esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Conjunctive Keywords Search over Encrypted E-Mail Data in Public Key Setting
###### Abstract  
Searchable public key encryption supporting conjunctive keywords search (SPE-CKS) enables data users to retrieve the encrypted data of interest from an untrusted server. Based on SPE-CKS, one can realize a multi-keywords search over the encrypted e-mails. In this paper, we propose an efficient SPE-CKS scheme by utilizing a keyword conversion method and the bilinear map technique. Our scheme is proven to be secure against chosen keyword attack under a standard security definition and can also withstand the keywords guessing attack. Furthermore, we design an experiment over a real world e-mail dataset to illustrate that our scheme has a better performance on time and space complexities than the previous schemes. A detailed analysis shows that our scheme is very practical for the encrypted e-mail system in the mobile cloud environment.  
searchable public key encryption; conjunctive keywords search; searching over encrypted data +
Footnote †: journal: Applied Sciences
## 2 Results and Discussion
### Effect of Diffusion Path (Z) at Variable Analyte Concentration in Sampled Air (Worst-Case Scenario)  
Time-weighted average sampling is conducted during long time periods (e.g., 24 h), during which concentrations of analytes in the sampled air can vary significantly. The apparent worst-case scenario can be when in the first half of sampling, concentration is much higher than during the second half. When the concentration of an analyte in the sampled air becomes close to or lower than the concentration near the fiber tip, the flux of analytes inside a protecting needle can go to a reverse direction resulting in desorption of analytes from a coating. However, this violates the main principle of TWA sampling: the rate of sampling should depend only on the concentration of an analyte in a sampled air. It means that if an analyte concentration in sampled air is zero, a rate of extraction should also be equal to zero. Thus, the aim of this part of the work was to model such a case and estimate the highest possible uncertainty of the TWA SPME sampling approach.  
As was assumed, desorption of dichloromethane, acetone, and benzene from a fiber started after concentrations of analytes dropped from 1.176 to 0.1176 \(\upmu\)mol\(\cdot\)m\({}^{-3}\) in the middle of the extraction process (Figure 7). Desorption of toluene was not observed because it has the highest distribution constant among all studied analytes. However, the toluene sampling rate after the drop of its concentration in sampled air was lower than theoretical. Recoveries of analytes at \(Z=10\) mm dropped from 65-82 to 52-70%, at \(Z=20\) mm from 78-90 to 67-79%, at \(Z=30\) mm from 85-93 to 73-82, at \(Z=40\) mm from 86-93 to 75-82% (Figure 7). Only at \(Z=40\) mm, it was possible to keep recovery of all analytes above 75%. Thus, if possible, for greater accuracy, sampling must be arranged so that no significant drop in concentration takes place. Such a drop can be observed, e.g., if the end of sampling is planned for the night when VOCs concentrations in ambient air are typically lower due to much lower road traffic and other human activities. Also, using shorter sampling times can minimize the risk of the reverse diffusion when ambient concentrations are predicted to drop significantly.  
Figure 6: Effect of diffusion path length on recoveries of four analytes (\(C=0.641\)\(\upmu\)mol\(\cdot\)m\({}^{-3}\)) after sampling for 100,000 s using 23 ga Car/PDMS fiber assembly.
## 4 Discussion  
### Reliability of Control-Free Nap-of-the-Object Photogrammetry Technique  
In previous research on Benggang erosion monitoring by UAV photogrammetry, most study areas were performed in regions with highly complex topography [8; 9; 15]. The traditional method of establishing image control points is excessively time-consuming, labor-intensive, and challenging in terms of field operations [13; 14; 16]. Though the accuracy achieved by UAV measurement coupled with GNSS RTK technology can fall below the centimeter level, it necessitates manually navigating hazardous terrain [35].  
In this study, control-free-image-based nap-of-the-object photogrammetry technology was applied to measure Benggang erosion. The positioning and measurement accuracy errors of the obtained DSMs all reached the subcentimeter level. The planar direction error was mainly concentrated between 0.01 m and 0.02 m, which is consistent with results for seismogenic faults measured via orthophotography, as discussed by [PERSON] et al. [36]. In this study, nap-of-the-object photogrammetry was found to reduce the accuracy error through multi-angle and close-distance photography compared with orthophotography in a single direction. The relatively low altitude of the UAV also allowed for more accurate data acquisition.  
