esa-sceva/satcom-chunk-collection huggingface dataset:
# Nanomaterials in Photocatalysis: An In-Depth Analysis of Their Role in Enhancing Indoor Air Quality
## 4 Photocatalytic Oxidation
### Light Intensity  
Light intensity is crucial for photocatalytic pollutant removal. Higher light intensity increases the photogeneration of charge carriers, enhancing photocatalytic activity [85]. The quantum yield of light absorbed during the photocatalytic process can be expressed as follows:  
\[\Phi_{\text{overall}}=\frac{\text{rate of reaction}}{\text{rate of absorption of radiation}}\]  
For example, increasing light power from 8 to 24 W improved the photocatalytic efficiency of USPNT for p-xylene removal (Figure 5a) [85]. Additionally, for toluene degradation, increasing infrared irradiation intensity from 95 to 116 mW/cm\({}^{2}\) enhanced the degradation rate for the Pt-rGO-TiO\({}_{2}\) photocatalyst (Figure 5b). Notably, the increase in efficiency was more significant between 95 and 105 W than between 105 and 116 W, suggesting that degradation efficiency does not depend linearly on light intensity [97].  
The effects of light intensity can be categorized into three main regimes:  
_Low Light Intensity:_ Adsorption of photons is linearly dependent on the light intensity, resulting in constant quantum efficiency.  
_Intermediate Light Intensity:_ Quantum efficiency decreases with the square root of light intensity.
## 4 Experimental Results
### Datasets and Quantitative Comparison Criterion  
The public dataset MSRA-1000 includes 1000 source images and 1000 corresponding manually generated binary ground truth images. The datasets have been widely utilized in the evaluation of salient object detection recently.  
In the process of evaluation, we first use _Precision_ and _Recall_ as the evaluated criteria. They are defined as follows:  
\[\mathit{Precision}=\frac{\sum_{i=1}^{h}\sum_{j=1}^{w}S(i,j)G(i,j)}{\sum_{i=1}^ {h}\sum_{j=1}^{w}S(i,j)} \tag{12}\]  
\[\mathit{Recall}=\frac{\sum_{i=1}^{h}\sum_{j=1}^{w}S(i,j)G(i,j)}{\sum_{i=1}^{h }\sum_{j=1}^{w}G(i,j)} \tag{13}\]  
where \(S(i,j)\) is the binary gray value converted from the final salient map, \(G(i,j)\) is the corresponding ground truth, and \(h\) and \(w\) are the height and width of the image, respectively.  
In addition, the \(F_{\beta}\) -measure and mean absolute error (MAE) are then evaluated as they have become increasingly popular in recent years. They are defined as follows:  
\[F_{\beta}=\frac{(1+\beta^{2})Precision\times Recall}{\beta^{2}\times Precision+Recall} \tag{14}\]  
\[\text{MAE}=\frac{1}{h\times w}\sum_{i=1}^{h}\sum_{j=1}^{w}\abs{Sal(i,j)-\text{ G}(i,j)} \tag{15}\]  
where \(\beta^{2}\) is set to 0.3 as usual [7,9], \(\text{\it Sal}(i,j)\)  is the saliency of each pixel, and \(\abs{Sal(i,j)-\text{G}(i,j)}\) is the distance between each pixel's \(\text{\it Sal}(i,j)\)  and the corresponding ground truth.
## 3 Results and Discussion
### Preparation and Properties of the Silica-Filled (PAA-co-PAM)-DPNR Composites
#### 3.3.1 Cure Characteristics
The swelling ratio decreased to 3.73 and 2.18 for P10-DPNR and P30-DPNR, respectively. At the same time, the swelling ratio tended to decrease with an increase in silica content, which was clearly observed in the case of the silica-filled (PAA-_co_-PAM)-DPNR composites. For example, the swelling ratio values were found to be 3.73, 3.57, and 3.20 for the P10-DPNR mixed with 0, 10, and 20 phr, respectively. The grafting of (PAA-_co_-PAM) onto the DPNR, together with the addition of silica, exhibited a noticeable increase in the delta torque and a decrease in the swelling ratio, indicating the high crosslink density in the structure. Their interactions are illustrated in Figure 10. The increase in crosslink density is probably due to the crosslinking reaction via sulfur vulcanization, together with the hydrogen bonding of the (PAA-_co_-PAM) copolymer chains and the interaction between the grafted polar functional groups and the silica [66].
