esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Parametric Study by Computational Analysis
### Influence of Single Parameters \(h\), \(t\), and \(a\)
By increasing the values of \(h\) (height) to 100 mm, \(t\) (thickness) to 16 mm, or \(a\) to 750 mm, it is possible to control the maximum tensile stress of the concrete overlay at approximately 0.71 MPa. Figure 17: The impact of changes in the \(a\) value on the bridge deck structure: (**a**) impact on stress of concrete overlay, (**b**) impact on displacement of concrete overlay, (**c**) impact on stress of crossbeam. However, it should be noted that just as a larger \(t\) value means more steel consumption, increasing the value of \(a\) will also result in greater consumption of concrete. Figure 18 illustrates how changes in \(h\), \(t\), and \(a\) affect the cross-sectional area of the concrete overlay. Increasing both \(a\) and \(t\) values will moderately increase the consumption of concrete while increasing the \(h\) value will significantly reduce the consumption of concrete. Additionally, increasing the \(h\) value will not have a significant impact on the amount of steel used. Therefore, among the three discussed size parameters (\(h\), \(t\), and \(a\)), controlling the \(h\) value appears to be the most effective method for the parameterized design of the BUSP structure in terms of both safety and economy. However, it is important to ensure that the \(h\) value is not excessively high, as this would result in a thin concrete overlay that may not meet the design requirements for crack resistance. Thus, a careful balance needs to be struck between optimizing the size parameters to achieve the desired structural performance while considering material consumption and meeting design standards for concrete durability.
## 2 TH-2 Spaceborne GNSS Data Assessment
### Quality of GNSS Observations
#### 2.3.2 Pseudorange Multipath Combination  
The pseudorange multipath combination (MPC) [26] is an important index to assess the quality of the pseudorange observations. Figure 6 shows the MPCs against the elevation angle of all signals for GPS and BDS2 from the TH-2A. As seen in the figure, the MPCs of the GPS L1 and L2 frequencies are within 3 m, while the MPCs of the BDS2 B1 and B3 are within 1 m in most cases. However, the elevation-dependent systematic bias can be noticed in the pseudorange observations of the BDS2 GEOs, IGSOs, and MEOs, which may be due to the satellite design [27, 28]. The MPCs characteristic for the GEOs are close to that for the IGSOs. The systematic variations of the B1 frequency for the BDS2 MEOs are more obvious than those of the other signals, which exceed 1 m close to the zenith. To avoid the precision degradation of the pseudorange caused by systematic biases, an elevation-dependent piecewise linear model, as proposed by [PERSON] [27], is applied to compensate for such bias in the BDS2 observations. The RMS of the MPCs for the BDS2 B1 and B3 frequencies are 0.20 and 0.13 m after the correction of the pseudorange observations, while the GPS L1 and L2 frequencies are 0.29 and 0.24 m. The results show that the pseudorange observations for BDS2 achieve a better quality than those for GPS.  
Figure 5: Average C/N\({}_{0}\) values of signals from TH-2A as a function of the elevation angle.  
Figure 6: _Cont_.
# Virtualized MME Design for IoT Support in 5G Systems
## 2 Background
### Internet of Things
Therefore, the main drawback of these new procedures for cellular IoT is the increased processing capacity required by the MME [17]. In addition to this transfer optimization, a new narrowband radio technology for IoT was developed in Release 13, called the Narrowband Internet of Things (NB-IoT). The objective of NB-IoT is to reduce complexity, increase coverage, achieve long battery lifetime and support a massive number of devices. To achieve these objectives, NB-IoT has low data rates, a 164-dB maximum coupling loss target for standalone mode operation and limited mobility support, and it allows only the half-duplex frequency-division duplexing operation. NB-IoT requires a reduced bandwidth of 180 kHz for both downlink and uplink and further protocol optimizations [18]. For UEs that support the NB-IoT, the 3 GPP agreed that the control plane CIoT Evolved Packet System optimization feature explained above will be mandatory [5]. For more information about NB-IoT, see [19].
