esa-sceva/satcom-chunk-collection huggingface dataset:
# A Modality-Enhanced Multi-Channel Attention Network for Multi-Modal Dialogue Summarization
## 5 Conclusions and Future Work  
This paper proposes a unified model that emphasizes symmetrical modality interaction in multi-modal dialogue summarization. Our experimental results demonstrate that MEMA produces summaries that are more fluent, informative, and relevant while maintaining a high level of symmetry across the modalities. By complementing the gap in current dialogue summarization research, which predominantly focuses on textual data and often neglects multi-modal content, our model sets a new standard for achieving balanced and symmetrical multi-modal integration.  
While MEMA demonstrates strong performance on the MDS dataset, it also has the potential for various real-world applications. For instance, in customer service, MEMA could be utilized to summarize customer interactions across different modalities (text, audio, and video), allowing for quick resolution and improved service efficiency. In medical consultations, the model could assist healthcare professionals by summarizing patient discussions, ensuring that vital information is captured and easily accessible. In the realm of video-based educational content, MEMA can help summarize lectures and instructional videos, providing concise, context-rich summaries that enhance learning outcomes for students. We leave this for future work.  
However, despite these advancements, factual inconsistencies are still a major problem. In the future, we aim to solve this problem from the following perspectives:  
* Implement a multi-modal consistency checking module to align and compare information across modalities, resolving discrepancies in real time.
* Integrate a fact verification component using external knowledge bases to ensure the accuracy of the generated summaries.
* Apply contrastive learning to improve the model's ability to distinguish between coherent and incoherent multi-modal data.
* Conduct targeted experiments and ablation studies to refine the model's components and enhance its robustness against misleading inputs.  
Methodology, M.L.; Software, M.L. and Y.L.; Supervision, X.Z.; Validation, M.L.; Writing--original draft, Y.L.; Writing--review and editing, X.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
###### Abstract
Unsupervised Single Underwater Image Restoration (USUIR) [22] is an unsupervised network with an encoder-decoder structure. It performs unsupervised recovery with a differentiable gradient layer and cyclic consistency using the \"homology\" assumption between corrupted and clean underwater images; however, if the gradient model or homology is invalid, the training becomes unstable and there is a risk of model collapse. In general, supervised deep-learning-based methods require a large number of referenced data, a need often met with distorted or synthetic data. The difficulty of obtaining referenced data in the underwater environment significantly limits the reliability and generalization ability of such methods. In this study, we focus on the negative effects of non-physical based methods, which lead to excessive contrast or color saturation, additional noise generation, and poor generalization ability. While a dedicated processing unit strengthens the image's structural details, we employ the unsupervised fusion network from to intelligently combine these features. This allows us to harness the powerful feature-learning capacity of deep learning, thereby achieving effective enhancement without the data dependency limitations of supervised models [23]. The main contributions of the proposed method are as follows:  
1. A two-stage Multi-Scale Detail Enhancement Unit (MSDE) is proposed to expose structural details in underwater images in a natural and distinct way. 1. First stage: a copy of the input image is added to the original image with a detail layer created with detail maps obtained at different scales; both the edge sharpness and structural details of the image are enhanced. 2. Second stage: the noise and artefacts that may occur as a result of the first stage are decomposed into subspaces with the Latent Low-Rank Representation (LatLRR) [24] method and unwanted effects are successfully attenuated. 2. Adaptive gamma correction [25] is applied to another copy of the image; thus, a sensitive and dynamic brightness enhancement is provided to the brightness level in the scene. 3. The two developed copies are combined using the MU-Fusion [26] network, an unsupervised learning method adapted to underwater images. Thus, a balanced final image covering both global contrast and local details is obtained. In addition, owing to the ability of MU-Fusion to dynamically learn information about the scene, the generalization ability of the proposed method to different underwater environments is increased. The rest of this paper is organized as follows: In Section 2, the proposed hybrid underwater image enhancement method is presented in detail and the main steps of the method are explained. In Section 3, the experimental setup, datasets, comparison metrics, the quantitative and qualitative results, and statistical tests are comprehensively discussed. Finally, in Section 4, the general conclusions are summarized and evaluations on future work are presented.
