esa-sceva/satcom-chunk-collection huggingface dataset:
## Data Processing  
### Input Data  
Input data for the identification system consist of digital images and parameters from the radar. The parameters from the radar are real numbers such as velocity of a flying bird in m/s and bearing (i.e., a heading: the horizontal angle between the direction of an object and that of true north) in degrees. All images for training the CNN are of wild birds in flight and they have been taken manually at the test location. There are also constraints concerning the area where the images have to be taken. Here, the area refers to the air space in the vicinity of the pilot wind turbine. We have used the wind turbine swept area (the diameter of the swept area is 130 m) as a suitable altitude level constraint for taking the images, because birds flying below or above the swept area are not in danger. At this stage, the images are only taken in the vicinity of 1350 m in lengthwise direction, which is the distance to the pilot wind turbine. There are 1164 images for each class and the number of classes is 8, thus the original training set size is 8\(\times\)1164 = 9312. We applied data augmentation as it is well-known method to increase performance of an image classifier. In addition, the original (i.e., not augmented) data set includes plenty of data examples of images with various portion of cloudiness as the background and also with clear sky as the background.  
The number of images of each class should be the same as a CNN is applied [12] and therefore the lowest number of images of the classes is used. The number of classes (which includes both key species) is 8 at this phase. The eight classes for training the CNN are the Common Goldeneye (_Bucephala clangula_), the White-tailed Eagle (_Haliaeetus albicilla_), the Herring Gull (_Larus argentatus_), the Common Gull (_Larus canvas_, the Lesser Black-backed Gull (_Larus fuscus fuscus_), the Black-headed Gull (_Larus ridibundus_, the Great Cormorant (_Philacrocorax carbo_) and Common/Arctic Tern (_Sterna hirundo/paradiseae_).
## 3 Proposed Methodology
### Transfer Learning Process
#### 3.2.1 Pretrained CNNs  
As presented in the previous section, transfer learning refers to the reuse of pretrained CNN architectures on a large dataset. In this study, we have analyzed the efficiency of four well-known CNN architectures, namely, AlexNet [9], GoogleNet [31], ResNet18 [32], and VGG16 [33] regarding classification precision and training time, as you can see below:  
* AlexNet: In 2012, AlexNet could outperform other prior architectures in ImageNet LSVRC-2012 competition, designed by the SuperVision group [9]. AlexNet includes five convolutional layers and three FC layers in which Relu is applied after every convolutional and FC layer. In addition, the dropout technique is applied before the first and the second FC layer [9].
* GoogleNet: GoogleNet won ILSVRC 2014 competition with a high precision close to human perception. Its architecture has taken benefits of several small convolutions to reduce the number of parameters drastically. It consists of a 22-layer deep CNN, but it decreased the number of parameters from 60 million (in AlexNet) to 4 million [31].
* VGG: Visual Geometry Group (VGG) is a CNN proposed by the University of Oxford [33] to improve AlexNet by replacing large kernel-sized filters with multiple 3 by 3 kernel-sized filters one after another. VGG16 was trained for weeks and was using NVIDIA Titan Black GPU [33].
* ResNet: Residual Neural Network (ResNet) presented an outstanding performance in ILSRVC 2015 [32]. The Residual network directly copies the input matrix to the second transformation output and sums up the output in final ReLU function [32].  
It should be taken into account that the output of the last layer of the convolutional structure before the classification layer has been used as the feature set for the designed classification; \"fc8\" for AlexNet and VGG16, \"loss3-classifier,\" and \"fc1000\" for GoogleNet and ResNet18, respectively. Notably, the input image size for AlexNet is 227 by 227 and 224 by 224 for the three other CNNs.
## 4 Simulation Study
### Effect of the Number of Pedestrians  
In this subsection, we present the simulation outcomes that illustrate the correlation between the number of pedestrians and the efficacy of the surveillance system, as indicated by the detection time \(T_{suc}\), out-of-range time \(T_{fail}^{out}\) and direction error time \(T_{fail}^{dir}\).  
In the simulation scenario, the number of pedestrians observed ranged from 10 to 500, whereas the number of surveillance cameras deployed was 10, 100, 200, 300, 400, or 500.  
