esa-sceva/satcom-chunk-collection huggingface dataset:
# Traffic Demand Estimations Considering Route Trajectory Reconstruction in Congested Networks
## 2 Observed Data Analysis and Processing
### Model Establishment  
The principle of Bayesian traffic demand estimation is to correct the prior OD demand distribution according to the observed data (including data after dimension unification of congested road networks and route travel time after trajectory reconstruction), to obtain the posterior OD demand distribution. Bayesian estimation methods use both observed information and historical information; a large amount of information helps to estimate more reliable traffic demand. The model expression is the following bi-level programming form.  
The upper-level:  
\[\hat{\mathbf{D}}=\underset{\mathbf{D}}{\text{argmax}}\frac{f\hat{\left( \mathbf{C}\right|\mathbf{D}\right)}p(\mathbf{D})}{\int f\hat{\left(\mathbf{C} \right|\mathbf{D}\right)}p(\mathbf{D})d\mathbf{D}} \tag{13}\]  
Constraint conditions: Formulas (7), (11) and (12).  
The lower-level: Traffic demand \(\mathbf{D}\) is allocated using the SUE model to obtain the OD-link ratio, \(\beta\), and road flow properties \(m_{a}\) and \(n_{a}\).  
\[f_{k}^{\omega}=q^{\omega}\cdot p_{k}^{\omega}(\mathbf{c}^{\omega})=q^{\omega} \cdot\Pr(c_{k}^{\omega}\leq c_{l}^{\omega},\forall l\in K^{\omega},l\
eq k| \mathbf{c}^{\omega}),\forall\omega\in W,k\in K^{\omega} \tag{14}\]  
where \(\mathbf{C}\) represents the vector form of the processed observed data, \(\hat{\mathbf{C}}=\left\{\cdots,\hat{c}_{s},\cdots\right\}\), including \(t_{a}\) of Equation (1) after dimension unification and \(x_{i}\) of Equation (5) after trajectory reconstruction; \(\Pr(\cdot)\) represents the probability operator; and \(\mathbf{c}^{\omega}\) is the vector form of route travel time \(c_{k}^{\omega}\), namely \(\mathbf{c}^{\omega}=\left\{\cdots,c_{k}^{\omega},\cdots\right\}\). Formula (9) can be used to calculate \(p_{k}^{\omega}\). In the upper-level model of the bi-level programming framework, the objective function (13) is the maximum posterior probability estimation of the traffic demand under road data observation. Maximum posterior probability is used to obtain the point estimation value of traffic demand. The lower-level model of the bi-level programming framework estimates variables in the upper-level model constraints, namely the OD-link ratio and road flow properties.
## Abbreviations  
List of Acronym  
_Abbreviations_  
PV photovoltaic  
Iph  
output current  
MPPT  
maximum power point tracker V  
terminal voltage  
P&O  
Perturb and Observe  
Rsh  
shunt resistor  
IC  
Incremental Conductance  
Rs  
series resistance  
PSC  
partial shading condition  
Io  
reverse saturation current of the cell  
P-V  
power voltage  
q  
electronics charge  
I-V  
current voltage  
V  
output voltage of the cell  
LMPP  
local maxima power point  
n  
ideality factor  
GMPP  
global maxima power point  
K
[PERSON]'s constant  
MPP  
maximum power point  
T  
absolute operating temperature  
FL
Fuzzy Logic Control  
Ns  
total number of cells in series  
PSO  
Particle Swarm Optimization  
Ish  
current flow through the shunt resistor  
PSCs
Partial shading conditions  
S
actual irradiance (W/m\({}^{2}\))  
PWM Pulse Width Modulation  
Sref  
