esa-sceva/satcom-chunk-collection huggingface dataset:
# An Effective Scheduling Algorithm for Coverage Control in Underwater Acoustic Sensor Network
## 3 Energy-Efficient Coverage Control Using Memetic Algorithm
### Node Sleep-Scheduling Mechanism Based on Memetic Algorithm  
The Memetic algorithm was first proposed by [PERSON] in 1989, which consists of an individual-based local search and a population-based global search to solve large-scale combinatorial optimization problems [27; 28]. The initial state of the population is generated randomly or in a specific way. The evolutionary process is operated by genetic algorithms including selection, crossover, and mutation. In ESACC, each individual represents a solution with a corresponding adaptability, which is given by the adaptive function. The adaptive function is used to evaluate the superiority of the solution. After completing the operation of the genetic algorithm, the local search further improves the superiority of the solution. In this way, a new batch of excellent individuals is generated, and the individuals in the new population are closer to the global optimal solution. Below, we describe the redundant node sleep-scheduling algorithm.
## 4 Conclusions  
This paper presented the nonlinear effects of an SMC based on a washout filter that considers CIL and CPL. For this purpose, an electronic circuit was modeled and simulated in MATLAB-Simulink to visualize the different nonlinear effects created when different types of loads are connected. The results show that the system regulates well the voltage with the three loads tested. The robustness of the system was checked by changing the control parameter \(k\), obtaining a steady-state error less than 3%. Additionally, the quantization, system discretization, and delay effects present in real systems were modeled to obtain results closer to those obtained with real circuits and that can be used in future research. A buck converter with a quasi-sliding control technique was placed as a CPL load, realizing that the system does not present unstable zones and a high steady-state error, and that the steady-state error is a little greater than the other two loads when the parameter \(k\) changes. From the bifurcation diagrams, it was observed that when the control parameter was around 0.5, the operation was close to the limit. Therefore, it is recommended to increase this value to avoid entering the zone where the steady-state error is greater. In the comparison of the performance of the three controllers with load variations, it was observed that the three controllers regulate well the voltage at the output with CIL; however, if the load is a combination of CIL and CPL, then the only controller that regulates well the voltage signal with small setting times and low steady-state error is the SMC with the washout filter.  
Further research will analyze the SMC in an MG environment, integrating the controllers with other types of PCs as DC-AC. Future work will also consider the integration of the filter with the main control of the MG and compare the control efficiency with other methods. A classification of nonlinear effects can also be performed based on a bifurcation analysis to identify the effectivity of the response of the controller and the dynamic behavior. In addition, experimental validation of the dynamic responses of the SMC with the washout filter is required to validate the benefits of the controller. Finally, theoretical and experimental research with three-phase loads and various types of motors may be controlled by using an SMC with a washout filter.  
Conceptualization, methodology, analysis, investigation, writing--review, and editing: M.M.-R., J.E.C.-B., and F.E.H. This research received no external funding. This work was supported by the Universidad Nacional de Colombia, Sede Medellin under the projects HERMES-34671 and HERMES-36911. The authors thank the School of Physics and the Department of Electrical Engineering and Automation for their valuable support in conducting this research. The authors declare no conflict of interest.
## 2 Materials and Methods
### Process Data Collection  
In-process data collection included process settings and sensor readings. The structure of the collected data can be seen in Table 1. After aligning the machine data and porosity data from CT scans, the voxelisation was carried out and each voxel was labelled by porosity (Yes/No) and porosity type (LOF/KH). The positional data were used only for alignment and not as inputs to the machine learning models.  
Figure 3: (a) Location of pillars on the L-PBF build plate. (b) Dimensions of each pillar and example embossing of top surface identifying an individual pillar.  
Figure 2: L-PBF process.  
Sensor values were obtained from Renishaw RenAM 500M machine sensors, namely the LaserView module and MeltView module, at a frequency of 100 kHz [36]. The LaserView module is designed to monitor the emissions from the 500W Ytterbium fibre laser. The module measures wavelengths ranging from 1050 nm to 1080 nm using a photodiode sensor. The MeltView module is designed to measure the emissions from the melt pool using two photodiode sensors with the MeltVIEWMeltPool sensor measuring the wavelengths ranging from 1090 nm to 1700 nm for the infrared spectrum and the MeltVIEWPlasma sensor measuring the emissions in the wavelength ranging from 700 nm to 1040 nm in the visible spectrum.  
