esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Feature Extraction
#### 3.2.5. Morphological Features
Slope: The slope characteristic in signal analysis denotes the maximal rate at which the signal alters over a specific time span. This feature sheds light on the abruptness or speed of changes within the signal. Investigating the slope within ECG and GSR signals enables the detection of abrupt variations or tendencies in physiological reactions. Notably sharp slopes might signal substantial transitions in physiological conditions, reflecting swift fluctuations in arousal or emotional states. The slope, or the signal's rate of change across a designated period, can be effectively estimated through differentiation. \[\text{Slope}=\frac{dx(t)}{dt} \tag{7}\]where \(\frac{dx(t)}{dt}\) represents the derivative of the signal with respect to time, approximating the signal's rate of change over a designated time interval. Energy: The energy of a signal is determined through the application of the Fourier transform [45]. \[H(e^{i\omega}) =\sum x[n]e^{-j\omega n} \tag{8}\] \[\text{Energy} =\sum|H(e^{i\omega})|^{2} \tag{9}\]  
\(H(e^{i\omega})\), represents the frequency response of a digital filter, with \(x[n]\) denoting the filter's coefficients. This expression is the standard way to characterize how the filter will affect signals of different frequencies. Figure 5 illustrates the energy profiles of ECG signals corresponding to five different emotional states.
## 6 Concluding Remarks  
A strong tracking adaptive IMM algorithm based on CKF was proposed to solve the problem of the low tracking accuracy or even failure of the traditional IMM algorithm under the high maneuverability of hypersonic targets.  
Firstly, the six-degree-of-freedom equation was established for the dynamics of HTV-2. The jump glide trajectory of HTV-2 was obtained by using the fourth-order Runge-Kutta method and the angle of attack model, and the radar measurement model was established according to the coordinate axis transformation relationship. After analyzing the structure of CKF, the gain matrix was adjusted in real time by adding multiple pairs of fading factors, and the strong tracking filter was combined with CKF to improve the filtering accuracy when the model is out of order. Aiming at the problem that the model dimension in the IMM is expanded, which makes Cholesky decomposition impossible in the CKF algorithm, SVD decomposition was proposed to replace Cholesky decomposition. Aiming at the shortcomings of the IMM in estimating the Markov matrix, an IMM with an adaptive transition probability was proposed. The transition probability was adaptively modified by posterior information to enhance the proportion of matching models.  
The multi-fading factor strong tracking algorithm proposed in this paper adjusts the filtering gain in real time and improves the tracking accuracy. The adaptive interaction multi-model proposed in this paper improves the matching degree of the motion state of the hypersonic target and speeds up the convergence of the algorithm. The SVD decomposition of CKF in this paper improves the stability of the algorithm. The simulation results show that the proposed algorithm enhanced the proportion of matching models in the IMM and improved the tracking accuracy by 16.51% and the convergence speed by 37.5%. It has engineering application value.  
Conceptualization, Y.L. ([PERSON]) and methodology, Z.L.; software, Y.L. ([PERSON]) and H.W.; writing--original draft preparation, Y.L. ([PERSON]); writing--review and editing, Y.L. ([PERSON]); supervision, Y.L. ([PERSON]) and S.N.; funding acquisition, Z.L.; All authors have read and agreed to the published version of the manuscript.  
National Natural Science Foundation of China, No. 61805283.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results
### Cell Morphology  
To qualitatively evaluate the early cell morphology, fluorescence microscopy experiments were conducted by marking the cytoskeleton and nuclei. As can be appreciated from Figure 2, no particular alterations or differences in cell morphology are observed between MC3T3s plated on HNS and SM surfaces at T1. On the other hand, a slight difference is observed in the level of spreading, which is greater at T2 than at T1, with cells seeded on  
Figure 1: Cell adhesion was evaluated on all samples at 12 min. The level of cell adhesion was measured by counting the number of adherent cells for each sample. Values represent mean \(\pm\) SEM. The symbol (*) indicates the statistical significance vs. HNS surface considering a \(p\)-value < 0.05.  
HNS surfaces appearing very slightly more elongated and less round than those plated on SM surfaces.  
In order to investigate the morphological differences of the MC3T3-E1 24 h after seeding on the different surfaces, different magnifications were considered (100, 200, and 600X). Cells were labeled in order to highlight the cytoskeleton, nucleus, and focal adhesion points through paxillin labeling.  
