esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
The Ward method applied in this study selects, at each stage of the analysis, from among all combinable aggregate pairs the one that results in a focus on minimal differentiation as a result of fusion (Tables 4-7).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Bank** & **C1** & **C2** & **C3** & **C4** & **C5** & **C6** & **C7** & **C8** & **C9** & **C10** & **C11** & **C12** \\ \hline C1 & 0.0 & 10.7 & 10.9 & 9.8 & 16.9 & 14.3 & 12.2 & 11.3 & 7.6 & 17.8 & 10.2 & 12.8 \\ C2 & 10.7 & 0.0 & 10.2 & 1.2 & 23.8 & 8.4 & 8.7 & 16.4 & 4.8 & 24.1 & 10.9 & 8.4 \\ C3 & 10.9 & 10.2 & 0.0 & 9.6 & 19.7 & 7.1 & 17.0 & 13.4 & 8.4 & 16.7 & 18.3 & 17.3 \\ C4 & 9.8 & 1.2 & 9.6 & 0.0 & 22.6 & 8.7 & 8.4 & 16.1 & 3.6 & 23.0 & 10.7 & 8.3 \\ C5 & 16.9 & 23.8 & 19.7 & 22.6 & 0.0 & 26.4 & 23.0 & 24.7 & 19.0 & 8.4 & 23.9 & 24.1 \\ C6 & 14.3 & 8.4 & 7.1 & 8.7 & 26.4 & 0.0 & 17.0 & 14.6 & 10.2 & 23.8 & 18.3 & 16.8 \\ C7 & 12.2 & 8.7 & 17.0 & 8.4 & 23.0 & 17.0 & 0.0 & 21.5 & 8.7 & 26.2 & 6.7 & 1.2 \\ C8 & 11.3 & 16.4 & 13.4 & 16.1 & 24.7 & 14.6 & 21.5 & 0.0 & 15.7 & 22.3 & 18.1 & 21.6 \\ C9 & 7.6 & 4.8 & 8.4 & 3.6 & 19.0 & 10.2 & 8.7 & 15.7 & 0.0 & 19.7 & 10.9 & 9.1 \\ C10 & 17.8 & 24.1 & 16.7 & 23.0 & 8.4 & 23.8 & 26.2 & 22.3 & 19.7 & 0.0 & 27.1 & 27.1 \\ C11 & 10.2 & 10.9 & 18.3 & 10.7 & 23.9 & 18.3 & 6.7 & 18.1 & 10.9 & 27.1 & 0.0 & 6.8 \\ C12 & 12.8 & 8.4 & 17.3 & 8.3 & 24.1 & 16.8 & 1.2 & 21.6 & 9.1 & 27.1 & 6.8 & 0.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Euclidean distances for equal weights for all variables.  
When analysing the dendrograms with regard to the differences between the individual nodes, four main clusters can be identified, in which clusters III (banks C_7, C_11 and C_12) and IV (banks C_5 and C_10) are identical for both methods. Although concentrations I and II involve different banks under both methods, the differences arise only from the various positions of the same banks. The biggest discrepancy between cluster IV and other aggregates means that this cluster is far from the other ones. In addition, one can identify close distances between banks C_2 and C_4, as well as banks C_7 and C_12.
## 4 Literature Review on DAS+PRS
### Experimental Procedure
#### 4.3.1 2-Class Classification
The database used for experiments consists of 10 h of recordings (1700 acoustic signals) corresponding to different machines carrying out different activities: big excavator moving, big excavator hitting, big excavator scrapping, small excavator moving, small excavator hitting, small excavator scrapping, plate compactor, and pneumatic hammer. These classes were further divided into threat and non-threat classes. Results showed a 68% of threat detection rate (true detection rate) and a 56% of false alarm rate. Other works such as [45] aimed to train and background noise classification, where an optical fiber cable of 17-km length was employed for the experiments. Two different signal recordings that amount to 1 h each were carried out. From these, 1500 s were used for SVM training, 1 h was used for CV, and the rest (2100 s) was employed for testing. Results showed an accuracy of 99.6%. It is important to note that in the cases where the authors report results evaluated on just a few _signals_, the statistical significance of the results is extremely low. As an example, consider the cases of [12; 13], where the tests were done on 5 data and the reported accuracy was 100%. If one of the evaluated data had failed, the accuracy would have dropped to 75%, and the confidence interval would be of \(\pm\)38% (for a confidence level of 95%), which is unacceptable.
