esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Production History Matching Using the Entire Production History  
At the beginning of the study, production history matching was done between the entire actual production data with the DCA models. The objective of this step was to find the DCA model that best fit the production data and produced the most accurate ultimate recovery value. The iterative least-squares fitting method was performed on the production data with the SEPD and the Arps hyperbolic decline equations to find the optimum (fitted) values of the decline parameters for each model. For the SEPD model, the parameters were initial flow rate (qj), an exponent parameter (n), and a characteristic time parameter (\(\tau\)), and for the Arps hyperbolic decline model, the parameters were initial flow rate (qj), a hyperbolic exponent (b), and the nominal 1/month decline rate (Dj).  
Table 3 represents the error analysis results from production history, matching between the entire production histories of the subject wells and the Arps hyperbolic decline model. Figure 4 shows an example of the matching with a well from one of the formations. The outcome of this production history matching is the decline parameters values for each well (qj, b, and Dj) assuming them to be constant through the entire production period. Table 4 represents the error analysis results from production history matching between the entire production histories and the SEPD model. Figure 5 shows an example of the matching with a well from one of the formations. The outcome of this production history matching is the decline parameter values for each well (qj, n, and \(\tau\)), assuming them to be constant through the entire production period. These resulting decline parameters were then used to calculate EUR for each well and compared with the actual EUR values measured using the cumulative production at the economic limit.  
Figure 4: Example of production history matching of entire production history data with the Arps hyperbolic decline model for a well in the Niobara formation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Decline Model** & **Formation** & **MAPE (\%)** &
\begin{tabular}{c} **Correlation** \\ **Coefficient (\(r\))** \\ \end{tabular} & **NRMSE (\%)** \\ \hline \multirow{5}{*}{SEDD} & Niobrara & 1.34 & 0.9997 & 1.70 \\ \cline{2-5}  & Barnett & 0.77 & 0.9998 & 1.32 \\ \cline{2-5}  & Haynesville & 2.46 & 0.9973 & 3.81 \\ \cline{2-5}  & Eagle Ford & 2.90 & 0.9989 & 4.44 \\ \cline{2-5}  & Bakken & 1.40 & 0.9992 & 2.69 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Error analysis results for production history matching using entire production history with the stretched exponential production decline (SEPD) model.  
Figure 5: Example of production history matching of entire production history data with the SEPD model for a well in the Barnett formation.
# Delay Management in Regional Railway Transport
## 1 Introduction  
For public service functionality (which includes accuracy and reliability), it is necessary to maintain the integrity of the transportation chain in passenger transport as part of the network for public transport planning. The integrity of the transportation chain is often disrupted by negative consequences for passengers (e.g., delay and loss of transfer connections) as they experience operational disruptions. Accordingly, it is necessary to ensure an acceptable level of operation regarding stable timetables so that transfer waiting times and planned transfer bindings may be achieved with tolerable levels of delay.  
The necessity of waiting for services due to delays in other services increases with growing intervals between connections. While waiting of the continuing services for delayed connecting services is not urgent during short intervals (i.e., up to 30 min) because the waiting time for the next service is relatively short, the significance of waiting grows up with longer intervals (i.e., 60-120 min) because waiting for the next service is longer (often longer than the travel time itself).  
This article deals with transfer waiting times, especially in passenger railway transport. The authors develop an original approach for determining transfer waiting times, based on a modified critical path method (CPM), which is understood to be an effective tool for the management of delays that are due to common operational disruptions (i.e., disruptions with only relatively small deviations from the timetable, not extraordinary interruptions causing, e.g., traffic suspensions). The main contribution of this approach (compared to existing approaches examined by the authors) is the determination of transfer waiting times by taking into consideration the operational influences between trains, without the need for railway operation simulation (which is often much too time-consuming). The CPM approach easily applied in cases where there are not many relevant disposal measures and their combinations are used; however, this is also one of the limitations of the approach. The anticipated impact of applying this approach in practice is that waiting times would not be merely estimated (which, unfortunately, is the common Czech praxis); rather, they would be calculated, so that delays caused by waiting for connecting trains do not interruptrelevant transfer bindings and the transportation chain remains compact. It is necessary, however, to mention that this approach does not include the passenger-time consideration.
