esa-sceva/satcom-chunk-collection huggingface dataset:
# Cybersecurity and Safety Co-Engineering of Cyberphysical Systems--A Comprehensive Survey
## 1 Introduction
In recent research, many proposals for security and safety co-engineering methods have appeared, and some survey articles have reviewed these proposed methods in varied degrees of depth and scopes. [PERSON] et al. [3] surveyed the differences and similarities between safety and security aspects focusing on their dependencies per application domain. [PERSON] et al. [4] conducted a survey of safety and security methods and analyzed methods for industrial control systems. Various safety and security risk assessment methods, categorized according to their application domain, were reviewed by [PERSON] et al. [5]. [PERSON] [6] surveyed existing methods for safety and security requirements engineering in CPSs. A systematic literature review was conducted by [PERSON] et al. [7] that focused on already developed and evaluated methods. [PERSON] et al. [8] provided a short survey, in which five integrated safety and security co-engineering methods were analyzed. Finally, [PERSON] and [PERSON] [2] provided an extended bibliography of research papers on safety and cybersecurity co-engineering since the early 90s without, however, analyzing them. In this paper we revisit previous surveys on cybersecurity and safety co-engineering approaches; we report on the results of a systematic literature survey of such approaches that have not been reviewed before; we define a multi-attribute taxonomy and we use it to analyze such approaches; and we discuss pertinent open issues and research challenges. The overall contribution of this paper is a comprehensive discussion on the recent advances in cybersecurity and safety co-engineering. A summary of the contributions of the paper follows:  
* A comprehensive review of sixty eight methods for cybersecurity and safety co-engineering, of which nine have not been reviewed before. * The development of a multi-attribute taxonomy of cybersecurity and safety co-engineering methods, encompassing inter alia all attributes used in previous surveys. * A discussion on open issues, not fully addressed by existing approaches for cybersecurity and safety co-engineering, that give rise to research challenges. The remainder of this work is structured as follows: Section 2 reviews related work; it is divided into two subsections, one on previous surveys and another on approaches not previously reviewed. In Section 3 we define a multi-attribute taxonomy of cybersecurity and safety co-engineering approaches and we employ it to analyze the reviewed approaches. In Section 4 we discuss the results of the analysis, and we identify issues not fully addressed by existing approaches that give rise to research challenges. Finally, in Section 5 we summarize our conclusions and we outline our future research plans.
## 3 Numerical Simulation
### Material Properties and Contact Settings  
The three-dimensional (3D) finite element model simulates the theory of elastic laminar system and satisfies the following basic assumptions [31]: (1) All layers, except for the soil, are composed of isotropic homogeneous linear elastic materials. (2) The soil layer is infinite in both horizontal and depth directions, whereas the structural layer of the pavement structure is limited in depth and remains infinite in the horizontal direction. (3) Each layer connection is continuous. Because this study is mainly concerned with the stability of the tunnel structure, the airport pavement structure is modeled using the linear elastic material properties considering the foregoing assumptions. The elastic-plastic Mohr-Coulomb failure criterion is used as the soil constitutive model to simulate the nonlinear behavior of soil under aircraft loads with Rayleigh damping and small strain damping based on the Rayleigh damping equation:  
\[[C]=\alpha[M]+\beta[K] \tag{8}\]  
Figure 6: A schematic of the mesh division.  
where \([M]\) is the mass matrix, and \([K]\) is the stiffness matrix. The Rayleigh damping factors, \(a\) and \(\beta\), can be calculated using the following equations:  
\[\alpha=\zeta\frac{2\overline{\omega_{1}}\overline{\omega_{2}}}{\overline{\omega_{ 1}}+\overline{\omega_{2}}} \tag{9}\]  
\[\beta=\zeta\frac{2}{\overline{\omega_{1}}+\overline{\omega_{2}}} \tag{10}\]  
where \(\zeta\) is the damping ratio, and \(\overline{\omega}_{1}\) and \(\overline{\omega}_{2}\) are the first and second intrinsic frequencies of the system, respectively. The main material properties of the numerical model selected according to the MH/T 5004-2010 specification for airport cement concrete pavement design and MH/T 5010-2017 specification for asphalt pavement design of civil airports [29,32] are listed in Table 1.  
