esa-sceva/satcom-chunk-collection huggingface dataset:
# Adsorption Separation of Various Polar Dyes in Water by Oil Sludge-Based Porous Carbon
###### Abstract  
The pollution caused by printing and dyeing wastewater is increasingly severe, posing significant harm to aquatic plants and animals. In this study, porous carbon was synthesized via the high-temperature pyrolysis of light and heavy organic matter present in oily sludge, utilizing low oil content sludge as the raw material and zinc chloride as a chemical activator. The results exhibited a significant increase in the specific surface area of the oily sludge-based porous carbon, from 4.95 m\({}^{2}\)/g to 10.95 m\({}^{2}\)/g. The effects of various parameters such as pH, amount of sorbent, dye concentration, temperature, and contact time on dye removal have been studied. The results showed that the oil sludge-based porous carbon exhibited high efficiency in removing Malachite Green from aqueous solutions, which has low polarity and remains consistently above 97%. The removal rate of Crystal Violet, which is more polar, was as low as 24.14%. The corresponding adsorption capacities were 33.41 mg/g for Malachite Green, 16.41 mg/g for Crystal Violet, and 13.56 mg/g for Methylene Blue. The adsorption capacity of OSC700 for three types of dyes was characterized by monolayer adsorption, primarily driven by chemical adsorption, with significant contributions from electrostatic and hydrophobic effects. The adsorption process was spontaneous, exothermic, and accompanied by an increase in entropy. For less polar substances, the adsorption on oily sludge-based porous carbon is primarily driven by aromatic functional groups on the carbon surface, hydrophobicity, \(\pi\)-\(\pi\) electron-donor-acceptor (\(\pi\)-\(\pi\) EDA) interactions, and surface hydrogen bond formation. In contrast, for more polar dyes, electrostatic and hydrophobic interactions dominate, with electrostatic adsorption being the predominant mechanism and minimal hydrogen bond formation during adsorption.  
oily sludge-based porous material; dyeing and printing wastewater; adsorption; adsorption; mechanism; hydrophobic interaction
# Spatialization and Analysis of China's GDP Based on NPP/VIIRS Data from 2013 to 2023
## 5 Discussion
### Dynamic Analysis of GDP Spatial and Temporal Changes
#### 5.2.2 Differences in Economic Development of Major Urban Agglomerations
The Jing-Jin-Ji region is dominated by the development of heavy industry. Due to China's attention and protection of the ecological environment in recent years, the economic development of this region has been limited to a certain speed. In addition, typical cities in each city are selected to analyze the changes in the high-value-GDP areas. As shown in Figure 8, this is a thematic map for spatial expansion research in Beijing, Tianjin, Nanjing, Shanghai, Hangzhou, and Suzhou in the Beijing-Tianjin-Hebei region, and Guangzhou and Shenzhen in the Pearl River Delta region. Taking Beijing as an example, the area of high-GDP-value areas was 1214.52 km\({}^{2}\) in 2013, and the area of high-GDP-value areas increased by 455.66 km\({}^{2}\) from 2013 to 2018, and the area of high-GDP-value areas increased by 485.27 km\({}^{2}\) from 2018 to 2023. The expansion speed of high-GDP-value areas from 2018 to 2023 is faster than that from 2013 to 2018, and high-GDP-value areas continue to expand to the surrounding suburbs.