As it is possible to appreciate in Figure 3, cells seeded on the HNS surface appear much more elongated and fusiform than those grown on the SM surface, which instead showed a greater spreading. Furthermore, the cells on the HNS surface have long, intertwining filopodia. Overall, as observed in the quantification of cellular areas, the cells grown on the SM surface have a significantly greater cellular area than those grown on the HNS surface. Observing qualitatively the labeling of paxillin (in red), no important differences in the arrangement of focal adhesions are appreciated, however, it is possible to notice a slight increase in the amount of red staining in the cells grown on the SM surface.  
Figure 2: Early cell morphology. Panel depicting the early morphological characterization performed on adherent MC3T3-E1 osteoblasts seed at 2 h (T1) and 6 h (T2) on HNS and SM surfaces. The cytoskeleton is labeled red with Rhodamine-Phalloidin. Cell nuclei are stained blue with DAPI.
# Improvement of ANFIS Model for Prediction of Compressive Strength of Manufactured Sand Concrete
## 3 Materials and Methods  
### Adaptive Neuro Fuzzy Inference System (ANFIS)  
Early introduced in the 1990s by [PERSON] [37], ANFIS is well-known as a hybrid AI model in merging ANN [38; 39; 40; 41] and Fuzzy Logic (FL) [42]. The ANFIS architecture consists of five principal layers such as fuzzification, rule, normalization, defuzzification and aggregation [37; 43; 44; 45; 46]. Based on such construction, the neural network exhibits the ability to identify the parameters of FL algorithm [47; 48; 49]. In ANFIS, the Takagi-Sugeno if-then rules and appropriate membership function are employed for the fuzzy inference system [50; 51]. Similar to ANN, hybrid ANFIS technique has also an ability to identify the nonlinear relationship between inputs and outputs [43; 52; 53]. It is worth noticed that ANFIS exhibits a better prediction efficiency than individual ANN or FL, as demonstrated in several investigations, such as in the works of [PERSON] et al. [54] or [PERSON] et al. [55]. However, there are several limitations in ANFIS model such as ANFIS is not powerful in searching the best firing strength (i.e., weight) [56; 57], which greatly impact the prediction effectiveness [47]. Various investigations have employed different optimization methods to find the weighs of parameters in a better way, for instance using Genetic Algorithm, Particle Swarm Optimization, Grey Wolf Optimizer or Invasive Weed Optimization [58; 59; 60; 61; 62; 63]. In this paper, we proposed the Teaching-Learning-based Optimization (TLBO) to optimize the parameter's weights in ANFIS.
## 4 Discussion
These advanced techniques would be suitable for applications that improve the SNR and examine brain function at high temporal resolution while maintaining spatial resolution. Furthermore, the evaluation of dynamic change in contrast agents is also important for clinical applications.
## 2 Overview of the Study Area  
### Study Area  
The study region, Heshan District, Taojiang County, and Anhua County of Yiyang City, is located in the central region of Hunan Province, China (Figure 1). The area is situated in 110\({}^{\circ}\)43\({}^{\prime}\)-112\({}^{\circ}\)43\({}^{\prime}\) east longitude and 27\({}^{\circ}\)58\({}^{\prime}\)-28\({}^{\circ}\)41\({}^{\prime}\) north latitude, covering 8297 km\({}^{2}\). With a subtropical continental monsoon humid climate, it is one of the four major rainstorm centers in Hunan Province, with average annual rainfalls of 1517.6 mm, 1700 mm, and 1672.9 mm, respectively. The overall terrain of Yiyang City is high in the west and low in the east, forming a narrow and long shape and sloping from south to north. According to the statistics of the 2022 population census, the survey region is relatively densely populated, with a permanent population of 2.325 million. Mountainous and hilly, the terrain is intensely cut, with very developed V-shaped valleys and steep slopes. The geological structure is complex, and the strata are relatively developed. The stratum lithology is mainly a gravel layer and upper clay, mudstone, sandstone, shale, silty soil, slate, limestone, basalt, and so on. The study area is particularly susceptible to rainfall-induced landslides, under the influence of the complex geological structures, long duration of heavy seasonal rainfall, and uneven scattering of steep slopes [22; 23].
## 6 Security Analysis  
This part primarily offers a discussion of the proposed scheme, which can meet all of the necessary safety needs between the SB and the CC. The subsequent condition is identical to the initial one.  
