esa-sceva/satcom-chunk-collection huggingface dataset:
# A Dermoscopic Inspired System for Localization and Malignancy Classification of Melanocytic Lesions
## 1 Introduction  
### Motivation  
Melanoma is one of the worst forms of skin cancer resulting in increased morbidity and huge medical expenditure almost up to $3.3 billion [1]. Although, simple observation aids in detection of the changes in the melanocytic nevi, the deadly disease can spread to other parts of the body by metastasizing. Skin tumor thickness mainly determines the spread of the disease. Melanoma prognosis is inversely proportional to the tumor thickness, since the survival rate relies on the tumor thickness. The greater the thickness, the lesser the survival rate. However, to measure the spread and thickness of the tumor biopsy is required, which is a painful experience to the patient. Additionally, careful observation of the melanoma characteristics can be performed, as the lesion is visible on the skin. However, it is further liable to metastasize and spread to lymph nodes thus incrementing the level of malignancy. According to the Fitzpatrick Skin classification there are six skin types [2]. The type I and type II are more prone to melanoma compared to the other skin types. Melanocytes produce a pigment termed as melanin, which gives thenatural color to the skin. There are two kinds of melanin, eumelanin and pheomelanin present in the dark skinned and lighter skinned population respectively. Eumelanin is insoluble and thus the skin darkening effects produced by eumelanin last relatively longer compared to the skin-reddening effects produced by pheomelanin. A Dermoscope aids the dermatologists in the primitive analysis of melanocytic skin lesions [2]. Owing to a dearth of experience and differences in visual perceptions, the prognosis of melanoma is still subjective, in spite of the availability of well-established medical methods. This fosters the need for an objective evaluation tool. Computer Aided Diagnostic (CAD), tools were introduced for dermoscopic images to provide quantitative and target assessment of the skin lesion to help clinicians in demonstrative and prognostic undertakings. Due to the inter and intra-observer variabilities, determination of melanoma is innately subjective. Thus, a CAD tool institutively eliminates the subjectivity in the diagnosis and prognosis of melanoma, and aids in early detection of melanoma in situ, thereby improving the accuracy of detection and reducing the mortality rate. This paper describes a clinical framework that can significantly identify the lesion properties and provide a diagnosis. The system incorporates the knowledge of an experienced dermatologist to co-relate the features extracted with their histopathological relevance. Additionally, it further incorporates certain statistical features to achieve promising results.
## 5 Conclusions  
Our present assessment shows the long-term productivity of soils in Hungary. Long-term productivity in this context means the mean productivity of the last three decades. A period of 20 to 30 years was found to be an adequate time scale for estimating the productivity of soil biomass and for identifying the weights of different factors in it, and also as prospective baseline and threshold values of soil health and soil quality indicators, which can be used in land degradation and soil improvement assessment. A new generalized biomass productivity map was created on a 100 m resolution, which can be implemented in the cadastral system and in multipurpose land use planning programs. The general map of productivity was produced from crop-specific productivity maps by applying biomass productivity models on the country-scale soil, climate and topography geodatabase. Soil properties and characteristics play the most important roles in wheat biomass productivity, while maize has a more significant relationship with precipitation. In the case of sunflowers, soil type and texture are less important factors. The spatial pattern of biomass potential is shown on the general productivity map at 100 m resolution. This map can be used to plan land use in general and agricultural production in croplands. Climate change largely affects the minimum and maximum temperatures, their variability and the amount of precipitation and its temporal distribution, which all have considerable impact on soil biomass productivity. The most important climatic variables for crops deserve particular attention in the next decade, particularly in developing adaptation strategies. We believe that our soil-climate-based land productivity models will help in developing new methods for such adaptation. However, in order to measure changes in biomass production potential, further assessment is required, including trend analysis and the analysis of the effects of changing combinations of soil properties. Nevertheless, the proposed methodology, in addition to possible applications in cadastral systems and in land use planning and agricultural development programs, is also applicable to the integrated monitoring of biomass productivity, which is in line with the goals related to the UN SDGs.  
Conceptualization, G.T., N.C. and B.S.; methodology, G.T., B.S., N.C. and G.S.; resources, L.P. and A.L.; writing--original draft preparation, B.S., G.T. and N.C.; writing--review and editing, N.C., B.S., T.H., A.L., J.M., L.P., G.S., K.T. and G.T.; visualization, N.C.; supervision, B.S. and G.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to private property.  
We thank the anonymous referees for their valuable recommendations and suggestions.  
The authors declare no conflict of interest.
