esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Design in Component Level  
The electrical elements of the system, such as the inverter and coil, should be designed so that, in addition to the appropriate dimensions of the coil, the inductance variation after placing the load is not too severe. In [10], cookware enhancements are shown to deliver a uniform temperature across the workpiece. Induction burners usually operate at frequencies between 25 kHz and 30 kHz for switching so that the requirements for the penetration depth and inaudibility of the device can be met.  
According to Equation (2), by choosing 27 kHz for the no-load oscillation frequency and fixing the appropriate dimensions of the coil, an inductance value of 76 uH for the coil and capacitance of 440 nF for the capacitor are chosen. The durability of the capacitors in handling high voltages along with temperature swings should be considered during the selection process, since when the energy of the inductor is transferred to the capacitor, it will cause a great surge in the capacitor's voltage. Another factor worth mentioning is that during the heavy operation of the inverter, the current constantly fluctuates between the inductor and capacitor, thus causing the capacitor to heat up. Therefore, film capacitors with polyester dielectric or polypropylene are selected [28; 29].  
In induction stoves, the ferrite cores under the main coil provide a low-reluctance path for the magnetic field, thus acting as a shield so that the magnetic flux can be concentrated on the load above. By neglecting their effect on the net inductance, we consider only the coil itself for now. In [6], the use of overlapped coils is discussed, which allows for a flexible working surface and simplicity of production. Generally, induction cooker coils comprise spiral, flat windings [30]. To calculate the inductance of such a coil, parameters such as the wire diameter, number of turns, distance between wires, and inner radius are introduced to the existing calculators [31]. A good estimation for making a viable coil is a 12 m long wire with a 2 mm diameter. High-copper alloys should be used in fabrication, since copper is not considered a suitable host for eddy currents due to its low electrical resistance. Figure 4 shows that the inductance measurement of the coil by the LCR meter is manifested. As can be seen, there is little difference between the theoretical and practical values [32].  
Due to heavy noise in the inverter, during switching and rapid capacitor charge, IGBTs are a better choice since MOSFETs are more sensitive to momentary changes in the working voltage, which can cause them to get damaged. IGBTs, on the other hand, are more tolerant of these changes and are therefore less likely to be damaged. Additionally, IGBTs have higher current ratings, which means that they can deliver more power. The STGWT20 IH12 SDF IGBT from STMicroelectronics, with a breakdown threshold voltage of 1200 Volts and a current rating of 15 Amperes, was chosen due to its suitable price and heat tolerance [33].
## 2 Materials and Methods
### Method Validation
#### 2.5.2 Accuracy and Precision  
In addition to LLOQ, three different levels of quality control samples (QCs) of the ERDA were analyzed in six replicates on the same day and in three successive days to evaluate intra-day and inter-days' accuracy and precision, respectively. According to the guidelines, precision (in the term of RSD%) and accuracy of the method should be within \(\pm\)15% for the all QCs and \(\pm\)20% for the LLOQ.
## 3 Types of Chemical Deteriorations in RC Structures  
In this study, the chemical deteriorations observed in reinforced concrete structures are categorized into two main groups based on their underlying mechanisms. The first group involves deteriorations caused by a cation exchange reaction. In this type of deterioration, ions from aggressive materials replace ions present in the concrete compounds, resulting in chemical changes and subsequent degradation of the concrete structure. The second group comprises deteriorations caused by the expansion of materials resulting from various chemical reactions. These reactions induce dimensional changes in the materials, exerting pressure on the surrounding concrete and leading to cracking, spalling, and other forms of structural damage. Understanding and identifying these different types of chemical deteriorations is crucial for developing effective maintenance and repair strategies to mitigate their impact and extend the service life of reinforced concrete structures. Addressing these deteriorations at an early stage is essential to ensure the structural integrity and durability of the concrete components.  
