esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Spatially Fixed Virtual Sensing Algorithms
### The adaptive LMS virtual microphone technique
The best estimation performance is theoretically achieved with an array of five physical sensors, however, this configuration was found to be ill-conditioned in experiments and a similar estimation performance was achieved with all three physical sensor configurations. Despite being calculated by exciting the secondary source only, the weights, \(\mathbf{w}\), in Eq. (18), are applied to both the primary and secondary disturbances. It has thus been assumed that the weights are optimal in the estimation of both disturbances. This however, may not always be true, especially in the near field of the secondary source where the spatial properties of the primary and secondary sound fields are very different [38]. As a result, [PERSON] [12] suggested that the optimal weights for the estimation of both the primary and secondary disturbances should be found separately, with the adaptive LMS virtual microphone technique being implemented as shown in Fig. 8.  
As shown in Fig. 8, the virtual sensing algorithm separates the physical error signals into their primary and secondary components using the vector of the physical secondary transfer functions \(\tilde{\mathbf{G}}_{pu}\). This vector of FIR or IIR filters is estimated in the preliminary identification stage. The primary component of the physical error signals is calculated as [12]  
\[\tilde{\mathbf{d}}_{p}(n)=\mathbf{e}_{p}(n)-\tilde{\mathbf{y}}_{p}(n)= \mathbf{e}_{p}(n)-\tilde{\mathbf{G}}_{pu}u(n). \tag{21}\]  
Once the primary and secondary weights have been estimated separately using Eq. (18), the pressure at the virtual location is estimated, as shown in Fig. 8, using  
\[\tilde{e}_{v}(n)=\tilde{d}_{v}+\tilde{y}_{v}=\mathbf{w}_{p}^{\mathrm{T}}\tilde {\mathbf{d}}_{p}(n)+\mathbf{w}_{u}^{\mathrm{T}}\tilde{\mathbf{y}}_{p}(n), \tag{22}\]  
where \(\mathbf{w}_{p}\) and \(\mathbf{w}_{u}\) are vectors containing the \(M_{p}\) optimal physical primary and secondary weights and \(\tilde{\mathbf{d}}_{p}(n)\) and \(\tilde{\mathbf{y}}_{p}(n)\) are vectors containing estimates of the primary and secondary disturbances at the \(M_{p}\) physical sensor locations.
# Slipping Trend Prediction Based on Improved Informer
## 4 Experiment
### Model Failure and Edge Case Analysis  
The improved Informer model achieved an overall accuracy of 94.75%, and outperformed RNN and LSTM in the experiments. However, certain issues remained, such as the potential failure of the model in some edge cases. As shown in Tables 3 and 4, with an increase in the prediction horizon, the accuracy, recall, and F1 score exhibit a gradual decline. This may be due to error accumulation, which can degrade model performance in long-sequence forecasting and reduce prediction reliability, particularly in edge cases. Furthermore, in practical scenarios, the proportion of idle states is not balanced. Particularly in multi-axle predictions, idle states constitute a relatively small portion of the dataset, leading the model to focus more on non-idle states. As a result, it may fail to detect rare idle-state edge samples. Additionally, although dropout was applied to mitigate overfitting, the dataset consists entirely of real-world data from the Train Control Unit (TCU). Some data may contain significant errors due to sensor noise interference. Compared with the improved Informer model, traditional RNN and LSTM, with their internal state transmission and gating mechanisms, exhibit certain advantages in capturing long-term dependencies and smoothing out short-term anomalies. Future research will further explore hybrid models or design specialized robustness enhancement strategies for these edge cases to improve reliability while maintaining real-time prediction capabilities.
## 3 Results and Discussion
### Batch Studies
#### 3.2.3 Effect of Dosage on MB Adsorption  
The information presented in Figure 8 demonstrates changes in \(q_{e}\) values with varying doses of NGC. It was noted that as the NGC dose increased, the adsorption capacity \(q_{e}\) decreased. Specifically, the decrease in \(q_{e}\) from 25.709 to 3.207 mg g\({}^{-1}\) was observed as the adsorbate dose rose from 0.001 to 0.008 g. This decline can be explained by the competition among adsorbates for adsorption and the distribution of the concentration gradient between the solute in solution and the solute at the adsorbate's surface. Moreover, the covering of adsorption sites due to a decrease in available surface area for adsorption and an increase in the diffusion pathway also played a role in this reduction. Consequently, the quantity of MB adsorbed per unit weight of NGC decreased, leading to a decrease in adsorption capacity [49; 50].  
