esa-sceva/satcom-chunk-collection huggingface dataset:
# Sillcom: A Communication-Efficient Privacy-Preserving Scheme for Indoor Localization
## 5 Sillcom\({}_{3R}\): Basic Design with Three Online Rounds
### Putting It All Together  
We now present an overall description of \(\text{Sillcom}_{3R}\) in Algorithm 4. First, after receiving the RSS shared fingerprints sent by the user and the DO, the three computing servers perform distance computation, which requires only local computation without RSS reconstruction. Then, through share conversion, the 3P-ASS shared distances from the distance computation are transformed into a format suitable for the secure shuffle algorithm. After two rounds of communication, the distance vector of \(m\) values is randomly shuffled. Finally, the shuffled distance vector undergoes pairwise comparisons based on FSS, and the sorting result is revealed in one round of communication to obtain the top-\(k\) values. The corresponding coordinates are then averaged to determine the user's location.  
```
0:RSS shared fingerprints \(F_{i}\) of \(RP_{i}\) and \(F_{C}\) of client, \(i\in[m]\).
0:Client gets location coordinates. Offline preprocessing:
1:\(P_{2}\) prepares DDCF keys, random permutations and random values. Online computing:
1:Servers perform \(\Pi_{SED}\) without RSS reconstruction to get \(m\) 3P-ASS shared distances.
2:Servers perform \(\Pi_{SC}\) to convert 3P-ASS shared distance vector into 2P-ASS shared.
3:Servers perform \(\Pi_{Shuffle}\) to randomly shuffle distance vector.
4:Servers perform \(\Pi_{rank}\) to get top-k RPs and get their average coordinates as client's indoor location.
```  
**Algorithm 4** Three online round \(\text{Sillcom}_{3R}\)
# Interference Alignment for Cognitive Radio Communications and Networks: A Survey
## 6 Open Research Challenges
### Algorithms Optimization of IA in CR Networks  
Algorithms optimization remains a hot topic for IA CR networks research as a number of features can be incorporated or further improved such as complexity, low SNR performance, CSS data fusion computation and robustness to CSI imperfections [53; 67; 110]. Emphasis on developing algorithms that will not only achieve IA by progressively reducing LIF at each receiver while keeping an allowable interference level to the PU, but will also be focusing on closer practical implementation in CR networks.
# AI Governance in Higher Education: Case Studies of Guidance at Big Ten Universities
## 2 Background  
### Responsible AI Governance  
As AI technology continues to advance and to be widely adopted, the governance of its development and implementation has become a key issue that is widely concerned and discussed by various stakeholders [14]. AI governance refers to the frameworks, policies, and practices designed to ensure that AI systems are developed, deployed, and managed in a way that aligns with ethical principles, legal requirements, and societal values. It encompasses the establishment of guidelines for responsible AI use, mechanisms for accountability, and processes for managing risks associated with AI technologies [15; 16; 17]. Organizations establish ethical guidelines for AI usage by engaging in a multifaceted process that involves the identification of core ethical principles, the development of practical frameworks, and the implementation of governance models that ensure adherence to these guidelines throughout the AI system's lifecycle [18; 19]. The process often begins with the recognition of the need for ethical AI principles that sustain human values and rights [20], acknowledging the complex and opaque nature of AI systems and their potential to impact fairness, accountability, and transparency [21; 22]. This recognition is supported by international and organizational efforts to publish principles of ethical AI, which aim to outline values and abstract requirements for AI development and deployment [23]. However, the effectiveness of these principles is contingent upon their translation into measurable and actionable guidelines that can be practically applied [24].  
To address the gap between ethical principles and their application, organizations are adopting frameworks like the hourglass model of organizational AI governance. This model emphasizes the need for governance at environmental, organizational, and AI system levels, connecting ethical principles to the AI system lifecycle to ensure comprehensive governance [25]. These collective efforts underline the importance of a structured approach to AI governance, ensuring that AI technologies are developed and deployed in ways that uphold ethical standards and promote social good [26]. HEIs also make prominent contributions to the process as research efforts are critical in assessing the impact of intelligent applications, minimizing harm, and promoting well-being and social good [27]. In addition, a comprehensive understanding of the impact of disruptive AI technologies on education and the development of frameworks is critical for society to make informed decisions about the use of Generative AI tools [28]. This study aims to bridge the gap between theoretical principles and practical application, offering a detailed analysis of whether and how HEIs implement responsible AI governance frameworks.
