esa-sceva/satcom-chunk-collection huggingface dataset:
# Comprehensive Evaluation of Conveyor Belt Impact Resistance Using Canonical Correlation Analysis
## 2 Materials and Methods
### Test Equipment and Test Samples
* A P1250 rubber textile (Continental Matador Rubber, Puchov, Slovakia) conveyor belt exhibits a tensile strength of 1250 N\(\cdot\)mm\({}^{-1}\); it contains 4 polyamide textile plies; its top cover layer is 6 mm thick; and its bottom cover layer is 4 mm thick. The belt is 18 mm thick. * A P2500 rubber textile (Continental Matador Rubber, Puchov, Slovakia) conveyor belt exhibits a tensile strength of 2500 N\(\cdot\)mm\({}^{-1}\); it contains 4 polyamide textile plies; its top cover layer is 8 mm thick; and its bottom cover layer is 4 mm thick. The belt is 22 mm thick. As for steel cord conveyor belts, their manufacture is based on the preparation of rubber mixtures and rubber layers and includes the process of laying the individual layers on top of each other and then pressing the material while applying the hot vulcanization bonding method. * A ST1250 steel cord (Continental Matador Rubber, Puchov, Slovakia) conveyor belt exhibits a tensile strength of 1250 N\(\cdot\)mm\({}^{-1}\); its top cover layer is 8 mm thick, and its bottom cover layer is 6 mm thick. The cord spacing is 13 mm and the cord diameter is 4.1 mm. The steel cord structure is (1 + 6) + 6(1 + 6). The structure of the cord core consists of 1 strand, while the whole cord structure contains 6 more strands. The steel cord is of a circular shape. The belt thickness is 18.1 mm, and its weight is 27 kg\(\cdot\)m\({}^{-2}\). * A ST2500 steel cord (Continental Matador Rubber, Puchov, Slovakia) conveyor belt exhibits a tensile strength of 2500 N\(\cdot\)mm\({}^{-1}\); its top cover layer is 12 mm thick, and its bottom cover layer is 8 mm thick. The cord spacing is 15 mm and the cord diameter is 6.8 mm. The steel cord structure is 7 \(\times\) 19 (7 strands; 19 wires in each strand). The belt thickness is 26.8 mm, and its weight is 41 kg\(\cdot\)m\({}^{-2}\). Both the rubber textile and the steel cord conveyor belts have cover layers of the same category type M (intended for the transport of abrasive sharp-edged multi-piece materials). Their elongation is 450%; their abrasiveness is 120 mm\({}^{3}\); and the Shore hardness is 60.8 Shore. The tested conveyor belts are used in the mining industry to transport large-piece materials that are poured from a bucket-wheel excavator in the brown coal mining process. The impact height simulated the height of the chutes used in a real-life facility, while the weight of the drop hammer simulated a specific weight of coal, and the impactor type simulated the shape of the transported material (coal and accompanying rocks). The experimental parameters were chosen based on the experience gained in our previous research as well as the requirements demanded by the industry [17; 18; 34].
# Energy-Efficient Clustering Multi-Hop Routing Protocol in a UWSN
## 4 Performance Evaluation
### Simulation Results
#### 4.2.3 Received Packets at the Sink Node  
We assume that all the sensed data of nodes will be received at the sink. In the network deployment, the network topology changes every 100 rounds, which may cause a failure to receive packets at the cluster head and cluster relay. This is because the distance from a cluster member to the cluster head is greater than the transmission range and the received packets at the cluster head or cluster member may fail as a result of a low level of received power. Therefore, the cluster must be re-established, and a new cluster head, cluster member, and cluster relay must be selected. In Figure 6, the total received packets at the sink are evaluated in four cases. In Figure 6,c, despite different transmission ranges, the received packets in EECMR are higher than in LEACH, EEBLC, or DBR. It is noted that the received packets at the sink increase when the transmission range increases. The number of total received packets in the EECMR scheme is occasionally different, which causes high jitters. Despite this, the increase in transmission range leads to a lower residual energy at the node, as shown in Figure 5. This can be considered a pay-off with respect to the transmission range and network performance.  
