esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
However, in terms of general large scale wind turbine SCADA system configuration, the sliding window time increment for the identification of wind turbine operating state health indicators based on SCADA data analysis may require tens of minutes or even one hour, which means that the currently obtained state health indicators were actually the states (operating data) tens of minutes or even one hour ago. Therefore, state identification was always delayed, and real-time performance was the reverse characteristic of this delay.
# Characterization of Slag Cement Mortar Containing Nonthermally Treated Dried Red Mud
## 3 Characterization of Slag Cement Mortar Containing RM and NTRM
### Experimental Results and Discussion  
#### 3.4.1 Compressive Strength  
Figure 6 and Table 5 show the compressive strengths of SC100, SC80 RM20, and SC80 NTRM20. For all curing ages, SC100 had the highest compressive strength, and SC80 NTRM20 had the lowest value. On the first day, the measured compressive strength was 6.26 MPa for SC100, 5.02 MPa for SC80 RM20, and 3.95 MPa for SC80 NTRM20. After 28 days, the compressive strengths of SC100, SC80 RM20, and SC80 NTRM20 were 30.14, 25.91, and 19.98 MPa, respectively, indicating that the differences in the compressive strength increased as the age increased. This tendency of decreasing compressive strength owing to the addition of RM was the same as the tendency reported in other previous studies [25; 26; 27; 28; 29]. However, this tendency was significantly different from that reported by [PERSON] et al. [10], who found that the density and compressive strength increased as the RM addition amount increased. This difference may have occurred because the RM used in this study had larger particle sizes than the RM used in the study of [PERSON] et al. [10], and thus, the pore filling was not sufficient to increase the compressive strength.  
Figure 6: Compressive strength of different RM-type mortar specimens.  
[PERSON] et al. [30] reported that mortar based on a binder, in which RM, coal refuse, fly ash, and OPC were mixed, could achieve a higher compressive strength than OPC mortar on a long-term basis because of pozzolanic reactions. The PSA and HCFA used in this study contained a large amount of CaO, and thus, the compressive strength could be expected to increase because of the hydration reaction. However, it appears that the strength of the mortar could not be increased because the CaO already present in PSA and HCFA reacted with moisture in the RM sludge during the NTRM production process and generated portlandite (Ca(OH)\({}_{2}\)), as shown in Figure 7. In this study, although SC80 NTRM20 had a lower compressive strength than SC100 and SC80 RM20, it was confirmed that it had sufficient strength to be used as a raw material for sidewalks, bike roads, and parking lots, as suggested by [PERSON] et al. [21].
# Distributed Global Function Model Finding for Wireless Sensor Network Data
## 4 Distributed Global Function Model Finding for Wireless Sensor Networks Data  
### Algorithm Idea  
In WSNs, because the number of sensors is very large and sensors are physically deployed in a very distributed fashion, traditional centralized function model finding algorithms will undoubtedly increase transmission bandwidth, network delay and probability of data packet loss, and also reduce the efficiency of function model finding. Meanwhile, centralized analysis for massive data in WSNs will also add pressure to the data storage so that traditional centralized function model finding algorithms are difficult to apply in wireless sensors networks.  
Grid is a high performance and distributed computing platform with good self-adaptability and scalability, and provides favorable computing and analysis capability for massive or distributed data sets. Grid could provide strong analysis and computing power with distributed data mining and knowledge discovery. In view of advantages of grid computing, on the basis of FF-GEP, this paper presents distributed global function model finding for wireless sensor networks data (DGFMF-WSND) which combines with global model generation and grid services.  
Suppose that data on each grid node are homogeneous and the attributes that are contained in each of datasets on the computing nodes are same in this paper. The algorithm idea is divided into some sub-processes. Firstly, algorithms proposed in this paper are wrapped as grid services and deployed on each grid node. Meanwhile, a local function model is obtained by performing FF-GEP algorithm service on each grid node in parallel. Lastly, the local function model of each node is transmitted to the specified node to generate a global model and returned to the user.
