esa-sceva/satcom-chunk-collection huggingface dataset:
# A Quantum-Secure Cryptographic Algorithm Integrating Fractals and Prime Numbers
## 6 Conclusions  
The purpose of this work was to develop a new cryptographic algorithm starting from our work in [1]. With respect to the algorithm in [1], we extended the number of fractals, we introduced a sub-algorithm for the choice of fractals that was random and rolling, and then we introduced a new multiscale sieve for prime generation, but the most relevant addition was the introduction of a sub-engine, which introduced two quantum properties useful for its crypto-agility, that is, quantum evolution of the key and quantum recombination. The NIST tests on the cryptographic key confirmed in all cases the randomness of the latter; this was also true for the new fractals added, Barnsley and Vicsek. Consequently, with the quantum security technique, an additional level of security is guaranteed by further varying the position of the components of the key and making it more resistant to the possibility of violation via quantum attack. This makes the security key applicable in the context of cryptography of the next generation.  
Conceptualization, G.I., E.B. and A.D.L.; Writing--review & editing, G.I., E.B. and A.D.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Frequency** & \
\begin{tabular}{c} **Block** \\ **Frequency** \\ \end{tabular} &
\begin{tabular}{c} **Cumulative** \\ **Sum** \\ \end{tabular} & **Longest Run** \\ \hline Cantor & 0.562347 & 0.384479 & 0.512504 & 0.336289 \\ \hline Sierpinski & 0.276303 & 0.379936 & 0.359890 & 0.845236 \\ \hline Mandelbrot & 0.425819 & 0.319567 & 0.116564 & 0.617954 \\ \hline Peano & 0.606979 & 0.757654 & 0.437202 & 0.782429 \\ \hline Barnsley & 0.635501 & 0.434880 & 0.218350 & 0.509799 \\ \hline Vicsek & 0.277132 & 0.381236 & 0.195538 & 0.782429 \\ \hline \end{tabular}
\end{table}
Table 1: \(p\)-values for each fractal.  
Figure 11: \(p\)-value comparison chart.
# The Influence of Mountain Height and Distance on Shape Factor of Wind Load of Plastic Tunnel
In those studies, field tests and wind tunnel tests were relatively demanding on the structural dimensions and the environment. Numerical simulation is currently an essential approach to wind engineering, with the advantage of simulating real environmental and prototype dimensions, a short finish time and low costs [42]. Therefore, the CFD numerical simulation method is used to analyze the wind characteristics or wind pressure distribution. [PERSON] et al. [31] and [PERSON] et al. [33] simulated the wind field characteristics in a valley terrain. [PERSON] et al. [34], [PERSON] et al. [35], [PERSON] et al. [36], and [PERSON] et al. [37] analyzed the wind pressure distribution on different types of greenhouses based on numerical simulation, but those studies are only suitable for greenhouses located on plain area. In general, fewer studies have been carried out on surface wind pressure patterns for greenhouses placed in valley areas. In this study, to obtain the wind pressure distribution law and the shape factor of wind load on a single-span arched plastic tunnel laid in a valley region, mountain models and a greenhouse model have been built in the same wind flow field through CFD. The verified turbulence model and grid division method are applied in the analysis. The influence of the height of the mountain and distance between mountains on the distribution law of the wind pressure of the greenhouse is discussed. Then, based on the partition method, the shape factors of wind load on each surface area of the greenhouse are studied, and a calculation model for the shape factor of wind load is proposed. The research results can offer a supplement to the design of greenhouses in valley area.
# Multi-Level Feature Dynamic Fusion Neural Radiance Fields for Audio-Driven Talking Head Generation
## 2 Related Work
### Audio-Driven Talking Head Generation  
The generation of lifelike talking heads using arbitrary audio is an active area of research in the fields of computer vision and computer graphics. The objective is to reconstruct an individual with top-notch images and seamless audio-visual consistency. Researchers have explored various techniques to create synchronized and realistic talking portrait videos. Traditional approaches [17; 18] include defining phoneme-to-mouth mapping rules and employing splicing techniques to adjust mouth shapes. Initial deep learning methods [19; 20; 21; 22] focus on generating images that match audio inputs to accomplish synchronized lip movements. Nonetheless, these strategies struggle with pose control when creating fixed-resolution images. Multi-stage strategies [23; 24; 25; 26; 27; 28; 29] incorporate 3D facial models and facial landmarks [30] as intermediate steps to enhance audio portrait synthesis, mitigating overhead pose control issues. Yet, these representation estimates might introduce inaccuracies, resulting in the loss of motion detail or image identity. Recently, diffusion models [31; 32; 33] have been used to boost lip synchronization and image quality, albeit with slow inference times. Additionally, 2D-based methods require improvements in  
Figure 1: Overview of the proposed MLP-NeRF.  
naturalness and audio-visual coherence regarding head posture control due to a lack of explicit 3D structure representation.  
