esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanical-Empirical Pavement Design Guide Applied to Portuguese Pavement Structures
## 3 Application of MEPDG to the Portuguese Pavement Structures  
### Introduction  
The MEPDG software was applied to the pavement structures recommended in the Portuguese Manual of Pavement Structures for the national road network [19]. Figure 3 presents the characteristics of the 16 different pavement structures (e.g., thickness, stiffness modulus, Poisson's ratio, and type of material) ordered according to their structural number [33; 34; 35]. These pavement structures were defined using the Shell pavement design method [36] considering an accumulated damage between 80% and 100%, with an additional verification performed with the University of Nottingham [37] and Asphalt Institute [38] pavement design methods.  
The Portuguese manual presents the 16 different flexible pavement structures for each combination between traffic and pavement foundation classes (Table 2). Each pavement foundation class corresponds to a stiffness modulus value. They are as follows: (1) 30 MPa for class F1; (2) 60 MPa for class F2; (3) 100 MPa for class F3; and (4) 150 MPa for class F4. No flexible pavement structures were defined for pavement foundation class F1 because, according to the Portuguese manual, this type of pavement foundation is not adequate for a flexible pavement with an asphalt base layer.  
The Portuguese manual [19] contains most of the data required by the MEPDG software, such as, for example, the percentage of the effective binder content and the air voids defined according to the type of layer and material. There are also minimum and maximum limit values for the effective binder content and the porosity, depending on the type of layer and material. The traffic data are also specified by the Portuguese manual, including the Average Annual Daily Truck Traffic (AADTT). Nevertheless, the Portuguese Road Network Agency (Infraestruturas de Portugal) has detailed traffic data for each segment of the national road network [39].  
Table 3 presents the threshold for each pavement performance indicator considered in this case study. The IRI has three different thresholds, which depend on the percentage of the total length of the project. These thresholds were based on Portuguese specifications [40], Portuguese Quality Control Plans (QCP), and pavement design practice. Within the scope of road concession contracts [41], the concessionaires need to submit to the Portuguese Institute of Mobility and Transports (IMT) a Quality Control Plan (QCP) and a Maintenance and Operation Manual (MOM). The QCP presents the thresholds for each pavement performance indicator that a highway concessionaire needs to fulfill in each year of the concession period, which is usually 30 years. Contractual infractions are penalized with  
Figure 3: Characteristics of pavement structures (adapted from [35]).  
fines, in which the global sum varies according to its gravity between EUR 5000 and EUR 100,000 or daily values that can vary between EUR 500 and EUR 5000.
# Analysis of Permanent Magnet Motors in High Frequency--A Review
## 2 High-Frequency Phenomena in Electrical Machines
### High-Frequency Phenomena in Conductors  
The skin effect is the first phenomenon that arises as the frequency increases. Eddy currents are induced in the current carrying conductor due to the self-created High-Frequency (HF) magnetic field. These Eddy currents cause a non-uniform current density inside the conductor, as the electrons are pushed to the outer region of the conductor. This way, the effective cross-section of the conductor is reduced with the frequency, as the current flows only in the skin depth of the conductor (see Figure 2). The skin depth can be computed applying the next well-known analytical expression:  
\[\delta=\sqrt{\frac{2}{\omega\mu\sigma}} \tag{1}\]  
where \(\omega\) is the current pulsation, \(\mu\) is the medium permeability and \(\sigma\) is the conductivity. According to Equation (1), it can be deduced that the higher the frequency is, the thinner the skin depth is.  
This surface distribution of the current increases the resistance and decreases the inductance due to the flux distribution inside the conductor. The skin effect is a local effect in each conductor, independent from the neighboring ones, so it can be analyzed simplifying the problem to a single conductor [76; 77]. As it depends on the frequency and geometry, it can be mitigated using a smaller conductor diameter than the skin depth.  
The neighboring effect between the conductors comprising a coil is defined as the proximity effect, and it is far more influential than the skin effect [52]. The basis is similar to the skin effect, but it considers the effect of induced Eddy currents in a conductor due to the high-frequency magnetic field created by the neighboring conductors. Thus, this phenomenon depends on the relative position between conductors and can be mitigated using windings with parallel strands or Litz wires, although they are complex to manufacture and they can generate unbalanced current distribution across the strands [78]. The distortion caused by this effect in the current density distribution of an electrical machine coil is shown in Figure 3. When those effects get together, the effective cross-section of a wire decreases further, leading to a higher resistance increase [55; 58].
