esa-sceva/satcom-chunk-collection huggingface dataset:
# Feature Extraction and Evaluation for Driver Drowsiness Detection Based on Thermoregulation
## 3 Results  
### Data Trends  
As described above, the average trend in the distal skin temperatures, heart rate, and sleepiness score was calculated for both the group of drowsy and non-drowsy subjects. These trends and the corresponding 95% confidence interval are visualized in Figure 4. We note that on average T\({}_{\text{nose}}\) and T\({}_{\text{wrist}}\) increased over the first three windows (+0.55 and +0.91 \({}^{\circ}\)C, respectively), followed by a decrease (-1.05 \({}^{\circ}\)C and -2.01 \({}^{\circ}\)C, respectively) in the drowsy subjects. The skin temperatures of the non-drowsy subjects showed no such pattern. When looking at the heart rate, we observed that there was a decreasing trend for both the drowsy and the non-drowsy subjects, yet the trend appeared to be more distinct for drowsy drivers with an average decrease of 10 bpm compared to 3 bpm for the group of non-drowsy drivers. The sleepiness scores showed a linear increase from on average 2.5 to 6.7 and 2.3 to 3.1 for the drowsy and non-drowsy group, respectively. Furthermore, the results of the [PERSON]'s ANOVA are presented in Table 1. From these tests, we note that for drowsy subjects, the means of the windows differed significantly from each other in each of the four variables (\(p<\)0.001). For the group of non-drowsy subjects, there was a significant difference in the mean of the heart rate between all nine windows, which indicated that the observed decreasing trend was significant (\(p=0.008\)). For the other three variables (T\({}_{\text{nose}}\), T\({}_{\text{wrist}}\), and SSS), the hypothesis that the means of all windows were equal was accepted.  
Figure 3: Schematic overview of the structure for the decision tree classification. (**a**) dT\({}_{\text{win 1}}\) refers to the slope of the temperature in one of the initial windows of the measurement (number 1, 2, or 3). Accordingly, dT\({}_{\text{win 1}}\) refers to the slope in either one of windows 4 to 9. As a result, the driver is labeled as “drowsy” when an increase in temperature is followed by a decrease. (**b**) dHR\({}_{\text{win 1}}\) indicates the slope of the heart rate inside one of the nine windows.
# Tunnel Geology Prediction Using a Neural Network Based on Instrumented Drilling Test
## 3 Methodology for Tunnel Geology Prediction Based on Instrumented Drilling Tests  
### Basic Neural Network  
To construct the complex nonlinear relationships between the drilling data parameters and the actual rock types, the basic neural network method was used. Figure 3 shows a schematic diagram of a basic neural network with a single hidden layer.  
The input layer takes the raw data or preprocessed data vector \(\left(x_{1},x_{2},\ldots,x_{i}\right)^{T}\) into the hidden layer using a weight value vector \(\left(w_{11},w_{12},\ldots,w_{ij}\right)^{T}\) where \(i\) means the input layer node label, \(j\) means the hidden layer node label, and superscript T means the transpose. The hidden layer processing units calculate the sum of the weighted values \(x_{i}w_{ij}\) and add the threshold value \(b_{j}\) (see Equation (1)).  
\[a_{j}=f\left(\sum_{i=1}^{N}w_{ij}x_{i}+b_{j}\right), \tag{1}\]  
where \(N\) means the number of input layer nodes.  
Figure 2: Drilling test records from Chainage no. YK282 + 214 to YK202 + 284.  
The basic neural network structure used in tunnel geology prediction had two hidden layers. There were 18 nodes in each hidden layer, the ReLU activation function was used in the normalization of each layer's output, and the cross-entropy loss function was used to evaluate the loss values. The drilling parameters used in training the neural network model were the drilling speed, torque, rotation speed, propulsion, blow energy, blow number, water supply rate, water supply pressure, and EV energy data.
# Ultra-Low-Loss Silicon Waveguides for Heterogeneously Integrated Silicon/III-V Photonics
## 4 Photonic Integrated Circuits with Ultralow Loss Silicon Waveguides
### Transition Taper to Standard Silicon Waveguides  
Figure 7 shows the schematic of an adiabatic taper for a low loss, low reflection transition of the optical mode from the 56 nm etched rib ULL waveguide to a deeper 231 nm etched rib waveguide, which has a smaller bend radius (50 \(\upmu\)m). The waveguide width is set at 1.8 \(\upmu\)m. The mode transition happens along the linear taper formed by etching on the Si slab. Figure 7a-c shows the simulated profiles of the waveguide's fundamental mode at points a, b and c denoted on the schematic figure.  
