esa-sceva/satcom-chunk-collection huggingface dataset:
# ANN Sizing Procedure for the Day-Ahead Output Power Forecast of a PV Plant
## 5 Case Study  
This study was lead on three different PV plants located in the northern part of Italy, each with different features. Nevertheless, it is important to highlight that this procedure has general validity, and can be easily extended to other PV plant locations with different data set availability [33; 34].  
The available hourly datasets considered here cover one year of measurements of the PV output power. The historical weather forecasts for the next day, which have been employed to train the network, are delivered daily by a weather service at 11 a.m. The historical hourly database of some weather parameters, such as the ambient temperature, global horizontal solar radiation, wind, etc., used to train the neural network. In addition, the deterministic global solar radiation under clear sky conditions is provided to the network for obtaining an hybrid method [7]. The number and type of these input parameters have been defined in [33; 34], where the comparison of the neural approach versus physical methods has been presented. As reported in the cited paper, physical approaches based only on temperature and irradiance are outperformed by neural networks considering an extended range of meteorological parameters.  
In order to properly train the network, the three groups of data (training, validation, and test) should be statistically consistent: for every physical parameter describing the data point, they should contain almost the same mean value, variability, and range. The samples' grouping is realized as follows: 70% for training, 15% for validation, and 15% for testing. The samples composing the three groups are randomly chosen from the whole dataset. The data are first divided into 365 blocks (one block per day); then, 255 days are assigned to training, 55 days to validation, and 55 days are left for the test. It has to be noted that each group has the same average number of daylight hours.  
Finally, for each \(p\)-th configuration of the network, we calculated a sufficiently large number of forecasts (i.e., 100) related to 55 days, and covering all of the possible meteorological conditions (test set). For each 55-day forecast, the corresponding \(NMAE_{\%}\) was evaluated. The \(NMAE_{\%}\)'s sample mean, its sample variance, and the width of its confidence interval for a given degree of confidence were also calculated as previously described. As the sample variance is different for each network configuration, also the related confidence intervals will have different amplitudes.
## 5 Results  
### Comparative Performance of Training and Testing Datasets  
Performance comparison of BBN models were made for training and testing datasets. The ratio of the number of liquefied case history records to non-liquefied case history records is 2.53:1 for the training dataset. It is obvious that there is a class imbalance problem. The ratio for testing and total datasets is 2.57:1 and 2.54:1, respectively, which indicates that there is approximately no sampling bias in the training and testing datasets. The BBN and C4.5 DT models were developed from the training dataset of 201 CPT case histories (144 case records of liquefaction and 57 case records of non-liquefaction) that has class imbalances and almost no sampling bias owing to the class ratio of 180:71 for 251 case histories.  
Table 5 shows that the BBN model has a higher OA, while OA alone cannot be utilized as a model performance measure; therefore, AUC of ROC, MCC, precision, recall, and F-measure were also used to select optimal model performance separately for liquefaction and non-liquefaction instances. For liquefaction, the BBN model has a higher recall, whereas the C4.5 DT model has maximum precision. However, when F-measure is computed, the BBN model has a highest value and moreover has the highest value of AUC and MCC, with respect to the C4.5 DT model. In the case of the non-liquefaction class the BBN model has a higher precision value, while the C4.5 DT model has a high recall value; however, the F-measure for BBN has higher value. BBN has shown to have a relatively good OA, MCC, AUC, and F-measure for liquefaction and non-liquefaction instances, offering better performance.  
The predictive performance of the BBN and C4.5 DT models are compared with the [PERSON] et al. [6] simplified procedure, referred to as CPT-YD; and the [PERSON] et al. evolutionary-based approach [31], referred to as CPT-RA, with the same testing dataset (36 case records of liquefaction and 14 case records of non-liquefaction). Table 6 clearly shows that BBN has the highest OA, AUC, and MCC. In the case of liquefaction and non-liquefaction classes, BBN has the highest scores in all parameters, barring one (the C4.5 DT model has the highest value of recall in the liquefaction class). The BBN model, thus, showed robust performance in comparison with C4.5 DT, CPT-YD, and CPT-RA.
