esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
### Geolocation Accuracy of the BNU-1 L1A Product  
The BNU-1 L1A images with 50% transparency are superimposed on the corresponding MODIS images in Figure 3. The sub-figures (a), (b), (c), and (d) correspond to the Sample Images A, B, C, and D shown in the red box in Figure 1. The prominent features in the images, such as coastlines, rocks, sea ice, etc., are blurred, indicating the mismatch of the geometric position between the BNU-1 images and the corresponding MODIS images. Obvious geolocation errors are observed in the BNU-1 L1A images. Table 1 shows the geolocation errors of the 42 scene BNU-1 L1A images. The errors of the BNU-1 L1A images range from 3 to 20 km, with an average of about 10 km. The geolocation errors of the sub-graphs in Figure 3a-d are 6544.83 m, 7919.60 m, 15,071.02 m, and 7778.63 m, respectively (Table 1).  
Figure 3: BNU-1 L1A images superimposed on the corresponding MODIS images. Red polygons refer to the outline of the Sample Image scenes shown in Figure 1. (**a**) Sample Image A; (**b**) Sample Image B; (**c**) Sample Image C; (**d**) Sample Image D. The images in the yellow boxes below are the enlarged versions of the part in the sample images.  
Figure 4 shows the distributions of the geolocation errors of the CPs in the X and Y directions for each image shown in Figure 3. The length and direction of the vectors in Figure 4 represent the magnitude and direction of the CPs' geolocation errors. The directions and the magnitude of geolocation errors for each image are not consistent (Figure 4). For example, the CPs' geolocation errors of Sample Image A, B, and D are less than 12 km, while most CPs' geolocation errors of Sample Image C are up to 19 km. And the geolocation errors in the middle part of Sample Image A are smaller than the errors in the edges of the image, while Sample Image B shows a quite different distribution of geolocation errors. In addition, the direction of the geolocation errors of the CPs shown in Sample Images B, C, and D are also different from the center to the periphery of the images. The CPs' geolocation errors within an image also vary significantly. For example, geolocation errors in Sample Image C are less than 2000 m in the center-west parts and more than 15,000 m in the east and southwest parts (Figure 4). The results illustrate that the distribution of the CPs' geolocation errors varies in each image and indicates that some local distortions exist in the BNU-1 L1A product.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Scene ID**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**RMSE**} \\ \cline{2-6}  & **L1A** & **L1B** & **Scene ID** & **L1A** & **L1B** \\ \hline \multirow{4}{*}{1 (A) 1  } & \multirow{4}{*}{270.19} & \multirow{4}{*}{1} & \multicolumn{2}{c}{Victoria Land} \\  & & & & & \\
2 & 8613.73 & 180.87 & 2 (B) & 7919.60 & 253.40 \\
3 & 5061.16 & 234.17 & 3 & 4683.40 & 277.69 \\
4 & 10,848.59 & 175.13 &
# A Novel OFDM Format and a Machine Learning Based Dimming Control for LiFi
## 2 Literature Review on OFDM Formats
There is a slight degradation in signal-to-noise ratio (SNR), but there is an improvement in the peak to average power ratio (PAPR). The efficiency of the HACO system is improved in [29], where PAPR is reduced compared to ACO. This system results in a low BER of \(10^{-7}\). AHO is a combination of ACO and PAM-DMT [21]. In AHO, the ACO signal is added to the inverted PAM-DMT signal. This scheme provides a wide dimming range with better spectral efficiency than HACO [21]. In HDAP, DCO symbols are placed in the lower subcarriers, while ACO and PAM-DMT symbols are placed in the higher odd and higher even subcarriers, respectively. This HDAP scheme is optically power efficient with a low PAPR and provides a wide dimming range.
