esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Research Results and the Analysis
The observed relatively small changes in the average relative values of the reinforcement stationary potential E\({}_{\text{st}}\) are visible in Figure 7a. The positive and negative values of this parameter disturb general analysis of the relative values. It should be remembered that, in in-situ studies, only negative values represent a reliable and significant range of results. Values that are greater than E\({}_{\text{st}}=-200\) mV indicate very low corrosion probability. Therefore, the trend of changes as a function of temperature was verified on specimens with advanced reinforcement corrosion and presented in a separate publication. The average value of the concrete cover resistivity (\(\Theta\)) at higher temperatures clearly decreases down to 25% of the value at T = 1 \({}^{\circ}\)C. As the changes are significant, the inference about the corrosion risk of the tested element will be different. Figure 7: Relative values of measured parameters on samples at different temperatures; (**a**) reinforcement stationary potential, (**b**) concrete cover resistivity, (**c**) corrosion current density. The most important factor enabling the determination of the reinforcement corrosion activity, i.e., the corrosion current density (i\({}_{\text{cor}}\)) changed by \(\sim\)100% at the temperature T = 21 \({}^{\circ}\)C in relation to the reference temperature (i.e., T = 1 \({}^{\circ}\)C). While the absolute values, regardless of the temperature, were insignificant and indicated low reinforcement corrosion activity, the observed relative changes of the i\({}_{\text{cor}}\) values in relation to those that were measured at the reference temperature are considerable. Therefore, the prediction of the reinforcement corrosion activity requires taking into account the temperature of the element being tested. Otherwise, the measurement will be inaccurate. There were extreme differences in the relative values of the measured parameters at individual points in relation to the average values at these points: the reinforcement stationary potential - Est \(\pm\) 200 \(\div\) 500 %, the concrete cover resistivity - \(\Theta\pm\) 40 \(\div\) 85%, and the corrosion current density - i\({}_{\text{cor}}\pm\) 25 \(\div\) 300%.
## 3 Results and Discussion
Another important inference is that regardless of the externally applied electric field, it does not significantly change the resonance frequency of the patch. Therefore, there is no need to go through reconfiguration while antenna efficiency can be controlled. In another study, an antenna with a diaphragm connection was produced to be used in the band from 14 to 16 GHz [109]. In this study, the patch is made of copper, and the surface resistance is 100 \(\Omega\). This property corresponds to the case where \(\tau=0.5\) ps  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference No. ** & **Antenna Type** & **Dimensions mm3 and Applications** & **Substrate, Conductive Element** & **Bandwidth (FBW\%)** & **Resonant Frequency** & **Antenna Gain and Efficiency** \\ \hline \multirow{4}{*}{[102]} & \multirow{4}{*}{Elliptical quasi-dipole antenna} & 46 \(\times\) 45 mm2.
# Kernel Learning by Spectral Representation and Gaussian Mixtures
## 3 Approximating Stationary Kernels
### Learning Locally Stationary Kernel, GaBaSR
#### 3.2.4. Learning Locally Stationary Kernels  
In order to learn locally stationary kernels, we use a similar process, but we compute \(\mathbf{\varphi}(x)\) by using Equation (6) instead of Equation (4). Equation (6) needs the variables \(\mathbf{\omega}_{i},i=1,2,...,M_{1}\) (approximating the \(\kappa_{2}\)) and \(\mathbf{v}_{k},k=1,2,...,M_{2}\) (approximation for \(\kappa_{1}\)). To learn the variables in \(\kappa_{2}\) we use the algorithm showed; however to learn the variables to approximate \(\kappa_{1}\), we approximate \(\kappa_{1}\) as a infinite Gaussian mixture. This means that we need to learn the variables \(Z_{j}^{\prime},\mathbf{\mu}_{k}^{\prime},\Sigma_{k}^{\prime}\) and \(\mathbf{v}_{k}\) that approximate the function \(\kappa_{1}\). Learning these variables is very similar on how we learn them from the stationary kernel with a slight modification:  
1. **Sample**\(Z_{j}^{\prime}\): Sampling \(Z_{j}^{\prime}\) is analogous to learning the stationary kernel but with \(\mathbf{v}_{k}\) instead of \(\mathbf{\omega}_{k}\).
2. **Sample**\(\mathbf{\mu}_{k}^{\prime},\Sigma_{k}^{\prime}\): This sample is analogous to the previous section but with \(\mathbf{v}_{k}\) instead of \(\mathbf{\omega}_{k}\).
