esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Research Methodology
### Multistate Markov Chain Model
#### 2.1.1 Conceptual Description of the Markov Chain  
The discrete state space \(E=\{S_{1},S_{2},S_{3},\ldots\}\) and the random process \(\{X(t)/X(t)\in E\), \(t>0\}\) on the discrete time parameters \(0\leq t_{1}<t_{2}<t_{3}<\cdots<t_{n}\) exist at any time. From the definition of conditional probability, the state probability \(\mathfrak{t}_{n}\) at different times can be obtained. The specific formula is expressed in Formula (1).  
\[P[X(t_{n})/X(t_{1})\cdots X(t_{t-1})]=S_{n}[X(t_{1})=S_{1}\cdots X(t_{t-1})=S_ {n-1}] \tag{1}\]  
If the random process is consistent with Formula (2), then the random process accords with the characteristic of no memory, and the random process is called a Markov chain.  
\[P[X(t_{n})/X(t_{1})\cdots X(t_{t-1})]=P[X(t_{1})=X(t_{n})/X(t_{t-1})] \tag{2}\]  
Assuming that the stochastic process is a homogeneous Markov process (3), the state statistics of the Markov chain determine the conditional probability \(P_{ij}(\Delta t)\), and \(P_{ij}(\Delta t)\) is the conditional probability of the transition from state \(S_{i}\) to state \(S_{j}\) in \(\Delta t\). The determination of conditional probability is one of the important links in the application of Markov chain theory.  
\[P\big{[}X(t+\Delta t)=S_{j}/X(t)=S_{i}\big{]}=P\big{[}X(\Delta t)=S_{j}/X(0)=S _{i}\big{]}=P_{ij}(\Delta t) \tag{3}\]  
For the state space composed of \(\mathfrak{n}\) states with the same time interval of \(\mathfrak{t}_{1},\mathfrak{t}_{2},\mathfrak{t}_{3},\ldots\mathfrak{t}_{n}\), the state transition probability can be expressed in the form of a matrix, as shown in Formula (4).  
\[P=\mathfrak{p}_{ij}=\begin{pmatrix}\mathbb{P}_{11}&\mathbb{P}_{12}&\cdots& \mathbb{P}_{1n}\\ \mathbb{P}_{21}&\mathbb{P}_{22}&\cdots&\mathbb{P}_{2n}\\ \vdots&\vdots&&\vdots\\ \mathbb{P}_{n1}&\mathbb{P}_{n2}&\cdots&\mathbb{P}_{nn}\end{pmatrix},P_{ij}\geq 0,\sum_{j=1}^{n}P_{ij}=1; \tag{4}\]  
Therefore, the state transition probability matrix \(P\) that meets the precision requirements can be obtained by collecting the state transition data between \(S_{m}\) and \(S_{m+1}\). At the same time, whenever a set of data samples is added, the probability value of only one row of matrix \(P\) will change, and the number of rows corresponds to the state of the system at the time of \(\mathfrak{t}_{m}\). Theoretically, the statistical value of matrix \(P\) will gradually approach its real value when the number of samples tends to infinity.
## 3 Results and Discussion
### Analysis of Influencing Factors on Carbon Emission Intensity
#### 3.2.5. Analysis of Chemical Carbon Emissions  
This study conducted a comprehensive analysis of the chemical carbon emissions from 98 STPs in East China in 2024, revealing the significance of chemical usage in the carbon footprint of STPs. The results showed that chemical carbon emissions accounted for 14.2% of the total carbon emissions from STPs, highlighting the non-negligible role of chemical usage as a source of carbon emissions.  
The proportion of carbon emissions from various agents is shown in Figure 9. Carbon sources and phosphorus removal agents were identified as the primary contributors to chemical carbon emissions, accounting for 27.8% and 18.9% of the total chemical carbon  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{3}{*}{**WWTPs**} & **Fossil Source** & \multirow{3}{*}{\begin{tabular}{c} **CH4 Emission** \\ **Intensity/** \\ **(kgCO2-eq/m3)** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **N2O Emission** \\ **Intensity/** \\ **(kgCO2-eq/m3)** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **Power** \\ **Intensity/** \\ **(kgCO2-eq/m3)** \\ \end{tabular} } & \multirow{3}{*}{\
\begin{tabular}{c} **Drug** \\ **Consumption** \\ **Intensity/** \\ **(kgCO2-eq/m3)** \\ \end{tabular} } & \multirow{3}{*}{
\begin{tabular}{c} **Total Emission** \\ **Intensity/** \\ **(kgCO2-eq/m3)** \\ \end{tabular} } \\ \hline WWTPs1 & 0.011 & 0.034 & 0.375 & 0.187 & 0.005 & 0.613 \\ WWTPs2 & 0.015 & 0.021 & 0.015 & 0.208 & 0.027 & 0.286 \\ WWTPs3 & 0.014 & 0.027 & 0.185 & 0.231 & 0.005 & 0.462 \\ WWTPs4 & 0.009 & 0.051 & 0.019 & 0.140 & 0.008 & 0.227 \\ \hline \end{tabular}
\end{table}
Table 7: Carbon emission of four wastewater treatment plants with different processes.  
