esa-sceva/satcom-chunk-collection huggingface dataset:
# Compact Design of Annular-Microstrip-Fed mmW Antenna Arrays
## 2 Theory and Analysis of Annular Microstrip Feeding Design
### Verification of the Basic Structure of Outer-Annular Microstrip-Fed Antenna Array
#### 2.2.2 Add-on Analysis with a Connector  
In the simulation, we set the feeding port as the wire port which is more consistent with the actual application scenario. However, in order to achieve a more precise experimental result, the coaxial feeding method is used in the experiment. So, the possible cause of the shift of the result is the 1 mm connector. Aiming at this point, we still use Altair FEKO to analyze the characteristics of the antenna under the coaxial feeding method. The verification model with coaxial feeding method is shown in Figure 9. We set the material of the coaxial feeding conductors as perfect electric conductor. The coaxial feeding port is composed of internal cylinder block and outer cylindrical surface. The internal diameter is \(D_{1}=0.005\) mm and the outer diameter is \(D_{2}=0.0146\) mm. The coaxial feeding method  
Figure 8: Simulation result and experimental result of the reflection coefficient of the first structure.  
Figure 7: Photo of the manufactured object after being soldered, (**a**) the top view, (**b**) the bottom view.  
penetrates the dielectric substrate to feed the antenna array. It is worth mentioning that the coaxial feeding method with edge port in the simulation is just a kind of approximation of the 1 mm connector because of the high complexity of the 1 mm connector. The comparation of the simulation result, experimental result, and the simulation result with coaxial feeding method is shown in Figure 10.  
As shown in Figure 10, after using the coaxial feeding method with edge port to replace the original wire feeding port, the resonance frequency moves 0.2 GHz to the right which has a similar trend as the experimental result. Additionally, the bandwidth appears to overlap with the experimental result, which means the results are almost coincident. It is necessary to state that various probable factors will influence the experimental result and enlarge the difference of resonant frequency in the experimental process, especially in such a high frequency band, such as the poor soldering, the change of the resistance because of the soldering tin, and the size of the dielectric substrate. Besides, the resonant frequency of our experimental result is still in the 60-GHz frequency band and the shift is far less than the possible deviation because of the manufacture craft and test process which means it makes no sense in focusing on such deviation in practical application. Thus, our experimental result can be verified.  
Figure 10: Comparation of the simulation result, experimental result, and the simulation result with coaxial feeding method.  
Figure 9: The side view and local detail of the verification model with coaxial feeding method.
# A Novel Method of Local Anode Effect Prediction for Large Aluminum Reduction Cell
## 5 Experimental Verification
### Simulation Experiment
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Area** & **Model** & **MAE** & **MSE** \\ \hline \multirow{3}{*}{Area1} & LSTM & 1.2834 & 10.7425 \\  & CNN-LSTM & 1.6688 & 7.2261 \\  & GRU-LSTM & 0.7674 & 5.3984 \\ \hline \multirow{3}{*}{Area2} & LSTM & 1.7317 & 11.0440 \\  & CNN-LSTM & 1.0992 & 7.7306 \\  & GRU-LSTM & 0.6360 & 4.9796 \\ \hline \multirow{3}{*}{Area3} & LSTM & 2.3139 & 10.5886 \\  & CNN-LSTM & 2.1652 & 7.1304 \\  & GRU-LSTM & 0.7543 & 5.2048 \\ \hline \multirow{3}{*}{Area4} & LSTM & 1.3038 & 11.4501 \\  & CNN-LSTM & 1.6883 & 7.4859 \\  & GRU-LSTM & 1.0851 & 5.2771 \\ \hline \multirow{3}{*}{Area5} & LSTM & 2.0312 & 11.7961 \\  & CNN-LSTM & 1.7280 & 7.9029 \\ \cline{1-1}  & GRU-LSTM & 0.6182 & 5.4486 \\ \hline \multirow{3}{*}{Area6} & LSTM & 1.5773 & 10.2655 \\ \cline{1-1}  & CNN-LSTM & 1.8256 & 7.1738 \\ \cline{1-1}  & GRU-LSTM & 0.4752 & 5.1290 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: 6 areas MAE, MSE of three models. Figure 10: Area 1 anode rod current fluctuation diagram. reduced by 5.344 and 2.128 on average, and the prediction results of fluctuating anode rod current in six areas are more accurate. According to the criteria for determining the anode effect level proposed in this paper, the local anode effect level of nine groups of anode effects is shown in Table 3, which is basically consistent with the industrial field conditions.
