esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Comparison of Different Decomposition Methods
### Selection of the Algorithm  
For each decomposition method, the CNOT gate counts are compiled in Table 1 and plotted in Figure 12. As an indication, the number of CNOT gates resulting from the decomposition of a one to five-qubit unitary gate is given, along with the general formulas for the number of CNOT gates resulting from the decomposition of an \(n\)-qubit gate, if such a formula were available.  
As can be seen in Table 1, the optimized version of QSD results in the fewest CNOT gates. The choice was therefore made to implement this decomposition, although not the optimized version. The optimizations from [16] can be implemented without any modifications to a base implementation of the algorithm.  
Figure 11: Decomposition of the \(L\) matrix in QSD [22].  
Figure 9: Recursive CSD decomposition [21].  
Besides that, QSD has several other advantages. The recursion is performed at general \(n\)-qubit gates rather than multi-controlled one-qubit gates, which makes it relatively simple to implement. If algorithmic implementations for three-qubit, four-qubit or five-qubit or bigger general gates are found, they can be easily implemented. The same goes for other specific optimizations. In addition, because the mathematical decompositions are done separately for each step in the recursion, rather than all at once at the beginning, any underlying structure in the beginning or intermediate matrices can be taken advantage of immediately, therefore potentially skipping many computational steps as well as decreasing the size of the resulting circuit.  
For these reasons, the choice was made to go with Quantum Shannon Decomposition for the implementation of unitary decomposition in OpenQL.
# Cryptography in Wireless Multimedia Sensor Networks: A Survey and Research Directions
## 2 Security Issues in Multimedia Sensor Networks
### Security Threats
In general, excessive sensing requests transmitted from intruders may rapidly drain the energy of nodes, besides prejudicing the service of valid requests. If sensor nodes are associated with sensing priorities according to their potential to retrieve relevant data [11; 18], attackers may create malicious information to change the way sensors are prioritized, for example creating fake events of interest. Such a kind of attack is not easily detected, and its impact on monitoring quality may be too severe. Overall, the many threats that can be presented in traditional wireless sensor networks may also occur in wireless multimedia sensor networks. However, the more stringent requirements of multimedia capturing, processing and transmission make them potentially more critical for WMSN. In such a way, security defenses are often required, and there are many ways to protect wireless multimedia sensor networks.
# A New SDN-Based Routing Protocol for Improving Delay in Smart City Environments
## 3 Machine Learning Algorithms
### Problem Formulation and Proposed Modeling  
Our routing application tends to reduce transmission delay and overhead packets in an intelligent transport environment using SDN to manage vehicle communication in smart cities efficiently. We integrate the supervised approach given by the Naive Bayes classifier in the routing solution. We use the Naive Bayes classifier giving consideration to its simple implementation with low computational complexity and a real-time prediction process. This ML algorithm predicts locating a vehicle in a specific area of the road network assigned to a particular RSU assuming the role of a virtual switch in SDN. Therefore, the controller will not broadcast packets-out to discover the vehicle's location, which will reduce the control packets exchanged between the controller and the various switches/RSU in the network. Localization of the node allows establishing a particular candidate route between a pair of nodes, depending on the current link delay. Based on the least current delay on each candidate route, we select the one with the best value, i.e., the lowest delay.  
\(X\) [\(x_{1},\ldots,x_{n}\)] vector indicates a problem instance where \(x_{1},\ldots,x_{n}\) are n characteristics related to the vehicle information defined and discussed in the next section. We assume that there is \(k\) class, \(C_{1},\ldots,C_{k}\) corresponding to areas including RSU/ Switch \(S_{j}\) with \(j=1..k\). Applying the Naive Bayes theorem defined in Equation (1) to our problem, allows us to define the conditional probability of our model, represented by \(P\) (\(C_{k}\mid X\)), which denotes the instance probability of \(C_{k}\) given \(X\), in the Equation (2) as follows:  
\[P\left(C_{k}\mid X\right) = P\left(C_{k}\mid x_{1},\ldots,x_{n}\right) = \frac{P\left(C_{k}\right)\cdot P\left(x_{1},\ldots,x_{n}\mid C_{k} \right)}{P\left(x_{1},\ldots,x_{n}\right)}\]  
According to the conditional independence hypothesis of the theorem, i.e., the features are mutually conditionally independent, then the posterior probability for each class becomes:  
\[P\left(C_{k}\mid X\right)=\frac{P\left(C_{k}\right)\cdot\prod_{j=1}^{n}P\left( x_{j}\mid C_{k}\right)}{\prod_{j=1}^{n}P\left(x_{j}\right)} \tag{3}\]  
Before executing the proposed approach in a real-time process, it should perform a training step in an offline manner by examining the set of data in the learning stage. In the first step, the model clams a dataset to learn the function that maps the future events with past knowledge occurrences. The creation and implementation of such a dataset are developed in the following section.