The combination of UAV control-free nap-of-the-object photogrammetry and geological monitoring, as evaluated by [PERSON] et al. [25] in the context of debris flow disasters, aligns with the results of the present study. However, the accuracy of the results in terms of planar and elevation errors surpasses that achieved by [PERSON] et al. [25]. This improvement can be attributed to the control-free images attained through nap-of-the-object photogrammetry, which, in contrast to the oblique photography employed in the aforementioned study, results in a more refined system capable of reducing errors in DOM images and DSMs. In summary, the results of this analysis demonstrate that control-free nap-of-the-object photogrammetry imaging is well suited to the measurement of Benggang erosion.
## 3 Materials and Methods
### Building Modeling Setup  
DesignBuilder v.6 program was selected for this study due to its user-friendly three-dimensional graphical interface. Additionally, this software utilizes the powerful EnergyPlus engine for building thermal simulations and energy consumption estimations. The building was modeled to accurately reflect the geometrical characteristics of the case study, as illustrated in Figure 2. To ensure the reliability of the results, validation of the simulation model was essential. Therefore, model validation was conducted at the outset of the research, calibrating the energy model by comparing simulated energy consumption data with actual consumption data from the chiller plant over the course of one year. This validation process included weather data for Dhahran, occupancy schedules and densities, the building's fabric characteristics, and the properties of the cooling chiller unit components (plant loop, chiller, pumps, and cooling tower).  
The annual cooling electricity consumption of the modeled building was 477,073.9 kWh. In contrast, the actual cooling electricity consumption was approximately 493,714.29 kWh, as obtained from field measurements, as shown in Table 1. This results in a discrepancy of 3.37% in energy consumption, which is considered an acceptable margin of error given the complexities of thermal performance modeling and the underlying assumptions involved. Furthermore, this aligns with the estimated annual cooling electricity consumption based on EUI data for the Eastern Province of Saudi Arabia, which is reported to be 182.4 kWh/m\({}^{2}\)/year [51], with 70% of this energy attributed to air conditioning in apartment buildings [52]. Thus, the EUI for air conditioning is calculated at 127.4 kWh/m\({}^{2}\)/year, leading to an estimated annual cooling energy consumption of 462,462 kWh. This indicates a discrepancy of 3.78% when compared to the field measurements in the examined case study.  
Figure 3: The water-cooled chiller system in the examined case study.
## 3 Methodology
### Rough Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)  
The weights of the criteria obtained from the previous rough AHP process are used to obtain the ranking by following the procedure for the rough TOPSIS method. The rough TOPSIS, a hybrid method for analyzing multiple-decision-maker problems, combines the rough set theory and the TOPSIS method [15; 24; 25]:  
Step D1: Develop a matrix by compiling the data of the decision-makers, such that the performance values of each alternative or rack are compared using the scale provided in Table A4 with respect to the criteria.  
Step D2: After obtaining the crisp value, calculate the rough numbers by following Equations (2)-(9) to construct the rough matrix for the TOPSIS analysis as follows:  
\[T=\left[\begin{array}{cccc}[1,1]&[x_{12}^{L},\ x_{12}^{U}]&\ldots&[x_{1m}^{ L},\ x_{1m}^{U}]\\ [x_{21}^{L},\ x_{21}^{U}]&[1,1]&\ldots&[x_{2m}^{L},\ x_{2m}^{U}]\\ \vdots&\vdots&\ddots&\vdots\\ [x_{m1}^{L},\ x_{m2}^{U}]&  
where \(Nx_{ij}^{L}\) and \(Nx_{ij}^{U}\) are the lower and upper limits of the standardized rough matrix, respectively.  
Step D4: Determine the weighted standardized rough matrix by multiplying the standardized rough number from the previous step with the weights obtained from the rough AHP method:  
\[Y_{ij}^{L}=W_{gi}^{L}\times Nx_{ij}^{L} \tag{16}\]  
\[Y_{ij}^{U}=W_{gj}^{U}\times Nx_{ij}^{U} \tag{17}\]  
Step D5: Obtain the positive and negative ideal solutions using Equations (18) and (19):  
\[V_{j}^{+}=\max_{i}(Y_{ij}^{U}),\;if\;J\;\epsilon\;B;\min_{i}(Y_{ij}^{L}),if\;J \;\epsilon\;C \tag{18}\]  
\[V_{j}^{-}=\min_{i}(Y_{ij}^{L}),\;if\;J\;\epsilon\;B;\max_{i}(Y_{ij}^{U}),\;if \;J\;\epsilon\;C \tag{19}\]  
where \(V_{j}^{+}\) and \(V_{j}^{-}\) denote the positive and negative ideal solution, respectively, and \(B\) and \(C\) represent the beneficial and cost criteria, respectively.  
