esa-sceva/satcom-chunk-collection huggingface dataset:
# A Secure and Efficient Multi-Factor Authentication Algorithm for Mobile Money Applications
## 6 Performance Analysis
### Computational Cost
However, the enrolment, authentication, and transaction are different between the two algorithms, and the matching computational efficiencies are compared accordingly. It was observed that the proposed algorithm provides better security than the schemes in [15; 16; 19; 20; 21; 93]. The result also shows that the proposed algorithm has a higher computation cost than the related scheme [93] because of the cryptographic functions (i.e., SHA-256, RSA, and Fernet encryption/decryption operations). Contrastingly, the algorithm in [93] only uses SHA-1 and ECC and stores the user's biometric template in the database, which takes time to retrieve compared to our proposed algorithm. The encrypted private key and biometric template are stored in the smartphone. Therefore, the proposed algorithm design in securing mobile money proves to be a reliable algorithm because of its efficiency. The proposed algorithm has a high communication overhead and computational cost compared to the existing schemes but provides strong security against many security attacks. This is because it uses multiple identifiers, such as PIN, OTP, biometric fingerprint, and a QR code, for user authentication compared to existing schemes that only use two factors to authenticate mobile money users. Further, the proposed algorithm uses cryptographic techniques such as SHA-256 to secure PIN and OTP, FIDO, which uses the public-key cryptography technique (RSA encryption) to secure biometric fingerprint, and Fernet encryption to secure a QR code and the records in the databases. These cryptographic techniques take extra time during hashing and encrypting/decrypting data. However, most of the existing schemes, such as [13; 14; 15; 16; 17; 18; 19], did not implement cryptographic techniques. The user enrolment, authentication, and transaction processes are simple and easy to use for novice users since the interfaces for the native G-MoMo application prototypes are simple, much like the existing schemes.
###### Abstract  
This study investigates the main insects and endophytic fungi that promote the formation of agarwood in _Aquilaria sinensis_ (Lour.) Spreng. and elucidates the effects and mechanisms of different 'insect + fungus' combinations on agarwood formation. The results showed that 16 strains of endophytic fungi were isolated from _A. sinensis. Fusarium solani, Penicillin chrysogenum, Fusarium equiseti_, and _Phaeocarcinomaum alvesii_ were identified as dominant fungi promoting agarwood formation, while _Nadezhdiella cantori_ was recognized as the dominant insect facilitating this process. The optimal 'insect + fungus' combination was _Nadezhdiella cantori + Fusarium equiseti_. The average agarotetrol contents were 0.046% and 0.054% in February and June, respectively, which were significantly higher than those in cold drilling, fungal-only, and insect-only treatments. RNA sequencing revealed 23,801 differentially expressed unigenes in cjYB1Z4 (optimal combination) versus control BMZ. Upreguulated unigenes were enriched in isoflavque biosynthesis, flavonoid biosynthesis, and sesquiterpenoid and triterpene biosynthesis. Fifty sesquiterpene-related differential unigenes encoded seven key enzymes in the MVA pathway, seven key enzymes in the MEP pathway, and seven terpene synthases. Co-expression network analysis indicated that transcription factors (e.g., _WRKY33, ABF, WKKY2_) potentially regulate agarwood sesquiterpene formation. This work elucidates preliminary effects and molecular mechanisms of insect- and fungi-induced agarwood formation in _A. sinensis_, advancing agarwood induction technology.  
agarwood; _Aquilaria sinensis_; fungal infection; dual fungal-insect induction; agarwood tetraol; transcriptome
# Bacterial Therapy of Cancer: A Way to the Dustbin of History or to the Medicine of the Future?
## 1 Introduction  
The history of a human disease such as cancer and the history of microorganisms, particularly bacteria, are closely intertwined. The relationship between bacterial infections and the treatment of \"swellings\", which were quite probably the cancerous tissue, have been documented by the Egyptian physician [PERSON] more than four thousand years ago [1, 2]. Some observations describing the facts of regression of a malignant tumor in people exposed to bacterial infection were mentioned in medical documents of the early 19 th century [3]. On the other hand, the infectious theory of carcinogenesis, which linked the occurrence and spread of tumors to bacterial or parasitic infection, was popular throughout the late 19 th century and the first third of the 20 th century. Many microbiologists have tried to find a microbial agent that causes cancer and prove its role in oncogenesis. Near the same time, the first anticancer \"vaccines\" were developed, based on inactivated or weakened bacteria. However, the results were poorly reproducible, while the vaccines were not universal (one \"vaccine\" could help against a certain type of cancer but was useless against another). As a result, the theory of bacterial (parasitic) oncogenesis was generally rejected by the scientific and medical communities to the middle of the 20 th century. Despite this, publications have continued to occur only sporadically until recently, when interest in this topic appeared again due to the numerous reports establishing theconnection of some types of cancer with bacterial agents [4; 5; 6; 7; 8; 9; 10; 11; 12]. _Helicobacter pylori_ was the first bacterium whose etiological role in gastric cancer was proven.  
