esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Beam-Forming Optimization for Active-RIS-Assisted Internet-of-Things Networks with SWIPT
## 3 Proposed Algorithm  
To transform the problem (P1) from a non-convex to a convex problem, the variable \(t_{k}\) is introduced below, so (P1) can be rewritten in the following form:  
\[\mathrm{(P2)}:\underset{\mathrm{w}_{k}\Theta,P\text{,}\rho_{k},t_{k}}{\text {maximize}}\ f_{2}=\sum\limits_{k=1}^{K}t_{k}^{2}, \tag{15}\]  
\[\text{s.t.}\frac{\left|\mathrm{h}_{k}^{H}\mathrm{w}_{k}\right|^{2}}{\sum \limits_{\begin{subarray}{c}i=1\\ i\
eq k\end{subarray}}^{K}\left|\mathrm{h}_{k}^{H}\mathrm{w}_{i}\right|^{2}+ \sigma_{v}^{2}\|\mathrm{h}_{r,k}^{H}\mathrm{P}\Theta\|^{2}+\sigma_{k}^{2}+ \frac{\delta_{k}^{2}}{\rho_{k}}}\geqslant\gamma_{k}, \tag{16}\]  
\[\sum\limits_{i=1}^{K}\|\mathrm{P}\Theta G\mathrm{w}_{i}\|^{2}+\sigma_{v}^{2}\| \mathrm{P}\Theta\|^{2}\leqslant p_{\text{max}}, \tag{17}\]  
\[\sum\limits_{k=1}^{K}\|\mathrm{w}_{k}\|_{2}^{2}\leqslant P_{B},\forall k, \tag{18}\]  
\[P_{k}^{L}\geqslant t_{k}^{2},\forall k, \tag{19}\]  
\[0<\rho_{k}<1,\forall k. \tag{20}\]  
The problem (P2) is not a convex problem because the variables are entangled with each other and there is a strong coupling relationship, so it cannot be solved by CVX, the most popular method for solving convex problems. To solve this problem, the optimization objective is first Taylor's first-order expansion, due to the strong coupling relationship between the variables, and then we use the overall BCD algorithm. After first fixing the RIS phase matrix \(\Theta\) and the signal strength increase coefficient P, Semidefinite Relaxation (SDR) and Successive Convex Approximation (SCA) are used to optimize the base station (BS) beam-former w, the PS ratio \(\rho_{k}\) and \(t_{k}\), iterating until convergence. The converged base station (BS) beam-former w, and PS ratio \(\rho_{k}\) and \(t_{k}\) are used as known quantities to design the RIS phase matrix \(\Theta\) and signal strength increase coefficient P. The outputs of the last two components are optimized iteratively with each other until convergence.
# Analyzing the Performance of the Multiple-Searching Genetic Algorithm to Generate Test Cases
## 6 Parametric Analysis
### Crossover Operation  
Crossover operations within MSGAs are used to solve for the optimal solution. The selection of a crossover operator depends on a given problem. Furthermore, the performance of a crossover operator depends on probability. Generally, crossover probability is configured at a high rate [33; 34]. A low crossover probability results in slow convergence to the optimal solution [35]. In this experiment, the crossover operators tested three basic functions (one-point, two-point, and uniform). The crossover probability was specified to be between 0.6 and 1, with increments of 0.1. Each crossover operation was independently executed 100 times while using a mutation probability of zero. When different crossover operators were used to solve a given problem with different optimal crossover probabilities, it was found that an overall uniform crossover outperformed both one-point and two-point crossovers (Table 5). Therefore, the best crossover parameter for this MSGA was a uniform crossover operator with a 0.8 crossover probability.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Pair of the Values** & **\(t\)-Test** & **Mann-Whitney (\(p\)-Value)** \\ \hline
50 vs. no. of parameters & \textless{}0.00001 & \textless{}0.00001 \\
50 vs. 30 & 0.021425 & \textless{}0.00001 \\
50 vs. 70 & 0.124037 & \textless{}0.00001 \\
50 vs. 100 & 0.012744 & \textless{}0.00001 \\
50 vs. 150 & 0.013221 & \textless{}0.00001 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistical comparison between population size 50 and the others.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Population Size**} & \multicolumn{2}{c}{**Best Value**} & \multicolumn{2}{c}{**Average Value**} & \multicolumn{2}{c}{**Worst Value**} \\ \cline{2-7}  & **Time (s)** & **No. of Generations** & **Time (s)** & **No. of Generations** & **Time (s)** & **No. of Generations** \\ \hline No. of parameters & 30.80 & 3 & 119.59 & 54.96 & 345.20 & 100 \\
