esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Coordination of PSS and BESS Controllers to Enhance Oscillatory Stability Using Proposed Method
### Power-System-Controller Modeling
#### 4.1.2.2 POD Controller for BESS  
The POD structure described in References [21; 35] was used in our study and has a similar structure to PSS. However, unlike most PSSs, input feedback signal for POD can be signals from remote locations, which are often more effective for oscillation damping than local signals [35].  
POD structure is very similar to PSS, and is shown in Figure 8. Lead-time constants for POD at the \(i^{th}\) location are \(T_{1i,pad}\), \(T_{3i,pod}\), and lag-time constants are \(T_{2i,pod}\), \(T_{4i,pod}\), the washout-time constant is \(T_{i,podw}\), the POD gain is \(K_{i,pod}\), and maximum and minimum power limits are \(P_{max}\) and \(P_{min}\), respectively.  
Unlike most PSSs, POD input feedback signal can be signals from remote locations that are often more effective for oscillation damping than local signals [35]. It is assumed that phasor-measurement units (PMU) that provide these remote signals were installed in our test system. However, there exists finite delay while transmitting the measured signal from PMU to POD control stations, it is commonly termed as communication delay or latency [36], and is modeled as \(e^{-sT_{d}}\) in our work [35]. Value of latency \(T_{d}\) is taken as 100 ms [35].  
The feedback remote signal can be any electrical variables, such as real and reactive power flow in lines, current flow in lines, bus voltages, and angular differences. Among these variables, we chose real power flow in lines \(P_{line,i}\) as input for PODs, as both reactive power and bus voltage have exciter and flux-decay components, and angular differences require an additional phase lead of 90 degrees, making them less suitable for feedback control [37]. The best input (among many real power flow in lines) for each POD controller is selected using the residue method [15; 20; 36]. Output from POD \(P_{mod}\) was chosen to modulate the real power control loop in contrast to the reactive power loop as BESSs are located at the ends of the transmission line of our test system given in Section 5.1, in which case, real power modulation is superior to reactive power modulation [38]. The values of \(P_{max}\), \(P_{min}\), and \(T_{i,podw}\) are 0.2 and 40.2 p.u. and 10 s, respectively [6]. The range of other POD parameters used in this paper is provided in Section 4.2.1.
## 6 Conclusions  
OFDR technology was used to measure the strain of PCC pile. A model test for a PCC pile was carried out. The numerical model was established using COMSOL, and the following conclusions were obtained.  
(1) Based on the model test, the method of layout, packaging, and protection of the optical fiber bonded to the surface of the PCC pile were developed and introduced in detail. The strain of the PCC pile was successfully monitored by OFDR technology. The strain curves of the PCC pile at different vertical loads have the same variation trends, and agree well with the results of finite element calculation.  
(2) The OFDR has higher spatial resolution and measuring accuracy, both of which can reflect the deformation characteristics of pile. The weak section and abnormal position of the pile can easily be detected. The strain gets smaller from the top to bottom of pile, and increases with loading. These show the superiority of OFDR in the strain monitoring of pile.  
(3) Wavelet transform is an effective method to deal with the data obtained by using OFDR, in terms of deleting noise signal. Combined with polynomial matching method, the variations of strain can be clearly shown.  
[PERSON] designed the project. [PERSON] and [PERSON] performed the experiment. [PERSON] did the numerical simulation. [PERSON], [PERSON] and [PERSON] analyzed the data and prepared the manuscript. [PERSON] reviewed and commented on the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 51508159), the Fundamental Research Funds for the Central Universities of Hohai University (No. 2016B06114, 2017 FB20614), China Postdoctoral Science Foundation (No. 2014T70468), the Natural Science Foundation of Jiangsu Province (No. BK20130832), the Key Laboratory of Hydraulic and Waterway Engineering of the Ministry of Education and National Engineering Research Center for Inland Waterway Regulation open foundation (SLK2015A01).  
The authors declare no conflict of interest.
