esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model Design
### Using the Fuzzy Inference System to Compute the Transmission Preference
#### 3.3.3 The Defuzzifier Component  
The proposed algorithm adopts the AND operation and the OR operation to compute the transmission preference of the node in an opportunistic social network according to the Mamdani fuzzy system. In our inference system, we first maximize each value of different fuzzy sets by the OR operation, and then calculate the minimum combination of these fuzzy values through the AND operation. Consequently, the OR operation is employed to obtain the maximum shaded area of each fuzzy value, and the AND operation is used to get the overlap shadow area of the six maximum shadows of two input variables. For example, as shown in Figure 3, when \(MS(u,v)=1/2\), it corresponds to \(f_{low}(1/2)=0.8066\). After the OR operation, the corresponding maximum shadow of the low membership function is shown as the pink area in Figure 3.  
In the Mamdani fuzzy system, the maximum shaded area accurately represents the controlling result of each membership function. As mentioned above, there are six membership functions for two input variables (the mobile and social similarities). Hence, there are six controlling results with different sizes for six membership functions in our fuzzy inference system. Moreover, the transmission preference can be gained by the minimum overlapping shadow of these six controlling results, and it represents the final recommendation result after the fuzzy inference of the mobile and social similarities.  
Figure 3: The controlling result of low membership function.  
Following the above example, when \(MS(u,v)=1/2\) and \(SS(\gamma_{u},\gamma_{v})=1/2\), the overlapping area of six controlling results is shown as the pink area in Figure 4 after the AND operation.  
\[T_{(u\pi)}=\frac{\sum\limits_{i=1}^{n}y_{i}\times x_{i}}{\sum\limits_{i=1}^{n}y _{i}} \tag{17}\]  
Finally, because the movement of the node in an opportunistic social network commonly obeys the normal distribution [28], we creatively used the centroid of the overlap shaded area to represent the transmission preference value. Moreover, the transmission preference value can be calculated by the above Equation (17), where \(x_{i}\) is the value of the node similarity between nodes \(u\) and \(v\), \(y_{i}\) is the membership degree of the node similarity between nodes \(u\) and \(v\), and \(n\) is the number of coordinates at the boundary of the overlapping shaded area in Figure 4. During the data transmission process in opportunistic social networks, the message carrier makes the optimal message delivery decision by collecting and comparing the transmission preference values of nodes in the network.
## 1 Introduction
However, a DAE with a fixed number of input layers cannot extract contextual information because of the local minima, the poor performance of the network in the unknown signal-to-noise ratios [14, 15, 21], the poor generalization, and the fact that the enhanced output has some residual noise. In the present paper, a new hybrid-composite system for hearing aid applications is proposed, called HC-DDAEs. The system encompasses two stages: (1) the Wiener filter, which attenuates the noise component and generates a clean speech signal; (2) a composite of three multilayer DDAEs with different frames for each DDAE, which map the output of the Wiener filter to clean features and enhance the speech based on the patient's hearing loss. The composite DDAEs include one, three, and five window lengths for each DDAE level, respectively. The output of each DDAE is connected to the input of the next DDAE,  
Figure 1: Different degrees of hearing loss. Intensity commonly ranges from \(-10\) to \(120\) dB. each of which is specified for an enhancement subtask of the complete enhancement. This method takes into account a hierarchical learning structure, which uses a smaller dedicated frame compared to a single DDAE to complete specific enhancement tasks rather than a single DDAE with a fixed number of layers to complete general enhancement tasks. The proposed composite DDAEs divide the training set into subsets that correspond to distinct subtasks. Each DDAE works as an expert in a specific enhancement task while its adjacent frames decide which DDAE should be used for each training case. The rest of this paper is organized as follows: Section 2 introduces the background knowledge about noise reduction based on autoencoders. Section 3 gives the background and context of auditory perception and hearing aids. Section 4 presents the proposed hearing aid system. Sections 5 and 6 describe our experimental setup and evaluation methodology for this study, respectively. Finally, Section 7 presents the conclusion of this work.
