esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Simulation
### Grid Independence  
A single-battery heat transfer model, Figure 7b, is applied to assess the grid independence of the computational domain. The meshes mainly consist of polyhedral meshes. Locale mesh refinement with prism layer meshes on the solid-liquid interface is applied within the narrow fluid domain to better compute the boundary layer flow and enhance accuracy. A grid independence study was conducted at the condition of \(\text{Q}_{\text{total}}=5\) L/min, I = 25 A, N = 0. The seven different levels of grid systems, with grid numbers ranging from 3000 to 260,000, are presented in Table 4. Figure 8 shows a grid sectional view at the mid-symmetry plane of grid level 1, 4, and 7. The Nusselt number Nu, pressure loss \(\Delta p\), and maximum temperature \(T_{\text{max}}\) on the battery surface were selected to be the parameters of validation. The results obtained by these systems are shown in Figure 9a. It can be seen that when the grid number exceeds 50,000 (i.e., when the grid number increases significantly), all three parameters change slightly. Thus, to balance the accuracy and computational cost, a level 4 grid system was selected for the numerical models.  
Figure 8: Sectional view of the grids at the mid-symmetry plane: (a) grid level 1; (b) grid level 4; (c) grid level 7.  
Figure 7: Geometry models: (a) battery cells with a cooling plate; (b) single-battery heat transfer model (grid level 1).  
To verify the numerical model, a single-pipe model is established under constant wall temperature boundary conditions to compare the results from the numerical simulation and theoretical calculations, according to Equations (10) and (18) in Section 3. The results are shown in Figure 9b, where the theoretical results are represented as Nu\({}_{0}\), \(\Delta\)p\({}_{0}\), and the numerical results are represented as Nu, \(\Delta\)p. The results show good agreement, and the relative errors are within 10%. Further, the Nusselt number results for the numerical simulation are larger than those for the theoretical simulation based on Sieder-Tate correlation. The main reason that the pipe appears to be a straight pipe in Equation (10) but has a serpentine-like shape in practice is to enhance the heat exchange. The heat transfer coefficient or Nusselt number of a curved pipe's surface is larger than that of a straight pipe under the same inlet conditions. Thus, the results are reasonable.
# Planar Microwave Resonant Sensors: A Review and Recent Developments
## 4 Prototype Examples
### Coupling Modulation Sensors
As it can be seen, two harmonic signals are needed in this case (injected through a combiner), one of them tuned to the resonance frequency of the resonators of the velocity chain, and the other one tuned to the resonance of the resonant elements of the direction chain. Rotor motion AM modulates each harmonic signal, and the corresponding envelope functions can be inferred by means of envelope detectors, after filtering each signal by means of a diplexer. Figure 19 shows the photograph of rotor (zoom view) and the stator, as well as a picture of the overall experimental set-up. The envelope functions inferred with such sensing system, corresponding to the angular velocity and direction detection, are shown in Figure 20 (note that the direction is detected by the increasing or decreasing sequence of pulses in the corresponding envelope function). In these angular displacement and velocity sensors based on the edge configuration, the number of cumulative pulses from a reference (REF) position provides the angle of rotation. Thus, the angular resolution (in degrees) is simply 360\({}^{\circ}\)/PPR, where PPR is the number of pulses per revolution. For the specific sensor system of Figure 19, with PPR = 1200, the angular resolution is thus 0.3\({}^{\circ}\), a competitive value. Further details of this sensor (dimensions, considered substrates, specific components, and instrumentation) are given in [30]. A detailed analysis of angular velocity resolution and error for these sensors, out of the scope of this paper, can be found in [29]. As highlighted in [29; 30], microwave rotary encoders represent a low-cost alternative to optical encoders. Moreover, microwave encoders can be of interest for operation in harsh and hostile environments (i.e., subjected to radiation, pollution, dirtiness, grease, etc. ), where the optical counterparts may offer a limited robustness. Note that the dynamic range of these rotation sensors for angle measurement is theoretically unlimited, as these sensors are based on pulse counting. This is a clear advantage as compared to other sensors based on symmetry disruption and implemented by means of a single resonant element [21; 22; 23; 26; 27]. This unlimited dynamic range plus the achievable angular resolution situates these sensors as excellent candidates for angular displacement and velocity sensing.
