esa-sceva/satcom-chunk-collection huggingface dataset:
# Segmentation of Rice Seedings Using the YCrCb Color Space and an Improved Otsu Method
## 2 Materials and Methods
### Improved Otsu Method for Rice Seeding Segmentation  
In this subsection, we discuss how to determine the threshold \(T\) mentioned in Equation (5) and subsequently use it to separate rice seedlings from the background. Threshold is widely used for image segmentation due to its simplicity. Until now, a number of threshold segmentation algorithms have been published in the literature. For example, [PERSON] et al. (2016) applied an adaptive threshold algorithm determined by entropy of a histogram to distinguish plants and soil.  
Figure 5: Relationship between Cg-Cr and Cg-Cb of the rice seedling and background sampling points.  
Figure 6: Distribution of 2 Cg-Cr-Cb of the rice seedling and background sampling points.  
[PERSON] et al. [23] introduced an automatic threshold method based on Gaussian distribution functions of the soil and plant pixel intensities. A survey considering a variety of automatic threshold techniques for plant segmentation was carried out by [PERSON] et al. [9], and this study showed that most threshold methods provide expected results for particular application, but it also suggested there is not a general method available for crop segmentation. The Otsu method [24] is one of the most commonly applied methods to threshold segmentation due to its simplicity and adaptability. The details of the Otsu method are described in Section 2.5.1.
# A Grey-Box Ensemble Model Exploiting Black-Box Accuracy and White-Box Intrinsic Interpretability
## 6 Experimental Results  
We conducted a series of experimental tests in order to evaluate the performance of the proposed Grey-Box model utilizing the classification accuracy and \(F_{1}\)-score as an evaluation metric with a 10-fold cross-validation.  
In these experiments, we compared the performance of Black-box, White-box and Grey-box models using five different labeled dataset ratios i.e., 10%, 20%, 30%, 40%, and 50%, in six datasets. The term \"ratio\" refers to the ratio of labeled and unlabeled datasets utilized for each experiment. The White-Box model utilizes a single White-Box base learner in self-training framework, the Black-Box model utilizes a single Black-Box base learner in self-training framework while the Grey-Box model utilizes a White-Box learner with a Black-Box base learner in self-training framework.  
Additionally, in order to investigate which Black-Box or White-Box combination brings the best results, we tried two White-Box and three Black-Box learners. For this task we have utilized the Bayes-net (BN) [25] and the random tree (RT) [26] as White-Box learners and the sequential minimal optimization (SMO) [27], the random forest (RF) [28], and the multi-layer perceptron (MLP) [29] as Black-Boxes for each dataset. Probably these classifiers are the most popular and efficient machine learning methods for classification tasks [30]. Table 2 presents a summary of all models used in the experiments.  
The experimental analysis was conducted in a two-phase procedure: In the first phase, we compared the performance of Grey-Box models against that of White-Box models, while in the second phase, we evaluated the prediction accuracy of the Grey-Box models against that of Black-Box models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Application Domain** & **\#Instances** & **\#Features** & **\#Classes** \\ \hline EduDataA & Education & 2260 & 5 & 4 \\ EduDataAB & Education & 2260 & 10 & 4 \\ Australian credit & Finance & 690 & 14 & 2 \\ Bank marketing & Finance & 4119 & 20 & 2 \\ Coimbra & Medicine & 116 & 10 & 2 \\ Wisconsin & Medicine & 569 & 32 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Benchmark datasets summary.