Figure 6: The impact of pH on MB adsorption onto NGC.  
Figure 7: The impact of concentration on the adsorption of MB on NGC.
# Global Resource Scheduling for Distributed Edge Computing
###### Abstract  
Recently, there has been a surge in interest surrounding the field of distributed edge computing resource scheduling. Notably, applications like intelligent traffic systems and Internet of Things (IoT) intelligent monitoring necessitate the effective scheduling and migration of distributed resources. In addressing this challenge, distributed resource scheduling must weigh the costs associated with resource scheduling, aiming to identify an optimal strategy amid various feasible solutions. Different application scenarios introduce diverse optimization objectives, including considerations such as cost, transmission delay, and energy consumption. While current research predominantly focuses on the optimization problem of local resource scheduling, there is a recognized need for increased attention to global resource scheduling. This paper contributes to the field by defining a global resource scheduling problem for distributed edge computing, demonstrating its NP-Hard nature through proof. To tackle this complex problem, the paper proposes a heuristic solution strategy based on the ant colony algorithm (ACO), with optimization of ACO parameters achieved through the use of particle swarm optimization (PSO). To assess the effectiveness of the proposed algorithm, an experimental comparative analysis is conducted. The results showcase the algorithm's notable accuracy and efficient iteration cost performance, highlighting its potential applicability and benefits in the realm of distributed edge computing resource scheduling.  
resource scheduling; distributed systems; edge computing +
Footnote †: journal: Applied Sciences
# Matched-Filter Thermography
## 2 Shortcoming of Conventional Active Thermography  
Despite the appealing attributes, the performance of conventional active thermography techniques is limited by the diffusive nature of heat conduction. That is, an inherent compromise needs to be made between inspection depth and depth resolution. In PT, in order to inspect deep into samples, larger energy needs to be deposited into the sample. Since the maximum excitation power is often limited by availability and complexity of high power excitation sources, and also by the maximum permissible exposure of the sample, larger energy deposition can practically be achieved by increasing the duration of pulses. However, increasing pulse duration results in superposition of transient thermal responses of closely spaced defects and thus deteriorates depth resolution. LIT suffers from a similar trade-off. That is, a deep inspection into samples can be achieved at low modulation frequency due to the increase in the thermal diffusion length. However, increase in thermal diffusion length also increases the thermal wavelength and thus depreciates depth resolution. Recently, incorporation of matched-filtering in thermography has been proposed [21; 22; 23; 24; 25] for overcoming the classic compromise of conventional active thermography in order to maintain depth resolution while inspecting deep in samples. The sections below review the fundamentals of matched-filter thermography based on the works of [PERSON] and [PERSON] [21; 22; 23; 26].
# Designing an Educational Metaverse: A Case Study of NTUniverse
## 3 Digitalization of NTU Campus
### Campus Modeling  
Covering about 200 hectares of land, the main campus of NTU hosts the College of Engineering; College of Science; Nanyang Business School; Lee Kong Chian School of Medicine; National Institute of Education; and the College of Humanities, Arts, and Social Sciences. There are 23 Halls of Residence on the NTU campus. As such, NTUniverse has a large number of schools and halls to be modelled.  
Various software tools are used in research on 3D modelling, such as SolidWorks and Maya, among others; these are used to elaborate the details of buildings, and to provide texture, labels, models, etc. These models are then imported to Omniverse after USD formatting. An alternative solution has been developed based on the Scan-to-BIM technology, in which laser scanners or cameras are used to digitally capture the physical space in the form of a 3D point cloud, followed by the creation of a building-information-models (BIM) model [17]. AI technology can then be applied to reconstruct the 3D models in the BIM format from the point cloud. In general, modeling of NTUniverse is primarily implemented using two methods: (i) 3D modeling for the external aspects of entire existing buildings, and (ii) Scan-to-BIM conversion for individual classrooms or laboratories, from laser scanned point clouds. For those structures or objects not yet in physical existence, 3D design is employed to create them (e.g., the future train system of the NTU campus).  
