esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Total RNA Isolation and Library Construction for Illumina Sequencing  
Total RNA was extracted from a pool of three independent biological replicates, each comprising approximately 0.2 g leaf samples. An ethanol precipitation protocol and the CTAB-PBIOZOL reagent were introduced to extract and purify the total RNA from garlic leaves according to the manual instructions. Total RNA was qualified and quantified by determining the optical density at 260 and 280 nm with a Nano Drop instrument (NanoDrop Technologies, Wilmington, DE, USA). Magnetic oligo-dT beads were utilized to purify mRNA. The sublimated mRNA was then broken into short pieces under high temperature conditions. The first-strand cDNA was synthesized by using a first strand reaction system, and the second-strand cDNA was synthesized as well. The reaction product was purified, and sticky ends were repaired. Afterwards, the base \"A\" was added to the 3'end of the cDNA, and the adapter was connected. The cDNA segments with adapters were propagated by PCR. The library quality and quantity were measured by using the Agilent 2100 bioanalyzer and quantitative real-time PCR (qRT-PCR). The cDNA library qualified was sequenced on an Illumina HiSeq 4000 platform with a 150-bp paired-end approach. The Bioproject accession number of the transcriptome data was PRJNA799598 in the NCBI Short Read Archive (SRA) database.
# The Real-Time Optimal Attitude Control of Tunnel Boring Machine Based on Reinforcement Learning
## 4 Case Study
### GEC Predictive Model  
Based on the matched training dataset of the excavation parameters and GEC, a fully connected DNN was used to establish the GEC predictive model. The 20 excavation parameters introduced in Section 2.2, including 2 excavation control parameters and 18 excavation response parameters, were used as the input. The model outputs four values corresponding to the probabilities of four GECs, because of four GECs in the training dataset introduced in Section 2.2. The hidden layer of the model structure was set to six layers, and the unit number of each layer adopted a symmetrical structure, set to 30, 60, 90, 60, 30, and 10, respectively. Each layer used the Relu function as the activation function and added a Batchnorm layer before the Relu function to prevent gradient explosion and disappearance. In order to output the probability, the Softmax function was used as the activation function of the output layer.  
In order to eliminate the difference between different inputs and improve the convergence efficiency, each input was normalized by the standardization method of standard deviation. One-hot encoding was used for GEC data to easily calculate the loss function. For this multi-classification problem, the categorical cross-entropy introduced in Section 3.3 was selected as the loss function. A total of 151,447 sets of matched data were selected from the total dataset and divided into a training set and test set according to the ratio of 4:1. The batch size was set to 5000, 35 epochs were trained, the learning rate was set to 0.001, and the optimizer used Nadam.  
After training, the training set and test set accuracy of the GEC predictive model were 0.9616 and 0.9453, respectively, which showed the effectiveness of the GEC predictive model. Taking the number of epochs as the abscissa, the change curve of the training set and the test set accuracy during the training are shown in Figure 5. In the early stage of training, the prediction accuracy of the model had a steep increase, while the prediction accuracy of the model had a slow increase in the later stage of training. The precision, recall, and F1-score of various GECs were analyzed statistically, as shown in Table 2. From the table, it can be seen that all indexes of each GEC have exceeded 0.83, which indicates that the GEC predictive model has sufficient credibility.  
Figure 5: Change curve of GEC predictive model accuracy.
## 4 Case Study
### Implementation of BLE-Based RTLS  
Due to budget restrictions, only a single floor of the commercial building was used in the case study. Bluetooth sensors (Figure 7a) were used to implement the RTLS for the RTLS. The installation included 26 Bluetooth sensors in the 34 th floor of the building (the locations of the sensors can be seen in Figure 7b), and the coverage area for each sensor had a radius of 5-10 m. A cloud server was used to determine the location of a smartphone user in the building based on the data exchanged between the smartphone and the Bluetooth sensors. The Bluetooth system considered in the case study was accurate to within a range of 1-5 m. The actual range in other settings depends on the quantity of sensors deployed and the locations of these sensors, as well as on the obstructions in the building (such as walls) and the material composition of these obstructions, the sensor power, and other factors. After this testing system was deployed, it was calibrated by comparing the coordinates for a building occupant that were calculated by the system to the actual coordinates of the occupant in the building. Since the building had a structure made of reinforced concrete, the partition walls on this floor of the building were not considered to be a serious obstruction. As tested, this system was able to achieve an accuracy of between 1.5 and 1.8 m.
