esa-sceva/satcom-chunk-collection huggingface dataset:
The Effect of Taper and Apical Diameter on the Cyclic Fatigue Resistance of Rotary Endodontic Files Using an Experimental Electronic Device  
[PERSON]  
1 Department of Stomatology, Faculty of Medicine and Dentistry, University of Valencia, 46010 Valencia, Spain; fausy@uves (V.F.-L.); [EMAIL_ADDRESS] (N.H.K.); celia.ruiz@uves (C.R.-S.); [EMAIL_ADDRESS] (I.F.-M.); [EMAIL_ADDRESS] (V.F.-M.)  
[PERSON]  
1 Department of Stomatology, Faculty of Medicine and Dentistry, University of Valencia, 46010 Valencia, Spain; fausy@uves (V.F.-L.); [EMAIL_ADDRESS] (N.H.K.); celia.ruiz@uves (C.R.-S.); [EMAIL_ADDRESS] (I.F.-M.); [EMAIL_ADDRESS] (V.F.-M.)  
[PERSON]  
1 Department of Stomatology, Faculty of Medicine and Dentistry, University of Valencia, 46010 Valencia, Spain; fausy@uves (V.F.-L.); [EMAIL_ADDRESS] (N.H.K.); celia.ruiz@uves (C.R.-S.); [EMAIL_ADDRESS] (I.F.-M.); [EMAIL_ADDRESS] (V.F.-M.)  
[PERSON]  
1 Department of Stomatology, Faculty of Medicine and Dentistry, University of Valencia, 46010 Valencia, Spain; fausy@uves (V.F.-L.); [EMAIL_ADDRESS] (N.H.K.); celia.ruiz@uves (C.R.-S.); [EMAIL_ADDRESS] (I.F.-M.); [EMAIL_ADDRESS] (V.F.-M.)  
[PERSON]  
2 Department of Endodon  
that the fracture incidence of NiTi endodontic rotary files increased in curved root canal systems, possibly related to the apical diameter [19] and taper of the NiTi endodontic rotary files [20], mainly at the point of the maximum curvature. This is because large file tapers accumulate a greater amount of internal stress during stress-compression cycles when flexed to accommodate the curvature of the root canal system [21]. However, an increase in the diameter of the NiTi endodontic rotary files can contribute to increasing the resistance to torsional fracture [5].  
In addition, many cyclic fatigue testing devices have been proposed in the literature; however, they differ mainly in the ability to reproduce the pecking movements of the operator statically or dynamically during the root canal shaping. Dynamic cyclic fatigue testing devices are recommended because they faithfully reproduce the operator pecking movements; moreover, [PERSON] et al. reported that the pecking movements performed by dynamic cyclic fatigue testing devices extended the fatigue lifetime of the NiTi endodontic rotary files, compared to static cyclic fatigue testing devices [22]. Therefore, a dynamic cyclic fatigue testing device was used in this study.  
The aim of this study was to analyze and compare the effect of the taper and apical diameter on the dynamic cyclic fatigue resistance of NiTi endodontic rotary files, with a null hypothesis (H0) stating that the taper and apical diameter would not affect the resistance of NiTi endodontic rotary files in dynamic cyclic fatigue.
