esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Stochastic Load Conditions
### Traffic Conditions

Table 4: Probabilistic properties of GVW for each type of vehicle (kN). Figure 3: Hourly traffic volume: (**a**) Fast lane; (**b**) slow lane. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Fast Lane**} & \multicolumn{2}{c}{**Slow Lane**} \\ \cline{2-5}
**Vehicle Type** & \multicolumn{2}{c}{**Number of**} & \multicolumn{2}{c}{**Occupancy Ratio**} & \multicolumn{2}{c}{**Number of**} & \\  & **Vehicles in 1 Day** & & **Vehicles in 1 Day** & & **Occupancy Ratio** \\ \hline V1 (2-axle mini-van) & 312 & 3.22 & 543 & 5.60 \\ V2 (2-axle sedan car) & 3326 & 34.28 & 463 & 4.77 \\ V3 (2-axle truck) & 305 & 3.14 & 635 & 6.55 \\ V4 (3-axle truck) & 346 & 3.57 & 680 & 7.01 \\ V5 (4-axle truck) & 402 & 4.14 & 643 & 6.63 \\ V6 (5-axle truck) & 383 & 3.95 & 702 & 7.24 \\ V7 (6-axle truck) & 321 & 3.30 & 642 & 6.62 \\ Total & 5395 & 55.60 & 4308 & 44.40 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Vehicle categories and the corresponding traffic lane occupancy ratios (\(\times\)100%). 2. Secondly, for each vehicle type, Latin hypercube sampling was conducted to obtain samples of GVW through the established distribution model (in Table 4), which was further assigned to each individual vehicle of that vehicle type. 3. Thirdly, the traffic density and the information of each individual vehicle (with the vehicle type and GVW) were put into the CA model for stochastic traffic simulation. As an illustration, Figure 4 displays the simulated traffic on the fast lane and slow lane under both free-flow and busy-flow traffic conditions. Each short line in Figure 4 represents one vehicle, and various colors are assigned to the short line to differentiate various vehicle categories. It is observed from Figure 4 that free-flow and busy-flow traffic exhibits different traffic patterns, i.e., the vehicles under free-flow traffic conditions (Figure 4a,c) drive smoothly without experiencing any traffic congestion, while the vehicles under busy-flow traffic conditions (Figure 4b,d) experience few times of traffic congestions. This is due to the fact that the traffic volume under busy-flow traffic condition is significantly higher than that under free-flow traffic conditions (refer to Figure 3), which could cause traffic congestions. In order to further evaluate the performance of the proposed CA-based stochastic traffic flow model, the predicted distributions of the gross vehicle weight in the simulated traffic flow were compared with the corresponding target distributions (refer to Table 4), as shown in Figure 5. It is observed from  
Figure 4: Simulated stochastic traffic flow. (**a**) Fast-lane: Free-flow; (**b**) fast-lane: Busy-flow; (**c**) slow-lane: Free-flow; and (**d**) slow-lane: Busy-flow. Figure 5 that the predicted GVW distributions match quite well with the target distributions for V1, V3, V5, and V7 vehicles. It is worth mentioning that the predicted GVW distribution for V2, V4, and V6 also match quite well with the target distribution. Therefore, the simulated stochastic traffic flow can accurately reflect the site-specific traffic statistics.
# Simulation of Cross-Correlated Random Fields for Transversely Anisotropic Soil Slope by Copulas
## 1 Introduction
[PERSON] [25] assumed that all random fields have similar autocorrelation, and then used the cross-correlation coefficient to characterize the CCRFs of \(c\) and \(\phi\). However, according to probability theory, correlation coefficient cannot accurately represent the correlation structure among random variables. Such treatment obviously does not cater to the real spatial distribution of soil parameters. More efforts must be made to describe the correlation problem involving multiple-parameter random fields. Copula theory has been gradually introduced to bridge the gap between individual margin and joint distribution [27; 28; 29]. It is capable of measuring complex interdependencies among random variables. To date, various related achievements have been made [9; 14; 15]. Presently, few studies have made attempts to expand copulas to characterize the relationship among multiple random fields [24; 30; 31]. [PERSON] et al. [24] proposed a framework of multivariate CCRF for geotechnical parameters. Subsequently, [PERSON] and [PERSON] [30] discussed the application of copula theory in random field simulation. On the basis of these, [PERSON] et al. [31] established a framework of multivariable CCRF under rainfall conditions by using copulas. However, similar studies on copula-based CCRF remain sparse. The performance and effectiveness of CCRF deserve to be further studied. On the other hand, previous studies mainly focused on Gaussian CCRFs. The effects of the non-Gaussian-dependent structure of soil parameters on anisotropic CCRF have seldom been reported. Therefore, more thorough efforts need to be taken to explore this controversial area. To compensate for the aforementioned drawbacks, this paper is mainly intended to propose a new CCRF simulation method based on copulas. The remainder of the paper is organized as follows: brief introductions of random field theory and the copula theorem are elaborated in Sections 2 and 3, respectively; Section 4 establishes four algorithms associated with different candidate copulas; the overall simulation procedure of transversely anisotropic CCRF is displayed in Section 5; then the performance of the proposed method is verified by means of an assumed soil slope and two engineering slope cases in Section 6; lastly, some outstanding conclusions are drawn in Section 7.