# Study on Shear Mechanism of Bolted Jointed Rocks: Experiments and CZM-Based FEM Simulations
## 3 Laboratory Experiments
### Experimental Results
#### 3.4.2 Effect of Joint Surface Roughness  
Bolt can limit excessive shear dislocation of joint surface and fasten rock on both sides of joint surface to improve the integrity and stability of the rock. Thus, studying the influence of bolt on shear strength of jointed rock is important. Our experimental results indicate that under the same conditions, different bolt materials have an important influence on the shear effect of bolted joints. In order to analyze the contribution of elongation to shear strength, we analyzed the relationship between shear stress and shear displacement for the tests. As shown in Figure 10a S1-(MG1-MG3), the shear strength of the bolted jointed rock is generally increased as the bolt becomes \"hard\" (lower elongation). It is worth noting that the peak shear strength of MG2 and MG3 bolts are similar. Figure 10b shows the experimental results of S2-(MG1-MG3) where we observe that the shear strength of the jointed rock bolted by MG1 is lower than that of the jointed rock bolted by MG2. However, unexpectedly, the shear strength of jointed rock bolted with MG3 high-strength bolt is not significantly improved, but rather slightly lower than MG1 and MG2 with relatively larger elongation and lower strength. The same trend holds true for the specimens bolted with S3-(MG1-MG3), and the order of peak shear strength is: MG2 (15% elongation) \(>\) MG1 (25% elongation) \(>\) MG3 (5% elongation).  
Figure 10: _Cont._  
Based on the above results, on one hand, it is clear that the use of the \"harder\" bolt is beneficial to improving the shear strength of jointed rock. On the other hand, it is counterproductive to simply use high-strength bolts for reinforcement, because the high strength bolt can bear lower deformation, which causes rock to break in the process of shearing. Thus, it reduces the shear strength of bolted jointed rock, and degrades the integrity of the surrounding rock. As far as the Huangdao underground oil storage carven is concerned, we can conclude that the bolt of MG2 (15% elongation) is superior to the other two bolted materials.
## 1 Introduction
These digital replicas enable farmers to accurately understand critical factors, such as soil health, moisture levels, and crop growth [28]. This approach goes beyond conventional practices, offering targeted interventions and resource optimization through the ability to rapidly adapt to evolving conditions and ensure resilience against uncertainties [29]. Likewise, considering that livestock farming is one of the leading sectors of the agricultural industry, adding to food safety [30], as well as creating work opportunities and income, DTs may emerge as a promising technology for addressing issues related to the intense nature of livestock production systems [30], which are typically characterized by high levels of input and output per farming unit [31]. To this end, by creating virtual counterparts of livestock and the environment, DTs can facilitate the real-time monitoring of individual animals, providing farmers with insights into feeding patterns, stress factors, and optimal breeding strategies, allowing for a proactive approach to health management as well as disease prevention, thereby ensuring not only productivity but also the welfare of the livestock [32]. Despite the prominent benefits that derive from the integration of the DTs technology into livestock production systems, the number of works in the literature that review recent advances in DTs technology (with a focus on their applications in livestock production systems) has been, to the best of our knowledge, rather limited to date. Therefore, this study is prompted by the objective of tracking the latest research works in the literature related to the application of the DTs technology in livestock production systems in the context of Agriculture 5.0, as well as to synthesize their outcomes in order to identify the opportunities and challenges regarding this topic. Following the introduction in Section 1, the rest of the paper is structured as follows: In Section 2, the concept of DTs is specified based on the recent definitions reported in the literature, and its integration in livestock farming is also studied. Then, in Section 3, the methodology followed for reviewing the literature is overviewed. The results of the search in the literature are presented in Section 4, along with an analysis of the recent applications of DTs in livestock production systems, and Section 5 discusses the results of this study and identifies the open issues for the implementation of DTs in the livestock sector. Finally, in Section 6, the conclusions of this study are presented, and some directions for future research are proposed.
# IoT: A Long-Reach Fully Passive Low-Rate Upstream PHY for IoT over Fiber
## 3 Results
### Digital Communication Channel Characterization (\(M_{3}\))  
In order to analyze the system performance in a field deployment, we tested the achieved BER as a function of the attenuation imposed by the transmission channel, illustrated in Figure 9 assuming 0.2 dB/km. As expected, for the highest baud rate (300 bps), FBG-based IoToF was less tolerant to link attenuation. The horizontal dashed line represents the reach of our FBG-based IoToF. This threshold value was based on the receiver sensitivity of narrowband wireless systems (usually defined at bit error rate (BER) of 1% [38; 39]). Figure 9 shows that even at 300 bps, reach can exceed 30 km.  
