esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Selected MPC Algorithms
### Periodic Control (Pmpc)  
The periodic control strategy focuses on fixing the time it takes for two consecutive similar commutations [12]. Since the time between two consecutive commutations does not affect the resulting values of \(T_{up}\) or \(T_{down}\), the overall result of changing this time is a change in the average output. Thus, the objective is to control the \(T_{up}\) and \(T_{down}\) for the system shown in Figure 3a in order to achieve a modulation behavior the same as the pulse width modulation (PWM) pattern.  
\[g_{p}=\lambda_{p}((T_{up}-T_{r})^{2}+(T_{down}-T_{r})^{2}). \tag{18}\]  
In the case of periodic control, the frequency is compared with a reference time \(T_{r}\), which is used for both the up and down events to achieve the desired performance through applying the states in order to minimize the error. A mathematical CF is given in (18), and it states how the up and down events can be evaluated and included in the main CF. The CF can be rewritten as the following:  
\[g_{p}=\lambda_{p}((K_{up}-K_{r})^{2}T_{s}^{2}+(K_{down}-K_{r})^{2}T_{s}^{2}). \tag{19}\]  
The algorithm predicts the state of \(K_{up}\) and \(K_{down}\) for all possible states, which are eight states in the case of VSC. It is worth mentioning that \(K_{r}\) is the reference frequency that is given to the MPC controller to reshape the spectrum around the frequency of interest and its multiples. The factor \(\lambda_{p}\) is used to mitigate the deviation as long as the stability of the overall system is preserved.
## 2 Resource Allocation Model
To distribute the resources in a fair way, our scheme introduces a \(max-job\) fair policy, which initially considers the _maximum_ number of jobs based on the demands on the dominant resource. Let us define \(U=\{U_{1},\ldots,U_{n}\}\) as the set of \(n\) users that content for the dominant resource \(\widehat{r}\). The users are sorted by increasing order of their demands for the dominant resource into vector \(V_{\widehat{r}}\) and then \(U_{i\max}\), the maximum number of job assigned to each user is computed as follows:  
\[U_{i\max}=\frac{T_{\widehat{r}}}{V_{\widehat{r}}},\text{ for all users }i. \tag{5}\]  
Figure 1: Resource allocation model with 3 resources, \(i=1\) is the dominant resource. Then, we find the sum of all the jobs computed in the first step, \(N=\sum\limits_{i=1}^{n}\frac{T_{i}}{V_{i_{\theta}}}\), and we find the fair resource allocation factor \(f\) for each of these jobs as follows:  
\[f=\frac{T_{f}}{N}. \tag{6}\]  
Finally, we use the fair resource allocation factor to compute the resources allocated fairly to each user \(i\), \(F_{i}\) as follows:  
\[F_{i}=f\times U_{(n+1-i)\max}. \tag{7}\]  
Let us use an example to illustrate the process described. Assume that 4 users content for their dominant resource, CPU, and the cloud system has 18 CPUs available and their demands are: 4 CPUs for \(U_{1}\), 9 CPUs for \(U_{2}\), 6 CPUs for \(U_{3}\) and 5 CPUs for \(U_{4}\). By sorting in ascending order, we have: \(V=[U_{1}=4,U_{4}=5,U_{3}=6,U_{2}=9]\). From (4), we obtain that \(U_{1\max}=\frac{18}{4}=4.5,\ U_{2\max}=\frac{18}{5}=3.6,U_{1\max}=\frac{18}{6} =3\), and \(U_{4\max}=\frac{18}{9}=2\). The sum of these jobs is \(N=4.5+3.6+3+2=13.1\) jobs. Then \(f=\frac{18}{13.1}=1.374\). Thus, from (6), we have:  
\[F_{1} = 1.374\times U_{4\max}=1.374\times 2=2.748,\] \[F_{2} = 1.374\times U_{3\max}=1.374\times 3=4.122,\] \[F_{3} = 1.374\times U_{2\max}=1.374\times 3.6=4.97,\text{ and}\] \[F_{4} = 1.374\times U_{1\max}=1.374\times 2=6.18.\]  
Since the values \([F_{1},F_{2},F_{3},F_{4}]\) correspond to users \([U_{1},U_{4},U_{3},U_{2}]\) (recall that the users have been sorted based on their requests, from Step 1). Thus, \(U_{1}\) will get 3 CPUs, \(U_{2}\) will get 6 CPUs, \(U_{3}\) will get 5 CPUs and \(U_{4}\) will get 4 CPUs. To conclude, our resource allocation policy first examines the job arrival rate that the system can afford by solving a system of equations like the one in Equation (4) and then it applies Equations (5)-(7) to fairly distribute the resources. In cases when the requesting jobs arrival rate is such that the system cannot handle, then the system reduces the resources assigned to each job by Equations (5)-(7), until the rates are regulated to affordable values.
