esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
Simple methods such as a simple average of the previous four weeks performed quite well based on the relation with previous lags. A load model developed using autoregression methods and a Holt-Winters-Taylor exponential smoothing method tended to outperform other models [20]. With proper analysis, simple and conventional methods can save time as well as computation power while forecasting. In short-term load forecasting, accuracy becomes a crucial factor, since it is used to optimize the day-to-day operational efficiency of electrical power delivery [21]. The forecasting is done on an hourly and day-to-day base prediction of load precision data, which drives the decision on the load and supply for the next day or hour. This prediction method plays a key role for power companies, as they need to develop load plans and short-term strategies in particular, which has attracted considerable attention particularly for building power management to avoid voltage fluctuations and blackouts [22]. The forecasting models vary within short-term load forecasting (STLF) [23] and classify the methods in four categories: similar pattern, variable selection, hierarchical, and weather station selection. It finds the similarities within the data such as the time, day, and weather conditions of the load profile to predict the future load of the specified time, whereas the variable selector selects the most suitable variable within the dataset while they can adequately capture the relationship between the available input data and the output (i.e., electrical demand). The models developed within these concepts identified the effects of the variables on the output, such as monthly effects, daily to weekly effects, weather effects as well as social and local effects. Similarly, the authors discussed the hierarchical methods, such as bottom-top and top-bottom, which showed load studying from the appliance level to the utility level [23]. Forecasting can be done at a different level to decide specific levels that may improve the accuracy of the results. Based on the available data, proper model selection must be done, as a huge dataset does not mean better forecasting, such that the selection of the wrong model for the selected dataset or selection of the wrong dataset can decrease the model's accuracy. Within a variety of models, current research has been conducted to identify the best forecasting model. One study [17] used a combined genetic algorithm (GA) with machine learning to choose the best features to be used for forecasting and finally used long short-term memory (LSTM) to obtain the best predicted output. Likewise, the authors of [7] combined multiple linear regression (MLR) with machine learning to perform short-term load forecasting, considering the MLR model to establish a relation with the input variables.
## 3 Results
### Detection
Table 4 summarizes recent studies, highlighting AI models, data types, and performance metrics used for DFU detection. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref.** & **Year** & **Study Aim** & **Data Type** & **Model Used** & **Code Availability** & **Hyperparameter** & **Training Protocols** & **Evaluation Metrics** \\ \hline \multirow{4}{*}{[60]} & \multirow{4}{*}{2021} & \multirow{4}{*}{Detection of DFUs} & \multirow{4}{*}{Image} & \multicolumn{4}{c}{AdaBoost, MobileNetV2, DenseNet201, ResNet50, InceptionV3} & \multirow{4}{*}{Transfer learning} & \multirow{4}{*}{Transfer learning} & \multirow{4}{*}{1. AdaBoost: 97.75\%} \\  & & & & & & & 80\%, testing: & 2. MobileNetV2: \\ \cline{4-7}  & & & & & & 20\%, & 92.50\% \\ \cline{4-7}  & & & & & & & 5-fold cross- & 3. DenseNet201: 94.01\% \\ \hline \multirow{4}{*}{[62]} & \multirow{4}{*}{2022} & \multirow{4}{*}{Detection of images} & \multirow{4}{*}{Image} & \multirow{4}{*}{DFINET} & \multirow{4}{*}{Not provided} & Learning rate: 0.0001, batch size: 16, epochs: 30, loss function: binary cross-entropy & Training: 70\%, testing: 20\%, testing: 10\% & \multirow{4}{*}{1. DGINET: 92.12\%} \\  & & & & & & & 20\%, & 1. ACoogleNet: 76.39\% \\ \cline{4-7}  & & & & & & 20\%, & & 3. VGG16: 83.54\% \\ \cline{4-7}  & & & & & & 10\% & 4. AlexNet: 77.54\% \\ \hline \multirow{4}{*}{[22]} & \multirow{4}{*}{2023} & \multirow{4}{*}{Detection of DFUs} & \multirow{4}{*}{Image} & \multirow{4}{*}{EfficientNet} & \multirow{4}{*}{Not provided} & EfficientNet (depth, width, resolution) & Training: 60\%, validation: 20\%, testing: 20\% & \multirow{4}{*}{1. OurSG16: 91.0\%} \\  & & & & & & 20\%, & & 3. DFUNet: 93.3\% \\ \cline{4-7}  & & & & & & 20\% & & 4. GoogleNet: 93.0\% \\ \cline{4-7}  & & & & & & & 5. EfficientNet: 99.0\% \\ \hline \multirow{4}{*}{[63]} & \multirow{4}{*}{2024} & \multirow{4}{*}{Detection and localizing of DFUs} & \multirow{4}{*}{Image} & YOLOw8m and Faster R-CNN & \multirow{4}{*}{Not provided} & Learning rate: 0.001, epochs: 100–150 & \multirow{4}{*}{10\%, testing: 10\%, transfer learning} & \multirow{4}{*}{1 F1 score: 0.780} \\  & & & & & & & 10\%, & \\ \cline{4-7}  & & & & & & & 10\%, & \\ \cline{4-7}  & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of AI-based detection models for DFU.
