esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancement of Text Analysis Using Context-Aware Normalization of Social Media Informal Text
## 3 Proposed Sentiment Analysis Method
### Methods Used for Text Normalization
#### 3.1.6 Sentence-Level Substitute Prediction (SLSP)  
Sentence-level normalization is also referred to as context-aware. After the candidate generation they are ranked with respect to their relevance in the intended context. At this stage we have a list of \(n\) candidates for each OOV word. This sub-module is to re-rank the list of candidates to find the best context-aware substitute for the target OOV word. We used the n-gram-based conditional probabilities dictionary for context understanding. We added the n-gram (tri-gram) probability to the features of the language model (LM) of each candidate substitute as context information. Each tri-gram contains a combination of the candidate, a previous word, and the next word of the target OOV word. We searched for the tri-grams, in the tri-gram dataset [74], and sentences corpus from Natural Language Corpus Data: Beautiful Data ([[http://norvig.com/ngrams/](http://norvig.com/ngrams/)]([http://norvig.com/ngrams/](http://norvig.com/ngrams/)), accessed on 22 March 2021), which are similar to each tri-gram of OOV word substituted by the suggested word. The suggestedword with highest number of occurrences in the extracted tri-grams is considered to be the correct substitution.
# A Comprehensive Hybrid Deep Learning Approach for Accurate Status Predicting of Hydropower Units
## 3 Methodology
### Proposed Deep Learning Model Framework  
We propose a hybrid model, as depicted in Figure 1. The model processes time series data by segmenting it into windows of defined length. These segmented windows are subsequently input into a TCN. Unlike traditional recurrent neural networks, the TCN architecture is capable of extracting high-dimensional features in parallel rather than sequentially. This parallel processing not only speeds up feature extraction but also strengthens the model's ability to independently identify key patterns in each time series, thereby boosting overall prediction accuracy.  
Figure 1: Framework diagram of the proposed hybrid model.  
Following feature extraction, the data are input into a parallel architecture combining REST-LSTM and GRU, referred to as the Dual-Path Memory Integration (DPMI) module. The DPMI module is configured in parallel to leverage their respective strengths in memory retention and temporal information processing. By harnessing these complementary capabilities, the model effectively captures and synthesizes memory-based features from multiple perspectives of the time series data.  
To integrate the outputs from the DPMI module, an adaptive weighting mechanism is employed. This method fine-tunes the influence of each network based on its predictive performance, thereby optimizing the model's overall accuracy.  
Additionally, the tuna swarm optimization algorithm is used to fine-tune the hyperparameters of the hybrid network according to the fitness function. The fitness function refers to the mean squared error (MSE) calculated as the loss function between the predicted and actual values in the training set. This optimization process ensures the identification and utilization of the most effective network parameters, which are then preserved and loaded into the final training model.  
The details of each model component are provided in the following sections.
## 2 System Model
### Transmit Signal for RSMA  
The RSMA scheme has various strategies that split user messages into common and private streams [3]. In this paper, the one-layer RS, which is the simplest and most-practical RSMA scheme, was assumed. At the BS, each user message is split by the message splitter, and the common messages of each user are combined into one common message by the message combiner. Then, the common message is encoded into a common stream \(s_{c}\) by a codebook, which decodes the common message for all users. However, the private message is independently encoded into private streams \(s_{1},s_{2},\cdots,s_{N_{K}}\). The encoded streams \(\mathbf{s}=[s_{c},s_{1},s_{2},\cdots,s_{N_{K}}]^{T}\in\mathbb{C}^{(N_{K}+1)\times 1}\) were assumed to have zero mean and unit variance. Then, the streams are precoded by the linear precoder, which is represented as \(\mathbf{P}=[\mathbf{p}_{c},\mathbf{p}_{1},\mathbf{p}_{2},\cdots,\mathbf{p}_{ N_{K}}]\in\mathbb{C}^{N_{T}\times(N_{K}+1)}\). Thus, the transmit signal is written as follows:  
