esa-sceva/satcom-chunk-collection huggingface dataset:
# FACTMS: Enhancing Multimodal Factual Consistency in Multimodal Summarization
## 6 Results and Analysis  
### Word Overlap Evaluation  
Table 2 shows the ROUGE and BLEU performance of the baseline models and FactMS on the HCSCL-dataset test set, measuring the degree of word overlap between the generated summaries and target summaries. Although FactMS introduces a large amount of textual and visual factual knowledge in the hope of enhancing factual consistency, it is still comparable to the most advanced MS model in terms of word overlap metrics. FactMS achieves the highest ROUGE-2 score (29.04), ROUGE-L score (41.87), BLEU-3 score (25.52), and BLEU-4 score (23.65). This indicates that FactMS retains the ability to generate fluent text summaries while focusing on the factual consistency problem.  
To comprehensively evaluate FactMS, we conducted comparative analyses with other methods on both the Daily Mail and CNN datasets. As shown in Table 3, FactMS consistently outperforms all metrics on the CNN dataset and surpasses other baselines on the Daily Mail dataset in terms of ROUGE-2 and ROUGE-L scores. These findings provide robust evidence that the superior performance of our model on the HCSCL dataset is not an isolated case, thereby validating the effectiveness of the proposed dual heterogeneous knowledge graph.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Methods** & **Rouge-1** & **Rouge-2** & **Rouge-L** & **Bleu-1** & **Bleu-2** & **Bleu-3** & **Bleu-4** \\ \hline PG [46] & 42.54 & 28.25 & 39.95 & 38.26 & 30.78 & 24.55 & 19.92 \\ FTSum [4] & 42.01 & 27.66 & 41.00 & 37.49 & 30.03 & 23.78 & 19.27 \\ MAtt [2] & 41.30 & 24.70 & 37.55 & 37.41 & 28.44 & 21.13 & 16.02 \\ MSMO [42] & 42.89 & 28.25 & 39.86 & 39.06 & 31.26 & 24.78 & 19.93 \\ MOF [43] & 42.41 & 28.10 & 39.60 & 38.04 & 30.55 & 24.38 & 19.71 \\ MSE [3] & 42.99 & 28.78 & 41.79 & 39.02 & 31.51 & 25.21 & 20.50 \\ VMSMO [44] & 42.68 & 28.35 & 41.34 & 38.75 & 31.20 & 24.75 & 19.99 \\ HCSCL [19] & **43.64** & 29.00 & 40.94 & **39.64** & **31.91** & 25.40 & 20.54 \\ \hline FactMS & 42.02 & **29.04** & **41.87** & 39.42 & 31.64 & **25.52** & **23.65** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of FactMS with baselines on HCSCL-dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Daily Mail Dataset**} & \multicolumn{4}{c}{**CNN Dataset**} \\ \hline  & **Method** & **Rouge-1** & **Rouge-2** & **Rouge-L** & **Rouge-1** & **Rouge-2** & **Rouge-L** \\ \hline Extractive & Lead-3 & 35.81 & 15.34 & 26.13 & 11.70 & 3.40 & 13.94 \\ Model & TextRank [49] & 24.20 & 9.11 & 22.14 & 21.95 & 7.12 & 19.25 \\ \hline Textual & PG [46] & 28.41 & 3.46 & 37.97 & 26.43 & 1.50 & 17.24 \\ Only & SS5 [45] & 20.42 & 1.63 & 15.12 & 26.88 & 1.60 & 18.82 \\ \hline Multimodal & MSMO [42] & 35.38 & 14.79 & 25.41 & 26.83 & 8.11 & 18.34 \\ Model & M2 SM [50] & **41.73** & 18.59 & 31.68 & **27.81** & 8.87 & 18.73 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of FactMS with baselines on Daily Mail and CNN dataset.
