esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Challenges and Prospects
### Integration with Machine Learning Algorithm  
In fact, in the process of comprehensive modeling using the discrete phase method of computational fluid dynamics, the fluid-particle flow mainly depends on the solution to the fluid equation. However, there are serious challenges in fluid analysis based on these equations. For example, N-S equations considering the momentum conservation of incompressible fluids include high-dimensional and nonlinear factors. These equations cannot provide closed-form solutions, and they limit the work of real-time optimization and control. Machine learning (ML) has achieved some success in solving complex flows. Problems including reduced order modeling, shape optimization and feedback control can be regarded as optimization and regression tasks [137]. For example, [PERSON] et al. combined CFD-DPM with support vector regression-particle swarm optimization (SVR-PSO) technology. SVR was used to train the CFD dataset, and PSO was used to optimize the dataset to maximize the fluid cooling in the microchannel [138]. Similarly, [PERSON] et al. optimized the structure of a vessel seawater desulphurization scrubber based on the CFD-DPM and SVM-GA methods [139]. In a word, machine learning capabilities are developing at an astonishing rate, and fluid mechanics has begun to give full play to the potential of this powerful method.
## 4 A Case Study of Hidake Tunnel in Japan
### Results and Discussions
In engineering practice, the purpose of visual testing is to identify the areas where there is a need for further internal testing and to monitor the development of concrete lining damage. Generally, when TCI is larger than \(20\,\times 10^{-5}\), further internal testing is required [33]. According to the fitting formula shown in Figure 11, the corresponding fractal dimension was identified as 1.2386. The two thresholds divided Figure 11 into four parts: I, healthy according to both indexes; II, unhealthy according to TCI but healthy according to D; III, healthy according to TCI but unhealthy according to D; IV, unhealthy according to both indexes. Combining these two indexes, more damaged spans could be determined (part III), and the distribution of cracks could be considered. In particular, it is possible to find the unhealthy spans that the cracks almost penetrate the entire lining or the cracks are connected together \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Span No.** & **TCI (10\({}^{-5}\))** & **D** & **Span No.** & **TCI (10\({}^{-5}\))** & **D** & **Span No.** & **TCI (10\({}^{-5}\))** & **D** \\ \hline  
& \\ \hline \end{tabular}
\end{table} 
Table 2: The results of Tunnel-lining Crack Index (TCI)and fractal dimension for all the 65 spans of Hidake Tunnel. Figure 11: The correlation between the TCI and the fractal dimension in field test. Moreover, if the TCI or D increased rapidly in a short period of time, further internal testing is required for these spans. The fractal dimension of the cracks is a promising method in the health assessment of tunnel lining as shown in these tests. However, the impact factor of different elements, including the density, width, and distribution of cracks, were not fully determined. More field test data and comparative study were need to interpretation the results of the fractal dimension. Nevertheless, the new health assessment method of tunnel lining is a good complement to the conventional visual inspection method.
# Stochastic Disruption of Synchronization Patterns in Coupled Non-Identical Neurons
## 4 Transients And Intermittent Synchronization
### Intermittent Synchronization
Classical noise-induced intermittency, typically observed near saddle-node bifurcations, is well known to exhibit universal scaling behavior: the mean laminar phase duration scales with an exponent of  
Figure 12: Characteristics of noise-induced intermittency in the stochastic system (2) with \(\gamma=-1.75\) and \(\Delta=0.002\). (**a**) Mean duration of laminar phases \(\langle\tau\rangle\) corresponding to co-directed behavior (\(z=1\)) as a function of noise intensity \(\varepsilon\) for various coupling strengths \(\sigma\). (**b**) Probability \(P(\varepsilon)\) of observing co-directed behavior as a function of noise intensity for different \(\sigma\) values. (**c**) Mean duration of synchronization intervals \(\langle\tau\rangle\) (with \(z=1\)) plotted against deviation \(d\) of \(\sigma\) from crisis bifurcation point \(\sigma_{*}=0.019011\), where \(\sigma=\sigma_{*}-d\). \(-1/2\), and the distribution of laminar durations follows a power law with an exponent of \(-3/2\)[40, 41, 42, 43]. However, as shown in Figure 12, the scaling behavior depends sensitively on both the coupling parameter and the noise intensity. This lack of universality suggests that the intermittency we observe is not simply a textbook example of noise-induced behavior but rather reflects a more complex