esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Computational Results for the TS/VNS-ATM Algorithms
This leads to another interesting conclusion: The majority of late orders from previous PPPs constitute a child whose father or fathers are on the current PPP. When released immediately, an additional \(p(i,j)\) is identified. If late orders had no correlation whatsoever with the remaining ones, then the performance of TS/VNS would degrade, especially for \(\delta=1\), since late orders occupy additional potential slots that could be used for proper orientation. The computational overhead of the TS/VNS module is correlated with the number of production orders released and not the total number of \(p(i,j)\) pairs. Overall running times are shallow and can be considered substantial only for the door lock assembly group and the casting phase. No clear conclusions can be drawn regarding the superiority of any single PDR in any group. Apparently, a general pattern can be identified for all functional groups. For example, in the casting phase, \(\delta=1\) LPT appears to perform remarkably well, whereas PSF comes in second, and SPT notes the worst performance of the 3. If \(\delta=0\), the values of \(w1,w2,w3\) do not change significantly, but the contribution of SPT increases. Indexes appear to be genuinely stochastic only in the cutting group, while \(w1,w2,w3\) appear to be entirely differentiated between Tables 7 and 8. The performance of the ATM module validates the approach taken by the authors in tackling this scheduling problem. In most cases of \(p(i,j)\) pairs, there is enough stock of \(i\) to commence production of \(j\) with the starting time \(rjm_{j}\) defined by the TS/VNS algorithm. The maximal number of wrongly oriented \(p(i,j)\) is 8 and is found in the assembly group if late orders are released. The vast amount of orders traversing this group makes additional order transferring by the planner inevitable in order to fully satisfy the BOM precedence constraints. In the other groups, minor adjustments need to be made in the TS/VNS schedule since, in most cases, an average number of 4 \(p(i,j)\) orientations necessitate fixing. The parameter \(\delta\) has a substantial impact on the ATM output, and this can be seen in both tables. If \(\delta=1\), more children are scheduled later than their relative fathers' minimum starting time. Empirical analysis showed that the number of wrong \(p(i,j)\) orientations is equal to the respective number if \(\delta=0\), plus the additional disoriented pairs that resulted when prioritization was given to late orders ensued by first and second-level dispatching.
## 3 Performance Analysis  
### Solar Fraction  
The SACS performance can be evaluated using solar fraction (solar coverage). This factor demonstrates the solar energy contribution in chilled water production [37]; the following equation enables the calculation of the solar fraction.  
\[SF=\frac{\dot{Q}_{s}}{\dot{Q}_{S}+\dot{Q}_{aux}} \tag{18}\]  
where \(\dot{Q}_{s}\) solar gained energy and \(\dot{Q}_{aux}\) is energy from the auxiliary heater. \(Q_{s}\) can be calculated by:  
\[\dot{Q}_{s}=\dot{Q}_{c}-\sum Q_{loss}^{\dot{}} \tag{19}\]  
where \(\dot{Q}_{c}\) is useful collectors' energy and \(\dot{Q}_{loss}\) is the system losses energy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Type TRNSYS** & **Parameters (Base Design Values)** \\ \hline Solar Collector & TYPE 71a & Apricus ETC-30 (Table 3) \\  & & Number of collectors (12) Inclination (30\({}^{\circ}\)) \\ \hline Hot water tank & TYPE 4a & Volume (50 L/m\({}^{2}\) collector) \\ \hline Auxiliary boiler & TYPE 6 & Efficiency (90\%) \\ \hline Absorption chiller & TYPE 107 & YAZAKI WFC SC10 (Table 5) \\ \hline Cooling tower & TYPE 51b & B.A.C. FXT-26 (Table 4) \\ \hline Weather data & TYPE 109—TMY2 & Location: Baghdad, Iraq \\ \hline Collector pump & TYPE 3b & Flow rate 50 (L/h)/m\({}^{2}\) of collector \\ \hline Collector pump control & TYPE 2b &
\begin{tabular}{c} Maximum accumulator temperature (90 \({}^{\circ}\)C) \\ Minimum collector gain (5 \({}^{\circ}\)C) \\ \end{tabular} \\ \hline Pipe & TYPE 31 & \\ \hline Flow mixer & TYPE 11h & \\ \hline Building & TYPE 56a & \\ \hline \hline \end{tabular}
\end{table}
Table 4: List of the most important components of the TRNSYS model.
