esa-sceva/satcom-chunk-collection huggingface dataset:
# End-to-End Multi-Modal Speaker Change Detection with Pre-Trained Models
## 5 Discussion  
The results across various configurations of the proposed **eMD** and **eMD\({}_{att}\)** models demonstrate consistent improvements in SCD. These gains can be attributed to three key design choices: multi-modal integration, fine-tuning of PLM, and the use of self-attention layers in combination with focal loss.  
Multi-modal integration proved to be effective across all settings. Models that jointly processed audio and text consistently outperformed those relying on a single modality. This advantage was especially apparent in imbalanced data scenarios, where speaker change points were rare. While audio captures acoustic variations, text offers semantic information, forming a more complete signal for identifying transitions.  
Fine-tuning the PLM further enhanced performance, particularly for the audio and multi-modal configurations. Models with the unfrozen pre-trained model adapted more effectively to the data, reducing both the FAR and MDR. These improvements were reflected in the overall F1-scores, suggesting that trainable backbones are more effective at capturing the complex interaction patterns and overlapping speech dynamics characteristic of multi-talker meetings.  
Introducing self-attention into the architecture, as in **eMD\({}_{att}\)**, further boosted performance. The attention mechanism enabled the model to weigh temporal features more flexibly, particularly around potential speaker transitions. Attention visualizations revealed clear distinctions in how the model processed speaker change versus normal segments. In speaker change segments, attention was distributed more broadly across time, suggesting increased uncertainty and the need for wider contextual integration. Normal segments, on the other hand, showed tightly focused attention patterns, indicating greater temporal stability. Higher attention entropy in speaker change segments indicates that the model is actively attending to a broader temporal context, which helps it capture the complex and distributed cues necessary for accurately detecting speaker transitions.  
Focal loss optimization played a critical role in improving performance under imbalance. By assigning more weight to challenging examples and reducing the influence of easy negatives, the model achieved better recall and a stronger balance between false alarms  
Figure 6: Comparison of attention entropy between speaker change and normal segments. Each point corresponds to a 1 s segment. Entropy is computed over the intra-segment attention matrix, where higher entropy indicates more dispersed attention across time frames. The orange line in each box indicates the median attention entropy within the corresponding group.  
and missed detections. This was particularly helpful in cases where standard loss functions failed to adequately address the rarity of speaker change events.
# Application of Blockchain Technology in Emergency Management Systems: A Bibliometric Analysis
## 5 Discussion  
### Theoretical Insights and Practical Applications  
The findings of the bibliometric analysis provide a comprehensive understanding of the evolving landscape of blockchain technology in emergency and crisis management, integrating both theoretical constructs and real-world applications. Through the identification and analysis of seven major research clusters, this study constructs a macro-level knowledge framework that illuminates the logical structure of the field and emphasizes key themes such as transparency, efficiency, trust, and interdisciplinary collaboration. The clusters span diverse yet interrelated domains such as humanitarian logistics, patient-centered healthcare, smart cities, emergency logistics, collaborative multi-agent systems, IoT networks, and next-generation communication infrastructures, thereby demonstrating blockchain's potential to support decentralized trust mechanisms, enhance data security, and improve operational coordination during crises. Theoretically, this study contributes to a better understanding of how blockchain redefines digital trust and coordination paradigms across complex systems. Practically, it offers insights into how blockchain-enabled solutions are being piloted or proposed in domains requiring resilient infrastructure and secure information flow. Moreover, this study highlights the persistence of critical challenges such as limited global collaboration, insufficient standardization, and a gap between theoretical frameworks and real-world implementations.  
Across all clusters, blockchain technology emerges as a foundational element of digital infrastructure that supports the three core pillars of modern emergency and crisis response systems: decentralization, transparency, and security. Moreover, the high silhouette values across clusters, ranging from 0.898 to 0.979, indicate strong thematic cohesion and maturity within each research stream.  
Below, we summarize the implications as integrated and organized by thematic domain.
