esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Possible Practical Applications  
In this section, some hypothetical situations are presented that support the importance of load transportation between aerial and ground vehicles.  
Rescue operations present numerous situations in which it is necessary to apply techniques related to cargo transport. In the first example, illustrated in Figure 1, let's assume that an ambulance is going to help a victim of a traffic accident in a very congested avenue or a hard-to-cross bridge. In such situation, a UAV can assist in these emergencies by transporting first aid equipment or even specific/appropriate medical devices between the nearest hospital to the accident site where the rescue service is being performed.  
To further complement some rescue operations, Figure 2 presents a didactic illustration of a situation in which a rescue team is trying to contain a fire in a burning building. In this hypothetical situation, there are civilians present inside the structure, and there is no easy access to the interior of the building due to the fire and the imminent risk of the structure collapsing. Once the rescue team is unable to access the interior of the building, immediate relief equipment can be sent to victims, such as a gas mask or water for example, aiming to increase the chances of rescuing a more significant number of victims still alive.  
Delivery Operations  
Drone deliveries are one of the main trends for the future of e-commerce. In fact, with the expansive growth of the industry in recent years, companies in the sector are looking for increasingly advanced solutions to stand out in this extremely competitive market. Given this, the delivery of products by using drones is a very promising strategy for the future [28].  
As another practical example in a real-world scenario, imagine that a ground delivery vehicle is in transit to deliver a large number of goods. Suppose now that this vehicle is crossing a long-span bridge, at the site of a mechanical problem, congestion due to an accident, or even civic maintenance on the bridge structure. In this situation, this vehicle will be stopped on the bridge until traffic is cleared, which may seem normal as this happens with high frequency in today's urban environments. However, if we suppose that vehicle is transporting hospital supplies, such as vaccines or even human organs, we have a situation in which every second of delay could risk lives. Thinking of such situations, it is proposed that a UAV goes to the ground vehicle that is stopped in traffic. Then, it flies carrying the cargo to a desired coordinate, where there will be a third vehicle waiting for it. Figure 3 illustrates such a situation.  
Figure 1: Example of situation of emergency delivery.  
Figure 2: Example of situation of rescue operation.  
After presenting some hypothetical applications related to suspended load transportation with a heterogeneous robot team, we present in the sequel our a cargo transportation strategy in cases in which a UGV cannot fulfill a delivery task, and a UAV comes to help it in the accomplishment of the mission.
## 2 The Graph Neural Network  
To use a graph as input in a neural network, the information associated with graph (e.g., node values, edges cost) should be converted to an appropriate form to be used as neural network input. Figure 2 illustrates how a graph can be represented with or without filtering. The greyed blocks transform the graph information, given as a vector (or matrix) that has on each line the data in a specific node. Each column represents specific information for a node. The way the graph is represented is presented below [19].  
Given a graph \(\mathbf{G}\), it is characterized by its adjacency matrix \(\mathbf{A}=[a_{ij}]\), \(i\), \(j=1\),..., \(N\) where \(a_{ij}\) indicates the existence of edge between nodes \(i\), \(j\) (1--presence, 0--absence)and \(N\) is the number of nodes. Also, a node in graph \(\mathbf{G}\) is characterized by the degree of the node which is the number of edges incident at the node. A diagonal matrix is defined as:  
\[\mathbf{D}=[d_{ij}],\;i,\;j=1,\;\ldots,N\;\textit{and}\;d_{ij}=0,\;if\;i\
eq j, \;d_{ii}=\sum_{k=1}^{N}a_{ik}\]  
A difference matrix \(\mathbf{L}=\mathbf{D}-\mathbf{A}\) is involved to build a polynomial as follows: \(\mathbf{p}(\mathbf{L})=w_{0}\mathbf{I}_{n}+w_{1}\mathbf{L}+w_{2}\mathbf{L}^{2 }+\ldots+w_{d}\mathbf{L}^{d}\). The polynomial \(\mathbf{p}(\mathbf{L})\) is a (n \(\times\) n) matrix. We note the coefficients as vector \(\mathbf{w}=\left[w_{k}\right]\), \(k=0,\,1,\;\ldots,\,d\).  
