esa-sceva/satcom-chunk-collection huggingface dataset:
# Multimodal Diarization Systems by Training Enrollment Models as Identity Representations +
## 5 Speaker Subsystem
### Identity Assignment Scoring  
The considered identity assignment block follows a state-of-the-art speaker recognition PLDA backend followed by score normalization and calibration stages. By means of the PLDA model, we estimate the score \(s_{ij}\), which represents the likelihood that the diarization cluster \(j\) shares the same speaker identity as the enrollment speaker \(i\). Then, these scores are normalized according to adaptive S-norm [45], using MGB as extra cohort. Finally, normalized scores are calibrated according to a threshold \(\epsilon\). Whenever the score \(s_{ij}\) overcomes the threshold, we consider that the cluster \(j\) contains audio from the enrolled person \(i\), being different otherwise. Threshold \(\epsilon\) is adjusted by assignment error rate (AER) minimization according to a calibration subset \(\Phi_{\text{calib}}\) and the enrollment embeddings \(\Phi_{\text{enroll}}\) as follows:  
\[\epsilon=\underset{\epsilon}{\arg\min}(\text{AER}(\Phi_{\text{calib}},\Phi_{ \text{enroll}},\epsilon)) \tag{6}\]  
where \(\Phi_{\text{calib}}\) and \(\Phi_{\text{enroll}}\) represent the set of embeddings from calibration as well as the enrollment speakers.  
Final AER labels are obtained according to these normalized and calibrated scores. The audio from cluster \(j\) is assigned to the \(i\)th enrolled identity with highest score if \(s_{ij}\) overcomes the calibration threshold. If \(s_{ij}\) is below the threshold for any enrolled identity \(i\), the cluster is assigned to the generic unknown identity. Mathematically, the assigned identity (\(\theta_{j}\)) for a subset of embeddings \(j\) with respect to the enrolled identity \(i\) is  
\[\theta_{j}=\left\{\begin{array}{ll}\underset{\text{Unknown}}{\arg\max}_{i}( s_{ij}|s_{ij}>\epsilon)&\text{if }\exists i|s_{ij}>\epsilon\\ \text{Unknown}&\text{if }\forall i,s_{ij}<\epsilon\end{array}\right. \tag{7}\]  
where \(s_{ij}\) stands for the normalized PLDA log-likelihood ratio score between the embedding \(j\) and the enrolled identity \(i\). By means of this decision-making, we do not exclude the possibility of assigning multiple diarization clusters to the same identity. This design choice is taken to allow the fix of some diarization errors.
# Uplink Transmit Power Control for Single-Carrier Grouped FDMA with Iterative Multiuser Detection
## 1 Introduction
Accordingly, we argue that it is desirable to have a sequential relation of SINRs of ordered users with a consistent relation between consecutive users. In this context, we consider a geometric sequence of the controlled powers of users in the same chunk. The common ratio of the geometric sequence is a design parameter of the proposed power control scheme, and an optimal ratio can be derived from the SINR analysis based on SNR-variance density evolution. The proposed method with the optimized common ratio achieves a significantly higher spectral efficiency than that achieved by the conventional SC-GFDMA and SC-FDMA systems, even with a reduced total transmit power. Compared to the previous studies, the novel observations of this paper can be summarized as follows:  
* A specific power control algorithm that exploits the non-orthogonal structure of SC-GFDMA is designed to increase the available spectral efficiency in each chunk. * Based on the SNR-variance density evolution, we provide a tractable analysis of SINR of users during the IMD. * The proposed power control significantly increases the spectral efficiency in each chunk. * The proposed power control efficiently reduces (transmit) power consumption at each chunk compared with the conventional SC-GFDMA schemes without power control. The primary symbols used in this paper are summarized in Table 1, and some notations used in this paper are summarized as follows. _Notation:_\(\mathbf{F}_{K}\) is the normalized \(K\times K\) DFT matrix, whose \((n+1,k+1)\)th entry is \(1/\sqrt{K}e^{-i2\pi mk/K}\) for \(n,k=0,1,...,K-1\), where \(i=\sqrt{-1}\); \(\mathbf{0}_{M\times N}\) and \(\mathbf{1}_{M\times N}\) are all-zero and all-one matrices of size \(M\times N\), respectively; \(\mathbf{I}_{K}\) is the \(K\times K\) identity matrix; \(\mathbf{e}_{n}=[\mathbf{0}_{1\times(n-1)}\ \mathbf{1}\ \mathbf{0}_{1\times(K-n)}]^{T}\) is a \(K\times 1\) unit vector with 1 as the \(n\)th element and 0 as the remaining elements; \(\Pr(\cdot)\) indicates the probability of an event, and \(\mathrm{E}(\cdot)\) and \(\mathrm{Cov}(\cdot)\) are the expectation and covariance operators, respectively; \((\cdot)^{*}\), \((\cdot)^{T}\), \((\cdot)^{H}\), and \([\cdot]_{n}\) denote the complex conjugate, transpose, conjugate transpose, and \(n\)th entry of a vector, respectively; \(\mathrm{Diag}(\mathbf{c})\), for a vector \(\mathbf{c}\), produces a diagonal matrix whose diagonal entries correspond to \(\mathbf{c}\) in the same order.
