esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Global Forecasting and Interpolation Performances
This clearly illustrates the relevance of NN-based representation, which fully embeds the direct and inverse mappings between the SST field and its patch-level representation. Iterating patch-level assimilation steps and global reconstruction steps as illustrated by the Algorithm 1 allows information propagation of assimilated patches in a global scale which helps outperforming AnDA schemes. Interestingly, Table 3 also reveals the importance of the EOF-based parameterization of the NN-based covariance model (19) in the improvement of interpolation results. **Qualitative analysis of the proposed schemes:** We further illustrate these conclusions through interpolation examples in Figure 4. The visual analysis of the reconstructed SST gradient fields emphasize the relevance of PB-NNKF-EOF scheme to reconstruct fine-scale details. While OI and  
Figure 3: Reconstruction and gradient RMSE times series for the selected models. PB-VE-DINEOF schemes tend to smooth out fine-scale patterns, the analog data assimilation may not account appropriately for patch boundaries. This typically requires an empirical post-processing step [22]. By contrast, the PB-NNKF-EOF scheme fully embeds this post-processing step through reconstruction network \(\mathcal{F}_{r}\) and learns its parameterization from data, which is shown here to greatly improve patch-based interpolation performance. The analysis of the spectral signatures in Figure 5 leads to similar conclusions with the PB-NNKF-EOF scheme being the only one to recover significant energy level up to 50 km. \begin{table}
\begin{tabular}{l c c c c
This paper first mentions the types of cyberattacks and vulnerabilities for industrial control systems. Then, it focuses on how adversarial training and deceptive patch solutions can be applied in industrial control systems and window size manipulation. Finally, trials and evaluations are performed on the ICS dataset to assess the effectiveness of this integrated approach. Deceptive patching technology increases security by changing the attack surface in a system and making it more challenging for an attacker to perform an attack successfully. Window size manipulation changes how network protocols function, making it more difficult for an attacker to observe and analyze the network traffic. Combining these two techniques enables the analysis of large datasets to detect and respond to potential attacks, preventing attacks from succeeding and disrupting the attacker's strategy. This paper explains in detail how the suggested artificial intelligence-based hybrid model functions, how it is implemented, and the consequences. Moreover, it discusses what this model means for future cybersecurity strategies and the roadmap that it presents for further research and development. The present study, which explains how deceptive patch solutions can be integrated with window size manipulation to increase cybersecurity in ICSs with the adversarial training method, is a significant step toward the better understanding and application of methods to create an effective cybersecurity strategy for industrial control systems. A summary of the novelties and contributions of the present study is presented below:  
* Analyzing the suitability of deceptive patching techniques for industrial control systems, developing novel methods, and optimizing the current methods;
* Warning system administrators about the presence of an attacker in the system with the early detection of attacks by assessing the effectiveness and performance of the methods developed;
* Reducing the risk of system damage by diverting attackers' attention from fundamental system components and security vulnerabilities and decreasing the risk of critical infrastructure damage;* Developing deceptive patching strategies for industrial control systems and applying dynamic and adaptive deception techniques;
* Ensuring real-time threat analysis and response capabilities for system security, analyzing aggressive behavior patterns specific to industrial control systems, and optimizing the deceptive patching strategies with these models. Our study is organized as follows. Section 2 presents the background. Section 3 describes the methods employed. Section 4 addresses the suggested approach. The experiments and their outcomes are presented in Section 5. Finally, the conclusions and future research are given in Section 6.
## 3 Results
### CFD Simulations  
The computed temperature distributions in the modules at different times (revealing thermal gradients and heat flow patterns during phase transitions) are shown in Figures 11 and 12. The simulated temperature curves at the reference measurement point are shown in Figure 13 and further demonstrate the accuracy of the numerical model. The simulation results exhibit a strong correlation with the experimental data, particularly in the key thermal events. Remarkably, the simulation accurately reproduces the time required for cooling from 22 \({}^{\circ}\)C to 6 \({}^{\circ}\)C (approximately 60 min), aligning closely with the experimental observations. This phase is critical, as it involves the dissipation of sensible heat, which sets the stage for the subsequent phase transition. Moreover, the model precisely captures the time required for the liquid-solid phase transition (which is the most critical in our case) and the remaining time until reaching 0 \({}^{\circ}\)C. This agreement between the experimental and numerical results not only validates the computational approach but also highlights its potential as a reliable tool for predicting thermal behavior in PCM-based systems.  