Step D6: Obtain the ranking using Equations (20) and (21):  
\[D_{j}^{+}=\left\{\sum_{J\;\epsilon\;B}\left(Y_{ij}^{L}-V_{j}^{+}\right)^{2}+ \sum_{J\;\epsilon\;C}\left(Y_{ij}^{U}-V_{j}^{+}\right)^{2}\right\}^{\frac{1}{ 2}} \tag{20}\]  
\[D_{j}^{-}=\left\{\sum_{J\;\epsilon\;B}\left(Y_{ij}^{U}-V_{j}^{-}\right)^{2}+ \sum_{J\;\epsilon\;C}\left(Y_{ij}^{L}-V_{j}^{-}\right)^{2}\right\}^{\frac{1}{ 2}} \tag{21}\]  
where \(D_{j}^{+}\) and \(D_{j}^{-}\) are the separation distance of each alternative from the positive and negative ideal solution, respectively. The closeness coefficient \(DA_{i}\) is evaluated for each value by the following equation:  
\[DA_{i}=\frac{D_{j}^{-}}{D_{j}^{-}+D_{j}^{+}} \tag{22}\]  
Finally, the ranking of the rack is obtained based on the descending order of the closeness coefficient.
# Simulation Study on Thermal Wake Characteristics of Underwater Vehicle under Rotary Motion
## 2 Mathematical Models and Numerical Simulation Methods
### Mathematical Model  
In order to improve the simulation accuracy, the overlapping mesh technology is used to simulate the underwater rotary motion of the underwater vehicle. The computing domain is used as the background grid, which is divided into structured grids by ICEM. As shown in Figure 2, the underwater vehicle, as a component grid, is divided into unstructured grids mixed with polyhedra and hexahedron by Fluent Meshing, and the circular discharge outlet of cooling water is meshed. The component mesh is inserted into the background mesh to complete the overlapping mesh construction as shown in Figure 3, which ensures the mesh quality during the component movement.  
Mesh independence verification verifies the sensitivity of the calculated results to the density of the mesh. The denser the grid, the more accurate the results and the longer the computation cycle, but the computing resources are limited. By constantly changing the density of the grid and observing the change of the calculation results, the grid independence can be verified if the change range is within the permissible range. The underwater vehicle was rotated for 30 s, and four sets of total grids with the number of units varying from 4.45 million, 5.45 million, 6.45 million and 7.45 million were adopted. The YZ section of 0.3 m behind the body at the initial position of the underwater vehicle was selected. Figure 4 shows the change of thermal wake center temperature on the section with the density of the grid. It can be seen that when the number of grids is greater than 6.45 million, the calculated thermal wake center temperature of the cross section will no longer change significantly with the increase in the number of grids, satisfying the grid-independent condition.  
Figure 1: Model diagram of underwater vehicle rotation in uniform fresh water environment.  
Figure 3: Overlapping grid of underwater vehicle e and sea waters.  
During the simulation calculation, the realizable k-\(\varepsilon\) turbulence model, using the finite volume method for the computational domain, is divided into a number of control volumes to solve the series of control equations. The engineering, turbulent kinetic energy, and specific dissipation rate are controlled using the second-order upwind scheme and coupled pressure-base and velocity-base coupling modes.
## 3 Hypotheses and Model Development
### Technology Readiness  
Technology Readiness captures the internal technical resources of the organization [112]. A meta-analysis [145] asserted the technology readiness importance for IS adoption and impact. [PERSON], [PERSON] [146] recommended technology readiness to be composed of technology infrastructure and IT skills.  
Before adopting CC technology, it is important to know the readiness of an HEI. The HEI needs to promote the technology readiness of CC, and the Internet bandwidth should be sufficient for cloud access by all students and teachers. Instructional content should be ready to run on the cloud. Teachers and students need to have appropriate devices and an adequate internet connection to support the CC initiative [147].  
