esa-sceva/satcom-chunk-collection huggingface dataset:
# Practical Inner Product Encryption with Constant Private Key +
## 4 Security Analysis of the Proposed Scheme
** This phase is the same as **Query Phase 1**. * **Guess. ** The adversary \(\mathcal{A}\) outputs a bit \(\beta^{\prime}\). The challenger \(\mathcal{C}\) outputs 1 if \(\mathcal{A}\) wins the game and outputs a random bit otherwise. Assume that the adversary \(\mathcal{A}\) wins the game with advantage \(\epsilon\): \[\left|\Pr[\beta^{\prime}=\beta]-\frac{1}{2}\right|\geq\epsilon.\] If \(C=g_{T}^{ab}\), then the view of the adversary is identical as that in real world. Thus, we have \[ \begin{array}{ll}&\Pr[\mathcal{C}(P,A^{\prime},g_{T},A,B,C=g_{T}^{ab})=1]\\ =&\Pr[\beta^{\prime}=\beta]\\ \geq&\frac{1}{2}+\epsilon.\end{array} \] However, if \(C\) is a random element in \(\mathbb{G}_{T}\), then the choice of \(\beta\) is independent from the adversary's view and we have \[ \begin{array}{ll}&\Pr[\mathcal{C}(P,A^{\prime},g_{T},A,B,C\stackrel{{ \xi}}{{\leftarrow}}\mathbb{G}_{T})=1]\\ =&\Pr[\beta^{\prime}=\beta]\\ =&\frac{1}{2}.\end{array} \] Therefore, the advantage of \(\mathcal{C}\) in solving the M-DDH\({}_{\mathbb{G}_{T}}\) problem is \[ \begin{array}{ll}&\left|\Pr[\mathcal{C}(P,A^{\prime},g_{T},A,B,C=g_{T}^{ ab})=1]\right.\\ -&\Pr[\mathcal{C}(P,A^{\prime},g_{T},A,B,C\stackrel{{\xi}}{{ \leftarrow}}\mathbb{G}_{T})=1]\right|\\ \geq&\left|\left(\frac{1}{2}+\epsilon\right)-\frac{1}{2}\right|\\ \geq&\epsilon.\end{array} \] This means that if there is an adversary winning the game with nonadvantage \(\epsilon\), then there is an algorithm \(\mathcal{C}\) solving the M-DDH\({}_{\mathbb{G}_{T}}\) problem with a probability greater than \(\epsilon\).
# Optimal Deployment of Container Weighing Equipment: Models and Properties
## 4 Model Verification and Analysis
### Sensitivity Analysis
#### 4.3.2 The Impact of the Number of Weighing Machines  
In this analysis, the baseline scenario involves 400 weighing machines. As the number of machines increases to 1013, there is a linear growth in the optimal objective function value, with a slope of 1000. This reflects the maximum weekly weighing capacity (\(M\)) of one machine, confirming that during this period, all machines are fully utilized to weigh containers at their origin ports. However, between 1014 and 1023 machines, the growth rate of the objective function value decelerates, suggesting that some machines are now weighing containers at transshipment or destination ports. Upon reaching 1024 machines, the objective function value stabilizes, indicating that the capacity to weigh all containers has been met. Figure 3 illustrates these dynamics.  
Table 3 and Figure 4 illustrate the distribution of weighing machines and containers across various ports when the total number of machines is 1014. At this juncture, the data indicate that containers begin to be weighed at their transshipment location, specifically at port 4. Consequently, the value of the optimal objective function is recorded at 1,013,902.  
Figure 4: Container weighing scheme with 1014 weighing machines.  
Figure 3: The optimal objective function value under different numbers of weighing machines.  
Table 4 and Figure 5 present the allocation of weighing machines and containers across each port, when there is a total of 1018 machines. Notably, several containers are weighed at their intermediate ports: 386 containers are weighed at their transshipment port 1, with 76, 404, and 97 containers weighed at ports 4, 8, and 10, respectively. Additionally, an interesting observation is that while 219 containers destined for port 3 are weighed there, surpassing the initial count of zero, containers transshipped at port 3 are not weighed at this location. This serves as empirical validation of Proposition 6.  
Table 5 and Figure 6 display the distribution of weighing machines and containers across ports when the total number of machines reaches 1024. Notably, even with an increase from 1023 to 1024 machines, all containers remain to be weighed at their respective ports of origin. This observation confirms that all containers  
Through a sensitivity analysis of the number of weighing machines, we determine the relationship between the changes in the number of machines and the corresponding shifts in the value of the objective function. This analysis has substantiated several propositions discussed earlier, illustrating examples of these relationships. Additionally, the analysis provides a visual representation of the variations in the number of weighing machines and the corresponding fluctuations in the number of containers weighed at each port.
