esa-sceva/satcom-chunk-collection huggingface dataset:
# An Approach to Analyze Vulnerability of Information Flow in Software Architecture
## 3 Proposed Approach
### Construction Approach of Service Invocation Diagram Based on Software Architecture  
From Section 3.3, we can see that two key points of security policies of information flow in architecture are the direction of information flow during service invocation and the security class of each service. As a result, the model constructed by the proposed approach should have corresponding descriptive ability. That is, the model should be able to reflect the direction and path of information flow in the architecture as well as the confidentiality and integrity level of the information carried by services. Software architecture design specification contains some diagrams that can reflect information interaction, such as data flow diagrams, flow charts, activity diagrams, and state diagrams, but these diagrams cannot be used for analysis directly. This is because the uncertainty of the future operation environment of the system, the complexity of the software, the cognitive limitation of the designer and service reuse in current service-oriented architectures make it impossible for all potential information flow and propagation to be included in these diagrams. To further explain this problem, we give an example, shown in Figure 4.  
In the architecture design specification of a piece of software, there is a statement that when performing a certain function, a calculation service X is supposed to pass the calculated value to another service Y. According to this description, a designer designs the information flow path as shown in Figure 4a. However, to achieve this in actual operation of the system, before passing the value to calculation service Y, calculation service X invokes storage service Z. Then Z invokes calculation service z and passes the value to y. From this process, we can see clearly that an implicit information flow X \(\rightarrow\) Z \(\rightarrow\) Y, which does not exist in the design phase, emerges, as shown in Figure 4b.  
So, it is not enough to use architecture design specification and architecture diagrams to analyze the vulnerability of information flow in software architecture. It is necessary to construct a model that can capable of depict all potential information flow and propagation based on architecture. In the 18 th century, [PERSON], a renowned mathematician, solved the famous problem of the seven bridges of Konigsberg by abstracting paths into a graph and hence set up graph theory. As a data structure, a graph can represent complex relationships between data elements that cannot be determined intuitively. In this paper, we model the information flow and propagation in architecture by use of graph theory.  
Figure 4: Data flow path of (**a**) architecture design phase vs. (**b**) data flow path in actual operation.
# System-Theoretic Process Analysis Based on SysML/MARTE and NuSMV
## 5 Conclusions
### Three Disadvantages of STPA-SN  
State machines and model checking contribute to the automation of STPA-SN, meanwhile, they also contribute to three disadvantages of STPA-SN:  
1. As STPA-SN describes the system behaviors with state machines, which are insufficient to describe the continuous behaviors, STPA-SN does not apply to the hybrid system that comprises both continuous and discrete dynamic behaviors.
2. STPA-SN is not competent to analyze an excessively complicated system, which leads to state explosion, exceeding the ability of model checking used by STPA-SN. However, since STPA is mainly applied to the high-level design of the system, this disadvantage might not be so prominent.
3. In STPA-SN, an SC must be converted into temporal logic for the identification of the loss scenario. If an SC cannot be converted into the temporal logic supported by the model checker, the loss scenario cannot be identified against the SC either.
# Lightweight Integrity Preserving Scheme for Secure Data Exchange in Cloud-Based IoT Systems
## 3 Problem Statement
### The Threats Model  
Instead of being trustworthy, the semi-trust server should be regarded as untrustworthy. It behaves in a trusted way for public integrity checking, but inspects the data it receives. Hence, it may initiate the following attacks [11]:  
* Replay attack. The server may generate the proof from the previous proof or other information, without retrieving the actual file.
* Forge attack. The server may forge the integrity checking key of the file and deceive the semi-trust server if the semi-trust server has reused the same challenge key of the same file.
* Replace attack. The server may choose another valid and uncorrupted pair of file and integrity checking parameter to replace the challenged of file, if it has already discarded the current file.
## 3 Simulation Results
### Effects of Different Load Increase Scenarios
#### 3.1.1 IEEE 30-Bus System  
The first test system is concerned with the IEEE 30-bus system that consists of 6 generators, 24 loads, and 41 lines [31].  
