esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
The hydrophobicity of the SSS coating can effectively improve the impermeability of concrete under high water pressure. The impermeability of coated concrete is influenced by multiple factors based on the experimental results. These factors include pore structure, water pressure, time, direction of water penetration, and crack width. As the water pressure increases, the depth of water seepage in the concrete also increases. During this process, water pressure changes the pore structure of the concrete. [PERSON] et al. [32] pointed out the relationship between porosity and the relative permeability coefficient. [PERSON] et al. [17] conducted permeability tests at 0 MPa, 1.4 MPa, and 2.4 MPa. Due to the low water pressure during the initial loading, the relative permeability coefficient of concrete first increased and then decreased. This study started from 1 MPa and the relative permeability coefficient continued to increase. It can be seen that a water pressure of 1 MPa is a condition for reducing the relative permeability coefficient of concrete. After the water pressure has been applied for a long time, the SSS coating and concrete are eventually penetrated. Whether in coatings or concrete, the permeation does not stop. [PERSON] et al. [54] and [PERSON] et al. [55] also verified this by calculating the weight loss rate of the coating and measuring the long-term water absorption rate of concrete, respectively. This study does not consider coating thickness, which is also an important factor in slowing down the penetration rate. The potential future exploration is the relationship between coating thickness and penetration rate. This is significant for determining the engineering quantity of coatings. When the coating is not on the concrete-facing surface, water enters the concrete from the other side. The SSS coating can indirectly reduce the impermeability of concrete by improving its airtightness. [PERSON] et al. [56] developed a silicone polymer coating that improved airtightness. Penetration testing showed that it reduced the gas permeability of concrete by 40-70%. This study also focuses on the damage of coatings in high-pressure  
Figure 8: The results of the SSS coating under different concrete crack widths.  
water environments. Coating damage is related to water pressure and crack width. The main types of damage are puncture damage and tear damage.
## 4 Mobile Peer-to-Peer Architectures
### Introduction  
As MANETs and P2P networks share central characteristics, combing both leads to a fully distributed architecture. Those MP2P networks consist of a MANET underlay combined with a P2P overlay. The P2P algorithm provides a lookup service on the MANET underlay. The major challenges in developing MP2P architectures are the overhead caused by the routing table maintenance combined with the strongly limited resources of the MANET underlay.
## 1 Introduction
Testing the feasibility of multiple spectral devices within real-world conditions is important to determine how spatial resolution impacts the ability to detect AMD. Furthermore, it is important to explore each sensor's resilience toward mixed pixels caused by nearby water bodies (often \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Instrument**} & \multirow{2}{*}{**Products**} & \multirow{2}{*}{**Wavelength Range**} & \multicolumn{2}{c}{**Interoperable Band**} \\  & & **(nm)** & **Ratios (with** \\  & & & **Central Wavelengths)** \\ \hline ASD TerraSpec Halo & Mineralogical scalars; Spectral & & \\ handheld & signature profiles (ASD binary & 350–2500 & Fe\({}^{3+}i^{*}\)scalar (742 nm/500 nm) \\ spectrometer [20; 21] & files); Ore mineralogy & & \\ UAV & 50 mm\({}^{2}\) (12-bit & 400–1000 & Bands1/bands2 (665 nm/560 nm) \\ Nano-Hyperspec [22] & hyperspectral products) & & \\ PlanetScope P\({}^{2}\)S2D & 3 m\({}^{2}\) (8-bit multispectral products) & 464–888 (4 bands) & Bands3/bands2 (666/566 nm) \\ Sentinel-2 2A/1C & 4 bands at 10 m\({}^{2}\); 6 bands at 20 m\({}^{2}\); 24 & 43–2190 (13 bands) & Bands4/bands3 (665/560 nm) \\
[24] & & multispectral products) & & \\ \hline \hline \end{tabular}  
* \(i\) = intensity  
\end{table} 
Table 1: Instruments used in a multi-satellite study configuration, with specifications and interoperable wavelengths. associated with AMD deposits). Sudden changes in soil moisture can lead to an overall reduction in soil reflectance for all wavelengths, as when soil moisture levels increase, reflection decreases for most soil types [25]. The use of binary water masking is often the go-to method to deal with water in mining environments, which is an indiscriminative method. This study aims to fill this research gap by observing the impacts that spatial resolution and waterbodies have on the detection and delineation of surface AMD deposits at a representative AMD site in Cornwall, UK, through the same-day data capture of ground-, air-, and space-borne spectroscopy.
