esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Results, Analysis, and Discussions
### Radial Configuration
* A remarkable aspect of the DSCA-BONMIN methodology is the existence of D-STATCOMs in the commercial and industrial zones (Figure 2), similar to the GAMS solvers and the genetic-convex methodology and unlike the DCCBGA methodology, where there are D-STATCOMs in each zone. Furthermore, the sizes of the D-STATCOMs located by the DCCBGA and DSCA-BONMIN are similar. The only representative differences are the change from node 25 to node 8, and a reduction of 14.39% for the first location, 11.17% for the second, and 24.39% for the third with regard to the size in each of the locations. These differences represent the improvement in the annual operating costs. Finally, Figure 5 shows the convergence graph of the methodology implemented for the 33-node IEEE test system in its radial version, from which the following can be concluded. The DSCA-BONMIN finds a value of 106,711.67 USD/year for this system in its first iteration. In the second iteration, it finds a lower objective function value, with 106,677.52 USD, demonstrating the efficiency of the methodology. Around iteration 19, the DSCA-BONMIN continues to reduce the value of the objective function, which indicates that the proposed methodology is rapidly converging to the global optimum. The above confirms that the DSCA-BONMIN is an effective and robust tool to solve the problem regarding the location and sizing of D-STATCOMs in distribution systems in order to reduce the annual operating costs. This makes the proposed methodology the best option to solve the studied problem in 33-node IEEE test systems with a radial topology and obtain the best solution from an economic point of view while respecting all of the system operator's constraints. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methodology** & **Node Location** & **Sizes (Mvar)** & **Annual Cost (USD/Year)** & **Reduction (\%)** \\ \hline Benchmark case & - & - & 130,580.82 & - \\ \hline XPRESS & [13; 16; 32] & [0.1822, 0.0727, 0.2328] & 112,376.45 & 13.94 \\ SBB, DICOPT, and LINDO & [13; 16; 32] & [0.1850, 0.0825, 0.4478] & 109,768.70 & 15.94 \\ Genetic-Convex [34] & [14; 30; 32] & [0.2896, 0.5593, 0.1177] & 109,455.96 & 16.18 \\ DCCBGA [47] & [14; 25; 30] & [0.2327, 0.1056, 0.5403] & 108,196.46 & 17.14 \\ \hline DSCA-BONMIN & [14; 8; 30] & [0.1992, 0.1174, 0.6721] & 106,528.98 & 18.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results obtained by the metaheuristic optimizers and the proposed methodology for the radial configuration.
## 2 Materials and Methods  
### Description of the Flux Sites  
As illustrated in Figure 1, in this study, we used the eddy covariance (EC)-based measurements of ecosystem carbon fluxes and climate data at the forest (CH-Dav: 2002-2005 and 2017-2020) and grassland (CH-Oe1: 2002-2005; CH-Cha: 2017-2020) sites before, during, and after the droughts of 2003 and 2018. These data can be accessed from [[http://www.europe-fluxdata.eu/](http://www.europe-fluxdata.eu/)]([http://www.europe-fluxdata.eu/](http://www.europe-fluxdata.eu/)) (accessed on 8 March 2023). Both sites belong to the Swiss FluxNet national eddy covariance network [3]. The climate of this region is temperate, with elevation as the confounding factor, particularly for the subalpine evergreen forest in Davos. The CH-Dav flux site (9\({}^{\circ}\)51\({}^{\prime}\)21.3\({}^{\prime\prime}\) E, 46\({}^{\circ}\)48\({}^{\prime}\)55.2\({}^{\prime\prime}\) N) is located in Davos, Switzerland, at an elevation of approximately 1600 m above sea level. The vegetation type is dominated by the evergreen needle-leaf forest of mostly Norway spruce. A 45-meter tall tower was equipped at this site. Multi-year mean temperature and annual precipitation are 3.4 \({}^{\circ}\)C and 992 mm, respectively [24]. The CH-Oe1 flux site (7\({}^{\circ}\)43\({}^{\prime}\)55.9\({}^{\prime\prime}\) E, 47\({}^{\circ}\)17\({}^{\prime\prime}\)8.1\({}^{\prime\prime}\) N) is located on the Central Swiss Plateau near Oensingen, Switzerland, at an elevation of about 452 m above sea level. The flux tower was built up in an intensively managed grassland at 1.2 m above ground with the dominant species of English ryegrass, meadow foxtail, and white clover. The grassland was cut typically four times per year. Multiyear mean temperature and annual precipitation are 9.5 \({}^{\circ}\)C and 1184 mm, respectively [25; 26]. The dominant vegetation for the low-elevation grassland site (CH-Cha) is a mixture of Italian ryegrass and white clover, predominantly used for fodder production and occasional winter grazing by sheep [20]. Multi-year mean precipitation and temperature are 1136 mm and 9.5 \({}^{\circ}\)C, respectively. The flux site (8\({}^{\circ}\)24\({}^{\prime}\)38\({}^{\prime}\) E, 47\({}^{\circ}\)12\({}^{\prime}\)37\({}^{\prime\prime}\) N) was operational since July of 2005.