As mentioned earlier, the components of data security include data integrity, privacy and confidentiality. Considering the broadcast nature of wireless and internet communications, the SG data may be interfered with and substituted by the adversary; such a system is considered quite risky in the event of a system outage. Therefore, the present model decrypts the data by utilizing a one-time session key. This unique random key is generated after the authentication stage and is altered periodically. This method of data encryption (one-time key-based) may meet all three data security properties, whereby any data alteration or adversary's replay attempt may simply be identified using a tag that is highly resistant to falsification.
# Predicting Putting Development Using Machine Learning Methods Based on RIOCHTrack Data
## 2 Methodology
### Evaluation Indicators  
To compare the accuracy of different algorithms for road rutting prediction, the coefficient of determination (R\({}^{2}\)), the root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE) were adopted as indicators for evaluating performance. R\({}^{2}\) represents the proportion of the dependent variable explained by the independent variable; the closer to 1 it is, the better the prediction effect. RMSE represents the deviation between the real and predicted values; the smaller the value is, the closer the predicted value is to the real value. MAE describes the actual error of the predicted value as well as the degree of dispersion of the data. The smaller the value is, the higher the accuracy of the model.  
MAPE is a relative measure that utilizes absolute values to avoid positive and negative errors canceling each other out.  
\[R^{2}=1-\frac{\sum_{i}^{n}\left(y_{i}-\hat{y_{i}}\right)^{2}}{\sum_{i}^{n} \left(y_{i}-\overline{y}\right)^{2}} \tag{5}\]  
\[\text{RMSE}= \sqrt{\frac{\sum_{i=1}^{n}\left(\hat{y_{i}}-y_{i}\right)^{2}}{n}} \tag{6}\]  
\[\text{MAE}= \frac{1}{n}\sum_{i=1}^{n}\left|y_{i}-\hat{y_{i}}\right| \tag{7}\]  
\[\text{MAPE}=\frac{100\%}{n}\sum_{i=1}^{n}\frac{\left|y_{i}-\hat{y_{i}} \right|}{y_{i}} \tag{8}\]  
where n represents the number of samples, \(y_{i}\) represents the test value, and \(\hat{y_{i}}\) represents the predicted value of the \(i\)-th sample. The higher the value of \(R^{2}\) is, the lower the RMSE, MAE, and MAPE values, indicating better prediction performance by the model.
## 2 Antenna Design  
The antenna is easy to fabricate on Rogers RT Duroid 5880 substrate due to its simple and symmetrical structure, but it requires precise alignment of the ground and the feed-plane.  
Feed-Plane: The single-element structural design of the MIMO antenna is shown in Figure 1, which comprises a circular patch with a radius of 1.1 mm, and this patch is covered from the top by another half-disk-shaped parasitic structure with an outer radius of 1.7 mm and an inner radius of 1.4 mm. There is a separation of 0.3 mm between the outer disk and the inner circle. Additionally, there are two slits with the dimensions 1.02 mm \(\times\) 0.3 mm; each is placed on either side of the feedline. The circular patch and the parasitic disk are fed by the feedline, which has the dimensions 4.12 mm \(\times\) 0.5 mm, which is placed in the center of the overall antenna structure, as shown in Figure 1b.  
Figure 1: Design structure of single-element antenna. (**a**) Single element of MIMO antenna design. (**b**) Feedline with circular and semi-circular patch.  
Ground Plane: The ground plane consists of a rectangular patch measuring 8.3 mm \(\times\) 3.1 mm, which is aligned just below the circular feed structure, as shown in Figure 1a. Two thin, rectangular-shaped, protruding stubs measuring 2 mm \(\times\) 0.3 mm are positioned on the extreme edges of the ground plane.  
Below, in Figure 2, the simulated result of the reflection coefficient (S\({}_{11}\)) is depicted for the single-element antenna. The simulation results are obtained by using Ansys HFSS software (2024 R1). It is observed that based on the \(-\)10 dB impedance bandwidth reference, excellent values of S\({}_{11}\) are achieved in the frequency range of 24.25 to 29.50 GHz, resulting in a bandwidth of 5.25 GHz. This completely covers the n257/n258/n261 band frequency of FR2.