# Phosphorus Mobilizing Enzymes of _Alnus_-Associated Etomycorrhizal Fungi in an Alaskan
## 4 Discussion
### Phytase Activity  
Although several studies have assessed the potential of EMF to mobilize P from organic sources [51; 18; 53], most have focused on acid phosphatase activity [56; 16; 58; 19; 59] and to a lesser extent phosphodiesterase active [6]. Phytase activity has rarely been measured on EMF root tips [27] and never before to our knowledge on _Alnus_, despite the reputedly large proportion of phytate in total soil P pools [8; 60]. Reasons behind this may relate to the importance of simple phosphomonoesters as a component of soil organic P. However, this phenomenon may also be methodological; assessing phytase activity on root tips and in soils is difficult. The method we used for measuring phytase activity on root tips has potential to further our understanding of the role of EMF in breaking down complex recalcitrant P molecules, which often make up large proportions of the organic P pool [8; 60; 61], and may become increasingly important where P is limiting. We were unable to resolve clear differences in phytase activity across succession, between horizons, or hosts; however, we note the high variability of this enzyme activity at all levels, which may suggest large differences among individual EMF species in the capacity to produce phytase.
# Energy-Saving Traffic Scheduling in Hybrid Software Defined Wireless Reschargeable Sensor Networks
## 6 Simulations and Results
In Figure 7, the minimum and maximum latency of ETS are the same as MTE when the amount of SDN nodes grows. The average latency of ETS is slightly higher than MTE when the ratio of SDN nodes is high because the flows may pass a little longer route when they are converged to save energy. Figure 8 shows the relationship between latency and the network load. For ETS, the average latency remains steady when the network load is under a certain threshold, and then grows slightly. In summary, the ETS algorithm do have a little longer average latency compared to the MTE algorithm. However, the increase in latency is so few that it will not influence the quality of service and can be omitted. Figure 9 shows the impact of the sink node's position on the energy consumption. We choose four locations for comparison: top right corner, top center, right corner and center in the network. The MTE algorithm and ETS algorithm with different SDN nodes are all compared. The simulation results show that the location of the sink has a direct impact on the energy saving effect, raising the demand on the optimization of sink node deployment. Considering that this issue has been fully investigated in various WSNs, we can directly adopt current solutions when the ETS algorithm is implemented. Figure 6: Energy consumption with different load. Figure 7: Latency with a different amount of SDN nodes. Consequently, the ETS algorithm can achieve obvious energy efficiency compared with the traditional MTE algorithm while ensuring feasible data delay.
# Getting over High-Dimensionality: How Multidimensional Projection Methods Can Assist Data Science
## 3 Ground Theory
### Taxonomies Used in the Multidimensional Projection Area
Close points to an instance in the high dimensional space must be projected as close as possible to that instance in the visual space. Generally, local methods build a family of transformations to project the data; therefore, instances are not mapped by a single transformation but using a set of local mappings in which each mapping will project a subset of data, preserving the local structures of the dataset. Local methods can rely on global mechanisms to perform multidimensional projections or combine global and local properties, generating hybrid approaches. For example, a small subset of data samples (often called control points) is defined and projected, relying on global relationships. From this initial mapping, the remaining instances are positioned based only on their neighborhood relationships concerning each control point [12], which maintains the essence of a local mapping. In summary, global techniques are often accurate in preserving distances average between objects, in global terms, but fail to preserve local relationships, besides being computationally more expensive. Moreover, according to [PERSON] et al. [6], preserving distances as a whole tends to distort small neighborhoods. Thus, when the purpose is to preserve neighborhoods, local techniques are more suitable than global ones.
# OTFS: A Potential Waveform for Space-Air-Ground Integrated Networks in 6G and Beyond
### Orthogonal Frequency Division Multiplexing (OFDM)  
Due to its increased spectral efficiency and resilience to multipath fading, OFDM has been the preferred modulation technique for most wireless communication systems,  
Figure 1: Space–Air–Ground Integrated Network architecture.  
including 4G, 5G, Wi-Fi, and digital broadcasting standards. OFDM operates in the time-frequency domain. It divides the available bandwidth into multiple orthogonal subcarriers, allowing the parallel transmission of data streams. Therefore, each subcarrier experiences a flat fading channel, making equalization in the frequency domain easier. Therefore, OFDM is highly efficient in frequency-selective fading channels.  