# Performance Analysis of a Keyword-Based Trust Management System for Fog Computing
## 3 Methodology
### Performance Evaluation Parameters
#### 3.3.2 Packet Delivery Ratio (PDR)  
During transmission, a certain number of packets are sent out and a certain number were successfully received. In computing a packet reception ratio, the relationship between data received by the top layer of receiving nodes and data transmitted by the top layer of sender nodes is described. For data packet loss modeling in our approach, we employ the well-known Gilbert-Elliott model, which has been around for a long time and has been widely used by the industry. Calculating data packet loss necessitates contrasting the connections between a good and a bad node. After that, the model's parameters are calibrated to match the data. The chance of transitioning from a good state into an undesirable one and the likelihood of migrating from an undesirable condition into a desirable state is estimated as a consequence of the outcomes of experiments conducted in a fog computing environment. The packet delivery ratio (\(PDR\)) is calculated using the formula:  
\[PDR=DR/DR+DL\]
# Mid-Infrared Multispectral Gaseous Stimulated Raman Scattering Laser
## 1 Introduction  
Mid-infrared (mid-IR) laser sources in the range of 2-6 \(\upmu\)m have important applications for atmospheric sounding, the detection of organic gaseous pollutants, biomedical surgery, defense technology, gas detection, and higher harmonics generation technology [1; 2; 3; 4; 5; 6; 7; 8]. The stimulated Raman scattering (SRS) process is an effective method of broadening the laser spectral range and generating new wavelength lasers [9; 10; 11]. It is very convenient to realize the down-conversion of laser frequencies and achieve a mid-IR Raman laser output by the SRS process. By using solid-state materials as Raman gain media, mid-IR lasers have been widely studied and reported [12; 13]. Large gain coefficients and the high thermal conductivity of solid crystals can be beneficial to realizing a high conversion efficiency with mid-IR Raman lasers. However, the development of high-energy Raman laser output is limited by the absorption of crystals in the mid-IR range and the low optical damage threshold. Moreover, the Raman shifts of solid crystals are about 1000 cm\({}^{-1}\), which is not large enough to generate wide-ranging mid-IR Raman lasers. Gas Raman gain media in hollow-core fiber (HCF) have also been demonstrated to generate mid-IR Raman lasers, but the strong loss of silica over wavelengths of 5 \(\upmu\)m limits the generation of silica anti-resonant hollow-core fiber Raman lasers [14; 15; 16; 17; 18]. With a much higher damage threshold, gaseous Raman media in free space can withstand large pump energy. However, it is very hard to generate mid-IR Raman lasers due to the low Raman gain of gaseous media.  
In this work, we reported the generation of mid-IR multispectral gaseous Raman lasers in free space by using a mixed gas of hydrogen (H\({}_{2}\)) and deuterium (D\({}_{2}\)) as the Raman gainmedium. The four-wave mixing (FWM) process contributed to dramatically reducing the threshold of mid-IR Raman laser generation. A 4432 nm Raman laser with a peak power of 121 kW was obtained, and multispectral mid-IR Raman lasers at wavelengths of 2071 nm, 2266 nm, 2604 nm, 2920 nm, 3322 nm, 3743 nm, 4432 nm, and 5431 nm were also generated.
