esa-sceva/satcom-chunk-collection huggingface dataset:
# Entropy Optimization in MHD Nanofluid Flow over an Exponential Stretching Sheet
## 2 Problem Formulation  
We investigated laminar free-forced convective mass-heat transportation along an exponentially stretching surface in viscous incompressible fluid in a magnetic field. We assumed that \(u\to U_{\infty}=ax^{m}\) is the stretching velocity, where \(m\) is the stretching rate, \(T_{w}\) is the temperature at the surface, and \(T_{\infty}\) is the ambient temperature, where \(T_{w}>T_{\infty}\). The magnetic field is aligned in \(y\)-direction to influence the velocity of the fluid, as shown in Figure 1.  
Figure 1: The flow schematic diagram. The steady, incompressible fluid flow equations for a viscous nanofluid flow under these assumptions can be obtained as follows; see [19; 20]:  
\[\frac{\partial u}{\partial x}+\frac{\partial v}{\partial y}=0, \tag{1}\] \[u\frac{\partial u}{\partial x}+v\frac{\partial u}{\partial y}= \frac{1}{\rho_{\infty}}\frac{\partial}{\partial u}\bigg{(}\mu\frac{\partial u }{\partial y}\bigg{)}+\frac{\sigma B_{0}^{2}}{\rho_{\infty}}\bigg{(}L_{ \infty}-u\bigg{)}+g\beta_{T}\bigg{(}T-T_{\infty}\bigg{)}+g\beta_{C}\bigg{(}C- C_{\infty}\bigg{)},\] (2) \[u\frac{\partial T}{\partial x}+v\frac{\partial T}{\partial y}= \alpha\frac{\partial^{2}T}{\partial y^{2}}+\frac{Q_{0}}{\rho_{\infty}c_{p}} \bigg{(}T-T_{\infty}\bigg{)}+\frac{16\sigma^{*}T_{\infty}^{3}}{3k^{*}\rho_{ \infty}c_{p}}\frac{\partial^{2}T}{\partial y^{2}}+\frac{v_{\infty}}{c_{p}} \bigg{(}\frac{\partial u}{\partial y}\bigg{)}^{2}+\frac{\sigma B_{0}^{2}}{ \rho_{\infty}c_{p}}u^{2}+\] \[\tau\bigg{[}D_{B}\frac{\partial C}{\partial y}\frac{\partial T}{ \partial y}+\frac{D_{T}}{T_{\infty}}\bigg{(}\frac{\partial T}{\partial y} \bigg{)}^{2}\bigg{]},\] (3) \[u\frac{\partial C}{\partial x}+v\frac{\partial C}{\partial y}= D_{B}\frac{\partial^{2}C}{\partial y^{2}}+\frac{D_{T}}{T_{\infty}}\frac{ \partial^{2}T}{\partial y^{2}}-R_{0}\bigg{(}C-C_{\infty}\bigg{)}, \tag{4}\]  
where \(u\) and \(v\) are the velocity components in \(x\) and \(y\)-directions, respectively; \(\rho_{\infty},U_{\infty},T_{\infty}\), \(v_{\infty}\), and \(C_{\infty}\) represent the density, velocity, temperature, kinematic viscosity, and concentration of the fluid in free stream, respectively; \(B_{0}\) is the external magnetic field applied in \(y\)-direction; \(g\) is the acceleration; \(\beta_{C}\) and \(\beta_{T}\) are the concentration and thermal expansions, respectively; \(\alpha\) represents the thermal diffusion; \(Q_{0}\) represents the heat generation rate; \(k^{*}\) is the mean absorption coefficient; \(c_{p}\) is the specific heat of nanoparticles; \(\sigma^{*}\) represents the [PERSON] constant; \(\sigma\) and \(\tau\) are the electrical conductivity and ratio of nanoparticle heat capacity, respectively; \(D_{B}\) is the Brownian mention; \(T\) and \(C\) are the temperature and concentration distributions of the fluid, respectively; \(R_{0}\) is a chemical reaction parameter; \(D_{T}\) represents the thermophoretic diffusion coefficient.