# Simultaneous Sensing of Touch and Pressure by Using Highly Elastic e-Fabrics
## 2 Materials and Methods
### Fabrications of the Sensor Layers  
The manufacturing process of the e-fabric skin-based soft capacitive pressure sensor is shown in Figure 1. The process consists of three main steps: (1) Preparation of the SWCNT ink as a conductive buffer layer and the stretchable silver paste as a conductive layer (Figure 1a), (2) using screen-printing technologies in order to fabricate the capacitive sensor layer based on the PET/SP fabric and the capacitive pressure sensor layer based on the PET/SP spacer fabric, and (3) integrating two fabricated layers with the robust electrical connections in order to complete the e-fabric-skin.  
In the first step, the SWCNT ink 0.1 wt.% (the weight percent of single-walled carbon nanotubes in water) was stirred and ultra-sonicated (2 h, 19.990 Hz) in a stirring machine (60-80 \({}^{\circ}\)C, 1000 rpm, 24 h), and the silver paste was stored in a fridge (4 \({}^{\circ}\)C) with the lids tightly sealed. The used fabric was a polyethylene terephthalate/spandex fabric with a 76/24 (76% polyethylene terephthalate, 24% polyurethane) ratio from SNt Co. Ltd., Seoul. The stretchable paste would be gently stirred before use to avoid incorporation of air bubbles. The PET/SP spacer fabrics have 3D manufactured textile structures in which two outer textile layers (PET/SP fabric layers) are connected by a layer of pile fibers (PET). The design of the construction has the properties of good breathability, crush resistance, pressure stability, and 3D appearance.  
In the second step, the SWCNT ink and silver pastes were applied on the PET/SP fabric (Figure 1b-g) and the PET/SP spacer fabric (Figure 1h-m) by screen-printing technologies at a speed of \(\sim\)30 mm/s with the printing tools, as shown in Figure S1 (Supplementary Materials). After printing the SWCNT layer, excess water in the fabrics was removed by the two-way drying machine. The drying conditions were set-up at the time of drying of 1-3 min, range of temperatures of 180-200 \({}^{\circ}\)C, and speed of the circulation fan of 1500 rpm. After printing the silver paste layer, the stretchable silver paste was dried, before curing, at 60-80 \({}^{\circ}\)C for 15 min to remove solvents in an IR or convection oven. The curing parameter used was 120-200 \({}^{\circ}\)C for 10 min.  
Finally, using a thin adhesive layer and a heat press in order to integrate two sensor layers together (Figure 1n), and to laminate the electric lines onto the surface, robust electrical connections were attained. Figure 1o,p show an actual image of the two sensor layers, as well as the application of the e-fabric-skin on the hand.
## 3 Mathematical and Problem Formulations
### Proposed Approach based on the Coordinated Control of PSS and UPFC  
The control structure presented in Figure 6 is used to manage the voltage injected by the UPFC, with input parameters including changes in active and reactive power and voltage. The output of the controller is \(V_{d}\) and \(V_{q}\). The UPFC's techno-economical structure includes various regulator gain blocks for both shunt and series converters. Figure 7 shows the PSS, which adds the output (\(Vs\)) to the excitation system reference voltage (\(V_{ref}\)). Phasor measurements are continuously taken and can be transmitted in real time to control centers via optical fiber communication. However, delays in transmission can cause performance degradation, so delays are considered in controller design. A 15 ms delay is considered for the PSS time constant, and a 50 ms delay is considered for the damping controller with a 15 ms sensor delay [15].  
Figure 6: Controller structure diagram for the series component of the UPFC.
