esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Moving Target Defense: Intelligently Affordable, Optimized and Self-Adaptive
## 3 Intelligent MTD Techniques
### Intelligent MTD Techniques
#### 3.2.2 Towards Optimized
[66] proposed a multi-objective reinforcement learning algorithm to minimize the attack surface of the system, mainly including the components, interfaces, and communication channels of the system. The configuration diversity was obtained by solving the optimal policy. They designed a system to generate a multi-objective Markov decision process, used three different multi-objective reinforcement learning algorithms to learn a set of optimal policies, and compared the resultant benefits, concluding that the multi-objective time-difference post-state algorithm could obtain optimal solutions. [PERSON] et al. [65] mentioned that in a multi-stage MTD game, both the attacker and the defender can use reinforcement feedback learning to attack through the risk assessment of the attack, system configuration, and adjustment of the system configuration leading to a shift in the attack surface, thus continuing to influence the risk assessment results and reaching a cycle until equilibrium. The point of reaching equilibrium is that neither the attacker nor the defender can further enhance their gains by picking other actions. This iterative convergence process from evaluation to policy adjustment is called generalized policy iteration and is a common policy solution in reinforcement learning dynamic planning. As can be seen, the above study has not yet considered the order of play between the defender and the network adversary and is simply understood as a general simultaneous play situation. However, this is inconsistent with most actual cyber-attack and defense situations. Usually, leader-follower games are the common way of cyber-attack and defense games. In addition, the author's dismissal of incomplete information about rational adversaries makes the whole theory out of practice, but we must admit that all practice requires the continuous evolution of the theory to be successful. [PERSON] et al. [60] proposed a multi-intelligent partially observable MTD Markov decision process model and formulated a two-person general sum game between the adversary and the defender. Based on the established adaptive MTD model, the authors proposed a multi-intelligent reinforcement learning framework based on a dual-prophecy machine algorithm to guarantee the convergence of strategies for both attackers and defenders to solve the game problem. [PERSON] et al. [61] proposed to model MTD as a leader-follower game between a multi-intelligent defender and a networked adversary by designing a Bayesian Stackelberg Markov game model that could model the uncertainty of attacker types and the nuances of the MTD system. On the algorithmic side, the authors applied the Bayesian Strong Stackelberg Q-learning method: an algorithm that learns the best movement strategy for the BSMG in a reasonable amount of time through interactions. We can learn that the latest intelligent MTD solutions have converged the best strategies in incomplete information.
## 3 Analysis and Results
### Qualitative Analysis
#### 3.2.1 The Content of Work and Time Spent on BIM Usage  
To understand the content of using BIM, the researchers collected information on the tasks involved in the construction phase BIM participation and the time spent on BIM. These tasks were detailed into additional detailed modeling, constructability review, process management, construction cost management, and site management. A total of 379 work items were collected from Project 1, and 410 work items were collected from Project 2 (Table 7).  
The results show that, in terms of the content of BIM work, Project 1 had a higher number of additional detailed modeling tasks (173 items), accounting for 45.65% of all work, followed by construction cost management (67 items), and the least was process management (41 items). Similarly, in Project 2, the highest number of tasks was related to additional detailed modeling (205 items), accounting for 52.35%, followed by process management (83 items), and the least was constructability review (19 items). In terms of time spent, Project 1 invested the most time in additional detailed modeling (675.5 h), accounting for 46.30% of all work. Process management and site management took the least time, constituting only about 8% of the total work time. Similarly, Project 2 spent the most time on additional detailed modeling (860.2 h), accounting for 52.35% of all work. The least time was spent on constructability review (56 h), representing only 3.41% of the total work time.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Project** & **Work Item** & **Count** & **Percentage (\%)** & **Time** & **Percentage (\%)** \\ \hline \multirow{8}{*}{Project 1} & Additional Detailed Modeling & 173 & 45.65 & 675.5 & 46.30 \\  & Constructability Review & 50 & 13.19 & 173.5 & 11.89 \\  & Process Management & 41 & 10.82 & 127.5 & 8.74 \\  & Construction Costs Management & 67 & 17.68 & 365.5 & 25.05 \\  & On-Site Management & 48 & 12.66 & 117 & 8.02 \\ \cline{2-6}  & Total & 379 & 100.00 & 1459.00 & 100.00 \\ \hline \multirow{8}{*}{Project 2} & Additional Detailed Modeling & 205 & 50.00 & 860.2 & 52.35 \\  & Constructability Review & 19 & 4.63 & 56.0 & 3.41 \\  & Process Management & 83 & 20.24 & 353.4 & 21.51 \\ \cline{1-1}  & Construction Costs Management & 55 & 13.41 & 243.0 & 14.79 \\ \cline{1-1}  & On-Site Management & 48 & 11.71 & 130.5 & 7.94 \\ \cline{1-1} \cline{2-6}  & Total & 410 & 100.00 & 1643.10 & 100.00 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The content of work and time spent on BIM usage.  