## 3 Results and Discussion
### Comparison with Available Studies
#### 3.3.1 Hindukush  
[PERSON] et al. [11] applied ensemble-based methods to the Hindukush region, for binary classification, achieving a 78.7% accuracy score. A hybrid neural networks and support vector regressor (SVR-HNN) was used by [PERSON] et al. [9] for the Hindukush region, for seismic prediction. The authors claimed to achieve 65% accuracy. A seismic classification system was presented by [PERSON] et al. [14], using machine learning algorithms, while attaining a 79% accuracy score. Table 8 evidently highlights that the proposed model performed better, with a 95.13% accuracy score against the extant approaches to the Hindukush region, on the basis of accuracy as well as precision, recall, and F1-score.
# State-of-the-Art Review of Structural Vibration Control: Overview and Research Gaps
## 3 General Limitations of Structural Control Systems
### Sensor and Controller Placement  
The practical constraints of installing sensors at all critical points for comprehensive feedback collection are formidable. Balancing the optimization of sensors and controller locations with limited resources can be a complex task, and using observers to construct a state vector from sparse measurements may introduce additional challenges. The strategic positioning of actuators and sensors not only enhances energy efficiency and cost-effectiveness but also plays a crucial role in influencing the stability and reliability of a control system. The methods for designing optimal damper placement can be categorized into three primary groups: evolutionary, analytical, and heuristic. A review of these methods has been outlined by [PERSON] et al. [44]. The authors recommend that more studies be conducted on damper placement for algorithms such as the artificial bee colony algorithm (ABCA) and the firefly algorithm (FA). [PERSON] and [PERSON] [45] presented a review of placement control devices for structural control techniques, stating that most of the available literature on semi-active control focuses on MR dampers. The study also states that the research on the optimal placement of control devices in semi-active and hybrid control systems remains largely unexplored. While structural control systems show promise in effectively mitigating external dynamic forces, such as those induced by winds and earthquakes, the precise optimization of device placement has not received extensive investigation.
_applied_  
_sciences_  
_Article_  
**A Novel Bacterial-Mediated Method for the Differentiation of**  
_Aedes_ **spp. Larvae in Mixed-Nursery Assays and Its Application in the Determination of Vegetative**  
**in the Determination of Vegetative**  
**Biocontrol Efficiency in Invasive Vector**  
**[PERSON] (c) and [PERSON] ([EMAIL_ADDRESS])**  
Microbiological Research Center (CMIC), Department of Biological Sciences, Universidad de Los Andes, Bogota 111711, Colombia; [EMAIL_ADDRESS]  
\({}^{\star}\) Correspondence: [EMAIL_ADDRESS]; Tel.: +57-(1)339-4949  
**Featured Application: The developed coloration method employed the use of a genetically modified strain of the**  
_Escherichia coli_ **bacterium, more specifically the pUC19 and red fluorescent protein (mRFP)-containing DHS\(\alpha\) strain. This bacterium was used in a coloration step previous to comparative mortality assays involving two**  
**species. This assessment allowed for the quick and easy differentiation between both**  
_Ae. albopictus_ **and**  
_Ae. aegypti,_ **and the results obtained shone light onto possible ecological variables to have in mind when applying biocontrollers in the wild. The evaluation of this method applied to differential mosquito lethality served as a proof of concept, and it is theorised that the same coloration method can be employed in similar comparative lethality studies relating to public health and beyond.**  
The establishment of _Aedes aegypti_ and the recent invasion of _Aedes albopictus_ has put Colombia at risk, as it now harbours two arboviral vector species. Studies have shown the susceptibility of _Aedes aegypti_ to the vegetative toxins produced by _Lysinobacillus sphaericus_. This study aims to determine the potential of two _L. sphaericus_ strains in the control of both _Aedes_ species present in Colombia, in both single and mixed species nurseries. Given the similarities between both species, there exists a need to differentiate them for the development of precise control strategies. Accordingly, mixed nurseries employed a red fluorescent protein (mRFP)-expressing _Escherichia coli_ strain as a differentiator. Coloration-aided differentiation facilitated the analysis of mixed nursery treatments and provided deeper scrutiny into the variables at play. This showed an increase in the biocontrol efficiency, pointing to probable ecological variables. Bioassays involving _Ae. albopictus_ showed a lower resistance to the one reported for _Ae. aegypti_ in single-species nurseries. The study concludes that the use of _E. coli_ strains expressing fluorescent proteins are useful tools to be employed in areas like public health entomology. Moreover, it was found that the use of _L. sphaericus_ strains for biocontrol of vector mosquitoes is a viable alternative to chemical insecticides.  