Figure 8: Distribution of COD (a) and TN (b) removal and carbon emission intensity.  
emissions, respectively [32]. This indicates that reducing the dosage of carbon sources and phosphorus removal agents is key to mitigating chemical carbon emissions from STPs. In contrast, disinfectants contributed the least to carbon emissions, with a share of only 10.4%, suggesting their relatively minor impact on chemical carbon emissions.  
Based on these findings, the following recommendations are proposed to help STPs reduce chemical carbon emissions:  
1. Optimize chemical dosage: Precisely control the addition of carbon sources and phosphorus removal agents to avoid overuse and reduce carbon emissions.
2. Adopt low-carbon alternatives: Explore and utilize low-carbon alternatives for carbon sources and phosphorus removal agents to lower overall carbon emissions.
3. Improve treatment processes: Enhance wastewater treatment processes to reduce reliance on carbon sources and phosphorus removal agents, thereby indirectly reducing carbon emissions.
## 3 Results  
### MESMA-Based Crop Classification  
The intermediate crop classification map obtained by MESMA and the confusion matrix are shown in Figure 7. This classification has an overall accuracy (OA) of 37%. Considering the producer accuracy, the best-classified crops are soybean and maize with 54% and 44% accuracy, respectively. On the other hand, lowest values of accuracy occur for tree crops (17%) and pasture (10%).  
The decision tree uses fractions to distinguish the maize class and separate the vetch from the other crops.
# Automatic Bridge Crack Detection Using a Convolutional Neural Network
## 3 Experimental Results and Discussion
### Evaluation and Discussion
#### 3.2.4 Discussion  
Compared with the traditional classification models, the advantages of the proposed model are mainly as follows:  
1. An atrous convolution with an atrous rate of 2 was introduced in the last three convolutional layers of the network, replacing the maxpooling layer, thus avoiding the loss of crack edge information caused by pooling and down-sampling. At the same time, the atrous convolution can also obtain a larger receptive field, so as to obtain more context information.
2. An ASPP module was introduced to detect the input feature map with multiple sampling rates, so as to obtain the receptive field and image context information of different scales, and to improve the network recognition accuracy. Generally, ASPP is used more in the fields of Semantic Segmentation and Object Detection, and we introduced the ASPP module to the field of crack detection first. It is experimentally demonstrated that the ASPP module works best when placed in the middle of the network (after conv3 in the proposed model). By adding ASPP in the middle of the network, multi-scale information can be better integrated, so that the image feature information can be effectively utilized.
3. The depthwise separable convolution was used to reduce computational complexity and improve computational efficiency, so that the network could efficiently learn the representative features of datasets. As shown in Table 7, the proposed model has a lower computational complexity and shorter runtime than other models in the table.
## 3 Results
### Contact Angle and Peel Tests  
Contact angle measurements performed before and after coating application revealed clear distinctions in surface wettability across the different formulations (Figures 6 and S18). The uncoated (pre-treated surface) chromatic fields, consistent with the hydrophobic nature of acrylic-based POSCA markers, displayed high contact angles averaging around 98\({}^{\circ}\), both before and after artificial ageing, with no significant variations detected. In contrast, the chitosan-only (CS) coating exhibited substantially lower contact angles, averaging approximately 62\({}^{\circ}\), indicative of a more hydrophilic surface. Following artificial ageing, this value remained largely unchanged (around 60\({}^{\circ}\)), suggesting stable surface behaviour but relatively low water repellency. The inclusion of nanocellulose in the coating formulations significantly influenced wettability. The CS/CNC1.5 coating showed a moderate increase in contact angle to about 74\({}^{\circ}\) before ageing, with only a slight reduction post-ageing (-71\({}^{\circ}\)). The CS/CNC3 formulation further increased the contact angle to approximately 80\({}^{\circ}\), which decreased marginally after ageing (-78\({}^{\circ}\)), maintaining its water-repellent characteristics. The CS/CNC6 formulation, which included the highest nanocellulose content, demonstrated the highest contact angles among the coated samples (-87\({}^{\circ}\) before ageing), and showed only a minimal drop to 83\({}^{\circ}\) after ageing, preserving its enhanced hydrophobic performance. Overall, contact angle measurements confirmed that all protective coatings retained their surface characteristics after artificial ageing, with no evidence of surface degradation or significant changes in wettability across the different chromatic fields.  