## 4 Case Study
### Reference Wind Farm  
In this work, an offshore wind farm, which is composed of 50 Enercon E82-2.0 MW WTs is selected as the studied case. The WT coordinates are listed in Table 10[31] while the technical and economic information of the wind farm is shown in Table 11[18]. It is worthy of note that there are usually limited types of cables that are available in the actual wind farm construction process. Hence, two scenarios are presented in this work to show the effectiveness of the proposed method in both an ideal situation and relatively realistic case.
# A Review of Detection Technologies for Underwater Cracks on Concrete Dam Surfaces
## 1 Introduction
The above researches systematically reviewed the crack detection techniques for civil infrastructures. However, due to the complexity of the underwater environment of dams, underwater crack detection technology is different from traditional civil infrastructure crack detection methods. It is necessary to review the technologies for underwater cracks on concrete dam surface. Therefore, this paper takes underwater dam surface crack detection technology as the research object, summarizes various underwater dam surface crack detection methods, and focuses on the image acquisition and image processing of image-based underwater dam face crack detection methods. This paper is organized as follows: Section 2 presents existing methods for underwater crack detection on dam surfaces. Section 3 introduces the underwater crack detection of dam surfaces using an underwater robot. Section 4 details the underwater image processing method. Section 5 gives a summary and outlook.
## 5 Deployment of PLC for Smart Metering and Smart Grid Systems
### Grid Aspects with Influence in PLC Systems Design
#### 5.2.2 Communication Characteristics of the Segments of the Grid  
The selection of any architecture is always to be considered related to the physical aspects of the grid, which impose conditions and affect performance of the PLC technologies on top of them.  
A first major effect to consider is the propagation characteristics of the power lines in each of the segments (MV and LV). Cable design and disposition (overhead lines favor propagation), and the isolation of the segment from the PoS (disturbing noisy loads are mostly connected to the LV, and do not affect the MV segment) need to be considered to see how to best favor PLC signal propagation (maximizing desired signal and minimizing existing noise).  
A second major effect is related to the interfaces between MV and LV grid, _i.e._, the transformers at the SSs. The effect of these transformers at the interfaces is to be considered if the PLC system is to be used in both the MV and LV segment. UNB PLC systems are known to be capable of traversing the MV to LV transformer at SSs. Thus, a simple PLC signal injection in the feeders at the primary substations can get this signal to appear at the PoS. This is simply not possible in BB PLC systems (typically in a frequency range from 2 MHz to 30 MHz [131]) and inconsistently in NB PLC systems [102]. The latter is a high-impact issue, as it is not possible to know in advance if the PLC signal is going to be coupled from the primary to the secondary winding of the transformers without any external coupler, and this can be a major deployment problem if no clear and general rule can be established. As a consequence, if we just make use of one technology without making use of the natural segmentation of the grid (MV and LV), the bandwidth will be shared in all the system (with the constraints and conditions of the MAC layer and the attenuation conditions of the different parts of the grid), reducing the available highest possible throughput.
# Cognitive Diagnosis Method via Q-Matrix-Embedded Neural Networks
## 3 Experiments and Results
### Agreement Evaluation Metrics  
In this section, we introduce two evaluation metrics: AAR and PAR. Suppose that the model predicts the skill mastery pattern \(\hat{\mathbf{\alpha}}_{i}=\{\hat{\mathbf{\alpha}}_{1},\hat{\mathbf{\alpha}}_{2},\ldots, \hat{\mathbf{\alpha}}_{IK}\}\) for student \(i\) and the true skill mastery pattern for student \(i\) is \(\mathbf{\alpha}_{i}=\{\alpha_{i1},\alpha_{i2},\ldots,\alpha_{iK}\}\), then AAR and PAR are defined as shown in Equations (14) and (16), respectively.  
\[AAR=\frac{1}{N\times K}\sum_{i=1}^{N}\sum_{k=1}^{K}\rho(\hat{\mathbf{\alpha}}_{ik},\mathbf{\alpha}_{ik}) \tag{14}\]where AAR represents the agreement rate between the model-predicted student mastery of each skill and the actual student mastery of each skill; \(N\) denotes the number of students; and \(K\) denotes the number of skills assessed. The function \(\rho(\cdot)\) is the Kronecker delta, which is defined as shown in Equation (15).  
\[\rho(a,b)=\left\{\begin{array}{ll}1,&\text{if }a=b\\ 0,&\text{if }a\
eq b\end{array}\right. \tag{15}\]  
The definition of PAR is shown in Equation (16).  
\[PAR=\frac{1}{N}\sum_{i=1}^{N}\prod_{k=1}^{K}\rho(\hat{\alpha}_{ik},\alpha_{ik}) \tag{16}\]  
where PAR represents the overall consistency between the model-predicted student skill mastery patterns and the actual student skill mastery patterns. In other words, the PAR value is 1 only when the model-predicted mastery status of each skill perfectly aligns with the actual mastery status observed in students.
