esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this article, we proposed a CFD digital twin framework. This framework is based on the IoT, CFD simulation, and artificial intelligence technology. It builds the twinned object of the physical entity through the integration of geometric models, rules models, physical models, and neural network models. Digital twins are designed to receive the operating data of physical entities and predict the CFD flow field in real-time to monitor the production status. It allows onsite staff to quickly obtain the CFD simulation results without CFD professional knowledge.  
Combined with the characteristics of POD and CNN, we proposed the POD-MCNN model. It performs well for testing data, new data, and fault data. When the key process parameters are abnormal, the model output is also obviously abnormal. It enables on-site workers to adjust the abnormal temperature in time. The model response only requires 0.2 s, which can provide a rapid response at the production site. The speed is greatly accelerated, saving CFD computing costs significantly. Compared with other models, this model has the highest accuracy in predicting the flow field of the push-plate kil. Through the standardized interface, the data generated by the simulation digital twin can be embedded into the visualization software such as unity to provide the real-time monitoring and evaluation of equipment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Network Model** & **Number of Parameters** & **Mean Square Error** \\ \hline FNN & 8 k & 0.00343 \\ CNN & 6 k & 0.00264 \\ MPCNN & 6 k & 0.00306 \\
**MCNN** & **6 k** & **0.00223** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparison of different models.  
In summary, the simulation digital twin development method proposed in this paper has good accuracy and generalization. Note that the twins in this paper are only used for 3D visual monitoring. The work that can be further explored includes combining the knowledge of production experts and guiding the parameters of the production process through the digital twin.  
Conceptualization, P.W.; Methodology, P.W. and L.J.; Resources, T.T.; Software, L.J.; Supervision, T.T.; Writing--original draft, L.J.; Writing--review and editing, L.J., W.Y. and Z.L. All authors have read and agreed to the published version of the manuscript.  
This study was funded by Chengdu Xingyun Zhilian Technology Co., Ltd. (No.92219331211).  
Not applicable.  
The authors declare no conflict of interest.
# Randomized Feature and Bootstrapped Naive Bayes Classification
## 2 Related Theory
### Decision Boundary  
For binary classification \((K=2)\), the decision boundary is defined as the set of points \(X\) where the posterior probabilities of the two classes are equal:  
\[P(Y=y_{1}|X)=P(Y=y_{2}|X). \tag{10}\]  
By substituting the Gaussian likelihoods and simplifying, the boundary is defined by  
\[\log P(Y=y_{1})-\log P(Y=y_{2})+\sum_{i=1}^{n}\Biggl{[}\frac{\mu_{i1}-\mu_{i2} }{\sigma_{i}^{2}}X_{i}+\frac{\mu_{i2}^{2}-\mu_{i1}^{2}}{2\sigma_{i}^{2}} \Biggr{]}=0, \tag{11}\]  
assuming equal variances for each feature across classes \(\left(\sigma_{ik}^{2}=\sigma_{i}^{2}\right)\).  
For multi-class classification \((K>2)\), the decision boundary between two classes \(y_{k}\) and \(y_{l}\) is determined by equating their posterior probabilities:  
\[P(Y=y_{k}|X)=P(Y=y_{l}|X). \tag{12}\]Taking the logarithm and simplifying yields  
\[\log P(Y=y_{k})-\log P(Y=y_{l})+\sum_{i=1}^{n}\left[\frac{\mu_{ik}-\mu_{il}}{ \sigma_{ik}^{2}}X_{i}+\frac{\mu_{il}^{2}-\mu_{ik}^{2}}{2\sigma_{ik}^{2}}\right]=0, \tag{13}\]  
This boundary partitions the feature space into regions associated with each class.
# Monitoring the Joint Area of Composite Membrane Materials
## 2 Materials and Methods  
### Materials  
Textile membranes are composed of a carrier fabric made of polyester (PET), polytetrafluoroethylene (PTFE), or glass fibers, and a multilayer polyvinyl chloride (PVC) coating on both sides. The fabric serves to bear the mechanical loads while the coating ensures the fixation of the fabric structure as well as its protection against external influences (e.g., UV radiation, moisture, aggressive media, etc.). Due to their structure and the described different purposes of individual components, textile membranes can be classified as flexible FRPs.  
