esa-sceva/satcom-chunk-collection huggingface dataset:
# Comprehensive Analysis of Maritime Cybersecurity Landscape Based on the NIST CSF v2.0
## 5 NIST CSF-Based Analysis of Maritime Cybersecurity
### Detect
#### 5.4.1 Continuous Monitoring  
Continuous monitoring, as defined by the NIST CSF v2.0, is crucial for observing both physical and digital assets for any abnormalities, indicators of compromise, or other potentially adversarial events. Regular monitoring of networks, physical environments, personnel activities, and technology usage are paramount for timely detection of adverse events. Moreover, keeping tabs on the activities and services rendered by external service providers can further aid in identifying any untoward activities. Finally, diligent monitoring of computing hardware, software, runtime environments, and their data can provide early detection of potentially harmful events, reinforcing the cybersecurity defenses of the organization and contributing to its overall resilience. The culmination of these efforts in the maritime sector can vastly improve maritime security, again emphasizing the crucial role of ongoing detection processes. In the context of continuous monitoring, understanding the mode of operation of cybersecurity threat actors is paramount. [PERSON] et al. (2012) meticulously explored this avenue, offering an analytical perspective on the cybersecurity threat landscape and homing in on the maritime industry and transportation systems. They dissected the techniques, tactics, and procedures (TTPs) employed by various threat entities, including Bear, Panda, and Buffalo, among others. This piece of research aligns cohesively with the principles of continuous security monitoring, which emphasize regular surveillance, analysis, and appraisal of the evolving cybersecurity threats to maritime domains. Continuous monitoring emphasizes the need to maintain an ongoing watchful eye over systems in order to rapidly identify anomalies or unusual activities. In the context of the maritime sector, this has additional layers of complexity due to the multifaceted and highly sophisticated technologies employed. Several of these critical technologies harbor vulnerabilities that can potentially impact maritime security, including the Automatic Identification System (AIS) and the Global Navigation Satellite System (GNSS). [PERSON] et al. (2013) have outlined several vulnerabilities inherent in these systems, importantly stressing issues pertaining to tampering and reliability. If these systems are compromised, it could lead to a chain of repercussions affecting the entire organization, further emphasizing the urgent need for robust detection processes. Moreover, the authors emphasized the importance of conducting frequent penetration tests to monitor the operation and ensure the reliability, resilience, and confidentiality of these critical technologies. Their research underlines the role of the human factor in understanding the vulnerabilities associated with these navigation systems. They provide
# Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks
## 4 The Proposed Clustering Protocol
### Network Model of the Proposed Method  
We assume a sensing network in which N sensing nodes are deployed independently and uniformly in a two-dimensional area _A(x,y)_, and the BS is located outside the field area. We are unaware about the prior knowledge of the area; in this situation, random node distribution could be a more effective strategy. In our designed WSN, we use the following random distribution algorithm to deploy the nodes in the sensing area:  
\[P\big{(}N(A)=K\big{)}=\frac{\big{(}\lambda|A|^{K}\exp^{-\lambda|A|}\big{)}}{K!} \tag{1}\]  
where \(\lambda\) is the node density and \(|A|\) is the area of the sensing field. In our designed network, nodes collect the information from the surroundings, and convey this information to the BS with the help of concerned CHs. So, in this way, there are three types of nodes in the network like field nodes, CH nodes, and the BS.  
Let N is the set of field nodes, and CHs is the set of the cluster head nodes. Where, \(N=\{j\ |\ j\geq 1\bigwedge j\leq j_{\max}\}\) and \(j_{max}\) is the maximum number of nodes. \(CHs=\{CH\ |\ CH\geq 1\bigwedge CH\leq CH_{\max}\}\), where \(CH_{max}\) is the maximum number of \(CHs\). Each field node is able to set up a link to the other field nodes or CH depending upon the situation. Through the above discussion, we have found this connectivity parameter:  
\[P_{(a,b)}=\begin{cases}1&\text{If $a$ establishes $a$ connection \ with $b$}\\ 0&\text{Otherwise}\end{cases} \tag{2}\]  
The \(a,b\in N\), and \(a\
eq b\). Here, \(b\) is either a member node or a CH. If \(b\) is a CH, then \(a\) is a member node. The quality of links is directly related to the received signal strength and the distance.