The first category of deteriorations can be further classified into three subgroups (referred to as types 1 to 3), while the second category consists of seven subgroups (referred to as types 4 to 10). These classifications of deteriorations allow for a comprehensive and detailed diagnosis of the specific type of deterioration. In total, there are 10 distinct types of chemical deteriorations observed in reinforced concrete structures.  
1. Deteriorations caused by cation exchange reaction-formation of soluble calcium salts.
2. Deteriorations caused by cation exchange reaction-formation of insoluble and non-expandable salts.  
* Deteriorations caused by cation exchange reaction-chemical attack by magnesium salts.
* Deteriorations caused by sulfate attack due to ingress of external sulfate ion into concrete.
* Deteriorations caused by sulfate attack due to ingress of internal sulfate ion into concrete.
* Deteriorations caused by alkali-silica reaction (ASR).
* Deteriorations caused by alkali-carbonate reaction (ACR).
* Deteriorations caused by hydration of magnesium oxide and crystalline calcium oxide.
* Deteriorations caused by reinforced concrete corrosion in concrete due to chloride ion diffusion.
* Deteriorations caused by reinforced concrete corrosion in concrete due to carbonation.
# Time-Dependent Alternative Route Planning: Theory and Practice +
## 4 Experimental Evaluation
### Experimental Results
[PERSON]'s algorithm was, again, replaced by TDD, and for the lower bounds required by ALT the constant free-flow minimum-travel-time distances were used (i.e., each arc has a scalar cost corresponding to its minimum traversal-time). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Network**} & \multirow{2}{*}{ \begin{tabular}{c} **Landmark** \\ **Set** \\ \end{tabular}  } & \multirow{2}{*}{\(N\)} & **Target** & **Total** & **Avg** & **Decision** & **Relative** & **Time** & **AG Size** \\ \cline{5-10}  & & & & **Dist. ** & **Dist. ** & **Edges** & **Error (\%)** & **(ms)** & 1/V’1, 1/E’1 \\ \hline \multirow{6}{*}{BE} & & 1 & 1.53 & 1.54 & 1.01 & 4.63 & 0.48 & 0.52 &  
DPP does not require prepossessing, while APP requires a linear-in-space and super-linear-in-time prepossessing phase for computing the lower bounds required by ALT. Regarding the computation of the alternative graph (column q-time in Table 2), both baseline approaches had a slow path collection phase. DPP constructs a subgraph \(H\) that is huge in size, using an expensive phase of pure TDD as there are no heuristics. APP improves the time of the path collection phase; however, the lower bounds are not tight for a time-dependent metric. In both cases, the achieved quality is high, at the cost of large processing times. From Tables 1 and 2, it is clear that, for all instances, the configurations of TDAG that achieve analogous aggregate quality required execution times that were about two orders of magnitude smaller than those of DPP. In particular, the achieved speedups were more than 102.7 for BE, 90.8 for GE, and 37.9 for EU. Similarly, the configurations of TDAG achieving similar values of the target function were faster than APP by about one order \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network** & **Method** & **Target Function** & **Query Time** & **Speedup** \\ \hline \multirow{4}{*}{BE} & DPP & 3.01 & 319.38 & \multirow{2}{*}{102.7} \\ \cline{2-4}  & TDAG vs. DPP & 2.97 & 3.11 & \\ \cline{2-4}  & APP & 4.21 & 134.73 & \multirow{2}{*}{2.1} \\ \cline{2-4}  & TDAG vs. APP & 4.22 & 64.44 & \\ \hline \multirow{4}{*}{GE} & DPP & 3.27 & 2623.36 & \multirow{2}{*}{90.8} \\ \cline{2-4}  & TDAG vs. DPP & 3.26 & 28.89 & \\ \cline{2-4}  & APP & 4.17 & 1860.80 & \multirow{2}{*}{4.8} \\ \cline{2-4}  & TDAG vs. APP & 4.15 & 384.15 & \\ \hline \multirow{4}{*}{EU} & DPP & 3.36 & 19,511.93 & \multirow{2}{*}{37.9} \\ \cline{2-4}  & TDAG vs. DPP & 3.30 & 514.45 & \\ \cline{2-4}  & APP & 3.89 & 10,266.29 & \\ \cline{2-4}  & TDAG vs. APP & 3.62 & 1237.80 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Speedups of TDAG over DPP (with TDD) and APP (with \(A^{*}\) and free-flow lower-bounds). Figure 4: The targetFunction, execution time, and ratio of targetFunction/time of TDAG at GE. In the first row of the horizontal axis, the tested combinations of \(N/|L|\) are shown. of magnitude, as the instance size increased. In particular, the speedups were 2.1 for BE, 4.8 for GE, and 8.3 for EU.
