esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Discussion
### Expert Feedback and Framework Refinements  
In addition to the quantitative evaluation, experts provided open-ended feedback (Q22) to enhance the framework's real-world applicability. Their insights highlighted several actionable areas for refinement. First, multiple experts stressed the need for real-world validation, recommending that future research includes case studies across various project types to assess the framework's scalability and universality. Second, the importance of training and workforce readiness was emphasized, including the involvement of Lean-trained personnel and employee assessments to support effective framework adoption. Economic feasibility was also raised as a concern, with one expert questioning the return on investment. This led to the recommendation of integrating a cost-modeling component to evaluate the economic viability of implementing emerging technologies compared to traditional methods. Additionally, issues of standardization and regulatory alignment were noted, particularly concerning software interoperability among stakeholders and variations in regional regulations. One expert also criticized the limited vertical integration within the framework and recommended strengthening phase-specific workflows beyond horizontal  
\begin{table}
\begin{tabular}{l  
BIM-Lean-Technology links. Experts unanimously stressed the importance of practicality, recommending modular implementation, cost-benefit toolkits, and real-world case studies, while affirming the framework's theoretical robustness. These insights will guide future refinements, prioritizing pilot deployments, workforce training, and regulatory alignment to bridge the gap between theory and practice.
# Attention-Based Background/Foreground Monocular Depth Prediction Model Using Image Segmentation
## 2 Related Works  
Early monocular depth estimation methods mainly relied on hand-crafted features. The authors of [38] predicted monocular depth using a linear model and depth cues, such as texture variations, gradients, and color. With the rise of deep learning, several deep learning-based monocular depth estimation methods have been proposed in recent years. Deep learning-based monocular depth estimation methods [39] can be divided into supervised learning and unsupervised learning methods. Some studies have implicitly modeled the relationship between semantic segmentation and depth to improve performance. In addition, some researchers have attempted to apply attention mechanisms to depth prediction neural networks. In the following sections, we introduce four categories of monocular depth prediction methods: supervised learning, unsupervised learning, joint learning, and depth prediction based on attention mechanisms.
# Factors Affecting the Stability of Loess Landslides: A Review
## 2 Relevant Measured Parameters
### Resistivity  
Numerous scientific studies have shown that the resistivity of a soil is mainly influenced by its water content. It decreases with increasing water content. In this particular order, soil resistivity is determined by water content, pore water conductivity, saturation, and soil type [59]. Water content has a strong effect on the salinity, gravel content and compactness of soils, and sandy soils [60]. A large decrease in resistivity can occur at a moisture content of 2%. The resistivity of all gravel is slightly higher than that of 50% gravel content. The resistivity of sand decreases with increasing bulk density for the same water content. Clearer results are obtained by measuring the resistivity of loess landslide layers in different environments using a modified probe electrode device and a digital DC exciter [61]. Drilling and trench probing techniques can be costly and cause some damage to the landslide. However, they can help to validate the accuracy of the results. It is necessary to determine the rock physical properties of the medium before directly converting soil volumetric conductivity to solute concentration using ERT and time-domain reflectometer (TDR) equipment. However, it is difficult to apply this method to certain conditions [5]. With increasing water content, temperature, metal ion content, compaction, and contaminant concentration, the resistivity of compacted loesses, contaminated loesses, and unsaturated soils increases [62; 63; 64]. Calibration of soil physical properties can effectively demonstrate the range of water content [65]. Figure 3 shows the resistivity distribution of the slopes in the Cuiying area. It can be seen that the surface resistivity is significantly higher than the subsurface resistivity. It also indicates the approximate area of low resistivity distributed underground. The characteristics of resistivity include low resistance for soil landslides, medium to high resistance for rock landslides, and high resistance for fractured rock landslides [32]. Nevertheless, resistivity shows a reverse pattern of change in laboratory model tests of moisture migration in remolded loesses under rainfall conditions, with infiltration fronts showing a \"Y\" shape in light rain and a \"D\" or \"A\" shape in heavy rain [66]. Changes in both soil water content and suction can be observed using four-dimensional electrical resistivity tomography data, which track temporal and spatial differences in electrical resistivity [10]. There is an exponential relationship between loess resistivity and water content. Hysteresis, with higher resistivity during moisture absorption and desorption at equivalent water content levels, is also observed [67].  
