esa-sceva/satcom-chunk-collection huggingface dataset:
# Design Procedure of a Mono-Anchor Head for 2360 MPa High-Strength Steel Strand
## 5 Parametric Study
### Performance Evaluation According to the Design Parameters  
In total, 72 models were analyzed to assess the behavior of the anchor head. With the combination of the diameter, height, and inclination angle shown in Table 6, two materials with 36 configurations were used for the models. Carburized 42 CrMo4 and C45 were used. The yield strengths were 1170 MPa and 340 MPa, respectively. The behavior of the anchor head according to the design variables was compared under 95% of the MUTS of the strand, the standard load suggested for a performance evaluation by PTI [11]. At this time, the maximum von-Mises stresses were compared in the anchor head in the triaxial state due to the axial stress and the circumferential and radial stresses. In addition, the plastic hoop strain at the top of the wedge hole, which is the performance parameter, was extracted and compared with the acceptance criterion.  
At the top of the wedge hole in the anchor head, the load was concentrated, the maximum stress occurred, and it showed different patterns depending on the configuration. Figure 18 shows the maximum stress according to the configuration of the anchor head for each material, and the resulting plastic strain is shown in Figure 19. The effect of the height was insignificant, and thus only the effects of the diameter and inclination angle are compared. The stress of the anchor head using the carburized 42 CrMo4 did not exceed the yield strength and only showed elasticity. Accordingly, plastic hoop strain did not occur at all. On the other hand, the stress of the anchor head using C45 exceeded the yield  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**D (mm)** & **H (mm)** & \(\Theta\) (\({}^{\circ}\)) & \(f_{y}\) (MPa)** \\ \hline
45 & 50 & 6.3 & 1167 \\
50 & 55 & 6.5 & 340 \\
55 & 60 & 6.7 & - \\
60 & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 6: Design parameters of the Anchor head.  
Figure 17: Hoop strain distribution in anchor head.  
strength locally and exceeded the allowable plastic hoop strain in all configurations. It can be implied that the carburized 42 CrMO4 anchor head has excessive strength, but the C45 anchor head needs improvement in strength for use with 2360 MPa high-strength steel strand.
# Improved Bootstrap Method Based on RBF Neural Network for Reliability Assessment
###### Abstract  
The investigation of the reliability of long-life equipment is typically hindered by the lack of experimental data, which makes accurate assessments challenging. To address this problem, a bootstrap method based on the improved RBF (radial basis function) neural network is proposed. This method utilizes the exponential function to modify the conventional empirical distribution function and fit right-tailed data. In addition, it employs the RBF radial basis neural network to obtain the distribution characteristics of the original samples and then constructs the neighborhood function to generate the input network. The expanded sample is used to estimate the scale and shape parameters of the Weibull distribution and obtain the estimated value of the \(MTBF\) (mean time between failures). The bias correction method is then used to obtain the interval estimate for the \(MTBF\). Subsequently, a simulation experiment is conducted based on the failure data of a CNC (computer numerical control) machine tool to verify the effect of this method. The results show that the accuracy of the \(MTBF\) point estimation and interval estimation obtained using the proposed method is superior to those of the original and conventional bootstrap methods, which is of major significance to engineering applications.  
b 2024 14 2001 1
## 2 Massive MIMO System Model and Signal Detection  
Consider a massive MIMO uplink system with \(N\) active BS antennas and \(K\) single-antenna users. Typically, the number of BS antennas greatly surpasses the number of users in massive MIMO configurations. Let \(s_{n}(1\leq n\leq K)\), comprising \(J(=\log_{2}Q)\) bits per symbol, denote the transmitted signal sent from the \(n\)th user, which is generated from a Q-ary constellation \(\mathcal{M}\). The coefficients of the channel between the \(K\) user terminals and the \(N\) BS antennas form a channel matrix, which is given by  
\[\mathbf{H}=\begin{bmatrix}h_{11}&h_{12}&\ldots&h_{1k}\\ h_{21}&h_{22}&\ldots&h_{2k}\\ \vdots&\vdots&\vdots&\vdots\\ h_{n1}&h_{n2}&\ldots&h_{nk}\end{bmatrix}\]  
where \(h_{nk}\) represents the channel gain or loss from the \(k\)th user to \(n\)th BS antenna. For simplicity, it is assumed that the receiver has perfect knowledge of the channel state information. Thus, the received signal at the BS can be represented as  
\[\mathbf{y}=\mathbf{H}\mathbf{s}+\mathbf{n}, \tag{1}\]  
where \(\mathbf{y}=[y_{1},y_{2},\ldots,y_{N}]\) is the received signal vector and \(\mathbf{n}\) denotes the Additive White Gaussian Noise (AWGN) vector, with each element independently and identically distributed (i.i.d.) following a circularly symmetric complex Gaussian distribution with zero mean and variance \(\sigma^{2}\), i.e., \(\mathbf{n}\sim\mathcal{CN}(0,\sigma^{2}\mathbf{I})\). This assumes that the noise is uncorrelated across antennas and users, with constant variance \(\sigma^{2}\).  
