esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Qualitative Analysis of Xiaoqu Liquor
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Compounds**} & \multicolumn{2}{c}{**RI**} & \multirow{2}{*}{**FD**} & \multirow{2}{*}{**Identification**} & \multirow{2}{*}{**Aroma Description**} \\ \cline{3-4} \cline{6-7}  & & & & & & & \\ \hline
1 & Acetaldehyde & 882 & - & 32 & MS, Std, Odor, RI & Fruity, grass \\
2 & Ethyl acetate & 895 & 576 & 8 & MS, Std, Odor, RI & Pineapple, apple, fruity \\
3 & 1,1-diethoxyethane & 897 & 721 & 32 & Std, Odor, RI & Fruity \\
4 & 3-methylbutylaldehyde & 923 & 624 & 1 & MS, Odor, RI & Grass, malt \\
5 & Ethyl 2-methylbutyrate & 965 & - & 2 & MS, Odor, RI & Nail enamel \\
6 & Sec-butanol & 1008 & - & 4 & Std, Odor, RI & Fruity \\
7 & Isobutyl acetate & 1014 & - & 2 & MS, Odor, RI & Fruity \\
8 & Ethyl butyrate & 1016 & 803 & 4 & MS, Std, Odor, RI & Apple, pineapple, fruity \\
9 & N-propanol & 1029 & 569 & 4 & Std, Odor, RI & Fruity, floral, grass \\
10 & Ethyl isovalerate & 1064 & 856 & 8 & MS, Std, Odor, RI & Apple \\
11 & Isobutanol & 1078 & 611 & 8 & MS, Std, Odor, RI & Fruity \\
12 & Ethyl valerate & 1122 & 903 & 2 & MS, Std, Odor, RI & Fruity, apple, strawberry \\
13 & Isoamyl acetate & 1131 & 883 & 32 & MS, Std, Odor, RI & Banana \\
14 & _N_-butanol & 1144 & 656 & 2 & MS, Std, Odor, RI & Pungent odor, mellow \\
15 & Isoamyl alcohol & 1193 & 763 & 4 & Std, Odor, RI & Friuity, floral, foul odor \\
16 & Ethyl hexanoate & 1219 & 1013 & 32 & MS, Std, Odor, RI & Fruity, collar aroma \\
17 & Isoamyl butyrate & 1254 & 2 & MS, Std, Odor, RI & Fruity, floral \\
18 & _N_-amyl alcohol & 1259 & 763 & 2 & MS, Std, Odor, RI & Fruity \\
19 & Dimethyl disulfide & 1352 & - & 16 & MS, Std, Odor, RI & Gas odor, rot vegetable odor, garlic odor \\
20 & 3-hydroxy-2-butanone & 1374 & 755 & 1 & MS, Std, Odor, RI & Burning, sweet \\
21 & Ethyl lactate & 1309 & 820 & 2 & MS, Std, Odor, RI & Fruity, grass \\
22 & N-hexanol & 1315 & 868 & 4 & MS, Std, Odor, RI & Grass, floral \\
23 & Ethyl heptanoate & 1317 & 1121 & 2 & MS, Std, Odor, RI & Pineapple, floral \\
24 & 2-nonanone & 1384 & 1032 & 4 & MS, Std, Odor, RI & Milk \\
25 & Trans-2-hexene-1-ol & 1400 & - & 1 & MS, Odor, RI & Grass \\
26 & Ethyl octanoate & 1413 & 1211 & 256 & MS, Std, Odor, RI & Pear, lithchi, fruity \\
27 & Acetic acid & 1431 & - & 2 & Std, Odor, RI & Sour, vinegar \\
28 & Furfural & 1449 & 831 & 1 & MS, Std, Odor, RI & Burning odor, nuts \\
29 & 1-octen-3-ol & 1455 & 990 & 16 & MS, Std, Odor, RI & Grass, fruity, dust \\
30 & Benzaldehyde & 1509 & 966 & 2 & MS, Std, Odor, RI & Apriotot, nut \\
31 & Ethyl nonanoate & 1513 & 1289 & 2 & MS, Std, Odor, RI & Sweet, fruity \\
32 & Propionic acid & 1525 & 698 & 4 & Std, Odor, RI & Water odor \\
33 & Ethyl caprate & 1617 & 1395 & 8 & MS, Std, Odor, RI & Pineapple, floral \\
34 & Butyric acid & 1618 & 811 & 4 & MS, Std, Odor, RI & Sweat odor, acidic odor, collar mud odor \\
35 & Phenylacetaldehyde & 1636 & 1041 & 4 & MS, Std, Odor, RI & Rose \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Volatile compounds in JPXQL determined by GC-O. Figure 1: Aroma intensity of JPXQL by GC-MS-O.
