esa-sceva/satcom-chunk-collection huggingface dataset:
# ANN Sizing Procedure for the Day-Ahead Output Power Forecast of a PV Plant
## 9 Conclusions  
This work has presented a detailed analysis of a method which has been defined for the setting up of an ANN in terms of number of trials in the ensemble forecast, the number of neurons, and their layout. This method has been employed for a day-ahead forecast with a recently developed PHANN approach.  
Figure 5: Mean \(NMAE_{\%}\) of 50 different forecasts as a function of the hidden layers’ sizes, with a constant ratio of neurons.  
Figure 6: Comparison between the mean \(NMAE_{\%}\) and the ensemble \(NMAE_{\%}\) of 50 trials as a function of the hidden layers’ sizes, kept with a constant ratio of neurons.  
On the basis of the proposed method, the settings minimizing the mean \(NMAE_{\%}\) of the 24 h ahead PV power forecast with a one-year historical dataset are: an ensemble size of ten trials, and a number of 120 neurons in a single layer ANN configuration. The above outlined method is meant to be adopted for setting the most suitable ANN parameters in view of the day-ahead forecast of any PV plant's output power.  
In this research activity, all of the authors were involved in the data analysis and preprocessing phase, the simulation, the results analysis and discussion, and the manuscript's preparation. All of the authors have approved the submitted manuscript. [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed analysis tools; all the authors equally contributed to the writing of the paper.  
The authors declare no conflict of interest.
# Bilinear CNN Model for Fine-Grained Classification Based on Subcategory-Similarity Measurement
## 4 Results
### Experiment and Analysis on Stanford Cars-196  
#### 4.3.1 Experimental Analysis of Improved Loss Function  
Figure 6 shows the comparison of our results with the method that solely trains with triplet or softmax loss. We experimentally observed that by jointly optimizing both loss functions, the convergence rate could be significantly improved. When using softmax loss alone, the convergence rate is much faster than only using triplet loss. This is because intraclass variance, which is essential for discovering visually and semantically similar instances, is not preserved in softmax loss, and more information leads to a slower convergence rate of triplet loss. By jointly minimizing both of them, the loss function can harvest augmented information from both sides, resulting in a fast convergence rate. We also compared our method with EmLS + FGFR [31], which also effectively learns fine-grained feature representations by jointly optimizing both classification and similarity constraints. However, EmLS + FGFR effectively generates fine-grained feature representations by embedding label structures, such as hierarchical labels or shared attributes. Overall, the convergence rates of the two methods are similar, which proves the practicability of our method.  
In addition, the relation between training error and the iteration number of two methods, B-CNN + weighted softmax and improved triplet loss, and B-CNN + weighted softmax loss on Cars-196, is shown in Figure 7. It can be seen that the convergence speed of B-CNN + weighted softmax and improved triplet loss is fast, which reaches convergence after about 6000 iterations, while the convergence speed of the B-CNN + weighted softmax loss training process is relatively slow.
## 1 Introduction  
### Motivation  
The widespread integration of renewable energy sources along with the extensive introduction of non-linear loads and semiconductor devices in the power system causes the degradation of power quality in terms of distortions in voltage, current, and frequency. Power quality disturbances (PQDs) lead to system protection malfunction and system instabilities and limit the lifetime of electrical equipment [1]. As a result, these disruptions significantly impact the reliability of the power grid. In smart city environments, where interconnected devices and systems rely on stable power, poor power quality can result in decreased productivity, compromised safety, and increased manufacturing costs [2]. In the context of a smart grid, as shown in Figure 1, power quality issues across the generation, transmission, and distribution systems are primarily caused by (1) intermittent renewable energy sources, (2) the presence of power electronics, 3) system faults, (4) variations in load, (5) switching operations, (6) imbalances in load distribution, and (7) non-linear loads. The non-linear loads include (1) variable-speed drives, (2) computers, (3) fluorescent lighting, and (4) other electronics. By addressing these issues, smart cities can ensure reliable energy delivery, enhance operational efficiency, and improve the overall quality of life for residents. Hence, compliance with power quality standards is integral to monitoring setups, which require accurate detection and classification of various kinds of PQDs, including sag, swell, flicker, interruption, harmonics, and transients. This critical problem motivates research scholars to design and investigate a framework for the correct classification of PQDs. In the context of smart cities, the integration of advanced PQD classification methods into smart grid communication systems is essential for ensuring grid stability. Smart grids require efficient and reliable data exchange between key components such as distributed energy resources, smart meters, and control systems. This enables quicker decision-making and response to grid anomalies, ultimately improving the resilience and efficiency of smart city infrastructures. Similarly, interoperability between diverse systems and devices is crucial for ensuring the smooth operation of smart grids. These devices must be able to communicate and function together seamlessly. A customary communication standard being followed in this work ensures compatibility across different platforms and enables smooth integration into existing infrastructures. Furthermore, the security of data traffic within smart grids is also a critical concern, especially given the increasing volume of sensitive information exchanged between components. By implementing robust encryption, access control, blockchain for data integrity, secure communication protocols, and regular security audits, this study ensures that both the PQD data and classification results remain secure throughout the entire classification process. These security measures support the proposed method and make it resilient to cyber threats in smart grid applications.