## 3 Results and Discussion
### Aromatization in Financial Services
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Authors/Year** & **Environment** & **Scent** & **Research Techniques** & **Subjects** & **Measures** \\ \hline [PERSON] et al. & Restaurants, & Congruent & Traditional & Not reported & Guest satisfaction— \\
**(2020)** & cafés, hotels & scent & & & brand image \\ \hline
**[PERSON] et al.** & & & & 330 customers—pre-test; & Ambient scents’ positive \\
**(2019)** & Railways & Pleasant & Traditional & 204 customers—1. & long-term and aftereffects \\  & & ambient scent & & experiment test and 100 + & on consumers’ situational \\  & & & & 74 customers 2. Experiment & service perception \\ \hline
**[PERSON] et al.** & Restaurant & Crunchy bread, & & & \\
**(2016)** & Restaurant & chicken soup & Traditional & Not reported & Increase in sales of \\ \hline
**Spence** & Restaurant/food & Appropriate & & & \\
**(2015)** & and beverage & product & & & \\  & sector & aromas & & analyzed & \\ \hline
**[PERSON] and** & & & & 2 studies— & The feasibility of scent as \\
**[PERSON]** & Business & Orange scent & Traditional & 45 surveyed students, & an effective promotional \\
**(2013)** & & & & 69 interviewed shoppers & tool for business \\ \hline
**[PERSON] and** & & & & & \\
**[PERSON]** & Services & Not reported & Not reported & Not reported & Examining various fields \\
**(2007)** & & & & & of this segment \\ \hline
**[PERSON] et al.** & & & & & \\
**(2020)** & Services & Not reported & Traditional & - & of this segment and the \\
**Randiwela and** & & & & & \\
**Alahakoon** & Restaurants & Food aromas & Traditional & - & Surveys to examine the \\
**(2016)** & & & & & \\  & & & & & impact of sensory \\ \hline
**[PERSON] et al.** & & & & & \\
**(2016)** & Restaurants & KFC food & & & \\  & & & & & on customers \\ \hline
**[PERSON]** & Fast-food & Restaurant & & & \\
**(2018)** & restaurants & food aromas & Traditional & 300 respondents & Survey—impact of \\  & & & & & aromas on customers \\ \hline
**[PERSON] and** & & Restaurants & Restaurant & & & \\
**[PERSON]** & Restaurants & food aromas & Traditional & 1600 respondents & Survey—impact of \\  & & & & & aromas on customers—spent time \\
**(2019)** & & & & & \\ \hline
**[PERSON] and** & & & & & \\
**[PERSON]** & Restaurants & Lavender and & & & \\
**(2020)** & Restaurants & Lavender and & & & \\  & & & & & \\  & & & & \\ \hline
**Güeguen and Petr** & & & & & \\
**(2005)** & Restaurants & Lavender and & & & \\  & & & & & \\  & & & & \\  & & & & & \\  & & & & customer’s image on \\  & & & & & experience in terms of \\  & & & & & money and length of time \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Studies related to aroma marketing in various segments of services.
## 4 Conclusions  
A mechanically robust SPME MN comprising a hypodermic needle with a CNT/CNC and PANI in-tube coating for dual electrochemical and GC-MS analysis was demonstrated. The success LbL in-tube coating was confirmed using Raman and electrochemical techniques, with excellent reproducibility in the fabrication process. The PANI@CNT/CNC SPME MN demonstrated enhanced sensitivity towards the adsorption of caffeine compared to a commercial SPME fiber, while showing excellent robustness with repeated use in hot temperatures of the GC-MS conditions. Moreover, the PANI@CNT/CNC SPME MN was used to accurately determine the caffeine content of coffee and tea samples via coupling to GC-MS and could be evaluated in the future for other food and environmental applications. Electrochemically, the PANI@CNT/CNC SPME MN was used to quantify 3-CQA in coffee and to semi-quantitatively probe contents in a ripening kiwi fruit through dual electrochemical and GC-MS analysis. Future work will involve extending evaluating for other analytes in different real world environmental and food samples. To increase the selectivity for the PANI@CNT/CNC SPME MN dual platform to afford specificity to different analytes, biomimetic molecular receptors such as molecularly imprinted polymers (MIPs) can be easily integrated. Indeed, there are many literature studies on the molecular imprinted conductive polymers prepared from functional monomers such as an aniline and pyrrole for. As such, the frugal PANI@CNT/CNC SPME MN opens opportunities for dual chemical analytics through electrochemistry and GC-MS.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/s23042317/s1](https://www.mdpi.com/article/10.3390/s23042317/s1)]([https://www.mdpi.com/article/10.3390/s23042317/s1](https://www.mdpi.com/article/10.3390/s23042317/s1)), Figure S1: EIS circuit fitting for the different stages of fabrication for the PANI@CNT/CNC SPME MN; Figure S2: Overlaid extracted ion chromatograms (EIC) of GC-MS analysis of green and earl grey tea samples (\(m/z\) = 194 \(\pm\) 0.30) acquired following SPME using the PANI@CNT/CNC SPME MN.  
Methodology, M.W.; Writing--original draft, S.V.R.; Supervision, S.M.M. All authors have read and agreed to the published version of the manuscript.  
S.M.M. research group acknowledges funding from Natural Sciences and Engineering Research Council of Canada (NSERC) in the discovery grant category.  