\[\mathbf{x}=\sqrt{P_{c}}\mathbf{p}_{c}s_{c}+\sum_{k=1}^{N_{K}}\sqrt{P_{p}} \mathbf{p}_{k}s_{k}, \tag{5}\]  
where \(P_{c}=(1-\alpha)P_{T}\), \(P_{p}=\frac{\alpha P_{T}}{N_{K}}\) is the power allocated to each stream, and \(\alpha\in(0,1]\) is the RS power-splitting ratio. In this paper, the SVD [22; 23] and ZF [24; 25; 26] precoding schemes was adopted at \(\mathbf{p}_{c}\) and \(\mathbf{p}_{k}\), respectively. The SVD precoder is given by the dominant left singular vector of the cascaded channel as follows:  
\[\mathbf{H}_{eff}^{T}=\mathbf{U}\mathbf{\Sigma}\mathbf{V}^{\mathbf{H}}, \tag{6}\]  
\[\mathbf{p}_{c}=([\mathbf{U}]_{1,1},[\mathbf{U}]_{2,1},\cdots,[\mathbf{U}]_{N_{ T},1})^{T}.\]  
Furthermore, the ZF precoder used in the private stream precoder \(\mathbf{P}_{p}=[\mathbf{p}_{1},\mathbf{p}_{2},\cdots\mathbf{p}_{N_{K}}]\) is given by the pseudo-inverse of the cascaded channel as follows:\[\begin{split}\mathbf{W}=\mathbf{H}_{\mathit{eff}}^{H}(\mathbf{H}_{ \mathit{eff}}\mathbf{H}_{\mathit{eff}}^{H})^{-1},\\ \mathbf{P}_{p}=\frac{\mathbf{W}}{\left\|\mathbf{W}\right\|}.\end{split} \tag{7}\]  
Hence, the received signal at the \(k\)-th UE can be rewritten as follows:  
\[y_{k}=\sqrt{P_{c}}\mathbf{h}_{k}\mathbf{\Phi}\mathbf{G}\mathbf{p}_{c}s_{c}+ \sqrt{P_{p}}\mathbf{h}_{k}\mathbf{\Phi}\mathbf{G}\mathbf{p}_{k}s_{k}+\sum_{j \
eq k}^{N_{K}}\sqrt{P_{p}}\mathbf{h}_{k}\mathbf{\Phi}\mathbf{G}\mathbf{p}_{j} s_{j}+n_{k}. \tag{8}\]
# Safety-Oriented System Hardware Architecture Exploration in Compliance with ISO 26262
## 4 Safety-Oriented System Hardware Architecture Exploration Framework
### System Hardware Architecture Exploration with Safety and Hardware Overhead Consideration  
The proposed system hardware architecture exploration is performed for two aspects: Safety and hardware overhead. For the former, the system hardware architecture needs to contain sufficient safety mechanism protection so that the safety metrics for the target ASIL can be achieved; for the latter, the additionally increased hardware overhead attributed to safety mechanism deployment needs to comply with the constraint specified by the system engineers. To fulfill the safety and hardware overhead requirements, we firstly propose an effective ASIL-oriented system hardware architecture exploration algorithm to determine a solution that meets the safety requirements, and then use a Hardware Overhead (HO)-oriented hardware architecture exploration algorithm to adjust the hardware architecture solution derived from the ASIL-oriented system hardware architecture exploration algorithm to satisfy the safety metrics and hardware overhead constraint simultaneously. These two algorithms will be introduced in Sections 4.3.2 and 4.3.3, respectively. Furthermore, we will demonstrate how to perform these two algorithms for a system with the simple example already shown in Section 4.2.  
In this work, the deployed safety mechanisms are categorized into three different levels, which are \"High\", \"Medium\", and \"Low\" and represent the diagnostic coverage (DC) estimated to be at least 99%, 90%, and 60%, respectively. Such categorized levels are adopted in ISO-26262. System engineers can also specify their required DC percentages for these three levels. If a hardware element belongs to the \(MCS_{k}\) marked as an MBP weak point and the \(Gap_{PMHF}(MCS_{k})\), \(Gap_{SPFM}(MCS_{k})\), and \(Gap_{LFM}(MCS_{k})\) cannot be reduced to be lower than 1, even when the safety mechanism (SM) with \"High\" DC is used to protect the element, then the target ASIL goal will never be fulfilled. Thus, system engineers should replace the hardware element with a superior one with a lower inherent failure rate or adjust the target ASIL.
