esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A Measurement Methodology (Development of Measurement Methodology)
### Measurement Methodology Using a Fixed-Wing Aircraft (Plane)  
Using a fixed-wing aircraft, the measurement speed and the amount of data collected increase significantly, due to the much higher aircraft speed and longer flight time (and distance). When using a \"Spartan\" type aircraft, the flight time is typically about 2-3 h (depending on the conditions), with a flight speed of 16 m/s.  
During measurements, the aircraft flies in circles around the antenna being measured, changing the altitude of the flight circle each time (Figure 15a).  
Typically, during the measurement, the altitude of the flight circle is changed every 5 m (other altitude change steps are also possible). In all flight stages, antenna radiation measurements are conducted. In this way, due to measurements at different altitudes, a 3D antenna measurement diagram is obtained (Figure 15b).  
The obtained 3D diagram provides much more information about the antenna's radiation compared to the 2D diagram (which is obtained using a rotary-wing aircraft). Additionally, in this case, it is not necessary to perform measurements by ascending vertically, as measurements taken during the circular flight at different altitudes ensure the detection of the radiation peak when processing the data.  
In all cases, the amount of measurement data obtained by the fixed-wing UAV is much larger (and obtained much faster) compared to using a rotary-wing aircraft. However, due to the simpler use of a rotary-wing aircraft (easy take-off and landing), both methods (or one of them) should be used, depending on the conditions.
## 5 Conclusions  
In this work, we presented and tested an RST-based algorithm to identify and map volcanic thermal features through ASTER TIR data, processed under the GEE platform. By analyzing results retrieved in three active volcanic areas (i.e., Etna, Shishadin and Shimodake), through comparison with the AVA database, we found that RASTer was capable of successfully identifying thermal anomalies (as indicated by the relevant number of unique detections), also providing accurate information about these features in terms of shape and spatial extent. These outcomes demonstrate that RASTer may support the monitoring of thermal volcanic activity from space, despite some limitations. Main factors that could affect the RASTer detections are in fact bush fires, when occurring along the volcano flanks, and clouds that may obscure the underlying thermal anomalies. The computation of the normalized index defined in Equation (3) according to the general RST detection scheme, along with the implementation of an iterative procedure devoted to filter out signal outliers under the GEE environment, should further increase the RASTer performance and will be the aim of future investigations.  
This study also encourages the RST implementation on data from previous Landsat missions and relative TIR sensors (Thematic Mapper and Enhanced Thematic Mapper plus). Those data should favor the investigation of thermal volcanic activities occurring in the past. Finally, this work opens new scenarios regarding the possible RST implementation on Landsat-8/TIRS (Thermal Infrared Sensor) data at 100 m spatial resolution, especially  
Figure 13: Thermal anomaly (red/yellow pixels) over Mt. Etna area detected by RASTer on 17 February 2021.  
in view of the launch of the next Landsat-9 satellite, which will improve the temporal resolution of Landsat observations from 16 to 8 days.  
Conceptualization, N.G. and F.M.; software, N.G.; methodology, V.T., N.P. and N.G., validation and formal analysis, N.G., F.M. and M.N.; writing--original draft preparation, N.G. and F.M.; writing--review and editing, N.P., M.N. and V.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available under the Google Earth Engine platform.  
The authors declare no conflict of interest.
# Using Artificial Neural Network Models to Assess Hurricane Damage through Transfer Learning
## 3 Transfer Learning
### Artificial Neural Network Models
#### 3.2.1 ResNet  
The ResNet architecture was developed with a deep residual learning framework to directly address the issue of degradation of training accuracy in deeper networks that begin to converge [26]. Identity shortcut connections within the ResNet architecture do not rely on parameters and allow a continuous flow of information between layers as well as additional learning of residual functions. Thus, the residual net framework allows for easier optimization of the residual mapping and increased accuracy from enhanced depth of the residual nets [26]. The 50-layer ResNet contains a 3-layer bottleneck design that results in a more efficient model when paired with the identity shortcuts. We incorporated the 50-layer ResNet architecture into our model to match the target input resolution and simplistic model structure.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Damage Detection Types** & **\# of Samples** & **Percentage** \\ \hline damaged roof & 365 & 45.625\% \\ damaged wall & 281 & 35.125\% \\ flood damage & 167 & 20.875\% \\ structural damage & 145 & 18.125\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of images for the building damage detection task. The selected 800 images were divided into four categories to characterize damage inflicted upon different kinds of objects or structures by historical hurricanes. The total number of object samples of 958 is larger than the number of images (i.e., 800) due to the fact that an individual image can include multiple damaged objects.