# Assessing the Applicability of Machine Learning Models for Robotic Emotion Monitoring: A Survey
## 2 Background
### Emotional Levels
#### 2.3.2 Continuous Emotions  
Some emotional models use dimensions for emotion instead. Amongst them, the most popular is the Valence-Arousal emotion model. This has two dimensions, as the name would suggest: valence and arousal. Valence ranges from feeling pleasant to unpleasant, and arousal ranges from feeling quiet to active [63]. Due to the lack of discretisation of these dimensions, the number of specific emotions that can be located on the valence-arousal plane is infinite. However, our typical everyday emotion models, such as [PERSON]'s 6-emotion model, can be mapped out onto this plane. Figure 2 shows an example of a correspondence between a discrete emotional model and the V-A (valence-arousal) plane.  
Figure 2: Valence-Arousal emotional model.  
Although mapping discrete emotions on continuous models is often performed, classifying emotions on the continuous labels presents an interesting challenge. The classification task, in this case, is exceptionally complicated since there are infinite points along any continuous dimension and hence infinite classes. However, researchers have come up with several ways to accomplish this, and one of them is through the segmentation of the V-A plane into compartments of interest. For example, splitting the plane into four quadrants: high valence + high arousal, high valence + low arousal, low valence + high arousal, and low valence + low arousal.
# Magnetic Resonance-Electrical Properties Tomography by Directly Solving Maxwell's Curl Equations
## 4 Discussion
### Limitations of the Proposed Method  
Two main limitations can be identified regarding the proposed method. Firstly, the imaged subject needs to be modelled in the EM field solver. As it is challenging to find solutions to the formed optimization problem with millions of unknowns, the following assumption was made: one type of tissue has uniform EP values. In electromagnetic computing, a segmented MR image is required as prior information to help assign initial EP values of corresponding FDTD cells representing the imaged subject. The structure of the imaged subject can be constructed by using image registration techniques or implementing segmentation of the obtained MR images [40, 41]. During optimization,  
Figure 8: The calculated magnitude and phase of the \(B_{x}\) field component from the experiment, (**a**,**d**): on the upper plane, which is 0.85 mm away from the central slice, (**b**,**e**): on the central slice, (**c**,**f**): on the lower plane, which is 0.85 mm away from the central slice.  
several or tens of unknown variables are only required to update. With a significant dimension reduction, superior convergence can thus be achieved. Owing to an ideal experimental setup involving birdcage coil and sample phantom, the FDTD model can match the experiment shown in this work. Compared with conventional EPT approaches with z-field independency and LHA, the full-wave EM calculation is capable of regularizing the EPT solution closer to measurements. Even with the \(\left|B_{1}^{+}\right|\) data only, the strong coupling mechanism of E- and H-fields in Maxwell's curl equation can still faithfully find the EP values. Lastly, the proposed EP solution is sensitive to the initial setup of the EP values. We expect that the accuracy of the proposed method for human tissue mapping could be much lower than the given simple phantom and simulation results, and further validation is thus required. On the other hand, the coil structure can be obtained using a reference-based approach [42; 43]. That is, using a phantom with pre-known dielectric properties, one can determine the coil information. The coil model can then be used for EPT studies. Secondly, [PERSON]'s curl equations are solved by the FDTD technique, which is usually time-consuming, and may take up to several hours to converge. In this study, to balance the spatial resolution and computational cost, an mm-level grid size for the FDTD solver was found to be effective to resolve the MRI-EPT problem. In this work, we have successfully employed GPU acceleration to enable an efficient implementation of the FDTD computing (in only tens of seconds). Through appropriately applying these techniques, it is then feasible to use a standard optimization technique to retrieve accurate EP distributions in practice.