## 3 Results
### Results at the Component Level
#### 3.1.2 Actual Thermal Transmittance Assessment  
The data collected in the previous Section show that environmental factors, such as rain and wind, can affect the performance of the insulation layer, even when it is placed on the inner side. Through precision and numerical simulations, it was possible to calculate the level of the water content reached in the insulating layer, and to what extent this moisture content affected the thermal transmittance of the wall. The variation in the thermal transmittance during the winter period is shown in Figure 7a,b for Monfero and Turin, respectively. The plots compare the \(U_{dry}\) value (indicated with X) with the \(U_{eff}\) one; the latter value was calculated considering the indoor/outdoor temperatures and the heat flux crossing the wall section. The importance of knowledge on the material properties (moisture-dependent thermal conductivity) should be pointed out, since the \(U_{eff}\) value is influenced to a great extent by the actual moisture content, \(mc\), in the insulating materials.  
The results show a difference between the value of Udry (thermal transmittance) calculated according to EN ISO 6946 [29] and the Ueff, max value obtained through the average method (EN 9869) from hourly data exported from the software. As the plot graph shows, Ueff > Udry due to the presence of moisture in the configurations. The phenomenon has a greater impact on the thermal plaster configurations than on the counter wall; this depends on the higher moisture storage capabilities of the plaster. This analysis shows that the installation of this material would have a different performance from that estimated according to EN ISO 6946. Moisture accumulation in the wall occurs for two primary reasons: a reduced permeability of an insulation limits the drying potential in the internal direction and the insulation layer lowers the temperature of the wall, thereby resulting in a reduced drying capacity and an increased probability of moisture condensing within the wall. It should be pointed out that although a dry counter wall (C) solution on average shows a better insulating performance than thermal insulating plasters, the 1D hygrothermal analysis did not take into account the effect of the thermal bridges of the window frame system. Therefore, under real conditions, it is expected that the insulation performance of a counter wall would partially be reduced, thus making the U-value results closer for the two analyzed solutions.  
Figure 7: Box plots: Thermal transmittance values of the wall, U [W/m\({}^{2}\)K], in the winter period (October-April) in Monfero (**a**) and Turin (**b**).
# Characteristics of Resonant Tunneling in Nanostructures with Spacer Layers
###### Abstract  
The effect of spacer layers on electron transport through two-barrier nanostructures was studied using the numerical solution of the time-dependent Schrodinger-Poisson equations with exact discrete open boundary conditions. The formulation of the problem took into account both the active region consisting of a quantum well and barriers, as well as the presence of highly doped contact layers and spacer layers. The use of the time formulation of the problem avoids the divergence of the numerical solution, which is usually observed when solving a stationary system of the Schrodinger-Poisson equations at small sizes of spacer layers. It is shown that an increase in the thickness of the emitter spacer leads to a decrease in the peak current through the resonant tunneling nanostructures. This is due to the charge accumulation effects, which, in particular, lead to a change in the potential in an additional quantum well formed in the emitter spacer region when a constant electric field is applied. The valley current also decreases as the thickness of the emitter spacer increases. The peak current and valley current are weakly dependent on the thickness of the collector spacer. The collector spacer thickness has a strong effect on the applied peak and valley voltages. The above features are valid for all three different resonant tunneling nanostructures considered in this study. For the RTD structures based on Al\({}_{0.3}\)Ga\({}_{0.7}\)As/GaAs, the optimized peak current value \(I_{p_{max}}=5.6\times 10^{9}\) A/m\({}^{2}\) and the corresponding applied voltage \(V_{p}=0.44\) V. For the RTD structures based on AlAs/In\({}_{0.5}\)Ga\({}_{0.2}\)As, \(I_{p_{max}}=14.5\times 10^{9}\) A/m\({}^{2}\) (\(V_{p}=0.54\) V); for RTD structures based on AlAs/In\({}_{0.55}\)Ga\({}_{0.47}\)As, \(I_{p_{max}}=45.5\times 10^{9}\) A/m\({}^{2}\) (\(V_{p}=1.75\) V).  