## 1 Introduction
Little research has been undertaken to establish a relationship between the British Pendulum Number (BPN) arising from the BPT and the surface coefficient of friction (\(\upmu\)) [23; 29]. Additionally, the BPT has been used to evaluate road and runway microstructure by several authors. Their main results are presented in the Table 1.  
[PERSON] et al. [24] used the BPT to evaluate the impact of superhydrophobic coatings on different pavements. Their main findings indicated that a superhydrophobic coating may delay ice formation and then enhance surface friction resulting in a higher BPN [24]. [PERSON] et al. [26] and [PERSON] et al. [28] used the BPT to evaluate the performance of chemical de-icers used on roads. Both studies have obtained results during natural field testing campaigns showing that chemical de-icers could lead to effective results by significantly increasing the surface BPN compared to the untreated surface condition. However, since the tests were conducted in natural conditions, it was hard to obtain repeatable results. [PERSON] et al. [27] used the BPN to evaluate the skid resistance of previously used concrete slabs subjected to ice. They obtained results showing that in both de-icing mode and anti-icing mode, the de-icer, namely calcium chloride, helped increase the BPN significantly. However, the way the ice was formed on the concrete slab was not representative of how it occurs under natural conditions, since the ice is formed by soaking the concrete substrate \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Ref. Authors** & **Condition** & **BPN** & **Note** \\ \hline \multirow{3}{*}{[PERSON] et al. [24]} & Dry & 70 & Rough superhydrophobic \\  & Wet & 60 & coating on a coarse surface \\  & Iced & 48 & \\ \hline \multirow{3}{*}{[PERSON] et al. [27]} & Dry & 74 & Coarse surface \\  & Wet & 64 & Complete ice cover \\  & Iced by soaking & 22 & \\ \hline \multirow{3}{*}{[PERSON] et al. [28]} & Dry & 11 & Coarse surface \\  & Wet with de-icer & 60 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main Results obtained using BPT with winter conditions. completely in water at cold temperatures leading to ice formations in the cavities and not on the top of the substrate. Finally, in the anti-icing mode, the chemical de-icer is applied before exposure to ice which contradicts the literature facts, suggesting that de-icers should be applied after exposure to ice. Based on a recent literature survey on the BPT, there is no work that includes the use of different winter contaminants in conjunction with common runway de-icing products [25; 26; 27; 28]. With the introduction of the new GRF and the fact that there is a gap in the evaluation of RDPs, the main goal of this study was to reproduce the cited winter conditions in the laboratory and on concrete cement substrates, to present a correlation between the GRF and the BPT, and to then propose a complete testing procedure of liquid RDPs in those conditions. The obtained results will be useful in determining preferable conditions for the use of RDPs.
## 3 Results and Discussion
### Photophysical Properties
Although reported as a crucial property in the phototherapeutic activity of a photosensitizer, as this ROS is highly relevant in biological systems [66], photosensitizing compounds with low singlet oxygen generation ability may exhibit consistent photodynamic effects resulting from the marked production of type I reactions [25; 67]. In Figure 2, the normalized Vis/NIR absorption spectra of the squaraine dyes **10**-**13** in acetonitrile, acetone, dichloromethane, dimethylformamide, dimethyl sulfoxide, 1,4-dioxane, ethanol, methanol and tetrahydrofuran are presented. The relevant spectra data are summarized in Table 2. Regardless of the solvent polarity, all compounds showed a narrow absorption band in the Vis/NIR region and high molar extinction coefficient values (4.0 \(<\) log \(\varepsilon\) (M\({}^{-1}\)\(\cdot\)cm\({}^{-1}\)) \(<\) 5.5) in agreement with \(\pi\)-\(\pi^{*}\) transitions. The introduction of the methoxy group and amines into the four-membered central ring had effects on the maximum absorption wavelength, proposing that the electronic levels involved in the \(\pi\)-\(\pi^{*}\) transition have different energies. Additionally, the formation of a hyposchromic shoulder, a typical photophysical characteristic of this core of dyes, is also observable regardless of the solvent. Figure 2: Vis/NIR absorption spectra of squaraine cyanine dyes **10**–**13** obtained in acetonitrile (ACN), acetone (ACT), dichloromethane (DCM), dimethylformamide (DMF), dimethyl sulfoxide (DMSO), 1,4-dioxane (DXN), ethanol (EtOH), methanol (MeOH) and tetrahydrofuran (THF). Absorbance was normalized and is presented as arbitrary units (a.u. ).