In Figure 6b,d, the received packets at the sink are shown according to the number of rounds. Due to the number of packets at the sensor node or the number of clusters at each round, the results in the timeline fluctuate. In total, the received packets at the sinks in EECMR are higher than other protocols, which results in high throughput and a more reliable network.  
Figure 6: \(Cont.\)
## 3 Results  
Crystallographic Distinctions between st-C\({}_{4}\)A\({}_{3}\)S and ss-C\({}_{4}\)A\({}_{3}\)S  
To distinguish the crystallographic distinctions between orthorhombic and cubic ye'elimite, XRD patterns of st-C\({}_{4}\)A\({}_{3}\)S (sintered at 1300 \({}^{\circ}\)C for 240 min) and ss-C\({}_{4}\)A\({}_{3}\)S (sintered at 1250 \({}^{\circ}\)C for 240 min) were compared, as shown in Figure 1. As can be seen, except for some low-intensity peaks for monocalcium aluminate (CaAl\({}_{2}\)O4) and calcium oxide (CaO), all peaks belonged to orthorhombic and cubic ye'elimite. Meanwhile, the patterns for st-C\({}_{4}\)A\({}_{3}\)S and ss-C\({}_{4}\)A\({}_{3}\)S tended to be similar, as the major peaks of both patterns overlap at 23.7\({}^{\circ}\), 33.7\({}^{\circ}\), 41.6\({}^{\circ}\), (angles refer to 20 in this paper) and so on. However, it should also be noted that, compared to ss-C\({}_{4}\)A\({}_{3}\)S, the pattern of st-C\({}_{4}\)A\({}_{3}\)S exhibited some additional characteristic peaks (such as the peaks at 18.1\({}^{\circ}\), 20.6\({}^{\circ}\), 35.8\({}^{\circ}\), and 37.3\({}^{\circ}\)), which can be observed in the zoomed-in images of Figure 1. The additional characteristic peaks were used to distinguish the st-C\({}_{4}\)A\({}_{3}\)S from the ss-C\({}_{4}\)A\({}_{3}\)S.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Models** & \(f(\alpha)\) & \(g(\alpha)\) \\ \hline D1 & One-dimensional diffusion & \(1/2\alpha\) & \(a^{2}\) \\ D2 & Two-dimensional diffusion & \(-1/\mathrm{ln}(1-\alpha)\) & \(\alpha+(1-\alpha)\mathrm{ln}(1-\alpha)\) \\ D3 & (Three-dimensional diffusion) & \(3(1-\alpha)^{2/3}/(2[1-(1-\alpha)^{1/3}]\mathrm{h}\) & \([1-(1-\alpha)1/3]^{2}\) \\ D4 & Ginsling-Brounstein (Three-dimensional) & \(3/(2[(1-\alpha)^{-1/3}-1]\mathrm{h}\) & \(1-(2\alpha/3)-(1-\alpha)^{2/3}\) \\ R3 & Model for interface reaction & \(3(1-\alpha)^{2/3}\) & \(1-(1-\alpha)^{1/3}\) \\ A3 & Avrami–Erogev & \(3(1-\alpha)[-\mathrm{ln}(1-\alpha)]^{2/3}\) & \([-\mathrm{ln}(1-\alpha)]^{1/3}\) \\ F1 & Crystal nucleation growth model & \(1-\alpha\) & \(-\mathrm{ln}(1-\alpha)\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Kinetic models for solid-state reactions [28].  
To further confirm the lattice parameters of synthesized st-C\({}_{4}\)A\({}_{3}\)$ and ss-C\({}_{4}\)A\({}_{3}\)$, the Rietveld method was employed to refine the patterns in Figure 1. The fitting result of ss-C\({}_{4}\)A\({}_{3}\)$ can be seen in Figure 2, and the obtained values of the lattice parameters are exhibited in Table 3. According to the results of the Rietveld refinement, the crystallographic characters of synthesized st-C\({}_{4}\)A\({}_{3}\)$ and ss-C\({}_{4}\)A\({}_{3}\)$ in this paper were consistent with those of previous studies.
