esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Ear Acoustic Authentication  
In ear acoustic authentication, biometric authentication is performed using a special earphone with a small embedded microphone, as shown in Figure 1. In this study, ear canal transfer characteristics are defined as the characteristics related to signal transmission between the earphone and microphone placed at the entrance of the ear canal. The ear canal transmission characteristics include the acoustic characteristics of the ear canal, electroacoustic conversion characteristics of the earphone, acoustoelectric conversion characteristics of the microphone, and characteristics depending on the positional relationships between the microphone and earphone. The ear canal transfer characteristics become the ear canal impulse response (ECIR) in the time domain and are represented by the ear canal transfer function (ECTF) in the complex frequency domain. An ECIR can be derived by measuring the acoustic characteristics using an impulse response measurement method with a time-stretched pulse signal or a maximum length sequence (MLS) signal. The acoustic characteristics differ for each user because they depend on the unique physical characteristics of the individual [18]. In ear acoustic authentication, focus is placed on the individuality of the ear canal transfer characteristics, and biometric authentication is performed by applying feature extraction processing to the discriminator.  
Figure 1: Sketch of outer ear with measurement device (earphone with microphone) for identification of ear canal acoustics.
## 5 Numerical Experiments and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Problem** & \(\mathbf{n\times m}\) & \(\mathbf{f}\) & (\(\mathbf{LB, UB}\)) & **GA** & **TS** & **AIA** & **ACO** & **ISFLA** \\ \hline MK01 & \(10\times 6\) & 2.09 & (36, 42) & 40 & 40 & 40 & 40 & 40 \\ MK02 & \(10\times 6\) & 4.1 & (24, 32) & 26 & 26 & 26 & 26 & 26 \\ MK03 & \(15\times 8\) & 3.01 & (204, 211) & 204 & 204 & 204 & 204 & 204 \\ MK04 & \(15\times 8\) & 1.91 & (48, 81) & 60 & 60 & 60 & 60 & 60 \\ MK05 & \(15\times 4\) & 1.71 & (168, 186) & 173 & 173 & 173 & 173 & 173 \\ MK06 & \(10\times 15\) & 3.27 & (33, 86) & 63 & **58** & 63 & **58** & **58** \\ MK07 & \(20\times 5\) & 2.83 & (133, 157) & **139** & 144 & 140 & 140 & **139** \\ MK08 & \(20\times 10\) & 1.43 & 523 & 523 & 523 & 523 & 523 & 523 \\ MK09 & \(20\times 10\) & 2.53 & (299, 369) & 311 & **307** & 312 & **307** & **307** \\ MK10 & \(20\times 10\) & 2.98 & (165, 296) & 212 & **198** & 214 & **198** & **198** \\ \hline \end{tabular}
\end{table} 
Table 1: Performance comparison between improved shuffled frog-leaping algorithm (ISFLA) and other algorithms. The numbers in bold are the best among the five approaches. TS, tabu search; AIA, artificial immune algorithm.
# Requirements for Crafting Virtual Network Packet Captures
## 2 Related Work  
The creation of network packets is discussed in various research works. In [21], the authors propose Moongen as a high-speed packet generator able to saturate a 10 GBit/s connection. Pktgen [22] is another software-based packet generator that aims to cover high-speed communication. In addition to this, different hardware-based generators such as [23] exist.  
Encapsulating protocols are able to tunnel given protocol information to transfer this information without any further interaction from a given point in a network to its intended destination. This is done for security reasons such as IPSec and Encapsulating Security Payload (ESP) [24], virtual private networks [25] or virtual environments [26]. Virtual environments such as software-defined networks (SDN), Docker [27] or Kubernetes [28, 29] are heavily based on encapsulating protocols [30]. Network packet captures freeze the transferred network traffic and, depending on the captured traffic, provide additional opportunities for training, learning and testing.  
The generation of own packet captures is done for various reasons such as testing of new network protocols, fuzzing, IoT design [31] or security implementations [32]. Because of this, [33] differentiates application-level, flow-level and packet-level traffic generators.  
A common technique is the replay of the network packets inside a virtual environment with tools such as tcpreplay, TCPivo [34] or OFRewind [35]. The tools provide great flexibility in repeating captured network traces but focus on the implementation or evaluation of high-performance networks. In contrast to these, tools with a forensic purpose such as [36] focus on making the replay as accurately as possible. When crafting network packets with encapsulation protocols as used in SDN, only _Encapcap_[20] is available. The definition of requirements typically depends on the intended purpose of the generator; e.g., in [37], the authors describe bandwidth, accuracy and precision as relevant requirements for network packet generators, but this research focuses on the live injection of the crafted network packets into a network or network analysis tool. In [38], the authors define five requirements supporting the reproducible traffic generation. In [39], the authors define seven requirements in virtual networks, but they focus on benchmarking and measurement.
