esa-sceva/satcom-chunk-collection huggingface dataset:
# Chemical Diversity of Mediterranean Seagrasses Volatilome
## 4 Discussion
### Major Compounds Released by the Four Studied Species  
For each species, the Shannon index average indicated specific major compounds for each species. For _C. nodosa_, the main compound was isopropyl myristate, already highlighted in BVOC produced by red algae (_Corallina elongata_ and _Polysiphonia denudate_[115; 117]), brown algae (_Ultra punctaria_[117]; _Cystoseira compressa_[118]), and green algae (_Codium tometosum_[119]).  
DMS was the main compound of _P. oceanica_ as shown by [72]. DMS is the most emitted sulfur compound by the oceans to the marine troposphere [33; 35] with annual emissions exceeding 107 t. It is a key component of the ocean sulfur cycle and has a global role in atmosphere-ocean feedback processes [120; 121]. DMS acts as a defense compound against herbivory, released in higher amounts during grazing on macroalgae, which reduces feeding and limits macroalgae consumption [119; 122].  
The two _Zostera_ species mainly produced alkanes: pentadecane for _Z. marina_ and heptadecane for _Z. noltei_. Pentadecane is commonly produced by brown macroalgae [123], while heptadecane has been mainly described in red macroalgae [68] with varying emissions according to the season [68; 123]. These alkanes decrease in response to heat shock [124]. Our results showed that the major compounds produced are FADs, particularly for _Z. marina_. These compounds could be plant membrane degradation products, especially as the analytical method used here required heating to 50 \({}^{\circ}\)C. This method is similar to those in studies on benthic organisms (e.g., macroalgae [69; 116; 119]). The optimal SPME temperature range is between 40 \({}^{\circ}\)C and 70 \({}^{\circ}\)C [125; 126], and many studies indicate that heat does not alter BVOC chemical profiles but primarily affects the abundance of highly volatile compounds [126; 127]. Moreover, the method used in the present study and previous ones [69; 72; 73] requires plants to be cut, thus inducing GLV production. GLVs are C6 aldehydes, alcohols, and esters forming a distinctive scent when leaves are damaged [128]. The only two GLVs recorded in this study are 3-hexenol in the two _Zostera_ species and hexanali in _Z. noltei_ and _C. nodosa_[129], suggesting that _Zostera_ spp. and _C. nodosa_ produce defense compounds or at least a reaction to herbivory. For _P. oceanica_, no GLV was identified in this study.
# On the Integration of VLBI Observations to GENESIS into Global VGOS Operations
## 3 Materials and Methods
### Simulations  
We use the Vienna VLBI and Satellite Software v3.3 (VieVS; [29]) to perform Monte Carlo simulations. Each 24 h schedule is simulated 1000 times. The observed minus computed observation delay is composed of four parts: tropospheric turbulence, clock error, measurement noise, and errors in the orbit determination of the GENESIS satellite. The delays in the troposphere include temporal and spatial correlation. A refractive index structure constant of \(C_{n}=1.8\times 10^{-7}\text{m}^{-1/3}\) and a scale height of 2000 m are assumed at all stations, along with an eastward wind velocity of 8 m/s [30]. Clock drifts are characterized by random walk and integrated random walk processes, with an Allan Standard Deviation of \(1\times 10^{-14}\) s after 50 min. Measurement noise is simulated as white noise with a standard deviation of 10 ps (\(\approx\)1.2 mm) for both geodetic sources and GENESIS [16; 28]. We assume that the orbit determined through satellite techniques contains errors in the satellite positions at the time of scans. For the simulation of orbit errors, we added a module to VieVS which is described in Section Orbit Errors. The simulated effects are put together to form the reduced observations  
\[\tau_{o}-\tau_{c}=(\tau_{trp,2}+\tau_{clk,2})-(\tau_{trp,1}+\tau_{clk,1})+\tau_ {wn}+\tau_{orb}, \tag{1}\]  
where \(\tau_{trp,i}\) is the tropospheric delay at station \(i\), \(\tau_{clk,i}\) is the clock drift at station \(i\), \(\tau_{wn}\) is the measurement noise per baseline observation, and \(\tau_{orb}\) is the delay due to an error in the satellite orbit position. Orbit errors are only simulated for observations of GENESIS.
# WRNFS: Width Residual Neuro Fuzzy System, a Fast-Learning Algorithm with High Interpretability
## 3 Results and Discussion
### Dataset 3: Electroencephalogram  
In this case, training data are publicly available in the Irving Machine Learning Library (UCI) of the University of California and includes 13-dimensional EEG data. EEG (Electroencephalogram) is a 12-input and 1-output brain wave data, instead of using biomedical formula to calculate, we use WRNFS to learn the characteristic of the evaluating system.  
