esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The imperative need for early detection and fast treatment led to intensive research focused on the better control of the risk factors and a more detailed understanding of the neoplastic process. In line with NMSC, sun exposure, particularly UV radiation, remains one of the most important risk factors [21]. The molecular basis is of great importance, and key events, such as _TP53_ gene mutations, are opening new, personalized therapeutic perspectives. The activation of wild-type p53 is a potential therapeutic strategy in cutaneous melanoma; several compounds, such as WIP1 inhibitor and MDM2-p53 binding antagonists, are showing promising results [22]. The detection of specific mutations, such as the V600E mutation in the _BRAF_ gene, has recently permitted the use of more precision treatment with specific inhibition therapy [23]. Despite advances in understanding the pathophysiology of the malignant process and the treatment of skin cancers, an important limitation remains regarding the diagnosis. Currently, skin biopsy followed by histopathological examination is the only gold standard diagnosis method accepted [24]; however, one must acknowledge its limitations. There are several techniques available, including punch, shave, incisional and excisional biopsy, the decision to choose one over the others being based on the type of lesion, site of lesion, and also on the proficiency of the dermatologist [25]. Regardless of the chosen approach, technique-related complications might occur, with pain, bleeding, scarring, and infection being the most frequently encountered. Moreover, the localization and wrong timing of the possible malign skin lesion makes the biopsy lose its sensitivity and specificity. In this context, complementary diagnostic methods are preferred, with the technique known as \"liquid biopsy\" increasingly gaining ground during recent years [26]. One of the main reasons for its popularity relies upon the possibility to study several cellular and molecular components from only small amounts of sample; circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) are some of the most studied particles. In line with the current development of new diagnostic techniques, this narrative review aims to offer fresh insights on the role of liquid biopsy in skin cancer detection and monitoring, a highly discussed topic nowadays, which is not yet fully elucidated. After describing the impact of this method on the clinical and therapeutic aspects of BCC, SCC, and MM, the authors focus mainly on the role of CTCs in the early detection and disease monitoring of these three most common skin cancers, reviewing also the utility of other relevant particles, such as ctDNA or exosomes. Finally, dielectrophoretic analysis is presented as a promising complementary diagnostic method, opening new perspectives for electrical and electrochemical impedance measurements to become a veritable screening and monitoring tool in daily clinical practice.
# Optimal Energy Management within a Microgrid: A Comparative Study
## 4 Explicit Model of OPF for Microgrid
### Equality Constraints Variables  
It should be taken into account that the decision variables must acquire admissible values; otherwise, the solution provided by the OPF model could not make sense from a practical point of view. For this reason, the decision variables \(y\) are limited during the interval of time \(T\) by means of the constraints set in Equation (29):  
\[\left\{\begin{array}{c}\underline{y}_{RD}\leq y_{RD}^{t_{z}}\leq\overline{y} _{RD}\\ \underline{y}_{MF}\leq y_{MF}^{t_{z}}\leq\overline{y}_{MF}\\ \underline{y}_{B}\leq y_{B}^{t_{z}}\leq\overline{y}_{B}\end{array}\right\} \forall t_{z}\in T \tag{29}\]  
where the set of decision variables associated to the distribution network is given by the magnitude and angle of the voltage phasor at each node, as well as the active powers exchanged with the main grid, such as \(y_{RD}=\left[V\in V_{j}^{t_{z}};\theta\in\theta_{j}^{t_{z}},P_{RP\;j}^{t_{z}} \right];\forall t_{z}\in T\) and \(\forall j\in N_{bCA}\), whereas the decision variables batteries are \(y_{B}=\left[P_{B\;k}^{t_{z}},P_{B\;k}^{t_{z}}\right]\forall t_{z}\in T\) and \(\forall k\in N_{B}\). Finally, the photovoltaic decision variables are \(y_{MF}=\left[V_{CD\;i}^{t_{z}},I_{CD\;i}^{t_{z}}\right];\forall t_{z}\in T\) and \(\forall i\in N_{MF}\).  