# Integrated Silicon Photonics for Enabling Next-Generation Space Systems
## 4 Space Radiation Effects  
The space environment contains a plethora of complex radiation phenomena, ranging from galactic cosmic rays that can cause single-event effects (SEE), to high-energy solar particles that can cause total-ionizing dose damage (TID) and displacement damage (DD) in semiconductor components [45; 46; 47; 48; 49; 50; 51; 52]. All radiation effects present reliability concerns for space systems, which makes silicon photonics potentially vulnerable to space radiation. The extent to which radiation can impede the functionality of a component depends on the radiation source (e.g., high-energy ion versus high-energy gamma), and the underlying operation physics of the component itself. Knowledge of how various components respond to radiation phenomena is important when considering a system's survivability in the space environment. It can also provide insight into radiation-hardening methods to make certain components more robust to specific types of radiation. This section gives an overview of the current progress of radiation effects in silicon photonics. As a reference for radiation levels, near-Earth space models for TID exposure, depending on the orbit of the spacecraft, suggest roughly 10-100 krad(Si) per year, while missions to Jupiter could experience radiation levels as much as several hundred krad(Si) over the entire mission lifetime for components that are within a radiation vault, and 50 Mrad(Si) for components outside of this vault [53; 54]. DD radiation damage is mostly caused by protons in space which, in near-Earth models, can reach fluxes of \(1\times 10^{5}\) cm\({}^{-2}\)s\({}^{-1}\) for energies \(>\)10 MeV [45; 46; 50]. However, since protons cause both TID and DD damage, it is preferable to test DD with neutrons to decouple these radiation effects, as neutrons only cause DD damage. The relationship between damage caused by neutrons to that of protons can be approximated by the non-ionizing energy loss (NIEL) scaling hypothesis and can be found in [55; 56]. For proton and neutron energies \(>\)10 MeV, the NIEL scaling is similar, which allows for proper DD testing with neutrons (which is the case for the DD studies presented in this review). Finally, the abundance of high-energy particles in our solar system that cause SEEs is quite dynamic based on our sun's activity and the element in question. However, heavy-ion models, such as CREME96 [57], can be used to capture these dynamics and predict the flux of ions such as H and Fe with maximum levels of \(10^{-4}\) and \(10^{-7}\) (cm\({}^{2}\)-s-sr-MeV/nuc)\({}^{-1}\), respectively. For a more in-depth analysis of space climatology, the interested reader is directed to [45].
# Research Progress on the Mechanisms and Control Methods of Rockbursts under Water-Rock Interactions
## 3 Research Progress on Water's Impact in Strain-Induced Rockburst Experiments
### Study of Strain-Induced Rockburst Tendencies under Different Moisture Contents
Additionally, there were clear disparities in the damage on the two opposite sidewalls within the same sample. In contrast, \"the sidewalls' damaged zones of SWC cubic hole specimens\" were characterized by a more consistent and evenly spread pattern along the hole's axis. There was also less disparity in the damage between the two opposing sides of the same sample. During the tests, NWC cubic hole samples exhibited significant sidewall damage and rapid plate separation, indicative of  
Figure 1: Stress–axial strain curves from uniaxial compression testing: (**a**) stress-strain curves of cylindrical samples with different water contents, and (**b**) a magnified view of the dotted line area [28]. rockbursts. In contrast, SWC cubic hole samples showed milder sidewall damage and minimal plate separation, indicating spalling. The comparison highlights clear differences in initial damage patterns based on moisture levels. Under SWC conditions, the rock's brittleness around the hole decreased, leading to reduced damage severity, consistent with uniaxial compression test results. From the above study, it was found that water affects hard rock materials and structures differently. For hard rock tunnels susceptible to rock bursts, applying water to the rock surface or injecting water into the surrounding rock can mitigate or even prevent the occurrence of rock bursts [62; 63; 64]. Figure 2: Failure mode of cylindrical samples with different water contents: (**a**) sample D1, (**b**) sample D2, (**c**) sample D3, (**d**) sample W1, (**e**) sample W2, and (**f**) sample W3 [28].
# A Trajectory Optimisation-Based Incremental Learning Strategy for Learning from Demonstration
###### Abstract  
The insufficient generalisation capability of the conventional learning from demonstration (LfD) model necessitates red demonstrations. In addition, retraining the model can overwrite existing knowledge, making it impossible to perform previously acquired skills in new application scenarios. These are not economical and efficient. To address the issues, in this study, a broad learning system (BLS) and probabilistic roadmap (PRM) are integrated with dynamic movement primitive (DMP)-based LfD. Three key innovations are proposed in this paper: (1) segmentation and extended demonstration: a 1D-based topology trajectory segmentation algorithm (1D-SEG) is designed to divide the original demonstration into several segments. Following the segmentation, a Gaussian probabilistic roadmap (G-PRM) is proposed to generate an extended demonstration that retains the geometric features of the original demonstration. (2) DMP modelling and incremental learning updating: BLS-based incremental learning for DMP (Bi-DMP) is performed based on the constructed DMP and extended demonstration. With this incremental learning approach, the DMP is capable of self-updating in response to task demands, preserving previously acquired skills and updating them without training from scratch. (3) Electric vehicle (EV) battery disassembly case study: this study developed a solution suitable for EV battery disassembly and established a decommissioned battery disassembly experimental platform. Unscrewing nuts and battery cell removal are selected to verify the effectiveness of the proposed algorithms based on the battery disassembly experimental platform. In this study, the effectiveness of the algorithms designed in this paper is measured by the success rate and error of the task execution. In the task of unscrewing nuts, the success rate of the classical DMP is S7.14% and the maximum error is 2.760 mm. After the optimisation of 1D-SEG, G-PRM, and Bi-DMP, the success rate of the task is increased to 100% and the maximum error is reduced to 1.477 mm.  
