esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Fracture Analysis
### Effect of Loading Rate  
Besides temperature, the fracture behavior of viscoelastic asphalt concrete may also be affected by the loading rate. In this section, the effect of loading rate on the fracture behavior of asphalt concrete was analyzed with the CZM-based FE model. In this analysis, the temperature was set to 0 \({}^{\circ}\)C. Two different loading rates, namely, 1 and 10 mm/min, were considered. Other parameters for numerical simulation were kept the same as that used in the model validation. Figure 13 shows the total number of damaged elements and the distribution of SDEG values at the peak load under different loading rates. As shown in Figure 13a, more damaged elements were induced at the peak load under higher loading rates. However, further statistical analysis of SDEG values showed that the average value of SDEG was lower under higher loading rates, as shown in Figure 13b. In other words, a higher loading rate may induce more, but minor, element damage. The main reason is that the stiffness of asphalt mortar is larger under a higher loading rate, so the CZM elements in asphalt mortar cannot bear much more stress through deformation. As a result, the excessive stress has to be transferred to adjacent elements and borne by these elements together. In this case, more elements are involved to resist the cracking, with less damage to each single element.  
Figure 12: Crack propagation path at (**a**) \(-10\)\({}^{\circ}\)C; (**b**) \(0\)\({}^{\circ}\)C.  
Figure 13: Effect of loading rate on fracture behavior: (**a**) total number of damaged elements; (**b**) SDEG distribution.
## 5 Communication Protocol Design  
Having discussed the design of the Marmote SDR platform debuting a simple FSK-based PHY layer design in Sections 3 and 4, this section focuses on the development and evaluation of a more advanced communication protocol PHY layer for WSN. A fundamental task of the wireless communication in a WSN is to transfer the sensory data from the low-complexity and power-constrained sensor nodes to a resourceful base station. This task has generally been achieved using sensor nodes equipped with highly integrated RF chips and base station nodes of essentially the same architecture, but attached to a laptop or desktop computer. The disadvantage of this approach is that it prescribes the use of a CSMA or TDMA access scheme throughout the network, including the base station vicinity, thus, it fails to take advantage of the increased resources usually available at the base station. However, if the PHY layer between a resourceful base station and the directly connected sensor nodes could be redefined, it would allow for experimentation with other multiple access schemes. One potentially beneficial candidate for WSNs is asynchronous DS-CDMA, which holds the following promises:  
* Collision-free multiple-access scheme that allows for simultaneous packet transmissions and reduced packet losses, due to multiple access interference
* Asynchronous, contention-free medium access that requires no synchronization (scheduling) between sensor nodes
* Asymmetric communication link shifts the processing burden from sensor nodes to the base station (simple transmitter and complex receiver architecture)
* Rejection of narrowband interference and jamming  
The Marmote SDR platform provides a means to experiment with novel, full-custom physical layer designs by exploiting the direct access to the baseband signals at the sensor nodes. Therefore, we developed a DS-CDMA-based protocol and evaluated its performance using a measurement setup with multiple wireless sensor nodes and a base station, as depicted in Figure 4. In this setup, the sensor nodeswere instances of the Marmote SDR platform and primarily acted as DSSS transmitters with uniquely assigned pseudo-noise (PN) code sequences. The \"powerful\" base station, on the other hand, was represented by a USRP N210 connected to a desktop computer, which ran a GNU Radio implemented receiver, listened for all the assigned PN code sequences and evaluated the protocol performance.  
The rest of this section presents the design of the spread-spectrum DS-CDMA communication protocol optimized for WSNs, along with its experimental evaluation.
