esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work  
Sequential models like recurrent neural networks (RNNs) handle longitudinal data, assuming registers are evenly time-spaced [5]. However, real-world EHRs could be irregular. Some methods like RETAIN include an extra feature indicating time intervals between visits [16, 24, 25]. Others aggregate data within time windows while maintaining certain temporal evolution. For instance, Patient2 Vec requires a consistent number of time periods across patients [14], unlike ZiMM [19]. Training on batches of patients with equal visit counts [26, 27] as well as zero padding and masking are also common. A systematic review up to 2022 found that models using longitudinal EHR data usually involved less than three hidden layers [18]. While 61.7% used architectures based on RNNs only, 27.2% used combinations of layers like convolutional neural networks (CNNs) or graph neural networks (GNNs), with or withoutRNNs [18]. Lately, temporal CNNs (TCNNs) and transformers have shown similar or better results than RNNs when working with longitudinal tabular data [28; 29].  
Attention mechanisms are a component that can be included in deep learning architectures capable of providing an intuition of the contribution of each input to the outcome. They were first introduced in natural language processing, specifically within sequence-to-sequence tasks like machine translation [9] and then expanded to other domains. Extended taxonomy reviews have been presented [30; 31]. In healthcare, many studies have included them, aiming to increase their transparency [18], often in medical imaging, where attention maps help to locate diseases [32; 33], and less often with longitudinal tabular data from EHRs. In these cases, in addition to transparency, attentional mechanisms also help to handle long temporal sequences more successfully. [PERSON] et al. (2016) introduced RETAIN, a model with a parallel attention mechanism for both variable and time dimension predicting heart failure using embedded features rather than the immediate input features [16]. Patient2 Vec by [PERSON] et al. (2018) employed a hierarchical attention mechanism that learned dedicated weights for the clinical events in each visit and aggregated weights for the visits to predict the next diagnosis [14]. Similarly, [PERSON] et al. (2020) proposed ZiMM, combining self-attention with recurrent layers into an encoder-decoder architecture to predict long-term and blurry relapses [19].  
While the role of attention mechanisms in explainability is currently debated [34; 35; 36] due to the lack of a consistent definition of explainability, their contribution to transparency is not discussed [35]. [PERSON] et al. performed a study to assess how good attention maps were based on the consensus with human explainability [37], following the recommendations from [PERSON] on human-centred artificial intelligence to provide a good explanation such as one that is plausible [38]. They defined some metrics to measure the consensus, such as the overlap between humans and attention mechanisms but recognized that the subjectivity of humans themselves could make it difficult to define these metrics.
## 4 Rice Grain Morphology and Nutritional Composition
### Nutritional Composition
#### 4.2.5 Phytochemical Composition  
Plants contain secondary metabolites such as flavonoids and phenolics that play a significant role in plant development, fertilization, pigmentation, UV protection, and defence against pathogens and environmental conditions. Coloured flavonoids (flavanols, isoflavuoids, flavones) are responsible for plant pigmentation. Due to the presence of flavonoids, pigmented rice exhibits cytotoxic, anti-tumour, anti-inflammatory, antioxidant, and neuroprotective activities [28]. Specifically, proanthocyanidins (red pigments) and anthocyanins (purple to blue pigments) are the two principal flavonoids present in pigmented rice [29]. The darker the grain colour, the higher the anthocyanin level, and therefore the higher the flavonoid content [30]. However, at present, colour measurement cannot be used as an indirect method to determine the phenolic content of cereal grains. Techniques such as high-performance liquid chromatography (HPLC) or spectrophotometric assays are still required for accurate quantification.  
The pigmented rice varieties contain various phenolic acids, such as ferulic acid, p-coumaric acid, sinapic acid, and hydroxybenzoic acid. Among these, ferulic, protocatechu, and p-coumaric acids are mostly cell wall-bound phenolic acids, while sinapic, ferulic (28%), and vanillic acids are the main soluble phenolic acids in black rice [31]. P-coumaric acid and ferulic acid are predominantly found in red rice bran, while gallic acid, protocatechuic acid, p-coumaric acid, and ferulic acid are accumulated in black rice bran. Phenolic compounds in rice exist in three forms: free, insoluble-bound phenolic attached to sugars and other molecules, and insoluble-conjugated phenolic compounds [32]. The majority of the phenolic compounds in pigmented rice is present in an insoluble-bound form; they are not easily discharged in the gut during digestion, and therefore not easily absorbed by the body (low bio-accessibility). Additionally, phenolic compounds often bind with dietary fibres in the bran layer through non-covalent or covalent bond interactions, resulting in lower bio-accessibility [33]. Phenolic compounds in rice reduce risk of developing chronic diseases and prevent oxidative stress-related diseases [29]. In a study conductedby [PERSON] et al. [34], pigmented rice (three varieties per pigment of red and purple) was determined to contain more total phenolic compounds (7.83-47.3 mg/L) and resistant starch (2.44-10.50%) than white flours.
