esa-sceva/satcom-chunk-collection huggingface dataset:
# Threat Analysis and Risk Assessment (TARA) Analysis of an Autonomous Emergency Braking (AEB) System
## 2 Related Work
### Background
#### 2.1.2 Security Standard  
A comprehensive review of various studies on similar systems highlights a significant lack of discussions or evaluations of AEB security according to established standards. While the literature presents a wide array of approaches, the role of standardization is critical in system analysis. ISO 24089 is a recent standard in the automotive sector, providing guidelines for software update management systems to ensure that vehicle software is securely updated throughout its lifecycle. Despite its relevance in maintaining software integrity, ISO 24089 primarily focuses on the processes for software updates and does not fully address all cybersecurity aspects, as discussed by [PERSON] et al. [9].  
On the other hand, ISO/SAE 21434 is the foundation of the automotive sector's solution, which offers a thorough framework for handling cybersecurity threats in every stage of a vehicle's lifecycle, from conception to decommissioning. It incorporates automotive-specific procedures, such as TARA, to detect, evaluate, and reduce risks. It is consistent with the risk management concepts included in ISO 27001 [10], as discussed by [PERSON] et al. [11], which are specifically aimed at automotive cybersecurity, offering a framework to evaluate and mitigate potential security risks. This allows for the development of safer, more secure mobility solutions, as shown by [PERSON] et al. [12]. In the context of AEB systems, the integration of ISO/SAE 21434 standards is essential for comprehensive security assessment. While ISO 24089 ensures secure software update management, ISO/SAE 21434 addresses broader cybersecurity concerns, together providing a holistic view of both the safety and security of AEB systems. As outlined in Table 1, these standards, although complementary, still leave some gaps in accounting for external environmental factors.  
The primary references selected for this study, particularly [7], as shown in Table 1, were chosen to conduct a comprehensive examination of AEB architecture and the application of the TARA methodology for cybersecurity.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Characteristic** & **ISO 24089** & **ISO/SAE 21434** \\ \hline Objective & Software update management & Cybersecurity \\ \hline Application & All software in vehicles & Electrical and electronic systems in networked vehicles \\ \hline Process & Secure software update management & Cybersecurity management \\  & throughout vehicle lifecycle & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between ISO 24089 and ISO/SAE 21434 standards.
## 3 Methods
### Overall Framework
#### 3.2.3 Fault Diagnosis Module  
Based on the synthetic data in Section 3.2.2, we used mixed data (including real data and synthetic data) to train BiLSTM, and then validated the trained model in the test set.  
LSTM is a special kind of recurrent neural network (RNN) that is proposed to solve the problem of the long-range dependence of RNNs. For a given one-dimensional sequence \(\mathbf{x}=(x_{1},x_{2},\cdots x_{n})\), the RNN model can be built using Formulas (9) and (10).  
\[\mathbf{h}_{t}=f(\mathbf{W_{xh}x}_{t}+\mathbf{W_{hh}h}_{t-1}+\mathbf{b_{h}}) \tag{9}\]  
\[\mathbf{y}_{t}=\mathbf{W_{hy}h}_{t}+\mathbf{b_{y}} \tag{10}\]  
where \(\mathbf{h}_{t}\) is the hidden layer sequence. \(\mathbf{y}_{t}\) represents the output layer sequence at time \(t\). \(\mathbf{W_{xh}}\) is the weight matrix between the input layer and the hidden layer. \(\mathbf{b_{h}}\) is the bias of the hidden layer. \(f\) represents the activation function.  
To solve the problem that RNN cannot handle long sequences, LSTM has improved RNN. The model structure diagram is shown in Figure 3. Its calculation method is as follows:  
\[\mathbf{i}_{t}=\sigma(\mathbf{W_{xi}x}_{t}+\mathbf{W_{hi}h}_{t-1}\;+\;\mathbf{ W_{ci}c}_{t-1}\;+\;\mathbf{b_{i}}) \tag{11}\]  
\[\mathbf{f}_{t}=\sigma(\mathbf{W_{xf}x}_{t}\;+\;\mathbf{W_{hf}h}_{t-1}\;+\; \mathbf{W_{cf}c}_{t-1}\;+\;\mathbf{b_{f}}) \tag{12}\]  
\[\mathbf{c}_{t}=\mathbf{f_{i}c}_{t-1}\;+\;\mathbf{i_{f}tanh}(\mathbf{W_{xc}x}_{ t}\;+\;\mathbf{W_{hc}h}_{t-1}\;+\;\mathbf{b_{c}}) \tag{13}\]  
\[\mathbf{o}_{t}=\sigma(\mathbf{W_{xo}x}_{t}\;+\;\mathbf{W_{ho}h}_{t-1}\;+\; \mathbf{W_{co}c}_{t}\;+\;\mathbf{b_{o}}) \tag{14}\]  
\[\mathbf{h}_{t}=\mathbf{o}_{t}\tau(\mathbf{c}_{t}) \tag{15}\]  
where, \(\mathbf{i}_{t}\), \(\mathbf{f}_{t}\), \(\mathbf{c}_{t}\), \(\mathbf{o}_{t}\) represent the input gate, forget gate, candidate memory cell, and output gate, respectively. \(\sigma,\tau\) are the sigmoid and hyperbolic tangent activation functions, respectively.  