learning from demonstration (LfD); dynamic movement primitives (DMP); incremental learning; broad learning system (BLS); probabilistic roadmap (PRM) 2024
# Potential of Waste Oyster Shells as a Novel Biofiller for Hot-Mix Asphalt
## 3 Results
### Differential Scanning Calorimetry (DSC)
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{6}{c}{**TGA/DTGA (\({}^{\circ}\)C)**} & \multirow{2}{*}{\(-\Delta\)W (wt \%)**} \\ \cline{2-2} \cline{5-8}  & **Stage 1** & **Stage 2** & & **Stage 3** & \(T_{\rm onset}\) & \(T_{\rm offset}\) & \(T_{\rm max}\) \\ \hline OSP & 25.21–641.31 & 641.31–710.76 & 710.76–999.98 & 641.31 & 710.76 & 695.89 & 54.91 \\ AP-5 OSP 0 wt \% & 25.39–362.23 & 362.23–457.56 & 457.56–999.97 & 362.23 & 457.56 & 432.27 & 14.29 \\ AP-5 OSP 5 wt \% & 25.25–369.64 & 369.64–460.60 & 460.60–999.98 & 369.64 & 460.60 & 437.14 & 13.01 \\ AP-5 OSP 10 wt \% & 25.14–374.47 & 347.47–460.72 & 460.72–999.98 & 374.47 & 460.72 & 437.80 & 14.11 \\ AP-5 OSP 15 wt \% & 25.15–362.42 & 362.42–458.33 & 458.33–99.98 & 362.42 & 458.33 & 433.65 & 12.56 \\ \hline \multicolumn{8}{c}{\(T_{\rm onset}\): onset of thermal degradation (\({}^{\circ}\)C). \(T_{\rm offset}\): offset of temperature of final loss (\({}^{\circ}\)C). \(T_{\rm max}\): maximum decomposition temperature (\({}^{\circ}\)C). \(\Delta\)_W_: carbonaceous residue at 1000 \({}^{\circ}\)C (wt \%).} \\ \end{tabular}
\end{table} 
Table 5: TGA and DTGA thermogram data of OSP, neat asphalt, and OSP/asphalt blends containing 5, 10, and 15 wt % OSP loading at heating rate of 10 \({}^{\circ}\)C min\({}^{-1}\). groups adsorbed is seemingly found to be dependent on the Braunauer-Emmett-Teller (BET) surface area of the filler but not on the chemical composition of the filler or bitumen [66]. The higher the BTE surface area (e.g., 10 and 15 wt % OSP), the greater the amount of polar groups adsorbed and the lower the glass transition temperature, and vise-versa (with 5 wt % OSP) [66]. The reduction in the glass transition temperature indicates that this adsorption phenomenon results in asphalt softening. The data deducted from DSC studies indicates that higher content of OSP will worsen the low-temperature performance of bitumen; however, lower content (\(\leq\)5 wt %) will improve it [67; 68]. Further BBR (i.e., bending beam rheometer) and DMA (i.e., dynamic mechanical analysis) studies are absolutely necessary to prove this assumption. Figure 16: STA (TGA/DSC) measurement of OSP (sample mass 11.151 mg) in Alumina crucibles at a heating rate of 10 K min\({}^{-1}\) in argon atmosphere (50 mL min\({}^{-1}\)). Figure 15: DSC thermograms of OSP, neat asphalt, and OSP-filled asphalts.