## 1 Introduction
When the central point of similarity (CPS) of the transformation coincides with a source field's singular point and a correct N value is used, the FDST of a potential field anomaly becomes zero or linear at all observation points. In each sliding window, a field measured at two different observation levels and a set of structural indices are needed. The three methods described above are among the few algorithms that account for a linear background. In terms of anti-interference ability against noise, MaGSOundFDST is better than the MaGSOundDST and DST algorithms, because it does not need derivatives of measured data. In terms of calculation speed, DST is faster [19; 20]. In general, the calculation accuracy of the Euler solutions that take into account linear background is better than that of the first-class algorithms. Third, taking into account a nonlinear background, [PERSON] [21] introduced an interference polynomial to the right-hand side of the Euler equation to reduce the influence of the background field. [PERSON] [22] proposed a Euler deconvolution with a nonlinear background. The background is approximated using a rational function, and a nonlinear optimization technique (e.g., precondition conjugate gradient) is applied to calculate the solutions. Up to now, few authors apply the Euler deconvolution with nonlinear background to real data. In this paper, we propose a new method to improve the accuracy of Euler inversion results, which is less susceptible to interference from nearby sources. The proposed method is based on the finite-difference algorithm and can estimate depth, shape, and the coefficient of the background field simultaneously. In addition, we applied the new approach to 2D and 3D datasets.
# Wideband UHF Antenna for Partial Discharge Detection
###### Abstract  
This paper presents a ultra-high frequency (UHF) antenna for partial discharge (PD) detection and the antenna sensor can be used near a conducting ground wire. The proposed UHF antenna has advantages of easy setup and higher-frequency detection over the high-frequency current transformer (HFCT) sensor. First, a variety of loop-shaped antennas are designed to compare each near field coupling capability. Then, a new UHF antenna is designed based on the loop-shaped antenna, which has the best near field coupling capability. Finally, the proposed UHF antenna is fabricated and measured. It provides a wide impedance bandwidth of 760 MHz (740-1500 MHz). Its simple setup configuration and wide bandwidth frequency response in the UHF band can provide a more efficient means for PD detection.  
partial discharge; ultra-high frequency (UHF) antenna +
Footnote †: journal: Applied Sciences
## 3 Materials and Methods
### Problem Definition  
Given a UWSN \(G(t)=(V,E(t))\) at time slot \(t\). As mentioned above, we ascertain the objective as minimizing the energy consumption of data forwarding with maximal Value of information within a given monitoring duration \(T\). Therefore, we aim to solve the problem of data forwarding by jointly considering timeliness and energy consumption.  
\[\min\sum_{t=0}^{T}\sum_{i=0}^{N}E_{c}(i,t) \tag{5}\]  
Figure 3: Orientation of sensor nodes.  
\[s.t.\] \[p_{i,t}^{*}=\arg\max_{p_{i,t}\in P_{i,t}}VoI(p_{i,t}) \tag{6}\] \[\forall v_{i}\in V,t\leq T,\ E_{r}(i,t)\geq 0\] (7) \[\forall v_{i}\in V,p_{i,t}\in P_{i,t},\ t_{l}\leq TTL\] (8) \[\forall v_{i}\in V,t\leq T,\ |\mathbf{C_{i}(t+1)}|\leq R_{i} \tag{9}\]  
As shown in Equation (6), \(p_{i,t}^{*}\) represents the candidate packet which has the highest value of information in \(v_{i}\) at time slot \(t\). Furthermore, if \(v_{i}\) is able to forward data to any neighbor at time slot \(t\), \(p_{i,t}^{*}\) will be delivered. In Equation (7), each sensor node has limited energy and is out of use when its residual energy hits the bottom at 0. The living time of packets cannot exceed the maximum living duration \(TTL\) as shown in Equation (8). In Equation (9), the moving range of each sensor node is limited to the length of its tethered wire \(R_{i}\).
