esa-sceva/satcom-chunk-collection huggingface dataset:
# Computational Fluid Dynamics Applied to
## 2 Background
### Rigid Mesh Motion or Sliding Meshes  
As the name suggests, the rigid mesh motion (RMM) approach involves moving the mesh rigidly in space. In this way, it is possible to use classical solvers since the apparent forces can be naturally generated by the mesh movement. In addition, through this method it is possible to consider field forces such as gravity. For instance, this approach is used in [31]. In this work, the air-oil behavior in a roller bearing is studied through the RMM approach. The rotational speed of the cage is imposed with a rotation of the mesh. Additional boundary conditions were then used to assign the correct speeds of to the other components.  
The RMM approach can be applied to rolling bearings where axis-symmetric elements rotate both around a main axis and around its own axis. However, this approach cannot be applied when the rotating bodies are not axis-symmetric because their rotation would result in a topological change of the mesh. To overcome this problem the sliding meshes or mesh sliding (MSL) approach can be exploited. The MSL approach is based on the concept of arbitrary mesh interface (AMI), i.e., interfaces through which non-conforming, disconnected but adjacent meshes can exchange data.  
By exploiting the MSL approach, it is possible to create a mesh with an axis-symmetric interface integral to a non-axis-symmetric component. In this way, the set of mesh and component can be considered a single axial-symmetric block that can rotate on its own axis and exchange data with a non-conforming mesh that has different motions trough a sliding interface. An application of MSL can be found in [32]. In this work, the churning power losses of ordinary a rotating gear is studied. Through the MSL approach the mesh deformation due to the topological changes (due to the rotation of the gear) are avoided. The application of MSL is common in the study of centrifugal pumps. Indeed, in many studies, the partition related to the impeller and the diffuser are connected through an AMI by exploiting the MSL approach [33; 34].  
However, the MSL approach is not able to solve the problem related to the topological changes requested by the gear engagement. For instance, in order to apply MSL to a planetary gearbox some geometrical simplifications are needed, e.g., downscaling of the gears in order to make the various partitions not overlapping [35].
## 3 Dual Sparsity Pruning Federated Learning Framework  
In this section, we introduce a novel dual pruning framework named DualPFL (Dual Pruning Federated Learning) and provide a detailed analysis of its components in optimizing the communication overhead in federated learning. The main notations are summarized in Table 1.  
We consider a federated learning framework comprising \(N\) client devices, where each client \(i\in\{1,\ldots,N\}\) owns a local dataset \(D_{i}=\left\{D_{i}^{x},D_{i}^{y}\right\}\). Here, the data samples \((x_{i},\ y_{i})\sim D_{i}\) are drawn from the global instance space X and the global label space Y, respectively. We can formulate the empirical risk minimization of the proposed DualPFL, formulated as follows:  
\[\underset{\mathbf{\theta}}{\text{min}}\mathcal{L}(\mathbf{\theta})=\sum_{i}w_{i}L(x_{ i};y_{i},\mathbf{\theta}_{i})+\lambda|\mathbf{M}_{i}|_{1}, \tag{1}\]  
where \(\mathcal{L}(\mathbf{\theta})\) represents the global loss function and \(L(\mathbf{\theta}_{i})\) is the local loss function of client \(i\). \(\lambda\) is the coefficient for L1 regularization and \(w_{i}\) refers to the weight coefficient for each device.
## 3 Results
### Prokaryotic Protein Expression and In Vitro Functional Validation of DcTPSb1  
Following expression, the proteins were purified, where supernatant, rinse solution, and eluent were separated out showcasing a single band on an SDS-PAGE gel, matching the expected size of DcTPSb1 (Figure 5a). To further verify its in vitro function, GPP was used as a substrate for incubation with recombinant DcTPSb1 protein. GC-MS detection of the resulting products revealed that the recombinant DcTPSb1 protein yielded three monoterpenes, linalool, ocimene, and (-)-\(\alpha\)-pinacol (Figure 5b). Notably, the three monoterpenes aforementioned were undetected in a heat-inactivated protein mixture, thereby confirming the specificity of DcTPSb1. These findings collectively indicated that DcTPSb1 from \(D\). _chrysotoxum_ exhibits the capability to catalyze the formation of linalool, ocimene, and (-)-alpha-Terpineol. Consequently, due to its ability to produce multiple terpen, DcTPSb1 was appropriately classified as a multi-product terpene synthase.  
