esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Rare Earth Solution Image Feature Extraction  
### Color Feature Extraction  
During the process of rare earth extraction production, the field environment is often harsh, which is not conducive to the direct acquisition of images. Therefore, an image acquisition device is utilized to first capture the image of the rare earth solution. Subsequently, operations such as image background segmentation and image filtering are performed. The central area of the image is selected for cropping, yielding a 128 \(\times\) 128 sample map. The first-order components of RGB are then extracted. RGB is a color space commonly employed by hardware devices. However, the RGB color space is influenced by light intensity and color offset, and there is a correlation between its color channels. Consequently, it is not suitable for feature extraction from rare earth solution images. Other color spaces, such as HSV and YUV, align more closely with human visual perception and can more effectively describe the brightness, saturation, and other color characteristics. The HSV space primarily describes the brightness, saturation, and hue of the color, while the YUV space describes the brightness and chrominance. Both are nonlinear transformations based on the RGB space, and their combination can more comprehensively describe color information. Their conversion relationships to the RGB color space are as follows:  
\[Max=\max(R,G,B) \tag{1}\]  
\[Min=\min(R,G,B) \tag{2}\]  
\[H=\left\{\begin{array}{l}0,\,M\mathrm{ax}=Min\\ 60^{\circ}\times\frac{G-B}{\mathrm{Max-Min}}+0^{\circ},Max=RandG\geq B\\ 60^{\circ}\times\frac{G-B}{\mathrm{Max-Min}}+360^{\circ},Max=RandG<B\\ 60^{\circ}\times\frac{G-B}{\mathrm{Max-Min}}+120^{\circ},Max=G\\ 60^{\circ}\times\frac{G-B}{\mathrm{Max-Min}}+120^{\circ},Max=B\\ \end{array}\right. \tag{3}\]  
\[S=\left\{\begin{array}{l}0,\,ifMax=0\\ \frac{Max-Min}{Max},otherwise\\ \end{array}\right. \tag{4}\]  
\[V=max(R,G,B) \tag{5}\]In the above formula, the values of R, G, and B are real between 0 and 1, Max is the largest of RGB, and Min is the smallest. If (R, G, B) is reduced to (0, 255), then RGB is converted to the YUV formula, shown below.  
\[\begin{bmatrix}Y\\ U\\ V\end{bmatrix}=\begin{bmatrix}0.299&0.587&0.114\\ -0.169&-0.331&0.5\\ 0.5&-0.419&-0.081\end{bmatrix}\begin{bmatrix}R\\ G\\ B\end{bmatrix}+\begin{bmatrix}0\\ 128\\ 128\end{bmatrix} \tag{6}\]
## 4 Results and Discussion
### Energy Efficiency: Possible Minimum Value  
Supply chains of agricultural products comprise road transport, IWT, and maritime transport. Road transport delivers products from farmers to river terminals or elevators. Its distances of haulage are relatively short. In many cases, they do not exceed 50 km [39]. IWT supplies seaports. Its distances can be up to some hundred kilometers. Further, marine ships deliver agricultural products to end consumers. In this study, we analyze only IWT.  
Transportation increases the costs of agricultural products and energy consumption due to the additional fuel consumption. Some characteristics of trucks are given in Table 3[40; 41]. The fuel economy depends on the truck make, trailers, cargo class, condition of roads, load factor, driver qualifications, etc. This indicator varies from 0.011 to 0.0251/tkm. Traction efficiency or cargo carried to a rated power ratio is calculated by Formula (3).  
To reduce energy intensity, both organizational and technical measures can be applied. Organization measures include the use of road trains (reduction of fuel consumption by 20... 25%), maintaining the proper technical condition of equipment, training of drivers, etc. Technical measures should include, first of all, the use of modern commercial vehicles. Adoption of relevant state standards will also contribute to this. Thus, in the United States, these measures have reduced the cost of road transportation per unit of mileage [42].  
IWT uses dry cargo vessels, tankers, barges, tugs, etc. Barges with a carrying capacity of up to 9.2 thousand tons are used to transport goods by rivers. They can be combined into trains carrying up to 40 thousand tons of cargo. Pusher tugs are used to move them. Their main characteristics are given in Table 4[43; 44; 45].  
