esa-sceva/satcom-chunk-collection huggingface dataset:
# NTPP-MVSNet: Multi-View Stereo Network Based on Neighboring Tangent Plane Propagation
## 2 Related Works
### Other Optimization  
To overcome the issue of low absolute resolution caused by fixed depth hypotheses, [PERSON] et al. [16] developed an adaptive depth sampling module (ATV) combined with uncertainty estimation. This module accurately determines each pixel's depth interval in three-dimensional space to produce more precise depth maps. [PERSON] et al. [25] introduced the PVSNet, the first-ever deep learning framework that captures the visibility information of different adjacent views, with the aim of improving the multi-view stereo algorithms' performance on datasets with strong viewpoint changes. In contrast to popular learning-based planar scanning algorithms that rely on approximating isotropic cost volumes, [PERSON] et al. [26] argued that planar scanning volumes are inherently anisotropic in both space and depth directions. To remedy this, they proposed P-MVSNet [26], a multi-view stereo algorithm that employs isotropic and anisotropic 3D convolutions. In addition to improving the accuracy of depth maps, efficiency is a desirable feature for multi-view depth estimation in real-world scenarios. Thus, [PERSON] et al. [27] created Fast-MVSNet, which utilizes a sparse-to-dense algorithm framework and optimizes the depth map further with a simple and effective Gaussian-Newton layer.
## 2 Background  
### Problem Description  
As mentioned in the Introduction, we aim to develop a data-dependent feature extraction method for weakly supervised domestic SED system. The target system and problem are designed in accordance with the Detection and Classification of Acoustic Scenes and Events (DCASE) 2020 Task 4 [34]. The target system aims to detect sound events with class, onset time, and offset time labels through a weakly-supervised training. In this training, some of the dataset annotations are omitted. More specifically, the dataset may be categorized into three types: strongly-labeled, weakly-labeled, and unlabeled data. The strongly-labeled data provide all the required information: sound class, onset time, and offset time annotations. The weakly-labeled data provide only part of the information, for example the sound class label only. The unlabeled data do not provide any information, only waveforms. A schematic diagram of the target system is presented in Figure 1.  
Our goal is not to design the system itself but to develop a data-dependent feature extraction method for the system. Thus, we focus on the non-negative matrix decomposition technique, which is a sparse representation tool for acoustic signal data.
# Local Batch Normalization-Aided CNN Model for RSSI-Based Fingerprint Indoor Positioning
## 2 Background  
### Batch Normalization and Its Limitations  
BN is a technique used to accelerate and stabilize the training of neural networks. It works by normalizing the inputs to each layer based on the statistics of a mini-batch, thereby mitigating internal covariate shift. This process not only speeds up training but also allows for the use of higher learning rates. Since its introduction, BN has been widely adopted in various network architectures, including ResNet [23] and Inception [24]. However, BN has some limitations, particularly its sensitivity to small batch sizes, which has led to the development of alternative normalization techniques. One such alternative is LN [20], which standardizes across the features rather than the batch dimension, making it more suitable for recurrent neural networks. Another alternative is GN [22], which divides features into groups, striking a balance between batch and layer normalization.  
The core idea behind BN, as introduced by [PERSON] and [PERSON] [16], is the standardization of layer inputs during the training phase to address internal covariate shift in deep neural networks. This process, outlined in Section 8.7.1 of _Deep Learning_ by [PERSON] et al. [25], involves the following key steps:  
1. Calculate the mean and variance: Compute the mean and variance for each mini-batch. \[\mu_{batch}=\frac{1}{m}\sum_{i=1}^{m}x_{i},\ \sigma_{batch}^{2}=\frac{1}{m} \sum_{i=1}^{m}(x_{i}-\mu_{\text{batch}})^{2}\] (1)
2. Normalize: Standardize the inputs using the calculated mean and variance to achieve zero mean and unit variance. \[\hat{x}=\frac{x-\mu_{batch}}{\sqrt{\sigma_{batch}^{2}+\epsilon}}\] (2)
3. Scale and shift: Introduce two learnable parameters, the scale factor (\(\gamma\)) and the shift factor (\(\beta\)), to adjust the normalized data. These adjustments allow the network to maintain its representational capacity. \[y=\gamma\hat{x}+\beta,\] (3) Variable Definitions: \(x_{i}\) is the input of the \(i\)-th sample in a mini-batch. \(x\) is the input value before normalization. \(\hat{x}\) is the normalized input. \(\mu_{batch}\) is the mean of the mini-batch. \(\sigma_{batch}^{2}\) is the variance of the mini-batch. \(m\) is the number of samples in the mini-batch. \(\epsilon\) is a small constant added to prevent division by zero. \(y\) is the final output of the BN process.  
