esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Characterization of Sorbent Material
#### 3.6.4 Scanning Electron Microscope (SEM)  
The physical structure of the adsorbent was characterized by SEM. Figure 7a represents the SEM image of treated bleaching clay with ethyl acetate; it shows irregular layer structure and roughness of the surface [29; 35]. The SEM image in Figure 7b indicated the surface of AHIBC adsorbent. It is obvious that the rough surface turned to being relatively bright after modifying treated bleaching clay with 3-amino-5-hydroxyprazole, suggesting that the organic reagent filled out most of the vacant pores. Yttrium ions seem to appear as bright spots on the surface of AHIBC, as displayed in Figure 7c, which emphasizes the adsorption of Y(III) on the sorption sites of AHIBC.  
Figure 6: FTIR spectra of (a) treated bleaching clay, (b) AHIBC, and (c) Y(III) loaded on AHIBC surface.  
The EDX analysis of treated bleaching clay is given in Figure 8a, and it has been found that both the Si and Al atoms are the main constituents; the peak of Si has high intensity due to SiO\({}_{2}\), the main component of clay. Fe and Mg are the minor substitute elements in this layer silicate mineral, whereas K, S, and Ti are in traces. Clearly, as is obvious in Figure 8b, the contents of metal elements of AHIBC are similar with high intensity of both carbon and oxygen atoms due to impregnation with 3-amino-5-hydroxypyrazole. Figure 8c illustrates the EDX analysis of Y(III) loaded on AHIBC. New peaks of Y(III) confirm that Yttrium ions were adsorbed on the surface of AHIBC. Thus, it may be reasonable to speculate that AHIBC has outstanding sorption properties and accessible sorption sites within its interlayer space.  
Lastly, the proposed mechanism for the ethyl acetate treatment and impregnation of spent bleaching clay with 3-amino-5-hydroxypyrazole, as well as the adsorption of Y(III) on AHIBC, is shown in Figure 9. According to the above characterizations, the adsorption of Y(III) on AHIBC adsorbent is chemisorption.  
Figure 8: EDX spectra of (**a**) treated bleaching clay, (**b**) AHIBC, and (**c**) Y(III) loaded on AHIBC.  
Figure 7: SEM images of (**a**) treated bleaching clay, (**b**) AHIBC, and (**c**) Y ions loaded on AHIBC.  
Figure 9: Proposed mechanism of Y(III) loaded on AHIBC.
## 4 Results and Discussion
### Attenuation Behavior of Ceramics and Rocks
Carrara marble and Santa Cecilia granite (Signature Hardware, Erlanger, KY, USA) are on opposite ends of the scale, showing low and high attenuation values, respectively. Marble showed a quadratic frequency spectrum with low attenuation. Although the marble microstructure was unavailable, AXM-5Q graphite, Macor, and marble showed differing spectral behavior, and all showed low attenuation values with fine structures. These results should be compared in detail to explore the cause of the low attenuation. For rocks, there is a long history of damping studies. In 1965, [PERSON] [27] listed Q (= 1/\(\upeta\)) values for 23 types of rocks. When the \(\upeta\) value for granite is calculated from his table, it is 12 times smaller than that from test C33 (see listing in Table 9). This is similar to the situation for ceramics, as discussed above. Ultrasonic work on rocks continues to date, but most geophysical attenuation studies have ignored the diffraction effects and transmission loss, making the results difficult to assess. The last three tests were on single crystals, with calcite and fluorite being natural crystals, and ammonium dihydrogen phosphate (ADP or H\({}_{6}\)NO\({}_{4}\)P) being factory grown. No attenuation was detectable for ADP, even though it had a 73 mm propagation distance. The other two crystals also showed low attenuation. For calcite, the wave path for the maximum output was along the [001] axis (at 75\({}^{\circ}\) from (001)). The low attenuation of these crystals is in line with similarly low attenuation levels of rock salt, as shown by [PERSON] et al. [117]. Using detailed analysis of AE signals in a large salt dome (with propagation distances up to 510 m), \(\upalpha\) values were found to be 0.002 to 0.34 dB/m at 1 to 100 kHz. Glacial ice also shows low attenuation [118]. This section presented the observed attenuation data for ceramics and rocks, as tabulated in Table 8, covering 40 tests. Most attenuation spectra showed linear dependence, with eight also showing the Rayleigh scattering effect. Two cases showed quadratic behavior, with marble showing only the quadratic spectrum. In mortar samples with varying void contents, the attenuation spectra were initially in-line with the Mason-McSkimin relation, eventually transitioning to the Datta-Kinra relation. Two graphite samples also showed contrasting behaviors of high and low attenuation, reflecting their coarse and fine microstructures. Granite and marble were another pair that showed large differences in attenuation.
