esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.2.1 Preprocessing  
Multiple imputation is a method that applies several univariate imputations to find the optimal imputed value [22]. In the present study, after generating \(m\) imputed datasets by applying multiple univariate imputations to incomplete data, the estimator and standard error were calculated using \(m\) imputed datasets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **Variable Name (Abbreviation)** & **Unit** & **MDL \({}^{*}\)** & **Type** \\ \hline Response & & & & 0: discharge \\ variable & Complex Odor & \(\text{OU}_{\text{E}}\text{m}^{-3}\) & & 1: no discharge \\ \hline \multicolumn{5}{c}{Ammonia (NH\({}_{3}\))} & ppm & 0.08 & Float64 \\ \multicolumn{5}{c}{Hydrogen sulfide (H\({}_{2}\)S)} & 0.06 & Float64 \\ Sulfur & Methyl mercaptan (MM) & 0.07 & Float64 \\ compounds & Dimethyl sulfide (DMS) & ppb & 0.08 & Float64 \\  & Dimethyl disulfide (DMDS) & 0.05 & Float64 \\ \hline \multicolumn{5}{c}{Accetic acid (ACA)} & 0.07 & Float64 \\  & & Propionic acid (PPA) & 0.34 & Float64 \\  & Isobutyric acid (IBA) & 0.52 & Float64 \\  & Volatile & Normality butyric acid (BTA) & 0.96 & Float64 \\  & Organic & Isovaleric acid (IVA) & 0.49 & Float64 \\ Explanatory & Compounds & Normality valeric acid (VLA) & ppb & 0.53 & Float64 \\ variables & (VCCs) & Phenol (Ph) & 0.09 & Float64 \\  & & P-Cresol (p-C) & 0.06 & Float64 \\  & & Indole (ID) & 0.40 & Float64 \\  & & Skatole (SK) & 0.38 & Float64 \\ \hline \multicolumn{5}{c}{In: inside of the pig barn} \\ \multicolumn{5}{c}{Out: outside of the pig barn} \\ \multicolumn{5}{c}{Boundary: site boundaries} \\ \multicolumn{5}{c}{Spring: March–May} \\ Season & & Summer: June–August \\ \multicolumn{5}{c}{Fall: September–November} \\ Winter: December–February \\ \hline \hline \end{tabular} \({}^{*}\) MDL = Method Detection Limit.  
\end{table}
Table 2: Summary of study variables.  
ANOVA is a widely used statistical method for group comparisons. It determines whether a difference exists between groups by testing the hypothesis of variance between the groups to be compared using a \(t\)-test. In this study, we used an ANOVA to verify whether a difference existed in the average amount of complex odor production by category. Correlation analysis [23] identifies the relationship between two or more quantitative variables. The range of the correlation coefficient, the result of the correlation analysis, is from \(-1\) to \(+1\). A correlation coefficient of \(+1\) indicates that the two variables have a perfectly positive linear relationship, \(-1\) indicates that they have a completely negative linear relationship, and \(0\) indicates that they have no linear relationship.
## 3 Results
### Participant Characteristics  
The participant characteristics are shown in Table 3. Of the 20 participants, 17 were female and 3 were male with ages ranging from 27 to 58 years. All eligible participants were randomized into two groups (\(n=10\) per group). The placebo group comprised one (10%) man and nine (90%) women, with ages ranging from 27 to 54 years, while two (20%) men and eight (80%) women were assigned to the probiotic group (33-58 years old). No patients withdrew from the study before the endpoint, and no serious adverse events were reported during the intervention period. No statistically significant differences were observed in the demographic characteristics of participants between the two groups at baseline. Effect of the Vegetable and Fruit Juice (VF)) with and without Robotic L. paracasei on Physical Parameters  
BW, BMI, and WC showed no considerable differences at baseline and endpoint within the placebo group. Moreover, within the probiotic group, no considerable changes were observed in BW or BMI; however, the WC exhibited a decreasing trend (\(p<0.10\); Table 4). Moreover, no significant differences were observed between the two groups in terms of BW, BMI, or WC, at the endpoint (Table 5). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{4}{c}{**Placebo Group**} & \multicolumn{4}{c}{**Probiotic Group**} \\ \cline{2-9}  & **Baseline** & **Endpoint** & **\% Change** & **\(p\)-Value** & **Baseline** & **Endpoint** & **\% Change** & **\(p\)-Value** \\ \hline BW (kg) & 62.62 \(\pm\) 3.20 & 62.08 \(\pm\) 3.15 & \(-\)0.86 & 0.255 & 65.01 \(\pm\) 2.53 & 64.57 \(\pm\) 2.57 & \(-\)0.68 & 0.192 \\ BMI (kg/m\({}^{2}\)) & 25.36 \(\pm\) 1.43 & \(25.16\pm\) 1.43 & \(-\)0.79 & 0.281 & \(26.25\pm\) 1.06 & 26.06 \(\pm\) 1.04 & \(-\)0.72 & 0.163 \\ WC (cm) & 81.50 \(\pm\) 2.46 & 81.10 \(\pm\) 2.26 & \(-\)0.49 & 0.343 & 86.00 \(\pm\) 3.02 & 85.20 \(\pm\) 3.02 & \(-\)0.93 & 0.087\({}^{*}\) \\ TC (mg/dL) & 202.60 \(\pm\) 9.46 & 203.00 \(\pm\) 6.40 & 0.20 & 0.955 & 203.80 \(\pm\) 5.11 & 192.40 \(\pm\) 4.53 & \(-\)5.59 & 0.041\({}^{**}\) \\ LDL-C (mg/dL) & 145.20 \(\pm\) 3.93 & 145.80 \(\pm\) 4.97 & 0.41 & 0.818 & 146.60 \(\pm\) 3.54 & 137.80 \(\pm\) 4.04 & \(-\)6.00 & 0.003 \({}^{***}\) \\ HDL-C (mg/dL) & 51.40 \(\pm\) 2.87 & 50.80 \(\pm\) 2.56 & \(-\)1.17 & 0.712 & 49.20 \(\pm\) 2.53 & 53.30 \(\pm\) 2.36 & 8.33 & 0.009 \({}^{***}\) \\ \hline \hline \end{tabular}  
