esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Result and Discussion
### Answer-Grading Process after Balancing Dataset
There is an increase in performance when using the augmented data, which is also consistent with our experimental results. The RMSE scores obtained from our experiments are indeed larger than those from [PERSON]'s research [20], but that does not have much impact because of other better evaluation metrics values. Moreover, the use of the T5-XL model would definitely require even larger and more expensive resources. The comparative F1-score and accuracy values were only obtained from research by [PERSON] et al. [13], and our best experimental results on average also exceed those results. [PERSON] et al. used the relatively small paraphrase-albert-small-v2 model, which is only about 40 MB in size. However, our recommended model can potentially perform better, due to differences in the datasets. [PERSON] et al. used the SPRAG dataset, which is a binary classification problem [13], while the Mohler dataset consists of grade labels ranging from 0 to 5. With a more complex dataset, even though it requires a larger model, the results can still compete with simpler models. Note also that the paraphrase-albert-small-v2 model has an average running time that is not significantly different from the all-distilroberta-v1 model, which is about seven times larger in size. The Pearson correlation scores from our recommended model can also achieve better results than the existing research. When the same dataset and the same hyperparameter fine-tuning process are implemented on larger models, it indeed produces better results, but this performance improvement is not proportional to the larger and more expensive computational cost. This research aims to find a simpler model with a proper fine-tuning process. The experiments have shown that relatively smaller-sized models with the proper fine-tuning can achieve a good performance. The data augmentation for balancing the dataset itself also contributes to a significant improvement in the performance results of this grading system. The all-distilroberta-v1 model, which is less than 300 MB in size, with the proper hyperparameter selection and combined with balancing the dataset, can compete with the results of larger and more complex models.
## 3 Results and Discussion
### Chemistry
#### 3.1.2 Determination of Some Physicochemical Properties
The C\({}_{18}\)-functionalized silica gel (stationary phase; SPh) and liquid binary mixtures of methanol (MeOH) in different volume concentrations (60-90%; \(v/v\)) with 5% increments and water [66] were employed as suitable mobile phases (MPhs) in order to estimate these \(\log k\) parameters (Tables S1 and S2). The highest \(t_{\rm r}\) and \(\log k\) data were determined for the most lipophilic compounds **6f** (\(t_{\rm r}=26.5828\) min, \(\log k=1.7516\)), **6e** (\(t_{\rm r}=24.7159\) min, \(\log k=1.7372\)), and **6g** (\(t_{\rm r}=23.1377\) min, \(\log k=1.6913\)) in the MPh with a 60% proportion (\(v/v\)) of the organic modifier. The increase in the amount of MeOH (\(v/v\)) lowered the values of both \(t_{\rm r}\) and \(\log k\) parameters as expected (Tables S1 and S2). The estimated \(\log k\) descriptors for **6a-g**, however, might not be considered universal parameters to adequately express their lipophilicity because of their being very notably influenced by particular chromatographic conditions [67]. The extrapolation of estimated solute retention (\(\log k\)) to the elution with 100% water (\(\log k_{\rm w}\)) was regarded as a much more adequate procedure (Table 2). The impact of lipophilicity, expressed with \(\log k_{\rm w}\), on the in vitro antimyobacterial activity of these compounds might be expected [31; 47]. The linear relationships between estimated \(\log k\) and \(\varphi_{\rm M}\) related to particular molecules were statistically extremely significant, as all calculated probabilities of obtaining \(F\)-ratio (_Prob \(>\) F_) values belonged to the interval from 0.0000 to <0.0010 (Table 2). Very acceptable quality, which characterized all suggested linear relationships providing desired extrapolated parameters (\(\log k_{\rm w}\)s), was adjusted with the reduced chi-square (\(\chi^{2}_{\rm red}\)) descriptor varying from 0.0004 (**6e**) to 0.0040 (**6c**). Next, the linearity of all observed models was defined with minimal _RSS_ (residual sum of squares) outputs from 0.0016 (**6e**) to 0.0201 (**6c**) as well as with the root mean square error (_RMSE_; Table 2) found between 0.0176 (**6e**) and 0.0635 (**6c**). The \(\log k_{\rm w}\) values of **6a-g** ranged from 4.1170 (**6a**) to 5.2184 (**6g**), considering the variably substituted piperazin-1-ium-1,4-diyl moiety. The lipophilicity of a 4'-CH\({}_{3}\) substituted molecule (**6b**; \(\log k_{\rm w}=4.8419\)) was higher than that containing a 4'-F atom (**6c**; \(\log k_{\rm w}=4.2672\)). This finding was in agreement with the calculated values of the hydrophobic substituent constant (\(\pi\)) of both substituents, which were as follows [68]: \(\pi_{\rm(4^{\prime}\text{-}CH3)}=0.53\) and \(\pi_{\rm(4^{\prime}\text{-}F)}=0.14\).
