esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Ultrasonic Guided Wave Signal Processing Methods
### Split Spectrum Processing  
SSP is sensitive to the different frequencies of UGWs at the desired and other modes, that is, the processed sub-band signal group contains the desired mode UGW with a similar amplitude and other mode UGWs with different amplitudes [10; 21; 27]. The schematic diagram of SSP (Figure 3) can be described as follows. First, the fast Fourier transform (FFT) is applied to transform an input signal \(x(t)\) in the time domain into a frequency-domain signal \(X(f)\), which is split into a sub-band signal group \(X_{i}(f)\) (\(i\) = 1,2,... \(N\); where \(N\) denotes the number of filters) in the frequency domain by an intersecting bandpass filter bank. Then, the inverse FFT (IFFT) on \(X_{i}(f)\) is employed and normalized to obtain a time-domain sub-band signal group \(y_{i}(t)\). Finally, the nonlinear signal recombination methods are utilized to produce an output signal \(y(t)\).  
The processing effect of SSP is closely connected with the filter band design, filter band parameters, and signal recombination method, of which the first two will be discussed in Section 4.1. The signal recombination methods considered of SSP are primarily based  
Figure 3: Schematic diagram of SSP.  
Figure 2: (**a**) Excitation signals in time and frequency domains, and dispersion curves of non-axisymmetric UGWs: (**b**) phase velocity and (**c**) group velocity.  
on order statistics and phase observation [30], which includes normalized minimization, mean, frequency multiplication, polarity thresholding (PT), and polarity thresholding with minimization (PTM). In the light of our previous research [31], the high resolution of sparse signals recombined by PT and PTM is likely to be obtained. PT can be expressed as:  
\[y_{PT}(t)=\begin{cases}x(t)&if\ y_{i}(t)<0\ or>0,\,\forall i\\ 0&otherwise\end{cases} \tag{2}\]  
PTM is similar with PT in form, which also requires that \(y(t)\) to take value (\(\min\ |\ y_{i}(t)|\)) only when the positivity and negativity of all sub-band signals at a certain time are consistent; otherwise, it is zero. Both PT and PTM can eliminate the coherent noise that is highly sensitive to the frequency, which is that the amplitude of the sparse signal recombined by PTM may be distorted, resulting in the inaccurate quantitative analysis results of defects.
# A Survey of Analog-to-Digital Converters for Operation under Radiation Environments
## Inventory
### Collected Data  
Selected ADCs target different applications with different key parameters: high linearity and resolution are mandatory in video acquisition, while larger bandwidths are prioritized in telecommunications. However, while comparing two radenv-ADCs for a particular application, secondary parameters could become critical when the key ones are similar. It is hence important to establish a common playground to compare selected ADCs, as we explain below.  
We have collected performance data and additional information from data-sheets [28; 29; 30; 31; 32; 33] (both commercial and SMD formats), radiation reports [1; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62] and scientific publications [20; 60; 61; 62]. We have considered a total of 22 fields for each ADC: power consumption P, differential and integral non-linearities DNL/INL, effective number of bits ENOB, Nyquist sampling rate \(f_{s}\), input Nyquist bandwidth \(f_{in}\), FOM\({}_{W}\), FOM\({}_{TID}\) for low and high dose rates, FOM\({}_{SET}\) for transients and upsets, operational temperature range (T range), TID for low and high dose rates LDR/HDR, LET\({}_{th}\) for latch-up events, Weibull curve limits for transients and upsets, SEE test method, technology and architecture.  
Electrical performance data (P, INL, DNL and ENOB) were collected at nominal operation (nominal bias and sampling rate at 25 \({}^{\circ}\)C). Power consumption data from data-sheets were limited to single core information (excluding IO cells, other ADC cores inside the same chip, additional functionalities, etc.) to enable comparison with scientific publications that usually refer to single core data. ENOB was considered for the best case frequency inside the first Nyquist band. For few cases (AD7712S, RHD5940, RHD5950, RHD5958, ADS1258 and VASP), neither the SNDR\({}_{max}\) nor the ENOB could be found. Therefore, the ENOB (needed for FOM\({}_{W}\) calculation) had to be inferred from their declared number of bits NB and DNL or INL (expressed in LSBs) with the formula:  
\[\text{ENOB}_{infer}=\text{NB}-log_{2}(1+max(|\text{DNL}|,|\text{ INL}|)) \tag{5}\]  
Since the actual ENOB comes from a dynamic measure, it would always be close but below the inferred ENOB (that comes from a quasi-static measure).  
