esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling the Response of Magnetorheological Fluid Dampers under Seismic Conditions
## 4 Results and Discussion
### Constant Pressure  
Figure 8 shows the static pressure measured across the annular orifice when a constant inlet pressure of 500 Pa and magnetic field strengths from 0 T to 1.5 T were applied. In the absence of a magnetic field, the resistance to shear flow experienced by the damper was due to the viscosity and the yield strength of the composite fluid. In this case, a linear pressure drop was observed between the inlet and the outlet of the computational domain, and the pressure drop across the annular orifice, between locations 1 and 2, was 53.82 Pa.  
In the cases where the annular orifice was magnetized, as the piston moved, a current was induced in the MR fluid. While in microscopic models, it has been shown that chain-like aggregates form and resist shear flow [2], in this macroscopic model, it was the resulting Lorentz force acting in opposition to the fluid flow that resulted in damping. This was demonstrated by the greater pressure differential  
Figure 8: Static pressure distribution along the length of the channel with various magnitudes of externally applied magnetic fields.  
between locations 3 and 4. The pressure drop increased to 177.55 Pa, 333.63 Pa, and 409.7 Pa for the \(B\) = 0.5 T, 1.0 T, and 1.5 T cases, respectively.  
When the magnetic field was increased from \(B\) = 0 T to \(B\) = 0.5 T, and again from \(B\) = 0.5 T to \(B\) = 1 T, there was a linear increase in the pressure differential across locations 3 and 4. However, as the magnetic field was increased to \(B\) = 1.5 T, the pressure difference asymptotically increased. This resulted from the MR fluid approaching its magnetic saturation point. Increases in magnetic field strength above this point will only serve to marginally increase the damping coefficient of the damper.  
In Figure 9, the unmagnetized case had a fluid velocity of 0.374 m/s at location 6 downstream from the inlet condition. In the magnetized cases, the fluid velocity was much lower downstream from the inlet with a deacceleration upon entering the annular orifice after location 3 and an acceleration upon exiting after location 4. This deceleration and acceleration of the fluid in the magnetized cases were noticeably absent from the unmagnetized case. The deceleration of the fluid was caused by the Lorentz force generated after location 3 and the re-acceleration was caused by the fluid entering a low-pressure zone after location 4. The downstream fluid velocities at location 6 were 0.284 m/s, 0.163 m/s, and 0.0946 m/s for the \(B\) = 0.5 T, 1.0 T, and 1.5 T cases, respectively. The lower velocity in the magnetized cases was characteristic of MR fluids and was caused by the increases in apparent viscosity and viscoelasticity of the fluid in the presence of magnetic fields [5].
## 3 Methodology
### Correlated Multi-Objective Energy-Efficient Routing Scheme for SDWSN-IoT
#### 3.2.3 The DOS-RL Routing Scheme
The Q-learning equation to update Q-values is designed as shown below in Equation (7): \[Q^{n}(s_{t},a_{t})\leftarrow(1-\alpha)\cdot Q^{n}(s_{t},a_{t})+\alpha\cdot[R^ {n}_{t+1}+\gamma\cdot\max_{\forall a\in A}\{Q^{n}(s_{t+1},a)\}].\] (7) To steer the exploration behavior of the learning agent by incorporating some heuristic knowledge on the problem domain, we introduce an extra reward \(F_{t+1}\) onto the reward received from the environment \(R_{t+1}\). The newly added shaping reward function \(F\) is included when updating the Q-learning rule as follows: \[Q^{n}(s_{t},a_{t})\leftarrow(1-\alpha)\cdot Q^{n}(s_{t},a_{t})+\alpha\cdot[R^ {n}_{t+1}+F_{t+1}+\gamma\cdot\max_{\forall a\in A}Q^{n}(s_{t+1},a)].