esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimized Polar Codes as Forward Error Correction Coding for Digital Video Broadcasting Systems
## 7 System Complexity Comparison  
Polar coding transformation can be represented as a graph with \(N[1+\log_{2}N]\) variables. This graph contains \([1+\log_{2}N]\) levels with \(N\) variables in each level. Computation starts at the source level and can be executed from one level to another. Thus, the total number of calculations needed for the polar code is \(N[1+\log_{2}N]\); the encoding and SCD complexity of polar code is \(O(N\log_{2}N)\) as proved in [1], CA-SCL decoding complexity is \(O(LN\log_{2}N)\) as proved in [22]. However, FA-SCL decoding complexity is \(O(\overline{L}N\log_{2}N)\), where \(\overline{L}\) is the mean of \(L\) under \(L_{max}\). It is seen that under \(L_{max}\) = 32, the mean \(\overline{L}\) = 2.04, which gives about 16 times complexity reduction with respect to CA-SCL decoding, but with similar performance [36].  
On the other hand, The encoding complexity of LDPC code is \(O\big{(}N+g^{2}\big{)}\), where \(g\) is the representation gap in the parity-check matrix in approximate lower triangular form [51] which differs for each code rate, and the decoding complexity is \(O(i_{max}(N\rho+(N-K)\gamma\ ))\), where \(i_{max}\) is the maximum number of iterations and \(\rho\), \(\gamma\) represent the average degree of the variable and parity nodes respectively in the parity check matrix [52], which also differ for each code rate. DVB-S2 QEF performance is achieved after a pre-determined number of iterations \(i_{max}=50\)[38]. Moreover, the computational complexity of BCH encoding, and decoding are \(O\big{(}t\sqrt{K_{bch}}\big{)}\), \(O\big{(}t\sqrt{N_{bch}}\big{)}\) respectively [53; 54; 55], where \(t\) is the BCH error correction capability.  
Table 7 summarizes the computational complexity of different channel coding methods. It is shown that polar code complexity is less than (LDPC + BCH) codes. Because, by comparison between mean \(\overline{L}\) = 2.04 and \(i_{max}=50\), the polar code decoding complexity is less than LDPC code decoding complexity by twenty-five times at least. Moreover, BCH code adds another layer of complexity to LDPC code. Therefore, polar code usage reduces the complexity of DVB systems.
## 3 EDIJP Framework
### Data Transmission between Workers  
There are two workers \(w_{i}\) and \(w_{j}\). In the original model, the execution of the sub-model in \(w_{i}\) requires the output of the sub-model in \(w_{j}\) as input. Six situations show the relationships between the input and output ranges of the two sub-models on two workers, which are contained in Figure 6. We assume that the output feature range of worker \(w_{j}\) is \([out_{s},out_{e}\), the input feature range of worker \(w_{i}\) is \([in_{s},in_{e}]\), and \(w_{j}\) needs to transmit data to \(w_{i}\). In Figure 6, the two dashed lines represent the range of input data that \(w_{i}\) wishes to obtain. If the range indicated by the dashed lines exceeds the output range of \(w_{j}\), such as Figure 6e,f, it indicates that \(w_{j}\) does not need to transmit data to \(w_{i}\). The data range sent from \(w_{j}\) to \(w_{i}\) follows the following equations.  
\[trans_{s}=\max\{out_{s},in_{s}\}, \tag{7}\]  
\[trans_{e}=\max\{out_{e},in_{e}\}, \tag{8}\]  
\([trans_{s},trans_{e}]\) is the data transmission scale from \(w_{j}\) output to \(w_{i}\). If \(trans_{e}<trans_{s}\), there is no data transmission between the two workers.  
Figure 5: The EDIJP overview.