# Interpretation of Bridge Health Monitoring Data from Satellite InSAR Technology
## 2 Case Study
### Dataset of SAR Images  
We analysed a dataset of SAR images acquired by the Italian satellite mission COSMO-SkyMed (CSK, 1 st generation), which have been granted free of charge by the Italian Space Agency (ASI) to the Department of Civil, Environmental and Mechanical Engineering at the University of Trento. CSK satellites carry X-band radar sensors with a frequency of 9.6 GHz and a wavelength of 3.1 cm. The revisit time of the CSK constellation (i.e., the number of days between two successive identical passes over the same area) is about 16 days; therefore, the temporal resolution is about 1-2 products per month for images of the same area in both ascending and descending geometry.  
Our dataset includes CSK images acquired in Stripmap HIMAGE mode--a medium-resolution mode with a nominal spatial resolution of 3 m--collected over long, continuous 40 km-wide swaths. The dataset counts 109 images acquired in descending orbit at UTC 5:13 p.m. and covers 8 years, from 1 May 2014 to 12 May 2021. The CSK satellite that acquired the images of our dataset has right-side looking-angle with an incidence angle \(\theta=33.9449^{\circ}\) (i.e., the angle between the LoS and the normal to the ground in our area) and a heading angle \(\beta=10.6095^{\circ}\) (i.e., the angle between the satellite orbit and the North-South direction). Figure 6a shows the footprint of our dataset. The investigated area is a \(4.2\times 4.2\) km\({}^{2}\) portion of this footprint, cropped from the original SAR images and shown in Figure 6b. It includes the A22 Po River Bridge and the villages of San Nicolo Po and Portiolo. Villages and artificial structures in this area contribute to the successful co-registration of SAR imagery. Figure 7 shows the \"Star Graph\" connecting the dataset, highlighting the temporal and normal baselines. The master image of the descending dataset corresponds to the date 24 July 2017; it is the barycentre of the images' graph and minimises the temporal and normal baselines, reducing the temporal and spatial decorrelation between images.  
Figure 6: (**a**) Footprint of our dataset (full frame) and (**b**) multi-temporal reflectivity map of the investigated area, which represents a portion of the dataset footprint cropped from the original SAR images.
# Comparison of EPIC-Simulated and MODIS-Derived Leaf Area Index (LAI) across Multiple Spatial Scales
###### Abstract  
Modeled leaf area index (LAI) in conjunction with satellite-derived LAI data streams may be used to support various regional and local scale air quality models for retrospective and future meteorological assessments. The Environmental Policy Integrated Climate (EPIC) model holds promise for providing LAI within a dynamic range for input into climate and air quality models, improving on current LAI distribution assumptions typical within atmospheric modeling. To assess the potential use of EPIC LAI, we first evaluated the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product collections 5 and 6 (i.e., Mc5, Mc6) with in situ LAI estimates upscaled at four 1.0 km resolution research sites distributed over the Albemarle-Pamlico Basin in North Carolina and Virginia, USA. We then compared the EPIC modeled 12.0 km resolution LAI to aggregated MODIS LAI (Mc5, Mc6) over a \(3\times 3\) grid (or \(36\) km \(\times 36\) km) centered over the same four research sites. Upscaled in situ LAI comparison with MODIS LAI showed improvement with the newer collection where the Mc5 overestimate of +2.22 LAI was reduced to +0.97 LAI with the Mc6. On three of the four sites, the EPIC/MODIS LAI comparison at 12.0 km resolution grid showed similar weighted mean LAI differences (LAI 1.29-1.34), with both Mc5 and Mc6 exceeding EPIC LAI across most dates. For all four research sites, both MODIS collections showed a positive bias when compared to EPIC LAI, with Mc6 (LAI = 0.40) aligning closer to EPIC than the Mc5 (LAI = 0.61) counterpart. Despite modest differences between both MODIS collections and EPIC LAI, the overestimation trend suggests the potential for EPIC to be used for future meteorological alternative management applications on a regional or national scale.  
leaf area index; EPIC; simulation; satellite; MODIS; evaluation; in situ 2020
# Newly Designed Identification Scheme for Monitoring Ice Thickness on Power Transmission Lines
## 3 New Scheme for Iced Transmission Line Identification
### Bounding Box Identification  
Bounding box identification can separate specific areas of the PTLI image that are considered more important than other objects. In the PTLI image with ROI coding, that area will have higher image quality than the background.  
