esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Noise-induced hearing loss (NIHL) is the second most common sensorineural hearing loss after age-related hearing loss [1]. According to the 2019 State of the UK Fitness Industry Report [2], the health and fitness industry continues to grow in UK; hence, the noise-induced hearing loss during gym activities should be taken seriously. There is evidence that the main noise in the gym comes from aerobic training and weight training. Existing studies have shown that average noise levels during aerobics in gym range from 78 to 106 dB, and 76% of readings reach or exceed 90 dB [3], which is very likely to cause hearing loss. Some class members have complained to instructors that they suffer from hearing loss or tinnitus. Whereas the database is massive for the NIHL in aerobics classes, the database is less impressive for exposure to the impulse noise caused by dropping barbells. Hearing loss is usually a cumulative process.  
At present, there is no research to show whether there is hearing loss due to the cumulative number of barbells drops during weightlifting activities. Through field measurements in a gym at an early stage of this study, four critical activities among many that produce the most noise could be identified for this research. The top four activities and some preliminary results are shown in Table 1. The noises were measured in a gym using a sound level meter (Duratool D03130) and calibrated mobile phone sensors. The peak noise levels were recorded from a number of heavy gym activities, and the mean value could be determined from those peaks. In this study, shock noise and vibrations were emphasized; thus, A-weighting was selected for comparisons.  
It can be seen from the data in Table 1 that there is a great risk of hearing loss in the above four activities, and the noise peak value even exceeds 100 dBA. Therefore, this project focuses on these four heavy gym activities.  
Barbells, one of the biggest culprits for audible noise in gyms, mainly cause intermittent noise and impulse noise. Compared with continuous exposure to steady-state noise, the situation of exposure to noncontinuous noise is much more complicated. This is partly because of the ability of the ear to recover from the noise exposure during quiet periods; in other words, even short rest periods may be helpful to prevent hearing loss [4]. However, studies have shown that repeated exposure to impulse noise can induce hearing loss. Therefore, the purpose of this project was to explore the dose value produced by the single drop of a barbell, as well as to study whether the cumulative dose has the risk of exceeding the safe value with repeated drops.
###### Abstract
However, these previously mentioned modulation techniques are not a proper solution for the problem. Since researchers are still working on it, this is the appropriate time to design a suitable topology aiming to reduce the overall cost, size, and total harmonic distortions. Therefore, these modulation techniques enhance design complexity and semiconductor costs that affect industrial applications at both lower and middle levels. As a result, the output of the conventional cycloconverter includes a large amount of harmonic distortion when used in these existing methods. Later on, microprocessor-based circuit and Field Programmable Gate Array (FPGA) technology had been introduced as a pulse generator circuit for higher frequency to lower frequency power conversion. This cycloconverter's output voltage contains the same amplitude sine pulse. The output voltage THD is therefore very high. However, the circuit based on the microcontroller is easy to implement and has a low cost [22; 23]. In particular, third harmonic distortion affects nearly every machine operating parameter: supply voltage, torque output, torque ripple, motor temperature, vibrations, load stress, etc. Through analyzing the third harmonic distortion, the performance of the cycloconverter is crucial before an induction machine is connected to it. Several cycloconverter investigations have shown different operating factors. In [24], the researchers examine the extent to which harmonic output voltage spectrum improvements have been made, but suggest a complex circuit scheduling and modulation strategy to switch signal generation. As a result, for variable speed applications, cycloconverters with lower harmonics levels at their output are becoming more appealing than traditional cycloconverters [25]. However,  
Figure 2: Output voltage with THDs of conventional single-phase to single phase step down cycloconverters (**a**) one\(-\)third and (**b**) one\(-\)fourth frequency conversion of 50 Hz. these cycloconverters are expensive and require skilled personnel for maintenance, and, therefore, they may not be cost-effective in all cases. To solve the above problems, this paper introduces a novel single-phase step-down Tiac based cycloconverter. The proposed topology uses five Tiac devices for (1/3) and (1/4) frequency conversion of 50 Hz with reduced THDs by using a low frequency modulation technique. The main contributions of this paper are as follows:  
* A novel Tiac based step down AC to AC converter has been proposed;
* Reduction in the conventional inverter-based AC to AC converter problems;
* Reduction in total harmonic distortion, size, and weight of the cycloconverter.
