esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multilevel Road Alignment Model for Spatial-Query-by-Sketch
## 4 Results and Discussion
### Extracting Main Roads  
All these sketch maps refer to the same place, but their similarity is low. This might be due to the fact that each participant reflected his/her particular understanding of the place [49]. Therefore, it was necessary to extract the main roads from the sketch maps. It should be noted that certain sketched roads such as those appearing in sketches S10 and S11 are schematic and do not reflect the shapes of the roads. These schematic roads only represent the accessibility or connection between two places/buildings. Therefore, the roads in these sketches were not included in our subsequent road-related matching calculations.  
The parameters of _degree centrality_ (\(DC\)) and _betweenness centrality_ (\(BC\)) were adopted, as described in Section 3.2.1, to extract the main roads from the sketches. Roads with high values of both \(DC\) and \(BC\) were considered as the main roads. Figure 16d presents all the roads in the experimental area, as extracted from OSM, and the main roads, which are central roads in the experiment area. Table 4 shows all the roads drawn in each sketch except sketch S7, and the main roads extracted from each sketch according to their \(DC\) and \(BC\). Only two roads were drawn in sketch S7, so both of them were extracted as main roads.  
From Table 4, the following observations could be made:  
* R2 and R19 were most frequently extracted as the main roads. These roads are consistent with the main roads in OSM, as shown in Figure 16d. The same ID was assigned to the same roads in each sketch to facilitate the analysis.
* The number of main roads extracted from each sketch was different, as shown in the lower part of the second column in Table 4. The maximum number was 6 (S5), and the minimum number was 2 (S7); however, almost half the roads in each sketch were extracted as the main roads.
* In sketches S3, S4, and S7, all the roads were extracted as the main roads. In S3, the \(DC\) and \(BC\) values were the same for R2 and R19 and for R0 and R21. In S4, all the roads except for R19 had the same \(DC\) and \(BC\). In S7, only two roads were drawn.
* In S5, R0, R1, R2, R3, R6, and R19 were selected as the main roads as they had the same values of \(DC\) and \(BC\). In S9, although R1, R2, R3, R6, and R15 had similar \(DC\) values, R1 was not chosen as a main road owing to its lower \(BC\). This aspect also holds for R0 and R16 in S8.
## 3 Performance Analysis
### Propagation Models  
As previously mentioned, propagation models are mathematical expressions used to determine losses along the path of a wireless signal, considering factors such as distance, frequency, and additional elements such as terrain irregularities, obstacles, diffraction, and signal reflection between the base station and the mobile device. These models are essential for understanding and optimizing the performance of wireless systems. In the context of the work conducted, a combination of empirical and deterministic models is utilized to address signal propagation in specific environments.  
Figure 3: Radiation pattern lobes to 2.4 GHz and 5 GHz.
# LightR: A Fault-Tolerant Wavelength-Routed Optical Networks-on-Chip Topology +
## 5 Conclusions  
In this paper, we proposed the first fault-tolerant WRONoC topology, LightR, which reserved two independent signal paths for each communication. When constructing the signal paths, we applied PSEs to minimize the MRR usage. With a \(4\times 4\) HashR as the basic building block, the LightR can easily be implemented to support a network with \(N\) nodes at any scale. Moreover, we proposed the first realistic fault model, which contained defective MRRs depending on the fault rate of the MRRs. Based on the fault model, we compared the LightR to typical state-of-the-art WRONoC topologies. According to the results, the LightR outperformed them in enhancing the reliability by reducing the number of error communications compared to the other topologies. In some cases, the LightR ensured that no communications failed, i.e., no data loss. Compared to a state-of-the-art fault-tolerant design method, RobustONoC, the LightR improved the reliability while decreasing the MRR usage by more than half.  
Figure 15: The average and worst-case SNR in Light, \(\lambda\)-router, GWOR, and LightR.  