The sensor data were then augmented with process settings initially set by the process engineers operating the AM machine to manufacture the part. These settings were extracted from the computer-aided manufacturing (CAM) tool used to create the machine toolpath instructions for each layer. These values include the pre-set laser power, pulse duration, point distance, and hatch distance (Table 1).
# Emerging Topics in Joint Radio-Based Positioning, Sensing, and Communications
## 3 Emerging Topics in JPSAC Context
### Waveform Design  
The research field of waveform design in its broader sense is not, strictly speaking, an emerging one, as researchers have focused their attention on waveform design in various wireless areas for decades. However, the novelty of the waveform design in the context of ISAC stems from the dichotomy between either designing--and then embedding--two simultaneous signal waveforms, one for communication and one for sensing targets, or designing a single waveform with dual functionality. The former approach is also known as RCC, while the latter is referred to as DFRC [69]. A few of the main open research questions relevant to this area involve determining which approach is more suitable for the desired application, how to optimally design such waveforms, and which are the most suitable optimization metrics and optimization-solving approaches. In the case of RCC, there will be interference between the two simultaneous waveforms, and this must be taken into account. Index Modulation (IM) is one recently proposed method for DFRC modulation design, in which the information-bearing signal (i.e., the communication waveform) is indexed or embedded in the radar waveform. IM can be carried out in the frequency, antenna, phase, or space domains as well as in a hybrid domain involving all four [69]. Typically, hybrid approaches can achieve a lower complexity than standalone ones, while spatial-path approaches achieve the best communication rates [69].
## 2 Materials and Methods
### Synthesis of ligands
#### 2.2.3 2-(2,3,4-Tri-O-benzyl-6-acetyl-\(\alpha\)-D-glucopyranosyl)-ethanol 9  
To a solution of **8** (3.53 g, 6.84 mmol) in CH\({}_{2}\)Cl\({}_{2}\) (100 mL, 0.07 M) at \(-\)78 \({}^{\circ}\)C, O\({}_{3}\) was bubbled until a pale blue color appeared. The reaction was followed by TLC (Hexane/EtOAc 8:2). At the end of the reaction, the excess of O\({}_{3}\) was removed by purging the mixture with a stream of argon at \(-\)78 \({}^{\circ}\)C and then Ph\({}_{3}\)P (17.1 mmol, 2.5 eq) was added. The mixture was stirred for 24 h at room temperature and then concentrated by rotary evaporation. The product was purified from the crude reaction by flash chromatography (silica gel, eluent: Hexane/EtOAc 8:2 to 7:3), affording aldehyde **9** (2.92 g, 5.63 mmol, 82% yield).  
\({}^{1}\)H NMR (400 MHz, CDCl\({}_{3}\)) \(\delta\) 9.72 (s, 1H, H2\({}^{\prime}\) (CHO)), 7.43-7.24 (m, 15H, CH Ar), 4.93 (d, J = 10.9 Hz, 1H, CH\({}_{2}\)Ph), 4.87 (d, J = 10.8 Hz, 1H, CH\({}_{2}\)Ph), 4.83 (d, J = 10.9 Hz, 1H, CH\({}_{2}\)Ph), 4.75-4.66 (m, 2H, CH\({}_{2}\)Ph), H1), 4.63 (d, J = 11.5 Hz, 1H, CH\({}_{2}\)Ph), 4.58 (d, J = 10.8 Hz, 1H, CH\({}_{2}\)Ph), 4.30-4.18 (m, 2H, H6), 3.82-3.74 (m, 2H, H2, H3), 3.74-3.63 (m, 1H, H5),3.53-3.43 (m, 1H, H4), 2.89 (dd, J = 15.8, 5.0 Hz, 1H, H1\({}^{\prime}\)a), 2.74 (ddd, J = 16.2, 8.6, 2.8 Hz, 1H, H1\({}^{\prime}\)b) and 2.05 (s, 3H, CH\({}_{3}\)CO).  