## 4 Adaptive Robust Model Free Control
### Simulation Setup  
To better clarify the significance of the proposed scheme, the conventional iPD-TDE [44] and FTSMC-TDE [45] algorithms were conducted for comparison. Equations (39) and (40) are referred to as these two existing algorithms, respectively.  
\[\tau_{u}=\bar{\mathrm{M}}(\bar{\theta}_{k}+K_{D}e+K_{P}e)+\hat{F}(t) \tag{39}\]  
\[\begin{split} s=e+\beta\dot{e}^{\frac{m}{n}}\\ \tau_{u}=-\bar{\mathrm{M}}((\beta\frac{m}{n})^{-1}\dot{e}^{2- \frac{m}{n}})-\bar{\mathrm{M}}(\gamma_{1}s+\gamma_{2}s^{\frac{m}{n}})+\\ \bar{\mathrm{M}}\dot{\theta}_{k}+\hat{F}(t)\end{split} \tag{40}\]  
The parameters of the four controllers are listed in Table 2. They are manually adjusted by trial-and-error.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{tabular}{c} **Parameter** \\ **(Equation)** \\ \end{tabular} & **iPD-TDE** & \begin{tabular}{c} **FTSMC** \\ **-TDE** \\ \end{tabular} & \
\begin{tabular}{c} **ANFTSMC** \\ **-TDE** \\ \end{tabular} &
\begin{tabular}{c} **FNN-** \\ **ANFTSMC** \\ **-TDE** \\ \end{tabular} \\ \hline \(\bar{\mathrm{M}}\) & 0.001 & 0.001 & 0.001 & 0.001 \\ \(K_{P}\) Equation (39) & 0.05 & & & \\ \(\hat{F}\) Equation (40) & 0.5 & & & \\ \(m\) Equations (12) and (40) & 5 & 5 & 5 & 5 \\ \(n\) Equations (12) and (40) & 3 & 3 & 3 & 3 \\ \(\gamma_{1}\) Equation (12) & 10 & 1 & 1 \\ \(\gamma_{2}\) Equation (12) & 1 & 0.1 & 0.1 \\ \(a\) Equation (12) & & 2 & 2 \\ \(\eta\) Equation (17) & & 20 & 20 \\ \(\sigma\) Equation (25) & & 0.01 & 0.01 \\ \(c\) Equation (28) & & & \([-3-10\,13;\) \\ \(b\) Equation (28) & & & \([22\,22\,22]\) \\ \(\kappa\) Equation (34) & & & \(0.1\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameters of four controllers.  
Figure 4: Block diagram of FNN-ANFTSMC-TDE.  
The APK was to follow the desired trajectory \(\theta_{d}=15*sin(\pi*t-\pi/2)+15\), as depicted in Figure 5 (dashed black line). The initial velocity and acceleration were set to zero. The sampling time \(T\) was 0.001 s. The elastic stiffness of the joint was set to 2.1 N/mm. The velocity of the prosthetic knee was calculated by \(\dot{\theta}_{k}(t)=(\theta_{k}(t)-\theta_{k}(t-T))/T\), and the corresponding acceleration could be evaluated as \(\ddot{\theta}_{k}(t)=(\theta_{k}(t)-2\theta_{k}(t-T)+\theta_{k}(t-2T))/T^{2}\) by numerical differentiation. From the results in Figure 5a-c, it can be concluded that these four TDC schemes can guarantee trajectory tracking under system uncertainties. The validity of the TDE algorithm was proved effectively. Compared with iPD-TDE and FTSMC-TDE, ANFTSMC-TDE and FNN-ANFTSMC-TDE have similar better convergences and the tracking error can be limited in \(|e|<0.051\).
