esa-sceva/satcom-chunk-collection huggingface dataset:
# Beyond Mitigation: New Metrics for Space Sustainability Assessment +
## 5 Conclusions  
This work has proposed a preliminary framework for the development of a novel KPI that encapsulates, in a concise manner, the primary parameters and design decisions influencing the space sustainability of a given constellation or mission. The essential components of the KPI have been defined and their mathematical expressions have been derived as functions of key constellation and service parameters. To illustrate its applicability, the proposed Space Sustainability Index (\(SSI\)) has been utilized to compare two prominent, currently operational mega constellations, Starlink and OneWeb. The findings underscore the critical role of orbital altitude in determining space sustainability.  
The \(SSI\) aspires to serve as an instrumental tool for optimizing the design of future constellations, with a specific emphasis on integrating sustainability considerations. The next stage in refining this KPI involves incorporating terms related to bandwidth and frequency  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **h = 550 km** & **h = 1200 km** \\ \hline \(e_{cov}\) & 0.2840 & 0.2541 \\ \hline \(e_{rate}\) & 0.4179 & 0.4179 \\ \hline \(e_{debris}\) & 0.02916 & 0.1924 \\ \hline \(SSI\)\% & 7.189\% & 23.11\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison assuming one constellation, with equal parameters and different altitudes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Starlink** & **Oneweb** \\ \hline \(e_{cov}\) & 0.0283 & 0.2541 \\ \hline \(e_{rate}\) & 0.2321 & 0.4179 \\ \hline \(SSI\)\% & 24.50\% & 30.61\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: SSI for Starlink and Oneweb.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Starlink** & **Oneweb** \\ \hline \(e_{cov}\) & 0.0283 & 0.2541 \\ \hline \(e_{debris}\) & 0.2850 & 0.3424 \\ \hline \(SSI\)\% & 24.50\% & 30.61\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison without considering collision avoidance/retrivial and assisted deorbiting.  
compatibility, accounting for enabling technologies such as network softwarization, high data-rate inter-orbit and intra-orbit links, and autonomous system capabilities.  
Conceptualization/Review and Editing, E.C.; Methodology/Review, M.D.S.; Software/Formal analysis/Original draft preparation, S.Q.; Methodology/Review and Editing, T.R., Conceptualization/Review, K.T.; Supervision, M.R. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the European Union - Next Generation EU under the Italian National Recovery and Resilience Plan (NRRP), Mission 4, Component 2, Investment 1.3, CUP B83D22001190006, partnership on \"Telecommunications of the Future\" (PE00000001--program \"RESTART\").  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflicts of interest.
## 4 Formulating the Expression of the Total Number of Transmissions and Receptions
### Formulating \(N_{r}^{p}\)
#### 4.2.2 Calculating \(N_{r2}^{q}\)  
Next, we formulate the expression of \(N_{r2}^{q}\). As presented in Figure 7a, the value of \(N_{r2}^{q}\) is the number of receive node in area \(S_{5}\), thus we have  
\[N_{r2}^{q}=C_{N_{t}^{q-1}}^{2}\times{p^{\prime}}_{L}\times N_{t}^{q}\times \frac{\overline{S}_{4}}{\pi r_{t}^{2}}\times N\times\frac{\overline{S}_{5}}{S}, \tag{43}\]  
