esa-sceva/satcom-chunk-collection huggingface dataset:
# Second-Harmonic Generation in Mie-Resonant GaAs Nanowires
## Appendix A  
The expression of the SH scattering efficiency in Equation (17) derives from the Lorentz reciprocity theorem and the concept of effective susceptibility [35; 36; 37]. In the undepleted pump approximation and in the absence of third-order nonlinear effects, the time-harmonic electromagnetic problem at the SH frequency (\(2\omega_{p}\)) is given by a distribution of volume currents localized inside the nanowire,  
Figure 8: SH scattering and absorption efficiency for a GaAs nanowire with \(r_{NW}=220\) nm with an input pump intensity \(I_{p}=2\) GW/cm\({}^{2}\). The three background colors indicate the three regions described in the text with the same color code of Figure 7.  
\[\mathbf{J}_{NL}=-i2\omega_{p}\mathbf{P}_{NL}=-i2\omega_{p}\bigg{(}\epsilon_{0} \chi_{xyz}^{(2)}E_{1,x}^{TM}E_{1,y}^{TM}\bigg{)}\hat{\mathbf{z}}, \tag{10}\]  
which radiate into the background medium and induce the unknown field \(\mathbf{E}_{SH}(R,\phi^{\prime})=E_{SH}\hat{\mathbf{z}}\) at a distance \(R\) from the nanowire in a direction tilted by an angle \(\phi^{\prime}\) with respect to the input pump, where a virtual detector is located. The problem is illustrated in Figure 11a. In Figure 11b, we also report the reciprocal problem, in which a line current source \(\mathbf{J}_{0}=I_{0}\delta(x-x_{0})\delta(y-y_{0})\hat{z}\), at the virtual-detector location, emits radiation of frequency \(2\omega_{p}\) and induces a z-polarized field \(\mathbf{E}_{1}^{TE}=E_{1,z}^{TE}\hat{z}\) inside the nanowire. The Lorentz reciprocity imposes that  
\[\int\mathbf{J}_{0}\cdot\mathbf{E}_{SH}dS=\int\mathbf{J}_{NL}\cdot\mathbf{E}_ {1}^{TE}dS \tag{11}\]  
where the surface integral is performed across the whole \(xy\) plane. The expression of the unknown field is therefore:  
\[E_{SH}=\frac{-i2\epsilon_{0}\omega_{p}}{I_{0}}\int_{NW}\chi_{xyz}^{(2)}E_{1,x} ^{TM}E_{1,y}^{TM}E_{1,z}^{TE}(\phi^{\prime})dS. \tag{12}\]  
If the source \(\mathbf{J}_{0}\) is located in the far-field region, then the phase fronts emitted by \(\mathbf{J}_{0}\) at the nanowire position are virtually planar. Under these circumstances, it is convenient to replace, in the reciprocal problem, the current \(\mathbf{J}_{0}\) with a z-polarized plane wave with amplitude \(E_{0}\) and traveling in the direction \(\phi^{\prime}\), as illustrated in Figure 11c. This plane wave induces an electric field \(E_{1,z}^{TE}\) (\(\phi^{\prime}\)) identical to that produced in the original reciprocal problem of Figure 11b. The plane wave excitation may be more practical for full-wave numerical simulations. An additional advantage is that all the internal fields in the integral of Equation (12) can now be retrieved analytically by using the expressions in Equations (12)-(13). The only missing piece of information is the relation between the plane wave amplitude \(E_{0}\) and the current \(I_{0}\). This can be retrieved by solving for the 2D Green's function for the Helmholtz equation at the SH frequency in the plane of scattering (\(xy\) plane), \(G_{2D}=\frac{i}{4}H_{0}^{(1)}(k_{SH}R)\).
