esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Finite Element Analysis for Bridge Structures with Damage and Deterioration
### Eigenvalue Analysis Results
#### 2.4.2 Pier Model  
Similarly, for the two cases (scouring-free and scouring pier models), eigenvalue analysis was conducted to obtain their modal frequencies and mode shapes. Knowing that the pier is a cantilever-like structure, the change in its bottom boundary due to scour conditions would similarly change its vibration properties. Therefore, the FEM model is simplified by omitting the superstructure with little loss of generality on the alteration of vibration behavior. Future and further work would be required to adequately model the boundary condition for the top of the pier with regard to mass and stiffness of the bridge deck.  
Table 4 gives the modal frequencies of the first three modes. All three modal frequencies in the scouring case were about 30% smaller than those in the scouring-free case; as expected, the modal frequencies dropped as scouring occurred.  
Figure 9 shows the first three mode shapes for both the scouring-free and scouring cases, and Figure 10 shows the relative amplitude for the three observation points and the two cases. It is observed that the 1 st mode is a longitudinal bending mode, and when scouring occurred, the relative amplitude in y-direction increased at Point No. 1 but decreased at Point No. 3. The 2 nd mode is a torsional mode, and when scouring occurred, the relative amplitude in y-direction increased at Point No. 1 and decreased at Point No. 3. The 3 rd mode is a lateral bending mode, and when scouring occurred, the relative amplitude changed little. In addition, the above three modes presented small z-direction amplitude as they are all bending modes, so the vibration in the z-direction is hardly useful in scouring detection.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Vibration Mode** & **Scouring Free** & **Scouring** \\ \hline
1 st & 4.59 & 3.05 \\
2 nd & 8.40 & 6.62 \\
3 rd & 9.90 & 7.14 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Modal frequencies of pier models (Unit: Hz).  
Figure 8: Vibration mode shapes of slab model (Intact: Case 0).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Vibration Mode** & **Case 0 (Intact)** & **Case 1** & **Case 2** & **Case 3** \\ \hline Mode 1: 1 st bending & 5.98 & 5.79 & 5.49 & 5.25 \\ Mode 2: 1 st torsion & 11.46 & 10.44 & 10.67 & 10.83 \\ Mode 3: 2 nd bending & 19.90 & 14.94 & 16.82 & 17.80 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Vibration frequencies of slab models (Unit: Hz).  
Application of MEMS Vibrational Power Generator for Detecting Damage and Deterioration in Bridge Structures
# All-Optical Method of Determining Laser Power from the Photomechanical Effect
## 4 Discussion
The simplicity of solution for a two-dimensional Green's function without loss at the boundary and the idealness of the fits at long time-scales allows the current model to be used for real-time power measurements in devices with current microprocessor capabilities. The method for measuring laser power via the photomechanical effect presented in this paper is well-suited for device applications. If the diffraction pattern is placed on the same side as the probe beam to create a reflected diffraction beam, then an enclosure can be designed with a port exposing a PDMS sample doped with an opaque dye. The linear CCD array that collected position data is the same type of electronic sensor found in most portable spectrometers, which are commonly used in optical experiments. Thus, a portable spectrometer could be designed to have a second port devoted to laser power measurements and use the same electronic sensor. Because Gaussian fitting along the linear CCD array requires so little memory and processing, cheap, low-power microprocessors can easily determine each gaussian peak in real time making this method viable for portable devices. For the experimentalist on a budget, the long and precise linear CCD array used in this study could be swapped out for the two-dimensional CCD array in a camera with the lens removed. The range of the adapted power sensor design would be decreased; however, alignment between the direction of the one-dimensional grating and the orientation of the linear CCD array's long axis would no longer be required. Additional analysis of the temperature profile over a large film's area, which is not exposed to the pump beam, is necessary for a user-friendly device. Materials or additives that significantly increase the thermal diffusivity will be necessary to create a laser power sensor with a large exposure surface. On the other hand, thin films of materials with a small thermal diffusivity could be used to map a pump beam's intensity profile by scanning over the film surface with a small probe laser. Finally, there will be a level of intensity at which the maximum strain varies nonlinear with the pump intensity; however we were only able to achieve a maximum of 15.2 mW of pump power after filtering the 1064 nm fundamental wavelength out of the 532 nm pump beam with a KG-1 filter. A higher power pump beam is required to determine the intensity level at which nonlinear processes occur for the Oil Red O-doped PDMS material used in this study.