30 & 35.40 & 3 & 88.73 & 18.29 & 431.30 & 100 \\
50 & 49.10 & 3 & 108.09 & 10.56 & 717.00 & 100 \\
70 & 72.80 & 2 & 145.05 & 7.71 & 1200.40 & 100 \\
100 & 106.60 & 3 & 181.31 & 5.88 & 391.50 & 13 \\
150 & 170.50 & 3 & 327.04 & 5.92 & 507.20 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Effect of population size for test case generation using the MSGA.  
We compared the uniform crossover operator with 0.8 with the others (Table 6). As shown in Table 6, the \(t\)-test indicates that there are five crossover operator values that are greater than 0.05; however, the Mann-Whitney (\(p\)-value) results are all less than 0.05. In considering the worst value, we can still conclude that the uniform crossover operator with 0.8 is better than the others.
## 2 Materials and Methods
### Data Collection  
Data were collected over a duration of 20 weeks. Hens were weighed at the beginning (week 19), at the middle (week 29), and at the end of the study period (week 39). Eggs were collected and weighed daily. Feed intake (per hen per day in grams) was recorded weekly. Egg quality (both external and internal) was measured at 29 and 39 WOA. The apparent total GE and CP digestibility of the dietary treatments was determined at 38 WOA. Hens of approximately similar body weight were selected (8 hens/treatment, 88 hens in total) to collect excreta samples by the total excreta collection method. An individual tray wrapped with aluminium foil paper was placed directly under the cage. Loose feathers and feed residues were removed from the tray, and then the collected excreta were transferred into polypropylene transparent zipper poly bags. This was conducted once daily between 9 a.m. to 1 p.m., over 3 consecutive days (72 h). The collected samples were then transported to the laboratory and mixed thoroughly before taking subsamples in 70 mL plastic containers, which were then stored at 4 \({}^{\circ}\)C. Approximately 5 g of fresh excreta sample was weighed into a preweighed crucible and dried in a forced air oven (Qualtex, Solidstat Temperature Control Oven, Model No. OM24 SE3, Morningside, QLD, Australia) at 105 \({}^{\circ}\)C for around 48 h (to constant weight), to remove the moisture for determination of DM. The remaining excreta subsample was preserved at \(-20\)\({}^{\circ}\)C for further analysis. Feed consumption of the individual hens was also recorded across the 3 days of the excreta collection period.  
A subgroup of hens (8 hens/treatment, 88 hens in total) were euthanised at 40 WOA by decapitation after electrical stunning. Blood samples were collected in silica-coated vacutainers (Becton, Dickinson UK. Limited, Plymouth, UK) containing serum separator polymer gel from the jugular vein during decapitation of the hens to measure serum uric acid levels. Right tibia samples (8 hens/treatment, 88 hens in total) were collected for evaluation of bone parameters. Bones were excised and defleshed manually by hand using a scalpel and scissors following collection, and then transferred to the laboratory in cool box. The fresh wet bones were weighed using a Discover Precision balance (FX-3000i, A & D Company Ltd., Tokyo, Japan) and then air-dried in a fume hood for 2 days, reweighed, and stored at 5 \({}^{\circ}\)C. Hen caecal digesta samples (8 hens/treatment, 88 hens in total) were collected to determine the relative total bacterial population. Caecal contents were collected into 2 mL Eppendorf tubes and then snap-frozen in liquid nitrogen. They were then stored at \(-20\)\({}^{\circ}\)C for further processing. The percentage of dirty eggs was also determined at 40 WOA to establish the correlation between the eggshell cleanliness and caecal microbial load. Daily hen mortality across the study was also recorded. Only one mortality in treatment RP-EAA-Arg was observed over the entire study.
