esa-sceva/satcom-chunk-collection huggingface dataset:
# A Unified Point Multiplication Architecture of Weierstrass, Edward and Huff Elliptic Curves on FPGA
## 2 Mathematical Background over \(GF(2^{m})\)
### Control Unit and Clock Cycle Calculation
Furthermore, the execution cost of the \(for\) loop of Algorithm 2 is \(15\times(m-2)\) cycles, and the cycle count is 3465. The implementation cost of the \(for\) loop of Algorithm 3 is \(38\times(m-2)\) cycles, and the cycle count is 8778. It is important to emphasize that our unified architecture takes \(m\) extra cycles to implement the iterative \(for\) loops of Algorithms 1-3. The reason is a conditional state to check the inspected key bit value based on the mode signal. As we stated earlier, the projective to affine conversions in Algorithms 1 and 2 require an inversion operation. As mentioned, the inversion requires \(m-1\) squares followed by 10 modular multiplications. In our unified architecture of Figure 1, a modular square and a multiplier take one clock cycle for execution. Using the quad-block variant of an Itoh-Tsujii algorithm, \(m-1\) squares can be executed in \(\frac{m-1}{2}\) cycles. Thus, the cycle count for one inversion over \(GF(2^{233})\) is 126 (116 cycles for \(m-1\) square computations and 10 cycles for multiplications). Additionally, 9 and 2 cycles are required to compute the remaining instructions of the projective to affine conversions of Algorithms 1 and 3. Consequently, if we accumulate the clock cycles to calculate the total counts, the Weierstrass, Edward, and Huff curves require 3607, 3472, and 8913 cycles to implement the corresponding Algorithms 1-3.
## 2 Materials and Methods
### Prediction Modeling Combining CFD and ANN  
#### 2.3.1 Artificial Neural Network (ANN)  
Artificial Neural Networks (ANNs) are computational models inspired by biological neural systems, such as the interconnected neurons in the human brain, designed to emulate synaptic signal transmission for complex data processing and pattern recognition [45]. Compared to other intelligent algorithms, ANN exhibits superior capabilities in handling intricate and nonlinear modeling tasks. As demonstrated in Sections 2.2 and 3.2, ANN-based predictions of jet-cleaning performance achieve the highest coefficient of determination (\(R^{2}\)) and lowest mean absolute error (\(MAE\)) among the evaluated methods. The ANN architecture, illustrated in Figure 4, comprises three layers: an input layer, two hidden layers, and an output layer, with structural parameters detailed in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Features** & **Description** \\ \hline Network topology & Feed-forward \\ Training algorithm & Back-propagation \\ Training set & 70\% \\ Test set & 15\% \\ Validation set & 15\% \\ Number of neurons in the input layer & 6 \\ Number of neurons in hidden-layer layers & 32 \\ Number of neurons in the output-layer layer & 3 \\ Maximum number of iterations & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Artificial neural network training and structural parameters.  
The activation function, one of the most critical components in an ANN, introduces nonlinear mapping capabilities to the network. The sigmoid function is defined as follows:  
\[f(x)=\frac{1}{1+e^{-x}}(-\infty<x<\infty) \tag{16}\]  
is commonly employed due to its differentiable, S-shaped output range (0, 1).
## 2 Materials and Methods
In the proposed IoT setup, the Azure services are responsible for the following tasks:  
* AI-driven quality control is associated with the ML model (Azure Custom Vision Service). The system uses AI to inspect casts for defects. This approach reduces human error and increases inspection speed;
* The Azure IoT Hub ensures IoT-enabled device communication. It serves as a communication backbone, managing device connectivity and allowing diverse devices, like ESP32-CAM and various equipment types (M5 Stack, Asus Thinker Board, Raspberry PI, etc. ), to exchange information and coordinate their actions. This is especially valuable in a manufacturing environment where different machines and sensors must work together seamlessly;
* The Azure Functions integrate serverless and event-driven architecture;
* Event Grid supports serverless and event-driven programming. This structure enables on-demand alert processing as production requirements fluctuate. Figure 2 illustrates a globally interconnected manufacturing system facilitated by Azure IoT Hub. In this setup, Factories 1, 2, and 3, each situated in distinct regions, stay connected through a unified IoT framework. When a defect surfaces at Factory 1 or 3, it is reported to Azure IoT Hub, serving as the central relay. From there, corrective actions are issued to other locations, such as Factory 2. This architecture demonstrates the scalability of IoT systems in supporting distributed manufacturing. This setup proposes a modern, automated solution that aligns with Industry 4.0 goals. It provides an innovative quality control framework for cast production with minimal human involvement. For the first time in the literature, the IoT infrastructure proposed in this study enables seamless communication between machinery located in different facilities, potentially in various countries, regardless of geographical distance. This capability is facilitated through Azure IoT Hub, which provides a unified platform for real-time data exchange and device management over long distances. This infrastructure supports distributed manufacturing environments via the global connectivity goals of Industry 4.0. Figure 1: Azure IoT Hub-based architecture for automated cast quality control in Industry 4.0.