# Investigating the Influence of Feature Sources for Malicious Website Detection
###### Abstract  
Malicious websites in general, and phishing websites in particular, attempt to mimic legitimate websites in order to trick users into trusting them. These websites, often a primary method for credential collection, pose a severe threat to large enterprises. Credential collection enables malicious actors to infiltrate enterprise systems without triggering the usual alarms. Therefore, there is a vital need to gain deep insights into the statistical features of these websites that enable Machine Learning (ML) models to classify them from their benign counterparts. Our objective in this paper is to provide this necessary investigation, more specifically, our contribution is to observe and evaluate combinations of feature sources that have not been studied in the existing literature--primarily involving embeddings extracted with Transformer-type neural networks. The second contribution is a new dataset for this problem, GAWAIN, constructed in a way that offers other researchers not only access to data, but our whole data acquisition and processing pipeline. The experiments on our new GAWAIN dataset show that the classification problem is much harder than reported in other studies--we are able to obtain around 84% in terms of test accuracy. For individual feature contributions, the most relevant ones are coming from URL embeddings, indicating that this additional step in the processing pipeline is needed in order to improve predictions. A surprising outcome of the investigation is lack of content-related features (HTML, JavaScript) from the top-10 list. When comparing the prediction outcomes between models trained on commonly used features in the literature versus embedding-related features, the gain with embeddings is slightly above 1% in terms of test accuracy. However, we argue that even this somewhat small increase can play a significant role in detecting malicious websites, and thus these types of feature categories are worth investigating further.  
malicious website detection; phishing; cybersecurity; machine learning; feature extraction Article
# Robust Structural Damage Detection Using Analysis of the CMSE Residual's Sensitivity to Damage
## 1 Introduction
It further raises the possibility that one can assume all \(N_{e}\) (\(N_{e}\geq N_{d}\)) structural elements are damaged in case of omitting any actual damage only if sufficient equations \(\left(N_{i}\times N_{j}\geq N_{e}\right)\) are constructed. Nevertheless, the disadvantages of constructing excessive equations are obvious. On the one hand, the damage identification result by solving a linear system assembled by different equations varies, leading to complicated decisions surrounding the actual damage state of the structure. On the other hand, the redundant equations do not contribute to damage detection but complicate the system, and exacerbate the damage identification result. The main purpose of this study is to address the excessive equations issue faced in the assemblage of the CMSE linear system of equations. From the standpoint of solving a linear system of equations, optimizing the assemblage of the system by eliminating redundant equations serves as an effective tool to improve identification of damage, which has been rarely investigated in previous research. The ITR method is adopted as a tool for solving the linear system of equations given by Equation (1), with some improvements to this method also presented. This study contributes towards supplying a guideline for the elimination of the redundant equations to enhance the robustness of the CMSE system. A sensitivity index that, to some extent, measures how the actual damage level vector satisfies each CMSE equation is derived by performing an analysis of the defined residual's sensitivity to damage. Finally, the numerical and experimental robustness of the proposed damage identification scheme against measurement noise is investigated.
_applied_  
_sciences_  
_Article_  
Experimental Investigation on Innovative Stress Sensors for  
Existing Masonry Structures Monitoring  
[PERSON] 1,\({}^{\star,\star}\)\({}^{\text{\textregistered}}\), [PERSON] 1,\({}^{\text{\textregistered}}\)\({}^{\text{\textregistered}}\), [PERSON] 1,\({}^{\text{\textregistered}}\), [PERSON] 1,\({}^{\text{\textregistered}}\), [PERSON] 2,\({}^{\text{\textregistered}}\), [PERSON] 3,\({}^{\text{\textregistered}}\) and [PERSON] 3,\({}^{\text{\textregistered}}\)  
\({}^{\text{\textregistered}}\) These authors contributed equally to this work.  
**Clication:** [PERSON], [PERSON]; [PERSON], [PERSON]; [PERSON], [PERSON]; [PERSON], [PERSON]; [PERSON], [PERSON]; [PERSON], [PERSON]; [PERSON], [PERSON] Experimental Investigation on  
Innovative Stress Sensors for Existing  
Masorry Structures Monitoring. _Appl. Sci._**2023**, _13_, 3712. [[https://doi.org/10.3390/app13063712](https://doi.org/10.3390/app13063712)]([https://doi.org/10.3390/app13063712](https://doi.org/10.3390/app13063712))  
\({}^{\text{\textregistered}}\) Academic Editor. [PERSON]  