3. **Sample**\(\mathbf{v}_{k}\): To sample \(\mathbf{v}_{k}\), we sample from \[\mathbf{v}_{k}^{*}\sim\mathcal{N}(\mathbf{v}_{k}^{*}|\mathbf{\mu}_{Z_{j}^{\prime}}^{\prime },\Sigma_{Z_{j}^{\prime}}^{\prime}).\] In order to compute \(P(\mathbf{y}|X,W,V)\), we use \(\mathbf{\varphi}(x)\) as a locally stationary kernel instead of a stationary kernel. This simple change allows to add more learning capabilities to GaBaSR.
# Influence of Recycled Precast Concrete Aggregate on Durability of Concrete's Physical Processes
## 1 Introduction
Likewise, [PERSON] [14] demonstrated the viability of using recycled aggregate from out-of-service railway superstructure wastes for the manufacture of a more ecological SCC that also fulfils the mechanical requirements of slab tracks. Another property that is usually affected by the incorporation of RA is the durability of concrete. In general, the incorporation of RA reduces the durability of concrete [15; 16], although [PERSON] [17] considers that the influence of RA is not significant since the durability is more dependent on the _w/c_ ratio. [PERSON] [18] investigated the influence of the curing conditions of concrete with RA on its durability, observing a decrease in terms of permeability when the concrete is exposed to an aggressive environment. The use of recycled aggregate causes a greater shrinkage in concrete compared to natural aggregate [19; 20; 21]. However, the greater or lesser variation in shrinkage can be influenced by other factors such as the dosage method, manufacturing procedure and curing conditions. For example, for a 100% substitution, [PERSON] [19] and [PERSON] [20], found an increase of between 10-35%, while the results obtained by [PERSON] [21] showed increases greater than 70% with respect to the control concrete. The results obtained in different studies have shown that the incorporation of AR, can affect other physical processes related to the durability of concrete, such as resistance to frost [22; 23; 24] or resistance to erosion [19; 25]. It can be seen that numerous studies have examined the use of concrete waste in the manufacture of new concrete, however, few studies relate to the use waste from the precast sector [6; 13; 26; 27],These studies are mainly focused on examining the influence of these wastes on the physical and mechanical properties of concrete [5, 26]. Taking these data into account, the objective of this work was to analyse the influence that the use of RA from precast elements has on the durability of SCC under physical actions such as shrinkage, freeze-thaw cycles, thermal shock cycles and abrasion. This study can be considered innovative since, as mentioned above, the studies carried out to date have mainly focused on analysing the influence of these RAs on the mechanical properties of concrete. A SCC has been manufactured with a minimum compressive strength of 30 and 45 MPa for use in precast elements. The degrees of substitution analysed were 20%, 50%, 100% of the natural aggregate. The results of this research show that it is possible to use the recycled aggregate coming from precast pieces in order to the manufacture of self-compacting recycled concrete in the same precast industry.
# Theranostic Nanoplatforms of Thiolated Reduced Graphene Oxide Nanosheets and Gold Nanoparticles
## 2 Materials and Methods
### Physicochemical Characterization
#### 2.7.3 Atomic Force Microscopy (AFM)  
Topographic AFM images were acquired using a Cypher S instrument (Asylum Research, Oxford Instruments, Santa Barbara, CA) operating in dynamic contact mode (also called intermittent contact, AC mode or tapping mode) tapping AC mode and equipped with XY scanner with scan range of 30/40 \(\upmu\)m (closed/open loop). Silicon tetrahedral tips, mounted on rectangular 30 \(\upmu\)m long cantilevers, were purchased from Olympus (AC240 TS, Oxford Instruments). The probes had nominal spring constant of 2 N/m and driving frequencies of 70 kHz. Samples were prepared by dropping 10 \(\upmu\)L of bwGO, rGOSH, AuNS, AuNR, and hybrids' aqueous dispersion on a freshly cleaved muscovite mica (Ted Pella, Inc.). After 5 min of incubation at RT, samples were washed with 1 mL of Millipore water and dried under a gentle nitrogen flow.