We used the third data set to show the importance of using small subsystems and the sustained effect of WRNFS. Taking the first 12-dimensional input data as input and the 13 th-dimensional stability as output, the regression model of 12-dimensional input and 1-dimensional output is obtained. A single ANFIS system can no longer solve this problem, so it must carry out dimension reduction in advance or use methods like WRNFS that is capable of solving the data sets in medium scale. According to the different input dimensions of low-dimensional systems, WRNFS with two inputs, three inputs, four inputs, and six inputs are established, respectively. The training results of each system are compared and shown in Table 3. From Table 3, when the input dimension of the low-dimensional system is reduced, the overall performance of the system is greatly improved, although the accuracy is slightly decreased.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**WRNFS**} \\ \hline System & \multicolumn{2}{c}{Total Input Numbers: 6} & \multicolumn{2}{c}{Membership Functions: 3} \\ \hline Low-D-ANFIS & \multicolumn{2}{c}{2} & \multicolumn{2}{c}{3} \\ Input Numbers & \multicolumn{2}{c}{3} & \multicolumn{2}{c}{2} \\ \hline Number of & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} \\ Low-D-ANFIS & \multicolumn{2}{c}{3} & \multicolumn{2}{c}{2} \\ \hline Group & Train & Test & Train & Test \\ \hline RMSE & 0.5227 & 2.0980 & 0.2683 & 167.1314 \\ \hline MRPE \({}^{\text{V}}\) & 7.47 & 20.95 & 3.78 & 196.45 \\ \hline Parameter & \multicolumn{2}{c}{135} & \multicolumn{2}{c}{270} \\ Numbers & \multicolumn{2}{c}{27} & \multicolumn{2}{c}{54} \\ \hline \hline \end{tabular} \({}^{\text{V}}\): MRPE: Mean Relative Percentage Error.  
\end{table}
Table 2: WRNFS in the 6-input system(3 MF).  
In Figure 7, the error figure of 12-input-WRNFS below shows that through the transmission of residual between low dimension systems, the error gradually decreases and seldomly becomes larger, which indicates the robustness and the sustained effect of the designed WRNFS.
###### Abstract
A common position embedding is Relative position encoding (RPE) [24], which is independent of the input features and can only be applied when the input size is fixed. Instead, to make it more effective and flexible, we design a dynamic contextual positional encoding module (DCPE) in which the encoding will be changed with the input feature maps. The DCPE takes two relative coordinates  
Figure 1: Different window partitions used in Swin-Transformer and our proposed LGViT. (**a**) For non-overlapping windows used in Swin-Transformer, the self-attention module is performed within each local limited window. (**b**) For overlapping windows used in LGViT, the feature map will be padded if needed and the self-attention module is conducted within each locally enhanced window. and the current query as input to compute the corresponding position encodings. The DCPE module can be integrated into the transformer blocks with an ignorable cost while making the position encodings applicable to arbitrary input sizes instead of fixed input sizes in RPE. We shall emphasize that our proposed LGViT is a universal architecture and can be applied to various visual tasks. For simplicity, we conduct experiments on three visual tasks (i.e., image classification, object detection, and semantic segmentation) on three benchmark datasets and achieve very promising results. In short, the main contributions of our model are listed as follows:  
* We propose a novel Local and Global Vision Transformer (LGViT) with a well-designed window-based self-attention mechanism. It contains a local self-attention module (LSA) based on overlapping windows to promote local interactions and a global self-attention module (GSA) with multi-scaled dilated pooling to obtain global contextual information. * We also design a dynamic contextual positional encoding module (DCPE) to make the relative position embedding more flexible and effective, i.e., applying to variable input size and changing with the input queries. * Extensive experiments strongly demonstrate that our proposed LGViT achieves outperformance on various visual tasks to state-of-the-art approaches.
# Indoor Localization Using Semi-Supervised Manifold Alignment with Dimension Expansion
## 5 Conclusions  
We have proposed a new semi-supervised manifold alignment approach with dimension expansion for indoor WLAN localization. Based on the innovative objective function constructed from the augmented physical coordinates and the corresponding RSS measurements, the neighborhood relations of the nearby locations can be well preserved in the corresponding manifolds. In addition, the localization performance has been carefully analyzed by considering different noise perturbations and system parameters, and meanwhile, the objective function corresponding to the multiple types of measurements used for localization has also been deduced. Finally, the testing results demonstrate that the proposed approach is able to achieve the satisfactory localization accuracy especially with limited location calibration load. In future, the application of the proposed approach to a large-scale indoor multi-floor environment forms an interesting topic.  
