esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The aim of the current study was to identify the specific differences in anthropometric and performance profiles between elite and sub-elite hurling players with respect to position and level of play. One hundred and thirty-seven (\(n=137\)) hurlers at the elite (\(n=61\)) and sub-elite (\(n=76\)) level completed a series of anthropometric [height, body mass, the sum of seven skindolds, adipose tissue percentage estimates (%AT), fat-free mass estimations (FFM)) and performance (countermovement jump height (CMI), CMJ peak power (CMI PP), CMJ relative peak power (CMJ RPP), acceleration (5, 10 and 20 m), and yo-yo intermittent recovery test level 1 (Yo-Yo IRT1)) assessments during the \"early in-season\" phase of the competition. Data were categorized into positions at both levels (full-backs, half-backs, midfielders, half-forwards, full-forwards). Multiple two-way ANOVA's were performed to determine the effect of two fixed factors (level and position) on several anthropometric and performance variables. Elite level players had a significantly lower sum of seven skindolds across all positions compared to the sub-elite (\(\eta^{2}=0.441\), _large_). At half-back, half-forward, and full-forward elite players had significantly lower %AT (\(\eta^{2}=0.087\)-\(0.167\), small-medium) and greater FFM (\(\eta^{2}=0.040\)-\(0.065\), small). Jump performance assessment showed elite players performed significantly better across all positions for CMJ (\(\eta^{2}=0.526\), _large_), CMJ PP (\(\eta^{2}=0.385\), _large_) and CMJ RPP (\(\eta^{2}=0.520\), _large_). When Yo-Yo IRT1 was considered, elite players completed an increased distance than the sub-elite across all positions (\(\eta^{2}=0.526\), _large_). The current data are the first to show differences in positional anthropometric and performance profiles between standards of play within hurling. Applied practitioners should consider these normative data when implementing training programs to maximize position-specific preparation for competition. Furthermore, these data could improve the considerations regarding the transitioning of players from the sub-elite to the elite level of play.  
team sports; playing standards; kinanthropometry; jump performance; intermittent running capacity
## 2 Related Work  
There have been several studies performed on spoken digits recognition in different languages. [PERSON] et al. proposed [24] the first complete audio visual database of spoken digits, known as Extended XM2 VTS, which is made up of recordings from 295 people of all ages and genders. Each speaker's audio recording consists of two continuous digit strings. [PERSON] et al. proposed the BANCA database [25], which has utterances in English, Spanish, Italian, and French, among other languages. The goal of this database was to put person verification to the test in a variety of settings, including controlled, degraded, and unfavorable. [PERSON] et al. [26] proposed Discrete Cosine Transform and Local Binary Patterns to conduct an audiovisual digit recognition challenge. [PERSON] et al. [27] created an in-house database for Marathi speech digits to explore lip reading movement and digit classification.Wazir et al. [28] proposed LSTM to accomplish the recognition task, and MFCCs were used to extract features from audio files to develop a speech recognition solution for Arabic numerals using RNN. This model is capable of classifying digits from noisy spoken digits. The results are promising, with a 69 percent of overall recognition accuracy. [PERSON] et al. [29] presented a novel, publicly available audio dataset of spoken digits in the Gujarati language, as well as some preliminary results. A comparison of the categorization algorithm with an English language database was also performed. Many works [12; 17; 22] considered Urdu language voice data for various applications. [PERSON] et al. in [17] described the frequency analysis of spoken Urdu numbers from 0 to 9. In this work, the authors collected voice samples from 15 speakers who spoke the same digit set from 0 to 9. The voice recordings were curtailed for 0.9 min. The data were recorded using a microphone connected to a Windows-based PC and saved into.wav file format. The initial processing of data was done using Simulink and MATLAB. Fourier descriptions and correlation calculations were performed using these same tools. The same words were delivered by the same and different people, and words were distinguished using correlation. A feed-forward neural network developed in MATLAB was used to classifythe voices with 100 percent accuracy. Although authors obtained maximum accuracy and found a trade-off between goal, epochs, and learning rate, the dataset used in this work was too small.  
All of the above works [12; 17; 21; 22] have used Urdu datasets with limited data (e.g., [17] used a dataset of 150 samples). In contrast, our dataset is comprehensive and diverse and consists of 25,518 samples, which is helpful for classification.