# Fatigue Characteristics of Fe-Based Shape-Memory Alloys
## Materials
### Recovery Stress of Fe-SMAs  
An activation experiment was conducted to confirm the recovery stress of the Fe-SMAs used in this study. [PERSON] et al. [15] reported that ettringite between cement and fine aggregates collapsed at a temperature of 160 \({}^{\circ}\)C or higher. Therefore, an appropriate heating temperature for using the Fe-SMAs as a prestressing material was determined to be 160 \({}^{\circ}\)C. Considering this, recovery stress experiments were performed on the alloys at a heating temperature of 160 \({}^{\circ}\)C and a prestrain level of 4%. The detailed recovery stress test procedure is presented in [PERSON] et al. [12]. Figure 2 shows the temperature-recovery stress relationship for each alloy derived from the recovery stress experiments. The maximum recovery stress (\(\sigma_{\text{rec\_max}}\)) and the final recovery stress (\(\sigma_{\text{rec\_final}}\)) of the A-type alloy were the same at 424 MPa, with little short-term relaxation. On the other hand, the maximum recovery stress and the final recovery stress of the B-type alloy were 377 MPa and 362 MPa, respectively and short-term relaxation of about 15 MPa occurred. In addition, the final stress of the B-type alloy was found to be about 17% smaller than that of the A-type alloy. On the other hand, it was reported in the study of [PERSON] et al. [12] that the B-type alloy has about twice the recovery strain compared to the A-type alloy. Therefore, it is judged that the A-type alloy is suitable for an element requiring a large recovery stress, and the B-type alloy is an alloy suitable for an element requiring a large recovery strain.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Specimen** & \begin{tabular}{c} **Elastic Modulus** \\ **(GPa)** \\ \end{tabular} & \begin{tabular}{c} **Yield** \\ **Strain** \\ \end{tabular} & \begin{tabular}{c} **Yield Stress** \\ **(MPa)** \\ \end{tabular} & \
\begin{tabular}{c} **Ultimate Strain** \\ \end{tabular} &
\begin{tabular}{c} **Ultimate Stress** \\ **(MPa)** \\ \end{tabular} \\ \hline A-type & 124.57 & 0.00666 & 599 & 0.22556 & 1140 \\ B-type & 123.26 & 0.00530 & 410 & 0.39510 & 1080 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical properties of the Fe-SMA [12].  
Figure 1: Stress–strain curves according to type of Fe-SMA [12].  
Figure 2: Recovery stress–temperature curves according to type of Fe-SMA at prestrain 0.04, heating temperature 160 \({}^{\circ}\)C [12].
## 1 Introduction and Background
### Geographic Data  
Geographic data typically include very large chunks of graphic data, e.g., popular digital 2D cartographic products are often enhanced with aerial/satellite imagery and 3D objects, as well as annotations and query capabilities to non-graphic information that may be coupled with location input. Furthermore, the vision of _digital earth_[10] led to true 3D representations that are enriched with multimedia and multi-sensory data [11] exceeding these already large volumes of data. [PERSON] _et al_. (2005) [12] list potential database volumes of some typical geographic applications as follows (p. 12):  
* _1 megabyte_: A single data set in a small project database
* _1 gigabyte:_ Entire street network of a large city or small country
* _1 terabyte:_ Elevation of entire Earth surface recorded at 30 m intervals
* _1 petabyte:_ Satellite image of entire Earth surface at 1 m resolution
* _1 exabyte:_ A future 3-D representation of entire Earth at 10 m resolution?  
For example, today a simple PNG (Portable Network Graphics) compressed shaded relief map of Switzerland is easily 20 MB [13] or Open Street Map dataset for Switzerland is 139 MB when compressed (bz2) and 1.7 GB uncompressed [14]. Considering these numbers; current geographic services on the Internet and mobile/wireless systems are impressive in speed. What such services can deliver today was hard to conceptualize a mere fifteen years ago (e.g., [15, 16, 17]). However, also today, when loading e.g., Google Earth [18], we have to wait even on \"this side\" of the _digital divide_ (a term that refers to the gap between communities in terms of their access to computers and the Internet, highquality digital content and their ability to use information communication technologies). There is a clear lag from the request time to the data viewing time; buildings slowly load, photo-textures come even later, terrain level of detail (LOD) switches are not seamless. Besides disturbing the user experience, such delays can also be financially costly. For example, when we are in a new location, mobile geo-location services for phones and other hand-held instruments are very convenient to have, but the data is so large that some of the geographic services quickly become prohibitively expensive [19, 20, 21].
