esa-sceva/satcom-chunk-collection huggingface dataset:
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 6 Efficient Power and Data Transfer in BackCom
### Power Sources in BackCom
Moreover, compared to Bluetooth, it improves the total number of transmitted bits. Likewise, HitchHike was presented for a low power BackCom system and can harvest power from ambient WiFi signals [34]. HitchHike enables the little-powered tag to reflect the signals from commodity WiFi transmitters on existing IEEE \(802.11b\) standards. Thus, the \(802.11b\) commodity receiver can decode backscattered signals as a standard WiFi packet. Later, a low power operating system for ultra-low power devices was proposed to scale down the buffer size in dynamic and ultra-low harvesting conditions [27]. [PERSON] et al. suggested a two-user cooperation procedure in a WPCN assisted by BackCom [132]. This procedure saves energy by reusing wireless power transfer for simultaneous data exchange during harvesting time. Furthermore, the authors present a battery-free BER modulated data transmission technology that works on ambient WiFi signals [133]. The harvest-then-transmit (HTT) protocol is used for wireless energy transfer by H-AP [134]. H-AP transmits energy to tags in the downlink and users can send their information utilizing harvested energy. The optimal harvesting ratio was obtained using the HTT protocol with non-dedicated sources  
Figure 30: Power consumption of active and BackCom devices. [54]. This harvesting ratio can enhance the expected throughput for decode-and-forward (DF) relay transmissions, as well as direct transmissions (DTs). A mixture of backscatter modulation and wireless power transfer with a diode-based design was proposed for the continuous flow of energy [135].
## 4 Conclusions and Future Work  
In this paper, a framework modification for the power normalized cepstral coefficient (PNCC) system was presented for extracting more robust speech features, specifically at low signal to noise ratios (SNRs), without affecting the system performance for undistorted speech. Experimental results were acquired for a part of TIDIGITS corpus for clean and additive white and AURORA environmental noises. The proposed PNCC dramatically exceeded the state-of-the-art PNCC method as well as the MFCC, RASTA-PLP, and GFCC systems.  
* Under clean conditions, the recognition accuracy for all systems was almost equivalent.
* The highest robust proposed PNCC features in comparison to MFCC and RASTA-PLP were achieved in the subway noise condition at SNR 5 dB, whereas, for GFCC and PNCC methods, they were obtained in the case of car noise at SNR 0 dB and \(-\)5 dB, respectively. The achieved recognition accuracies for the proposed PNCC system were 86.89% in the subway noise condition at SNR 5 dB, 79.32% in the car noise condition at SNR 0 dB, and 53.58% in the car noise condition at SNR \(-\)5 dB.  
Many different adaptations, tests, and experiments are suggested for future work. They concern the more in-depth analysis of specific mechanisms, trying different methods to enhance the proposed  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **RASTA–PLP** & **PNCC** & **Proposed Method** \\ \hline Pre-emphasis & & 205 & 205 \\ Windowing & 205 & 205 & 205 \\ FFT & 2560 & 2560 & 2560 \\ Magnitude squared & 128 & 128 & 128 \\ Time power calculation & & 25 & 125 \\ Spectral integration & 2477 & 2492 & 2739 \\ ANS filtering & & 100 & \\ Channel bias minimizing & & & 125 \\ Equal loudness pre-emphasis & 128 & & \\ Temporal masking & & 120 & \\ Weight averaging & & 120 & 120 \\ IDFT & 120 & & \\ LPC and cepstral recursion & 115 & & \\ DCT & & 240 & 240 \\ \hline
**Sum** & **5733** & **6195** & **6447** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of multiplications and divisions in each frame [15].  
system's performance or to evaluate the proposed system's behavior under different conditions. For instance, the performance of the proposed system can be examined with a more extensive vocabulary and different languages datasets. Moreover, noisier conditions need to be evaluated, such as reverberation noise effects, colored noises, background music, and mixtures of environmental noises.  
[PERSON] and [PERSON] proposed the idea, conceived and designed the simulations, analyzed the data, and wrote the paper. [PERSON] reviewed the paper and simulation results.  
This research received no external funding.  
The authors declare no conflict of interest.