## 4 Discussion
### Cooperative Detect and Avoid Approach  
A simple collision avoidance approach based on horizontal and vertical safety distances was tested within the cooperative system. Despite the straightforward approach, collisions between all simulated and real participants could be avoided during the flight tests. Using the remaining energy reserves as a decision basis for initiating a collision avoidance manoeuvre (in this case the transition to hover flight) allows transparent prioritisation and grants preference to the manned aircraft even in manned/unmanned constellations. Position data received via ADS-B and forwarded into the UTM network is given a SoC of 0 % (as this data field is not part of the ADS-B standard), so all UAVs give priority to this type of traffic.  
In the test flights, the UAVs shared their positions at a frequency of 2 Hz and received traffic information from the UTM server once a second. This enabled a collision avoidance for flight speeds of up to 5 m/s. For higher velocities, greater safety distances or higher update rates are required. In addition, the agility of UAVs should be included in the respective collision avoidance procedure. However, due to the variable packet transit times in the LTE network and the associated reception delays, the safety distances were sometimes undercut. A definition of minimum and maximum packet transit times can further increase the reliability of this collision avoidance method.
## 1 Introduction
The wavelet packet (WP) is a popularization of the traditional wavelet, which can divide the frequency domain adaptively [26]. There has been much research performed on structural vibration response analysis based on the WP technique [27; 28; 29; 30]. Typically, the structural vibration response signal is mixed in with a lot of noise and WP is an efficient way to deal with the white noise. To improve the denoising effects of the GNSS-RTK signal, we propose a two-step denoising method, which integrates the merits of both CEEMDAN and WP and is called CEEMDAN-WP. Modal parameters identification is an important part of structural health monitoring [31; 32]. Structural dynamic deformation monitoring and modal parameter identification under ambient excitation do not need to predict the input signal and do not damage the structure nor affect its normal operation. Simply via the output response can the structural operating modal parameters can be obtained. [PERSON] [33] advanced a modal parameter identification method named the random decrement technique (RDT), which is based on output signals only. In the cases where the ambient excitation is stationary and random, theoretically like white noise, RDT can convert the ambient responses to random decrements [34; 35; 36]. Recently, RDT has been employed in extracting modal parameters from structural nonstationary response [37; 38; 39]. Therefore, it may be feasible to employ RDT in structural dynamic characteristics analysis under ambient excitation. This paper primarily focuses on studying the dynamic response of a long-span concrete-filled steel tubular arch bridge based on the GNSS-RTK technique. Above all, the stability test was conducted to assess the accuracy of GNSS-RTK receivers and, secondly, the FEM of the bridge was established to provide a reference with which to compare the field measurement results. Subsequently, a combined denoising method (CEEMDAN-WP) is proposed in order to process the monitoring signals from the GNSS-RTK receivers. Finally, the structural modal parameters are identified through FFT and RDT.
# Developing Quantum Trusted Platform Module (QTPM) to Advance IoT Security
## 3 Quantum Trusted Platform Module (QTPM)  
### QTPM Design  
A QTPM combines quantum technology with TPM to enhance IoT security. Using a quantum random number generator, the QTPM can address the inherent limitations of classical random number generators (RNGs) that are vulnerable to biases or attacks. The use of quantum technology ensures a more robust and truly random generation of keys, mitigating RNG-related security risks. With QTPM, IoT devices can achieve higher levels of trust, confidentiality, and integrity, ensuring highly secure communication and data protection.  
In this paper, we focus on the implementation of QTPM using a software-based TPM (STPM) framework. STPM is a software emulation of a hardware-based TPM (HTPM), which can support key generation, secure storage, and cryptographic operations. For a system with a lack of HTPM support, STPM can provide a more adaptable and accessible alternative to data security. STPM is useful for testing and development purposes, but it may not offer the same level of protection as HTPM. STPM is generally less secure than HTPM due to its software-based operation, making it more susceptible to attacks, such as code injection or key extraction. In future work, we plan to develop QTPM at the hardware chip level.  