However, in high-mobility (time-varying) scenarios, as expected in SAGINs, the performance of OFDM degrades significantly. This is due to the sensitivity of OFDM to time-varying channels with high Doppler effects, leading to inter-carrier interference (ICI). One of the reasons is that the orthogonality between subcarriers, which is crucial for interference-free detection in OFDM, is disrupted by the rapid time-varying nature of the wireless channel [3]. This occurs as Doppler shifts induced by high mobility cause the spectral spreading of the transmitted signals, leading to overlap or interference between adjacent subcarriers, known as inter-carrier interference (ICI) [4].  
Another critical issue is the need for frequent transmission of pilot symbols and channel estimation to compensate for the effects of the rapidly changing channel conditions [5]. This results in pilot overhead, which reduces the spectral efficiency of the OFDM scheme in these scenarios. Due to the challenges faced by OFDM in time-varying high-mobility channels, researchers are exploring alternative waveforms like OTFS, to meet the extreme requirements of SAGINs for 6G and beyond.
# Reviewing 6D Pose Estimation: Model Strengths, Limitations, and Application Fields
## 4 Models
### Non-Real-Time Models
[81], published in 2022, presents a vision system for UAV-based logistics that integrates 2D image and 3D point cloud data to detect, segment, and estimate 6D object poses, demonstrating its effectiveness through experiments on the YCB-Video and SIAT datasets and highlighting future improvements for real-time deployment. The model StereoPose [82], published in 2023, presents StereoPose, a stereo image-based framework for category-level 6D pose estimation of transparent objects, integrating a back-view NOCS map, a parallax attention module for feature fusion, and an epipolar loss to enhance stereo consistency, with validation on the TOD dataset and potential applications in robotics. The model SwinDePose [83], published in 2023, proposes SwinDePose, a fusion network that extracts geometric features from depth images, combines them with point cloud representations, and predicts poses through semantic segmentation and keypoint localization, validated on the LineMod, Occlusion LineMod, and YCB-Video datasets. The model Yolo 7 [84], published in 2023, presents an improved YOLOv7-based 6D pose estimation method that extends prediction networks, modifies the loss function, and incorporates keypoint interpolation to enhance pose accuracy while reducing reliance on precise 3D models, validated on public and custom datasets. The model developed by [PERSON] et al. [85], published in 2024, introduces an open-vocabulary 6D object pose and size estimation framework that utilizes pre-trained DinoV2 and text-to-image diffusion models to infer NOCS maps, enabling generalization to novel object categories described in human text, supported by the large-scale OO3D-9D dataset. The model RNNPose [86], published in 2024, presents RNNPose, a recurrent neural network-based framework for 6D object pose refinement that iteratively optimizes poses using a differentiable Levenberg-Marquardt algorithm, leveraging descriptor-based consistency checks for robustness against occlusions and erroneous initial poses, validated on multiple public datasets. The model developed by [PERSON] et al. [87], published in 2024, presents a cross-modal fusion network for 6D pose estimation that extracts and integrates RGB and depth features using transformer-based fusion, demonstrating improved accuracy in occluded and truncated environments through evaluations on Occlusion Linemod and Truncation Linemod datasets.