The requirements presented in Table 1 and in Figure 1 can be met by the developed simple and low-cost setup [11; 12] (AOM \(\sim\)100 euros, commercial value of FBG). Actually, our PHY prototype for IoToF exceeds LoRa and Sigfox reach wide area transmission by providing 300 bps raw transmission for more than 30 km long links. There is plenty of room for improvements. More elaborated E/A structures would improve the acoustic channel. The use of forward error corrector and more powerful broadband source would extend reach beyond hundred of kilometers. More elaborated modulation and coding strategies, than elementary M-FSK, can boost IoToF spectral efficiency, improving the end-user bit rate.  
Figure 8: (**a**) 8-FSK encoded data symbols, (**b**) modulation and received signal for 8-FSK in back to back configuration.
## 3 Precision Analysis of GNSS-Derived elevations
### Possibility of Substituting GNSS-Derived Height Determination for Sea-Crossing Leveling
This differed from that of spirit leveling, unlike section USS04B-USS13B, where the two were similar. The comparison results of the elevation differences at these two sections among the surveying methods are summarized in Table 4.  
In terms of surveying time, there is a large difference between the sea-crossing leveling and GNSS-derived height determinations. For both sections, two days of GNSS surveying were conducted for the GNSS-derived height determination. On the other hand, seven and six days were spent on sea-crossing leveling for sections USS04B-USS13B and TBM1-TBM2, respectively. Although fewer repetitions were conducted for section TBM1-TBM2, sea fog caused huge inconsistencies in the observations. Thus, re-surveying was conducted, so one day's worth of observations was lost. These kinds of environmental effects frequently occur during sea-crossing leveling, which uses optical instruments. These environmental effects can cancel surveying. GNSS-derived height determination, however, is largely unaffected by environmental effects. Therefore, it would be a more efficient way to determine the elevations of stations located in the region that refer to local vertical datum, considering its effectiveness, shorter surveying time, and similar level of precision. However, the precision of the local geoid model represents a risk factor in this regard. The local geoid model was developed by adjusting the gravimetric geoid to the GNSS/Leveling data that are generally located inland. Therefore, the precision is relatively high inland, but it could decreas in offshore areas. It is, thus, necessary to continuously improve the precision of the geoid model over coastal and island areas to enable more generalized usage of GNSS-derived elevation in the field of surveying.
## 2 Materials and Methods
### Methods
In GF, 110 DAS was considered in location 1 in years 1, 2, and 3 and location 2 in year 2, while the dates at 105 DAS were used in year 4 in location 1 and years 1, 3, and 4 in location 2 (no available images at 110 DAS). The Pearson correlation coefficient (R), the ratio between the covariance of two variables and the product of their standard deviations, was used to determine linear correlations among yield, all Sentinel-2 bands, and VIs at each year and location (years and locations were not mixed in a single matrix). The analysis included two layers; on the first layer (first row in the correlation matrix), the yield was correlated with each spectral band or VIs as described in Equation (8). On a second layer (subsequent rows), a collinearity study between spectral bands, between spectral bands and VIs, and between VIs was performed, following the structure of Equation (9). \[y_{\text{yield}}=a_{0}+a_{1}\cdot x_{\text{SR or VI,j}} \tag{8}\]  
Figure 3: Scheme to summarize Sentinel-2 image selection in all locations and years. where \(y_{\text{yield}}\) is the yield acquired by the combine; \(a_{0}\), \(a_{1}\) are the regression coefficients; and \(x_{\text{SR or VI,ij}}\) is the surface reflectance (SR) or Vegetation Index (VI) value for a given image i at each spectral band or VI j.  
\[y_{\text{SR or VI,ij}}=a_{0}+a_{1}\cdot x_{\text{SR or VI,ij}} \tag{9}\]  
where \(y_{\text{SR or VI,ij}}\) is the surface reflectance (SR) or Vegetation Index (VI) value for a given image i at each spectral band or VI j; \(a_{0}\), \(a_{1}\) are the regression coefficients; and \(x_{\text{SR or VI,ij}}\) is also the surface reflectance (SR) or Vegetation Index (VI) value for a given image i at each spectral band or VI j. **Section 3:** Study of alternatives to linear correlations between yield and bands or VIs in the three main phenological stages. The Spearman rank correlation coefficient was determined as an alternative to linear correlations [64]. In addition to linear correlations, this coefficient allows us to find monotonic nonlinear correlations. Thus, the third section aimed to test the existence of correlations other than or better than the linear correlations obtained for the Pearson R. The analysis was carried out in bands and VIs with no strong collinear correlations after the results of Section 2. In Figure 4, a workflow emphasizes the inputs and outputs for each section.
