esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
Endodontic pretreatment aims to eliminate previous filling materials and infected dentin responsible for the persistence of bacterial biofilms within the root canal system [19,20]. These pathogenic microorganisms can adhere to root canal surfaces or infiltrate denitnal tubules, particularly when initial instrumentation is inadequate or fails to reach all \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{10}{c}{**(a)**} \\ \hline \multirow{3}{*}{**Reciproc Blue**} & \multicolumn{3}{c}{**Single Cone (\(n\) = 11)**} & \multicolumn{3}{c}{**Guttafusion (\(n\) = 10)**} & \multicolumn{3}{c}{**Difference**} \\ \cline{2-10}  & **Mean** & **SD** & **95\% CI** & **Mean** & **SD** & **95\% CI** & **Mean** & **_p-Value_ & **95\% CI** \\ \hline RB40 & 6.2 & 11.9 & \(-\)1.8, 14.2 & 7.3 & 9.3 & 0.6, 13.9 & \(-\)1.1 & 0.822 & \(-\)10.9, 8.8 \\ RB50 & 5.1 & 10.7 & \(-\)2.1, 12.3 & 5 & 8 & \(-\)0.8, 10.7 & 0.1 & 0.977 & \(-\)8.6, 8.8 \\ \hline \multicolumn{10}{c}{**(b)**} \\ \hline \multirow{3}{*}{**Reciproc Blue**} & \multicolumn{3}{c}{**Single Cone (\(n\) = 11)**} & \multicolumn{3}{c}{**Guttafusion (\(n\) = 10)**} & \multicolumn{3}{c}{**Difference**} \\ \cline{2-10}  & **Mean** & **SD** & **95\% CI** & **Mean** & **SD** & **95\% CI** & **Mean** & **_p-Value_ & **95\% CI** \\ \hline RB40 & 3.1 & 5.2 & \(-\)0.4, 6.5 & 3 & 4.5 & \(-\)0.3, 6.2 & 0.1 & 0.958 & \(-\)4.3, 4.6 \\ RB50 & 2.4 & 4.8 & \(-\)0.8, 5.7 & 2 & 3 & \(-\)0.2, 4.1 & 0.5 & 0.798 & \(-\)3.2, 4.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: (**a**) Percentage of remnant area after retreatment with Reciproc Blue (RB) 40 and 50, by obturation technique. (**b**) Percentage of remnants volume after retreatment with Reciproc Blue (RB) 40 and 50, by obturation technique. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{10}{c}{**All**} & \multicolumn{3}{c}{**Single Cone**} & \multicolumn{3}{c}{**Guttafusion**} & \multicolumn{3}{c}{**Difference**} \\ \cline{2-10}  & \(n\) & \(r\) & \(p\)-Value & \(n\) & \(r\) & \(p\)-Value & \(n\) & \(r\) & \(p\)-Value & \(n\) & \(r\) & \(p\)-Value \\ \hline After instr. & 23 & 0.35 & 0.104 & 11 & 0.21 & 0.531 & 12 & 0.47 & 0.120 & 23 & \(-\)0.26 & 0.538 \\ After RB40 & 21 & 0.59 & 0.005 * & 11 & 0.89 & \(<\)0.001 * & 10 & 0.10 & 0.776 & 21 & 0.78 & 0.012 * \\ After RB50 & 20 & 0.83 & \(<\)0.001 * & 11 & 0.96 & \(<\)0.001 * & 9 & 0.02 & 0.961 & 20 & 0.94 & \(<\)0.001 * \\ After RB40 (\%) & 21 & 0.63 & 0.002 * & 11 & 0.94 & \(<\)0.001 * & 10 & 0.12 & 0.734 & 21 & 0.81 & 0.002 * \\ After RB50 (\%) & 21 & 0.80 & \(<\)0.001 * & 11 & 0.99 & \(<\)0.001 * & 10 & 0.37 & 0.297 & 21 & 0.62 & \(<\)0.001 * \\ \hline \hline \end{tabular}  
* \(p\)-value \(\leq\) 0.05. Abbreviations: CBCT, cone beam computed tomography; RB, Reciproc Blue.  