# Traffic Light Cycle Configuration of Single Intersection Based on Modified Q-Learning
## 4 Experimental Result
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method Time Periods**} & \multicolumn{2}{c}{**Exhaustive Search**} & \multicolumn{2}{c}{**Modified Q-Learning**} & \multicolumn{2}{c}{**Comparison**} \\ \cline{2-7}  & **Delay (S)** & **Steps** & **Avg. Delay (S)** & **Avg. Steps** & \(E\) & \(I\) \\ \hline \multirow{2}{*}{Huanhe N. Rd. \& \&} & 8:00-9:00 a.m. & 27.0 & & 29.1 & 29.1 & 7.8\% & 1141\% \\ \cline{2-7}  & 5:00-6:00 p.m. & 68.0 & & 71.1 & 26.1 & 4.6\% & 1283\% \\ \cline{2-7}  & 8:00-9:00 a.m. & 31.3 & & 34.1 & 33.1 & 8.9\% & 991\% \\ \cline{2-7}  & Nanjing W. Rd. & 5:00-6:00 p.m. & 6.9 & & 7.05 & 29.6 & 2.2\% & 1120\% \\ \cline{2-7}  & 8:00-9:00 a.m. & 9.9 & & 9.90 & 26.4 & 0.0\% & 1267\% \\ \cline{2-7}  & 5:00-6:00 p.m. & 7.7 & & 7.7 & 25.4 & 0.0\% & 1321\% \\ \hline \multicolumn{2}{c}{Average} & 25.13 & 361 & 26.49 & 28.28 & 5.4\% & 1176\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The delay and processing steps for exhaustive search and modified Q-learning schemes. Figure 15: The results of vehicle delay by using our proposed modified Q-learning mechanism on Yanping N. Rd. and Minsheng W. Rd. (**a**) from 8:00 a.m. to 9:00 a.m. (**b**) from 5:00 p.m. to 6:00 p.m.  
come out with local optimal results, and this occurrence produces around 8.9% of the error rate as compared to the optimal solution. On the contrary, if the difference between East-West and North-South light cycle times is insignificant, the error rate \(E\) is also small. In this case, the proposed model can find global optimal or approximate global optimal results. Therefore, the proposed mechanism is capable of achieving good enough results with significantly fewer computational steps as compared to the Exhaustive search scheme.
# Immunological Aspects of EBV and Oral Mucosa Interactions in Oral Lichen Planus
## 1 Introduction
Studies show that the development of the disease is determined not by the direct effect of the virus but by the host's immune system responding to the presence of the virus particles. This means that the pathogenic pathways involved are not entirely clear [36]. Contradictory studies have been conducted on the involvement of the Herpes group viruses (HSV) in the aetiology of OLP. For example, despite the positive HSV-PCR test and elevated HSV IgG level, no link between HSV and OLP was stated [37]. A study of HHV-7 supported the hypothesis mentioned by the authors on the association of this virus with lichen planus pathogenesis [38]. A significant association is found between human papilloma virus (HPV) and OLP, suggesting that there is evidence that HPV could have a significant causal role in both OLP and its malignant progression. Studies have shown that the extent of this correlation may vary depending on factors such as geographic population, clinical types of OLP, and HPV genotypes [39]. Additionally, there appears to be an increased risk of both Epstein-Barr virus (EBV) and HPV infection in individuals with OLP, as indicated by the study of [PERSON] et al. 's [40] finding of statistical significance in the number of EBV (\(n\) = 23; 35%) and HPV (\(n\) = 14; 21%) infection positive cases in OLP, contrary to the number of HSV positive cases (\(n\) = 6; 9%). However, [PERSON] et al. [31] found that after the review of the 43 chosen articles, direct activity could not be confirmed by EBV, HSV-1, cytomegalovirus CMV, or HPV regarding the etiopathogenesis of oral lichen planus or its potential malignant transformation. Nevertheless, relatively more studies have shown interest in the role of EBV in the origin of OLP [32; 39; 41; 42; 43; 44]. It is still unclear what the target antigen is and how this process manifests itself at the molecular level. The purpose of this review is to provide a summary of information supporting the role and pathways of EBV and other involved cells in inducing OLP as an autoimmune lesion.
## 5 Conclusions  
Based on the conventional cold mass suspension rod of a superconducting magnet, a novel type of suspension rod structure is studied, which can be used to suspend and fix many kinds of cold mass components of the superconducting magnet. It could be concluded that the tie rod body of the four structures uses the same T300 carbon fiber and the same processing technology. The maximum tensile stress of the material along the fiber length direction is about 2290 MPa. The experimental test results show that the new tie rod structure makes maximum use of its material strength, which is about 85.14% of the tensile strength of the material. There is no obvious stress concentration in the whole rod body, and the utilization rate of type B tie rod of the racetrack structure and type C tie rod of the cylindrical structure is 70.23% and 71.92%, respectively. The contact surface between the carbon fiber rod body and the metal connector shows obvious stress concentration at different degrees.  
