esa-sceva/satcom-chunk-collection huggingface dataset:
# SemKoRe: Improving Machine Maintenance in Industrial IoT with Semantic Knowledge Graphs
###### Abstract  
The recent focus on sustainability and improved efficiency requires innovative approaches in industrial automation. We present SemKoRe, a knowledge graph developed to improve machine maintenance in the industrial domain. SemKoRe is vendor-agnostic, it helps Original Equipment Manufacturers (OEMs) to capture, share and exploit the failure knowledge generated by their customers machines located around the world. Based on our interactions with actual customers, it usually takes several hours to days to fix a machine-related issue. During this time, production stops and incurs cost in terms of lost production. SemKoRe significantly enhances the maintenance process by reducing the failure diagnostic time, and by centralizing machine maintenance knowledge fed by the experts and technicians around the world. We developed flexible architecture to cover our customers' varying needs, along with failure and machine domain ontologies. To demonstrate the feasibility of SemKoRe, a proof-of-concept is developed. SemKoRe gathers all failure related data in the knowledge graph, and shares it among all connected customers in order to easily solve future failures of the same type. SemKoRe received the approval of several substantial clients located in USA, UK, France, Germany, Italy and China, associated with various segments such as pharmaceutical, automotive, HVAC and food and beverage.  
failure diagnostics; industry 4.0; industrial internet of things (IIoT); knowledge graph; machine maintenance; semantic web
# Energy-Efficient Amplifiers Based on Quasi-Floating Gate Techniques
## 2 Quasi-Floating Gate Transistors
### Wideband Capacitive Coupling Using Quasi-Floating Gate Transistors
For transient operation with input frequencies larger than \(1/\tau\) (in practice from near DC), C acts as a floating battery with voltage \(V_{BIAS}\)-\(V_{DC}\), with \(V_{DC}\) the DC voltage at input \(V_{sig}\). This is because C cannot be discharged rapidly through the pseudo-resistor. The signal is thus level-shifted by \(V_{BIAS}\)-\(V_{DC}\) and attenuated by factor \(\alpha\) when it reaches the gate. In practice, nonlinearity of the pseudo-resistor may lead to nonlinear distortion and offset in the gate voltage. These issues are more relevant for large voltage swings at the pseudo-resistor terminals and depend on the implementation of the pseudo-resistors. Some of the most common fixed and tunable pseudo-resistors are shown in Figures 2 and 3, respectively. The simplest pseudo-resistor is a diode-connected PMOS transistor operating in deep subthreshold, as shown in Figure 2a [5; 35; 36]. It is very compact, but the resistance obtained cannot be modified once fabricated, and it is very sensitive to PVT variations. Moreover, the I-V characteristics are asymmetric, so it becomes nonlinear for large voltage swings. These drawbacks are usually of minor concern when the pseudo-resistor is used in high-gain amplifiers operating in negative feedback, as long as the resistance remains high enough for the QFG transistor to process the lowest frequency of the input signal \(V_{sig}\) in Figure 1c. When symmetric I-V characteristics are required, elementary devices can be mirrored either in series or in parallel, as shown in Figure 2b,c, respectively, for the case of two elements [8]. The series connection is useful when large resistance values for wide voltage ranges are needed. Some applications require tunable pseudo-resistors, where tuning can be achieved using a DC voltage [8; 12] or a DC current [13], as shown in Figure 3. Further details and examples of pseudo-resistors can be found in [8].