In order to demonstrate the functionality of textile sensors under realistic load conditions, they were mounted on a commercially available textile membrane. The material used for the investigations was Polyplan 668 Dessin 8 HB from the manufacturer Sattler PRO-TEX GmbH. This is a polyester panama weave fabric, PVC-coated on both sides and painted with a weldable polyvinylidene dil fluoride (PVDF) varnish variant [17].  
Various materials were considered as potential candidates for the sensor yarn. Their suitability was examined by means of cyclic yarn tensile tests in addition to simultaneous measurements of the yarns' electrical resistance (see below for process description). Chapter 3 will present the test results of two yarn types, i.e., a carbon black-filled silicone yarn from LeMur S.p.A and a silver-coated polyamide yarn provided by Stacex Produktions- und Vertriebs GmbH (see Table 1 for properties).  
In addition to conductive yarns, a non-electrically conductive embroidery yarn was used in the embroidery process (see below for process description). A standard polyester embroidery yarn obtained from Brildor S. L., type PB40, with a yarn count of 135 \(\times\) 2 dtex [20] was selected.
# Deep-Learning Multiscale Digital Holographic Intensity and Phase Reconstruction
## 3 Experiment and Result Analysis
### Experimental Results and Analysis
#### 3.3.4 Comparison of Mimo-Net and Y-Net at 64 \(\times\) 64 Scale  
The reconstructed results of the Mimo-Net and Y-Net models were compared using images at 64 \(\times\) 64-scale, the enlarged comparison figures are shown in Figure 8.  
Figure 8 shows that the intensity image of Y-Net network reconstruction is incomplete, with burrs and protrusions at the edge of the phase image, while the intensity and phase images of Mimo-Net network reconstruction are relatively complete, and the reconstruction effect is also improved. Table 5 presents the PSNR and SSIM values of the images reconstructed by the Mimo-Net network and the Y-Net network compared to the original images.  
Table 5 shows that the strength and phase images reconstructed by the Mimo-Net network model have improved PSNR values by 2.3839 dB and 1.0823 dB, respectively, compared to the Y-Net network. The SSIM values have also increased by 0.0455 and 0.0377, respectively. Therefore, the Mimo-Net network exhibits better reconstruction performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Intensity of Y-Net** & **Intensity of Mimo-Net** & **Phase of Y-Net** & **Phase of Mimo-Net** \\ \hline PSNR/dB & 29.7061 & 32.4975 & 31.3524 & 31.5966 \\ SSIM & 0.9499 & 0.9676 & 0.9239 & 0.9403 \\ \hline \hline \end{tabular}
\end{table}
Table 4: \(128\times 128\) scale PSNR and SSIM.  
Figure 8: Comparison of Mimo-Net and Y-Net network reconstruction results at 64 \(\times\) 64 scale.  
Figure 7: Comparison of Mimo-Net and Y-Net network reconstruction results at 128 \(\times\) 128 scale.
# Call Redistribution for a Call Center Based on Speech Emotion Recognition
###### Abstract  
Call center operators communicate with callers in different emotional states (anger, anxiety, fear, stress, joy, etc.). Sometimes a number of calls coming in a short period of time have to be answered and processed. In the moments when all call center operators are busy, the system puts that call on hold, regardless of its urgency. This research aims to improve the functionality of call centers by recognition of call urgency and redistribution of calls in a queue. It could be beneficial for call centers giving health care support for elderly people and emergency call centers. The proposed recognition of call urgency and consequent call ranking and redistribution is based on emotion recognition in speech, giving greater priority to calls featuring emotions such as fear, anger and sadness, and less priority to calls featuring neutral speech and happiness. Experimental results, obtained in a simulated call center, show a significant reduction in waiting time for calls estimated as more urgent, especially the calls featuring the emotions of fear and anger.  