Since it is hard to discern SETs from SEUs during SEE tests of an ADC, radiation data were collected and classified as originally reported. In case that multiple data are available, the worst case is considered.
## 3 System Architecture and Methodology
### System Architecture & Physical Layout
Intuitively, there would be a slight trade-off in real-time performance, but we believed that a wireless connection remained a promising solution. Moreover, a wired connection is impossible in real ATC scenarios. Therefore, it was reasonable to deploy a wireless network for connection. A detailed analysis of performance in latency is presented at a later stage of this paper.
# NFV-Enabled Efficient Renewable and Non-Renewable Energy Management: Requirements and Algorithms
## 6 Analysis of Heuristic Strategies  
The \(\mathcal{NP}\)-hard nature of the problem (Section 4.4) and the intractability of OptTs for large values of \(N\) or \(T_{s}^{k}\) (Section 5.3) motivates the development low complexity strategies to find feasible solutions within a reasonable computing time. In this context, Section 4.4 has proven that the \(P_{RES}\) minimization falls into the category of a \(1/0\) Knapsack Problem. Specifically, it can be categorized as a multiple-choice Knapsack Problem [27], due to the production of the different variations \((VarServ)\) belonging to the same service. Subsequently, the existing literature proposes a number of techniques and strategies for efficiently solving this problem [27; 28], taking into account that, in some cases, the reduction of the execution time is achieved by relaxing the exact solution. Based on the literature surveyed and to cover as far as possible the main categories of methods or techniques that were developed for solving the \(1/0\) Knapsack Problem (in our case, the \(P_{RES}\) minimization problem), we have examined four strategies. According to the degree of difficulty in implementation, the examined strategies are: (i) a Pre-partition strategy (FastTs), based on a divide-conquer approach of the total number of analyzed services; (ii) a Greedy strategy, based on a constructive algorithm in which the best \(VarServ\) is selected based on a performance criterion (e.g., tradeoff \(P_{RES}\) and \(AR\)); (iii) a metaheuristic Genetic algorithm that evolves as a function of the analyzed population and genetic operator (crossover and mutation) applied; and, (iv) a Dynamic programming approach, which is an optimization and programming technique that can be applied for efficiently solving a variety of computational problems. Table 4 summarizes the features and implementation challenges of the possible heuristic strategies.  
Based on a preliminary assessment of the strategies presented in Table 4, in this article we have decided to analyze, in detail, the Pre-partitioning strategy, as shown in Section 7, because its implementation requires few modifications on the algorithm OptTs. Besides, preliminary results show that this method can produce high-quality solutions in a reduced time when compared to the optimal strategy. The implementation and evaluation of the other strategies will be addressed in detail in future work.  
Figure 7: Example of growth rate of OptTs based on \(T_{s}\). Parameter: \(T_{s}^{k}=\{0,\cdots,10\}\).
# Electronic and Transport Properties of Epitaxial Graphene on SiC and 3C-SiC/Si: A Review
## 5 Fine-Tuning of Transport Properties
### Top-Gate Graphene Field-Effect Transistors
Generally, the oxides such as SiO\({}_{2}\), HfO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\), and Y\({}_{2}\)O\({}_{3}\) are used as the gate dielectrics, but these tend to become amorphous with scaling and interactions with the graphene can occur, which makes the high-quality defect-free interface formation difficult [150; 151; 152]. Hexagonal boron nitride (h-BN) forms a weak van der Waals interaction with the graphene (lattice mismatch only 2% [153]) and has been utilized as an ideal insulator for the graphene FET formation [151]. Due to the lower value of a dielectric constant (k\(-\)4), h-BN is usually combined with a high k-dielectric layer such as Y\({}_{2}\)O\({}_{3}\) formed via atomic layer deposition on h-BN [154]. The scaling (\(\sim\)1.3 nm) of h-BN results in excessive gate leakage current within the FET. To overcome this, [PERSON] et al. [150] utilized epitaxial calcium fluoride of \(\sim\)2 nm thickness as a gate insulator with low leakage current and on/off ratio up to 10\({}^{7}\). Furthermore, the epitaxial growth of graphene-h-BN heterostructures on copper via chemical vapor deposition has been demonstrated by [PERSON] et al. [155].