# A Quantum-Resistant FHE Framework for Privacy-Preserving Image Processing in the Cloud
## 4 Experimental Evaluation
### Security Analysis
#### 4.7.5 Quantum Threat Timeline Analysis  
Our framework is designed with \"crypto-agility\" in mind, meaning that we can adapt to the evolving landscape of post-quantum cryptography as needed [23].  
Currently, we are in a relatively comfortable position regarding quantum threats. Today's quantum computers operate with around 1000 to 2000 physical qubits and still suffer from high error rates, making them far from able to break current cryptographic systems [24]. Conservative estimates suggest that quantum computers capable of breaking traditional encryption, often called Cryptographically Relevant Quantum Computers (CRQCs), are still many years away from realization [25]. Meanwhile, our framework is fully operational today and already provides quantum-resistant security through its fully homomorphic encryption (FHE) foundation, which is based on the hardness of lattice problems [26].  
Looking ahead, we must stay prepared for changes in the cryptographic landscape. The U.S. National Institute of Standards and Technology (NIST) has published the first standards for post-quantum algorithms, such as ML-KEM (Kyber), and it has indicated plans for a new round to standardize additional algorithms in the future [27]. Our architecture is designed to be flexible; we can substitute Kyber with future alternatives as they become standardized. The security of FHE is expected to remain quantum-resistant unless major algorithmic breakthroughs fundamentally alter the understanding of lattice-based cryptography, an event considered unlikely by experts [26; 28].
# A QoS-Guaranteed and Congestion-Controlled SDN Routing Strategy for Smart Grid
## 3 Design of QCORS Algorithm
### Network Model  
In this section, we present our network model and the optimization framework in combination with the current SG architecture. To better utilize the Lyapunov optimization [38, 44] in our framework, we transform delay into queue length to simplify the analysis.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Abbreviations** & **Definitions** \\ \hline AMI & Advanced Metering Infrastructure \\ DR & Demand Response \\ DA & Distribution Automation \\ HAN & Home Area Network \\ LABERO [33] & LoAd-BalancEd Routing with OpenFlow \\ LOBUS [32] & LOad-Balancing over UnStructured networks \\ NAN & Neighborhood Area Network \\ QoS & Quality of Service \\ QCORS (our work) & QoS-guaranteed and Congestion-controlled OpenFlow Routing Strategy \\ SG & Smart Grid \\ WAN & Wide Area Network \\ \hline \hline \end{tabular}
\end{table}
Table 1: The abbreviations and the corresponding definitions.  
Suppose that the SDN controller has optimized the network \(k\) times in the duration \(T\), then the \(T\) is divided into \(k\) time slots. Each slot is denoted as \(t\).  
Given a communication network model, the network topology is defined as a undirected graph composed of communication links and nodes, such as \(G=\{N,L\}\). Among them, \(N=\{n_{1},\cdots,n_{i},\cdots,n_{m}\}\) represents the set of switches or terminals in the network, where \(m\) is the total number of nodes. In addition, \(L\) represents the set of physical links. Given the source node and destination node \(n_{s}\) and \(n_{d}\) respectively. The packet transmission path \(P_{sd}^{t}\) (suppose \(n_{s}\) and \(n_{d}\) are not directly connected) at time \(t\) consist of a series of node pairs, such as \(P_{sd}^{t}=\{(n_{s},n_{s1}),\cdots,(n_{j},n_{i}),(n_{i},n_{k}),\cdots,(n_{d1}, n_{d})\}\). It is noted that every element in \(P_{sd}^{t}\) is physically connected. For the target node \(n_{i}\), \(I(n_{i}^{t})=\{n_{s},\cdots,n_{j}\}\) represents the set of predecessors whereas \(O(n_{i}^{t})=\{n_{k},\cdots,n_{d}\}\) represent the set of successors.