Not applicable.  
All relevant data is included in the manuscript and in the Supporting Information.  
The authors declare no competing interest.
## 4 Experiments
### Evaluation
#### 4.1.2 Comparison  
The object detection performance of the AYOLO algorithm is visually represented in the Figure 10, where bounding boxes with confidence levels are shown. The model successfully detected multiple poppy plants within a single image with high confidence levels ranging from 80% to 97%. This high confidence suggests that AYOLO performs well in accurately detecting and classifying poppy plants, even within a dataset containing only 80 images.  
When compared with other state-of-the-art models like YOLOv5, YOLOv8, and YOLOX, AYOLO exhibited competitive performance, particularly excelling in balancing accuracy and inference speed. This can be attributed to AYOLO's hybrid architecture that leveragesthe fast processing ability of YOLO and the attention mechanisms from Vision Transformers to enhance precision.  
YOLOv5: AYOLO performed similarly in terms of inference speed but slightly better in terms of accuracy (mAP). While YOLOv5 is well-optimized for general-purpose object  
Figure 10: Poppy.  
detection, AYOLO's specialized architecture for smaller datasets like the poppy plant images made it more efficient in this specific task.  
YOLOv8: While YOLOv8 achieves high accuracy, AYOLO matched it with a marginally better real-time performance due to optimizations that minimize processing latency.  
YOLOX: AYOLO outperformed YOLOX in terms of accuracy in detecting poppy plants, with mAP values approximately 5-10% higher across similar datasets. YOLOX was faster in inference speed, but AYOLO maintained an excellent trade-off between speed and accuracy, ideal for practical UAV applications.  
DETR (Detection Transformer): DETR, known for its success on large datasets, struggled to perform as well on the smaller poppy dataset used in this study. AYOLO's custom architecture allowed it to outperform the DETR in terms of accuracy and localization precision, making it a better choice for small-scale object detection tasks.
# Hybrid Crow Search Algorithm-LSTM System for Enhanced Stock Price Forecasting
## 3 Proposed Method
### Data Acquisition and Generation  
First, basic historical stock data were obtained, as listed in Table 1.  
These five sets were used for data generation to obtain the following three datasets:  
1. Technical indicator: All indicator types were included in the dataset. We considered the 54 indicators belonging to the four categories defined in the Technical Analysis Library (TA-lib) ([[https://ta-lib.org/](https://ta-lib.org/)]([https://ta-lib.org/](https://ta-lib.org/)), accessed on 3 December 2024), including momentum-indicator functions for trend determination, overlap study functions to calculate the overlap between moving averages of multiple indicators, volume indicators to estimate transaction-volume trends, and price transform. These were combined with the basic stock-price information to form the first part of the dataset, as shown in Figure 5.
2. Price-fluctuation limits: This dataset was generated by limiting the daily stock-price fluctuation to \(\pm\)10% of the previous day's closing price, with a 0.2% step. The second part of the dataset included 101 prices in addition to the basic stock-price data, as shown in Figure 6.
3. VMD (variation mode decomposition) subsignal sequences: These were generated using the closing price as the input signal, which was decomposed into six subsignals IMF (Intrinsic Mode Functions) IMF1-IMF6 using VMD. Subsequently, these subsignals replaced the closing price in the original stock-price data, generating six new subsignal sequences, which constituted the third part of the dataset, as shown in Figure 7.  
Owing to the requirements for generating technical indicators, the first 87 data points were used as primary data. To maintain temporal consistency, each of the three generated datasets included 2317 data points per stock for subsequent preprocessing.
# Deep Neural Networks for Spatial-Temporal Cyber-Physical Systems: A Survey
## 3 DNNs in CPS-Based Spatial-Temporal Data
### DNN Techniques
#### 3.3.3 Gnn  
GNN is an architecture designed to operate on graph-structured data, such as traffic networks, social networks, molecular structures, etc. The framework incorporates information about the graph's structure and the relationships between nodes into their computations. The fundamental idea behind GNNs is to represent each node in the graph as a vector or tensor and use message passing between nodes to update these representations based on the graph's structure. At each iteration of the message-passing process, each node aggregates information from its neighbors and updates its representation based on a learned update function. There are different variations on the basic GNN architecture, depending on the specific problem being addressed [54]. For example, some GNNs use graph convolutional layers to learn local features of the graph, while others use attention mechanisms to learn global relationships between nodes. Some GNNs are designed to handle dynamic graphs that change over time, while others are designed for heterogeneous graphs with nodes and edges of different types. GNNs have been applied to address various problems, including traffic prediction, social network analysis, recommendation systems, and drug discovery, among others.