Pushers with barges have the lowest theoretical energy consumption per freight tkm (Figure 6). We used our calculations and data from the International Energy Agency and the International Union of Railways [46]. We used Formula (2) to calculate the specific energy consumption.  
Traction efficiency of IWT is higher compared to road transport (Tables 3 and 4) and railways. Electric trains have the traction efficiency within the range from 0.68 to 1.35 [47]. Specific energy consumption of IWT is lower compared to road transport (Figure 4). The use of barges (and, accordingly, pushers) with a capacity of more than 3 thousand tons reduces fuel consumption and the costs of waterway transportation.
## 6 Conclusions
Importantly, future research should investigate explainability techniques such as LIME (local interpretable model-agnostic explanations) or SHAP (Shapley additive explanations) to illuminate the model's decision-making process. This would not only foster trust, but also deepen our understanding of the complex links between physiological signals and emotions. Ultimately, such an approach would ensure that VR environments are not only emotionally intelligent, but also finely personalized for individual users. Finally, we believe that this research's value extends beyond its current discoveries, setting a solid foundation for future explorations aimed at realizing the full potential of emotionally intelligent virtual environments. This research has raised questions that need further investigation. Further research that incorporates additional biosignals, such as electroencephalography (EEG) and electromyography (EMG), could provide more comprehensive data for emotion classification, particularly for distinguishing between closely related emotional states. It would be interesting to explore more advanced feature extraction techniques and machine learning models, including deep learning approaches such as recurrent neural networks (RNN) or long-short-term memory (LSTM) networks, to improve the temporal analysis of physiological signals. Furthermore, future studies should consider the integration of the model into more diverse and dynamic virtual environments, testing its applicability across different user populations and real-world scenarios. This would help to refine the system's accuracy and generalizability, broadening its potential applications in areas such as education, therapy, and entertainment. Conceptualization, M.F.A. and M.Y. ; formal analysis, E.E.A. ; investigation, M.F.A. ; data curation, M.Y. ; methodology, M.F.A. and M.Y. ; project administration, H.E.I. ; resources, H.E.I. ; software, H.E.I; supervision, M.Y. ; validation, H.E.I. ; visualization, M.F.A. ; writing--originaldraft, E.E.A. ; writing--review and editing, M.Y. and H.E.I. All authors have read and agreed to the published version of the manuscript. This research was supported by the Gazi University Scientific Research Projects Unit (GAZI, BAP (2023), Project Number: 8179). Not applicable. This study did not involve humans or animals. Not applicable. This study did not involve humans. The dataset used in study is publicly accessible at [[https://www.kaggle.com/datasets/lumaatabaa/vr-eyes-emotions-dataset-vreed](https://www.kaggle.com/datasets/lumaatabaa/vr-eyes-emotions-dataset-vreed)]([https://www.kaggle.com/datasets/lumaatabaa/vr-eyes-emotions-dataset-vreed](https://www.kaggle.com/datasets/lumaatabaa/vr-eyes-emotions-dataset-vreed)) (accessed on accessed on 1 January 2020). The relevant codes for this research are available upon request. The authors, [PERSON] (E.E.A), [PERSON] (M.F.A), [PERSON] (M.Y. ), and [PERSON] (H.E.I. ), declare that they have no competing interests. The following abbreviations are used in this manuscript:
# Accurate Energy Consumption Modeling of IEEE 802.15.4e TSCH Using Dual-Band OpenMote Hardware
## 5 Evaluation  
In this section, the accuracy of the model is verified. First, the charge drawn per slot type for both radios is calculated and compared to the measured values. Afterwards, the accuracy of the charge drawn during a slot frame is validated using a small-scale test network. The developed packet size-aware model is also compared to a state-of-the-art model to show the accuracy improvement when including the packet size in the model. Finally, using the measured charge consumption values  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**CPU State** & **Radio State** & **Consumption (mA)** \\ \hline  & & **CC2538** & **CC1200** \\ \hline Active & Sleep & 13.97 & 15.06 \\ Active & Idle & 13.97 & 17.49 \\ Active & Listen & 31.14 & 40.13 \\ Active & RX & 26.94 & 50.63 \\ Active & TX & 31.47 & 54.26 \\ Sleep (PM\_NOACTION) & Sleep & 10.06 & 11.42 \\ Sleep (PM\_NOACTION) & Idle & 10.06 & 13.82 \\ Sleep (PM\_NOACTION) & Listen & 27.18 & 36.18 \\ Sleep (PM\_NOACTION) & RX & 23.16 & 46.73 \\ Sleep (PM\_NOACTION) & TX & 27.55 & 50.24 \\ Sleep (PM2) & Sleep & 0.00156 & 0.27 \\ Sleep (PM2) & Idle & 0.00156 & 2.64 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Current drawn during different device states.  
for both frequency bands' communication, several TSCH network simulations were conducted to observe the energy consumption effects in an end-to-end context.