## 2 Experimental Setup  
The experimental setup used in this paper is shown in Figure 1. The experiments are operated under pure nitrogen and air, which is mixed using nitrogen and oxygen with a ratio of 8:2 by adjusting the mass flow controller (MFC; $48$-$300, HoribaStec, Wuxi, China), and the purity of nitrogen and oxygen are both higher than 99.999%. Moreover, the total gas flow rate is 200 mL/min during the experiment. The current probe (Tektronix TCP312, 100 MHz, Shanghai, China), the high voltage probe (Tektronix P6015A, 1000\(\times\), 3.0 pF, 100 M\(\Omega\), Shanghai, China), and the oscilloscope (Tektronix, MDO3034, 350 MHz, Shanghai, China) constitute the electrical diagnosis system, which is used to record the voltage and current waveforms of the discharge. Moreover, the discharge modes and the optical emission spectrum from the generated reactive species are captured by the ICCD camera (Andor New iStar DH334T, Belfast, UK), optical fiber and monochromator (Andor SR-750i, grating groove 2400 lines/mm, blazed wavelength 300 nm, Belfast, UK), respectively, and display on the computer finally. These four devices compose the optical diagnosis system. The discharge reactor, which includes the high voltage electrode, the ground electrode, the dielectric plate, the dielectric materials, and the closed chamber, is driven by the nanosecond pulsed power (HVP-20P, Xi'an Smart Maple Electronic Technology Co. Ltd., Xi an, China). The discharge characteristics, such as the rising and falling time of the voltage pulse, pulse duration, pulse peak voltage, and the pulse repetition rate, can be self-adjusted. Furthermore, the ICCD camera and nanosecond pulsed power can be triggered by a digital delay generator (DG 645, Stanford Research Systems, Sunnyvale, CA, USA) to obtain the time resolved information of the discharge and the reactive species. Figure 1b shows the enlarged view of the electrode structure. The shape of the high voltage electrode is a hollow needle tube (304 white steel), and the inner and outer diameters are 0.90 mm and 1.26 mm, respectively. The dielectric materials, with a diameter of 2 mm, are alumina cylinders, which are always used as the support of the catalyst. To simplify the traditional packed bed reactor, three cylinders are placed (as Figure 1b shows), and the needle electrode is placed directly above the upper cylinder. The dielectric plate, with a thickness of 1 mm, is placed on the ground electrode (304 white steel) and it is made of alumina. The 2 mm-thick reactor wall is made of quartz to observe the discharge and to collect the optical emission spectrum conveniently. In addition, the \"top\" and \"bottom\", marked in Figure 1b, are two spatial positions used to study the distributions of the discharge and reactive species.
## 6 Challenges
This involves considerations for efficient data compression, prioritization mechanisms, and adaptive resource allocation strategies. Overall, navigating the challenges of QoS-based performance, costs, and resource constraints involves a holistic approach that integrates technological advancements, cost-effective solutions, and resource-efficient algorithms. Collaborative efforts among healthcare practitioners, technologists, and researchers are crucial for developing sustainable and effective systems that meet QoS requirements in patient health monitoring through cloud-connected IoT platforms.
## 6 Discussion and Findings
### Discussion of Experiments with Infectious Disease Models  
The model described in the first experiment, which extends previous studies and takes into account the incubation period of the infectious disease and viral mutations, shows in Figure 18 the response of individuals to the infectious level of the disease. As a result, secondary infections occur or infectious diseases start to re-emerge after the majority of the population begins to act freely without quarantine. The main parameter examined in this study is \(\delta\), which expresses the extent to which the population is aware of the individual costs of infection. This parameter has little effect on the final number of infections but has the greatest effect on the number of infections at the peak of the epidemic. For higher values of the parameter \(\delta\), the number of people infected at the peak of infection is lower, but multiple peaks of infection occur. The occurrence of multiple peaks of infection overlaps with the actual increase and decrease in the number of infected people in Japan. In the  
Figure 24: Increase and decrease in inventory quantity.  
Figure 25: Effect of ordering policy \((\alpha,\ \beta)\) on inventory quantity.  
current crisis of infectious disease epidemics, it is extremely important to reduce the number of people infected during the peak period in order to prevent the collapse of the healthcare system. Therefore, it is effective to spread awareness of the risk of disease, which can spread the increase in the number of infected patients over a long period of time and reduce the number of infected patients at the peak of infection.  