## 3 Retrieval
### Setup
#### 3.1.5 A Priori Error Covariance Matrix \(\mathbf{S}_{a}\)  
The a priori error covariance matrix defines the uncertainties of the a priori state vector elements and their correlations. Its dimensionality is \(n\times n\). Except for the CO\({}_{2}\) and H\({}_{2}\)O profile layers, we assume \(\mathbf{S}_{a}\) to be diagonal. As described by [PERSON] et al. (2016), we compute the CO\({}_{2}\) layer-to-layer covariances by comparing randomly chosen SECM2016 profiles with corresponding CT2016 model profiles. The CO\({}_{2}\) layer variances have been up-scaled so that the a priori XCO\({}_{2}\) uncertainty becomes 10 ppm (1 ppm \(\widehat{=}\) 2.5% without scaling). This ensures retrievals to be dominated by the measurement but not the a priori. We estimated the H\({}_{2}\)O layer-to-layer covariances by analyzing H\({}_{2}\)O day-to-day variations of ECMWF analysis profiles. CO\({}_{2}\) and H\({}_{2}\)O a priori error covariances are shown in Figures 4 and 5. All other (diagonal) elements of \(\mathbf{S}_{a}\) are listed in row \(\sigma\vec{x}_{a}\) of Table 1.  
Figure 4: CO\({}_{2}\) a priori error covariance computed from randomly chosen SECM2016 profiles and corresponding CT2016 profiles. The CO\({}_{2}\) layer variances have been up-scaled so that the a priori XCO\({}_{2}\) uncertainty becomes 10 ppm (1 ppm without scaling). **Left**: Layer-to-layer correlation matrix of the a priori uncertainty. **Right**: 1\(\sigma\) a priori uncertainty.  
Figure 5: As Figure 4 but for H\({}_{2}\)O and estimated from day-to-day variations of ECMWF analysis profiles (without variance scaling as done for CO\({}_{2}\)).
# Extracellular Vesicles for Regenerative Medicine Applications
## 3 Pre-Clinical Applications of EVs in Tissue Engineering and Regenerative Medicine
### Respiratory System  
Major challenges for TERM in the context of the respiratory system involve inhibiting the pro-fibrotic and pro-pulmonary hypertension pathways central to the healing of damaged lung tissue. MSC- and lung-derived EVs have been considered as potential therapies for the mitigation of these adverse healing pathways. Specifically, MSC-derived exosomes have been shown to inhibit pulmonary hypertension and suppress hyperproliferative and fibrotic pathways [86]. In addition, lung-derived microvesicles have been shown to be particularly useful in TERM as there is evidence that these EVs can reprogram marrow cells into a pulmonary epithelial phenotype which could help provide a stable cell source for lung engineering [87; 88].
