esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of ANN in Predicting the Cantilever Wall Deflection in Undrained Clay
###### Abstract  
The main objective of this study is to propose an artificial neural network (ANN)-based tool for predicting the cantilever wall deflection in undrained clay. The excavation width, the excavation depth, the wall thickness, the at-rest lateral earth pressure coefficient, the soil shear strength ratio at mid-depth of the wall, and the soil stiffness ratio at mid-depth of the wall were selected as the input parameters, whereas the cantilever wall deflection was selected as an output parameter. A set of verified numerical data were utilized to train, test, and validate the ANN models. Two commonly used performance indicators, namely, root mean square error (RMSE) and mean absolute error (MAE), were selected to evaluate the performance of the proposed model. The results indicated that the proposed model can reliably predict the cantilever wall deflection in undrained clay. Moreover, the sensitivity analysis showed that the excavation depth is the most important parameter. Finally, a graphical user interface (GUI) tool was developed based on the proposed ANN model, which is much easier and less expensive to be used in practice. The results of this study can help engineers to better understand and predict the cantilever wall deflection in undrained clay.  
cantilever wall deflection; machine learning; artificial neural network; graphical user interface; undrained clay +
Footnote †: journal: Applied Sciences
# Genetic Algorithms to Maximize the Relevant Mutual Information in Communication Receivers +
## 2 Preliminaries
### Flexible Parametrized Mappings
#### 3.1.1 Clustering by Simple Exact Nearest Neighbor Search  
The first class of parametrized mappings of \(\mathbf{y}\) onto \(t\in\mathcal{T}=\{0,1,\ldots,|\mathcal{T}|-1\}\) that we consider determines the outgoing \(t\) for an incoming \(\mathbf{y}\) as  
\[t=f_{\theta}(\mathbf{y})=\arg\min_{t^{\prime}\in\mathcal{T}}\{\,d(\mathbf{y}, \theta_{t^{\prime}})\}, \tag{5}\]  
where \(d(\mathbf{y},\theta_{t})\) is some properly defined, but at the same time, arbitrary distance measure between an incoming vector \(\mathbf{y}\) and an optimized parameter vector \(\theta_{t}\) of the same dimension \(N\) as \(\mathbf{y}\). In this article, we will consider the Euclidean distance \(d_{\text{E}}(\mathbf{y},\theta_{t})\) and the Manhattan distance \(d_{\text{M}}(\mathbf{y},\theta_{t})\) from Section 2.3, but we want to stress that the proposed method can deal with arbitrary distances. This mapping is characterized by \(|\mathcal{T}|\) such parameter vectors \(\theta_{t}=[\theta_{t,0},\theta_{t,1},\ldots,\theta_{t,N-1}]\) which we compactly gather in a long vector \(\theta=[\theta_{0},\theta_{1},\ldots,\theta_{|\mathcal{T}|-1}]\). As each vector \(\theta_{t}\) has length \(N\), there are \(N\cdot|\mathcal{T}|\) parameters \(\theta_{m}\) in \(\theta\). Clearly, the approach is very much inspired by a vector quantizer which we aim to design with a genetic algorithm such that it maximizes the mutual information \(\text{I}(\mathcal{X};\mathsf{T})\).  
Figure 3: Exemplary application of a parametrized mapping \(t=f_{\theta}(\mathbf{y})\) for the quantization and demodulation of an AWGN channel output under PSK modulation. The system output \(t\in\mathcal{T}\) shall be highly informative about the transmitted modulation symbol \(x\in\mathcal{X}\).  
In its simplest form, the considered mapping can be implemented by calculating all possible distances \(d(\mathbf{y},\boldsymbol{\theta}_{t})\,\forall t\in\mathcal{T}\) and choosing the vector \(\boldsymbol{\theta}_{t}\) with the smallest distance. This approach is sometimes also termed the naive nearest neighbor search [14], but for small values of \(|\mathcal{T}|\) it offers a quite practical solution to identifying the nearest neighbor. The integer index \(t\) of the closest found vector then is the output of the system.
