esa-sceva/satcom-chunk-collection huggingface dataset:
# Marine Bacteria Associated with Colonization and Alteration of Plastic Polymers
###### Abstract  
The aim of this work was molecular identification of bacteria associated with marine sand at the drift line, where most plastic debris is deposited, and evaluation of the alteration of plastic polymers by them. Bacterial communities growing on plastic polymer surfaces may differentially cause surface alteration through exopolysaccharideae production. This alteration can be analyzed by changes in spectra regions of colonized polymers compared to uncolonized polymers using Fourier Transform Infrared Spectroscopy (FTIR). In this study, bacteria located in sand at the drift line above sea water, where microplastics are most abundant, were isolated and identified through 16S rRNA. Six of the identified species produced exopolysaccharideaeMICS, namely _Bacillus thuringiensis_, _B. cereus_, _Bacillus_ sp. _Proteus penneri_, _Acaligenes faecalis_ and _Myroides gitamensis_. These bacteria species were inoculated into plates, each containing two frequently reported types of polymers at the drift line. Specifically, the two types of plastic polymers used were polypropylene and polystyrene spheres in whole and mechanically crushed states. Differences in bacterial growth were reported as inferred from weight increase of polypropylene and polystyrene spheres after 1-year long culture. Results also showed that _Acaligenes faecalis_, _Bacillus cereus_ and _Proteus penneri_ colonized polypropylene spheres and modified spectra regions of FTIR. It is concluded that bacteria located in sand can be considered plastic-altering bacteria as changes in FTIR-spectra of polymers can be related to bioalteration.  
bacteria; EPS; FTIR; in vitro colonization; plastic polymer Article
# Research Progress and Future Prospects of Key Technologies for Dryland Transplanters
## 2 Classification of Transplanters
### Semi-Automatic Transplanter
#### 2.2.2 Chain-Clip Semi-Automatic Transplant  
The chain-clamp translanter consists of a soil covering wheel, transmission sprocket group, furrow opener, frame, seedling clamping and conveying device, seedling rack, and other components. During operation, when the clamping device rotates above the clamping slide via the sprocket, operators manually place seedlings onto the grippers. As the device moves along the sprocket to the clamping slide, the seedling grippers close and carry seedlings through the slide. Upon exiting the slide, the grippers open, allowing seedlings to fall into the furrow created by the furrow opener. Subsequently, the soil covering wheel compacts the soil around the seedlings, completing the transplanting process.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mechanism Configurations** & **Mechanism** & **Advantage** & **Disadvantage** & **Reference** \\ \hline \hline \multirow{4}{*}{link configuration} & \multirow{4}{*}{\
\begin{tabular}{c} Basic mechanisms feature simple \\ structures, low production costs, \\ and ease of manufacturing \\ and maintenance. \\ \end{tabular} } & With limited motion, these & \multirow{4}{*}{
\begin{tabular}{c} With limited motion, these \\ mechanisms result in poor \\ seedling verticality and \\ low adaptability. \\ \end{tabular} } & [PERSON] et al. [60] \\  & & & \\ \cline{1-1}  & & &  
Based on the agronomic requirements for sweet potato cultivation, Nantong Fulaivei Company designed the 2 ZL-2 chain-clamp sweet potato transplanter [37] (Figure 6a). The transplanting mechanism clamps sweet potato seedlings via a slideway, opens the seedling gripper at the lowest point, and releases them into the planting furrow to complete transplantation. It features stable transplanting depth and plant spacing, low manufacturing cost, and an operating speed of 30-40 plants/min, but it suffers from low seedling verticality and high damage rate. [PERSON] [66] developed a semi-automatic chain-clip pineapple large-seedling transplanter with a self-balancing system (Figure 6b), which is tractor-towed and requires manual seedling feeding. [PERSON] [67] designed a chain-clip chili pot-seedling transplanter, using a combination of seedling ejector and chain-clip planting mechanisms for plug-seedling transplanting, resulting in a low transplanting success rate.