\({}^{13}\)C NMR (101 MHz, CDCl\({}_{3}\)) \(\delta\) 199.49 (C2\({}^{\prime}\) (CHO)), 170.83 (CH\({}_{3}\)CO), 138.29, 137.67 and 137.65 (Cq Ar), 128.66, 128.64, 128.61, 128.20, 128.18, 128.14, 127.99 and 127.94 (Cr), 81.90 (C2), 78.88 (C3), 77.30 (C4), 75.54 (CH\({}_{2}\)Ph), 75.07 (CH\({}_{2}\)Ph), 73.67 (CH\({}_{2}\)Ph), 70.95 (C5), 69.58 (C1), 63.20 (C6), 41.26 (C1\({}^{\prime}\)) and 20.95 (CH\({}_{3}\)CO).  
MS: \(m/z=519.6\) [M+H]\({}^{+}\).
# Usability Evaluations Employing Online Panels Are Not Bias-Free
## 1 Introduction
### Research Project  
Usability and User Experience, widely discussed in the academic field [26], and defined by the International Organization for Standardization [27], are nowadays key issues for developing effective digital products and services [28].  
However, studies on the quality of data provided by OPs have focused most exclusively on survey responses for market, social, psychological, and medical research. Studies focusing on the quality of data provided by PRs when they are involved in usability testing are lacking.  
This research project is aimed at extending the literature on online panels, exploring the relationship between the level of expertise of the OPs and the usability ratings. Therefore, we selected two types of users from the international online platform UNGUESS whose mission is finding and sharing users' feedback through online usability and debugging research ([[https://www.unguess.io](https://www.unguess.io)]([https://www.unguess.io](https://www.unguess.io)), accessed 15 July 2022):  
* Professional respondents (PR)--Highly experienced users of online studies, who have performed a large number of debugging (where the goal is to find as many bugs as possible in digital products) and usability tests. They accurately report and describe many bugs, browsing and interacting with the digital product in-depth and repeatedly;
* Non-professional respondents (NPR)--Users with negligible or no experience with online studies, who have never done neither debugging or usability testing.  
In addition to the two categories described above, an additional category of users was recruited based on a survey conducted on social networks and employed exclusively in study 3:Non-Crowd Respondents (NCR)--Users who are not part of any online panel and have no previous experience in usability evaluation.  
The PRs group is consistent with [PERSON]'s definition [16]. However, as argued by [PERSON], [PERSON], and [PERSON] [23], there are different criteria in the literature for defining PRs and consequently NPRs. As for the purposes of this research project, both categories were created by setting a threshold that allowed us to clearly distinguish between the two groups. PRs were those individuals who had served in at least 30 online studies (10 in the last 6 months), whereas NPR were individuals freshly included in the panel and not yet involved in any study. This research project was conducted in accordance with the tenets of the Declaration of Helsinki and each participant signed an informed consent explaining the general purpose of research and its risks. Access to the UNGUESS platform was granted for data collection by one of the authors.  
The project had two objectives:  
* Investigating the impact of prior experience with online studies (high/low) on perceived usability ratings expressed during different types of tests (debugging/usability);
* Investigating the impact of the type of test carried out (debugging/usability) on the perceived usability ratings expressed by users with two different levels of experience with online studies (high/low).
## 4 Experimental Results  
### Datasets  
**The DengFeng-City dataset** was collected in DengFeng City, China, via a DJI M350 drone with standard oblique photogrammetry techniques and a 6 cm ground sampling distance (GSD), and it contains 1000 images with a resolution of \(768\times 1280\) pixels. This dataset primarily includes scenes such as roads, buildings, forests, and rivers. During the training process, we selected three scenes to validate the effectiveness of the method proposed in this paper. Each scene included 60 cropped images from different perspectives.  
**The XinMi-Village dataset** is a rural scene dataset that we collected using a DJI M350 drone in Xinmi, China. The interval between its shooting and the DengFeng dataset was more than three months, and it contains 1500 images with the same resolution. Similarly, the dataset also includes three scenes--an intersection, a country road, and a stadium--each of which also contains 60 images from different perspectives. The relevant information for drone image acquisition is shown in Figure 3.  