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### AGB Maps Derived from a Combination of Field Biomass, LiDAR Data and Optical and/or Radar Data
#### 3.4.6 Estimated Deforested Area Biomass in Tropical America, Africa, and Asia, 2000  
[PERSON] et al. [91] provided predeforestation aboveground biomass estimates at 30 m resolution for the year 2000 for the pantropical forests where deforestation occurred between 2000 and 2012. The biomass map was a subset of the continental biomass maps in deforestation areas suggested by [PERSON] et al. [45] and the additional national tree cover loss/deforestation data. The continental biomass maps at 30 m spatial resolution were obtained by expanding methodology in [PERSON] et al. [18]. The statistical relationship established with field measured biomass and GLAS LiDAR metrics, as described by [PERSON] et al. [18], was applied to estimate biomass at GLAS footprints. Then a random forests model was trained by estimated GLAS biomass as well as geospatial datasets including Landsat 7 Enhanced Thematic Mapper Plus (ETM+) top-of-atmosphere reflectance and tree canopy cover from the Global Forest Change dataset [45], elevation and climate data, to obtain an overall biomass map.
## 2 Related Works
### Watermarking Deep-Learning-Based Algorithm  
Several deep learning (DL) watermarking algorithms have been proposed [3; 11; 13]. HiDDeN [13] uses an algorithm for digital image watermarking that applies a neural network to train an end-to-end model. The algorithmic network allows the adaptive balancing of watermark properties, such as capacity, confidentiality and robustness, to different types of noise by adjusting model parameters or noise layers during training. HiDDeN presents excellent advantages in robust digital image watermarking because new attacks can be directly added to the training process to improve the generalization of the algorithm. The model can robustly hide all kinds of image deformations by considering several different types of image attack layers, unchanged watermarked images, dropouts, crop-outs, cropping, Gaussian and JPEG.  
StegaStamp [11] also presented an end-to-end algorithm based on deep learning. StegaStamp learns the watermark information embedding and extracts model resistant image perturbations to simulate the distorted space produced by real printing and photography, including changes in illumination, shading, movement, perspective, rotation, occlusion and viewing distance. Specifically, the researchers simulated five different types of attack layers, including perspective distortion, blurring, color manipulation, Gaussian noise and JPEG, and applied various kinds of image distortions. Finally, robust information extraction performance over multiple printers, screen-shooting and cameras was demonstrated in the experiments.  
ReDMark [3] developed a deep end-to-end propagation watermarking algorithm that can flexibly transform the space and learn a new watermarking model. The model consists of CNN with residual blocks to achieve watermark embedding and blind extraction in the simulation environment. The whole network system consists of three main modules: embedding watermark convolution layers, simulating differentiable attack layers and blind extracting watermark convolutional layers. The developed model simulated multiple types of image attacks as a differentiable attack layers to benefit end-to-end training. The watermarking information can be spread in a wide area of the image to enhance the security and robustness of the watermarking algorithm.  
StegaStamp used CNN networks that learn a robust encoding and decoding algorithm in the spatial domain and simulate the distortions caused by physically displaying and imaging and image perturbations to train the network for outstanding resistance to screen-shooting attacks. Many researchers proposed the watermarking-based spatial domain and presented weak robustness. ReDMark proposes a deep end-to-end diffusion watermarking framework, which can learn watermarking algorithms in the frequency domain space. However, ReDMark is unable to resist the screen-shooting attacks. The proposed hybrid ReDMark framework and screen-shooting attacks, similar to StegaStamp's distortion [11], is used to train a robust screen-shooting-resilient watermarking.
## 2 Materials and Methods
### Ammonium Nitrogen Adsorption Experiments  
To evaluate the ammonium nitrogen adsorption performance of the biochar samples, a series of adsorption experiments were conducted using a 100 mL conical flask containing 25 mL NH\({}_{4}\)Cl aqueous solution. Typically, 0.1 g biochar was added to 25 mL 100 mg/L NH\({}_{4}\)Cl aqueous solution, and the mixture was placed in an electrically thermostatic shaker (HNY-100B, Tianjin Honour Instrument Co., Ltd, Tianjin, China) at 200 rpm at 30 \({}^{\circ}\)C for 6 h. The pH of the NH\({}_{4}\)Cl aqueous solution was adjusted in the range from 3 to 10 by adding NaOH and HCl. After adsorption, the residual ammonium solution was filtered through a 0.45 \(\upmu\)m membrane, and the ammonium concentration in the solution was analyzed using the Nesslerization method with the help of a UV-vis spectrophotometer (Shimadzu UV-2500, Japan) at 420 nm [23]. The ammonium adsorption capacity was calculated based on the following equation:  
\[Q_{t}=(C_{0}-C_{t})V/m \tag{1}\]  
where \(C_{0}\) is the initial ammonium nitrogen concentration (mg/L), \(C_{t}\) is the residual ammonium nitrogen concentration (mg/L) after biochar adsorption at time \(t\), \(V\) (mL) is the solution volume for the adsorption experiment, and \(m\) (g) is the adsorbent mass used in the adsorption experiment.  