###### Abstract
The systems are evaluated and compared in terms of their ability to handle next generation multi-carrier orthogonal frequency division multiplexing (OFDM) and the single-carrier 32 amplitude phase shift keying (32-APSK) formats. For both systems, and both waveforms, the critical impact of the optical source linewidths (and resulting mm-wave phase noise) is investigated in terms of overall performance--revealing important system limitations in all cases. Furthermore, through theoretical analysis, PNC receiver design rules are established, allowing mm-wave system optimization for given optical linewidths, optical carrier and sideband frequency spacing and target performance (in terms of EVM or signal-to-noise ratio (SNR)). The outline of the paper is as follows: Section 2 outlines the potential of photonic assisted mm-wave communications in a networking context and Section 3 discusses the waveforms which hold importance for these networking scenarios. Section 4.1 details the developed hybrid photonic/mm-wave platform and Section 5 provides an analysis of system performance. Finally, Section 6 concludes the work.
## 5 Subgrade Defect Intelligent Identification
### Field Verification  
Based on research into GPR signatures and experiments in intelligent target recognition for roadbed defects, an on-site test was conducted on a selected road surface. The purpose was to further apply and test the intelligent defect recognition algorithm system developed in this study. The on-site testing utilized a Swedish MALA-450 GPR with a 450 MHz antenna and a time window of 50 ns. According to the GPR on-site detection results, a suspected case of roadbed-cavity-induced surface subsidence was identified. The on-site test results are shown in Figure 21.  
Figure 22 shows the radar image after gain processing. Intelligent recognition experiments were conducted on the on-site radar detection images using the two algorithms, the SSD and YOLO v3, with the results shown in Figure 23. Both algorithms successfully identified the roadbed cavity defect and its extent, with the red boxes in the images indicating the predicted results. The SSD algorithm yielded a prediction probability of 0.96 and a smaller prediction range, while the YOLO v3 algorithm yielded a prediction probability of 0.98 and a larger prediction range. These results demonstrate that the SSD and YOLO v3 target detection algorithms, when trained on the GPR roadbed defect image dataset developed in this study, could effectively identify and classify defects in shallow-layer radar detection images of roadbeds and predict their locations and extents. Compared to the SSD, the YOLO v3 algorithm exhibited superior performance in intelligently recognizing the location, extent, and classification of the roadbed cavity defect in the GPR images.  
Figure 21: Field roadbed defect test.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Detection Algorithm** & **Defect Type** & **Precision** & **Recall** & **F1** & **AP** & **mAP** \\ \hline \multirow{3}{*}{YOLO v3} & Void & 81.03\% & 82.46\% & 0.82 & 84.66\% & \multirow{3}{*}{76.69\%} \\  & Cavity & 74.07\% & 81.63\% & 0.78 & 80.26\% & \\  & Loose & 76.76\% & 62.64\% & 0.69 & 65.15\% & \\ \hline \multirow{3}{*}{SSD} & Void & 81.43\% & 50.00\% & 0.62 & 76.66\% & \multirow{3}{*}{75.07\%} \\  & Cavity & 80.00\% & 65.31\% & 0.72 & 73.00\% & \\ \cline{1-1}  & Loose & 77.86\% & 58.62\% & 0.67 & 75.54\% & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation index results for different target detection and recognition tests.
