esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Traditional Layout Design for Robotic Mobile Fulfillment System with Multiple Workstations
## 5 Numerical Experiments
### Comparison of Traditional and Flying-V Layouts
#### 5.5.1 Total Travel Distance  
We compare the traditional and flying-V layout in terms of total travel distance by varying number of workstations, warehouse length and width, as in Tables 12 and 13 where Diff. (%) is the percentage of travel distance saving compared to traditional layout. Both tables show that the flying-V layout outperforms the traditional layout in terms of travel distance, and the saving ratio depends on the warehouse size and workstation number. To be specific, in Table 12, about 9\(\sim\)26% distance saving can be observed with a length-to-width ranging from 0.6 to 1.6. When given the number of picking aisles, the advantage of distance saving dwindles as the length increases. In Table 13, around 8\(\sim\)18% distance saving can be observed when the length-to-width ratio ranges from 0.8 to 1.33.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(\mathbf{\theta}\) & \(\mathbf{K=3}\) & \(\mathbf{K=4}\) & \(\mathbf{K=5}\) & \(\mathbf{K=6}\) & \(\mathbf{K=7}\) & \(\mathbf{K=8}\) & **Space Use (\%)** \\ \hline \multicolumn{8}{c}{\(N=32,L=80\)} \\ \hline \(\frac{5}{36}\pi\) & 76,901.7 & 71,593.3 & 66,284.9 & 63,549.6 & 61,934.3 & 60,824.3 & 45.28 \\ \(\frac{5}{36}\pi\) & 75,857.4 & 70,067.8 & 64,281.5 & 61,665.9 & 60,147.6 & 59,027.0 & 45.24 \\ \(\frac{5}{36}\pi\) & 75,828.51 & 69,477.0 & 63,236.3 & 60,795.3 & 59,303.6 & 57,811.8 & 45.42 \\ \(\frac{11}{36}\pi\) & 77,297.00 & 69,594.9 & 63,743.1 & 61,283.7 & 58,824.4 & 56,682.0 & 45.57 \\ \(\frac{11}{36}\pi\) & 75,768.65 & 68,592.6 & 63,706.0 & 60,709.2 & 57,779.5 & 56,033.5 & 43.60 \\ \hline \multicolumn{8}{c}{\(N=40,L=120\)} \\ \hline \(\frac{5}{36}\pi\) & 204,151.1 & 191,361.1 & 178,571.2 & 171,840.2 & 168,269.2 & 165,519.2 & 46.75 \\ \(\frac{7}{36}\pi\) & 201,360.3 & 187,379.6 & 173,437.2 & 166,905.9 & 163,516.0 & 160,815.9 & 46.75 \\ \(\frac{7}{36}\pi\) & 200,535.1 & 185,138.6 & 169,937.4 & 163,692.0 & 160,468.0 & 157,244.0 & 46.88 \\ \(\frac{11}{36}\pi\) & 202,678.5 & 184,440.6 & 169,065.8 & 163,464.1 & 158,475.0 & 153,485.8 & 46.98 \\ \(\frac{11}{36}\pi\) & 202,169.7 & 185,125.5 & 172,327.0 & 163,726.9 & 155,126.9 & 150,602.5 & 45.67 \\ \hline \hline \end{tabular}
\end{table}
Table 11: The influence of aisle angle on distance and space use.  
The tremendous distance saving shown in the flying-V layout can be explained by the inserted crossing aisles, as they enable diagonal paths rather than orthogonal ones. When the warehouse length increases, the vertical path dominates the diagonal path, weakening the advantage of distance saving. Likewise, more workstations may also eliminate distance saving, as presented in the tables.
## 4 Experiments and Discussion
### Implementation Environment  
For the experiment, all implementations were analyzed on a PC with Intel Xeon CPU E5-2696 v5 @ 4.40 GHz, 512 GB RAM, and NVIDIA GeForce GTX 1080 24 GB. The ML- and DL-based prediction models were developed using the Tensorflow [45], Keras [46], and scikit-learn [47] packages in Jupyter Notebook [48] with Python (Version 3.6) [49].