**Author Contributions:** Conceptualization, investigation, visualization, software, writing--original draft, Z.Z.; Validation and writing--review and editing, M.L.; Supervision, funding acquisition and writing--review and editing, T.-M.T.; Writing--review and editing, U.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Project Number 496766278.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Results and Discussions  
To validate the performance of the proposed MPC-UKF technique, detailed simulation-based analysis and through comparisons are performed in MATLAB/Simulink. Every aspect of the simulation results is analyzed and visually obtained in graphs. Real-time PMSM parameters are selected for the PMSM system simulations to replicate the actual setup, as shown in Table 3.
## 3 Methodologies  
### Study Scope  
In relation to the building Life-Cycle Assessment (LCA) method, the building energy consumption is divided into three phases which are the materialization phase, the operation phase, and the end of life phase (Figure 6). Existing references indicate that the ratio of energy consumption between the three stages is of the order of 20%, 65%, 15% [54; 55; 56]. It is clear that building energy consumption during the operation phase is the dominant component. In this phase, energy consumption includes lighting, space cooling, space heating, ventilation, appliances, and water heating.  
This simulation is focused on the building energy consumed during the operation phase. This energy can be further divided into six categories, namely lighting, space heating, space cooling, ventilation, appliance and water heating. It should be emphasized that the energy required for space heating and space cooling is directly related to the building structure and materials, while the energy used for water heating, appliances and lighting is related to the behavior and habits of the building users.
# Two-Factor Authentication Scheme for Mobile Money: A Review of Threat Models and Countermeasures
## 2 Related Work
### Types of Authentication Factors  
There are mainly three types of authentication factors available to verify both online and offline users.  
* Single-Factor Authentication (SFA) Single-factor authentication was defined by [PERSON] [32] and [PERSON] [33] as the process via which a person seeking access requests an authenticating party to attest their personality by availing a single attribute linked to the identity, for example, the use of a PIN to unlock a phone. Many companies accepted SFA because it is simple and user-friendly [34]. However, this form of authentication cannot be applied to financial institutions and other relevant transactions because it is vulnerable to shoulder surfing attacks, brute force attacks, social engineering attacks, and impersonation attacks [33].
* Two-Factor Authentication (2 FA) Two-factor authentication was defined by [PERSON] [33] as the process via which a person seeking access requests an authenticating party to attest their personality by availing two attributes such as something you know and either something you have or something you are that are linked to the identity. Mobile money authentication, for example, relies on 2 FA. In 2 FA, the attacker must have the two identifiers for access [33]. Nevertheless, the 2 FA is susceptible to eavesdropping, MITM attack, and forgery or Trojan horse attack and is not fully effective against phishing [34].
* Multifactor Authentication (MFA) [PERSON] [33] defined MFA as the process via which a person seeking access requests an authenticating party to attest their personality by availing multiple identifiers such as something you know, something you have, and something you are that are linked to the identity to grant access. In MFA, three factors such as knowledge, ownership, and biometric are all used together during authentication [33]. Many computing devices and critical services are adopting MFA because it provides higher levels of security against the different attacks [34; 35]. MFA becomes more successful when one of the authentication factors is separated physically from the device from which the client accesses the application or resource [36]. The use of biometrics in the MFA process helps to improve identity proof and has a profound effect on the security of the system [34; 37].
## 4 Experimental Results
### Actual Test Results  
In this section, the proposed approach is further validated using field signals. The field signals are obtained from reference [34] and collected using two Yagi-Uda antennas. One antenna is directed towards a DVB-T tower located in Sendai, Japan, and the other antenna is directed towards two ships entering the harbor. The signal has a carrier frequency of approximately 509 MHz, a sampling rate of 2.048 MHz, and an integration time of 0.07 s. To improve the experimental results of the field signal, the extensive cancellation algorithm (ECA) [46] is used for clutter suppression in the surveillance signal, and the radon fourier transform (RFT) [47] algorithm is used for motion compensation. The CAF result of the field signals is shown in Figure 7. It can be observed that the widening of the strong target peak covers the weak target peak in the Doppler domain, resulting in difficulties in detecting the weak target.  
Under different values of \(R\) and \(C\), the sparse recovery results of the field signals are shown in Figure 8.  