# tRNA and tsRNA: From Heterogeneity to Multifaceted Regulators
## 3 Chemical Modifications: Guardians of tRNA Integrity and Function
### RNA Modification on tRNA
#### 3.1.6 Pseudouridine (\(\Psi\))  
As the most prevalent modification, \(\Psi\) is present on various types of RNAs across three kingdoms [59; 109]. On tRNA, \(\Psi\) is catalyzed by different \(\Psi\) synthases with specific tRNA base locations. Among these locations, \(\Psi\)55 is present in nearly all tRNAs at the position of the TYC loop and plays pivotal roles in stabilizing tRNA tertiary structures and maintaining tRNA biological function in translation. In human tRNA, \(\Psi\)55 is catalyzed by nuclear TruB Pseudouridine Synthase Family Member 1 (TRUB1), mitochondrial TRUB2, and PUS10 [110]. Notably, PUS10 has two isoforms: one is cytoplasmic PUS10, which is responsible for the pseudouridinylation of \(\Psi\)54 and \(\Psi\)55; the other is the nuclear isoform of PUS10, which is catalytically inactive but has a specific binding affinity to tRNAs that contain unmodified U54/A54U55, thereby inhibiting nuclear TRUB1 activity on conversing U55 to \(\Psi\)55 [111]. The other positions of \(\Psi\) modifications on human tRNA are usually catalyzed by the PUS family (PUS1; PUS7) [110; 112], which usually occurs on U1, U8, U26-28, U34, and U36.  
As mentioned above, one important role of pseudouridinylation is to ensure the proper folding and stabilization of tRNA tertiary structures. The Watson-Crick base pairs of \(\Psi\) are the same as U, which is adenine A, but with greater thermodynamic stability than U-A pairs. For example, \(\Psi\)39 of tRNALys was reported to increase the melting temperature by 5 \({}^{\circ}\)C, thus stabilizing tRNA from degradation. However, there is an exception whereby the \(\Psi\)8 modification that is catalyzed by PUS7 can reduce tRNA stability. After the knockout of PUS7 in human stem cells, although the overall amount of tRNA is unchanged, a reduction in \(5^{\prime}\)tsRNA from tRNA\({}^{\text{Ala}}\), tRNACys, and tRNAVal can be observed [113], while the overexpression of PUS7 enhances the biogenesis of \(5^{\prime}\)tsRNAAla/Cys and \(5^{\prime}\)tsRNAVal with \(\Psi\)8 modification, which further sequesters the translation initiation factors and significantly represses the cellular translation rate.
## 3 Data and Methodology
### Applied Techniques
#### 3.1.4 Vs30 Values and Soil Classification  
The shear wave velocity for the uppermost 30 m, often denoted as Vs30, can be determined using the Vs profile obtained by inversion of the HVSR and dispersion curves. The Vs30 value is determined using Equation (2) (Eurocode 8, [51]):  
\[\text{Vs30}=\frac{30}{\sum_{i=1}^{N}\frac{H_{i}}{V_{i}}} \tag{2}\]Here, \(H_{i}\) denotes the thickness of the \(i\)th layer in meters, \(V_{i}\) represents the shear wave velocity of the \(i\)th layer in meters per second (m/s), and \(N\) indicates the total number of layers from the surface down to a depth of 30 m.  
Different contour maps have been generated by linear interpolation to represent the different key factors related to site effects, including \(f_{0}\), \(A_{0}\), \(Kg\), depth to bedrock, and Vs30. In the case of Vs30 values, the generated contour map serves as a basis for site classification according to the National Earthquake Hazards Reduction Program (NEHRP) code [52]. Three-dimensional and 2D geo-models constitute valuable tools for representing multisource data retrieved from boreholes, seismic measurements, etc. [53; 54; 55; 56].
Activity Characteristic of the Two Buried Faults in Zhongwei Basin along the Northeastern Margins of Tibetan Plateau, China, by Means of Shallow Seismic and Composite Drilling Section Exploration  
Chao Liu  
short). Ningxia overlaps the junction of three large active blocks, the Tibetan Plateau, the Alashan Block and the Ordos Block (Figure 1B). There are many active faults and destructive earthquakes in Ningxia [20,21], which are affected by the relative movements of the three blocks. The Tianjingshan Fault splay eastward off the north side of the Haiyuan Fault, where the 1709 \(M\)7.5 Zhongwei earthquake produced a left-lateral surface rupture 30-60 km [22]. The Zhongwei Basin lies on the north side of Tianjingshan Fault, the Yellow River passes there. At present, the existence of buried faults in the Zhongwei Basin and their activity characteristics have been considered only rarely, but these issues need to be addressed urgently, which is a possible seismic hazard and a threat to urban security of Zhongwei City.  