emotion recognition; intelligent speech signal processing; affective computing; human computer interaction; supervised learning Article
## 2 Theoretical Background
### EEG Measurement of Cognitive Load and Flow State  
EEGs have been commonly used in learning technology and educational studies [11; 12; 14]. In particular, several research studies, such as [28], have attempted to measure cognitive load. The EEG measurement of cognitive load may be conducted using working memory [29]. In one study [19], (\(\theta+\alpha\))/(10 \(\times\) low\(\_\gamma\)) was found to be an effective index for measuring the extent of cognitive load through portable EEG devices. In relation to task difficulty, EEG signal analyses reveal a high degree of discrimination between the dynamic changes in mental workload [18; 30; 31; 32]. The main effect of task difficulty on EEG-measured cognitive load estimates was correlated with learning performance or intelligence [33].  
For the flow state, two studies [15; 16] also used an EEG for real-time flow measurement in students and the determination of its indicators, namely, high levels of attention and engagement. In addition to these indicators, the said study also observed the positive effect of several factors, such as the balance between challenges and skills (BCS), overall test performance (i.e., the present study's LT-tp), and momentary test performance (i.e., the present study's ST-tp), on flow experiences. Although cognitive load was not considered in the abovementioned study, other works suggested a possible connection between the flow state and cognitive load. One study [34] indicated that the flow state could alleviate negative experiences, while another study [35] stated that the reduction of cognitive load could promote engagement, possibly leading to the flow state. In line with this, a study [2] revealed that cognitive load negatively impacts flow. However, the EEG measurement of cognitive load in relation to the flow state and its implications remains unexplored, hence the present study's focus.
# Lip2 Speech: Lightweight Multi-Speaker Speech Reconstruction with Gabor Features
## 2 Literature Review  
Initial research into speech reconstruction faced computational limitations [5; 12; 13]. A full reconstruction was hard to achieve, and so the focus was on low dimensional estimation such as using DCT visual information to estimate filterbank audio features [12; 13; 14; 15]. However, increased computational power and more advanced machine-learning models allowed for improved estimation. Early research [12] reconstructed audio from video usinga deep-learning network trained on visual features from the mouth region. Recent work by [PERSON] et al. [2] utilised an end-to-end deep neural network to learn auditory spectrograms from raw facial pixels. Similarly, [PERSON] et al. [16] proposed a sequence-to-sequence architecture that learned individual speaking styles from an unconstrained dataset. However, those approaches are speaker-dependent, which limits their application to a small group of individuals, as each speaker requires a separate model.  
Several studies [17; 18; 19; 20] have attempted to address the problem of multi-speaker speech reconstruction by adding additional information such as speech or output text to distinguish between speakers. However, if the supplementary information is not present, speakers can not be distinguished. In addition, despite this extra information, these studies [17; 18; 19] generally suffer from the problem of low speech-quality reconstruction when reconstructing speech from multiple speakers. Furthermore, the system structure developed in these studies [17; 18; 19; 20] is highly complex, which requires a large amount of data and lengthy training, leading to high computational costs. Therefore, there is a need for more lightweight and transparent multi-speaker speech reconstruction models to overcome the limitations of existing models. These models should maintain high speech-quality reconstruction performance without the requirement for additional information, simplifying the system structure.
## 2 Experimental Procedure
### Casting of Specimens  
The mixing method for the NWC was as follows: The fine aggregate that was processed into a saturated surface-dry condition was added to a double-shaft mixer, and the cement was then placed in the mixer and fully dry-mixed at a speed of 45 rpm for approximately 1 min. Next, the water mixed with the superplasticizer was put into the mixer and mixed for approximately 1.5 min. The crushed stone that was processed into a saturated surface-dry condition was then placed into the mixer and mixed for another 1-2 min to obtain fresh concrete with uniform consistency. As for the LWAC, the mixing procedure was roughly the same, but the main difference was that the LWA had to be dried before it was mixed. This is because the water absorption rate of LWA varies greatly and is unstable. When immersed in water in a dry state, the instantaneous water absorption rate in the first few minutes is approximately 25-30% of the water absorption rate in 24 h. However, after 30 min, the water absorption rate is much slower. Therefore, when mixing the LWAC, the additional water consumption was calculated according to the 30 min water absorption rate of the LWA.  