resonant tunneling; peak current; spacer layers; open boundary conditions; Schrodinger-Poisson equations
# Design and Diagnostics of High-Precision Accelerator Neutrino Beams
## 10 Mitigation Techniques of Beam Systematics at the Neutrino Detectors
### Energy Unfolding  
A high precision ND is also an excellent tool to correct for the spectral shape of the beam. As noted above, the beam is not monochromatic and the spectrum at FD is quite different than the spectrum at ND. To correct for the mismatch, the ND must rely on a good reconstruction of the neutrino energy. Unlike the quasi-elastic events mentioned above, the reconstruction of the energy cannot be performed analytically and depends on the actual number of final state particles that are reconstructed. Particle losses or mis-identification contribute to biases that affect both the long-baseline oscillation experiments [152] and the short-baseline cross-section experiments [9; 153; 154]. The main source of uncertainty comes from final-state interactions of particles inside the nucleus, undetected neutral particles, and particles produced outside the acceptance of the detector. Two novel techniques have been recently proposed to overcome this limitation: the PRISM concept for long-baseline experiments and the narrow-band off-axis technique for short-baseline experiments. Both techniques exploit some a priori knowledge of the neutrino energy coming from the source to sidestep the use of final state particles and reduce the biases.
# Enhancing Weather Forecasting Integrating LSTM and GA
## 3 Data and Methods
### Dataset  
This work considers weather data collected from two locations: Pinhao town and Quinta de Santa Barbara. Quinta de Santa Barbara is located at coordinates 41.172753\({}^{\circ}\) N, \(-\)7.549335\({}^{\circ}\) W. The data were collected from a sensor located in Quinta de Santa Barbara between September 8 and 30 December 2022, totaling a sampling length of 114 days. However, for a more robust forecast, a larger input dataset is advantageous to improve the validation, stability, generalization, and accuracy of the forecasts. To this end, climate data from the Portuguese Institute for Sea and Atmosphere (IPMA) station, located at coordinates 41.172775\({}^{\circ}\) N, \(-\)7.548972\({}^{\circ}\) W, were also used, as it is in the same geographical area as the study site, as shown in Figure 1. This additional dataset comprises a sample of 4748 days, covering the period from 1 January 2010, to 31 December 2022.  
When analyzing the data from Pinhao, reading errors of the sensor were eliminated for each of the variables studied. Since the data were collected daily, any outlier value for a particular day was replaced with the average value for that day in the other years.  
In turn, the range of values measured at Quinta de Santa Barbara was presented at 15-minute intervals. Thus, for each of the variables studied, the hourly values were averaged, and then, the average of these values was used to obtain the daily values.
## 2 Methods and Materials
### Numerical Experimental Model  
We assumed an imaging area with a lattice system of n = 50, that is, a 50 \(\times\) 50-pixel grid with a spherical head phantom with a diameter of 46 pixels placed within this area. Assuming that the diameter of the head phantom was 20 cm, the actual size of each pixel was 0.432 cm\({}^{2}\). Furthermore, a cancerous lesion (i.e., radiation source) was placed within the phantom. The lesion size was set to 4 \(\times\) 4 pixels (=1.742 cm\({}^{2}\)). Figure 1 shows a case where the cancerous lesion was positioned slightly to the upper left of the center.  
In Figure 1, the black areas represent regions outside the head, the grey areas indicate normal cells, and the white areas denote tumor cells. If the brightness levels of the black and white regions are set to 0 and 1, respectively, the brightness level of the grey areas is 0.31.  
The ratio of boron accumulation in tumor cells to that in normal cells (T/N ratio) was equal to the ratio of the pixel values (source intensity) of tumor cells to that of normal cells; in this system, it was calculated as 1/0.31 (=3.2) [24].  