# CoMP-Aware BBU Placements for 5G Radio Access Networks over Optical Aggregation Networks
## 3 Problem Formulation
### Objective Function  
The multi-objective function is formulated in Equation (1), consisting of two terms. The first term minimizes the number of BBU pools, while the second minimizes the number of used wavelengths in the transport network:  
\[min\left(\beta\sum_{i}K_{i}+\gamma\sum_{ij}\sum_{mn}Y_{ij}^{mn}\right) \tag{1}\]  
where, \(\beta\) and \(\gamma\in[0.1]\) can be tuned to select the primary and secondary objectives of the optimization function.
# CoKnowEMe: An Edge Evaluation Scheme for QoS of IoMT Microservices in 6G Scenario
## 1 Introduction
### Beyond 5G Networks towards 6G Revolution  
A promising communication paradigm is represented by 6G that will have the ability to give a boost to the ongoing process of telecommunications evolution and to rule the entire health field from 2030 onward [13]. We are witnessing an increasing effect in both number of connected devices and demand for high-data rate services. In fact, with a growing number of mobile users and bandwidth-intensive services and applications, worldwide research activity is mainly interested in defining new paradigms for the next-generation 6G systems, in order to take into consideration all the last trends of this evolution, such as densification, higher rate, massive antenna, complexity, artificial intelligence (AI), computing and sensing [9; 10; 11; 12]. It will revolutionize several fields toward a society of fully automated remote management systems including industry, health, roads, oceans and space jointly with the millions of sensors integrated into cities, vehicles, homes, industries, food, toys and other environments to provide a smart life and automated systems [8].  
The 6G technology has the ambition to provide new directions to deal with future network challenges. It will address the constraints and the performance requirements of the applications and innovative services which need highly increasing resources, through innovative approaches [10]. Major challenge arises from the health related systems, since the new communication technology enables to envision the future healthcare in terms of well-being of people. The healthcare systems have to manage a wide variety of diseases, the increase of aging populations, the management of pandemics and jointly the effects of people awareness [14] and an increased number of treatments and patients [15]. The proliferation of devices ranging from simple sensors to sophisticated ones and the different network scenarios as cellular, vehicular, Wi-Fi, Internet of Things (IoT) and Internet of Everything (IoE) systems, shape a fully connected network of millions of people and billions of machines. In this context edge intelligence [16; 17; 18] will be a key enabling factor for future networks to improve performances, functions and services. In the edge application development the distributed and complex approach will be represented by the microservices able to develop modular lightweight application components. In the healthcare scenario, this context is suitable for enabling the Internet of Medical Things (IoMT) [19].
## 2 System Model and Problem Description  
### Network Topology  
In our envisioned scenario, the MLSCN contains four kinds of nodes, which are satellites, HAPs, UAVs, and ground stations. Space nodes of the same type can build intra-layer networks, which means that the MLSCN has a total of three intra-layer networks. The space nodes of adjacent layers can be connected through inter-layer links; UAVs are connected to HAPs through Platform-Vehicle Links (PVLs), and HAPs are connected to satellites through Satellite-Platform Links (SPLs). When the destination nodes, i.e., the ground stations, are within the visual range of the space nodes, the MLSCN can build links to the ground, which are Vehicle-Ground Links (VGLs), Platform-Ground Links (PGLs), and Satellite-Ground Links (SGLs), respectively.  
Taking into account the complexity of data acquisition in the hot-event case, UAVs are the only data source nodes in the MLSCN in Figure 1. A UAV cluster consists of multiple UAVs, and UAVs in the same cluster can connect to each other. Although the flexibility of UAVs ensures that it can perform high-intensity data collection for specific areas, the limited range makes it difficult to efficiently deliver data to the destination nodes. At this point, the HAP network can act as a data tuple to assist UAVs to transfer data more efficiently. With a wider coverage, satellites can assist MLSCN in reducing the number of hops and improving the transmission efficiency.  
Figure 1: Multi-Layer Space Communication Network.