\(\gamma\) and \(\beta\) are learnable parameters that scale and shift the normalized data, respectively.
# Permanent Deformation Mechanism of Steel Bridge Deck
###### Abstract  
Unlike conventional asphalt pavements, steel bridge deck pavement (SBDP) is directly constructed on orthotropic steel deck plates characterized by relatively low flexural stiffness, rendering it more susceptible to rutting deformation under elevated temperatures and repeated loading. To investigate the mesoscopic mechanism underlying rutting formation in SBDP, a three-dimensional (3D) discrete-continuous coupled model of a steel-asphalt composite structural specimen (SACSS) was developed and employed to conduct virtual rutting simulations, which were subsequently validated against laboratory test results. The impact of surface cracking on rutting progression was then explored. In addition, the spatial motion and contact interactions of particles during the rutting process were monitored and analyzed. The influence of steel plate stiffness on the rutting resistance of SBDP was also evaluated. The numerical analyses yielded the following key findings: (1) Under three steel-asphalt interface bonding (SAIB) failure conditions (0%, 17%, and 100%), the virtual simulation results exhibited strong agreement with experimental trends in rutting depth over time, thereby confirming the validity and reliability of the coupled modeling approach. (2) At 30 \({}^{\circ}\)C, the presence of surface cracks is found to increase the rutting depth by 35.77%, whereas this effect is mitigated at 45 \({}^{\circ}\)C. (3) The meso-mechanical mechanisms governing rutting deformation in SBDP are further elucidated under different temperature conditions. (4) Moreover, at elevated temperatures, the use of a steel plate with an elastic modulus of 206 MPa effectively inhibit rutting development. This study offers mesoscopic-level insights into the effects of temperature, SAIB conditions, steel plate stiffness, and surface cracking on the macroscopic rutting behavior of SBDP, thereby providing a theoretical foundation for the design and optimization of long-lasting SBDPs.  
Road and Railway Engineering Research Institute, Hohai University, Nanjing 210098, China; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]  
30 May 2025  
[PERSON], [PERSON], Y. Permanent Deformation Mechanism of Steel Bridge Deck Pavement Using Three-Dimensional Discrete-Continuous Coupling Method on the Mesoscopic Scale. _Appl. Sci._**2025**, _15_, 6187-6187
# Reversal of the Word Sense Disambiguation Task Using a Deep Learning Model
## 4 Debugging and the Knowledge Base Management System
### Clustering  
This work hypothesized that a deep neural network (DNN) trained on comprehensive natural language processing tasks can implicitly capture semantic relationships that are useful for word sense disambiguation. We propose an approach where the embedded vectors learned by such a DNN are clustered, with each cluster potentially representing a distinct sense. By comparing these data-driven clusters with the existing sense inventory of a knowledge base (e.g., WordNet), discrepancies between the two can be identified. These inconsistencies may indicate the need for refinements of either the knowledge base's sense definitions or the DNN's architecture and training data.  
To facilitate WSD debugging and refinement of the knowledge base, we propose a clustering-based approach to analyze the embedded vectors. This method empowers knowledge engineers to make informed decisions regarding the merging, deletion, or creation of synsets. Two clustering algorithms were considered for this purpose: principal component analysis and neighborhood component analysis. While KMeans was used for all clustering tasks throughout this project for consistency, future work will explore the potential benefits of utilizing alternative algorithms in the context of WSD debugging to assess their impact on efficacy.  
The initial phase of clustering entails determining the optimal number of clusters. The algorithm unfolds through several key steps:  
1. **Selection of the method of vector projection**: The clustering algorithm offers flexibility in the methods of vector projection, including principal component analysis or neighborhood component analysis.
2. **Vector projection**: Embedded vectors undergo projection, a crucial preparatory step for subsequent calculations.
