esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Opportunities and Open Research Areas in VANET Multimedia
### Video Streaming Techniques in VANETs  
The authors in [55] thoroughly examine challenges and present viable solutions for real-time video transmission in vehicular ad hoc networks (VANETs), particularly in the context of assisted driving applications. Their specific emphasis lies in vehicle-to-vehicle (V2V) communication, advocating for the utilization of the IEEE 802.11p standard, purpose-built for vehicular communication. Researchers explore the viability of real-time video transmission and conduct a comprehensive analysis of the system's performance, taking into account factors like delay, throughput, and video quality. The proposed research aligns well with the study by [55], as it also addresses the challenges of multimedia transfer in VANETs, with a specific emphasis on static audio-visual datasets, with an advantage in terms of bandwidth in comparison to video streaming and image transfer through beacon messages. While [55] concentrates on real-time video transmission using the IEEE 802.11p standard, it overlooks the underlying transmission bandwidth issues that arise due to large data transmission.  
Despite the resource challenges faced by [56] in conducting vehicle-to-pedestrian (V2P) communications, researchers have shown that the transmission of essential messages and audio warnings can enhance situational awareness and promote safer interactions between vehicles and pedestrians in DSRC. Compared to video, which consumes more bandwidth due to the transmission of visual data [57], audio-visual messages can effectively convey information using fewer network resources. This is particularly important in DSRC, where bandwidth is a valuable and limited resource. Effectively addressing these challenges and optimizing multimedia transmission techniques can unlock the full potential of multimedia transmission in VANETs, leading to substantial improvements in the safety and efficiency of vehicular transportation.  
The use of audio-visual communication in DSRC has the potential to align not only with the goal of providing efficient and effective communication; however, as emphasized by [58], it can facilitate easy identification and help the public engage in identifying criminals during emergency notifications while ensuring driver safety and minimizing distractions. By leveraging the combination of pictures, text, and audio, audio-visual communication, it offers a promising solution that strikes a balance between conveying valuable information and maintaining the driver's focus on the road, all while utilizing less bandwidth.
# Late Fusion-Based Video Transformer for Facial Micro-Expression Recognition
## 4 Experimental Results  
### Dataset and Metrics for the Whole Experiment  
We used the SMIC [1] and the CASME II [2] dataset for the evaluation of the proposed method. There are 100 Hz 164 videos classified into positive (51), negative (70), and surprise (43) emotions, built by 16 subjects in SMIC. Each video shows the upper body is different in length, has 33.7 frames on average. The other dataset, CASME II, consists of 200 Hz 247 videos classified into disgust (64), happiness (32), others (99), repression (27), and surprise (25), built by 26 subjects. The average number of frames is 67.2. All video has three color channels: red, green, and blue.  
Since the dataset has an imbalance distribution of emotion labels, we used three balanced metrics to reduce the bias: accuracy (Acc), unweighted average recall (UAR) [22], and unweighted F1 score (UF1) [23].  
\[Acc.=\frac{\sum_{c=1}^{C}TP_{c}}{\sum_{c=1}^{C}N_{c}}, \tag{9}\]  
\[UAR=\frac{1}{C}\sum_{c=1}^{C}\frac{TP_{c}}{N_{c}}, \tag{10}\]  
\[UF1=\frac{1}{C}\sum_{c=1}^{C}\frac{2 TP_{c}}{2 TP_{c}+FP_{c}+FN_{c}}, \tag{11}\]  
where \(C\) is the number of classes, and \(N_{c}\) is the number of samples for each class. _TP_, _FN_, and _FP_ are the true positive, false negative and false positive, respectively.
# Security in Cloud-Native Services: A Survey
## 6 Secure Software Development Lifecycle (SDLC)
### Security Risk Profiling and Mitigation
**Containers:** Containers are a form of virtualization that allows for lightweight and portable deployment of applications, but they also introduce new attack surfaces and security risks. First of all, containers are built using base images, which can contain vulnerabilities that attackers can exploit. In addition, the container runtime environment can be vulnerable to attacks if not configured securely. Furthermore, container orchestration platforms [138], such as Kubernetes, can introduce security risks if not configured securely. Containers may be subject to compliance requirements, such as HIPAA or PCI DSS. An example of container security is depicted in Figure 10.  