With the rise of NeRF, scholars have started utilizing it to tackle the 3D head structure issue in audio-driven talking head generation. Initial attempts fused NeRF into talking head generation tasks; however, this method used basic NeRF, which was sluggish and consumed much memory. For example, AD-NeRF [6] required approximately 10 s to render a single image. SSP-NeRF [34] introduced a semantic sampling method to enhance local motion modeling. For real-time video production, RAD-NeRF [11] employed Instant-NGP [8] to boost visual quality and inference speed, though the MLP network module added computational complexity and training challenges. ER-NeRF [12] designed a three-plane hash encoder to reduce computational demands by pruning spatial regions and minimizing hash collisions, although it might miss features in areas rich with details. Based on this, ER-NeRF++ [35] employs a deformation grid transformer to facilitate the reuse of spatial features across different regions, thereby enhancing the synchronization between audio and facial movements. Wav2 NeRF [36] renders 32 \(\times\) 32 features instead of directly rendering 512 \(\times\) 512 features, significantly reducing computational complexity and rendering time. The paper suggests a practical and expressive MLP-NeRF framework for talking head generation. The framework dramatically improves the accuracy and consistency of 3D facial representation, overcoming some issues with earlier approaches.
## 3 Results and Discussions
### 3.10 pH Effect  
The cations of the solution pH are very important because they influence the charge of the sorption process, thus affecting the process's extent [58]. Figure 9 shows the effect of pH ranging from 1 to 9 for both adsorbents. The removal capacity of ibuprofen increases as the pH increases from 1 to 5 on both adsorbents, with a maximum capacity observed at pH 5. The adsorption due pH increases were due to the lower electrostatic repulsion between ibuprofen and the activated carbon surface. With a pH greater than 7, the uptake for ibuprofen decreased. Less ibuprofen uptake in neutral to basic condition might due to be competition between protons and ibuprofen for site availability [36].
## 5 The Molecular Mechanisms Behind the Resistance of Biofilms to Antimicrobial Agents
### Diminished Rate of Growth and Decreased Metabolic Activity  
Bacterial biofilm cells' diminished growth rate and modified metabolic functions play a crucial role in their heightened antibiotic resistance. The biofilm matrix presents a complex landscape where nutrient and oxygen levels fluctuate significantly, leading to unique metabolic conditions for each cell. The presence of these variations facilitates antibiotic tolerance and persistence, which permits specific subpopulations of bacteria to endure antimicrobial treatments [123; 124]. The restricted availability of nutrients and reduced metabolic activity commonly observed in bacteria associated with biofilms enhance resistance mechanisms, significantly complicating biofilm infection eradication [125; 126].  
Comparative studies reveal significant disparities in microbial growth rates between bacteria associated with biofilms and those in a planktonic state. Studies indicate that bacteria within biofilms exhibit a growth rate of roughly one-third that of planktonic cells during the exponential growth phase. The diminished growth rate noted in _P. aeruginosa_ biofilms aligns with observations in multiple bacterial species, highlighting the slower replication of cells associated with biofilms [126]. The diminished growth observed, particularly within the deeper layers of the biofilm, compromises the effectiveness of numerous antibiotics, which are typically formulated to target cells that are actively dividing.  
Within biofilms, bacterial cells demonstrate notable metabolic diversity, especially when comparing the inner and outer layers of the biofilm architecture. Cells situated in the center of the biofilm encounter restricted oxygen levels, leading to decreased growth rates and lowered metabolic activity. This metabolic inactivity makes these cells more resistant to antibiotics, focusing on active cellular functions. Cells at the biofilm surface benefit from enhanced access to oxygen and nutrients, facilitating increased metabolic activity and higher replication rates [127].  
Genetic studies have clarified the connection between reduced growth rates and antibiotic resistance in biofilm-associated bacteria. For example, in _Proteus mirabilis_, the downregulation of specific genes, including _pstS_, _sodB_, and _funC_, is closely linked to slower growth and diminished metabolic activity. The alterations facilitate biofilm development, establishing a safeguarding environment that improves bacterial resilience to antimicrobial therapies. This genetic and metabolic adaptation is directly linked to heightened antibiotic resistance, underscoring biofilm communities' resilience and capacity to circumvent therapeutic interventions [128].
