esa-sceva/satcom-chunk-collection huggingface dataset:
# Computer-Aided Diagnosis of Skin Diseases using Deep Neural Networks
## 3 Results
### Results on DermNet
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{2}{c}{**Precision (\%)**} & \multicolumn{2}{c}{**Sensitivity (\%)**} & \multicolumn{2}{c}{**Specificity (\%)**} & \multicolumn{2}{c}{**F-1 Score (\%)**} \\ \cline{2-9}  & **Exp-1** & **Exp-2** & **Exp-1** & **Exp-2** & **Exp-1** & **Exp-2** & **Exp-1** & **Exp-2** \\ \hline ACROS & 81.39 & 81.66 & 85.86 & 87.39 & 98.90 & 98.94 & 83.56 & 84.43 \\ AKBCC & 79.17 & 81.45 & 77.24 & 79.75 & 98.19 & 98.43 & 78.20 & 80.59 \\ ATO & 71.95 & 75.76 & 75.34 & 77.08 & 98.57 & 98.83 & 73.61 & 76.41 \\ BUL & 75.72 & 74.08 & 60.61 & 64.71 & 99.35 & 99.26 & 67.33 & 69.08 \\ CEL & 61.60 & 64.18 & 44.88 & 50.14 & 99.40 & 99.42 & 51.92 & 56.30 \\ ECZ & 75.19 & 78.41 & 81.59 & 83.79 & 96.69 & 97.24 & 78.26 & 81.01 \\ WXA & 62.99 & 65.17 & 64.39 & 67.00 & 98.88 & 98.97 & 63.68 & 66.07 \\ ALO & 76.96 & 81.19 & 85.64 & 84.10 & 99.70 & 99.78 & 81.07 & 82.62 \\ HER & 77.87 & 77.99 & 71.12 & 74.19 & 99.33 & 99.32 & 74.34 & 76.04 \\ PIG & 69.57 & 73.31 & 68.50 & 71.87 & 98.72 & 98.91 & 69.03 & 72.59 \\ LUPUS & 69.61 & 74.60 & 59.38 & 63.64 & 99.20 & 99.35 & 64.09 & 66.68 \\ MEL & 82.85 & 83.46 & 80.63 & 83.46 & 99.36 & 99.38 & 81.72 & 83.43 \\ NAIL & 89.64 & 89.08 & 88.71 & 90.01 & 99.00 & 98.95 & 89.17 & 89.53 \\ POI & 76.81 & 75.33 & 56.84 & 61.39 & 99.62 & 99.57 & 65.33 & 67.65 \\ PSO & 78.39 & 79.61 & 78.65 & 81.91 & 97.09 & 97.26 & 78.52 & 80.75 \\ SCA & 74.51 & 77.42 & 62.19 & 70.70 & 99.22 & 99.27 & 67.80 & 73.91 \\ SEB & 79.14 & 85.16 & 86.10 & 87.15 & 96.47 & 97.69 & 82.48 & 86.14 \\ SYS & 68.61 & 72.35 & 72.06 & 72.79 & 98.38 & 98.67 & 70.29 & 72.57 \\ TIN & 80.97 & 80.97 & 83.70 & 85.73 & 97.66 & 97.68 & 82.31 & 83.28 \\ URT & 75.67 & 78.21 & 75.09 & 75.85 & 99.62 & 99.68 & 78.38 & 77.01 \\ VASCT & 83.30 & 84.77 & 72.80 & 76.62 & 99.47 & 99.51 & 77.70 & 80.49 \\ VASCP & 72.43 & 77.24 & 74.34 & 75.75 & 99.03 & 99.26 & 73.37 & 76.49 \\ WARTS & 77.76 & 81.97 & 81.02 & 82.76 & 97.76 & 98.29 & 79.36 & 82.36 \\
**Weighted Average** & **71.81** & **79.82** & **77.53** & **79.94** & **98.14** & **98.40** & **77.34** & **79.80** \\ Standard Deviation & 06.46 & 05.89 & 11.20 & 09.83 & 00.95 & 00.75 & 08.42 & 07.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance Metrics for 23-ary Classification of DermNet using Ensemble. Exp-1: Training on 23 classes and testing on 23 classes without using disease ontology. Exp-2: Training on 622 classes and testing on 23 classes using disease ontology. Refer to Table 1 for full-form of class abbreviations. (MCA) around 51%. Classification accuracy and AUC of individual models for 622-ary classification are given in Table A2 in Appendix A.