# People Detection Using Artificial Intelligence with Panchromatic Satellite Images
###### Abstract  
The detection of people in urban environments from satellite imagery can be employed in a variety of applications, such as urban planning, business management, crisis management, military operations, and security. A WorldView-3 satellite image of Prague was processed. Several variants of feature-extracting networks, referred to as backbone networks, were tested alongside the Faster R-CNN model. This model combines region proposal networks with object detection, offering a balance between speed and accuracy that is well suited for dense and varied urban environments. Data augmentation was used to increase the robustness of the models, which contributed to the improvement of classification results. Achieving a high level of accuracy is an ongoing challenge due to the low spatial resolution of available imagery. An F1 score of 54% was achieved using data augmentation, a 15 cm buffer, and a maximum distance limit of 60 cm.  
people detection; CNN; Faster R-CNN; data augmentation; satellite images; WorldView 2024, 14, 8555. [[https://doi.org/10.3390/app14188555](https://doi.org/10.3390/app14188555)]([https://doi.org/10.3390/app14188555](https://doi.org/10.3390/app14188555))
# A Decision Support System for Melanoma Diagnosis from Dermoscopic Images
## 6 Performance Evaluation  
### Evaluation Metrics  
Therefore, to evaluate and measure the effectiveness of the implemented CAD system in classifying a dermoscopic image of skin lesions, some of the performance measurement metrics indicated in the International Skin Imaging Collaboration (ISIC) challenge were considered. Accuracy, sensitivity, specificity, and precision were assessed by adopting the following expressions:  
\[Sensitivity\;(Se)=\frac{TP}{TP+FN} \tag{6}\]  
\[Specificity\;(Sp)=\frac{TN}{TN+FP} \tag{7}\]  
\[Precision\;(Pr)=\frac{TP}{TP+FP} \tag{8}\]  
\[Accuracy\;(AC)=\frac{TP+TN}{TP+FP+TN+FN} \tag{9}\]  
\[F1\;score=\frac{2 TP}{2 TP+FP+FN} \tag{10}\]  
in which:  
* \(TP\) (true positives) is the number of melanomas correctly classified inside the database.
* \(FN\) (false negatives) is the number of melanomas that the algorithm is not able to correctly classify and, consequently, considers non-melanomas.
* \(FP\) (false positives) is the number of non-melanomas that the algorithm is not able to correctly classify and, consequently, considers melanomas.
* \(TN\) (true negatives) is the number of non-melanomas correctly classified inside the database.  
According to the intended use of the developed CAD system, some of the measures above mentioned play a role of relative significance and impact; hence, they are prioritized: for example, sensitivity is considered to be relevant if the failure to detect the disease can have serious health and survival consequences, such as in treatable malignancies [43; 44]. Broadly speaking, a trade-off between the various measures is inevitable based on the impact of FP and FN diagnoses and on disease prevalence [45; 46]
###### Abstract  
Vanadium dioxide (VO\({}_{2}\)) with reversible metal-insulator transition (MIT) is one of the most promising energy-efficient materials. Especially for VO\({}_{2}\)-based smart windows, the visible transmittance and solar modulation ability are the most critical parameters. However, VO\({}_{2}\) thin films that are directly deposited onto glass substrates are of poor crystallinity and MIT performance, limiting the practical applications of VO\({}_{2}\)/glass heterostructures. In this paper, a buffer layer of Cu\({}_{50}\)Zr\({}_{50}\) was introduced to build a novel Zr-based thin film metallic glass (VO\({}_{2}\)/Cu\({}_{50}\)Zr\({}_{50}\)/glass) with multilayer structures for thermochromic applications. It is observed that the insertion of a Cu\({}_{50}\)Zr\({}_{50}\) buffer layer with appropriate thickness results in a clear enhancement of crystalline quality and MIT performance in the VO\({}_{2}\)/Cu\({}_{50}\)Zr\({}_{50}\)/glass thin films, compared with the single-layer VO\({}_{2}\)/glass thin films. Moreover, the VO\({}_{2}\)/Cu\({}_{50}\)Zr\({}_{50}\)/glass bi-layer films exhibit better optical performance with enhanced solar modulation ability (\(\Delta T_{sol}\) = 14.3%) and a high visible transmittance (\(T_{\text{vis}}\) = 52.3%), which represents a good balance between \(\Delta T_{sol}\) and \(T_{\text{vis}}\) for smart window applications.  
