esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
CST microwave studio is deployed to simulate the proposed metamaterial in the frequency range 1-14 GHz. Figure 13 demonstrates the scattering parameters (reflection and transmission coefficients). Numerical simulation yields four resonance frequencies at 2.35, 7.72, 9.23 and 10.68 GHz with magnitudes of \(-43.23\) dB, \(-31.05\) dB, \(-44.58\) dB and \(-31.71\) dB, respectively. These frequency bands cover the S, C, and X bands. Moreover, the response for the reflection coefficient (\(S_{11}\)) shows at 3.33 GHz, 7.92 GHz and 10.39 GHz with magnitudes of \(-36.30\) dB, \(-13.84\) dB and \(-15.23\)dB, respectively. It is evident that every resonance of the transmission coefficient (\(S_{21}\)) is followed by bandwidths of 0.36 GHz, 0.46 GHz, 1.42 GHz and 0.30 GHz at the concerned S, C, and X bands, respectively. It  
Figure 11: Equivalent electric al circuit model of the proposed metamaterial unit cell.  
Figure 12: The transmission coefficient (\(S_{21}\)) of the proposed unit cell simulated by CST and ADS software.  
is also evident that each resonance of the transmission coefficient \((S_{21})\) is tracked by a reflection coefficient \((S_{21})\) minimum. Subsequently, the frequency of each value of \(S_{21}\) minimum is always lower than the concerned value of the \(S_{11}\) minimum frequency. In this regard, it can be concluded that every resonance can be treated as electrical resonance in the proposed metamaterial unit cell [43]. The permittivity \((\varepsilon)\), permeability \((\mu)\) and refractive index \((n)\) extracted by applying the RRM (robust retrieval method) in CST and NRW in MATLAB are shown in Figures 14-16, respectively. Figure 14 shows that the permittivity of the designed MM varies from the positive value to the negative. Again, the values of \(S_{21}\) begin when the magnitudes of permittivity fluctuate from maximum to minimum values. Moreover, the starting positive minimum value of \(\mu\) occurs at the minimum resonance frequency shown in Figure 15. This tendency continues over the whole resonance frequency range. A graph of the refractive index is presented in Figure 16, which reveals negative refractive indices in the frequency ranges of 2.36-3.19 GHz, 7.74-7.87 GHz, 9.26-10.33 and 10.70-11.81 GHz. The negativity of refractive index is a function of frequency that can be utilized to increase the gain and directivity of antennae, whereas the \(\varepsilon\)-negative property is deployed to enhance the bandwidth [44; 45]. Finally, a brief comparison is shown on the basis of some important parameters in Table 3.  
Figure 14: Graph of real and imaginary magnitudes of permittivity \((\varepsilon)\) for the unit cell.  
Figure 13: Graph of the S parameters \((S_{11}\) and \(S_{21})\) of the unit cell.
## 3 Performance Analysis
### Bit Error Rate Analysis  
The conditional probability of error that can be achieved with \(M\)-PSK and \(M\)-QAM at a certain SNR \(\gamma\) value is provided by [41]\(P_{\text{BER}}(\gamma)=\text{a}Q(\sqrt{b\gamma})\). \(Q(.)\) denotes the Gaussian Q function and can be expressed as \(Q(x)=(1/\pi)\int\limits_{0}^{\pi/2}\exp\left(-\frac{x^{2}}{2\sin^{2}\theta} \right)d\theta\). BER expressions for \(M\)-PSK and \(M\)-QAM are presented in Table 1.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Modulation Type** & **a** & **b** & \(P_{\text{BER}}\) \\ \hline \(M\)-PSK & \(\frac{2}{\log_{2}M}\) & \(2\sin^{2}(\frac{\pi}{M})\) & \(\frac{aQ(\sqrt{2b\gamma})}{\log_{2}M}\) \\ \hline \(M\)-QAM & \(\frac{4}{\log_{2}M}\) & \(\frac{3}{M-1}\) & \(\frac{4a}{\log_{2}M}\times\sum\limits_{i=0}^{\sqrt{M}/2}(2i-1)\sqrt{2b\gamma \log_{2}M}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: BER expressions for \(M\)-PSK and \(M\)-QAM modulations.  
