esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Strategy and Study Selection  
A systematic literature search was conducted on the following databases: MEDLINE via PubMed via MedLine, Cochrane Library, EMBASE and Web of Science Core Collection (All databases). The search for grey literature included ProQuest (Database, EBooks and Technology for Research), HSRProj and Onegrey. The search was performed until 6 March 2023, independently by two reviewers. The Search Strategies are detailed in Table S1. No restrictions on publication date were applied but language filters (English, Portuguese, and Spanish) were considered. A manual search of the reference list of the retrieved studies selected for critical appraisal was also conducted.  
Results of all database searches were collated and uploaded to Mendeley v1.19.5 (Mendeley Ltd., Elsevier, Amsterdam, The Netherlands), and all duplicates were removed. Subsequently, the titles and abstracts were independently screened by two reviewers and assessed according to the eligibility criteria. Any disagreements were resolved by a third review author. If the article did not have the information for the authors to make the decision, the article was collected for reading the full text.  
The following inclusion criteria were used: (1) Participants: adults (aged 18 years and over), male or female, with or without a diagnosis of osteoporosis, and with or without bone fractures; (2) papers where a correlation coefficient was calculated to assess the relationship between BMD measured with CBCT and the lumbar spine or femoral neck BMD measured by DEXA; (3) Study design: randomized controlled trials (RCTs), controlled clinical trials (CCTs) and cohort studies that show the correlation coefficient between DEXA and CBCT; 4) studies in humans. The following exclusion criteria were considered: (1) Participants: children or animal studies; (2) papers where a correlation coefficient between DEXA and CBCT technologies was not calculated; (3) Study design: case reports, case series, editorials, conference abstracts, opinion letters, book chapters; (4) studies with missing data.  
The full text of relevant papers was screened by two independent reviewers and whenever a consensus was not reached, a third researcher was consulted.
## 4 Proposed Fault-Prone Software Requirements Specification Detection Model
### Ambiguous Classification Model
#### 4.1.4 Phase 3: Ambiguity Classification
The F-measure score, which shows the balance of precision and recall, 0.9664 and 0.9598 respectively, was also high, indicating the model's ability to balance the classification of ambiguities. Therefore, the GloVe_CNN_RNN_LSTM model is considered the highest-performing model and will be chosen for the next stage of the project. The proposed GloVe_CNN_RNN_LSTM algorithm utilizes a sequential neural network architecture comprising an embedding layer with parameters such as max features and embedding size. A dropout layer is also applied to remove some context from the input dataset. Conv1D and MaxPooling layers are then added to the architecture to extract higher-level features and expedite the process. Additionally, RNN and LSTM layers are employed to capture long-term dependencies between word sequences and grasp the context of the input sentence. To enhance the efficiency of the hybrid model, dropout and dense layers are added in the final stages. Two activation functions, namely Relu in Conv-1D and Softmax in dense layers, are used. The suggested model is compiled with the optimizer Adam. Table 7 shows the performance of the GloVe_CNN_RNN_LSTM algorithm for each class of ambiguity in the software requirements. The algorithm achieved a high precision score of 0.9860, 0.9826, and 0.9739 for pragmatic, lexical, and syntactic ambiguities, respectively. The recall score, which measures the true positive rate of the correctly classified instances, was also high for all ambiguity classes, ranging from 0.9621 to 0.0.9848. The f-measure, which is the harmonic mean of precision and recall, was also high for all classes, with a minimum value of 0.9628 for semantic ambiguity and a maximum value of 0.9854 for pragmatic ambiguity. These results indicate that the GloVe_CNN_RNN_LSTM algorithm is effective in classifying different types of ambiguity in software requirements, with a high level of accuracy, recall, and f-measure for each ambiguity class. Therefore, we utilized this algorithm as the basis for developing a reliable and effective ambiguity classification model for software requirements. An accuracy of 0.9907 is considered reliable as it combines advanced techniques such as GloVe embeddings, CNN, RNN, LSTM, and expert validation. These techniques enable the algorithm to effectively classify and detect ambiguity in software requirements by capturing patterns and nuances in the data. The involvement of subject matter experts adds credibility to the accuracy score, ensuring alignment with their domain knowledge.
