esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multi-Agent Deep Reinforcement Learning System for Government Interoperability
## 1 Introduction
Utilizing autonomous agents to embody different administrative departments, the system enables smooth coordination, immediate information sharing, and enhanced workflows, tackling significant issues in public administration. The incorporation of DRL allows agents to consistently learn from their interactions, adjust to changing governance requirements, and enhance decision-making procedures, thus increasing the effectiveness and responsiveness of local government services. By utilizing the JADE's organized communication protocols, the system guarantees uniform and effective task execution, minimizing bureaucratic hold-ups and improving inter-departmental cooperation. Furthermore, the MAS framework streamlines essential municipal tasks like document management, adherence to regulations, and information sharing, resulting in quicker and more precise service provision. The suggested method functions as a scalable simulation platform, enabling municipalities to evaluate and enhance interoperability strategies prior to actual implementation. By promoting digital transformation in public administration, this system enhances the modernization of governance via AI-powered adaptability, ensuring municipal services are more responsive, efficient, and in tune with community requirements. The combination of an MAS and DRL fosters a more advanced, technology-oriented governance framework, enhancing public service provision and reinforcing collaboration between departments. The structure of the paper is as follows: Section 2 provides an overview of the key literature and earlier research that support this study. In Section 3, we describe the methodology used in this study, detailing the methods and techniques applied. Section 4 outlines the main characteristics of the system that was developed and executed according to the specified methodology. Section 5 presents the results generated by the system, where we assess them in relation to the goals established in this study. In Section 6, we offer an analysis of the results, examining their significance and implications. Section 7 outlines the key conclusions derived from the research conducted. Ultimately, Section 8 highlights directions for future research and development stemming from the results of this study.
###### Abstract  
With the proliferation of the Internet, network complexities for both commercial and state organizations have significantly increased, leading to more sophisticated and harder-to-detect network attacks. This evolution poses substantial challenges for intrusion detection systems, threatening the cybersecurity of organizations and national infrastructure alike. Although numerous deep learning techniques such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and graph neural networks (GNNs) have been applied to detect various network attacks, they face limitations due to the lack of standardized input data, affecting model accuracy and performance. This paper proposes a novel preprocessing method for flow data from network intrusion detection systems (NIDSs), enhancing the efficacy of a graph neural network model in malicious flow detection. Our approach initializes graph nodes with data derived from flow features and constructs graph edges through the analysis of IP relationships within the system. Additionally, we propose a new graph model based on the combination of the graph neural network (GCN) model and SAGEConv, a variant of the GraphSAGE model. The proposed model leverages the strengths while addressing the limitations encountered by the previous models. Evaluations on two IDS datasets, CICIDS-2017 and UNSW-NB15, demonstrate that our model outperforms existing methods, offering a significant advancement in the detection of network threats. This work not only addresses a critical gap in the standardization of input data for deep learning models in cybersecurity but also proposes a scalable solution for improving the intrusion detection accuracy.  
intrusion detection system (IDS); graph neural network (GNN); deep learning; flow-based characteristic; feature engineering +
Footnote †: journal: Medical Imaging  
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Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
## 3 PiFM Measurement for a Single-Molecule
### Microscopic Interaction between Molecule and Plasmon
In Figure 3c, when the tip is at the center of the molecule, the dipoles of the two diagonal elements in the y-axis direction are in phase with the plasmon but the other four elements are in the opposite phase; therefore, they cancel each other out and the molecule cannot be excited. In contrast, in Figure 3d, when the tip is at the center of the molecule, all dipoles of the molecule are in phase with the plasmon, so the PiFM image appears at the center of the molecule. In Figure 3e, the structure of the dipoles is similar to Figure 3d, but when the tip is at the center of the molecule, the four dipoles on the sides from the y-axis are orthogonal with the plasmon; thus, the PiFM image is a little weaker at the center and shows a torus-like structure. In this section, although we analyzed the PiFM measurements of a simple model that mimics a six-membered ring molecule, we obtained a qualitative understanding from the perspective of the interaction between the polarization structure inside the molecule and the localized plasmon. The results we obtained facilitate the understanding of PiFM measurements in a realistic analysis and experiments. Figure 2: Photoinduced force spectra of the six-membered molecule. The solid lines are the force spectra for the respective tip positions, and the dashed line represents the force spectrum for the tip on the substrate, that is, in the absence of the molecule. Figure 3: (**a–e**) are PiFM images for the resonance states. The green squares represent the positions of each element of the six-membered molecule. (**f–j**) represent the dipole structures of the molecule corresponding to the PiFM image above. The circles represent the elements of the six-membered molecule, and the red arrows represent the polarization vectors.
