esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Three-Stage Quantum Cryptography  
In the BB84 protocol and its many variants, each qubit is transmitted in one of four different states. In contrast, in the three-stage protocol, the qubit can be in an arbitrary quantum state during the transmission. The qubit remains in its quantum state in each stage. No classical information exchange is needed for the three-stage protocol. In comparison, in previous protocols, the classical information is exchanged after qubit transmission in one direction.  
In the three-stage protocol, the qubit state \(\left|X\right\rangle\) can be in one of the two orthogonal states, such as \(\left|0\right\rangle\) and \(\left|1\right\rangle.\) Alternative orthogonal states, e.g., \(\dfrac{1}{\sqrt{2}}\left\langle\left|0\right\rangle-\left|1\right\rangle\right\rangle\) and \(\dfrac{1}{\sqrt{2}}\left\langle\left|0\right\rangle+\left|1\right\rangle\right\rangle,\) can also be used. The orthogonal states of \(\left|X\right\rangle\) represent \(0\) and \(1\) respectively. The orthogonal states, as well as details such as which state represents \(1\) and \(0\), are agreed upon prior to the transmission.  
[PERSON] and [PERSON] will apply secret rotation operators \(R_{A}(\alpha)\) and \(R_{B}(\beta)\) on the qubit state \(\left|X\right\rangle.\)\(R_{A}(\alpha)\) and \(R_{B}(\beta)\) are commutative, which means \(R_{A}(\alpha)R_{B}(\beta)\left|X\right\rangle=R_{B}(\beta)R_{A}(\alpha)\left| X\right\rangle.\)  
The summarized steps shown in Figure 1 are described as follows:  
* Step 1: [PERSON] applies a unitary operator \(R_{A}(\alpha)\) on quantum information \(\left|X\right\rangle\) and sends the qubit to [PERSON].
* Step 2: [PERSON] applies another unitary operator \(R_{A}(\beta)\) on the received qubit state \(R_{A}(\alpha)\left|X\right\rangle\), thereby giving \(R_{B}(\beta)R_{A}(\alpha)\left|X\right\rangle\) and sends it back to [PERSON]. \(R_{A}(\alpha)\) and \(R_{B}(\beta)\) should be commutative transformations.  
Figure 1: Schematic diagram of the three-stage protocol.  
* Step 3: [PERSON] applies \(R_{A}(-\alpha)\) (transpose of complex conjugate of \(R_{A}(\alpha)\)) on the received qubit state to get \(R_{A}(-\alpha)R_{B}(\beta)R_{A}(\alpha)\big{|}X\big{\rangle}=R_{B}(\beta)\big{|}X \big{\rangle}\) and sends it back to [PERSON].
* Step 4: Then [PERSON] applies \(R_{B}(-\beta)\) on \(R_{B}(\beta)\big{|}X\big{\rangle}\) to get the information\(\big{|}X\big{\rangle}\).  
The angles of polarization rotation that both sides ([PERSON] and [PERSON]) select to apply to the information bits are arbitrary and independent values which vary from 0 to 180 degrees. The eavesdropper cannot obtain correct information without the knowledge of the correct polarization rotation. Moreover, both [PERSON] and [PERSON] do not need to exchange the encryption angle before the transmission. All they need to do is to apply their independent angle to the information (potentially, one unique rotation angle for each bit of information), and reverse the process independently. Then the receiver can recover the original information.