###### Abstract
Furthermore, the majority of the studies in this field rely on retrospective database analyses, effectively tracing career trajectories and identifying potential performance indicators [5; 8; 9], yet they often overlook significant additional aspects such as the interpersonal experiences among coaches in talent identification and selection and the athletes themselves [12]. In that regard, a landmark study conducted by [PERSON] and colleagues meticulously tracked the evolution of performance across various track cycling disciplines, providing a comprehensive evaluation of its distinct features over a span of 20 years [5]. This extensive investigation encompassed both genders and included an analysis of the performance trends within both the elite and junior categories, offering valuable insights into the sport's development. While this study stands as a notable contribution [5], the majority of research on this topic has predominantly focused on road cycling. Within this context, the performance of youth cyclists, particularly starting from the junior category, has emerged as a promising indicator for predicting future success within the elite category of road cycling [8; 9; 11]. Furthermore, several hypotheses have been proposed regarding this topic, including the exploration of the potential influence of the relative age effect (RAE) or birthplace effect, which may confer advantages or disadvantages in achieving future success. Due to the classification based on birth year, athletes born at different extremes within the year may present significant variations in physical, physiological, and psychological development in youth categories [13]. In the realm of road cycling, which predominantly emphasizes endurance, the RAE appears to primarily impact early performance, with its effects diminishing as athletes progress beyond the junior category [9; 14]. Furthermore, hypotheses have also been raised concerning the gender differences in the predictive power of youth performance, considering the divergent developmental paths of males and females [15; 16]. However, to the authors' knowledge, studies investigating this specific topic in the realm of cycling, particularly in track cycling, remain scarce or non-existent. Therefore, the main objective of the present study was to explore the significance of early performance in predicting future success, as well as the role of various publicly available performance indicators in forecasting the performance of male and female track cyclists across different disciplines. Furthermore, the study aims to complement the quantitative analysis based on publicly available databases by incorporating the insights and perspectives of international-level coaches and athletes through open interviews, thereby enriching the findings.
## 1 Introduction
However, since the formulation in [7] was purposely developed for damped axially symmetric waveguides it is limited to them. The formulation proposed in [8], can also deal with waveguides with generic cross section. However, the damping is simply introduced by adding an imaginary \"stiffness\" proportional to real \"stiffness\", simulating thus a sort of hysteretic material for the waveguide. Herein, a damped spectrally formulated plane triangular finite element with constant strain description (CST) is proposed to extend the formulation in [7] to waveguides of arbitrary cross-section. Damping is accounted analytically starting from the damped bulk properties of the material. The proposed algorithm can thus provide the dispersion curves as well as calculate the time transient response in waveguides of arbitrary cross-section accounting for any linear viscoelastic constitutive model that can be formulated in the frequency domain. The formulation is next adopted to analyze the problem of guided waves propagating in octagonal damped bars that to the authors' best knowledge has never been addressed. Octagonal bars, having a generic cross section, are representative of any arbitrary cross section waveguide and therefore useful to prove the reliability of the formulation. In addition since an octagonal bar has a behavior somehow similar to the one well-known of an equivalent rod with circular cross-section, the formulation and its results were controlled by means of the algorithm developed in [5] for axially symmetric waveguides. Finally, stress waves in octagonal bars are of interest since they can reveal some useful information on the behaviour of guided waves propagating in octagonal tubes, largely used for highway signal marks and lighting poles. These structures present two critical regions that are susceptible of corrosion because of the deposit of water: the base of the pole at the street level and the underground zone where the pole joins the concrete foundation. While the base of the pole can be rapidly inspected in several ways, the screening of the underground zone presents more difficulties. Therefore, a guided waves based non-destructive technique that operating from the outside near surface is capable to reveal the state of corrosion in the underground zone could be really appealing. Here, the basis for the numerical simulation are set. In this study several cases have been investigated to show the performance of the proposed procedure by considering different source-receiver distances and different excitations.