mechanism tied to the geometry of chaotic transient basins. Moreover, a key distinguishing feature is that, for strong coupling, we observe an increase in the probability of laminar (synchronous) phases with increasing noise intensity in the weak noise regime (see Figure 12 for \(\varepsilon=0.02\)), implying a stabilizing role of noise, which contrasts with the typical destabilizing effect seen in many other systems exhibiting noise-induced intermittency. Finally, in Figure 13, we present the largest Lyapunov exponent \(\Lambda\), which serves as a key indicator of system stability. This exponent provides a quantitative criterion for distinguishing between regular dynamics (\(\Lambda<0\)) and chaotic behavior (\(\Lambda>0\)). Figure 13a shows \(\Lambda\) as a function of noise intensity \(\varepsilon\) for various coupling strengths \(\sigma\), while Figure 13b illustrates the dependence of \(\Lambda\) on both control parameters, \(\sigma\) and \(\varepsilon\). Figure 13b also displays the curve \(\Lambda=0\), which delineates the transition between ordered (\(\Lambda<0\)) and chaotic (\(\Lambda>0\)) dynamics. The transition is not abrupt but rather gradual, as seen in the plots of \(\Lambda(\varepsilon)\) for fixed \(\sigma\) in Figure 13a. From these graphs, one can estimate the threshold noise intensity at which the system transitions from order to chaos. For example, for \(\sigma=0.015\), the Lyapunov exponent crosses zero near \(\varepsilon\approx 0.03\), marking the onset of frequent chaotic bursts--consistent with the behavior observed in Figure 8a. This gradual increase in \(\Lambda\) reflects a noise-driven destabilization of regular three-cycles.
# Fungi of the _Trichoderma_ Genus: Future Perspectives of Benefits in Sustainable Agriculture
## 5 Application of _Trichoderma_ Fungi in Promoting Plant Growth  
Many secondary metabolites and volatile substances produced by _Trichoderma_ sp. exhibit, in addition to antifungal properties, the ability to promote plant growth. These compounds are responsible for activating signaling pathways leading to specific reactions in the plant which include the induction of the defense system, the production of substances that reduce stress caused by environmental factors, the synthesis of phytohormones, and other substances that stimulate plant growth and development. In addition, these microorganisms convert hard-to-reach nutrients present in the soil into soluble compounds, which are then taken up and used by plants [21; 22; 34; 55; 124; 125] (Figure 3).
# Improved Ship Detection Algorithm from Satellite Images Using YOLOv7 and Graph Neural Network
## 4 Results
### Learning Rate  
The weights of the updated gradient error are affected by the learning rate. It specifically regulates the number of errors that the model's weights will take into account when it is updated. The learning rates of 0.001 and 0.01 are examined. In comparison to a learning rate of 0.01, the learning rate of 0.001 results in a substantially higher accuracy as shown in Table 6.  
Figure 7 demonstrates a few results of ship detection from HRSID using our approach, YOLOv7+GNN. A comparison of YOLOv7 and other object detection algorithms such as YOLOv4, YOLOv5, and many more with respect to speed and accuracy has been presented by [PERSON] [21].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No. of** & **No. of** & **Size of Batch** & **Epoch** & **Learning Rate** & **Accuracy** \\ \hline
2200 & 1200 & 16 & 10 & 0.001 & 93.4\% \\
2200 & 1200 & 16 & 10 & 0.01 & 86.73\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison between different learning rates.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No. of** & **No. of** & **Size of Batch** & **Epoch** & **Learning Rate** & **Accuracy** \\ \hline
2200 & 1200 & 16 & 75 & 0.001 & 91.06\% \\
3000 & 600 & 16 & 75 & 0.001 & 91.41\% \\
3600 & 200 & 16 & 75 & 0.001 & 91.87\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy comparison between training and testing images.  
Figure 7: Ship detection result using our approach, YOLOv7 + GNN.
## 3 Proposed Method
### Solution Encoding and Population Initialization  
A candidate solution x integrates all key parameters required to enable data reduction and to recognize a particular gesture using the LM-WLCSS method.  
As previously noted, the sample at time \(t\) is an \(n\)-dimensional vector \(x(t)=[x_{1}(t)\ldots x_{n}(t)]\), where \(n\) is the total number of features characterizing the sample. Focusing on a small subset of features could significantly reduce the number of required sensors for gesture recognition, save computational resources, and lessen the costs. Feature selection has been encoded as a binary valued vector \(\mathbf{p}_{c}=\left\{p_{j}\right\}_{j=1}^{n}\in[0,1]^{n}\), where \(p_{j}=0\) indicates that the corresponding features is not retained whereas \(p_{j}=1\) signifies that the associated feature is selected. This type of representation is very widespread across literature.  