irradiance under the reference condition (W/m\({}^{2}\))  
_Levy flight parameters_  
Iph,ref  
light current under the reference condition  
\(l\)  
length of the flight  
\(\mu_{\text{Isc}}\)  
manufacturer supplied temperature coefficient of short-circuit current (A/K)  
\(\lambda\)  
variance  
T\({}_{\text{C,ref}}\)  
actual reference temperature (K)  
q  
infinite variance  
Ego  
band gap energy  
P\({}_{\text{a}}\)  
probability  
_Boost converter parameters_  
n  
total number of particles  
R\({}_{\text{load}}\)  
output resistance  
\(\alpha\)  
step size  
R\({}_{\text{m}}\)  
value of the resistance at the maximum power point  
f\({}^{\prime}\)  
fitness function  
D  
duty cycle  
\(v_{\text{best}}\)  
best particle  
R\({}_{\text{m(max)}}\)  
resistance at maximum power point at maximum irradiance  
\(v_{\text{i}}\)  
current particle  
D\({}_{\text{min}}\)  
minimum duty cycle  
\(\beta\)  
beta  
beta  
\(R_{\text{load(opt)}}\)  
optimum load resistance  
P
Levy multiplying coefficient  
R\({}_{\text{m(min)}}\)  
resistance at maximum power point at minimum irradiance  
\(G_{\text{best}}\)  
global best particle  
D\({}_{\text{max}}\)  
maximum duty cycle  
\(\Gamma\)  
integral gamma function  
V\({}_{\text{om}}\)  
output voltage  
\(\pi\)  
3.14159  
V\({}_{\text{mG}}\)  
maximum voltage at maximum irradiance  
\(\upsilon\)  
coefficient  
\(\Delta\)i\({}_{\text{L}}\)  
inductor ripple current  
\(\sigma_{\text{u}}\)  
variable  
f  
switching frequency  
\(\sigma_{\text{v}}\)  
variable  
C\({}_{\text{in}}\)  
minimum input capacitance  
List of symbol  
C\({}_{\text{in}}\)  
input capacitor  
F
V\({}_{\text{mp}}\)  
maximum power voltage
V  
L inductor  
H
Isc  
short circuit current  
A  
\(f_{\text{s}}\)  
switching frequency
Hz  
Imp  
maximum power current  
A  
\(C_{\text{out}}\)  
output capacitor  
F  
Ns  
number of series cells  
\(P_{\text{max}}\)  
maximum power  
W  
s
time in second
s  
V\({}_{\text{oc}}\)  
open circuit voltage  
V
# Impact Load Sparse Recognition Method Based on Mc Penalty Function
## 3 Impact Load Sparse Identification Model
### Impact Load Sparse Identification Model of MC Penalty Function
The formula for MC penalty function is as follows:  
\[\mathbf{\phi}(\mathbf{f};a)=\left\{\begin{matrix}|\mathbf{f}|-\frac{\mathbf{a}}{2}\mathbf{f} |\mathbf{f}|\ \leq\  
As can be seen from Figure 1, the difference between MC penalty function and L1 norm is that MC penalty function does not take the positive and negative number of x as the judgment condition, but introduces the value a as the segmentation point, and adds a constraint term on the basis of \(\mid\)x\(\mid\), which is no longer zero in other cases. In this way, compared with the L1 norm, the numerical value will not be blindly changed to zero in the iterative process. More information is retained to a certain extent. It can be concluded from Figure 2 that when a is closer to 0, the MC penalty function is closer to the L1 norm. When a is closer to 1, the stability constraint area of the MC curve is larger and larger. It is not difficult to see that when a = 1 or 0.5, the constraint effect of the MC penalty function is the best. However, considering the stability and preventing over-fitting problems, a = 0.5 is selected as the proximal operator parameter of MC penalty function.  