# Local Crossover: A New Genetic Operator for Grammatical Evolution
## 2 Materials and Methods
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**DATASET** & **NNC** & \(N_{cr}=25\) & \(N_{cr}=50\) & \(N_{cr}=100\) \\ \hline APPENDICITIS & 13.70\% & 14.00\% & 14.50\% & 14.70\% \\ \hline AUSTRALIAN & 14.51\% & 14.46\% & 14.13\% & 13.97\% \\ \hline BALANCE & 22.11\% & 22.29\% & 17.76\% & 18.05\% \\ \hline CIRCULAR & 13.64\% & 11.90\% & 9.38\% & 8.46\% \\ \hline CLEVELAND & 50.10\% & 49.69\% & 48.90\% & 49.17\% \\ \hline DERMATOLOGY & 25.06\% & 20.51\% & 18.20\% & 16.29\% \\ \hline ECOLI & 47.82\% & 47.79\% & 47.39\% & 47.52\% \\ \hline FERT & 19.00\% & 18.70\% & 19.20\% & 18.70\% \\ \hline HABERMAN & 28.03\% & 28.27\% & 28.43\% & 26.70\% \\ \hline HAYES-ROTH & 35.93\% & 31.54\% & 27.77\% & 27.69\% \\ \hline HEART & 15.78\% & 15.07\% & 16.00\% & 14.67\% \\ \hline HEARTATTACK & 19.33\% & 20.13\% & 19.73\% & 18.50\% \\ \hline HOUSEVOTES & 3.65\% & 3.30\% & 3.26\% & 3.13\% \\ \hline GLASS & 57.10\% & 55.38\% & 54.62\% & 54.29\% \\ \hline IONOSPHERE & 11.12\% & 10.63\% & 10.71\% & 9.89\% \\ \hline LIVERDISORDER & 33.71\% & 32.03\% & 32.53\% & 31.12\% \\ \hline MAMMOGRAPHIC & 17.78\% & 17.72\% & 17.64\% & 17.12\% \\ \hline PARKINSONS & 12.21\% & 12.53\% & 12.79\% & 11.58\% \\ \hline PIMA & 27.99\% & 27.26\% & 27.68\% & 26.09\% \\ \hline POPFAILURES & 6.74\% & 6.33\% & 6.91\% & 6.35\% \\ \hline REGIONS2 & 25.52\% & 26.20\% & 25.47\% & 24.82\% \\ \hline SAHEART & 30.52\% & 30.61\% & 29.81\% & 29.58\% \\ \hline SEGMENT & 54.99\% & 53.07\% & 49.24\% & 42.90\% \\ \hline SPIRAL & 48.39\% & 48.08\% & 48.20\% & 48.34\% \\ \hline STUDENT & 5.78\% & 5.40\% & 5.20\% & 4.10\% \\ \hline TRANSFUSION & 25.34\% & 25.26\% & 24.80\% & 24.47\% \\ \hline WDBC & 6.95\% & 6.82\% & 7.39\% & 6.59\% \\ \hline WINE & 14.35\% & 11.82\% & 11.77\% & 9.88\% \\ \hline Z\_F\_S & 14.17\% & 12.60\% & 13.50\% & 9.98\% \\ \hline Z\_O\_N\_F\_S & 49.18\% & 48.20\% & 46.24\% & 44.73\% \\ \hline ZO\_NF\_S & 14.14\% & 12.72\% & 12.18\% & 10.42\% \\ \hline ZONF\_S & 3.14\% & 3.18\% & 2.82\% & 2.58\% \\ \hline ZOO & 9.20\% & 8.20\% & 8.10\% & 7.50\% \\ \hline
**AVERAGE** & **23.54\%** & **22.78\%** & **22.19\%** & **21.21\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The effects of the parameter \(N_{cr}\) to the NNC machine learning model. The classification datasets were used in this experiment. In all experiments, the value \(p_{cr}\) was set to 0.025.
## 4 Simulation Results and Performance Evaluation
### The Optimization Results of the Control Parameters for SMC Control  
Figure 9 presents the optimization results for SMC control parameters. For the SMC, aiming for an expected peak time of 10 ms and an expected overshoot of 5% leads to good control performance. The control parameters for the SMC controller are chosen to yield these expected outcomes: \(\alpha_{2}/\alpha_{1}=599.1465\) and \(\alpha_{3}/\alpha_{1}=1.8844\times 10^{5}\). The optimized SMC control parameters obtained through GA are \(\alpha_{2}/\alpha_{1}=1.6291\times 10^{3}\) and \(\alpha_{3}/\alpha_{1}=1.8410\times 10^{5}\).  
