esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### IR and NMR Analysis  
The synthesized copolymers were characterized by FTIR and \({}^{1}\)H NMR to confirm their molecular structures. The FTIR spectra of all PEAs (supplementary materials, Figures S4-S18) clearly show the characteristic absorption band for ester carbonyl stretching at 1732 cm\({}^{-1}\). In addition, the stretching and deformation bands of the conjugated double bond can be found at 1631 cm\({}^{-1}\) and 814 cm\({}^{-1}\), indicating that the unsaturated double bond does not get attacked during the reaction. For PEAs with higher amide content, the FTIR spectra show two small vibrations at 1538 and 3306 cm\({}^{-1}\), corresponding to the amide and the N-H bonds.  
The \({}^{1}\)H NMR spectra exhibit all the characteristic signals for PEAs from itaconic acid. Fo series one, the transesterification can be witnessed by a change of the signals compared to the virgin polyesters and oligoamides (Figure 1). During the transesterification step, the terminal OH-groups of the hexanediol undergo a nucleophilic attack on the ester bonds of the oligoamide. As a result, the signals of the CH\({}_{2}\) of the terminal hexanediol (signal 9\({}^{\prime}\) at 3.65 ppm) are being reduced with the increasing amount of oligoamide. In addition, the ratio of the CH\({}_{2}\) signals of the hexanediol next to the itaconic acid (signal 4 and 9 at 4.09 and 4.15 ppm) are changing, as new ester bonds with adipic acid are being formed as a result of the transesterification, which have the same chemical shift as the signals of the ester of the unconjugated ester group of the itaconic acid. Furthermore, during the reaction, molecules of ethylene glycol are liberated over time and can also be found in the NMR at 3.73 ppm, which should result in a slight increase of molecular weight of the PEAs. Finally, the signal of the inhibitors can be seen at 6.75 and 3.76 ppm, respectively.  
For the PEAs, synthesized via pseudo-one-pot pathway, the NMR spectra also show the characteristic signals described for the first series. However, in all three PEAs, two additional signals appear at 6.80 and 2.32 ppm, which correspond to the mesaconic moieties formed during the condensation product (Figure 2). The mesaconic moiety can be formed at elevated temperatures by isomerization of the itaconic ester in the presence of a base (Scheme 4). This isomerization indicates that during the first step of the reaction, the amine is not completely consumed, and a small amount of free amine is still present in the second reaction step, which is sufficient to catalyze the isomerization. Unfortunately, the free amine was not detectable during the reaction by means of FT-IR. The NMR spectra of the PEA-101 and 102 can be found in the supporting information.  
Figure 2: \({}^{1}\)H NMR spectra of PEA-103.  
For the PEAs synthesized via pseudo-one-pot pathway, the NMR spectra also show the characteristic signals described for the first series. Figure 2 shows the NMR spectra of PEA-103. All other spectra can be found in the supporting information.
## 3 Materials and Methods
### Experimental Design and Statistical Analysis  
A Box-Behnken experimental design (BBD) with 17 runs (5 center points) and RSM were employed to investigate the effects of initial moisture content, incubation temperature, and incubation time on the total peptide content of HOC and to determine the optimum conditions maximizing TBC [67; 127]. The lower (\(-1\)) and upper (\(+1\)) levels of SSF conditions were selected based on preliminary experiments. An average of two replicates of total peptide content (mg tryptone eqv./g DM of solid substrate) was taken as the response variable.  
The results of the conducted experiments were used to express the relationship between the predicted response and the process parameters by a second-order polynomial regression model (Equation (2)).  
\[Y=\beta_{0}+\sum_{i=1}^{k}\beta_{i}X_{i}+\sum_{i=1}^{k}\beta_{ii}X_{i}^{2}+ \sum_{-1}^{k}\sum_{j>1}^{k}\beta_{ij}X_{i}X_{j} \tag{2}\]  
In Equation (1), \(Y\) represents the predicted response, \(\beta_{0}\) is the constant term, \(\beta_{1}\) is the linear coefficient, \(\beta_{ii}\) is the quadratic coefficient, and \(\beta_{ij}\) is the interaction coefficient. The experiments were performed, and variance analysis was statistically conducted using Design Expert (11.0.0 trial version, Stat-Ease, Minneapolis, MN, USA) software. The significance of the model was evaluated according to ANOVA, lack-of-fit test, and multiple determination coefficients tests. In the regression analysis, the variables having a \(p\)-value lower than 0.05 were accepted to have significant effect on the TPC.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Factors**} & \multirow{2}{*}{**Unit**} & \multirow{2}{*}{**Sign**} & \multicolumn{4}{c}{**Actual Factor Levels**} \\ \cline{3-5}  & & & **(\(-1\))** & **(0)** & **(\(+1\))** \\ \hline Initial moisture content & (\%) & X\({}_{1}\) & 30 & 50 & 70 \\ Incubation temperature & (\({}^{\circ}\)C) & X\({}_{2}\) & 20 & 30 & 40 \\ Incubation time & (day) & X\({}_{3}\) & 2 & 3.5 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The investigated factors and their levels in the Box–Behnken experimental designs.  