## 3 Results
### Common Method Bias Test and Mediation Analysis
#### 3.4.2. Mediation Effect Analysis  
To explore the mediating roles of perceived teacher support and academic self-efficacy, Models 4 and 6 of the PROCESS macro in SPSS were used, applying a bias-corrected percentile bootstrap method with 10,000 resamples. The results are shown in Table 3. Based on the significant direct effect of digital technology use on teaching quality, three indirect pathways were tested: Digital technology use \(\rightarrow\) perceived teacher support \(\rightarrow\) teaching quality; Digital technology use \(\rightarrow\) academic self-efficacy \(\rightarrow\) teaching quality; Digital technology use \(\rightarrow\) perceived teacher support \(\rightarrow\) academic self-efficacy \(\rightarrow\) teaching quality. The estimated effect sizes for these mediation pathways were 0.303, 0.068, and 0.096, respectively. The corresponding 95% confidence intervals were [0.238, 0.374], [0.038, 0.103], and [0.061, 0.134], all of which excluded zero, indicating statistical significance. These findings demonstrate that perceived teacher support and academic self-efficacy function not only as independent mediators but also as function as a chain mediating effect. Therefore, Hypotheses 2, 3, and 4 were supported.  
\begin{table}
\begin{tabular}{c l c c c c c c} \hline \hline  & & **Mean** & **SD** & **1** & **2** & **3** & **4** \\ \hline
1 & Digital technology use & 29.827 & 10.707 & 1 & & & & \\
2 & Perceived teacher support & 69.006 & 10.390 & 0.463 ** & 1 & & & \\
3 & Academic self-efficacy & 83.867 & 12.400 & 0.478 ** & 0.695 ** & 1 & & \\
4 & Teaching quality & 99.387 & 17.558 & 0.497 ** & 0.647 ** & 0.590 ** & 1 \\ \hline \multicolumn{8}{c}{Note: ** \(p<0.01\).**} \\ \end{tabular}
\end{table}
Table 2: Correlation analysis.  
Figure 7: Interaction effects of paired digital tools on predicted teaching quality scores. (**A**) Management software and Websites; (**B**) Management software and Multimedia materials; (**C**) Management software and Multimedia courseware; (**D**) Websites and Multimedia materials; (**E**) Websites and Multimedia courseware; (**F**) Multimedia materials and Multimedia courseware.
## 4 Illustrative Example  
The sequence of operations performed to hide the data and to restore the image is schematically shown in Figure 4. An original image of size \(512\times 512\) pixels is considered for illustration. An encryption key is used to generate a similar-sized pseudo-random matrix. The image and the pseudo-random matrix are bit-wise XORed to obtain an encrypted image. The illustration shown in Figure 4 considers the processing of the image as independent \(4\times 4\) pixel blocks. A series of binary numbers form the secret information, out of which 2 bits of information are embedded on each independent \(4\times 4\) block. As shown in the example, the 2-bit data which we need to hide in the selected block are \(11\). To embed 11, we perform a local diagonal swapping of the pixels (refer to Equation (9)) on each non-overlapping \(2\times 2\) pixel block of the currently selected \(4\times 4\) block. This makes the initially encrypted block embedded with secret data 11. Now the information embedded \(4\times 4\) block is placed at the corresponding position of the marked image \(E^{\prime}\). The marked image \(E^{\prime}\) is transmitted to the receiver.  
Figure 4: Illustration of the proposed scheme.  
The receiver also processes the received image \(E^{\prime}\) as independent \(4\times 4\) pixel blocks. In the illustrative example, we considered the same \(4\times 4\) data-embedded block to justify how we restore the cover image and extract the exact hidden data. The receiver generates four different versions of the data-embedded \(4\times 4\) block, which are \(V^{0},V^{1},V^{2}\), and \(V^{3}\). Bit-wise XOR over these blocks with the corresponding block from the pseudo-random matrix performs the decryption operation. The pseudo-random matrix is created using the decryption key. We compute the smoothness measure of all these four decrypted blocks to recover the correct block (refer to Algorithm 3). The restored block is the one with the lowest smoothness measure. Say, if the decrypted block recovered is computed from version \(V^{P}\), then the hidden data extracted are \(P\) where \(P\in{0,1,2,3}\). As shown in the example, the fourth decrypted version of the block is the block with the lowest measure. Hence, this block is the correct block recovered. This version of the decrypted block is computed from \(V^{3}\), which corresponds to the data 11. In a similar fashion, all the remaining blocks of the marked image are processed to restore the original image and the complete hidden message.