# Infrared Dim and Small Target Detection Based on the Improved Tensor Nuclear Norm
## 3 Infrared Patch Tensor (IPT) Model
### A Priori Model Construction Based on the Structural Tensor  
The traditional infrared dim and small target detection algorithm can only process local feature information, ignoring the global information of the image, resulting in unsatisfactory background modeling and target extraction. The infrared dim and small target detection algorithm based on a low rank and sparse theory build the target and background models from the global information through the characteristics of the low rank in the background and the sparse theory in the target. The algorithm has better robustness, but in complex scenes, there are strong noise signals and edge contours; the strong noise signals and edges are also globally sparse, leading to a high signal-to-noise ratio in the final obtained target image. For different structure tensor situations under different local features, [PERSON] (2016) proposed a local feature extraction method to introduce local features as weights into the IPT model and a reweighted infrared block tensor (RIPT) model to realize the combination of local features and global features, where the local feature weights are calculated as follows:  
\[W_{LS}=\exp\biggl{(}h\frac{(\lambda_{1}-\lambda_{2})-d_{\text{min}}}{d_{\text{ max}}-d_{\text{min}}}\biggr{)} \tag{7}\]  
where \(W_{LS}\) is the local structure weight, \(d_{\text{max}},d_{\text{min}}\) are the maximum and minimum values of \(\lambda_{1}-\lambda_{2}\), respectively, and \(h\) is a stretching parameter. The above local feature weights can only describe the background information and cannot describe the target information; [PERSON] (2017) proposed a new local feature extraction method based on this to consider both the target signal and the background information where the structure weights are calculated as follows:  
\[W_{t}(x,y)=\frac{\lambda_{1}\lambda_{2}}{\lambda_{1}+\lambda_{2}} \tag{8}\]  
\[W_{b}(x,y)=\text{max}(\lambda_{1},\lambda_{2}) \tag{9}\]  
where \((x,y)\) denotes the position of the image element point in the image; \(W_{t}\) is the corner point intensity function, which is used to describe the weight value function of the corner point feature information, as shown in Figure 3b; \(W_{b}\) is the maximum feature value \(\lambda_{1},\lambda_{2}\); and it is used to describe the weight value function of the background feature information, as shown in Figure 3c. The final a priori weight value function is obtained by multiplying these two weight value functions together, as follows (2017):  
\[W_{p}(x,y)=W_{t}(x,y)\cdot W_{b}(x,y) \tag{10}\]  
where \(W_{p}\) denotes the final a priori weight function, as shown in Figure 3d.  
Figure 3: Local a priori extraction method proposed by the PSTNN algorithm. (**a**) Infrared image containing dim and small targets; (**b**) weighted image describing corner point information; (**c**) weighted image describing background information; (**d**) final obtained weighted image.
# Denial-of-Service Attacks on Permissioned Blockchains: A Practical Study
## 2 Background
### HLF Architecture
#### 2.2.4 Ordering Service  
The ordering service is responsible for determining the sequence of transactions and creating blocks. It ensures all peers receive transactions in the same order, maintaining consistency across the network. Unlike public blockchains that use Proof of Work (PoW) or Proof of Stake (PoS), Fabric uses consensus mechanisms like Raft and Kafka [23], which prioritize efficiency and low latency. The ordering service does not validate transactions but simply orders them, separating consensus from execution.
## 6 Conclusions  
1. Data acquisition system was improved using NI(r) LabVIEW for monitoring the frequency, temperature, and displacement during the tests. Fully reversed tension-compression sinusoidal mode (\(R=-1\)) at 20 kHz was proved through the opposite phase of vibration on the top and bottom of the specimen. Elongation results obtained by a laser sensor and thermal expansion results calculated by an infrared camera were in good agreement.
2. The \(S\)-\(N\) curve of Inconel 718 in the VHCF regime at 20 kHz with self-heating phenomenon was presented. The propagation duration continued from several to tens of seconds. More than 99% of fatigue life was consumed in initiation. Fatigue life of aged Inconel 718 was sensitive to loading levels. A difference of 100 MPa was found, corresponding to the fatigue life between \(10^{6}\) and \(10^{9}\) cycles.
3. Influence factors on VHCF life were analyzed. Good agreement appeared between test data with self-heating phenomenon and others with cooling. Self-heating phenomenon as well as specimen temperature change were not decisive factors on VHCF strength for superalloy Inconel 718, as long as the temperature increase was far less than the design temperature limitation.
4. One, single initiation site was observed, regardless of the loading level and total fatigue life cycles. Initiation was situated at the subsurface facet or persistent slip bands in the direction of the maximum shear stress. Quasi-cleavage fracture emerged initially in transgranular ductile mode with crack extension in a depth of 80 \(\upmu\)m from the specimen surface. Then the crack tended to trace the grain boundaries in an intergranular manner after growing beyond 150 \(\upmu\)m in cleavage-dominated mixed mode.  