The second experiment was conducted to verify the number of hospital beds available by comparing the number of hospital beds for COVID-19 infection with the number of patients in the actual hospitals. However, the number of infected patients shown by the model differs greatly from the actual number of infected patients in Japan. Therefore, it is necessary to modify the parameters of the model to make it comparable with the actual data.
# Use of Reservoir Sediments to Improve Engineering Properties of Dune Sand in Oman
## 1 Introduction
[PERSON] et al. [21] added plastic and non-plastic fines to the poorly graded sands in varying quantities to study the changes in compaction parameters. They concluded that the addition of fines up to a certain amount increases the maximum dry unit weight of poorly graded sands and the amount of increase depends on the uniformity coefficient value of the sand. In past, various additives have also been employed to improve the properties of dune sand, such as the use of cement and cement bypass dust [21], bentonite [22], ash [23], marble waste [24], biopolymer [1], biopolymer and fibers [25] and fiber reinforcement [26]. Likewise, [PERSON] et al. [27] have reported that the addition of more than 20% sand to fines has a noticeable effect on the quick undrained shear strength of the mix. [PERSON] et al. [28] improved the sand by the addition of kaolin clay and reported the critical and limiting fine content, porosity and concept of interfine void ratio. They found that the critical fine content of binary mixture was 40% and limiting fine content was around 80% to achieve optimum compression index and shear modulus of treated soil. [PERSON] et al. [24] added fine marble waste powder to dune sand and reported an enhancement in geotechnical properties at a 15% treatment ratio. [PERSON] et al. [29] reported a decrease in maximum and minimum void ratios with the addition of fines to a certain level (transition zone) up to which fines fill the void. Beyond the transition zone, the void ratio increases because of the replacement of sand particles with fines. It is therefore anticipated that making a binary mixture of fine sediment with poorly graded dune sands has a significant potential to improve their mechanical behavior. This will not only improve the sand lacking fines but also provide a useful application of sediments deposited on the bed of reservoirs. This study provides detailed experimental results and the derived conclusions on using reservoir sediments to improve engineering properties of dune sands in Oman.
# An Algorithm for Solving Robot Inverse Kinematics Based on FOA Optimized BP Neural Network
## 3 Algorithm Principle
### Fruit Fly Optimization Algorithm  
The scholar [PERSON] proposed a fruit fly optimization algorithm after long-term observation of fruit flies [32]. Compared with other organisms, fruit flies have a particularly sensitive sense of vision and smell. During their activities, fruit flies perceive the taste of food through smell, and use their unique vision to move in the direction where other fruit flies gather.  
The fruit fly optimization algorithm is to find the global optimum through the foraging evolution of Drosophila fruit flies. Foraging for fruit flies is divided into two procedures. The first procedure is relying on a strong sense of smell to initially locate the food source and quickly approach the food source; the second procedure is discovering where food and companions gather through vision and fly in that direction. The basic optimization process of the fruit fly optimization algorithm is the fruit fly population starts from a given initial position, and performs an olfactory search according to the given flight direction and step distance. Then, the approximate direction of the optimal solution and the distance between the fruit fly and the optimal solution are preliminarily determined by the concentration, and finally the position of the optimal solution is reached through visual search. The fruit fly optimization algorithm has a good global optimization ability, and can meet the optimization problems in different fields.  
According to the foraging process of fruit flies, the fruit fly algorithm performs optimization according to the following necessary steps:  
1. Set the size of the fruit fly population and initialize the position of the fruit fly population randomly;
2. Set the direction and distance for individual fruit flies to search randomly through smell: \[\begin{array}{l}X_{i}=X_{axis}+\text{RandomValue}\\ Y_{i}=Y_{axis}+\text{RandomValue}\end{array}\] (9)  
Figure 5: BP neural network topology.  
3. The location of the food is unknown. First, calculate the distance \(D(i)\) from the origin, and use the reciprocal of the distance \(D(i)\) as the taste concentration judgment value \(S(i)\): \[D(i)=\sqrt{X_{i}^{2}+Y_{i}^{2}}\] (10) \[S(i)=1/D(i)\]
4. Substitute \(S(i)\) into the taste concentration judgment function fitness, the taste concentration \(Smell(i)\) at the individual position of the fruit fly can be obtained: \[Smell(i)=\text{Fitness}(S(i))\] (11)
5. Find the fruit fly with the best taste concentration Smell in the population: \[[bestSmell,bestindex]\ =\ \text{minimum}(Smell)\] (12)
6. Keep the X and Y coordinates of the best individual fruit fly, and the best taste concentration value;
7. Repeat steps (2) to (5) for iterative optimization, and judge whether the taste concentration of the current iteration is better than that of the previous iteration, and if so, proceed to the next iteration after step (6).
