esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Cytotoxicity Assays  
To assess the cytotoxicity of the six SLEnS variants to A6 cell lines, two different viability assays were performed, namely, the thiazolyl blue tetrazolium bromide (MTT) and resazurin assays, according to the National Institute of Health's (NIH) guidelines [27], which were adapted to the A6 cell line. Briefly, cells were plated on 96-well plates at a density of \(1\times 10^{4}\) cells per well (Countess(r) II FL Automated Cell Counter (ThermoFisher Scientific(r))) and allowed to adhere overnight at 25 \({}^{\circ}\)C. On the following day, test solutions of each SLEnS variant were made, immediately before exposure, through the dilution of the initially prepared stock solution (3300 mg/L of SLEnS and 6600 mg/L of LAS) in complete cell culture medium. Then, the cell lines were exposed to the following SLEnS concentrations, which were selected based on prior range finding tests: 0.132, 0.264, 0.396, 0.528, 0.66, 0.792, 0.924, 1.056, 1.188, and 1.32 mg/L of SLEnS. The duration of the tests was 48 h, with observations of the viability of the cells made after 24 h and 48 h of exposure. Three independent assays were performed, each containing four replicates per concentration and type of SLEnS. For the MTT assay, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolzolium bromide (TCI, Brussels, Belgium) was diluted in PBS (pH 7.36) at a concentration of 5 mg/mL, while for the resazurin assay, 7-hydroxy-3H-phenoxyazin-3-one-10-oxide sodium salt (Serva, Heidelberg, Germany) was diluted in PBS of pH 7.36 at a concentration of 0.15 mg/mL, and both reagents were sterilized by filtration (pore: 0.22 um), stored at \(-20\) \({}^{\circ}\)C, and protected from light. After the exposure period, the cells  
Figure 1: Chemical structure of sodium lauryl ether sulphate (SLEnS) with and without ethylene oxide units.  
were washed with PBS, 100 \(\mu\)L of 10% MTT solution was added to each well, and the plates were incubated for 4 h at 25 \({}^{\circ}\)C. At this stage, the cells with an active metabolism transform the MTT to purple-colored formazan. Then, the MTT solution was removed and DMSO was added to the wells. The amount of formazan produced was measured through absorbance (at 570 nm and 690 nm using MultiSkan Spectrum ThermoFisher Scientific(r)). The cell viability was calculated as a percentage of the control. After the 24 and 48 h exposure periods, 10 \(\mu\)L of resazurin was added to each well, and the cells were incubated for 4 h at 25 \({}^{\circ}\)C. After this period, the media were transferred to black 96-well plates. Viable cells will reduce resazurin to resorufin. The fluorescence was measured (Ex. 530.0--Em. 590.0 nm) with a fluorescence spectrophotometer (Hitachi F-7000; @Hitachi, Ltd.; WVR, Lisbon, Portugal). The cell viability was calculated as a percentage of the control.
# A Scheduling Scheme for Improving the Performance and Security of MU-MIMO Systems
## 5 Numerical Results and Discussions
### Comparison of the Proposed Scheduling Scheme with Other Schemes from Previous Works  
Now, the presented proposal is compared with the scheduling schemes described in Section 4.1, that is the [PERSON] norm-based scheduling algorithm and the fair channel allocation algorithm in terms of the mean BER. In particular, the [PERSON] norm-based  
Figure 5: BER as a function of \(E_{b}/N_{0}\), parameterized by \(U_{t}\), and by the Rician factor \(K\), considering BPSK modulation, \(A=4\), \(U=4\), and \(e=0.1\).  
Figure 6: BER as a function of \(e\), parameterized by \(U_{t}\), considering BPSK modulation, \(A=6\), \(U=6\), \(E_{b}/N_{0}=10\) dB and a channel with Rician factor \(K=2\).  
algorithm is known to be one of the suboptimal allocation schemes with best trade-off between implementation complexity and performance [27], for this reason, it is considered for comparative purposes. Moreover, the fair algorithm seeks an equitable transmission of all users in the MIMO system, as a consequence, it is also an interesting scheme to be compared with the algorithm proposed in this work.  
