esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Conclusions  
The quality of structural topologies depends on the density of mesh discretization. On the other hand, the use of fine mesh for a whole structure in order to obtain a topology with smooth boundaries implies a high computational cost. The remedy for that can be the implementation of the adaptive mesh refinement. Refining mesh in an adaptive way allows for reduction of the number of elements without losing the accuracy of results. Choosing this approach in this paper, a Cellular Automata approach with the refined mesh adaptation has been proposed in order to generate structural topologies. The fine grid was implemented mainly in the so-called grey regions, where it has to be decided whether a cell becomes solid or void. The results of topology generation for illustrative examples confirmed the accuracy of the approach presented in this paper.  
The benefits of applying the Cellular Automata algorithm with the refined mesh adaptation can be summarized as follows:  
* high-resolution optimal topologies can be generated with a relatively low computational cost
* the algorithm presented is versatile, which allows for its easy combination with any solver built on a finite element method
* using Cellular Automata does not require additional density filtering
* boundaries of the resulting structures are smooth
* in the case of a fine mesh, thin and slender parts of structural topology do not appear
* the so-called grey areas are eliminated without using any additional filtering
* the mesh dependency phenomenon has not been observed
* topologies generated are free of the checkerboard effect.  
Although the discussion in this paper has been restricted to generation of plane topologies, it has to be stressed that the proposed concept can be extended to spatial problems. Both the form of the Cellular Automata local rules and the possibility of refinement of the meshing procedure offered by the ANSYS solver allow adjustment to 3D tasks.  
It is also worth noting that the proposed approach can also be extended to the implementation of the adaptive mesh refinement based on solution errors, e.g., equivalent stress error or deformation error, which allows for improvement in the numerical accuracy of the structural analysis.  
Figure 38: Selected middle topologies and the final one for the structure under self-weight. For the last iteration the grid has not been shown to improve the clarity of the picture.  
**Author Contributions:** As far as conceptualization, methodology, software, investigation and writing the paper are concerned, [PERSON] and [PERSON] contributed equally to this work. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by NATIONAL SCIENCE CENTRE, POLAND; grant no 2018/02/X/ST8/01203.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A High-Robust Sensor Activity Control Algorithm for Wireless Sensor Networks
### Contributions  
The three contributions of this research are as follows:  
1. This paper proposes a sensor control algorithm with fast convergence. According to the application requirements, an algorithm that can provide fast convergence can avoid the waste of sensor power resources. SACA also uses random access technology, such that the sleep sensors do not need to receive the feedback packet from the base station and can truly achieve the purpose of dormancy and power saving. In the Gur Game algorithm, all sensors must receive feedback packets from the base station to switch their states.
2. The SACA algorithm is highly scalable and can be used in large-scale network environments. The sensor network is often composed of hundreds or thousands of sensor nodes. In the Gur Game, when the ratio of the target value to the total number of sensors is too large or too small, the number of active sensors cannot reach the target value.
3. SACA has a strong self-organization ability algorithm and can be applied in severe network environments, such as a network environment with a high death ratio (high damage ratio). Because sensors are usually deployed in dangerous areas, it is not possible to repair or add new sensors. Therefore, an algorithm with strong self-organization capabilities can enable sensors to operate in a severe network environment in real time, self-adjust, or maintain a specific target value.  
The organization of the paper is the following. Section 2 describes the Gur Game and ACK methods, and Gur Game-based control algorithms for solving different problems. Section 3 describes preliminary assumptions of SACA. Section 4 describes the operation details of SACA, adjustment procedure for the weight value of sensors, and transform procedure of the state of sensors. Section 5 is the experimental analysis. We compare the differences among SACA, ACK, and Gur Game algorithms based on the algorithm's convergence time, success rate, and self-organization ability of sensor nodes. In the last section, we present the conclusion and future work.
