esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Feature Analysis  
Different from previous studies, we did not study cartilage volume as a whole, instead, we treated the thickness information from each CDI informative location as an individual feature. Nine CDI locations on the medial tibia generated a 9-dimensional feature space (Figure 2). These features provided input to the machine learning method ANN to predict if any thickness change occurred one year later. For each informative location, the thickness change over a year (subtracting baseline data from 12-month data) was computed to decide the output category. The corresponding class label was 'changed' or 'no-change'. The label 'changed' or 'no change' is marked according to the difference of the thickness values between baseline year and 12-month follow-up year. In clinical studies, 'changed' and 'no change' represent a disease that has progression or no progression, which is an important sign to evaluate the effect of a treatment.  
When using CDI, we compared the computed thickness values through CDI measurement at each of the nine locations. For example, for CDI position #1, if the baseline year thickness is 8, and the follow-up year thickness is 6, this position is marked as a 'changed'; if the absolute difference between the thickness values of the baseline and follow-up year is equal to 0 or 1, we mark the position as 'no-change'. These nine features were further divided into 3 groups; each group contained 3 CDI features from the top, center and bottom horizontal positions, respectively. As illustrated in Figure 2, the top group contained CDI locations indexed 7, 8, and 9. The middle group contains the three CDI locations indexed 4, 5, and 6. The bottom group contained the CDI locations indexed 1, 2, and 3. We have trained separate ANN models for the combined nine CDI features as well as the three subgroups, respectively.  
To obtain the features from manual segmentation, we first measured the thickness for each pixel along the cartilage boundary in one slice, and then saved the thickness values into a vector. In order to register different slices, we normalized thickness vectors into the universal length [1; 150]. The unified vectors represented the thickness information of baseline manual segmentation. In order to compare with the CDI features, we extracted the same three corresponding slices on the medial tibia that were used for CDI features, denoted by X1, X2, and X3 in Figure 2. Our goal is to predict the thickness change at each pixel along the unified vector after 12 months, by using the baseline thickness information in the neighborhood as input features. In specific, for each pixel, we defined a mask including its left and right neighbors as the features for ANN; the output is 'changed' or 'no change' according to the difference between baseline and 12-month thickness vectors at this location. We mark 'no change' if the absolute difference of thickness value between the baseline and the follow-up years is equal to 0 or 1 and 'changed' otherwise.
## 2 Materials and Methods  
### Materials  
TiO\({}_{2}\) nanopowder (commercial grade, Aeroxide TiO\({}_{2}\)-P25, average particle size 21 nm, specific surface area 50 \(\pm\) 15 m\({}^{2}\cdot\)g\({}^{-1}\), 80/20 anatase/rutile), ethanol (absolute, \(\geq\)99.8%), glycerol (ACS reagent, \(\geq\)99.5%), formic acid (puriss. p.a., ACS reagent, \(\geq\)98%), lactic acid (85%, FCC), ethylene glycol (anhydrous, 99.8%) were purchased from Sigma Aldrich (Pty Ltd.), while cuprous oxide (Cu\({}_{2}\)O, powder) and methanol (99.9%) are purchased from Carlo Erba Reagents (Cornaredo, MI). All reagents are used as received. Doubly glass-distilled water is used.
# Research on Co-Interactive Model Based on Knowledge Graph for Intent Detection and Slot Filling
###### Abstract
Therefore, they are prone to cause errors in entity recognition without supplementary knowledge in the medical field, especially for domain-specific entities or abbreviations. When modelling intention detection and slot filling jointly, incorrect results from entity recognition can directly affect intention detection, subsequently leading to a decline in the overall performance of both intention detection and slot filling. Moreover, in the specialized domain, current joint models for intent recognition and slot filling [9; 10; 11] merely associate the relevant information between intents and slots. They have not proposed effective solutions to address the issues that may arise in the joint process of intent recognition and slot filling, such as domain-specific entity recognition, specific abbreviations, and easily confused entity recognition. In order to effectively utilize domain-specific knowledge in the joint modelling of intent detection and slot filling, we propose a co-interactive model based on a knowledge  
Figure 1: Example of intent detection and slot filling. graph (CIMKG) for intent detection and slot filling. To enhance the overall performance of intent detection and slot filling tasks in specific domains, the model constructs a representation of the relationship between these two tasks by integrating triples sourced from the knowledge graph into utterances while taking into account the interdependencies that exist between these two tasks.