**Acknowledgments:** The authors wish to thank the reviewers for the careful review and valuable suggestions. This work was supported in part by the Program for Changjiang Scholars and Innovative Research Team in University (IRT1299), National Natural Science Foundation of China (61301126), Special Fund of Chongqing Key Laboratory (CSTC), Fundamental and Frontier Research Project of Chongqing (cstc2015 cyjBX0065) and Postgraduate Scientific Research and Innovation Project of Chongqing (CYS16157).  
**Author Contributions:** [PERSON] and [PERSON] conceived of and designed the experiments. [PERSON] performed the experiments and analyzed the data. [PERSON] helped with the data analysis. [PERSON] helped with the paper revision.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Performance Analysis of BDS-5G Combined Precise Point Positioning
## 3 Results and Discussion
### Dynamic PPP  
#### 3.3.1 Quantity  
We performed a static imitation dynamic model solution based on the same experimental setting and data described in Section 3.2. In the dynamic solution process, we considered an STD error of less than 0.3 m for 30 consecutive epochs as convergence.  
Figures 10 and 11 show the results of dynamic PPP for different numbers of 5G base stations involved in localization in Scenes 1 and 2 (from top to bottom, the convergence of XYZ directions and tropospheric zenith wet delay). During the convergence of the dynamic PPP, the convergence rate of each direction and the tropospheric zenith wet delay tended to accelerate as the quantity of base stations increased.  
Figure 10: Convergence for different numbers of base stations in Scene 1 (dynamic precise point positioning [PPP]), (**a**) X direction, (**b**) Y direction, (**c**) Z direction, (**d**) Zenith wet delay.  
The mean convergence time and STD of the error variation for the six initializations in a day are presented in Figure 12. The convergence times for both scenes in the dynamic PPP solution mode showed an overall decreasing trend as the number of base stations increased. In Scene 1, when three base stations were introduced, the convergence time reduced to less than 10 min in all directions. The convergence time decreased by 50.01%, 62.50%, and 55.42% in the XYZ directions, respectively, when five base stations were introduced compared with three base stations. In Scene 2, when three base stations were introduced, the convergence times in the XY directions were reduced to less than 10 min. However, although the convergence speed in the Z direction was 50% faster than that in the case of no base station, it still took 30 min to converge, and we consider that this was due to the poor distribution of 5G base stations in the Z direction. When five base stations were introduced, they were able to converge in all directions within 5 min.  
Figure 11: Convergence for different numbers of base stations in Scene 2 (dynamic precise point positioning [PPP]), (**a**) X direction, (**b**) Y direction, (**c**) Z direction, (**d**) Zenith wet delay.  
Figure 12: Convergence speed (**a**) and standard deviation (STD) (**b**) of the errors for different numbers of base stations under Scene 1 and Scene 2 (dynamic precise point positioning [PPP]).  
The STD of the errors also decreased after convergence. In Scene 1, the STD of the errors decreased by 69.82%, 63.23%, and 51.91% in the XYZ directions, respectively, when five base stations were introduced compared with no base stations. In Scene 2, compared with no base stations, the STD of the errors decreased by 73.13%, 76.88%, and 42.60% in the XYZ directions, respectively, when five base stations were involved in the positioning.  
From this, we can see that in dynamic PPP, the introduction of 5G observations can speed up the convergence time and contribute to the reduction of error STD after convergence. When there are few available satellites, 5G observations can be used as a supplement to obtain better results.
###### Abstract  
The simultaneous removal of nitrogen oxides (NO\({}_{x}\)) and sulfur dioxide (SO\({}_{2}\)) by absorption is considered to be one of the most promising technologies for flue gas treatment, and sulfite is the main component of the absorption solution. To understand the chemical behaviors of the NO\({}_{2}\) absorption in sulfite solution, the absorption time dependences of concentrations of nitrogen and sulfur compositions in both gas phase and liquid phases were investigated by flue gas analyzer, Ion chromatography (IC), Gas chromatography (GC), and Fourier transform infrared spectrometry (FTIR) methods using a bubbling reactor. The mass equilibrium of the N and S compositions were also studied. The results indicate that sulfite concentration plays a vital role in NO\({}_{2}\) absorption. The main absorption products are NO\({}_{3}\)\({}^{-}\) and NO\({}_{2}\)\({}^{-}\), and NO\({}_{2}\)\({}^{-}\) can be converted into NO\({}_{3}\)\({}^{-}\) in the presence of oxygen. Besides, about 4% to 9% by-products of S compositions are formed, and 4% to 11% by-products of N compositions such as NO, N\({}_{2}\), N\({}_{2}\)O\({}_{5}\), N\({}_{2}\)O, and HNO\({}_{3}\) in the gas phase were detected in the emissions from the bubbling reactor. On the basis of N and S compositions, a possible pathway of NO\({}_{2}\) absorption in sulfite solution was proposed.  
