esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Nowadays in modern industrial applications, where the power supply efficiency is more important than the output noise performance, DC-DC converters are widely used in order to fulfill the requirements. Yet, component selection and precise estimation of parameters can improve the converter's performance, leading to smaller and more efficient designs. Hence, metaheuristic optimization algorithms can be applied using the mathematical model of DC-DC converters, in order to optimize their performance through an optimal inductance selection. Therefore, this work presents a novel design methodology for DC-DC converters, where the inductance selection is optimized, in order to achieve an optimal relation between the inductance size and the required energy. Moreover, a multi-objective metaheuristic optimization is presented through the Earthquake Algorithm, for parameter estimation and component selection, using the inductance of a buck DC-DC converter as a case study. The experimental results validate the design methodology, showing ripple improvement and operating power range extension, which are key features to have an efficient performance in DC-DC converters. Results also confirm the Small-Signal Model of the circuit, as a correct objective function for the parameter optimization, achieving more than 90% of accuracy on the presented behavior.  
multi-objective optimization; DC-DC converters; energy harvesting; EA; ITAE; small-signal; cascade controllers; nature-inspired; geo-inspired +
Footnote †: journal: Applied Sciences
## 5 Conclusions
Thus, the proposed framework might emerge as an intelligent technology for application in the field of damage inspection. The framework was studied for small-scale bridges with concrete structures that can be photographed by humans. Nonetheless, it can even be applied for suspension and cable-stayed bridges, or large bridges, if the member is a concrete structure. As the inference time of the proposed model is approximately 0.2 s, real-time detection is possible. However, because it uses approximately 2.5 G of GPU memory (VRAM) per model and 300-350 w power (case of RTX 3090), the model cannot be deployed on a mobile device. However, using an edge computing device (e.g., jetson nano, etc. ), it is possible to immediately detect and analyze images input from the image sensor by loading the model (requires 5-10w power). As another approach, the images collected by imaging equipment (smartphone, UAV, etc.) can be transmitted to the network and detected at the DL server. However, network latency must be considered. Future research can investigate and develop a model capable of improving the instance segmentation performance based on an ensemble of multiple models, as well as a model to automatically detect damaged objects in a bridge optimized through parameter tuning for each member/model. Furthermore, a quantification model that enables automatic estimation of the size of damaged objects can be developed. Conceptualization, S.-S.H. ; Software, S.-S.H. and C.-H.H. ; Validation, S.-S.H. and S.-W.C.; Resources, S.-W.C.; Data curation, C.-H.H. ; Writing--original draft, S.-S.H. and C.-H.H. ; Writing--review & editing, S.-S.H., S.-W.C. and B.-K.K. ; Supervision, B.-K.K. ; Project administration, B.-K.K. ; Funding acquisition, B.-K.K. All authors have read and agreed to the published version of the manuscript. Research for this paper was carried out under the KICT Research Program (project no. 202020217-001, Development of DNA-based Smart Maintenance Platform and Application Technologies for Aging Bridges) funded by the Ministry of Science and ICT. DIV2K dataset--Super Resolution Benchmark Dataset (link: [[https://data.vision.ee.ethz.ch/cvl/DIV2K/](https://data.vision.ee.ethz.ch/cvl/DIV2K/)]([https://data.vision.ee.ethz.ch/cvl/DIV2K/](https://data.vision.ee.ethz.ch/cvl/DIV2K/)), accessed on 30 October 2022). The authors declare no conflict of interest.
## 4 Methods
### Object Tracking and Real-Time Streaming  
Object tracking is an extension of object detection in the domain of videos, and as such we leverage our proposed object detection model as part of the tracking algorithm. However, running our object detection model on each video frame is a naive approach that is rather inefficient in terms of bandwidth efficiency and latency. Typically, object-tracking algorithms require an initial prediction (bounding box) for the first frame and the positions of relevant objects throughout the subsequent frames are estimated by exploiting the spatiotemporal locality in videos. New predictions need to be periodically generated in order to minimize the error in the tracking algorithm. The key idea is that the estimation  
Figure 2: The onboard _Model Backbone_ module extracts feature maps. The _Noise Module_ injects noise from \(\mathcal{N}(\mu,\sigma^{2})\), and the _Selector Module_ chooses a subset of the feature maps. The drone sends the processed feature maps and additional metadata to the cloudlet for further processing.  
process of object tracking is significantly faster than repeated object detection inference while being sufficiently accurate.  