**Infrastructure-as-Code (IaC):** Here, we refer to provisioning and managing infrastructure resources using machine-readable definition files rather than manual processes; although IaC provides many benefits like automation, version control, repeatability, and consistency [139], security should be a key consideration when implementing IaC since any vulnerability or weakness in IaC code can be automatically propagated across all infrastructure instances. An example of IaC is depicted in Figure 11.  
Figure 10: Container security. Figure 9: Software Composition Analysis (SCA). Several principles should be considered, including:  
* The principle of least privilege: To ensure that each user has only the minimum access required to perform their tasks. * Define who is authorized to run the scripts and who is not. * Limit the permissions of authorized IaC users to what is necessary to perform their tasks. * IaC scripts should ensure that the permissions granted to the various resources created are limited to what is required. * Network segmentation: The resources and their related dependencies are all secured within a private subnet. * Data encryption: A technique used to authenticate and encrypt data sent between two services. Mutual Transport Layer Security (mTLS) ensures that traffic is secure and trusted in both directions between a client and server, providing an additional layer of security for users who log in to a network or applications. Secure SDLC [140] is critical towards building secure, reliable, resilient software to withstand cyber threats and attacks. By incorporating security into every phase of the software development process, we can minimize the risk of security incidents and, at the same time, comply with regulatory requirements. It is of major importance to understand how these mechanisms should be implemented in the context of cloud-native environments. This has been an increasingly important research topic for cloud-native applications, especially in the case of applications that are characterized by traits such as the vast amount of involved data (e.g., audio/video), the simultaneous presence of multiple users, and low-latency & high-bandwidth requirements.
## 4 Experimental Design and Results
### Case 2: The Performance of HEMOGWO on SCOS Problems
The results indicate that the HEMOGWO algorithm achieved the highest rank on both metrics, significantly outperforming the other compared algorithms. This strongly supports its superior overall performance in solving multi-objective optimization problems. SMOGWO ranked second in both GD and IGD, demonstrating its strong competitiveness in handling complex multi-objective tasks, particularly in balancing convergence and solution distribution. MOGWO ranked fourth, suggesting that while it inherits strengths from the original gray wolf optimizer, it is slightly inferior to HEMOGWO and SMOGWO in terms of convergence accuracy and solution  
diversity. In contrast, MOEAD_DE and MOPSO ranked fifth and sixth, respectively, reflecting clear limitations in convergence speed, solution uniformity, and proximity to the true Pareto front. Notably, the rankings across GD and IGD were highly consistent for all algorithms, further confirming the complementarity and reliability of these two metrics in evaluating multi-objective optimization performance. Finally, all \(p\)-values from the tests were well below the 0.05 threshold, providing strong evidence of statistically significant performance differences among the algorithms. Finally, to quantitatively assess the computational cost of each algorithm across varying scales of SCOS problems, this study presents a statistical analysis of their runtime, as illustrated in Figure 4. The experimental results show that MOPSO consistently exhibits the highest computational efficiency across all test scales, with runtimes significantly lower than those of the other algorithms. This efficiency is primarily attributed to its simple and effective particle update mechanism. MOGWO and MOEAD_DE also demonstrate slightly lower runtimes compared to HEMOGWO, indicating relatively lower computational complexity. In contrast, the HEMOGWO algorithm, due to the incorporation of a hybrid differential evolution strategy enhanced by chaotic mapping and Levy flight  
Figure 3: The best solution distribution. mechanisms, involves a more sophisticated search process, resulting in relatively higher computational costs. However, when considered alongside the GD and IGD performance metrics, HEMOGWO clearly outperforms the other algorithms in terms of solution accuracy and stability. Although its runtime exceeds that of MOGWO, MOEAD_DE, and MOPSO, the additional computational cost remains within a reasonable and acceptable range for practical applications that demand high-quality solutions. Notably, although SMOGWO and NSGA-III exhibit performance levels close to that of HEMOGWO, their execution times are significantly longer. In particular, the average computational cost of NSGA-III is approximately three times that of HEMOGWO, which limits its applicability in resource-constrained environments.