# Transcription of _Listeria monocytogenes_ Key Virulence Genes on Tomato, Cucumber and Carrot
## 1 Introduction
The significance of these studies is highlighted by the fact that prior exposure to environmental stimuli affects invasiveness [18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32] and that upregulation of virulence associated genes observed during such exposure is correlated with increased invasion efficiency [13; 21]. Tomatoes, cucumbers and carrots are very important vegetables, both in terms of nutritional value ([[https://fdc.nal.usda.gov/](https://fdc.nal.usda.gov/)]([https://fdc.nal.usda.gov/](https://fdc.nal.usda.gov/)) (accessed on 20 May 2021)) and commercial importance ([[http://www.fao.org/faostat/en/#data/QV](http://www.fao.org/faostat/en/#data/QV)]([http://www.fao.org/faostat/en/#data/QV](http://www.fao.org/faostat/en/#data/QV)) (accessed on 20 May 2021)). Many authors have studied the prevalence of _L. monocytogenes_ in these commodities. In cucumbers, it has been reported below 7% [33; 34; 35]; however, [PERSON] et al. [36], [PERSON] et al. [37] and [PERSON] et al. [38] reported detection of the pathogen in the 80%, 43.8% and 23.4%, respectively, of the samples assessed. A rather high prevalence has been reported for tomatoes. More accurately, [PERSON] et al. [34] reported 13.3%, [PERSON] et al. [37] 21.9% and [PERSON] et al. [38] 19.67%. However, many studies have reported its absence [33; 39; 40; 41]. Similar is the case of carrots, [PERSON] et al. [37] reported a prevalence of 24.2% while [PERSON] et al. [38] of 9.02%. On the contrary, [PERSON] et al. [33], [PERSON] et al. [34] and [PERSON] et al. [42] reported absence in the samples assessed. Outbreaks linked to the consumption of tomatoes and cucumbers usually involve _Salmonella_ serovars as etiological agents. On the other hand, illnesses related to carrot consumption have been mostly assigned to Norovirus, _Sliegella_ ssp. and botulism (data retrieved from outbreakdatabase.com (accessed on 20 May 2021)). However, based on the rather high prevalence values and their widespread and general applicability, their implication in _L. monocytogenes_ outbreaks would also be reasonable, either through direct consumption or through cross-contamination. So far, only one recorded outbreak has been partially linked to tomato consumption. More accurately, [PERSON] et al. [43] reported that the _L. monocytogenes_ outbreak that took place in 1979 in Boston and involved 23 patients from eight hospitals, could be assigned to the consumption of celery, lettuce and tomatoes. The aim of the present study was to provide information regarding the transcriptional response of _L. monocytogenes_ during subsistence on the surface of tomato, cucumber and carrot, stored at 4, 10 and 30 \({}^{\circ}\)C. More accurately, data regarding the relative transcription of _sigB_, _prfA_, _pICA_, _pICB_, _hly_, _inLA_, _inIB_, _inLC_ and _inI_] could improve the accuracy of risk assessment studies and enable the development of commodity-specific control strategies.
## 2 Methods  
### ITO:TiO\({}_{2}\):Air Layer Refractive Index  
According to the Drude model to describe the plasmonic response [17], the frequency dependent complex dielectric function of ITO can be written as:  
\[\varepsilon_{ITO,\omega}=\varepsilon_{1,\omega}+ie\varepsilon_{2,\omega} \tag{1}\]  
where  
\[\varepsilon_{1,\omega}=\varepsilon_{\infty}-\frac{\omega_{P}^{2}}{(\omega^{2 }-\Gamma^{2})} \tag{2}\]  
and  
\[\varepsilon_{2,\omega}=\frac{\omega_{P}^{2}\Gamma}{\omega(\omega^{2}-\Gamma^ {2})} \tag{3}\]  
where \(\omega_{P}\) is the plasma frequency and \(\Gamma\) is the free carrier damping. The plasma frequency is \(\omega_{P}=\sqrt{Ne^{2}/m^{*}\varepsilon_{0}}\), with \(N\) number of charges, \(e\) the electron charge, \(m^{*}\) the effective mass, and \(\varepsilon_{0}\) the vacuum dielectric constant. For ITO, we use \(N=2.49\times 10^{26}\) charges/m\({}^{3}\) (and we start from this value when we increase the number of carriers in this study), \(m^{*}=0.4\times m_{0}\), with \(m_{0}=9.1\times 10^{-31}\) kg. For the free carrier damping, we use \(\Gamma=0.1132\) eV. The parameters for ITO nanoparticles are taken from [9,18]; the value of the high frequency dielectric constant, \(\varepsilon_{\infty}=4\), is taken from [19].  