## 3 Results
### Economic Analysis of the Crop Production  
An economic analysis of the value of the different treatments to the farmer is presented in Table 4. The analysis aimed to compare the total cost of production up to the point of harvesting and sale of the grain and plant biomass. The net profit and the benefit-cost ratio was used to show the profitability of the different treatments. The combined treatment with farmyard manure and gypsum (FYM + G50) was the most profitable treatment for wheat, with a PKR 97,165 (USD 433) profit and a benefit-cost ratio of 2.32 in year 3. For maize, the most profitable treatment was green manure and gypsum (GM + G50), with a profit of PKR 60,969 (USD 272) and a benefit-cost ratio of 1.83 in year 3. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Parameters** & **Units** & **Control** & **G100** & **FYM + G50** & **PM + G50** & **GM + G50** \\ \hline pH & - & 9.1 \(\pm\) 0.1 a & 8.5 \(\pm\) 0.2 b & 8.2 \(\pm\) 0.3 ab & 8.3 \(\pm\) 0.3 ab & 8.4 \(\pm\) 0.5 ab \\ EC & dS m\({}^{-1}\) & 5 \(\pm\) 0.4 a & 4 \(\pm\) 0.5 b & 4 \(\pm\) 0.9 b & 4 \(\pm\) 0.6 b & 4 \(\pm\) 0.6 b \\ SAR & - & 18 \(\pm\) 4 a & 12 \(\pm\) 2 a & 12 \(\pm\) 1 a & 12 \(\pm\) 2 a & 12 \(\pm\) 2 a \\ OM & \% & 0.4 \(\pm\) 0.1 c & 0.7 \(\pm\) 0.3 b & 1 \(\pm\) 0.1 a & 1 \(\pm\) 0.2 a & 1 \(\pm\) 0.2 a \\ CEC & cmol kg\({}^{-1}\) soil & 7.7 \(\pm\) 0.5 c & 8.3 \(\pm\) 0.1 b & 8.5 \(\pm\) 0.5 a & 8.7 \(\pm\) 0.6 a & 8.7 \(\pm\) 4 a \\ TN & g kg\({}^{-1}\) & 0.04 \(\pm\) 0.1 c & 0.09 \(\pm\) 0.04 ab & 0.1 \(\pm\) 0.5 b & 0.2 \(\pm\) 0.5 a & 0.1 \(\pm\) 0.07 b \\ MBC & mg kg\({}^{-1}\) & 65.1 \(\pm\) 2 c & 90.2 \(\pm\) 4 b & 217.3 \(\pm\) 3 a & 206.1 \(\pm\) 4 a & 208.3 \(\pm\) 5 a \\ SR & mmol m\({}^{-2}\) s\({}^{-1}\) & 21.2 \(\pm\) 4 c & 27.1 \(\pm\) 4 b & 25.1 \(\pm\) 3 ab & 37.1 \(\pm\) 2 a & 27.4 \(\pm\) 4 b \\ DHA & \(\mu\)g TPF g\({}^{-1}\) h\({}^{-1}\) & 330 \(\pm\) 11 d & 400 \(\pm\) 21 cd & 571 \(\pm\) 20 c & 689 \(\pm\) 6 a & 620 \(\pm\) 10 b \\ \hline \end{tabular}
\end{table} 
Table 3: Post-harvest soil analysis. Note: EC = electrical conductivity, SAR = sodium adsorption ratio, OM = organic matter, CEC = cation exchange capacity, TN = total nitrogen, MBC = microbial biomass carbon, SR = soil respiration, DHA = dehydrogenase activity. Rounded-off values after \(\pm\) represents standard error (\(n\) = 3). Different letters indicate statistical difference (LSD, \(p\) \(<\) 0.05).
# Analysis of Climate-Oriented Researches in Building
## 3 Results and Discussion
### Results of Cluster Analysis
#### 3.2.9 Cluster 9--Energy Simulation, Conservation and Meteorological Data  
This cluster, with 39 publications, is the largest, has a central location among the other clusters (Figure 4), and deals with energy conservation. All studies in this cluster are presented in Table S4 and include studies on the simplification of methodologies for estimating energy consumption in buildings [141; 142]; the relationship between thermal comfort [61] and visual comfort [143] with energy conservation in buildings; and comparisons of the energy efficiency of simulated dwellings according to the use of national building standards, both in Italy and Spain, for different building climate zones, showing the most favourable energy regulations [144]. Due to its size, and to facilitate the analysis of the results, this cluster has been manually divided into five sub-themes, which will be analysed below: (i) Multiparametric and multi-objective optimisations; (ii) Heat and energy recovery ventilators; (iii) Schedule occupancy and occupant behaviour; (iv) Renewable energy systems; (v) Meteorological data and climate change.  