We consider a Rayleigh flat-fading channel model in this work. This model is adopted due to its tractability and wide use in the theoretical analysis of massive MIMO systems [29]. It assumes that the channel coefficients are i.i.d. complex Gaussian random variables, which is a reasonable assumption for rich scattering environments. While the Rayleigh model simplifies the analysis, it can also be extended to correlated fading channels, making the results more generalizable.  
Information bits transmitted by different users cause multiuser interference at the receiver side in multiuser massive MIMO uplink systems. Therefore, a signal detector is necessary at the BS to estimate the transmitted data from the noisy received data. The ML detector exhibits exponential complexity with respect to the number of decision variables, making it excessively intricate in systems with a large number of antennas. Hence, linear detection methods are usually employed in large MIMO systems. The estimation of signals at the BS using the linear MMSE criterion is given by [30]  
\[\mathbf{\hat{s}}=\left(\mathbf{H}^{H}\mathbf{H}+\frac{\sigma^{2}}{E_{s}} \mathbf{I}_{\mathbf{K}}\right)^{  
Solving the linear MMSE problem requires solving a system of linear equations \(\mathbf{Ax}=\mathbf{b}\), where iterative methods that circumvent matrix inversion can be applied for signal detection in massive MIMO systems.
# Enhancing Botnet Detection in Network Security Using Profile Hidden Markov Models
## 4 Methodology and Implementation
### Profile Hidden Markov Model (PHMM)  
The data were split into training and testing sets in an 80:20 ratio, maintaining an equal representation of botnet and normal traffic using stratified sampling. Data imbalance was handled using SMOTE for oversampling. The PHMM required additional pre-processing steps, including scaling data to the range 0 to 1, converting data into uppercase alphabetical strings using binning and forming sequences for input. The sequences were stored in FASTA format for PHMM input. Testing involved calculating scores for each trained PHMM and classifying based on the maximum score. Sequences that did not match any of the seven PHMM models were classified as normal traffic.  
The test data consisted of 2231 normal traffic sequences and 2852 botnet samples. The PHMM, being a more complex model than the HMM, required more data for training and achieved an accuracy of 97%.  
The FASTA files served as input to the PHMM model, which utilized the HMMER library for training seven separate PHMMs, one for each botnet type.  
Here, we outline a step-by-step recap:  
The dataset was divided into training and test data subsets.  
The training-to-test data split ratio was set at 80:20.  
Balancing of the dataset was applied.  
The original pre-processed data were scaled to a range of 0 to 1.  
Data were converted to uppercase alphabetical strings (A-Z) using a process called binning.  
These features were concatenated to form sequences.  
The sequences were then converted into FASTA format files.  
One separate PHMM was trained for each of the seven distinct botnet types.  
Scores were calculated for each trained PHMM.  
Classification was performed based on the maximum score.  
Sequences that did not match any of the seven PHMM models were classified as normal traffic.  
The PHMM was applied to classify data as either botnet or normal.  
The PHMM was applied to classify seven malware families plus normal traffic.
## 4 Conclusions  
Reduced tillage is becoming more popular every year. Therefore, research that can improve the quality of shallow tillage with disc implements, as well as reduce traction force and energy consumption, is very important. Biopreparations can be one of the useful tools that have a positive impact on the physical properties of the soil and the technical parameters of tillage machines.  