Figure 8: _Cont._  
Figure 7: The CAF result of the field signals.  
As shown in Figure 8, under different sub-block partitioning schemes, the sparse recovery approach separates the two targets in the delay-Doppler domain. It enables the detection of the weak target without being affected by the non-ideal factors of CAF.
## Appendix A Beam Wander a Gaussian-Beam Wave Propagating through Non-Kolmogorov Turbulence  
_Appendix A.1. Beam Wander a Gaussian-Beam Wave Propagating through Non-Kolmogorov Turbulence in the Free Troposphere_  
For integration purposes, the normalized distance variable \(\xi=1-z/L\) is used and Equation (12) becomes  
\[\left\langle\rho_{c}^{2}\right\rangle=4\pi^{2}k^{2}W^{2}L\int_{0}^{1}\int_{0} ^{\infty}\kappa\Phi_{n}(\kappa,\xi)H_{LS}(\kappa,\xi)\left(1-e^{\Lambda Lx^{2 }\xi^{2}/k}\right)dxd\xi. \tag{13}\]  
The last term in the large-scale filter function Equation (13) is dropped to emphasize the refractive nature of beam wander, and then it is expressed as  
\[H_{LS}(\kappa,\xi)=\exp\Bigl{[}-\kappa^{2}W_{0}^{2}\bigl{(}\Theta_{0}+ \overline{\Theta}_{0}\xi\bigr{)}^{2}\Bigr{]}. \tag{14}\]  
In addition, here, the geometrical optics approximation is used and the last term in Equation (13) becomes the following form,  
\[1-e^{\Lambda Lx^{2}\xi^{2}/k}\cong\frac{\Lambda Lx^{2}\xi^{2}}{k},Lx^{2}/k\ll 1. \tag{15}\]  
Substituting Equation (5), the power spectrum of non-Kolmogorov turbulence in the free troposphere, and Equation (14) into Equation (13) yields  
\[\left\langle\rho_{c}^{2}\right\rangle = 0.592 kL^{2}W^{2}\Lambda\int_{0}^{1}\xi^{2}\widehat{C}_{nF}^{2}( \xi)\int_{0}^{\infty}\kappa^{-1/3}\Biggl{[}1-\exp\Biggl{(}-\frac{\kappa^{2}} {\kappa_{0}^{2}(h)}\Biggr{)}\Biggr{]} \tag{16}\] \[\times \exp\Bigl{[}-\kappa^{2}W_{0}^{2}\bigl{(}\Theta_{0}+\overline{ \Theta}_{0}\xi\bigr{)}^{2}\Bigr{]}dxd\xi.\]  
Using the integral identity  
\[\int_{0}^{\infty}\exp(-st)t^{x-1}dt=\frac{\Gamma(x)}{s^{x}}, \tag{17}\]and integrating the variable \(\kappa\) in Equation (A4) yields  
\[\langle\rho_{Tc}^{2}\rangle = 1.59L_{1}^{3}W_{0}^{-2/3}\int_{0}^{1}\ C_{nf}^{2}(\xi)\xi^{2}\] (A6) \[\times \Bigg{\{}\frac{1}{|\Theta_{0}+\overline{\Theta}_{0}\xi|^{2/3}}- \Bigg{[}\frac{\kappa_{0}^{2}(h)W_{0}^{2}}{1+\kappa_{0}^{2}(h)W_{0}^{2}(\Theta_ {0}+\overline{\Theta}_{0}\xi)^{2}}\Bigg{]}^{1/3}\Bigg{\}}d\xi.\]  
Here it should be noted that the formula \(\Lambda=2L_{1}/\text{kW}^{2}\) is used in the derivation.
## 3 Tactile Internet
### End-to-End Latency Analysis  
One of the key challenges for this technology is reducing latency with high-reliability communication to match human sensory perception [29]. When we touch something, the touch feel is immediate as the sensed signal is sent to the brain in a fraction of a second by neurons. Tactile Internet needs to be designed in the same way. Although current 5G technology enables URLLC, mMTC, and eMBB [30] to have real-time applications, however, to have real-time immersive experiences, such as with a sense of touch or muscle movements, the delay should be in order of a millisecond.  