## 2 Experimental Details
### Characterization  
The composite samples were subjected to standard metallurgical procedures, including grinding and polishing, and subsequently cut (as cylindrical compression test specimens) using wire EDM following standard procedure. The overall phase composition was evaluated with X-ray diffraction (XRD) using Rigaku(tm) DMAX X-ray diffractometer [operating at 40 kV-40 mA and Cu-K\(\alpha\) radiation] (Tokyo, Japan). The reactions during the composite formation were investigated using differential scanning calorimetry (DSC). Microstructure and composition area mapping were then investigated using an electron microscope (SEM) equipped with energy-dispersive X-ray spectroscopy (EDS). The SEM device fitted with an electron backscattered diffraction (EBSD) detector was used to obtain inverse pole figure maps (IFF) with step size 0.5 \(\upmu\)m by an EDAX Trident(tm) system (Sparks Glencoe, MD, USA) along with system-integrated FEI Scios(tm) field emission gun (FEG) scanning electron microscope (FE-SEM) (Sunnyvale, CA, USA). The EBSD data were processed using an EDAX OIM Analysis(tm) software ([[https://www.edax.com.cn/products/ebsd/oim-analysis](https://www.edax.com.cn/products/ebsd/oim-analysis)]([https://www.edax.com.cn/products/ebsd/oim-analysis](https://www.edax.com.cn/products/ebsd/oim-analysis)), accessed on 4 May 2025) and a detailed description of the technique has been reported elsewhere [27]. The hardness was measured using a Vickers hardness tester at a load of 100 g [ASTM E10-14 standard [39]]. The room temperature (RT) compression test (strain rate (\(\dot{e}\)) of 0.5 \(\times\) 10\({}^{-3}\) s\({}^{-1}\)) specimens with dimensions 20 \(\times\) 5.6 \(\times\) 5 mm\({}^{3}\) were evaluated using a servo-hydraulic UTM (100 kN load cell) [ASTM-E08 standard [40]]. The tests continued until the failure of the specimen. The fracture surfaces of the post-compressed specimens were examined using SEM to understand the mode of failure.
## 4 Findings and Discussions  
### A General Bibliometric Outlook  
In order to capture a holistic perspective within the scope of this study, the authors did not set a specific time framework. Accordingly, from 1999 to 2022, the research corpus includes publications spread over more than two decades. Over 276 publications, a total of 769 authors contributed to the literature on AI in online distance education studies. Of these, 42 of the publications are single-authored.  
As shown in Figure 1, though studies on artificial intelligence in online distance education date back to the late 1990s and early 2000s, interest started increasing in the second decade of the 2000s, with a notable focus by 2015 and a peak by 2021.  
The most-cited publications are usually on predicting behaviors and designing interventions, respectively (see, for example, [19; 20; 21]). The most productive countries are China (n = 88), India (n = 46), and the United States (n = 17) (see Figure 2).  
In terms of disciplinary outlook, the top five subject areas constituted most of the research corpus (see Figure 3). Expectedly, technical subject areas such as computer science, engineering, and mathematics outweigh other research areas. Surprisingly, only around 13.3% of studies are in social sciences. This finding implies that most of the publications have a specific interest in the technical dimensions and less interest in pedagogical aspects.
