esa-sceva/satcom-chunk-collection huggingface dataset:
# Advancing Smart Zero-Carbon Cities: High-Resolution Wind Energy Forecasting to 36 Hours Ahead
## 3 Research Methodology  
The core aim of this paper is to develop a robust system for wind speed forecasting (WSF) and wind power forecasting (WPF) using pattern recognition models across various time scales. To achieve the study's aim, this study applied a quantitative approach as illustrated in Figure 3. This research process outlines a systematic approach for enhancing wind speed and power prediction through AI. The research process begins with a literature review to understand current methodologies and identify gaps. A hypothesis is formed that optimized AI could enhance prediction accuracy using real-world data from the Gabl El Zayt wind farm. These AI models are validated using performance metrics. Finally, the validated models are integrated into the planning and operation of smart, zero-energy cities, showcasing their practical application in sustainable urban development.  
The research methodology of this paper is grounded in the analysis of an extensive actual dataset comprising over 200,000 data points, collected from a high-wind area in the Middle East. The dataset includes detailed records of wind generation and meteorological parameters such as wind speed, wind direction, temperature, air pressure, and humidity. These data points are sampled at intervals aligned with the forecasting time horizons, ranging from 10 min to 36 h. For instance, a typical training record might include inputs such as historical wind speed measurements (e.g., 8.5 m/s, 9.2 m/s) at specific timestamps, temperature (e.g., 25 \({}^{\circ}\)C), and air pressure (e.g., 1015 hPa), alongside corresponding wind power outputs (e.g., 2 MW). This comprehensive dataset enables the training and evaluation of 12 AI algorithms, facilitating multi-horizon predictions and ensuring robust model validation. By leveraging such a rich dataset, the study ensures the reliability and scalability of the proposed wind speed-power forecasting system under varying climatic and operational conditions.  
The methodology in Figure 4 outlines a structured process for developing ML models for wind speed and power prediction. The dataset is split into training (70%), validation (15%), and testing (15%) subsets. Pre-processed data undergo 5-fold cross-validation to assess accuracy and robustness. Hyperparameter optimization fine-tunes models using validation data, iteratively refining parameters based on metrics like MAPE and MSE. The best-performing model is then selected for accurate and efficient forecasting.  
Figure 4: Wind speed and power integration methodology.  
Figure 3: Key research steps.
They also reported the same phenomenon for the reused powder materials and ferrite materials such as 316 L.SsteelPal et al. [54] attributed the formation of such defects to several physical actions as well as thermodynamic effects in the molten pool induced by laser and support structures. They suggested optimization of laser processing parameters, mainly the laser scanning speed (\(v\)), to reduce the formation of powder clustering in the initial layers of LPBF-processed Ti-6 Al-4V. Such observations urge the need for support topology optimization, taking into consideration the factors like available area, loading, and constraint conditions [39]. (3) Reducing the overall production cost: The addition of support structures, compared to a solid support, decreases the overall fabrication cost because it is associated with a lower required amount of powder as well as post-process operations. In general, the main focus of the currently available support generator tools has been to decrease the amount of required supports and offer faster, cheaper, and more efficient AM processing [55; 56]. These well-known and commercially available support generator software include Magics (Materialize, Michigan, USA), Sunata (Atlas 3D, Plymouth, IN), and 3 DXpert (3 DXpert, Rock Hill, SC). Generally, these packages offer several default support structure types for users with some control on the geometrical parameters of support structures (e.g., the wall thickness and gap). Due to the ease of use, these software packages have successfully been used by researchers in the LPBF design stage for generating support structures. Overall the primary concern when designing support structures has always been to minimize the overall production cost and time, as well as lowering the risk factors associated with the fabrication, such as residual stress, the warping effect, and part collapse. However, no work has been conducted to enhance the quality of the as-fabricated main part through analyzing various factors including the microstructure behavior, material properties and fabrication cost. Inspired by this motivation, this study focused on the effect of support structure on the quality of parts considering the mentioned aspects. Specifically, the influence of support structure shape (i.e., Pin-type, Angled-type, Cone-type) and geometry (i.e., support wall thickness, and gap) was investigated on the composition, hardness, microstructure, and material/time consumption for LPBF-fabricated IN718 parts.
