esa-sceva/satcom-chunk-collection huggingface dataset:
### Ion Channels  
Several studies have analyzed the action of biophysical stimulation on the cell membrane. In particular, their action at the level of signal transduction has been demonstrated. PEMFs and CCEFs control the release of Ca\({}^{2+}\) at the intracellular level, which, in turn, promotes the synthesis of growth factors (such as BMPs and TGF-\(\beta\)1) and various cell matrix proteins. In this way, biophysical stimulation promotes cell repair and mineralization [12; 13].  
The main fields of use currently are as follows [2]:  
* Delayed union and non-union.
* Fractures with associated risk factors (open fractures, severe soft tissue damage, patient-specific factors).
* Joint-replacement surgery.
* Stress fractures.
* Bone marrow edema.
* CRPS-I.
* Cartilage repair surgery.
* Inflammatory and catabolic processes at the joint.
* Increasing bone mineral density.  
In the bone-repair process an improvement in the timing of bone consolidation and fracture healing was observed [2; 7; 9]. For this reason, its use in recent fractures or surgical osteotomies has progressively increased in recent years.  
Regarding the problem of fracture non-union, an increase in bone healing of between 73% and 85% is reported in the literature [14; 15]. Even the presence of infectious processes does not compromise the validity of these treatments. Furthermore, the cost-benefit ratio supports the choice of therapy with PEMFs.  
Several reviews in the literature report a valid reaction of cartilage tissue to electromagnetic biophysical stimuli [2]. Moreover, within in vivo studies it emerged that real benefits in cartilage structural improvement occur in cases of early osteoarthritis (OA) and less so in forms in which greater damage to the structural matrix is already present.  
The modulation with PEMFs or CCEFs on the pathways mediated with TGF-\(\beta\) produces positive effects on the joint inflammatory process through an improvement in insulin growth factor-1 production in favor of anabolic processes and decreases the release of catabolic ones. In vivo, it translates into a lower production of matrix-degrading enzymes and consequent preservation of tissue components. These benefits appear particularly valid in recent and early forms of arthritis [16]. For the reasons mentioned above, the application of PEMFs after joint surgery is becoming widespread, obtaining positive results both on the tissue-healing process and on the clinical aspect such as pain relief and functional recovery [17].  
The rise in the number of applications and the greater propensity to use biophysical therapy has led to a significant increase in the amount of literature on the subject.  
The objective of this review is to take into consideration several recent studies regarding the application of PEMFs and Capacitively Coupled Electric Fields (CCEFs) for the improvement of bone and cartilage tissue conditions. For this reason, heterogeneous studies present in the literature were included with the aim of obtaining detailed information that can be used in a clinical context.
# RBC Storage Lesion Studies in Humans and Experimental Models of Shock
## 11 The Effects of Temperature on RBC Storage Lesion  
We next examined the influence of storage temperature on RBC viability using ex vivo human and canine RBCs as well as in vivo radiochromium-labeled RBC transfusions in canines [20]. The 1-6 \({}^{\circ}\)C range of temperature variation allowed currently during RBC storage has not changed in over 70 years. Since refrigeration is a key regulatory factor in RBC storage, administration, and transport, a critical re-examination of the effect of storage temperature with contemporary RBC storage practices was long overdue. Our studies revealed that the current practice standards for blood storage does not adequately account for an important interaction between storage time and temperatures that may also confound chromium RBC viability testing and prove clinically relevant.  
At the upper bounds of the refrigeration temperature range (4-6 \({}^{\circ}\)C), we found that canine and human RBCs have lower RBC viability and increased hemolytic by-products released at near the end of their shelf-life (35 days) when compared to blood stored at lower temperatures (closer to 2-3 \({}^{\circ}\)C). RBC hemolysis occurs early within the storage bag and continues as it is stored at 4-6 \({}^{\circ}\)C and in vivo after transfusion. Our data suggest that at these higher storage temperatures there is more RBC metabolic activity than at the lower temperatures, resulting in increased lactate levels, more acidosis, lower glucose concentrations, and greater stress to RBC, causing more hemolysis when transfused. This continued hemolytic process can result in shorter intervals between transfusions, the need for additional transfusions, and increased iron deposition for chronically transfused patients.  
During the chromium-labelling process (which uses a test sample from a larger unit of blood), the washing procedure, combined with the higher temperature storage, causes lysis of a cohort of the most fragile cells, which results in the test sample taken not being representative of RBC unit being transfused [16]. Storage of RBCs at refrigeration temperatures closer to 2 \({}^{\circ}\)C may result in a better product for transfusion and more accurate results on chromium RBC viability testing.