## 1 Introduction
### Equivalent Inelastic SDOF System for Steel Frames with Hysteretic Dampers
#### 2.2.3 Inelastic Springs Equivalent to Dampers  
This section describes the methodology used to estimate the skeleton curve of the inelastic springs that are equivalent to the dampers in the equivalent SDOF system with multi-springs.  
The force versus drift curve of the damper at the \(i\)th story of the MDOF system and the \(i\)th inelastic spring of the equivalent SDOF system are shown in Figure 2a,b, respectively.  
As shown in the top portion of Figure 2a, by considering only the first mode, a lateral drift \(\Delta_{i}\) of the \(i\)th story of the MDOF system can be expressed using the drift \(\overline{\delta}\) of the equivalent SDOF system, as shown in the top portion of Figure 2b. This can be expressed as follows:  
\[\Delta_{i}=s\phi_{1,i}^{E}\cdot\Gamma_{1}^{E}\cdot\overline{\delta} \tag{12}\]  
where \({}_{s}\phi_{1,i}^{E}\) signifies the first elastic mode vector of the \(i\)th story as obtained from EVA. Thus, the inter-story drift \(\delta_{i}\) at the \(i\)th story of the MDOF system can be expressed as follows:  
\[\delta_{i}=\Delta_{i}-\Delta_{i-1}=\left({}_{s}\phi_{1,i}^{E}-s\phi_{1,i-1}^{E }\right)\cdot\Gamma_{1}^{E}\cdot\overline{\delta} \tag{13}\]  
It is assumed that the energy \(E_{d,i}\) dissipated by the damper at the \(i\)th story of the MDOF system is equivalent to the energy \(\overline{E}_{d,i}\) dissipated by the \(i\)th inelastic spring of the equivalent SDOF system.  
Based on this assumption and Equation (13), in the range of \(\delta_{i}<dy_{d,i}\) that corresponds to the yield deformation of the damper at the \(i\)th story of the MDOF system, the stiffness \(\overline{k}_{d,i}\) of the \(i\)th inelastic spring of the equivalent SDOF system can be expressed as follows:  
\[\overline{k}_{d,i}=k_{d,i}\left({}_{s}\phi_{1,i}^{E}-{}_{s}\phi_{1,i-1}^{E} \right)^{2}\left(\Gamma_{1}^{E}\right)^{2} \tag{14}\]  
where \(k_{d,i}\) denotes an elastic stiffness of the damper at the \(i\)th story.  
Based on a previous assumption that indicates \(E_{d,i}=\overline{E}_{d,i}\) and Equations (13) and (14), when \(\delta_{i}=dy_{d,i}\), the yield deformation \(\overline{dy}_{d,i}\) of the \(i\)th inelastic spring of the equivalent SDOF system can be expressed as follows:  
\[\overline{dy}_{d,i}=\frac{dy_{d,i}}{\left({}_{s}\phi_{1,i}^{E}-s\phi_{1,i-1}^{ E}\right)\Gamma_{1}^{E}} \tag{15}\]  
Figure 2: Skeleton curves of the dampers and inelastic springs: (**a**) damper force versus inter-story drift curve of the damper at the \(i\)th story in the MDOF system and (**b**) force versus drift curve of the \(i\)th spring in the SDOF system.  
Based on a previous assumption \(E_{d,i}=\overline{E}_{d,i}\) and Equations (13)-(15), in the range of \(\delta_{i}>dy_{d,i}\), a post-elastic stiffness ratio \(\overline{\alpha}_{d,i}\) of the \(i\)th inelastic spring of the equivalent SDOF system can be determined as follows:  
\[\overline{\alpha}_{d,i}=\alpha_{d,i} \tag{16}\]  
where \(\alpha_{d,i}\) signifies the post-elastic stiffness ratios of the damper at the \(i\)th story.
