esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Investigation of the Time-Lag Relationship Between Subsurface Strain and Surface Subsidence  
To investigate the response relationship between subsurface strain evolution and surface subsidence, this study conducted a time-lagged cross-correlation (TLCC) analysis between distributed optical fiber strain data (represented by the average value) from the 5-50 m depth interval and cumulative surface subsidence over the same period. The strain data were obtained from a distributed fiber-optic sensing system, covering the period from January 2021 to December 2022 (a total of 24 months). To enhance the robustness of the statistical analysis, the Spearman rank correlation coefficient was employed to account for potential nonlinearities in the data. Notably, the strain variations recorded by the fiber-optic system were most pronounced in the 200-400 m depth interval (Figure 9), where significant strain accumulation was observed across multiple years. This indicates a dynamic evolution of the stress state in the deeper subsurface, which also supports the rationale for selecting the 5-50 m interval for correlation analysis with surface deformation.  
The TLCC results show a clear peak in correlation strength at a lag of -3 months, where the strain data lead the subsidence by three months, with a maximum Spearman correlation coefficient of \(\rho\) = 0.858 (Figure 10). This indicates a strong positive correlation and suggests that changes in the subsurface strain field can be detected approximately three months prior to observable surface deformation, highlighting the predictive and precursor nature of strain signals. This phenomenon is likely driven by groundwater-level fluctuations that alter pore pressure fields, leading to soil compaction and strain accumulation, with surface settlement subsequently occurring as a macroscopic response.  
Figure 9: Annual average subsurface strain profiles from 2021 to 2022, as measured by distributed fiber-optic strain sensing.  
Moreover, correlation coefficients remain relatively high (around 0.8) for lag times between 0 and +2 months, albeit slightly lower than the peak. This further confirms the predictive potential of subsurface strain monitoring. These findings imply that distributed fiber-optic strain sensing has significant potential for use in early warning systems and subsidence mechanism studies. In areas subject to multiple driving forces--such as groundwater extraction and soil compaction--its ability to capture fine-scale deformation with high temporal and spatial resolution offers a valuable complement to InSAR, especially in addressing the limitations of traditional techniques in detecting lagged responses.
# A Survey on Malleability Solutions for High-Performance Distributed Computing
## 4 MPI Malleability
### Elastic Execution of MPI Applications
It is also responsible to manage the parameters on which the reconfiguration decision is founded, such as runtime performance or power consumption. * Adaptive Batch Scheduler (ABS) is responsible for efficient job scheduling and dynamic reconfiguration decisions for running jobs. It maintains two separate priority-ordered queues, one for rigid and the other for malleable applications. These components are executed concurrently in two independent threads of the node. Figure 2: Elastic MPI—overview of the interactions between the SLURM daemons [21]. Source: [[https://mediatum.ub.tum.de/doc/1539159/file.pdf](https://mediatum.ub.tum.de/doc/1539159/file.pdf)]([https://mediatum.ub.tum.de/doc/1539159/file.pdf](https://mediatum.ub.tum.de/doc/1539159/file.pdf)) (accessed on 7 May 2022). Reprinted with permission from [21]. Copyright 2020, [PERSON]. The interaction between them is event-triggered and occurs every _SchedulerTick_ second (defined in the configuration file) when a job is submitted or after job completion. * When an SRUN is launched in a node, a unique port number to a particular thread is communicated to the ERS, on which specific messages will be received. * Thus, the reconfiguration of a job can be summarized in the following steps: 1. ERS sends a reconfiguration signal to the SRUN instance. 2. If a shrinking is notified by ERS, SRUN informs the corresponding SLURMD daemons about the processes to terminate. 3. If an expansion is triggered, SRUN notifies the corresponding SLURMD daemons about the number of processes to create. 4. When children processes are executing MPI_Probe_adapt, SLURMD notifies SRUN and the state of the job is changed to _ADAPTING_. 5. After the execution of MPI_Comm_adapt_commit in all processes, SRUN sends a reconfiguration complete message to ERS. 6. Finally, ERS sends to SRUN the updated job credentials and updates the job state back to _RUNNING_. Figure 3 depicts how these stages are followed. The scheduling policies can be based on different criteria. In [22], the evaluation on a cluster of 6480 compute nodes using the standard benchmark and synthetic elastic applications were made. In this experimentation, the authors used two different scheduling strategies:  
* The performance-aware strategy considers the MTCT ratio (the time spent in MPI calls versus the time doing relevant computation), as an intuitive heuristic. Figure 3: Elastic MPI—overview of interactions between different SLURM daemons during a reconfiguration with the elastic execution [21]. Source: [[https://mediatum.ub.tum.de/doc/1539159/file.pdf](https://mediatum.ub.tum.de/doc/1539159/file.pdf)]([https://mediatum.ub.tum.de/doc/1539159/file.pdf](https://mediatum.ub.tum.de/doc/1539159/file.pdf)) (accessed on 7 May 2020). Reprinted with permission from [21]. Copyright 2020, [PERSON]. * The power-aware strategy requires the correct management of the RAPL counters, specifically updating automatically system files that store energy values. All this information is stored and managed by ERS component.
