esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodological Background
### Particle Swarm Optimization (PSO)
#### 2.3.2 Fitness Function of PSO  
In different applications, the suitable fitness functions are different, depending on the optimization problem defined by the corresponding application. For example, in the shortest path problem, the smallest sum of the distances from a location to all destinations is the best solution. Thus, the fitness function of the shortest path problem is designed to calculate the total distance of a location from all destinations. The smaller the output value of the fitness function, the closer the location is to the optimal location.  
However, PSO suffers from premature convergence, especially in high-dimensional multimodal problems. The convergence speed decreases as the number of iterations increases. These facts lead to difficulties for the particles to achieve the best global fitness values. Therefore, in the next section, we introduce SSO.
## 3 Results and Discussion
### Energy Independence  
The optimal installation of a 33-kW solar PV array, coupled with lithium-ion battery storage, enabled the site to generate and store its own energy. Since the primary objective of this research is to reduce the mining site's dependence on grid power, the designed system is able to meet a significant portion of the site's energy demands using EMS during daylight hours and to provide stored energy during non-solar periods, such as at nighttime. Accordingly, Table 2 shows the system's energy consumption split where the solar PV system generated approximately 31,132 kWh annually, significantly reducing reliance on grid electricity. Here, peak grid power consumption dropped by 87%, indicating the productivity of the PV-battery hybrid system in meeting site demand. Further, Figures 7 and 8 present the customer's weekly consumption profile and maximum demand respectively. This shows that the demand from the grid side can be reduced significantly following the solar project installation system.  
fluctuating grid electricity prices. However, from Table 3, the proposed hybrid LSTM-classical optimization method presents the system's cost savings approximately USD $2769 with least computation time 1.153 s and high accuracy 99.247% when compared with existing other algorithms. The results are derived from rigorous simulations ensuring fair comparison. The details of the applied existing algorithms are given in Appendix A. Further, Figure 9 shows the distributed grid power cost, finance cost and net savings before and after the optimization method. Subsequent to the applied optimization, the grid power cost is reduced from USD $15,375 to USD $6747. Accordingly, after implementing the optimal solar PV and battery system, annual electricity costs are reduced by 57%.
# Generative Models in Medical Visual Question Answering: A Survey
## 4 Advances in Generative MedVQA
### Automated Dataset Building and Data Augmentation
The synthetic dataset is generated through a multi-stage pipeline: first, 914,960 medical images are extracted from PubMed, filtering out non-medical images using semantic retrieval and classification models. Next, GPT-4V reformats image-text pairs into VQA-style structured data, producing two types of synthetic data: Alignment VQA, where predefined medical questions are paired with AI-generated answers, and Instruction-Tuning VQA, where GPT-4V generates diverse clinical interactions in 10 different medical scenarios (e.g., doctor-patient discussions, intern-specialist dialogues). This approach ensures rich and diverse synthetic medical knowledge, improving model alignment for real-world medical tasks. The synthetic dataset is further validated through medical expert reviews, confirming higher accuracy, completeness, and relevance than previous reformatted datasets. **Data Augmentation** enhances existing datasets by introducing variations that improve model robustness. The CGMVQA framework [6] employs augmentation techniques on both images and texts to diversify the training data. For visual data, methods such as rotation, scaling, and cropping are applied to simulate real-world variations in imaging. For textual data, tokenization and paraphrasing are used to create diverse representations of clinical questions and annotations. These augmentation strategies not only improve the model's ability to generalize across different scenarios but also reduce its susceptibility to overfitting. **Retrieval-Based Enrichment** focuses on integrating multimodal prompts to provide contextually rich training data. The MPR framework [23] uses retrieval methods to curate relevant text and image pairs from large medical corpora. By augmenting the training process with these contextually enriched prompts, the model gains a deeper understanding of complex medical queries. For instance, when answering a question about organ abnormalities, the retrieval-based approach supplements the query with similar cases, relevant anatomical details, and diagnostic guidelines, enabling the model to generate more accurate and informed responses. In summary, data augmentation and automated dataset building are critical for scaling MedVQA systems and improving their performance. Techniques such as synthetic dataset generation, augmentation on existing datasets, and retrieval-based enrichment address the limitations of annotated data scarcity while enhancing model robustness and generalization. As these methods continue to evolve, they will play a pivotal role in advancing the capabilities and clinical applicability of MedVQA technologies.