Sub-Theme 1--Multiparametric and Multi-Objective Optimisations  
This first sub-theme presents 13 studies on the optimisation of buildings for energy conservation through four different approaches in the use of energy-efficient measures (Table 4): (i) building envelopes, (ii) architectural parameters (building orientation and geometry), (iii) building service systems and equipment, and (iv) internal conditions (thermal/lighting comfort, building occupation, occupant behaviour and indoor environmental conditions).  
It can be seen that the older publications considered a limited number of parameters for optimising buildings and only with an energy purpose. With the progress of research, the number of parameters and measures analysed has increased significantly, and secondary objectives of optimisation have also been developed, be they economic, ecological or health-oriented. These objectives were later integrated with the energy aspect of the optimisation. Recently, there has been a trend towards a decrease in the number of parameters and optimisation techniques, through the search for more efficient methods and parameters for different types of buildings, in different climate zones [65].
## 5 Discussion
The first step that clusters the text corpus into topics has a complexity of \(O(((NT)^{\tau}(N+\tau)^{3}))\)[23], where \(N\) is the number of words in the corpus, \(T\) is the number of topics in the corpus and \(\tau\) is the number of topics in a document. This has polynomial run-time if \(\tau\) is a constant. 2. The second step that performs context incorporation has a complexity of \(O(m*n*k*k)\), where \(m\) is the number of training topics, \(n\) is the number of test topics and \(k\) is the number of words in a topic (\(k=10\) in our case). 3. The overall complexity is thus \(O(((NT)^{\tau}(N+\tau)^{3}))+O(m*n*k*k)\). The second step does not affect the overall complexity of the algorithm asymptotically. The two major computational tasks performed by our algorithm are performing topic modeling to extract topics and computing semantic distances. We mentioned earlier that the iterative nature of LDA can scale it up to millions of documents. Calculation of semantic distances using WordNet can be performed in polynomial time. Calculation of semantic distance using the web is restrictive in practice,as search engines throttle automated queries (e.g., Google permits only up to 1000 queries per day from an IP address). In order to tackle this challenge, we suggest the use of a dictionary which enlists the number of search queries returned for a list of common terms by a search engine. Hence, we believe that with the use of map-reduce framework to perform clustering and with the use of a pre-computed Internet semantic dictionary, it is possible to apply our technique on much larger datasets. To conclude, in this paper we have proposed that, by augmenting topic modeling techniques with contextual information derived from semantic networks, we can improve the detection of deviant topics in large scale text logs. We were able to validate this empirically and build a system that could accommodate the human judgment of anomalies as well. Our results show both reductions of false positives and detection of previously undetected anomalies in existing datasets. Extension of our approach to online settings could significantly improve existing techniques of sentiment extraction being researched using social network feeds [24]. Also, since anomaly detection here occurs at a topic level, it is possible to implement privacy-preserving tracking of intra-organizational communication using systems built around our basic concept.
## 2 Materials and Methods
### Models' Development  
The bioaccumulation factor (BAF) is used to measure the accumulation of zinc (Zn) in rice grains, and, based on the soil properties, relevant predictive models, and corresponding theoretical framework, a predictive model for the \(\mathrm{BAF}_{\mathrm{Zn}}\) of rice grains is established [24].  