Since this study focuses on the dynamic response of the tunnel structure, the contact between the soil layers is simplified to be fully contacted, and the excavation layer model is divided by direct cutting. The interaction between the soil and tunnel structure has been considered in modeling. Hard contact is set as the normal contact, and the \"penalty function\" is set as the tangential contact between the two; the friction coefficient used is 0.577 [33].
## 3 Dependable Wireless System  
### System Configuration  
Figure 1 shows the proposed dependable wireless system. The terminal is an integrated terminal with multiple RATs such as cellular, UWB, Bluetooth, wireless local area network (LAN), and so on. Integrated terminals are commonplace because many cellular phones have cellular, Bluetooth, wireless LAN, and the iPhone 11 and 12 already have UWB chips, U1 chips, and many future cellular phones will have UWB chips. UWB is a technology which enables both communication and ranging [25]. Integrated terminals are equipped with an FEC coder-decoder (CODEC), and the encoded information is divided and communicated using multiple RATs simultaneously. Even if some RATs are attacked, the transmitted information can be recovered using encoded information received by other RATs.  
Even if some RATs are wiretapped, the transmitted information cannot be recovered using less encoded information. Thus, the proposed system is a realistic system that can be implemented with cellular phones to improve the information-theoretic security capability against wiretapping and attacks.
# Analyzing the Performance of the S3 Object Storage API for HPC Workloads
## 2 Materials and Methods
### Utilizing S3 APIs in Data Centers  
Data centers traditionally utilize parallel file systems such as Lustre [27] and GPFS. These storage servers do not natively support the S3 API. In a first iteration, we explore MinIO to provide S3 compatibility on top of the existing infrastructure. MinIO offers various modes, one of which is a gateway mode providing a natural deployment mode: in this mode, any a S3 request is converted to POSIX requests for the shared file system. We show the effect of the object size for the different MinIO modes, and we compare the obtained results to the native REST protocol and Lustre. To achieve the convergence between HPC and the Cloud, it needs to be possible to move workloads seamlessly between both worlds. We aim to allow the huge number of S3 compatible applications, the possibility to benefit from HPC performance while using the same compatibility offered by the S3 interface. Therefore, we create an I/O library called S3 Embedded based on libs3, where parts of the S3 stack were replaced or removed to optimize the performance, this library also performs the translation between S3 and POSIX inside the application address space. Our aim is to make it compatible with standard services, competitive with the HPC I/O protocols, and optimized for use in distributed environments. Additionally, the extended library S3 remote is introduced, it is intended to provide a local gateway on each node--an independent process similar to MinIO--but uses a binary protocol over TCP/IP for local communication instead of HTTP as HTTP might be the cause of the observed performance issues. In Section 4, we assess the performance of S3 Embedded/S3 Remote inside HPC.
## 4 Conclusions  
In the present work, the efficiency of PLE extraction for the recovery of phytochemicals from artichoke by-products was studied. The proposed extraction system by PLE obtained 9 extracts under different extraction conditions delimited by the technical limits of the PLE extractor. The analytical characterization of these extracts allowed the detection of 23 compounds, most of them were phenolic and other polar bioactive compounds previously identified in artichoke samples. The individual phenolic compounds recovery through the different PLE extraction conditions was estimated by considering the peak area of each compound in the chromatogram (three replicates), which permits a comparison of the same compound between extracts. This information could establish the best extraction condition for each compound or family. The best PLE parameters, for both phenolic acids and flavonoids, consist in high percentages of ethanol and medium-high temperatures. In addition, it should be pointed into light that at the same temperature, the recovery of individual phenolic compounds is improved when the solvent composition provides the lowest value of dielectric constant.  
**Supplementary Materials:** The following are available online at [[https://www.mdpi.com/article/10](https://www.mdpi.com/article/10)]([https://www.mdpi.com/article/10](https://www.mdpi.com/article/10)) 3390/app11003788/s1, Table S1: Peak areas of the identified compounds in _C. scolymus_ pressurized liquid extraction (PLE), extracts expressed as mean \(\pm\) standard deviation of the three analyses replicates. Table S2: Statistical data of the PLE extraction conditions for individual phenolic compounds.  