# Efficient Health Data Transmission Method in a Wireless Body Area Network for Rural Elderly
## 5 Experiment and Analysis  
### Simulation Settings  
In the environment of Intel(r)Core(tm) I7-9700U [EMAIL_ADDRESS] GHz with 16 GB RAM, the algorithm was verified on the MATLAB platform. The deployment location of the WBAN is shown in the Figure 2. To facilitate human action, the sink was placed at the waist. Taking the sink as the origin, the ray parallel to the ground as the X-axis, the ray perpendicular to the ground as the Y-axis, and the ray perpendicular to the X-axis and Y-axis as the Z-axis, a three-dimensional coordinate system of the WBAN for the rural elderly was established.  
In order to approach the channel state of the WBAN when the elderly was really active, we set the path loss as [26]:  
\[PL(dB)=-10\lg(25.8e^{-2d}-71.3)+3.8 \tag{14}\]  
The rural elderly moves frequently. In order to facilitate work, the clothe material will be rough and will easily cause large signal loss. Therefore, the maximum environmental attenuation rate in this model was set to 2. Through the signal loss model, the connectivity probability between links was initialized, and the actual connectivity probability of the links during each transmission was simulated. Other algorithm parameters are described in Table 3.  
Assuming that the height of the elderly was 165 cm (\(h=23.5\) cm) [27], the human body was in a relatively stable state such as standing/sleeping at the beginning, and all nodes were on the plane formed by the X-axis and the Y-axis, then the position coordinates of all nodes could be obtained, as listed in Table 4.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Number of Sensor Nodes & 5 \\ Number of Sink & 1 \\ \(T_{window}\) & 10 \\ Initial energy E0 & 0.5 J \\ \(E_{TXelc}\) & 50 nJ \\ \(E_{amp}\) & 100 pJ \\ Packet Size & 1040 bits \\ Transmission Range & 0.5 m \\ \hline \hline \end{tabular}
\end{table}
Table 3: Detailed parameters.  
Figure 2: Deployment diagram of the nodes in the WBAN.  
When simulating walking, the position of the sensors changed in a period of 2 s. The specific position changes of the sensors in a period are shown in Table 5.
## 4 Algorithm Design
### \(N\)-Ary Number Encoded ADENSGA for Node Placement Problem
\begin{table}
\begin{tabular}{l l} \hline Length and width of the monitoring area, \(x_{m},y_{m}/m\) & 100 \\ \hline Coordinates of the sink node, \((x_{sink},y_{sink})/m\) & (50,175) \\ \hline Total number of nodes, \(n\) & 50 \\ \hline Sensing range of the node, \(r_{s}/m\) & 12 \\ \hline Size of the population, \(P_{size}\) & 20 \\ \hline Crossover probability constant 1 for the node with appropriate node density, \(pc1\) & 0.5 \\ \hline Crossover probability constant 2 for the node with high node density, \(pc2\) & 0.9 \\ \hline Mutation probability, \(p_{m}\) & 0.1 \\ \hline Theoretical maximum number of overlapping, \(Num_{max}^{*}\) & 5 \\ \hline Initial energy of nodes, \(E_{o}/l\) & 0.5 \\ \hline Energy consumption coefficient of node movement, \(E_{d}/(l/m)\) & \(5\times 10^{-6}\) \\ \hline Energy consumption coefficient of data transmission and reception, \(E_{dec}/(l/bit)\) & \(50\times 10^{-9}\) \\ \hline Energy consumption coefficient of data fusion, \(E_{D,A}/(l/bit)\) & \(5\times 10^{-9}\) \\ \hline Energy consumption coefficient of free space power amplification loss, \(E_{fs}/[l/(bit\cdot m^{2})]\) & \(10\times 10^{-12}\) \\ \hline Energy consumption coefficient of multi-path attenuation power amplification loss, \(E_{mp}/[l/(bit\cdot m^{2})]\) & \(0.0013\times 10^{-12}\) \\ \hline Data length, \(l/bit\) & 4000 \\ \hline Node movement range coefficient, \(a_{limit}\) & 0.1 \\ \hline Round number of WSN, \(r\) & 300 \\ \hline \end{tabular}
\end{table} 
Table 1: Parameter Settings. Figure 3: The optimal results for different values of \(N\) of node placement 2. Figure 2: The optimal results for different values of \(N\) of node placement 1. Table 2 shows that the running time and \(D\) have little relation with \(N\). This is because that the running time of the algorithms mainly influenced by the predicting running of WSNs in \(f_{2}\). The influence of encoding length on running time can be negligible. Furthermore, the any number \(N\) may slightly affect the precision of the solutions, but will not affect the total moving distance \(D\) of all the nodes.