Today, the burden of infections that are associated to human cancer is steadily increasing. The International Agency for Research on Cancer (IARC) estimated that one-in-five cancer cases worldwide are caused by infection [13].  
Having become interested in this topic, the authors initially planned to write a thorough retrospective review including the little-known investigations of scientists from Russia (namely [PERSON] and [PERSON], see below). However, as the review was being written, it became clear that the future of bacterial cancer therapy is much more interesting than its past. Therefore, the main purpose of this review became to create for the readers a bridge from the first doctors and scientists searching for a \"cancer microbe\" to researchers of the 21 st century designing a bacteriobot. Unfortunately, since the volume of the article is not unlimited, we can only provide a quick look at current trends and achievements in this field of science without claiming to be exhaustive.
# Reflective Fiber Surface Plasmon Resonance Sensor for High-Sensitive Mercury Ion Detection
## 3 Performance Test
### Repeatability Analysis  
Figure 11a shows the wavelength shifts of the resonant spectra during the detection of Hg\({}^{2+}\) solutions with different concentrations. After each detection of the Hg\({}^{2+}\) concentration, the sensor was immersed in dilute hydrochloric acid solution with pH = 3.1. After about 10 min, the tracking wavelength could be basically restored to its original position. It can be seen that when the sensor was immersed in solution of different concentrations, the spectral wavelength shifts differently. The main reason is that when the pH of the tested solution was less than 4, the -NH\({}_{2}\) and -COOH in the sensitive materials were protonated, resulting in the release of Hg\({}^{2+}\) bound to it, which guarantees the regeneration and reuse of the sensor. Experimental results show that the sensor has no large loss after 10 times being reused and shows good repeatability performance. The same sensor was also used to  
Figure 9: Resonant spectra (**a**) and linear fitting result (**b**) of the reflective fiber SPR sensor in the Hg\({}^{2+}\) concentration measurement.  
detect Hg\({}^{2+}\) solution of a 50 uM concentration with five cycles in a month. In each cycle, the sensor was firstly inserted into the Hg\({}^{2+}\) solution of a 50 uM concentration until the resonant wavelength reached equilibrium, and then the sensor was immersed in dilute hydrochloric acid solution with pH = 3.1 until the resonant wavelength recovered to the original wavelength. Figure 11b shows the experimental results. In each cycle, the wavelength shifts of the sensor at equilibrium were 20 nm, 20 nm, 21 nm, 19 nm, and 21 nm, respectively. A small difference between the measurement results of the five cycles exists. Besides, the rising velocities of the five cycles are different. It was apparent that there was a small difference between the measurement results of the five cycles. Besides, the rising velocities of the five cycles were different. As the long internal time between the first cycle and the fifth cycle (30 days), the potential influencing factor may be the temperature disturbance (the temperature variation in our lab may be 10 \({}^{\circ}\)C), which will influence the response velocity of the sensor. Besides, the variation of temperature will change the physicochemical properties of the CS/PAA sensitive film and the sensing properties of the SPR sensor, which will then induce measurement error in the mercury ion sensor. To solve this temperature crosstalk problem, one feasible method is to measure the mercury ion concentration and temperature simultaneously, and then compensate the influence of temperature [23; 24]. We will focus on this problem in the future.  
Figure 11: Repetitive experiment of the SPR Hg\({}^{2+}\) sensor in different Hg\({}^{2+}\) concentrations (**a**) and the same Hg\({}^{2+}\) concentration (**b**).  
Figure 10: Resonant spectra (**a**) and wavelength shifts (**b**) of the reflective fiber SPR sensor in different metal ions.
## 3 O-MAC Protocol Design Goals and Architecture
### Dynamic Operational Node  
The DON is selected based on the residual energy, data-forwarding capacity and buffer allocation resources. We assign a weight to each factor. The node with the highest weight is selected as the DON. Once the DON completes 10 cycles, a base station broadcasts the message inside the network for the selection of a new DON. After receiving the message from the base station, the nodes with substantial data-forwarding capacity are declared as the candidate DONs. Subsequently, each sensor node determines its residual energy and buffer allocation resources.  
