esa-sceva/satcom-chunk-collection huggingface dataset:
## Results and Discussion
### Chemometrics Approaches to Identify Chemical Descriptors of Strawberry Cultivar  
To investigate the potential of volatile metabolites as chemical descriptors to discriminate between strawberry cultivars grown in soilless systems, various complementary chemometrics approaches were employed in the present study. Principal component analysis (PCA) was first applied for a rapid exploration and visualization of the data trends. The PCA model obtained, with three principal components (PC), was able to explain up to 69.4% of the total variance. As shown in the corresponding scores plot (Figure 4A), the three strawberry varieties were slightly separated along the first principal component, but significant intra-cultivar variability was also observed. The PC1 was positively associated with levels of ethyl butanoate, ethyl hexanoate, hexyl hexanoate, linalool, geraniol and furaneol (Figure 4B). That is, the volatile content was in general higher in Festival samples, located in the right side of the PCA plot with positive loadings, in line with results presented in Table 1. On the other hand, the main contributors to PC2 were trans-2-hexen-1-ol, 1-hexanol, methyl butanoate, mesifurane and cis-nerolidol, thereby being responsible for the variability detected within Camarosa strawberries and, consequently, their wide spread on PC2 (Figure 4A). In a second step, linear discriminant analysis (LDA) was applied to build classification models for the predefined groups and to select the variables with higher discriminant power. For this purpose, the dataset was randomly divided into two groups, i.e., training and validation sets, accounting for 80% and 20% of the total number of samples, respectively. The training set was used to build the model, whereas the validation set enabled the testing of its performance. To validate the recalling rate (effectiveness of classification in the training set) and the prediction ability (effectiveness of classification in the validation set) of the LDA model, both training and validation sets were repeated at least ten times with different constitutions. The average percentage of correctly classified cases in the recalling and prediction tests obtained from these 10 runs was used as a measure of the method performance. Using this method, two significant discriminant functions (roots) were obtained by stepwise LDA (Wilks' lambda \(p\)-values 0.0000 and 0.0001, respectively), which explained 68% and 32% of the total variability of the data, respectively. As shown in Table 3, the most discriminant variables were geraniol, hexyl hexanoate, and trans-2-hexen-1-al.
# Channel-Hopping Using Reinforcement Learning for Rendezvous in Asymmetric Cognitive Radio Networks
## 4 Simulation Results
### Discussion, Limitations, and Further Works  
Through experiments, it was confirmed that the proposed reinforcement learning method demonstrates higher performance from the perspective of EITR in asymmetric cases compared to existing methods. Since each SU has its own agent, the overall complexity does not increase even as the number of SUs grows. However, as the number of the available channels for each SU increases, even with the use of an FCN network, the dimensions of the hidden layers increase, leading to greater complexity. This is because the state retains as much information as possible from previous channel exploration attempts to make appropriate decisions. In the future, it will be necessary to optimize this complexity through state embedding optimization techniques to prevent significant increases in complexity.  
Figure 9 shows an ineffective search pattern generated based on reinforcement learning. Two SUs are attempting to rendezvous based on this pattern, but even after 2000 attempts, they have not successfully rendezvoused. The current search pattern from reinforcement learning includes periods where the SUs stay in place, but it tends to search sequentially. If the index difference between the channels that the two SUs are currently exploring is large, one SU would need to stay for an extended period. This pattern delays the rendezvous, stemming from a lack of diversity in action.  
This limitation is also related to the issue of oscillations in the average reward during episode training. In this experiment, we only applied methods such as utilizing a  
Figure 8: TTR Distributions for asymmetric scenarios.  
Figure 9: Detection pattern example: Ineffective instance.  
replay buffer for episodes and separating the target network from the learning network to accelerate the convergence of the average reward. It is expected that performance can be further improved in the future through optimizations using various proximal policy optimization algorithms that enhance learning stability, such as gradient clipping. Additionally, the rigidity of this pattern could potentially be resolved by modifying the design of the actions. Currently, an approach that explores all channels collectively is used. If the action design could be adjusted to enable a hierarchical exploration by dividing the channel range into several subsets, it could help overcome the current limitations of sequential channel exploration.