## 1 Introduction
### Edge Computing in UAV Networks  
With traditional cloud computing, data have to be sent all the way to the cloud, which often has a high time latency, resulting in slow processing rates. To solve this issue, edge computing (EC) has been proposed. With EC, the data can be processed on the edge, which is not only much closer to where the data are being created but also better than the original approach for preserving privacy [5; 6]. In EC, local data centers and servers are deployed by a service provider at the edge of mobile networks, such as the base stations of radio access networks [2]. These data centers often have sufficient computational resources to be able to handle heavy tasks. One of the key considerations of EC is overcoming network latency.  
Because many Internet of Things (IoT) applications and UAVs require fast response time (i.e., the time required to offload and compute data), sending data all the way to the cloud as in traditional cloud computing may result in serious problems in practice. For instance, a safety control system that operates an industrial machine may need to stop immediately if a human is too close. A delayed response time may cause serious harm to the person or damage the machine. Autonomous vehicles/UAVs face the same problem because they usually require a fast response time below 20 ms [7]. This short response time cannot be achieved by traditional cloud computing, but moving the processing of the sensor or device to the edge (i.e., EC) can be a promising and viable way to achieve the desired response time.  
Another major concern for traditional cloud computing is its cost. As we can see in Figure 1, with EC, the data can be properly filtered and processed before they are sent to the cloud such that the network cost of data transmission is effectively reduced while also reducing the cost of cloud storage. For example, a mobile temperature device sensor that reports a reading of 20 \({}^{\circ}\)C every second might not be informative, but once it starts reading 40 \({}^{\circ}\)C, the data become more informative. If the traditional cloud architecture is combined with EC, then the users can reduce the latency by uploading continuously to the close edge server. When uploads reach a set number, the edge server can filter these data and then send it to the cloud for storing. However, in EC, because UAVs are most often deployed in the public to gather data [7; 8; 9], some privacy concerns still exist; for example, the data can be exposed to malicious secrecy attacks or overhearing by eavesdroppers and/or possibly by other mobile devices as well as UAVs. In particular, during the offloading process from UAVs to the edge server, data are vulnerable to an attack.
## 2 Literature Review
### Digital Technologies and Agricultural Value Chains in the Context of Society 5.0 Sustainability  
Digital technology provides avenues for services and new opportunities in achieving sustainability and transformation of the agricultural value chains [24]. Digital technologies include networks such as 4G and 5G, cloud computing, and the Internet of Things (IoT), which are rapidly becoming integrated into virtually all agricultural processes with a focus on enhancing efficiency, effectiveness, and sustainability [17]. IoT, in particular, provides farmers with digital solution tools that address numerous challenges in agriculture [11]. For instance, farmers connect to their farms at any time from anywhere through IoT-enabled devices [49]. The concept of Society 5.0 combines digital technologies with social innovation to promote a human-centric society [50]. Society 5.0's salient features, consisting of technology, systems, and people, as well as the interaction among these features, promote the digital economy in the agricultural sector. Table 2 summarizes the features of Society 5.0 that envision innovative human-centered smart applications that are knowledge-driven to meet sustainable solutions to differentiated physical and social needs in addressing societal challenges [51].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Description** \\ \hline Human-centric approach & People are at the center of the development process to create sustainable, inclusive, and prosperous societies [52; 53]. \\ \hline Integration of digital technologies & Digital technologies, including artificial intelligence, the Internet of Things, and big data to solve social problems and enhance the quality of life [54; 55]. \\ \hline Collaboration & Stakeholders’ collaborations, including government, industry, academia, and civil society, play a significant role in creating innovative solutions to social problems such as the lack of employment [56]. \\ \hline Sustainability & Creating sustainable societies that balance economic growth with environmental and social considerations [57]. \\ \hline Inclusivity & Innovation spurs the development of new business models to address social problems and create new opportunities [58]. \\ \hline Digital empowerment & Empower individuals and communities by providing access to information and resources and promoting participation in decision-making processes [59]. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Features of Society 5.0 for an inclusive digital economy in the agricultural value chain (Authors’ analysis).  
