esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparing PlanetScope and Sentinel-2 Imagery for Mapping Mountain Pines in the Sarntal Alps, Italy
## 2 Materials and Methods
### Data
#### 2.2.2 Sentinel-2 Imagery  
Complementary to PlanetScope, we obtained multi-temporal images from the Sentinel-2 Multispectral Instrument (MSI). The European Space Agency's (ESA) Sentinel-2 mission consists of two identical satellites (Sentinel-2A & Sentinel-2B) with a repetition rate of 5 days. The MSI sensor has thirteen spectral bands in the wavelength range between blue  
Figure 1: **(a)** Study area in Sarntal Valley with reference data points for mountain pines and other land cover classes. Blue stars indicate the locations of the field photography seen in **c,d**. Background: Orthophoto 2020 (Autonomous Province of Bolzano); **(b)** location of study area in South Tyrol, Italy. Basemap and Hillshade: OpenStreetMap, SRTM; **(c,d)** mountain pine fields in Durnholz Valley with visible clear-cuts. Pictures were taken during a field visit in July 2021 and **(e)** typical appearance of young, regrowing mountain pine. Picture was taken near location **(d)**.  
(\(\lambda\) = 443 nm) and shortwave infrared (SWIR) (\(\lambda\) = 2190 nm) in three different geometric resolutions (10 m, 20 m, 60 m) [39].  
We downloaded four cloud-free Sentinel-2 L2A (surface reflectance) images from the summer period of 2020 (2 June 2020; 7 July 2020; 26 August 2020; 5 September 2020) that were close in time to the acquisition dates of PlanetScope. For comparability, we selected only the bands (Blue, Green, Red and NIR) with a pixel size of 10 m. We cropped the Sentinel-2 images to the extent of the study area and masked snow and cloud cover using an overall brightness threshold of these four spectral bands. The resulting data gaps were then filled with the observations closest in time.
## 7 Conclusions and Future Work
We implemented proposed wound monitoring solution in three different environments i.e., home, park and industrial area to check working of proposed EEMS and NN model, results showing that proposed solution predict environmental feasibility for wound at home with 99% accuracy, in parks and industrial area with 96% accuracy rate. We also provided wound monitoring solution to users of different age to verify patient's quality of experience with proposed solution, results showed that mostly patients of different ages found solution feasible to use. There are many future perspectives of research which could considered for enhancements in proposed healthcare approach. * Proposed approach could implement with other machine leaning techniques in future e.g., KNN, Decision Tress, Fuzzy system etc. ;
* In future, current wound monitoring system could reproduce by adding sensors for detection of wound interior microenvironment;
* Proposed approach could enhance to cover larger patients range by adding patient profile in wound care solution to consider effect of other health concern in wound healing. Conceptualization, H.S. and I.S.B. ; methodology, I.B and R.U.-A. ; software, U.F. ; validation, B.K. and R.K.; investigation, H.S. ; writing--original draft preparation, H.S., I.S.B. ; writing--review and editing, R.U.-A., R.K., W.A. ; visualization, R.K.; supervision, I.S.B. This research received no external funding. We are thankful to the [PERSON] at Civil Hospital, Bahawalpur for providing in technical details and helping in testing of the work. The authors declare no conflict of interest.
## 3 Methods and Data
### Data
Distances to rivers were categorized into seven classes: <400 m; 400\(-\)800 m; 800\(-\)1200 m; 1200\(-\)1600 m; 1600\(-\)2000 m; 2000\(-\)2400 m; and >2400 m. Rainfall was categorized into five classes at 100 mm intervals: <700 mm; 700\(-\)800 mm; 800\(-\)900 mm; 900\(-\)1000 mm; and >1000 mm.
# Band-Pass Sampling in High-Order BOC Signal Acquisition
## 4 Performance Analysis  
In this section, we will analyse the detection performance and the computational complexity.
# Faults and Diagnosis Methods of Permanent Magnet Synchronous Motors: A Review
## 3 Fault Diagnosis Methods
### Data-driven Intelligent Diagnosis Algorithm
#### 3.3.4 Deep Learning  
Before using a machine learning classification algorithm such as SVM, it is usually necessary to extract feature vectors as the input by using a signal processing method such as WT. However, for the deep learning method proposed in recent years, it can automatically learn the ability to represent and classify raw data with features [103], which can reflect the deep features of the original data. Deep learning models used in the field of fault diagnosis includes deep stacking network (DSN) [104], deep belief networks (DBN) [105], long short-term memory (LSTM) [106] and so on. Among them, the most commonly used method is convolutional neural networks (CNNs).  
