esa-sceva/satcom-chunk-collection huggingface dataset:
# The BCL Method for DSSC: Basis and Applications +
## 3 Mapping Electronic Density in the Ligand Excited State
### Theoretical Background  
In the literature, the introduction of numerical parameters called \"descriptors\" has been used for the study of chemical reactivity. These parameters can be global or local. The global descriptors consider the molecule as a whole, while the so-called local descriptors explain the role of specific sites in the chemical skeleton, helping in this way to understand the selectivity when used in chemical reactions.  
As the focus of the present study was to understand the effect of a quinonic group in the acceptor capacity of the ligand, and how an added charge would distribute along the corresponding ligand, the introduction of global and local descriptors was considered for the first time in DSSC dyes. Specifically, a global descriptor, the electrophilicity [10], a measure of the acceptor power of the neutral ligand, and the Fukui function [11], a local descriptor that shows how the spin density of the formed radical is distributed on the whole molecule, were used. A brief description of both parameters is given below.  
The capability of a ligand to accept one electron is measured by its electron affinity. However, if only partial electron transfer occurs, it is necessary to define a property considering the maximum electron flow that would lower the total binding energy. The total capacity for accepting charge of the molecule is identified with the electrophilicity index \(E^{+}\):  
\[E^{+}=\frac{\mu^{2}}{2\eta} \tag{1}\]  
By considering [PERSON] theorem for vertical excitations, the ionization potential can be estimated by the energy of the HOMO orbital, while electron affinity is related to the negative of the LUMO.  
Since the electrophilicity index is a global quantity giving information about the whole molecule, it is necessary for our ultimate purpose to specify how the accepted charge will be distributed along the different atoms of the molecule. This can be achieved by introducing a local descriptor, \(f_{\alpha}\), like the Fukui function, for a radical,  
\[f_{\alpha}=\rho_{\mathrm{sa}}=\rho_{\alpha}(\uparrow)-\rho_{\alpha}(\downarrow) \tag{2}\]  
where \(\rho_{\mathrm{sa}}\) is the spin density on atom \(\alpha\) obtained through a Mulliken's population analysis; it is subject to a normalization condition \(\sum\limits_{\alpha}f_{\alpha}=1\) by setting \(\rho_{\alpha}(\uparrow)=\rho_{\mathrm{SOMO}}\) and \(\rho_{\alpha}(\downarrow)=\rho_{HOMO}\); the \(\Delta\rho=\rho_{SOMO}-\rho_{HOMO}\) difference is also a measure of the Fukui function for a single excitation. It is noteworthy that, for the case of a radical with an unfilled shell, the Fukui function is defined as the response of the electronic density to a change on the spin polarization.  
All theoretical calculations were performed with the ADF package.
# An Innovative and Efficient Method for Reverse Design of Wheel-Rail Profiles
## 6 Conclusions  
This paper presents a reverse design method for wheel-rail profiles by taking the rolling radius difference as the target. This method consists of three parts, i.e., the profile designs of wheels, symmetric rails and non-symmetric rails. This method is deduced from the mapping relation between a rolling radius difference and a wheel-rail profile gradient. The designed wheel profile is a smooth curve created from a multi-sectional quadratic curve. Dichotomy is adopted during numerical calculation in order to improve the calculation efficiency.  
The three reverse design examples (the profile designs of wheels, symmetric rails and non-symmetric rails) prove the effectiveness and efficiency of the method. This method is also suitable for optimization design of wheel profiles. The contrast analysis shows that when the lateral displacement reaches 6 mm, the maximum contact stress will be distributed evenly and can be decreased by 184.4 MPa compared to that of existing profiles. The critical speed can be increased by 10.8% compared to that of existing profiles. The running stability can be improved by around 7%.  
This method is suitable for design of new-type wheel-rail profiles and optimization of existing profiles. This method, though having many advantages, still needs to be improved and optimized. The most important thing for this method is how to find the ideal rolling radius difference in order to design wheel-rail profiles satisfying the requirements and therefore more efforts are needed.  