## 1 Introduction
Previous researches, using residence time distribution (RTD) data which evaluate the mixing of industrial columns, showed that mixing of cleaner process is similar to fully mixer conditions [26; 28; 29; 30]. Especially, [PERSON]'s research group has made advances in the design and modeling in the mixing of industrial columns. They reported that the large and small tanks in series (LSTS) as well as the two parallel perfect mixers models were remarkably flexible compared to the other structures, and that the RTD in flotation columns cannot be described by a unique model structure [31; 32]. It was also observed that the mixing regime in flotation columns could not be described by a single model structure nor could be related to the cross-sectional shapes or size of columns [31]. Flotation efficiency can be determined by factors, such as size fractions, flows of liquid and solids, and bubble rise velocity, regarding flotation kinetics in continuous operation [33]. Furthermore, dispersion and mixing are the most important characteristics for column geometry [23; 26]. Several studies have been conducted for the design and scaleup of column flotation by applying the axial dispersion model and the drift flux model [10; 24; 25]. [PERSON] et al. [14] reported that values of axial mixing coefficient (\(E\)), vessel dispersion number (\(N_{d}\)), bubble diameter (\(D_{b}\)), column height (\(H_{c}\)), and gas holdup (\(\varepsilon_{g}\)) were assumed, and the column diameter was calculated as 5.42 (cm). Considerable research efforts have been mostly made in terms of the large and the industrial columns. In particular, there are few existing researches that applied and compared various models, such as plug flow, fully mixed tanks, and axial mixing in CPT column flotation for coal. This paper discusses the estimation of flotation rate constants based on various models that describe different residence time distributions, including those that describe axial dispersion, in an attempt to identify the most suitable model for column flotation. In addition, we aim to develop an empirical relationship of vessel dispersion number (\(N_{d}\)) that can be applied for designing and scaling up of the flotation columns under operating conditions for coal beneficiatio
# Code Edit Recommendation Using a Recurrent Neural Network
## 5 Evaluation Setup
### Experimental Procedure
#### 5.3.2 Hyperparameter Set-Up  
In CERNN, we have five main hyperparameters as shown in Table 2. Regarding the sliding window described in Section 4.3.2, we set up the window size to 3 (W = 3). Additionally, there is the embedding size that determines the size of the vectors that are used to convert program elements into several numeric values. We set up the embedding size to 512. Next, the batch size represents the size of the training data to be used at once. Regarding the batch size, it is generally known that 32 is approximately the most effective value. Therefore, we set up the batch size to 32. Then, the epoch is the number of training repetitions. We repeatedly changed the epoch value from 100 to 600, increasing it by 50. From the experiment, we selected one of the optimal values, 500, for the epoch value. Finally, a threshold is used to determine which program elements should be recommended. We changed the threshold value from 84 to 95, increasing it by 1, and selected one of the optimal values, 91, for the threshold.  
The reasons why we set up the values of hyperparameters as in Table 2 are as follows. In order to compare our approach CERNN with MI-EA [1], we set the representative window size to 3, which is the same size as [PERSON] et al.'s [1]. Regarding the epochs and the threshold, we varied their values and observed the trends of the recommendation accuracy. Figure 11 shows the recommendation accuracy obtained from the five projects mentioned earlier, by varying the epochs value changes from 100 to 600, increasing it by 50. The curve in Figure 11 shows that the recommendation accuracy is high when the number of epochs ranges from 400 to 600. We also checked the weighted average recommendation accuracy of the five projects and found that the curve is close to flat from 100 to 600. Therefore, we concluded that our set-up of the epochs value to 500 is reasonable.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Default Value** \\ \hline window\_size & 3 \\ embedding\_size & 512 \\ batch\_size & 32 \\ epoch & 500 \\ threshold & 0.91 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Values of hyperparameters.  
Figure 11: Average recommendation accuracy from varying epochs value with five projects.  
Figure 12 shows the average recommendation accuracy of the five projects, by varying the threshold value from 84% to 95%, increasing it by 1%. The curve in Figure 12 shows that the recommendation accuracy (F-score) is highest from 0.91 to 0.94. We also checked the weighted average recommendation accuracy of the five projects, and we found that the highest points are at 91 and at 93. Therefore, we found that our set-up of the threshold value to 0.91 is reasonable.