## 1 Introduction  
Skeletal maturation is important in orthodontics in order to predict growth peak and completion as well as growth acceleration and deceleration periods. This information is useful mainly in cases of skeletal discrepancies and for clinical decisions regarding timing for orthognathic surgery or treatment initiation time [1, 2, 3, 4, 5, 6]. Additionally, skeletal maturation may be useful for age estimation in living individuals or human cadavers. Identification of an unknown deceased person is important in individual cases and in mass disasters (Disaster Victim Identification, DVI). In these cases, it is crucial to reconstruct the biological profile of a specimen (by gathering information such as age and gender), even when identification is impossible. In both cases, age estimation is based on dental and skeletal maturity [7, 8, 9, 10, 11].  
Several biological indices have been proposed in order to assess skeletal maturity including, stature changes, chronological and dental age, appearance of secondary gender characteristics, and ossification of hand and wrist as well as maturation of the cervical vertebrae (CVM) [12, 13, 14, 15, 16, 17, 18]. Hand wrist and cone beam computed tomographic images may be helpful to assess skeletal maturity [5, 19]. However, most studies have been conducted with lateral cephalometric radiographs [1, 5, 18, 20, 21]. The latter are often included in the initial diagnostic records required for orthodontic diagnosis and treatment planning. The CVM estimation from lateral cephalometric radiographs shows high reliability in comparison to the methods based on the growth increments of the mandible and the ossification of hand and wrist [5; 18; 21; 22; 23]. Two studies stand out as the most influential in this research field [1; 21]. Both evaluate the shape of the cervical vertebrae and the morphology of their lower borders in order to assess the maturation stage. A recent systematic review reported that both these methods are reliable enough to replace the hand-wrist radiograph in predicting the pubertal growth spurt [24].  
Unfortunately, no scientific consensus has been reached regarding the method's reproducibility and accuracy. Some studies reported high reproducibility and accuracy; however, others observed discordance in the assessments of the maturation stage among the examiners [2; 25; 26; 27; 28]. Recent systematic reviews have identified several studies that present methodological weaknesses related to study design (blinding, randomization, sample size), statistical analysis and interpretation of the results [5; 24]. Consequently, further research concerning the reproducibility of the CVM method is needed [2; 5].  
The aims of this study were to investigate (1) the reproducibility of the Cervical Vertebral Maturation (CVM) method according [PERSON] et al. [1] and (2) the potential for chronological age estimation using this method.
## 5 Performance Evaluation  
In this section, the efficacy of our proposed scheme is validated through simulation. The performance of the proposed QEPRA and First Fit Satisfaction (FFS) and First Fit Profit (FFP) algorithms are compared with some of the existing schemes in the literature. Here, the proposed system is assimilated with the NvG (news vendor game-based resource allocation) scheme [9] and QoSM (Quality-of-Service-aware dynamic BBU-RRH mapping) methodology [6].
