esa-sceva/satcom-chunk-collection huggingface dataset:
Mining activity and decommissioned mining districts represent one of the major anthropogenic sources of PTEs in the environment, as the result of the accumulation and dispersion of mining wastes enriched in PTEs. [PERSON] et al. [4] investigated the use of portable X-ray fluorescence (pXRF) as a geochemical technique in multi-elemental screening analysis for the management of contaminated sites. This technique was employed to analyze soils and mining wastes from two different decommissioned mining sites in NE Italy, and its results were compared with those obtained via a traditional destructive approach and analysis, through inductively coupled plasma mass spectrometry (ICP-MS). The authors found that pXRF represents an effective and accurate tool for identifying the location of the most critical sources of contamination, which are characterized by the highest PTE concentrations. The accumulation of PTEs and their potential mobility represent two of the main environmental issues facing coastal environments. The study of [PERSON] et al. [5] is focused on PTE occurrence, sources, and potential environmental risk in surface sediments from the Aviles estuary and the nearby coastal area (north of Spain), which has been highly affected by anthropogenic activities. The authors provided a preliminary scenario of the geochemical status of both coastal and estuarine sediments, identifying the main estuary channel as the most critical area. There, sediments showed notable concentrations of Cd, Hg, Pb and Zn, with subsequent significant ecological risk due to PTE mobility and potential bioaccumulation. Among the PTEs found in aquatic systems, mercury (Hg) is a focus of global concern due to its high toxicity and the bioaccumulation potential of its most toxic form, methylmercury (MeHg), in the aquatic food chain. [PERSON] et al. [6] reported the results of a long-term survey within the framework of the WFD/2000/60/CE, focused on sediments, water and biota in the Marano and Grado Lagoon (northern Adriatic Sea, Italy). The authors found that this environment represents one of the most contaminated areas of the Mediterranean Sea, showing Hg concentrations in sediment which exceeded both the threshold limit for good quality, and that for potential eco-toxicological effects. Although speciation analyses testified to the scarce mobility of Hg from the sediment to the overlying water column, the Hg-contaminated sediments still represent an environmental issue of concern in terms of MeHg production and its bioaccumulation in fish, mostly in the most contaminated sector of the lagoon. This was especially evident in the case of detritivore feeding species, showing a significant correlation between edible parts and the total Hg concentration in sediment. Although submissions for this Special Issue have been closed, current research focused on the impact of PTEs and other contaminants in the environment will continue to address new challenges in order to provide useful management and remediation strategies for contaminated sites. The authors declare no conflict of interest.
## 4 Performance Optimization and Testing
### Coalesced Access  
Global memory, as the largest device memory in DCU, is often used to store large-scale data. To facilitate data management, data layout is necessary. There are typically two data layout modes: Array of Structures (AOS) and Structure of Arrays (SOA). In AOS mode, multiple structures are placed in an array, where each array element represents a structure object. Each structure object contains variables of different data types. In SOA mode, arrays of different data types are placed in a structure. The data layout mode typically affects how threads in the DCU access data. Taking the example of the particle distribution function on the grid, the AOS and SOA data layouts and thread access modes are illustrated in Figures 17 and 18, respectively.  
In Figure 17, the particle distribution function on the grid is stored in the AOS format. It stores all the particle distribution functions for one grid first and then sequentially stores the particle distribution functions for the next grid. When threads simultaneously access the particle distribution function in the 0 direction, the accessed memory addresses are not contiguous. For example, in the D2Q9 model, when thread 1 and thread 2 execute the same memory operation instruction on the distribution functions at grid 1 and grid 2, the address space interval generated is 9 \(\times\) sizeof (double) bytes. Therefore, the AOS memory layout is obviously not suitable for DCU parallelization. In Figure 18, the particle distribution function on the grid is stored in the SOA format. It first stores the distribution functions for all grids in one propagation direction and then sequentially stores the distribution functions for the next direction. When threads simultaneously access the particle distribution function in the 0 direction, the accessed memory addresses are contiguous. During thread execution, due to the threads in a thread bundle accessing contiguous address space, the SOA storage format meets the requirement of coalesced access in DCU, improving the efficiency of the program execution.  