* \({}^{1}\)mg eq GA represents milligram equivalent gallic acid.  
\end{table} 
Table 4: Physical examination parameters and biological markers in plasma and feces of the participant at the baseline (Day 0) and endpoint (Day 30). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Nutrition** & **Amount (Per 100 mL)** \\ \hline Zinc & 0.06 mg \\ Copper & 0.02 mg \\ Chloride & 9.39 mg \\ Pantothenic acid & 0.04 mg \\ Total polyphenol & 41.99 mg eq GA \({}^{1}\) \\ Naringenin & 636.82 ug \\ Quercetin & 1088.34 ug \\ Lutein & 48.76 ug \\ Lycopene & 239.10 ug \\ \hline \hline \end{tabular}  
* \({}^{1}\)mg eq GA represents milligram equivalent gallic acid.  
\end{table} 
Table 2: _Cont._
[45] catalogued and reviewed various approaches for satellite image fusion such as intensity-hue-saturation [46], principal component analysis [47], wavelet decomposition [48], high-pass filter (HPF) [49], sparse representation [50] and area-to-point regression kriging (ATPRK) methods [51]. Recently, machine learning techniques such as deep learning [52] and random forest (RF) [53] have gained popularity in satellite image fusion. Considering its advantages and past performance [54], an RF algorithm is proposed here for the multi-sensor data alignment in the virtual constellation of L8, S2A, and ASTER. Our approach for multi-sensor data integration is to first designate the most ubiquitous sensor with the longest data record, in this case L8, as the baseline. The second component of our approach is to investigate baseline and peer sensors for similar spectral features and to develop candidates for multi-sensor inequality alignment and finally fusion. Third, the hierarchical fusion workflow is established to produce enhanced cloud-free NDVI that mimics the L8 product. There are two sub-approaches for this task. The first is to resample and interpolate the 20 m S2A Level-1C data and the 15 m ASTER Level-1B data to the 30 m spatial resolution of L8 Level-1B. S2A Level-1C top-of-atmosphere (TOA) reflectance is geometrically and radiometrically rectified with orthorectification to generate accurate geolocated products. ASTER Level-1B data contain calibrated at-sensor radiances, which are geometrically corrected and geolocated. The process for resampling is straightforward but effectively discards some of the valuable 20 m information obtained by S2A and 15 m information obtained by ASTER. Since our objective is to enrich the existing L8 with available complementary data, we retained all spatial and spectral characteristics of L8 and modified the other two satellites accordingly. The fusion of L8 with S2A and ASTER data can increase the spatial coverage of data available for continuous monthly monitoring. This is especially beneficial in CW areas where cloud and water vapor mask a high percentage of the data [55]. The second option was to compute NDVI from each sensor first before the co-registration and scaling. This approach is called 'index then blend (IB)' [56; 57]. The IB approach has been found to be computationally more accurate because it mitigates error propagation compared to the alternative [56]. This paper presents a virtual constellation that integrates NDVI data from three satellite sensors that have similar spectral features: L8, S2A, and ASTER. The combined imagery enables NDVI observations of CW at 30 m spatial resolution similar to L8 with more available spatio-temporal coverage. The novelty in the current study lies in the use of four years of coincident NDVI imagery to train components of the virtual constellation to predict NDVI for any selected date irrespective of season, thus making the model robust and adaptive to seasonal and inter-annual changes. The virtual constellation model will serve as a unique tool for coastal managers to continuously monitor long term CW changes.