## 3 System Description
### Data Gathering Procedure  
The data is gathered in three steps: first, from MN to PN (MN\(\rightarrow\)PN); next, from PN to AUV (PN\(\rightarrow\)AUV); and finally, from AUV to sink (AUV\(\rightarrow\)sink). The MN\(\rightarrow\)PN is carried out constantly except during PN\(\rightarrow\)AUV. During DGT, the AUV travels to each cluster and obtains the list of PNs from the CH. Then, the AUV visits every PN in the order of the list, to take the data-gathering step PN\(\rightarrow\)AUV. After visiting the last cluster, the AUV returns to the original tour-starting point **P1**, and then ends the DGT with the step AUV\(\rightarrow\)sink.
## 2 Background on MRAM and Its Variation
### Switching Variation of MRAM  
We swept the write time of each of the 10 chips to identify the Cumulative Distribution Function (CDF) of write time for \(0\to 1\) switching. Figure 7 shows the CDFs of three chips that are found to possess highest/median/lowest write times. To realize PUF, the write latency of each chip should be chosen to write 50% of the bits to get uniform 0/1 distribution. From Figure 7 it is obvious that write time varies from chip to chip. This could be useful for TRNG application. From the experimental results and analysis, we note the following:  
Figure 5: MRAM (**a**) write and (**b**) read waveforms. Note that with 12 ns of write enable (low) signal, data (0x0C) is written successfully (to address 0x00 0180).  
Figure 6: Experimental setup. The MRAM PCB (inset) is mounted on top of Basys3 for noise reduction.  
(a) The write latency can be arranged in increasing order as, 0 \(\to\) 0, 1 \(\to\) 0, 0 \(\to\) 1, 1 \(\to\) 1 (i.e., writing 0 \(\to\) 0 takes shortest time and 1 \(\to\) 1 takes longest time (\(T_{(1\to 1)}\))).  
(b) For writing 1 \(\to\) 1 with \(t_{n\%ite}\) \(<\) 10 ns and 10 ns\(<t_{n\%ite}\)\(<T_{1\to 1}\), the stored data becomes 0 and stochastic respectively. From this observation we conclude that the chip initializes the data to 0 before writing a 0 or 1. We believe that, this observation is rooted at the toggle MRAM implementation which toggles the bit (irrespective of the existing state) during write.  
(c) Consecutive writes of 1 s with the same short write time reduces number of 1 s in the data (consequence of (b)). For example, if the addresses are flushed with all 0 s and then written with all 1 s with write time to flip 75% bits (which is \(<T_{1\to 1}\)) and the writing is repeated with same time (without flushing in between), the number of 1 s in the data reduces to \(\approx\)49%, \(\approx\)34% and then remains fairly constant (Figure 8). This can be useful for TRNG (further explained in Section 4).  
(d) Columns 0 and 7 are biased to data 0, columns 1 and 2 are biased to data 1 and column 3-6 mainly shows 50% switching probability (Figure 9) although we select the write time to flip 50% of the bits for the entire memory array. Therefore, columns 0, 1, 2 and 7 cannot be used for PUF and TRNG. If every cell needs to be biased at their own 50% switching probability, then write time for each cell in a row/column needs to be controlled individually which is practically impossible.  
(e) Error-free read can be performed with 23.3 ns under 3.0 V-3.6 V and 25 C-60 C. The highest read latency is 23.3 [EMAIL_ADDRESS] V, 60 C and lowest read latency is 22.47 [EMAIL_ADDRESS] V, 25 C.  
Figure 8: Number of consecutive writes vs. percentage of 1s in the data.  
Figure 7: Cumulative Distribution Function (CDF) of write success of 3 chips (out of 10). These 3 chips exhibit maximum write time variation.