## 3 The VSWT-PMSG Control System
### Grid Side Controller  
Figure 9 shows a block diagram of the grid side controller system. In this control strategy, the control system based on the d-q rotating reference frame is implemented, which has the same rotational speed as the grid voltage. The three phase grid currents (\(I_{ga}\), \(I_{gb}\), \(I_{gc}\)) and the grid voltages (\(V_{ga}\), \(V_{gb}\), \(V_{gc}\)) are transformed into the d-q rotating reference frame by using Park transformation. The Phase Locked Loop (PLL) [15] is used to extract the grid side phase angle (\(\theta_{g}\)).  
The controller is divided into two cascade loop control, one for active power and the other is for the reactive power. When grid voltages on the stationary reference frame are transformed into the d-q rotating reference frame, \(V_{gd}\) is set to constant and \(V_{gq}\) is set to zero. Therefore, the active and reactive power delivered to the grid can be controlled separately by the d-  
In grid fault condition, the fault detector is activated when the grid voltage decreases under 0.9 pu. The detector sends the control signal command (_Ctrl_) to trigger the DC link protection. At the same time, the active power transfer to the grid is set to zero.
# Markov-Based Malware Propagation Modeling and Analysis in Multi-Layer Networks
## 3 Malware Propagation Modeling in Multi-Layer Networks
### Modeling Malware Propagation via a Markov Random Field
All the results that will be presented for each type of network have been calculated on average in 25 different scenarios. In terms of attacks, we focus on non-smart (random) malware attacks, starting with a single intruder (infected), where the infected node is unable to use any side information to help spread the malware. The intruder and the infected nodes communicate only through point-to-point contacts, and susceptible nodes are infected with the same probability of infection when they come in contact with infected/malicious nodes. Consequently, such non-smart attacks can be considered as \"blind\" (random) attacks against vulnerable one-hop neighbors. Random attack vulnerability analysis is just one of the many applications that the MRF framework can host. At this point, we need to note an important aspect of the MRF framework presented above. Malware propagation may have some preferential directions, e.g., an Android-basedmalware will follow the \"direction\" of other Android devices but not the direction of iOS devices. MRFs can be applied equally well in directed graphs [63]. An MRF is essentially defined on a neighborhood system, namely the set of all neighborhoods formed by the nodes of the graph. In case of a directed process, the underlying graph will be directed, but each node will be equally fit to define its neighborhood, on which it will be possible to perform the distributed Gibbs sampling with simulated annealing. The rest of the model can remain unchanged.
# Enhancing Street-Crossing Safety for Visually Impaired Pedestrians with Haptic and Visual Feedback
## 5 Discussion
### Analysis of Confidence Levels in Relation to Decision Time and Crossing Behavior
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study** & **Feedback** & **State** & **Full Confidence** & **High Confidence** & **Low Confidence** \\  & & & **(100\%)** & **(90–99\%)** & **(<90\%)** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{\begin{tabular}{c} Haptic Feedback \\ Visual Feedback \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} Dynamic \\ Static \\ \end{tabular} } & 3.65 & 4.24 & 4.79 \\  & & 3.45 & 4.15 & 3.82 \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{\
\begin{tabular}{c} Haptic Feedback \\ \end{tabular} } & Dynamic & 3.53 & 2.88 & 3.27 \\  & & Static & 3.26 & 3.10 & 2.99 \\ \cline{2-5}  & \multirow{2}{*}{
\begin{tabular}{c} Visual Feedback \\ \end{tabular} } & Dynamic & 4.79 & 4.59 & 4.32 \\  & Static & 3.36 & 3.48 & 2.89 \\ \hline \end{tabular} Note: each value represents the mean decision time (measured in seconds) calculated from all decisions made under the corresponding feedback type and confidence level.  