In this work the IP, GA-IP, and patternsearch-IP methods provided by the MATLAB(r) optimization solvers are used to solve the microgrid OPF problem given by Equations (26)-(29). Since these methods can directly deal with the whole set of constraints as Equations (27)-(29) [29; 30; 31], the active \(\Delta P_{RD\;i}^{t_{z}}\) and reactive \(\Delta Q_{RD\;k}^{t_{z}}\) power balance constraints in Equation (27) do not need to be handled and solved by a power flow algorithm external to the optimization solvers. Externally solving these constraints imposes the need of an extra power flow algorithm, which in addition should be substantially upgraded to take into account the new variables and constraints introduced by the models of renewable distributed generation and storage systems. In this way, the use of the three methods provided by the MATLAB(r) optimization solvers yields a simple approach for solving the optimization problem as Equations (26)-(29) in unified way.
# Ultrasound Elastography: Methods, Clinical Applications, and Limitations: A Review Article
## 3 Elastography Techniques
### A: Strain Imaging
#### 3.1.2 Strain Elastography  
On the other hand, SE is a qualitative or semi-quantitative imaging technique that assesses and compares relative stiffness between tissues when an external force is present [38]. Because it uses external stimuli, it is considered one of the most challenging elastography techniques. Strain elastography is divided into two methods: excitation or manual (compression). Manual compression can be helpful in assessing superficial organs and their pathology. In this method, the operator applies manual compression and pressure to the region of interest, which works best in identifying a superficial pathology in the thyroid or breast tissue. Meanwhile, the second method uses information from tissue displacement resulting from internal physiologic motion and is not dependent on superficially applied compression, unlike the first method, which makes it beneficial in the assessment of deeper organs such as the cardiovascular and respiratory systems [39].
## 3 Results and Discussion
### PCA and PCR Results of the Data Set
The correlation coefficient (R 0.477) and coefficient of determination (R\({}^{2}\) 0.200) indicate sufficient strength of the linear trend between the variables in the linear regression model according to the equation:  
\[\text{PC UV/Vis}=\beta_{0}+\beta_{1}\text{ PC Electrochemical + }\beta_{2}\text{PC Organic matter + }\beta_{3}\text{PC Dissolved solids + }\varepsilon \tag{9}\]  
However, significance levels of the ANOVA (0.273) and coefficients \(\beta\) (0.093; 0.773; 0.229; 0.143) were statistically insignificant (\(p>0.05\)) and model of PCR was unreliable probably because of correlations of many predictors [17; 21; 43; 44]. Therefore, further statistical analysis was performed by partial least square (PLS) model [10; 14; 34; 37; 44].
# Performance Evaluation of Graph Neural Network-Based RouteNet Model with Attention Mechanism
## 1 Introduction
Thus, our solution involves modifying the architecture of the RouteNet model by incorporating an attention layer. This additional layer aims to refine the model's predictive capabilities and accuracy in predicting network performance metrics. To evaluate the performance of the modified RouteNet model, a comparative analysis with the original RouteNet model is conducted. By comparing the results, we aim to measure the impact and effectiveness of incorporating the attention layer in enhancing the model's overall performance and prediction accuracy. Hence, there is a need to explore new approaches for training GNN-based models in networking applications, by utilizing recent advancements in semi-supervised learning. The major contributions of this research are as follows:  
* Enhancement of the RouteNet model internal architecture by incorporating the attention mechanism, enhancing its delay prediction. * Evaluation of the predictive capabilities of the original RouteNet with the attention-enhanced version. The rest of this paper is organized as follows: Section 2 presents a background study with related work, Section 3 provides the methodology of this research, and Section 4 presents the results and analysis, while Section 5 concludes the paper.
# Q-LBR: Q-Learning Based Load Balancing Routing for UAV-Assisted VANET
## 4 Proposed Q-LBR Design
### Network Load Estimation  
#### 4.2.1 Ground Network Congestion Identifier  
It is extremely important to determine how a URN identifies ground network congestion according to the traffic load. In brief, each GVN estimates the GNCI from itself by using the queue load. This bitwise information is delivered to the URN using overhearing or broadcast messages. Then, URN computes the ratio of GNCI (\(GNCI_{ratio}\)) in its time interval by using the number of \(GNCI_{i}\) instances with a value of '1' from GVN \(i\).  