Experimental research showed that the use of different biopreparations for shallow tillage (100 \(\pm\) 20 mm) with disc narrows resulted in a reduction of the machine traction force by up to 20% at all investigated velocities (8 \(\pm\) 0.15 km h\({}^{-1}\), 10 \(\pm\) 0.17 km h\({}^{-1}\) and 12 \(\pm\) 0.20 km h\({}^{-1}\)).  
Continuous treatment of the same soil with biopreparations for two years significantly reduced the soil density by about 0.09 g cm\({}^{-3}\) and increased the total soil porosity by about 6.49%. It had a significant effect on the reduction of the machine's traction force during shallow tillage.  
Research has shown that biopreparations sprayed after the resumption of plant vegetation in spring did not significantly affect the cutting force of winter wheat residues, and the force required to cut one straw varied from 22 to 34 N. Plant residues were cut at a depth of 16-20 mm, regardless of the composition of the biological preparation.  
The results of this research allow recommending the use of biopreparations in agricultural production, improvement of soil physical properties, and reduction of the traction force required for tillage, while saving fuel and the cost of all winter wheat production.  
Conceptualization, E.S., K.L., V.N. and A.J.; methodology, E.S. and V.N.; validation, E.S.; formal analysis, K.L.; investigation, K.L. and V.N.; data curation, K.L., E.S., V.N. and A.J.; writing--original draft preparation, K.L.; writing--review and editing, K.L., E.S. and V.N.; supervision, E.S.; funding acquisition, E.S. and A.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Vytautas Magnus University, Agriculture Academy.  
Figure 7: The impact of different biopreparations on winter wheat cutting force and cutting depth: Treatment 1—composition of the biopreparation No. 1; Treatment 2—No. 1 and No. 2 mixture; Treatment 3—composition of the biopreparation No. 2; Treatment 4—No. 1 and No. 3 mixture; Treatment 5—composition of the biopreparation No. 4; Treatment 6—No. 1 and No. 4 mixture; Treatment 7—composition of the biopreparation No. 3. The same letters indicate no significant difference.
## 3 Results and Discussion
### Cu Uptake and Accumulation in Soybeans  
The Cu contents in shoots, roots, and soil samples in the control were 10.89 \(\pm\) 1.24, 8.29 \(\pm\) 5.76, and 38.32 \(\pm\) 1.96 mg/kg dry weight, respectively (Figure 6). In shoots, CuO NP treatments reduced the Cu contents slightly but not significantly (Figure 6A). In roots, at 1 mg/kg, the 50 nm CuO NP treatment decreased the Cu contents by 21.1%, while the 20 and 50 nm CuO NP treatments at 10 mg/kg increased the Cu contents by 5.5% and 14.5%, respectively, relative to the control (Figure 6B). [PERSON] et al. reported that CuO NP (40 nm) suspensions at 2 mg/L increased the Cu contents in roots [49]. In addition, [PERSON] et al. also reported that CuO NPs (40 nm) were observed in the meristematic cells of the root zone and increased the Cu contents by 32.3-fold in dry roots as compared to the control [50]. In the soil, the addition of CuO NPs increased the Cu contents in a dose-dependent fashion after harvesting. At 1 mg/kg, the 20 and 50 nm CuO NP treatments increased the Cu contents by 12.6% and 28.6%, while increasing them by 28.2% and 31.4% at 10 mg/kg, respectively, relative to the control (Figure 6C), suggesting that at the same use dose, the 50 nm CuO NP treatments resulted in more Cu accumulation in the soil than the 20 nm CuO NP treatment. Other nutrient contents in soybean shoots, roots, and soil samples across all the CuO NP treatments are provided in Table S5.  
A previous study indicated that the NP size can determine the uptake and utilization of elements by plants and that smaller nanoparticles (\(\leq\)20 nm) are more likely to be taken up and accumulated by plants [51]. Thus, higher Cu contents in shoots were observed in the 20 nm CuO NP treatments. However, the larger NP size might induce the formation of large pores in root cell walls [52; 53] and subsequently enter the plant tissues via the wounds, which could explain the reasons that the 50 nm CuO NP treatments increased the Cu contents in roots more than the 20 nm treatments.  