The wavelength dependent refractive index of TiO\({}_{2}\) can be written [20]:  
\[n_{TiO_{2}}(\lambda)=\Big{(}4.99+\frac{1}{96.6\lambda^{1.1}}+\frac{1}{4.60 \lambda^{1.95}}\Big{)}^{1/2} \tag{4}\]  
where \(\lambda\) is the wavelength [in micrometers, and \(\varepsilon_{TiO_{2}}(\lambda)=n_{TiO_{2}}^{2}(\lambda)\)].  
To determine the dielectric function of the layer made of ITO nanoparticles, TiO\({}_{2}\) nanoparticles, and air--ITO:TiO\({}_{2}\):air (we call it \(\varepsilon_{eff1,\omega}\))--we use a generalized Rayleigh mixing formula for a three-phase mixture [21]:  
\[\frac{\varepsilon_{eff1,\omega}-\varepsilon_{Air}}{\varepsilon_{eff1,\omega}+2 \varepsilon_{Air}}=\Big{(}f_{ITO}+f_{TiO_{2}}\Big{)}\frac{f_{ITO}(\varepsilon _{ITO,\omega}-\varepsilon_{Air})+f_{TiO_{2}}t_{12,\omega}\Big{(}\varepsilon _{TiO_{2},\omega}-\varepsilon_{Air}\Big{)}}{f_{ITO}(\varepsilon_{ITO,\omega} +2\varepsilon_{Air})+f_{TiO_{2}}t_{12,\omega}\Big{(}\varepsilon_{TiO_{2}, \omega}+2\varepsilon_{Air}\Big{)}} \tag{5}\]  
with  
\[t_{12,\omega}=\frac{3\varepsilon_{ITO,\omega}}{\varepsilon_{TiO_{2},\omega}+2 \varepsilon_{ITO,\omega}} \tag{6}\]  
where \(f_{ITO}\) is the volume fraction (filling factor) occupied by ITO nanoparticles and \(f_{TiO_{2}}\) is the volume fraction occupied by TiO\({}_{2}\) nanoparticles. The frequency dependent complex refractive index of the ITO:TiO\({}_{2}\):air layer is determined considering that \(n_{eff1,\omega}=\sqrt{\varepsilon_{eff1,\omega}}\).
# Aquasafe: A Remote Sensing, Web-Based Platform for the Support of Precision Fish Farming
## 3 Results
### User Experience  
The Aquasafe user interface was evaluated by relevant experts in the field, including scientists and stakeholders, through a user satisfaction survey. Participants were given specific scenarios and step-by-step instructions and were asked to provide feedback on the amount of time it took to complete each task, the clarity of the instructions, their overall satisfaction with the system, and any suggestions for improvement. The survey results showed that 27.3% of users were able to complete all tasks in under ten minutes, 54.5% took between 10 and 20 min, and 18.2% took longer than 20 min (Figure 8). The majority of users found the instructions clear and easy to follow (82%), and over half of the participants reported being quite (28%) or completely (55%) satisfied with their interaction with the platform and the results they achieved (82%) (Figure 9). However, some users did express difficulty with navigation and searching for specific information, as well as exporting results. Despite these minor criticisms, the overall feedback from users was positive, with suggestions for further improvements to the platform.  
Figure 6: Visualization of the layer corresponding to “Chlorophyll-a”.  
Figure 7: Model results and forecast about fish growth parameters in Agrilia fish farm for the first 3 days of February 2023.
## 2 Packet-Based Traversal Time Hop Count Analysis (TTHCA)
### Packet Traversal Time (PTT) Measurement  
As highlighted in Section 2.1, RTT is an inaccurate measure for determining the distance between two communicating nodes since packet processing delays on intermediate nodes have high variations. Conversely, the propagation speed of a data packet is nearly constant making the air traversal time of a packet much more accurate for distance estimations. The wormhole detection algorithm in TTHCA is thus based on the analysis of PTT rather than RTT.  