## 2 GPS Attitude Baseline Determination  
It has been known for many years that GPS multi-antenna systems are well suited for the attitude determination of mobile objects [18, 19]. The accuracy of a GPS attitude angle depends on the baseline length, which is limited to _ca._ 1 m for small UAVs, and the accuracy of the baseline coordinates. To achieve sub-cm accuracies of the baseline coordinates, which lead to sub-degree attitude accuracies for a 1-m baseline, carrier phases have to be observed, and the carrier phase ambiguities have to be fixed to their correct integer value. For kinematic applications, this ambiguity resolution should be fast and reliable at the same time.  
In recent years, many different approaches have been investigated to allow for the ambiguity resolution in the attitude determination. For example, motion-based methods rely on the motion of the vehicle or changes in the receiver-satellite-geometry [19,20]. Many other procedures make use of the LAMBDA (Least-squares AMBiguity Decorrelation Adjustment) method [21]. Due to the integration of the known baseline length or geometry in the ambiguity objective function, this procedure has been improved for the attitude determination [22, 23, 24, 25]. Further improvements are possible, when also inertial sensor readings are used to reduce the number of candidates in the ambiguity search space [16,26, 27, 28, 29, 30, 31]. For example, in [PERSON] _et al._[29], an instantaneous ambiguity resolution procedure based on the ambiguity function method (AFM) [32], aided by MEMS gyroscopes, is presented. [PERSON] _et al._[28] applied an enhanced approach of the extended LAMBDA method, which has been developed by [PERSON] _et al._[16]. To accelerate the ambiguity resolution, they use the baseline length and magnetic field observations as constraints. However, as described in [31], a fast or even instantaneous ambiguity resolution with a constrained LAMBDA method can be difficult, when the ambiguity float solution is systematically wrong, due to poor GPS measurements under challenging GPS measurement conditions in urban areas.  
In our approach, we aim for an instantaneous and reliable single-frequency GPS ambiguity resolution during UAV flights. Therefore, we use the information from additional sensors (GPS position, inertial sensors and magnetic field observations) and the known baseline length to improve the ambiguity float solution. Afterwards, we perform a search in the ambiguity domain, using the modified LAMBDA method (MLAMBDA) [33]. For a further search and also a validation step, we perform a transformation into the coordinate domain, where the AFM and further checks are also applied.
## 5 Translational Temperature Determination in HAI-Trap
### Correlation between Ions' TOF Distribution and Their Translational Temperature
Using the time-of-flight (TOF) trace corresponding to the tomography from Figure 6, and the linear fit for temperatures above 60 K in Figure 9, one can calculate the temperature of the \"benchmark measurement\" as 370(12) K. This is in line with the predicted value by the Monte-Carlo simulations. Since the tomography measurements are time-consuming, the calibration between the TOF variance and temperature of the ion ensemble provides a fast and reliable thermometry method to determine external or translational temperature of the ions trapped in a rf trap. Figure 8: Heatmap representation for the density distribution of ions in the trap. (**a**) Homogeneous ion density distribution at 373 K. (**b**) Ion density distribution at 30 K exhibiting regions of global maxima called “pockets”. The depth of the pockets is calculated from the energy of ions \(E\) via the relation \(T=\frac{2E}{k_{\mathrm{B}}}\) to \(\sim\)60 K. The axes used are corresponding to the coordinates shown in Figure 2.