## 5 Annotation-Free Transcription Alignment
### Transcription Alignment  
After training a fully convolutional object detection system on synthetic training data, it is applied to the dataset of historical Vietnamese handwriting. For each scanned page, the bounding boxes \(\mathcal{B}\) of the detected characters and the existing transcription \(T[n,m]\) with \(n\) columns and \(m\) rows are provided as input to the proposed alignment method, which is described in Algorithm 2. \(T[i,j]\) with \(1\leq i\leq n\), \(1\leq j\leq m\), denotes the \(j\)th character of the \(i\)th column in the transcription, according to the column breaks present in the transcription.  
First, the median bounding box \(b_{m}\) is determined with respect to the larger box side \(max(width,\;height)\) (Line 1). Afterwards, the following three types of outliers are removed from \(\mathcal{B}\) (Lines 2-4):  
* Boxes that deviate more than \(\sigma_{size}\) in both width and height from \(b_{m}\).
* Boxes that overlap more than \(\sigma_{overlap}\) with another box and have the smaller intersection over union (IoU) with \(b_{m}\) when compared with the other box.
* Boxes that are less than \(\sigma_{border}\) pixels away from the image border.  
Next, k-means clustering is applied to the remaining \(\mathcal{R}\) boxes using the \(x\) center coordinates and \(k=n\), in order to obtain columns (Line 5). Among all columns that have exactly \(m\) elements and are thus expected to be correctly identified, the median \(column_{m}\) is determined with respect to the sum of squared distances of the \(y\) center coordinates (Line 6). Similarly the median \(row_{m}\) is determined using k-means clustering of \(\mathcal{R}\) in vertical direction (Lines 7 and 8).  
The median column and the median row have a common crossing box \(b_{c}\) and span a grid of \(n\times m\) cells. To build the set of aligned boxes \(\mathcal{A}\), each cell is visited once (Lines 9-11). In Line 12, the \((x,y)\) center position is calculated as:  
\[x = column_{m}[j].x-(b_{c}.x-row_{m}[i].x) \tag{1}\] \[y = column_{m}[j].y-(b_{c}.y-row_{m}[i].y) \tag{2}\]If there is one or several boxes in \(\mathcal{R}\) that include this center point (Line 13), the closest such box is selected as the alignment result for the cell (Line 14). Otherwise, the translated box \(column_{m}[j]\) of the median column is used (Line 16), ensuring that each cell has an alignment result. This procedure is useful to fill gaps in the detection result around the position \((x,y)\).  
Finally, the character of the bounding box is set to the corresponding character from the transcription and the box is added to \(\mathcal{A}\) (Lines 18-19), which is returned as the final result. Figure 2(c) illustrates the resulting alignment for \(\sigma_{size}=0.2\), \(\sigma_{overlap}=0.1\), and \(\sigma_{border}=5\), which are used as default values in this article.
# Enhancing the SRAM PUF with an XOR Gate
## 1 Introduction  
In modern cryptography, a combination of asymmetric and symmetric encryption protocols, such as Advanced Encryption Standards (AES) and Rivest-Shamir-Adleman (RSA), are commonly used to secure digital communications or hardware authentication devices. Although effective for many applications, these methods have inherent vulnerabilities, such as the need to store private keys in non-volatile memory, which can be non-invasively stolen [1, 2]. The security of such systems can be enhanced by using Physically Unclonable Functions (PUFs), which can generate keys for both symmetrical and asymmetrical encryption, thereby eliminating the need to store private keys. The approach offers multiple options, such as key generation on demand, one-time keys, multi-factor authentications, or random number generation.  
PUFs leverage the inherent randomness of the manufacturing process to create unique digital 'fingerprints' that behave as a one-way function, mapping challenges to responses. These 'fingerprints' are stochastic and can be used to generate cryptographic keys or random numbers. PUFs are relatively simple to use and consume little power, making them highly desirable in cryptographic applications.  
Among the various PUF technologies, a Static Random-Access Memory (SRAM) PUF is the most prevalent, as electronic devices worldwide already ship with SRAM chips. SRAM is mass-produced, which indicates an existing supply chain and affordable manufacturing costs. Several benefits exist when using SRAM to create a PUF, including simplicity, energy efficiency, and ubiquity. The downside is that SRAM PUFs suffer from a limited number of challenge-response pairs with large bit-error rates [3], vulnerability to cloning [4], and aging problems [5, 6].  
