esa-sceva/satcom-chunk-collection huggingface dataset:
A detailed investigation of the rainfall-induced landslide mechanisms taking place in the Southern Apennines, Italy, would require an accurate hydro-mechanical characterisation of the fissured clays outcropping on the slopes not only when fully saturated but also when partially saturated. While the hydro-mechanical behaviour of fully saturated fissured clays has been already thoroughly investigated [9; 10; 22; 23; 24], discussions about the behaviour of fissured clays when partially saturated have been rarely reported in the literature. In this respect, interesting contributions are provided, for example, by [25; 26; 27], but these studies refer to compacted and/or reconstituted fissured clays. The water retention behaviour of natural unsaturated fissured clays has not been investigated yet in great detail, except for a few contributions, including [12; 13]. The latter present water retention data of the Paola Doce fissured clay outcropping on the Pisciolo slope (Figure 1), which is representative of the fissured clays often involved in instability phenomena in the Southern Apennines, Italy [12].  
Figure 1: Undisturbed sample of Paola Doce fissured clay taken at 1.2 m depth at Pisciolo (**a**) and details of its meso-structure (**b**).  
The present paper is intended to address this gap of knowledge by extending the study presented in [12; 13]. First of all, some of the key micro- to meso-structural features of the Paola Doce fissured clay forming the slope cover at Pisciolo were analysed, also based on Scanning Electron Microscope (SEM) micrographs of an undisturbed sample taken at 1.4 m depth. Subsequently, water retention data of the same material obtained through laboratory tests are presented, showing how the state of the fissured clay herein analysed changes, during the year, as a result of its interaction with vegetation and atmosphere. Water retention data were collected not only on undisturbed samples (i.e., with reference to in situ conditions) but also while subjecting the same samples to drying paths. All these data were interpreted based on the preliminary micro- to meso-structural investigation reported in the paper, linking the water retention behaviour of the material to its fissured structure. The water retention data herein presented refer to suction measurements performed with two different methods, i.e., high-capacity tensiometers and filter paper technique, which are capable of measuring sutions up to around 1.8 MPa and 30 MPa, respectively [28]. A comparison between the results obtained with these two methods is also discussed with reference to some of the laboratory data reported.
## 1 Introduction
The idea is to replace the nonlinear system by an equivalent frequency locus and use it jointly with the uncertain linear systems to compute the constraint region for a stabilizing controller using the QFT loop shaping. The multiplier theory is extended for a 3-DOF non-interfering control structure in [16] as shown in Figure 3. In this figure, \(G\) and \(E\) represent the feedback controller and the AW controller, respectively. The control structures in Figures 2 and 3 are equivalent, with \(C=G/(I-E)\) and \(H=E/(I+E)\). The difficulty in searching for the multiplier (infinite dimension) [16] and the restriction involved in designing the AW controller (pole-zero cancellation, strictly proper) [13] are the main motivation for this work. In other words, the main aim of this article is to present a QFT-based AW design to alleviate the effects (divergent limit cycle for conditionally stable system) of saturation nonlinearity in uncertain linear SISO systems using the 3-DOF non-interfering structure (direct manner). Consequently, the technical novelty and contributions of this work are summarized as follows:  
1. An anti-windup control design approach is presented using the QFT method for an uncertain linear time invariant (LTI) SISO system with input amplitude saturation. 2. Design constraint for the AW compensator to avoid the limit cycle and the saturation compensation are developed and translated into frequency domain bounds on the Nichols chart for synthesis purpose. 3. The technical novelty is the development of the proposed method directly on the 3-DOF non-interfering control structure, shown in Figure 3, as opposed to the indirect method [13]. With this framework, the proposed method does not require any restriction on the AW controller unlike the method in [13], in which one must take care of unstable pole-zero cancellation in designing the AW controller. The effectiveness of the proposed method is validated through a comparative work between the proposed method and existing methods [13; 16] using a benchmark problem. It is found to perform  
Figure 3: A 3-DOF non-interfering control structure. Figure 2: An interacting 3-DOF control structure. better than the existing methods, in terms of lesser overshoot than [13] and faster response time as compared to [16]. The remainder of this paper is divided as follows. Section 2 states the problem, and the proposed design method for the AW controller is discussed in the same section. The proposed design approach is demonstrated on a benchmark example in Section 3 and comparisons with the existing methods are given in the same section. Conclusions are drawn in Section 4.