## 3 Numerical Simulations
### Loading Method  
In the experiment, the stress wave amplitude could be varied by adjusting the swing angle of the pendulum. However, it was difficult to vary the frequency. To study the effect of the stress wave characteristics on the stress fields of the hole, a half-sine stress wave (consistent with the experimental monitoring results) was directly input into the incident bar (see Figure 7). The damping inside the incident rod and reflecting rod was set to zero to prevent attenuation of the stress wave in the incident process. Meanwhile, the damping of the sample was identified as 0.1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Particle-Based Properties** & **Value** \\ \hline Bulk density/(kg/m\({}^{3}\)) & 2200 \\ Ball radius/m & \(3\times 10^{-4}\)–\(5\times 10^{-4}\) \\
**Parallel-bond modulus** & \\ K ratio/(kn/ks) & 1 \\ pb-cohesion/MPa & 10 \\ pb-tension/MPa & 8 \\ pb\_fa/\({}^{\circ}\) & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Micro-parameters of specimen after calibration.  
Figure 5: Schematic diagram of SHPB simulation model.  
Figure 6: Failure mode and stress–strain of calibrations. (**a**) Failure modes, (**b**) Stress–strain.  
Comparisons between the simulated and experimental failure models with an impact velocity of 4.3 m/s and a hole diameter of 8 mm are shown in Figure 8. It is evident that tensile failure occurred in the 90\({}^{\circ}\) and 180\({}^{\circ}\) directions around the hole. In addition, a nearly vertical tensile microcrack appeared on the left end, and the failure mode was completely consistent with the experiment. This indicated the rationality of the simulation method.
# A Review of Test Stimulus Compression Methods for Ultra-Large-Scale Integrated Circuits
## 5 Enhancing Encoding Compression Efficiency  
### Traditional Preprocessing Methods  
Due to the limited scope for improving the efficiency of encoding compression, extensive research has been conducted on test data preprocessing methods for enhancing encoding compression efficiency. Traditional preprocessing methods involve studying the inter-row similarity, inter-column similarity, and two-dimensional similarity of test data in the test set. For inter-row similarity, researchers employ techniques such as test vector segmentation [92], differencing [93], and reordering [94] to compactly arrange similar test vectors, which aids in extending the length of symbols between consecutive test vectors to achieve the purpose of enhancing encoding efficiency. However, this method overlooks the length of symbols that are not encoded in the test vectors. For inter-column similarity, researchers partition and reorder scan cells [95], placing similar columns next to each other in the test set to extend the length of encoded symbols within the test vectors but neglecting the length of symbols between consecutive test vectors. For two-dimensional similarity, which refers to both inter-row and inter-column similarity, researchers propose a two-dimensional reordering of the test machine, namely, rearranging all rows first based on inter-row similarity and then rearranging all columns based on inter-column similarity. This method optimizes the encoding length from the overall perspective of the test data, significantly improving the effectiveness of double run-length encoding. However, these preprocessing methods also have significant drawbacks, such as introducing signal delays when the order of scan cells is changed and reconnected, which can affect test quality.  
The traditional preprocessing methods described above do not consider the limitation of test power consumption. However, during testing, the significant switching activity of integrated circuits can lead to a surge in power consumption, which not only affects operational reliability but also performance. When the test power consumption exceeds the tolerance range of the integrated circuit, it can cause irreversible damage to the circuit. Therefore, researchers have proposed techniques such as scan chain reordering [96], test compression [97], and ATPG [98] to address the power consumption issues in the reordering process. These preprocessing methods for test data can effectively reduce test power consumption and optimize encoding compression [10], but the issue of signal delays introduced by wiring modifications between scan cells still persists. To address this problem, [PERSON] [99] proposed a two-dimensional reordering of tests based on Hamming distance and completed a two-dimensional re-adjustment based on irrelevant bits. This approach can effectively reduce the number of logic transitions for test data with a high proportion of irrelevant bits, but the adjustment effect is not ideal for test data with a large number of deterministic bits.
## 4 Node Activity Scheduling
### Principles  
In this paper, SERENA is specified as a generic solution able to adapt to various environment or architecture constraints as well as different application requirements such as:  
* Broadcast transmission required or not.
* Types of unicast transmissions:
* general: any node can transmit messages to any other node in the network;
* tree-based: a node transmits a message either to its parent or to its children only.