# Single-Phase, Bidirectional, 7.7 kW Totem Pole On-Board Charging/Discharging Infrastructure
## 2 Brief Detail and Parameter Design
### Inductor Design  
In this proposed BDCI, the grid interfacing inductor (\(L_{inf}\)) has a major role to play. \(L_{inf}\) will be the common interfacing inductor for both G2V as well as V2G mode. Therefore, its value should withstand the maximum current. Additionally, the \(L_{inf}\) suppresses the current harmonics and acts as a power factor (PF) booster in PFC operation. The value of \(L_{inf}\) is calculated as follows [45]:  
\[L_{inf}=\frac{V_{g}^{2}}{\%I_{ripp}P_{rat}f_{SW}}\bigg{(}1-\frac{V_{g,peak}}{ V_{o}}\bigg{)} \tag{1}\]  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{2}{c}{**G2V/Charging Mode**} \\ \hline \(V_{g}\) and \(f_{l}\) & 230 V and 50 Hz \\ \(V_{o}\) or \(V_{DC-link}\) & 400 V \\ \(P_{rat}\) & 7.7 kW \\ \(f_{SW}\) & 65 kHz \\ \(\Delta I_{ripp}\) & 20\% \\ \(\Delta V_{DC-link}\) & 5\% \\ \(\eta\) target & \(>\)95\% \\ \(PF\) target & \(>\)98\% \\ \hline \multicolumn{2}{c}{**V2G/Discharging Mode**} \\ \hline \(V_{DC-link}\) & 400 V \\ \(V_{g}\) and \(f_{l}\) & 230 V and 50 Hz \\ \(P_{rat}\) & 7.7 kW \\ \(f_{SW}\) & 65 kHz \\ \(THD\) & \(<\)5\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: System specifications for G2V and V2G mode.  
Figure 2: Circuit diagram of the bidirectional charging/discharging infrastructure.  
where \(V_{g},V_{g,peak},V_{o},I_{ripp},P_{rat}\), and \(f_{SW}\) are the grid voltage, peak grid voltage, output voltage or DC-link voltage, inductor ripple current, rated power and switching frequency, respectively.
# On the Use of Parsing for Named Entity Recognition
## 2 Named Entity Recognition
### Evaluation Measures for NER Systems  
Three measures are usually considered for evaluating NER systems: precision, recall, and F-measure. In the context of NER, the entities labeled in the test data set are considered as ground truth. To compute the metrics, we must consider the numbers of true positives (_TP_), false positives (_FP_), and false negatives (_FN_) with respect to said ground truth, where:  
* A true positive is counted for each entity that is returned by a NER system and also appears in the ground truth;
* A false positive is counted for each entity that is returned by a NER system but does not appear in the ground truth;
* A false negative is counted for each entity that is not returned by a NER system but does appear in the ground truth.  
Precision, \(P\), refers to the percentage of a system's results that correspond to correctly-recognized entities and is computed as indicated below in Equation (1). Recall, \(R\), refers to the percentage of the total entities in a text that are successfully recognized by a system and is computed as indicated in Equation (2). Finally, _F-measure_ combines precision and recall by means of their harmonic mean, as shown in Equation (3).  
\[P=\frac{TP}{TP+FP}, \tag{1}\]  
\[R=\frac{TP}{TP+FN}, \tag{2}\]  
\[\textit{F-measure}=\frac{2\times P\times R}{P+R}. \tag{3}\]  
Nowadays, NER is far from being a solved task. Even in a well-studied and resource-rich language like English, state-of-the-art approaches obtain _F-measures_ around 80% in some biomedical data sets (e.g., 80.5% on GENIA [95] ([PERSON] et al., 2020) or 77.1% on the ShARe/CLEF eHealth Task 1 Corpus [96]). As such, NER is currently a highly active topic in NLP research, the subject of frequent shared tasks with a high number of participants. Examples from 2020 are the Spanish CAMTEMIST-NER shared task, with 23 participating teams, featured in IberLEF 2020 [97]; or the English W-NUT-2020 Task 1, with 13 participants, featured in EMNLP 2020 [98].
