esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Formulas for quantitatively describing the coloration mechanisms of Ao-ike Pond were derived. Based on these formulas, three coloring mechanisms were anticipated; (i) pond bottom irregular reflection, (ii) density fluctuation scattering by water, and (iii) white Mie scattering due to suspended solids. In order to evaluate the physical model parameters, image analyses were carried out. The targets were RGB data of the images under little-shielded solar radiation onto the pond surface, and integrated RGB data from the optical path trajectories included in the sunbeam images. The following results were obtained:  
Figure 20: (a) R value, (b) G value and (c) B value simulations for a sunbeam image (14:35 (JST) of 5 June 2021) as shown in Figure 19d, which are drawn separately for each contribution of pond bottom irregular reflection and in-pond scattering. The label “Total”: Pond bottom irregular reflection and in-pond scattering, “Irr”: Pond bottom irregular reflection only, “ES”: Water density fluctuation scattering only, “Mie”: Mie scattering contribution only. The graphs (**a–c**) are depicted in the relative coordinate system.  
1. The major causes producing the blue coloration of Ao-ike Pond are density fluctuation scattering by water and white Mie scattering by suspended solids for both the images with little-shielded solar radiation onto the pond surface and the sunbeam images.
2. In the calculations for the density fluctuation scattering of water, the pond coloration by the mechanism was calculated by applying the water absorption coefficient and the density fluctuation scattering coefficient of water, of which the values were cited in the previous literature. The coefficients were also obtained by other measurements, and this study can thus be the first case of theoretical reproduction of the digital camera images with blue colorations.
3. The analyses of the sunbeam image were performed by integrating the RGB values of the image along the axes perpendicular to the light trajectories and by theoretically reproducing the RGB values. Consequently, the RGB integral values were fairly well reproduced and the images with sunbeams were also well simulated. The theoretical reproduction of sunbeams onto the pond is considered to be the first instance as well.  
In conclusion, regarding the coloration of Ao-ike Pond, the involvement of the coloration mechanism was clarified by analyzing two types of images, i.e., an image with little-shielded solar radiation over the pond surface and an image of sunbeams passed through the tree leaves above the pond.  
By analyzing the sunbeam images, the contribution of irregular reflected light from the pond bottom and that of scattered light inside the pond were clearly separated, and thereby much information was obtained. Hence, the analytical method in this study is judged to be greatly useful for lake coloration studies in the future.  
The authors declare no conflict of interest.
## 2 System Structure and Operation Principle
Once the two optical signals are sent into the BPD for photoelectric conversion and the frequency-converted fundamental signal and the IMD3 component are obtained, the output signals of the upper link can be expressed as  
\[i_{fun-up} =b^{2}A^{2}J_{1}^{4}(am_{1})-2b^{2}A^{2}J_{1}(am_{1})J_{1}(m_{2}) \cos(\omega_{IF_{1}}+\omega_{IF_{2}}+\omega_{LO})t+b^{2}A^{2}J_{1}^{2}(m_{2}) \tag{4}\] \[i_{IMD_{3}-up} =b^{2}A^{2}J_{1}^{2}(am_{1})J_{2}(am_{1})-2b^{2}A^{2}J_{2}(am_{1} )J_{1}(am_{1})J_{1}(m_{2})\cos(2\omega_{IF_{1}}-\omega_{IF_{2}}+\omega_{LO})t\] (5) \[+b^{2}A^{2}J_{1}^{2}(m_{2})\] \[\text{and the output signals of the lower link can be expressed as}\] \[i_{fun-down} =A^{2}J_{1}^{4}(m_{1})-2A^{2}J_{1}(m_{1})J_{1}(m_{2})\cos(\omega_ {IF_{1}}+\omega_{IF_{2}}+\omega_{LO})t+A^{2}J_{1}^{2}(m_{2})\] (6) \[i_{IMD_{3}-down} =A^{2}J_{1}^{2}(m_{1})J_{2}(m_{1})-2A^{2}J_{2}(m_{1})J_{1}(m_{1}) J_{1}(m_{2})\cos(2\omega_{IF_{1}}-\omega_{IF_{2}}+\omega_{LO})t\] (7) \[+A^{2}J_{1}^{2}(m_{2})\] Ignoring the DC term, the IMD3 component of the BPD output can be expressed as \[i_{IMD_{3}}=\left[2b^{2}A^{2}J_{2}(am_{1})J_{1}(am_{1})J_{1}(m_{2})-2A^{2}J_{2} (m_{1})J_{1}(m_{1})J_{1}(m_{2})\right]\cos(2\omega_{IF_{1}}-\omega_{IF_{2}}+ \omega_{LO})t \tag{8}\]  