## 2 Results
### Effects of Different Postharvest Calcium on Metabolomics of Fresh-Cut Apples During Storage
The contents of fruit alcohols (Figure 5c) and aldehydes (Figure 4d) in the T2 treatment were higher compared to other treatments during storage, reaching their maximum on days 9 and 12, which were 43.78% and 41.49% higher than those of the CK treatment, respectively. The total aroma substances (Figure 5a) and esters (Figure 5b) in T2-treated fruits were higher than those in other treatments throughout the storage process. The total aroma content reached its maximum on day 9, which was 31.40% higher than the lowest in the CK group. The ester content reached its maximum on day 6, which was 61.79% higher than in CK. This indicates that the T2 treatment improved the alcohol and aldehyde components in the fruit's aroma during storage, thus increasing the total aroma and ester content of the fruit, which demonstrates a positive role in maintaining or enhancing apple flavor. As shown in Table 3, the sorbitol-related calcium treatment significantly increased metabolites linked to the flavonoid biosynthesis pathway (querc  
Figure 5: Effects of different calcium treatments after harvest on aroma and volatile components of fruit during storage. (**a**) Content of total volatiles. (**b**) Content of ester substances. (**c**) Content of alcohols. (**d**) Content of aldehydes. CK, T1, T2, and T3 represent the different treatments of control, calcium chloride, sorbitol-related calcium, and calcium nitrate. The error bars represent the SD of three biological replicates. Different lowercase letters in the same developmental stage indicate a significant difference at \(p<0.05\). metabolism (hydroxyprosol, tyrosol), and isoflavonoid biosynthesis (isoflavones), all of which play critical roles in fruit pigmentation, enzymatic browning regulation, flavor modulation, and inhibiting microbial activity. Sorbitol-chelated calcium effectively improved fruit aroma quality, with its total aroma substances and ester accumulation consistently outperforming other treatments, demonstrating a positive role in maintaining or enhancing apple flavor. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Volatile Compound**} & \multirow{2}{*}{ \begin{tabular}{c} **Retention** \\ **Index** \\ \end{tabular}
## 2 Introduction to Full-Color Holographic System Module  
The full-color holographic system is divided into four stages: acquisition, preprocessing, hologram generation, and reconstruction. The system is shown in Figure 1.  
In the first stage, we use the Kinect depth camera to collect color and depth information of objects, thus generating a 3D point cloud model. The creation of a point cloud can be seen as a sampling of a continuous surface, where the spherical waves from each point source are superimposed on the hologram plane. Due to the relatively large amount of data directly collected from 3D objects, redundant information should be filtered in this step, namely, salient object detection [25].  
In the second stage, the collected color and depth information is pre-processed. For the 3D point cloud model, it is separated into red, green, and blue channels by depth-layer weighted prediction.  
In the third stage, the color component values are characterized based on the depth information of real objects to generate effective depth grids. Each object is composed of different grids, which contains color information of different R/G/B channels. The fast Fourier transform calculates the grid of different channels to generate the hologram.  
Figure 1: Schematic diagram of full-color holographic system.  
At this stage, our algorithm performs Taylor expansion of the radial value r in the convolution-based on Rayleigh-Sommerfeld diffraction, thus effectively improving the computational speed of hologram generation while ensuring the reconstruction quality in an acceptable range.  
The fourth stage is the full-color reconstruction module. Fresnel diffraction is used to calculate the propagation of light waves in the near-field region to clearly reconstruct real 3D objects.
## 2 System Model  
We consider transmission of electromagnetic (EM) signal over the communication channel using OFDM technique. The system model under consideration is a linear cascaded model. The entire bandwidth of the system is conventionally split into \(K\) orthogonal subcarriers. Let \(x(t)\) denote the transmitted EM signal, and \(z(t)\) be the received EM signal. We consider the impulse responses of four parameters in modeling the end-to-end channel-\(\mathbf{h_{d}(t)}\), \(\mathbf{a(t)}\), \(\mathbf{b(t)}\), and \(\mathbf{\theta}\). Suppose there are \(L_{1}\) propagating paths from the BS to the \(n\)th IRS element, then \(\mathbf{a(t)}\) can be modeled as [29]:  