where \(\overline{S}_{4}\) denotes the expected area of the black region, \(\overline{S}_{5}\) denotes the expected area of the shadow region, and \({p^{\prime}}_{L}\) denotes the probability that the distance between two nodes satisfies \(0\leq d(O,O^{\prime})\leq r_{t}\). Thus, we have the following:  
\[{p^{\prime}}_{L}=p\{d(O,O^{\prime})\leq r_{t}\}=\frac{\pi r_{t} ^{2}}{S}=\pi r_{t}^{2}. \tag{44}\]  
As shown in Figure 7a, the area \(S_{4}\) equals twice the area of region ACD minus the half intersection area of circle A and B, i.e., \(\overline{S}_{2}/2\), plus the half intersection area of circle \(O\) and \(O^{\prime}\), i.e., \(\overline{S^{\prime}}_{2}/2\). The area of region ACD equals to the area of sector ACD plus the area of sector OAC minus the area of triangle OCA; thus,  
\[S_{ACD}=\pi r_{t}^{2}\times\frac{\overline{s}+\arcsin\left( \frac{d}{\pi_{t}}\right)}{2\pi}+\pi r_{t}^{2}\times\frac{\overline{s}}{2\pi}- \frac{1}{2}\times r_{t}\times\frac{\sqrt{3}\pi_{t}}{2}=\frac{r_{t}^{2}}{2} \Bigl{[}\frac{\pi}{3}+\arcsin\left(\frac{d}{2r_{t}}\right)\Bigr{]}+\frac{\pi }{6}r_{t}^{2}-\frac{\sqrt{3}}{4}r_{t}^{2}. \tag{45}\]  
\[S_{4}=S_{ACD}-\frac{\overline{S}_{2}}{2}+\frac{\overline{S^{\prime}}_{2}}{2} =\frac{r_{t}^{2}}{2}\biggl{[}\frac{\pi}{3}+\arcsin\left(\frac{d}{2r_{t}} \right)\biggr{]}+\frac{\pi}{6}r_{t}^{2}-\frac{\sqrt{3}}{4}r_{t}^{2}-\frac{ \overline{S}_{2}}{2}+\frac{\overline{S^{\prime}}_{2}}{2}. \tag{46}\]Combining Equations (39) and (46), we obtain  
\[\begin{array}{l}\overline{S}_{4}=\int_{0}^{r_{t}}2\times\left[\frac{r_{t}^{2}}{3 }\Big{(}\frac{\pi}{3}+\arcsin\frac{x}{2r_{t}}\Big{)}+\frac{\pi r_{t}^{2}}{6}- \frac{\sqrt{3}r_{t}^{2}}{4}\right]f_{2}(x)dx-\frac{\pi r_{t}^{2}}{8}+\frac{1}{2 }\Big{(}\pi-\frac{3\sqrt{3}}{4}\Big{)}r_{t}^{2}\\ =\frac{\pi r_{t}^{2}}{2}-\frac{\pi r_{t}^{2}}{8}+\frac{1}{2}\Big{(}\pi-\frac{3 \sqrt{3}}{4}\Big{)}r_{t}^{2}=\frac{7\pi r_{t}^{2}}{8}-\frac{3\sqrt{3}r_{t}^{2} }{8},\end{array} \tag{47}\]  
where \(2\int_{0}^{r_{t}}x\arcsin\frac{x}{2r_{t}}dx=\Big{(}\frac{\sqrt{3}}{2}-\frac{ \pi}{6}\Big{)}r_{t}^{2}\). According to the method in [28], we can get the approximate value of \(\overline{S}_{5}\), i.e., \(\overline{S}_{5}\simeq S_{5,\max}=\pi r_{t}^{2}/6\). Finally, combining Equations (43), (44) and (47), we obtain  
\[N_{r2}^{q}=\frac{C_{N_{t}^{q-1}}^{2}NN_{t}^{q}}{6}\Bigg{(}\frac{7\pi}{8}-\frac {3\sqrt{3}}{8}\Bigg{)}. \tag{48}\]
## 1 Introduction  
### Skin Cancer  
Cancer is the first or second leading cause of premature death in 134 of 183 countries (i.e., ages 30-69) [1]. In 2020, more than 19 million new cases of cancer were diagnosed, and approximately 10 million deaths are expected [2]. According to the World Health Organization, the total annual economic cost of cancer is estimated to exceed 1 trillion euros [2]. These figures are expected to continue to rise due to increasing population and life expectancy. Cancers of the skin are the most common cancer type in humans. The incidence of specifically both melanoma and non-melanoma skin cancers is increasing worldwide, especially in the older White and Asian populations due to the exposure to ultraviolet light (sun exposure) [3]. Melanoma incidence rates have tripled in Europe over the past four decades, similar to the United States. Early detection and treatment of skin cancer can improve outcomes while having less impact on the quality of life of the patient while the mortality rate is decreased. However, traditional methods of skin cancer diagnosis, such as visual examination and biopsy, can be invasive and may not detect all cases of skin cancer [4].  