## 6 An Example for the Usage of the Proposed Method
* For BCs of 3 SIF, \(K_{u}\) is very small and stable irrespective of \(e_{x}/b\) when \(\beta=1\), 2, and \(\beta=3\) (or 4) with \(d/b\) less than 0.4. When \(\beta=3\) or 4 and \(d/b\) is bigger than 0.4, \(K_{u}\) increases as the hole moves away from the loading edge towards its center along \(x\)-axis. Therefore, it is recommended to place the big hole (\(d/b\geq 0.4\)) close to the middle area of the plate in \(x\)-axis, especially when \(d/b\) is bigger than 0.4. * For a small hole size, there is little effect of the hole position \(e_{y}/b\) on buckling coefficient \(K_{u}\) regardless of the BCs (4S or 3S1F), and that effect becomes more pronounced as \(d/b\) increases. So it is recommended to put the holes near the middle of the plate for 4S and the simple support edge for 3S1F in \(y\)-axis. * As shown in Table 2, the buckling stresses and ultimate loads calculated by the proposed method of this paper combining with the winter equation are very close to the experimental values. **Acknowledgments:** The major source of funding for this project was provided by the School of Civil Engineering, Southeast University, China. The authors would also like to express their gratitude to National 973 Plan Program (No. 2012 CB026200) for financial support and to Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education for technical support. **Author Contribution:** [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] contributed analysis tools; [PERSON] wrote the paper. [PERSON], [PERSON] and [PERSON] have made substantial contributions to revise the manuscript. **Conflicts of Interest:** The authors declare no conflict of interest. **Glossary/Nomenclature/Abbreviations** \begin{tabular}{l l} \(a\),\(b\) & the lengths in the longitudinal (transverse) directions \\ \(t_{\rm s}\) & the thicknesses of the steel plate \\ \(t_{f}\) & the total thicknesses of FRP sheet \\ \(n_{f}\) & FRP Layers' number \\ \(t_{f0}\) & single layer of FRP sheet's thickness \\ \(d\) & diameter of the hole \\ \(e_{x\psi}\) & the distance along \(x(y)\)-axis from the center of the hole to the left(bottom) edge \\ \(E_{g}\),\(E_{f}\) & the elastic modulus of steel and FRP \\ \(v_{s}\),\(v_{f}\) & the Poisson ratio of steel and FRP \\ \(\beta\) & Plate aspect ratio, equal to b/a \\ \(k\) & the buckling coefficient \\ \(D_{0}\), \(D_{t}\) & the flexural rigidity of plate without FRP and strengthened with FRP \\ \(f_{\sigma}\) & the critical stress of the plate \\ \(P_{\sigma}\) & the critical load of the plate \\ \(P_{u}\) & the ultimate load of the plate \\ \(\alpha\) & reinforcement index of FRP, calculated by Equation (8) \\ \(\gamma\) & reinforcement index of FRP, calculated by Equation (9) \\ \(\omega\) & reinforcement index of FRP, calculated by Equation (16) \\ \(K_{u}\) & the modified buckling coefficient \\ \end{tabular}
## 4 Materials and Methods  
### Overview  
A detailed description of Materials and Methods is provided elsewhere [36]. Briefly, below are the summaries of particular approaches used. Research vineyards were located at South Dakota State University (SDSU, Brookings, SD, USA) and Iowa State University (ISU, Ames, IA, USA). Grape clusters were randomly selected, and volatiles from the same clusters were sampled from veraison to harvest. Veraison is defined as when half of the clusters have changed to their ripe color and is shown as the first time point in Results. Collection of volatiles from whole clusters and single berries was completed in 2012 and 2013 seasons, respectively. Berry chemistry data (i.e., Brix, pH, ambient temperatures, and titratable acidity (IA only)) is provided in Supplementary Material. Volatiles from crushed berries were collected at the same time as in-vivo sampling for both growing seasons. A SPME (65 \(\upmu\)m polydimethylsiloxane (PDMS)/divinylbenzene (DVB)) fibers were used for on-site sampling at vineyards and for headspace extraction from crushed berries. No internal standard was used. However, trip blanks (i.e., ambient air samples collected at each vineyard) and sampling vial blanks(for destructive sampling) were used to account for potential interfering volatiles. Four replicates (vines) were sampled per site and cultivar at each time point.