# Study on Noise Reduction and Data Generation for sEMG Spectrogram Based User Recognition
## Abbreviations  
Surface electromyography (sEMG), electromyogram (EMG), electrocardiogram (ECG), electroencephalogram (EEG), one-dimensional (1D), short-time Fourier transform (STFT), convolutional neural network (CNN), mean absolute value (MAV), slope sign change (SSC), root mean square (RMS), waveform length (WL), variance (VAR), integrated EMG (IEMG), zero crossing (ZC), Fourier transform (FFT), k-nearest neighbor (KNN), long short-term memory (LSTM), two-dimensional (2D), generative adversarial network (GAN).
## 4 Scenario Based Wind Curtailment Scheduling  
### Random Variable Discretization for Absolute Power Limit  
For a scenario-based stochastic UC model, the continuous probability distribution model of wind generation is not computationally practical. Instead, it is more reasonable to consider discretization of the continuous probability distribution of the wind generation with a number of representative segments. This paper uses a discretized random variable [11] of wind generation with \(N_{m}\) segments, which equally divides the available wind generation region. The discrete random variable of wind generation \(s_{i,t}(m)\) is defined as a value at the center of the \(m\)th segment, and its probability is calculated by integrating the continuous probability within the corresponding segment.  
With this discrete random variable of wind generation, the absolute power limit can be expressed as follows:  
\[l_{W,i,t}=\sum_{m=1}^{N_{m}}\big{(}s_{i,t}(m)-s_{i,t}(m-1)\big{)}\cdot u_{L,i,t}( m), \tag{23}\]  
where \(s_{i,t}(m)\) is the discrete random variable of wind generation of wind farm \(i\) at time \(t\); \(u_{L,i,t}(m)\) is the binary variable that indicates whether the absolute power limit is greater than \(s_{i,t}(m)\) or not, and \(u_{L,i,t}(m)\) is restricted to be lower than or equal to \(u_{L,i,t}(m-1)\). In the same way, \(p_{AW,i,t}^{s}\), \(p_{AW,i,t}^{s}+w_{AW}(p_{AW,i,t}^{s})\), and \(p_{AW,i,t}^{s}-dv_{AW}(p_{AW,i,t}^{s})\) can be also expressed in terms of the discrete random variable of wind generation and also the binary variables \(u_{AW,i,t}^{s}(m)\), \(u_{w,i,t}^{s}(m)\), and \(u_{dv,i,t}^{s}(m)\), respectively.  
With the discrete random variable of wind generation, the expected wind power with wind curtailment in (3) can be rewritten as follows:  
\[p_{W,i,t}^{f}=\sum_{m=1}^{N_{m}}\bigg{\{}(s_{i,t}(m)-s_{i,t}(m-1))\cdot u_{L, i,t}(m)\cdot\sum_{n\geq m}^{N_{m}}\text{pr}(s_{i,t}(n))\bigg{\}}. \tag{24}\]  
Absolute power constraints for scenarios in (19) can be expressed as follows:  
\[p_{W,i,t}^{s}=\sum_{m=1}^{N_{m}}\big{\{}(s_{i,t}(m)-s_{i,t}(m-1))\cdot u_{AW, i,t}^{s}(m)\ \cdot u_{L,i,t}(m)\big{\}}. \tag{25}\]  
Note that the minimum of two binary variables is equal to their product. In the same way, the maximum upward and downward variation in wind generation for scenario \(s\) of (6) and (7) can be rewritten in the following MIP forms:  
\[uv_{W}(p_{AW,i,t}^{s})=\sum_{m=1}^{N_{m}}\big{\{}(s_{i,t}(m)-s_{i,t}(m-1)) \cdot u_{w,i,t}^{s}(m)\cdot u_{L,i,t}(m)\big{\}}-p_{W,i,t}^{s}, \tag{26}\]  
\[dv_{W}(p_{AW,i,t}^{s})=p_{W,i,t}^{s}-\sum_{m=1}^{N_{m}}\Big{\{}(s_{i,t}(m)-s_ {i,t}(m-1))\cdot u_{dv,i,t}^{s}(m)\cdot u_{L,i,t}(m)\Big{\}} \tag{27}\]  
All constraints of the wind curtailment scheduling problem can be transformed into MIP forms with the help of discrete random variable of wind generation.