# Manufacturer Channel Encroachment and Evolution in E-Platform Supply Chain: An Agent-Based Model
## 5 Discussion
### Results Discussion  
In this part, based on the simulation results of the above five scenarios, the specific influence and enlightenment of different factors on the e-commerce supply chain are discussed.  
Figure 17: The profits of: (**a**) manufacturers; (**b**) the e-commerce platform. (\(t=100\)).  
First, an increase in quality preference reduces the number of dual-channel manufacturers. Since e-commerce platforms have complete pre-sales and after-sales services and a good user base, consumers are more willing to trust mature e-commerce platforms when they have high quality preferences. Therefore, when a manufacturer establishes a direct sales channel, taking into account the quality needs of consumers, channel maintenance can be strengthened, and advertising can be increased to increase consumers' trust in direct sales channels. In addition, consumers' high-quality preference can bring higher utility for themselves, so it can be used as an incentive to the supply side of the market, and through high quality requirements, product providers can improve products with high quality and low price.  
Consumers' channel preference for e-commerce platforms is a big blow to dual-channel manufacturers. As consumers prefer to shop on e-commerce platforms, manufacturers' strategy of establishing direct sales channels may not bring them benefits. In order to satisfy customer expectations, manufacturers should, on the one hand, pay attention to the maintenance of sales channels on the platform where channel preference is high. On the other hand, by marketing and other strategies to generate demand for direct sales channels, manufacturers may also build consumers' trust in their own channels. E-commerce platforms need to continue to provide consumers with a good shopping experience in order to maintain consumers' trust and preference for platform channels.  
As an important component of operating costs, the manufacturer's direct sales channel maintenance costs have an important impact on channel selection. It is not difficult to understand that higher maintenance costs will force manufacturers to abandon dual-channel operations. At the same time, the increase in cost makes the price of products rise accordingly, reducing the utility of consumers. Therefore, in order to achieve omnichannel operations and improve supply chain efficiency, manufacturers should pay attention to cost control and improve the efficiency of developing direct sales channels.  
Finally, the reduction in e-commerce platform commissions can reduce the cost burden of manufacturers and make them more capable of developing direct sales channels. In consideration of improving the performance of the entire supply chain, e-commerce platforms should assume their own corporate social responsibilities, and can establish a coordination mechanism to reduce manufacturer commissions, thereby increasing the richness of the market, realizing an omnichannel, and improving consumer utility and supply chain efficiency.
## 5 Experimental Results
### Real Data in Native Network Form  
NBR-Clust using VAT, integrity, toughness, tenacity and scattering number was applied on four sets of data in native network form. The first network represents a set of books about US politics, compiled by [PERSON] [57]. The graph's nodes represent 105 books sold by Amazon (Seattle, WA 98121, USA), categorized by [PERSON] as liberal, neutral, or conservative. The edges represent frequent co-purchasing of books by the same buyers, as indicated by the _customers who bought this item also bought these other books_ feature on Amazon. For comparison, Girvan-Newman results for this graph were obtained from [64], and Louvain modularity [60] was calculated using Gephi software (version 0.9.2, The Gephi Consortium, from gephi.org, accessed on 30 July 2018). As shown in Table 1, VAT, integrity, toughness and tenacity all achieved accuracies of 80% or higher on this dataset. Both Girvan-Newman and Louvain modularity attained a clustering accuracy of 84%.  