In the first phase of the simulation, single load change case is tested wherein the load at the specific bus is increased proportionally until the load flow equations fail to converge. In this simulation, the load at bus 30 is selected. Figure 7 shows the comparison with \(\lambda^{\max}\) and CPU time with the proposed method and conventional CPFLOW method, where \(\lambda^{\max}\) denotes the critical point at which voltage collapses. From the shown figure, one can see that both the proposed approach and conventional CPFLOW approach can achieve the actual value \(\lambda^{\max}=1.8777\). The CPU time, however, is quite different from each method. Specifically, the proposed method takes 0.23 s in the execution, while the conventional CPFLOW method takes 0.86 s.  
In the second phase of the simulation, a multiple load change case is considered. In this case, all the loads in the test system are simultaneously increased with a constant load power factor. The trajectories of searching \(\lambda^{\max}\) using the two compared methods are given in Figure 8. It indicates that the proposed method quickly finds the accurate solution of \(\lambda^{\max}=2.9493\) in 0.29 s. Meanwhile, for the conventional CPFLOW method, it still requires 1.15 s to reach the same point.  
Figure 6: Summary of scenarios and events during the experiments. IEEE: Institute of Electrical and Electronics Engineers.
# Automatic Detection of Sensitive Data Using Transformer-Based Classifiers
###### Abstract  
The General Data Protection Regulation (GDPR) has allowed EU citizens and residents to have more control over their personal data, simplifying the regulatory environment affecting international business and unifying and homogenising privacy legislation within the EU. This regulation affects all companies that process data of European residents regardless of the place in which they are processed and their registered office, providing for a strict discipline of data protection. These companies must comply with the GDPR and be aware of the content of the data they manage; this is especially important if they are holding sensitive data, that is, any information regarding racial or ethnic origin, political opinions, religious or philosophical beliefs, trade union membership, data relating to the sexual life or sexual orientation of the person, as well as data on physical and mental health. These classes of data are hardly structured, and most frequently they appear within a document such as an email message, a review or a post. It is extremely difficult to know if a company is in possession of sensitive data at the risk of not protecting them properly. The goal of the study described in this paper is to use Machine Learning, in particular the Transformer deep-learning model, to develop classifiers capable of detecting documents that are likely to include sensitive data. Additionally, we want the classifiers to recognize the particular type of sensitive topic with which they deal, in order for a company to have a better knowledge of the data they own. We expect to make the model described in this paper available as a web service, customized to private data of possible customers, or even in a free-to-use version based on the freely available data set we have built to train the classifiers.  
G 2  
items are usually relatively simple to retrieve using dictionaries or regular expressions, considering that they usually appear within well-established and easy-to-read data structures.
* Sensitive data, on the other hand, include personal data such as racial or ethnic origin, political opinions, religious or philosophical beliefs, trade union membership, data relating to one's sexual life or sexual orientation, as well as data on present and future physical and mental health. This includes information on healthcare services regardless of the source, for example a doctor. These data types are hardly structured, and most frequently they are part of a document such as an email message, a review or a post. This makes it extremely difficult to know if a company is in possession of sensitive data, with the risk that they will not be properly protected.  
The goal of this project is to develop a model capable of automatically detecting whether a document contains sensitive data, as well as the specific type of sensitive topic contained therein: politics, religion, health or sexual habits.
## Data and Software  
### Datasets  
The first dataset was the infrared spectral data, which was collected by a Tensor 27 spectrophotometer (Bruker, Inc., Ettlingen, Germany). It contained sample data for five classes of _Tegillarca granosa_, four of which were polluted by lead (Pb), copper (Cu), cadmium (Cd), and zinc (Zn) heavy metals in varying concentrations, and one class consisting of healthy (non-polluted) _Tegillarca granosa_. Each class had 30 samples. Heavy metal pollution can lead to changes in components of body tissues such as proteins, enzymes, and lipids etc. Each heavy metal can have its own unique influences and toxicological profiles, and hence it is important to recognize specific classes of heavy metal pollution [20]. Reference [20] can be referred to for specific data descriptions and further details.  
