esa-sceva/satcom-chunk-collection huggingface dataset:
# Mobility, Citizens, Innovation and Technology in Digital and Smart Cities
## 3 Cities Transportation Trends
### Blockchain for Managing Cities' Transportation Data and Contracts
#### 3.5.4 Applications for Carpooling and Ride-Sharing  
Along with the advance of novel technologies, new interactive ways of transportation have emerged such as carpooling and ride-sharing, which changes the traditional format of getting around inside the cities [188]. A new range of users has been engaged in these new forms of transportation while new applications arose. With such technologies, we have the possibility of sharing destination, costs and personalizing our trip in a much easier way. Furthermore, it shows up as the first time in the modern society in which P2P online transportation [189] emerges in the concept of IoV.  
On the users' side, it gives them the possibility of choosing their preferences about routes and even car models [190], such as opting for a ride-sharing with an electric car, e-scooters or motorcycles and bikes. For example, a given person can even ride a vehicle in a given street street and left it in another random point or station. These decentralized transportation systems are beneficial due to the volume of cars on the runway, being useful in the peak hours, during parking, reducing congestion and, in a sustainable view, reducing pollution [191; 192]. Summed up with the possibility of learning from the data [193], these decentralized methodologies gain a huge potential to provide profits to service providers.  
Usually, this carpooling and ride-sharing are offered by dedicated online platforms, in mobile applications and websites [194]. In this sense, this type of transport can be seen as a flexible and tailored solution that solves a wide dimension of transport problems [191]. In connection with this line of reasoning, we can say that the evolution of these systems is related to DLT technologies and DApps. There is an open field of privacy concerns, transparency and digital payments that will cross its roots with such applications. In this sense, we believe that integration of such decentralized transportation system will soon have an intersection with cryptocurrencies and smart contracts. The flow of payments between users and those who offer the service should be more automatic, offering rules that are clear to both sides. In this sense, those who offer the service would not control identities and money anymore, they would be just a channel (a book of offers) in which services would flow a negotiation protocol with rules guided by smart contracts.
# Reliable Semantic Communication System Enabled by Knowledge Graph
## 4 Experimental Results
### Experimental Result Analysis
#### 4.2.2 Comparison with Other Semantic Communication Models  
To validate that our proposed model is more competitive than existing research, we compared it with the scheme from [23], which adopts an end-to-end deep learning-based communication system for text transmission (named DeepNN). Figure 12 shows the relationship between the SNR and the semantic similarity performance over the AWGN channel. From Figure 12, our proposed model outperforms DeepNN across the entire SNR region. The reasons are two-fold. First, by using triplets as semantic basic symbols, our proposed model can extract lossless semantics. Moreover, the important triplets are allocated more transmission resources in our proposed model, which effectively protects the importance of the semantics. However, DeepNN uses a fixed bit length to encode sentences of different lengths, resulting in a partial loss of semantics.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Strategies** & **Time Complexity/s** \\ \hline Huffman + LDPC & 2.7324 \\ Proposed model without AT and SR & 3.1638 \\ Proposed model without AT & 3.7742 \\ Proposed model & 3.8539 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The time complexity of all strategies.  
Figure 11: **(a)** Semantic similarity versus the SNR over the AWGN channel. **(b)** Semantic similarity versus the SNR over the Rayleigh fading channel.
## 2 Formulation of the Shape Optimization Problem
### Shape Optimization Under Static Loads  
The research object of this paper is performance-based shape optimization, and the shape optimization with the objective of strain energy optimization is in line with the goal of this paper, so it is selected as the optimization method. In this method, the applied distributed load is equivalently converted to concentrated nodal loads, with the total load remaining constant during the optimization process. Under the uniformly distributed load, the shape optimization of the structure is performed with the objective of minimizing the structural strain energy. This process can be mathematically formulated as follows:  
\[\left\{\begin{aligned} find\ \mathbf{X}\\ min\mathbf{C}(\mathbf{X})=\frac{1}{2}\mathbf{F}^{\mathrm{T}} \mathbf{U}\\ \mathbf{X}\in\left[\mathbf{X}^{\mathrm{L}},\mathbf{X}^{\mathrm{U}} \right]\end{aligned}\right. \tag{1}\]  
where \(\mathbf{X}=(\mathbf{x}_{1},\mathbf{x}_{2},\mathbf{x}_{3}\ldots\mathbf{x}_{w})\) represents the design variables, with \(w\) denoting the number of nodes. Each component of \(\mathbf{X}\) corresponds to nodal coordinates; for example, \(\mathbf{x}_{1}\) includes the \(x\)-, \(y\)-, and \(z\)-coordinates of Node 1. Here, \(C\) is the structural strain energy, \(\mathbf{F}\) is the load vector, \(\mathbf{U}\) is the nodal displacement vector, and \(\mathbf{X}^{\mathrm{L}}\) and \(\mathbf{X}^{\mathrm{U}}\) are the lower and upper bounds of the design variables, respectively.
# Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System
## 4 Materials and Methods
### Evaluation Measures  
In order to evaluate the accuracy of the proposed model, performance measures are calculated including _precision_, _accuracy_, _F1 score_, etc. The accuracy of the model is important to determine, especially in the medical field, where an accurate prediction is desirable.  
\[Accuracy=\frac{Number\ of\ correctly\ classified\ predictions}{Totalpredictions} \tag{5}\]  
\[Precision=\frac{TP}{TP+FP} \tag{6}\]  
\[Recall=\frac{TP}{TP+FN} \tag{7}\]  
\[F1\text{-}score=2\times\frac{precision\times recall}{precision+recall} \tag{8}\]  
where _TP_, _TN_, _FP_, and _FN_ represent true positive, true negative, false positive, and false negative, respectively.
# An Energy Efficient Routing Approach for Cloud-Assisted Green Industrial IoT Networks
## 6 Experimental Results and Evaluation
### Simulation Results and Discussion
It can be seen that there is a correlation between DODAG repair and routing overhead because when the route failure detects, each node reset the trickle timer for DODAG construction, which leads to a large number of DIO message generation. However, in both cases, RAROF outperforms OF-ER and OF-EC, and hence enhance energy efficiency. Similarly, Figure 7d reveals that RAROF constructs topology with lower hop distance than OF-ER and OF-EC. We believe this achievement is because of the rank calculation technique of RAROF. In heterogeneous low power and lossy networks, the routing technique is one of the main affecting factors for enhancing the performance of emergency monitoring IIoT application. The sensitivity of the  
Figure 6: Lifetime of the network; (**a**) lifetime in terms of reporting interval, (**b**) lifetime over time. Figure 7: Overhead comparison of the objective functions (OFs); (**a**) average parent changes per node, (**b**) routing packets overhead, (**c**) destination orientated directed acyclic graph (DODAG) repair execution in the network, and (**d**) DODAG depth in hops. application in terms of resource availability and reliability are extensively analyzed broadly in this paper. According to our study, we presume the proposed RAROF is very promising for mission-critical IoT applications due to its energy efficiency, stability, and reliability. Before our conclusion, we summarize the several relevant features of our proposition. The proposed multi-queuing traffic differentiation approach proved its effectiveness, and the approach of dealing with traffic sensitivity using the timer was reasonable. The strength and efficiency of IEMS rely on the RAROF provision of duty cycle adaptation and quantifying node's resource availability. The inter-packet interval, traffic generation rate, and transmission range of the node have a direct impact on the network performances, which can lead to more energy consumption, reliability degradation, and faster network partition. In addition, resource-ignoring path selection techniques can also affect the resource-constrained nodes, thereby disrupting overall network performance. In contrast to the default single metric based rank calculation and route selection technique, RAROF is based on multiple metrics including the duty cycle. Therefore, our resource aware route selection approach is more efficient and have significant influence on the network performance. Overall, with RAROF, the routing performance can be significantly improved for IoT based applications, and it is preferable than the existing OFs.