The average BER can be computed as follows:  
\[\hat{P}_{\text{BER}}=\int\limits_{0}^{\infty}P_{\text{BER}}(\gamma)f_{\gamma}( \gamma)\,d\gamma=\frac{\text{a}}{\pi}\!\!\int\limits_{0}^{\infty}\int\limits_{0 }^{\pi/2}\!\exp\!\left(-\frac{b\gamma}{2\sin^{2}\theta}\right)\!f_{\gamma}( \gamma)\,d\gamma d\theta. \tag{27}\]
# Enhancing Far-Field Speech Recognition with Mixer: A Novel Data Augmentation Approach
## 5 Results
### Main Results
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Whisper-** & **Whisper-** & **Whisper-** & **Whisper-** & \\  & **Small** & **Medium** & **Large-v2** & **Large-v3** & **Avg.** \\ \hline Params & 0.4977 M & 1.1327 M & 2.2118 M & 2.2118 M & / \\ \hline Testing & **46.98** & 42.76 & 46.11 & 42.04 & 44.47 \\ \hline Finetune & 59.07 & 49.36 & / & / & / \\ \hline LoRA & 52.65 & 39.70 & 42.24 & 37.41 & 43.00 \\ \hline +SpecAugment [5] & 62.94 & 39.14 & 40.56 & 41.84 & 46.12 \\ \hline +Mixer & 49.15 & **38.46** & **38.50** & 36.07 & **40.55** \\ \hline +Mixer-C & 51.29 & 39.82 & 40.29 & **35.27** & 41.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: WER (%) of four different Whisper versions under different training settings. “Params” denotes the trainable parameters of LoRA. capabilities and a larger model capacity; otherwise, it may confuse the model's learning. However, Mixer is also superior to SpecAugment and does not overly confuse the model's learning.
## 1 Introduction  
Fractals are patterns that look similar at different scales in which each small part resembles the whole. Fractal patterns are seen in many natural structures, including biological structures. In fact, many human organs display fractal properties. For instance, trabecular bone structures have fractal or fractal-like properties [1]. Fractal analysis is a mathematical method that is increasingly used to study the complexity of fractal structures due it to not being affected by imaging conditions, such as projection angle; its easy accessibility; and the fact that it provides a quantitative outcome, the fractal dimension (FD), for structural complexity [2; 3]. A higher FD is associated with a more complex fractal structure; therefore, a higher FD demonstrates a more complex trabecular bone pattern [4]. In dentistry, fractal analysis is most frequently applied to panoramic and preapical radiographs to detect local or systemic disorders that can potentially change the fractal pattern [3]. Fractal analysis has been used to study various areas, such as in evaluating the relation between jaw bone structure and lactation [4], evaluating trabecular changes in periodontitis [5; 6], detectingosteoporosis [7], implant osseointegration prediction [8], evaluating the effect of histogram algorithms on periapical radiographs [9], and investigating mandibular trabecular bone in bruxism [2], as it has the potential to determine the jaw bone internal trabecular pattern [3].  
Bruxism is a disorder and an oral habit characterized by nonfunctional grinding and clenching of the teeth that occurs in 12.8% of adult individuals [10; 11; 12]. Bruxism can happen during sleep (nocturnal bruxism) or while awake (diurnal bruxism). Self-reports, clinical examination, and polysomnographic or electromyographic records of patients are used for diagnosis of bruxism [2; 13]. Bruxism and other parafunctional habits result in condylar resorption as a consequence of mechanical stress [14], and it can cause TMD, disc displacement, and masticatory muscle pain [15].  
Studies show that trabecular bone structure is affected in patients with bruxism in the mandibular cuddle, genital angle, and the region between the second premolar and first molar [2; 13]. However, these studies have conflicting results. In one study, FD value measurements were only statistically different between bruxism and control groups in the condylar regions; FD values were lower in the right cuddle in individuals with bruxism than in non-bruxer individuals [2]. Another study reported that, in individuals with bruxism, the gonal bone region FDs on both sides were significantly lower compared with control patients, while no significant difference was observed between the FD values of the condylar region between the groups [13]. This study aimed to evaluate the trabecular pattern of the mandibular cuddle, angle, and dental region in panoramic radiographs of individuals with and without bruxism using fractal analysis and a larger sample size.