The advantage of this approach is that it provides a sufficient number of options to each node to be declared the DON based on the set criteria. We describe the residual energy process of each node in each region. Let us assume that single-hop communication is used among the sensor nodes to detect events and transmit the information. Each node forwards data at some specified distance within the region and located within the N * N area of the WSN. We determine the residual energy of two types of nodes: the DON and the non-dynamic operational nodes (NDONs), which can be expressed as described below.  
A DON requires three types of messages for communication, with their corresponding consumed energies: \" E\({}_{\text{sch}}\)\" for scheduling, \"E\({}_{\text{adv}}\)\" for the advertisement and \"E\({}_{\text{dat}}\)\" for sending the data. Thus, the energy consumed by a DON for scheduling can be computed as follows:  
\[E_{sch}=\frac{n\big{(}\Delta C_{p}\times R_{e}\big{)}+n\big{(}A_{e}\times\Delta C _{p}\big{)}}{2M_{e}}+r^{2}\ (N-1) \tag{13}\]  
Equation (13) provides the energy consumed in scheduling the nodes.  
\[E_{adv}=\frac{\big{(}\Delta C_{p}\times R_{e}\big{)}+\big{(}A_{e}\times\Delta C _{p}\big{)}}{2M_{e}} \tag{14}\]  
Equation (14) provides the energy consumed in advertising the message for the interior and exterior communication.  
\[E_{dat}=\frac{\big{\{}n\big{(}\Delta D_{p}\times R_{e}\big{)}+n\big{(}A_{e} \times\Delta D_{p}\big{)}\big{)}^{2}}}{2M_{e}}+\ r^{2}\ (N-1) \tag{15}\]  
Equation (15) provides the energy consumed in receiving the data from the region's nodes (interior communication) and in forwarding the data to either the base station or adjacent DON of another region.
## 3 Disturbance-Rejection Controller Design Based On Linear Extended State Observer (LESO)
### Controller Design  
We used a backstepping method to design the nonlinear attitude-tracking controller. Consider the following Lyapunov candidate function:  
\[V_{1}=k_{1}\ln(1+\tilde{\xi}^{T}\tilde{\xi})+\frac{1}{2}\tilde{\omega}^{T}J \tilde{\omega} \tag{24}\]  
for which the derivative of \(V_{1}\) is  
\[\begin{split}\dot{V}_{1}&=\frac{2k_{1}\tilde{\xi}^{T }}{1+\tilde{\xi}^{T}\tilde{\xi}}H(\tilde{\xi})\tilde{\omega}+\tilde{\omega}J \dot{\tilde{\omega}}\\ &=k_{1}\tilde{\xi}^{T}\tilde{\omega}+\tilde{\omega}^{T}(v+d)\\ &=\tilde{\omega}^{T}(k_{1}\tilde{\xi}+v+d)\end{split} \tag{25}\]  
We can select control input \(v\) as  
\[v=-k_{1}\tilde{\xi}-k_{2}\tilde{\omega}-d_{e} \tag{26}\]  
where \(d_{e}=Jz_{2}\) is the estimation of disturbance \(d\). We then get the final control input  
\[\tau=-k_{1}\tilde{\xi}-k_{2}\tilde{\xi}-d_{e}+[S(\omega)J\omega+I_{A}\dot{ \omega}+C(\omega)\omega+D(\omega)]+J(-S(\tilde{\omega})\tilde{R}\omega_{d}+ \tilde{R}\dot{\tilde{\omega}}_{d}) \tag{27}\]  
Noting \(\tilde{d}=d-d_{e}\), we then get  
\[\dot{V}_{1}\leq||\tilde{\omega}||||\tilde{d}||-k_{2}||\tilde{\omega}||^{2} \tag{28}\]
# An Improved ABC Algorithm and Its Application in Bearing Fault Diagnosis with EEMD
## 2 The Improved Artificial Bee Colony Algorithm  
### Artificial Bee Colony Algorithm  
Artificial bee colony algorithm is a kind of swarm intelligence optimization method that was proposed by [PERSON] in 2005 [19]. It has few control parameters and good optimization ability. Therefore, it attracts many scholars' attention. The optimization performance of artificial bee colony is better than that of differential evolution algorithm, genetic algorithm, and particle swarm algorithm [20].  