## 6 Conclusions  
The chaotic dual watermarking scheme in the spatial domain has been implemented to enhance information security successfully. Firstly the chaotic logistic system was applied for generating chaotic sequences as secret keys, so that pixel shuffling and chaotic watermarking in the spatial domain can be made to embed watermarks. The host image was shuffled and embedded using a secure chaotic sequence of a variable length. Secondly the hyper-chaotic system was used to generate another watermark for block scrambling to further strengthen information security via dual chaotic watermarking. Experimental results verified the information security and integrity authentication of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**RGB Relative Entropy** & **Red** & **Green** & **Blue** \\ \hline Source A & & & \\ Chaotic Watermarking & 0.0041 & 0.0176 & 0.0079 \\ Dual Chaotic Watermarking & 0.0047 & 0.0455 & 0.0177 \\ \hline Source B & & & \\ Chaotic Watermarking & 0.0220 & 0.0132 & 0.0021 \\ Dual Chaotic Watermarking & 0.0285 & 0.0222 & 0.0039 \\ \hline Source C & & & \\ Chaotic Watermarking & 0.0089 & 0.0115 & 0.0113 \\ Dual Chaotic Watermarking & 0.0122 & 0.0131 & 0.0133 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantitative Metric of Relative Entropy.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**RGB Discrete Entropy** & **Red** & **Green** & **Blue** \\ \hline Source A & 2.6470 & 2.6116 & 2.3542 \\ Chaotic Watermarking & 2.6551 & 2.6241 & 2.3564 \\ Dual Chaotic Watermarking & 2.6761 & 2.6443 & 2.3861 \\ Source B & 2.5103 & 2.4543 & 2.6716 \\ Chaotic Watermarking & 2.5277 & 2.4689 & 2.6985 \\ Dual Chaotic Watermarking & 2.5872 & 2.5522 & 2.7729 \\ Source C & 2.1757 & 2.1772 & 2.2911 \\ Chaotic Watermarking & 2.2327 & 2.2338 & 2.3550 \\ Dual Chaotic Watermarking & 2.2931 & 2.3444 & 2.4673 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative Metric of Discrete Entropy.  
the proposed chaotic dual watermarking scheme. To conduct quantitative analysis, two tests of NPCR and UACI in the spatial domain were carried out initially, followed by two tests of discrete entropy and relative entropy analyses being implemented in the frequency domain. The case studies using suboptimal hashing with the variable length gave rise to highly confidential chaotic watermarked images, which depicted the feasibility of potential applications for secure digital image watermarking.  
Conceptualization: [PERSON] and [PERSON] conceived of the presented idea. Methodology: [PERSON], [PERSON] and [PERSON] developed the theoretical formalism. Software: [PERSON] and [PERSON] designed the codes and performed the numerical simulations. Validation: [PERSON], [PERSON] and [PERSON] verified the analytical methods. Formal Analysis: [PERSON] and [PERSON] carried out analytic calculations. Writing-Original Draft: [PERSON] prepared the draft manuscript. Writing-Review: [PERSON] conducted initial review. Visualization: [PERSON] provided feedback on visual appeal of all figures.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Results
### \(\Delta\Delta C_{T}\) Method Results  
The \(\Delta\Delta C_{T}\) method calculates the fold change of a gene of interest versus a reference gene. Crucially, it is the only way to interpret qRT-PCR data from the raw threshold cycle (C\({}_{T}\)) values. This is why we considered it important to validate the reference genes using the same method, as this would be used when analysing if a gene of interest was changing under ES conditions. The fold changes for the reference genes were plotted versus the gene determined to be the most stable by NormFinder for each of the seeding densities using the \(\Delta\Delta C_{T}\) method (Figure 5).  
The \(\Delta\Delta C_{T}\) results showed a significant increase in GAPDH expression while ACTB also exhibited an upwards trend in expression, relative to PPIA at 5000 cells/cm\({}^{2}\) (Figure 5a). The \(p\)-value for ACTB was 0.06, therefore it was close to being significant, but failed to reach significance possibly because of the relatively large standard deviation in the values.  
In contrast, YWHAZ, 18S, and B2M did not show any significant differences in the ES groups versus PPIA and the control cells (Figure 5a). At the seeding density of 10,000 cells/cm\({}^{2}\), the expression of none of the potential reference genes were significantly altered following ES (Figure 5b). However, the most consistent pair were PPIA and YWHAZ, with an average fold change in the ES group of 1.08 and 0.90 respectively (Figure 5a,b).  
