esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The study of fractures in the subsurface is very important in unconventional reservoirs since they are the main conduits for hydrocarbon flow. For this reason, a variety of equivalent medium theories have been proposed for the estimation of fracture and fluid properties within reservoir rocks. Recently, the Galvin model has been put forward to model the frequency-dependent elastic moduli in fractured porous rocks and has been widely used to research seismic wave propagation in fractured rocks. We experimentally investigated the feasibility of applying the Galvin model in fractured tight stones. For this proposal, three artificial fractured tight sandstone samples with the same background porosity (11.7% \(\pm\) 1.2%) but different fracture densities of 0.00, 0.0312, and 0.0624 were manufactured. The fracture thickness was 0.06 mm and the fracture diameter was 3 mm in all the fractured samples. Ultrasonic P- and S-wave velocities were measured at 0.5 MHz in a laboratory setting in dry and water-saturated conditions in directions at 0\({}^{\circ}\), 45\({}^{\circ}\), and 90\({}^{\circ}\) to the fracture normal. The results were compared with theoretical predictions of the Galvin model. The comparison showed that model predictions significantly underestimated P- and S- wave velocities as well as P-wave anisotropy in water-saturated conditions, but overestimated P-wave anisotropy in dry conditions. By analyzing the differences between the measured results and theoretical predictions, we modified the Galvin model by adding the squirt flow mechanism to it and used the Thomsen model to obtain the elastic moduli in high- and low-frequency limits. The modified model predictions showed good fits with the measured results. To the best of our knowledge, this is the first study to validate and calibrate the frequency-dependent equivalent medium theories in tight fractured rocks experimentally.  
artificial fractured tight stones; equivalent medium theories; rock physics; experimental validation; anisotropy +
Footnote †: journal: Applied Sciences
# Influence of Training Parameters on Real-Time Similar Object Detection Using YOLOv5s
## 1 Introduction
In this paper, the influence of training parameters has been analyzed for one of the new and still not fully investigated object detection algorithms, YOLOv5, which has also been analyzed. An analysis of related works [17; 18] has shown that, usually, other similar research focuses only on a few Yolov5 hyperparameters, such as the learning rate, momentum, and weight selection, but other hyperparameters usually have not been changed. The main reason is that, in object detection tasks, the training of each model usually takes at least a few days. Therefore, in this research, we select the best learning hyperparameters that have been used in related works and focus on parameters related only to the training dataset: the image color, batch size, image resolution, and learning time. In our case, when the dataset has a large number of classes and the dataset items have similar features, it is important to investigate the dataset parameters and which parameters have the highest influence in such a case. The models have been trained and tested in real-time object detection using a mobile device. For this reason, the smaller version of YOLOv5 has been used, YOLOv5s, that is suitable for such types of tasks. The structure of this paper is as follows. In Section 2, the related works are reviewed. In Section 3, the latest YOLO group algorithms have been overviewed. The new dataset and the experimental investigation are presented in Section 4. In Section 5, the discussion and limitations of the research are presented. Section 6 concludes the paper.
# Advances in Engineering and Application of Optogenetic Indicators for Neuroscience
## 1 Introduction  
A central and astonishing feature of the nervous system is the capacity for learning and remembering, which are inherently dynamic processes. Advances in genetically-encoded sensors enable the real-time observation of key signaling molecules in a cell-type and circuit-specific manner within intact brain tissue and in vivo. Genetic strategies allow targeted expression of optogenetic tools [1; 2] to a specific cell type (using specific promoters [3] or in transgenic animals expressing DNA recombinases in a specific cell type [4]) or an anatomically distinct circuit (using intersectional or retrograde viral labeling strategies [5; 6]). Additionally, genetically-encoded probes are the only technique available to observe precisely the same cells longitudinally, permitting long-term monitoring of specific cellular processes, up to months [7; 8]. Judiciously selecting suitable spectral properties of optical sensors potentially enables the visualization of the activity and interactions of distinct cell types [9]. Thus, genetically-encoded indicators are an indispensable tool for visualizing neuronal activity in a cell type and circuit-specific manner while minimizing disturbance to the complex cellular milieu of the brain. Advances in these sensors allow noninvasive and longitudinal monitoring of key neural processes, which is essential for understanding how information is transmitted and processed.  