# Changes of Gait Patterns after Correction of Refractive Error in the Elderly with Cataracts
## 3 Results
### Analysis of Correlation between the Factors of Visual Measurements and Gait Patterns  
An analysis was performed to determine the correlation between each gait pattern and the spherical equivalent power, astigmatic diopter power, and improvement of visual acuity lines after the participants wore correction glasses, as shown in Table 2. No clear correlation was observed between any factors affecting the gait pattern that were measured in this study and the subjects' refractive error component and increase in corrected visual acuity.
# A Novel Method for Tool Identification and Wear Condition Assessment Based on Multi-Sensor Data
###### Abstract  
The development of industry 4.0 has put forward higher requirements for modern milling technology. Monitoring the degree of milling tool wear during machine tool processing can improve product quality and reduce production losses. In the machining process of machine tools, many kinds of tools are usually used, and the signal characteristics of various sensors of different tools are different. Therefore, before the tool wear assessment, this paper identified the tool type according to the spindle current data. After the tool type recognition, this paper evaluates the tool wear degree according to the tool force data, vibration data, acoustic emission signal, and other multi-sensor data. Firstly, the Elman neural network and Adaboost algorithm are combined to construct the Elman_Adaboost strong predictor. Then, the variance and mean of seven sensor data were selected as the characteristic quantities to input the strong predictor. Finally, three wear quantities were obtained to measure the wear degree of the tool. The method proposed in this paper is implemented by Matlab, and the validity of this method is verified using the competition data provided by PHM (Prognostics and Health Management) Society. The results show that the average evaluation accuracy of the same tool wear is more than 92%, and that of the similar tool wear is more than 85%.  
tool wear; Elman_Adaboost; multi-sensor; data processing +
Footnote †: journal: Applied Sciences
# Guard Band Protection Scheme to Facilitate Coexistence of 5G Base Stations and Radar Altimeters
## 2 Modelling for Guard Band Protection and Analysis
### Mathematical Model for Guard Band Protection
Notably, the various propagation losses that occur when the interfering signal of the 5G base station reaches the radar altimeter are expressed as [20]  
\[\mathrm{L_{total,i}}=\mathrm{PL_{i}}+\mathrm{L_{body}}+\mathrm{L_{polar}}+ \mathrm{L_{cable}}, \tag{10}\]  
where \(PL_{i}\) is the path loss [23], which depends on the distance between the \(i\)th 5G base station and radar altimeter, antenna height of the 5G base station, and building height. \(L_{\mathrm{body}}\), \(L_{\mathrm{polar}}\), and \(L_{\mathrm{cable}}\) denote the body loss, polarisation loss, and cable loss, respectively, assumed to be constants. ACIR is the ratio of the total interference received by the radar altimeter between ad-jacent channels, defined as [20]  
\[\mathrm{G_{BS,i}}=\mathrm{A_{element}}+\mathrm{A_{array}}, \tag{2}\]0  
where the calculations of ACLR and ACS are outlined in Sections 2.2 and 2.3, respectively. The aggregate received interference by the radar altimeter is expressed as  
\[\mathrm{G_{BS,i}}=\mathrm{A_{element}}+\mathrm{A_{array}}, \tag{2}\]1  
where \(N_{\mathrm{BS}}\) is the number of 5G base stations within the coverage of the radar altimeter, calculated as  
\[\mathrm{G_{BS,i}}=\mathrm{A_{element}}+\mathrm{A_{array}}, \tag{2}\]2  
where \(h_{\mathrm{RA}}\) is the altitude of the airplane, and \(R_{\mathrm{BS}}\) is the cell radius of the 5G base station.