Recent studies on CC adoption using DOI did find that technological readiness still has a significant impact on the adoption of CC [18; 33; 39; 84; 147]. Therefore, it is hypothesized that:  
**Hypothesis 6 (H6).**_Technology readiness positively influences CC adoption in HEIs._
# A Modified TCP BBR to Enable High Fairness in High-Speed Wireless Networks
## 4 Evaluation of Experiments
### Throughput  
We first tested the throughput of the CCAs in a WiFi network environment. The Web service was set up on the server via Nginx, and 10 Gbps files were prepared for download. The CCAs were tested in the same period in each experiment. The statistical results are shown in Figure 4. Except for CUBIC, the download speeds of the algorithms are the same, which improves the transfer speed. CUBIC has the slowest speed. In the experimental process, we found that the speed of BBQ would increase rapidly after the beginning, and then, the speed would decrease significantly, which could be seen in many experiments. On the other hand, BBR and BBR-PG download speeds are relatively stable. In the beginning, the download speed rose rapidly, then fell back slightly, and finally remained stable. In Figure 4, the speeds of several algorithms increase slightly compared to the small buffer results shown in Figure 4. Overall, BBR and its variants are effective in improving throughput by about 40% over CUBIC.  
Then, we compare the performance of the CCAs in our extensive simulations for 5G. Figure 5 shows the average throughput and latency of each of the algorithms at different loss rates and buffer sizes. As the loss rate rises, the throughputs of all algorithms decline. BBR and its variants are only capable of maintaining approximately 75% bandwidth utilization when the packet loss rate is 10%. In particular, the throughputs of BBQ and BBRv3 are significantly reduced due to the inability of these algorithms to detect the bandwidth with an appropriate \(pg\). Yinker's throughput is maintained due to its utilization of the loss rate and congestion degree to regulate the \(pg\), which in turn can resist packet loss. In contrast to Yinker, BBR-PG attains a high throughput by modifying the \(pg\) in the ProbeBW cycle from a fixed interval to adaptive regulation, thereby counteracting the throughput degradation observed in the lossy network. This further suggests that a fixed \(pg\) cannot adapt to the dynamic characteristics of a cellular network, in contrast to an adaptive \(pg\).  
Figure 4: Transmission speed in WiFi networks.  
BBR-PG exhibits the lowest latency performance among all CCAs. This is because, as it employs RTT to regulate the _pg_, it can effectively limit the queue length. Consequently, it attains a lower latency when the channel is stable. CUBIC maintains a continuous filling of the buffer until a packet loss occurs. Upon the occurrence of a packet loss, the throughput of CUBIC diminishes. Even in the absence of packet loss, CUBIC's throughput performance is considerably inferior to that of other products. This is because CUBIC is designed for wired networks and cannot adapt to highly dynamic networks such as 5G.
# OFF-The-Hook: A Tool to Detect Zero-Font and Traditional Phishing Attacks in Real Time
## 5 Methodology
### Machine Learning Classifier (4 th Layer)
#### 5.5.3 Features Extraction  
The initial stage of developing a machine learning system to detect phishing emails involved a comprehensive research phase to identify effective features. The 10 characteristics listed in Table 6 were selected based on their prevalence and effectiveness as demonstrated in multiple sources, including scientific articles, technical reports, and the widely recognized literature.  
In particular, the presence of phishing keywords and the features of safe keyword presence were derived through a combination of word frequency analysis (through word clouds) and manual refinement using domain expertise. Only frequently occurring and semantically relevant terms, those commonly associated with phishing intent (e.g., urgency, threats, and log-in prompts) or legitimate communication were retained. This ensured statistical significance and contextual validity in the final set of keywords.  
In phishing_keyword_presence and safe_keyword_presence, we conducted a word cloud analysis to find the most repeated words in phishing and ham emails. Then we chose the first 20 correct and meaningful words that were repeated frequently. The results of the cloud analysis are shown in Figures 6 and 7. Figure 6 highlights the most common words found in safe emails, while Figure 7 displays the most common terms identified as phishing emails based on their occurrence.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset Source** & **Class** & **Total Count** & **Training Set (80\%)** & **Validation Set (20\%)** \\ \hline
**[PERSON] and** & & & & \\
**[PERSON] [30]** & & & & \\ \hline
**Cyber Cop. [31]** & Ham & 3039 & 2431 & 608 \\ \hline
**Total** & - & 4053 & 3242 & 811 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Dataset sources and class distribution for training and validation sets.  