The IBM TPM, which uses a deterministic random bit generator, is a widely used STPM that has been extensively adopted in experimental or commercial applications. Specifically, the IBM TPM software implementation typically employs a SHA-1 or SHA-256-based DRBG as defined by the National Institute of Standards and Technology (NIST) in its Special Publication 800-90A. The seed is derived from an entropy source and is used to produce a stream of random numbers. However, since it is algorithmic and deterministic, the same seed will always produce the same output, which brings a fundamental limitation. Random numbers are generated from a seed relying on secure cryptographic hash functions (such as SHA-1 or SHA-256). These hash functions add complexity to the number generation process, making it difficult (but not impossible) for an attacker to reverse the process and determine the seed. The DRBG needs to periodically reseed itself with new entropy to improve randomness and mitigate predictability. IBM TPM is generally secure. However, its quality and security heavily depend on the entropy source, and it remains vulnerable to seed predictability or entropy source weaknesses, especially if attackers can compromise the underlying software or access the seed.
## 3 Results and Discussion
### Environmental Characterization
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Mine Tailings** & \multicolumn{4}{c}{**Contact Time Liquid/Solid 24 h**} & \multicolumn{4}{c}{**Contact Time Liquid/Solid 48 h**} \\  & **pH** & **Conductivity (m/S/cm)** & **NaOH (mL)** & **pH** & **Conductivity (m/S/cm)** & **NaOH (mL)** \\ \hline TSF1 & 3.46 & 68.9 & 5.601 & 5.12 & 58.7 & 0.613 \\ TSF2 & 3.48 & 67.7 & 5.566 & 5.32 & 54.4 & 0.530 \\ TSF3 & 3.94 & 57.3 & 5.101 & 5.64 & 37.4 & 0.242 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Physicochemical characteristics of the static tests of the TSF tailings. Figure 14: Environmental characterization of the TSF tailings based on NNP and NP/AP for a 48 h test (adapted from [59]). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Contact Time Liquid/Solid 24 h**} & \multicolumn{4}{c}{**Contact Time Liquid/Solid 48 h**} \\  & **TSF1** & **TSF2** & **TSF3** & **TSF1** & **TSF2** & **TSF3** \\ \hline
5 (\(\%\)) & 1.01 & 1 & 0.42 & 1.01 & 1 & 0.42 \\ Paste pH & _7.77_ & _7.92_ & 8.25 & & & \\ NP (Mg CaCO/\(\prime\)) & 48.49 & 48.58 & 49.75 & 60.96 & 61.175 & 61.89 \\ AP (Mg CaCO/\(\prime\)) & 31.56 & 31.25 & 31.32 & 31.56 & 31.25 & 13.12 \\ NNP (Mg CaCO/\(\prime\)) & 16.93 & 17.33 & 36.63 & 29.4 & 29.93 & 48.77 \\ NP/AP & 1.53 & 1.55 & 3.79 & 1.93 & 1.96 & 4.72 \\ Environmental classification of mine drainage & Uncertain & Uncertain & Non-acid generator & Uncertain & Uncertain & Non-acid generator \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Static test results of the TSF tailings.
# Intrusion Detection Method Based on Preprocessing of Highly Correlated and Imbalanced Data
## 2 Materials and Methods
The result of each model was averaged for regression, and the majority voted for classification by the meta-model. The effectiveness of bagging was achieved because the basic algorithms trained on different subsamples turned out to be quite different, and their errors were mutually compensated during voting, as well as due to the fact that outliers may not fall into some training subsamples. In this work, meta-algorithms based on bagging, Random Forest [23], Adaboost [29], and Gradient Boosting [30] are researched as models, which are the most effective in detecting intrusions in computer systems and networks. To compare the results of the ensemble classification models, the following algorithms were used: SVM, KNN, Naive Bayes, and Perceptron. The problem statement of classification can be described as follows:  
Let input data be the set of marked pairs \(X_{1}=(x_{i1},\,x_{i2},\,\ldots,\,x_{im})\), where \(x_{i}\) is the set of features, and \(y_{i}\) is a classifying label. Every \(x_{i}\) is characterized by a set of features F.  
