esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Results for Poland
Their impact is so multifaceted that it covers almost all types of transport, except for pipeline transport. Adverse weather conditions can lead to a decrease in the speed of transport vehicles, an increase in the duration of delays and the number of road traffic accidents, infrastructure damage, and more. All these consequences can result in significant losses, the generalized classification of which, by weather conditions and types of transport, is presented in Table 3.  
Thus, the generalization of the consequences of adverse weather conditions for the effective functioning of the transport industry can lead to the following:  
- A decrease in the capacity of transport networks due to factors that prevent their full operational utilization;  
- Economic losses caused by damage to cargo and transport infrastructure and the necessity of their restoration;  
- A deterioration in the safety of transport industry workers and passengers. From the perspective of existing climate zones, Ukraine and Poland are located within the area of all the listed weather risks. Therefore, during the economic-mathematical modeling of the transport industry's activities, these factors must be taken into account. Accordingly, the modeling period in this study is set as one year. Investigating the correlation between a country's economic expansion and the transportation industry requires an examination of well-established inter-industry linkages. As seen in Figure 1, Ukraine and Poland have different GDP values and total production structures. Each industry has its own needs for transport services, depending on the specifics of economic activity and technological development. Therefore, scenario modeling should consider these factors and the differences between Ukraine and Poland. For this purpose, in this study, we used the Leontief model [17] based on statistical tables of \"Cost-Output\" that are included in the system of national accounts [33]. Using Equations (8) and (9), scenarios of a 1% increase in final demand for each industry's products were calculated, with a simultaneous assessment of the relevant shifts in expanding transport service volumes for Poland, as illustrated in Figure 4. The difference was that the transport industry breakdown considered ground and pipeline transport, water and air transport, and warehousing. It should be noted that, in Poland, various sectors of the economy are much more closely related to transport logistics. The leader remains the manufacturing industry, where the elasticity coefficient is 0.5-0.6%. That is, every additional percentage of economic growth in the industry automatically increases the total demand for transportation by at least 0.5%. There is also a high dependence on trade and other types of activities.
## 3 Results and Discussion
### Reaction Mechanism  
The reactions of organic removal begin with the FA decomposition and then O\({}_{2}\) reduction to H\({}_{2}\)O\({}_{2}\). Control experiments in Figure 7a confirmed the effect of FA and O\({}_{2}\), and the results also suggested the removal of AR 73 in the FA/air/FeNi-Pd@CN system cannot be ascribed to adsorption. Once H\({}_{2}\)O\({}_{2}\) was produced, Fenton reaction could happen due to the existence of Fe and Ni; thus, the system involves many reactive oxygen species (ROS), such as H\({}_{2}\)O\({}_{2}\), hydrogen superoxide radical (HO\({}_{2}\bullet\)), and hydroxyl radical (\(\bullet\)OH) [37; 38]. Scavengers including isopropanol (IPA, 3 M) and benzoquinone (BQ, 1 mM) are added in the reaction system to capture \(\bullet\)OH and HO\({}_{2}\bullet\), respectively. The results in Figure 7b show that \(\bullet\)OH and HO\({}_{2}\bullet\) have a strong inhibitory effect in varying proportions. ESR spin trapping experiments with dimethylpyridine N-oxide (DMPO), as the trapping agent also shows typical signals of DMPO-\(\bullet\)OH and DMPO-\(\bullet\)O\({}_{2}\)\({}^{-}\) in Figure 7c. HO\({}_{2}\bullet\) may participate in the Fe\({}^{3+}\) reduction and is a crucial ROS in this system. The activity of Fe towards Fenton reaction is higher than Ni; thus, it is not surprising that the removal rate of AR 73 catalyzed by Fe-Pd@CN almost coincides with that of Ni-Pd@CN, despite the latter having more H\({}_{2}\)O\({}_{2}\) in the system. Other experiments also pointed out that the presence of Ni could increase the \(\bullet\)OH generation, probably facilitating the reduction in Fe\({}^{3+}\) to Fe\({}^{2+}\)[39].  