The standard ABC algorithm classifies the bee colony into three kinds by simulating the honey collecting mechanism of actual bees: collecting bees, observing bees, and scouting bees. The goal of the entire colony is to find the source of the most nectar. The collecting bees use the previous honey source information to find a new honey source and share the information with the observing bees. The observing bees waiting in the hive search for a new honey source based on the information shared by the bee. The scouting bee randomly searches for a new valuable source of honey near the hive.  
Assuming the solution is a \(D\)-dimensional vector, \(D\) is the number of the optimized parameters. The specific principle is: First, initialize \(S_{N}\) honey sources, which represent \(S_{N}\) solutions. The numbers of collecting bees and observing bees are also \(S_{N}\). Each collecting bee searches within the neighborhood of the corresponding honey source to find new honey source according to formula (1):  
\[X_{ij}^{\prime}=X_{ij}+\phi_{ij}(X_{ij}-X_{kj}), \tag{1}\]  
where: \(k\in\{1,2,\cdots,S_{N}\}\), \(j\in\{1,2,\cdots,D\}\), \(k\
eq i\); \(\phi_{ij}\in[-1,1]\), which is a random number used to control the neighborhood range of \(X_{ij}\). The closer to the best solution, the smaller the neighborhood range.  
Then the collecting bee compares the new honey source with the old one, using greedy principles to select honey source with high fitness value and shares the information with the observing bee. The observing bees select the honey sources according to the following probability calculation formula:  
\[p_{i}=\frac{F_{i}}{\sum\limits_{n=1}^{S_{N}}F_{n}}, \tag{2}\]  
where, \(p_{i}\) is the probability of observing bee's choice of honey source and \(F_{i}\) is the fitness value of the \(i\)th solution.  
Based on \(p_{i}\) and roulette wheel selection, the observing bee selects one solution to update using formula (1) and the greedy criterion is also used to select a better solution with higher fitness. At this stage, only the selected solution can be updated.  
If a honey source has not been improved after _limit_ cycles, then the solution will be abandoned. The bee at this position is transformed into a scouting bee, and a new solution is randomly generated according to Equation (3):  
\[X_{i}=X_{\min}+rand(0,1)(X_{\max}-X_{\min}). \tag{3}\]
# Four-Qubit Cluster States Generation through Multi-Coin Quantum Walk
## 2 Generating Cluster States through Multi-Coin Quantum Walk
### GHZ States Generation  
Before we generate 4-qubit cluster state, it is necessary to generate 3-qubit GHZ state through quantum walk first. Therefore, we consider the generation of GHZ state using 2 pairs of Bell states. The system state of 4 qubits is  
\[|\varphi(0)\rangle=\frac{1}{2}(|00\rangle+|11\rangle)_{01}(|00\rangle+|11 \rangle)_{23} \tag{5}\]  
and our target is to generate GHZ state between qubit 0, 2, and 3, which means that our method is deterministic rather than post-selection based. In the case of 2-complete graph, we only need to go through two quantum walks to obtain the GHZ state, and its evolution is shown in Figure 1, where the circle represents the qubit and the straight line represents the entanglement. Through quantum walks described in Equations (6) and (7), GHZ state formed between qubit 0, 2, and 3 can be obtained. In the implementation of 2-step quantum walks, qubit 0 serves as a walker and qubit 1, 2, and 3 as coins.  
\[|\varphi(1)\rangle =S_{1}^{2-com}(C_{1}\otimes I)|\varphi(0)\rangle=\frac{1}{2}(|00 \rangle+|01\rangle)_{01}(|00\rangle+|11\rangle)_{23} \tag{6}\] \[|\varphi(2)\rangle =S_{2}^{2-com}(C_{2}\otimes I)|\varphi(1)\rangle=\frac{1}{2}(|000 \rangle+|111\rangle)_{023}(|0\rangle+|1\rangle)_{1} \tag{7}\]  
In the first step (Equation (6)), to be specific, we take qubit 0 as walker and qubit 1 as coin whose operator is \(C_{1}=I\). In the second step (Equation (7)), we also take qubit 0 as walker but qubit 2 as coin whose operator is \(C_{2}=I\). After performing X-basis measurement on qubit 1, we can achieve GHZ state between qubit 0, 2, and 3. This process is simulated using IBM Quantum Experience and the result is shown in Figure 2. It can be seen that GHZ states are generated between targeted qubits. This also proved that GHZ states can be prepared through quantum walk only by using Bell states.  
Figure 1: Process of generating GHZ state with 2 pairs of Bell states through quantum walk.  