# Digitalization and Dynamic Criticality Analysis for Railway Asset Management
## 1 Introduction and Background
The technique facilitates proactive maintenance planning, reducing unexpected failures and ensuring the continued operability of the network. A failure in any of these can disrupt service and compromise security. Criticality analysis identifies vulnerable elements and prioritizes their maintenance, minimizing risks of unforeseen failures [8]. In other networks, complex network analysis (CNA) and graph Fourier transform (GFT) are applied to optimize asset management and enhance criticality assessment [9; 10]. Additionally, machine learning techniques are employed to predict failures and detect anomalies [11]. Each network's unique characteristics influence the selection of criticality analysis models and the corresponding data. Table 1 provides a summary of these network characteristics. While both rail and utility networks use criticality analysis methodologies and advanced digital tools, there are vital differences in the criteria and approaches used to assess criticality. In Table 2, some of the \"Key elements\" that are considered in this context are reviewed and compared, highlighting the differences between one type of network and another. Criticality analysis in rail and service networks shows that although the methodologies and technologies used for analysis may be similar, the objectives and criteria for each system vary significantly depending on the nature of the network. In rail networks, the focus is on operational safety and service continuity, where the failure of critical components can directly impact passenger safety and system operation, unlike utility networks that prioritize resilience and quality of service, focusing on ensuring continuity of supply. Digitalization, including technologies such as digital twins and predictive analytics, playsa key role in both contexts, enabling greater efficiency in maintenance planning and asset management. However, the challenge remains in the effective implementation of these technologies, especially in utility networks with older or less digitized infrastructure. The objective of this research is to develop a digitalized data model that enables more accurate and real-time criticality analysis in railway maintenance management. By integrating an ETL process and advanced technologies such as digital twins and asset attribute characterization (AAC), this study aims to optimize the identification and prioritization of critical assets, thereby enhancing operational efficiency, resource allocation, and safety. Section 2 presents a comprehensive review of the relevant literature to establish the theoretical framework for the proposed model, drawing on recent advancements in digitalization and predictive maintenance. The methodology is developed in Section 3, including the ETL process and the model for criticality assessment. A case study focusing on a commuter rail network is shown in Section 4. In Section 5, the results are analyzed, and the practical implications are discussed, with recommendations for future research. Finally, the conclusions are exposed in Section 6.
# A Review of Requirements for Gamma Radiation Detection in Space Using CubeSats
###### Abstract  
Initially intended as student-led projects at universities and research institutions, the CubeSats now represent a unique opportunity to access space quickly and in a cost-effective fashion. CubeSats are standard and miniaturized satellites consisting of multiple identical units with dimensions of about \(10\times 10\times 10\) cm\({}^{3}\) and very limited power consumption (usually less than a few W). To date, several hundreds of CubeSats have been already launched targeting scientific, educational, technological, and commercial needs. Compact and highly efficient particle detectors suitable for payloads of miniaturized space missions can be a game changer for astronomy and astroparticle physics. For example, the origin of catastrophic astronomical events can be pinpointed with unprecedented resolution by measuring the gamma-ray coincidence signals in CubeSats flying in formations, and possibly used as early warning system for multi messenger searches. In this paper, we will discuss and analyze the main features of a CubeSat mission targeting intense and short bursts of gamma-rays.  