# Distributed Kernel Extreme Learning Machines for Aircraft Engine Failure Diagnostics
## 1 Introduction
Compared with the traditional network, ELM has much faster training and testing speed and better or similar generalization performance [28; 29]. Due to the random configuration of parameters, ELM has advantages of high computational efficiency and easy implementation, but at the same time, the random characteristics also lead to fluctuating and unstable network performance [30]. [PERSON] et al. proposed the KELM method by introducing kernel functions into ELM [31]. Similar to the SVM method, the training sample error and weight minimization are considered comprehensively in KELM, where the tradeoffs are represented by regular parameter. Extensive experiments have shown that KELM has better generalization ability than ELM in most cases, and tends to perform better for dealing with classification as well as regression problems [32; 33]. However, the original KELM has two obvious limitations. Firstly, due to the inverse calculation of an \(n\times n\) matrix, the overall computational complexity increases cubically with \(n\). Thus the training process would be computationally impossible when the number of training samples is too large even if the problem is linearly solvable. In addition, the network constructed by KELM contains all the training samples, and such a network would be too complex due to lack of sparsity, which is likely to lead to overfitting problem. Due to the limiting onboard computing power of aircraft engine, the data driven methods on board are required to have high computational efficiency and good real-time performance [34]. Aiming at the above lack of original KELM, we proposed a novel DKLEMs algorithm, where the network structure of the original KELM is replaced by some distributed sub-networks, and then the fusion of information form each sub-network is conducted subsequently. The new network structure includes feature input layer, distributed machines layer, fusion layer and output layer. In order to verify the validity of distributed kernel extreme learning machines (DKELMs) algorithm, performance comparison among the proposed algorithm, the original KELM algorithm and SVM algorithm are carried out in both regression and classification benchmark dataset. Finally, for practical verification of the proposed algorithm, experiments are carried out on performance degeneration estimation and failure patterns identification for an aircraft engine. The remainder of this paper is organized as follows: In Section 2 we will briefly review the original KELM algorithm and further propose the distributed KELM framework. The fusion approach of distributed machines for both classification and regression problems are presented and the derivation is given in detail in Section 3. In Section 4, performance evaluation on regression and classification benchmark datasets are conducted, where DKELMs are compared with original KELM and SVM algorithms. In Section 5, the proposed DKELMs algorithm is applied into failure diagnostics of aircraft engine. Conclusions are given in Section 6.
## 2 Literature Review
also choose a robust method to study uncertain demands from the patients and illustrate the need for a trade-off between a deterministic optimal solution that is particularly susceptible to any small disruption and an extremely robust solution that is very expensive and often \"underused\" [28]. Resilient approaches must then be deployed at the operational level to repair the routes in the case of high-impact disruption. Among the classic recourse strategies for  
Figure 1: Difference between variation and disruption and between robust solution and resilient approach. stochastic VRP, most of them imply a return to the depot [29], which is not helpful for HHCRSP. Similarly, rescheduling strategies for routing problems may not be suited to HHC applications; [PERSON] et al. study two alternative strategies to solve a VRP with time windows and stochastic service times, which both imply skipping customers [30]. To the best of our knowledge, only three articles propose reactive and resilient solutions to counter high-impact disruptions in the field of HHC. [PERSON] et al. use a multi-agent system to deal with unexpected events, such as vehicle breakdown, where visits are dynamically reassigned to another vehicle [31]. [PERSON] et al. use a similar two-level architecture with an offline module that assigns caregivers to patients and an online multi-agent module that takes local decisions to optimize the routes [32]. No change in the caregiver-patient assignment is allowed, which prevents any collaboration in case of disruption, and none of these two articles take into account time windows. Yet, we consider temporal constraints as a key element to HHCRSP because delays in care delivery may not only have an impact on the quality of care but also on the satisfaction of the patient. In the study by [PERSON] et al., patients can cancel their appointments or require new visits during the execution of the routes, so they are re-optimized in real-time with a tabu-search, with the objective of minimizing deviations from the original plan [33]. The possibility of calling in additional workers and the cancellations of requests guarantee a stable workload. In our article, the perturbations on care durations have a major impact on the workload of the caregivers and, thus, on the feasibility of the routes and the quality of care. In summary, the HHCRSP has been studied for a long time and increasingly in a stochastic environment, where many robust solutions have been proposed in the literature, but few resilient approaches have been published. Moreover, it is difficult to find a use case that is clear, realistic, and available to the scientific community. For this reason, in this article, we propose to examine a realistic use case, the result of numerous interviews, and we analyze and compare two resilient approaches to a baseline one to counter the disruptions of home care routes.