Figure 8: The optimization results for PI control parameters: (**a**) The output voltage of the DAB; (**b**) the evolution of control parameters; (**c**) Bode diagram of the closed-loop system with pre- and post-optimized control parameters.  
Figure 9a illustrates the output voltage of the DAB, showing a reduced voltage drop from the pre-optimized maximum drop value of 925 V to 950 V after optimization. Moreover, the maximum drop rate decreases from 7.50% to 5.00% following optimization, signifying an improved stability. Figure 9c depicts the Bode diagram of the DAB system, showcasing an increase in phase margin from 127\({}^{\circ}\) to 155\({}^{\circ}\) after optimization, further highlighting the enhanced stability achieved by the optimized parameters.
## 3 Results  
The search and selection processes are described in Figure 1 The systematic literature search and the additionally performed hand search found 38 reviews with the used search terms. Twenty-two articles were excluded after title and abstract screening because they were narrative reviews [2; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20], did not concern the use of air powder water jet technology in dentistry [21], did not investigate APWJT for tooth/implant cleaning [22; 23; 24], did not report clinical in vivo data [19; 25; 26; 27] or their full text was not available in English [28].  
After full-text screening, five other reviews were excluded because of in vitro data [30] reporting only on patient-related outcomes [31] or because their conclusion was not focused on the use of APWJT measures [32; 33; 34]. One study was excluded after the full-text screening because of the low methodological quality [35].  
Figure 1: Modified PRISMA 2020 flow diagram of study selection [29].  
Finally, 10 systematic reviews were included in this umbrella review. The main study characteristics of the included systematic reviews are shown in Table 1. The funding of the included studies is shown in Appendix A Table A1. A quality assessment according to AMSTAR 2 [36] was performed (Figure 2).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Author** & **Year** & **No. of Included Studies** & **Meta-Analysis** & **Type of Included Studies** & **Follow-Up** \\ \hline [PERSON] et al. [37] & 2022 & 8 & Yes & RCT, CT & 3 m-12 m \\ \hline [PERSON] et al. [38] & 2022 & 6 & Yes & RCT & 6 m-12 m \\ \hline [PERSON] et al. [39] & 2021 & 7 & Yes & RCT, CT & 3 m-12 m \\ \hline [PERSON] et al. [40] & 2021 & 13 & No & RCT & 1 w-12 m \\ \hline [PERSON] et al. [41] & 2021 & 80 & Yes & RCT, CT & 3 m-12 m \\ \hline [PERSON] et al. [42] & 2021 & 17 & Yes & RCT & 1 w-6 m \\ \hline [PERSON] et al. [43] & 2019 & 6 & No & RCT & 2 w-12 m \\ \hline [PERSON] et al. [44] & 2018 & 8 & Yes & RCT, CT & 1 w-12 m \\ \hline [PERSON] F \& [PERSON] et al. [7] & 2015 & 5 & Yes & CT & 3 m-12 m \\ \hline [PERSON] et al. [45] & 2012 & 9 & No & RCT & 6 m-12 m \\ \hline \end{tabular}
\end{table}
Table 1: Characteristics of the included studies (CT: controlled clinical trial; m: month; RCT: randomized controlled trial; w: week).  
Figure 2: Quality assessment of included systematic reviews according to AMSTAR 2 (RoB: risk of bias) [36; 37; 38; 39; 40; 41; 42; 43; 44].
# Enhancing Task-Oriented Dialogue Modeling through Coreference-Enhanced Contrastive Pre-Training
## 4 Experiment
### Downstream Tasks and Evaluation  
**The intent recognition** task involves predicting a single intent label from multiple categories for each utterance.  
\[P_{\text{int}}=Softmax(W_{c}*G(U))\in R^{I}, \tag{7}\]  
where \(G\) encodes the utterance \(U\) and \(I\) represents the number of possible intents. \(W_{c}\in R^{1\text{-}d_{b}}\) is a linear mapping, and \(d_{b}\) denotes the hidden size (typically 768).  
**The dialogue act prediction** task is the prediction of multiple dialogue acts of the next system response.  
\[P_{da}=Sigmoid(W(G(x)))\in R^{N}, \tag{8}\]  
where \(W\in R^{d_{b}*N}\) denotes a weight matrix. We train the model using binary cross-entropy loss and set a threshold (typically 0.5) to predict act triggers.  