_E. coli_; larvae differentiation; biological control; _Lysinobacillus sphaericus_; vegetative cell; _Aedes_ spp.
## 2 Materials and Methods
### Phylogenetic Analyses  
_S. mycoparsitica_ transcripts (i.e., Sm68082_c0_g1_i1 for biotrophy-associated secreted protein; Sm105692_c0_g1_i1, Sm72487_c0_g1_i2, Sm24426_c0_g1_i1, Sm52544_c0_g2_i1, Sm60015_c0_g1_i1, Sm107436_c0_g1_i1, and Sm72276_c0_g1_i2 for ABC-B; Sm78934_c5_g2_i8, Sm78625_c4_g2_i1, Sm78625_c4_g2_i3, Sm78625_c4_g2_i5, Sm75748_c0_g1_i1, Sm80145_c2, g9_i1, Sm75748_c0_g1_i3, Sm75748_c0_g1_i2, Sm76779_c0_g2_i1, and Sm79181_c0_g2_i4 for ABC-C; Sm80002_c1_g4_i2, Sm76040_c0_g1_i1 for ABC-F; Sm79490_c2_g2_i2 for ABC-G) of this _Fusarium_-specific mycoparsite were used to compare a biotrophy-associated protein and ABC-B, -C, -F, and -G transporters. Different fungi representing biotrophic, chemibiotrophic, necrotic, endophytic, and hemiedophytic lifestyles were selected for comparisons. Transcriptome (RNA sequencing) data of _S. mycoparsitica_ are deposited in NCBI with currently available accession PRJNA757970.  
The phylogenetic ABC transcript analyses of _S. mycoparsitica_ as well as evolutionary relationships of this biotrophic mycoparsite with different fungal and other eukaryotes were investigated. The evolutionary history was inferred using the minimum evolution method [39]. The optimal phylogenetic trees with the sum of branch length equal to 2.46085065, 11.74499404, 53.69656248, 2.19433433, and 1.13037743 were shown. The trees were drawn to scale, with branch lengths in the same units as those of the evolutionary distances used to infer the phylogenetic trees. The evolutionary distances were computed using the maximum composite likelihood method [40] and were in the units of the number of base substitutions per site. The ME trees were searched using the close-neighbor-interchange (CNI) algorithm [41] at a search level of 1. The neighbor-joining algorithm [42] was used to generate the initial trees. All ambiguous positions were removed for each sequence pair (pairwise deletion option). There was a total of 1002, 7567, 458, 4870, and 3364 positions in the final datasets of the presented phylogenetic trees The evolutionary analyses were conducted in MEGA X [43].
## 4 Active Distribution Network Scheduling Model  
The scheduling model established in this paper considers energy storage, controllable distributed generation, switches, and demand-side loads. Therefore, the decision variables include the active power of energy storage, the active power of DGs, the state of switches, and the incentive price of demand-side loads.
# SAHA: A String Adaptive Hash Table for Analytical Databases
## 1 Introduction
### Problems  
Implementing efficient hash tables for analytical databases is nontrivial, due to the huge design space they belong to and their critical performance impacts on a query's runtime. [PERSON] et al. [9] gives an in-depth analysis of how to implement hash tables for integer keys. They proposed more than 10 conditions to check in order to find the best hash table implementation. However, there is little information in an analytical database to fulfill those checks, and processing string keys is more difficult than integers because strings have variable lengths. Meanwhile, string data in the real world is commonly used as a key column in many queries, such as URL domains, nicknames, keywords, RDF (Resource Description Framework) attributes and IDs, to name a few. These datasets share a common property: the average length is short. There are also some string datasets, such as package delivery addresses--web search phrases that have long average string length. Nevertheless, neither  
Figure 3: Two main implementations of hash tables.  
distribution gets optimized in current analytical databases, and there is no easy way to tell beforehand how the actual length distribution of a string dataset looks in every subset to choose a suitable hash table implementation. In summary, we discuss the following three problems to process analytical workloads on string datasets:  
1. When processing string datasets, different optimizations should be applied based on the distribution of string length. There is no single hash table that can fit all cases of string data.