# A Comparison of Pooling Methods for Convolutional Neural Networks
## 4 Advantages and Disadvantages of Pooling Approaches
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Pooling Methods**} & \multirow{2}{*}{**Architecture**} & \multirow{2}{*}{**Activation Function**} & \multicolumn{4}{c}{**Error Rate of Different Datasets**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Reference**} \\ \cline{3-3} \cline{5-8}  & & & **MNIST** & **CIFAR-10** & **CIFAR-100** & \\ \hline Rank-Based & & & & & & \\ Average Pooling & Network in Network (NIN) & Leaky RELU & & 9.48 & 32.18 & \\ (RAP) & & & & & & \\ \hline Rank-Based & & & & & & \\ Weighted Pooling & Network in Network (NIN) & Leaky RELU & & 9.34 & 32.47 & \\ (RWP) & & & & & & \\ \hline Rank-Based & & & & & & \\ Stochastic Pooling & Network in Network (NIN) & Leaky RELU & & 9.84 & 32.16 & \\ (RSP) & & & & & & \\ \hline Rank-Based & & & & & & \\ Weighted Pooling & Network in Network (NIN) & RELU & & 10.62 & 35.62 & \\ (RWP) & & & & & & \\ \hline Rank-Based & & & & & & \\ Stochastic Pooling & Network in Network (NIN) & RELU & & 9.48 & 36.18 & \\ (RSP) & & & & & & \\ \hline Rank-Based & & & & & & \\ Average Pooling & Network in Network (NIN) & (Parametric) & & 8.75 & 34.86 & \\ (RAP) & & & & & & \\ \hline Rank-Based & & & & & & \\ Weighted Pooling & Network in Network (NIN) & (Parametric) & & 8.94 & 37.48 & \\ (RWP) & & & & & & \\ \hline Rank-Based & & & & & & \\ Average Pooling (RAP) (Includes Data Augmentation) & & & & & & \\ \hline Rank-Based & & & & & & \\ Weighted Pooling (RWP) (Includes Data Augmentation) & & & & & & \\ \hline Rank-Based & & & & & & \\ Stochastic Pooling (RSP) (Includes Data Augmentation) & & & & & & \\ \hline Rank-Based & & & & & & \\ Stochastic Pooling (RSP) & Network in Network (NIN) & RELU & & & & \\ (Parametric) & & & & & & \\ \hline Rank-Based & & & & & & \\ Stochastic Pooling (RSP) & Network in Network (NIN) & RELU (Parametric) & & 7.74 & 33.67 & \\ (RSP) & & & & & & \\ Augmentation & & & & & & \\ \hline \hline \end{tabular} 
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Pooling Methods**} & \multirow{2}{*}{**Architecture**} & \multirow{2}{*}{**Activation Function**} & \multicolumn{4}{c}{**Error Rate of Different Datasets**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Reference**} \\ \cline{3-3} \cline{5-8}  & & & **MNIST** & **CIFAR-10** & **CIFAR-100** & \\ \hline Rank-Based & & & & & \\ Average Pooling (RAP) & & & & & \\ \hline Rank-Based & & & & & \\ Weighted Pooling & Network in Network (NIN) & Leaky RELU & & 9.48 & 32.18 & \\ (RWP) & & & & & \\ \hline Rank-Based & & & & & \\ Weighted Pooling & Network in Network (NIN) & Leaky RELU & & 9.34 & 32.47 & \\ (R
## 2 ECMA-368 Standard  
The physical layer of ultra wideband using MB-OFDM is described by ECMA-368 in Wireless personal area (WPAN) and is also advised for vehicular networks. It is allocated into the unlicensed 3.1-10.6 GHz frequency band. It also adopts 53,3 Mb/s, 80 Mb/s, 106,7 Mb/s, 160 Mb/s, 200 Mb/s, 320 Mb/s, 400 Mb/s, and 480 Mb/s as data rates. In Figure 1, the ECMA-368 band is shown; this band is split into six groups of bands. Band groups 1 to 4 contain three bands each, covering the bands 1 to 12. Band group 5 consists two bands 13 and 14. Band group 6 contains the bands 9, 10 and 11. Band group 1 is used for mandatory mode and the rest of the bands groups are dedicated for future use. The center frequency \(f_{c}\) is related to the band number \(n_{b}\) by: \(f_{c}=2904+528*n_{b},\ n_{b}=1...14\) (MHz) [19]. The transmitted MB-OFDM symbols are time-interleaved across the 14 bands according to the specified time-frequency code (TFC) [19].  