## 4 Experimental Setup
### Experimental Settings  
In the case of EFT-ILS, we use the best configuration in [11] (see Table 3). The GRASP-CPA uses a few extra parameters. One of them is the \(\alpha\) value of the GRASP algorithm, for which we carried out extensive experimentation with \(\alpha=\{0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0\}\), finding that the best values for these instances were 1.0 and 0.9 without a clear dominance. Thus, we decide to use 1.0 and 0.9 as \(\alpha\) values randomly with an equal probability before the \(GRASP\)_Construction_ (see line 5 of Algorithm 5). We use the same Local Search, perturbation process, and perturbation probability \(p_{m}\) in both algorithms for a fair comparison. Finally, to ensure the CPA communication process, we set the probability of recombination \(p_{r}\) as 100%. In both algorithms, the first phase has two stopping criteria, 50 iterations or a maximum of 5 min, while the second phase uses a stopping criterion of 100,000 objective function evaluations. Every instance from Section 4.1 is run 100 independent times. The complete experimental parameter settings are shown in Table 3. We made available the algorithm implementations at [39; 40].
## 2 System Identification Methods
### Performance Evaluation of Models  
To evaluate the output performance of systems identifications applied for the structure behavior, three criteria were used in this study:  
First: The correlation coefficient (\(R^{2}\)) provides information for linear dependence between observations and corresponding predictions [25]. \(R^{2}\) is defined in Equation (9) and ranged between 0 and 1; the higher value of \(R\) is the better quality of the model.  
\[R^{2}=\frac{\sum_{i=1}^{n}\left(y-\ddot{y}\right)\left(y_{s}-\ddot{y}_{s} \right)}{\sqrt{\sum_{i=1}^{n}\left(y-\ddot{y}\right)^{2}\sum_{i=1}^{n}\left(y_ {s}-\ddot{y}_{s}\right)^{2}}} \tag{9}\]  
Figure 1: The two inputs adaptive neuro-fuzzy inference system (ANFIS) model architecture (A and B are a membership functions (MFs)).  
where \(y\) and \(y_{s}\) are observed and predicted performance of structures at time \(t=1,\ldots,n\); \(n\) is the number of observations; and \(\ddot{y}\) and \(\ddot{y}_{s}\) are the mean values of the observations and predictions, respectively.  
Second: The mean absolute error (MAE) which describes the average magnitude of the errors. MAE is defined in Equation (10) where smaller value of MAE means better quality of the model.  
\[\text{MAE}=\frac{\sum_{i=1}^{n}\text{abs}\left(y-y_{s}\right)}{n} \tag{10}\]  
Third: The root mean square error (RMSE) which considered the average magnitude of the errors by giving more weight on large errors. The RMSE defined as in Equation (11) where smaller value of RMSE means better quality of the model.  
\[\text{RMSE}=\sqrt{\frac{\sum_{i=1}^{n}\left(y-y_{s}\right)}{n}} \tag{11}\]
## 3 Results and Discussions
### Thermo- and pH-Responsive Behavior in Aqueous Solution
#### 3.2.2 pH-Responsive Behavior of SiO\({}_{2}\)-\(g\)-PAA  
The hydrodynamic diameter and zeta potential of the SiO\({}_{2}\)-\(g\)-PAA nanoparticles were investigated at the pH range from 8 to 12 (Figure 6). The result shows that the hydrodynamic diameter of the SiO\({}_{2}\)-\(g\)-PAA nanoparticle increases with pH, and its zeta potential decreases with increasing pH, indicating that the SiO\({}_{2}\)-\(g\)-PAA nanoparticles are pH-responsive. PAA polymer chains possess -COOH groups and the pKa of the PAA is 4.5 [37], meaning that at pH above 4.5, the -COOH groups in the polymer chains are dissociated, and PAA becomes more negatively charged with increasing pH. Further, in our previous study, the isoelectric point of the silica surface was found around pH = 2, above which the silica surface is negatively charged [38]. As expected, at the investigated pH range, the zeta potential of SiO\({}_{2}\)-\(g\)-PAA nanoparticle is negatively charged and decreases with increasing pH (Figure 6b). The hydrodynamic diameter of the SiO\({}_{2}\)-\(g\)-PAA nanoparticles increases with increasing pH (Figure 6a), attributing to the PAA polymer shell becoming more swollen due to the electrostatic repulsive force between PAA polymers increases at a higher pH.  
Figure 5: Hydrodynamic diameter of SiO\({}_{2}\)-\(g\)-POEGMA nanoparticle as a function of temperature. The error bars correspond to standard deviations from three measurements.  