\[\hat{\mathbf{f}}=\underset{f}{\text{minimize}}||\mathbf{H}\mathbf{f}-\mathbf{y}||_{2}^{2}+ \lambda\ \sum_{i=1}^{m}\ \mathbf{\phi}(\mathbf{f}\colon\text{a}) \tag{25}\]  
where, \(m\) represents the length of the impact signal. By further simplifying Equation (25), the final MC penalty function model can be obtained:  
\[\hat{\mathbf{f}}=\underset{f}{\text{minimize}}||\mathbf{H}\mathbf{f}-\mathbf{y}||_{2}^{2}+ \sum_{i=1}^{m}\ \mathbf{\phi}_{\lambda,\text{a}}(\mathbf{f}) \tag{26}\]
# Minimum Interference Channel Assignment Algorithm for Multicast in a Wireless Mesh Network
## 4 Simulations  
We evaluate MICA by comparing it with the MCM algorithm. The reason why MICA is proposed is that it eliminates the drawback of MCM (HCP) by examining every pair of nodes in the network. Our simulation tool is QualNet 4.5 (SCALABLE Network Technologies, Culver City, CA, USA) [30]. For the first experiment, we measure an average number of packets received by multi-receivers and the standard deviation of average packets in different network topologies. In the experimental setting, the number of multi-receivers is fixed and a variety of multicast trees are used. For the second test, QualNet computes an average number of packets received by multi-receivers and an end-to-end packet delay with the different number of multi-receivers. In the second simulation setup, we use the same  
Figure 3: Channel assignment example by MICA.  
network topology during the entire simulation. Whenever carrying out the second experiment, only the number of multi-receivers is changed.
## 2 Algorithm and Methodology
### Evaluation Indexes
Then, the sum of the error squares is calculated, and the function minimizing this sum is determined as the contour fitting curve. Curve 1 in Figure 4b illustrates the contour fitting curve for the eyes in the open state, while Curve 1 in Figure 4d depicts the contour fitting curve for the mouth in the closed state. To normalize the openness of the eyes and mouth, the area enclosed by the fitted contours and the area of their circumscribed circle are calculated. The process for constructing the circumscribed circle and calculating the enclosed area is as follows:  
1. Determining the center of the circumscribed circle: The x-coordinates of the vertices of the fitted parabolas, \(x_{1}=-b_{1}/2a_{1}\) and \(x_{2}=-b_{2}/2a_{2}\), are used to compute the center \(C=(x_{c},y_{c})\) of the circle: \[x_{c}=\frac{x_{1}+x_{2}}{2},\quad y_{c}=\frac{y_{\text{upper}}^{\text{max}}+y_ {\text{lower}}^{\text{min}}}{2}\] (3)
2. Radius of the circumscribed circle:  
Figure 4: Eye and mouth feature points and their contour fitting curves and external circles. (**a**) Feature points of the left eye; (**b**) Contour fitting curve for the left eye in the open state; (**c**) Feature points of the mouth; (**d**) Contour fitting curve for the mouth in the closed state. The radius \(R\) is determined as the maximum Euclidean distance from the center to the sampled points along both parabolas:  
\[R=\underset{i}{\max}\sqrt{\left(x_{i}-x_{c}\right)^{2}+\left(y_{i}-y_{c}\right)^ {2}} \tag{4}\]
3. Area of the circumscribed circle: The area of the circumscribed circle is given by: \[A_{2}=\pi R^{2}\] (5)
4. Fitted area between the contours: The area between the upper and lower parabolic contours is computed using the definite integral over the fitting interval: \[A_{1}=\int_{x_{min}}^{x_{max}}\left[y_{1}(x)-y_{2}(x)\right]dx\] (6)
5. Normalized aperture metric: The final normalized aperture metric is defined as: \[\eta_{\text{NA}}=\frac{A_{1}}{A_{2}}\] (7)  
The degree of eye and mouth opening and closing is calculated by Equation (7) and is used as a normalized index for the calibration of opening size. \(A_{1}\) represents the area enclosed by the fitted contour curve of the eyes and mouth, whereas \(A_{2}\) represents the area of their circumscribed circle. In Figure 4b, Curve 2 represents the circumscribed circle of the eye's feature points in the open state. In Figure 4d, Curve 2 represents the circumscribed circle of the mouth's feature points in the closed state. The normalized aperture indicator \(\eta_{NAE}\) for the eyes is shown in Section 3.1. The normalized aperture indicator \(\eta_{NAM}\) for the mouth is shown in Section 3.1.