The method for determining the bounding box is: if the iced transmission line forecast image is \(r(x,y)\), then \(r_{i}(x,y)\) are the connected components (regions) contained in \(r(x,y)\), with \(i=1,\ 2,\ldots,n\); \(n\) is the number of regions; or \(r_{i}(x,y)\in r(x,y)\). A bounding box is obtained for each \(r_{i}(x,y)\) based on the iced line forecast image. A bounding box is an imagined box that encloses a specified area that surrounds \(r_{i}(x,y)\). The bounding box for each \(r_{i}(x,y)\) is determined based on the spatial coordinates of the upper-left (UL), upper-right (UR), lower-left (LL), and lower-right (LR) pixels. Suppose the size of the image \(r_{i}(x,y)\) is \(M\times N\), where M is the number of pixel rows and N is the number of pixel columns. The illustration of the bounding box \(r_{i}(x,y)\) is shown in Figure 8. The flow for determining the bounding box for each region \(r_{i}(x,y)\) is shown in Figure 9. Yellow boxes  
Figure 7: The flowchart for image segmentation refinement using mathematical morphology.  
indicate connected component bounding boxes (regions). The bounding box obtained only surrounds the transmission line icing area (Figure 10a). The bounding box's size is adjusted again by widening it to cover the entire area of the line icing. Each bounding box spatial coordinate (UL, UR, LL, and LR) is widened by adding k pixels (Figure 10b). The k value is obtained from the average iced transmission line pixel width.
# Fuzzy Decision Algorithm for Health Impact Assessment in a 5G Environment
## 2 Overview of Open Questions, Guidelines, Recommendations, and Standards
### Health Effects from Non-Thermal Effects  
In the last decade, many non-expert reports have caused numerous debates about the biological effects of RF-EMF. Following the scientific literature that deals with the adverse non-thermal effects of RF-EMF, we come across different findings and conclusions depending on the authors, the type of studies, the type of research, research financiers, etc. [5]. Many review papers are based on available studies regarding RF's impact on health in the range from 6 GHz to 100 GHz [5; 6; 8; 9; 10; 11; 12; 13]. The state of the art of research can be classified into four key directions:  
1. Diseases caused by carcinogens;
2. Effects on the nervous system and neurobehavioral disorders;
3. Short-term symptoms: headache, fatigue, and dizziness;
4. Other effects (reproductive and developmental effects).  
The first two research directions refer to \"in vivo\" studies, \"in vitro\" studies, and epidemiological studies [14]. The authors singled out several \"objective\" references in order to better explain the non-thermal effects of RF.  
Many open questions can be found on the WHO website [13]. What are the potential health risks of 5G? The answer is: \"To date, and after much research performed, no adverse health effect has been causally linked with exposure to wireless technologies. Health-related conclusions are drawn from studies performed across the entire radio spectrum but, so far, only a few studies have been carried out at the frequencies to be used by 5G\". Millimeter wave and THz applications do not significantly affect the average exposure of humans. In these frequency ranges, low radiation power causes low penetration depth, i.e., the negative impact of radiation is mainly reflected on the surface tissues.  
Many in vivo and in vitro studies (the various power densities from 0.02 to 450 mV/cm\({}^{2}\)) have dealt with comprehensive analysis of cells, genes, proteins, etc. None of the relevant studies pointed to significant negative effects of radiation from the mentioned frequency range [11; 15; 16].  
However, as the number of studies grows, it must be noted that the paradigm slowly shifts from apriori non-acceptance to a new understanding and acceptance of scientific explanations and results of clinical studies. Another decade should pass so that conclusions can be drawn more reliably about the potential health hazards of high-frequency EMFs.
