esa-sceva/satcom-chunk-collection huggingface dataset:
# Grouping Sensors for the Key Distribution of Implicit Certificates in Wireless Sensor Networks
## 2 Related Work
The turbo-aggregate method achieves O(\(nLogn\)) for a secure aggregation in a network with n users, and it can speed up the network's dispatching efficiency [37]. Energy consumption is another issue in data aggregation. The facility derived from data aggregation is not fully utilized, and much power is consumed during communication. For energy efficiency in WSN, a fuzzy-based node arrangement is used to select the parent node and format the tree topology. Secure Reference-based Data Aggregation (SRDA), in which each sensor node compares its sensed data with the averaged value of previous sensed data (\"reference value\"), has been proposed [39]. Each node transmits and encrypts only the differential value between the sensed data and the reference value to reduce bandwidth and power consumption. The main disadvantage of this scheme is that only the cluster root can aggregate sensed data; thus, the aggregation effectiveness is reduced. In [40], the authors proposed \"Concealed Data Aggregation\" to build upon privacy homomorphism, which can directly perform calculations on encrypted data. Thus, all encrypted data are directly fed into the aggregation function, and the aggregated result is delivered to the BS. However, this scheme cannot render high-level security. The logical key hierarchy is used to speed up the encryption and decryption for implementing an effective key-numbering approach [41; 42]. In Ref. [43], the logic operations are used to develop the lightweight authenticated group-key distribution scheme to speed up the encryption and decryption operation. In Ref. [12], a promising scheme called implicit security was proposed. The term \"implicit security\" implies that data protection comes from the partitioning of data \(d\) into pieces using mathematical polynomial operations (instead of relying on cryptography). The time complexity for this implicit security scheme is only O(\(n\)) for data partitioning or recovery. In this study, we used this scheme to partition and reconstruct our encryption key. Compared with existing key management approaches, the algorithm proposed herein employs the aggregating method to group keys, and the bi-directed search improves the computation performance. In contrast to previous studies, we account for data aggregation and develop clustering algorithms with the minimum number of encryption/decryption operations to generate a group key. This enables secure communication among nodes within a group and increases scalability.
# Investigating Models for the Transcription of Mathematical Formulas in Images
## 5 Results
### Natural Language Processing Models  
To create transcriptions from LaTeX codes, we evaluated seven NLP models plus the online service _MathIAX_ ([[https://mathjax.github.io/MathJax-demos-web/speech-generator/](https://mathjax.github.io/MathJax-demos-web/speech-generator/)]([https://mathjax.github.io/MathJax-demos-web/speech-generator/](https://mathjax.github.io/MathJax-demos-web/speech-generator/)) convert-with-speech.html, accessed on 21 January 2024) by comparing their resulting transcriptions with the transcriptions in the references of the _Formula2 Text_ test set. Figure 11 visualizes the performances of the analyzed NLP models in terms of TER. The blue bars display again the performances of the original pre-trained models. The green bars show the performances of the models that we fine-tuned using the _Formula2 Text_ validation set. Fine-tuning was not possible for _ChatGPT_, _GPT-3.5 Turbo_, _GPT-4_, and _MathJAX_ at the time of our experiments from mid March to mid August 2023.  
We see that the fine-tuned versions of _T5-Base_ (34.95%), _BART-Base_ (35.44%), and _FLAN-T5-Base_ (35.60%) outperform the other models by far. However, their original pre-trained versions are significantly worse, in the range of 54.44-56.94%. _ChatGPT_ achieves 39.95% TER, _GPT-3.5 Turbo_ 43.28%, and _GPT-4_ 46.01%. Worst performances are obtained by _MathJAX_ (56.59%) and _GPT-2-Medium_ (76.69). The fine-tuned model of _GPT-2-Medium_ has a TER of 63.59%, while its original pre-trained version achieves a TER of 76.69%.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **TER [\%]** & **TER [\%]** & **\(\Delta\) Improvement [\%]** \\  & **Pre-Trained** & **Fine-Tuned** & **Relative** \\ \hline TrOCR-Base & 83.75 & 27.71 & +202.24 \\ LaTeX-OCR & 24.85 & **23.45** & +5.97 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Impact of fine-tuning the mathematical expression recognition models.  