## 2 Materials and Methods
### Isolation Procedures for Major Food- and Water-Borne Bacteria Using Selective Media  
#### 2.3.1 Swab Samples Procedure  
Under aseptic conditions, each swab sample was placed in 9 mL of sterile buffered peptone water. The buffer composition included casein peptone at a concentration of 10 g/L, di-sodium hydrogen phosphate 12 H\({}_{2}\)O at a concentration of 9 g/L, potassium dihydrogen phosphate at a concentration of 1.5 g/L, and sodium chloride at a concentration of 5 g/L (Merck KGaA, Darmstadt, Germany). The sample was thoroughly mixed using a vortex mixing device [31]. To create a series of ten-fold dilutions of 10-8, 1 mL of the original suspension was mixed with 9 mL of buffered peptone water. Eosin Methylene Blue agar (EMB) was used to isolate _E. coli_, _Salmonella Shigella_ Agar (SSA) for _Salmonella_, Cetrimide Agar (CA) for _Pseudomonas_, and Baird Parker Agar (BPA) for _Staphylococcus aureus_ (_S. aureus_) isolation. All selective media used in this study were purchased from Sigma-Aldrich, Burlington, MA, USA. The media were prepared according to the manufacturer'sinstructions, poured into sterile Petri dishes, gently stirred, and allowed to set. Then, 0.1 mL of the diluted sample was spread on the prepared medium using the standard spread plate method. The plates were then turned over and kept at 37 \({}^{\circ}\)C for 48 to 72 h [32] to observe whether each successive dilution allowed aerobic growth.
# Gaining Insights into Conceptual Models: A Graph-Theoretic Querying Approach
## 1 Introduction  
Systems engineering, business processes, and workflows are few examples of domains that benefit greatly from conceptual modelling, which expresses them formally and visually. The increase in the complexity of systems, reflected in their models, is manifested in the ever-increasing number of components participating in the model's structure and behavior and the sheer amount of interdependencies. This complexity renders simple inspection of the model insufficient for answering questions and queries related to relationships among the components, such as \"what if\", explaining cause-and-effect interactions, and gap analysis. The high density of the model elements and their interconnections makes it difficult to holistically grasp and comprehend the entire system. The dynamic, operational aspects of the system represented by the model also become complex and hard to follow and predict. As a result, legacy systems, even those that are somehow modeled, areoften left unchanged, as refactoring them poses risks and might not be worth the effort. For evolving systems that undergo continuous changes, impact analysis is very important for decision-making and introducing changes to control flows. Tackling such complex tasks requires analytical queries. To avoid hazardous situations, organizations must model their newly developed or existing systems so they are amenable to being queried effectively and efficiently to enable judicious decision-making and risk assessment. Despite intensive search, we have not found literature on how to systematically query conceptual system models to gain technological, scientific, or business insights.  
We present a methodology for querying a conceptual model of a (possibly very complex) system to answer questions related to issues such as the structural relations among system objects, possible execution scenarios, cause-and-effect, and \"what if\" questions about the behavior of the system.  
The article is structured as follows: In Section 2, we briefly introduce Object-Process Methodology (OPM) ISO 19450, the conceptual modeling language used to represent the model to be queried. Section 3 contains a survey of the database and query approaches and types used in academia and industry. Adopting a graph-theoretic approach for model querying, in Section 4 we describe the conversion of an OPM model into a graph representation and its storage in a graph database. In Section 5, we demonstrate the power of OPM model querying in systems engineering and business processes. The implementation of various query types is elaborated in Section 6, and in Section 7 we conclude and propose future work.