## 3 Simulation Results
### Simulation Results in a Circuit with FOC at Rated Motor Shaft Rotation Frequency
Therefore, the use of the Bessel filter in the compensator to damp higher harmonics of the supply voltage in the traction drive output section with FOC is impractical. The presence of filters with adapted cut-off frequency in the compensator compared to a fixed frequency leads to:  
* Increase in drive efficiency. * Decrease in torque ripple factor both in stable mode (Table 6) and during motor acceleration. * Decrease in THD. * Decrease in convergence time both for stable mode and for motor acceleration. In this paper, we assumed that the load for the traction drive is a constant static moment. Moreover, this moment was determined without considering the weight of the locomotive, the weight of the train, and the profile of the track section. Additionally, the operational factors that cause stochastic changes in the voltage in the DC link and the load on the motor shaft were not considered. The authors acknowledge that when considering operational factors, the parameters of the traction drive with the compensator calculated in this study will undergo quantitative changes. The purpose of this study was to provide a methodology for assessing the effect of filters with different types of approximations on the efficiency of the compensator in the traction drive system and to choose the optimal technical \begin{table}
\begin{tabular}{l c c c} \hline \hline  & \multicolumn{3}{c}{**Parameter**} \\ \cline{2-4}
**Control Strategy** & **THD, \%** & \
\begin{tabular}{c} **Torque Ripple** \\ **Factor k\({}_{\text{p}}\), \%** \\ \end{tabular} &
\begin{tabular}{c} **Convergence Time** \\ **for Stable Mode** \\ **t\({}_{\text{conv}}\), \%** \\ \end{tabular} \\ \hline \multicolumn{4}{c}{Adaptive filtering methods} \\ LSM & 10.07 & 15.96 & 1.27 \\ NLSM & 16.11 & 28.62 & 1.14 \\ LLSM & 10.66 & 15.37 & 1.12 \\ Kalman filter & 9.62 & 11.81 & 0.93 \\ Wiener filter & 0.38 & 0.04 & 0.62 \\ \hline \multicolumn{4}{c}{Higher harmonic damping algorithm} \\ With a Butterworth filter & 1.42 & 0.3 & 0.186 \\ With a Chebyshev Type I filter & 1.38 & 0.15 & 0.175 \\ With a Chebyshev Type II filter & 1.47 & 0.151 & 0.172 \\ With a Bessel filter & 1.43 & 0.157 & 0.539 \\ With an elliptic filter & 1.39 & 0.145 & 0.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparative performance analysis of traction drive systems equipped with reactive power compensators under different control strategy implementations. solution when synthesizing compensation circuits. The assessment of the effect of the filters with different approximation types on the efficiency of the compensator in a traction drive system with due consideration of operational factors, is a topic for further studies.
# A Weighted Histogram-Based Tone Mapping Algorithm for CT Images
## 1 Introduction
### Tone Mapping
[24] models visual adaptation for global tone mapping which is followed by a second stage local contrast enhancement. The Mantiuk'08 operator [25] iteratively minimizes the visible distortion of the image measured by an error metric, and takes into account both the display properties and the properties of HVS. Color perception plays a central role in retinex-based TMO [26] and in the so-called iCAM model which is a 'next-generation color appearance model' [27]. For further details, we refer the interested reader to the literature [28; 29]. Our proposed method builds on spatially weighted histogram equalization. It can be seen as an effective tone mapping approach or as a method for local contrast enhancement. Our aim was to avoid halos and artefacts of local histogram methods and ensure good local contrast. While histogram methods have been used for medical image processing since the 1980s, the effectiveness of tone mapping operators has also been demonstrated on medical images, for instance [PERSON]'s method [21]. The literature on histogram methods and tone mapping operators is vast; further details can be found, for instance, in [30].