In order to ensure an adiabatic transition from fundamental mode of the 56 nm etched waveguide to the 231 nm etched waveguide, the length of the linear taper must be sufficiently long. As shown in Figure 8a, at 1550 nm wavelength, high order modes in the deeper rib waveguide are excited if the taper length is shorter than 25 \(\upmu\)m. With a taper longer than 50 \(\upmu\)m, both mode expansion and finite-difference time-domain (FDTD) simulations show that essentially 100% transmission in the fundamental mode is achieved. The reflection power is also simulated and is shown to be lower than \(-\)60 dB level. To be safe, the taper length of 200 \(\upmu\)m is chosen in practice. The wavelength dependence of the transmission efficiency and reflection power are again simulated in FDTD and shown in Figure 8b. Larger than 99.8% transmission and lower than \(-\)55 dB reflection should be achievable over the whole C + L band.  
Figure 7: Schematic of the taper converting the optical mode from an ULL waveguide to standard rib (231 nm etch depth) waveguide. Figures (**a**–**c**) show the waveguide mode profiles at locations labeled a—ULL silicon waveguide, b—central part of the taper, and c—standard silicon waveguide along the taper structure.
## 4 Results
### Final LAI Maps  
The best performing ML regression model has been applied to map cotton LAI with Sentinel-2 imagery, as analyzed in previous sections, we use the GBRT model to map cotton LAI in the study area. The fields in the study area are regular, we conducted a manual vectorization based on Google Earth image data to obtain accurate agricultural fields boundaries, and Landsat 8 image data were collected to update the fields, as Google Earth image data has poor temporal information. Totally, 40,211 fields were collected by this way in the study area. Figure 11 displayed the agricultural fields boundaries. A per-field crop classification was performed to extract cotton fields. The final cotton LAI map was obtained by masking the results using the extracted cotton fields. Figure 12 presents the final cotton  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metrics in the Radar Chart** & **Corresponding Description** \\ \hline M1 & Fitness (\(\mathbf{R^{2}}\)) \\ M2 & Accuracy (RMSE) \\ M3 & Accuracy (MAE) \\ M4 & Computational efficiency \\ M5 & Sensitivity to training sample size (\(\mathbf{R^{2}}\)) \\ M6 & Sensitivity to training sample size (RMSE) \\ M7 & Sensitivity to training sample size (MAE) \\ M8 & Sensitivity to spectral bands (\(\mathbf{R^{2}}\)) \\ M9 & Sensitivity to spectral bands (RMSE) \\ M10 & Sensitivity to spectral bands (MAE) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Metrics used in a radar chart and the corresponding descriptions.  
Figure 10: Comprehensive comparison of five machine learning regression algorithms (MLRAs), with different metrics using a radar chart.  
LAI map. From Figure 12, it is clear to find that there is an increasing trend of cotton LAI growth, as revealed by Figure 2.  
In the early June cotton LAI map, some areas have LAI values much larger than the whole map (green color), as well as LAI values much smaller found in mid-July and mid-August (orange color), these fields are some other crops rather than cotton, and it can be ascribed to the misclassification.
