esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 PWC-Based \(m\)CAP-DM with 2D Chaotic Encryption
### PWC-Based mCAP  
The schematic diagrams of the PWC encoding and decoding are illustrated in Figure 2a,b, respectively. Firstly, the quadrature amplitude modulation (QAM) constellation symbols corresponding to two paired subbands in an \(m\)CAP signal, i.e., subband 1 and subband 2, can be expressed by  
\[x_{\text{SB1}}=a_{n}+jb_{n}, \tag{14}\]  
\[x_{\text{SB2}}=c_{n}+jd_{n}, \tag{15}\]  
where \(a_{n}\) and \(b_{n}\) are the I and Q parts of subband 1, and \(c_{n}\) and \(d_{n}\) are the I and Q parts of subband 2, respectively. In the encoding process, an angle rotation is firstly performed to obtain the following rotated constellation symbols:  
\[x_{\text{SB1},\theta}=x_{\text{SB1}}e^{j\theta}=(a_{n}\cos\theta-b_{n}\sin \theta)+j(a_{n}\sin\theta+b_{n}\cos\theta), \tag{16}\]  
\[x_{\text{SB2},\theta}=x_{\text{SB2}}e^{j\theta}=(c_{n}\cos\theta-d_{n}\sin \theta)+j(c_{n}\sin\theta+d_{n}\cos\theta), \tag{17}\]  
where the rotation angle \(\theta\) is usually set to \(\theta=45^{\circ}\)[15]. Then, the I and Q components of each rotated constellation symbols are interleaved. After interleaving, the transmitted signal of each subband can be obtained by  
\[x_{\text{SB1},\text{PWC}}=\text{Re}(x_{\text{SB1},\theta})+j\text{Re}(x_{ \text{SB2},\theta})=(a_{n}\cos\theta-b_{n}\sin\theta)+j(c_{n}\cos\theta-d_{n} \sin\theta), \tag{18}\]  
\[x_{\text{SB2},\text{PWC}}=\text{Im}(x_{\text{SB1},\theta})+j\text{Im}(x_{ \text{SB2},\theta})=(a_{n}\sin\theta+b_{n}\cos\theta)+j(c_{n}\sin\theta+d_{n} \cos\theta), \tag{19}\]where \(\text{Re}(\cdot)\) and \(\text{Im}(\cdot)\) denote the operations to extract the I and Q parts of a complex-valued input, respectively.  
In the decoding process, as shown in Figure 2b, the I and Q parts of the corresponding received signals \(y_{\text{SBI,PWC}}\) and \(y_{\text{SB2,PWC}}\) are firstly separated and then deinterleaving is executed correspondingly. Finally, an angle rotation is further performed to recover the transmitted QAM constellation symbols.
# Detecting and Mitigating Attacks on GPS Devices
###### Abstract  
Modern systems and devices, including unmanned aerial systems (UASs), autonomous vehicles, and other unmanned and autonomous systems, commonly rely on the Global Positioning System (GPS) for positioning, navigation, and timing (PNT). Cellular mobile devices rely on GPS for PNT and location-based services. Many of these systems cannot function correctly without GPS; however, GPS signals are susceptible to a wide variety of signal-related disruptions and cyberattacks. GPS threat detection and mitigation have received significant attention recently. There are many surveys and systematic reviews in the literature related to GPS security; however, many existing reviews only briefly discuss GPS security within a larger discussion of cybersecurity. Other reviews focus on niche topics related to GPS security. There are no existing comprehensive reviews of GPS security issues in the literature. This paper fills that gap by providing a comprehensive treatment of GPS security, with an emphasis on UAS applications. This paper provides an overview of the threats to GPS and the state-of-the-art techniques for attack detection and countermeasures. Detection and mitigation approaches are categorized, and the strengths and weaknesses of existing approaches are identified. This paper also provides a comprehensive overview of the state-of-the-art on alternative positioning and navigation techniques in GPS-disrupted environments, discussing the strengths and weaknesses of existing approaches. Finally, this paper identifies gaps in existing research and future research directions.  
global positioning system; GPS security; GPS jamming; GPS spoofing; GPS-denied; environments +
Footnote †: journal: Sensors
## 3 Materials and Methods
### Numerical Simulations  
Two-dimensional (2-D) elastic wave propagation in concrete was simulated to generate ultrasonic scanning data and evaluate the proposed data processing approach. Two concrete models--pristine (full depth) and delaminated (shallow depth) concrete models--were simulated. A MATLAB-based finite-difference time-domain (FDTD) acoustic wave simulation toolbox \(k\)-wave was used in this study [19]. A laptop computer with a 6-core processor (Intel Core i7-8750H) and 16 GB RAM was used for the FDTD simulations. The total computation time was 23 min for the pristine concrete model and 12 min for the delamination model.  
