esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Prediction of MMAD Values Change  
In order to fit a good model, the least-squares technique was used to calculate the coefficient values of the expressed rate constant at various combustion temperatures and orders. Figure 6a,b shows the _MMAD_ values' 3D surface plots by the first- and second-degree models or Equations (3) and (4). The \(x\)-axis and \(y\)-axis present the para rubber wood moisture content and the burning time period, respectively, while the \(z\)-axis shows the _MMAD_ values. These results show that the moisture content of para rubber wood affected the _MMAD_ values when compared with the burning time. Table 2 presents the \(p\) coefficient values calculated by the least-squares method. As seen in Figure 6a,b, at the combustion period, the response of both models presents a decreased trend of _MMAD_ values as the combustion time increases, although only the clear response of the second-degree model presented the effect of moisture content on decreasing the _MMAD_ value as the combustion time progressed, relating to the results. Comparing the _MMAD_ data between the experimental results and prediction values, the second-degree model had a better fit in predicting than the first-degree model; this result is confirmed by the higher mean of the coefficient of determination (Figure 7a,b), respectively. Although the second-degree model had a better fit in prediction than the first-degree model, the mean of the coefficient of determination, or \(R^{2}\), was 0.8267, which is a rather low value. It is probable that in the future, high (third, fourth, fifth, sixth) polynomial degree models will be a very interesting research method to study.  
Figure 5: _TPMC_ emission from the para rubber wood combustion for different wood moisture contents.
## 3 Results
### Temporal and Spatial Dynamics of NEP/WUE/CUE
#### 3.2.2 NEP/WUE/CUE Spatial Patterns  
The total annual NEP during the study period was about 0.076 PgC (1 Pg = 10\({}^{15}\) g), with an overall pattern of being high in the southeast and low in the northwest (Figure 7a). The vast majority of the area is a carbon sink, accounting for 96.51% of the MRYR. The distribution of 100-200 gCm\({}^{-2}\) is the largest, accounting for about 24.32%, followed by 200-300 gCm\({}^{-2}\), accounting for about 22.37%. The carbon sources are distributed in and around the Maowusu Sand. This may be related to the arid climate in these areas. Moreover, the vicinity of rivers and lakes are also carbon sources. The variations in the trends of the vegetation's NEP, WUE, and CUE in the MRYR are shown in Figure 8. The vegetation's NEP in the MRYR ranges from \(-\)16.09 to 20.62 gCm\({}^{-2}\)a\({}^{-1}\), with an overall significantly increasing trend (Figure 8a,d), and a decreasing trend is only observed for the croplands sporadically distributed near rivers and lakes (Figure 8a). The increase in the NEP is not significant in some of the areas in the eastern and southern parts of the MRYR (Figure 8d), even though these areas are covered with more vegetation throughout the year.  
Within the study area, 29.32% of the area had a WUE lower than 0.4 gCm\({}^{-2}\)mm\({}^{-1}\), 56.63% of the area had a WUE in the range of 0.4-0.8 gCm\({}^{-2}\)mm\({}^{-1}\), and 14.05% of the area had a WUE greater than 0.8 gCm\({}^{-2}\)mm\({}^{-1}\) (Figure 7b). The high WUE values were distributed along the border of Gansu and Shaanxi and the southern part of the MRYR, and the low values were mainly found in the arid northwestern part of the MRYR. The uneven variations in environmental factors and the spatial heterogeneity of the vegetation types led to the large differences in the WUE trends across the region. The trend of WUE  
Figure 6: Interannual variation in NEP, WUE, and CUE in MRYR and under different land types, 2001–2022. (**a–e**) Represent MRYR grasslands, croplands, shrubs, and forests, respectively.  
Figure 7: Spatial distribution of mean NEP (**a**), WUE (**b**), and CUE (**c**), 2001–2022.  
change in the MRYR was between \(-0.41\) and \(0.43\) gC m\({}^{-2}\) mm\({}^{-1}\)a\({}^{-1}\) (Figure 8b), with an increasing trend for about 78.75% of the area. Of these, the areas with significant and highly significant increases accounted for 7.18% and 44.73% of the total (Figure 8e), mainly the grasslands in the arid northwestern region and some of croplands within Shanxi.  