Figure 4: Different tissues of \(D\). _chrysotoxum_, include leaf, stem, root (**a**) and three developmental stages of flowers (**b**). (**c**) Expression levels of _DcTPSb1_ in different D. _chrysotoxum_ tissues 18S served as the internal standard. HB, bud stage; BK, half blooming stage; SK, full blooming stage. Each bar represents the mean \(\pm\) standard error of three independent biological replicates. The one-way ANOVA was performed to identify the differences among experimental groups, and different lowercase letters indicated significant differences \(p<0.001\).
# Vitamin D and Rehabilitation after Stroke: Status of Art
## 3 Genetic and Epigenetic of Vitamin D Pathway
### Gene Polymorphisms
#### 3.1.1 Cyp2r1  
Three independent studies reported that individuals with a GG or GA allele for the _CYP2R1_ rs10741657 polymorphism showed lower levels of 25(OH)D when compared to AA genotyped individuals [25; 26; 75].  
These dates were confirmed by a meta-analysis (total of 52,417 participants) that showed the association between risk of vitamin D deficiency and allele G of rs10741657 [29].  
Moreover, _CYP2R1_ rs10741657 together with rs12794714, rs1562902, and rs10766197 genetic polymorphisms are responsible for the variability in levels of 25(OH)D in elderly women with vitamin D insufficiency, and these genotypes do not allow the response to vitamin D3 supplementation [27]. Another study conducted on the Danish healthy population showed that higher serum 25(OH)D concentrations are detected in non-carriers of risk alleles of _CYP2R1_ rs10741657 and rs10766197, with respect to carriers of all risk alleles [28].  
Furthermore, the rs10766197 _CYP2R1_ polymorphism is associated with risk of multiple sclerosis (MS) especially in men with disease progression [34], with coronary artery disease incidence [26] and with ischemic stroke [33].
## 3 Automatic Recognition Algorithm of VLPT
### Unsupervised Clustering  
Unsupervised clustering is a machine learning task that aims to partition data samples into similar groups or clusters without requiring prior knowledge of labels or class information. Unlike supervised learning, where models rely on external guidance, unsupervised learning performs clustering by discovering the inherent structure and similarities within the data [38].  
In unsupervised clustering tasks, algorithms automatically identify and learn similarities between data samples. They assign similar data points to the same cluster while assigning dissimilar data points to different clusters. Unsupervised clustering finds wide applications in many fields and domains, including data mining, image analysis, text mining, recommendation systems, and more. Unsupervised clustering models belong to a class of models in unsupervised learning used to discover underlying categories or cluster structures in unlabeled datasets. Here are several common clustering models: K-means clustering [39], hierarchical clustering [40], density-based spatial clustering of applications with noise (DBSCAN) [41], spectral clustering (SC) [42], etc.  
The computational complexity of K-means clustering is relatively low, and it is computationally efficient. The trained model can be pre-trained and used to predict new samples. Spectral clustering is a clustering algorithm based on graph theory. It constructs a graph using similarity between data samples and performs dimensionality reduction and clustering based on graph features. Spectral clustering, in contrast to traditional clustering algorithms such as K-means, does not generate a model that can be used to predict new samples.  
Therefore, in this experiment, combining the idea of dimensionality reduction with spectral clustering, we use the K-means model to train and predict the reduced matrix. First, we perform dimensionality reduction and clustering on feature vectors by considering them as column vectors. Then, we preprocess and reduce a set of clear VLPT spectrograms, as shown in Figure 1b, which serve as training samples for the K-means model. We use the trained model to predict new samples.  
Unsupervised clustering model K-means clustering: Using selected cluster centers as initial centers, run the allocation and update steps of the K-means algorithm iteratively until the convergence condition is met. K-means++ is an improved version of the K-means algorithm that enhances the selection of initial cluster centers to improve the accuracy and stability of clustering results. Here are the steps of the K-means algorithm:  
1. Randomly select a data point as the first cluster center.
2. For each data point, calculate the squared distance to the cluster centers already selected and use it as a weight.
3. Based on distance weights, select the next cluster center with a higher probability. For each data point, normalize the weights, and then select the next cluster center probabilistically.