Figure 2 illustrates the overall structure of the simulated concrete models, and the numerical simulation parameters are summarized in Table 1. The simulated pristine concrete model contains 1024 mm \(\times\) 250 mm concrete between two air layers. The delaminated concrete model has a concrete layer with a reduced thickness (60 mm). In both models, a concrete and two air layers are surrounded by perfectly matched layers (PMLs) [20] to eliminate unwanted boundary reflections, as shown in Figure 2a. An out-of-plane excitation force with an excitation signal, as shown in Figure 2b (three-cycle tone burst centered at 30 kHz), was applied at the top surface of the concrete. The corresponding out-of-plane velocity responses were collected across 900 sensing points with a spacing of 1 mm, as shown in Figure 2a. Note that the generated wave responses across the sensing points represent ultrasonic scanning measurement data across a one-dimensional scanning measurement grid.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Material Properties**} \\ \hline  & P-wave speed (m/s) & S-wave speed (m/s) & Mass density (kg/m\({}^{3}\)) \\ \hline Concrete & 3727 & 2282 & 2400 \\ Air & 343 & 240 \\ \hline  & & \\ \hline Grid spacing (dx and dy) & 1 mm & \\ Time step (dt) & 0.0268 us & \\ Time duration (T) & 1 ms & \\ Number of sensing points & 900 & \\ Sensor spacing & 1 mm & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the numerical simulation parameters for the pristine and delaminated concrete models. An increased mass density was used for the air layer to improve the simulation stability.  
Figure 2: **(a)** Overall structure of the numerical simulation model and **(b)** the excitation signal. The pristine and delaminated concrete cases were modeled as concrete with thicknesses of 250 mm and 60 mm, respectively. In both cases, a concrete and two air layers are surrounded by perfectly matched layers (PMLs) to eliminate boundary reflections.
# Cooperative Crossing Cache Placement in Cache-Enabled Device to Device-Aided Cellular Networks
## 6 Conclusions  
In this paper, we proposed a cooperative crossing cache placement scheme that maximizes the total offloading probability of cache-enabled D2D-aided cellular networks. The proposed scheme establishes device availability, which indicates the possibility of transmitting the desired content within the required sending time. The scheme derives the available transmission probability of a device that would join the cooperation and indicates that some D2D transmitters may not be able to participate in the cooperative transmission to the requesting user, insofar as they are serving other users or remaining silent for some other reason. Based on the caching strategy and the availability of transmission devices, we then proposed three cooperation content offloading strategies while guaranteeing successful content transmission, in which content delivery traffic can be offloaded via the local cache, a D2D link, or else via a cellular link. Finally, we optimized the proposed cache placement scheme by exploiting a DC structure, with an easily implemented low-complexity algorithm through DC programming. Compared to other cache placement schemes, the proposed scheme showed the best performance in terms of the offloading probability.  
In the future work, we will consider cache placement based on incentive mechanisms [37] for the cooperation content offloading, in order to motivate cache-enabled users to transmit content by taking maximal permissible battery consumption into account. With the allowed energy cost, the D2D transmitter can serve the desired requesting user. Moreover, we will characterize the relationship between the energy cost and delay.  
**Author Contributions:** [PERSON] designed and developed the proposed scheme and performed the simulation and analysis. [PERSON] provided critical comments and helped write the paper.  
**Funding:** This work is supported in part by the Beijing Municipal S&T project under Grant Z181100003218003, in part by the National Nature Science Foundation of China Project under Grant 61701042 and 61428102, and in part by the 111 Project of China under the Grant B16006.  