Figure 6: The H\({}_{2}\)O\({}_{2}\) production ability over as-prepared catalysts (Catalyst dosage: 2 g/L; FA: 46.6 mmol/L; air; 200 mL/min).  
Figure 7: (**a**) Degradation of FeNi-Pd@CN in different environments (AR 73 200 mg/L), (**b**) the H\({}_{2}\)O\({}_{2}\) production ability over as-prepared catalysts, and (**b**) effect of IPA, BQ on the AR 73 degradation efficiency. (**c**) ESR DMPO-\(\bullet\)OH and methanol-\(\bullet\)O\({}_{2}\)\({}^{-}\) spectra of FeNi-Pd@CN catalyst (catalyst dosage: 2 g/L; FA: 46.6 mmol/L; air; 200 mL/min).
## 2 Materials and Methods
### Development of Models  
In this article, five ML models were implemented using the same database and inputs. The first model implemented was a simple Decision Tree (DT) regressor. A DT can be unstable since small variations in the data might result in a completely different generated tree. A single tree usually exhibits high variance and tends to overfit, not generalizing well to new data [71]. This problem is attenuated by using several trees within an ensemble. Ensemble methods seek to combine the predictions of several base estimators to improve generalizability, accuracy and robustness over a single estimator. The other four models implemented in this study involve Ensemble Tree (ET) methodologies. For the sake of the readers, the following subsections briefly present an overview of each implemented method. Training data exist in the form of a training set \(\{(x_{i},y_{i})\}_{i=1}^{n}\), in which \(x_{i}\in\mathbb{R}^{p}\) represents the input features and \(y_{i}\in\mathbb{R}\) represents the torsional capacity for \(p\) inputs and \(n\) samples.
# Reliability Prediction of Mixed-Signal Module Based on Multi-Stress Field Failure Mechanisms
## 6 Reliability Prediction of the Communication Module Under the Mission Profile
### Key Component Performance Degradation Reliability Prediction
By substituting all the parameters into the long-term NBTI degradation model and considering the distribution of \(t_{\text{ox}}\), the degradation curves of the threshold voltage over time for the 122,900 PMOS transistors are calculated. The average degradation curve is shown in Figure 21.  
By considering the failure threshold of the threshold voltage, the time at which the threshold voltage reaches 1.2 V in each degradation curve is considered the lifetime of the PMOS under the effect of NBTI. The lifetimes of the 122,900 PMOS transistors are then calculated, and the lifetime histogram is plotted, as shown in Figure 22. The histogram follows a normal distribution, which proves the accuracy of the selected model parameters and simulation conditions. A sufficient amount of virtual data helps to reduce the impact of sample errors on the results. Using the 122,900 PMOS lifetimes, the reliability curve of PMOS is plotted, as shown in Figure 23. Within the present operational cycles, the performance degradation of PMOS is evident, especially after prolonged operation, where the reliability decreases significantly. Compared to historical experimental data, the lifetime results are consistent. Figure 21: Threshold voltage degradation curve of PMOS in FPGA. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter Name** & **Parameter Value** & **Parameter Name** & **Parameter Value** \\ \hline \(\epsilon_{\text{ox}}\) (F/m) & 3.45 \(\times\) 10\({}^{-11}\) & \(E_{\text{a}}\) (eV) & 0.49 \\ \(K\) (C\({}^{-0.5}\cdot\)nm\({}^{-2.5}\)) & 8 \(\times\) 10\({}^{5}\) & \(\xi_{1}\) & 0.9 \\ \(E_{\text{o}}\) (V/nm) & 0.335 & \(\xi_{2}\) & 0.5 \\ \(T_{0}\) (s\(\cdot\)nm\({}^{2}\)) & 1 \(\times\) 10\({}^{-10}\) & \(n\) & 1/6 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: NBTI degradation model parameters. Similar to the reliability-prediction process for FPGA considering NBTI, the reliability curve for SRAM units is also plotted, as shown in Figure 24.  
Figure 23: Reliability curve of PMOS in FPGA. Figure 22: Lifetime histogram of PMOS in FPGA.