In this review, we describe advances from the past three years in the engineering and application of genetically-encoded fluorescence indicators of neuronal activity. We focus on indicators for calcium, voltage, and neurotransmitters that show acceptable performance for in vivo imaging. We also provide an overview of new sensors--which potentially enable fundamentally newkinds of measurements--and their molecular design, biochemical and spectral characteristics, and current limitations.
# Evaluation of Bol Corrosion Degree Based on Non-Destructive Testing and Neural Network
## 5 Experiment Results and Analysis
### Comparison with the Latest Methods  
In order to evaluate our method better, we compared the MS-CNN with other models. Since there is limited research on the waveform recognition of anchor bolt corrosion, we selected methods that also recognize time series for comparison. The compared methods included SqueezeNet, TI-CNN [19], CNN-LSTM [20], and CNN-BiLSTM-SE [21], as shown in Figure 16 and Table 3. It should be noted that during the comparison process, due to the differences in dataset size between this study and the literature, we adjusted the convolutional kernel size and stride of some models. The training results for each method are shown in Table 5.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Training Set** & **Test Set** & **Degree of Corrosion** \\ \hline Dataset 1 & 101 & 25 & 0.72\% \\ Dataset 2 & 101 & 25 & 1.54\% \\ Dataset 3 & 101 & 25 & 2.39\% \\ Dataset 4 & 101 & 25 & 3.12\% \\ Dataset 5 & 102 & 26 & 3.81\% \\ Dataset 6 & 102 & 26 & 4.46\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Dataset description.  
Figure 16: Comparison of training for different models. (**a**) Training accuracy of different methods based on raw signals. (**b**) Training time of different methods.  
Figure 15: Training of MS-CNN. (**a**) CNN model training progress chart. (**b**) Confusion matrix results for multi-scale CNN model recognition.  
From the comparison results, it can be seen that MS-CNN has the highest recognition accuracy, reaching 99.4%. The CNN-LSTM has the fewest model parameters and takes only 18 s for training, with a recognition accuracy of 96.1%. The CNN-BiLSTM-SE model incorporates a bidirectional long short-term memory network (BiLSTM) to effectively model long-term dependencies in sequence data, achieving an accuracy of 98.6%. The recognition accuracy of the SqueezeNet model is not ideal, possibly because it fails to capture the temporal correlations in the ultrasonic guided wave recognition scenario sufficiently. The TI-CNN model shows slow improvement in accuracy at the beginning of training as it only introduces dropout to enhance the model's generalization ability, and it takes longer time than the MS-CNN to achieve the same 98% recognition accuracy. The multi-scale model requires doubling the number of convolutional kernels in adjacent paths, leading to a rapid increase in parameter count and training time as the number of paths increases. Although the MS-CNN does not show significant advantages in terms of model parameters and training time, it greatly improves the recognition accuracy.