## 3 Results and Discussion
### Gc-Ms
Fatty acid production is determined in part by the initial composition of must [59] and therefore possibly by varietal origin. Malvazija istarska wines stood out with low concentrations of decanoic and octanoic acid. Among esters, Pospip was clearly differentiated from the other monovarietal wines by the highest concentration of 2-phenethyl acetate, which could have been related to the higher concentration of its precursor 2-phenylethanol found in this wine. However, it was stated previously that precursor concentrations do not significantly determine the concentrations of acetate esters formed by _Saccharomyces cerevisiae_, with the expression of alcohol acetyl transferase gene in yeast as a limiting factor [60]. Concentration of 2-phenethyl acetate in all the investigated wines was higher than the corresponding threshold of 250 ug/L [37], suggesting its floral odor participated in the aroma of all the wines. The major ethyl and acetate esters are among the most important volatile compounds for the fresh fruits aroma of young white wines to which they significantly contribute by commonly multiply surpassing their rather low odor perception thresholds, such as 30 ug/L for isoamyl acetate, 20 ug/L for ethyl butyrate, 5 ug/L for ethyl hexanoate, and 2 ug/L for ethyl octanoate [37]. The highest concentration of linear middle-chain ethyl esters and acetates other than 2-phenylethyl acetate, although in some cases without statistical significance, was noted in Malvazija istarska wines. Pospip was also relatively abundant in these esters, except for ethyl hexanoate which was found in the lowest concentration in this and in Marastina wines. Although hexanoic acid is mainly formed in fermentation, grapes also contain non-negligible concentration. This means that the concentration of ethyl hexanoate in wine is probably partly influenced by the concentration of its precursor, hexanoic acid, in grapes [4], so the lower concentration of ethyl hexanoate in Pospip and Marastina could have been influenced by a genotype.
# Real-Time Prediction of Transarterial Drug Delivery Based on a Deep Convolutional Neural Network
## 2 Methods
### Model Training  
Model training is an iterative process, where the difference between the target (\(\psi\)) and the prediction (\(\hat{\psi}\)) are minimized. The difference is generally in the form of a loss function:  
\[J=\frac{1}{N}\sum_{n=1}^{N}\big{(}\psi(s_{n},d_{n})-\hat{\psi}(s_{n},d_{n}) \big{)}^{2}+\lambda\parallel W\parallel_{2} \tag{4}\]  
where \(s_{n}\) and \(d_{n}\), respectively, represent the blood vessel geometry and the injection position of the n-th blood vessel. \(\psi(s_{n},d_{n})\) refers to the numerical simulation results while \(\hat{\psi}(s_{n},d_{n})\) stands for the network prediction results. W denotes all the weights of the network layers; \(\lambda\) is the regularization coefficient; \(\lambda W_{2}\) is the L2 regularization term for preventing model overfitting. Compared with average relative error, L2 is more sensitive to outliers and more conducive to neural networks to capture the variations in minor features in data.  
The Adaptive Moment Estimation (ADAM) optimization algorithm is proposed [42] and the mini-batch strategy is adopted in the optimization process. The hyperparameters of the network training optimization algorithm are listed in Table 1.  
Figure 4: Architecture of the two CNN models: (**a**): Model 1; (**b**): Model 2.
## 2 Numerical Model Construction
### Microscopic Parameter Calibration  
In the numerical simulation, the values of the fine-scale parameters are key to the simulation results. First, considering the contact friction and lack of cementation between the rockfill particles, a linear contact model is used to describe the mechanical properties of the rockfill materials. Second, to better describe and respond to the mechanical properties of the rockfill materials, the model parameters \(E\), \(k_{\text{n}}\), \(k_{\text{s}}\) and \(\mu\)[36], which have a greater influence on them, are selected. A blind adjustment of the microscopic parameters during the calibration process will increase the difficulty of the test to a large extent. This paper quickly determines the range of parameter values on the basis of mastering the intrinsic connection between the microscopic parameters and macroscopic indices (e.g., the effective modulus \(E\) has the highest degree of influence on the elastic modulus, \(k_{\text{n}}\) and \(k_{\text{s}}\) have a significant influence on the Poisson's ratio \(\
u\), and the coefficient of friction \(\mu\) mainly affects the peak strength [37]). Finally, minor parameter fine-tuning was carried out according to the indoor test results until the simulation results were in good agreement with the indoor results, and the following microscopic parameters were finally determined, as shown in Table 1.  