## 2 Composition of the \"Target Situational Awareness\" Test System and Experimental Methods
### Carrier Aircraft and Pod Being Tested
#### 2.2.4 Design of the Pod  
The proposed block diagram of the pod is shown in Figure 7, which is composed of a radome, a seeker, a network of cables, an electrical connector and a connecting cable, a cabin, a control device, an inertial measuring device, water-cooling equipment, a storage battery, a GPS station and its antenna, a telemetry antenna, and a data recorder. The radome was locked by radial screws via the joints between the cabins, and the radome was located at the head of the pod, which was subject to pneumatic heating and overload during the test to ensure the normal operation of the seeker. The seeker was placed in the radome to intercept and track the target aircraft selected by the test system, as well as to provide the target information to the control device. The control device conducted calculations for the pod and issued control instructions to each piece of equipment according to the defined time sequence in the test and responded to the carrier's instructions. The storage battery provided the required power supply for the pod, the water cooling equipment offered a cooling path for the seeker to operate for a long time, and the data recorder oversaw data recording on the pod for analysis after the test.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Electrical Interface** & **Signal Format** \\ \hline
1 & In-place status & - \\
2 & Interlock loop & - \\
3 & DC A & 45 V \\
4 & DC A loop & - \\
5 & Multiplex bus A & 1553B \\
6 & Multiplex bus B & 1553B \\
7 & AC B1 & 150 V \\
8 & AC B2 & 150 V \\
9 & AC B3 & 150 V \\
10 & AC neutral wire & - \\
11 & Structural grounding & Connected to the shell of the carrier body \\
12 & Satellite signal & GPS \\
13 & Telemetry forwarding signal & - \\ \hline \hline \end{tabular}
\end{table}
Table 7: Design of the electrical interface.  
Figure 7: Composition of the pod.
## 3 Results  
### Numerical Data Validation  
The velocity profiles calculated by the models, as well as the reference ones found in [37], are presented in Figures 2 and 3. Figure 2 depicts the profiles of the x component of velocity over a vertical, center line, for the Reynolds numbers of 1000 (a), 10,000 (b) and 20,000 (c), and Figure 3 shows the equivalent plots of the y component of velocity over a horizontal, center line. As can be seen, our results replicate the reference model reasonably well. The distance between our results and the reference model slightly increases with the Reynolds number, but the flow structure remains the same, as can be noticed by the curvatures, especially near the cavity walls.
# Quantum Compressive Sensing: Mathematical Machinery, Quantum Algorithms, and Quantum Circuitry
## 5 Projection
### Entangled Born Machine  
A bottleneck in the application of both the Decomposition and Rodeo projection protocols is the number of trials required to obtain the appropriate syndrome measurement. For any given \(s\) and \(y\), the probability \(p\) to measure \(|s\rangle\) varies. Any one instance of our protocol requires a number of trials scaling with \(p^{-1}\). The expected value of the number of trials is a weighted average over all possible \(|s\rangle\):  
\[\langle N_{\text{trials}}\rangle=\sum_{|s\rangle}pp^{-1}=2^{m}. \tag{14}\]  
Thus, the number of trials is exponential with the number of measurements--an extremely unfavorable scaling. Worse yet, rarer signals that coincide with the most interesting features would require even more trials.  
In light of the above, one important area of further research is to identify deterministic projection protocols. One possible line of inquiry is to consider multiple, entangled copies of \(\Psi\). For example, assume \(|\Psi\rangle\) is in the following state:  
\[|\Psi\rangle=a\ |0\rangle|\Psi_{x}\rangle+b|1\rangle|\Psi_{1}\rangle. \tag{15}\]  
Further, assume that our target syndrome is \(|s\rangle=|0\rangle\), and we aim to isolate \(|\Psi_{x}\rangle\). Given this state, we can only hope that measuring the first qubit yields \(0\), as in the first two projection protocols described above. However, given the entangled state  
\[|\Phi\rangle=\frac{a+b}{\sqrt{2}}|0\rangle|\Psi_{x}\rangle|\Psi_{1}\rangle+ \frac{a-b}{\sqrt{2}}|1\rangle|\Psi_{1}\rangle|\Psi_{x}\rangle \tag{16}\]  
one can measure the first qubit: if it is \(0\), \(|\Psi_{x}\rangle\) is prepared in the first copy; if it is \(1\), we take the second copy instead. This method requires just one trial, and guarantees success. However, it instead requires an exponential number of _copies_, and it is not clear how to prepare the entangled state \(|\Phi\rangle\), given a protocol to prepare the single state \(|\Psi\rangle\). We leave further investigation of this approach to future work.