# RAC1B: A Rho GTPase with Versatile Functions in Malignant Transformation and Tumor Progression
## 5 Biological Functions of RAC1B
### Cell Motility and Migration  
Indirect evidence that RAC1B is involved in regulating cell motility already came from the EMT-promoting role of RAC1B (see Section 5.4) and from a study showing that downregulation of the splicing factor ESRP1 is closely associated with a motile phenotype of cancer cells and induction of RAC1B [14]. More direct evidence was first provided by a study showing that directed cell movement initiated by RAC1B is dependent upon p120 catenin (p120(ctn)), an armadillo domain protein involved in multiple cellular functions [21]. As mentioned earlier, EGF-induced ubiquitination of hnRNP A1 together with the activation of SRPKs results in the upregulation of RAC1B to promote cell motility [13]. Indirect regulation of RAC1B alternative splicing may underlie the influence of the neural crest-associated transcription factor FOXD1 on invasion and migration. Knockdown of RAC1B significantly impaired cell migration and invasion capacities of multipotent neural crest cells and metastatic melanomas [38]. Recently, it was shown that RAC1B can mediate ARHGAP11A-induced migration and invasion of HCC cells in vitro and metastasis formation in vivo [22].  
While the majority of studies suggested RAC1B to be a promigratory factor, RAC1B may also be able to act as a migration inhibitor, e.g., in cell motility driven by TGF-\(\beta\). Data obtained from different cell lines including pancreatic ductal epithelial cells and highly metastatic human breast  
Figure 2: Differential role of RAC1B in epithelial-mesenchymal transition (EMT). Whether RAC1B stimulates or inhibits EMT depends on the EMT-inducing agent. For instance, MMP3 has been reported to promote EMT and TGF-\(\beta\)1 to inhibit it. The role of EMT induced by other mechanisms, e.g., ionizing radiation, still needs to be determined.  
carcinoma cells revealed that Rac1 and RAC1B can significantly modulate TGF-\(\beta\)1-induced cell migration. This effect is associated with antagonistic functionalities whereby RAC1 promotes and RAC1B inhibits TGF-\(\beta\)1-dependent random cell migration (chemokines) [39; 40]. Data also suggest that TGF-\(\beta\)1-dependent chemokinesis may be coupled to a senescent phenotype in normal breast epithelial cells [39].  
Cell migration requires the formation of lamellipodia, a process which is mainly controlled by RAC1. Whether RAC1B, too, can induce the formation of lamellipodia remains contradictory. Shortly after the discovery of RAC1B it was reported that RAC1B induces the formation of lamellipodia in fibroblasts [2]. The same group later found that expression of neither wild-type RAC1B nor constitutively active RAC1B-Q61L led to significant induction of lamellipodia in SW480 human colon cancer cells [8]. Whether cell type-specific differences account for these discrepant observations needs further clarification.
## 3 Results
### Physicochemical Properties of Skin Care Products  
Three stable O/W emulsions containing probiotic ingredients were obtained. The Lactobacillus casei strain and microspheres containing _L. casei_ were used as the active ingredients. A system consisting of sodium benzoate combined with sodium salts of levulinic and anisic acid was used as a preservative. Samples were submitted to an accelerated stability study by centrifugation and to a thermal stress test in order to confirm that the homogeneity of the samples had not been altered. The obtained formulations were characterised by pH corresponding to the physiological skin pH. The mean droplet size of the internal phase of the emulsions was in the range of 8.6-12.4 \(\upmu\)m. Table 4 describes the physicochemical properties of the prepared emulsions.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample No.**} & \multicolumn{2}{c}{**Immediately**} & \multicolumn{2}{c}{**After 7 Days**} & \multicolumn{2}{c}{**After 30 Days**} \\ \cline{2-7}  & **Cell Viability \({}^{*}\)** & **Log** & **Cell Viability** & **Log** & **Cell Viability** & **Log** \\  & **[log CFU/g]** & **Reduction** & **[log CFU/g]** & **Reduction** & **[log CFU/g]** & **Reduction** \\ \hline
1P & 9.34 \(\pm\) 0.2 & 0.26 & 6.18 \(\pm\) 0.8 & 3.42 & 6.06 \(\pm\) 0.25 & 3.54 \\
2P & 7.29 \(\pm\) 0.8 & 2.31 & 3.08 \(\pm\) 0.2 & 6.51 & 2.30 \(\pm\) 0.3 & 7.30 \\
3P & NG & 9.60 & NG & 9.60 & NG & 9.60 \\
4P & 3.26 \(\pm\) 0.4 & 6.34 & NG & 9.60 & NG & 9.60 \\
5P & 3.94 \(\pm\) 0.15 & 5.66 & NG & 9.60 & NG & 9.60 \\
6P & 3.90 \(\pm\) 0.5 & 5.70 & 3.50 \(\pm\) 0.15 & 6.10 & 2.08 \(\pm\) 0.7 & 7.52 \\ \hline \hline \end{tabular} Note. – no growth, \({}^{*}\) cell viability is the mean value of two replicates \(\pm\)SD.  