3. **Determination and evaluation of clusters as follows.** 3.1. Application of the KMeans clustering algorithm: Use of the KMeans algorithm on the projected vectors enables the delineation of clusters and the assignment of each sample to its corresponding cluster. 3.2. Metric calculation: Two pivotal metrics, namely the silhouette score and the adjusted mutual information score, are computed for each potential configuration of the clusters, providing quantitative insights into the quality and coherence of the resulting clusters.
4. **Selection of the optimal cluster**: The ultimate determination of the ideal number of clusters hinges upon selecting the configuration yielding the highest product of the silhouette score and the adjusted mutual information score, indicative of optimal clustering performance.  
This systematic approach to determination of the clusters not only ensures robustness and reproducibility but also facilitates informed decision-making for knowledge engineers engaged in WSD debugging tasks. Furthermore, the incorporation of diverse vector design methods and comprehensive evaluation metrics underscores the methodological rigor that is essential for advancing the field of natural language processing.
## 4 Solution  
### Formulation of the Continuous State Problem  
The service provider's optimization problem is a dynamic programming problem with discrete states. In practice, the customer population \(N\) is so large that the traditional backward induction solution is hard to implement. For this reason, we proceed to analyze the continuous state problem in limiting case \(N\to+\infty\).  
To characterize the percentage of customers already in data plan \(i\) at the beginning of period \(t\), we define \(\theta_{i,t}=\lim\limits_{N\rightarrow+\infty}S_{i,t}/N\). For convenience, we define \(\theta_{0,t}=\lim\limits_{N\rightarrow+\infty}(N-\sum_{i=1}^{m}S_{i,t})/N\). Moreover, we let \(\overline{V}_{t}(\theta_{t})\) be the service provider's maximum average revenue per customer from period \(t\) to the end, starting at state \(\theta_{t}=(\theta_{0,t},\theta_{1,t},\cdots,\theta_{m,t})\). To simplify the expression, we let:  
\[\rho_{i}=\sum_{k=1}^{k_{i}^{*}}[F_{i}(v_{i}+kv_{i}^{s})-F_{i  
Summing (2) and (3) over \(t\) and plugging them into the formulation of Data Plan 2 **(DP2)**, we obtain:  
\[\overline{V}_{t}(\theta_{t})=\lim_{N\to\infty}\frac{V_{t}(S_{t})}{N},\quad\forall \ t.\]  
Due to the strong law of large numbers, we can reformulate \(\theta_{i,t+1}\) and \(\theta_{0,t+1}\) as follows.  
\[\theta_{i,t+1} = \lim_{N\to\infty}\frac{S_{i,t+1}}{N} \tag{4}\] \[= \lim_{N\to\infty}\frac{S_{i,t}+Y_{i,t}-L_{i,t}}{N}\] \[= \theta_{0,t}\cdot\lambda p_{i,t}+\theta_{i,t}\cdot(1-q_{i,t}), \quad\forall\ i,t.\] \[\theta_{0,t+1} = 1-\sum_{i=1}^{m}\theta_{i,t+1}\] (5) \[= 1-\sum_{i=1}^{m}\left\{\theta_{0,t}\cdot\lambda p_{i,t}+\theta_{ i,t}\cdot(1-q_{i,t})\right\}\] \[= \theta_{0,t}\cdot(1-\sum_{i=1}^{m}\lambda p_{i,t})+\sum_{i=1}^{m }\theta_{i,t}\cdot q_{i,t},\quad\forall\ t.\]  
Combining (4) and (5), we have the transition function \(\theta_{t+1}=A_{t}(\theta_{t},C_{t})\cdot\theta_{t}\).  
The arguments above can be summarized in the following theorem.  
Theorem 1: _The optimal objective value in **(DP2)** equals the optimal objective value in **(DP1)**: \(\overline{V}_{1}(\theta_{1})=\lim_{N\to\infty}V_{1}(S_{1})/N\)._  
Theorem 1 suggests that when the customer population becomes large enough (which is easily satisfied in real applications), we can approximately solve the SP's dynamic programming problem with discrete states by solving a related problem with a continuous state.