# Rough Estimator Based Asynchronous Distributed Super Points Detection on High Speed Network Edge
## 6 Experiments and Analysis
### Experiment Data  
In this paper, four groups of high-speed network traffic are used. Two of the four sets of data come from the 10 Gb/s Caida [28]. The other two groups are from the network boundary of the 40 Gb/s CERNET in Nanjing network [29]. The Caida data acquisition dates are 19 February 2015 and 21 January 2016 (denoted by _Caida_ 2015\(2\)19 and _Caida_ 2016_01_21), and the data acquisition dates of the two groups of CERNET Nanjing network were 23 October 2017 and 8 March 2018 (denoted by _IPtas_ 2017_10_23 and _IPtas_ 2018_03_08). The collection time of the four groups of data is one hour from 13:00. The collected data are raw IP Trace. Caida data collected Trace between Seattle and Chicago. In this paper, the IP on Seattle side is defined as a, and the IP on Chicago side is defined as b. IPtas data collects traces between CERNET Nanjing network and other networks. In this paper, the IP in Nanjing network is a, and in the other network is b. In the experiment of this section, the length of time window is 5 min, and the threshold of super point is set to 1024. Therefore, each group of experimental data contains 12 time windows. Table 2 lists the statistical information of each experimental data. The number of a in Caida data is more than the number of a in IPtas data, which is 1.85 times more on average. However, the average cardinality per a in Caida data is less than that in IPtas data, only 21.389% of the latter. The number of packets per second determines the number of IP address pairs that need to be processed per second. Therefore, packet speed (in millions of packets per second, Mpps) is a key attribute. As can be seen from Table 2, the average packet speed of IPtas data is 3.89 times that of Caida data. Therefore, Caida data and IPtas data represent two different types of network data sets, which can test the effect of the algorithm more comprehensively.
# Blockchain-Based Federated Learning: A Survey and New Perspectives
## 3 Blockchain vs. Federated Learning
### Reasons Why Blockchain Enables Federated Learning
#### 3.3.3 Reputation Incentives  
The blockchain consensus mechanism provides a consensus protocol for the data stored on the chain, improving the efficiency of FL model training. The concept of blockchain-based reputation-aware fine-grained FL is proposed in [56] to ensure trusted collaborative training in mobile edge computing systems. [PERSON] et al. [57] proposed a blockchain-based payment system for FL, where the contribution of each FL participant is evaluated through a Proof of Shapley (PoSap) consensus protocol. In addition, the characteristics of the blockchain itself can provide an incentive system for data and promote a reasonable distribution of rewards. In [29], [PERSON] et al. discussed an incentive mechanism based on contribution points to fairly reward FL participants for contributing their local data, to achieve the authenticity of FL model aggregation and to provide incentives for FL participants. [PERSON] et al. [58] proposed a blockchain-based federated learning framework and protocol to transparently evaluate the contribution of each participant. The framework preserves the privacy of all parties during the model building phase and transparently evaluates contributions based on model updates.
## 2 Method and Data
### Main Data for GLOPEM-CEVSA Model Input and Validations  
The GLOPEM-CEVSA model inputs include the interpolated meteorological data, the remote sensed FPAR and vegetation classification data, and soil data (e.g., soil texture, soil available water and soil carbon). Other auxiliary data included digital elevation model (DEM) data and vegetation carbon storage data.  
The DEM was used in the meteorological data interpolation, and was obtained from Shuttle Radar Topography Mission (SRTM) with an original 90-m spatial resolution and was resampled to 8-km resolution. Soil texture and soil carbon storage were obtained from the Harmonized World Soil Database (HWSD) of the International Institute of Applied System Analysis (IIASA) and the Food and Agriculture Organization (FAO). The soil carbon storage data was used to initialize the soil respiration sub-module, and the soil texture data was used to calculate the parameters of soil water content.