Figure 2: The framework of our proposed ACGAN-SN model for fault diagnosis with S & I data.  
On the basis of LSTM, BiLSTM realizes the context-based judgment of the model by stacking two layers of LSTM so that the model has the ability to understand the overall data [31].  
To make the one-dimensional data meet the input size of BiLSTM, two embedding layers and a one-dimensional flip layer are used to transform the signal dimension and then input it into the BiLSTM model. The input feature dimension of the model is 32, and the feature dimension of the hidden layer is 64. The number of recurrent neural network layers is 2, and the bidirectional LSTM is stacked, which is easy to implement in PyTorch. Then, two fully connected layers are used to output the model prediction results to classify the data. The structural parameter settings of the fault diagnosis module are shown in Table 2.
# Enhancements and Challenges in CoAP--A Survey
## 4 Enhancements in CoAP
### Congestion Control Mechanisms in CoAP
#### 4.1.3 CoCoA  
To overcome the problems in default CoAP congestion control, CoCoA introduces an adaptive RTO calculation feature in addition to a Variable Backoff Factor (VBF) instead of BEB and an RTO aging mechanism. CoCoA defines two RTO estimators: a weak RTO and a strong RTO estimator [6]. The strong RTO estimator calculates the RTO values for the next message transmission based on the measurement of strong RTT whereas the weak RTO estimator calculates the RTO values based on the weak RTT. The strong RTT is the RTT value obtained after the first successful transmission while the weak RTT value is the RTT value obtained after at least one retransmission. The calculation for the final overall RTO is based on the values of weak and strong RTT and RTO values. For detailed calculations of overall RTO, the reader is referred to [6].  
For the purpose of backoff value CoCoA applies a VBF according to the network status information in order to avoid long idle delays. Depending on the initial RTO value of a transmission, a VBF is applied to retransmissions. If initial RTO is very small i.e., less than 1 sec, a larger VBF is applied and for large RTO value i.e., greater than 3 s, a smaller backoff is applied. For a value in between 1 and 3 s, the value of VBF is set to an optimum value of 2, in correspond to BEB. The values of VBF are mentioned in [6].  
Another issue concerning the aging of RTO values is covered by CoCoA. The RTO aging mechanism is applied in the case of small and large RTO estimations. For an RTO estimation value below 1 s and above 3 s, if new RTT measurements are not made for 16 or 4 times the current RTO value respectively, CoCoA uses the RTO aging mechanism and changes the RTO value in a way that it approaches the default initial value.  
The CoCoA outperforms default CoAP congestion control mechanism; however, there is an ambiguity in the weak RTT estimator calculation. Moreover, the weak RTT value may vary in each consecutive calculation as the it is unknown that after how many retransmissions does the ACK of a message is received. This leads to long overall RTOs and in turn, high idle delays.