# Reliable and Energy-Efficient LEO Satellite Communication with IR-HARQ via Power Allocation
## 3 Outage Analysis and Solution to the Optimization Problems  
To maximize the energy efficiency, the key is to calculate the outage probability in (17) and (24). In this section, we propose the NI-FFT method to calculate it accurately. Based on this, the VPA-GA method is designed to optimize the transmission power in each IR-HARQ round.
###### Abstract  
_Chlamydomonas reinhardtii_ is a green microalgae used as a model organism associated with biotechnological applications, yet its nutritional value has not been assessed. This study investigates the nutritional capacity of _C. reinhardtii_ as an additional value for this species beyond its known potential in biofuels and bio-products production. The composition of key nutrients in _C. reinhardtii_ was compared with _Chlorella_ and _Spirulina_, the species widely regarded as a superfood. The results revealed that the protein content of _C. reinhardtii_ (46.9%) was comparable with that of _Chlorella_ (45.3) and _Spirulina_ (50.4%) on a dry weight basis. _C. reinhardtii_ contained all the essential amino acids with good scores based on FAO/WHO values (0.9-1.9) as in _Chlorella_ and _Spirulina_. Unsaturated fatty acids predominated the total fatty acids profile of _C. reinhardtii_ were ~74 of which ~48% are \(n\)-3 fatty acids. Alpha-linolenic acid (ALA) content in _C. reinhardtii_ (42.4%) was significantly higher than that of _Chlorella_ (23.4) and _Spirulina_ (0.12%). For minerals, _Spirulina_ was rich in iron (3.73 mg/g DW) followed by _Chlorella_ (1.34 mg/g DW) and _C. reinhardtii_ (0.96 mg/g DW). _C. reinhardtii_, unlike the other two species, consisted of selenium (10 ug/g DW), and had a remarkably lower heavy metal load. Moreover, _C. reinhardtii_ contained relatively high concentrations of chlorophyll (a + b) and total carotenoids (28.6 mg/g DW and 6.9 mg/g DW, respectively) compared with _Chlorella_ (12.0 mg/g DW and 1.8 mg/g DW, respectively) and _Spirulina_ (8.6 mg/g DW and 0.8 mg/g DW, respectively). This study confirms that, based on its nutrient credentials, _C. reinhardtii_ has great potential as a new superfood or ingredient for a food supplement.  
**Keywords:**_Chlamydomonas; Spirulina; Chlorella_; superfood; Omega-3; selenium; microalgae
## 1 Introduction
Furthermore, the MAC layer challenges, energy consumption, coexistence and issues concerning channel modeling are also analyzed and summarized. An interesting and systematic study of a machine-to-machine (M2M) system for mobile health (mHealth) applications is introduced in [8]. That is, the different PHY and MAC design approaches to develop efficient mHealth applications for WBANs are surveyed and discussed. In addition, the integration challenges between diverse communication technologies and highlighted different design approaches for end-to-end connectivity through some examples of practical implementations are presented. On the other hand, a comprehensive review of the different MAC protocols developed for WBANs is presented in [9]. The review discusses the design requirements of the MAC protocols for WBANs with emphasis on energy minimization and also investigates the existing protocols with a focus on their strength and weakness. A number of MAC protocols have been proposed for WBANs, but few have been developed for CRBANs. Therefore, the development of a MAC protocol for CRBANs is a promising area of research. In this paper, we survey and compare recent advances and development trends in MAC protocols for CRBANs and then address open research issues and challenges. The rest of this paper is organized as follows: in Section 2, we briefly discuss important MAC design issues in CRBANs. Section 3 is devoted to a review of existing MAC protocols for CRBANs with regard to significant protocol features. In Section 4, we compare the reviewed MAC protocols in detail. Crucial open research issues are discussed in Section 5, and we offer our conclusions in Section 6.