# Molecular Dynamics-Based Study of Graphene/Asphal Mechanism of Interaction
## 2 Models Establishment and Simulation Methods
### Molecular Dynamics Simulation Methods
#### 2.2.6 Mean Square Displacement  
Self-healing of asphalt is accompanied by diffusion and miscibility of asphalt molecules, which will move with the force field and tend to diffuse from the original position during kinetic simulation. Mean square displacement (\(MSD\)) has been used reflect the displacement change of certain molecules with the increase in the simulation time [26]. The diffusion coefficient (\(D\)), which is calculated by the slope of the \(MSD\) curve, has been used to characterize the diffusion or motion capacity of molecules [27]. The \(MSD\) curve is obtained from the system at an equilibrium state. The \(MSD\) and \(D\) are calculated in Equations (5) and (6).  
\[MSD(t)=\left|r_{i}(t)-r_{i}(0)\right|^{2} \tag{5}\]  
\[D=\underset{t\rightarrow\infty}{lim}\frac{\left|r_{i}(t)-r_{i}(0)\right|^{2}} {6t}=\underset{t\rightarrow\infty}{lim}\frac{MSD(t)}{6t}=\frac{m}{6} \tag{6}\]  
where \(MSD(t)\) presents the root mean square displacement of particle \(i\) at moment \(t\); \(r_{i}(0)\) and \(r_{i}(t)\) are the initial and current positions, respectively; \(t\) is the time; and m is the slope of the \(MSD(t)\) function. The unit of \(D\) and \(m\) is \(\AA^{2}\)/ps, which is equivalent to \(10^{-8}\) m\({}^{2}\)/s.
## 6 Security Analysis
### Informal Analysis
#### 6.3.5 Session Key Disclosure Attack  
Even if \(\mathcal{A}\) has obtained values, as mentioned above, \(\mathcal{A}\) cannot generate the session key \(SK\). The \(SK\) comprises the hash function with secret random numbers \(r_{i}\) and \(r_{j}\). However, \(\mathcal{A}\) cannot extract random numbers, because they are masked with secret parameters \(MV_{i}\) and \(RB_{j}\). Moreover, \(MV_{i}\) and \(RB_{j}\) are also masked with random numbers. Therefore, \(\mathcal{A}\) does not know about the session key \(SK\).
## 3 Results and Discussion
### Photometric Analysis  
Table 3 presents the total phenolic content (TPC), as well as the antiradical and the antioxidant activities, of the flavored EVOO samples. The TPC values of the EVOO samples flavored with marjoram were significantly higher compared to the unflavored EVOO and ranged from 179.18 to 249.84 mg GAE/kg oil, depending on the amount of marjoram added and on the storage time. The antiradical activity of the flavored EVOO oils, expressed as mg Trolox equivalents (TE) per kg of oil, was not significantly affected by the addition of marjoram compared to the unflavored EVOO; however, the antiradical activity of the EVOO flavored with 40 g L\({}^{-1}\) dry marjoram decreased after 14 days of storage. Similarly, the incorporation of marjoram into the EVOO did not have a significant impact on the antioxidant activity measured by ferric-reducing antioxidant power (FRAP) assay compared to the unflavored EVOO; nevertheless, the antioxidant activity of the flavored EVOO samples at both addition levels decreased after 14 days of storage.  
Some studies report conflicting results for the impact of flavoring agents on the total phenolic content, no matter if they belong to the same family. More specifically, for _Lamiaceae_ plants, the application of dry basil 15% by means of ultrasound technology [10], dry laverned 5% \(w/w\) and dry mint 5% [11], resulted in a decrease in the phenolic compounds content, whereas the addition of dry oregano 10 g/kg, dry thyme 10 g/kg [58] and dry rosemary 10% \(w/w\)[10] caused an increase in the total phenolic content. The above contradictory findings could be attributed to a series of factors, including the flavoring technique and its conditions, as well as the type, the quantity and the chemical composition of the flavoring agent [13]. Concerning the antioxidant activity, [PERSON] et al. (2009) [42] studied the use of some flavoring agents (oregano, pepper, lemon, rosemary and garlic) and concluded that the antioxidant content was higher in the flavored than in the non-flavored olive oils, and it was more pronounced after olive oil storage.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Olive Oil Samples**} & **Total Phenolic Content (TPC)** & **Antiradical Activity** & **Antioxidant Activity by Ferric** \\  & **Expressed as mg Gallic Acid** & **Expressed as mg Trolox** & **Reducing Antioxidant Power (FRAP)** \\  & **Equivalents (GAE)/kg Oil** & **Equivalents (TE)/kg Oil** & **Assay Expressed as mg** \\  & & & **of FeSO\({}_{4}\) x 7H\({}_{2}\)O/kg Oil** \\ \hline C\_0 & 117.75 \(\pm\) 17.10 a & 204.81 \(\pm\) 13.54 ab & 1354.74 \(\pm\) 99.71 ab \\ A\_0 & 179.18 \(\pm\) 11.43 b & 192.42 \(\pm\) 14.42 a & 1581.28 \(\pm\) 136.20 b \\ B\_0 & 215.98 \(\pm\) 7.63 cd & 238.74 \(\pm\) 14.05 b & 1402.36 \(\pm\) 98.43 ab \\ A\_14 & 183.80 \(\pm\) 17.30 bc & 205.10 \(\pm\) 17.12 ab & 1420.26 \(\pm\) 97.31 ab \\ B\_14 & 249.84 \(\pm\) 8.02 d & 199.17 \(\pm\) 7.02 a & 1236.74 \(\pm\) 50.54 a \\ \hline \hline \end{tabular}
\end{table}
Table 3: Total phenolic content and antiradical and antioxidant activities of the EVOO samples.