# Efficient Massive MIMO Detection for M-QAM Symbols
###### Abstract  
Massive multiple-input multiple-output (MIMO) systems significantly outperform small-scale MIMO systems in terms of data rate, making them an enabling technology for next-generation wireless systems. However, the increased number of antennas increases the computational difficulty of data detection, necessitating more efficient detection techniques. This paper presents a detector based on joint deregularized and box-constrained dichotomous coordinate descent (BOXDCD) with iterations for rectangular m-ary quadrature amplitude modulation (M-QAM) symbols. Deregularization maximized the energy of the solution. With the box-constraint, the deregularization forces the solution to be close to the rectangular boundary set. The numerical results demonstrate that the proposed detector achieves a considerable performance gain compared to existing detection algorithms. The performance advantage increases with the system size and signal-to-noise ratio.  
box-constrained dichotomous coordinate descent; massive MIMO; negative diagonal loading regularization; signal detection; quadrature amplitude modulation +
Footnote †: journal: _Entropy_  
1
Footnote 1: email: {[[https://doi.org/10.3390/e25030391](https://doi.org/10.3390/e25030391)]([https://doi.org/10.3390/e25030391](https://doi.org/10.3390/e25030391))}
## 2 Data
### CryoSat-2  
We used 35 CryoSat-2 altimeter tracks that were near-coinciding with the Sentinel-1 images to analyze waveform response and behavior for different ice types and small-scale features. The SIRAL altimeter aboard CryoSat-2 is a \(\mathrm{K_{u}}\)-band (13.575 GHz \(\widehat{=}\) 2.2 cm) altimeter that operates in SAR (delay/Doppler) mode over sea ice. Sequences of radar bursts are emitted by the radar forming a  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Date** & **Number of Images** & **Number of Sequences** & **Mean Time Lag [min]** & **Min/Max Time Lag [min]** \\ \hline Feb 2016 & 3 & 1 & 61 & - \\ Mar 2016 & 12 & 6 & 27 & 3/54 \\ Nov 2016 & 6 & 3 & 46 & 28/69 \\ Dec 2016 & 9 & 4 & 27 & 2/40 \\ Jan 2017 & 5 & 2 & 44 & 27/61 \\ Jan 2018 & 4 & 2 & 55 & 53/56 \\ Feb 2018 & 28 & 14 & 33 & 4/74 \\ Mar 2018 & 6 & 4 & 44 & 26/75 \\ Total & 73 & 36 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of Sentinel-1 data and time lags with respect to CryoSat acquisitions.  
Figure 1: Beaufort Sea study area: Sentinel-1 images are shown in blue and the red lines represent the CryoSat-2 altimeter ground tracks. The inset in the upper left corner shows the location of the study area in the Arctic.  
stack of waveforms that view the same area on the ground from different viewing angles. The range corrected waveforms are averaged, and these multi-looked waveforms are provided alongside so-called stack parameters that describe the angular behavior of delay time integrated power [10]. The pulse limited footprint size in SAR mode is approximately 300 m along-track and 1.5 km across-track [22]. The distance between range bins of the multi-looked waveforms is 23.42 cm and a full waveform of 256 bins covers a range window of 60 m, whereas the range resolution is about 47 cm [23]. The high orbital inclination of the satellite (92 \({}^{\circ}\)) allows observations up to 88 \({}^{\circ}\) N in the Arctic. CryoSat data provided by ESA is used in this study. Waveform parameters are obtained from level 1b Baseline C data and freeboard measurements are taken from level 2 data. For the analysis in this study no further processing of the data is performed.
## 2 Materials and Methods
### Conventional FL Model  
In conventional FL, a system comprises a central server and \(N\) clients, each holding a local dataset \(\mathcal{D}_{i}\) drawn from a potentially distinct distribution \(P_{i}\), reflecting the non-IID nature of real-world scenarios. The objective is to collaboratively train a global model parameterized by \(w\in\mathbb{R}^{d}\) without sharing raw data. This is typically formulated as an optimization problem:  
\[\min_{w\in\mathbb{R}^{d}}\left\{f(w):=\frac{1}{N}\sum_{i=1}^{N}f_{i}(w)\right\}, \tag{1}\]  
where \(f_{i}(w)=\mathbb{E}_{\mathcal{D}_{i}\sim\mathcal{R}_{i}}[\tilde{f}_{i}(w; \mathcal{D}_{i})]\) represents the expected loss over client \(i\)'s data distribution, and \(\tilde{f}_{i}(w;\mathcal{D}_{i})\) is the loss for a specific data sample \(\mathcal{D}_{i}\). A widely adopted approach to solve this is FedAvg [1], where at each round \(t\): (1) The server broadcasts the global model \(w_{t}\) to a subset of clients; (2) Each selected client \(i\) performs local updates (e.g., via stochastic gradient descent) on \(w_{t}\) using \(\mathcal{D}_{i}\) to obtain a local model \(w_{i,t+1}\); and (3) The server aggregates these updates as \(w_{t+1}=\sum_{i\in\mathcal{S}_{t}}\frac{|\mathcal{D}_{i}|}{\sum_{i\in\mathcal{ S}_{t}}|\mathcal{D}_{i}|}w_{i,t+1}\), where \(\mathcal{S}_{t}\) is the sampled client subset. While effective under IID conditions, FedAvg struggles with non-IID data, as local objectives diverge, leading to client drift and poor generalization [8].