## 2 Materials and Methods
### Inter-Target Resolution  
For inter-target distances of 2, 5, and 10 mm and different target sizes, three separate one-way repeated ANOVA measures and linear regressions were used to understand the effect of inter-target distance on inter-target identification in target sizes of 2 \(\times\) 2, 5 \(\times\) 5, 10 \(\times\) 10 mm, respectively. Post-hoc testing via the Bonferroni method was used to identify significant differences between each group. The significant level was set to 0.05. The R-value was used to understand the relationship between the inter-target separation and target resolution.
# Predicting Output Power for Nearshore Wave Energy Harvesting
## 5 Results and Discussion
### Wave Tank Experiment
#### 5.2.1 Data Segmentation  
To carry out classification and regression, it was necessary to segment data for the analysis because the data were dynamically changing. We adopted a windowing approach; however, the appropriate size of the window should be found by trial and error [15]. To find the most appropriate window size, we performed several experiments with different window sizes by applying three types of ML algorithms: RF, SVM and ANN.  
The data in the wave tank experiment were obtained with a sampling frequency of 12 Hz. We considered seven window sizes: 12, 18, 24, 26, 32, 36 and 72, which corresponded to window durations of 1, 1.5, 2, 2.16, 2.67, 3 and 6 s, respectively. Table 3 shows the numbers of the training and testing datasets. It is obvious that the window size dictated the number of samples because the total number of available data is constant.  
Figure 8 shows the performance depending on the various window sizes defined in Table 3. The best result for the prediction model was found when the window size was 72. With this, the number of training datasets was 111 and that of testing datasets was 48. We used this window size (72) for the remainder of the wave tank experiment.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metric** & **Definition** \\ \hline MAE & \(=\frac{1}{n_{\text{s}}}\sum\limits_{i=0}^{n_{\text{s}}-1}|\hat{y}_{\text{i}}-y _{\text{i}}|\) \\ \hline MSE & \(=\frac{1}{n_{\text{s}}}\sum\limits_{i=0}^{n_{\text{s}}-1}(\hat{y}_{\text{i}}-y _{\text{i}})^{2}\) \\ \hline R\({}^{2}\) & \(=1-\frac{\sum_{i=0}^{n_{\text{s}}-1}(\hat{y}_{\text{i}}-y_{\text{i}})^{2}}{\sum \limits_{i=0}^{n_{\text{s}}-1}(y_{\text{i}}-y)^{2}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation metrics (n\({}_{\text{s}}\): number of samples).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Window Size** & **12** & **18** & **24** & **26** & **32** & **36** & **72** \\ \hline Number of training & 657 & 438 & 329 & 303 & 246 & 220 & 111 \\ Number of testing & 282 & 189 & 142 & 131 & 106 & 95 & 48 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Window size vs. the numbers of training and testing datasets.
## 3 Results
### Accuracy Assessment
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Area** & **Satellite Images** & **Overall Accuracy** & **Producer’s Accuracy for Water** & **User’s Accuracy for Water** \\ \hline \multirow{6}{*}{Haditha Dam} & Landsat 5 & 98.56\% & 100.00\% & 94.99\% \\ \cline{2-5}  & Landsat 7 & 98.57\% & 100.00\% & 95.07\% \\ \cline{2-5}  & Landsat 8 & 99.38\% & 100.00\% & 97.87\% \\ \cline{2-5}  & Sentinel-2 & 98.32\% & 100.00\% & 93.87\% \\ \cline{2-5}  & Monthly\_S2 & 97.85\% & 99.30\% & 96.62\% \\ \hline \multirow{6}{*}{Mosul Dam} & Landsat 5 & 98.01\% & 98.63\% & 94.74\% \\ \cline{2-5}  & Landsat 7 & 98.59\% & 100.00\% & 95.22\% \\ \cline{2-5}  & Landsat 8 & 98.36\% & 99.24\% & 95.08\% \\ \cline{2-5}  & Sentinel-2 & 98.36\% & 99.24\% & 94.99\% \\ \cline{2-5}  & Monthly\_S2 & 98.41\% & 98.93\% & 96.47\% \\ \hline \multirow{6}{*}{Hamrin Dam} & Landsat 5 & 95.94\% & 99.22\% & 88.00\% \\ \cline{2-5}  & Landsat 7 & 98.61\% & 100.00\% & 95.37\% \\ \cline{1-1} \cline{2-5}  & Landsat 8 & 97.58\% & 95.33\% & 96.70\% \\ \cline{1-1} \cline{2-5}  & Sentinel-2 & 98.18\% & 98.35\% & 96.39\% \\ \cline{1-1} \cline{2-5}  & Monthly\_S2 & 98.43\% & 100.00\% & 97.17\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Error matrix for areas and satellites used, with accuracy statistic for the reclassification based on the Normalised Difference Water Index (NDWI\({}_{\text{xu}}\)). Errors of commission were slightly more frequent, but again, mostly tied to the medium resolution of the images used, sometimes unable to distinguish mixed pixels in areas of finer sediments at the edges of the lakes. It should be mentioned that no built-up pixels were incorrectly registered as water, thus confirming the correct choice of the NDWI\({}_{\text{xu}}\) for these regions (see Section 2.4.1). Nonetheless, as shown in [PERSON]; [PERSON], 2013 [81], commission errors can be potentially lowered by experimenting with different thresholds. However, this should be evaluated for each area in order to find the respective optimum threshold, which is something beyond the scopes of this paper.