**Author Contributions:** Conceptualization, J.L.-S. and I.B.-L.; methodology, J.L.-S. and R.G.-M.; software, L.L.-S.; validation, [PERSON]-S., [PERSON] and [PERSON]-[PERSON]; formal analysis, L.L.-S.; investigation, L.L.-S., P.G.-G. and D.Q.; data curation, L.L.-S.; writing--original draft preparation, L.L.-S; writing--review and editing, [PERSON]-S. and [PERSON]-L.; visualization, [PERSON]-M.; supervision, [PERSON]-S., [PERSON]-[PERSON] and [PERSON]-M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All the data generated by this research have been included in the article. For any assistance it is possible to contact with the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Results
### Performance Analysis Based on Clustering Algorithms  
In experiment 1, various unsupervised approaches are compared by employing popular clustering models such as k-means, k-medoids, and random. The technique is built on clustering, in which customers with similar perspectives on the business are grouped to allow for churn or non-churn prediction categorization. The performance of clustering techniques is shown in the form of a bar graph in Figure 2. The x-axis indicates the clustering method and the y-axis reflects the performance values including accuracy, recall, precision, and f-measure achieved by a different method. Figure 2a represents the performance of the Orange dataset while Figure 2b represents the performance of clustering techniques for the Cell2 Cell dataset. The bar graph shows k-medoids technique reach 75.44% and 75.56% accuracies for the Orange and Cell2 Cell dataset, respectively. However, through this comparison, we can see that the k-medoids method outperforms other methods.
## 4 Results
### Fracture Development Characteristics
#### 4.3.2 Microscopic Fracture Characteristics  
At the microscopic level, the development of each lithologic fracture is different from that of the macroscopic fracture. As shown in Figure 7, intergranular fractures and suture lines of felsic shale are developed, with good connectivity and fracture widths ranging from 10 to 200 nm, which are good seepage channels. The fractures developed in the dolomitic felsic shale are mainly particle edge fractures, which communicate with the dissolution pores, and the suture lines are relatively developed, with widths ranging from 30 nm to 300 nm. The length span of the fractures is large, with the short fractures only 500 nm and the long fractures up to 18 \(\upmu\)m. The fracture types of clay-bearing felsic shale are mainly clay mineral shrinkage fractures, and well-developed fractures are occasionally seen. The width of fractures is between 10 and 200 nm, and the fractures are relatively isolated with few communication pores. The fracture connectivity is the worst among the four lithologies. The structural fractures of siltstone interlayers often develop along the edge of mineral particles, and the dissolution pores are mainly communicated through the mineral edge fractures, with the width of the fractures between 20 and 200 nm. The fractures have good connectivity.  
Figure 6: Statistical chart of core fracture density.
## 5 Outlook
### Methods and Applications  
Only abrupt land cover changes were detectable with early bi-temporal change detection methods and binary change maps were the only products, as interannual changes were not the main concern. In addition, change detection implementation constituted an independent stage after classification, when comparing land cover classes at different times. Today, different change targets are detectable, resulting from abrupt changes such as natural disasters or gradual changes such as shifts in vegetation phenology. New studies tend to use more data than before and in general, there are an increasing number of studies that used all available data. These studies implemented methods that enabled the detection of changes in near real-time. Although five studies in our database used neural networks, a few studies applied deep learning techniques for Landsat change detection. [PERSON] et al.  
(2018) used RNN-based transfer-learning to detect annual urban changes from 1984 to 2016 [69]. The feature representations learned from a few training samples were directly transferred to new target images by combining transfer learning and RNN. The lack of long-term training data, especially in large-scale studies, limited the usage of deep learning techniques. Advanced flexible classifiers have been frequently used and time series change metrics have become an input for the classification of types of land cover changes. The sustainability of Landsat data resulted in the inclusion of Landsat change detection products in many international agreements and plans. A vast majority of applications using Landsat change detection and the results have become a reference point for comparison with products from other sensors or non-remote sensing methods. With the availability of cloud computing platforms, large-scale global products have been increasingly available. The current availability of different data sources caused that recent studies mostly focus on specific applications and the development of new methods for these applications.