# A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges
## 4 Vehicular Ad-Hoc Networks (VANETs)
### Vehicle-To-Infrastructure (V2I) Communication  
It is also known as roadside-to-vehicle communication (RVC) and allows the vehicles to interact with the RSUs. It helps to detect traffic lights, cameras, lane markers and parking meters [1]. The communication of vehicles with the infrastructure is ad-hoc, wireless and bidirectional [10]. The data collected from the infrastructure are used for traffic supervision and management. They are used to set different speed variables allowing the vehicles to maximise fuel efficiency as well as control the traffic flow [4]. Depending on the infrastructure, the RVC system can be divided into the Sparse RVC (SRVC) and the Ubiquitous RVC (URVC) [23]. The SRVC system provides communication services at hotspots only, for example to detect available parking spaces or gas stations, whereas the URVC system provides coverage throughout the road, even at high speeds. Therefore, the URVC system requires a large investment to ensure network coverage [23].
# Effect of Laser Treatment on Intrinsic Mechanical Stresses in Titanium and Some of Its Alloys
## 3 Results and Discussion
### Mechanical Properties
In [38], residual stresses were shown to be substantially related, among the others, to the dimensions of macroscopic and microscopic stresses, intergranular interactions caused by neighboring grain morphologies and orientations, the presence of different phases or precipitates, variations within individual grains, and can be attributed to, e.g., dislocations and twinning. Moreover, it is also significantly influenced by the depth at which the stresses are measured. The research technique we used is in accordance with the results obtained in earlier studies [42], proving that high tensile stresses can appear in the Ti6 Al4V titanium alloy melted with the Nd:YAG laser, measured using the X-ray diffraction method.
## 3 Adaptive Background Calibration Method
### Cascade Calibration Structure  
Exploiting the presented calibration structure and mismatch estimation method can suppress the error signal and improve the performance greatly in the weakly nonlinear systems. However, the calibration performance decreases with the increase of nonlinearity because the difference between \(y[n]\) and \(x[n]\) increases. A cascade calibration structure is employed to further improve the compensation performance.  
Figure 5: Adaptive calibration structure exploiting the filtered-X least mean square (FxLMS) algorithm.  
Figure 6 shows the \(i\)-th stage calibration scheme of the cascade architecture. The calibrated output is denoted as \(y_{i}^{c}[n]\) (\(y_{0}^{c}[n]=y[n]\)), which is expressed as:  
\[y_{i}^{c}[n]=x[n]+\vec{T}_{n}^{T}(\mathbf{C}-\hat{\mathbf{C}}_{i}[n])\vec{P}_{y_{i-1}[n ]}+\vec{T}_{n}^{T}\mathbf{C}(\vec{P}_{x[n]}-\vec{P}_{y_{i-1}[n]}). \tag{23}\]  
When the nonlinear parameter \(\hat{\mathbf{C}}_{i}[n]\) converges to \(\mathbf{C}\), the residual error signal for the \(i\)-th stage output is:  
\[e_{i}^{r}[n]=\vec{T}_{n}^{T}\mathbf{C}(\vec{P}_{x[n]}-\vec{P}_{y_{i-1}[n]}). \tag{24}\]  
Exploiting the fact that the power of error signal is much less than the power of input signal, the vector \(\vec{P}_{y_{i-1}[n]}\) can be approximated by:  
\[\vec{P}_{y_{i-1}[n]}\approx\vec{P}_{x[n]}+\vec{P}_{x[n]}e_{i-1}^{r}[n] \tag{25}\]  
where:  
\[\vec{P}_{x[n]}^{r}=[2x[n]\;3x^{2}[n]\;\cdots\;Lx^{L-1}[n]]^{T}. \tag{26}\]  
Thus, the residual error of the \(i\)-th stage calibrated output is rewritten as:  
\[e_{i}^{r}[n]\approx\vec{T}_{n}^{T}\mathbf{C}\vec{P}_{x[n]}^{r}e_{i-1}^{r}[n]. \tag{27}\]  
In weakly nonlinear systems, \(|\vec{T}_{n}^{T}\mathbf{C}\vec{P}_{x[n]}^{r}|<1\) is guaranteed, i.e., \(|e_{i}^{r}[n]|<|e_{i-1}^{r}[n]|\), which means the power of the residual error is decreasing using the cascade calibration structure. To be more specific, the magnitude of the residual error signal \(e_{i-1}^{r}[n]\) and \(e_{i}^{r}[n]\) is the order of \(O((c_{k,l})^{i})\) and \(O((c_{k,l})^{i+1})\), respectively. If the nonlinearity-induced error is the only error source, the signal to noise and distortion ratio (SNDR) of the \(i\)-th stage calibrated output will decrease 20 dB per decade of the mismatch strength faster than the \((i-1)\)-th stage output. This phenomenon is verified by the simulation result shown in Section 4.4.