# Lithium-Ion Battery Capacity Prediction with GA-Optimized CNN, RNN, and BP
###### Abstract
Optimizing these parameters is crucial because it directly impacts the model's ability to accurately predict battery health and lifespan. Traditional methods of parameter selection can be time-consuming and may not explore the full potential of the model's architecture. To address this, the use of a genetic algorithm (GA) is proposed for parameter optimization. Genetic algorithms are powerful search heuristics that mimic the process of natural selection, effectively exploring a large parameter space to identify the most suitable configurations for the models. Therefore, optimizing these parameters using a genetic algorithm can improve the efficiency and accuracy of the models. In this study, the parameters of the CNN, RNN, and BP algorithms, which are among the models used in prior studies, were optimized using a genetic algorithm, resulting in improved prediction accuracy. Consequently, the following contributions were acquired. 1. Accuracy is enhanced using the most optimal parameters, rather than random or standard parameters, when establishing the parameters of the algorithms via the genetic algorithm. 2. A total of four features are extracted for charging and discharging times. Using a relatively small number of features compared to the literature, the processing load and potential error situations are reduced. The resulting high performance has increased the importance of this process. 3. Capacity prediction is conducted to estimate the capacity of the battery at each specific location. Consequently, these data provide information on both battery health and remaining battery life. 4. Compared to the literature, accurate results are produced with fewer data, validating the usefulness and correctness of the approach. The succeeding parts of the paper will be structured as follows. Section 2 outlines the general flow of the investigation, including the methodology, data information, and feature extraction. The algorithms utilized in the research are presented in Section 3. The results are provided in Section 4. Conclusions and future work are addressed in Section 5.
# Self-Organizing Control of Mega Constellations for Continuous Earth Observation
## 4 Conclusions  
In view of the numerous satellites in mega constellations, this paper innovatively introduces the self-organizing concept of mega constellation control. The emphasis is on realizing continuous Earth observation within the designed latitude zones so that the combination of multiple sub-constellations can provide global remote sensing services. A novel continuous coverage constraint has been presented that relates to intersatellite motion only, including the RAAN drift range and relative motion-bound drift range between two adjacent coplanar satellites. The configuration requirements for mega constellations have been studied, and most ongoing or planned mega constellations satisfy the proposed coverage constraint. Considering RAAN, the relative motion-bound and semi-major axis constraints, the corresponding artificial potential functions as well as the self-organizing rules have been designed. As a highlight, the scale-independent relative orbital elements are used as control variables in mega constellation control because these elements have high precision for near-circular orbits and arbitrary intersatellite ranges. The stability of satellite control using quadratic artificial potential functions has been proven. One key finding is that if the time derivative of the control variable is the same as that of the expected value under two-body Keplerian motion condition, the corresponding quadratic artificial potential function is stable. The self-organizing control simulations of coplanar satellites and the GW-2 sub-constellation have been carried out. The simulation results indicate the goodness of control effect for the small single-step velocity increment and low accumulated control consumption, and the mega constellation satisfies continuous coverage under control.  
Conceptualization: Y.X., Y.Z. and, Z.W.; funding acquisition: Z.W. and L.F.; Methodology: Y.X.; validation verification: Y.X. and Y.H.; writing--original draft: Y.X.; writing--review and editing: Y.X. and Y.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China of funder grant number No. U20B2056 and the Huzhou Distinguished Scholar Program of funder grant number ZJIH-KY0016. The APC was funded by Zhejiang University.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 28: Multiplicity of coverage of the GW-2 sub-constellation at the initial and final moments.