4. Repeat steps 2 and 3 until K cluster centers are selected.
## Materials and Methods
### Experimental System Design
Moreover, the borehole depth in the prototype is mostly about 60-100 m. The soil thickness that the model experiment simulated is 30 m. Consequently, the fluid flow rate is determined to be \(\left(V_{f}\right)_{m}=C_{Vf}\cdot\dfrac{30}{60\sim 100}\cdot\left(V_{f} \right)_{p}\approx\left(\dfrac{1}{2}\sim\dfrac{1}{3}\right)\dfrac{\left(V_{f} \right)_{p}}{30}\) where \(\left(V_{f}\right)_{m}\) is the flow flux in the model; \(\left(V_{f}\right)_{p}\) is the flow flux of heat exchanger in the field, and the value is mainly about \(0.6\sim 1.5\) m\({}^{3}\)/h varied in different circumstances. Therefore, the value of the flux in the model experiment is about \(0.67\sim 1.0\) L/min. In our model tests 1.0 L/min is chosen for the testing flux which adds up to 0.06 m\({}^{3}\)/h (heat flux in model test). Based on the Fourier number and analysis above, all the similarity constants are summarized in Table 1.  
Winter circulation system consists of a cryostat tank with inlet and outlet silicone hoses, connecting with U-tube in model soil (Figure 3), while the summer case is running by a water chamber (with heater inside), a water pump, and a flow meter (Figure 3). 2 cm-thick polyethylene foam is pasted on walls of model box for insulation material. It notes that the flux of circulation flow should be controlled to 1.0 L/min in this model according to the similarity ratio so that the same heat flux per surface area compared in the prototype can be promised. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Details** & **Similarity Ratio** \\ \hline \hline \multirow{4}{*}{Model} & Model box & \(C_{l}=1/30\) \\ \cline{2-3}  & Model soil & \(C_{l}=1/30\), \(C_{\gamma}=1\), \(C_{k}=1\), \(C_{\alpha_{o}}=1\) \\ \cline{2-3}  & U-shaped tube (the ground & Cross-sectional: \(C_{l_{1}}=1\) \\  & heat exchanger) & Vertical: \(C_{l_{2}}=1/30\) \\ \hline \multirow{4}{*}{Heat circulation system} & \multirow{4}{*}{Heat transfer parameters} & \(C_{\alpha}=1\), \(C_{V_{f}}=\left(C_{l_{1}}\right)^{2}\cdot C_{l_{2}}=1/30\) \\  & & \(C_{q}=1\) where \(q\) is the heat flux \\  & & \(q=2\pi r_{e}Q=-\dfrac{2\pi a_{p}ku_{f}}{r_{e}a\ln r_{e}-k}\) \\ \hline \multirow{2}{*}{Output} & Thermal response parameters & \(C_{\lambda}=1\), \(C_{p}=1/30\), \(C_{t}=C_{l}^{2}=\left(1/30\right)^{2}=1/900\) \\ \hline \hline \end{tabular} Note: \(\gamma\) is the bulk density; \(k\) is the coefficient of permeability, and \(\alpha_{o}\) is the coefficient of compressibility.  
\end{table} 
Table 1: Similarity ratios used in the model experiment.
## 3 Results and Discussion  
### Effects of Mixing Ratios on Biogas Production  
To assess the biogas yield potential from various combinations of FW and MA, the daily biogas yield and corresponding experimental and calculated biogas yield were evaluated (Figure 1). The daily biogas yield showed a similar profile across various ratios; it reached a peak after 1 to 2 days of digestion, then decreased significantly. Most of the biogas was produced in the first 7 days. The maximum and minimum values of daily biogas production were 213.25 and 40.07 mL/g VS for FW and MA as sole substrates, while the peak values were 165.43, 176.42, and 191.28 mL/g VS at FW/MA ratios of 1:3, 1:1, and 3:1, respectively. Biogas yield was accompanied by the degradation of organic matter in the substrate. These results confirmed that FW was rich in easy biodegradable substrates (protein and carbohydrate), which exhibited efficient biogas production. In contrast, the lower biogas yield seen for MA may be due to limitations caused by the rigidity of MA cell walls and the presence of cellulose [18]. This could be attributed to rich biodegradable substrates in FW, which were more easily hydrolyzed than the refractory substances (cellulose) surrounded by the rigid cell wall in MA [18]. Previous studies have also shown that cellulose content negatively correlates with biogas potential during anaerobic digestion [33; 34]. Overall, the higher proportion of FW led to a greater biogas yield.  