Figure 1: The schematic of the large dynamic frequency up-conversion system and the spectrum at different locations. If the coefficient of the IMD3 is equal to 0, the cancellation of the IMD3 components can be realized,  
\[b^{2}J_{2}(am_{1})J_{1}(am_{1})=J_{2}(m_{1})J_{1}(m_{1}) \tag{9}\]  
Under the condition of small-signal modulation, \(J_{0}(m)\approx 1\), \(J_{1}(m)\approx m/2\) and \(J_{2}(m)\approx m^{2}/8\), Equation (9) can be simplified as  
\[i_{IMD_{3}-up} =b^{2}A^{2}J_{1}^{2}(am_{1})J_{2}(am_{1})-2b^{2}A^{2}J_{2}(am_{1} )J_{1}(am_{1})J_{1}(m_{2})\cos(2\omega_{IF_{1}}-\omega_{IF_{2}}+\omega_{LO})t\]0  
It can be seen from Equation (10) that when the power distribution ratio of optical carrier and IF signal between two paths is appropriately allocated to match the condition, the IMD3 component in the frequency conversion process can be effectively suppressed.
## 4 Numerical Results and Discussion
### Uplink Coverage Probability
Hence, a trade-off exists between price and performance in the QEFC scenario. Figure 12: Simulation results of uplink coverage probability with target SINR \(-5.6\) dB in rural (dashed) and urban (solid) scenario.
## Materials and Methods
### DM Test and Forecasting Effectiveness  
The DM test, which focuses on the difference in the forecasting precision between the proposed hybrid model and other traditional methods, is described as follows:  
\[\begin{array}{l}H_{0}:E(d_{n})=0,\forall n\\ H_{1}:E(d_{n})\
eq 0,\exists n\end{array} \tag{10}\]  
The value of the DM test is described as follows:  
\[\begin{array}{l}\text{DM}=\frac{\sum\limits_{h=1}^{k}\left(L\left(\varepsilon _{t+h}^{(i)}\right)-L\left(\varepsilon_{t+h}^{(j)}\right)\right)/k}{\sqrt{S^{ 2}/k}}s^{2}\end{array} \tag{11}\]  
where \(\varepsilon_{t+h}\) is the forecasting error.  
\(S^{2}\) represents the estimation value for the variance of \(d_{h}=L\left(\varepsilon_{t+h}^{(i)}\right)-L\left(\varepsilon_{t+h}^{(j)}\right)\). \(L\) is the loss function, which represents the accuracy of the forecasting.  
Among the loss functions, absolute deviation error loss and square error loss are widely applied. They are described as follows:  
Absolute deviation error loss:  
\[L\left(\varepsilon_{t+h}^{(i)}\right)=\left|\varepsilon_{t+h}^{(i)}\right| \tag{12}\]  
Square error loss:  
\[L\left(\varepsilon_{t+h}^{(i)}\right)=\left(\varepsilon_{t+h}^{(i)}\right)^{2} \tag{13}\]  
If no significant differences are found between the performances of the included methods, the null hypothesis will be rejected.  
The null hypothesis is described as follows:  
\[\left|\text{DM}\right|>z_{\alpha/2} \tag{14}\]  
where \(z_{\alpha/2}\) represents the critical value of the standard value distribution when the value of significance is \(\alpha\).  
Figure 2: The flowchart of the proposed forecasting method.  
Forecasting effectiveness, which evaluates the performance of the proposed model using the sum of the squared errors and the mean squared deviation of the forecasting results is also applied. The forecasting effectiveness is described as follows [33].  
The \(k\)th order forecasting effectiveness unit is described as:  
\[\begin{array}{c}m^{k}=\sum\limits_{n=1}^{N}Q_{n}A_{n}^{k}\\ \sum\limits_{n=1}^{N}Q_{n}=1\end{array} \tag{15}\]  
where \(Q_{n}\) stands for the discrete probability distribution when the time is \(n\). \(A_{n}\) represents the forecasting accuracy.  