###### Abstract
On-chip antennas with multi GigaHertz bandwidths in millimeter-wave (mm-wave) bands, specifically, in the unlicensed 60 GHz band, are fabricated and demonstrated [23; 24; 25; 26]. Using such on-chip antennas embedded in the chip [27] or waveguides [28] Wireless Network-on-Chip (WiNoC) architectures are shown to improve energy efficiency and bandwidth of on-chip data communication in multicore chips [29]. Recently, their feasibility in energy-efficient chip-to-chip data communication has also been investigated [30]. In addition to that, wireless interconnects do not require laying out physical interconnects eliminating additional place-and-route steps in the design process. Figure 2 shows a multi-chip system with multicore chips showing the propagation of the radiation for inter-chip wireless communication without the need for physical waveguides. The cores are interconnected using an intra-chip interconnection network and routers in the interconnection network are equipped with a wireless antenna and transceiver to enable the inter-chip wireless interconnects. In this paper, we will discuss and present the advances in design methodologies, challenges, and opportunities of such wireless interconnection architectures for of multi-chip systems. We present the state-of-the-art in design methodologies for topologies, physical layer, MAC, and routing protocols for wireless multi-chip systems. We will discuss challenges and opportunities in these aspects from a variety of perspectives, including design, electronic design automation (EDA), testing and qualitative benefits which are not easily measurable. Finally, we present some paradigm-shifting applications that might be enabled by high-speed wireless inter-chip communication and the implications of developments of complementary technologies such as Internet-of-Things (IoT), Industry 4.0, flexible electronic devices, and neuromorphic computing. Figure 1: (**a**) Conceptual view of the interposer system (**b**) peak bandwidth and energy with respect to system size.
# Development of Fingerprint Identification Based on Device Flow in Industrial Control System
## 3 Device Traffic Fingerprint Extraction and Identification
### Fuzzy Matching to Identify Unknown Devices  
Because the exact matching conditions are strict, the query sequence may not match any device fingerprint, so fuzzy pattern matching will be used for the final device identification. For device fingerprint Finger, query pattern sequence \(\mathfrak{q}\), and allowable error threshold \(\tau\), fuzzy pattern matching is to find all subsequences \(Trav[i,j]\) satisfying \(sim(q,Trav[i,j])\leq\tau\), where \(sim\) is Similarity function, where \(1\leq i,j\leq n\), \(Trav\) is the original sequence corresponding to the device fingerprint. The process of fuzzy matching is shown in the Figure 5. The sequence is first preprocessed to shard it into multi-segment subsequences, and then the multi-segment subsequences are accurately matched, and the error values between the matching sequence and the original sequence are counted. If the error value is higher than a certain threshold, it is directly judged that the matching failed,  
Figure 4: FM index matching process.  
otherwise its matching score is calculated for the judgment of the final device. This section, we will first outline the overall process of fuzzy matching, then optimize the two processes of fragmentation and computing errors, and finally describe how to calculate the matching score of fuzzy matching.  
If the query sequence q is divided into \(\tau+1\) slices, because the error threshold is an integer, it is impossible for all \(\tau+1\) slice sequences to have errors, and at least one slice sequence is an exact match. First, the query pattern sequence q is evenly divided into \(\tau+1\) slices, and then all subsequences of the slice sequence that exactly match in the fingerprint sequence are found. For this work, please refer to Section 3.2. Considering a sharding sequence \(q[i,j]\) of the query sequence q, the sharding divides the query sequence q into three parts \(q[1,i-1]\), \(q[i,j]\), and \(q[j+1,n]\). The sequence can also be divided into three parts, \(Trav[1,i-1]\), \(Trav[i,j]\), and \(Trav[j+1,n]\), which can be verified by checking the left and right subsequences, respectively. \(Trav_{l}^{m}\) and \(Trav_{r}^{m}\) represent the longest left and right extensions, respectively. First, Equation (5) is used to calculate the similarity between all suffixes of the longest extension on the left \(Trav_{l}^{m}\) and \(q[1,i-1]\).  