## 2 Bridge Dynamics in a VBI System and the Data Reconstruction Algorithm
### Signal Processing Methods
#### 2.3.3 Transfer-Reconstruction Process  
In this research, the low-frequency bridge responses induced by vehicle loading were reconstructed using a transfer matrix that was trained with a ResNet, leveraging high-frequency bridge responses. The flowchart of the entire algorithm is presented in Figure 7.  
Initially, a high-pass filter was employed to isolate the high-frequency free vibration components of the bridge. The remaining data consisted of temperature-induced bridge responses (TBR) and driving-force-induced responses, separated with the above interception method. These high-frequency responses were subsequently harnessed to train a ResNet, simulating the inter-sectional relationships for high-frequency responses across various locations, such as Location 1, Location 2, and Location 3. Following the training, the calibrated network (ResNet) was then deployed to reconstruct the absent low-frequency responses at Location 3, utilizing the separated low-frequency responses at Location 1 and Location 2. It should be emphasized that throughout the entire process, there was no need  
Figure 6: ResNet configuration.  
for explicit global stiffness matrices, mass matrices, or mode shape vectors, despite these parameters typically being required to calculate the transfer matrix, as in Equation (12). Instead, the ResNet was employed to directly estimate the transfer matrix using the input measurement data from Location 1 and Location 2 to predict the output data at Location 3. It was anticipated that, compared with model-based methods, the proposed approach would offer greater efficiency.
# A Low-Loss Circuit with High-Pass Low-Pass Broadband Flat Negative Group Delay Characteristics
## 2 Circuit Design and Analysis with High-Pass and Low-Pass Negative Group Delay
### Low-Pass Flat Negative Group Delay Circuit
#### 2.2.4 Low-Pass Negative Group Delay Circuits with Different \(L_{2}\) Values  
When \(C_{1}\), \(R_{1}\), \(L_{1}\), \(R_{2}\), and \(L_{3}\) in the circuit remain unchanged from the values in Table 8, and the \(L_{2}\) values are sequentially changed to 0.9 nH, 3.9 nH, and 5.9 nH, we observe the changes in the circuit transfer function and group delay. The circuit results in the three scenarios are shown in Table 12 and Figures 23 and 24. Through observation, it is found that, as the value of inductance \(L_{2}\) increases, the group delay of the high-pass circuit at 0 MHz decreases, but the difference is small. Changing the value of inductance \(L_{2}\) also has a small impact on the bandwidth of the flat negative group delay. At the same time, the insertion loss of the circuit does not change with the value of inductance \(L_{2}\).
# JIT Production Strategy and Maintenance for Quality Deteriorating Systems
## 3 Problem Statement
### Production Inventory Control Problem  
The central premise of the approach is the incorporation of a JIT production strategy into the production control policy, where such JIT strategy consists of a zero-inventory policy. The aim of such an approach is to enhance company competitiveness through lower inventory levels and to reduce the total incurred cost. To make things concrete, the JIT strategy is combined with a derivation of the so-called Hedging Point Policy, where the production rate at any instant of time \(t\) is controlled with the following expressions:  
If \(a(t)\leq B_{JIT}\):  
\[u^{*}(1,x,a)=\begin{cases}u_{max}&ifx(t)<0\\ d/(1-\beta(a))&ifx(t)=0\\ 0&ifx(t)>0\end{cases} \tag{8}\]  
If \(a(t)>B_{JIT}\):  
\[u^{*}(1,x,a)=\begin{cases}u_{max}&ifx(t)<Z_{p}\\ d/(1-\beta(a))&ifx(t)=Z_{p}\\ 0&ifx(t)>Z_{p}\end{cases} \tag{9}\]  
The critical point of the proposed production policy is to implement a JIT production strategy when the production unit is under the age \(B_{JIT}\), since at such age the effects of deterioration are considered to be non-significant. Thus, there is no need to maintain stock as protections against shortages and defectives, justifying a zero-inventory policy. Then, as the machine ages and surpasses age \(B_{JIT}\), a threshold inventory strategy is implemented to maintain an amount of inventory \(Z_{p}\) as a protection to mitigate the effect of increasing defectives and other disruptions encountered in production.