Figures 7 and 8 show the mean BER as a function of \(E_{b}/N_{0}\), parameterized by \(e\), and by the scheduling algorithm using \(A=4\), \(U=4\), and \(U_{t}=5\) in a Rician fading channel with \(K=2\). In particular, Figure 7 considers BPSK modulation and Figure 8 considers 16-QAM. In the figures, notice that both for \(e=0\) and \(e=0.05\), the BER obtained with the presented proposal is lower than that obtained with the Frobenius norm-based and fair allocation algorithms. This occurs because the proposed algorithm allows the system to obtain diversity, which is presented as a performance gain. On the other hand, the mean BER with the Frobenius norm-based algorithm is slightly lower than that obtained with the fair allocation algorithm. For \(e=0.05\), the BER obtained with all the algorithms is considerably affected by the imperfect channel estimation, and this effect is greater when 16-QAM is used. However, for both BPSK and 16-QAM, the presented proposal obtains the lowest BER in this scenario. In addition,  
In the scenarios of Figures 7 and 8, the number of complex operations performed by each algorithm does not change, since they are independent of the modulation order. Thus, from (20), the number of complex operations performed by the proposed algorithm is 750, from (15), the Frobenius norm-based algorithm carries out 320 complex operations and, from (16), the fair allocation algorithm performs 80 complex operations. Thus, although the proposed algorithm is the one that performs a greater number of operations, it is also interesting to observe that, for both modulation schemes, the BER obtained with the presented proposal in the imperfect channel estimation scenario with \(e=0.05\) is lower than the BER obtained with the other algorithms operating in a perfect channel estimation scenario (\(e=0\)) for certain values of \(E_{b}/N_{0}\).
# Delayed Combination of Feature Embedding in Bidirectional LSTM CRF for NER
## 4 Experimental Setup
### Model Setup  
When training our models, we used two different numbers of iterations without early stopping: (1) 200 epochs for all model variants except the model with ELMo and (2) 120 epochs for the model with ELMo. This is because the performance of the model with ELMo was rarely improved but the model took quite a long time in training at each epoch when further increasing the number of epochs.  
We evaluated our model after every epoch with the F1 score on the validation set and selected the best validation F1 scored model within the number of epochs, which was used for evaluating the test set. Due to the randomness, we did the same experiments five times and averaged them. The following equation calculates the F1 score:  
\[F1\ score=\frac{2\times Precision\times Recall}{(Precision+Recall)} \tag{4}\]  
Here, precision refers to the ratio of correct named entities found in the NER system, and recall is the ratio of named entities that are retrieved by the NER system. Our goal is to show the effectiveness of the delayed combination and the CNN-based sentence encoding using the dictionary. To achieve this goal, we organized three groups of experiments as follows:  
1. The experiments for comparison between the delayed and the early combinations of FastText (FT) and character-level CNN or LSTM word encoding (we refer to these as Delayed-BiLSTM-CRF (FT + CNN or LSTM) and Early-BiLSTM-CRF (FT+ CNN or LSTM), respectively). The results are given in Section 5.1.
2. The experiments with the model equipped with our dictionary representation (we refer to this model as Delayed-BiLSTM-CRF (FT + CNN or LSTM + Dic)). The results are given in Section 5.2.  
\begin{table}
\begin{tabular}{l l c c} \hline \hline
**Layer** & **Hyper-Parameter** & **CoNLL 2003** & **OntoNotes 5.0** \\ \hline Character-level CNN & Filter size & 3 & 3 \\  & Number of filters & 30 & 30 \\  & Max word length & 25 & 30 \\  & Character embedding dimension & 100 & 30 \\ Character-level LSTM & Max word length & 60 & 97 \\  & Character embedding dimension & 100 & 30 \\  & Hidden units & 128 & 128 \\ CNN with dictionary & Filter size & 3 & 3 \\  & Number of filters & 30 & 30 \\  & Max sentence length & 100 & 100 \\  & Character embedding dimension (With BI tags) & 8 & 22 \\  & Character embedding dimension (Without BI tags) & 4 & 11 \\ BiLSTM with ELMo & ELMO embedding (Dim) & 1024 & - \\  & Number of bidirectional LSTM layers & 2 & 2 \\  & Hidden units & 128 & - \\ BiLSTM with FastText & Word embedding (Dim) & 300 & 300 \\  & Number of bidirectional LSTM layers & 3 & 3 \\  & Hidden units & 128 & 128 \\ \hline  & Dropout & 0.5 & 0.5 \\  & Optimizer & Nadam & Nadam \\  & Learning rate & 0.002 & 0.001 \\  & Mini-batch size & 200 & 200 \\  & Epochs & 200 & 120 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The hyper-parameter values used in our experiments.  
