esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Optimal Resource Allocation  
In order to reduce the computational complexity of **P1** which is a mixed integer programming problem, we apply the time-sharing strategy which introduces the factors \(\left\{\tau_{n,k,t}\right\}\), for \(n\in\mathcal{N}\), \(k\in\mathcal{K}\), \(t=1,2\). We assume that \(\tau_{n,k,i}\) indicates a part of time that subcarrier \(n\) is allocated to relay \(k\) over hop \(t\). The variables \(\left\{\tau_{n,k,t}\right\}\) satisfy \(\sum_{k}^{K}\rho_{n,k,t}=1,\forall n,t\). It is proved in [33; 34] that the optimal results obtainedby the time-sharing strategy are almost identical to those achieved by integer channel allocations. Hence, the optimization problem can be rewritten by  
\[\begin{split}\text{({\bf P2}):}&\max_{\{\mathbf{\tau},\mathbf{ \rho}\}}\ \sum_{k=1}^{K}r_{k}\\ \text{s.t.}&\text{C7}:\sum_{n=1}^{N}\tau_{n,k,t}\ln \left(1+\frac{h_{n,k,t}p_{n,k,t}}{\sigma^{2}}\right)\geq r_{k},\forall k,t\\ &\text{C8}:\sum_{n=1}^{N}\left(1-\tau_{n,k,1}\right)\xi\left(h_{ n,k,1}p_{n,k,1}+\sigma^{2}\right)\geq\sum_{n=1}^{N}\tau_{n,k,2}p_{n,k,2}, \forall k\\ &\text{C9}:\sum_{k=1}^{K}\tau_{n,k,t}=1,\forall n,t\\ &\text{C2},\text{C3},\text{C4},\text{C5},\text{C6}.\end{split} \tag{10}\]  
The total variables to be optimized in **P2** include subcarrier arrangement \(\mathbf{\tau}=\{\tau_{n,k,t}\}\), the power allocation \(\mathbf{\mathsf{p}}\). Since the time-sharing condition is satisfied [35], the optimization problem is convex, thus, the Lagrange dual method can be utilized to obtain an asymptotically optimal solution. In the following sub-sections, we illustrate the detailed derivation of the optimal resource allocation by applying the dual method.
## 2 Methods
### LM-Based Hydro-Optical Retrieval Algorithm
This is done through calculating \(T\) using the retrieved concentration vector and the adopted hydro-optical model and the parametric relationship \(T=f(a,\ b_{b})\). The reconstructed spectrum \(T\) is then compared with the initial spectrum \(S\) derived from the acquired satellite image. The pixel becomes flagged if the difference \(\left(S-T\right)^{2}\) surpasses a certain threshold (\(10^{-5}\) sr\({}^{-1}\)) established experimentally.
###### Abstract  
The accurate classification of traffic data is challenging for network management and security, especially in imbalanced situations. The limitation of the existing convolutional neural networks is that they have problems such as overfitting, instability, and poor generalization when used to classify imbalanced datasets. In this paper, we propose a new imbalanced encrypted traffic classification model. The proposed model is based on the improved convolutional block attention module (CBAM) and re-weighted cross-entropy focal loss (CEFL) function. The model exploits the redefined imbalance degree to construct a weight function, which is used to reassign the weights of the categories. The improved CBAM based on the redefined imbalance degree can make the model pay more attention to the characteristics of the minority samples, and increase the representation ability of these samples. The re-weighted CEFL loss function can be used to expand the effective loss gap between minority and majority samples. The method is validated on the public ISCX Tor 2016 dataset. The experimental results show that the performance of the new classification model is better than the baseline methods, and the proposed method can remarkably push the precision of the minority categories to 93.28% (14.63%), recall to 91.71% (16.98%), and F1 score to 92.49% (16.23%).  
imbalance ratio; convolutional neural network; attention module; loss function +
Footnote †: journal: Applied  
**Citation:** [PERSON]; [PERSON]; [PERSON] A New Imbalanced Encrypted Traffic Classification Model Based on CBAM and Re-Weighted Loss Function.  