## 5 Results and Discussion
### Analysis and Recommendations
#### 5.5.2 Shenzhen City and Regional Synergy Optimization  
As a multi-centered city, Shenzhen generally follows the development pattern of \"taking industry as the first step, followed by the gradual construction of residential and commercial facilities\". Each region has its own unique development characteristics and functional positioning, and the following conclusions and recommendations on the development of administrative districts can be drawn from the perspectives of urban development and regional planning to ensure the synergistic development of Shenzhen's regions and cities.  
As the core commercial CBDs of Shenzhen, the Nanshan District and Futian District remain the backbone of the city's economic development. These two districts are not only the concentration of financial, commercial, and high-tech enterprises but also the core area of urban development. North of the Bao'an District, due to its border with Dongguan City,government and corporate offices, commercial residences, and other influences in this area show a more pronounced spatial heterogeneity, which may be mainly due to the differences between Shenzhen and Dongguan Cities in terms of their economic development patterns and industrial structures. The two regions should engage in economic cooperation on many fronts to promote industrial synergy and optimize regional resource allocation to achieve more balanced and sustainable development. The Longgang District is mostly industrial land and faces a demand for industrial upgrading and urban renewal. By increasing the proportion of office land and raising the population density per unit of office land, it can effectively promote the diversification of the regional economy and increase passenger traffic. The south side of the Nanshan and Bao'an Districts, due to the large number of residential areas, office locations, and schools, needs to take a series of measures to solve the problem of large passenger flow in the morning peak promptly, such as optimizing the public transport network, increasing investment in transport facilities, and improving traffic management facilities to improve the travel efficiency of the residents. For administrative districts farther away from the downtown commercial CBD area, especially the Longhua District, the traffic congestion situation is a concern; the Longhua District and other districts farther away from the downtown commercial CBD area can be considered within the scope of the opening of a new rail station or other modes of transportation to improve the accessibility of the site to improve the region's subway station passenger flow, which has a very significant role.
## 4 Results and Discussion
### Histogram Analysis
However, the similarly lower values for the MSE and PSNR obtained when applying the BFV and the BGV algorithms over the biometric images indicate that through the implementation of these schemes, a suitable level of security and efficiency can be achieved. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Statistical Metrics**} \\ \hline  & **MSE** & \multicolumn{2}{c}{**PSNR**} & \multicolumn{2}{c}{**SSIM**} \\ \hline
**BFV** & **BGV** & **BFV** & **BGV** & **BFV** & **BGV** \\ \hline
0.0574 & 0.0649 & 27.897 & 27.896 & 0.87244 & 0.87244 \\
0.0532 & 0.0676 & 28.141 & 27.959 & 0.92221 & 0.92221 \\
0.0665 & 0.0755 & 28.100 & 27.911 & 0.91185 & 0.91185 \\
0.0280 & 0.0520 & 27.818 & 27.962 & 0.86908 & 0.86908 \\
0.0443 & 0.0288 & 27.726 & 27.820 & 0.95722 & 0.95722 \\
0.0408 & 0.0549 & 27.827 & 27.968 & 0.91097 & 0.91097 \\
0.0455 & 0.0583 & 27.830 & 27.978 & 0.77493 & 0.77493 \\
0.0615 & 0.0691 & 28.056 & 27.922 & 0.92674 & 0.92674 \\
0.0543 & 0.0684 & 27.888 & 27.976 & 0.91349 & 0.91349 \\
0.0375 & 0.0513 & 27.793 & 27.891 & 0.82885 & 0.82885 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Qualitative parameter analysis. Similar behavior to the previously calculated parameters can be observed for the SSIM index values presented in Table 2, where the values calculated for both cryptographic algorithms are slightly different for each image. The obtained results for the SSIM index approaching 1 indicate a high level of resemblance based on the structural features involved in the present analysis and a close relation between the analyzed images and highlight the efficiency and strength of both homomorphic encryption algorithms. Considering the values obtained during the simulations using the MSE, PSNR, and SSIM parameters, we deduce that both algorithms are suitable for image encryption, and can be integrated into the proposed authentication mechanism to protect the biometric information.