The modeling flow is described as follows: (1) modeling (FBX file) of buildings on the NTU campus or reconstruction of classrooms or laboratories via LiDAR scanning; (2) compiling the models for direct import into Omniverse (USD file); (3) exploring the as-is state of the NTU campus via Omniverse navigation features, i.e., panning and rotation of camera angles; (4) separating or ungrouping layers of objects, e.g., building components; (5) improving texture of objects, features, and buildings in the campus model based on their respective materials, e.g., roof tiles, aluminum cast, and bricks; (6) importing botany models across the campus as generic greeneries and adding additional shapes for better replication accuracy, i.e., missing building objects/components, texture maps, and geometry; (7) creating labels for key/important buildings within the campus, consisting of naming boards  
Figure 3: Summary of the NTUniverse design.  
created using USD Composer, and the text on the naming boards using the Omniverse Extension \"3D Text\"; and (8) creating interactive avatars augmented with motion capabilities that simulate real-world scenarios. Figure 4 shows the created digital twin of the entire NTU campus in NTUniverse, and Figure 5 illustrates two selected buildings modelled in NTUniverse.
# HRU-Net: High-Resolution Remote Sensing Image Road Extraction Based on Multi-Scale Fusion
## 3 Experiments and Results
### Computational Efficiency  
In the field of deep learning, the number of parameters and floating point operations per second (FLOPs) serve as crucial measures for evaluating the complexity and computational demands of a model. A higher number of parameters might indicate a model's ability to learn more intricate patterns, but it could also lead to overfitting and extended training time. A comparison of the computational efficiency of different methods is shown in Table 6. Our model, the HRU-Net, demonstrated a balanced attribute with respect to both parameter quantity and FLOPs--neither being the simplest nor the most complex. Therefore, while significantly improving segmentation performance, the HRU-Net did not introduce an excessive number of parameters or noticeably increase the training time. This reflects that our model not only prioritizes performance but also focuses on computational efficiency and model generalizability.
# Parameter-Matching Multi-Objective Optimization for Diesel Engine Torsional Dampers
## 3 Results  
Figure 7 demonstrates the Pareto frontier solutions obtained through iterative optimization, revealing a positive correlation between the torsional damper's rotational inertia and the maximum additional stress in shaft systems. Post-crowding distance calculation identifies damper parameters with damping coefficients approaching the upper bounds of specified ranges. Two optimal parameter sets emerge:  
For stress suppression: rotational inertia of 2.2-3.3 kg.m\({}^{2}\) (inertia ratio of 0.1-0.15) with torsional stiffness of 1.3-1.7 (MN.m/rad).  
Figure 7: Pareto optimization torsional damper parameter diagram.  
For nodal response control: rotational inertia of 5.5-6.6 kg\(\cdot\)m\({}^{2}\) (inertia ratio of 0.25-0.3) with torsional stiffness of 1.5-1.9 (MN\(\cdot\)m/rad).  
To validate the accuracy of the Pareto predictions, this study compares Pareto-optimized parameters with computational results. As shown in Table 2, the parameters derived from Pareto optimization demonstrate less than 1% deviation from computational results in vibration suppression effectiveness, confirming the validity of Pareto-optimized torsional damper parameters.  
Table 3 compares parameters between Pareto-optimized solutions and dual-pendulum model dampers with equivalent inertia ratios. The data demonstrate close alignment between high-inertia-ratio Pareto solutions and theoretical dual-pendulum model parameters. However, low-inertia-ratio solutions show significant discrepancies in torsional stiffness--the optimized configuration increases the stiffness adjustment ratio by approximately 20% while achieving a 5% reduction in additional stress.
## 2 Materials and Methods
### Soil Bacterial Community Diversity Analysis  
Genomic DNA from the microbial community was meticulously extracted utilizing the E.Z.N.A.(r) soil DNA Kit (Omega Bio-tek, Norcross, GA, USA), adhering to the manufacturer's prescribed instructions. The resultant DNA extract underwent scrutiny on a 1% agarose gel, and its concentration and purity were judiciously assessed using a NanoDrop 2000 UV-vis spectrophotometer (Thermo Scientific, Wilmington, NC, USA). The amplification of the bacterial 16S rRNA gene's hypervariable region V3-V4 was executed with the primer pairs 338F (5'-ACTCCTACGGGAGGCAGCAG-3') and 806R (5'-GGACTACHVGGGTWTCTAAT-3') employing an ABI GeneAmp(r) 9700 PCR thermocycler (ABI, Los Angeles, CA, USA). Subsequently, the purified amplicons were amalgamated in equimolar proportions and subjected to paired-end sequencing on an Illumina HiSeq PE300 platform or NovaSeq PE250 platform (Illumina, San Diego, CA, USA) in strict adherence to standardized protocols, as implemented by Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China) [16].