## 6 Relating Cancer Diagnosis and Dates
### Linking Dates to Cancer Entities  
Cancer entities and dates extracted in a previous step have to be correctly linked. The challenge is how to relate a certain cancer entity to a particular date appearing in the same sentence. To achieve this task, we propose to analyze the syntactic structure of the sentence and the entity events that appear in this sentence. Thus the syntactic parse tree of the sentence is generated, and traversing this tree; it is possible to relate the date to the cancer entity. In particular, a date is linked to a cancer entity if some of the following conditions are true:  
1. A cancer entity or an event entity is an ancestor of a date in the dependency path of the sentence.
2. The cancer entity and a date entity appear contiguously or belong to the same predicate in the sentence.  
Figure 6: Scope using a Parse tree.  
The sentence contains event entities: in this case, the event, the cancer entity, and the date are linked if some of the prior conditions are met.  
Figure 7 depicts the process to link a cancer entity to a date entity for the sentence: _\"Paciente diagnosticado con cancer de pulmon el 12 October 2016, tratado con cirugia el 21 March 2017\"_. (Patient diagnosed with lung cancer on 12 October 2016, treated with surgery on 21 March 2017).  
As one can see, the date _\"12 October 2016\"_ is linked to the concept _\"cancer de pulmon\"_ because they are in the same predicate. Moreover, the event _\"diagnosticado\"_ is also linked to this date. After linking these concepts, a triplet (_\"cancer de pulmon, 12 October 2016, diagnosticado\"_) is created. On the other hand, the date _\"21 March 2017\"_ is linked to the event _\"tratado\"_ because this event is an ancestor of this date.
## 2 Materials and Methods
### Methods
#### 3.1.1 Influence of the Parameters  
The influence of the solvents (acetone, ethanol, ethyl acetate), the solid/liquid ratio, the temperature, and the time used in the extraction on the parameters (lycopene, DPPH, polyphenols, and flavones) can be seen in Table 6. According to the calculations, the highest amount of lycopene was 3.36 mg/100 g, which was obtained using acetone as a solvent, while the highest level of DPPH activity (82.93%) was observed in the sample extracted with ethanol. For polyphenol and flavone content, the best solvent for extraction was acetone. Acetone had the greatest influence, giving the best extraction results. The solvent was a parameter that significantly influenced the content of flavones (\(p<0.001\)). The limitations of the solvents used in this study are related to their toxicity; their evaporation prior to their addition to foods is a mandatory process. According to [PERSON] et al., if the raw by-product is treated with the greener solvent solution, it is possible to obtain extracts with 25% more lycopene when acetone/n-hexane are used than with ethyl acetate/ethyl--lactate (30:70 %/v). The use of deep eutectic solvents allows practical issues related to the high volatility of the traditional organic solution to be overcome. In the case of \(\beta\)-carotene, the acetone/n-hexane (1:1 %/v) solvent extracted 40% more than the ethyl acetate/ethyl--lactate (30:70 %/v) solvent. On the other hand, the DL-menthol/lactic acid solution showed difficulties in the interaction between the solvents in the mixture and the by-product due to its viscosity, thus lowering the extraction potential, with 12.32 ug/g and 492.46 ug/g, respectively, for lycopene and \(\beta\)-carotene (DL-menthol/lactic acid (8:1 mol/mol)) [25]. Influence of Solvent/Liquid Ratio on Parameters  
The ratio of solvent to liquid plays a critical role in the extraction process of tomato pomace, exerting a significant influence on various parameters of the resulting extract [9]. This can lead to higher yields of desired compounds such as antioxidants, phenolic compounds, and other bioactive substances found in tomato pomace [26].