X-ray and gamma ray detectors; particle detectors; detectors for astronomy; astrophysics; gravitational waves; silicon photomultipliers; electronic detector readout +
Footnote †: journal: Appl. Sci.  
11 February 2021  
10 March 2021  
**Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.**  
**Cobeyright:** 2020 by the authors. Licence MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Highly Porous Organic Polymers for Hydrogen Fuel Storage
## 3 Highly Porous Organic Polymers for H\({}_{2}\) Storage
### Brief comparison of highly porous organic polymers with MOFs  
To compare the performance of different materials in H\({}_{2}\) storage, the experimental conditions for measuring the H\({}_{2}\) uptake should be same, or at least similar, that is, under the same temperature and pressure. Unfortunately, the reported results available were obtained at different conditions. Although the temperatures for the experiments were at either 77 K or room temperature, the pressures for H\({}_{2}\) measurement varied significantly, ranging from 1 bar to hundreds of bar. Such discrepancy in the experimental conditional makes the direct comparison impossible. Nevertheless, we selected the results under the similar conditions, close to 77 K/1 bar, to make a rough comparison, as shown in Figure 12. Since the pressure is not exactly same, e.g. some 1 bar, and other 1.13 bar as seen in Table 3, we present the general ranges of pore size vs. H\({}_{2}\) uptake, rather than individual definite data points in the figure.  
Figure 12: Brief comparison between highly porous polymers and MOFs in H\({}_{2}\) adsorption.  
It is interesting to see that polymers with intrinsic microporosity (PIMs) outperform the other highly porous polymers in H\({}_{2}\) adsorption at ambient pressures. PIMS even outperform MOFs when under these conditions, despite the latter ones possess much larger internal surface and pore volumes. This analysis clearly indicates that the number of ultra micropores determines the total H\({}_{2}\) adsorption, which is very consistent with the theoretical predictions.
# Multi-Relational Graph Convolution Network for Service Recommendation in Mashup Development
## 3 Multi-Relational GCN Based Service Recommendation Model
### Overall Framework
#### 3.2.3 Prediction  
As we perform GCN on a fused graph with \(L\) layers, we obtain multiple layers of feature representations for each mashup \(m\) and service \(s\), namely \(h_{m}^{(1)}\), \(\ldots\), \(h_{m}^{(L)}\) and \(h_{s}^{(1)}\), \(\ldots\), \(h_{s}^{(L)}\). Since the feature representations obtained in different layers reflect the information aggregated from different nodes, we concatenate them to constitute the final representation for the mashup \(m\), i.e., \(h_{m}=h_{m}^{(0)}\parallel\cdots\parallel h_{m}^{(L)}\), and \(\parallel\) is the concatenation operation. Similarly, for service \(s\), \(h_{s}=h_{0}^{(0)}\parallel\cdots\parallel h_{s}^{(L)}\). Note that \(h_{m}^{(0)}\) and \(h_{s}^{(0)}\) are the initial feature vectors for mashup \(m\) and service \(s\), which are the rows of the feature matrix \(H^{(0)}\). In this case, we enrich the initial representations for mashups/services with representations from all layers of GCN capturing the higher-order relational information.  
Once the final representations of mashup and service are obtained, we use the inner product to estimate the composition preference of mashup \(m\) to service \(s\). Since we aim to output the possibility of service s being selected as a component of mashup \(m\), we use the sigmoid activation function to constrain the value between 0 and 1:  
\[\hat{r}_{ms}=\sigma(h_{m}^{T}\cdot h_{s}). \tag{3}\]
# Identification of Mode Shapes Based on Ambient Signals and the IA-VMD Method
## 3 The Iterative Adaptation VMD Method
### The Estimation of Mode Number \(K\)
The formula for calculating the total energy value of the oscillation signal is as follows:  
\[E_{IMF}=\sum_{i=1}^{n}E_{imf}(i) \tag{11}\]  
(3) In [24], it can be found that the oscillation characteristic parameters of modal components with a high proportion of energy can provide a reference for the analysis of the sampling signal characteristics. When the calculated modal energy weight \(\eta_{i}\) is larger than the threshold value \(\varepsilon\), the modal component is extracted and set as the main oscillating mode. Otherwise, this component is ignored. The formula for calculating the energy weight of the component signal is as follows:  
\[\eta_{i}=\frac{E_{imf}(i)}{E_{IMF}}\times 100\% \tag{12}\]  
(4) The intrinsic modal components with higher signal energy are screened out by the signal energy method, and the intrinsic modal component value \(K\) is updated. Additionally, the intrinsic modal components finally obtained correspond to the main operating modes of the system. The operating characteristic parameters of the system can be obtained by parameter identification of the dominant intrinsic modal components. The modal component with higher signal energy has a higher oscillation amplitude and weaker damping. The power of these modal components varies greatly and is easy to diverge, which poses a great threat to the safe and stable operation of power systems. The decomposition and research of dominant inherent modal components is the key to power system stability analysis. If the frequencies of the two dominant modal components are close, traditional VMD may lead to the problem of mode aliasing. By using IA-VMD of the dominant modal components, the problem of mode aliasing can be solved eventually, and the dominant modal components with clearer oscillation mode decomposition can be more easily identified in the next step.