\end{table}
Table 3: The influence of preservation systems on the viability of the _L. casei_ strain.  
In the next stage of the research study, the rheological properties of the prepared compositions were checked. Viscosity curves of the obtained emulsions with probiotic ingredients are shown in Figure 4.  
The data presented in Figure 4 indicate that the viscosity of the products decreases with the increase in the shear rate, which is characteristic of non-Newtonian, shear-thinning fluids. The obtained results show that the addition of the active ingredient did not significantly change the viscosity of the composition.
It should be stressed that in our previous studies [19; 20], we assumed the application of a single type of OADM/MUX devices, which simplifies the MILP formulation by getting rid of the power budget constraints from the model, but it does not allow for the flexible use of OADMs. Our focus was on the minimization of the number of TPs instead of the optimization of the network cost [19]. To our best knowledge, the problem of the physical layer-aware and cost-oriented optimization of OADM placement in packet-optical xHaul networks implemented using passive WDM devices has not been studied in the literature so far. Additionally, the MILP model presented in this work was not considered in prior studies. The remainder of the article is structured as following. Section 2 reviews related works. Section 3 presents the network model and main assumptions of the network scenario. In Section 4, the considered xHaul network planning problem is formulated as an MILP optimization problem. In Section 5, the numerical results are presented and discussed. Finally, Section 6 concludes the work.
## 3 Evaluation Approach
### Vertical Handover Decisions
#### 3.3.2 Topsis  
The selected network is the one that is closest to the ideal solution and farthest from the worst case solution. These networks are defined as the network with the best and worst values for each metric. For a metric of performance, the best value is the largest. For a cost metric, the best value is the lowest. The steps of TOPSIS are cited in [20]:  
\begin{table}
\begin{tabular}{l|c|c|c|c} \hline
**Traffic Class** & **Ber** & **Jitter** & **Delay** & **Throughput** \\ \hline
**Conversational** & 0.07968 & 0.55464 & 0.31956 & 0.04610 \\
**Streaming** & 0.05104 & 0.13444 & 0.29493 & 0.51957 \\
**Interactive** & 0.50385 & 0.27509 & 0.04608 & 0.17496 \\
**Background** & 0.68037 & 0.17644 & 0.10390 & 0.03926 \\ \hline \end{tabular}
\end{table}
Table 3: Importance Weights Per Class.  
Step 1:Calculate the weighted normalized decision matrix as follows:  
\[v_{ij}=w_{ij}\cdot n_{ij} \tag{3}\]  
Step 2:Determine the \"positive ideal\" and \"negative ideal\" alternatives. They are given by:  
\[A^{+}=\{v_{1}^{+},v_{2}^{+},...\},v_{j}^{+}=\max_{i}(v_{ij}) \tag{4}\]  
\[A^{-}=\{v_{1}^{-},v_{2}^{-},...\},v_{j}^{-}=\min_{i}(v_{ij})\]  
Step 3:Calculate the distances. The separation of each alternative \(A^{i}\) from the ideal solution \(A^{+}\) and the separation of each alternative \(A^{i}\) from the ant-ideal solution \(A^{-}\) are calculated as:  
\[d_{i}^{+}=\sqrt{\sum_{j=1}^{M}|v_{i}^{+}-v_{ij}|} \tag{5}\]  
\[d_{i}^{-}=\sqrt{\sum_{j=1}^{M}|v_{i}^{-}-v_{ij}|}\]  
Step 4:Calculate the relative proximity to the ideal solution. The relative proximity of \(A_{i}\) with regards to \(A^{+}\) and \(A^{-}\) is given by \(R_{i}\) and can be expressed as:  
\[R_{i}=\frac{d_{i}^{+}}{d_{i}^{+}+d_{i}^{-}} \tag{6}\]  
where \(R_{i}\) is the best while it is most proximate to 0; \(R_{i}\in[0,1]\).