# Zero-Shot Recognition Enhancement by Distance-Weighted Contextual Inference
## 4 Experiments
### Experiments on Imagenet Categories
#### 4.2.3 Results
SM, CS and HM imply the semantic, cosine and harmonic similarity measurements, respectively. 16.36 (40%), and 20.55 (54%) in the classic setting, respectively and 3.31 (95%), 3.31 (324%), 3.31 (670%), and 4.54 (53%) in the generalized setting, respectively. The enhancements in the generalized setting are mostly higher than those in the classic setting, which indicates that the influence of contextual inference increases as the number of target categories increases. That in turn implies that the surrounding objects do have a common relationship, and thus, the context induces predictions of relevant types of categories among the various types of the entire categories. CS shows better performance than SM in the classic setting but poor performance in the generalized setting. In several evaluations, it has no effect at all. We attribute that difference to the properties of the external knowledge sources. A semantic embedding space is trained from a text corpus in a sub-symbolic manner; thus, it contains less intuitive information, such as concept relations or category types, than a symbolic knowledge graph, which explains the relatively poor effectiveness of CS when the number of categories is large. Moreover, for SJE, LatEm, and ConSE, the calibrated prediction performances are mostly the same in the generalized setting, which indicates that the visual inference results are not referred in the predictions in those cases. In other words, our experiments have verified the importance of considering contextual information. * Top-n Result  
The top-N evaluation is conducted on the HM\({}_{path}\) method which shows generally flat performance in the previous hit@1 evaluation (Table 3). Our proposed method enhances performance compared with the baselines even in the top-n. As n increases, the rate of performance improvement tends to decrease slightly. In particular, according the performance of the ConSE, baseline at top-n is relatively higher than top-1, the improvement rate becomes lower. In other words, it implies that context plays a particularly important role in giving the correct category the highest ranking. Moreover, the significant effectiveness of similarity-based contextual inference is validated in the top-n prediction results. * Ablation Study  
We conduct an additional experiment to validate whether a nearby object is highly relevant to the target object. The ablation study is performed by excluding the module that applies the defined distance calculation from the entire model. For the contextual inference, the HM\({}_{path}\) calibration is applied with a normal average instead of the distance-weighted average in Equation (9). As seen in Table 4, even with only the similarity measurement, our proposed approach outperforms the existing models by a large margin. In most cases, however, the ablation study confirms that distance weighting boosts performance, verifying the validity of our defined distance calculation.
###### Abstract  
Singular value decomposition (SVD) is a widely used and powerful tool for signal extraction under noise. Noise attenuation relies on the selection of the effective singular value because these values are significant features of the useful signal. Traditional methods of selecting effective singular values (or selecting the useful components to rebuild the faulty signal) consist of seeking the maximum peak of the differential spectrum of singular values. However, owing to the small number of selected effective singular values, these methods lead to excessive de-noised effects. In order to get a more appropriate number of effective singular values, which preserves the components of the original signal as much as possible, this paper used a difference curvature spectrum of incremental singular entropy to determine the number of effective singular values. Then the position was found where the difference of two peaks in the spectrum declines in an infinitely large degree for the first time, and this position was regarded as the boundary of singular values between noise and a useful signal. The experimental results showed that the modified methods could accurately extract the non-stationary bearing faulty signal under real background noise.  
Keywords:singular value decomposition; incremental spectrum of singular entropy; curvature spectrum; noise-reduction; adjacent maximum peaks +
Footnote †: journal: Applied Sciences
## 4 Experimental Results
### Experiment Analysis
#### 4.2.2 Real Data--Retail  
We varied the support level from 0.05% to 0.01% for the FDCNB-MINING and FLR-mining algorithms in six different simulation cases of the network bandwidth environment to study the environment effects on the ratios of execution times. The data [18] was retail-market data from an anonymous Belgian retail store. The number of transactions, number of items, and the average transaction length were 88,162, 16,470, and 10, respectively. Table 1 describes the six network bandwidth environment cases.  