# Power Optimization in Multi-Tier Heterogeneous Networks Using Genetic Algorithm
## 2 Related Work
An accurate prediction of power consumption in distributed areas necessitates a trained deep learning model. This requires the optimization of the hyperparameters for a provided environment. To address this issue, a technique was presented in [22] to identify the best parameter values for learning. Furthermore, genetic algorithms were utilized to optimize the layer parameters of the deep learning models. The paper [23] presented a technique for tuning the trainable layers of pre-trained models using a genetic algorithm. This approach was applied to a classification task on single-channel image datasets, consisting of grayscale images and log-Mel spectrograms generated from preprocessed audio signals.
## 3 Results and Discussion
### UV-Vis Analysis of Nanoparticle Dispersion
For the 0.75% SiO\({}_{2}\) sample, the initial values are 2.631, 2.546, and 2.402. Higher sonication times (2.5 h) maintain higher absorbance values for longer periods. For the 1.0% SiO\({}_{2}\) sample, the initial values are 2.426, 2.316, and 2.193. Again, the trend of better stability with increased sonication time is observed. Across all concentrations, peak absorbance values decrease over the 77-day period. The decrease is more pronounced in the initial days (up to 15 days) and tends to stabilize somewhat after 30 days. Samples with longer sonication times (2.5 h) generally show better stability, retaining higher absorbance values over time. Lower concentrations (0.25% and 0.50%) have higher initial absorbance values compared to higher concentrations (0.75% and 1.00%). Higher concentrations benefit more from longer sonication times, showing slower decreases in absorbance. Figure 8: UV-Vis Absorbance of nanolubricants at different weight concentrations of nanoparticles and various sonication times: (**a**) SiO\({}_{2}\)/Mistral 15W40 nanolubricants after preparation; (**b**) SiO\({}_{2}\)/PLANTO MOT SAE 10W40 nanobiolubricants after preparation; (**c**) SiO\({}_{2}\)/Mistral 15W40 nanolubricants after 77 days; (**d**) SiO\({}_{2}\)/PLANTO MOT SAE 10W40 nanobiolubricants after 77 days. Figure 9: UV-Vis evaluation of SiO\({}_{2}\)/Mistral 15W40 nanolubricants for 77 days at different concentrations with different sonication times. The numerical indices displayed in Figures 9 and 10 refer to Table 4. Each index represents specific combinations of nanoparticle concentration percentages and sonication times.
# A New Technical Ear Training Game and Its Effect on Critical Listening Skills
## 4 Evaluation: Training Benefit in Speech Understanding
### Methods  
The authors formed two listener groups: the Control group with 4 participants who studied advanced topics in audio engineering for 14 weeks (without the closed-loop audiomotor training from the proposed game) and the Treatment group with 4 participants who only trained themselves with the proposed game (thus with the closed-loop audiomotor training) for the same 14 weeks. We measured and compared the two groups' speech understanding in noise (SiN), which is a non-trained auditory skill to investigate whether the training benefit has been transferred to a non-trained skill. The two groups' performances were first compared with the Reference group with 34 participants who did not receive any training at all. SiN performance was measured through the Coordinate Response Measure (CRM) corpus by [PERSON] et al. [42]. The background maskers are speeches that compete and interfere with a given focal target speech. Figure 7 illustrates a task where all listeners are asked to attend to the focal cue of \"Ready Tiger\" and understand the color and number. In the figure above, the correct answer would be \"red\" and \"5\", to which the listeners need to respond by selecting the corresponding button on a tablet (by pressing the red-colored 5 square on the screen). As for the other task attending to the background cue, the correct answer would have been \"blue\" and \"2\".  
The experiment also adapted spatial separation of background from focal. Two recent studies [28, 43] showed that the spatial location of background sounds is an important experimental variable for auditory selective attention. The experiment incorporates this factor by testing six different positions. We used the correct answer ratio of this CRM as a metric of listeners' SiN performance.  