Figure 11: Temperature distribution after 30 min (**left**) and 60 min (**right**).  
Figure 12: Temperature distribution after 360 min (**left**) and 540 min (**right**).  
Figure 13: Simulated temperature behavior of PLA and PLA–metal modules.  
The simulation results provide detailed insights into the spatial distribution of heat absorption and release within the modules, offering valuable information for optimizing thermal management designs. Figures 11 and 12 illustrate regions of effective heat absorption and dissipation, clearly identifying the thermal behavior throughout the cooling and phase transition processes. These figures highlight areas within the module where heat absorption is most pronounced, particularly during the initial cooling phase. The regions with the highest core temperature are identified, indicating zones where the material stores the maximum amount of thermal energy before the onset of phase transition. Understanding these areas is crucial, as they represent the sections of the module that are most thermally active and, therefore, most influential in the overall heat management performance. The simulation also highlights the most critical areas of the heat exchanger, where heat dissipation is most effective. These critical regions are essential for optimizing the module's design, as they are key to enhancing the efficiency of thermal regulation. By identifying where heat is released most efficiently, the simulation provides a roadmap for targeting design improvements, such as enhancing thermal conductivity or adjusting the material composition in these regions to maximize performance.
## 4 Experimental Results
### Haze Removal  
As mentioned above, pivotal estimates (transmission map \(t\) and \(AL\)) of dehazing system were calculated through a high-performance strategy. Consequently, haze removal operation can be attained easily by employing the atmospheric degradation model in Equation (1) as:  
\[J(x)=\frac{I(x)-A(x)}{t(x)}+A(x) \tag{12}\]  
where \(J(x)\) and \(I(x)\) represent recovered image and hazy image, respectively. \(t(x)\) and \(A(x)\) are the estimated transmission map and \(AL\), respectively. Figure 12 shows the practical results achieved by the proposed dehazing system.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{CMCNN\({}_{tr}\)} & **Nbre-U** & 1-U & 2-U & 3-U & 4-U & 5-U & 6-U \\ \cline{2-8}  & **MSE-Val** & 1.96 & 1.42 & 1.06 & 0.62 & 0.61 & 0.61 \\ \hline \multirow{2}{*}{CMCNN\({}_{t}\)} & **Nbre-U** & 1-U & 2-U & 3-U & 4-U & 5-U & 6-U \\ \cline{2-8}  & **SSIM-Val** & 0.87 & 0.94 & 0.94 & 0.94 & 0.95 & 0.95 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of D-units and their corresponding average values of MSE and SSIM in each subNetwork.  
Figure 11: The values of MSE and SSIM in terms of number of used D-units for some tested images: (left) MSE; and (right) for SSIM.  
Figure 12: Visual results of proposed model: (**a**) hazy images; (**b**) rough transmission map \(t_{r}\); (**c**) refined transmission map \(t\); and (**d**) dehazed images.
# A Lightweight and Practical Anonymous Authentication Protocol Based on Bit-Self-Test PUF
## 3 Proposed Scheme
### Lightweight Helper Data Algorithms  
Based on the BST-APUF, we propose a lightweight HDA that can be used to extract stable cryptographic keys, or for security authentication. The proposed HDA schematic diagram process is shown in Figure 4. In the key generation process, the original challenge \(c\) will be input into the LFSR to generate a challenge collection of the BST-PUF, and the BST-PUF will generate the original output \(r_{m1}\) and the reliability flag \(F\). The reliability flag \(F\) is used as the helper data \(h_{1}\) in this HDA to perform the key extraction and recovery operation. Moreover, the \(r_{m1}\) and h1 will be input to the robust response extractor to generate the highly reliable robust response \(r_{c1}\). Although, after extracting, the PUF response \(r_{c1}\) is reliable enough, the disclosure of the helper data in the response error correction process can lead to a large entropy loss, and the stronger the error correction capability, the relatively  
Figure 3: Extraction of robust response.  