# Retrieval of Biophysical Crop Variables from Multi-Angular Canopy Spectroscopy
## Results
### Improved Look-Up-Table Inversions  
Different techniques were explored to find an optimal setting for the inversion of the PROSAIL RTM. At first, the number of best fits (n\({}_{\text{bf}}\)) was fixed to 100 and artificial noise was added to the modelled spectra (Table 5). For the LAI retrieval, performance was better, the lower the level of additive noise was assumed (rRMSE = 0.19 for \(\sigma\) = 0.0%). LCC on the other hand could be better retrieved with noise levels of up to 5% (best rRMSE = 0.27). Inverse multiplicative noise created similar results that were almost independent of the absolute level of \(\sigma\). Interestingly, in contrast to LCC, for LAI inverse multiplicative noise yielded slightly better results than the additive noise type. A parameter-specific tendency can be assumed. Therefore, by default an inverse multiplicative noise level of \(\sigma\) = 2.0% was used for the first and additive noise of the same level for the second inversion run for all analyses.  
Figure 8: Canopy Chlorophyll Content, as a multiplication of LAI and LCC, combines the performance of the two underlying parameters. Results are shown for nadir (**a**), backscatter (**b**) and forward scatter (**c**) observations. The slope of the regression line is indicated as \(m\).  
In the second experiment, \(\mathrm{n_{bf}}\) and the cost function type (cft) were varied (Table 6). For LAI, the overall best result was achieved for MAE and \(50\leq\mathrm{n_{bf}}\leq 200\) (rRMSE = 0.18). The MAE generated 3% lower rRMSE in average. NSE as a cost function did not pass the slope threshold test for any of the experiments and was omitted in the analysis of the results. Also, for LCC, the choice of a merit function seemed to be of minor importance in comparison to the number of best fits taken into account for the retrieval. The more LUT members were considered, the better the performance in terms of rRMSE at the expense of precision at the extreme ranges, leading to a reduced regression slope. RMSE\({}_{\mathrm{cft}}\) performed slightly better than MAE, again suggesting a parameter-specific behavior of the LUT inversion.
# Smart Antenna for Application in UAVs
## 4 Control System Development
### Control Algorithm  
The P-ESPAR antenna control system was delineated by a set of criteria. These criteria were established by the needs that the communication determines according to the flight route of the UAV. In this way, the algorithm defines, by degree of priority, the variation of the wolf and finally the change of the route of the UAV.  
The behavior of the P-ESPAR antenna was one of the main criteria of the algorithm. In a first phase, it was necessary to realize the capacity of the lobe to change the antenna radiation to perceive how necessary it would be to influence the route of the UAV. However, the orientation and inclination of the UAV according to the various axes are also very important factors to be considered by the algorithm of the control system.  
The procedures related to the algorithm mechanism are presented in the flowchart in Figure 17, with the several steps described below.  
1. Control system communicates with the central processor of the UAV to request the co-ordinates of the base station;
2. after receiving the co-ordinates of the base station, the control system again communicates with the central processor of the UAV and requests the current co-ordinates, the inclination, and the height of the UAV;
3. after receiving current co-ordinates, the control-system algorithm calculates the distance between the UAV and the base station;
4. if the distance is less than or equal to 40 km, the control system selects the slope that least interferes with the route of the UAV and calculates the minimum variations required for its route;
5. the control system communicates with the central processor of the UAV, sending information about its orientation and positioning. According to the base station, the UAV must be perfectly perpendicular and parallel to the ground. It also sends information on changing the slope of the UAV;
6. after changing the route according to the communication needs, the algorithm checks the combination of capacities relative to the angle of the selected radiation lobe, and the control system communicates with the digital potentiometer to inject to the terminals of the diodes the corresponding voltages;
7. after completing the previous steps, the necessary conditions for the transmission of 10 s are fulfilled. At the end of the transmission, we return to Step (2).