# Round-Trip Time Ranging to Wi-Fi Access Points Beats GNSS Localization
## 13 Nature of the Measurement Error  
One striking feature of Figure 4 is the different \"fine structure\" of the BGU tracks (green) and the GNSS tracks (red). Part of the difference is the result of GNSS positions being updated regularly at 1 s intervals, while the BGU estimates of position are computed irregularly, several times per second, as RTT responses come in.  
The GNSS tracks are smoother but appear to have a relatively large offset or bias that persists over time. The offset changes when different satellites come into view, as is apparent in some sections of the path that were traversed more than once a few minutes apart. The smoothness of the tracks is most likely the result of some form of smoothing of the raw measurements, such as Kalman filtering. This filtering also manifests itself as a lag that causes the GNSS along-track error to be greater than the GNSS across-track error.  
The BGU tracks, which appear more \"jittery\", could be made smoother using some form of filtering as well. But filtering introduces lag, which is undesirable when navigating in real-time. Unlike the GNSS tracks, the BGU tracks tend to pass through nearly the same positions when the path is traversed a second time. This is because the \"position-dependent error\" [19] does not change much with time.
## 2 Mathematical Modeling of PV Cell
### Modified Equivalent Circuit of the Double-Diode PV Model  
The refined version of the double-diode model (DDM) is the modified double-diode model (MDDM). In the MDDM, additional resistance is incorporated in series with the basic DDM which represents that the resistance at grain boundaries is greater than resistance within crystallites. The accuracy of the modified single-diode model is uncertain; therefore, further investigation was carried out to search for more sophisticated models. A modified two-diode model is adopted to solve this problem, using two diodes attached parallel to the current source, as shown in Figure 3. The modified double-diode model is the modified version of the modified single-diode model, which takes the effect of recombination into account by introducing another diode in parallel.  
In this model, two unknown diode-quality factors are introduced. As a result, there is an increase in the number of equations which makes calculations more complicated [37].  
Figure 3: Schematic diagram of the modified double-diode PV model.  
Figure 2: Schematic diagram of the modified single-diode PV model.  
Hence, at a low insolation level, this model exhibits better accuracy. Equation (4) represents the performance equation of the modified double-diode cell model.  
\[I=I_{ph}-I_{d}\bigg{(}e^{\frac{V+IR_{ge}}{WN_{T}}}-1\bigg{)}-I_{d1}\bigg{(}e^{ \frac{V+IR_{ge}-I_{d}IR_{g}}{WN_{T}}}-1\bigg{)}-\frac{V+IR_{ge}}{R_{sh}} \tag{4}\]  
Therefore, considering all aspects of mathematical calculation, and the number of iterations, the modified single-diode model tends to have quick results due to fewer complex calculations.
# SAPBERT: Speaker-Aware Pretrained BERT for Emotion Recognition in Conversation
## 7 Discussion
### Ablation Study  
In this ablation study, we analyze the effects of each pre-training objective. '-' denotes the model pre-trained without the objectives. For example, '- Speaker Classification' means the SAPBERT pre-trained with Masked Utterance Regression and Last Utterance Generation tasks only.  
As shown in Table 6, the performance drops whenever any of the pre-training objective is excluded. When the Masked Utterance Regression and Last Utterance Generation objectives are excluded individually, the performance drops by 2.71%, 1.9%, (MUR) and 3.01%, 2.17% (LUG) on both datasets. This means that these objectives can be effective for capturing the both coherence and the whole context of conversations. In particular, when the Speaking Classification objective is excluded, the performance on the IEMOCAP dataset drops considerably by 4.38%.  