# A Performance Analysis of Internet of Things Networking Protocols: Evaluating MQTT, CoAP, OPC UA
## 3 An Overview on IoT Communication Protocols and Frameworks
### Comparison of IoT Communication Approaches
#### 3.8.10 Complexity  
Today the available approaches take into consideration aspects such as small header size, lightweight implementation, and the possibility to run in embedded devices. CoAP, for instance, has an average header size of 4 bytes; requires 10 KiB of RAM, and 100 KiB of code space [14]. AMQP has a very short header size (2 bytes) and, similarly to MQTT, it holds a lightweight implementation that can run in devices with less than 64 Kb of RAM. DDS holds a lightweight implementation (CoreDX DSS [28]) which can run in Android devices. The MicroDDS implementation of DDS, which provides publishing support only, runs on 8-bit microcontrollers with 2 KB of SRAM, and 32 KB of ROM. DDS RTI Connext Micro implementation runs on 16-bit ARM. OPC UA servers can run in devices with just 15 KB of RAM and 10 KB of ROM. NDN for RIoT has been implemented in a very light way when compared to 6 LowPAN [29].
# Envisat RA-2 Individual Echoes: A Unique Dataset for a Better Understanding of Inland Water
###### Abstract  
The exploitation of synthetic aperture properties in nadir-looking radars is opening new scenarios in the framework of satellite radar altimetry. Both recent and upcoming missions including Cryosat-2, Sentinel-3, Sentinel-6 and SWOT take benefit from the coherent processing of radar data, aimed at improving range measurements in particular contexts, such as ice, open ocean, coastal zone, and even inland waters. This work investigates the possibilities offered by current and future satellite radar altimetry missions for the study of inland water bodies, probing into the peculiarities of the expected radar returns and their potential usage. In this regard, signals collected by the RA-2 instrument (Radar Altimeter 2) onboard the Envisat mission offer an unprecedented possibility, even with a relatively low pulse repetition frequency, to analyze the peculiarities of actual signals for detecting and ranging small water surfaces. In particular, the RA-2 instrument offers a global archive of Individual Echoes (IEs), collected at the native sampling rate of 1795 Hz, in addition to the 18 Hz data obtained by incoherent averaging, which are typically delivered to the users as standard products. RA-2 shares with future radar platforms such as Sentinel-6 a continuous and interleaved working modality, as was recommended by the scientific community in designing next missions' requirements. This is a further reason to consider the usage of RA-2 IEs as particularly attractive. Whilst only available for a small percentage of the earth's surface, sufficient IE data exist to study the height retrieval capability of these echoes, in particular for what concerns small water bodies, where we show that enough coherence is exhibited for focusing relatively narrow surfaces and range them correctly. A peculiar aspect of this work lies in the assumption that most of the returned echoes (in RA-2 IEs) are specular. A theoretical framework is developed according to this assumption, which is validated by investigating real RA-2 data and observing their related specular features. In particular, we discuss how specular echoes are expected to be very common in inland altimetry, and are most often associated with small to medium size lakes and rivers. This paper illustrates the expected electromagnetic behavior of specular water targets by exploiting the classical radar cross-section (RCS) theory for specular surfaces. Results from the model are compared with real IE data in three selected case studies, regarding two rivers of variable width and one flood plain, in order to check different hydrological regimes. The model very closely matches the data in all cases, making the results of this validation activity very promising. In particular, we demonstrate the feasibility of using satellite radar altimetry in rivers much smaller than what was considered possible until now.  
inland water altimetry; coherence; Doppler processing; Envisat; Individual Echoes 20179 9, 605; doi:10.3390/rs9060605
###### Abstract
Furthermore, we enable the detection of pantograph horns, sparks, and other features through the pruned RefineDet method [20], reconstructing the three-dimensional trajectory of the pantograph slide to acquire the pantograph catenary contact parameters, which include pantograph pose, center line offset, PCS detachment, and arcing detection. Finally, we construct a dataset with more than 16,000 frames including both left and right images. Within the dataset there are arcs, occlusions, and a variety of illumination conditions. The results using the dataset show that our method has obvious positioning advantages in positioning accuracy and stability under long-time operation compared with the other state-of-the-art tracking methods, and the output multiple contact positioning indicators also meet the expectations of actual railway operation. The main contributions of this study are as follows. * Based on the parameter adaptive perception, a reliable analysis framework of the PCS contact state that satisfies the safety supervision of train outdoor operation is proposed. * By constructing closed-loop tracking, ensuring that the tracking parameters can be updated according to the feedback of collected image, long-term stable, high-speed, and high-precision contact point positioning is achieved. * Through the use of a pruned RefineDet [20]--an optimized deep learning object detection method, we achieved robust real-time positioning of pantograph horns and sparks. * The method we proposed can effectively obtain the PCS contact parameters and contact state analysis during the long-term outdoor operation of trains, supporting the safe and stable operation of railways. The rest of this paper is organized as follows. An overview of the feature tracking method and contact analysis is given in Section 2, Section 3 describes the implementation of the image feature localization method, and Section 4 describes the contact state analysis formula through the obtained image feature. Section 5 provides the operation effect of the integrated system on the actual data set and we report the findings of targeted analysis. Finally, the summary of the full text is given.