_Appl. Sci._**2022**, _12_, 9631. [[https://doi.org/10.3390/app12199631](https://doi.org/10.3390/app12199631)]([https://doi.org/10.3390/app12199631](https://doi.org/10.3390/app12199631))  
Academic Editor: [PERSON]  
Received: 11 August 2022  
Accepted: 23 September 2022  
Published: 25 September 2022  
**Published:** **Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
## 3 Methodology
### Data Analysis, Rigor, and Transparency  
Following the method implemented by previews studies (e.g., [39; 40] we conducted a literature review and identified three main categories for our study. Then we combined image restoration theory, social identity theory, and procedural justice to develop interview questions shown in Table 3. These perspectives helped identify key dimensions of employee responses and guided the interpretation of emerging patterns during analysis. To mitigate potential elite bias, at least two interviews were conducted in each participating company, one with a manager or senior executive (e.g., CISO, IT director, or business unit head), and one with an operational or administrative employee. This approach allowed us to capture diverse perspectives across organisational levels. In addition, interview data were triangulated with company reports and public disclosures to enhance the reliability of the findings. For the analysis of the results, the responses sent in the form (Appendix A) were first taken and related to the following framework designed and proposed in Table 3 for our purpose. During the data collection step, we paid close attention to the consistency and authenticity of participants' responses, using triangulation across cases and sources to strengthen the validity of the finding [34]. Questions exploring emotional reactions and trust dynamics were included to capture employees' self-reported perceptions, which are essential for understanding the social and psychological dimensions of post-breach contexts. Then, to analyse the data, coding was performed using the taxonomy developed [41] and the Atlas T.I. software version 25, which allowed for the identification of patterns in the interviews (see Table 4). \begin{table}
\begin{tabular}{c c} \hline \hline
**1. Technical Aspects of Cybersecurity** & (TA.1) Ransomware \\  & (TA.2) Phishing \\ Attack Taxonomy & (TA.3) Data exfiltration \\  & (TA.4) Supply chain attacks \\  & (TA.5) Social engineering \\  & (PR.1) Implementation of containment protocols \\ Risk Response Plans & (PR.2) Data backup and recovery \\  & (PR.3) Vulnerability assessment \\  & (PR.4) Cyberattack simulations \\  & (DC.1) Early incident detection \\ Detection and Containment & (DC.2) Network traffic monitoring \\  & (DC.3) Disconnection of compromised devices \\  & (DC.4) SOC (security operations centre) response \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Coding for data analysis. Once patterns were identified, relationships were established with the variables defined in the situational crisis communication theory (SCCT) of [42] (see Appendix B) and the image restoration theory [3] (see Appendix C) to analyse the extent to which each variable of the coded taxonomy data set was associated with a theme or factor that allows cyberattacks and data leaks to be related to the impact on employees and the reputation of organisations. The coding allowed us to identify patterns and relationships between the different levels of analysis. Below is a summary of the frequency of the codes in each dimension (see Table 5).
# Security Performance Analysis of Full-Duplex UAV Assisted Relay System Based on SWIPT Technology
## 2 System Models and Transmission Protocols
### System Model
The unit of \(K_{ij}\) is dB and the expression [17] for \(K_{ij}\) is:  
\[K_{ij}(\theta_{ij})=k_{m}+(k_{M}-k_{m})\frac{2\theta_{ij}}{\pi} \tag{3}\]  
where \(k_{m}\) and \(k_{M}\) are constants, determined by the environmental parameters and transmission rate, and \(\theta_{ij}\triangleq\frac{180}{\pi}arcsin\frac{H}{\left\|d_{ij}\right\|}\) is the elevation angle between the two nodes in radians. When \(\theta_{ij}\) is 0, it indicates that the two nodes are located in the same horizontal plane, and when \(\theta_{ij}\) converges infinitely to \(\frac{\pi}{2}\), it indicates that the two nodes converge to the same vertical plane. \(d_{ij}\) is the Euclidean distance between any two points, \(d_{ij}=\left\|W_{i}-W_{j}\right\|\). Considering the hindered LOS model, the path loss (PL) component related to the elevation angle is used [20]:  
\[\alpha_{ij}\left(\theta_{ij}\right)=\frac{\alpha_{L}-\alpha_{N}}{1+\omega_{1} \cdot exp\left(-\omega_{2}\left(\theta_{ij}-\omega_{1}\right)\right)}+\alpha_ {N} \tag{4}\]  
where \(\alpha_{L}\) and \(\alpha_{N}\) denote the PL indices of the LOS link and NLOS link between two nodes, respectively. \(\omega_{1}\) and \(\omega_{2}\) are constants that change according to the specific application environment. When the signal transmitted by node \(i\) reaches the receiver of the node through the intermediate wireless medium, the large-scale fading is expressed as \(L_{ij}\triangleq d_{ij}{}^{-\alpha_{ij}}\), and the elevation angle between the nodes, the height of the UAV, and the environmental factors all affect the large-scale fading. The large-scale fading problem is neglected when node U and node B receive self-interfering signals in full-duplex mode.