# A Novel Approach to Enhance Crude Oil Recovery Ratio Using Selected Bacterial Species
## 3 Results
### Effect of Temperature and Crude Oil Concentrations on the Growth of the 3 Bacterial Isolates  
Pure cultures of the three selected bacteria were supplemented with different concentrations of crude oil \(-\)1.0, 2.0, and 3.0 (\(v/v\)) and incubated at 37 and 55 \({}^{\circ}\)C under controlled conditions. All species exhibited more favorable growth at temperatures of 55 \({}^{\circ}\)C than at 37 \({}^{\circ}\)C, and hydrocarbon concentrations at 2.0%. The optimum growth rates of the different isolates on 2.0% (\(v/v\)) of crude oil were calculated and showed that the _Bacillus_ species and _Pseudomonas putida_ grew quicker than the other isolate, _Nocardi_ _cryiacigeorgica_, on hydrocarbons of 2.0% (\(v/v\)) at 55 \({}^{\circ}\)C. The maximum specific growth rate (\(\upmu\)max) for the _Bacillus_ species was 1.88 h\({}^{-1}\), _Pseudomonas putida_ was 1.734 h\({}^{-1}\), and the _Nocardi_ _cryiacigeorgica_ was 0.764 h\({}^{-1}\) (Table 3). Therefore, both _Bacillus_ species and _Pseudomonas putida_ gave the highest growth rate and were selected for further investigation.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Ref. Cod** & **Compound Name** & **Chemical Name** & **Chemical Formula** & **Approximate \%** \\ \hline
44-1415 & Lepidocroccite & Iron Oxide Hydroxide & FeO(OH) & 58.04 \\
19-0932 & Microcline Potassium Aluminum Silicate & KAlSi\({}_{3}\)O\({}_{8}\) & 41.46 \\
06-0615 & Wustite & Iron Oxide & FeO & 0.50 \\ \hline \end{tabular}
\end{table}
Table 1: Minerology composition of soil sample using XRD analysis.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Isolates** & **Species as Close Relatives** & **Accession No.** & **Similarity (\%)** \\ \hline HKI & _Pseudomonas putida_ & FM211694.1 & 100.0 \\ HKII & _Bacillus_ sp. & AF526907 & 99.1 \\ HKIII & _Nocardi cynicicgeorgica_ & AF282889.1 & 99.1 \\ \hline \end{tabular}
\end{table}
Table 2: Identification of selected hydrocarbon-degrading bacterial isolates by 16S rDNA gene sequence.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Bacterial Species** & **Concentration in vol \%** & **Growth Rate \(\upmu\) h\({}^{-1}\) at 37 \({}^{\circ}\)C** & **Growth Rate \(\upmu\) h\({}^{-1}\) at 55 \({}^{\circ}\)C** \\ \hline \multirow{4}{*}{_Bacillus_ species} & 1.0 & 0.397 & 1.252 \\  & 2.0 & 0.445 & **1.89** \\  & 3.0 & 0.161 & 0.784 \\  & 1.0 & 0.208 & 1.514 \\ _P. putida_ & 2.0 & 0.651 & **1.734** \\  & 3.0 & 0.119 & 0.772 \\  & 1.0 & 0.205 & 0.786 \\ _N. cyriacigeorgica_ & 2.0 & 0.206 & **0.764** \\  & 3.0 & 0.155 & 0.713 \\ \hline \end{tabular}
\end{table}
Table 3: Growth rates of the three bacterial strains at different optimum concentrations of Bouri crude oil and different temperatures.