The parameters outlined in Table 1 were used for large-scale triaxial numerical simulations, and a relative comparison of the numerical test simulations and the indoor triaxial test results is shown in Figure 6. The simulation results of the bias stress-axial strain relationship of the numerical simulation test) agree well with the indoor test results, and the stress(strain curve) evolution trend is basically the same. This alignment indicates that the microscopic-scale parameters calibrated in this study can effectively capture the deviatoric  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Materials** & \(E\)**/Pa** & \(k_{\text{n}}\)**/(N\(\cdot\)m\({}^{-1}\))** & \(k_{\text{s}}\)**/(N\(\cdot\)m\({}^{-1}\))** & \(\mu\) & \(\
u\) & **Film Thickness/(mm)** \\ \hline Particles & \(55\times 10^{9}\) & \(7.85\times 10^{8}\) & \(6.28\times 10^{8}\) & \(0.6\) & & \\ Boundary & \(7.86\times 10^{6}\) & — & — & \(0\) & \(0.47\) & \(5\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter setting for the particle flow numerical simulation.  
stress-axial strain relationship of the rockfill materials. The purpose of this paper is to study the deformation characteristics and variations in the characteristic state parameters of rockfill materials. Therefore, large-scale triaxial numerical simulation tests were conducted at three densities of 0.30, 0.60, and 0.90, corresponding to initial pore ratios of 0.368, 0.336, and 0.215, respectively, under confining pressures of 300, 600, and 900 kPa.
# Simultaneous Electrochemical Removal of Selenium and Strontium from Aqueous Solution
## 3 Results  
The results from all 102 experiments, as detailed in Appendix A Table 11, indicate that Se reached a 100% removal efficiency in several experiments, while the highest removal efficiency for strontium peaked at approximately 98%. Table 3 provides an overview of selected experiments--those that achieved the lowest and highest efficiencies for each element. Notably, the lowest efficiencies for Sr removal were observed using Fe electrodes for a short duration and without any pH adjustment (specifically in runs 54, 3, and 62). Runs 80, 48, and 84 yielded the highest removal efficiencies, suggesting that higher applied currents and the maximum range of pH values favored Sr removal. These results also suggest that Se was generally easier to remove than Sr. The complete removal of Se was achieved in experiments without a pH adjustment, 25 min duration, and 15 or 25 A of applied current. Considering the performance for both elements, run 48 achieved the top performance with 97.92% and 99.96% removals of Sr and Se, respectively. This indicates that the optimal operational parameters included 25 A of applied current, four Fe electrodes, 15 min of operational time, and the middle range of pH values. Generally, increasing the current strength and extending the duration enhanced the removal efficiencies of both elements. The choice of electrode material and the pH level also significantly influenced the outcomes, with the maximum pH range favoring better removal rates for Sr.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Run**} & **Factor A** & **Factor B** & **Factor C** & **Factor D** & **Factor E** & **Response 1** & **Response 2** \\ \cline{2-7}  & **I (A)** & **A (m2)** & **t (min)** & **Material** & **pH** & **\%Sr** & **\%Se** \\ \hline
54 & 5 & 6 & 5 & Fe & No pH adjustment & 8.39 & 86.67 \\
3 & 15 & 8 & 5 & Fe & No pH adjustment & 11.21 & 96.31 \\
62 & 5 & 8 & 15 & Fe & No pH adjustment & 18.89 & 90.14 \\
65 & 5 & 6 & 5 & Al & No pH adjustment & 22.86 & 56.24 \\
24 & 5 & 6 & 5 & Al & Middle range & 57.30 & 64.33 \\
30 & 5 & 6 & 5 & Al & Maximum range & 71.85 & 67.62 \\
80 & 25 & 6 & 25 & Fe & Maximum range & 96.10 & 99.91 \\
84 & 25 & 6 & 25 & Al & Maximum range & 97.96 & 98.03 \\
48 & 25 & 4 & 15 & Fe & Middle range & 97.92 & 99.96 \\
13 & 25 & 6 & 25 & Fe & No pH adjustment & 84.67 & 100 \\
29 & 15 & 4 & 25 & Fe & No pH adjustment & 90.62 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental runs with the lowest and highest Se and Sr removal efficiencies.