# A Game Theoretic Framework for Surplus Food Distribution in Smart Cities and Beyond
## 2 Materials and Methods
### Strategyproofness  
**Lemma 2**.: _FDRM-CA is strategyproof for donors and receivers._  
Proof.: While there can be many places in FDRM-CA for donors to misreport, they can never gain from the same location. Firstly, say a donor \(\mathbb{D}_{i}\) misreports the donor's preference list as \(\mathbb{R}_{k}\succ\mathbb{R}_{j}\). Since the CA-DTB method awards the best available option from this preference list, misreporting only gains the donor \(\mathbb{D}_{i}\) and the option \(\mathbb{R}_{k}\). The only way a donor \(\mathbb{D}_{i}\) can gain is if \(\mathbb{R}_{k}\) becomes unavailable in the _extracted and updated_ preference list of \(\mathbb{D}_{i}\). However, that is a run-time phenomenon not affected by the preference list of the donor, and hence not affected by manipulation. The above line of thinking can be extended to other input details of the donors such as location and type of food. Note that misreporting the amount of food donated by \(\mathbb{D}_{i}\) does not affect the order in which receivers are chosen for matching, it only changes the count of receivers assigned to the donor \(\mathbb{D}_{i}\). Secondly, donors cannot influence the choice of their neighbourhood as it depends on the run-time availability of volunteers (\(\mathbb{V}\)) and the details of their transport. Thirdly, ties are broken primarily in favour of the earliest food donation or requirement timings, which can be misreported. Consider a donor \(\mathbb{D}_{i}\) who misreported food pickup time to a time (\(t_{a}\)) before the food is available for pickup (\(t_{b}\); \(t_{a}<t_{b}\)). Since, food pickup is a volunteer-mediated activity and volunteers only have a window of time available, the availability of a volunteer \(\mathbb{V}_{j}\) at a misreported time (\(t_{a}\)) does not guarantee the donor the availability of any volunteer at the donor's truthful pickup time (\(t_{b}\)). Worst case scenario, no volunteers may be available at the truthful pickup time (\(t_{b}\)). This concludes the proof. The study can prove, along similar lines, that FDRM-CA is strategyproof for receivers as well.
## 3 Geological and Geotechnical Conditions of the Area  
### Regional Geology and Tectonics  
In the Himalayas, the main lithospheric plates, along with microplates, in the region are under constant movement; consequently, the height of the Himalayas is continuously increasing. This movement also activates strike-slip activities along faults away from the collision zone. Because of these activities, the Himalayan region is separated into different litho-tectonic units in terms of extensional and compressional faulting, which range from the northwest to the southeast [12]. These units include the Higher Himalaya, Trans-Himalaya, Sub-Himalaya, Tethyan Himalaya, Lesser Himalaya, and major bounding faults [13]. Figure 3 shows the location of the project, which illustrates that the project was constructed under significantly difficult geological and geotechnical environments between the Hazara-Kashmir Syntaxis (HKS), which is an antiformal structure in the Sub-Himalaya, and it is bounded by two branches of the Main Boundary Thrust (MBT) fault [14; 15]. The location of the project shows that two major faults, the MBT and the Muzzaffarabad fault (MF), bounded the headrace tunnel, and the axis of the tunnel is sub-perpendicular to the faults (Figure 1).  
The MBT is one of the major faults in the western part of the Himalayas, with a displacement of approximately 10 mm per year; moreover, it runs along the entire length of the Himalayan range. This fault separates the younger Sivalik formations (the upper portion of the Sub-Himalayas) and the older formations of the Lesser Himalayas [16]. Performing underground excavations and ensuring their stability under this uncertain environment are challenging tasks for projects using conventional and mechanized tunneling methods, owing to the unexplored in situ geological environment and active tectonics of the region. The seismically active MF was responsible for the disastrous 7.6 magnitude earthquake that occurred on 8 October 2005. More than 87,000 people were killed, and millions were left homeless, owing to this natural disaster. The epicenter of the earthquake was located approximately 30 and 20 km from the project and Muzaffarabad city, respectively [17].