\] (8) To avoid changes on the optimal policy, F is implemented as the difference of some potential value, \(F_{t+1}=\gamma\phi(t+1)-\phi(t)\) over the state space, where \(\phi\) is a potential function that provides some hints on states. In our study, we define \(\phi\) as the Normalized Euclidean Distance between the current state(s) and the goal state(G) expressed as: \[\phi=1-\frac{\text{distance}(S_{t},G)}{\max_{\forall x\in S}(\text{distance}(x,G))},\] (9) where \(\text{distance}(S_{t},G)\) is the Euclidean distance between the current state \(S_{t}\) and the goal state \(G\), and \(\max_{\forall x\in S}(\text{distance}(x,G))\) represents the maximum distance between any state \(x\) in the state space \(S\) and the goal state \(G\). The potential function guides the agent towards the goal state by giving bigger rewards to states that are closer to the goal and smaller rewards for states that are farther away. 5. _In DOS \(Q\)-routing_: To find the best action-value of the Q-function, the learning agents use an action selection mechanism to trade-off between the exploitation and exploration of available action space. To achieve this, our proposed scheme uses the \(\epsilon\)-greedy exploration and exploitation method, whereby \(\epsilon\in[0,1]\), allowing the agent to exploit with probability \(pr=\epsilon\) and explore with probability \(pr=1-\epsilon\). The agent action selection is determined by a randomly generated number \(x\in[0,1]\), of which if \(x<\epsilon\), the agent exploits it by taking an action that returns the most expected optimal value; otherwise, it explores it by selecting a random action based on the most confident objective \(o_{best}\) as observed from the current state by the learning agent as shown below:  
\[\text{actionSelection}= \begin{cases}\max_{a\in A}\{Q(s,a,o_{\text{best}})\},& \text{if }x<\epsilon\\ \text{randomAction}(Q(s,a,o_{\text{best}}),\dots,Q(s,a_{n},o_{\text{best}})),& \text{otherwise}\end{cases} . \tag{10}\]
# Adaptive Multi-Channel Clustering in IEEE 802.11s Wireless Mesh Networks +
Assuming at least two WLAN interfaces per mesh node, which allow parallel communication on separate channels within the backbone, the data collected in the cluster can be synchronized on an independent base channel. Compared to the centralized single-channel case, the path lengths of the transmissions are shortened while communication parallelism and resilience are increased. Consequently, this paper presents a distributed clustering approach that preserves full compatibility with the standard 802.11s specification without introducing any modifications to the WLAN protocol stack. The developed solution CHaChA (Clustering Heuristic and Channel Assignment) integrates the link and path information of the 802.11s link layer to derive topology information and use it directly for cluster formation and channel selection. CHaChA increases the scalability of common single-channel 802.11s networks by efficiently employing additional mesh interfaces to create independent sub-networks (clusters) on orthogonal channels. Thereby, unrestrained network-wide connectivity is ensured on a base channel via a dedicated interface. The created clusters, featuring smaller collision and broadcast domains, allow for the isolation of distributed applications, thereby increasing overall communication parallelism and relieving stress on the base channel. This article considerably extends a previous work [17] where we introduced the basic concepts of CHaChA and executed an early prototype evaluation. Using the status monitoring of mesh networks as application example, the general performance advantages resulting from clustering were demonstrated. The work at hand is largely based on further results of the corresponding author's PhD thesis [18], not yet published in any conference or journal. It first presents the detailed concepts for initial clustering and channel selection in IEEE 802.11s mesh networks. We substantially extend CHaChA by mechanisms for cluster adaptation to dynamic topology changes that are likely to occur during long-term network operation and maintenance. These mechanisms include automatic cluster joining of subsequently installed nodes, isolation and fault detection, and node roaming for cluster balancing. The concepts are implemented as software prototype and are practically validated in a 25-node real-world mesh testbed. The remainder of this article is organized as follows: Section 2 outlines the basic principles of the IEEE 802.11s WLAN mesh specification and its Linux implementation. In Section 3, we classify and discuss related work in the area of combined clustering and channel selection for WLAN mesh networks. Section 4 illustrates the extended concepts of our cross-layer approach CHaChA. In Section 5, we describe our practical prototype implementation, which is thoroughly evaluated in Section 6. Finally, we give a summary and conclusion in Section 7 and briefly state future research directions.