## 3 Results and Discussion
### Effect of Thermal Processing on Photosynthetic Pigments in Broccoli Extracts
\begin{table}
\begin{tabular}{c c c c c c} \hline
**mg/kg fw** & **BO** & **BL** & **ST** & **AF** & **PF** \\ \hline Chlorophyll \(a\) & 7.27 \(\pm\) 2.59 a & 12.26 \(\pm\) 2.05 a & 11.93 \(\pm\) 1.58 a & 10.53 \(\pm\) 4.99 a & 13.20 \(\pm\) 5.48 a \\ Chlorophyll \(b\) & 4.18 \(\pm\) 3.25 a & 3.32 \(\pm\) 0.54 a & 5.84 \(\pm\) 4.80 a & 4.20 \(\pm\) 1.08 a & 15.93 \(\pm\) 11.22 a \\ Carotenoids & 3.65 \(\pm\) 0.03 ab & 4.02 \(\pm\) 0.92 ab & 3.53 \(\pm\) 1.17 ab & 6.73 \(\pm\) 2.89 a & 1.77 \(\pm\) 0.43 b \\ Porphyrins & 31.73 \(\pm\) 25.84 a & 23.70 \(\pm\) 5.41 a & 43.86 \(\pm\) 37.87 a & 53.38 \(\pm\) 6.38 a & 125.85 \(\pm\) 83.70 a \\ \(\beta\)-Carotene & 0.43 \(\pm\) 0.03 a & 0.51 \(\pm\) 0.14 a & 0.54 \(\pm\) 0.02 a & 0.56 \(\pm\) 0.26 a & 0.17 \(\pm\) 0.00 a \\ Lycopene & 0.47 \(\pm\) 0.22 b & 0.81 \(\pm\) 0.16 ab & 0.80 \(\pm\) 0.04 ab & 0.91 \(\pm\) 0.19 a & 0.72 \(\pm\) 0.17 ab \\ \hline \end{tabular} Values represent average \(\pm\) standard deviation of three biological replicates. Different letters indicate a significant difference among the values in a row (one-way ANOVA, Duncan’s test, \(p\leq\) 0.05). fw = fresh weight; BO = broccoli boiled; BL = broccoli blanched; ST = broccoli steamed; AF = broccoli air-fried; PF = broccoli blanched and then pan-fried.  
\end{table} 
Table 2: Effect of different thermal processing techniques on photosynthetic pigments in broccoli extracts.
## 3 Control Strategy Design
### Pump Control  
To ensure the working performance of the cylinder and energy saving, the VFPB method is applied into the pump by controlling the speed of the motor. This signal is combined by two control signals, the velocity feed forward signal \(U_{P}^{VF}\) and the position feedback signal \(U_{P}^{PB}\). In which the \(U_{P}^{VF}\) signal is the required speed to guarantee that the cylinder receives the requisite amount of oil to move in accordance with the design trajectory. The \(U_{P}^{VF}\) signal can be calculated as follows:  
\[U_{P}^{VF}=\frac{Q_{need}}{D_{p}} \tag{14}\]  
where \(D_{P}\) is the pump displacement, and \(Q_{need}\) is the requisite amount of oil to supply on the cylinder. Depending on the metering mode, the amount of oil required is different to ensure energy saving and can be calculated as provided by Equation (15):  
\[Q_{need}=\left\{\begin{matrix}\dot{x}\Big{|}A_{a}&\text{PE mode}\\ \dot{x}\Big{|}A_{b}&\text{PR mode}\\ (A_{a}-A_{b})&\text{HSRE mode}\end{matrix}\right. \tag{15}\]  
In addition, the position feedback signal \(U_{P}^{PB}\) is added by using FLC to compensate for the effect of various factors such as leakage, friction, delayed response of the pump, etc. This fuzzy set has two inputs as the displacement error of cylinder \(e(t)\) and the target velocity \(v_{r}(t)\); an output variable which is the motor speed, shown in Figures 6 and 7 depicts the specifics of the triangular-type membership functions, in which \"neg-big\" (NB), \"neg-middle\" (NM), \"neg-small\" (NS), \"zero\" (Z), \"pos-small\" (PS), \"pos-middle\" (PM), and \"pos-big\" (PB) are used for displacement error input, whereas \"neg-small\" (NS), \"zero\" (Z), and \"pos-small\" (PS) are described for velocity input. The membership function of the output of FLC is divided into \"zero\" (Z), \"pos-small\" (PS), \"pos-middle\" (PM), \"pos-big\" (PB), and \"pos-large\" (PL). Table 3 shows the logic rule of this FLC based on the linguistic values corresponding to the membership functions.  
Figure 6: The fuzzy logic controller for pump control.