# Deep Learning-Based Hip X-ray Image Analysis for Predicting Osteoporosis
## 3 Research Methods
### Experimental Design
#### 3.4.3 Deep Learning Model Training  
In this study, the model training was divided into two parts: image segmentation and image classification. Image segmentation was performed using radiographic images of the femoral neck, greater trochanter, Ward's triangle, and total hip. The labeled images of the four areas were input into the U-Net and U-Net++ models for training, and the results of the two model experiments were compared after binary segmentation of the images was generated by U-Net and U-Net++. Regarding the U-Net and U-Net++ models, we utilized the Adam optimizer, cross entropy loss function, batch size set to 2, and learning rate = 0.00001. For image classification, the X-ray images of the four parts of the body were divided into two categories according to the above-mentioned bone density and T-score indexes, and the experiments were conducted to compare the segmented and non-segmented images, and then VGG16, ResNet50, and DenseNet121, which are the innovative pre-trained models of deep learning, were used for the image classification experiments in this study after fine-tuning the models. Moreover, VGG16, ResNet50, and DenseNet121, which are innovative deep learning pre-trained models in recent years, were fine-tuned  
\begin{table}
\begin{tabular}{c c} \hline \hline
**T-Score Value** & **Degree of Osteoporosis** \\ \hline T-score \(>-2.5\) & not suffering from osteoporosis \\ \hline T-score \(\leq-2.5\) & suffering from osteoporosis \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification basis of the experimental T-score in this study.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Evaluation Indicators** & **Description** \\ \hline Accuracy & Percentage of correctly diagnosed positive and negative patients in all cases. \\ \hline Sensitivity & Known as the True Positive Rate (TPR), the proportion of patients who are positive who are diagnosed as positive indicates the detection rate of symptoms. \\ \hline Specificity & Known as the True Negative Rate (TNR), the proportion of patients with a negative diagnosis who are negative indicates the detection rate of asymptomatic patients. \\ \hline F1-score & The F1-score is used to comprehensively assess the performance of a model. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Definition of model evaluation indicators.  
to be used as the image classification experiments in this study. In this study, we used the Adam optimizer, cross-entropy loss function, batch size set to 16, and a lower learning rate of 0.000001 in the image classification experiments. All these parameter settings were determined as the optimal parameters through multiple rounds of testing. Additionally, we added a dropout layer and a dense layer to the model structure. The purpose of the dropout layer is to avoid the over-simulation problem in the subsequent experiments, and the dense layer is used as the output layer to generate various types of probability values with the softmax function.
# Development of Energy Storage Systems for High Penetration of Renewable Energy Grids
## 3 Frequency Regulation Technology Model  
The WECC second-generation generic mode is adopted to design proposed control strategies in this section. The battery energy storage system model is at the utility-scale level and operated using a four-quadrant control mode. The battery energy storage system topology is a three-phase universal topology. Figure 3 shows the block diagram of the model. The battery energy storage system model consists of the renewable energy plant control (REPC_A) model, the renewable energy electrical control (REEC_C) model, and the renewable energy generator/converter control (REGC_A) model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Control Method** & \begin{tabular}{c} **Considered Components** \\ **and Characteristics** \\ \end{tabular} & **Response Parameters** \\ \hline Virtual synchronous machine & \
\begin{tabular}{c} Stator windings, rotor windings, \\ excitation windings, damping \\ \end{tabular} & x \\ \hline Swing equation emulation & X & Rotational inertia \\ \hline Synchrotronver &
\begin{tabular}{c} Stator winding impedance, stator \\ winding phase voltage, \\ rotor motion back EMF \\ \end{tabular} & Rotational inertia, damping constant \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of virtual inertia control method.  
The control block diagram of the REPC_A model includes the active power control block and the reactive power control block. The important parameters of the active power control block of the REPC_A model include the deadband range, droop gain, PI controller settings, and command value range settings. Figure 4 depicts the control block diagram of the REPC_A model.  
The control block diagram model of the REEC_C model includes the active power control block and reactive power control block. The important parameter in the active power control block is the protection and determination of the state of charge (SOC). The reactive power control block utilizes flag selection to adjust different functions. As shown in Figure 5, it represents the control block diagram model of the REEC_C model.  
Figure 4: The block diagram of the REPC_A model [27; 28].  
Figure 3: The block diagram of the battery energy storage system [26].  
The control block diagram model of the REGC_A model: This model primarily includes converter response time constants, low-voltage logic management, low-voltage active power management, and high-voltage reactive power management. The converter response time constants are used to adjust the response speed of the converter. Low-voltage logic management is employed to control the behavior of the converter under low-voltage conditions. Low-voltage active power management prevents low voltage by regulating active power. High-voltage reactive power management prevents overvoltage by regulating reactive power. As shown in Figure 6, it represents the control block diagram model of the REGC_A model.