## 3 Results and Discussion
### Analysis of the Effect of Helmholtz Resonators on Micro-Pressure Waves
#### 3.2.2 Analysis of Micro-Pressure Wave AMPLITUDE Reduction Effect  
The Helmholtz resonator is observed to be effective in reducing the micro-pressure wave amplitude. This is determined by comparing the micro-pressure wave amplitude data measured at measurement points M-1, M-2, and M-3 in the conventional tunnel and new 94-cavity tunnel. According to the CEN European Standard [30], the values measured at 20 m and 50 m at the exit of the tunnel are the micro-pressure wave amplitude values (i.e., at measurement points M-2 and M-3). As shown in Figure 11 and Table 3, the new 94-cavity tunnel with Helmholtz resonators shows a 31.87% and 33.23% decrease in the micro-pressure wave amplitude at M-2 and M-3, respectively, when compared with the numerical simulation results of the conventional tunneling scheme. By comparing the initial compressional gradient curves (Figure 12), it can be observed that the initial compressional gradient in the new 94-cavity tunnel exhibits a trend similar to that of the conventional tunnel, but with a 7.06% reduction in amplitude.  
Figure 11: Effect of different solutions on micro-pressure waves. (**a**) M-2; (**b**) M-3.  
However, by comparing the values measured at the tunnel wall measurement points in the conventional tunnel with those in the new 94-cavity tunnel, it can be observed that the maximum tunnel wall pressure increases from 6149.85 Pa to 7176.44 Pa, an increase of 16.69%. This indicates that the presence of the Helmholtz resonator leads to an increase in pressure at the tunnel wall. The root cause of this phenomenon is the increased blockage ratio of the tunnel section owing to the presence of Helmholtz resonators [31; 32], which increase the blockage ratio of the new 94-cavity tunnel by 18.9% when compared to that of the original tunnel. This in turn results in an increase in pressure.
# A Survey on MLLMs in Education: Application and Future Directions
## 3 Applications of MLLMs in Education  
The advent of multimodal large language models (MLLMs) has ushered in a new era in educational technology, offering innovative solutions that enhance both teaching and learning experiences. Using the ability to process and generate content across multiple modalities, such as text, images, audio, and video, MLLMs are transforming the educational landscape [34]. This section explores the diverse applications of MLLMs in education, highlighting how these advanced AI models are being integrated into various educational tools and platforms to address longstanding challenges and meet the evolving needs of learners and educators.  
Table 2 summarizes several key areas where MLLMs are making a significant impact:  
* **Adaptive learning platforms**: Examining how MLLMs enable personalized learning experiences by dynamically adjusting instructional content to meet individual learners' needs, preferences, and performance levels.
* **Virtual tutors and chatbots**: Exploring the role of MLLMs in developing intelligent virtual assistants that provide personalized support, guidance, and interactive learning opportunities through natural language conversations.
* **Intelligent content creation**: Investigating how MLLMs automate and enhance the development of educational materials, including textbooks, lesson plans, assessments, and multimedia resources, thereby increasing efficiency and accessibility.
* **AI-powered learning management systems (LMSs)**: Analyzing the integration of MLLMs into LMS platforms to enhance content delivery, personalize learning paths, and facilitate more engaging and interactive educational experiences.
* **AI-based insight and predictive analytics for educators**: Discussing how MLLMs provide educators with actionable insights by analyzing multimodal educational data, enabling early identification of at-risk students and informed decision making.
* **Grading and assessment tools**: Assessing the application of MLLMs in automating grading processes, providing objective evaluations, and delivering detailed, personalized feedback across various types of student work.  
Through these explorations, the section aims to demonstrate the transformative potential of MLLMs in education. By presenting case studies, technological insights, and practical applications, we seek to highlight how MLLMs not only enhance learning outcomes and engagement but also support educators in delivering high-quality, personalized instruction. The objective is to provide a comprehensive understanding of how MLLMs are shaping the future of education and to inspire further research and implementation in this promising field.