## 4 Discussion
### Limitations and Strengths  
Admittedly, these findings should be cautiously interpreted, as the current research is limited in certain respects. Perhaps the most important limitation is the cross-sectional design, which prevents the establishment of causal associations. Notably, the limited number of male individuals in the sample did not allow for a balanced distribution of the sexes. Additionally, the majority of the sample was overweight or obese, which complicated the understanding of the impact of BMI on associations in older individuals.  
However, despite the aforementioned limitations, this research possesses several strengths. Cognitive function was assessed across multiple dimensions using validated questionnaires. The physical tests employed to evaluate physical function are validated and suitable for the target population. Furthermore, cognitively relevant covariates were included, and the age of the sample is crucial for the early detection of MCI.
## 4 Data Generation  
To generate measurement data, a procedure shown in Figures 18 and 19 is used. Here, zinc-air cells are continuously charged or discharged with an alternating current of 1 A, 2 A, or 3 A. This means that the charging process starts with 1 A. A short delay is made until the cell voltage has become familiar with the current and a measurement of the impedance spectrum is started. After that, the charging current is increased to 2 A; again a short delay is made and the next impedance measurement is started. The same is performed for 3 A  
Figure 16: Samples of measured impedance spectra. The frequency range is from 100 mHz to 10 kHz.  
Figure 17: Schematic of the electrode changer board used to drive three-electrode technology cells with EISmeter impedance spectroscope.  
and then it starts again at 1 A. As expected, this also results in a change in the battery voltage.  
Meanwhile, the applied charge is counted so that, in this scenario without any breaks, the actual state of charge is known and can be used as target values for supervised learning. Once the cell is full, the discharge process starts using the identical technique. Therefore, discharging processes have been recorded where the discharge current was regularly alternated between 1 A, 2 A, and 3 A. The resulting voltage curve and the corresponding discharge current for one discharge cycle are shown in Figure 19.  
After several charge and discharge cycles, there is no random division of the resulting data into training data and test data, but the measurements at 1 A and 3 A form the training data set and the measurements at 2 A form the test data set. This ensures that the results correspond to an actual generalization of the working point. The hyperparameters are optimized by cross-validating the training set only to prevent data leakage. Of course, the feature vector must be extended to include the DC current value, so that the following feature vector is now obtained:  
\[x_{i}=[I_{\text{bat,DC}},\Re\{Z_{i,1}\},\ldots,\Re\{Z_{i,m}\}, \Im\{Z_{i,1}\},\ldots,\Im\{Z_{i,m}\},\\ |Z_{i,1}|,\ldots,|Z_{i,m}|,\phi(Z_{i,1}),\cdots,\phi(Z_{i,m})]. \tag{23}\]
## 4 Our Prototype
### Usability Evaluation  
To evaluate the prototype, we conducted a usability session with eight participants (average age: \(29.6\pm 9.1\), min: 22, max: 50). Even if the sample size was limited, it is important to remember that a small sample size, typically around five participants, is often sufficient to uncover the majority of usability issues, according to the human-computer interaction literature based on the works of [PERSON] [42]. By conducting our initial testing with eight participants, we aimed to achieve remarkable efficiency in identifying and addressing the vast majority of existing problems within the user interface. The session aimed to assess the User Interface usability using the System Usability Scale (SUS) [43]. The SUS is a widely recognized tool for evaluating the usability of a system. It consists of a 10-item questionnaire with five response options for respondents, ranging from \"Strongly agree\" to \"Strongly disagree\". Participation was voluntary, and all participants were informed about their rights regarding participation and data privacy in accordance with the European General Data Protection Regulation. We recruited participants through snowball sampling, and although we could only enlist eight users, the scientific literature suggests that six users can uncover approximately 90% of usability issues under specific conditions [43].  
During the session, participants were asked to interact with two new Uls--Floor Map and Emergency Analysis--and to complete predefined tasks, such as determining whether a laboratory was empty or not. After completing these tasks, participants filled out an online questionnaire consisting of the ten SUS items, rated on a 5-point Likert scale (0 = strongly disagree to 4 = strongly agree). The overall SUS score was 89 out of 100, which is significantly above the average benchmark score of 68, indicating high usability.  