CNNs usually consist of convolutional layers, ReLU layers, pooling layers, and a fully-connected layer. In the training process, the input samples are forwardly propagated for feature learning and classification, and the errors are reversely propagated until the model is finally completed. It is a bite like NN but can reduce the tendency to overfit.  
Since CNN is a method developed in image processing, some researchers first converted the features extracted by time-frequency analysis technology into two-dimensional, and then input them into 2D-CNN for diagnosis [107]. [PERSON] et al. proposed a method to convert the raw time-domain signal data into 2-D gray images, which eliminates the need for additional feature extraction steps [108]. According to their comparison, the CNN-based fault diagnosis algorithm is more accurate than the sparse filter, DBN and SVM. However, it has the disadvantage of being too time consuming and has high hardware requirements. On the other hand, [PERSON] et al. directly input the time domain signal collected in the motor into the 1D-CNN for fault diagnosis [109]. [PERSON] et al. compared this method with the method of using WPT to extract features and then inputting them into ANN. The results show that CNN has a slightly better accuracy in the fault diagnosis of PMSM [110]. Therefore, research on fault diagnosis algorithms for PMSM based on deep learning is promising.
## 2 Related Work
### SIN Dynamic Topology Model
#### 2.2.3 Dynamic Topology Model  
SIN is different from a simple network, and the complexity of its compositional structure and the diversity of its functions make it a more complex topology. SIN contains multi-nodes and multiple information exchange relationships, and it forms a subnetworks intertwined situation. Therefore, according to the set of nodes and edges, a static topology model \(G\) of SIN is established, and the mathematical representation is as follows:  
\[G:=\ <V,E> \tag{5}\]  
where, symbol \(V\) represents limited sets of network nodes, and \(E\) (\(E\subseteq V\times V\)) represents limited sets of network edges. Based on Equation (5), a time factor introduced can define the dynamic topology model of SIN, a five-tuples is used to describe the model in this article, and its mathematical representation is:  
\[G_{i}:=\ <V_{i},Av_{i},E_{i},Ae_{i},t_{i}> \tag{6}\]  
where, symbol \(t_{i}\) represents an ordered time point, and \(0\leq i\leq T\). Symbol \(T\) represents the set of the time point, symbols \(V_{i}\) and \(E_{i}\) represent a finite set of nodes and edges at time \(t_{i}\), respectively; and symbols \(Av_{i}\) and \(Ae_{i}\) represent the node attribute set and edge attribute set at time \(t_{i}\), respectively. Meanwhile, defining these time points does not need to be equally spaced, that is, they may change at consecutive times between two time points \((t_{i+1}-t_{i})\).
## 7 Experimental Results  
This section reports the results obtained by exploiting the record and replay approach for assessing the performance of the GBPT, which in this case embeds a consumer-grade GNSS receiver. Data were recorded from vehicular data collections by following a qualitative classification between urban and suburban environments, among the generic user environments described in [24]. The advantages and disadvantages of the use of the record and replay approach are highlighted for these two different operational environments.
# Real-Time 3D PET Image with Pseudoinverse Reconstruction
## 2 Materials and Methods
### Real Data  
#### 2.2.1 Preclinical PET/CT Scanner  
We used the six ring version of the SuperArgus PET/CT scanner (Figure 4. (left)). This configuration consists of 24 arrays of 13 \(\times\) 13 crystals with 1.55 mm pitch size. The number of sinogram bins was 175 (radial) \(\times\) 128 (angular). Radial bins were 0.5 mm in size, configuring a 8.75 cm transaxial FOV. A total of 195 slices of 0.775 mm each were used in the axial direction. The maximum ring difference [28] was set to 97 and a span factor of 19 was applied, resulting in a total of 1185 sinograms. The axial pseudoinverse matrices \(A_{z}^{\intercal}(z_{1}z_{2},z)\) had a size of \(1185\times 195\). For the 2D matrices, 175 voxels were chosen for the image FOV. The size of 2D matrices for the SuperArgus PET/CT was around 2.6 Gb. Both \(\sigma_{z}\) and \(\sigma_{xy}\) of the Gaussian distributions of the TOR were 0.6 mm.  