## 3 Analysis and Results of CGAN-IFCM
### Comparison Between CGAN-IFCM with Other Approaches using Wilcoxon Signed-Rank Test  
We conducted analysis on the performance of proposed CGAN-IFCM and various approaches in Sections 3.1-3.5. To determine if CGAN-IFCM statistically outperformed other approaches, adopting a \(t\)-test is not suitable because both TNR and TPR are bounded by 0% and 100% which does not follow normal distribution [46; 47]. Non-parametric Wilcoxon signed-rank test [48; 49] was chosen to confirm if CGAN-IFCM is statistically significant compared with other approaches. We assumed the significance level is 0.05. H\({}_{0}\) and H\({}_{a}\) were denoted as null hypothesis and alternative hypothesis, respectively. Equivalent accuracy (EQA) was defined as the weighted sum of TNR and TPR of the model. Table 11 summarizes the results of Wilcoxon signed-rank test. The subscript of EQA denotes the method of voice disorder detection model. Results reveal that the \(p\)-values of all cases are less than 0.05 so that the proposed CGAN-IFCM outperforms aforesaid approaches, presented in Sections 3.1-3.5. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Database**} & \multirow{2}{*}{**Methodology**} & **Types of** & **Performance** \\  & & **Cross-Validation** & **TNR**: 80.2\%; TPR: 74.8\% \\ \cline{2-4} \multirow{2}{*}{SVD} & SVM (radial basis kernel function) & 10-fold & TNR: 79.1\%; TPR: 73.4\% \\ \cline{2-4}  & Proposed CGAN-IFCM & & TNR: 92.2\%; TPR: 88.9\% \\ \hline \multirow{2}{*}{VOICED} & RF & & TNR: 78.5\%; TPR: 77.1\% \\ \cline{2-4}  & SVM (radial basis kernel function) & 10-fold & TNR: 76.9\%; TPR: 75.4\% \\ \cline{2-4}  & Proposed CGAN-IFCM & & TNR: 89.4\%; TPR: 90.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance comparison between CGAN-IFCM and two typical algorithms (random forest and support vector machine) for a multi-class detection model of voice disorder detection.
# A Preemptive-Resume Priority MAC Protocol for Efficient BSM Transmission in UAV-Assisted VANETs
## 4 Problem Formulation and Performance Metrics
### UAV Queuing Delay
This paper selects two representative cases for illustration, as follows:  
As shown in Figure 7, for Scenario 1, \(\lambda_{\rm{v1}}<\lambda_{\rm{v2}}<\lambda_{\rm{v3}}\) is selected, where the vehicle with the slowest speed segment has the highest arrival rate and the largest number of vehicles. For Scenario 2, \(\lambda_{\rm{v1}}>\lambda_{\rm{v2}}>\lambda_{\rm{v3}}\) is selected, where the vehicle with the highest speed segment has the highest arrival rate and the largest number of vehicles. The remaining scenarios, as they are not representative, are not discussed in this paper. In our system, we define \(\rm{T_{ku}}\) as the average communication delay between each vehicle within the coverage range of the UAV across different priority levels. According to the equation \(\rm{\rho_{i}}=\lambda_{i}E(S_{i})\), we can obtain the relationship between system utilization rate as follows:  
\[\rho_{1}=\lambda_{v1}E(S_{1}), \tag{31}\]  
\[\rho_{2}=\lambda_{v2}E(S_{2}), \tag{32}\]  
\[\rho_{3}=\lambda_{v3}E(S_{3}). \tag{33}\]  
By substituting Equation (30), we can rearrange the equation as follows:  
\[E(WT_{1})=\frac{\frac{1}{2}E(S_{1}^{2})\lambda_{v1}}{1-\rho_{1}}\ \, \tag{34}\]  
\[E(WT_{2})=\frac{\frac{1}{2}\big{(}E\big{(}S_{1}^{2}\big{)}\lambda_{v1}+E(S_{2 }^{2})\lambda_{v2}\big{)}}{(1-\rho_{1})(1-\rho_{1}-\rho_{2})}\, \tag{35}\]  
\[WT_{1}=\frac{R_{1}}{1-\rho_{1}}\, \tag{27}\]0
## 4 Delay and Energy Consumption Evaluation
### Simulation Parameters  
To validate the models and evaluate the delay and energy consumption of the FSA variants we use the parameters summarized in Table 1. These values have been selected according to the IEEE 802.15.4 standard [22] and the Texas Instruments CC2520 radio transceiver [23] specifications. In particular, all the packets are composed of a physical layer preamble, a MAC header, a payload (114 bytes) and a 2-byte CRC (Cyclic Redundancy Code). However, in FSA-FBP the length of the FBP payload is set to attach 2 bits per slot, allowing to inform the status of each slot. In contrast, for DFSA and FSA-ACK the coordinator responds to each data packet decoded successfully in each slot with an ACK.
# Alternative Pedicle Screw Design via Biomechanical Evaluation
## 3 Mechanical Strength of the New Design
### Experimental Testing of Bending Strength  
#### 3.1.1 Experimental Setup  
Experimental tests were performed based on the ASTM F1717 standard [14]. The model fused structure consisted of two vertebrae, four pedicle screws, and a linking mechanism. The model vertebrae with a pentagonal top view were made from ultra-high molecular weight polyethylene (UHMWPE) because of its high toughness and good self-lubrication, which are good characteristics for the bending test jig [15]. The pedicle screws were fastened to the model vertebrae and tightened with appropriate torque using a torque wrench [16]. The distance between the top and bottom screws was 76 mm, which is similar to the actual distance between two human vertebrae. The linking mechanism (two rods) was inserted into the U-shaped groove of the screw head.  