The PTT between a source and a destination node is calculated during the AODV route discovery process by firstly measuring the RTT and then subtracting all packet processing delays incurred by intermediate nodes. A timer is started at the source node when a RREQ message is broadcasted and stopped when the corresponding RREP is received, giving the RTT. Each intermediate node receiving a RREQ measures \(\mathit{\Delta T}_{\mathit{RREQ}}\) and stores the measured value in its local memory. The destination node measures the time from receiving a RREQ until sending a RREP (\(\mathit{\Delta T}_{\mathit{RREQ}}+\mathit{\Delta T}_{\mathit{RREP}}\)) and adds this to the new parameter \(\mathit{\Delta T}_{\mathit{TOT}}\) in the AODV RREP packet. Upon receiving an RREP, all intermediate nodes measure \(\mathit{\Delta T}_{\mathit{RREP}}\) and increment \(\mathit{\Delta T}_{\mathit{TOT}}\) in the RREP packet by both \(\mathit{\Delta T}_{\mathit{RREP}}\) and the previously measured \(\mathit{\Delta T}_{\mathit{RREQ}}\) so: \(\mathit{\Delta T}_{\mathit{TOT}}=\mathit{\Delta T}_{\mathit{TOT}}+\mathit{ \Delta T}_{\mathit{RREP}}+\mathit{\Delta T}_{\mathit{RREQ}}\). When the source node receives the RREP, it calculates the PTT for the route as:  
\[\mathit{PTT}=\frac{RTT\ -\ \mathit{\Delta T}_{\mathit{TOT}}}{2} \tag{1}\]  
In TTHCA, accurate PTT measurement must be provided by each intermediate node to enable the source node to effectively detect wormhole infected routes. In [18], IEEE 802.11b compliant hardware was shown to be able to measure data packet traversal times with an average error of between 3.9 ns and 7 ns depending on the network environment _i.e._, indoors or outdoors. In Section 3.3, it will be proven TTHCA can tolerate much higher error measurement margins, resulting in no extra hardware being required to perform the time measurements.
## 3 Results
### Cyberattacks in Smart Grids: Challenges and Implications of Cybersecurity
#### 3.1.4 Vulnerable Components and Cascading Consequences of Cyberattacks in the Smart Grid  
A smart grid's susceptibility to cyberattacks spans multiple components [189], including control systems such as supervisory control and data acquisition (SCADA) systems and energy management systems (EMS), communication networks, smart meters, customer systems, distributed energy resources (DER) (e.g., solar panels, energy storage), and data systems. Interconnected and reliant on information technology, these areas are vulnerable, potentially causing operational disruptions, data issues, privacy breaches, and physical harm. Cyberattacks can also trigger cascading effects, disrupting control systems, communication networks, device operations, data integrity, and DER stability, possibly leading to a chain of attacks [190, 191]. These cascades underscore the need for comprehensive cybersecurity measures that address interdependencies to ensure the overall resilience and security of the smart grid system.
# 5G Network Slice Isolation
## 1 Introduction
have identified network slice isolation as the core concept that impacts the network slice security. Since the traditional approaches, such as traffic isolation, encryption and firewalls, are not providing sufficiently satisfactory performance in countering the related cyber threats, they have envisioned some possible artificial intelligence mechanisms to enhance the network slice security. Unfortunately, little progress has been reported in the suggested approaches. One of the main reasons for this lack of achievement can be the absence of a deep and thorough understanding of the security model in slice isolation. The goal of this article is to demystify the appropriate defense mechanisms and provide an adequate isolation approach in different points of a network slice. The isolation point must be selected based on the characteristics of the network slice and the MNO's network infrastructure strategy. The main novel contributions of our work are: (1) identified security challenges in deploying NSaaS, (2) proposed a multi-layer model to decompose the network slicing security complexity, (3) analyzed the impact of network slice isolation point selection and (4) proposed a framework to optimize the selection of network slice isolation points. This article is organized as follows: Section 2 provides the principles of network slicing and network slice types of characteristics. Section 3 discusses the challenges of network slicing security when deploying an NSaaS platform. We use a multi-layer approach to explain the complexity layer-by-layer, then identify that the precision of the network slice isolation would affect the defense and performance of the network slice. Subsequently, we develop a mathematical model of network slice isolation relating to the level of control that the MNO and tenant would apply to the cost of deployment network slice relationships in Section 4. Finally, we conclude the paper in Section 5.