# Intelligent Monitoring Systems for Electric Vehicle Charging
## 4 Intelligent EV Charging Monitoring System
### Core Monitoring Functions
Detection mechanisms should analyze multiple parameters--such as current reduction patterns, voltage stability metrics, and thermal characteristics--to ensure accurate phase identification [19; 31]. The transition to the slow charging phase can be detected through analysis of current tapering patterns and voltage stability [11]. Similarly, the completed charging state should be confirmed through multiple mechanisms, including sustained current reduction below a predefined threshold (typically 1/20 of the maximum charging current) and, where available, direct charging completion signals from the vehicle [3; 17]. The monitoring system should maintain a state history with temporal resolution adapted to the specific charging phase [24; 32]. During rapid charging phases, state information could be recorded at short intervals, while slower charging phases may use adaptive recording intervals tailored to the operational context [3; 46]. This state history should encompass charging curve characteristics, environmental parameters, and power quality metrics, enabling detailed post-session analysis and long-term charging pattern evaluation [29; 50]. As a paramount consideration, all monitoring functions should incorporate fault tolerance [18]. Redundant measurement paths and real-time consistency checking could mitigate the impact of sensor failures or communication issues [11; 65]. When detected, measurement anomalies should trigger automated cross-validation routines that identify the source of the error, allowing the system to maintain monitoring continuity, albeit in a potentially degraded operation mode [19; 21]. The system architecture targets high monitoring availability through hierarchical failover mechanisms and self-healing capabilities [49]. While this multifaceted approach ensures robust monitoring, careful consideration of the associated data processing and storage demands remains essential [9; 76]. The distributed processing architecture, outlined in Section 4.1.2, should handle these demands efficiently [49; 64]. However, future investigation into data compression techniques and tiered storage solutions will be crucial for long-term scalability [63; 78].
## 3 Results and Analysis of Influencing Factors
### The Impact of Drill String Properties on Signal Attenuation
#### 3.5.2 The Impact of Drill String Magnetic Permeability on Signal Attenuation  
The magnetic permeability of the drill string is one of its key characteristics. When studying its impact on drill string performance, we set the electrical conductivity of the drill pipe at 1 \(\times\) 10\({}^{7}\) S/m and focused on the parameter of relative magnetic permeability in the model. Since drill pipes are mainly composed of iron and alloys, we set the range of their relative magnetic permeability between 1 and 1000 to more accurately simulate and analyze their performance in practical applications.  
According to Figure 8, with the increase in relative magnetic permeability, the attenuation of the magnetic field also shows a significant increase. Taking the transmission distance of 100 m as an example: when the relative magnetic permeability of the drill pipe \(\mu\) = 1, the signal attenuation is 95.01 dB; at \(\mu\) = 10, the attenuation further intensifies to 132.99 dB; and at \(\mu\) = 1000, the signal attenuation reaches even greater, at 158.7 dB.  
This phenomenon is closely related to the material of the drill string. If the drill string is made of materials with high magnetic permeability (such as iron, steel, and other ferromagnetic materials), electromagnetic waves experience greater attenuation while transmitting around the drill string, leading to reduced efficiency of long-distance propagation and increased transmission difficulty. In the design of communication equipment, the influence of the drill pipe's magnetic permeability on the propagation characteristics of electromagnetic signals must be fully considered.
## 5 Cutter Spacing Optimization and Bit Rock Breaking Simulation  
To optimize the hybrid cutter layout, it is crucial to prudently select the spacing between the sequential rows of cutters. The cutter spacing parameters, as illustrated in Figure 6, encompass four parameters. The manual modification of these parameters for simulation purposes can yield an excessive number of permutations and, possibly, the absence of the desired results. To circumvent this issue and maximize the rock-breaking efficiency of the hybrid cutter arrangement, the Box-Behnken response surface methodology [34] was implemented for the optimization of cutter spacing.