Our results suggest that GAPDH may be a poor choice for a reference gene at seeding densities below 10,000 cells/cm\({}^{2}\). The significant increase noted in GAPDH contradicts the NormFinder results, which placed GAPDH as the second most stable gene consistently at both seeding densities. This highlights the importance of using more than one method to  
Figure 4: The NormFinder stability value results for the most stable gene across both unstimulated control cells and cells stimulated for 60 min per day. A separate NormFinder analysis was performed for cells seeded at (**a**) 5000 cells/cm\({}^{2}\), and (**b**) 10,000 cells/cm\({}^{2}\).  
determine reference gene stability. The selection of a poor reference gene could easily mask a significant upregulation of a gene of interest.  
Despite the lack of significance, ACTB may also not be an appropriate reference gene for studies involving ES due to its observed increase in expression and high variability at 5000 cells/cm\({}^{2}\). It is important to recognise that this study only focused on an electric field strength of 100 mV/mm and a direct current set-up in a 2D culture environment. Within the system, there are many variables at play which all could influence the reference gene activity. Based on this precise experimental set-up, ACTB and GAPDH will be avoided in future, especially at lower seeding densities. In contrast, PPIA and YWHAZ would appear to be a stable pair regardless of the number of cells seeded prior to ES.
# A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication
## 3 Proposed TL-Based UWA OFDM Receiver
### Pre-Trained Model  
#### 3.1.1 Data Collection and Training  
The data collection and processing pipeline for pre-training a deep learning model in an underwater acoustic communication system is meticulously structured to ensure diversity and robustness. Figure 4 illustrates the OFDM communication. As the transmitted signal traverses the underwater channel, it undergoes complex propagation effects such as multipath fading, Doppler shifts, and attenuation, varying across different underwater conditions. Figure 4 showcases five distinct channel environments, BCH, NOF, NCS, KAU1 and KAU2. Figure 5 extends this concept by demonstrating how the received signal, rather than the transmitted signal, is used as the input feature for model training. Each channel (BCH, NOF, NCS, KAU1, and KAU2) introduces a distinct variation in the received signal due to different propagation conditions. The received signals from each environment are collected separately, forming variations in the dataset. Instead of training a model for each  
Figure 3: Comparison of individual data training in DL vs. knowledge reuse in TL.  
Figure 2: Traditional deep learning-based UWA OFDM communication system, reproduced from [22], _Applied Acoustics_, _2019_.  
channel separately, the received signals are combined into one dataset, making it very diverse and complete. This data aggregation is necessary for pre-training a deep learning model with high generalization performance over different underwater conditions. For further assurance of dataset diversity, the received signal is collected across a range of SNR: \(-5\) dB, \(5\) dB, and \(15\) dB. This is necessary so that for each SNR level, the received signal in each channel is recorded separately to make sure that the model sees different noise conditions and varying levels of signal degradation. Also, the data consists of more than \(43\),\(000\) samples representing both the real and imaginary parts of the received signal. It leads to a comprehensive dataset that helps in training the model on all aspects of underwater conditions, improving the robustness and adaptability to unseen environments.
## 4 Experiment Setup  
### Dataset  
Our analysis focuses on S&P 500 data from 2012 to 2016 (Table 1), chosen for their market stability and diverse economic conditions. For efficiency, we selected the top 100 stocks by trading volume from the 2012 S&P 500 index. This selection means our stock portfolio is based on these 100 stocks, allowing for a targeted and effective training approach for our deep reinforcement learning models. To ensure robust model training and validation, we divide our dataset into distinct subsets:  
* **Training Set (2012-2014):** The initial phase of our analysis utilises data from 2012 to 2014 as the training set. This period is crucial for training our models, tuning hyperparameters, and optimising algorithms based on historical market trends.
* **Validation Set (2015):** Data from 2015 are used as the validation set. The primary purpose of this phase is to compare different time window hyperparameters and select the optimal ones for application in our models. This step is critical for assessing the performance of our models under different market conditions and ensuring that they can generalise well to new data.
* **Testing Set (2016):** Finally, the 2016 data serve as our testing set. In this phase, we apply the models refined through training and validation to this unseen dataset. This allows us to compare the performance of various models and configurations, providing insights into their effectiveness and robustness.  