# An Integrated Photorefractive Analog Matrix-Vector Multiplier for Machine Learning +
## 2 Materials and Methods
### Photorefractive Effect
#### 2.1.4 Experimental Setup for Two-Wave Mixing  
The free-space experimental setup for two-wave mixing is outlined in Figure 7. This setup is used to characterize the photorefractive performance of the thin-film semi-insulating GaAs samples (Section 2.2).  
The photorefractive GaAs thin-film sample (purple) faces the beam path perpendicularly. The GaAs crystal axes are aligned in a way so that the incoming s-polarized light generates a p-polarized grating diffraction signal [12]. A linear polarizer in front of the detector is set at an azimuthal angle shifted away from p-polarization by a few degrees. Thereby, the s-polarized transmitted beam is mostly blocked, and interference with the p-polarized diffraction signal is enabled. Partially blocking the propagating beam enables higher detector gain for an improved signal-to-noise ratio.  
Two focusing lenses are deployed in front of the sample in order to shrink the laser spot size. The benefit is twofold: relatively small photonic circuit structures can be probed, and the increase in intensity lowers the photorefractive response time. A faster response time enables a faster experimental repetition rate.
## 4 Fragility Analysis Based on IDA Method
### Seismic Fragility Analysis
This underscores the superior seismic performance and enhanced anti-overturning capabilities of hybrid base-isolated structures during stronger earthquakes. To quantify the fragility comparison between the two structural types by using shear strain and TP value as DMs, a fragility matrix summarizing the fragility curve data was developed, as presented in Table 8. Under limit states LS1 to LS4, the maximum fragility differences between hybrid base-isolated and base-isolated structures are 19.5%, 25.3%, 25.3%, and 28.1% for the \(\gamma\)-based DM, and 3.5%, 8.6%, 11.3%, and 12.5% for the TP-based DM. This variation is attributed to the increase in PGV with higher seismic intensities. Since the viscous dampers in hybrid base-isolated structures are velocity-dependent, their damping force escalates with PGV, enhancing energy dissipation and reducing failure probabilities under stronger seismic events. Consequently, hybrid base-isolated structures demonstrate improved anti-overturning performance and reduced overturning effects at higher seismic intensity. Figure 14: Comparison of fragility curves under different damage measures: (**a**) The shear strain is \(\gamma\) as the DM. (**b**) The shear strain is TP as the DM. When the DM surpasses the threshold associated with LS4, the structure transitions to the DS5 collapse state. Consequently, the fragility curve for LS4 holds significant importance. As illustrated in Figure 15, a comparative analysis is conducted between the fragility curves representing LS4 for two damage metrics: the TP value and shear strain. The results reveal that the fragility curve for hybrid base-isolated structures demonstrates a smoother trend compared with conventional base-isolated structures, indicating reduced structural fragility and enhanced anti-overturning performance. To better evaluate the improvement in anti-overturning capability of hybrid base-isolated structures and perform a quantitative comparison of the collapse resistance between the two types of base-isolated structures, this study adopts the Collapse Margin Ratio (CMR) method for assessing structural collapse resistance, as recommended by FEMA P58 [57]. The CMR is defined as the ratio of the seismic intensity corresponding to a 50% probability of collapse to the seismic intensity corresponding to the design earthquake. In this study, considering the adopted seismic intensity levels, the BDBE acceleration peak corresponds to the design earthquake acceleration peak. Therefore, the expression for the CMR is  
\[CMR=Sa(T,5\%)_{50\%}/Sa(T,5\%)_{BDBE} \tag{10}\]  
The CMR for base-isolated structures and hybrid base-isolated structures with two different damage measures are summarized in Table 9. The results indicate that hybrid base-isolated structures exhibit consistently higher CMR values compared with conventional base-isolated structures, reflecting a significant enhancement in their collapse resistance. Furthermore, the viscous dampers demonstrate effective energy dissipation under BDBE, contributing to improved structural performance.