# A 1 km Global Carbon Flux Dataset Using In Situ Measurements and Deep Learning
## 2 Materials and Methods
### Target Carbon Fluxes and Predictors
#### 2.1.2 Remote Sensing Data  
The remote sensing data including FAPAR and LAI are from Copernicus Global Land Service (CGLS) version 2 dataset [32] with 1 km resolution, covering from January 1999 to June 2020. The interval of the data is 10 days: the first 10 days of a month, the 11 th to 20 th days of the month, and after the 20 th day of the month. The LAI_MAX, LAI_MIN, and LAI_COUNT variables were calculated according to LAI, and they were annual scale. For every year in our 10-day fitting, we applied the same values of these variables. Remote sensing data were extracted according to the location of each flux site. That is, if a site is located in a grid of remote sensing data, the corresponding values are extracted.
## 4 Methodology  
### Formal Description of the Task  
The intent detection problem is a typical example of a supervised text classification task. Formally, such a task is determined as follows:  
Let \(D=\{d_{1}\), \(d_{2}\),..., \(d_{n}\}\) be a set of documents (questions/statements--an input from a user). Let \(C=\{c_{1}\), \(c_{2}\),..., \(c_{m}\}\) be a set of intents (classes). In this research, a closed-set and single-label classification problem is tackled because each \(d_{i}\in D\) can be labeled with only one \(c_{j}\in C\). Let function \(\eta\) be a classification function that maps documents from the determined domain into their correct classes: \(D\to C\). Let \(D^{L}\subset D\) be a set of documents for which intents are known. Thus, (\(d_{i}\), \(c_{j}\)) pairs are labeled instances used to train a model. Let \(\Gamma\) be a classification method (i.e., classifier, its  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Intent**} & \multirow{2}{*}{**Numb. of Instances**} & \multicolumn{6}{c}{**Numb. of Words**} \\ \cline{3-6}  & & **EN** & **ET** & **LT** & **LV** & **RU** \\ \hline \multicolumn{6}{c}{**Training dataset**} \\ \hline Change & & & & & & \\ Password & 2 & 19 (15) & 16 (15) & 15 (13) & 15 (12) & 18 (15) \\ \hline Delete Account & 7 & 50 (20) & 36 (19) & 40 (19) & 38 (17) & 42 (21) \\ \hline Download & 1 & 7 (7) & 5 (5) & 5 (5) & 4 (4) & 5 (5) \\ \hline Export Data & 2 & 16 (13) & 13 (12) & 17 (15) & 13 (11) & 19 (16) \\ \hline Filter Spam & 6 & 43 (36) & 38 (37) & 43 (39) & 42 (38) & 42 (37) \\ \hline Find Alternative & 7 & 40 (33) & 35 (30) & 34 (29) & 35 (30) & 38 (32) \\ \hline None & 2 & 12 (12) & 11 (11) & 11 (11) & 17 (17) & 14 (14) \\ \hline Sync Accounts & 3 & 29 (22) & 23 (21) & 26 (24) & 26 (21) & 26 (22) \\ \hline \multicolumn{6}{c}{**Testing dataset**} \\ \hline Change & & & & & & \\ Password & 6 & 50 (37) & 42 (34) & 42 (31) & 42 (33) & 46 (37) \\ \hline Delete Account & 10 & 75 (36) & 56 (36) & 65 (35) & 63 (36) & 70 (36) \\ \hline Export Data & 3 & 35 (29) & 23 (23) & 32 (28) & 28 (26) & 30 (26) \\ \hline Filter Spam & 14 & 141 (83) & 98 (76) & 123 (86) & 129 (92) & 134 (88) \\ \hline Find Alternative & 16 & 104 (67) & 99 (69) & 94 (71) & 87 (63) & 89 (67) \\ \hline None & 4 & 35 (33) & 26 (26) & 32 (32) & 31 (31) & 34 (32) \\ \hline Sync Accounts & 6 & 61 (45) & 49 (40) & 53 (46) & 50 (39) & 58 (45) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistics about the **webapps** dataset.  
architecture, parameter set) that, from labeled instances, can learn an accurate model (which is the approximation of \(\eta\)).  
The aim of our solving intent detection task is to offer a classification method \(\Gamma\) that can find the best approximation of \(\eta\), achieving as high an intent detection accuracy as possible on unseen instances, i.e., on instances \(D\)-\(D^{T}\) that were not used for training.