Therefore, it can be observed that in the specific tasks of BIM applications in the construction phase of railway projects, the main focus is on additional detailed modeling, which nearly constitutes half of all BIM work and is also the most time-consuming part of the work.
## 5 Numerical Results and Discussion
### Identifying the Impact of Effective Velocity Duration on Damage Degree of Hydraulic Tunnels  
It is critical to have an estimated damage index to completely analyze the degree of damage to both the local and the global linings when determining the spatial variation effect, as shown in Figure 10. Based on the work of [PERSON] et al. [36], the detailed lining local tensile damage index (LLTDI) and the global tensile damage index (LGTDI) of the concrete lining are written as follows:  
\[LLTDI_{V1}=\left(\frac{v_{i}}{V_{i}}\right) \tag{8}\]  
\[LGTDI=\frac{\sum\limits_{i=1}^{n}{LLTDI_{V1}E_{i}}}{\sum\limits_{i=1}^{n}{E_{ i}}} \tag{9}\]  
where \(v_{i}\) is the damage cracking volume of crucial section \(i\) of the concrete lining; \(V_{i}\) is the volume of crucial section \(i\); \(E_{i}\) is the element damage dissipation energy in the cracked element of crucial section \(i\), and n is the number of crucial sections \(i\). Based on the valuation of the degree of structural damage and lifeline performance of America [43], the damage level of the tunnel is divided into criteria, which are generally considered as follows: (1) DS1 (LGTDI = 0-0.09), the tunnel structure is in an elastic state and no damage has occurred; (2) DS2 (LGTDI = 0.09-0.15), the tunnel shows slight tensile damage; (3) DS3 (LGTDI = 0.15-0.5), the tunnel has moderate damage as the lining shows obvious cracks; (4) DS4 (LGTDI = 0.5-0.85), the tunnel shows severe tensile damage and the lining structure shows a large number of cracks and a large amount of dripping or spraying water along the cracks or weak structural surfaces, requiring the repair of the lining as a whole; (5) DS5 (LGTDI = 0.85-1), the lining is completely damaged and loses its functionality.  
The connection between the degree of damage to the critical section and the overall section of the concrete lining and the various EVDs is demonstrated in Figure 11. Furthermore, the EVDs classified as 5% to 95% exhibit a significant correlation with the degree of damage to critical parts. At the same time, the other two fixed threshold values show a slight correlation. Nevertheless, as the EVD of the NFGM increases, the degree of damage of crucial sections still presents a gradually increasing trend. At the same time, it can be inferred that the limit range of the fixed threshold value may significantly affect the relationship between the EVD and the damage degree of the crucial sections. As shown in Figure 11d, the LGTDI for most of the concrete lining is between 0.09 and 0.5, and the lining is slightly or moderately damaged due to the limitations of the seismic intensity. Obviously, the maximum value of the LGTDI is 0.89 for the severely damaged lining, which has the longest effective velocity duration for the seismic input.  
Figure 9: The cumulative damage energy dissipation of the concrete lining is subjected to the different duration of NFGMs.  
Figure 11: The LLTDI and LGTDI for concrete lining under NFGM with different EVDs. (**a**) arch section (**b**) sidewall section (**c**) bottom section (**d**) overall.  
Figure 10: Schematic diagram of the damage volume [36].
## 4 Findings and Discussion
### Implications to Virtual Commerce Application Design  
Researchers can leverage the findings summarized in Tables 8 and 9 to establish some generic design guidelines for virtual commerce applications. A guideline can consist of the following steps as a reference.  