###### Abstract  
The arid terrestrial ecosystem carbon cycle is one of the most important parts of the global carbon cycle, but it is vulnerable to external disturbances. As the most direct factor affecting the carbon cycle, how land cover change affects vegetation carbon sources/sinks in arid terrestrial ecosystems remains unclear. In this study, we chose the arid region of northwest China (ARNWC) as the study area and used net ecosystem productivity (NEP) as an indicator of vegetation carbon source/sink. Subsequently, we described the spatial distribution and temporal dynamics of vegetation carbon sources/sinks in the ARNWC from 2001-2018 by combining the Carnegie-Ames-Stanford Approach (CASA) and a soil microbial heterotrophic respiration (R\({}_{\rm H}\)) model and assessed the effects of land cover change on them through modeling scenario design. We found that land cover change had an obvious positive impact on vegetation carbon sinks. Among them, the effect of land cover type conversion contributed to an increase in total NEP of approximately 1.77 Tg C (reaching 15.55% of the original value), and after simultaneously considering the effect of vegetation growth enhancement, it contributed to an increase in total NEP of approximately 14.75 Tg C (reaching 129.61% of the original value). For different land cover types, cropland consistently contributed the most to the increment of NEP, and the regeneration of young and middle-aged forests also led to a significant increase in forest carbon sinks. Thus, our findings provide a reference for assessing the effects of land cover change on vegetation carbon sinks, and they indicated that cropland expansion and anthropogenic management dominated the growth of vegetation carbon sequestration in the ARNWC, that afforestation also benefits the carbon sink capacity of terrestrial ecosystems, and that attention should be paid to restoring and protecting native vegetation in forestland and grassland regions in the future.  
net ecosystem productivity; Carnegie-Ames-Stanford approach; scenario design; cropland expansion; afforestation 2023
## 3 Calculation of Transmission Distance in the Ideal State
The system architecture's furthest transmission distance will be calculated as follows: The parameters in Table 2 can be expressed as follows:  
\[R\ =\ \Big{[}(n_{glass}\ -\ n_{air})/(n_{glass}\ -\ n_{air})]^{2} \tag{3}\]  
\[P_{in}\ -\ \Big{\{}2\ \times\ L_{glass}+X\ \times\ L_{mirror}\ +\ [1.5\ \times\ (X+1)\ \times\ L_{water}\ ]\ +\ \mathrm{R}\Big{\}}=\ P_{min} \tag{4}\]  
where \(P_{in}\) is the input optical power. \(L_{glass}\) is the loss of light through the glass. Assuming that all the mirrors are placed in the water tank, the light source will only penetrate the water tank glass twice to minimize the loss caused by penetrating the water tank glass, and the total loss is 0.7 dB. \(L_{mirror}\) is the loss of signal light reflected once by a mirror. The \(X\) value is the number of mirrors that the laser source meets; Note that when the beam light is injected into the tank through the glass wall, there is Fresnel loss, which is given for normal incidence as a reflectivity (in power) R = 0.04 (\(-\)\(-\)14 dB) in case the refractive index associated with the glass is around 1.5. This amount of light is lost by reflection before entering the bottom polarizer and to the PD. Assuming the light beam is collimated, so there is no divergence in the transmission process, it perfectly converges on the focus at the receiving end and is received by the photodetector. \(D_{total}\) is the total transmission distance, so Equation (5) can be obtained:  
\[D_{total}=\ 1.5\ +\ X\times 1.5\ (\mathrm{m}) \tag{5}\]By substituting the values in Table 2 into Equation (4), \(X\)'s value is 19, meaning that up to 19 mirrors can be used in this architecture. The system's maximum transmission distance can be up to 30 m in ideal condition by taking the \(X\) value into Equation (5). However, this is the calculation result in the ideal state. In the actual state, the increase of the propagation distance will result in laser beam diameter increase and received optical power decrease to shorten the effective propagation distance. A laser beam collimator may be applied to reduce beam divergence on the UWOC system. In Table 2, a minimum received power for a PD to obtain 10 \(\times\) 10\({}^{-3}\) BER under 1.25 Gbps blue laser transmission was provided. Because there is optical loss induced by the glass and/or mirror, the values and factors need to be considered and included.
