esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Experimental Results and Analysis
### Safety Discussion
#### 7.3.2 Brute-Force Cracking Attacks  
Brute-force cipher breaking is a method of cracking a cipher by systematically trying all possible keys. The size of a cipher's secret key space determines its strength, which is the set of all possible keys in a cryptosystem. A larger secret key space makes it more difficult to crack the cipher since more time and computational resources are required to exhaust all possible keys. Therefore, the security of a cipher depends on the size of its secret key space.  
For the DES algorithm, the secret key length is 56 bits, which gives \(2^{56}\) possiblescret keys. This size is relatively small, making cracking difficult.  
In contrast, the AES algorithm can have a secret key length of up to 256 bits, which gives \(2^{256}\) possible secret keys, making it highly resistant to attacks. Similarly, RC4 can take a long time and be more difficult to crack.  
The present chunking algorithm can use up to 99 sharing codes, with each code occupying 6 bits, resulting in a secret key length of 594 bits and a secret key space of \(2^{594}\), which surpasses the secret key space of DES and AES. This makes it highly difficult to crack, and as the length of the sharing code increases, the difficulty of cracking increases exponentially.  
Figure 19: Change in the average information entropy of the ciphertext under one key.  
Figure 18: Change in the average information entropy of a ciphertext under one plaintext.  
Experimental tests have shown that with only a few sharing codes, a brute-force simulation analysis of this chunking disorder algorithm can decrypt the plaintext in a short time. Due to the small number of sharing codes, a cracker can easily attempt all possible combinations of sharing codes to find the correct one and decrypt the plaintext. However, as the length of the sharing code increases, cracking becomes much more challenging. As the length of the sharing code increases, the number of sharing code combinations grows exponentially. The cracker needs more time and computational resources to try all possible sharing code combinations to find the correct one and decrypt the plaintext. Therefore, the security of this chunking disorder algorithm increases as the length of the sharing code increases. Figure 20 illustrates difficulty of brute-force cracking.
## 4 Network Communication
### State-of-the-Art Research on Network Communication
#### 4.1.2 Network Topology  
The network topology affects the system performance features, such as power consumption, traffic load, node robustness, selection of MAC protocol, and routing protocol. The common network topologies include star topology, mesh topology, ring topology, and bus topology. Both ring topology and bus topology are not fit for deploying on a complex dynamic human body. According to the literature statistics, there is almost no application systems using these two topologies because the BSN scale is small and sink nodes need to gather body information to send it to remote computers. Hence, the star topology has been very popular since BSNs came into being [101, 102, 103]. All the nodes directly communicate with the sink node. The network communication protocol realization is relatively simple, and sometimes even a routing protocol is unnecessary for data transmission, so the star topology is widely used. In practical applications, Personal Digital Assistants (PDAs) are often used as sink nodes which communicate with other nodes, completing data processing, data transferring, and part of decision-making [104]. The star topology has some disadvantages which limit its further development in BSNs, for it is difficult for the nodes far away from the sink node to communicate with it due to energy limitations. Besides, the nodes located in different sides of body will fail in building connections because of low data reception rates [91]. Different from the star topology, the mesh topology with more complex realization but more superior performance, is more suitable for multi-hop networks. It can reduce path losses caused by diffraction. Each node just needs a small amount of energy to communicate with its neighbors, and power consumption of whole network is well-distributed. Currently many researchers are focused on multi-hop networks, in which couples of new technologies are well applied, such as energy acquisition, human body channel, _etc._, so as to provide better BSN services. The differences between star topology and mesh topology are summarized in Table 2[29].
# Marine Diesel Engine Fault Detection Based on Xilinx ZYNQ SoC
###### Abstract  
Marine diesel engines are the preferred power equipment for ships and are the most important component among the numerous electromechanical devices on board. Accidents involving these engines can potentially cause immeasurable damage to the vessel, making fault detection in marine diesel engines crucial. This design enables the detection and reporting of faults in marine diesel engines at the earliest possible time through the computation of convolutional neural networks, which is of great significance for ensuring the safe navigation of ships. For this functionality, the Xilinx ZYNQ-7000 XC/ZZ010 is selected as the main control chip, and the LoRa wireless network is used as the transmission module. The FreeRTOS embedded operating system is ported, with sensor data collection completed on the PS side of the ZYNQ chip and algorithm acceleration calculations on the PL side. Data are then transmitted to the host computer via the LoRa module paired with a custom protocol. Experimental test results show that the program provides stable data transmission, with each module of the algorithm generally accelerating by more than 95% and an accuracy rate of 92.86%. Additionally, the host computer can display the received data in real time. The custom protocol's header also allows for precise judgments about the completeness and origin of messages, facilitating the expansion of other SOC's message uplink and the host computer's message downlink.  