## 3 Proposed Model
### TYM Model  
Based on the linear regression technique on the different parameters we have formulated a new model called the TYM model. This model shows that the path loss value is highly dependent on these parameters: distance between the transmitter (TX) and receiver (RX), temperature, humidity, window size, and the frequency. The TYM model is represented in Equation (7).  
\[PL=96.7-0.562*S_{win}-0.366*f+0.092*d-0.187*h+0.257*t \tag{7}\]  
**Definition 7.**_(PL) is path loss, (\(S_{win}\)): window size, (\(f\)): Frequency, (\(d\)): distance between sender and receiver, (\(h\)): humidity, (\(t\)): temperature, and the number before every variable and its coefficient._  
Based on the linear regression technique on the window size parameter, using a TYM model in Figure 4, shows the regression of the path loss by the window size with coefficients (0.562). If the size of the window is big, the path loss will be low, which means the relation is negative.  
Figure 4: Regression of path loss by the window size.  
Based on the linear regression technique on the window size parameter, Figure 5 shows the regression of the path loss between the distance and the receiver with coefficients (0.092). If the distance between the sender and the receiver is very close, the path loss will be low, which means that the relation is positive.  
Based on the linear regression technique on the humidity parameter, Figure 6 shows the regression of the path loss by the humidity with coefficients (0.187). The relative humidity increases, and drops based on (RSSI), and is depended on a positive relationship, while the trend of AH indicates a negative correlation.  
Figure 5: Regression of Path Loss by Distance.  
Figure 6: Regression of Path Loss by Humidity.  
Based on the linear regression technique on the temperature parameter, Figure 7 shows the regression of the path loss by the temperature with coefficients (0.257). When the temperature increases, the signal strength (RSSI) drops, and if the temperature decreases, the signal strength increases. This outcome specifies a negative relationship between the signal strength and the temperature.  
Based on the linear regression technique on the frequency parameter, Figure 8 shows the regression of the path loss by the frequency with coefficients (0.366). When the frequency increases, the signal strength (RSSI) drops, and when the frequency decreases, the signal strength increases. This outcome specifies a negative relationship between the signal strength and the frequency.  
Figure 8: Regression of Path Loss by Frequency.  
Figure 7: Regression of Path Loss by Temperature.  
Figure 9 shows the standard coefficients for all parameters using the linear regression technique and TYM model with a coefficient (96.7).
## 3 Technical Background
### Mathematical Foundation  
FVIM [28] introduced an innovative exploration strategy into the optimization process by the use of four vectors (Alpha, Beta, Gamma, and Delta), as shown in Equations (1) and (2), with each vector representing a distinct subset of top-performing solutions within the search space. This multi-vector structure allows FVIM to conduct a more effective exploration compared to traditional single-point search methods [28].  
\[\mathbf{X}_{n,i}=\mathbf{P}_{n,i}+(\alpha\times 2\times\xi_{1}-\alpha)\times \left|\xi_{2}\times\mathbf{P}_{n,i}-\overline{\mathbf{P}}_{i}\right|\times \begin{cases}1,&\text{if }\xi_{3}<0.5,\\ -1,&\text{otherwise}\end{cases},\quad\text{for }n\in\{1,2,3,4\}. \tag{1}\]\[\overline{\mathbf{p}}_{i}=\frac{\mathbf{X}_{1,i}+\mathbf{X}_{2,i}+\mathbf{X}_{3,i}+ \mathbf{X}_{4,i}}{4} \tag{2}\]  
where, \(\mathbf{X}_{n,i}\) represents the updated position for the \(n^{\text{th}}\) best agent in the \(i^{\text{th}}\) dimension. \(\mathbf{P}_{n,i}\) is the current position of the \(n^{\text{th}}\) best agent in the \(i^{\text{th}}\) dimension. \(\overline{\mathbf{P}}_{i}\) represents the current average position of all agents in the \(i^{\text{th}}\) dimension. \(\alpha\) is a coefficient. \(\xi_{1}\), \(\xi_{2}\), and \(\xi_{3}\) represent random numbers uniformly distributed in [0, 1].  