The chitosan-only coating (CS) exhibited the lowest adhesion values, with an average peel strength of 1.2 N/cm before ageing and a reduced value of 0.9 N/cm after artificial ageing (Figure 7).  
In this formulation, slight delamination was visually observed at the film edges post-ageing, indicating decreased mechanical stability under stress. Incorporation of nanocellulose into the chitosan matrix resulted in a measurable improvement in adhesion. The CS/CNC1.5 formulation demonstrated moderate adhesion values, with 1.5 N/cm before ageing and 1.3 N/cm after ageing, reflecting a good balance between stability and removability. The CS/CNC3 and CS/CNC6 formulations, which contained higher concentrations of nanocellulose, exhibited the highest adhesion forces, with average values reaching 2.0 and 2.2 N/cm before ageing, respectively. Following artificial ageing, these values showed only minor reductions (1.8 and 2.0 N/cm), confirming the strong cohesion and durability of the protective layer even under accelerated degradation conditions. The results indicate that nanocellulose significantly enhances the mechanical performance of chitosan-based coatings, particularly in terms of adhesion to acrylic-painted substrates.
## 3 Results and Discussion
### Stability Study
#### 3.2.3 Thermal Analysis by DSC  
DSC was used in this study to monitor changes in the thermal stability and crystallinity of the samples over time after storage under stress conditions. The initial melting point of KTP was 97.83 \({}^{\circ}\)C, while in the physical mixture (PM)--comprising raw materials of KTP, stabilizers, MNT, and leucine--it was 96.50 \({}^{\circ}\)C (Figure 7A). In sample F1, the melting point was reduced to 91.50 \({}^{\circ}\)C, mostly due to the milling process. Moreover, the less intensive peaks in sample F1 compared to KTP raw indicated that some particles partially converted to amorphous after spray drying. After storage (Figure 7B), the endothermic peak's position and intensity remained consistent, with melting points recorded at 90.84 \({}^{\circ}\)C after 1 month and 90.39 \({}^{\circ}\)C after 3 months. Overall, the DSC curves indicate that the thermal stability of  
\begin{table}
\begin{tabular}{c c} \hline
**Sample** & **Particle Size (\(\upmu\)m)** \\ \hline F1\_0m & 2.32 \(\pm\) 0.12 \\ \hline F1\_1m & 2.35 \(\pm\) 0.39 \\ \hline F1\_3m & 2.47 \(\pm\) 0.89 \\ \hline \end{tabular}
\end{table}
Table 2: The particle sizes of the F1 sample as freshly prepared and after storage according to the SEM images. Sampling was conducted immediately after preparation (F1—0m), after 1 month (F1—1m) and 3 months (F1—3m) of storage.  
Figure 6: SEM images of F1 sample at (**a**) 0 months, (**b**) 1 month, and (**c**) 3 months.  
the samples remained generally unchanged throughout the testing period, which is in line with previously published results [28].
# A Wideband Hybrid Envelope Tracking Supply Modulator with Slew-Rate-Enhanced Linear Amplifier
## 1 Introduction  
As the demand for data transmission rates continues to increase with modern communication technology, the increasing bandwidth and peak-to-average power ratio (PAPR) pose new challenges for the linearity and efficiency of power amplifiers. Envelope tracking (ET) technology is widely used, aiming to address this challenge by focusing on how to enhance the efficiency while ensuring the linearity of PAs [1; 2; 3; 4]. Different from conventional power amplifiers with a fixed drain supply voltage, ET technology utilizes a dynamic drain voltage supply that tracks the envelope waveform [1]. As illustrated in Figure 1, where the green shaded region represents power losses on the power transistor, ET technology significantly reduces power dissipation on the power transistor, thereby improving efficiency [5; 6; 7; 8]. HETSM, composed of switching converters (SWC) and LA and incorporating the advantages of both, has become the mainstream in research and applications and has been widely reported on in recent works [9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19].  