## 3 Results and Discussion
Further work including intermediate sintering temperatures (e.g., 950 \({}^{\circ}\)C) is ongoing to extend the observed trends. The electrochemical performance of our AC-EPD-fabricated LFP electrode is comparable to previously reported Li/LATP/LFP systems with similar mass loading [21; 22; 23; 24]. This validates that our system offers a suitable performance baseline for probing interfacial kinetics and charge storage mechanisms, which is the central focus of this study. Further, to evaluate the scalability of the AC-EPD method, we also tested LFP electrodes with a mass loading of \(\sim\)2 mg\(\cdot\)cm\({}^{-2}\) using a liquid electrolyte, as shown in Figure S2 (Supporting Information). These higher-mass-loading electrodes demonstrated stable cycling and good rate performance, confirming that the interface-sensitive design remains effective under more practical loading conditions. Unlike prior works that primarily emphasized performance optimization, our platform enables mechanistic analysis by combining a reproducible AC-EPD fabrication process with interface-sensitive evaluation methods. It is also important to emphasize that, in the liquid electrolyte configuration, the EC/DEC solvent system can decompose on the LFP surface to form a cathode-electrolyte interphase (CEI), typically composed of LiF, Li\({}_{2}\)CO\({}_{3}\), and organic moieties. While this CEI can protect the electrode, it may also increase interfacial resistance over time. In contrast, the solid-state LATP interface does not undergo similar solvent-driven decomposition; however, it is subject to mechanical and chemical interfacial instability, especially under high-temperature sintering or prolonged cycling. For example, interfacial reactions such as Ti reduction or phosphate interdiffusion can occur, which degrade ionic conductivity and cycle stability. Therefore, although solid-state systems can suppress solvent-driven SEI formation, maintaining interfacial integrity requires the careful control of interface chemistry and processing conditions.
## 3 System Model  
The model of a wireless transmission system with an energy harvesting transmitter is shown in Figure 1. Supposing that the data transmission time has \(N\) time slots, the length of each time slot is unit 1, the transmitter will harvest \(E_{i}\) units of energy at the beginning of time slot \(i\). The energy \(E_{i}\) collected at each time slot is independent and obeys uniform distribution. The transmit power in time slot \(i\) is \(p_{i}\); when \(p_{i}>0\), the circuit consumption power is \(a\) mW, and the total power consumed is  
\[P_{all}=\left\{\begin{array}{ll}p_{i}+a,&p_{i}>0\\ 0,&p_{i}=0\end{array}\right\} \tag{1}\]  
In (1), \(p_{i}\) is defined as  
\[p_{i}=\frac{E_{i}-s_{i}+r_{i}-al_{i}}{l_{i}},\;\;i=1,\ldots,N \tag{2}\]  
where \(l_{i}(0\leq l_{i}\leq 1)\) is the actual transmission time, \(s_{i}\) is the amount of energy stored in the battery, \(r_{i}\) is the amount of energy extracted from the battery and \(al_{i}\) is the consumption.  
There are energy losses when the energy is stored in the battery, supposing that the storage efficiency is \(\alpha\) (\(0\leq\alpha\leq 1\)). If the energy collected by the time slot is used up to transmit the data, there are no energy storage losses; if the collected energy is not used up at the current time slot, the remaining energy \(s_{i}\) is stored in the battery, the amount of energy loss is \((1-\alpha)s_{i}\), and only \(\alpha s_{i}\) units of energy can be used for future. The amount of energy in the battery in time slot \(i\) can be written as  
\[B_{i}=\sum_{j}^{i}\left(\alpha s_{j}-r_{j}\right)\geq 0,\;\;i=1,\ldots,N \tag{3}\]  
For the additive white Gaussian noise channel, the instantaneous transmission rate \(R(p_{i})\) is  
\[R(p_{i})=\frac{1}{2}\log(1+h_{i}p_{i}) \tag{4}\]  
In (4), \(h_{i}\) is the channel fading coefficient in time slot \(i\).  
In this paper, the average throughput of the system is maximized by optimizing the transmit power of each time slot and the factors such as energy arrivals, channel states, circuit consumption and storage losses are considered comprehensively.  
Figure 1: System model.