## 5 Results and Discussion  
### Surrogate Model  
Based on the wind tunnel test results, the Kriging model was constructed for the average output power, as shown in the three-dimensional and two-dimensional cartesian coordinates in Figure 6. The response surface fit well with the sampling points. The variation of the average output power with the aerodynamic shape variables can be observed intuitively. For the two-variable optimization problem, 50 sample points were enough to establish a surrogate model with high accuracy. For instance, [PERSON], [PERSON], [PERSON], and [PERSON] [49] constructed an ordinary Kriging using 90 samples. [PERSON] and [PERSON] [50] constructed a multi-fidelity Kriging model with 50 samples for a two-variable aerodynamic shape optimization of civil structures.  
\begin{table}
\begin{tabular}{c c} \hline \hline \(U\) (m/s) & \(S_{CFy}\) (\%) \\ \hline
1 & 16.2 \\
2 & 13.3 \\
3 & 12.3 \\
4 & 11.8 \\
5 & 11.5 \\
6 & 11.3 \\
7 & 11.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Random uncertainty values for \(C_{Fy}\) using the TSM.  
According to the experimental results, the average output power of the WEH with shape 11 was maximum for the variables \(a_{1}=0.20\) and \(a_{2}=0.63\). The arc angle \(\beta\) and tail length \(L_{t}\) were \(0.40\pi\) and \(1.26D\), respectively. The WEH with shape 18 had an excellent performance when \(a_{1}=0.35\) and \(a_{2}=0.57\). The arc angle \(\beta\) and tail length \(L_{t}\) were \(0.70\pi\) and \(1.14D\), respectively. The surrogate model had a peak region for the average output power \(P_{e}\), as in the red colored area with an inclination of \(45^{\circ}\) shown in Figure 6. The WEHs corresponding to the peak region output have abundant power because the bluff body of the WEHs is prone to gallop. For the other areas in Figure 6, the output power declines rapidly and decays to zero. Based on the surrogate model, the impacts of the shape modification on the efficiency of the WEH are further evaluated in the subsequent sections.
## 7 Conclusions  
This paper proposes an authenticated certificateless group key agreement method without pairing based on blockchain for smart grids, which mainly solves the key management problem of large-scale intelligent gateway nodes with limited computing power. Then, the KGC key distribution process is uploaded to the blockchain. This means KGC cannot prevent the distribution of the partial keys, which are stored in the blockchain, thus realizing the effective supervision of KGC. Furthermore, KGC stores the relevant system parameters in the blockchain to prevent malicious attackers from modifying the parameters, which would result in the collapse of the key management system. We also realize a certificateless key management scheme without bilinear pairing based on blockchain by  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Operation** & **Description** & **Time (ms)** \\ \hline \(T_{mul}\) & It is the time to perform one multiplication point operation & 1.5605 \\ \(T_{add}\) & It is the time to perform one add point operation & 0.0058 \\ \(T_{cert}\) & It is the time to perform a certificate generation operation & 6.4352 \\ \(T_{mode}\) & It is the time to perform one modular exponentiation operation & 0.386 \\ \(T_{bp}\) & It is the time to perform one bilinear pairing operation & 20.1456 \\ \(T_{h}\) & It is the time to perform one hash function operation & 0.0006 \\ \(T_{BC}\) & It is the time to perform one operation of uploading data to & 225 \\  & the blockchain & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The execution time of different cryptographic elements.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Computations** & **Computation Cost (ms)** \\ \hline
[5] & 12 \(T_{mul}+3\)\(T_{add}+10\)\(T_{h}\) & 18.7494 \\
[11] & 7 \(T_{cert}+2\)\(T_{mode}+2\)\(T_{bp}+10\)\(T_{h}\) & 86.1156 \\
[19] & 8 \(T_{mul}+2\)\(T_{add}+9\)\(T_{h}+T_{BC}\) & 237.5018 \\
**This article** & 12 \(T_{mul}+2\)\(T_{add}+6\)\(T_{h}\) & 18.7412 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of computation.  
combining the group key technology based on the logical key tree. This means that our scheme has lower computational overhead compared to similar schemes. Security and performance analysis show that the proposed approach can achieve lower computational overhead and greater safety compared to similar key management schemes.  