Figure 3: Two phases of Q-LBR.  
Figure 2: Q-learning based load balancing (Q-LBR) framework.  
For a more detailed explanation, \(q_{ground_{i}}(t)\), given by Equation (3), which indicates the queue load of each GVN, is calculated as the ratio of the maximum queue length (\(MQL_{i}\)) to the average queue length (\(AQL_{i}(t)\)) corresponding to time \(t\) of GVN \(i\).  
\[q_{ground_{i}}(t)=\frac{AQL_{i}(t)}{MQL_{i}} \tag{3}\]  
Based on the result of \(q_{ground_{i}}(t)\), each GVN calculates the weighted moving average \(Q_{ground_{i,k}}(t)\), given by Equation (4), in the window size \(k\) from GVN \(i\).  
\[Q_{ground_{i,k}}(t)=\frac{\sum_{k=0}^{n}w_{k}q_{ground_{i}}(t-k)}{\sum_{k=0}^{ n}w_{k}} \tag{4}\]  
Each ground node \(i\) determines whether the result of \(Q_{ground_{i}}(t)\) exceeds the GVN load threshold \(Q_{ground_{th}}\), given by Equation (5), and marks the value of \(GNCI_{i}(t)\) with a '1' or '0' in the packet header.  
\[GNCI_{i}(t)=\left\{\begin{array}{cc}1&\mbox{if }Q_{ground_{i}}(t)>Q_{ground_{ th}}\\ 0&\mbox{otherwise}\end{array}\right. \tag{5}\]  
The URN receives \(GNCI_{i}(t)\) of each ground node through an overhearing or broadcast messages and then calculates \(GNCI_{ntio}(t)\), given by Equation (6), which is the ratio of the congested GVN to the total number of GVNs, \(N\).  
\[GNCI_{ratio}(t)=\frac{\sum_{i=0}^{N}GNCI_{i}(t)}{N(t)} \tag{6}\]
## 4 Wear and Corrugation Simulation  
### Wear Model  
The contact solutions in the presence of corrugation (as previously discussed) are used to calculate the frictional work, which is considered proportional to wear [9; 18; 22]. This section examines whether corrugation will grow under the given conditions by comparing the phases of wear and corrugation. To consider a whole sleeper, the applied corrugation of length \(L_{2}\) in Figure 2a is extended along the rail to start at \(x\) = 0.87 m to cover both the middle sleeper span and sleeper support. The frictional work is calculated in the contact patch for each element for a whole wheel passage, i.e., from element entry until leaving contact. The wear of an element is as follows:  
\[w(x,y)=kW_{f}(x,y)=k\sum_{i=1}^{N}\tau_{i}(x,y)v_{i}(x,y)\Delta t \tag{5}\]  
where \(k\) is the wear coefficient, \(W_{f}(x,\,y)\) is the frictional work, \(\tau_{i}(x,\,y)\) and \(v_{i}(x,\,y)\) are the local tangential stress and slip, respectively, and \(N\) is the number of time steps \(\Delta t\) during which the element passes through the contact patch. The wear coefficient \(k\) is a constant that depends on the material, lubrication and temperature among other factors [8; 9; 53; 54; 55]. In this work, the calculated wear is normalized with respect to the average value of the wear under the smooth rail condition.  
Figure 7: Size of the slip zone as a function of the corrugation amplitude and slip distribution along the longitudinal axis \(y\) = 0 mm at the four positions. (**a**) Size of slip zone; (**b**) slip along the longitudinal axis \(y\) = 0 mm.  
This normalization is performed because the phase angle between wear and corrugation determines whether the corrugation will grow, which represents our main concern in this paper and is not directly affected by the wear coefficients. It is part of further research to include measurements of the wear coefficients to guide maintenance.