# Satellite Network Security Routing Technology Based on Deep Learning and Trust Management
## 3 Problem Modeling
### Trust Evidence Data Cleaning
To enable the node to dynamically adjust the direct trust value of the neighbor node, the corresponding reward and punishment function can be set:  
\[DR_{ij}(t_{n+1})=(1-v)DR_{ij}(t_{n})+v\cdot DR_{ij}(t_{n+1}) \tag{4}\]The reward and penalty function uses parameter \(v\) to adjust the ratio between the past direct trust value and the next time point direct trust value. This allows satellite network nodes to dynamically assess the behavior of nodes and, consequently, evaluate whether the node's behavior is secure. For the setting of two direct trust values, \(m\{T\}\) can be used to compare the difference with zero. If it is less than zero, a larger value will be assigned to \(v\), so that the direct trust of the satellite node to the target node will soon slip down; if it is greater than zero, a smaller value will be assigned to v, so as to improve the direct trust value of the target node. For the calculation of the recommendation trust vector \(IR_{ij}\), we can use the direct trust vector of all neighbors of the evaluated target node. The recommended trust vector can be obtained by designing a special routing package to request the direct trust evaluation of the neighbor nodes. Finally, comprehensive trust vectors synthesize the direct trust value and the recommended trust value through the following formula to obtain the comprehensive trust value. \[\left\{ \begin{array}{l}m_{ij}^{*}(A)=((m_{n1,j}(A)\oplus\ldots)\oplus m_{ nk,j}(A))\oplus m_{ij}(A),A\in\Omega\\ m_{ij}^{*}(\varnothing)=0\end{array} \right. \tag{5}\]  
In Formula (5), the following two formulas are used for calculation when different evidence is synthesized:  
\[m_{1}(A)\oplus m_{2}(A)=\frac{\sum\limits_{X\cap Y=A}m_{1}(X)m_{2}(Y)}{1-K} \tag{6}\]  
\[K=\sum\limits_{X\cap Y=\sigma}m_{1}(X)m_{2}(Y) \tag{7}\]  
Finally, a comprehensive trust vector can be obtained through D-S evidence theory, and then its comprehensive trust value can be obtained. To obtain the dataset of training variational autoencoder networks, trust vectors need to be designed. The trust vector is formed by recording the comprehensive trust value with length \(l_{w}\), and a large number of such trust vectors can be accumulated for the training of deep learning network models. Since the VAE-based anomaly detection model requires the input of normal data, a large number of trust vectors can be generated as normal datasets without malicious node interference at the initial stage of satellite network construction, and these data can be input into the variational autoencoder for model training.
# FREDA: Few-Shot Relation Extraction Based on Data Augmentation
## 2 Related Work
### Few-Shot Relation Extraction
The following are some of the more representative models in FSRE using the prototype network approach: Proto-HATT [17] improves model robustness and accelerates model convergence by designing instance-level and feature-level attention schemes; MLMAN [18] encodes the instances in the query set and support set and leverages the interaction of local and instance-level information between them to obtain richer semantic representation; REGRAB [44] incorporates global relation graphs with Bayesian meta-learning into the model; TD-proto [45] introduces relation and entity descriptions to enhance prototype networks; ConceptFERE [23] incorporates an entity multiconcept selection module to enhance key entity concept features; CTEG [46] fine-tuning by assigning pseudolabels to untagged data using the domain; HCRP [20] introduce three modules for hybrid prototype learning, contrast learning, and adaptive focus loss to improve the model; SimpleFSRE [21] simplifies the model using a direct join method of relation information; PRM [22] proposes a parameter-free prototype correction method; and MapRE [47] adds a framework that takes into account label uncertainty and label-aware semantic mapping information. However, these models have inferior domain adaptation capabilities. Moreover, the above studies using the prototype network approach usually do not focus on the interaction information between the support set instances and the query set instances but simply average the embedding matrix of the support set instances for each relation class to obtain the class prototype, failing to exploit the interaction information between instances. Some of the models that have achieved good results also improve the performance of FSRE tasks by incorporating external information and do not take full advantage of the information in the dataset. We, therefore, propose a data augmentation-based model FREDA for the FSRE task. Firstly, we extract the relation triple information from the support set to fully use the triple information and augment the triple information in the support set instances. Secondly, we successfully reduce the deviation between the obtained and expected relation prototypes by designing an interactive attention module that computes the instances between the support set, the query set, and the relation triple interactively, focusing on the highly interactive parts between the instances. Our method is efficient in capturing similar features between the query set and the support set to obtain more discriminative relation prototypes, and it also gives excellent results in datasets with different domains with strong domain adaptation capability.
