esa-sceva/satcom-chunk-collection huggingface dataset:
# A Cryptography-Powered Infrastructure to Ensure the Integrity of Robot Workflows
## 4 Application and Evaluation in a Real Robot System
### Results  
In this section, we show the results of our tests and measurements we took on (1) the overall process and (2) the signing and verification procedures. For the first we captured the network traffic caused by the single components in order to get the data overhead produced by the compliance infrastructure. Regarding the second we benchmarked the signature generation process as well as the signature verification process. In our implementation we use SHA-256 for hashing and a RSA-4096 key pair for generating the signature.
## 2 Materials and Methods  
### Numerical Simulation of Fiber Parameters  
The PCF (produced by fiberware, Mittweida, Germany) investigated here had a hole diameter of d = 1 \(\pm\) 0.3 \(\upmu\)m and a hole-to-hole distance of \(\Lambda\) = 3 \(\pm\) 0.2 \(\upmu\)m, which was measured with a MIRA 3 TESCAN scanning electron microscope (SEM). Similar fibers were shown to allow only single-mode propagation of light over wide spectral ranges in the core [9]. The fibers investigated here, however, deviated significantly from the theoretical, cleanly periodic structure of the PCF and to gain a greater understanding of the influence of this fact on the SC generation in these fibers, the optical modes of the PCF were numerically simulated using the following technique. Firstly, a grayscale image of the cross-section of the PCF was taken on the SEM and numerically converted into a binary image by setting a defined threshold value. In this way, the binary image numerically filtered noise, particles and scratches, producing a clean representative glass, no-glass image.  
This binary image of the fiber's cross-section was used (taking the known index of refraction for glass and air) as the input to solve the vector Helmholtz equation. The fiber was made from high-purity synthetic quartz glass with low OH content (F300 Heraeus Quarzglas, Hanau, Germany). An numerical routine was implemented similar to the one found in Ref. [14], which was modified to also consider polarization. At a given wavelength, a sparse eigenvalue routine was used to solve the resulting system for both the fundamental mode, as well as the higher order cladding modes of the fiber. This routine provided a few of the spatial eigenfunctions, pictured in Figure 3b-f, and their corresponding propagation constants, \(\beta(\lambda)\). For each mode, the group delay per unit length in Figure 4 was calculated based on this determined \(\beta\) by:  
\[T_{g}=\frac{1}{c}\bigg{(}n_{eff}-\lambda\frac{dn_{eff}}{d\lambda}\bigg{)}, \tag{1}\]  
where \(n_{eff}=\beta/k_{0}\) is the effective index of refraction, \(k_{0}\) is the free-space wavenumber, and \(c\) is the speed of light.
## 5 Conclusions  
The modified characteristic formulas in Equations (1) and (2) with kink effects (phonon disturbances) are provided to fit the as-measured current-voltage curves. The kinks are always located at the joint of the triode region and the saturation region. The authors noticed many conclusive points, presented as follows: (1) Three parameters (k\({}_{\rm N}\), Vth, \(\lambda\)) were determined and found to be different at different V\({}_{\rm GS}\). (2) \(\delta\) deviation helps improve the fitting to be as complete as possible, and may help distinguish a FinFET structure from a planar MOSFET one, as shown in Figures 1b, 2b, 3b and 4b. (3) The mobility at certain fixed Gate voltages, which is proportional to k\({}_{\rm N}\), was constant but varied at various Gate biases. For FinFET and MOSFET in Figure 6, k\({}_{\rm N}\) was found to be almost linearly dependent on (V\({}_{\rm GS}\)-Vth)\({}^{-1/3}\), and so was mobility. (4) The determined Vth was found to be V\({}_{\rm GS}\)-dependent too. An empirical equation, which takes advantage of the definition of V\({}_{\rm tho}\), is proposed in order to find \(\beta\) and thus the concentration (p) of doped boron, which may be used to calculate the thickness of the strong inversion layer in Figure 7. (5) In Figure 8, the early voltage-associated \(\lambda\) (\(\,\rm{|V_{A}}^{-1}\,\rm{|}\,\)), which is inversely linearly dependent of (V\({}_{\rm GS}\)-Vth), becomes flatter as (V\({}_{\rm GS}\)-Vth) becomes larger. This indicates that a higher field results in more depletion of the substrate which depresses the leakage current. (6) k\({}_{\rm N}\) was inversely proportional to the channel length, as verified in Figure 9 at a specific gate bias, e.g., V\({}_{\rm GS}\) = 1.00 V.  