\end{table} 
Table 7: Linear correlation between areas (mm\({}^{2}\)) measured on the X-rays and volume (mm\({}^{3}\)) obtained through digital reconstruction of CBCT images, overall and by obturation technique.
## 2 Related Concepts
### Sharding Protocol  
Network sharding, transaction sharding, and state sharding are the state-of-the-art mechanisms that perform blockchain sharding protocol in the modern world [46]. Network sharding divides the entire blockchain network into several shards so that different shards can process part of the transactions in the entire blockchain at the same time. Transaction sharding assigns transactions to different shards and allows them to be executed concurrently. State sharding separates and saves the entire ledger in shards, which can reduce the storage burden of network nodes.  
Sharding protocol can be decomposed into the following stages: shard configuration, intra-shard consensus, cross-shard protocol, and reconfiguration [31]. The shard configuration stage determines the shards to which the nodes belong and the transactions that each shard will process [22; 34; 36]. After completing the previous step, the validator nodes in the same shard pass messages according to the internal consensus protocol to reach a consensus on the entire shard. Researchers divide intra-shard consensus into two types: PoW-based [28; 29] and BFT-based [7; 22; 34; 35]. The cross-shard protocol uses transaction-related shards as the basic unit for processing cross-shard transactions to generate blocks that contain cross-shard transaction state transitions [34; 35]. The reassignment step shuts down validator nodes after one shard epoch and swaps to other shards to maintain the integrity of each shard and avoid attacks from adversaries that slowly adapt [22; 35].  
Although experimental setups or methodologies for validating different technologies may vary, throughput and latency remain common metrics for evaluating protocol performance [31].
# A Four-Port Dual-Band Dual-Polarized Antenna for Ku-Band Satellite Communications
###### Abstract  
This paper presents the development of a four-port dual-band dual-polarized antenna for transmitting/receiving (Tx/Rx) applications in Ku-band satellite communications. The antenna consists of two antenna elements sharing a common radiating aperture, a low-band element formed by an \(L\)-probe proximity-fed square-ring radiator for operation at the Rx band of 10.7-12.75 GHz, and a high-band element realized by a stacked-patch radiator for operation at the Tx band of 13.75-14.5 GHz. Within a compact multilayer structure, the antenna achieves wide dual-band operation, with each band having the ability to simultaneously operate with dual polarization. An antenna prototype is fabricated and tested to verify its performance. The experimental results show that the proposed antenna achieves an impedance bandwidth of 9.28-12.96 GHz (13.21-15.32 GHz), an isolation value between two orthogonal polarizations of 12 dB (12.4 dB), and a peak gain of 6.63 dBi (5.42 diBi) at the low band (high band). Tx/Rx isolation of more than 14 dB is achieved in both the Rx band and Tx band.  
