esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Basic Principles
### Preprocessing
#### 2.4.5 Whitening  
Closely related to normalization, whitening refers to a more generalized method, where a transformation is applied to the input data so that the diagonal elements of the covariance matrix are all one (also called _siphering_). This method reduces the correlations among the input data and improves the efficiency of the learning algorithm. The most common methods are _Principal Component Analysis_ (_PCA_), _Zero-Phase Component Analysis Whitening_ (_ZCA_), and _Cholesky Decomposition_[74; 75].  
Principal Component Analysis, the most popular procedure for decorrelating data, can be used to reduce the dimensionality of data while maximizing the variance of data. With regard to two-dimensional data structures, e.g., images, this is achieved by determining the covariance matrix, which is decomposed using SVD into two orthogonal matrices, \(\mathbf{U}\) and \(\mathbf{V}^{\mathrm{T}}\), and one diagonal matrix, \(\mathbf{S}\), where the diagonal matrix contains the eigenvalues. By taking only the first \(n\) components of the eigenvector matrix along with their corresponding eigenvalues, it is possible to obtain a compressed version of the original image. Here, it is used to compute the conversion matrix, \(\mathbf{W}_{\mathrm{PCA}}\), which can be multiplied with the original matrix to achieve decorrelation:  
\[\mathbf{\Sigma} =\mathrm{Cov}(\mathbf{X})=\mathbf{U}\mathbf{S}\mathbf{V}^{\mathrm{T}} \tag{10}\] \[\mathbf{W}_{\mathrm{PCA}} =(\mathbf{S}+\mathbf{I}\mathbf{e})^{-1/2}\mathbf{U}^{\mathrm{T}}\] (11) \[\mathbf{X}_{\mathrm{PCA}} =\mathbf{W}_{\mathrm{PCA}}\mathbf{X} \tag{12}\]  
The small constant, \(\mathbf{\epsilon}\), which is usuallyaround \(1.0\times 10^{-5}\), is inserted to avoid large coefficients caused by the reciprocals of very small eigenvalues. The zero-phase transformation  
\[\mathbf{W}_{\mathrm{ZCA}} =\mathbf{U}(\mathbf{S}+\mathbf{I}\mathbf{e})^{-1/2}\mathbf{U}^{\mathrm{T}} \tag{13}\] \[\mathbf{X}_{\mathrm{ZCA}} =\mathbf{W}_{\mathrm{ZCA}}\mathbf{X} \tag{14}\]  
is a whitening procedure, where, in contrast to PCA, the transformation leads to uncorrelated data with unit variances, and it is computed using PCA and an additional multiplication with the eigenvector matrix, \(\mathbf{U}\).
# A Review on Reconfigurable Liquid Dielectric Antennas
## 3 Liquid-Assisted Antennas
### Other Tuning Techniques
#### 3.3.2 Gravitational Tuning  
Due to the nature of liquid, it can conform to any shape of the vessel in such a way that its potential energy is minimized. It means that under normal conditions the surface of liquid is flat and perpendicular to the direction of gravity. This gives an opportunity for another degree of tunability. For example, in Reference [26], the authors proposed to use gravity for frequency tuning of metasurfaces. In one of the examples, a partially filled elliptical cylindrical container was used as a meta-cell. When its orientation changes, the liquid inside is redistributed due to gravity such that the effective shape of the metasurface changes which results in a different transmission response. This gravitational technique can be applied for the liquid-based antenna design as well. For example, in Reference [106], a cylindrical DRA filled with two types of liquids was used to stir the radiation pattern. The advantage of such technique is that no liquid pumping is required unlike in any of the previously discussed designs.