**Acknowledgments:** We thank [PERSON] for helpful conversations during the research that led to this paper, and anonymous reviewers for offering valuable feedback.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Methods of Extraction of Bioactive Substances from Grape and Winery By-Products
### Ultrasound Assisted Extraction (UAE)  
Ultrasound assisted extraction (UAE) is a convenient alternative to conventional (SEL), increasing extraction efficiency in a single step. Ultrasonic irradiation is simple, inexpensive, and efficient, reducing extraction time without high temperatures or long shaking times. Its efficiency is due to sonication, which enhances hydration and fragmentation processes [53].  
The phenomenon of acoustic cavitation answers the basic principle of ultrasound. In the process, when a sound wave meets a liquid medium, longitudinal waves are generated that lead to expansion in the molecules of the medium. In this way, bubbles, negative pressure, and heating are created. To achieve dynamic equilibrium, the ultrasonic intensity must be maintained constant during bubble formation and collapse. After collapse, jets are created due to pressure and high temperature, causing shock waves to be driven towards the solid. In this way the solvent penetrates towards the solid and the mass transfer rate increases [54]. The benefits of UAE include ease of application, reduction of extraction costs through the technique of ultrasonication, and shortened processing times. In addition, the method is more environmentally friendly since it uses small amounts of sustainable solvents, requires less energy, and is carried out at a lower temperature [55].  
[PERSON] et al. (2014) [55], conducted research to optimize ultrasound-assisted extraction (UAE) for maximizing extraction yield, total phenolic content (TPC), and individual phenolic compounds from grape pomace seeds. The study utilized response surface methodology (RSM) to analyze the effects of extraction temperature, time, and the liquid-to-solid ratio. The results indicated that UAE provided higher TPC values and stronger antioxidant activity compared to conventional solvent extraction, achieving a 23.76% increase in extraction yield and 34.54% stronger antioxidant activity. This highlights UAE as a promising technique for extracting phenolic compounds suitable for food and pharmaceutical applications. [PERSON] et al. (2014) focused on optimizing the ultrasonic-assisted extraction of pectins from grape pomace using citric acid. The research employed a Box-Behnken design to determine the optimal conditions, leading to high yields of pectin with a significant molecular weight and degree of esterification. The study found that the use of citric acid in combination with ultrasoundincreased extraction efficiency, resulting in a yield that was 20% higher than those of non-ultrasonic methods [56].
## 4 Results and Discussion
### Validation of Inversion GPP  
Due to the lack of spatially explicit measured data, the regional inversion GPP was validated by comparing the output of three independent models, the Vegetation Photosynthesis Model (VPM) [70], the Boreal Ecosystems Productivity Simulator (BEPS) model [71], and the MODIS MOD17A2 GPP products [72]. The VPM model used two improved vegetation indices (EVI and Land Surface Water Index (LSWI)) and is capable of tracking seasonal dynamics and inter-annual variations in GPP of evergreen needle leaf forest at a sub-monthly temporal resolution. The BEPS model is a daily process model that can estimate NPP (also GPP) for large areas by using remote sensing, gridded daily meteorological data, and soil data. The algorithm of MODIS GPP (MOD17A2) is an application of the radiation conversion efficiency concept that predicts daily GPP, using satellite-derived FPAR (from MOD15) and independent estimates of PAR and other surface meteorological fields.  
The GPP data output from the models have different spatial resolutions. In our study, data were reported with a spatial resolution of 30 m. The model outputs from the VPM have a spatial resolution of 500 m, and the BEPS and MOD17A2 have a spatial resolution of 1000 m. Due to the spatial resolution differences, and the uncertainty of the spatial re-sampling method, data spatial matching was problematic. We chose to contrast their spatial patterns with a combining pixel value frequency distribution analysis. One cloud-free image acquired on DOY 166 in 2008 was selected to validate our result with the three independent models.  
Figure 9: C fluxes spatial patterns in DOY of 166 in 2008 inversed by Hyperion image with narrowband bio-indicator scaled photochemical reflectance index (SPRI)* normalized difference vegetation index (NDVI).