## 3 Results
### Data Fusion and Reconstruction  
After implementing the virtual constellation model, the spatial coverage of L8 NDVI over the study area showed improvement. In February 2018 (Figure 8), L8 had visible coverage of only 48.1% of the study area. The coverage percentages improved to 100% after fusion with S2A. ASTER did not contribute to this fusion due to complete cloud coverage in its February 2018 image. In September 2016, both S2A and ASTER contributed to the increased spatial coverage of fused L8 NDVI. Before implementing the virtual constellation model, L8 had a visible coverage of only 9.89%. The visible coverage percentage improved to 33.75% after fusion with S2A and then to 70.86% after fusion with ASTER. Recall that the sharp image boundaries in Figure 8 represent the absence of an adjacent scene for that month. Additionally, it is important to note that the S2A and ASTER imagery shown in Figure 8 are in their raw NDVI state, prior to the RF adjustment to be L8 compatible.  
Figure 7: Comparison of observed NDVI differences between sensors and subsequent adjustments for 1000 randomly selected pixels in May 2018. Subfigure (**A**) shows Observed S2A versus Observed L8, (**B**) Random Forest Adjusted S2A versus Observed L8, (**C**) Observed ASTER versus Observed L8, and (**D**) Random Forest Adjusted ASTER versus Observed L8.
## 3 Case Study
### Input Data and Data Collection  
This study recorded hourly energy consumption data, time-related feature data, and meteorological feature data for the building between 1 June 2022 and 31 August 2022. Considering the target of the research is a large public building, the activation of internal air conditioning and lighting is influenced by time-related and meteorological features. The feature data points are illustrated in Table 1. Four time-related features were selected for this study, including the date within each month, time within each day, the type of workday, and whether it was a weekend. Meteorological feature data were primarily collected through a local weather station, including temperature, humidity, barometric pressure, wind speed, total solar irradiance at the previous moment, total solar irradiance, and energy consumption at the previous moment. The aforementioned feature datasets were utilized to predict the energy consumption of this public building.  
Figure 7: Hourly energy consumption data of large public building.
## 2 Methods
### Data Processing and Statistical Analysis
#### 2.4.2 Gaze Entropy
The transition probability \(P_{ij}\) from the ith AOI to the \(j\)th AOI is obtained by dividing the number of movements from the \(i\)th AOI to the \(j\)th AOI by the total number of movements between all AOIs. Markov entropy has the advantage of including information on the direction of eye movement [21]. Markov entropy, like Shannon entropy, has a limitation in that it cannot take into account information about how long the eye stayed at each AOI. Dwell time entropy is a method to reflect the time stayed on each AOI, and it has the same calculation formula as Shannon entropy. However, \(P_{i}\) is defined as the ratio of the fixation time of the \(i\)th AOI to the total fixation time of all AOIs [16]. Heat map entropy is a method of calculating entropy using a Gaussian distribution under the assumption that the heat map has a Gaussian distribution from the point where an eye stays the longest to the point where the eye stays the least. [PERSON] et al. [24] defined the joint probability distribution function of the heat map by assigning different weights according to the fixation duration. \[\widetilde{f}_{XY}(x,y)=\sum_{f=1}^{f_{num}}d_{f}\times\frac{1}{2\pi\sigma^{2 }}exp-(\frac{(x-x_{f})^{2}+(y-y_{f})^{2}}{2\sigma^{2}}) \tag{3}\]  
Here, \(\sigma\) is a standard deviation, meaning visual angle in eye tracking, and refers to the range of pixels that the user can perceive when viewing the screen; \(f_{num}\) is the number of fixations; and \(d_{f}\) is the weight of each fixation distribution. Using this combined probability distribution function, heatmap entropy can be calculated as follows:  
\[H=-\sum_{xy}\widetilde{f}_{XY}(x,y)\cdot\log\widetilde{f}_{XY}(x,y) \tag{4}\]
# A Review of Time-Scale Modification of Music Signals +
## 8 Conclusions  
In this article, we reviewed time-scale modification of music signals. We presented fundamental principles and discussed various well-known time and frequency-domain TSM procedures. Additionally, we pointed out more involved procedures proposed in the literature, which are--to varying extents--based on the fundamental approaches we reviewed. In particular, we discussed a recent approach that involves harmonic-percussive separation and combines the advantages of two fundamental TSM methods in order to attenuate artifacts and improve the quality of time-scale modified signals. Finally, we introduced some applications of TSM, including music synchronization and pitch-shifting, and gave pointers to freely available TSM implementations.  