# Dual-Probe Near-Field Phaseless Antenna Measurement System on Board a UAV
## 5 Conclusions  
Several improvements of a UAV-based system for the on-site antenna measurements were presented in this contribution. The use of a dual-channel radiofrequency power detector allows for the simultaneous acquisition of the NF measurements on two measurement surfaces, thus reducing the positioning and geo-referring uncertainties with respect to the case in which two independent flights are needed for the acquisition on each surface. However, the introduction of a dual-band multiconstellation RTK unit improves GNSS-RTK reliability. Both advances also yield smaller flight and deployment times, as well as more accurate measurements compared to previous prototypes of the implemented system. These features would make measurements at higher frequency bands feasible.  
The results prove the capability of the improved system for the characterization of high directive antennas, thus broadening the field of application where UAV-based antenna measurement systems can be introduced.
# Synchronous Remote Calibration for Electricity Meters: Application and Optimization
## 5 Conclusions  
This paper introduces a collaborative distributed remote calibration model designed to address the increasing calibration demands of electricity meters, particularly for current and voltage sensors. Leveraging synchronization, we decompose the synchronized task scheduling problem within two containers into two interdependent aspects: device-based and sequence-based optimization, subsequently developing a hybrid genetic algorithm that integrates these components to ensure efficient and flexible resource utilization.  
Experimental evidence demonstrates that our algorithm outperforms comparable algorithms within its category, further emphasizing its potential to enhance resource efficiency in distributed remote calibration systems. Furthermore, the analysis of the hybrid genetic algorithm provides valuable insights and practical solutions to the complex task scheduling challenges in dual-container synchronization, optimizing both execution time and system reliability.  
Future research could investigate methods for effectively handling task dependencies by enhancing the algorithm's capacity to dynamically adjust execution order, ensuring efficient resource utilization while minimizing conflicts and delays. Furthermore, examining heterogeneous task scheduling and prioritization could further enhance the algorithm's efficiency and adaptability, particularly in environments with varying resource availabilityand task complexities. In addition, incorporating real-time data and feedback mechanisms could enable the algorithm to adapt more effectively to changing conditions, further optimizing performance.  
Conceptualization, Z.Z. and J.X.; methodology, Z.Z. and C.Z.; software, F.L. and G.W.; validation, X.H. and C.D.; formal analysis, H.G. and T.D.; investigation, Z.Z. and J.X.; resources, State Grid Hubei Information & Telecommunication Company and Huazhong University of Science and Technology; data curation, X.H. and C.D.; writing--original draft preparation, Z.Z. and J.X.; writing--review and editing, H.G. and T.D.; visualization, F.L. and G.W.; supervision, J.X.; project administration, J.X.; funding acquisition, J.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the Key Research and Development Program of Hubei Province, China under grants 2024 BAB016, 2024 BAB031, 2023 BAB074, and 2022 BAA038.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
Authors [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] were employed by the company State Grid Hubei Information & Telecommunication Company. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# An Analysis Method for Interpretability of CNN Text Classification Model
###### Abstract  
With continuous development of artificial intelligence, text classification has gradually changed from a knowledge-based method to a method based on statistics and machine learning. Among them, it is a very important and efficient way to classify text based on the convolutional neural network (CNN) model. Text data are a kind of sequence data, while time sequentiality of the general text data is relatively weak, so text classification is usually less relevant to the sequential structure of the full text. Therefore, CNN-based text classification has gradually become a research hotspot when dealing with issues of text classification. For machine learning, especially deep learning, model interpretability has increasingly become the focus of academic research and industrial applications, and also become a key issue for further development and application of deep learning technology. Therefore, we recommend using the backtracking analysis method to conduct in-depth research on deep learning models. This paper proposes an analysis method for interpretability of a CNN text classification model. The method proposed by us can perform multi-angle analysis on the discriminant results of multi-classified text and multi-label classification tasks through backtracking analysis on model prediction results. Finally, the analysis results of the model can be displayed using visualization technology from multiple dimensions based on interpretability. The representative data set IMDB (Internet Movie Database) in text classification is verified by examples, and the results show that the model can be effectively analyzed when using our method.  
text classification; convolutional neural network; interpretability analysis; visualization +
Footnote †: journal: Pattern Recognition Letters
# IoT-Based Small Scale Anomaly Detection Using [PERSON]'s Q Test for e-Health Data
## 4 Results  
We obtained the IoTDixon normal distribution and evaluate them against (i) histogram with density curve (top left), (ii) plot of data points (top right), (iii) box plot (bottom left), and (iv) QQ plot (bottom right) for each of the six data packets. Figure 2 shows the packet wise evaluation of normality for following (a) x1 dataset, (b) x2 dataset, (c) x3 dataset, (d) x4 dataset, (e) x5 dataset, and (f) x6 dataset. We also present the overall normality evaluation for the whole dataset comprising 42 samples as in Figure 3.  