## 5 Conclusions  
This study proposed a physically-based grassland aboveground biomass (AGB) retrieval method which relies on the basic understanding that grassland is herbaceous, and therefore AGB can be represented as the product of leaf dry matter content (Cm) and leaf area index (LAI), i.e., AGB = Cm \(\times\) LAI. The lookup table (LUT) inversion of PROSAIL model against MCD43A4 imagery was used to estimate Cm and LAI and finally converted to AGB. The proposed method was validated in the Zoige Plateau, China. Results show that it could reproduce the reference AGB map, which is generated by upscaling the field measurements, in terms of magnitude (with RMSE and R-RMSE of 60.06 g:m\({}^{-2}\) and 18.1%, respectively) and spatial distribution. The estimated AGB time series also agreed reasonably well with the expected temporal dynamic trends of the grassland in our study area. Our study paved new way to monitor grassland AGB dynamics at regional and larger scales without the support of field measurement.  
Conceptualization, A.L. and G.Y.; Methodology, G.Y.; Writing--original draft preparation, L.H.; Writing--review and editing, G.Y.; Visualization, X.N.; Field measurement implementation, J.B.  
This research was funded by National Natural Science Foundation of China (no. 41631180 and no. 41571373), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA19030303),the National Key Research and Development Program of China (no. 2016 YFA0600103 and 2016 YFC0500201-06), and the 135 Strategic Program of the Institute of Mountain Hazards and Environment, CAS (SDS-135-1708).  
The authors are very thankful for Land Processes Distributed Active Archive Center (LPDAAC) for the free access to MCD43A4 product. We also acknowledge Prof [PERSON]'s team for sharing the PROSAIL model code.  
The authors declare no conflict of interest.
# Clustering Moving Object Trajectories: Integration in CROSS-CPP Analytic Toolbox
## 2 Related Work
### Clustering of Trajectories
#### 2.1.2 Spatial Clustering of Trajectories  
Once the distances among trajectories are computed and stored, different clustering algorithms may be applied. Each of them offers pros and cons, and the best solution is domain-specific.  
This project focuses on geometric properties of the trajectories, defined by characteristics like spatial distribution, proximity or length. The predominant algorithms are those based on density such as DBSCAN [16] or K-means [17], where the proximity is the most important feature. One of the drawbacks of K-means is its tendency to form spherical clusters, which is inadequate for clustering several streamlines as in the present case.  
For clusters with irregular or non-spherical shapes, one of the most efficient algorithms is the spectral clustering [18]; it uses the eigenvalues, also referred to as the spectrum.  
Quickbundles [13], presented before, is a clustering algorithm developed to reduce the dimensionality of brain streamlines datasets provided by tractography. It stands out due to its linear complexity with the number of streams and its interpretability.
# Time-Division Color Holographic Projection in Large Size Using a Digital Micromirror Device +
## 2 Methods
### Pixel Separation Method  
The pixel separation method [11] is a method for preparing multiple images such that the pixels of the original image are separated at certain intervals, and superimposing the reconstructed images by rapidly switching the CGHs of each separated image. Separating the pixels reduces undesired interference between side robes of adjacent reconstructed pixels.  
Figure 2 shows how the pixels of an original image are separated. We group \(n\times m\) pixels and then generate the \(n\times m\) separated images. Figure 2a shows a case where a single group consists of 6 pixels (\(2\times 3\)). Figure 2b shows the separated image of pixels assigned \"1\" in Figure 2a. We generate six pixel-separated images as shown in Figure 2b-g, and calculate the CGHs for each image.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Values** \\ \hline CGH resolution & 1920 \(\times\) 1080 pixels \\ CGH pixel pitch & 7.56 \(\upmu\)m \\ Wavelength of RGB lasers & Red: 632 nm, Green: 520 nm, Blue: 450 nm \\ Projection distance & 3.0 m \\ \hline \end{tabular}
\end{table}
Table 1: Calculation parameters.  
Figure 2: Original and pixel-separated images: (**a**) original image, (**b**) separated image of number 1, (**c**) separated image of number 2, (**d**) separated image of number 3, (**e**) separated image of number 4, (**f**) separated image of number 5, and (**g**) separated image of number 6.