The polynomial \(p(\mathbf{L})\) is used as a filter to calculate the neural network inputs (features). The generic algorithm for the training phase of the graph neural network is illustrated below (the vector \(\mathbf{x}\) represents the initial values in the nodes of the graph):  
\[\begin{array}{l}\textit{Initialization}\\ \mathbf{h}^{(0)}=\mathbf{x}\\ \textit{while}\;(\textit{error}\;>\;\text{minimum error})\;\textit{do}\\ \mathbf{g}^{(k)}=\mathbf{p}^{(k)}\times\mathbf{h}^{(k-1)}\\ \mathbf{h}^{(k)}=\sigma(\mathbf{g}^{(k)})\\ \textit{output}=\textit{PREDICT}(\mathbf{h}^{(k)})\\ \textit{error}=\left|output-\;\text{target}\right|\\ \textit{do end}\end{array}\]  
The vector \(\mathbf{w}\) represents the polynomial coefficients and \(\mathbf{x}=\left[\begin{array}{  
Consider a slight modification in the definition of adjacency matrix, as follows: \(\mathbf{A}=[\frac{a_{ij}}{N}]\), \(i,j=1,\,\ldots,N\). Then, the non-zero terms in the diagonal matrix \(\mathbf{D}\) are changed to \(d_{ii}=1\). If \(w_{i}=0\), \(i=\,2,\,3,\,\ldots,\,d\) then \(\mathbf{p}(\mathbf{L})=w_{0}\mathbf{l}_{n}+w_{1}\mathbf{L}=w_{0}\mathbf{l}_{n}+ w_{1}(\mathbf{D}-\mathbf{A})\) and \(\mathbf{g}^{(k)}=\mathbf{p}^{(k)}\times\mathbf{h}^{(k-1)}=\)\(w_{0}\mathbf{l}_{n}\times\mathbf{h}^{(k-1)}+w_{1}\mathbf{I}_{n}\times\mathbf{h}^{(k-1)}-w_{1} \mathbf{A}\times\mathbf{h}^{(k-1)}=(w_{0}+w_{1})\cdot\mathbf{h}^{(k-1)}-w_{1} \mathbf{A}\times\mathbf{h}^{(k-1)}\) which represents the formulas for GCN.
# Machine Learning Algorithm for Delay Prediction in IoT and Tactile Internet
## 5 Performance Evaluation  
The algorithms of ML have proven successful in network traffic prediction. The prediction error is a kind of bad prediction, and more precisely, if there are no error measurements, the prediction method is optimal. Therefore, our goal of reducing the errors after adjusting the connection weights can be evaluated in minimizing the errors. In this work, we used three different ML-based functions to measure the prediction accuracy, including MSE, RMSE, and MAPE, to estimate the prediction accuracy. MSE (Equation (5)) is a loss function that measures the mean squared error, where the error is the difference between expected and observed values. RMSE is equal to the root of MSE, as shown in Equation (6).  
Moreover, MAPE measures the average absolute percentage of error using absolute values (Equation (7)). MAPE has two benefits: first, the fundamental measures prevent the positive and negative errors from eliminating each other. Second, because the percent error is independent of the measurement of the reliable variables, you can use this scale to compare predictive accuracy among time sequence data of different magnitudes.  
\[\text{MSE}=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2} \tag{5}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}} \tag{6}\]  
\[\text{MAPE}=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{y_{i}-\hat{y}_{i}}{y_{i}}\right| \tag{7}\]  
where \(n\) is the number of data points, \(y_{i}\) is the observed value, and \(\hat{y}_{i}\) is the predicted value.
# A Novel Hybrid Model for the Prediction and Classification of Rolling Bearing Condition
## 4 Experimental Analysis
### Experimental Results and Analysis
#### 4.2.5 Discussion  
Considerable achievements have been made in time series forecasting [52] and classification [53] for the deep learning-based methods; however, these methods highly depend on the input scale level. Meanwhile, it is worthy to notice that the deep learning-based methods show accuracy associated with the scale level, and the more scales contribute, the more accurate and stable the performance becomes. Therefore, the solution to acquire high performance is costly in terms of time to train and test. This fact indicates that the augmentation of computation becomes the problem caused by its scale level. Moreover, most of the deep learning-based methods cannot deal with a small sample of data; in other words, accuracy and stability cannot be guaranteed in this case.  