For long-distance applications, the bottleneck is the speed of the signals used for communication [26; 31]. The optical fiber network in which light signals are used for the transmission and reception of data is already deployed in most parts of the world, providing data rates in the order of Tbps. Similarly, the signal can be transmitted wirelessly. Table 1 shows an approximate sample estimate of end-to-end delay for signal transmission between Mumbai and New York through submarine optical fiber networks. Table 2 gives an estimate for wireless transmission. The estimates show that the approximate one-way end-to-end delay is more than 76 ms for optical fiber transmission and more than 50 ms for wireless transmission. These end-to-end delays are estimated on the basis of the transmission distance and velocity of optical and RF signals, assuming some processing, queuing, and transmission delays at intermediate devices. For Tactile Internet applications, the delay needs to be less than one millisecond for a real-time immersive experience.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Transmission Media** & **Type of Signal** & **Maximum Velocity** & **Propagation Delay** & **Processing Delay * & **Queuing Delay * & **Transmission Routes)** & **Approx. Total End-to-End Delay * \\ \hline Optical fiber & Light & 200 km/msec & 75 ms & 0.2 ms & 1 ms (for light traffic) 20 ms (for heavy traffic;/congestion) & 0.3 ms & 76.203 ms (for heavy traffic;/congestion) & 76.203 ms (for heavy traffic;/congestion) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Approximate end-to-end delay from Mumbai to New York for one-way transmission through a submarine fiber-optic cable.  
In such a case, fundamental physics constrains the lower bound on latency, primarily controlled by the velocity of the signal and the medium of transmission. Information cannot travel faster than this universal constant. For medium- and long-distance applications, the required sub-millisecond latency of Tactile Internet is impossible, and it becomes a barrier for applications that require latency of more than 1 ms.
# Hardware Model Checking Algorithms and Techniques
## 2 Background and Notation
### Temporal Logics
#### 2.9.2 Computation Tree Logic
: _Given a transition system \(\mathcal{TS}\), an invariant property asserts that the condition described by a given propositional formula \(\varphi\) over \(AP\), called an invariant condition, always happen for \(\mathcal{TS}\) (\"some invariant condition \(\varphi\) always happens\")._  
Invariant properties can be described in LTL as formulas \(\mathbf{G}\varphi\) with the invariant condition \(\varphi\) a formula over \(AP\). Similarly, invariant properties in CTL take the form \(\mathbf{AG}\varphi\) with the invariant condition \(\varphi\) a formula over \(AP\). **Example 5**. : _The corresponding invariant properties of the safety properties defined in Examples 3 and 4 are as follows:_  
\[\psi =\mathbf{G}(\
eg a_{0}\vee\
eg a_{1}) \tag{20}\] \[\psi =\mathbf{AG}(\
eg a_{0}\vee\
eg a_{1}) \tag{21}\]  
_in LTL and CTL, respectively._  
Properties management [36; 37] goes beyond the scope of this paper, but it is still a topic as relevant as the proper choice of the actual model checking algorithm to be used given a specific scenario.
## 5 Limitations and Prospects  
There are some limitations to this study, which are caused by the complexity of calculations and the difficulty of data acquisition. Specifically, when selecting the most similar \(K\) nodes, it is necessary to calculate the similarity between the target node and all the remaining nodes, and select the \((K-1)\) nodes with the highest similarity to generate the KNN graph with the target node. This process requires a large quantity of calculations, which increases the running time of the entire model. Secondly, in order to be able to predict the random fluctuations in cellular network traffic, more multi-modal data should be considered, such as weather, traffic conditions, and holiday information. However, due to the large differences in data distribution between different countries and regions, it is necessary to use data according to the specific situation in practice.  