Figure 2: Model of Bit-Self-Test PUF.  
larger the entropy loss [36]. It even causes the PUF response to be unavailable for key generation when the entropy value is too low. Therefore, the robust response of the PUF needs to be processed by the entropy extractor to generate the high-entropy key \(K\).  
In the key recovery process, the original challenge \(c\) and the helper data \(h_{1}\) are, respectively, input to the LFSR and the robust response extractor. Because the PUF output is susceptible to environmental factors, for the same challenge collection \(c_{1}\) generated by the LFSR, the original output \(r_{m2}\) of the PUF is not exactly the same as the PUF output \(r_{m1}\). However, after processing by the \(h_{1}\), the generated robust response \(r_{c2}\) has no more than \(10^{-9}\) bit error rate with \(r_{c1}\), which ensures the stability and high efficiency of key recovery.
# A Multi-Objective Modified PSO for Inverse Kinematics of a 5-DOF Robotic Arm
## 3 Multi-Objective Inverse Kinematics Problem Statement
### Managing the Orientation of the End-Effector  
Euler angles convention \((\alpha,\beta,\gamma)\) is a classical standard used in robotics, essentially to specify orientations. The Euler convention, as illustrated in Figure 3, represents orientations as follows:  
* Frame 1 rotates first by an angle \(\gamma\) around \(\overrightarrow{X_{0}}\), of frame 0;
* Then Frame 2 rotates of \(\beta\) around \(\overrightarrow{Y_{0}}\);
* Lastly, Frame 3 rotates around \(\overrightarrow{Z_{0}}\) by an angle \(\alpha\).  
Figure 3: Euler convention rotations \(R_{X}(\gamma),R_{Y}(\beta)\) and \(R_{Z}(\alpha)\).  
The end-effector orientation by be represented by Euler angular convention, which allows a more generic and interpretable representation of the orientation. [PERSON]'s rotations toward the \((X,Y,Z)\) base frame are, respectively, \((\gamma,\beta,\alpha)\). They may be computed on Equations (11)-(13), terms which are retrieved from the homogeneous transformation matrix detailed in Equation (9).  
\[\beta=\arctan_{2}(-r_{13},\sqrt{r_{11}^{2}+r_{21}^{2}}) \tag{11}\]  
\[\alpha=\arctan_{2}(\frac{1}{r_{21}}cos(\beta),\frac{1}{r_{11}}cos(\beta)) \tag{12}\]  
\[\gamma=\arctan_{2}(\frac{1}{r_{32}}cos(\beta),\frac{1}{r_{33}}cos(\beta)) \tag{13}\]  
where the \(r_{ij}\) terms are those appearing in the homogenous transformation matrix of the arm detailed in Equation (9).  
Mono-objective optimization of the orientation aims to minimize the Euclidean distance of the generated solution toward a target orientation, as in Equation (14).  