# Inhibited Channel Potential of 3D NAND Flash Memory String According to Transient Time
## 4 Conclusions  
Owing to the structural characteristics of 3D NAND flash memory, the selected string channel floats under certain circumstances. As a result, the NLSB phenomenon occurs, and the channel potential of the selected WL increases because of this phenomenon. Regarding the structure, WL7, WL8, and WL9 were all erased from the 16-layer NAND flash memory, and all adjacent cells were applied separately as P1, P2, and P3. A comparative analysis of the channel potential according to time t = 0 s, 30 ms, 100 ms, 300 ms, and 1 s confirmed that the channel potential decreased over time when \(V_{th}\) of the adjacent cell was P1, P2, and P3. However, when the \(V_{th}\) of an adjacent cell changes, the rate of the decrease in the WL8 channel potential also changes. The higher the \(V_{th}\) of the adjacent cell, the faster the decrease in the channel potential of WL8. This phenomenon means that the decrease in the channel potential is different because of the influence on the \(V_{th}\) of the adjacent cell. In P3, which has the fastest rate, the selected WL channel potential decreases the fastest and becomes like unselected WL. The reason for this phenomenon is the fact that the more electrons that become trapped in the nitride, the more holes there will be in the spacer and channel. Owing to the influence of these holes, the electrons in the selected WL channel move easily to the adjacent WL channel. These results suggest that the rate of the channel potential decrease is influenced by the electrons in the nitride. The more electrons present in the nitride, the faster the rate of decrease in the channel potential. This analysis shows why a lower \(V_{th}\) is more resistant to program disturbance. Program disturbance is a problem with the reliability of the device. However, lowering \(V_{th}\) to reduce program disturbance can cause other problems. Therefore, it is necessary to find other ways than to lower \(V_{th}\). This study will assist the scientific community to find other ways.  
Data curation, software, and writing (original draft preparation), T.C.; project administration, H.K.; project administration, funding acquisition, supervision, and visualization, M.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National R&D Program through the National Research Foundation of Korea (NRF) funded by Ministry of Science and ICT (2021M3F3A2A03017693), and in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2018 RIA6A1A03023788).  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.  
Figure 5: Electron concentration profiles for various transient time of (**a**) P1 and (**b**) P3, whereas (**c**) is graph of electron concentration when WL9 is programmed as P3.
# On the Use of Knowledge Transfer Techniques for Biomedical Named Entity Recognition +
## 4 Results and Discussion of MTM-CNN
### Statistical Analysis of MTM-CNN  
The performance of the models was analyzed statistically, and the Friedman test was applied to the models' outputs. The Friedman test is suitable when three or more comparisons are drawn [55; 56]. Figure 7 shows that the difference between the results produced by the proposed models and their variants was statistically significant. All MTMs results were significantly better than the STM-CNN results. The result of the MTM-CNN\({}^{\clubsuit}\) (GENIA-POS and CoNLL used at the innermost layer) was found significantly better with respect to all approaches except for the \(MTM\)-\(CNN^{\clubsuit}\) (GENIA-POS and CoNLL used at the outermost layer).  
The output ranks of the Friedman test were considered to analyze which models were statistically superior to other model(s). Figure 8 shows the models according to their statistical ranks where the leftmost one represents the best model which decreases from left  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Datasets** & **MTM-CNN** & **MTM-CNN\({}^{\clubsuit}\)** & **MTM-CNN\({}^{\clubsuit}\)** & **MTM-CNN\({}^{in}\)** \\ \hline AnatEM & 86.9 & 87.1 & **87.3** & 86.6 \\ BC2 GM & 80.8 & **81.4** & 81.3 & 81.2 \\ BC4 CHEMD & 87.3 & **88.6** & 88.4 & 87.9 \\ BC5 CDR & 87.8 & 88.1 & **88.3** & 88.0 \\ BioNLP09 & 88.7 & 88.7 & 88.7 & **88.9** \\ BioNLP11 EPI & 84.7 & 84.6 & **84.9** & 84.5 \\ BioNLP111D & 87.6 & **88.0** & 87.6 & 87.5 \\ BioNLP13 CG & 84.2 & 84.4 & 84.5 & **84.6** \\ BioNLP13 GE & 79.8 & 79.4 & **80.0** & 80.0 \\ BioNLP13 PC & 88.8 & 88.7 & **89.0** & 88.7 \\ Ex-PTM & **83.1** & 81.4 & 81.5 & 81.1 \\ CRAFT & 80.9 & 83.6 & **84.1** & 83.5 \\ JNLPBA & **74.0** & 72.4 & 72.6 & 72.4 \\ LINNAEUS & 87.7 & **88.9** & 88.3 & 88.4 \\ NCBI-disease & 85.6 & 85.7 & **86.0** & 85.7 \\ Average & 84.5 & 84.7 & **84.8** & 84.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results comparison of proposed multitask learning approach with different auxiliary tasks and at different levels of the main model.  
to right. It can be seen that the proposed MTM-CNN\({}^{\bigstar^{in}}\) was significantly better than the rest of the approaches. Using the auxiliary tasks at the innermost layer was found the most effective, producing significantly better results with respect to most of the other approaches.