Figure 5: Soil urease (S-UE, **A**), soil neutral phosphatase (S-NP, **B**), soil polyphenol oxidase (S-PPO, **C**), and soil sucrose (S-SC, **D**) enzyme activity in the 20 and 50 nm CuO NP treatments at 1 and 10 mg/kg for three weeks. Error bars represent the standard error of 5 replicates, and the different letters in each panel represent significantly different values at \(p<0.05\).
# Automatic Ontology-Based Model Evolution for Learning Changes in Dynamic Environments
## 3 Ontology-Based Model Evolution Approach
### Ontology-Based Model Integration Module
#### 3.4.1 Alignment Phase  
To handle the ambiguity associated with formalization before the evolution of initial ontology-based model evolution, an alignment is essential to discover the correspondences between the refined local ontology and the initial ontology-based model. Therefore, the relations among the terms, which can be concepts, relations, properties, or individuals, should be mined before the merging activity as much as possible. For this purpose, we propose an automatic alignment approach consisting of two stages: an initial syntactic similarity measure followed by a semantic similarity measure. In the first stage, a simple distance computation between two strings labeling two terms is performed. Secondly, a semantic similarity is used to compute the extent of similarity between the term pairs regarding the likeliness of their meaning. This opens the door to exploring WordNet as a way of finding semantic similarities, since WordNet can determine the semantic distance between two terms' names by considering synonyms or the relation between the hypernym and hyponym. Given two terms \(T_{1}\) and \(T_{2}\), their similarity sim (\(T_{1}\), \(T_{2}\)) is calculated according to the following equation:  
\[sim(T_{1},\ T_{2})=\left\{\begin{array}{rl}1,if\ \text{the term}\ T_{1}\ \ \text{is part of the syntest of the term}\ T_{2}\ \ \text{or vice versa}\\ 0.5\ if\ T_{1}\ \ \text{is a hypernym or a hyponym of}\ T_{2}\ \ \text{or vice versa}\\ 0,otherwise\end{array}\right., \tag{1}\]  
In this work, the combination of both syntactic similarity and the semantic similarity between terms intends to improve the overall alignment performance. At the end, the alignment activity provides a set of matches between the terms extracted from the refined local ontology and the initial ontology-based model according to the chosen strategy. This matching is defined in the form of triplets (\(T_{1}\), \(T_{2}\), \(r\)), where \(T_{1}\) and \(T_{2}\) are the terms of the two ontologies and \(r\) is a type of relation such as equality, generalization, or specialization.  
Figure 4: Non-taxonomic relation refinement method.
## 3 Experimental Studies and Results
### Experiment 1: The Robot Tells a Story  
To reproduce the pattern of complex gaze behavior on the robot, we developed a model in Python in which different scripts change their activation in time, and the script with the highest activation takes control of the robot's eyes. Figure 3 shows the functioning of the model with three scripts: (a) to think--this state changes sinusoidally over time; (b) to speak--the model received data about the starting time of the utterance and the phrase accent; (c) to pay attention to the person--this state increases linearly if the robot was not looking at the person and resets its activation as soon as the robot looks at the person. Every 40 milliseconds, the system calculates the leading script and allows it to set the direction of gaze. This model was used in one of the experimental conditions and was compared to some simple models of gaze management, which are described later. The eyelids and mouth did not move in this experiment. The robot detected the position of the human face within the environment and directed its gaze at the location of the human's eyes so that the subjects could freely move in the space in front of the robot.  
In the experiment, the participants (\(n=14\), six females, mean age of 27.2 years) evaluated the contribution of the gaze model to the robot's attractiveness (a preliminary report was given in a recent paper [36]). During the experiment, the robot narrated eight short stories. Each participant had to evaluate the robot on two modes of behavior (or conditions) according to [PERSON]'s semantic differential scales. In Condition 1, the robot  
Figure 3: Gaze control model via the competition of control states; gaze direction is controlled by the leading state.  
switched its gaze direction every 6 s from left to right, in turn, not looking at the user. In Condition 2, the gaze direction was controlled by the leading script, as mentioned above, and the robot could direct its gaze to the user. The activation of each of these scrips varied in time. When the leading state was changed, the new leader changed the gaze direction of the robot, as shown in Figure 3. The robot's utterances were accompanied by iconic gestures, which were similar in the two experimental conditions.  