Conceptualization, H.-C.Y. and S.-C.C.; methodology, H.-C.Y., S.-C.C., H.-Y.Y. and Y.-T.Y.; software, H.-C.Y. and S.-C.C.; validation, H.-C.Y. and S.-C.C.; formal analysis, H.-C.Y. and S.-C.C.; investigation, H.-C.Y. and S.-C.C.; resources, H.-C.Y. and S.-C.C.; data duration, H.-C.Y. and S.-C.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
**Institutional Review Board Statement:** Not available.  
**Informed Consent Statement:** Not available.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
Figure 9: k\({}_{\rm N}\) for four FinFET transistors is linearly dependent of the inverse of channel length.
# ACT-FRCNN: Progress Towards Transformer-Based Object Detection
## 6 Conclusions  
An object detection solution based on ACT-FRCNN, which demonstrates robust performance through a transformer backbone, is presented in the proposed research. As described in these findings, alternative architectures, like transformers, are more promising than adaptive clustering transformers (ACTs) for advancing complex vision tasks. Transformer-based models have demonstrated impressive capabilities in pre-training on large datasets without reaching saturation. ACT-FRCNN demonstrates the advantages of using transformers in vision tasks. For instance, in the NYUDv2 dataset, ACT-FRCNN achieved ODS, OIS, and AP scores of 81.3%, 82.0%, and 85.1%, respectively, outperforming the previous best methods by 3.9%, 3.1%, and 3.2%, respectively. These results highlight the advantages of transformer-based models, specifically ACT, in vision tasks. This marks an important milestone, as it is the first exploration of transformer-based architectures for solving various vision problems in the research community. As a result of the success of ACT-FRCNN, further research and development in computer vision using transformer  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Method** & **Year** & **ODS** & **OIS** & **AP** \\ \hline SE [64] & 2014 & 0.695 & 0.708 & 0.679 \\ SE+NG+ [57] & 2014 & 0.706 & 0.734 & 0.738 \\ OEF [58] & 2015 & 0.651 & 0.667 & - \\ Semi Contour [59] & 2016 & 0.680 & 0.700 & 0.690 \\ HED [60] & 2015 & 0.720 & 0.734 & 0.734 \\ RCF [61] & 2017 & 0.729 & 0.742 & - \\ AMH-Net [62] & 2017 & 0.744 & 0.758 & 0.765 \\ LPCB [52] & 2018 & 0.739 & 0.754 & - \\ BDCN [45] & 2019 & 0.748 & 0.763 & 0.790 \\ PiDiNet [54] & 2021 & 0.733 & 0.747 & - \\ EDTER [55] & 2022 & 0.774 & 0.789 & 0.797 \\ \hline
**ACT-FRCNN (Our)** & **0.813** & **0.820** & **0.831** \\ \hline \end{tabular}
\end{table}
Table 6: _Cont._based models is encouraged. Future research ideas might be built around novel transformer kinds and transformer topologies modified for object-detecting applications. Lastly, more advanced techniques seem promising to improve the results and performance rates of the particular method, such as Conditional Generative Adversarial Networks (CGANs), or conduct additional research into the suitability of transformers in combination with other machine learning frameworks, like federated learning.  
The study idea and its execution were contributed to by all authors. Methodology, formal analysis, project administration, data curation, and English language editing performed by [PERSON], [PERSON], M.A.Z., A.R., S.U. and H.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
All procedures performed in studies were in accordance with the ethical standards of the institutional and/or national research committee.  
Data Availability StatementData are contained within the article.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Automatic Speech Recognition for Uyghur, Kazakh, and Kyrgyz: An Overview
## 4 Advances in Speech Recognition for Uyghur, Kazakh, and Kyrgyz
### Technical Development
#### 4.2.3. Kyrgyz  
In contrast, research on Kyrgyz speech recognition started even later. Until 2018, researchers [150] from Xinjiang University reported their study based on a CNN model. In this study, they trained an initial CNN model with Uyghur data and, then, adapted it to a Kyrgyz model using their private data consisting of 5 h of speech recorded from 40 speakers. Again, the value of these studies is limited as they were not based on an open benchmark.  