Figure 3 shows that the ratio of the execution times of the two algorithms (here denoted FDCNB-MINING/FLR-mining) was close to 1 in Case 1. In Case 2, where there were severe changes in the network bandwidth environment, the FDCNB-MINING algorithm only required 34.14% of the execution time of the FLR-mining algorithm when the support level was 0.01%. In Cases 3 to 6,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Support (\%)** & **0.07** & **0.06** & **0.05** & **0.04** & **0.03** \\ \hline Case1 & 0.9947 & 0.9970 & 1.0028 & 1.0002 & 1.0066 \\ Case2 & 0.5960 & 0.6056 & 0.5921 & 0.5764 & 0.5179 \\ Case3 & 0.9707 & 0.9573 & 0.9396 & 0.9656 & 0.9575 \\ Case4 & 0.9188 & 0.9154 & 0.9107 & 0.9395 & 0.9105 \\ Case5 & 0.8583 & 0.8781 & 0.8542 & 0.8742 & 0.8739 \\ Case6 & 0.8350 & 0.8265 & 0.7878 & 0.7485 & 0.7140 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Proportions of execution time for FDCNB-MINING/FLR-mining while varying support from 0.03% to 0.07% and for Case 1 to Case 6.  
Figure 2: Effect of varying support from 0.03% to 0.07% on proportions of execution time in FDCNB-MINING and FLR-mining.  
our proposed FDCNB-MINING method required about 82% of the execution time of the FLR-mining algorithm. Table 3 shows the detailed execution time ratios against support levels. In frequent pattern mining, a lower support threshold resulted in a much higher number of frequent patterns and is therefore one of the performance bottlenecks. We observed that the proportion of execution time of FDCNB-MINING to that of FLR-mining decreased with the decrease in support threshold from 0.05% to 0.01% for most of the cases, meaning that the proposed FDCNB-MINING was more scalable than FLR-mining. The average proportion of execution time of FDCNB-MINING to that of FLR-mining was about 78%.
# Forecasting Multivariate Time Series
### Data Profiling
#### 2.2.2 Stationarity  
A time series whose traits are not subject to time is stationary. Thus, the presence of a trend or seasonality indicates the series is not stationary. The observed property at different times gets affected by trend and seasonality. A time series has three components, which are season, trend, and noise. The season can be defined as the reiterating short-term cycle in the series. The trend is the slope in the data, which can rise or decline with time, whereas the noise is the random change in the series. It is often considered challenging to deal with time series data that contain seasons or trends. Plotting the time series gives instant information about the presence of a season and a trend.  
Figure 2: Preptimage: Sequential workflow for time series data preprocessing and model selection. Here, # represents the comment.  
There are two common approaches to deal with seasonal time series. One method is to split the series by removing the seasonal and trend component and then use the series for analysis or use any other transformation technique to obtain the nonseasonal representation of the series. The other way is to use specially designed forecasting techniques for seasonal series like SARIMA.
## Materials and Methods  
### Synthesis and Characterization of ZrO\({}_{2}\) Nanoparticles  
Zirconium oxide nanoparticles (ZrO\({}_{2}\)) were synthesized according to the literature by the co-precipitation method [24; 25; 26], using octahydrate zirconium chloride (Sigma-Aldrich, 98% purity, Saint Louis, MO, USA) doped at 7.5% mol of YO\({}_{1.5}\), yttrium chloride, anhydrous, YCl3 (Sigma-Aldrich, 99.9% purity, Saint Louis, MO, USA) as precursor salt, and ammonium hydroxide NH\({}_{4}\)OH as a precipitant. Each precursor salt was dissolved separately in deionized water with constant agitation for 30 min, and then they were mixed with continuous stirring for another 30 min. Subsequently, ammonium hydroxide 25 wt % was added. The pH of the solution was maintained \(\sim\)9; the addition of this was done drop by drop, to avoid a strong agglomeration in the solution. After precipitation, the slurry was stirred for another 30 min and then heated at 80 \({}^{\circ}\)C for a certain time under continuous stirring. The resultant gel was filtered, and then washed with deionized water until no Cl\({}^{-}\) was detected. The obtained powders were calcinated at 500 \({}^{\circ}\)C for 1 hour, according to the equilibrium phase diagram of ZrO\({}_{2}\)-YO\({}_{1.5}\)[16]. Samples were analyzed by X-ray diffraction (Bruker AXS D8 Advance, diffractometer, at a voltage of 40 kV, current of 40 mA and using Cu K\(\alpha\) radiation with a wavelength of 1.54 A, Cancun, QR, Mexico) to determine the present phases, and crystallite size was calculated using the D. Scherrer formula from the peak intensities of the X-ray diffraction (XRD) patterns [27].  