# Analysis of a Collision-Energy-Based Method for the Prediction of Ice Loading on Ships
## 5 Discussion and Conclusions
In this study, in order to enable a direct comparison between the calculated and measured loads, we estimate the ice crushing strength directly as a function of the ice salinity, brine volume, ice temperature, ice density and indentation rate. To validate the calculated ice crushing pressure values, we compare them with corresponding full-scale measurements. We find a good agreement between our calculated values and the corresponding measurements, both showing a negative relationship between ice crushing pressure and contact area. As such, the present study does not aim to influence any existing regulations directly. However, by analysing the effects of the generalizing assumptions behind the PC rules, this work provides motivation and support for a goal-based design in line with the Polar Code. We believe that the present study contributes to a better understanding of the application of the Popov Method in design cases outside of the conditions formally considered in standard PC rules. For instance, to help to assess ice loading in pack ice in ship-ice floe interactions, or to assess ice loads on the aft or mid-body when specific contact geometries must be applied. Because many of the actual ship-ice interaction parameters related to the measured ice loads, such as contact geometry and load length, are not fully known, the study cannot be considered a validation of the Popov Method. Nevertheless, it is promising that, for specific combinations of the assumptions, we obtained a good agreement with the measurements. Thus, we conclude that the Popov method has the potential to become a useful tool for goal-based design in the context of the Polar Code. It should be noted that this study is limited to assessing the effects of various assumptions when applying the Popov Method to estimate ice loading on ships. As such, it does not aim to directly compare PC design loads with full-scale measurements. Specifying a PC design load requires considering ice loads calculated for at least four sub-regions in the bow area along the waterline [38]. Because our applied full-scale data is limited to ice loads measured on two frames on the bow of the ship, it does not support a fair comparison. Therefore, comparing PC design loads with full-scale measurements remains a topic for future research. Another topic for future research is to address numerous unknowns related to the ship-ice interaction and sea-ice material properties, such as the actual ship-ice contact geometry and line load distribution in various types of ship-ice collisions. Formal analysis, S.I. ; Methodology, S.I. ; Supervision, P.K. ; Writing--original draft, S.I. and M.B. ; Writing--review & editing, M.B., S.E.H. and P.K. This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement number: 723526. We thank [PERSON] and [PERSON] for the helpful discussion. **Conflicts of Interest:** The authors declare no conflict of interest.
# High Performance MRAM with Spin-Transfer-Torque and Voltage-Controlled Magnetic Anisotropy Effects
## 2 MTJ Fundamental and Physical Modeling  
### Stt-Mtj  
Bi-directional flowing of the write current is used in STT-MTJ. In order to have a successful operation, write current must be greater than the threshold current (or critical current) \(\mathrm{I_{C0}}\), so that the STT mechanism allows MTJ to perform magnetism switching between parallel and anti-parallel states. Physically-based MTJ compact models have been realized in [27]:  
\[\mathrm{I_{C0}}=\alpha\frac{\mathrm{\gamma e}}{\mathrm{\mu_{B}g}}(\mathrm{\mu_ {0}M_{S}})\mathrm{H_{K}V}=2\alpha\frac{\mathrm{\gamma e}}{\mathrm{\mu_{B}g}} \mathrm{E} \tag{1}\]  
where \(\mathrm{H_{k}}\) is the effective anisotropy field, \(\mathrm{\mu_{0}}\) is free space permeability, \(\mathrm{M_{s}}\) is the saturation magnetization, a is the constant of magnetic damping, \(g\) is the ratio of gyromagnetic, \(e\) is the elementary charge, \(\mathrm{V}\) is the volume of the free layer, \(\mathrm{\mu_{B}}\), \(g\) and \(\mathrm{E}\) are the Bohr magneton, the factor of spin polarization efficiency and the energy of oxide barrier, respectively. The average switching probability and switching operation time \(\tau_{1}\) could be expressed by  
\[\mathrm{Pr}\Big{(}\mathrm{t_{pulse}}\Big{)}=1-\mathrm{exp}\bigg{(}-\frac{ \mathrm{t_{pulse}}}{\tau_{1}}\bigg{)} \tag{2}\]  
\[\frac{1}{\tau_{1}}=\bigg{[}\frac{2}{\mathrm{C+ln}(\mathrm{\pi^{2}\Delta})} \bigg{]}\frac{\mathrm{\mu_{B}P}}{\mathrm{em}\Big{(}1+\mathrm{P^{2}}\Big{)}}( \mathrm{I_{write}-I_{C0}}) \tag{3}\]  
where \(\mathrm{t_{pulse}}\) is the write current pulse duration, \(\mathrm{C}\) is Euler's constant, \(\Delta\) is the thermal stability factor, \(\mathrm{m}\) is the magnetization moment, and \(\mathrm{P}\) is the tunneling spin polarizations. MTJ with a high TMR can guarantee easy access to peripheral CMOS blocks, e.g., sensing and control circuits [28; 29; 30; 31; 32]. The real value of TMR (TMR(V)) can be adjusted by changing zero bias (TMR(0)) and \(\mathrm{V_{h}}\) (half of TMR(0)):  
\[\mathrm{TMR(V)}=\mathrm{TMR(0)}\;(1+\mathrm{V^{2}/V_{h}}^{2})^{-1} \tag{4}\]
## 3 Numerical Simulation Analysis
### Parameter Analysis
#### 3.3.3 Effect of Target Material Strength on Penetration Performance  
The projectile impact speed was set at 890 m/s. Penetration tests were carried out on five targets with uniaxial compressive strengths of 80 MPa, 100 MPa, 120 MPa, 140 MPa and 160 MPa. An elastoplastic model was selected for the projectile. The internal damage and penetration depth of the target are shown in Figure 15. The maximum acceleration of the target is shown in Figure 16.  