## 4 Discussion  
To the best of our knowledge, this is the first study to examine temporomandibular and cervical dysfunctions immediately after root canal therapy. Our results demonstrate  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Patients with** & **Patients without** & \\  & **Limitation (\(\leq\)35 mm)** & **Limitation (\(\leq\)35 mm)** & \\  & **after Endodontic** & **after Endodontic** & **\(p\)-Value** \\  & **Intervention** & **Intervention** & \\  & **(\(n\) = 5)** & **(\(n\) = 24)** & \\ \hline Gender & & & \\ (male/female) & 2/3 & 14/10 & 0.453 \\ \hline Age & 51.2 (13.7) & 44.46 (17.0) & 0.371 \\ \hline Weight & 66.8 (7.9) & 72.0 (15.0) & 0.463 \\ \hline Height & 165.2 (9.6) & 168.0 (8.1) & 0.493 \\ \hline Smoker (yes/no) & 3/2 & 6/18 & 0.124 \\ \hline Mouth opening & 40.4 (3.0) & 47.5 (6.0) & 0.016 * \\ \hline TMD Pain Screener & 2.20 (2.39) & 1.67 (2.01) & 0.614 \\ \hline JFLS20 & 1.18 (0.88) & 0.76 (1.21) & 0.474 \\ \hline Helkimo Index & 3.20 (2.78) & 0.67 (1.24) & 0.003 * \\ \hline HIT-6 & 41.20 (6.57) & 42.46 (8.96) & 0.769 \\ \hline NDI & 5.60 (4.83) & 4.00 (5.68) & 0.563 \\ \hline \hline \end{tabular}  
\end{table}
Table 4: Descriptive and comparative values of patients with/without limitation in mouth opening after endodontic intervention.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Baseline** & \
\begin{tabular}{c} **Post-Endodontic** \\ **Intervention** \\ \end{tabular} & \(p\)**-Value** &
\begin{tabular}{c} **Effect Size** \\ **(Cohen’s d)** \\ \end{tabular} \\ \hline \multirow{2}{*}{PPTs TMJ} & R & 3.39 \(\pm\) 0.97 & 2.77 \(\pm\) 0.90 & \textless{}0.001 * & 0.8427 \\ \cline{2-6}  & L & 3.33 \(\pm\) 1.02 & 2.77 \(\pm\) 0.76 & \textless{}0.001 * & 1.0249 \\ \hline \multirow{2}{*}{PPTs} & R & 2.68 \(\pm\) 0.93 & 2.1 (1.2-4.1) & 0.015 * & 0.5420 \\ \cline{2-6}  & L & 2.81 \(\pm\) 0.85 & 2 (1–4) & \textless{}0.001 * & 1.0502 \\ \hline \multirow{2}{*}{PPTs} & R & 3.2 (1.5–7.2) & 2.9 (1.5-4.9) & 0.028 * & 0.4292 \\ \cline{2-6}  & L & 3 (1.4–7.8) & 2.5 (1.5-4.5) & 0.001 * & 0.5668 \\ \hline \multirow{2}{*}{PPTs Upper trapezius} & R & 2.9 (1.2–9.2) & 2.6 (1.3–7.2) & 0.022 * & 0.4366 \\ \cline{2-6}  & L & 3.4 (1–9.4) & 2.7 (1.5–7.3) & 0.003 * & 0.5249 \\ \hline \multirow{2}{*}{PPTs} & R & 2.6 (0.9–6.9) & 2.5 (1.5–4.1) & 0.270 & 0.3213 \\ \cline{2-6}  & L & 2.8 (1.7–8) & 2.2 (1.6–4.3) & 0.001 * & 0.5404 \\ \hline \multirow{2}{*}{PPTs Subocipital} & R & 3.36 \(\pm\) 1.27 & 3.093 \(\pm\) 1.22 & 0.004 * & 0.5855 \\ \cline{2-6}  & L & 3.36 \(\pm\) 1.21 & 2.817 \(\pm\) 0.69 & 0.004 * & 0.5871 \\ \hline \hline \end{tabular}  
* VAS = Visual Analogue Scale; PPTs = Pressure Pain Thresholds; R = Right; L = Left;
* = Statistically significant (\(p\) value \(<\) 0.05).  