**The dialogue state tracking** task aims to predict slot values from a predefined ontology for each (domain, slot) pair with high scores.  
\[P_{dst}=Cosine(F_{j}(G(x)),G(v_{i}{}^{j}))\in R^{I}, \tag{9}\]  
where \(x\) represents the dialogue history, and \(v_{i}{}^{j}\) denotes the \(i\)-th slot value for the \(j\)-th (domain, slot) pair. \(G\) encodes the input utterances to obtain the representation. The \(Cosine\) similarity function computes the probability of potential values for the \(j\)-th (domain, slot) pairs. \(F_{j}\) serves as the slot projection layer for the \(j\)-th slot, and the number of layers in F matches the count of the (domain, slot) pairs. The model is trained using the cross-entropy loss summed across all pairs.
## 2 Existing Equivalent Methods for APMSM  
### Three-Dimensional Structure of Axial Flux Permanent Magnet Synchronous Motors  
The basic electromagnetic structure of a common axial flux permanent magnet motor is shown in Figure 1 (this article takes a double stator single rotor 12-slot, 10-pole motor as an example), which includes the stator core, winding, rotor permanent magnet, and air gap. The air gap between the stator core and the rotor permanent magnet is the motor's air gap. As the main magnetic flux of an axial flux motor passes through the motor's air gap along the axis direction of the motor, it is necessary to use a three-dimensional finite element model to calculate the electromagnetic performance of this type of motor. However, for designers, three-dimensional finite element calculation has extremely strict requirements for computers, requiring the use of high main frequency and large memory scientific research service station computers to achieve calculation. Low-configuration conventional computers usually cannot run calculation programs. To solve this problem, scholars have adopted an equivalent simplified approach to process the axial magnetic flux single machine, reducing its dimensionality from a three-dimensional model to a two-dimensional model, thereby reducing equipment requirements and improving computational efficiency.
## 5 Experiment
### System Performance Analysis  
In this section, the measurements focus on latency, bandwidth usage, and throughput. Latency is measured by sending ten packets in ping command. As seen in Figure 19, the round-trip time (RTT) in the existing CEA system is consistent. The RTT is between 9 and 15 ms, with an average latency of 11 ms. In the proposed CEA system, the RTT fluctuates. Because of network congestion, the sixth response goes up to 12 ms. Then, it drops to 2 ms. However, the average latency is 3 ms, which is less than the existing CEA system. Transferring data by using the proposed CEA system is faster than doing so in the existing CEA system.  
The measurement of bandwidth usage refers to the upload and download operations in the proposed CEA system. We use the file sizes of 100 kB, 1 MB, and 10 MB, multiplying by 54 as the average number of transactions in the existing CEA system. As seen in Figure 20, the upload operation consumes more bandwidth than does the download operation. The highest bandwidth usage for uploading 540 MB is 0.084 kB/s, whereas downloading 540 MB is only 0.024 kB/s. Neither uploading nor downloading consumes much bandwidth.  
Figure 19: Latency comparison.  
Figure 20: Bandwidth usage.  
Furthermore, this paper measures the bandwidth capacity of the proposed CEA system, using speedtest-cli [18]. The proposed CEA system handles 16.64 Mbits/s for downloading and 11.20 Mbits/s for uploading. In addition, iPerf [19] is used to measure the approximate packet size that can be transferred. It shows that the proposed CEA system can transfer a 25.7 Mbyte packet size with an average of 15.5 Mbits/s during 13.9 s. The throughput supports the proposed CEA system for uploading and downloading a maximum packet size of 540 MB.
## 2 General Overview of the System  
The proposed system is composed of three main parts: Remote Terminal Units (RTU), Base Terminal Units (BTU), and the Central Server (CS). The RTUs are designed based on a high performance microcontroller and designed to monitor the climatic variables: Air Temperature (AT), Relative Humidity (RH), Barometric Pressure (BP), Solar Radiation (SR), Wind Direction (WD), Wind Speed (WS) and Rain (R). Each RTU interacts with other RTUs as they integrate a wireless local area network (WLAN); they are responsible for collecting data from the meteorological sensors and then for sending this data to the BTUs by Radio Frequency (RF) in the 900 MHz frequency band designed for Industrial Scientific and Medical (ISM) applications. The BTU has the task to address the requests from the RMS to the corresponding RTU and to send back the data collected by them. This information is sent with intervals of 10 min through a satellite link in the Ku band. Data sent by the BTUs is stored in the CS where historical data is generated and presented to the final user in a portal web in Graphic User Interface (GUI). Figure 1 illustrates a general overview of the system.