The average score for each SUS question is illustrated in Figure 9. Notably, questions \"I thought the system was easy to use\" (item #3 in SUS) and \"I needed to learn a lot of things before I could get going with the system\" (item #10 in SUS) received exceptionally high scores of, respectively, 3.75 out of 4 and 0.125 out of 4. This suggests that participants found the system easy to use, well-integrated, and effective for monitoring building evacuations.  
Figure 8: Emergency Analysis—detailed reports.
# CNN-Based Illumination Estimation with Semantic Information
## 2 Technical Approach
### The Proposed DCNN Architecture
Furthermore, the concatenation preserves all the features within the block so that the network can learn to combine features generated on different scales. Figure 2 depicts the proposed DCNN architecture. To explain in more detail, the top half of the figure illustrates the whole process of the proposed DCNN architecture. The blue boxes are not all residual networks. There are six residual networks: two consisting of four layers and four consisting  
of three layers. A residual network is marked with its structure on its top right, which looks like a superscript. The bottom half of the figure gives explanatory notes and illustrates the two types of residual networks in detail. As in the explanatory notes, a convolutional layer is described in black; and the top s stands for a stride and the bottom n indicates the n by n filter kernel size, with a symbol * to the middle left. A dilated convolution is described in red; and the top d stands for dilated convolution with a stride of 1 and the bottom n indicates the n by n filter kernel size, with a symbol * to the middle left. For instance, 1 and 1 with a symbol * in black translates as a convolutional layer with a stride of 1 and a 1 1 filter kernel in size. As another example, 2 and 3 with the symbol * in red mean that the rate r of dilated convolution is 2, as in Equation (10), and the filter kernel size is 3 3. The emphasis of using the proposed DCNN architecture is on increasing the accuracy of estimating the local semantic information, which is vital to the final performance of the network, and training the network to optimally combine the local estimates by adaptively using the corresponding g and c, as in Equation (2), for each local region, which will suppress the impact of ambiguous patches.
# Intention Recognition for Multiple AUVs in a Collaborative Search Mission
## 2 Related Works
### Model-Based Intent Recognition Methods
#### 2.1.2 Expert Systems  
Expert systems encode domain knowledge into rule-based systems to infer intent. [PERSON] et al. proposed an expert system based on belief rules (BRB) to deal with classification problems under uncertainty [24]. [PERSON] analyzed the naval posture and threat assessment process and designed an expert system using real-time knowledge to implement naval battlefield situation assessment [25]. [PERSON] et al. improved the combat intention recognition expert system to solve the problem of insufficient expert knowledge [26].  
Expert systems offer significant advantages in intent recognition by leveraging domain knowledge and rule-based inference to provide highly interpretable and reliable results, particularly in scenarios requiring transparency and trust. They excel in handling uncertainty through probabilistic methods like Bayesian networks and belief rule-based systems, ensuring robust performance even with incomplete or noisy data. However, expert systems also face limitations, such as a strong dependence on manually crafted rules and domain expertise, which can hinder scalability and adaptability to dynamic or complex environments. Additionally, their computational complexity may increase significantly with the expansion of rule sets, limiting their applicability in large-scale or highly dynamic scenarios.
## 3 Design of a Digital Satellite-Borne Base Station for Quick Functional Reconfiguration
### VNF Migration Model and Optimization Problem  
The VNF migration problem in NFV networks is presented in this subsection, and a network model is created to address it. This model describes the dynamic changes in demand in NFV networks, the occupancy of VNFs on different types of node resources, and the circumstances that lead to VNF migration. To lessen the pressure on the controller for the SFC end-to-end performance limits in VNF migration, equivalent conversion is carried out.
# Dynamic Line Rating--An Effective Method to Increase the Safety of Power Lines
## 2 Problem Identification--Materials and Methods
### Data of the Chosen Span  
The chosen span is a section at one end of a tension span, meaning one boundary lattice tower is a suspension while the other is a tension one. The length of the span is 390 m, and according to the catenary curve and terrain profile analysis, the most critical sag point is 180 m from the tension tower.  