# Cycle Generative Adversarial Network Based on Gradient Normalization for Infrared Image Generation
## 3 Methodologies  
In order to generate better IR images, we must not only consider the feature extraction capability of the generator to prevent too much detail information from being lost. In addition, we also need to consider the model collapse problem of cycle generative adversarial networks during the model training process, which directly affects the effect of infrared image generation. Therefore, this paper will focus on solving the quality problem of infrared image generation and preventing model collapse. the specific contributions of this paper are as follows:  
1. A new generative network with ResNet is used instead of the traditional U-Net network structure, because the feature extraction capability of ResNet is much higher than that of U-Net. The new generative network makes the generated image features richer as well.  
2. In this paper, we introduce the spatial attention and channel attention mechanisms to the ResNet network structure. The spatial attention and channel attention mechanism enhances the textured detail information of image generation and reduces the loss ofdetails. In doing so, this solves the problems of severe textural information loss and low generated-image quality as observed in the traditional method.  
3. To address the problem of the model being unstable and prone to collapse during the training process of cycle generative adversarial networks, we introduce a gradient normalization module to the discriminator to stabilize the training process of the model and increase its convergence speed in the training process. In addition, the gradient normalization module ensures that the model does not easily collapse.
## 3 Results and Discussion
### Leakage Current Increase Rate and Degradation Mechanism  
To assess the electrical performance of two different cooling methods, we compared the leakage current of an undamaged package (fresh) at the operating voltage of 1.2 V with that of packages that were exposed to stress at 3.0 V and 3.1 V over time [Figure 6a]. At 3.0 V, the leakage current of the immersion cooling package increased by approximately 13% after 30 min and 15% after 1 h relative to fresh. In contrast, the leakage current of the air cooling package increased by -59% after 30 min and 74% after 1 h. At 3.1 V, the leakage current of the package under immersion cooling increased by 74% after 30 min, while that of the package in the air environment increased by 234%. The leakage current caused by voltage stress was approximately three times lower in the immersion cooling environment than in the air.  
Based on these results, it is noteworthy that the immersion cooling environment provided superior cooling performance to that of air, effectively reduced electrical and thermal stress caused by elevated voltage and enhanced the electrical stability of the package. Furthermore, the following mechanism explains this effect. When elevated voltage was applied to DRAM, heat generated by electrical stress and thermal acceleration was not effectively dissipated in the air environment, resulting in a significant increase in leakage current. As a result, the package leakage current increased rapidly, leading to oxide degradation and instability of the semiconductor device, which in turn caused an additional increase in leakage current [Figure 6b]. In contrast, in the immersion cooling environment, the heat generated by electrical stress was dissipated more effectively, minimizing further  
Figure 5: I-V curve characteristics with (**a**) 3 V applied over time in coolant, (**b**) 3 V applied over time in air, (**c**) 3.1 V applied over time in coolant, and (**d**) 3.1 V applied over time in air. Measurements were taken for fresh samples and after exposure to voltage stress for 10 min, 30 min, and 1 h (except for 3.1 V, which was measured up to 30 min).  
increases in leakage current and enhancing the reliability of the semiconductor package [Figure 6c].  
These results indicate a nonlinear correlation between stress voltage and leakage current growth rate, which is consistent with degradation mechanisms such as hot carrier injection and time-dependent dielectric breakdown. The observed current increase under air environment, particularly at 3.0 V, suggests that this condition surpasses the critical stress limit for DRAM packages. As such, it is recommended that operating voltages be maintained below 2.8 V to prevent accelerated degradation in air-cooled systems.