The present work has been supported by the National Natural Science Foundation of China (51425804, 51608459, 51778542, U1734207 and 51378439).  
[PERSON] conceived the method; [PERSON] and [PERSON] built the mathematical model and analyzed the results; [PERSON] wrote the paper. [PERSON] supervised the entire work; [PERSON] and [PERSON] wrote part of code.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dynamic Response** & **CHN60/LMa** & **CHN60/Optimized Wheel** \\ \hline Lateral wheel-rail force of the first wheelset at outer rail side (kN) & 2.3 & 2.1 \\ Friction power of the first wheelset at outer rail side (Nm/s) & 258 & 243 \\ Derailment coefficient of the first wheelset at outer rail side & 0.028 & 0.025 \\ Lateral displacement of the first wheelset (mm) & 6.5 & 8.5 \\ Attack angle of the first wheelset (rad) & \(1.14\times 10^{-4}\) & \(1.22\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Maximum dynamic parameters for vehicle curve negotiation.  
Figure 24: Vehicle curve negotiation performance indexes. (**a**) Lateral displacement of the first wheelset; (**b**) Yaw angle of the first wheelset; (**c**) Wheel friction force of the first wheelset at the outer rail side; (**d**) Derailment coefficient of the first wheelset at the outer rail side; (**e**) Lateral wheel-rail force of the first wheelset at the outer rail side.
## 3 Network Anomaly Detection
### Header and Payload Analysis  
Many network attacks cannot be detected with string matching, as they do not present a signature nor a pattern in the payload. It means that, for every attack, different payloads can be used so the signature matching process fail. In such cases, to detect these attacks, another approach has to be taken. One of them is to analyze packet headers content that may present anomalies, providing evidence of either an attack or a probe is taking place.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Scenario** & **Description** & **Supporting References** \\ \hline Web application attack detection & Application layer attacks, such as Structured Query Language (SQL) injection, cross-site scripting and cross-site request forgery, are based on commands sent to the server by the attackers through forms and, therefore, regular expression may be used to detect characters that may identify these attacks. & [45; 46; 47] \\ \hline Worms detection & Many worms, such as SQL Slammer and Nimda, have well-known signatures and, therefore, using regular expression to detect their occurrence in the network is effective. & [6; 48; 49] \\ \hline Web inspection & Inspecting Hypertext Transfer Protocol (HTTP) packets for specific web contents or web pages accessed by specific users. & [15; 50; 51] \\ \hline File inspection & Search for specific contents in files transmitted in a File Transfer Protocol (FTP) session. & [52; 53; 54] \\ \hline DNS inspection & Inspecting Domain Name System (DNS) packets for specific accessed domains and possible malicious payload. & [54; 55; 56] \\ \hline E-mail inspection & Search for specific words, in the Multipurpose Internet Mail Extensions (MIME) standard, contained in e-mails, along with attachments. & [32; 53; 57] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Possible scenarios where DPI is applicable.  
The following subsections present some review on how packet fields can be used in a network attack. Although attacks in the physical layer do exist, they are not in the scope of this review because its detection is not achieved with the inspection of the packets' contents.
# Responses of Medicinal and Aromatic Plants to Engineered Nanoparticles +
## 4 Effect of Metal-Based NPs on Medicinal and Aromatic Plants
### NPs of Other Metals
#### 4.6.5 CeO\({}_{2}\) NPs  
_Coriandrum sativum_ L. plants germinated and cultivated for 30 days in soil amended with 125 mg/kg CeO\({}_{2}\) NPs were characterized with longer roots, pronouncedly increased CAT activity in shoots, and APX activity in roots, and changed the chemical environment of carbohydrates in shoots, suggesting impact of CeO\({}_{2}\) NPs on the nutritional properties of cilantro [278]. Green synthesized organometallic CeO\({}_{2}\) nanostructures with mean size 38 \(\pm\) 5 nm applied at a dose of 4 mg/L increased callus induction of _Berberis lycium_ Royle to 90%, whereas using a dose of 20 mg/L direct shoot regeneration was improved to 79% [279]. On the other hand, CeO\({}_{2}\) NPs (\(\approx\)20.28 nm) and bulk CeO\({}_{2}\) (\(\approx\)4.24 um) particles applied at doses 12.5-100 ppm for 4 h exhibited cytotoxic and genotoxic effects on the root meristem cells of _A. cepa_ reflected in decreased MI values, considerably increased chromosomal aberrations including chromosome laggards, disturbed anaphase-telophase, stickiness, bridges, and caused DNA damage [280].