For large bandwidth requirements, it becomes increasingly challenging for envelope tracking supply modulators to improve the bandwidth and slew rate while maintaining high efficiency [9; 20; 21]. Works in [22; 23] proposed an envelope slew-rate reduction algorithm and demonstrated its beneficial effect on improving the linearity of ET PA. These works indicate the importance of meeting the signal slew-rate requirements for HETSM. Techniques such as adaptive biasing and super-class-AB recycling folded cascode are used to improve the slew rate without increasing the quiescent bias current in studies [20; 24; 25; 26]. These techniques optimize the slew rate by amplifying the input-stage current duringthe transition state. Consequently, the large current needs to be transferred to the final stage via the front stages [9]. Works [9; 27; 28] introduced the auxiliary circuit injecting slew-rate-enhanced current, which is proportional to the input voltage of LA, to the gate of the output stage without increasing the input-stage current. However, the quiescent current of the auxiliary SRE circuit should be large to achieve a high slew rate.  
In this work, a wideband HETSM with a slew-rate-enhanced LA is proposed. In the LA with SRE structures, a parallel auxiliary current path is introduced to the gate of the class-AB output stage, significantly accelerating the charging and discharging processes of the Miller capacitor without modifying the operating point of the remaining LA. The current delivered via this SRE path shows nonlinear characteristics, increasing sharply with changes in input voltage, thus reducing the quiescent current levels. The rest of this paper is organized as follows: Section 2 describes the principles and implementation of the recommended HETSM, including the LA with the proposed SRE structure. Measurement results are illustrated in Section 3, and the conclusion is summarized in Section 4.
# Low-Temperature Plasma as an Approach for Inhibiting a Multi-Species Carigenic Biofilm
## 3 Results
### Confocal Scanning Laser Microscopy (CSLM)  
To demonstrate the effect of LTP treatment, Figure 6 presents the confocal scanning laser microscopy (CSLM) representative images, which show the morphology and structural organization of multi-species biofilm after treatment with LTP for the highest tested exposure time (120 s) or exposed to the negative control. The images show live cells, which are stained green (SYTO 9), and dead cells, which are stained red (propidium iodide). Samples treated with LTP for 120 s present a visually larger area covered in dead cells when compared to the negative control, which can be visualized in the overlaid images (P120 live/dead).  
Figure 5: Mean values and standard deviations \(\log_{10}\) CFU/mL of multi-species biofilms formed by _S. mutans_, _S. gordonii_, and _S. sanguinis_, chlorohexidine (positive control), or 0.89% NaCl solution (negative control). Different letters indicate significant statistical difference (n = 9; \(p\leq 0.05\); ANOVA, Tukey’s Test), F30, F60, and F120 (argon gas—flow control treatment for 30 s, 60 s, and 120 s); P30, F60, and P120 (LTP treatment for 30 s, 60 s, and 120 s); PC (positive control); and NC (negative control).  
Figure 6: Confocal laser scanning microscopy (CLSM) of multi-species biofilms formed by _S. mutans_, _S. gordonii_, and _S. sanguinis_ exposed to the negative control or treated with LTP for 120 s; 25X 0.95 zoom. Biofilms are stained with Live/Dead BacLight Viability kit. Live cells are in fluorescent green (SYTO 9) and dead cells in fluorescent red (propidium iodide). (A) Negative control (NC), live/dead overlaid images and (B) LTP for 120 s (P120, live/dead overlaid images).
## 1 Introduction
Firstly, the reaction mechanism of urea hydrolysis type MICP technology was introduced, considering different influencing factors, including temperature, PH value, nutrient solution concentration, and calcium ion concentration. Secondly, the structural characteristic and the soil water characteristic curve (SWCC) of unsaturated soil and their influence on the physical and mechanical properties of unsaturated soil are introduced in detail. Thirdly, the changes of the soil microstructure before and after mineralization by MICP technology are analyzed and compared from the macro and micro levels. The influence of MICP technology on the permeability of unsaturated soil and SWCC is also discussed. Furthermore, the relationship between the hydraulic and mechanical properties of unsaturated soil is briefly introduced. Finally, it provides the existing problems, environmental challenges, and further research aspects in the application of MICP.