# Tracking Dynamic Northern Surface Water Changes with High-Frequency Planet CubeSat Imagery
## 4 Results
### Temporal Changes in Water Inundation Area
In low-relief wetland environments where a small change in water level can cause a large change in in unundation extent, temporal correlations between changing river discharge and individual lake surface areas are indicative of floodplain connectivity [8]. To quantify these correlations, for each lake object with at least 5 satellite observations and an observed maximum area >10,000 m\({}^{2}\), we calculated Pearson's R between the Yukon River discharge at Eagle and Planet time series of surface water extent [8]. Of the 268 lakes meeting these requirements, 100 (37%) are correlated with Yukon River discharge at the 90% confidence level and 60 (22%) are correlated at the 95% confidence level. Using a non-parametric Mann-Kendall test [42] we also assessed how many of these 268 lakes display statistically significant trends over the summer season. We find 65 (24%) of lakes have a statistically significant (\(p<0.1\)) decreasing linear trend over the summer season, 2 (1%) display increasing trends, and 201 (75%) display no trend. As nearly all lakes decreased in extent, the percent of lakes with statistically significant trends reflects lakes which changed linearly with little bidirectional variability between observations. Based on the observed correlation to river discharge, we characterized the 268 study lakes into three categories: connected, disconnected and stable, examples of which are shown in Figure 8. Connected lakes are interpreted as being hydrologically connected to the Yukon River [8] and thus immediately responsive to changes in its discharge. Here we defined connected lakes as having a surface area standard deviation >10% and a statistically significantly (\(p<0.1\)) correlation to river discharge. 77 lakes (28%) were classified as connected. In some cases, such as the example shown in Figure 8b, there may be a visible channel connecting the lake to the river system, but in many connected lakes no such channel is visible in the imagery. Disconnected lakes are uncorrelated with river discharge, but still experience substantial area changes over the summer season. These lakes typically steadily decrease in surface area throughout the summer season and do not respond to the summer high stage event. Here they were defined as lakes with a surface area standard deviation >10% and no correlation to Yukon River discharge, with 128 lakes (48%) classified as disconnected. Finally, stable lakes are interpreted as being  
Figure 7: Changes in lake surface areas between (column 1, (**a,d**)) 23 June and 1 October, (column 2, (**b,e**)) 23 June and 17 August and (column 3, (**c,f**)) 17 August and 1 October. In the top row individual water bodies are represented as circles with the sizes of the circles scaled by the area of the change. Bottom row shows the pixel-based change in water fraction. Location of inset shown in top row. Red represents water loss, blue represents water gain.
# Integrated Fusion Network for Hyperspectral, Multispectral and Panchromatic Data Fusion
###### Abstract  
The fusion of traditional HSI mainly involves the fusion of HSI with MSI or PAN. Nevertheless, the combination of the former is constrained by the inadequate spatial resolution of MSI, and therefore the enhancement of the spatial resolution of HSI is inadequate. The latter is constrained by the substantial disparity in spectral information between PAN and HSI, leading to pronounced distortion of high-resolution HSI. To address this issue, this paper proposes IFNPMH, which is an integrated fusion network for used for the fusion of HSI, MSI, and PAN. The IFNPMH algorithm is capable of efficiently extracting spatial information from MSI and PAN, as well as spectral information from HSI. It combines the spatial and spectral information to produce fused images that possess precise spectral information and distinct spatial details. Specifically, IFNPMH incorporates the CBAM attention mechanism module to enhance feature representation capabilities. Finally, this article proposes a loss function that combines spectral and spatial information loss in IFNPMH training. The effectiveness of the proposed IFNPMH is demonstrated by extensive testing in comparison to other sophisticated approaches. This model has shown promising performance in terms of qualitative visual effects and quantitative evaluation metrics.  