# Improving Data Communication of Enhanced Loran Systems Using 128-_ary_ Polar Codes
## 2 Materials and Methods
### The Proposed 128-ary Polar Coding Scheme for eLoran  
To enhance the error performance of the eLoran data communication system, this section introduces an encoding strategy based on 128-_ary_ polar codes. This approach is specifically designed to operate in conjunction with the existing 128-_ary_ PPM scheme, thereby maintaining compatibility with the system's established positioning functions. At the receiver, a corresponding Soft-Soft (SS) demodulation technique is developed to provide the necessary soft information for the decoder employed for the 128-_ary_ polar codes.  
It is important to note that the primary aim of this investigation is to demonstrate the viability and potential benefits of substituting the conventional RS code with the 128-_ary_ polar code in the eLoran context. As such, the specific 128-_ary_ polar code encoding/decoding algorithms, construction method, and rate-matching techniques presented here serve as  
Figure 2: PPM signal for one pulse.  
an initial framework and are not necessarily optimized and further refinements may yield additional performance gains.
# Research on Compressed Sensing Spectrometry Based on Electro-Optical Transmittance Coding
## 3 Experiment
### Transmittance Coding Experiment  
A halogen tungsten lamp was used as the light source, and the LiNbO\({}_{3}\) electro-optic modulator was driven by a DC high voltage to calibrate the transmittance. In the calibration experiment, the voltage increases from 0 to 2000 V in step of 1 V. The partial spectral transmittance calibration results were selected and recorded, as shown in Figure 4.  
The calibration results in Figure 4 reveal that the transmittance can be flexibly adjusted according to the driving voltage, and the number of peaks in the spectral transmittance increases when increasing the driving voltage. Multiple and different transmittance peaks were achieved. This provides sufficient elements to construct the coding measurement matrix \(\Phi\). The sequential forward floating selection (SFFS) algorithm was employed to select the most irrelevant transmittance from the values of spectral transmittance regulation to form the coding measurement matrix \(\Phi\) and realize spectral transmittance coding [17]. The correlation coefficient (\(\mu_{(\Phi)}\)) is regarded as the characteristic criterion (\(J\)) for selecting the elements of the matrix \(\Phi\), and the criterion (\(J\)) is \(J=1/\mu_{(\Phi)}\). The spectral transmittance with measurement times from 2 to 200 form the electro-optical transmittance coding measurement matrix \(\Phi\). Their characteristic criterion (\(J\)) was obtained and is shown in Figure 5.  
Figure 3: Compressed sensing spectrometry system: (**a**) Appearance; (**b**) Internal structure.  
Note that \(M\) external electric field voltages must be measured to realize an uncorrelated coding measurement matrix \(\Phi\) to meet the non-correlation as much as possible. As shown in Figure 5, before 30 measurements, the correlation coefficient of the matrix \(\Phi\) changes considerably with an increase in the number of measurements. When the number of measurements increases from 30 to 50, the change is gradual. In addition, the correlation of the measurement matrix changes negligibly, for more than 50 measurements.
# Evaluating Convolutional Neural Networks and Vision Transformers for Baby Cry Sound Analysis
## 2 Methodology
### Classification
#### 2.4.2 Multiple-Input CNN with CQT and Mel Spectrograms (Multi-CNN)  
The multiple-input CNN model was designed to take advantage of both Constant-Q Transform (CQT) and Mel spectrograms for cry sound analysis. The architecture consists of parallel convolutional neural networks, with each network processing one type of spectrogram. The CQT network and Mel network are connected at the feature fusion layer, where the extracted features from both networks are combined. This fusion enables the model to capture complementary information from both spectrogram types. Each network consists of several convolutional layers, followed by activation functions and pooling layers to extract hierarchical features from the input spectrograms. The number of filters, kernel sizes, and pooling sizes are carefully selected to balance the model's capacity andcomplexity. Batch normalization and dropout layers are also included to regularize the model and prevent overfitting. The output of the convolutional layers is flattened and fed into fully connected layers, which gradually reduce the dimensionality and extract more abstract representations. Finally, a softmax activation function is applied to generate the class probabilities, indicating the predicted cry class.  
1. _Convolutional layers for Mel spectrograms_: The Mel spectrogram branch consists of three convolutional layers (32, 64, and 128) with ReLU activation, followed by max-pooling layers for spatial dimension reduction. The architecture aims to capture hierarchical features from Mel spectrograms.
2. _Convolutional layers for CQT spectrograms_: The CQT spectrogram branch mirrors the structure of the Mel branch, featuring three convolutional layers (32, 64, and 128) with ReLU activation and max-pooling layers. This design choice ensures a consistent approach for feature extraction from both types of spectrograms.
3. _Merging of branches_: The outputs of the Mel and CQT branches are concatenated, creating a unified feature representation that combines the distinctive information from both types of spectrograms. This merged tensor serves as input to the subsequent dense layers.
4. _Dense layers for classification:_ The merged tensor undergoes dense layers with 256 and 128 neurons, each followed by dropout layers with a rate of 0.5. These layers facilitate high-level feature learning and classification. The softmax activation function in the output layer ensures the model's ability to classify baby cries into five distinct classes.  
