esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Impact Assessment  
The impact assessment was carried out using the Product Environmental Footprint (PEF) standard method [12], integrated within the SimaPro 9.5.0.0 software (PRe Consultants, Amersfoort, The Netherlands). This approach estimated 16 mid-point impact categories utilizing various models:  
- Climate Change (CC): Bern Model and 100-Year Global Warming Potentials [54].  
- Ozone Depletion (OD): EDIP Model and Ozone Depletion Potentials [55].  
- Ionizing Radiation (IR): Human Health Effect Model [56].  
- Photochemical Ozone Formation (PhOF): LOTOS-EUROS Model [57].  
- Particulate Matter (PM): UNEP Model [58].  
- Acidification (A): Accumulated Exceedance Model [59].  
- Freshwater Eutrophication (FWE) and Marine Eutrophication (ME): EUTREND Model [60].  
- Terrestrial Eutrophication (TE): Accumulated Exceedance Model [59].  
- Freshwater Eco-Toxicity (FWET), Non-Cancer Human Toxicity (NC-HT), and Cancer Human Toxicity (C-HT): USEtox Model [61].  
- Land Use (LU): LANCA\({}^{\oplus}\) v 2.2 Baseline Model [62].  
- Water Use (WU): Available Water Remaining (AWARE) Model [63].  
- Resource Use, Fossils (RUF) and Resource Use, Minerals and Metals (RUMM): CML2002 Model [64].  
Given the global nature of supply chains, the European PEF standard method normalized each impact category against global impacts (listed in ref. [65]). These normalizedscores were then weighted according to the scheme developed in ref. [66] and, finally, linearly combined to produce the so-called Product Environmental footprint (PEF).
# Target Tracking Based on a Nonsingular Fast Terminal Sliding Mode Guidance Law by Fixed-Wing UAV
## 4 Simulation Results
### Constant Speed Target  
Let the target move following an \(S\)-shape trajectory in the barrier free environment with the initial heading angle \(\psi_{t}=-45^{\circ}\) and the constant speed \(v_{t}=2\) m/s. The UAV's initial heading angle \(\psi_{a}=70^{\circ}\). After 2000 s simulation time, we get the trajectories which are depicted in Figure 7. Getting the similar results as the stationary target tracking: the NFTSM algorithm has better convergence rate than the TSM algorithm.  
Combined with Figures 7 and 8, we can find that the TSM algorithm has a slow heading adjustment compared with the NFTSM algorithm. The overshoot still exists by NFTSM\({}_{1}\) at the start state, but it is improved after 75 s. Due to \(v_{t}<v_{a}\) and the azimuth angle \(\lambda\) being considered as free variables, the UAV can loiter above the target to keep the predetermined distance.  
The simulation time of this constant speed target tracking is 2000 s. As is shown in Figure 7(a), we can find that all three of the methods will converge to zero in finite-time. However, there is much difference in convergence time: the NFTSM algorithm can make the distance error \(r\) less than 0.0001 m in 38.8 s. NFTSM\({}_{1}\) algorithm can make \(r\) less than 0.0001 m in 81.6 s. However, after 320 s, the TSM algorithm just converges to close to 0.18 s. Therefore, the proposed method is not only applicable for  
\begin{table}
\begin{tabular}{l l l l l} \hline
**Algorithm** & **Case (\({}^{\circ}\))** & **Simulation Time (s)** & **Convergence Time (s)** & **Distance Error \(r\) (m)** \\ \hline NFTSM & \(\psi_{a}=30\) & 80 & 31.2 & less than 0.0001 \\ TSM & \(\psi_{a}=30\) & 80 & 80 & \(-0.742\) \\ NFTSM\({}_{1}\) & \(\psi_{a}=30\) & 80 & 37.3 & less than 0.0001 \\ \hline NFTSM & \(\psi_{a}=130\) & 100 & 72.9 & less than 0.0001 \\ TSM & \(\psi_{a}=130\) & 100 & 100 & no convergence \\ NFTSM\({}_{1}\) & \(\psi_{a}=130\) & 100 & 100 & shock \\ \hline \end{tabular}
\end{table}
Table 3: The convergence time of stationary target tracking.  
Figure 6: The results of tracking a stationary target with \(\psi_{a}=130^{\circ}\).  
the stationary target tracking but is also suitable for the moving target tracking, and the effectiveness is guaranteed.