Figure 10: Results of the mathematical expression recognition models.  
Table 2 demonstrates the impact of fine-tuning the NLP models. We see that fine-tuning with our validation set helps to achieve relatively lower TERs between 20.60% and even 62.44%. We assume that these differences between the pre-trained and the fine-tuned versions have to do with the fact that the training data of the pre-trained models contained few or no data for the generation of transcriptions from LaTex code.
# An Attention-Based Deep Convolution Network for Mining Airport Delay Propagation Causality
###### Abstract
Given the powerful nonlinear representation ability of deep learning methods, they have been widely used in transportation [33; 34; 35], energy [36; 37], economics [38; 39], medical [40; 41], and other fields. Recently, in the research on nontemporal causality discovery, the research community has proposed some deep learning models, such as causal effect estimation based on variational autoencoders [42], functional causal model learning based on causal generative neural networks [43], and reconstruction of causal graphs based on structure-independent models [44]. Causal relationship mining methods can be divided into experimental-based methods and observation-based methods. As the airport network is dynamic and complex, it is not feasible to intervene in the actual operation of the airport. Since the delay time series contains the causal law of delay propagation, this paper will study the construction of an airport delay causality network from the perspective of data-driven and airport networks. It is well known that deep convolutional neural networks can characterize the delay time series of a single airport very well. Therefore, under the framework of a deep convolutional network, this paper integrates the delay time series of different airports by introducing an attention mechanism, and establishes a deep convolutional network based on the attention mechanism. The attention mechanism score parameter, like the convolutional network parameter, is learned from the data and can quantitatively characterize the impact of one airport's delay on another airport. Further, according to the characteristics of the strength of the airport, the score of the attention mechanism can be used to preliminarily screen the airport pairs with delay propagation causality. Finally, direct causality is verified based on a \(t\)-test and propagation delay analysis. The causality of delay propagation in China's airport network is analyzed, and the experimental results obtained are consistent with some mainstream experiences. The rest of this paper is organized as follows: Section 2 elaborates on the problem of delay propagation causality; the proposed method will be introduced in Section 3, including a delay prediction model based on deep convolutional networks and causality verification based on the attention mechanism and \(t\)-test. Section 4 discusses the propagation mechanism of airport network delays by case. Finally, Section 5 summarizes the content of this paper.
## 3 Analysis of Literature Resources
### AI Techniques to Support APS Systems
#### 3.2.2 Reinforcement Learning and Deep Learning Techniques
To address these limitations, hybrid approaches are being explored that integrate DRL with classical optimization methods or embed domain knowledge to improve learning performance and generalization. In job shop scheduling, DRL agents interact with evolving schedules--known as Temporary Scheduling-- and use DNNs to approximate value functions, enabling informed real-time decisions [55; 58]. A prominent DRL algorithm is the Deep Q-Network (DQN), which integrates Q-learning with DNNs to solve complex, dynamic problems like job scheduling [54; 59]. In applying DRL to production scheduling in complex job shops, cooperative DQN agents, derived from one of the most widely used RL techniques, are employed [33]. The DQN agents are trained in an RL environment, using DNNs to make real-time decisions. The DQN agents learn from historical data and aim to maximize efficiency by optimizing production scheduling and resource allocation even in NP-hard problems according to user-defined, flexible objectives [18; 53]. QN agents operate in uncertain environments without manual objective decomposition, allowing for global optimization and autonomous scheduling. In practice, cooperative multi-agent DQN systems are trained offline in simulations using historical data and then deployed online once optimal solutions are embedded in trained networks [33]. Each DQN agent manages its assigned workstation, refining decision rules based on observed outcomes and peer agent behaviors. Lot positions are randomized during training to ensure data diversity, though the quality of results can still depend on data sufficiency [33; 54]. Despite being computationally intensive, the approach offers strong scalability and flexibility, allowing legacy systems to be updated within hours and adapting to changing objectives without manual reprogramming [33; 56]. DQN agents also enable decentralized, self-learning production systems, which align well with Industry 4.0 goals. By leveraging a cooperative Multi-Agent Structure (MAS), DQN solutions effectively tackle the complexity and local optimization challenges of job shop scheduling [18; 33; 60].