In 2019, the Common Voice database was published, which contains a small amount of Kyrgyz data. This public data aroused the interest of researchers, and since then, Kyrgyz has become the research object of extremely low-resource language speech recognition [151; 152]. For example, [PERSON] et al. [153] in 2020 verified the performance of transfer learning on these data. They firstly used 360 h of LibriSpeech data to pre-train a contrastive predictive coding (CPC) model and, then, fine-tuned it with 1 h of Kyrgyz data. The performance of the fine-tuned model was 41.2% for the CER. In the same year, [PERSON] et al. [84] trained a Wav2 Vec 2.0 model with 56,000 h of data from 53 languages and, then, fine-tuned it with 1 h of Kyrgyz data, reducing the CER to 6.1%. Furthermore, [PERSON] et al. [91] proposed a completely unsupervised learning model, which used 1.8 h of unlabeled Kyrgyz data and obtained a CER of 14.9%. Table 6 presents the performance on the Common Voice-Kyrgyz dataset. In this table, CPC and XLSR are \"few-shot\" systems, where a large amount of extra data were used to pre-train the model, and then, one hour of Kyrgyz data were used to perform the fine-tuning. The pre-trained models are CPC and Wave2 Vec2.0, respectively. The last Wav2 Vec2.0 system is fully unsupervised, with Wav2 Vec2.0 as the feature front-end and a GAN as the decoder.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**ID**} & \multirow{2}{*}{**Models**} & \multirow{2}{*}{**LM**} & \multirow{2}{*}{**SpeedPerturb**} & \multirow{2}{*}{**SpecAug**} & \multicolumn{2}{c}{**Valid**} & \multicolumn{2}{c}{**Test**} \\ \cline{5-8}  & & & & & **CER (\%)** & & **WER (\%)** & **CER (\%)** & **WER (\%)** \\ \hline
1 & DNN-HMM & Yes & Yes & No & 5.2 & 14.2 & 4.6 & 13.7 \\
2 & & Yes & Yes & Yes & 5.3 & 14.9 & 4.7 & 13.8 \\ \hline
3 & & No & No & 9.9 & 32.0 & 8.7 & 28.8 \\
4 & & Yes & No & No & 7.9 & 20.1 & 7.2 & 18.5 \\
5 & & Yes & Yes & No & 5.7 & 15.9 & 5.0 & 14.4 \\
6 & & Yes & Yes & Yes & 4.6 & 13.1 & 4.0 & 11.7 \\ \hline
7 & & No & No & No & 6.1 & 22.2 & 4.9 & 18.8 \\
8 & & Yes & No & No & 4.5 & 13.9 & 3.7 & 11.9 \\
9 & & Yes & Yes & No & 3.9 & 12.3 & 3.2 & 10.5 \\
10 & & Yes & Yes & Yes & 3.2 & 10.0 & 2.8 & 8.7 \\ \hline \hline \end{tabular}
\end{table}
Table 5: SOTA results tested on KSC [114]. LM represents the language model; SpeedPerturb and SpecAug represent speed perturbation and SpecAugment, respectively.
## 3 Explicit MPC Controller Design
### Offline Solution of Explicit MPC Model  
For the above MPQP problem, because \(J^{*}(x(k))\) is a convex set, the solution \(Z\), under the Karush-Kuhn-Tucker (KKT) conditions, is the global minimum. At this time, the corresponding KKT condition is:  
\[\begin{split} HZ+G^{T}\lambda&=0,~{}~{}\lambda \in R^{q}\\ \lambda_{i}\Big{(}G^{i}Z-W^{i}-E^{i}x\Big{)}&=0,~{}~{ }i=1,\dots,q\\ \lambda&\geq 0\\ GZ-W&-Ex\leq 0\end{split} \tag{27}\]  
In the formula, \(i\) represents the ith row, \(\lambda\in R^{q}\) is the Lagrangian operator, and \(q\) is the number of constraints, which can be solved:  
\[Z=-H^{-1}G^{T}\lambda\lambda\Big{(}-G^{i}H^{-1}G^{T}\lambda-W^{i}-E^{i}x \Big{)}=0 \tag{28}\]  
The above formula can be combined as \(Z=H^{-1}\big{(}G^{\xi}\big{)}^{T}\Big{(}G^{\xi}H^{-1}\big{(}G^{\xi}\big{)}^{T }\Big{)}^{-1}\big{(}W^{\xi}+E^{\xi}x\big{)}\). This formula is valid for all \(x\in CR_{i}\). \(CR_{i}\) is called the critical region for the current state value \(x\). For this critical region, it can be obtained by the following method:  
\[\Big{(}W^{\xi}+E^{\xi}x\Big{)}\leq W+ExGH^{-1}\Big{(}G^{\xi}\Big{)}^{T}\Big{(} G^{\xi}H^{-1}\Big{(}G^{\xi}\Big{)}^{T}\Big{)}^{-1} \tag{29}\]  
Lagrange multipliers are non-negative, so:  
\[\Big{(}G^{\xi}H^{-1}\Big{(}G^{\xi}\Big{)}^{T}\Big{)}^{-1}\Big{(}W^{\xi}+E^{ \xi}x\Big{)}\leq 0 \tag{30}\]So the critical region \(CR_{i}\) is:  
\[CR_{i}=\{x\in R^{q}|\text{Meet the conditions \eqref{eq:CR_i} \eqref{eq:CR_i}}\} \tag{31}\]  
That is, according to the current engine system state \(x(k)\), the corresponding critical region \(CR_{i}\) can be found, and the current optimal control parameters can be obtained by solving the linear function of the critical region \(z\). As the control quantity \(u\) is a single-valued function of \(z\), a further simplification can be written as:  