Received: 16 February 2023  
Revised: 10 March 2023  
Accepted: 11 March 2023  
Published: 14 March 2023  
**Copyright:**\(\copy\) 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).  
**Abstract:** Historical masonry structures often suffer gradual deterioration that in many cases can compromise the safety levels and the operating conditions of the buildings. In this context, Structural Health Monitoring (SHM) is an effective tool for the prediction of the structural behaviour and the state of conservation of buildings. Although many monitoring systems have recently been proposed, there is a lack of practical application of low-cost systems. This paper presents an experimental study based on the use of two innovative stress sensors--capacitive stress sensor and ceramic stress sensor--for the monitoring of existing masonry elements. In order to reproduce the actual conditions of onsite masonry, sensors are post-installed in the mortar joints of two series of pre-stressed specimens made of calcarenite stone masonry and clay brick masonry. The best practice of post-installation of the two sensors is investigated. The reliability of the proposed sensors is evaluated through comparison with data recorded from classical measurement devices.  
**Keywords:** SHM, masonry; monitoring; capacitive sensor; piezo-resistive sensor
# A Method to Reduce Eddy Current Loss of Underwater Wireless Power Transmission by Current Control
## 3 Circuit Analysis in Seawater and Methods Proposed
Additionally, from Equations (21) and (22), the eddy current loss of underwater WPT system can be expressed as  
\[P_{ecl}=P_{p_{ecl}+}P_{S_{ecl}}=\int\limits_{V}\sigma(|\mathbf{E}_{P}|^{2}+| \mathbf{E}_{P}|^{2}+2|\mathbf{E}_{P}||\mathbf{E}_{S}|\cos(90^{\circ}+\theta_ {diff}))dV \tag{37}\]  
It can be seen that increasing the phase difference of the electric field intensity on two sides can reduce the eddy current loss when the modulus on the primary side and the secondary side is constant. Since it is assumed that the components in the compensation topology circuit in this paper are ideal components, the resistance on the wire can be ignored because it is much less than that of the coupling coil. As a result, the energy loss of the underwater WPT system can be approximately expressed as  
\[P_{loss}\approx I_{P}^{2}(R_{P}+R_{R_{rel}})^{2}+I_{S}^{2}(R_{S}+R_{S_{ecl}})^ {2}=I_{P}^{2}R_{P_{S}}^{2}+I_{S}^{2}R_{S_{S}}^{2} \tag{38}\]  
Ensuring that the total output of the system remains unchanged, the total power transmitted from the primary side to the secondary side can be obtained as  
\[P_{t}=\omega M_{sea}I_{P}I_{S}=P_{out}+I_{S}^{2}R_{S_{S}}^{2} \tag{39}\]  
In this paper, it is assumed that the ratio of the modulus of the current in the primary and secondary coils is \(\alpha\), and thus we can obtain  
Figure 8:0  
Figure 8:1  
The energy transmission efficiency of the system can be expressed as  
Figure 8:2  
For a two-coil system with symmetrical distribution, the equivalent resistance of the primary side coil and the secondary side coil is approximately equal, and so we can obtain  
\[\left\{ \begin{array}{c}\eta=f(\alpha,\tau)=1-\frac{\alpha^{2}+1}{\alpha^{2} +\alpha\tau}\\ \tau=\frac{\omega M_{sa}}{R_{S_{S}}}\end{array} \right. \tag{43}\]The introduced coefficient \(\tau\) is defined as the mutual inductance quality factor. When the parameters of the coils and working environment are determined, \(\tau\) is a constant, and the following equations can be listed:  
\[\left\{ \begin{array}{l}\frac{\partial\eta}{\partial t}=0\\ \frac{\partial^{2}\eta}{\partial a^{2}}<0\end{array} ,\alpha>0\right. \tag{44}\]  
Additionally, we can obtain  
\[\alpha_{0}=\frac{1+\sqrt{1+\tau^{2}}}{\tau} \tag{45}\]  
Therefore, when the system works under the condition that the coil parameters and environmental parameters remain unchanged, changing the current modulus value of the primary side and the secondary side to make their ratio equal to \(\alpha_{0}\) can reduce the eddy current loss, which improves the energy transmission efficiency of the system. In this section, two methods to reduce the eddy current loss of underwater WPT systems are proposed. This is realized by controlling the modulus ratio and phase difference of the current in the primary-side and secondary-side coils. Therefore, we can adjust the parameters of the components in the compensation circuit on both sides to control the current.
