Grid [39]. A summary of the main characteristics of GLASS LAI and the SMAP soil moisture product used in this study is shown in Table 1.