Keywords:dual-band antenna; dual-polarized; Ku-band; satellite communications; shared aperture +
Footnote †: journal: Applied
# Graphene Quantum Dots Doped PVDF(TBT)/PVP(TBT) Fiber Film with Enhanced Photocatalytic Performance
## 2 Experimental Section
### Characterization  
The microstructure of the resulting fiber film was measured with a scanning electron microscope (SEM, JSM-6390, JEOL, Tokyo, Japan). The morphology and size of the GQDs were observed using a transmission electron microscopy (TEM, H-7650, Hitachi, Tokyo, Japan). The absorbance of the reaction solution can be characterized by an ultraviolet-visible spectrophotometer (UV-Vis 3100, Mapada, Shanghai, China). Among them, the scanning wavelength range was 200 nm to 800 nm, and the resolution was 1 nm. The absorption spectrum of the prepared photocatalyst was analyzed using a T9 type UV-Visible spectrophotometer (DRS, T9, Persee, Beijing, China) with a scanning wavelength range of 200 nm to 800 nm. An X-ray diffractometer (XRD, Rigaku Smart Lab (3 kW), Hitachi, Japan.) was used to confirm the substance elements in the sample. The Brunauer-Emmett-Teller (BET, Autosorb iQ3, Quantachrome, Shanghai, China) measures the specific surface area of a photocatalyst by N\({}_{2}\) adsorption-desorption. The fluorescence characteristics of GQDs were measured using a fluorescence spectrometer (PL, F-4600, Hitachi, Tokyo, Japan). X-ray photoelectron spectroscopy (XPS, EscaLab 250 Xi, Thermo Fisher Scientific, Waltham, MA, USA) was measured at a 01 emission angle using a monochromatic Al Ka source (Ephoton = 1486.6 eV), 10 mA filament current, and 14.7 keV filament voltage source energy calibration.
# HCLE: Hybrid Clustering Approach for Extending WBAN Lifetime
## 3 Preliminaries
### Performance Metrics
#### 3.4.3 Energy Consumption  
Energy consumption is the most important parameter that needs to be considered during the design and development of WBANs. The analysis of residual energy is essential because the energy consumption of a single node directly affects the stability period and reliability of the network. Due to the limited energy resources of the sensor nodes in WBANs, it is vital to optimize energy consumption to prolong the operational lifetime of the network. The first-order radio model [63] is often used to analyze energy consumption in WBANs, which is explained in Section 3.2.
# Accumulative Roll Bonding--A Review
## 5 Computational Studies
Their simulations validated the solution framework and confirmed the development of a through thickness gradient of texture and anisotropy in an ARBed sheet after two cycles. A principal challenge in FE modeling of ARB process is growing the number of elements by a factor of two in each ARB cycle due to a large thickness reduction during each rolling cycle. The distortion of the mesh is also challenging and needs to be fixed. Besides the efforts made on FE modelling of ARB process, some other studies were focused on predicting the mechanical properties of ARBed materials. [PERSON] and [PERSON] [153] developed a grain refinement and strengthening model for oxide-free Al alloys processed by SPD to address ARBed Al alloys. Their model accurately predicted the grain refinement and hardness of an ARBed alloy. [PERSON] et al. [154] simulated the evolution of material strength of an ARB-processed titanium alloy that was then validated using experimental data exhibiting an acceptable fit in whole range of ARB cycles. To further validate the model for a wider range of materials, five different materials were also investigated and the results confirmed the ability of the proposed method in predicting the strengthening behavior of all the considered materials. [PERSON] and [PERSON] [155] proposed an analytical model for necking and fracture of the hard layer during the ARB substantiating that the critical strain for necking and fracture increased with increasing ARB ratio, strength coefficient ratio  
Figure 23: (**a**) equivalent strain through sheet thickness for varied friction coefficients [144], (**b**) the equivalent plastic strain contours obtained at 300 \({}^{\circ}\)C for different roller/sheet friction coefficients [147]. and work-hardening exponent of the hard phase while it decreased with increasing the work-hardening exponent of the soft phase.
# Two-Step Classification with SVD Preprocessing of Distributed Massive Datasets in Apache Spark
## 4 Implementation
### Analysis Cases  
For each segment of both datasets, a series of classification analyses was performed.  
* **Decision trees** were optimized based on the depth of the trees with values ranging between 2 and 30.
* **Random forest** was optimized in terms of the number of trees in the forest with values ranging between 1 and 60, having a step equal to two and maximum tree height equal to 10.
* **Logistic regression** was implemented for binomial and multinomial.
* **Gradient-boosted tree** was optimized based on the depth of the trees with values ranging between 2 and 4 and the total number of trees equal to 150. It must be noted that this type of classifier was only used with the Higgs dataset because it is considered as a binomial classifier.