Table 1 presents the MB-OFDM characteristics. An IFFT (Inverse fast Fourier transform) of 128 points generates the MB-OFDM symbol. Between 128 sub-carriers, 100 are for data, 12 pilots, 10 guard subcarriers, five zero guards and the DC. The subcarrier frequency spacing \(\Delta f=4.125\) MHz can fulfill the requirement of orthogonality in the OFDM system. The data rates are tuned by four possible forward error correction (FEC) codings, which are convolutional codes using 1/3, 1/2, 5/8 and 3/4 as coding rates.  
Eventually, the duration of each transmitted MB-OFDM symbol containing 165 samples is Ts = 312.5 ns. Figure 2[19] shows how the PHY (Physical) service interface and the MAC are connected  
Figure 1: The ECMA-368 band groups.  
by using a Physical Layer Convergence Protocol (PLCP) sublayer, and how a PSDU (PHY Service Data Unit) is converted to a PPDU (PLCP Packet Data Unit).  
The PLCP Preamble: contains the packet of synchronization and channel estimation sequences. The PLCP Header: contains information needed on both PHY and MAC layers, for example: MAC header, PHY header, Reed-Solomon parity bits. The PSDU: contains essentially the data packets.  
ECMA-368 uses two types of modulations:  
1. QPSK modulation used with data rates of 53.3 up to 200 Mb/s.
2. Dual-Carrier Modulation (DCM) used with data rates of 320 up to 480 Mb/s, in this modulation the Bits are divided into groups of 200 bits, and further grouped into 50 groups of 4 reordered bits. Then, the DCM modulation uses a matrix H to execute a mapping of the two QPSK symbols into two DCM symbols which form two 16-QAM constellations [20].
# Dynamics and Control of Typical Orbits around Saturn
## 3 Repeating Ground Track Orbits and Orbital Maintenance
The density of simplified atmospheric model, in the range of semi-major axis \(a\in[62,\!268,\!62,\!468]\) km, is \(\rho\in[3.7\times 10^{-12},4.7\times 10^{-12}]\) kg/m\({}^{3}\)[8]. Then we find the Figures 7 and 8 which illustrate the the control compensation of semimajor axis and control period of repeating ground track orbits. From Figure 7, we know the semimajor compensation \(\Delta a\) for \(a\in[62,\!268,\!62,\!468]\) km varies from \(5200\) m to \(5800\) m. The corresponding compensation period \(\Delta T_{\text{C}}\) in Figure 8 varies from \(18\) h to \(16\) h. Compared to Jupiter [29], Saturnian atmospheric drag is about 2 orders of magnitude greater than Jovian atmospheric  
\[+\frac{9 nJ_{2}^{2}R_{e}^{4}}{4p^{4}}\sqrt{1-e^{2}}(-\frac{3}{2} \sqrt{1-e^{2}}-\frac{19}{3}-\frac{26}{3}e^{2}-\frac{35}{4}\frac{e^{4}}{1-e^{2}} +\frac{25J_{4}}{4J_{2}^{2}}e^{2}),\]4 The repeating ground track orbit around Saturn for \(a=61539\) km, \(i=133.2052\) deg, \(e=0.001\), \(\omega=45\) deg, \(\Omega=30\) deg, and repetition parameter \(Q=2.49\). drag when the spacecraft is at the same altitude. As a result, it is essential to execute the semimajor axis compensation for repeating ground track orbits around Saturn.
## 2 Materials and Methods
### Experimental Design and Optimization of Plasma Treatment Parameters  
In this study, we investigated the effect of two important plasma treatment parameters, namely, treatment time and discharge power, on the mechanical properties of hemp-fiber-reinforced epoxy (HFRE) composites. The above-mentioned parameters were chosen and retained from previous reports as they have significant influence on the surface modification of natural fibers.  
The length of the time exposed to the plasma environment directly correlates with the degree of surface modification and is known as treatment time. Increased treatment duration provides more time for interaction between the reactive plasma species and the fiber surface, facilitating deeper changes in the chemistry and morphology of surface. Yet, treatment times higher than optima can lead to fiber degradation and reduction in mechanical properties. Discharge power, in contrast, affects the density and activity of plasma species. Increasing discharge powers create a harsher plasma environment, which enhanced the modification effects on the surface. However, thermal degradation of the fibers can occur at discharge powers that are too high and deteriorate structural integrity.  