The datasets for the pre-training and for the ERC task present different patterns. All the conversations in Mutual and DailyDialog datasets for the pre-training contain two-speaker conversations with one-by-one turn taking. On the other hand, IEMOCAP contains continuous utterances of the same speaker in some cases, while MELD has multi-speaker conversations with relatively few utterances per speaker. The performance of the SC excluded model does not drop with MELD as much as with IEMOCAP. This can be attributed to the difference in the conversation pattern and the number of speakers between the pre-training datasets and MELD. The result of the ablation study for Speaker Classification indicates that the model is well trained for our purposes, not overfitting the position of utterances in the conversation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**F-1 Score**} \\ \cline{2-3}  & **IEMOCAP** & **MELD** \\ \hline SAPBERT & 67.16 & 64.18 \\ - Speaker Classification (SC) & 62.78 (\(\downarrow\)**4.38**) & 63.09 (\(\downarrow\)1.09) \\ - Masked Utterance Regression (MUR) & 64.45 (\(\downarrow\)2.71) & 61.17 (\(\downarrow\)3.01) \\ - Last Utterance Generation (LUG) & 65.26 (\(\downarrow\)1.9) & 62.01 (\(\downarrow\)2.17) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of ablation study on IEMOCAP and MELD.  
Figure 11: The heatmap of the confusion matrix on MELD dataset; ang = angry, dis = disgusting, sur = surprise, fea = fear, neu = neutral. X-coordinate: model’s prediction and y-coordinate: the ground truth.
## 3 Stress Estimation Based on the Monitored Strain
### Establishment of the Forward Model
#### 3.1.2 Stress-Dependent Strain  
Stress-dependent strain includes the elastic instantaneous strain \(\varepsilon_{e}(t)\) and creep strain \(\varepsilon_{c}(t)\). According to the classical creep theory, the stress-dependent strain can be expressed as  
\[\varepsilon_{D}(t)=\varepsilon_{e}(t)+\varepsilon_{c}(t)=\sigma(t_{0})J(t,t_{ 0})+\int_{t_{0}}^{t}J(t,\tau)d\sigma(\tau) \tag{13}\]  
where \(\sigma(t)\) is the stress of the concrete at the age of \(t\) and \(J(t,t_{0})\) is the creep function of concrete at the age of \(t\) with an initial loading age of \(t_{0}\).  
By discretizing the time history \(t_{0}\)-\(t_{n}\) as \(n+1\) time points \(t_{0}\), \(t_{1}\),..., \(t_{n}\), Equation (7) can be approximately expressed as  
\[\varepsilon_{D}(t)\cong\sigma(t_{0})J(t,t_{0})+\frac{1}{2}\sum_{i=0}^{n-1} \big{[}J(t_{n},t_{i+1})+J(t_{n},t_{i})\big{]}[\sigma(t_{i+1})-\sigma(t_{i})]. \tag{14}\]The uncertainty of the evaluation model for the concrete strain is one of the most important factors that causes the error between the evaluation results and the monitored results of the concrete strain. The uncertainty can be taken into account by introducing an uncertainty factor for the creep effect \(\alpha_{c}\) and an uncertainty factor of shrinkage effect \(\alpha_{s}\). Consequently, Equation (6) can be expressed as  
\[\varepsilon_{M}=\varepsilon_{e}+\alpha_{c}\varepsilon_{c}(t)+\xi[\alpha_{s} \varepsilon_{s}(t)+\varepsilon_{T}]. \tag{15}\]
# Machine Learning-Based Framework for Predicting Creep Rupture Life of Modified 9 Cr-1 Mo Steel
## 2 Materials and Methods
### Feature Screening
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Data** & **Abbreviation** & **Description** & **Minimum** & **Maximum** & **Mean** &