## 2 Literature Review and Hypotheses Development
### Intention to Purchase  
Intention to purchase refers to a consumer's willingness to buy a specific product [5; 36]. Scholars have argued that intention to purchase is directly linked to business revenue growth, highlighting its importance as a key indicator for measuring the effectiveness of marketing strategies [6; 37]. Consequently, intention to purchase has been widely used as a dependent variable in food marketing research. For example, [PERSON] and [PERSON] [6] examined factors influencing the intention to purchase green food, while [PERSON] et al. [38] explored consumer behavior related to organic food using intention to purchase as a key metric. Similarly, [PERSON] and [PERSON] [39] focused on intention to purchase in their investigation of meat consumers, and [PERSON] et al. [9] utilized intention to purchase as a key explanatory variable in the context of edible insect products. Based on this body of literature, it is evident that intention to purchase is commonly employed to assess consumer behavior in the food business sector. Regarding the consumer perception, [PERSON] and [PERSON] [28] found the healthiness elevates the intention to purchase food. [PERSON] et al. [7] also demonstrated that healthiness is positively associated with consumer decision making in the domain of grocerant where functions both supermarket and restaurants together. [PERSON] et al. [8] revealed the positive impact of perceived healthiness from the Nutri-score positively affects the intention to purchase food. Based on the literature review, this research proposes the research hypothesis as follows:  
**Hypothesis 1.**_Healthiness positively affects the intention to purchase propolis products._
## 3 Results
### Evaluation of Ultimate Tensile Strength  
Figure 4 shows the measured values of ultimate tensile strength at constant plunger speed (4.5 m\(\cdot\)s\({}^{-1}\)) in the filling chamber for the different levels of pressure or holding pressure in the mold cavity (22, 28, and 32 MPa). As ultimate tensile strength is a key parameter for evaluating the mechanical properties of castings, the results provide important information on the effect of different process parameters on the mechanical strength of the castings.  
The experimental data (Figure 4) demonstrate the relationship between the change in pressure or holding pressure in the mold cavity and the measured ultimate tensile strength of the castings. The graph indicates that as the pressure or holding pressure in the mold cavity increases, there is a corresponding increase in the values of ultimate tensile strength. The upward trend observed in Figure 4 suggests that higher pressure or holding pressure in the mold cavity during the die casting process leads to the improved ultimate tensile strength of the castings. This indicates that increasing the pressure helps to enhance the mechanical properties and strength of the castings. In the case of higher-pressure values, the undesired phenomenon of slight mold opening occurred with consequent injection in the dividing plane during the forcing-in process. The measured values can be used to determine the optimal pressure or holding pressure levels in the mold cavity for achieving the desired ultimate tensile strength of the castings. For instance, if the desired ultimate tensile strength is 200 MPa, the results in Figure 4 can be used to select the appropriate pressure or holding pressure level that can produce castings with the desired strength. Overall, the evaluation of ultimate tensile strength is crucial in assessing the mechanical properties of castings and optimizing the die casting process for producing high-quality castings.
## 4 Discussion
### Algorithm Comparison
In our study, the R-squared of the stepwise regression models were in the range of 0.57-0.86, which is similar to other studies [16; 19; 24; 32; 53]. Other researcher have explored the prediction of lake transparency using machine-learning algorithms, showing that such algorithms are indeed more easily available with higher accuracy than traditional algorithms [19; 20; 21; 22]. The combination of band and band ratio provided better estimates and higher R-squared for SD estimation in all models compared to a single band or band combinations, which is consistent with the findings of many studies [15; 16; 17; 18; 19; 24; 32; 53]. In terms of the wave and band ratio combinations, the SVM model and the stepwise regression model have similar performance under a 1-day time window, and it can be speculated that the data exhibit a degree of linearity under this condition and are shown by both models. However, as the time window expands, the two models under this condition show different trends, with the SD interpretation ability of the regression model gradually decreasing, while the SVM model tends to stabilize, which we believe is due to the lack of the high-dimensional linear identification ability of the regression model. Additionally, the SVM had better performance than the regression model in the face of multiple and complex data. Although the MLP had very suitable performance in all time windows, its accuracy decreased substantially compared to the SVM model and the regression model when estimating data beyond its own learning capability (using a 1-day time window for training and a 7-day time window for validation), which means that the model was not applicable to inferring the transparency of a wider region beyond the learning region. Machine-learning models, compared with traditional empirical algorithms, can estimate lake SD better when using the same set of spectral variables and the same training set. With the same set of spectral variables and training dataset, the SVM and MLP models had lower MAE and RMSE and higher R-squared values than the stepwise regression models (Tables S1 and S2). This clearly demonstrates that machine-learning models outperform empirical algorithmic models (with higher accuracy and better fit) in explaining the relationship between remotely sensed reflectance and lake SD. In addition, although the empirical and SVM algorithms are not as accurate as the MLP algorithm, both algorithms produce acceptable accuracy in the face of unlearned data. More importantly, they are less limited by the amount of observed data. Therefore, empirical regression and SVM models can be considered useful candidates for lakes with limited field monitoring data. The MLP model performs well in predicting the SD; however, this approach requires a large in situ dataset for model training and is more suitable for large-scale lake datasets.