## 1 Introduction
### Amharic Language
#### 1.1.1 Pre-Processing and Normalization  
Amharic is written in Geez alphabets called Fidel or (_&A_). In traditional Amharic writing, each word and sentence is supposed to be separated using a unique punctuation mark, namely the Ethiopic comma ('). However, the modern writing system uses a single space to separate words. Using a single space suffices to split the majority of texts into tokens. However, there are some punctuation marks, such as: (1) The Ethiopic full stop (*) used to mark the end of a sentence. (2) The Ethiopic comma (') and the Ethiopic semicolon (') that are equivalent to their English counterparts. (3) The Ethiopic question mark (') that is used to mark the end of questions in Amharic. Moreover, most people also use Latin punctuation marks such as comma, semicolons, question marks, and exclamation marks, even mixing with the Amharic punctuation marks.  
For a properly written Amharic text, splitting sentences can be accomplished using the Amharic end of sentence marks (*), question marks, or exclamation marks. However, it might be also the case that people use two Ethiopic commas or two Latin commas to mark the end of a sentence. In the worst case, the Amharic sentence can be delimited with a verb (placed at the end of the sentence) without putting any punctuation marks. As far as we know, there is no proper tool to tokenize words and segment sentences in Amharic. As part of this work, we make available our Amharic segmenter within the FLAIR framework.  
Moreover, some of the \"Fidels\" in Amharic have different representations, for example, the \"Fidel\" \(u\) (ha) can have more than four representations (such as **v**, **a**, **a**, **a**, **a**, and so on). As the Amharic script originates from the Geez script, the use of different Fidels implied a change in meaning. However, the inherent meaning of the different Fidels became irrelevant in modern writing systems, so users can write with the similar-sounding Fidels interchangeably. These lead to texts written, especially in online communication such as news and social media communications, with different writing styles where the different Fidels are used randomly. For NLP processing, an arbitrary representation of words might pose a serious problem, for example the word _u_o- (man) and _w_o- (man) might have different embeddings while being the same word. To address this problem, we have built an Amharic text normalization tool that will normalize texts written with different \"Fidel\" sharing the same sound to a majority class.
## 2 Dispersion-Managed Link and WDM System Modeling
### WDM Transmitters, Receivers, and Midway OPC  
The transmission part of each WDM channel consists of a pseudorandom bit sequence (PRBS) for data generation, a distributed feedback laser diode (DFB-LD) for light source, and an external modulator. First, an independent 40 Gb/s 127 (=\(2^{7}-1\)) PRBS is generated and then modulates the intensity of the light emitted by DFB-LD in the external modulator. Following ITU-T recommendation G.694.1, the center wavelength of each DFB-LD is assumed to be from 1550 to 1568.4 nm with 100 GHz (0.8 nm) intervals. The output of the modulator is assumed to be return-to-zero (RZ) pulses. The output electric field of the RZ format is assumed to be chirp-free with a second-order super-Gaussian pulse with a 10 dB extinction ratio (ER) and a duty cycle of 0.5.  
The RZ pulses of 24 channels are multiplexed in the multiplexer (MUX) and propagated through the former half of the dispersion-managed link. However, the propagated signals are deteriorated by the chromatic dispersion and Kerr nonlinearities. The distorted optical signals are phase-conjugated in the midway OPC through the four-wave mixing (FWM) processes of the input waves and the pump light. Highly nonlinear dispersion-shifted fiber (HNL-DSF) was selected as a nonlinear medium for phase conjugation. The power and wavelength of the pump light for generating the phase-conjugated waves were assumed to be 18.5 dBm and 1549.75 nm, respectively. Consequently, each wavelength of the conjugated 24 channels in the midway OPC was then arranged from 1549.5 to 1528.5 nm (\(-0.8\) nm intervals).  
The conjugated WDM signals are propagated through the remaining half of the total link and then demultiplexed. We assumed that each receiver follows the direct detection method. The receiver bandwidth was assumed to be 0.65 times 40 Gb/s. The WDM receiver comprises an EDFA with a noise figure of 5 dB, an optical filter, a PIN diode as the photodetector, a pulse-shaping Butterworth filter, and a decision circuit.