# GIS-Based Optimal Route Selection of Submarine Cables Considering Potential Seismic Fault Zones
## 2 Materials and Methods
### Optimal Route Selection  
The advanced capabilities of GIS that enable the use of Python programming language, through the Python site package for analysis and data management, ArcPy, or Visual Basic for Applications (VBA), have been utilized to combine the developed GIS model with the results obtained from the numerical analyses. In particular, a Python script is created to automatically export the results from the output database of the ABAQUS FE software, in terms of absolute axial cable strains. The maximum value of the axial strain is computed utilizing a Python function in the aforementioned script. The script is embedded into the GIS model, while the absolute maximum axial strain is compared with the allowable cable strain via a simple if statement that is included in the script. If the resulting cable distress is lower than the indicative limit, the route can be selected as the optimal one for the specific criteria/weights of the AHP. Conversely, if the cable strain is greater than the limit, different scenarios are examined and the process of the smart decision support tool is iteratively performed, as shown in the flowchart of Figure 1. It is important to note that the interaction between two different software (i.e., GIS and ABAQUS) constitutes a challenging topic that is under constant development.
## 6 Development Trends and Prospects
### Cost  
With the development of sensors, edge computing, and low-power technology, fault detection equipment is developing towards being more portable and low-cost. Lightweight detection equipment is not only suitable for installation on production sites but also for remote monitoring and harsh environments. The development of portable devices is expected to make it easier for small- and medium-sized enterprises to adopt intelligent fault detection and further promote the application of technology.
# Application of Machine Learning Techniques to Predict a Patient's No-Show in the Healthcare Sector
## 4 Model Building
### Holdout and Cross Validation  
The model must be trained on a consistent number of observations to refine its prediction ability to train the model to classify new patterns. If possible, two distinct datasets are the best choice: one for training and a second to be used as a test. In this case, as two dedicated datasets were not available, the original dataset was split in one part for training (70%) and another used for testing (30%), called the holdout method [41].  
The train_test_split function, available in the SKLearn library, splits the data in both training and testing subsets. In the dataset split step, we need to keep the same distribution of target variables within both the training and test datasets. It is necessary to avoid that a random subdivision can change the proportion of the classes present in the training and test datasets from that in the original. Thus, even after the process of oversampling described in Section 4.1, we apply the parameter \"stratify\" to the train_test_split function to preserve the proportion of classes.  
We applied the cross-validation technique to prevent overfitting problems and to estimate the performance of the model. According to [42], in this technique, a dataset is  
Figure 14: Class imbalanced target variable.  
Figure 15: Class balanced after oversampling process.  
randomly divided into \(k\) disjoint folds of approximately equal size, and each fold is in turn used to test the model derived by a classification algorithm from the other \(k1\) fold. Then, the performance of the classification algorithm is evaluated using the average of the \(k\) accuracies resulting from the \(k\)-fold cross-validation. There is no defined rule for choosing \(k\), although splitting the data into 5 or 10 parts is more common. Figure 16 shows an overview of the cross-validation method.
## 6 Numerical Simulation
### Tracking of Simple-Maneuvering Target
#### 6.1.2 Scenario 2: Constant-Jerk Target  
In this scenario, the true motion of a target is set as  
\[x_{\mathrm{tk}}=16(kT)^{3}. \tag{53}\]  
Figure 4a shows the simulation results for the low gain case (\(\delta=0.1\)). The steady-state errors of the \(\alpha\)-\(\beta\)-\(\gamma\) and \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filters were 960.4 m and 8.29 m, respectively. Because the \(\alpha\)-\(\beta\)-\(\gamma\) filter assumes a constant acceleration model, the tracking of the target moving with jerk includes large bias errors. Furthermore, the \(\alpha\)-\(\beta\) filter theoretically diverges for tracking with constant jerk. In contrast, the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter can accurately track a target moving with jerk even when the gain is low.  
Figure 4b shows the simulation results for the high gain case (\(\delta=1.5\)). The steady-state errors of the \(\alpha\)-\(\beta\)-\(\gamma\) and \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filters were 66.2 m, and 24.5 m, respectively. Owing to the relatively high gain setting, the \(\alpha\)-\(\beta\)-\(\gamma\) filter can achieve a higher accuracy to some extent. However, the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter also achieves a better accuracy. These results clearly demonstrate the advantage and disadvantage of the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter implied by the theoretical analysis.  
Figure 4: Simulation results for target with constant jerk. (**a**) Low gain case; (**b**) High gain case.  
Figure 3: Simulation results for target with constant acceleration. (**a**) Low gain case; (**b**) High gain case.