## 3 System Architecture
### Privacy-Preserving Mechanisms  
To enhance data confidentiality beyond simple decentralization, the architecture optionally includes:  
* Differential privacy (DP): Local model updates may be perturbed with calibrated noise before upload. This technique helps obscure the contribution of individual data points by adding statistically controlled noise, thereby protecting user-level information during federated training.
* Secure aggregation: The coordinator can apply cryptographic aggregation protocols to prevent reconstruction of individual updates.
* Transport layer security (TLS): All communication between edge devices and the aggregator is encrypted. These mechanisms ensure that even in a 5G-enabled high-throughput environment, the system complies with data minimization principles and user privacy expectations. The 5G connectivity layer complements these mechanisms by supporting faster encrypted communication and federated coordination without compromising user data integrity.
## 6 Discussion  
In this paper, we considered the problem of the classification of received symbols in 2 WRC according to a many-to-one hierarchical map given from a WPLNC scenario with the employment of an ANN as a classifier. It was shown that for a slowly varying phase of the wireless channel, it is possible to utilize previously trained weights of the ANN as prior information. An effort was made to minimize the resources required for the training of the system, and the number of resources required for training was optimized. The simulation results showed that the utilization of the prior information was beneficial and improved the overall classification results. Three different procedures of training were proposed, implemented, and evaluated. The procedure denoted P3 in the text, where the weights of the ANN were not re-initialized with consecutive changes of the relative fading parameter, was identified to be the best one.  
Future research will focus on (1) the optimization of the parameters of the proposed methods, where the challenge is recognized in the number of parameters that the ANN model contains and a more systematic approach would be desired, (2) the implementation of the approach for over-the-air transmission, which seems straightforward using the concept of software-defined radio (however on-line processing of data opens timing issues in practical networks), and (3) exploiting of the principle for more complex networks, where new challenges will emerge. As noted previously, the presented scenario is still on the edge, which can be handled by traditional, analytically derived approaches. However, these are bearable only for simple network topologies. Hopefully, we shall be able to extend the approach described in this paper to more complex scenarios. These extensions might focus on  
Figure 7: Comparison of different proposed approaches together with reference solutions. Procedure P3 with \(I=3\) performed the best. The validation dataset of 100,000 samples was used.  
more complex modulation schemes, an increased number of network nodes, and relaxed knowledge of the network topology.  
Development of the algorithms and writing, J.K.; supervision and editing, J.S.; validation and editing, P.H. All authors read and agreed to the published version of the manuscript.  
This work was supported by the Grant Agency of the Czech Technical University in Prague, Grant No. SGS20/068/OHK3/1T/13, B2.3 (6.11.2020).  
The authors would like to thank the two anonymous reviewers for their helpful comments, which improved the text.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
2 WRC & 2-way relay channel \\ WPLNC & wireless physical layer network coding \\ ANN & artificial neural network \\ ML & machine learning \\ SNR & signal-to-noise ratio \\ BPSK & binary phase shift keying \\ BER & bit-error rate \\ \end{tabular}
## 3 Impact of Power Amplifier Nonlinearities on the System Performance
As discussed later, this also provides advantages regarding potential distortions introduced by the AM/PM characteristic of the amplifier.