The comparison of runtime for simulating lid-driven cavity flow using the two memory layouts is shown in Figure 19. Through comparative analysis, it is found that the SOA mode provides a performance improvement of over 25% compared to the AOS mode, further enhancing the computational performance of the algorithm.  
Figure 17: AOS data layout and thread access.  
Figure 18: SOA data layout and thread access.
# Detail Guided Multilateral Segmentation Network for Real-Time Semantic Segmentation
## 4 Experimental Results
### Implementation Details  
The experiment is conducted under the PyTorch framework, and the network is trained and tested under a device with GeForce RTX 2080 Ti GPU. Referring to [15; 24], the \"poly\" learning rate is used in the experiments, \(lr=lr_{i}\times\left(1-\frac{epoch}{\max\_epoch}\right)^{power}\), where the power is set to 0.9 and the initial learning rate is set as 0.01. In the training phase, we choose the stochastic gradient descent (SGD) optimizer, where the momentum is set to 0.9 and the weight decay is set to \(1\times 10^{-5}\). Due to the differences between the two datasets, different training parameters are set when using different public datasets to train the model. Specifically, since the image quality of Cityscapes is high and the original image resolution is 1024 \(\times\) 2048, we set the input resizing to 512 \(\times\) 1024, and the training epoch is set to 350. For CamVid with relatively few samples and low image resolution, we do not resize the images and set the training epoch to 250.
# Topological Corner Modes in Graphene-Coated Nanowire Waveguide
## 4 Conclusions  
In summary, we have numerically explored the corner modes of SPPs in 2D arrays constituted of graphene-covered nanowire waveguides. The waveguide arrays are sequenced in a breathing kagome lattice, including the triangle and rhombic lattices. We show the triangle arrays are able to support three corner modes while the rhombic array only supports a single corner mode. The numerical results indicate the corner modes relate to the bulk topology and are determined by the corner shape as well. In a triangle lattice, the structure has three identical corners and can support three degenerate corner modes when the bulk topology is nontrivial. At the same time, the corner modes also depend on the corner shape. In the rhombic waveguide arrays, the nanowire has four corners but the corner modes only exist at one acute-angled corner. Moreover, the edge modes coexist with the corner modes when the bulk topology is nontrivial. Comparing the spectra of the effective mode index and field distributions, we show the corner modes experience a smaller modal wavelength, longer propagation  
Figure 5: The typical mode profiles (\(|\textbf{E}|\)) in rhombic nanowires. (**a**) Edge mode and (**b**) corner mode.  
distance, and smaller mode volume than the edge modes. Our study enriches the study of high-order topologically protected modes and may be utilized to control light flows beyond the diffraction limit.  
Conceptualization, Q.L. and S.K.; software, N.L. and C.D.; formal analysis, N.L.; investigation, S.K.; writing--original draft preparation, N.L.; writing--review and editing, S.K.; visualization, N.L. and C.D. All authors have read and agreed to the published version of the manuscript.  
Fundamental Research Funds for the Central Universities (2662018 JC008), Hubei Provincial Natural Science Foundation of China (2017 CFB684), Program for Distinguished Middle-aged and Young Innovative Research Team in Higher Education of Hubei, China (T201806).  
The authors declare no conflict of interest.
# A Novel Cross-Correlation Algorithm Based on the Differential for Target Detection of Passive Radar
## 3 Result  
### Simulation Data, Simulation Results, and Analysis  
In this part, the transmitted signal is set as Gaussian white noise with length \(N\). It is assumed that there is only the target echo and Gaussian white noise in the echo channel. Two targets are synthesized in the surveillance channel. The detailed parameter values are shown in Table 1.