For the evaluation of the image quality we used the IQ NEMA and Derenzo phantoms [60]. The rod diameters (Derenzo phantom) were 1.2, 1.6, 2.4, 3.2, 4, and 4.8 mm.  
Two animal acquisitions injected with fluorodeoxyglucose (FDG) were also used to evaluate the performance of the PINV (PINV\({}_{z}\) + 2D-PINV) methodology: a 200 g rat and a cardiac acquisition of a 15 g mouse. Over the cardiac mouse acquisition, a quantitative comparison of 2D-PINV and FBP in real preclinical acquisitions was made. A profile over the heart was fit to two Gaussians (one to each side of the heart). The average of the standard deviation of both Gaussians is shown and the relative improvement of 2D-PINV over FBP was calculated as \(|\sigma_{2D-PINV}-\sigma_{FBP}|/\sigma_{FBP}\). Moreover, we used a first pass acquisition of a mouse injected with FDG to demonstrate the real-time capabilities of PINV reconstructions.  
Acquisitions were performed at the Instituto de Investigacion Sanitaria Gregorio Maranon (Madrid, Spain). All experimental procedures were done in compliance with the European Communities Council Directive 2010/63/EU and submitted for approval to the Institutional Animal Care and Use and Ethics Committee of the Hospital General Universitario Gregorio Maranon (HGUGM), supervised by the Comunidad de Madrid, according to the Annex X of the RD 53/2013. The subjects were kept at the animal housing facilities of the Unidad de Medicina y Cirugia Experimental (UMCE-HGUGM) in Madrid, Spain.
# Reducing Detrimental Communication Failure Impacts in Microgrids by Using Deep Learning Techniques
## 3 Link Loss Effects
### Smart ESS for Link Loss Effects Mitigation  
When the link is lost the ESS becomes disconnected from the EMS and therefore is not able to update its setpoints. In this case the ESS can either operate with its last received setpoint or shut down in order to prevent harmful charge/discharge operation. In either of these cases, the optimum MG operation and internal power balancing are no longer maintained.  
It is also important to mention that link-losses can happen in different parts of the communication system with dissimilar consequences. For example, if it occurs on the links that transmit the measurements from the inverters to the EMS and the rest of the communication system is operating then false data will be delivered to the ESS. Here, we are interested in investigating total communication failures and other scenarios are out of the scope of this research.  
A feasible solution to tackle this problem is to provide and load the ESS with extra information so it can anticipate its upcoming power references. This is compatible to the aforementioned MG structure since all the computations are performed on the external EMS, so in order to obtain the smartness feature in the ESS, load/generation forecastings should be performed on the EMS and extra/redundant information should be transmitted through the communication link.
# Fusion of Linear and Mel Frequency Cepstral Coefficients for Automatic Classification of Reptiles
###### Abstract  
Bioacoustic research of reptile calls and vocalizations has been limited due to the general consideration that they are voiceless. However, several species of geckos, turtles, and crocodiles are able to produce simple and even complex vocalizations which are species-specific. This work presents a novel approach for the automatic taxonomic identification of reptiles through their bioacoustics by applying pattern recognition techniques. The sound signals are automatically segmented, extracting each call from the background noise. Then, their calls are parametrized using Linear and Mel Frequency Cepstral Coefficients (LFCC and MFCC) to serve as features in the classification stage. In this study, 27 reptile species have been successfully identified using two machine learning algorithms: K-Nearest Neighbors (kNN) and Support Vector Machine (SVM). Experimental results show an average classification accuracy of 97.78% and 98.51%, respectively.  
biological acoustic analysis; bioacoustic taxonomy identification; reptile vocalization; frequency cepstral coefficients; SVM; kNN +
Footnote †: journal: Applied Sciences
## 2 Kinematics of Wheeled Mobile Robots
### Friction Forces for Mecanum-Wheeled Mobile Robot  
Here are key points regarding the friction of mobile robots equipped with Mecanum wheels. Mecanum wheels enable a robot to move in any direction--forward, backward, sideways, and diagonally--without changing the orientation of the robot. This is achieved by varying the direction and speed of rotation of each wheel. The rollers are typically mounted at a 45-degree angle to the wheel's rotational axis. This angle is critical in determining the direction of frictional forces, which in turn affects the robot's movement [21].  