## 4 Proposed Scheme  
### Overview  
The proposed scheme consists of six steps: system setup, ciphertext generation, trapdoor generation, verification and aggregation, search, and decryption. During the setup phase, the system creates and publishes public parameters. Consequently, the \(dg_{i}\) and \(dr\) must generate their respective pairs of public and private keys, denoted as \((Y_{i},y_{i})\) and \((X,x)\), respectively. To create environmental data ciphertexts (\(E\)), each \(dg_{i}\) encrypts its unique original environmental data using a distinct key during the ciphertext generation stage. \(k\) ciphertexts are employed to encrypt the associated \(k\)s. The file server (FS) is responsible for maintaining these ciphertexts and supplying a search function for the \(dr\). Subsequently, the FS sends pair \((E,k)\) to the \(dr\). Additionally, \(k\) ciphertexts (\(\{E_{5 ij},E_{6 ij}\}_{j\in[1,n]}\)) are transmitted to the blockchain to facilitate easy access to the related files by the authorized \(dr\). To safeguard \(k\) privacy, the \(dr\) first encrypts \(k\) during the trapdoor creation stage to generate a token (\(\tau\)). To ensure its accuracy, the \(dr\) simultaneously creates a \(k\) proof (\(\varphi_{i}\)). They then pass \(\tau,\varphi_{i}\)) to the \(dg_{i}\), which checks whether the token's \(k\) is part of the approved \(k\) set. If the verification is unsuccessful, the token is canceled. Nevertheless, if the verification is successful, each \(dg_{i}\) sends verification result (\(T_{i}\)) back to the \(dr\) and the authorization (\(T_{i},\tau,X\)) to the blockchain's aggregation \(SC\). Subsequently, the request \((X,\tilde{k})\) is sent to the aggregate \(SC\) by the \(dr\). When \((T_{i},\tau,X)\) and \((X,\tilde{k})\) are received, the aggregation \(SC\) produces the searchable and symmetric keys \((P_{agg},S_{agg})\) and retrieves corresponding keys that include the keyword (\(\tilde{k}\)). These keys are then combined to create the symmetric key \((PP_{agg},SS_{agg})\) and a single searchable key. Additionally, the aggregated \(SC\) produces an encoded symmetric \(SP_{agg}\) and a trapdoor (\(T_{i}\)). The true trapdoor (\(T_{i}\)) is subsequently obtained by the decryption routine(\(dr\)) using their private key (\(x\)) after it sends \((T_{0},SP_{agg})\). Lastly, the file server (FS) receives the actual trapdoor (\(T_{i}\)). The FS uses \(T_{i}\) to query \(k\) ciphertexts during the search phase. If the matching \(k\) ciphertexts are discovered, the FS forwards relevant environmental ciphertexts to the \(dr\). The \(dr\) confirms the accuracy of the environmental data ciphertexts (\(E_{i}^{\prime}\)) during the decryption and verification phase. The \(dr\) decrypts \(E_{i}^{\prime}\) and retrieves the original environmental data (\(d_{i}^{\prime}\)) if the verification is successful. Additionally, they must verify the decrypted environmental data (\(d_{i}^{\prime}\)) once more. The \(dr\) obtains the intended original environmental data if this verification is successful. If not, the \(dr\) terminates the procedure for those environmental data. Some frequently used notations in this work are listed in Table 1.
# Quantum PSO-Based Optimization of Secured IRS-Assisted Wireless-Powered IoT Networks
### Related Work  
#### 1.1.1 Wireless-Powered IoT Networks  
For most IoT applications, UDs are characterized by a low energy capacity and are traditionally powered by finite energy sources or batteries, requiring regular maintenance to sustain the operational lifespan. This can be costly, and in some cases impractical, particularly for UDs deployed in harsh environments or inaccessible infrastructure [10]. While recent research has made strides in improving energy efficiency (EE), addressing the energy constraints of UDs remains a significant challenge in IoT networks.  
With the advancement of radio frequency (RF) EH technologies, WPINs have emerged as a promising energy-efficient solution for UDs [11]. In WPINs, UDs are powered by a stable wireless energy source, such as a power station (PS), during the wireless energy transfer (WET) phase. UDs use the energy for information transmission to an AP during the uplink wireless information transmission (WIT) phase [12; 13]. In this setup, the PS in a WPIN serves as an alternative to conventional battery-powered devices, significantly enhancing the energy efficiency of UDs and eliminating the high costs associated with regular maintenance and battery replacement. With regulated energy supplies, WPINs can significantly enhance the EE of UDs, extending their operational lifetime compared to their classic battery-powered counterparts. In the past few years, WPINs have been applied to various scenarios, such as artificial noise (AN)-assisted interference alignment networks combined with WET [14], as well as the use of unmanned aerial vehicles for energy and information transfer [15]. Thus, a generic transmission protocol, harvest-then-transmit, was proposed to optimally schedule downlink and uplink transmission time slots to enhance network throughput [16].