## 3 Results  
First, the analysis focused on evaluating the willingness to consume. This revealed that the source of the protein turned out to be statistically insignificant (F (1.518) = 2328, \(p\) = ns, \(\eta^{2}\) = 0.002), which makes it impossible to confirm hypothesis H1a. However, labelling the product with the \"high protein\" Nutrition Claim influenced consumption willingness (F (1.518) = 4.531, \(p\) = 0.034, \(\eta^{2}\) = 0.009), which confirms hypothesis H2a. In this case, the interaction turned out to be statistically insignificant (F (1.518) = 0.162, \(p\) = ns, \(\eta^{2}\) = 0.001), which contradicts hypothesis H3a. Second, the analysis concerned evaluation of individual product attributes. When assessing how environmentally friendly the product is, the results of the analysis indicated that both variables: the origin of the protein (F (1.518) = 38.681,  
Figure 1: Experimental stimulus. \(p<0.001\), \(\eta^{2}=0.069\)) and the protein-related Nutrition Claim (F (1.518) = 6.658, \(p=0.01\), \(\eta^{2}=0.012\)), had statistically significant effects on the independent variable, which is in line with Hypotheses H1b and H2b. Generally, plant-based patties were perceived as more environmentally friendly (M = 5.49) than the meat-based ones (M = 4.64). Nonetheless, in this case, the interaction turned out to be statistically insignificant (H3b). The assessment revealed that the source of protein determines the perception of a product as natural (F (1.518) = 15.518, \(p<0.001\), \(\eta^{2}=0.029\)). Plant-based patties were perceived as more natural (M = 5.33) than the meat-based ones (M = 4.81), which confirms hypothesis H1c. The remaining dependencies were statistically insignificant; thus, it should be concluded that Hypotheses H2c and H3c were not confirmed. The overall product quality perception was only influenced by the protein claim (F (1.518) = 7.227, \(p=0.007\), \(\eta^{2}=0.014\)). Overall, product quality assessment revealed that only the claim affected this variable, confirming the H2d Hypothesis. In other cases, the differences were statistically insignificant, which contradicts Hypotheses H1d and H3d. A similar situation as in the case of H1c, H2c and H3c was noted in the case of evaluating whether the product is perceived as healthy. In this situation, however, the protein source effect was stronger (F (1.518) = 25.761, \(p<0.001\), \(\eta^{2}=0.047\)). Plant-based patties were perceived as more healthy (M = 5.49) than the meat-based ones (M = 4.84), which confirms hypothesis H1e. The remaining dependencies were statistically insignificant, which means that Hypotheses H2e and H3e were not confirmed. All results are presented in Table 1.
# High Spatial and Temporal Resolution Bistatic Wind Lidar
## 3 Validation and Characterisation  
Since the development of PTB's high-resolution bistatic lidar started in 2010, several comparative measurements have been conducted in order to characterise this remote sensing device over a range of different operating conditions. Some of these measurements are described in the following sections.
## 2 CRKG in Three Typical Duplex Modes
### Time Division Duplex (TDD) Mode  
TDD is wildly used in the existing Long Term Evolution (LTE)-Advanced and emerging 5G New Radio mobile networks, e.g., in TD-LTE, wireless local area networks, Bluetooth and ZigBee. The uplink and downlink transmissions are in different time slots over the same frequency band in TDD systems. The non-simultaneous sampling time is the most challenging problem that hinders the realization of a CRKG scheme in practical TDD systems.  
Fortunately, as the uplink and downlink transmission operate at the same carrier frequency, their propagation paths are almost the same if the surrounding environment does not change much between the two channel measurements slots [18; 19] and if the transceiver chains for up- and downlink transmission are similar or equal. The channel impulse response (CIR) of a radio wave within the coherence time is considered to be not varying [20; 21]. The coherence time is calculated by dividing the coherence length by the phase velocity of light in a medium. For example, the coherence time is about 5 ms when the system runs at a carrier frequency of 2.4 GHz and the moving speed is 10 m/s. If the sampling time difference is deliberately kept smaller than the channel coherence time, the responses of uplink and downlink channels are highly correlated, which satisfies the key generation requirement. Next, interpolation filters are employed to find the value of a signal at unobserved points that lie in between two samples which are known [22]. The reciprocity of interpolated measurements are improved and the effect of the normalized Doppler frequency on the correlation coefficient is reduced. Therefore, many CRKG applications and prototypes have been reported in TDD systems, e.g., by employing WiFi [23; 24], ZigBee [25], and Bluetooth [26].  