# Context-Aware End-User Development Review
## 2 Background: End-User Developing Context-Aware Applications
### Context and Context-Awareness  
Enabling technologies, resources, and services for context-aware applications is diverse--all physical or virtual things could be regarded as context [16]. Ubiquitous technologies and IoT increase ambient computational capabilities, making even more resources and services available for applications [17]. Relevant to user and application interaction, context is any information used to characterize the situation of an entity (entity is a person, a place, an object, or an application) [18], including the characteristics of the entity and application's domain [19]. In autonomic computing, entities also represent the self as context, working with locally accessible information that includes their settings, operations, and semantics [20]. Computational systems represent the context through context management processes when the context is available. The system then applies different data processing and reasoning techniques, providing adaptation (e.g., ambient digital support) through context-awareness.  
Context-awareness produces a reaction or pro-action based on the context gathered from the self-system and the environment; or integrating knowledge from the entities; the awareness interprets synchronous, asynchronous, situation-based, and social-based information changes. [PERSON] and [PERSON] introduced context-aware computing as a capability of the applications to discover and react based on variation in the environment [21]. [PERSON] and [PERSON] extended the scope of awareness, highlighting the context's significance, where information and services are relevant to the user's task [22]. [PERSON] et al. considered the scope of context-aware agents' mission performing their micro assessments [16].  
Due to the diversity of resources in environments and available technologies, new techniques/metaphors are available to approach non-software experts to develop context-aware applications. This review regards context as a digital representation of real conditions, considering internal and external system aspects that provide features for context-awareness.
## 4 Results and Discussion
### Effects of Defect Size on Gas Chamber Pressure and PDR
As noted in Section 2, the model can be adapted to predict the pressure decay in a membrane gas chamber. The system features are taken into account through several parameters: the defect size \(D_{0}\), the orifice coefficient \(\mu\), the elastic pressure \(P_{e}\), the modified coefficient \(\omega\), and the real surface/bubble contact diameter \(d_{w}\). The following hypothesis can be put forward to explain this difference, taking into account the fact that the model resolution is very sensitive to the defect size, orifice coefficient, and elastic pressure. Firstly, the real original defect size was not absolutely equal to the diameter of the acupuncture needle, and the instantaneous defect size during the pressure decay process would have been affected by the membrane elastic pressure and applied pressure. While a constant surface/bubble contact surface diameter was considered in the model, the experimental contact surface diameter \(d_{w}\) varied with bubble formation time [13]. In addition, the experimental orifice coefficient \(\mu\) might have been be underestimated or overestimated because the downstream of the defect outflow in this study was not gas but water. Moreover, the elastic pressure \(P_{e}\) changes with gas chamber pressure, but in this simulation, the elastic pressure was set to be constant. Therefore, there would be a predictable error between measured data and simulated values.
## 5 Discussion
* Decreasing (respectively increasing) the false alarm cost causes the decrease (respectively increase) and increase (respectively decrease) of the number of standard deviations between the central value and the control limits and the increasing (respectively decreasing) of the sampling interval and the sampling size. Thus, the cost of a false alarm primarily acts on the control limits to protect the system against false alarms. * The decrease (respectively increase) of preventive maintenance duration at each out-of-control point generates the decrease (respectively increase) of the five decision variables. If the preventive maintenance time increases, the system reacts to ensure excellent process quality while increasing the number of inspections and the sampling interval. However, the optimal number of preventive maintenance decreases when the process is under control. * The preventive and corrective maintenance costs have a slight trend in the model variables. The decrease (respectively increase) in the number of preventive maintenance corresponding to the number of assignable causes, \(n_{pc}\), generates a decrease (respectively increase) outstanding at the decision variables. In the case of a decrease of the parameter \(n_{pc}\), the system reacts to reduce the number of inspections and the sample size and consequently, increases the sampling interval. The lower and upper limits of control are getting closer since the process is more stable due to fewer assignable causes and preventive maintenance actions. The sensitivity analysis results clearly show that the system reacts optimally with the variation of the problem parameters.