# Importance of Event Binary Features in Stock Price Prediction
## 3 Design and Development of the Stock Price Prediction Model
### Model 2: Event Binary Features Model  
The second model we have proposed uses event binary features. Stock chart analysts observe charts and capture price fluctuation events. For example, the intersection of the moving average lines and the point of receiving the support and resistance are considered to be very important. In addition to the chart analysts, many people consider these points as an event and make decisions to buy or sell stocks. Indeed, many studies have shown statistically and numerically that this is the starting point for price increases or decreases. In this paper, these points are represented as binary values and used as features. The characteristic of binary data is that although the value is simple, it has an event in reality. Therefore, we anticipate that using this data will be very effective for improving performance. Model 2 consists of the event binary feature and the price feature, with a total of 250 features. The detailed configuration is shown in Tables 4 and 5. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Feature Descriptions** & **Formula** & **Number of Features** \\ \hline Gradient of the moving & \(GndR_{t}^{\text{s}}=\frac{\text{MA}_{t}^{\text{s}}-\text{MA}_{t}^{\text{s}} }{\text{MA}_{t}^{\text{s}}}x100\) (\(k=5,10,20,60,120\)) & 13 \\ average/volume moving & \(VGndR_{t}^{\text{s}}=\frac{\text{VMA}_{t}^{\text{s}}-\text{VMA}_{t}^{\text{s}} }{\text{VMA}_{t}^{\text{s}}}x100\) (\(k=5,10,20\)) & \\ \hline Sum of gradient of 60 and 120 days during past 40 days & \(SumGk=\sum\limits_{n=0}^{20}GndR_{t}^{\text{s}}(k=60,120)\) & 2 \\ \hline Rate of change of volume & \(RoCVMA_{t}^{\text{s}}=\frac{\text{VMA}_{t}^{\text{s}}}{\text{VMA}_{t}^{\text{ s}}}\) (\(k=60,120\)) & 60 \\ moving average of the k days & \((\,j=1,2,3,4,5,7,9,11,13,15,18,21,24,27,30,34,38,\) & 60 \\  & \(42,46,50,55,60,65,70,75,80,85,9
## 1 Introduction
Alternatively, we can compare two clocks by microwave frequency links in space. Even if the two clocks are not intervisible, we can abridge them by a satellite and measure their geopotential difference [32]. This method is regarded as the satellite frequency signal transmission (SFST) approach, which is discussed in detail in [33; 34; 35]. Given the assumption that the stability of the OACs is \(1\times 10^{-18}\) within an hour, simulation experiments show that the precision of the geopotential difference between two stations on the ground can reach several centimeters in height [34]. Although its precision is slightly lower than that of the OFL approach, the SFST approach is more convenient and economical. It is promising for use in global VHS unification and IHRS realization. In this paper, we propose an approach for unifying the global VHS by providing examples of connecting two different arbitrary local VHSs using the SFST approach. In Section 2, we briefly describe the concept of the height reference system and the SFST method. In Section 3, we described the setup of the simulation experiments. Data processing is presented in Section 4. In Sections 5 and 6, we provide the results and conclusions for this work and potential improvements and applications for the future.
## 4 Rare Earth Flotation from Secondary Resources
### Electroflotation  
Electroflotation (Figure 3c) is an advanced flotation technique that uses electrolysis to generate microbubbles. Stainless steel and aluminum are the most common and inexpensive cathode materials used, while titanium with ruthenium-titanium oxide coating is preferred as the anode [146; 147; 148].  
In the past ten years, electroflotation has often been applied to higher concentration REEs (50-250 M) and suitable for the leachate of the hydrometallurgical processes in chloride, carbonate, sulfate, and nitrate media [147]. It is also important to note that in electroflotation, the formation of poorly soluble compounds of metal salts (e.g., metal-hydroxides, metal-sulfides, metal-phosphates) is essential before floating REE ions to the surface via bubbles [149].  
Surfactants used in electroflotation include cationic (Septapav and DDAC), anionic (NaDDS), and non-ionic (PEO-1500) types (Table 5). It has been reported, however, that electroflotation efficiency depends on the type of electrolyte, which could be related to the effects of REE ionic charges. In a chloride electrolyte, anionic surfactant (NaDDS) is outperformed by other surfactants, especially for the recovery of Sc at neutral pH. Meanwhile, cationic surfactant is better when REE ions are found in carbonate and nitrate electrolytes. In some cases, flocculants (i.e., C494, C496, N300, M345, and A137) were used instead of surfactants to enlarge the precipitated metal compounds and enhance their probability of collision with bubbles [146; 150; 151].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
# Spectrogram Classification Using Dissimilarity Space
## 4 Clustering
### Spectral  
The spectral clustering technique splits data into groups using the data's undirected similarity graph represented by a similarity matrix (also called an _adjacency matrix_). In the similarity graph, every-node is an observation, and two nodes are connected by an edge if their similarity is larger then a certain threshold, which is often 0. The algorithm uses four mathematical expressions:  
* _Similarity Matrix_: a square symmetrical matrix that represents the similarity graph. Letting \(M\) be the similarity matrix, each cell value \(m_{ij}\) is the similarity value of two connected nodes in the graph, which, in turn, represent the spectrogram pairs \((s_{i},s_{j})\).