# IoT Implementation of Kalman Filter to Improve Accuracy of Air Quality Monitoring and Prediction
## 1 Introduction
By flexibly arranging inexpensive sensors throughout the monitoring area, the system monitors the concentration of six air pollutants such as SO\({}_{2}\), NO\({}_{2}\), CO, O\({}_{3}\), PM2.5 and PM10 in real time. According to the dynamic characteristics of different air pollutants, the KF algorithm constructs the short-term dynamic prediction model via 100 iterations of the initial sampling data with error. Therefore, the instantaneous prediction of pollutant concentrations is achieved, and the sensor accuracy is improved from the algorithm level on the edge side. This process of correcting the monitoring data to obtain the best predictive value is a local process, which is used as a concrete example of edge computing. Raspberry Pi (RPi) 3B is a low-power, low-cost card computer with a built-in quad-core 1.2 GHz 64 bit processor, which has very good computing and processing power. Therefore, this paper chooses RPi as the carrier of edge computing and the central node of the sensor network. After collecting the monitoring data from various sensors, RPi runs the KF algorithm to obtain the best prediction results of various air pollutant concentrations, and then uploads the monitoring data and prediction results to the cloud through the Wi-Fi module. The cloud stores data and sends feedback to client requests. Through this marginalized low-cost wireless solution, farmers and agricultural experts can collect more accurate concentrations of air pollutants, and analyze the current and subsequent AQ through the client, which provides a more real-time, accurate and scientific basis for taking appropriate control measures.
## 5 Performance Analysis  
The performance analysis of the proposed resource allocation scheme is explained in this section by using continuous-time Markov chains (CTMC) with multiple dimensions.
###### Abstract  
Evaluating the learning process requires a platform for students to express feedback and suggestions openly through online reviews. Sentiment analysis is often used to analyze review texts but typically captures only overall sentiment without identifying specific aspects. This study develops an aspect-based sentiment analysis (ABSA) model using IndoBERT, a pre-trained model tailored for the Indonesian language. The research uses 10,000 student reviews from Indonesian universities, processed through data labeling, text preprocessing, and splitting, followed by model training and performance evaluation. The model demonstrated superior performance with an aspect extraction accuracy of 0.973, an F1-score of 0.952, a sentiment classification accuracy of 0.979, and an F1-score of 0.974. Experimental results indicate that the proposed ABSA model surpasses previous state-of-the-art models in analyzing sentiment related to specific aspects of educational evaluation. By leveraging IndoBERT, the model effectively handles linguistic complexities and provides detailed insights into student experiences. These findings highlight the potential of the ABSA model in enhancing learning evaluations by offering precise, aspect-focused feedback, contributing to strategies for improving the quality of higher education.  
aspect-based sentiment analysis; bert; hyper-parameter; learning evaluation; natural language processing; student reviews +
Footnote †: journal: Applied sciences  
1
## 4 Results and Discussion
### Spectrum Curves Seasonal Variation of Forest Canopy in the Footprint
#### 4.1.3 Reasons for the Spectrum Seasonal Variation  
The vegetation canopy reflectance spectrum was subject to many controlling factors, notably, concentrations of canopy pigments, effects of variations in number of leaf layers (LAI), orientation of leaves (leaf angle distribution (LAD)), canopy ground coverage, presence of non-leaf elements, and areas of shadow and soil/litter surface reflectance. These variables influenced the reflectance spectrum in different wavelength regions [48]. The pigment played a dominant role in visible and red edge regions (400-800 nm). Canopy structural factors have dominant effects in the near-infrared region (750-1500 nm) [53].  
Due to the influence of the snow cover below and above the forest canopy, the spectrums of remote sensing pixels were mixed by the reflectance of forest canopy and snow. Snow has a high reflectance in the visible (VIS) and near-infrared (NIR) wavelengths when compared to other natural targets [54]. Variations in snow reflectance can induce large changes in the scene reflectance, even though visible snow-covered ground accounted for only half of the observed area as a result of the blocking effect of the trees [55]. The ratio of the snow cover to canopy cover can influence on spectrums of pixels in C flux footprint. A few of reflectance curves were much higher than the whole spectrums curves during the winter.  
The existence of peaks, including the red edge peak, in the FDR curves can be attributed to the characteristic absorption spectrum of chlorophyll [56], with a possible contribution from chlorophyll fluorescence [57]. The red edge peak feature can be used to identify bio-indicators.