For the adsorption kinetic study, ammonium concentrations at different time points (e.g., 0.5, 1, 1.5, 2, 3, 4, 5, and 6 h) were determined. For the adsorption isotherms, 0.1 g biochar was added to 25 mL NH\({}_{4}\)Cl aqueous solution with different concentrations (50, 100, 200, 400, and 600 mg/L). All the adsorption tests were performed in triplicate.
## 3 Results
### Spatial Variation Trends of NDVI  
The spatial distribution of annual and seasonal NDVI trends from 2000 to 2018 is depicted in Figure 3. Spring NDVI trends exhibited significant regional differences, with vegetation improvement in the southwest and degradation in parts of the central and eastern areas (Figure 3a,a'). Improvement zones covered 51% of the area, including 29% with significant and 22% with slight improvement.  
Summer trends showed widespread vegetation improvement, particularly in the east and south, with some central areas degrading (Figure 3b,b'). Improvement zones accounted for 74% of the area, with 43% significant and 31% slight improvement. Autumn trends mirrored summer, with broad improvement but scattered degradation in the north and central regions (Figure 3c,c'). Improvement zones covered 69% of the area, including 31% significant and 38% slight improvement. Winter trends diverged, showing extensive degradation, especially in the west. Degradation zones covered 31% of the area, with 29% slight degradation, while 43% showed no significant change (Figure 3d,d').  
Overall, annual NDVI trends indicated widespread vegetation improvement, particularly in the east and south, with some degradation in the west, north, and central areas (Figure 3e,e'). Improvement zones accounted for 72% of the area, including 47% significant and 25% slight improvement. From 2000 to 2018, vegetation coverage in the West Liao River Basin primarily improved, though trends varied significantly by season and region.
## 3 Model for Urban Area Air Quality Analysis Based on CFD Simulations
### CFD Simulations  
The software used in this study was Autodesk CFD [35], but the method is not limited to specific software. The Reynolds-averaged Navier-Stokes (RANS) model and the Chen-Kim k-\(\varepsilon\) turbulence model [36] were adopted for the computation. These models are used in most research on fluid dynamics in built environments [4; 37; 38] for their relative precision and speed. The finite element method was used for numerically solving differential equations, and the modified Petrov-Galerkin method was used as the upwind method. The mesh resolution was set to 0.5 m and the mesh was divided into hexahedral elements. The material used for the fluid was standard air material and the analysis was set as steady state. The building and ground material was set as generic concrete material. The computational domain and boundary conditions were based on the Architectural Institute of Japan standards and the COST group guidelines [39; 40]. In Figure 3, the domain configuration is illustrated. Parameter H represents the height of the tallest building of the urban block. The vertical and lateral extent is 10H, except for the side toward the outlet. The wind speed boundary condition is defined at the inlet. The outlet is set to uniform zero-gradient conditions. The geometry of the urban block and ground have a no-slip condition assigned. Possible wind angles were set from 0 to 337.5 degrees with an increment of 22.5 degrees. Each wind direction corresponds to a specific air velocity as an input parameter. The turbulence kinetic energy (TKE) tolerance was set to \(10^{-4}\). Eight  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline
**Wind** & **Windless** & **NE** & **ENE** & **ENE** & **NNE** & **SW** & **SSW** & **S** & **WSW** & **WNW** & **SE** & **ESE** & **WW** & **SSE** & **NW** & **N** & **NNW** \\ \hline relative frequencies (\%) & 87 & 28 & 35 &  
RANS simulations were performed for each urban block based on wind direction and velocity. On average, the mesh was divided into 150 k hexahedral elements, and the value for parameter H ranged from 10 to 109 m.