However, it is still a critical open issue to apply CRKG to the TDD scenarios with a very short coherence time. The coherence time is very short in high mobility scenarios, i.e., when terminals move fast or if the multi-path environment changes fast, e.g., moving robots, vehicles, drones, etc. Constrained by bandwidth and switching time, the sampling time difference cannot be infinitely small. When the  
Figure 3: CRKG in three typical duplex modes.  
sampling time difference is significantly greater than the coherence time, the uplink and downlink channel responses have very low similarity. Further investigations are required to address this issue.
# Some Approaches for Light and Color on the Surface of Mars
## 3 Results
### Modeling Light on Mars' Surface from CCT
#### 3.2.2 Comparison with Spectral Irradiances Reported by [PERSON] [7]  
As indicated before, current cameras in rovers working on Mars' surface only provide measurements of spectral irradiances at a few wavelengths within the visible range, which prevents accurate computations of tristimulus values (the first quantitative specification of color) for any object on Mars' surface. From some measurements provided by rovers, [PERSON] [7] compiled a set of 50 spectral irradiances (not given in his paper) and used them to propose five specific Mars illuminants, named M46, M48, M50, M52, and M54 (the last two numbers in these designations indicate the CCTs in Kelvin of these illuminants divided by 100). From Figure 4 in reference [7], we used WebPlotDigitizer v4.2 [26] to obtain numerical values of five Mars illuminants proposed by [PERSON], and computed their corresponding CCTs using the method indicated in [17]. From these five CCTs, we computed sol-light illuminants using our model in Section 3.2.1. The illuminants proposed by [PERSON] and those obtained from our model are shown in Figure 13a. Specifically, Table 2 shows information about the similitude between the five pairs of spectra shown in Figure 13a (i.e., between spectral irradiances at each temperature for [PERSON] and our current model). Figure 13b shows that small differences between total spectral irradiances from the model in Section 3.2.1 and [PERSON] illuminants are similar for the five CCTs and are mainly located in the region below 450 nm, approximately.  
Figure 13: Total spectral irradiances on Mars’ surface from the model in Section 3.2.1 with indicated CCTs (thin lines) and those proposed by [PERSON] [7] (thick lines) (**a**). Differences between total spectral irradiances from the model in Section 3.2.1 and from those proposed by [PERSON] (subtraction: _our model–[PERSON] model_) (**b**).
Because the deep and large foundation pit of the comprehensive transportation hub project has the characteristics of large engineering quantity, heavy labor, high cost, tight construction period, and complex structure, it is difficult to complete the time cost optimization using traditional optimization methods. At present, there is relatively little research on the combination of BIM technology and the GA for optimizing the construction cost of super large deep foundation pits in comprehensive transportation hub projects, taking into account the time value of funds and the rewards and punishments of the construction period. Therefore, this article conducted relevant research. Based on the construction project of a deep and large foundation pit of a comprehensive transportation hub project, a dynamic optimization model for time-cost is established. It utilizes BIM technology to obtain, store, transmit, and update relevant data, and fully utilizes the advantages of genetic algorithm such as rigorous quantitative analysis and accurate calculation. By solving the model, the dynamic optimal solution is found. The research results of this article have solved the dynamic optimization problem of time cost for complex deep and large foundation pits, making the optimization results more in line with reality. The combination of BIM technology and genetic algorithm makes optimization faster and more accurate. The optimization method studied in this article can be generalized and applied, providing a theoretical reference for time-cost optimization of similar projects.