## 2 Smartphone Global Navigation Satellite System (GNSS) Data Collection and Analysis
### Data Collection  
Data collection experiments were conducted on the rooftop of an office building in Central South University, China. We adopted the short-baseline static mode, which is approximately 5 m for the baseline length. The reference station was equipped with a survey-grade antenna (TRM 57971.00) and a geodetic receiver (Trimble Net R9), and the rover station was a Xiaomi Mi 8 smartphone running the Android 9.0 operating system. This smartphone supports five constellations with dual-frequency signals (i.e., GPS L1/L5, GLONASS G1, BDS B1, Galileo E1/E5a, and QZSS L1/L5), and its duty cycle [6], which is a battery-saving mechanism in the smartphone GNSS module, can be turned off. In this study, we disregarded the QZSS constellation. To obtain the smartphone GNSS observation data for analysing and solving the data in post time, we used the Geo++ RINEX Logger (v2.1.3) [20] Android mobile application. During measurement, users only need to turn on the \"Dual GPS\" positioning function and click start logging to start recording the observation data. The application automatically stores the GNSS data in RINEX format, which is convenient for users to directly process GNSS data. Approximately 5 h of data were collected on 15 November 2020 at a sampling interval of 1 s. The first 3 h was used for analysis, and the results of the last 2 h were consistent with the results of the first 3h, so we do not use the last 2 h to reduce the calculation time. It is considered that the sampling rate of 1 Hz is high enough; hence, 3 h is sufficient to guarantee the required amount of data.  
Although mobile phone manufacturers do not publish the location of the smartphone GNSS antenna, [PERSON] et al. [21] estimated the antenna position of Xiaomi Mi 8 through relative positioning with centimetre-level accuracy. The antenna phase centre is on the left side of the front of the phone (Figure 1). To facilitate centring, we used the top centre of the smartphone as the antenna reference point (ARP), whose distance from the phase centre is shown in Figure 1.  
The fixed control point (FCP) shown in Figure 2 was used as reference, and its accurate coordinates were obtained using two geodetic receivers through relative positioning. To ensure full GNSS signal reception, we placed the smartphone vertically using a holder. The FCP was taken as the centring point, and the centring accuracy was within 1 cm. We measured the distance between the smartphone ARP and the FCP along three directions(i.e., east, north, and up) and the angle between the orientation perpendicular to the screen and the true north (28.0\({}^{\circ}\)). Hence, we calculated the offset along the east, north, and up directions to obtain the overall phase centre offset provided in Table 1.
# Genetic Algorithms to Maximize the Relevant Mutual Information in Communication Receivers +
## 2 Preliminaries
### Genetic Algorithm Optimization  
In Sections 3.1.1-3.1.3, different approaches to the problem of finding the (approximate) nearest neighbor \(\mathbf{\theta}_{t}\) of the system input \(\mathbf{y}\) from Figure 2 were proposed and described. Our intention is using the described approaches to implement the mapping \(t=f_{\mathbf{\theta}}(\mathbf{y})\). In doing so, the parameters \(\mathbf{\theta}=[\mathbf{\theta}_{0},\mathbf{\theta}_{1},\dots,\mathbf{\theta}_{|\mathcal{T}| -1}]\) shall be tuned, such that the mutual information \(\mathrm{I}(\mathrm{X};\mathrm{T})\) is maximized for a given \(|\mathcal{T}|\). This naturally raises the question of how we can determine optimum parameters \(\mathbf{\theta}\). We propose to perform the optimization of the parameters \(\mathbf{\theta}=[\mathbf{\theta}_{0},\mathbf{\theta}_{1},\dots,\mathbf{\theta}_{|\mathcal{T}| -1}]\) for the considered mappings and irrespective of the used distance function \(d(\mathbf{y},\mathbf{\theta}_{t})\) with a genetic algorithm for various reasons explained in the following. Afterwards, we describe how to perform the parameter optimization with a genetic algorithm.
# Soliton Content of Fiber-Optic Light Pulses
## 3 Extensions to the Nonlinear Schrodinger Equation
### Cases Involving Several Solitons
#### 3.3.2 Soliton Gas and Crystal  
Another situation in which soliton propagation is subject to periodic perturbation was described in a series of experiments about a synchronously-driven fiber resonator [47] (using non-DM fiber). Free propagation in the fiber alternated with interference at an input coupler. As a result, relatively long input pulses organized themselves into a pattern that contained a multitude of short pulses;  
Figure 7: Evolution of a dispersion-managed soliton over two dispersion map periods.  
each of those fulfilled the soliton condition Equation (4). The pattern could be either chaotic (soliton gas) [48] or periodic (called soliton crystal) [49], depending on the drive power.  