The bioaccumulation factor of Zn is calculated using the following formula:  
\[\mathrm{BAF}_{\mathrm{Zn}}=\frac{\mathrm{C}_{\mathrm{Rice\ Zn}}}{\mathrm{C}_{ \mathrm{Soil\ Zn}}} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Samples** & **Item** & **Method** & **Detecting Limit** \\ \hline \multirow{8}{*}{Soil} & \(\mathrm{SiO_{2}}\) & & 0.05 * \\  & \(\mathrm{TFe_{2}O_{3}}\) & & 0.02 * \\ \cline{1-1}  & \(\mathrm{Al_{2}O_{3}}\) & & 0.03 * \\ \cline{1-1}  & \(\mathrm{CaO}\) & X-ray Fluorescent Spectroscopy (XRF) & 0.02 * \\ \cline{1-1}  & \(\mathrm{Mn}\) & & 10 \\ \cline{1-1}  & \(\mathrm{P}\) & & 50 \\ \cline{1-1}  & \(\mathrm{Zn}\) & & 4 \\ \cline{1-1}  & \(\mathrm{TOC}\) & \(\mathrm{Volumetric\ method\ (VOL)}\) & 0.1 * \\ \cline{1-1}  & \(\mathrm{pH}\) & \(\mathrm{Ion\text{-selective\ electrode\ (ISE)}}\) & 0.08 ** \\ \hline \multirow{2}{*}{Rice} & \multirow{2}{*}{Zn} & Inductively coupled plasma mass & \multirow{2}{*}{0.05} \\  & & & spectrometry (ICP-MS) & \\ \end{tabular}
\end{table}
Table 1: Sample analysis method scheme and detection limit.  
where \(\text{BAF}_{\text{Zn}}\) is the bioaccumulation factor for Zn, \(\text{C}_{\text{Rico}\,\text{Zn}}\) is the zinc content in the rice grains, and \(\text{C}_{\text{Soil}\,\text{Zn}}\) is the zinc content in the corresponding root soil.  
Before constructing the model, the input features are logarithmically transformed and normalized. This preprocessing step is aimed at making the data have similar scales and an approximately normal distribution, which accelerates the convergence of the machine learning algorithms and improves the prediction accuracy [12].
# Positioning by Multicell Fingerprinting in Urban NB-IoT Networks +
## 5 Performance Evaluation
### Results for the Rome Dataset
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Strategy**} & \multirow{2}{*}{**RF Parameter**} & \multicolumn{2}{c}{**Original**} & \multicolumn{2}{c}{**Interpolated**} \\ \cline{3-6}  & & \(k\) & **Error [m]** & \(k\) & **Error [m]** \\ \hline _Net_ & N/A & 40 * & 148.4 & 40 * & 149.1 \\ \hline \multirow{3}{*}{_Enhanced Net_} & RSSI & 3 & 85.3 & 3 & 69.5 \\ \cline{2-6}  & SINR & 2 & 63.4 & 2 & 39.9 \\ \cline{2-6}  & **RSRP** & 2 & **55.9** & **1** & **36.5** \\ \cline{2-6}  & RSRQ & 3 & 64.0 & 2 & 43.0 \\ \hline \multirow{3}{*}{_Coverage_} & RSSI & 2 & 76.6 & 3 & 36.4 \\ \cline{2-6}  & SINR & 2 & 74.0 & 2 & 29.3 \\ \cline{2-6}  & **RSRP** & 1 & **68.7** & **2** & **26.9** \\ \cline{2-6}  & RSRQ & 2 & 74.0 & 2 & 28.6 \\ \hline \multirow{3}{*}{_Weighted Coverage_} & RSSI & 4 & 57.4 & 4 & 35.6 \\ \cline{2-6}  & SINR & 3 & 49.7 & 2 & 29.4 \\ \cline{2-6}  & **RSRP** & 3 & **49.0** & **2** & **26.8** \\ \cline{2-6}  & RSRQ & 3 & 49.2 & 2 & 28.7 \\ \hline \multirow{3}{*}{_Single Cell_[7]} & RSSI & 19 & 213.5 & 25 & 215.9 \\ \cline{2-6}  & **SINR** & 9 & **135.3** & **13** & **117.5** \\ \cline{1-1} \cline{2-6}  & RSRP & 12 & 151.5 & 20 & 142.9 \\ \cline{1-1} \cline{2-6}  & RSRQ & 10 & 137.3 & 17 & 124.9 \\ \hline \hline \end{tabular}  
* Although the optimal \(k\) is at the edge of the considered range, the results show that the error hits a floor for \(k>40\).  