ZYNQ; marine diesel engine; lora; convolutional neural network; fault diagnosis Article
## 1 Introduction
Moreover, the use of n-pulse modulation to encode data into light signals for transmission is proposed to diminish the complexity of the reception routine concerning the current state of the art.This modulation technique, specifically adapted to the event transition detection DVS operation, enables the reception of separable symbols at the receiver's end. As a result, the decoding process becomes highly effective and computationally efficient, only requiring counting event transitions to demodulate the data. Through this approach, this work demonstrates the achievement of a below-FEC-limit data link, showcasing the potential of this technique. The remainder of this paper is structured as follows: Section 2 describes the current state-of-the-art optical communication systems using event cameras. Section 3 provides background about event cameras, DVS pixels, and their main parameters. Furthermore, Section 4 formalizes the used encoding and decoding techniques, whilst Section 5 describes the methodology defined to obtain the results presented in Section 6. Finally, Section 7 concludes the paper.
## 4 Conclusions  
This paper compares the detection accuracy and resolution of the time-domain DAS, the frequency-domain DAS, and the DORT-MUSIC damage imaging method. The results show that, compared to traditional time-domain imaging methods, the frequency-domain imaging method that incorporates dispersion relationships exhibits significantly higher imaging accuracy. Additionally, this work represents experimental demonstration of DORT-MUSIC's near-diffraction-limit resolution (0.79A) in guided wave imaging, validating [PERSON]'s theoretical predictions [28] under realistic SNR constraints, where evanescent waves generated during multiple scattering events carry subwavelength information that is recoverable through time-reversal subspace processing. In experimental environments with higher noise levels, the resolution is constrained by the degradation of signal subspace orthogonality, as predicted by [PERSON] et al. [38]. Our experimental setup utilized an idealized aluminum plate with known dispersion characteristics engineering applications in complex structures and high-noise environments. The quantified trade-offs between computational complexity, resolution, and noise robustness provide practical criteria for method selection in industrial applications, thus advancing ultrasonic guided wave imaging in structural health monitoring.  
Conceptualization, C.X. and B.X.; methodology, C.X. and Y.L.; software, G.X.; validation, C.X. and S.Z.; formal analysis, C.X.; investigation, S.Z.; resources, C.X.; data curation, C.X.; writing--original draft preparation, C.X.; writing--review and editing, Y.L.; visualization, C.X.; supervision, Y.L.; project administration, Y.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (NNSFC) (Grant Nos. 62071205, 62271235 and 12072133) and the Research Innovation Program for College Graduates of Jiangsu Province (Grant Nos. KYLX15_1045).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Detection Method of Partial Discharge on Transformer and Gas-Insulated Switchgear: A Review
## 4 Partial Discharge Measurement Using Different Types of Sensors on the Power Transformer and GIS
### Mechanism in Electric-Electromagnetic Sensors
#### 4.1.1 PD Detection in Gas-Insulated Switchgear  
In SF6, a common insulating gas, the rise time of the PD pulse current is less than a nanosecond (ns). The electromagnetic wave generated by PD primarily lies in the ultra-high-frequency (UHF) range. The antenna and sensor can capture the electromagnetic signal generated by PD as it passes through the GIS tank. If the frequencies of the UHF signals are high enough, they will not be interfered with by other radio or cellular transmissions. Using the data on the PD phenomenon in SF6 gasoline, the GIS can simply evaluate breakdown risk [123]. When used in combination with the acoustic method of measurement, the UHF PD measuring technique may enhance PD positioning accuracy. The power dissipation generated by the metallic container can greatly attenuate the PD electromagnetic wave. Experimental observations have indicated the presence of a remarkably low attenuation coefficient, typically measuring around 2 dB/km (with theoretical estimates ranging from 1 dB/km to 2 dB/km) [120]. A quintessential measurement configuration for GIS-based UHF PD detection is shown in Figure 12. A computer, digitizer, pre-amplifier, as well as UHF field probe sensor round out the system. A pre-amplifier with a 25 dB gain and a 1 GHz bandwidth is used to enhance the output. The digitizer samples the UHF signal at 20 ns intervals with a trigger delay during the test [124].  
Figure 12: Instrumentation for PD measurements and a typical setup for modeling the measurement on GIS [124].