* Immediate acknowledgement of unicast transmissions required or not. In case of immediate acknowledgement, the receiver uses the time slot granted to the sender to send its acknowledgement. Thus, the sender can retransmit immediately in case of unsuccessful receipt, as long as the maximum number of retries is not reached. Hence, immediate acknowledgement ensures shorter delivery delays, allows the sender to free its message quicker and avoids the receiver to store its acknowledgement in a queue before transmission.
* Minimizes the delay needed to collect or disseminate data in a tree or more generally in a hierarchical directed acyclic graph: a DAG where nodes are grouped into hierarchical levels (e.g., nodes that are at \(d\) hops from a given sink belong to level \(d\)).  
In SERENA, any node \(N\) proceeds as follows:  
* **Determination of \(\mathcal{N}(N)\)**, the set of nodes that cannot have the same color as \(N\). This set is built according to the application constraints previously specified. For instance, in case of general communications with broadcast and without immediate acknowledgement, the set \(\mathcal{N}(N)\) is the set of nodes up to two hops from \(N\). If the immediate acknowledgement is supported, \(\mathcal{N}(N)\) is the set of nodes up to three hops from \(N\).
* **Computation of \(priority(N)\)**. The priority of node \(N\) is computed. It determines the order according which nodes are colored. In the case of general communications, it is equal to the cardinal of \(\mathcal{N}(N)\), denoted \(|\mathcal{N}(N)|\). In the case of a tree, it is equal to the number of descendants of \(N\) in the tree. The choice of the priority tends to minimize the number of colors needed.
* **Coloring rules**: they are two:
* A node \(N\) colors itself if and only if it has the highest priority among the uncolored nodes in \(\mathcal{N}(N)\).
* When it colors itself, node \(N\) takes:
* the smallest color unused in \(\mathcal{N}(N)\) in the general case,
* the smallest color unused in \(\mathcal{N}(N)\) higher than the color of its parent in case of a tree or a hierarchical DAG structure. Colors are represented by natural integers.
* During the coloring algorithm, **each node \(N\) sends periodically** a \(Color\) message to its one-hop neighbors. This message contains all the information needed to determine the set \(\mathcal{N}(N)\), know the priority and color of any node in \(\mathcal{N}(N)\) and detect message loss.
## 3 Results
### Anti-Inflammatory Assay
#### 3.4.4 Histopathological Findings on the Ear
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Index**} & \multicolumn{2}{c}{**Thickness (\(\upmu\)m)**} & \multicolumn{2}{c}{**Cells (Numbers/mm\({}^{2}\))**} & \multirow{2}{*}{**Collagen Fiber**} \\
**Groups** & **Total** & **Epidermis** & **Dermis** & **Inflammatory** & **Maxst** & **(\(\upmu\)/mm\({}^{2}\) of Dermis)** \\ \hline Controls & & & & & & \\ \(\mathrm{Intact}\) & \(103.41\pm 11.47\) & \(8.98\pm 0.93\) & \(54.86\pm 11.97\) & \(15.20\pm 4.66\) & \(69.00\pm 15.48\) & \(78.31\pm 9.75\) \\ Xylene & \(264.48\pm 30.02\)\({}^{f}\) & \(9.07\pm 1.16\) & \(132.28\pm 22.16\)\({}^{f}\) & \(263.40\pm 55.50\)\({}^{f}\) & \(8.20\pm 4.16\)\({}^{a}\) & \(26.74\pm 6.58\)\({}^{f}\) \\ Reference & & & & & & \\ \(\mathrm{DEXA}\) 1 mg/kg & \(99.55\pm 9.57\)\({}^{h}\) & \(8.50\pm 1.65\) & \(52.88\pm 13.72\)\({}^{h}\) & \(18.10\pm 10.54\)\({}^{h}\) & \(61.30\pm 12.65\)\({}^{c}\) & \(77.43\pm 12.79\)\({}^{h}\) \\ AR 400 mg/kg & \(166.51\pm 17.59\)\({}^{h}\) & \(8.77\pm 0.71\) & \(75.41\pm 8.63\)\({}^{h}\) & \(72.30\pm 14.17\)\({}^{h}\) & \(45.20\pm 10.52\)\({}^{c}\) & \(64.73\pm 10.85\)\({}^{h}\) \\ KOG 400 mg/kg & \(158.83\pm 13.35\)\({}^{h}\) & \(8.76\pm 0.82\) & \(71.55\pm 5.05\)\({}^{h}\) & \(69.00\pm 12.00\)\({}^{h}\) & \(42.40\pm 6.72\)\({}^{c}\) & \(67.59\pm 4.70\)\({}^{h}\) \\ SKOG & \(105.94\pm 13.74\)\({}^{h}\) & \(9.15\pm 1.09\) & \(51.58\pm 6.97\)\({}^{h}\) & \(29.20\pm 8.26\)\({}^{h}\) & \(61.00\pm 10.92\)\({}^{c}\) & \(81.51\pm 7.58\)\({}^{h}\) \\
200 mg/kg & \(128.25\pm 15.08\)\({}^{h}\) & \(8.28\pm 1.18\) & \(61.37\pm 6.03\)\({}^{h}\) & \(48.40\pm 14.21\)\({}^{h}\) & \(58.20\pm 8.53\)\({}^{h}\) & \(76.84\pm 5.22\)\({}^{h}\) \\