# Federated Reinforcement Learning Based AANs with LEO Satellites and UAVs
## 3 System Design
### System Concept  
Figure 1 shows the concept of the proposed system. The proposed system includes the backbone based on LEO satellites and FRL-enabled UAVs, and provides public network services. In the satellite communication system, the satellites cover wide area to provide services to many devices with limited communication resources. Due to the satellites' limited resources, it is difficult for the satellite communication system to provide services to all wireless devices in areas with dense traffic demands. To increase the communication resources, UAVs are deployed to the areas with dense traffic and they provide network services to the devices which receive communication services with insufficient resource from the satellites. Each UAV takes off from its initial location and then autonomously and independently changes its position by considering the traffic distribution on the ground. Then, the UAVs properly perform the data routing to satellites by considering the service capability of satellites and the distance from them. The UAVs determine their movement based on their own neural network which is trained using our FRL algorithm. A huge amount of computation is required for the method of calculating the optimal location points with considering the amount of traffic changing in real time and the movement of satellites. Thus, this method does not guarantee real-time performance. In addition, such method cannot deal with unexpected factors or situations. In comparison, using the trained neural network, it is possible to determine the next movements of UAVs by considering the situations and to take appropriate countermeasures against the changes of various factors around UAVs immediately.
# The Use of Biological Sensors and Instrumental Analysis to Discriminate COVID-19 Odor Signatures
## 4 Discussion
### Advantages and Disadvantages  
In contrasting the use of canines and instrumental analysis to detect COVID-19 infection, the disadvantages of one approach appear to be compensated by the other in terms of mobility, speed of detection, development time, and performance rate. The HS-SPME-GC-MS method was developed using conventional benchtop GC-MS instrumentation with the HS-SPME procedure requiring heat baths and stabile working surfaces to prevent damage to the fibers while in use. The canines on the other hand are quite mobile once trained, they can easily be taken to new areas and be prepared to conduct their duties in under an hour where the movement of a GC-MS unit would require a day's time in pull-down and set-up.  
When viewing speed of detection, the canine method boasts a high-throughput rate with all five dogs being able to screen a sample in under ten minutes. In contrast, the HS-SPME method requires roughly 25 h for a sample to be analyzed and interpreted. In practice, samples are analyzed in batches to increase the efficiency of this method, the average per sample time equates to 3 h. While the canine method is much faster in its deployment, the average training time (per canine) was reported to be 31 weeks with an additional 5 weeks of testing. The HS-SPME-GC-MS method was developed in 16 weeks, owing8 weeks to sample analysis and another 8 weeks to data interpretation and computational analysis. While the canine method may be faster to deploy, the HS-SPME-GC-MS method was quicker to develop.  
In terms of ease of use, both methods require the participation of a trained expert with an analytical chemist being required to operate and interpret the results of a HS-SPME-GC-MS procedure and a trained canine handler being needed to conduct the canine association trials. Neither method is designed to be performed by a lay person.  
Finally, when comparing performance at detecting COVID-19 infection, the final HS-SPME-GC-MS model demonstrated perfect performance with a LOOCV performance of 100% sensitivity and 100% specificity compared to the overall canine performance of 63% sensitivity and 90% specificity. The general performance of these two approaches is noted to be within a similar, competitive range as that of other previously published COVID-19 detection methods (Table 7). All in all, the disadvantages posed by one method were seen to be an excelling point in the paired approach, supporting the argument for a dual investigation of disease detection through canine and instrumental avenues.