The three additional datasets included [PERSON]'s karate club [59], college football [2] and the Chesapeake Bay food web [58]. The accuracy results for these datasets are shown in Table 1. With the karate club, integrity achieves a high accuracy, while VAT and toughness are less successful. Like integrity, [PERSON] and Louvain both cluster the karate club at 97%. VAT and integrity achieve high accuracy with the football dataset, correctly classifying at least 89% of the teams according to conference. Girvan-Newman clusters this dataset at 90% accuracy, and Louvain clusters it at 91%. VAT and integrity are the most successful of all methods with the noisy and difficult food web dataset, where both correctly cluster 73% of the nodes. [PERSON] clusters the food web at 70%, and Louvain clusters it at 67% accuracy. Note also that, except in two cases with college football, all graphs are clustered by NBR-Clust in one iteration.
# Autonomous Multiple Tramp Materials Detection in Raw Coal Using Single-Shot Feature Fusion Detector
## 5 Results and Analysis
### Visualization of Detection Results  
In order to more intuitively understand the detection performance of our proposed network, in Figure 10, we show the visual detection results of tramp materials in raw coal. In order to effectively evaluate the detection performance of the TMRC-SSD network, we set the classification threshold score at 0.75. In Figure 10a-j, the images on the left and the right represent the original image and the TMRC-SSD network detection effect respectively. The red boxes indicate the detected gangue, the green boxes indicate the detected bolt, the purple boxes indicate the detected stick, the yellow boxes indicate the detected iron sheet, and the blue boxes indicate the detected iron chain. Due to the limitations of the basic SSD network, the detection performance for small targets is poor. However, our proposed network uses feature fusion and multi-scale prediction strategies to improve detection performance. Experimental results show that this method can detect most objects well. For  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **TMRC-SSD(-3)** & **TMRC-SSD(-2)** & **TMRC-SSD(-1)** & **TMRC-SSD** \\ \hline +MDCS & & ✓ & ✓ & ✓ \\ +4 CLFF & & & ✓ & ✓ \\ +AFM & & & & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 6: Models with various designs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Small Object** & **Medium Object** & **Large Object** \\ \hline SSD & 0.7438 & 0.9325 & 0.9598 \\ Yolov3 & 0.9142 & 0.9707 & 0.9602 \\ Yolov3-tiny & 0.8856 & 0.8983 & 0.9326 \\ DSSD & 0.8163 & 0.9422 & 0.9174 \\ Faster RCNN & 0.6839 & 0.9163 & 0.9658 \\ TMRC-SSD & 0.9557 & 0.9692 & 0.9706 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance with different components for three scales of objects.  
smaller targets in the scene, such as the iron chain in the upper right corner of Figure 10h, a good detection effect has been achieved.
# Self-Reporting Technique-Based Clinical-Trial Service Platform for Real-Time Arrhythmia Detection
###### Abstract
The self-reporting technique allows patients to record their subjective symptoms and other health information that wearable devices cannot reliably measure while monitoring objective vital signs [22]. It also allows clinicians to rapidly recognize the risky situations of patients, thus leading to timely and effective treatments. The self-reporting technique becomes feasible by connecting the mobile technologies and hospital information systems (HISs). Multiple vital signs, biosignals, and self-reported information are achievable in the mobile environment by communicating with wearable devices. These data are transmitted to the HIS, wherein automatic diagnosis and data saving take place. The novelty of this study is in the implementation of self reporting, automatic diagnosis, full data display and storage functions on a single data-acquisition platform. In particular, a self-reporting technique-based clinical-trial service platform was proposed for real-time arrhythmia detection. This platform facilitates the acquisition of a wide variety of health information and the provision of such data to clinicians in remote locations simultaneously. These advantages increase the efficiency of clinical trials by reducing clinician workload, and assist clinicians in providing appropriate intervention. Furthermore, this platform can be utilized for additional purposes, such as telemedicine services. The remainder of this paper describes the technical realization of the platform, its performance evaluation, and clinical importance.
# IoTactileSim: A Virtual Testbed for Tactile Industrial Internet of Things Services
## 1 Introduction
Figure 2: An overview of the relation between IoT, IIoT, tactial IoT, tactile IIoT, Industry 4.0, and Industry 5.0.  