From the perspective of geometric size, the width of the type D tie rod is only 13.76% of the type A tie rod, the hole is smaller when the rod is worn over 50 K cold screen, and the radiation heat leakage from the cold screen opening to the 4 K end is reduced by 45%. The main way of heat transfer from the 300 K end to the 4 K end of the cold mass suspension structure is the heat leakage of the rod body. Because the above four pull rods have the same length of 300 K-4 K, the thermal conductivity power is only related to the cross-sectional area of the rod body. On the premise of meeting the tensile strength demand, the cross-sectional area of the type D pull rod is only 83.78% of the first three, which helps to accelerate the cooling process of the magnet. At the same time, compared with the control group A, the 50 K cold screen opening size was reduced by 5196 mm\({}^{2}\), and the heat radiation was reduced by about 45% under the premise of using the heat reflection film to shield the opening hole. Consequently, the research scheme effectively solves the issues existing in the traditional tie rod structure.  
Methodology, J.C.; software, J.L.; validation, Z.F.; writing--original draft preparation, S.W.; visualization, Y.W.; supervision, H.W. All authors have read and agreed to the published version of the manuscript.  
This study was financially supported by the National Key R&D Program of China (2022 YFC2404001), National Natural Science Foundation of China (52325701, 52293421, 52293422) and Beijing Natural Science Foundation (L245010).  
The data are available from the corresponding author on reasonable request.  
The authors declare no conflicts of interest.
## 3 Background and Overview of LEACH
### Direct Transmission (DT)  
DT is the basic routing protocol used in WSNs to route the data from the sensing field directly, without any intermediate node or AP (Access Point), towards the base station. The deployed nodes in the field transmit the sensed data directly to the BS without any intermediate or neighbor node intervention. The nodes located far away from the BS will consume more energy, as opposed to the closer ones, because the energy consumption is directly proportional to the distance separating the sender node from  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Operation** & **Energy Dissipaton** \\ \hline Transmitter Electronics \(\mid\mid E_{Tx-dec}\) & \\ Receiver Electronics \(\mid\mid\mid E_{Rx-dec}\) & 50 nJ/bit \\ \((E_{Tx-dec}=E_{Rx-dec}=E_{dec})\) & \\ \hline Transmitter Amplifier (\(\in_{amp}\)) & 100 pJ/bit/m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Radio characteristics.  
the BS. Such transmission operation will rapidly deplete the nodes' battery, which in turn leads to degradation of overall network lifetime. The BS is considered as a remote monitoring station equipped with sufficient power for processing the received data from the sensing filed.  
To better understand the energy consumption in DT, consider a linear simple network model composed of \(n\) sensor nodes separated by uniformly distributed distance \(r\). Equations (5)-(7) describe the energy consumption needed for transmitting \(l\) bits long message from the sensing field to the BS in this network. For communication and data transmission, DT uses first order radio model explained in Section 3.1[4,18].  
\[E_{direct}=E_{Tx-elec}(l,d=n\times r) \tag{5}\]  
\[E_{direct}=E_{elec}\times l+\in_{amp}\times l\times(n\times r)^{2} \tag{6}\]  
\[E_{direct}=l\times(E_{elec}+\in_{amp}n^{2}r^{2}) \tag{7}\]
# Simultaneous Prevention of Rotational and Translational Slip for a Humanoid Robot
## 6 Conclusions  
Humanoid robots may encounter slip while walking in a real environment. Even a slight slip can make a robot walk unstably or even fall. As such, a humanoid robot must be able to avoid slipping.  
This paper proposes a method of preventing rotational and translational slip simultaneously for a humanoid robot. A three mass model, which takes into account the effect of swing leg on robot slip, is utilized for slip prevention. As for rotational slip prevention, a bionic walking pattern generator including arm swing and waist rotation is proposed to reduce the inertial moments in the yaw direction. This rotational slip prevention method studies from the yaw moment compensation mechanism of human walking. As for translational slip prevention, a novel reaction force ratio reduction control including compensating CoM acceleration is proposed to reduce the ratio of tangential force to normal reaction force. When the ratio of tangential force to normal reaction force is smaller than the coefficient of the ground friction, the translational slip is prevented. The simulations and experiments confirm that the humanoid robot can avoid both rotational and translational slip with the proposed slip prevention strategy.  