###### Abstract  
Hyperspectral remote sensing (RS) provides unique possibilities to monitor peatland vegetation traits and their temporal dynamics at a fine spatial scale. Peatlands provide a vital contribution to ecosystem services by their massive carbon storage and wide heterogeneity. However, monitoring, understanding, and disentangling the diverse vegetation traits from a heterogeneous landscape using complex RS signal is challenging, due to its wide biodiversity and distinctive plant species composition. In this work, we aim to demonstrate, for the first time, the large heterogeneity of peatland vegetation traits using well-established vegetation indices (VIs) and Sun-Induced Fluorescence (SIF) for describing the spatial heterogeneity of the signals which may correspond to spatial diversity of biochemical and structural traits. SIF originates from the initial reactions in photosystems and is emitted at wavelengths between 650-780 nm, with the first peak at around 687 nm and the second peak around 760 nm. We used the first HyPlant airborne data set recorded over a heterogeneous peatland area and its surrounding ecosystems (i.e., forest, grassland) in Poland. We deployed a comparative analysis of SIF and VIs obtained from differently managed and natural vegetation ecosystems, as well as from diverse small-scale peatland plant communities. Furthermore, spatial relationships between SIF and VIs from large-scale vegetation ecosystems to small-scale peatland plant communities were examined. Apart from signal variations, we observed a positive correlation between SIF and greenness-sensitive VIs, whereas a negative correlation between SIF and a VI sensitive to photosynthesis was observed for large-scale vegetation ecosystems. In general, higher values of SIF were associated with higher biomass of vascular plants (associated with higher Leaf Area Index (LAI)). SIF signals, especially \(\text{SIF}_{760}\), were strongly associated with the functional diversity of the peatland vegetation. At the peatland area, higher values of \(\text{SIF}_{760}\) were associated with plant communities of high perennials, whereas, lower values of \(\text{SIF}_{760}\) indicated peatland patches dominated by _Sphagnum_. In general, \(\text{SIF}_{760}\) reflected the productivity gradient on the fen peatland, from _Sphagnum_-dominated patches with the lowest SIF and fAPAR values indicating lowest productivity to the _Carex_-dominated patches with the highest SIF and fAPAR values indicating highest productivity.  
Keywords:HyPlant; Sun-Induced Fluorescence (SIF); peatland; spectral vegetation indices; NDVI; SR; EVI; PRI; fAPAR; LAI; spectral fitting method; airborne campaign +
Footnote †: journal: Remote Sensing
# 5G New Radio Open Radio Access Network Implementation in Brazil: Review and Cost Assessment
## 5 Implementation Strategies  
### Government Initiatives and Policies  
The Brazilian government typically supports new technologies that are highly attractive, have positive effects on the existing landscape, advance in other government sectors, creating initiative plans to accelerate the introduction of technology and facilitating its implementation and evolution. In addition, laws can be adapted to support the deployment of new technologies, removing regulatory barriers.  
The general telecommunication law, Article 19 th, Lines I and XXXII, establishes that ANATEL may implement national policies and periodically revalidate them to promote market competition and technological evolution. In 2021, the government created a policy rule called \"Portaria n.\" 1.924 - MCOM/2021, de 29 de Janeiro de 2021,\" which, as per the second article was established to incentivize the network's openness to promote interoperability and multiple manufacturers in solution integration [5].  
Spectrum assignment strikes a balance between government objectives, MNO requirements, and consumer welfare. Some decisions were based on supporting the current network, attracting players, and guaranteeing excellence for users [45]. The crucial aspects are as follows:  
* Longer license terms;
* Secondary spectrum market;
* Unlimited renewal terms.
## 4 Experiments
### DSNet and DSNet-RNN Structures  
The specific configuration of the proposed model is described in Table 1. The extracted normalized log mel spectrogram image was used for input to the neural networks. A convolution layer was used to produce feature maps for dense blocks. These networks consisted of four dense blocks each with four convolution layers and one bottleneck layer. The convolution layers consisted of three consecutive operations: \(3\times 3\) convolution, batch normalization and ReLU. We used \(1\times 1\) convolution layer to reduce channels. An SE block and a max pooling layer were placed after each dense block. For segment-level prediction, two dense layers were applied in the DSNet, and Bi-GRU and a dense layer were applied in the DSNet-RNN. Finally, the segment-level predictions were aggregated through the global pooling layer for clip-level prediction. We set \(\sigma_{ps}=0.1\) in Equation (7) and \(\lambda=0.01\) in Equation (8) to train the DSNet-RNN. The parameter size of the DSNet is 0.32M, which is similar to that of the baseline CNN. The DSNet-RNN has more parameters than the others due to the Bi-GRUs used for structured prediction.