# Optimizing Energy Forecasting Using ANN and RF Models for HVAC and Heating Predictions
## 2 Methodology
### Model Development (RF)  
Random Forests (RFs) have emerged as a powerful and effective machine learning method for both classification and regression problems. The method is an ensemble ap  
\begin{table}
\begin{tabular}{c c c} \hline
**Component** & **Equation** & **NO. Equation** \\ \hline Input Layer: The network takes input features & \(x\ =\ [x_{1},x_{2},\ldots,x_{n}]\) & (1) \\ First Hidden Layer: Each neuron j in the first hidden layer & \(z_{j}^{(1)}\ =\ \sum\limits_{i=1}^{n}w_{ij}^{(1)}x_{i}+b_{j}^{(1)}\) & (2) \\ This sum is then passed through an activation function f & \(a_{j}^{(1)}\ =\ f\Big{(}z_{j}^{(1)}\Big{)}\) & (3) \\ Second Hidden Layer: Each neuron k in the second hidden layer takes the outputs from the first hidden layer as inputs & \(z_{k}^{(2)}\ =\ f\Big{(}z_{k}^{(2)}\Big{)}\) & (4) \\ Output Layer: The output layer produces the final prediction & \(\widehat{y}\ =\ f\Big{(}\sum\limits_{k=1}^{p}w_{k}^{(3)}a_{k}^{(2)}+b^{(3)} \Big{)}\) & (6) \\ Backpropagation Algorithm & \(w_{ij}^{(l)}\ =\ w_{ij}^{(l)}-\eta\frac{\partial L}{\partial w_{ij}^{(l)}}\) & (7) \\ \(b_{j}^{(l)}\ \gets b_{j}^{(l)}-\eta\frac{\partial L}{\partial b_{j}^{(l)}}\) & \\ \hline \end{tabular}
\end{table}
Table 3: Mathematical model for ANN.  
proach that averages the predictions of many decision trees, each trained on a different random subset of the data and/or feature space. Aggregating the predictions from many decision trees allows Random Forests to make better predictions, be less subject to overfitting, and provide more stable predictions relative to a single decision tree. In addition to their other strong points, Random Forests--and the decision trees they use--provide useful interpretability that comes with built-in feature importance metrics. This means that practitioners using Random Forests can easily identify which variables in their model are most important. They demonstrate remarkable flexibility in that they can collect both numerical and categorical dataset labels without as much intensive processing required. Because of this flexibility, Random Forest performs consistently well across different problem domains. This flexibility and overall strong performance has led to a proliferation of Random Forest applications, making it particularly popular and widely used for real-world applications, particularly in industries such as financial services and healthcare that rely on operational decisions for critical tasks, such as fraud detection, customer segmentation, and medical diagnosis, where practitioners require an acceptable trade-off of model complexity or interpretability and, ultimately, predictive performance [44, 45].
# Fast Image Reconstruction Technique for Parallel Phase-Shifting Digital Holography
## 2 Theory
When the convolution theorem is used, the complex amplitude distribution of the object wave calculated by the angular spectrum method is given as follows:  
\[v(x,y)=\text{IFT}\bigg{[}\text{FT}[u_{0}(x,y)]\text{FT}[f(x,y)]\exp\bigg{(} \frac{i2\pi}{\lambda}z_{r}\sqrt{1-\left(\lambda f_{x}\right)^{2}-\left(\lambda f _{y}\right)^{2}}\bigg{)}\bigg{]}, \tag{4}\]  
where FT represents the Fourier transform, and \(u_{0}(x,y)\) is given as follows:  
\[u_{0}(x,y)=[l_{0}(x,y;0)-l_{0}(x,y;\pi)]+i[l_{0}(x,y;\pi/2)-l_{0}(x,y;3\pi/2)]. \tag{5}\]  
The above explanation is based on conventional phase-shifting techniques [13, 31]. Figure 3 depicts the proposed technique. The image extractions and interpolations are removed. The image captured by the image sensor is multiplied by the complex-number matrix represented by \(C\) to obtain \(u_{0}(x,y)\), and the Fourier transform is performed. Then, the Fourier transform of the interpolation kernel is multiplied, which is followed by the multiplication of the phase term. Finally, the inverse Fourier transform is performed to obtain the reconstructed image. The proposed technique completely removes the four image-interpolation calculations from the calculation procedure. However, two multiplication operations using \(C\) and the Fourier transform of the kernel are added. The Fourier transform of the kernel can be calculated in advance. Image interpolation generally requires more time than image multiplication. Therefore, the proposed technique can reduce the calculation time for the parallel phase-shifting technique. When the Fourier transform of the interpolation kernel is calculated in advance, the calculation time required for the proposed technique does not depend on the size of the interpolation kernel. The kernel size depends on the interpolation algorithm, and the  
Figure 3: Proposed calculation procedure for parallel phase-shifting digital holography. kernel sizes usually increase with the increase in the quality of the image interpolation algorithms. Research [32] has shown that the root mean square error of the reconstructed images decreased when interpolation algorithms of high accuracy were used, such as the bicubic interpolation (having kernel size of 7 \(\times\) 7). The proposed technique can be combined with any diffraction calculation using a Fourier transform, other than the angular spectrum technique. The proposed technique assumes that the interpolation process is space-invariant. Recently, space-variant techniques [33] have been developed for more precise image interpolation. Because these interpolation processes cannot be expressed using the convolution operation, the proposed technique cannot be used.