The prepared test mechanism can be seen in Figure 3. Cubic top and bottom jigs were also assembled with the model structure, fused by using two pins that allowed free rotation without resisting movement (Figure 3a). Each of the fixtures was placed in a material testing machine (MTS 858 Table Top System, MTS System Corp., USA; Figure 3b); the bending tests were performed by pressing the jigs at a rate of 25 mm/min until fracturing occurred. In all tests, the compression distance and load were recorded (especially the load that caused fracturing).
## 4 Materials and Methods
### Pulse Amplitude Modulated (PAM) Fluorometry  
Modulated chlorophyll fluorescence measurements were made on attached leaves using a FluoroPen FP100 PAM (Photo System Instruments, Brno, Czech Republic). All measurements in the dark-adapted state were made after the darkening of the leaves for at least 30 min. The OJIP transient (or Kautsky curves) depicts the rate of reduction kinetics of various components of PS II. This is obtained when a dark-adapted leaf is illuminated with a saturating light intensity of 3500 umol m\({}^{-2}\) s\({}^{-1}\). Then it exhibits a polyphasic rise in fluorescence (OJIP): level O represents all the open reaction centres at the onset of illumination with no reduction of Q\({}_{\rm A}\) (fluorescence intensity lasts for 10 ms); the O to J transient indicates the net photochemical reduction of Q\({}_{\rm A}\) (the stable primary electron acceptor of PS II) to Q\({}_{\rm A}\)\({}^{-}\) (lasts for 2 ms); the J to I transition is due to all reduced states of closed RCs such as Q\({}_{\rm A}\)\({}^{-}\) Q\({}_{\rm B}\)\({}^{-}\), Q\({}_{\rm A}\) Q\({}_{\rm B}\)\({}^{2-}\) and Q\({}_{\rm A}\)\({}^{-}\) Q\({}_{\rm B}\) H\({}_{2}\) (lasts for 2-30 ms); P-step coincides with a maximum concentration of Q\({}_{\rm A}\)\({}^{-}\) Q\({}_{\rm B}\)\({}^{2}\) with plastoquinol pool maximally reduced. It also reflects a balance between the light incident at the PS II side and the rate of utilization of the chemical (potential) energy and the rate of heat dissipation [77]. Table 2 summarizes all the parameters that were calculated from the fluorometric analysis.
# Multi-Objective Optimization for High-Dimensional Maximal Frequent Itemset Mining
## 2 Related Concepts
### Frequent Itemset  
The mathematical description of frequent itemset mining is detailed as follows: based on the above text, \(D=\{T_{1}\), \(T_{2}\), \(T_{3}\),..., \(T_{n}\}\) was set as the big data transaction set for mining, and \(I=\{\textit{l}_{1}\), \(\textit{l}_{2}\),..., \(\textit{l}_{m}\}\) was the set of all items. If a set \(X\subset I\) is composed of several items and the probability of occurrence (\(P(X)\)) of \(X\) in the transaction set \(T_{i}\) of \(D\) is higher than the preset threshold, then \(X\) is called the frequent itemset. \(\mid X\mid\) expresses the number of items in the itemset. If \(\mid X\mid\) = \(k\), then it is called the \(k\)-itemset. Given a minimum support threshold (_min_support_), the frequent itemset mining aims to find all the frequent itemsets whose _support_ is higher than the threshold. Based on this definition, the following properties can be determined:  
**Property 1.**_Given any non-void itemset \(X\), \(Y\subseteq I\), support (\(X\)) \(\geq\) support (\(Y\)) if \(X\subseteq Y\). Therefore, \(X\) is the frequent itemset if \(Y\) is a frequent itemset, and \(Y\) is not a frequent itemset if \(X\) is not a frequent itemset._  
**Definition 1.**_Closed frequent itemset. When the itemset \(X\) is a frequent itemset and no superset \(Y\) of \(X\) is found in the transaction sets \(D\) to make support (\(X\)) = support (\(Y\)), then \(X\) is a closed frequent itemset. The closed frequent itemset expresses lossless compression and has no loss of information about support. All frequent itemsets and corresponding supports can be obtained from the closed frequent itemset through the inversion mode._  
**Definition 2.**_Maximal frequent itemset. If the itemset \(X\) is a frequent itemset and any superset of \(X\) is not a frequent itemset, then \(X\) is a maximal frequent itemset. The expression of the maximal frequent itemset is a lossy compression, and it loses information about the support of the frequent itemset. Whether an itemset is frequent can be judged according to the maximal frequent itemset. However, the corresponding supports cannot be obtained._