_Algorithms_**2015**, \(8\), 366-379; doi:10.3390/a8030366  
_algorithmics_  
**ISSN 1999-4893**  
www.mdpi.com/journal/algorithms  
_Article_  
Identification of Dual-Rate Sampled Hammerstein Systems with a Piecewise-Linear Nonlinearity Using the Key Variable Separation Technique  
**[PERSON], [PERSON] 2 and [PERSON],* \({}^{1}\)**  
\({}^{1}\) College of Automation Engineering, Qingdao University, 308 Ningxia Road, Qingdao 266071, China; E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) College of Engineering, Ocean University of China, 238 Songling Road, Qingdao 266100, China; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-130-1241-6030.  
Academic Editor: [PERSON]  
_Received: 5 May 2015 / Accepted: 15 June 2015 / Published: 24 June 2015_  
**Abstract:** The identification difficulties for a dual-rate Hammerstein system lie in two aspects. First, the identification model of the system contains the products of the parameters of the nonlinear block and the linear block, and a standard least squares method cannot be directly applied to the model; second, the traditional single-rate discrete-time Hammerstein model cannot be used as the identification model for the dual-rate sampled system. In order to solve these problems, by combining the polynomial transformation technique with the key variable separation technique, this paper converts the Hammerstein system into a dual-rate linear regression model about all parameters (linear-in-parameter model) and proposes a recursive least squares algorithm to estimate the parameters of the dual-rate system. The simulation results verify the effectiveness of the proposed algorithm.  
**Keywords:** Hammerstein system; dual-rate; key variable separation technique; polynomial transformation; least squares
# Development of a Generic Numerical Transtibial Model for Limb-Prosthesis System Evaluation
## 3 Results
### Contact Pressure  
The maximum contact pressure at the limb-liner interface was determined for both the TSB and PTB sockets and for all load cases. The numerical values of the peak pressure are shown in Figure 7.  
Figure 6: Meshed numerical model of a transtibial limb-prosthesis system and graphical representation of the convergence study evaluated by average contact pressure at the limb-liner interface. The red dot indicates the final mesh.  
Figure 7: Peak contact pressure between the silicone liner and residual limb for the PTB (blue) and TSB (orange) sockets and all load cases.  
A pressure distribution was also considered in addition to the maximum contact pressure. A significant difference can be observed between the PTB and TSB sockets, as well as between the load cases. Figure 8 shows the pressure distribution for donning and the single-leg stance load step. Similarly, Figure 9 shows the pressure distribution for the P5 I and P5 II load steps at the settling test force. Note that the scale is different for each load step, ranging from 0 to 90 kPa for donning and 0 to 106 kPa for the single-leg stance, while a higher contact pressure of 0 to 128 kPa and 0 to 192 kPa can be observed for P5 I and P5 II, respectively.  
Figure 8: Contact pressure distribution for donning and the single-leg stance load steps.
# GenTrajRec: A Graph-Enhanced Trajectory Recovery Model Based on Signaling Data
## 2 Related Work  
### Trajectory Prediction  
Trajectory prediction is quite popular in recent research, which has been focused on humans [6; 7; 8] and vehicles [9; 10]. For instance, some researchers focused on a segment of a road or a single scene while others may focus on the movement of city dwellers. Traditional methods like Kalman filter [11] and Monte Carlo [12], might be computationally intensive and cannot handle multimodal distributions. Then, some machine learning methods like support vector machines [13] greatly improve positioning accuracy. However, these methods did not take the surroundings, such as traffic lights or maps, into consideration. Methods like DBN [14] can solve this problem, but they cannot be used for scenes like lane changing as well as lane maintaining.  