## 2 Materials and Methods
### Optimization System Modelling  
A In this study, Elekta microSelectronTM (Nucletron, Elekta AB, Stockholm, Sweden) HDR after-loader unit with 192 Ir source was used. OncentraTM (Oncentra, version 3.5, Elekta AB, Stockholm, Sweden) treatment planning system (TPS) was used for making brachytherapy plans. The voxel sizes of the T2 MRI data used for dose calculations as before were 0.5 \(\times\) 0.5 \(\times\) 4.0 mm\({}^{3}\). Here, MR-compatible TR applicator sets (MR ring applicator, Elekta AB, Stockholm, Sweden) of two differently sized (small and large) rings were used, each with a 30-degree angle, 2.5 mm source position separation, and with a distance from the first source to the applicator tip defined as 7 mm. Traditionally, an optimization algorithm needs specific input data such as digitized positions, relevant anatomical structures and an applicator path to calculate a dose distribution matrix. Regions of interest (ROIs) including the high-risk clinical target volume (HRCTV), intermediate-risk clinical target volume (IRCTV), bladder, rectum, bowel, and sigmoid were manually contoured on each first-fraction image by oncologists and delineation was based on MRI findings. The contour-point lists for each structure were extracted in sequence from the Digital Imaging and Communications in Medicine-Radiation Therapy (DICOM-RT) structure files using C++ program language. Also, applicator paths were reconstructed and ex tracted as radiation source positions in the treatment-planning system. The source dwell positions provided in the applicator coordinate system were pre-defined according to the applicator ring structure. Overall, volume delineation and dose constraints were made in accordance with GEC-ESTRO recommendations and EMBRACE protocol guidelines for both the nominal and robust plan [18; 43; 44]. Given the above facts, we only used positional information of anatomical structures and source positions from the first fraction image to make the optimization model; then, after determining the positional characteristics of the source relative to the dose, calculations were completed with care in all robust optimization processes (Figure 1).
# Impact of Dataset Size on Classification Performance:
###### Abstract  
The problem of detecting the number of samples in a dataset is to determine the number of samples in a dataset. The problem of detecting the number of samples in a dataset is to determine the number of samples in a dataset. on the performance of the classification algorithms, which makes it an open problem in the area that needs more investigation. Several studies have emerged recently that address the issue of small datasets from different perspectives, including enhancing the performance of classification models on limited datasets [8; 9; 10; 11] and proposing varying approaches to augment the training set [12; 13; 14; 15; 16]. For example, in the former category, authors in [8] proposed two methods for neural network (NN) training on small datasets using Fuzzy ARTMAP neural networks [10]. In [11], a novel particle swarm optimization-based virtual sample generation (PSOVSG) approach was proposed to iteratively produce the most suitable virtual samples in the search space. The performance of PSOVSG is tested against other three methods and had superior results. In the latter category, [PERSON] et al. [12] proposed a non-parametric method for learning trend similarities between attributes and then using them to predict the respective ranges in which attribute values can be situated when other attribute values are provided. Another study [13] generated data based on the Gaussian distribution by utilizing the smoothness which states that, if two inputs are close to each other, their outputs will be close as well. In [14], the authors learned the relationship between the dataset features to generate new data attributes using the fuzzy rules. Other studies [15; 16; 17] have proposed the extending attribute information (EAI) method to investigate the applicability of extracting features from small datasets by applying the similarity-based algorithms using fuzzy membership function on seven different data sets. Authors in [18] proposed the sample extending attribute (SEA) method to extend a suitable quantity of attributes for improving the learning performance of small datasets and preventing the data from becoming sparse. Research on the subject has been mostly restricted on increasing the accuracy of the classification algorithms on limited size datasets, little attention has been paid to study the impact of the dataset size on the performance of the classification algorithms. However, the proposed solutions suffer from multiple issues, such as data replicates [13], unscalability [8; 10], and noise [13; 19]. Similar studies to our work exist in the literature, where the main aim is to investigate the extent to which the size of the dataset can impact the classification performance in different domains such as sentiment classification [2; 20], object detection [21], plant disease classification [22], and information retrieval [23]. Table 1 summarizes the most relevant related works.
## 4 Performance Analysis of the MA-SM
### Analysis of the MA-SM Performance for Configuration III  
Based on the last configuration in Table 1, the size of the set \(\Gamma\) is equal to 8. Therefore, there are 64 codeword error events. The event \(\gamma_{i}\leftrightarrow\gamma_{i}\), where the spatial symbol is received without error, is repeated eight times. This leads to a single \(\Omega\) term, denoted by \(\Omega_{1}\) in Table 4. Note that \(\Omega_{1}\) is the only term that does not depend on the rotation angles. Also, since the probability of the error event \(\gamma_{i}\leftrightarrow\gamma_{j}\) is equal to \(\gamma_{j}\leftrightarrow\gamma_{i}\), there are 28 other \(\Omega\) terms, each is repeated twice. Out of the 28 terms, 19 have a similar form to \(\Omega_{2}\), where there are two terms of the squared Euclidean distance between two rotated signal symbols and two power terms of a signal symbol. The remaining nine terms include a single term of the squared Euclidean distance between two rotated signal symbols and four power terms, as in \(\Omega_{8}\). Table 4 gives a partial list of the \(\Omega\) expressions.  