Future work will consist in carrying out more experiments on various coefficients of friction between the foot sole and the ground surface and studying the slip prevention strategy in a more complex environment, such as slopes and soft and uneven ground.  
For this research article, [PERSON], [PERSON] and [PERSON] conceived the idea; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper; [PERSON], [PERSON] and [PERSON] reviewed and edited the paper.  
This work was supported in part by the National Natural Science Foundation of China under Grant 61533004, Grant 61320106012, and Grant 61703043.  
The authors declare no conflict of interest.  
Figure 10: Yaw moment and reaction force ratio in humanoid walking. Blue lines are yaw moment and reaction force ratio without proposed control. Red lines are yaw moment and reaction force ratio with proposed control.
## 3 Least Squares Estimation Model for GNSS Positioning and Outlier Correction
### Least Squares Position Estimation  
In the classical GNSS-based position estimation process, LSE recursively calculates the offset of the receiver's position relative to the linearization point by using measured pseudorange errors. Let \(\Delta\rho\) denotes the pseudorange error by taking the difference between the measured pseudorange \(\widetilde{\rho}\) and the predicted pseudorange \(\hat{\rho}\):  
\[\Delta\rho=\widetilde{\rho}-\hat{\rho} \tag{1}\]  
\(\hat{\rho}\) is the geometric distance between the estimated receiver position and the satellite position:  
\[\hat{\rho}_{l}=\sqrt{\left(x_{l}^{s}-x^{u}\right)^{2}+\left(y_{l}^{s}-y^{u} \right)^{2}+\left(z_{l}^{s}-z^{u}\right)^{2}}+cdt_{clk} \tag{2}\]  
\(\left[x_{l}^{s},y_{l}^{s},z_{l}^{s}\right]\) are the position coordinates of the \(l\_th\) satellite, \(\left[x^{u},y^{u},z^{u}\right]\) are the estimated receiver's coordinates and \(dt_{clk}\) is the receiver clock correction. \(c\) is the speed of light. The relation between \(\Delta\rho\) and \(\Delta X^{u}\) (difference between the predicted \(\tilde{X}^{u}\) and the true \(\tilde{X}^{u}\) receiver positions) is expressed as,  
\[\Delta\rho=\mathbf{H}\Delta X^{u}+\xi \tag{3}\]  
\(\mathbf{H}\) is the geometry matrix and \(\xi\) is the measurement noise vector. The noise might include uncompensated atmospheric delay, multipath error, satellite clock and ephemeris errors, and the Gaussian white noise. The receiver position is recursively computed,\[\Delta X^{u}=\left(\mathbf{H}^{T}\mathbf{H}\right)^{-1}\mathbf{H}^{T}\Delta\rho \tag{4}\]  
\[\hat{X}_{i}^{u}=\hat{X}_{i-1}^{u}+\Delta X_{i}^{u} \tag{5}\]  
\(\hat{X}_{i}^{u}\) is the estimated position for \(i\)-\(th\), \(\hat{X}_{i-1}^{u}\) is the estimated position in last iteration and \(\Delta X_{i}^{u}\) is the new estimated position error.
###### Abstract  
Pain assessment is used to improve patients' treatment outcomes. Human observers may be influenced by personal factors, such as inexperience and medical organizations are facing a shortage of experts. In this study, we developed a facial expressions-based automatic pain assessment system (FEAPAS) to notify medical staff when a patient suffers pain by activating an alarm and recording the incident and pain level with the date and time. The model consists of two identical concurrent subsystems, each of which takes one of the two inputs of the model, i.e., \"full face\" and \"the upper half of the same face\". The subsystems extract the relevant input features via two pre-trained convolutional neural networks (CNNs), using either VGG16, InceptionV3, ResNet50, or ResNeX450, while freezing all convolutional blocks and replacing the classifier layer with a shallow CNN. The concatenated outputs in this stage is then sent to the model's classifier. This approach mimics the human observer method and gives more importance to the upper part of the face, which is similar to the Prkachin and Soloman pain intensity (PSPI). Additionally, we further optimized our models by applying four optimizers (SGD/ADAM/RMSprop/RAdam) to each model and testing them on the UNBC-McMaster shoulder pain expression archive dataset to find the optimal combination, InceptionV3-SGD. The optimal model showed an accuracy of 99.10% on 10-fold cross-validation, thus outperforming the state-of-the-art model on the UNBC-McMaster database. It also scored 90.56% on unseen subject data. To speed up the system response time and reduce unnecessary alarms associated with temporary facial expressions, a select but effective subset of frames was inspected and classified. Two frame-selection criteria were reported. Classifying only two frames at the middle of 30-frame sequence was optimal, with an average reaction time of at most 6.49 s and the ability to avoid unnecessary alarms.  