Figure 3: Resistivity model of the Cuiying Mountain region, Lanzhou, China.
# Proactive Scheduling and Routing of MRP-Based Production with Constrained Resources
## 3 Proactive Scheduling and Routing in MRP-Based Production Planning  
In MRP-based planning, the system calculates the required dates and quantities of components and raw materials needed to fulfill customer orders using data on inventory levels, product structure, lead times, and other relevant information. It aggregates these requirements by component and date, and then transforms them into production and purchase orders. At this point, the source/customer order/that generated the requirement typically becomes untraceable. The system then transfers component orders for execution. Using technological data, including routes, it allocates the necessary resources/machines/employees/to these orders at defined times, thereby generating a schedule for their execution. In MRP II/ERP systems, these allocations are not always permissible. To address this, the Capacity Resource Planning (CRP) module verifies and adjusts them before passing the schedule to the Shop-Floor Control (SFC) module (see Figure 1). None of these stages, i.e., MRP, CRP, or SFC, incorporates optimization. Building on this, we propose our own approach (Section 4).  
Our approach departs from the above-mentioned methods by integrating a comprehensive MILP model with features tailored to the operational constraints of MRP-driven production. In contrast to earlier solutions, our model considers not only machine use but also a wide array of additional resources (e.g., labor, tools, software), allowing for partial and time-specific utilization. It also introduces dynamic routing and scheduling, precise traceability of component-level demand origins, and the ability to evaluate order feasibility under various failure or shortage scenarios. Furthermore, it supports proactive and optimized decision-making. In addition, a proprietary proced  
Figure 1: The functional scope of the proposed approach in relation to MRP II/ERP modules.  
the modeled problem, enabling efficient computation without sacrificing model fidelity. This makes our approach both practical and cost-effective when compared to expensive APS systems. Table 1 provides a general comparison of the proposed approach with existing alternatives.
## 1 Introduction
### Comparative Analysis of Communication Systems  
The comparative analysis, conducted herein, of the available communication technologies underscores the strengths and weaknesses of microwave-, optical-, and terahertz-based FSO systems. The table below provides a detailed comparison.  
Table 1 highlights the prospects of terahertz-bandwidth FSO systems in enhancing ultra-fast communication within the dimensions of bandwidth and security.  
Figure 1: Toward terahertz bandwidth in free-space optical communication.  
This review provides a comprehensive analysis of THz-FSO communication systems, addressing key challenges and advancements that have not been extensively covered in previous surveys. Unlike the existing reviews, which primarily focus on either THz communication or FSO systems separately, our study emphasizes the hybrid integration of THz and FSO technologies to enhance high-speed wireless communication. One of the key contributions of our work is the detailed discussion of the security aspects of these technologies, an area that has received limited attention in THz-FSO research. We examine potential vulnerabilities, interception risks, and the robustness of THz-FSO links against eavesdropping and jamming attacks. Additionally, we provide an in-depth analysis of integration challenges, including hardware constraints, alignment issues, and optimization techniques for hybrid THz-FSO networks. Furthermore, this study introduces new insights into atmospheric absorption models, dispersion effects, and THz device limitations. While previous surveys have briefly mentioned attenuation in regard to THz frequencies, our review quantifies the impact of atmospheric absorption above 500 GHz. It identifies the 680 GHz transmission window as an optimal band for THz-FSO communication. We also provide a detailed discussion on atmospheric dispersion, explaining how humidity, temperature fluctuations, and pressure variations affect THz signal propagation, which leads to pulse broadening and reduced spectral efficiency. To mitigate these effects, we explore adaptive signal processing, AI-driven equalization, and multi-band switching techniques. Additionally, our review offers a comprehensive assessment of the limitations of THz devices, including their operating temperatures, their detector sensitivity, the output power of light sources, and their modulation bandwidth constraints. Unlike previous reviews, which often provide a generalized discussion of THz sources, we analyze quantum cascade lasers (QCLs), difference frequency generation (DFG) techniques, and novel THz photonic mixers with respect to their feasibility for practical deployment in THz-FSO systems. This review serves as a valuable resource for researchers and engineers, providing quantitative analysis, mitigation strategies, and future research directions to overcome the existing limitations in THz-FSO hybrid networks.