We then generated pseudo-projection data. With a true image size of 50 \(\times\) 50 pixels, we utilized 50 detectors per projection angle, \(\Delta\theta\), resulting in \(m(90/\Delta\theta)\) projection angles in total. The setup is illustrated in Figure 2. A sinogram was created using the 50\(\times\)\(m\) projection data, and the image was reconstructed using both the proposed and ML-EM methods. Each reconstruction was performed for over 100 iterations. After applying a Gaussian filter [25] to the reconstructed images, we evaluated the performance using the MAE and SSIM.
# Feature Deep Continuous Aggregation for 3D Vehicle Detection
## 4 Experiments and Results
### 3D Vehicle Detection  
In the research regarding 3D object detection, AP is best used to verify the performance of the method because it represents the performance not under a certain confidence threshold, but under all confidence thresholds. In practical application, a certain confidence threshold is usually selected (threshold when the recall is 0.1) to balance the accuracy rate and the false detection rate.  
According to the official performance evaluation protocol of the KITTI 3D object detection benchmark, 3D vehicle detection results were evaluated using the AP\({}_{\text{3D}}\) and AP\({}_{\text{BEV}}\) at a 0.7 IoU threshold for three modes: easy, moderate, and hard.  
We compared our proposed method with other fusion-based methods--MV3D, F-PointNet, and AVOD-FPN--and the comparison results were summarized in Table 1.  
It is shown that our proposed network outperformed all other fusion-based methods in terms of 3D object detection, with a noticeable margin of 0.97% 3D AP on the easy setting, 2.38% on the moderate setting, and 0.78% on the hard setting in comparison to the second-best performing method, AVOD-FPN. Similarly, the performance on orientation (average heading similarity, AHS) had the same tend. It was 1.09%, 2.46%, and 0.85% higher than that of AVOD-FPN for the three settings, respectively. However, for the AP\({}_{\text{BEV}}\), FDCA was not significantly higher than the AVOD-FPN (note that in AVOD-FPN, the performance of the AP\({}_{\text{BEV}}\) is not provided; the results in Table 1 were obtained by running AVOD-FPN). This revealed that our innovations ultimately improved the performance of 3D object detection by improving the accuracy of the height regression. Meanwhile, the location error (average distance) of the detected vehicles was calculated to be 0.163 m. Compared with AVOD-FPN, which had the best performance in real-time, our architecture only slightly increased the runtime by 0.01 s on the GXT 1080 Ti GPU. Figure 12 shows the visual results of AVOD-FPN and our proposed method in both image and 3D point cloud space. It can be seen that the missing vehicle in AVOD-FPN was detected correctly in our architecture and the regression accuracy of the 3D bounding boxes of our architecture was higher than that of AVOD-FPN.
# Three-Dimensional Probabilistic Semi-Explicit Cracking Model for Concrete Structures
## 4 Numerical Results
### Second Step of the Strategy  
Figure 10 showcases surface views of three macrocrack patterns associated with the three meshes during the final stage of the simulation, characterized by an imposed displacement of 1560 \(\upmu\)m. It is apparent that Mesh 3 exhibits a straighter crack pattern with a longer macrocrack length. Table 4 outlines the mean values of the numerical crack lengths corresponding to the three mesh refinements, denoted as \(CL_{NUM}\). These numerical mean values are to be juxtaposed with the experimental ECL, which stands at 2.09 m.  
Analyzing the findings presented in Table 4, the following conclusions emerge: for Mesh 1 and Mesh 2, the optimal configurations are, respectively, S5 (\(\sigma_{L}(G_{IC})=30\times G_{IC}\)) and S6 (\(\sigma_{L}(G_{IC})=40\times G_{IC}\)). In these instances, the numerical crack length consistently appears slightly smaller but remains in close proximity to the equivalent crack length. Conversely, for Mesh 3, drawing a definitive conclusion is less straightforward. As mentioned in Section 3.3, in this scenario, the mean numerical crack length might be larger if the crack  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Mesh 1**} & \multicolumn{2}{c}{**Mesh 2**} & \multicolumn{2}{c}{**Mesh 3**} \\ \cline{2-7}  & **S4** & **S5** & **S5** & **S6** & **S6** & **S7** \\ \hline \(CL_{NUM}\) & 1.975 & 2.011 & 1.97 & 2.026 & 2.075 & 2.113 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the mean numerical crack length (m).  