The discretization scheme \(\mathcal{L}_{c}=(L_{1},L_{2},\ldots,L_{m})\) is represented by a variable-length vector, where \(m\) is a positive integer uniformly chosen in the range \([K_{c}^{lower},K_{c}^{upper}]=[10,70]\). The upper limit of this decision variable is purposely larger than necessary to improve diversity. These limits are selected by trial and error. Each discretization point \(L_{i}=(z_{1},z_{2},\ldots,z_{n})\in[0,1]^{n}\), \(i\in\{1,\ldots,m\}\), is a \(n\)-dimensional point uniformly chosen in the training space of the gesture \(c\).  
Amongst the abovementioned LM-WLCSS parameters, only the SearchMax window length \(W\!F_{c}\), the penalty \(P_{c}\), and the coefficient \(h_{c}\) of the threshold have been included into the solution representation.  
1. \(W\!F_{c}\) controls the latency of the recognition process, i.e., the required time to announce that a gesture peak is present in the matching score. \(W\!F_{c}\) is a positive integer uniformly chosen in the interval \([W\!F_{c}^{lower},W\!F_{c}^{upper}]=[5,15]\). By fixing the reward \(R_{c}\) to 1, the penalty \(P_{c}\) is a real number uniformly chosen in the range \([0,1]\); otherwise, gestures that are different from the selected template would be hardly recognizable.
2. The coefficient \(h_{c}\) of the threshold is strongly correlated to the reward \(R_{c}\) and the discretization scheme \(\mathcal{L}_{c}\). Since it cannot easily be bounded, its value is locally investigated for each solution.
3. The backtracking variable length \(W\!B_{c}\) allows us to retrieve the start-time of a gesture. Although a too short length results in a decrease in recognition performance of the classifier, its choice could reduce the runtime and memory usage on a constrained sensor node. Since its length is not a major performance limiter in the learning process and it can easily be rectified by the decider during the deployment of the system, it was fixed to three times the length of the longest gesture occurrence in \(c\) in order to reduce the complexity of the search space.  
Hence, the decision vector x can be formulated as follows:  
\[\mathrm{x}=(\mathbf{p}_{c},\mathcal{L}_{c},P_{c},W\!F_{c},h_{c}). \tag{11}\]
# An Unsupervised Image Denoising Method Using a Nonconvex Low-Rank Model with TV Regularization
## 3 Proposed Method
### Updating \(Z\)  
Similarly, to solve for variable \(\mathbf{Z}\), we minimize over \(h(\mathbf{L},\mathbf{S},\mathbf{Z},\mathbf{\Lambda}_{1},\mathbf{\Lambda}_{2})\) with the remaining optimization variables \((\mathbf{L},\mathbf{S},\mathbf{\Lambda}_{1},\mathbf{\Lambda}_{2})\) fixed:  
\[\begin{split}\mathbf{Z}^{i+1}&=\operatorname*{arg\, min}_{\mathbf{Z}}h\Big{(}\mathbf{L}^{i+1},\mathbf{S}^{i+1},\mathbf{Z}, \mathbf{\Lambda}_{1}^{i},\mathbf{\Lambda}_{2}^{i}\Big{)}\\ &=\operatorname*{arg\,min}_{\mathbf{Z}}\tau\|\mathbf{Z}\|_{TV}+ \Big{\langle}\mathbf{\Lambda}_{2}^{i},\mathbf{L}^{i+1}-\mathbf{Z}\Big{\rangle} +\frac{\mu}{2}\Big{\|}\mathbf{L}^{i+1}-\mathbf{Z}\Big{\|}_{\text{F}}^{2}\\ &=\operatorname*{arg\,min}_{\mathbf{Z}}\tau\|\mathbf{Z}\|_{TV}+ \frac{\mu^{i}}{2}\Big{\|}\mathbf{Z}-\left(\mathbf{L}^{i+1}-\frac{\mathbf{ \Lambda}_{2}^{i}}{\mu^{i}}\right)\bigg{\|}_{\text{F}}^{2}\end{split} \tag{18}\]  
We define \(\mathbf{Q}=\mathbf{L}^{i+1}-\frac{\mathbf{\Lambda}_{1}^{i}}{\mu^{i}}\) and \(\mathbf{Q}=[\mathbf{Q}_{1},\mathbf{Q}_{2},\cdots,\mathbf{Q}_{n}]\in\mathbb{R}^ {m\times n}\) where optimization (18) can be rewritten as:  
\[\operatorname*{arg\,min}_{\mathbf{Z}_{j}}\tau\Big{\|}\mathbf{Z}_{j}\Big{\|}_{ TV}+\frac{\mu}{2}\|\mathbf{Z}_{j}-\mathbf{Q}_{j}\|_{\text{F}}^{2} \tag{19}\]  
In this paper, we used the fast gradient-based algorithm introduced in [44] to solve (19).