Thus, to support a low-latency bandwidth-efficient framework suitable for real-time video analytics, an appropriate amount of frame sampling needs to be incorporated. Instead of transmitting the features of every video frame, the onboard model transmits the features of only a fraction (e.g., 10%) of all the video frames. Thus, if the camera on the drone captures video footage at 10-30 frames per second, the drone transmits the features of only 1 to 3 frames in a second. The video frames selected for transmission are uniformly sampled from all the video frames recorded. In our system, we implement a lightweight object-tracking algorithm on the drone and periodically query the cloudlet for object detection predictions. The tracking algorithm is local to the drone for two reasons: (i) to maintain bandwidth efficiency by eliminating constant transmission of bounding box information to the drone, and (ii) to preserve privacy because the cloudlet cannot access the raw video frames necessary for object tracking.
## 7 Conclusions  
In this paper we propose SVD-SCMA in conjunction with 8-APSK to improve the spectral efficiency of the uplink in 5G systems. The results show that SVD-SCMA has better BER with adverse SNR conditions (<8 dB) than algorithms using SM and MIMO for signal transmission. SVD-SCMA uses only one transmitting and receiving antenna, whereas previous works required antenna arrays with multiple radiating elements. The performance of the neural network receiver could be improved by modifying Adam [24,28] with a long-term memory of the gradients used to update the parameters of the neural network adaptively.  
The central hypothesis of this work is to increase the spectral efficiency (up to three bits) of the uplink through the design of codebooks (SVD-SCMA) and keeping the configuration of one transmitting and one receiving antenna. Preliminary tests that we have carried out show that with this configuration so far, it is not possible to increase the spectral efficiency to four bits, so the next step in this research is to use SVD-SCMA to encode three bits and use SM or MIMO to transmit more bits in the spatial plane and thus increase the number of bits transmitted simultaneously as well as using GPU to optimize receiver array operations. Combining SVD-SCMA in conjunction with SM/MIMO could increase spectral efficiency to more than 3 bits, resulting in a higher transmission rate for the eMBB segment of 5G communication systems and beyond.  
Conceptualization and methodology, S.V.-B.; mathematical modeling, J.L.L.-B. and S.V.-B.; simulation and validation, S.V.-B. and J.L.L.-B.; writing--original draft preparation, S.V.-B.; editing, J.L.L.-B. All authors have read and agreed to the published version of the manuscript. This research was funded by Instituto Politecnico Nacional, Mexico, Project number 20211324.  
Not applicable for studies not involving humans or animals.  
Not applicable for studies not involving humans.  
Figure 7: BER performance comparison.  
Data Availability Statement: Data available on request. The data presented in this study are available on request from the corresponding author. The data are not publicly available because this work is part of an ongoing funded project.  
The authors declare no conflict of interest.
## 2 Patients and Methods  
The original dataset of 1243 patients was reduced to 430 due to several reasons, including the unavailability of sufficient-quality CT scans, unclear tumor boundaries, and artifacts during image processing. Strict quality control ensured that only patients with the clearest images were included, which limited the dataset size.  