In the future, we will improve the existing group key based on the logical key tree. The group key scheme combined in this paper cannot deal with the joining and leaving of a large number of intelligent gateway nodes; thus, the existing group key scheme should be improved and designed to efficiently complete the joining and leaving of a large number of nodes. The future method can achieve more efficient management of intelligent gateway node keys.  
Writing--original draft, Z.W.; Writing--review & editing, R.H. and S.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The National Key R&D Program of China (2018 YFB1800500).  
Not applicable.  
The authors declare no conflict of interest.
# Autocorrelation Matrix Knowledge Distillation: A Task-Specific Distillation Method for BERT Models
## 4 Experiments
### Datasets  
We evaluated the proposed method using the GLUE dataset. The GLUE benchmark includes a variety of natural language understanding tasks, designed to comprehensively evaluate how well a model can handle different linguistic phenomena [33]. These tasks include Multi-Genre Natural Language Inference (MNLI), which involves determining the relationship between a premise and a hypothesis [34]; Quora Question Pairs (QQP), which evaluates the semantic equivalence between question pairs [35]; Question Answering Natural Language Inference (QNLI), which determines whether a given context contains the answer to a question; Stanford Sentiment Treebank (SST-2), which involves the sentiment classification of movie review sentences [36]; Microsoft Research Paraphrase Corpus (MRPC), which assesses whether sentence pairs are paraphrases [37]; and Recognizing Textual Entailment (RTE), which involves a determination of whether one sentence entails another [38].  
For the machine reading comprehension task, we used the Stanford Question Answering Dataset (SQuAD v1.1) for evaluation [39]. This dataset, created by [PERSON] et al. in 2016, contains 100,000 question-answer pairs collected through crowdsourcing, with the task of finding the text snippet within a Wikipedia passage that answers the question.  
SQuAD v2.0 builds on this by introducing cases where no clear answer is available, making the task more realistic and requiring the model to determine whether an answer exists and appropriately handle cases with no answer [40].
## 4 Results and Discussion
### Experimental Results
#### 4.2.1 Experiments without Shield Gas Chamber
Measurements were carried out directly above the hole plate in the working distance of the respective nozzle. To clarify whether the present oxygen concentration can be described by the ratio of carrier gas volume flow to shield gas volume flow, the oxygen concentration was plotted versus this ratio in Figure 12.  
Figure 12 illustrates the robustness of the coaxial nozzle to an increasing carrier gas flow to shield gas volume flow ratio. For a ratio of the carrier gas volume flow to shield gas volume flow of less than 0.4, the oxygen values of the two nozzles are in a similar range. From this ratio, the three-jet nozzle has a significantly higher oxygen concentration compared to the coaxial nozzle. Only for the highest ratio (1.6) does the coaxial nozzle perform worse. But in practice, this ratio is of no relevance, because such a high carrier gas flow is not used due to the high argon consumption and the induced turbulence in the shielding gas bell. Furthermore, it can be seen that the three-jet nozzle at a gas ratio of 0.53 performs worse with higher shielding gas flows (15 L/min and 22.5 L/min) with regard to a low oxygen concentration. This anamolism can be explained by the Venturi effect. Due to the higher flow rate of the shielding gas fluid, the static pressure of the fluid is lower than the pressure of the ambient air. This negative pressure allows oxygen to be sucked into the shielding gas bell from the ambient air. Summing up, Figures 8 and 9 show that the present spatial oxygen concentration follows the nozzle geometry and is more sharply limited for the coaxial nozzle. Figure 10 illustrates the oxygen increase under the nozzle with increasing carrier gas flow; this is more noticeable for the three-jet nozzle. This demonstrates the greater robustness of the coaxial nozzle to carrier gas volume flow variations. Figure 11 shows the decrease in oxygen concentration with increasing shielding gas flow. Figure 12 shows that from a carrier gas volume flow to shielding gas volume ratio of equal to/greater than 0.4, the coaxial nozzle performs significantly worse with respect to present oxygen concentration. It can thus be concluded that for oxidation-critical materials such as titanium and aluminum, the coaxial nozzle has significant advantages over a three-jet nozzle regarding oxidation.