remote sensing images; image fusion; convolutional neural networks; sharpening; attention mechanism Article  
* 2025
## 4 Results and Analysis
### The Effect of Natural Frequency  
The effects of resonance in numerous engineering problems are well documented and it is recognized that certain frequencies can cause catastrophic effects in structures such as bridges. This is also the case in electronics systems, and this test evaluates the circuit board and components against resonant effects. This was carried out by sweeping through a range of frequencies (10-2000 Hz) at an amplitude equivalent to a 10 G force. The test evaluates where the resonance was the greatest and displays the effects in terms of damage to the board at the frequencies of interest.  
It can be seen from Figure 9a-f that the resonant effects are small at the lower frequencies from 29.17 to 42.12 Hz. However, as the frequency increases, the damage incurred becomes much greater. At 212.75 and 222.69 Hz, the effects of resonance become especially pronounced in terms of stress/strain, and board/component failure would be eventually inevitable. Thus, mitigation would be needed, either in the form of avoidance of these frequencies (difficult in reality), or damping applied to the mounting of the board.
# Auditory Coding of Reaching Space
## 2 Materials and Methods
### Stimulus Material  
A human model was equipped with four inertial sensors (MTx miniature inertial 3 DOF orientation tracker; Xsens Technologies BV, Enschede, The Netherlands) on the right arm. The sensors (size: \(38\,\mathrm{mm}\times 53\,\mathrm{mm}\times 21\,\mathrm{mm}\), weight: \(30\) g) were attached on the middle of the right shoulder, the right upper arm, the right lower arm, and the back of the right hand. The inertial sensors provided 3D acceleration data (up to eighteen times of the acceleration of gravity), 3D rate of turn (up to \(1200^{\circ}\)/s), three degrees of freedom orientation and a sampling rate up to \(100\) Hz depending on the number of sensors used [52; 53]. Data of the inertial sensors were recorded while the model was executing reaching actions with the digitizing pen towards one of nine different target positions on the tablet (see Figure 3).  
Figure 3: The originally invisible target point positions on the digitizing tablet. The lengths of the axes correspond to the dimension of the digitizing tablet.  
Figure 2: The experimental arrangement for the measurements in the \(yz\)-plane.  
In a next step, a forward kinematic model of the right arm was used to determine trajectory and velocity information of the right metacarpophalangeal joints [54]. Four specific kinematic parameters were chosen to be mapped on four different parameters of a sound (patch 100 \"jupiter lead\" from \"Preset D Group\", SonicCell, Roland) [54]: (a) The absolute velocity of the right metacarpophalangeal joints was mapped on the volume of the sound (the faster the movement, the louder the sound). (b) The position of the right metacarpophalangeal joints on the \(y\)-axis was mapped on the pitch (the higher the metacarpophalangeal joints in space, the higher the sound). The pitch was modulated by two octaves from G2 to G4. (c) The position of the right metacarpophalangeal joints on the \(z\)-axis was mapped on the spectral composition (the higher the value, the less overtones the sound has; standard MIDI Continuous Controller No. 74, controls the cutoff frequency of the low-pass filter for an overall brightness control). (d) The position of the right metacarpophalangeal joints on the \(x\)-axis was mapped on the stereo characteristics (the further the metacarpophalangeal joints were on the left side from the acting subject's perspective, the louder the sound was on the left audio channel and the quieter the sound was on the right audio channel and vice versa). In this way, sonification-recordings (1.0-1.65 s) of different reaching actions towards target positions were generated. Additionally, the position of the right metacarpophalangeal joints when having reached the target point with the digitizing pen was transformed into sound by using the three above mentioned positional mappings (b, c, and d). In this way, the instruction sounds were formed. The participants' real-time sonification feedback was created in the same way.