The mechanism of these vectors is central to FVIM's functionality. The search space is navigated using the Alpha, Beta, Gamma, and Delta vectors, which are dynamically updated according to their fitness values. Each vector is responsible for guiding a segment of the population, promoting a diversified exploration approach. The Alpha vector takes the lead in the search, followed in the hierarchy of Beta, Delta, and Gamma, with each focusing on exploring different regions of the solution space. This hierarchical model is instrumental in maintaining a strategic balance between discovering new areas and capitalizing on known promising regions [28]. A key attribute of FVIM is its search strategy. FVIM incorporates dynamic parameters that adjust the influence of each vector on the search agents. This adaptability allows FVIM to navigate various regions of the solution space effectively, be they sparsely or densely populated with potential solutions [28].  
The balance between exploration and exploitation is another critical aspect of FVIM. Exploration is facilitated through the stochastic nature of the vector updates, ensuring FVIM avoids premature convergence to local optima. In contrast, exploitation is managed by concentrating the search around the best solutions identified by the vectors. This equilibrium between exploration and exploitation is essential in FVIM's capability to uncover high-quality solutions, enhancing the overall efficacy of the optimization process [28].
# Locality-Sensitive Hashing for Information Retrieval System on Multiple GPGPU Devices
## 6 Results and Comparison
### Comparisons  
Table 3 compares the differences of our proposed system with other recent LSH distributed systems. PLSH [21] can support a dynamic data set by using a buffer for storing the new data/items that need to be added to the database. However, the PLSH system requires an interval updating process to add this buffer into the stable database. During that time, the temporary data/items cannot be reached for the NNS problem. Our method employs a single LSH data cluster per each node/device, which is more compatible with GPGPU memory. Further, SES-LSH [22] stores multiple LSH data clusters to increase performance by searching only in selected data clusters. This approach means that our method achieved more accuracy by fully shuffling the queries into all data clusters. We used more network connections among nodes and GPUDirect to reduce the computation of the Master node. This resulted in a reduction in the performance of our system, but meant our system had the best accuracy compared to other similar systems.
# Cybersecurity and Safety Co-Engineering of Cyberphysical Systems--A Comprehensive Survey
## 4 Discussion
Existing work on dynamic security and dynamic safety risk assessment can be leveraged to this end. * _Model type:_ Most of the safety analysis approaches are based on formal models. Security techniques on the other hand tend to focus on qualitative analysis. Therefore, an approach able to handle the complexity of CPS by leveraging both graphical models and systematic perspectives would allow the consolidation of advantages of both worlds. * _Holistic approach:_ The human factor in relation with CPSs is often overlooked. In fact, CPSs, particularly safety/security critical ones need to be considered and studied as socio-technical systems. This calls for a holistic approach towards safety and security co-engineering, that would encompass the whole ecosystem into which the CPS under study is expected to operate, and would involve all the relevant stakeholders in the process. To this end, future methods should enjoy previously mentioned attributes such as _scalability, communication_, and _model type_, in order to facilitate the analysis of CPSs when both technical and human aspects are considered. Particularly, such methods should be able to handle the complexity (_scalability_) derived from the human-machine interaction; communicate the results by providing reports and leveraging software tools (_communication_); and provide graphical models of the system under study (_model type_) to facilitate the analysis and the validation of the results.