Figure 6: Hydrodynamic diameter (**a**) and zeta potential (**b**) of SiO\({}_{2}\)-\(g\)-PAA nanoparticle as a function of pH, at 25 \({}^{\circ}\)C. The error bars correspond to standard deviations from three measurements.
## 4 Results and Discussion
### Hardened Concrete Properties
#### 4.3.4 Splitting Tensile Strength  
The splitting tensile strength of the concrete specimens containing recycled glass aggregates is of particular importance, as it can be an early indication of the occurrence of the alkali-silica reaction. The alkali-silica gel present within concrete containing glass aggregates can expand if sufficient moisture is present, creating tensile stress that can result in the formation of cracks within the concrete structure [45, 46]. The splitting tensile strength for each of the concrete mixtures aged 7, 14, and 28 days is presented in Figure 14. It followed a similar trend as the compression strength with 15% substitution showing the highest 28-days strength of 3.2 MPa. It is shown that with glass substitutions, the early days strength was smaller, which may be an indication of a slight delay in reaching the peak strength. Nonetheless, the highest strength of 3.2 MPa recorded for 15% substitution may suggest that such a delay in reaching peak strength is not that significant for small volume proportions of glass sand. This study indicated that glass substitution up to 30% of sand could produce mechanical strength equivalent to the control mix. However, with the further increase in recycled glass content, the tensile strength of the respective mixtures was significantly reduced. The relative increase in strength at 28 days can be attributed to a further developed stage of the hydration process as a result of the decreased permeability of the glass in earlier stages [14, 47]. It is known that the bond strength at the interfacial transition zone is improved for a small proportion of substitution of recycled glass and decreased at higher proportions of substitutions [45].
## 2 Problem Formulations
### Path-Following  
As distinguished from the trajectory tracking problem, the path-following problem aims at moving the system along a geometric reference without any pre-specified timing information. It is assumed that the parametrized regular curve in the two-coordinate space [30] is as given in Equation (2).  
\[Path=\left\{y\in\mathbb{R}^{n}|\theta_{p}\in[\theta_{0},\theta_{1}]\mapsto y=p (\theta_{p})\right\} \tag{2}\]  
Here, the scalar variable \(\theta_{p}\) is called the path parameter, and Path: \(\mathbb{R}\mapsto\mathbb{R}^{n}\) is a parametrization of Path. The geometric curve \(p(\theta_{p})\) is satisfied with the characteristic of local bijectivity. The map \(p\): \(\mathbb{R}\mapsto\mathbb{R}^{n}\) is assumed to be sufficiently often continuously differentiable. As shown in Figure 1, the path with the coordinate points can be considered as \(p_{k}(\theta_{p}(t))\), \(k\in N\), where the time \(t\) of \(\theta_{p}\) is set arbitrarily. The direction line can be virtually set as the tangent line of the path at the point. In this paper, the discrete coordinate point of the path is \([\begin{array}{cc}\theta_{p}&\rho(\theta_{p})\end{array}]\) and the mobile robot's desired position \(p=[\begin{array}{cc}\theta_{p}&\rho(\theta_{p})\end{array}]^{T}\) can be considered as the path with a time law [1].  
The position error of the mobile robot for path-following can be expressed by the tracking error expression [29] in Equation (3).  
\[\tilde{q}_{e}=\left[\begin{array}{c}\tilde{x}_{e}\\ \tilde{y}_{e}\\ \tilde{\theta}_{e}\end{array}\right]=\left[\begin{array}{ccc}\cos\tilde{ \theta}&\sin\tilde{\theta}&0\\ -\sin\tilde{\theta}&\cos\tilde{\theta}&0\\ 0&0&1\end{array}\right]\left[\begin{array}{c}\theta_{p}-\tilde{x}\\ \rho(\theta_{p})-\tilde{y}\\ \theta_{path,r}-\tilde{\theta}\end{array}\right] \tag{3}\]  
Figure 1: Mobile robot path-following schematic.  
The goal of path-following is to guarantee that the position error converges, i.e., \(\lim_{t\to 0}\tilde{q}_{e}(t)=0\).