Skin cancer screening now commonly includes a total body skin examination (TBSE) in which dermatologists use non-invasive methods like dermoscopy to differentiate between malignant and benign skin lesions [5]. The diagnosis of melanoma is typically made through the use of the ABCDE classification, which stands for Asymmetry, Border irregularities, Color differences, large Diameter, and Evolution over time [6]. However, even when performed by skilled dermatologists, this method has a relatively low specificity and moderate sensitivity [7]. Dermoscopy with polarized light can improve sensitivity and specificity, but invasive excisions are still often necessary to exclude false-negative findings [8]. Other non-invasive approaches for cancer diagnosis include for example confocal microscopy, multispectral imaging, and photoacoustic imaging... [9]. CT/PET are invasive approaches as they make use of X-rays and/or contrasting or radioactive liquid.  
The purpose of this article is to give an overview of the different skin-cooling techniques for dynamic infrared thermography skin cancer diagnosis that are used in the literature. Over time, researchers used a diversity of skin cooling techniques in their research to diagnose skin cancer with infrared thermography. The different cooling techniques are tested on a human skin-tissue-mimicking phantom to be able to compare them accurately. The cooling effect of the different techniques is visualized and monitored with an infrared camera and thermistors to find the optimal cooling technique.
# Effect of Boundary Conditions on the Mechanical Behavior of the Geogrid-Soil Interface
###### Abstract  
Geogrid-reinforced structures are extensively adopted in various engineering fields. At present, the influence of boundary conditions was not considered in design methods, bringing hidden dangers to the safety of the structure. In the current study, a series of pullout tests were carried out on high-density polyethylene (HDPE) geogrid-reinforced coarse sand. The magnitude and growth pattern of pullout resistance and the variation laws of interfacial shear strength indexes under four types of boundary conditions were analyzed. Additionally, the boundary reduction coefficient (\(BRC\)) was introduced to establish the relationship between rigid and flexible boundary for the design of the structure. The tests results showed that the boundary conditions cannot be ignored in the design of structures, especially in the front. When the normal loading was up to 120 kPa, the \(BRC\)-top and \(BRC\)-positive could be taken as 0.9 and 0.5, respectively, and verified by fitting results. The boundary conditions affected the pullout resistance, while the vertical loading corresponding to the maximum pullout resistance was not related to boundary conditions. Investigating the interaction of the geogrid-soil under different boundary conditions can help to improve the understanding of the behavior of reinforced soil structure, and to achieve a more efficient and economical design.  
geogrid; pullout test boundary conditions +
Footnote †: journal: Applied Sciences
## 4 MAC Layer in WMSN
### Research Issues  
In this section we presented a set of representative research efforts regarding MAC for WMSNs: single-channel/multi-channel and/or scheduled/contention-based proposals appear in the literature. In our opinion, multi-channel MAC protocols are more suitable for WMSNs. We have also identified different considered topologies. Although it can be interesting to exploit to some extend the use of multi-tier WMSNs in the design of MAC protocols, this should not be used to simplify the problems that the transmission of multimedia content represents. In this sense, papers that consider a flat topology are more research challenging.  
We discussed the cross-layer design dependencies between MAC layer and other layers of the communication stack especially the physical layer, in the case of using UWB technology. The adoption of the UWB technology as the underlying transmission technique in WMSNs and the potential challenges in this area, appear as an interesting research topic. We believe that this research area will attract the attention of many researchers and boost the applicability of UWB in multimedia networking.  
In overall, we think that cross-layering is essential for efficient MAC designs in WMSNs, together with queue-management and traffic classification/prioritization as long as QoS is required for multimedia traffic.
## 4 Results
### Petrological Characteristics
#### 4.1.2 Lithofacies Division  
Lithofacies represent the rocks or rock combinations formed in specific sedimentary environments, reflecting characteristics such as mineral composition and structure [11; 34; 35; 36]. In the Mahu Depression, the Fengcheng Formation reservoirs are primarily composed of siliceous shale, with some intervals containing interbedded siltstone layers. Based on this geological background, we initially categorized the reservoirs into siltstone interlayers and shale formations based on grain size.  