# Incorporating Synonym for Lexical Sememe Prediction: An Attention-Based Model
## 3 Methodology
### Score Sememes from Synonyms  
In the study, words with similar semantics are grouped into the same set, which we refer to here as synonym set, \(\mathrm{T}=\left\{\mathrm{w_{1}},\mathrm{w_{2}},\ldots,\mathrm{w_{i}},\ldots, \mathrm{w_{j}},\ldots,\mathrm{w_{n}}\right\}\), where \(\mathrm{w_{i}}\) denotes a word. Any two words, \(w_{i}\) and \(w_{j}\), in the same synonym set are synonymous.  
A score function is defined to score all the candidate sememes of unlabeled word \(w\), in which high-scored sememes will be predicted as the sememes of \(w\). For incorporating the knowledge in pre-trained word vectors, the distance of words in the pre-trained vector space is employed in the function. The function, using synonyms, can be formulated as Equation (1):  
\[Score_{SPS}\big{(}\mathrm{s_{j}},\ w\big{)}=\sum_{w_{i}\in\mathrm{T}}\cos(w,\ w_{i}) \cdot M_{ij}\cdot c^{r_{i}} \tag{1}\]  
where \(M\) is the matrix representing the relationship between words and sememes, and can be calculated as  
\[M_{ij}=\left\{\begin{array}{ll}1\ s_{j}\ \text{is sememe of}\ w_{i}\\ 0\  
named ASPSW (Attention-based Sememe Prediction combining Synonym and Word embedding), is derived, and its score function can be calculated as Equation (4):  
\[Score_{ASPSW}\big{(}s_{j},w\big{)}=Attn\big{(}s_{j},w,W\big{)}\ =\ \sum_{W_{i}\in Y}^{n}a_{i}^{Attn} Score\big{(}s_{j},w\big{)} \tag{4}\]  
where \(a_{i}^{Attn}\) denotes the weights of contributions to different knowledge for different sememes in the joint model. The difference can be adjusted according to the distance in the word embedding space. Based on this, the weights of the contributions of different knowledge can be calculated by dynamically adjusting the score weights of the knowledge from Synonym and pre-trained word vector:  
\[a_{i}^{Attn}=\left\{\begin{array}{l}\frac{1}{2-log\big{(}[Sim_{ww}-Sim_{y}] \big{)}}\ \text{if}\ w_{i}\in W\\ \frac{1-log\big{(}[Sim_{ww}-Sim_{y}]\big{)}}{2-log\big{(}[Sim_{ww}-Sim_{y}] \big{)}}\ \text{if}\ w_{i}\in T\end{array}\right. \tag{5}\]  
\[Sim_{ww}=\frac{1}{K}\ \sum_{w_{i}\in W}cos(w,\ w_{i}) \tag{6}\]  
\[Sim_{ww}=\frac{1}{N}\ \sum_{w_{i}\in T}cos(w,\ w_{i}) \tag{7}\]  
where \(T\) is the synonym set of word w; \(W\) presents the top \(K\) similar words set of \(w\) in embedding space respectively, where \(K\) is a hyperparameter; \(Sim_{ww}\) and \(Sim_{ww}\) represent the average semantic similarity between new words and similar words in word embedding and synonyms, respectively; \(cos(w,\ w_{i})\) is the cosine similarity between \(w\) and \(w_{i}\) according to their embedding vectors.