The areas with a CUE greater than 0.5 accounted for only 8.71% of the MRYR and were distributed in the eastern part of Gansu; the areas with 0.3-0.5 accounted for 50.60%; and the areas with less than 0.3 accounted for 40.70%, and were mainly distributed among the croplands in the east-central part of the MRYR and the grasslands in the northern part of the study area (Figure 7c). Nearly 90% of the areas showed a CUE with an increasing trend (Figure 8c), which was significant in the grasslands and for most of the croplands (Figure 8f).
## 2 Dielectric Resonator Antenna
The CP DRA has gained much attention due to the combined features of both circular polarization and a dielectric resonator. The advantages and disadvantages of DRAs are summarized in \[f_{0}=\frac{c}{2\pi\sqrt{\varepsilon_{r}}}\sqrt{k_{x}^{2}+k_{y}^{2}+k_{z}^{3}} \tag{1}\]1  
* DRAs are problematic for design with a specific frequency compared to microstrip antennas. It is difficult to form and modify a complicated-shaped DR in order to compensate for manufacturing tolerances or fabrication errors. * Due to the proximity of resonant frequencies of various modes, the resonant frequencies and the field patterns are not only for the desired modes of operation but also for other undesired modes. * DRAs require precise alignment and assembly of the DR because any marginal misalignment between the DR and the feeding network will affect the antenna's performance due to the shorter wavelengths and the smaller antenna size. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Advantages** & **Disadvantages** \\ \hline \hline  • DRAs offer a high degree of flexibility over a wide range of frequency bands, thus making it practical for many applications and requirements.  • DRAs have a small dissipation loss as they feature a high dielectric constant with no conducting parts. Therefore, DRAs can handle high power.  • DRAs consist of ceramic material. Ceramics offer an excellent stability of temperatures. Therefore, they enable the DRA to operate in a wide temperature range.  • DRAs have simple geometries such as rectangular, cylindrical, and hemispherical, which are readily available on the market and can be easily fabricated.  • Various modes can be excited within the DRA element depending on the shape of the resonator. Various modes will generate different radiation patterns for different coverage requirements.  • The excitation method of DRA is simple and easy to integrate with current technologies.  • DRAs have a much wider impedance bandwidth compared to microstrip antennas because the DRA radiates over the entire antenna surface except for the grounded part.  • The operating bandwidth and radiation characteristics of DRAs can be varied by choosing a suitable dielectric constant and the dimensions of the resonator.  • DRAs can be used at microwave frequencies and higher such as terahertz (THz). \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Advantages and disadvantages of DRAs. In early years, DRAs were typically constructed of ceramic materials characterized by a high permittivity and high Q factor (between 20 and 2000). However, the fabrication of ceramic DRAs is quite challenging because the ceramic material is hard and difficult to machine [33]. A new approach is to use polymer-based materials such as plastic poly vinyl chloride (PVC), which make the fabrication process easier due to the natural softness, and results in a wide impedance bandwidth due to the very low permittivity of polymers [39]. One of the main advantages of DRAs is that various feeding techniques can be used to excite the radiating modes of a dielectric resonator.
# Source Anonymity against Global Adversary in WSNs Using Dummy Packet Injections: A Survey
## 3 Dummy Packets Techniques
### Efficient Privacy Preservation (TESP)  
The _TESP_[48] technique uses cluster heads to filter fake packets that are being transmitted to the sink. _TESP_ consists of three phases: In the first phase, the sink provides public and private keys to each node in the network. The keying mechanism contributed by the sink is based on elliptic curve cryptography, which is preferred over the traditional asymmetric key algorithms. In the second phase, nodes are deployed, organized, and assigned to clusters. All clusters have a cluster head that connects to all other cluster heads as well as to the sink in a tree structure manner. The sink is placed at the root of the tree. Neighboring nodes have a symmetric key between them to communicate securely. In the final phase, each sensor node checks its buffer for real packets. If there is no real packet, the node generates a fake packet encrypted by the public key of the cluster head. However, if the node has a real packet in its buffer, this packet is encrypted by the sink's public key. When a packet reaches the cluster head, if the cluster head is able to decrypt the packet successfully, that means it is a fake packet. Therefore, the packet is dropped immediately. Each cluster head collects all packets from its cluster members. Then, the cluster head waits for a higher cluster head or the sink to ignite the data collection signal. Once the cluster head receives the signal, it sends the collected data after re-encryption to the higher cluster head or the sink. In the case a higher cluster head received data from a lower cluster head, it adds its collected data to the incoming data. This process is repeated until all collected data reaches the sink.