This research uses 12,000 sample points to verify the performance of the prediction and classification. The biggest challenge is that the data used for training are much less. The reason for this is that our proposed method is conducted to monitor the rolling bear operating condition. Moreover, our strategy for condition monitoring is also effective while simultaneously relieving time resource problems. A detailed description of the comparative method's parameters is given as follows. The LSTM [52] is utilized to forecast the future trend of the subseries, and \"Sigmoid\" and \"Adam\" are utilized as the activation function and optimizer, respectively. The network is updated by minimizing the mean square error for \(e_{t}\) with a gradient descent algorithm. The configurations of the three one-dimensional CNN [53] for classifying layers are: Conv1D \((16,64,16)\), Conv1D \((32,3,1)\), Conv1D \((64,3,1)\). After the convolution operation, the Batch Normalization is carried out. \"Relu\" is applied as an activation function. Then, the convolution kernel \((2\times 1)\) is employed in the maximum pooling operation. To more clearly illustrate the advantages of our proposed method on time series forecasting using the same small sample data set, a comparison of quantitative criteria for prediction results is presented in Table 6. Our proposed method outperforms the CNN-based method in classification, whose averaged accuracy is higher than 91.88%. Therefore, both the performance of prediction and classification confirm the advantages of our proposed method.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Domain** & \multicolumn{3}{c}{**Classification Accuracy**} & \multicolumn{1}{c}{**Average Accuracy**} \\ \hline  & IF & BF & OF & Normal & \\ Time domain & 94\% & 86\% & 98\% & 96\% & 93.5\% \\ Frequency domain & 78\% & 68\% & 88\% & 98\% & 83\% \\ Time-frequency domain & 98\% & 88\% & 98\% & 96\% & 95\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the classification results by using different features.
# Magnetic Nanofluid Droplet Impact on an AAO Surface with a Magnetic Field
## 4 Conclusions  
So far, no study has been conducted to investigate the dynamic behavior of magnetic nanofluid droplets on different morphological surfaces under the action of an external magnetic field. Therefore, this study has experimentally studied the dynamic characteristics of magnetic nanofluid droplet impingement on an alumina surface with/without an anodic oxidation process in an applied magnetic field. First, a water-based Fe\({}_{3}\)O\({}_{4}\) nanofluid was synthesized by a co-precipitation process and stabilized using oleic acid as a surfactant. Next, an anodic oxidation treatment was performed to prepare an anodic aluminum oxide (AAO) surface with adjustable wettability, and static contact angles of magnetic nanofluid droplets on surfaces with and without anodic oxidation treatment were then obtained by a contact angle meter. Finally, the influence of surface morphology on the dynamic characteristics of magnetic nanofluid droplets impacting on the two surfaces under an applied magnetic field was investigated by using a high-speed camera. According to the results of static contact angle experiments, an aluminum sheet can be processed to obtain a more hydrophilic surface. From the dynamic images, it can be seen that the degree of adhesion between the liquid and solid on the processed surface without a magnetic field is slightly greater. If an external magnetic field is applied, the effect of AAO surface morphology will be enhanced. As a consequence, the AAO structure exerts a significant influence on the dynamic impact phenomenon of a magnetic nanofluid droplet on a solid surface with a magnetic field. This study may benefit research areas such as smart coating, spray cooling, spray painting, and jet impingement.  
**Author Contributions:** [PERSON] and [PERSON] designed the experiments; [PERSON] performed the experiments and collected the experimental data; [PERSON] and [PERSON] analyzed the data and wrote the paper.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Performance Analysis of the MPS Beamformer
### Impact of Array Rounded Corners Structures on the MPS Beamformer Performance  
The rounded corners planar arrays have the advantages of azimuth-independent beam scanning as well as lower sidelobe levels. These array designs have slightly different sidelobes patterns and distribution where they tend to be distributed in circular planes rather than perpendicular planes that exist in the square and rectangular arrays designs. Practically, and with uniform feeding, the rounded corners planar arrays have approximately a SLL of about \(-17\) dB which is lower than the square or rectangular arrays by \(2.5\) dB. The rounded corners planar array can be optimized also using the MPS beamformer where the proposed algorithm is not sensitive to sidelobes distribution and, indeed, can be applied to any array design provided that the original array factor has identified sidelobe peaks to be nulled out. Figure 8a shows a rounded corner square array formed by trimming \(8\) elements at each corner by setting \(\delta_{T}=0.8\)\(\delta_{MAX}\) for a \(16\times 16\) square array. The normalized array factor is depicted in Figure 8b using uniform feeding where the sidelobes tend to be distributed circularly around the main lobe while the optimized normalized array factor to achieve \(-50\) dB SLL is shown in Figure 8c after running \(71\) iterations using three major sidelobe groups with \(\beta=0.1\) as demonstrated in Figure 8d.  