# Nanosheets of CuCo\({}_{2}\)O\({}_{4}\) As a High-Performance Electrocatalyst in Urea Oxidation
## 3 Results and Discussion  
### Structural Characterization  
X-ray diffraction analysis confirmed a high phase purity of the synthesized CuCo\({}_{2}\)O\({}_{4}\), as seen in Figure 2a. All of the diffraction peaks matched well with the JCPDS card no. 78-2177, which corresponded to the pure cubic spinel phase of CuCo\({}_{2}\)O\({}_{4}\) with a lattice parameter value of a = 8.128 A [16; 17]. Scanning electron microscopy was employed to investigate the sample morphologically. High-magnification SEM images confirmed that CuCo\({}_{2}\)O\({}_{4}\) was uniformly grown on Ni foam, with a flower-shaped structure composed of nanosheets in different orientations and a smooth surface, revealing a three-dimensional porous structure (Figure 2b). This uniform flower-shaped structure may have been the result of the Faradic reaction. The average diameter of the flower structure was about 5 \(\upmu\)m, and it was interconnected with the nanosheets that had a thickness of 160 nm. TEM images of CuCo\({}_{2}\)O\({}_{4}\) powder scraped from the Ni foam at various magnifications are shown in Figure 3. TEM images reveal nanorod-like structures with good crystallinity. X-ray photoelectron spectroscopy of the CuCo\({}_{2}\)O\({}_{4}\) is shown in Figure 3. The XPS survey spectra confirmed the presence of copper, cobalt, oxygen, and carbon elements (Figure 4a). The high-resolution XPS spectrum for Cu 2p showed the existence of 2p\({}_{1/2}\) and 2p\({}_{3/2}\) peaks of copper (Figure 4b) [8; 18]. The high-resolution XPS spectrum for Co 2p is shown in Figure 4c. The two main peaks of Co 2p\({}_{3/2}\) and Co 2p\({}_{1/2}\) were observed around 779.6 and 794.7 eV, respectively, suggesting the existence of both Co\({}^{2+}\) and Co\({}^{3+}\)[8; 18]. Considering the Co 2p peak shape, this was consistent with spectra of Co\({}_{3}\)O\({}_{4}\) standards given by [PERSON] et al. [19] and [PERSON] et al. [20]. These studies showed a 2p\({}_{3/2}\) peak binding energy of 779.9 eV and a broad plateau-like satellite structure to higher binding energies between the 2p\({}_{3/2}\) and 2p\({}_{1/2}\) peaks. The high-resolution spectrum for O 1s is shown in Figure 4d, which shows two peaks centered at 529.4 and 531.3 eV,  
Figure 1: Illustration of the formation of CuCo\({}_{2}\)O\({}_{4}\) on Ni foam.  
respectively. The peak around 529.4 eV corresponded to metal-oxygen bonding, while the second peak corresponded to a high number of defect sites with low oxygen coordination in the material with a small particle size [21].  
Figure 4: _Cont._  
Figure 3: TEM images of CuCo\({}_{2}\)O\({}_{4}\) at various magnifications.  
Figure 2: (**a**) XRD pattern and (**b**) SEM image of CuCo\({}_{2}\)O\({}_{4}\).
# LDPC Codes on Balanced Incomplete Block Designs: Construction, Girth, and Cycle Structure Analysis
## 3 Girth and Cycle Structure Analysis of BIBD-LDPC Codes
### A Method for Counting Short Cycles of BIBD-LDPC Codes
#### 3.3.2 Number of Cycles of Length 8
Thus, both \(P_{21}\) and \(P_{22}\) are zero, and the total number of invalid cases (\(P_{2}\)) is  
\[P_{21} =\frac{v(v-1)}{\kappa(\kappa-1)}\times\frac{\kappa(\kappa-1)(\kappa -2)(\kappa-3)}{4!}\]1  
Hence, the number of cycles of length \(8\) (\(N_{8}\)) for \(\kappa=2\) is  
\[P_{21} =\frac{v(v-1)}{\kappa(\kappa-1)}\times\frac{\kappa(\kappa-1)(\kappa -2)(\kappa-3)}{4!}\]2 \[P_{21} =\frac{v(v-1)}{\kappa(\kappa-1)}\times\frac{\kappa(\kappa-1)(\kappa -2)(\kappa-3)}{4!}\]3  
Based on the above analysis, it can be observed that for \(\kappa=2\) and \(\kappa=3\), the number of cycles of length \(8\) (\(N_{8}\)) is consistent with the expression for \(N_{8}\) derived in (4) with \(\kappa\geq 4\). Thus, the general expression for \(N_{8}\) in (4) remains valid for \(\kappa=2\) and \(\kappa=3\), demonstrating its generality across different block sizes. This confirms that the cycle-counting method is robust and applicable to a wide range of BIBD-LDPC codes.
# Large-Scale Multimodal Piano Music Identification Using Marketplace Fingerprinting +
## 3 Results
### Experimental Setup  
We perform experiments on four different retrieval tasks. Below, we describe the data used in our experiments and the evaluation methodology.  
All four tasks use the same searchable database. The database consists of all solo piano sheet music in IMSLP. The database has 31,384 PDFs, 29,310 unique pieces, and 374,758 sheet music pages. This is the same database as [28].  