The second dataset was LIBS data obtained by developed equipment (composed of a Q-switched Nd:YAG laser with a spectrometer). The LIBS data was acquired from another batch of four classes of _Tegillarca granosa_ samples. Three of the classes comprised samples polluted by Pb, Cd, and Zn heavy metals in a certain concentration range and one class of healthy (non-polluted) samples. Each class included 30 samples. The body of a _Tegillarca granosa_, being a biological sample, contains a large number of organic as well as inorganic elements, and, consequently, the corresponding LIBS data contained massive amounts of information. In addition, the LIBS data also had large dimensions, reaching tens of thousands. To further increase the complexity, the same heavy metal elements in different valence states lead to many spectral lines. The LIBS data were often also affected by base-line drift [21; 22], a problem commonly seen in spectral analysis, and they were susceptible to noise as well. These difficulties brought substantial challenges to the recognition algorithm in classification recognition. For specific data descriptions, please refer to Reference [23].
## 2 Related Work
### Literature Review
#### 2.2.2 Studies with Sentiment Analysis  
Moving beyond traditional models, [PERSON] et al. [9] examined cryptocurrency price prediction by integrating sentiment analysis and tweet volume into their models. While their findings indicated that sentiment analysis using VADER was unreliable due to neutral or positively biased tweets, tweet volume emerged as a robust predictor.  
[PERSON] et al. [8] further demonstrated the importance of sentiment by analysing Google Trends and Twitter data. Their work established that Bitcoin prices were partially influenced by public sentiment, providing a foundational understanding of the interplay between sentiment and market dynamics.  
[PERSON] et al. [10] first utilised a pre-trained NLP model, FinBERT, for sentiment analysis in financial news. They incorporated this to make a hybrid FinBERT-LSTM model that combined sentiment analysis from financial news headlines with sequential data analysis of historical prices. Their model significantly improved predictive accuracy, achieving notable reductions in MAE and testing loss.  
[PERSON] et al. [18] investigated the utility of Twitter sentiment and tweet volume in predicting Bitcoin price direction and magnitude. They employed models like Bi-LSTM and CNN and introduced a voting classifier that refined predictions by aligning direction and magnitude outputs.  
[PERSON] et al. [7] took a nuanced approach by integrating prospect theory and sentiment variance into a logistic regression framework. Their study highlighted the differential impacts of positive and negative sentiments on Bitcoin price fluctuations, emphasizing the need for refined sentiment metrics.  
More recently, [PERSON] et al. [13] proposed a hybrid HyBiLSTM model, combining ARIMAX, GARCHX, and Bi-LSTM for Bitcoin price prediction. Their inclusion of social and economic variables, along with a SHAP analysis for feature importance, demonstrated the effectiveness of hybrid models in capturing both linear and non-linear dynamics.
# Federated Learning for Secure In-Vehicle Communication
## 2 Backgrounds and Preliminaries
### Vehicle-to-Everything Communication  
V2X (Vehicle-to-Everything) communication allows vehicles to exchange real-time data with surrounding infrastructure, extending their perception beyond onboard sensors. This capability enhances CAV services, contributing to improved road safety and traffic efficiency [7]. V2X includes C-V2X and DSRC, where C-V2X uses cellular networks for direct communication, while DSRC operates on IEEE 802.11P protocols. Both are essential for intelligent transportation systems. Edge computing in V2X minimizes data transfer and improves reaction times. The ETSI has established standards for edge computing to enhance data processing and communication efficiency. Edge computing should also support real-time data aggregation and distribution via RSUs. The edge serves as an interface between vehicles and the cloud, with edge servers providing local computing and storage close to vehicles [25]. A practical Vehicular Edge Computing (VEC) deployment scenario is introduced in [26], featuring a three-tier communication network that seamlessly connects the cloud, base stations (BS), roadside units (RSUs), and vehicles. Vehicle-to-Vehicle (V2V) communication relies on DSRC, LTE, and WiFi to facilitate direct data exchange between vehicles, ensuring low-latency interactions. Vehicle-to-Roadside (V2R) communication utilizes DSRC, LTE, and WiFi, enabling efficient connectivity between vehicles and RSUs for improved data dissemination and traffic management. RSU-to-Cloud (R2C) communication employs Ethernet or optical fiber, ensuring high-speed, reliable data transmission between RSUs and cloud infrastructure for large-scale data processing and decision-making. This hierarchical communication framework enhances real-time data processing, traffic coordination, and autonomous vehicle decision-making, making it a cornerstone for intelligent transportation systems.