* **Multilayer perceptron** with two, three, and four levels of hidden layers. For the case of two levels, the numbers of nodes were \(15-14\), \(20-15\), \(25-20\) respectively, whereas for case of three levels the numbers of nodes were \(25-20-15\), \(35-25-20\), \(40-30-20\), and for the case of four levels \(30-25-20-15\), \(35-30-25-20\), and \(40-35-30-25\). Moreover, the number of epochs was set equal to 1000.
* **One-Vs-Rest** was only used with the PAMAP dataset.
## 4 Experiments and Results  
To evaluate the performance of the proposed LDE model, we used GENIA [18], ACE 2004 [19], ACE 2005 [20] nested NER data, and CoNLL 2003 English [21] flat NER data (details in Appendix A, Tables A2-A6). These datasets were selected as they represent the most widely adopted benchmarks in nested and flat NER research communities. For nested NER, GENIA, ACE 2004, and ACE 2005 are considered the de facto standard evaluation datasets, as nested NER research has received significantly less attention compared to flat NER, and the available comparative studies predominantly utilize these specific datasets for evaluation. For flat NER evaluation, CoNLL 2003 English serves as the most established benchmark dataset in the field, ensuring fair comparison with existing methods across both nested and flat NER paradigms. The label descriptions for the LDE model were written by referring to the named entity tags in each dataset as long sentences containing various words.
# Monitoring of Possible Activities of Yangsan Fault Zone Using GNSS
## 2 Materials  
### GNSS Data  
Table 1 shows the list of domestic GNSS stations used in this study for data, and a global network of IGS (International GNSS Service) stations is summarized in Table 2. The location maps of stations are plotted in Figures 2 and 3 (domestic and IGS stations, respectively). A total of 24 domestic observation stations were collected from 17 July 2018, to 31 December 2021, which was obtained from the National Geographic Information Institute (NGII, 15 stations) and Korea Institute of Geoscience and Mineral Resources (KIGAM, 9 stations). In addition, the IGS stations were selected to be evenly spread all around the world (a total of 25 stations), which were used as a reference to be aligned with the global reference frame by minimally constraining to the known coordinates. The GNSS data are provided in a Receiver INdependent EXchange (RINEX) format for 24 h at 30-second intervals.
## 3 Modeling Approaches
### Evaluation of the Models' Performance  
#### 3.7.1 Statistical Index-Based Evaluations  
When mapping landslide susceptibility, accurately evaluating the model's performance is crucial for ensuring the reliability of the resulting maps. In this study, we chose a set of statistically based evaluation metrics to compare the predictions of different models, including positive and negative predictive rates, _sensitivity_, _specificity_ and _accuracy_. The positive and negative predictive rates (_PPR_ and _NPR_) represent the accuracy of predicting landslide and non-landslide events, respectively, based on the classification of the pixels. _Sensitivity_ measures the accuracy of classifying landslide pixels correctly, while _specificity_  
Figure 6: Structure of the Adaptive Boosting ensemble model.  