# Improving Congestion Control of TCP for Constrained IoT Networks
## 3 Overview of uIP TCP and CoCoA
### CoCoA  
We briefly describe the main features of CoCoA below.  
**Weak RTO estimator.** [PERSON]'s algorithm does not take RTT samples from retransmitted TCP segments in order to estimate RTT accurately. However, to raise the chances of measuring RTT, CoCoA takes an RTT sample even when the ACK is triggered by a retransmitted CON message. The RTO estimator using retransmitted messages is called the weak RTO estimator, which is differentiatedfrom the strong RTO estimator that uses only CON messages acknowledged without retransmissions. CoCoA ignores RTT samples obtained after the third retransmission to avoid taking a much larger RTT estimate than the actual RTT. The strong RTO estimator is calculated as follows:  
\[\text{E}_{\text{strong}}=\text{SRTT}+\text{max}(\text{G},\,\text{4}\times\text{ RTTVAR}),\]  
\[\text{RTO}=0.5\times\text{E}_{\text{strong}}+0.5\times\text{RTO},\]  
where \(G\) is the clock granularity. The weak RTO estimator is calculated as follows:  
\[\text{E}_{\text{weak}}=\text{SRTT}+\text{max}(\text{G},\,\text{RTTVAR}),\]  
\[\text{RTO}=0.25\times\text{E}_{\text{weak}}+0.75\times\text{RTO}.\]  
It is notable that, for the weak estimator, the RTT variance multiplier is set to 1 instead of 4 to avoid increasing RTO excessively, and that a lower weight is assigned to the weak estimator to reduce the effect of inaccurate measurements.  
**Variable backoff factor.** Since the weak estimator of CoCoA may have a larger RTT estimate than the actual one, the binary exponential backoff mechanism (BEB) may cause network underutilization. To address this problem, CoCoA uses a variable backoff factor (VBF) as follows:  
\[\text{VBF}(\text{RTO})=\left\{\begin{array}{ll}3&\quad\text{if RTO}<a\\ 2&\quad\text{if $a\leq\text{RTO}\leq b$}\\ 1.3&\quad\text{if RTO}>b\end{array}\right.\]  
where \(a\) and \(b\) are the thresholds. CoCoA sets \(a\) and \(b\) to 1 s and 3 s, respectively.  
**RTO aging.** Furthermore, CoCoA uses an RTO aging mechanism to avoid keeping an RTO which is no longer valid. If the RTO is too small or too large and is not updated for a while, CoCoA tries to make the RTO move close to the default value.
## 3 Numerical Simulation Results and Discussions
### Variation Characteristics of Circulating Air Mixing Ratio  
The circulating air mixing ratio is represented by \(\varphi_{\rm c}=m_{\rm c}/m_{\rm out}\), where \(m_{\rm c}\) is the amount of pollutants that is carried by the circulating air into the inlet tunnel from the outlet tunnel, and \(m_{\rm out}\) is the total amount of gaseous pollutants discharged from the outlet tunnel. Therefore, it can be perceived that \(\varphi_{\rm c}\) indicates the degree of impacts of exhaust emission from the outlet tunnel on the inlet tunnel.  
3.2.1 The Relationship between Circulating Air Mixing Ratio and the Air Velocity at the Inlet/Outlet  
The flow field structure depicted in Figure 9g-i shows that both reducing \(u_{\rm in}\) and increasing \(u_{\rm out}\) can weaken the circulating air. Existing literature [13] has also shown that when \(u_{\rm in}=u_{\rm out}\), for closely spaced twin tunnels with aligned inlet and outlet air tunnel portals, \(\varphi_{\rm c}\) does not change with the change of air velocity value but is related to the ratio of \(u_{\rm in}\) and \(u_{\rm out}\). Figure 10 shows the variation of \(\varphi_{\rm c}\) with air velocity at the closely spaced twin tunnels with staggered inlet and outlet. It can be seen that regardless of the relative position of the inlet and outlet, when \(u_{\rm in}=u_{\rm out}\), \(\varphi_{\rm c}\) is not affected by the value of air velocity, and that the variation of \(\varphi_{\rm c}\) does not exceed 0.3%.  
Figure 10: Impacts of air velocity \(u\) on circulating air mixing ratio \(\varphi_{\rm c}\).  