## 3 Data
### Standardized Precipitation Europotranspiration Index (SPEI) Data  
SPEI is estimated according to the cumulative sum of difference between PPT and potential ET on different time scales. Its basic principle is the same as the standardized precipitation index (SPI) [35]. Among them, PPT data were provided by GPCC and the potential ET data were from National Oceanic and Atmospheric Administration. SPEI has three different time scales: 3 months, 6 months and 12 months. The 3-month scale is associated with variations in soil moisture: agricultural drought; the 6-month scale is associated with variations in streamflow: hydrological drought; the 12-month scale is associated with variations in groundwater storage: hydrogeological drought [36]. In our study, SPEI gridded data with the spatial solution \(1^{\circ}\times 1^{\circ}\) spanned the period from January 2003 to December 2018.
###### Abstract  
Organic leafy vegetables face challenges related to potential substitution with non-organic products and vulnerability to dehydration and deterioration. To address these concerns, visible and near-infrared spectroscopy (VIS-NIR) combined with linear discriminant analysis (LDA) was employed in this study to rapidly distinguish between organic and non-organic leafy vegetables. The organic category includes organic water spinach (_Ipomaa aquatia [PERSON]_), amaranth (_Amananthus tricolor_ L.), lettuce (_Lactuca sativa_ var. _ramosa_ Hort), and pakchoi (_Brassica napus_ var. _chinnensis_ (Linnaeus) [PERSON]), while the non-organic category consists of their four non-organic counterparts. Binary classification was performed on the reflectance spectra of these vegetables' leaves and stems, respectively. Given the broad range of the VIS-NIR spectrum, stability selection (SS), random forest (RF), and analysis of variance (ANOVA) were used to evaluate the importance of the wavelengths selected by genetic algorithm (GA). According to the GA-selected wavelengths and their SS-evaluated values and locations, the significant bands for leaf spectra classification were identified as 550-910 nm and 1380-1500 nm, while 750-900 nm and 1700-1820 nm were important for stem spectra classification. Using these selected bands in the LDA classification, classification accuracies of over 95% were achieved, showcasing the effectiveness of utilizing the proposed method to rapidly identify organic leafy vegetables and the feasibility and potential of using a cost-effective spectrometer that only contains necessary bands for authenticating.  
rapid authentication; organic leafy vegetables; visible and near-infrared spectroscopy; wavelength selection; linear discriminant analysis +
Footnote †: journal: Applied Sciences
## 4 Mathematical Model
### System Power Chain  
Figure 2 shows the system power chain, illustrating the power transfer structure of the typical multicopter UAV [15]. The flight control system (FCS) includes both avionics and additional servo actuators for flight control. Usually, the avionics of the multicopter UAV is simply configured with a flight control computer, navigation sensors, and telemetry modems. The payload includes all of the additional equipment for missions. In the case of multicopter UAVs, typical payloads are cameras and gimbals for the aerial photography, LiDAR sensor for airborne laser scanning, and devices for transporting special cargo.  
The total power consumption (\(P_{total}\)) of the multicopter UAV is calculated, as shown in Equation (1), where \(P_{sys}\) is the power usage of the UAV system and \(P_{batt}\) is the power loss of the battery. The power usage of the system is divided into the required rotor power (\(P_{rotor}\)) and the power consumption in the FCS and payload (\(P_{fc}\), \(P_{payload}\)), as shown in Equation (2). The required rotor power is the sum of the power consumed by the motor (\(P_{motor}\)) and the ESC loss (\(P_{esc}\)). The calculation of the required rotor power is defined as Equation (3), where \(N_{rotor}\) is the number of rotors. Assuming that the output of all rotors is constant and stable under steady state flight, such as hovering or cruise flight conditions, Equation (3) can be simplified as Equation (4). The power that is consumed by the FCS and payload is considered to be constant.  