This study presents a new design for an SRAM PUF that addresses some of the challenges currently faced by SRAM PUF designs. The proposed design incorporates two SRAM devices to enhance the entropy and number of CRPs while retaining the ability to generate reliable bits. To evaluate the effectiveness of this design, we collect several critical PUF metrics, such as inter-chip distance, bit-error rate (BER), latency, and entropy. The data gathered from these metrics provide insights into the reliability and security of the proposed design.  
The first section of this paper goes over the background of SRAM and PUFs. It discusses the techniques used to create an SRAM PUF, along with our contributions to the field using the XOR SRAM PUF. The following section will discuss the devices used, along with the actual design of the XOR SRAM PUF and how key generation was performed. Afterward, the characterization section will discuss the benchmarks used to compare the XOR SRAM PUF and how it retains the desirable properties of the SRAM PUF while improving several other aspects. Finally, potential future work will be considered to improve the design further to be more applicable to the current SRAM PUF usage.
# An Intrusion Resistant SCADA Framework Based on Quantum and Post-Quantum Scheme
###### Abstract  
The rapid emergence of quantum computing threatens current Supervisory Control and Data Acquisition (SCADA) security standards, mainly, American Gas Association (AGA)-12. Therefore, researchers are developing various security schemes based on either quantum or post-quantum algorithms. However, the efficiency of quantum algorithms impacts the security of the post-quantum digital signature scheme. We propose an intrusion resistant algorithm exploiting and applying quantum principles in the post-quantum signature algorithm. We use the Bennett 1992 (B92) protocol, a quantum key distribution scheme, to obtain the cipher, and the practical Stateless Hash-based Signatures (SPHINCS)-256 protocol to obtain a post-quantum signature. However, instead of Chacha-12, a well-known cryptographically secure pseudo-random number generator, we apply a quantum random number generator to obtain a truly random Hash to Obtain Random Subset (HORS) signature with Tree (HORST) secret key used in SPHINCS-256. We have implemented the design in Python with the Quantum Information Toolkit. We have validated the proposed algorithm using the Probabilistic Model Checking for Performance and Reliability Analysis (PRISM) and Scyther tools. Moreover, the National Institute of Standards and Technology (NIST) statistical tests show that the proposed algorithm key pairs have randomness of 98% and RSA and ECDSA are below 96%.  
Network security; quantum cryptography; qubits; post-quantum cryptography +
Footnote †: journal: Applied  
1 Faculty of Computer Science, Dalhousie University, Halifax, NS B3H 4R2, Canada; [PERSON]]  
2 Research and Development, Cistel Technology, Ottawa, ON K2F 7V7, Canada; [EMAIL_ADDRESS]  
3 Research and Development, Technologie Sanstream, Catineau, QC J8V 2V5, Canada; [EMAIL_ADDRESS]
## 6 Experiment
### Simulation Experiment B
As shown in Table 4, in Maps 1, 2, 3, and 6, although the running time of Mul-ACO was not the shortest, the gap between the running time of Mul-ACO and the shortest running time was no more than 0.3 s. In some complex maps (Map4 and Map5), the running time of Mul-ACO was even about 0.1 s-0.8 s less than other algorithms, which provides the possibility for trajectory planning in dynamic environments. \begin{table}
\begin{tabular}{c c c c} \hline
**Map NO.** & **Name** & **Start Point** & **End Point** \\ \hline
1 & X-type & (0.5, 0.5) & (25.0, 23.0) \\
2 & Z-type & (1.0, 24.0) & (25.0, 10.0) \\
3 & Complex1 & (0.5, 0.5) & (26.0, 23.0) \\
4 & Complex2 & (0.5, 0.5) & (29.0, 24.0) \\
5 & Complex3 & (1.0, 0.5) & (29.0, 27.0) \\
6 & Vortex & (28.0, 20.0) & (13.0, 15.0) \\ \hline \end{tabular}
\end{table} 