## 3 Localization Algorithm  
In the proposed concept, the user is localized based on two types of data coming from the system infrastructure: RSS measurements performed in BLE radio interface and ranging results from proximity sensors. The concept assumes use of different algorithms for the possible scenarios of user localization, which are:  
* the user is not in the area covered by any of the proximity sensors and is localized using only BLE RSS measurement results (Extended Kalman Filter based algorithm),
* the user's tag is off or disabled and he is localized based solely on ranging data,
* the user wears the tag as normal and is present in at least one of the proximity sensors beams (hybrid algorithm in one of two versions: loosely or tightly coupled).
## 3 Proposed VAE Using Context Information
The proposed model assumes that images are generated from the condition-invariant feature \(\mathbf{z}_{p}\) and the condition-sensitive feature \(\mathbf{z}_{c}\). is sensitive to environmental influences is concentrated in \(\mathbf{z}_{c}\), and condition-invariant information is compressed into \(\mathbf{z}_{p}\). Therefore, \(\mathbf{z}_{p}\) can be used as a feature of an image which is robust to environmental changes. If not only context information \(\mathbf{c}\) but also structural information \(\mathbf{p}\) is provided, we propose a model named _CP-VAE_ as shown in Figure 5, which improves the independence between \(\mathbf{z}_{p}\) and \(\mathbf{z}_{c}\) of the previous model. The generative model is modified to \(p_{\theta,\varphi,\varphi}(\mathbf{x},\mathbf{p},\mathbf{c}|\mathbf{z}_{p}, \mathbf{z}_{c})\), and factorized as the following:  
\[p_{\theta,\varphi,\varphi}(\mathbf{x},\mathbf{c}|\mathbf{z}_{p},\mathbf{z}_{ p},\mathbf{z}_{c}) =p_{\theta}(\mathbf{x}|\mathbf{z}_{p},\mathbf{z}_{c})\cdot p_{\varphi}( \mathbf{p}|\mathbf{z}_{p})\cdot p_{\varphi}(\mathbf{c}|\mathbf{z}_{c}) \tag{4}\]  
where \(\theta\), \(\psi\), and \(\varphi\) are parameters of the generative model to generate \(\mathbf{x}\), \(\mathbf{p}\) and \(\mathbf{c}\), respectively. The variational lower bound is also modified as follows:  
\[ \begin{split}\mathcal{L}(\theta,\phi,\psi,\varphi;\mathbf{x}, \mathbf{p},\mathbf{c})\\ =\mathbb{E}_{q_{\phi}(\mathbf{z}_{p},\mathbf{z}_{c}|\mathbf{x})} [\log p_{\theta}(\mathbf{x}|\mathbf{z}_{p},\mathbf{z}_{c})+\log p_{\bar{ \varphi}}(\mathbf{p}|\mathbf{z}_{p})+\log p_{\varphi}(\mathbf{c}|\mathbf{z}_{ c})]\\ -D_{KL}(q_{\phi}(\mathbf{z}_{p},\mathbf{z}_{c}|\mathbf{x})||p_{ \theta}(\mathbf{z}_{p},\mathbf{z}_{c})p_{\psi}(\mathbf{z}_{p})p_{\bar{\varphi} }(\mathbf{z}_{c}))\end{split}  \tag{5}\]  
The difference from the previous model is that \(\mathbf{z}_{p}\) generates not only the image \(\mathbf{x}\), but also the position information vector \(\mathbf{p}\). Since \(\mathbf{z}_{p}\) generates the structural information vector \(\mathbf{p}\), the independence between \(\mathbf{z}_{p}\) and \(\mathbf{z}_{c}\) is enhanced, and the more robust condition-invariant feature \(\mathbf{z}_{p}\) can be extracted to recognize places under substantial environmental changes. However, this model has a limitation in that it requires a fairly strong assumption that the training data are aligned with the same places in order to obtain the position vector information \(\mathbf{p}\).