A schematic representation of friction forces acting on a Mecanum-wheeled robot for linear movement is presented in Figure 2a. A representation of friction forces acting on the robot for lateral movement is presented in Figure 2b and for rotational movement in Figure 2c.  
For an enhanced mathematical model of a Mecanum-wheeled robot, it is crucial to incorporate both rolling and sliding friction, and to calculate the friction torque with distinctions between linear and rotational movements. The model must consider the roller's angle \(\alpha\) in calculating sliding friction. Analyzing resistive friction between the Mecanum wheel and the ground is complex due to the robot's omnidirectional movement and the wheel's angled roller design. Each Mecanum wheel's resistive friction is evaluated separately, involving both static and dynamic friction [21]. Static friction, present between stationary surfaces, must be overcome to initiate movement. Dynamic friction, occurring during relative motion between surfaces, must be overcome to maintain movement. Typically, the coefficient of static friction exceeds that of dynamic friction. Sliding friction \(\text{F}_{\text{fl}}\) arises when surfaces rub together, while rolling friction \(\text{F}_{\text{fr}}\) resists a wheel's rolling motion on a surface.  
Rolling friction for a Mecanum wheel is calculated as:  
\[\text{F}_{\text{fr}}=-\mu_{\text{r}}\cdot\text{N}\cdot\text{cos}\left(\alpha \right)\cdot\text{sgn}(\
u_{\text{x}}), \tag{18}\]  
where \(\alpha\) is the roller angle.  
Sliding friction for a Mecanum wheel is calculated as:  
\[\text{F}_{\text{fl}}=-\mu_{\text{s}}\cdot\text{N}\cdot\text{sin}\left(\alpha \right)\cdot\text{sgn}(\
u_{\text{Y}}). \tag{19}\]  
The direction depends on the wheel's movement velocity (v) relative to the ground, projected along the roller in a parallel direction [21]. The advantage of the Mecanum wheel is that because of the roller, the sliding force imparts a rotational movement to the wheel's roller, and thus the sliding force depends on the rolling friction force of the roller.  
Friction Forces in Different Directions:
###### Abstract  
Civil infrastructure monitoring with the aim of early damage detection and acquiring the data required for urban management not only prevents sudden infrastructure collapse and increases service life and sustainability but also facilitates the management of smart cities including smart transportation sectors. In this context, smart geosynthetics can act as vital arteries for extracting and transmitting information about the states of the strain, stress, damage, deformation, and temperature of the systems into which they are incorporated in addition to their traditional infrastructural roles. This paper reviews the wide range of technologies, manufacturing techniques and processes, materials, and methods that have been used to date to develop smart geosynthetics to provide rational arguments on the current trends and utilise the operational trends as a guide for predicting what can be focused on in future researches. The various multifunctional geosynthetic applications and future challenges, as well as operational solutions, are also discussed and propounded to pave the way for developing applicable smart geosynthetics. This critical review will provide insight into the development of new smart geosynthetics with the contribution to civil engineering and construction industries.  
civil infrastructures monitoring; smart geosynthetics; damage detection; sustainability; stress and strain detection; smart cities +
Footnote †: journal: Sustainable Energy  
00203 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 4 Topology-Based Routing Protocols in FANETs
### Static Routing Protocol  
The static routing table is suitable for networks with a consistent topology, but it is insufficient for FANETs. To communicate, each UAV's routing table is calculated and populated in advance of the flight and then saved in the UAV. It should be emphasized that routing tables cannot be changed, limiting UAVs to communicating with a small number of other UAVs or ground-based base stations. This routing protocol is well suited for nondynamic networks and is designed to be fault-tolerant. Static protocols are unable to function normally in the event of link failures, causing disruptions throughout the network.  
MLHR is a statically routed protocol that was created to address the network's scalability issue. FANETs are arranged into clusters, each of which has a cluster-head (CH) that serves as the cluster's representative. Each CH has unique external and internal connections via UAVs with a direct communication range. This type of routing may be appropriate for FANETs if the mobility of UAVs is predefined in terms of swarms or a high number of UAVs are present in a large network. Hierarchical design is used to expand the operating area and size of the network [108].  
DCR is a data-centric static and multicast routing protocol. This is possible when a data packet or message is requested by a group of UAVs and distributed reactively, such as in one-to-many transmissions. DCR is used in cluster-based FANETs to serve a variety of applications that distribute explicit data for a specific mission area. DCR is built on a publish-subscribe strategy that automatically connects data publishers and subscribers.  