A major goal of this contribution was to present fundamental concepts in the field of TSM. Beyond discussing technical details, our main motivation was to give illustrative explanations in a tutorial-like style. Furthermore, we aimed to foster a deep understanding of the strengths and weaknesses of the various TSM approaches by discussing typical artifacts and the importance of parameter choices. We hope that this work constitutes a good starting point for becoming familiar with the field of TSM and developing novel TSM techniques.  
This work has been supported by the German Research Foundation (DFG MU 2686/6-1). The International Audio Laboratories Erlangen are a joint institution of the Friedrich-Alexander-Universitat Erlangen-Nurnberg (FAU) and Fraunhofer Institut fur Integrierte Schaltungen. We also would like to thank [PERSON] for his careful proofreading and comments on the article.  
The authors contributed equally to this work. The choice of focus, didactical preparation of the material, design of the figures, as well as writing the paper were all done in close collaboration.  
The authors declare no conflict of interest.
## 2 Methods
### Audio and EGG Signals Annotation and Analysis  
The beginning (LA1) and end (LA2) of the laryngeal articulations (see Figure 4) were annotated with Praat version 6.0.05 and saved together with the audio and EGG signals in a binary format. The criteria used to annotate the beginning and the end of the LA1 and LA2 intervals were as follows: beginning of LA1--the instant at which the EGG signal intercepts the x-axis (amplitude = 0) after a negative deflection resulting from the adduction of the vocal folds; end of LA1--moment at which the EGG signal returns to intercept the x-axis; beginning of LA2--instant at which the amplitude of the EGG signal initiates a noticeable increase after an interval in which its mean amplitude is very close to 0, resulting from the adduction of the vocal folds; end of LA2--instant at which the EGG signal intersects the x-axis again before a negative deflection.  
Figure 3: Audio signals, EGG, SpO2, and HR of the first three repetitions of participant DO.  
Figure 4 shows the audio signals, EGG and the annotation, in the Praat Editor, of the laryngeal articulations (LA1 and LA2) of one of the files.  
The annotations were later exported in ASCII format and read by specific scripts in the Matlab 8.5.0.197613 (R2015a) environment where the following parameters were extracted: Duration of laryngeal articulation LA1 (LA1 dur); duration of the laryngeal articulation LA2 (LA2 dur); duration of the interval between the end of the laryngeal articulation LA1 and the beginning of the laryngeal articulation LA2 (LA1 LA2 dur); and peak-magnitude-to-RMS ratio (crest factor) of the EGG signal, calculated for the intervals LA1 (LA1 peak2 rms) and LA2 (LA2 peak2 rms) with peak2 rms function of Signal Processing Toolbox Version 7.0 (R2015a) (MathWorks, Natick). The peak2 rms function calculates the ratio between the maximum value and the root-mean-square (RMS) value of an array. The resulting value allows one to analyse how extreme the peak of the waveform is in the range used for its calculation.  
The parameter values were finally exported to an Excel 2013 spreadsheet and IBM SPSS Statistics version 22, for statistical analysis.
# Cold Boot Attacks on the Supersingular Isogeny Key Encapsulation (SIKE) Mechanism
## 6 Conclusions  
This research paper addressed the question of the viability of cold boot attacks on SIKE. To this end, we reviewed SIKE's reference implementation as it was submitted to the NIST Post-Quantum Cryptography Standardization Process. Furthermore, we presented a dedicated key-recovery algorithm for SIKE in this setting and showed, through simulations, that our algorithm can reconstruct the secret key for SIKE, configured with any SIKE parameters, for varying values \(\beta\in\{0.001,0.01,\ldots,0.1\}\) and \(\alpha=0.001\), by only performing a \(2^{30}\) enumeration. We stress these success rates from our algorithm can be improved as long as there are more available resources to run it. Additionally, we showed that our algorithm could be sped-up by integrating a quantum key search algorithm with it, which brings the computation power of quantum computing into the post-processing phase of a side channel attack. Moreover, as a future work, we may extend our work to include a resource estimation of quantum gates resulting from running our quantum key-recovery algorithm against SIKE or other cryptographic primitives in the post-processing phase.  
**Author Contributions:** Conceptualization, [PERSON]-[PERSON]; methodology, [PERSON]-[PERSON]; software, [PERSON]-[PERSON] and [PERSON]-[PERSON]; validation, [PERSON]-[PERSON] and [PERSON]-M.; formal analysis, [PERSON]-P. and [PERSON]-M.; investigation, [PERSON]-P.; resources, [PERSON]-P.; data curation, [PERSON]-P. and [PERSON]-M.; writing--original draft preparation, [PERSON]-P. and [PERSON]-M.; writing--review and editing, [PERSON]-[PERSON] and [PERSON]-M.; visualization, [PERSON]-P. and [PERSON]-M.; supervision, [PERSON]; project administration, [PERSON]-[PERSON]; funding acquisition, [PERSON]-[PERSON]; All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Universidad del Norte grant number 2019-029 and the APC was funded by Universidad del Norte.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results  
This section presents the experimental results including the GOCE real data description and preprocessing in Section 3.1, the noise analysis of satellite accelerations obtained from both the orbit differentiation method and carrier phase differentiation method in Section 3.2, and the accuracy assessment of the derived gravity field models in Section 3.3.