measures the accuracy of classifying non-landslide pixels correctly. _Accuracy_ represents the overall correctness of the resulting models for classifying both landslide and non-landslide pixels. They can be expressed by the following equations:  
\[\textit{Positive predictive rate}=\frac{\textit{TP}}{\textit{TP}+\textit{FP}} \tag{6}\]  
\[\textit{Negative predictive rate}=\frac{\textit{TN}}{\textit{TN}+\textit{ FN}} \tag{7}\]  
\[\textit{Sensitivity}=\frac{\textit{TP}}{\textit{TP}+\textit{FP}} \tag{8}\]  
\[\textit{Specificity}=\frac{\textit{TN}}{\textit{TN}+\textit{FP}} \tag{9}\]  
\[\textit{Accuracy}=\frac{\textit{TP}+\textit{TN}}{\textit{TP}+\textit{TN}+ \textit{FP}+\textit{FN}} \tag{10}\]
## 3 Results and Discussion
### Electrochemical Studies
Consequently, the inhibitor Na2 HPO4 either alone or in combination with MBT provides the best protection to mild steel against corrosion in WSS solution (Table S2). Analyzing the evolution of the EIS fitting parameters \(R_{\rm ct}\), \(C_{\rm dl}\) of the mild steel panels after their subjection in 3.5 wt % NaCl solution for 96 h in the presence or absence of corrosion inhibitors (Figure 23), it can be noticed that that the system including both the inhibitors Na2 HPO4 and MBT reveals the highest \(R_{\rm ct}\) values together with the lowest \(C_{\rm dl}\) values for all the exposure time intervals. Therefore, the combination of both inhibitors Na2 HPO4 and MBT performs the best protection to mild steel against corrosion in 3.5 wt % NaCl solution (Table S3). Furthermore, it appears that all the systems including inhibitors exhibit higher \(R_{\rm ct}\) values compared to the _Steel-NaCl-blank_, indicating effective corrosion protection to mild steel. Figure 23: The evolution of the mean values of the EIS fitting parameters as a function of time for mild steel panels after submission in 3.5 wt % NaCl: (**a**) \(R_{\rm ct}\), (**b**) \(C_{\rm dl}\). Figure 18:0 The evolution of the mean values of the EIS fitting parameters as a function of time for mild steel panels after submission in WSS: (**a**) \(R_{\rm ct}\), (**b**) \(C_{\rm dl}\). The tabulated values of \(\eta\)(%) derived from Equation (1) after 24, 48, 72, and 96 h of mild steel samples submission either in WWS solution or into 3.5 wt % NaCl solution, in the presence or absence of inhibitor, are illustrated in Tables S2 and S3, respectively. The results reveal that for all the time intervals, the highest values of \(\eta\)(%) appear for the systems including both inhibitors Na\({}_{2}\)HPO\({}_{4}\) and MBT, denoting enhanced corrosion inhibition to mild steel. Furthermore, according to the obtained \(\eta\)(%) values, it may be remarked that the system containing the Na\({}_{2}\)HPO\({}_{4}\) provides also improved protection to mild steel against corrosion. Finally, it should be noted that for the _Steel-WSS-MBT_ sample, the \(\eta\)(%) values could be calculated for 48, 72, and 96 h since the corresponding \(R_{\rm ct}\) values are lower than those of the _Steel-WSS-blank_ sample. The same result was revealed for the _Steel-WSS-Na\({}_{2}\)HPO\({}_{4}\)_ sample after 96 h of subjection in WSS solution as well as for the _Steel-NaCl-MBT_ after 48 h of exposure to 3.5 wt % NaCl solution. These outcomes clearly denote that the presence of the inhibitor MBT into the WWS solution cannot provide adequate corrosion protection to mild steel after 48 h.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Integron Features** & **Resistance/Mobile Integons** & **Chromosomal Integons** \\  & **(Example of Class 1 Interon)** & **(Example of IntVchA)** \\ \hline Genetic context & Mobile genetic elements & Chromosome \\  & (transposons, plasmids, ICEs) & _intI1_ (_intI4_) \\ Integrase gene & _intI1_ & _intI4_ (_intI4_) \\ Number of gene cassettes & Dozens & Hundreds \\ Nature of gene cassettes & Antibiotic resistance & Unknown functions, metabolic functions, antibiotic resistance (less frequent) \\ Cassette recombination sites & _attC_ (variable in sequence and size) & VCR (\(\sim\)120-bp repeat) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main differences among resistance/mobile and chromosomal integrons. lies in the efficient and elegant modulation of gene cassette and integrase expression [13; 16], which is tightly coupled with bacterial cell physiology [13; 17; 18; 19; 20; 21; 22]. The subsequent integration events lead to an in tandem stacking of gene cassettes, of which only those closest to Pc promoter are expressed, while more distal cassettes remain silenced. Considering that gene cassettes can be independently mobilized by the integrase, the inserted cassette array can be modified by excisions, reassortments, and novel acquisition events [23]. In this case, silenced or new cassettes can be recruited to the first position, allowing bacteria to rapidly adapt to novel environmental challenges, such as multiple-antibiotic treatment regimes, without causing a fitness cost burden to the cell. Consequently, in a bacterial population harboring integrins with different cassette arrangements, any new variant will promptly express genes that could be involved with advantageous phenotypes, such as multidrug resistance [24]. Therefore, the variable and diverse cassette content of an integron reflects the retrospect of adaptive events and represents a reservoir of functions that can be recalled on demand [13]. This review aimed to provide an up-to-date overview of the fine-tuning regulation of integrase and gene cassette expression at both transcriptional and translational levels, and of the intricate relationship between integrins and cell physiology components that feature these platforms as powerful tools for genome innovation and bacterial adaptation.