\[l=\min_{1\leq i\leq Trav_{l}^{m}}sim(q[1,-1],Trav[i,Trav_{l}^{m}]) \tag{5}\]  
Then, Equation (6) is used to calculate the similarity between all suffixes of the longest extension on the right \(Trav_{r}^{m}\) and \(q[j+1,n]\).  
\[r=\min_{1\leq i\leq Trav_{r}^{m}}sim(q[j+1,n],Trav[i,Trav_{r}^{m}]) \tag{6}\]  
If there is an approximate sequence \(Trav[i,j]\) of the sequence q, then there must be \(l+r\leq\tau\). The time complexity of verification on the left and right is \(O(\tau*(|q_{l}+q_{r}|))=O(\tau*(|q|-|p|))\).
# The Early Detection of Cardiac Fatigue: Could the HRV Be Used as a Physiological Biomarker by AI?
###### Abstract  
Background:Physical activity is vital for promoting health and rehabilitation, and ensuring cardiovascular safety during such activities is paramount. Electrocardiography (ECG) and its longitudinal monitoring remain crucial for the early detection of cardiac diseases. Recent advancements in nonlinear RR analysis and machine learning offer promising approaches to identifying subtle precursors of cardiac pathologies in monitoring systems using simple heart rate (HR) wearable sensors. Therefore, using HR sensors in human activity recognition (HAR) is recommendable. After defining fatigue in a cardiological context, and focusing on an AI-based methods suite for HAR, the main research question of this scoping review is as follows: \"Can RR time series be successfully used as physiological biomarkers for the early detection of cardiac fatigue?\" The reported data on assessment of fatigue are focused on the last two decades. The aim of this scoping review was to collect, present and discuss the existing literature on the effectiveness of AI-based methods for processing RR time series as a predictive biomarker for cardiac fatigue compared to commonly used questionnaires for this outcome in adult populations.  
Methods:Queries were conducted in the PubMed, Scopus and Google Scholar databases for the time period 2005-2025. Only research articles and review papers were considered suitable candidates. **Results:** Data from 10 papers were considered, related to the information researched. **Conclusions:** Information on HRV-based objective measures is quite scarce and there is an urgent need to adopt a multidisciplinary approach and to improve advanced AI-based nonlinear analyses to differentiate cardiac physiological status from cardiac pathological status.  
physical activity; fatigue; wearable devices; heart rate variability; human activity recognition; artificial intelligence; machine learning
## 1 Introduction
PSO is a meta-heuristic as it makes few or no assumptions about the problem being optimized and can search very large spaces of candidate solutions [30; 31]. Compared with the other evolutionary algorithm such as artificial fish swarm algorithm [32], genetic algorithm [33; 34], artificial bee colony algorithm [35; 36], and simulated annealing algorithm [37], the most important advantages of PSO are the few parameters needed to adjust and the easy implementation. IBPSO follows the action of chromosomes in genetic algorithm (GA) and sparsely calculates the eigenvectors of the input AHs. In many practical problems, sparse signals can provide enough information, such as head image recognition [38], sound location [39], and target tracking [40]. In the proposed scheme, first the equivalent AHs sparse dataset is obtained by the IBPSO as the eigenvector input of the DNNs, and then the optimization training is conducted on two DNN modules for 112 Gbps PM-RZ-QPSK and 112 Gbps PM-NRZ-16 QAM signals, respectively, through PSO to obtain the accurate estimation of OSNR. Experimental results obtained show that the proposed method can achieve a higher precision estimation of OSNR than the existing methods. In addition, this technology does not require additional monitoring equipment or any modifications to the transmitter. As the dimension of the input signal set AHs decreases, the corresponding neural network structure is simplified correspondingly, and the estimation accuracy is still relatively high. The rest of this paper is structured as follows. Section 2 introduces the OSNR monitoring based on the conventional DNN and the improved binary particle swarm optimization and deep neural network (IBPSO-DNN) method trained with AHs in detail. Section 3 describes the structure of the OSNR monitoring experimental configuration of PM-RZ-QPSK and PM-NRZ-16 QAM signals. Some simulation results and discussion are presented in Section 4. Finally, Section 5 sums up the conclusions.