## 7 Experimental Investigation
### Results
Figure 14c shows the harmonic content of the output current with a THD of 4.46%. V\({}_{\text{AB}}\), as presented in Figure 14a,b, displays that nine-level waveforms have been produced. Similarly, V\({}_{\text{AN}}\) and I\({}_{\text{A}}\) also reflect satisfactory performances. In the second condition, the experiment without the tuning scheme was conducted. The conventional controller is designed with constant K\({}_{\text{pU}}\) and K\({}_{\text{PL}}\) as well as fixed K\({}_{\text{LU}}\) and K\({}_{\text{IL}}\) which are, respectively, 2 and 0.001, regardless of the change in V\({}_{\text{in}}\). For further examination of the controller's performance during the step response and steady-state condition, Figure 15 is provided as an illustration. The results show that all capacitor voltages are able to be kept around their respective references. The rise time, fall time, peak time and settling time are recorded as 9, 13, 12 and 200 ms, respectively. The percent overshoot is calculated to be around 20%. THD of the output current is 4.92%, as displayed in Figure 16c. It can be noticed that these values are worse than those when the online tuning mechanism is applied. Figure 13: Experimental results of the capacitor voltages (V\({}_{1}\), V\({}_{2}\), V\({}_{3}\) and V\({}_{4}\)) with online tuning for a step response with V\({}_{\text{in}}\) changes between 60 V and 200 V. **(a)** Step-up response **(b)** Steady-state condition **(c)** Step-down response. Figure 14: Experimental results of inverter’s waveforms (output current I\({}_{\text{A}}\) phase voltage V\({}_{\text{AN}}\), line-to-line voltage V\({}_{\text{AB}}\) and input voltage V\({}_{\text{in}}\)) with online tuning for a step response with V\({}_{\text{in}}\) changes between 60 V and 200 V. (**a**) Step-up response, (**b**) step-down response, (**c**) output current harmonics. Figure 15: Experimental results of the capacitor voltages (V\({}_{1}\), V\({}_{2}\), V\({}_{3}\) and V\({}_{4}\)) without online tuning for a step response with V\({}_{\text{in}}\) changes between 60 V and 200 V. (**a**) Step-up response, (**b**) steady-state condition, (**c**) step-down response.
# Yeast-Based Magnetic Biocomposite for Efficient Sorption of Organic Pollutants
## 4 Conclusions  
A biocomposite with magnetic properties was successfully synthesized by encapsulating biochar containing Fe\({}_{3}\)O\({}_{4}\) particles and _Yarrowia lipolytica_ yeast in calcium alginate beads. The obtained biocomposite can be used as an efficient, cheap, eco-friendly biological adsorbent for purifying the aqueous phase contaminated with organic dyes. Moreover, YL-M-L-AC had superparamagnetic properties and the magnetization in a 1.5 T field is 10.56 A-m\({}^{2}\)/kg, which makes it easy to separate it from the solution after adsorption. Experimental studies of MB adsorption show that the adsorption isotherm and kinetics data are well-fitted to the Freundlich isotherm model (R\({}^{2}\) = 0.996 and ARE = 3.41%) and the pseudo-second-order kinetics model (R\({}^{2}\) = 0.9989 and ARE = 1.14%), respectively). The sorption capacity of the biocomposite against MB increased with the contact time (q\({}_{\rm t}\) = 10.13-32.10 mg/g) and the initial dye concentration (C\({}_{0}\) = 50-250 mg/dm\({}^{3}\)). Studying the thermodynamic studies, it is found that the process is exothermic and the increase in temperature resulted in a decrease in sorption capacity, and that the process is spontaneous. The resulting magnetic biocomposite could be reused five times, exhibiting a desorption rate ranging from approximately 90% to approximately 84%. For this reason, YL-M-L-AC can be considered an environmentally friendly and economically viable bioadsorbent for removing methylene blue from the liquid phase in purification processes. In light of these advantages, the possibility of using this type of adsorbent in the removal of organic pollutants from industrial wastewater should be tested in the future. Only such research would fully demonstrate its potential for application.  
Conceptualization, P.S.; Methodology, J.C.; Validation, P.S.; Investigation, P.S. and J.C.; Writing--original draft, P.S.; Writing--review & editing, J.C.; Supervision, P.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Not applicable. Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request. The authors declare no conflict of interest.
# PC12 and THP-1 Cell Lines as Neuronal and Microglia Model in Neurobiological Research
## 3 Results and Discussion
### THP-1 Cells and Amyloid \(\beta\) (25-35)  
Incubation of THP-1 cells with amyloid \(\beta\) (25-35) caused a significant decrease in mitochondrial metabolic activity (measured in the MTT assay) in all tested cases except for 0.001 \(\upmu\)M A\(\beta_{25-35}\) concentration in differentiated cells (up to 16.4% or 15.2% at 5 \(\upmu\)M A\(\beta_{25-35}\), respectively). However, the differences depending on cell differentiation were small and statistically insignificant (Figure 7A).  