The rationale behind this structured approach to dataset splitting is to ensure that our models are well-trained on historical data and thoroughly validated before being tested. By comparing the performance of models trained and validated on different subsets, we can more accurately assess their predictive capabilities and adaptability to market changes. This systematic approach significantly enhances the reliability and validity of our findings.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Period** & **Date Range** & **\#Examples** \\ \hline Training & 2 January 2012 to 31 December 2014 & 754 \\ Validation & 4 January 2015 to 31 December 2015 & 252 \\ Testing & 2 January 2016 to 30 December 2016 & 252 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of training, validation and testing datasets.
# A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images
## 4 Performance Evaluation
### Dataset  
In this research, we have used two different brain MRI datasets for the evaluation of the proposed technique. The first main dataset used is the 'Figshare brain tumor dataset' obtained from [38] and is one of the largest available datasets for brain tumor detection. It contains a total of 3064 real brain MRI samples, collected from 233 different subjects, among which, samples belonging to the meningioma class are 708, pituitary 930, and glioma 1426. The size of each image is 512 \(\times\) 512 pixels. The second is the Brain MRI Dataset is obtained from [39] and is relatively small. It contains a total of 253 MRI samples of sizes of 845\(\times\)845 pixels, among which, 155 MRI samples contain tumors. Both datasets are publicly available. The MRI samples are diverse in terms of structural complexity, acquisition angle, devices, noise, and bias field-effect, etc. The reason for using a T1-weighted MRI image dataset is that it is contrast-enhanced. Thus, it provides a better distinction of the areas affected by the tumor, and they are popular for treatment planning.
## 4 Materials and Methods
### RNA Extraction and RT-qPCR Analysis  
Reverse transcription quantitative PCR validation and expression analysis of _FPS_ genes were performed using transcriptome data from _E. hirta_. Total RNA from five tissues (root, stem, leaf, flower, and latex) of wild-type _E. hirta_ was extracted using a SteadyPure Plant RNA Extraction Kit (AG21019; Aikerui, Changsha, China). The concentration and integrity of the RNA samples were determined using a NanoDrop 2000 spectrophotometer and gel electrophoresis. cDNA was synthesized by reverse transcription using the PrimeScript(tm) RT Reagent Kit and the gDNA Eraser Kit (Takara, Dalian, China). RT-qPCR primers for _EhFPS_ genes were designed using the online Integrated DNA Technologies website ([[https://www.idtdna.com/scitools/Applications/RealTimePCR/default.aspx](https://www.idtdna.com/scitools/Applications/RealTimePCR/default.aspx)]([https://www.idtdna.com/scitools/Applications/RealTimePCR/default.aspx](https://www.idtdna.com/scitools/Applications/RealTimePCR/default.aspx)) accessed on 21 November 2024) (Table S3). RT-qPCR was performed using _ACT1.2373_ as the internal reference gene and the reverse-transcribed cDNA obtained above as the template, according to the instructions of the 2 \(\times\) Q3 SYBR qPCR Master Mix (Universal) kit (2205,TOLOBIO, Shanghai, China). The relative expression level of _EhFPS_ was calculated using the 2\({}^{-\Delta\Delta\mathrm{Ct}}\) method, and the results were plotted using the data statistics software Prism v. 10.