###### Abstract
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Authors** & **Title** & **Models** &
\begin{tabular}{c} **Number of** \\ **Input Features** \\ \end{tabular} \\ \hline \hline \multirow{4}{*}{[PERSON] et al. [1]} & A data-driven approach to predict the success of bank telemarketing & - Logistic regression & \multirow{4}{*}{22} \\  & & - Decision tree & \\  & & - Artificial neural networks & \\  & & - Support vector machine & \\ \hline \multirow{4}{*}{[PERSON] et al. [2]} & Predicting the success of bank telemarketing using deep & \multirow{4}{*}{- Deep convolutional neural networks} & \multirow{4}{*}{16} \\  & convolutional neural network & & \\ \hline \multirow{4}{*}{[PERSON] et al. [3]} & Predicting customer response to bank direct telemarketing campaign & \multirow{4}{*}{- Artificial neural networks - Decision tree - Logistic regression} & \multirow{4}{*}{16} \\ \hline \multirow{4}{*}{[PERSON] et al. [4]} & Optimizing the prediction of telemarketing target calls by a classification technique & \multirow{4}{*}{21} \\  & & - Decision tree & \\ \cline{1-1}  & & - Artificial neural networks & \\ \cline{1-1}  & & - Support vector machine & \\ \hline \multirow{4}{*}{[PERSON] et al. [5]} & Business analytics in telemarketing: cost-sensitive analysis of bank campaigns using artificial neural networks & \multirow{4}{*}{16} \\ \cline{1-1}  & Deep learning-based methods for processing data in telemarketing-success prediction & \multirow{4}{*}{- Long short-term memory - Gated recurrent unit - Simple recurrent neural networks} & \multirow{4}{*}{20} \\ \cline{1-1}  & & - Random forest & \\ \hline \multirow{4}{*}{Authors of the present study} & Predicting Success of Outbound & \multirow{4}{*}{- Support vector machine - Gradient boosting machine - eXtreme gradient boosting - 210} \\ \cline{1-1}  & Telemarketing in Insurance Policy & \multirow{4}{*}{20} \\ \cline{1-1}  & Loans Using an Explainable & & \\ \cline{1-1}  & Multiple-Filter Convolutional & \multirow{4}{*}{- light gradient boosting machine - Deep neural networks - Deep convolutional neural networks} & \multirow{4}{*}{210} \\ \cline{1-1}  & & - Deep neural networks - Deep convolutional neural networks & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of present work with previous studies on predicting telemarketing success. machine learning (ML) modeling cannot achieve suitable predictive performance. Also, basic deep neural networks (DNNs) composed of only fully connected layers are likely to be subject to overfitting. Therefore, we propose a deep learning model to prevent overfitting even when many variables are used.
## 2 Background
### The State-of-the-Art Models  
While the initial models mimicking binaural hearing in humans allowed for the localization of only single sources under anechoic conditions [19; 20], the state-of-the-art algorithms are capable of localizing several sources simultaneously, both in anechoic and reverberant environments [9; 21; 22; 23; 24]. While some of the advanced models still employ a form of feature extraction combined with traditional classification algorithms such as Gaussian mixture models [21], the algorithms developed more recently commonly utilize deep learning techniques [22; 23; 24; 25]. Binaural signals can be either used directly at the input of the deep neural networks [26] or indirectly through some forms of signal preprocessing. Such preprocessing may involve a traditional feature extraction [21], calculation of the cross-correlation function from the left and right channel signals [22; 23; 24; 25], or converting sounds into \"images\" in the form of spectrograms [27]. The first and the last approaches were also adopted in this study.  
While the state-of-the-art algorithms allow for the accurate localization of simultaneously sound-emitting audio sources in binaural signals, they have major drawbacks, preventing them from being directly applicable to the automatic classification of scenes in the binaural recordings of music. Firstly, these algorithms typically require an a priori knowledge on the \"number\" and \"characteristics\" of the individual sources in an analyzed scene [21; 22; 23; 24]. Such information is normally unavailable for real-life music recordings reposited in the Internet. Secondly, to reduce the rate of front-back localization errors, some algorithms include mechanisms mimicking the head movements. While such algorithms can be applied to the systems equipped with robotic dummy-head microphones [28] or to the systems involving adaptive synthesis of binaural signals [23], they cannot be applied to the recordings obtained with a static head methodology (a scenario pertinent to this study). Finally, the above algorithms were developed and tested using either speech or contrived noise signals. Since they were not intended for audio (music) signals, it is unknown whether they could be \"transferred\" and applied to music signals.