# Multi-UAVs Communication-Aware Cooperative Target Tracking
## 3 Information Fusion Based on Extended Information Filtering
### Extend Information Filter and Information Fusion Phase
When \(r_{j}=r_{\min}\) and the communication between UAVs is perfect, which means \(c_{rj}=c_{\varphi j}=0\), we can rewrite Equation (45) as:  
\[\det(J_{i})= \frac{1}{4}\cdot\left(\sum\limits_{j\in N_{i}^{+}}\left(\frac{1} {\sigma_{r}^{2}}+\frac{1}{r_{\min}^{2}\sigma_{\varphi}^{2}}\right)\right)^{2} \tag{46}\] \[-\left(\frac{1}{\sigma_{r}^{2}}-\frac{1}{r_{\min}^{2}\sigma_{ \varphi}^{2}}\right)^{2}\left(\left(\sum\limits_{j\in N_{i}^{+}}\left(\cos 2 \varphi_{j}\right)\right)^{2}+\left(\sum\limits_{j\in N_{i}^{+}}\left(\sin 2 \varphi_{j}\right)\right)^{2}\right)^{2}\]Thus, when the communication is perfect, the necessary and sufficient condition to get the maximum \(\det(J_{i})\) is:  
\[\sum\limits_{j\in N_{i}^{+}}\left(\cos 2\varphi_{j}\right)=0\,\&\sum\limits_{j \in N_{i}^{+}}\left(\sin 2\varphi_{j}\right)=0. \tag{47}\]  
Then, from Equation (47), when \(N\geq 3\), we get the following observation configuration:  
\[\varphi_{i,j}=\frac{2}{N}\pi \tag{48}\]  
where \(\varphi_{i,j}\) means the angle between UAV \(i\) and \(j\). This is shown in Figure 3. We know from Equation (48) that when the communication is perfect, UAVs tend to be dispersed around the target to get a better observation. But, due to the communication noise between UAV \(i\) and \(j\), using the observation configuration of Equation (48) cannot get the maximum of \(\det(J_{i})\), which can be seen from Equation (45). So, in order to reduce communication noise, the UAVs tend to get as close as they can. When they are getting too close, the communication noise is very low, and as a result, we can consider the communication is perfect. But from Equation (48) we can see that it is not conducive to observation when they are getting too close. Therefore, the objective function in Equation (45) can make UAVs reach a compromise between observation and communication in a natural way.
# Fibromyalgiagia and Orofacial Health: Administration of a Dental Disease Assessment Questionnaire
## 3 Results  
The questionnaire was submitted to 107 sequential patients and completed by 100% of them. No patient refused to fill it out. Moreover, 7.5% of the patients were male, and 92.5% of the patients were female. The mean age of the patients who filled out the questionnaire was 51 years (18 to 72).  
Only 11.2% of the participants responded that they were aware of the correlation between fibromyalgia and oral health; 60.4% said they suffered from gum bleeding during oral hygiene procedures; 89.7% of the participants reported suffering from pain in the facial muscles; 44.9% of the patients experience noises in the temporomandibular joint when opening or closing the mouth; 71% of the patients grind their teeth at night, and 30.8% during the day; 98.1% of the patients reported waking up with tired or sore facial muscles; and 98.1% of the patients reported suffering from pain in the cervical area.  
The average FAS index value of the patients was 25, with a minimum of 19 and a maximum of 32. The statistical analysis showed statistically significant correlations between the FAS index and symptoms such as gum bleeding, teeth grinding during the day, and neck pain (_p_ < 0.05). The correlations between the FAS index and joint noise upon opening the mouth, as well as dissatisfaction with one's smile, also proved to be highly significant (_p_ < 0.001).  
The results of questions 3 to 11 of the questionnaire are summarized in Table 1 and Figure 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **n, (\%)** & **n, (\%)** & **n, (\%)** \\ \hline
**Question** & **Yes** & **No** & **1 Do Not Know** \\ \hline (3) Are you aware of the correlation between fibromyalgia and oral health? & 12, (11.2) & 95, (88.8) & 0 \\ \hline (4) Do your gums bleed when you brush your teeth? & 64, (59.8) & 42, (39.3) & 1, (0.9) \\ \hline (5) Do you suffer from facial pain? & 96, (89.7) & 11, (10.3) & 0 \\ \hline (6) Do you experience noises when opening your mouth? & 48, (44.9) & 58, (54.2) & 1, (0.9) \\ \hline (7) Do you grind your teeth when you sleep? & 76, (71) & 31, (29) & 0 \\ \hline (8) Do you grind your teeth during the day? & 33, (30.8) & 72, (67.3) & 2, (1.9) \\ \hline (9) Do you find yourself waking up with fatigued facial muscles. or headaches? & 105, (98.1) & 2, (1.9) & 0 \\ \hline (10) Do you suffer from neck pain? & 105, (98.1) & 2, (1.9) & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Questionnaire results regarding oral health.  
The distribution of the statistically significant correlations between the FAS index and the symptoms is represented in the boxplots in Figures 2-5.  
Figure 1: Patients’ smile satisfaction (1 being completely unsatisfied and 5 being completely satisfied).  
Figure 2: Boxplot graph regarding the correlation between gum bleeding and FAS index.  
Figure 4: Boxplot graph regarding the correlation between teeth grinding and FAS index.  
Figure 3: Boxplot graph regarding the correlation between temporomandibular joint noises and FAS index.