## 4 Conclusions  
We developed a new process for obtaining thick SnS films with a high growth rate of a-SnS\({}_{2}\) film and the advantages of the ALD method, which was used to produce 500 nm thick SnS films from a-SnS\({}_{2}\) films. The a-SnS\({}_{2}\) films were entirely transformed into SnS films by vacuum annealing at 400 \({}^{\circ}\)C for 10 min. The transformation from a-SnS\({}_{2}\) film into SnS film was identified using XRD and XPS analysis. In the measurement of optical properties by UV-vis, the SnS film had an absorption coefficient between \(3.6\times 10^{4}\) and \(1.1\times 10^{5}\) cm\({}^{-1}\) in the visible region, and the optical bandgap of the obtained SnS film was 1.51 eV. The uniformity of the SnS film in the process was \(\pm 2.61\%\) on a four-inch scale SiO\({}_{2}\)/Si wafer. In addition, we investigated the effect of seed layer to optimize the characteristics of the SnS film for various applications. The SnS films with crystalline SnS and SnS\({}_{2}\) seed layers exhibited various film properties. The SnS film using the SnS seed had small grain size and high film density due to the low surface energy of the SnS seed. In the case of the SnS film using the SnS\({}_{2}\) seed, volume expansion of the film occurred in vertically grown SnS grains. The vertical grain growth of the SnS film was caused by lattice mismatch between the crystallized SnS grains and the SnS\({}_{2}\) seed.  
Conceptualization, H.C.; methodology, H.C., N.L. and H.P.; formal analysis, H.C., Y.C. ([PERSON]) and H.Y.; data curation, H.C., Y.C. ([PERSON]) and K.K.; writing--original draft preparation, H.C., Y.C. ([PERSON]), J.K. and S.S.; writing--review and editing, H.C., N.L. and H.P.; project administration, H.J.  
Figure 6: (**a**) Water contact angle analysis of SiO\({}_{2}\) substrate and seed layers. (**b**) Grain growth behavior of SnS films at the interface with SiO\({}_{2}\) substrate and seed layers.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **A (No Seed)** & **B (Using SnS Seed)** & **C (Using SnS\({}_{2}\) Seed)** \\ \hline Bulk concentration (cm\({}^{-3}\)) & \(2.24\times 10^{19}\) & \(1.32\times 10^{19}\) & \(3.90\times 10^{19}\) \\ Resistivity (f1m) & \(4.96\times 10^{-2}\) & \(6.67\times 10^{-2}\) & \(5.13\times 10^{-2}\) \\ Mobility (cm\({}^{2}\)/Vs) & 32.67 & 11.50 & 8.97 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Electrical properties of SnS films using various seed layers by the Hall effect measurement.  
**Funding:** This work was supported by the Nano Material Technology Development Program (2014M3A7B4049367) through the National Research Foundation (NRF) of Korea funded by the Ministry of Science and ICT (MSIT), Korea.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Distributed Ledger Technologies and Their Applications: A Review
## 2 Distributed Ledger Technology
### Tangle
#### 2.2.4 Scalability  
IOTA addresses two main issues associated with blockchain, which are scalability and miners. In IOTA, each node must confirm transactions that have not yet been confirmed in addition to issuing a transaction. It begins by selecting two unconfirmed transactions, named tips, and then verifies there are no conflicts between them and adds its transaction to the network using a light Proof-of-Work algorithm. This technique makes IOTA's Tangle incredibly scalable. For every transaction that is added to the Tangle, two others are being confirmed. This means that the network doesn't slow down when there are several new transactions. The IOTA network becomes faster when more transactions happen. This means that IOTA can handle almost an unlimited number of transactions per seconds while traditional blockchain can only handle a few transactions per second. However, there is also another aspect to scalability which everybody tends to forget, which is storage. In Blockchain, users need a full copy of the chain before they can start adding new transactions, IOTA's Tangle is much more light weight. Users do not need a full copy of the Tangle to add transactions. They only need a small part of the Tangle to create and verify transactions. This makes it much more future proof and, crucially, IOTA has no miners. The concept of mining does not exist on the IOTA network [87].