# Multi-Granularity User Anomalous Behavior Detection
## 5 Experiments
### Experimental Settings
#### 5.1.3 Evaluation Metrics  
The main experiment in this paper is a binary classification problem for user behavior samples. The trained model classifies the test samples into two categories: normal behavior (positive example data) and anomalous behavior (negative example data). We use the confusion matrix counting model to classify the normal behavior samples and anomalous behavior samples. The confusion matrix for the binary classification task contains the four data metrics True Positive (TP, correctly classified normal behavior samples), True Negative (TN, correctly classified anomalous behavior samples), False Negative (FN, misclassified anomalous behavior as normal behavior samples), and False Positive (FP, misclassified normal behavior as anomalous behavior sample). The five indicators commonly used in this study were calculated based on the four indicators in the confusion matrix as follows:  
* Precision (_PRE_): Proportion of correctly predicted normal behavior as a percentage of actual positively predicted samples; the closer the value of this indicator is to 1, the more effective the model is: \[PRE=\frac{TP}{TP+FP}\] (1)
* Detection rate (_DTR_): also known as Recall; proportion of predicted correct normal behavior to total normal behavior--the closer the value of this indicator is to 1, the more effective the model is: \[DTR=\frac{TP}{TP+FN}\] (2)
* False positive rate (_FPR_): The probability that a sample of anomalous behavior will be misdiagnosed as a sample of normal behavior, which is a measure of the model'sability to discriminate between negative case samples; the closer the value of this indicator is to 0, the more effective the model is:  
\[FPR=\frac{FP}{FP+TN} \tag{3}\]
* F1-Score (\(F1\)): Harmonized mean of precision and recall, reflecting a combination of both; the closer the value of this indicator is to 1, the more effective the model is: \[F1=2\times\frac{Precision\times Recall}{Precision+Recall}\] (4)
* Accuracy (\(ACC\)): The proportion of correctly predicted behavioral samples to the total test set; the closer the value of this indicator is to 1, the more effective the model is: \[ACC=\frac{TP+TN}{TP+TN+FP+FN}\] (5)  
The coarse-grained detection module of MG-UABD is used to detect anomalous users, and the fine-grained detection module is used to detect anomalous behaviors of anomalous users. The results of the anomaly detection experiments shown in the following section combine the behavioral detection results of all users to get the overall results of the behavioral data test set and compare them with the baseline model.
## 5 Conclusions  
In this paper, a fast prediction model based on the GA-PSO-BP neural network for the hydroforming of aluminium alloy variable diameter tubes was established. The main conclusions are as follows:  
1. Compared with the PSO-BP prediction model and the traditional BP neural network prediction model, the GA-PSO-BP prediction model can control the coefficient of determination \(R^{2}\) around 97% for the three forming parameters. The maximum mean absolute error (MAE) and mean square error (RMSE) are 0.111 and 0.162; the minimum mean absolute error (MAE) and mean square error (RMSE) are 0.005 and 0.008.
2. The optimum combination of process parameters is obtained after solving the following: the internal pressure is 40 MPa, the axial feed is 4 mm, and the friction coefficient is 0.15. After forming, the minimum wall thickness of the part is 1.27 mm, the maximum wall thickness is 1.53 mm, and the maximum height of expansion is 5.11 mm. The maximum thinning rate was 15.1 percent, and the maximum thickening rate was 1.8 percent. The end result is without visible defects and meets the criteria for tube hydroforming results.