# A Multi-Fidelity Successive Response Surface Method for Crashworthiness Optimization Problems
## 4 Multi-Fidelity Metamodeling
### Background on Gaussian Process  
Within the context of black-box expensive-to-evaluate functions, a common goal is to discern the connection between the design variables and a generic black-box function \(f\). This task can be accomplished by initially retrieving a dataset \(\mathcal{D}\) of input and output observation pairs (for the sake of clarity, we recall that \(\mathcal{D}=\{(x_{p},y_{p})|p=1,\ldots,n_{p}\}=\{x,y\}\)) and then fitting a regression model to the collected observations. Before conditioning on such observations, a GP is defined by its mean function and kernel function (or covariance function):  
\[f(\mathbf{x})\sim\mathcal{GP}(\mu(\mathbf{x}),k(\mathbf{x}_{i},\mathbf{x}_{j})). \tag{12}\]  
It is common practice to assume that, before conditioning GP on the observations, the mean function is zero across the variable domain, especially since uncertainty about the mean can be accommodated by including an additional term in the kernel [32]. As a result, the structure that a GP can capture is entirely dictated by its covariance function \(k\). This function, which depends on a vector of hyperparameters \(\mathbf{\theta}\), yields the positive-definite symmetric covariance matrix \(\mathbf{K}\in\mathbb{R}^{n_{p}\times n_{p}}\). By maximizing the log-marginal likelihood (Equation (13)), it is possible to calculate vector \(\mathbf{\theta}\):  
\[\log p(\mathcal{D}|\mathbf{\theta})=-\frac{1}{2}\mathbf{y}^{T}\mathbf{K}^{-1}\mathbf{y}-\frac {1}{2}\log|\mathbf{K}|-\frac{n}{2}\log 2\pi. \tag{13}\]  
Once the hyperparameters are identified, it is possible to infer the posterior distribution by conditioning the joint Gaussian distribution to make predictions on unseen data:  
\[p(f_{*}|x_{*},\mathcal{D},\mathbf{\theta})\sim\mathcal{N}(f_{*}|\mu_{*}(x_{*}), \sigma_{*}^{2}(\mathbf{x}_{*})) \tag{14}\]  
\[\mu_{*}(x_{*})=\mathbf{k}_{np}\mathbf{K}^{-1}\mathbf{y} \tag{15}\]  
\[\sigma_{*}^{2}(\mathbf{x}_{*})=\mathbf{k}_{**}-\mathbf{k}_{*n}\mathbf{K}^{-1}\mathbf{k}_{*n}^{T}, \tag{16}\]  
where \(\mathbf{x}_{*}\) refers to a new given input, \(f_{*}\) to its prediction, \(\mathbf{k}_{**}=k(\mathbf{x}_{*},\mathbf{x}_{*})\), and \(\mathbf{k}_{*n}=[k(\mathbf{x}_{*},\mathbf{x}_{1}),\ldots,k(\mathbf{x}_{*},\mathbf{x}_{n_{p}})]\). The posterior mean \(\mu_{*}\) and its related uncertainty, namely the posterior variance \(\sigma_{*}^{2}\), can be, therefore, employed to make predictions. For more details on the conditioning of the Gaussian distribution and some examples of covariance functions, we suggest that the reader refer to the work of [PERSON] and [PERSON] [7].
## Results
### Effect of Dealcoholization on Volatile Compounds
#### 3.2.4 Trepenics and C\({}_{13}\)-Norisoprenoids  
Terpenes are known to impart floral and fruity aromas to the wine. They can be produced by yeasts during alcoholic fermentation when glucosidases bind freely to glycosylated precursors [86]. The concentration of terpen and C13-norisoprenoid compounds (citronellol, geraniol, nerol, \(\beta\)-damascenone, linalool, \(\alpha\)-terpineol, and geranyl acetone) in wines followed a similar trend as esters and higher alcohols. The total concentrations of terpen in dealcoholized and original wines were within the ranges of 7 ug/L to 112 ug/L in white wine, 5 ug/L to 63 ug/L in rose wine, and 2 ug/L to 47 ug/L in red wine. Both RO and VD significantly reduced the concentrations of all terpenic and C13-norisoprenoid compounds after dealcoholization. Between the dealcoholized fractions of RO and VD, the highest concentrations of terpen were found in the dealcoholized fractions obtained with RO, with \(\beta\)-damascenone showing the highest concentration. Furthermore, geranyl acetone, belonging to the C13-norisoprenoids, was significantly affected by RO and VD dealcoholization, with RO leading to losses between 35% and 100% and VD leading to losses between 92% and 100% in all wines. Contrarily, VD resulted in a 100% loss of citronellol, \(\alpha\)-terpineol, and nerol in white, rose, and red wines, respectively. Given the strong hydrophobicity of these terpen, a greater reduction, roughly equivalent to that of esters, was to be expected due to both their chemical affinity for the membrane and their high volatility upon heating. Losses of terpen were also reported after ethanol reduction of up to \(-5\%\)\(v/v\) in red wines by a polypropylene hollow fiber membrane contactor method [8]. However, the terpene losses were lower compared to those observed in this study. The difference in terpene losses between the two studies could be due to the degree of ethanol removal, the nature of the non-volatile matrix, and/or the dealcoholization method and operating parameters used in these studies.