# Line Scan Spatial Speckle Contrast Imaging and Its Application in Blood Flow Imaging
## 4 Conclusions  
In summary, we have upgraded the laser speckle imaging system, which consists of three imaging modes and can be switched flexibly and instantly. The first one is surface illumination laser speckle contrast imaging (SI-LSCI), and the other two are line scan temporal speckle contrast imaging (LS-TSCI) and line scan spatial speckle contrast imaging (LS-SSCI). We validated the combined model of Lorentzian and Gaussian line shapes by the flow phantoms. Using SI-LSCI, we experimentally studied the speckle contrast varied with the exposure time. The experimental results suggested 4 ms is a proper exposure time for imaging the blood flow of chick embryos. To compare the performance of LS-TSCI and LS-SSCI, chick embryos imaging experiments were conducted. The experimental results demonstrated that LS-SSCI, which maintains high temporal resolution and is less affected by motion artifacts, can provide better image quality for in vivo imaging blood flow of chick embryos than LS-TSCI. Furthermore, we applied LS-SSCI to measure blood flow change during the vascular clipping of chick embryos. LS-SSCI shows great potential in diagnosing vascular diseases such as angiogenesis, angiostenosis, or angiemphraxis.  
[PERSON] and [PERSON] designed the studies. [PERSON] designed the line scan speckle contrast imaging system. [PERSON] and [PERSON] set up and configured the laser speckle imaging system. [PERSON] prepared samples and performed imaging experiments. [PERSON] and [PERSON] analyzed the images. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Ministry of Education--Singapore (MOE2019-T2-2-094, R-397-000-327-114).  
Not applicable.  
The authors declare that there are no conflicts of interest related to this article.
# Leveraging Energy Harvesting and Wake-Up Receivers for Long-Term Wireless Sensor Networks
## 2 Related Work  
To the best of our knowledge, no previous works have proposed a joint focus on both EM and communication with ULP WuRx. However, many papers deal with either one or the other topic. Hence, this section is split into two subsections on the two topics. The related work regarding energy management for EH-WSNs is first given, followed by a presentation of state-of-the-art MAC protocols leveraging ULP WuRx.
# Fine-Grained Detection of Pavement Distress Based on Integrated Data Using Digital Twin
## 3 Methodology  
This paper proposes a refined pavement distress detection method based on the digital twin model and deep-object detection algorithms. The method consists of two stages: the generation of an integrated digital twin-based pavement distress dataset and the refined detection of deep object network-based pavement distress detection method. In the first stage, the digital twin model is generated by a UAV-based pavement background modelling and a virtual disease rendering based on a lightweight physics engine and is used to simulate the damaged state of the pavement. The virtual inspection data generated by the digital twin model is integrated with the small-scale real data captured by the UAV in a certain proportion to obtain a rich pavement-distress dataset required for training deep learning models. In the second stage, six deep-object detection networks are fully trained based on the integrated dataset for accurate and efficient detection of different types of pavement distress, and the performance of the deep-object detection networks is evaluated in terms of the data integration ratio and complex deployment scenarios. The architecture of the method is shown in Figure 1.  
Figure 1: Total architecture of refined detection algorithm for asphalt pavement distress based on integrated data using digital twin.
# Ferry Scheduling Optimization Considering Arrival Time Uncertainty and In-Place Time Differences
## 3 Problem Transformation and Model Solving  
In the previous section, the ferry scheduling problem considering the difference in in-place time under the determination of the arrival time of ferry service is analyzed. A  
Figure 3: The different splitting processes for ferry services, with \(u_{j}=2\).  
nonlinear dynamic programming model with the number of ferry usage as the optimization objective is established. In the decision cycle, the estimated arrival and departure times of flights deviate too much from the actual times. The effect of this deviation on the scheduling scheme cannot be ignored. Therefore, the ferry scheduling problem is a ferry dynamic scheduling problem under stochastic conditions.
## 1 Introduction  
Phosphorus, essential for all Earth's organisms, holds significant ecological influence. Simultaneously, it poses environmental risks, notably contributing to water body eutrophication [1]. Algal overgrowth is observed when total phosphorus in a water body exceeds 0.02 mg/L and total nitrogen exceeds 0.2 mg/L [2]. Consequently, managing phosphorus concentrations is critical for point source control. Major contributors include domestic sewage, industrial and livestock wastewater, runoff from mountainous forests and cultivated land, and minor amounts from rainfall and snowfall. The discharge of phosphorus-containing wastewater significantly pollutes water environments and ecosystems.  
Current wastewater phosphorus removal methods primarily encompass biological methods [3, 4], chemical precipitation [5], adsorption [6], membrane separation [7], crystallization [8], and so forth. The adsorption method involves utilizing the porous nature or extensive surface area of an adsorbent for the physical or chemical adsorption of phosphate, leading to surface deposition and achieving phosphorus removal objectives [9]. The adsorption method is characterized by its efficiency, recyclability, ease of operation, low cost, and minimal sludge production, making it a widely applicable and economical approach to phosphorus removal [10].  