# AUV Trajectory Planning for Optimized Sensor Data Collection in Internet of Underwater Things
## 1 Introduction
To quantify the importance of sensor data, considering both its value and timeliness, the concept of the Value of Information (VoI) is introduced. High-VoI data must be delivered to the surface promptly before its value significantly decays, thereby enabling timely responses and informed decision-making [1; 8; 9; 10]. Significant delays in delivering such data can prevent timely intervention. Notably, the rate of VoI attenuation tends to increase for more critical data and decrease for less important information [1; 6; 7; 8]. Therefore, the trajectory planning of the AUV responsible for data collection must consider the data importance value and urgency as primary objectives, ensuring that critical nodes are visited early and high-value data are retrieved before their importance diminishes. To address the need for the timely collection of high-value data in a UASN, this study proposes a novel VoI-based trajectory planning method for a single AUV operating in collaboration with a USV. The AUV's trajectory is formulated as an optimization problem that aims to maximize the total residual VoI collected from sensor nodes by the time it is delivered to the USV. Solving this problem yields both the AUV's optimal path and the USV's starting and ending positions, which together maximize the total residual VoI available upon mission completion. To address the optimization problem, the forward Dynamic Programming (DP) approach [21] is employed. This recursive method solves multistage decision problems by decomposing a complex task into smaller, interconnected subproblems that are addressed sequentially. The DP method is selected for its ability to guarantee globally optimal waypoint sequencing, in contrast to heuristic or greedy methods that may offer faster convergence but lack optimality guarantees [22]. This property is essential for maximizing residual VoI in time-critical underwater missions. In this study, the forward DP algorithm identifies the optimal sequence for visiting the nodes, ensuring that data are collected and delivered before their value significantly decays. The method offers both computational efficiency and reliability, making it well-suited for missions where node positions and information values are known in advance. Moreover, to further enhance the method, the AUV's energy consumption is addressed through careful path design. Since the underwater travel distance has a significant impact on energy usage, minimizing the path length is essential [23]. To this end, a path design that accounts for the AUV's kinematic constraints is integrated to determine energy-efficient motion. This approach not only reduces energy consumption but also helps preserve residual VoI by minimizing delays caused by inefficient movements along the trajectory.
## 3 Results and Discussion
### Weight Loss Measurements  
Table 2 shows the effect of addition of different concentrations of AO and HERA on mild steel corrosion in 1 M HCl. These results show that increasing the tested inhibitor concentration leads to a decrease in corrosion rate, whereas the inhibition efficiency increases. Moreover, the corrosion rate also depends on the molarity of the aggressive media: in our study the \(\mathrm{C_{R}}\) is 8.3 mm/year whereas [PERSON] et al. found that \(\mathrm{C_{R}}\) of 0.5 M hydrochloric acid is 3.07 mm/year [33]. At the same concentration (2 g/L), AO is more efficient (87%) than HERA (84%). This result could be attributed to the extraction mode, i.e., HERA was extracted using n-hexane (non-polar solvent) which has a great affinity for non-polar molecules. On the other hand, AO may contain polar and non-polar species. The  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **\% (Area Calculations)** \\ \hline
**Compd.** & RT & AO & HERA \\ \hline Myristic acid (C14:0) & 20.39 \(\pm\) 0.02 & 0.69 \(\pm\)0.13 & 0.24 \(\pm\)0.07 \\ \hline Palmitoleic acid (C16:1) & 22.41 \(\pm\) 0.03 & 0.41 \(\pm\)0.09 & 0.15 \(\pm\) 0.03 \\ \hline
**Palmitic acid** (C16:0) & 22.62 \(\pm\) 0.06 & 15.87 \(\pm\) 0.17 & **15.37 \(\pm\)0.41** \\ \hline
**Linoleic acid** (C18:2 n-6) & 24.38 \(\pm\) 0.08 & **60.55 \(\pm\) 0.16** & **32.26 \(\pm\) 0.33** \\ \hline
**Stearic acid** (C18:0) & 24.64 \(\pm\) 0.05 & **20.24 \(\pm\) 0.17** & 5.14 \(\pm\) 0.12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of AO and HERA.  