### Impediments in Channel Coding for URLLC  
One of the main characteristics of 5G NR is the use of two new error-correcting codes for control and data channels of the enhanced mobile broadband (eMBB) use case. Low-density parity-check (LDPC) codes are adopted for data channels replacing Turbo codes, whereas polar codes are adopted for control channels [9]. For URLLC applications, the channel coding requirement for low latency is short-block-length codes, while a low error rate is necessary for ultra-reliability. Although LDPC, Turbo, and polar codes perform near channel capacity at large block lengths, their performance degrades at small block lengths [10]. Further, in current over-the-air communication, data are transmitted in packets, and the encoding/decoding algorithms of contemporary codes are very time consuming.  
Figure 1: Classification of universal decoding techniques.  
Also, most of the state-of-the-art decoders like successive cancellation list (SCL) can decode cyclic redundancy check assisted polar codes (CA-Polar) with near maximum likelihood (ML) performance, yet large list sizes result in strong data dependency and suffer from a huge decoding delay [11]. This contradicts the low-latency requirement in URLLC, thus necessitating the application of universal decoding techniques.
## 4 Experimental Results and Discussion
### Results
#### 4.4.2 Robustness for Various Attacks
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & & \multicolumn{2}{c}{**BER (\%)**} \\ \cline{4-6}
**Attack Type** & **Attack** & **Strength** & \multicolumn{2}{c}{**WM1**} & \multicolumn{2}{c}{**WM2**} & \multicolumn{2}{c}{**WM3**} \\  & & & **Random** & \multicolumn{2}{c}{**(Average)**} & \multicolumn{2}{c}{**(\(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{ \mathbf{ \mathbf{ \mathbf{   }}}}}}}}}}}\))**} \\ \hline  & No attack & - & 0.7015 & 0.6696 & 0.6696 \\ \hline \multirow{4}{*}{Gaussian filtering} & 3 \(\times\) 3 & 1.5944 & 1.7538 & 2.0089 \\  & 5 \(\times\) 5 & 7.5255 & 7.3023 & 8.4503 \\  & 7 \(\times\) 7 & 11.5115 & 11.2883 & 11.5115 \\  & 9 \(\times\) 9 & 18.463 & 19.1964 & 17.5064 \\ \hline \multirow{4}{*}{Arverage filtering} & 3 \(\times\) 3 & 4.273 & 3.9541 & 4.1135 \\  & 5 \(\times\) 5 & 5.2296 & 5.3571 & 4.7832 \\ \hline \multirow{4}{*}{Median filtering} & 3 \(\times\) 3 & 8.4184 & 7.8763 & 7.8763 \\  & 5 \(\times\) 5 & 10.5548 & 10.8418 & 11.0969 \\ \hline \multirow{4}{*}{Pixel-value} & 0.01 & 0.861 & 0.7972 & 0.7972 \\  & 0.03 & 1.1798 & 1.0204 & 1.1161 \\  & 0.05 & 1.4031 & 1.2436 & 1.3393 \\  & noise addition & 0.07 & 1.8176 & 1.5306 & 1.8495 \\  & 0.09 & 2.5829 & 3.1250 & 2.0408 \\ \hline \multirow{4}{*}{Gaussian noise addition} & \(\sigma\) = 0.01 & 0.8291 & 0.7972 & 0.7334 \\  & \(\sigma\) = 0.03 & 1.977 & 1.6582 & 1.977 \\  & \(\sigma\) = 0.05 & 6.0906 & 6.8878 & 5.6441 \\  & \(\sigma\) = 0.08 & 11.9898 & 12.8508 & 13.3291 \\ \hline \multirow{4}{*}{Sharpening} & 5-point stencil & 3.2844 & 3.6671 & 3.5714 \\  & 9-point stencil & 3.9222 & 4.6237 & 4.3686 \\ \hline \multirow{4}{*}{JPEG} & 90 & 0.9566 & 0.8610 & 0.7653 \\  & 70 & 4.2411 & 3.9860 & 4.2411 \\  & 50 & 8.0676 & 7.9082 & 9.0561 \\  & 30 & 14.8916 & 15.1148 & 14.8278 \\  & 10 & 31.4732 & 31.8240 & 33.4184 \\ \hline \multirow{4}{*}{Rotation} & 15 & 2.0727 & 1.8814 & 1.9133 \\  & 30 & 4.9107 & 4.8151 & 5.2296 \\  & 45 & 5.0383 & 5.1339 & 5.7398 \\  & 60 & 3.8265 & 3.6671 & 4.7194 \\  & 75 & 1.7857 & 1.8176 & 1.9452 \\ \hline \multirow{4}{*}{Crop} & 