A design of experiments (DOE) approach was adopted to optimize these parameters and study their interaction effects on the mechanical properties of HFRE composites using Minitab 21 software. By choosing a full factorial design with factors treatment time and discharge power each at 3 levels, it resulted in an overall of 22 experimental runs listed in Table 1. The levels for treatment time were 10, 30, and 50 min, while the levels for discharge power were 140, 160, and 180 W. The discharge power levels were chosen based on preliminary experiments and literature recommendations within the ranges to achieve adequate surface modification with limited fiber degradation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameter (unit)**} & \multicolumn{3}{c}{**Level of Parameters**} \\ \cline{2-4}  & **Low** & **Intermediate** & **High** \\ \hline Discharge Power (W) & 140 & 160 & 180 \\ Treatment Time (min) & 10 & 30 & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 1: DBD plasma treatment parameters.  
Figure 2: Test specimens for tensile testing according to technical standard.  
The response variable, tensile strength of the HFRE composites, was measured for each experimental run to evaluate the effects of the plasma treatment parameters. The obtained data were analyzed using analysis of variance (ANOVA) to determine the statistical significance of each parameter and their interactions. The optimization of the plasma treatment parameters was performed using response surface methodology (RSM) to identify the optimal settings for achieving the highest tensile strength.
## 2 Materials and Methods
### Microbial Analysis Methods  
Microbial samples were collected from the inoculated activated sludge during the operation of each reactor. For PN1, samples were collected on days 6, 32, and 50. For PN2, samples were collected on days 6, 13, 22, 33, and 50, including points where the influent conditions were changed (with day 6 marking the occurrence of nitrification). Genomic DNA was extracted using the DNeasy(r) PowerSoil(r) Kit (QIAGEN, Venlo, Netherlands Hilden, Germany), following the manufacturer's instructions. The extracted DNA samples were stored at \(-\)20 \({}^{\circ}\)C until they were used for next-generation sequencing (NGS) analysis. The purity and quantity of the DNA were measured using a Nanodrop spectrophotometer (ND-1000, Thermo Fisher Scientific, Waltham, MA, USA). Polymerase chain reaction (PCR) was performed to amplify the V3-V4 region of the 165 rRNA gene (Table 2). Bacterial sequencing was then conducted using the Illumina MiSeq platform (Macrogen Co., Seoul, Republic of Korea). Quality control of the raw sequences was performed to remove ambiguous or potential chimeric sequences. Sequences with over 97% similarity were clustered to derive Operational Taxonomic Units (OTUs). The representative sequences of each OTU were classified based on the most similar bacteria using the National Center for Biotechnology Information (NCBI) database. Diversity indices, such as Chao1, Shannon, and InvSimpson, were calculated to assess the microbial diversity.
# Review on System Identification and Mathematical Modeling of Flapping Wing Micro-Aerial Vehicles
## 3 System Identification and Mathematical Modeling of Flapping Wing Micro Air Vehicles (FWMAVs)
### Tailless Flapping Wing Micro Air Vehicles
#### 3.2.1 System Identification of In-Flight Dynamics of Robotic Bee
The available data from the flight maneuvers were down-sampled using cubic spline to improve the continuity and a 3 rd order Chebyshev-type-II low pass filter was used to smooth the trajectories. The cutoff frequency of the filter was 50 Hz. The angular velocities were calculated via the use of rotation matrices and missing states were reconstructed via differentiation. The body fixed frame data were recovered via projecting the available states into the body coordinate frame. The estimated parameters were categorized into three for lateral dynamics, altitude dynamics, and rotational dynamics.
## 4 Evaluation
### Performance Evaluation Metrics
#### 4.3.2 PDR  
_PDR_ represents the ratio between packets received by the destination nodes and packets sent by the source nodes. This metric reflects the adaptability of solution under different traffic intensity.  
\[PDR=\frac{\sum_{i=0}^{N-1}Pr_{n_{i}}}{\sum_{i=0}^{N-1}\left(Pr_{n_{i}}+Pd_{n_{ i}}\right)} \tag{16}\]  
Figure 5: Protocol architecture of DGRL.