# A Novel Energy-Saving Route Planning Algorithm for Marine Vehicles
## 3 Methods
### Principle of Ant Colony Algorithm
#### 3.1.2 Pheromone Concentration  
Pheromone concentration is the key of the ant colony cooperation. The global pheromone concentrations are all equal at the initial stage. As ants continue exploring, they will leave pheromones on the route that they traveled, which would guide subsequent ants to choose the advantageous path. Therefore, after all ants complete a cycle, the pheromone content on each path is updated, and the updated rules are as follows.  
\[\tau_{ij}(NC+1)=(1-\rho_{\alpha})\tau_{ij}(NC)+\Delta\tau_{ij}(NC) \tag{14}\]  
\[\Delta\tau_{ij}(NC)=\sum\limits_{k=1}^{m}\Delta\tau_{ij}^{k}(NC) \tag{15}\]  
where \(\rho_{\alpha}\) is the pheromone volatile factor, \(\rho_{\alpha}\in(0,1)\). The pheromone increment on the path between node \(i\) and node \(j\) in the \(NC\) generation is \(\Delta\tau_{ij}(NC)\). \(\Delta\tau_{ij}^{k}(NC)\) is the pheromone content left by ant \(k\) on the path between node \(i\) and node \(j\) during the iteration.
## 3 Integration of Multi-Domain MEC Environments
### MEC Architecture
The MEO is also in charge of on-boarding the application packages. Finally, the MEC architecture is completed by operation support systems (OSS). These OSSs can receive requests from external entities, either from the user application lifecycle manager proxy (UA-LCM proxy), or the customer facing service (CFS) portal, for multi-access edge application instantiation, termination or relocation, determining if such requests can be granted, and in that case, forwarding granted requests to the orchestrator. These OSSs will also allow the network operator to trigger management and control actions, including the configuration of policies for the execution of the applications. Apart from these functional blocks and components, the MEC architecture defined a number of reference points among them. These have been summarized in Table 2, presenting the components involved as well as the main scope of each reference point. In this paper we will analyze the impact of the multi-domain approach on those reference points for the different scenarios evaluated. Figure 1: European Telecommunications Standards Institute (ETSI) multi-access edge computing (MEC) reference architecture. From all these reference points, ETSI MEC does not intend at this stage to further specify a number of them, such as Mm5, Mm7, Mm8, Mm9, Mx1 and Mp2.
# Characteristics of Summer Flash Drought and Its Effect on Maize Growth in Liaoning Province, China
## 4 Discussion
### Characteristics of Flash Droughts in Typical Years and Effects on Maize Growth  
Considering the summer flash droughts of 1989, 1997, and 2018 as typical examples, the EDDI was used to evaluate the occurrence and development of flash droughts, as well as their effect on maize growth. The duration of the flash droughts in 1989 and 2018 was relatively short, occurring from late July to late August, whereas the duration of the flash drought in 1997 was longer, lasting from mid-June to late August, and therefore developed into a seasonal drought. No large-scale high-temperature event was observed before the flash drought in the three typical years, but all three flash droughts were accompanied by regional or even widespread high-temperature events. In particular, during the summer of 2018, Liaoning experienced the longest duration and widest impact of extreme high-temperature events since 1951 [35]. Before the occurrence of the flash drought, all three typical years were characterized by a precipitation deficit. Although drought events had not yet occurred, the cumulative precipitation deficits caused flash droughts to develop rapidly. The flash drought was further exacerbated by the combined effects of a heat wave and water deficit.  
Because summer flash droughts occur during the crucial developmental period of maize, maize growth, dry matter accumulation, and grain formation are strongly impacted. The jointing-silking stage is the most crucial period of water demand during the entire growth cycle of maize, which has weak tolerance to adverse environments. Exposure to drought may reduce crop yield by almost 30%, while long-duration drought stress during tasseling and ear formation results in yield loss of 66-93% [25; 36]. [PERSON] et al. [37] reported that the probability of maize yield reduction may be 60% under combined severe droughts and high temperatures, which is greater than that under a single extreme event. The present study revealed that, in all three typical years of flash drought, different degrees of decline in the GPP of maize were observed during the development of the flash drought, and different degrees of yield reduction were recorded among the three years. The degree of decline in the GPP and yield of maize was influenced by the stage and duration of the flash drought. The year 1997 experienced the longest flash drought, which persisted for the entire maize jointing-milk stage and resulted in the peak severe reduction in yield. The duration of the flash drought in 2018 was shorter, and the greater severity of the drought coincided with the maize tasseling stage, resulting in a minimal reduction in yield. The flash drought in 1989 was of the same duration as that in 2018 and coincided with the jointing-heading stage of maize development. However, the drought severity range in 1989 was greater than that of 2018, so the maize yield reduction was more severe than that of 2018.