In this paper, we present the evidence of shallow seismic exploration to indicate the existence of two buried faults in the Zhongwei Basin, which are called the Hetan-Guotan buried fault and the Zhenjing-Zhenbei buried fault. Then, composite drilling section exploration can effectively determine the precise positions of two buried faults and the burial depths of uppermost point. Additionally, we analyze the activity characteristics of buried faults according to the results of sample dating test. The results show that the two buried faults became inactive at the end of Middle Pleistocene and have been inactive since the Late Pleistocene.  
Figure 1: The tectonic setting of Ningxia. (**A**) Regional tectonic map on the Tibetan Plateau. The black box shows the location of (**B**). (**B**) Topographic and Seismotectonic map of the Ningxia and the surrounding area. Black bold lines in the map represent major active faults. Red solid circles represent the destructive earthquake epicenters (\(M\geq 5\)). Dashed black box shows the location of Figure 2A.
# SCM-IoT: An Approach for Internet of Things Services Integration and Coordination
## 4 Case Study  
An application to home automation of the SCM-IoT model is described below to demonstrate its flexibility in describing interactions in IoT systems. The shared storage model allows describing complex events that require the combination of data generated by various sensors to make decisions, such as, for example, to notify emergency systems when very high temperatures are observed with the presence of smoke and toxic gases, as well as to determine the room where the residents of a house are.
## 2 Results
### Expression of SrTPSs under Multiple Stress Conditions  
In general, plant _TPSs_ are induced by environmental stresses [21]. To investigate if _SrTPSs_ are responsive to different environmental stresses, stress-related phytohormones, such as methyl jasmonate (MeJA), salicylic acid (SA), and abscisic acid (ABA), as well as wounding and dehydration stresses were applied. Figure 6A shows that _SrTPS1_ and _SrTPS2_ exhibited an early response to MeJA, SA, and ABA in leaves, whereas _SrTPS4_ was gradually induced under MeJA and SA treatment and reached its highest at 6 h of ABA treatment. In the roots of Stevia, when _SrTPS2_ remained nearly unaltered or slightly decreased upon all phytohormone treatment, _SrTPS4_ was gradually induced only by ABA but not by MeJA and SA (Figure 6B). On the other hand, _SrTPS1_ and _SrTPS5_ transcripts were induced early and slightly late by MeJA and SA, respectively (Figure 6B). Note that _SrTPS3_ and _SrTPS5_ in leaves were omitted due to undetectable levels of transcripts by phytohormone treatments, and _SrTPS3_ in roots was not responsive to any phytohormone treatment.  
Figure 6: Expression levels of Stevia _TPSs_ under various environmental stresses. (**A**,**B**) hormone treatments, (**C**,**D**) wounding, (**E**,**F**) dehydration, (**A**,**C**,**E**) leaf, (**B**,**D**,**F**) root. MeJA, methyl jasmonate; SA, salicylic acid; ABA, abscisic acid. The housekeeping gene _actin_ was used for normalization. Data are the mean \(\pm\) standard deviation of three readings. Statistical significance of the measurements was determined by Student’s \(t\)-test (* \(p<0.05\), ** \(p<0.01\), *** \(p<0.005\)).  
Similar results were obtained for _SrTPS1_, _SrTPS2_, and _SrTPS4_ in wounded leaves (Figure 6C). However, in roots, we were only able to find _SrTPS2_ induced by wounding (Figure 6D). The level of _SrTPS4_ and _SrTPS5_ transcripts remained unchanged, and others were very low levels or not detectable in roots.  
Under dehydration, the levels of _SrTPS1_ and _SrTPS2_ in leaves gradually increased and stayed high at 12 h (Figure 6E). Interestingly, the transcript levels of _SrTPS1_, _SrTPS2_, and _SrTPS5_ were highest at 6 h in roots. Significantly, the induction levels of _SrTPS1_ and _SrTPS2_ were more than 20 and 160 times under dehydration conditions, respectively (Figure 6F). Unlike other _SrTPSs_, the expression of _SrTPS4_ was slightly downregulated by dehydration in both leaves and roots (Figure 6E,F).  
All conditions for stress treatments in this study were verified by the expression of homologs of Arabidopsis stress-induced marker genes, such as _Ethylene-Responsive element-binding Factor 1_ (_ERF1_) for wounding and MeJA [22], _Glutathione S-Transferase 6_ (_GST6_) for SA [23], and _Responsive to ABA 18_ (_RAB18_) for dehydration and ABA [24; 25], which were highly upregulated under the above-mentioned stress conditions (Supplementary Figure S6).