## 2 Materials and Methods
### Network Architectures
#### 2.3.1 U-Net  
The first architecture tested is a standard U-Net (Figure 3), containing three convolutional blocks in both the encoder and the decoder, connected by a bottleneck and the characteristic skip connections of the U-Net architecture [41]. Each block in the encoder has two convolutional layers with ReLU activation and a max-pooling operation to reduce the resolution of the input to the next block. In the decoder, a transposed convolution with a stride greater than one is used as an upsampling method to increase the resolution of the intermediate results, which are concatenated with the output of the convolutional layers of the corresponding encoder block. Then, two convolutional layers are used as processing units that also reduce the depth channels of the intermediate results. Skip connections grant the decoder access to positionally precise, lightly processed image information, to use jointly with the heavily processed, less positionally precise, higher level information coming from the bottleneck. This makes the U-Net a very appealing architecture for this  
Figure 2: Example image with its segmentation mask and associated pixel weight map. (**a**) Original image and its segmentation mask. (**b**) Segmentation mask. (**c**) Pixel weight map.  
application as positionally precise information might be required to produce reliable results. The encoder and decoder are connected by a bottleneck, where the most complex, high-level, and less positionally accurate information should be obtained. All convolutions use \(3\times 3\) kernels, as is customary in FCNs. Other sizes were tested, but the best results were obtained with small kernels. The network has a total of 2.1 million parameters and dropout is included as regularization method.
# Enhancing Image Dehazing with a Multi-DCP Approach with Adaptive Airlight and Gamma Correction
## 2 Related Work
### Deep-Learning-Based Approach  
Recent advancements in image dehazing and restoration have been propelled by the deep-learning-based approach, particularly leveraging Convolutional Neural Networks (CNNs) and Transformers [16]. These techniques, as noted in the literature [16], have significantly improved image clarity and texture preservation.  
[PERSON] [11] stands out in this domain by amalgamating CNNs, Swin-Transformers [17], and 3D positional embedding with the deep convolutional prior (DCP). This integration aims to enhance clarity in foggy images, yielding superior results on benchmark datasets. Similarly, Restormer [12], a Transformer model designed for high-resolution image restoration, emphasizes global connections and detailed texture preservation. This model has demonstrated outstanding performance across various image restoration tasks.  
Despite their effectiveness, deep-learning-based approaches necessitate extensive training data and encounter challenges associated with data biases. Addressing these issues remains crucial for further improving these models' robustness and generalization capabilities.
## 3 Experimental Results  
### PD Experimental Results  
According to the IEC 60270 [20] standard, the PD is recorded when PD quantity exceeds the ambient discharge. The incipient discharge voltage was 30 kV. With increasing applied voltage, the PD quantity increases, as shown in Figure 4.  
The \(\varphi\)-q when the voltage was gradually increased from 30 kV to 60 kV is shown in Figure 5. At 30 kV, the discharge quantity was between 10 pC and 100 pC. The waveform diagram shows that the discharge signal phase is concentrated between 60\({}^{\circ}\) to 90\({}^{\circ}\) and 240\({}^{\circ}\) to 330\({}^{\circ}\), which is a typical tip corona discharge.  
Figure 3: Schematic diagram of the sample oil-paper test bushing and condenser core. (**a**) Defective foil end layer of sample bushing; (**b**) Schematic diagram of condenser core.  
According to Figure 6, with the higher voltage, the higher PD occurred and was more concentrated between 90\({}^{\circ}\) to 150\({}^{\circ}\) and 240\({}^{\circ}\) to 330\({}^{\circ}\). The higher discharge quantity was between 60\({}^{\circ}\) and 150\({}^{\circ}\), which is a typical characteristic of tip discharge and surface discharge according to the paper [21] results. At 40 kV, the discharge reaches 1000-2000 pC and a current sound starts around the bushing. As the voltage rises to 50 kV, it reached to 3600-6000 pC. At 60 kV the discharge starts at 1880-2100 pC. After 15 min of maintenance discharge, it became 20,000-30,000 pC, and then, after 15 min, the discharge went back to 18,000-21,000 pC. It was assumed that the PD increased during the gradual deterioration of the discharge.  