## 2 Materials and Methods
### Procedures
#### 2.3.1. Densification Process from Single Briquetting Unit  
Densification experiments were carried out in the single briquetting unit according to the configuration in Figure 2. The briquette cyanobacteria sample under the compressive load of 3 tons is shown in Figure 6. The made briquette cyanobacteria were a cylinder with a diameter of about 1 cm and a height of about 1 cm. The height would decrease with the increase in compressive load, while the diameter remains unchanged due to the fixed size of the stainless steel cylinder model in the single briquetting unit (Figure 2). Three kinds of factors are analyzed, including moisture content and compressive load. In order to study the influence of the moisture content, the cyanobacteria samples on a received basis are dried in an oven at 60 \({}^{\circ}\)C for different drying times. According to the detected moisture content, the samples of moisture content 5, 10, 15 and 20% were taken out for studying. The compressive load was also varied from 1 ton, 3 tons, 5 tons to 7 tons. All of the tests are conducted with cyanobacteria samples of 0.5 g. The forming rate and relax density are two assessment indexes for the densification performance of briquette cyanobacteria. In addition, some pressure crushing tests of the briquette cyanobacteria samples were conducted by the pressure crushing detector (Figure 3). The compressive force data per 0.1 s were recorded to study the original mechanical strength of briquette cyanobacteria.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reactor** & **Stage** & **Factors** & **Condition** & **Quality Attributes** \\ \hline Single briquetting unit & Densification process & Moisture content & (5–20\%) & Forming rate \\  & Compressive load & (1–7 tons) & Relax density \\  & & & & Crushing pressure \\ \hline  & & & (5–20\%) & \\  & Stage 1: Drying & Moisture content & (1–7 tons) & \\  & Stage 2: & Compressive load & 180 \({}^{\circ}\)C (drying) & Forming rate \\ analysis (TGA) reactor & Devolatilization & Heating rate & 500 \({}^{\circ}\)C & Relax density \\  & & & (Devolatilization) & \\  & & & (10–25 \({}^{\circ}\)C/min) & \\ \hline  & & & Batch feeding & Carbon conversion rate \\  & Thermochemical & Temperature & (750–900 \({}^{\circ}\)C) & Gas yield \\  & transformation & Hematite & Hematite 30 g & Reaction stability \\  & including char burnout & SEM analysis & N\({}_{2}\) 1.5 L/min+steam & NO emission \\  & Cycle number & & 0.8 g/min & Phosphorus phase \\ \hline \hline \end{tabular}
\end{table}
Table 3: Facts and quality attributes for briquette cyanobacteria in three reactors.
# Skin Lesion Classification Using a Deep Ensemble Model
###### Abstract  
Skin cancer, particularly melanoma, is a severe health threat that necessitates early detection for effective treatment. This research introduces a skin lesion classification system that harnesses the capabilities of three advanced deep learning models: VGG16, Inception-V3, and ResNet-50. By integrating these models into an ensemble, the system leverages their individual strengths to improve classification accuracy and robustness. Every model in the ensemble brings its distinctive contribution, having undergone pre-training on ImageNet and subsequent fine-tuning using dermoscopic images. We evaluated our ensemble approach using the ISIC 2018 dataset, a standard benchmark for skin lesion classification. By employing a weighted averaging method to merge predictions from the three models, our ensemble model demonstrated superior performance. The results show an improvement in classification accuracy, achieving an accuracy of 91% on the original dataset and 97% on the dataset balanced by oversampling compared to the individual models. This system was then evaluated using the HAM10000 dataset. The results using the HAM10000 dataset show an improvement in classification accuracy, achieving an accuracy of 90% on the original dataset and 96% on the dataset balanced by oversampling compared to the individual models. This enhanced performance highlights the effectiveness of our ensemble method in capturing diverse features of skin lesions, leading to more accurate diagnoses. Our findings suggest that this approach can significantly assist dermatologists in early and precise skin cancer detection, potentially improving patient outcomes.  