## 2 Materials and Methods
### Identifying Graph Topologies from Observations of Stationary Graph Signals
#### 2.1.3 Proximal Gradient Algorithm for Batch Topology Identification
\tag{12}\]  
The resulting entry-wise separable nonlinear map nulls the diagonal entries of \(\mathbf{S}_{k+1}\), sets the edge weights corresponding to \(\Omega\) to the observed values \(s_{ij}\), and applies a non-negative soft-thresholding operator to update the remaining entries. Note that for symmetric \(\mathbf{S}\), the gradient (9) will also be a symmetric matrix. So if the algorithm is initialized with, say, a very sparse random symmetric matrix, then all subsequent iterates \(\mathbf{S}_{k}\), \(k\geq 1\), will be symmetric without requiring extra projections. The resulting iterations are tabulated under Algorithm 1, which will serve as the basis for the online algorithm in the next section. ```
0:\(\hat{\mathbf{C}}_{\mathbf{y}}\), \(\mu>0\). 1: Initialize \(\mathbf{S}_{0}\
eq\mathbf{0}\) as a sparse, random symmetric matrix, \(\gamma=1/[4\mu\lambda_{\max}^{2}(\hat{\mathbf{C}}_{\mathbf{y}})]\), \(k=0\). 2:while not converged do
3: Compute \(\
abla g(\mathbf{S}_{k})=\mu\big{[}(\mathbf{S}_{k}\hat{\mathbf{C}}_{\mathbf{ y}}-\hat{\mathbf{C}}_{\mathbf{y}}\mathbf{S}_{k})\hat{\mathbf{C}}_{\mathbf{y}}- \hat{\mathbf{C}}_{\mathbf{y}}(\mathbf{S}_{k}\hat{\mathbf{C}}_{\mathbf{y}}- \hat{\mathbf{C}}_{\mathbf{y}}\mathbf{S}_{k})\big{]}\). 4: Take gradient descent step \(\mathbf{D}_{k}=\mathbf{S}_{k}-\gamma\
abla g(\mathbf{S}_{k})\). 5: Update \(\mathbf{S}_{k+1}=\text{prox}_{\gamma\|\cdot\|_{1},\mathcal{S}}\) (\(\mathbf{D}_{k}\)) via the proximal operator in (12). 6:\(k=k+1\). 7:endwhile
8:return\(\mathbf{S}^{\star}=\mathbf{S}_{k}\). ```  
**Algorithm 1** Proximal gradient (PG) for batch topology identification. The computational complexity is dominated by the gradient evaluation in (9), incurring a cost of \(\mathcal{O}(\|\mathbf{S}_{k}\|_{0}N^{2})\) per iteration \(k\). The iterates \(\mathbf{S}_{k}\) tend to become (and remain) quite sparse at early stages of the algorithm by virtue of the soft-thresholding operations (a sparse initialization is useful to this end), hence reducing the complexity of the matrix products in (9). As \(k\to\infty\) the sequence of iterates (11) provably converges to a minimizer \(\mathbf{S}^{\star}\) [cf. (8)]; see e.g., [46]. Moreover, \(F(\mathbf{S}_{k})-F(\mathbf{S}^{\star})\to 0\) due to the continuity of the composite objective function \(F(\cdot)\)--a remark on the convergence rate is now in order. **Remark 1**. : _Results in [47] assert that convergence speedups can be obtained through the so-termed accelerated (A)PG algorithm; see [12] for specifics in the context of ISTA that are also applicable here. Without increasing the computational complexity of Algorithm 1, one can readily devise an accelerated variant with a (worst-case) convergence rate guarantee of \(\mathcal{O}(1/\sqrt{\varepsilon})\) iterations to return an \(\varepsilon\)-optimal solution measured by the objective value \(F(\cdot)\) (cf. Algorithm 1 instead offering a \(\mathcal{O}(1/\varepsilon)\) rate)._
# Generic Language for Partial Model Extraction from an IFC Model Based on Selection Set
## 2 Related Work
### Partial Model Extraction According to User-Defined Requirements  
Due to the multidisciplinary, multi-stage and multi-party nature of building projects, business tasks require different information. The studies related to partial model extraction can be divided into two types: the schema level-based and the instance level-based methods. The former method is to develop a definition format with various mappings for data exchange, and the latter directly deals with the data within the original model. The schema level-based method refers to extract model data according to a predefined model data structure. Consequently, it is necessary to define all possible transformations in advance. The instance level-based method focuses on the specific information of project objects, and this method enables the extraction of some designated model data according to the user-defined requirements. In the IFC file, the specific information of objects means corresponding IFC instances. This method gives users enough flexibility and well-understanding, but more complex querying algorithms are required than the schema level-based method.