# Recent Trends and Progress in Corrosion Inhibitors and Electrochemical Evaluation
## 3 Types of Corrosion Inhibitors
### Inorganic Corrosion Inhibitors
#### 3.2.2 Cathodic Inhibitors  
The cathodic corrosion inhibitors stop the metal surfaces from undergoing the cathodic reaction during the corrosion process. These inhibitors have metal ions that precipitate as insoluble compounds at the cathodic sites. Here, a dense, adherent coating forms on the metal surface, limiting the diffusion of reducible species in these regions. Because of the oxygen diffusion and electron conductivity in these regions, these inhibitors have higher cathodic inhibitors because they react with the water's hydroxide ions to generate insoluble hydroxides. In order to safeguard the metal surfaces' cathodic sites, the generated insoluble hydroxides are deposited there. Oxides and salts of antimony, arsenic, and bismuth that are deposited on the cathodic area in acid solutions can be used as additional examples of cathodic inhibitors. These inhibitors are well known for reducing the emission of hydrogen ions [56].
# DTI Histogram and Texture Features as Early Predictors of Post-Radiotherapy Cognitive Decline
## 2 Materials and Methods
### Statistical Analysis  
All statistical analyses were performed using R software (version 4.0.3, R Foundation for Statistical Computing, Vienna, Austria). To identify significant changes in DTI upon radiotherapy, each DTI feature was initially compared between the two time points (baseline and immediately after radiotherapy) using paired Wilcoxon signed-rank tests. Non-parametric statistics (paired Wilcoxon signed-rank tests in this study) were chosen due to the small sample size (n = 19). Non-parametric tests do not require distributional assumptions, and are generally preferred for small samples [30; 31].  
Multiple linear regression models were reconstructed for each cognitive domain using those \(\Delta\) features that showed significant changes (\(p\) < 0.05) on the Wilcoxon signed-rank tests as predictors. Given the mathematical relationship among interdependent DTI indices, where MD can be derived from AD and RD as MD = (AD + 2 RD)/3, we implemented separate analytical approaches between MD and AD or RD to avoid issues of collinearity. Specifically, we developed two distinct model configurations; models containing \(\Delta\)MD features were analyzed separately from models containing \(\Delta\)AD and \(\Delta\)RD features. Regression diagnostics included examining residual plots to verify the assumptions of normality, homoscedasticity, and independence. Both unstandardized (\(\beta\)) and standardized (Std. \(\beta\)) coefficients, as well as the correlation coefficient (r), were calculated to facilitate comparisons among predictors and evaluate the strength of association with cognitive outcome. Relationships between significant features and cognitive outcomes were visualized using scatterplots with regression lines and 95% confidence intervals.  
To control for multiple comparisons, we applied the Benjamini-Hochberg false discovery rate (FDR) correction procedure. \(p\) values were ordered from the smallest to the largest, with critical values calculated as (i/m) \(\times\)\(\alpha\) (where i = rank, m = total comparisons, and \(\alpha\) = 0.05). Values below their corresponding critical threshold were considered significant after correction.
# Study on the Optimization of Pile Length of Micropiles in Soil Landslides
## 2 Methods
### Optimization of Micropiles Length Based on Numerical Simulation
#### 2.4.1 Mathematical Model for Pile Length Optimization
In which \(PL_{\text{min}},PL_{\text{max}}\) represent the upper and lower bounds of the design variable pile length, respectively. Calculations will be performed by the design method of minipiles. 3. The constraint function is determined as the stability safety coefficient of the slope after the micropile treatment, denoted as Fos. Different from the conventional optimization design method, using the safety coefficient as the index for evaluating the design scheme is not to obtain the maximum value of it as the design objective but to set a specification-allowed safety coefficient [Fs] index as the lower limit of the constraint objective function. That is, the reasonable solution must be satisfied for pile length optimization (The factor of safety is obtained by strength discounting method in numerical simulation. ):  
\[Fos\geq[F_{\text{s}}] \tag{13}\]
# SLA-DQTS: SLA Constrained Adaptive Online Task Scheduling Based on DDQN in Cloud Computing
## 3 Proposed Online Task Scheduling Model
### System Model  
Our task scheduling system considers a common cloud computing scenario, SaaS. It includes users who submit tasks, service providers, and IaaS providers [36]. Service providers build their resource pools from VM instances rented by IaaS providers, and provide users with services through the network. Service providers receive various types of tasks submitted by end-users through the network. An efficient and flexible task scheduler that can take into account multiple goals is necessary to balance the profit of the service provider against the user experience (e.g., response time and throughput).  