## 3 Results and Discussion
### Effect of Neutralization on Thermophilic Liquid Anaerobic Digestion  
To explore the applicability of different neutralization treatments for thermophilic L-AD, the cumulative methane yields and daily methane yields of the reactors are presented in Figure 3. The cumulative methane yields of all reactors with different neutralization treatments ranged between 186.7-225.3 mL\({}_{\text{N}}\):gSV\({}_{\text{feed}}\)\({}^{-1}\), while the CN had the highest yield with a significant (\(p<0.05\)) increase of 20.6% over that of RM. However, the peak of CN's daily methane production occurred the latest, on day 6, even 2 days later than that of RM. [PERSON] et al. (2015) did not find any AD initiation lag when using lime for pretreatment, seemingly because they removed the fermentation inhibitors by washing with water, while CO\({}_{2}\) neutralization did not. The inhibitors were derived from the side products released during the lignocellulosic pretreatment process, and they can have a negative impact on the enzymatic activity, growth, and metabolism of the microbial community associated with the AD process ([PERSON] et al., 2017). Similarly to the BMP test, the cumulative methane production of LDN was not significantly (\(p>0.05\)) different from that of RM. [PERSON] et al. (2018) found that LD would cause the consumption of soluble substrate, which could explain our experimental results. However, the earliest peak in the daily methane production of all reactors was observed at LDN, likely due to the destructive effect of microorganisms in the LD on the dense structure of the CS, which was confirmed by a previous report ([PERSON] et al., 2018).  
Figure 3: Effect of different neutralization treatments on the (**a**) cumulative methane yield; and (**b**) daily methane yield during 30 days thermophilic L-AD of lime-treated corn stover. Data are shown as mean \(\pm\) SD, \(n=3\).
# Evaluation of 5G Positioning Performance Based on UTDoA, AoA and Base-Station Selective Exclusion
## 2 Signal Model
### LTE Positioning Signal Structure
#### 2.1.2 Uplink Signal  
In uplink transmission, there is not any dedicated pilot for positioning, so the Sounding Reference Signal (SRS) is selected for this purpose. In 5G NR, the SRS generation is implemented according to 3 GPP TS 38.211 [15]. The generation of the SRS includes two steps: Zadoff-Chu sequence generation and mapping. Mapping of the signal is done with an interleaving factor of two subcarriers (FR = 2). SRS is transmitted by the UE for uplink channel sounding, which includes the channel estimation (in the frequency domain) and synchronization. The SRS is an uplink OFDM signal filed with a Zadoff-Chu sequence on different subcarriers. It is transmitted as OFDM symbols, which are allocated in specified frequency (subcarrier) and time (slot) positions in 5G NR subframes. Each radio frame consists of 10 subframes and 20 slots. Each slot comprises of seven Single-Carrier Frequency Division Multiple Access (SC-FDMA) symbols with the case of the normal Cyclic Prefix (CP) length configuration. In the frequency domain, resources are grouped in units of 12 subcarriers, occupying a total of 180 kHz with subcarrier spacing of 15 kHz. SRS is transmitted at the last symbol of the slot with full system band area.
# Energy Management Integrated Circuit for Multi-Source Energy Harvesters in WBAN Applications
## 6 Discussion
Today, energy harvesters have difficulty in commercialization because of their low efficiency and small amount of energy harvested. Photovoltaic (PV) cells to harvest the energy in sunlight are considered the only harvester commercialized as a field of industry. Fortunately, the amount of energy provided can be overcome by using multiple energy sources as described above. For their use in autonomous systems, the improvement of efficiency in energy harvesters remains a challenge. Various types of energy sources suitable for use in WBAN wearable devices should be surveyed. From the technology proposed herein, low energy harvesters can be considered practical energy sources, and more energy sources in or around the human body are worth investigating. In future, more wearable devices will be installed inside and outside of human bodies for health monitoring and more convenient living. Such wearable devices will require more energy from the environment surrounding them. Energy harvesters and energy management technology are expected to be the most feasible solutions for supplying energy to wearable devices. In the near future, based on energy harvesting systems, disengaging wearable devices from the limitation of battery life is expected to be realized.