# A Real-Time Safety Helmet Wearing Detection Approach Based on CSYOLOv3
## 4 Experiment
### Experimental Results and Analysis  
#### 4.3.1 Comparison of Results  
In this paper, the novel CSYOLOv3 algorithm achieved high performance in SHW detection of actual construction site compared with the original YOLOv3 model, which can be seen from Table 1. Moreover, the comparison with tiny-YOLOv3 was made in order to ensure the reliability of the proposed algorithm. For the SHW detection, YOLOv3 only obtains the mAP value of 42.5% while the CSYOLOv3 in this paper achieves the mAP value of 67.05%, and tiny-YOLOv3 reaches the mAP value of 38%. In terms of detection speed, the frame rate of the CSYOLOv3 has reached 25, while the original YOLOv3 has reached 19. Tiny-YOLOv3 is faster than the other two models with fps 27. Therefore, in a comprehensive view, the performance of CSYOLOv3 is most suitable for practical engineering application in aspects of accuracy and speed.  
The training loss curves of the two algorithms are demonstrated in Figure 5(a), of which the horizontal axis and vertical axis represent training time and the loss value of the final training respectively. Figure 5(a) expresses that CSYOLOv3 model's loss value is smaller than that of the original YOLOv3 eventually, among which the loss value of CSYOLOv3 model is 0.713 while that of original YOLOv3 model is above 2.369. Two critical performance indicators are precision and recall respectively, with the curves illustrated in Figure 5(b),c. The horizontal axis in Figure 5(b),c expresses the training time, while the vertical axis represents the value of precision and recall respectively. Similarly, it can be seen that the two indicators of CSYOLOv3 outperform that of YOLOv3, with precision 85.41% for CSYOLOv3 and 76.78% for YOLOv3, and recall 67.3% for CSYOLOv3 and 45.58% for YOLOv3. The mAP curves of the two model is displayed in Figure 5(d). Figure 5(d) displays that the highest mAP value of the CSYOLOv3 model is 67.05%, while the highest mAP value of original YOLOv3 model is 42.5%, demonstrating that the improved algorithm has better detection performance than YOLOv3.
## 3 Plastics Used for Food Packaging
### Selected Plant-derived Polymers
#### 3.3.3 Polysaccharides  
Packaging films based on carbohydrate sources are generally transparent and homogeneous films with effective oxygen barriers at intermediate to low humidity and good mechanical properties. According to their hydrophilic character, they have poor water vapor barrier qualities, and they are relatively sensitive to moisture. Furthermore, the films obtained from several polysaccharides are brittle usually due to interactions between the polymer chains. This leads to limited applications of polysaccharide- and protein-based coatings and films [184, 2, 106]. To meet the required properties, a pretreatment such as plasticization with small molecular weight-compatible constituents, blending, or chemical modification is needed [130]. While considering mechanical properties, the tensile strength \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Permeability**} & \multicolumn{3}{c}{**Mechanical Properties**} \\ \cline{2-7}
**Film Composition** & **O2** & **CO2** & **H2O Vapor** & **Tensile Strength** & **Elongation at Break** & **References** \\ \cline{3-7}  & & & [g.mm/(m2-d.atm)] * & **[MPa]** & **[\%]** & \\ \hline Soy protein isolate & & & & 1710 & & \\ film with glycerol & NR & NR & (23 °C, 50\% RH) & 6.97 & 113.94 & [180] \\ (casting method) & & & & & \\ Whey protein & & & & & & \\ isolate film & 92.448 g/(m2-d) & NR & (25 °C, 75\% RH) & 3 & 13 & [181] \\ with glycerol & (25 °C, 90\% RH) & & & & & \\ Pumpkin oil cake & 16.06 cm3/(m2-d.atm) & 21.15 cm3/(m2-d.atm) * & NR & 0.86–6.56 & 22.2–196.61 & [165] \\  & (23 °C; unknown RH) & (23 °C; unknown RH) & & & & \\ Fish gelatin film & NR & NR & (24 °C, 50\% RH) & 9.08 & 44.93 & [182] \\  & & & & & & \\ Beef skin gelatin & 0.8–4.7 \(\times\) 10\({}^{-4}\) & & & & & \\ films with corn & cm3-mm/(m2-d.atm) & NR & (23 °C, 50\% RH) & 1.43–5.37 & 1.68–2.60 & [183] \\ oil (extrusion) & (23 °C, 50\% RH) & & & & & \\ \hline \hline \end{tabular}  
* In some data, the units were normalized. NR—Data not reported.  