The first retrieval task uses cell phone pictures of physical sheet music as queries. We use the Sheet-MIDI Retrieval dataset [26]. This dataset contains 2000 different cell-phone images from 25 different composers and 200 different piano pieces. The cell phone images are taken of physical pages of piano sheet music placed in various locations. There are 40 train and 160 test pieces. The dataset includes 10 images per piece, resulting in 400 train queries and 1600 test queries. We use the same train/test split as in [33].  
The second retrieval task uses scanned sheet music images as queries. This dataset is generated from the corresponding PDFs from the Sheet-MIDI Retrieval dataset. Each PDF page is converted to an image and used as a query. The PDFs are converted to PNG files at 300 dpi and resized so that the width of the image is 2550 pixels. The train queries consist of 217 single page scans from the 40 train pieces, and the test queries consist of 863 single page scans from the 160 test pieces.  
The third retrieval task uses MIDI files as queries. The data is taken from the MAESTRO dataset [44]. This dataset consists of audio and MIDI pairs. The audio files are recordings from a piano competition where the pianos were equipped to record MIDI note events. For each (audio, MIDI) pair, we manually found the matching piece in the IMSLP dataset. There are a total of 1276 MIDI files, and we use 20% (256) of the MIDI files for training and 80% (1020) for testing. From each MIDI file, we take \(K=5\) random samples of duration \(L\) seconds. We consider \(L=10,20,30,40,50,60\). The end result is a collection of 6 datasets, one for each value of \(L\).  
The fourth retrieval task uses audio recordings as queries. The audio dataset is also taken from the MAESTRO dataset and is prepared similarly to the MIDI data. There are a total of 1276 audio files, and we use 20% (256) of the audio files for training and 80%(1020) for testing. From each audio file, we take \(K=5\) samples of duration \(L\) seconds. We consider \(L=10,20,30,40,50,60\).  
All tasks are evaluated along two different axes: retrieval accuracy and runtime. Each query matches a single unique piece in the IMSLP database. Therefore, we evaluate retrieval accuracy using mean reciprocal rank (MRR). The MRR is calculated as \(\text{MRR}=\frac{1}{N}\sum\limits_{i=1}^{N}\frac{1}{R_{i}}\), where \(R_{i}\) indicates the rank of the true matching item for the \(i^{th}\) query and \(N\) indicates the total number of queries. We also measure the runtime required to process each query and report the average and standard deviation of query runtimes.
## 3 Results and Discussion
### Research on the ROI of Hyperspectral Image Data  
The regional growing methods were used to select a rapeseed image by EVNI 4.5, which was primarily conducted by combining pixels with similar properties [36]. First, a seed point should be designated as the starting point of growth, and the pixels in the area around the seed point should be compared with the seed point. Then, we combined the four similarity points, which continued to grow outward until the pixels did not meet the conditions. After identifying the region of similar spectral characteristics, the average spectrum was calculated as the average of the spectra of all pixels enclosed in this region. In this work, a four-neighborhood region-growing algorithm was used to mask the image. The ROI and spectral data were shown in Figure 3.  
Figure 3c showed the average spectrum. It was reported that the wavelengths of fat were 1210 nm and 1715-1750 nm, which was associated with the third overtone -CH stretch. The protein was related to the wavelengths of 2052 nm and 2300 nm and corresponded to the N-H functional groups [37]. Meanwhile, erucic acid ranged from 1333-1837 nm and glucosinolates were 1333-1837 nm and 2173-2355 nm [38]. This study demonstrated that the species, variety, and production area of samples were the important factors in NIR analysis, which will increase the heterogeneity of the spectrum and improve the robustness of the calibration model [37].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & **Group** & **Number** & **Min.** & **Mean** & **Max.** \\ \hline \multirow{2}{*}{Crude protein content} & Training set & 120 & 18.66 & 24.29 & 29.60 \\  & Test set & 30 & 18.86 & 24.78 & 29.12 \\ \hline \multirow{2}{*}{Oil content} & Training set & 120 & 33.24 & 42.15 & 49.37 \\  & Test set & 30 & 34.54 & 43.32 & 49.11 \\ \hline \multirow{2}{*}{Erucic acid} & Training set & 120 & 0.10 & 16.37 & 50.20 \\  & Test set & 30 & 0.10 & 15.40 & 47.87 \\ \hline \multirow{2}{*}{Glucosinolates} & Training set & 120 & 14.63 & 68.97 & 168.96 \\  & Test set & 30 & 15.87 & 71.43 & 155.54 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive statistics for crude protein content, oil content, erucic acid content, and glucosinolate content.