This innovative neural network architecture strategically leverages the complementary information embedded in Mel and CQT spectrograms. By synergistically combining these spectrogram types, the model is primed to discern intricate patterns within baby cries, enhancing its ability to capture nuanced acoustic features. This sophisticated fusion ensures a more comprehensive representation of infant vocalizations and signifies a pivotal step towards refining baby cry analysis for advanced well-being monitoring systems.
# Battery Surface and Edge Defect Inspection Based on Sub-Regional Gaussian and Moving Average Filter
## 5 Conclusions  
This study investigates the method for battery surface and edge defect inspection, especially for the concave and convex defects that affect battery quality. Sub-regional Gaussian and MAF is innovatively proposed. The partition processing method fully considers the characteristics of uneven distribution of edge illumination, processing the image sub-region to improve the detection rate of edge defects. The proposed method has been proven to have higher defect detection accuracy and lower miss detection rate and performed very efficiently in edge defect detection compared with the traditional unpartitioned processing method which processes horizontally or vertically on the whole picture directly (Figure 13). Table 1 shows that the accuracy rates are approximately 20% higher than that obtained without the use of the partition processing algorithm. In addition, Table 2 summarizes the performances of our method and other recently reported metal surface defect inspection methods. It can be seen that our method exhibits a high accuracy and low detection time, which is comparable with the best results reported in the literature. Furthermore, our method has been applied to actual factory inspection; the detection speed required by the factory is four batteries per second, and the detection accuracy is not less than 92%. Therefore, some methods like local binary patterns (LBP), deep convolutional neural network(DCNN), and polynomial filtering do not satisfy the speed requirement although they meet accuracy requirement. The proposed method can satisfy both requirements in Visual Studio 2013, has better real-time performance and can guaranteed the accuracy. The results also show that the proposed method is effective in detecting metals surface defects and has further research significance.  
Conceptualization, B.Z. and H.H.; methodology, B.Z.; software, B.Z.; validation, B.Z., H.H. and G.X.; formal analysis, G.X.; investigation, B.Z.; resources, H.H.; data curation, D.X.; writing--original draft preparation, B.Z.; writing--review and editing, B.Z.; visualization, B.Z.; supervision, H.H.; project administration, H.H.; funding acquisition, H.H.  
This research was funded by the National Major Scientific Instruments and Equipment Development Project of Ministry of Science and Technology of China, grant number 2013 YQ220749. Thanks for the technical support given from my teacher and classmates. The authors declare no conflict of interest.
## 1 Introduction
In a comparative study among various SAHP systems [19], it was found that the use of a thermal photovoltaic is the best solution energetically, while the use of a photovoltaic (PV)-driven heat pump is the optimum choice financially. Moreover, another study [20] concluded that the use of thermal photovoltaics is the best solution for a SAHP in a radiant floor system with phase-change materials. [PERSON] et al. [21] investigated the utilization of nanofluids at a thermal photovoltaic that feeds a SAHP and it a 4.8% energy efficiency enhancement was found by applying the water/Cu nanofluid. The previous bibliographic works show that there is an important interest in different SAHP systems in the literature. Most of the investigations are focused on the use of different solar systems, while there is a not-so-deep analysis of different heat pump configurations. In this direction, this study comes to investigate a SAHP with a high-temperature heat pump that can be coupled with radiative terminal units for achieving adequate thermal comfort conditions in the indoor space. The investigated problem is an important one because it tries to exploit an abundant renewable energy source with the pioneering technology of high-temperature heat pumps with environmentally friendly refrigerants. Flat-plate collectors connected with a thermal storage tank are used for feeding a high-temperature heat pump which produces heating at 70 \({}^{\circ}\)C. Moreover, there is an auxiliary electrical thermal resistance inside the storage tank for keeping the working fluid (glycol/water mixture 50-50%) at a temperature over 12 \({}^{\circ}\)C. This fact eliminates any danger of freezing, as well as avoids the extremely low temperatures in the tank that can reduce the system COP. The analysis is conducted for the location of Athens in Greece. The thermal loads are calculated with a model in TRNSYS [22], the thermodynamic analysis takes place with a created model in Engineering Equation Solver [23], while the total configuration is examined with a developed code using the inputs from the aforementioned software. The analysis is parametric, and it also includes an optimization part with energy and economic criteria. The final results can lead to critical conclusions regarding the optimal design, from energy and economic points of view, of the suggested configuration for application in real buildings. The contribution of the present work in the literature can be summarized with the following bullet points:  
* An investigation of a high-temperature heat pump and not a conventional one as is usually studied in the literature. * The use of a detailed model for the heat pump modeling by exploiting the available data from the manufacturers. * The use of dynamic results for the accurate estimation of the heating loads of the building. * A multi-objective evaluation of the system with energy and economic criteria aiming to determine a global optimal and sustainable design. * To provide detailed work about a solar heating system with high efficiency for the Greek climate conditions.