\[minf_{2}(Q)=\frac{|\gamma-\gamma_{t}|+|\alpha-\alpha_{t}|+|\beta-\beta_{t}|}{3} \tag{14}\]
## 2 Theoretical Foundation and Hypotheses Development
### Environmental Factors  
Sustainable Development Goals  
Sustainable development goals, also known as global goals, are a set of 17 interconnected objectives aimed at providing a shared blueprint for peace and prosperity [33]. The World Bank and the United Nations have recognized financial inclusion as a factor contributing to the achievement of seven of the 17 SDGs [34]. Although developed countries diligently pursue SDG goals, the International Monetary Fund (IMF) has taken the lead in helping developing countries achieve these SDGs too. For example, the IMF strives to alleviate poverty in low-income developing nations by providing financial assistance [35]. Access to financial services and resources is a critical step toward alleviating poverty and inequality and ensuring sustainable national development [36]. Despite technical advancements and innovations, the World Bank [22] reports that more than a billion and a half adults worldwide do not have a bank account. A few studies have highlighted the remark  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Themes** & **Studied** & **References** \\ \hline Individuals and public & Individuals and public perception & [14; 20; 23; 26] \\ adoption & of CBDCs & [14; 20; 23; 26] \\ \hline Technological & How the design of CBDCs can & \\ characteristics & benefit users and nations’ & [15; 17; 24; 25] \\  & financial systems & \\ \hline Commercial adoption & Business perception of CBDCs & [27] \\ \hline Country adoption & Macro-financial implications of & \\  & CBDC adoption & [16; 18; 19; 28] \\ \hline Relation of CBDCs with & Directional predictability between & \\ crypto-assets & CBDC shocks and CBDC attention & [10; 11; 12; 29] \\  & index on cryptocurrencies & [10; 11; 12; 29] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Some key studies on CBDCs.  
able potential of CBDCs to achieve universal financial inclusion [37; 38]. [PERSON] et al. [39] contend that CBDCs can help promote green finance and sustainable development by improving payment efficiency and financial access. The establishment of a CBDC also indirectly contributes to SDGs by reducing the transactional loop and increasing the efficiency of fund sourcing for investments in activities and initiatives that promote sustainable development [40]. Consequently, we believe that countries that pursue SDG goals diligently are likely to adopt CBDCs earlier. Therefore, we propose the following hypothesis:  
**H2.**_SDG rank is positively associated with the country's adoption of CBDCs._
# Improving Symbolic System-Level Synthesis by Solver Coordination and Domain-Specific Heuristics
## 4 System Model
### Platform Model  
Our platform model is a versatile abstract model which enables modeling of several crucial aspects of heterogeneous parallel architectures, e.g., inhomogeneous performance of processing elements and the on-chip communication infrastructure. Since tile-based architectures interconnected by a NoC are considered to be a scalable reference design for massively parallel hardware platforms [3; 4], these type of platforms are of our special interest and therefore in the main focus of our platform model.  
We model the topology of tile-based many-core hardware platforms as a directed graph \(g_{p}=(\mathbf{R},\mathbf{E}_{p})\), the platform graph.  
The platform graph's set of vertices \(\mathbf{R}\) summarizes all potentially shared resources of the hardware architecture whereas the directed edges \(\mathbf{E}_{p}\subseteq\mathbf{R}\times\mathbf{R}\) represent interconnections between shared resources. Shared resources \(\mathbf{R}=\mathbf{R}_{pe}\cup\mathbf{R}_{ni}\cup\mathbf{R}_{trtr}\) can be categorized into processing elements (PEs) \(\mathbf{R}_{pe}\), network interfaces (NIs) \(\mathbf{R}_{ni}\) and routers \(\mathbf{R}_{trtr}\) of the NoC. As part of our synthesis approach, we resolve resource contention on all shared resources via time-triggered scheduling at design time. This requires the computation and communication resources of a platform to be in sync, e.g., via a global clock. Note that in a more elaborate platform model, we also model more details of the routers \(\mathbf{R}_{trtr}\), namely the internal switching of the routers to capture different NoC implementations. We omit further details here for brevity and refer the interested reader to [29].  
A processing element \(r\in\mathbf{R}_{pe}\) is able to perform computations and is linked to the NoC of the platform by a network interface \(\tilde{r}\in\mathbf{R}_{ni}\). As an auxiliary hierarchical element we introduce tiles \(\mathbf{R}_{i}\). A tile \(r^{\prime}\in\mathbf{R}_{t}\) integrates exactly one PE, one NI and local memory (not explicitly modeled) that can be accessed by both the PE and the NI of the same tile.  
We assume that the transport of data through the NoC follows a store-and-forward routing protocol, i.e., packets are atomic entities that are transferred between routers on a per-hop basis [3].  