Figure 9: Flow structure at the portals of twin tunnels with small spacing.  
Figure 11 shows the variation of \(\varphi_{\rm c}\) with \(u_{\rm in}/u_{\rm out}\) when \(u_{\rm in}\
eq u_{\rm out}\). It can be seen that \(\varphi_{\rm c}\) increases gradually with the increase of \(u_{\rm in}/u_{\rm out}\) despite the relative position of the inlet and outlet. By further comparing the numerical calculation results of \(\varphi_{\rm c}\) in the tunnel with aligned inlet and outlet air tunnel portals with the scaled model test results in the literature [13], it can be found that the trend of the simulated data is congruent with the test results in the literature, but the values of the simulation are generally lower than the data in the literature. This is caused by the inconsistency between the front slope gradient (\(15^{\circ}\)) of the tunnel portal for the numerical simulation in this paper and the front slope gradient (\(90^{\circ}\)) of the scaled model in the previous study.  
In addition, according to Figures 10 and 11, \(\Delta l\) and \(d\) have a great influence on the circulating air; with the decrease of \(\Delta l\) and the increase of \(d\), \(\varphi_{\rm c}\) drops significantly.
## 2 Materials and Methods
### Preparation and Characterization of Biomass and Nanoparticles
#### 2.1.3 XRD Analysis  
X-ray diffraction (XRD) is a technique that allows obtaining information about the crystalline nature of the precipitates formed during the biosorption stage. For this reason, the samples analyzed by FT-IR were also analyzed by XRD to identify the presence of crystalline precipitates, indicating the presence of metallic nanoparticles in the biomass. A Malvern Panalytical Empyrean instrument (Malvern, UK) was used under the conditions described in [PERSON] et al. [11]. In parallel, the technique was also used to identify the presence of CuO-NPs after the synthesis stage with cellular extract of the microorganism studied. In both cases, the theoretical size of the crystal was calculated from the most intense peak and using the Debye-Scherrer equation (Equation (1)).  
\[d=\frac{k\lambda}{\beta cos\theta} \tag{1}\]  
where \(d\) is the mean diameter (nm) of the crystals, \(k\) is Scherrer's constant, which has a value of 0.9, \(\lambda\) is the wavelength (nm) of the incident X-rays, \(\beta\) is the width of the mean XRD peak height expressed in radians, and \(\theta\) is the Bragg diffraction angle.
# Capacity-Operation Collaborative Optimization for Wind-Solar-Hydrogen Multi-Energy Supply System
## 2 System Description  
### WSH-MES System  
Figure 1 illustrates the sophisticated WSH-MES system, a composite of five unique subsystems: PV, WP, CSP, PEMFC.  
PV and WP Subsystems: These subsystems primarily supply electricity to consumers. Surplus power is directed to the PEME subsystem for the conversion into hydrogen. Both subsystems include inverters, with the PV subsystem consisting of solar panels and the WP subsystem containing a wind turbine. The electrical outputs from the solar panels and wind turbine are connected to the PEME subsystem and the users through their respective inverters.  
CSP and PEMFC Subsystems: Serving as peak power sources, they share the electrical load with the PV and WP subsystems, ensuring a balanced energy distribution.  
PEME Subsystem: Specialized in hydrogen production, the hydrogen generated here is used in the PEMFC subsystem to create electricity.  
CSP Subsystem Components: This subsystem encompasses a solar field (SF), electric heater (EH), hot salt tank, superheater, steam generator, preheater, cold salt tank, high-pressure cylinder, low-pressure cylinder, and steam turbine. The SF's heat transfer end connects to the hot salt tank, and the latter's high-temperature molten salt output links to the superheater, steam generator, preheater, and cold salt tank. Together, these elements  
Figure 1: Diagram of WSH-MES system.  
heat the feed water, producing superheated steam for the high-pressure cylinder. The cold salt tank's chilled molten salt output feeds into the SF, and the steam from the high-pressure cylinder flows to the low-pressure cylinder, driving the steam turbine and generator to deliver electricity to the user.
