esa-sceva/satcom-chunk-collection huggingface dataset:
# Formal Analysis of Ratchet Protocols Based on Logic of Events
## 3 Formal Analysis of the Signal Protocol
### Formal Verification Framework  
The formal verification process for the Signal protocol's strong authentication properties, as illustrated in Figure 2.  
Figure 2 presents the logical flowchart of the verification process based on the extended Logic of Events theory (LoET). It outlines a stepwise framework: modeling the ratchet phases with LoET, proving unidirectional and bidirectional strong authentication, and determining matching session validity through axiomatic rules and causal consistency. This framework is then translated into a concrete procedural logic, as detailed in the following steps.  
1. Use LoET and its extensions to formally describe the ratcheting mechanism of the Signal protocol. Define the actions between initiator A and responder B, specify the basic sequence, and identify the strong authentication properties to be verified.
2. Prove the unidirectional strong authentication property. Assume both A and B are honest. For any instance of the basic sequence, identify matching events and exclude non-matching ones.  
Figure 1: Message exchange diagram of the Signal protocol between A and B through server S.  
Check whether the matching events satisfy the session conditions. If so, prove the matching session; otherwise, continue filtering. If only weak matching is found, analyze the sequence length and verify strong matching based on the axioms.
4. After completing the unidirectional proof, verify the bidirectional property. The protocol is secure only if both directions hold. If any event fails even weak matching, the protocol lacks strong authentication and is vulnerable to impersonation.
## 4 Potential IIoT Applications
### Energy Applications  
By employing NTNs, various electric grid automation and metering applications can be handled, whereas the incorporation and productive use of the major energy resources and the electric grid infrastructure can be supported by involving smart sensors, smart meters, and smart control [76]. In oil and gas production sites, where adverse environments and burdensome operating conditions are observed, NTNs can be also beneficial by monitoring and transmitting sensor data related to wellhead production, drilling control, distribution logistics, pipeline monitoring, and asset security. The measured operational parameters are initially transmitted to a control room. Then, using SCADA, set-points are arranged and control settings are expedited. By exploiting intelligent NTNs in energy systems together with sophisticated sensors, companies can remotely collect meaningful data and thus attain real-time vital insights into the E2E operational condition of their projects, e.g., solar panels and wind turbines, depending on the environmental conditions, and ensure that the systems work at the maximum possible efficiency. Moreover, by using remote installations of UAVs, the human inspection of the facilities would be avoided.
## 3 Proposed Method
### Noise Detection Strategy  
As mentioned before, DSRE suffers from the wrong labeling problem whether the data comes from a corpus or web table. Especially when there is the only one sentence in the bag, if the sentence is labeled incorrectly, this noise problem will greatly damage the performance of the model. Unlike previous works that just relied on an attention module to alleviate noise, we propose a noise-detection strategy to detect and revise the noise label in the training process. Specifically, since RE aims to predict the relation between pairs of entities, we combine the embeddings of entities into our model. We employ the similarity between entity embeddings and sentence representation to estimate whether the current sentence can imply a relation between the target entity pair. If the current positive relation label does not exist in the sentence, the label will be modified to NA and the sentence is treated as invalid in the training of current relations. In this way, the noisy labeling problem of distant supervision will be largely eliminated.  
Motivated by the translation-based knowledge graph embedding approaches [22; 23; 24], we utilize the difference vector between the target entity pair as an additional feature. Specifically, we use \(\mathbf{r}_{ht}=\mathbf{t}-\mathbf{h}\) to reflect some information of relation between entities, where \(\mathbf{h},\mathbf{t}\in\mathbb{R}^{d^{\prime\prime}}\) are pre-trained word embeddings.  
Here, we adopt the Euclidean distance to measure the similarity of \(\mathbf{r}_{ht}\) and the sentence representations. A two-layer feed-forward network is then applied to the distance, which is formally denoted as  
\[sml=\mathbf{W}^{f2}\sigma(\mathbf{W}^{f1}d(\mathbf{As},\mathbf{r}_{ht})+ \mathbf{b}^{f1})+\mathbf{b}^{f2} \tag{6}\]  
where \(\mathbf{W}^{f1}\in\mathbb{R}^{d^{\prime\prime}\times 1}\), \(\mathbf{W}^{f2}\in\mathbb{R}^{1\times d^{\prime\prime}}\), \(\mathbf{b}^{f1}\in\mathbb{R}^{d^{\prime\prime}}\), \(\mathbf{b}^{f2}\in\mathbb{R}^{1}\) and \(\mathbf{A}\in\mathbb{R}^{d^{\prime\prime}\times(3\times d^{\prime})}\) are learnable parameters; \(\sigma(\cdot)\) is the sigmoid function; and d(\(\_)) is the Euclidean distance function.  