Similar phenomena have also been reported from fiber lasers [50; 51; 52], which, however, are not described by the NLSE, as that does not consider nonlinear gain and loss. An equation including these effects is the cubic-quintic complex Ginzburg-Landau equation. One finds that there are soliton-like solutions of this equation that are called dissipative solitons [53; 54; 55].
## 3 Results
### Damage Spatial Correlation  
Considering both directions of probability calculations, the enhanced and deblurred \(\mu\)CT imaging showed a greater spatial correlation of damage with BSE than the baseline \(\mu\)CT imaging (BSE-labeled damage occurring near \(\mu\)CT labeled damage and vice versa). As expected, spatial correlation probability between image pairs (BSE and baseline \(\mu\)CT, BSE and enhanced \(\mu\)CT, BSE and deblurred \(\mu\)CT) increased with larger inflation radii (Figure 5). Post-hoc pairwise comparison showed the probability of finding \(BaSO_{4}\)-labeled pixels in enhanced \(\mu\)CT images near \(BaSO_{4}\)-labeled pixels in BSE images was significantly greater than in the baseline \(\mu\)CT images for inflation radii of 10-40 \(\mu\)m (\(p\) = 0.0002-0.045, Figure 5a). When BSE images were inflated (Figure 5a), the deblurring algorithm showed no significant differences compared to enhanced \(\mu\)CT at any inflation radii (\(p\) = 0.32-0.99). The probability of spatial correlation approached 1 when the BSE images were inflated (Figure 5a), as the DAF of BSE images also approached 1 (due to the inflation of many small, distributed pixels throughout the specimens), increasing the likelihood of finding a \(\mu\)CT pixel in this area.  
The probability of finding a \(BaSO_{4}\)-labeled pixel in a BSE image spatially close to one in a \(\mu\)CT image was significantly different for each \(\mu\)CT modality at all inflation radii. Post-hoc pairwise comparison showed the deblurring algorithm to have the highest probability of spatial correlation compared to baseline and enhanced images at all inflation radii (\(p\) < 0.0001-0.002, Figure 5b). For both directions, there were no significant differences across \(\mu\)CT modalities when no convolution was applied (\(p\) = 0.082-0.99, radius = 0 \(\mu\)m).  
Figure 4: Damage area fractions (\(BaSO_{4}\) area/bone area) of \(BaSO_{4}\) label fields in \(\mu\)CT and BSE images before (radius = 0 \(\mu\)m) and after convolution inflating the label fields at six radii. The data represented are from eleven sagittal BSE slides from rat L1 vertebrae (healthy = 5, osteolytic = 3, mixed = 3). * \(p\) < 0.05, ** \(p\) < 0.01, *** \(p\) < 0.001, *** \(p\) < 0.001, Nemenyi pair-wise comparison.
# Tracking Extended Targets: Novel TPMB Filter Driven by Model and Data Collaboration
###### Abstract
Due to the decrease in tracking accuracy of adaptively modeled maneuvering targets under noise and clutter, we have developed the ASCKF. This involves adaptively estimating the covariance of process and measurement noise, ensuring that the algorithm's performance does not depend on noise statistics and is more robust when these statistics are unknown. The rest of the paper is organized as follows. Section 2 introduces the background on state and trajectory parameters, the trajectory set, and TPMB. Section 3 provides the BP cycle for TPMB marginal posterior probabilities and the principles of factor graphs, further detailing the adaptive gating improvement for factor graphs and the ASCKF mixed particle filtering method. The implementation of ASCKF for unknown noise is presented in Section 4. Simulation results are provided in Section 5, and the conclusion is given in Section 6.