First, business objectives identification. The high-level objectives can be firstly identified among six dimensions of the influential factors in Figure 3: technology attitude, brand attitude, product attitudes, enjoyment, trust, and user satisfaction. With the high level objectives, more concrete objectives can be derived following the models in Figures 4-9 from the outcomes all the way down to influential factors. This can help narrow the search space for applicable design decisions.  
Second, design requirements analysis. The design artifact categories in Table 8 can be mapped to five types of design requirements: forms of immersive technology, interface, performance, intelligence, and function, which are listed in Table 10. Based on this categorization, researchers should analyze the pairwise relations of design requirements and business objectives. This can be achieved by the mappings of impacted final outcomes,influential factors, and design artifacts categories in Table 9. For example, the goal of user trust can be converted to the interface and function requirements. The interface requirement consists of all design artifacts except for accessibility in the client design category in Table 9, and the function requirement consists of all design artifacts except for navigability in the function design category.  
Finally, design pattern determination. A design pattern is a reusable alternative to some design requirements. After determining design requirements, the design artifacts in Tables 8 and 9 can be treated as some useful design patterns. Moreover, their implementations in the corresponding design papers can be referred to in practices.  
Through these three steps, the applicable design patterns to the business objectives can be recognized, and the most cost-effective and efficient plan to perform the application design can be determined. To illustrate, consider an application to achieve positive product response, i.e., improvement on the dimension of product attitude. Then, Table 7 suggests that product attitude can be achieved by an authentic, informative, and vivid 3D display. Therefore, forms of immersive technology and function are the most prominent requirements, and by referring to Table 9, the appropriate design patterns can be AR-based realistic product rendering and AR-based information presentation.  
In the future, researchers can implement application prototypes for these design patterns, which should be confronted with customers in virtual commerce, to empirically evaluate their effectiveness and obtain first-hand understanding of their priority, importance, implication to the high level design objectives, and so on.
# A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence
## 2 Overview of Healthcare Applications
### mHealth
Ultra-portable electrocardiogram patches (Zio Patch by iRhythm Technologies, San Francisco, CA, USA) that can last up to 14 days without recharging and can, in some scenarios, provide better diagnostic yield than Holter monitoring;
5. Sleep apnea screening can be undertaken with wrist-worn reflective photoplethysmography, which has a promising correlation to standard polysomnography;
6. Artificial intelligence algorithms can be used in conjunction with the data collected from wearables, automatically detecting multiple conditions, without the need for manual interpretation. In [31], we are presented with an attempt to create a model with the exclusive purpose of predicting psychotic relapses in conditions such as schizophrenia spectrum disorders. The data required for this work were obtained from a system composed of an Android application and a cloud-based platform. An encoder-decoder neural network was used to provide anomaly detection based on the collected passive sensing data. Similarly to the previous table, in Table 2 we are presented with mHealth implementations, their results and the technologies used. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors** & **Work Description** & **Results** & **Technologies Employed** \\ \hline \multirow{4}{*}{[29]} & Emotional state prediction & Personalized models; data collected through eB2 & \\  & through machine learning & MindCare; 943 users selected; limitations of the & Artificial \\  & techniques & study include missing observations and & intelligence/machine learning, \\  & techniques & sporadically reported emotional states & smartphones \\  & & information. & \\ \hline \multirow{4}{*}{[30]} & Cardiac monitoring system & Review of real-world use of arrhythmia and & Artificial intelligence, remote \\  & based on smart wearables & other cardiovascular devices & patient monitoring, wearables, \\ \cline{2-4}  & Predicting psychotic relapse & Better prediction of anomalies in patients with & Artificial \\  & in patients with schizophrenia & SSDs; 20,137 days of data collected through & intelligence/machine learning, \\  & spectrum disorders (SSDs) & CrossCheck study; anticipated challenges during & smartphones, Android \\  & deployment. & & application CrossCheck \\ \hline \multirow{4}{*}{[32]} & Signal quality assessment & Signal quality classification with good accuracy; & \\  & algorithm to classify the signal & challenges: signal quality misjudgment, most & \\  & quality of ECG and & SQAs were not conducted by daily life use of & Wearable device (SensEco) \\ \cline{1-1}  & respiratory & wearable devices, best methods are supervised & \\ \cline{1-1}  & & ML models. & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: mHealth applications.