# LEO Constellation-Augmented Multi-GNSS for 3D Water Vapor Tomography
## 5 Conclusions
This research was funded by the National Science Fund for Distinguished Young Scholars (No. 41825009), Wuhan Science and Technology Project (No. 2018010401011270), Changjiang Scholars Program, Hubei Province Natural Science Foundation of China (No. 2018 CFA081), The Funds for Creative Research Groups of China (Grant No. 41721003), Open Fund of Hunan Natural Resources Investigation and Monitoring Engineering Technology Research Center (No: 2020-9), Hubei University of Science and Technology Cultivation Project (No: 2016-18X057). The numerical calculations in this paper have been done on the supercomputing system in the Supercomputing Center of Wuhan University. Not applicable. **Data Availability Statement:** The ERA5 data is available at [[https://cds.climate.copernicus.eu/cdsapp#](https://cds.climate.copernicus.eu/cdsapp#)]([https://cds.climate.copernicus.eu/cdsapp#](https://cds.climate.copernicus.eu/cdsapp#))!/dataset/reanalysis-era5-pressure-levels?tab=overview (accessed on 3 August 2021). The site coordinate data of CORS is available at [[https://www.geodetic.gov.hk/en/index.htm](https://www.geodetic.gov.hk/en/index.htm)]([https://www.geodetic.gov.hk/en/index.htm](https://www.geodetic.gov.hk/en/index.htm)) (accessed on 3 August 2021). The Sounding data is available at [[https://weather.uwyo.edu/wyoming/](https://weather.uwyo.edu/wyoming/)]([https://weather.uwyo.edu/wyoming/](https://weather.uwyo.edu/wyoming/)) (accessed on 3 August 2021). **Acknowledgments:** The authors would like to thank the ECMWF for providing layered meteorological data.
# Detecting Cyber Threat Event from Twitter Using IDCNN and BiLSTM
## 4 Experiments Setup
### The Datasets and Experimental Environment  
_The Data Collection._ To verify our method's validity, we collected lots of tweets using Twitter's API over a period from 1 January 2019 to 1 June 2020 using the keywords seeds related to network security for model training. Such as \"DDOS\", \"ransomware\", \"data breach\", \"phishing\", and others. We also collect the tweets from the user of \"@[PERSON]\" as the raw samples that are not related to cybersecurity, the raw data can be seen in the file([[https://github.com/das-lab/Cyberthreat-Detection](https://github.com/das-lab/Cyberthreat-Detection)]([https://github.com/das-lab/Cyberthreat-Detection](https://github.com/das-lab/Cyberthreat-Detection))).  
_The Data Preprocessing._ In order to train more accurate models, the raw tweets need to be fully preprocessed. We removed the tweets with repetitive content and remove meaningless characters in tweets, including emoticons and remove the words that appeared less than twice in all tweets. We replace the URL that appears in the tweet. Then, we tokenize tweets using the Natural Language Toolkit (NLTK). Moreover, we use the Stanford CoreNLP to extract named entities. After preprocessing, the labeled dataset is balanced as 2525 cyber threat event tweets and 2410 non-cyber threat event tweets.  
_The Experimental Environment._ The experimental environment is shown in Table 1. We show the Python library we used and the operating system version and associated configuration of the machine.
# An Enhanced Precoder for Multi User Multiple-Input Multiple-Output Downlink Systems
###### Abstract  
Recently, as the demand for data rate of users has increased, wireless communication systems have aimed to offer high throughput. For this reason, various techniques which guarantee high performance have been invented, such as massive multiple-input multiple-output (MIMO). However, the implementation of huge base station (BS) antenna array and decrease of reliability as the number of users increases are chief obstacles. In order to mitigate these problems, this paper proposes an adaptive precoder which provides high throughput and bit error rate (BER) performances to achieve the desired data rate in multi user (MU) MIMO downlink systems which have a practical BS antenna array (up to 16). The proposed scheme is optimized with a modified minimum mean square error (MMSE) criterion in order to improve BER gain and reduce data streams in order to obtain diversity gain at low signal to noise ratio (SNR). It is shown that the BER and throughput performances of the proposed scheme are improved.  