## 4 Discussion
### The Role of Support Leg in Ball Spin  
Ball spin decreased with greater support knee flexion-extension ROM for both preferred and non-preferred leg kicks. Based on the previously defined ROM calculation method, the maximum knee angle occurred at kicking leg toe-off, while the minimum value was observed after SLTG [15; 19; 28]. Therefore, the knee flexion-extension ROM primarily represents the knee joint's adjustment capacity prior to SLTG. Previous studies have investigated the relationship between knee flexion-extension ROM during the support phase (from SLTG to ball impact) and ball velocity, reporting no significant correlation [21]. Expanding upon these findings, this study further demonstrates that knee ROM before the support phase (i.e., pre-landing ROM) also does not affect ball velocity, but it has a negative effect on ball spin. This may be because a greater knee ROM before SLTG allows for more effective pre-landing adjustments, enhances support leg stability at ground contact, and facilitates a more efficient ball-foot impact during the instep kick, thus ultimately reducing ball spin. This effect was statistically significant in professional players (both male and female), where the preferred leg kicks exhibited greater knee flexion-extension ROM than the non-preferred leg kicks. This finding likely reflects an advantage of the preferred leg kicks in the support leg adjustment capacity, allowing for more precise control of support leg positioning and improved energy transfer efficiency within the kicking kinetic chain.  
Ball spin increased with greater hip adduction-abduction ROM of the support leg for preferred leg kicks. The maximum angle occurred at ball impact, while the minimum was observed after SLTG [13, 15]. Following ground contact, the support leg primarily undergoes hip adduction [13, 22], and a greater hip adduction-abduction ROM indicates excessive hip adduction during the support phase, which may limit counterclockwise pelvic rotation and reduce the effectiveness of the proximal-to-distal force transmission chain, ultimately influencing the spin and stability of the ball upon impact.  
Coaches can implement neuromuscular training to improve knee flexion-extension control, especially in the non-dominant leg [37]. Exercises that enhance hip joint stability, promote greater support leg control, reduce excessive hip adduction, and optimize energy transfer throughout the kicking motion could help players improve their ball spin [17, 38]. This will also help provide a more controlled and stable base for the kicking leg.
# A Review on Hot-IP Finding Methods and Its Application in Early DDoS Target Detection
## 2 Hot-IP Finding Methods
### Methods for Finding High-Occurrence-Frequency Elements
#### 2.1.5 Count-Sketch  
The Count-Sketch algorithm was proposed by [PERSON] et al. for the first time in 2002 and the improved version of the algorithm was introduced in 2004 [12]. Sketch is a terminology used to represent a data structure, or a linear mapping of the input frequency vector. Count-Sketch is used to find \(k\) elements that have a high occurrence frequency in the data stream. Sketch data structure is compact and the algorithm's processing speed is fast.  
Count-Sketch uses two input variables, where \(\varepsilon\) is the error rate and \(\delta\) is the error probability. To store counter variables in the processing, Count-Sketch uses an array sized \(w\times d\), where \(d\) is the number of hash functions and \(w\) is the space size used by a hash function.  
The Count-Sketch algorithm consists of three stages: Stage 1 is for variable initialization; Stage 2 is a loop to process the data stream; and Stage 3 is for processing of the output result. The details of this algorithm can be found in [12].
# Primary User Traffic Pattern Based Opportunistic Spectrum Handoff in Cognitive Radio Networks
## 5 Conclusions  
Spectrum Handoff is a vital part of the spectrum management process in CRNs. It is the method of moving the ongoing communication of a CRU to a vacant channel at the arrival of PU without a glitch. In this research, a new handoff (PUTPOSH) approach has presented--on the one hand, it maximizes the channel utilization and the throughput while on the other hand, it minimizes the overall service time and the number of handoffs in CRNs. In PUTPOSH, firstly, an energy detection sensing scheme was employed for the announcement of a PU in its licensed spectrum band. Secondly, a PUTPOSH pattern was projected, where a CRU intelligently switched between reactive and proactive handoffs depending upon the overall service time. Hence the presented approach has achieved the aids of both proactive and reactive schemes. The comparison of the PUTPOSH with the existing proactive, reactive and hybrid handoff strategies has also conducted. The results show that the PUTPOSH approach gets good performance in terms of overall service time--having a superior rate than the proactive and 12.8% robust than the reactive handoff. In terms of the number of handoffs, the PUTPOSH gives 14.28% better results than proactive (FLSH), reactive (DFHC) and hybrid (DSA) approaches. The PUTPOSH scheme produces 20% better results than the static spectrum access and throughput in CRNs. PUTPOSH can professionally reduce the overall service time and the number of fruitless handoffs of a CRU while keeping the channel busy and the system throughput at the highest level.  