Subsequently, we use a ternary division method based on differences in mineral content, specifically whether the content of siliceous minerals, carbonate minerals, and clay minerals exceeds 50%, to classify the shale lithofacies [34]. The shale lithofacies are  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Depth (m)**} & \multicolumn{4}{c}{**Siliceous Minerals (\%)**} & \multicolumn{4}{c}{**Carbonate Minerals (\%)**} & \multirow{2}{*}{**Others (\%)**} \\ \cline{2-2} \cline{6-7}  & \multirow{2}{*}{**Quartz**} & **Potash** & **Plagioclase** & & & **Clay** & \\  & & **Feldspar** & **Feldspar** & **Calcite** & **Dolomite** & **Minerals (\%)** & **Others (\%)** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 1: Mineral composition data of well Maye-1 based on XRD experiment.  
Figure 2: Image of petrological features. (**a**) Mineral composition ratio at different depths and (**b**) shale lithofacies division.  
categorized into four major types: felsic shale, dolomitic shale, clayey shale, and mixed shale. Based on the mineral content, the three major lithofacies excluding mixed shale are further subdivided. Felsic shale is subdivided into felsic shale, dolomitic felsic shale, and clay-bearing felsic shale based on the content of the other two minerals. Dolomitic shale is subdivided into dolomitic shale, felsic dolomitic shale, and clay-bearing dolomitic shale. Clayey shale is subdivided into clayey shale, felsic clayey shale, and dolomitic clayey shale (Figure 2b). In total, the shale is classified into four major categories and ten subcategories of lithofacies. Based on this criterion, the shale lithofacies in the Fengcheng Formation of the Mahu Depression primarily consist of felsic shale, dolomitic felsic shale, and clay-bearing felsic shale. Combined with the interbedded siltstone interlayers, the lithofacies in the study area are ultimately classified into these four types.
# Posterior Probability-Based Symbol Detection Algorithm for CPM in Underwater Acoustic Channels
## 3 Proposed Algorithms
### PS-FDE for Binary CPM Signals  
The overarching discourse on PS-FDE is extended to encompass a binary CPM signal with a \(1/2\) modulation index [29]. The signal is approximated as the summation of a single PAM signal following Laurent decomposition. Consequently, the soft symbol output obtained from FDE is transformed through the following mapping:  
\[\hat{\mathbf{b}}=\mathbf{b}\exp(j\frac{\pi}{2}(n+1)), \tag{26}\]  
\[\hat{\mathbf{v}}=\mathbf{v}\exp(j\frac{\pi}{2}(n+1)), \tag{27}\]  
where \(\mathbf{b}\) is the equalized signal after FDE, \(\hat{\mathbf{b}}\) is the mapped symbol, and \(\hat{\mathbf{v}}\) is the mapped pseudo-symbol with a different \(l\).  
The information in \(\hat{\mathbf{b}}\) are separated into real (\(\mathrm{Re}(\mathbf{b})\)) and imaginary (\(\mathrm{Im}(\mathbf{b})\)) parts. The \(\mathrm{Re}(\mathbf{b})\) corresponds to the information set of the \((n+l)^{\mathrm{th}}\) symbol when \(l\) is odd, whereas the \(\mathrm{Im}(\mathbf{b})\) corresponds to the information set of the \((n+l)^{\mathrm{th}}\) symbol when \(l\) is even. The symbol information associated with the \(n\)th interval primarily dominates the energy within that interval and \(\text{Re}(\hat{b}_{n}^{(0)})\in\{\pm 1\}\) after normalization. Consequently, the initial estimated log-likelihood ratio \(\text{LLR}(r_{n})\) is formulated as follows:  