Figure 10: Comparison of the macrocrack pattern obtained for the three meshes—frontal view.  
length at the specimen's surface surpasses that in its interior. Evidence supporting faster crack propagation at the specimen's surface is evident in Figure 11. This figure provides a comparison between the macrocrack pattern obtained for the two surfaces and a cut view, illustrating the interior of the specimen.  
Considering both Figures 10 and 11, the most suitable configuration for Mesh 3 appears to be S7 (\(\sigma_{L}(G_{IC})=50\times G_{IC}\)). The observations from Table 4 and Figure 11 imply that the macrocrack pattern is better defined with the most refined mesh. This inference aligns with the logical progression that as mesh refinement increases, precision in the information also improves. However, this enhancement in precision comes at the expense of increased computational cost in terms of simulation runtime.
## 5 Results and Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sl. No.** & **Author(s)** & **Publication** & **Approach** & **Dataset** & **Preprocessing** & **Results** \\  & **Year** & & & & & \\ \hline
1. & [PERSON] et al. [63] & 2019 & Two convolutional layers and one dense layer & ENSIST & X & 87.1\% \\ \hline
2. & [PERSON] et al. [64] & 2017 & CNN with dropout and Gabor Filters. & CMATERdb. 3.1.1 & Raw images passed to & 94.78\% \\ \hline
3. & [PERSON] et al. [55] & 2019 & A CNN with 3 convolutional layers and & ENSIST & X & 95.3\% (Letters) \\  & & two capsule layers. & ENSIST & X & 99.77\% (Digits) \\ \hline
4. & [PERSON] et al. [66] & 2019 & Deep convolutional extreme learning machine. & ENSIST Digits & X & 99.77\% (Digits) \\ \hline
5. & [PERSON] et al. [67] & 2018 & Deep belief Network (DEN), Stacked Auto & CMATERdb 3.11 & 600 images are rescaled to 32 \(\times\) & 99.13\% (Digits) \\  & & encoder (AE), DenseNet & & 32 pixels & 98.18\% (Spatial Character) \\ \hline
6. & [PERSON] et al. [68] & 2020 & Three CNN were combined into a single feature & UC Moret, AID, and & & \\  & & map for classification. & NWPU-RESISC45 & X & UC Moret 98.8\% \\ \hline
7. & [PERSON] et al. [69] & 2020 & 1. CNN, 2 Modified Level CNN (MLCNN) and & & & \\  & & 3. Acnet CNN (ACNN). & Own dataset of 38.750 images & X & MLCNN 99.9\% \\ \hline
8. & \
\begin{tabular}{c} [PERSON] and [PERSON] [70] \\ \end{tabular} & 2018 & K-NN classifier and Neural Network & Own dataset with 30 samples &
\begin{tabular}{c} RGB to pay conversion, sheev \\ correction, filtering, \\ morphological generation \\ \end{tabular} & 78.6\% \\ \hline
9. & [PERSON] et al. [51] & 2020 & Introduced an intelligent character recognition & IAM & & \\  & (UG) system & RRIES lexicon & X & 99\% \\ \hline
10. & [PERSON] et al. [71] & 2018 & Convolutional Neural Network (CNN) architecture & 1000-character samples & Resiared all images to & 99.40\% \\ \hline
11. &
\begin{tabular}{c} [PERSON], \\ and [PERSON] [72] \\ \end{tabular} & 2019 & Deep Neural Network (DNNs) & Own dataset of 400 types of pictures & Normalized to 52 \(\times\) 52 pixels. & 93.3\% \\ \hline
12. & [PERSON] et al. [73] & 2019 & 1-D CNN & ICDAR-2013 & Chinese character images & 98.1\% (ICDAR-2013) \\  & & & AS2016 & rescaled into 60 \(\times\) 60 pixel size. & 97.1\% (AMKC-UCAD16) \\ \hline
13. & [PERSON] et al. [45] & 2019 & CNN (5 convolutional layers, 2 max pooling layers, and fully connected layers) & HFL-Tamil-is-o-chur & RGB to pay conversion & 97.7\% \\ \hline
14. & [PERSON] et al. [74] & 2019 & Divide and Mapping (DAMM) & Own dataset with 1.66,105 images & Resiare all images to 128 \(\times\) 128. & 99.13\% \\ \hline
15. & [PERSON] et al. [75] & 2021 & Support vector machine (SVM) classifiers & MNIST, CENPARMI & X & 94\% \\  & & network graphical methods. & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of different learning models’ approaches based on dataset, preprocessing, and accuracy.