## 4 Simulations and Analysis
### Analysis of Periodic Current Distortion  
The experimental results indicate that the waveform of the periodic fault current is directly related to the liquid conductivity, meaning that the ion concentration in the liquid influences the degree of current distortion. When the cable core meets the liquid, it acts as an electrode, and the charge distribution on its surface causes the rearrangement of ions within the liquid. Under the influence of the electric field, positive and negative ions in the liquid undergo directional migration, forming a current. Oppositely charged ions accumulate on the surface of the cable core, thereby affecting both the electric field strength and the magnitude of the current.  
During half a cycle, as the voltage gradually increases, the electric field strength near the surface of the cable core (under positive polarity) intensifies. At this stage, negative ions in the liquid migrate toward the surface of the cable core under the influence of the electric field, while positive ions move toward the grounded liquid end. The higher the ion concentration, the greater the number of ions migrating per unit time, resulting in a larger current. As the voltage approaches its peak, many ions rapidly accumulate on the metal surface of the cable core. These accumulated ions generate an electric field component opposite to the externally applied electric field, weakening the electric field within the defect and hindering further ion migration in the liquid, thereby reducing the current. A higher ion concentration leads to a greater accumulation of ions, producing a stronger opposing electric field, which causes the current to decrease more quickly and significantly. After the voltage surpasses its peak and begins to decrease, the ion accumulation rate slows, while the rate at which ions leave the cable core surface relatively increases. As a result, the number of accumulated ions on the electrode surface stops increasing and instead begins to decrease. With fewer accumulated ions, the strength of the opposing electric field generated by these ions gradually weakens. Under the remaining external electric field, the ions in the solution can gradually resume migration, leading to a renewed increase in current. The entire physical process is illustrated in Figure 17.
## 2 Materials and Methods
### Taguchi Method  
The Taguchi method is a statistical approach used to improve product quality in industry by optimizing process parameters and reducing the need for extensive experiments. Using an orthogonal array, the Taguchi design examines how variations in input factors impact experiment outputs with a minimal number of trials. Key steps include identifying factors and their levels, conducting experiments with orthogonal tables, calculating the signal-to-noise (S/N) ratio, creating output tables, and using analysis of variance (ANOVA) analysis. The S/N ratio helps to measure deviations in quality characteristics, ranking factors with \"higher is better\", \"lower is better\", and \"nominal is best\" quality types. ANOVA analysis quantifies data variability, showing how each factor's settings influence outcomes. The final phase is a confirmation experiment to validate the chosen process settings.
## 4 Results
### Sequence-Specific Clustering Properties
#### 4.4.1 Cornith Gulf  
In Table 6, the inverted parameters for the 26 clusters of dataset D1 with \(N\geq 30\) are given. We adopt the generic values of Omori law (\(p\) and \(c\)) for clusters with \(N<80\) to increase the stability of the inverted parameters. We observe relatively high background rates for most of the sequences and low \(a\) values, in particular, \(a<1\) for 10 out of the 26 clusters.  
Concerning the 2013 Aigion earthquake swarm and its subsequent swarms (clusters C6, C9 and C10), we observe relatively low productivity values of the ETAS model (\(a=1.31\), \(1.29\), \(1.13\), Table 6) in accordance with studies suggesting pore-fluid pressure as the main triggering mechanism during the excitation [63]. Clusters C11 and C12 are part of the same swarm (Figure S4) that occurred offshore Psathopyrgos. Their relatively high background rates (\(\mu=1.34\), \(1.92\)) show that a significant part of the clustered seismicity cannot be explained by the empirical laws of the triggering part of the ETAS model. Cluster 14 is part of a major swarm that began on 8 June 2014 (Figure S4). [PERSON] et al. [65] did not find high diffusion rates that are related to fluid pore pressure. However, the large background rate found in our study (\(\mu=4.86\)) and the low \(a\) value (\(a=0.92\)) suggest the existence of a non-typical mainshock-aftershock sequence, with more complex triggering mechanisms being responsible, such as as aseismic creep. Similarly, the largest cluster in the dataset, the C15, located offshore Nafpaktos, is characterized by relatively high background rate (\(\mu=1.32\)) and low productivity (\(a=1.38\)), more typical values for swarm activity. In contrast, clusters C18 and C19 that are more typical mainshock-aftershock sequences with a distinct in magnitude event in the initiation of the sequence (Figure S5), have low  
Figure 8: (**a**) The number of events triggered by an earthquake of magnitude \(M_{i}\) at CG (blue), NAS (orange) and CII (brown), respectively. (**b**) The temporal distribution of triggered aftershocks.  