## 4 Results
### Qualitative Analysis
#### 4.2.3 Mint Functions  
Mint functions can be a normal part of an ERC-20 smart contract, and are usually used to generate tokens for that contract. However, they can also have malicious intent depending on how a smart contract is coded or if a developer of a contract has renounced their ownership. After investigating the tokens which appeared to have mint functions, it was found that two types were used maliciously. The first is a normal mint function which could be included in a standard ERC-20 contract; however, with a non-malicious developer, if this was in the contract, they would usually call a renounce ownership function, which would make the owner of the contract a null address so that they could not call the mint function. The other mint function, which was more prevalent, would be a hidden one. This would be a function named something unrelated to minting tokens, but would operate as such. Sometimes a user's address would be included in the code so that even if they renounced ownership of a contract, they would be whitelisted to run the mint command.  
One of the tokens analysed which included a malicious function was \"Emperor Shibab-0x6b27b276e34f5F01 FC733 fBFA96C65072 db89A41\", where it is possible to see a \"Hidden Mint Function\" which was \"liquidityFees(uint256 amount)\", as seen in Figure 8. This function allows the owner of the smart contract to mint any \"amount\" by writing to the smart contract, and it will then add the minted amount to the user's balance. As ownership of the contract has not been revoked, the function, when called by the malicious user, allowed them to mint 10,000,000,000,000,000,000 tokens, as shown in Figure 9, which was far greater than the amount originally put in the liquidity pool. Due to the token also having no maximum transaction, they were then able to instantly drain the liquidity pool, stealing 1.5 BNB worth of other users' money, alongside the original liquidity amount.  
Figure 8: Emperor Shiba hidden mint functions.  
Another token that used a hidden mint function was \"Doge Sendor-0 xbb0e5e2f546e2e7 6528 afde557 ab63 bea14b0 ee4\". This token manipulated a usually safe function \"ManualSendFunds\", shown in Figure 10, which should allow a user to send funds to another user. However, this function was set to only the owner of the contract being able to call it with the \"onlyOwner\" function and allowed the transfer of funds over the number of tokens already minted, enabling more tokens to be generated than are in the liquidity pool. This allowed users' funds to be taken similarly to through Emperor Shiba, even though the original liquidity was sent to a null address.  
\"MOONSQUASHINU\", another malicious token, was found using a generic mint function called \"mint\", which allows for the same effect as the previous two tokens analysed. This shows that not every user would investigate the contract for a mint function, as funds were still stolen from users for this. However, hidden mint functions became much more prevalent in later years, which assumes users became aware of the usual malicious mint techniques.
# Molecular Targets of Minor Cannabinoids in Breast Cancer: In Silico and In Vitro Studies
## 2 Results
### Molecular Docking Results
The main amino acid residues of the target are shown as gray sticks, while the cannabinoids are shown as colorful sticks. function played by this steroid receptor in sensitive ER\({}^{+}\) tumors, the compounds studied should, for therapeutic purposes, act as AR agonists [87]. Therefore, we docked the nine selected cannabinoids on an agonistic structure of this receptor and the results showed that the AR binding site appears to be small to properly accommodate these cannabinoids. Nevertheless, they established interactions, mainly hydrophobic, with several residues, including Met745, Trp741, Leu704, Met895, Met342, and Thr877 (Figure 6). Furthermore, they were stabilized by hydrogen bonds with Thr877, Leu704, and Met742. Thr877 has been known to be important for the agonist's stabilization [88; 89; 90]. In fact, **CBD** docking in AR showed the lack of ability of this compound to interact with Thr877 (Figure 6) and our previous in vitro studies have demonstrated that **CBD** acts as an AR antagonist with inverse agonist properties [44]. Following this, we postulate that **CBDA**, **CBCA**, and **CBGVA**, by establishing this bond with Thr877, may act as AR agonists, while the others may act as AR antagonists. Figure 6: Molecular docking of the nine minor cannabinoids, **CBG** (**A**,**B**), **CBDV** (**C**,**D**), **CBN** (**E**,**F**), **CBDB** (**G**,**H**), **CBDA** (**I**,**J**), **CBDM** (**K**,**L**), **CBCA** (**M**,**N**), **CBGVA** (**O**,**P**), and **CBC** (**Q**,**R**), as well as **CBD** (**S**,**T**), in AR. The images represent both 2D and 3D AR/cannabinoid interactions. AR structure  
has the PDB code 2 AMA and is represented in as cartoon. The main amino acid residues of the target, including Met745, Leu704, Trp741, Met895, Met742, and Thr877, are shown as gray sticks, while the cannabinoids are shown as different colorful sticks. Hydrogen bonds are represented as black dashed lines in the 3D representations and as purple arrows in the 2D maps.