Figure 15 shows that when the strength of the target was not greater than 100 MPa, the diameter of the compacted zone was large, and the cracks on the section were fine and dense. When the strength of the target was greater than 120 MPa, the diameter of the compacted zone was small, and the cracks were small and wide. When the target strength was not greater than 120 MPa, the penetration depth decreased linearly with increasing target strength. When the target strength was greater than or equal to 140 MPa, the penetration depth decreased gradually with increasing target strength. The penetration depth decreased sharply from 120 MPa to 140 MPa. When the target strength varied from  
Figure 15: Relation between penetration depth and target strength.  
80 MPa to 160 MPa, the penetration depth decreased in three stages (i.e., gently decreased, sharply decreased and gently decreased).  
Figure 16 shows that the penetration acceleration increased with increasing target strength, and the mode of increase was also three-staged. The first stage was from 80 MPa to 120 MPa, which was a gentle growth stage. The second stage was from 120 MPa to 140 MPa, which was a period of rapid growth. The third stage was from 140 MPa to 160 MPa, which was a flat growth stage, in which the acceleration growth rate was similar to that of the first stage. The trend of the acceleration increasing with the target strength was similar to that of the depth decreasing with the target strength in Figure 16, which was in a three-section form.
## 4 Performance Analysis
### Results and Discussion
#### 4.3.4 Case Study  
To accurately and directly observe the ability of the MDA, the RC task is selected as the case study, and the medical triplets are recognized from the sentences from the BITEmrRC dataset for exploration. The sentence from the BITEmrRC dataset is shown in Table 8 and its augmented data are shown in Table 9, which contains the text data and the label data. First,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{3}{c}{**CHIP2020**} & \multicolumn{3}{c}{**BITEmrRC**} \\ \cline{2-7}  & **Original** & **EDA** & **MDA** & **Original** & **EDA** & **MDA** \\ \hline Multi-Head attention & 47.10 & 47.92 & 49.21 & 70.27 & 70.90 & 72.38 \\ ETL-Span & 49.46 & 60.17 & 51.38 & 71.8 & 72.62 & 74.33 \\ RoBERTa + CasRel & 58.27 & 59.11 & 60.18 & 86.01 & 86.45 & 89.37 \\ \hline \hline \end{tabular}
\end{table}
Table 6: F1-score of the RC task on the CHIP2020 dataset and the BITEmrRC dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Methods**} & **NER** & **RC** \\ \cline{2-3}  & **F1** & **F1** \\ \hline Ours & 86.64 & 89.37 \\ -MKR & 84.48 & 87.01 \\ -WI & 85.83 & 88.49 \\ -WS & 86.07 & 88.74 \\ -WD & 86.11 & 88.71 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The results of the ablation experiments.  
from the perspective of text data, the sentence is a piece of descriptive language, which not only contains complex medical nouns but also contains the writing rules of medical orders. Hence, the traditional text data augmentation is adverse to keeping the structure of medical terms. Moreover, the rules of medical orders can be broken. However, with the help of the MDA, the context of all nouns is consistent with the semantics of the original data. Moreover, the MDA also increases the diversity of synthetic sentences. To be specific, the original entities of 'hypesthesia' (\" \" \" \" \" \" \" \" \" \" \" \" \" \" \" \" \" \" \" \" \" \" \" \" \" \" \" \" \"