The experiments with the model additionally equipped with ELMo encoding (we refer to this model as Delayed-BiLSTM-CRF (FT + CNN or LSTM + Dic + ELMo)). The results are given in Section 5.3.
## 2 Materials and Methods
### Finite Element Simulation  
In order to study the effect of lung inflation on the ablation area, a finite element method combining electromagnetic field and biological heat transfer was used to establish a simulation model of lung airflow during microwave ablation. The model takes into account changes in several key physical parameters, including the relative permittivity, conductivity, and density of lung tissue.
## 3 Experiments and Results
### Implementation Details  
All experiments were implemented based on Pytorch. We trained and tested the network on NVIDIA Tesla P100. We adopted ResNet-101 pretrained on the ImageNet dataset as the backbone network. In our experiments, the stochastic gradient descent (SGD) was used as an optimizer, with a momentum of 0.9 and weight decay of 0.0005. The batch size was set to 1, the initial learning rate was set to 0.001, and the training epochs was set to 12. The learning rate dropped by 0.1 after 8 and 11 epochs, respectively.
## 4 Discussion
### Methodological Limitations and Research Gaps  
One of the primary challenges identified in the literature is the variability in the sourcing and processing of CWP and insufficient information on the source of CWP. Although many studies indicate the origin of the CWP used, a significant number lack comprehensive characterisation of the material or fail to detail the processing methods employed in making the waste CWP. This complicates meaningful comparisons of CWP from various sources.  
Another critical limitation is the inconsistent durability testing procedures. While most researchers follow recognised standards like ASTM or EN, some adopt modified or undefined procedures without sufficient explanation. These methodological inconsistencies reduce the reliability of comparative analyses and obscure a clear understanding of the durability performance of the CWP. Furthermore, although short-term performance indicators, such as compressive strength results at 28 days, are commonly reported, there is a notable lack of long-term assessments. Given the importance of long-term performance in evaluating the durability and viability of CWP as an SCM, comprehensive long-term durability testing is essential.  
Additionally, the highest concrete strength level assessed in the current literature is generally limited to 40 MPa. High-strength concrete (above 40 MPa) has not been adequately investigated. Further research is therefore recommended to evaluate the performance of CWP in high-strength-concrete applications.  
Lastly, most studies compare the performance of CWP solely with Portland cement. To better assess its effectiveness, it is advisable to compare CWP with conventional SCMs such as GGBS and fly ash.
# UAV Detection with Transfer Learning from Simulated Data of Laser Active Imaging
## 4 Methodology
### GloU Loss  
The conventional deep learning algorithms usually use MSE loss to directly regress the center coordinates as well as the length and width of the bounding box. Directly estimating the coordinates of these points is the way that treats these points as independent variables, and ignores the integrity of the object. To deal with this problem, the researchers adopted intersection over union (\(IoU\)) loss as an optimization objective, which is scale-invariant. However, \(IoU\) loss also has a serious problem: if there is no intersection area between the predicted box and the ground truth box, the gradient of the loss would be zero and cannot be optimized during the iteration. In order to solve this problem, we adopt the generalized \(IOU\) (\(GloU\)) proposed in [31] as the bounding box regression loss. The formula of \(GloU\) is as follows:  
\[GloU=IoU-\frac{|A_{C}-U|}{A_{C}} \tag{8}\]  
where \(IoU\) denotes the ratio between the intersection and union of two boxes; \(U\) is the union of the two boxes and \(A_{C}\) represents the smaller circumscribed rectangle of the two boxes. Similar to \(IoU\), \(GloU\) is also insensitive to scale. \(L_{GloU}=1-GloU\) can be the loss. Compared to \(IoU\) loss, \(GloU\) loss always has a non-zero gradient whether there is an overlapping area between two bounding boxes or not. Therefore, it is the proper policy to choose \(GloU\) loss as the optimization objective in our work.
## 4 Energy Consumption  
In this section, we derive expressions to determine the average energy consumed by an RN per cycle, considering the different periods in which a cycle is divided as defined in Section 2.  