100 mg/kg & \(160.57\pm 18.68\)\({}^{h}\) & \(8.73\pm 0.92\) & \(71.69\pm 10.31\)\({}^{h}\) & \(77.70\pm 21.78\)\({}^{h}\) & \(42.80\pm 10.09\)\({}^{c}\) & \(66.43\pm 10.37\)\({}^{h}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Effects of AR, KOG, and SKOG on the histomorphometry of ear in mice.
# An Adaptable Train-to-Ground Communication Architecture Based on the 5G Technological Enabler SDN
## 4 Simulation Scenario
### Use Cases  
Two use cases are considered to test different configurations of the communication network and obtain a relative comparison of delay and data rate measurements. Table 1 shows a summary of the configuration of each use case.  
The goal of these use cases is to measure the performance of the novel communication architecture. In consequence, use case A is configured according to a legacy architecture that can nowadays be found in any railway network, such as the one depicted in Figure 1. However, in order to compare equivalent mechanisms, we set a layer-2 network with switches running Spanning Tree Protocol (STP) instead of a more complex layer-3 network based on routers executing MPLS. Alternatively, the configuration of use case B represents the proposed architecture according to Section 3.
## 2 Description of Studied Area  
The international airport Poprad-Tatry is situated on the outskirts of the town Poprad, in the region of Presov, Northern Slovakia (Figure 1). Poprad lies in the Poprad basin that is surrounded from the north by the High Tatras and the mountain range Spisska Magura, from the east by the mountain range Levocske vrchy and from the south by the mountain range Kozie chrbty and the Hornad basin. From the west, the Poprad basin continues to the Liptov basin (Figure 2). With an altitude of 570 m above sea level (the bottom of the basin), the Poprad basin is the highest intermountain basin in Slovakia. The airport itself is situated at an altitude of 718 m above sea level, which is what makes it one of the highest-placed airports in Europe. The massive and rugged mountain terrain that encircles the basin influences the climatic conditions in the area significantly. The orientation of the basin, its openness to the west, as well as the mountain barrier of the High Tatras determine the major direction of wind blowing in this region [52].  
Figure 1: The location of the meteorological station in Slovakia (upper); in Poprad (lower). Source: [53; 54].  
The meteorological station Poprad (indication 11,934) lies in close proximity to the airport. The mast for wind measurements is within the measuring plot of standard dimensions--20 \(\times\) 20 m. The GPS coordinates are 49.06806, 20.24972 (refers to the measuring plot); the altitude of the station is 694 m above sea level. According to the internal instruction of the Slovak Hydrometeorological Institute (SHMI), the measuring plot must be flat, without depressions, covered with grass. The weather station is surrounded by a big garden; the general surroundings are absolutely free. The height for measuring the wind direction and speed, respectively, at the monitoring station was standardised to 10 m above the ground. For measuring the wind characteristics, there are currently used Vaisala automatic instruments and GILL ultrasonic instruments. The anemometers have a 2-year calibration interval. In meteorological practice, the direction and the power of a wind vector are recorded separately as the wind direction and the wind speed. The direction of wind is determined by the direction from which the wind blows. It is measured by wind vanes at meteorological stations and recorded as the average direction of wind in the past 10 min [53].