Keywords:computer vision; deep learning; inceptionv3; pain assessment; ResNet-50; ResNeX450; transfer learning; VGG16 +
Footnote †: journal: Applied
## 2 Basic Concepts of DQ0-Based Models
### Summary of Power Definitions  
We will briefly recall the power expressions obtained so far. The (total) three-phase power of a unit is given by  
\[P_{3\phi}=v_{a}i_{a}+v_{b}i_{b}+v_{c}i_{c}=\frac{3}{2}\left(v_{d}i_{d}+v_{q}i_{ q}+2v_{0}i_{0}\right), \tag{59}\]  
where \(v_{a}i_{a}\), \(v_{b}i_{b}\), and \(v_{c}i_{c}\) are the instantaneous single-phase powers, and \(P_{3\phi}\) is the instantaneous three-phase power. These equalities are mathematical identities (not approximations). In addition, if the system is quasi-static, then the active power is the average power over a line cycle (of a single phase):  
\[P(t)=\frac{1}{T}\int_{t-T/2}^{t+T/2}v_{a}(\tau)i_{a}(\tau)\mathrm{d}\tau= \frac{1}{T}\int_{t-T/2}^{t+T/2}v_{b}(\tau)i_{b}(\tau)\mathrm{d}\tau=\frac{1}{ T}\int_{t-T/2}^{t+T/2}v_{c}(\tau)i_{c}(\tau)\mathrm{d}\tau, \tag{60}\]  
where \(T\) is the period of the AC signals.  
The active and reactive powers relate to \(dq0\) quantities by  
\[\begin{split} P(t)&=\mathrm{Re}\left\{V(t)I^{*}(t) \right\}=\frac{1}{2}\left(v_{d}(t)i_{d}(t)+v_{q}(t)i_{q}(t)\right),\\ Q(t)&=\mathrm{Im}\left\{V(t)I^{*}(t)\right\}=\frac{ 1}{2}\left(v_{q}(t)i_{d}(t)-v_{d}(t)i_{q}(t)\right).\end{split} \tag{61}\]  
Moreover, since we assume that quasi-static systems are balanced, the instantaneous (three-phase) power is constant over a line cycle:  
\[P_{3\phi}=v_{a}i_{a}+v_{b}i_{b}+v_{c}i_{c}=\frac{3}{2}\left(v_{d}i_{d}+v_{q}i_ {q}+2v_{0}i_{0}\right)=3P(t). \tag{62}\]  
This last equality is one of the greatest advantages of three-phase systems. While a single-phase system provides alternating power, a balanced three-phase system provides constant power over a line cycle. As a result, three-phase devices with constant power output do not need to store significant energy, and can be made small and efficient. This idea is illustrated in Figure 7.  
Figure 7: While a single-phase system provides alternating power, a balanced three-phase system provides constant power over a line cycle.
###### Abstract  
Line loss is inherent in transmission and distribution stages, which can cause certain impacts on the profits of power-supply corporations. Thus, it is an important indicator and a benchmark value of which is needed to evaluate daily line loss rates in low voltage transformer regions. However, the number of regions is usually very large, and the dataset of line loss rates contains massive outliers. It is critical to develop a regression model with both great robustness and efficiency when trained on big data samples. In this case, a novel method based on robust neural network (RNN) is proposed. It is a multi-path network model with denoising auto-encoder (DAE), which takes the advantages of dropout, L2 regularization and Huber loss function. It can achieve several different outputs, which are utilized to compute benchmark values and reasonable intervals. Based on the comparison results, the proposed RNN possesses both superb robustness and accuracy, which outperforms the testing conventional regression models. According to the benchmark analysis, there are about 13% outliers in the collected dataset and about 45% regions that hold outliers within a month. Hence, the quality of line loss rate data should still be further improved.  
benchmark calculation; daily line loss rate; low voltage transformer region; robust neural network +
Footnote †: journal: Applied Sciences