esa-sceva/satcom-chunk-collection huggingface dataset:
# Coupled Spatiotemporal Characterization of Monsoon Cloud Cover and Vegetation Phenology
## 1 Introduction
### Example Study Area
Through time, example grassland and forest pixels (G and H) show clear phenological differences in maximum greenness and rate of senescence. The EOF analysis presented next will map these phenological differences among pixels on the basis of covariance. Figure 1: Index map of Jabal Dhofar. Topography (**A**, 30 m SRTM) limits the extent to which monsoon moisture can penetrate inland when blown northward from the Arabian Sea. The landscape is dry before the annual monsoon (**B**). Precipitation is then delivered to Jabal Dhofar and the coastal plain (**C**), resulting in extensive green-up during and immediately following the monsoon (**D**). Green vegetation, non-photosynthetic vegetation/soil, and clouds can be differentiated on the basis of Normalized Difference Vegetation Index (NDVI), as seen in the inset image histogram (**D**). This is due to differences in spectral reflectance between the visible red (0.65 mm) and the near infrared (0.85 mm), as shown by example field spectra (**E** and **F**). NDVI time series from example grassland (**G**) and forest (**H**) pixels show clear differences in phenology. These differences are quantified by covariance of the image time series, providing the basis for mapping with Empirical Orthogonal Function (EOF) analysis. The studies referenced above have documented the difference in plant species composition between the forests and grasslands, as well as various aspects of the regional micrometeorology and ecohydrology. However, to our knowledge, neither the dynamics of the relationship between precipitation and phenology, nor the variability within ecozones have yet been comprehensively characterized in a spatiotemporally explicit way. This analysis of Jabal Dhofar thus serves as an example of the type of information that spatiotemporal analysis can contribute to the base of scientific knowledge obtained from more traditional ecological and hydrological methods.
## 3 Experiments and Discussions
### Preparations
#### 3.1.3 Comparison Methods and Performance Indicators  
To comprehensively evaluate the effectiveness of the proposed method, different types of algorithms are selected for comparison. Firstly, the conventional techniques are selected as the baselines, including the semi-supervised k-nearest neighbors (KNN\({}_{semi}\)) [35] and the semi-supervised k-means (KMeans\({}_{semi}\)) [36], both based on the absolute distance. Subsequently, the dimensionality reduction technique is utilized for semi-supervised problem in the same experimental condition. Robust principal component analysis (RPCA) [37] and non-negative matrix factorization (NMF) [38] are two widely adopted techniques for dimension reduction. RPCA assumes that the observed data consists of the clean low-rank component and a sparse noise part and finds the low-rank component of the data by solving a rank-minimization objective function. NMF model decomposes the data matrix into two non-negative matrices to search for the low-dimensional representation. Laplacian eigenmaps (LE) [23] search for the best low-dimensional mapping according to the manifold Gaussian kernel in the data. At last, two state-of-the-art algorithms are also employed. ABNMTF is a semi-supervised non-negative matrix tri-factorization model that learns the similarity graph via adaptive k-nearest neighbors [39]. Deep Autoencoder-like Nonnegative Matrix Factorization (DANMF) consists of an encoder and decoder to learn the hierarchical mapping of the original data [40].  
There are several evaluation indicators designed for evaluating the performance of semi-supervised classification algorithms. Four common indicators, namely accuracy (Acc), precision (Pre), recall (Rec) and F1 score, are chosen in this paper. In the pattern recognition task, the prediction of a single sample can fall into one of four situations: true positive (\(TP\)), false negative (\(FN\)), false positive (\(FP\)) and true negative (\(TN\)). Accuracy is calculated as the ratio of correctly classified samples to the total number of samples, defined as follows:  
\[Accuracy=\frac{TP+TN}{TP+FN+FP+TN}. \tag{21}\]  
Precision indicates the proportion of correctly classified positive samples to the total number of samples determined as positive by the algorithm. It is defined as follows:  
\[Precision=\frac{TP}{TP+FP}. \tag{22}\]  
Recall refers to the proportion of correctly classified positive samples to the total number of positive samples in the dataset. It is defined as follows:  
\[Recall=\frac{TP}{TP+FN}. \tag{23}\]  
The F1 score is the harmonic mean of precision and recall. It is calculated using the following formula:  
\[F1=\frac{2\times Precision\times Recall}{Precision+Recall}. \tag{24}\]  
The values of these indicators are recorded as percentages. In general, a higher value indicates a better performance in clustering tasks.