# Impact of Disruption on Ship Emissions in Port: Case of Pandemic in Long Beach
## 5 Conclusions  
The COVID pandemic has affected the shipping industry and the process of achieving environmental sustainability for ships. This study examines the COVID pandemic impact on port-wide ship emissions, using the POLB as an example, and the findings of this study are summarised as follows:  
1. This study gives an optimised approach for calculating ship emissions and proposes a solution to the problem of erroneous emission estimations caused by lacking data for some ships. The method can be applied to future ship emission monitoring and policy design.
2. This study details the vessel emissions in the POLB region before and after the pandemic. Changes in ship emissions are systematically analysed in four dimensions: different emissions, different ship types, different ship engines, and different ship states.
3. This study assesses the shore power policy and demonstrates the significance of its implementation through calculations. The implementation of the shore power policy can be extended to many ports in many countries to achieve environmental sustainability development in the shipping industry. We also envisage new rules such as optimising ship scheduling in port areas, changing the flow of port operations, and increasing the demand for fuel. The analysis of emissions for various vessel types and operating modes presented in this study can be used to establish and modify rules.  
Lastly, our study has limitations, such as the weather-related variation in the results and the estimating methodologies employed by some vessels with incomplete ship information. In addition, some vessels lack AIS data, and smaller vessels in the port vicinity may disable their AIS equipment. In future research, satellite remote sensing and radar data can be employed to measure ship emissions more precisely.  
Methodology, Z.H., J.S.L.L. and M.L.; Software, Z.H. and M.L.; Conceptualisation, J.S.L.L.; Visualisation, Z.H.; Writing--origin and editing, Z.H.; Validation, J.S.L.L.; Writing--review and editing, J.S.L.L.; Supervision, J.S.L.L.; Formal analysis, M.L.; Investigation, M.L. All authors have read and agreed to the published version of the manuscript.  
Project 04 SBS000097C120 at Nanyang Technological University, Singapore.  
Not applicable.  
Data Availability StatementData analysed during this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.
# Monitoring Forest Phenology in a Changing World
###### Abstract  
Plant phenology is strongly interlinked with ecosystem processes and biodiversity. Like many other aspects of ecosystem functioning, it is affected by habitat and climate change, with both global change drivers altering the timings and frequency of phenological events. As such, there has been an increased focus in recent years to monitor phenology in different biomes. A range of approaches for monitoring phenology have been developed to increase our understanding on its role in ecosystems, ranging from the use of satellites and drones to collection traps, each with their own merits and limitations. Here, we outline the trade-offs between methods (spatial resolution, temporal resolution, cost, data processing), and discuss how their use can be optimised in different environments and for different goals. We also emphasise emerging technologies that will be the focus of monitoring in the years to follow and the challenges of monitoring phenology that still need to be addressed. We conclude that there is a need to integrate studies that incorporate multiple monitoring methods, allowing the strengths of one to compensate for the weaknesses of another, with a view to developing robust methods for upscaling phenological observations from point locations to biome and global scales and reconciling data from varied sources and environments. Such developments are needed if we are to accurately quantify the impacts of a changing world on plant phenology.  