2. Short strings are cost-ineffective when stored with pointers, and it is a waste of space to store saved hash values for short strings.
3. Hashing long strings while doing insertion or lookup in hash tables is cache unfriendly.
## 4 Solution Algorithms
### An Adaptive Large Neighborhood Search Algorithm with Efficient Initial Solution
#### 4.6.2 Solution Destruction  
In the destruction phase, we put forward four different removal methods to maintain the diversity during the searching process. Each removal method aims to remove a predefined number of warehouses or stations from current assignment. We denote the removed nodes as unassigned nodes.  
* Random-remove This operator is very simple but important for exploring larger search space [57], the main idea is to randomly choose warehouses or stations as target nodes, then remove them. In this paper, we remove N/2 target nodes, where N means the total number of warehouses and stations.
* Worst-remove This worst-remove is adapted from the worst-remove proposed by [56]. The main idea is to remove the nodes that have a larger impact on the solution quality. Specifically, the Equation (1) is calculated before and after removal, the larger difference it is, the higher probability that considered nodes (denoted as worst nodes) are removed.
* Break-removeInspired by the work of [58], where they design break-remove operator in the ALNS to solve the gate assignment problem. This operator is carried out in two steps, firstly, we sort all the DCs by the average cost \(c_{l},l\in L\) in descending order. \(c_{l}\) is calculated as the sum of transportation cost related to DC \(l\). Secondly, we select the DC with maximum average cost and all the nodes (warehouses or stations) assigned to the selected DC are removed.  
1. Exchange  
This operator can be regarded as a special type of destroy operator, the solution can be updated without using repair operator. In this paper, we adopt the two exchange and can be further divided into three situations: exchange for warehouses, exchange for stations, and both. In the algorithm, they are three types of separate destroy operators. Take the exchange for warehouses as an example, two warehouses are selected, if they have different DCs then exchange them. Otherwise, reassign them to other DCs.
## 4 Metasurfaces for OAM Beam Generation
### Transmissive Metasurfaces
When the structure is illuminated by circularly polarized incident wave, two vortex beams with OAM modes \(l=1\) and \(l=-2\) are excited in different diffraction directions. The dual OAM beams metasurface achieve 71% efficiency at 29.4 GHz and over 50% in a frequency bandwidth of 13%. In Reference [80], the authors proposed a mechanism for producing distinct OAM modes in orthogonal circular polarized (CP) transmitted fields, which breakthrough the inherent symmetric restriction between orthogonal CP waves (Figure 13d). The vortex beam carrying an OAM with mode \(l=1\) was generated in the cross-polarized output field under RHCP illumination, while the OAM with mode \(l=3\) is produced through cross-polarization under LHCP illumination. The polarization state of incident wave acts as the important role for altering the distinct OAM modes, and hence dual mode vortex beams have been generated conveniently and effectively. Figure 13: (**a**) OAM metasurfaces based on multi-layer CP conversion unit [77]. (**b**) Vortex beams multiplexing based on CP wave [79]. (**c**) Multiple OAM modes by active metasurface [78]. (**d**) CP wave decoupled OAM metasurface [80].
## 4 Real-Time Orbit and Clock Offset Performance  
### Real-Time Orbit Performance  
The satellite orbit product performance is evaluated between real-time and final products in terms of 'along', 'cross' and 'radial' directions; the evaluation methodology is referenced in [32]. The real-time GNSS satellite orbit accuracies in along, cross and radial directions on the day of year (DOY) 319 in 2022 are depicted in Figure 7. The mean accuracies of real-time GNSS satellite orbit in three directions and the three-dimensional (3D) Root Mean Square (RMS) are counted in Table 3.  
As can be seen from Figure 7, for GPS, the satellite orbit accuracy difference for different satellites is small, and the orbit accuracy for most satellites is better than 0.05 m for along, cross and radial directions, while the orbit accuracy of some satellites is larger than 0.05 m for radial direction, such as the G14 satellite. The orbit accuracy of the BDS-2 satellites is poor, particularly in some of the IGSO satellites, which is mainly because of the worse geometry configuration of IGSO satellites [33]. Moreover, the solar radiation pressure model is mainly for MEO satellites, which does not fully satisfied the precise orbit determination of IGSO satellites. Compared with BDS-2, the real-time orbit accuracy of BDS-3 has been significantly improved due to their better geometry configuration and the  
Figure 6: PDOP for Huawei Mate40 and P40.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**System** & **Along** & **Cross** & **Radial** & **3D RMS** \\ \hline GPS & 0.064 & 0.029 & 0.025 & 0.075 \\ BDS-2 MEO & 0.127 & 0.055 & 0.030 & 0.142 \\ BDS-2 IGSO & 0.770 & 0.173 & 0.409 & 0.889 \\ BDS-3 MEO & 0.123 & 0.047 & 0.051 & 0.141 \\ BDS-3 IGSO & 1.513 & 0.329 & 1.091 & 1.895 \\ GLONASS & 0.132 & 0.036 & 0.042 & 0.143 \\ Galileo & 0.067 & 0.031 & 0.034 & 0.081 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The average accuracy of the real-time orbit (unit: m).  