## 3 Results and Discussion
### MALDI-TOF/TOF Analysis and Protein Identification
Further study to identify the proteins corresponding to the 24 undetected spots is required. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Spot** & **Protein Name** & **Experimental** & **Protein** & **GenBank** & **Regulation** & **Fold Change** \\
**No.** & & **P1/MW (kDa)** & **Score** & **Accession No.** & **in PH3-R** & **Fold Change** \\ \hline
6602 & cytochrome oxidase subunit I, partial & 7.33/42.48 & 29 & gi1 6979442 & Up-regulated & 18.8 \\
3701 & cytochrome P450 71A1 [_Culex quinnuefuscatus_] & 4.96/75.00 & 17 & gi1170072409 & Up-regulated & 13.0 \\
2705 & heat shock 70 kDa protein cognate 5 [_Tribolium castanemul_] & 4.74/66.08 & 74 & gi1 91077414 & Up-regulated & 2.7 \\
8304 & triosephosphate isomerase [_Tribolium castanemul_] & 7.95/21.72 & 118 & gi1 189236533 & Up-regulated & 9.7 \\
8501 & citrate synthase 2, mitochondrial [_Tribolium castanemul_] & 7.99/37.55 & 109 & gi1 91083623 & Up-regulated & 530.2 \\
8702 & delta-1-pyrroline-5-carboxylate dehydrogenase, mitochondrial [_Tribolium castanemul_] & 8.07/55.03 & 61 & gi1 91084231 & Up-regulated & 595.9 \\
6606 & ATP synthase subunit beta, mitochondrial [_Hydra vulgaris_] & 7.21/51.61 & 26 & gi1 221132854 & Up-regulated & 100.3 \\
7202 & hypothetical TosaGA2\_TC001380 & 7.53/21.68 & 33 & gi1 270002362 & Up-regulated & 19.7 \\
8702 & protein TosaGA2\_TC00622 [_Tribolium castanemul_] & 8.09/143.07 & 15 & gi1 270012119 & Up-regulated & 144.3 \\
7401 & glyceraldehyde-3-phosphate dehydrogenase, partial [_Timeritis reducat_] & 7.36/28.13 & 180 & gi1 161088548 & Up-regulated & 3.9 \\
4704 & beta-glucuronidase [_Tribolium castanemul_] & 5.19/144.10 & 21 & gi1 91089483 & Down-regulated & 0.18 \\
2512 & protein MLP1-like [_Tribolium castanemul_] & 4.80/37.21 & 22 & gi1 642919153 & Down-regulated & 0.73 \\
2704 & glycogen phosphorylase [_Tribolium castanemul_] & 4.74/90.39 & 68 & gi1 91081301 & Down-regulated & 0.19 \\
3503 & DNA primase large subunit [_Tribolium castanemul_] & 4.96/39.05 & 16 & gi1 189241229 & Down-regulated & 0.65 \\
1705 & beta-actin, partial [_Gammarus minus_] & 4.50/66.84 & 85 & gi1 384402814 & Down-regulated & 0.39 \\ \hline \end{tabular}
\end{table} 
Table 1: Identification of differentially expressed proteins using MALDI TOF/TOF.