drones; ecosystem change; methods; monitoring; phenology; remote sensing; UAVs +
Footnote †: journal: Physics Letters B
## 2 Aero-Thermal Modeling of Rotating Detonation Combustors
### Heat-Transfer Analysis: URANS vs. Reduced-Order Model with Heat-Flux Correlation
\[A=0.123e^{-1.561H_{i}}\bigg{(}\frac{M_{e}a_{0}\theta_{tr}}{v_{0}}\bigg{)}^{-0. 268}\bigg{(}\frac{T_{e}}{T_{ref}}\bigg{)}^{0.732}\bigg{(}\frac{T_{e}}{T_{0}} \bigg{)}^{3.268}, \tag{7}\]0 (**a**) 2D detonation tube: pressure flow field; (**b**) wall heat-flux evolution with 2D URANS vs prediction based on the correlation [18]. The dependence of the numerical accuracy with the first cell size (in terms of y+), time discretization and total number of cells was retrieved based on the Richardson extrapolation method [36]. The mesh independence study for the current manuscript was performed on a 2D mesh to determine the discretization requirements for the 3D test case. Figure 9a displays the log-log plot of the extrapolated numerical error of the maximum heat-flux level (at the shock front) as a function of the nondimensional wall distance, y+. For values of y+ smaller than 0.5, which relate to a dimensioned wall distance of 2.0 \(\times\) 10\({}^{-8}\) m, the relative error is less than 10%. On Figure 9b, the extrapolated numerical error on the maximum heat flux is shown as a function of the timestep per iteration. For values smaller than 1.0 \(\times\) 10\({}^{-8}\) s, the relative difference with regard to the 0.5 \(\times\) 10\({}^{-9}\) s case is less than 4%. For these reasons, we opted for a timestep of 1.0 \(\times\) 10\({}^{-8}\) s and a first wall distance of 2.0 \(\times\) 10\({}^{-8}\) m.  
Furthermore, a grid independence study was performed to determine the grid density based on a grid convergence study [36; 37]. Figure 9c plots the extrapolated convergence error for three different grid sizes. The average heat flux on the bottom plate was chosen as the key variable. To obtain a feasible calculation time with an acceptable error, the middle mesh with 60 radial grid points was chosen with an extrapolation error of 10.95%. Finally, we applied the heat-flux methodology to our reduced-order model. First, the streamlines and the corresponding path lines were computed to track the static and the absolute stagnation conditions (Me, Te, a\({}_{0}\) and T\({}_{0}\)) to retrieve the instantaneous heat flux at each spatial location via Equation (14).
# SAM-PAY: A Location-Based Authentication Method for Mobile Environments +
## 4 Design of the SAM-PAY Authentication Method
### OTC Usage in SAM-PAY Authentication Method  
In our approach, the OTC is generated by the SP (acting as a verifier) and is sent to the UT, which will be used transparently in the authentication phase. Since the SP needs to be aware of some important data related to the UT (such as user or terminal identification data), the user must register first with an SP. In the registration phase, the client provides the SP with several data, like personal data (name, surname, birthplace, fiscal code), phone number, and the IMEI code of the UT, uniquely identifying the UT device in the cellular network, the username and the password used by the user to authenticate to the SP, contract expiry date, bank account number (if a payment operation needs to be performed in the service) and other optional data like the subscription type (silver, gold), etc. In addition, in the registration phase, the user also sets two secret keys: key \(KS_{e}\) and key \(KS_{a}\), which will be used, respectively, for encryption and authentication purposes. Furthermore, we assumed that when certifying the location of the UT, the \"legitimate\" user controls the device when (or immediately before) the evidence about the location has been acquired. In practice, we do not separate the location authentication (that assures the truthfulness of the claimed or presumed location) from the entity authentication, which helps corroborate the veracity of a claimed or presumed party's identity.  
Since the UT (and implicitly the user controlling the UT) is authenticated based on the knowledge of the OTC, the ownership of the UT, and the location (of the UT), we lookedfor a solution aimed at combining these authentication factors. In practice, in SAM-PAY, the user will be able to \"recover\" the OTC only if they are in a certain location (within the range of the Toleration Distance) and have access to the UT. To combine the location information and the OTC, we used a modified LDEA geo-encryption algorithm, described further below.