# Plant Exosomal Vesicles: Perspective Information Nanocarriers in Biomedicine
## 3 Mechanism of Nanovesicles Formation  
The mechanism of nanovesicle formation in plants is not yet completely studied, but according to the available data, it is generally similar to the biogenesis of mammalian exosomes (Figure 1). The formation of exosomes is tightly connected with the cellular membrane transport and is initiated by the formation of an early endosome through the invagination of the plasma membrane cooperating with the regulatory components of the Golgi trans-network [26]. The early endosome undergoes maturation accompaniedby invagination of the endosomal membrane with the formation of intraluminal vesicles (ILVs) that selectively accumulate various intracellular biomolecules. In mammals, this process is controlled by a special endosomal sorting complex, required for their transport (ESCRT), consisting of four proteins, ESCRT-0, -I, -II, and -III, and vacuolar protein sorting 4 (VPS4) ATPase [26]. As a result of these processes, the formation of a multivesicular body (MVB) occurs, which is a late stage in the development of endosomes. Plants contain most of the ESCRT proteins, as well as VPS4/SKD1 homologs, suggesting the common functions for membrane modifications and endosomal trafficking [27]. However, a certain part of the exosomes has the potential to be formed in an ESCRT-independent way [28]. For example, the formation of some ILVs was dependent on sphingolipid ceramide produced by neutral sphingomyelinases [29]. The protein sorting in exosomes can be mediated by major histocompatibility complex class II enriched in the lipid microdomains [30]. In the case of plants, no alternative mechanisms for the exosome biogenesis are currently known. At the next stage of the biogenesis, MVB merges with the plasma membrane or with lysosomes. In the case of fusion with the plasma membrane, the nanosveicles are released into the extracellular space [31]. The fusion process is regulated itself by two protein complexes from the GTPase family Rab and soluble NSF attachment receptor (SNARE), which are localized on the outer MVB membrane and remain there after the fusion for the next MVB [32; 33]. Upon the fusion with lysosomes, the enzymatic decomposition of MVB occurs [34].  
Figure 1: Mechanism of nanosveicles formation. 1—early endosome formation through the invagination of the plasma membrane cooperating with the regulatory components of the Golgi trans-network; 2—endosome maturation accompanied by invagination of the endosomal membrane with the formation of intraluminal vesicles that selectively accumulate various intracellular biomolecules by a special endosomal sorting complex: ESCRT-0, -I, -II, -III, and vacuolar protein sorting 4 (VPS4) ATPase; 3—formation of a late endosome or multivesicular body (MBT); 4a—fusion with the plasma membrane accompanied by the release of exosomes, regulated by two protein complexes from the GTPase family Rab and soluble NSF attachment receptor; 4b—fusion with lysosome and further degradation.
# XTTS-Based Data Augmentation for Profanity Keyword Recognition in Low-Resource Speech Scenarios
## 2 Proposed Methods
### Data Augmentation  
Typically, data augmentation entails generating new data by applying subtle modifications to the original dataset, thereby expanding the volume of training data for model development. In contrast, this study employs the publicly available AISHELL-3 dataset [14] as the source for target audio files. We apply VAD technology to concatenate speech segments from these files, ensuring compliance with the 10 s uninterrupted speech requirement recommended by XTTS [6]. Next, we generate synthetic speech using a customized keyword list. This is followed by integrating a small set of real human-recorded keyword speech samples with the synthetic data to complete the augmentation process, facilitating subsequent model training. The detailed architecture is illustrated in Figure 2.