There exists an unknown fitness function--a mapping \(f\): \(F\to Y\), the values of which are only known for a finite set of training samples \((X,\,Y)=\{(x_{1},y_{1}),\,\ldots,(x_{m},\,y_{m})\}\). The task itself consists of the formation of such a meta-algorithm \(f\) that is able to classify an arbitrary object \(x\in X\) and to adjust the values of its parameters \(w\) in such a way to bring the predicted value \(\hat{y}\) closer to the actual value \(y\):  
\[F\;(f\;(w,\;x),\;\hat{y})\;\to\;y.\]  
The model's performance was evaluated using accuracy (1), precision (2), recall (3), and F1 score (4). \[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{1}\]  
\[Precision=\frac{TP}{TP+FP} \tag{2}\]  
\[Recall=\frac{TP}{TP+FN} \tag{3}\]  
\[F1\;score=\frac{2}{\frac{1}{Precision}+\frac{1}{Recall}}=\frac{TP}{TP+0.5( FP+FN)} \tag{4}\]  
In addition, an important criterion for the quality of the model is its training time and recognition time.
# Translating Speech to Indian Sign Language Using Natural Language Processing
## 10 Conclusions  
Through this paper, we have presented a user-friendly audio/text to Indian Sign Language translation system specially developed for the hearing- and speaking-impaired community of India. The main aim of the system is to bring a feeling of inclusion among the hearing-impaired community in society. The system does not only help the person who is suffering from a disability but would also be beneficial for the hearing people who want to understand the sign language of a hearing-impaired person so that they can communicate with them in their language. The core of the system is based on natural language processing and Indian Sign Language grammar rules. The integration of this system in areas such as hospitals, buses, railway stations, post offices, and even in video conferencing applications, etc., could soon be proved a boon for the hearing-impaired community in India.  
In the future, the features of the system could be enhanced by integrating reverse functionality, i.e., an Indian Sign Language to audio/text translation system which could open the path for a two-way communication system. In addition, the database of the system could be increased to enhance the literacy and scope of the system.  
Conceptualization: D.T. and P.S. ([PERSON]); Methodology: P.S. ([PERSON]); Formal analysis and investigation: P.S. ([PERSON]); Writing--original draft preparation: D.T. and N.N.; Writing--review and editing: C.V. ([PERSON]); Funding  
Figure 21: Case 10. Available video for ‘good evening’.  
Figure 20: Case 9. Available video for ‘you’.  
acquisition: none; Resources: P.S. ([PERSON]); Supervision: P.S. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 1 Protein Glycoslation and the Golgi
Synthesis of \(O\)-glycans starts in the early _cis_-Golgi or in a translational compartment (ERGIC, with characteristics of the ER), and is completed via the sequential addition of glycans while the secretory protein moves through the _cis_-, _medial_- and _trans_-Golgi cisternae. Efficient protein \(N\)- and \(O\)-linked glycosylation is strictly dependent on the precise distribution of glycosyltransferases, glycosylatases and nucleotide sugar transporters which should be maintained in the correct Golgi compartment as the secretory cargo protein passes through the Golgi. How Golgi enzymes are retained in the Golgi membranes is strictly regulated by vesicle trafficking across the Golgi [19]. So far, more than 100 monogenic congenital disorders of glycosylation (CDGs) have been identified and associated with a wide variety of symptoms such as neurological deficit, liver disfunction, and skin and bone alterations [20; 21]. Traditionally CDGs have been divided into two groups: CDG-I affect the synthesis and/or transfer of the dolichol pyrophosphate oligosaccharidescharide precursor of \(N\)-linked glycoproteins, while CDG-II affect the processing of \(N\)-linked glycans or the biosynthesis of \(O\)-linked glycans [22]. Many pathogenic mutations in intra-Golgi trafficking regulators have been associated  
Figure 1: Examples of glycans that are elaborated in the Golgi. The diagram depicts \(N\)- and \(O\)-linked glycans attached to glycoproteins and proteoglycans. Abbreviations are: Xyl, xylose; Gal, galactose; GalNAc, \(N\)-acetylgalactosamine; Man, mannose; NeuAc, \(N\)-Acetylheuraminic acid; GlcNAc, \(N\)-acetylglucosamine; Fuc, Fucose; Glc, glucuronic acid; IdoA, iduronic acid. with CDG II. This review will highlight the strict relationship between the Golgi trafficking machinery and protein glycosylation.