# Diagnosis and Treatment of MODY: An Updated Mini Review
## 4 MODY Subtypes and Their Treatments
### Hnfia-mody (mody 3)  
MODY 3 is a common MODY type and is caused by _HNF1A_ gene mutations [40]. The _HNF1A_ gene is found in the liver, kidney, intestine, and pancreatic \(\beta\)-cells and it has been shown to control the expression of insulin genes in mature \(\beta\)-cells as well as the glucose transport _GLUT2_ genes [7; 48; 47]. Mutations in the _HNF1A_ gene can cause impaired dimerization, resulting in a malfunction of the molecular mechanisms and diabetes mellitus. _HNF1A_-MODY displays a glycemic pattern that includes mild fasting hyperglycemia and extremely high glucose levels after glucose administration [34]. _HNF1A_-MODY is characterized by transient neonatal hyperinsulinemic hypoglycemia, progressive hyperglycemia throughout childhood, and the onset of diabetes mellitus by the age of 25 years [24]. Insulin secretion gradually decreases in patients with _HNF1A_-MODY, with glucose control deteriorating over time and necessitating treatment. Furthermore, 63% of patients develop diabetes before the age of 25, 79% before the age of 35, and 96% before the age of 55 [34; 37]. Individuals with _HNF1A_-MODY are treated based on their age and HbA1c levels [49]. At first, _HNF1A_-MODY is treated with a low-dose diet and sulfonylures, but insulin administration is required at later disease stages or during pregnancy [34; 50; 51]. Glucagon-like peptide-1 receptor agonists (GLP-1 Ras) have been shown to effectively manage _HNF1A_-MODY, which was validated in a clinical trial [52; 53; 54; 55].
# Five-Axis Finish Milling Machining for an Inconel 718 Alloy Monolithic Blisk
## 3 Finish Machining Strategy and Method
### Cutting Speed
#### 3.1.2 Cutting Signal Capture and Analysis  
During the machining process, cutting force signals were captured by the sensory tool holder. Because the toolpath involved side milling using a side rake, the tool was mainly subjected to lateral forces. The bending moment was thus used as the basis for evaluating  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Cutting Speed** & **Effective Cutting Speed** & **Feed per Tooth** & **Depth of Cut** & **Time** \\  & \(V_{e}\) **(m/min)** & \(V_{e}\) **(m/min)** & \(Fz\) **(mm/tooth)** & \(A_{p}\) **(mm)** & **(min/s)** \\ \hline Exp. 1 & 26 & 18.33 & 0.05 & 0.05 & 56:25 \\ \hline Exp. 2 & 36 & 25.38 & 0.05 & 0.05 & 40:53 \\ \hline Exp. 3 & 46 & 32.43 & 0.05 & 0.05 & 32:08 \\ \hline Exp. 4 & 56 & 39.48 & 0.05 & 0.05 & 26:26 \\ \hline Exp. 5 & 66 & 46.53 & 0.05 & 0.05 & 22:30 \\ \hline Exp. 6 & 76 & 53.59 & 0.05 & 0.05 & 19:43 \\ \hline Exp. 7 & 91 & 64.16 & 0.05 & 0.05 & 16:48 \\ \hline Exp. 8 & 96 & 67.69 & 0.05 & 0.05 & 16:06 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Machining parameters I.  
Figure 7: The effective cutting speed \(V_{e}\) for inclined milling.  
the cutting condition. The toolpath progressed from the periphery of the blade to the hub on a layer-by-layer basis along the blade surface, as illustrated in Figure 2. The bending moment signals captured in each experiment were recorded on the basis of the cutting layer, as shown in Figure 8. The bending moments in Experiments 1-3 and 5-6 were stable, while the value raised in Experiment 4 and continued to rise in Experiments 7 and 8. It is mentioned in the literature that cutting speed and cutting force are not linearly related [17]. At medium cutting speeds, because of the material properties of Inconel 718, the cutting force will first decrease, then increase slightly as the cutting speed changes, and become stable. But when the cutting speed exceeds the critical value, the cutting temperature will increase. The tool edge will be welded, and the front edge will be seriously worn. The cutting force will increase significantly. It can be speculated that the cutting speed in Experiment 7 exceeded the critical value, indicating that the cutting conditions were inappropriate. The abnormal increase in Experiment 4 may come from machine vibration or cutting chatter.