# Research on a Real-Time Driver Fatigue Detection Algorithm Based on Facial Video Sequences
## 2 Approach  
The overall structure of our research is shown in Figure 1. The methodology consists of the following two parts:  
_Offline Training_: In order to realize driver fatigue recognition accurately, off-line training is carried out. Driver fatigue state is generally recognized by facial features, such as the opening and closing of eyes and the degree of opening the of mouth when yawning [26; 27; 28; 29]. Thus, we firstly collected the driver's facial feature images of the driver wearing glasses or sunglasses, such as the opening and closing of eyes and the opening degree of the mouth. Then, a tasks-constrained deep convolutional network (TCDCN) algorithm is applied to the driver's facial feature recognition for each image collected. The advantage of this algorithm is to conduct multiple-task learning (including gender, whether wearing glasses or sunglasses or not, and face posture) at the same time. These auxiliary attributes can help to locate facial feature points better. Furthermore, two types of percentage of eye closure time (PERCLOS) methods were used, as shown in Figure 1. The PERCLOS1 is the value calculated when the eyes open with or without glasses and PERCLOS2 is the value calculated when the state of the driver's eyes, with or without glasses are closed, and the ratio of the mouth's height and width can be calculated in real time. Finally, we take the above three types of data (i.e., PERCLOS1 when the eyes are open, PERCLOS2 when the eyes are closed, and the ratio of the mouth's height and width) as samples, and train them with AdaBoost classifier to determine whether the eyes are open or closed, and whether the mouth is open or closed. AdaBoost is the abbreviation of adaptive boosting. It is a machine learning meta algorithm produced by [PERSON] and [PERSON]. Its core idea is to train different classifiers (weak classifiers) for the same training set, and then combine these weak classifiers to form a stronger final classifier [30].  
_Online Testing_: Is a real-time module to detect driver's fatigue from live video sequences. All the video images are imported into TCDCN to recognize the driver's face, and then the eye and mouth feature landmarks are obtained in real time [31]. Then, the off-line trained AdaBoost classifier is used to determine whether the driver's eyes and mouth are open or closed in the current frame [32]. At last, we classify whether the driver is sleepy or not according to the condition that the mean duration of closure time (or open mouth time) exceeds the selected threshold in a period of time. Besides, in order to identify driver fatigue more accurately, TCDCN can eliminate the influence of wearing glasses, head posture, and other factors.
# Revolution or Evolution? Technical Requirements and Considerations towards 6G Mobile Communications
## 8 Future Directions in 6G Technology Development
### Security, Privacy, and Trust  
The 5G and 6G services and technologies are expected to take the world by storm in the near future, offering better services and technologies to end-clients, communications, businesses, governments, etc. Such developments will develop an enormous attack vector attributed with high risk towards businesses and individuals. Nevertheless, the proper execution of 6G networks seems difficult considering the outdated security techniques, privacy, integrity, availability, and authenticity in 4/5G. However, better execution of 6G technologies is possible by integrating a security by-plan to meet the evolving and complex needs associated with data privacy, location privacy, identity privacy, and metadata privacy. In conclusion, we can infer that future research in the domain of 6G must focus on the development of better and cheaper security and protection features [145].
## 2 Experimental Procedures
### Thermal Stability Analysis
#### 2.5.3 Thermal Conductivity  
The quick thermal conductivity meter QTM-500 (Kyoto Electronic Manufacturing Company, Tokyo, Japan) consisted of a thermocouple, and a single heater wire was used. Its operating principle was based on the temperature variation (increase or rise) when applying constant electric power to the heater and thus the wire. This rise in temperature has an exponential shape. The temperature values occurred during the experiment were recorded and plotted in a linear form (taking the logarithm of the exponential one). Various important properties were derived and drawn from the linear plot of the temperature values. Therefore, from the linear plot obtained and the angle, the thermal conductivity is determined using Equation (5):  
\[\lambda=\frac{q.\ln\frac{t_{i+1}}{t_{i}}}{4\pi(T_{i+1}-T_{i})} \tag{5}\]  
where; \(\lambda\) = specimen thermal conductivity (W/mK), \(q\) = generated heat per unit length of specimen/time (W/m), \(t_{i}\) and \(t_{i+1}\) = measured time length (s), \(T_{i}\) and \(T_{i+1}\) = temperature values at \(t_{i}\) and \(t_{i+1}\), respectively (K). The test was performed according to the standards ASTMC177 [32] and in different directions to derive the average values.