## 3 Results
### Physical Function Outcomes  
#### 3.2.1 Adjusted Intent to Treat Analysis  
Figures 2-5 display the posture, gait, and TUG, as well as the 360\({}^{\circ}\) turn and 5x sit-to-stand, respectively. Two measures improved in the OEP plus usual care group. Firstly, single-task gait stride length increased from baseline to 6 months in the OEP plus usual care group (0.59 m to 0.75 m, +0.16 m; \(p\) = 0.03). Secondly, TUG dual-task turn velocity increased from baseline to 6 months in the exercise (71.19\({}^{\circ}\)/s to 90.15\({}^{\circ}\)/s; +18.96 \({}^{\circ}\)/s; \(p\) = 0.0003) group. Several variables showed similar results in both groups. Single-task gait lower leg double support average showed a significant decrease from baseline to 6 months in both the exercise (35.51% to 11.94%; \(-\)23.57%; \(p\) < 0.0001) and usual care (34.04% to 15.07%; \(-\)18.97%; \(p\) < 0.0001) groups. Both the exercise (38.69% to 32.37%; \(-\)6.32%; \(p\) = 0.02) and usual care groups (39.78% to 33.88%; \(-\)5.90%; \(p\) = 0.0495) showed a significant decrease from baseline to 6 months in dual-task gait lower leg double support average. Single-task gait speed showed a statistically significant increase from baseline to 6 months in both the exercise (0.41 m/s to 0.63 m/s; +0.22 m/s; \(p\) = 0.01) and usual care (0.44 m/s to 0.68 m/s; +0.24 m/s; \(p\) = 0.047) groups. TUG single-task turn velocity increased from baseline to 6 months in the exercise (81.23\({}^{\circ}\)/s to 105.24\({}^{\circ}\)/s; +24.01 \({}^{\circ}\)/s; \(p\) = 0.001) and usual care (87.88\({}^{\circ}\)/s to 100.24\({}^{\circ}\)/s; +12.26 m/s; \(p\) = 0.03) groups. TUG dual-task duration decreased from baseline to 6 months in the exercise (40.86 s to 29.32 s; \(-\)11.54 s; \(p\) = 0.01) and usual care (41.13 s to 31.69 s; \(-\)9.45 s; \(p\) = 0.03) groups. Interestingly, in the adjusted ITT analysis, post hoc pairwise comparisons after 6 months revealed faster gait speed during dual-tasking in the usual care compared to the OEP plus usual care group (0.35 m/s to 0.77 m/s; +0.42 m/s; \(p\) = 0.01). Mid-swing leg elevation during dual-tasking increased from baseline to 6 months (0.53 cm
# Opto-Electronic Oscillators for Micro- and Millimeter Wave Signal Generation
## 2 Current Progress of the Common Topologies of the OEO
### Progress of the OEO toward Better Long-Term Stability  
Long-term stability is another important characteristic of the OEO. The electrical bandpass filter and the optical fiber are major components of the OEO that are temperature-dependent [75]. For the frequency drift of a non-temperature-stabilized OEO operating at a 10 GHz central frequency, 8 ppm/K was measured [75]. One of the useful approaches is the temperature stabilization of the optical fiber and the electrical bandpass filter of the OEO loop [75]. With this solution, 0.1 ppm/K was achieved. In 2016, [PERSON] et al. brought another approach, i.e., the OEO with a feedback control loop [24; 76]. The configuration of the feedback control loop is shown in Figure 6.  
In the feedback control loop, the frequency discriminator controls the temperature of the laser by measuring the refractive index of the optical fiber. A proportional-integral (PI) controller is used to control the temperature of the laser. Using the feedback control loop, a frequency drift of 0.05 ppm/K was achieved for a single-loop OEO operating at 3 GHz. In 2017, the optical delay stabilization system (ODSS) was introduced [77] for active fiber delay stabilization at a different wavelength than used in the oscillator loop. The outstanding result of a 0.02 ppm/K frequency drift was achieved for a 3 GHz OEO. However, the frequency of the oscillator can be increased, but this does not affect the stability result because the stabilization is performed at an independent wavelength.  