## 5 Influence of Spatial Step in EMR Methods  
### Issue of Spatial Step in the Fault Estimation Stage  
During the fault estimation stage of the EMR-based fault location methods, the significance of the spatial step (\(\Delta\)g) cannot be overlooked. Firstly, a comparison is presented between the EMR energy metric and the correlation estimator method while assessing this issue. For both techniques, a simulation considering a certain spatial step was performed; in the EMR energy metric, the simulation was run after the fault occurrence, and in the correlation estimator, it was already completed before the fault event, which is why it is a time-efficient approach and, hence, preferred.  
For illustration purposes, \(x_{f}=8.25\) km along the line length of 10 km was considered. The results are shown in Figures 16 and 17 for the EMR energy metric and the correlation estimator method, respectively. Four different values of the spatial steps were selected in the fault estimation stage, e.g., \(\Delta\)g = 1000 m (solid blue line), 500 m (dashed purple line), 100 m (dotted-dashed red line), and 10 m (dotted black line). From the figures, it can be seen that, with a larger spatial step, there was location error in the GFL (\(x_{g}\)), and as the spatial step became smaller, the fault detection accuracy increased.  
Moreover, once again, the advantage of the correlation estimator metric was confirmed by the results shown in Figure 17, where CCv = 1.0000 as the deciding criterionfor the accurate guess of the test fault location, i.e., \(x_{f}=x_{g}\). Unlike the EMR energy metric, as shown in Figure 16, there is no clear and distinctive definition to decide between the true and false estimations. As it uses a global normalisation, i.e., En = 1 (normalised to the maximum value) as the fault location criterion, so all the GFLs (\(x_{g}\)) could be the best candidates for the true fault location.
# Transportation Mode Selection Using Reinforcement Learning in Simulation of Urban Mobility
## 3 Materials and Methods
### Simulation of Urban Mobility (SUMO)  
Here, a standardized simulation platform would be helpful. The desire to win the support of other universities was the second justification for making the simulation open source, and Figure 4 shows an example [41]. The Traffic Control Interface, or TraCI for short, is a Python API that treats the SUMO simulation like a server and lets users obtain data from it or alter a traffic simulation. This is one of the most significant components of SUMO. SUMO traffic simulations can be integrated with other systems or libraries thanks to the interface TraCI offers. This webinar explains how urban traffic management can benefit from the integration of reinforcement learning with SUMO via TraCI [18].  
SUMO is a free, open-source traffic simulation and transportation modeling program widely used for urban planning, traffic management, and autonomous vehicle research. It offers tools like NETEDIT for creating network models, SUMO-GUI for interactive simulation control, and TRACI for integration with external applications. With its comprehensive database, SUMO enables the development of custom scenarios and realistic traffic simulations. Its key features include high accuracy, flexibility, scalability for large-scale simulations, real-time data analysis, and support for diverse traffic scenarios. Being open-source and highly extensible, SUMO is a preferred choice for traffic simulation and transportation modeling.
## 4 Experiments
### Evaluation Metrics
#### 4.3.3 Robustness and Error Case Analysis  
To evaluate the model's real-world applicability, we conducted a series of stress tests under degraded input conditions and performed a detailed analysis of misclassified  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Model Configuration** & \begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **F1-Score** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Inference Speed** \\ **(FPS)** \\ \end{tabular} \\ \hline Density-Only & 74.5 & 76.3 & 50 \\ \hline Trajectory-Only & 77.2 & 79.1 & 48 \\ \hline Semantic-Only & 72.8 & 74.9 & 53 \\ \hline Density + Trajectory & 81.6 & 83.4 & 45 \\ \hline Density + Semantic & 78.9 & 80.7 & 47 \\ \hline Trajectory + Semantic & 76.5 & 78.2 & 49 \\ \hline
**Full Model** & **91.7** & **88.2** & **40** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparison in ablation study.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt}} \hline \hline
**Metric** & **Value** \\ \hline Accuracy & 0.917 \\ \hline Precision & 0.892 \\ \hline Recall & 0.873 \\ \hline F1-score & 0.882 \\ \hline AUC-ROC & 0.920 \\ \hline Mean end-to-end latency & 71 ms \\ \hline \hline \end{tabular} Note: Metrics are computed frame-wise on all 19,408 annotated frames (30 Itaewon videos), the AUC uses a 0–1 threshold sweep (0.01 step), and latency—measured on a single-batch RTX 308—covers decoding, inference and post-processing.  