## 4 Numerical Results and Discussion
### Stability Analysis of Small Clear-Distance Tunnels under Different Excavation Schemes
#### 4.1.1 Analysis of Surface and Surrounding Rock Deformation  
The ramp excavated to the target section was selected, and the support time for analysis was completed. Figure 5 shows the vertical displacement of the surrounding rocks in the various excavation methods. The overall vertical displacements of the surrounding rocks were close. Given that the right tunnel was excavated first and the left ramp was excavated later, the overall vertical displacement exhibited uneven settlement on the same horizontal plane and eventually settled to the antecedent tunnel. The different excavation schemes had different disturbance methods and times relative to the surrounding rocks, resulting in different vertical displacement trends of the surrounding rocks. The comparison of the vertical displacements of the surrounding rocks above the ramp showed that the order of influence range from small to large was as follows: full section method < CD method and double wall heading method < step method. The excavation of full section method was completed in one step, the influence range of which on vertical displacement was less than the methods using multi-step. Although the CD and double wall heading methods provided more disturbances to the surrounding rocks than the other methods did, the ground deformation did not extend to the vault due to the vertical bearing of the temporary support.  
Figure 6 shows the horizontal displacement of the surrounding rocks under the various excavation methods. The temporary support exerted an obvious effect on controlling the horizontal displacement. Under the step and full section methods, due to the large excavation range at one time, the surrounding rocks around the tunnel had a large range of horizontal displacement, which easily produced stress concentration on the lining that was not conducive to the overall stress of the lining structure. In the CD and double wall heading methods, the overall horizontal displacement was relatively average, which helped improve the integrity of the lining structure and the stability of the overall displacement of the surrounding rocks.  
Figure 5: Overall vertical displacement of surrounding rock (unit: m): (**a**) Step method; (**b**) Full section method; (**c**) CD method; (**d**) Double wall heading method.
## 5 Discussion
### Power Loss  
As explained in Section 4.3, the proposed scheme requires a smoothing reactor of 50 mH, thereby leading to power loss during the normal state. Considering that the resistance value of a 2 mH reactor equals 18.9 m\(\Omega\)[42], the power loss \(P_{L}\) is expressed as Equation (49).  
\[P_{L}=\frac{4I_{dN}^{2}R_{L}L_{\tau}}{P_{capkN}}\times 100\% \tag{49}\]  
Figure 19: FCL ability comparison among existing methods: (**a**) Arm current in the resistance-based method, (**b**) DC line current in the resistance-based, (**c**) Arm current in the inductance-based method, (**d**) DC line current in the inductance-based method, (**e**) Arm current in the proposed method, (**f**) DC line current in the proposed method.  
where \(I_{dN}\) is the rated DC line current, \(R_{L}=0.00945\)\(\Omega\)/mH is based on Reference [43], \(L_{r}\) is the smoothing reactor, and \(P_{cabltN}\) is the rated DC line power. Thus, the smoothing reactor in the proposed scheme causes 0.135% power loss in comparison with the rated DC line power, thus denoting a promising economic efficiency.
## 3 Materials and Methods
### Data Preprocessing  
Data preprocessing was performed to ensure data quality and enhance the reliability of model training, involving missing data handling, data normalization, and data splitting.  
Missing values were addressed using median imputation to prevent data loss and minimize outliers [61].  
\[X^{\prime}=X_{i}(if\ X_{i}\ \
eq\ Nan),\ X^{\prime}=median(X)(if\ X_{i}=Nan)\]  
Data normalization was achieved using StandardScaler to adjust scale differences among features, thus preventing overfitting and improving training speed.  
\[X^{\prime}=(X-\mu/\sigma)\]  
\(X^{\prime}\ :\) Normalized data, \(X\): Original data, \(\mu\): Mean, \(\sigma\): Standard deviation.  
The data were split into training data (80%) and test data (20%), and five-fold cross-validation was applied to validate the generalization performance of the model.