\[U_{*}=F_{i}x(k)+G_{i}x(k)\in CR_{i},i=1,2,3\ldots N_{r} \tag{32}\]  
This is a piecewise affine explicit relationship, where \(CR_{i}\) is the i-th state partition, and \(F_{i}\). \(G_{i}\) are the control variable constant matrix corresponding to the partition. Then the optimal control quantity of the system at this time is:  
\[u(k)^{*}=\left[I\ 0\ldots 0\right]_{N\times 1}U_{*} \tag{33}\]  
The optimal control problem of the system is transformed into a calculation of the PWA function with the system state as the independent variable, and the optimal control input as the dependent variable.
# Comparative Analysis of Volatile Flavor Compounds in Seven Mustard Pastes via HS-SPME-GC-MS
## 2 Results and Discussion
### Identification of Volatiles in MPs
required that the content of AITC should exceed 98% as a flavor compound. Therefore, allyl chloride might be introduced by adding AITC, which further indicates that AITC might be added in all seven MPs. Dehydroacetic acid was only detected in MP 6, which is a permitted preservative listed in the Chinese Food Additive Use Standard GB 2760-2014 [20]. However, ITC flavor compounds have antimicrobial and bacteriostatic properties [21], and preservatives are not required during MP production, so dehydroacetic acid detected might be from other raw materials used in the production of MP 6. Other than MP 4, 3-butenemitrile was detected in all samples. The precursors of both nitrile and ITC are thioglucosides, which can be readily degraded to ITCs under neutral conditions and to nitrile under acidic conditions. It was reported that the precursors of 3-butenemitrile and benzeenpropanenitrile are sinigrin and gluconasturitin, respectively [22]. To determine the contribution of volatiles identified to the overall odor profile of MPs, OAVs were calculated according to their concentrations and thresholds in water [13], as shown in Table S2. The odorants with OAV \(\geq\) 1 were determined as the potent odorants of MPs, the number of which were 9, 11, 10, 9, 8, 7, and 6 in the 7 MPs, respectively. The odorant contributing greatly to the aroma of MP was AITC, followed by carbon disulfide, which was a degradation product of AITC which also affects the aroma quality of MPs. Although the potent odorants varied among samples, the OAV of AITC and carbon disulfide were much higher than those of the other aroma components, making the overall aroma of different MP samples similar.
# Automatic Detection of Airway Invasion from Videofluoroscopy via Deep Learning Technology
## 4 Discussion
In most of the false negative images, the shadow of the penetrating bolus appeared less opaque, which can be explained by either a small volume of airway invasion or an insufficient amount of contrast medium. If a small penetration volume is the cause of such an error, it may not have clinical significance because a small amount of PAS two penetration can also be present in normal subjects. Regarding the contrast medium, an adequate and standardized amount is necessary for human inspection, as well as machine-based inspection. Thus, it is believed that in either case, the clinical significance of the false negatives generated by the present model should be minimal. The present study has a few limitations. The size of the dataset was limited and consisted of randomly selected VFSS files. The distributions of various properties, such as gender, age, severity of dysphagia, image quality, bolus consistency, and amount of contrast medium, should have been taken into account. A larger, balanced dataset will be required to make the model more robust for general clinical application. In addition, the labeling of the dataset was reviewed and confirmed by only two physiatrists, which may not be sufficient. The reliability of the model should be examined with a larger number of human observers, preferably through a prospective controlled study. Finally, the sole target of model development was the detection of airway invasion, neglecting the many other variables that might also be valuable for the evaluation of dysphagia. Further studies are expected to reveal the utility of deep learning models for investigating other variables, including the time elapsed for food passage and the motions of various anatomical structures.