# The Sensitivity Analysis of Parameters in the 1D-2D Coupled Model for Urban Flooding
[21] also provided supporting evidence that Morris-Sobol methods improve computational feasibility while maintaining analytical depth. Compared to existing studies that apply only the Morris or Sobol methods, the present study builds upon this research by demonstrating that their combined application provides a more balanced approach. Previous studies utilizing Morris alone often lack interaction assessments, while studies relying solely on Sobol require high computational costs. By integrating Morris and Sobol, this study maximizes efficiency while retaining analytical rigor, making it a robust method for urban flood model optimization. While there is a substantial body of literature on sensitivity analysis, the majority of studies have focused on one-dimensional models, with relatively few sensitivity analyses conducted on coupled models. This paper employs the Morris method and the Sobol method to perform sensitivity analyses on 12 parameters of the SWMM. It does so with the objective of exploring the influence mechanisms of the parameters on the amount of water accumulation under different rainfall conditions. The Morris method is used to quickly screen important parameters with high computational efficiency, while the Sobol method is employed to reveal the interaction and nonlinear effects of parameters through global analysis. By integrating these two methods, one can achieve a balance between efficiency and accuracy, ensure the reliability of the screening results, and provide support for subsequent in-depth analysis, thereby establishing a more accurate data foundation for early warning of urban flooding.
## 3 Results and Discussion
### Microcosm Testing Results
In summary, under the same oil content and fixed TCE concentration, only the SOE+FL and SOE test groups could reach an eventual TCE removal rate of 99%. The addition of FL accelerated TCE degradation. The groups with emulsions alone required a longer time for degradation to gradually reach a reducing condition in the environment. Adding FL shortened the time required to achieve the reducing condition, so microbes could start anaerobic reductive dechlorination earlier. Moreover, Figure 5b reveals that the SOE+FL group generated the highest concentration of cis-1,2-DCE, suggesting that the SOE+FL group had the greatest reductive dechlorination efficiency. Therefore, the subsequent in situ biological field test was conducted using SOE+FL. On day 90, the amount of representative strains of DHC and DCA1 in the SOE+FL group was 5.64 \(\times\) 10\({}^{3}\) and 6.17 \(\times\) 10\({}^{3}\) gene copies/L, respectively.
## 2 POD Strategy and Data Usage  
### POD Strategy  
The reduced dynamic approach is employed to determine the orbits of GRACE-FO satellites. The Position And Navigation Data Analyst (PANDA) software [47], developed at the GNSS research center of Wuhan University, is modified and used for the POD process. The POD strategy is listed in Table 1. The macro model of GRACE-FO satellites [44] is applied to model the non-gravitational forces, which mainly result from atmospheric drag, solar, and earth radiation pressure. The atmospheric density values required for atmospheric drag modeling are obtained with the DTM94 model [48]. To account for deficiencies in DTM94 and the macro model, drag coefficient is estimated freely once per orbital revolution. Solar radiation pressure is calculated based on the satellite macro model and a scale factor is estimated per orbit determination arc. Additionally, one cycle per revolution (CPR) empirical accelerations in along-track and cross-track directions are estimated to compensate for deficiencies in the adopted force models. Other estimated parameters include receiver clock offsets and carrier phase ambiguities. The GRACE-FO antenna PCOs are applied according to the VGN1B product [44]. PCV corrections for IF carrier phase observations are calculated using the method described in Section 3.1.