improved onboard atomic clock and signal designation. For the BDS-3 IGSO satellites, i.e., C38, C39 and C40, their orbit accuracy is significantly worse than that of BDS-3 MEO satellites. The performance of the real-time orbit among the Galileo satellites is small, and the accuracy in three directions is better than 0.1 m, except for individual satellites. The difference in the real-time orbit accuracy for GLONASS satellites is smaller in along and cross directions, while it is larger in radial directions.  
Figure 7: \(Cont.\)  
It becomes clear that from the statistics given in Table 3, the real-time orbit performances of GPS and Galileo are superior, the BDS MEO and GLONASS satellites are the next best, and the BDS IGSO satellites are the worst.
# Investigation of Using CAPTCHA Keystroke Dynamics to Enhance the Prevention of Phishing Attacks
## 5 Experimental Evaluation of the Proposed System
### Main Experiment  
Seventy-five participants participated in a controlled laboratory experiment to evaluate the proposed system. The participants comprised undergraduate students studying different subjects at Qassim University, all with different levels of typing skill. In addition, all were familiar with text-based CAPTCHAs. These participants were divided into two groups: genuine users and phishers. The group of genuine users consisted of 30 participants, while the phishing group contained 45 participants. The number of participants and their division into groups was very similar to the method adopted in most of the previous studies, for example, study of Ref. [40]. Thus, the results of these studies were all considered to be equally credible. Nonetheless, a higher number of participants was sampled in this current work compared to previous studies. The following subsections explain the actual experimental setup and procedure.
## 3 Maintenance and Lifespan Management
### Developing the Means that Enable Signals to be Digitally Acquired
#### 3.1.1 Sensors and Parameters  
[PERSON] et al. (2016) pointed out that the passive optical transformer has excellent measurement quality, in the same way for the active optical transformer. It seems that analog transformers present certain improvements over digital ones, as shown by [PERSON] and [PERSON] (2016); specifically, the output bandwidth in digitals has more limitation than analog ones.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Summary of Connection to the Enterprise Architecture**} & \multicolumn{2}{c}{**Operation and Control: Enterprise Architecture**} \\ \cline{2-3}  & **Coordinating the Operation with the External Systems** & **The Handling and Interoperability of Large Database** \\ \hline
**Database** & [138; 141; 142] & [137; 139; 140; 143] \\ \hline
**Customers and Exchange** & [145; 146; 148] & [149] \\
**Between Networks** & [145; 146; 147; 148] & [149] \\ \hline
**Technical and Non–Technical Losses** & [150; 151; 152; 153; 154; 155; 156; 157; 158; 159; 160; 161; 162] & - \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of Connection to the Enterprise Architecture.  
[PERSON] and [PERSON] [165] demonstrate that it is necessary: intelligent transformers, integrative information platform, sequence control, intelligent alarm and smart switches.  
It is necessary to have new sensors in the infrastructure, to allow the SG to self-diagnose and self-heal, as presented by [PERSON] [166]. [PERSON] et al. [167] present a partial discharge measurement system using a fluorescent fiber sensor.  
Some authors propose the development of a new IED for protections in substations, such as the one presented by [PERSON] and [PERSON] [168]. A new fault recognition system is proposed by [PERSON] et al. [169], and study the signal-to-noise ratio of the electric line. By means of intelligent sensors, it is possible to design a new self-healing strategy, as shown by [PERSON] et al. [170].  
[PERSON] et al. [171] propose a dynamic generator estimator, which is based on certain captured parameters; the method is based on Newton-Raphson. [PERSON] et al. [172] and [PERSON] et al. [173] present a novel method for estimating parameters based on weighted least squares method and a new approach in estimating important parameters based on Kalman filter, respectively. The new sensors will allow new advances in the development of the SG, and they will also allow the development of new applications based on new monitored variables.