## 6 Conclusions  
Based on the PUPSO algorithm, a geometry optimization approach for PCSH wind turbine towers has been proposed in this paper. During the optimization procedure, several working conditions, including wind and earthquakes as well as combinations of these factors, are considered. The LCOE of the PCSH tower is treated as the objective function and the geometry variables for the optimization of the PCSH tower include the dimensions of the PC and steel segments of the PCSH wind turbine tower. Based on this analysis, a geometrically optimal result was obtained, and the following findings can be made:  
1. The proposed PUPSO algorithm performs better when compared with the traditional PSO algorithm and the UPSO. The computation time is greatly reduced by using parallel algorithms. Fulfilling the design constraints of relevant specifications and industry standards, the PUPSO algorithm provides an optimal design for the PCSH wind turbine towers with considerably improved computational efficiency.
2. The levelized cost of energy (LCOE) of the PCSH wind turbine tower in a life cycle perspective is considered as the objective function as an alternative to the direct investment. The LCOE of the optimized PCSH wind turbine clearly decreases when compared with the benchmark tower and increases the material utilization rate of the tower. The optimized PCSH wind turbine tower can be an economic alternative for wind farms with lower LCOE requirements. The height of the steel segment of the optimized PUPSO tower is recommended to be 30% of the total height of the PCSH wind turbine tower.
3. The optimized tower can provide better dynamic behavior to avoid the resonance caused by wind turbine excitation.
4. The optimization results for PCSH wind turbine towers provide valuable references in practice for PCSH wind turbine tower design in mountainous areas. This paper, based on a linear hypothesis and limited deformation, has been conducted as the preliminary optimization. Because of the nonlinearity present in prestressed concrete towers, nonlinear calculations should be investigated in the future.  
Conceptualization, B.X. and H.G.; methodology, B.X.; software, H.C.; validation, Z.L.; investigation, Z.L.; data curation, H.C.; writing--original draft preparation, Z.L.; writing--review and editing, B.X. and H.G.; visualization, Z.L.; supervision, H.G.; funding acquisition, B.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (NSFC) under Grants No. 51478175, the Scientific Research Funds of Huaqiao University (Grant number 605-50Y18016), and the International Science and Technology Cooperation and Exchange Fund Projects of Ministry of Science and Technology of China (2014 DFE70230).  
Not applicable.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**PSO Computation (s)** & **UPSO Computation (s)** & **PUPSO Computation (s)** \\ \hline
432,759 & 451,480 & 212,801 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Comparison of computational time with three computing methods.
# Improved Likelihood Probability in MIMO Systems Using One-Bit ADCs
## 4 Proposed Method
### Proposed Receiver
16 end for
```  
**Algorithm 1** Proposed Method  
**Remark (dependent parameters):** The combined weights of the proposed method are trained from the realized samples offline. Thus, the statistical property of the realized samples is important to determine the combining weights \((\alpha,\beta)\). This statistical property differs from parameters, such as the signal-to-noise ratio (SNR), channel, number of data blocks, number of transmit and receive antennas, modulation order, and coding schemes (see Figure 2). In addition to the parameters above, the parameters that affects the system performance can be input to train the estimated likelihood probability. Thus, the optimal weights \((\alpha,\beta)\) should be trained by changing the values of the parameters. **Remark (memory):** Owing to the dependent parameters, many inputs are required for the proposed method. Specifically, the data symbol index \(n\) significantly increases the memory size. Saving all the parameters every data symbol requires a large memory size, and updating the weights every data symbol increases communication latency. This is because frequent weight updates inevitably lead to increased memory access time. To address this issue, the weights \((\alpha,\beta)\) are updated only after the end of data block \(d\). This update method significantly reduces the memory size and the number of weight update. For example, when \(N_{d}=128\) and \(D=40\), a total of 10,280 weights should be saved in the memory, with \(10,280\) weight updates. However, the proposed method saves only 80 weights in memory, resulting in a corresponding reduction to 80 weight updates. The updated method is described in Algorithm 1. **Remark (complexity):** To provide a comparison of complexity, Table 3 summarizes the number of real multiplications required to obtain the estimated likelihood probability. For comparison, conventional RL method [32] is introduced. In the conventional RL method, the main complexity arises from calculating the optimal policy, and the corresponding number of real multiplications is listed. In contrast, the proposed method does not require this calculation and only necessitates updating the likelihood probability at the end of each data block. Table 3 presents an example of the complexity for each block when considering \(N_{t}=4\), \(N_{r}=8\), \(M=2\), \(N_{d}=256\), and \(D=40\). In this example, the number of real multiplications required to obtain the estimated likelihood probability is approximately \(1.43\times 10^{-4}\) times that of the conventional RL method.