The ROS level in the THP-1 cultures incubated with A\(\beta_{25-35}\) was, like the metabolic activity, dependent on the A\(\beta_{25-35}\) concentration (Figure 7B). At the concentrations of 1 and 5 \(\upmu\)M, the level of free radicals was significantly higher than in control, and the differences between undifferentiated and differentiated cells were also statistically significant--the level of ROS in differentiated cells was 7.3-10.1% higher.  
The FHA assay showed a significant increase in the amount of DNA damage in THP-1 cells differentiated to microglia at all A\(\beta_{25-35}\) concentrations, while in undifferentiated  
Figure 6: Morphology of THP-1 cells used in the study: (**A**) cells incubated in the primary medium; (**B**) cells incubated in differentiation medium for 5 days.  
\begin{table}
\begin{tabular}{c c c l} \hline \hline  & **Undifferentiated THP-1 Cells** & & **Differentiated THP-1 Cells** \\ \hline \(\bullet\) & Monocytic cells & \(\bullet\) & Macrophages (microglia) \\ \(\bullet\) & Growing in suspension & \(\bullet\) & Cells adhere to the surface of the wells \\ \(\bullet\) & Higher expression of CD14 & \(\bullet\) & Decreased expression of CD14 \\  & & \(\bullet\) & Can phagocytes opsonized particles \\  & & \(\bullet\) & Produce cytokines IL-1\(\beta\) and TNF-\(\alpha\) upon \\  & & TLR2 or TLR4 stimulation \\ \hline \hline \end{tabular}
\end{table}
Table 3: Characteristic properties of undifferentiated and differentiated THP-1 cells [35,37,38,39].  
cells, at 0.1-5 \(\upmu\)M A\(\beta_{25\text{-}35}\) (Figures 7C and 8). Except for the highest concentration of 5 \(\upmu\)M, the level of DSBs in differentiated cells was statistically significantly higher (by 14.3-38.9%), especially at 0.001 \(\upmu\)M A\(\beta_{25\text{-}35}\) to which undifferentiated THP-1 cells were completely insensitive.
# Automated Pixel-Level Deep Crack Segmentation on Historical Surfaces Using U-Net Models
## 1 Introduction
* Developing an automated pixel-level detection approach through assessing various U-Net deep learning architectures for handling the problem of deep crack segmentation on historical surfaces. * Investigating two loss functions, namely Dice and cross-entropy (CE), in addition to a third hybrid loss function, for training and enhancing the performance of the proposed approach. * Constructing an expert-annotated primary dataset of crack images on historical surfaces, collected over two years from a historical location in Historic Cairo, Egypt. * Applying a contrast stretching method for handling the impacts of different environmental conditions on images of historical surfaces. * Building an extra semi-supervised pixel-level map generation module for annotating historical surface images, to avoid the cost of pixel-by-pixel manual annotation. The remainder of this paper is organized as follows. Section 2 introduces the state-of-the-art literature related to the pixel-level crack detection problem. Section 3 describes the different phases of the proposed pixel-level crack detection approach, namely the data acquisition, data preparation, pixel-level map generation, and pixel-level crack segmentation phases. Section 4 presents details of the crack image datasets and discusses the obtained experimental results. Section 5 discusses the research conclusions and addresses recommendations for future research.
# A Novel Method for Building Displacement Based on Multipopulation Genetic Algorithm
## 3 Introducing BDMPGA
### Genetic Operations  
There are several operations in the BDMPGA, such as selection, crossover, and mutation operation. The selection operation selects fine individuals from the parent population according the selective probability. These individuals have a chance to produce the child individuals. The probability of an individual being selected is related to the fitness value. The higher the fitness value of an individual is, the greater the probability of being selected is. The selection methods include roulette wheel selection, tournament, and other methods. This study adopts the roulette wheel selection method, namely, one individual is selected by its fitness ratio. The probability of an individual \(X_{i}\) being selected is calculated by the following equation:  
\[P(X_{i})=\frac{f(X_{i})}{\sum_{i=1}^{N}f(X_{i})} \tag{1}\]  
where \(f(X_{i})\) is the fitness value of individual \(X_{i}\), \(P(X_{i})\) is the probability of individual \(X_{i}\) being selected, and \(N\) is the number of individuals in one population.  
The crossover operation is the most important genetic operation in the BDMPGA, which defines the procedure of generating new individuals by exchanging information in parent individuals. The child individuals inherited the excellent characteristics of the parent generation. This operation determines the global search ability of the algorithm. As individuals are coded in real numbers, the crossover operation adopts the arithmetical crossover strategy [39] in the BDMPGA. The arithmetical crossover is calculated according to the following equation:  
\[\left\{\begin{array}{l}x_{kj}=x_{lj}*(1-\alpha)+x_{kj}*\alpha\\ x_{lj}=x_{kj}*(1-\alpha)+x_{lj}*\alpha\end{array}\right. \tag{2}\]  
where \(x_{kj}\) represents the \(j\)th gene in individual \(X_{k}\), \(x_{lj}\) represents the \(j\)th position on individual \(X_{l}\), and \(\alpha\) is the random number in the interval [0, 1].  