We define an _update supercycle_ as a set of \(N_{sc}\) consecutive cycles. For simplicity, we assume that \(Cc\) SNs transmit one _SYNC_ packet every \(N_{sc,c}\) cycles \(c=1,2\), i.e., one _SYNC_ packet per _update supercycle_. Also, they might receive one _SYNC_ packet per cycle in the remaining \(N_{sc,c}-1\) cycles. To avoid missing _SYNC_ packets from neighboring nodes, occasionally, an SN keeps _awake_ for a whole cycle. As these cycles are different from the _normal_ cycles, which include a _sleep_ period after an _active_ period, we refer to them as _awake_ cycles.  
We define a _hypercycle_ as a set of \(N_{aw}\) consecutive _update supercycles_, i.e., \(N_{aw}\cdot N_{sc}\) consecutive cycles. We also assume that a \(Cc\) SN follows _awake_ cycles during a complete _supercycle_ (\(N_{sc,c}\) consecutive cycles), whereas it follows _normal_ cycles during the other \(N_{aw,c}-1\)_update supercycles_ of the _hypercycle_. Clearly, these definitions of the _normal_ and _awake_ cycles have an impact on the energy consumed by an SN.  
Only the energy consumed by the radio transceiver of an SN is considered by the model. The energy consumed due to the specific sensing tasks of SNs, being application-specific, has not been taken into account.  
Along the next Sections we determine the energy consumed by RN1 and RN2. The energy consumed by any other node of the same class is the same as the one consumed by the corresponding RN.
# Analytic Model for U-Nb Liquidus and U-6 Nb Melting Curve
The high-\(T\) U-rich portion of the U-Nb phase diagram can be considered as reliable, since the results of several experimental studies that it is based on, including those of [PERSON] of 1982 [14], as well as the more recent unpublished LANL MST-6/Sigma data from [PERSON] [11], are consistent with each other within experimental uncertainties. Nonetheless, taking into account a potential uncertainty associated with the high-\(T\) Nb-rich portion of the U-Nb phase diagram, the knowledge of the (ambient) melting behavior of the U-Nb system remains incomplete. In particular, it has not yet been adequately modeled based on general thermodynamics principles or using an equation-of-state (EOS) approach. The present article directly addresses this deficiency. Thus, the clarification of the behavior of the ambient \(T_{m}\) of U-Nb alloy as a function of the Nb content requires further study. Here, we present a theoretical model for the melting curve (liquidus) of a mixture and apply it to the U-Nb system, which is being considered as a mixture of pure U and Nb. To this end, we calculate the melting curve (the \(P\) dependence of \(T_{m}\)) of U using the so-called Z method implemented with quantum molecular dynamics (QMD) simulations with the first-principles-based code VASP. We also consider the generalization of this model to a nonzero \(P\) and derive the melting curve of U-6 Nb as an example.
## 5 Conclusions  
1. In this paper, the Lagrange method is used to dynamically model the double-transition shift for heavy-duty mining truck automatic transmission. The vehicle powertrain system and the controller based on the multi-objective genetic algorithm are built-in Matlab/Simulink. The dynamic analysis of two sets of clutches for DTS is carried out.
2. Obtained in the multi-objective genetic algorithm, the Pareto optimal solution shows that the lowest root mean square of shift jerk and the energy loss of clutches are 4.05 m/s\({}^{-3}\) and 22.4 \(K\), respectively.
3. The simulation results of five strategies of double-transition shift show that overlapping the inertia phases of both clutch groups will cause the fluctuation amplitude of the output torque to increase. On the contrary, the shift jerk can be more stable at \(j_{\rm max}\leq 14.2\) m/s\({}^{-3}\), which is reduced by 56.3%, when the inertial phases are not overlapped. The torque phase overlaps with the inertia phase of another group of clutches, effectively reducing the shift time to about 1.1s and thus decreasing the clutch energy loss by 13.4%, which is \(W=27.1\)\(K\).  
Figure 9: Simulation results of 2–3 power-on double-transition upshift for Strategy 5. (**a**) Output shaft and clutch torque. (**b**) Clutch speed and speed ratio. (**c**) The jerk of shift process. (**d**) The clutch energy loss of shift process.  