# The Domain Mismatch Problem in the Broadcast Speaker Attribution Task
## 4 Methodology
### The Identity Assignment Block  
The considered identity assignment block consists of a speaker recognition architecture, composed of the same clustering PLDA model, followed by score normalization and calibration stages. First, the previously mentioned PLDA backend, by means of its likelihood ratio, scores how likely a subset of embeddings \(j\) from the audio under analysis with a common speaker resemble the enrollment speaker \(i\). These scores \(s_{ij}\) are then normalized by means of the adaptive S-norm [55]. The normalization cohort consists of MGB 2015 labeled data. Finally, the normalized scores are calibrated according to a threshold \(\gamma\) prior to the decision making. Those scores overcoming the threshold are considered the _target_, i.e., the test speaker and the enrollment speaker are the same person. This threshold is adjusted by AER minimization in terms of a calibration subset \(\mathbf{\Phi}_{\text{calib}}\) and the enrollment embeddings \(\mathbf{\Phi}_{\text{enroll}}\) as follows:  
\[\gamma=\operatorname*{arg\,min}_{\gamma}(\text{AER}(\mathbf{\Phi}_{\text{calib}}, \mathbf{\Phi}_{\text{enroll}},\gamma)) \tag{14}\]  
where \(\mathbf{\Phi}_{\text{calib}}\) and \(\mathbf{\Phi}_{\text{enroll}}\) represent the set of embeddings from calibration as well as the enrollment speakers. The last step in the identity assignment block is the decision making.  
Any subset of embeddings is assigned to the enrolled identity with the highest score only if this value overcomes the calibration threshold, being assigned to the generic unknown identity otherwise. Mathematically, the assigned identity (\(\theta_{j}\)) for a subset of embeddings \(j\) with respect to the enrolled identity \(i\) is:  
\[\theta_{j}=\left\{\begin{array}{ll}\arg\max_{i}(s_{ij}|s_{ij}>\gamma)&\text{ if }\exists i|s_{ij}>\gamma\\ \text{Unknown}&\text{ if }\forall i,s_{ij}<\gamma\end{array}\right. \tag{15}\]  
where \(s_{ij}\) stands for the normalized PLDA log-likelihood ratio score between the embedding \(j\) and the enrolled identity \(i\).
## 4 Results and Discussion
### Positional Accuracy Test Results Depending on Camera Orientation  
To analyze the effect of individual camera orientation on positional accuracy, all other parameters were set the same, including the direction of flight longitudinally to the study area. This resulted in three cases (two, four, and seven) where the camera orientation axis was taken perpendicular (90\({}^{\circ}\)), oblique (70\({}^{\circ}\)), and both together. As shown in Figure 11, the results showed that for the longitudinal flight direction, the positioning accuracy of the vertical acquisition angle (90\({}^{\circ}\)), which was 6 cm, was better than that in the other cases. The worst positioning accuracy (36 cm) was obtained with an oblique detection angle (70\({}^{\circ}\)). This difference was very reliable because at an oblique acquisition angle (70\({}^{\circ}\)), the geometric distortions increased at large distances, as in the case of the longitudinal flight direction.  
On the other hand, if all parameters were set equal and the direction of flight was chosen as traversing relative to the study area, and the only variable was the camera orientation, then three cases were obtained (three, five, and ten). The results showed that the overall accuracy of the three cases in this test was almost the same (12 cm). The horizontal accuracy was better with a vertical acquisition angle (90\({}^{\circ}\)), while the vertical accuracy was better with an oblique acquisition angle (70\({}^{\circ}\)) (Figure 12).  
Figure 11: The impact of camera orientation (longitudinal flight direction).  
Figure 10: The impact of flight direction (acquisition angle = 70\({}^{\circ}\)).  