Phase-locked loops (PLLs) are alternative solutions that are widely used in practice to improve the long-term stability of the OEO signal. In this case, the OEO signal is locked by the PLL signal [78]. The typical configuration of an OEO with a PLL is shown in Figure 7.  
For an OEO with a PLL configuration, a stable reference signal is required to improve the long-term stability. [PERSON] proposed another approach to improving the long-term stability that involves a fiber-delay monitoring mechanism [79]. This mechanism monitors the fiber delay using an injected probe signal. A thermal drift of \(10^{-7}\) s was achieved after 4000 seconds with the monitoring mechanism.  
Figure 6: OEO with a feedback-control loop.
# Intelligent Caching with Graph Neural Network-Based Deep Reinforcement Learning on SDN-Based ICN
## 4 Proposed Methodology
### GNN Architecture  
Our methodology utilizes a GNN for node-level Q-value predictions. The model architecture, depicted in Figure 2, operates on a network graph consisting of node embeddings and an adjacency matrix.  
The GNN takes as input the graph structure data \(G=(\mathcal{N},\mathcal{E},\mathcal{S})\), where \(\mathcal{N}\) represents the set of nodes, \(\mathcal{E}\) denotes the set of edges, and \(\mathcal{S}\) represents the network state. With this input, the GNN model generates Q-value predictions for each action of every node, allowing us to estimate the outcome of each action through a single forward propagation of the GNN model.  
For the GNN architecture, our approach utilizes four GraphSage layers [45]. Each layer has different hidden embedding dimensions, specifically 1024, 512, 256, and C. GraphSage is an inductive framework that leverages sampling and aggregation techniques to generate node embeddings. It allows for efficient embedding generation even for previously unseen data. By incorporating four GraphSage layers, we can aggregate information from up to four-hop neighbouring nodes at each step. This enables the GNN to capture the network structure and traffic patterns, resulting in more informative node embeddings for Q-value prediction.  
The aggregation process in GraphSage is described by the equation  
\[h^{k}_{N(v)}=AGG_{k}(h^{k-1}_{u},\forall u\in N(v)), \tag{7}\]  
where \(N(v)\) represents the one-hop neighbours of node \(v\), and \(h^{k-1}_{u}\) is the embedding of node \(u\) at the previous \((k-1)^{\text{th}}\) step. In each step, the GNN aggregates the embeddings of the one-hop neighbours of a node \(v\) from the previous step to obtain \(h^{k}_{N(v)}\). The aggregation function \(AGG\) is typically permutation-invariant, meaning that it is not affected by the ordering of the aggregated embeddings. In our approach, we use a mean aggregator, which calculates the element-wise mean of the vectors \(h^{k-1}_{u},\forall u\in N(v)\).  
After the aggregation step, the GNN performs concatenation by combining the embeddings of each central node from the previous \((k-1)^{\text{th}}\) step with the embeddings of its neighbouring nodes from the current \(k^{\text{th}}\) step. The concatenated embeddings are then fed into a fully connected layer with a nonlinear activation function:  
\[h^{k}_{v}=\sigma\Big{[}W^{k}\cdot\text{CONCAT}\Big{(}h^{k-1}_{v},h^{k}_{N(v)} \Big{)}\Big{]}, \tag{8}\]  
where \(W^{k}\) represents a learned matrix specific to the \(k^{\text{th}}\) step, \(\sigma\) denotes the rectified linear activation function (ReLU), and \(h^{k}_{v}\) corresponds to the embedding of the central node \(v\) at the \(k^{\text{th}}\) step. The CONCAT operation refers to the concatenation of the embeddings \(h^{k-1}_{v}\) and \(h^{k}_{N(v)}\).  
Figure 2: Model architecture.