# A Generalized Spatial Modulation System Using Massive MIMO Space Time Coding Antenna Grouping
## 1 Introduction
Section 2 presents the GSM system model with a new modification by increasing antenna number and using Quadrature Amplitude Modulation (QAM) modulation. Then, the GSM-STC is presented using two subgroups of antennas and with QAM modulation. The simulation results for the modified GSM and GSM-STC technique are displayed and discussed in Section 3. This is followed by the main conclusions in Section 4, showing the advantages of the proposed system.
# Service Reliability Based on Fault Prediction and Container Migration in Edge Computing
## 5 Performance Evaluation
### Experimental Results and Evaluation Analysis  
This experiment first explored two typical time-series machine algorithm models, LSTM and GRU, and then compared their precision and recall. Fault-based container migration strategies mainly explore the scale of the edge network and the number of faulty devices for container migration. We evaluated the migration cost and the impact of load balancing on the process. Similarly, [PERSON] et al. [19] compared their proposed Genetic Algorithm Container Migration Strategy (GACMS) with the Greedy Container Migration Strategy (GCMS) and RCMS.
## 3 Experimental Validation and Analysis
### Training of LSTM Model and Prediction Results  
#### 3.4.1 Experimental Settings  
In this study, the experiments were implemented in Python 3.7 on a PC with AMD Ryzen 9 5900 HX with Radeon Graphics 3.30 GHz and NVIDIA GeForce RTX 3050 Ti Laptop GPU, with a memory size of 16.00 GB. The clustering algorithm and the CEEMDAN algorithm were implemented in MATLAB 2022b.  
For the LSTM prediction task targeting GNSS displacement and acceleration data, we divided the dataset into 70% for training, 15% for testing, and 15% for validation to ensure effective training and objective evaluation of the model. After normalizing the training set, it was input into the model for training. The Adam [47] optimization algorithm was employed, and through iterative exploration of various parameter combinations, the optimal hyperparameters were determined based on performance on the validation dataset. For GNSS displacement prediction, the final selected parameters are as follows: an initial learning rate of 0.001, 1 layer in the network, 370 hidden units, a dropout probability of 0.5, and decay rates of 0.99 for the first order and 0.999 for the second order. For acceleration prediction, the final selected parameters are as follows: an initial learning rate of 0.005, 2 layers in the network, 490 hidden units, a dropout probability of 0.5, and decay rates of 0.99 for the first order and 0.999 for the second order. To prevent overfitting during the training of the LSTM model, the learning rate was reduced to one-tenth of its initial value after 250 iterations.  
Figure 12: Reconstructed acceleration sequences.
## 4 Case Studies
### Evolution of Spatiotemporal Grid Failures of IEEE 14-Bus System During Typhoon Maria  
Based on the spatiotemporal parameters of Typhoon Maria in Section 4.2, the evolution of spatiotemporal grid failures of IEEE 14-bus system is obtained by calculating the fault probability matrix \(Pr(B)\) in (13). \(Pr(B)\) is calculated and then visualized as a heatmap in Figure 3.  
As shown in Figure 3, the failure probability of each line increases over time. Lines 9, 16, and 17 have significant high-failure probabilities, because they are the first lines that suffer from the typhoon during its passage. Among them, Line 17 has the highest fault probability (0.78) since it is closest to the typhoon and has the greatest cumulative fatigue damage. Additionally, as the wind weakens, the cumulative damage caused by Typhoon Maria to subsequent lines decreases, and the fault probability of these lines less affected by Typhoon Maria remain at a low level (less than 0.1). The range \((0.1,0.7)\) of \(Pr_{0}\) is partitioned into seven equal parts with the interval 0.1, i.e., \(Pr_{0}=\{0.1,0.2,0.3,\cdots,0.7\}\). Then, based on (15), seven on-off line status matrices are calculated to construct the set \(\Omega_{\mu}\) of 10 emergency scenarios.  