## 3 Results and Discussion
### Warping Process  
Some researchers believe that due to the lack of constraints on the other side of the struts for horizontal overhang structures, deformation gradually accumulates and eventually leads to failure [33]. However, the experimental results showed that a structure with an overhanging length of less than 3.0 mm can be formed successfully. When the overhang structure is in the early stage of manufacturing, as shown in Figure 13a,b, the  
Figure 12: Residual stress: (**a**) Layer 1; (**b**) Layer 2; (**c**) Layer 3; and (**d**) Layer 4.  
residual stress in the forming process of the first few layers of the powder exceeds the material strength, and there is no entity to constrain its deformation because the bottom of the overhang area is supported by the powder at this time, making the deformation of the first few layers larger. The results in Figure 12 also confirm this conclusion: the low residual stress area at the edge of the part was mainly caused by the lack of constraints and deformation, which released stress. In the subsequent powder spreading process, the warping deformation of the part affected the powder spreading process of LPBF, as shown in Figure 13c,d. During the subsequent process, the laser directly heated the previously formed part area in the warping area because of the lack of powder, resulting in local remelting. The remelting results in a smoother surface [44]. The corresponding situation was also found in the experimental group, and there are obvious differences between the surface quality of the warped area and the top surface of the part. As the manufacturing progressed, the experiment and simulation results both showed that the condition of the subsequent forming layer gradually improves. This is because as more solids are generated, the thermal conductivity and stiffness of the powder support area tend to be the same as that of the solid support area in the same layer. As shown in Figure 8d, the temperature change of each point tends to be the same. Heat can be transferred to the formed area at the bottom of the overhanging area, which can also provide a certain constraint for the subsequent forming layer so that the top warpage gradually improves. However, when the overhang length exceeded 3.0 mm, the distance between the overhang edge portion and the solid pillar was too long, resulting in warpage that exceeded the thickness of the powder layer. Such a large deformation seriously affected the subsequent powder spreading process and caused damage to the scraper; therefore, the processing needed to be stopped.
## 3 Solution of the Problem
### Calculation of the Velocity Field  
Applying the Laplace transform to Equation (17), and taking into consideration the corresponding initial and boundary conditions, we find that:  
\[\frac{\partial^{2}\overline{u}(y,q)}{\partial y^{2}}-a_{1}q^{\alpha}\overline {u}(y,q)=-a_{1}a_{2}\frac{1}{q}\exp\bigl{(}-y\sqrt{a_{3}q^{\alpha}}\bigr{)};\, y\,>\,0 \tag{28}\]  
where the Laplace transform \(\overline{u}(y,q)\) of \(u(y,t)\) has to satisfy the conditions:  
\[\overline{u}(0,q)=0;\ \overline{u}(y,q)\ \to\ 0\ \text{as}\ y\to\infty. \tag{29}\]  
A particular solution of Equation (28) is:  
\[\overline{u}_{p}(y,q)=\frac{a_{1}a_{2}}{a_{1}-a_{3}}\frac{1}{q^{\alpha+1}} \exp(-y\sqrt{a_{3}q^{\alpha}}), \tag{30}\]  
while its general solution is:  
\[\overline{u}(y,q)=A\exp\bigl{(}y\sqrt{a_{1}q^{\alpha}}\bigr{)}+B\exp\bigl{(} -y\sqrt{a_{1}q^{\alpha}}\bigr{)}+\frac{a_{1}a_{2}}{a_{1}-a_{3}}\frac{1}{q^{ \alpha+1}}\exp\bigl{(}-y\sqrt{a_{3}q^{\alpha}}\bigr{)}. \tag{31}\]  
Considering the conditions of (29), it results that:  
\[\overline{u}(y,q)=\tfrac{a_{1}a_{2}}{a_{1}-a_{3}}\tfrac{1}{q^{\alpha+1}}[\exp (-y\sqrt{a_{3}q^{\alpha}})-\exp(-y\sqrt{a_{1}q^{\alpha}})];\,0<\alpha<1. \tag{32}\]Applying the inverse Laplace transform to Equation (32), using the convolution theorem and the Equality (A1), we find that:  
\[u(y,t)=\frac{a_{1}a_{2}}{a_{1}-a_{3}}\frac{1}{\Gamma(1-\beta)}\int\limits_{0}^{t }\frac{1}{(t-s)^{\beta}}\Big{[}\Psi\Big{(}1,\frac{\beta-1}{2};-y\sqrt{a_{3}s^{ \beta-1}}\Big{)}-\Psi\Big{(}1,\frac{\beta-1}{2};-y\sqrt{a_{1}s^{\beta-1}}\Big{)} \Big{]}ds, \tag{33}\]  
where \(\beta=1-\alpha\).  