## 4 Recommendations for Practice  
The conducted research analyzed the impact of factors on mitigation effect and made comparisons among the factors with regard to the sensitivity, which provided insights for improving the design of stone columns in practice. The analysis framework is also valuable and referable in relevant investigations.  
The simulations present that stone columns were effective in reducing the peak values of EXPPR and accelerating the dissipation of pore pressure in surrounding soils. The stone column's permeability ratio was found to be a critical factor in this mitigation effect. The increase in replacement ratio can also lead to a reduction in the EXPPRs, but it is less significant than the permeability ratio. In contrast, the change of diameter and shear stiffness in the selected boundary exert a trivial impact on the peak values of EXPPRs. Similar conclusions were obtained in terms of the PGAs. The permeability ratio plays the most significant role in affecting the PGAs, which is followed by the replacement ratio, while the influence of diameter and shear stiffness is negligible. Additionally, the results regarding PGA manifested a pronounced difference against those regarding EXPPR, that the increase of permeability ratio and replacement ratio would result in larger PGAs. Such phenomena can be explained by the suppressing of vibration isolation due to the strengthened liquefaction resistance of surrounding soils.  
The obtained results suggest that to mitigate the liquefaction risk by reducing the EXPPR, it is vital for stone columns to have a reasonable permeability ratio and replacement ratio. The increase of permeability is more effective to reduce the risk. Furthermore, the design can be further improved by adjusting the diameter as well as shear stiffness for economic reasons. However, it should be noted that the recommendations are made on the basis of the soils with particular mechanical properties in the investigated engineering case. Furthermore, because the influence of the factors is studied by changing the parameters in a limited range, more analyses are needed when the design parameters exceed the current investigated domain.  
Overall, the conducted research does not consider the effects of construction processes on the surrounding soils (e.g., densification), for which the positive impact of replacement ratio may be underestimated and more efforts can be made in this aspect for further research. Besides, the drainage capacity of stone columns, which are suggested to be critical for liquefaction mitigation in the current study, could be undermined in the long run due to multiple effects such as clogging. As not addressed hereby, more attention should be given to these effects to properly evaluate the long-term serviceability of stone columns and improve the design and construction in practice.
## 6 Simulation Results
### Complexity Analysis  
The number of messages exchanged in the network is analyzed as following: as illustrated in Section 5.2, the first hop of data transmission needs only 1 message exchange; The second hop needs three message exchanges including two action messages to be sent forward and one transition message to be back propagated; The third hop needs five message exchanges which comprise three action messages and two transition messages, and so on, so that the \(n_{th}\) hop needs \(2n-1\) messages to be exchanged in total, which contains \(n\) action messages and \(n-1\) transition messages. We assume that \(N\) SUs uniformly distribute over a square area in our experiment, which means that the average route length can be approximated to \(\sqrt{N}\). So the number of message exchange can be calculated as \(\left[\frac{1}{2}\left(1+\left(2\sqrt{N}-1\right)\right)\times\sqrt{N}\right] /(1-P_{error})=N/(1-P_{error})\), where \(P_{error}\) is the average estimation error when the algorithm converges. That is, the complexity of message exchange for every route is \(o(N/(1-P_{error}))\).  
Then the time complexity of the joint design algorithm is taken into consideration. In the experiment, the neural network has three layers. The number of neurons in the first and third layer is equal to the dimension of the states \(n_{feature}\) and the size of action space \(n_{action}\), respectively, and the number of the second layer is set to be \(n_{2}\). The feed-forward calculation for one sample needs two matrix operations, which requires \(n_{feature}\times n_{2}\) and \(n_{2}\times n_{action}\) times of computation. So the time complexity for one sample is \(o\left(n_{feature}\times n_{2}+n_{2}\times n_{action}\right)=o(n_{2})\). We assume that the algorithm converges after \(K\) episodes. Since the average route length is approximated to \(\sqrt{N}\), the average steps of one episode is \(\sqrt{N}/(1-P_{error})\), which means that the number of total samples is calculated as \(K\times\sqrt{N}/(1-P_{error})\). Therefore, the time complexity of the joint design algorithm is \(o\left(n_{2}\times K\times\sqrt{N}/(1-P_{error})\right)\).