In cloud computing, tasks submitted by users are allocated to cloud nodes based on a set scheduling strategy. Hence, the choice of scheduling strategy is important to the task completion time and cost. Figure 2 shows the online dynamic task scheduling model of a typical cloud computing scenario. The user can submit a task at any time. After submission, the scheduling center allocates a task to the task waiting queue of the VM according to its demand and the scheduling strategy. We proposed SLA-DQTS for this process. It uses the virtual machine and task information obtained by the scheduler to determine the VM that executes the task. After the decision, the task is added to the task waiting queue of the selected VM. We stipulate that each VM maintains its task waiting queue, and processes tasks in a first-come-first-served manner and space share model according to the queue. The VM processes the tasks in a first-come-first-served manner according to the task queue. In this article, tasks whose number and resource requirements are unknown are dynamically submitted to the scheduler. We take scheduling time, cost, and SLA as optimization goals, and design a scheduling plan that can adapt to different loads.  
In our problem model, users can submit any number of tasks at the same time, which do not require consistent resource requirements, and can be of multiple types. Therefore, the submission time, number, and type are unpredictable. Application service providers can change the numbers of VMs according to the load, so we consider the adaptation of the model to a variable number of VMs.  
The scheduling center can obtain the status information of all VMs, including tasks in the queue. After a task arrives, the scheduling center reads its resource demand, calculation amount, and expected completion time, and matches it to a VM. At the end of each batch of tasks, the scheduling center records decisions and task execution-related results, such as VMs assigned to tasks, starting and ending execution times, and costs.
## 3 Materials and Methods
### In Vitro Release Studies of Active Substances from Hydrogels  
CBD and/or TER were/was loaded into the hydrogel by physical mixing using the following procedure: A total of 5.0% (m/m) of CBD and TER in distilled water/Tween 80 mixture (2.0% (w/v) was added to three hydrogel samples (H-TER, H-CBD-TER, H-CBD). The hydrogels were left sealed for 24 h. The mixtures were dried under vacuum at room temperature to obtain an active-substance-loaded hydrogel film. The in vitro release of CBD or TER from the hydrogels was performed in a buffer (pH 7.4 or 5.5) containing 2.0% (\(w/v\)) Tween 80 at 37 \({}^{\circ}\)C under stirring. Vials containing hydrogel films were filled with 5.0 mL of a buffer, sealed, and left at 37.0 \({}^{\circ}\)C for a specified time. The solutions were then removed for further testing and replaced by fresh buffer.  
The amount of (cannabidiol) CBD being released was measured by UV-Vis spectroscopy (detected at the wavelength of 220 and 280 nm) [80; 81].  
The amount of (\(\alpha\)-terpineol) TER being released was measured by UV-Vis spectroscopy (detected at the wavelength of 205 nm due to the \(\pi\)-\(\pi^{\star}\) transition of the double bond in TER) [82].  
The release data points were subjected to zero-order kinetics, first-order kinetics, and Korsmeyer-Peppas models. Calculations were made based on the formulas mentioned below [36].  
\[\text{Zero-order}:\ F=kt \tag{3}\]  
\[\text{First-order}:\ \log F=\log F_{0}-\frac{kt}{2.303} \tag{4}\]  
\[\text{Korsmeyer-Peppas model}:\ F=kt^{n}(F<0.6) \tag{5}\]  
where the following definitions hold:  
\(F\) is the fraction of the drug released from the matrix after time;  
\(F_{0}\) is the initial amount of the drug;  
\(k\) is a model constant, and \(n\) is the drug release exponent in the Korsmeyer-Peppas model.