## 3 Methodology
### Analysis Methods
#### 4.1.1 Alarm-Set Usage
First, the Kruskal-Wallis \(H\) test on the set-alarm hours (_avg_ring_hour_) reveals a significant difference among the tasks, but its effect size was small, indicating that most of the alarms were concentrated in the morning hours [\(H(3)=2558.71\), \(p<0.001\), \(\epsilon^{2}=0.008\)]. Even though the magnitude of the difference is not that large, we could find a tendency of the task preference in the set-time. _normal_task_'s average hours (about 8 a.m.) tended to be slightly later than those of the other tasks (vs. _picture_task_: \(d=0.281\), vs. _shake_task_: \(d=0.119\), vs. _math_task_: \(d=0.181\)). Interestingly, hard tasks (i.e., _picture_task_, _shake_task_, and _math_task_) tended to be more concentrated at the earlier hours than _normal_task_. Similarly, we also found a small effect size in the comparison of the weekday use proportion (_prop_weekday_), indicating that most of the set alarms were concentrated on weekdays regardless of the wake-up task types [\(H(3)=1828.93\), \(p<0.001\), \(\epsilon^{2}=0.006\)]. Approximately 82-85% of the alarms went off on weekdays, and such a tendency was not entirely different depending on the task type. **Set Consistency. ** We compared three variables, _sd_daily_alarm_, _sd_ring_hour_, and _prop_recur_, in terms of the set consistency. First, we found a significant difference in the variability of the number of a daily alarm-set (_sd_daily_alarm_) and its moderate effect size [\(H(3)=12,485.94\), \(p<0.001\), \(\epsilon^{2}=0.041\)]. _normal_task_ tended to be more inconsistent in the number of the daily set alarms (_vs. picture_task_: \(d=0.671\), _vs. shake_task_: \(d=0.354\), _vs. math_task_: \(d=0.302\)). _picture_task_ showed strong consistency in the number of the daily set alarms compared to the other tasks (_vs. shake_task_: \(d=0.371\) and _vs. math_task_: \(d=0.428\)). Similarly, there were significant and moderate differences in the variability of the ringing hours (_sd_ring_hour_) [\(H(3)=11,731.86\), \(p<0.001\), \(\epsilon^{2}=0.039\)]. It was found that the users set _normal_task_'s ringing hour more inconsistently than the other tasks (vs. _picture_task_: \(d=0.482\), vs. _shake_task_: \(d=0.332\), vs. _math_task_: \(d=0.349\)). Finally, we compared the proportion of weekly-recurrent alarms over wake-up tasks. The results showed a small effect size, indicating that most of the alarms were set to recur weekly regardless of the task type [\(H(3)=2736.596\), \(p<0.001\), \(\epsilon^{2}=0.009\)]. Approximately 73-76% of the alarms were set to recur.
# Hole Concealment Algorithm Using Camera Parameters in Stereo 360 Virtual Reality System
## 3 Algorithm Proposed to Conceal a Hole
### Concealment Based on Intrinsic and Extrinsic Matrixes  
In this subsection, we derive the relationship between the stereo views, where the camera parameters of the warped pictures are utilized. In Figure 4, \(I_{L}\) and \(I_{R}\) are the warped images after they have been taken by the left and right cameras, respectively. Based on the geometric relation between the camera coordinate and world coordinate systems [27], the \((X,Y,Z)\) of point P in the world coordinate system is mapped to the \((x_{L},y_{L})\) and \((x_{R},\,y_{R})\) of points \(p_{L}\) and \(p_{R}\) in \(I_{L}\) and \(I_{R}\), respectively. The relation is represented as follows.  
\[P=\text{R}(I_{R})K(I_{R})^{-1}p_{R} \tag{5}\]  
\[P=\text{R}(I_{L})K(I_{L})^{-1}p_{L} \tag{6}\]  
Combining (5) and (6) gives  
\[\text{R}(I_{R})K(I_{R})^{-1}p_{R}=\text{R}(I_{L})K(I_{L})^{-1}p_{L} \tag{7}\]  
\[p_{R}=K(I_{R})\text{R}(I_{R})^{-1}\text{R}(I_{L})K(I_{L})^{-1}p_{L} \tag{8}\]  
Therefore, the relation between the pixels in \(I_{L}\) and \(I_{R}\) can be represented with a \(3\times 3\) matrix \(H(I_{L}\to I_{R})\) as follows.  
\[p_{R}=H(I_{L}\to I_{R})p_{L} \tag{9}\]  
where  
\[H(I_{L}\to I_{R})=K(I_{R})\text{R}(I_{R})^{-1}\text{R}(I_{L})K(I_{L})^{-1} \tag{10}\]  
Figure 4: Relationship between world coordinates and picture coordinates.  