## 3 Ice-Templating of Nanocellulose Suspensions
### Effect of the Suspension Formulation
#### 4.2.5 Nanofibre Slenderness  
The nanofibre slenderness \(r=l/d\) was shown to have a significant effect on the foam shrinkage. As previously mentioned, [PERSON] et al. [56] observed that the foam shrinkage increased drastically when \(\mathrm{C_{i}}\) was lower than a critical concentration \(\mathrm{C^{*}}\). This critical concentration \(\mathrm{C^{*}}\) was found to depend on the nanofibre slenderness \(r\) and colloidal interactions between the particles [56; 100]. Using volume excluded concepts, the authors showed that \(\mathrm{C^{*}}\) probably corresponded to a percolation threshold below which nanocellulose networks were no longer connected and lost their mechanical integrity. The percolation threshold that corresponds to a critical number of bonds or interactions per nanofibre \(z^{*}\), was estimated using the statistical tube model for fibrous networks of homogeneously distributed straight fibres [150; 151; 152]:  
\[z^{*}=4\phi^{*}\bigg{(}\frac{2}{\pi}r\Phi_{1}+\Phi_{2}+1\bigg{)}\approx 2 \tag{1}\]  
where \(\phi^{*}\) is the critical nanofibre volume content and \(\Phi_{1}\), \(\Phi_{2}\) are the orientation distribution functions (\(\Phi_{1}=\pi/4\) and \(\Phi_{2}=1/2\) for isotropic fibrous microstructures [150]). Using this analytical estimate, the authors found that \(z^{*}\approx 2\). Below \(z^{*}\), particle depletion or migration phenomena that are induced by ice formation could be facilitated, in particular near the sample external surface [56].  
Further, the morphology of the pores/cells can also vary significantly with the nanofibre slenderness. [PERSON] et al. [120] prepared ice-templated foams using two types of electrostatically stabilised nanocellulose suspensions, namely an aqueous suspension of long and slender CNFs (\(r\approx 100\)) (Figure 16a) and a suspension of CNCs with much lower aspect ratio (\(r\approx 35\)) (Figure 16b). Interestingly, the authors observed that the suspensions of slender CNFs lead to foams with tubular-like porous structures (Figure 16c), whereas foams produced from the shorter and more rigid CNCs displayed lamellar porous structures (Figure 16d). The difference in pore morphology could be related to the ability of CNFs to entangle and form highly connected and cohesive fibrous networks that would restrain the growth of large, lamellar ice crystals.  
Figure 15: SEM micrographs of CNC foams’ cross-sections (perpendicular to the ice growth direction) showing the various pore morphologies that can be obtained depending on both the amount of added hydrazide-functionalised poly(oligoethylene glycol methacrylate) H-POEGMA and CNC concentration. (Adapted with permission from [PERSON] et al. [148]. Copyright 2016 American Chemical Society).
## 3 Research Design
### Spatial Analysis Framework for Visual Attention
#### 3.3.3 Boundary Construction and Linear Interpolation  
To accurately construct the boundary of the fixation envelope, this study proposes a position calculation method based on linear interpolation. This method determines precise boundary positions using two complementary functions, with its visual representation shown in Figure 6a-c. The process is based on the linear relationship between consecutive extremum points, using analytical geometry methods to derive exact coordinates.  