## 4 Studies of the Molecular Profiling of EVs as Potential Biomarkers
### miRNA Profiling Analysis of EVs in Multiple Cancers  
Tumor-derived EV microRNAs (miRNAs) have received much attention as biomarker candidates for non-invasive diagnostics, given their role in tumor progression and metastasis. Analysis of serum exosomal miRNA expression profiles of CRC patients revealed that miR-19a and miR-92a were significantly upregulated compared to HCs [190] (Table 3). Moreover, let-7a, miR-1229, miR-1246, miR-150, miR-21, miR-223, and miR-23a were detectable at significantly higher levels in colon cancer cell lines and serum samples from CRC patients [191] (Table 3). A recent report demonstrated that miR-1246 and other miRNA markers were highly enriched in EVs derived from pancreatic cancer patients [192] (Table 3). In addition, miR-1246, miR-1290, miR-375 [193], miR-141 [194], and others [195]were detectable in EVs isolated from plasma and serum samples of patients with prostate cancer, respectively. Furthermore, other works highlighted miR-21-5p and miR-92a-3p as emerging diagnostic biomarkers for HCC [196] (Table 3). Analysis of plasma-derived EVs from lung cancer patients identified miR-320, miR-126 [197], as well as let-7f, miR-146, miR-203, miR-106a, and miR-20b [198].  
It has been reported that EV miRNAs confer high accuracy in identifying BC; in particular, a decreased expression of miR-142-5p and miR-150-5p were significantly associated with more advanced tumor grades (grade III), while the decreased expression of miR-142-5p and miR-320a was associated with a larger tumor size (<20 mm) [199] (Table 3). Some significant miRNAs derived from EVs were also associated with the severity of BC, including miR-939 implicated in drug resistance, miR-338, and others involved in TME [200; 201; 202] (Table 3). miRNA profiling of EVs has also revealed an increased expression of miR-200 and miR-18, while a decreased expression of miR-100 and miR-125b has been reported in different histotypes of ovarian carcinomas, as compared to normal ovarian tissue [203]. Furthermore, Let-7 miRNA family members represent a diagnostic potency marker to manage follicular nodules in the thyroid gland [204]. miR-423-5p, miR-484, miR-142-5p, and miR-17-5p were discovered to be dysregulated in GC or implicated in GC tumorigenesis/metastasis in EVs isolated from serum samples [205]. [PERSON] and coworkers (2020) identified eight candidate miRNAs that may mediate EV-associated angiogenesis in glioblastoma, including miR-148a and miR-9-5p [206]. Different studies involving miRNAs associated with tumor progression and diagnostics are summarized in Table 3.  
Several approaches are being used to create a miRNA profile to classify different cancer histotypes, nonetheless, there is a deregulated expression of specific miRNAs. Some of them have found them as regularly loaded in EVs and associated with cancer progression, which could be used as biomarkers and in therapy design. However, the development of new technology is required to advance in this aspect.
## 2 Linear or Linear-like Formulations of Nonlinear Systems
### State Dependent Coefficient Parameterization  
The state dependent coefficient (SDC) parameterization is a method that transforms a nonlinear to a linear-like system by factorizing the nonlinear functions [24]. Thus, Equation (1) can be rewritten as  
\[\dot{\mathbf{x}}=\mathbf{f}(\mathbf{x})+\mathbf{g}(\mathbf{x})\mathbf{u}= \mathbf{A}(\mathbf{x})\mathbf{x}+\mathbf{B}(\mathbf{x})\mathbf{u},\ \mathbf{y}=\mathbf{h}(\mathbf{x})= \mathbf{C}(\mathbf{x})\mathbf{x} \tag{11}\]  
where \(\mathbf{f}(\mathbf{x})=\mathbf{A}(\mathbf{x})\mathbf{x}\), \(\mathbf{g}(\mathbf{x})=\mathbf{B}(\mathbf{x})\), and \(\mathbf{h}(\mathbf{x})Define the new state, control and output the same as the original ones:  
\[\mathbf{z}=\mathbf{x},\ \mathbf{v}=\mathbf{u},\ \mathbf{w}=\mathbf{y} \tag{12}\]  
which lead to the new state and output equations as Equation (2). Note that Equation (2) in this scheme is a set of linear-like equations. The major benefit of this scheme is design flexibility, but the stability and controllability it yields depend on the form of factorization.  
Regarding control design, the state-dependent Riccati equation (SDRE) control is mostly applied to Equation (2). The design procedure of the control scheme is similar to the linear quadratic regulator (LQR), but the Riccati equations become functions of the states. This is the reason the control scheme is called the SDRE control, and the difficulty of the control scheme is solving the state-dependent Riccati equations.