vanadium dioxide thin film; Cu\({}_{50}\)Zr\({}_{50}\) buffer layers; metal-insulator transition; thermochromic property; smart window +
Footnote †: journal: Applied Physics Letters
# Review of Weed Detection Methods Based on Computer Vision
## 4 Weed Detection and Identification Methods Based on Deep Learning
### Other Deep Learning Methods
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Ref.** & **Crop** & **Types** & **Architecture** & \multicolumn{2}{c}{**Comparison Group**} & **Highest Accuracy** \\ \hline
[151] & Not specified & RGB & \begin{tabular}{c} Convolutional neural \\ network \\ \end{tabular} & \begin{tabular}{c} Propose a low-cost Weighted Identification System (WIS) using \\ RGB images taken by drones as training data and applying \\ CNN to build the identification model. \\ \end{tabular} & \
\begin{tabular}{c} 1.CNN-WIS \\ 2.12b \\ 3.HOG \\ \end{tabular} &
\begin{tabular}{c} 98.8\% \\ (CNN-WIS) \\ \end{tabular} \\
[152] & Lettuoc & Multis-pectral & \begin{tabular}{c} Region proposal \\ network \\ \end{tabular} & \begin{tabular}{c} A false green image was generated, which is the union of \\ the red, green, and near infrared bands, in order to highlight \\ vegetation. \\ \end{tabular} & \
\begin{tabular}{c} 1.Mask R-CNN \\ 2.1 HOG-SVM \\ \end{tabular} &
\begin{tabular}{c} (Precision) 98\% \\ (Mask R-CNN) \\ \end{tabular} \\
[25] & Rice & RGB & \begin{tabular}{c} Fully convolutional \\ network \\ \end{tabular} & \
\begin{tabular}{c} 3.5 MO,O3 \\ \end{tabular} &
\begin{tabular}{c} (Mask R-CNN) \\ 2.1 HOG-SVM \\ \end{tabular} \\
[144] & Corn, lettuce, \\
[2020] & rdish & RGB & \begin{tabular}{c} Graph convolutional \\ network \\ \end{tabular} & \begin{tabular}{c} Proposed a 3-best semantic segmentation method based on \\ FCN. Could effectively classify the pixels of face seedlings, \\ background, and needs in ride field images. \\ \end{tabular} & \
\begin{tabular}{c} 1.SegNet \\ 2.1 CNN \\ 3.1 CNN \\ \end{tabular} &
\begin{tabular}{c} 92.7\% \\ (SegNet) \\ \end{tabular} \\
[144] & Corn, lettuce, \\
[2020] & rdish & RGB & \begin{tabular}{c} Graph convolutional \\ network \\ \end{tabular} & \begin{tabular}{c} 1.0 CNN-ResNet101 \\ 2.2 CNN-VGG \\ \end{tabular} & \
\begin{tabular}{c} 2.5 CNN-VGG \\ (GCN-ResNet101) \\ 3.1 CNN-AlexNet \\ \end{tabular} &
\begin{tabular}{c} 97.8\% \\ (GCN)-ResNet101 \\ \end{tabular} \\
[30] & Mase, common \\
\begin{tabular}{c} 1.H Hybrid Network \\ (AgroAVNET) \\ \end{tabular} \\
[10] & Mase, common \\
[2018] & \begin{tabular}{c} Meise, common \\ wheat, sugar beet \\ \end{tabular} & \begin{tabular}{c} RGB \\ (Weight Network) \\ \end{tabular} & \begin{tabular}{c} Hybrid Network \\ (Weight Network) \\ \end{tabular} & \
\begin{tabular}{c} AgroAVNET is a hybrid model of AlexNet and VGGNET. \\ (AgroAVNET) \\ 2.1 HOG-SVM \\ \end{tabular} &
\begin{tabular}{c} 1.H Hybrid Network \\ 98.23\% \\ (Hybrid Network) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the typical deep learning methods.