## 4 Simulation Experiments
### Comparative Analysis  
To comparatively evaluate the torque prediction performance of different modeling methods, three representative approaches--RLS, MLP, and the proposed BLS-PSO--are illustrated in Figure 10. From top to bottom, the subplots correspond to RLS, MLP, and BLS-PSO predictions versus the ground truth.  
RLS (Top): Although the Recursive Least Squares (RLS) method captures the general trend of the torque curve, it exhibits noticeable deviations in regions with sharp fluctuations. This indicates limited adaptability to the nonlinear and non-smooth characteristics of the friction torque.  
MLP (Middle): The Multi-Layer Perceptron (MLP) achieves better performance than RLS in capturing the high-frequency variations. However, it still suffers from overshooting and irregular fitting in certain regions, especially when the signal is noisy or dynamically changing.  
BLS-PSO (Bottom): In contrast, the proposed BLS-PSO method demonstrates significantly improved prediction accuracy, with its output curve closely tracking the true torque across the entire sample index range. The alignment between the green prediction line and the black ground truth curve is notably tighter, validating the effectiveness of combining Broad Learning with PSO-based adaptive optimization in handling nonlinearity and model uncertainty.  
This comparative result highlights the superior generalization and noise robustness of the proposed method, making it a more suitable choice for real-time robotic torque modeling applications.  
As shown in Figure 11, the proposed BLS-PSO method yields consistently smaller prediction errors across the test samples. Compared to RLS and MLP, BLS-PSO demonstrates improved stability and reduced peak deviations, particularly in mid and late test intervals. The results validate its capability to accurately capture nonlinear dynamics and suppress modeling noise.  
Table 2 summarizes the numerical results of the three evaluated methods. The proposed BLS-PSO achieves the lowest RMSE (0.10893) and highest smoothness (0.55865), indicating superior prediction accuracy and reduced output vibration. Although its computation time (0.00036 s) is slightly higher than RLS, it remains significantly faster than MLP. The results confirm that BLS-PSO offers the best trade-off among accuracy, robustness, and efficiency, making it well suited for real-time robotic applications.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **RMSE** & **Smoothness** & **Time** \\ \hline RLS & 0.11723 & 0.60058 & 0.0001024 \\ MLP & 0.11116 & 0.56249 & 0.044911 \\ BLS-PSO & 0.10893 & 0.55865 & 0.0003557 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative comparison of three methods.  
Figure 11: Smoothed prediction error comparison among RLS, MLP, and BLS-PSO methods.
## 4 Discussion  
Based on the present study, it can be concluded that the fracture behavior of the Brazilian disc is not only affected by its loading condition, but also by its material properties. This may serve as an explanation for the discrepancy in some of the existing findings, which are stated in the introduction. In addition, it should be noted that the anisotropy of rocks will also have a significant effect on the failure mode of the Brazilian discs, according to [PERSON] et al. [37]. The tested materials in this work were treated as approximately isotropic. Therefore, the key findings from this study are not suitable for those rock types that show anisotropy or transverse isotropy. Furthermore, although the ISRM loading type is the most suitable test method for the Brazilian test in this paper, it may lead to an invalid failure mode for rock materials with high stiffness and tensile to compressive strength ratios. For such rock types, it is necessary to explore loading configurations that are suitable for them, which is another research direction that will be explored later.  