# Multi-Target Detection Algorithm for Fusion Images Based on an Attention Mechanism
###### Abstract  
Due to the inherent limitations of visible-light sensors, monitoring systems that rely solely on single-modal visible-light images exhibit reduced accuracy, posing safety concerns in applications such as autonomous driving. Infrared and visible-light image fusion technology addresses this issue by generating composite images that integrate complementary information from both modalities, thereby enhancing perception robustness. This study focuses on target detection in fused images. Given that targets in such images are often small and severely occluded, we propose an optimized detection framework to overcome these challenges. Specifically, we improve the YOLOv8 baseline model by introducing a dedicated small-object detection layer, incorporating the Global Attention Mechanism (GAM), and refining the loss function. Experimental results show that our method achieves a 5.0% improvement in mAP and a 6.5% gain in recall over the original YOLOv8. Furthermore, comparative experiments on fused and single-modal inputs demonstrate that fused images yield the highest detection accuracy. These results confirm that leveraging fused inputs significantly enhances detection accuracy and robustness in complex environments.  
image fusion; target detection; global attention mechanism; deep learning Article  
1
# Assessing and Priorities Delay Factors of Prefabricated Concrete Building Projects in China
## 1 Introduction
First, there is a lack of implementing mathematical models in the comprehensive investigation of relevant delay factors in prefabricated building projects, especially in the Chinese construction industry. Second, there is an urgent need to rank the delay factors and quantify relationships among the factors for prefabricated building projects. Recently, several studies have applied multi-criteria decision making techniques, such as Technique for Order Preference by Similarity, Analytic Hierarchy Process (AHP) and Data Envelopment Analysis, for project risk assessment [23; 24; 25; 26]. Analytic network process (ANP) is a generalisation of AHP, and is capable of making more accurate predictions with better priority calculations in cases of networks with dependent criteria [27]. Clearly, ANP can better provide benefits in calculating criteria priorities and their relations. Meanwhile, decision-making trial and evaluation laboratory (DEMATEL) is a useful method for analysing and quantifying cause-effect relationships. Thus, integrating DEMATEL with ANP can effectively identify the critical attributes of strategy implementation and evaluate weights of the business environment criteria [28]. Consequently, many fields are showing increasing interest in utilising the DEMATEL-ANP method for multi-criteria decision-making. For example, [PERSON] and [PERSON] [29] adopted a DEMATEL-ANP method to find an appropriate solution regarding municipal solid waste management in metropolitan Manila. In 2011, [PERSON] [30] developed the DEMATEL-ANP method to evaluate knowledge management capability of a firm, while the same methodology was also used to select health-care organisations [31]. Moreover, [PERSON] and [PERSON] [32] examined the key factors in determining location selections for wind farms based on the combination method. More recently, the method was also applied in the renewable energy resources selection in Turkey [28]. However, a synthesised risk assessment based on the combination method has not yet been established in construction projects of prefabricated concrete buildings. Thus, this paper adoptsthe DEMATEL-ANP method to quantify cause-and-effect relationships of delay factors and prioritise them in terms of their importance. The assessment measure can be beneficial for decision makers to minimise schedule delay risks in prefabricated concrete building projects.
###### Abstract  
The proliferation of fifth-generation (5G) networks has opened up new opportunities for the deployment of cellular vehicle-to-everything (C-V2X) systems. However, the large-scale implementation of 5G-based C-V2X poses critical challenges requiring thorough investigation and resolution for successful deployment. This paper aims to identify and analyze the key challenges associated with the large-scale deployment of 5G-based C-V2X systems. In addition, we address obstacles and possible contradictions in the C-V2X standards caused by the special requirements. Moreover, we have introduced some quite influential C-V2X projects, which have influenced the widespread adoption of C-V2X technology in recent years. As the primary goal, this survey aims to provide valuable insights and summarize the current state of the field for researchers, industry professionals, and policymakers involved in the advancement of C-V2X. Furthermore, this paper presents relevant standardization aspects and visions for advanced 5G and 6G approaches to address some of the upcoming issues in mid-term timelines.  
cellular V2X; use case; 5G; 6G; cellular positioning; deployments; 3 GPP +
Footnote †: journal: _Sensors_**2023**, 23, 7031. [[https://doi.org/10.3390/s23167031](https://doi.org/10.3390/s23167031)]([https://doi.org/10.3390/s23167031](https://doi.org/10.3390/s23167031))
# Novel Phenotypic Elements of Type IV Collagenopathy Revealed by the _Drosophila_ Model
###### Abstract  
Type IV collagen is proposed to be a key molecule in the evolvement of multicellular animals by forming the architectural unit basement membrane, a specialized form of the extracellular matrix. Functions of the basement membrane include guiding organ regeneration, tissue repair, modulation of cell differentiation, apical-basal polarity identification, cell migration and adhesion, regulation of growth factor signaling gradients, maintenance of tissue architecture and compartmentalization. Type IV collagenopathy is a devastating systemic disease affecting the circulatory, renal and visual systems and the skeletal muscles. It is observed in patients carrying mutations in the _COLAA1_ gene, which codes for the ubiquitous basement membrane component. _Col4a1_ mouse mutants display the human symptoms of type IV collagenopathy. We chose the _Drosophila melanogaster_ model as we recorded dominant, temperature-sensitive mutations in the cognate _col4a1_ gene of the fruit fly and demonstrated phenotypic elements which have not yet been explored in humans or in mouse models. In this paper we show a transition of the Z-discs, normally bordering each sarcomere, to the level of M-discs significantly penetrant in the mutants, uneven distribution of fused mitochondria in the Malpighian tubules of the excretory organ and a loss of sarcomere structure in the visceral muscles in the gut of mutants. Our observations demonstrate the systemic nature of the _col4a1_ mutations in the fruit fly. However, these traits are elements of the type IV collagen-associated pathology and may provide insights into approaches that can alleviate symptoms of the disease.  