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} \\ \hline Inputs & C & Carbon/wt.\% & 0.080 & 0.110 & 0.096 & 0.010 \\  & Mn & Manganese/wt.\% & 0.350 & 0.560 & 0.432 & 0.049 \\  & P & Phosphorus/wt.\% & 0.005 & 0.021 & 0.011 & 0.005 \\  & S & Sulfur/wt.\% & 0.001 & 0.009 & 0.002 & 0.002 \\  & Ni & Nickel/wt.\% & 0.040 & 0.460 & 0.141 & 0.092 \\  & Cr & Chromium/wt.\% & 8.310 & 9.000 & 8.530 & 0.175 \\  & Mo &olybdenum/wt.\% & 0.860 & 0.990 & 0.918 & 0.032 \\  & N & Nitrogen/wt.\% & 0.030 & 0.084 & 0.051 & 0.010 \\  & Al & Aluminum/wt.\% & 0.001 & 0.023 & 0.010 & 0.007 \\  & V & Vandolumn/wt.\% & 0.180 & 0.230 & 0.207 & 0.015 \\  & Nb &obium/wt.\% & 0.060 & 0.090 & 0.075 & 0.006 \\  & N Temp & Normalizing & 1040 & 1065 & 1051.624 & 5.859 \\  & N Time & Normalizing Time/min & 10 & 90 & 33.271 & 26.986 \\  & T Temp & Temperature & 750 & 790 & 771.949 & 9.984 \\  & T Time & Temperature/\({}^{*}\)C & 30 & 120 & 55.916 & 17.941 \\  & Form & Material Form & 1 & 3 & 1.473 & 0.653 \\  & Test Stress & Test Stress/MPa & 30 & 450 & 162.018 & 91.278 \\  & Test Temp & Test Temperature/\({}^{*}\)C & 450 & 725 & 599.258 & 61.697 \\ Outputs & Ig(life) & Logarithm of creep life & \(-1.706\) & 5.091 & 3.536 & 0.954 \\  & Crep life & Crep life/h & 0.02 & 123,442.1 & 14,613.5 & 21,862.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the statistical description of collected information. the creep life are acquired and the importance of each feature is determined. Finally, these selected features are utilized to establish the machine learning models for creep rupture life prediction.
## 3 Optimized Dual-Channel Data Storage and Access Method
### The Matrix Block Three-dimensional Mapping  
The matrix block three-dimensional mapping method makes full use of the feature of cross-bank priority data access in DDR3 chips [38]. First of all, the SAR raw data matrix is divided into several sub-matrices of equal size. Then, a three-dimensional mapping method is used to map the continuous sub-matrices to different rows in different banks [32; 33; 34].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Performance** & **Range** & **Azimuth** \\ \hline Writing efficiency & 92.25\% & 8.51\% \\ Reading efficiency & 94.64\% & 12.12\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance of the conventional synthetic aperture radar (SAR) data storage method.  
Figure 5: Schematic diagram of conventional SAR data matrix storage method. (**a**) The SAR data matrix; (**b**) each row in the matrix is stored within multiple rows in Double-Data-Rate Three (DDR3) by using the conventional mapping method.  
This method can maximize the equilibrium of the two-dimensional access bandwidth of the data matrix and meet the real-time requirements of the system.  
The SAR data matrix can be described as \(A(x,y)\), \(0\leq x\leq N_{A}-1\), \(0\leq y\leq N_{R}-1\). Where, \(N_{A}\) is the number of data in the azimuth direction of the matrix, and \(N_{R}\) is the number of data in the range direction of the matrix. As the SAR imaging algorithm requires multiple FFT operations, both \(N_{A}\) and \(N_{R}\) are positive integer powers of two.  
The matrix is chunked into \(M\times N\) sub-matrices of the same shape, and the sub-matrix is recorded as \(A_{m,n}\), where \(M\) and \(N\) are the signs of the sub-matrix in the azimuth direction and range direction, as shown in Figure 6. The size of the sub-matrix is \(N_{a}\times N_{r}\), thus, \(N_{a}=N_{A}/M\), \(N_{r}=N_{R}/N\). The data of the sub-matrix are mapped to a row in DDR3, so that \(N_{a}\times N_{r}=C_{n}\), where \(C_{n}\) is the number of columns in DDR3.  
After the matrix is divided into various sub-matrices, each sub-matrix is mapped to a row of DDR3 through three-dimensional mapping method, as shown in Figure 7. The three-dimensional mapping method maps consecutive sub-matrices to different rows in different banks by using the priority access feature of cross-bank data in DDR3, effectively improving data access efficiency [38].  
Figure 6: Schematic diagram of matrix block.  
Figure 7: Schematic diagram of three-dimensional mapping for SAR data matrix (take a 16,384 \(\times\) 16,384 matrix as an example). (**a**) The SAR raw data matrix; (**b**) the SAR raw data matrix is chunked into many sub-matrices; (**c**) each sub-matrix is mapped to a row in DDR3 by using the three-dimensional mapping method.