\end{table} 
Table 7: Decision time (s) analysis across confidence levels and feedback types.
## 3 Results and Discussion
In contrast, a one-order-of-magnitude reduction in the WVTR (\(\sim\)0.19 g/m\({}^{2}\)/day) was obtained from the PET substrate which was coated with the film using the TMS-O\({}_{2}\)-NH\({}_{3}\) gas mixture at a flow ratio of 0.25. The marked enhancement on the barrier property was closely linked to the release of the stress residing in the deposited film as a consequence of the introduced nitrogen atoms in the deposited film under the TMS-O\({}_{2}\)-NH\({}_{3}\) gas mixture which functioned to fill the voids in the deposited film, and thereby to improve the quality and density of the SiO\({}_{x}\) film. Accordingly, the film that had the lowest internal compressive stress synthesized using a gas flow ratio of 0.5 coated onto the PET substrate could significantly optimize the resulting WVTR value to about 0.06 g/m\({}^{2}\)/day. However, a significant degradation in the WVTR (\(\sim\)0.57 g/m\({}^{2}\)/day) was measured from the PET substrate coated with the film synthesized from the gas flow ratio of 0.75 which also corresponded to the increase in the residual compressive stress (\(\sim\)250 MPa). The film deposited under the TMS-NH\({}_{3}\) glow discharge, which had a large compressive stress, was almost ineffective in enhancing the barrier property of the PET substrate as evidence of the high WVTR (\(\sim\)3.43 g/m\({}^{2}\)/day). Figure 5: Surface roughness of the films synthesized from the gas flow ratios of (**a**) 0; (**b**) 0.25; (**c**) 0.5; (**d**) 0.75; and (**e**) 1, respectively. Figure 6: Surface morphologies of the films synthesized from the gas flow ratios of (**a**) 0; (**b**) 0.5; and (**c**) 1, respectively (the inset figures show the tape-peeling test result). Figure 7: The relationship between the surface roughness and internal stress residing in the deposited film as a function of the gas flow ratio.
## 5 Conclusions  
According to the decomposition-ensemble principle, a novel three-step decomposition-ensemble learning model integrating EEMD and RNN was proposed for landslide displacement prediction. The experimental results from the Muyubao landslide in the Three Gorges Reservoir area demonstrate that the proposed EEMD-RNN decomposition-ensemble learning model is capable of increasing prediction accuracy and outperforms traditional decomposition-ensemble learning models (including EMD-LSTM, EEMD-SVM, and EEMD-ELM) in terms of prediction accuracy. Moreover, the GRU- and LSTM-based models perform better than standard RNN by providing equally satisfactory performance in terms of predicting accuracy. Due to the simpler structure, GRU is more efficient than standard RNN and LSTM. Therefore, in practical application, EEMD-GRU learning model is more suitable for medium-term to long-term horizon displacement prediction of reservoir landslide in the Three Gorges Reservoir area. In addition to landslide displacement prediction, the proposed EEMD-RNN decomposition-ensemble learning model can also be extended to other difficult forecasting tasks in geosciences with extremely complex nonlinear data characteristics.  
The work was carried out in collaboration between all the authors. [PERSON] and [PERSON] guided and supervised this research; [PERSON], [PERSON], [PERSON] and [PERSON] performed the field investigation; [PERSON] wrote the original draft; and [PERSON] and [PERSON] reviewed and edited the draft. All authors have contributed to, seen, and approved the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Major Program of National Natural Science Foundation of China (Grant No. 42090055), Chongqing Geo-disaster Prevention and Control Center (Grant No. 20C0023), Science and Technology Projects of the Huaneng Lancang River Hydropower Co., Ltd. (Grant No. HNKJ18-H24), and the National Natural Science Foundation of China (Grant Nos. 41702328 and 42090055). All support is gratefully acknowledged.  
Not applicable.  
The data used in this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.