## 2 Materials and Methods  
### Calcite Material  
Calcite material used in this study was produced using limestone rock as a starting material. Limestone came from Jurassic deposits in Bukowa mine (Bukowa, Poland) operated by the company Lhoist Bukowa Poland. The mineral calcite constituted 95% of the material by mass [62]. The grain size of the initial material was \(\leq\)4 cm, with a particle diameter d\({}_{90}\) of 3120 \(\upmu\)m and specific surface area (SSA) of 7.2 m\({}^{2}\) g\({}^{-1}\) (determined with the BET method; see Section 2.4 for details). To increase its SSA and affinity for the OP, the limestone material was further ground in a laboratory ball mill (Pulveriette 6; Fritsch, Idar-Oberstein, Germany) for 30 min at a grinding speed of 350 rpm using 37 balls of the total mass of 500 g (corresponding to the sample mass/ball mass ratio of 10:1). Resulting material parameters were grain size of d\({}_{90}\) = 55 \(\upmu\)m and SSA = 15 m\({}^{2}\) g\({}^{-1}\). In a simple batch test, this material removed 7-20 times more OP compared to the initial limestone sample, depending on the reaction time employed (Figure 2).
## 4 Discussion  
This paper introduces a methodology for controlling the game interface using SCM and Masseter EMG. Two models for translating raw EMG signals to input commands have been explored: the equation and the ML model. The proposed equation accentuates the differences that may exist between the SCM muscle tension and hence has a stable performance instead of the quasi-tension between the muscles, as illustrated in Figure 4. Thus, it has good performance for the input signal close to zero compared to the conventional method. The proposed equation model provides a dynamic interface according to other methods proposed in [18,21].  
SCM muscles were used due to their near-uniform symmetry in left and right turns. Figure 5 shows that the data obtained can be used without a lot of calibration and standardization processes. The ML algorithm used was Ensemble due to its high accuracy (93.9%) and prediction time, as shown in Table 1. The generalization of the trained model was satisfied with all subjects reviewed without the need for recalibration or retraining, which improved usability and ease of setup (in plug-and-play settings) as recommended in playing the game using bio-signals [41,48,49,50].  
The results in Figure 7 confirm the ease of use of the system. The angle input in Figure 7 for the equation model is continuous. Therefore, the model achieves both direction and speed control in the game interface. In the ML model, the direction of the paddle control was accurate, but the speed control was achieved by the accumulation of movement in the same direction. In this step, the ML model had a more stable directional stability and poorer speed control compared to the equation model.  
In a 2D environment, the completion time of the subject indicates the difficulty of the task. This was found to be related to the number of vertical turns the user had to make, as confirmed by the correlation coefficient (r(8) = 0.892). From these results, the most challenging scenes were 10, 9, and 3. In the setup, Bite EMG controlled vertical movement (up and down). The subject had to double-click to change the orientation of the player, which may have contributed to the increase in difficulty along the vertical axis.  
As shown in Table 4, the completion time of the test groups was uniform, with a slight difference. Using one-way ANOVA, the mean reaction time for the female to male subjects was significant (\(p\) = 0.0299). On average, female subjects reacted 0.41 s faster than males. However, reaction time reported a low correlation with completion time (r = 0.316). On the other hand, male subjects completed the scene 12.91 s faster than female subjects.
# Thermal and Thermomechanical Characterization of Polypropylene-Seed Shell Particles Composites
## 3 Results
### XRD Results  
Figure 7 shows the X-ray diffraction patterns of the pristine PP and the PP-JA composites. It can be seen that PP shows characteristic peaks at 14, 17, 18, and 21\({}^{\circ}\) that correspond to the monoclinic crystalline phase alpha with planes 110, 040, 130, and 111, respectively, in addition to two small peaks at 25 and 28\({}^{\circ}\), that were reported with plans 060 and 220, and attributed to the same monoclinic structure of the PP [36; 37]. In the PP-JA composites at 4, 6, 8, and 10, differences with respect to the pristine PP are identified, with the appearance  
Figure 6: DMA thermograms for PP and PP-PI composites. (**a**) Storage modulus versus temperature, and (**b**) tan \(\delta\) versus temperature.  
of small peaks at 16\({}^{\circ}\), which are associated with \(\upbeta\)-type crystals with 300 planes [21; 24; 36]. Previous works report that the \(\upbeta\) phase of PP is identified by the appearance of a peak at 16\({}^{\circ}\)[22; 26; 38]. The presence of the \(\upbeta\) phase in the PP-JA composites can be related to the presence of lignocellulosic materials that promote its appearance, due to a nucleating effect; however, other works indicate that the lignocellulosic materials do not influence the presence of the \(\upbeta\) phase. PP has \(\upbeta\) crystals, but the presence of these are due to the high shear stresses that are reached during processing [22]. [PERSON] et al. [39] report that the \(\upbeta\) phase in PP provides a better performance and that using nucleating agents from wastes, such as eggshell, would be a good option.  