# Automated Generation of Hybrid Metaheuristics Using Learning-to-Rank
## 3 Preliminaries
### Metaheuristic Algorithms
#### 3.2.3 Genetic Algorithm  
The Genetic Algorithm [28] is a stochastic optimization method based on evolutionary mechanisms. The GA iteratively evolves a population to gradually approach the optimal solution of a problem. Its core procedure includes the following steps:  
1. Population Initialization: Randomly construct initial population, with each \(X_{i}\) representing a possible solution to the problem. Individuals are often encoded using binary strings, real-valued vectors, or other formats.
2. Fitness Evaluation: Compute the fitness of every individual, where the fitness function \(F(X_{i})\) assesses the quality of each corresponding solution.
3. Selection: Select individuals for the next generation via their fitness. Roulette wheel and tournament selection are two common selection procedures. Individuals with high fitness are more likely to be selected.
4. Crossover Operation: With a crossover probability (\(croRate\)), combine the genes of two parent individuals to generate new offspring, simulating genetic recombination in biological reproduction. In this study, we adopt single-point crossover for real-valued encoding. One parent is the current individual \(X_{p}\), and another \(X_{q}\) is randomly selected from the population. For these two parent individuals \(X_{p}=(x_{p}^{1},x_{p}^{2},...,x_{p}^{D})\) and \(X_{q}=(x_{q}^{1},x_{q}^{2},...,x_{q}^{D})\), we randomly select a crossover point \(c\in[1,D-1]\) and generate offspring as \(X_{\text{offspring}}=(x_{p}^{1},...,x_{p}^{c},x_{q}^{c+1},...,x_{q}^{D})\), where the first part is taken from parent \(X_{p}\) and the second part from parent \(X_{q}\). A single-point crossover is performed to generate a new individual, which replaces the current one.
5. Mutation Operation: With a mutation probability (\(mutRate\)), randomly modify the genes of individuals to enhance population diversity and reduce the risk of premature convergence. In our implementation, mutation is performed by randomly selecting several dimensions of an individual and replacing their values with new random values drawn uniformly from the allowed range of each dimension. This method introduces diversity by directly altering certain genes within their feasible bounds.
6. Population Update: Replace the current population with newly generated individuals to form the next generation. Individuals that are not selected for crossover or mutation are retained unchanged (i.e., Replication Operation). This replication mechanism helps preserve high-quality individuals and accelerates convergence. However, excessive replication may reduce population diversity and increase the risk of premature convergence to local optima.
7. Termination Condition: The method ends when the maximum iterations are achieved or the fitness fulfills the predefined condition and the best solution is output.
# Behavior-Based Control Architecture for Legged-and-Climber Robots
## 4 Control Architecture  
A new control architecture that considers safety under unforeseen circumstances is needed to guide legged-and-climber robots. The proposed control architecture is characterized as a behavior-based control, hierarchical and centralized. As shown in Figure 4, the architecture is split in the Executive, the Planner and the User Interface. The Planner is divided into three main levels, which make use of complementary modules located in the Executive. The architecture includes a User interface, with which the user may control the behavior of the robot and observe the state of the robot and the legs. Each level of the Planner has a set of critical and given objectives:  
1. **Level 1**: Corresponds to the nominal and continuous behavior without checking the safety at any moment. This level is responsible for the body movement in the desired direction, through the performance of the robot legs.
2. **Level 2**: Corresponds to behaviors about movements under expected situations, having considered basic safety issues. It is responsible for determining if a movement may still be developed.