# Effective PSCCH Searching for 5G-NR V2X Sidelink Communications
## 3 Proposed Effective PSCCH Searching
### Correlation Analysis  
An analysis of the correlations performed in the Blind algorithm, line 3, is completed in this Section. If the received signal has a PSCCH present in a subchannel \(n\) with DMRS using an index \(i\), it would be expected to have a stronger correlation peak when \(\text{corr}(\mathbf{s}_{\tau x,v},\mathbf{d}_{PSCCH}^{nj})\) is conducted. We observed it by transmitting a signal with a PSCCH on the subchannel \(n=1\) and DMRS using the index \(i=1\). This test is conducted for a scenario where the signal-to-noise-ratio (SNR) is set to \(-10\,\mathrm{dB}\). The remaining parameters are \(N_{RB}^{SL}=50\), \(N_{subChSize}^{SL}=10\), \(N_{subCh}^{SL}=5\), \(N_{PSCCH}=10\), \(T_{PSCCH}=3\), \(N_{PSSCH}=1\), \(N_{tx}=16\), and \(N_{rx}=16\). The results are shown in Figure 4.  
As we can see in Figure 4a, the peak associated to the right subchannel, 0.267 is much higher than the other peaks associated to other subchannels, 0.103, 0.101, 0.098, and 0.092. Thus, the use of this information can be very effective in determining the right subchannel. Moreover, we can also see in Figure 4b, that the peak associated to the right index \(i\) is higher than the other peaks, and thus we can also use this information in determining the right index \(i\).
## 3 Feature Selection Algorithms
### Gain Ratio, Filter and Wrapper  
Typically, the GR gives priority to the features that have large values, making the GR biased towards the large number of values. The combination of feature 1 and feature 2 has better potential than feature 1 or feature 2 alone, meaning that the combination is better than the constituent features. Split information is mostly used to deviate from the bias [29]. Feature selection can be filter, wrapper or hybrid. Filters select the best subsets of features by ranking all the features, and features with high classification performance are chosen at the detriment of others. Hence, there is no dependency among the subsets of features that are chosen. Filters are computationally fast since there is no interaction among the selected subsets of features. On the other hand, wrappers are computationally expensive since, for each subset of features that is selected, the learning algorithm must be included to determine the performance of the chosen features. In this study, NSGA2 developed by [PERSON] et al. [27] and NSGA3 introduced by [PERSON] et al. [30], along with the GR as the filter evaluation criterion, are used to solve the issue of dependency among selected subsets of features. GR-based filters are now becoming popular in solving various multi-objective optimization problems in feature selection [31].
# Big Data Collection in Large-Scale Wireless Sensor Networks
## 2 Overview of WSNs
### WSN Application Areas
#### 2.8.3 Security Applications  
Some institutions can be monitored in real time by WSNs to prevent possible theft or fire. Another example is the application of sensor networks in intelligent transportation systems for monitoring roads or railways to prevent accidents [50]. In the military domain, these networks are used for surveillance inside and outside the theater of operations and in espionage. In the security field, sensor networks can significantly contribute to the reduction of financial expenses dedicated to the security of locations and human beings. Thus, the integration of sensors in large structures, such as bridges or buildings, could help detect cracks and alterations in the structure following an earthquake or theaging of the structure. Deploying a motion-sensor network can be an alarm system that can detect intrusions into a surveillance zone [41].
## 4 Experimental Results
### Platooning Flight  
Figure 11 presents the outcomes of UAVs maintaining a fixed formation distance. By configuring a route that enables both UAVs to navigate at a constant distance, the follower UAV can track the 60 GHz band communication spot created by the leading UAV. Then, their communication link can be sustained, consequently extending the available communication time. We successfully transmitted data exceeding 10 GB within a 30 s platooning flight duration. Since the relative positions of the two UAVs are invariant, they exhibit constant data transmission characteristics similar to hovering. This flight method can perform stable data transmission while the UAVs navigate to the desired coordinates, and has the advantage of enabling both movement of the aircraft and high-volume data exchange.  
In situations where hovering is impractical because of the necessity of movement, or when using aircraft types incapable of hovering, pass-by communication offers an effective approach for data transmission without interrupting aircraft mobility. Conversely, when the need arises to transmit larger-volume data, the communication link time can be extended through the platooning flights described here. Thus, employing these distinct methods properly according to the situation is beneficial and efficient.