# Comparative Study of Blue Light with Ultraviolet (UVC) Radiation on Betacoronavirus 1
###### Abstract  
The ongoing coronavirus pandemic requires more effective disinfection methods. Disinfection using ultraviolet light (UV), especially longer UVC wavelengths, such as 254 and 270/280 nm, has been proven to have virucidal properties, but its adverse effects on human skin and eyes limit its use to enclosed, unoccupied spaces. Several studies have shown the effectiveness of blue light (405 nm) against bacteria and fungi, but the virucidal property at 405 nm has not been much investigated. Based on previous studies, visible light mediates inactivation by absorbing the porphyrins and reacting with oxygen to produce reactive oxygen species (ROS). This causes oxidative damage to biomolecules, such as proteins, lipids, and nucleic acids, essential constituents of any virus. The virucidal potential of visible light has been speculated because the virus lacks porphyrins. This study demonstrated porphyrin-independent viral inactivation and conducted a comparative analysis of the effectiveness at 405 nm against other UVC wavelengths. The betacoronavirus 1 (strain OC43) was exposed to 405, 270/280, 254, and 222 nm, and its efficacy was determined using a median tissue culture infectious dose, i.e., TCID50. The results support the disinfection potential of visible light technology by providing a quantitative effect that can serve as the basic groundwork for future visible light inactivation technologies. In the future, blue light technology usage can be widened to hospitals, public places, aircraft cabins, and/or infectious laboratories for disinfection purposes.  
visible light; 405 nm; virucidal; inactivation; betacoronavirus; irradiation; far-UVC; 222 nm +
Footnote †: journal: Applied Sciences
# COVID-19 Diagnosis in Chest X-rays Using Deep Learning and Majority Voting
###### Abstract  
The COVID-19 disease has spread all over the world, representing an intriguing challenge for humanity as a whole. The efficient diagnosis of humans infected by COVID-19 still remains an increasing need worldwide. The chest X-ray imagery represents, among others, one attractive means to detect COVID-19 cases efficiently. Many studies have reported the efficiency of using deep learning classifiers in diagnosing COVID-19 from chest X-ray images. They conducted several comparisons among a subset of classifiers to identify the most accurate. In this paper, we investigate the potential of the combination of state-of-the-art classifiers in achieving the highest possible accuracy for the detection of COVID-19 from X-ray. For this purpose, we conducted a comprehensive comparison study among 16 state-of-the-art classifiers. To the best of our knowledge, this is the first study considering this number of classifiers. This paper's innovation lies in the methodology that we followed to develop the inference system that allows us to detect COVID-19 with high accuracy. The methodology consists of three steps: (1) comprehensive comparative study between 16 state-of-the-art classifiers; (2) comparison between different ensemble classification techniques, including hard/soft majority, weighted voting, Support Vector Machine, and Random Forest; and (3) finding the combination of deep learning models and ensemble classification techniques that lead to the highest classification confidence on three classes. We found that using the Majority Voting approach is an adequate strategy to adopt in general cases for this task and may achieve an average accuracy up to 99.314%.  