3. **Level 3**: Corresponds to the critical safety checks to ensure that the robot is not in a hazardous situation. This level is vitally important in robots like the one in question here, where the goal is to allow it to walk safely on the wall and ceiling.  
There is a hierarchical relationship between the different levels in that the higher level is able to disable the lower level. Dependencies occur from top to bottom; in other words, what happens at the upper level is unknown by lower levels. The agents of the same level are in a situation of equality, so they need a synchronization mechanism in case two behaviors are mutually exclusive. A token synchronization has been used to do this: the agent with the token is the one that can be executed. When it stops executing, it will drop the token and other behavior will be free to catch it.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Leg**} & \multicolumn{3}{c}{**Joint Angles (rad)**} & \multicolumn{3}{c}{**Feet Position (mm)**} \\ \cline{2-7}  & \(\mathbf{q_{0}}\) & \(\mathbf{q_{1}}\) & \(\mathbf{q_{2}}\) & \(\mathbf{x}\) & \(\mathbf{y}\) & \(\mathbf{z}\) \\ \hline
1 & \(-0.1\) & \(-0.13\) & \(0.33\) & \(28\) & \(6\)  
Each behavior has its own functionalities, inputs, outputs, and implementation features. Architecture modularity allows developers to add more, increasing control capabilities.
## 3 Results and Discussion
### Conductivity of GO/CCs  
#### 3.2.1 Effect of Water Loss on Electrical Conductivity  
Figure 4a shows the relationship between the average current intensity of the specimens and the water loss for different GO concentrations in GO/CCs. For WL \(\leq\) 6%, the current intensity decreased linearly with the increase in WL, and the data of resistivity variation with WL for five specimens, 0%, 0.025%, 0.05%, 0.075%, and 0.1%, were fitted linearly; the results are shown as a straight line in Figure 4a. The R\({}^{2}\) values were 0.98633, 0.98542, 0.98863, 0.99341, and 0.99151, respectively. When WL \(\geq\) 6%, the decrease in the current intensity became slower and showed a tendency to form an arc. Figure 4b shows the change in volume resistivity of GO/CCs with water loss. When GO/CCs are saturated with water, the volume resistivity is about 50 \(\Omega\cdot\)m. With the loss of water, the resistivity rises stepwise to about 70, 150, and 400 \(\Omega\cdot\)m. After a WL of more than 6%, the variability increases dramatically, with the resistivity rising to 1200 \(\Omega\cdot\)m. After a WL of more than 8%, the resistivity increases to 10,000 \(\Omega\cdot\)m. Therefore, the volume resistivity of GO/CCs changes the most when the WL exceeds 8%. Therefore, the WL greatly influences the electrical conductivity of GO/CCs, and the increase in electrical conductivity of specimens with different GO concentrations is slightly different. In contrast to the change in GO concentration, water loss has a greater effect on electrical conductivity. According to [PERSON] et al., the addition of GO (0.05%) reduces the electrical resistivity of the cement, and the conduction mechanism in the cement changes from ionic conduction to combined ionic and electronic conduction [6]. The microstructure of the cement specimen contains numerous microscopic pores, allowing for a higher retention of pore water. In a water-saturated state, ions dissolved in this pore water can conduct between these pores, leading to ionic conductivity. Moisture is a carrier of ionic conductivity, and with increasing WL, the moisture connected in the microscopic pores gradually decreases and ion migration becomes more difficult. Thus, the ionic conductivity in the material decreases linearly with decreasing moisture content [17]. Figure 4 illustrates that the electrical conductivity of GO/CCs at higher moisture content levels is mainly determined by the ionic conductivity in the pore water. Compared with the specimens with 0% GO concentration, the decreasing trend of current intensity of the specimens with 0.025-0.1% GO concentrations demonstrated similar results, thus indicating that ionic conductivity is a dominant factor in the electrical conductivity of GO/CCs.