\end{table}
Table 5: Overall Classification Performance of the Itaewon Dataset.  
cases. Specifically, we simulated visual and auditory degradations, including Gaussian blur (\(\sigma=3\)) and motion blur, to mimic low-resolution or fast camera motion, random occlusions covering up to 20% of pedestrian regions, and additive audio noise (SNR = 10 dB) combined with overlapping voices to replicate chaotic environments. Despite these perturbations, the full multimodal model maintained an F1 score of 0.821, demonstrating moderate resilience, primarily attributed to redundancy across modalities. Among the three branches, the semantic module was most affected by noise, with an F1-score drop of approximately 9.8%, while the trajectory and density fusion streams were more robust, with less than a 5% reduction. In addition to these robustness tests, we analyzed 50 false-positive and 50 false-negative frames from the Itaewon dataset. False positives were often triggered by transient crowd compressions--such as people entering or exiting narrow pathways--where no actual panic behavior occurred. Conversely, false negatives typically involved partially occluded panic behaviors like sudden running or screaming, which were only captured by a single modality (e.g., audio-only or motion-only), resulting in insufficient evidence for classification. These observations suggest that, while the system is generally reliable, future improvements should consider incorporating confidence-aware fusion mechanisms and temporal consistency modeling to further reduce misclassifications under uncertain conditions.
# Iterative Annotation of Biomedical NER Corpora with Deep Neural Networks and Knowledge Bases
## 1 Introduction
Distant supervision [24] is a completely automatic approach and exploits the knowledge extracted from knowledge bases (KBs) such as thesauri or a dictionary, assuming that if a string in text is included in a KB, then that string can be automatically annotated as an entity. This approach has no human cost, but the resulting corpus usually suffers from incomplete and noisy annotations. Incomplete annotations are named entities not listed in the KB, which will not be automatically annotated in the training corpus. On the other hand, a noisy annotation is a partial identification of a named entity, due to the presence in the KB of only an entity part (e.g., missing some words of that entity) or due to slight differences between the entity listed in the thesaurus and the one in the corpus (e.g., the use of a synonym of one of the words in multi-word entity, or a plural version of the same word). In this paper a methodology that leverages both AL and DS for the annotation of B-NER clinical corpora is proposed, addressing some of the issues of both approaches to improve the quality and the speed of the annotation process. Firstly, an AL-based annotation is performed, exploiting a deep-learning NER architecture as an automatic classifier. Then, biomedical KBs are used for DS annotation and dataset expansion through data augmentation, with the purpose of mitigating the class imbalance problems [25] that could affect the annotations obtained through AL. In the experimental assessment the contribution of different pretrained Word Embedding (WE) models trained on a closed biomedical domain corpus as input of the DNN is also analysed, in particular comparing the contribution of word2 vec [26], FastText [27] and ELMo [28] with a fine-tuned BERT model [29] pretrained on a general domain corpus. The proposed approach was used to easily and rapidly create an Italian language B-NER annotated corpus with very little effort with respect to a fully manual annotation procedure. The obtained corpus was evaluated on the aforementioned B-NER task, achieving performances comparable with the state of the art, as demonstrated in the experimental assessment. In summary, the main contributions of this paper are:* An automatic annotation methodology for B-NER corpora based on AL and DS techniques;
* An analysis of the contribution of different clinical closed-domain WE models (including word2 vec, FastText and ELMo models), compared to a fine-tuned BERT model trained on a general-domain document collection;
* The annotation of an Italian clinical B-NER corpus. The paper is organised as follows: in the next Section 2, an overview of the recent related works is presented, mainly focusing on methods for the annotation of texts from clinical and biomedical domains. Then, the details of the proposed approach are described in Section 3. In Section 4, the experimental assessment and the obtained results are shown and discussed and, finally, in Section 5 the final considerations, conclusions and future works are highlighted.