The synthesis of an effective adsorbent is pivotal to the success of the adsorption method. Adsorption efficiency can be notably enhanced through appropriate modifications to the adsorbent, such as employing lanthanide modification [11]. Lanthanide-modified adsorbents have demonstrated substantial improvement in both adsorption capacity and the rate of phosphate removal [12].  
This study employs the rare-earth metal lanthanum to modify the efficient composite industrial residue phosphorus removal material (EPRC), the group's former research outcome, to create lanthanum-modified enhanced composite phosphorus removal material (La-EPRC). Initial experimental analysis identifies the optimal modification method. Subsequently, La-EPRC undergoes static adsorption experiments to assess phosphorus removal efficacy, exploring stability during removal and material regeneration feasibility.
## 6 Discussion  
In this section, we attempt to put into perspective the results of our approach. While comparing to other multimodal systems is not easy because each one addresses a different task with different technologies, we can say that the best accuracy results obtained with multimodal classification across various areas is around 80%. For example, the best predicted class in the emotion classification of the work by [PERSON] et al. (2018) was the class \"happy\" with an accuracy of 79.08%, and the speech six-intention classification model achieved a 83.10% average accuracy [35]. Better results around 93% were achieved in music genre classification when using audio, text and images [36]. As we commented in Section 2.3, we must also stress that these systems work in limited application contexts and, more importantly, they use datasets that include professional transcription services [34] or manually transcribed text [35].  
The performance of our model was below the best accuracy achieved by the aforementioned approaches as we faced several limitations such as the use of a small dataset that contained a low number of samples (34 lesson recordings), potential labeling errors as well as the errors inherent in automated transcriptions.  
On the other hand, we also offer some advantages over other models:  
* Our proposal is independent of the audio recording system and does not need proprietary audio recorders such as in LENA [10].
* Our model is independent of the automated transcription tool and can be used with the transcriptions provided by any service such as YouTube, Apple's Siri, Zoom video communications or others. In our case, we used the open-source MLLP transcription and translation platform [40; 41], which turned out to translate scientific and technical terms better than YouTube.
* The use of transformer-based models broadens the applicability of the classification architecture to different thematic contexts and different languages. Our model is thus applicable to a large variety of subjects of different natures and is extensible to other languages.
## 3 Proposed Methodology
### Indexing Strategy Based on Quadtree
#### 3.2.2 Indexing Algorithm
Input: the root node of the quadtree \(node\), x-coordinate of the top left corner \(lx\), y-coordinate of the top left corner \(ly\), x-coordinate of the bottom right corner \(ry\), condition judgment function \(F\) Output: decompressed data
3.1. ifF(node) == true
4. or\(node\_top\_left\_child==\mathbf{null}\)
5. or\(node\_top\_right\_child==\mathbf{null}\)
6. or\(node\_bottom\_left\_child==\mathbf{null}\)
7. or\(node\_bottom\_right\_child==\mathbf{null}\)then
8. \(res\gets\) decompress\(\_data\)(\(node\_data\))
9. //Slicing operation
10. \(res\gets\)res[lx-\(node\_lx\_rx\)-\(node\_lx\_x\)+1, ly-\(node\_ly\_ry\)-\(node\_ly\)+1]
11. returnres
12. endif
13. \(top\_left\_res\leftarrow\mathbf{null}\)
14. \(top\_right\_res\leftarrow\mathbf{null}\)
15. \(bottom\_left\_res\leftarrow\mathbf{null}\)
16. \(bottom\_right\_res\leftarrow\mathbf{null}\)
17. if\(lx\leq node\_top\_left\_child.rx\)and\(ly\leq node\_top\_left\_child.ry\)then
18. \(top\_left\_res\leftarrow\)range\(\_query\)(\(node\_top\_left\_child\),
19. \(lx\),
20. \(lx\),
21. \(ly\),
22. \(\min(rx\), \(node\_top\_left\_child.rx)\),
23. \(\min(ry\), \(node\_top\_left\_child.ry))\)
24. endif
25. if\(lx\leq node\_top\_right\_child.rx\)and\(ry\geq node\_top\_right\_child.ly\)then
26. \(top\_right\_res\leftarrow\)range\(\_query\)(\(node\_top\_right\_child\),
27. \(lx\),
28. \(\max(ly\), \(node\_top\_right\_child.ly)\),
29. \(\min(rx\), \(node\_top\_right\_child.rx)\),
30. \(ry\))
31.  endif
32. if\(rx\geq node\_bottom\_left\_child.lx\)and\(ly\leq node\_bottom\_left\_child.ry\)then
33. \(bottom\_left\_res\leftarrow\)range\(\_query\)(\(node\_bottom\_left\_child\),
34. \(\max(lx\), \(node\_bottom\_left\_child.lx)\),
35. \(ly\),
36. \(rx\),
37. \(\min(ry\), \(node\_bottom\_left\_child.ry))\)