## 4 Materials and Methods
### Test-Retest Study  
To evaluate the repeatability of the kidney-MRSI method, test-retest studies were conducted. The studies were performed on a 3 Tesla Siemens Prisma scanner (Siemens Healthineers, Erlangen, Germany). Subjects were asked to lie on a spine coil in a head-first supine position before a flexible coil (18-channel body) was placed above their abdominal region. The dedicated coils were used instead of the scanner integrated coil to improve the signal-to-noise ratio (SNR).  
The acquisition protocol included two sequences: (1) a high-resolution T2-HASTE MRI sequence to provide structural reference images and (2) the proposed DW-CRT [41] MC FID-MRSI acquisition, which is used for fat fraction quantification [27].  
The high-resolution T2-HASTE MRI reference images were acquired with TE/TR = 82/1200 ms, FA = 150\({}^{\circ}\), number of averages = 1, spatial resolution = 0.9 \(\times\) 0.9 \(\times\) 4 mm\({}^{3}\), FOV = 280 \(\times\) 280 mm\({}^{2}\), and echo train length = 83.  
DW-CRT MRSI was prescribed using a Hanning window and the following parameters: acquisition delay = 4 ms, TR = 1000 ms, FA = 90\({}^{\circ}\), number of averages = 1, FOV = 240 \(\times\) 240 mm\({}^{2}\) (one slice), slice thickness = 10 mm, matrix size = 48 \(\times\) 48, extractable voxel resolution = 5 \(\times\) 5 \(\times\) 10 mm\({}^{3}\) (0.25 mL nominal spatial resolution), TA = 192 s, number of rings = 24, points-per-ring = 64, temporal samples = 512, spatial interleaves = 4, time acquire = 96 s, and spectral bandwidth = 1250 Hz. No respiration navigation/triggering was used. This resulted in total acquisition duration of 3 min and 16 s. To enhance static field (B\({}_{0}\)) homogeneity, the left kidney area was shimmed before acquiring MRSI data. The typical full width at half maximum (FWHM) was 24.5 \(\pm\) 0.4 Hz.  
After a 30 min break outside the scanner, the subject returned to the scanner table and was repositioned before the repeat scan was acquired using the same scanning protocol. For repeatability purposes, MRSI data were obtained from an axial slice that demonstrated the same anatomy, marked by the kidney hilum (Figure 1).
###### Abstract  
The mechanical behavior of nanocomposites consisting of highly ordered nanoporous nickel (HONN) and its carbon nanotube (CNT)-reinforced composites (CNHONNs) subjected to a high temperature of 900 K is investigated via molecular dynamics (MD) simulations. The study indicates that, out-of-plane mechanical properties of the HONNs are generally superior to its in-plane mechanical properties. Whereas the CNT shows a significant strengthening effect on the out-of-plane mechanical properties of the CNHONN composites. Compared to pure HONNs, through the addition of CNTs from 1.28 wt% to 5.22 wt%, the weight of the composite can be reduced by 5.83% to 2.33% while the tensile modulus, tensile strength, compressive modulus and compressive strength can be increased by 2.2% to 8.8%, 1% to 5.1%, 3.6% to 10.2% and 4.9% to 10.7%, respectively. The energy absorption capacity can also be improved due to the existence of CNTs. Furthermore, the MD simulations provide further insights into the deformation mechanism at the atomic scale, including fracture in tension, pore collapse in compression and local changes in lattice structures due to stacking faults.  