Recently, deep learning methods have become more popular because of their performance. Some methods use reinforcement learning. For instance, [PERSON] [9] introduced a target-driven trajectory prediction framework and modeled the likelihood of a trajectory given a target. While graph neural networks are becoming more popular in representation learning, [PERSON] [15] proposed an edge-enhanced graph attention network, a three-channel framework that can deal with the heterogeneity of the target agents and traffic participants, and used graph neural networks to model the interactions among traffic individuals and the interactions between traffic individuals and their surroundings. However, all these methods focus on a small area, such as a road segment, and haven't been used on a city-scale scene.
# Quality-Aware Resource Model Discovery
## 5 Evaluation
### Results  
First, we discovered a transition system from the collected event log with an abstraction such that: \(rep^{e}(\sigma)=\{[\beta_{a}(\sigma(|\sigma|))\times\beta_{o}(\sigma(|\sigma|) )]\}\) and \(rep^{e}(e)=[\beta_{a}(e)\times\beta_{o}(e)]\). After that, we identified the superior and inferior cases by applying the user-defined weight as 0.85; thus, we obtained 20% of the superior and inferior cases, respectively. Figure 9 depicts the model that integrates the derived transition system and the superior and inferior cases.  
According to the number of activities included in the log, the integrated model had 120 vertical lines (i.e., 118 activities and artificial start and end), which included multiple states, i.e., manufacturing resources. For example, in the leftmost vertical line, four resources were engaged: one blue, one red, and two gray nodes. After that, we performed the rest of the proposed methodology, i.e., statistical analysis with all significance level thresholds as 0.05 and model simplification with _simplifiedIM1_. Figure 10 presents the quality-based simplified resource model.  
Similar to Figure 9, we could recognize that Figure 10 was also composed of 120 vertical lines. However, the number of states for each line was dramatically decreased in this figure. For example, in the 51 st line, i.e., the longest vertical line in Figure 9, the number of states became 12, while it was 93 in the previous figure. Furthermore, it was discovered what resources had a significant effect on the quality in each activity. In the 51 st activity, six resources displayed as blue nodes, produced high quality, while five resources, i.e., the red nodes, were connected to poor quality. Note that the corresponding nodes had statistically significant effects on quality compared to other ones.
## 3 Results
### Experimental Results
As an example, consider the received signal from 50 kHz test case on 4% CSA pipe, is shown in Figure 19. The pipe end signal is a pure reflection of the excitation sequence where limited changes should be observed due to the coherent noise. The retrieved signal from the pipe end observes a 5 kHz shift in centre frequency. Furthermore, the generated sequence can also have a greater number of cycles which will result in higher maximum energy in an iteration's power spectrum in comparison to the reference. Therefore, because of these significant changes which are caused by generation of the sequences, it is not recommended to use higher than 42 kHz excitation frequencies with this algorithm. Figure 17: Signal-to-noise ratio (SNR) of the defect signal in each experimental test case. Figure 18: Results achieved using the algorithm where (**a**) shows the detection amplitude of defect signal and (**b**) shows the detection amplitude of the outlier. Each line represents a defect with different CSA size. The red dotted line represents the amplitude threshold for filtering the outliers. The only frequencies capable of detecting 2% CSA defect are 34, 36 and 40 kHz. With regards to Figure 20, which demonstrates the defect to outlier detection ratio, the detection amplitude achieved from defect from 34 and 40 kHz is higher by a factor of 1.5 and 2.38 than the outliers', while, in the case of 36 kHz, this factor is only 0.36 which means the amplitude for defect detection is smaller. Nonetheless, for defects with higher CSA size, the detected defect to outlier amplitude ratio from tested frequencies in the range of 34 to 42 kHz is almost always higher than a factor of 2. This suggests that by setting an amplitude threshold, defects above 3% CSA size can be detected without any outliers. This threshold is shown in Figure 18 by the red dotted line which in this case is set as 0.2. It should also be borne in mind that doing so, would remove the detection of 2% CSA defect, especially in the case of 40 kHz since even though the values are smaller than this threshold, but the amplitude of defect detection (0.19) is approximately twice than those of outlier (0.08). The only two frequencies which do not detect any outliers in any of the tests are 30 and 38 kHz. Nonetheless, using 38 kHz, 2% CSA defect and using 30 KHz, both 2% and 3% CSA defects are not detected. Figure 19: **(a)** The signal received from pipe end using 50 kHz and **(b)** its corresponding power spectrum. The red lines (dotted, +) show the references achieved from excitation sequence and the black lines (solid, x) show the results from each iteration. Figure 20: The ratio of detection amplitude of defect to outliers. In cases where the defect is not detected, the ratio is set as zero.