* _Degree Matrix_: a diagonal matrix obtained by summing the similarity matrix rows. The degree matrix is defined by the equation \[D_{g}(i,i)=\sum_{j}m_{ij},\] where \(D_{g}\) is the degree matrix, and \(m_{ij}\) is a value of the similarity matrix.
* _Laplacian Matrix_: another way of representing the similarity graph that is defined as \[L=D_{g}-M.\] Here are the steps required by the spectral algorithm:
* For each spectrogram in the dataset, define a local neighborhood. There are different ways such a neighborhood can be defined. The MATLAB implementation defaults to the nearest-neighbor method. Once the neighborhood is defined, compute the pairwise similarities of each spectrogram in the neighborhood using some distance metric.
* Calculate the Laplacian matrix \(L\).
* Create a matrix \(V\) containing columns \(v_{1},...,v_{k}\), where the columns are the \(k\) eigenvectors that correspond to the \(k\) smallest eigenvalues of the Laplacian matrix. The eigenvalues of the matrix are also called _spectrum_, hence the algorithm's name.
* Treating each row of \(V\) as a pattern, perform k-means clustering or k-medoids clustering.
* Assign the original spectrograms in the dataset to the same clusters as their corresponding rows in \(V\).
## 5 Engineering Test Validation
### Experimental Results Analysis  
Figure 18 shows the RUL prediction comparison between the proposed method, CNN-BiGRU, and TCN-BiGRU, while Table 9 presents the quantitative results comparison of the three different methods.  
Through the analysis and validation of engineering test data, the method proposed in this study demonstrates significant advantages in key performance indicators. The experimental results indicate that, in the context of bearing RUL prediction, the mean values of the root mean square error (RMSE) and mean absolute error (MAE) for the proposed method are reduced by 7.8% compared to the CNN-BiGRU baseline model and decreased by 14.6% relative to the TCN-BiGRU model. Furthermore, regarding the comprehensive prediction performance scores, the average scores show improvements of 7.8% and 9.3 percentage points, respectively, when compared to the two benchmark methods. These results suggest that the proposed method significantly enhances the accuracy of remaining useful life (RUL) prediction.
# Formal Modeling of IoT and Drone-Based Forest Fire Detection and Counteraction System
## 4 Specifying Model Formally Using VDM-SL  
The formal specification using VDM-SL of the forest fire and counteraction system is defined in this segment. Various constructs, such as sets, invariants, composite elements, and pre- and post-conditions, are used to construct the formal definition. There are two parts of the proposed model: static and dynamic. The static model defines the data types, while the dynamic model specifies the functions, operations, and state.
## 4 Results and Discussion
### Consensus on Benefits
#### 4.2.1 Visualization  
Seventeen papers agreed that the use of AR application for communication enabled the visualization of information and site activities. This visualization can be achieved through various devices such as head-mounted displays, cellphones, or tablets that support AR applications. Within an uncontrolled environment, [PERSON] et al. (2019) mentioned that AR provided better visualization results which allowed the users to interact with reality more intuitively [40]. In addition, visualization of the site helps in understanding and preventing design issues and monitoring construction activities [41]. This shows the advantage of AR for exchanging information in real time to provide the experience of site visits and in-person meetings in addition to the verbal conversion and discussion needed on site. Therefore, this benefit is related to the AR application, independent of the type of environment and type of project.
## Materials and Methods
### Atomic Force Microscopy (AFM) Measurements  
To obtain true height measurements of enamel surface topography before and after bleaching treatment, we used correlative optical-atomic force microscopy with a commercial AFM (NanoWizard 4 XP BioScience, Bruker, Billerica, MA, USA) equipped with a motorized stage for precise sample repositioning (HybridStageTM, Bruker), and coupled with an upright optical microscope (AxioZomm v.16, Zeiss, Jena, Germany).  