To further compare the co-digestion performance of FW and MA, the calculated biogas yield (CBY) and biogas potential improvement (BPI) were calculated and evaluated. Interestingly, the cumulative experimental biogas yield (EBY) was notably higher than the CBY at various ratios (Figure 1b). A 2.04-26.86% improvement in biomass yield was observed in the co-digestion system of FW and MA, with the higher BPI observed at the mixing ratio of FW/MA = 1:1 (9.03%) and FW/MA = 1:3 (26.86%). This suggests a positive co-metabolism (synergistic) effect for the co-digestion of FW and MA. Similarly, [PERSON] et al. [35] evaluated the optimal mixing ratio for the co-digestion of FW and MA (Spirulina) and reported the highest co-digestion synergy (8.7%) observed with a mixture of 75% MA and 25% FW. The performance enhancement obtained by the co-digestionsystem could be explained by trace elements (Cu, Fe, Zn, Ca, etc.) being released after algal cell wall hydrolysis, where they subsequently serve as nutrients for the microbial communities [36]. In addition, co-metabolism was observed for MA and lipid waste [37], which could potentially enhance lipid degradation derived from FW. Taken together, these results further confirmed that MA are an excellent substrate for co-digestion with FW to achieve efficient waste disposal [32; 38].
## 2 Materials and Methods
### Measurement of Thiobarbituric Acid-Reactive Substances (TBARS)  
TBARS were measured following the method by [PERSON] et al. [16] with modifications. Briefly, 5 g of each sample was homogenized (SHG-15D, SciLab Co. Ltd., Seoul, Korea) in 12.5 mL of TCA solution containing 20% trichloroacetic acid in 2 M phosphoric acid and adjusted to 25 mL with 3 rd-distilled water. Next, homogenate samples were centrifuged at 1500 rpm for 10 min, and the upper layer was collected and filtered using filter paper No. 2 (55 mm; Advantech, Tokyo, Japan). The supernatant was then mixed with a 0.005 M thiobarbituric acid solution at a ratio of 1:1 (\(v/v\)) and incubated at 95 \({}^{\circ}\)C for 30 min in a water bath (JSWB-22 TL, JS Research Inc., Gongju City, Korea). The samples were then left to cool to room temperature. Samples (200 \(\mu\)L) and the blank group (3 rd-distilled water) were placed on a 96-well plate and measured at 530 nm using a SPECTROstar Nano Microplate Reader (S/N601-0618, BMG Labtech Ltd., Ortenberg, Germany). Malondialdehydehehyde (MDA) bis (dimethyl acetal) was used as the standard.
## 4 Results
### Performance Analysis by Video Type
#### 4.2.8 Out of View (VE)  
This group comprises images in which the drone is not visible within the scene, presenting a particular challenge for detection models. If a model indicates the presence of a drone in these images, it incurs an error known as a false positive. In this case, the model's efficiency is assessed using the True Negative Rate (TNR), as the emphasis is on the models' ability to refrain from detecting non-existent drones. This group contains a total of 2022 images.  
ViTDet is the best-performing model in this group, demonstrating a superior ability to avoid false detections when no target is present (Table 11). This characteristic enhances its reliability in scenarios where minimizing false positives is crucial. In this case, the example illustrates a 0% match if a prediction is made, as the model attempts to compare the predicted bounding box with one that does not exist. Figure 11 presents an example of a result in this category.
# An Improved Routing Approach for Enhancing QoS Performance for D2D Communication in B5G Networks
## 4 Results and Discussion
### Energy Consumption  
Figure 9 illustrates energy variation consumed by the network devices for the MBLCR and its counterpart routing schemes throughout the network simulation time. The simulation results show that the MBLCR performs better than the other routing schemes owing to the reason that the MBLCR selects the relay devices with the exploitation of low ETX value acquired from the probe packets. Besides, the MBLCR evaluates the link quality value with the exploitation of the ETX parameter that can reduce the adverse flooding effect of RREQ messages in the wireless network. Thus, the ETX parameter minimizes the probability of frequent route failures, resulting in lower energy consumption in the route computational procedure. Nevertheless, as the device speed increases, the energy consumed by the network devices also increases. Figure 9 shows that energy consumption increases from 54.264 mAh to 57.064 mAh for the MEQSA-OLSRv2, from 55.098 mAh to 58.247 mAh for the MP-OLSRv2, from 53.058 mAh to 55.131 mAh for EMBLR, and from 52.164 mAh to 54.1245 mAh for the MBLCR when the device speed elevates from 10 m/s to 60 m/s.  
Figure 8: Packet drops with various device speed.