In the following section we address the (potential heterogeneous) processing capabilities of resources modeled within our application model. This requires to introduce the associated entities of the application model properly.
# Positioning Information Privacy in Intelligent Transportation Systems:
## 2 Solutions for Spotting of Vehicles on the Road
### Verifiable Multilateration  
Generally, verifiable multilateration is a technique based on the measurement of the difference in distance between two or more stations at known locations by broadcast signals at known times. Recently, researchers have proposed a number of positioning and distance estimation techniquesin [63; 77]. This method requires to install a set of base stations controlled by a central authority, for example, a cellular or Low-Power Wide-Area Wireless Technology (LPWA) operator. If the base stations can uniquely compute the vehicle location using these distance bounds, and if this location falls into the triangular pyramid formed between the verifiers, then they conclude that the vehicle location is correct. Equivalently, only three verifiers are needed to define the vehicle location in two dimensions; the verifiers still consider the location correct if it can be uniquely computed and if it falls in the triangle formed between them [63]. This approach poses a number of problems, namely:
* Simultaneous reception: In this method, the vehicle communicates with at least three base stations in order to obtain its current coordinates. Certainly, in the real world, it is impossible to get three signals perfectly synchronized.
* Security: In the communication process, it is vital not to allow the attacker to receive the transmitted data. In [78; 79] the potential cyber-attacks specific to automated vehicles are investigated. Therefore, it is necessary to provide a secure transfer of information between the station and the vehicle.
* Confidence or trustability of the access nodes: Before transmitting the information, it is required to ensure that the base station is trustworthy valid by employing mutual authentication. Some of the approaches to mutual authentication were described in [80; 81]. This problem could also be addressed with conventional role-based models [82].
* Anonymity: In some situations, the base station is not supposed to obtain any information about the vehicle, neither the identification nor the location. In this paper, we show several protocols fulfilling mobile node anonymity requirement.
# An Interactive Method for Detection of Process Activity Executions from IoT Data +
## 6 Discussion
### Summary of Discussions  
To summarize the discussions, we explicitly address the research question introduced in Section 1 that guided the development of the data analysis method to increase the process awareness of IoT data by identifying activity executions (cf. Section 2.1). The research question asks for necessary steps to analyze a given IoT data set with the goal of finding a sequence of process activity executions. Following the visual information-seeking mantra, we have derived a general 8-step analysis method from a given, comprehensive IoT data set (cf. Section 4). In essence, the CPS components have to be evaluated regarding their relevance in the process executions, and then specific patterns indicating the start and end of activity have to be found within the sensors and actuator values (represented as time series) of the relevant CPS components. From the evaluation of applying the developed method to an unknown data set, we have learned that the steps of the method should guide the data analysis, but do not represent a strict sequence that should be followed. Cross-references with already identified activities in other CPS components and iterations in the detection of activity boundaries help with the disambiguation of activities and specification of activity start and end more precisely. During the analysis, the process analyst gains additional process knowledge and develops an increasing understanding of the executed processes, which helps in refining the detected activities.  
The developed method is useful for activity detection in IoT domains that feature a manageable amount of sensors and actuators emitting low-level IoT data at a frequency that is reasonable for a data analyst to perform a visual analysis (cf. Sections 6.2 and 6.3). In deriving the analysis method, we explicitly focused on the post-mortem analysis of IoT data for building a knowledge base regarding the activity-IoT data correlations. In the initial phases of data analysis, many steps rely on domain expertise and manual decisions regarding the relevance of components, correlations and domain-specific patterns among sensors and actuators as well as granularity and labels of activities (cf. Table 6). Once a sufficient part of the data set was labeled with identified activity executions, this knowledge can be used as ground truth to increase the levels of (supervised) automation for all steps significantly. The newly introduced concept of _activity signatures_ that contain the sequence of sensor/actuator values as time series within the boundaries of an identified, labeled activity facilitates this automation. Using the derived patterns and correlations that are part of the knowledge base, we are also able to move into the detection of activity executions at runtime, which will be part of our future work.