0.9 & 0.7015 & 1.1798 & 0.9247 \\  & 0.7 & 2.1365 & 4.3367 & 13.4566 \\  & 0.5 & 14.6365 & 16.7411 & 11.4796 \\  & 0.3 & 24.9681 & 21.3967 & 29.1773 \\  & 0.1 & 39.6365 & 48.5013 & 38.361 \\ \cline{2-6}  & \multirow{4}{*}{Cropout} & 0.1 & 2.2003 & 3.0293 & 1.7538 \\  & & 0.3 & 9.0561 & 12.4362 & 9.088 \\ \cline{1-1}  & & 0.5 & 17.0281 & 24.2666 & 20.9184 \\ \cline{1-1}  & & 0.7 & 24.0434 & 33.4184 & 25.4783 \\ \cline{1-1}  & & 0.9 & 34.8533 & 44.9298 & 37.3724 \\ \hline \multirow{4}{*}{Dropout} & 0.9 & 0.9247 & 0.9247 & 1.0523 \\ \cline{1-1}  & 0.7 & 2.4554 & 2.2003 & 2.3278 \\ \cline{1-1}  & 0.5 & 6.25 & 5.4528 & 5.5166 \\ \cline{1-1}  & 0.3 & 14.8916 & 15.5612 & 15.1148 \\ \cline{1-1}  & 0.1 & 34.1199 & 37.3087 & 34.7577 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average bit error ratio (BER) values of extracted watermarks resulting from various attacks.
## 3 Results
### Tunnel Support Design by RMi System on Discontinuous Ground Using the Q-System Support Chart  
As shown in Table 6, \(SL\approx 1/SRF\)[22]. If this relation for \(SL\) characterization in highly stressed, jointed rock mass is used, the already heavier supports suggested by the _RMi_ system would be further increased. Since the \(SRF\) calculation was based on the \(Q\)-system support chart, an attempted has been made to use it for the _RMi_ system, again in this particular case of a highly stressed, jointed rock mass.  
The modified \(Q\) system from Equation (2) can be rewritten as Equation (24) using \(c=J_{r}/J_{a}\). Similarly, the ground condition factors in Equation (13) can be rewritten as Equation (25) by introducing \(N_{j}\) and \(C_{0}\) into it from Equation (14), and replacing \(V_{b}\) in terms of \(RQD\) using Equation (17). The constants \(a\) and \(b\) depend on \(jR/jA\):  
\[Q_{c}=\left(\frac{c}{100}\right)\times\left(\frac{RQD}{J_{n}}\right)\times \left(\frac{\sigma_{c}}{SRF}\right)\times\left(J_{w}\right) \tag{24}\]  
\[G_{c}^{*}=a\times\left(\frac{\sigma_{c}}{SRF}\right)\times\left(\frac{1}{\left( 110-RQD\right)^{b}}\right)\times\left(\frac{N_{j}}{C_{0}}\right), \tag{25}\]  
where \(SRF=f\) (\(\sigma_{c},\sigma_{c}/\sigma_{1}\) and \(RQD/J_{n}\)), and the constants \(a\), \(b\), and \(c\) are given in Table 7, depending on the case number as in Table 4.  
As \(J_{w}\) and \(C_{0}\) are not properly included in the _RMi_ and \(Q_{c}\) systems, respectively [15; 18], taking \(J_{w}\) = one (dry condition) and \(C_{0}\) = one (favorable joint orientation), the above two equations are based on the same parameters as the relation of \(N_{j}\), and the number of joint sets is given in Equation (26) [23]:  
\[N_{j}=\frac{3}{n_{j}}, \tag{26}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Stress Level (SL)** & **Approximate Overburden** & **Rating** \\ \hline Very low & \(<\)10 m & 0.1 \\ Low & 10–35 m & 0.5 \\ Moderate & 35–350 m & 1 \\ High & \(>\)350 m & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Stress levels and their ratings per _RMi_ system.  
Figure 9: Actual and _RMi_-suggested support differences in term of (**a**) rock bolt spacing (**b**) shotcrete thickness (positive value on the x-axis indicates that the recommended support is heavier than the actual).  
where \(\eta_{j}\) is the number of joint sets.  
The ratio between \(G_{c}^{*}\) and \(Q_{c}\) can be developed from the available data using Equations (24) and (25), keeping the limitations of the data of the four projects in this study.  