Keywords:nanoporous nickel; carbon nanotube/nickel composite; molecular dynamics; lightweight; strengthening effect +
Footnote †: journal: Applied Physics Letters
## 4 Aircraft Tracking and Activity Recognition
### Aircraft Activity Recognition  
The velocity of each tracked aircraft was calculated every 25 frames using the multi-cycle backward difference method. As shown in Figure 9, the current speed was taken to be the total moving distance over the latest 50 frames divided by the total time, \(t_{total}\). The calculation formula is as follows.  
\[\mathrm{V}_{\mathrm{M}}^{t}=\frac{\|\mathrm{P}_{\mathrm{M}}^{t}-\mathrm{P}_{ \mathrm{M}}^{t-50}\|}{t_{total}} \tag{4}\]  
Here, \(\mathrm{V}_{\mathrm{M}}^{t}\) is the current speed, \(\mathrm{P}_{\mathrm{M}}^{t}\) is position of the tracked aircraft at the current frame, \(\mathrm{P}_{\mathrm{M}}^{t-50}\) is the position of the aircraft 50 frames away from the current frame, and \(t_{total}\) is the time elapsed while the aircraft moved from position \(\mathrm{P}_{\mathrm{M}}^{t-50}\) to \(\mathrm{P}_{\mathrm{M}}^{t}\). Thus, we can obtain a relatively stable speed measurement of the aircraft every other 25 frames. Finally, the recognition result of the aircraft's activity can be given based on the speed of the aircraft:  
\[\left\{\begin{array}{rl}\mathrm{Parking}&0\leq\mathrm{V}_{\mathrm{M}}^{t} \leq(2\times\mathrm{GSD}\times 3.6)\ \ \mathrm{km/h}\\ \mathrm{Taxing}&(2\times\mathrm{GSD}\times 3.6)\ \mathrm{km/h}<\mathrm{V}_{ \mathrm{M}}^{t}\leq 230\ \ \ \mathrm{km/h}\\ \mathrm{Flying}&230\ \mathrm{km/h}<\mathrm{V}_{\mathrm{M}}^{t}\leq 1100\ \mathrm{km/h}\end{array}\right. \tag{5}\]  
Here, we considered the possible positioning error caused by matching aircraft in the rotated search area image. If the movement of the aircraft is less than two pixels, the aircraft is considered to be in the parking state. If its speed exceeds 230 km/h, the aircraft is considered to be in the flying state. In other cases, it is considered to be in the taxiing state.
# Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights
## 2 Technical Background of NB-IoT
### The Standardization and Technology of NB-IoT
#### 2.3.3 NB-IoT Comparison with Other IoT Technologies  
In Table 5[1; 1; 7; 16], the comparison between NB-IoT, other licensed, and unlicensed band IoT is presented. The IoT operating in the licensed bands include eMTC, while LoRaWAN, SigFox, Ingenu, etc., are for the unlicensed band.  
Comparisons between NB-IoT and other LPWAN have been carried out to evaluate some features. These features include the cost of deployment, coverage range, interference immunity, power, and spectral efficiencies[6; 7; 16; 81; 82]. However, the results obtained showed that these technologies have some distinctive features that are suitable for specific purposes or applications. In some cases, trade-offs are required by the user for their deployments. An example is the deployment of NB-IoT in industries. Such decisions would leverage the existing infrastructure, security, and coverage of the LTE. Meanwhile, a decision that chooses LoRa, would require additional network infrastructure for deployment.  
Figure 12: NB-IoT modes of deployment.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **eMTC** & **NB-IoT** & **LoRa** & **SigFox** \\ \hline Spectral Bandwidth & 1.4 MHz & 180 KHz & 7.8–500 KHz & 200 KHz \\  & 700–900 & 700–900 & 868 & 868 \\ Spectral Frequency Band (MHz) & Cellular & Cellular & ISM & ISM \\  & Licensed & Licensed & Unlicensed & Unlicensed \\ Spectral Efficiency & High & High & Very Low & Very Low \\ Data Rate & \textless{}1 Mbps & \textless{}50 kbps & \textless{}10 kbps & 100 bps \\ Coverage Area & \textless{}10 km & \textless{}15 km & \textless{}10 km & \textless{}12 km \\ Terminal Cost (\$-2020) & 3.3 & 2-3 & 2.64 & 2.64 \\ Power Efficiency & Medium-High & Medium-High & Very High & High \\ Interference Immunity & Medium & Medium & Very High & Medium \\ Standard & 3 GPP Release 14 & 3 GPP Release 13 & LoRaWAN2 & ETSI \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison between NB-IoT frequency band and other bands.