## 5 Summary and Recommendations
\begin{tabular}{l l} \(a\) & constant in Best DSD approximation (Equation (10)) \\ \(a_{1}\) & constant in droplet terminal velocity equation (\(f\)(droplet radius)) (Equation (2)) \\ \(A\) & Weibull distribution scale parameter (Equation (19)) \\ AEP & annual energy production \\ ARM & Atmospheric Radiation Measurement site operated by the US Department of Energy \\ ASL & above sea level \\ \(B\) & constant in droplet terminal velocity equation (\(f\)(droplet radius)) (Equation (2)) \\ \(c\) & intercept of regression equation (Equation (2)) \\ \(c_{1}\) & density correction factor in the approximation for the terminal fall velocity for \\ \(c_{1}\) & deformed droplets (\(f\)(ambient pressure)) (Equation (12)) \\ \(C_{D}\) & drag coefficient for terminal velocity of nail stones (Equation (3)) \\ CoCoRaHS & Community Collaborative Rain, Hail and Snow network \\ \(D\) & hydrometeor diameter \\ \(D_{i}\) & hydrometeor diameter class \\ \(dD\) or \(\Delta D\) & hydrometeor diameter interval \\ DiveN & UK Disdrometer Verification Network \\ \(D_{0}\) & median droplet diameter \\ \(D_{m}\) & mass-weighted droplet mean diameter (Equation (8)) \\ \(D_{max}\) & hailstone maximum diameter \\ \(dN\)/\(dD\) or \(N\)(\(D\)) & number density--i.e., concentrations of hydrometeors per cubic meter as a function of diameter normalized for a fixed diameter interval \\ DNV & Det Norske Veritas \\ DoE & US Department of Energy \\ DSD & droplet size distribution \\ DTU & Danish Technical University \\ ECMWF & European Centre for Medium-Range Weather Forecasts \\ ERA5 & fifth generation ECMWF atmospheric reanalysis of the global climate \\ \(F\) & area field of view of the disdrometer \\ FAR & False Alarm Rate \\ \(F_{\mathit{eff}}\) & effective sampling area of the disdrometer \\ \(g\) & gravitational acceleration \\ GCHN & Global Historical Climatology Network \\ GW & GigaWatt (10\({}^{9}\) Watts) \\ GPCC & Global Precipitation Climatology Centre \\ GPM & Global Precipitation Measurement \\ GWEC & Global Wind Energy Council \\ HR & Hit Rate \\ IEA Wind TCP & International Energy Agency Wind Technology Collaboration Programme \\ IMERG & Integrated Multi-satellite Retrievals for the GPM \\ \(k_{B}\) & constant in Best DSD approximation (Equation (10)) \\ \(k\) & Weibull distribution shape parameter (Equation (19)) \\ \(L\) & length of disdrometer viewing area \\ LCoE & levelized cost of energy \\ LEE & leading edge erosion \\ LEP & leading edge protection \\ LPM & Thies Laser Precipitation Monitor \\ \(L\)WC & liquid water content of air \\ MW & MegaWatt (10\({}^{6}\) Watts) \\ \(M_{n}\) & nth moment of the measured hydrometeor size distribution (Equation (17)) \\ \(m\) & slope of regression equation (Equation (18)) \\ MRR & Micro-rain RADAR \\ NH & Northern Hemisphere \\ \end{tabular}