To generate the true value of the ground DSM corresponding to the two datasets, we utilized a DJI M350 drone equipped with an L2 sensor, employing an orthogonal acquisition method to collect LiDAR point cloud data. The point cloud density was 207 points per square meter, and the drone's flight speed was 10 m per second. Accuracy validation of the DSM was performed using existing control points within the survey area. The results showed that both planar and elevation accuracies were within 1 cm, meeting the precision requirements of this study.  
Figure 3: The study location along the with general collection pattern, flight lines for the drone imagery.
# Measurement of Absolute Acoustic Nonlinearity Parameter Using Laser-Ultrasonic Detection
## 3 Experimental Setup  
### Test Specimens  
Two test specimens fabricated using copper and Al6061-T6 bulk materials were prepared to validate the performance of the proposed laser-ultrasonic detection method. The material properties such as density, wave speed, and acoustic impedance for the two specimens were considerably different from each other. Detailed values are presented in Table 1. A general milling machine polished the specimen surfaces; the arithmetic average of their roughness (Ra) measured by a commercial roughness tester was approximately 0.64 \(\upmu\)m. This level is approximately ten times greater than that of the optically flat surface required for other capacitive receivers or the [PERSON] and Fabry-Perot interferometers [9]. Both specimens were of the same thickness of 20 mm. Note that when the wave propagation distance (i.e., specimen thickness) is greater than a certain level (called the Fresnel zone [32]), ultrasonic diffraction occurs independent of the material nonlinearity and increases the measurement error. Here, because the wave propagation distance (20 mm) is within the Fresnel zone (copper: 48 mm, Al6061-T6: 37 mm), the diffraction effect is not considered. Images of the test specimens are displayed in Figure 2.
## 3 Materials and Methods
### Model Construction  
#### 3.2.1 STL-DCSInformer-ETS Hybrid Model  
To enhance the accuracy of time-series forecasting [29; 30], this paper proposes a combined prediction model based on STL decomposition, the DCSInformer model, and the ETS model, as illustrated in Figure 1. STL (seasonal and trend decomposition using Loess) is a robust non-parametric time-series decomposition method that divides the series into three components: trend (trend), seasonality (seasonal), and remainder (remainder) [31; 32]. The decomposition formula is expressed as:  
\[Y_{t}=T_{t}+R_{t}+S_{t} \tag{11}\]By decomposing the series into these components, STL effectively reduces the complexity of model fitting, allowing subsequent models to concentrate on the distinct characteristics of each component, thereby improving predictive accuracy.  
For medium- and long-term time-series forecasting [33; 34], the Informer model excels, particularly in uncovering latent time-series associations within high-dimensional data. Its sparse self-attention mechanism significantly reduces computational complexity while enhancing its ability to capture both long-term and short-term dependencies. To better address the requirements of medium- and long-term forecasts, this paper employs an enhanced version of the Informer model, DCSInformer. By incorporating dilated causal convolution and multi-scale feature extraction mechanisms, DCSInformer captures features across different time scales more effectively, achieving a balanced approach to processing long-term trends and short-term fluctuations. The ETS (AAM) model is specifically designed to handle seasonal data with clear cyclical patterns, making it highly effective for capturing and forecasting seasonal fluctuations. For macroeconomic indicators like GDP growth rates or CPI, the ETS model accurately identifies seasonality trends and integrates them into sales forecasts, enhancing overall prediction precision.  
The hybrid model proposed in this paper further refines this process by precisely decomposing time-series data into trend, seasonal, and remainder components. This approach significantly improves the accuracy of long-term forecasts, particularly within the Chinese market.