\[\mathbf{a_{n}(t)}=\sum_{l=1}^{L_{1}}\sqrt{\alpha_{n}^{l}}e^{-2\pi f\ell t}\delta(t -\tau_{n}^{l}) \tag{1}\]  
where \(\alpha_{n}^{l}\in\) [0,1] is the path propagation loss, \(\tau_{n}^{l}\geq\)0 represents the delay of the \(l\)th path and \(\delta(t)\) represents the Dirac delta function. Likewise, \(\mathbf{b(t)}\) can be modeled as [29]:  
\[\mathbf{b_{n}(t)}=\sum_{l=1}^{L_{2}}\sqrt{\beta_{n}^{l}}e^{-2\pi f\ell t}\delta(t -\tau_{n}^{l}) \tag{2}\]  
We place a constraint on the values that the IRS elements can take; they can only take values from the set \(\{1,-1\}\). The superimposed channel response in the time domain can be given by [30]:  
\[\mathbf{h_{\theta}}=\mathbf{h_{d}}+(\mathbf{a*b})*\mathbf{\theta} \tag{3}\]  
here \({}^{*}\) represents the convolution operation. The received signal is then given by:  
\[z(t)=\mathbf{h_{d}(t)}+(\mathbf{h_{\theta}*x})(t)+\mathbf{\omega}(t) \tag{4}\]  
The discrete-time form of Equation (4) can be represented as follows:  
\[z[k]=\sum_{m=-\infty}^{\infty}x[m]\mathbf{h_{\theta}}[k-m]+\mathbf{\omega}[k] \tag{5}\]  
where \(h_{\theta}[0],...,h_{\theta}[M-1]\) represents the non-zero components of the finite impulse response (FIR) filter with M \(\geq\) 1 terms. Taking the Fourier Transform of (5) and with \(\mathfrak{F}\) representing the Fourier Transform, we obtain:  
\[\mathfrak{F}(z)=(\mathbf{H_{d}}+\mathbf{V\theta})\mathfrak{F}(x)+\mathbf{\mathcal{N}}_{ \mathbb{C}} \tag{6}\]
# Machine Learning- and Deep Learning-Based Multi-Model System for Hate Speech Detection on Facebook
## 2 Literature Review
To address these gaps, this study collects and annotates a large-scale dataset of Roman Urdu hate speech from Facebook comments, a resource currently lacking in the field. We employ six ML models (Logistic Regression, SVM, Naive Bayes, Random Forest, KNN, Gradient Boosting) and four DL models (CNN, RNN, LSTM, GRU) to evaluate their effectiveness in hate speech detection. Our experimental results reveal that CNN and LSTM outperform all other models, achieving 95.1% and 96.2% accuracy, respectively. Furthermore, our work introduces improved preprocessing techniques, including phonetic normalization and optimized word embeddings (FastText and Word2 Vec) to better handle Roman Urdu's spelling variations and code-mixing issues. By integrating state-of-the-art deep learning methods with ML feature engineering, we provide a more robust, scalable, and linguistically informed approach to Roman Urdu hate speech detection, setting the foundation for future research in low-resource languages.
## 4 Simulation and Experiment Validation
### Amplitude Root Mean Square Analysis of Simulation and Measured Signals  
Verify that this model has good computational results, the root mean square (RMS) processing is applied to the vibration signal in the z-axis direction. The \(X_{RMS}\) calculation formula is as follows:  
\[X_{RMS}=\sqrt{\frac{1}{N_{f}}\sum_{i=1}^{N_{f}}x_{i}^{2}} \tag{22}\]  
where, x\({}_{i}\) is the response of the i-th vibration signal, and N\({}_{f}\) is the length of the vibration signal.  
Tables 2 and 3 show the root mean square values of the simulation signals in three directions for the spalling axial position \(\tau=0^{\lx@math@degree}\) and \(\tau=15^{\lx@math@degree}\). It can be seen from the comparison of the vibration root mean square values at the same fault location under radial load that the root mean square value is highest in the x-direction and lowest in the z-direction. The root mean square value in the z-direction at the axial position \(\tau=0^{\lx@math@degree}\) of the spalling fault is 0. When the axial position is \(\tau=15^{\lx@math@degree}\), the root mean square value of the z-direction vibration signal increases to 0.615. There is no change in the y-direction, while the value in the x-direction decreases by 22.9%. Comparing Table 3, which shows the root mean square values of the measured signals in three directions for the spalling axial positions \(\tau=0^{\lx@math@degree}\) and \(\tau=15^{\lx@math@degree}\), the simulation and experimental results are close to each other. The simulation and experimental loading conditions for the two bearings are the same. Thus, it shows that when the axial position of the spalling fault is far away from the center of the raceway, the vibration signal in the x-direction will be weakened, the vibration  
Figure 14: Simulation and measured signal envelope diagrams. (**a**) Envelope diagram of the non-center position simulation signal (**b**) Envelope diagram of the non-center position measured signal.  
signal in the z-direction will be enhanced, and the vibration signal in the y-direction will not be affected.