## 7 Experiments
### Dataset Splitting at a 7:3 Ratio
#### 7.3.1 Convergence Results  
Figure 10 presents the convergence result for average loss under the condition where the samples were randomly distributed between the training and testing sets at a 7:3 ratio. According to Figure 10, the WRN_k8 model, which increased the width of the entire network, effectively reduced the number of epochs required for initial convergence. However, as the model size expanded and the number of parameters doubled, the required time increased by a factor of at least two. Additionally, model WRN_b256, which applied a batch size of 256, reached a similarly low loss with approximately 320 epochs, and thus was quicker than model WRN_k8. Generally, systems with larger batch sizes are more stable and exhibit greater accuracy during testing. However, model WRN_kernel5, in which the size of some kernels was expanded, was less stable than the original model WRN_k4.  
As shown in Figure 11, the WRN_ex3D model which employed the integrated structure of 3D convolution, exhibited stable convergence with approximately the same number of  
Figure 10: Convergence results of average loss under the condition where the samples were randomly distributed between training and testing sets at a 7:3 ratio.  
epochs as model WRN_b256 for convergence. In other words, this model required fewer parameters and was relatively quick. Moreover, the limit of loss convergence was low.
# Nanomaterials-Based Ion-Imprinted Electrochemical Sensors for Heavy Metal Ions Detection: A Review
## 3 Application of Nanomaterials in HMIIECSs
### Carbon-Based Nanomaterials
#### 3.1.3. Graphitic Carbonitride Nanomaterials  
Graphitic carbonitride (g-C\({}_{3}\)N\({}_{4}\)) is a typical two-dimensional semiconductor with the core composed of carbon and nitrogen atoms of triazine or hydrazine, and has a hexagonal carbon ring layer similar to most carbon materials [115]. It has the characteristics of wide band gap (2.7 eV), easy preparation, good thermal stability, controllable electronic structure, and its 2D layered structure is conducive to charge carrier transfer [116]. The planar conjugated structure also helps to disperse metals or oxides and prevent agglomeration of nanomaterials [117]. Because of its unique physicochemical properties, large surface area, abundant active centers, and nanostructure design, g-C\({}_{3}\)N\({}_{4}\) is widely used as a catalyst in light-emitting devices (LEDs), photocathodes, and sensors [118].  
[PERSON] et al. designed an electrochemical sensor for Hg\({}^{2+}\) by composing IIP and g-C\({}_{3}\)N\({}_{4}\)[119]. Hg\({}^{2+}\)-IIP nanomaterials were prepared by precipitation polymerization using a functional monomer (itaconic acid, ITA), then was used to modify the CPE together with g-C\({}_{3}\)N\({}_{4}\), and the modified electrode was employed for Hg\({}^{2+}\) analysis. The results obtained by SWASV method were compared with those obtained by unmodified CPE, and it was found that modified CPE had a higher propensity for Hg\({}^{2+}\). The modified electrode was found to have a suitable linear response in the range of 0.06 to 25.0 nM, with an excellent LOD of 18 pM (S/N = 3). A detailed list of carbon nanomaterials-based HMIIECSs for various HMIs was given in Table 3.  
Figure 10. (**a**) Functionalization of GQD (**b**) synthesis of the imprinted polymer material GQDTU-IIP. Reprinted with permission from Ref. [114]. Copyright 2021, the Springer Nature.
# In Silico SwissADME Analysis of Antibacterial NHC-Silver Acetates and Halides Complexes
## 2 Materials and Methods  
The studied complexes [26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68], represented in the simplified molecular-input line-entry system (SMILES) format, were subjected to SwissADME analysis. The analyzed structures were divided into two groups: active compounds **A** (136 entries) and superior complexes **S** (61 entries) (Figures 1 and 2), and compared to the entire dataset **A** + **S**. The results are presented in the corresponding box and whisker plots (Figures 3-13) and BOILED-Egg charts (Figure 14). The complete results are available in the Supplementary Materials.  
All the calculations in this paper were performed using Spartan software package from Wavefunction, Inc. [69]. The geometries were fully optimized in vacuo using the DFT EDF2 method with a 6-31+G\({}^{*}\) basis set. Frequency calculations were conducted for all structures to confirm they correspond to energy minima [70]. The LUMO absolute values presented in Figure 15 [square root of e/au\({}^{3}\)] were mapped on the isodensity surface (0.002 e/au\({}^{3}\)).  
Figure 1: _Cont._  
Figure 1: NHCs-silver acetate complexes. NHCs–silver acetate complexes. Active complexes (**A**) are shown in black, superior complexes (**S**) are shown in red.