# Research on an Index System for the Earthquake Disaster Resistance Capability of Highway Tunnels
## 2 Analysis of Seismic Damage Scenes in Highway Tunnels
### Analysis of the Influencing Factors of Earthquake Disasters in Portal Sections of Highway Tunnels
#### 3.3.2 Construction Joint Cracking  
Construction joint cracking refers to the cracking of the connecting parts of adjacent lining structures under the action of seismic force. As the connection between two adjacent main structures, the construction joint is different from the main structure in the material and is prone to damage caused by external forces. During the earthquake, the six construction joints of Shaohuoping Tunnel all had circumferential cracks, and one of them was staggered; construction joint cracking occurred in Ma'anshi Tunnel; almost all the construction joints of Youyi Tunnel cracked, and the maximum width of the joints reached 30 mm. The main reason for the above damage is that the tunnel is subjected to a large stress caused by the earthquake, and the construction joint link strength of the construction joint cannot adapt to the bearing capacity of the main structure.  
It is concluded that the main reason for the construction joint cracking is that the construction joint is located at the connection part of the two main structural members and the link strength of the construction joint is insufficient, resulting in structural damage, which is a tunnel structural factor.
## 2 Materials and Methods  
### Data Sources  
This study utilizes datasets composed of post-earthquake images from two distinct locations. The first dataset includes UAV images captured after a magnitude 4.5 earthquake in Baoxing County, Ya'an City, Sichuan Province, on 1 June 2022. The second consists of post-earthquake remote sensing images acquired after a 6.2 magnitude earthquake on 22 June 2022 in Khost Province, Afghanistan [20]. The original remote sensing images of Afghanistan can be downloaded (license required) from the following URL: [https://resources.maxar.com/](https://resources.maxar.com/), accessed on 27 June 2022. Initially, a region of interest (ROI) was extracted from the original images. Subsequently, the region of interest (ROI) was divided into patches of various sizes based on the image resolution and the architectural characteristics of the buildings. Every patch was then evenly adjusted to dimensions of \(512\times 512\) pixels. Table 1 provides detailed information on the datasets.  
All buildings were categorized into four levels as follows: intact, slightly damaged, severely damaged, and collapsed. The criteria for judging the four categories were as follows: slightly damaged buildings are those where the roof exhibits uneven color tones due to partial tile loss, resulting in visible leaking areas. Severely damaged buildings have the characteristics of the overall outline of the building remaining intact, but one side of the house shows partial wall collapse, forming local ruins in the imagery. Fallen debris displays significant variations in brightness and color tones, indicating a collapse degree of 10-50%.  
The outline of the collapsed buildings is incomplete, and the roof texture and color tones appear asymmetrical. There is a noticeable contrast between the collapsed corners of the walls and the roof texture in terms of brightness and color tones, indicating a collapse degree of over 50%. The proportion of intact, slightly damaged, severely damaged, and collapsed is 52%, 22%, 16%, and 10%, respectively. Table 2 shows an example of four building damage levels in the UVA and remote sensing images. Lastly, Table 3 illustrates the disparity in resolution between drone imagery and satellite remote sensing imagery. Notably, within the demarcated region highlighted by the red box, a significant discrepancy exists in the pixel area occupied by a single residential structure in the two image types [21].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Source** & **Resolution** & **Year** & **Number of Samples** \\ \hline Afghanistan Dataset & WordView3 & \(\leq\)0.31 m & 2022 & 5601 \\ Baoxin Dataset & UAV & \(\leq\)0.1 m & 2022 & 4020 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Image detail information on the datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Image Category** & **Intact** & **Slightly Damaged** & **Severely Damaged** & **Collapsed** \\ \hline UAV & & & \\ Remote sensing & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Examples of four building damage levels in UVA and remote sensing images.
## 3 Dataset and Methodology
### Input Parameters
#### 3.1.3 Sediment Supply and Water Discharge  
Sediment supply and water discharge values are important input parameters in forward stratigraphic modelling, as they transport sediments and fill up the given accommodation space in siliciclastic settings. The majority of modelling studies use today's reported world river values [18; 41] to achieve realistic results in ancient settings, e.g., [28; 42; 43]. This approach is useful in ancient settings; however, real-time Fraser River discharge and sediment supply values are used in this study, as well as river location and width.  
In the model, river discharge values vary between 4500 m\({}^{3}\)/s and 6000 m\({}^{3}\)/s (Table 1). These values are slightly higher than today's average values, mainly because the reported values represent recent real-time data and do not consider the fact that the discharge rate was most likely higher in the past due to the extensive glacial melting, particularly close to the early stages of the delta progradation. Nevertheless, these values are lower than today's maximum discharge rate.  
Although annual river discharge values are known, it is also important to consider long-term variations along the river systems. In the Fraser River, extreme flooding events take place every 500 years [44]. The last extreme flood was reported in 1894 [45]. Thus, in the model, short-term high-energy floods (HEST) are applied to represent extreme floods [46].