3. The primary innovations of this study are focused on algorithmic advancements, along with the application of the proposed model to the field of plastic forming. The proposed methodology is not limited to hydroforming processes but can also be extended as a reference for other plastic-forming technologies such as tube bending and forging. Further research is required to determine optimal process parameters, such as through NSGA multi-objective optimisation algorithms, extension to rupture prediction, integration with real-time control, etc.  
Methodology, investigation, funding acquisition, conceptualisation supervision, Y.X.; software, investigation, validation, writing, X.Z.; methodology, investigation, conceptualisation, writing--reviewing and editing, W.X.; conceptualisation, writing--reviewing and editing, S.Z.; methodology, Y.T.; writing--reviewing and editing, L.C. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the National Natural Science Foundation of China [grant number 52405439] and the China Postdoctoral Science Foundation [grant number 2024M76339].The funder is [PERSON].  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Optical-OFDM VLC System: Peak-to-Average Power Ratio Enhancement and Performance Evaluation
## 2 Multicarrier Modulation Schemes Based on the VLC System
### Ado-Ofdm  
A hybrid technique combines data arrangement between even and odd subcarriers, known as ADO-OFDM. It operates similarly to ACO-OFDM by utilizing the odd subcarriers, but it also incorporates even subcarriers, resembling DCO-OFDM [31]. Figure 4 provides an overview of the block diagram of ADO-OFDM. As mentioned earlier regarding the disadvantages of ACO and DCO-OFDM, ADO-OFDM is employed to enhance the performance of these OFDM schemes. It involves mapping the symbols of \(S(W)\), where \(W\) ranges from 1 to \(N/2~{}-~{}1\). \(S(W)\) is applied to H.S as denoted as \(X(K)\). \(X(K)\) is divided into two parts as dedicated in Equation (13) for odd subcarriers and Equation (14) for even subcarriers [31; 32; 33].  
\[X(K)=\big{[}0,~{}S_{1},~{}S_{2},\ldots,\ldots,S_{N/2-1},0,~{}S_{N/2-1}^{*}, \ldots\ldots,S_{2}^{*},~{}S_{1}^{*}\big{]}, \tag{12}\]  
\[X_{ACO}(K)=\big{[}0,~{}S_{1},~{}0,~{}S_{4},0,\ldots\ldots,0,S_{N/4},0,~{}S_{N/4 }^{*},0,\ldots\ldots,S_{2}^{*},0,~{}S_{1}^{*}\big{]}, \tag{13}\]  
\[X_{EVEN}(K)=\Big{[}0,~{}0,S_{\frac{N}{2}+1},~{}0,~{}S_{\frac{N}{2}+2},0, \ldots\ldots,0,S_{\frac{N}{2}-1},0,~{}0,~{}0,~{}S_{\frac{N}{2}-1}^{*},0,\ldots \ldots,S_{\frac{N}{2}+2}^{*},0,~{}S_{\frac{N}{2}+1}^{*},0\Big{]}, \tag{14}\]  
Figure 3: Flip-OFDM block diagram based on a VLC system.  
Both \(X_{ACO}(K)\) and \(X_{EVEN}(K)\) components are applied to IFFT operation as follows:  
\[x_{ACO}(n) = \frac{1}{\sqrt{N}}\sum_{k=0}^{N-1}X_{ACO}(k)\ exp\bigg{(}j\frac{2\pi }{N}kn\bigg{)},\ \ \ n=0,1,\ldots,N-1 \tag{15}\] \[x_{EVEN}(n) = \frac{1}{\sqrt{N}}\sum_{k=0}^{N-1}X_{EVEN}(k)\ exp\bigg{(}j\frac{ 2\pi}{N}kn\bigg{)},\ \ \ n=0,1,\ldots,N-1 \tag{16}\]
## 4 Discussion
### Changes in Blood Values  
We observed a tendency for lower IGF-1 (t(12) = 2.049, \(p\) = 0.065, r = 0.53). This is in accordance with review by [PERSON] et al. [11] that concluded IGF-1 tends to be lower in low EA state in male and female athletes. The review looked into three papers that analysed male athletes. None of them actually measured EA: [PERSON] et al. [12] observed a 54-h ultra-endurance event and energy balance, [PERSON] et al. [13] designed a study by measuring body composition and using questionnaires and [PERSON] et al. [14] observed bodybuilders. They did not measure EA directly, they estimated energy restriction from self-reported training logs and intermittently measured 3-days dietary intake in seven athletes and seven controls. We can speculate that a longer duration or a bigger sample size in our study would lead to significantly different IGF-values. We suggest further research is needed to actually prove low EA is associated with IGF-1.  