# Multi-Material Topology Optimization Taking into Account the Position of Material Interfaces in 3D
## 3 Methods
### Extended ITED
#### 3.6.2 Optimization Problem  
The interface energy listed in Equation (14) is transformed into the following objective function by integration over the interface \(\Gamma\) analogously to [22] using the smooth interface integral:  
\[J_{\text{i}ted}=\int_{\Omega}H(\varphi_{1})\check{\delta}(\varphi_{2})W_{\text{ i}ted}d\Omega, \tag{17}\]  
where \(H(\varphi_{1})\) is the Heaviside function, \(\check{\delta}(\varphi_{2})\) the smooth delta function and is defined as follows:  
\[\check{\delta}(\varphi_{2})=\begin{cases}\frac{3}{4\Delta}\Big{(}1-\big{(} \frac{\varphi_{2}}{\Delta t}\big{)}^{2}\Big{)}&,|\varphi_{2}|\leq\Delta l\\ 0&,|\varphi_{2}|>\Delta l.\end{cases} \tag{18}\]  
The complete optimization problem is as follows:  
\[\begin{split}&\min_{\mathbf{\eta}}w_{c}J_{c}+w_{\text{i}ted}J_{\text{ i}ted}+w_{\text{r}i}\int_{\Omega}d\Gamma+w_{\text{r}s}\int_{\partial\Omega}d \varsigma\\ \text{s.t.}& a(\mathbf{u},\mathbf{\psi})-l(\mathbf{\psi})=0,\quad\forall\mathbf{\psi}\in U _{\text{a}d}\\ & f_{V1}\leq\overline{J}_{V1}\\ & f_{V2}\leq\overline{J}_{V2}\\ & v^{min}\leq\eta_{i}\leq v^{max},\quad i=1,\dots,n_{d}\end{split} \tag{19}\]  
where \(J_{c}=\int_{\Omega}\frac{1}{2}\mathbf{\epsilon}(\mathbf{u})\cdot\mathbb{C}[\mathbf{ \epsilon}(\mathbf{u})]d\Omega\) is the compliance. The third and fourth terms are two regularization components that can be set independently of each other. The third term regularizes the interface \(\Gamma\) between the two materials and thus ensures the solvability of the optimization problem. The fourth regularizes the entire part surface \(\partial\Omega\) and thus ensures strut-like structures [15]. \(w_{c}\), \(w_{\text{i}ted}\), \(w_{\text{r}i}\) and \(w_{\text{r}s}\) are the scaling factors of the individual terms.  
The weak form of the boundary value problem of elastostatics and the two volume boundary conditions act as constraints in the optimization problem. Here, \(\overline{J}_{V1}\) and \(\overline{J}_{V2}\) are the target volume fraction of the first and second material, respectively.  
The design variable vector \(\mathbf{\eta}=[\eta_{1}^{1},\dots,\eta_{n_{d}}^{1},\eta_{1}^{2},\dots,\eta_{n_{d} }^{2}]^{T}\) contains the normal velocities of both LSF at each node in the LS-mesh. The components of \(\mathbf{\eta}\) are bounded by the minimum \(v^{min}\) and maximum \(v^{max}\) normal velocity.
# Towards Automatic Landslide-Quake Identification Using a Random Forest Classifier
## 3 Results and Discussion
### Influence of Attributes between the Time and Frequency Domains  
Figure 9a shows that the accuracies of the attributes in the time domain were higher than those in the frequency domain. For confirmation purposes, classifiers containing attributes in time and frequency domain were built separately (a TD classifier and an FD classifier), and the results of classification are shown in Table 4. The TD classifier had a higher accuracy (87.7%) than that of the FD classifier (84.7%), in agreement with the test of single-attribute classifiers. Comparing the results between the TD and FD classifiers, the sensitivities for earthquakes had no significant difference. However, the misclassification of landslide and noise cases increased obviously. For landslide cases, the sensitivity of the FD classifier was 6.1% lower than that of TD classifier, and for noise cases, the sensitivity was 3.7% lower. Since the TD classifiers were better for discriminating the cases of landslides from noise and the FD classifier was better for discriminating earthquakes from noise, a classifier combining attributes in the time and frequency domains together could provide better classification of all three cases.