Knowing the ratio, Table 8 can be used to adjust \(G_{c}^{*}\) for joint orientation and corresponding \(Q_{c}\). This \(Q_{c}\) value is then adjusted for \(J_{w}\) and used for the recommended support from the \(Q\)-system support chart. Ultimately, the groundwater factor is included in the \(RMi\) system and \(C_{0}\) is included in the \(Q\) system.
# Performance and Challenges of Service-Oriented Architecture for Wireless Sensor Networks
## 7 Service-Oriented Architectures Approaches for WSNs
### SOA Model for Sensor Networks  
The Service-Oriented Model is designed for WSNs with internet (IP network) through different components such as Application Agent (AA), Resource Manager (RM), Register Agent (RA), and multi-gateways [78]. The architecture of these components performs as a service provider and the CQM (Complex Query Management) that exists among the WSN and internet from the gateway can be separated. This design provides a flexible architecture by using multi-gateways with RM. The architecture provides suitable mechanisms that guarantee all data from the sensors is transmitted correctly to the subscribed users. The system requires data to be located closest to the users and the filtering mechanism to be closest to the source. This mechanism should use a method to keep this data in a secure manner. The drawback of this approach is that it does not test in real-time [78].
## 4 Results and Discussion
### Results of Landslide Detection
Creating accurate objects through the optimized segmentation process allowed the use of spatial, orthophoto, and textural features for feature detection. Landslides should be differentiated from non-landslides based on the accurate segmentation of spatial and textural features. The selection of relevant features in landslide detection relies on the experience of the analysts. Thus, a feature selection method is crucial for accurate and reliable landslide detection. The optimal features selected via the CFS method simplified landslide detection by the NN model. Computation time and reliance on the expert knowledge of the analyst were reduced. Moreover, the optimized parameters of the NN models improved the performance of the models, reduced the complexity of the models, and decreased overfitting in the training sample.
## 2 Related Works
Different values of other indicators (for passengers in particular) were determined for roads with the same IRI values; therefore, alternative roughness or vehicle/passenger response indicators (such as the RMS value of acceleration) could be used for specific pavements. Some significant aspects of comfort evaluation in the cabins of different bus topologies were also highlighted as an IRI limitation [59]. First, it was argued that, in the case of similar IRI values, it was possible that there could be three different road profiles and that passengers would experience different vibration conditions. Then, insufficient sensitivity to lateral motions (acceleration) was proven based on experimental data. Due to the limited IRI evaluation of specific or unpaved road surfaces, dynamic response-based devices or road profile scanners have been used [45]. Devices added onto a vehicle have shown a high enough correlation with the IRI and allowed real-time measurements. However, evaluations of different roughness indicators have shown that nearly all the roughness measuring devices employed around the world could produce measurements on the same scale, as long as that scale was suitably selected; therefore, the IRI was defined [60; 61]. The World Bank developed an IRI after conducting the International Road Roughness Experiment (IRRE) in Brazil in 1982. The aim of the experiment was to harmonise the existing indices for evaluating pavement roughness. Due to its stability over time and transferability over the world, it has become the most widely employed pavement index (not only for roughness assessment) in developed countries [62; 63; 64; 65] and in developing countries [66; 67; 68]. Therefore, it is preferable to adopt an existing and known index to gravel roads instead of developing a new one. Road infrastructure in different regions has been developed unevenly as there are various gravel coatings in suburban and rural areas. The majority of studies on vehicle-road interaction have focused on asphalt pavement. Evaluations of gravel road conditions have commonly been performed using methodologies developed several decades ago, and therefore new solutions are needed.
## 2 Materials and Methods
### Waste Collection Practices in Saudi Arabia  
The general practice of the collection of MSW in Saudi Arabia is based on a communal system, where open collection containers (5 m\({}^{3}\)) are kept in public places and the nearby residents and commercial users dump their comingled wastes (mixed solid waste) in the container. Generally, old shopping bags or trash plastic bags are used for bagging the waste and dumping into the container [45]. In some places, curbside collections are observed, where small barrel-type containers (0.25 m\({}^{3}\)) are used to collect the residents' disposed waste. Compaction or semi-compaction waste vehicles are used for curbside collection of waste, assisted by the manual emptying of waste containers to the compaction vehicles. The local municipality employs contractors to collect the waste, which is hauled either to the transfer station or directly to the landfill site. Most of the landfilled sites have no facilities for the collection of landfill gas, with some exceptions of Madinah and Jeddah (new and old) landfills [46].  