Figure 4: PD quantity of capacitor screen process defect with equal gradient voltage.  
Figure 5: Phase diagram of sample bushing with foil layer defects at different voltages. (**a**) \(\varphi\)-q diagram of 30 kV discharge; (**b**) \(\varphi\)-q diagram of 40 kV discharge; (**c**) \(\varphi\)-q diagram of 50 kV discharge; (**d**) \(\varphi\)-q diagram of 60 kV discharge.  
the insulated paper and then decreased after the gradual formation of discharge carbonization channels on the insulation paper. Through the voltage-phase diagram, it can be seen that part of the PD signal was also concentrated between 0\({}^{\circ}\) and 90\({}^{\circ}\), which is characteristic of air bubble discharge between the metal electrodes. The local overheating of the insulated paper and the high electric field strength are presumed to result in the moisture and gas in the insulating paper forming bubbles and clinging to the surface of the insulating paper, and the PD intensified the oxidation and decomposition of the insulating paper to produce other substances which adhered to the surface of the insulating paper [22].  
As shown in Figure 7, the electric capacity was reduced by about 1.5 pF after high partial discharge, indicating partial insulation failure in the insulated paper due to insulation deterioration or even partial breakdown of the insulation paper.
# High-Speed VCSEL-Based Transceiver for 200 GbE Short-Reach Intra-Datacenter Optical Interconnects
## 2 Materials and Methods
### Materials and Architecture of the Devices
#### 2.1.3 Linear Receiver Architecture and Assembly  
The linear receiver board was based on previously reported designs [31; 32; 33], which comprised a two-channel linear TIA (Transimpedance Amplifier) array (Figure 3a) that operates in tandem with a surface-illuminated photodiode array. Commercial 25 Gb/s photodiodes were used for the assembly. The TIA exploited a multi-stage low-noise linear data path architecture that converted the photocurrent to the respective voltage value, amplified it, and fed it to a 50 \(\Omega\) output buffer. The saturation between the different amplifier stages was obviated by using an event-driven gain-bandwidth control loop that provided the necessary linearity and bandwidth for PAM-4 detection. The fabrication of the TIA chip was based on 130 nm SiGe BiCMOS technology and had a footprint of \(1\times 3.7\) mm. The assembly rested on a PCB that featured a cavity to align the top of the TIA with the top of the PCB to minimize the bond wire length.
## 2 Materials and Methods
### Methods
#### 2.2.3 Calibration and Validation  
After quality control, 29,056 pieces of FLUXNET site data remained, of which 19,459 pieces were randomly chosen for calibration and the remaining data (9597 pieces) were for validation. Data for the calibration and validation both are globally distributed and cover all vegetation types. Figure 2 shows the validation of the three approaches. We also calculated LUE with MOD17 GPP algorithm [79] and compared with in situ data. The results show that our LUE and GPP products were better than MOD17 results which had lower coefficients of determination (R\({}^{2}\)) and higher root-mean-square error (RMSE), with R\({}^{2}\) of 0.098 for LUE and 0.558 for GPP, and RMSE of 0.545 gC/MJ for LUE and 2.575 gC/m\({}^{2}\)/d for GPP. The R\({}^{2}\) of LUE equaled 0.183 and 0.240 for parameterization approaches V1 and V2, respectively. The RMSE of LUE dropped from 0.508 to 0.487 gC/MJ after considering the CI. The results suggested that the addition of the CI to the parameterization approach slightly improved the accuracy of LUE. The R\({}^{2}\) and RMSE of LUE by the Cubist regression approach equaled 0.538 and 0.352 gC/MJ, respectively. In comparison with the former two approaches, the Cubist regression tree approach had great advantages in gaining a higher R\({}^{2}\) and a lower RMSE. The three approaches all underestimated LUE, especially when the FLUXNET LUE exceeded 2.0 gC/MJ. However, the underestimation problem was alleviated for GPP, with the R\({}^{2}\) ranging from 0.63 to 0.78 and the RMSE ranging from 1.79 to 2.33 gC/m\({}^{2}\)/d. The LUE in the FLUXNET sites was equal to the GPP in sites divided by the corresponding MODIS FPAR. The uncertainty in the LUE at the sites might result from the error of MODIS FPAR. However, the predicted GPP was calculated by multiplying the FPAR. There was a certain degree of offset of GPP error after multiplication and division operations. In addition, the seasonal variations in solar radiation enhanced the accuracy of GPP estimation.  