The k-order forecasting effectiveness is presented as:  
\[H\Big{(}m^{1},m^{2},\cdots,m^{k}\Big{)} \tag{16}\]  
The first order forecasting effectiveness is defined as \(H\big{(}m^{1}\big{)}=m^{1}\) while the second-order forecasting effectiveness is the difference between the standard deviation and expectation, which is shown as:  
\[H\Big{(}m^{1},m^{2}\Big{)}=m^{1}\bigg{(}1-\sqrt{m^{2}-\left(m^{1}\right)^{2}} \bigg{)} \tag{17}\]
# Effect of Metallic Waste Addition on the Physical and Mechanical Properties of Cement-Based Mortars
###### Abstract
Nevertheless, this negative effect can be variable depending on the amount of metallic fibres added to the mortar matrix and on the type of mechanical test used to evaluate the effect of fibres [20]. Recently there has been a lot of interest in the development of innovative cement-based materials with crack self-sensing capabilities [21; 22] or general self-sensing properties [23; 24]. These materials are based on the addition of electrically conductive fibres or particles to the cement matrix and the modification of its electrical properties, without compromising the mechanical performance of the composite material. In this context, [PERSON] et al. [25] concluded that the variation of the electrical properties of cementitious materials reinforced with metallic fibres depends on the amount and morphology of the added fibres and also on their physico-mechanical properties, moisture content, and their distribution and connectivity inside the material. The existing literature has identified some effects of the metallic waste on cement mortars. Nonetheless, there are still many open research questions that need to be addressed in order to understand the associated phenomena. For example, the interaction between the cementitious matrices and the waste metallic inclusions is not yet clear. Another less researched area concerns the optimum amounts and types of metallic waste to yield cement-based composites with adequate properties. Therefore, the main objective of this paper is to evaluate the influence of the type and amount of metallic waste on the physical and mechanical properties of cement mortars designed with self-monitoring purposes. Additionally, this paper corresponds to a second part of the work recently published by [PERSON] et al. [18] related to the measurement of the electrical and thermal properties of cement-based mortars containing recycled metallic waste.
## 4 Data Processing and Features
### Deep Learning
#### 5.2.3 Long Short-Term Memory (LSTM)  
The Recurrent Neural Network (RNN) is used to learn patterns that happen one after the other, but it has a problem with vanishing gradient. There is a variation of the RNN known as the LSTM that does not suffer from the issue of vanishing gradient. It consists of a series of repeated memory modules, each of which consists of three gates [3; 10; 18; 26; 29; 34; 37; 54; 57; 68].  
**Advantages of using LSTM**[85]  
* Capable of self-learning data interactions and patterns.
* Analyses data interactions and hidden patterns to make effective predictions.
* Capable of retaining knowledge for an extended period of time.  
**Disadvantages of using LSTM**[85]  
Due to the correlation between the number of memory cells and the dimensions of recurrent weight matrices, it is difficult to index the memory while it is being written to or read from.
###### Abstract  
To improve the overall mechanization level of rapeseed production in China, especially in some hilly regions where ground machinery cannot enter the fields or can only enter with very low economic benefits, a special aerial seeding system for rapeseed based on a miniature air-assisted centralized metering device was designed and tested in this study. Unlike existing commercial aerial seeding systems, the proposed seed meter was a miniaturized version derived from the traditional air-feeding seed meter on ground planners. The new version contained a redesigned seed feeding component to overcome problems of serious air backflow to the seed box and difficult seed feeding after miniaturization. Three groups of experiments were designed and conducted to optimize the parameters of the seed meter and test its performance. Results from the orthogonal experiment showed that the seed feeding component performed best when the seed layer thickness was 45 mm, the rotational speed of the gear disc was 45 r/min, and the airflow pressure was 2450 Pa. Results from the static workbench test showed that the designed seeding system had a maximum average total sowing efficiency of 537.17 g/min, with the maximum values of the stability variation coefficient of total seeding rate (seven ports) and the consistency variation coefficient between each port was 2.37% and 4.89%, respectively. Field tests further proved that the designed aerial seeding system could work stably, uniformly, and efficiently, so that the agronomic requirements of rape crop planting could be well met.  