In the future, we can consider using clustering methods (such as K-means and mutual information) to cluster nodes and generate a new graph for nodes in each cluster to input into the model for prediction to reduce the complexity of calculations. In addition, with the gradual popularization of artificial intelligence models, models currently used in popular fields, such as natural language processing, can be applied to the field of cellular traffic prediction. For example, the proposed TimeGPT model is based on Transformer. Therefore, it is a meaningful work to combine the spatio-temporal convolution used in this paper with the large model. In addition, from the perspective of exploring multi-modal features, more features that contribute to cell flow prediction can be combined to improve prediction accuracy. This is because multi-modal features can provide a richer information representation for the model.
## 3 Numerical Simulation
Regardless of the standard deviation of the measurement noise, the least squares (LS) method performs better than the maximum likelihood estimation (MLE) method for solving position equations, which stem from two principal advantages: (i) enhanced robustness to potential model misspecifications, particularly in handling non-Gaussian measurement noise, and (ii) superior computational stability, as LS provides closed-form solutions that avoid convergence ambiguities inherent to MLE's iterative optimization. Moreover, the LS framework circumvents the convergence challenges of MLE in non-convex optimization landscapes, avoiding suboptimal local minima or divergent iterations in MLE. Figure 4 compares the accuracy of the joint AOA and TDOA positioning scheme and the single-method interference positioning. It can be observed that the accuracy of the joint positioning scheme achieves superior accuracy by leveraging complementary spatial and temporal information and mitigating estimation ambiguity. Single-method interference positioning schemes rely solely on either angular or temporal measurements, which are inherently limited by geometric dilution of precision (GDOP) and sensitivity to noise. In contrast, the joint scheme combines AOA's directional precision with TDOA's robustness to synchronization errors, effectively constraining the solution space and mitigating individual measurement uncertainties. This fusion not only improves localization consistency but also enhances resilience to multipath effects and sensor placement biases, leading to superior accuracy. Figure 3: Accuracy of optimal solutions to the position equation based on least squares and maximum likelihood estimation.
## Materials and Methods
### Hyperspectral Imaging System  
A laboratory-based push-broom HSI system (shown in Figure 2) was used to acquire the hyperspectral images of tomatoes. The system comprised a line scan image spectrograph (Headwall Photonics, Fitchburg, MA, USA) that covered the spectral range 948-2494 nm with 7.5 nm spectral resolution, mercury cadmium telluride (MCT) detectors to detect the radiation reflected back from the samples, a high-performance camera with a detector sized 320 \(\times\) 256 pixels (Headwall Photonics, Fitchburg, MA, USA), 1.4/25 C-mount lens (Navitar, Inc., Rochester, NY, USA), a stepper motor to move the conveyer, data acquisition software, and a display unit.  
The tomatoes were illuminated with diffused light provided by four tungsten-halogen lamps (Light Bank, Ushio Inc., Tokyo, Japan) with fiber optics to illuminate the samples (each 100 W, 12 V) located at the circumference of a diffusing dome, at equal distances from one another. To improve  
Figure 1: Key steps in the full procedure for predicting the moisture content (MC), pH and soluble solid content (SSC) in intact tomatoes.  
Figure 2: Schematic of the hyperspectral imaging system.  
the signal-to-noise ratio, the samples were set to move at 0.2 mm/scan through the conveyor unit, and to be able to cover the spatial shape of the samples, the exposure time was set at 7500 us, and the distance of the samples from the camera was set at 40 cm. Tomato samples were placed on a cup and transferred to the conveyer belt to be scanned line by line. The movement of a sample fixed to the translation stage was controlled by the step interval and the number of steps. Both spectral and spatial information were obtained from the samples when it was in the range of the camera during movement through the conveyor unit. The hyperspectral images of tomato samples were saved in a raw format as a three-dimensional (3-D) hypercube consisting of two spatial dimensions and one spectral dimension. The dimensions of the hypercube were 320 pixels in the \(x\)-direction, \(n\) pixels in the \(y\)-direction (based on the length of the sample), and 208 bands in the \(k\)-direction. The system components and image acquisition process were controlled by software developed using Microsoft (MS) Visual Basic (Version 6.0) operated in the MS Windows operating system.