###### Abstract  
The aim of this study was to propose a methodology that provides a detailed description of the argillic zone of a hydrothermal field, based on satellite multispectral data. More specifically, we developed a method based on spectral unmixing where hydroxyl-bearing alteration is represented by a single endmember (representing clays) and the three (nearly) non-altered primary volcanic lithologies, namely, two types of lava flows (basic and acidic compositions) and the loose materials (alluvial/beach deposits, scree, pyroclastic deposits, etc.), are represented by three endmembers. We also used one endmember representing elemental sulfur that is present in fumarolic vents hosted by active hydrothermal craters. The methodology was applied in the south part of Lakki plain inside the Nisyros volcano caldera (Greece), using Sentinel-2, Landsat-8/OLI, and ASTER satellite multispectral datasets. Specifically, it was applied separately to each one of the three datasets. The spectral unmixing results, combined with the relative geological map, provide quantitative estimations of the primary volcanic and loose material areas affected by alteration. In addition, pixels with high abundance values of hydroxyl-bearing alteration corresponded to mapped areas with strong hydrothermal alteration. The developed methodology is superior to conventional approaches (e.g., alteration spectral index) in terms of its ability to describe the overall pattern of the hydrothermal field. The most accurate results were taken when applied to ASTER or Sentinel-2 MSI data.  
hydrothermal alteration; Nisyros volcano; linear spectral unmixing; alteration mapping; ASTER; Landsat-8/OLI; Sentinel-2 MSI Article
## Conclusions: Low Temperature  
After analyzing the tests with a temperature variation between \(22\,^{\circ}\)C and \(9\,^{\circ}\)C with three elements in linear regression and a beacon rate of \(30\) s, it can be concluded that:  
* The inclusion of the temperature (see Figure 4) to obtain the clock skew value improves the performance. As a consequence, the average synchronization error decreases.
* Temperature-based methods improve the average synchronization error between \(104.17\)% and \(118\)% compared to FTSP.
# 3D Face Profilometry Based on Galvanometer Scanner with Infrared Fringe Projection in High Speed
## 2 Principles  
### The Principle of Fringe Projection Profilometry  
A typical FPP system consists of a camera and a projector, as illustrated in Figure 1. The 3D shape can be measured using the principles of triangulation with the matching computation for the correspondence among A, B, and C. The FPP approach is basically to encode the relationship of A, B and C by using the phase-shifting profilometry, which also acquires the highest measurement accuracy with a high resolution since it works in good conditions of illumination and surface reflection [2].  
A sequence of fringes is projected onto an object surface by a projector and then the modulated images are captured by the camera; the modulated fringe images can be represented as  
\[I_{n}(x,y)=I^{\prime}(x,y)+I^{\prime\prime}(x,y)\cos[\phi(x,y)+2\pi n/N], \tag{1}\]  
where \(n=0,1,2,\ldots,N-1,I^{\prime}(x,y)\) is the average intensity, \(N\) is a step of phase shift, \(I^{\prime\prime}(x,y)\) is the intensity modulation, and \(\phi(x,y)\) is the phase function. In Reference [27], the wrapped phase map can be solved by  
Figure 1: The schematic diagram of a typical fringe projection profilometry (FPP) setup, which consists of a camera and a projector. **A**, **B** and **C** are the points of the same phase point on the pattern, the object surface, and the captured image, separately.  
\[\phi(x,y)=\arctan\frac{\sum\limits_{n=0}^{N-1}I_{n}(x,y)\sin(2\pi n/N)}{\sum \limits_{n=0}^{N-1}I_{n}(x,y)\cos(2\pi n/N)}, \tag{2}\]  
Typically, in the phase-shifting process, \(N\) is chosen to be as high as possible to improve the three-dimensional measurement accuracy. For fast 3D measurements, a three-step (i.e., \(N=3\)) phase-shifting algorithm is used for the FPP. The phase provided from Equation (2) is wrapped in the range from \(-\pi\) to \(\pi\), so a phase unwrapping algorithm has to be adapted to obtain a continuous phase map by removing \(2\pi\) discontinuities. Mathematically, the relationship between the unwrapped phase \(\Phi(x,y)\) and wrapped phase \(\phi(x,y)\) can be described as  
\[\Phi(x,y)=\phi(x,y)+2\pi k(x,y) \tag{3}\]  
The key is to determine the variable \(k(x,y)\) of the absolute fringe order. Here, the order \(k(x,y)\) is an integer number which can be calculated pixel-by-pixel from a set of fringe patterns with different frequencies with a certain regularity and such methods are shown in subsequently.