Results and a Preliminary Discussion  
In the experimental setting, the subjects significantly more often (\(p<0.01\), Chi-Square) preferred the robot controlled by the balancing gaze model (Condition 2). This robot was significantly more often described as friendly, attractive, calm, emotional, and attentive (\(p<0.01\), Chi-Square). The results allow us to evaluate the contribution of the gaze direction to the positive perception of the robot and prove the effectiveness of the developed model of gaze behavior in the situation of human-machine interactions.
Assessing the interaction between the foot and the ground through stabilometry offers valuable insights into postural control strategies. Such information enables clinicians to identify dysfunctions and develop targeted interventions to improve balance, reduce falls, and enhance posture [16]. Posture and balance are particularly significant in the context of sports [19]. Athletes rely on optimal sensory integration and musculoskeletal coordination to achieve high performance while minimizing the risk of injury. Differences in postural stability between athletes and non-athletes suggest that long-term, high-intensity training may enhance sensory integration and motor responses. For example, volleyball players exhibit greater centre-of-pressure sway during visual deprivation tasks, indicating a reliance on visual input to maintain balance [20]. This may reflect adaptations that improve dynamic visual acuity and postural strategies in sport-specific scenarios [20]. Similarly, football players demonstrate playing position-specific differences in balance performance, with midfielders showing greater postural stability than defenders and forwards [21; 22]. Given the physical demands of football and the increased incidence of muscle and anterior cruciate ligament injuries, particularly in the post-COVID-19 era, preventive exercise programs have gained renewed relevance for maintaining musculoskeletal integrity and reducing injury risk [23; 24]. This study examines the relationship between visual acuity, refractive errors, and postural stability in elite football players. Few studies have specifically targeted elite football players, a population characterized by unique visual and motor demands, such as rapid visual processing under high-speed and unstable conditions. Our integrated approach combines comprehensive optometric assessments with objective postural stability analysis using the Cyber-Sabots(tm platform, evaluating participants under controlled visual conditions (eyes open and closed). This methodological approach enables a detailed examination of how refractive errors and visual acuity specifically affect postural control in elite athletes. These athletes rely heavily on visual input to guide motor responses and maintain balance. Furthermore, subtle visual impairments, such as uncorrected refractive errors, may compromise postural control and, consequently, performance. Investigating this population offers valuable insights into visuo-postural interactions under elite-level physiological and perceptual demands. This study aims to determine how visual acuity and refractive errors impact postural stability. We hypothesized that both greater refractive errors and reduced visual acuity would be significantly associated with increased postural sway, indicating reduced postural stability.
## 5 Energy Result Analysis and Reliability Evaluation  
### Energy Result Analysis  
#### 5.1.1 Analysis of the Reason for the Difference in Energies  
It can be seen from the previous section that the transferred energies obtained by different devices and methods with the same ignitor are quite different. Among them, the energy measured by the aluminum honeycomb device is the smallest, while the energy measured by the piston device has the largest value. The authors try to analyze the reasons why, as follows.  
When measuring the transferred energy, the friction is often ignored due to the small magnitude compared to the thrust. However, in the aluminum honeycomb device, in order to make the pin-rod perfectly compress the aluminum honeycomb, additional masses such as a compression adjustment lever and a pressing plate are added to the pin-rod, and thus the friction increases a lot, so that the work achieved by the friction cannot be ignored, thereby causing the transferred energy measured by the aluminum honeycomb device to be smaller than the others.  
Moreover, the aluminum honeycomb type, the absorbing cup type and the pin-rod type of devices all use the same pin puller housing, so that the initial volume of the ignition chamber is the same. However, with the movement of the pin-rod, the volume changes are no longer the same, which result in the pressure produced by the high-temperature and high-pressure gas also being different. Naturally, the transferred energies measured by the three different types of device are quite different.  
The parameters in the piston device, such as the initial volume, piston cross-sectional area, piston mass, and movement stroke, are totally different from those of the other three devices. The purpose of setting this device is to compare whether the energy measurement results are different with the same ignitor and different test parameters. At the same time, it can also provide a new and different idea for measuring the transfer energy of the ignitor. According to the results, it can be seen that the variation coefficient of the results measured by the piston device is quite small, which indicates that the measurement device has a high stability.