# BA-YOLO for Object Detection in Satellite Remote Sensing Images
## 4 Experiments
### Implementation Details  
In this paper, BA-YOLO is implemented in the PyTorch framework and is currently being trained and tested on an NVIDIA Tesla A100 GPU with 40 GB of memory. The YOLOv8 model is initially trained on the COCO dataset. To save training time, we employed a transfer learning strategy by directly using a pre-trained YOLOv8 model. During the training phase, we utilized the pre-trained YOLOv8 backbone network model from the COCO dataset for transfer learning. We employed the SGD optimizer with an initial learning rate of 0.01. The number of training epochs was set to 150, and the first 3 epochs used a learning rate warm-up strategy, which is a common technique for optimizing learning rates. The learning rate gradually decreased from 0.1 to 0.01 during these initial 3 epochs. Learning rate warm-up helps stabilize the training of deeper models. The batch size was set to 8, the probability for Mosaic data augmentation was set to 1.0, and the probability for Mixup data augmentation was set to 0.7. Additionally, during the final few epochs of training, we disabled Mosaic data augmentation, which contributed to improving the model's performance. Other detection models, such as Faster R-CNN, RetinaNet, SSD, YOLOv3, YOLOv5, and others, were validated using the default settings provided in their respective literature.
# Temperature Effects on Vibration-Based Damage Detection of a Reinforced Concrete Slab
## 4 Damage Identification in Test Structure  
In the above experiments and analysis, the ARX model, which can reflect the relationship between natural frequencies and variations of temperature, is obtained, and it can be exploited for the damage identification problem. Theoretically, the simulated frequencies of the experimental slab can be output when the measuring temperature data are input to the model. However, if the large discrepancy is observed between the simulated and measured frequencies, something abnormal may happen and cannot be explained by temperature effects. For instance, if the measured natural frequencies are lower, the slab may be damaged. However, the significant deviation is a rather subjective criterion to judge the state of a structure. In this respect, some more objective statistical criterion needs to be discussed; if there is an abnormal change, such as a crack in structures [40; 41], the procedure of damage identification will be carried out to identify whether the structure is damaged.
## 3 Results
### Number of Stages  
The change in the number of NLI stages (\(N\)) has been demonstrated in reference 18 (\(N=2\)) and 19 (\(N=3\)) under different NLI conditions, such as the DSF and SMF lengths. Here, we increase the stage number under identical NLI conditions and measure the JSI spectrum and its bandwidth. As seen in Figure 5a-d, varying the stage number does not change the maximum island frequency but the FWHM bandwidth of islands. \(\lambda_{p}\) is fixed at 1550.92 nm, \(L_{\text{DSF}}\) is 100 m, and \(L_{\text{SMF}}\) is 94 m. This condition makes the second island away from the pump frequency by 400 GHz, as seen in Figure 4b. Figure 5a-d are the measured JSI for various Ns. Insets show the JSI filtered by the signal/idler filters. Note that the second island is anchored at the center of the signal and idler DWDM filters even if we change the stage number from \(N=2\) to \(N=5\).  
Figure 5e is the normalized diagonal JSI spectra for various stage numbers against the frequency detuning from the pump frequency. The black dashed line indicates the signal filter spectrum, and the \(m=2\) island is centered on the filter spectrum. The normalized diagonal JSI spectra in Figure 5e show that the bandwidth of each island gets narrower as the stage number increases, but the peak frequency is stationary. Figure 5f shows the FWHM bandwidth of each island against the stage number. The curved lines are the theoretical predictions with considering the DSF phase shift. The FWHM bandwidth decreases as the stage number increases. The measured data matches well with the theoretical predictions except for the \(N=5\) case. We believe that the non-uniformity of the DSF sections causes this discrepancy, but further study is necessary. The measured \(p\) values are 0.82, 0.96, 0.98, and 0.97 for \(N=2\), 3, 4, and 5, respectively. Therefore, we could obtain the photon pairs with high spectral purity by adding the proper NLI stages.  