\end{table}
Table 3: _Cont._that 90 min of root canal therapy can produce a significant reduction in mouth opening, PPTs, and cervical mobility, as well as an increase in MTrPs.
## 3 Methods
### Environmental Impact of Aggregates
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Classification**} & \multicolumn{2}{c}{**Std. and**} & \multirow{2}{*}{**F-R30**} & \multirow{2}{*}{**F-S30**} & \multirow{2}{*}{**F-B30**} & \multirow{2}{*}{**C-R30**} & \multirow{2}{*}{**C-R30**} & \multirow{2}{*}{**C-R30**} \\  & & & & & & & & & \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Cement \\ \end{tabular} } & Density (g/cm3) & 3.15 & 3.15 & 3.15 & 3.15 & 3.15 & 3.15 & 3.14 \\  & Blaine (cm2/g) & 3450 & 3602 & 3450 & 3266 & 3390 & 3200 \\  & NFA 1 & Density (g/cm3) & 2.60 & 2.58 & 2.61 & 2.59 & 2.53 & 2.63 \\  & Absorption Ratio (\%) & 2.40 & 0.92 & 1.57 & 2.33 & 0.46 & 1.50 \\ \multirow{2}{*}{
\begin{tabular}{c} Aggre- \\ gate \\ \end{tabular} } & NCA 2 & Absorption Ratio (\%) & 2.65 & 2.60 & 2.63 & 2.72 & 2.59 & 2.75 \\  & & Absorption Ratio (\%) & 0.6 & 0.67 & 0.36 & 1.32 & 0.58 & 1.00 \\ \cline{5-10}  & & Aggregate Type & Waste Glass & Recycled & Slag & Bottom Ash & Recycled & Bottom Ash \\  & RBA 3 & Density (g/cm3) & 2.50 & 2.51 & 2.62 & 1.70 & 2.55 & 2.30 \\  & Absorption Ratio (\%) & 1.13 & 2.68 & 3.49 & - & 3.00 & 3.30 \\  & & Naphthalene & & & & & & \\ \cline{5-10}  & Chemical Admixtures & Sulfonic & Polycarbonate & Polycarbonate & Naphthalene & Polycarbonate & Naphthalene \\  & & Acid & & & & & & & \\ \cline{5-10}  & Source & [PERSON] [35] & [PERSON] et al. & [PERSON] et al. & [PERSON] et al. & [PERSON] et al. [40] & [PERSON] [41] \\ \hline \multicolumn{2}{c}{\({}^{1}\) Natural Fine Aggregate. \({}^{2}\) Natural Coarse Aggregate. \({}^{3}\) Recycled or By-product Aggregate.} & & & & \\ \end{tabular}
\end{table} 
Table 3: Physical Properties of the Materials used in the Concrete Mixture proportions. In this study, the six environmental impacts (ADP, GWP, ODP, AP, POCP, and EP) associated with each aggregate as provided by ezEPD [42], a program for environmental product declaration from the Korea Environmental Industry & Technology Institute, and the German OKOBAUDAT [34] DB were compared based on the LCI DB aggregate information presented in Table 1. In this instance, for a simple comparison of the environmental impact of each aggregate, mass in kg was used as the reference unit for sand and gravel to reflect the average unit volume weight of aggregates (1750 kg/m\({}^{3}\)).