Looking at the validation result in each type of vegetation (Figure 3), it is easy to find that the DBF, GRA and WSA gained high-quality LUE and GPP. In contrast, the two parameterization approaches obtained weak relationships between FLUXNET and estimated LUE in CRO, EBF, MF, CSH and OSH. In CRO, crop species were complicated and might contain C3 plants, C4 plants or both. The different features between C3 and C4 crops [6] introduced errors in CRO. The vegetation in EBF showed few variations in different seasons; therefore, it was difficult to retrieve solid correlations. In addition, EBF was distributed in both high-latitude and low-latitude areas where the maximum LUE and the optimal temperature varied greatly. These factors might lead to the uncertainty in the LUE and GPP in EBF. In MF, OSH and CSH, the main cause was the heterogeneity of plants.  
Figure 1: The flow chart of global Light use efficiency (LUE) and gross primary productivity (GPP) by 3 approaches.
## 3 Methodology
### Bias Correction Methods
#### 3.1.5 Distribution Mapping (DM) of Precipitation and Temperature  
In the DM method, the distribution function of the simulated GCM model data is corrected with that of the distribution function of the observed data. The DM method adjusts the mean, standard deviation, and quantiles. In addition, it retains the extreme data values [31]. This method assumes that the observed data and model-simulated raw data of variables follow the same distribution function, which leads to the addition of new unnecessary biases.  
For precipitation, the gamma distribution function [32] with shape parameter \(\alpha\) and scale parameter \(\beta\) is used for distribution (10) and has been verified to be effective [31; 32]:  
\[f_{\gamma}(x\mid\alpha,\ \beta)=x^{\alpha-1}\times\frac{1}{\beta^{\alpha}\times \lceil(\alpha)\ }\times e^{\frac{-x}{P}};x\geq 0,\ \alpha,\ \beta>0 \tag{10}\]  
where \(x\) is the observed variable; \(\lceil(.)\) is Gamma function; \(\alpha\) is form parameter; and \(\beta\) is scale parameter.  
As previously discussed regarding the LOCI method (Section 3.1.2), a precise threshold value is used to define a wet day as a large number of drizzle days are recorded in the raw GCM-simulated precipitation data, causing distortion in the distribution of raw data. The bias correction is performed like LOCI-corrected precipitation data, \(P_{LOCI,m,d}\) using (11):  
\[P_{cor,m,d}=F_{\gamma}^{-1}\big{(}F_{\gamma}(P_{LOCI,m,d}\big{|}\ \alpha_{LOCI,m},\beta_{LOCI,m})\ \big{|}\ \alpha_{obs,m},\beta_{obs,m}\big{)} \tag{11}\]  
where \(F_{\gamma}(\ )\) and \(F_{\gamma}^{-1}(\ )\) are the gamma CDF (cumulative distribution function) and its inverse; \(\alpha_{LOCI,m}\) and \(\beta_{LOCI,m}\) are the fitted gamma parameters for the LOCI-corrected precipitation in a given month m; and \(\alpha_{obs,m}\) and \(\beta_{obs,m}\) are the fitted gamma parameters for observed data.  
For temperature, the Gaussian cumulative distribution function shown in (12), or normal distribution with mean \(\mu\) and standard deviation \(\sigma\), is assumed to fit temperature best [4]:  
\[f_{N}(x\mid\mu,\;\sigma)=\frac{1}{\sigma\;\times\sqrt{2\pi}}\;\times e^{\frac{- (x-\mu)^{2}}{2\sigma^{2}}};\;x\epsilon\;R \tag{12}\]  
Similarly, the corrected temperature can be estimated using (13):  
\[T_{cor,m,d}=F_{N}^{-1}\big{(}F_{N}(T_{raw,m,d}\big{|}\;\mu_{raw,m},\sigma_{raw, m})\;\big{|}\;\mu_{obs,m},\sigma_{obs,m}\big{)} \tag{13}\]  
where \(F_{N}(\;)\) and \(F_{N}^{-1}(\;)\) are the Gaussian CDF and its inverse; \(\mu_{raw,m}\) and \(\mu_{obs,m}\) are fitted and observed means for the raw and observed temperature data at a given month, m; and \(\sigma_{raw,m}\) and \(\sigma_{obs,m}\) are fitted and observed standard variation for the raw and observed temperature time series at a given month, m.