# Concept Drift Detection Based on Deep Neural Networks and Autoencoders
## 3 Methodology
### Concept Drift Detection
```
1:The pretrain output matrix \(a^{(L)}=[a_{1}^{(L)},a_{2}^{(L)},\ldots,a_{m}^{(L)}]\), the output stream \(o^{(L)}=[o_{1}^{(L)},o_{2}^{(L)},\ldots,o_{l}^{(L)},\ldots]\) obtained from process stream data, the number of neurons in the autoencoder hidden layer \(L_{A}\), the loss function \(\mathcal{L_{A}}\), the learning rate \(\eta_{A}\), the optimizer \(optim_{2}\), the autoencoder iterations \(A_{p}\). 2:Drift position. 3:Process train data \(a^{(L)}\) and test data \(o^{(L)}\). 4:for\(epoch=1\) to \(A_{p}\)do
5:for\(a_{i}^{(L)}\) in \(a^{(L)}\)do
6: Calculate the prediction with \(\hat{a_{i}}^{(L)}\). 7: Calculate the loss \(\mathcal{L_{A}}(a_{i}^{(L)},\hat{a_{i}}^{(L)})\) between prediction \(\hat{a_{i}}^{(L)}\) and true data \(a_{i}^{(L)}\). 8: Loss backward and update all the parameters. 9:endfor
10:endfor
11:Calculate the train reconstruction error \(e_{a}\) with \(a^{(L)}\). 12:Calculate the mean \(\mu\) with Equation (1) and variance \(\sigma\) to obtain _lower_threshold_ and _upper_threshold_ with \(e_{a}\) with Equation (2) and Equation (3). 13:\(F\_list=[]\)
14:for\(i,o_{i}^{(L)}\) in \(o^{(L)}\)do
15: Calculate the test reconstruction error \(e_{s}\). 16:if\(e_{s}\leq\textit{lower\_threshold}\) or \(e_{s}\geq\textit{upper\_threshold}\)then
17:\(F\_list.\)append(i)
18:endif
19:endfor
```  
**Algorithm 4** Concept drift detection  
The time complexity of the DNN+AE-DD during the model pretraining phase (as shown in Figure 3) is \(O\left(T\times N\times\sum_{l=1}^{L}n_{l-1}\times n_{l}\right)\), where \(T\) represents the number of iterations, \(N\) denotes the size of the data, \(n_{l-1}\) refers to the number of neurons in the previous layer, \(L\) is the number of layers in the network, and \(n_{l}\) represents the number of neurons in the current layer. The time complexity for the autoencoder in Figure 5 is \(O(T\times N\times d\times h)\), where \(d\) represents the input dimension and \(h\) denotes the hidden layer dimension. The overall time complexity of the DNN+AE-DD is \(O(L\times H^{2}\times N)\), and the space complexity is \(O(L\times H^{2})\), where \(H\) represents the number of neurons per layer.