absorption; nitrogen oxides; sulfite solution; chemical behaviors; product analysis +
Footnote †: journal: Applied Physics Letters
# Drone Deployment Algorithms for Effective Communication Establishment in Disaster Affected Areas
## 3 Proposed Methods
### Region Filling  
Region filling is the core part of drone deployment; here, we implement a strategy to cover a rectangular region without any voids using circles of different radii. Each circle represents the coverage area of each drone. A drone deployed at a particular height is considered to have a cone-shaped coverage region, as shown in Figure 3. Usually, when a rectangular region is filled using circles, there will be voids in between. However, we use overlapping circles to avoid leaving any regions without network coverage. As this leads to wastage or redundancy of coverage, we aim to minimizing this wastage, and thereby the required number of drones.  
We define \(H_{max}\) as the maximum altitude (in km) where a drone can be positioned such that any mobile device connected to it has sufficient network strength. Furthermore, \(H_{max}\) must be greater than the tallest obstacle within the whole region. While the region  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Description** \\ \hline \(m\) & Length of the region \\ \(n\) & Breadth of the region \\ \(H_{min}\) & Minimum altitude of a drone \\ \(H_{max}\) & Maximum permissible height of a drone \\ \(h_{td}\) & Threshold height for drone deployment \\ \(SH_{x,y,r}\) & Height of the lowest point in a circular region with center at \((x,y)\) and \\  & radius \(r\) (\(SH\) is a sub Height Matrix, a subset of \(H\)) \\ \(B\) & Initial energy of a drone \\ \(W\) & Ratio of horizontal lift to drag ratio to vertical lift to drag ratio \\ \(C\) & Energy dissipation rate per kilometer \\ \(\alpha\) & Coverage angle of network drone in degrees \\ \hline \hline \end{tabular}
\end{table}
Table 1: The various parameters of a drone.  
Figure 3: Front view and top view of network coverage of a drone deployed at a particular height.  
filling algorithm performs filling coverage in two dimensions, it incorporates terrain data at various points for the practical deployment of drones.
## 3 Results and Discussion
### Physical Properties of Mandarin Juice Powders
#### 3.1.4 Flowability and Cohesiveness of Powders  
The [PERSON]'s index is related to the powder flowability, while the Hausner's ratio indicates the cohesiveness of the spray-dried powders. From Table 2, the [PERSON]'s index showed a relatively bigger range among all of the spray-dried powders (from 17.14 to 22.05%), while the [PERSON]'s ratio for all samples only varied slightly from each other (from1.21 to 1.28). No significant differences were observed for these two indices for the samples produced from the flavedo-removed juices (i.e., A2 vs. A3) and the whole fruit juices (i.e., RS2 vs. RS3). The flowability of microcapsules is highly related to the particle  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **A1 Powder** & **A2 Powder** & **A3 Powder** & **RS1 Powder** & **RS2 Powder** & **RS3 Powder** \\ \hline
**Water Activity (Aw)** & 0.25 \(\pm\) 0.00 a & 0.17 \(\pm\) 0.01 b & 0.20 \(\pm\) 0.01 a & 0.17 \(\pm\) 0.01 b & 0.20 \(\pm\) 0.003 a & 0.14 \(\pm\) 0.01 c \\
**Bulk density**, \(\rho_{\mathrm{bulk}}\) (**g/mL)** & 0.56 \(\pm\) 0.01 c & 0.61 \(\pm\) 0.01 a & 0.60 \(\pm\) 0.01 a & 0.57 \(\pm\) 0.01 c & 0.58 \(\pm\) 0.01 b & 0.60 \(\pm\) 0.01 ab \\
**Tapped density**, \(\rho_{\mathrm{up}}\) (**g/mL)** & 0.71 \(\pm\) 0.00 a & 0.75 \(\pm\) 0.02 ab & 0.73 \(\pm\) 0.02 b & 0.69 \(\pm\) 0.00 d & 0.72 \(\pm\) 0.02 c & 0.76 \(\pm\) 0.02 a \\
**Carr’s index (\%)** & 21.48 \(\pm\) 0.02 a & 18.37 \(\pm\) 0.33 b & 19.00 \(\pm\) 0.31 c & 17.14 \(\pm\) 0.00 c & 21.02 \(\pm\) 1.41 b & 22.05 \(\pm\) 1.38 a \\
**Hausner’s ratio** & 1.27 \(\pm\) 0.02 a & 1.23 \(\pm\) 0.01 b & 1.22 \(\pm\) 0.01 b & 1.21 \(\pm\) 0.00 b & 1.27 \(\pm\) 0.02 a & 1.28 \(\pm\) 0.02 a \\
**Solubility (\%)** & 74.19 \(\pm\) 1.13 a & 74.07 \(\pm\) 1.62 a & 73.82 \(\pm\) 1.02 a & 75.30 \(\pm\) 1.56 a & 74.82 \(\pm\) 1.40 a & 73.84 \(\pm\) 1.28 a \\
**Hygroscopicity** & 