###### Abstract
Additionally, in the extant literature, ANN models have been extensively used for solar radiation prediction, therefore, the performance of these hybrid models is compared with that of the developed ANN model. The specific objective of this study is:  
* The development of two novel hybrid neural network models for solar radiation prediction. * Integration of CNN with other models to enhance prediction robustness and accuracy. * Development of solar radiation predictive hybrid models adaptable to different climatic conditions. * Comparison of these hybrid models and ANN model performances. The hybrid models are applied for solar radiation prediction in ten countries from the five geopolitical zones in Africa. This study is important as the models developed will be instrumental in the calculation/estimation of solar-powered systems' performance and subsequently increase solar energy utilization for electricity generation and other purposes. Thereby reducing the lack of access to electricity in Africa significantly. The development of these models is justified in the subsequent section. The materials and methods (including the data preparation, case study, model development, etc.) used in this study are reported comprehensively in Section 2. The results from this study are discussed and compared with existing works of literature in Section 3 while the concluding remarks from the entire study are highlighted in Section 4.
# Monitoring the Health and Residence Conditions of Elderly People, Using LoRa and The Things Network
## 4 Materials and Methods
### System Concept and Prototype  
The prototype was built to test the proposed system as shown in Figure 6. We can observe the temperature, humidity, GPS location displayed on the screen, and the distance to the residence. In addition, the accelerometer data are also collected.  
Naturally, the prototype in Figure 6 is not usable by an older adult as it is, with the prototyping boards requiring careful development and miniaturization work to avoid unnecessary energy consumption. However, it is the next step in technology transfer to a company interested in marketing the product. It is not our primary goal, so our focus is on demonstrating the capabilities of the components used and the software developed to achieve the goals.  
The collected data are sent via LoRa communication to the Gateway and the TTN. Data received by TTN do not remain on the system unless a Data Integration is configured.  
Figure 6: Functional prototype in operation powered with USB Power Bank.  
To store the data, we can set up a Data Integration connection on the TTN in a Data Storage account, which allows data to be stored temporarily for seven days in the free version. To have further access to the data, The Things Network access key authorization must be set to make queries to the storage system. The result is obtained in JSON format. For the data to be entered into the database developed to support the system, it is necessary to extract and convert the relevant data and subsequently insert them into the appropriate tables.  
Figure 7 shows the data obtained in the query executed over the Data Storage database, integrated with TTN.  
From this API (Application Program Interface), we can obtain the data in other applications by programming the connection to the database, periodically executing triggers to the database query to populate the global database for each user and each device.
# High-Gain Waveguide-Fed Circularly Polarized Spidron Fractal Aperture Antenna
## 2 Antenna Design  
Figure 1 presents the geometry of the proposed antenna, with Figure 1a showing an exploded three-dimensional (3-D) view. The antenna includes a Taconic RF-35 dielectric substrate (\(h_{sub}\) = 0.51 mm, \(\varepsilon_{r}\) = 3.5, and tan\(\delta\) = 0.0018), a ground plane mounted on the bottom layer of the dielectric substrate, a superstrate, and a WR-90 waveguide-to-coax adapter. The superstrate is a Rogers RT/duroid 6010 material (\(h_{sup}\) = 0.64 mm, \(\varepsilon_{r}\) = 10.2, and tan\(\delta\) = 0.0023). The Spidron fractal aperture is etched on the ground plane of the dielectric substrate. The dimensions of the substrate and the superstrate with four truncated corners are identical to those of the flange of the adapter, and these components can also serve as radome layers to protect the antenna aperture. The substrate is directly attached to the flange, and the superstrate is located above the ground plane at a distance of \(S\) (see Figure 1b).  
Figure 1: Topology of the proposed antenna: (**a**) exploded 3-D view; (**b**) side view; (**c**) Spidron fractal aperture.  