Figure 5: The measured JSI for various number of stage (i.e., number of DSF sample): (a) 2, (b) 3, (c) 4, and (d) 5. Inset indicates ISI after signal/idler filter which is 400-GHz detuned 100-GHz DWDM filter (3 dB-bandwidth \(\sim\) 80 GHz)). (**e**) The plot of the diagonal line. The black dashed line indicates signal/idler filter (flat-top spectral shape). (**f**) Full-Width at Half-Maximum (FWHM) bandwidth of each peak against the number of stages. Lines are theoretical predictions extracted from the calculated JSI by using Equation (1).
# Solar Ultraviolet Irradiance Characterization under All Sky Conditions in Burgos, Spain
## 3 Results and Discussion  
### Sky Classification  
Definition of (clear, intermediate, and overcast) sky types is usually achieved through meteorological and climate indices. Among others, the clearness index, \(k_{t}\) (ratio of global solar irradiation to extraterrestrial solar irradiation) [67; 68; 69; 70], relative sunshine, \(S\), [71; 72], [PERSON]'s clearness index, \(\varepsilon\), and sky brightness, \(\Delta\), [73; 74], have been proposed as sky type classifiers. A previous study concluded [66] that limited results for sky classification can be obtained using meteorological indices. In 2003, the CIE adopted 15 standard sky types (five clear, five intermediate, and five overcast skies) in order to create a standard for sky classification [61]; these were defined by the International Commission on Illumination (_Commission Internationale de L'Eclairage_ or CIE), and each one was perfectly characterized in terms of energy and daylight, and widely considered as an adequate representation of empirical sky conditions [75; 76; 77; 78; 79; 80]. Therefore, the CIE standard for sky taxonomy was selected as the reference for atmospheric conditions. A complete description of the CIE standard sky classification model and the procedure for obtaining a CIE standard sky classification from sky scanner measurements can be found in previous works [62; 63; 81; 82]. Figure 2 shows the frequency of occurrence of each CIE standard sky type in Burgos, Spain, during the experimental campaign. Skies in Burgos are predominantly clear, the most frequent sky type in the city being CIE standard sky type 13 (cloudless polluted with a broader solar corona), with a Frequency of Occurrence (FOC) higher than 18%. As can be observed in Figure 3, the fifteen CIE categories are grouped into overcast (CIE categories 1 to 5), partly cloudy (6 to 10), and clear skies (11 to 15); the clear sky conditions have the highest FOC (44.63%). This result concurs with previous experimental campaigns developed in Burgos between 2016 and 2022 [82; 83]. As CIE standard sky classification data around the world are quite scarce, an alternative sky classification model, based on the \(k_{t}\) classification, was prepared. A comparison with CIE taxonomy is shown in Figure 3.
## 2 System Model
### Position Estimation  
#### 2.2.1 Transmitter Mobility Model  
The following describes a model representing the random movement of a balloon. In this model, the balloon undergoes random drift and fluctuations in the \(x,y,\) and \(z\) directions due to environmental factors such as air currents and sea breezes.  
\[x_{s}(t)=x_{s0}+\mu_{x}t+\sigma_{x}W_{x}(t), \tag{1}\]\[y_{s}(t)=y_{s0}+\mu_{y}t+\sigma_{y}W_{y}(t), \tag{2}\]  
\[z_{s}(t)=z_{s0}+\mu_{z}t+\sigma_{z}W_{z}(t). \tag{3}\]  
In this context, \(\mu_{x}\), \(\mu_{y}\), and \(\mu_{z}\) represent the drift coefficients in the \(x\), \(y\), and \(z\) directions, respectively, while \(\sigma_{x}\), \(\sigma_{y}\), and \(\sigma_{z}\) denote the intensity of Brownian motion in each direction. The terms \(W_{x}(t)\), \(W_{y}(t)\), and \(W_{z}(t)\) represent independent standard Brownian motions, which account for the random components of the balloon's movement [15].