# Highly Sensitive Non-Enzymatic Detection of Glucose at MWCNT-CuBTC Composite Electrode
###### Abstract  
A novel electrochemical glucose sensor was developed, based on a multiwall carbon nanotubes (MWCNTs)-copper-1,3,5-benzetricarboxylic acid (CuBTC)-epoxy composite electrode, named MWCNT-CuBTC. The electrode nanocomposite was prepared by a two-roll mill procedure and characterized morphstructurally by scanning electron microscopy (SEM). The CuBTC formed defined crystals with a wide size distribution, which were well dispersed and embedded in the MWCNTs. Its electrical conductivity was determined by four-point probe contact (DC) conductivity measurements. The electroactive surface area, determined using cyclic voltammetry (CV), was found to be 6.9 times higher than the geometrical one. The results of the electrochemical measurements using CV, linear sweep voltammetry (LSV), differential pulse voltammetry (DPV), chronoamperometry (CA) and multiple pulse amprometry (MPA) showed that the MWCNT-CuBTC composite electrode displayed high electrocatalytic toward the oxidation of glucose and, as a consequence, very high sensitivity. The best sensitivity of 14,949 \(\upmu\)AmM\({}^{-1}\)cm\({}^{-1}\) was reached using MPA at the potential value of 0.6 V/SCE, which was much higher in comparison with other copper-based electrodes reported in the literature. The good analytical performance, low cost and simple preparation method make this novel electrode material promising for the development of an effective glucose sensor.  
CuBTC-multiwall carbon nanotube-based electrochemical sensors; HKUST; glucose; electrochemical detection; differential pulse voltammetry; multiple pulse amprometry 2020 10 8419 10.3390/app10238419 26 November 2020
## 3 Results and Discussion
### Analysis Results of the Fatigue Strength under Operational Loads
#### 3.3.1 Influences of Changes in the Throat Depth of Fillet Welds  
Calculated results under combined conditions of supernormal loads suggest that the fillet welds A8 of traction rod brackets on the bogie frame satisfy requirements for the static strength in UIC615-4. Calculated results under combined conditions of operational loads indicate that except for models with cracks, the maximum stress and minimum stress in calculation results of other models are both in the Goodman diagrams, meeting the requirements for fatigue strength. The maximum stress amplitudes at weld roots in models of fillet welds A6 and A8 of the first and second traction rod brackets are separately illustrated in Figures 14 and 15.  
When the throat depth of fillet welds increases from 6 to 8 mm, the maximum stress amplitudes of defect-free welds of type A and type B traction rod brackets separately decrease by 5% and 14%, respectively. As the throat depth increases from 6 to 8 mm, if the welds contain the maximum clearance at weld roots, maximum incomplete fusion of side walls, maximum incomplete fusion at weld roots, and the maximum porosity permissible in ISO 5817, the maximum stress amplitude of the type A traction rod bracket separately decreases by 13%, 29%, 24%, and 25%; meanwhile, that of the type B traction rod bracket separately reduces by 17%, 18%, 11%, and 1%. This can be explained as follows: fillet welds with a greater throat depth are more tolerant of defects. It is worth noting that when throat depth enlarges, the maximum stress amplitude of fillet welds with incomplete fusion of side walls exhibits the maximum decrease.  
Figure 13: (**a**) Stress amplitudes at weld roots of type A traction rod brackets; (**b**) Stress amplitudes at weld roots of type B traction rod brackets.