## 6 Discussion
Additionally, by measuring the electrical resistance with a multimeter instead of using specialized electrodes, variations in the readings could have been generated. These peculiarities in MRE construction were part of the behavior of the prototypes. With regard to the energy-harvesting tests with the piezoelectric sensor merged with a cantilever beam, data in Figure 27 show that the cantilever with a 7.5 cm length generated the highest voltage levels when the input signal had frequencies of 7 and 8 Hz. Under these conditions, the piezoelectric array was able to generate 1.60 V, which was the highest obtained voltage. The experimental trials included testing with an array of three piezoelectric transducers in parallel (Figure 13), and this configuration allowed for generation of a greater voltage than that generated with a single piezoelectric element. The detected opportunity area was the signal-conditioning circuit with the diode and capacitor. The required voltage to energize both elements was too high compared to the voltage generated by the cantilever, and all the collected energy was lost. Although the voltage signals contained noise, a decrease or increase in voltage was observed for the different experiments, and, owing to that behavior, preliminary conclusions could be generated. Moreover, having generated similar maximum voltage values for the testing conditions contributes to the reproducibility of the experiments. Even though the complete prototype was tested with a strain-gauge transducer and powered up with a power supply, the results were promising and reinforced two research lines in smart sensors--how to increase the collected energy in a cantilever beam with embedded piezoelectric sensors, and how to reduce the required energy to rectify and retain the gathered voltage for further storage in the smart sensor. For the CPU, wireless communication, and IoT capabilities, this study offers a manner of passing the data of the smart-sensor subsystems and data post-processing to cloud computing. For the tests, decisions were made regarding the CPU, and the best option was WEMOS-D1-R1, which was programmed in the familiar Arduino development environment. The ESP8266 Wi-Fi module was the implemented wireless approach, which was compatible with the WEMOS-D1-R1 through the Arduino integrated development environment. Moreover, IoT capabilities included the revision of commercial options to send data to the cloud, store them in Google sheets, and display them on a dashboard developed in ClicData software. The complete prototype (Figure 15) included the design and manufacture of protective cases and the communication connection among the subsystems. Although these last two activities were not the contribution of this research, they were also important due to the time dedicated to design and to manufacture the complete prototype of the smart sensor.
## 5 Paradigms of AI for Physical Layer Optimization and System Design
### AI for End-to-End Multi-Antenna Techniques  
In conventional physical layer modules, secure transmission design usually relies on non-interactive random-coding arguments and mainly focuses on the establishment of theoretical achievements. In this case, it is possible to achieve a good security performance by demonstrating the existence of PLS codes, such as low-density parity-check (LDPC) codes, lattice codes and polar codes. However, the design of explicit security using these conventional methods is less reliable in practical and realistic communication systems [151]. To be more specific, the optimization problems of the physical layer of beyond 5G networks are non-deterministic polynomial (NP) problems and are becoming more challenging as there is a series of barriers between different signal processing blocks, such as non-orthogonal multiple-access (NOMA) channel encoding and signal detection, the space-time processing of multiple user MIMOs and channel decoding for polar or LDPC codes [152; 153]. In such scenarios, LDPC codes have a higher decoding complexity in large blocks or under poor noise conditions, while polar codes require multiple iteration convergences to achieve optimal performance. In order to cope with the existing gaps, AI and ML techniques have been applied to simplify and handle physical layer modules [154; 155; 156; 157]. Specifically, deep neural network (DNN) algorithms have been widely implemented in various signal processing function blocks, such as MIMO detection [158], polar code decoders [139], modulation recognition [159], etc. AI can be mainly applied to CSI processing, receiver design and end-to-end link design at the physical layer. For example, neural networks in deep learning have been used to learn the compressed representations of high-dimensional CSI in wireless communication, thereby reducing the CSI feedback overhead. For the physical layer modules, there have been many related AI-based works that have addressed the design and optimization problems of end-to-end multi-antenna systems. In [160], DNN was employed for a joint optimization scheme in end-to-end communication. In contrast to conventional channel equalizer and channel decoder approaches that follow non-interactive schemes, the DNN-based joint optimization consists of an adaptive learning (encoding) module and an end-to-end communication module, which are interactive. This approach is very useful for reducing the computational cost of training and online tasks.