We perform KS-test on each of the data packets. The cumulative distribution plots for each of the six data packets are shown in Figure 4, where x1, x2, x3, x4, x5, and x6 datasets are considered separately. All the six data packets are considered as normally distributed for proceeding the [PERSON]'s Q test.  
We perform the Dixon test on each of the 6 packets: x1, x2, x3, x4, x5, and x6. We find the Q statistic value for each of them as shown in Table 1. The probability of the Dixon test is shown as P. The position of selected anomaly from each data packet is mentioned under the POS column, and the corresponding value of anomaly point is shown under the anomaly column.  
We find that value 76.37 is statistically significant where \(P=0.012<\alpha=0.05\), thus rejecting the null hypothesis for x6 data packet. In other data packets, no such significance is observed; thus, no outlier is statistically detected.  
The proposed work is performed for the first time to showcase the use of the [PERSON]'s Q-test for detecting point anomalies from a small-scale dataset. In this study, we used a health dataset as a case study to validate the applicability of the proposed methodology. This approach can be deployed at a resource-constrained IoT-edge device connected to a health sensor as a proof of concept for the purpose of detection of point anomalies from a small-scale dataset. IoT devices are less processing capacity aware; thus, such systems should be fed with a lightweight scheme with a minimal amount dataset at their vicinity. Doing so can certainly improve the existing scenario of large-scale dataset-aware anomaly detection schemes toward minimalistic processing consumption deployments. Thus, the proposed technique can support at the edge real-life implications where small numbers of samples are collected and mitigated for localized anomaly detection. This can further minimize the overhead of a high amount anomaly eradication procedure in the later phase of application.  
Figure 3: IoTDixon complete dataset normal distribution evaluation (**i**) histogram with density curve (top-left), (**ii**) plot of data points, (**iii**) box plot, and (**iv**) QQ plot.  
Figure 2: IoTDixon normal distribution evaluation (i) histogram with density curve (top-left), (ii) plot of data points, (iii) box plot, and (iv) QQ plot. (**a**) x1 dataset, (**b**) x2 dataset, (**c**) x3 dataset, (**d**) x4 dataset, (**e**) x5 dataset, and (**f**) x6 dataset.
# Inter-UAV Routing Scheme Testbeds
## 2 Ad Hoc Routing
### Mobility Models
#### 2.2.1 Gauss-Markov  
Gauss-Markov is a time-dependent mobility model capable of accurately describing a FANET's movement, with individual node's paths being independent from one another. Random initial positions are defined for each UAV and the velocity vector is recomputed at each time instance depending solely on previous UAV speed and position [53].
# Indole-3-Acetic Acid Action in Outdoor and Indoor Cultures of _Spirulina_ in Open Raceway Reactors
## 2 Materials and Methods
### Evaluation of Cell Growth  
At the initiation and end of the experiments, assessments were conducted to determine the biomass concentration, pH levels, fluorescence, and cell morphology. Biomass concentration was gauged through optical density measurements at 670 nm, utilizing a spectrophotometer (Shimadzu UV/VIS UVmini-1240 spectrophotometer, Kyoto City, Japan), aided by a calibration curve correlating optical density with biomass dry mass [29].  
The optical density data enabled the determination of the final biomass concentration and subsequently facilitated calculating the final biomass productivity. pH measurements were taken using a digital pH meter (Mettler Toledo FiveGoTM, Greifensee, Switzerland). Fluorescence measurements were taken with a fluorimeter (AquaPen AP 100, Photon System-Instrument, Drasov, Czech Republic) on day 0 of cultivations (Fv/Fm initial) and the last day of experiments (Fv/Fm final). Samples of 3 mL were dark adapted for 15 min in the cuvettes before the measurement. A wavelength of 620 nm excitation was used. Cell morphology was assessed using an electron microscope (AxioCan ERc 5s Microscope camera, Zeiss, Germany).