## 3 Results and Discussion
### Performance of Studied Asphal Mixtures
#### 3.4.3 Stiffness Modulus of Studied Asphalt Mixtures  
Based on the test results of stiffness modulus and phase angle, carried out for a range of frequencies (0.1 to 10 Hz) at different temperatures, it is possible to predict the same parameters over a larger range of frequencies, from a reference temperature (20 \({}^{\circ}\)C), using the time-temperature superposition principle. The resulting curves (master curves) are presented in Figure 11 and cover a frequency range far beyond the experimentally accessible values. They were obtained by translation of each isotherm curve (0, 10, 30, 40 \({}^{\circ}\)C) based on the reference curve of 20 \({}^{\circ}\)C. In order to evaluate the applicability of this  
Figure 10: Evolution of the rut depth of the studied mixtures in the wheel tracking test (WTT) (the inset presents the resulting wheel tracking slope in air (WTS\({}_{\text{AIR}}\)) values).  
method, the horizontal translation factor should be overlapped by viscoelastic functions. The Arrhenius function was used for the construction of these master curves, and this process was performed using IRIS 9.0 software.  
The conventional mixture M1 has lower values for the stiffness modulus at lower frequencies (or high temperatures), which is in agreement with the permanent deformation results, followed by M2 (PMB mixture without PEX). The influence of the addition of PEX on stiffness modulus can be observed by comparing the results of M1 and M3 (50/70 pen bitumen, with and without PEX, respectively), these results showed that the addition of PEX increases the stiffness modulus, in a more noticeable way at low frequencies (or high operating temperatures). The same is observed for the mixtures with the PMB 45/80-60 binder (M2 and M5), and there is even an increase of 0.5% in the binder content. Looking at the results obtained for mixture M4, one could expect to obtain stiffness modulus values lower than those of mixture M3, due to its higher binder content. However, that behaviour was not observed, possibly due to the existence of a higher amount of bitumen available to interact with the PEX portion that was able to melt during the mixing operation, increasing its stiffness.  
In terms of phase angle, only the conventional mixture (M1) showed values above 45\({}^{\circ}\) (tan \(\delta\) higher than 1), i.e., M1 is the mixture that provides a more viscous behaviour, followed by mixture M2, while mixtures with PEX have a more elastic behaviour.  
Generally, it becomes evident that PEX increased the stiffness moduli and reduced the phase angle values in comparison with the mixtures without this waste material.
# An Efficient Parallel Algorithm for Detecting Packet Filter Conflicts
###### Abstract  
Advanced network services, such as firewalls, policy-based routing, and virtual private networks, must rely on routers to classify packets into different flows based on packet headers and predefined filter tables. When multiple filters are overlapped, conflicts may occur, leading to ambiguity in the packet classification. Conflict detection ensures the correctness of packet classification and has received considerable attention in recent years. However, most conflict-detection algorithms are implemented on a conventional central processing unit (CPU). Compared with a CPU, a graphics processing unit (GPU) exhibits higher computing power with parallel computing, hence accelerates the execution speed of conflict detection. In this study, we employed a GPU to develop two efficient algorithms for parallel conflict detection: the general parallel conflict-detection algorithm (the GPCDA) and the enhanced parallel conflict-detection algorithm (the EPCDA). In the GPCDA, we demonstrate how to perform conflict detection through parallel execution on GPU cores. While in the EPCDA, we analyze the critical procedure in conflict detection as to reduce the number of matches required for each filter. In addition, the EPCDA adopts a workload balance method to enable load balancing of GPU execution threads, thereby significantly improving performance. The simulation results show that with the 100 K filter database, the GPCDA and the EPCDA execute conflict detection 2.8 to 13.9 and 9.4 to 33.7 times faster, respectively, than the CPU-based algorithm.  