# A Review on AI for Smart Manufacturing: Deep Learning Challenges and Solutions
## 4 Challenge: Data Secrecy
### Elimination Approaches  
The straightforward solution for privacy protection is to directly eliminate the privacy-sensitive information from raw data. The traditional commonly-used anonymization techniques are K-anonymity [104], l-diversity [105], and t-closeness [106]. To achieve k-anonymity, any individual in the datasets should be indistinguishable from at least \(k-1\) other individuals. There are two methods for K-anonymity, i.e., suppression and generalization. Suppression removes values of some entries in datasets and generalization groups the entries into categories. [PERSON] et al. apply a generalization mechanism to face deidentification, in which the individual faces are replaced using the average face from the same class [107]. L-diversity is an extension of k-anonymity and aims to ensure that there are at least \(L\) distinct values for each attribute in one equivalent class. The two main approaches for l-diversity are suppression and generalization. There are two other versions of l-diversity, i.e., entropy l-diversity and recursive l-diversity (see [105]). The main ideas are to reduce the granularity of a data representation. T-closeness is an extension of l-diversity in which the distance between the distribution of sensitive attributes in the equivalent class and the distribution of the attribute is larger than the threshold. Therefore, the attribute values of each equivalent class cannot offer more than the global table.  
\begin{table}
\begin{tabular}{c c c} \hline
**PPML Techniques** & **Applied Scenarios** & **Applied Objects** \\ \hline Elimination-based Approaches & Cloud & Data \\ Homomorphic Encryption & Cloud & Data, Model \\ Functional Encryption & Cloud & Data, Model \\ Differential Privacy & Cloud, Edge & Data, Model \\ Federated Learning & Edge & Architecture \\ \hline \end{tabular}
\end{table}
Table 3: Privacy-preserving machine learning techniques and their usages. Data: data preparation and storage, model: model training and inference, architecture: deep model architectures.
## 5 Cryptosystems Choice
### Cryptosystem for Vendor'S Key-Pair
#### 5.1.2 Boldyreva Signatures  
Boldyreva digital signatures [22] are discrete-logarithm based and implemented over a so-called _Gap Diffie-Hellman_ (GDH) group. In a GDH group, the _Diffie-Hellman problem_ is difficult, namely, given \(g^{a}\) and \(g^{b}\), computing \(g^{ab}\) is assumed to be hard. However, the _Decisional Diffie-Hellman problem_ is easy to solve, namely, given \(g^{a}\),\(g^{b}\), and \(g^{c}\), it is easy to decide whether \(c=ab\).  
A GDH group of large prime order \(q\) has to be chosen. Let \(g\) be a generator of such group. A private key is generated by choosing a random \(x\in\{0,\ldots,q-1\}\). The corresponding public key is computed as \(y=g^{x}\).  
A digital signature over a digest \(M\) is computed as \(S=M^{x}\).  
A digest-signature tuple \(\{M,S\}\) is validated under public key \(y\) by checking whether \(\{M,y,S\}\) is a GDH-tuple, that is, \(\log_{g}M\cdot\log_{g}y=\log_{g}S\).  
This signature scheme allows the computation of blind signatures throughout the following procedure:  
[PERSON] chooses \(r\in\{0,\ldots,q-1\}\) and computes \(\overline{M}=M\cdot g^{r}\). Then she sends \(\overline{M}\) to [PERSON].  
[PERSON] computes \(\overline{S}=\overline{M}^{x}\) and sends \(\overline{\sigma}\) back to [PERSON].  
Finally, [PERSON] computes \(S=\overline{S}\cdot y^{-r}\) which is a digital signature over \(M\).  
Given the group generator \(g\) and a public key \(y\) (\(y=g^{x}\)), a Boldyreva digest-signature tuple is simulated by taking a random integer \(t\) and computing \(M=g^{t}\) and \(S=y^{t}\). Tuple \(\{M,S\}\) is a simulated digest-signature tuple. The construction in Section 3.3 allows us to link simulated signatures to pseudo-random pieces of data.