# Algebraic Dynamic Programming on Trees
## 4 Single-Tape DP on Trees and Forests
### The Minimum Evolution Problem  
Given a tree \(T\) with leaf set \(X\), vertex set \(V(T)\) and edge set \(E(T)\). The relation \(chd(v)\) returns the set of children of a vertex \(v\) in a tree. \(\mathcal{A}\) is the alphabet of the vertex labels. The problem is described as given a labelling \(\ell:X\rightarrow\mathcal{A}\), and a similarity function \(w:\mathcal{A}\times\mathcal{A}\rightarrow\mathbb{R}\), find an extension \(\tilde{\ell}:V(T)\rightarrow\mathcal{A}\) such that: (i) \(\tilde{\ell}(v)=\ell(v)\) on \(X\) and (ii) \(w(\ell):=\sum_{\{u,v\}\in E(T)}w(u,v)\) is maximal. Maximizing similarity amounts to minimizing the number of evolutionary events that occur along the edges of the (phylogenetic) tree, explaining the traditional name of the problem. This combinatorial optimization problem is known as the \"Minimum Evolution\" or \"Small Parsimony\" problem.  
A well-known solution to Minimum Evolution Problem, known as [PERSON]'s Algorithm Sankoff (1963), consists of computing for each complete subtree of \(T\), which by construction is rooted in a vertex \(v\in V\), and each possible label \(a\in\mathcal{A}\) of \(v\), the score of the best scoring labeling of that subtree. Let us call this quantity \(S_{v}(a)\). It satisfies the recursion  
\[S_{v}(a) =\begin{cases}0&\text{if }\ell(v)=a\\ -\infty&\text{otherwise}\end{cases}\qquad\text{for all }v\in X \tag{2}\] \[S_{v}(a) =\sum_{c\in\text{chd}(v)}\max_{\ell(c)}\left(S_{c}(\ell(c)) \right)+w(a,\ell(c)))\qquad\text{for all }v\in V(T)\setminus X\]  
The initialization for \(v\in X\) enforces that a score different from \(-\infty\) is obtained only for configurations where the label \(a\) at leaf \(v\) coincides with the input \(\ell(v)\). Otherwise, \(v\) incurs a score of \(-\infty\) that propagates upwards through the recursion for the inner nodes. Probabilistic versions of the algorithm assign probabilities proportional to \(\exp(score)\) to each configuration, and thus a probability of \(0\) to any solution in which the leaf labels deviate from the input. The grammar to [PERSON]'s algorithm, Equation (2), is given by  
\[T\to x\shortmid F\qquad\qquad F\to T\circ F\bigm{|}\$ \tag{3}\]  
We remark that the same grammar can also be use to describe [PERSON]'s solution to the Small Parsimony problem [PERSON] (1963) for binary trees, which was later generalized by [PERSON] (1963) for arbitrary trees. Even though the same grammar is used, the scoring algebra is completely different in the two algorithms, see Appendix C.
# Mutual Authentication Protocol for D2D Communications in a Cloud-Based E-Health System
## 6 Performance Analysis
### Communication Cost  
The evaluation of the communication costs considered messages exchanged over an insecure channel and parameters and their respective costs in bits (see Table 6).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Cost** \\ \hline Random Number/Identity/Timestamp & 48 bits \\ Bilinear Pairing/Hash & 160 bits \\ Symmetric Key & 128 bits \\ Signature (symmetric algorithm) & 512 bits \\ \hline \hline \end{tabular}
\end{table}
Table 6: Cost of each parameter exchanged in bits.  
Figure 7: Computational cost comparison.  
The message exchange over an insecure channel was analyzed in each of the four common phases performed by our protocol and those of [PERSON] et al. [7] and [PERSON] et al. [6]. Table 7 shows comparisons of each phase and a comparison of the total communication cost of each protocol.  
Our protocol required the lowest communication cost, hence, the best communication cost, due to the reduced number of parameters exchanged and choice of small parameters to be exchanged (identities, random numbers, timestamps) and the adaptation to D2D communication that offloads part of the traffic outside the 3 GPP spectrum. The proposals of [PERSON] et al. [7] and [PERSON] et al. [6] required higher communication costs, because of the exchange of some costly signature parameters. Our protocol achieved the best performance, revealed by security and performance analyses.  
Figure 8 shows the best performance of the proposed protocol regarding communication costs. It considers that 30% of devices perform offload and use D2D communication to execute their mutual authentication. Reducing costs in the traditional 3 GPP network.