Then, we correct the original relation label \(r\) using the similarity score \(sml\) as follows:  
\[r^{\prime}=\{\begin{subarray}{c}r,\text{ if }sml\in[\phi,\phi]\\ \text{NA, otherwise}\end{subarray} \tag{7}\]  
where \(\phi\) is a pre-defined threshold. If the similarity score \(sml\) is less than the threshold \(\phi\), we think the sentence can express the current relation \(r\). Otherwise, \(r\) will be modified to NA.
###### Abstract
In this paper, a shape optimization method for PMSMs for EVs using MLP, a type of deep learning, is proposed. To maximize the performance of a PMSM, the average torque and total harmonic distortion (THD) of the back electromotive force (EMF) were set as the multi-objective function, and the efficiency and torque ripple were set as constraints. FEA was used to improve the accuracy of the design results, and DOE was performed by adjusting the design variables. Based on the DOE results, metamodels were generated by applying the proposed MLP and the conventional Kriging model. Shape optimization was performed by applying the generated metamodels and a hybrid metaheuristic algorithm (HMA), and the results were compared. FEA for the optimized model was conducted to verify the validity of the shape optimization design results. To verify the structural stability, mechanical stress analysis was performed on the optimal model. In addition, the changes in the PMSM performance were examined while changing the weighting of the multi-objective function. The overall process of this study is represented in the flowchart shown in Figure 1.  
Figure 1: Flowchart of the overall research procedure.
# Optimization Design for Sparse Planar Array in Satellite Communications
## 4 Sparse Planar Array Optimization Design Method
### Intelligent Optimization Methods
#### 4.2.2 Sparse Area Array Optimization Based on Artificial Intelligence Methods
This type of method will become the mainstream method of intelligent reconfigurable antenna design in the future, with good application prospects.
## 3 Results and Discussion
### PdM Assessment of Case Study
#### 3.1.7 Step 7: Prediction Techniques and Priority Index
In this step, the detection priority index in Table 3 is used to assign the relevant category based on technician experience and sensor manufacturer. For example, vibration sensor provides medium detectability (score 2.5) and medium earliness (score 2), which results in a total detection level of \(5\) (\(2.5\times 2=5\)). 4. Estimate the effectiveness of the diagnosis technique, which covers the diagnostic ability and diagnostic informativeness. In this step, the diagnosis priority index in Table 4 is used to assign the relevant category based on technician experience and diagnosis solution providers. For example, the studied compressor is monitored by a vibration sensor that is tracked over time to identify deviations in sensor output. Thus, this diagnosis technique is assigned to offer a medium level of diagnosis \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Precision of Deterioration Prognosis and RUL Prediction**} \\ \hline
**Predictive Horizon** & **Low (1)** & **Medium (2.5)** & **High (5)** \\ \hline Low (1) & 1 & 2.5 & 5 \\ \hline Medium (2) & 2 & 5 & 10 \\ \hline  & & & 15 \\ High (3) & 3 & 7.5 & (Full accuracy and predictive horizon) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Prognosis priority index. ability (score 2.5) and a medium level of diagnostic informativeness (score 2), which results in a total diagnosis level of 5 (2.5 \(\times\) 2 = 5). * Estimate the effectiveness of the prognosis technique, which covers the prediction precision and predictive horizon. In this step, the prognosis priority index in Table 5 is used to assign the relevant category based on technician experience and prognosis solution providers. For example, the studied compressor is monitored by a vibration sensor, and two prognosis methods are used (regression and expert knowledge). Thus, this prognosis technique combination is assigned to offer a low level of prediction precision (score 1) and a medium level of predictive horizon (score 2), which results in a total prediction level of 2 (1 \(\times\) 2 = 2). * Estimate the overall predictive maintenance effectiveness index by multiplying the coverage index (i.e., relative value) with the detection level (0-15), diagnosis level (0-15), and prognosis level (0-15). For example, the designed PdM specifications for the studied compressor scores 46 (0.92 \(\times\) 5 \(\times\) 5 \(\times\) 2 = 46) out of 3375 (1 \(\times\) 15 \(\times\) 15 \(\times\) 15 = 3375). Therefore, the PdM effectiveness percentage for the studied case is assigned a value of 1.37% (46/3375 = 0.0137).