###### Abstract  
With the increasing demand for space missions, space robots have become the focus of research and attention. As a typical representative, the free-floating dual-arm space robot has the characteristics of multiple degrees of freedom, a floating base, and dynamic coupling between the manipulator and the base, so its modeling and control are very challenging. To address these challenges, a novel dynamic modeling and control method is proposed for a free-floating dual-arm space robot. First, an explicit dynamic model of a free-floating dual-arm space robot is established based on the explicit canonical multi-rigid-body dynamic modeling theory and combined with the concept of a dynamic equivalent manipulator. The establishment process of this model is not only simple and canonical to avoid the definition and calculation of many intermediate variables, but the symbolic result expression of the model also has the characteristics of iteration, which is convenient for computer automatic modeling. Next, aiming at addressing the problem of trajectory tracking and the base attitude stability of a free-floating dual-arm space robot with parameter perturbation and external disturbance, an improved nonlinear model predictive control method introducing the idea of sliding mode variable structure is proposed. Theoretical analysis shows that the proposed controller has better robustness than the traditional nonlinear model predictive controller. Then, an in-orbit service task is designed to verify the effectiveness of the proposed dynamic modeling and control strategy of the free-floating dual-arm space robot. Finally, the dynamic modeling and control methods proposed are discussed and summarized. The proposed methods can not only realize the tracking of the desired trajectory of the arms of the free-floating space robot, but can also realize the stable control of the base of the free-floating space robot. This paper provides new insights into the difficult problems regarding the dynamics and control of free-floating dual-arm space robots.  
free-floating dual-arm space robot; dynamic modeling; sliding mode; nonlinear model predictive control Article
## 3 Results  
In the first phase, we applied the 3D-ZM algorithm to extract statistical features from GM images, which generates 372 statistical features per subject. Next, these features were applied to a robust feature selection based on the IDPSO algorithm. The setting param  
Figure 3: Proposed IDPSO.  
eters regarding the IDPSO algorithm were as follows: population size =100, probability of mutation = 0.04, specific iteration is set to maximum iteration divided by two (1000 in 2000 iteration), \(C_{1}\) = 2, \(C_{2}\) = 1.5, \(C_{3}\) = 2, \(K_{1}\) and \(K_{2}\) are set to 1 and 10. Table 3 shows the results achieved based on different scenarios. Using GM voxel intensities, pMCI patients were distinguished from sMCI patients with 50.88% accuracy (SEN 46.67%, SPE 54.55%, AUC 0.51). The 3D-ZM-based feature extraction slightly improved the prediction accuracy (ACC 53.10%, SEN 50.48%, SPE 55.37%, and AUC 0.52). As can be seen from Table 3, feature selection based on the traditional PSO algorithm significantly improved the prediction performance when compared to classification results using raw statistical features extracted with the 3D-ZM-based feature extraction technique or raw GM voxel intensities (ACC 68.58%, SEN 71.43%, SPE 66.12%, and AUC 0.62). However, the best classification performance was achieved using the proposed 3D-ZM-based feature extraction technique and the IDPSO with 75.66% accuracy (SEN 83.81%, SPE 68.60%, and AUC 0.76). Our accuracy of 75.66% for identifying pMCI patients from sMCI patients is very comparable with state-of-the-art approaches using baseline MRI data [13]. Figure 4 displays the receiver operating characteristic (ROC) curves belonging to the different scenarios, whereas Figure 5 shows the confusion matrixes.  
Figure 4: Receiver operating characteristics curves of MCI conversion prediction using different scenarios.  
Figure 5: The confusion matrixes for pMCI vs. sMCI task using different scenarios.
## 1 Introduction
### Methodology
#### 1.1.1 Validation  
The FEM model benchmarking used a case study of field tests carried out on a 2.2 m diameter open-ended pile driven offshore in China [22]. The piles had a thickness of 0.03 m and a depth of 57.4 m below the seabed. Soil was characterised using a mechanical cone penetrometer (CPT) equipped with soundings for shear wave velocity. Cone penetration test, CPT, tip resistance, \(\mathrm{q_{c}}\), was converted to undrained shear strength, \(\mathrm{S_{u}}\), values using Equation (5):  
\[\mathrm{S_{u}}=0.07\mathrm{q_{c}}+2 \tag{5}\]  
The undrained shear strength profile was checked by retrieving soil samples and doing CIUC triaxial tests, and the results compare well with the \(\mathrm{S_{u}}\) profile, validating the use of Equation (5).  
Table 1 presents the soil properties established from the SCPT sounding and the model parameters utilized in the FEM of the field-test results.