# A Learned Reduced-Rank Sharpening Method for Multiresolution Satellite Imagery
## 3 Results
### Fusion Evaluation
Visual comparisons show that the proposed method gives very clear results for both the 20 and 60 m bands from the S2 and the sharpened 30 m bands from L8, as seen in the bottom rows of Figure 10, with obvious improvements to fine detail compared with the inputs in the top row. The SLSharp image is slightly softer, while the ATPRK results are rougher and exhibit more artifacts.
# Discrimination of Spatial Distribution of Aquatic Organisms in a Coastal Ecosystem Using eDNA
## 2 Materials and Methods
### Data Analysis and Statistics  
OTUs assigned by meta-barcoding were classified into habitat types (marine, freshwater and estuarine), trophic level (first consumer [filter feeder], second consumer [carnivore], producer and symbiotic) and ISR (indigenous, non-indigenous). Canonical correspondence analysis (CCA) of OTUs exhibiting \(>\)1% relative abundance was performed using the PAST 3.0 program [34] to evaluate relationships between abundance of OTU sequences (based on genus level identification) and environmental factors (temperature (Temp.), salinity, total phosphorus (TP), total nitrogen (TN), chlorophyll-\(a\) (Chl-\(a\)), total organic carbon (TOC), total inorganic carbon (TIC), total carbon (TO), elemental carbon (EC)). All CCA results were constructed using relative abundance data, with natural logarithms transformation (In \(1+\) X) used for sample normalization. Linear relationships (Pearson correlations) were calculated between the above environmental factors and classified OTU sequences, based on habitat using XLSTAT version 2018.6.54467 (64 bit) as a plug-in for the Microsoft Excel program [35].
# Measurement Repeatability of Rail Wheel Loads Caused by Rolling Surface Damages
## 1 Introduction
To ensure the railway transport safety, decision-makers of rail companies need, inevitably, to monitor the geometrical parameters of the track and the condition of the vehicle wheel rolling surfaces. Wheel damage (flats, crumbling, cavities, cracks, wear, out-of-roundness, etc.) endangers traffic safety, i. e., the risk of derailment. The intensive damaging of the rails and track as a whole track geometry also increases. Mathematical models of the impact of wheel flat damage on the rail usually developed using the reverse Laplace transformation methodology. These models were later developed by researchers from Ukraine and other countries [15]. It is noted in the literature that it is not entirely clear how short-term loads affect the systemic wear of the wheel surface. It is preliminarily known that the wear processes are influenced by such factors as surface irregularities and roughness, material hardness, and elasticity [11]. Due to the large number of factors, processes are modeled by simplifying them [16]. As an example, one can give the fact that elastic and plastic deformations and the formation of metal cracks as a result of them are not always taken into account when modelling processes [17]. In order to create a more reliable wheel wear prediction tree, it is necessary to evaluate not only one or two factors, but many more, perhaps a dozen or a few tens [18]. Both when examining wheel damage and when examining the causes of traffic accidents, it can be seen that wheel damage frequently causes traffic accidents (on the railway, traffic accidents are considered not only accidents with drastic consequences, but also minor incidents when train traffic is stopped for a short time) [19]. Flats and cracks are the most common damage of the rolling surface of the wheel to rail [20]. As rolling stock speeds increase, increasing dynamic forces affect the development of damages [21]. According to the impact on traffic safety, it is accepted to preliminarily divide wheel-rolling surface damage into two types [22]:  
1. Damages that cause a short-term loss of wheel-rail adhesion (cracks, flats, bends);
2. Damages that do not cause loss of wheel-rail adhesion (e.g., uneven wear). Some of the wheel damage may go unnoticed during the external inspection due to the human factor, so it is necessary to improve the automatic damage detection systems [23]. Different diagnostic systems associate the magnitude of the wheel surface damage differently with the impact force (the data processing algorithms used for this purpose may differ). This study aimed to determine whether the repeatability of force measurement is more determined by the nature of the wheel damage or the driving speed, and to determine whether it is possible to detect the laws of the dependence of the repeatability of this measurement on the cause.