## 2 Materials and Methods
### Conceptual Site Model Reconstruction
In Figure 5, the groundwater levels that were monitored from March 2016 to present in two piezometers that are located in the flooded zone are represented. One of the two piezometers is monitored continuously through a data logger, while in the other, the groundwater level is manually recorded approximately every 15 days. The graph clearly shows a very fast response of the groundwater to no single rain events--especially when these exceed 20 mm--which is probably due to a very small effective porosity of the sediments hosting the groundwater body, but is undoubtedly caused by the fact that the zone is a stagnation area where the outflow is reduced due to the hydrogeological setting. Single events like the heavy rains in November and December of 2017 do not create great piezometric level variation as in the other cases, however an interruption in the downward trend can be observed. Figure 3: Cross Section 4 and Section C passing through the Grandate plain. Generally, after finding out and outlining the stagnation zones based on the flow network reconstruction, it is essential to understand what the physical phenomena are that control the origin and presence of zero-velocity areas. This knowledge is useful for correctly interpreting the model results that are related to the behavior of the aquifer and the effects of potential drainage systems on groundwater levels (Figure 6). In fact, in order to achieve optimal results in different pluviometrical and hydrological conditions, the design of drainage interventions in these kinds of areas requires more  
Figure 4: Piezometric map of the first aquifer and measured points with their groundwater level (head expressed in m a.s.l.). Encircled in red is the zone that was flooded in 2014. Piezometric lines 305 m a.s.l. and 295 m a.s.l. mark the low gradient zone (stagnation area). Figure 5: Monitoring data for the two points located in the flooded zone. than the usual preliminary hydrogeological analysis of the most suitable water well location and a correct evaluation of the withdrawal and the water level lowering. In this study, the piezometric map, which was constructed by interpolating the November 2015 survey data (Figure 4), shows that a stagnation area would probably be present in the Grandate plain. For this reason, the case of Grandate--which has a hydrogeological setting that is similar to that of an extensive part of the Lombard territory, as indicated in the already mentioned studies [31; 32; 33; 34]--has been examined in detail through the implementation of a numerical model. The implementation phase was carried out taking into account the hypothesis of the stagnation area existence which was supported by the piezometric map and the geological reconstruction of the area. The mathematical model then allowed a better understanding of the factors that affect groundwater flow and helped to evaluate the most suitable interventions for avoiding future groundwater flooding.
Also, in-control data are used to obtain a \(100\) (\(1-a\))% confidence region of (\(\delta,\gamma\)), which is employed to conduct statistical tests for accuracy index \(\delta\) and precision index \(\gamma\). The statistical testing results of these two indices can be concluded as follows: if the process cannot reach the six-sigma quality level, then the deviation direction of the process can be seen from the testing result of accuracy index \(\delta\), which can be provided to the industry for improvement reference; meanwhile, according to the testing result of precision index \(\gamma\), we can decide whether to reduce the process variation. It is clear that this study aims to monitor the process mean and try to make it fall within the target value using two important parameters of the normal process, namely accuracy index \(\delta\) and precision index \(\gamma\). In the meantime, the process variation can be supervised so that the process can meet the requirement of the \(k\)-sigma quality level. In order to decrease the risk of misjudgment incurred by sampling errors, a statistical testing model of these two indices is developed to individually evaluate whether the process accuracy and process precision can reach the required level, as well as decide whether to make improvements at the same time, in order to raise the process quality level of products. Concerning the rest of the paper, it is arranged in the following sections. In Section 2, expected values and standard deviations of the estimators for the accuracy index and the precision index are first derived from normal approximation rules. Then, the control limits between the accuracy index and the precision index are established based on the principles of three-sigma control charts, so as to provide the basis for the industry to monitor the process. In Section 3, the in-control data are retrieved to gain a 100 (\(1-\alpha\))% confidence region of \((\delta,\gamma)\), and then statistical tests are conducted with this confidence region for accuracy index \(\delta\) and precision index \(\gamma\). The statistical testing results of these two indices are regarded as the basis of determining whether to improve the process. In Section 4, based on the testing results of accuracy index \(\delta\) and precision index \(\gamma\), suggestions about process improvement are made, including how to correct process deviation directions and whether to reduce process variation. In Section 5, an empirical example is presented to demonstrate the application of the model proposed by this study. In Section 6, conclusions are made.
## 4 Security Analysis and Simulation Verification
### Formal Simulation Verification of Protocol Security
#### 4.3.2 Protocol Security Verification Results  
Based on the modeling performed by the protocol, the security of the protocol is verified using the execution results of the ProVerif tool. The verification results of the ProVerif tool are shown in Figure 5.  
Figure 5: ProVerif verification results.  
The ProVerif security verification shows that the authentication process of the proposed protocol cannot be destroyed by attackers and the session key cannot be obtained by attackers. The protocol security satisfies these requirements.