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Description** \\ \hline \multicolumn{2}{c}{**Background force models**} \\ \hline Static gravity field model & EIGEN\_06C (130 \(\times\) 130) [49] \\ \hline Solid earth and pole tides & IERS Conventions 2010 [50] \\ \hline Ocean tides & FES2004 (30 \(\times\) 30) [51] \\ \hline Ocean pole tides & Desai [52] (30 \(\times\) 30) \\ \hline Third body perturbations & JPL’s DE405 \\ \hline General relativistic effects & IERS Conventions 2010 [50] \\ \hline Solar radiation pressure & Macro model [44] \\ \hline Atmospheric drag & Macro model [44], DTM94 [48] \\ \hline \multicolumn{2}{c}{**Input data and products**} \\ \hline GPS Observations & Undifferenced IF code and carrier phase \\ \hline Sample interval & 10 s \\ \hline Elevation mask & 0\({}^{\circ}\) \\ \hline GPS orbit & CODE’s final GPS orbit \\ \hline GPS clock and hardware bias & CODE’s 5 s clock and OSB products \\ \hline GPS antenna correction & igs14.atx \\ \hline GRACE-FO antenna correction & PCO applied [44] \\ \hline Phase wind-up & Applied [53] \\ \hline \end{tabular}
\end{table}
Table 1: POD strategy of GRACE-FO satellites.  
According to the ambiguity resolution methods, three kinds of orbits are calculated. The first orbit is calculated with float ambiguities (donated as FA solution hereafter), the second solution is constrained by integer DD ambiguities (DD AR solution), and the third orbit is generated with the constraint of integer SD ambiguities (SD AR solution).
# Local Community Detection Based on Small Cliques
## 2 Density-Based Local Community Detection Algorithms
### Lfm  
The LFM algorithm [5] maximizes the fitness function  
\[f(C):=\frac{\mathrm{vol}_{w}(C)-\mathrm{cut}_{w}(C)}{\mathrm{vol}_{w}(C)^{ \alpha}}\]  
where \(\alpha\) is a real-valued, positive parameter. In each iteration, it adds the node that leads to the highest improvement of \(f(C)\) to the community \(C\) and then removes every node whose removal increases \(f(C)\). The parameter \(\alpha\) can be used to influence the size of the detected communities. Large values of \(\alpha\) correspond to small communities while small values correspond to larger communities. The authors report [5] that values below \(0.5\) usually lead to just one community while values larger than \(2\) led to the smallest communities. Further, they recommend \(\alpha=1\) as a natural choice which is also the parameter we use in our experiments.  
Their proposed LFM algorithm is actually an algorithm for detecting overlapping communities that cover the whole graph by iteratively expanding a random seed node that has not yet been assigned to a community into a new community. We only consider the basic building block of local expansion here.  
Implementation Details  
For LFM, we use the same data structure as GCE M for adding nodes to the community. In order to allow removals by simply scanning over the nodes of the community once, we also store internal and external (weighted) degrees of all nodes in the community. The running time is not so easy to state in terms of the community size as nodes may be added and removed again. Each addition of a node \(u\) needs time \(\mathcal{O}(\deg(u)+|C|+|S(C)|)\) as we scan first the shell to find \(u\) and then the community to find a possible candidate for removal. Each removal of a node \(u\) needs time \(\mathcal{O}(\deg(u)+|C|)\) as we need to scan \(C\) again to find other nodes that possibly need to be removed. Further, every time we add a node to the shell we might also need to calculate its total weighted degree unless it has been pre-computed.