Figure 8 illustrates the XRD pattern for PP and PP-PI composites; there is similar behavior to PP-JA composites, with the presence of a peak at 16\({}^{\circ}\), which is associated with the presence of the \(\upbeta\)-type crystals of PP with 300 planes.This peak is more evident for composite PP-4-PI, as this material shows the highest % crystallinity calculated by DSC. Some works report that \(\upbeta\)-crystals of PP melts at 150 \({}^{\circ}\)C, but others report the presence of \(\upbeta\)-crystals with the presence of a peak at 16\({}^{\circ}\)[28]. Others attribute the presence of the \(\upbeta\)-form of PP to the shearing forces between the polymer matrix and particles, which can be reached during composites preparation.
###### Abstract  
The use of artificial intelligence (AI) in dermatology is skyrocketing, but a comprehensive overview integrating regulatory, ethical, validation, and clinical issues is lacking. This work aims to review current research, map applicable legal regulations, identify ethical challenges and methods of verifying AI models in dermatology, assess publication trends, compare the most popular neural network architectures and datasets, and identify good practices in creating AI-based applications for dermatological use. A systematic literature review is conducted in accordance with the PRISMA guidelines, utilising Google Scholar, PubMed, Scopus, and Web of Science and employing bibliometric analysis. Since 2016, there has been exponential growth in deep learning research in dermatology, revealing gaps in EU and US regulations and significant differences in model performance across different datasets. The decision-making process in clinical dermatology is analysed, focusing on how AI is augmenting skin imaging techniques such as dermatoscopy and histology. Further demonstration is provided regarding how AI is a valuable tool that supports dermatologists by automatically analysing skin images, enabling faster diagnosis and the more accurate identification of skin lesions. These advances enhance the precision and efficiency of dermatological care, showcasing the potential of AI to revolutionise the speed of diagnosis in modern dermatology, sparking excitement and curiosity. Then, we discuss the regulatory framework for AI in medicine, as well as the ethical issues that may arise. Additionally, this article addresses the critical challenge of ensuring the safety and trustworthiness of AI in dermatology, presenting classic examples of safety issues that can arise during its implementation. The review provides recommendations for regulatory harmonisation, the standardisation of validation metrics, and further research on data explainability and representativeness, which can accelerate the safe implementation of AI in dermatological practice.  
dermatology; artificial intelligence; deep learning; classification; image analysis; AI regulations 2025  
157856
## 2 Materials and Methods
### Experimental Setup  
#### 2.2.1 Infrared Camera  
The used thermal camera is an Optris Xi400 with microscope optics (Optris GmbH, Berlin, Germany) long-wave infrared camera (8-14 \(\upmu\)m). The type of detector is an uncooled microbolometer with a pixel pitch of 17 \(\upmu\)m and a thermal sensitivity (NETD) of <80 mK. The thermal camera is equipped with manual motor focus and microscope optics (18\({}^{\circ}\times\) 14\({}^{\circ}\)) with a minimal focus distance of 90 mm and maximum focus distance of 110 mm. The setup is shown in Figure 1. The thermal camera is used to capture 16-bit RAW thermal image sequences in 382 \(\times\) 288 resolution at a frame rate of 27 Hz. Microscope optics specifically allows focusing extremely close on a subject such that tiny details from the skin lesion become prominent features with high resolution. A one-point non-uniformity correction (NUC) is applied on the raw images at a reference temperature of 15 \({}^{\circ}\)C using a blackbody. The infrared camera was positioned at 100 mm above the tissue-mimicking phantom. The field of view at the object surface is 34.2 mm \(\times\) 25.7 mm with a pixel size of 90 \(\upmu\)m and a 3 \(\times\) 3 pixel size of 0.27 mm.