# Digital 3D Technologies for Humanities Research and Education: An Overview
## 2 Related Work
### Current State of Empirical Research on 3D Technologies in the Humanities  
#### 2.2.1 Research on Structures and Topics  
Various attempts have been made during the last decades to evaluate, quantify, and qualify the usage of digital 3D modelling for particular fields of humanities by using social empirical methods. Most of these approaches focus on a qualitative analysis, e.g., by expert boards or surveys. The EPOCH network of excellence (2004-2008) employed focus group discussions and perspectives on digital 3D techniques in cultural heritage studies [5]. While qualitative approaches are appropriate to identify and explain [39] phenomena in terms of evolutions, current states, and perspectives, they show only limited usefulness for quantifying uncovered phenomena or investigating scientific structures. The VIA project organized a series of workshops and questionnaire-based surveys to investigate a situation specifically on visualization in archaeology in the UK [40]. From 2012, the Enumerate project has performed bi-annual monitoring of digitization activities of cultural heritage institutions within the EU area--primarily focusing on museums and archives [41; 42]. Vice versa, the DARIAH DIMPO is periodically monitoring the digital humanities community [43]. Recently, several monitoring actions were conducted by the European Commission (c.f. [44; 45]), e.g., to investigate a state of digital competency in cultural institutions [46]. Against the background of the COVID pandemic, several associations surveyed the consequences for cultural institutions and their digital transition (c.f. e.g., [47], an overview: [48]). Research regarding scholarly behavior often relies on analyzing the publication record. With regards to a scholarly area of visual digital humanities and its adjacent fields like digital heritage, Hicks et al. [49] stated that publication and research habits are widely spread between single disciplines in the (digital) humanities. Similarly, Leydesdorff et al. [50] examined the disciplinary canon in humanities and digital humanities by employing bibliometric methods. With regards to a scholarly community within the digital humanities, Terras [51] reported that until 2006, especially US-, Canada-, and UK-based researchers contributed to academic discourse. Similarly, Grandjean performed a social network analysis of Twitter to map the digital humanities community [52]. Specifically for digital heritage, Scollar [53] investigated the Conference on Computer Application in Archaeologies (CAA) from 1973 until 1996. Secondly, information habits of visual digital humanities scholars are the focus of various studies. Since older investigations found large differences in information behavior between scholars in different disciplines [54], nowadays, many scholars in art history and architecture rely heavily on digital information and perform visual search strategies [55; 56].
[PERSON] and [PERSON] [13] applied combined L1 and L2 penalties to feature selection in large datasets, demonstrating significant improvements in the obtained classification performance, which has applications in financial dataset analysis. Thus, compressed sensing (CS) and the LASSO algorithm share the use of sparsity and L1 penalties to select a small subset of significant features from the given data. While CS focuses on reconstructing a signal from incomplete data [5], the LASSO algorithm focuses on feature selection in a predictive modelling context (regression or classification) [4; 14]. In this work, various feature selection methods will be analyzed, and we will introduce techniques that are commonly used in compressed sensing but which can also be adapted for feature selection in classification problems, especially when the number of features is very large and the number of examples is relatively small. On the other hand, we will not perform an extensive comparative analysis with other studies using the same datasets, as the main objective was to identify the most effective feature selection method, not to compare classification performance. Although there are studies that have used the same datasets, our focus was solely on analyzing the studied methods' performance in selecting the most relevant features for the given context. A comprehensive comparative analysis of our classification results with those of other studies would have diverted attention from the primary goals of our research. Therefore, we opted for a narrower focus on feature selection methods, ensuring a deeper analysis of the selected methods, and did not extend the analysis to compare our classification methods with those reported in other works, a task that would involve additional complexity related to the classification methods that were used. This paper is structured into six sections. Section 2, titled Background, presents the mathematical foundations underlying the feature selection methods inspired by compressed sensing, explaining how mathematical concepts from sparse signal recovery can be applied to feature selection and identifying the most representative features of a dataset. Section 3 describes the datasets used and the methods applied in this study, which is followed by the presentation of the obtained results in Section 4. Section 5 is dedicated to discussion, and Section 6 presents the general conclusions of this study.