# Optimal Prefix Free Codes with Partial Sorting +
## 5 Discussion  
We described an algorithm computing an optimal prefix free code for \(n\) unsorted positive weights in time within \(O(n(1+\log\alpha))\subseteq O(n\log n)\), where the alternation \(\alpha\in[1..n{-}1]\) roughly measures the amount of sorting required by the computation of such an optimal prefix free code. This result is a combination of a new algorithm inspired by [PERSON]'s 1976 algorithm ([PERSON], 1976), and of a minor extension of [PERSON] et al.'s 1988 results about deferred data structures supporting rank and select queries on multisets ([PERSON] et al., 2018). In theory, such a result has the potential to improve over previous results, whether the \(\Theta(n\log n)\) complexity suggested by [PERSON] in 1952 or more recent improvements for specific classes of instances ([PERSON], 1952; [PERSON], 1952). In practice, it does not seem to be very promising, at least when computing optimal binary prefix free codes for mere texts: on such data, preliminary experiments show the alternation \(\alpha\) to be polynomial in the input size, the number \(n\) of weights (their logarithms are within a constant factor of each other). The situation might be different for some other practical application, but in any application, the alternation \(\alpha\) would have to be asymptotically bounded by any polynomial of the input size \(n\), in order for the GDM algorithm to yield an improvement in running time by more than a constant factor.  
The results described above yield many new questions, of which we discuss only a few in the following sections: how those results relate to previous results (Section 5.1); about the potential (lack of) practical applications of our results on the practical computation of optimal binary prefix free codes (Section 5.2); and about problems similar in essence to the computation of optimal prefix free codes, but where optimizing the computational complexity might have more of a practical impact (Section 5.3).
## 2 QPSK-Oriented All-Optical Matching System and Noise Analysis
### Noise in the QPSK-Oriented All-Optical Matching System
The input QPSK optical signal splits into two paths, with the upper path passing through the PSA module in the upper arm, where the power at symbols \(\pi/2\) and \(3\pi/2\) is compressed to zero before interfering with Target 1, although residual power of \(P\) remains at positions 0 and \(\pi\). Similarly, the lower path undergoes processing through the PSA module in the lower arm, which compresses the power at symbols 0 and \(\pi\) to zero prior to interference with Target 2, resulting in destructive interference that nullifies power at \(\pi/2\), while constructive interference amplifies \(3\pi/2\) to power \(4P\), yet residual power of \(P\) persists at the mismatched positions (0 and \(\pi\)). To mitigate the impact of mismatch noise, [16] employed two cascaded AND gates for processing. However, this configuration requires four EDFAs. The entire system ultimately utilizes seven EDFAs, with some amplifiers exhibiting gain coefficients exceeding 20 dB, which introduces significant amplifier noise. In VPItransmissionMaker simulations, the noise figure (NF) is used to describe the noise introduced by the EDFAs. Most commercially available EDFAs have an NF below 5 dB, and Alnair Labs' Ultra-Low-Noise Optical Amplifier (LNA-220) can reduce the NF below 4 dB [31]. Therefore, for subsequent simulations, we set the NF of the EDFAs to 4 dB to model the influence of the amplifier noise on the system.
# Detail-Preserving Shape Unfolding
## 5 Experimental Details  
The proposed mesh unfolding method is tested on a non-rigid 3D watertight meshes dataset [47]. This dataset provides a wide range of non-rigid shape classes. Each class contains 20 different poses and each mesh has about 9500 vertices. To improve the computational efficiency, each mesh is first simplified to about 2000 vertices. Then we use tetgen [48] or [49] to generate the corresponding tetrahedral meshes. We obtain about 513 tetrahedral meshes after removing the failed generation. All experiments are performed on a laptop with Intel Core \(i7-4790K\) CPU and 32 G RAM.
# The HD-Domain Metalloprotein Superfamily:
### HD-Domain Hydrolases: The PDE Subfamily
#### 1.3.1 HD-Domain PDEs Acting on DNA  
Clustered regularly interspaced short palindromic repeats (CRISPR)-associated systems (Cas) are major players in prokaryotic adaptive immunity and RNA-based defense [50; 51]. Type I CRISPR-Cas utilize a multicomponent system and recruit a single nuclease, Cas3, for the degradation of invader nucleic acids. The Cas3-associated gene can encode for a protein that has only the HD-domain (Cas3\({}^{\prime}\), I-A subtype), or more commonly, an N-terminal HD-domain fused to a Superfamily 2 helicase (Cas3). Type III-B CRISPR-Cas utilize Cas10 (Cmr2) for RNA-activated ssDNA cleavage [26].  