# Unsupervised Novelty Detection Using Deep Autoencoders with Density Based Clustering
## 4 Experimental Study
### Experimental Result

Table 2: AUC score comparison between Algorithm 1 and Algorithm 3. The highest values of AUC scores are marked in bold. 13 methods have sometimes indicated memory errors in KPCA-CDF, KPCA-GMM and KPCA-DBC algorithms depending on sample number. For our proposed DAE-DBC algorithm, all of the data has been successfully tested. Table 4 shows the time performance of algorithms used in our experimental study and the unit of the table is seconds for all columns. However, for the small dataset, the complexity time of the SVM based (OC-SVM, eta-SVM and Robust-SVM) and PCA based (Robust-PCA, PCA-CDF, KPCA-CDF, PCA-GMM, KPCA-GMM and KPCA-DBC) algorithms work faster than the Autoencoder based (DAE-Kmeans and DAE-DBC) algorithms. Our proposed algorithm achieves higher accuracy compared with the other algorithms. For the large dataset such as Suttle, our proposed method works faster than SVM based methods. As a conclusion, the accuracy complexity of our proposed algorithm is higher than other algorithms and the computation time of our algorithm is constant for all datasets. Figure 3 shows the ROC curves of compared 13 methods on 20 different benchmark datasets from ODDS. The DAE-DBC method shows higher AUC scores on 9 of total 20 datasets than other methods. The x-axis is False Positive Rate and the y-axis is True Positive Rate in Figure 3 ROC curves of compared methods. \begin{table}
Figure 3: _Cont._  
Figure 3: ROC curves of compared methods. (**a**) Annthyroid, 6 dimension, novelty ratio 0.08; (**b**) BreastW, 9 dimension, novelty ratio 0.53; (**c**) Arrhythmia, 274 dimension, novelty ratio 0.17; (**d**) Cardio, 21 dimension, novelty ratio 0.1; (**e**) Glass, 9 dimension, novelty ratio 0.04; (**f**) Ionosphere, 33 dimension, novelty ratio 0.56; (**g**) Letter, 32 dimension, novelty ratio 0.06; (**h**) Lympho, 18 dimension, novelty ratio; (**i**) Mnist, 100 dimension, novelty ratio 0.1; (**j**) Musk, 72 dimension, novelty ratio; (**k**) Optdigits, 64 dimension, novelty ratio 0.03; (**l**) Pendigits, 16 dimension, novelty ratio 0.02; (**m**) Pima, 8 dimension, novelty ratio 0.5; (**n**) Satellite, 36 dimension, novelty ratio 0.46; (**o**) Satimage, 36 dimension, novelty ratio 0.01; (**p**) Suttle, 9 dimension, novelty ratio 0.07; (**q**) Thyroid, 6 dimension, novelty ratio 0.02; (**r**)Vertebral, 6 dimension, novelty ratio 0.14; (**s**) Wbc, 30 dimension, novelty ratio 0.05; (**t**) Wine, 13 dimension, novelty ratio 0.08. In order to demonstrate the effectiveness of components together, we have tested multiple variants for replacing components with different algorithms by this experiment. In other words, we used to reduce dimension and get the reconstruction error using AE, then we have made experiments to create PCA-DBC, KPCA-DBC version replacing AE by PCA technique and create DAE-Kmeans version replacing density-based clustering with the K-means algorithm. The DAE-DBC algorithm works better than its other versions and the-state-of-the-art methods that are proposed for other researches.
## Nomenclature  
\(A\)heat transfer area (m\({}^{2}\))  
\(Bo\)Bond number = \((\rho_{\rm L}-\rho_{\rm V})gd_{\rm i}/\sigma\)  
\(c_{\rm p}\)isobaric specific heat (J kg\({}^{-1}\) K\({}^{-1}\))  
\(d\)tube diameter (m)  
\(f\) friction factor  
\(F_{\rm FO}\)[PERSON]'s modified Froude number  
\(G\)refrigerant mass velocity (kg m\({}^{-2}\)s\({}^{-1}\))  
\(g\)gravitational acceleration (m s\({}^{-2}\))  
\(Ga\)Galileo number = \(gd_{\rm i}^{3}p_{\rm L}^{2}/\mu_{\rm L}^{2}\)  
\(h_{\rm fg}\)latent heat of condensation (J kg\({}^{-1}\))  
\(L\)heat transfer length (m)  
\(m\)mass flow rate (kg s\({}^{-1}\))  
\(Nu\)Nusselt number = \(\alpha d_{\rm i}/\lambda_{\rm L}\)  
\(Nu_{\rm B}\)free convection Nusselt number  
\(Nu_{\rm F}\)for forced convection Nusselt number  
\(P\)pressure (Pa)  
\(Ph\)phase change number = \(c_{\rm P}(T_{\rm r}-T_{\rm wi})/h_{fg}\)  
\(Pr\)Prandtl number = \((c_{\rm P}\mu)/\lambda\)  
\(Q\)heat transfer rate of each subsection (W)  
\(Re_{\rm L}\)liquid Reynolds number = \(G(1-x)d_{\rm i}/\mu_{\rm L}\)  
\(Re_{\rm V}\)vapor Reynolds number = \(G\,x\,d_{\rm i}/\mu_{\rm V}\)  
\(T\)temperature (K)  
\(T_{\rm i}\)dimensionless temperature difference  
\(x\)vapor quality  
**Greek Symbols**  
\(\alpha\)heat transfer coefficient (W m\({}^{-2}\) K\({}^{-1}\))  