## 4 Data Preparation for Neural Networks
### Feature Selection  
The process of feature selection involves reducing the number of input variables used in a model to only the most relevant ones. In this context, relevance refers to the link between the input quantities and the target used for training. This procedure has three main aims:  
* By retaining key features and eliminating irrelevant ones, it boosts a model's predictive performance;
* It guards against overfitting by reducing data complexity and focusing on crucial attributes;* Models with fewer features are quicker to train and less resource-intensive, which is ideal for large datasets and real-time applications, reducing computational costs.  
In order to select the features for the neural network responsible for predicting sideslip angle, a methodology was employed that combined concepts and equations of vehicle dynamics [16] with the channels that can be obtained through sensors that are easily installed, purchased, and used on various types of vehicles, as explained in Section 2. Using this information, the most relevant acquired channels were selected.
# Towards Flexible Integration of 5G and IIoT Technologies in Industry 4.0: A Practical Use Case
## 2 Related Work
### 5G, NFV and SDN in Industrial Environments
In regards to solutions aimed at providing additional flexibility and dynamicity to IIoT networks through the adoption of NFV and SDN technologies, the authors of [21; 22] proposed the introduction of network slicing mechanisms in IIoT scenarios with a large amount of sensors deployed for different monitoring purposes. They focused on applying network slicing to address varying Quality of Service requirements of different IIoT applications. This differs from our intentions of leveraging network slicing to rather enable a dynamic assignment of these IIoT applications to different business processes.
## 4 The Seismic Performance Parameters of RCS Composite Joints Under Floor Effect Are Studied
### The Influence of Floor Thickness on Seismic Performance of Nodes
#### 4.2.1 Destructive Morphology  
By comparing the stress contour maps and equivalent plastic strain distributions of the steel beams at various slab thicknesses (Figure 18), it can be observed that the modes of failure exhibit limited variation. Buckling and plastic hinges appear at the lower flange of the steel beam and the bearing plate, with stress concentrations occurring near the bolt holes of the web plate close to the column. As the slab thickness increases, the composite beam's sectional stiffness improves, enhancing the joint's resistance to deformation. Consequently, the plastic range of the steel beam decreases, and the area of plastic deformation in the concrete slab also diminishes accordingly.
## 1 Introduction  
Induced pluripotent stem (iPS) cells were first established in 2006 by [PERSON] and [PERSON] [1]. iPS cells were generated from mouse embryonic or adult fibroblasts after retrovirally introducing genes encoding four transcription factors (Oct3/4, Sox2, Klf4 and c-Myc). iPS cells are prime candidates for cell sources of cell regeneration therapy, because they retain their pluripotency. Two methods to differentiate iPS cells into target cells are known: embryoid body (EB)-mediated differentiation or direct differentiation of iPS cells. In this study, the EB-mediated differentiation method was used. Outgrowth cells (OGCs) from EBs can be used to differentiate further them into other types of cells. Several studies have reported that cell differentiation is influenced by a variety of physicochemical factors, such as matrix composition and mechanical stress.  
Oxygen tension is also known to be an important factor for iPS cell differentiation. The in situ oxygen tension is considerably lower than atmosphere levels. Thus, many studies on cell differentiation under hypoxia conditions have been reported, but little is known about the effects of high oxygen tension for the differentiation of cells. On the other hand, hyperbaric oxygen therapy is an effective treatment for severe bone fractures, and it promotes quick and complete healing of bones. However, few studies have focused on the effects of higher oxygen tension on the formation of EBs from iPS cells and iPS cell differentiation.  