Now, using the identitiy (A2) from the Appendix A, we can present our solution in an interesting, but equivalent, form:  
\[u(y,t)=\frac{a_{1}a_{2}}{a_{1}-a_{3}}\bigg{\{}^{c}D_{t}^{\beta}\left[t\Psi \bigg{(}2,\frac{\beta-1}{2};-y\sqrt{a_{3}t^{\beta-1}}\bigg{)}\right]-^{c}D_{t} ^{\beta}\left[t\Psi\bigg{(}2,\frac{\beta-1}{2};-y\sqrt{a_{1}t^{\beta-1}}\bigg{)} \right]\bigg{\}}, \tag{34}\]  
in terms of the Caputo derivative of the Wright functions.
## 3 Results and Discussion
### Autogenous Shrinkage
This tendency was also observed in Figure 7. The cumulative heat release of CRC01 was higher than that of CRC00 at early ages, but they became similar at 48 h. The difference in cumulative heat release between CRC05 and CRC03 also increased after four h, but it decreased after 48 h. CRC05 exhibited the highest hydration rate due to the shortest induction period, and it also showed the fastest reduction in IRH. Figure 11: SEM images of CRC05. Figure 13 shows the relationship between the autogenous shrinkage and IRH. The graph is divided into two stages. In the first stage, autogenous shrinkage increased with no change in RH, which is referred to as the water-vapor saturated stage. The water-vapor saturated stage was also observed in other studies, and it is affected by the water-to-binder ratio [54; 55; 56]. In the second stage, the RH decreased. In stage I, the CRC00 sample exhibited the largest autogenous shrinkage, and autogenous shrinkage decreased as the CNT content increased. In stage I, the shrinkage increased due to the change in bulk strain, regardless of the hydration reaction. It appears that the bulk strain decreased as the CNT content increased, owing to the bridge effect of the CNTs. In stage II, the change in autogenous shrinkage was similar regardless of the CNT content. The slope of stage II is affected by the IRH change caused by the hydration reaction. In the graph, the slope of stage II was similar regardless of the CNT content. This indicates that CNTs affected the hydration reaction, but the effect of the hydration reaction on autogenous shrinkage was not significant. Consequently, the reduction in autogenous shrinkage due to the increase in CNT content resulted from the decrease in bulk strain caused by the bridge effect of CNTs. Figure 12: IRH of CNT-reinforced cement composites. Figure 13: The relationship between autogenous shrinkage and IRH.
## 5 Conclusions  
In summary, a novel hybrid OAM-DM-WDM-OFDM-PON system is proposed, which can support two OAM (\(LG_{I=0,p=2}\) and \(LG_{I=0,p=4}\)) modes multiplexing. With the combi  
Figure 11: BER curves of the system on the OOK and QPSK modulation versus transmission distance at (a) upstream link; (b) downstream link.  
Figure 10: The upstream receiver eye diagram at (a) B2B; (b) 50 km MMF transmission distance.  
nation of OAM-DM, WDM, and OFDM modulation, the communication capacity of the system is significantly improved. Meanwhile, the simulation results effectively prove the compatibility of OAM with WDM and OFDM techniques. Benefiting from the orthogonality of OAM modes, each LG mode over 80 km of MMF is successfully recovered at the receiver side. Besides, the measured BER of multiplexed LG mode system based on different wavelengths, as well as a single LG mode system with the same wavelength, is verified to be less than \(3.8\times 10^{-3}\) in our OAM-DM-PON system. Compared with QPSK and OOK modulation, the proposed hybrid system with OFDM modulation is preferable for improving transmission capacity which can up to 40 Gbps with BER values below the FEC threshold. The simulation results demonstrate that the hybrid OAM-DM-WDM-OFDM-PON system has a strong ability to realize capacity enhancement.  