Figure 8: MPS beamformer performance for rounded-corners planar arrays: (**a**) array structure, (**b**) normalized array power gain for the uniform feeding case in dB, (**c**) normalized array power gain after \(71\) iterations to achieve \(-50\) dB SLL, and (**d**) the convergence profile of the MPS beamformer to achieve the SLL in (**c**).
# Evaluation of Deployment Challenges of Wireless Sensor Networks at Signalized Intersections
## 5 Radioplanning Analysis
These results, along with the different planes of received power achieved for the whole scenario with the 3D Ray Launching Code, gives precise results but no homogeneous. As stated before and it is represented in Figure 7,  
Figure 16: Aerial view of the radial lines, which are represented in Figure 15, along the X-axis and \(Y\)-axis. Figure 15: Comparison of radial of received power (dBm) along the \(X\)-axis with the receiver sensitivity (a) ZigBee XBee Pro and ZigBee XBee for \(Y=6\) m; (b) ZigBee XBee Pro and ZigBee XBee for \(Y=14\) m; (c) BLE system; (d) Classic Bluetooth; (e) 802.11p Radio for \(Y=6\) m; (f) 802.11p Radio for \(Y=14\) m.  
the received power can be approximated for regular coverage zones. Depending of these results, and considering also the capacity of the system, an optimal design of the wireless sensor network coverage can be obtained. In order to represent a practical example, a schematic view of a final implementation with omnidirectional antennas for the different systems analyzed in the considered scenario is shown in Figure 17. It must be pointed out that the different radio coverage of the different circles has been obtained according to simulation results represented in Figure 15, and also taking into account the complete planes of received power, which have been obtained for the whole scenario and several examples have been represented in Figure 5 (\(XY\) planes), Figure 6 (\(YZ\) planes) and Figure 7 (\(XY\) planes divided by zones). Another important parameter which is important when designing a WSN is capacity. Once coverage levels are satisfied, we must have adequate capacity in order to have a good quality of service in the communication link. The channel capacity depends on the number of users who are  
Figure 17: Radioplanning coverage for different technologies within the considered environment. connected to the same communication link at the same time, the data rate of the transceivers and the number of gateways in which the information is gathered. Figures 18 and 19 show the channel capacity vs. the number of users for different data rates considered and for different number of gateways. It is observed as expected that the channel capacity increases for the same number of users, for every data rate considered, if the information is gathered with a higher number of gateways. Figure 19: Channel capacity (bps) vs. the number of users for different data rates for eight gateways considered. Figure 18: Channel capacity (bps) vs. the number of users for different number of gateways considered. (a) Data Rate = 250 Kbps; (b) Data Rate = 13 Mbps; (c) Data Rate = 18 Mbps; (d) Data Rate = 27 Mbps. The great difference in the channel capacity depending on the data rate considered is also very significant. This leads us to conclude that it is highly important to adequately fix the number of gateways in the design phase, depending of the data rate of the transceivers and the expected number of users.
## 3 Convective-Tracking Method and Dataset  
### Spatial Distributions  
In the current study, we focus on the sudden local convective storms observed over China and nearby regions (a domain bracketed from 70\({}^{\circ}\)E to 140\({}^{\circ}\)E and 15\({}^{\circ}\)N to 60\({}^{\circ}\)N, see Figure 1), which can be fully covered by H08/AHI data. This region has complex spatial and temporal structures. It covers both subtropical and mid-latitude regions, and its rainfall is often concentrated on long strips stretching for thousands of kilometers, affecting China, Japan, South Korea and the surrounding seas. During the East Asian summer monsoon, the impact of floods on human life and the economy is large, as finer seasonal space-time structures combined with narrow rivers are more sensitive to inter-annual variations [32]. Note that this area of interest includes some typical climate belts, complex atmospheric circulation, and various terrain, such as the tropical monsoon region, the subtropical monsoon climate region, the Qinghai-Tibet plateau climate region, etc. In summertime, the warm and humid air flow from the tropical ocean provides sufficient water and seasonal precipitation to the North of the area, resulting in a large number of strong convective systems, which is conducive to the establishment of a rich data set of strong convection systems [33].  