We obtained topography images using the \"contact mode\" in deionized water over 20 \(\times\) 20 \(\upmu\)m\({}^{2}\) scanning areas. To ensure consistent results, we used the same probe (HQ:CSC17/Hard/Al BS, MikroMasch, Tallinn, Estonia) with a wear-resistant coating to maintain a consistent tip geometry/size over multiple measurements, resulting in comparable topographic features.  
AFM data were processed using the Mountains(r) software from Digital Surf, Besancon, France. To numerically express and compare changes in surface roughness, we calculated  
Figure 2: Experimental design: I: Following the extraction of 5 lower central incisors, enamel portions measuring 8 \(\times\) 8 mm, compatible with the analysis chamber of the atomic force microscope, are excised. One section is reserved solely as a control sample for nanoindentation and the others intended for treatment with bleaching agents. II: Surface cleaning is carried out using air flow and 14 \(\upmu\)m erythroid powder (EMS). III: The samples are placed in the bioreactor, where they are reconditioned in the oral environment for 1 week. IV: Prior to the bleaching treatments, the surfaces are analysed with atomic force microscopy (AFM). V: Enamel surfaces undergo bleaching treatments, following the times and protocols prescribed by the manufacturers and outlined in Table 1. VI: The second step of AFM analysis is performed on the same points previously analysed, and the results are compared to obtain the measurements necessary for a reference statistic on any surface changes. VII: Finally, the same samples are subjected to nanoindentation to evaluate any mechanical changes in the enamel material before and after the bleaching treatment.  
the \"Developed Interfacial Area Ratio\" (Sdr) parameter according to ISO 25178-2:2012 standard. The Sdr parameter expresses the percentage of the additional surface area contributed by surface topography compared to an ideal plane the size of the scanned region. For a flat surface, Sdr is 0%, and it increases with the complexity of the surface topography [21].
# A Small Implantable Compact Antenna for Wireless Telemetry Applied to Wireless Body Area Networks
## 3 Antenna Design and Simulation
### Antenna Parameter Analysis  
The antenna designed in this study introduces rectangular slots of various shapes on the radiating patch to achieve compactification and bandwidth enhancement, ensuring good impedance matching for the implantable antenna. To better investigate the impact of slotting on antenna performance, this paper conducts a study and analysis of the parameters of the S-shaped slot.  
The influence of the S-shaped slot with different widths on the antenna's reflection coefficient is depicted as shown in Figure 7a. When \(\mathrm{l}_{1}=0.2\) mm, the antenna's resonant frequency is relatively high and does not fully cover the ISM band (2.4-2.48 GHz). However, when \(\mathrm{l}_{1}=0.3\) mm, the antenna's resonant frequency decreases, and the operating frequency band fully covers the ISM band, with an increase in the antenna's bandwidth. If the width of the slot is further increased, the antenna will not resonate at the corresponding operating frequency band. Analysis indicates that the width of the S-shaped slot affects the antenna's resonant frequency and operating bandwidth, with optimal performance achieved when its width is 0.3 mm. With the increase of s, the antenna's resonant frequency shifts. When the length of the S-shaped slot is increased to 4.4 mm, the antenna's resonant frequency is more ideal. However, further increases in length result in a narrower bandwidth and poorer impedance matching. Therefore, it is analyzed that altering the length of the S-shaped slot affects the antenna's resonant frequency, impedance matching, and operating bandwidth.  
From the analysis of the parameters of the S-shaped slot, it is evident that both the length and width of the slot impact the antenna's operating bandwidth and impedance matching. Consequently, by conducting relevant parameter analysis, the optimal values  
Figure 6: Reflection coefficients of the antenna with varying silicone layer thicknesses.  
Figure 7: Analysis of the impact of different antenna parameters on the reflection coefficient; (a) \(\mathrm{l}_{1}\), (b) \(\mathrm{s}_{1}\).  
for the designed antenna parameters can be determined. All parameters of the antenna designed in this paper are presented in Table 2.