The study protocol was firstly approved by the Institutional Review Board of the Ankara University Faculty of Medicine (AUFM) (no: 12-97-21), thus removing the need to seek informed consent. Between 2012 and 2020, testing of 1243 patients determined to have NSCLC according to histological analysis was performed to identify the \(EGFR\) mutation status in their tumor tissues. We included patients with histopathologically confirmed adenocarcinoma or NSCLC-NOS; thoracic spiral contrast-enhanced CT performed within 4 weeks preoperatively or biopsy available from the electronic archive of the AUFM; a tumor suitable for segmentation; no prior malignancy; and no previous radiotherapy or chemotherapy treatment before the primary thoracic CT (Figures A1 and A2). We excluded patients for whom CT scans of the primary tumor were unavailable, for whom the diagnosis was unclear due to artifacts, who had tumor atelectasis in which tumor boundaries could not be clearly delineated, or where the \(EGFR\) mutation status was unknown. This resulted in the identification of 430 patients in total, who were assigned to either a wild-type (EGFR-WT, n = 372) or an \(EGFR\) mutant (\(EGFR\)m, n = 58) group (Table 1). Patients identified as having mutations in exons 18, 19, 20, or 21 of the \(EGFR\) gene were categorized into the \(EGFR\)m group.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **All Cases** & **EGFR-WT** & **EGFR** & **\(p\) Value** \\  & **\% (n)** & **\% (n)** & **\% (n)** & **\% (n)** \\ \hline
**Patient number** & 430 & 86.51(372) & 13.49 (58) & \\ \hline
**Age (mean) \(\pm\) SD** & 62.15 \(\pm\) 10 & 62.06 \(\pm\) 9.8 & 62.7 \(\pm\) 11.3 & NS \\ \hline
**Gender (n, \%)** & & & & \textless{}0.001 \\ \hline Female & 26.7 (115) & 21 (78) & 63.8 (37) & \\ \hline Male & 73.3 (315) & 79 (294) & 36.2 (21) & \\ \hline
**Smoking (n = 274)** & & & & \textless{}0.001 \\ \hline
**Current Smoker** & 25.2 (69) & 24.1 (66) & 1.1 (3) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Clinicopathological features of patients.
# Enhancing Federated Intrusion Detection with Class-Specific Dynamic Sampling
## 3 DS-FedIDS
### Non-IID Data Characteristics  
In conventional intrusion detection datasets, traffic often appears aggregated into a uniform format. By contrast, our split across _CDN_, _IoT_, _Database_, _Office_, and _Industrial_ yields markedly different traffic profiles:  
* CDN: Primarily _HTTP(S)_ flows and large-volume data transfers, with a prevalence of _tcp_ connections.
* IoT: Often dominated by _DNS_, _MQTT_, or lightweight request-response traffic; smaller packet sizes and frequent connections.
* Database: Fewer protocols in use (often _tcp_ only), with specialized services (e.g., SQL-based).
* Office: Balanced among _web_, _mail_, and _file-sharing_ services. Attack types vary widely but are generally less frequent than in high-exposure networks.
* Industrial: Typically large or specialized data bursts, sometimes with older or proprietary protocols, and medium-sized packets.
## 3 Energy-Sensitive X-ray and Gamma-ray Imaging in Wide FoV
### Photon-Counting Sensitivity, Quantum-Imaging Detection  
The hybrid architecture of Timepix, the high granularity and the integrated per-pixel signal electronics enable to operate the detector at room temperature while effectively suppressing noise to provide dark-current free operation and quantum-counting sensitivity of single particles [29]. Timepix operates as a radiation camera [26] registering single particles and visualizing individual pixelated tracks (called _clusters_) produced in the detector sensor. Such clusters exhibit a wide range of variability in terms of cluster size (number of pixels), morphology (shape contour) and deposited energy which are to a large extent determined by the particle type, deposited energy and direction [29]. Tracks produced by X-rays appear as small, i.e., a few pixel clusters. Tracks produced by gamma rays are registered as larger curly narrow clusters. Charged particles produce a wide morphology of clusters [29]. Analysis of this spectral-tracking response of the pixel detector enables the resolution of the registered particles into various broad-classes of particle-type events [29].  