**Author Contributions:** Conceptualization, H.Z. and X.Z. methodology, J.Y. and W.Z.; software, H.Z. and X.Z.; validation, H.Z. and X.Z.; writing--original draft preparation, H.Z.; writing--review and editing, H.Z., X.Z., J.Y. and W.Z.; supervision, W.Z.; project administration, J.Y. and W.Z.; funding acquisition, J.Y. and W.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 51905031, the Fundamental Research Funds for the Central University of China, grant number FRF-TP-18-036A1, and the National Key Research and Development Program of China, grant number 2018 YFC0604402.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Review of Unmanned Aerial Vehicle Swarm Communication Architectures and Routing Protocols
## 2 Communication Architectures
### Remarks  
As discussed, we know that UAV swarm communication architecture technology has already made great progress. When faced with different mission scenarios, there are different communication architectures to choose from. Table 2 summarizes the advantages and disadvantages of the four mentioned architectures. Centralized communication architecture is suitable for scenarios where the UAV swarm is small, and the task is relatively simple. Each individual UAV requires a long-range communication link with the infrastructure. The decentralized communication architecture expands communication coverage through a multi-hop network. The dedicated gateway UAV is responsible for U-T-I communication. The \"single-group swarm Ad hoc network\" architecture is appropriate for a swarm of the same type UAVs, while \"multi-group swarm Ad hoc network\" and \"multi-layer swarm Ad hoc network\" architectures can be deployed using different types of UAVs. In a \"multi-group swarm Ad hoc network\", communication between two different groups can also suffer from delays. In addition, in terms of robustness, \"multi-layer swarm Ad hoc network\" architecture is a relatively reliable system because it overcomes SPOF.  
Figure 5: Schematic of multi-layer swarm Ad hoc network.  
When discussing the advantages and disadvantages of the UAV swarm communication architectures, we often focus on the requirements of high coverage and maintaining connectivity. High coverage plays an important role in gathering intelligence and analyzing situations. Only by maintaining connectivity can the real-time communication of the swarm be guaranteed. However, UAV swarms often perform tasks in unknown environments, and the appearance of threats and obstacles is random in time and space. Therefore, it is essential that members withdraw or rejoin. This leads to the impossibility for a disruption-free connectivity. This mainly depends on signal attenuation due to propagation loss. To achieve an uninterrupted connection, on the one hand, the distance in the UAV swarm ad hoc network configuration should not exceed the sensitivity of the receiver, and must be limited to the minimum signal-to-noise ratio (SNR) or receive signal strength indicators (RSSI) range, respectively Inside [42]. On the other hand, the wisdom of bird flocks and fish schools in nature is worth learning from. UAVs in a swarm should be able to react cognitively to changes of the environment to adapt their movement to positions with channel characteristics. This is undoubtedly beneficial for achieving uninterrupted connections. In addition, existing researchers have used the high-efficiency bandwidth of 4G wireless networks to improve the connection stability of UAV swarm in wide coverage areas [43]. The emerging 5G wireless network will provide higher technical support for these studies.
## 3 Materials and Methods  
### Samples and Chemicals  
The 16 cultivars of rice (_Oryza sativa_ L.) analyzed in the present study were categorized as non-pigmented (white), black, or red, in accordance with their pericarp color (Figure S3), as follows: WR: Purple check (PC), Dongjin (DJ), and Heugdaegu (HDG); BR: Heughyang (HH), Heuginjuju (HJJ), Heugnam (HN), Josengheugchal (JSHC), Maligate Pirurutong (MP), Suwon 493 (SW 493), and Suwon 505 (SW 505); RR: Angmi (AM),Goryeong 8 (GR8), Hongjinju (HoJJ), Hanyangjo (HYJ), Siga-Chata (SC), and Jagwangdo (JGD). The rice samples were obtained from the Agricultural Genetic Resources Center at the National Academy of Agricultural Science (Suwon, Korea). The seeds were harvested in 2016 and the rice samples were manually hulled. The samples were pulverized on the same day using a mortar and a pestle. The rice powder was stored in a refrigerator at \(-20\)\({}^{\circ}\)C prior to analysis. Pyridine, \(N\)-methyl-N-(trimethylsilyl) trifluoroacetamide (MSTFA), methoxyamine hydrochloride, ribitol, 5\(\alpha\)-cholestane, and fatty acid methyl ester (FAME) mixture (C\({}_{8}\)-C\({}_{24}\)) were obtained from Sigma-Aldrich Corp. (St. Louis, MO, USA). Peonidin-3-O-glucose and cyanidin-3-O-glucose were purchased from Extrasynthese (Genay, France). All chemicals and reagents used in the study were HPLC grade.