Osteo-differentiation is a process that happens gradually. Several specific markers can be examined to determine the differentiation state of the cells [2]. Runt-related transcription factor 2 (Runx2) is an early osteo-differentiation marker that is critical for the osteo-differentiation process [3]. Runx2 regulates the differentiation of mesenchymal progenitor cells into pre-osteoblasts. These bone progenitor cells initiate the production of collagen type 1, the most abundant type of collagen in the body [4]. Collagen type 1 is the principal organic component of the bone extracellular matrix. [PERSON] et al. [5] suggested that collagen type 1 is involved in mediating the signaling cascade associated with enhancing the expression of the differentiated phenotype of osteoblasts and accelerating the mineralization of the matrix in vitro. Forming EBs followed by harvesting OGCs and differentiating them into the osteogenic lineage requires the expression of type 1 collagen and Runx2 [2].  
The aim of this study was to investigate the effects of oxygen tension (hyperoxia, normoxia and hypoxia) on the size of EBs and on iPS cell differentiation in terms of the expression of collagen type 1 and Runx2, which are markers of early osteogenesis.
## 1 Introduction  
### Open Jet Wind Tunnel  
A wind tunnel is an aerodynamic testing facility, usually consisting of a series of pipes, which creates a controlled flow of air to simulate the atmospheric conditions experienced by moving objects. Wind tunnels generally consist of a test section, a contraction section, a drive section, a settling chamber, diffusers, and corners (see Figure 1). The test section is the area where aerodynamic measurements are made and is the central component of the wind tunnel. The drive section is responsible for driving the airflow motion and serves as the primary energy source within the wind tunnel. Other components of the wind tunnel are primarily used to regulate and maintain optimal operating conditions for the airflow.  
Depending on the configuration of the test section, wind tunnels can be classified into open-jet wind tunnels and closed wind tunnels [1]. The test section of a closed wind tunnel, together with other sections of the tunnel, forms a continuous closed channel. In contrast, an open-jet wind tunnel creates a free jet in front of the test section and uses uniform airflow within the core region for aerodynamic testing. The airflow from the nozzle is directed through a collector into the first diffuser downstream of the test section, as shown in Figure 1. The test section of an open-jet wind tunnel is typically enclosed by a sealed chamber known as a plenum [1, 2, 3] or test hall [2, 4]. The plenum of an open-jet wind tunnel used for acoustic testing is typically equipped with sound-absorbing wedges on its interior walls and is commonly referred to as either an anechoic plenum or a chamber [2].  
Open jet wind tunnels find extensive applications in automotive wind tunnels [5; 6; 7; 8], acoustics wind tunnels [7; 9; 10; 11], and certain specialized wind tunnels [12] due to their low blockage ratio, ease of test model installation and implementation, and large variable angle range of models compared to closed wind tunnels.
# Three-Dimensional Modeling of the Xichang Crust in Sichuan, China by Machine Learning
## 2 Data and Methods
### Methods  
The traditional geophysical modeling method is to establish a relationship between velocity structure and geological body based on a large number of borehole data, seismic profile, magnetotelluric, etc. [43]. First, based on the above multi-source data, a reliable velocity structure model suitable for modeling was selected through comparative analysis of different velocity structure model results. The rock mass structure and layer structure were extracted to establish a regional crustal medium framework model combined with geological evolution data and velocity structure characteristics. Secondly, the fault initialization model is established based on the fault data on the surface, fault initial model data, and focal mechanism solution provided by the Sichuan-Yunnan experiment site. The relocated earthquake catalog established a complex fault model with a more fine structure using supervised classification, cluster analysis, and other machine learning algorithms to extract morphological fault features. Thirdly, combined with the seismic precision location data, the equivalent seismic energy particles were obtained by using the magnitude-energy conversion relationship, and the seismic energy clusters were obtained by using the unsupervised clustering ML method. The kernel density of the energy clusters was calculated respectively, and the density isosurfaces were extracted to obtain the source structure model. At last, based on the above three models, We use the Gempy library ([[https://www.gempy.org/](https://www.gempy.org/)]([https://www.gempy.org/](https://www.gempy.org/)), accessed on 30 January 2022) for model calculation, and the crustal medium structure model was generated. From this we derive the geometric model to be used for numerical simulation of seismic waves. The complete workflow is displayed in Figure 2.  
Figure 1: Seismicity and seismotectonic setting in the study area.