The Spidron fractal structure was initially utilized in the design of a wideband CP slot antenna [13]. In subsequent works [14; 15], a dual-band CP patch antenna and a wideband CP dielectric resonator antenna with Spidron fractal geometry were successfully designed. In the design proposed here, a Spidron fractal system with nine iterations is implemented. As shown in Figure 1c, the proposed Spidron fractal aperture is formed by merging two nine-iterated Spidron fractal slots (here, Spidron fractal slot #1 and Spidron fractal slot #2). These two Spidron fractal slots share a common edge, as indicated by the dashed line (see Figure 1c). The Spidron fractal aperture is positioned such that vertex A of Spidron fractal slot #2 is located at the position of \((-dx,dy)\) with respect to the global coordinate system. A simulation of the antenna was conducted using the ANSYS HFSS simulation software (ANSYS, Inc. Canonsburg, PA, USA). The proposed antenna was optimized to achieve right-handed circular polarization (RHCP) around the designed frequency of 11.1 GHz. The optimized dimensional parameters are given in Table 1.
## 6 Conclusion  
The ultimate goal of WSNs is to increase the sensing precision, accuracy of sensed information and to reduce redundant data of communications. The aim of precipitation monitoring, for example, is to improve the accuracy and real-time of precipitation data in the basin as well as to reduce the information loss of precipitation details. Existing hydrological stations must be built based on the conditions of good power supply, communications and transportation. The data from a single hydrographic station are often used to represent the precipitation information in its radius of a few hundred meters or even a few kilometers, and it would seriously affect the accuracy of regional precipitation information. WSNs for monitored environment avoid the dependence of monitor sites on the hydrological infrastructure, and geographical and terrain factors can be fully taken into account in the deployment of sensor nodes. The precipitation information collected via sensors can be representative for meteorological environment of the monitored region. In addition, the reduced cost of building stations and the reduced size of nodes make it possible to deploy more nodes. Based on requirements, the amount of precipitation information within the same area can be several, several hundred times or even higher than that of the existing hydrological station network. The decisions supported by that amount of information are much more accurate than by the traditional network.  
In wireless sensor networks, the performance of routing protocols determines the overall performance of the network, and the WSN routing protocols have always been a hot topic. This paper takes outdoor environmental monitoring applications such as meteorology and hydrology and wetland ecology field as the background. The paper proposes an improved LEACH routing algorithm based on the differential evolution algorithm (DE_LEACH routing algorithm) to address the shortcomings of the traditional LEACH routing protocol. The improved algorithm uses the simple and fast search features of the differential evolution algorithm (DE) to optimize multiple objectives for the selection process of cluster heads to improve energy efficiency and stability of the application system. The simulation results prove that the DE_LEACH routing algorithm proposed in this paper can effectively prevent blind nodes in normal clustering routing algorithms, improve the life cycle of large-scale WSNs as well as the quality, and make WSN routing protocols with the improvement in this paper more suitable for outdoor environmental monitoring applications such as meteorology and hydrology, wetland ecology field than other routing protocols.  
The present research results are mainly on how to avoid an unreasonable distribution of cluster heads and how to prevent the emergence of blind nodes. Further studies will focus on how to improve the selection for each parameter to adaptively meet the needs of various applications in order to reduce the computing amount as much as possible.
###### Abstract  
Applications of stereo imagery acquired by cameras onboard unmanned aerial vehicles (UAVs) as practical forest inventory tools are hindered by the unavailability of ground surface elevation. It is still a challenging issue to remove the elevation of ground surface in leaf-on stereo imagery to extract forest canopy height without the help of lidar data. This study proposed a method for the extraction of forest canopy height through the synthesis of UAV stereo imagery of leaf-on and leaf-off, and further demonstrated that the extracted forest canopy height could be used for the inventory of deciduous forest aboveground biomass (AGB). The points cloud of the leaf-on and leaf-off stereo imagery was firstly extracted by an algorithm of structure from motion (SFM) using the same ground control points (GCP). The digital surface model (DSM) was produced by rasterizing the point cloud of UAV leaf-on. The point cloud of UAV leaf-off was processed by iterative median filtering to remove vegetation points, and the digital terrain model (DTM) was generated by the rasterization of the filtered point cloud. The mean canopy height model (MCHM) was derived from the DSM subtracted by the DTM (i.e., DSM-DTM). Forest AGB maps were generated using models developed based on the MCHM and sampling plots of forest AGB and were evaluated by those of lidar. Results showed that forest AGB maps from UAV stereo imagery were highly correlated with those from lidar data with R\({}^{2}\) higher than 0.94 and RMSE lower than 10.0 Mg/ha (i.e., relative RMSE 18.8%). These results demonstrated that UAV stereo imagery could be used as a practical inventory tool for deciduous forest AGB.  