## 4 Discussion  
In traditional approaches, urbanization was often understood as a linear and physical process, including top-down land-use planning [51]. Recently, however, researchers have examined the nonlinear and socioeconomic properties of urban expansion with advanced modeling algorithms such as machine learning (ML) and artificial intelligence (AI) models [19; 52]. While these techniques greatly improved the accuracy of predicting urban expansion, their black-box nature has been pointed out as a major limitation in understanding the relative importance of influencing factors of urbanization [53; 54].  
From this point of view, our study is novel in the existing literature from several perspectives. First, the study adopted the XAI approach by integrating the XGBoost-SHAP model in predicting the urban expansion in South Korea. It enabled the interpretation of magnitude and direction of influencing factors in predicting the urban expansion, which has not been thoroughly investigated in previous studies using the ML and AI techniques [27; 30]. As a result, the vicinity to green area and the existence of harsh topographic environments, such as slope and elevation, were found to be the most influencing factors that prevent the expansion of urbanized areas.  
In addition, the study takes a theoretical step forward from previous studies, in that it examines the relative effects of social and economic factors in predicting the urban expansion. More specifically, the study shows that the level of economic development tends to more promote urbanization compared to the density of populations. It complements the existing studies' findings that the urban expansion has been mainly dependent on population growth [55; 56]. It is also noteworthy that the legislative ECVAM grade was found to have more significant impact on urban expansion than the ecological grade. This suggests that the environmental regulations on land use can affect the spatial pattern of urban expansion.  
Based on the study's findings, we suggest several policy implications for the cities' sustainable development. First, planners and practitioners in the urban planning field need to narrow down the spatial extent of urban expansion based on the geographical and topographical features of the target regions. In other words, scientific judgement on whether a certain area will be urbanized or not should be determined prior to developing strategies for the urban expansion control. Second, the designation of appropriate legal restrictions on development can be effective tools in managing the level of urban expansion. Based on our findings, the authorities can establish site-specific conservative zones to control excessive urbanization. Last, urban planners should be aware that the economic level of a certain city can be an important predictor of urban expansion. In order to prevent the spatial imbalance of urbanization across the country, it is necessary to prepare appropriate measures for economically underdeveloped regions, such as attracting companies or promoting the tourism industry.
## 5 Validation
### Results of Validation--Link 2  
From the analysis of the validation results for link 2 (Table 4) based on the above indicators, it is clear that the values of the indicators achieve even worse results than in the case of link 1 (this may be due to oscillations that originate from the interconnection of individual parts of the system for link 2 as for link 1). The highest value of the indicator NRMSE = 18.14% was achieved for the model when the input joint angle was 30\({}^{\circ}\), but MAE achieved the best result at the initial joint angle of 10\({}^{\circ}\) (MAE = 0.5655).  
Since the course of the measured data for link 2 has a different character compared to the courses of link 1, and at the same time, a larger oscillation is present, these facts are also reflected in the actual outputs of the model. In Figures 17-20, where the model outputs of validation for the joint angle 10-40\({}^{\circ}\) for link 2 are presented, it is possible to see to what extent over time the model output (blue solid line) was able to approach an average of the measured values (red dashed line). Since the output of the model moves many times close to the zero value of the joint angle, in all cases, the shape of the error (black dotted line) is similar to the course of the mean response. Similarly, for both link 1 and link 2, at the moment when the mean response and thus also the output model approaches zero, the error value is minimized.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Angle of Rotation** & **MAE** & **NRMSE** \\ \hline
10\({}^{\circ}\) & 0.5655 & 14.94\% \\
20\({}^{\circ}\) & 1.1078 & 16.36\% \\
30\({}^{\circ}\) & 1.7524 & 18.14\% \\
40\({}^{\circ}\) & 2.8270 & 16.88\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of validation for link 2.  
Figure 16: Model output of validation for the joint angle 40\({}^{\circ}\)—link 1.  
Figure 15: Model output of validation for the joint angle 30\({}^{\circ}\)—link 1.  
Figure 19: Model output of validation for the joint angle \(30^{\circ}\)—link 2.  
Figure 17: Model output of validation for the joint angle \(10^{\circ}\)—link 2.  
Figure 20: Model output of validation for the joint angle \(40^{\circ}\)—link 2.  
Based on the insufficient results obtained in the validation phase and presented either in the form of MAE and NRMSE criteria parameters or by time dependences of the model outputs alone, compared to the average of measured values and the error between model output and real setpoint, was as next step selected estimation of model parameters. Parameter estimation is an efficient tool that uses mathematical models to accurately describe the behavior of systems. This phase is described in the following two sections.