## 3 Field Quantization
### Linearized Quantization
#### 3.2.4 Special Case  
As discussed above, the Hamiltonian \(\mathbb{H}_{3}\) can be made identically zero [56; 57; 82; 83] to access the quadratic interaction terms \(\mathbb{H}_{4}\) directly. There is an interesting condition on the ratio of optical to mechanical frequencies, which could be sought here. Let  
\[\omega=\sqrt{\eta R}\Omega, \tag{59}\]  
in which \(\eta\) is a constant to be determined later. This allows the \(\mathbb{H}_{4}\) to be written as  
\[\mathbb{H}_{4}=2\hbar\beta\biggl{[}-\frac{1}{\eta}\mathfrak{P}^{2}\mathbb{Q}^ {2}+\mathfrak{x}^{2}\Bigl{(}\mathbb{P}^{2}+\mathbb{Q}^{2}\Bigr{)}\biggr{]}= \hbar\beta\biggl{[}\frac{1}{2\eta}\Bigl{(}\hat{b}^{\dagger}-\hat{b}\Bigr{)}^ {2}\Bigl{(}\hat{a}^{\dagger}+\hat{a}\Bigr{)}^{2}+\Bigl{(}\hat{b}^{\dagger}+ \hat{b}\Bigr{)}^{2}\Bigl{(}\hat{a}^{\dagger}\hat{a}+\hat{a}\hat{a}^{\dagger} \Bigr{)}\biggr{]}. \tag{60}\]  
Further expansion of results in (Appendix B)  
\[\mathbb{H}_{4,int}=2\hbar\beta|\overline{a}|\biggl{[}\biggl{(}1+\frac{1}{2\eta }\biggr{)}\Bigl{(}\hat{b}^{\dagger^{2}}+\hat{b}^{2}\Bigr{)}+\biggl{(}1-\frac{ 1}{2\eta}\biggr{)}\hat{m}\biggr{]}\times\Bigl{(}\hat{a}^{\dagger}+\hat{a} \Bigr{)}. \tag{61}\]  
Then, for the choice of \(\eta=\frac{1}{2}\), that is \(\omega\cong 0.69\Omega\), one may reach the desired interaction quadratic Hamiltonian, linearized in the electromagnetic operators  
\[\mathbb{H}_{4,int}=\hbar 2J\Bigl{(}\hat{b}^{\dagger^{2}}+\hat{b}^{2}\Bigr{)} \Bigl{(}\hat{a}^{\dagger}+\hat{a}\Bigr{)}, \tag{62}\]  
where the interaction rate is \(J=\lambda=2\beta|\overline{a}|\). When expanded in its four terms and after the replacement \(\hat{c}=\frac{1}{2}\hat{b}^{2}\) (Appendix C), one may immediately recognize the Hamiltonian of the type  
\[\mathbb{H}_{4,int}=\hbar J\Bigl{(}\hat{c}\hat{a}^{\dagger}+\hat{c}^{\dagger} \hat{a}\Bigr{)}+\hbar\lambda\Bigl{(}\hat{c}^{\dagger}\hat{a}^{\dagger}+\hat{c} \hat{a}\Bigr{)}. \tag{63}\]The first parenthesis represents the Hopping or Beam-Splitter term, while the second is normally referred to as the dissipation. Interestingly, the above could have been further linearized in mechanical operators to obtain  
\[\mathbb{H}_{4,int}=i\hbar\mathcal{J}\left(e^{-i\theta}\hat{b}^{\dagger}+e^{i \theta}\hat{b}\right)\left(\hat{a}^{\dagger}-\hat{a}\right)=i\hbar\mathcal{J} \left(e^{i\theta}\hat{b}\hat{a}^{\dagger}-e^{-i\theta}\hat{b}^{\dagger}\hat{a} \right)+i\hbar\mathcal{J}\left(e^{-i\theta}\hat{b}^{\dagger}\hat{a}^{\dagger}-e ^{i\theta}\hat{b}\hat{a}\right). \tag{64}\]  
where \(\mathcal{J}=2J\Big{|}\bar{b}\Big{|}\). This latter form, may find application in non-reciprocal optomechanics [84].