After each group of concrete was mixed, its properties were tested immediately. Eight slab specimens (600 (length) \(\times\) 400 (width) \(\times\) 150 mm (thickness)) and six cylindrical specimens (diameter: 100 \(\times\) height 200 mm) were then cast for each group of concrete. There were embedded thermocouples and pressure gauges inside the slab specimen. The detailed configuration of the thermocouples (measuring temperature up to 1000 \({}^{\circ}\)C) and pressure gauges (model: MN1-6-M-B02C-1-4-D, its measuring range is 0-20 MPa) is shown in Figure 2. The specimen was disassembled after 24 h and then placed in a curing room with a temperature of 23 \(\pm\) 2 \({}^{\circ}\)C. The specimens were not taken out until the day before the test age (28 days) for the high-temperature test and cylinder compressive strength test. For the test conditions in the air-dried state, the specimens were allowed to dry in the air for 24 h before the high-temperature test was performed. As for the test conditions of the oven-dried state, the specimen was first placed in an oven at 105 \({}^{\circ}\)C until its weight did not change.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Mix ID**} & \multicolumn{4}{c}{**Compressive Strength (MPa)**} \\ \cline{2-5}  & \multicolumn{2}{c}{**7 Days**} & \multicolumn{2}{c}{**28 Days**} \\ \hline  & 33.0 & & 39.7 & \\ LWAC & 31.0 & (32.9) & 36.1 & (38.3) \\  & 34.8 & & 39.0 & \\ \hline  & 28.2 & & 40.0 & \\ NWC & 26.6 & (27.4) & 40.3 & (39.9) \\  & 27.3 & & 39.3 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Compressive strength of the concrete.  
Figure 2: Installation of thermocouples and pressure gauges in the slab specimens: (a) before pouring and (b) after pouring.
## 2 Datasets and Methods
### Datasets  
#### 2.2.1 GF-3 and OHS  
The Chinese Gaofen-3 (GF-3) satellite carried a SAR sensor and was launched on 10 August 2016 [30]. The GF-3 supports operations in single-polarization (HH or VV), dual-polarization (HH+HV or VH+VV), and quad-polarization (HH+HV+VH+VV) modes with respect to 12 different observing modes [49], and has been widely used for water conservancy, land and ocean monitoring, and other vital applications [50; 51].  
Figure 1: Location of the study area. The base map is derived from the 10 m false color OHS composite image on 23 March 2020 (red = band 28; green = band 14; blue = band 8). The red rectangular box on the false color image represents the study area. The blue and green boxes in the inset indicate the coverage of OHS hyperspectral remote sensing and GF-3 SAR remote sensing images, respectively.  
QPSI is a quad-polarization strip imaging mode with a spatial resolution of 8 m that captures four polarization channels by transmitting and receiving horizontal and vertical waves and can improve the classification accuracy of ground targets [33]. The detailed parameters of GF-3 data selected for this study are shown in Table 1.  
The Zhuhai-1 satellite constellation of Orbita company includes four Orbita hyperspectral satellites (OHS-A, OHS-B, OHS-C, and OHS-D) that have the same hardware configuration and operating status as well as strong hyperspectral data acquisition abilities [52]. The hyperspectral sensor CMOSMSS (Complementary Metal-Oxide-Semiconductor Multispectral Scanner System) mounted on the OHS satellites can acquire images with a resolution of 10 m, a width of 150 km, and 32 spectral segments [53]. The detailed parameters and spectral range of the OHS bands used for this study are shown in Tables 1 and 2.
# Accurate Estimation of Air Pollution in Outdoor Routes for Citizens and Decision Making
## 3 Air Quality Issues and Its Monitoring Network
### Accessing to AQ Monitoring Data  
According to Directive 2008/50/EC, in the cities, there are a number of AQ official stations monitoring polluting gases, as shown in Figure 1. This information is usually open and available. In practice, we have different options to access the AQ dataset.  