## 4 Experiments
### Comparison with State-of-the-Art Methods
#### 4.3.1 Quantitative Comparison
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**CUFED5 [3]**} & \multicolumn{1}{c}{**Sun80 [34]**} & \multicolumn{1}{c}{**Urban100 [35]**} & \multicolumn{1}{c}{**Manga109 [36]**} & \multicolumn{1}{c}{**WR-SR [6]**} \\ \cline{3-6}  & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** \\ \hline \multirow{5}{*}{SISR} & SRCNN [10] & 25.33/0.745 & 28.26/0.781 & 24.41/0.738 & 27.12/0.850 & 27.27/0.767 \\  & EDSR [12] & 25.93/0.777 & 28.52/0.792 & 25.51/0.783 & 28.93/0.891 & 28.07/0.793 \\  & RCAN [15] & 26.06/0.769 & 29.86/0.810 & 25.42/0.768 & 29.38/0.895 & 28.25/0.799 \\  & SRGAN [16] & 24.40/0.702 & 26.76/0.725 & 24.07/0.729 & 25.12/0.802 & 26.21/0.728 \\  & Enet [37] & 24.24/0.695 & 26.24/0.702 & 23.63/0.711 & 25.25/0.802 & 25.47/0.699 \\  & ESRGAN [18] & 21.90/0.633 & 24.18/0.651 & 20.91/0.620 & 25.53/0.797 & 26.07/0.726 \\  & RankSRGAN [19] & 22.31/0.635 & 25.60/0.667 & 21.47/0.624 & 25.04/0.803 & 26.15/0.719 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative comparison between SISR model and RefSR model (using reconstruction loss, adversarial loss, perceptual loss). The best and second best PSNR/SSIM values are marked in bold and underlined in the table, respectively.
## 3 Evaluation Objectives
### Concept-Driven Evaluation Studies  
Most studies [16; 25; 26; 27; 28; 29; 30; 31; 32; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 73; 74; 75; 81; 82] in our sample are driven by research on explanation models and their representation. The objective is to comprehend what constitutes a high-quality explanation that increases human cognition and decision-making.  
The research in this category aims to investigate a common understanding of the explanation quality of existing XAI frameworks such as LIME or SHAP [63]. Often, novel explanation concepts and approaches are evaluated, including example-based explanations (normative and comparative explanations) [48], consequence-oriented and contrastive explanations [81], question-answering pipelines [36; 61], data-centric explanations [36], or argumentative explanations [28]. Also, design principles, such as the implementation of the right to explanation [43], or novel interface concepts, such as interactive explanations [38; 51], have been evaluated in this kind of research.  
To improve generalizability, most studies in this category carry out domain-independent evaluations using fictional and illustrative scenarios. The goal is to obtain insights into how various explanation models and features impact the overall functionality and effectiveness of explanation systems. To reduce confounding effects, these studies typically prefer experimental designs conducted under controlled conditions that abstract away from specific real-world contexts. Regarding the tension between rigor and relevance [83], evaluation studies in this category are often methodologically sound yet lack ecological validity as fictional and simplified tasks were often used that were disconnected from real-world applications and did not involve real users.