\(\Delta P\)total pressure drop (Pa)  
\(\Delta P_{\rm F}\)frictional pressure drop (Pa)  
\(\Delta P_{\rm M}\)acceleration pressure drop (Pa)  
\(\lambda\)thermal conductivity (W m\({}^{-1}\) K\({}^{-1}\))  
\(\mu\)viscosity (Pa s)  
\(\xi\)void fraction  
\(\rho\)density (kg m\({}^{-3}\))
# SLNER: Chinese Few-Shot Named Entity Recognition with Enhanced Span and Label Semantics
## 2 Related Work
### Span-Based Method  
Previously, the majority of research work treated NER as a sequence-labeling task, where entity classification is performed at the token level. These methods often employ the BIO tagging scheme, where each character's category is determined to achieve entity recognition for Chinese NER tasks, as shown in Figure 2. As a representative example, [PERSON] et al. [24] utilized BiLSTM as an encoder to learn contextual representations, then employed a conditional random field (CRF) as a decoder to label tokens. Additionally, leveraging the power of pretrained language models such as ELMo [25] and BERT [5] significantly improved the performance of NER.  
While the aforementioned token-based NER methods have achieved considerable success, they suffer from two inherent limitations: token-wise label dependency and difficulty handling nested entities. As shown in Figure 2, this method may fail to correctly classify nested entities such as \"Taojie greengrocer\" and \"Hami Melon\". Additionally, if errors occur during the labeling process, incorrect beginning tags (B) or internal tags (I) may affect subsequent labeling, resulting in the erroneous tagging of the entire entity.  
Therefore, recent work has increasingly embraced span-based approaches to address NER tasks. This method involves partitioning all possible spans in a sentence into predefined types (e.g., PER or LOC) and determining whether a given text span belongs to a particular category, as shown in Figure 3. [PERSON] et al. [26] adopted a biaffine attention model to assign scores to all potential spans and achieved state-of-the-art performance on both flat and nested English NER datasets. [PERSON] et al. [27] also employed a span-based framework for Chinese NER datasets, effectively addressing the issue of nested entities. [PERSON] et al. [28] and [PERSON] et al. [29] proposed span-level metric learning to bypass the token-wise label dependency problem while still explicitly utilizing phrase representations. [PERSON] et al. [30] introduced a span-based prototype network and a global boundary matrix to learn explicit span boundary information.  
Figure 2: Traditional sequence-labeling NER method.  
In this study, we also utilize a span-based approach for named entity recognition and extensively explore the internal information of entity word spans to obtain enhanced span feature representations.
# Seismic Behavior of Steel Plate-Concrete Shear Walls with Holes
## 3 Experimental Analysis
### Effects of Axial Compression Ratio on the Hysteretic Performance of SPSW Specimens  
The axial compression ratio is an important factor that influences the hysteretic performance of SPSW specimens. In this section, axial compression ratios in the low-cycle reciprocal tests of three SPSW specimens with holes (SCW-3, SCW-4, and SCW-5) are set at 0.15, 0.3, and 0.5, respectively. The ultimate loads and displacements of specimens are shown in Table 4. Figure 7 illustrates the hysteretic curves of different SPSW specimens, whereas Figure 8 shows the skeleton curves.  
In the loading process of the three specimens, steel plates develop low sounds, and internal concretes begin to crack at 350 kN when the axial compression ratio is 0.5. When the axial compression ratios are 0.15 and 0.3, steel plates develop low sounds at 500 kN and 150 kN, respectively. In summary, the concrete begins to crack early under large axial compression ratios. With the continuous increase of loads, SCW-3 and SCW-4 show a reduction stage of loads and good ductility. When loads increased to 1217 kN, the root plate of SCW-4 bent, and the test was terminated. SCW-4 thus shows poor ductility, as verified from the skeleton curve. Table 4 shows that the ultimate bearing capacity of components is positively related with axial compression ratio, but deformation resistance and ductility are negatively correlated. Therefore, attention should be paid to the sudden failure of structures under high axial compression ratio.