# Transfer Learning for Operator Selection: A Reinforcement Learning Approach
## 2 Background and Related Work
### Artificial Bee Colony Algorithm  
The outmost optimisation framework used in this study is a swarm intelligence algorithm, which is the artificial bee colony algorithm (ABC). It is a population-based metaheuristic and evolutionary technique developed inspired by the foraging behavior of honey bees when seeking a quality food source [6]. There is a population of food positions, which refers to solution set in the ABC algorithm, and the artificial bees modify their positions over time to reach high-quality food. In order to find the optimal solution, the algorithm employs a group of agents known as honeybees. It is one of the efficient nature-inspired optimisation algorithms for solving continuous problems. Other swarm intelligence algorithms include ant colony optimisation (ACO) [7], which has been successfully used to solve discrete problems, and particle swarm optimisation (PSO) [8], which is a population-based stochastic optimisation algorithm that has been successfully used to solve continuous problems. There are three types of bees in the ABC algorithm, namely employer, onlooker, and scout bees. Employer bees are assigned to each food source in the first phase, and they use Equation (1) to try to increase the quality of the food source. The second stage involves onlooker bees attempting to enhance the most promising solutions, which are assigned probability values based on fitness function values using Equations (2) and (3). In the final step, onlooker bees turn into scout bees, who replace the non-improved food source with a new random viable solution.  
\[v_{i}=x_{i}+rand(-1,1)(x_{i}-x_{n}) \tag{1}\]  
\[p(x_{i})=\frac{F(x_{i})}{\sum_{j=1}^{N}F(x_{j})} \tag{2}\]  
\[F(x_{i})=\begin{cases}\frac{1}{1+f(x_{i})}&f(x_{i})\geq 0\\ 1+|f(x_{i})|&\text{otherwise}\end{cases} \tag{3}\]  
where candidate, current, and neighbour solutions are represented by \(v_{i}\), \(x_{i}\), and \(x_{n}\), respectively.  
In this study, we will focus on using the ABC algorithm, which is widely used in various industries to solve a variety of problems, including combinatorial and binary problems. For the sake of brevity, further literature details have not been considered because the major goal of the proposed research is to emphasise building adaptive operator selection using one of the state-of-the-art machine learning techniques and investigate if transfer learning can be achieved in this respect.
# Ten Years of TerraSAR-X Operations
## 8 Performance Monitoring
### Instrument Monitoring  
Instrument Monitoring collects and visualizes information about the actual state of the radar instruments and performs automatic checks of measurements against a parameter limit matrix. For that purpose, housekeeping data, which is down-linked via S-band, is checked and analyzed both, automatically and manually, in order to examine instrument health. In critical cases, dedicated procedures are immediately initiated according to the instrument handbook. The instrument-specific housekeeping data is replicated and is stored in a central database, where it is one of the main sources of information for long-term monitoring. The instrument onboard timeline is continuously reviewed w.r.t. data take and command execution state, SAR data downlink history, etc.
## 1 Introduction
### Structure of the Article  
The research article is structured as follows: A summary of the existing literature is presented in Section 2. The system model is described in Section 3. A detailed description of our proposed solution based on the genetic algorithm (GA)-assisted linear combination (LC) of higher-order cumulants (HOCs) is discussed in Section 4. Section 5 presents a performance analysis in the form of a discussion on exhaustive simulation results and a comparison with the state of the art. The research article is then concluded in Section 6, and some research directions for extending this work are pointed out.