## 9 Future Trends and Research Directions
Currently, several regional and international organizations are working toward defining regulatory frameworks, but inconsistencies remain in power limits, modulation schemes, and environmental compliance. The IEEE 802.15.7m standard, ref. [86] an extension of the original IEEE 802.15.7 for visible light communications, has been adapted to address underwater optical wireless communication. It specifies modulation techniques such as OOK (On-Off Keying) and OFDM (Orthogonal Frequency Division Multiplexing) for line-of-sight (LOS) and non-line-of-sight (NLOS) configurations. However, it does not yet account for dynamic underwater conditions like turbidity and salinity variations, limiting its applicability in real-world deployments. The International Telecommunication Union (ITU) has introduced ITU-T G.9991 [87], which provides guidelines for high-speed underwater optical links, including channel modeling and error correction techniques. While this standard offers a foundation for interoperability, its scope is primarily limited to shallow-water applications, leaving deep-sea and high-turbidity environments insufficiently addressed. In China, the GB/T 34000-202X draft standard [88] focuses on underwater optical sensor networks, specifying emitter power limits and receiver sensitivity thresholds. However, this standard is still under review and has not yet been ratified, creating uncertainty for manufacturers and researchers. Meanwhile, the European Union's Marine Strategy Framework Directive (MSFD) imposes strict ecological regulations on underwater communication technologies, requiring UVLC systems to minimize disruption to marine life. While environmentally conscious, these restrictions complicate power-level optimization for long-range communication. Efforts to unify these standards are underway, led by organizations such as the Underwater Wireless Optical Communication Alliance (UWOA), which published a 2024 whitepaper advocating for cross-border compatibility. Additionally, the ISO/TC 43/SC 3 [89] working group is developing guidelines for underwater acoustics and optical communications, though progress has been slow due to differing regional priorities. The absence of a universally accepted standard leads to fragmentation in UVLC deployments. For instance, a system compliant with China's GB/T regulations may not meet the EU's ecological constraints, while IEEE-based designs might struggle in turbid coastal waters. Future standardization efforts must address the following:  
1. Power and safety limits tailored to different water conditions (clear vs. turbid). 2. Adaptive modulation schemes that dynamically adjust to environmental changes. 3. Ecological impact assessments to ensure marine life protection without sacrificing performance. Until a cohesive global framework is established, researchers and industry stakeholders must navigate a patchwork of regional regulations, slowing innovation and deployment scalability.
# Dynamic UAV Inspection Boosted by Vehicle Collaboration Under Harsh Conditions in the IoT Realm
## 4 Algorithm
### Static Planning and Scheduling Algorithm
#### 4.1.2 Partitioning Routes Within Anchor Points  
After K-means algorithm, each data collection point is assigned exactly one docking anchor point, meaning that each docking anchor point \(a_{i}\) has a corresponding set of task points \(E_{i}\). In this section, we describe the planning of routes for the task point sets of each anchor point and the output of a set of UAV flight routes. A diagram illustrating the division of routes within anchor points is shown in Figure 3.  
In Figure 3, the gray areas represent clustered anchor-task sets, where each set is associated with one and only one docking anchor point, while the others are candidate anchor points. From the figure, it can be observed that within each gray area, routes are planned between various task nodes, forming multiple UAV flight routes.  
This study employed a greedy approach to partition routes within anchor points. The algorithm for dividing routes within anchor points is outlined in Algorithm 2. First, an empty set of routes \(routes\), the route \(route\), and the current position are initialized. Then, a temporary task point set \(Q\) is set and the task points from the task point set \(E_{i}\) are added to \(Q\). Each time, a task point is randomly selected from the temporary task point set \(Q\) and added to the current single route \(route\). It is checked whether traveling from the anchor point through the route \(route\) and returning to the anchor point exceeds the UAV's maximum flight distance \(d_{max}\). If it is exceeded, the task point is removed from the route, added to the route set \(routes\), and the route \(route\) is reset. This step aims to maximize the collection of task points in a single UAV flight while satisfying the UAV's flight distance constraint. The maximum iteration count is set to \(max\_iter\), and the process continues until the temporary task point set \(Q\) is empty, at which point, the UAV flight route set at that anchor point is output. Here, \(location\) is used to record the position of the last node in the current route.  