# Developing an I4.0 Cyber-Physical System to Enhance Efficiency and Competitiveness in Manufacturing
## 4 Case Study--A Cold-Chain Insulated Packaging Manufacturing Process  
This section presents a case study of implementing, testing, and validating the framework with a cold-chain insulated packaging manufacturer. The assembly line consists of three material rolls: bubble wrap, tape, and branding label, all of which unwind at constant rates and are fed into an insulated bag-making machine. The process involves halving the bubble wrap, lining it with tape and labels, and heat sealing it into specified dimensions. However, the manual replacement of these material rolls significantly increases the operation times and contributes to prolonged machine downtime and material waste. The timing of roll replacement is critical; premature replacement leads to unnecessary waste, while late replacement results in faulty products and extra time in reloading the materials across all the tension bars within the machine. To address these issues, our proposed framework began with a thorough assessment of the current state of the assembly line, identifying key physical parameters that need to be measured. The goal was to develop a CPS incorporating relevant sensing technology that can alert personnel precisely when a roll is about to deplete, minimising waste and downtime. In addition, the framework emphasised the importance of data management. By monitoring relevant information, the company can make data-driven decisions to enhance productivity and efficiency in its packaging manufacturing processes.
# On Training Data Selection in Condition Monitoring Applications--Case Azimuth Thrusters
## 5 Conclusions  
This study demonstrated the effects of training data size and distribution on automated training subset selection with a case study focusing on condition monitoring of azimuth thrusters based on a previously introduced algorithm and real measurement data. Two approaches to the segmentation of training data distribution were compared and five hypothesis tests for normal distribution were studied as a part of training subset selection. The selection of subsets was successful when the dataset was segmented flexibly into numerous subsets with varying sample sizes and ranges instead of using only a few subset candidates. The proposed 'adaptive training subsets' approach to dataset segmentation proved to be adaptable to all tested datasets exhibiting varying distributions and sizes. As a part of training subset selection, the normal distribution tests based on the empirical distribution functions, such as Kolmogorov-Smirnov or Anderson-Darling tests, were practical because a broad proportion of the entire operating area became covered in the training subsets. With the other hypothesis tests, the selected ranges were narrow and few. For the future development of automated condition monitoring, model training would benefit from research on sensitivity enhancement in fault detection and research on the reliability of data under the effects of varying operating situations.  
Conceptualization, R.-P.N.; methodology, R.-P.N.; software, R.-P.N.; validation, R.-P.N.; formal analysis, R.-P.N.; investigation, R.-P.N.; resources, S.A.B.; data curation, R.-P.N.; writing--original draft preparation, R.-P.N.; writing--review and editing, R.-P.N., M.R. and S.A.B.; visualization, R.-P.N.; supervision, M.R.; project administration, S.A.B. and R.-P.N.; funding acquisition, R.-P.N. and M.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Business Finland during Reboot IoT Factory project, grant number 4356/31/2019.  
Not applicable.  
Data Availability Statement: Data sharing is not applicable to this article.  
We thank the CMS team of Kongsberg Maritime Finland Oy and the project partners for the collaboration.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
The distributions of the studied shaft speed values in the range 100-600 rpm are shown here as histograms with 20-rpm bar width.
# A Review of Autonomous Tugboat Operations for Efficient and Safe Ship Berthing
## 4 Autonomous Tugboat Element Technologies  
To achieve autonomous berthing using tugboats, a range of technologies are required, including perception technology to allow each tugboat to recognize and understand its surrounding situation, allocation of various tasks within a group, maintenance of formation, decision-making technology for path planning, modeling technology considering multibody dynamics such as motherships and tugboats, and precise control technology for tugboat groups during berthing. This chapter reviews the development trends thus far and the necessity of each technology by dividing it into recognition, perception, decision-making, modeling, and control technologies for berthing ships using autonomous tugboats.