# Post-Quantum Authentication in the MQTT Protocol
## 1 Introduction
### NIST Postquantum Standardization  
The National Institute of Standards and Technology (NIST) ispart of the U.S. Department of Commerce. NIST is responsible for providing standards and support for technological advancements in the United States. Due to the international nature of technology, NIST standards are used around the world. In 2016, NIST began a competition for new, quantum-resistant (postquantum) cryptographic standards to be proposed ([[https://csrc.nist.gov/projects/post-quantum-cryptography](https://csrc.nist.gov/projects/post-quantum-cryptography)]([https://csrc.nist.gov/projects/post-quantum-cryptography](https://csrc.nist.gov/projects/post-quantum-cryptography)) (accessed on 7 February 2023)). The areas identified as needing new standards are encryption, key establishment, and digital signatures, as these are the areas that will no longer be secure in their current form with the use of quantum computers ([[https://www.nist.gov/news-events/news/2016/12/nist-asks-public-help-future-proof-electronic-information](https://www.nist.gov/news-events/news/2016/12/nist-asks-public-help-future-proof-electronic-information)]([https://www.nist.gov/news-events/news/2016/12/nist-asks-public-help-future-proof-electronic-information](https://www.nist.gov/news-events/news/2016/12/nist-asks-public-help-future-proof-electronic-information)) (accessed on 7 February 2023)).  
Table 1 shows the five security levels that NIST has used as benchmarks for the proposed postquantum cryptographic schemes ([[https://csrc.nist.gov/Projects/Post-Quantum-Cryptography/Post-Quantum-Cryptography-Standardization/Evaluation-Criteria/Security-](https://csrc.nist.gov/Projects/Post-Quantum-Cryptography/Post-Quantum-Cryptography-Standardization/Evaluation-Criteria/Security-)]([https://csrc.nist.gov/Projects/Post-Quantum-Cryptography/Post-Quantum-Cryptography-Standardization/Evaluation-Criteria/Security-](https://csrc.nist.gov/Projects/Post-Quantum-Cryptography/Post-Quantum-Cryptography-Standardization/Evaluation-Criteria/Security-))(Evaluation-Criteria) (accessed on 7 February 23)). Our implementations consider security levels 2, 3 and 5, corresponding to being as difficult to break as a collision search on a 256-bit hash function, a key search on a block cipher with a 192-bit key and a key search on a block cipher with a 256-bit key, respectively.  
NIST Lightweight Protocol Standardization  
In February 2023, NIST announced their selected suite of lightweight cryptography algorithms to be used for IoT devices. The selected suite is called ASCON [20]. This competition was first proposed in 2019, and of the 57 candidates, only one was selected for standardization. One of the variants in the ASCON suite may have some level of protection against quantum attacks. However, this was not one of the aspects NIST took into account in the selection of this cryptographic suite [21]. The main focus was to find an algorithm that would work for most resource-constrained devices, as the implementation is very compact.
## 2 Related Works
Those studies help identify a base architecture for IoMT integration where data are collected by heterogeneous IoMT devices and forwarded to gateways and local servers near an IoMT solution (at the network edge) or adapters, connectors or agents (instantiated either at the network edge, or in the cloud), which transform the heterogeneous data formats into a unified one. The collected data are then loaded to the cloud through services or in place when the adapters, connectors, or agents are instantiated in the cloud. Finally, the external applications access the data through application services, or according to an agent-based approach. The architecture shows several shortcomings regarding the functional requirements previously defined, particularly those related to massive data processing and data standardization that uses an EHR definition. On the other hand, [PERSON] et al. [28] proposed a big data-based smart pervasive healthcare system framework that offers knowledge-extraction capabilities over a data repository. Differently from the other approaches, it does not consider data sharing for third party applications and a standardized EHR. [PERSON] et al. [29] analyzed the use of data-processing platforms in multiple OpenEHR-based EHRs following two approaches (Figure 1) that consider three layers, namely data acquisition, data storage, and information delivery. The first considers the Extract-Transform-Load (ETL) process from multiple EHR files into a data warehouse that serves an online analytical processing (OLAP) [30] server which provides analyses and visualization tools. The second approach enables the application of data mining techniques through Hadoop Map Reduce [15] supported by data stored in a Hadoop distributed file system (HDFS), populated with EHRs records after a data wrangling process. As with ETL, data wrangling (DW) [31] applies data profiling, cleaning, transformation, integration and visualization operations to improve the quality of data, and corrupted, repeated or incoherent EHR records are filtered towards better quality data for data mining applications. However, the platforms are closed and do not provide functionalities for data interchange with external applications, which incurs in the above-defined prerequisites for IoMT. Figure 1: Data warehousing, OLAP and big data approaches (adapted from [29]). Table 1 shows a summary of the related works addressed in this section. No approach could cover the functionalities elicited for IoMT, which together with the concepts introduced below have motivated us to propose a new platform for IoMT.