For the management of non-hazardous industrial wastes in Saudi Arabia, MODON (Saudi Authority for Industrial Cities and Technology Zones) and Royal Commission for Juhail and Yanbu (RCJY) are the responsible organizations. MODON offers non-hazardous waste collection services from factories through its own contractor, who dumps the wastes in the landfill. On the other hand, RCJY encourages the industries under its jurisdiction to recycle or reuse industrial waste.  
The composition of municipal solid waste in Saudi Arabia varies based on community to community, rural/urban, and agricultural/non-agricultural areas. In urban areas, food waste is the largest fraction of MSW, which is about 50.6% [47], followed by plastic (5-17%), textiles (6.4%), glass (4.6%), and metals (8.1%) [48]. In areas where agricultural activities including date palm farming are prominent, a considerable quantity of green waste (about 6.8%) is observed [45]. The composition of municipal solid waste used in this paper for estimation of CH\({}_{4}\) generation from landfill is shown in Figure 2. On the other hand, industrial waste is mainly composed of solid and liquid chemical waste, plastic, ferrous and non-ferrous metal, wood, paper, rubber, textiles, and electronic waste, as shown in Figure 2.
## 2 Materials and Methods
### Construction of the B-Camera
#### 2.2.2 Microcontroller Unit  
The main tasks of the microcontroller unit are to read the sensors, perform basic processing and to send the result to the PDU. We employed an Arduino microcontroller  
Figure 1: A block diagram of the B-Camera showing its subsystems and their interactions. It consists of an array of magnetometers and a multiplexer (implemented as an Arduino shield), a microcontroller unit (Arduino), a communication unit (Bluetooth or USB port) and a processing and display unit (implemented in a smartphone or a computer).  
because of its availability, affordability and its openness. We read the data using the four digital channels of the microcontroller and translated the integer (scaled) field values into re-scaled proper values in gauss units. The results were then sent to the PDU by using a USB port, if the PDU was implemented on a computer, or by using a Bluetooth channel for the smartphone-based PDU.
## Results and Discussion
### SEM Analysis  
The SEM was used to further observe the microscopic morphology of the composite coatings. Figure 8a-e and a'-e' show the surface and cross section microscopic structure of WEP, PGT-0.5%/WEP, PGT-1%/WEP, PGT-2%/WEP, and PGT-3%/WEP, respectively. It can be seen from Figure 8a that the surface of the WEP coating is rough and there are a large number of micropores, the WEP coating cross section (Figure 8a') exhibits a relatively smooth river-shaped fracture surface, and the fracture surface has many microporous structures, which may be due to the micropores remaining on the surface after the resin curing. With the nano PDA@GO-TiO\({}_{2}\) composites added to the surface of the coating, the micropores gradually decrease among them, the surface of PGT-2%/WEP coating (Figure 8d) has a single microstructure and high regularity. In addition, it can be seen from the cross-section of PGT-2%/WEP coating (Figure 8d') that nano-PDA-GO-TiO\({}_{2}\) composite exhibits good compatibility with resin. This is because DA forms a polydopamine (PDA) layer on the surface of GO by self-polymerization and deposition, which improves the compatibility of GO with waterborne epoxy resin and enhances the interfacial adhesion between GO and epoxy resin. However, PGT-3%/WEP coating (Figure 8e) has defects (microracks) in the coating, which may be due to the accumulation of excess nano-PDA @GO-TiO\({}_{2}\) composites outside the voids of the resin space structure, which cannot be completely covered by the resin. This can be confirmed by the cross-section of the PGT-3%/WEP coating (Figure 8e').  
Figure 7: Transmission electron microscopy (TEM) images of GO (**a,b**) and nano-PDA@GO-TiO\({}_{2}\) composites (**c–e**).
## 2 Optimization Methodology
### Contour Tracking Control
#### 2.1.4. Fitness Functions  
Fitness function is required in the optimization process to transform the contour tracking error into a single arithmetic value for optimization process. In the present work, three types of fitness functions (ISE, IAE, and MSDAE) are adopted and respectively given by:  
\[IAE =\int\limits_{0}^{\infty}|e_{c}|dt \tag{6}\] \[ISE =\int\limits_{0}^{\infty}e_{c}^{2}dt \tag{7}\]  
\[MSDAE =|\overline{e_{c}}|+\sigma|e_{c}|+max(|e_{c}|) \tag{8}\]  
where \(e_{c}\) is the contour tracking error, \(|\overline{e_{c}}|\) is the mean value of contour tracking error, \(\sigma|e_{c}|\) is the standard deviation of contour tracking error, and \(max(|e_{c}|)\) is the maximum value of contour tracking error.