The morphological analysis was carried out by scanning electron microscopy (SEM) in a JEOL JSM-6010 PLUS/LA microscope, (Chetumal, QR, Mexico), equipped with energy dispersive X-ray spectroscopy (EDX).
## 1 Introduction
### Literature Review  
#### 1.1.1 Grouping Strategy under Preventive Maintenance  
Preventive maintenance helps keep equipment in a desired state with the purpose of preventing failure and unscheduled stoppages associated with high corrective costs that are often higher the preventive maintenance cost. Therefore, preventive maintenance is a set of maintenance activities scheduled at predefined time intervals according to criteria intended to reduce the probability of failure or degradation of asset functionality [10].  
According to [PERSON] [11], maintenance tasks and responsibility begin at the design stage of a plant. They also identify 16 different design tasks benefiting the performance of a plant. Hence, \"design for maintenance\" is one of the preliminary tasks to implement in a plant. In fact, maintainability has been considered a design attribute for a long time [12].  
Following design, planning is the next step, which is defined as the process to determine future decisions and needed actions to achieve predefined objectives and goals [13]. Depending on the addressed time horizon, is possible to identify three basic levels of planning:  
1. Long-term planning, covering a horizon of several years;
2. Medium-term planning, covering horizons from approximately one month to a year;
3. Short-term planning, covering daily and weekly horizons.  
In the context of maintenance scheduling, there are several criteria to allocate resources and time; nevertheless, the system is modelled under a functional view. Moreover, the behaviour or operation of a system is defined by the interactions between the elements within the system; these are economic dependencies, structural dependencies, and stochastic dependencies. These dependencies condition the optimal specific actions for each component, and therefore, the advantages of grouping or ungrouping rely on these dependencies.  
Usually, the most important challenge when optimising a maintenance schedule arises from the stochastic models that describe the behaviour or degradation of a component, together with the combinatorial problem associated with grouping maintenance tasks. Among these, it is possible to identify two types [14]:  
1. Stationary models used in stable situations where the planning horizon may even be infinite;
2. Dynamic models wherein planning and its rules may change over the horizon according to short-term data.  
The latter kind (dynamic model) is of particular interest for this article because in a dynamic planning model, the available resources change in the short term, whereas procurement planning usually responds to a long-term plan. Hence, in the short term, resources can be a limitation, especially when grouping tasks.
# Concerto: Dynamic Processor Scaling for Distributed Data Systems with Replication
## 4 Design of Concerto
### Replication Lag Analysis  
The replication lag refers to the delay between a write occurring to the master and being reflected on the replica. When the replica has long write latency, the replication lag inevitably increases, leading to the provision of more stale data in the replica and increasing the risk of data losses in the failure. In addition, a long replication lag indicates to the master that a problem occurs in data transmission, causing it to re-transmit data to the replicas. This is termed partial synchronization, and it unnecessarily increases both the master-node burden and the network traffic.  
However, our experimental analysis shows that it is unlikely for the above situation to occur in practice. Figure 3 shows the propagation rate of the master and the data processing time in the replica with and without the scaling down of the processor. This result is obtained by measuring the duration of each component in Redis, running the memtier benchmark with one million SET operations. Each operation has a 16 B key and a 1 KB value. We run Redis with a single master with two replicas. The numbers are the averages of the overall results. In the figure, we find that the processing speed of the replica greatly exceeds the propagation rate even when the processor scales down. The write request from the master arrives once every 77.69 us on average, while the corresponding processing time takes 10.98 us and 22.28 us on the replica when the processor runs at full speed and when  
Figure 2: Read latency analysis and overall architecture of Redis with Concerto.  
it scales down, respectively. Because the master is responsible for more functions than the replica, such as responding to client and synchronizing replicas, it has much longer processing time per request than the replica.  
Figure 4 shows the write path of the master and of the replica, respectively. The master initially reads the data from the kernel buffer to the query buffer and applies it to the database. Subsequently, it maintains the data within the two buffers: backlog buffer and propagate buffer. They are needed to propagate the data safely to the replicas. In contrast, the replica only performs the first two steps. As a result, the master undertakes data copying six times in total, while the replica does this only three times. For this reason, the propagation rate of the master lags the processing speed of the replica, even when the replica runs on the scaled-down processor. This outcome shows that, although the write latency increases by nearly two times, it can be hidden because write processing is finished before the next request.