## 1 Introduction
This use of cameras as information gathering devices, not only decreases the infrastructure inversion, but it also increases the system security. If the signal has been intercepted, an additional signal processing will be required in order to extract the relevant information. Additionally, for security cameras, such as from CCTV systems, the communication is already protected by design. Moreover, the use of OCC in WSN allows the implementation of Multiple Input Multiple Output (MIMO) systems as well as Space Division Multiple Access (SDMA) solutions [16; 17]. In this work, the interference that another emitter close by the particular transmitter of an OCC system can introduce into the communication link is experimentally characterized. Specifically, working with RGB multi-LED transmitters, such as LED matrices, and commercial middle range cost cameras. For this purpose, the Normalized Power Signal to Interference Ratio (NPSIR) is presented in order to measure the chromatic interference independently of the chosen camera or the optical power transmitted by the LEDs. Some functions for easily determining the 2D pixel representation of real separation distances are deduced and validated. These functions are also used for projecting proportional distances into a real scenario, as shown in Figure 1, where a WSN is deployed in a small office with three sensors transmitting in different wavelengths using OCC with On-Off Keyingmodulation (OOK). Finally, the corresponding Signal to Interference Ratio (SIR) of the sensors over each other is determined in function of the NPSIR obtained from the experimental characterization. This paper is organized as follows: in Section 2 the fundamental theory is presented; the materials and methods of the experiments are detailed in Sections 3 and 4 present the obtained results. A real case scenario simulation is described in Section 5 along with the corresponding results. The discussion, conclusions, future works and possible applications are presented in Section 6.
# Lightweight Transformer Network for Ship HRRP Target Recognition
## 2 Proposed Model
### Local RNN Module  
The local RNN module aims at extracting local features and makes full use of the advantages of RNN in short sequence feature extraction. With a short sequence consisting of three time steps as a single RNN input, the structure of the local RNN module is shown in Figure 2. The white circles represent the zero sequence, the blue circles represent the original input sequence, and the light blue circles represent the output sequence. The white circles and blue circles form the input of the local RNN module.  
Figure 1: Lightweight Transformer model structure. The feature extraction module consists of a local RNN module and a Transformer encoder modified by GLTs. The classifier consists of a linear layer and a SoftMax function.  
Take the sequence containing \(k\) time steps in HRRP as the input of a single RNN as an example. The pre-processed HRRP sequence containing \(T\) time steps is \(\mathbf{X}=[\mathbf{x_{1}},\mathbf{x_{2}},\ldots,\mathbf{x_{T}}]\). A traditional RNN takes a sequence of \(T\) time steps and inputs them in time order to obtain a hidden state vector characterizing the target feature. Since RNN has the problem of gradient vanishing when processing long sequences, the local RNN module is used to alleviate the gradient vanishing problem while extracting local features from short sequences. The local RNN module is essentially extracting correlation features between neighbouring distance units within the HRRP sequences. The local RNN module first complements the left side of the input sequence \(\mathbf{X}\) with \(k-1\) zero vectors according to the set short sequences size \(k\) to form a new input sequence \(\mathbf{X}^{\prime}\).  
\[\mathbf{X}^{\prime}=\left[\mathbf{0},\underbrace{\ldots}_{k-1}\mathbf{0},\mathbf{x_{1}},\mathbf{ x_{2}},\ldots,\mathbf{x_{T}}\right] \tag{4}\]  
The sliding window of size \(k\), sliding through the distance of \(1\) from left to right in turn, forms \(T\) short sequences, each containing \(k\) time steps in \(\mathbf{X}^{\prime}\). The \(T\) short sequences are fed into \(T\) RNNs, each of which outputs a hidden state vector characterizing the feature, and the \(T\) hidden state vectors are stitched into a new hidden state vector.  
\[\mathbf{h_{t}}=RNN(\mathbf{x_{t-k+1}},\mathbf{x_{t-k+2}},\ldots,\mathbf{x_{t}}) \tag{5}\]  
\[\mathbf{H}=[\mathbf{h_{1}},\mathbf{h_{2}},\ldots,\mathbf{h_{T}}] \tag{6}\]  
Adding a local RNN module before Transformer's encoder can make up for Transformer's disadvantage in local feature extraction, and act as a positional encoding. The features extracted by the improved model contain more information and are more comprehensive and more generalizable.
# Innovative Integration of Severe Weather Forecasts into an Extended Arrival Manager
## 5 Case Studies  
In the framework of the SINOPTICA project, three different severe events were selected to be analyzed. These events were identified during the summer season, when temperatures are higher and a larger amount of energy is available for the triggering of thunderstorms and the development of deep convective systems. The selected events affected the major airports of the Po Valley in northern Italy, with Milano Malpensa (MXP) having 28.8 million passengers affected in 2019 and Bergamo Orio al Serio (BGY) having 13.9 million passengers affected:  
1. 11 May 2019 in MXP: A squall line hitting the Malpensa airport producing a large amount of hail. There was no landing for over 1 h at the airport and eight flights were diverted;
2. 7 July 2019 in BGY: General instability affected the Po Valley with intense thunderstorms. Strong downbursts caused some diversions and huge flight delays;
3. 6 August 2019 in MXP and BGY: High atmospheric instability with thunderstorms and hail hitting the Bergamo and Malpensa airports in the late afternoon. Several cancellations and diversions occurred during the event.