background rates (\(\mu=0.05,\ 0.76\)) and relatively high productivity rates (\(a=1.77,\ 1.80\)). The two clusters near Itea Gulf show contradictory results, in particular, the first one, \(C13\), is characterized by a high background rate (\(\mu=3.41\)), whereas the second, \(C17\), which occurred four months later, exhibits a much smaller background value (\(\mu=0.35\)) more typical for mainshock-aftershock sequences. However, biases can exist in the inversion of the parameters for clusters with a small number of events, so we should be cautious with the inference.
## 3 Results and Discussion
### Influence of the Descriptors Selection  
The last part of this section is devoted to the study of the sensitivity of classification algorithms and to the influence of the choice of descriptors on the data segmentation with artificial data sets. This data set is artificially generated from actual acoustic emission data [13]. The data set contains 4 clusters (2000 signals each) that are representative of actual experimental data (Figure 10). This data set, being very similar to real data, will illustrate the notion of relevant descriptor. This data set is initially described by 18 descriptors then by 3 relevant descriptors (amplitude, In [counts to peak], In [energy]). The user according to the known structure of the data has defined the relevant parameters. When the 18 descriptors are considered, the best-obtained segmentation is still a 3-cluster solution. Only one cluster is correctly identified with 1985 signals. The average Silhouette for this group is greater than 0.6, while for the other two groups, the average Silhouette is less than 0.5. Considering only the three relevant descriptors (amplitude, In [counts to peak], In [energy]), the 4-cluster solution corresponding to the actual data structure is obtained. The four clusters have an average silhouette greater than 0.6 and there are only 120 misclassified signals. In the unsupervised classification, it is impossible for the user to visually determine the relevant parameters from an experimental point of view. The use of the correlation matrix of the 18 descriptors only allows the selection of uncorrelated descriptors and does not give any indication of their relevance.
# A Review on the Thermal Hazards of the Lithium-Ion Battery and the Corresponding Countermeasures
## 4 Thermal Hazards of Lithium-Ion Battery
### Thermal Hazards of Typical Commercial Lithium-Ion Batteries
#### 4.4.1 Lithium cobaltate oxide  
At present, LCO cathode material still occupies a major part in the 3C field, while the safety is relatively poor, so it is gradually being replaced by other positive electrode materials [167; 168]. The active surface of the LCO cathode material can catalyze the decomposition of the carbonate electrolyte then release a large amount of heat, thus causing the thermal runaway of battery [169; 170]. [PERSON] et al. studied the overcharge capacity degradation mechanism of LCO/graphite battery and found that the dissolution of current collector led to changes in the structure of the SEI layer and electrodes, thereby reducing the battery capacity and increasing the risk of thermal runaway [171]. In view of the safety problem associated with LCO, researchers have proposed a variety of solutions. [PERSON] et al. [172] recommended a phosphazene additive N\({}_{3}\)P\({}_{3}\)(OPh)F\({}_{5}\) (PPPN), which could be oxidized to form a protective cathode electrolyte interface (CEI) before the electrolyte, greatly improving the safety of battery. [PERSON] et al. [173] coated and doped LCO-positive electrode materials by adding Al and Ti elements, which greatly improved the thermal stability, increased the thermal runaway critical temperature and reduced the heat release. With the increasing demand for high-voltage LCO batteries, further studies are needed for the modification of LCO cathode materials, safe electrolytes and additives at high voltage.