## 1 Introduction
One of the commonly used remote sensing products to indicate vegetation health is normalized distribution of vegetation index (NDVI) which can present the relative greenness of vegetation cover over a large region. Health of vegetation cover and thus the carbon sequestration from vegetation can be attributed to a number of environmental factors, e.g., landforms, soil types, and meteorological conditions [26; 27]. Climatic factors are included by most carbon cycle models [28]. For example, the Carnegie-Ames-Stanford Approach (CASA), a light use efficiency (LUE) model which has been widely applied to map NPP over space, takes input of monthly precipitation and temperature. Similarly, vegetation cannot maximize carbon sequestration without favorable natural conditions such as landforms [29], soil properties [30], and biome groups [31]. Remote sensing can invert key environmental variables, including land cover and land use [32], terrain properties [33], climate changes (e.g., precipitation, temperature, and solar radiation) [34; 35], soil properties [36], and atmospheric contents [37]. Vegetation productivity is dependent on a variety of environmental factors as well as anthropogenic activities. While it is unrealistic to numerate all the influential factors, meteorological factors such as temperature; precipitation; sun radiation; soil properties such as soil classes and soil moisture; landforms; and plant types are generally recognized as the principal contributors or constraints on vegetation growth [38]. This study tries to quantify, in an urban context, the potential of vegetation carbon uptake, given that land management practices (LMPs) are freely transferable across a region. We assume that for a set of available LMPs, some promote vegetation growth while others do not, depending on various urban environments. Carbon uptake from urban vegetation can be improved either by adopting LMPs that favor vegetation growth to less productive areas or discontinuing or replacing LMPs that show a negative effect on vegetation growth. We define optimal LMPs as any land management practices that help vegetation sequester more carbon and enhance carbon uptake potential by filling the gap between the capacity of carbon uptake before and after the implementation of the optimal LMPs. This definition acknowledges that optimal LMPs pertain to locations characterized by unique environmental conditions. Urban areas are characterized by a number of ecosystem types, e.g., forests, grasslands, croplands, rangelands, wetlands, and human settlements [39]. We selected Guangzhou, a metropolitan region in southern China as the case study and assessed the potential of carbon uptake from vegetation by combining segmentation of homogeneous urban environments and neighborhood-based analysis.
## 5 Establishment, Training, and Testing of Deep Learning Model
### Model Inference and Case Validation  
To verify the applicability and reliability of UNet in practical contaminant detection tasks, this section presents a comprehensive case study. Figure 18 displays the original image based on high-density electrical method inversion, the ground truth annotation, and the segmentation results from the deep learning model for a specific site. The target anomalies to be identified are the two white regions in Figure 18c. After feeding the original image into the UNet recognition network, the output is shown in Figure 18d. By overlaying the identification results with the original image (where pink represents the localized contaminant plumes and blue denotes the target contaminants to be localized), it is evident that the network achieves robust segmentation and localization performance.  
Traditional NAPL identification approaches, which provide high specificity but are time-consuming, invasive, and costly, primarily rely on direct sampling. Geophysical methods such as ERT have also been applied by interpreting resistivity anomalies manually; however, this process is often subjective and labor-intensive, especially for large datasets. Geostatistical methods, like kriging or Bayesian approaches, require prior assumptions about spatial continuity and distribution, which may not hold in complex contaminated sites. In contrast, the proposed UNet-based model enables rapid and automated identification of resistivity anomaly zones associated with potential NAPL contamination. This significantly improves efficiency and reproducibility, particularly in field-scale surveys. Future work will focus on integrating additional site knowledge and multi-physics data to enhance classification performance.