# Improved Exponential Phase Mask for Generating Defocus Invariance of Wavefront Coding Systems
###### Abstract  
Wavefront coding is an effective way to extend the depth of field of optical imaging systems. The invariant defocusing imaging feature can be obtained by adding a phase mask with a suitable form to the aperture of a typical optical system. Traditional exponential phase mask defocusing optical characteristics exhibit strong invariance in the frequency domain, but the point spread function (PSF) variation is significant in the image plane To reduce PSF position deviation, we presented an improved exponential phase mask. The phase function of the improved mask is obtained by analyzing the relationship between Taylor expansion and wave aberration. Numerical analysis and imaging simulation are used to evaluate the performance of the proposed phase mask to that of other standard phase masks. The simulation results show that the improved exponential phase mask has a stronger defocus invariance of the modulation transfer function (MTF), and the position deviation of the PSF has been effectively controlled.  
wavefront coding; hybrid imaging system; phase mask; extend the depth of focus +
Footnote †: journal: Applied Sciences
## 2 Related Work
### Distributed Frameworks for Spatio-Temporal data Queries Processing  
Due to the explosive growth of spatio-temporal data, the domain of distributed execution of spatial queries has gained considerable concern. In [15], a novel framework, known as STARK, is recommended for spatio-temporal data management, which includes spatial partitioners, different modes for indexing, filter, join, and clustering operators. In contrast to existing solutions, STARK is considered an integrated Spark program and provides more flexible and comprehensive operators. Several application scenarios of this framework are presented in [16]. Moreover, the authors in [17] introduce a new abstraction called IndexTRDD to manage trajectory segments, which exploits a global and local indexing mechanism to accelerate trajectory queries. Also, they adaptively update the partition structure based on the change of data distribution to alleviate the partitioning overhead.  
In [18], a scalable system for massive trajectory data management is elaborated, which modifies the three core layers of \(ST\)-Hadoop. Authors in [19] evaluate the distributed execution of spatial SQL queries in GeoSpark and STARK system. Moreover, in order to address the challenges of high velocity location data, authors propose a distributed in-memory spatio-temporal data processing system, which includes a distributed in-memory index and storage infrastructure built on a distributed in-memory programming paradigm [20]. The location records are distributed across a cluster of nodes using the producer-consumer model.
## 3 Methods  
### Synthetic Dataset  
A synthetic data set referenced to [Chl] was developed to test the ZTT model and develop an expression for the \(f_{L}\) term (see Section 4.1). Twenty values of [Chl] were assumed, logarithmically spaced between 0.01 and 30 mg m\({}^{-3}\). Total absorption coefficient (\(a\)) was represented by a sum of four components:  
\[a(\lambda)=a_{w}(\lambda)+a_{ph}(\lambda)+a_{d}(\lambda)+a_{g}(\lambda). \tag{19}\]  
Pure water absorption \(a_{w}\) was taken from Reference [61]. Phytoplankton absorption \(a_{ph}\) was calculated from chlorophyll concentration [Chl] and from spectrally averaged absorption coefficients of micro- and pico-plankton (\(\overline{a}_{micro}(\lambda)\) and \(\overline{a}_{pico}(\lambda)\), respectively) [62]:  
\[a_{ph}(\lambda)=[Chl]*\Big{(}\Big{[}S_{f}\overline{a}_{pico}(\lambda)\Big{]}+ \Big{[}\Big{(}1-S_{f}\Big{)}\overline{a}_{micro}(\lambda)\Big{]}\Big{)}, \tag{20}\]  
where \(S_{f}\) = [0.25, 0.5, 0.75] is the shape mixing factor. Non-algal particulate absorption \(a_{d}\) was given by [63]:  
\[a_{d}(\lambda)=a_{d}(440)*e^{-0.011(\lambda-440)}, \tag{21}\]  
where \(a_{d}(440)\) is equal to \(R_{d}\)\(a_{ph}(440)\), with \(R_{d}\) = [0.05, 0.1, 0.5, 1, 2]. Similarly, we used an exponentially decaying expression for the absorption of chromophoric dissolved organic matter, \(a_{g}\)[64]:  
\[a_{g}(\lambda)=a_{g}(440)*e^{-0.014(\lambda-440)}, \tag{22}\]  
where \(a_{g}(440)\) is equal to \(R_{g}\)\(a_{ph}(440)\), with \(R_{g}\) = [0.1, 0.3, 0.5, 1, 2].  