Figure 6: GUI of the memory (M) of a mixing balance module. Players are to memorize computer-generated mix balance of one to six sound sources (depending on the level) and replicate. (**Left panel**) is the UI of the level 1, where players need to memorize a single sound source, its level, and the pan-pot position, and subsequently match them using given controllers (fader and knob). (**Right panel**) is the level 16 where players need to do the same task for three sound sources.
## 7 Discussion
### Agenda for Future Research  
The A/B testing conducted in this study elicited responses to four questions associated with selections made for each circuit. The results demonstrated comparable success rates across both circuits, suggesting a lack of discernible influence of advertising on decision-making. Notably, many drivers make choices based on personal preferences, encompassing elements such as brand recognition, visually appealing stimuli, and other unspecified factors. Although a fraction of respondents acknowledged some impact of advertisements, these responses were subjective, thus necessitating caution in their interpretation. It is plausible to posit that the influence of advertising may operate at a subconscious level.  
The analysis of participants' responses revealed that drivers consciously register minimal outdoor advertisements. This limited conscious registration can be attributed to the fleeting nature of their exposure, characterized by an average fixation time of merely 0.3 s and often restricted to a single fixation. Consequently, the advertised logo or product often goes unnoticed, resulting in a lack of conscious recognition during decision-making. The evidence suggests that decisions are predominantly guided by personal experiences with specific goods or services and by the influence of word-of-mouth reviews from others. Consequently, it appears that outdoor advertising exerts only a marginal impact on individuals' decision-making processes.  
The eye-tracking measurements employed in this research are pivotal for assessing its validity, as they guarantee the accuracy of the collected data. Notably, a significant disparity exists between the measurements obtained from the inner and outer circuits. The \"urban\" inner circuit records considerably fewer outdoor advertisements compared with the \"circular\" outer circuit. Nevertheless, it is observed that, on average, drivers notice approximately one-tenth of the large-format outdoor advertisements encountered. It is important to acknowledge that this value is contingent upon numerous factors. Nonetheless, it can be stated that a lower number of recorded ads corresponds to a weaker impact on purchase decisions.  
By evaluating measurements conducted under three different conditions, certain instances reveal the potential for external advertising to sway driver choices. However,it is important to note that this potential remains relatively weak when compared with the overall number of ads surveyed. This observation underscores the need for further measurements and exploration. Notably, all instances of possible influence were registered on the outer circuit, indicating the influence exerted while driving in town. Conversely, within the city, specifically on the inner circuit, no instances of possible impact through external advertising were recorded. It is crucial to avoid unequivocal assertions of the absence of influence; nonetheless, determining the magnitude of impact solely based on eye-tracking measurements is inherently challenging.
## 2 Beyond 100 Gb/S Era
### The Need for per Wavelength AIR Enhancement  
The 100 G standards for short-reach optical transceivers are defined by the IEEE 802.3. The 100 GBase long-range (LR4) defines the optical interface for four wavelengths at about 1310 nm based on a channel spacing of 4.5 nm with an achievable transmission range of 10 km. The 100 GBase-ER4 can operate over a 40 km range [36; 62]. The 100 G Ethernet local area network (LAN) interface standardization prompts the need for the enhancement of the per-wavelength AIR of the short-reach systems to 100 Gb/s and beyond. This is an effort to support and meet the growing increase in Internet traffic [9; 24]. For instance, 100 Gb/s Ethernet frames' transmission over 2 to 10 km SMF has been standardized as 100 GBASE-LR4 in the IEEE 802.3 ba. In this context, four colors in the 1300 nm band are employed on a LAN-WDM grid with a 4.5 nm channel spacing and with individual channels supporting 25 Gb/s of intensity-modulated data. Besides the LR4 standard, 100 GBASE-SR4 offers specifications to support Ethernet frames over \(\sim\)100 m of multimode fiber. This is based on the employment of four fiber lanes with each of the lanes delivering 25 Gb/s in the 850 nm window. Furthermore, with respect to the IEEE 802.3 ba standard, there are a number of commercial 100 G transceivers on the market. These transceiver modules are C form factor pluggable (CFP) such as CFP and CFP2, which are the industry standard MSA form factors that are designed for optical communication applications. They are intended to support 40/100 G and are compliant with the 40 and 100 GBase IEEE 802.3 ae standards [63]. There are some efforts toward 400 G and beyond (1 Tb/s) Ethernet. For instance, CFP8, which is a form factor being specified by the CFP MSA for 400 G Ethernet solutions, has been presented. The two 400 G transceivers designed for this purpose are CFP8-FR8 and CFP8-LR8 [64; 65].