After the model compatibility was ensured, the validation of the assumptions used in ANOVA was performed with statistical calculations. In the optimization, the \"desirability function\" method, which is based on the simultaneous solution of the models determined for each response, was used. Experimental validation of the determined optimum conditions was performed by the production and analysis under the specified conditions. The criteria for the solution were \"in range\" between the upper and lower limits of the investigated factors and \"maximize\" for the response. _Error_ (%) between the experimental (\(y_{exp}\)) and predicted (\(y_{pre}\)) values was calculated by Equation (3).  
\[Error\ (\%)=\frac{\left|y_{exp}-y_{pre}\right|}{y_{exp}}\times 100 \tag{3}\]
# Responses of Seasonal Indicators to Extreme Droughs in Southwest China
## 3 Results
### Variations in Climate  
The spatial averaged variables of meteorology including precipitation, air temperature, evapotranspiration and solar radiation are given in Figure 7. An obvious reduction in precipitation and increase in evapotranspiration were found in drought years (2009 and 2011), while high air temperature and large evapotranspiration were found in another drought year (2006). The sharp drop of precipitation in 2009 was the consistent with the decrease in SWA. Despite the slight increase in precipitation in 2010, the effects of long-term water scarcity on vegetation and surface water were sustained (Figures 4 and 5).  
Figure 6: Variations of vegetation indices and indicator of carbon cycle: (**a**) Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), (**b**) Gross Primary Productivity (GPP).  
Figure 7: Variations of (**a**) precipitation, (**b**) temperature, (**c**) evapotranspiration and (**d**) solar radiation.  
In 2009, with the dramatic decrease in precipitation, abnormally high solar radiation, high temperature and large evapotranspiration exacerbated the drought and pushed it to an extreme state. In 2010, the slight increase in precipitation and the significant decline in evapotranspiration and solar radiation alleviated the drought. However, rainfall declined again to a recorded low value and evapotranspiration dramatically increased in 2011, which caused a new drought event, despite the fact that air temperature and solar radiation dropped to a certain degree. Compared to vegetation and surface water which show cyclical response to droughs, meteorological variables could quickly change drought trends in a short time. PDSI derived from the meteorological variables, showed two drought periods from 2000 to 2017 (Figure 8). The comparison of the periods extracted from SWA, LOS and PDSI indicated the generally similar pattern, but with a minor difference. PDSI and LOS showed a slight relief in 2004 and 2011, while SWA has no observable signals.
## 3 P2P Trading Platform
### Welfare Metrics  
The last step for the suggested P2P platform is to determine the welfare of the market participants. This research suggests three metrics to evaluate the performance of the market: (i) _The prosumers' welfare_--the increase in the revenue of the prosumers selling their excess PV energy; (ii) _the customers' welfare_-- the decrease in the payment of thecustomers interacting/trading with the prosumers; and (iii) _the social welfare_--the overall improvement in the well-being of the market participants [28]. To develop the mathematical relations, the original market value is first defined for the customers and prosumers as follows:  
\[C_{1}=\sum_{i\in\text{C}}D_{i}R_{\Delta t_{i}},\ \ \ C_{2}=\sum_{j\in P}G_{j}F_{ \Delta t_{i}} \tag{21}\]  
where \(C_{1}\) and \(C_{2}\) represent the original market value of the customers and prosumers without the P2P, respectively. The prices for the P2P trading are given as follows:  