## 4 Mathematical Description for Linear Bearings
### Wear and Preload of Linear Ball Bearing with Four-Point Connection  
Bearing wear is determined by the degradation of the bearing raceway surface layer after a certain number of cycles [17; 18]. The parameter that describes the reliability of a bearing is efficiency. The three stages of wear progress of the bearing raceways shown in Figure 7 can be identified. The first stage is the bedding-in. The next stage is the main operation of the bearing (bearing life stage) and the final stage is accelerated wear. The final stage of bearing operation occurs when microcracks, adhesion or a lack of lubrication occur [19]. A key factor in bearing design is the selection of the appropriate preload value to increase bearing life. According to [PERSON] and [PERSON] [20; 21], preload distribution plays a key role in bearing life. The load in the bearing is caused by preload, which is determined by external loads and rotational speed. In a linear bearing, preload is caused only by an external load.  
Figure 7: Bearing raceway wear intervals [20].  
The value of preload in linear bearings is obtained by the tolerances of the raceways and the accuracy of the balls in the bearing. The preload in a linear bearing affects the final value of the resistance to movement and the natural frequency. A high preload value increases the stiffness of the connection, which affects the natural frequency, increases the resistance of linear motion, but also reduces the durability. Linear ball bearings with four-point connection have a preload value that is twice as low as that of linear ball bearings with two-point connection, while maintaining the same tolerances for raceways and bearing balls.  
The reaction force of the ball with the raceway is due to the preload, as shown in Figure 8. The sum of the reaction force of the ball with the raceways should correspond to the value of the preload (Equation (13)) [22; 23; 24]. For the sake of simplification, it can be assumed that the distribution of reaction forces in contact between the ball and individual raceways is the same [22]. In addition, the value of the reaction force of the ball with the raceway depends on the angle of contact (Equation (14)).  
\[\mathrm{F_{N}}=\mathrm{F_{n1}}+\mathrm{F_{ro2}}=\mathrm{F_{n2}}+\mathrm{F_{ro1}} \tag{13}\]  
\[\mathrm{F_{r}}\text{-}\cos\alpha=\mathrm{F_{ri1}}+\mathrm{F_{ro2}}+\mathrm{F_{ ri2}}+\mathrm{F_{ro1}} \tag{14}\]  
where  
\(\mathrm{F_{ri1}},\mathrm{F_{ri2}},\mathrm{F_{ro1}},\mathrm{F_{ro2}}\)--reaction forces on a specific contact surface, where ri is the inner raceway and ro is the outer raceway,  
\(\mathrm{F_{N}}\)--preload,  
\(\mathrm{F_{r}}\)--external load,  
\(\alpha\)--contact angle between the bearing ball and the raceway.
## 4 Driving Simulation Experiment
### Experimental Results
#### 4.3.3. Perclos  
This study uses the eyelid closure degree during simulated driving--measured by PERCLOS--as a core physiological indicator of fatigue to evaluate changes in alertness under different fatigue states. A higher PERCLOS value typically indicates greater driver fatigue and lower alertness. Based on the PERCLOS data collected from 30 participants across various experimental conditions, a two-way repeated-measures ANOVA was conducted to examine the effects of the fatigue level and voice interaction type on the physiological responses associated with fatigue (Figure 5).  