# Femtosecond Laser Processing of Biodegradable Polymers
## 6 Laser-Triggered Molecular Release from Biodegradable Polymers  
Because the degradation rate of biodegradable polymers can be enhanced by laser irradiation, laser-triggered release and laser-controlled-sustained release of drug molecules have been investigated. Laser-based methods, among other methods, have advantages in spatial characteristics and applicability to catheter-based systems using optical fibers. Most studies for controlling molecular release using lasers take advantage of metal nanoparticles as an absorber [62] because many polymers show little absorption of visible and near-infrared light. A limited number of papers reported molecular release without doping materials. [PERSON] et al. studied the release of embedded fluorescence molecules from biodegradable polymers using nanosecond laser pulses at a wavelength of 248 nm [63]. The acceleration of molecular release by the degradation was reported (Figure 7). They found that laser irradiation decreased the crystallinity of the polymer surface, resulting in the acceleration of release. As for studies using femtosecond lasers, [PERSON] et al. reported that femtosecond laser irradiation at 400 nm wavelength accelerates the release of loaded-fluorescent molecules from PLGA, whereas 800 nm laser irradiation does not induce a comparable degree of change in the release of loaded molecules [42]. This may be due to the different degree of change in the degradation rate depending on the laser wavelength, as described in Section 3. [PERSON] et al. reported the burst release of embedded molecules triggered by femtosecond laser pulses [64]. The release of fluorescence molecules encapsulated in biodegradable microcapsules was significantly enhanced by laser irradiation. The rupture of biodegradable microcapsules was also observed after laser irradiation.
## 2 Materials and Methods
### Statistical Analysis  
The data are presented as mean \(\pm\) standard error of mean (SEM). Statistical analysis was performed using the software GraphPad Prism version 10.3.1.509 (GraphPad Software Inc., La Jolla, CA, USA). Unpaired \(t\)-tests were performed to examine statistical differences between groups. The differences were considered significant if \(p<0.05\).  
\begin{table}
\begin{tabular}{l l l} \hline
**Gene** & **Accession Number** & **Primer Sequence** \\ \hline GAPDH & NM\_002046 & Forward primer: ACCCACAGTCCATGCCCATGC \\  & & Reverse primer: TCCACCACCCTGITGTCTG \\ ALP & NM\_000478 & Forward primer: GCTGTAAGGACATGCGCCTAC \\  & & Reverse primer: CCTGGGTTTTCTGACTCTCA \\ Col 1 & NM\_000088 & Forward primer: GATTCCCTGACCTAAAGGGTGC \\  & & Reverse primer: AGCCCTCCTCATTTTGCCAGCA \\ OCN & NM\_199173 & Forward primer: GCCTCTGAAAGCGTGG \\  & & Reverse primer: CTCTCTGAAAACGCATGCGTGTCA \\ Runx 2 & NM\_004348 & Forward primer: CCCCAGGAAACCACCAT \\  & & Reverse primer: GCCTGGGCCCACAATCTC \\ OPN & NM\_000582 & Forward primer: CGAGGTGATGGTGTTTTATG \\  & & Reverse primer: GCACCATTCAAACTCCCCCTTTC \\ BSP & NM\_004967 & Forward primer: GGCA
# An Access Control Model for Preventing Virtual Machine Hopping Attack
## 4 Methods
### PVMH Model Design
#### 4.2.3 Access Matrix Mapping  
In our framework designed with the PVMH model, the access matrix is stored as a binary file in the virtual machine manager, while a backup file is also stored in the privileged virtual machine. Each element of the access matrix is a one-dimensional ordered tuple (SID, OID, R, A, W, Flag) where SID is the subject security ID number, OID is the object security ID number, R is read-only access attribute, A is write-only access attribute, W is read-write access attribute, and Flag is used to indicate whether the rule is valid; \"1\" is valid and \"0\" is invalid. The security ID number is set by the system administrator, and the ID number of the virtual machine in the cloud platform is allocated by the cloud system when the virtual machine starts up. For a virtual machine, both the SID and the OID are equal to its security ID number, that is, SID = OID = ID. In the cloud platform, the ID number is a 13-bit binary number, and R, A, and W are represented by 1-bit binary numbers. When R (or A or W) is set to 1, the subject has the access attribute for the object, and when the value is set to 0, the subject does not have the access attribute for the object. It can be seen that the six-element tuple has a total of 30 binary digits (13 bits for SID and OID, 1 bit for R, A, and W, and 1 bit for Flag), and the default access matrix is sorted by pair (SID, OID) in ascending order, which is very efficient for searching later.