## 3 Background and Theory
### Notch Filtering  
The initial step in preprocessing our EEG data involves applying a notch filter [23], which is commonly used to eliminate a targeted frequency from EEG signals. This specialized filter introduces a pronounced, narrow suppression, known as a \"notch\", at a designated frequency. By configuring this filter, we can reduce interference in the signals being studied using factors such as the sampling rate \(F_{S}\), the powerline frequency \(f0\), and the quality factor \(Q\). The transfer function \(H(z)\) represents the digital notch filter mathematically, as expressed in Equation (1):  
\[H(z)=\frac{Y(z)}{X(z)}=\frac{\sum_{m=0}^{M}b_{m}z^{-m}}{1+\sum_{n=1}^{N}a_{n}z ^{-n}} \tag{1}\]  
where the following apply  
* \(b_{m}\) are the feedforward coefficients (numerator);
* \(a_{n}\) are the feedback coefficients (denominator);
* \(M\) is the order of the numerator;
* \(N\) is the order of the denominator;
* \(X(z)\) is the Z-transform of the input signal;
* \(Y(z)\) is the Z-transform of the output signal.
# A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications
## 5 Discussion and Future Directions
### Deep Learning Algorithms  
Deep learning algorithms are used in many fields, including agriculture and remote sensing. The base learners in current ensemble models are mostly statistical and conventional ML methods, and the possibility of combining deep learning models in several ways is worthy of investigation. Deep learning algorithms have been used as base learners in some studies with the intention of increasing prediction accuracy. For example, [PERSON] et al. [70] used three CNNs to predict reference evapotranspiration time series and developed ensemble models consisting of the three CNNs. The CNN ensembles produced predictions with high accuracy and low variance. [PERSON] et al. [47] developed a heterogeneous ensemble learning approach that combined three deep learning models (a deep belief network (DBN), a CNN and a deep residual network (ResNet)) to map landslide susceptibility in the Three Gorges reservoir area in China.  
Deep learning algorithms well capture nonlinear relationships between target and sensor signals, so an ensemble of various deep learning algorithms should produce more accurate predictions than a single algorithm. The three principal ensemble methods we described in this study, particularly stacking, provide a framework for leveraging different algorithms. Future studies will investigate optimal combinations of deep learning algorithms in various applications.
# Scale-Model Experiment on the Delayed Failure Mechanism of Deep Tunnels in Brittle Rock
## 5 Delayed Failure Time Simulation of the Scale-Model Testing  
### Delayed Failure Mechanism and Model Review of the Brittle Rock  
The lifetime of rocks is governed by sub-critical and critical crack growth. Above the critical value crack propagation speed reaches high values up to the speed of surface waves. Below a critical value given by a specific value for the stress intensity factor (or energy release rate or J-Integral) the crack growth is called sub-critical. The delayed failure time, called time-to-failure is defined as the time from the start of the sub-critical crack growth to the start of the critical crack growth. The common implementation on rock delayed failure is an exponential relation (Equation (5)) based on the rate process theory; the average time-to-failure is related to the applied stress [23,24].  
\[t_{f}=t_{0}\text{exp}\left(-b\frac{\sigma}{\sigma_{0}}\right) \tag{5}\]  
where \(\sigma_{0}\) is a reference stress, \(t_{0}\) is the time-to-failure under stress level \(\sigma_{0}\), \(b\) is a constant.  
Figure 8: The stresses monitoring data: (**a**) isotropic geostress; (**b**) anisotropic geostress.  
Figure 9: The strain monitoring data (isotropic geostress only): (**a**) \(\sigma_{r}\); (**b**) \(\sigma_{\theta}\).  
Equation (5) states that over the range of \(\sigma_{L}<\sigma<\sigma_{N}\) for a given material having a characteristic value, the logarithm of the normalized time-to-failure is shown to be a \"universal\" linear function of the normalized applied stress. Hillig provides the upper cutoff \(\sigma_{N}\) and the lower threshed \(\sigma_{L}\), where \(\sigma_{N}\) is the failure stress determined at liquid nitrogen temperature, \(\sigma_{L}\) is termed \"the fatigue limit\", and in rock mechanics it is also termed long-term strength \(f_{3}\)[25].  
During the design of our similar material, elastic and plastic characteristics are considered; however, the complex laws for time-dependent parameters need to be involved in similar. The time-to-failure parameters of our similar material are independent with the real tunnel. However, the parameters were tested by the creep time-to-failure tests and the parameters can be found in Table 3.