Conceptualization--M.Z.; methodology--T.W. and Z.Z.; validation--L.L. and G.L.; supervision--W.C.; writing--original draft preparation, M.Z.; writing--review and editing, T.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Acknowledgments:** Author [PERSON] highly appreciates the support of [PERSON] and [PERSON] from Universite Paris X Nanterre for their assistance in fatigue experiments, of [PERSON] from Arts et Metiers ParisTech for the profound discussion, and of [PERSON] from Onera for the specimen supply.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Nomenclature**  
BSE back-scattered electrons _Cs_ stress-amplitude coefficient EDS energy dispersive X-ray spectroscopy \(f\) frequency of loading FCC face-centered cubic _Nf_ number of cycles OM optical microscope \(R\) stress ratio PSB persistent slip band \(S\) stress amplitude RB rotary bending _\(\sigma_{0.2}\)_ yield strength SEI secondary electrons _ours_ ultimate strength SEM scanning electron microscope SIF stress intensity factor VHCF very high cycle fatigue
## 4 Artificial Neural Network Classifiers Used to Select EA Signals  
### Feedforward Neural Network  
In a forward non-linear neural network (FNN), the flow of information (signals) is unidirectional. Its structure consists of the input layer, two or more hidden layers, and the output layer (Figure 5). All layer inputs can be linked only with the neurons in the preceding layer. Input signals \(\mathbf{x}\) in a neuron are added to weights \(\mathbf{w}\). Neuron \(y\)'s output signal is calculated using a non-linear activation function \(\varphi\), according to the relationship [38]:  
\[y=\varphi\Bigg{(}\sum_{m=1}^{M}w_{m}x_{m}\Bigg{)} \tag{6}\]  
Figure 4: Influence of the position of different window lengths against the AE signal on the teaching properties’ values: kurtosis (**a**); skewness (**b**); energy (**c**); upper envelope (**d**); the recorded signal wavelength is in the background.  
The unipolar sigmoid function is among the most commonly-used activation functions:  
\[\varrho(z)=\frac{1}{1+e^{-z}} \tag{7}\]  
alongside a bipolar function (hyperbolic tangent):  
\[\varrho(z)=\frac{1-e^{-z}}{1+e^{-z}} \tag{8}\]  
For learning feedforward neural network (FFN), the Levenberg-Marquardt algorithm was used. This method is one of the most effective learning algorithms. It has high convergence when network weights are near the optimal solution (as the Gauss-Newton method) and when the network is far from the optimal solution (as the descent gradient method). Detailed information about the Levenberg-Marquardt algorithm can be found in [39].
## 4 Results and Discussion
### Baseline Models  
We construct a baseline model from three perspectives for the experimental evaluation: (1) an extraction method for char-level embeddings. We use pretraining models based on BERT, such as XLNet [18], BERT-BASE (BERT), BERT-WWM (WWM) [40], and ERNIE3.0 [16], to extract char-level embeddings representations during the preprocessing. (2) An extraction method for document-level embeddings. We use models such as HAs (HA or Bi-LSTM + HA) and FastText (FT) [41] to extract document-level embeddings representations for classification based on char-level embeddings. (3) Whether to enable transfer learning between classifiers. That is, whether transfer learning (TL) is enabled for classifiers using HA models in the classifier tree. Therefore, we design 13 models based on these elements from these three perspectives. Of these, the first one, XLNet + HAs + FC + TF, is the HTMC-PGT model proposed in this paper, and the remaining 12 models are the baseline models (see Table 2).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Index**} & **Classifier** & **Parent** & \multirow{2}{*}{1 C I} & \multirow{2}{*}{1 D I} & \multicolumn{2}{c}{**Training Set**} & \multicolumn{2}{c}{**Valid Set**} \\ \cline{5-8}  & **No.** & & **Classifier** & & **Total** & **PL** & **Total** & **PL** \\ \hline  
\hline \end{tabular}
\end{table}
Table 1: Summary of datasets for each classifier in the classifier tree *.
## 6 Case Study
### Results of Billing Mechanism  
After a day's operation, the MCO should redistribute the cost of each MG according to the exchanged energy via DC-links. The results of billing mechanism are shown in Figure 8, where \(trading\)(1-2) represents the cost transacted from \(MG1\) to \(MG2\), and \(trading\)(3-2) represents 6205640 the cost transacted from \(MG3\) to \(MG2\). In the first two hours, \(MG1\) has to buy energy from \(MG2\) to meet the heavy load demand at a higher price. From 3:00 to 9:00, the fluctuating heavy load demand in \(MG2\) leads to a high cost paid to both \(MG1\) and \(MG3\). From 10:00 to 18:00, there is plenty of solar and wind energy and light load in \(MG2\), thus \(MG1\) and \(MG3\) can purchase electricity from \(MG2\) to meet their growing load demands. After 21:00, although the exchanged powers are sharply reduced, the transacted cost still maintain on a high level due to higher generation cost in this time period.