\end{table} 
Table 7: Minimum average positioning error and corresponding optimal \(k\) for the proposed strategies and the _Single Cell_ approach in [7] using data collected for Operator 88 in the Rome dataset in its original and interpolated versions. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Strategy**} & \multicolumn{4}{c}{**Original**} & \multicolumn{4}{c}{**Interpolated**} \\ \cline{2-10}  & \multicolumn{2}{c}{**Op. 1**} & \multicolumn{2}{c}{**Op. 10**} & \multicolumn{2}{c}{**Op. 88**} & \multicolumn{2}{c}{**Op. 1**} & \multicolumn{2}{c}{**Op. 10**} & \multicolumn{2}{c}{**Op. 88**} \\ \cline{2-10}  & \(k\) & **E [m]** & \(k\) & **E [m]** & \(k\) & **E [m]** & \(k\) & **E [m]** & \(k\) & **E [m]** \\ \hline _Net_ & 25 & 146.8 & 32 & 137.6 & 40 & 148.4 & 35 & 147.2 & 32 & 137.5 & 40 & 149.1 \\ \hline _Enhanced Net_ & 1 & 82.8 & 2 & 62.1 & 2 & 55.9 & 2 & 47.5 & 2 & 33.2 & 1 & 36.5 \\ \hline _Coverage_ & 4 & 80.1 & 2 & 70.9 & 1 & 68.7 & 2 & 34.8 & 2 & 26.7 & 2 & 26.9 \\ \hline _Weighted Cov._ & 3 & 69.3 & 2 & 58.1 & 3 & 49.0 & 2 & 35.0 & 2 & 26.8 & 2 & 26.8 \\ \hline _Single Cell_[7] & 32 & 163.3 & 20 & 153.4 & 9 & 135.3 & 40 & 155.6 & 25 & 149.6 & 13 & 117.5 \\ \hline \hline \end{tabular}  
\end{table} 
Table 8: Minimum average positioning error (E) and corresponding optimal \(k\) for the proposed strategies and the approach in [7] all using RSRP as RF parameter in the three networks detected in Rome using the original and interpolated datasets.
# Discrete-Time First-Order Plus Dead-Time Model-Reference Trade-off PID Control Design
## 4 Proportional-Integral-Derivative Parameter Design
### Algorithm  
The proposed design procedure is summarized as the following algorithm:  
1. The plant parameters and the controller parameters are normalized.
2. For the normalized systems, the constrained optimization problem is solved such that the reference and disturbance responses are optimized, respectively, and the optimal controller parameters are obtained.
3. Based on the obtained optimized parameters, the tuning rule for the controller parameters is designed.
4. Using the tuning rule, the practical PID parameters are decided.  
When the tuning rule is obtained once, the PID parameters are calculated without solving the constrained optimization problem.
## 3 Epidemiology and Diagnosis
### Incidence Associated with Surgical Intervention
#### 3.2.2 Dental Implant  
A previous study identified the presence of dental implants as a potential risk factor for MRONJ, with implant treatment reported to trigger MRONJ in certain cases [39; 40]. However, contrasting evidence from another systematic review suggests relatively high implant survival rates, reporting 97%, 99%, and 91% for groups not using antiresorptive drugs, those taking oral BP, and those receiving intravenous BP, respectively [41]. Despite these high survival rates, recent reviews caution that surgical interventions, including dental implants and regenerative therapies, carry inherent risks, particularly for patients on antiresorptive medications like BP and denosumab [17]. These interventions are known triggers for MRONJ, with an increased risk in scenarios involving bone manipulation or stress [17]. Compounding this concern is the significant lack of detailed information on regenerative therapies, including the types of grafting materials used, the surgical protocols followed, and the metrics of success and failure rates [17]. Details specific to implant treatments, such as the timing of insertion, implant dimensions, and the loading protocol, are also inadequately documented [17]. This paucity of information hampers the ability of clinicians to fully assess and effectively mitigate the risks associated with these surgical interventions for each patient.