The mutation operation defines the procedure by which offspring individuals are altered randomly with a small probability after the crossover operation. The mutation operation determines the local search ability of the BDMPGA, which guarantees the population diversity and facilitates convergence at the global optimum and avoids premature convergence. The nonuniform mutation is adopted in the BDMPGA, which searches the entire space at the beginning and only local search searches at the end and can help to find better points quickly. The nonuniform mutation is calculated by the following equation:  
\[x_{ij}=\left\{\begin{array}{l}x_{ij}+\left(x_{ij}-x_{max}\right)*f(gen),\;r \geq 0.5\\ x_{ij}+\left(x_{min}-x_{ij}\right)*f(gen),\;r<0.5\end{array}\right. \tag{3}\]  
where \(x_{max}\) is the upper bound of gene \(x_{ij}\), \(x_{min}\) is the lower bound of gene \(x_{ij}\), \(f(gen)=r_{2}*(1-gen/MaxGen)^{2}\), \(r_{2}\) is a random number, \(gen\) is the current iteration number, \(MaxGen\) is the maximum iteration number, and \(r\) is the random number in the interval [0, 1].
## 3 Influence Metrics
### Quality of Service Metrics Analysis
#### 4.3.5 Path Length  
As illustrated in Figure 11, the average number of hops taken by packets is influenced by the node selection procedure. While this effect may not be readily apparent from the graph, our algorithms have exhibited a slight decrease in the number of hops, surpassing S&W by more than 3%, and significantly outperforming the Epidemic or Prophet v1 and v2 routing protocols. This reduction in hops is accomplished through our meticulous node selection process and the principles we employ to determine packet forwarding. Consequently, the packets take fewer diversions along their routing path.
## 3 Initial Mapping to Line
### Hilbert Curve Mapping  
For certain graphs, geographic/geometric information is available for each vertex. It is fairly easy, then, to construct an ordering using a space-filling curve--prime examples are Peano, Morton and Hilbert curves but we focus on the latter in this work. These methods are known to capture proximity well: nodes that are close in the space are expected to be placed nearby on the line.  
There has been extensive study of the applicability of these methods in solving large-scale optimization problems in parallel; see, for example, References [30; 31].  
Not only can this algorithm be used on its own without any cut guarantees but it can also be employed to break a big instance down into smaller ones that we can afford to run more intensive computations on. Both applications were known previously.  
The previous works do not offer any theoretical guarantees on the quality of the cut generated from Hilbert curves. However, certain assumptions on the distributions of edge lengths and node positions let us bound the resulting cut ratio and show that it is significantly less than the result of random ordering [32; 33; 34]. This is observed in our experiments, too.
## 5 Performance Evaluation
### Experimental Results
#### 5.3.3 The Influence of the Request Arrival Rate  
Finally, we investigated the performance of three algorithms when the time interval between adjacent inference tasks changed. We study the performance of our method under high task loads by increasing the arrival rate of the request. As the time interval increases, the number of idle kernels for the next arriving task also increases, resulting in a decrease in the likelihood and duration of task queuing, thereby reducing the average latency of all tasks. This survey is conducted on 10 edge servers, each equipped with a specific number of cores. As shown in Figure 7, the experimental results reveal the performance indicator trends of three algorithms: for the average delay, as the time interval increases, the greedy algorithm always searches for the optimal solution, the possibility and duration of task queuing decrease, and the average delay continues to decrease. And the band distribution graph in Figure 7 demonstrates the consistency of our algorithm's performance over other baseline algorithms. For average energy consumption, increasing the time interval can only change the queue delay, so the impact on average energy consumption is minimal. Overall, compared to the two baseline algorithms, our algorithm consistently maintains excellent performance and consistent results. In Figure 7, we compare the performance of our method with that of fixed partition granularity. It is evident that our algorithm has significant advantages over fixed partitioning in terms of latency, energy consumption, and  
Figure 6: The influence of the data size.  
overall metrics, highlighting the advantage of our dynamic partitioning in selecting the optimal partition granularity for each task. Table 3 presents specific experimental data on latency and energy consumption as the request arrival rate increases.