## 5 Solution of the Design and Testing
### Positioning and Construction
This complication imparts heightened challenges to the development of a positioning system reliant on signal strength for distance measurement. To delineate this signal attenuation model accurately, a stochastic variable is introduced to elucidate the nuanced relationship between the measured distance and the ultimate positioning, stemming from signal drift [60], as articulated in Equation (6):  
\[PL(d)=PL(d_{0})+10n\times log\left(\frac{d}{d_{0}}\right)+X_{\sigma} \tag{6}\]  
(2)  
Tree point positioning  
The maximum likelihood method is employed to ascertain the position of the target object. The underlying concept revolves around gauging the likelihood of the label coordinates (x, y) on the plane, extrapolated from the positions of three reference points. As expressed in Equation (7), where \(\sigma_{\text{(x,y)}}=\min\,\sigma_{\text{(x,y)}}\):  
\[\sigma_{\text{(x,y)}}=\left|\sqrt{\left(x-x_{A}\right)^{2}+\left(y-y_{A} \right)^{2}-r_{A}\right)}\right|+\left|\sqrt{\left(x-x_{B}\right)^{2}+\left(y -y_{B}\right)^{2}-r_{B}}\right)\right| \tag{7}\]  
In this formulation, r\({}_{\text{A}}\), r\({}_{\text{B}}\), and r\({}_{\text{C}}\) denote the distances estimated by A, B, and C, respectively. Subsequently, the minimum \(\sigma_{\text{(x,y)}}\) is utilized to ascertain the position of the target object, as illustrated in Figure 38.  
(3)  
Sample comparison method  
This positioning approach harnesses the principles of reinforcement learning (RL) and leverages the characteristics of signal strength, juxtaposing each training location in the positioning database with the corresponding entries. Figure 38: Utilized to ascertain the position of the target object. Effectively mitigating positioning errors arising from the fluctuating environmental signal strength, this methodology necessitates a division of the positioning process into the reinforcement learning framework, as depicted in Figure 39.  
* Training phase  
When initiating with the determination of coordinates for training positions, approximately 50 to 100 intensity signals are collected from neighboring ZigBee devices at these training locations. During this phase, the database accumulates numerous records structured as \(x_{1}\), \(y_{1}<\)ss\({}_{1}\), ss\({}_{2}\), ss\({}_{3}\)..., ss\({}_{n}>\) in the database, where x and y denote the coordinates of the training position. * Positioning stage  
The ZigBee tags are compared to discern the training position where the signal sample, received during the localization of the target object, may manifest. (4) Nearest Neighbor in Signal Strength:  
In the positioning stage, predicated on the sample signal S = \(<\)ss\({}_{1}\), ss\({}_{2}\), ss\({}_{3}\),..., ss\({}_{n}>\), the geometric distance (Euclidean distance) of each characteristic vector in the positioning module is calculated, as shown in Equation (8):  
Figure 36:0  
where, \(i\)_in_\(c_{j}^{i}=<\)\(c_{1}^{i}\), \(c_{2}^{i}\)...\(c_{n}^{i}\)\(>\) signifies the training position, capturing the average of all signals received from device j.
## 5 Discussion  
Contextualizing data and observations is a major challenge for operators of industrial plants. The present article addressed this challenge by presenting examples of the psychological literature that deals with the questions how people sample information, integrate different information elements, categorize objects and events, and reason about causes. In each area, characteristic limitations and biases were reported, which should form the basis for the conceptualization of support strategies. Based on the literature section, we extracted three groups of functional requirements for digital transformation technologies: they should connect data to models of the system, provide and integrate data from different sources, and process and integrate data over time.  
A variety of technologies can address these requirements and support contextualization. At the core of this endeavor is the generation and combination of formal models of physical and functional system relations. This can be achieved by Semantic Web technologies such as ontologies and Linked Data. These models can provide the basis for information sampling and integration. A model-based integration of data from different sources calls for standards such as OPC UA that provide semantic modelling capabilities and can be combined with other technologies, for instance to enable real-time communication. Machine learning can find patterns in complex datasets by matching dynamic and historical data, but an important requirement is that it should be explainable. Moreover, operator experience can be made available via case-based reasoning. Again, such approaches would greatly benefit from a combination with formal models of system relations. Thus, the integration of different technologies provides immense potential to support contextualization.