Lowering EA for 25% in the same sample (unpublished data, baseline published in [5]) resulted in lower haemoglobin values (t(12) = 2.652, \(p\) = 0.022), but there was no correlation to EA. It seems that in more severe conditions, we were able to show the correlation of EA and haemoglobin. Also, the lower the EA, the lower the testosterone. We failed to find any differences in testosterone function as detected by morning erections. It might suggest that lowered EA must persist for a longer time frame or EA must be lower in order to change functional effect of testosterone. It is not yet clear how LEA affects hypothalamic-pituitary-gonadal axis in men [11]. Studies so far have not been consistent. Conclusions that lower EA might be associated with lower testosterone were made according to seven studies. Five of them did not measure EA at all [15; 16; 17; 18; 19] and two papers looking into testosterone and EA [10; 20] did not use objective methodology for EA. [PERSON] et al. [10] used the most accurate approach to detect EA from the studies mentioned. They concluded that questionnaires provide better estimation of energy for health than measuring EA. In contrast to that study, our research measured EEE instead of estimating it. What is more, their study was designed as low EA being <30 kcal/kg FFM/day, which is an unconfirmed data derived from studies in females. We can certainly confirm that direct measurement of EA is challenging. However, this is the only way to provide direct link between hormonal changes and EA. To the best of our knowledge our paper provides novel finding that reducing EA is associated with lower testosterone. Objective measurement in our sample proved that suboptimal energy state can result in negative health consequences.  
Finally, although we cannot present direct evidence of energy deficiency causing compromised immunity, we can report that 3/12 (25%) cases experienced an upper respiratory infection that resulted in inability to train.
## 1 Introduction
[25] found that the summation of differences between the red and near-infrared reflectance in a time series of Landsat images of alfalfa was significant. Also, the average values of the near-infrared and red bands during the growing season were remarkably higher for alfalfa than for other crops. Based on these findings, a new vegetation index was constructed to achieve efficient automatic mapping of alfalfa. Based on the growth phenological characteristics of different crops, a time-weighted dynamic time warping (TWDTW) similarity matching algorithm was used to calculate the similarity distance between each image element to be classified and the crop standard sequence to achieve early identification of winter wheat [26]. [PERSON] et al. [27] successfully realized automatic early season mapping of winter wheat by phenological indicators such as NDVI integration, NDVI maximum, the relative rate of change, and a series of winter wheat discriminative classification rules, and then by a threshold method. However, they have integrated image data from the entire growth period, with mapping not completed until the end of the season. Different crops have unique growth and development rules. Over time, growth rates and spectral characteristics may differ [28; 29], which brings a good opportunity for the early identification of crops. [PERSON] et al. [30], combined with the knowledge of crop growth and development, designed the index of vegetation index variation in the early and late growth period of winter wheat, established the winter wheat extraction model, which efficiently and quickly realized the multi-year continuous mapping of winter wheat in ten provinces of North China, the main production area of China, with the overall recognition accuracy up to 92%. However, there is also a lack of analysis on which periods have greater growth differences and which features contribute more during these key periods. Therefore, our research objective is to mine the differential crop growth information from the early growing season, then construct multi-temporal indicators that can effectively highlight the differences in their growth processes and explore its impact on early crop identification in typical black soil areas in Northeast China. Our contributions are:  
(1) Design indicators that can reflect crop growth characteristics and construct a crop growth characteristics dataset composed of a sequence of spectral bands and their derived indices for the early growing season. (2) Explore the potential ability to use different growth datasets to differentiate crops at early growth stages and analyze their spatial and temporal variations. (3) Achieve remote sensing recognition of crops in different periods based on different classifier models, and summarize the earliest identifiable date and identifiable accuracy of different crops.