# Yeast-Based Magnetic Biocomposite for Efficient Sorption of Organic Pollutants
## 3 Result and Discussion
### Methylene Blue Adsorption
#### 3.2.4 Thermodynamic Studies  
MB removal experiment was performed on YL-M-L-AC at 25-40 \({}^{\circ}\)C, at every 5 \({}^{\circ}\)C. The obtained experimental data suggest that the dye removal capacity of the biocomposite depends on temperature. As the process temperature increases, the MB removal capacity decreases, which is consistent with the studies of other researchers using alginate beads in MB removal [82]. Figure 10 shows the modified Langmuir isotherms of MB removal on YL-M-L-AC at the tested temperatures. On their basis, K\({}_{\rm ML}\) was calculated and presented  
Figure 9: Kinetic models for MB adsorption on YL-M-L-AC, C\({}_{0}\) = 250 mg/dm\({}^{3}\), T = 25 \({}^{\circ}\)C.  
in Table 5 along with other thermodynamic parameters. The \(\Delta\)G\({}^{\circ}\) calculation results show that the MB dye adsorption process using YL-M-L-AC is spontaneous and possible. Additionally, the increasing \(\Delta\)G\({}^{\circ}\) value at higher temperatures suggests that the adsorption process is more favorable at lower temperatures [61]. Depending on the value of \(\Delta\)G\({}^{\circ}\), it can be expected that the process may have a different character. The range from \(-\)20 to 0 kJ/mol indicates physical adsorption (\(-\)16.9 kJ/mol--15.9 kJ/mol), while from \(-\)400-80 kJ/mol suggests chemical adsorption [83]. The \(\Delta\)H\({}^{\circ}\) value for the MB removal process on YL-M-L-AC is \(-\)36.45 kJ/mol, which suggests that the adsorption process is exothermic and physical adsorption (\(\Delta\)H\({}^{\circ}\) < 40 kJ/mol--physical adsorption, >40 kJ/mol--chemical adsorption). The \(\Delta\)S\({}^{\circ}\) value was negative for the biocompposite, suggesting a reduction in random collisions and particle irregularities during the adsorption process and suggesting some structural changes in YL-M-L-AC [84; 85; 86].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Kinetic Model**} & \multicolumn{4}{c}{**Parameters**} \\ \hline \multirow{2}{*}{Pseudo-first order rate} & ARE [\%] & R\({}^{2}\) & q\({}_{1}\) [mg/g] & k\({}_{1}\) [1/min] \\ \cline{2-5}  & 6.22 & 0.9761 & 30.46 & 1.783 \\ \hline \multirow{2}{*}{Pseudo-second order rate} & ARE [\%] & R\({}^{2}\) & q\({}_{2}\) [mg/g] & k\({}_{2}\) [g/(mg-min)] \\ \cline{2-5}  & 1.14 & 0.9989 & 32.37 & 0.0837 \\ \hline \multirow{2}{*}{[PERSON]} & ARE [\%] & R\({}^{2}\) & \(\alpha\) [mg/(g\(\cdot\)min)] & \(\beta\) [g/mg] \\ \cline{2-5}  & 9.90 & 0.9439 & 765.8 & 0.243 \\ \hline \multirow{2}{*}{Weber–Morris} & ARE [\%] & R\({}^{2}\) & 1 & K\({}_{\rm id}\) \\ \cline{2-5}  & 18.86 & 0.8057 & 17.5 & 3.70 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters of kinetic models for MB adsorption on YL-M-L-AC, C\({}_{0}\) = 250 mg/dm\({}^{3}\), T = 25 \({}^{\circ}\)C.  