# Two-Stage Stochastic Programming for Precast Module Water Transportation: A Case Study in Hong Kong
## 4 Case Study
### Sensitivity Analysis  
The results and analysis in Section 4.3 reveal that the key to minimizing transportation costs lies in the balanced use of both booked and temporarily rented barges for transporting modules. The prices of booking and temporarily renting barges for a round trip can greatly affect the total transportation cost, highlighting the need for further analysis to assist contractors in making informed decisions. Therefore, a sensitivity analysis about the influence of \(p_{k}/c_{k}\) on the total transportation cost was conducted. All parameters, except for \(p_{k}\), were held unchanged and the rate of \(p_{k}\) and \(c_{k}\) ranges from 1.0 to 5.0, meaning that the value of \(p_{k}\) varies from equal to \(c_{k}\) to \(5c_{k}\), and the results are shown in Figure 4.  
Figure 4: Sensitivity with the ratio \(p_{k}\) and \(c_{k}\).  
Figure 3: The transportation costs are solved by the two-stage stochastic programming and three deterministic models.  
Figure 4 demonstrates that the two-stage stochastic programming model [M1] consistently devises the most efficient transportation plan, regardless of changes in the cost ratio of \(p_{k}\) to \(c_{k}\). [DM1] exhibits the highest sensitivity to increases in \(p_{k}\), because it relies entirely on temporarily rented barges for transporting modules. [DM1] and [M1] achieve the same best results when the ratio of \(p_{k}\) and \(c_{k}\) is 1.4 or lower. This indicates that relying solely on temporary barge rentals is the most effective strategy to address uncertainty when the cost of a single round trip by temporary rental is comparable to the cost of booking in advance. The optimal transportation cost calculated by [DM3] remains constant because it fulfills the transportation task entirely through booked barges, thus changes in \(p_{k}\) do not influence the optimal result. When the value of \(p_{k}\) rises to approximately 2.4 times \(c_{k}\), the transportation cost outlined by [DM3] becomes less than that of [DM1]. Moreover, the transportation cost for [M1] and [DM3] equilibrate at approximately \(p_{k}=4.0c_{k}\), and beyond this point, costs no longer increase with rising \(p_{k}\) value. At this stage, the transportation plan ceases to incorporate temporarily rented barges for completing round trips. This indicates that when \(c_{k}\) is excessively high, avoiding renting barges temporarily is more economical. With the increase of \(p_{k}\), the cost disparity between [M1] and [DM2] widens, underscoring the effectiveness of [M1]. While no clear superiority exists among the three deterministic models, each exhibits strengths depending on the value of \(p_{k}\). However, even in ranges where the three deterministic models perform optimally, the solutions they produce still fall short of those obtained by [M1].
# An Improved Algorithm of Drift Compensation for Olfactory Sensors
## 4 Results
### Experimental Data Distribution Analysis  
The difference in the data distribution of different data is the best manifestation of the sensor drift problem. In order to more intuitively reflect the different distributions of different batches of data, the PCA method is used to reduce the dimensionality of the data. This part reduces the data to two dimensions and presents the dimensionality-reduced data points by means of a dot plot. Figure 2 is a dot plot of all batches of data in the dataset after PCA dimensionality reduction:  
It can clearly be seen from Figure 2 that the uneven distribution of data in different batches of datasets is caused by sensor drift; especially when comparing Batch1 with Batch4, Batch5, Batch8, and Batch9, it is found that this situation is more obvious. In addition, we also found that there is no certain law in the change of distribution. It can be seen that the sensor drift is random, not in a fixed direction. It is for these reasons that the classification model that has been trained on one dataset often has a poor recognition effect on the new dataset.  
The main idea of the DTSWKELM algorithm proposed in this study is to find a new domain for mapping between the source domain data and the target domain data by calculating the MMD. Make the new source domain data more similar to the target domain data and then perform model training and recognition on the new data. Figure 3 shows the distribution of different batches after domain transformation, where Batch1 is selected as the source domain data, and other batch data are respectively used as the target domain data.  
Figure 3: Dot diagram of 10 batches of data after PCA dimensionality reduction after domain transformation.  
Figure 2: Dot diagram of 10 batches of data after PCA dimensionality reduction.  