Society 5.0 refers to a human-centered integration of the cyber and physical space, intending to balance economic advancement with the resolution of social problems [60]. Hence, to be inclusive and empower individuals and communities through the integration of digital technologies, stakeholders must collaborate and create digital economy solutions to achieve a sustainable agricultural value chain (SDG3) [61].
## 3 Results
### Steering Angle Estimation  
#### 3.2.1 Random-Walk Simulation of Possible Course Alterations  
The above Markov chains are in discrete seconds. The random-walk simulation was conducted in ten million seconds to cover most altering cases. A total of 166,305 course alterations were extracted, as shown in Figure 8, and their trajectories are shown in Figure 9 using distinguishable colors for each.  
Figure 9 depicts the various course alterations in trajectories, with the ship proceeding from the same position with the initial course of zero degrees. Each trajectory was generated by trigonometric functions using random-walk simulated course alterations with a thirteen-knot speed, the initial speed set in the simulated ship handling training.
# Parallel Makespan Calculation for Flow Shop Scheduling Problem with Minimal and Maximal Idle Time
## 2 Literature Overview
### Parallel Computing
GPGPU devices are often used due to the high number of parallel cores they provide, especially when multiple such devices are used together as a part of a computer cluster. However, GPGPUs have a different memory and thread organization compared to CPUs, requiring care in order for the parallel algorithms to be effective. For example, in paper [52], a GPGPU technique for reducing the number of memory accesses was proposed. The technique was then applied to a TS method to solve the FSSP. The results showed speedups of up to 140. As another approach, the authors of paper [53] proposed a modified GA method with islands and a lower-higher structure. This duality was designed to match and take advantage of the lower (block of threads) and higher (grid of blocks) organization scheme of GPGPUs. The obtained results proved competitive quality with reduced running time when compared with a reference method. Similar results were obtained in paper [54], where a parallel GA method for an energy-efficient dynamic flexible FSSP was proposed, reducing the running time up to 25 times. An interesting example was shown in paper [20], where many GPGPU devices, for a total of 2 million CUDA cores, and the branch-and-bound method were used to solve 11 previously unsolved Taillard instances to optimality and update bounds on a further eight instances. For the largest instances, 64 years' worth of CPU time was computed in just 13 h.  
Finally, while authors usually focus on one type of parallel device--GPGPUs or multicore/manycore processors--there are exceptions. This was the case in paper [55], where the authors considered a GA method that employed a hybrid GPGPU + CPU + SSE configuration and used it to solve a large-size flexible FSSP. Aside from the speedups and improved quality, the results indicated the importance of SSE vectorization. For a further reading on the use of parallel computing methods in scheduling, please refer to [56].
## 3 Results and Discussion
### Electronic Properties of Mo-doped Nb (100) Surface  
To examine the interaction between atoms, we investigated the electronic properties of the Mo-doped Nb (100) surface. The calculated density of states (DOS) of H atom adsorbed at the bridge, hollow, and top sites are shown in Figure 5a-c, respectively. The Partial DOS (PDOS) peaks at about \(-30\) eV with respect to the Fermi level are mainly contributed by Nb-\(p\) and Mo-\(p\) states for Mo-doped Nb (100) surface. The PDOS peaks near \(-5\) eV are contributed by the H-\(s\) state. In the case of H1 model (Figure 5b), an obvious peak of Nb-\(d\) is observed at \(-5\) eV, but it is absent in the case of B1 and T1 models, indicating that the hybridization between H-\(s\) and Nb-\(d\) states in B1 model is adjusted by Mo-doping.
# Evaluating Swarm Robotics for Mining Environments: Insights into Model Performance and Application
## 5 Results and Discussions
### Mining Efficiency and Operational Performance  
Strengthening supply chain operations and boosting profits can be achieved through efficient, high-production mining. In this study, each swarm model's performance in ore exploration, extraction, and transportation was tested using 5, 10, and 15 robots with 20 randomly distributed blocks. For testing all considered swarm models for mining efficiency and robustness under a consistent and controlled environment, a 15 m by 15 m arena was used for all of swarm models as a standard scenario. Figure 10 shows how each model optimizes mining operations based on efficiency and operational performance.  