## 5 Implementation and Evaluation
### Complexity Analysis for the Water Cycle Detection Approach
#### 5.1.1 Water Cycle Parameter Settings  
We investigated the evolution of WC solutions under several parameter settings. These parameters were chosen for the WC scheme because the WSN-DS dataset differs from other datasets, and we needed to map the best results according to the fitness function mentioned earlier in (11). The \(di_{max}\) was set as 1E03. The most important parameters are \(M_{sr}\) and \(M_{pop}\). To clarify, \(M_{sr}\) represents the total number of rivers (i.e., parameters defined by the user) plus the individual sea, while \(M_{pop}\) represents the number of raindrops (i.e., the preliminary population of features). Consequently, this section highlights the impact of changes in individual parameters. Table 4 examines three contrasting scenarios (Mpop = 2, 4, and 8).  
Experimental research showed that the specific relationship between \(M_{sr}\), \(M_{pop}\) and the number of features provides the best results. In each scenario examined, the highest number of iterations was set at 100 for each run. The best result was selected based on the fitness function value. The best scenario was the seventh one, which had \(M_{pop}\) = 8 and \(M_{sr}\) = 3. Regarding other benchmark feature selection methods, we set their parameters as shown in Table 5. The rest of their parameters remained the defaults. In addition, for all methods, we defined a population size of eight and a maximum iteration limit of 100.
## 2 Test Method
### Intelligent Detection Algorithm  
As shown in Figure 2, the SSD network architecture is based on the VGG16 backbone, with an additional six convolutional layers to capture more feature maps. By leveraging multi-scale feature detection, the SSD can maintain high detection accuracy even when the input image resolution is relatively low, achieving a good balance between detection speed and accuracy.  
The SSD network model converts the object detection problem into a regression problem. It detects larger objects using high-resolution feature maps and smaller objects using low-resolution feature maps. By directly extracting the object image features at different scales during the convolution stage, the SSD network significantly improves the detection speed. Moreover, the SSD uses the anchor box concept from Faster R-CNN, setting different scales in the anchor boxes at each unit to classify and locate objects, thus reducing the computational burden and enhancing the precision of small-object detection. Multi-scale feature detection allows the SSD to maintain high detection accuracy even with a lower input image resolution. The end-to-end design also simplifies the training process. The SSD achieves a detection speed of 59 FPS and an average detection accuracy of 74.3%, obtaining a good balance between speed and accuracy.  
YOLO v3 is the third version of the You Only Look Once (YOLO) series and uses the Darknet53 residual network structure. As shown in Figure 3, the YOLO network  
Figure 2: SSD target detection algorithm network architecture.  
architecture consists of 24 convolutional layers, 4 pooling layers, and 2 fully connected layers. The convolutional layers are used to extract image features, the pooling layers enhance target feature information, and the fully connected layers predict the position of the target in the image and estimate the probability values of the target's class attributes.  
It performs object detection using pre-selected boxes on feature maps with three different scales, combining deep and shallow image information to obtain semi-rich composite feature maps, thus enhancing the detection accuracy for small objects. YOLO v3 replaces the Softmax layer with multiple independent logistic classifiers for multi-label classification, and it uses a binary cross-entropy loss function to ensure effective object detection under multi-label conditions.