\[C_{1}^{{}^{\prime}}=\sum_{i\in\text{C}}(\sum_{j\in P}p_{ij}t_{ij}+ E_{c,j}R_{\Delta t_{i}}) \tag{22}\] \[C_{2}^{{}^{\prime}}=\sum_{j\in P}(\sum_{i\in\text{C}}p_{ij}t_{ij}+ E_{c,j}F_{\Delta t_{i}}) \tag{23}\]  
where \(C_{1}^{{}^{\prime}}\) and \(C_{2}^{{}^{\prime}}\) are the P2P trading values. By including the transaction fee constraint, the updated prices \(C_{1}^{{}^{\prime}}\) and \(C_{2}^{{}^{\prime}}\) can be written as follows:  
\[C_{1}^{{}^{\prime}}=\sum_{i\in\text{C}}(\sum_{j\in P}p_{ij}t_{ij}+ E_{c,j}R_{\Delta t_{i}})+\underbrace{\sum_{i\in\text{C}}(\sum_{j\in P}\eta_{c}( \gamma_{i}d_{i}t_{ij}))}_{\text{customers\;free}} \tag{24}\]  
\[C_{2}^{{}^{\prime}}=\sum_{j\in P}(\sum_{i\in\text{C}}p_{ij}t_{ij}+ E_{c,j}F_{\Delta t_{i}})-\underbrace{\sum_{j\in P}(\sum_{i\in\text{C}}(1-\eta_{c} )(\gamma_{j}d_{j}t_{ij}))}_{\text{prosumers\;free}} \tag{25}\]  
The welfare quantities can be determined as follows:  
\[W_{c}=\frac{C_{1}-C_{1}^{{}^{\prime}}}{C_{1}}\times 100\%,\ \ \ W_{p}=\frac{C_{2}^{{}^{ \prime}}-C_{2}}{C_{2}}\times 100\% \tag{26}\]  
\[W_{s}=\left|\frac{(C_{1}-C_{2})-(C_{1}^{{}^{\prime}}-C_{2}^{{}^{\prime}})}{(C _{1}-C_{2})}\right|\times 100\%, \tag{27}\]  
where \(W_{c}\), \(W_{p}\), and \(W_{s}\) are the customers, prosumers, and social welfare, respectively.
## 4 Experiment
### Results  
As shown in Table 1, we evaluated the prediction performance of the proposed DualET on three different PV stations. For short-term (6-h) PV power prediction, DualET achieved the best results on three error metrics: MAE, RMSE, and SMAPE. Compared with the results of other models on all stations, DualET achieved a relative MAE and RMSE reduction of 22.53% and 22.75%, respectively, which is a significant improvement. The average reduction in MAE was even more than 53%, compared with the traditional ARIMA, and 27.72% compared with the popular LSTM. Among these baselines, the transformer-based models, i.e., Transformer, Informer, and Autoformer, were better than ARIMA and LSTM models. Moreover, our DualET still outperformed the competitive transformer-based models and yielded a relative MAE reduction of 10.64%.  
The prediction results of different models are presented in Figures 4 and 5. It obviously shows that the number of deviation points predicted by the statistical model ARIMA is much larger than those predicted by other DNN-based models, which indicates that ARIMA is unsuitable for short-term prediction up to several hours, especially for the nonstationary PV power series. As shown in Figure 5, the LSTM model has a more scattered distribution of points than the transformer-based models, which indicates the significance of transformer  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Models** & **Metrics** & **Station0** & **Station1** & **Station2** \\ \hline ARIMA & MAE & 3.6934 & 3.7727 & 3.6343 \\  & RMSE & 5.3633 & 6.3809 & 5.4976 \\  & SMAPE & 0.8881 & 0.9019 & 0.9135 \\ \hline LSTM & MAE & 2.3500 & 2.2612 & 2.6287 \\  & RMSE & 3.0866 & 2.9739 & 3.4570 \\  & SMAPE & 0.7218 & 0.6888 & 0.7635 \\ \hline Transformer & MAE & 2.0780 & 2.0678 & 1.8413 \\  & RMSE & 2.8468 & 2.7659 & 2.5875 \\  & SMAPE & 0.7110 & 0.6482 & 0.6274 \\ \hline Informer & MAE & 1.8274 & 1.8393 & 1.8564 \\  & RMSE & 2.5874 & 2.5773 & 2.5940 \\  & SMAPE & 0.6549 & 0.6232 & 0.6498 \\ \hline Autoformer & MAE & 1.9989 & 2.0690 & 1.9771 \\  & RMSE & 2.7379 & 2.7834 & 2.7700 \\  & SMAPE & 0.6905 & 0.6764 & 0.6810 \\ \hline DualET & MAE & **1.6904** & **1.7576** & **1.7657** \\  & RMSE & **2.2845** & **2.4205** & **2.5087** \\  & SMAPE & **0.6335** & **0.5931** & **0.6258** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Prediction performance of the proposed DualET.