aerial seeding; winter rapeseed; multi-rotor UAV; air-assisted centralized metering device +
Footnote †: journal: Applied Sciences
## 4 Results and Discussion
Figure 11 illustrates the nodes' (UAVs') agreement approval probability in order to assess the blockchain performance for block execution on the chain. It shows the cumulative execution probability [60] when the number of blocks is increased. The percentile of executed confirmations can be estimated by using the percentile analysis on the generated blocks. This metric is utilized to evaluate the risk of confirmation failures by using the ground station node. According to Figure 11, the cumulative probabilities of a successful confirmation are 0.547 (using the PoW consensus protocol) and 0.663 (using the PoS consensus protocol) after their first block generation. After generating four blocks, this value settles at 0.901 (for the PoW) and is higher than 0.953 (for the PoS). The comparison of the cumulative execution probability using the PoW and PoS consensus protocols reveals the superiority of the PoS. The PoS is therefore considered a priority for some UASs to ensure the execution of a new block on the chain with higher probability from this point of view, while the PoW outperforms the PoS in terms of reliability. The blockchain designed for consensus processing can also be assessed from the perspective of reliability. With this aim, the blockchain's error rates while the blocks are generated were considered as performance indices. The error rates were measured in relationship to the block execution failures while PoW and PoS consensus mechanisms were in operation. This experiment was performed regarding both leader-based and leader-free approaches, in which the leader-free consensus protocol allowed UAS-4s to reach an agreement for airspace allocation without relying on a central authority, and vice versa for the leader-based protocol [61]. The error rates using different consensus protocols and the number of UASs are listed in Table 3.  
Figure 11: Cumulative execution probability with an increasing number of blocks using the PoW (blue) and PoS (red) consensus protocols. As shown in Table 3, a flight zone was arranged using three different total numbers of UASs (10, 100, and 1000) for the blockchain reliability analysis. The airspace was partitioned into five shards. The results show that a consensus process with a leader reduces the reliability of both the PoW and the PoS, while leader-free consensus is more reliable as the error rate is higher. The results also reveal that the error rates when using PoW's leader-free consensus mechanism were lower than those of PoS, regardless of the number of UASs in the flight zone. Therefore, the PoW leader-free consensus strategy was better than PoS for airspace allocation.
# Railway Bridge Condition Monitoring Using Numerically Calculated Responses from Batches of Trains
## 2 Model Description
### Bridge Model  
The bridge/culvert is modelled using a finite element discretization (Figure 3). Behind the walls (abutments), it is filled with soil which is represented by an additional layer of springs with stiffness \(k_{soil}\). On top of the beam there is a track which is also modelled as a beam (of much less flexural rigidity). The track beam is supported on a sprung mass system which represents pads, sleepers, and a ballast.  
Figure 2: Track model.  
The vehicle and track/bridge model are coupled together via the wheel/rail interaction, i.e., the DOFs of the wheels and the DOFs of the rail are combined. Each of the subsystems can be defined by a set of equations of motion. The coupling of the subsystems is addressed by the coupled equations of motion of the complete model. The equations of motion are solved using the Newmark-\(\beta\) numerical integration scheme.
## 4 Optimization Design Scheme and Results Analysis
### Optimization Design Scheme
Different thickness ratio coefficients were obtained by taking different values for these two key variables, so that the thickness coefficient ratio contained different ranges from 0.8 to 1.8. By comparing the hydraulic performance of the multiphase pump with different schemes, such as the head coefficient and efficiency, analyzing the internal flow condition and the aggregation degree of gas phase in the impeller passage of different schemes, the rule and relation between the mixture transportation characteristics of the multiphase pump and the value of coefficient was finally summarized. By adjusting the value of the thickness ratio coefficient and analyzing the simulation results of different thickness ratio coefficient schemes, the optimization design of the impeller was achieved, the impeller model, which had higher hydraulic performance, could be found. Finally, a multiphase pump with higher mixture transportation performance would be obtained.
# Improving Geotechnical Properties of Expansive Subgrade Using Sugar Cane Molasses and Cement
## 4 Results and Discussion
### CBR Swell of Expansive Clay Soils  
Soil treated with molasses exhibits a reduction in CBR swell compared to the 9.66% observed in untreated soil, as shown in Figure 15 and Table 6. Additionally, it is noted that the swelling potential decreased with increasing molasses content up to 4%, but began to rise again beyond 8% molasses content, leading to an increase in swelling potential. [PERSON] et al. [39] observed a similar trend in his study on the effects of can molasses on the strength of expansive soil, finding a comparable relationship between molasses content and swelling potential in terms of CBR swell, although with lower CBR swell values than those reported in this research. This discrepancy could be attributed to differences in the clay mineralogy of the expansive soils [40]. The addition of molasses and cement significantly reduced CBR swell. Furthermore, it is observed that the swelling potential of expansive soils treated with molasses and cement decreases as the percentage of stabilizers is increased. These reduced swell characteristics are generally attributed to the lower water affinity of calcium-saturated clay and the formation of a cementitious matrix that resists volumetric expansion [41]. Figure 16 shows dial gauges mounted on top of molds soaked in water to measure swelling after a 4 day period.  