type IV collagen; mutation; multisystem disease; _Drosophila_ model 1
## 3 Design and Fabrication of the Prototype FSM
### Control System  
A DSP system (DS1103, dSPACE) was used to control the motion of the mirror in the lateral, axial, and tip-tilt directions. The 16 bit AD/DA converters were used, and the sampling frequency was 20 kHz. The lateral displacement, measured by two eddy current displacement sensors (PU-05, AEC), was feedbacked to the magnetic suspension. Four capacitance type displacement sensors (C23-B, Lion Precision) precisely measure the axial displacements at 90 degree intervals at the same radius on the surface of the dummy mirror. The tip-tilt and axial displacements of the dummy mirror are calculated using the measured signals and these were feedbacked to the controller. The sensor signal noise is reduced using 2 nd-order low-pass filters with a cutoff frequency of 5 kHz. PID controllers are used for the tip-tilt, axial, and lateral movement control. The control parameter  
Figure 8: The test rig using the magnetic suspension mechanism: (**a**) The configuration of the test rig; (**b**) top view of the test rig without the displacement sensors; (**c**) side view of the suspended part including the dummy mirror.  
values were determined experimentally to extend the servo bandwidth as much as possible and enable a stable touch-up.  
The four VCMs and four electromagnets are each connected to independent voltage amplifiers (Output range: \(-28\) to \(+28\) V, DCV-304, Uniel-Denshi). Voltage control is applied in the lateral and axial directions. In order to increase the servo bandwidth, current feedback using current transducers (LA25-NP, LEM Corp., Tokyo, Japan) was applied to control the tip-tilt motion movement.
## 4 Results and Discussions
### Results
#### 4.1.2 Estimation of Computational Time  
Various timings were estimated in relation to feature-extraction and classification for the top performing dataset (i.e., Dataset 15). The mean timings per character w.r.t. feature extraction and dimensionality reduction are compiled in Table 13.  
Referring to Table 13, the net time required was 4.27 msec. for the construction of a hybrid feature-vector from an input image. The timings related to character-classification through a feature-vector are summarized in Table 14.  
Referring to Tables 13 and 14, the proposed model took total mean time of 10.65 ms and 21.54 msec. in recognition of a test character from the image using MLP and SVM classifiers, respectively.
## 4 System Architecture
### Cyclic-Prefix (CP)  
The cyclic-prefix (CP) is important in the OFDM system to mitigate inter-symbol interference. The length of CP is determined by the environment channel's symbol length and time delay. The cyclic prefix is generated by copying the bottom part of the OFDM symbol to the top of the OFDM symbol. Figure 2 shows the creation and insertion cyclic-prefix to the OFDM symbol.
# GDPR and Large Language Models: Technical and Legal Obstacles
## 1 Introduction
### Right to Erasure and LLMs  
Article 17 [8] of the GDPR, known as the \"Right to Erasure\" or \"Right to be Forgotten\", allows individuals to request the deletion of their personal data under specific conditions [8]. This right is intended to give individuals control over their personal information and to ensure that outdated or irrelevant data is removed from processing systems [4].  
Implementing the Right to Erasure in the context of LLMs is particularly challenging. Once trained, the model parameters encapsulate the information learned during training, making it difficult to remove specific data points without retraining the entire model. In addition, the aggregation of data across numerous sources complicates the identification and extraction of individual data points [31; 37]. Potential solutions include developing machine unlearning techniques that enable models to \"forget\" specific data without complete retraining, retraining the model from scratch while excluding the data subject's information, or designing modular model architectures where specific data segments can be isolated and removed [31; 56]. However, these approaches remain in early stages and require further research to be practical for large-scale LLMs [41; 42].