Total backscattering \(b_{b}\) was represented by the sum of water and particulate components:  
\[b_{b}(\lambda)=b_{bp}(\lambda)+b_{bw}(\lambda). \tag{23}\]  
To derive particulate backscattering \(b_{bp}\), total particulate scattering \(b_{p}\) was first empirically estimated at 550 nm [65]:  
\[b_{p}(550)=0.416[Chl]^{0.766}, \tag{24}\]  
and then extrapolated spectrally [66]:  
\[b_{p}(\lambda)=b_{p}(550)*\left(\frac{\lambda}{550}\right)^{v([Chl])}, \tag{25}\]  
where \(v([\text{Chl}])=0.5(\log[\text{Chl}]-0.3)\) when \(0.01\leq[\text{Chl}]\leq 2\) mg m\({}^{-3}\) and \(v([\text{Chl}])=0\) when \([\text{Chl}]>2\) mg m\({}^{-3}\). The empirical relationship from [41] between the particulate backscattering ratio \(\widetilde{b}_{bp}\) and \([\text{Chl}]\) was then used to derive \(b_{bp}\):  
\[b_{bp}=b_{p}\widetilde{b}_{bp}=b_{p}*0.0096[Chl]^{-0.253}. \tag{26}\]  
Pure seawater backscattering \(b_{bw}\) was calculated according to [PERSON] et al. [52].
# Pas: Privacy-Preserving Authentication Scheme Based on SDN for VANETs
## 2 Relate Works
### Identity-Based Cryptography-Based Authentication Schemes  
Different from traditional asymmetric cryptography mechanism, IBC takes the identity information of the sender instead of the certificate for message authentication, which addresses the issue of storing and guaranteeing the security of vehicle certificates in traditional asymmetric cryptography authentication schemes. Ref. [16] provides an anonymous authentication scheme to protect vehicle privacy and trajectory. The proposed scheme assures that additional authentication beyond the threshold will lead to the revocation of illegal vehicles. Meanwhile, Ref. [16] designs a role-splitting mechanism to guarantee that any vehicles cannot be framed by a single corrupted authority. Meanwhile, identity-based cryptography is adopted to facilitate communication and storage efficiently. Ref. [17] presents an IBS-based scheme for mutual authentication in VANETs. The scheme adopts online and offline signatures to achieve V2I and V2V authentication, respectively, which meets conditional privacy and non-repudiation. However, as part of the asymmetric cryptography mechanism, IBC-based authentication schemes have to face the challenge of the limited computation and storage capacities of vehicles.
# Exploring the Impact of AI-Based Cyber Security Financial Sector Management
## 5 Conclusions  
Improved privacy, scalability, risk reduction, data protection, and attack avoidance belong to some of the many benefits of the proposed CS-FSM paradigm, which is powered by AI to revolutionize cyber security in the financial sector. Using Enhanced Encryption Standard (EES) and the KNN algorithm, the research demonstrates the efficacy of these tools in foreseeing and averting intrusions. Positive outcomes in terms of data security, privacy, and risk reduction have been observed. This research shows how critical it is to implement AI algorithms into banking sector cyber security solutions. The system has analytical values of 96.1% for data privacy, 97.2% for scalability, 98.7% for risk reduction, 95.4% for data protection, and 94.3% for attack avoidance. Information security can be  
Figure 9: The contrast between attack avoidance.  
improved further by incorporating blockchain technology, which should be the subject of future studies. As a whole, this innovative approach is an improvement over standard cyber security procedures and is effective in strengthening online safety in the banking industry.  