Figure 13 shows the sparse point cloud, dense point cloud, 3D model, DEM, and orthomosaic of the study area for the eleventh case.  
Figure 12: The impact of camera orientation (traversal flight direction).  
Figure 13: Geospatial products for the eleventh case: (**a**) sparse point cloud, (**b**) dense point cloud, (**c**) 3D model, (**d**) DEM, (**e**) orthophoto mosaic.
# The Online Reservation Problem
## 3 Algorithms  
We begin this section by presenting Lemma 1 that will be the basis for deriving upper bounds on CRs of algorithms in subsequent proofs. Subsequently, in Section 3.1, we analyze CRs of all greedy algorithms for the ORP. In Sections 3.2 and 3.3, we analyze the CRs of algorithms \(D\) and \(R\), respectively. Lemma 1 is based on the notions of allocation scheme and allocation, which we define next.  
Definition 1: Given two sets of jobs, \(B\) and \(C\), an allocation scheme from \(B\) to \(C\) is a function \(w\) that maps the Cartesian product \(B\times C\) to the set of non-negative real numbers such that for any job \(b\in B\), \(\sum\limits_{c\in C}w(b,c)\) is less than or equal to \(d_{b}\), the duration of job \(b\). Furthermore, \(w(b,c)\) is called the allocation from job \(b\) to job \(c\).  
**Lemma 1**: _Given Algorithm \(A\in\mathcal{A}_{D}\) and an instance \(L\), let \(B_{L}\) be the set of jobs accepted by \(A\) and \(C_{L}\) be an offline optimal set. If we are given a positive real number \(a\) and if for every instance \(L\in\mathcal{L}(\Delta)\), we are given an allocation scheme \(w_{L}\) from \(B_{L}\) to \(C_{L}\) such that for any job \(c\in C_{L}\), \(d_{c}/\left(\sum\limits_{b\in B_{L}}w_{L}(b,c)\right)\leq a\) holds, then \(a\) is an upper bound on the CR of Algorithm \(A\)._  
**Sketch of the Proof.** The proof involves algebraic arguments, which are presented in Appendix B. The intuition behind the lemma is that if for a given instance \(L\), we can allocate the durations of jobs that are accepted by Algorithm \(A\) to jobs in the offline optimal set such that each job in the offline optimal set is allocated at least \(1/a\) of its duration, then \(OPT(L)/v_{A}(L)\leq a\). Furthermore, if we can do the same for every instance \(L\in\mathcal{L}(\Delta)\), then \(r_{A}\leq a\).  
Intuitively, Lemma 1 uses the concept of allocation scheme to check whether the algorithm under consideration accepts jobs with enough duration vis-a-vis the offline optimal set for all possible instances. Hence, if one comes up with appropriate allocation schemes, Lemma 1 implies an upper bound for the algorithm. We use this idea multiple times for proving the various results in the paper.
# Flying Watchdog-Based Guard Patrol with Check Point Data Verification
## 3 Proposed Flying Watchdog Architecture
### PGP Detection Model
The edge detector is comprised of a multi-stage process that generates a binary edge map, where white pixels denote detected edges, as evidenced in encrypted LED pattern detection results. This comprehensive approach, encompassing Gaussian smoothing, gradient calculation, non-maximum suppression, and hysteresis thresholding, emerges as a robust and productive technique for precise edge detection within digital images. Following the successful edge detection phase, our contouring algorithm takes the helm, systematically identifying connected components of edge pixels. This process assembles neighboring pixels that collectively delineate a common object boundary, giving rise to distinct groups. These groups, or contours, are subsequently represented as sequences of (x, y) coordinates, effectively outlining the boundaries of objects inherent in the binary image. Contours are invaluable assets in our endeavor to discern the LED pattern. They encompass a wealth of information, including metrics such as area and centroid, which can be calculated mathematically via contour analysis. This holistic approach, underpinned by the fusion of thresholding and contouring techniques, constitutes the bedrock for the efficacious detection and analysis of encrypted LED patterns within images, employing the versatile OpenCV framework.