Figure 10: Nonlinear fitting plots of the modified Langmuir model, t = 24 h, C\({}_{0}\) = 50–250 mg/dm\({}^{3}\).  
Ultimately, the adsorption of MB onto YL-M-L-AC appears to be a complex process, combining both physical and chemical adsorption.
## 3 System Model and Problem Formulation
### End-To-End Traffic Transmission Model with Service Function Constraints  
According to the NTN networking architecture proposed by [PERSON] [25], as shown in Figure 1, the end-to-end traffic transmission modes of the LEO-MSN can be divided into three types: (a) the gNB and the NGC are both on the ground, and the user terminal is relayed to the ground station for processing via the satellite; (b) the gNB is on the satellite and the NGC is on the ground, the user terminal is connected to the satellite node, and the data are transmitted back to the ground; (c) the gNB and the NGC are both on the satellite, and data on the traffic are processed on the satellite without landing. The constraints for completing the end-to-end service transmission in different deployment modes are different, in which the network nodes containing the gNB and NGC functions must be passed through. The path can be expressed as \(Path_{Src,Dist}=\{V_{Src}\rightarrow\cdots\to VF_{i}\cdots \to VF_{FN}\cdots\to V_{Dist}\}\), where \(VF_{i}\) denotes the ith functional node through which the end-toend service transmission passes, and \(FN\) denotes the total number of functional nodes of the SFC in the path.  
The transmission delay of traffic \(d\) with path \(Path_{Src,Dst}\) can be defined as follows:  
\[Delay_{d}=\sum_{e_{ij}\in Path_{d}}\frac{Bv_{d}}{R(i,j)} \tag{13}\]  
where \(Bv_{d}\) represents the data size of traffic \(d\), and \(R(i,j)\) represents the transmission rate of the link \((i,j)\).
## 5 Fracture Analysis
### Effect of Temperature  
As a viscoelastic material, asphalt concrete could exhibit different fracture behavior at different temperatures. In this section, some critical fracture parameters were analyzed with the CZM-based FE model developed in this paper at different temperatures. The loading rate was 5 mm/min in the numerical simulation. Two different temperatures, namely, 0 \({}^{\circ}\)C and \(-\)10 \({}^{\circ}\)C, were considered. The other parameters for numerical simulation were kept the same as that used in model validation. The simulation results showed that asphalt concrete tends to crack earlier at lower temperatures. In detail, for the above simulations, cracks were observed in the specimen when the applied displacement reached 0.812 mm at \(-\)10 \({}^{\circ}\)C. However, for the same specimen at 0 \({}^{\circ}\)C, no cracks were observed until the applied displacement reached 1.141 mm (Figure 8). To find the reason for the above phenomenon, the total number of elements with cohesive damage or adhesive damage under the peak loads were counted. Then the percentage of damaged elements in all elements was calculated and shown in Figure 11.  
As shown in Figure 11, although the peak load at \(-\)10 \({}^{\circ}\)C was higher than that at 0 \({}^{\circ}\)C, the cohesive-damaged elements and adhesive-damaged elements at \(-\)10 \({}^{\circ}\)C were both less than that at 0 \({}^{\circ}\)C. The reason for this is that the initial strength of the viscoelastic elements was larger at lower temperatures so that these elements could bear more stress before total failure. Figure 11 also shows that the percentage of adhesive-damaged elements in all damaged elements was higher at lower temperatures, while the percentage of cohesive-damaged elements in all damaged elements was lower at lower temperatures. This indicates that the cracking path might be closer to the aggregates at lower temperatures. However, since the percentage of adhesive-damaged elements only increased by 2.1%, the influence of temperature on the cracking path should not be very significant. This is proved by Figure 12, which shows the simulated crack propagation paths for the same specimen at 0 \({}^{\circ}\)C and \(-\)10 \({}^{\circ}\)C. It can be seen that
## 3 System Design and Development
### Field Operator System
\begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{1}{c}{**Module**} & \multicolumn{2}{c}{**Description**} \\ \hline MR Simulation & - & Spatially align BIM virtual objects in MR \\  & - & Move virtual objects \\ \hline Office Manager Information Management & - & Track FOS, OMS device ID \\  & - & Control dynamic IP address of OMS device \\ \hline  & - & Create Mixed Reality Capture (MRC) video (Combine the real-world image \\ FOS Communication & - & of existing artifacts in the construction site and BIM virtual images) \\  & - & Send the MRC to the OMS \\  & - & Voice communication \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Field Operator System modules. Figure 2: Spatial alignment process using QR code. Figure 5 shows the functions of FOS modules. The FOS Communication module creates MRC, the combined image of the real-world image obtained from the HMD camera and the BIM object image retrieved from the Communication Server, and sends it to OMS. The resolution of the MRC is 960 \(\times\) 540, which is transmitted in real-time at 30 FPS to OMS. Furthermore, this module assists both users of FOS and OMS in communicating by voice. Information communication with OMS is implemented through the dynamic IP addresses of OMS and FOS devices of the Office Manager Information Management module.