# Medical Image Segmentation Network Based on Dual-Encoder Interactive Fusion
## 3 Methodology
### Global-Local Feature Fusion Module  
To enhance the integration of features from disparate encoders, we have developed a GLFF module. This module capitalizes on both global and local information to facilitate a more effective amalgamation of features. Specifically, the feature outputs from the Transformer, denoted as \(F_{t}\), and the CNN, denoted as \(F_{c}\), are combined through concatenation to yield the fused feature \(F_{M}\):  
\[F_{M}=Concat[F_{t},F_{c}] \tag{10}\]  
The GLFF module incorporates a channel attention mechanism [38] that prioritizes salient channel features while attenuating the less relevant ones. Initially, average pooling is applied along the spatial dimension to derive the channel-wise representation of the fused features, denoted as \(F_{M_{avg}}\in\mathbb{R}^{C\times 1\times 1}\). Following this, a nonlinear transformation is applied to \(F_{Mavg}\) using the sigmoid function \(\sigma\) and a multi-layer perceptron (MLP), resulting in the generation of channel attention weights \(\varphi\in\mathbb{R}^{C\times 1\times 1}\). These weights are then applied element-wise to the concatenated features \(F_{M}\) to highlight significant channel characteristics:  
\[F_{M_{avg}}=AvgPool(F_{M}) \tag{11}\]  
\[\varphi=\sigma(W_{2}\delta(W_{1}F_{M_{avg}})) \tag{12}\]  
\[F_{M_{C}A}=F_{M_{avg}}\otimes\varphi \tag{13}\]  
Here, \(W_{1}\) and \(W_{2}\) are the weight matrices corresponding to the fully connected layers, with \(\delta\) denoting the ReLU activation function.  
Subsequently, the integrated features are modulated using the global semantic information and local detail knowledge derived from the GLFF module, leading to a richer feature representation:  
\[F_{Fusion}=F_{M_{C}A}+\gamma((\omega_{t}+\omega_{c})\otimes F_{M_{C}A}) \tag{14}\]
## 4 Simulation Results
### Buildings  
Table 5 shows the values that define the thermodynamic sorption cycle for each working pair used in the residential application. Figure 6 shows the energy efficiencies of short-term storage, as the mass of metal present in the adsorbent bed varies. In general, the most promising values are associated with the use of water as a refrigerant; in particular, the most promising working pairs are the composite CaCl\({}_{2}\)-silica/water (CM09) solutions and the zeolite DDZ70 UOP/water (CM13). The first one offers the best performance in all cases (from \(\phi\) = 0 to \(\phi\) = 3). The second one has a good efficiency for low percentages of metal mass (\(\eta\)0 = 3.05, \(\eta\)1 = 2.88), but undergoes a significant reduction as \(\phi\) increases (equal to 23.7%). Methanol or ethanol-based solutions generally show lower efficiencies, except for the composite LiCl-silica/methanol solution (CM04), which is characterized by a rather limited reduction in performance as the metal mass increases: for \(\phi\) = 3 this is the working pair with the best performance after CM09, with a storage energy efficiency of 2.57. Figure 7 instead shows the efficiency values in the case of long-term storage (ideally infinite). The composite CaCl\({}_{2}\)-silica/water (CM09) is still the best choice for \(\phi\) = 0, but has a very critical reduction in the presence of metal mass. The LiCl-silica/methanol mixture (CM04) appears to be even more promising than in the case of short-term storage: it is the working pair with the highest performance for \(\phi\) > 1. Despite presenting good efficiency values in the absence of metal mass and for a short time, ethanol-based solutions (CM06, CM08) tend to strongly degrade their performance as \(\phi\) and storage time increase. Figure 8 shows the trend of the storage efficiency of the four most promising working pairs as a function of \(\phi\), for both cases considered: the water-based working pairs (CM09, CM12, CM13) are more sensitive to the presence of a metal mass in the adsorbent bed, with a significant reduction in performance compared to the methanol-based pairs (e.g., CM04). For example, in the transition from \(\phi\) = 0 to \(\phi\) = 3, water-based solutions show a reduction in efficiency of 19.4%, compared to 13.8% for methanol-based solutions (for long-term storage, the percentages increase to 25.6% and 33.4%, respectively).