# Stock Price Prediction Using a Frequency Decomposition Based GRU Transformer Neural Network
## 4 Experiment
### Dataset
#### 4.1.2 Csi-300  
We choose 100 stocks from CSI-300 as our dataset. Our dataset includes 21 trading days in April, 2021. There are 3 h and 57 min active trading hours in each trading day. We update the LOB every 3 s. Hence, there are 4740 time steps of LOB records each day.  
In addition, we employ the data normalization to transform the raw data into an appropriate range for deep learning algorithms. The data normalization maps the data into interval \([0,1]\) according to the following formula:  
\[\hat{P}=\frac{P-P_{min}}{P_{max}-P_{min}}, \tag{14}\]  
where the \(P\) is the price before normalization, \(P_{max}\) and \(P_{min}\) are the maximum and minimum price before normalization. \(\hat{P}\) is the price after the normalization.
## 2 Materials and Methods
### Data
#### 2.2.1 Digital Elevation Models
The TanDEM-X DEM [28] is a publicly available 3\({}^{\prime\prime}\) mesh size DEM released by the German Aerospace Center DLR ([[https://download.geoservice.dlr.de](https://download.geoservice.dlr.de)]([https://download.geoservice.dlr.de](https://download.geoservice.dlr.de)), accessed on 18 September 2020). Smaller mesh size versions of the TanDEM-X DEM of \(\approx\)12 m and 30 m (1\({}^{\prime\prime}\)) are not available for the public, but only for scientific research in limited areas. The data were collected by X-band interferometry with the TanDEM-X/TerraSAR-X satellite constellation mission after 2010. Apart from TanDEM-X, where heights are expressed as ellipsoidal heights, other models provide heights above the EGM96 geoid (as an approximation for mean sea level). We refer to them as physical heights without distinguishing between normal or orthometric heights.
# Near Real-Time Freeze Detection over Agricultural Plots Using Sentinel-1 Data
## 4 Results
### Comparison with ERA5-Land Dataset
The freezing detection results using VV were similar to VH for ERA5-Land soil temperatures between 1 and 3 \({}^{\circ}\)C. However, on 12 March 2019 (morning), when both ERA5-Land soil and air temperatures were roughly 1.3 \({}^{\circ}\)C, our algorithm detected 29% of plots being mild-to-moderately frozen using VV against ~7% using VH. Finally, on 31 January 2019, the reported soil temperature by ERA5-Land at Sentinel-1 acquisition time (~6 PM) was ~0 \({}^{\circ}\)C, but our algorithm did not detect any plots as frozen using either VH or VV. However, on this date, the reported ERA5-Land air temperatures from several hours before S1 acquisition, and up-to S1 acquisition time (~6 P.M.), was relatively high (~3 \({}^{\circ}\)C). Therefore, we suspect, given the uncertainties on ERA5-Land soil temperatures and the warm air temperatures, that no freezing occurred on this date and time.
# Reutilization of Recycled CDW Sand in Mortars, Paving Blocks, and Structural Concrete
## 3 Results and Discussion
### Properties of the Developed Mortars
#### 3.2.2. Compressive Strength of the Mortar Samples  
Figures 11a and 11b present the compressive strength and RS of the developed mortars, respectively. Compressive strength progressively decreased as the CSW content increased. The RS remained above 80% with NA substitutions of up to 50 vol.% regardless of curing age, and above 70% for all the mortars cured for 28 days, which indicates a strength loss of less than 30%.  
Figure 10: Properties of the mortars made with the NA and CDW aggregates: (**a**) workability; (**b**) density.  