# A Secure and Lightweight Group Mobility Authentication Scheme for 6 LoWPAN Networks
## 2 Related Works
Its primary goal is facilitating efficient and secure group authentication in 6 LoWPAN networks. The performance evaluation of CBAS demonstrates that it has effectively decreased the registration time, signaling cost, and handoff authentication delay compared to the SGMS. However, it does not provide PFS, whereas key management weaknesses may be compromised during the handover process to compromise the integrity of session keys, thereby challenging the PFS. In addition, due to authentication overhead, devices may not be able to successfully transition to a new network, effectively causing DoS attacks. Our analysis shows that there are a few solutions to target group security in PMIPv6-based 6 LoWPANs. Despite significant interest in the 3 rd Generation Partnership Project (3 GPP), there has been a lack of focus on security issues [3]. Moreover, in the schemes that use the aggregated MAC technique, the identities of each mobile node are transmitted in plain text over an insecure channel and, therefore, cannot protect the privacy of the node. Additionally, these schemes are vulnerable to DoS attacks since all members must be legal for the MAC to be successfully verified. The adversary can deliberately provide fake MAC addresses to the group leader, resulting in the intentional failure of the entire group verification process. It is important to balance security, privacy, and efficiency in IoT and mobile networks. Authentication schemes that rely on static identities have historically been vulnerable to tracking attacks, replay attacks, and identity theft [1]. Several studies [4,5] have addressed this issue by utilizing one-time user identities, ensuring that each session uses a fresh, unlinkable identity each time. In resource-constrained environments such as 6 LoWPAN-based IoT networks, alias-based authentication enhances privacy while maintaining strong security guarantees. Similar to conventional authentication schemes that require individual message authentication, aggregated MAC-based authentication has been proposed as an efficient alternative. The use of aggregated MACs allows multiple authentication messages to be verified simultaneously, which reduces processing overhead and increases scalability [6]. Consequently, a secure and lightweight group authentication scheme (SL_GAS) is proposed for highly constrained sensor nodes in PMIPv6-based 6 LoWPAN. By contrast with existing schemes, SL_GAS uses both formal and informal security analysis techniques, such as Scyther, SVO logic, and adversary models (DY, CK, and eCK), to validate the system's security properties. Through these evaluations, we ensure that our systems are resistant to impersonation, replay, MITM, and privileged insider attacks. We also guarantee that our systems are untraceable and secure. Using ticket-based authentication, as well as secret parameters, one-time alias identities, temporary IDs, and an aggregated MAC with tags, providing superior security and efficiency for highly constrained PMIPv6-based 6 LoWPAN networks.
# Exploring the Spatiotemporal Alterations in China's GPP Based on the DTEC Model
## 4 Discussion
### Uncertainties and Limitations  
The DTEC model distinguishes the different light-use efficiencies of C3 and C4 plants in GPP calculations; however, due to the lack of large-scale, long-term, and high-resolution C3 and C4 vegetation type data, we can use only a static map of C3 and C4 vegetation for GPP estimation, which affects the estimation accuracy of GPP. The atmospheric CO\({}_{2}\) concentration is an important factor affecting photosynthetic efficiency because vegetation adjusts leaf chemistry in response to elevated CO\({}_{2}\) concentrations [68], leading to changes in photosynthetic capacity [69]. Accounting for the CO\({}_{2}\) fertilization effect (CFE) in models can improve GPP estimates to reproduce long-term changes [70], and studies [71] have also shown that accounting for the CFE can better capture long-term trends in elevated GPP in the tropics; the CFE can also better explain and quantify the causes of global vegetation greening [72]. CFE should be incorporated into models in future studies.
# AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection
## 3 AI-Driven Blockchain Solutions
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Aspect** & **Consensus Mechanisms** & **Smart Contracts** & **Privacy Protection** & **Data Retrieval** \\ \hline
**Performance** & Low TPS, high latency [108] & Execution inefficiency [109], high gas costs [110] & High computational overhead (e.g., ZKP, FHE) [111] & Slow query speed, limited scalability \\ \hline
**Security** & 51\% attack risk, PoS centralization & Vulnerabilities, irreversible deployment & TEE vulnerabilities, MPC security concerns & Data integrity risks, potential manipulation \\ \hline
**Decentralization** & Validator oligopoly risk & Platform dependence (e.g., Ethereum) & Requires trusted third parties (e.g., MPC and FHE) & Often relies on centralized indexing \\ \hline
**Scalability** & Limited node expansion, slow finality & High on-chain storage and computation costs & ZKP and MPC scalability issues & High storage costs, inefficiency in large datasets \\ \hline
**Privacy** & Transparent transactions, weak financial privacy & Code and execution transparency & High cost for privacy-preserving computations & Publicly accessible data may expose user behavior \\ \hline
**Energy Consumption** & High in PoW, moderate in PoS [112] & Gas fees fluctuate, inefficient execution & Cryptographic computations are energy-intensive & Storage and verification require significant resources \\ \hline
**Usability** & High confirmation time, node reliability required Difficult to regulate, irreversible transactions & Difficult to upgrade and fix vulnerabilities Hard to modify contracts per legal requirements & Complex, high technical threshold & Slower than traditional databases \\ \hline
**Regulatory Compliance** & Highly making costs [113] & High gas fees, expensive audits & Expensive cryptographic computations & High storage and retrieval costs \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of blockchain core technologies. in Figure 7 below. The diagram shows multiple applications of AI in blockchain, including smart contract vulnerability detection, consensus algorithm optimization, data privacy protection, and efficient data storage. The security, scalability, and privacy protection of blockchain are improved by techniques such as deep learning, decision trees, federated learning, and optimized Merkle trees [115].
## 2 Terrain Model
### Hardness Data  
The hardness layer (\(H\)) stores information about materials' hardness (durability) or resistance to the erosion forces and is correlated with the materials' layers (Figure 3). The binding of materials' indexes to hardness records can be done by external files or such data can be generated by the normalisation of the materials' layer. The layer collects data in a range between \(0\leqslant H(p)\leqslant 1\) where \(H(p)\) means the value of the layer in the point \(p\) and \(H(p)\in\mathbb{R}\). The value 0.0 means the material is completely vulnerable to erosion and value 1.0 means that it is non-erodible. The numerical value of records can be associated with geological scales of mineral hardness (Table 1) [41]. Entering hardness records completes the initial model preparation (Figure 1).  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Type**} & \multicolumn{2}{c}{**Scale of Mineral Hardness**} \\ \cline{2-3}  & **Absolute** & **Normalised** \\ \hline none & 0 & 0.0 \\ talc & 1 & 0.001 \\ gypsum & 3 & 0.002 \\ calcite & 9 & 0.006 \\ fluorite & 21 & 0.014 \\ apatite & 48 & 0.032 \\ orthoclase & 72 & 0.048 \\ quartz & 100 & 0.067 \\ topaz & 200 & 0.133 \\ corundum & 400 & 0.267 \\ diamond & 1500 & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Exemplary comparison of materials’ hardness according to the geological scale of mineral hardness.  
Figure 9: Materials layer generated by the Linear Fault Formation (LFF) method interpreted as a grey-scale map (grid resolution: \(1024\times 1024\); fault height (each): \(\Delta h=1\); total faults: 9999). (**a**) Geometric layer. (**b**) Initial map. (**c**) \(mat=3\). (**d**) \(mat=5\). (**e**) \(mat=7\).