# Analysis of Pre-Driver and Last-Stage Power--Ground-Induced Jitter at Different PVT Corners
## 3 Jitter Analysis under PVT Corners: Simulation Setups and Results  
### IO Buffer Circuit and PVT Corners  
The considered driver circuits with their respective PDNs for PGSIJ analysis and evaluation are shown in Figure 4. The driver is composed of four cascaded inverters in series and designed in 0.35 \(\mu\)m technology. The nominal supply voltage \(\big{(}V_{DDQ}/V_{DD}\big{)}_{Nom}=3.3\) V. The first three inverters combinedly represent the pre-driver stage, whereas the last stage is composed of the front-end inverter. The driver's output-induced jitter is investigated by estimating the pre-driver and last-stage jitter contributions independently. Then, the jitter induced by the shared and decoupled PGSV configurations are also studied, as shown in Figure 5. This work assumes that PGSV are the sum of sinusoidal voltage sources which are applied around a dc voltage. The IO buffer is simulated with a 500 Mbps input data rate at different corners. Each corner has a specific level of \(V_{DD}/V_{DDQ}\) and a defined temperature: slow-slow (SS), typical-typical (TT), and fast-fast (FF) corners as described in Table 1.  
In the decoupled case, the pre-driver is powered by \(v_{dd}(t)=V_{DD}^{corner}+v_{dd}^{noise}(t)\) and \(v_{ss}(t)=V_{ss}^{noise}(t)\). Moreover, the last stage is powered by \(v_{ddq}(t)=V_{DDQ}^{corner}+v_{ddq}^{noise}(t)\) and \(v_{ssq}(t)=v_{ssq}^{noise}(t)\). In the shared case, P/G terminals of the pre-driver and last stage are shorted and a single voltage noise is used at the P/G supplies. The peak-to-peak (p2p) AC noise applied to the PGSV is defined to be within 20% of \(\left(V_{ddq}/V_{dd}\right)_{Nom}\) (e.g., 660 \(mVpp\)).
## 4 Discussion
Furthermore, the study did not investigate the effects of different tool materials (e.g., carbide, high-speed steel) or geometries (e.g., helix angle, rake angle) on AE signal response. The omission of these variables restricts the robustness and scope of the results, particularly for applications requiring a high customization of tool profiles in wood machining. The experiment was conducted using only four combinations of spindle speed and feed rate. While these settings do cover basic high and low operational extremes, the narrow range of cutting conditions constrains the ability to fully characterize the relationship between AE parameters and process dynamics. Real-world milling operations may involve more aggressive or varied conditions, including deeper cuts, interrupted cuts, or variable depth profiles across complex geometries. Although the study adhered to the EN 14584 standard for AE sensor calibration, sensor placement and configuration remain potential sources of limitation. The AE sensors were attached directly to the MDF surface using adhesive, which may alter the propagation path of acoustic waves and introduce damping or signal distortion. The adhesive interface and variability in sensor contact pressure could affect signal fidelity, particularly for high-frequency components. External environmental factors such as ambient temperature, humidity, and background vibration can significantly influence AE signal detection and interpretation. The dynamic behavior of the CNC machine itself--vibrations from spindle bearings, servo motors, or machine bed resonance--could introduce extraneous AE signals or interfere with the true acoustic signature of the cutting process. These machine-generated noises may be indistinguishable from material-related AE unless careful baseline measurements and filtering are applied. Although numerous AE parameters can be extracted from raw signals (e.g., count rate, rise time, duration, energy, frequency centroid), the study focused primarily on RMS and MAX_sig (signal envelope). While MAX_sig is frequently used in the literature, this reductionist approach may oversimplify the rich information content of AE signals. The findings of this study have significant implications for the development of adaptive control systems in CNC wood milling. By incorporating AE signals into ACS, it is possible to dynamically adjust machining parameters, such as feed rate and spindle speed, in response to real-time changes in the machining environment. This adaptability can lead to improved machining efficiency, extended tool life, and enhanced product quality. Furthermore, the integration of AE-based monitoring systems can facilitate predictive maintenance strategies by identifying early signs of tool wear or process anomalies. This proactive approach can reduce unplanned downtime and maintenance costs, contributing to more sustainable and cost-effective manufacturing practices.