## 3 Simulation Studies  
To demonstrate the effectiveness of the proposed approach for cooperative hunting by multi-AUV in surface-water environment, a simulation is conducted in MATLAB R2016a (The MathWorks, Inc., MA 01760-2098 UNITED STATES). In order to easy realization, the assumptions are as follows. (1) The turning radius of the AUV is ignorable in surface-water environment, thus the AUV is assumed to be able to move omni-directionally. (2) AUV is assumed to be able to recognize each other and identify their targets by the sonar. (3) The AUV velocity is set at a value more than the target velocity. (4) AUVs are capable of communicating with each other.  
In this simulation, there are six AUVs, two targets, and several static obstacles of different size and shape. The area of the environment is \(120\times 120\) (m\({}^{2}\)). AUVs and targets are allowed to move within the given space. Among them, targets move at random, and AUVs move at the proposed algorithm. When one target moves into any AUV's sensing range, this target is regarded as being found. Figure 2 shows the conditions where the target is successfully surrounded by hunting AUVs. When all targets have been surrounded by at least four AUVs, the targets are regarded as being caught, and the hunting task ends.
## 5 Interaction Model  
Figure 5 illustrates the sequence of connectivity among the Task Input module, Input Buffer, Algorithm Controller and Visualization using MVC views. It also provides a general overview of the operation of the configuration. The Tasks Unit is a controller module which first initiates random tasks generations. This module creates tasks based on user input and stores them in a CSV file. The CSV file is then loaded to the Input buffer, and virtual instances of the tasks are created which are ready to be scheduled using an algorithm which is the role of the algorithm unit. The algorithm unit loads the tasks instances from the input buffer and applies any algorithms specified in the request. Application users can interact with the visualisation module of the system. The user login to the system and make a GET request to understand the response of input tasks using algorithms described earlier. The result is presented in HTML format and is being rendered to the App User by MVC view. It can be seen from Figure 5 that the tasks have been passed via terminal or some sensors and the scheduler schedules the task and the result has been communicated back using Restful web services.  
The focus of this paper is two-fold; first, to design and implement a web-based graphical interface tool aimed to simulate various algorithms and to present the result of algorithms more cleanly and robustly. Second, the use of the proposed system in an IoT environment where multiple physical tasks are scheduled, and their performance is tracked and monitored using the proposed system.  
Figure 5: Sequence of interaction of different modules of Proposed Tool.  
Figure 6 demonstrates that input tasks are admitted for scheduling using any scheduling algorithm. In the first case, virtual tasks are added to assess and analyse the behaviour of input task sets for any chances of missing the deadline. The virtual tasks are not real tasks but rather simulated tasks to carry out the feasibility analysis of the scheduling algorithm under a certain task load. In the second case, once it is confirmed that tasks are guaranteed to meet the deadline, physical tasks are added, and the results based on the input algorithm is visualised. The proposed Web-based application is used to aid in the visualisation of results. The results of both iterations are then evaluated and analysed in better UX interfaces.
# SMT Assembly Inspection Using Dual-Stream Convolutional Networks and Two Solder Regions
## 6 Convolutional Neural Network (CNN) Structure
### Single-Stream CNN
At this time, since the calculation of two \(3\times 3\) convolution layers is \(3^{2}+3^{2}=18\), the computational efficiency per receptive field is better than that of the \(5\times 5\) convolution layer. For these reasons, we used a \(3\times 3\) kernel size. We used padding because it maintains the size of the output feature map. With the \(3\times 3\) convolution layer, the size of the output feature map decreases. If padding size is set to '0', the output has very small feature maps in the early stages of CNN. It will adversely affect the performance of CNN. Here, we set the padding size to '1' to prevent degradation of CNN performance. Common pooling methods include max pooling and average pooling. Max pooling selects the maximum value from the kernel, and average pooling calculates the average of the kernel values for pooling. In this paper, ReLu is used as the activation function, and a number of '0' values are output due to the ReLu function. At this time, if average pooling is used, a down-scale weighting phenomenon occurs in which strong weights are reduced by the average operation, potentially leading to overall performance degradation in the model. Therefore, we used max pooling in the proposed method, which is less affected by down-scale weighting. \({}^{I}R\), \({}^{I}G\), and \({}^{I}B\) are obtained by extending the solder region. The image size increases with the margin used, with the size of the single-stream CNN using it increasing proportionally. In addition, the extended regions of \({}^{I}R\), \({}^{I}G\), and \({}^{I}B\) include various background regions in the PCB such as the silk line. This may hinder the performance of the CNN since the background region is included in the defect classification.