## 2 Materials and Methods
### Hyperspectral Image Acquisition  
A lab-based visible-near-infrared HSI system was used in reflectance mode for HSI acquisition. The wavelength range of the HSI sensor was 400-1000 nm. The HSI system consisted of four main parts: (1) the camera (Specim FX 10e, Middleton Spectral Vision; Middleton, WI, USA); (2) illumination system (Via-Spec(tm) II Halogen Reflectance Illumination-MRC-920-045); (3) sample conveying acquisition system (Lab Scanner Setup 40 \(\times\) 20-MRC-313-004-05); and (4) the Lumo Scanner software, which allows the user to set up, adjust, and acquire data for the supported Specim spectral camera. Figure 2 shows the essential parts and units of the designed HSI system.  
The 90 samples used for the HSI analysis were placed on the sample base and conveyed to the camera sensor for capturing the image by line scanning. Before image acquisition, each steak was allowed to bloom (oxymyoglobin formation) for 20 min after cutting from the ribeye roll sub-primal. All images were subjected to a complete set of image interrogation and analysis tools to aid the imaging process. The collected resolution of each HSI spectral image was 1024 \(\times\) 2056 pixels on 448 bands. Considering the signal-to-noise ratio, the conveyor platform speed was set to 28 mm/s and the distance between the  
Figure 1: Representation of the three beef classes utilized for this study.  
Figure 2: The designed hyperspectral imaging system.  
sample and the camera lens was 25.5 cm. For system calibration, a Teflon bar (20 \(\times\) 2 cm) with \(\sim\)99% reflectance (white) area was first placed on the scanning bed and captured as a calibration image. The following equation was used to calibrate the hyperspectral images:  
\[\mathrm{I}=\frac{\mathrm{I}_{0}-\mathrm{D}}{\mathrm{W}-\mathrm{D}} \tag{1}\]  
where I is the calibrated image, I\({}_{0}\) is the raw hyperspectral image, W represents a mean value of the white reference, and D represents a mean value of the dark reference.
# Multi-Media and Multi-Band Based Adaptation Layer Techniques for Underwater Sensor Networks
## 7 Proposed Scheme
### Medium Selection Mechanism
#### 7.4.2.3 Scenario for Medium Selection Mechanism  
In Figure 10, two scenarios in which the multi-media approach can be used are presented: (1) Tsunami monitoring, and (2) Diver networks monitoring. In both scenarios, the data needs to reach the control room much faster, and that data should be reliable. So, the distance is used as the criteria for selecting the medium. The distance between the nearby nodes is calculated using RSSI values and the Manhattan method. Table 3 shows the RSSI value received as dB from the nearby node. For example, S1 receives -33 dB from node S4, which is one of the nearby nodes of S1. So, its distance is 4 m, based on the Manhattan method.  
Figure 10: Surveillance using the multi-media mechanism.  