## 3 Results
### Study of the \(ESV\) in Guizhou Province
#### 3.1.2 Spatial Changes in the ESV in Guizhou Province  
Based on the land-use layer of Guizhou Province from 1985 to 2020, the ESV was linked to it to spatialize the ESV of Guizhou (Figure 4).  
Figure 4: The spatial pattern of ESV in Guizhou Province from 1985 to 2020. Note: In the figure, (**a**) is 1985, (**b**) is 1990, (**c**) is 1995, (**d**) is 2000, (**e**) is 2005, (**f**) is 2010, (**g**) is 2015, (**h**) is 2020.  
From Figure 4, it can be seen that the ESV in Guizhou Province showed an overall distribution characteristic of low in the middle, and high in the surrounding areas, especially in the east and southeast, as most of the forest in Guizhou Province is distributed in these areas. There was also a small concentrated distribution in the north, mainly provided by the bamboo forest in Chishui City, with the reputation of being the bamboo town in China. According to statistics, from 2000 to 2020, the area of bamboo forest in Chishui City increased by more than 50,000 hectares, accounting for more than 28% of the area of Chishui City. The central ESV contiguous low value area was concentrated in Anshun City and Guiyang City, because the Guiyang and Anshun areas are flatter, with more intense human activities and have more arable land distributed there. Meanwhile, there were also a few contiguous ESV in Weining County, Bijie City, in the west. This is mainly because Weining County, the second most populous county in Guizhou Province, focused on agricultural development with agricultural land accounting for approximately 89% of the county area [54], while the forest and grassland areas, the main contributors to the ESV, were less distributed. The distribution of the ESV in other regions was more scattered and fragmented.
# Adaptive Navigation Based on Multi-Agent Received Signal Quality Monitoring Algorithm
###### Abstract  
In the era of industrial evolution, satellites are being viewed as swarm intelligence that does not rely on a single system but multiple constellations that collaborate autonomously. This has enhanced the potential of the Global Navigation Satellite System (GNSS) to contribute to improving position, navigation, and timing (PNT) services. However, multipath (MP) and non-line-of-sight (NLOS) receptions remain the prominent vulnerability for the GNSS in harsh environments. The aim of this research is to investigate the impact of MP and NLOS receptions on GNSS performance and then propose a Received Signal Quality Monitoring (RSQM) algorithm. The RSQM algorithm works in two ways. Initially, it performs a signal quality test based on a fuzzy inference system. The input parameters are carrier-to-noise ratio (CNR), Normalized Range Residuals (NRR), and Code-Carrier Divergence (CCD), and it computes the membership functions based on the Mamdani method and classifies the signal quality as LOS, NLOS, weak NLOS, and strong NLOS. Secondly, it performs an adaptive navigation strategy to exclude/mask the affected range measurements while considering the satellite geometry constraints (i.e., \(DOP\leq 2\)). For this purpose, comprehensive research to quantify the multi-constellation GNSS receiver with four constellation configurations (GPS, BeiDou, GLONASS, and Galileo) has been carried out in various operating environments. This RSQM-based GNSS receiver has the capability to identify signal quality and perform adaptive navigation accordingly to improve navigation performance. The results suggest that GNSS performance in terms of position error is improved from 5.4 m to 2.3 m on average in the complex urban environment. Combining the RSQM algorithm with the GNSS has great potential for the future industrial revolution (Industry 5.0), making things automatic and sustainable like autonomous vehicle operation.  
multi-agent communication; swarm intelligence; NLOS; RSQM; adaptive navigation +
Footnote †: journal: _electronics_  
Article  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] Adaptive Navigation Based on Multi-Agent Received Signal Quality Monitoring Algorithm
## 6 Discussion  
In this section, we discuss two limitations of our method and explore future directions for practical implementation.  