\end{table} 
Table 3: Barrier and mechanical properties of protein-based polymers. of polysaccharide-based films are similar to those of synthetic polymers; differences are observed in elongation at break (Table 4) [82]. For several polysaccharides, the film-forming properties and especially their potential for edible packaging has been studied, including starch, cellulose, and its derivatives, alginate and chitosan (Figure 5) [161; 185; 186; 187].
## 3 Methodology
### Outlier and Extraneous Point Removal  
Height-based filtering serves as an effective means for removing redundant points and performing coarse target extraction; however, it is insufficient for eliminating outliers or extraneous points. Such points not only increase computational burden but more critically, they compromise the accuracy of normal vector and curvature computations. Figure 6 illustrates the impact of stray light caused by multiple reflections and diffuse reflection on the point cloud. As shown in Figure 6a, stray light introduces numerous outliers along the edges of the magnetic tile's surface (highlighted by the red dashed region). If these outliers are included in normal vector and curvature estimation, they cause significant deviations, leading to the misclassification of standard edge points as defects. Figure 6b presents the effective point cloud after filtering, while Figure 6c shows the removed outliers and noise. Figure 6d depicts a raw point cloud of a circuit board before stray-light suppression, where stray light distorts the board's overall shape and introduces substantial noise and outliers (marked by the red dashed region). In contrast, Figure 6e shows the corresponding point cloud after stray-light suppression, revealing a significant reduction in noise and outliers and a clearer representation of key features. The laser scanner employed in this study not only integrated stray-light suppression but also incorporated multiple correction functions, including position, height, and deformation correction, ensuring high-quality data acquisition.  
After evaluating various filtering techniques, we adopted the method proposed by [PERSON] et al. [69]. This approach leverages a KD-tree to identify the \(k\) nearest neighbors of a given point \(p\), computes the mean distance \(\mu\) and standard deviation \(\sigma\) of these neighbors, and removes points outside the range \(\mu\pm\alpha\sigma\), where \(\alpha\) is chosen based on the density of the point cloud. Furthermore, downsampling also contributes to the removal of outliers  
Figure 5: Height-based target extraction: (a) original copper-plated PCB; (b) raw point cloud of the chip; (c) copper-plated circuit after substrate removal.  
Figure 6: Impact of stray light on the point cloud: (a) magnetic tile with noise and outliers; (b) magnetic tile after noise and outlier removal; (c) filtered noise and outliers; (d) point cloud before stray-light suppression; (e) point cloud after stray-light suppression; red dashed lines: noise and outliers.  
and extraneous points. Related techniques can be found in the works of [PERSON] et al. [68], [PERSON] et al. [70], [PERSON] et al. [71], and [PERSON] et al. [72].
###### Abstract  
Background: The global space economy has grown remarkably, witnessing a 10-fold increase in active satellites during the last 15 years. This growth was accompanied by both the increase in geopolitical tensions feeding huge investments (the New Space Race), on the one hand, and the transformation, shifting from a domain historically dominated by government-led programs to one partially energized by commercial players and innovative business models (\"New Space\"), on the other hand. Objective: To assess the space economy's current state and future prospects by considering its economic and social dimensions. Methods: Over 120 scholarly articles and \"grey\" literature positions (e.g., industry reports) were reviewed. The review was structured by a modified Structure-Conduct-Performance framework originally developed by industrial organization (IO) scholars. Findings: Outer space creates extremely harsh conditions for placing and operating objects in orbits, which results in high launching costs, steep reliability standards, capital intensity, and risks that are unmatched in most terrestrial industries. One of the main motivations to venture into this harsh domain was, and still is, the desire to dominate or the fear of being subjugated by others. This \"original sin\", born of geopolitical rivalries, continues to cast a shadow over the space economy, channeling the majority of public space budgets into military-related programs. Moreover, many space technologies have a dual-use feature. Not surprisingly, governments are still the major source of demand, dominating midstream in the space value chain. This triad--harsh physics, great power rivalry, and a state-centric midstream--produces a specificity of the sector. In the recent two decades, new entrants (called \"New Space\") have begun altering market structure, resulting in new conduct patterns focused on pursuits towards serial production, reusability, and lowering costs. Performance outcomes are mixed. While some efficiency gains are unprecedented, some doubts about market power and negative externalities arise. The assessment of the space economy's performance is a challenge, as such, due to the blurred boundary between political objectives (supplying public goods, mitigating negative externalities) and economic optimization. Such trade-offs are becoming even more complicated considering the potential conflict between national and global perspectives. The paper offers a preliminary, descriptive study of the space economy through the lens of the modified S-C-P framework, laying basic foundations for the future, possibly more rigorous research of the increasingly important space economy.  