## 4 Discussion
### Light Pathway  
After passing through the photosensitizer, light will diffuse into the surrounding tissues, which, relative to common clinical applications, are the gingiva, bone and/or dentin. As such, the optical properties of these, which are wavelength-dependent, should be thoroughly examined and addressed. Specifically, in the 350-800 nm wavelength range, water absorption is negligible for all structures. On the contrary, in gingival tissue, absorption bands of oxyhaemoglobin can be observed at 415 nm, 542 nm and 576 nm [45]. From 650 nm to 800 nm, scattering is the predominant interactive outcome. For the dentin and bone, the spectrum is mainly described by the scattering effects of hydroxyapatite and collagen [45; 46; 47; 48].  
The above optical effects (absorption and scattering) will determine the light penetration depth of these tissues. This term plays a pivotal role concerning the correct dose required during phototherapy [49]; the wavelengths of our interest are 445 nm, 635 nm, 660 nm and 808 nm. According to [PERSON] et al. [45], the penetration depth of human gingiva will range from 1 mm (at 450 nm), 4.6 mm (at 650 nm) and up to 6.9 mm (at 800 nm). As for dentin, these indices will be 8.9 mm (at 450 nm), 18.7 mm (at 650 nm) and 55 mm (at 800 nm). Regarding the bone, what is particularly notable is the knowledge that it represents similar characteristics in terms of its composition to dentin [46; 49]; however, it is slightly softer than dentin [49]. Therefore, it can be assumed that these indices will be quite similar.  
An undesired effect concerning the light pathway during photodynamic therapy is the presence of competitive absorbers of the photosensizers. Haemoglobin, which is the main absorber, and other proteins can decrease the effectiveness of the performed therapy [4; 34]. These highly absorbing molecules, especially within the visible region, may lead to local deviations from the calculated light distribution. Furthermore, \"unevenly distributed\" absorbers can give rise to local \"cold spots\" for the fluence rate [34].  
An additional fact that may cause complications regarding the irradiance applied is heterogeneously distributed light scattering, which is expected to occur at the boundaries of different tissues, leading to either \"hot\" or \"cold\" spots. Moreover, the effect of scattering can decrease light penetration in such tissues [50].
# AHC: Advanced Network-Hiding Access Control Framework
## 2 Related Work
### Port Knocking  
Port Knocking [11] is a method proposed in the early 21 st century to enhance network service security. It leverages a simple but powerful concept: access requests are authenticated through a series of specific port \"hits\" before a network connection is established [15]. The core advantage of this method is that it can achieve secure access control to services without revealing the existence of the service, thereby effectively hiding network services and reducing the potential attack surface. Port Knocking can be implemented in a variety of ways [16; 17]. The most common method is to use a specialized daemon (such as knocked) to run on the server and listen for knocking attempts on the network. Port Knocking should be viewed as part of a multi-layered defense strategy rather than a comprehensive security solution. When designing and implementing a knock-knock strategy, one should consider how it can be combined with other security measures (such as strong password policies, two-factor authentication, encrypted communications, etc.) to enhance overall security [18]. However, Port Knocking technology has shortcomings such as a single point of failure, complex operation, and poor compatibility.