# Body Height Trajectories in Pediatric Competitive Athletes from 46 Different Sport Types
## 3 Results  
The study database consisted of 4044 height measurements from 2276 different participants, with 688 athletes having repeated measurements (Table 1). The median time between repeated measurements was 1.9 years (interquartile range: 1.0-2.9) with a maximum of 9.3 years. The study participants trained in 46 different sport types, and the most prevalent ones were basketball (\(n\) = 425), tennis (\(n\) = 266), cycling (\(n\) = 258), triathlon (\(n\) = 200), track and field (\(n\) = 190), gymnastics (\(n\) = 137), football (\(n\) = 133), swimming (\(n\) = 119), handball (\(n\) = 81), judo (\(n\) = 66), volleyball (\(n\) = 65) and horsebrak riding (\(n\) = 56).  
The age at the first clinic visit ranged from 5 to 18 years; hence, our first analysis served to examine if older athletes were taller than expected, signifying some form of selection bias towards higher athletes during adolescence. Figure 1 presents the height SDS across the age span of the whole cohort. Analyzing all study participants together, no meaningful correlation was found between height SDS and age at first visit (r = 0.059, \(p\) = 0.005).  
Focusing on the 688 participants with repeated measurements, GEE analyses found that there was no significant change in height SDS up to >4 years of measurements (B = \(-\)0.010, 95%CI = \(-\)0.048-0.028, \(p\) = 0.62) (Figure 2). In subgroup analyses, no significant change in height SDS was present in each of the two sexes when analyzed separately (males: B = \(-\)0.027, 95%CI = \(-\)0.069-0.015, \(p\) = 0.20; females: B = 0.028, 95%CI = \(-\)0.047-0.010, \(p\) = 0.46). No significant change in height SDS over time was seen when participants were divided into subgroups of first measurement age (i.e., <9, 9-12 and 12+ years) and adjusted for sex. However, when examining athletes from the most common sport types separately, significant reductions in height SDS over time were seen among athletes that trained in swimming, cycling and triathlon (Table 2) by a magnitude equivalent to approximately 1-2 cm/year. No significant changes in height SDS over time were seen among athletes that trained in gymnastics, tennis, basketball or football.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **All Cohort** & **Recurring Participants** \\  & **(\(n\) = 2276)** & **(\(n\) = 688)** \\ \hline Age at first & 12.3 \(\pm\) 2.6 & 11.4 \(\pm\) 2.4 \\ measurement (years) & 71.3\% & 70.9\% \\ Percent males & 153 \(\pm\) 16 & 149 \(\pm\) 16 \\ Height SDS & 0.30 \(\pm\) 1.05 * & 0.40 \(\pm\) 1.04 * \\ BMI (kg/m\({}^{2}\)) & 19.3 \(\pm\) 3.7 & 18.6 \(\pm\) 3.1 \\ BMI SDS & 0.17 \(\pm\) 0.97 * & 0.18 \(\pm\) 0.94 * \\ \hline \hline \end{tabular}
\end{table}
Table 1: Baseline clinical and anthropometric data of the study’s participants.  
Figure 1: Height SDS presented as the mean +95% confidence interval across age groups for all study participants (\(n\) = 2276).  
Figure 2: Height SDS changes, presented as the mean +95% confidence interval, during follow-up measurements since the first clinic visit (\(n\) = 688).