The \(H(I_{L}\to I_{R})\) can be calculated when \(K(I_{R})\), \(\text{R}(I_{R})\), \(\text{R}(I_{L})\), and \(K(I_{L})\) are known. If \(I_{L}\) and \(I_{R}\) are replaced by \(B_{L}^{c}\) and \(B_{R}^{c}\), respectively, then (10) becomes  
\[H(B_{L}^{c}\to B_{R}^{c})=K(B_{R}^{c})\text{R}(B_{R}^{c})^{-1}\text{R}(B_{L}^{c })K(B_{L}^{c})^{-1} \tag{11}\]  
In the proposed algorithm based on (11), each pixel in a hole is filled with a pixel in the right ERP, where \(H(B_{L}^{c}\to B_{R}^{c})\) provides the location of the pixel in the right ERP to replace a particular pixel in a hole. This means that we can conceal the hole if \(K(B_{R}^{c})\), \(\text{R}(B_{R}^{c})\), \(\text{R}(B_{L}^{c})\), and \(K(B_{L}^{c})\) are known.  
On the other hand, when the hole is found in the right ERP, the following equation is used instead of (11).  
\[H(B_{L}^{c}\gets B_{R}^{c})=K(B_{L}^{c})\text{R}(B_{L}^{c})^{-1}\text{R}( B_{R}^{c})K(B_{R}^{c})^{-1} \tag{12}\]  
Figure 5 shows an example of concealing each pixel in a hole when the hole is found in the left ERP.
# A Survey of Candidate Waveforms for beyond 5G Systems
## 4 Performance Results and Discussion
### Final Discussion  
To sum up this discussion, Table 3 summarizes and grades the different MC waveform formats analyzed throughout this paper for each KPI. This table considers the simulation results from previous subsections and the grades are presented taking into consideration a performance comparison between each waveform candidate and OFDM.  
There are three categories of traffic types in 5G and beyond, which exhibit different characteristics under different propagation scenarios. They are eMBB, mMTC and URLLC.  
eMBB mainly focuses on high transmission bit rate and spectral efficiency. Therefore, the main positive KPIs that a waveform contender should have is regarding the spectral efficiency, OOB emissions, robustness to both time and frequency selectivity and efficient MIMO implementation. From this perspective, by analyzing Table 3, the most suitable waveforms to be the heir of OFDM are GFDM and TIBWB-OFDM with WTO, due to their ability to provide a very high spectral efficiency, with relatively low OOB emissions, along with a considerable robustness upon transmission in time and frequency selective channels (although MIMO implementations in GFDM transceivers require more complex channel equalization schemes).  
Concerning the mMTC service requirements, the used waveform should support a huge number of devices, with simple synchronization and low power consumption. Hence, waveforms with low PAPR and computation complexity, along with an efficient MIMO implementation should prevail. Pure MC waveforms always exhibit high PAPR values. However, F-OFDM is the most fitting candidate to replace OFDM in DL transmissions since its characteristics are better adapted to fulfill the UE receiver's low complexity requirement. If we consider UL transmissions, TIBWB-OFDM with WTO is a suitable candidate to replace current OFDM systems, since it presents a similar transmitter's complexity to OFDM with much better power and spectral efficiencies, and because the receiver's computation complexity criteria is less critical at BS.  