# An Improved Anti-Interference Precoding of Large-Scale Fading System Based on Channel Inversion
## 5 Simulation
The noise figures of the BS and user were 5 and 10, respectively; the bandwidth was 20 MHZ. In the simulation, different numbers of base station antennas were used, \(M\) was set to 100, 1000, 10,000, and 1,000,000. Channel transmission rate can be calculated by (36). The simulation results show the lowest achievable rate in a cellular network group. In Figure 5, the simulation results are compared with those of the ZF-LSFP that are provided in [4]. The comparison results show that when M reached the value of \(10^{6}\), the achievable channel transmission rate of the A-ZF-LSFP reached the value of \(10^{0}\), and the performance of channel transmission rate increased by 60%. When M was between \(10^{2}\) and \(10^{4}\), the channel transmission rate of the A-ZF-LSFP increased by 90%. Figure 4: Relative distribution of all users in the area. As Figure 6 shows, when the number of antennas was 100, the probability that the unimproved optimized precoding could achieve a channel transmission rate greater than \(10^{0}\) was about 0.8, and the probability of reaching the value of \(10^{1}\) was zero; when the number of antennas was 1000, the probability that the O-LSFP could achieve a channel transmission rate greater than \(10^{0}\) was about 0.9, and the probability of reaching the value of \(10^{1}\) was close to zero. As Figure 7 shows, when the number of antennas was 100, the probability that the optimized precoding with channel inversion processing could achieve a channel transmission rate greater than \(10^{0}\) was about 0.9, and the probability of reaching the value of \(10^{1}\) was 0.05; when the number of antennas was 1000, the probability that the A-O-LSFP could achieve a channel transmission rate greater than \(10^{0}\) was about 0.96, and the probability of reaching the value of \(10^{1}\) was about 0.5. In the simulations, when the number of antennas was a few hundreds, the transmission rate of the A-O-LSFP was increased by at least 10%. Figure 5: CDF of A-ZF-LSFP channel can achieve transmission rate. Figure 6: CDF of O-LSFP can achieve transmission rate. Figure 8 shows that when the number of antennas was 100, the probability that the optimized precoding with regular channel inversion processing could achieve a channel transmission rate greater than \(10^{0}\) was about 0.7, and the probability that it could reach the value of \(10^{1}\) was 0.15; when the number of antennas was 1000, the probability that the MMSE-A-O-LSFP could achieve a channel transmission rate greater than \(10^{0}\) was about 0.9, and the probability of reaching the value of \(10^{1}\) was about 0.25. The simulation results showed that when the number of antennas was about 100, the MMSE-A-O-LSFP had a probability of 15% to reach 10 times the transmission rate gain, but there was still a 10% attenuation probability. The MMSE-A-O-LSFP had a probability of reaching 10 times the transmission rate gain of 25%, and there was no attenuation of the transmission rate gain, and the improved performance is completely improved.
## 3 Results and Discussions  
### Foam Flow Characteristics around Injection Well TK467  
The N\({}_{2}\) foam was injected into the three-dimensional visualized fracture-vuggy physical model though well TK467. The injected rate of N\({}_{2}\) foam is 12 mL/min. The oil-water interface (OWI) height around the TK467 is higher than the borehole's height owing to the synergism of injected water and bottom water (Figure 3a). The injected N\({}_{2}\) foam preferentially migrates to the upper part of the fracture-vuggy unit controlled by well TK467 because of the density difference of water and N\({}_{2}\) foam. Figure 3a shows that the N\({}_{2}\) foam enters the upper fracture along the wellbore. The foam can accumulate in the upper fracture through continuous foam injection, making N\({}_{2}\) foam migrate into the vug, as shown in Figure 3b. The N\({}_{2}\) cap is formed after the N\({}_{2}\) foam in the upper fracture is defoamed (Figure 3c). The N\({}_{2}\) cap with pressure acts on the gas-oil interface (GOI) and pushes the oil belt moving downward, resulting in a decrease of OWI height. Additionally, the N\({}_{2}\) foam can also accumulate in the vug and inhibit the rise of the OWI, as shown in Figure 3d. Compared to Figure 3a, Figure 3d shows that the oil film is significantly detached where the N\({}_{2}\) foam spreads, which is attributed to the IFT reduction with surfactants' help.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Well Name** & **Well Height (cm)** & **Well Type** & **Coordination** \\  & & & **Number** \\ \hline TK411 & 10.8 & Vug well, production well & 3 \\ TK467 & 18.0 & Fracture well, convert well & 1 \\ S48 & 14.8 & Vug well, production well & 4 \\ T401 & 12.2 & Vug well, production well & 2 \\ TK426 & 9.2 & Fracture well, production well & 4 \\ \hline \hline \end{tabular}  
* Tips: The coordination number refers to the fractures number connecting to the reservoir unit where the borehole locates. The well TK467 is a convert well, while the other four wells are production wells all the time.  