This work can be extended in a number of means; (1) a situation can be considered where a CRU can transfer its respective data on numerous accessible primary channels concurrently, (2) a management tool amongst the CRUs retrieving the primary spectrum holes can be developed, (3) CRU can interconnect to exchange the information regarding the movement of the PU and their stopover as well on a conforming channel to make the communication more reliable, and (4) spectrum handoff requires frequent spectrum sensing and channel information that takes a significant amount of power. Hence the energy-efficient spectrum sensing and mobility are still open challenges in CRNs.  
Conceptualization, K.A.; Data curation, M.K.B., N.A.M. and M.Z.; Formal analysis, I.H. and I.A.K.; Funding acquisition, M.K.B., S.N., A.D. and N.A.M.; Methodology, K.A., I.H. and I.A.K.; Resources, M.Z.; Software, K.A.; Supervision, I.H. and A.D.; Validation, M.K.B., S.N., A.D. and I.A.K.; Visualization, N.A.M.; Writing--original draft, K.A.; Writing--review & editing, I.H., S.N., M.Z. and I.A.K. All authors have read and agreed to the published version of the manuscript.  
Figure 11: Comparison of proposed handoff and a hybrid approach (dynamic spectrum aggregation (DSA)) schemes in terms of handoff delay.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Social-Aware Peer Selection for Device-to-Device Communications in Dense Small-Cell Networks
## 4 Formulation of Social-Aware D2D Peer Selection Algorithm and Performance Indicators
### Performance Indicators
#### 4.2.1 Signal to Noise Ratio  
We incorporate \(\Delta\) to compute \(\gamma\) at \(Rx\) (i.e., \(\gamma\) (\(Rx\))), which is related to the transmission power, path-loss coefficient, additive white Gaussian noise (AWGN) with zero mean, and the variance \(\sigma\)[19] and is given by  
\[\gamma_{(Rx)}=\ \frac{P_{(Tx,Rx)}\ h_{(Tx,Rx)}\ \Delta_{(Tx,Rx)}}{\sigma^{2}\ D_{(Tx, Rx)}^{\beta}}, \tag{15}\]  
where \(P_{(Tx)}\) is the transmission power of \(Tx\), \(h_{(Tx,Rx)}\) is the Rayleigh coefficient, \(\Delta_{(Tx,Rx)}\) is the cumulative closeness coefficient between \(Tx\) and \(RX\), and \(\beta\) is the coefficient of path-loss.
## 2 Antenna Designing and Operation Mechanism
### Design of UWB Antenna  
The UWB antenna element (Tachism) with a KAYI shape is presented in Figure 1, and dimension values of different parameters of the proposed antenna is presented in Table 1. The proposed antenna elements are suitable for UWB applications along with notching characteristics. The UWB antenna specifications can be achieved by placing a single patch element on top of the substrate and carving a defected KITE shape on the ground plane. A resonance point in simulation affects the UWB performance parameters at some frequencies within the whole band. The impedance bandwidth matching of these bands can be enhanced by placing symmetrical semi-circular curves of diameter C = 1 mm on top of the KITE shape which is at the ground plane. The reflection coefficient of UWB elements of MIMO design is presented in Figure 2. The result shows that the antenna is working excellently for a specified UWB band. Additionally the extended bandwidth is achieved for the entire 3-20 GHz frequency band.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Values (mm)** & **Parameter** & **Values (mm)** \\ \hline \(L\) & 30 & \(W\) & 30 \\ \hline \(L1\) & 14 & \(W1\) & 2.5 \\ \hline \(L2\) & 8.5 & \(W2\) & 2.5 \\ \hline \(L3\) & 4 & \(W3\) & 1 \\ \hline \(L4\) & 4 & \(W4\) & 15 \\ \hline \(L5\) & 22 & \(C\) & 1 \\ \hline \end{tabular}
\end{table}
Table 1: Optimized dimensional values of different parameters of the proposed UWB-MIMO antenna system with a three notch band using only two notching elements.  
Figure 1: Geometry of the proposed \(1\times 3\) MIMO antenna: (**a**) Top view of three radiating elements, each loaded with two notching elements; (**b**) \(L_{u}\) and \(U_{u}\) shape as notching elements to suppress WiMax and WLAN, respectively; (**c**) Back view of antenna in a kite shape along with two semicircle loading to achieve batter matching.