Firstly, the computational complexity is crucial for practical applications. The proposed MADRL approach functions in a decentralized manner. Each UAV agent infers its action using on-board processor and executes the action subsequently. In addition, the multi  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Reward** & **CFE Score** \\ \hline ms-SDRGN & \(\mathbf{6025.82}\pm 911.13\) & \(\mathbf{0.8911}\pm 0.1699\) \\ ms-SDRGN-ms & \(5198.48\pm 877.04\) & \(0.8674\pm 0.1985\) \\ ms-SDRGN-Soft & \(5337.79\pm 814.41\) & \(0.8592\pm 0.1791\) \\ ms-SDRGN-1 GAT & \(5523.05\pm 905.01\) & \(0.8102\pm 0.1762\) \\ ms-SDRGN-2 GAT & \(4984.57\pm 837.96\) & \(0.7956\pm 0.1729\) \\ ms-SDRGN-GRU & \(4318.14\pm 815.62\) & \(0.8017\pm 0.1878\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation study of ms-SDRGN method.  
Figure 7: The evaluation results in environments with different scale factors: (**a**) mean episodic reward and (**b**) CFE score. (‘w/o local CNN encoder’ denotes using linear encoder to process local observations).  
scale convolutional encoder introduced in Section 4.1 becomes the major computational burden for embedded processors. Therefore, future works will focus on introducing more computationally efficient spatial feature extractors.  
Secondly, hand-crafted reward function limits the scalability. The heuristic reward function designed in Section 4.4 is customized for SAG-MCS simulation environment. When migrated to other application scenarios, the reward function requires modification case to case. Inverse reinforcement learning can be a solution for agents to infer reward functions from expert trajectories [53].
# Generic Methodology for Field Calibration of Nacelle-Based Wind Lidars
## 2 Two Plausible Calibration Concepts: The White- or the Black-Box
### White Box  
An alternative methodology to the black box consists in calibrating the reconstruction algorithms' inputs. This method will be further referred to as 'white box' calibration. These inputs are \(V_{LOS}\) along the different LOS and beam localisation quantities [15]. The white box calibration requires knowledge of the reconstruction algorithms and being able to:  
* calibrate the LOS positioning: e.g., by calibrating the lidar's internal inclinometers (if any), by verifying the geometry of the trajectory (opening angle between each LOS and the optical centreline), by verifying the measurement range;
* calibrate the lidar-measured \(V_{LOS}\);
* propagate inputs' uncertainties to the lidar-reconstructed wind characteristics.
# Cross-Layer and Energy-Aware AODV Routing Protocol for Flying Ad-Hoc Networks
## 4 Materials and Methods
### Cooperative MAC Design for Cross-Layer Approach
To conserve additional energy, if the transmitter does not have the data to be broadcast and receives no packets, a timeout forces the recipient to sleep again. Only fire-impacted nodes change their MAC to the emergency mode when specific node sensors detect fire. The nodes are fired nodes that emit FIRE signals: their one-hop neighbors get the messages, their ancestors receive an emergency flag data packet, and one-hop neighbors for their ancestors, while others remain in normal mode. With the following rules, a node changes the MAC:  
(1) A slot owner wakes up at the start of their slot transmission. If the packet has a high priority to send, it instantly transmits the packet but otherwise enables the one-hop neighbors to compete for the slot. (2) At the beginning of each slot, all non-owners wake up for potential contention or the receipt of packets. If the owner of a high-priority packet detects no channel activity in t0, it will fight the slot at the time of t1 by sending a SLOT_REQUEST message to the owner of the slot. A SLOT_ACKNOWLEDGEMENT answers the owner. (3) The slot owner with low-precedence packets may only utilize its slot if it does not receive any SLOT_REQUEST from its neighbors during t0 + t1. (4) A non-owner with low priority slots may compete if no actions are perceived during t0 + t1 + t2. The slot contests during t3 by sending the slot owner a SLOT_REQUEST. The owner answers with a SLOT_ACKNOWLEDGEMENT. The original emergency packet is delivered in a scheduled slot to prevent a node from supplying a sleeping parent with an emergency packet. This allows for node ancestors to switch to MAC on receipt of the packet. The initial delay in emergencies is the same as normal. In the event of a false alarm, a fire node will broadcast a FALSE_ALARM message, enabling its one-hop neighbors to restore their MAC to normal mode. The node's ancestors on their way to the base station in emergency mode will convert their MAC into regular mode until after n cycles of collecting. They obtain emergency packets. Figure 5: The construction of cooperative MAC design.