As demonstrated in Figure 6a, the detected time \(T_{suc}\) is shown across various numbers of surveillance cameras for multiple counts of pedestrians ranging from 10 to 500. Upon investigation, it was established that the detected time \(T_{suc}\) exhibited similar values even as the number of pedestrians increased. This finding reaffirms the notion that the number or  
Figure 5: Performance evaluation of the surveillance system in terms of \(T_{suc}\), \(T_{fail}^{out}\), and \(T_{fail}^{dir}\) as the number of surveillance cameras is increased from 10 to 500.  
density of surveillance cameras serves as the primary influencer of performance in terms of \(T_{suc}\), not the number of pedestrians.  
To illustrate, the detected time ranged from 7.7 to 8.3 s, 182.1 to 183.7 s, and 253.5 to 258.4 s, respectively, when the number of surveillance cameras was 10, 300, and 500. Furthermore, it is important to note that the standard deviation values of the detected time were only 0.2, 0.7, and 1.3 s, respectively, when the number of surveillance cameras was 10, 300, and 500 respectively.  
Figure 6: Performance analysis of the surveillance system according to various number of pedestrians in terms of (**a**) detected time, (**b**) out of range time, and (**c**) direction error time.  
The results for the additional performance metrics, \(T_{fail}^{out}\) and \(T_{fail}^{dir}\), exhibit similarities. In Figure 6b,c, we present the out-of-range time \(T_{fail}^{out}\) and the direction error time \(T_{fail}^{dir}\) in relation to various numbers of surveillance cameras and multiple numbers of pedestrians ranging from 10 to 500.  
For instance, when there were 10, 300, and 500 surveillance cameras, the out-of-range time ranged from 591.7 to 592.3 s, 416.3 to 417.2 s, and 341.6 to 346.5 s, respectively. Furthermore, the standard deviation values of the out-of-range time were only 0.2, 0.5, and 1.3 s, respectively, when there were 10, 300, and 500 surveillance cameras.  
The direction error time was observed to range from 45.9 to 47.5 s, 371.6 to 373.8 s, and 332.2 to 338.4 s, respectively, when the surveillance camera count was 10, 300, and 500. The standard deviation values of the recorded time were only 0.5, 0.6, and 1.4 s, respectively, for the same surveillance camera counts.  
In conclusion, it can be inferred that the number of pedestrians does not significantly influence the performance of the surveillance system in terms of \(T_{suc}\), \(T_{fail}^{out}\), and \(T_{fail}^{dir}\). Therefore, for subsequent simulations in this paper, the number of pedestrians was fixed at 100, and its effect is no longer considered.
# Hybrid Empirical and Variational Mode Decomposition of Vibratory Signals
## 4 Cases Studies
### Case 1: Estimation of Vibratory Components Using EMD, VMD, and the Hybrid EMD-VMD Method
The inclusion of average values in the table facilitates an intuitive understanding of the general performance and effectiveness of each method across different metrics. This structured presentation not only aids in assessing the relative strengths and limitations of each technique but also provides a solid foundation for further discussion and analysis. By comparing the aggregated results, readers can discern patterns and the efficacy levels of each strategy, contributing to more informed decisions in future applications of these methods in similar contexts. Figure 12: Comparison of estimated amplitudes of the harmonic vibratory components \(\widehat{F}_{i}\) between EMD, VMD, and the hybrid method EMD–VMD. To strengthen the results and the performance of the method, the mean squared error (MSE) was used as a measure of accuracy. The MSE quantifies the average of the squares of the errors--that is, the average squared difference between the estimated values and the reference values. The formula for calculating MSE is given by  
\[\text{MSE}=\frac{1}{n}\sum_{i=1}^{n}(\text{Reference}_{i}-\text{Method}_{i})^{2} \tag{1}\]  
where \(\text{Reference}_{i}\) is the reference value, \(\text{Method}_{i}\) is the estimated value from each method, and \(n\) is the number of observations. The MSE values, comparing the estimates from each method (EMD, VMD, and Hybrid EMD-VMD) to the reference values, are summarized in Table 3. These results offer insights  
Figure 13: Comparison of estimated frequencies of the harmonic vibratory components \(\widehat{\Omega}_{i}\) between EMD, VMD, and the hybrid method EMD–VMD. into the relative accuracy and effectiveness of the decomposition techniques applied in this study. The MSE values indicate the degree of deviation of the estimated frequencies and amplitudes from the reference values. Lower MSE values represent higher accuracy of the decomposition method. The hybrid method consistently shows the lowest MSE values across all components, demonstrating its superior performance in accurately extracting the oscillatory components compared to the EMD and VMD methods.