# Communication-Efficient Vertical Federated Learning
## 5 Results
### Performance using PCA  
Tables 2-5 provide a comparison of the proposed method's performance with centralized training and guest client training with its local data. Furthermore, they demonstrate how the proposed method's performance is affected when PCA is not performed on the local data of guest client C1.  
\begin{table}
\begin{tabular}{l l l} \hline  & **Accuracy** & **F1 Score** \\ \hline Centralized Training with unpartitioned data & 81.45 & 81.46 \\ Guest party with local data & 68.88 & 68.87 \\ Performance after PCA at all clients & 78.46 & 78.44 \\ Performance after PCA at all clients except C1 & 62.22 & 62.19 \\ \hline \multicolumn{3}{c}{**Overview of Feature Dimensions**} \\  & **Client 1** & **Client 2** & **Client 3** \\ \hline Feature dimension before PCA & 26 & 34 & 43 \\ Feature dimension after PCA & 20 & 24 & 38 \\ \hline \end{tabular}
\end{table}
Table 2: Experimental Results on Adult Income Dataset.  
\begin{table}
\begin{tabular}{l l l l} \hline  & **Accuracy** & **F1 Score** \\ \hline Centralized Training with unpartitioned data & 86.81 & 86.21 \\ Guest party with local data & 81.31 & 81.10 \\ Performance after PCA at all clients & 81.49 & 81.51 \\ Performance after PCA at all clients except C1 & 81.24 & 81.23 \\ \hline \multicolumn{3}{c}{**Overview of Feature Dimensions**} \\  & **Client 1** & **Client 2** & **Client 3** \\ \hline Feature dimension before PCA & 4 & 5 & 4 \\ Feature dimension after PCA & 2 & 3 & 3 \\ \hline \end{tabular}
\end{table}
Table 3: Experimental Results on Heart Disease Prediction Dataset.  
The experimental results using the proposed method with PCA show that the aggregated model outperforms the local model of the guest client across all datasets. However, if PCA is not performed on the local data for the guest client, performance varies across datasets. For example, the accuracy and F1 score improve for the _Wine Quality Dataset_ (Table 4), but decrease for the other datasets. This might be a result of the reduction of noise in the dataset due to the application of PCA on it.
# Generative Aspect Sentiment Quad Prediction with Self-Inference Template
## 4 Experimental Setup
### Baselines  
To assess the effectiveness of our approach compared to previous methods, we selected several strong baseline methods:  
**HGCN-BERT+BERT-Linear**: HGCN [21] jointly extracts aspect categories and sentiment polarities, utilizes BERT to extract corresponding aspect terms and opinion terms [22], and applies a linear layer for final aggregation.  
**HGCN-BERT+BERT-TFM** Modification of the above model with the final linear layer replaced by Transformer blocks (BERT-TFM).  
**TASO-BERT-Linear**: TAS [8], originally designed for extracting unified triples of aspect categories, aspect terms, and sentiment polarities, is extended to TASO for handling ASQP tasks. Linear classification layers are used for prediction.  
**TASO-BERT-CRF**: A variant of the TASO model with a Conditional Random Field layer in the prediction stage.  
**TAS-BERT-ACOS**: On the basis of the TAS method, cai [9] designed a two-step pipeline approach that incorporates BERT to extract quadruples from ACOS data.  
**Extract-Classify-ACOS**: This method first extracts aspect terms and opinion terms from the original sentence and then classifies aspect categories and sentiment polarities based on these extracted terms [9].  
**GAS**: A generative baseline [23], modified by [10] to directly generate aspect sentiment quadruplets as the target sequence in the generative model.  
**Seq2 Path**: Transforming the generation order of sentiments into the path of a tree, using a constrained beam search, automatically selecting valid paths with the help of additional tokens [24].  
**PARAPHRASE**: This method extracts (at, ac, sp, ot) by paraphrasing the original sentence as \"ac is sp because at is ot\" [10].  
**DLO**: Considering the impact of the order of generating each element in the quadruplet in generative models [11], 24 template orders were experimented with. The final template order was chosen based on the overall quadruplet extraction performance on the dataset.  
**ILO**: Similar to DLO, after experimenting with 24 template orders, the template order for each instance was chosen individually based on its own performance.