Before starting the measurement, four measurement planes were defined, as illustrated in Figure 1.  
Figure 1: Measuring positions in the selected span.  
Out of the four measuring planes, three play a prominent role. Measuring plane 1 extends directly below the tension tower's insulator suspension points, the height of which above ground is nearly constant. There is a dirt road in the 2 nd measurement plane, which should be treated as a critical case for pedestrian and agricultural vehicles. Measurement plane 4 is the plane containing the transmission line's points with the smallest clearance (180 m from the tension tower) at the maximum design operating temperature of 60 \({}^{\circ}\)C. Measuring plane 3 was placed at equal distances from planes 2 and 4.
###### Abstract  
Organic Rankine cycle (ORC) systems are some of the most suitable technologies to produce electricity from low-temperature waste heat. In this study, a non-regenerative, micro-scale ORC system was tested in off-design conditions using R134a as the working fluid. The experimental data were then used to tune the semi-empirical models of the main components of the system. Eventually, the models were used in a component-oriented system solver to map the system electric performance at varying operating conditions. The analysis highlighted the non-negligible impact of the plunger pump on the system performance Indeed, the experimental results showed that the low pump efficiency in the investigated operating range can lead to negative net electric power in some working conditions. For most data points, the expander and the pump isentropic efficiencies are found in the approximate ranges of 35% to 55% and 17% to 34%, respectively. Furthermore, the maximum net electric power was about 200 W with a net electric efficiency of about 1.2%, thus also stressing the importance of a proper selection of the pump for waste heat recovery applications.  
micro-scale ORC; off-design performance; experimental modeling; volumetric pump modeling; low-grade waste heat recovery applications Article
# Interactions in Augmented and Mixed Reality: An Overview
## 2 Conceptual Framework Definition
### Modality  
A user interface [57] is based on information inputs and outputs (IO) via bidirectional human-computer communication channels. As input or output, we consider any human actions that convey meaning for interaction to a computer or any intentional augmentation or alteration of the human perceptual modalities. Every independent channel is called a modality, and every system that uses only one of these channels for IO is called a unimodal system. Systems that incorporate more than one of the modalities above are called multimodal. We define six modalities that allow information IO: visual-based, audio-based, haptic-based, taste-based, smell-based, and sensor-based modalities. As the taste and smell-based modalities are not reviewed in this paper, we do not provide definitions for them. Therefore, we define the visual, audio, haptic, and sensor-based modalities as follows:  
* Visual-based: The visual-based modality includes any state changes that a camera sensor can capture, convey meaning and can be used to describe the user's intention to interact or present visual feedback.
* Audio-based: The audio-based modality contains all actions and feedback that include sound perception and sound stimuli.
* Haptic-based: The haptic-based modality defines all interactions that can be perceived through the sense of touch or executed through graspable-tangible objects.
* Sensor-based: Finally, the sensor-based modality includes all interactions requiring any sensor to capture information regarding an action or transmit feedback back to the user, besides visual, auditory, haptic, taste, and smell inputs/outputs. An example of this modality includes the pressure detection method.
# Using Transfer Learning to Realize Low Resource Dungan Language Speech Synthesis
## 2 Models and Methods  
The proposed framework of transfer learning-based low-resource Dungan speech synthesis, which is shown in Figure 1, including a feature extraction module, a pre-trained Mandarin acoustic model, a transfer learning-based Dungan acoustic model training module, and a WaveRNN vocoder-based speech synthesizer.  
The feature extraction module extracts acoustic features such as Mel Spectrogram from speech signals and speech synthesis units sequence from sentences. We have developed a complete Dungan language text analyzer to extract speech synthesis units with prosodic features to map Dungan sentences onto unit sequences. Given that both the Mandarin and Dungan languages utilize initials and finals as their core speech synthesis units, the resulting unit sequence incorporates these elements and pertinent prosodic information, including syllable tones and sentence-level prosodic boundary labels.  