minimum mean square error (MMSE); multi user multiple-input multiple-output (MU-MIMO); block diagonalization (BD)
## 3 Methods
### A CNN-BiLSTM Method with Multi-Head Self-Attention
#### 3.4.2 Bidirectional LSTM Network  
LSTM occupies a place in the field of sequential signal analysis by sharing weights, and the weights between its hidden layer and output layer can be recycled at any time [28]. It is a chain model for processing time series and can effectively compensate for the vanishing gradient problem. Compared with the classical unidirectional EEG signal extraction method, the bidirectional EEG signal extraction method can extract dynamic information from both earlier and later segments of the EEG signal sequence [29]. An LSTM unit consists of three gate control units of the forget gate and input gate and the calculation formulas are defined by Equations (3)-(8):  
\[f_{t}=\sigma(W_{f}\cdot[h_{t-1},x_{t}])+b_{f}), \tag{3}\]  
\[i_{t}=\sigma(W_{i}\cdot[h_{t-1},x_{t}])+b_{i}), \tag{4}\]  
\[\widetilde{\mathcal{C}}_{t}=\tanh(W_{\mathrm{C}}\cdot[h_{t-1},x_{t}])+b_{C}), \tag{5}\]  
\[C_{t}=f_{t}\times C_{t-1}+i_{t}\times\widetilde{\mathcal{C}}_{t}, \tag{6}\]  
\[O_{t}=\sigma(W_{\mathrm{O}}\cdot[h_{t-1},x_{t}])+b_{O}), \tag{7}\]  
\[h_{t}=\tanh(C_{t})\times O_{t}, \tag{8}\]  
where, \(x_{t}\) is the time series at time \(t\), \(C_{t}\) is the cell state, \(\widetilde{C}_{t}\) is the temporary cell state, \(\sigma\) is the sigmoid function, \(W\) is the weight matrix, \(b\) is the bias vector of the corresponding weight, \(h_{t}\) is the hidden state, \(f_{t}\) is the forget gate, \(i_{t}\) is the memory gate, and \(O_{t}\) is the output gate. The forget gate selects the retained features and inputs both the information of the previous state and the current state information into the Sigmoid function. The memory gate is responsible for updating the state of the LSTM cell, after which the input gate controls the output value to the next LSTM cell. Unlike the above single LSTM, the output formulas of the bidirectional LSTM are defined by Equation (9):  
\[y_{t}=\sigma(W_{h}\cdot[h_{t},h_{t}^{\prime}])+b_{h}), \tag{9}\]  
The BiLSTM network adds a backward layer to learn the future sentiment information, which is an extension of the past sentiment information. The BiLSTM merges the gating architecture and bidirectional characteristics perfectly so that more information can be remembered and processed by the two LSTM units [30]. The network structure of BiLSTM is shown in Figure 3. The time series is input to the model, the forward layer connects the feature information in the past series with the present information, the backward layer connects the future, and finally, the predicted value is output by Equation (9).  
Figure 3: Structure of BiLSTM.
## 2 Background
### Consumer Purchase Model  
Although a detailed discussion of consumer behavior research is beyond the scope of this paper, a solid understanding of this discipline, especially on individual purchase behavior, can be beneficial to the systematic review process. Generally speaking, consumer purchase decisions can be described by the five-stage classical model (see Figure 1), which serves well as a theoretical foundation despite having been refined over time [53] and has been adopted in the information system discipline to study consumer behaviors in e-commerce [54].  
Among the five stages, the purchase decision stage refers to a consumer's willingness to fulfill a purchase [55], thus, it is perceived as the most relevant factor to the actual  
Figure 1: The classical model of consumer purchase decision making.  
purchase [56, 57]. Mostly, purchase decision is measured by purchase intention, i.e., the strength of consumer's willingness to buy a particular product, service, or products and services from a particular brand [58, 59, 60]. Furthermore, a purchase decision can be impacted by the activities and factors of all the stages, as a consumer may not strictly follow their sequential orders during a purchase [53].  
Moreover, the classical model can be inspected from the view of the stimulus-organism-response (SOR) model [61]. The SOR view has been widely adopted in the research on consumer behavior. In social commerce, the classical model was regarded as the response [54]. Ref. [62] applied SOR to study the effect of authentic experience (stimulus) on the evaluation of VR tourism promotion (response) through cognitive and affective responses (organism). In terms of purchase-related research, [63] studied how unusual purchase (response) was caused by online information exposure (stimulus) during the early stage of the COVID-19 pandemic, while [64, 65, 66] studied various components of the stimulus and the organism that caused impulse buying behaviors. Moreover, a stimulus-response model was adopted in marketing research, creating the so-called \"buyer's black box\" model [67], in which the marketing mix [68] and the marketing environment [69] was regarded as stimuli, the classical model, together with buyer characteristics, was merged into the \"buyer's black box\", and buyer's responses were measures of the purchase decision process (see Table 2).  