# The Genetic and Imaging Key to Understanding Bicuspid Aortic Valve Disease
## 2 Genetic Components of BAV
### Other Relevant Genes  
The review also identified other significant genes associated with BAV. ADAMTS16 was studied by [PERSON] et al. (2024), who found that extracellular matrix disorganization caused by ADAMTS16 deficiency leads to Bicuspid Aortic Valve with raphe formation [29]. FBN1, typically associated with Marfan Syndrome, was also implicated in BAV. [PERSON] et al. (2014) identified common and rare variants in non-syndromic BAV cases [30]. MMP-2 was studied by [PERSON] et al. (2019), who found increased MMP-2 levels and decreased miR-29A in BAV patients, indicating a role in extracellular matrix destruction and aneurysm development [31]. ACTA2 was studied by [PERSON] et al. (2017), who found that ACTA2 mutations were not significant contributors to BAV aortopathy [32]. Table 1 further indicates all the genetic components involved. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Authors** & \begin{tabular}{c} **Number of** \\ **Participants** \\ \end{tabular} & **Study Design** & \begin{tabular}{c} **Sequencing** \\ **Technology** \\ \end{tabular} & **Main Findings** \\ \hline NOTCH1 & [5] & 11 family members & Family Study & Targeted Sequencing & Mutations in NOTCH1 cause a spectrum of developmental aortic valve anomalies and severe valve calcification. \\ \hline \multirow{4}{*}{[17]} & \multirow{2}{*}{\begin{tabular}{c} 428 probands with \\ LS-CHD and their \\ families \\ \end{tabular} } & \multirow{2}{*}{Cohort Study} & \multirow{2}{*}{Direct Sequencing} & \multirow{2}{*}{\begin{tabular}{c} NOTCH1 mutations highly \\ penetrant (75%), most of which \\ were BAV. \\ \end{tabular} } \\  & & & & & \\ \cline{1-1} \cline{3-5}  & \multirow{2}{*}{\begin{tabular}{c} 4 cases from two \\ families \\ \end{tabular} } & \multirow{2}{*}{Family Study} & \multirow{2}{*}{\
\begin{tabular}{c} Targeted \\ Sequencing \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} Heterozygous NOTCH1 deletion \\ associated with a spectrum of \\ congenital heart defects from BAV \\ to complex anomalies. \\ \end{tabular} } \\  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Studies Identifying Genetic Components of BAV Disease.
# On Combining Feature Selection and Over-Sampling Techniques for Breast Cancer Prediction
## 5 Conclusions  
Feature selection aims at selecting representative features from a given training set, whereas over-sampling is for re-balancing the class imbalanced training set. In this paper, the order of combining feature selection and over-sampling for breast cancer prediction are compared in terms of classification accuracy. In order to assess the performances of different combination approaches, the information gain (IG) and the genetic algorithm (GA) as the filter and wrapper-based feature selection methods and the synthetic minority over-sampling technique (SMOTE) were employed for creation of the combinations. Moreover, two breast cancer datasets with significantly different class imbalance ratios and number of features were used for the experiments.  
Regarding the experimental results, for the highly imbalanced dataset containing a large number of features, performing both feature selection and over-sampling can cause  
Figure 4: The type I errors of different approaches over the Breast Cancer Wisconsin Dataset.  
Figure 3: AUC rates of different approaches over the Breast Cancer Wisconsin Dataset.  
the SVM classifier provide higher AUC rates than performing feature selection and over-sampling alone as well as at the baseline. In particular, it is recommended to execute feature selection first and over-sampling second. On the contrary, for the dataset with the low imbalance ratio and small number of features, performing over-sampling alone is the better choice.  
Conceptualization, C.-H.C. and M.-W.H.; methodology, C.-H.C. and C.-F.T.; software, W.-C.L.; validation, C.-F.T., W.-C.L., and M.-W.H.; formal analysis, C.-H.C., C.-F.T., and M.-W.H.; resources, C.-H.C.; data curation, C.-F.T.; writing--original draft preparation, C.-H.C., C.-F.T., and M.-W.H.; writing--review and editing, M.-W.H. and C.-H.C.; supervision, C.-H.C.; project administration, M.-W.H.; funding acquisition, W.-C.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology of Taiwan, grant MOST 109-2410-H-182-012 and Chang Gung Memorial Hospital, Linkou, grant BMRPH13.  