# Optimize Rotating Wind Energy Rotor Blades Using the Adjoint Approach
## 1 Introduction
However, these works showed the application either for low Reynolds numbers, for non-rotating fields or using porosity of numerical cells to impact the shape of [PERSON]. Thus, the usage of the adjoint approach in wind energy needed further development and investigation. The adjoint solver for ducted flows could be used in the field of diffuser-augmented wind turbines. The generalized actuator disc theory, proposed by [PERSON] [13] and extended by [PERSON] and [PERSON] [14], could be used within OpenFOAM to compute optimal difuser geometries with maximal effective diffuser efficiency. In this work, we focus on the optimization of rotor blade geometries of wind turbines in open flow. Therefore, the adjoint equations in OpenFOAM were adopted for external flow and the resulting gradients were verified against finite differences by [PERSON] [15; 16]. It was shown in [PERSON] [16] that the adjoint approach can be used in wind energy relevant cases, in that work shown for the optimization of an airfoil slat. In a previous work, the adjoint optimization in OpenFOAM has been tested against other optimization strategies, like finite differences and Nelder-Mead. The independence from the amount of design parameters was shown there, as well as the applicability for wind energy relevant profiles [17]. In this work, the adjoint approach is used for rotating geometries and large Reynolds numbers. To the authors' knowledge, this combination poses a novelty and offers new areas of application. It should be noted that the rotational flow around rotor blades is complex and it is generally already challenging to achieve low residuals for such flows using stationary flow solvers. In addition, the NREL Phase VI turbine is stall regulated and the chosen velocity already corresponds to the rated velocity. Thus, the flow is separated at the inner blade region, leading to poor convergence behavior of the flow field. The adjoint field is driven by the flow field so that the errors in the flow field transfer directly to the adjoint field and decrease the convergence quality of it. This challenging set-up is used purposely to thoroughly test the optimization tool, its convergence and applicability. The paper is structured as follows. First, the theory of the underlying optimization is given, including the gradient estimation via the adjoint approach and their processing is explained in Section 2. In Section 3, the simulation set-up in OpenFOAM is shown, as well as the optimization set-up and cases. Finally, results of the optimization are given in Section 4 followed by a conclusion in Section 5.
# Feasibility of Using Low-Cost Dual-Frequency GNSS Receivers for Land Surveying
## 2 Materials and Methods
### Experiment Setup and Processing Strategies
#### 2.3.2 RTK and NRTK  
The performance of RTK and NRTK modes was evaluated in multiple configurations and the coordinates directly estimated by the mobile receiver were used. Firstly, a dual receiver configuration, in which both the base station and the mobile receiver were u-blox evaluation boards, was used. The antenna of the mobile receiver was mounted on a 0.93 m long arm, which spins around the antenna of the base station. The horizontal distance and height difference between the two receivers was analyzed in each epoch during 17 rotations and compared with a nominal value. As the orientation of the antenna of the mobile receiver with respect to the antenna of the base station was changing over time, patterns indicating either multipath effect or irregularities in the antenna phase center variation were searched.  
Seven control points having known coordinates were selected and measurements in the following positioning modes were performed: (a) RTK using a geodetic grade receiver (WROC station) as a base station, (b) NRTK using network differential correction data transmitted by the Leica HxGn Smartnet service ([[https://www.smartnetleica.pl/smartnet/](https://www.smartnetleica.pl/smartnet/)]([https://www.smartnetleica.pl/smartnet/](https://www.smartnetleica.pl/smartnet/))), based on Virtual Reference Station (VRS) concept, (c) RTK using a low-cost receiver as a base station. In each positioning mode, two measurement series were performed at each point. After every single measurement, the re-initialization of the receiver was carried out, in order for the ambiguities to be independently computed. The accuracy achieved by the low-cost receiver was analyzed in each positioning mode.