# Reinforcement-Learning-Based Path Planning: A Reward Function Strategy
## 2 Proposed Methodology
### Controller Design for Path Tracking  
#### 2.3.1 Kinematic Model of a Differential Drive Robot  
The locomotion system, the environment in which it operates, and the number of wheels determine the type of movement of a system [30; 31]. Differential drive is obtained by independently turning the two wheels, which are connected to the same axis but operate independently. There is often an additional support wheel to improve stability.  
Figure 7 shows a differential drive robot on coordinates \(x-y\); for kinematic analysis, the following is assumed:  
* The robot is treated as a rigid body, as evidenced by its elements.
* Displacement is minimized in a perpendicular direction to the rolling.
* There is no translational displacement between the wheel and the floor.  
The \(x\) and \(y\) velocities of the robot depend on the linear velocity, \(v\), and are represented as follows:  
\[\dot{x}=v\cdot\cos\theta \tag{14}\]  
\[\dot{y}=v\cdot\sin\theta \tag{15}\]  
For the angular velocity, \(\dot{\theta}\), we have the following expression:  
\[\dot{\theta}=\omega \tag{16}\]  
The kinematic model of a differential drive robot is represented in Equation (17):  
\[\begin{bmatrix}\dot{x}\\ \dot{y}\\ \dot{\theta}\end{bmatrix}=\begin{bmatrix}\cos\theta&0\\ \sin\theta&0\\ 0&1\end{bmatrix}\begin{bmatrix}v\\ \omega\end{bmatrix} \tag{17}\]  
Considering the kinematic model, it is possible to differentiate equations that describe the movement of the robot, where \(vr\) is the velocity of the right wheel and \(vl\) is the velocity of the left wheel, both in \(\mathrm{rad}/\mathrm{s}\), \(R\) represents the radius of the wheel, and \(L\) represents the distance between wheels. The lineal velocity, \(v\), and angular velocity, \(\omega\), are shown in Equations (18) and (19), respectively [32]:  
Figure 7: Arbitrary top view of the platform with respect to the global reference frame.  
\[v=R\frac{(vr+vl)}{2} \tag{18}\]  
\[\omega=R\frac{(vr+vl)}{L} \tag{19}\]
###### Abstract  
The pH-responsive behavior of six triple-helix DNA nanoswitches, differing in the number of protonation centers (two or four) and in the length of the linker (5, 15 or 25 bases), connecting the double-helical region to the single-strand triple-forming region, was characterized at the atomistic level through Adaptively Biased Molecular Dynamics simulations. The reconstruction of the free energy profiles of triple-forming oligonucleotide unbinding from the double helix identified a different minimum energy path for the three diprotic nanoswitches, depending on the length of the connecting linker and leading to a different per-base unbinding profile. The same analyses carried out on the tetraprotic switches indicated that, in the presence of four protonation centers, the unbinding process occurs independently of the linker length. The simulation data provide an atomistic explanation for previously published experimental results showing, only in the diprotic switch, a two unit increase in the pKa switching mechanism decreasing the linker length from 25 to 5 bases, endorsing the validity of computational methods for the design and refinement of functional DNA nanodevices.  
D +
Footnote †: journal: Applied Sciences  
0000-0002-4000-0000-0000-0000-0000-0000-0000-00000-00000-00000-00000-00000-00000-000000-000000-000000-000000-000000-000000-000000-0000000-000  
(O6-MeG) modified triple helix-based nanoswitch was designed, with the ability to monitor the activity of DNA repair enzymes. [25]  
In this context, the full understanding of the DNA triplex-based devices' atomistic behavior represents an important aspect for a fine prediction of the rules governing their structure/dynamics/function relationship. In the last few years, we demonstrated the importance of Molecular Dynamics (MD) simulations coupled to experiments in describing and predicting the atomistic behavior of DNA nanoswitches [26; 27] integrated into complex nanostructures [28]. Recently, it was experimentally shown that the pH-responsive behavior of a nucleic acid nanoswitch, which can form an intramolecular triplex structure through hydrogen bonds (Hoogsteen interactions) between a hairpin double helix (DH) and a single-strand triplex-forming oligo (TFO) with two protonation centers, strongly depends on the length of the linker connecting the two domains (Figure 1A) [29]. Furthermore, it has also been demonstrated that the linker-dependent modulation can be dissipated by the introduction of four protonation centers in the single stranded triplex-forming portion [29]. In this work, we apply Adaptively Biased Molecular Dynamics (ABMD) simulations to describe the effect of varying the length of the linker from 5 to 25 bases in regulating the pH-dependent conformational unbinding of the TFO from the DH, simulating three diprotic and three tetraprotic DNA nanoswitches. The results provide an atomistic depiction of the mechanism of unbinding, and they indicate that enhanced sampling techniques can be a valuable tool to rationally design the function of these devices, allowing a correct prediction of their real-world applications.