# Mini-Review: Potential of Diatom-Derived Silica for Biomedical Applications
## 3 Diatom Biosilica Sources  
Diatom-derived silica can be obtained either from living cultures or fossil diatoms (diatomite, e.g., chalky deposits of skeletal remains). The energy required for diatom growth is sustained by either led-based (i.e., low energy demanding) artificial light or sunlight. Furthermore, the nutrients required for algal growth, such as nitrates, phosphates, silicates, vitamins, and some trace elements, can be purchased for a relatively cheap price or even obtained from wastewaters. To avoid both the costs of artificial illumination and the seasonal variability of sunlight, cells can also be grown heterotrophically [44; 45; 46; 47], although organic substrates are to be supplied in this case. However, only a small number of species are able to grow in the dark [48; 49], and organic compounds can promote bacterial growth leading to culture contaminations and to a decrease in cell growth. Biosilica is obtained after cell dewatering (i.e., centrifugation or filtration of the whole culture), followed by a purification process that is usually based on treatments with strong acids and/or high temperatures (see below). Besides, the limited motility of diatoms (due to the lack of flagella) and the \"heavy\" cell wall (due to the presence of a high silicon amount) enhance the spontaneous sinking of cells, limiting the volume to harvest and, thus, costs of biomass collection.  
Diatoms generally exhibit fast growth rates and high lipid and biomass productivities, [50] which can be further enhanced by tuning growth conditions [51; 52], making diatoms promising candidates for mass culturing. However, to the best of our knowledge, no diatom-based industrial plants (i.e., indoor or outdoor systems of algal culturing) are focusing on biosilica production as their main activity. Follow-up studies are thus required to lay the foundations for the industrial production of silica-based biomaterials.  
The most abundant source of biosilica that does not foresee the induction of living cultures is diatomite, which can be easily crushed into a fine powder to become a marketable product, namely, diatomaceous earth (DE). Diatomite is made of frustules of dead diatom cells, usually found in benthic environments. The harvesting of fossil frustules, which are naturally present in benthic environments, is cost-effective and makes diatomite a promising starter for the industrial production of biosilica. However, the composition of DE is variable and the purity is often lower than that of living culture-derived frustules. The quality and abundance of these impurities vary upon environmental and aging conditions [18]. DE, generally made of ca. 80-90% of silicon and of clay minerals [53], is used as a raw material for different kinds of applications, such as agricultural fertiliser, sorbent for pollutants, and filler in plastics and paints to improve the strength of construction materials. In addition, DE is also employed to filter impurities and as an abrasive agent in cleaning and polishing products.