The publisher initiates data transmission, which is intercepted directly or indirectly by the intended UAVs. The publisher initiates the data broadcast, which is intercepted either indirectly or directly by the intended UAVs [109].  
LCAD is a dedicated static routing mechanism for FANETs. Before UAVs take off, LCAD configures the navigation path on the ground. UAVs act as a link between a pair of source and destination ground stations by collecting, transporting, and transmitting data packets. If the UAVs carrying the data packets are not heading in the correct direction, other UAVs might take over and deliver the data packets. LCAD is also used in delay-tolerant networks (DTNs) and is occasionally used in search and rescue (SAR) applications. This approach is secure and has a high throughput. However, the method's primary disadvantage is that transmission delay may be significant due to the great distances involved [110].
## 5 Experiments and Results
### Test Case 2-Five-Generator Test System with System Losses  
The second test case was a five-generator system. Simulation was carried out for a total demand of 730 MW. This test case included the added effect of steam valve operation and thereby resulting in non-convex optimization problem. Transmission line losses were neglected for this experiment. The data for the test system is given in Tables 6 and 7, the results are presented in Table 8.  
For this experiment \(\mathcal{C}_{1}\) and \(\mathcal{C}_{2}\) were reduced to u(0,1). \(G\) was set at 6 and \(H\) was set at 350. In this test case MBA produced the best results in comparison to GA, PSO and BA. The mean attained by MBA is $23.58 and $34.56 lower than PSO and BA respectively. It also achieved the lowest standard deviation which is 8.5% and 10.8% lower that PSO and BA.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Generator** & **1** & **2** & **3** & **4** & **5** \\ \hline Pmin (MW) & 50 & 20 & 30 & 10 & 40 \\ Pmax (MW) & 300 & 125 & 175 & 75 & 250 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Generator active power limits.  
\begin{table}
\begin{tabular}{c c|c c c c c} \hline \hline
**No.** & **\(a\)** & **\(b\)** & **\(c\)** & **\(d\)** & **\(e\)** \\ \hline
1 & 0.0015 & 1.8 & 40 & 200 & 0.035 \\
2 & 0.0030 & 1.8 & 60 & 140 & 0.040 \\
3 & 0.0012 & 2.1 & 100 & 160 & 0.038 \\
4 & 0.0080 & 2.0 & 25 & 100 & 0.042 \\
5 & 0.0010 & 2.0 & 120 & 180 & 0.037 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Fuel cost coefficients.  
\begin{table}
\begin{tabular}{c c|c c c c c} \hline \hline  & & **P1 (MW)** & **P2 (MW)** & **P3 (MW)** & **P4 (MW)** & **PS (MW)** & **Cost (\$/h)** \\ \hline \multirow{4}{*}{**GA**} & **Best** & 234.82 & 100.92 & 112.80 & 71.46 & 209.99 & **2068.06** \\  & **Avg.** & 253.89 & 91.39 & 127.71 & 49.37 & 207.62 & **2357.02** \\  & **Std** & 29.12 & 19.19 & 27.23 & 15.85 & 23.30 & **111.80** \\  & **Best** & 229.51 & 102.98 & 112.67 & 75.00 & 209.81 & **2029.63** \\
**PSO** & **Avg.** & 248.71 & 94.28 & 126.39 & 55.04 & 205.56 & **2165.08** \\  & **Std** & 30.85 & 19.33 & 27.77 & 23.06 & 26.58 & **104.24** \\  & **Best** & 229.14 & 101.30 & 114.05 & 74.26 & 211.23 & **2042.88** \\
**BA** & **Avg.** & 250.69 & 90.03 & 133.26 & 53.18 & 202.84 & **2176.06** \\  & **Std** & 38.69 & 26.89 & 32.47 & 27.81 & 28.00 & **106.92** \\  & **Best** & 231.06 & 99.59 & 113.48 & 74.42 & 211.44 & **2032.23** \\
**MBA** & **Avg.** & 256.94 & 96.53 & 131.55 & 47.81 & 197.14 & **2141.50** \\  & **Std** & 35.60 & 17.06 & 29.09 & 29.65 & 30.24 & **95.39** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Economic dispatch comparison.