Table 3: Simulation environment description. Figure 9: The trajectories generated by ACO, MH-ACO, MF-ACO, and MuL-ACO. (**a**) Map. NO.13: X-type Map; (**b**) Map. NO.2: Z-type map; (**c**) Map. NO.3: Complex1 map; (**d**) Map. NO.4: Complex2: map; (**e**) Map. NO.5: Complex3 map; (**f**) Map. NO.6: Vortex map. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Name** & **Map NO.** & **Length** & **Iterations** & **Turns** & **Height Difference** & **Time (s)** \\ \hline \multirow{6}{*}{ACO} & 1 & 60.80 & 40 & 38 & 7.59 & 2.02 \\  & 2 & 67.41 & 39 & 25 & 16.71 & 2.73 \\  & 3 & 41.2 & 38 & 22 & 7.36 & 1.41 \\  & 4 & 43.60 & 32 & 17 & 6.48 & 1.63 \\  & 5 & 43.0 & 30 & 14 & 7.45 & 1.71 \\  & 6 & — & — & — & — \\ \hline \multirow{6}{*}{MH-ACO} & 1 & 56.82 & 27 & 15 & 2.29 & 2.30 \\  & 2 & 66.30 & 21 & 13 & 9.31 & 2.53 \\  & 3 & 44.21 & 28 & 14 & 6.72 & 1.54 \\  & 4 & 43.40 & 25 & 8 & 5.10 & 1.72 \\  & 5 & 43.60 & 23 & 12 & 1.55 & 1.74 \\  & 6 & 78.40 & 19 & 31 & 8.15 & 2.57 \\ \hline \multirow{6}{*}{MF-ACO} & 1 & 54.27 & 15 & 10 & 3.44 & 1.93 \\  & 2 & 74.10 & 12 & 14 & 9.98 & 2.87 \\  & 3 & 44.57 & 23 & 15 & 5.54 & 1.96 \\  & 4 & 43.41 & 18 & 9 & 3.33 & 2.28 \\  & 5 & 46.80 & 13 & 7 & 4.38 & 2.39 \\  & 6 & 73.62 & 16 & 22 & 6.93 & 2.54 \\ \hline \multirow{6}{*}{Mul-ACO} & 1 & 46.38 & 6 & 2 & 1.51 & 2.11 \\  & 2 & 60.21 & 5 & 6 & 5.34 & 2.65 \\ \cline{1-1}  & 3 & 42.88 & 5 & 9 & 2.18 & 1.61 \\ \cline{1-1}  & 4 & 39.70 & 7 & 2 & 0.42 & 1.52 \\ \cline{1-1}  & 5 & 43.10 & 8 & 3 & 1.56 & 1.55 \\ \cline{1-1}  & 6 & 55.93 & 9 & 7 & 5.88 & 2.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Simulation results of ACO, MH-ACO, MF-ACO, and Mul-ACO. Figure 10: Comparison of trajectory performance. (**a**) length, (**b**) number of turns, (**c**) number of iterations, (**d**) the height difference of the trajectory.
# Survey of Blockchain-Based Applications for IoT
## 12 Industry-Specific Applications of Blockchains in the IoT
### Transportation  
Blockchains and the IoT can power autonomous vehicles by securing communication among vehicles, infrastructure, and cloud platforms. Smart contracts facilitate toll payments, parking fees, or even peer-to-peer car sharing without intermediaries. For instance, a charging station can dynamically adjust pricing based on demand and vehicles equipped with blockchain wallets can execute payments instantly and securely. This eliminates the need for centralized billing systems and ensures a seamless, automated user experience.  
Payment channels are instrumental in enhancing the scalability of these applications. Payment channels, a type of layer 2 solution, allow multiple transactions to occur off-chain between two parties, with only the final state recorded on the blockchain. This reduces the load on the main blockchain network, enabling it to handle high volumes of micropayments for tolls, parking, and charging without congestion or high transaction fees, as shown in the following examples.  
* A vehicle can use a payment channel to prepay for multiple small transactions (e.g., tolls on a long trip), settling the total on-chain only when the channel is closed;
* Parking or charging providers can aggregate payments across multiple vehicles, reducing operational overhead.
## 2 Study Area and Data
### Ground Synchronous Measurement
#### 2.3.3 Auxiliary Parameter Measurement  
During the synchronization experiment, the real-time kinematic (RTK) of Starfish iRTK5 was applied to accurately acquire the latitude and longitude coordinates, as well as the elevation of the centers of the seven automatic CR deployed on the ground, where the accuracies of the RTK plane measurement and elevation were better than 10 and 15 mm, respectively. After the automatic CR is adjusted to the required azimuth and elevation angle through program control automatically, the azimuth and elevation angle of CR were reviewed using the traditional measurement method, i.e., using the north marker and level, respectively, to ensure the accuracy of the equipment alignment parameters (Figure 6a,b) and respond to the effectiveness of the automatic CR. Among them, the measurement accuracy of the north marker was 0.1\({}^{\circ}\), and the measurement accuracy of the level was 0.2\({}^{\circ}\)  
Figure 5: Schematic of trihedral CR azimuth and elevation angle calculation (an example of the ascending orbit left look). These charts are: (**a**) azimuth angle calculation, and (**b**) elevation angle calculation.