###### Abstract
However, some concerns still exist regarding general health practice issues, such as which health parameters must be measured, the frequency of the optimal measurements and the performance measure of the new health technologies for observing patients [15; 16; 17]. Consequently, ML techniques can be used to effectively analyze and efficiently generate actionable insights from vital signs data or through the combination of additional data from the patient's health records. It has been established in medical studies that timely discovery and prompt intervention are very crucial measures to avoid declining a patient's medical condition [18]. Both the cost and optimization of healthcare systems could be achieved through the early prediction of patient health outcomes. Similarly, the availability of Electronic Health Record (EHR) systems offers abundant and unique prospects for biomedical studies, whereby analytics and predictive modelling are at the core [19]. Disease existence prediction or recognition [20], critical condition assessment [21], evaluating a condition that may require the intervention of life-support [22] and the existence of a specific medical outcome [23; 24] are a few examples of tasks that are related to health that could be the objectives of such models. Additionally, it is possible to integrate these with EHR systems to automate real-time health warning systems [25]. There exist several instances where AI techniques are implemented in a variety of medical-care-related fields [26; 27; 28]. Many studies are available on the application of AI techniques for a particular disease, environment, health domain and outcome, and in some cases, specific ML algorithms are simply the focus. For instance, [PERSON] et al. [29] reviewed the application of ML techniques in medicine and healthcare. [PERSON] et al. [30] in their study investigated the usage of deep learning (DL) techniques to process EHR datasets. Several DL techniques for exploring different sources of data and their targeted applications were analyzed. Likewise, [PERSON] and [PERSON] [31] studied the use of data mining techniques for the diagnosis and prediction of heart diseases while [PERSON] et al. [32] investigated the deployment of data mining techniques for the diagnosis and prediction of diabetes. Hence, the objective of this review is to investigate and analyze the process of integrating AI techniques with clinical data acquired through wearable sensors. Consequently,a unique taxonomy is developed to highlight the benefits and challenges associated with the application of wearable sensors and AI techniques in the conventional healthcare delivery system. The remainder of this paper is arranged as follows: the research approach and methods are explained in Section 2. The application of wearable sensors in healthcare is presented in Section 3. Section 4 discusses the deployment of AI in healthcare, while the benefits and challenges of the application of AI in healthcare are addressed in Section 4. The limitations encountered in this study are explained in Section 5, and finally, the conclusions of this review are presented in Section 6.
## 4 PSO Techniques for Adaptive Equalization
### Comparative Study of PSO Variants for Adaptive Filtering  
PSO offers various enhancements and variations that can be applied to improve its performance and applicability in different domains. The ring topology in PSO enables particles to communicate and share information with their immediate neighbors, facilitating local exploration and exploitation. Dynamic multi-swarm PSO divides the population into multiple swarms that adapt dynamically to explore different regions of the search space, achieving a balance between exploration and exploitation. Fully informed PSO enhances the global exploration capability by allowing particles to have knowledge of the best solution found by their neighbors. Dynamic neighborhood PSO allows particles to change their set of neighbors dynamically, improving adaptability to changing problem conditions. The hybridization of PSO with other optimization techniques or problem-solving methods combines the strengths of different algorithms, leading to robust optimization in complex problem domains. Cooperative particle swarm PSO involves multiple swarms working together, facilitating knowledge exchange and cooperative behavior to tackle large-scale optimization problems. Self-organizing hierarchical PSO organizes particles in a hierarchical structure, allowing for efficient exploration and coordination across different levels. Comprehensive learning in PSO incorporates additional learning mechanisms or problem-specific knowledge, enhancing the algorithm's efficiency and convergence toward optimal solutions. These enhancements and variations in PSO broaden its capabilities and make it applicable to a wide range of optimization problems across diverse domains. HPSO combines the strengths of PSO with other optimization techniques or problem-solving methods, making it a powerful and versatile approach for solving complex optimization problems. While it is not accurate to claim that hybrid PSO is universally better than all other optimization methods, it offers several advantages that make it highly effective in many scenarios. A critical summary of the advantages and limitations of PSO variants is provided in Table 3.