Figure 14: Stages of sample preparation for CBR testing: (**a**) hand-mixing of soil, molasses, and cement; (**b**) sample pouring in CBR mold for compaction; (**c**) layer-by-layer compaction of soil mix with molasses and cement in CBR mold by modified proctor hammer; (**d**) trimming and leveling of the mix by spatula; (**e**) prepared mold placed in the machine for CBR testing; (**f**) tested CBR samples.  
Figure 16: Dial ages placed on top of mold soaked in water for swell measurement after 4 days.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Soil Type** & \begin{tabular}{c} **Mold Height** \\ **(mm)** \\ \end{tabular} & \
\begin{tabular}{c} **Change in Length** \\ **in mm During Soaking** \\ \end{tabular} &
\begin{tabular}{c} **CBR Swell** \\ **(\%)** \\ \end{tabular} & \% Change** \\ \hline S\({}_{0,0}\) & \(116.43\) & \(11.25\) & \(9.66\) & \(0.00\) \\ S\({}_{4,4}\) & \(116.43\) & \(8.5\) & \(7.30\) & \(-24.43\) \\ S\({}_{4,8}\) & \(116.43\) & \(8\) & \(6.87\) & \(-28.88\) \\ S\({}_{4,2}\) & \(116.43\) & \(6.75\) & \(5.80\) & \(-39.95\) \\ S\({}_{8,4}\) & \(116.43\) & \(7.4\) & \(6.36\) & \(-34.16\) \\ S\({}_{8,8}\) & \(116.43\) & \(6.6\) & \(5.67\) & \(-41.30\) \\ S\({}_{8,12}\) & \(116.43\) & \(5.2\) & \(4.47\) & \(-53.72\) \\ S\({}_{12,4}\) & \(116.43\) & \(7\) & \(6.01\) & \(-37.78\) \\ S\({}_{12,8}\) & \(116.43\) & \(4.95\) & \(4.25\) & \(-56.0\) \\ S\({}_{12,12}\) & \(116.43\) & \(4.45\) & \(3.82\) & \(-60.45\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: CBR swell results with additives.  
Figure 15: CBR swells for treated and untreated soils.
# Geometric Detail-Preserved Point Cloud Upsampling via a Feature Enhanced Self-Supervised Network
###### Abstract  
The upsampling of point clouds is a common task to increase the expressiveness and richness of the details. The quality of upsampled point clouds is crucial for downstream tasks, such as mesh reconstruction. With the rapid development of deep learning technology, many neural network-based methods have been proposed for point cloud upsampling. However, there are common challenges among these methods such as blurring sharper points (e.g., corner or edge points) and producing points gathered together. These problems are caused by similar feature replication or insufficient supervised information. To address these concerns, we present SSPU-FENet, a self-supervised network consisting of two modules specifically designed for geometric detail-preserved point cloud upsampling. The first module, called the feature enhancement module (FEM), aims to prevent feature blurring. This module retains important features such as edges and corners by using non-artificial encoding methods and learning mechanisms to avoid the creation of blurred points. The second module, called the 3D noise perturbation module (NPM), focuses on high-dimensional feature processing and addresses the challenges of feature similarity. This module adjusts the spacing of reconstructed points, ensuring that they are neither too close nor too far apart, thus maintaining point uniformity. In addition, SSPU-FENet proposes self-supervised loss functions that emphasize global shape consistency and local geometric structure consistency. These loss functions enable efficient network training, leading to superior upsampling results. Experimental results on various datasets show that the upsampling results of the SSPU-FENet are comparable to those of supervised learning methods and close to the ground truth (GT) point clouds. Furthermore, our evaluation metrics, such as the chamfer distance (CD, 0.0991), outperform the best methods (CD, 0.0998) in the case of 16\(\times\) upsampling with 2048-point input.  
point cloud upsampling; self-supervised learning; feature enhancement; perturbation learning 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vol., pp. 1-10. External Links: Document Cited by: SS1.