The UAV flight route set generated through these steps represents a feasible greedy solution, but may not be optimal. Further iterations are required in subsequent steps to refine and produce the optimal UAV flight route solution.
# Training and Inference of Optical Neural Networks with Noise and Low-Bits Control
## 2 Architecture
### Noise  
Some factors could affect the accuracy of the chip in reality. Among them, quantum-limit noise is the root of the fundamental limit of optical devices [3,14]. As we mentioned in Section 2.1, shot noise would be produced in the balanced detector during transmission.  
In a neural network, each layer of neurons \(x_{i}\) is transmitted to the next layer of neurons \(x_{i+1}\). Each neuron is a homodyne detector that interferes with the broadcast signal to the weighted signal \(A_{ij}\)[16,19]. \(A_{ij}\) and \(x_{i}\) multiply and accumulate (MAC), as shown in Equation (1):  
\[x_{i+1}=f(\sum_{j}A_{ij}x_{i}) \tag{1}\]  
\(x_{i}\) is the input of the current layer, where \(x_{i+1}\) is the output of the current layer. As reminded in Section 2.1, input vector \(x_{i}\) is encoded temporally as pluses. Then, the weights enter into channels in the form of time coding, the same as input vectors. These data will be processed optically by MAC calculations. The nonlinear activation function is implemented by electrical methods. Finally, we get the output. Power consumption can be calculated by \(P(t)=|E(t)|^{2}\).  
Assuming that the input signal and the weight signal have a perfect spatiotemporal mode match, this can be normalized so that \(|\overline{x}_{i}|^{2}\), \(|\overline{A}_{ij}|^{2}\) correspond to the number of photons per pulse. When a pulse with an amplitude of \(u\) enters, the output current can be described by Poisson distribution: \(\frac{Q}{e}\sim Poisson\Big{(}|u|^{2}\Big{)}\). Each photocurrent \(Q(\pm)\) is the sum of many Poisson random variables. In the useful limit of many photons per neuron (although not necessarily per MAC), this will approximately lead to a Gaussian random variable as follows:  
\[\frac{Q_{i}^{(\pm)}}{e}=\sum_{j}\frac{1}{2}\big{(}\overline{A}_{ij}\pm \overline{x}_{ij}\big{)}^{2}+w_{i}{}^{(\pm)}\Bigg{(}\sum_{j}\frac{1}{2}\big{(} \overline{A}_{ij}\pm\overline{x}_{ij}\big{)}^{2}\Bigg{)}^{1/2} \tag{2}\]  
where \(w_{i}(k)\sim N(0,1)\) are Gaussian random variables.  
Then, the next layer of neurons \(x_{i+1}\) with the influence of noise can be represented in Equation (3) [3].  
\[x_{i+1}=f(\sum_{j}A_{ij}x_{i}+w_{i}\frac{\|A\|\|x\|}{\sqrt{N^{2}N^{\prime}}} \frac{\sqrt{N}}{\sqrt{n_{MAC}}}) \tag{3}\]  
where \(||\cdot||\) is the 2-norm, \(n_{MAC}\) is the number of photons per MAC, \(N\) is the number of input neurons, and \(N^{\prime}\) is the number of output neurons. \(n_{MAC}\) is related to the total energy consumption of the layer which is given by \(Etot=NN^{\prime}n_{MAC}\). We can figure out that the total energy consumption between computation layers is \(1.64\times 10^{7}J\).  
In the previous work, we simulated a layer and multi-layer model with granular noise [15]. To get closer to reality, the noise effect expressed by Equation (3) was added to the simulation process exactly following the procedure mentioned in Section 2.1. The result in Figure 4 shows that when the \(photon/n_{MAC}\) is large enough, the error rate is not affected.