The position of interaction, deposited energy or time of interaction of single particles are individually registered in high-spatial resolution and in wide range in terms of radiation fluxes, energy and direction with particle-type resolving power [29]. Combined with the registration and visualization of single particle tracks in the semiconductor sensor matrix, the Timepix detector provides highly sensitive _quantum-imaging_ detection to individual particles in a mixed-radiation field [30]. Together with enhanced radiation shielding of the spacecraft payload, use of the Timepix detector enables discrimination of background and unwanted events. The per-pixel spectral response of Timepix enables to provide energy-sensitive imaging of the detected X rays and gamma rays. Registered particles can be thus spectrally characterized, filtered, and further processed to produced selective energy-binned images as well as deposited energy spectra, particle fluxes, dose rates and LET spectra with particle-type resolving power [29].
## 4 The Research Results
### Descriptive Statistics of AT Availability and UT's Categorized Unavailability
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Vehicle’s Make**} & \multicolumn{2}{c}{**Number of**} & \multirow{2}{*}{**\(M(AT)\) (\%)**} & \multirow{2}{*}{**\(M(AT)\) (\%)**} & \multirow{2}{*}{**\(SD(AT)\) (\%)**} & \multirow{2}{*}{**\(V(AT)\)**} & \multirow{2}{*}{**Min. (\%)**} & \multirow{2}{*}{**Max. (\%)**} \\  & & & & & & & & & \\ \hline
1 & A & 53 & 88.07 & 89.84 & 6.36 & 0.07 & 61.39 & 96.13 \\
2 & B & 20 & 83.49 & 84.50 & 8.62 & 0.10 & 50.00 & 96.45 \\
3 & C & 27 & 92.73 & 94.27 & 4.88 & 0.05 & 70.55 & 97.90 \\
4 & D & 10 & 81.03 & 82.89 & 9.92 & 0.12 & 31.82 & 95.53 \\
5 & E & 10 & 87.97 & 89.50 & 7.71 & 0.09 & 50.00 & 98.41 \\
6 & F & 22 & 91.55 & 92.05 & 5.41 & 0.06 & 71.24 & 100.00 \\
7 & G & 20 & 87.18 & 87.16 & 6.07 & 0.07 & 61.48 & 97.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Descriptive statistics for the buses’ \(AT\) availability indicators. Figure 1: Frame chart of the buses’ \(AT\) availability indicators. The results of the calculations indicate that the share of individual categories of downtime varied greatly. There was a large share of the NB category--\"current repair\", \(M(UT_{k})=39.41\%\div 63.11\%\). On the other hand, the smallest share in the total time of downtime was WS downtime--\"engine replacement\" within the range \(UT_{k}=0.04\div 2.0\).
## 5 Analysis of Recent Smart Campus and University Deployments
### Smart Campus and University Applications
Although smart cards have been used for a long time by universities [111], they can still provide useful services for a smart campus, like information retrieval, mobile payments, library usage, access control, or e-learning [112; 113; 114]. Instead of a smart card, the latest developments suggested the use of the Near-Field Communications (NFC) interface of a smartphone to provide the mentioned smart campus applications [115]. Due to the multiple potential applications of smart cards, some authors also proposed to mine the data collected from the student transactions to infer their behavior [116; 117]. * Teaching and Learning applications. The technologies embedded into a smart campus/university can also help students to learn through their mobile phones [118] or have an ubiquitous user-centered personalized learning and training experience with advanced analytics [119; 120]. These technologies also allow teachers to make use of specific learning services (e.g., online programming contests [121]), sophisticated online teaching platforms [122], and to implement novel teaching paradigms like Flipped Classroom [123] or amplification [124]. * Research and innovation activities. Smart campus/university technologies can be used to encourage collaboration and cooperation among people (e.g., international networks of living labs). For instance, crowdsourcing can be used to collect data of people with different profiles (e.g., students, teachers, researchers, and administrative staff) and create large-scale datasets for further research and novel applications [125]. * Community-based knowledge transfer applications. Smart campus and university technologies can be explored to benefit the global community [126], either by increasing their awareness about sustainability issues [127] or by making citizens actively involved as central players of smart environments [128]. * Location-aware applications. In many situations the information given to smart campus users depends on their location. Such a location-aware data may include information on content, activities, projects, services, tools, knowledge, or events [129; 130; 131; 132]. * Security services. Smart campus managers can make use of the multiple sensors and recording devices to monitor the campus status and increase physical security through video surveillance [133] and location-aware applications [134]. In addition, it is essential to protect the privacy of campus user data when making use of wireless communications [135] and preventing cyber-attacks [66].