## 3 Numerical Modelling
### Verification  
To verify the reliability and accuracy of the established finite element, a smaller model with totally identical modeling criteria was set up according to the experiments conducted by [PERSON] et al. (2018). The validated experiments include three segments and four bolts, which can properly reflect the overall characteristics of the proposed numerical method in this paper.  
The length, width, and depth of the segments on the sides are 350, 1600, and 500 mm, while those of the middle segment are 2700, 1600, and 500 mm, respectively, as shown in Figure 11. Besides, the material properties of the segments and bolts are the same as those described in Section 3.2. The two circumferential joints are subjected to shear force during the experiments, which is simulated in the verified model. More detailed information about the verified experiments can be directly found in ([PERSON] et al., 2018).  
Additionally, a convergence study is also carried out by comparing calculation results obtained from numerical models with different mesh criteria, i.e., large mesh, medium mesh, and small mesh. Figure 12 illustrates the results of the numerical analysis against those of the experiments. It can be seen that the overall behavior of the results from the two different approaches shows good agreement, no matter which mesh size is utilized. However, the numerical results with large mesh size (nearly 40 cm) are not accurate enough, whereas those with medium mesh size (nearly 20 cm) and small mesh size (nearly 5 cm) both exhibit satisfactory reliability compared with the corresponding experimental results.  
Figure 11: Verified numerical model.  
Figure 10: Layout of Jack Thrust.  
The aforementioned verification study provides confidence for the reliability and accuracy of the proposed numerical method so that it can be used to further investigate the effect of the jack thrust on the mechanical characteristics of the segmental lining structures. Besides, the mesh of the first three segmental rings adopts a small size, while the other ones uses a medium size. In this way, both the accuracy and computational efficiency can be considered during the numerical analysis.  
Figure 12: Comparison between numerical and experimental results. (**a**) Small mesh size. (**b**) Medium mesh size. (**c**) Large mesh size.
## 3 System Model
### Energy Model  
The energy consumption of the UAVs consists of the communication energy and the propulsion energy. Since the communication energy consumption is relatively small, we only consider the propulsion energy in this paper. According to [34], the propulsion energy is used for keeping the UAV hovering, flying, and accelerating, which is related to the speed of the UAV. The propulsion energy of the rotary-wing UAV can be expressed as  
\[E_{u}= P_{0}(1+\frac{3V_{t}^{2}}{U_{tip}^{2}})+P_{1}(\sqrt{1+\frac{V_{t}^{4}}{4 v_{0}^{4}}}-\frac{V_{t}^{2}}{2v_{0}^{2}})^{\frac{1}{2}} \tag{5}\] \[+\frac{1}{2}d_{0}\rho s_{0}A_{r}V_{t}^{3},\]  
where \(P_{0}\) and \(P_{1}\) represent the blade profile power and derived power, respectively, \(V_{t}\) is the flying speed of the UAV in time slot \(t\), \(U_{tip}\) is the tip speed of the rotor blade of the UAV, \(v_{0}\) is the mean rotor induced velocity in the hovering state, \(d_{0}\) is the fuselage drag ratio, \(\rho\) is the density of air, \(s_{0}\) is the rotor solidity, and \(A_{r}\) represents the area of the rotor disk. In particular, the energy consumption, when hovering, is \(E_{u}=P_{0}+P_{1}\). Therefore, the energy consumption generated of the \(i\)-th decision can be expressed as  
\[E_{i}=E(v)T_{f}^{i}+E(0)T_{c}^{i}, \tag{6}\]  
where \(E(v)\) is the UAV flight energy consumption and \(E(0)\) is the hovering energy consumption. In this paper, it is assumed that \(E_{m}ax\) is the initial energy consumption of the UAV.