# Dynamic Collaborative Optimization Method for Real-Time Multi-Object Tracking
## 3 Method
### Dynamic Motion Model
#### 3.4.1 Enhanced State Transition  
To capture the complex motion patterns of a target, we define a six-dimensional state vector \(\mathbf{s}=[x,y,v_{x},v_{y},a_{x},a_{y}]^{T}\), explicitly incorporating acceleration components to improve the modeling of non-uniform motion. The enhanced state transition matrix is designed as follows:  
\[\mathbf{A}=\begin{bmatrix}1&0&\Delta t&0&0.5\Delta t^{2}\eta&0\\ 0&1&0&\Delta t&0&0.5\Delta t^{2}\eta\\ 0&0&1-\alpha_{v}&0&\Delta t\eta&0\\ 0&0&0&1-\alpha_{v}&0&\Delta t\eta\\ 0&0&0&0&\eta&0\\ 0&0&0&0&0&\eta\end{bmatrix},\quad\eta=e^{-\beta\Delta t} \tag{16}\]  
Here, the motion damping factor \(\alpha_{v}=0.1\) introduces a velocity self-decay mechanism, while the time decay coefficient \(\beta=0.05\) controls the persistence of acceleration effects. Together, they effectively mitigate the divergence issue in long-term extrapolation. This design ensures the model maintains short-term prediction accuracy while avoiding the common trajectory divergence phenomenon in long-term predictions, making it particularly suitable for scenarios with frequent target direction changes.
# Maximum Entropy Approach to Massive Graph Spectrum Learning with Applications
## 8 EGS for Estimating Cluster Number
### Large Real-World Networks  
For large datasets with \(n\gg 10^{4}\), where the Cholesky decomposition becomes completely prohibitive even for powerful machines, we can no longer define a ground-truth using a complete eigen-decomposition. Nevertheless, we present our findings for the number of clusters in the DBLP (\(n=317\),080), Amazon (\(n=334\),863) and YouTube (\(n=1\),134,890) networks [26] in Table 4, where we use a varying number of moments. We see that for both the DBLP and Amazon networks, the number of clusters \(N_{c}\) seems to converge with  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(n_{e}\) & **9** & **30** & **90** & **240** \\ \hline Lanc & \(\mathbf{3.2\times 10^{-3}}\) & \(1.4\times 10^{-2}\) & \(1.8\times 10^{-2}\) & \(2.89\times 10^{-2}\) \\ EGS & \(9.7\times 10^{-3}\) & \(\mathbf{6.4\times 10^{-3}}\) & \(\mathbf{5.8\times 10^{-3}}\) & \(\mathbf{3.5\times 10^{-3}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fractional error in cluster number detection for synthetic networks using EGS and Lanczos methods with 80 moments. \(n_{c}\) denotes the number of clusters in the network and \(n=30\times n_{c}\) the number of nodes. Best results given in **bold font**.  
increasing moments number \(m\), whereas for YouTube such a trend is not visible. This can be explained by looking at the approximate spectral density of the networks implied by maximum entropy. For both DBLP and Amazon (Figure 6a,b respectively), we see that our method implies a clear spectral gap near the origin, indicating the presence of clusters. Whereas for the YouTube dataset, shown in Figure 6c, no such clear spectral gap is visible and hence the number of clusters cannot be estimated accurately.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Moments** & **40** & **70** & **100** \\ \hline DBLP & \(2.2\times 10^{4}\) & \(8.5\times 10^{3}\) & \(8.3\times 10^{3}\) \\ Amazon & \(2.4\times 10^{4}\) & \(1.1\times 10^{4}\) & \(1.2\times 10^{4}\) \\ Youtube & \(4\times 10^{3}\) & \(1.3\times 10^{4}\) & \(1.9\times 10^{4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Cluster number detection by EGS in the DBLP (\(n=\) 317,080), Amazon (\(n=\) 334,863) and YouTube (\(n=\) 1,134,890) datasets.  
Figure 4: Log error of cluster number detection using EGS and Lanczos methods on large synthetic networks with (**a**) 201,600 nodes and 305 clusters and (**b**) 404,420 nodes and 1355 clusters, and on small-scale real-world networks (**c**) Email network of 1003 nodes and (**d**) NetScience network of 1589 nodes.