The honeybee and firefly models have outperformed the ant and baseline models in efficiency. For the case of five robots, the honeybee model completed harvesting in 311 s, making it by 55.4% more efficient than the baseline model's 698 s. The firefly model completed the same task in 453 s, making it by 35.1% more efficient than the baseline and by 19.5% more efficient than the ant model, which completed the task in 562 s. The baseline model is less efficient due to simultaneous multitasking in exploration, harvesting, and transportation, making it better suited for simpler mining tasks such as surface ore extraction and short-distance mineral transport. The ant model, inspired by leafcutter ants' division of labor, improves upon the baseline through task specialization among robots. However, without integrated communication in the ant model, a poor performance by a specialized group can cause delays, highlighting the need for synchronized activities to prevent idleness. The firefly model utilizes bioluminescence communication, where a detection robot's light beam guides the picking robot to the ore. This direct signaling reduces idle time and enhances task coordination and rapid positioning. The honeybee model, inspired by the honeybee's foraging, has a scout robot that identifies resources and communicates with the master robot, which then coordinates the harvesting robots.  
Figure 10: Efficiency and operational performance of swarm models across varying swarm sizes. The boxes are representing the interquartile range (IQR), the solid line inside the box represents the median, the whiskers represent the data range, the X cross marks the mean (average) value, and the dots represent individual data points.  
This hybrid control system significantly reduces the time required for ore location and extraction, optimizing exploration and harvesting through improved positioning. Overall, the role specialization and direct communication strategies in these models, especially the honeybee model's hybrid system, can greatly enhance mining efficiency and robustness, particularly in environments requiring precise operations. It worth noting that tests on mining scalability and adaptability simulated for various size environments are presented and discussed under Section 5 of this paper.
## 4 Optimal Layout for Shear Connectors
### Stepwise Uniform Distribution for Shear Connectors
When adopting a 2-stepped rectangle distribution, the interface slip of the beam end decreases by 14.17% for the four kinds of load, while the interface slip decreases by 21.59% for the 3-stepped rectangle distribution, and the interface slip decreases drastically, by 28.02%, for the 5-stepped rectangle distribution. Nevertheless, it can be clearly seen from Figure 9(a) that the location at which maximum interface slip occurs gradually moves from the beam end toward the mid-span when using the 5-stepped rectangle distribution method, and this phenomenon is also apparent in Figure 9(b). The main reason for this is that cross-sectional shear force is uniformly distributed along the composite beams for the first type of load and is relatively evenly distributed along the beam for the second type of load; but compared with the 3-stepped rectangle distribution, the 5-stepped rectangle distribution results in several too many shear connectors concentrated at the two ends of the beam. In this way, the number of shear connectors in the region close to the mid-span is too small to resist interface slip, leading to the movement of the maximum interface slip from either end of the beam towards the mid-span. However, the 3-stepped rectangle distribution facilitates application in practice, because this type of distribution has fewer segments than the 5-stepped rectangle distribution, and, most importantly, the 3-stepped rectangle distribution method is able to effectively decrease the interface slip. For these reasons, it is reasonable to select the 3-stepped rectangle distribution method as an optimal layout method for the stepwise uniform distribution of shear connectors. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Load Types** & **I** & **II** & **III** & **IV** \\ \hline Uniformly slip \(s_{1}\) (mm) & 1.31 & 1.62 & 1.90 & 2.11 \\
2-stepped rectangles slip \(s_{2}\) (mm) & 1.12 & 1.40 & 1.63 & 1.83 \\
3-stepped rectangles slip \(s_{3}\) (mm) & 1.02 & 1.27 & 1.49 & 1.66 \\
5-stepped rectangles slip \(s_{4}\) (mm) & 0.94 & 1.17 & 1.37 & 1.52 \\ \((s_{1}-s_{2})/s_{1}\) (\%) & 14.17 & 13.33 & 13.95 & 13.47 \\ \((s_{1}-s_{3})/s_{1}\) (\%) & 21.59 & 21.48 & 21.33 & 21.53 \\ \((s_{1}-s_{4})/s_{1}\) (\%) & 27.96 & 27.96 & 27.65 & 28.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of beam end interface slip under four load cases. Figure 9: Distribution of stiffness for every type of stepped rectangle.