In this study, the rank of convective storm system is divided into three types on the basis of amount of quantitative precipitation, including (1) slight convective storm system with the maximum rain rate less than 2.5 mm/h, (2) medium-strength convection storm system with the maximum rain rate from 2.5 to 16 mm/h, and (3) severe convective storm system with the maximum rain rate exceeding 16 mm/h. The two instantaneous rain rates of 16 and 2.5 mm/h stem from a common classification criterion of heavy rainfall over China by the National Meteorological Center (NMC) of CMA and a standard definition of moderate rain by the American Meteorological Society (AMS) [34].
## 3 Results and Discussion
### Comparison among the Soxhlet, Percolation, and Thermosonication Techniques
This great difference between total electricity consumption was due to the relationship between the mass of phenolic extracted and the mass of ethanol used in the extraction techniques. A greater mass of solvent was used by Soxhlet and percolation extraction techniques. Therefore, the phenols extracted were more diluted in the ethanol. Moreover, although the Soxhlet and percolation techniques extracted a similar amount of solids from the raw material, the electricity cost was directly related to the process's total electricity consumption (Table 3), representing a cost of four times and two times higher (Soxhlet and percolation, respectively) than thermosonication (Figure 8b). However, it also demonstrated that the thermosonication technique was more selective in extracting the interest compounds. In addition to being a faster technique, thermosonication was also economically most advantageous, since the extract obtained was more concentrated in phenolic compounds. Figure 8: Electricity costs of each extraction technique to obtain: (**a**) 1 kg of fennel extract, (**b**) 1 kg of fennel dried solids, and (**c**) 1 kg of fennel phenolic compounds. Cost calculated considering the electrical energy price in Brazil per kWh (BRL 0.53) converted to dollars (USD 0.09). The cost of ethanol evaporation to obtain the dried solids and phenolic compounds was not considered.
# Pipeline Damage Identification Based on Additional Virtual Masses
## 3 Numerical Simulation  
A pipe structure model with fixed bearings at both ends is used to verify whether virtual masses can be used to increase the sensitivities of natural frequencies to local damages, as well as to illustrate the application principles of the proposed approach.
## 2 Materials and Methods
To have a survey aligned to the reference frame at the centimeter level, an official benchmark with known coordinates should be available in the area. Alternatively, an ad hoc benchmark should be surveyed using geodetic class GNSS receivers. Each of the tests described below was performed on the same day, in the same session, while keeping the master receiver in the same position, so that no issues due to the initialization process would bias the results. All tests were performed by repeating several runs (namely 24, 31, and 26) to guarantee a sufficient redundancy to calculate some statistics. Moreover, the choice of repeating the same test several times allowed evidencing unusual behaviors of the navigation system, which would not have been spotted by performing simpler testing, or, on the other hand, which would have dramatically affected the computed statistics. The full potentiality of the Ublox-F9P receiver was used, enabling data acquisition from the four available GNSS constellations, namely GPS, GLONASS, Galileo, and BeiDou. In the analysis of the results, there will be frequent reference to the working conditions of the RTK as \"fix\", \"float\", or \"single\". It is worth noting that with the term \"fixed solution\", we indicate the condition in which the receiver fixes as natural numbers the \"initial phase ambiguities\" of the GNSS carrier signals [26], thus allowing the system to work at its best precision. Without further details, the phase ambiguity is the number of whole cycles in the GNSS carrier signal that occurred between its departure and its reception by the receiver at the instant the satellite starts being tracked. This number remains constant over time until the satellite signal is lost, even if re-gained later, and constitutes one of the unknowns in the positioning problem. The condition of \"float solution\" implies the ambiguities to be defined only as real numbers, which usually leads to meter-level precisions, whereas what we will refer to as a \"single solution\" is technically a solution obtained without using phase observations. This latter case can be due to the impossibility of the rover receiving the master station's corrections or lacking satellite signals. Finally, \"multipath\" [27] is the phenomenon that wrongly lengthens the measurement of the satellite-receiver distance because of signal reflection by metal/regular surfaces close to the GNSS antenna.