The crystal structure of the _Thermobifida fusca_ Cas3 shows a diiron active site (Figure 4), yet no activity with this cofactor has been demonstrated [23]. Cas3 proteins can be promiscuously activated by various metal ions, with most being activated by Ni or Co and, to a lesser extent, by other divalent metal ions (with the exception of Mg and Ca) [23; 25]. In contrast, the _Pyrococcus furiosus_ Cmr2 (dinuclear) and the Cas3\({}^{\prime}\) proteins (mononuclear in the published structures) exhibit an Mg-dependent PDE activity [23; 25]. The nuclearity of the Cas3\({}^{\prime}\)'s may be an artifact due to the larger flexibility of the  
Figure 4: Active sites of HD-domain PDEs. HD-domain PDEs utilize the conserved HD motif “H...HD...H...HH...D” to bind a di- or trinuclear metal center. The metal ions coordinated in their active sites are zinc (purple), magnesium (light green), nickel (dark green), or iron (orange). Small red spheres represent water molecules.  
protein polypeptide, making binding of the second metal ion more transient. Of note, no correlation between helicase and PDE activities has been demonstrated to date.
# Revealing the Spatial Pattern of Weather-Related Road Traffic Crashes in Slovenia
## 2 Materials and Methods  
### Databases and Data Preprocessing  
National road traffic crash data from 2006 to 2018 were obtained from the Slovenian Traffic Safety Agency web platform [27], which operates under the Ministry of the Interior. Traffic load data for the same time window were downloaded from the Slovenian Open Data Portal (OPSI) owned by the Ministry of Infrastructure [28]. These data are collected at predefined locations but can be interpolated by applying Spatial Analysis along Networks (SANET) tools [29]. Municipal level socioeconomic data (registered personal vehicles, motor vehicles, trailers, trucks, buses, the number of adult persons, regional road length, highway length) were provided by the Statistical Office of the Republic of Slovenia [30] and the Ministry of Finance (municipal development coefficient (MDC)). Climatic and bioclimatic spatial data (resolution = 30 s), version 2.1 (1970-2000), were obtained from the WorldClim database [31]. In order to link all listed data with administrative units (municipalities), vector data were downloaded from the STAGE web application provided by the Statistical Office of the Republic of Slovenia.  
The obtained data on road traffic crashes were initially filtered according to their classification (severe injury, death) and/or individual weather situation (rain, snow, wind, fog). In the next step, the standardized road traffic crash rate variable per weather type was calculated [32] since municipalities differ in population density and size:  
\[\text{AR\_P}=\text{num. traf. accid./tot. pop. }\times 100.000 \tag{1}\]
## 3 Results and Discussion
### Calibration Test  
In this study, calibration tests in the static state for both vehicle-mounted CWML and a reference Lidar (the fixed Lidar) were carried out. The Lidars were set to simultaneously measure the wind field under static conditions. The reference Lidar (KC-WL-2004) has the same specifications as those of the KC-WL-2006. The reference Lidar (KC-WL-2004) was verified through a verification test with a radiosonde. In addition, the calibration experimental site was selected on the roof of block C4 in the Innovation Industrial Park (117.12\({}^{\circ}\)E, 31.85\({}^{\circ}\)N). The two Lidars were kept five meters apart to ensure identical measurements on a 30 min time basis (see Figure 12). Vertical wind profiles were observed for 24 h from 00:00 on 29 July to 00:00 on 30 July in 2021, to study the stability of the vehicle-mounted CWML system.  
Figure 13 shows the wind profiles measured by the KC-WL-2006 Lidar and the reference Lidar in the horizontal direction during the calibration test. The maximum values of the standard deviation of magnitude and the direction of horizontal wind speed measured using two instruments were 0.58 m/s and 3.29\({}^{\circ}\), with average values of 0.26 m/s and 1.41\({}^{\circ}\), respectively. The calibration results of wind direction and speed exhibit good consistency in the detection distance. In low-altitude measurement, wind speed and direction near the ground are affected by terrain, which induces certain differences. Alternatively, with increasing detection height (more than 2500 m), the energy of backscattering signals received by the two Lidars decreases, thereby increasing the measurement errors for wind speed and direction. The main purpose of this experiment was to verify the stability of KC-WL-2006 Lidar before the formal vehicle-mounted experiment and in preparation for navigation monitoring.  
Figure 12: Calibration test.