# Comparison between Laser and Stamping without Die (SWD) for Micro Tapered Hole Forming
## 3 Experimental Method and Procedure
### Laser Processing  
The laser processing experiment is as shown in Figure 8a. The laser source is a Nd:YAG laser (LOTIS TII LS-2134 UTF, Tokyo Instruments, Inc., Edogawa-ku, Japan) with second harmonic of a 532 nm wavelength. In order to discuss the effect of the defocusing distance on the hole morphology, the focusing lens focal length of 120 mm is changed; The laser processing has not specified the long and short focal lengths. The defocusing distance ranges from +0.5 mm (positive defocus) to \(-\)0.5 mm (negative defocus) with an increment of 0.1 mm, as shown in Figure 8b.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Condition** \\ \hline Workpiece & Al6061 C2680 \\ Punch angle & 23.5\({}^{\circ}\) \\ Stamping speed & 0.5 mm/min \\ Stamping depth (\(Z\)) & 300, 310, 320, 330, 340, 350, 360, 370, 380 \(\upmu\)m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Micro stamping parameters.  
Figure 7: Schematic diagram of processing mode (a) stamping depth definition (b) working point position.  
The processing parameters of the laser drilling experiment are as shown in Table 3. The sample is the same as the Al6061 sample for the experiment in Section 3.1. The laser powers used in the experiments are 20, 30, 40 mW, which is measured by a powermeter (Ophir-NOVAII, Ophir Optics, Inc., North Andover, MA, USA).  
We will discuss the change of required pulses for perforation, inlet, and outlet diameter with different defocusing distance in the section later.
# Experimental Evaluation of Unicast and Multicast CoAP Group Communication
## 2 Group Communication
### Hybrid Group Communication
#### Summary  
In summary, our proposed hybrid group communication solution for CoAP is an extension to our previously proposed unicast-based solution. By extending it with multicast support, we achieved the increased flexibility of our solution. It is now up to the creator of the entity to decide whether communication reliability with the members is essential. When it is not essential and the user selects to create a multicast entity, the EM automatically configures the multicast group and lets the members join it. This minimizes manual and error-prone group membership configurations. Since group membership management is done via unicast CoAP messages, it is reliable, and the user can be informed about its results.
## 4 Discussion
### Model Implications and Wider Adoption Potential  
The developed model is capable of predicting accurately the bending response change of a pallet segment when the payload characteristics are modified. This model and the characterization of the effects that influence load bridging are capable of providing a valuable input to packaging engineers and designers. Such a wide and in-depth analysis of how the payload characteristics affect pallet bending was not possible to produce without the models presented here. In the past, in order to investigate the effect of any of the aforementioned factors, researchers needed to physically build a unit load, conduct laboratory testing, and compare the different results. This process requires extensive cost and monetary investment. Researchers and designers can now evaluate the effect that design decisions, such as making a unit load taller or using a pallet with a higher friction coefficient, can have in the pallet performance. The utilization of this methodology can streamline the packaging design process and allow for engineers to better consider the pallet and packages interactions to create more efficient systems.  
The model developed presents an opportunity to improve the pallet and unit load design methodology. Accounting for the factors identified can allow designers to select potentially problematic unit loads and further investigate.  
The results presented provide a guideline towards the change in trends of pallet deflection as payload configuration is modified. It is not the intent of the author to directly modify the safe carrying capacity of pallets. Pallet designs must be properly verified by the currently accepted methodologies. This model allows for the identification of payloads that can potentially generate larger deformation. Pallet designs must be tested with such payloads.
## 3 Network Model Construction
### Residual Shrinkage Network Construction  
Soft thresholding is a fundamental operation in many signal denoising methods. It sets features with absolute values below a predefined threshold to zero while shrinking larger values toward zero. The threshold, a hyperparameter that must be carefully tuned, directly influences the denoising efficacy. The input-output relationship of soft thresholding is defined as  
\[z=\begin{cases}x-\tau&x>\tau\\ 0&-\tau\leq x\leq\tau\\ x+\tau&x<-\tau\end{cases} \tag{4}\]
# A New Compound Staged Gelling Acid Fracturing Method for Ultra-Deep Horizontal Wells
## 2 Results and Discussion
### Field Application  
In the Sichuan carbonate gas reservoir, a total of 12 horizontal wells have been stimulated by staged acid fracturing, and the production is 2.1 times higher than that of general stimulation. Among them, the drilling depth of well QY1019 is 6823 m, the horizontal section length is 1185 m, the reservoir section length is 901 m, the average porosity of the reservoir is 3.2%, and the average permeability is 0.31 mD. In 2021, the QY1019 well completed the compound staged acid fracturing stimulation through pack + temporary plugging. A total of 480 m\({}^{3}\) antibigenic acid, 1440 m\({}^{3}\) gelling acid, 800 kg fiber, and 800 kg temporary plugging particles were injected into the formation. In order to verify the effectiveness of staged acid fracturing for the temporary plugging, two kinds of chemical tracers were added before and after the injection of the temporary plugging agent. The chemical tracer interpretation results showed that new fractures were formed after the addition of a temporary plugging agent. Net pressure fitting shows that, compared with conventional acid fracturing, the fracture length and conductivity of alternating injection acid fracturing increased by 78.3% and 52.5% respectively. The daily gas production is 1.58 \(\times\) 10\({}^{4}\) m\({}^{3}\)/d, which is 2.4 times of the test daily gas production of the adjacent wells.