# Germanium Single Crystals for Photonics
## 9 Acousto-Optic Effects in Germanium
Sometimes, Ge is used in the 5-6 \(\upmu\)m range, as well as in the 2-3 \(\upmu\)m range, where the dominant material paratellurite (TeO\({}_{2}\)) has absorption bands [97]. The high thermal conductivity of Ge allows the creation of thermally stable designs of various acousto-optic devices based on Ge single crystals, consuming tens of watts of control power with a diffraction efficiency at a wavelength of 10.6 \(\upmu\)m of more than 90% [52]. Water-cooling is usually used to thermally stabilize the working crystal in these devices. In modern technology, laser acousto-optic modulators based on Ge are used for pulse control of laser radiation [53]. The disadvantage of germanium is that the material does not have high AO quality, and to achieve high efficiency of AO interaction, it requires the application of extremely high acoustic power, reaching 200 W. In addition, this requires the use of large, by acousto-optic standards, crystals with water-cooling. In addition, germanium is optically isotropic, and it is difficult to create good AO deflectors and filters on its basis.
## 5 Airborne Measurements
### Other Airborne Techniques  
In extremely strong wind conditions, such as tornadoes and tropical cyclones, all measurement techniques presented so far have constraints. Although sonic anemometers can measure winds up  
Figure 9: Histograms of \(G\) observed at the 62-m level in a meteorological mast over the Baltic Sea about 8 km south of Helsinki, Finland (**a**), and \(G\) based on horizontal low-level flights over Arctic sea ice and open water (**b**). Stability groups are defined as vu/u: very unstable/unstable; nu/n/ns: near neutral unstable/neutral/near neutral stable; vs/s: very stable/stable. Stabilities are defined as in Figure 6. The results are based on the [PERSON] hypothesis [90]. Redrawn with permission from [40].  
to about 33-65 ms\({}^{-1}\)[98; 99] and cup anemometers up to 75 ms\({}^{-1}\)[100], the reported extreme gusts seldom exceed 40-50 ms\({}^{-1}\) because of power outages and/or anemometer or supporting structure failures caused by wind-borne debris [101; 102]. An alternative method to measure extreme winds is dropwindsonde, which measures the wind based on the location of the sonde tracked by GPS [103]. Wind speed can be measured with an accuracy of 0.5-2 ms\({}^{-1}\) and a vertical resolution of about 5 m along the path across the entire troposphere from about a 12-km height down to 4-10 m above the sea surface (for safety reasons, they can only be operated over the sea), and the measured wind speed range is up to 150 ms\({}^{-1}\). Dropwindsondes can provide unique information for example of high wind speeds in tropical cyclones [104]. Although these measurements can provide information on the most extreme winds in the atmosphere, the measurements represent the (almost) instantaneous state of the atmosphere similar to radio soundings and therefore do not necessarily capture the absolute maximum wind speed at each measurement height. In other words, the measured wind speeds do not correspond to wind gusts as defined in this work (Section 2).
# Research on an ID-PCA Early Fault Detection Method for Rolling Bearings
## 4 Experimental Results and Analysis
### Simulation Results and Analysis of Rolling Bearing Rolling Body Faults
#### 4.2.2 Performance Comparison  
This section compares the experimental results of I-PCA, DE-PCA, DV-PCA, and ID-PCA by analyzing the failure detection rate and detection delay of the incipient failure experimental results of the rolling bearing rolling body.  
Figure 7: Experimental results of ID-PCA fault detection method for the rolling bearing rolling body: (**a**) the result of \(T_{21}^{2}\); (**b**) the result of \(SPE_{21}\); (**c**) the result of \(T_{22}^{2}\); (**d**) the result of \(SPE_{22}\); (**e**) the result of \(T_{23}^{2}\); (**f**) the result of \(SPE_{23}\); (**g**) the result of \(T_{24}^{2}\); (**h**) the result of \(SPE_{24}\).  
Table 3 shows the missing detection rate, fault detection rate, and detection delay of the rolling bearing rolling body fault by I-PCA, DE-PCA, DV-PCA, and ID-PCA methods. We can obtain that the missing detection rate of I-PCA is 18%, and the fault detection rate of I-PCA is about 50%. We can obtain that the I-PCA method has almost no early failure detection capability. The missing detection rate of DE-PCA and DV-PVA is about 15%, and the fault detection rate of DE-PCA and DV-PVA is about 20%. Although the detection ability has been improved, the detection effect of rolling element faults is still not ideal. ID-PCA has a missing detection rate of 2% and a fault detection rate of 4%, which is significantly lower than other methods and has a certain incipient fault detection capability. From the perspective of detection delay, although the detection delay of the I-PCA and DV-PCA methods is also zero, the missing detection rate and the false detection rate are poor. Based on the above comparison results, it can be seen that the IDPCA method proposed in this paper has a good effect on the rolling element fault.