###### Abstract
When one mine is being exploited, buildings within the mining rights area of another mine may also be affected. Therefore, the mining methods and backfill schemes used by both mines must consider the overall surface stability of the mining area. This paper primarily focuses on the overlapping mining areas of the Baojia, Yinzhushan, and Yinhai mining zones as research subjects to study the impact of backfilling on surface stability. Backfill mining, as a green and efficient mining method, is highly effective in improving ore recovery rates and controlling surface deformation [16; 17]. However, the cost investment and economic benefits of different backfill schemes vary significantly, which increases the complexity of selecting backfill mining plans [18; 19; 20]. [PERSON] et al. [21] studied the impact of different backfill schemes on roof stability through laboratory experiments, theoretical analysis, and numerical simulations. They found that the stability of the backfill roof was optimal when the upper and lower layered mining methods were arranged vertically or nearly vertically. [PERSON] et al. [22] optimized the backfill scheme for the Renjiazhuang mining area using numerical simulation software and discovered that the backfill ratio is a critical factor influencing backfill effectiveness. An appropriate backfill ratio can significantly reduce surface subsidence in goat areas. [PERSON] et al. [23] investigated the influence of different backfill strengths on surface subsidence patterns using the Donggou Coal Mine as a case study. They found that surface subsidence decreased as the depth of the ore body increased. [PERSON] et al. [24] used numerical simulation software to study the impact of backfilling on surface subsidence in the Xincheng Gold Mine. They concluded that backfilling effectively reduces surface subsidence and the range of ground pressure activities. They also emphasized the need for dynamic monitoring of surface subsidence through the establishment of monitoring points under complex geological conditions. In summary, this paper focuses on a silver-lead-zinc mine in Jiangxi as the research subject under complex engineering geological conditions. Based on laboratory rock mechanics parameter testing, the study uses FLAC3D finite difference numerical simulation software to simulate surface subsidence and horizontal displacement during the mining and backfilling processes, providing practical recommendations for mine backfilling. The goal of this study is to optimize the filling rate of mines in multi-mineral rights intersection areas by using numerical simulation methods and evaluate its impact on surface stability.
## 2 Study Area and Data
### Data  
#### 2.2.1 Land Cover Classification Sample  
The training set, validation set and test set used in this study were derived from the 1 km CNLUCC dataset of the Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences [52]. These data were resampled to a resolution of 0.05\({}^{\circ}\) by the method of majority resampling [53]. The dataset has been in existence every five years since 1980, and there is a lack of 1985 classification data. Due to the accuracy of the CNLUCC dataset and the mixed pixel problems caused by majority resampling, to ensure the reliability and credibility of the sample, we conducted a sample screening. When eight pixels around a pixel were the same, the cell in the center of the selection was considered to be a more reliable sample.  
In this study, the 7-year 0.05\({}^{\circ}\) land cover classification data of 1980, 1990, 1995, 2000, 2005, 2010 and 2015 were filtered through reliability, and the 7-year samples were integrated and randomly disordered. From each category, 10% of the data were randomly extracted as a test set. The remaining samples were sample-tuned by a stratified SMOTE algorithm to obtain a training set. The sample distribution is shown in Table 1.
## 3 Materials and Methods
### Detailed Subsystem Feature Extraction Process
#### 3.1.1 System State Variables and Feature Representation  
\[X(t)=\{x_{1}(t),x_{2}(t),\ldots,x_{n}(t)\} \tag{1}\]  
where \(x_{i}(t)\) represents the \(i\)-th state variable at time \(t\), including the environmental quality, economic benefit, and social welfare indices. A dedicated Squeeze-and-Excitation module is applied to each subsystem (denoted by \(SE_{\text{env}}\)) so that the channel-wise relevance can be learned in a task-specific fashion:  
* **Environmental subsystem feature extraction**: Considering the climate and environmental change (\(C_{e}(t)\)); ecosystem condition (\(E_{c}(t)\)); air, water, and soil pollution (\(P_{e}(t)\)); and resource consumption (\(R_{c}(t)\)): \[E(t)=SE_{\text{env}}(C_{e}(t),E_{c}(t),P_{e}(t),R_{c}(t))\] (2)
* **Economic subsystem feature extraction**: Considering the tourism revenue (\(R_{t}(t)\)), infrastructure investment (\(I_{f}(t)\)), employment opportunities (\(E_{m}(t)\)), and tourism market growth (\(M_{g}(t)\)): \[V(t)=SE_{\text{eco}}\Big{(}R_{t}(t),I_{f}(t),E_{m}(t),M_{g}(t)\Big{)}\] (3)* **Social subsystem feature extraction**: Considering the resident satisfaction (\(S_{r}(t)\)), tourist experience (\(T_{e}(t)\)), cultural heritage protection (\(C_{p}(t)\)), and social carrying capacity (\(S_{c}(t)\)): \[S(t)=SE_{\text{soc}}\big{(}S_{r}(t),T_{e}(t),C_{p}(t),S_{c}(t)\big{)}\] (4)