COVID-19; X-ray; deep learning; classification; majority voting; Pneumonia; VGGNet; EfficientNet; ResNet; MobileNet; inception; densenet +
Footnote †: journal: Applied Sciences
# Multi-Constraint and Multi-Policy Path Hopping Active Defense Method Based on SDN
## 2 Related Works
In summary, researchers have conducted extensive research on path switching, but there are still some shortcomings. Currently, in studies on generating path switching spaces, most methods use SMT constraint solving, which has a high computational time complexity for large-scale and highly complex networks. The time complexity increases exponentially with the addition of nodes, making this method impractical for large-scale networks. Furthermore, existing research either imposes overly stringent constraints on generating path switching spaces or lacks sufficient constraints. Overly stringent constraints can result in a reduced path switching space, leading to some paths being repeatedly used in a short period. Insufficient constraints may render path switching paths unusable or cause key nodes to be repeatedly used in the short term. Lastly, current research lacks a variety of triggering mechanisms for path switching, which limits adaptive adjustments of switching strategies and switching periods for different attacks. This limitation reduces the diversity, unpredictability, and effectiveness of path switching. Building upon these challenges, this paper introduces SDADM. Firstly, it employs DFS to explore all paths between communicating ends. Subsequently, it applies constraints on the remaining path bandwidth, key nodes, path similarity, and selection period to select alternative paths. Finally, it adjusts the selection of switching paths adaptively based on the remaining path bandwidth and key node weights as the probability of path selection.
## 4 Simulations and Performance Evaluations
Simulations are conducted to assess the impact of a single variable as well as different combinations. Additionally, performance differences between virtual clients implemented on Windows computers and hardware clients of Raspberry PI are carefully analyzed. The introduction of pre-trained models and alternative learning paradigms such as Transfer Learning (TL) are also considered.
# State Estimation of Over-Sensored Systems Applied to a Low-Cost Robotic Manipulator
## 4 State Estimation
### Link Model Estimation  
To verify the correctness of the sensor models, the estimated state of the link model, which is composed of rotation matrices, had to be converted into the angles of the joints. These angles could then be compared to the baseline created by dead-reckoning of the encoder measurements. The matrix that should be converted to a vector is \({}_{i}\dot{R}(k)\) for joint \(i\). This can be obtained from the state matrices as:  
\[{}_{i}\dot{R}(k)={}_{i}\dot{R}^{T}{}_{i}\dot{R}^{T}(k)\;{}_{n}^{i}R(k) \tag{61}\]  
Figure 5: Structure used for accuracy tests with its interest points marked.  
Since the rotation of joint \(i\) is around the \(z\) axis of frame \(i\) for all joints, the expected rotation vectors should have their \(x\) and \(y\) components (\(\theta_{ix}\) and \(\theta_{iy}\)) close to zero, and the \(z\) component (\(\theta_{iz}\)) should match the baseline angle (\(\theta_{ib}\)).
# Bio-Sourced and Biodegradable Membranes
## 4 Green Membrane Technology Applications
### Food Packaging  
In recent decades, a number of studies have been conducted on the development and application of biodegradable membranes/films in food packaging due to their antimicrobial, non-toxicity, antifungal, controlled permeability, good flexibility, lightness, and transparency [10; 231; 232; 233; 234; 235; 236; 237]. Antimicrobial packaging is a promising method to improve food safety by preventing the growth of pathogenic, spoilage microorganisms, and certain bacteria [231; 238]. Among the biodegradable materials, chitosan and bacterial cellulose have shown excellent antimicrobial properties and have been extensively used in food packaging [232; 233; 235; 237]. The chitosan membranes also proved to have a low permeability to O\({}_{2}\), N\({}_{2}\), and CO\({}_{2}\) and moderate permeability to water [223]. PLA is the most used biodegradable material for food packaging applications [239]. However, there are some limitations to using PLA films in the food industry, including its mechanical stability, antimicrobial, and barrier performance. In order to overcome those limitations, several techniques have been implemented, and PLA films have been modified with other biodegradable materials such as PHB, tea polyphenols (TP), and limonene [239; 240; 241; 242; 243]. PVA is another biodegradable polymer, commonly used with CS to fabricate the composite filmfor food packages owing to its good film-forming property [145; 244; 245]. Water and water vapor resistance property is a key factor for membranes/films employed in food packaging applications. Incorporating nanocellulose into polymeric membranes could improve the water barrier feature of biodegradable films by the creation of nanocrystal networks that prevent swelling and water absorption [234]. According to [PERSON] et al. [246], the nanocellulose filled PVA films (1% nanocellulose loading) showed a 9.5% improvement in water vapor barrier property after 166 h of testing. An overview of biodegradable membranes in food packaging is presented in Table 1.