## Methods
### Research Design
#### 2.2.2 Semi-Structured Interview  
In addition to the questionnaires, the on-board operators underwent a semi-structured interview. It was subdivided into three parts. In an introductory section, an interviewer queried the on-board operator on personal and sociodemographic data including education and training, previous job positions, and work experience. Next, a detailed analysis of workspace and task design, as well as scenarios and incidents that occur in their work as an on-board operator was conducted. Finally, the interviewer asked for an assessment of the workspace and the shuttle HMI they perform monitoring and intervention tasks with. This manuscript focuses on the interview sections on assessing the workspaces and HMIs.  
As the quality of the responses to the initial questions was heterogenous between on-board operators and projects, a post-hoc approach of categorizing qualitative data was applied. In this approach, a deductive method was used, which proved to be useful in gaining comprehensive insights into the text-based material of the interview study. The first step was to review and paraphrase the operators' responses in order to make them comparable and categorizable. Second, categories were derived deductively from the literature. These categories, which were specifically defined in the area of usability and HMI, provided a theoretical framework and clear definitions. This allowed existing knowledge and established concepts to be applied to the research material, providing a solid foundation for analysis. For the HMI assessment in particular, the established Work Design Questionnaire (WDQ [28]) with its Work Context Scale was used as a scaffold to categorize operator responses on this topic (see Table 2 for used constructs and their definitions). The same scale was used as a questionnaire to collect quantitative data, contributing to methodological triangulation. Once categorization was completed, a valence rating with the three levels \"positive (+)\" \"neutral (o)\" and \"negative (-)\" was added, in order to classify each operator statement as favorable to, indifferent to, or in objection to the addressed aspect of the workspace and HMI, respectively. In order to ensure the intersubjective reliability of the rating, the entirety of the material was reviewed by two independent raters. These raters independently classified the operators' responses according to the previously established criteria. After this phase was completed, the ratings were compared and any differences in interpretation were discussed. [PERSON]'s kappa was calculated to quantify interrater reliability, ensuring that the results were consistent.
# Performance and Challenges of Service-Oriented Architecture for Wireless Sensor Networks
## 5 The Taxonomy of Middleware Architectures for WSNs
### Service-Oriented Architecture Approach
#### 5.6.3 Context Awareness Applications  
The middleware has been designed for context awareness applications and testing for healthcare and smart environments [38; 39; 40; 41].
## 2 Materials and Methods  
### Soil Sampling and Analyses  
Rhizosphere soils, which were the soils firmly attached to the root and bulk soils, which were loose soils away from the plant [9], were collected from two sunflower fields in South Africa: Palmietfontein (26\({}^{\circ}\)19\({}^{\prime}\)39.94\({}^{\prime\prime}\) S: 26\({}^{\circ}\)52\({}^{\prime}\)52.57\({}^{\prime\prime}\) E) and Bloemhof (26\({}^{\circ}\)17\({}^{\prime}\)46.1\({}^{\prime\prime}\) S: 26\({}^{\circ}\)58\({}^{\prime}\)19.83\({}^{\prime\prime}\) E). Palmietfontein has an average rainfall of 200 mm and an average annual temperature of 22 \({}^{\circ}\)C, and the month of collection, June 2018, had an average temperature of 11 \({}^{\circ}\)C. Bloemhof has an average rainfall of 350 mm and an average annual temperature of 20 \({}^{\circ}\)C, and the temperature at the month of collection was 9.9 \({}^{\circ}\)C. Rhizosphere soil samples from Palmietfontein (R1) and Bloemhof (R2) and bulk soil samples from Palmietfontein (B1) and Bloemhof (B2) were collected in triplicate at 0-20 cm depth using 2 cm soil auger [17]. Samples were collected in sterile bags, after which they were placed in a cooler box and brought to the laboratory for analysis.  
Phosphorus (P) was analyzed using the P Bray method, while total carbon (C) and nitrogen (N) were determined using a TruSpec elemental determinator [9]. pH meter using the ratio 1:2.5 (soil/water) was used to measure the soil pH according to [PERSON] et al. [18]. Organic matter (OM) content was measured using the Walkley-Black method. The soil moisture was determined by a procedure described by [PERSON] et al. [19],while the hydrometer technique was used for the particle size analysis [20]. The particle size classes used to assign texture were those of the United States Department of Agriculture (USDA) for sand (0.5-2.0 mm), silt (0.002-0.05 mm), and clay (<0.002 mm). Organic carbon was analyzed using dichromatic digestion [21]. Potassium (K) and calcium (Ca) were analyzed by methods described by [PERSON] et al. [22].