Not applicable.  
The data that support the findings of this study are openly available in [[https://www.kdd.org/kdd-cup/view/kdd-cup-2008](https://www.kdd.org/kdd-cup/view/kdd-cup-2008)]([https://www.kdd.org/kdd-cup/view/kdd-cup-2008](https://www.kdd.org/kdd-cup/view/kdd-cup-2008)) (accessed on 15 February 2021) and [[https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+%28](https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+%28) original%29]([https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+%28](https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+%28) original%29) (accessed on 15 February 2021).  
The work was supported in part by the Ministry of Science and Technology of Taiwan under Grant MOST 109-2410-H-182-012 and in part by Chang Gung Memorial Hospital, Linkou under Grant BMRPH13.  
The authors declare no conflict of interest.
## 3 Results
### Prediction of Environmental Satisfaction (Thermal and Air Quality)
Table 8 presents the results of three regression models that predicted the effect of various independent variables on overall air quality satisfaction as an outcome variable. Model 1 indicated that odour problems that contributed to respondents' dissatisfaction with the indoor air quality, particularly from tobacco smoke (\(B\) = 0.366, \(p\) < 0.1), external sources (\(B\) = 0.894, \(p\) < 0.01), home furnishings (\(B\) = \(-\)1.519, \(p\) < 0.05) and some other sources (\(B\) = 0.503, \(p\) < 0.1) could significantly predict the overall air quality satisfaction levels. When housing condition variables were added to the regression Model 2, the odour problems from the external sources (\(B\) = 0.866, \(p\) < 0.1) still significantly predicted the overall air quality satisfaction levels, including the duration of the residence (\(B\) = 0.067, \(p\) < 0.05). After including the socio-economic and demographic factors in Model 3, the predicting role of odour problems from the external sources (\(B\) = 1.057, \(p\) < 0.05), the duration of the residence (\(B\) = 0.051, \(p\) < 0.1), alcohol consumption (\(B\) = 0.484, \(p\) < 0.01) and smoking habit (\(B\) = \(-\)0.251, \(p\) < 0.01) had a statistically significant effect on overall air quality satisfaction. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{2}{c}{**Model 1**} & \multicolumn{2}{c}{**Model 2**} & \multicolumn{2}{c}{**Model 3**} \\ \cline{2-7}  & **B** & **SE** & **B** & **SE** & **B** & **SE** \\ \hline Constant & 1.917 *** & 0.218 & 1.616 *** & 0.284 & 1.209 ** & 0.506 \\ OD\_tob & 0.366 * & 0.192 & 0.394 & 0.516 & 0.656 & 0.517 \\ OD\_equip & \(-\)0.260 & 0.288 & \(-\)0.254 & 0.590 & \(-\)0.069 & 0.587 \\ OD\_food & \(-\)0.349 & 0.221 & \(-\)0.276 & 0.526 & \(-\)0.046 & 0.530 \\ OD\_furn & \(-\)1.519 ** & 0.620 & \(-\)1.369 & 0.829 & \(-\)1.065 & 0.824 \\ OD\_perfu & 0.847 & 0.812 & 1.025 & 0.937 & 1.504 & 0.937 \\ OD\_produ & 0.259 & 0.541 & 0.310 & 0.712 & 0.522 & 0.709 \\ OD\_out & 0.894 *** & 0.208 & 0.866 * & 0.498 & 1.057 ** & 0.498 \\ OD\_other & 0.503 * & 0.269 & 0.402 & 0.272 & 0.427 & 0.271 \\ L\_dura & & & 0.067 ** & 0.028 & 0.051 * & 0.029 \\ H\_floor & & & 0.017 & 0.041 & 0.029 & 0.041 \\ Num\_Odo & & & \(-\)0.030 & 0.492 & \(-\)0.253 & 0.492 \\ Age & & & & 0.036 & 0.051 \\ Gender & & & & 0.094 & 0.172 \\ Smo\_stat & & & & \(-\)0.251 *** & 0.090 \\ Al\_stat & & & & 0.484 *** & 0.132 \\ Emp\_stat & & & & \(-\)0.014 & 0.038 \\ Edu\_stat & & & & \(-\)0.023 & 0.073 \\ R\({}^{2}\) & 0.081 & 0.093 & & 0.129 \\ \(\Delta\)R\({}^{2}\) & & & 0.012 & & 0.036 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Multiple regression analysis with overall satisfaction with air quality as outcome variable.