Based on the classical model of consumer decision making and the SOR model, a holistic view of individual consumer purchase in virtual commerce can be established. Purchase decisions can be regarded as the response, and the characteristics of virtual commerce belonging to either immersive technologies or consumer behaviors can be captured as stimulus or organism.
## 4 Numerical Experiment Results and Discussions
### Performance of Optimal Filters for Two Noise Incidences  
In practical cases, the incoming noise could be more complicated. One typical example is that there are multiple noise sources producing plane wave noise from different directions. In this subsection, we evaluate the performance of the optimal filter in the case of two plane waves. The amplitudes of the two plane waves are set to be 2:1, but their directions are randomized. We run the simulation for 20 cases and illustrate the noise reduction performance of the exact, full-rank (case 1), and overdetermined (case 1 with 181 incidences) optimal filters. The full-rank optimal filter suffers from significant performance degradation, whereas the overdetermined optimal filter yields a very close performance compared to the exact optimal filter, as shown in Figure 13. The sound pressure distribution of the 2D sound field in the FEM simulation for two noise sources at 1000 Hz is exemplified in Figure 14, showing that the global control is achieved in both the exact and overdetermined optimal filters, but not in the full-rank method. The overdetermined optimal method has thus far been robust to a single noise source impinging from any incidence and two noise sources simultaneously from random incidence angles, which can be extended to multiple source incidences.  
Figure 11: Noise reduction performance of full-rank optimal filter case 1 and overdetermined optimal filter case 1, at frequency (**a**) 400 Hz, (**b**) 1000 Hz.  
Figure 12: Noise reduction performance of three overdetermined optimal filter cases, at frequency (**a**) 400 Hz, (**b**) 1000 Hz.
## 4 Results and Discussion
### Radiological Hazards Parameters
#### 4.3.4 Absorbed Dose Rate (D\({}_{\mathrm{air}}\)) and Annual Effective Dose (AED)  
The exposure to the emitted gamma rays from uranium, thorium, and potassium over 1 m from the surface can be described by the absorbed dose (D\({}_{\mathrm{air}}\)). The following Formula (5) was used to calculate the values of D\({}_{\mathrm{air}}\)[51], which are presented in Table 3:  
\[\mathrm{D_{air}}=0.430\ A_{U}+0.666\ A_{Th}+0.042\ A_{K} \tag{5}\]  
The D\({}_{\mathrm{air}}\) values of the Wadi sediments varied between 50.2 and 197.3 nGy h\({}^{-1}\) with a mean value of 1185 nGy h\({}^{-1}\) that was greater than the worldwide average of 59 nGy h\({}^{-1}\)[1].  
Figure 5: The distribution (%) of calculated radium equivalent content, internal hazard index, external hazard index, and gamma level index compared to the reported limit.  
Consequently, restrictions must be applied to prevent the use of Wadi sediments materials in the infrastructure fields. Therefore, the public's exposure to emitted gamma radiation is assessed by two exposure scenarios, both indoors and outdoors. The public spends 20% and 80% of their time outdoors and indoors, respectively. The AED was detected utilizing Equation (6) and is listed in Table 3[16; 52]:  
\[\text{AED}\left(\text{mSv}/\text{y}\right)=\text{D}_{\text{air}}\times\text{ F}\times\text{T}\times\text{DCF}\times 10^{-6} \tag{6}\]  
F represents the occupancy factor, T is the exposure time to gamma radiation (8760 h), and DCF represents the dose conversion factor (0.7 Sv/Gy). The corresponding statistical values of the annual outdoor effective dose (\(\text{AED}_{\text{out}}\)) and annual outdoor effective dose (\(\text{AED}_{\text{in}}\)) are presented in Table 3. The mean values of \(\text{AED}_{\text{out}}\) and \(\text{AED}_{\text{in}}\) are 0.1 and 0.6 mSv y\({}^{-1}\), respectively and are 1.5 times higher than the recommended limits of 0.07 and 0.41 mSv y\({}^{-1}\)[1]. The presence of uranium and thorium minerals such as uraninite, thorianite, and monazite can result in exposure to high doses that cause adverse health impacts, such as the deterioration of tissues and deoxyribonucleic acid (DNA) in the genes, as well as cancer and cardiovascular disease [53].