The results show a significant main effect of the fatigue level on the eyelid closure degree (\(p<0.001\)). As fatigue severity increased, PERCLOS significantly rose, indicating that PERCLOS is an effective indicator of the fatigue level. The voice interaction type also exhibited a significant main effect (\(p<0.001\)), with the highest average PERCLOS observed under low-stimulation voice conditions and the lowest under high-stimulation voice conditions, demonstrating that stronger interactive stimuli help alleviate ocular fatigue. Furthermore, a significant interaction effect between the two factors was found (\(p<0.05\)). Notably, under severe fatigue, a high-stimulation voice reduced PERCLOS by approximately 5.2% on average, showing a clear arousal effect; whereas in the no-fatigue state, differences among voice types were not statistically significant.  
Additionally, exploratory comparisons of eyelid closure trends among different driver types revealed that long-haul truck drivers experienced the most pronounced PERCLOS reduction under a high-stimulation voice; taxi drivers responded more positively to a medium-stimulation voice; and private car owners showed weaker responses to a high-stimulation voice during mild fatigue, preferring milder interaction styles. Although these  
Figure 5: PERCLOS of drivers under different fatigue levels (ns: not significant (\(p>0.05\)); *: \(p<0.05\); ***: \(p<0.001\)).  
differences did not reach statistical significance (\(p>0.05\)), they suggest potential individualized response tendencies in a fatigue intervention across driver groups.
# Application Research of Negative Pressure Wave Signal Denoising Method Based on VMD
## 2 Methodology
### Signal Noise Reduction Based on VMD
#### 2.2.2 Optimization of VMD Algorithm Decomposition Layer Number  
A one-dimensional random sequence \(x(n)=(x_{1},x_{2},\ldots,x_{i},\ldots,x_{n})\) can be assumed if the probability of obtaining \(x_{i}\) obeys the probability distribution \(p(x_{i})\). Then, the information entropy \(H(x)\) of \(x(n)=(x_{1},x_{2},\ldots,x_{i},\ldots,x_{n})\) is [23]:  
\[H(x)=-\sum\limits_{i=1}^{n}p(x_{i})\log p(x_{i}) \tag{5}\]  
The larger the \(H(x)\), the higher the uncertainty of the probability distribution \(p(x_{i})\).  
Figure 1: Schematic diagram of leak location based on NPW.  
After using VMD to decompose signals, the effective IMF components are used to reconstruct the noise reduction signal. However, the algorithm needs to predetermine the decomposition number \(k\) of IMF, and it will affect the determination of the center frequency \(\{\omega_{k}\}\). Therefore, the noise reduction effect of each \(k\) value corresponding to the reconstructed signal is different. The optimization of \(k\) value is particularly important for signal noise reduction. Considering that information entropy is a dimensionless index, this index has a simple principle and can be used as a basis for the selection of system parameters. When using VMD to denoise signals, noise signals appear more disordered than pressure signals, and their uncertainty is greater. Therefore, the information entropy can be used as a characterization of the noise content in the signal [23]. When the information entropy reaches the minimum, the noise in the reconstructed signal is considered to be the minimum. The \(k\) value is optimal.  
Before the optimal \(k\) can be calculated, the reconstructed signals corresponding to different \(k\) values should be obtained first. Therefore, in order to achieve signal reconstruction, it is necessary to screen the effective IMF components.
## 4 Experiment, Results and Analysis  
### Main Result  
In this section, we assess the EER and Cavg of our novel B-transformer model and compare them with the results obtained in previous studies.  
Table 1 presents the experimental results of the language identification system based on the Cross-Entropy loss function. The table provides the experimental results of using the X-vector, XSA-LID, CNN-trans (our baseline), conformer, and our proposed B-transformer model on three different test sets of varying durations from the NIST LRE 2017 dataset. According to the data in Table 1, it is evident that transformer-based models have significant advantages over traditional models based on time-delay neural networks and convolutional neural networks. This advantage is mainly due to the attention mechanism of the transformer. It can focus on areas of interest and model the dependencies of global context. Compared to the transformer-based CNN-trans and conformer models,  
our proposed model shows further performance improvement. This is mainly attributed to the dual-branch structure in the B-transformer, which can model both local and global contextual dependencies simultaneously, and the subsequent merging module also plays a crucial role in combining local and global information.