\[\text{LLR}(r_{n})=\ln\{\frac{\exp(\widetilde{\text{Pr}}(\text{Re}(b_{n})=1))}{ \exp(\widetilde{\text{Pr}}(\text{Re}(b_{n})=-1))}\}. \tag{28}\]  
The estimated \(\text{LLR}(r_{n})\) after the information merging is expressed as follows:  
\[\begin{array}{ll}\text{LLR}(r_{n})^{(k)}&=\kappa^{(k)}\times\ln\{\frac{\exp (\widetilde{\text{Pr}}(\text{Re}(b_{n})=1+\sum\limits_{l\in\text{even}}A_{n}^{ (l)}))}{\exp(\widetilde{\text{Pr}}(\text{Re}(b_{n})=-1+\sum\limits_{l\in \text{even}}A_{n}^{(l)}))}\}\\ &+\sum\limits_{l\in\text{odd}}\left[\kappa^{(l)}\times\ln\frac{\sum\limits_{ (\xi,\eta)\approx\{1\}}\exp(\widetilde{\text{Pr}}(\text{Re}(b_{n-2l})=\xi)+ \widetilde{\text{Pr}}(\text{Im}(b_{n-l})g_{n-l}=\eta))}{\sum\limits_{(\xi, \eta)\approx\{-1\}}\exp(\widetilde{\text{Pr}}(\text{Re}(b_{n-2l})=\xi)+ \widetilde{\text{Pr}}(\text{Im}(b_{n-l})=\eta))}\right]\\ &\text{where }l=\{1-L_{m},\ldots,-1,1,\ldots L_{m}-1\}\text{. }\end{array} \tag{29}\]  
If \(\mathbf{b}\) follows a Gaussian distribution, the first term of (29) is rewritten as follows:  
\[\ln\frac{\exp[\widetilde{\text{Pr}}(\text{Re}(b_{n})=1+\sum_{l\in\text{even} }A_{n}^{(l)})]}{\exp[\widetilde{\text{Pr}}(\text{Re}(b_{n})=-1+\sum_{l\in \text{even}}A_{n}^{(l)})]}=\ln\frac{\exp[\widetilde{\text{Pr}}(\text{Re}(b_{ n})=1)]}{\exp[\widetilde{\text{Pr}}(\text{Re}(b_{n})=-1)]}-\sum\limits_{l\in\text{ even}}\left(\frac{2A_{n}^{(l)}}{\left(\sigma^{(0)}\right)^{2}}\right) \tag{30}\]  
where \(A_{n}^{(l)}\) is computed according to \(\text{LLR}(r_{n-l})\):  
\[A_{n}^{(l)}=c(0,0;l)\times[\text{Pr}(r_{n-l}=1)-\frac{1}{2}]. \tag{31}\]  
The received data \(\mathbf{\hat{a}}\) are obtained by  
\[\hat{a}_{n}=-r_{n}r_{n-1}. \tag{32}\]
# Gated Recurrent Attention for Multi-Style Speech Synthesis
## 2 Backgrounds  
### Gated Recurrent Unit  
GRU is a RNN that is motivated by the long short-term memory (LSTM) [27; 28], but simpler to compute and implement [25]. GRU updates the recurrent hidden state \(h_{t}\) to be a linear interpolation between the previous recurrent hidden state \(h_{t-1}\) and the candidate recurrent hidden state \(\tilde{h}_{t}\) as follows,  
\[h_{t}=\left(1-z_{t}\right)h_{t-1}+z_{t}\tilde{h}_{t} \tag{1}\]  
where an update gate \(z_{t}\) controls how much the unit updates its hidden state. The update gate \(z_{t}\) is determined by  
\[z_{t}=\sigma\left(W_{z}x_{t}+Uz_{t}h_{t-1}\right) \tag{2}\]  
given an input \(x_{t}\) at time \(t\) in the input sequence \(x=\left(x_{1},x_{2},...,x_{T}\right)\) with \(\sigma\) representing the logistic sigmoid function. In Equations (2)-(4), \(W_{*}\) and \(U_{*}\) are parameter matrices. The candidate recurrent hidden state is obtained by  
\[\tilde{h}_{t}=\phi\left(Wx_{t}+U\left(r_{t}\odot h_{t-1}\right)\right) \tag{3}\]  
where \(\phi\) is a nonlinear activation function such as \(\tanh\), \(r_{t}\) is a reset gate, and \(\odot\) denotes element-wise multiplication [26]. The reset gate \(r_{t}\) is determined according to  
\[r_{t}=\sigma\left(W_{r}x_{t}+U_{r}h_{t-1}\right). \tag{4}\]  
Unlike the vanilla RNN, which always replaces the recurrent hidden state with the current input and the previous recurrent hidden state, i.e.,  
\[h_{t}=\phi\left(x_{t},h_{t-1}\right), \tag{5}\]  
GRU maintains the existing context \(h_{t-1}\) and add the new content \(\tilde{h}_{t}\) as in Equation (1) [26]. This additive nature of GRU makes it easy for each unit to remember the existence of a specific feature in the input sequence for long-time-steps.