Since Tacotron2 is one of the most popular encoder-to-decoder speech synthesis frameworks, and the WaveRNN vocoder can generate natural speech, we use Tacotron2 to train acoustic models and WAVRNN to convert spectrogram to waveform for both the Dungan language and Mandarin. The Mandarin acoustic model is pre-trained with a large-scale Mandarin corpus, while the Dungan language model is transferred from the Mandarin acoustic model with a small-scale Dungan corpus.  
In the speech synthesis stage, the WaveRNN vocoder generates Dungan or Mandarin speech from the input of Dungan or Chinese sentences. The text analyzer first generates the context-dependent labels from the input sentence. Then, the speech synthesis unit sequences (initials and finals with their prosodic information) are fed into the Mandarin or Dungan acoustic model to generate the Mel spectrogram. The WaveRNN vocoder is finally used to generate the speech waveforms from the Mel spectrogram. We use a home-grown Chinese text analyzer for Chinese text analysis.
## 3 Results  
### Relationship between cos (SZA\({}_{0}\)) and Apparent SIF Yield  
The tower-based and satellite experimental data were used to investigate the relationship between the apparent \(SIF_{yield}\) (calculated using the SIF observed at the top of the canopy) and cos (SZA\({}_{0}\)). As illustrated in Figure 2, there was a seasonal change in the apparent \(SIF_{yield}\), which was correlated with cos (SZA\({}_{0}\)) in the maize crop and evergreen forest ecosystems. Therefore, it was concluded that cos (SZA\({}_{0}\)), which is a proxy for the solar radiation intensity, could be used to describe the seasonal variation in the apparent \(SIF_{yield}\).  
Figure 1: Flowchart for the production of aggregated, 0.05\({}^{\circ}\) globally continuous TanSat SIF.  
From Figure 2a,c it can be seen that the value of apparent SIF\({}_{\text{yield}}\), reaches its peak in July. As illustrated in Figure 2b,d, the apparent SIF\({}_{\text{yield}}\) also increases as cos (SZA\({}_{0}\)) increases at the crop sites (slope = 0.68, R\({}^{2}\) = 0.32) and the NR forest site (slope = 0.22, R\({}^{2}\) = 0.41). The different values of the slope may be due to the different types of ecosystem and number of samples. As has been widely observed previously, the value of apparent _SIF\({}_{\text{yield}}\)_ at the canopy level was higher for cropland than for forest [72,73].  
The relationship between apparent _SIF\({}_{\text{yield}}\)_ and cos (SZA\({}_{0}\)) was also investigated using the TROPOMI SIF data acquired from February 2018 to February 2019. As in-situ APAR data were not available for TROPOMI SIF, _SIF\({}_{\text{normalized}}\)_ (as defined in Equation (3)) for dense vegetation (NDVI \(>\) 0.85) was used as a proxy of the apparent _SIF\({}_{\text{yield}}\)_. Only forest pixels lying within the area of dense vegetation (NDVI \(>\) 0.85) were selected. The dense vegetation regions at DOY 210 and 365 in 2018 were illustrated in Figure 3.  
Then, we calculate _SIF\({}_{\text{normalized}}\)_. The values of cos (SZA\({}_{0}\)) were divided into 10 levels ranging from 0.35 to 0.95. The results showed an obvious rise in _SIF\({}_{\text{normalized}}\)_ with cos (SZA\({}_{0}\)) at the global scale (Figure 4), indicating the important influence of the solar radiation intensity on apparent _SIF\({}_{\text{yield}}\)_.  
Figure 3: Dense regions of the world in July (DOY 210) and December (DOY 365) 2018.  
Figure 2: The seasonal change in apparent _SIF\({}_{\text{yield}}\)_ and its relationship with cos (SZA\({}_{0}\)) using the tower-based observations made at the HL, DM and Aurora maize sites (**a,b**) and the NR forest site (**c,d**). The apparent _SIF\({}_{\text{yield}}\)_ is the fluorescence quantum yield, DOY is the day of year, and cos (SZA\({}_{0}\)) is the cosine of the solar zenith angle at noon, used to represent the solar radiation intensity under clear-sky conditions. The black line is the fitted line.