# PCCNoC: Packet Connected Circuit as Network on Chip for High Throughput and Low Latency SoCs
## 6 Conclusions  
The proposed design, called PCCNoC, provides an NOC architecture for a heterogeneous multi-core real-time system on a chip. PCCNoC inherits the packet switching advantages of flexible routing of packet switching and scalability in addition to the low latency of a circuit switch network. Furthermore, it is buffer-free, which is the key advantage of circuit switching. The data transferred on PCCNoC is based on a circuit, and the data are absolutely in order, meaning that the re-order induced latency area overhead is eliminated. This scheme can support multiple parallel transmissions at the same time. It is a hybrid NoC that uses packet transmission to establish a data path and lock this data path into a temporary circuit.  
Compared with traditional packet switched networks, PCCNoC has 97% lower latency and 242% higher throughput, which is particularly suitable for complex heterogeneous multi-core real-time SoC, such as baseband systems in base stations. Compared with other related works, PCCNoC uses the same resource for packet switching and circuit switching, resulting in reduced area and power consumption. Moreover, it has multicasting and broadcasting functions, expanding the possible network-on-chip application scenarios, and in the future could be equipped with a CRC and timer to ensure QoS.  
Conceptualization, D.L., P.H. and X.Z.; methodology, D.L. and X.Z.; software, X.Z.; resources, X.Z.; data curation, X.Z. and P.H.; writing--original draft preparation, X.Z.; writing--review and editing, D.L., P.H. and X.Z.; visualization, X.Z.; supervision, D.L., X.Z.; project administration, D.L., P.H. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Hainan University project funding KYQD (ZR)1974.  
Not applicable.  
The data that support the findings of this study are available on request from corresponding author.  
The authors want to thank the editor and anonymous reviewers for there valuable suggestions for improving this paper. The authors are grateful to the State Key Laboratory for the Utilization of Marine Resources in the South China Sea, School of Information and Communication Engineering, Hainan University for administrative and equipment support. The authors would like to express their gratitude for Ultichip Communications Technology Company Limited for technical support.  
The authors declare no conflict of interest.
## 4 Results
### GHG Emission Reduction Results
#### 4.2.2 The Total GHG Emission of FFBS in Life Cycle  
According to the data from bike operators, relevant studies, and the Ecoinvent database [19; 40], the weight of each bike is about 20 kg, including 15.181 kg steel, 0.171 kg aluminum, 1.105 kg plastic, 2.143 kg paper, 1.400 kg rubber, 0.35 kg electrical equipment, 0.15 kg battery, and 0.02 kg photovoltaic panel, etc. Producing a bike requires 0.001 m\({}^{3}\) of water, 0.37 kWh of electricity, and 0.85 m\({}^{3}\) of natural gas. According to the recovery efficiency setting in the disposal phase, taking Nanjing as an example, the GHG emission of a FFBS is 54.209 kg CO\({}_{2}\)-eq.  
The operation phase is mainly divided into two parts: rebalancing and maintenance. First of all, according to the obtained scheduling statistical data, shown in Table 1, assuming that the daily average scheduling distance of small trucks, three-wheel electric vehicles, and Iveco vehicles is 100, 185, and 70 km, respectively. The GHG emission generated by the daily scheduling of each FFBS is 0.012 kg CO\({}_{2}\)-eq. Hence, in the service life of FFBS, the GHG emission generated by the scheduling is 13.384 kg CO\({}_{2}\)-eq. Due to lack of corresponding data, the average GHG emission per FFBS maintenance was 0.319 kg CO\({}_{2}\)-eq [40]. Therefore, in the whole operation phase, each FFBS emits an average of 13.727 kg CO\({}_{2}\)-eq. In the disposal phase, it is assumed that 90% of the recyclable metals can be reused, and the remaining parts are all disposed as waste for incineration. According to the formula, the average emission of 8.774 kg CO\({}_{2}\)-eq will be generated in each FFBS disposal phase.  
It is calculated that in the life cycle of FFBS, the three phases of production, operation, and disposal will generate GHG emissions of 76.710 kg CO\({}_{2}\)-eq. of which the production phase generates the most GHG emissions.