Figure 9: Medium selection mechanism.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
**RSS/sensor Nodes** & **S1** & **S2** & **S3** & **S4** & **S5** & **S6** & **S7** & **S8** & **S9** & **S10** & **S11** & **S12** & **S13** & **SN** \\ \hline S1 & 0 & \(-\)55.0 & - & \(-\)33.1 & \(-\)17.66 & - & - & - & - & - & - & - & - & - \\ S2 & \(-\)55 & 0 & \(-\)46.3 & - & - & - & -37.1 & \(-\)42.1 & - & - & - & - & - & - \\ S3 & \(-\) & \(-\)46.3 & 0 & - & - & - & - & \(-\)43 & - & - & - & - & - & - \\ S4 & \(-\)32.0 & - & - & 0 & \(-\)41 & - & - & - & \(-\)44.9 & - & - & - & - & - \\ S5 & \(-\)77.6 & - & - & -41 & 0 & \(-\)31 & - & - & \(-\)55.0 & - & - & - & - & - \\ S6 & - & - & - & - & \(-\)30.1 & 0 & - & - & \(-\)43 & -40.11 & - & - & - & - \\ S7 & - & \(-\)39 & - & - & - & - & - & 0 & - & - & - & \(-\)40.01 & \(-\)18 & - \\ S8 & - & \(-\)42 & \(-\)43 & - & - & - & \(-\)40.11 & 0 & - & - & - & \(-\)33.66 & - & - \\ S9 & - & - & - & - & - & \(-\)43 & - & - & 0 & - & - & - & \(-\)45 \\ S10 & - & - & - & - & - & \(-\)40.1 & - & - & 0 & - & - & \(-\)30.99 & \(-\) \\ S11 & - & - & - & - & - & - & - & - & - & - & 0 & - & \(-\)37.01 & \(-\)45 \\ S12 & - & - & - & - & - & - & \(-\)40.01 & \(-\)33.88 & - & - & - & 0 & \(-\)17 & - \\ S13 & - & - & - & - & - & - & \(-\)13.00 & - & - & - & \(-\)37.01 & \(-\)17 & 0 & \(-\)41 \\ SN & - & - & - & - & - & - & - & \(-\)45 & \(-\)30.9 & \(-\)45 & - & - & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: RSSI values of nearby nodes based on distance.  
Figure 11 shows the multi-media method which is applicable for reliable and fast routing underwater. The result is based on source to destination routing using the Manhattan method. In this approach, multiple media can be used for communications between nodes. In our working scenario, there are a number of sensor nodes from S1 to S13, and SN is considered as the sink node. Here, S1, S2, and S3 are the source nodes that are to transmit the data. In this case, it will choose the most efficient method for transmitting data. For example, S1 will transmit data through S1\(\rightarrow\)S1-S5-S6-S10-SN using the Manhattan approach. Also, this approach will be used in different media for communication between sensor nodes based on Sections 7.4.2.1 and 7.4.2.2.
# Enhanced Pet Behavior Prediction via S2 GAN-Based Heterogeneous Data Synthesis
## 4 Experiment
### Results of Data Preprocessing  
#### 4.2.1 Video Preprocessing  
Video data preprocessing involved creating sequences of x and y coordinates where the skeleton was extracted in each frame. To improve the accuracy of skeleton extraction, open datasets, such as the StandardExtra dataset [32] and the Animal Pose dataset [33], were used for dog-pose estimation. The Stanford extra dataset is labeled for joint coordinates using the Stanford dogs dataset containing images of over 120 breeds. The animal pose dataset provides joint coordinates for dogs, cats, cows, horses, and sheep; herein, only data for dogs were extracted. The number of data points used for dog detection is shown in Table 6.  
For skeleton extraction, the YOLOv8 model adopted herein was differentiated by size into nano (n), small (s), medium (m), large (l), and extra-large (x) variants. Using the prepared dog-pose dataset, training was conducted on the m, l, and x models; the results are shown in Figure 9. With batch sizes of 16 and 100 epochs, the YOLOv8x model demonstrated the best performance across various scales and, thus, was selected. The results of pose estimation on a portion of the collected dataset using the trained YOLOv8x model are shown in Figure 10.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**Utilized Data**} & \multirow{2}{*}{**Total**} \\ \cline{2-3} \cline{5-4}  & **Stanford Extra** & & **Animal Pose** \\ \hline Training Data & 14,355 & 1070 & 15,425 \\ Validation Data & 2009 & 159 & 2168 \\ Test Data & 4216 & 542 & 4758 \\ \hline Total & 20,580 & 1771 & 22,351 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Number of utilized data points in image data preprocessing.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Behaviors** & **Total** \\ \hline
0 & Standing on Two Legs & 486 \\
1 & Standing on All Fours & 4755 \\
2 & Stitting on Two Legs & 5762 \\
3 & Stitting on All Fours & 5079 \\
4 & Lying Down & 1502 \\
5 & Lying & 935 \\
6 & Walking & 1995 \\
7 & Sniffing & 776 \\
8 & Eating & 1986 \\ \hline Total & & 23,276 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Amount of data per behavior used in the experiment.  
Figure 9: Learning results by YOLOv8 model. (**a**) Represents the loss value for validation data, and (**b**) shows the performance results for mAP (mean Average Precision) at the 50–95 range.