\end{table}
Table 4: Well parameters of three-dimensional visualized fracture–vuggy model.  
A graph depicting the evolution of the OWI position around the well TK467 with time is plotted in Figure 4. The slope of this curve represents the moving rate of the OWI. The injected foam accumulates in the fracture-vuggy unit's top controlled by TK467 and then acts on the residual attic oil, leading to the OWI's height decrease. The decline rate of the OWI height keeps stable at 0.0357 mm/s until the OWI decreases to the cylinder base tank.
## 2 Methods  
### Shallow Seismic Exploration  
For shallow seismic exploration, a multiple time coverage reflection wave method was used, which not only helped suppress interference and improve the signal-to-noise ratio of seismic data, but also helped determine the existence and form of the fault via a visual image of the subsurface structure. This shallow seismic data acquisition uses the 428 XL digital seismograph produced by Sercel, Nantes, France. All equipment parameters satisfy the requirements of shallow seismic exploration. The seismograph has a high sampling rate, wide recording frequency, large dynamic range and real-time correlation processing of the controllable source data. Two 28 ton vibrators manufactured in the USA, which have strong anti-interference properties, are used to excite seismic waves. Geophone is one of the most important components of the seismic acquisition system. The natural frequency of the geophone used in this acquisition is 60 Hz. Processing of seismic data is conducted  
Figure 2: The location of shallow seismic exploration and composite drilling section exploration. (**A**) The distribution of buried faults and shallow seismic survey lines in Zhongwei Basin. The epicenter of the 1709 \(M\)7.5 earthquake on the Tianjingshan Fault is highlighted. (**B**) The location map of boreholes and composite drilling section exploration (Black bold line) in Xietan Village. (**C**) The location map of boreholes and composite drilling section exploration (Black bold line) in Lizuil Village.  
with the Focus software package. The data processing methods mainly include format conversion and data decompiling, de-noising before superposition, deconvolution, statics correction, dynamic correction, common midpoint point (CMP) stacking, inverse filtering after superposition and so on. In the survey, we completed Xietan Village's Survey Line and Lizui Village's Survey Line (location shown in Figure 2A), 4.22 km and 3.0 km, respectively. The observational system and acquisition parameters of seismic data are listed in Table 1. Through two shallow seismic exploration profiles, the locations and properties of two buried faults in the range of the control line were identified and the depths of uppermost points were found. These shallow seismic profiles lay a solid foundation for the detection of buried faults.
# A Decision Support System for Crop Recommendation Using Machine Learning Classification Algorithms
## 3 Materials and Methods
### Gradient Boosting  
Gradient Boosting is an ensemble method that sequentially trains the models. Here, each model tries to rectify the previous model. Each time, the new model gets trained based on the loss observed by applying the log-likelihood function in the previous old model. The location-based crop dataset shall be used in recommending, with the help of the gradient boosting technique.  
In this approach, at first, a base model was used for prediction based on a crop dataset. Equation (11) shows the current model \(f_{i}(x)\) for sample \(i\) evaluated at \(x\), \(Yi\) is the predicted value, \(\gamma\) is the adjustment for prediction value by the min loss function, and \(L(Yi,\gamma)\) is the loss function.  
\[f_{i}(x)=minloss\ \gamma\sum_{i=1}^{n}L(Yi,\gamma) \tag{11}\]  
Equation (12) shows the loss function \(L\), the number of samples \(n\), the predicted probability for crops \(p_{i}\), and the true binary label \(y_{i}\).  
\[L=-\sum_{i=1}^{n}y_{i}\cdot\log(pi)+(1-p_{i})\log(1-p_{i}) \tag{12}\]  
Equation (13) shows that each model \(f_{m+1}(x)\) is constructed by adding a weak learner tree \(h_{m}(x)\) to the ensemble \(f_{m}(x)\). There are \(m\) samples, and we need to fit the \(h_{m}(x)\) at each iteration of \(m\). So, the new weak learner is predicted for our model.  
\[f_{m+1}(x)=f_{m}(x)+\gamma\cdot h_{m}(x) \tag{13}\]  
Finally, Equation (14) shows the prediction probability \(p\), a sigmoid function to the sum of all the individual models and linear score \(f(x)\).  
\[probability\ p=sigmoid(f(x))=\frac{1}{\left(1+e^{-f(x)}\right)} \tag{14}\]