# Human Factor Issues in Remote Ship Operations: Lesson Learned by Studying Different Domains
## 4 Results
### Lack of Direct Sensory Information  
Industries are investing in smart technologies and are deploying sensor networks to collect data for supporting decisions. [PERSON] et al. suggest leveraging the industry 4.0 concept to collect data about the health status of equipment to predict safety incidents and avoid possible accidents [60]. In remote ship operations, there is no direct, physical connection between the ship and operator, and operators receive no direct information from the vessel's surroundings. This may lead to a loss of kinetic feeling and visual perception [22]. This lack of direct sensory information is one of the important human factor challenges in the field of remote ship operation. [PERSON] et al. [7] stated that ship sense is not only about the collection and use of presented information by the navigational equipment. Furthermore, they wrote that some tasks are mainly visual, for instance navigating in close proximity to other objects or harbor maneuvers. This refers to the challenge related to the lack of direct sensory information during remote ship operation via the SCC. This factor primarily affects SA.  
Studies in other domains also reference the lack of sensory information. A study of UAV operations identified distance and the resulting loss of sensory information about obstacles as factors that hinder optimal human performance [16]. Studies of remote container crane operations also highlight this challenge [43; 45].
# Side-Channel Power Analysis Based on SA-SVM
## 2 Materials and Methods  
### Data Preprocessing  
DPA Contest is a globally recognized standard competition in the field of cryptographic security, and the latest version is DPA Contest v4 [28]. Since complete encryption was not used in this experiment, DPA Contest v4.1 was selected as the dataset. We chose 1000 power traces from the DPA Contest v4.1 (DPAv4.1), with 435,000 feature values in each sample. Because the mask recovery phase was not considered in this study, we converted the dataset to an unprotected scheme with known masks. We decided to only target \(H(k^{*})\), the HW output of the first mask \(\mathsf{S}\)-box of the first round.  
\[H(k^{*})=HW(Sbox(P_{1}\ \oplus\ k^{*}\ )\ \oplus\ M_{known\ mask}) \tag{1}\]  
where \(H\in\{c_{1},c_{2},\ldots,c_{C}\},C\) is the number of classifications required. \(P_{1}\) is the first plaintext byte and \(k^{*}\) is the real subkey.  
Not all power traces and encrypted data are correlated. Usually, the Pearson correlation coefficient is used to select those features that are related to encrypted data.  
\[\text{Pearson}(x,y)=\frac{\sum_{i=1}^{N}((x_{i}-x)(y_{i}-y))}{\sqrt{\sum_{i=1} ^{N}(x_{i}-x)^{2}}\sqrt{\sum_{i=1}^{N}(y_{i}-y)^{2}}} \tag{2}\]  
We computed the absolute Pearson correlation between power traces sampling points and \(H(k^{*})\). The correlation coefficient's absolute value shows the degree of correlation, and the results are shown in Figure 1.  
Figure 1 shows notable peaks that span from around 100,000 to between 200,000, and peaks observed at approximately 250,000 points. The points corresponding to the spikes are highly correlated with intermediate \(H(k^{*})\). Power traces of DPAv4.1 have an extremely high correlation coefficient. The peak points shown in Figure 1 are listed in Table 1. There are 192 points of interest with absolute values of correlation coefficient larger than 0.5, and 4 points of interest with absolute values greater than 0.9. Having a fewer number of interest points (POIs) is not necessarily better. Although reducing the number of POIs  
Figure 1: Raw traces and Pearson correlation coefficients.  
speeds up computation and decreases information redundancy, it also results in the loss of useful information. Choosing the appropriate number can help us to crack the key more effectively.
## 4 Results
### Computational Performance  
The whole method was implemented under Matlab R2017b and the experiments were performed in a laptop equipped with Intel(r) CoreTM i7-7500U CPU (2.7 GHz, 4 Mb Cache, 2 cores), 16 GB DDR4 RAM and a GPU NVIDIA(r) GeForce 940 MX (2 GB dedicated). The networks' training for both tasks (classification and segmentation) took less than 2 h for each CNN. Processing of an entire exam in testing phase took from 30 s to 100 s depending on the scar size, being always faster than the experts' scar delineation (which took from 1 to 5 min according to the size, the complexity of the scar shape and the size of the gray zone that render the segmentation difficult). Exams that required less amount of time to be automatically processed correspond to the ones that required less time to be manually delineated.