## 7 Results and Discussion
### Discussion  
This sub-section proposes a comparison between the results obtained by the MALBP and related SA solving algorithm proposed in this manuscript and the most promising literature contributions that target similar assembly line-related problems. In particular, the literature is distinguished by a set of different original contributions that consider several features of MALBPs very relevant to adequately model a real industrial assembly system. However, their main limitation is represented by the problem dimension of the analyzed case studies, which is often insufficient in terms of task number. Indeed, only two of them face the challenges determined by big size problems, namely [PERSON] [13] and [PERSON] et al. [40]. They propose two case studies with more than 100 tasks, the largest available in the literature so far. On the contrary, this paper offers a novel practical problem distinguished by 665 tasks, which may be considered the biggest available in the literature for multi-manned assembly lines. This dataset offers unique opportunities to researchers who are willing to validate novel models and methods with very large and real case studies.  
Furthermore, the considered instances proposed by [PERSON] [13] and [PERSON] et al. [40] are distinguished only by few specific features of MALBPs; in particular, they lack to consider both the equipment that needed to execute certain tasks and the workpiece different mounting positions. The two features often distinguish real-world case studies and should not be neglected. The MALBP proposed in this manuscript fully considers these aspects and the related consequences, e.g., the need for a meta-heuristic algorithm as the presented SA to solve the complex optimization problem.  
The remarkable assembly workers average saturation rate of 82% obtained by [PERSON] [13] is even improved by the model and procedure developed in this manuscript and adopted to solve the presented case study to a value of 89% despite considering as constrains additional limitations, such as mounting position restrictions, equipment sharing, and worker cooperation within the same station.
# Deep Learning-Based Crack Detection on Cultural Heritage Surfaces
## 2 Materials and Methods
### Evaluation Indices  
A confusion matrix serves as a performance evaluation tool for object detection models, illustrating the relationship between predicted and actual observations. It offers valuable insights into model performance across various categories and helps identify potential error types [46; 47; 48]. Several key evaluation metrics can be derived from the confusion matrix:  
Accuracy measures the proportion of correct predictions made by the classification model:  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{2}\]  
Precision represents the proportion of correctly predicted positive samples among all instances predicted as positive:  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{3}\]  
Recall quantifies the proportion of actual positive samples that are correctly identified by the model:  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{4}\]  
F1-Score is the harmonic mean of precision and recall:  
\[\text{F1}-\text{Score}\ =\frac{2}{\frac{1}{\text{Precision}}+\frac{1}{\text{ Recall}}} \tag{5}\]  
where True Positive (TP) refers to correctly predicted positive samples, False Positive (FP) denotes negative samples incorrectly predicted as positive, True Negative (TN) represents correctly predicted negative samples, and False Negative (FN) indicates positive samples incorrectly predicted as negative.  
Intersection over Union (IoU), also known as the Jaccard Index, is a widely used evaluation metric for semantic segmentation models such as SegNet. IoU quantifies the accuracy of predicted bounding boxes by computing the ratio of the intersection to the union of the predicted and ground-truth bounding boxes. This metric provides a quantitative assessment of a model's ability to accurately localize objects within an image. By applying a predefined IoU threshold, predictions can be classified as successful or unsuccessful, facilitating model evaluation and comparison. The IoU value ranges from 0 to 1, with a commonly used threshold of 0.5, indicating that a prediction is considered correct only if the predicted bounding box overlaps with the ground-truth bounding box by at least 50% [48; 49; 50; 51]. The IoU formula is defined as follows:  
\[\text{IoU}=\frac{\text{Area of Intersection}}{\text{Area of Union}} \tag{6}\]