###### Abstract  
This study investigated the predictive performance of three regression models--Gradient Boosting (GB), Random Forest (RF), and XGBoost--in forecasting mortality due to endocrine, nutritional, and metabolic diseases across Italian provinces. Utilizing a dataset encompassing air pollution metrics and socio-economic indices, the models were trained and tested to evaluate their accuracy and robustness. Performance was assessed using metrics such as coefficient of determination (\(r^{2}\)), mean absolute error (MAE), and root mean squared error (RMSE), revealing that GB outperformed both RF and XGB, offering superior predictive accuracy and model stability (\(r^{2}\) = 0.55, MAE = 0.17, and RMSE = 0.05). To further interpret the results, SHAP (SHapley Additive exPlanations) analysis was applied to the best-performing model to identify the most influential features driving mortality predictions. The analysis highlighted the critical roles of specific pollutants, including benzene and socio-economic factors such as life quality and instruction, in influencing mortality rates. These findings underscore the interplay between environmental and socio-economic determinants in health outcomes and provide actionable insights for policymakers aiming to reduce health disparities and mitigate risk factors. By combining advanced machine learning techniques with explainability tools, this research demonstrates the potential for data-driven approaches to inform public health strategies and promote targeted interventions in the context of complex environmental and social determinants of health.  
remote sensing; machine learning; nutritional disease; endocrine disease; metabolic disease; exposure; explainable artificial intelligence; air pollution; public health; socio-economic indices +
Footnote †: journal:  
Article
## 2 Problem Outlines
### The Objective Function
#### 2.3.8 The Uncertainty Modeling for PV Sources  
The nature of solar power and photovoltaic irradiance can be represented by various types of distribution functions. One of the most efficient types for this purpose is the lognormal probability distribution function (PDF), which effectively and precisely characterizes the intensity of irradiance on a typical day. The PDF of solar irradiance (\(\zeta_{\rm s}\)) trailing the lognormal distribution function with a mean value of \(\mu_{\rm s}\) and standard deviation of \(\sigma_{\rm s}\) that is presented in Equation (20).  
\[f\Big{(}\zeta_{\rm s}\Big{|}\mu_{s},\sigma_{\rm s}^{2}\Big{)}=\frac{1}{\sigma_{ \rm s}^{2}\sqrt{2\pi}}\exp\left\{-\left(\frac{(\log(\zeta_{\rm s})-\mu_{\rm s} )^{2}}{2\sigma_{\rm s}^{2}}\right)\right\}\hskip 28.452756 pt\forall\zeta_{\rm s}>0 \tag{20}\]  
The solar power generation (\(P_{PV}\)) depends on the solar radiation intensity, also known as solar irradiance, which is shown by \(\zeta_{\rm s}\). The generation curve can be different based on different items such as the location and type of installation, the technology of solar panels, as well as the ambient temperature. A typical photovoltaic irradiance-power curve is given in Equation (21).  
\[P_{PV}(\zeta_{\rm s})=A_{PV}\eta_{pv}\zeta_{\rm s} \tag{21}\]  
\[\eta_{pv}=\eta_{0}[1-0.0042\ (\frac{\zeta_{s}}{18}+T_{a}-20)]\ \eta_{\rm inv} \tag{22}\]  
The amount of power generation by a solar panel depends on different factors, which can be estimated by Equations (21) and (22). Above, \(\eta\) stands for efficiency, and \(T_{a}\) denotes the ambient temperature.  
In order to model the intermittencies, a dataset of historical records associated with the solar radiation. Such irradiance datasets can effectively be matched with a lognormal or Weibull distribution function. These distribution models usually have high mathematical compatibility with various natural phenomena. The PDF helps to generate randomly derived samples within the occurrence range while specifying a confidence range to eliminate low-probable cases. For each scenario, a random value is derived from the lognormal model. If the number of scenarios is high, various scenario reduction techniques can be employed that merge similar cases into one class. Based on the model objectives, the operation state can be adjusted within the confidence range with respect to the level of risk chosen by the operator. The operator can arrange the operation point in a risk-averse zone to ensure a reliable and secure operation, although it incurs higher costs. On the other hand, the operation of the grid, with respect to the whole uncertainties, can be stated in a high-risk zone by ignoring less-probable incidences to boost the profitability and the economy of the model.
## 2 Study Area, Methods, and Data Sources
### Methodology  
The study schema is composed of two parts, i.e., assessing RCU by applying neighborhood analysis and exploring the driving factors by the regression models through OLS and GWR (Figure 2). Much work related to RCU has been detailed in the previous work [18]. Both parts in the workflow are described in detail in this section.