# Hashcash Tree, a Data Structure to Mitigate Denial-of-Service Attacks
## 7 Conclusions  
Hashcash trees combine features of the hashcash algorithm with those of hash trees. Labels are obtained by running the hashcash algorithm and therefore are moderately hard to compute (exponential on the length of the prefix) and easy to verify (one hash value computation). Labels of internal nodes depend on child nodes, and therefore parallel computation is limited. Moreover, the root is a commitment for the tree, which does not need to be fully transmitted to the verifier. In fact, the verification involves a logarithmic number of nodes because the selected leaf is known to the prover only after disclosing the commitment. The associated client puzzle protocol relies on two parameters for controlling the prover's effort. The number of computed hash values grows exponentially on the prefix length and linearly on the size of the hashcash tree. The empirical analysis suggests that the prefix length can be fixed to 4 or 5, with hashcash trees of size between 127 and 4095, to generate puzzles solvable in a few seconds. Improving parallel computation resistance is an interesting future line of research and will require the introduction of synchronization points in the computation of labels at the same level of the tree. Other future lines of research include the definition of a (one-phase) challenge-response protocol, for example, by self-imposing the selected leaf based on the hash value of the root node, and the definition of a solution-verification protocol, constructing the hashcash tree based on publicly-available data from the server.  
**Funding:** This research was partially funded by Italian Ministry of Research (MUR) under PNRR project FAIR \"Future AI Research\", CUP H23C22000860006, under PNRR project Tech4 You \"Technologies for climate change adaptation and quality of life improvement\", CUP H23C22000370006, and under PNRR project SERICS \"SEcurity and RIghts in the CyberSpace\", CUP H73C2200088001; by Italian Ministry of Health (MSAL) under POS project RADIOAMICA, CUP H53C22000650006; by the LAIA lab (part of the SILA labs), and by GNCS-INdAM.  
Data Availability Statement: No new data were created or analyzed in this study. Data sharing is not applicable to this article.
## 2 Materials and Methods
### Video Content Analysis  
As there was no available checklist evaluating the quality of online video content on MADs, a customized MAD scoring scheme was a priori developed specifically for this study (Figure 1). The aim of that checklist was to assess videos on the presence of content domains and comprehensiveness. The specific criteria included: (1) a definition of MAD, (2) indications about the use of MAD, (3) contraindications about the use of MAD, (4) instructions on the usage of MAD, (5) adverse effects of MAD, (6) comparison of treatment alternatives (CPAP, surgery, etc.), (7) the need to consult a dentist qualified in dental sleep medicine, (8) the diagnostic and therapeutic process of OSA described as a multidisciplinary care team endeavor, and (9) the cost and duration of therapy with MAD. The presence of any individual component was equated to the score of 1, whereas its absence was equated to the score of 0. Based on the presence or absence of information, a video could reach a score between 0 and 9. We categorized the videos into three levels of usefulness based on the scores: 'useful' (3+),'moderately useful' (1-2) and 'not useful' (0).
# Histogram-Based Descriptor Subset Selection for Visual Recognition of Industrial Parts
## 1 Introduction  
Computer vision, in the last years, has gained much interest in many fields, such as autonomous driving [1], medical [2], face recognition [3], object detection [4], and object segmentation [5]. Perception is also regarded as one of the key enabling technologies for extending the robot capabilities, preferentially targeting flexibility, adaptation, and robustness, as required for fulfilling the industry 4.0 paradigm [6]. Although in most fields large and complex datasets can be obtained, detection of industrial parts has a lack of datasets. One of the reasons is that most of the time in industrial context, the aim is to detect an object from which usually the CAD is available. However, sometimes there is a need of detecting diverse, complex, and tiny objects [7] and lack of time to generate a robust dataset (taking pictures and labeling). One of the solutions is to generate simulated data to train the models but usually there is a significant gap transferring that learned knowledge to reality.  