also provide some reference tactile data traces, software, and hardware to evaluate newly developed kinesthetic and tactile codecs. In [15], a framework for tactile cyber physical systems was proposed, which is specifically for physical remote environments and based on network simulator NS3. It provides an interface for robotic experiments, along with haptic communication modules. However, the authors ignored the extensibility of the proposed testbed for other haptic-driven applications. Similarly, the authors in [16; 17] proposed a generic testbed framework for different TI use cases. A data-driven experiment setup was proposed in [16] to provide a common playground for testing haptic applications. The proposed haptic communication testbed at the Otto-von-Guericke University of Magdeburg (OVGU-HC) focused on providing experiment testbed for long-distance haptic-enabled teleoperation systems, in addition to small scale wireless haptic-driven applications. The OVGU-HC presents experiment automation and data collection utilizing experiment description language (DES-Cript). The proposed OVGU-HC did not work standalone, is a part of the MIoT-Lab, and is just used to gather hepatic experiment information. Moreover, it utilized domain-specific language DES-Cript [18], and did not provide an open-source facility to the research and development community. The study in [17] presents a two-level classification of the TI applications based on controlled environment and master-slave integrations to develop a generic testbed, with a focus to ensure compatibility for all these classified applications, which is named as TI- eXtensible Testbed (XT). To demonstrate the potential of the TXT, they discuss H2M haptic communication in the virtual and physical environment. However, they ignored the explanation on how to characterize the network impairments (delay, jitter, and packet losses) and investigate the performance of the haptic-driven IIoT application. Therefore, there is a strong need for a testbed that offers flexibility, scalability, open-source availability, tailored to examine network impairments, communication flow, and extensible for TI IIoT use cases. In this regard, we proposed a virtual testbed called IoTactileSim to investigate tactile IIoT services from QoS and QoE perspectives. The IoTactileSim employs Software Define Network (SDN) and edge computing at the core network to tactile industrial application. The following section presents the main contribution of the proposed IoTactileSim testbed.
## 4 Results and Discussion
### The Effect of Original Image Correction  
Figure 3 shows the result of our method in the underwater image segmentation. As shown in Figure 3, the coordinate points \(\{[\text{X},\text{Y}]:[77,41]\}\) and \(\{[\text{X},\text{Y}]:[152,57]\}\) are located at the saliency target edge in Figure 3b. However, in Figure 3c, the coordinate point at the same position is inside the target instead of on the target edge. This error is caused by the deviation in the saliency detection. Therefore, it is necessary to use the original image to supplement the missing information. This paper used the local image fitting method to extract the contour information of the original image. It then used the contour information to correct the deviation caused by saliency detection. The result of the correction is shown in Figure 3e. As shown in Figure 3e, the missing contour information of the saliency image is accurately supplemented, and the background information is filtered out.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Experiments** & **local Intensity** & **Global Intensity** \\ \hline A & ✓ & \\ B & & ✓ \\ C (our fusion intensity) & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 2: The comparative experiment of the local-global intensity.  
Figure 2: The contour extraction results. (**a**) Original saliency image. (**b**) The result of the local intensity fitting. (**c**) The result of the global intensity fitting. (**d**) The result of our method.
## 3 Experimental Results and Discussion
### Evaluation of Fall Detection
#### 3.2.2 Fall Detection in Various Fall Scenes  
To verify our method more quantitatively, we tested all the datasets (38 videos consisting of 4271 frames in total) including confounding scenes and several normal walking, sitting, and standing scenes that might increase errors. Table 3 represents the numerical results of the experiments. It is observed that the accuracy is slightly lower than the results presented in Table 2, which is sufficiently outstanding to detect various fall postures on the street. Additionally, we performed an experiment with the appearance of several people in one scene to measure the robustness of the fall detection algorithm, and the results showed successful fall detection of a single target person in the presence of multiple other people (two to four) without losing tracking as shown in Figure 18.
###### Abstract  
As brain-computer interfaces (BCI) must provide reliable ways for end users to accomplish a specific task, methods to secure the best possible translation of the intention of the users are constantly being explored. In this paper, we propose and test a number of convolutional neural network (CNN) structures to identify and classify single-trial P300 in electroencephalogram (EEG) readings of an auditory BCI. The recorded data correspond to nine subjects in a series of experiment sessions in which auditory stimuli following the oddball paradigm were presented via earphones from six different virtual directions at time intervals of 200, 300, 400 and 500 ms. Using three different approaches for the pooling process, we report the average accuracy for 18 CNN structures. The results obtained for most of the CNN models show clear improvement over past studies in similar contexts, as well as over other commonly-used classifiers. We found that the models that consider data from the time and space domains and those that overlap in the pooling process usually offer better results regardless of the number of layers. Additionally, patterns of improvement with single-layered CNN models can be observed.  
convolutional neural networks (CNN); auditory brain-computer interface (BCI); P300; virtual sound; electroencephalogram (EEG); pool strategies; classification Article