Regarding URLLC, service requirements include high reliability and low latency as the main focus. Thus, the KPIs that a waveform should have are an acceptable BER performance, low computation complexity and low processing delay. From Table 3, F-OFDM is the most suitable waveform when the main issue is the latency. This is typically observed in real time applications, such as online gaming or video applications. However, if the main focus is on reliability, the TIBWB-OFDM with and without WTO are the main contenders, due to their ability to provide reasonable BER performances.
## 7 Simulation Results
### Low Battery SOC Scenario  
In the second scenario (Figure 13), the initial SOC of the batteries did not correspond to a fully charged state, where it has been set to 50% of the maximum value. Figure 13a,b report the same quantities described in the previous case. In the second case, while the _SOC_ was decreasing, the minimum threshold was reached (after 21 min) and then some limitations in power output were triggered. Such limitations lead to lower power provided by the batteries, which tends to zero after some minutes (Figure 13b). At the same time, the range extender started generating the maximum available power (100 kW) and maintained this steady-state condition until the _SOC_ values of the batteries reached acceptable values. By considering this configuration, the range extender powered the motor drives according to the traction limitations and the batteries in order to charge them. In such a condition, the system achieved a new equilibrium point in terms of power consumption, although the reference velocity was still 200 km/h. Considering Equation (39), \(P_{ batteries}\) is equal to zero and the equilibrium is represented by \(P_{drives}=P_{RE}\).  
Figure 13: Simulation results of the second scenario in terms of the speed of the vehicle and SOC (**a**) and total power of the motor drives and total power of the batteries (**b**).
# Improvement of Recycled Aggregates Properties by Means of CO\({}_{2}\) [PERSON]
## 4 Results and Discussions
### Impact of Accelerated Carbonation on the Properties of RCAs  
Accelerated carbonation has an impact on an RCA's properties. In fact, after accelerated carbonation, the phases of portlandite are consumed and CaCO3 is formed, as is presented in Tables 5 and 6. This consumption affected the decrease in the SFSA, because the SFSA represents phases that can be carbonated.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **RB 1–4** & **RB 10–20** & **RB 10–20** & **RB 1–4 C** & **RB 1–4 C** & **CB 1–4** \\  & **(15\% CO2)** & **(100\% CO2)** & **(15\% CO2)** & **(100\% CO2)** & **(100\% CO2)** & **CB 1–4** \\ \hline CO2 uptake (g/kg) & 8.6 & 14.9 & 20.4 & 12.6 & 17.3 & 49.9 \\ SFSA (\%) & 17.1 \(\pm\) 7.1 & 17.9 \(\pm\) 2.9 & 12.8 \(\pm\) 8.2 & 20.3 \(\pm\) 6.4 & 14.8 \(\pm\) 5.9 & 23.1 \(\pm\) 3.8 \\ Portlandite rate (\%) & \(\approx\)0 & \(\approx\)0 & \(\approx\)0 & 0.4 \(\pm\) 0.1 & \(\approx\)0 & 0.7 \(\pm\) 0.1 \\ CaCO3 rate (\%) & 41.5 \(\pm\) 0.2 & 37.2 \(\pm\) 0.1 & 47.0 \(\pm\) 0.3 & 40.4 \(\pm\) 0.5 & 34.5 \(\pm\) 0.4 & 37.2 \(\pm\) 0.4 \\ Porosity (\%) & 6.7 & 13.2 & 14.7 & - & - & - \\ WAC (\%) & 2.8 & 6.7 & 6.6 & 3.5 & 3.1 & 3.0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Properties of carbonated RCAs (RCAs from demolition concretes).  
Figure 12: Correlation between optimum water content and water absorption coefficient of an RCA.  
Tables 5 and 6 also present the porosity and the WAC of RCAs after carbonation. The uptake of CO2 enhanced the two properties by the filling of pores with CaCO3, and there was a relation between the increase in the CO2 uptake and the decrease in porosity and the WAC. For OC 1-4, after 21.8 g/kg of CO2 uptake, the porosity decreased by 35%, and the WAC decreased by 40%. For OC 12-20, the porosity decreased by 30%, and the WAC decreased by 15%. Thus, the CO2 uptake, influenced by the different parameters of carbonation, had an impact on the RCA properties.