## 3 Results  
In total, 12 patients (6 male and 6 female) were included in this study: 4 patients received secukinumab (IL-17A); 7 patients, izekizumab (IL-17A); and 1 patient, bimekizumab (IL-17A and -F inhibitor) as biologic treatment [1]. The patients' age ranged from 25 to 84 years (mean age: 52.9 years; sd: 16.5 years). One patient (bimekizumab) had to interrupt therapy due to candida esophagitis after the completion of the study. There were no serious adverse effects observed among the other patients. The location of the SP was in the lower extremities of six patients and upper extremities including the scapular region in the other six patients. The mean time between the first study visit (V0) and second study visit (V1) was 5.4 (sd: 1.6) weeks, and that between V1 and the third study visit (V2) was 8.1 (sd: 3.0) weeks.  
Figure 3: Three-dimensional (3D) line-field confocal optical coherence tomography (vertical image size: 1.2 mm \(\times\) 0.4 mm; 3D image size: 1.2 \(\times\) 0.5 \(\times\) 0.5 mm\({}^{3}\)) stack with reconstruction of the artificial intelligence (AI) model-generated segmentation of skin layers (blue = skin surface, red = boundary between stratum corneum and stratum granulosum, green = dermo-epidermal junction).
## 2 Materials and Methods
### Background
#### 2.1.2 Data Rate Allocation Issues Related to Range and Power Consumption
Thus, when an ED joins the network, it is likely possible that it is using a much lower data rate than is actually required. Overall, in the general case, the network server-side algorithm converges to the ideal data rate for an ED more quickly. The total convergence time for EDs eventually becomes extended as the network gets bigger because of the duty-cycle regulations applied to the GW [14]. Furthermore, an evaluation on Multitech's ADR algorithm [7] showed that when it is enabled, this causes a negative impact on the PDR, especially for EDs that are deployed far away. This scheme mainly aims to optimize throughput and dominantly assign two spreading factors, either SF12 or SF7 BW250. When ADR is disabled and long-range EDs are incorrectly set up with a high data rate, which is not designed for long range, a situation occurs in which an ED can join the network but cannot effectively communicate. The joining process will adjust the spreading factor used during the joining procedure, but once the ED is joined, it will revert back to the preconfigured spreading factor. With that high data rate, it experiences very high packet loss and regularly will attempt to rejoin the network. This cycle repeats with the ED being able to join the network but not being able to transmit data successfully. In addition, note that when the link margin estimate is marginal between two assignment decisions, oscillations can occur as ADR requests are repeatedly sent, instructing the ED between different configurations [7]. Generally, if an ADR scheme is enabled, the network server transmits the LinkADRReq MAC command to control data rate (DR) and transmission power (TX) of an ED. The LinkADRReq provides only a subset of eight DR and six TX settings, which is not a utilization of the 6720 potential transmission settings combinations on the ED part [13]. Notice, that even in short coverage scenarios, the Semtech ADR is partially failed on a large scale (>1000 EDs) because it assigns on each ED the SF7 so there are quite a number of collisions [1]. Most of the current ADR approaches are nothing more than simple distance-based ED configuration mechanisms. A lot of times this fact results in non-optimal network performance, but offers an assurance within basic deployments that EDs are at least capable of communicating with GWs [9]. These remarks show how ADR convergence time and missing data can lead to loss of valuable time-on-air and drain power without reason and thus limit the overall performance on range, on capacity and on energy balance of a LoRaWAN network.