## 2 Experimental Investigations
### Experimental Results and Discussion
#### 2.3.4. Curing Tests  
Adhesion  
The adhesion results for the cured products after curing with catalytic infrared radiation are shown in Figure 9. A rating higher than 4B is generally considered a qualified product [14], and the rating standards are shown in Table 4. With increasing curing temperature and curing time, the adhesion level of the coating increased. This was due to the enhanced degree of bonding between the coating and the substrate. When the curing temperature was 200 \({}^{\circ}\)C and the curing time was 5 min, an adhesion rating of 5B was obtained, meeting engineering requirements. With a curing temperature of 230 \({}^{\circ}\)C or 240 \({}^{\circ}\)C, a curing time of only 2 min was required to achieve an adhesion rating of 5B. [PERSON] et al. [14] used NIR radiation to cure a solvent-blended acrylic resin with an amine crosslinker coating, which did not meet the 4B standard. However, they combined an NIR curing system with a convective oven to obtain a coating that met this standard. The maximum allowable temperature for solvent-based coatings is generally low, and the high-power density of an infrared heater may induce thermal stress between the substrate and the coating, resulting in defects, such as local cracks and film deletion.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Grade** & **Area Removed** &
\begin{tabular}{c} **Cross-Cut Area of Six** \\ **Parallel Lines** \\ \end{tabular} \\ \hline
5B & None & \\
4B & Less than 5\% & \\
3B & 5–15\% & \\
2B & 15–35\% & \\
1B & 35–65\% & \\
0B & Greater than 65\% & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification chart for paint adhesion tests [14].  
Figure 9: Coating adhesion test results.  
In contrast, powder coatings' maximum allowable temperature is generally high, and they do not have a solvent component. Therefore, powder coatings are more suitable for infrared curing. However, excessively high coating temperatures inevitably change other physical properties, such as color and gloss.
## 2 Materials and Methods
### Mathematical Model
#### 2.2.9 Iterative Calculation Algorithm  
The iterative procedure is based on the application of the control Equations (32) and (33) in order to \"balance\" the applied mathematical procedure. \"Balance\" is necessary because the following variables are used in the calculations (Table 5): \(T_{abs}\) [K] (variable 3, i.e., V3), \(T_{o}\) [K] (variable 4, i.e., V4) and wind speed \(W\) [m/s] (variable 5, i.e., V5).  
\[q_{abs-gt}\ =\ h_{abs-gt,rad}\big{(}T_{abs}-T_{gt}\big{)} \tag{32}\]  
\[q_{gt-o}\ =\ \Big{(}T_{gt-o,rad}-T_{gt-o,conv}\Big{)}\big{(}T_{gt}-T_{o}\big{)} \tag{33}\]  
where [40,41]\(q_{abs-gt}\) [W/m\({}^{2}\)] is the specific heat flux exchanged between the absorber and the glass tube, \(h_{abs-gt,rad}\) [W/m\({}^{2}\)K] is the radiation heat transfer coefficient between the absorber and the glass tube, \(q_{gt-o}\) [W/m\({}^{2}\)] is the specific heat flux exchanged between the glass tube and the ambient air, \(h_{gt-o,rad}\) [W/m\({}^{2}\)K] is the radiation heat transfer coefficient between the glass tube and the ambient air and \(h_{gt-o,conv}\) [W/m\({}^{2}\)K] is the convection heat transfer coefficient between the glass tube and the ambient air.  
The iterative procedure takes place in the following stages:  
1. variables \(T_{abs}\), \(T_{o}\) and \(W\) are adopted from Table 5;
2. the glass tube temperature \(T_{gl}\) [K] is assumed;
3. the results of the initial simulations are printed: \(Q_{net}\), \(Q_{loss}\), \(Q_{heat}\) and \(\eta\);
4. the results of the control Equations (32) and (33) are printed;
5. the fulfillment of the condition \(\mid q_{abs-gt}\)\(q_{gt-o}\mid\)\(\leq\) 0.01 W/m\({}^{2}\) (from Equations (32) and (33)) is checked.
6. If the condition from point 5 is met--the final simulation results are printed: \(Q_{net}\), \(Q_{loss}\), \(Q_{heat}\) and \(\eta\);
7. if the condition from point 5 is not met--the value of \(T_{gl}\) [K] is changed and the entire process is repeated until the condition from point 5 is met.