# Implementation of Adaptive Real-Time Camouflage System in Visible-Light Band
###### Abstract  
This paper presents an adaptive camouflage system in visible band, featuring a dominant color feature-matching algorithm and pulse width modulation (PWM)-based display driving circuit. The control system consists of three parts, namely, the background sensing part, the central processing part, and the physical driving waveform generation part. Images of the local environment are sampled by the background sensing part, and then the dominant color feature matching algorithm is conducted to select a proper camouflage image that matches the local environment. Consequently, the cholesteric liquid crystals (CLCs) display using amplitude adjustable AC voltage, which is modulated by the physical driving waveform generation unit. The experimental results show that the matching degree of the proposed algorithm was 2.47 times that of the conventional hue (H), saturation (S), and value (V) histogram camouflage evaluation method, while the output peak wavelength of the reflective band can be adjusted from 604 to 544 nm according to the ambient color profile.  
adaptive camouflage system; dominant color matching algorithm; visible band +
Footnote †: journal:  
image that possesses the highest similarity with the environment. Conventionally, the digital images of the target and environment are converted into grayscale formats, and then grayscale histograms are calculated and compared for calibrations. Although the conventional camouflage-synthesis method is commonly used for compact representation and reduced complexity, there are some problems to be solved, such as inefficiency in representing images with few dominant colors and low image registration accuracy [7]. For the third step, it is important to choose proper display devices to mimic the local environment. However, the traditional display devices are heavily dependent on backlight modules, which bring difficulty to adjust the color and textures in real time [4].  
In this paper, we present a dominant color feature-matching algorithm for image matching in the HSL space. The proposed algorithm performs better than the method of grayscale histogram calculation in terms of complexity, matching efficiency, and accuracy. The reflective-type cholesteric liquid crystals (CLCs) were chosen as display device, avoiding the use of backlight module. The rest of this paper is organized as follows. The second section presents the architecture of an adaptive camouflage system, which is based on dominant color feature matching algorithm and CLC display. In the third section, the matching result between the camouflage image and local environment is evaluated. Finally, conclusions are given in the fourth section.
## 3 Methodology  
### Study Design, Instruments, and Data Sampling  
First of all, this study developed research hypotheses based on the literature review and collected data to test these hypotheses. The measurement items for the survey also were formulated after an in-depth literature review. Fifteen measures borrowed from  
Figure 1: Proposed conceptual model. Notes: 1. H = Hypothesis. 2. The bolded construct is added in the original model of the unified theory of acceptance and use of technology.  
[PERSON] et al. [21] and [PERSON] [73] were used to measure the extended UTAUT. Three items to indicate attitude were derived from [PERSON] [34] and [PERSON] et al. [74]. Three items used to express intentions to use were taken from [PERSON] et al. [75] and [PERSON] and [PERSON] [7]. Word-of-mouth intentions were manifested using three items that were cited from [PERSON] et al. [76] and [PERSON] et al. [50]. Lastly, health consciousness was associated with three items that were adapted from [PERSON] [77] and [PERSON] [78]. A five-point Likert scale ranging from (1) strongly disagree to (5) strongly agree was used to assess the items.  
In addition, this study investigated the acceptance intentions of potential users, because robotic caddies have not yet been widely commercialized. A pretest was conducted by administering questionnaires to 50 amateur golfers in South Korea by using an online survey before conducting the main survey. A brief video, which lasted about two minutes, was shown to them prior to the survey in efforts to assist the participants comprehend the concept of a robotic golf caddy better, and it is illustrated in Appendix A. The Cronbach's alpha values of the constructs were all greater than 0.70. This indicates a high level of reliability [79]. A total of 3912 individuals with experience playing golf within the last three months were invited to participate in the survey via email. Out of the 405 respondents who completed the survey, 14 were omitted due to multivariate outliers, and thus 391 respondents were statistically analyzed.