To make matter worse, industrial parts are usually texture-less. This means that many of the most used recognition methods cannot deal with them. One of the methods to deal with texture-less objects are Convolutional Neural Networks. Nowadays, computer vision researches are mainly focused on using Convolutional Neural Networks (CNN) [8; 9; 10]. One of the disadvantages of the CNNs is the need of a large dataset to train them. Even if it is possible to use the CNN trained on other fields in industry [11], there is still a need of a large enough training dataset to obtain good results. Feature descriptors based on classical methods have been very useful and thoroughly spread in the literature previous to CNN. One of the benefits of using this approach is that there is no need of a large training set to obtain good results. Actually, there are many image descriptors and each of them has its advantages and disadvantages.  
Our approach is based in the idea that the combination of different descriptors leads to a better performance, taking advantage of the benefits of each descriptor to deal with the two problems mentioned before (lack of a large dataset and texture-less objects). The crux of the matter is to select the descriptors that contribute to achieve a better result and discard those that do not provide any improvement. Our method achieves a classification quality similar to state-of-the-art methods on the experiments done.  
In Section 2, we present a background of the description methods, classifiers, and features subset selection techniques. In Section 3, we explain the combination of the descriptors and the image classification. The experiments done and their results are gathered in Section 4. Finally, in Section 5, the conclusions are summarized.
# Metal Additive Manufacturing for Satellites and Rockets
## 6 Antennas  
Telecommunication satellites need to enable high data transmission rates, resulting in the necessity to have large bandwidth and high power levels, which require special antenna designs with many horns per antenna. These complex design requirements suggest the use of additive manufacturing. [PERSON] et al. compared conventional and additive manufacturing of waveguides and radio frequency (RF) components, combining different components to reduce mass and power loss [94]. They found elliptical cross-sections to be advantageous for waveguides, with lower losses as compared to conventional rectangular waveguides. While linear polarization showed good antenna quality, the accuracy of circularly polarized waveguide components has to be further increased.  
[PERSON] et al. used direct metal laser sintering of AlSi\({}_{10}\)Mg to prepare antenna feed arrays for the Ka band based on high-efficiency horns, which are typically used as feed elements in high-throughput satellites using multibeam antennas [95]. The antenna feed array constructed according to typical limitations of 3D printing, such as reduced use of support that can easily be removed by machining, rounding off sharp corners, etc., is depicted in Figure 8, mounted on a plate for tests in an anechoic chamber. The authors successfully performed RF tests at 27-30 GHz and found a first natural frequency of 502 Hz, which is in the correct range for space payload systems. Furthermore, mechanical tests showed adequate results, making such antenna feed arrays suitable for communication satellites.  
Figure 8: Direct metal laser sintered antenna feed array in an anechoic chamber. Reprinted from [95], with permission from Elsevier.  
Instead of antennas, [PERSON] et al. investigated additively manufactured fifth- and sixth-order waveguide filters for the Ku/K band [96]. They compared filters prepared by selective laser melting of steel 1.2709 with silver plating, Ti\({}_{6}\)Al\({}_{4}\)V with silver plating, and the usual AlSi\({}_{10}\)Mg with an SLA printed polymer with copper plating and found the latter be problematic due to the adhesion between metal plating and polymer base. All the prototypes, on the other side, showed sufficient RF reflection and transmission in the pass and stop bands, respectively, with the aluminum based waveguide filter showing the lowest insertion loss. Similarly, the group optimized dual-polarization waveguide components for the Ka-band, prepared by selective laser melting [97].  
Other research groups concentrated on metamaterial filters for the K-band [98], waveguide antennas for the Ku band [99; 100] or other bands [101; 102]. Generally, here the mechanical and thermal requirements are less rigorous in comparison with the aforementioned applications; thus, most studies are based on state-of-the-art metal additive manufacturing, taking into account design for the respective technique and possibly a postprocessing step to avoid too high roughness. Because of these reduced requirements, additively manufactured antennas can be expected to be used in the near future.