The feature phishing_keyword_presence is equal to one if there are five or more phishing keywords in the same email; otherwise, it is equal to zero. The same applies to safe_keyword_presence with the presence of safe words.
## 2 The Examined Numerical Methods  
### The Leapfrog-Hopscotch Method for the Heat Conduction Equation  
To use the leapfrog-hopscotch method, or any other odd-even hopscotch method, the space domain must be discretized using a special, so-called bipartite mesh. This means that the mesh is divided into two disjoint subsets. The nodes or cells belong to the first and second subsets that are labeled as odd and even, respectively. The main requirement is that all the immediate neighbors of the odd cells must be even and vice versa, just like on a checkerboard. We describe it in the case of a 1D interval \(x\in[x_{0},\ x_{N}]\), \(L=\ x_{N}-x_{0}\) on which an equidistant grid is constructed with coordinates \(x_{0},\ x_{1}\,\ \ldots,\ x_{N}\) of nodes, so \(x_{j}=x_{j-1}+\Delta x,\ j=1,\ldots,N,\ \ \Delta x=L/N\). The time domain is \(t\in[t^{0},\ t^{\text{fin}}]\) and it is discretized as usual: \(t^{j}=t^{0}+jh\,\ j=1,\ldots,\ T\,\ hT=t^{\text{fin}}-t^{0}\), where \(h\) is the time step size. The mesh ratio can be defined as \(r=\frac{\alpha h}{\Delta x^{2}}\), where \(\alpha\) is defined after Equation (1). In all stages, the following version of the theta formula (obtained from the central difference formula for the space derivative) is used as a starting point:  
\[u_{i}^{n+1}=u_{i}^{n}+r\Big{[}u_{i-1}^{n}+u_{i+1}^{n}-2\theta u_{i}^{n}-2(1- \theta)u_{i}^{n+1}\Big{]}, \tag{4}\]  
where \(\theta\in[0,1]\). The space-time structure of the algorithm is presented in Figure 1, where one can see that the neighbors \(u_{i\pm 1}^{n}\) are almost always taken into account at the time level half way between the actual old and new time levels. The first stage has the length of a halved time step, and it calculates new values for the odd nodes using \(\theta=0\), thus we have the formula  
\[u_{i}^{\
icefrac{{1}}{{2}}}=\frac{u_{i}^{0}+r/2\big{(}u_{i-1}^{0}+u_{i+1}^{0 }\big{)}+\
icefrac{{h}}{{2}}\cdot q_{i}}{1+r}, \tag{5}\]  
symbolized by thick red arrows in the figure. Then a full time step is made with \(\theta=\
icefrac{{1}}{{2}}\) for the even nodes using  
\[u_{i}^{1}=\frac{(1-r)u_{i}^{0}+r\Big{(}u_{i-1}^{\
icefrac{{1}}{{2}}}+u_{i+1} ^{\
icefrac{{1}}{{2}}}\Big{)}+hq_{i}}{1+r}, \tag{6}\]  
Figure 1: Space–time structure of the leapfrog-hopscotch (LH) scheme. Odd and even nodes are symbolized by light and dark dots, respectively.  
After this, full time steps are taken alternately for the odd and even nodes with Formula (6), symbolized by blue and green arrows in the figure. Finally, a half-length time step (orange arrows) must close the calculations with \(\theta=\
icefrac{{1}}{{2}}\) for the odd nodes  
\[u_{i}^{T}=\frac{(1-\
icefrac{{r}}{{2}})u_{i}^{T-1/2}+\
icefrac{{r}}{{2}}\big{(}u_{ i-1}^{T}+u_{i+1}^{T}\big{)}+\
icefrac{{h}}{{2}}\cdot\
icefrac{{q_{i}}}{{2}}}{1+ \
icefrac{{r}}{{2}}}. \tag{7}\]  
The key point here is that the latest values of the \(u\) function are always used, which means that the time indices of the node variables have to be set according to this logic, in which the figure can help. For example, when the odd node value \(u_{i}^{1+1/2}\) is calculated, \(u_{i-1}^{1}\), \(u_{i+1}^{1}\), and \(u_{i}^{1/2}\) are used, etc.