38. endif
39. if\(rx\geq node\_bottom\_right\_child.lx\)and\(ry\geq node\_bottom\_right\_child.ly\)then
30. \(bottom\_right\_res\leftarrow\)range\(\_query\)(\(node\_bottom\_right\_child\),
31. \(\max(lx\), \(node\_bottom\_right\_child.lx)\),
32. \(ly\),
33. \(rx\),
34. \(\min(rx\), \(node\_bottom\_right\_child.rx)\),
35.  endif
36. if\(rs\geq node\_bottom\_right\_child.lx\)and\(ry\geq node\_bottom\_right\_child.ly\)then
37. \(bottom\_right\_res\leftarrow\)range\(\_query\)(\(node\_bottom\_right\_child\),
38. \(\max(lx\), \(node\_bottom\_right\_child.lx)\),
39. \(ly\),
40. \(\min(rx\), \(node\_bottom\_right\_child.rx)\),
41. \(ry\))
42. endif
43. //Splicing operation
44. \(res\leftarrow\)[\([top\_left\_res,top\_right\_res]\), [\(bottom\_left\_res,bottom\_right\_res]\)]
45. returnres ```  
**Algorithm 2**range_query
## 3 Proposed Hardware Architecture
### Total Computational Requirements  
We rely on Equation (4) as the foundation for calculating the clock cycles (CCs) needed for one PM computation. In this equation, the term \"Initial\" pertains to the initialization phase of Algorithm 1. Variable \"m\" is a key determinant representing the key length. Additionally, \"Inv\" characterizes the inversion operation, as defined in Equation (4). To present a clear and organized breakdown of these CCs, we structure Table 2 as follows:  
\[Clock\,cycles=Initial+12(m-1)+Inv. \tag{4}\]  
The first column in the table specifies the key length. In the second column, we provide the number of clock cycles for executing the initialization phase of Algorithm 1. The third column comprehensively accounts for the total CCs required to perform both point additions (PA) and point doublings (PD). Moving on to the fourth column, we outline the CCs essential for the inversion operation (\"Inv\"). Lastly, the final column in Table 2 shows the total CCs, offering a holistic view of the computational demands for effectively implementing Algorithm 1. This structured presentation enables decision makers (designers) to gain a clear understanding of the computational requirements according to different key lengths. In other words, it serves as a valuable reference for resource allocation and informed decision making within our cryptographic system, ensuring efficient and effective operations.
## 4 Experimental Environment and Methodology in Smart Farming Environment  
### Environment  
Figure 5 provides an overview of the **experimental data collection setup** within the smart farming environment. This includes IoT-enabled sensors and control systems that continuously monitor soil conditions in real time to automate irrigation. The key components of the system include soil sensors, gateways, and data storage units, all of which support efficient environmental monitoring and data-driven decision making.  
These components are described as follows:  
* **Sensors and control units**: The smart farm is equipped with various soil sensors and irrigation controllers. Soil sensors monitor real-time data such as moisture, temperature, and humidity levels. Based on these data, the irrigation system is managed by controlling solenoid valves to maintain optimal soil conditions for crop growth.
* **Gateway**: The gateway acts as a central hub to collect data from soil sensors and transmit the data to a remote server. The gateway also communicates with control units to remotely adjust the irrigation system based on the data analysis.
* **Data collection system**: This system stores and manages the gathered sensor data in a centralized database. This system supports real-time data collection and storage, allowing machine learning models to be trained on up-to-date environmental data.  
Figure 6 shows the **smart farm system used for the performance evaluation**, highlighting its ability to monitor and adjust environmental factors in real time. Using advanced algorithms, this system distinguishes soil textures by analyzing characteristic changes and dynamically adjusting irrigation based on temperature and humidity variations. This functionality optimizes water usage and ensures that crops grow under ideal conditions, promoting sustainable and efficient agricultural practices.  
Figure 5: Experimental data collection environment.  
Figure 6: Smart farm system for performance evaluation.  
Through the combination of sensors, gateways, and data storage systems, this smart farming setup provides a comprehensive approach to monitoring and managing agricultural environments. By utilizing this IoT-driven framework, the system achieves precise control over environmental variables, facilitating sustainable farming practices and supporting more efficient resource allocation. The collected data serve as a foundation for developing and evaluating machine learning models to diagnose and predict agricultural conditions in real time.