The limitation of this work is that all the results are simulated. Furthermore, our research team will continue to apply it to practical environment and investigate the effects of multi-mode crosstalk on the OAM-DM-PON system to improve communication quality. In the future, the transmission capacity of optical access networks is expected to be greatly increased by introducing more OAM modes and advanced multiplexing/modulation formats.  
Supervision, Q.D. and X.G.; Writing--original draft, L.Z.; Writing--review & editing, H.L., J.L. ([PERSON]), X.C., Z.D., Q.Y. and J.L.([PERSON]). The authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Shandong Natural Science Foundation, China (No. ZR2017 MF070); The National Natural Science Foundation for Young Scholars of China (No. 61801267); The National Natural Science Foundation of China (No. 61471224); Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talents (No. skr20-1-008); Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talents (No. skr-D-0104060540915); Shandong Natural Science Foundation, China (No. ZR2020 MF014); Youth Fund of Natural Science Foundation of Shandong Province (No. ZR202103060092).  
Not applicable.  
The authors would like to thank [PERSON] for his active guidance and valuable suggestions. The authors are also grateful who have participated in this research work.  
The authors declare no conflict of interest.
# Effect of Local Gas Flow in Full Penetration Laser Beam Welding with High Welding Speeds
## 3 Results
### 3.3. _Spatter Reduction by Use of Local Gas Flow_
#### 3.3.1 Influence of Flow Rate
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Experimental Welding Parameters**} \\ \hline
**Welding Speed in m/min** & **8** & **10** & **12** & 16 \\ \hline
**Laser Power in W** & 2700 & 3100 & 3700 & 4800 \\ \hline
**Flow Rate in L/min** & 8.0 & 6.4 & 4.8 & 4.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Defect-free welding parameters at lowest loss of mass. The spatter formation mechanism of the reference process at 12 m/min (single wave regime) is characterized by the formation of a melt pool swelling at the rear side of the keyhole (see Section 3.1). Due to a propagating movement of the melt in welding direction (Frame 1 Figure 5a), the keyhole is temporarily constricted (Frame 2 Figure 5a). Following, the transferred impulse of the upwards rising metal vapor flow to the rear sided melt is significantly increased. Consequently, this leads to a filamentary bulging of the top-sided melt (Frame 3, Figure 5a). If the kinetic energy of the propagating melt exceeds the condition of spatter formation (see Equation (2)), the spatter is detached from the melt pool (Frame 4, Figure 5a). Comparing the reference process to the applied local gas flow, the formation of a rear-sided melt pool swelling could be observed as well (Frame 1, Figure 5b). However, the melt pool swelling occurs less frequent. Following, the formation of spatter is reduced. Based on the stabilized keyhole rear wall, a reverse melt flow into the downstream melt pool is initiated. Due to the additional transferred impulse of the gas to the melt pool, the accelerated melt may have a higher kinetic energy [27], which can be justified as reason for the melt pool elongation. However, in the case of a melt pool propagation, the aperture of the keyhole is also temporarily affected (Frame 2, Figure 5b). Spatters are detached in the same way as described on the reference process as a result (Frame 3, Figure 5b). In contrast to the reference process, the detached spatter mostly has a significantly lower kinetic energy. Thus, the spatters are re-transported to the melt pool due to a more curved spatter trajectory (Frame 4, Figure 5b).
## 5 Application of Marine Accident Occurrence Prediction Model and Results
### Reduction in MTSA-Related Accidents upon Implementation of MTSA
#### 5.5.2 Annual Number of Cases of Accidents after Application of MTSA (ANA)  
The average number of MTSA-related accidents occurring within a 3-NM radius of past MTSA-assessed project sites over the last six years is defined as the anticipated annual MTSA-related accident prediction. As mentioned in Section 3.1, marine accident data should be based on accident data following the MTSA according to the marine development project cycle. Therefore, the annual average number of MTSA-related accidents from 2017 to 2022 in the MTSA-related route information was derived. The results of the calculation of the number of accident cases after the application of the MTSA (ANA) are shown in Table 12.