# Generating Synthetic Training Data for Supervised De-Identification of Electronic Health Records
## 4 Results
### Does the Synthetic Data Resemble the Properties of Real Data?
#### 4.1.1 Are the Synthetic PHI Annotations Well-Formed and Realistically Represented?  
The syntactic quality of PHI annotations is good across all corpora. Between 97% and 99% of the annotations were well-formed (Table 5). We observe that the LSTM-based generators are slightly more consistent than the GPT-based generators. With respect to the distribution of PHI types, we observe that LSTM-based corpora stay closer to the real distribution (Figure 5). The GPT-2 model with beam-search decoder shows a pronounced bias for \"Date\" while the GPT-2 model with p-sampling boosts some of the rare PHI tags. Additionally, we note that the GPT-p corpus has substantially more PHI annotations (105 k) than the other corpora (24 k-33 k, Table 4). We analyze the impact of this in context of the downstream task (Section 4.2). A detailed report on the PHI frequencies per corpus can be found in Appendix B.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **NUT**[10] & **LSTM-p** & **LSTM-Temp** & **GPT-p** & **GPT-Beam** \\ \hline Tokens & 445,586 & 976,637 & 977,583 & 1,087,887 & 1,045,359 \\ Vocabulary & 30,252 & 23,052 & 29,485 & 12,149 & 8026 \\ PHI instances & 17,464 & 32,639 & 31,776 & 105,121 & 24,470 \\ Sentences & 43,682 & 70,527 & 72,140 & 128,773 & 83,634 \\ Avg. tokens & 10.2 & 13.8 & 13.6 & 8.4 & 12.5 \\ per sentence & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary statistics of the synthetic corpora in reference to a real corpus (NUT).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **LSTM-p** & **LSTM-Temp** & **GPT-p** & **GPT-Beam** \\ \hline Well-formed PHI tags & 99.97\% & 99.89\% & 97.75\% & 98.84\% \\ Malformed PHI tags & 0.03\% & 0.11\% & 2.25\% & 1.16\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: A comparison of PHI tag consistency across synthetic corpora.
## 3 Background
### Security Requirements
#### 3.3.6 Access Control  
Access control is comparable to the police in that it ensures that every participating node performs its functions according to its roles and privileges. An efficient system must have an access control panel.
# Edge-Aided Reliable Data Transmission for Heterogeneous Edge-IoT Sensor Networks
## 3 System Design
### Framework  
We propose an edge-aided data transmission scheme for sensor networks. Figure 2 shows the framework. We employ the edge servers to improve the transmission efficiency in two ways: data forwarding and data retransmissions.  
**Data forwarding.** When an edge server overhears a packet transmission nearby, it first finds out the destination nodes of the current packet. Then, the edge server estimates the potential benefits if it transmits this packet. On one hand, the destination nodes have more chance to receive the target packets; On the other hand, the non-destination nodes in the edge's communication range experience severe interference. The estimation of the potential benefits must consider both the improved gains and possible costs on all nodes in its communication range. The details of the forwarding mechanism are discussed in Section 3.3.  
**Retransmissions.** The edge servers overhear nearby transmissions and identify those repeating retransmissions. When some packets are retransmitted multiple times, it is highly possible that the link is temporarily down or is of poor quality. In such cases, the edge nodes may adaptively help retransmit the packets to the target destination node(s). The details of the retransmission mechanism are discussed in Section 3.4.  
With the forwarding and retransmission schemes, the efficiency and reliability of data transmissions in sensor networks are significantly improved.
## 2 Materials and Methods
### Evaluation of Antioxidant Activity
#### 2.4.3 Free Radical Scavenging Ability by DPPH Assay  
The antioxidant activity of _G. hederacea_ extracts was determined based on the free radical scavenging activity of the DPPH assay (2,2-diphenyl-1-picrylhydrazyl radical), which was modified slightly from the method described by [PERSON] et al. (2016) [46]. The working solution was prepared by preparing a methanolic solution of DPPH (0.1 mM). An aliquot of 2850 \(\mu\)L of this solution was mixed with 150 \(\mu\)L of the sample, the standard (Trolox) under different concentrations or the blank (methanol). The reaction mixture was thoroughly mixed before incubation in the dark for 30 min at room temperature. After that, the absorbance at 517 nm was measured using a Varian Cary 300 bio UV-VIS spectrophotometer (Agilent, Santa Clara, CA, USA). Low absorbance of the reaction mixture indicated high free radical scavenging activity. The experiment was repeated three times, and the findings presented are averages of the three values. The scavenging activity was estimated based on the percentage of DPPH radical scavenged according to the following equation:  
\[\text{DPPH inhibition \%= (B-S/B)\times 100} \tag{4}\]  
where B is the absorbance of the blank and S is the absorbance of the sample.