# An Affinity Propagation-Based Self-Adaptive Clustering Method for Wireless Sensor Networks
## 5 Performance Evaluation  
### Simulation Parameters  
Matlab as a powerful project software has been widely used in automatic control, machine design and mathematical statistics. Researchers can solve the complicated engineering problem efficiently using the integrated toolbox in Matlab. Additionally, Matlab can dynamically simulate operation of the system and conveniently visualize the data. Matlab is run with version of R2016a in a personal computer equipped with an Intel Core I5 central processing unit (CPU) to test the performance of the proposed APSA. The simulator randomly generates the sensors in a specific area with the same initial energy. A round is used as the period of the network and in each round, a sensor needs to upload a data package to the base station via single or multi-hop communication. According to [19], considering the discriminability and run time of the simulation results, the initial energy \(E_{init}\) of each node is set as 2J and the data aggregative energy \(E_{DA}\) is 5 nJ/bit/signal. All the relevant parameters used in the simulation are listed in Table 2.  
An assumption is made that the sensors can communicate with the other nodes in their transmission range. In each round, each node generates a data package which contains the monitored information of surroundings and the target of the network is to gather all the packages of sensors. In the simulation, 50 sensor nodes are firstly deployed in a \(100\times 100\) m\({}^{2}\) sensor field in a random way. Then a BS is set at the center of the monitoring area. The AP algorithm is executed to search the optimal initial cluster centers and adopt the modified K-medoids to form clusters. After the clusters are formed, the BS collects data at regular intervals. Generally, the normal node transmits the monitored data directly to CH if the CH is in its one-hop transmission range. Otherwise, it will choose a relay node to forward its data package to CH by greedy algorithm. In a greedy algorithm, node chooses a neighbor which is closer to the sink compared to itself as the relay node. After the data is received by CH, the CH compresses the data and forwards it to the BS.
# Optimal Siting and Sizing of FACTS in Distribution Networks Using the Black Widow Algorithm
## 5 Numerical Results
### Comparative Analysis with Literature Reports  
Table 5 presents the numerical results obtained when D-STATCOMs (SVCs) are installed in the IEEE 33- and 69-bus grids.  
The numerical results in Table 5 show the following:  
1. The VSA and the proposed BWO approach find the same optimal objective function value for both test feeders, corresponding to reductions of about 12.63% and 13.97% for the test feeders, respectively. However, the main advantage of the proposed BWO approach is that it requires a lower processing time to solve the problem regarding the  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{5}{c}{**IEEE 33-Bus Grid**} \\ \hline
**Method** & **Location** & **Size (Mvar)** & \(z_{\text{cost}}\) (USD/Year) & **Reduction (\%)** & **Time (s)** \\ \hline Benchmark case & — & — & 112,740.90 & — & — \\ COUENNE & [16; 17; 18] & [0.0109, 0.0224, 0.2065] & 107,589.50 & 4.56 & 3.03 \\ BONMIN & [17; 18; 30] & [0.0339, 0.0227, 0.2395] & 102,447.29 & 9.13 & 7.59 \\ GA/PSO & [14; 30; 31] & [0.1599, 0.3497, 0.1117] & 98,511.63 & 12.62 & 6417.91 \\ VSA & [14; 30; 32] & [0.1599, 0.3591, 0.1072] & 98,497.90 & 12.63 & 59.64 \\ \hline BWO & [14; 30; 32] & [0.1599, 0.3591, 0.1072] & 98,497.90 & 12.63 & 44.08 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Numerical results in the IEEE 33- and 69- grids when compared to the proposed BWO approach and the literature reports.  
optimal location of D-STATCOMs in both test feeders, with values of about 44.08 s and 176.74 s, respectively. ii. The combination of the GA and PSO algorithms exhibited an adequate performance in both test feeders, reaching the exact solution of the BWO approach in the case of the IEEE 69-bus grid, as well as a near-optimal solution for the IEEE 33-bus grid. However, the main complication of this approach lies in its required processing times, which are higher than 6400 s in both simulation scenarios. This behavior is expected given that the GA approach defines the nodes where the D-STATCOMs must be located. In addition, the PSO approach is entrusted with solving the optimal reactive power flow problem to determine their optimal sizes. This implies that there are two combinatorial optimization methods working under a master-slave connection, thus increasing the total processing times required to solve the studied problem. iii. The exact solution of the MINLP model with BONMIN and COUENNE showed these to be stuck in locally optimal solutions in the case of the IEEE 33-bus grid, and they did not reach a possible solution point for the IEEE 69-bus grid. This behavior shows the high complexity of the model that represents the problem under study, which is due to its nonlinearities and non-convexities, thus confirming the need for efficient master-slave optimizers, such as the proposed BWO approach.