## 4 Discussion  
### Spatial Correlations between Objects within Eco-Environments  
[PERSON]'s First Law of Geography posits that all things are spatially related, with closer entities exhibiting stronger connections [32]. This principle underscores the importance of selecting an appropriate evaluation basic unit for EEQ, as it can significantly affect the assessment result. For example, the MW-_RSEI_ evaluation model [19] considered the effect on the EEQ of the spatial correlation among things within a region by dividing the study area into multiple evaluation units, and the evaluation result was more detailed. Therefore, it is necessary to consider the influence of the basic evaluation unit on the EEQ.  
However, many existing studies take the entire study area as an evaluation unit, neglecting the internal relations among features. To more accurately represent the ecological environment conditions, it is essential to consider the spatial components of the study area as evaluation units. For instance, evaluating the EEQ of one city based on each town as one separate evaluation unit can provide a more granular perspective. However, from the geographical standpoint, it may be advantageous to select sub-watershed units constituting the study area as the evaluation units, as this is more consistent with geographical processes. The quantity and shape of the sub-watersheds depend on the setting of the threshold of the drainage area. Reference [33] delved into the relationship between the lengths of drainage networks, different DEM resolutions, and drainage area thresholds to identify the optimal grid cell size and drainage area threshold for extracting the sub-watersheds of the study area. According to the results, it is vital to choose the appropriate DEM resolution and drainage threshold to evaluate the EEQ based on each sub-watershed as the evaluation unit.
## 2 Materials and Methods
### Finite Element Modeling
#### 2.3.2 Mesh Generation  
Mesh generation is a critical step in establishing finite element models. The quality of the mesh has a significant impact on the accuracy and precision of the simulation results, as well as on the convergence and efficiency of the computation. Free mesh generation is one of the most automated mesh division techniques. It can automatically divide the surface into triangular or quadrilateral meshes and generate tetrahedral meshes in the volume. When facing complex geometric models, the free mesh has good adaptability, significantly saving computation time. The properties of the resulting element stiffness matrix remain unchanged, while accuracy and efficiency are improved. The RSS base solid model is relatively complex, so free mesh generation was used for this modal analysis. To improve the reliability of the simulation results, a cell size of 5 mm was used for the overall structure, while a finer mesh with a cell size of 1 mm was applied to the key component, the tangential support plate. A total of 523,982 nodes and 159,125 elements were defined, and the mesh division result for the RSS base is shown in Figure 2.
## 4 Simulations and Performance Estimation  
In this section, a CV-QKD system is demonstrated under the optimal parameters (e.g., modulation variance and repetition frequency) for the orthogonal dual-polarization joint time and frequency division multiplexing model established in Section 2 by numerical simulations.
## 3 Results and Discussion
### Changes of NPP in the Future
The NPP in tropical and subtropical forests would also increase, but to a lesser extent. As shown in Figure 9, there are small differences in the spatial pattern of NPP simulated using projected climates under different scenarios during 2011-2020. During 2046-2055, the NPP pattern would vary more significantly under AR5 scenarios than under AR4 scenarios. NPP in temperate forests would increase obviously overall, NPP in the Greater and Lesser Xing'an, Changbaishan, and Hengduan mountain areas would increase from 500-600 g C m\({}^{-2}\) yr\({}^{-1}\) during 2011-2020 to 600-800 g C m\({}^{-2}\) yr\({}^{-1}\) during 2046-2055. NPP in subtropical forests in the southeastern coastal areas of China would also increase from 600-700 g C m\({}^{-2}\) yr\({}^{-1}\) during 2011-2020 to 700-900 g C m\({}^{-2}\) yr\({}^{-1}\) during 2046-2055. However, the NPP increases in the Northeast Plain, North China Plain, and middle-lower Yangtze River plain would be small from the period of 2011-2020 to the period of 2046-2055. The patterns of simulated NPP under four different scenarios would show distinguishable differences during 2091-2100. The NPP in most forests would continue to increase under the A2 and RCP85 scenarios, but would have no increase or even decline under the B1 and RCP45 scenarios.