# CFD-Simulink Modeling of the Inflatable Solar Dryer for Drying Paddy Rice
## 2 Materials and Methods
### Simulation of Airflow Distribution in the ISD
#### 2.2.3 CFD Model Simulation  
The simulation of the airflow distribution in the ISD was carried out using ANSYS Fluent 19.1 (ANSYS Workbench, ANSYS, Inc., Canonsburg, PA, USA) installed on a HP Z420 Workstation (Hewlett-Packard, Palo Alto, CA, USA) with an Intel Xeon CPU E5-1660 3.30 GHz processor and RAM 32 GB. The simulations were performed in a steady state and the k-realizable turbulence model was used with enhanced wall treatment. The governing equations were computed by applying a pressure-based segregated solver and the default under-relaxation factors set as solution controls. The materials used were subdivided into the fluid (drying air across the ISD) and the solid (paddy rice). The properties of incompressible gas were applied. Based on the experimental data, the calculated inlet velocity was 5 m s\({}^{-1}\). The assumptions considered in the ISD simulation were nonadiabatic and no-slip walls, as well as no shrinkage or deformation of paddy rice kernels during drying. The paddy rice bulk was considered as a porous medium.
###### Abstract  
With the increasing power and speed of laser welding, in-process monitoring has become even more crucial to ensure process stability and weld quality. Due to its low cost and installation flexibility, acoustic process monitoring is a promising method and has demonstrated its effectiveness. Although its feasibility has been the focus of existing studies, the temporal resolution of acoustic emissions (AE) has not yet been addressed despite its utmost importance for realizing real-time systems. Aiming to provide a benchmark for further development, this study investigates the relationship between duration and informativeness of AE signals during high-power (3.5 kW) and high-speed (12 m/min) laser beam butt welding. Specifically, the informativeness of AE signals is evaluated based on the accuracy of detecting and quantifying joint gaps for various time windows of signals, yielding numerical comparison. The obtained results show that signals can be shortened up to a certain point without sacrificing their informativeness, encouraging the optimization of the signal duration. Our results also suggest that large gaps (\(>\)0.3 mm) induce unique signal characteristics in AE, which are clearly identifiable from 1 ms signal segments, equivalent to 0.2 mm weld seam.  
laser beam butt welding; acoustic process monitoring; nondestructive testing (NDT); joint gap detection; temporal resolution; clustering; neighborhood components analysis (NCA)
## 2 Materials and Methods
### Bacterial Species and Culture Conditions Used  
_Legionella pneumophila_ ATCC 33152 and _Salmonella enterica subsp. enterica_ serovar Enteritidis ATCC 25928 (later referred to as _S. enteritidis_) were used to evaluate the antimicrobial activity for DWDSs and food contamination settings, respectively. Starting from a \(-20\,^{\circ}\)C bacterial stock, \(10\) uL of _S. enteritidis_ were plated on Tryptic Soy Agar (Microbiol, Macchiaareddu, Italy) and aerobically incubated overnight at 37 \({}^{\circ}\)C. L. _pneumophila_ ATCC 33152 was cultivated on Buffered Charcoal Yeast Extract Agar (BCYE--Oxoid Ltd., United Kingdom) for ten days at 37 \({}^{\circ}\)C and 2.5% CO\({}_{2}\). Bacteria inocula were prepared by resuspending bacterial colonies into a sterile saline solution (NaCl, 0.9% \(w/v\)) until reaching a concentration of 0.5 McFarland units (equal to 1.5 \(\times\)\(10^{8}\) colony forming units/mL (CFU/mL)) determined with a densitometer (BioSan, Medical-Biological Research and Technologies, Riga, Latvia). From each bacterial inoculum, serial dilutions in a sterile saline solution were performed to obtain \(10^{4}\) and \(10^{3}\) CFU/mL final concentrations.