forest aboveground biomass; stereo imagery; unmanned aerial vehicles; UAV; AGB; leaf-on; leaf-off; inventory +
Footnote †: journal: Remote Sensing, 11, 889; doi:10.3390/rs11070889
## 3 Results
### Detector Comparison
Both detectors produced images with comparable quality. However, the Xineos detector provided additional benefits in terms of a larger FOV and faster frame rate (40 fps) \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Adipose**} & \multicolumn{2}{c}{**Glandular**} \\ \cline{2-5}  & **Hamamatsu** & **Xineos** & **Hamamatsu** & **Xineos** \\ \hline x\({}_{\text{res}}\) (\(\upmu\)m) & 152 \(\pm\) 9 & 169 \(\pm\) 7 & 147 \(\pm\) 4 & 164 \(\pm\) 2 \\ y\({}_{\text{res}}\) (\(\upmu\)m) & 172 \(\pm\) 11 & 183 \(\pm\) 7 & 175 \(\pm\) 6 & 186 \(\pm\) 5 \\ SNR & 6.0 \(\pm\) 0.6 & 7.6 \(\pm\) 0.5 & 7.4 \(\pm\) 0.9 & 9.3 \(\pm\) 0.3 \\ SNR/res (\(\upmu\)m\({}^{-1}\)) & 0.037 \(\pm\) 0.004 & 0.043 \(\pm\) 0.002 & 0.046 \(\pm\) 0.006 & 0.053 \(\pm\) 0.002 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Measured spatial resolution, SNR, and SNR/res for the PCT images produced using data from the Hamamatsu and the Xineos detectors, respectively, of the adipose and glandular tissues in a mastectomy sample scanned at 32 keV (6 m sample-detector distance, MGD = 4 mGy). Figure 6: Comparison of the line pattern visibility for PCT images obtained using data from the Hamamatsu and the Xineos detectors as a function of the spatial frequency of the resolution test phantom (expressed as line pairs/cm). compared to Hamamatsu. Moreover, the frame rate of the Xineos detector can be further increased, in its Mag2 (50%) imaging mode, to a maximum of 54 fps.
## 2 Selected Wireless Sensor Networks Standards: State-of-the-art
### IEEE 802.15.4-based Standards  
In comparison with Bluetooth(r), the IEEE 802.15.4 standard presents lower data rates, but requires also lower energy budgets. According to [6], the standard is appropriate for infrequent exchanges of small packets, when power consumption is an important issue. The standard is therefore suited for _process automation applications_, where continuous production streams are monitored.  
The IEEE 802.15.4 physical layer is common to all the following standards and operates in the 2.4 GHz frequency band as well as the 868 MHz and 915 MHz bands in Europe and North America, respectively. The 2.4 GHz frequency band is divided into 16 channels with a maximal data rate of 250 Kbits/s per channel and separated by a 5 MHz gap, while the 915 MHz band is divided into 10 channels with a maximal data rate of 40 Kbit/s each. The single channel in the 868 MHz frequency band presents a  
Figure 3: WISA superframe structure [16].  
data rate of 20 Kbit/s. However, the effective data rates are smaller than the announced nominal values in reality, as mentioned in [6]. In addition to WISA, we have selected the WirelessHART, ISA100.11a, ZigBee, ZigBee PRO, and 802.15.4e Factory Automation MAC Layer technologies. These technologies address the complete protocol stack from the physical layer to the application layer (Table 1) and thus provide a complete system, except for the 802.15.4e FA MAC and the WISA technology that mainly focus on the data link layer.