osr 202215237. [[https://doi.org/10.3390/a15070237](https://doi.org/10.3390/a15070237)]([https://doi.org/10.3390/a15070237](https://doi.org/10.3390/a15070237))  
1
# Enabling Multi-Mission Interoperable UAS Using Data-Centric Communications
## 4 Data-Centric Communications in UAS Scenarios
### QoS Negotiation  
In general, the dissemination and retrieval of information do not uniquely involve the identification, location, and request of the desired data. Complementary to the aforementioned procedures, a consumer interested in a given content must contact its corresponding producer, not only to identify \"what\" is being solicited, i.e., which content to deliver, but also to specify \"how\" it should be received by the soliciting endpoint. To illustrate this, we return to our previous example where an operator of a ground mobile station (e.g., a firefighter participating in a fire extinction) aims at visualizing the video recorded from a given UAV. In addition to identifying the video component to be activated and propagating an appropriate request towards the corresponding UAV, the video player application in the ground station should indicate a set of additional parameters that must be observed by the publisher (i.e., the UAV) to guarantee the usefulness of the data to the receiver, particularly the set of video codecs supported by the receiver application, the desired video coding rate, and thetype of distribution, by considering some non-functional (sometimes fundamental) elements such as transport reliability or security support. It is important to understand that actual data dissemination will also be determined by the capabilities and resources available at the publisher equipment at runtime. Returning to the previous example, a commercial UAV product may only support a limited set of video codecs and coding rates, and may not even implement a security solution for the delivery of telemetry information.  
Several well-known examples of network protocols in the TCP/IP world have been used in different contexts to negotiate and establish data communication quality. A consolidated mechanism in the scope of multimedia networking is the Offer/Answer model of the Session Description Protocol (SDP) [23], which allows two endpoints to agree on the description of a multimedia session with diverse unicast and/or multicast data streams. However, existing TCP/IP-based approaches present limitations to define requirements and parameters that may be of interest in the considered scenarios, such as the minimum frequency for data transmission (e.g., a firefighter in the ground unit may specify a minimum refresh threshold for the telemetry information delivered by a UAV unit), or deadline constraints that must be observed at the receiver (e.g., the time delay to receive critical a sensed data not to exceed certain predefined value). Alternatively, the utilization of topics in DDS allows the receiver to flexibly identify a given content with specific format characteristics in a standardized way. In particular, DDS offers a flexible platform to negotiate a large set of QoS characteristics for publisher-to-subscriber communications (detailed information in Section 3).
# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 3 VBDA Architectures and Core Technologies
### VBDA Core Technologies
#### 3.2.2 AI-Driven Video Analytics
For instance, retrieval-augmented generation techniques have been shown to improve the realism and contextual relevance of synthesized video sequences in simulation-based training and safety-critical environments [31]. Additionally, vision-language models (VLMs) such as CLIP, VideoBERT, and BLIP are now integral to cross-modal VBDA tasks [81; 135; 136]. These models enable zero-shot video retrieval, caption generation, and semantic alignment between visual and textual modalities, supporting applications in forensic search, intelligent surveillance, and human-AI interaction [32]. Beyond mainstream models, the integration of multi-object tracking (MOT), SSL-enhanced anomaly detection, and hybrid spatiotemporal architectures point to emerging directions in adaptive video intelligence [5]. These techniques provide deeper behavioral insight, enabling more robust reasoning in dynamic environments. Overall, the AI landscape in VBDA demonstrates a clear shift from conventional deep learning models to more flexible, multi-modal, and privacy-aware architectures. This transition aligns with the design principles of the proposed ViMindXAI platform, which emphasizes scalable learning, decentralized intelligence, and explainable analytics across diverse video-driven domains.
# Future Is Unlicensed: Private 5G Unlicensed Network for Connecting Industries of Future
## 3 Opportunities of NR-U for Industry 4.0  
WiFi has always been famous for industrial applications due to its easy-to-use and fast deployment characteristics. In many cases, WiFi remains an excellent solution to some extent. However, the use of private LTE over WiFi networks for industries gives the advantage of radio performance in terms of range/link budget, spectral efficiency/capacity, configurable QoS, mobility, interoperability, high to low rate scaling, spectrum options, security, and road map to 5G and beyond. Looking at the evolving practice of LTE, the technological revolution of 4G seemed to be more focused on richer multimedia content for the individual users, while the NR-U technology of 5G holds the potential to revolutionize the industry. Industries and enterprises have always been in the priority group for adopting the leading technology. Hence, 5G NR-U tends to transform the future cyber-physical industries in the following ways (Figure 2).