Figure 1: GC-MS chromatogram of AO and HERA.  
efficiency of AO could be justified by the positive influence of adding salt to the dough while it has been hand-pressed. This suggestion was proved by [PERSON] et al. (2018). They found out that the presence of chloride ions stabilizes the inhibitor's adsorption on the metal surface and consequently the inhibition efficiency was improved. The same results are illustrated graphically in Figure 2.  
Even though the gravimetric measurements give an idea about the efficiency of a corrosion inhibitor, it is still incapable of approaching the corrosion mechanisms. For this particular reason scientist use other techniques such as EIS and polarization curves.
## 4 Toward Cognitive Connectivity: The Case of Dynamic Bandwidth Management
### On the Integration of the Bandwidth Management into the COGNIFOG Stack
This could be achieved by means of simple decision-making procedures or, alternatively, through an AI module responsible for providing the optimal configuration based on constraints, requirements, and making use of past information.
## 3 Modeling and Control Strategy Design
### Battery Model  
The battery model calculates the instantaneous SOC of the battery pack and provides the other parameters required by the energy management control strategy, such as the open-circuit voltage, the internal resistance, and the SOP of the battery. For the convenience of control implementation, the Rint model is chosen instead of the equivalent circuit model with several RC circuits, and the model in shown in Figure 5.  
Figure 4: Closed-loop driver model.  
The Rint model consists of a voltage source reflecting the open-circuit voltage of the battery, and a resistor representing the internal resistance of the battery. When the battery is discharging, the output voltage will be lower than the open-circuit voltage because the voltage drops on R. Similarly, the output voltage will be higher than the open-circuit voltage when the battery is charging. By defining a positive value for the current when discharging, the output power of the battery can be calculated by:  
\[p_{b}=UI=(U_{OC}-IR)I \tag{4}\]  
By solving this equation, the output current of the battery can be obtained as shown in Equation (5).  
\[I=\frac{U_{OC}-\sqrt{U_{OC}^{2}-4 Rp_{b}}}{2R} \tag{5}\]  
In this equation, the open-circuit voltage, \(U_{OC}\), is a function of SOC, which is experimentally calibrated and stored as a lookup table in the model. The internal resistance, \(R\), is a function of both the temperature and the SOC, which is also calibrated through battery charge/discharge tests and stored as a 2D lookup table in the model.  
Normally, the battery capacity is defined in the unit of Ah; SOC can be calculated by the ampere-hour integral method, as shown in Equation (6):  
\[SOC=SOC_{0}-\frac{1}{C_{b}}\int\eta_{C}I(t)dt \tag{6}\]  
where \(SOC_{0}\) is the initial SOC, \(C_{b}\) is the total capacity of the battery, and \(\eta_{C}\) is the Coulomb efficiency of the battery. The physical interpretation of Coulomb efficiency shows that \(\eta_{C}\) depends on the temperature and charge/discharge rate, and it is difficult to obtain an accurate value at every moment. Therefore, in this study, we calculate SOC based on energy:  
\[SOC=SOC_{0}-\frac{1}{E_{b}}\int U_{OC}(t)I(t)dt \tag{7}\]  
where \(E_{b}\) is the total energy when the battery is fully charged. The integral term is the total power of the battery, including output power and the Joule heat power generated by the battery's internal resistance, by taking the open-circuit voltage instead of output voltage as the integrated part.  
Finally, the battery model should provide the SOP information, which includes the maximum discharge power and maximum charge power. In the same way as for the current, we define the maximum discharge power with a positive value denoted by \(p_{\text{Jmax}}\), and the maximum charge power is defined with a negative value, denoted by \(p_{\text{Jmin}}\). Both values are SOC- and temperature-dependent, and they are stored in the model as 2D lookup tables.  
Figure 5: Rint model of the battery.
## 3 Experiments
### Data Preprocessing
#### 3.2.2. Data Normalization  
From Figure 4, it can be found that there are differences in the vibration magnitudes of different samples. To eliminate the differences in the magnitudes of different samples and improve the classification performance [29], a min-max normalization method [30] expressed by Equation (12) was used to normalize all sample magnitudes to the range of 0 to 1.  
\[\hat{X}=\frac{X-\min\left(X\right)}{\max\left(X\right)-\min\left(X\right)} \tag{12}\]