The results obtained in this study differ from those reported by [PERSON] et al. [16], who prepared mortars using RAs composed of 46% old mortar, 32% ceramics, and 22% basaltic coarse aggregates. While [PERSON] et al. [16] observed a slight increase in the 28-day compressive strength at replacement rates of 25% and 50%, the compressive strength of the reference mix in their study was significantly lower, approximately 2 MPa. In contrast, the results obtained by [PERSON] and [PERSON] [34] were more consistent with those of the present study, showing a progressive decrease in both compressive and flexural strength as the replacement rate of RAs increased. Although they used recycled sand of a different composition (primarily recycled concrete), the compressive strength of the reference mortar (52 MPa) was closer to that of the mortars prepared in this study using CEM II/B-M(Q-L) 42.5R (\(\sim\)55 MPa). A more pronounced decrease in compressive strength was observed by [PERSON] et al. [33], who employed recycled sand predominantly composed of calcite and quartz, with traces of cement compounds. This reduction is partly attributed to a significant decrease in the mix's consistency.
# COSMIC-2 Mission Summary at Three Years in Orbit
## 2 Mission Status and Milestones
### Program Milestones
This release also includes TGRS high-rate scintillation data (downlinked when the onboard computed S4 index exceeds a configurable threshold and there is sufficient downlink capacity), ground computed scintillation amplitude and phase indices for the high-rate observations, and ancillary radio frequency interference information. The scintillation data were verified to successfully geolocate ionospheric irregularity regions using back-propagation analysis, and via comparisons to GOLD ultraviolet imagery [26].
## 3 Results
### Seasonality Analysis  
The estimates of the length of the season (LOS), indicated by different productivity metrics, differed by as much as 48 days (Figure 9a). \(\text{SIF}_{\text{d}}\) indicated the shortest growing season (132 days), followed by \(\text{SIF}_{\text{i}}\) (138 days), GPP (145 days), EVI (152 days), and NDVI (170 days). The SOS and EOS timings estimated with \(\text{SIF}_{\text{i}}\), \(\text{SIF}_{\text{d}}\), GPP, and EVI were within two weeks of one another, indicating shifts of a few days in the season start and end times among the metrics (Figure 9a). GPP had the earliest start and end dates. In contrast, NDVI had a similar SOS, but the EOS was two weeks or more after the \(\text{SIF}_{\text{i}}\), \(\text{SIF}_{\text{d}}\), GPP, and EVI EOS. NDVI indicated the longest growing season due to the later EOS (18 to 38 days longer).  
The proportion of integrated production attributed to the base and pulse-integrated production for \(\text{SIF}_{\text{i}}\), \(\text{SIF}_{\text{d}}\), and GPP was comparable (base = 0.11, 0.08, and 0.13; pulse = 0.89, 0.92, and 0.87, respectively; Figure 9b). Normalized estimates of base and pulse-integrated production estimated by \(\text{SIF}_{\text{i}}\), \(\text{SIF}_{\text{d}}\), and GPP indicated that the base production for the study area was between 0.08 and 0.13 of the total production (or about 8-13%) and the seasonal pulse-integrated production was responsible for 0.87 to 0.92 (87% to 92%) of the deciduous forest production (Figure 9b). In contrast, NDVI indicated that the base production was 0.65 and the pulse was 0.35 (65% and 35%, respectively). EVI had an intermediate estimate of the base (0.38) and pulse (0.62) integrated production. \(\text{SIF}_{\text{y}}\) seasonality metrics are excluded from the seasonality results because repeated tests of seasonality calculations for \(\text{SIF}_{\text{y}}\) did not yield consistent or reliable results, which was likely due to the lack of seasonal cycles present for \(\text{SIF}_{\text{y}}\).  
Figure 8: Relative importance of environmental variables on \(\text{SIF}_{\text{i}}\), \(\text{SIF}_{\text{d}}\), NDVI, and EVI. Relative importance of coefficients for proxies: (**a**) \(\text{SIF}_{\text{i}}\), (**b**) \(\text{SIF}_{\text{d}}\), (**c**) \(\text{SIF}_{\text{y}}\), (**d**) GPP, (**e**) NDVI, and (**f**) EVI.
# Biomolecules from Microalgae and Cyanobacteria: Applications and Market Survey
## 3 Current Uses of Microalgae and Cyanobacteria in Industry
### Use of Microalgae and Cyanobacteria for Biofuel Applications
In addition to Solazype, there are many industries producing energy based on algae in the United States, such as Algenol, Blue Marble Production, Sapphire Energy, Culture Biosystems, Genifuels, Seambiotic, Solix Biofuels, and Algae Production Systems, and in other countries, such as Proviron in Belgium (Hemiksem, Belgium).