# Probabilistic Analysis of Infinite Slope Stability Considering Variation in Soil Depth
## 3 Results and Discussion
### Sensitivity Analysis  
In slope stability analysis, sensitivity analysis is performed to evaluate the influence of input parameters on slope stability. There are various methods for sensitivity analysis, and local sensitivity analysis (LSA) is often used to identify which parameters have the most significant impact on slope stability [17; 45; 46; 47]. This method assesses how incremental changes in each input parameter influence the \(F_{\rm S}\) while keeping other parameters constant. However, LSA provides sensitivity assessments only around a specific nominal point and can overlook non-linear relationships and interaction effects, leading to limited insights in complex systems. Unlike LSA, global sensitivity analysis (GSA) examines the effects of variations in all parameters across their entire range of possible values and provides a more comprehensive understanding of parameter impact. Therefore, GSA was conducted to assess the effect of parameter uncertainty on the \(P_{f}\) in the slope stability analysis. [PERSON]'s correlation coefficient was employed as a metric to quantitatively evaluate the sensitivity of random variables. Figure 13 shows the changes in sensitivity (Spearman's correlation coefficient) of \(c\), \(\phi\), and \(d_{g}\) with saturation depth.  
Figure 12: CDF of the minimum \(F_{\rm S}\) for a saturation depth of 2.5 m.  
Figure 13: Changes in sensitivity of random variables according to saturation depth: (**a**) Case 2; (**b**) Case 3; and (**c**) Case 4.  
Soil depth was found to have a negative correlation with \(P_{f}\), and its overall effect on \(P_{f}\) was greater compared with that of the soil shear strength. Although Case 1 and Case 2 exhibited similar \(P_{f}\), some differences were observed in the sensitivity of each random variable. The variability of soil depth was assumed to follow a uniform distribution (Case 4), with the sensitivity of soil depth on \(P_{f}\) being the highest.
# PD Detection and Analysis Triggered by Metal Protrusion in GIS Through Various Methods
## 1 Introduction  
GAS-insulated switchgear (GIS) is widely used in power systems because it has the advantages of high reliability, being environmentally friendly and especially saving space. However, some unexpected defects may occur in GIS during the manufacturing, transportation or installation processes, which threaten the safe operation of GIS. Among these defects, metal protrusion is a common defect, including metal protrusion on the conductor and metal protrusion on the enclosure [1].  
Because the working electric field is very high in GIS and the field is concentrated at the metal protrusion, partial discharge (PD) happens at the protrusion. Therefore, the protrusion defect can be effectively detected by detecting the PD signals.  
According to the different physical quantities detected, PD detection methods are grouped into the electrical detection method, acoustic detection method, optical methodand chemical detection method [2; 3; 4; 5; 6]. Among these methods, pulse current [7; 8; 9], ultrahigh frequency (UHF) [10; 11] and ultrasonic [12; 13; 14] methods are widely used. The pulse current, ultrasonic and UHF methods measure the pulse current, ultrasonic and electromagnetic signals, respectively. The results from these three methods for the same PD detection may be different due to the different physical quantities detected.  
The three methods have been widely applied in partial discharge research. [PERSON] [15] measured partial discharge signals from defects such as metal protrusions on GIS conductor surfaces using the pulse current method. [PERSON] [16] employed built-in and external UHF sensors (Hyundai Heavy Industries Co., Ltd., Ulsan, Republic of Korea) to detect the partial discharge characteristics of metal protrusion defects under different voltage levels and explored their development patterns. [PERSON] [17] conducted experimental investigations on partial discharge and its progression in defects such as metal protrusions using UHF and ultrasonic detection methods, studying their development patterns. These three detection methods are also extensively used in partial discharge identification and localization [18; 19; 20; 21].  
The existing research not only predominantly focuses on single or dual detection methods, but also lacks systematic comparative analysis of the differences among pulse current, ultrasonic and UHF methods. In this study, we set tested the metal protrusion on the enclosure and the metal protrusion on the conductor of different sizes in GIS. We also used the pulse current, ultrasonic and UHF methods to detect the PD signals at the same time to analyze the defect characteristics, and the sensitivities of different methods to detect protrusion defects were obtained. The results of this study will be helpful for PD detection and analysis in the field.