In the DTSWKELM algorithm, in the process of domain conversion, the information of the source domain data is preserved as much as possible. In this experiment, Batch1 is all used as the source domain data. Therefore, its distribution has not changed much, and the above figure only shows a diagram of Batch1. From the figure, we can see that the distribution of Batch2-10 data changed significantly after domain transformation, and it is closer to the distribution of Batch1, in which Batch2 and Batch8 are more obvious. It can be seen from this that the domain transformation part of the algorithm plays a role. It can effectively reduce the distribution difference between different batches of data, so that the new source domain data are more similar to the target domain data. Thus, the semi-supervised learning problem in different domains caused by the sensor drift problem is transformed into a semi-supervised learning problem in the same domain.
# Experimental Study on the In-Plane Seismic Performance of a New Type of Masonry Wall System
## 2 Outline of the Experiment
### Test Program  
A loading system for the static cyclic in-plane tests is shown in Figure 7. Lateral loads in the positive and negative directions were applied to the left end of the upper beam with hydraulic actuators. As mentioned above, since the reference building of this study is a single-story storage building, the axial load was considered as the weight of the upper beam (13.7 kN, axial stress, \(\sigma_{0}\) = 0.04 N/mm\({}^{2}\)). Figure 8 shows a lateral loading protocol that was controlled by a drift angle \(R\), defined as a lateral drift \(\Delta\) at the top-center of the specimen divided by the height from the bottom of the specimen, \(H\), as shown in Figure 7. As shown in Figure 8, the peak drift angles of 0.0005, 0.001, 0.002, 0.004, 0.0067, 0.01, 0.015, 0.02, 0.03, 0.04 and 0.05 rad., that is, 0.05, 0.1, 0.2, 0.4, 0.67, 1.0, 1.5, 2.0, 3.0, 4.0 and 5.0% rad., were planned, and two cycles for each peak drift were imposed. After severe damage was found, the specimen was pushed over to collapse.  
Figure 8: Lateral loading protocol.  
Figure 7: Loading system (unit: mm).  
The measurement system is shown in Figure 9. As shown in Figure 9, 14 displacement transducers were used: one relative lateral displacement transducer, five lateral displacement transducers at each layer of wall, two vertical displacement transducers of both ends of the specimen, four sliding displacement transducers of both ends of the top and bottom, and two diagonal deformation transducers of the wall. The 3-axis strain gauges for calculation of the principal strain were attached on all blocks of the only Specim PS, but the strain data were not measured because of the trouble of the measuring equipment. Furthermore, the maximum crack widths at peak loads and residual crack widths at unloaded stages were carefully measured.
## 6 Results  
### Reduction of Oscillations Caused by Platform Pitch Displacement  
In the simulation experiments, the external forces caused by the waves are initially set to zero, and the initial tower displacement is also considered null; the initial platform pitch is \(5^{\circ}\). The performance of the passive TMD and the semi-active TMD based on the vibration's amplitude is now evaluated.  
In this context, the optimized parameters for the passive TMD are spring constant \(k_{TMD}=4868\) N/m and damping coefficient \(d_{TMD}=2419\) Ns/m, respectively.  
Figure 10 compares the system responses for three cases: with the optimized passive TMD (orange line), without a TMD (blue line), and with the reference TMD presented in [11] (red line).  
Figure 10: Platform pitch angle and TTD without TMD (blue line), with the reference TMD (red line) and with the optimized TMD (orange line).  
A 35% reduction was achieved in the standard deviation of the tower displacement (\(\sigma\)TTD\({}_{\text{FA}}\)), decreasing from 0.487 without TMD to 0.317 with the TMD tuned by the Genetic Algorithm (GA). Using the OpenFAST TMD, the suppression rate was 28.6%, while the GA-optimized TMD achieved a 34.9% suppression rate. This clearly demonstrates an improvement in oscillation suppression during free decay tests. The platform pitch also showed similarly promising results, with significantly enhanced stability of the barge when using the improved TMD.  
After evaluating the performance of the optimized passive TMD, this work investigates whether parameter variation based on amplitude--using the optimized amplitude-based semi-active TMD--could offer further improvements. Figure 11 presents the TTD (tower top displacement) and platform pitch angle for both the optimized passive TMD (dotted red line) and the optimized amplitude-based semi-active TMD (green line). The comparison was conducted for an initial platform pitch of 5\({}^{\circ}\).  
As can be seen in the results, employing a semi-active control strategy based on the amplitude of the oscillations reduces the tower top displacement (\(\sigma\)TTDFA) by an additional 16% compared to the case of GA-optimized passive control, yielding a standard deviation of 0.266 versus 0.317 for the passive TMD. The vibration suppression rate achieved by the semi-active control as presented here is 45.4%. This reduction is significant, as it contributes to lowering structural loads, thereby mitigating fatigue and wear on the wind turbine components.