# Long-Term Forecasting of Electrical Loads in Kuwait Using Prophet and Holt-Winters Models
## 3 Results and Discussion
### Model Accuracy
The Holt-Winters model showed a generally increasing trend of MAPE values with the increase in the forecast horizon. These results are evidence that both models exhibit a great extent of accuracy in long-term energy forecasting. Figure 7: The coefficient of determination R\({}^{2}\) and the fitting characteristics of real and simulated data for both models.
## 3 Results and Discussion
### Application of Pectin-Based Electrospun Film in Multilayers
Figure 9: Visual aspect of the films based on pectin and poly(3-hydroxybutyrate-\(co\)-3-hydroxyvalerate) (PHBV): (**a**) PHBV/PHBV multilayer; (**b**) PHBV/solvent-casted pectin/PHBV; (**c**) PHBV/electrospun pectin/PHBV. Finally, the permeance of the multilayer films of PHBV/PHBV, PHBV/electrospun pectin/PHBV, and PHBV/solvent-casted pectin/PHBV multilayer films to water and limonene vapors was measured. Permeance is the permeability expression with elimination of the thickness factor used to ascertain the barrier of multilayer structures. On account of comparing the permeance values of multilayer films, thicknesses of the inner and outer layers remained in the same range. Table 6 shows the water and limonene permeance values and the thickness of each layer and the whole structure. In terms of water vapor, the PHBV/PHBV and PHBV/solvent-casted pectin/PHBV multilayer films exhibited somewhat higher permeance values, that is, 5.00 \(\pm\) 0.83 \(\times\) 10\({}^{-10}\) kg\(\cdot\)m\({}^{-2}\cdot\)Pa\({}^{-1}\cdot\)s\({}^{-1}\) and 3.94 \(\pm\) 0.56 \(\times\) 10\({}^{-10}\) kg\(\cdot\)m\({}^{-2}\cdot\)Pa\({}^{-1}\cdot\)s\({}^{-1}\), respectively, than the PHBV/electrospun pectin/PHBV multilayers, that is, 1.75 \(\pm\) 0.14 \(\times\) 10\({}^{-10}\) kg\(\cdot\)m\({}^{-2}\cdot\)Pa\({}^{-1}\cdot\)s\({}^{-1}\). As a result, the incorporation of the cast but more in particular of the electrospun pectin interlayer into PHBV provided enhanced barrier properties against water vapor, even though pectin has intrinsically a hydrophilic character. The reason why the cast film did not have comparable barrier performance to the electrospun film may be ascribed to the lower adhesion at the interphase of the cast material and also to the fact that the casting usually results in less dense film materials due to the slow forming process. Limonene permeance analysis was also carried out since this organic vapor is often used as an standard for aroma barrier and is also used as an indication for gas barrier properties. From the results, one can observe that the multilayers containing the pectin-based interlayers significantly reduced the limonene permeance, having both the electrospun and solution-casted pectin films the same performance. Limonene permeability is strongly governed by solubility in PHA. Indeed, solubility of limonene in PHBV is relatively high and 100-\(\upmu\)m PHBV films can uptake up to 12.7 wt% of limonene [79]. Therefore, it can be considered that the presence of the high barrier to organic vapors, pectin interlayer blocked the passage of the aroma molecules due to its inherent low solubility to limonene. The developed multilayers containing the pectin-based interlayers showed lower permeance than monolayers of polylactide (PLA) electrospun film, that is, 2.62 \(\pm\) 1.54 \(\times\) 10\({}^{-10}\) kg\(\cdot\)m\({}^{-2}\cdot\)Pa\({}^{-1}\cdot\)s\({}^{-1}\), and PET electrospun film, that is, 0.64 \(\pm\) 0.11 \(\times\) 10\({}^{-10}\) kg\(\cdot\)m\({}^{-2}\cdot\)Pa\({}^{-1}\cdot\)s\({}^{-1}\)[80].