## 4 Comparisons of Test Results and Code Predictions
### Moment Capacities  
Table 5 shows the comparisons of the flexural cracking moment capacity (\(M_{\sigma r}\)) and ultimate moment capacity (\(M_{n}\)) of the beams in the F-series specimens with nominal predictions obtained using the flexural design approach specified in code provisions. The beams with steel bars possessed higher moment capacities than the predictions by the ACI 318-14 procedure, yielding ratios between experiments and predictions that exceeded 1.06 for \(M_{\sigma r}\) and 1.31 for \(M_{n}\). However, the beams with GFRP bars also had a lower flexural crack resistance than the predictions calculated by the ACI 440.1R-15 equations, as ACI 440.1R-15 specifications for \(M_{\sigma r}\) are identical to ACI 318-14. Meanwhile, the beam element of the specimen F-N-G exhibited non-conservative results when predicting \(M_{n}\) using the ACI 440.1R-15 procedure. This may be attributed to the fact that the beam did not reach its own moment capacity owing to the anchorage failure of longitudinal GFRP bars. The beam element of the specimen F-H-G had higher \(M_{n}\) than the predictions by the ACI 440.1R-15 procedure.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & \multicolumn{4}{c}{**Moment Capacity (kN-m)**} & \multicolumn{4}{c}{**Comparison**} \\ \cline{2-7}  & \(M_{\sigma(EXP)}\) & \(M_{n(EXP)}\) & \(M_{\sigma(PRE)}\) & \(M_{n(PRE)}\) & \(M_{\sigma(EXP)}\)/\(M_{\sigma(PRE)}\) & \(M_{n(EXP)}\)/\(M_{\sigma(PRE)}\) \\ \hline F–N–C & 72.3 & 169.6 & 64.0 & 124.2 & 1.13 & 1.37 \\ F–N–G & 60.0 & 134.0 & 64.0 & 151.9 & 0.94 & 0.88 \\ F–H–C & 96.8 & 453.9 & 91.1 & 345.4 & 1.06 & 1.31 \\ F–H–G & 72.6 & 353.1 & 91.1 & 300.8 & 0.80 & 1.17 \\ \hline \hline \end{tabular} Notes: \(M_{\sigma(EXP)}\) is the measured initial flexural cracking strength; \(M_{n(EXP)}\) is the measured moment capacity; \(M_{\sigma(PRE)}\) is the initial flexural cracking strength calculated using code equations; \(M_{n(PRE)}\) is the moment capacity calculated using the equivalent stress block specified in ACI 318-14 for beams with steel bars and ACI 440.1R–15 for beams with GFRP bars.  
\end{table}
Table 5: Comparisons of measured moment capacities and predictions by code provisions.  
Figure 10: Comparison between experimental values and predictions calculated from \(I_{e}\) specified in code provisions.
# LSTM Recurrent Neural Network for Hand Gesture Recognition Using EMG Signals
## 2 Methodology
### Recurrent Neural Network (RNN) Model
#### 2.3.2 Long Short-Term Memory (LSTM) Layer  
The recurrent layer is made of Long Short-term Memory (LSTM) units, which is one of the state of the art units used in RNNs due to its capacity to memorize long term dependencies. Studies have shown that LSTM units outperform other state of the art recurrent units in complex dataset [61; 62; 63; 64], making them a reliable choice for the recurrent layer architecture. The structure of a single LSTM unit can be seen in Figure 6.  
Equations (3)-(8) show the vectorized implementation of each one of the items that make up the LSTM structure.  
\[\tilde{c}^{(l)}=tanh(W_{c}[a^{  
where:  
\(X^{\langle t\rangle}\) is an input vector containing the value of each EMG channel or the activation values of the previous dense layer in time \(t\).  
\(a^{\langle t-1\rangle}\) represents the activations of the LSTM units in the previous time \(t-1\).  
\(c^{\langle t-1\rangle}\) represents the memory values in the previous time \(t-1\); \(a^{\langle t\rangle}\) are the activations in the current time \(t\).  
\(c^{\langle t\rangle}\) are the new memory values for time \(t\).  
\(W_{x}\) represents the LSTM unit weights for each gate and \(b_{x}\) represents the LSTM unit bias for each gate.  
It is important to note that \([a^{\langle t-1\rangle},X^{\langle t\rangle}]\) is a matrix form by the concatenation of the previous time activations \(a^{\langle t-1\rangle}\) and the current inputs \(X^{\langle t\rangle}\).  
The output layer integrates a SOFTMAX activation function to classify which gesture is being performed. The SOFTMAX activation function is one of the most widely used functions for multi-class classification problems because of its performance in predicting the probability of each class [55; 65; 66]. The SOFTMAX function predicts the probability of a class \(C_{j}\) given an input \(X\) as shown in Equation (9). Its mathematical expression can be seen in Equation (10).  
\[\hat{y}_{j}=P(C_{j}|X) \tag{9}\]  
\[\hat{y}_{j}=\frac{t_{j}}{\sum_{j=1}^{\sum_{j=1}^{C}t_{j}}};\quad t_{j}=e^{a_{j}} \tag{10}\]  
where:  
\(\hat{y}_{j}\) is the output probability for the class \(j\).  
\(C\) is the total number of classes that are being predicted.  
\(a_{j}\) represents the linear combination of the weights and the previous layer activations, and can be defined as \(a_{j}=Wa^{\langle t-1\rangle}\).  
Its important to note that the SOFTMAX output layer has the the same units or nodes as the number of classes that are being predicted.