# Nanoformulation of Peptides for Pharmaceutical Applications: In Vitro and In Vivo Perspectives
## 6 Mechanism of Nanoformulated Peptides in Disease Theranostics
### Cell-Penetrating Peptide  
The cell-penetrating peptide (CPP) possesses the ability to permeate through the plasma membrane of the cells for facilitating the internalization of impermeable molecules [154]. The effectiveness of CPPs can help in decreasing unwanted side effects and the administration dosage to be utilized as an efficient drug carrier. Moreover, CPPs were incorporated into therapeutic drugs in two ways--a stand-alone delivery system and a conjugated-with-nanoparticle delivery system. Thus, the nanoformulation of CPPs has shown an enhancement in the delivery of chemotherapeutic agent [147; 155]. Further, [PERSON] et al. (2018) developed cell-penetrating \(\alpha\)-helical polypeptide (PVBLG-8/siRNA/PLG@PLG (PSPP) NPs)-based metastable nanoparticles to deliver systemic siRNA for anticancer treatment. High siRNA encapsulation efficiency is achieved with PLG to act as a stabilizer to facilitate the complexation between positively charged PVBLG-8 and negatively charged siRNA. Additional PLG also enhances the serum stability of the NPs for their better circulation. Deeper penetration is observed in acidic conditions (the tumor microenvironment is more acidic) compared to neutral conditions due to the de-shielding of the PLG at the outer layers of NPs, which can lead to the exposure of cationic PVBLG-8 and improve tumor accumulation. The study also showed that the peptide formulation possesses an enhanced inhibition ability against tumor growth with PSPP NPs, especially to improve their in vitro and in vivo antitumor efficacies [156]. Another research study by [PERSON] et al. reported that CPP-loaded nanobubbles (NB) synergized with ultrasound-targeted microbubble destruction (UTMD) technology were able to improve the delivery of EGFR-targeted siRNA (siEGFR) for TNBC therapy. The proliferation level of MDA-MB-231 TNBC cells with the CPP-NB (siEGFR)+ ultrasound (US) group has led to the lower proliferation of cancer cells, compared to other groups (siEGFR + US, NB + US, CPP-NB + US, NBsiEGFR + US, CPP- NBsiEGFR) after 48 and 72 h of transfection for effectively inhibiting tumor cell proliferation. Moreover, it is evident from the in vivo studies that the TNBC xenograft tumors possess significant abilities to inhibit the CPP-NB (siEGFR) + US group [157]. Cell-penetrating peptides formulated withnanoparticles were also identified to be beneficial for the treatment of other diseases in the future [158].
# A Sentence-Matching Model Based on Multi-Granularity Contextual Key Semantic Interaction
## 4 Experimental Results and Analysis
### Implementation  
The model architecture and system experiments in this study were implemented using the PaddleNLP framework. The framework's pre-trained models and their initialized weights were utilized. The experiments were conducted using two NVIDIA GeForce RTX 3090 GPUs (Taiwan Semiconductor Manufacturing Company Limited, Hsinchu, Taiwan), each with a memory capacity of 24 GB.  
In terms of the model architecture, for the input encoding stage, this study utilized 768-dimensional BERT-base, 312-dimensional ERNIE 3.0-nano, and 768-dimensional ERNIE 3.0-base for text embedding, respectively. For the feature extraction stage, the BiGRU unidirectional hidden dimension was set to 150. The multi-scale CNN adopts convolutional filters of size (3, 4, 5), while the multi-level CNN uses convolutional filters of size (3, 4). The feature map dimension for both was 300. The multi-head self-attention employs 20 heads. In the interaction fusion stage, the hidden dimension of the FNN was set to 150.  
Regarding model training, the maximum sentence length was set to 128, and the batch size was 32. The optimization was performed using AdamW with \(\beta_{1}\) = 0.9 and \(\beta_{2}\) = 0.999. The learning rate was set to 5 \(\times\) 10\({}^{-5}\), and the dropout parameter was 0.1. The model was trained for 3 epochs. The evaluation metric used was accuracy (Acc), and the best-performing result was chosen as the experimental outcome.  
In terms of model testing, the evaluation metric used was accuracy (Acc), and the optimal result was chosen as the experimental outcome. The following definitions were used: True Positives (_TP_s) represent the cases where the original sentences were matched and correctly classified, while True Negatives (_TN_s) indicate the cases where the original sentences were unmatched and correctly classified. False Positives (_FPs_) refer to the cases where the original sentences were unmatched but mistakenly classified, and False Negatives (_FN_s) denote the cases where the original sentences were matched but erroneously classified. The accuracy was calculated using the following formula:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{30}\]