# Depletion-of-Battery Attack: Specificity, Modelling and Analysis
## 1 Introduction
Sensor energy consumption is increased and leads to quick battery exhaustion. Radio communications is usually among the highest energy drains on WSN nodes. At the same time, the signal-to-noise ratio is improved. Hence, the quality of signal reception is improved. The sensor transmission range is increased. Hence, packet latency can be reduced. System throughput is increased right up to the crash, which happens when a group of sensors fails and the network becomes disconnected. The attack effect looks like physical destruction. According to the International Telecommunications Union definition, DoS attacks focus on preventing legitimate users of a service from using the service. In this case, the goal of the attack is not fast service degradation. Moreover, the quality of service can be temporarily improved. Therefore, it is not a DoS attack. The target of the attack is the equipment (more exactly, the sensor battery), and the goal of attack is to disable the battery. The attack tools can be similar to a DoS situation. It can be malware or relaxed jamming. Therefore, it is not a physical attack. Here, by the term \"physical attack\", we mean that an attacker commits unauthorized physical actions to completely disable a sensor node (sensor node destroying, damaging of some key component of sensor equipment, thieivery). Thus, we get a special type of attack. We name it the depletion-of-battery (DoB) attack. This paper is organized as follows. In the next two sections, we discuss the related works and the feasibility of rash depletion of battery power. Next, we describe a model for sensor battery behavior when there is no attack. We then modify this model, taking into account the specifics of a DoB attack experienced due to packets flood. Next, we consider the DoB attack itself, which is caused by excessive power in the transmit signal. Thus, we propose theoretical tools for the evaluation of energy consumption under DoB attacks. A counteracting technique against the attack is discussed as well. Performance analysis and a brief conclusion finalize the paper.
## 1 Introduction  
Today, the size of data is getting bigger and the internet speed is getting faster. This causes a lot of data to be encrypted quickly. In line with this, hardware is developing rapidly, and the development of hardware enables fast operation and provides various functions.  
ARMv8 is the latest 64-bit ARM architecture. ARMv8 supports Advanced Single Instruction Multiple Data (ASIMD), which is also known as NEON engine. ASIMD is an instruction that can perform arithmetic operations on multiple data in parallel. In [1], the parallel encryption of AES block ciphers [2] is performed, showing a 5% performance improvement over the ASIMD-based Linux kernel implementation. In [3], they presented optimized format alignment and round function layer for SM4 block cipher on ARMv8 architectures. In [4], they utilized TBL/TBX instructions to perform fast multiplication for format-preserving encryption on ARMv8 architectures.  
Graphics processing units (GPUs) have become an integral part of today's computing systems. Parallel implementations of block ciphers using GPU capabilities are steadily progressing. Recently, the parallel implementation of block cipher using ARX structure was introduced in [5], and parallel implementation of AES using SPN structure was introduced in [6, 7].  
The ARIA block cipher [8] was developed in 2003. The algorithm is Korean standard, IETF standard (RFC 5794), and part of the TLS/SSL protocol. The ARIA block cipher is designed with an SPN (substitution permutation network) structure. It is designed as a substitution layer using four different Sboxes, a diffusion layer, and an addroundkey layer [9]. Previous studies implemented ARIA block ciphers on low-end processors. Few implementation works have been carried out on high-end processors.  
In this paper, we firstly present parallel implementations of the ARIA block cipher on ARMv8 processor. For the efficient implementation of the substitution layer, we explored two approaches of 4-PT (plaintext) and 16-PT, where 4-PT and 16-PT indicate that when the block size of the encryption algorithm is 128-bit, 4 (4 \(\times\) 128-bit) and 16 (16 \(\times\) 128-bit) blocks are encrypted in parallel. Secondly, we optimized parallel implementations of the ARIA block cipher on GPU (Nvidia GTX 3060). GPUs provide several types of memory space. In this paper, each implementation is evaluated by loading and using Sbox in different memory types, including global, shared, and constant. Furthermore, a method using extended Sbox to solve the bank conflict problem is introduced in [6]. We also explored this technique for ARIA block cipher in this paper.