Given the linear relationship between two extremum points, the point position calculation is defined as follows:  
\[P^{\prime}\ =\ \big{(}\text{LimitX}(P_{1},P_{2},Y),\text{LimitY}(P_{1},P_{2 },X)\big{)}\]  
where the LimitX function calculates the X-coordinate corresponding to a specific Y value:  
\[\text{LimitX}(P_{1},P_{2},Y)\ =\ aY+b\]  
where \(a\ =\ (x_{2}-x_{1})/(y_{2}-y_{1})\) and \(b\ =\ x_{1}-(ay_{1})\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(k\) & **Silhouette** & **WCSS** \\ \hline
2 & 0.497 & 5334.41 \\
3 & 0.463 & 3719.29 \\
4 & 0.455 & 2855.24 \\
5 & 0.432 & 2280.45 \\
6 & 0.418 & 1890.55 \\
7 & 0.402 & 1609.02 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Silhouette coefficients and WCSS values for different cluster numbers (k = 2 to 7).  
Figure 5: Validation curves showing (**a**) silhouette coefficient and (**b**) within-cluster sum of squares (WCSS) across \(k=2\)–7, supporting the trade-off choice of \(k=5\).  
Similarly, the LimitY function calculates the Y-coordinate corresponding to a specific X value:  
\[\text{LimitY}(P_{1},P_{2},X)\ =\ a^{\prime}X+b^{\prime}\]  
where \(a^{\prime}\ =\ (y_{2}-y_{1})/(x_{2}-x_{1})\) and \(b^{\prime}\ =\ y_{1}-a^{\prime}x_{1}\).  
Finally, the fixation envelope can be represented by the following closed polygon:  
\[P^{\prime}\ =\ (x_{\text{min}},y_{\text{min}})\cup(x_{\text{max}},y_{\text{min }})\cup(x_{\text{max}},y_{\text{max}})\cup(x_{\text{min}},y_{\text{max}})\]  
In Figure 7, the blue and orange polygons represent different categories of fixation envelopes, while extremum points (\(P_{1}\), \(P_{2}\), \(P_{3}\), \(P_{4}\)) are marked as red circles. This method not only simplifies the analysis of large-scale fixation data, but also provides a solid foundation for subsequent quantitative spatial analysis. The algorithm can automatically filter out invalid data points (e.g., zero-coordinate points) potentially caused by instrument errors or blink events, ensuring the reliability and stability of the analysis results.
## 4 System Architecture
### Hardware Resource Usage  
After implementing the hardware design with the Vivado tools, a test was performed to assess the impact of the beam numbers in the utilization of FPGA resources. The test was performed for 5 different beam numbers, from 1 to 20 beams.  
Table 1 contains the utilization results achieved by the different implementations. Only the look-up tables (LUTs), flip-flops (FFs), and DSP48E2 results are shown since they are the ones that vary in relation to the beam number. Both the absolute number of components and the percentage in relation to the available resources are shown. The implementation is occupying a small area of FPGA and there is a linear increment of resources used in relation to the number of beams used. DSP48E2 usage is the more crucial since there are fewer available DSPs overall. This will lead to routing problems if a larger number of beams is needed due to the disparity of utilization. It is important to note, however, that these values are for a 300 MHz frequency, meaning that if a larger implementation is needed, the reduction of clock rate will consequently reduce the number of resources used.
# Design and Testing of an Intelligent Control System for Maize Picking Harvest
## 3 Experimental Testing and Analysis of the Developed System  
### Materials  
Field experiments were conducted at Zhoukou, Henan Province, on 18 September 2020, to evaluate the maize picking performance with the intelligent control system. The proposed intelligent control system was tested on a Zoomlion 4 YZL-8 BZ maize combine harvester. Table 2 presents the relevant parameters of the machine. The intelligent control system was programmed in CODESYS V3.5. Two ultrasonic sensors (type UKF1600-G18-VN7L-Q12) were used with a maximum detection distance of 1600 mm and a repeatability accuracy of 5%. The speed sensors at the header were coaxial with the hydraulic motors of the pulling rollers, and the vehicle speed sensor was fixed on the main reducer housing. The stroke of the hydraulic cylinder lifting the header was 500 mm, and the cylinder diameter was 60 mm. Thus, the header height could be adjusted from 50 to 1180 mm. The displacement of the driving motor at the pulling rollers was 161.1 mL/r, so the rotational speed of the pulling rollers could be adjusted in the range of 0-1500 r/min. The displacement of the piston pump was 100 mL/r, and the driving speed of the maize harvester could reach 0-10 km/h in first gear and 10-25 km/h in second gear.