In Table 4, the term with more occurrences of the rotation angles has more impact on the end result of the optimization process. For instance, \(\Omega_{8}\) is a function of only two rotation angles each with a single frequency. On the other hand, \(\Omega_{2}\) is a function of \(\theta_{1}\) and \(\theta_{2}\), where each angle occurred twice. We can therefore conclude that \(\Omega_{2}\) has more impact on the obtained optimal rotation angle that does \(\Omega_{8}\).  
Figure 3: The value of the weighted \(\Omega\) terms that depend on the rotation angle (left y-axis) and the value of the rotation-independent \(\Omega_{1}\)(right y-axis) for \(n_{R}=3\) and 16-QAM (**left sub-figure**) and 64-QAM (**right sub-figure**).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Modulation** & \multicolumn{3}{c}{**16-QAM**} & \multicolumn{3}{c}{**64-QAM**} \\ \hline \(\theta\) & optimal & \(\theta_{i}=0\) & [20] & optimal & \(\theta_{i}=0\) & [20] \\
**Cost** & 31195 & 35883 & 31313 & \(1.4027\times 10^{7}\) & \(1.5975\times 10^{7}\) & \(1.4225\times 10^{7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: A comparison between the minimum value, maximum value, and the one corresponding to the angles in [20] of the cost function in (21) for 16 and 64-QAM with \(n_{R}=3\).  
Table 5 shows the obtained optimal rotation angles for QPSK and 16-QAM and several values of receive antennas. The values of the cost function (the sum term in (13) with \(B=29\), \(f_{1}=8\), and \(f_{i}=2\), for \(i=2,\cdots,29\)) is also included using the optimal angles, the angles given in [20] and the all-zero angles. In all scenarios, the optimal rotation angles derived in this paper reduced the values of the cost function. We remarked that several sets of rotation angles produce almost the same value of the multi-objective cost function. Table 5 lists a single example. Unlike in the case of configuration I and II, it is difficult to obtain a closed-form expression for the rotation angles.
# Comparison of Methods to Identify and Monitor Mold Damages in Buildings
## 4 Discussion
### Contribution of Dust DNA Analyses for Assessment of Indoor Moisture Problems  
Previous studies have revealed that moisture problems and subsequent mold growth, i.e., an additional indoor fungal source, significantly modify the overall composition of fungal spores in indoor dust samples [24; 25; 26]. In contrast, other studies have suggested that the effects of moisture damage are less evident. The response to moisture damage may be observed as changes in the relative abundance of a few less common taxa rather than in the overall community structure [27]. [PERSON] et al. [25] demonstrated that a specific mold growth significantly influenced the richness and ecology of airborne fungi in distant parts of the same building, where the relative abundances of mold-damage indicator taxa were inversely correlated to the distance to the mold growth. In our study, the genera detected in the mold samples were largely present in the dust with scarce abundance. Interestingly, the overlap of the OTUs between the sample types (mold and dust) was slightly higher in the damaged rooms than in the central rooms, which indicated some local spread of moisture indicator fungi in the study apartments. However, this difference was not statistically significant, and there was no clear association between their relative abundances in the dust and the distance to the mold growth. As anticipated by [PERSON] at al. [25], multi-building studies with such a limited number of samples as ours may not be the appropriate approach to identify these spatial patterns due to the inherent factors affecting each individual building.  
Fungal species growing directly on moist building materials (i.e., moisture-damage indicator fungi) should not be considered as part of the baseline fungi in healthy indoor environments, as the presence of moist structures is an abnormal condition in buildings [12; 13]. Therefore, in the assessment of moisture problems, identifying the characteristic baseline fungi of the study region is as important as the identification of moisture indicator fungi. These kinds of studies should also account for the temporal variability of indoor mycobiota because the outdoor seasonality has been recognized as a key driving factor [37]. Diverse dust DNA analyses have been proposed for the detection and monitoring of mold damages in buildings, including various qPCR-based environmental relative moldiness indices (ERMI and its Finnish adaptation FERMI) [47; 48] as well as a DNA metabarcoding approach [26]. The latter study, based on the ITS metabarcodes from dust samples, detected significant differences in the community composition between moldy vs. no mold houses in three different climatic regions in the USA. They proposed a combined use of an HTS and machine learning modeling for an accurate classification of buildings according to the potential presence of mold growth [26].