Figure 2: Multicopter UAV system power chain structure.  
\[P_{total}=P_{sys}+P_{butt} \tag{1}\]  
\[P_{sys}=P_{rotor}+P_{fcs}+P_{payload} \tag{2}\]  
\[P_{rotor}=\sum_{n=1}^{N_{water}}\left(P_{motor_{n}}+P_{esc_{n}}\right) \tag{3}\]  
\[P_{rotor}=N_{rotor}\cdot\left(P_{motor}+P_{esc}\right) \tag{4}\]
## 1 Introduction
[21] discussed the influence of initial modulus and stress path on resilient modulus by comparing the strength and deformation of two coarse-grained soils. In addition to stress states and physical properties of subgrade materials, resilient modulus is significantly affected by external environmental factors, especially F-T cycles. For materials in seasonally frozen areas, the performance reduction caused by F-T cycles should be considered in construction design. [PERSON] et al. [22; 23] measured the resilient modulus after one F-T cycle and recognized a significant modulus reduction. Moreover, he found that after F-T cycles, the porosity of compacted soils became larger, but the porosity of loose soils became smaller. [PERSON] et al. [24] defined Su1%, which represents the stress at the axial strain of 1% in unconfined compression tests. Their studies demonstrated that the performance reduction effect exhibited after the first F-T cycle is about 50% when Su1% is greater than 103 kPa. The obvious reduction effect was also verified by [PERSON] et al. based on measuring properties of several road materials after F-T cycles [25]. Existing experimental studies about the influence of F-T cycles on soil property have provided valuable results. The physical and mechanical property of soils modified by multiple additives such as FA and OSA are unknown and worth studying. To evaluate the performance of a modified material as subgrade filling in seasonally frozen regions, it is necessary and crucial to master the variation characteristics of its dynamic resilient modulus after multiple F-T cycles. In this study, a novel subgrade material obtained by modifying silty clay (SC) with OSA and FA was produced. The dynamic resilient moduli of unmodified SC and the modified SC under different stress states and F-T cycles were measured. The influence of various factors such as confining pressure, deviator stress, and F-T cycles on the resilient modulus for the two materials were analyzed. The damage ratio and its fitting equation were adopted to describe the relationship of resilient modulus versus F-T cycles. To better investigate the influence of F-T cycles on resilient modulus for test soils, SEM testing was conducted to analyze the variation characteristics of microstructure and pore distribution of soils.
# A Road Crack Detection Method Based on Residual and Attention Mechanism
## 3 Experimental Results and Analysis
### Ablation Experiment  
In order to verify the effectiveness of the BasicBlock, BottleNeck, and CBAM modules proposed in this paper, five ablation experiments were designed on the CrackTree260, CrackLS315, and DeepCrack datasets. The first group was UNet. The second group used the BasicBlock module instead of traditional convolution to extract features in the encoder structure of UNet. The third group added a BottleNeck module to the encoder structure of UNet to extract features based on the second group. The fourth group introduced the CBAM module in the skip connection part of the UNet network to make the model more focused on small cracks. The fifth group was based on UNet and included three modules: BasicBlock, BottleNeck, and CBAM.  
Tables 2-4 show the ablation experimental results for the CrackTree260, CrackLS315, and DeepCrack datasets, respectively. By comparing the results, we can conclude that the network model with the added BasicBlock module and BottleNeck module enhanced the ability to extract crack features, thereby improving network performance. The overall crack area segmentation effect was better than UNet. The CBAM module can enhance the model's ability to obtain global features of cracks, while also improving the model's ability to extract features from crack edges and small cracks. The network model that simultaneously introduced three modules significantly exceeded the baseline network model UNet in various indicators. Among them, the accuracy was improved by 9.3% and F1 by 9.4% on the CrackTree260 dataset, while the accuracy was improved by 5.3% and F1  
Figure 8: Detection results of four methods in CrackTree260, CrackLS315, and DeepCrack datasets, from top to bottom, correspond to 2 images selected from each dataset. (**a**) Source Image. (**b**) Ground-truth. (**c**) Detection results of UNet. (**d**) Detection results of Ref. [21]. (**e**) Detection results of Ref. [22]. (**f**) Our method.  
**Acknowledgments:** The authors are indebted to the four anonymous reviewers for their professional comments.  
**Conflicts of Interest:** The authors declare no conflicts of interest.