## 1 Introduction
In Reference [6], an aperture is moved across the object and the corresponding diffraction patterns are sequentially recorded. A phase mask has been applied to modulate the wavefront in References [7; 8] and the phase information could be recovered from diffraction patterns recorded at different wavelengths. The latter suffers from the problem of dispersion effects that result in different responses of the object under investigation with respect to the wavelength employed. Therefore, each of the recorded wavelength's corresponding speckle patterns may hold different information, which hinders or makes the application of the iterative phase retrieval approach impossible. In Reference [9], not the distance between sensor and object but the distance between point source and object was changed. Hence, compared to Reference [5], the lateral resolution did not suffer since it was the same for all diffraction patterns recorded. Moreover, the retrieval algorithms could be simplified via the application of the direct Fresnel transform. In this paper, the optical performance of the curved illumination phase retrieval algorithm with regard to the optical resolution, phase sensitivity, and signal to noise ratio (SNR) was investigated and compared to digital off-axis holography using the same sensor and same distance to the object. Furthermore, the curved illumination phase retrieval method was applied for the label-free discrimination of cancerous and healthy human renal tissue.
# Mod2 VQLS: A Variational Quantum Algorithm for Solving Systems of Linear Equations Modulo 2
## 4 Solving the Linear System Using a Variational Quantum Algorithm  
Now we propose a Variational Quantum Algorithm (VQA) designed to solve the linear system \(Ax\equiv b\mod 2\). As with any VQA, there are two main ingredients: a variational ansatz and a cost function that serves as the optimization objective. The ansatz gives a circuit pattern or template with gate parameters (typically angles) that can be varied and optimized. In this report we consider one objective function and compare two different ansatze: a rotations ansatz specially designed for the task, and a more generic brickwork ansatz.  
The system as a whole is called Mod2 VQLS, which stands for Modulo 2 Variational Quantum Linear Solver. Figure 5 illustrates the full variational circuit evaluated by Mod2 VQLS when solving the linear system described in Example 1. In this case, the brickwork layout variational ansatz described in Section 4.2 is used, with 4 layers.  
Our cost function here measures the overlap between the projector \(\ket{\psi(\theta)}\bra{\psi(\theta)}\) and the subspace orthogonal to \(\ket{b}\), which is given by  
\[\widehat{C}=\text{tr}\big{(}\ket{\psi(\theta)}\bra{\psi(\theta)}\ket{\text{ id}-\ket{b}\bra{b}}\big{)}.\]  
We note that this cost function has appeared before in [9] as Equation (3) in the setting of solving square linear systems with _real_ entries.  
Some algebra shows that \(\widehat{C}\) can be evaluated by computing the expected energy of an Ising Hamiltonian:  
\[\widehat{C}=\text{tr}\big{(}\ket{\psi}\bra{\psi}\ket{\text{id}-\ket{b}\bra{b} }\big{)}=\bra{\psi}\ket{\text{id}-\ket{b}\bra{b}}\ket{\psi}=1-\ket{\bra{b} \ket{\psi}}^{2}, \tag{3}\]  
where we have omitted the dependence on \(\theta\) for simplicity of notation. Notice that the second term is the expected value of the rank-1 Ising Hamiltonian \(\ket{b}\bra{b}\) with eigenvalue 1 corresponding to the eigenstate \(\ket{b}\), computed with respect to the variational state \(\ket{\psi}\). Although it may be difficult to express this Hamiltonian as a linear combination of tensor products of Pauli matrices, we do not need to construct or even to know it explicitly; all we need is an oracle that can lazily evaluate the Hamiltonian's eigenvalues.