## 4 Conclusions  
In this study, we introduce a gas source localization model utilizing the ViViT. The model processes time series diffusion data to predict the source grid by classifying the grid with the highest probability of gas occurrence within the covered area. Through extensive experimentation using the NBC-RAMS simulator, we generate large datasets of gas diffusion under varied experimental conditions and meteorological environments. Subsequently, we train and evaluate the model using these data. Our findings demonstrate that the ViViT outperforms other deep learning models in processing time series gas data, showcasing a superior estimation performance. Leveraging a transformer architecture, the ViViT exhibits robust classification performance even in scenarios heavily influenced by weather conditions or when observations are incomplete. Furthermore, we conduct an  
Figure 5: (**a**) Accuracy and (**b**) the number of parameters of models according to sequence length changes.  
analysis of accuracy and parameter count across various input sequence lengths. The ViViT not only achieves a high estimation performance with a small number of frames but also maintains high computational efficiency by keeping the number of learned parameters constant regardless of sequence length. These results underscore the effectiveness of the ViViT as a model for gas source localization, particularly in situations demanding a rapid response in real-world environments, such as gas leaks or attacks. In future work, since the data generated through simulation is limited to one type of gas even though various experimental conditions are used, we aim to conduct experiments by generating gas dispersion data with various volatile characteristics. Additionally, we intend to explore the application of the ViViT in real-world scenarios, leveraging data collected from gas diffusion experiments conducted in real environments.  
Conceptualization, H.-D.J., S.K. and H.N.; methodology, H.-D.J.; software, H.-D.J.; validation, H.-D.J., S.K. and H.N.; formal analysis, H.-D.J.; investigation, H.-D.J., S.K. and H.N.; resources, H.-D.J., S.K. and H.N.; data curation, H.-D.J., S.K. and H.N.; writing--original draft preparation, H.-D.J. and S.K.; writing--review and editing, D.E.C.; visualization, H.-D.J.; supervision, D.E.C.; project administration, D.E.C.; funding acquisition, D.E.C. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to legal restrictions.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Comprehensive Document Summarization with Refined Self-Matching Mechanism
## 4 Experiments
### Comparison Baselines  
We compared the RSME with strong baselines from previous state-of-art abstractive and extractive summarization systems.  
_Abstractive_ :  
**ConvS2S**: [PERSON] et al. [19] innovatively applied the convolutional neural network the sequence to sequence (seq2 seq) model and improved on several tasks, including the abstractive summarization.  
**PGN + Cov**: [PERSON] et al. [15] integrated pointer and coverage mechanism into seq2 seq-based abstractive system for solving out of vocabulary (OOV) and repetition problems during generation.  
**Fast-abs**: [PERSON] et al. [23] proposed a novel sentence-level policy gradient method to bridge the sentence selection network and sentence rewriting network in a hierarchical way.  
_Extractive_ :  
**Lead-3**: The most common baseline model, which selects the lead three sentences in the document as a summary.  
**HSSAS**: [PERSON] et al. [18] used the hierarchical structure self-attention mechanism to create sentence and document representation.  
**Refresh**: [PERSON] et al. [20] directly optimized the evaluation metric ROUGE through a reinforcement learning objective function.  
**BanditSum**: [PERSON] et al. [12] proposed to regard the extractive method as a context bandit problem, and using a policy gradient reinforcement learning algorithm to select the sentences with maximize ROUGE.  
**SWAP-NET**: [PERSON] et al. [5] proposed a two-level pointer network architecture for modeling the interaction of keywords and highlighted sentences respectively.