Figure 4 shows the on-off statuses of 20 lines over 24 h of Typhoon Maria's passage under different \(Pr_{0}\). As for each heatmap, the horizontal axis represents the period number; and the vertical axis represents the line number. \(state(i,j)=0\) indicates that line \(j\) at period \(i\) is disconnected, while \(state(i,j)=1\) indicates that line \(j\) at period \(i\) is connected. Figure 4a shows that small \(Pr_{0}=0.1\) leads to more line failures indicated by 0 in the heatmap  
\begin{table}
\begin{tabular}{c c c c c} \hline \(\mathbf{v_{out}}\) & \(\mathbf{v_{\sigma}}\) & \(\mathbf{v_{m}}\) & \(\mathbf{W_{\max}^{norm}}\) & \(\mathbf{W_{\max}}\) \\ \hline
25 m/s & 20 m/s & 53 m/s & 0.7 kN/m2 & 2.1 kN/m2 \\ \hline \end{tabular} The system data of IEEE-14 bus system is presented in Table 5.  
\end{table}
Table 4: Typical parameters of identifying abnormal states in emergency dispatch.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Line ID** & **From Bus** & **To Bus** & **Reactance** & **MVA Rating** \\ \hline  
\hline \end{tabular}
\end{table}
Table 5: Line data of IEEE-14 bus system.  
compared with other \(Pr_{0}\) values. When the decision maker does not hold a conservative stance and only pays attention to those high-probability events by setting the threshold \(Pr_{0}=0.7\), only lines 9 and 17, which are heavily affected by Typhoon Maria, experience the outage accidents.  
Figure 4: Emergency scenarios under (**a**) scenario 1 \(Pr_{0}=0.1\) (**b**) scenario 2 \(Pr_{0}=0.3\) (**c**) scenario 3 \(Pr_{0}=0.5\) (**d**) scenario 4 \(Pr_{0}=0.7\).  
Figure 3: Fault probability of lines during Typhoon Maria.
# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 2 Literature Overview in VBDA
### Problem Taxonomy and Applications of Video Processing in VBDA
#### 2.4.5 Content-Based Video Retrieval and Understanding  
Semantic video retrieval enables efficient access to relevant content in massive video repositories, supporting applications in surveillance triage, content moderation, and multimedia recommendation. Recent advances in cross-modal learning have led to models like Show and Tell [80], VideoBERT [81], and Frozen in Time [82], which align visual andtextual modalities to support tasks such as zero-shot captioning and search. Large-scale datasets such as YouTube-8M [83], ActivityNet [84], and MSR-VTT [85] have been pivotal for training and evaluation.
## 2 Materials and Methods
### Selected Species
#### 2.1.1 Arundo donax  
Colloquially known as common reed, _Arundo donax_ (Figure 1) is a perennial herbaceous species from the Poaceae family, characterized by its aquatic habitat and rhizomatous growth pattern [17]. Notably, it has garnered infamy as one of the most warned against  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Species** & **Type** & **Family** & **Raw Matter** & **Moisture (\%)** \\ \hline _Arundo donax_ & Herbaceous & Poaceae & Leaves & 70.12 \\ _Tradescantia fluminensis_ & Herbaceous & Comelinaceae & Stem & 87.79 \\ \hline _Phylotacca americana_ & Herbaceous & Comelinaceae & Complete & 91.92 \\ \hline _Phylotacca americana_ & Herbaceous & Phytolaccaceae & Leaves & 80.22 \\ \hline _Eucalyptus globulus_ Labill & Arboreal & Myrtaceae & Stem & 67.25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parts of raw matter used for handsheet testing and pulping and moisture.  
invasive species due to its fast-growing capacities, particularly prevalent in warm-climate regions encompassing Oceania, Africa, and the Americas [23].  
The native origins of _Arundo donax_ are believed to be rooted in Eurasian regions characterized by Mediterranean and subtropical climates. However, the precise provenance of its invasive populations remains enigmatic [24]. It was first documented in Galicia in 1852, introduced for cultivations purposes [17].  
Beyond its invasive characteristics, _Arundo donax_ boasts notable mechanical attributes, rendering it suitable for applications in the field of carpentry [25]. Moreover, it assumes significance in terms of environmental conservation, serving as a viable source of raw material for bioenergy production and biochemical processes [26]. Research has unveiled the potential of its chemical constituents in diverse sectors, ranging from industrial applications to medical domains, as prospective antimicrobial agents [23] and antioxidants [17; 18]. Furthermore, there is evidence to suggest that the chemical compounds derived from _Arundo donax_ possess the capacity to ameliorate soil and aquatic ecosystems afflicted by industrial pollution, enhancing their fertility [23].