## 3 Methodology
### BIM Query Dataset  
The dataset comprises 1680 samples, categorized into two main types: precise queries and fuzzy queries, to comprehensively assess the model's parsing capabilities in different contexts. The natural language queries were constructed based on representative usage examples drawn from mainstream BIM software systems. These examples reflect frequently encountered information retrieval needs during real-world BIM operations. The query design was informed by domain knowledge embedded in these application scenarios to ensure practical relevance and authenticity. The precise query dataset contains 1440 samples, each adhering strictly to IFC standard logic, with clear expression and well-structured format. These samples are primarily used to evaluate the model's accuracy and stability when handling explicit query requirements. In contrast, the fuzzy query test set includes 240 samples, which simulate natural language inputs from non-expert users. This dataset is primarily used to test the model's robustness and generalization in real-world interaction scenarios. These queries may involve semantic ambiguity, incomplete expression, or colloquial descriptions. All queries in the dataset are divided into 12 standardized query types, corresponding to common query needs on BIMserver, covering core tasks in BIM data extraction. This design ensures both systematic and representative data, aiding in the training and evaluation of the model's performance across different query categories and providing a high-quality testing benchmark for intelligent query systems in the BIM domain. The precise query dataset is further subdivided into 1200 training samples and 240 test samples, while the fuzzy query test set is exclusively used for validating the model's flexibility in handling ambiguous queries. Initially, all query types were manually created and then augmented using GPT to generate 100 queries per type. The fuzzy query test set specifically aims to evaluate the system's matching capability for fuzzy queries. All the source code and dataset can be obtained from [[https://github.com/liubingru66/BIMcoder](https://github.com/liubingru66/BIMcoder)]([https://github.com/liubingru66/BIMcoder](https://github.com/liubingru66/BIMcoder)) (accessed on 4 March 2025). As shown in Table 1, the dataset structure is as follows. To better manage and understand the diversity of BIM queries, the 12 types of IFCQL query instructions can be classified based on the query target object, the query method, and its content. Grouping these queries according to their querying methods allows for clearer
## 5 Tie Strength Applied to MANETs
### Computation of Average Tie Strength of a Forwarding Path in a MANET  
We define five integer values to measure the strength of a tie (_tie strength_) between an individual and each one of the other individuals in a MANET. This way, TS takes integer values from 0 to 4, where 4 represents the strongest tie (high interaction and/or trust) and 0 represents the weakest tie (very poor or no interaction and/or untrusted relationship). We have chosen five values following the same structure traditionally done in the mean opinion score (MOS) metric used to assess subjective quality of service (QoS) in multimedia applications [28]. Notice that the tie strength (TS) is asymmetrical (i.e., \(TS_{u}(v)\
eq TS_{v}(u)\)) and that the tie between a given user and itself is 0 (i.e., \(TS_{u}(u)=0\)).  
Finally, we define the average tie strength \(\overline{TS_{k}}\) of a path \(k\) by means of the geometric mean of each \(TS_{k,j}\) between every node \(j\) of the path and its neighbor \(j+1\), from a source \(S\) to its destination \(D\), see Equation (8), for a MANET general scheme represented in Figure 3. \(NF_{k}\) stands for the number of forwarding nodes that compose path \(k\).  
\[\overline{TS_{k}}=\sqrt[N_{k}]{\prod_{j=1}^{NF_{k}}TS_{k,j}(j+1)}\,,\ \ 0\leq TS_{k,j}\leq 4 \tag{8}\]
# Estimating the Optimal Overall Slope Angle of Open-Pit Mines with Probabilistic Analysis
## 2 Methods of Slope Stability Analysis
### Numerical Modelling Set Up  
Numerical modelling has been conducted using RoScience RS2D software, adopting elastoplastic Mohr-Coulomb failure criterion (M-C). The geomechanical properties for the three different rock masses, which are used in this analysis, are listed in Table 1. It is noteworthy to emphasize that this study does not represent a real case study. However, it uses representative geological properties and general open-pit mine geological features. Also, it is conducted to demonstrate a method to propose an optimal slope angle while maintaining a factor of safety (**FOS**).