17.45 \(\pm\) 0.14 c & 17.71 \(\pm\) 0.21 bc & 18.20 \(\pm\) 0.19 ab & 18.02 \(\pm\) 0.40 bc & 17.53 \(\pm\) 0.48 abc & 18.53 \(\pm\) 0.46 a \\
**(g moisture/100 g solids)** & & & & & & & \\ \hline \hline \end{tabular}  
* A1—peel-removed mandarin fruit from Afourer cultivar; A2—flavedo-removed mandarin fruit from Afourer cultivar; A3—whole Afouer mandarin fruit; RS1—peel-removed mandarin fruit from [PERSON] cultivar; RS2—flavedo-removed mandarin fruit from Richard Special cultivar; RS3—Whole [PERSON] mandarin fruit; Values are presented as mean \(\pm\) standard deviation (\(n=3\)). Different letters (a–d) in the same row indicate significant statistical difference (\(p<0.05\)) as analysed by [PERSON]’s test.  
\end{table}
Table 2: Properties of spray-dried mandarin juice powders.  
size. Smaller particle size represents s higher surface area to mass ratio, and it is often responsible for good flowability in powder samples [33]. The flowability and cohesiveness of powders could also be influenced by the moisture content of the samples. A higher moisture content could be associated with a greater force between particles, due to the formation of liquid bridge, thus contributing to a poorer flowability [23].
## 4 Results
### Case Study Application to Singtel Building
#### 4.4.2 Fixture Prediction Accuracy  
Table 11 compares the ML model's projections to both actual fixture needs and baseline building code requirements. The machine learning approach yields minimal error rates (2.63-4.76%), whereas code-based estimates overshoot actual usage by 12.07-19.05%, often due to generous safety margins.  
(The analysis incorporated international benchmarks and standards (detailed comparative analysis available in Appendix B).
# 6G Use Cases and Scenarios: A Comparison Analysis Between ITU and Other Initiatives
## 13 Results and Discussion
### Analysis 2: 6G Use Cases from Other Initiatives vs. ITU 6G Usage Scenarios  
With this analysis, we compare the 6G use cases from other initiatives with the ITU 6G usage scenarios. The results of this analysis indicate how the 6G use cases from initiatives are closer to the ITU 6G usage scenarios. Even in this case, for each ITU 6G usage scenario, we have evaluated the Hamming distance (according to (1)) concerning each row in Figure 11, which corresponds to the 6G use cases proposed by the other initiatives. Results are reported in Appendix B.2 with the associated colors. As shown in Figure 6, the best matches in terms of Hamming distance (i.e., 0.00 value) occur when a fully connected world (HEXA) is compared with ubiquitous connectivity (ITU). Other very good matches are also obtained, for example, if immersive experience (HEXA) and immersive communication (ITU) use cases are compared. In fact, from a technological point of view, these two use cases adopt the same mixed reality (XR) technology to enable human interaction within a virtual environment through 3D images and videos.  
Figure 6 gives a graphic representation (in terms of stacked bars) of the variability of the Hamming distance among the 6G use cases proposed by other initiatives compared with the ITU 6G usage scenarios.  
In Table 12, we extracted the very good matches obtained from the comparison between the ITU 6G usage scenarios and the other initiatives. The fully connected world and ubiquitous connectivity use cases are characterized by the best match (Hamming distance equal to zero). The comparison between the Immersive experience and immersive communication use cases provides a very good match (Hamming distance equal to 0.04), as well as distributed sensing and communications with massive communication, and network evolution with AI and communication. Finally, the comparison between distributed sensing and communications and integrated sensing and communication, as well as network evolution vs. ubiquitous connectivity gives a Hamming distance value equal to 0.08.  
As shown, in several comparisons, the use cases proposed by other initiatives are closer to the ITU 6G usage scenarios.  
Figure 5: Good/very good/acceptable matches between the proposed 6G use cases and the vertical market sectors.