s +
Footnote †: journal: World  
2025  
6
## 3 System Model
### Routing Phase  
Once the clusters are formed and the TDMA schedule is fixed, the data transmission phase can begin. The sensor nodes periodically collect the data samples \(X=[L_{1},...,L_{N}]\) (networked data) [35], and transmit it during their allocated transmission time to the CH. We assume that the sensed data is highly correlated in space domain. We use distributed wavelet transform (DWT) to sparsify the networked data \(X\) and DWT is applied to the sampled data. DWT [36] is successfully applied to sparsify the network data [35,36] acquired by the sensors deployed in an irregular grid. Once the BS knows the locations of all sensor nodes, DWT basis can be computed. DWT replaces the 2-D set of measurements with a set of transform coefficients that, for piecewise smooth fields, are sparser than the original data:  
\[X=\mathrm{TS}, \tag{29}\]  
where \(\mathrm{S}\in\mathbb{R}^{N}\) is the transform coefficient vector which contains \(K(K\ll N)\) nonzeros, and \(\mathrm{T}\in\mathbb{R}^{N\times N}\) is the DWT basis. After receiving the data from the cluster members, the CHs compress the collected data using CS. The received signal vector at CH can be written as:  
\[Y=\Phi X=\Phi\mathrm{TS}, \tag{30}\]  
where \(\Phi\) is the sampling matrix whose entries are \(i.i.d\) Gaussian with zero mean and unit variance. Subsequently CHs transmit measurements \(Y\) to the BS independently. Finally, the BS decodes the networked data \(X\) from \(Y\) using the basis pursuit solver in Sparselab toolbox of Matlab.  
It is well known that transmission on wireless channels is much more error prone than on wired channels. Physical phenomena like reflection, diffraction, and scattering of waveforms, partially in conjunction with moving nodes or movements in the environment, lead to fast fading and inter-symbol interference. Path loss, attenuation, and the presence of obstacles lead to slow fading. In addition, there are noise and interference from other nodes/systems working in overlapping or neighboring frequency bands. Thus, transmission errors are inherent in wireless communications because of these instability of wireless channels, which is due to many reasons, for example, channel fading, time-frequency coherence, and inter-band interference [37]. To overcome the problem of recovery of transmitted data packet in case of error, we propose to resend the corrupted portion of the packet as illustrated in Figure 4.
## 2 Results and Discussion
### Geosmin and MIB Degradation  
The degradation efficiency towards geosmin and MIB was assessed with the synthetized materials and with the benchmark P25. The removal of the two molecules was extremely fast for all the photocatalysts used with the complete disappearance of the organic molecules in less than 10 min, as shown in Figure 3. In this case, the heterojunction impregnated with Cu\({}_{2}\)O showed a better photoactivity compared to ZC and pristine ZnO, leading to the abatement of geosmin in 7 min and to the degradation of MIB in 5 min.  
In light of the obtained data, the higher photocatalytic efficiency of ZC and ZCC materials compared with pristine ZnO and benchmark P25 can be explained in terms of a better absorption of light in the visible region and increased charge separation and electron stabilization in the f orbitals of Ce\({}^{4+}\). Hence, as shown in previous studies [30; 38], the interface between ZnO and CeO\({}_{2}\) allows for more photoinduced charge carriers to be stabilized in the solid due to the transfer of electrons from the valence band of ZnO to the empty and localized 4f levels of Ce\({}^{4+}\), which is reduced to Ce\({}^{3+}\).  
In addition, the presence of Cu\({}_{2}\)O increases the photon absorption in the visible region due to its band gap, and previous UV-Vis analysis and EPR measurements revealed the presence of residual traces of unreduced Cu(II) dispersed on the semiconductor surface, which could play a role in photodegradation reactions.