In fact, the test specifications recommended by ASTM and ISRM are feasible in this study if only the final results of the Brazilian test (i.e., the tensile strength values) are considered. Additionally, the similar tensile strength values of the rocks measured by these two experimental methods can be found in some existing experimental results [39; 41]. However, most scholars believe that the Brazilian disc test overestimates the tensile strength of rocks compared to the direct tensile test [55; 56; 57]. In this regard, the Brazilian test with the Chinese standard has occasionally been considered a suitable method for measuring the tensile strength of rocks, due to its lower load carrying capacity compared with other loading methods [40]. Of course, this may be unreasonable based on the key findings of this paper.  
The macroscopic crack behavior of the rock specimens shows a good agreement with the results from the analysis performed by DIC. For example, the crushed regions at the ends of the failed discs indicate the existence of severe stress concentration in these regions. However, it is not recommended to evaluate the validity of the Brazilian test only by visually observing the post-fracture pattern of the tested specimen. As shown in Figure 10b, although the sandstone and marble specimens tested by the Type II test specification both show an ideal splitting mode, they are actually invalid failure modes based on the results of the analysis performed by DIC.  
Finally, it has to be pointed out that it is difficult for us to perform the direct tension test in our laboratory because of its demanding requirements for testing [12; 58]. Therefore, there is no way to compare the results obtained from this paper with the results obtained from the direct tensile test. This is a deficiency, and is also the next research direction of this work.
# Semi-Supervised Method for Underwater Object Detection Algorithm Based on Improved YOLOv8
###### Abstract  
Deep learning-based object detection technology is rapidly developing, and underwater object detection, an important subcategory, plays a crucial role in various fields such as underwater structure repair and maintenance, as well as marine scientific research. Some of the major challenges in underwater object detection are the relatively limited availability of underwater image and video datasets and the high cost of acquiring high-quality, diverse training data. To address this, we propose a novel underwater object detection method, SUD-YOLO, based on the Mean Teacher semi-supervised learning strategy. More specifically, it combines a small number of labeled samples with a large number of unlabeled samples, using the teacher model to guide the generation of pseudo-labels. In addition, a multi-scale pseudo-label enhancement module is developed specifically to address the issue of low-quality pseudo-labels. To overcome the model's difficulty in learning underwater image feature extraction, we integrate a receptive-field attention mechanism with local spatial features and then design a lightweight detection head based on the task alignment concept to further improve the model's feature extraction capability. Experimental results on the DUO dataset show that, by using only 10% of the labeled data, the proposed method achieves an average precision of 50.8, which is an improvement of 11.0% over the fully supervised YOLOv8 algorithm, 11.3% over the fully supervised YOLOv11 algorithm, 9.3% over the semi-supervised Efficient Teacher algorithm, and 3.4% on the semi-supervised Unbiased Teacher algorithm, while only 20% of the computational cost is required.  
underwater object detection; semi-supervised object detection; receptive-field attention; pseudo-labels 202515065  
122
# Two-Channel Information Fusion Weak Signal Detection Based on Correntropy Method +
## 2 Fundamental Theory
### Definition of Correntropy  
The CE between any two random variables \(X\) and \(Y\) is defined as  
\[V_{\sigma}(X,Y)=\mathrm{E}[k_{\sigma}(X-Y)] \tag{4}\]where \(\mathbb{E}[\bullet]\) represents expectation.  