# Analysis of Dynamic Response of a Two Degrees of Freedom (2-DOF) Ball Bearing Nonlinear Model
## 4 Recurrence Analysis
### Recurrence Plots (RPs) Method
For analysis of each case, the short time series consisting of 1500 data points was taken for analysis, as the dynamic response is based on the mathematical model and the general character of the deformations is repeatable after the staring procedure. In Table 3, the parameters for the phase space reconstruction are collected, values of a calculated time delay with \(MI\) function are on a relatively high level denoting the nonlinear character of the response obtained. The calculated threshold \(\varepsilon\) is rather on a constant level for 4 cases, taking into account the constant value of the recurrence rate. By observation of the recurrence plots (Figure 7), it is possible to distinguish periodic and chaotic motion of the ball bearing for specific eccentricity. For small values of the eccentricity, the periodic character of response is observed, confirming the course of the deformation time series (Figure 5a-c). For the eccentricity \(ecc\) = 0.3, the periodic character of the response is vanishing, it is evidenced by broken diagonal lines, observance of additional very-short diagonal lines in the perpendicular direction, and single isolated points. The last two cases have a strongly non-regular character with numerous empty zones (indicating losses of correlations) and black regions informing about responses trapped to characteristic states in the corresponding phase space. In those cases, the distances between lines are modulated in unexpected ways informing about multiple unstable orbits that form the chaotic attractor. The square-like structures confirm the presence of intermittences are  
Figure 6: The mutual information (MI) (**a**) denotes the time delay (\(\tau\)) of the phase-space trajectory by the first minimum (red-dashed line). The first minimum of the False Nearest Neighbors (FNN) (**b**) denotes the dimension (\(nt\)) in the phase space. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Eccentricity** & **0.05** & **0.1** & **0.2** & **0.3** & **0.35** & **0.4** \\ \hline Time delay (\(\tau\)) & 14 & 11 & 14 & 11 & 15 & 17 \\ Dimension (\(m\)) & 11 & 12 & 11 & 3 & 3 & 4 \\ Threshold (\(\varepsilon\)) & 0.62 & 0.59 & 0.60 & 0.43 & 0.42 & 0.59 \\ \hline \end{tabular}
\end{table} 
Table 3: The parameters for the reconstruction of recurrence plots (RPs) by the constant value of recurrence rate, \(RR\) = 0.05.  
observed [79; 80]. For the last three cases, the stronger influence of vertical structures is visible in contrast to the periodic solutions.
## 3 Results
### Accuracy Analyses of Experimental Results
#### 3.2.2 Accuracy Analyses via Neural Network  
The experimental results in Tables 2-4 show that the average accuracy (correct rate) is not consistent across the five electrodes of each participant, and there are high individual differences among the users. Therefore, we use a NN technology for classification and identification to identify the best prediction function for each participant and perform the validation with a 5-fold cross-validation method (Table 7). The accuracy rendered in boldface is the best among the three components of the ERPs for each participant, respectively.  
The average accuracy across all the participants via the NN analysis using the data from component N2P3 was 78.96%, which is better than the accuracies of the other two components.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**\(\boldsymbol{\alpha}\) = 0.01, N = 480**} \\ \hline
**Component** & **Case** & **\(\boldsymbol{T}\)-Value** & **\(\boldsymbol{p}\)-Value** \\ \hline N2P3 & T4 vs. Pz & \(-\)1.809 & 0.084 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Paired samples t-test results of average accuracies for every trial between the best and worst electrodes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & & **Unit \%** \\ \hline
**Subjects** & **N200** & **P300** & **N2P3** \\ \hline N01 & 70.00 & 95.00 * & 90.00 \\ N02 & 85.00 * & 55.00 & 85.00 * \\ N03 & 60.00 & 75.00 & 80.00 * \\ N04 & 70.00 & 60.00 & 85.00 * \\ N05 & 65.00 & 65.00 & 70.00 * \\ N06 & 65.00 & 60.00 & 85.00 * \\ N07 & 70.00 * & 70.00 * & 70.00 * \\ N08 & 60.00 & 80.00 * & 80.00 * \\ N09 & 60.00 & 65.00 & 80.00 * \\ N10 & 80.00 & 70.00 & 85.00 * \\ N11 & 50.00 & 80.00 * & 65.00 \\ N12 & 55.00 & 70.00 & 75.00 * \\ N13 & 75.00 * & 65.00 & 75.00 * \\ N14 & 70.00 & 75.00 & 80.00 * \\ N15 & 70.00 & 90.00 * & 75.00 \\ N16 & 45.00 & 70.00 & 75.00 * \\ N17 & 80.00 & 55.00 & 85.00 * \\ N18 & 75.00 & 80.00 & 90.00 * \\ N19 & 70.00 & 65.00 & 85.00 * \\ N20 & 65.00 & 80.00 & 85.00 * \\ N21 & 65.00 & 85.00 * & 85.00 * \\ N22 & 60.00 & 65.00 & 80.00 * \\ N23 & 70.00 * & 65.00 & 70.00 * \\ N24 & 65.00 * & 50.00 & 60.00 \\ \hline Average & 66.67 & 70.42 & 78.96 * \\ \hline \hline \end{tabular}
\end{table}
Table 7: Average accuracies based on the NN analysis.