## Nomenclature
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multirow{2}{*}{**Layer**} & **s** & \(\lambda\) & \(\rho\) & **c** & **R** & **U** \\ \cline{3-8}  & & **[m]** & **[W m\({}^{-1}\) K\({}^{-1}\)]** & **[kg m\({}^{-3}\)]** & **[J kg\({}^{-1}\) K\({}^{-1}\)]** & **[m\({}^{2}\) K W\({}^{-1}\)]** & **[W m\({}^{-2}\) K\({}^{-1}\)]** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Wood \\ external wall \\ \end{tabular} } & Liminar inner layer & - & - & - & 0.125 & \\  & Plasterboard & 0.0125 & 0.210 & 700 & 1000 & 0.060 & \\  & Wood fiber & 0.100 & 0.039 & 150 & 2100 & 2.564 & \\  & Cross laminated timber & 0.100 & 0.130 & 500 & 1600 & 0.769 & 0.150 \\  & Wood fiber & 0.120 & 0.039 & 150 & 2100 & 3.077 & \\  & Plaster & 0.015 & 0.900 & 1800 & 1000 & 0.017 & \\  & Liminar outer layer & - & - & - & - & 0.040 & \\ \hline \multirow{4}{*}{\begin{tabular}{c} Internal wall \\ with insulation on \\ the inner surface \\ \end{tabular} } & Liminar inner layer & - & - & - & - & 0.125 & \\  & Plasterboard & 0.0125 & 0.210 & 700 & 1000 & 0.057 & \\  & EPS with graphite & 0.120 & 0.031 & 20 & 1450 & 3.871 & \\  & Bricks & 0.280 & 0.778 & 1800 & 840 & 0.360 & 0.224 \\  & Plaster & 0.015 & 0.900 & 1800 & 1000 & 0.017 & \\  & Liminar outer layer & - & - & - & - & 0.040 & \\ \hline \multirow{4}{*}{\begin{tabular}{c} Internal wall \\ with insulation on \\ the inner surface \\ \end{tabular} } & Liminar inner layer & - & - & - & - & 0.125 & \\  & Plasterboard & 0.015 & 0.900 & 1800 & 1000 & 0.017 & \\  & Hollow brick & 0.080 & 0.400 & 775 & 840 & 0.200 & 2.069 \\  & Plaster & 0.015 & 0.900 & 1800 & 1000 & 0.017 & \\  & Liminar outer layer & - & - & - & - & 0.125 & \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Floor \\ \end{tabular} } & Liminar inner layer & - & - & - & - & 0.125 & \\  & Ceramic tiles & 0.010 & 1.000 & 2300 & 840 & 0.010 & \\  & Blockes & 0.280 & 0.778 & 1800 & 840 & 0.360 & \\  & Plaster & 0.015 & 0.900 & 1800 & 1000 & 0.017 & \\  & Liminar outer layer & - & - & - & - & 0.040 & \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Floor \\ \end{tabular} } & Liminar inner layer & - & - & - & - & 0.125 & \\  & Plasterboard & 0.015 & 0.900 & 1800 & 1000 & 0.017 & \\  & Liminar outer layer & - & - & - & - & 0.125 & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Stratigraphy and thermal properties of the structures of the well-insulated masonry buildings (WIE and WII).
## 3 Results and Discussion
### Effect of the Sulfonate Group Content on Water/oil IFT Values for the Systems Containing SPSNP  
Figure 10 shows the IFT results as function of the sulfonate group and OADA contents. This graph presents the IFT values which correspond to the final value in the experiment (Spinning Drop measurements) when the IFT versus time curve reached an equilibrium plateau. SPSNP synthesized in the presence of a greater amount of surfactant (20 CMC of OADA) have shown to be more effective in the reduction of water/oil interfacial tension when mineral oil was used as the oil phase. This tendency can be explained by a larger surfactant content retained in the nanoparticles' structure, as indicated by the 03 and 07 samples analysis as described in Table 2. Further investigations on retained surfactant quantities are described in Section 3.8.  
In addition, taking into consideration that the substitution of styrene monomers by sulfonated styrene causes a reduction in particle size (as shown in Figure 9), it could be expected a higher interfacial activity for these systems, since they show greater area/volume ratio. Low concentrations of St-S favor the reduction of interfacial tension, because smaller nanoparticles are more effective in displacing other molecules at the interface [16]. However, at higher sulfonate group concentrations, this tendency is not observed. The nanoparticles become more soluble in the aqueous medium to such an extent that they do not migrate to the water/oil interface any longer. Therefore, the number of active substances (nanoparticles + surfactant system or free nanoparticles or free surfactant molecules) at the water/oil interface is reduced, increasing the interfacial tension. Considering a balance of these factors, it was determined which system would be derived from the optimized conditions. The system that showed  
Figure 9: Influence of St-S content on SPSNP particle size prepared with different OADA concentrations: 10 CMC and 20 CMC.  
minimal particle size and interfacial tension in the range of ultra-low IFT was the one synthesized with 0.012 mol % St-S content and an OADA concentration of twenty times the CMC.