## 3 Methodology
### Byte-Level Byte Pair Encoding (BBPE)  
With the help of more processing power, data, and improved algorithms, natural language processing--the study of how to properly handle natural text by computers--has lately made significant strides. It is the core of various application cases, including machine translation, conversational bots, and opinion mining, among others. Various ML models can combine with NLP techniques and perform well for distinct tasks such as text classification [18].  
Currently, advanced natural language translation models are constructed on top of character-based vocabularies, such as words, subwords, or characters. Byte pair encoding (BPE) is one of the character-based encoding mechanisms that works on frequent character occurrences (i.e., characters that occurs the most in the text) and merging (merging the frequently occurred characters to represent as one character). However, BPE won't work efficiently for character-rich languages like Japanese and Chinese. Therefore, more advancedmodels such as the Roberta uses byte-level BPE (BBPE), where the text is represented at the byte level and uses the vocabulary of 256 bytes [54,55].  
Studies such as [54] have observed that BBPE is better than BPE. BBPE has advantages such as, it takes less computational power and can work efficiently on almost all languages for tokenization. Thus, to utilize the above-mentioned advantages of BBPE we have used Roberta-based Emorberta model for the emotion detection task. Moreover, BBPE is essential to learn the contextual meaning of known (i.e., the words that are in the text vocabulary and that the ML model is aware of) and unknown (i.e., the words that are not in the text vocabulary and that the ML model can eliminate or miss to learn) words in the text data to further detect the emotions.
# A Minimum Mean-Square-Error (MMSE) Decoder for Quasi-Orthogonal Space-Time Block Code
## 4 Simulation Results  
In this section, the BER performance of the proposed algorithm is illustrated through computer simulation for a Rayleigh fading channel. The modulation type was presumed to be the quadrature phase shift keying and the channel was assumed to be a zero-mean white Gaussian or correlated with the variance of \(\frac{1}{2}\). The coefficient of the channel was perfectly known in the receiver [5]. The background noise \(\mathbf{w}\) is added to \(\mathbf{H}\mathbf{x}\).  
Figure 1 compares the BER performance of the proposed algorithm at various forgetting factors in a Rayleigh fading channel. As can be seen, the BER performance can change slightly according to the  
forgetting factor in the proposed algorithm. Thus, from the next simulation, the parameter is selected as \(\zeta=0.995\).  
Figure 2 shows the BER performance of the proposed algorithm compared to that of the conventional QO-STBC algorithms under various signal-to-noise ratios (SNRs) from 6 dB to 22 dB for the four antennas system in a Rayleigh fading channel [5]. As shown in Figure 2, the proposed algorithm illustrates the lowest BER in all SNRs. In addition, the proposed algorithm is verified in the three antennas environment [5]. Figure 3 compares the BER of the proposed algorithm with that of the conventional algorithms in a Rayleigh fading channel. As a consequence, the proposed algorithm is more useful than the other algorithms.  
Figure 1: BER performance of the proposed algorithm at various forgetting factors for the four antennas system in a Rayleigh fading channel.  
Figure 2: Bit-error-rate (BER) performance of the conventional quasi-orthogonal space–time block code (QO-STBC) and proposed algorithm as a function of the signal-to-noise ratio (SNR) for the four antennas system in a Rayleigh fading channel.  
Figures 4 and 5 compare again the conventional algorithms and proposed algorithm under a correlated Rayleigh fading channel (the correlation coefficient is 0.9) [13]. The proposed algorithm still achieves a better BER than the conventional algorithms.  
Figure 4: BER performance of the conventional QQ-STBC and proposed algorithm as a function of the SNR for the four antennas system in a correlated Rayleigh fading channel (the correlation coefficient = 0.9).  
Figure 3: BER performance of the conventional QQ-STBC and proposed algorithm as a function of the SNR for the three antennas system in a Rayleigh fading channel.