# Influence of Sulphate Attack on Properties of Modified Cement Composites
## 1 Introduction
In a number of studies [36; 37], it was found that the use of an electrolyte such as calcium nitrate reduces the degree of structure crystallization of concrete in the long term due to the interaction of nitric acid salt with aluminum-containing phases of Portland cement forming calcium hydronitroaluminates and calcium hydroxymitrates. Due to their shielding ability, these compounds slow the recrystallization of cement matrix. Despite the compacting effect and the increase in water resistance with the addition of calcium nitrate, its use in concretes exposed to aggressive media is limited because it enhances the corrosion processes of the solid phase of cement matrix [7; 38; 39]. Analysis of the durability of cement-based composites is difficult because no unambiguous criterion currently exists for evaluating this parameter, in contrast to the mechanical and electrical properties. Despite certain successes in laboratory studies of destructive processes [12; 13; 40], the degree of reliability of quantitative analysis remains low due to the high coefficient of variation of experimental data. To save resources, and to improve the accuracy and sensitivity of the obtained data, a stochastic dynamic model for assessing the effect of internal fluctuations on the destructive process of hydrosulfate corrosion was proposed [11]. The proposed model focuses on the analysis of three aspects: the process of sulphate ion transfer [7]; chemical reactions caused by the process of sulphate corrosion [6]; and the combination of the hydraulic pressure of the solution and the chemical reaction between the sodium sulphate solution and the components of solid phase of concrete [2]. At present, several basic methods exist for studying hydrosulphate corrosion: the use of stochastic dynamic reactions of nonlinear systems [11], the potentiometric method [41],the Moskvin and Kind methods [7], etc. The potentiometric method of analysis is widely used because it allows the capabilities of the analysis of organic and inorganic substances to be expanded with the provision of the most sensitive and accurate data. The aim of the study was to evaluate the effect of hydrosulphate corrosion on the electro-conducting properties of cement composite by means of the potentiometric method and titrimetric analysis of a mono-normal solution of sodium sulphate, with subsequent assessment of the morphological changes in the material structure.
# A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting
## 3 The Architecture of Deep Learning Algorithms
### Gated Recurrent Unit (GRU)  
The GRU is considered to be a less sophisticated RNN [108] than LSTM, which can be more simply applied and computed. GRU and LSTM are equivalent in terms of their capacity to recall and capture long-term dependencies. The computational time is faster and the complexity is lower in GRU compared to LSTM, since there are fewer features [67]. Moreover, GRU can effectively learn long-term dependencies data, which resolves the issue of vanishing gradient that arises when using simple RNN. Furthermore, GRU is deemed as a variant of LSTM due to the similarities between its function mechanism and design. This observation can be seen when both GRU and LSTM use the gate mechanism to control the flow of information by combining the input and forget gates into a single update gate. However, unlike LSTM, GRU has only two gates, which are the update gate (\(z_{t}\)) and the reset gate (\(r_{t}\)). These two gates determine what information from the past should be preserved for the future and what past information should be deleted, respectively. Furthermore, GRU models suffer from delayed convergence and low learning efficiency, resulting in extensive training and even under-fitting. Figure 7 depicts the structure of GRU, and the relationship between its inpute and outpute is presented in Equations (19)-(21):  
\[R_{t}=\sigma\ (U_{t}\cdot h_{t-1}+b_{r}+W_{r}\cdot X_{t}) \tag{19}\]  
\[z_{t}=\sigma\ (b_{2}+W_{z}\cdot X_{t}+U_{z}\cdot h_{t-1}) \tag{20}\]  
\[A_{t}=\tan h\ (W_{h}\cdot X_{t}+U_{h}\cdot b_{h}+(r_{t}*h_{t-1})) \tag{21}\]  
\[h_{t}=(1-Z_{t})\ *\ h_{t-1}+Z_{t}*A_{t} \tag{22}\]  
At the current time step (\(t\)), the reset gate is denoted as \(R_{t}\), the update gate is denoted as \(z_{t}\), the memory component is denoted as \(A_{t}\), the activation function is denoted as \(\tan h\), and the final memory is denoted as \(h_{t}\).
## 3 Numerical Results
### Impact of the Content's Popularity Pattern  
We analyze the impact of the content's popularity pattern on the algorithms' performance in Figure 3. Namely, we vary the shape parameter \(\alpha\) of the contents popularity with the value 0.46 to 1.16. As expected, with increase of \(\alpha\), the active MECSs decrease in the scheme, and the energy efficiency improves as the popularity distribution gets steeper. When \(\alpha\) is high, the vast majority of user requests refer to a small number of contents. Clearly, caching the above contents provides significant benefits to the provider. To conclude, when the content popularity distribution is highly concentrated (i.e., \(\alpha>1\)), our algorithm achieves at least 1.5 times higher performance than \(\alpha<1\).