For limited length data \(\{(x_{i},y_{i})\}_{i=1}^{N}\), the CE can be rewritten as (5):  
\[\hat{V}_{N,\sigma}(X,Y)=\frac{1}{N}\sum_{i=1}^{N}k_{\sigma}(x_{i}-y_{i}) \tag{5}\]  
where \(k_{\sigma}(x_{i}-y_{i})\) is a kernel function, and \(\sigma\) is the size of the kernel. Gaussian kernel [22] is the most commonly used expressed as (6):  
\[k_{\sigma}(x-x_{i})=\frac{1}{\sqrt{2\pi\sigma}}\exp\left(-\frac{\|x-x_{i}\|^{ 2}}{2\sigma^{2}}\right) \tag{6}\]  
where \(\|\cdot\|^{2}\) means L2 norm.
## 2 Materials and Methods  
### Humidity Control Experiments in Airtight Boxes  
To test the proposed configuration, the experimental setup of Figure 1b was implemented. In an airtight confined box volume, an ionic membrane was attached to its wall, while silica gel desiccants bags were placed in 3D-printed columns attached to the cover of the box. The insertion of the desiccant in the box could be achieved quickly by opening the caps of the columns without opening the lid of the whole box, thus minimizing humidity level variations inside it. The humidity inside the box was increased externally using a humidifier through a tube that was attached to a side of the box, and it was sealed by a valve when the desired humidity had been reached. The humidity and the temperature inside the box were monitored by a USB-powered sensor with 0.1% resolution, and the results were plotted in real time.  
Two boxes were used for the experiments. The first one was a commercial 70 L airtight plastic box from IRIS-OHYAMA while the second one was a 230 L in-house-built box made from Perspex material. The two boxes are shown in Figure 2a,b, respectively. The 230 L box was covered with a copper mesh for future developments, and it is not relevant to this experiment. The 230 L box was sealed with RTV silicone sealant, which was cured and outgassed for 2 weeks before installing the membrane and initiating the experiments. For the 70 L box, two columns of 0.33 L were filled with silica gel desiccants, while for the 230 L, four columns were used. The ionic membrane (model M-5J1R, dehumidifying capacity: 8 g/day) was purchased from Westside International Ltd. (Oxford, UK) and powered with a BWM3.3 SX-U1A power supply. The ionic membranes from Westside International Ltd. make use of a solid-state polymer as the electrolytic layer. The A DUAL 200S humidifier from LEVOIT was used to increase the humidity in the two boxes. The humidity and the temperature were recorded and logged using a TSPO1 sensor from Thorlabs.  
The dehumidifying ability of the silica gel desiccants, the ionic membrane and their combination was investigated in separate experiments. For the 70 L box, two initial humidity regimes were studied. The first experiments were conducted with low initial humidity levels (34-43%), achieved without the use of the humidifier, over a span of 20 h for each configuration. Next, high initial humidity levels were used, by increasing the humidity to approximately 73% and then turning the humidifier off. For the 230 L box, the initial humidity levels were close to 80% and measurements were conducted for all three configurations over the span of 40 h.  
Figure 2: **(a)** The 70 L airtight box; **(b)** 230 L airtight box.
# SDR-Based 28 GHz mmWave Channel Modeling of Railway Marshaling Yard
## 3 Software-Defined Radio-Based Channel Modeling
### Corresponding Algorithms
#### 3.3.2. Discrete Algorithm of Cross-Correlation  
Define \(h\) as a discrete sequence, whose index can be negative. \(N\) is the number of elements in input sequence \(X\), \(M\) is the number of elements in sequence \(Y\), and assuming  
\[x_{j}=0,j<0\;\text{or}\;j\geq N \tag{2}\]  
\[y_{j}=0,j<0\;\text{or}\;j\geq M \tag{3}\]  
\(h\) is calculated as:  
\[h_{j}=\sum_{k=0}^{N-1}x_{k}\times y_{j+k} \tag{4}\]  
where \(j\in[-(N-1),(M-1)]\).  
The cross-correlation is calculated as:  
\[Rxy_{i}=h_{i}-(N-1) \tag{5}\]  
where \(i\in[0,N+M-2]\).  
Figure 14. Process flow of the measurement system.