skin lesion classification; VGG16; ResNet-50; Inception-V3; ensemble
## 3 Results
### Physiological Variables  
#### 3.4.1 Electrodermal Activity  
ANOVA for EDA\({}_{\text{mean}}\) showed a significant interaction between condition and moment (F (2.88) = 3.331, \(p\) = 0.040, \(\eta\)2\(p\) = 0.070, \(\beta\)-1 = 0.508). As is reflected in Table 2, both groups had a similar mean of EDA during T1. However, in the case of EG, a significant descent in EDA was observed during the intervention, followed subsequently by a significant increase during T2. However, post hoc analysis did not reveal any significant difference between groups.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{2}{c}{**T1**} & \multicolumn{2}{c}{**T2**} \\  & & **M (SD)** & & **M (SD)** \\ \cline{2-5}  & **EG** & **CG** & **EG** & **CG** \\ \hline
**Total Performance** & 1.67 (0.76) & 1.75 (0.70) & 0.97 (0.39) ** & 1.55 (0.26) \\ \hline
**Self-efficacy** & & & & \\ Success & 72.41 (16.40) & 78.18 (15.31) & 67.93 (14.23) & 64.55 (21.76) \\ Task Importance & 55.93 (27.17) & 65.45 (21.76) & 58.66 (21.23) * & 56.82 (21.90) \\ Confidence & 65.17 (19.93) & 80.91 (16.59) & 68.97 (18.39) * & 62.27 (24.86) \\ \hline
**Task appraisal** & & & & \\ Task Difficulty & 7.79 (0.86) & 7.18 (0.95) & 7.45 (1.54) * & 7.86 (1.12) \\ Stress & 0.81 (0.16) & 0.74 (0.24) & 0.86 (0.10) & 0.89 (0.06) \\ Perceived Effort & 7.69 (1.07) & 7.35 (1.66) & 7.69 (1.22) & 7.96 (1.18) \\ Frustration & 7.07 (2.18) & 6.61 (3.01) & 6.45 (2.81) & 6.00 (2.70) \\ \hline \hline \end{tabular} Note: Total Performance, Self-efficacy (Success, Confidence, Task Importance), and Task appraisal (Task Difficulty values, Stress, Perceived Effort, and Frustration) are represented as M and SD. Differences between condition groups (EG and CG) were calculated by repeated measures ANOVA analyses. The stress variable was transformed using log10. * \(p\) < 0.05, ** \(p\) < 0.01.  
\end{table}
Table 1: Score differences on Total Performance, Self-efficacy, and Task Appraisal in T1 and T2 between EG and CG.
###### Abstract  
Gold nanoparticles (AuNPs) used in pharmaceutical treatments have been shown to effectively deliver a payload, such as an active pharmaceutical ingredient or image contrast agent, to targeted tissues in need of therapy or diagnostics while minimizing exposure, availability, and accumulation to surrounding biological compartments. Data sets collected in this field of study include some toxico- and pharmacodynamic properties (e.g., distribution and metabolism) but many studies lack information about adsorption of biological molecules or absorption into cells. When nanoparticles are suspended in blood serum, a protein corona cloud forms around its surface. The extent of the applications and implications of this formed cloud are unknown. Some researchers have speculated that the successful use of nanoparticles in pharmaceutical treatments relies on a comprehensive understanding of the protein corona composition. The work presented in this paper uses a suite of data analytics and multi-variant visualization techniques to elucidate particle-to-protein interactions at the molecular level. Through mass spectrometry analyses, corona proteins were identified through large and complex datasets. With such high-output analyses, complex datasets pose a challenge when visualizing and communicating nanoparticle-protein interactions. Thus, the creation of a streamlined visualization method is necessary. A series of user-friendly data informatics techniques were used to demonstrate the data flow of protein corona characteristics. Multi-variant heat maps, pie charts, tables, and three-dimensional regression analyses were used to improve results interpretation, facilitate an iterative data transfer process, and emphasize features of the nanoparticle-protein corona system that might be controllable. Data informatics successfully highlights the differences between protein corona compositions and how they relate to nanoparticle surface charge.  
protein corona; gold nanoparticle; proteomics; data visualization; physicochemical characterization +
Footnote †: journal: Applied Physics and Chemistry
# A Study on DNN-Based Practical Model for Predicting Spot Color
## 3 Results
### Preprocessing Validation  
In this study, an experiment was designed to validate the performance and effectiveness of the proposed preprocessing method. The main objective was to determine the differences in the performance of the prediction model, specifically focusing on the mean loss value on the evaluation set, when preprocessing was applied versus when it was not. Two models with identical architecture and hyperparameter settings were used. The first model was trained on the raw data set, while the second model used the data set that had undergone the proposed preprocessing. Based on this experimental setup, both models underwent 30 independent training and evaluation iterations. The mean loss values from these iterations were then compared for statistical analysis.  
A \(t\)-test was conducted to statistically compare the mean loss values of the two models. As shown in Table 4, the results showed a significant decrease in the mean loss value for the model trained with the preprocessed data. Specifically, the model using preprocessed data consistently showed a lower mean loss of 0.4692 compared to its counterpart, with a \(p\)-value less than 0.05, highlighting the statistical significance of the observed difference. These results show the effect of the proposed preprocessing technique in reducing the loss of the prediction model, thus demonstrating its importance in the field of ink color prediction.