## 3 Motivation  
Some practical challenges arise when implementing dynamic service discovery, because the attributes are dynamic. Ignoring these challenges comes at a definite cost. A service that is best suited for a particular user context can be penalized due to an inferior evaluation mechanism. Using our telescope example, we examine how these challenges arise and specifically why they constrain the applicability of the aggregation and skyline techniques. Assume there are hundreds of telescope web services available around the world. To a user interested in photographing a particular star, only a subset of these services is of particular use. Some telescopes are located in the wrong hemisphere. With others, the user may not have a service-level agreement (SLA) that permits her to use that telescope. By considering all such attributes, ideally, one can reduce the set of telescopes to a small subset that is functionally equivalent. This is referred to as functional selection: capturing a set of predefined values. Consider Table 1 depicting attributes and their values captured on four different days for three different telescope services. In this table, the functional attributes are assumed to be satisfied, denoted by a Y for yes, on all four days. After functional selection, the service subset needs to be ranked and possibly further pruned with respect to its dynamic, quantitative QoS attributes [15, 16].  
The values of all of these attributes fluctuate over time, perhaps even wildly, which complicates the matter of selecting the web service most likely to deliver the highest quality photographs. A knowledge base can maintain an expert-derived threshold value for each attribute, in each domain, to ensure an adequate quality of service.  
Because the values are captured or possibly computed at run time, they may not always be available, perhaps they did not arrive on time or were lost due to network problems. Consider what happens when the observatory weather station cannot be reached, the service provider does not provide a value for contrast or a power outage takes out the sensor that measures these values. In these scenarios, the telescope should not necessarily be discounted from consideration, but neither the skyline nor the aggregation function is well-defined over these null values.  
Null values are not the only concern. Consider visibility: even on a superb day, there can be patchy cloud cover. A particular measurement of visibility, one that captures the precise value at an exact moment in time, might signify that the visibility is poor, when in fact, there is only a momentary lapse before the cloud passes over. Another issue is latency: perhaps a particular telescope is generally slow to respond, but right after the service has been restarted, there are few connected users; a measurement at this time would overestimate the quality of the service.
## 4 Performance Evaluation  
In this section, we firstly evaluate the code phase measurement accuracy of open-loop tracking method with the code search grid sizes shown in Figure 5 for different \(C/N_{0}\) and verify the grid size calculated based on EWPRE is optimal. Afterwards, the performance of the proposed ASCA-OLTA is assessed with the GPS L1 CA signal data collected in open air area by off-line processing. Since positioning accuracy is directly related to the pseudo range calculated from the code phase measurement, by utilizing the data collected in the open air area, the positioning accuracy of the open loop tracking methods with different inherent detection grid sizes are compared with the proposed method when \(C/N_{0}\) varies. The method that uses EMLD is also considered here. Finally, with the help of the data collected from the urban area, the total performance of these methods in urban area is evaluated.
## 4 Derivation of Performance Indices and Stability Conditions
### Tracking Performance Index  
The filter is required to track complicated motions. In second-order trackers, when tracking a target moving with constant acceleration, steady state bias error occurs as a result of the difference between the motion model and the actual target motion. This provides an index of the tracking performance for an accelerating target. When the true target position \(x_{\rm tt,k}=a_{\rm c}(kT)^{2}/2\) (\(a_{\rm c}\) denotes a constant acceleration) and measurement errors are not considered, the steady state predicted error is expressed as [11]:  
\[e_{\rm fin}=\lim_{k\to\infty}(x_{\rm tt,k}-x_{\rm p,k}). \tag{25}\]  
which is called the tracking performance index.  
For the \(\alpha\)-\(\beta\)-\(\eta\)-\(\theta\) filter, the tracking performance index becomes:  
\[e_{\rm fin}(\alpha,\beta,\eta,\theta)=\lim_{z\to 1}(1-z^{-1})E_{\rm P}(z)= \frac{2-2\eta-\theta}{2(\alpha\theta-\beta\eta+\beta)}a_{\rm c}T^{2}. \tag{26}\]  
The derivation of \(e_{\rm fin}\)\((\alpha,\beta,\eta,\theta)\) is given in Appendix B. Note that when \(\eta=\theta=0\),  
\[e_{\rm fin}(\alpha,\beta,0,0)=a_{\rm c}T^{2}/\beta, \tag{27}\]  
which is the tracking performance index of the conventional \(\alpha\)-\(\beta\) filter [12].