In particular, in the case of Valencia city [6], there are two options, both in open-data. The first option is to download the data directly using a specific URL for each station [7]. Within this option, we get flat file formats (CSV, JSON, and Excel) or geographic file formats (GEOJSON, Shapely, and KML). In the second option, we can use a specific API to obtain this data. We chose this option because it allows us to automate the retrieving process  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Index Level** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline PM2.5 & 10 & 20 & 25 & 50 & 75 & 800 \\ \hline PM10 & 20 & 40 & 50 & 100 & 150 & 1200 \\ \hline O\({}_{3}\) & 50 & 100 & 130 & 240 & 380 & 800 \\ \hline NO\({}_{2}\) & 40 & 90 & 120 & 230 & 340 & 1000 \\ \hline SO\({}_{2}\) & 100 & 200 & 350 & 500 & 750 & 1250 \\ \hline \hline \end{tabular}
\end{table}
Table 2: European Air Quality Index (E-AQI) level and maximum allowed per pollutant (in \(\upmu\)g/m\({}^{3}\)) per index level.  
Figure 2: Location of the official AQ monitoring stations in Valencia city (Spain) [7] with their European AQI colour indicator (19 July 2023, 10:00 a.m.).  
using Opendatasoft API [20]. In other infrastructures, we have similar alternatives since these data are usually publicly available.  
Thus, the dataset from Valencia city is made up of 11 records from these 11 different AQ monitoring stations, as shown in Figure 2 and mentioned in Section 3.2, from different zones of Valencia. For each one, we get the following pollutants: NO\({}_{2}\), CO, SO\({}_{2}\), O\({}_{3}\), PM10 and PM2.5. Notice that this information is updated in the system every 10 min according to ISO 11771:2010, ISO 37122:2019 and European Regulation Directive 2008/50/EC.
# Data Knowledge Dual-Driven Rate of Penetration Prediction Method for Horizontal Wells
## 3 Methodology
### Data Pre-Processing  
Data pre-processing is important for modeling ROP prediction models. The data quality determines the upper limit of the accuracy of the data-driven model, which is similar to ROP prediction. Similar to previous studies, noise reduction and outlier removal are also performed on the data in this paper. The difference is, in this paper, a drag and torque (D&T) model is introduced to correct the drilling parameters for the drilling characteristics of horizontal wells, which is also part of the data pre-processing.
## 3 RPL Overview
### RPL Topology and Control Messages  
As shown in Figure 2, an RPL DODAG is a DAG graph rooted and pointed at a single destination while the DODAG root has no edges. A DODAG graph is identified uniquely using a combination of DODAG ID and RPL Instance ID as the RPL instance can contain more than one DODAG each one of them has an DODAG ID. Each node in the DODAG has a rank value which expresses the node position with respect to the DODAG root node. Rank values strictly increase towards down direction and decrease in the up direction as it becomes closer to the root node. DODAG Root is responsible for the routes aggregation and DODAG construction. Traffic towards the root node is considered as Multi_point to Point (MP2P) while the traffic from root node to leaves nodes considered as Point to Multi_point (P2 MP). In our proposed scenarios, we consider RPL topology with one DODAG root and one instance.  
RPL protocol has three types of ICMPv6 control messages, which are defined as follows:  
* DODAG Information Object (DIO): The DODAG root (border router node) issues DIO message in a multicast form to construct a new DODAG. The DIO message structure contains all information concerning the network that allows any node to find an RPL instance, select a DODAG parent set, learn about its configuration parameters, and finally build the DODAG.  
* Destination Advertisement Object (DAO): As the DODAG is being constructed each node in the DODAG sends this message to propagate and populate a node rank and routing tables' information to their predecessor nodes that support the downward route traffic (traffic towards leaves nodes).
* DODAG Information Solicitation (DIS): These messages are sent by any node to trigger the others to send DIO messages to this node and this happened only when that node didn't receive a correct DIO message for a long time.