CS-FSM provides a revolutionary approach to cyber security in the financial sector, improving privacy, scalability, risk reduction, data protection, and attack avoidance. The usage of EES and KNN, two examples of AI algorithms, offers a holistic perspective on cyber security that is lacking from more conventional approaches. The proposed solution is a considerable upgrade over current cyber security practices for dealing with cyber threats in the banking sector. The overall findings and conclusions prove the originality and usefulness of the proposed method for boosting financial sector cyber security.  
There are, however, limitations to this research, such as the small sample size and narrow focus of the analysis. The report also glosses over the feasible consequences and probable costs of the proposed solution. Despite this, the successful use of AI algorithms into banking sector cyber security solutions is warranted due to the favorable results in data security, privacy, and risk reduction.  
This research was funded by Deanship of Scientific Research at Majmaah University for supporting this work under Project Number No: R-2023-407.  
Not applicable.  
The author declares no conflict of interest regarding the present study.
# Energy-Aware System Design for Autonomous Wireless Sensor Nodes: A Comprehensive Review
## 5 Energy Saving on Network Level  
Wireless communication consumes a big part of node energy for both the sending mode and the listening mode depending on the sending distance and the number of necessary hops to reach the sink. In this section, we provide a comprehensive review of the energy saving techniques at network level aiming to maintain the network with maximum node life. We provide an overview of methods and refer to several studies that we judge important and relevant, providing the references to help retrieve more details.  
Many energy saving techniques can reduce the energy consumption of nodes significantly and lower the requirements for the energy harvesting supply. They can increase the whole network life with a relatively high system reliability at low costs. Energy saving techniques can be classified into four main techniques: radio optimization, sleep/wake-up schemes, energy efficient routing protocols and data reduction (see Figure 10).
## 3 Results and Discussion
### Characterization of the Tomato Extract  
Total phenolic and carbohydrate compounds are important components of tomato extracts involved in the reduction and stabilization steps of AgNP biosynthesis. The aqueous tomato extract prepared by subcritical water presents a dark brown color. The carbohydrate and phenolic contents were 22.8 \(\pm\) 2.9 mg GE/mL and 2.1 \(\pm\) 0.4 mg GAE/mL, respectively. It is known that an increase in the temperature causes an increase in the ionic product of water [21], making it more reactive and able to catalyze the hydrolysis of the carbohydrate fraction of green tomatoes. Furthermore, the dielectric constant of water decreases with an increase in the temperature, thus allowing water to extract phenolic acids with a wide range of polarities. However, no comparison with other green tomato extracts can be made, as this is the first study of such an extract. Most of the studies regarding biogenic AgNP synthesis focus on nanoparticle characteristics and not on extract characteristics. Moreover, other biomolecules, such as proteins, amino acids, alkaloids, enzymes, saponins, tannins, terpenoids, and vitamins, present in the green tomato extract could also contribute to the formation and stabilization of the AgNP [31].
# RARZ: Ring-Zone Based Routing Protocol for Wireless Sensor Networks
###### Abstract  
Sensor networks are handicapped by limited resources in the form of energy, processing, and memory. This paper proposes a new multi-hop energy efficient protocol, namely a routing algorithm using the ring-zone (RARZ) model. The protocol is lightweight, takes routing decisions based on the remaining energy of nodes, and performs location-based routing without the need for the nodes to know their respective positions. The protocol partitions the network into concentric rings around the base station. Each node assigns itself to a particular ring, known by a ringID. Multi-hop routing is performed and nodes within inner rings carry data for the outer rings towards the base station. Simulation results show that RARZ outperforms the address-light integrated MAC routing protocol (AIMRP), ad hoc on-demand distance vector (AODV) and Flooding in terms of end-to-end delay, average hop count, and energy consumption.  
clustering algorithms; energy efficiency; routing protocol; ring zone; wireless sensor networks +
Footnote †: journal: Applied