Figure 2: Generating GHZ states through 2-step multi-coin quantum walk: (**a**) Quantum circuit generating GHZ states and (**b**) simulation results. CNOT gates are indicated by blue circles in the circuit.
# XR Technology Deployment in Value Creation
## 4 Deployment Purposes of XR Technologies in Value Creation
### Collaboration  
Another key area of XR deployment in value creation is collaboration through XR technologies. In particular, the characteristics of XR system compositions provide a high degree of immersion and the interaction of multiple users in one or more virtual spaces, which have a high potential to enable collaboration across spatial and temporal boundaries [87].  
[PERSON] et al. identified the potential for value creation through XR deployment for collaboration by providing virtual access to physically inaccessible scenarios or increasing the efficiency of existing analog collaboration scenarios [63]. [PERSON] et al. introduced a method to enable the locally distributed asynchronous collaboration of multiple parties in immersive environments, allowing the collaborators to avoid inconsistent scene states within their individual scenes [62]. In terms of VR, [PERSON] et al. provide a solution for a collaborative e-learning environment, accessible with multiple mobile and web-based VR devices [61]. [PERSON] et al. developed a concept for web-based VR technology that enables collaboration between technicians and product designers through communication, coordination, control, and integration within the product validation process [68]. The benefits of value creation are improved maintainability and reduced development times, resulting in reduced overall costs [68]. Deploying XR technologies for AR provides major benefits for value creation by enabling multiple people to view, discuss, and interact with 3D models [51]. [PERSON] et al. furthermore created a concept for remote maintenance, connecting trained operators off-site with unskilled people (e.g., clients) on-site in real-time, avoiding sending maintenance experts on-site, and enabling an increase in the number of key accounts that can be served by one maintenance expert [67].  
The XR technology deployment purpose of collaboration can be understood as enabling multiple people from one or more value creation organizations to work together through XR technologies. This includes bridging spatial and temporal distances to increase productivity by saving time and the efficient transfer of relevant information and know-how via XR technologies; the value-adding role of XR technologies in the context of collaboration thereby resides with the creation of both synchronous and asynchronous interaction between stakeholders.
# Identifying the Initial Corrosion Fatigue Failure Based on Dropping Electrochemical Potential
## 3 Results and Discussion
### Relating the Electrical Potential to Fatigue Failure
Precision-turned specimens  
Figure 5: Representative samples of potential pathways of different X2 CrNiMoN22-5-3 specimens measured with the Ag/AgCl wire electrode in an insulated fatigue testing setup. typically had potentials about 50 mV higher than polished specimens [19] (indicating less susceptibility to corrosion processes), because at sufficient high stress amplitudes the lifespan of precision-turned surfaces improves with higher stress amplitudes and short test durations. Note that at long test durations and low stress amplitudes, polished surfaces minimize stress concentration near micro-notch bases, reducing crack initiation and delaying early failures. (As detailed in prior studies summarized in [19], polished surfaces with an Rz of approximately 1.4-1.59 \(\upmu\)m exhibit better resistance to degradation than turned surfaces with an Rz of about 3.2 \(\upmu\)m.)  
Immediate failure did not occur, instead, the electrochemical potential generally dropped over a 15- to 30 min period (Figure 5). Electrochemical fluctuations in potential which may follow, displayed in Figure 6, signal local damage to the passivation layer and may suggest a secondary flaw or microcrack [19]. Most likely, alternating passivation and degassivation reactions at the steel's surface, caused by contact with geothermal brine, progressively weaken the material.
# Coal Mine Water Inflow Prediction Model Based on Multi-Factor Pearson Correlation Analysis
## 1 Introduction
Therefore, the present paper develops a coal mine water inflow prediction model based on multi-factor Pearson correlation analysis, where the factors with strong correlations with water inflow are selected to train the prediction model, while the factors with weak correlations with water inflow are neglected to improve the accuracy and efficiency. According to a comparison of the present model with the previous models, the accuracy of predicting coal mine water inflow is improved significantly. The present study is organized as follows. Firstly, the CNN-BiLSTM-Attention model is presented. Next, a Pearson correlation analysis of coal mine water inflow is conducted, where correlation coefficients of different factors are calculated. Then, to validate the performance of the present prediction model, a case study is conducted, where the predicted coal mine water inflow is close to the collected coal mine water inflow. Lastly, the present prediction model is compared to other prediction models; the present prediction model can predict the magnitude and development trends of coal mine water inflow in the next 8 h more accurately.